0s autopkgtest [12:42:59]: starting date and time: 2024-03-26 12:42:59+0000 0s autopkgtest [12:42:59]: git checkout: 4a1cd702 l/adt_testbed: don't blame the testbed for unsolvable build deps 0s autopkgtest [12:42:59]: host juju-7f2275-prod-proposed-migration-environment-4; command line: /home/ubuntu/autopkgtest/runner/autopkgtest --output-dir /tmp/autopkgtest-work.ggp6iigi/out --timeout-copy=6000 --setup-commands 'dhclient || true; ln -s /dev/null /etc/systemd/system/bluetooth.service; printf "http_proxy=http://squid.internal:3128\nhttps_proxy=http://squid.internal:3128\nno_proxy=127.0.0.1,127.0.1.1,login.ubuntu.com,localhost,localdomain,novalocal,internal,archive.ubuntu.com,ports.ubuntu.com,security.ubuntu.com,ddebs.ubuntu.com,changelogs.ubuntu.com,launchpadlibrarian.net,launchpadcontent.net,launchpad.net,10.24.0.0/24,keystone.ps5.canonical.com,objectstorage.prodstack5.canonical.com\n" >> /etc/environment' --apt-pocket=proposed --apt-upgrade r-cran-systemfit --timeout-short=300 --timeout-copy=20000 --timeout-build=20000 --env=ADT_TEST_TRIGGERS=r-base/4.3.3-2build1 -- lxd -r lxd-armhf-10.145.243.5 lxd-armhf-10.145.243.5:autopkgtest/ubuntu/noble/armhf 22s autopkgtest [12:43:21]: testbed dpkg architecture: armhf 24s autopkgtest [12:43:23]: testbed apt version: 2.7.12 24s autopkgtest [12:43:23]: @@@@@@@@@@@@@@@@@@@@ test bed setup 31s Get:1 http://ftpmaster.internal/ubuntu noble-proposed InRelease [117 kB] 32s Get:2 http://ftpmaster.internal/ubuntu noble-proposed/restricted Sources [8504 B] 32s Get:3 http://ftpmaster.internal/ubuntu noble-proposed/multiverse Sources [56.0 kB] 32s Get:4 http://ftpmaster.internal/ubuntu noble-proposed/main Sources [496 kB] 32s Get:5 http://ftpmaster.internal/ubuntu noble-proposed/universe Sources [4019 kB] 33s Get:6 http://ftpmaster.internal/ubuntu noble-proposed/main armhf Packages [669 kB] 33s Get:7 http://ftpmaster.internal/ubuntu noble-proposed/main armhf c-n-f Metadata [2492 B] 33s Get:8 http://ftpmaster.internal/ubuntu noble-proposed/restricted armhf Packages [1372 B] 33s Get:9 http://ftpmaster.internal/ubuntu noble-proposed/restricted armhf c-n-f Metadata [116 B] 33s Get:10 http://ftpmaster.internal/ubuntu noble-proposed/universe armhf Packages [4075 kB] 34s Get:11 http://ftpmaster.internal/ubuntu noble-proposed/universe armhf c-n-f Metadata [7776 B] 34s Get:12 http://ftpmaster.internal/ubuntu noble-proposed/multiverse armhf Packages [49.1 kB] 34s Get:13 http://ftpmaster.internal/ubuntu noble-proposed/multiverse armhf c-n-f Metadata [116 B] 36s Fetched 9502 kB in 3s (3173 kB/s) 36s Reading package lists... 43s sh: 1: dhclient: not found 45s tee: /proc/self/fd/2: Permission denied 66s Hit:1 http://ftpmaster.internal/ubuntu noble-proposed InRelease 66s Hit:2 http://ftpmaster.internal/ubuntu noble InRelease 66s Hit:3 http://ftpmaster.internal/ubuntu noble-updates InRelease 66s Hit:4 http://ftpmaster.internal/ubuntu noble-security InRelease 67s Reading package lists... 68s Reading package lists... 68s Building dependency tree... 68s Reading state information... 68s Calculating upgrade... 69s The following packages were automatically installed and are no longer required: 69s linux-headers-6.8.0-11 python3-distutils python3-lib2to3 69s Use 'apt autoremove' to remove them. 69s The following packages will be REMOVED: 69s libapt-pkg6.0 libarchive13 libatm1 libcurl3-gnutls libcurl4 libdb5.3 libelf1 69s libext2fs2 libgdbm-compat4 libgdbm6 libglib2.0-0 libgnutls30 libgpgme11 69s libhogweed6 libmagic1 libnetplan0 libnettle8 libnpth0 libnvme1 libparted2 69s libpcap0.8 libperl5.38 libpng16-16 libpsl5 libreadline8 libreiserfscore0 69s libssl3 libtirpc3 libuv1 linux-headers-6.8.0-11-generic 69s The following NEW packages will be installed: 69s libapt-pkg6.0t64 libarchive13t64 libatm1t64 libcurl3t64-gnutls libcurl4t64 69s libdb5.3t64 libelf1t64 libext2fs2t64 libgdbm-compat4t64 libgdbm6t64 69s libglib2.0-0t64 libgnutls30t64 libgpgme11t64 libhogweed6t64 libmagic1t64 69s libnetplan1 libnettle8t64 libnpth0t64 libnvme1t64 libparted2t64 69s libpcap0.8t64 libperl5.38t64 libpng16-16t64 libpsl5t64 libreadline8t64 69s libreiserfscore0t64 libssl3t64 libtirpc3t64 libuv1t64 linux-headers-6.8.0-20 69s linux-headers-6.8.0-20-generic xdg-user-dirs 69s The following packages have been kept back: 69s multipath-tools 69s The following packages will be upgraded: 69s apparmor apt apt-utils base-files bind9-dnsutils bind9-host bind9-libs 69s binutils binutils-arm-linux-gnueabihf binutils-common bolt bsdextrautils 69s bsdutils btrfs-progs coreutils cryptsetup-bin curl dbus dbus-bin dbus-daemon 69s dbus-session-bus-common dbus-system-bus-common dbus-user-session dhcpcd-base 69s dirmngr dmsetup dpkg dpkg-dev e2fsprogs e2fsprogs-l10n eject fdisk file ftp 69s fwupd gawk gcc-13-base gcc-14-base gir1.2-girepository-2.0 gir1.2-glib-2.0 69s gnupg gnupg-l10n gnupg-utils gpg gpg-agent gpg-wks-client gpgconf gpgsm gpgv 69s groff-base ibverbs-providers inetutils-telnet info initramfs-tools 69s initramfs-tools-bin initramfs-tools-core install-info iproute2 jq keyboxd 69s kmod kpartx krb5-locales libapparmor1 libaudit-common libaudit1 libbinutils 69s libblkid1 libblockdev-crypto3 libblockdev-fs3 libblockdev-loop3 69s libblockdev-mdraid3 libblockdev-nvme3 libblockdev-part3 libblockdev-swap3 69s libblockdev-utils3 libblockdev3 libbpf1 libbrotli1 libcap-ng0 libcom-err2 69s libcryptsetup12 libctf-nobfd0 libctf0 libdbus-1-3 libdebconfclient0 69s libdevmapper1.02.1 libdpkg-perl libevent-core-2.1-7 libexpat1 libfdisk1 69s libfido2-1 libftdi1-2 libfwupd2 libgcc-s1 libgirepository-1.0-1 69s libglib2.0-data libgssapi-krb5-2 libgudev-1.0-0 libgusb2 libibverbs1 69s libjcat1 libjq1 libjson-glib-1.0-0 libjson-glib-1.0-common libk5crypto3 69s libkmod2 libkrb5-3 libkrb5support0 libldap-common libldap2 69s liblocale-gettext-perl liblzma5 libmagic-mgc libmbim-glib4 libmbim-proxy 69s libmm-glib0 libmount1 libnghttp2-14 libnsl2 libnss-systemd libpam-modules 69s libpam-modules-bin libpam-runtime libpam-systemd libpam0g libplymouth5 69s libpolkit-agent-1-0 libpolkit-gobject-1-0 libproc2-0 libprotobuf-c1 69s libpython3-stdlib libpython3.11-minimal libpython3.11-stdlib 69s libpython3.12-minimal libpython3.12-stdlib libqmi-glib5 libqmi-proxy 69s libqrtr-glib0 librtmp1 libsasl2-2 libsasl2-modules libsasl2-modules-db 69s libseccomp2 libselinux1 libsemanage-common libsemanage2 libsframe1 libslang2 69s libsmartcols1 libsqlite3-0 libss2 libssh-4 libstdc++6 libsystemd-shared 69s libsystemd0 libtext-charwidth-perl libtext-iconv-perl libtirpc-common 69s libudev1 libudisks2-0 libusb-1.0-0 libuuid1 libvolume-key1 libxml2 libxmlb2 69s libxmuu1 linux-headers-generic logsave lshw lsof man-db motd-news-config 69s mount mtr-tiny netplan-generator netplan.io openssh-client openssh-server 69s openssh-sftp-server openssl parted perl perl-base perl-modules-5.38 69s pinentry-curses plymouth plymouth-theme-ubuntu-text procps psmisc 69s python-apt-common python3 python3-apt python3-cryptography python3-dbus 69s python3-distutils python3-gdbm python3-gi python3-lib2to3 python3-minimal 69s python3-netplan python3-pkg-resources python3-pyrsistent python3-setuptools 69s python3-typing-extensions python3-yaml python3.11 python3.11-minimal 69s python3.12 python3.12-minimal readline-common rsync rsyslog shared-mime-info 69s sudo systemd systemd-dev systemd-resolved systemd-sysv systemd-timesyncd 69s tcpdump telnet tnftp ubuntu-pro-client ubuntu-pro-client-l10n udev udisks2 69s usb.ids util-linux uuid-runtime vim-common vim-tiny wget xxd xz-utils zlib1g 69s 236 upgraded, 32 newly installed, 30 to remove and 1 not upgraded. 69s Need to get 100 MB of archives. 69s After this operation, 85.0 MB of additional disk space will be used. 69s Get:1 http://ftpmaster.internal/ubuntu noble-proposed/main armhf motd-news-config all 13ubuntu8 [5098 B] 69s Get:2 http://ftpmaster.internal/ubuntu noble-proposed/main armhf base-files armhf 13ubuntu8 [73.9 kB] 69s Get:3 http://ftpmaster.internal/ubuntu noble-proposed/main armhf bsdutils armhf 1:2.39.3-9ubuntu2 [102 kB] 69s Get:4 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libbrotli1 armhf 1.1.0-2build1 [319 kB] 70s Get:5 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libgssapi-krb5-2 armhf 1.20.1-6ubuntu1 [119 kB] 70s Get:6 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libkrb5-3 armhf 1.20.1-6ubuntu1 [320 kB] 70s Get:7 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libkrb5support0 armhf 1.20.1-6ubuntu1 [31.5 kB] 70s Get:8 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libk5crypto3 armhf 1.20.1-6ubuntu1 [78.6 kB] 70s Get:9 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libcom-err2 armhf 1.47.0-2.4~exp1ubuntu2 [21.9 kB] 70s Get:10 http://ftpmaster.internal/ubuntu noble-proposed/main armhf zlib1g armhf 1:1.3.dfsg-3.1ubuntu1 [49.2 kB] 70s Get:11 http://ftpmaster.internal/ubuntu noble-proposed/main armhf librtmp1 armhf 2.4+20151223.gitfa8646d.1-2build6 [51.3 kB] 70s Get:12 http://ftpmaster.internal/ubuntu noble-proposed/main armhf udisks2 armhf 2.10.1-6 [276 kB] 70s Get:13 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libudisks2-0 armhf 2.10.1-6 [143 kB] 70s Get:14 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libblkid1 armhf 2.39.3-9ubuntu2 [160 kB] 70s Get:15 http://ftpmaster.internal/ubuntu noble-proposed/main armhf liblzma5 armhf 5.6.0-0.2 [117 kB] 70s Get:16 http://ftpmaster.internal/ubuntu noble-proposed/main armhf kmod armhf 31+20240202-2ubuntu4 [91.8 kB] 70s Get:17 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libkmod2 armhf 31+20240202-2ubuntu4 [44.9 kB] 70s Get:18 http://ftpmaster.internal/ubuntu noble-proposed/main armhf systemd-dev all 255.4-1ubuntu5 [103 kB] 70s Get:19 http://ftpmaster.internal/ubuntu noble-proposed/main armhf systemd-timesyncd armhf 255.4-1ubuntu5 [36.0 kB] 70s Get:20 http://ftpmaster.internal/ubuntu noble-proposed/main armhf dbus-session-bus-common all 1.14.10-4ubuntu2 [80.3 kB] 70s Get:21 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libaudit-common all 1:3.1.2-2.1 [5674 B] 70s Get:22 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libcap-ng0 armhf 0.8.4-2build1 [13.5 kB] 70s Get:23 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libaudit1 armhf 1:3.1.2-2.1 [44.3 kB] 70s Get:24 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libpam0g armhf 1.5.3-5ubuntu3 [62.0 kB] 70s Get:25 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libselinux1 armhf 3.5-2ubuntu1 [70.9 kB] 70s Get:26 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libcurl4t64 armhf 8.5.0-2ubuntu8 [296 kB] 70s Get:27 http://ftpmaster.internal/ubuntu noble-proposed/main armhf curl armhf 8.5.0-2ubuntu8 [219 kB] 70s Get:28 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libpsl5t64 armhf 0.21.2-1.1 [55.7 kB] 70s Get:29 http://ftpmaster.internal/ubuntu noble-proposed/main armhf wget armhf 1.21.4-1ubuntu2 [317 kB] 70s Get:30 http://ftpmaster.internal/ubuntu noble-proposed/main armhf tnftp armhf 20230507-2build1 [98.6 kB] 70s Get:31 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libpcap0.8t64 armhf 1.10.4-4.1ubuntu2 [137 kB] 70s Get:32 http://ftpmaster.internal/ubuntu noble-proposed/main armhf tcpdump armhf 4.99.4-3ubuntu2 [425 kB] 70s Get:33 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libsystemd-shared armhf 255.4-1ubuntu5 [2009 kB] 70s Get:34 http://ftpmaster.internal/ubuntu noble-proposed/main armhf systemd-resolved armhf 255.4-1ubuntu5 [289 kB] 70s Get:35 http://ftpmaster.internal/ubuntu noble-proposed/main armhf sudo armhf 1.9.15p5-3ubuntu3 [936 kB] 70s Get:36 http://ftpmaster.internal/ubuntu noble-proposed/main armhf rsync armhf 3.2.7-1build1 [413 kB] 70s Get:37 http://ftpmaster.internal/ubuntu noble-proposed/main armhf python3-cryptography armhf 41.0.7-4build2 [788 kB] 70s Get:38 http://ftpmaster.internal/ubuntu noble-proposed/main armhf openssl armhf 3.0.13-0ubuntu2 [975 kB] 70s Get:39 http://ftpmaster.internal/ubuntu noble-proposed/main armhf openssh-sftp-server armhf 1:9.6p1-3ubuntu11 [35.5 kB] 70s Get:40 http://ftpmaster.internal/ubuntu noble-proposed/main armhf openssh-client armhf 1:9.6p1-3ubuntu11 [890 kB] 70s Get:41 http://ftpmaster.internal/ubuntu noble-proposed/main armhf openssh-server armhf 1:9.6p1-3ubuntu11 [503 kB] 70s Get:42 http://ftpmaster.internal/ubuntu noble-proposed/main armhf linux-headers-6.8.0-20 all 6.8.0-20.20 [13.6 MB] 70s Get:43 http://ftpmaster.internal/ubuntu noble-proposed/main armhf linux-headers-6.8.0-20-generic armhf 6.8.0-20.20 [1287 kB] 70s Get:44 http://ftpmaster.internal/ubuntu noble-proposed/main armhf linux-headers-generic armhf 6.8.0-20.20+1 [9610 B] 70s Get:45 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libssl3t64 armhf 3.0.13-0ubuntu2 [1558 kB] 70s Get:46 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libnss-systemd armhf 255.4-1ubuntu5 [148 kB] 70s Get:47 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libudev1 armhf 255.4-1ubuntu5 [166 kB] 70s Get:48 http://ftpmaster.internal/ubuntu noble-proposed/main armhf systemd armhf 255.4-1ubuntu5 [3502 kB] 71s Get:49 http://ftpmaster.internal/ubuntu noble-proposed/main armhf udev armhf 255.4-1ubuntu5 [1852 kB] 71s Get:50 http://ftpmaster.internal/ubuntu noble-proposed/main armhf systemd-sysv armhf 255.4-1ubuntu5 [11.9 kB] 71s Get:51 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libpam-systemd armhf 255.4-1ubuntu5 [216 kB] 71s Get:52 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libsystemd0 armhf 255.4-1ubuntu5 [410 kB] 71s Get:53 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libpam-modules-bin armhf 1.5.3-5ubuntu3 [47.0 kB] 71s Get:54 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libpam-modules armhf 1.5.3-5ubuntu3 [261 kB] 71s Get:55 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libpam-runtime all 1.5.3-5ubuntu3 [40.8 kB] 71s Get:56 http://ftpmaster.internal/ubuntu noble-proposed/main armhf dbus-user-session armhf 1.14.10-4ubuntu2 [9962 B] 71s Get:57 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libapparmor1 armhf 4.0.0-beta3-0ubuntu2 [45.0 kB] 71s Get:58 http://ftpmaster.internal/ubuntu noble-proposed/main armhf gcc-14-base armhf 14-20240315-1ubuntu1 [47.0 kB] 71s Get:59 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libgcc-s1 armhf 14-20240315-1ubuntu1 [41.5 kB] 71s Get:60 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libstdc++6 armhf 14-20240315-1ubuntu1 [714 kB] 71s Get:61 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libexpat1 armhf 2.6.1-2 [65.9 kB] 71s Get:62 http://ftpmaster.internal/ubuntu noble-proposed/main armhf dbus-system-bus-common all 1.14.10-4ubuntu2 [81.5 kB] 71s Get:63 http://ftpmaster.internal/ubuntu noble-proposed/main armhf dbus-bin armhf 1.14.10-4ubuntu2 [37.1 kB] 71s Get:64 http://ftpmaster.internal/ubuntu noble-proposed/main armhf dbus armhf 1.14.10-4ubuntu2 [28.1 kB] 71s Get:65 http://ftpmaster.internal/ubuntu noble-proposed/main armhf dbus-daemon armhf 1.14.10-4ubuntu2 [109 kB] 71s Get:66 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libdbus-1-3 armhf 1.14.10-4ubuntu2 [190 kB] 71s Get:67 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libmount1 armhf 2.39.3-9ubuntu2 [171 kB] 71s Get:68 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libseccomp2 armhf 2.5.5-1ubuntu2 [49.5 kB] 71s Get:69 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libdevmapper1.02.1 armhf 2:1.02.185-3ubuntu2 [135 kB] 71s Get:70 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libuuid1 armhf 2.39.3-9ubuntu2 [34.4 kB] 71s Get:71 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libcryptsetup12 armhf 2:2.7.0-1ubuntu2 [238 kB] 71s Get:72 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libfdisk1 armhf 2.39.3-9ubuntu2 [196 kB] 71s Get:73 http://ftpmaster.internal/ubuntu noble-proposed/main armhf mount armhf 2.39.3-9ubuntu2 [134 kB] 71s Get:74 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libblockdev-utils3 armhf 3.1.0-1build1 [16.9 kB] 71s Get:75 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libvolume-key1 armhf 0.3.12-7build1 [38.4 kB] 71s Get:76 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libjcat1 armhf 0.2.0-2build2 [30.4 kB] 71s Get:77 http://ftpmaster.internal/ubuntu noble-proposed/main armhf parted armhf 3.6-3.1build2 [39.4 kB] 71s Get:78 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libparted2t64 armhf 3.6-3.1build2 [143 kB] 71s Get:79 http://ftpmaster.internal/ubuntu noble-proposed/main armhf python3.12 armhf 3.12.2-4build3 [645 kB] 71s Get:80 http://ftpmaster.internal/ubuntu noble-proposed/main armhf python3.12-minimal armhf 3.12.2-4build3 [1942 kB] 71s Get:81 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libpython3.12-stdlib armhf 3.12.2-4build3 [1906 kB] 71s Get:82 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libpython3.12-minimal armhf 3.12.2-4build3 [816 kB] 71s Get:83 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libsasl2-modules-db armhf 2.1.28+dfsg1-5ubuntu1 [19.0 kB] 71s Get:84 http://ftpmaster.internal/ubuntu noble-proposed/main armhf python3.11 armhf 3.11.8-1build4 [589 kB] 71s Get:85 http://ftpmaster.internal/ubuntu noble-proposed/main armhf python3.11-minimal armhf 3.11.8-1build4 [1795 kB] 71s Get:86 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libpython3.11-stdlib armhf 3.11.8-1build4 [1810 kB] 71s Get:87 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libpython3.11-minimal armhf 3.11.8-1build4 [826 kB] 71s Get:88 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libsqlite3-0 armhf 3.45.1-1ubuntu1 [599 kB] 71s Get:89 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libtext-iconv-perl armhf 1.7-8build2 [12.7 kB] 71s Get:90 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libtext-charwidth-perl armhf 0.04-11build2 [8962 B] 71s Get:91 http://ftpmaster.internal/ubuntu noble-proposed/main armhf perl-modules-5.38 all 5.38.2-3.2 [3110 kB] 71s Get:92 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libdb5.3t64 armhf 5.3.28+dfsg2-6 [661 kB] 71s Get:93 http://ftpmaster.internal/ubuntu noble-proposed/main armhf python3-gdbm armhf 3.12.2-3ubuntu1.1 [17.1 kB] 71s Get:94 http://ftpmaster.internal/ubuntu noble-proposed/main armhf man-db armhf 2.12.0-3build4 [1196 kB] 71s Get:95 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libgdbm6t64 armhf 1.23-5.1 [30.3 kB] 71s Get:96 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libgdbm-compat4t64 armhf 1.23-5.1 [6208 B] 71s Get:97 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libperl5.38t64 armhf 5.38.2-3.2 [4101 kB] 71s Get:98 http://ftpmaster.internal/ubuntu noble-proposed/main armhf perl armhf 5.38.2-3.2 [231 kB] 71s Get:99 http://ftpmaster.internal/ubuntu noble-proposed/main armhf perl-base armhf 5.38.2-3.2 [1671 kB] 71s Get:100 http://ftpmaster.internal/ubuntu noble-proposed/main armhf liblocale-gettext-perl armhf 1.07-6ubuntu4 [15.0 kB] 71s Get:101 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libnettle8t64 armhf 3.9.1-2.2 [187 kB] 72s Get:102 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libhogweed6t64 armhf 3.9.1-2.2 [187 kB] 72s Get:103 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libgnutls30t64 armhf 3.8.3-1.1ubuntu2 [1046 kB] 72s Get:104 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libldap2 armhf 2.6.7+dfsg-1~exp1ubuntu6 [172 kB] 72s Get:105 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libcurl3t64-gnutls armhf 8.5.0-2ubuntu8 [290 kB] 72s Get:106 http://ftpmaster.internal/ubuntu noble-proposed/main armhf shared-mime-info armhf 2.4-1build1 [470 kB] 72s Get:107 http://ftpmaster.internal/ubuntu noble-proposed/main armhf gir1.2-girepository-2.0 armhf 1.79.1-1ubuntu6 [24.8 kB] 72s Get:108 http://ftpmaster.internal/ubuntu noble-proposed/main armhf gir1.2-glib-2.0 armhf 2.79.3-3ubuntu5 [182 kB] 72s Get:109 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libgirepository-1.0-1 armhf 1.79.1-1ubuntu6 [106 kB] 72s Get:110 http://ftpmaster.internal/ubuntu noble-proposed/main armhf python3-gi armhf 3.47.0-3build1 [219 kB] 72s Get:111 http://ftpmaster.internal/ubuntu noble-proposed/main armhf python3-dbus armhf 1.3.2-5build2 [94.7 kB] 72s Get:112 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libnetplan1 armhf 1.0-1 [113 kB] 72s Get:113 http://ftpmaster.internal/ubuntu noble-proposed/main armhf python3-netplan armhf 1.0-1 [22.5 kB] 72s Get:114 http://ftpmaster.internal/ubuntu noble-proposed/main armhf netplan-generator armhf 1.0-1 [58.7 kB] 72s Get:115 http://ftpmaster.internal/ubuntu noble-proposed/main armhf initramfs-tools-bin armhf 0.142ubuntu23 [20.3 kB] 72s Get:116 http://ftpmaster.internal/ubuntu noble-proposed/main armhf initramfs-tools-core all 0.142ubuntu23 [50.1 kB] 72s Get:117 http://ftpmaster.internal/ubuntu noble-proposed/main armhf initramfs-tools all 0.142ubuntu23 [9058 B] 72s Get:118 http://ftpmaster.internal/ubuntu noble-proposed/main armhf netplan.io armhf 1.0-1 [64.3 kB] 72s Get:119 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libxmlb2 armhf 0.3.15-1build1 [57.0 kB] 72s Get:120 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libqrtr-glib0 armhf 1.2.2-1ubuntu3 [15.4 kB] 72s Get:121 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libqmi-glib5 armhf 1.35.2-0ubuntu1 [908 kB] 72s Get:122 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libqmi-proxy armhf 1.35.2-0ubuntu1 [5732 B] 72s Get:123 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libpolkit-agent-1-0 armhf 124-1ubuntu1 [15.3 kB] 72s Get:124 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libpolkit-gobject-1-0 armhf 124-1ubuntu1 [44.1 kB] 72s Get:125 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libglib2.0-0t64 armhf 2.79.3-3ubuntu5 [1414 kB] 72s Get:126 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libfwupd2 armhf 1.9.15-2 [123 kB] 72s Get:127 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libarchive13t64 armhf 3.7.2-1.1ubuntu2 [330 kB] 72s Get:128 http://ftpmaster.internal/ubuntu noble-proposed/main armhf fwupd armhf 1.9.15-2 [4350 kB] 72s Get:129 http://ftpmaster.internal/ubuntu noble-proposed/main armhf apt-utils armhf 2.7.14 [210 kB] 72s Get:130 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libapt-pkg6.0t64 armhf 2.7.14 [986 kB] 72s Get:131 http://ftpmaster.internal/ubuntu noble-proposed/main armhf apt armhf 2.7.14 [1368 kB] 73s Get:132 http://ftpmaster.internal/ubuntu noble-proposed/main armhf ubuntu-pro-client-l10n armhf 31.2.2 [19.4 kB] 73s Get:133 http://ftpmaster.internal/ubuntu noble-proposed/main armhf ubuntu-pro-client armhf 31.2.2 [216 kB] 73s Get:134 http://ftpmaster.internal/ubuntu noble-proposed/main armhf keyboxd armhf 2.4.4-2ubuntu15 [111 kB] 73s Get:135 http://ftpmaster.internal/ubuntu noble/main armhf libnpth0t64 armhf 1.6-3.1 [6940 B] 73s Get:136 http://ftpmaster.internal/ubuntu noble-proposed/main armhf gpgv armhf 2.4.4-2ubuntu15 [224 kB] 73s Get:137 http://ftpmaster.internal/ubuntu noble-proposed/main armhf gpg armhf 2.4.4-2ubuntu15 [524 kB] 73s Get:138 http://ftpmaster.internal/ubuntu noble-proposed/main armhf gpg-wks-client armhf 2.4.4-2ubuntu15 [87.4 kB] 73s Get:139 http://ftpmaster.internal/ubuntu noble-proposed/main armhf gnupg-utils armhf 2.4.4-2ubuntu15 [158 kB] 73s Get:140 http://ftpmaster.internal/ubuntu noble-proposed/main armhf gpg-agent armhf 2.4.4-2ubuntu15 [235 kB] 73s Get:141 http://ftpmaster.internal/ubuntu noble-proposed/main armhf gpgsm armhf 2.4.4-2ubuntu15 [241 kB] 73s Get:142 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libreadline8t64 armhf 8.2-4 [129 kB] 73s Get:143 http://ftpmaster.internal/ubuntu noble-proposed/main armhf gawk armhf 1:5.2.1-2build2 [415 kB] 73s Get:144 http://ftpmaster.internal/ubuntu noble-proposed/main armhf fdisk armhf 2.39.3-9ubuntu2 [135 kB] 73s Get:145 http://ftpmaster.internal/ubuntu noble-proposed/main armhf gpgconf armhf 2.4.4-2ubuntu15 [115 kB] 73s Get:146 http://ftpmaster.internal/ubuntu noble-proposed/main armhf dirmngr armhf 2.4.4-2ubuntu15 [346 kB] 73s Get:147 http://ftpmaster.internal/ubuntu noble-proposed/main armhf gnupg all 2.4.4-2ubuntu15 [359 kB] 73s Get:148 http://ftpmaster.internal/ubuntu noble-proposed/main armhf python3-apt armhf 2.7.7 [162 kB] 73s Get:149 http://ftpmaster.internal/ubuntu noble-proposed/main armhf pinentry-curses armhf 1.2.1-3ubuntu4 [36.7 kB] 73s Get:150 http://ftpmaster.internal/ubuntu noble-proposed/main armhf python3-yaml armhf 6.0.1-2build1 [117 kB] 73s Get:151 http://ftpmaster.internal/ubuntu noble-proposed/main armhf python-apt-common all 2.7.7 [19.8 kB] 73s Get:152 http://ftpmaster.internal/ubuntu noble-proposed/main armhf python3-setuptools all 68.1.2-2ubuntu1 [396 kB] 73s Get:153 http://ftpmaster.internal/ubuntu noble-proposed/main armhf python3-pkg-resources all 68.1.2-2ubuntu1 [168 kB] 73s Get:154 http://ftpmaster.internal/ubuntu noble-proposed/main armhf dpkg armhf 1.22.6ubuntu4 [1229 kB] 73s Get:155 http://ftpmaster.internal/ubuntu noble-proposed/main armhf python3-minimal armhf 3.12.2-0ubuntu1 [27.1 kB] 73s Get:156 http://ftpmaster.internal/ubuntu noble-proposed/main armhf python3 armhf 3.12.2-0ubuntu1 [24.1 kB] 73s Get:157 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libpython3-stdlib armhf 3.12.2-0ubuntu1 [9802 B] 73s Get:158 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libsmartcols1 armhf 2.39.3-9ubuntu2 [117 kB] 73s Get:159 http://ftpmaster.internal/ubuntu noble-proposed/main armhf bsdextrautils armhf 2.39.3-9ubuntu2 [78.7 kB] 73s Get:160 http://ftpmaster.internal/ubuntu noble-proposed/main armhf groff-base armhf 1.23.0-3build1 [946 kB] 73s Get:161 http://ftpmaster.internal/ubuntu noble-proposed/main armhf readline-common all 8.2-4 [56.4 kB] 73s Get:162 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libgpgme11t64 armhf 1.18.0-4.1ubuntu3 [120 kB] 73s Get:163 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libblockdev-crypto3 armhf 3.1.0-1build1 [20.3 kB] 73s Get:164 http://ftpmaster.internal/ubuntu noble-proposed/main armhf e2fsprogs-l10n all 1.47.0-2.4~exp1ubuntu2 [5996 B] 73s Get:165 http://ftpmaster.internal/ubuntu noble-proposed/main armhf logsave armhf 1.47.0-2.4~exp1ubuntu2 [21.9 kB] 73s Get:166 http://ftpmaster.internal/ubuntu noble-proposed/main armhf dhcpcd-base armhf 1:10.0.6-1ubuntu2 [186 kB] 73s Get:167 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libblockdev-fs3 armhf 3.1.0-1build1 [34.4 kB] 73s Get:168 http://ftpmaster.internal/ubuntu noble/main armhf libreiserfscore0t64 armhf 1:3.6.27-7.1 [66.2 kB] 73s Get:169 http://ftpmaster.internal/ubuntu noble-proposed/main armhf btrfs-progs armhf 6.6.3-1.1build1 [852 kB] 73s Get:170 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libext2fs2t64 armhf 1.47.0-2.4~exp1ubuntu2 [201 kB] 73s Get:171 http://ftpmaster.internal/ubuntu noble-proposed/main armhf e2fsprogs armhf 1.47.0-2.4~exp1ubuntu2 [571 kB] 73s Get:172 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libblockdev-loop3 armhf 3.1.0-1build1 [6502 B] 73s Get:173 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libblockdev-mdraid3 armhf 3.1.0-1build1 [13.3 kB] 73s Get:174 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libblockdev-nvme3 armhf 3.1.0-1build1 [17.5 kB] 73s Get:175 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libnvme1t64 armhf 1.8-3 [67.5 kB] 73s Get:176 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libblockdev-part3 armhf 3.1.0-1build1 [16.4 kB] 73s Get:177 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libblockdev-swap3 armhf 3.1.0-1build1 [8894 B] 73s Get:178 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libblockdev3 armhf 3.1.0-1build1 [42.9 kB] 73s Get:179 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libgudev-1.0-0 armhf 1:238-3ubuntu2 [13.6 kB] 73s Get:180 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libxml2 armhf 2.9.14+dfsg-1.3ubuntu2 [595 kB] 73s Get:181 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libbpf1 armhf 1:1.3.0-2build1 [146 kB] 73s Get:182 http://ftpmaster.internal/ubuntu noble-proposed/main armhf iproute2 armhf 6.1.0-1ubuntu5 [1060 kB] 73s Get:183 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libelf1t64 armhf 0.190-1.1build2 [49.9 kB] 73s Get:184 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libtirpc-common all 1.3.4+ds-1.1 [8018 B] 73s Get:185 http://ftpmaster.internal/ubuntu noble-proposed/main armhf lsof armhf 4.95.0-1build2 [248 kB] 73s Get:186 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libnsl2 armhf 1.3.0-3build2 [36.5 kB] 73s Get:187 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libtirpc3t64 armhf 1.3.4+ds-1.1 [73.2 kB] 73s Get:188 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libmbim-proxy armhf 1.31.2-0ubuntu2 [5748 B] 73s Get:189 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libmbim-glib4 armhf 1.31.2-0ubuntu2 [216 kB] 73s Get:190 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libjson-glib-1.0-common all 1.8.0-2build1 [4210 B] 73s Get:191 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libjson-glib-1.0-0 armhf 1.8.0-2build1 [61.2 kB] 73s Get:192 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libnghttp2-14 armhf 1.59.0-1build1 [68.1 kB] 73s Get:193 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libssh-4 armhf 0.10.6-2build1 [169 kB] 73s Get:194 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libusb-1.0-0 armhf 2:1.0.27-1 [48.7 kB] 73s Get:195 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libgusb2 armhf 0.4.8-1build1 [34.6 kB] 73s Get:196 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libmm-glib0 armhf 1.23.4-0ubuntu1 [214 kB] 73s Get:197 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libprotobuf-c1 armhf 1.4.1-1ubuntu3 [17.7 kB] 73s Get:198 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libsasl2-2 armhf 2.1.28+dfsg1-5ubuntu1 [49.7 kB] 73s Get:199 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libibverbs1 armhf 50.0-2build1 [57.9 kB] 73s Get:200 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libfido2-1 armhf 1.14.0-1build1 [75.8 kB] 73s Get:201 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libproc2-0 armhf 2:4.0.4-4ubuntu2 [49.0 kB] 73s Get:202 http://ftpmaster.internal/ubuntu noble-proposed/main armhf procps armhf 2:4.0.4-4ubuntu2 [700 kB] 74s Get:203 http://ftpmaster.internal/ubuntu noble-proposed/main armhf coreutils armhf 9.4-3ubuntu3 [1280 kB] 74s Get:204 http://ftpmaster.internal/ubuntu noble-proposed/main armhf util-linux armhf 2.39.3-9ubuntu2 [1216 kB] 74s Get:205 http://ftpmaster.internal/ubuntu noble-proposed/main armhf file armhf 1:5.45-3 [21.1 kB] 74s Get:206 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libmagic-mgc armhf 1:5.45-3 [307 kB] 74s Get:207 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libmagic1t64 armhf 1:5.45-3 [81.4 kB] 74s Get:208 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libplymouth5 armhf 24.004.60-1ubuntu6 [140 kB] 74s Get:209 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libpng16-16t64 armhf 1.6.43-3 [166 kB] 74s Get:210 http://ftpmaster.internal/ubuntu noble-proposed/main armhf bind9-host armhf 1:9.18.24-0ubuntu3 [47.4 kB] 74s Get:211 http://ftpmaster.internal/ubuntu noble-proposed/main armhf bind9-dnsutils armhf 1:9.18.24-0ubuntu3 [149 kB] 74s Get:212 http://ftpmaster.internal/ubuntu noble-proposed/main armhf bind9-libs armhf 1:9.18.24-0ubuntu3 [1148 kB] 74s Get:213 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libuv1t64 armhf 1.48.0-1.1 [82.9 kB] 74s Get:214 http://ftpmaster.internal/ubuntu noble-proposed/main armhf uuid-runtime armhf 2.39.3-9ubuntu2 [41.7 kB] 74s Get:215 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libdebconfclient0 armhf 0.271ubuntu2 [10.8 kB] 74s Get:216 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libsemanage-common all 3.5-1build4 [10.1 kB] 74s Get:217 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libsemanage2 armhf 3.5-1build4 [84.5 kB] 74s Get:218 http://ftpmaster.internal/ubuntu noble-proposed/main armhf install-info armhf 7.1-3build1 [60.5 kB] 74s Get:219 http://ftpmaster.internal/ubuntu noble-proposed/main armhf gcc-13-base armhf 13.2.0-21ubuntu1 [48.3 kB] 74s Get:220 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libss2 armhf 1.47.0-2.4~exp1ubuntu2 [14.7 kB] 74s Get:221 http://ftpmaster.internal/ubuntu noble-proposed/main armhf dmsetup armhf 2:1.02.185-3ubuntu2 [81.1 kB] 74s Get:222 http://ftpmaster.internal/ubuntu noble-proposed/main armhf eject armhf 2.39.3-9ubuntu2 [43.2 kB] 74s Get:223 http://ftpmaster.internal/ubuntu noble-proposed/main armhf krb5-locales all 1.20.1-6ubuntu1 [13.8 kB] 74s Get:224 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libglib2.0-data all 2.79.3-3ubuntu5 [46.6 kB] 74s Get:225 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libslang2 armhf 2.3.3-3build1 [478 kB] 74s Get:226 http://ftpmaster.internal/ubuntu noble-proposed/main armhf rsyslog armhf 8.2312.0-3ubuntu7 [460 kB] 74s Get:227 http://ftpmaster.internal/ubuntu noble-proposed/main armhf vim-tiny armhf 2:9.1.0016-1ubuntu6 [665 kB] 74s Get:228 http://ftpmaster.internal/ubuntu noble-proposed/main armhf vim-common all 2:9.1.0016-1ubuntu6 [385 kB] 74s Get:229 http://ftpmaster.internal/ubuntu noble/main armhf xdg-user-dirs armhf 0.18-1 [17.3 kB] 74s Get:230 http://ftpmaster.internal/ubuntu noble-proposed/main armhf xxd armhf 2:9.1.0016-1ubuntu6 [62.5 kB] 74s Get:231 http://ftpmaster.internal/ubuntu noble-proposed/main armhf apparmor armhf 4.0.0-beta3-0ubuntu2 [562 kB] 74s Get:232 http://ftpmaster.internal/ubuntu noble-proposed/main armhf ftp all 20230507-2build1 [4724 B] 74s Get:233 http://ftpmaster.internal/ubuntu noble-proposed/main armhf inetutils-telnet armhf 2:2.5-3ubuntu3 [90.7 kB] 74s Get:234 http://ftpmaster.internal/ubuntu noble-proposed/main armhf info armhf 7.1-3build1 [127 kB] 74s Get:235 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libxmuu1 armhf 2:1.1.3-3build1 [8004 B] 74s Get:236 http://ftpmaster.internal/ubuntu noble-proposed/main armhf lshw armhf 02.19.git.2021.06.19.996aaad9c7-2build2 [310 kB] 74s Get:237 http://ftpmaster.internal/ubuntu noble-proposed/main armhf mtr-tiny armhf 0.95-1.1build1 [51.7 kB] 74s Get:238 http://ftpmaster.internal/ubuntu noble-proposed/main armhf plymouth-theme-ubuntu-text armhf 24.004.60-1ubuntu6 [9818 B] 74s Get:239 http://ftpmaster.internal/ubuntu noble-proposed/main armhf plymouth armhf 24.004.60-1ubuntu6 [142 kB] 74s Get:240 http://ftpmaster.internal/ubuntu noble-proposed/main armhf psmisc armhf 23.7-1 [176 kB] 75s Get:241 http://ftpmaster.internal/ubuntu noble-proposed/main armhf telnet all 0.17+2.5-3ubuntu3 [3682 B] 75s Get:242 http://ftpmaster.internal/ubuntu noble-proposed/main armhf usb.ids all 2024.03.18-1 [223 kB] 75s Get:243 http://ftpmaster.internal/ubuntu noble-proposed/main armhf xz-utils armhf 5.6.0-0.2 [271 kB] 75s Get:244 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libctf0 armhf 2.42-4ubuntu1 [87.7 kB] 75s Get:245 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libctf-nobfd0 armhf 2.42-4ubuntu1 [88.0 kB] 75s Get:246 http://ftpmaster.internal/ubuntu noble-proposed/main armhf binutils-arm-linux-gnueabihf armhf 2.42-4ubuntu1 [2925 kB] 75s Get:247 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libbinutils armhf 2.42-4ubuntu1 [464 kB] 75s Get:248 http://ftpmaster.internal/ubuntu noble-proposed/main armhf binutils armhf 2.42-4ubuntu1 [3078 B] 75s Get:249 http://ftpmaster.internal/ubuntu noble-proposed/main armhf binutils-common armhf 2.42-4ubuntu1 [217 kB] 75s Get:250 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libsframe1 armhf 2.42-4ubuntu1 [13.1 kB] 75s Get:251 http://ftpmaster.internal/ubuntu noble-proposed/main armhf bolt armhf 0.9.6-2build1 [138 kB] 75s Get:252 http://ftpmaster.internal/ubuntu noble-proposed/main armhf cryptsetup-bin armhf 2:2.7.0-1ubuntu2 [214 kB] 75s Get:253 http://ftpmaster.internal/ubuntu noble-proposed/main armhf dpkg-dev all 1.22.6ubuntu4 [1074 kB] 75s Get:254 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libdpkg-perl all 1.22.6ubuntu4 [268 kB] 75s Get:255 http://ftpmaster.internal/ubuntu noble-proposed/main armhf gnupg-l10n all 2.4.4-2ubuntu15 [65.8 kB] 75s Get:256 http://ftpmaster.internal/ubuntu noble-proposed/main armhf ibverbs-providers armhf 50.0-2build1 [27.4 kB] 75s Get:257 http://ftpmaster.internal/ubuntu noble-proposed/main armhf jq armhf 1.7.1-3 [65.2 kB] 75s Get:258 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libjq1 armhf 1.7.1-3 [156 kB] 75s Get:259 http://ftpmaster.internal/ubuntu noble/main armhf libatm1t64 armhf 1:2.5.1-5.1 [20.0 kB] 75s Get:260 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libevent-core-2.1-7 armhf 2.1.12-stable-9build1 [82.3 kB] 75s Get:261 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libftdi1-2 armhf 1.5-6build4 [25.7 kB] 75s Get:262 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libldap-common all 2.6.7+dfsg-1~exp1ubuntu6 [31.3 kB] 75s Get:263 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libsasl2-modules armhf 2.1.28+dfsg1-5ubuntu1 [61.3 kB] 75s Get:264 http://ftpmaster.internal/ubuntu noble-proposed/main armhf python3-distutils all 3.12.2-3ubuntu1.1 [133 kB] 75s Get:265 http://ftpmaster.internal/ubuntu noble-proposed/main armhf python3-lib2to3 all 3.12.2-3ubuntu1.1 [79.1 kB] 75s Get:266 http://ftpmaster.internal/ubuntu noble-proposed/main armhf python3-pyrsistent armhf 0.20.0-1build1 [53.0 kB] 75s Get:267 http://ftpmaster.internal/ubuntu noble-proposed/main armhf python3-typing-extensions all 4.10.0-1 [60.7 kB] 75s Get:268 http://ftpmaster.internal/ubuntu noble-proposed/main armhf kpartx armhf 0.9.4-5ubuntu6 [31.5 kB] 76s Preconfiguring packages ... 76s Fetched 100 MB in 6s (16.9 MB/s) 76s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 58619 files and directories currently installed.) 76s Preparing to unpack .../motd-news-config_13ubuntu8_all.deb ... 76s Unpacking motd-news-config (13ubuntu8) over (13ubuntu7) ... 76s Preparing to unpack .../base-files_13ubuntu8_armhf.deb ... 76s Unpacking base-files (13ubuntu8) over (13ubuntu7) ... 76s Setting up base-files (13ubuntu8) ... 77s motd-news.service is a disabled or a static unit not running, not starting it. 77s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 58619 files and directories currently installed.) 77s Preparing to unpack .../bsdutils_1%3a2.39.3-9ubuntu2_armhf.deb ... 77s Unpacking bsdutils (1:2.39.3-9ubuntu2) over (1:2.39.3-6ubuntu2) ... 77s Setting up bsdutils (1:2.39.3-9ubuntu2) ... 77s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 58619 files and directories currently installed.) 77s Preparing to unpack .../0-libbrotli1_1.1.0-2build1_armhf.deb ... 77s Unpacking libbrotli1:armhf (1.1.0-2build1) over (1.1.0-2) ... 77s Preparing to unpack .../1-libgssapi-krb5-2_1.20.1-6ubuntu1_armhf.deb ... 77s Unpacking libgssapi-krb5-2:armhf (1.20.1-6ubuntu1) over (1.20.1-5build1) ... 77s Preparing to unpack .../2-libkrb5-3_1.20.1-6ubuntu1_armhf.deb ... 77s Unpacking libkrb5-3:armhf (1.20.1-6ubuntu1) over (1.20.1-5build1) ... 77s Preparing to unpack .../3-libkrb5support0_1.20.1-6ubuntu1_armhf.deb ... 77s Unpacking libkrb5support0:armhf (1.20.1-6ubuntu1) over (1.20.1-5build1) ... 77s Preparing to unpack .../4-libk5crypto3_1.20.1-6ubuntu1_armhf.deb ... 77s Unpacking libk5crypto3:armhf (1.20.1-6ubuntu1) over (1.20.1-5build1) ... 77s Preparing to unpack .../5-libcom-err2_1.47.0-2.4~exp1ubuntu2_armhf.deb ... 77s Unpacking libcom-err2:armhf (1.47.0-2.4~exp1ubuntu2) over (1.47.0-2ubuntu1) ... 77s Preparing to unpack .../6-zlib1g_1%3a1.3.dfsg-3.1ubuntu1_armhf.deb ... 77s Unpacking zlib1g:armhf (1:1.3.dfsg-3.1ubuntu1) over (1:1.3.dfsg-3ubuntu1) ... 77s Setting up zlib1g:armhf (1:1.3.dfsg-3.1ubuntu1) ... 77s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 58619 files and directories currently installed.) 77s Preparing to unpack .../librtmp1_2.4+20151223.gitfa8646d.1-2build6_armhf.deb ... 77s Unpacking librtmp1:armhf (2.4+20151223.gitfa8646d.1-2build6) over (2.4+20151223.gitfa8646d.1-2build4) ... 77s Preparing to unpack .../udisks2_2.10.1-6_armhf.deb ... 77s Unpacking udisks2 (2.10.1-6) over (2.10.1-1ubuntu2) ... 77s Preparing to unpack .../libudisks2-0_2.10.1-6_armhf.deb ... 77s Unpacking libudisks2-0:armhf (2.10.1-6) over (2.10.1-1ubuntu2) ... 77s Preparing to unpack .../libblkid1_2.39.3-9ubuntu2_armhf.deb ... 77s Unpacking libblkid1:armhf (2.39.3-9ubuntu2) over (2.39.3-6ubuntu2) ... 77s Setting up libblkid1:armhf (2.39.3-9ubuntu2) ... 77s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 58619 files and directories currently installed.) 77s Preparing to unpack .../liblzma5_5.6.0-0.2_armhf.deb ... 77s Unpacking liblzma5:armhf (5.6.0-0.2) over (5.4.5-0.3) ... 77s Setting up liblzma5:armhf (5.6.0-0.2) ... 77s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 58619 files and directories currently installed.) 77s Preparing to unpack .../0-kmod_31+20240202-2ubuntu4_armhf.deb ... 77s Unpacking kmod (31+20240202-2ubuntu4) over (30+20230601-2ubuntu1) ... 77s dpkg: warning: unable to delete old directory '/lib/modprobe.d': Directory not empty 77s Preparing to unpack .../1-libkmod2_31+20240202-2ubuntu4_armhf.deb ... 77s Unpacking libkmod2:armhf (31+20240202-2ubuntu4) over (30+20230601-2ubuntu1) ... 77s Preparing to unpack .../2-systemd-dev_255.4-1ubuntu5_all.deb ... 77s Unpacking systemd-dev (255.4-1ubuntu5) over (255.2-3ubuntu2) ... 78s Preparing to unpack .../3-systemd-timesyncd_255.4-1ubuntu5_armhf.deb ... 78s Unpacking systemd-timesyncd (255.4-1ubuntu5) over (255.2-3ubuntu2) ... 78s Preparing to unpack .../4-dbus-session-bus-common_1.14.10-4ubuntu2_all.deb ... 78s Unpacking dbus-session-bus-common (1.14.10-4ubuntu2) over (1.14.10-4ubuntu1) ... 78s Preparing to unpack .../5-libaudit-common_1%3a3.1.2-2.1_all.deb ... 78s Unpacking libaudit-common (1:3.1.2-2.1) over (1:3.1.2-2) ... 78s Setting up libaudit-common (1:3.1.2-2.1) ... 78s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 58618 files and directories currently installed.) 78s Preparing to unpack .../libcap-ng0_0.8.4-2build1_armhf.deb ... 78s Unpacking libcap-ng0:armhf (0.8.4-2build1) over (0.8.4-2) ... 78s Setting up libcap-ng0:armhf (0.8.4-2build1) ... 78s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 58618 files and directories currently installed.) 78s Preparing to unpack .../libaudit1_1%3a3.1.2-2.1_armhf.deb ... 78s Unpacking libaudit1:armhf (1:3.1.2-2.1) over (1:3.1.2-2) ... 78s Setting up libaudit1:armhf (1:3.1.2-2.1) ... 78s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 58618 files and directories currently installed.) 78s Preparing to unpack .../libpam0g_1.5.3-5ubuntu3_armhf.deb ... 78s Unpacking libpam0g:armhf (1.5.3-5ubuntu3) over (1.5.2-9.1ubuntu3) ... 78s Setting up libpam0g:armhf (1.5.3-5ubuntu3) ... 78s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 58618 files and directories currently installed.) 78s Preparing to unpack .../libselinux1_3.5-2ubuntu1_armhf.deb ... 78s Unpacking libselinux1:armhf (3.5-2ubuntu1) over (3.5-2build1) ... 78s Setting up libselinux1:armhf (3.5-2ubuntu1) ... 78s dpkg: libcurl4:armhf: dependency problems, but removing anyway as you requested: 78s curl depends on libcurl4 (= 8.5.0-2ubuntu2). 78s 78s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 58618 files and directories currently installed.) 78s Removing libcurl4:armhf (8.5.0-2ubuntu2) ... 78s Selecting previously unselected package libcurl4t64:armhf. 78s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 58613 files and directories currently installed.) 78s Preparing to unpack .../libcurl4t64_8.5.0-2ubuntu8_armhf.deb ... 78s Unpacking libcurl4t64:armhf (8.5.0-2ubuntu8) ... 78s Preparing to unpack .../curl_8.5.0-2ubuntu8_armhf.deb ... 78s Unpacking curl (8.5.0-2ubuntu8) over (8.5.0-2ubuntu2) ... 78s dpkg: libpsl5:armhf: dependency problems, but removing anyway as you requested: 78s wget depends on libpsl5 (>= 0.16.0). 78s libcurl3-gnutls:armhf depends on libpsl5 (>= 0.16.0). 78s 78s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 58619 files and directories currently installed.) 78s Removing libpsl5:armhf (0.21.2-1build1) ... 78s Selecting previously unselected package libpsl5t64:armhf. 78s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 58614 files and directories currently installed.) 78s Preparing to unpack .../libpsl5t64_0.21.2-1.1_armhf.deb ... 78s Unpacking libpsl5t64:armhf (0.21.2-1.1) ... 78s Preparing to unpack .../wget_1.21.4-1ubuntu2_armhf.deb ... 78s Unpacking wget (1.21.4-1ubuntu2) over (1.21.4-1ubuntu1) ... 78s Preparing to unpack .../tnftp_20230507-2build1_armhf.deb ... 78s Unpacking tnftp (20230507-2build1) over (20230507-2) ... 78s dpkg: libpcap0.8:armhf: dependency problems, but removing anyway as you requested: 78s tcpdump depends on libpcap0.8 (>= 1.9.1). 78s 79s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 58620 files and directories currently installed.) 79s Removing libpcap0.8:armhf (1.10.4-4ubuntu3) ... 79s Selecting previously unselected package libpcap0.8t64:armhf. 79s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 58609 files and directories currently installed.) 79s Preparing to unpack .../00-libpcap0.8t64_1.10.4-4.1ubuntu2_armhf.deb ... 79s Unpacking libpcap0.8t64:armhf (1.10.4-4.1ubuntu2) ... 79s Preparing to unpack .../01-tcpdump_4.99.4-3ubuntu2_armhf.deb ... 79s Unpacking tcpdump (4.99.4-3ubuntu2) over (4.99.4-3ubuntu1) ... 79s Preparing to unpack .../02-libsystemd-shared_255.4-1ubuntu5_armhf.deb ... 79s Unpacking libsystemd-shared:armhf (255.4-1ubuntu5) over (255.2-3ubuntu2) ... 79s Preparing to unpack .../03-systemd-resolved_255.4-1ubuntu5_armhf.deb ... 79s Unpacking systemd-resolved (255.4-1ubuntu5) over (255.2-3ubuntu2) ... 79s Preparing to unpack .../04-sudo_1.9.15p5-3ubuntu3_armhf.deb ... 79s Unpacking sudo (1.9.15p5-3ubuntu3) over (1.9.15p5-3ubuntu1) ... 79s Preparing to unpack .../05-rsync_3.2.7-1build1_armhf.deb ... 79s Unpacking rsync (3.2.7-1build1) over (3.2.7-1) ... 79s Preparing to unpack .../06-python3-cryptography_41.0.7-4build2_armhf.deb ... 79s Unpacking python3-cryptography (41.0.7-4build2) over (41.0.7-3) ... 79s Preparing to unpack .../07-openssl_3.0.13-0ubuntu2_armhf.deb ... 79s Unpacking openssl (3.0.13-0ubuntu2) over (3.0.10-1ubuntu4) ... 79s Preparing to unpack .../08-openssh-sftp-server_1%3a9.6p1-3ubuntu11_armhf.deb ... 79s Unpacking openssh-sftp-server (1:9.6p1-3ubuntu11) over (1:9.6p1-3ubuntu2) ... 79s Preparing to unpack .../09-openssh-client_1%3a9.6p1-3ubuntu11_armhf.deb ... 79s Unpacking openssh-client (1:9.6p1-3ubuntu11) over (1:9.6p1-3ubuntu2) ... 79s Preparing to unpack .../10-openssh-server_1%3a9.6p1-3ubuntu11_armhf.deb ... 79s Unpacking openssh-server (1:9.6p1-3ubuntu11) over (1:9.6p1-3ubuntu2) ... 80s Selecting previously unselected package linux-headers-6.8.0-20. 80s Preparing to unpack .../11-linux-headers-6.8.0-20_6.8.0-20.20_all.deb ... 80s Unpacking linux-headers-6.8.0-20 (6.8.0-20.20) ... 82s Selecting previously unselected package linux-headers-6.8.0-20-generic. 82s Preparing to unpack .../12-linux-headers-6.8.0-20-generic_6.8.0-20.20_armhf.deb ... 82s Unpacking linux-headers-6.8.0-20-generic (6.8.0-20.20) ... 84s Preparing to unpack .../13-linux-headers-generic_6.8.0-20.20+1_armhf.deb ... 84s Unpacking linux-headers-generic (6.8.0-20.20+1) over (6.8.0-11.11+1) ... 84s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 89772 files and directories currently installed.) 84s Removing linux-headers-6.8.0-11-generic (6.8.0-11.11) ... 84s dpkg: libssl3:armhf: dependency problems, but removing anyway as you requested: 84s systemd depends on libssl3 (>= 3.0.0). 84s libssh-4:armhf depends on libssl3 (>= 3.0.0). 84s libsasl2-modules:armhf depends on libssl3 (>= 3.0.0). 84s libsasl2-2:armhf depends on libssl3 (>= 3.0.0). 84s libpython3.12-minimal:armhf depends on libssl3 (>= 3.0.0). 84s libpython3.11-minimal:armhf depends on libssl3 (>= 3.0.0). 84s libnvme1 depends on libssl3 (>= 3.0.0). 84s libfido2-1:armhf depends on libssl3 (>= 3.0.0). 84s libcryptsetup12:armhf depends on libssl3 (>= 3.0.0). 84s dhcpcd-base depends on libssl3 (>= 3.0.0). 84s bind9-libs:armhf depends on libssl3 (>= 3.0.0). 84s 84s Removing libssl3:armhf (3.0.10-1ubuntu4) ... 84s Selecting previously unselected package libssl3t64:armhf. 84s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78622 files and directories currently installed.) 84s Preparing to unpack .../libssl3t64_3.0.13-0ubuntu2_armhf.deb ... 84s Unpacking libssl3t64:armhf (3.0.13-0ubuntu2) ... 84s Setting up libssl3t64:armhf (3.0.13-0ubuntu2) ... 84s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78635 files and directories currently installed.) 84s Preparing to unpack .../libnss-systemd_255.4-1ubuntu5_armhf.deb ... 84s Unpacking libnss-systemd:armhf (255.4-1ubuntu5) over (255.2-3ubuntu2) ... 84s Preparing to unpack .../libudev1_255.4-1ubuntu5_armhf.deb ... 84s Unpacking libudev1:armhf (255.4-1ubuntu5) over (255.2-3ubuntu2) ... 84s Setting up libudev1:armhf (255.4-1ubuntu5) ... 84s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78635 files and directories currently installed.) 84s Preparing to unpack .../systemd_255.4-1ubuntu5_armhf.deb ... 84s Unpacking systemd (255.4-1ubuntu5) over (255.2-3ubuntu2) ... 85s Preparing to unpack .../udev_255.4-1ubuntu5_armhf.deb ... 85s Unpacking udev (255.4-1ubuntu5) over (255.2-3ubuntu2) ... 86s Preparing to unpack .../libsystemd0_255.4-1ubuntu5_armhf.deb ... 86s Unpacking libsystemd0:armhf (255.4-1ubuntu5) over (255.2-3ubuntu2) ... 86s Setting up libsystemd0:armhf (255.4-1ubuntu5) ... 86s Setting up libkmod2:armhf (31+20240202-2ubuntu4) ... 86s Setting up libsystemd-shared:armhf (255.4-1ubuntu5) ... 86s Setting up systemd-dev (255.4-1ubuntu5) ... 86s Setting up systemd (255.4-1ubuntu5) ... 86s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78635 files and directories currently installed.) 86s Preparing to unpack .../systemd-sysv_255.4-1ubuntu5_armhf.deb ... 86s Unpacking systemd-sysv (255.4-1ubuntu5) over (255.2-3ubuntu2) ... 86s Preparing to unpack .../libpam-systemd_255.4-1ubuntu5_armhf.deb ... 86s Unpacking libpam-systemd:armhf (255.4-1ubuntu5) over (255.2-3ubuntu2) ... 86s Preparing to unpack .../libpam-modules-bin_1.5.3-5ubuntu3_armhf.deb ... 86s Unpacking libpam-modules-bin (1.5.3-5ubuntu3) over (1.5.2-9.1ubuntu3) ... 86s Setting up libpam-modules-bin (1.5.3-5ubuntu3) ... 87s pam_namespace.service is a disabled or a static unit not running, not starting it. 87s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78635 files and directories currently installed.) 87s Preparing to unpack .../libpam-modules_1.5.3-5ubuntu3_armhf.deb ... 87s Unpacking libpam-modules:armhf (1.5.3-5ubuntu3) over (1.5.2-9.1ubuntu3) ... 87s Setting up libpam-modules:armhf (1.5.3-5ubuntu3) ... 87s Installing new version of config file /etc/security/namespace.init ... 87s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78633 files and directories currently installed.) 87s Preparing to unpack .../libpam-runtime_1.5.3-5ubuntu3_all.deb ... 87s Unpacking libpam-runtime (1.5.3-5ubuntu3) over (1.5.2-9.1ubuntu3) ... 87s Setting up libpam-runtime (1.5.3-5ubuntu3) ... 87s (Reading database ... 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(Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78633 files and directories currently installed.) 87s Preparing to unpack .../libgcc-s1_14-20240315-1ubuntu1_armhf.deb ... 87s Unpacking libgcc-s1:armhf (14-20240315-1ubuntu1) over (14-20240303-1ubuntu1) ... 87s Setting up libgcc-s1:armhf (14-20240315-1ubuntu1) ... 87s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78633 files and directories currently installed.) 87s Preparing to unpack .../libstdc++6_14-20240315-1ubuntu1_armhf.deb ... 87s Unpacking libstdc++6:armhf (14-20240315-1ubuntu1) over (14-20240303-1ubuntu1) ... 87s Setting up libstdc++6:armhf (14-20240315-1ubuntu1) ... 88s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78633 files and directories currently installed.) 88s Preparing to unpack .../0-libexpat1_2.6.1-2_armhf.deb ... 88s Unpacking libexpat1:armhf (2.6.1-2) over (2.6.0-1) ... 88s Preparing to unpack .../1-dbus-system-bus-common_1.14.10-4ubuntu2_all.deb ... 88s Unpacking dbus-system-bus-common (1.14.10-4ubuntu2) over (1.14.10-4ubuntu1) ... 88s Preparing to unpack .../2-dbus-bin_1.14.10-4ubuntu2_armhf.deb ... 88s Unpacking dbus-bin (1.14.10-4ubuntu2) over (1.14.10-4ubuntu1) ... 88s Preparing to unpack .../3-dbus_1.14.10-4ubuntu2_armhf.deb ... 88s Unpacking dbus (1.14.10-4ubuntu2) over (1.14.10-4ubuntu1) ... 88s Preparing to unpack .../4-dbus-daemon_1.14.10-4ubuntu2_armhf.deb ... 88s Unpacking dbus-daemon (1.14.10-4ubuntu2) over (1.14.10-4ubuntu1) ... 88s Preparing to unpack .../5-libdbus-1-3_1.14.10-4ubuntu2_armhf.deb ... 88s Unpacking libdbus-1-3:armhf (1.14.10-4ubuntu2) over (1.14.10-4ubuntu1) ... 88s Preparing to unpack .../6-libmount1_2.39.3-9ubuntu2_armhf.deb ... 88s Unpacking libmount1:armhf (2.39.3-9ubuntu2) over (2.39.3-6ubuntu2) ... 88s Setting up libmount1:armhf (2.39.3-9ubuntu2) ... 88s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78633 files and directories currently installed.) 88s Preparing to unpack .../libseccomp2_2.5.5-1ubuntu2_armhf.deb ... 88s Unpacking libseccomp2:armhf (2.5.5-1ubuntu2) over (2.5.5-1ubuntu1) ... 88s Setting up libseccomp2:armhf (2.5.5-1ubuntu2) ... 88s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78633 files and directories currently installed.) 88s Preparing to unpack .../libdevmapper1.02.1_2%3a1.02.185-3ubuntu2_armhf.deb ... 88s Unpacking libdevmapper1.02.1:armhf (2:1.02.185-3ubuntu2) over (2:1.02.185-3ubuntu1) ... 88s Preparing to unpack .../libuuid1_2.39.3-9ubuntu2_armhf.deb ... 88s Unpacking libuuid1:armhf (2.39.3-9ubuntu2) over (2.39.3-6ubuntu2) ... 88s Setting up libuuid1:armhf (2.39.3-9ubuntu2) ... 88s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78633 files and directories currently installed.) 88s Preparing to unpack .../0-libcryptsetup12_2%3a2.7.0-1ubuntu2_armhf.deb ... 88s Unpacking libcryptsetup12:armhf (2:2.7.0-1ubuntu2) over (2:2.7.0-1ubuntu1) ... 88s Preparing to unpack .../1-libfdisk1_2.39.3-9ubuntu2_armhf.deb ... 88s Unpacking libfdisk1:armhf (2.39.3-9ubuntu2) over (2.39.3-6ubuntu2) ... 88s Preparing to unpack .../2-mount_2.39.3-9ubuntu2_armhf.deb ... 88s Unpacking mount (2.39.3-9ubuntu2) over (2.39.3-6ubuntu2) ... 88s Preparing to unpack .../3-libblockdev-utils3_3.1.0-1build1_armhf.deb ... 88s Unpacking libblockdev-utils3:armhf (3.1.0-1build1) over (3.1.0-1) ... 88s Preparing to unpack .../4-libvolume-key1_0.3.12-7build1_armhf.deb ... 88s Unpacking libvolume-key1:armhf (0.3.12-7build1) over (0.3.12-5build2) ... 88s Preparing to unpack .../5-libjcat1_0.2.0-2build2_armhf.deb ... 88s Unpacking libjcat1:armhf (0.2.0-2build2) over (0.2.0-2) ... 88s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78633 files and directories currently installed.) 88s Removing libgpgme11:armhf (1.18.0-4ubuntu1) ... 88s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78627 files and directories currently installed.) 88s Preparing to unpack .../parted_3.6-3.1build2_armhf.deb ... 88s Unpacking parted (3.6-3.1build2) over (3.6-3) ... 88s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78627 files and directories currently installed.) 88s Removing libparted2:armhf (3.6-3) ... 88s Selecting previously unselected package libparted2t64:armhf. 88s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78621 files and directories currently installed.) 88s Preparing to unpack .../00-libparted2t64_3.6-3.1build2_armhf.deb ... 88s Unpacking libparted2t64:armhf (3.6-3.1build2) ... 89s Preparing to unpack .../01-python3.12_3.12.2-4build3_armhf.deb ... 89s Unpacking python3.12 (3.12.2-4build3) over (3.12.2-1) ... 89s Preparing to unpack .../02-python3.12-minimal_3.12.2-4build3_armhf.deb ... 89s Unpacking python3.12-minimal (3.12.2-4build3) over (3.12.2-1) ... 89s Preparing to unpack .../03-libpython3.12-stdlib_3.12.2-4build3_armhf.deb ... 89s Unpacking libpython3.12-stdlib:armhf (3.12.2-4build3) over (3.12.2-1) ... 89s Preparing to unpack .../04-libpython3.12-minimal_3.12.2-4build3_armhf.deb ... 89s Unpacking libpython3.12-minimal:armhf (3.12.2-4build3) over (3.12.2-1) ... 89s Preparing to unpack .../05-libsasl2-modules-db_2.1.28+dfsg1-5ubuntu1_armhf.deb ... 89s Unpacking libsasl2-modules-db:armhf (2.1.28+dfsg1-5ubuntu1) over (2.1.28+dfsg1-4) ... 89s Preparing to unpack .../06-python3.11_3.11.8-1build4_armhf.deb ... 89s Unpacking python3.11 (3.11.8-1build4) over (3.11.8-1) ... 89s Preparing to unpack .../07-python3.11-minimal_3.11.8-1build4_armhf.deb ... 89s Unpacking python3.11-minimal (3.11.8-1build4) over (3.11.8-1) ... 89s Preparing to unpack .../08-libpython3.11-stdlib_3.11.8-1build4_armhf.deb ... 89s Unpacking libpython3.11-stdlib:armhf (3.11.8-1build4) over (3.11.8-1) ... 89s Preparing to unpack .../09-libpython3.11-minimal_3.11.8-1build4_armhf.deb ... 89s Unpacking libpython3.11-minimal:armhf (3.11.8-1build4) over (3.11.8-1) ... 89s Preparing to unpack .../10-libsqlite3-0_3.45.1-1ubuntu1_armhf.deb ... 89s Unpacking libsqlite3-0:armhf (3.45.1-1ubuntu1) over (3.45.1-1) ... 89s Preparing to unpack .../11-libtext-iconv-perl_1.7-8build2_armhf.deb ... 89s Unpacking libtext-iconv-perl:armhf (1.7-8build2) over (1.7-8build1) ... 89s Preparing to unpack .../12-libtext-charwidth-perl_0.04-11build2_armhf.deb ... 89s Unpacking libtext-charwidth-perl:armhf (0.04-11build2) over (0.04-11build1) ... 89s Preparing to unpack .../13-perl-modules-5.38_5.38.2-3.2_all.deb ... 89s Unpacking perl-modules-5.38 (5.38.2-3.2) over (5.38.2-3) ... 90s dpkg: libperl5.38:armhf: dependency problems, but removing anyway as you requested: 90s perl depends on libperl5.38 (= 5.38.2-3). 90s 90s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78624 files and directories currently installed.) 90s Removing libperl5.38:armhf (5.38.2-3) ... 90s dpkg: libdb5.3:armhf: dependency problems, but removing anyway as you requested: 90s iproute2 depends on libdb5.3. 90s apt-utils depends on libdb5.3. 90s 90s Removing libdb5.3:armhf (5.3.28+dfsg2-4) ... 90s Selecting previously unselected package libdb5.3t64:armhf. 90s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78099 files and directories currently installed.) 90s Preparing to unpack .../libdb5.3t64_5.3.28+dfsg2-6_armhf.deb ... 90s Unpacking libdb5.3t64:armhf (5.3.28+dfsg2-6) ... 90s Preparing to unpack .../python3-gdbm_3.12.2-3ubuntu1.1_armhf.deb ... 90s Unpacking python3-gdbm:armhf (3.12.2-3ubuntu1.1) over (3.11.5-1) ... 90s Preparing to unpack .../man-db_2.12.0-3build4_armhf.deb ... 90s Unpacking man-db (2.12.0-3build4) over (2.12.0-3) ... 90s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78105 files and directories currently installed.) 90s Removing libgdbm-compat4:armhf (1.23-5) ... 90s Removing libgdbm6:armhf (1.23-5) ... 90s Selecting previously unselected package libgdbm6t64:armhf. 90s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78095 files and directories currently installed.) 90s Preparing to unpack .../libgdbm6t64_1.23-5.1_armhf.deb ... 90s Unpacking libgdbm6t64:armhf (1.23-5.1) ... 90s Selecting previously unselected package libgdbm-compat4t64:armhf. 90s Preparing to unpack .../libgdbm-compat4t64_1.23-5.1_armhf.deb ... 90s Unpacking libgdbm-compat4t64:armhf (1.23-5.1) ... 90s Selecting previously unselected package libperl5.38t64:armhf. 90s Preparing to unpack .../libperl5.38t64_5.38.2-3.2_armhf.deb ... 90s Unpacking libperl5.38t64:armhf (5.38.2-3.2) ... 90s Preparing to unpack .../perl_5.38.2-3.2_armhf.deb ... 90s Unpacking perl (5.38.2-3.2) over (5.38.2-3) ... 91s Preparing to unpack .../perl-base_5.38.2-3.2_armhf.deb ... 91s Unpacking perl-base (5.38.2-3.2) over (5.38.2-3) ... 91s Setting up perl-base (5.38.2-3.2) ... 91s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78626 files and directories currently installed.) 91s Preparing to unpack .../liblocale-gettext-perl_1.07-6ubuntu4_armhf.deb ... 91s Unpacking liblocale-gettext-perl (1.07-6ubuntu4) over (1.07-6build1) ... 91s dpkg: libnettle8:armhf: dependency problems, but removing anyway as you requested: 91s libhogweed6:armhf depends on libnettle8. 91s libgnutls30:armhf depends on libnettle8 (>= 3.9~). 91s libcurl3-gnutls:armhf depends on libnettle8. 91s libarchive13:armhf depends on libnettle8. 91s 91s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78626 files and directories currently installed.) 91s Removing libnettle8:armhf (3.9.1-2) ... 91s Selecting previously unselected package libnettle8t64:armhf. 91s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78619 files and directories currently installed.) 91s Preparing to unpack .../libnettle8t64_3.9.1-2.2_armhf.deb ... 91s Unpacking libnettle8t64:armhf (3.9.1-2.2) ... 91s Setting up libnettle8t64:armhf (3.9.1-2.2) ... 91s dpkg: libhogweed6:armhf: dependency problems, but removing anyway as you requested: 91s libgnutls30:armhf depends on libhogweed6 (>= 3.6). 91s 91s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78627 files and directories currently installed.) 91s Removing libhogweed6:armhf (3.9.1-2) ... 91s Selecting previously unselected package libhogweed6t64:armhf. 91s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78622 files and directories currently installed.) 91s Preparing to unpack .../libhogweed6t64_3.9.1-2.2_armhf.deb ... 91s Unpacking libhogweed6t64:armhf (3.9.1-2.2) ... 91s Setting up libhogweed6t64:armhf (3.9.1-2.2) ... 91s dpkg: libgnutls30:armhf: dependency problems, but removing anyway as you requested: 91s libldap2:armhf depends on libgnutls30 (>= 3.8.2). 91s libcurl3-gnutls:armhf depends on libgnutls30 (>= 3.8.2). 91s fwupd depends on libgnutls30 (>= 3.7.3). 91s dirmngr depends on libgnutls30 (>= 3.8.1). 91s apt depends on libgnutls30 (>= 3.8.1). 91s 91s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78628 files and directories currently installed.) 91s Removing libgnutls30:armhf (3.8.3-1ubuntu1) ... 91s Selecting previously unselected package libgnutls30t64:armhf. 91s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78619 files and directories currently installed.) 91s Preparing to unpack .../libgnutls30t64_3.8.3-1.1ubuntu2_armhf.deb ... 91s Unpacking libgnutls30t64:armhf (3.8.3-1.1ubuntu2) ... 91s Setting up libgnutls30t64:armhf (3.8.3-1.1ubuntu2) ... 91s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78647 files and directories currently installed.) 91s Preparing to unpack .../libldap2_2.6.7+dfsg-1~exp1ubuntu6_armhf.deb ... 91s Unpacking libldap2:armhf (2.6.7+dfsg-1~exp1ubuntu6) over (2.6.7+dfsg-1~exp1ubuntu1) ... 91s dpkg: libcurl3-gnutls:armhf: dependency problems, but removing anyway as you requested: 91s libfwupd2:armhf depends on libcurl3-gnutls (>= 7.63.0). 91s fwupd depends on libcurl3-gnutls (>= 7.63.0). 91s 91s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78647 files and directories currently installed.) 91s Removing libcurl3-gnutls:armhf (8.5.0-2ubuntu2) ... 91s Selecting previously unselected package libcurl3t64-gnutls:armhf. 91s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78640 files and directories currently installed.) 91s Preparing to unpack .../00-libcurl3t64-gnutls_8.5.0-2ubuntu8_armhf.deb ... 91s Unpacking libcurl3t64-gnutls:armhf (8.5.0-2ubuntu8) ... 91s Preparing to unpack .../01-shared-mime-info_2.4-1build1_armhf.deb ... 91s Unpacking shared-mime-info (2.4-1build1) over (2.4-1) ... 91s Preparing to unpack .../02-gir1.2-girepository-2.0_1.79.1-1ubuntu6_armhf.deb ... 91s Unpacking gir1.2-girepository-2.0:armhf (1.79.1-1ubuntu6) over (1.79.1-1) ... 92s Preparing to unpack .../03-gir1.2-glib-2.0_2.79.3-3ubuntu5_armhf.deb ... 92s Unpacking gir1.2-glib-2.0:armhf (2.79.3-3ubuntu5) over (2.79.2-1~ubuntu1) ... 92s Preparing to unpack .../04-libgirepository-1.0-1_1.79.1-1ubuntu6_armhf.deb ... 92s Unpacking libgirepository-1.0-1:armhf (1.79.1-1ubuntu6) over (1.79.1-1) ... 92s Preparing to unpack .../05-python3-gi_3.47.0-3build1_armhf.deb ... 92s Unpacking python3-gi (3.47.0-3build1) over (3.47.0-3) ... 92s Preparing to unpack .../06-python3-dbus_1.3.2-5build2_armhf.deb ... 92s Unpacking python3-dbus (1.3.2-5build2) over (1.3.2-5build1) ... 92s Selecting previously unselected package libnetplan1:armhf. 92s Preparing to unpack .../07-libnetplan1_1.0-1_armhf.deb ... 92s Unpacking libnetplan1:armhf (1.0-1) ... 92s Preparing to unpack .../08-python3-netplan_1.0-1_armhf.deb ... 92s Unpacking python3-netplan (1.0-1) over (0.107.1-3) ... 92s Preparing to unpack .../09-netplan-generator_1.0-1_armhf.deb ... 92s Adding 'diversion of /lib/systemd/system-generators/netplan to /lib/systemd/system-generators/netplan.usr-is-merged by netplan-generator' 92s Unpacking netplan-generator (1.0-1) over (0.107.1-3) ... 92s Preparing to unpack .../10-initramfs-tools-bin_0.142ubuntu23_armhf.deb ... 92s Unpacking initramfs-tools-bin (0.142ubuntu23) over (0.142ubuntu20) ... 92s Preparing to unpack .../11-initramfs-tools-core_0.142ubuntu23_all.deb ... 92s Unpacking initramfs-tools-core (0.142ubuntu23) over (0.142ubuntu20) ... 92s Preparing to unpack .../12-initramfs-tools_0.142ubuntu23_all.deb ... 92s Unpacking initramfs-tools (0.142ubuntu23) over (0.142ubuntu20) ... 92s Preparing to unpack .../13-netplan.io_1.0-1_armhf.deb ... 92s Unpacking netplan.io (1.0-1) over (0.107.1-3) ... 92s Preparing to unpack .../14-libxmlb2_0.3.15-1build1_armhf.deb ... 92s Unpacking libxmlb2:armhf (0.3.15-1build1) over (0.3.15-1) ... 92s Preparing to unpack .../15-libqrtr-glib0_1.2.2-1ubuntu3_armhf.deb ... 92s Unpacking libqrtr-glib0:armhf (1.2.2-1ubuntu3) over (1.2.2-1ubuntu2) ... 92s Preparing to unpack .../16-libqmi-glib5_1.35.2-0ubuntu1_armhf.deb ... 92s Unpacking libqmi-glib5:armhf (1.35.2-0ubuntu1) over (1.34.0-2) ... 93s Preparing to unpack .../17-libqmi-proxy_1.35.2-0ubuntu1_armhf.deb ... 93s Unpacking libqmi-proxy (1.35.2-0ubuntu1) over (1.34.0-2) ... 93s Preparing to unpack .../18-libpolkit-agent-1-0_124-1ubuntu1_armhf.deb ... 93s Unpacking libpolkit-agent-1-0:armhf (124-1ubuntu1) over (124-1) ... 93s Preparing to unpack .../19-libpolkit-gobject-1-0_124-1ubuntu1_armhf.deb ... 93s Unpacking libpolkit-gobject-1-0:armhf (124-1ubuntu1) over (124-1) ... 93s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78651 files and directories currently installed.) 93s Removing libnetplan0:armhf (0.107.1-3) ... 93s dpkg: libglib2.0-0:armhf: dependency problems, but removing anyway as you requested: 93s libmm-glib0:armhf depends on libglib2.0-0 (>= 2.62.0). 93s libmbim-proxy depends on libglib2.0-0 (>= 2.56). 93s libmbim-glib4:armhf depends on libglib2.0-0 (>= 2.56). 93s libjson-glib-1.0-0:armhf depends on libglib2.0-0 (>= 2.75.3). 93s libgusb2:armhf depends on libglib2.0-0 (>= 2.75.3). 93s libgudev-1.0-0:armhf depends on libglib2.0-0 (>= 2.38.0). 93s libfwupd2:armhf depends on libglib2.0-0 (>= 2.79.0). 93s libblockdev3:armhf depends on libglib2.0-0 (>= 2.42.2). 93s libblockdev-swap3:armhf depends on libglib2.0-0 (>= 2.42.2). 93s libblockdev-part3:armhf depends on libglib2.0-0 (>= 2.42.2). 93s libblockdev-nvme3:armhf depends on libglib2.0-0 (>= 2.42.2). 93s libblockdev-mdraid3:armhf depends on libglib2.0-0 (>= 2.42.2). 93s libblockdev-loop3:armhf depends on libglib2.0-0 (>= 2.42.2). 93s libblockdev-fs3:armhf depends on libglib2.0-0 (>= 2.42.2). 93s libblockdev-crypto3:armhf depends on libglib2.0-0 (>= 2.42.2). 93s fwupd depends on libglib2.0-0 (>= 2.79.0). 93s bolt depends on libglib2.0-0 (>= 2.56.0). 93s 93s Removing libglib2.0-0:armhf (2.79.2-1~ubuntu1) ... 93s Selecting previously unselected package libglib2.0-0t64:armhf. 93s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78622 files and directories currently installed.) 93s Preparing to unpack .../libglib2.0-0t64_2.79.3-3ubuntu5_armhf.deb ... 93s libglib2.0-0t64.preinst: Removing /var/lib/dpkg/info/libglib2.0-0:armhf.postrm to avoid loss of /usr/share/glib-2.0/schemas/gschemas.compiled... 93s removed '/var/lib/dpkg/info/libglib2.0-0:armhf.postrm' 93s Unpacking libglib2.0-0t64:armhf (2.79.3-3ubuntu5) ... 93s Preparing to unpack .../libfwupd2_1.9.15-2_armhf.deb ... 93s Unpacking libfwupd2:armhf (1.9.15-2) over (1.9.14-1) ... 93s dpkg: libarchive13:armhf: dependency problems, but removing anyway as you requested: 93s fwupd depends on libarchive13 (>= 3.2.1). 93s 93s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78647 files and directories currently installed.) 93s Removing libarchive13:armhf (3.7.2-1ubuntu2) ... 93s Selecting previously unselected package libarchive13t64:armhf. 93s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78641 files and directories currently installed.) 93s Preparing to unpack .../libarchive13t64_3.7.2-1.1ubuntu2_armhf.deb ... 93s Unpacking libarchive13t64:armhf (3.7.2-1.1ubuntu2) ... 93s Preparing to unpack .../fwupd_1.9.15-2_armhf.deb ... 93s Unpacking fwupd (1.9.15-2) over (1.9.14-1) ... 93s Preparing to unpack .../apt-utils_2.7.14_armhf.deb ... 93s Unpacking apt-utils (2.7.14) over (2.7.12) ... 93s dpkg: libapt-pkg6.0:armhf: dependency problems, but removing anyway as you requested: 93s ubuntu-pro-client depends on libapt-pkg6.0 (>= 1.9~). 93s python3-apt depends on libapt-pkg6.0 (>= 2.7.11). 93s apt depends on libapt-pkg6.0 (>= 2.7.12). 93s 93s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78648 files and directories currently installed.) 93s Removing libapt-pkg6.0:armhf (2.7.12) ... 93s Selecting previously unselected package libapt-pkg6.0t64:armhf. 93s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78599 files and directories currently installed.) 93s Preparing to unpack .../libapt-pkg6.0t64_2.7.14_armhf.deb ... 93s Unpacking libapt-pkg6.0t64:armhf (2.7.14) ... 93s Setting up libapt-pkg6.0t64:armhf (2.7.14) ... 93s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78649 files and directories currently installed.) 93s Preparing to unpack .../archives/apt_2.7.14_armhf.deb ... 93s Unpacking apt (2.7.14) over (2.7.12) ... 93s Setting up apt (2.7.14) ... 94s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78649 files and directories currently installed.) 94s Preparing to unpack .../ubuntu-pro-client-l10n_31.2.2_armhf.deb ... 94s Unpacking ubuntu-pro-client-l10n (31.2.2) over (31.1) ... 94s Preparing to unpack .../ubuntu-pro-client_31.2.2_armhf.deb ... 94s Unpacking ubuntu-pro-client (31.2.2) over (31.1) ... 94s Preparing to unpack .../keyboxd_2.4.4-2ubuntu15_armhf.deb ... 94s Unpacking keyboxd (2.4.4-2ubuntu15) over (2.4.4-2ubuntu7) ... 94s dpkg: libnpth0:armhf: dependency problems, but removing anyway as you requested: 94s gpgv depends on libnpth0 (>= 0.90). 94s gpgsm depends on libnpth0 (>= 0.90). 94s gpg-agent depends on libnpth0 (>= 0.90). 94s gpg depends on libnpth0 (>= 0.90). 94s dirmngr depends on libnpth0 (>= 0.90). 94s 94s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78649 files and directories currently installed.) 94s Removing libnpth0:armhf (1.6-3build2) ... 94s Selecting previously unselected package libnpth0t64:armhf. 94s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78644 files and directories currently installed.) 94s Preparing to unpack .../libnpth0t64_1.6-3.1_armhf.deb ... 94s Unpacking libnpth0t64:armhf (1.6-3.1) ... 94s Setting up libnpth0t64:armhf (1.6-3.1) ... 94s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78650 files and directories currently installed.) 94s Preparing to unpack .../gpgv_2.4.4-2ubuntu15_armhf.deb ... 94s Unpacking gpgv (2.4.4-2ubuntu15) over (2.4.4-2ubuntu7) ... 95s Setting up gpgv (2.4.4-2ubuntu15) ... 95s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78650 files and directories currently installed.) 95s Preparing to unpack .../gpg_2.4.4-2ubuntu15_armhf.deb ... 95s Unpacking gpg (2.4.4-2ubuntu15) over (2.4.4-2ubuntu7) ... 95s Preparing to unpack .../gpg-wks-client_2.4.4-2ubuntu15_armhf.deb ... 95s Unpacking gpg-wks-client (2.4.4-2ubuntu15) over (2.4.4-2ubuntu7) ... 95s Preparing to unpack .../gnupg-utils_2.4.4-2ubuntu15_armhf.deb ... 95s Unpacking gnupg-utils (2.4.4-2ubuntu15) over (2.4.4-2ubuntu7) ... 95s Preparing to unpack .../gpg-agent_2.4.4-2ubuntu15_armhf.deb ... 95s Unpacking gpg-agent (2.4.4-2ubuntu15) over (2.4.4-2ubuntu7) ... 95s Preparing to unpack .../gpgsm_2.4.4-2ubuntu15_armhf.deb ... 95s Unpacking gpgsm (2.4.4-2ubuntu15) over (2.4.4-2ubuntu7) ... 95s dpkg: libreadline8:armhf: dependency problems, but removing anyway as you requested: 95s gpgconf depends on libreadline8 (>= 6.0). 95s gawk depends on libreadline8 (>= 6.0). 95s fdisk depends on libreadline8 (>= 6.0). 95s 95s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78650 files and directories currently installed.) 95s Removing libreadline8:armhf (8.2-3) ... 95s Selecting previously unselected package libreadline8t64:armhf. 95s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78638 files and directories currently installed.) 95s Preparing to unpack .../libreadline8t64_8.2-4_armhf.deb ... 95s Adding 'diversion of /lib/arm-linux-gnueabihf/libhistory.so.8 to /lib/arm-linux-gnueabihf/libhistory.so.8.usr-is-merged by libreadline8t64' 95s Adding 'diversion of /lib/arm-linux-gnueabihf/libhistory.so.8.2 to /lib/arm-linux-gnueabihf/libhistory.so.8.2.usr-is-merged by libreadline8t64' 95s Adding 'diversion of /lib/arm-linux-gnueabihf/libreadline.so.8 to /lib/arm-linux-gnueabihf/libreadline.so.8.usr-is-merged by libreadline8t64' 95s Adding 'diversion of /lib/arm-linux-gnueabihf/libreadline.so.8.2 to /lib/arm-linux-gnueabihf/libreadline.so.8.2.usr-is-merged by libreadline8t64' 95s Unpacking libreadline8t64:armhf (8.2-4) ... 95s Setting up libreadline8t64:armhf (8.2-4) ... 95s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78658 files and directories currently installed.) 95s Preparing to unpack .../00-gawk_1%3a5.2.1-2build2_armhf.deb ... 95s Unpacking gawk (1:5.2.1-2build2) over (1:5.2.1-2) ... 95s Preparing to unpack .../01-fdisk_2.39.3-9ubuntu2_armhf.deb ... 95s Unpacking fdisk (2.39.3-9ubuntu2) over (2.39.3-6ubuntu2) ... 95s Preparing to unpack .../02-gpgconf_2.4.4-2ubuntu15_armhf.deb ... 95s Unpacking gpgconf (2.4.4-2ubuntu15) over (2.4.4-2ubuntu7) ... 95s Preparing to unpack .../03-dirmngr_2.4.4-2ubuntu15_armhf.deb ... 95s Unpacking dirmngr (2.4.4-2ubuntu15) over (2.4.4-2ubuntu7) ... 95s Preparing to unpack .../04-gnupg_2.4.4-2ubuntu15_all.deb ... 95s Unpacking gnupg (2.4.4-2ubuntu15) over (2.4.4-2ubuntu7) ... 95s Preparing to unpack .../05-python3-apt_2.7.7_armhf.deb ... 95s Unpacking python3-apt (2.7.7) over (2.7.6) ... 95s Preparing to unpack .../06-pinentry-curses_1.2.1-3ubuntu4_armhf.deb ... 95s Unpacking pinentry-curses (1.2.1-3ubuntu4) over (1.2.1-3ubuntu1) ... 95s Preparing to unpack .../07-python3-yaml_6.0.1-2build1_armhf.deb ... 95s Unpacking python3-yaml (6.0.1-2build1) over (6.0.1-2) ... 95s Preparing to unpack .../08-python-apt-common_2.7.7_all.deb ... 95s Unpacking python-apt-common (2.7.7) over (2.7.6) ... 95s Preparing to unpack .../09-python3-setuptools_68.1.2-2ubuntu1_all.deb ... 96s Unpacking python3-setuptools (68.1.2-2ubuntu1) over (68.1.2-2) ... 96s Preparing to unpack .../10-python3-pkg-resources_68.1.2-2ubuntu1_all.deb ... 96s Unpacking python3-pkg-resources (68.1.2-2ubuntu1) over (68.1.2-2) ... 96s Preparing to unpack .../11-dpkg_1.22.6ubuntu4_armhf.deb ... 96s Unpacking dpkg (1.22.6ubuntu4) over (1.22.4ubuntu5) ... 96s Setting up dpkg (1.22.6ubuntu4) ... 96s Setting up libpython3.12-minimal:armhf (3.12.2-4build3) ... 96s Setting up libexpat1:armhf (2.6.1-2) ... 96s Setting up python3.12-minimal (3.12.2-4build3) ... 97s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78655 files and directories currently installed.) 97s Preparing to unpack .../python3-minimal_3.12.2-0ubuntu1_armhf.deb ... 97s Unpacking python3-minimal (3.12.2-0ubuntu1) over (3.12.1-0ubuntu2) ... 97s Setting up python3-minimal (3.12.2-0ubuntu1) ... 97s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78655 files and directories currently installed.) 97s Preparing to unpack .../python3_3.12.2-0ubuntu1_armhf.deb ... 98s Unpacking python3 (3.12.2-0ubuntu1) over (3.12.1-0ubuntu2) ... 98s Preparing to unpack .../libpython3-stdlib_3.12.2-0ubuntu1_armhf.deb ... 98s Unpacking libpython3-stdlib:armhf (3.12.2-0ubuntu1) over (3.12.1-0ubuntu2) ... 98s Preparing to unpack .../libsmartcols1_2.39.3-9ubuntu2_armhf.deb ... 98s Unpacking libsmartcols1:armhf (2.39.3-9ubuntu2) over (2.39.3-6ubuntu2) ... 98s Setting up libsmartcols1:armhf (2.39.3-9ubuntu2) ... 98s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78655 files and directories currently installed.) 98s Preparing to unpack .../0-bsdextrautils_2.39.3-9ubuntu2_armhf.deb ... 98s Unpacking bsdextrautils (2.39.3-9ubuntu2) over (2.39.3-6ubuntu2) ... 98s Preparing to unpack .../1-groff-base_1.23.0-3build1_armhf.deb ... 98s Unpacking groff-base (1.23.0-3build1) over (1.23.0-3) ... 98s Preparing to unpack .../2-readline-common_8.2-4_all.deb ... 98s Unpacking readline-common (8.2-4) over (8.2-3) ... 98s Selecting previously unselected package libgpgme11t64:armhf. 98s Preparing to unpack .../3-libgpgme11t64_1.18.0-4.1ubuntu3_armhf.deb ... 98s Unpacking libgpgme11t64:armhf (1.18.0-4.1ubuntu3) ... 98s Preparing to unpack .../4-libblockdev-crypto3_3.1.0-1build1_armhf.deb ... 98s Unpacking libblockdev-crypto3:armhf (3.1.0-1build1) over (3.1.0-1) ... 98s Preparing to unpack .../5-e2fsprogs-l10n_1.47.0-2.4~exp1ubuntu2_all.deb ... 98s Unpacking e2fsprogs-l10n (1.47.0-2.4~exp1ubuntu2) over (1.47.0-2ubuntu1) ... 98s Preparing to unpack .../6-logsave_1.47.0-2.4~exp1ubuntu2_armhf.deb ... 98s Unpacking logsave (1.47.0-2.4~exp1ubuntu2) over (1.47.0-2ubuntu1) ... 98s Preparing to unpack .../7-dhcpcd-base_1%3a10.0.6-1ubuntu2_armhf.deb ... 98s Unpacking dhcpcd-base (1:10.0.6-1ubuntu2) over (1:10.0.6-1ubuntu1) ... 98s Preparing to unpack .../8-libblockdev-fs3_3.1.0-1build1_armhf.deb ... 98s Unpacking libblockdev-fs3:armhf (3.1.0-1build1) over (3.1.0-1) ... 98s dpkg: libreiserfscore0: dependency problems, but removing anyway as you requested: 98s btrfs-progs depends on libreiserfscore0 (>= 1:3.6.27). 98s 98s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78662 files and directories currently installed.) 98s Removing libreiserfscore0 (1:3.6.27-7) ... 98s Selecting previously unselected package libreiserfscore0t64. 98s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78657 files and directories currently installed.) 98s Preparing to unpack .../libreiserfscore0t64_1%3a3.6.27-7.1_armhf.deb ... 98s Unpacking libreiserfscore0t64 (1:3.6.27-7.1) ... 98s Preparing to unpack .../btrfs-progs_6.6.3-1.1build1_armhf.deb ... 98s Unpacking btrfs-progs (6.6.3-1.1build1) over (6.6.3-1.1) ... 98s dpkg: libext2fs2:armhf: dependency problems, but removing anyway as you requested: 98s e2fsprogs depends on libext2fs2 (= 1.47.0-2ubuntu1). 98s 98s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78663 files and directories currently installed.) 98s Removing libext2fs2:armhf (1.47.0-2ubuntu1) ... 98s Selecting previously unselected package libext2fs2t64:armhf. 98s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78656 files and directories currently installed.) 98s Preparing to unpack .../libext2fs2t64_1.47.0-2.4~exp1ubuntu2_armhf.deb ... 98s Adding 'diversion of /lib/arm-linux-gnueabihf/libe2p.so.2 to /lib/arm-linux-gnueabihf/libe2p.so.2.usr-is-merged by libext2fs2t64' 98s Adding 'diversion of /lib/arm-linux-gnueabihf/libe2p.so.2.3 to /lib/arm-linux-gnueabihf/libe2p.so.2.3.usr-is-merged by libext2fs2t64' 99s Adding 'diversion of /lib/arm-linux-gnueabihf/libext2fs.so.2 to /lib/arm-linux-gnueabihf/libext2fs.so.2.usr-is-merged by libext2fs2t64' 99s Adding 'diversion of /lib/arm-linux-gnueabihf/libext2fs.so.2.4 to /lib/arm-linux-gnueabihf/libext2fs.so.2.4.usr-is-merged by libext2fs2t64' 99s Unpacking libext2fs2t64:armhf (1.47.0-2.4~exp1ubuntu2) ... 99s Setting up libcom-err2:armhf (1.47.0-2.4~exp1ubuntu2) ... 99s Setting up libext2fs2t64:armhf (1.47.0-2.4~exp1ubuntu2) ... 99s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78672 files and directories currently installed.) 99s Preparing to unpack .../e2fsprogs_1.47.0-2.4~exp1ubuntu2_armhf.deb ... 99s Unpacking e2fsprogs (1.47.0-2.4~exp1ubuntu2) over (1.47.0-2ubuntu1) ... 99s Preparing to unpack .../libblockdev-loop3_3.1.0-1build1_armhf.deb ... 99s Unpacking libblockdev-loop3:armhf (3.1.0-1build1) over (3.1.0-1) ... 99s Preparing to unpack .../libblockdev-mdraid3_3.1.0-1build1_armhf.deb ... 99s Unpacking libblockdev-mdraid3:armhf (3.1.0-1build1) over (3.1.0-1) ... 99s Preparing to unpack .../libblockdev-nvme3_3.1.0-1build1_armhf.deb ... 99s Unpacking libblockdev-nvme3:armhf (3.1.0-1build1) over (3.1.0-1) ... 99s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78672 files and directories currently installed.) 99s Removing libnvme1 (1.8-2) ... 99s Selecting previously unselected package libnvme1t64. 99s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78665 files and directories currently installed.) 99s Preparing to unpack .../0-libnvme1t64_1.8-3_armhf.deb ... 99s Unpacking libnvme1t64 (1.8-3) ... 99s Preparing to unpack .../1-libblockdev-part3_3.1.0-1build1_armhf.deb ... 99s Unpacking libblockdev-part3:armhf (3.1.0-1build1) over (3.1.0-1) ... 99s Preparing to unpack .../2-libblockdev-swap3_3.1.0-1build1_armhf.deb ... 99s Unpacking libblockdev-swap3:armhf (3.1.0-1build1) over (3.1.0-1) ... 99s Preparing to unpack .../3-libblockdev3_3.1.0-1build1_armhf.deb ... 99s Unpacking libblockdev3:armhf (3.1.0-1build1) over (3.1.0-1) ... 99s Preparing to unpack .../4-libgudev-1.0-0_1%3a238-3ubuntu2_armhf.deb ... 99s Unpacking libgudev-1.0-0:armhf (1:238-3ubuntu2) over (1:238-3) ... 99s Preparing to unpack .../5-libxml2_2.9.14+dfsg-1.3ubuntu2_armhf.deb ... 99s Unpacking libxml2:armhf (2.9.14+dfsg-1.3ubuntu2) over (2.9.14+dfsg-1.3ubuntu1) ... 99s Preparing to unpack .../6-libbpf1_1%3a1.3.0-2build1_armhf.deb ... 99s Unpacking libbpf1:armhf (1:1.3.0-2build1) over (1:1.3.0-2) ... 99s Preparing to unpack .../7-iproute2_6.1.0-1ubuntu5_armhf.deb ... 99s Unpacking iproute2 (6.1.0-1ubuntu5) over (6.1.0-1ubuntu2) ... 99s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78673 files and directories currently installed.) 99s Removing libelf1:armhf (0.190-1) ... 99s Selecting previously unselected package libelf1t64:armhf. 99s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78668 files and directories currently installed.) 99s Preparing to unpack .../libelf1t64_0.190-1.1build2_armhf.deb ... 99s Unpacking libelf1t64:armhf (0.190-1.1build2) ... 99s Preparing to unpack .../libtirpc-common_1.3.4+ds-1.1_all.deb ... 99s Unpacking libtirpc-common (1.3.4+ds-1.1) over (1.3.4+ds-1build1) ... 99s Preparing to unpack .../lsof_4.95.0-1build2_armhf.deb ... 99s Unpacking lsof (4.95.0-1build2) over (4.95.0-1build1) ... 99s Preparing to unpack .../libnsl2_1.3.0-3build2_armhf.deb ... 99s Unpacking libnsl2:armhf (1.3.0-3build2) over (1.3.0-3) ... 99s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78673 files and directories currently installed.) 99s Removing libtirpc3:armhf (1.3.4+ds-1build1) ... 99s Selecting previously unselected package libtirpc3t64:armhf. 99s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78667 files and directories currently installed.) 99s Preparing to unpack .../00-libtirpc3t64_1.3.4+ds-1.1_armhf.deb ... 99s Adding 'diversion of /lib/arm-linux-gnueabihf/libtirpc.so.3 to /lib/arm-linux-gnueabihf/libtirpc.so.3.usr-is-merged by libtirpc3t64' 99s Adding 'diversion of /lib/arm-linux-gnueabihf/libtirpc.so.3.0.0 to /lib/arm-linux-gnueabihf/libtirpc.so.3.0.0.usr-is-merged by libtirpc3t64' 99s Unpacking libtirpc3t64:armhf (1.3.4+ds-1.1) ... 99s Preparing to unpack .../01-libmbim-proxy_1.31.2-0ubuntu2_armhf.deb ... 99s Unpacking libmbim-proxy (1.31.2-0ubuntu2) over (1.30.0-1) ... 100s Preparing to unpack .../02-libmbim-glib4_1.31.2-0ubuntu2_armhf.deb ... 100s Unpacking libmbim-glib4:armhf (1.31.2-0ubuntu2) over (1.30.0-1) ... 100s Preparing to unpack .../03-libjson-glib-1.0-common_1.8.0-2build1_all.deb ... 100s Unpacking libjson-glib-1.0-common (1.8.0-2build1) over (1.8.0-2) ... 100s Preparing to unpack .../04-libjson-glib-1.0-0_1.8.0-2build1_armhf.deb ... 100s Unpacking libjson-glib-1.0-0:armhf (1.8.0-2build1) over (1.8.0-2) ... 100s Preparing to unpack .../05-libnghttp2-14_1.59.0-1build1_armhf.deb ... 100s Unpacking libnghttp2-14:armhf (1.59.0-1build1) over (1.59.0-1) ... 100s Preparing to unpack .../06-libssh-4_0.10.6-2build1_armhf.deb ... 100s Unpacking libssh-4:armhf (0.10.6-2build1) over (0.10.6-2) ... 100s Preparing to unpack .../07-libusb-1.0-0_2%3a1.0.27-1_armhf.deb ... 100s Unpacking libusb-1.0-0:armhf (2:1.0.27-1) over (2:1.0.26-1) ... 100s Preparing to unpack .../08-libgusb2_0.4.8-1build1_armhf.deb ... 100s Unpacking libgusb2:armhf (0.4.8-1build1) over (0.4.8-1) ... 100s Preparing to unpack .../09-libmm-glib0_1.23.4-0ubuntu1_armhf.deb ... 100s Unpacking libmm-glib0:armhf (1.23.4-0ubuntu1) over (1.22.0-3) ... 100s Preparing to unpack .../10-libprotobuf-c1_1.4.1-1ubuntu3_armhf.deb ... 100s Unpacking libprotobuf-c1:armhf (1.4.1-1ubuntu3) over (1.4.1-1ubuntu2) ... 100s Preparing to unpack .../11-libsasl2-2_2.1.28+dfsg1-5ubuntu1_armhf.deb ... 100s Unpacking libsasl2-2:armhf (2.1.28+dfsg1-5ubuntu1) over (2.1.28+dfsg1-4) ... 100s Preparing to unpack .../12-libibverbs1_50.0-2build1_armhf.deb ... 100s Unpacking libibverbs1:armhf (50.0-2build1) over (50.0-2) ... 100s Preparing to unpack .../13-libfido2-1_1.14.0-1build1_armhf.deb ... 100s Unpacking libfido2-1:armhf (1.14.0-1build1) over (1.14.0-1) ... 100s Preparing to unpack .../14-libproc2-0_2%3a4.0.4-4ubuntu2_armhf.deb ... 100s Unpacking libproc2-0:armhf (2:4.0.4-4ubuntu2) over (2:4.0.4-4ubuntu1) ... 100s Preparing to unpack .../15-procps_2%3a4.0.4-4ubuntu2_armhf.deb ... 100s Unpacking procps (2:4.0.4-4ubuntu2) over (2:4.0.4-4ubuntu1) ... 100s Preparing to unpack .../16-coreutils_9.4-3ubuntu3_armhf.deb ... 100s Unpacking coreutils (9.4-3ubuntu3) over (9.4-2ubuntu4) ... 100s Setting up coreutils (9.4-3ubuntu3) ... 100s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78679 files and directories currently installed.) 100s Preparing to unpack .../util-linux_2.39.3-9ubuntu2_armhf.deb ... 100s Unpacking util-linux (2.39.3-9ubuntu2) over (2.39.3-6ubuntu2) ... 100s Setting up util-linux (2.39.3-9ubuntu2) ... 101s fstrim.service is a disabled or a static unit not running, not starting it. 101s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78679 files and directories currently installed.) 101s Removing libatm1:armhf (1:2.5.1-5) ... 101s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78674 files and directories currently installed.) 101s Preparing to unpack .../file_1%3a5.45-3_armhf.deb ... 101s Unpacking file (1:5.45-3) over (1:5.45-2) ... 101s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78674 files and directories currently installed.) 101s Removing libmagic1:armhf (1:5.45-2) ... 101s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78664 files and directories currently installed.) 101s Preparing to unpack .../libmagic-mgc_1%3a5.45-3_armhf.deb ... 101s Unpacking libmagic-mgc (1:5.45-3) over (1:5.45-2) ... 101s Selecting previously unselected package libmagic1t64:armhf. 101s Preparing to unpack .../libmagic1t64_1%3a5.45-3_armhf.deb ... 101s Unpacking libmagic1t64:armhf (1:5.45-3) ... 101s Preparing to unpack .../libplymouth5_24.004.60-1ubuntu6_armhf.deb ... 101s Unpacking libplymouth5:armhf (24.004.60-1ubuntu6) over (24.004.60-1ubuntu3) ... 101s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78675 files and directories currently installed.) 101s Removing libpng16-16:armhf (1.6.43-1) ... 101s Selecting previously unselected package libpng16-16t64:armhf. 101s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78665 files and directories currently installed.) 101s Preparing to unpack .../libpng16-16t64_1.6.43-3_armhf.deb ... 101s Unpacking libpng16-16t64:armhf (1.6.43-3) ... 101s Preparing to unpack .../bind9-host_1%3a9.18.24-0ubuntu3_armhf.deb ... 101s Unpacking bind9-host (1:9.18.24-0ubuntu3) over (1:9.18.21-0ubuntu1) ... 101s Preparing to unpack .../bind9-dnsutils_1%3a9.18.24-0ubuntu3_armhf.deb ... 101s Unpacking bind9-dnsutils (1:9.18.24-0ubuntu3) over (1:9.18.21-0ubuntu1) ... 102s Preparing to unpack .../bind9-libs_1%3a9.18.24-0ubuntu3_armhf.deb ... 102s Unpacking bind9-libs:armhf (1:9.18.24-0ubuntu3) over (1:9.18.21-0ubuntu1) ... 102s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78676 files and directories currently installed.) 102s Removing libuv1:armhf (1.48.0-1) ... 102s Selecting previously unselected package libuv1t64:armhf. 102s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78671 files and directories currently installed.) 102s Preparing to unpack .../libuv1t64_1.48.0-1.1_armhf.deb ... 102s Unpacking libuv1t64:armhf (1.48.0-1.1) ... 102s Preparing to unpack .../uuid-runtime_2.39.3-9ubuntu2_armhf.deb ... 102s Unpacking uuid-runtime (2.39.3-9ubuntu2) over (2.39.3-6ubuntu2) ... 102s Preparing to unpack .../libdebconfclient0_0.271ubuntu2_armhf.deb ... 102s Unpacking libdebconfclient0:armhf (0.271ubuntu2) over (0.271ubuntu1) ... 102s Setting up libdebconfclient0:armhf (0.271ubuntu2) ... 102s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78677 files and directories currently installed.) 102s Preparing to unpack .../libsemanage-common_3.5-1build4_all.deb ... 102s Unpacking libsemanage-common (3.5-1build4) over (3.5-1build2) ... 102s Setting up libsemanage-common (3.5-1build4) ... 102s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78677 files and directories currently installed.) 102s Preparing to unpack .../libsemanage2_3.5-1build4_armhf.deb ... 102s Unpacking libsemanage2:armhf (3.5-1build4) over (3.5-1build2) ... 102s Setting up libsemanage2:armhf (3.5-1build4) ... 102s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78677 files and directories currently installed.) 102s Preparing to unpack .../install-info_7.1-3build1_armhf.deb ... 102s Unpacking install-info (7.1-3build1) over (7.1-3) ... 102s Setting up install-info (7.1-3build1) ... 102s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78677 files and directories currently installed.) 102s Preparing to unpack .../00-gcc-13-base_13.2.0-21ubuntu1_armhf.deb ... 102s Unpacking gcc-13-base:armhf (13.2.0-21ubuntu1) over (13.2.0-17ubuntu2) ... 102s Preparing to unpack .../01-libss2_1.47.0-2.4~exp1ubuntu2_armhf.deb ... 102s Unpacking libss2:armhf (1.47.0-2.4~exp1ubuntu2) over (1.47.0-2ubuntu1) ... 102s Preparing to unpack .../02-dmsetup_2%3a1.02.185-3ubuntu2_armhf.deb ... 102s Unpacking dmsetup (2:1.02.185-3ubuntu2) over (2:1.02.185-3ubuntu1) ... 102s Preparing to unpack .../03-eject_2.39.3-9ubuntu2_armhf.deb ... 102s Unpacking eject (2.39.3-9ubuntu2) over (2.39.3-6ubuntu2) ... 102s Preparing to unpack .../04-krb5-locales_1.20.1-6ubuntu1_all.deb ... 102s Unpacking krb5-locales (1.20.1-6ubuntu1) over (1.20.1-5build1) ... 102s Preparing to unpack .../05-libglib2.0-data_2.79.3-3ubuntu5_all.deb ... 102s Unpacking libglib2.0-data (2.79.3-3ubuntu5) over (2.79.2-1~ubuntu1) ... 102s Preparing to unpack .../06-libslang2_2.3.3-3build1_armhf.deb ... 102s Unpacking libslang2:armhf (2.3.3-3build1) over (2.3.3-3) ... 102s Preparing to unpack .../07-rsyslog_8.2312.0-3ubuntu7_armhf.deb ... 102s Unpacking rsyslog (8.2312.0-3ubuntu7) over (8.2312.0-3ubuntu3) ... 102s Preparing to unpack .../08-vim-tiny_2%3a9.1.0016-1ubuntu6_armhf.deb ... 102s Unpacking vim-tiny (2:9.1.0016-1ubuntu6) over (2:9.1.0016-1ubuntu2) ... 102s Preparing to unpack .../09-vim-common_2%3a9.1.0016-1ubuntu6_all.deb ... 102s Unpacking vim-common (2:9.1.0016-1ubuntu6) over (2:9.1.0016-1ubuntu2) ... 103s Selecting previously unselected package xdg-user-dirs. 103s Preparing to unpack .../10-xdg-user-dirs_0.18-1_armhf.deb ... 103s Unpacking xdg-user-dirs (0.18-1) ... 103s Preparing to unpack .../11-xxd_2%3a9.1.0016-1ubuntu6_armhf.deb ... 103s Unpacking xxd (2:9.1.0016-1ubuntu6) over (2:9.1.0016-1ubuntu2) ... 103s Preparing to unpack .../12-apparmor_4.0.0-beta3-0ubuntu2_armhf.deb ... 103s Unpacking apparmor (4.0.0-beta3-0ubuntu2) over (4.0.0~alpha4-0ubuntu1) ... 103s Preparing to unpack .../13-ftp_20230507-2build1_all.deb ... 103s Unpacking ftp (20230507-2build1) over (20230507-2) ... 104s Preparing to unpack .../14-inetutils-telnet_2%3a2.5-3ubuntu3_armhf.deb ... 104s Unpacking inetutils-telnet (2:2.5-3ubuntu3) over (2:2.5-3ubuntu1) ... 104s Preparing to unpack .../15-info_7.1-3build1_armhf.deb ... 104s Unpacking info (7.1-3build1) over (7.1-3) ... 104s Preparing to unpack .../16-libxmuu1_2%3a1.1.3-3build1_armhf.deb ... 104s Unpacking libxmuu1:armhf (2:1.1.3-3build1) over (2:1.1.3-3) ... 104s Preparing to unpack .../17-lshw_02.19.git.2021.06.19.996aaad9c7-2build2_armhf.deb ... 104s Unpacking lshw (02.19.git.2021.06.19.996aaad9c7-2build2) over (02.19.git.2021.06.19.996aaad9c7-2build1) ... 104s Preparing to unpack .../18-mtr-tiny_0.95-1.1build1_armhf.deb ... 104s Unpacking mtr-tiny (0.95-1.1build1) over (0.95-1.1) ... 104s Preparing to unpack .../19-plymouth-theme-ubuntu-text_24.004.60-1ubuntu6_armhf.deb ... 104s Unpacking plymouth-theme-ubuntu-text (24.004.60-1ubuntu6) over (24.004.60-1ubuntu3) ... 104s Preparing to unpack .../20-plymouth_24.004.60-1ubuntu6_armhf.deb ... 104s Unpacking plymouth (24.004.60-1ubuntu6) over (24.004.60-1ubuntu3) ... 104s Preparing to unpack .../21-psmisc_23.7-1_armhf.deb ... 104s Unpacking psmisc (23.7-1) over (23.6-2) ... 104s Preparing to unpack .../22-telnet_0.17+2.5-3ubuntu3_all.deb ... 104s Unpacking telnet (0.17+2.5-3ubuntu3) over (0.17+2.5-3ubuntu1) ... 104s Preparing to unpack .../23-usb.ids_2024.03.18-1_all.deb ... 104s Unpacking usb.ids (2024.03.18-1) over (2024.01.30-1) ... 104s Preparing to unpack .../24-xz-utils_5.6.0-0.2_armhf.deb ... 104s Unpacking xz-utils (5.6.0-0.2) over (5.4.5-0.3) ... 104s Preparing to unpack .../25-libctf0_2.42-4ubuntu1_armhf.deb ... 104s Unpacking libctf0:armhf (2.42-4ubuntu1) over (2.42-3ubuntu1) ... 104s Preparing to unpack .../26-libctf-nobfd0_2.42-4ubuntu1_armhf.deb ... 104s Unpacking libctf-nobfd0:armhf (2.42-4ubuntu1) over (2.42-3ubuntu1) ... 104s Preparing to unpack .../27-binutils-arm-linux-gnueabihf_2.42-4ubuntu1_armhf.deb ... 104s Unpacking binutils-arm-linux-gnueabihf (2.42-4ubuntu1) over (2.42-3ubuntu1) ... 104s Preparing to unpack .../28-libbinutils_2.42-4ubuntu1_armhf.deb ... 104s Unpacking libbinutils:armhf (2.42-4ubuntu1) over (2.42-3ubuntu1) ... 104s Preparing to unpack .../29-binutils_2.42-4ubuntu1_armhf.deb ... 104s Unpacking binutils (2.42-4ubuntu1) over (2.42-3ubuntu1) ... 104s Preparing to unpack .../30-binutils-common_2.42-4ubuntu1_armhf.deb ... 104s Unpacking binutils-common:armhf (2.42-4ubuntu1) over (2.42-3ubuntu1) ... 104s Preparing to unpack .../31-libsframe1_2.42-4ubuntu1_armhf.deb ... 104s Unpacking libsframe1:armhf (2.42-4ubuntu1) over (2.42-3ubuntu1) ... 104s Preparing to unpack .../32-bolt_0.9.6-2build1_armhf.deb ... 104s Unpacking bolt (0.9.6-2build1) over (0.9.6-2) ... 104s Preparing to unpack .../33-cryptsetup-bin_2%3a2.7.0-1ubuntu2_armhf.deb ... 104s Unpacking cryptsetup-bin (2:2.7.0-1ubuntu2) over (2:2.7.0-1ubuntu1) ... 105s Preparing to unpack .../34-dpkg-dev_1.22.6ubuntu4_all.deb ... 105s Unpacking dpkg-dev (1.22.6ubuntu4) over (1.22.4ubuntu5) ... 105s Preparing to unpack .../35-libdpkg-perl_1.22.6ubuntu4_all.deb ... 105s Unpacking libdpkg-perl (1.22.6ubuntu4) over (1.22.4ubuntu5) ... 105s Preparing to unpack .../36-gnupg-l10n_2.4.4-2ubuntu15_all.deb ... 105s Unpacking gnupg-l10n (2.4.4-2ubuntu15) over (2.4.4-2ubuntu7) ... 105s Preparing to unpack .../37-ibverbs-providers_50.0-2build1_armhf.deb ... 105s Unpacking ibverbs-providers:armhf (50.0-2build1) over (50.0-2) ... 105s Preparing to unpack .../38-jq_1.7.1-3_armhf.deb ... 105s Unpacking jq (1.7.1-3) over (1.7.1-2) ... 105s Preparing to unpack .../39-libjq1_1.7.1-3_armhf.deb ... 105s Unpacking libjq1:armhf (1.7.1-3) over (1.7.1-2) ... 105s Selecting previously unselected package libatm1t64:armhf. 105s Preparing to unpack .../40-libatm1t64_1%3a2.5.1-5.1_armhf.deb ... 105s Unpacking libatm1t64:armhf (1:2.5.1-5.1) ... 105s Preparing to unpack .../41-libevent-core-2.1-7_2.1.12-stable-9build1_armhf.deb ... 105s Unpacking libevent-core-2.1-7:armhf (2.1.12-stable-9build1) over (2.1.12-stable-9) ... 105s Preparing to unpack .../42-libftdi1-2_1.5-6build4_armhf.deb ... 105s Unpacking libftdi1-2:armhf (1.5-6build4) over (1.5-6build3) ... 105s Preparing to unpack .../43-libldap-common_2.6.7+dfsg-1~exp1ubuntu6_all.deb ... 105s Unpacking libldap-common (2.6.7+dfsg-1~exp1ubuntu6) over (2.6.7+dfsg-1~exp1ubuntu1) ... 105s Preparing to unpack .../44-libsasl2-modules_2.1.28+dfsg1-5ubuntu1_armhf.deb ... 105s Unpacking libsasl2-modules:armhf (2.1.28+dfsg1-5ubuntu1) over (2.1.28+dfsg1-4) ... 105s Preparing to unpack .../45-python3-distutils_3.12.2-3ubuntu1.1_all.deb ... 105s Unpacking python3-distutils (3.12.2-3ubuntu1.1) over (3.11.5-1) ... 105s Preparing to unpack .../46-python3-lib2to3_3.12.2-3ubuntu1.1_all.deb ... 105s Unpacking python3-lib2to3 (3.12.2-3ubuntu1.1) over (3.11.5-1) ... 105s Preparing to unpack .../47-python3-pyrsistent_0.20.0-1build1_armhf.deb ... 105s Unpacking python3-pyrsistent:armhf (0.20.0-1build1) over (0.20.0-1) ... 105s Preparing to unpack .../48-python3-typing-extensions_4.10.0-1_all.deb ... 106s Unpacking python3-typing-extensions (4.10.0-1) over (4.9.0-1) ... 106s Preparing to unpack .../49-kpartx_0.9.4-5ubuntu6_armhf.deb ... 106s Unpacking kpartx (0.9.4-5ubuntu6) over (0.9.4-5ubuntu3) ... 106s Setting up pinentry-curses (1.2.1-3ubuntu4) ... 106s Setting up motd-news-config (13ubuntu8) ... 106s Setting up libtext-iconv-perl:armhf (1.7-8build2) ... 106s Setting up libtext-charwidth-perl:armhf (0.04-11build2) ... 106s Setting up libibverbs1:armhf (50.0-2build1) ... 106s Setting up systemd-sysv (255.4-1ubuntu5) ... 106s Setting up libapparmor1:armhf (4.0.0-beta3-0ubuntu2) ... 106s Setting up libatm1t64:armhf (1:2.5.1-5.1) ... 106s Setting up libgdbm6t64:armhf (1.23-5.1) ... 106s Setting up bsdextrautils (2.39.3-9ubuntu2) ... 106s Setting up libgdbm-compat4t64:armhf (1.23-5.1) ... 106s Setting up xdg-user-dirs (0.18-1) ... 106s Setting up ibverbs-providers:armhf (50.0-2build1) ... 106s Setting up linux-headers-6.8.0-20 (6.8.0-20.20) ... 106s Setting up libmagic-mgc (1:5.45-3) ... 106s Setting up gawk (1:5.2.1-2build2) ... 106s Setting up psmisc (23.7-1) ... 106s Setting up libjq1:armhf (1.7.1-3) ... 106s Setting up libtirpc-common (1.3.4+ds-1.1) ... 106s Setting up libbrotli1:armhf (1.1.0-2build1) ... 106s Setting up libsqlite3-0:armhf (3.45.1-1ubuntu1) ... 106s Setting up libsasl2-modules:armhf (2.1.28+dfsg1-5ubuntu1) ... 106s Setting up libuv1t64:armhf (1.48.0-1.1) ... 106s Setting up libmagic1t64:armhf (1:5.45-3) ... 106s Setting up rsyslog (8.2312.0-3ubuntu7) ... 106s info: The user `syslog' is already a member of `adm'. 106s apparmor_parser: Unable to replace "rsyslogd". apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 106s 107s Setting up binutils-common:armhf (2.42-4ubuntu1) ... 107s Setting up libpsl5t64:armhf (0.21.2-1.1) ... 107s Setting up libnghttp2-14:armhf (1.59.0-1build1) ... 107s Setting up libreiserfscore0t64 (1:3.6.27-7.1) ... 107s Setting up libctf-nobfd0:armhf (2.42-4ubuntu1) ... 107s Setting up libnss-systemd:armhf (255.4-1ubuntu5) ... 107s Setting up krb5-locales (1.20.1-6ubuntu1) ... 107s Setting up file (1:5.45-3) ... 107s Setting up kmod (31+20240202-2ubuntu4) ... 107s Setting up lshw (02.19.git.2021.06.19.996aaad9c7-2build2) ... 107s Setting up libldap-common (2.6.7+dfsg-1~exp1ubuntu6) ... 107s Setting up libprotobuf-c1:armhf (1.4.1-1ubuntu3) ... 107s Setting up xxd (2:9.1.0016-1ubuntu6) ... 107s Setting up libsframe1:armhf (2.42-4ubuntu1) ... 107s Setting up libelf1t64:armhf (0.190-1.1build2) ... 107s Setting up libkrb5support0:armhf (1.20.1-6ubuntu1) ... 107s Setting up linux-headers-6.8.0-20-generic (6.8.0-20.20) ... 107s Setting up eject (2.39.3-9ubuntu2) ... 107s Setting up apparmor (4.0.0-beta3-0ubuntu2) ... 107s Installing new version of config file /etc/apparmor.d/abstractions/authentication ... 107s Installing new version of config file /etc/apparmor.d/abstractions/crypto ... 107s Installing new version of config file /etc/apparmor.d/abstractions/kde-open5 ... 107s Installing new version of config file /etc/apparmor.d/abstractions/openssl ... 107s Installing new version of config file /etc/apparmor.d/code ... 107s Installing new version of config file /etc/apparmor.d/firefox ... 107s apparmor_parser: Unable to replace "lsb_release". apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 107s 107s apparmor_parser: Unable to replace "kmod". apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 107s 107s apparmor_parser: Unable to replace "nvidia_modprobe". apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 107s 108s sysctl: cannot stat /proc/sys/kernel/apparmor_restrict_unprivileged_userns: No such file or directory 108s Reloading AppArmor profiles 108s /sbin/apparmor_parser: Unable to replace "1password". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "MongoDB Compass". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "Discord". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "QtWebEngineProcess". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "brave". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "buildah". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "busybox". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "cam". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "ch-checkns". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "ch-run". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "chrome". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "vscode". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "devhelp". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "epiphany". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "crun". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "evolution". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "element-desktop". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "firefox". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "flatpak". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "github-desktop". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "goldendict". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "ipa_verify". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "kchmviewer". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "keybase". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "lc-compliance". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "geary". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "linux-sandbox". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "loupe". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "lxc-attach". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "lxc-create". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "libcamerify". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "lxc-destroy". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "lxc-execute". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "lxc-stop". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "lxc-unshare". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "lxc-usernsexec". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "mmdebstrap". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "msedge". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "notepadqq". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "obsidian". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "nautilus". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "opam". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "pageedit". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "polypane". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "podman". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "opera". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "qcam". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "privacybrowser". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "qmapshack". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "rootlesskit". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "rpm". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "rssguard". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "qutebrowser". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "sbuild-abort". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "runc". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "QtWebEngineProcess". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "plasmashell". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "sbuild-apt". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "sbuild-adduser". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "sbuild". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "sbuild-createchroot". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "sbuild-clean". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "sbuild-destroychroot". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "sbuild-hold". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "sbuild-distupgrade". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "sbuild-checkpackages". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "sbuild-update". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "lsb_release". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "sbuild-unhold". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "sbuild-upgrade". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "signal-desktop". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "slack". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "scide". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "slirp4netns". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "steam". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "stress-ng". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "surfshark". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "systemd-coredump". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "thunderbird". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "toybox". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "trinity". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "sbuild-shell". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "tup". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "tuxedo-control-center". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "userbindmount". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "unprivileged_userns". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "vdens". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "kmod". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "nvidia_modprobe". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "virtiofsd". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "vivaldi-bin". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "vpnns". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "wpcom". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "uwsgi-core". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "unix-chkpwd". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "/usr/bin/man". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "ubuntu_pro_apt_news". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "rsyslogd". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s /sbin/apparmor_parser: Unable to replace "tcpdump". /sbin/apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 108s 108s Error: At least one profile failed to load 108s Setting up libglib2.0-0t64:armhf (2.79.3-3ubuntu5) ... 108s No schema files found: doing nothing. 108s Setting up libglib2.0-data (2.79.3-3ubuntu5) ... 108s Setting up vim-common (2:9.1.0016-1ubuntu6) ... 108s Setting up gcc-13-base:armhf (13.2.0-21ubuntu1) ... 108s Setting up libqrtr-glib0:armhf (1.2.2-1ubuntu3) ... 108s Setting up libslang2:armhf (2.3.3-3build1) ... 108s Setting up libnvme1t64 (1.8-3) ... 108s Setting up mtr-tiny (0.95-1.1build1) ... 108s Setting up gnupg-l10n (2.4.4-2ubuntu15) ... 108s Setting up librtmp1:armhf (2.4+20151223.gitfa8646d.1-2build6) ... 108s Setting up libdbus-1-3:armhf (1.14.10-4ubuntu2) ... 108s Setting up xz-utils (5.6.0-0.2) ... 108s Setting up perl-modules-5.38 (5.38.2-3.2) ... 108s Setting up libproc2-0:armhf (2:4.0.4-4ubuntu2) ... 108s Setting up libblockdev-utils3:armhf (3.1.0-1build1) ... 108s Setting up libpng16-16t64:armhf (1.6.43-3) ... 108s Setting up systemd-timesyncd (255.4-1ubuntu5) ... 109s Setting up libevent-core-2.1-7:armhf (2.1.12-stable-9build1) ... 109s Setting up udev (255.4-1ubuntu5) ... 109s Setting up libss2:armhf (1.47.0-2.4~exp1ubuntu2) ... 109s Setting up usb.ids (2024.03.18-1) ... 109s Setting up sudo (1.9.15p5-3ubuntu3) ... 109s Setting up dhcpcd-base (1:10.0.6-1ubuntu2) ... 109s Setting up gir1.2-glib-2.0:armhf (2.79.3-3ubuntu5) ... 109s Setting up libk5crypto3:armhf (1.20.1-6ubuntu1) ... 109s Setting up logsave (1.47.0-2.4~exp1ubuntu2) ... 109s Setting up libfdisk1:armhf (2.39.3-9ubuntu2) ... 109s Setting up libdb5.3t64:armhf (5.3.28+dfsg2-6) ... 109s Setting up libblockdev-nvme3:armhf (3.1.0-1build1) ... 109s Setting up libdevmapper1.02.1:armhf (2:1.02.185-3ubuntu2) ... 109s Setting up libblockdev-fs3:armhf (3.1.0-1build1) ... 109s Setting up python-apt-common (2.7.7) ... 109s Setting up mount (2.39.3-9ubuntu2) ... 109s Setting up dmsetup (2:1.02.185-3ubuntu2) ... 109s Setting up uuid-runtime (2.39.3-9ubuntu2) ... 110s uuidd.service is a disabled or a static unit not running, not starting it. 110s Setting up libmm-glib0:armhf (1.23.4-0ubuntu1) ... 110s Setting up groff-base (1.23.0-3build1) ... 110s Setting up libplymouth5:armhf (24.004.60-1ubuntu6) ... 110s Setting up dbus-session-bus-common (1.14.10-4ubuntu2) ... 110s Setting up kpartx (0.9.4-5ubuntu6) ... 110s Setting up jq (1.7.1-3) ... 110s Setting up procps (2:4.0.4-4ubuntu2) ... 111s Setting up gpgconf (2.4.4-2ubuntu15) ... 111s Setting up libpcap0.8t64:armhf (1.10.4-4.1ubuntu2) ... 111s Setting up libcryptsetup12:armhf (2:2.7.0-1ubuntu2) ... 111s Setting up libgirepository-1.0-1:armhf (1.79.1-1ubuntu6) ... 111s Setting up libjson-glib-1.0-common (1.8.0-2build1) ... 111s Setting up libkrb5-3:armhf (1.20.1-6ubuntu1) ... 111s Setting up libpython3.11-minimal:armhf (3.11.8-1build4) ... 111s Setting up libusb-1.0-0:armhf (2:1.0.27-1) ... 111s Setting up libperl5.38t64:armhf (5.38.2-3.2) ... 111s Setting up tnftp (20230507-2build1) ... 111s Setting up libbinutils:armhf (2.42-4ubuntu1) ... 111s Setting up dbus-system-bus-common (1.14.10-4ubuntu2) ... 111s Setting up libfido2-1:armhf (1.14.0-1build1) ... 111s Setting up openssl (3.0.13-0ubuntu2) ... 111s Setting up readline-common (8.2-4) ... 111s Setting up libxml2:armhf (2.9.14+dfsg-1.3ubuntu2) ... 111s Setting up libxmuu1:armhf (2:1.1.3-3build1) ... 111s Setting up dbus-bin (1.14.10-4ubuntu2) ... 111s Setting up info (7.1-3build1) ... 111s Setting up liblocale-gettext-perl (1.07-6ubuntu4) ... 111s Setting up gpg (2.4.4-2ubuntu15) ... 111s Setting up libgudev-1.0-0:armhf (1:238-3ubuntu2) ... 111s Setting up libpolkit-gobject-1-0:armhf (124-1ubuntu1) ... 111s Setting up libbpf1:armhf (1:1.3.0-2build1) ... 111s Setting up libmbim-glib4:armhf (1.31.2-0ubuntu2) ... 111s Setting up rsync (3.2.7-1build1) ... 111s rsync.service is a disabled or a static unit not running, not starting it. 111s Setting up libudisks2-0:armhf (2.10.1-6) ... 111s Setting up bolt (0.9.6-2build1) ... 112s bolt.service is a disabled or a static unit not running, not starting it. 112s Setting up gnupg-utils (2.4.4-2ubuntu15) ... 112s Setting up initramfs-tools-bin (0.142ubuntu23) ... 112s Setting up libctf0:armhf (2.42-4ubuntu1) ... 112s Setting up cryptsetup-bin (2:2.7.0-1ubuntu2) ... 112s Setting up python3.11-minimal (3.11.8-1build4) ... 113s Setting up tcpdump (4.99.4-3ubuntu2) ... 113s apparmor_parser: Unable to replace "tcpdump". apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 113s 113s Setting up apt-utils (2.7.14) ... 113s Setting up gpg-agent (2.4.4-2ubuntu15) ... 113s Setting up libpython3.12-stdlib:armhf (3.12.2-4build3) ... 113s Setting up libblockdev-mdraid3:armhf (3.1.0-1build1) ... 113s Setting up wget (1.21.4-1ubuntu2) ... 113s Setting up libblockdev-swap3:armhf (3.1.0-1build1) ... 113s Setting up plymouth (24.004.60-1ubuntu6) ... 113s update-rc.d: warning: start and stop actions are no longer supported; falling back to defaults 113s update-rc.d: warning: start and stop actions are no longer supported; falling back to defaults 114s Setting up libxmlb2:armhf (0.3.15-1build1) ... 114s Setting up btrfs-progs (6.6.3-1.1build1) ... 114s Setting up libpython3.11-stdlib:armhf (3.11.8-1build4) ... 114s Setting up python3.12 (3.12.2-4build3) ... 115s Setting up libblockdev-loop3:armhf (3.1.0-1build1) ... 115s Setting up gpgsm (2.4.4-2ubuntu15) ... 115s Setting up inetutils-telnet (2:2.5-3ubuntu3) ... 115s Setting up e2fsprogs (1.47.0-2.4~exp1ubuntu2) ... 115s update-initramfs: deferring update (trigger activated) 115s e2scrub_all.service is a disabled or a static unit not running, not starting it. 115s Setting up libparted2t64:armhf (3.6-3.1build2) ... 115s Setting up linux-headers-generic (6.8.0-20.20+1) ... 115s Setting up dbus-daemon (1.14.10-4ubuntu2) ... 115s Setting up libmbim-proxy (1.31.2-0ubuntu2) ... 115s Setting up vim-tiny (2:9.1.0016-1ubuntu6) ... 115s Setting up libnetplan1:armhf (1.0-1) ... 115s Setting up man-db (2.12.0-3build4) ... 116s Updating database of manual pages ... 117s apparmor_parser: Unable to replace "/usr/bin/man". apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 117s 117s man-db.service is a disabled or a static unit not running, not starting it. 117s Setting up libblockdev3:armhf (3.1.0-1build1) ... 117s Setting up fdisk (2.39.3-9ubuntu2) ... 117s Setting up libjson-glib-1.0-0:armhf (1.8.0-2build1) ... 117s Setting up libblockdev-part3:armhf (3.1.0-1build1) ... 117s Setting up libsasl2-modules-db:armhf (2.1.28+dfsg1-5ubuntu1) ... 117s Setting up libftdi1-2:armhf (1.5-6build4) ... 117s Setting up perl (5.38.2-3.2) ... 117s Setting up plymouth-theme-ubuntu-text (24.004.60-1ubuntu6) ... 117s update-initramfs: deferring update (trigger activated) 117s Setting up gir1.2-girepository-2.0:armhf (1.79.1-1ubuntu6) ... 117s Setting up dbus (1.14.10-4ubuntu2) ... 117s A reboot is required to replace the running dbus-daemon. 117s Please reboot the system when convenient. 118s Setting up shared-mime-info (2.4-1build1) ... 118s Setting up libgssapi-krb5-2:armhf (1.20.1-6ubuntu1) ... 118s Setting up ftp (20230507-2build1) ... 118s Setting up keyboxd (2.4.4-2ubuntu15) ... 118s Setting up libdpkg-perl (1.22.6ubuntu4) ... 118s Setting up libsasl2-2:armhf (2.1.28+dfsg1-5ubuntu1) ... 118s Setting up libssh-4:armhf (0.10.6-2build1) ... 118s Setting up libpam-systemd:armhf (255.4-1ubuntu5) ... 118s Setting up libpolkit-agent-1-0:armhf (124-1ubuntu1) ... 118s Setting up libgpgme11t64:armhf (1.18.0-4.1ubuntu3) ... 118s Setting up netplan-generator (1.0-1) ... 118s Removing 'diversion of /lib/systemd/system-generators/netplan to /lib/systemd/system-generators/netplan.usr-is-merged by netplan-generator' 118s Setting up initramfs-tools-core (0.142ubuntu23) ... 118s Setting up binutils-arm-linux-gnueabihf (2.42-4ubuntu1) ... 118s Setting up libarchive13t64:armhf (3.7.2-1.1ubuntu2) ... 118s Setting up libldap2:armhf (2.6.7+dfsg-1~exp1ubuntu6) ... 118s Setting up libpython3-stdlib:armhf (3.12.2-0ubuntu1) ... 118s Setting up systemd-resolved (255.4-1ubuntu5) ... 119s Setting up python3.11 (3.11.8-1build4) ... 120s Setting up telnet (0.17+2.5-3ubuntu3) ... 120s Setting up initramfs-tools (0.142ubuntu23) ... 120s update-initramfs: deferring update (trigger activated) 120s Setting up libcurl4t64:armhf (8.5.0-2ubuntu8) ... 120s Setting up bind9-libs:armhf (1:9.18.24-0ubuntu3) ... 120s Setting up libtirpc3t64:armhf (1.3.4+ds-1.1) ... 120s Setting up e2fsprogs-l10n (1.47.0-2.4~exp1ubuntu2) ... 120s Setting up iproute2 (6.1.0-1ubuntu5) ... 120s Setting up openssh-client (1:9.6p1-3ubuntu11) ... 120s Setting up libgusb2:armhf (0.4.8-1build1) ... 120s Setting up libcurl3t64-gnutls:armhf (8.5.0-2ubuntu8) ... 120s Setting up parted (3.6-3.1build2) ... 120s Setting up libqmi-glib5:armhf (1.35.2-0ubuntu1) ... 120s Setting up python3 (3.12.2-0ubuntu1) ... 120s Setting up binutils (2.42-4ubuntu1) ... 120s Setting up libjcat1:armhf (0.2.0-2build2) ... 120s Setting up dpkg-dev (1.22.6ubuntu4) ... 120s Setting up dirmngr (2.4.4-2ubuntu15) ... 120s Setting up dbus-user-session (1.14.10-4ubuntu2) ... 120s Setting up python3-cryptography (41.0.7-4build2) ... 121s Setting up python3-gi (3.47.0-3build1) ... 121s Setting up python3-typing-extensions (4.10.0-1) ... 121s Setting up lsof (4.95.0-1build2) ... 121s Setting up python3-pyrsistent:armhf (0.20.0-1build1) ... 121s Setting up libnsl2:armhf (1.3.0-3build2) ... 121s Setting up gnupg (2.4.4-2ubuntu15) ... 121s Setting up python3-netplan (1.0-1) ... 121s Setting up curl (8.5.0-2ubuntu8) ... 121s Setting up libvolume-key1:armhf (0.3.12-7build1) ... 121s Setting up bind9-host (1:9.18.24-0ubuntu3) ... 121s Setting up python3-lib2to3 (3.12.2-3ubuntu1.1) ... 121s Setting up python3-pkg-resources (68.1.2-2ubuntu1) ... 122s Setting up python3-distutils (3.12.2-3ubuntu1.1) ... 122s python3.12: can't get files for byte-compilation 122s Setting up openssh-sftp-server (1:9.6p1-3ubuntu11) ... 122s Setting up python3-dbus (1.3.2-5build2) ... 122s Setting up python3-setuptools (68.1.2-2ubuntu1) ... 123s Setting up gpg-wks-client (2.4.4-2ubuntu15) ... 123s Setting up openssh-server (1:9.6p1-3ubuntu11) ... 123s Replacing config file /etc/ssh/sshd_config with new version 124s Created symlink /etc/systemd/system/ssh.service.requires/ssh.socket → /usr/lib/systemd/system/ssh.socket. 125s Setting up libblockdev-crypto3:armhf (3.1.0-1build1) ... 125s Setting up python3-gdbm:armhf (3.12.2-3ubuntu1.1) ... 125s Setting up python3-apt (2.7.7) ... 125s Setting up libfwupd2:armhf (1.9.15-2) ... 125s Setting up python3-yaml (6.0.1-2build1) ... 125s Setting up libqmi-proxy (1.35.2-0ubuntu1) ... 125s Setting up netplan.io (1.0-1) ... 125s Setting up bind9-dnsutils (1:9.18.24-0ubuntu3) ... 125s Setting up ubuntu-pro-client (31.2.2) ... 125s apparmor_parser: Unable to replace "ubuntu_pro_apt_news". apparmor_parser: Access denied. You need policy admin privileges to manage profiles. 125s 127s Setting up fwupd (1.9.15-2) ... 127s fwupd-offline-update.service is a disabled or a static unit not running, not starting it. 127s fwupd-refresh.service is a disabled or a static unit not running, not starting it. 127s fwupd.service is a disabled or a static unit not running, not starting it. 127s Setting up ubuntu-pro-client-l10n (31.2.2) ... 127s Setting up udisks2 (2.10.1-6) ... 127s vda: Failed to write 'change' to '/sys/devices/pci0000:00/0000:00:01.3/0000:04:00.0/virtio2/block/vda/uevent': Permission denied 127s vda1: Failed to write 'change' to '/sys/devices/pci0000:00/0000:00:01.3/0000:04:00.0/virtio2/block/vda/vda1/uevent': Permission denied 127s vda15: Failed to write 'change' to '/sys/devices/pci0000:00/0000:00:01.3/0000:04:00.0/virtio2/block/vda/vda15/uevent': Permission denied 127s vda2: Failed to write 'change' to '/sys/devices/pci0000:00/0000:00:01.3/0000:04:00.0/virtio2/block/vda/vda2/uevent': Permission denied 127s loop0: Failed to write 'change' to '/sys/devices/virtual/block/loop0/uevent': Permission denied 127s loop1: Failed to write 'change' to '/sys/devices/virtual/block/loop1/uevent': Permission denied 127s loop2: Failed to write 'change' to '/sys/devices/virtual/block/loop2/uevent': Permission denied 127s loop3: Failed to write 'change' to '/sys/devices/virtual/block/loop3/uevent': Permission denied 127s loop4: Failed to write 'change' to '/sys/devices/virtual/block/loop4/uevent': Permission denied 127s loop5: Failed to write 'change' to '/sys/devices/virtual/block/loop5/uevent': Permission denied 127s loop6: Failed to write 'change' to '/sys/devices/virtual/block/loop6/uevent': Permission denied 127s loop7: Failed to write 'change' to '/sys/devices/virtual/block/loop7/uevent': Permission denied 128s Processing triggers for ufw (0.36.2-5) ... 128s Processing triggers for systemd (255.4-1ubuntu5) ... 128s Processing triggers for install-info (7.1-3build1) ... 128s Processing triggers for libc-bin (2.39-0ubuntu6) ... 128s Processing triggers for initramfs-tools (0.142ubuntu23) ... 130s Reading package lists... 130s Building dependency tree... 130s Reading state information... 131s The following packages will be REMOVED: 131s linux-headers-6.8.0-11* python3-distutils* python3-lib2to3* 131s 0 upgraded, 0 newly installed, 3 to remove and 1 not upgraded. 131s After this operation, 86.5 MB disk space will be freed. 131s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 78647 files and directories currently installed.) 131s Removing linux-headers-6.8.0-11 (6.8.0-11.11) ... 132s Removing python3-distutils (3.12.2-3ubuntu1.1) ... 132s Removing python3-lib2to3 (3.12.2-3ubuntu1.1) ... 134s autopkgtest [12:45:13]: rebooting testbed after setup commands that affected boot 172s autopkgtest [12:45:51]: testbed running kernel: Linux 5.15.0-101-generic #111-Ubuntu SMP Wed Mar 6 18:01:01 UTC 2024 197s autopkgtest [12:46:16]: @@@@@@@@@@@@@@@@@@@@ apt-source r-cran-systemfit 206s Get:1 http://ftpmaster.internal/ubuntu noble/universe r-cran-systemfit 1.1-30-1 (dsc) [2203 B] 206s Get:2 http://ftpmaster.internal/ubuntu noble/universe r-cran-systemfit 1.1-30-1 (tar) [1040 kB] 206s Get:3 http://ftpmaster.internal/ubuntu noble/universe r-cran-systemfit 1.1-30-1 (diff) [2516 B] 207s gpgv: Signature made Wed Jun 28 12:43:54 2023 UTC 207s gpgv: using RSA key F1F007320A035541F0A663CA578A0494D1C646D1 207s gpgv: issuer "tille@debian.org" 207s gpgv: Can't check signature: No public key 207s dpkg-source: warning: cannot verify inline signature for ./r-cran-systemfit_1.1-30-1.dsc: no acceptable signature found 207s autopkgtest [12:46:26]: testing package r-cran-systemfit version 1.1-30-1 209s autopkgtest [12:46:28]: build not needed 212s autopkgtest [12:46:31]: test run-unit-test: preparing testbed 221s Reading package lists... 221s Building dependency tree... 221s Reading state information... 221s Starting pkgProblemResolver with broken count: 0 221s Starting 2 pkgProblemResolver with broken count: 0 221s Done 222s The following additional packages will be installed: 222s fontconfig fontconfig-config fonts-dejavu-core fonts-dejavu-mono 222s fonts-glyphicons-halflings fonts-mathjax libblas3 libcairo2 libdatrie1 222s libdeflate0 libfontconfig1 libfreetype6 libgfortran5 libgomp1 libgraphite2-3 222s libharfbuzz0b libice6 libjbig0 libjpeg-turbo8 libjpeg8 libjs-bootstrap 222s libjs-highlight.js libjs-jquery libjs-jquery-datatables libjs-mathjax 222s liblapack3 liblerc4 libnlopt0 libpango-1.0-0 libpangocairo-1.0-0 222s libpangoft2-1.0-0 libpaper-utils libpaper1 libpixman-1-0 libsharpyuv0 libsm6 222s libtcl8.6 libthai-data libthai0 libtiff6 libtk8.6 libwebp7 libxcb-render0 222s libxcb-shm0 libxft2 libxrender1 libxss1 libxt6t64 littler node-normalize.css 222s r-base-core r-cran-abind r-cran-backports r-cran-bdsmatrix r-cran-bit 222s r-cran-bit64 r-cran-boot r-cran-brio r-cran-broom r-cran-callr r-cran-car 222s r-cran-cardata r-cran-caret r-cran-cellranger r-cran-class r-cran-cli 222s r-cran-clipr r-cran-clock r-cran-codetools r-cran-collapse r-cran-colorspace 222s r-cran-conquer r-cran-cpp11 r-cran-crayon r-cran-curl r-cran-data.table 222s r-cran-desc r-cran-diagram r-cran-diffobj r-cran-digest r-cran-dplyr 222s r-cran-e1071 r-cran-ellipsis r-cran-evaluate r-cran-fansi r-cran-farver 222s r-cran-forcats r-cran-foreach r-cran-foreign r-cran-formula r-cran-fs 222s r-cran-future r-cran-future.apply r-cran-generics r-cran-ggplot2 222s r-cran-globals r-cran-glue r-cran-gower r-cran-gtable r-cran-hardhat 222s r-cran-haven r-cran-highr r-cran-hms r-cran-ipred r-cran-isoband 222s r-cran-iterators r-cran-jsonlite r-cran-kernsmooth r-cran-knitr 222s r-cran-labeling r-cran-lattice r-cran-lava r-cran-lifecycle r-cran-listenv 222s r-cran-littler r-cran-lme4 r-cran-lmtest r-cran-lubridate r-cran-magrittr 222s r-cran-maptools r-cran-mass r-cran-matrix r-cran-matrixmodels 222s r-cran-matrixstats r-cran-maxlik r-cran-mgcv r-cran-minqa r-cran-misctools 222s r-cran-modelmetrics r-cran-munsell r-cran-nlme r-cran-nloptr r-cran-nnet 222s r-cran-numderiv r-cran-openxlsx r-cran-parallelly r-cran-pbkrtest 222s r-cran-pillar r-cran-pkgbuild r-cran-pkgconfig r-cran-pkgkitten 222s r-cran-pkgload r-cran-plm r-cran-plyr r-cran-praise r-cran-prettyunits 222s r-cran-proc r-cran-processx r-cran-prodlim r-cran-progress r-cran-progressr 222s r-cran-proxy r-cran-ps r-cran-purrr r-cran-quantreg r-cran-r.methodss3 222s r-cran-r.oo r-cran-r.utils r-cran-r6 r-cran-rbibutils r-cran-rcolorbrewer 222s r-cran-rcpp r-cran-rcpparmadillo r-cran-rcppeigen r-cran-rdpack r-cran-readr 222s r-cran-readxl r-cran-recipes r-cran-rematch r-cran-rematch2 r-cran-reshape2 222s r-cran-rio r-cran-rlang r-cran-rpart r-cran-rprojroot r-cran-sandwich 222s r-cran-scales r-cran-shape r-cran-sp r-cran-sparsem r-cran-squarem 222s r-cran-statmod r-cran-stringi r-cran-stringr r-cran-survival 222s r-cran-systemfit r-cran-testthat r-cran-tibble r-cran-tidyr 222s r-cran-tidyselect r-cran-timechange r-cran-timedate r-cran-tzdb r-cran-utf8 222s r-cran-vctrs r-cran-viridislite r-cran-vroom r-cran-waldo r-cran-withr 222s r-cran-writexl r-cran-xfun r-cran-yaml r-cran-zip r-cran-zoo unzip 222s x11-common xdg-utils zip 222s Suggested packages: 222s fonts-mathjax-extras fonts-stix libjs-mathjax-doc tcl8.6 tk8.6 222s libjs-html5shiv elpa-ess r-doc-info | r-doc-pdf r-mathlib r-base-html 222s r-cran-roxygen2 r-cran-rmarkdown r-cran-ff r-cran-aer r-cran-bbmle 222s r-cran-cluster r-cran-cmprsk r-cran-coda r-cran-covr r-cran-emmeans 222s r-cran-epir r-cran-gam r-cran-gee r-cran-geepack r-cran-glmnet r-cran-gmm 222s r-cran-hmisc r-cran-irlba r-cran-interp r-cran-ks r-cran-lavaan r-cran-leaps 222s r-cran-lsmeans r-cran-maps r-cran-mclust r-cran-metafor r-cran-modeldata 222s r-cran-multcomp r-cran-network r-cran-ordinal r-cran-psych r-cran-robust 222s r-cran-robustbase r-cran-rsample r-cran-spdep r-cran-spatialreg 222s r-cran-spelling r-cran-survey r-cran-tseries r-cran-bradleyterry2 222s r-cran-ellipse r-cran-mlbench r-cran-party r-cran-pls r-cran-randomforest 222s r-cran-rann r-cran-rstudioapi r-cran-slider r-cran-kernlab r-cran-mvtnorm 222s r-cran-vcd r-cran-shiny r-cran-shinyjs r-cran-png r-cran-jpeg r-cran-viridis 222s r-cran-tinytest r-cran-markdown r-cran-th.data r-cran-magick r-cran-sf 222s r-cran-getopt r-cran-rgeos r-cran-spatstat.geom r-cran-raster 222s r-cran-polyclip r-cran-plotrix r-cran-spatstat.linnet r-cran-spatstat.utils 222s r-cran-spatstat r-cran-clue r-cran-dbi r-cran-formattable r-cran-nanotime 222s r-cran-palmerpenguins r-cran-units r-cran-vdiffr r-cran-inline r-cran-sem 222s r-cran-bench r-cran-blob r-cran-here r-cran-htmltools r-cran-runit 222s Recommended packages: 222s javascript-common r-recommended r-base-dev r-doc-html r-cran-covr 222s r-cran-mockery r-cran-spelling r-cran-earth r-cran-mda r-cran-mlmetrics 222s r-cran-fastica r-cran-kernlab r-cran-themis r-cran-htmltools 222s r-cran-htmlwidgets r-cran-rmarkdown r-cran-rstudioapi r-cran-whoami 222s r-cran-xts r-cran-bench r-cran-decor r-cran-lobstr r-cran-later 222s r-cran-httpuv r-cran-webutils r-cran-nanotime r-cran-gh r-cran-dbi 222s r-cran-dbplyr r-cran-rmysql r-cran-rpostgresql r-cran-rsqlite 222s r-cran-unitizer r-cran-rhpcblasctl r-cran-r.rsp r-cran-markdown 222s r-cran-hexbin r-cran-hmisc r-cran-mapproj r-cran-maps r-cran-multcomp 222s r-cran-profvis r-cran-ragg r-cran-sf r-cran-svglite r-cran-vdiffr 222s r-cran-xml2 r-cran-devtools r-cran-modeldata r-cran-roxygen2 r-cran-usethis 222s r-cran-testit r-cran-mlbench r-cran-httr r-cran-bslib r-cran-formatr 222s r-cran-gridsvg r-cran-jpeg r-cran-magick r-cran-png r-cran-reticulate 222s r-cran-rgl r-cran-sass r-cran-tikzdevice r-cran-tinytex r-cran-webshot 222s node-highlight.js r-cran-ellipse r-cran-fields r-cran-geepack r-bioc-graph 222s r-cran-bookdown r-cran-igraph r-cran-lavasearch2 r-cran-mets r-cran-optimx 222s r-cran-polycor r-cran-lintr r-cran-tidyverse r-cran-base64enc 222s r-cran-r.devices r-cran-runit r-cran-bitops r-cran-mathjaxr r-cran-mockr 222s r-cran-remotes r-cran-aer r-cran-spdep r-cran-urca r-cran-doparallel 222s r-cran-itertools r-cran-logcondens r-cran-webfakes r-cran-pbmcapply 222s r-cran-furrr r-cran-shiny r-cran-commonmark r-cran-cba r-cran-pingr 222s r-cran-gbrd r-cran-ddalpha r-cran-dials r-cran-rann r-cran-rcpproll 222s r-cran-rsample r-cran-rspectra r-cran-splines2 r-cran-dichromat r-cran-gstat 222s r-cran-deldir r-cran-terra r-cran-raster r-cran-setrng r-cran-formattable 222s r-cran-pkgdown r-cran-zeallot r-cran-mime r-cran-renv libfile-mimeinfo-perl 222s libnet-dbus-perl libx11-protocol-perl x11-utils x11-xserver-utils 222s The following NEW packages will be installed: 222s autopkgtest-satdep fontconfig fontconfig-config fonts-dejavu-core 222s fonts-dejavu-mono fonts-glyphicons-halflings fonts-mathjax libblas3 222s libcairo2 libdatrie1 libdeflate0 libfontconfig1 libfreetype6 libgfortran5 222s libgomp1 libgraphite2-3 libharfbuzz0b libice6 libjbig0 libjpeg-turbo8 222s libjpeg8 libjs-bootstrap libjs-highlight.js libjs-jquery 222s libjs-jquery-datatables libjs-mathjax liblapack3 liblerc4 libnlopt0 222s libpango-1.0-0 libpangocairo-1.0-0 libpangoft2-1.0-0 libpaper-utils 222s libpaper1 libpixman-1-0 libsharpyuv0 libsm6 libtcl8.6 libthai-data libthai0 222s libtiff6 libtk8.6 libwebp7 libxcb-render0 libxcb-shm0 libxft2 libxrender1 222s libxss1 libxt6t64 littler node-normalize.css r-base-core r-cran-abind 222s r-cran-backports r-cran-bdsmatrix r-cran-bit r-cran-bit64 r-cran-boot 222s r-cran-brio r-cran-broom r-cran-callr r-cran-car r-cran-cardata r-cran-caret 222s r-cran-cellranger r-cran-class r-cran-cli r-cran-clipr r-cran-clock 222s r-cran-codetools r-cran-collapse r-cran-colorspace r-cran-conquer 222s r-cran-cpp11 r-cran-crayon r-cran-curl r-cran-data.table r-cran-desc 222s r-cran-diagram r-cran-diffobj r-cran-digest r-cran-dplyr r-cran-e1071 222s r-cran-ellipsis r-cran-evaluate r-cran-fansi r-cran-farver r-cran-forcats 222s r-cran-foreach r-cran-foreign r-cran-formula r-cran-fs r-cran-future 222s r-cran-future.apply r-cran-generics r-cran-ggplot2 r-cran-globals 222s r-cran-glue r-cran-gower r-cran-gtable r-cran-hardhat r-cran-haven 222s r-cran-highr r-cran-hms r-cran-ipred r-cran-isoband r-cran-iterators 222s r-cran-jsonlite r-cran-kernsmooth r-cran-knitr r-cran-labeling 222s r-cran-lattice r-cran-lava r-cran-lifecycle r-cran-listenv r-cran-littler 222s r-cran-lme4 r-cran-lmtest r-cran-lubridate r-cran-magrittr r-cran-maptools 222s r-cran-mass r-cran-matrix r-cran-matrixmodels r-cran-matrixstats 222s r-cran-maxlik r-cran-mgcv r-cran-minqa r-cran-misctools r-cran-modelmetrics 222s r-cran-munsell r-cran-nlme r-cran-nloptr r-cran-nnet r-cran-numderiv 222s r-cran-openxlsx r-cran-parallelly r-cran-pbkrtest r-cran-pillar 222s r-cran-pkgbuild r-cran-pkgconfig r-cran-pkgkitten r-cran-pkgload r-cran-plm 222s r-cran-plyr r-cran-praise r-cran-prettyunits r-cran-proc r-cran-processx 222s r-cran-prodlim r-cran-progress r-cran-progressr r-cran-proxy r-cran-ps 222s r-cran-purrr r-cran-quantreg r-cran-r.methodss3 r-cran-r.oo r-cran-r.utils 222s r-cran-r6 r-cran-rbibutils r-cran-rcolorbrewer r-cran-rcpp 222s r-cran-rcpparmadillo r-cran-rcppeigen r-cran-rdpack r-cran-readr 222s r-cran-readxl r-cran-recipes r-cran-rematch r-cran-rematch2 r-cran-reshape2 222s r-cran-rio r-cran-rlang r-cran-rpart r-cran-rprojroot r-cran-sandwich 222s r-cran-scales r-cran-shape r-cran-sp r-cran-sparsem r-cran-squarem 222s r-cran-statmod r-cran-stringi r-cran-stringr r-cran-survival 222s r-cran-systemfit r-cran-testthat r-cran-tibble r-cran-tidyr 222s r-cran-tidyselect r-cran-timechange r-cran-timedate r-cran-tzdb r-cran-utf8 222s r-cran-vctrs r-cran-viridislite r-cran-vroom r-cran-waldo r-cran-withr 222s r-cran-writexl r-cran-xfun r-cran-yaml r-cran-zip r-cran-zoo unzip 222s x11-common xdg-utils zip 222s 0 upgraded, 209 newly installed, 0 to remove and 1 not upgraded. 222s Need to get 161 MB/161 MB of archives. 222s After this operation, 316 MB of additional disk space will be used. 222s Get:1 /tmp/autopkgtest.coZgcK/1-autopkgtest-satdep.deb autopkgtest-satdep armhf 0 [712 B] 222s Get:2 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libfreetype6 armhf 2.13.2+dfsg-1build2 [331 kB] 223s Get:3 http://ftpmaster.internal/ubuntu noble/main armhf fonts-dejavu-mono all 2.37-8 [502 kB] 223s Get:4 http://ftpmaster.internal/ubuntu noble/main armhf fonts-dejavu-core all 2.37-8 [835 kB] 223s Get:5 http://ftpmaster.internal/ubuntu noble-proposed/main armhf fontconfig-config armhf 2.15.0-1.1ubuntu1 [37.4 kB] 223s Get:6 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libfontconfig1 armhf 2.15.0-1.1ubuntu1 [113 kB] 223s Get:7 http://ftpmaster.internal/ubuntu noble-proposed/main armhf fontconfig armhf 2.15.0-1.1ubuntu1 [189 kB] 223s Get:8 http://ftpmaster.internal/ubuntu noble/universe armhf fonts-glyphicons-halflings all 1.009~3.4.1+dfsg-3 [118 kB] 223s Get:9 http://ftpmaster.internal/ubuntu noble/main armhf fonts-mathjax all 2.7.9+dfsg-1 [2208 kB] 223s Get:10 http://ftpmaster.internal/ubuntu noble/main armhf libblas3 armhf 3.12.0-3 [123 kB] 223s Get:11 http://ftpmaster.internal/ubuntu noble/main armhf libpixman-1-0 armhf 0.42.2-1 [184 kB] 223s Get:12 http://ftpmaster.internal/ubuntu noble/main armhf libxcb-render0 armhf 1.15-1 [15.2 kB] 223s Get:13 http://ftpmaster.internal/ubuntu noble/main armhf libxcb-shm0 armhf 1.15-1 [5852 B] 223s Get:14 http://ftpmaster.internal/ubuntu noble/main armhf libxrender1 armhf 1:0.9.10-1.1 [16.5 kB] 223s Get:15 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libcairo2 armhf 1.18.0-1ubuntu1 [482 kB] 223s Get:16 http://ftpmaster.internal/ubuntu noble/main armhf libdatrie1 armhf 0.2.13-3 [16.9 kB] 223s Get:17 http://ftpmaster.internal/ubuntu noble/main armhf libdeflate0 armhf 1.19-1 [41.3 kB] 223s Get:18 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libgfortran5 armhf 14-20240315-1ubuntu1 [312 kB] 223s Get:19 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libgomp1 armhf 14-20240315-1ubuntu1 [125 kB] 223s Get:20 http://ftpmaster.internal/ubuntu noble/main armhf libgraphite2-3 armhf 1.3.14-2 [72.7 kB] 223s Get:21 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libharfbuzz0b armhf 8.3.0-2build1 [446 kB] 223s Get:22 http://ftpmaster.internal/ubuntu noble/main armhf x11-common all 1:7.7+23ubuntu2 [23.4 kB] 223s Get:23 http://ftpmaster.internal/ubuntu noble/main armhf libice6 armhf 2:1.0.10-1build2 [36.4 kB] 223s Get:24 http://ftpmaster.internal/ubuntu noble/main armhf libjpeg-turbo8 armhf 2.1.5-2ubuntu1 [123 kB] 223s Get:25 http://ftpmaster.internal/ubuntu noble/main armhf libjpeg8 armhf 8c-2ubuntu11 [2148 B] 223s Get:26 http://ftpmaster.internal/ubuntu noble/universe armhf libjs-bootstrap all 3.4.1+dfsg-3 [129 kB] 223s Get:27 http://ftpmaster.internal/ubuntu noble/universe armhf libjs-highlight.js all 9.18.5+dfsg1-2 [385 kB] 223s Get:28 http://ftpmaster.internal/ubuntu noble/main armhf libjs-jquery all 3.6.1+dfsg+~3.5.14-1 [328 kB] 223s Get:29 http://ftpmaster.internal/ubuntu noble/universe armhf libjs-jquery-datatables all 1.11.5+dfsg-2 [146 kB] 223s Get:30 http://ftpmaster.internal/ubuntu noble/main armhf liblapack3 armhf 3.12.0-3 [2085 kB] 223s Get:31 http://ftpmaster.internal/ubuntu noble/main armhf liblerc4 armhf 4.0.0+ds-4ubuntu1 [152 kB] 223s Get:32 http://ftpmaster.internal/ubuntu noble/main armhf libthai-data all 0.1.29-2 [158 kB] 223s Get:33 http://ftpmaster.internal/ubuntu noble/main armhf libthai0 armhf 0.1.29-2 [15.1 kB] 223s Get:34 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libpango-1.0-0 armhf 1.52.1+ds-1 [206 kB] 223s Get:35 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libpangoft2-1.0-0 armhf 1.52.1+ds-1 [36.4 kB] 223s Get:36 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libpangocairo-1.0-0 armhf 1.52.1+ds-1 [24.9 kB] 223s Get:37 http://ftpmaster.internal/ubuntu noble/main armhf libpaper1 armhf 1.1.29 [12.5 kB] 223s Get:38 http://ftpmaster.internal/ubuntu noble/main armhf libpaper-utils armhf 1.1.29 [8170 B] 223s Get:39 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libsharpyuv0 armhf 1.3.2-0.4build2 [13.6 kB] 223s Get:40 http://ftpmaster.internal/ubuntu noble/main armhf libsm6 armhf 2:1.2.3-1build2 [15.1 kB] 223s Get:41 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libtcl8.6 armhf 8.6.14+dfsg-1 [903 kB] 223s Get:42 http://ftpmaster.internal/ubuntu noble/main armhf libjbig0 armhf 2.1-6.1ubuntu1 [24.9 kB] 223s Get:43 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libwebp7 armhf 1.3.2-0.4build2 [183 kB] 223s Get:44 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libtiff6 armhf 4.5.1+git230720-4ubuntu1 [178 kB] 223s Get:45 http://ftpmaster.internal/ubuntu noble/main armhf libxft2 armhf 2.3.6-1 [36.8 kB] 223s Get:46 http://ftpmaster.internal/ubuntu noble/main armhf libxss1 armhf 1:1.2.3-1build2 [7580 B] 223s Get:47 http://ftpmaster.internal/ubuntu noble/main armhf libtk8.6 armhf 8.6.14-1 [681 kB] 223s Get:48 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libxt6t64 armhf 1:1.2.1-1.2 [145 kB] 223s Get:49 http://ftpmaster.internal/ubuntu noble/main armhf zip armhf 3.0-13 [162 kB] 223s Get:50 http://ftpmaster.internal/ubuntu noble/main armhf unzip armhf 6.0-28ubuntu3 [163 kB] 223s Get:51 http://ftpmaster.internal/ubuntu noble/main armhf xdg-utils all 1.1.3-4.1ubuntu3 [62.0 kB] 223s Get:52 http://ftpmaster.internal/ubuntu noble-proposed/universe armhf r-base-core armhf 4.3.3-2build1 [26.6 MB] 224s Get:53 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-littler armhf 0.3.19-1 [80.8 kB] 224s Get:54 http://ftpmaster.internal/ubuntu noble/universe armhf littler all 0.3.19-1 [2472 B] 224s Get:55 http://ftpmaster.internal/ubuntu noble/universe armhf node-normalize.css all 8.0.1-5 [10.8 kB] 224s Get:56 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-abind all 1.4-5-2 [63.6 kB] 224s Get:57 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-backports armhf 1.4.1-1 [101 kB] 224s Get:58 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-bdsmatrix armhf 1.3-6-1 [291 kB] 224s Get:59 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-bit armhf 4.0.5-1 [1054 kB] 224s Get:60 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-bit64 armhf 4.0.5-1 [462 kB] 224s Get:61 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-boot all 1.3-30-1 [619 kB] 224s Get:62 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-brio armhf 1.1.4-1 [37.0 kB] 224s Get:63 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-cli armhf 3.6.2-1 [1363 kB] 224s Get:64 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-generics all 0.1.3-1 [81.3 kB] 224s Get:65 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-glue armhf 1.7.0-1 [153 kB] 224s Get:66 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-rlang armhf 1.1.3-1 [1658 kB] 224s Get:67 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-lifecycle all 1.0.4+dfsg-1 [110 kB] 224s Get:68 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-magrittr armhf 2.0.3-1 [154 kB] 224s Get:69 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-fansi armhf 1.0.5-1 [611 kB] 224s Get:70 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-utf8 armhf 1.2.4-1 [136 kB] 224s Get:71 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-vctrs armhf 0.6.5-1 [1310 kB] 224s Get:72 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-pillar all 1.9.0+dfsg-1 [464 kB] 224s Get:73 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-r6 all 2.5.1-1 [99.0 kB] 224s Get:74 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-pkgconfig all 2.0.3-2build1 [19.7 kB] 224s Get:75 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-tibble armhf 3.2.1+dfsg-2 [415 kB] 224s Get:76 http://ftpmaster.internal/ubuntu noble-proposed/universe armhf r-cran-withr all 3.0.0+dfsg-1 [228 kB] 224s Get:77 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-tidyselect armhf 1.2.0+dfsg-1 [218 kB] 224s Get:78 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-dplyr armhf 1.1.4-1 [1510 kB] 224s Get:79 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-ellipsis armhf 0.3.2-2 [35.0 kB] 224s Get:80 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-purrr armhf 1.0.2-1 [499 kB] 224s Get:81 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-stringi armhf 1.8.3-1 [859 kB] 224s Get:82 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-stringr all 1.5.1-1 [290 kB] 224s Get:83 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-cpp11 all 0.4.7-1 [266 kB] 224s Get:84 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-tidyr armhf 1.3.1-1 [1152 kB] 224s Get:85 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-broom all 1.0.5+dfsg-1 [1729 kB] 225s Get:86 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-ps armhf 1.7.6-1 [310 kB] 225s Get:87 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-processx armhf 3.8.3-1 [334 kB] 225s Get:88 http://ftpmaster.internal/ubuntu noble-proposed/universe armhf r-cran-callr all 3.7.5-1 [429 kB] 225s Get:89 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-cardata all 3.0.5-1 [1819 kB] 225s Get:90 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-mass armhf 7.3-60.0.1-1 [1118 kB] 225s Get:91 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-lattice armhf 0.22-5-1 [1341 kB] 225s Get:92 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-nlme armhf 3.1.164-1 [2252 kB] 225s Get:93 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-matrix armhf 1.6-5-1 [3762 kB] 225s Get:94 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-mgcv armhf 1.9-1-1 [3205 kB] 225s Get:95 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-nnet armhf 7.3-19-2 [110 kB] 225s Get:96 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-pkgkitten all 0.2.3-1 [25.1 kB] 225s Get:97 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-rcpp armhf 1.0.12-1 [1968 kB] 225s Get:98 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-minqa armhf 1.2.6-1 [104 kB] 225s Get:99 http://ftpmaster.internal/ubuntu noble/universe armhf libnlopt0 armhf 2.7.1-5build2 [152 kB] 225s Get:100 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-desc all 1.4.3-1 [359 kB] 225s Get:101 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-digest armhf 0.6.34-1 [180 kB] 225s Get:102 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-evaluate all 0.23-1 [90.2 kB] 225s Get:103 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-jsonlite armhf 1.8.8+dfsg-1 [438 kB] 226s Get:104 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-crayon all 1.5.2-1 [164 kB] 226s Get:105 http://ftpmaster.internal/ubuntu noble-proposed/universe armhf r-cran-fs armhf 1.6.3+dfsg-1build1 [226 kB] 226s Get:106 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-pkgbuild all 1.4.3-1 [209 kB] 226s Get:107 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-rprojroot all 2.0.4-1 [124 kB] 226s Get:108 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-pkgload all 1.3.4-1 [207 kB] 226s Get:109 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-praise all 1.0.0-4build1 [20.3 kB] 226s Get:110 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-diffobj armhf 0.3.5-1 [1115 kB] 226s Get:111 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-rematch2 all 2.1.2-2build1 [46.5 kB] 226s Get:112 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-waldo all 0.5.2-1build1 [120 kB] 227s Get:113 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-testthat armhf 3.2.1-1 [1666 kB] 227s Get:114 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-nloptr armhf 2.0.3-1 [363 kB] 227s Get:115 http://ftpmaster.internal/ubuntu noble-proposed/universe armhf r-cran-rcppeigen armhf 0.3.4.0.0-1 [1401 kB] 227s Get:116 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-statmod armhf 1.5.0-1 [294 kB] 227s Get:117 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-lme4 armhf 1.1-35.1-4 [4115 kB] 228s Get:118 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-numderiv all 2016.8-1.1-3 [115 kB] 228s Get:119 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-xfun armhf 0.41+dfsg-1 [415 kB] 228s Get:120 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-highr all 0.10+dfsg-1 [38.3 kB] 228s Get:121 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-yaml armhf 2.3.8-1 [99.0 kB] 228s Get:122 http://ftpmaster.internal/ubuntu noble/main armhf libjs-mathjax all 2.7.9+dfsg-1 [5665 kB] 228s Get:123 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-knitr all 1.45+dfsg-1 [917 kB] 229s Get:124 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-pbkrtest all 0.5.2-2 [182 kB] 229s Get:125 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-sparsem armhf 1.81-1 [902 kB] 229s Get:126 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-matrixmodels all 0.5-3-1 [361 kB] 229s Get:127 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-survival armhf 3.5-8-1 [6104 kB] 229s Get:128 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-matrixstats armhf 1.2.0-1 [478 kB] 229s Get:129 http://ftpmaster.internal/ubuntu noble-proposed/universe armhf r-cran-rcpparmadillo armhf 0.12.8.1.0-1 [857 kB] 229s Get:130 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-gtable all 0.3.4+dfsg-1 [191 kB] 229s Get:131 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-isoband armhf 0.2.7-1 [1477 kB] 229s Get:132 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-farver armhf 2.1.1-1 [1346 kB] 229s Get:133 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-labeling all 0.4.3-1 [62.1 kB] 229s Get:134 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-colorspace armhf 2.1-0+dfsg-1 [1538 kB] 229s Get:135 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-munsell all 0.5.0-2build1 [208 kB] 229s Get:136 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-rcolorbrewer all 1.1-3-1build1 [55.4 kB] 229s Get:137 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-viridislite all 0.4.2-2 [1088 kB] 229s Get:138 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-scales all 1.3.0-1 [603 kB] 229s Get:139 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-ggplot2 all 3.4.4+dfsg-1 [3411 kB] 229s Get:140 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-class armhf 7.3-22-2 [86.8 kB] 229s Get:141 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-proxy armhf 0.4-27-1 [180 kB] 230s Get:142 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-e1071 armhf 1.7-14-1 [551 kB] 230s Get:143 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-codetools all 0.2-19-1 [90.5 kB] 230s Get:144 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-iterators all 1.0.14-1 [336 kB] 230s Get:145 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-foreach all 1.5.2-1 [124 kB] 230s Get:146 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-data.table armhf 1.14.10+dfsg-1 [1816 kB] 230s Get:147 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-modelmetrics armhf 1.2.2.2-1build1 [120 kB] 230s Get:148 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-plyr armhf 1.8.9-1 [832 kB] 230s Get:149 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-proc armhf 1.18.5-1 [961 kB] 230s Get:150 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-tzdb armhf 0.4.0-2 [512 kB] 230s Get:151 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-clock armhf 0.7.0-1.1 [1773 kB] 230s Get:152 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-gower armhf 1.0.1-1 [206 kB] 230s Get:153 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-hardhat all 1.3.1+dfsg-1 [554 kB] 230s Get:154 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-rpart armhf 4.1.23-1 [657 kB] 230s Get:155 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-shape all 1.4.6-1 [770 kB] 230s Get:156 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-diagram all 1.6.5-2 [656 kB] 230s Get:157 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-kernsmooth armhf 2.23-22-1 [89.9 kB] 230s Get:158 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-globals all 0.16.2-1 [117 kB] 230s Get:159 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-listenv all 0.9.1+dfsg-1 [112 kB] 230s Get:160 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-parallelly armhf 1.37.1-1 [364 kB] 230s Get:161 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-future all 1.33.1+dfsg-1 [634 kB] 230s Get:162 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-future.apply all 1.11.1+dfsg-1 [171 kB] 230s Get:163 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-progressr all 0.14.0-1 [338 kB] 230s Get:164 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-squarem all 2021.1-1 [179 kB] 230s Get:165 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-lava all 1.7.3+dfsg-1 [2166 kB] 230s Get:166 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-prodlim armhf 2023.08.28-1 [406 kB] 230s Get:167 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-ipred armhf 0.9-14-1 [383 kB] 230s Get:168 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-timechange armhf 0.3.0-1 [182 kB] 230s Get:169 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-lubridate armhf 1.9.3+dfsg-1 [1009 kB] 230s Get:170 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-timedate armhf 4032.109-1 [1229 kB] 230s Get:171 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-recipes all 1.0.9+dfsg-1 [1964 kB] 230s Get:172 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-reshape2 armhf 1.4.4-2build1 [110 kB] 230s Get:173 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-caret armhf 6.0-94+dfsg-1 [3433 kB] 230s Get:174 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-conquer armhf 1.3.3-1 [460 kB] 230s Get:175 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-quantreg armhf 5.97-1 [1520 kB] 230s Get:176 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-sp armhf 1:2.1-2+dfsg-1 [1445 kB] 230s Get:177 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-foreign armhf 0.8.86-1 [237 kB] 230s Get:178 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-maptools armhf 1:1.1-8+dfsg-1 [1363 kB] 230s Get:179 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-forcats all 1.0.0-1 [369 kB] 230s Get:180 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-hms all 1.1.3-1 [96.5 kB] 230s Get:181 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-clipr all 0.8.0-1 [53.5 kB] 230s Get:182 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-prettyunits all 1.2.0-1 [162 kB] 230s Get:183 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-progress all 1.2.3-1 [91.9 kB] 230s Get:184 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-vroom armhf 1.6.5-1 [822 kB] 230s Get:185 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-readr armhf 2.1.5-1 [752 kB] 230s Get:186 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-haven armhf 2.5.4-1 [327 kB] 230s Get:187 http://ftpmaster.internal/ubuntu noble-proposed/universe armhf r-cran-curl armhf 5.2.0+dfsg-1build1 [175 kB] 230s Get:188 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-rematch all 2.0.0-1 [18.3 kB] 230s Get:189 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-cellranger all 1.1.0-3 [102 kB] 230s Get:190 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-readxl armhf 1.4.3-1 [721 kB] 230s Get:191 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-writexl armhf 1.5.0-1 [143 kB] 230s Get:192 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-r.methodss3 all 1.8.2-1 [84.0 kB] 230s Get:193 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-r.oo all 1.26.0-1 [955 kB] 230s Get:194 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-r.utils all 2.12.3-1 [1386 kB] 231s Get:195 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-zip armhf 2.3.1-1 [116 kB] 231s Get:196 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-openxlsx armhf 4.2.5.2-1 [1927 kB] 231s Get:197 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-rio all 1.0.1-1 [529 kB] 231s Get:198 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-car all 3.1-2-2 [1692 kB] 231s Get:199 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-collapse armhf 2.0.10-1 [3050 kB] 231s Get:200 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-formula all 1.2-5-1 [158 kB] 231s Get:201 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-zoo armhf 1.8-12-2 [984 kB] 231s Get:202 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-lmtest armhf 0.9.40-1 [396 kB] 231s Get:203 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-misctools all 0.6-28-1 [99.9 kB] 231s Get:204 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-sandwich all 3.1-0-1 [1484 kB] 231s Get:205 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-maxlik all 1.5-2-1 [1550 kB] 231s Get:206 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-rbibutils armhf 2.2.16-1 [677 kB] 231s Get:207 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-rdpack all 2.6-1 [742 kB] 231s Get:208 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-plm all 2.6-3-1 [2141 kB] 232s Get:209 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-systemfit all 1.1-30-1 [1174 kB] 232s Preconfiguring packages ... 232s Fetched 161 MB in 9s (16.9 MB/s) 232s Selecting previously unselected package libfreetype6:armhf. 232s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 58436 files and directories currently installed.) 232s Preparing to unpack .../000-libfreetype6_2.13.2+dfsg-1build2_armhf.deb ... 232s Unpacking libfreetype6:armhf (2.13.2+dfsg-1build2) ... 232s Selecting previously unselected package fonts-dejavu-mono. 232s Preparing to unpack .../001-fonts-dejavu-mono_2.37-8_all.deb ... 232s Unpacking fonts-dejavu-mono (2.37-8) ... 232s Selecting previously unselected package fonts-dejavu-core. 232s Preparing to unpack .../002-fonts-dejavu-core_2.37-8_all.deb ... 232s Unpacking fonts-dejavu-core (2.37-8) ... 232s Selecting previously unselected package fontconfig-config. 232s Preparing to unpack .../003-fontconfig-config_2.15.0-1.1ubuntu1_armhf.deb ... 233s Unpacking fontconfig-config (2.15.0-1.1ubuntu1) ... 233s Selecting previously unselected package libfontconfig1:armhf. 233s Preparing to unpack .../004-libfontconfig1_2.15.0-1.1ubuntu1_armhf.deb ... 233s Unpacking libfontconfig1:armhf (2.15.0-1.1ubuntu1) ... 233s Selecting previously unselected package fontconfig. 233s Preparing to unpack .../005-fontconfig_2.15.0-1.1ubuntu1_armhf.deb ... 233s Unpacking fontconfig (2.15.0-1.1ubuntu1) ... 233s Selecting previously unselected package fonts-glyphicons-halflings. 233s Preparing to unpack .../006-fonts-glyphicons-halflings_1.009~3.4.1+dfsg-3_all.deb ... 233s Unpacking fonts-glyphicons-halflings (1.009~3.4.1+dfsg-3) ... 233s Selecting previously unselected package fonts-mathjax. 233s Preparing to unpack .../007-fonts-mathjax_2.7.9+dfsg-1_all.deb ... 233s Unpacking fonts-mathjax (2.7.9+dfsg-1) ... 233s Selecting previously unselected package libblas3:armhf. 233s Preparing to unpack .../008-libblas3_3.12.0-3_armhf.deb ... 233s Unpacking libblas3:armhf (3.12.0-3) ... 233s Selecting previously unselected package libpixman-1-0:armhf. 233s Preparing to unpack .../009-libpixman-1-0_0.42.2-1_armhf.deb ... 233s Unpacking libpixman-1-0:armhf (0.42.2-1) ... 233s Selecting previously unselected package libxcb-render0:armhf. 233s Preparing to unpack .../010-libxcb-render0_1.15-1_armhf.deb ... 233s Unpacking libxcb-render0:armhf (1.15-1) ... 233s Selecting previously unselected package libxcb-shm0:armhf. 233s Preparing to unpack .../011-libxcb-shm0_1.15-1_armhf.deb ... 233s Unpacking libxcb-shm0:armhf (1.15-1) ... 233s Selecting previously unselected package libxrender1:armhf. 233s Preparing to unpack .../012-libxrender1_1%3a0.9.10-1.1_armhf.deb ... 233s Unpacking libxrender1:armhf (1:0.9.10-1.1) ... 233s Selecting previously unselected package libcairo2:armhf. 233s Preparing to unpack .../013-libcairo2_1.18.0-1ubuntu1_armhf.deb ... 233s Unpacking libcairo2:armhf (1.18.0-1ubuntu1) ... 233s Selecting previously unselected package libdatrie1:armhf. 233s Preparing to unpack .../014-libdatrie1_0.2.13-3_armhf.deb ... 233s Unpacking libdatrie1:armhf (0.2.13-3) ... 233s Selecting previously unselected package libdeflate0:armhf. 233s Preparing to unpack .../015-libdeflate0_1.19-1_armhf.deb ... 233s Unpacking libdeflate0:armhf (1.19-1) ... 233s Selecting previously unselected package libgfortran5:armhf. 233s Preparing to unpack .../016-libgfortran5_14-20240315-1ubuntu1_armhf.deb ... 233s Unpacking libgfortran5:armhf (14-20240315-1ubuntu1) ... 233s Selecting previously unselected package libgomp1:armhf. 233s Preparing to unpack .../017-libgomp1_14-20240315-1ubuntu1_armhf.deb ... 233s Unpacking libgomp1:armhf (14-20240315-1ubuntu1) ... 233s Selecting previously unselected package libgraphite2-3:armhf. 233s Preparing to unpack .../018-libgraphite2-3_1.3.14-2_armhf.deb ... 233s Unpacking libgraphite2-3:armhf (1.3.14-2) ... 233s Selecting previously unselected package libharfbuzz0b:armhf. 233s Preparing to unpack .../019-libharfbuzz0b_8.3.0-2build1_armhf.deb ... 233s Unpacking libharfbuzz0b:armhf (8.3.0-2build1) ... 233s Selecting previously unselected package x11-common. 233s Preparing to unpack .../020-x11-common_1%3a7.7+23ubuntu2_all.deb ... 233s Unpacking x11-common (1:7.7+23ubuntu2) ... 233s Selecting previously unselected package libice6:armhf. 233s Preparing to unpack .../021-libice6_2%3a1.0.10-1build2_armhf.deb ... 233s Unpacking libice6:armhf (2:1.0.10-1build2) ... 233s Selecting previously unselected package libjpeg-turbo8:armhf. 233s Preparing to unpack .../022-libjpeg-turbo8_2.1.5-2ubuntu1_armhf.deb ... 233s Unpacking libjpeg-turbo8:armhf (2.1.5-2ubuntu1) ... 233s Selecting previously unselected package libjpeg8:armhf. 233s Preparing to unpack .../023-libjpeg8_8c-2ubuntu11_armhf.deb ... 233s Unpacking libjpeg8:armhf (8c-2ubuntu11) ... 233s Selecting previously unselected package libjs-bootstrap. 233s Preparing to unpack .../024-libjs-bootstrap_3.4.1+dfsg-3_all.deb ... 233s Unpacking libjs-bootstrap (3.4.1+dfsg-3) ... 233s Selecting previously unselected package libjs-highlight.js. 233s Preparing to unpack .../025-libjs-highlight.js_9.18.5+dfsg1-2_all.deb ... 233s Unpacking libjs-highlight.js (9.18.5+dfsg1-2) ... 233s Selecting previously unselected package libjs-jquery. 233s Preparing to unpack .../026-libjs-jquery_3.6.1+dfsg+~3.5.14-1_all.deb ... 233s Unpacking libjs-jquery (3.6.1+dfsg+~3.5.14-1) ... 233s Selecting previously unselected package libjs-jquery-datatables. 233s Preparing to unpack .../027-libjs-jquery-datatables_1.11.5+dfsg-2_all.deb ... 233s Unpacking libjs-jquery-datatables (1.11.5+dfsg-2) ... 233s Selecting previously unselected package liblapack3:armhf. 233s Preparing to unpack .../028-liblapack3_3.12.0-3_armhf.deb ... 233s Unpacking liblapack3:armhf (3.12.0-3) ... 233s Selecting previously unselected package liblerc4:armhf. 233s Preparing to unpack .../029-liblerc4_4.0.0+ds-4ubuntu1_armhf.deb ... 233s Unpacking liblerc4:armhf (4.0.0+ds-4ubuntu1) ... 233s Selecting previously unselected package libthai-data. 233s Preparing to unpack .../030-libthai-data_0.1.29-2_all.deb ... 233s Unpacking libthai-data (0.1.29-2) ... 234s Selecting previously unselected package libthai0:armhf. 234s Preparing to unpack .../031-libthai0_0.1.29-2_armhf.deb ... 234s Unpacking libthai0:armhf (0.1.29-2) ... 234s Selecting previously unselected package libpango-1.0-0:armhf. 234s Preparing to unpack .../032-libpango-1.0-0_1.52.1+ds-1_armhf.deb ... 234s Unpacking libpango-1.0-0:armhf (1.52.1+ds-1) ... 234s Selecting previously unselected package libpangoft2-1.0-0:armhf. 234s Preparing to unpack .../033-libpangoft2-1.0-0_1.52.1+ds-1_armhf.deb ... 234s Unpacking libpangoft2-1.0-0:armhf (1.52.1+ds-1) ... 234s Selecting previously unselected package libpangocairo-1.0-0:armhf. 234s Preparing to unpack .../034-libpangocairo-1.0-0_1.52.1+ds-1_armhf.deb ... 234s Unpacking libpangocairo-1.0-0:armhf (1.52.1+ds-1) ... 234s Selecting previously unselected package libpaper1:armhf. 234s Preparing to unpack .../035-libpaper1_1.1.29_armhf.deb ... 234s Unpacking libpaper1:armhf (1.1.29) ... 234s Selecting previously unselected package libpaper-utils. 234s Preparing to unpack .../036-libpaper-utils_1.1.29_armhf.deb ... 234s Unpacking libpaper-utils (1.1.29) ... 234s Selecting previously unselected package libsharpyuv0:armhf. 234s Preparing to unpack .../037-libsharpyuv0_1.3.2-0.4build2_armhf.deb ... 234s Unpacking libsharpyuv0:armhf (1.3.2-0.4build2) ... 234s Selecting previously unselected package libsm6:armhf. 234s Preparing to unpack .../038-libsm6_2%3a1.2.3-1build2_armhf.deb ... 234s Unpacking libsm6:armhf (2:1.2.3-1build2) ... 234s Selecting previously unselected package libtcl8.6:armhf. 234s Preparing to unpack .../039-libtcl8.6_8.6.14+dfsg-1_armhf.deb ... 234s Unpacking libtcl8.6:armhf (8.6.14+dfsg-1) ... 234s Selecting previously unselected package libjbig0:armhf. 234s Preparing to unpack .../040-libjbig0_2.1-6.1ubuntu1_armhf.deb ... 234s Unpacking libjbig0:armhf (2.1-6.1ubuntu1) ... 234s Selecting previously unselected package libwebp7:armhf. 234s Preparing to unpack .../041-libwebp7_1.3.2-0.4build2_armhf.deb ... 234s Unpacking libwebp7:armhf (1.3.2-0.4build2) ... 234s Selecting previously unselected package libtiff6:armhf. 234s Preparing to unpack .../042-libtiff6_4.5.1+git230720-4ubuntu1_armhf.deb ... 234s Unpacking libtiff6:armhf (4.5.1+git230720-4ubuntu1) ... 234s Selecting previously unselected package libxft2:armhf. 234s Preparing to unpack .../043-libxft2_2.3.6-1_armhf.deb ... 234s Unpacking libxft2:armhf (2.3.6-1) ... 234s Selecting previously unselected package libxss1:armhf. 234s Preparing to unpack .../044-libxss1_1%3a1.2.3-1build2_armhf.deb ... 234s Unpacking libxss1:armhf (1:1.2.3-1build2) ... 234s Selecting previously unselected package libtk8.6:armhf. 234s Preparing to unpack .../045-libtk8.6_8.6.14-1_armhf.deb ... 234s Unpacking libtk8.6:armhf (8.6.14-1) ... 234s Selecting previously unselected package libxt6t64:armhf. 234s Preparing to unpack .../046-libxt6t64_1%3a1.2.1-1.2_armhf.deb ... 234s Unpacking libxt6t64:armhf (1:1.2.1-1.2) ... 234s Selecting previously unselected package zip. 234s Preparing to unpack .../047-zip_3.0-13_armhf.deb ... 234s Unpacking zip (3.0-13) ... 234s Selecting previously unselected package unzip. 234s Preparing to unpack .../048-unzip_6.0-28ubuntu3_armhf.deb ... 234s Unpacking unzip (6.0-28ubuntu3) ... 234s Selecting previously unselected package xdg-utils. 234s Preparing to unpack .../049-xdg-utils_1.1.3-4.1ubuntu3_all.deb ... 234s Unpacking xdg-utils (1.1.3-4.1ubuntu3) ... 234s Selecting previously unselected package r-base-core. 234s Preparing to unpack .../050-r-base-core_4.3.3-2build1_armhf.deb ... 234s Unpacking r-base-core (4.3.3-2build1) ... 234s Selecting previously unselected package r-cran-littler. 234s Preparing to unpack .../051-r-cran-littler_0.3.19-1_armhf.deb ... 234s Unpacking r-cran-littler (0.3.19-1) ... 234s Selecting previously unselected package littler. 234s Preparing to unpack .../052-littler_0.3.19-1_all.deb ... 234s Unpacking littler (0.3.19-1) ... 234s Selecting previously unselected package node-normalize.css. 234s Preparing to unpack .../053-node-normalize.css_8.0.1-5_all.deb ... 234s Unpacking node-normalize.css (8.0.1-5) ... 234s Selecting previously unselected package r-cran-abind. 235s Preparing to unpack .../054-r-cran-abind_1.4-5-2_all.deb ... 235s Unpacking r-cran-abind (1.4-5-2) ... 235s Selecting previously unselected package r-cran-backports. 235s Preparing to unpack .../055-r-cran-backports_1.4.1-1_armhf.deb ... 235s Unpacking r-cran-backports (1.4.1-1) ... 235s Selecting previously unselected package r-cran-bdsmatrix. 235s Preparing to unpack .../056-r-cran-bdsmatrix_1.3-6-1_armhf.deb ... 235s Unpacking r-cran-bdsmatrix (1.3-6-1) ... 235s Selecting previously unselected package r-cran-bit. 235s Preparing to unpack .../057-r-cran-bit_4.0.5-1_armhf.deb ... 235s Unpacking r-cran-bit (4.0.5-1) ... 235s Selecting previously unselected package r-cran-bit64. 235s Preparing to unpack .../058-r-cran-bit64_4.0.5-1_armhf.deb ... 235s Unpacking r-cran-bit64 (4.0.5-1) ... 235s Selecting previously unselected package r-cran-boot. 235s Preparing to unpack .../059-r-cran-boot_1.3-30-1_all.deb ... 235s Unpacking r-cran-boot (1.3-30-1) ... 235s Selecting previously unselected package r-cran-brio. 235s Preparing to unpack .../060-r-cran-brio_1.1.4-1_armhf.deb ... 235s Unpacking r-cran-brio (1.1.4-1) ... 235s Selecting previously unselected package r-cran-cli. 235s Preparing to unpack .../061-r-cran-cli_3.6.2-1_armhf.deb ... 235s Unpacking r-cran-cli (3.6.2-1) ... 235s Selecting previously unselected package r-cran-generics. 235s Preparing to unpack .../062-r-cran-generics_0.1.3-1_all.deb ... 235s Unpacking r-cran-generics (0.1.3-1) ... 235s Selecting previously unselected package r-cran-glue. 235s Preparing to unpack .../063-r-cran-glue_1.7.0-1_armhf.deb ... 235s Unpacking r-cran-glue (1.7.0-1) ... 235s Selecting previously unselected package r-cran-rlang. 235s Preparing to unpack .../064-r-cran-rlang_1.1.3-1_armhf.deb ... 235s Unpacking r-cran-rlang (1.1.3-1) ... 235s Selecting previously unselected package r-cran-lifecycle. 235s Preparing to unpack .../065-r-cran-lifecycle_1.0.4+dfsg-1_all.deb ... 235s Unpacking r-cran-lifecycle (1.0.4+dfsg-1) ... 235s Selecting previously unselected package r-cran-magrittr. 235s Preparing to unpack .../066-r-cran-magrittr_2.0.3-1_armhf.deb ... 235s Unpacking r-cran-magrittr (2.0.3-1) ... 235s Selecting previously unselected package r-cran-fansi. 235s Preparing to unpack .../067-r-cran-fansi_1.0.5-1_armhf.deb ... 235s Unpacking r-cran-fansi (1.0.5-1) ... 235s Selecting previously unselected package r-cran-utf8. 235s Preparing to unpack .../068-r-cran-utf8_1.2.4-1_armhf.deb ... 235s Unpacking r-cran-utf8 (1.2.4-1) ... 235s Selecting previously unselected package r-cran-vctrs. 235s Preparing to unpack .../069-r-cran-vctrs_0.6.5-1_armhf.deb ... 235s Unpacking r-cran-vctrs (0.6.5-1) ... 235s Selecting previously unselected package r-cran-pillar. 235s Preparing to unpack .../070-r-cran-pillar_1.9.0+dfsg-1_all.deb ... 235s Unpacking r-cran-pillar (1.9.0+dfsg-1) ... 235s Selecting previously unselected package r-cran-r6. 235s Preparing to unpack .../071-r-cran-r6_2.5.1-1_all.deb ... 235s Unpacking r-cran-r6 (2.5.1-1) ... 235s Selecting previously unselected package r-cran-pkgconfig. 235s Preparing to unpack .../072-r-cran-pkgconfig_2.0.3-2build1_all.deb ... 235s Unpacking r-cran-pkgconfig (2.0.3-2build1) ... 235s Selecting previously unselected package r-cran-tibble. 235s Preparing to unpack .../073-r-cran-tibble_3.2.1+dfsg-2_armhf.deb ... 235s Unpacking r-cran-tibble (3.2.1+dfsg-2) ... 235s Selecting previously unselected package r-cran-withr. 235s Preparing to unpack .../074-r-cran-withr_3.0.0+dfsg-1_all.deb ... 235s Unpacking r-cran-withr (3.0.0+dfsg-1) ... 235s Selecting previously unselected package r-cran-tidyselect. 235s Preparing to unpack .../075-r-cran-tidyselect_1.2.0+dfsg-1_armhf.deb ... 235s Unpacking r-cran-tidyselect (1.2.0+dfsg-1) ... 235s Selecting previously unselected package r-cran-dplyr. 235s Preparing to unpack .../076-r-cran-dplyr_1.1.4-1_armhf.deb ... 235s Unpacking r-cran-dplyr (1.1.4-1) ... 235s Selecting previously unselected package r-cran-ellipsis. 235s Preparing to unpack .../077-r-cran-ellipsis_0.3.2-2_armhf.deb ... 235s Unpacking r-cran-ellipsis (0.3.2-2) ... 235s Selecting previously unselected package r-cran-purrr. 235s Preparing to unpack .../078-r-cran-purrr_1.0.2-1_armhf.deb ... 235s Unpacking r-cran-purrr (1.0.2-1) ... 235s Selecting previously unselected package r-cran-stringi. 235s Preparing to unpack .../079-r-cran-stringi_1.8.3-1_armhf.deb ... 235s Unpacking r-cran-stringi (1.8.3-1) ... 236s Selecting previously unselected package r-cran-stringr. 236s Preparing to unpack .../080-r-cran-stringr_1.5.1-1_all.deb ... 236s Unpacking r-cran-stringr (1.5.1-1) ... 236s Selecting previously unselected package r-cran-cpp11. 236s Preparing to unpack .../081-r-cran-cpp11_0.4.7-1_all.deb ... 236s Unpacking r-cran-cpp11 (0.4.7-1) ... 236s Selecting previously unselected package r-cran-tidyr. 236s Preparing to unpack .../082-r-cran-tidyr_1.3.1-1_armhf.deb ... 236s Unpacking r-cran-tidyr (1.3.1-1) ... 236s Selecting previously unselected package r-cran-broom. 236s Preparing to unpack .../083-r-cran-broom_1.0.5+dfsg-1_all.deb ... 236s Unpacking r-cran-broom (1.0.5+dfsg-1) ... 236s Selecting previously unselected package r-cran-ps. 236s Preparing to unpack .../084-r-cran-ps_1.7.6-1_armhf.deb ... 236s Unpacking r-cran-ps (1.7.6-1) ... 236s Selecting previously unselected package r-cran-processx. 236s Preparing to unpack .../085-r-cran-processx_3.8.3-1_armhf.deb ... 236s Unpacking r-cran-processx (3.8.3-1) ... 236s Selecting previously unselected package r-cran-callr. 236s Preparing to unpack .../086-r-cran-callr_3.7.5-1_all.deb ... 236s Unpacking r-cran-callr (3.7.5-1) ... 236s Selecting previously unselected package r-cran-cardata. 236s Preparing to unpack .../087-r-cran-cardata_3.0.5-1_all.deb ... 236s Unpacking r-cran-cardata (3.0.5-1) ... 236s Selecting previously unselected package r-cran-mass. 236s Preparing to unpack .../088-r-cran-mass_7.3-60.0.1-1_armhf.deb ... 236s Unpacking r-cran-mass (7.3-60.0.1-1) ... 236s Selecting previously unselected package r-cran-lattice. 236s Preparing to unpack .../089-r-cran-lattice_0.22-5-1_armhf.deb ... 236s Unpacking r-cran-lattice (0.22-5-1) ... 236s Selecting previously unselected package r-cran-nlme. 236s Preparing to unpack .../090-r-cran-nlme_3.1.164-1_armhf.deb ... 236s Unpacking r-cran-nlme (3.1.164-1) ... 236s Selecting previously unselected package r-cran-matrix. 236s Preparing to unpack .../091-r-cran-matrix_1.6-5-1_armhf.deb ... 236s Unpacking r-cran-matrix (1.6-5-1) ... 236s Selecting previously unselected package r-cran-mgcv. 236s Preparing to unpack .../092-r-cran-mgcv_1.9-1-1_armhf.deb ... 236s Unpacking r-cran-mgcv (1.9-1-1) ... 236s Selecting previously unselected package r-cran-nnet. 236s Preparing to unpack .../093-r-cran-nnet_7.3-19-2_armhf.deb ... 236s Unpacking r-cran-nnet (7.3-19-2) ... 236s Selecting previously unselected package r-cran-pkgkitten. 236s Preparing to unpack .../094-r-cran-pkgkitten_0.2.3-1_all.deb ... 236s Unpacking r-cran-pkgkitten (0.2.3-1) ... 236s Selecting previously unselected package r-cran-rcpp. 236s Preparing to unpack .../095-r-cran-rcpp_1.0.12-1_armhf.deb ... 236s Unpacking r-cran-rcpp (1.0.12-1) ... 236s Selecting previously unselected package r-cran-minqa. 236s Preparing to unpack .../096-r-cran-minqa_1.2.6-1_armhf.deb ... 236s Unpacking r-cran-minqa (1.2.6-1) ... 236s Selecting previously unselected package libnlopt0:armhf. 236s Preparing to unpack .../097-libnlopt0_2.7.1-5build2_armhf.deb ... 236s Unpacking libnlopt0:armhf (2.7.1-5build2) ... 236s Selecting previously unselected package r-cran-desc. 236s Preparing to unpack .../098-r-cran-desc_1.4.3-1_all.deb ... 236s Unpacking r-cran-desc (1.4.3-1) ... 236s Selecting previously unselected package r-cran-digest. 236s Preparing to unpack .../099-r-cran-digest_0.6.34-1_armhf.deb ... 236s Unpacking r-cran-digest (0.6.34-1) ... 236s Selecting previously unselected package r-cran-evaluate. 236s Preparing to unpack .../100-r-cran-evaluate_0.23-1_all.deb ... 236s Unpacking r-cran-evaluate (0.23-1) ... 237s Selecting previously unselected package r-cran-jsonlite. 237s Preparing to unpack .../101-r-cran-jsonlite_1.8.8+dfsg-1_armhf.deb ... 237s Unpacking r-cran-jsonlite (1.8.8+dfsg-1) ... 237s Selecting previously unselected package r-cran-crayon. 237s Preparing to unpack .../102-r-cran-crayon_1.5.2-1_all.deb ... 237s Unpacking r-cran-crayon (1.5.2-1) ... 237s Selecting previously unselected package r-cran-fs. 237s Preparing to unpack .../103-r-cran-fs_1.6.3+dfsg-1build1_armhf.deb ... 237s Unpacking r-cran-fs (1.6.3+dfsg-1build1) ... 237s Selecting previously unselected package r-cran-pkgbuild. 237s Preparing to unpack .../104-r-cran-pkgbuild_1.4.3-1_all.deb ... 237s Unpacking r-cran-pkgbuild (1.4.3-1) ... 237s Selecting previously unselected package r-cran-rprojroot. 237s Preparing to unpack .../105-r-cran-rprojroot_2.0.4-1_all.deb ... 237s Unpacking r-cran-rprojroot (2.0.4-1) ... 237s Selecting previously unselected package r-cran-pkgload. 237s Preparing to unpack .../106-r-cran-pkgload_1.3.4-1_all.deb ... 237s Unpacking r-cran-pkgload (1.3.4-1) ... 237s Selecting previously unselected package r-cran-praise. 237s Preparing to unpack .../107-r-cran-praise_1.0.0-4build1_all.deb ... 237s Unpacking r-cran-praise (1.0.0-4build1) ... 237s Selecting previously unselected package r-cran-diffobj. 237s Preparing to unpack .../108-r-cran-diffobj_0.3.5-1_armhf.deb ... 237s Unpacking r-cran-diffobj (0.3.5-1) ... 237s Selecting previously unselected package r-cran-rematch2. 237s Preparing to unpack .../109-r-cran-rematch2_2.1.2-2build1_all.deb ... 237s Unpacking r-cran-rematch2 (2.1.2-2build1) ... 237s Selecting previously unselected package r-cran-waldo. 237s Preparing to unpack .../110-r-cran-waldo_0.5.2-1build1_all.deb ... 237s Unpacking r-cran-waldo (0.5.2-1build1) ... 237s Selecting previously unselected package r-cran-testthat. 237s Preparing to unpack .../111-r-cran-testthat_3.2.1-1_armhf.deb ... 237s Unpacking r-cran-testthat (3.2.1-1) ... 237s Selecting previously unselected package r-cran-nloptr. 237s Preparing to unpack .../112-r-cran-nloptr_2.0.3-1_armhf.deb ... 237s Unpacking r-cran-nloptr (2.0.3-1) ... 237s Selecting previously unselected package r-cran-rcppeigen. 237s Preparing to unpack .../113-r-cran-rcppeigen_0.3.4.0.0-1_armhf.deb ... 237s Unpacking r-cran-rcppeigen (0.3.4.0.0-1) ... 237s Selecting previously unselected package r-cran-statmod. 237s Preparing to unpack .../114-r-cran-statmod_1.5.0-1_armhf.deb ... 237s Unpacking r-cran-statmod (1.5.0-1) ... 237s Selecting previously unselected package r-cran-lme4. 237s Preparing to unpack .../115-r-cran-lme4_1.1-35.1-4_armhf.deb ... 237s Unpacking r-cran-lme4 (1.1-35.1-4) ... 237s Selecting previously unselected package r-cran-numderiv. 237s Preparing to unpack .../116-r-cran-numderiv_2016.8-1.1-3_all.deb ... 237s Unpacking r-cran-numderiv (2016.8-1.1-3) ... 237s Selecting previously unselected package r-cran-xfun. 237s Preparing to unpack .../117-r-cran-xfun_0.41+dfsg-1_armhf.deb ... 237s Unpacking r-cran-xfun (0.41+dfsg-1) ... 237s Selecting previously unselected package r-cran-highr. 237s Preparing to unpack .../118-r-cran-highr_0.10+dfsg-1_all.deb ... 237s Unpacking r-cran-highr (0.10+dfsg-1) ... 237s Selecting previously unselected package r-cran-yaml. 237s Preparing to unpack .../119-r-cran-yaml_2.3.8-1_armhf.deb ... 237s Unpacking r-cran-yaml (2.3.8-1) ... 237s Selecting previously unselected package libjs-mathjax. 237s Preparing to unpack .../120-libjs-mathjax_2.7.9+dfsg-1_all.deb ... 237s Unpacking libjs-mathjax (2.7.9+dfsg-1) ... 238s Selecting previously unselected package r-cran-knitr. 238s Preparing to unpack .../121-r-cran-knitr_1.45+dfsg-1_all.deb ... 238s Unpacking r-cran-knitr (1.45+dfsg-1) ... 238s Selecting previously unselected package r-cran-pbkrtest. 238s Preparing to unpack .../122-r-cran-pbkrtest_0.5.2-2_all.deb ... 238s Unpacking r-cran-pbkrtest (0.5.2-2) ... 238s Selecting previously unselected package r-cran-sparsem. 238s Preparing to unpack .../123-r-cran-sparsem_1.81-1_armhf.deb ... 238s Unpacking r-cran-sparsem (1.81-1) ... 238s Selecting previously unselected package r-cran-matrixmodels. 238s Preparing to unpack .../124-r-cran-matrixmodels_0.5-3-1_all.deb ... 238s Unpacking r-cran-matrixmodels (0.5-3-1) ... 238s Selecting previously unselected package r-cran-survival. 238s Preparing to unpack .../125-r-cran-survival_3.5-8-1_armhf.deb ... 238s Unpacking r-cran-survival (3.5-8-1) ... 238s Selecting previously unselected package r-cran-matrixstats. 238s Preparing to unpack .../126-r-cran-matrixstats_1.2.0-1_armhf.deb ... 238s Unpacking r-cran-matrixstats (1.2.0-1) ... 238s Selecting previously unselected package r-cran-rcpparmadillo. 238s Preparing to unpack .../127-r-cran-rcpparmadillo_0.12.8.1.0-1_armhf.deb ... 238s Unpacking r-cran-rcpparmadillo (0.12.8.1.0-1) ... 238s Selecting previously unselected package r-cran-gtable. 238s Preparing to unpack .../128-r-cran-gtable_0.3.4+dfsg-1_all.deb ... 238s Unpacking r-cran-gtable (0.3.4+dfsg-1) ... 238s Selecting previously unselected package r-cran-isoband. 239s Preparing to unpack .../129-r-cran-isoband_0.2.7-1_armhf.deb ... 239s Unpacking r-cran-isoband (0.2.7-1) ... 239s Selecting previously unselected package r-cran-farver. 239s Preparing to unpack .../130-r-cran-farver_2.1.1-1_armhf.deb ... 239s Unpacking r-cran-farver (2.1.1-1) ... 239s Selecting previously unselected package r-cran-labeling. 239s Preparing to unpack .../131-r-cran-labeling_0.4.3-1_all.deb ... 239s Unpacking r-cran-labeling (0.4.3-1) ... 239s Selecting previously unselected package r-cran-colorspace. 239s Preparing to unpack .../132-r-cran-colorspace_2.1-0+dfsg-1_armhf.deb ... 239s Unpacking r-cran-colorspace (2.1-0+dfsg-1) ... 239s Selecting previously unselected package r-cran-munsell. 239s Preparing to unpack .../133-r-cran-munsell_0.5.0-2build1_all.deb ... 239s Unpacking r-cran-munsell (0.5.0-2build1) ... 239s Selecting previously unselected package r-cran-rcolorbrewer. 239s Preparing to unpack .../134-r-cran-rcolorbrewer_1.1-3-1build1_all.deb ... 239s Unpacking r-cran-rcolorbrewer (1.1-3-1build1) ... 239s Selecting previously unselected package r-cran-viridislite. 239s Preparing to unpack .../135-r-cran-viridislite_0.4.2-2_all.deb ... 239s Unpacking r-cran-viridislite (0.4.2-2) ... 239s Selecting previously unselected package r-cran-scales. 239s Preparing to unpack .../136-r-cran-scales_1.3.0-1_all.deb ... 239s Unpacking r-cran-scales (1.3.0-1) ... 239s Selecting previously unselected package r-cran-ggplot2. 239s Preparing to unpack .../137-r-cran-ggplot2_3.4.4+dfsg-1_all.deb ... 239s Unpacking r-cran-ggplot2 (3.4.4+dfsg-1) ... 239s Selecting previously unselected package r-cran-class. 239s Preparing to unpack .../138-r-cran-class_7.3-22-2_armhf.deb ... 239s Unpacking r-cran-class (7.3-22-2) ... 239s Selecting previously unselected package r-cran-proxy. 239s Preparing to unpack .../139-r-cran-proxy_0.4-27-1_armhf.deb ... 239s Unpacking r-cran-proxy (0.4-27-1) ... 239s Selecting previously unselected package r-cran-e1071. 239s Preparing to unpack .../140-r-cran-e1071_1.7-14-1_armhf.deb ... 239s Unpacking r-cran-e1071 (1.7-14-1) ... 239s Selecting previously unselected package r-cran-codetools. 239s Preparing to unpack .../141-r-cran-codetools_0.2-19-1_all.deb ... 239s Unpacking r-cran-codetools (0.2-19-1) ... 239s Selecting previously unselected package r-cran-iterators. 239s Preparing to unpack .../142-r-cran-iterators_1.0.14-1_all.deb ... 239s Unpacking r-cran-iterators (1.0.14-1) ... 239s Selecting previously unselected package r-cran-foreach. 239s Preparing to unpack .../143-r-cran-foreach_1.5.2-1_all.deb ... 239s Unpacking r-cran-foreach (1.5.2-1) ... 239s Selecting previously unselected package r-cran-data.table. 239s Preparing to unpack .../144-r-cran-data.table_1.14.10+dfsg-1_armhf.deb ... 239s Unpacking r-cran-data.table (1.14.10+dfsg-1) ... 239s Selecting previously unselected package r-cran-modelmetrics. 239s Preparing to unpack .../145-r-cran-modelmetrics_1.2.2.2-1build1_armhf.deb ... 239s Unpacking r-cran-modelmetrics (1.2.2.2-1build1) ... 239s Selecting previously unselected package r-cran-plyr. 239s Preparing to unpack .../146-r-cran-plyr_1.8.9-1_armhf.deb ... 239s Unpacking r-cran-plyr (1.8.9-1) ... 239s Selecting previously unselected package r-cran-proc. 239s Preparing to unpack .../147-r-cran-proc_1.18.5-1_armhf.deb ... 239s Unpacking r-cran-proc (1.18.5-1) ... 239s Selecting previously unselected package r-cran-tzdb. 239s Preparing to unpack .../148-r-cran-tzdb_0.4.0-2_armhf.deb ... 239s Unpacking r-cran-tzdb (0.4.0-2) ... 239s Selecting previously unselected package r-cran-clock. 239s Preparing to unpack .../149-r-cran-clock_0.7.0-1.1_armhf.deb ... 239s Unpacking r-cran-clock (0.7.0-1.1) ... 239s Selecting previously unselected package r-cran-gower. 239s Preparing to unpack .../150-r-cran-gower_1.0.1-1_armhf.deb ... 239s Unpacking r-cran-gower (1.0.1-1) ... 239s Selecting previously unselected package r-cran-hardhat. 239s Preparing to unpack .../151-r-cran-hardhat_1.3.1+dfsg-1_all.deb ... 239s Unpacking r-cran-hardhat (1.3.1+dfsg-1) ... 239s Selecting previously unselected package r-cran-rpart. 239s Preparing to unpack .../152-r-cran-rpart_4.1.23-1_armhf.deb ... 239s Unpacking r-cran-rpart (4.1.23-1) ... 239s Selecting previously unselected package r-cran-shape. 239s Preparing to unpack .../153-r-cran-shape_1.4.6-1_all.deb ... 239s Unpacking r-cran-shape (1.4.6-1) ... 240s Selecting previously unselected package r-cran-diagram. 240s Preparing to unpack .../154-r-cran-diagram_1.6.5-2_all.deb ... 240s Unpacking r-cran-diagram (1.6.5-2) ... 240s Selecting previously unselected package r-cran-kernsmooth. 240s Preparing to unpack .../155-r-cran-kernsmooth_2.23-22-1_armhf.deb ... 240s Unpacking r-cran-kernsmooth (2.23-22-1) ... 240s Selecting previously unselected package r-cran-globals. 240s Preparing to unpack .../156-r-cran-globals_0.16.2-1_all.deb ... 240s Unpacking r-cran-globals (0.16.2-1) ... 240s Selecting previously unselected package r-cran-listenv. 240s Preparing to unpack .../157-r-cran-listenv_0.9.1+dfsg-1_all.deb ... 240s Unpacking r-cran-listenv (0.9.1+dfsg-1) ... 240s Selecting previously unselected package r-cran-parallelly. 240s Preparing to unpack .../158-r-cran-parallelly_1.37.1-1_armhf.deb ... 240s Unpacking r-cran-parallelly (1.37.1-1) ... 240s Selecting previously unselected package r-cran-future. 240s Preparing to unpack .../159-r-cran-future_1.33.1+dfsg-1_all.deb ... 240s Unpacking r-cran-future (1.33.1+dfsg-1) ... 240s Selecting previously unselected package r-cran-future.apply. 240s Preparing to unpack .../160-r-cran-future.apply_1.11.1+dfsg-1_all.deb ... 240s Unpacking r-cran-future.apply (1.11.1+dfsg-1) ... 240s Selecting previously unselected package r-cran-progressr. 240s Preparing to unpack .../161-r-cran-progressr_0.14.0-1_all.deb ... 240s Unpacking r-cran-progressr (0.14.0-1) ... 240s Selecting previously unselected package r-cran-squarem. 240s Preparing to unpack .../162-r-cran-squarem_2021.1-1_all.deb ... 240s Unpacking r-cran-squarem (2021.1-1) ... 240s Selecting previously unselected package r-cran-lava. 240s Preparing to unpack .../163-r-cran-lava_1.7.3+dfsg-1_all.deb ... 240s Unpacking r-cran-lava (1.7.3+dfsg-1) ... 240s Selecting previously unselected package r-cran-prodlim. 240s Preparing to unpack .../164-r-cran-prodlim_2023.08.28-1_armhf.deb ... 240s Unpacking r-cran-prodlim (2023.08.28-1) ... 240s Selecting previously unselected package r-cran-ipred. 240s Preparing to unpack .../165-r-cran-ipred_0.9-14-1_armhf.deb ... 240s Unpacking r-cran-ipred (0.9-14-1) ... 240s Selecting previously unselected package r-cran-timechange. 240s Preparing to unpack .../166-r-cran-timechange_0.3.0-1_armhf.deb ... 240s Unpacking r-cran-timechange (0.3.0-1) ... 240s Selecting previously unselected package r-cran-lubridate. 240s Preparing to unpack .../167-r-cran-lubridate_1.9.3+dfsg-1_armhf.deb ... 240s Unpacking r-cran-lubridate (1.9.3+dfsg-1) ... 240s Selecting previously unselected package r-cran-timedate. 240s Preparing to unpack .../168-r-cran-timedate_4032.109-1_armhf.deb ... 240s Unpacking r-cran-timedate (4032.109-1) ... 240s Selecting previously unselected package r-cran-recipes. 240s Preparing to unpack .../169-r-cran-recipes_1.0.9+dfsg-1_all.deb ... 240s Unpacking r-cran-recipes (1.0.9+dfsg-1) ... 240s Selecting previously unselected package r-cran-reshape2. 240s Preparing to unpack .../170-r-cran-reshape2_1.4.4-2build1_armhf.deb ... 240s Unpacking r-cran-reshape2 (1.4.4-2build1) ... 240s Selecting previously unselected package r-cran-caret. 240s Preparing to unpack .../171-r-cran-caret_6.0-94+dfsg-1_armhf.deb ... 240s Unpacking r-cran-caret (6.0-94+dfsg-1) ... 240s Selecting previously unselected package r-cran-conquer. 240s Preparing to unpack .../172-r-cran-conquer_1.3.3-1_armhf.deb ... 240s Unpacking r-cran-conquer (1.3.3-1) ... 240s Selecting previously unselected package r-cran-quantreg. 240s Preparing to unpack .../173-r-cran-quantreg_5.97-1_armhf.deb ... 240s Unpacking r-cran-quantreg (5.97-1) ... 240s Selecting previously unselected package r-cran-sp. 240s Preparing to unpack .../174-r-cran-sp_1%3a2.1-2+dfsg-1_armhf.deb ... 240s Unpacking r-cran-sp (1:2.1-2+dfsg-1) ... 240s Selecting previously unselected package r-cran-foreign. 241s Preparing to unpack .../175-r-cran-foreign_0.8.86-1_armhf.deb ... 241s Unpacking r-cran-foreign (0.8.86-1) ... 241s Selecting previously unselected package r-cran-maptools. 241s Preparing to unpack .../176-r-cran-maptools_1%3a1.1-8+dfsg-1_armhf.deb ... 241s Unpacking r-cran-maptools (1:1.1-8+dfsg-1) ... 241s Selecting previously unselected package r-cran-forcats. 241s Preparing to unpack .../177-r-cran-forcats_1.0.0-1_all.deb ... 241s Unpacking r-cran-forcats (1.0.0-1) ... 241s Selecting previously unselected package r-cran-hms. 241s Preparing to unpack .../178-r-cran-hms_1.1.3-1_all.deb ... 241s Unpacking r-cran-hms (1.1.3-1) ... 241s Selecting previously unselected package r-cran-clipr. 241s Preparing to unpack .../179-r-cran-clipr_0.8.0-1_all.deb ... 241s Unpacking r-cran-clipr (0.8.0-1) ... 241s Selecting previously unselected package r-cran-prettyunits. 241s Preparing to unpack .../180-r-cran-prettyunits_1.2.0-1_all.deb ... 241s Unpacking r-cran-prettyunits (1.2.0-1) ... 241s Selecting previously unselected package r-cran-progress. 241s Preparing to unpack .../181-r-cran-progress_1.2.3-1_all.deb ... 241s Unpacking r-cran-progress (1.2.3-1) ... 241s Selecting previously unselected package r-cran-vroom. 241s Preparing to unpack .../182-r-cran-vroom_1.6.5-1_armhf.deb ... 241s Unpacking r-cran-vroom (1.6.5-1) ... 241s Selecting previously unselected package r-cran-readr. 241s Preparing to unpack .../183-r-cran-readr_2.1.5-1_armhf.deb ... 241s Unpacking r-cran-readr (2.1.5-1) ... 241s Selecting previously unselected package r-cran-haven. 241s Preparing to unpack .../184-r-cran-haven_2.5.4-1_armhf.deb ... 241s Unpacking r-cran-haven (2.5.4-1) ... 241s Selecting previously unselected package r-cran-curl. 241s Preparing to unpack .../185-r-cran-curl_5.2.0+dfsg-1build1_armhf.deb ... 241s Unpacking r-cran-curl (5.2.0+dfsg-1build1) ... 241s Selecting previously unselected package r-cran-rematch. 241s Preparing to unpack .../186-r-cran-rematch_2.0.0-1_all.deb ... 241s Unpacking r-cran-rematch (2.0.0-1) ... 241s Selecting previously unselected package r-cran-cellranger. 241s Preparing to unpack .../187-r-cran-cellranger_1.1.0-3_all.deb ... 241s Unpacking r-cran-cellranger (1.1.0-3) ... 241s Selecting previously unselected package r-cran-readxl. 241s Preparing to unpack .../188-r-cran-readxl_1.4.3-1_armhf.deb ... 241s Unpacking r-cran-readxl (1.4.3-1) ... 241s Selecting previously unselected package r-cran-writexl. 241s Preparing to unpack .../189-r-cran-writexl_1.5.0-1_armhf.deb ... 241s Unpacking r-cran-writexl (1.5.0-1) ... 241s Selecting previously unselected package r-cran-r.methodss3. 241s Preparing to unpack .../190-r-cran-r.methodss3_1.8.2-1_all.deb ... 241s Unpacking r-cran-r.methodss3 (1.8.2-1) ... 241s Selecting previously unselected package r-cran-r.oo. 241s Preparing to unpack .../191-r-cran-r.oo_1.26.0-1_all.deb ... 241s Unpacking r-cran-r.oo (1.26.0-1) ... 241s Selecting previously unselected package r-cran-r.utils. 241s Preparing to unpack .../192-r-cran-r.utils_2.12.3-1_all.deb ... 241s Unpacking r-cran-r.utils (2.12.3-1) ... 241s Selecting previously unselected package r-cran-zip. 241s Preparing to unpack .../193-r-cran-zip_2.3.1-1_armhf.deb ... 241s Unpacking r-cran-zip (2.3.1-1) ... 241s Selecting previously unselected package r-cran-openxlsx. 241s Preparing to unpack .../194-r-cran-openxlsx_4.2.5.2-1_armhf.deb ... 241s Unpacking r-cran-openxlsx (4.2.5.2-1) ... 241s Selecting previously unselected package r-cran-rio. 241s Preparing to unpack .../195-r-cran-rio_1.0.1-1_all.deb ... 241s Unpacking r-cran-rio (1.0.1-1) ... 241s Selecting previously unselected package r-cran-car. 241s Preparing to unpack .../196-r-cran-car_3.1-2-2_all.deb ... 241s Unpacking r-cran-car (3.1-2-2) ... 241s Selecting previously unselected package r-cran-collapse. 241s Preparing to unpack .../197-r-cran-collapse_2.0.10-1_armhf.deb ... 241s Unpacking r-cran-collapse (2.0.10-1) ... 242s Selecting previously unselected package r-cran-formula. 242s Preparing to unpack .../198-r-cran-formula_1.2-5-1_all.deb ... 242s Unpacking r-cran-formula (1.2-5-1) ... 242s Selecting previously unselected package r-cran-zoo. 242s Preparing to unpack .../199-r-cran-zoo_1.8-12-2_armhf.deb ... 242s Unpacking r-cran-zoo (1.8-12-2) ... 242s Selecting previously unselected package r-cran-lmtest. 242s Preparing to unpack .../200-r-cran-lmtest_0.9.40-1_armhf.deb ... 242s Unpacking r-cran-lmtest (0.9.40-1) ... 242s Selecting previously unselected package r-cran-misctools. 242s Preparing to unpack .../201-r-cran-misctools_0.6-28-1_all.deb ... 242s Unpacking r-cran-misctools (0.6-28-1) ... 242s Selecting previously unselected package r-cran-sandwich. 242s Preparing to unpack .../202-r-cran-sandwich_3.1-0-1_all.deb ... 242s Unpacking r-cran-sandwich (3.1-0-1) ... 242s Selecting previously unselected package r-cran-maxlik. 242s Preparing to unpack .../203-r-cran-maxlik_1.5-2-1_all.deb ... 242s Unpacking r-cran-maxlik (1.5-2-1) ... 242s Selecting previously unselected package r-cran-rbibutils. 242s Preparing to unpack .../204-r-cran-rbibutils_2.2.16-1_armhf.deb ... 242s Unpacking r-cran-rbibutils (2.2.16-1) ... 242s Selecting previously unselected package r-cran-rdpack. 242s Preparing to unpack .../205-r-cran-rdpack_2.6-1_all.deb ... 242s Unpacking r-cran-rdpack (2.6-1) ... 242s Selecting previously unselected package r-cran-plm. 242s Preparing to unpack .../206-r-cran-plm_2.6-3-1_all.deb ... 242s Unpacking r-cran-plm (2.6-3-1) ... 242s Selecting previously unselected package r-cran-systemfit. 242s Preparing to unpack .../207-r-cran-systemfit_1.1-30-1_all.deb ... 242s Unpacking r-cran-systemfit (1.1-30-1) ... 242s Selecting previously unselected package autopkgtest-satdep. 242s Preparing to unpack .../208-1-autopkgtest-satdep.deb ... 242s Unpacking autopkgtest-satdep (0) ... 242s Setting up libgraphite2-3:armhf (1.3.14-2) ... 242s Setting up libpixman-1-0:armhf (0.42.2-1) ... 242s Setting up libsharpyuv0:armhf (1.3.2-0.4build2) ... 242s Setting up libpaper1:armhf (1.1.29) ... 242s 242s Creating config file /etc/papersize with new version 242s Setting up fonts-mathjax (2.7.9+dfsg-1) ... 242s Setting up liblerc4:armhf (4.0.0+ds-4ubuntu1) ... 242s Setting up libjs-mathjax (2.7.9+dfsg-1) ... 242s Setting up libxrender1:armhf (1:0.9.10-1.1) ... 242s Setting up libdatrie1:armhf (0.2.13-3) ... 242s Setting up libxcb-render0:armhf (1.15-1) ... 242s Setting up fonts-glyphicons-halflings (1.009~3.4.1+dfsg-3) ... 242s Setting up unzip (6.0-28ubuntu3) ... 242s Setting up x11-common (1:7.7+23ubuntu2) ... 243s Setting up libdeflate0:armhf (1.19-1) ... 243s Setting up libnlopt0:armhf (2.7.1-5build2) ... 243s Setting up libxcb-shm0:armhf (1.15-1) ... 243s Setting up libpaper-utils (1.1.29) ... 243s Setting up libgomp1:armhf (14-20240315-1ubuntu1) ... 243s Setting up libjbig0:armhf (2.1-6.1ubuntu1) ... 243s Setting up zip (3.0-13) ... 243s Setting up libblas3:armhf (3.12.0-3) ... 243s update-alternatives: using /usr/lib/arm-linux-gnueabihf/blas/libblas.so.3 to provide /usr/lib/arm-linux-gnueabihf/libblas.so.3 (libblas.so.3-arm-linux-gnueabihf) in auto mode 243s Setting up libfreetype6:armhf (2.13.2+dfsg-1build2) ... 243s Setting up fonts-dejavu-mono (2.37-8) ... 243s Setting up libtcl8.6:armhf (8.6.14+dfsg-1) ... 243s Setting up fonts-dejavu-core (2.37-8) ... 243s Setting up libjpeg-turbo8:armhf (2.1.5-2ubuntu1) ... 243s Setting up libgfortran5:armhf (14-20240315-1ubuntu1) ... 243s Setting up libwebp7:armhf (1.3.2-0.4build2) ... 243s Setting up libjs-highlight.js (9.18.5+dfsg1-2) ... 243s Setting up libharfbuzz0b:armhf (8.3.0-2build1) ... 243s Setting up libthai-data (0.1.29-2) ... 243s Setting up libxss1:armhf (1:1.2.3-1build2) ... 243s Setting up libjs-jquery (3.6.1+dfsg+~3.5.14-1) ... 243s Setting up node-normalize.css (8.0.1-5) ... 243s Setting up xdg-utils (1.1.3-4.1ubuntu3) ... 243s update-alternatives: using /usr/bin/xdg-open to provide /usr/bin/open (open) in auto mode 243s Setting up libjs-bootstrap (3.4.1+dfsg-3) ... 243s Setting up libjpeg8:armhf (8c-2ubuntu11) ... 243s Setting up libice6:armhf (2:1.0.10-1build2) ... 243s Setting up liblapack3:armhf (3.12.0-3) ... 243s update-alternatives: using /usr/lib/arm-linux-gnueabihf/lapack/liblapack.so.3 to provide /usr/lib/arm-linux-gnueabihf/liblapack.so.3 (liblapack.so.3-arm-linux-gnueabihf) in auto mode 243s Setting up fontconfig-config (2.15.0-1.1ubuntu1) ... 243s Setting up libjs-jquery-datatables (1.11.5+dfsg-2) ... 243s Setting up libthai0:armhf (0.1.29-2) ... 243s Setting up libtiff6:armhf (4.5.1+git230720-4ubuntu1) ... 243s Setting up libfontconfig1:armhf 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245s Setting up r-cran-zip (2.3.1-1) ... 245s Setting up r-cran-viridislite (0.4.2-2) ... 245s Setting up r-cran-sparsem (1.81-1) ... 245s Setting up r-cran-statmod (1.5.0-1) ... 245s Setting up r-cran-nnet (7.3-19-2) ... 245s Setting up r-cran-clipr (0.8.0-1) ... 245s Setting up r-cran-proxy (0.4-27-1) ... 245s Setting up r-cran-r6 (2.5.1-1) ... 245s Setting up r-cran-pkgkitten (0.2.3-1) ... 245s Setting up r-cran-numderiv (2016.8-1.1-3) ... 245s Setting up r-cran-magrittr (2.0.3-1) ... 245s Setting up r-cran-littler (0.3.19-1) ... 245s Setting up r-cran-fs (1.6.3+dfsg-1build1) ... 245s Setting up r-cran-rcpp (1.0.12-1) ... 245s Setting up r-cran-curl (5.2.0+dfsg-1build1) ... 245s Setting up r-cran-codetools (0.2-19-1) ... 245s Setting up r-cran-brio (1.1.4-1) ... 245s Setting up r-cran-boot (1.3-30-1) ... 245s Setting up r-cran-diffobj (0.3.5-1) ... 245s Setting up r-cran-rematch (2.0.0-1) ... 245s Setting up r-cran-rlang (1.1.3-1) ... 245s Setting up r-cran-matrixstats (1.2.0-1) ... 245s Setting up r-cran-listenv (0.9.1+dfsg-1) ... 245s Setting up littler (0.3.19-1) ... 245s Setting up r-cran-xfun (0.41+dfsg-1) ... 245s Setting up r-cran-withr (3.0.0+dfsg-1) ... 245s Setting up r-cran-backports (1.4.1-1) ... 245s Setting up r-cran-processx (3.8.3-1) ... 245s Setting up r-cran-praise (1.0.0-4build1) ... 245s Setting up r-cran-generics (0.1.3-1) ... 245s Setting up r-cran-iterators (1.0.14-1) ... 245s Setting up r-cran-abind (1.4-5-2) ... 245s Setting up r-cran-digest (0.6.34-1) ... 245s Setting up r-cran-yaml (2.3.8-1) ... 245s Setting up r-cran-gower (1.0.1-1) ... 245s Setting up r-cran-evaluate (0.23-1) ... 245s Setting up r-cran-timedate (4032.109-1) ... 245s Setting up r-cran-highr (0.10+dfsg-1) ... 245s Setting up r-cran-foreach (1.5.2-1) ... 245s Setting up r-cran-prettyunits (1.2.0-1) ... 245s Setting up r-cran-fansi (1.0.5-1) ... 245s Setting up r-cran-cardata (3.0.5-1) ... 245s Setting up r-cran-mass (7.3-60.0.1-1) ... 245s Setting up r-cran-collapse (2.0.10-1) ... 245s Setting up r-cran-bdsmatrix (1.3-6-1) ... 245s Setting up r-cran-data.table (1.14.10+dfsg-1) ... 245s Setting up r-cran-glue (1.7.0-1) ... 245s Setting up r-cran-foreign (0.8.86-1) ... 245s Setting up r-cran-writexl (1.5.0-1) ... 245s Setting up r-cran-bit (4.0.5-1) ... 245s Setting up r-cran-cli (3.6.2-1) ... 245s Setting up r-cran-rbibutils (2.2.16-1) ... 245s Setting up r-cran-lifecycle (1.0.4+dfsg-1) ... 245s Setting up r-cran-rprojroot (2.0.4-1) ... 245s Setting up r-cran-bit64 (4.0.5-1) ... 245s Setting up r-cran-progressr (0.14.0-1) ... 245s Setting up r-cran-shape (1.4.6-1) ... 245s Setting up r-cran-r.methodss3 (1.8.2-1) ... 245s Setting up r-cran-jsonlite (1.8.8+dfsg-1) ... 245s Setting up r-cran-pkgconfig (2.0.3-2build1) ... 245s Setting up r-cran-sp (1:2.1-2+dfsg-1) ... 245s Setting up r-cran-utf8 (1.2.4-1) ... 245s Setting up r-cran-colorspace (2.1-0+dfsg-1) ... 245s Setting up r-cran-parallelly (1.37.1-1) ... 245s Setting up r-cran-stringi (1.8.3-1) ... 245s Setting up r-cran-cpp11 (0.4.7-1) ... 245s Setting up r-cran-plyr (1.8.9-1) ... 245s Setting up r-cran-rcolorbrewer (1.1-3-1build1) ... 245s Setting up r-cran-isoband (0.2.7-1) ... 245s Setting up r-cran-diagram (1.6.5-2) ... 245s Setting up r-cran-gtable (0.3.4+dfsg-1) ... 245s Setting up r-cran-zoo (1.8-12-2) ... 245s Setting up r-cran-matrix (1.6-5-1) ... 245s Setting up r-cran-kernsmooth (2.23-22-1) ... 245s Setting up r-cran-knitr (1.45+dfsg-1) ... 245s Setting up r-cran-mgcv (1.9-1-1) ... 245s Setting up r-cran-lmtest (0.9.40-1) ... 245s Setting up r-cran-rcpparmadillo (0.12.8.1.0-1) ... 245s Setting up r-cran-tzdb (0.4.0-2) ... 245s Setting up r-cran-globals (0.16.2-1) ... 245s Setting up r-cran-maptools (1:1.1-8+dfsg-1) ... 245s Setting up r-cran-vctrs (0.6.5-1) ... 245s Setting up r-cran-rcppeigen (0.3.4.0.0-1) ... 245s Setting up r-cran-pillar (1.9.0+dfsg-1) ... 245s Setting up r-cran-ellipsis (0.3.2-2) ... 245s Setting up r-cran-minqa (1.2.6-1) ... 245s Setting up 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Setting up r-cran-readr (2.1.5-1) ... 245s Setting up r-cran-waldo (0.5.2-1build1) ... 245s Setting up r-cran-tidyr (1.3.1-1) ... 245s Setting up r-cran-recipes (1.0.9+dfsg-1) ... 245s Setting up r-cran-readxl (1.4.3-1) ... 245s Setting up r-cran-haven (2.5.4-1) ... 245s Setting up r-cran-caret (6.0-94+dfsg-1) ... 245s Setting up r-cran-testthat (3.2.1-1) ... 245s Setting up r-cran-broom (1.0.5+dfsg-1) ... 245s Setting up r-cran-conquer (1.3.3-1) ... 245s Setting up r-cran-rio (1.0.1-1) ... 245s Setting up r-cran-nloptr (2.0.3-1) ... 245s Setting up r-cran-quantreg (5.97-1) ... 245s Setting up r-cran-lme4 (1.1-35.1-4) ... 245s Setting up r-cran-pbkrtest (0.5.2-2) ... 245s Setting up r-cran-car (3.1-2-2) ... 245s Setting up r-cran-systemfit (1.1-30-1) ... 245s Setting up autopkgtest-satdep (0) ... 245s Processing triggers for man-db (2.12.0-3build4) ... 246s Processing triggers for install-info (7.1-3build1) ... 246s Processing triggers for libc-bin (2.39-0ubuntu6) ... 278s (Reading database ... 79749 files and directories currently installed.) 278s Removing autopkgtest-satdep (0) ... 284s autopkgtest [12:47:43]: test run-unit-test: [----------------------- 286s BEGIN TEST KleinI.R 287s 287s R version 4.3.3 (2024-02-29) -- "Angel Food Cake" 287s Copyright (C) 2024 The R Foundation for Statistical Computing 287s Platform: arm-unknown-linux-gnueabihf (32-bit) 287s 287s R is free software and comes with ABSOLUTELY NO WARRANTY. 287s You are welcome to redistribute it under certain conditions. 287s Type 'license()' or 'licence()' for distribution details. 287s 287s R is a collaborative project with many contributors. 287s Type 'contributors()' for more information and 287s 'citation()' on how to cite R or R packages in publications. 287s 287s Type 'demo()' for some demos, 'help()' for on-line help, or 287s 'help.start()' for an HTML browser interface to help. 287s Type 'q()' to quit R. 287s 287s > library( "systemfit" ) 287s Loading required package: Matrix 288s Loading required package: car 288s Loading required package: carData 288s Loading required package: lmtest 288s Loading required package: zoo 288s 288s Attaching package: ‘zoo’ 288s 288s The following objects are masked from ‘package:base’: 288s 288s as.Date, as.Date.numeric 288s 288s 288s Please cite the 'systemfit' package as: 288s Arne Henningsen and Jeff D. Hamann (2007). systemfit: A Package for Estimating Systems of Simultaneous Equations in R. Journal of Statistical Software 23(4), 1-40. http://www.jstatsoft.org/v23/i04/. 288s 288s If you have questions, suggestions, or comments regarding the 'systemfit' package, please use a forum or 'tracker' at systemfit's R-Forge site: 288s https://r-forge.r-project.org/projects/systemfit/ 288s > library( "sandwich" ) 288s > options( warn = 1 ) 288s > options( digits = 3 ) 288s > 288s > data( "KleinI" ) 288s > eqConsump <- consump ~ corpProf + corpProfLag + wages 288s > eqInvest <- invest ~ corpProf + corpProfLag + capitalLag 288s > eqPrivWage <- privWage ~ gnp + gnpLag + trend 288s > inst <- ~ govExp + taxes + govWage + trend + capitalLag + corpProfLag + gnpLag 288s > system <- list( Consumption = eqConsump, Investment = eqInvest, 288s + PrivateWages = eqPrivWage ) 288s > restrict <- c( "Consumption_corpProf + Investment_capitalLag = 0" ) 288s > restrict2 <- c( restrict, "Consumption_corpProfLag - PrivateWages_trend = 0" ) 288s > 288s > for( dataNo in 1:5 ) { 288s + # set some values of some variables to NA 288s + if( dataNo == 2 ) { 288s + KleinI$gnpLag[ 7 ] <- NA 288s + } else if( dataNo == 3 ) { 288s + KleinI$wages[ 10 ] <- NA 288s + } else if( dataNo == 4 ) { 288s + KleinI$corpProf[ 13 ] <- NA 288s + } else if( dataNo == 5 ) { 288s + KleinI$invest[ 16 ] <- NA 288s + } 288s + 288s + # single-equation OLS 288s + lmConsump <- lm( eqConsump, data = KleinI ) 288s + lmInvest <- lm( eqInvest, data = KleinI ) 288s + lmPrivWage <- lm( eqPrivWage, data = KleinI ) 288s + 288s + for( methodNo in 1:5 ) { 288s + method <- c( "OLS", "2SLS", "SUR", "3SLS", "3SLS" )[ methodNo ] 288s + maxit <- ifelse( methodNo == 5, 500, 1 ) 288s + 288s + cat( "> \n> # ", ifelse( maxit == 1, "", "I" ), method, "\n", sep = "" ) 288s + if( method %in% c( "OLS", "WLS", "SUR" ) ) { 288s + kleinModel <- systemfit( system, method = method, data = KleinI, 288s + methodResidCov = ifelse( method == "OLS", "geomean", "noDfCor" ), 288s + maxit = maxit ) 288s + } else { 288s + kleinModel <- systemfit( system, method = method, data = KleinI, 288s + inst = inst, methodResidCov = "noDfCor", maxit = maxit ) 288s + } 288s + cat( "> summary\n" ) 288s + print( summary( kleinModel ) ) 288s + if( method == "OLS" ) { 288s + cat( "compare coef with single-equation OLS\n" ) 288s + print( all.equal( coef( kleinModel ), 288s + c( coef( lmConsump ), coef( lmInvest ), coef( lmPrivWage ) ), 288s + check.attributes = FALSE ) ) 288s + } 288s + cat( "> residuals\n" ) 288s + print( residuals( kleinModel ) ) 288s + cat( "> fitted\n" ) 288s + print( fitted( kleinModel ) ) 288s + cat( "> predict\n" ) 288s + print( predict( kleinModel, se.fit = TRUE, 288s + interval = ifelse( methodNo %in% c( 1, 4 ), "prediction", "confidence" ), 288s + useDfSys = methodNo %in% c( 1, 3, 5 ) ) ) 288s + cat( "> model.frame\n" ) 288s + if( methodNo == 1 ) { 288s + mfOls <- model.frame( kleinModel ) 288s + print( mfOls ) 288s + } else if( methodNo == 2 ) { 288s + mf2sls <- model.frame( kleinModel ) 288s + print( mf2sls ) 288s + cat( "> Frames of instrumental variables\n" ) 288s + for( i in 1:3 ){ 288s + print( kleinModel$eq[[ i ]]$modelInst ) 288s + } 288s + } else if( methodNo == 3 ) { 288s + print( all.equal( mfOls, model.frame( kleinModel ) ) ) 288s + } else { 288s + print( all.equal( mf2sls, model.frame( kleinModel ) ) ) 288s + } 288s + cat( "> model.matrix\n" ) 288s + if( methodNo == 1 ) { 288s + mmOls <- model.matrix( kleinModel ) 288s + print( mmOls ) 288s + } else { 288s + print( all.equal( mmOls, model.matrix( kleinModel ) ) ) 288s + } 288s + if( methodNo == 2 ) { 288s + cat( "> matrix of instrumental variables\n" ) 288s + print( model.matrix( kleinModel, which = "z" ) ) 288s + cat( "> matrix of fitted regressors\n" ) 288s + print( round( model.matrix( kleinModel, which = "xHat" ), digits = 7 ) ) 288s + } 288s + cat( "> nobs\n" ) 288s + print( nobs( kleinModel ) ) 288s + cat( "> linearHypothesis\n" ) 288s + print( linearHypothesis( kleinModel, restrict ) ) 288s + print( linearHypothesis( kleinModel, restrict, test = "F" ) ) 288s + print( linearHypothesis( kleinModel, restrict, test = "Chisq" ) ) 288s + print( linearHypothesis( kleinModel, restrict2 ) ) 288s + print( linearHypothesis( kleinModel, restrict2, test = "F" ) ) 288s + print( linearHypothesis( kleinModel, restrict2, test = "Chisq" ) ) 288s + cat( "> logLik\n" ) 288s + print( logLik( kleinModel ) ) 288s + print( logLik( kleinModel, residCovDiag = TRUE ) ) 288s + if( method == "OLS" ) { 288s + cat( "compare log likelihood value with single-equation OLS\n" ) 288s + print( all.equal( logLik( kleinModel, residCovDiag = TRUE ), 288s + logLik( lmConsump ) + logLik( lmInvest ) + logLik( lmPrivWage ), 288s + check.attributes = FALSE ) ) 288s + } 288s + 288s + cat( "Estimating function\n" ) 288s + print( round( estfun( kleinModel ), digits = 7 ) ) 288s + print( all.equal( colSums( estfun( kleinModel ) ), 288s + rep( 0, ncol( estfun( kleinModel ) ) ), check.attributes = FALSE ) ) 288s + 288s + cat( "> Bread\n" ) 288s + print( bread( kleinModel ) ) 288s + } 288s + } 288s > 288s > # OLS 288s > summary 288s 288s systemfit results 288s method: OLS 288s 288s N DF SSR detRCov OLS-R2 McElroy-R2 288s system 63 51 45.2 0.371 0.977 0.991 288s 288s N DF SSR MSE RMSE R2 Adj R2 288s Consumption 21 17 17.9 1.052 1.026 0.981 0.978 288s Investment 21 17 17.3 1.019 1.009 0.931 0.919 288s PrivateWages 21 17 10.0 0.589 0.767 0.987 0.985 288s 288s The covariance matrix of the residuals 288s Consumption Investment PrivateWages 288s Consumption 1.0517 0.0611 -0.470 288s Investment 0.0611 1.0190 0.150 288s PrivateWages -0.4704 0.1497 0.589 288s 288s The correlations of the residuals 288s Consumption Investment PrivateWages 288s Consumption 1.0000 0.0591 -0.598 288s Investment 0.0591 1.0000 0.193 288s PrivateWages -0.5979 0.1933 1.000 288s 288s 288s OLS estimates for 'Consumption' (equation 1) 288s Model Formula: consump ~ corpProf + corpProfLag + wages 288s 288s Estimate Std. Error t value Pr(>|t|) 288s (Intercept) 16.2366 1.3027 12.46 5.6e-10 *** 288s corpProf 0.1929 0.0912 2.12 0.049 * 288s corpProfLag 0.0899 0.0906 0.99 0.335 288s wages 0.7962 0.0399 19.93 3.2e-13 *** 288s --- 288s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 288s 288s Residual standard error: 1.026 on 17 degrees of freedom 288s Number of observations: 21 Degrees of Freedom: 17 288s SSR: 17.879 MSE: 1.052 Root MSE: 1.026 288s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.978 288s 288s 288s OLS estimates for 'Investment' (equation 2) 288s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 288s 288s Estimate Std. Error t value Pr(>|t|) 288s (Intercept) 10.1258 5.4655 1.85 0.08137 . 288s corpProf 0.4796 0.0971 4.94 0.00012 *** 288s corpProfLag 0.3330 0.1009 3.30 0.00421 ** 288s capitalLag -0.1118 0.0267 -4.18 0.00062 *** 288s --- 288s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 288s 288s Residual standard error: 1.009 on 17 degrees of freedom 288s Number of observations: 21 Degrees of Freedom: 17 288s SSR: 17.323 MSE: 1.019 Root MSE: 1.009 288s Multiple R-Squared: 0.931 Adjusted R-Squared: 0.919 288s 289s 289s OLS estimates for 'PrivateWages' (equation 3) 289s Model Formula: privWage ~ gnp + gnpLag + trend 289s 289s Estimate Std. Error t value Pr(>|t|) 289s (Intercept) 1.4970 1.2700 1.18 0.25474 289s gnp 0.4395 0.0324 13.56 1.5e-10 *** 289s gnpLag 0.1461 0.0374 3.90 0.00114 ** 289s trend 0.1302 0.0319 4.08 0.00078 *** 289s --- 289s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 289s 289s Residual standard error: 0.767 on 17 degrees of freedom 289s Number of observations: 21 Degrees of Freedom: 17 289s SSR: 10.005 MSE: 0.589 Root MSE: 0.767 289s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 289s 289s compare coef with single-equation OLS 289s [1] TRUE 289s > residuals 289s Consumption Investment PrivateWages 289s 1 NA NA NA 289s 2 -0.32389 -0.0668 -1.2942 289s 3 -1.25001 -0.0476 0.2957 289s 4 -1.56574 1.2467 1.1877 289s 5 -0.49350 -1.3512 -0.1358 289s 6 0.00761 0.4154 -0.4654 289s 7 0.86910 1.4923 -0.4838 289s 8 1.33848 0.7889 -0.7281 289s 9 1.05498 -0.6317 0.3392 289s 10 -0.58856 1.0830 1.1957 289s 11 0.28231 0.2791 -0.1508 289s 12 -0.22965 0.0369 0.5942 289s 13 -0.32213 0.3659 0.1027 289s 14 0.32228 0.2237 0.4503 289s 15 -0.05801 -0.1728 0.2816 289s 16 -0.03466 0.0101 0.0138 289s 17 1.61650 0.9719 -0.8508 289s 18 -0.43597 0.0516 0.9956 289s 19 0.21005 -2.5656 -0.4688 289s 20 0.98920 -0.6866 -0.3795 289s 21 0.78508 -0.7807 -1.0909 289s 22 -2.17345 -0.6623 0.5917 289s > fitted 289s Consumption Investment PrivateWages 289s 1 NA NA NA 289s 2 42.2 -0.133 26.8 289s 3 46.3 1.948 29.0 289s 4 50.8 3.953 32.9 289s 5 51.1 4.351 34.0 289s 6 52.6 4.685 35.9 289s 7 54.2 4.108 37.9 289s 8 54.9 3.411 38.6 289s 9 56.2 3.632 38.9 289s 10 58.4 4.017 40.1 289s 11 54.7 0.721 38.1 289s 12 51.1 -3.437 33.9 289s 13 45.9 -6.566 28.9 289s 14 46.2 -5.324 28.0 289s 15 48.8 -2.827 30.3 289s 16 51.3 -1.310 33.2 289s 17 56.1 1.128 37.7 289s 18 59.1 1.948 40.0 289s 19 57.3 0.666 38.7 289s 20 60.6 1.987 42.0 289s 21 64.2 4.081 46.1 289s 22 71.9 5.562 52.7 289s > predict 289s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 289s 1 NA NA NA NA 289s 2 42.2 0.462 40.0 44.5 289s 3 46.3 0.518 43.9 48.6 289s 4 50.8 0.341 48.6 52.9 289s 5 51.1 0.396 48.9 53.3 289s 6 52.6 0.397 50.4 54.8 289s 7 54.2 0.359 52.0 56.4 289s 8 54.9 0.327 52.7 57.0 289s 9 56.2 0.350 54.1 58.4 289s 10 58.4 0.370 56.2 60.6 289s 11 54.7 0.606 52.3 57.1 289s 12 51.1 0.484 48.9 53.4 289s 13 45.9 0.629 43.5 48.3 289s 14 46.2 0.602 43.8 48.6 289s 15 48.8 0.374 46.6 50.9 289s 16 51.3 0.333 49.2 53.5 289s 17 56.1 0.366 53.9 58.3 289s 18 59.1 0.321 57.0 61.3 289s 19 57.3 0.371 55.1 59.5 289s 20 60.6 0.434 58.4 62.8 289s 21 64.2 0.425 62.0 66.4 289s 22 71.9 0.666 69.4 74.3 289s Investment.pred Investment.se.fit Investment.lwr Investment.upr 289s 1 NA NA NA NA 289s 2 -0.133 0.607 -2.498 2.231 289s 3 1.948 0.499 -0.313 4.208 289s 4 3.953 0.449 1.735 6.171 289s 5 4.351 0.371 2.192 6.510 289s 6 4.685 0.349 2.540 6.829 289s 7 4.108 0.329 1.976 6.239 289s 8 3.411 0.292 1.301 5.521 289s 9 3.632 0.389 1.460 5.804 289s 10 4.017 0.447 1.801 6.233 289s 11 0.721 0.601 -1.638 3.080 289s 12 -3.437 0.507 -5.704 -1.169 289s 13 -6.566 0.616 -8.940 -4.192 289s 14 -5.324 0.694 -7.783 -2.865 289s 15 -2.827 0.373 -4.988 -0.667 289s 16 -1.310 0.320 -3.436 0.816 289s 17 1.128 0.347 -1.015 3.271 289s 18 1.948 0.243 -0.136 4.033 289s 19 0.666 0.312 -1.456 2.787 289s 20 1.987 0.366 -0.169 4.143 289s 21 4.081 0.332 1.948 6.214 289s 22 5.562 0.461 3.334 7.790 289s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 289s 1 NA NA NA NA 289s 2 26.8 0.354 25.1 28.5 289s 3 29.0 0.355 27.3 30.7 289s 4 32.9 0.354 31.2 34.6 289s 5 34.0 0.269 32.4 35.7 289s 6 35.9 0.266 34.2 37.5 289s 7 37.9 0.266 36.3 39.5 289s 8 38.6 0.273 37.0 40.3 289s 9 38.9 0.261 37.2 40.5 289s 10 40.1 0.247 38.5 41.7 289s 11 38.1 0.354 36.4 39.7 289s 12 33.9 0.363 32.2 35.6 289s 13 28.9 0.429 27.1 30.7 289s 14 28.0 0.376 26.3 29.8 289s 15 30.3 0.371 28.6 32.0 289s 16 33.2 0.310 31.5 34.8 289s 17 37.7 0.305 36.0 39.3 289s 18 40.0 0.238 38.4 41.6 289s 19 38.7 0.357 37.0 40.4 289s 20 42.0 0.321 40.3 43.6 289s 21 46.1 0.335 44.4 47.8 289s 22 52.7 0.502 50.9 54.5 289s > model.frame 289s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 289s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 289s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 289s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 289s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 289s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 289s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 289s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 61.0 289s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 289s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 289s 10 57.8 21.7 21.1 45.3 5.1 211 41.3 67.0 64.5 289s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 289s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 289s 13 45.6 7.0 11.4 34.3 -6.2 213 29.0 44.3 53.4 289s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 289s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 289s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 289s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 289s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 289s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 289s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 289s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 289s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 289s trend 289s 1 -11 289s 2 -10 289s 3 -9 289s 4 -8 289s 5 -7 289s 6 -6 289s 7 -5 289s 8 -4 289s 9 -3 289s 10 -2 289s 11 -1 289s 12 0 289s 13 1 289s 14 2 289s 15 3 289s 16 4 289s 17 5 289s 18 6 289s 19 7 289s 20 8 289s 21 9 289s 22 10 289s > model.matrix 289s Consumption_(Intercept) Consumption_corpProf 289s Consumption_2 1 12.4 289s Consumption_3 1 16.9 289s Consumption_4 1 18.4 289s Consumption_5 1 19.4 289s Consumption_6 1 20.1 289s Consumption_7 1 19.6 289s Consumption_8 1 19.8 289s Consumption_9 1 21.1 289s Consumption_10 1 21.7 289s Consumption_11 1 15.6 289s Consumption_12 1 11.4 289s Consumption_13 1 7.0 289s Consumption_14 1 11.2 289s Consumption_15 1 12.3 289s Consumption_16 1 14.0 289s Consumption_17 1 17.6 289s Consumption_18 1 17.3 289s Consumption_19 1 15.3 289s Consumption_20 1 19.0 289s Consumption_21 1 21.1 289s Consumption_22 1 23.5 289s Investment_2 0 0.0 289s Investment_3 0 0.0 289s Investment_4 0 0.0 289s Investment_5 0 0.0 289s Investment_6 0 0.0 289s Investment_7 0 0.0 289s Investment_8 0 0.0 289s Investment_9 0 0.0 289s Investment_10 0 0.0 289s Investment_11 0 0.0 289s Investment_12 0 0.0 289s Investment_13 0 0.0 289s Investment_14 0 0.0 289s Investment_15 0 0.0 289s Investment_16 0 0.0 289s Investment_17 0 0.0 289s Investment_18 0 0.0 289s Investment_19 0 0.0 289s Investment_20 0 0.0 289s Investment_21 0 0.0 289s Investment_22 0 0.0 289s PrivateWages_2 0 0.0 289s PrivateWages_3 0 0.0 289s PrivateWages_4 0 0.0 289s PrivateWages_5 0 0.0 289s PrivateWages_6 0 0.0 289s PrivateWages_7 0 0.0 289s PrivateWages_8 0 0.0 289s PrivateWages_9 0 0.0 289s PrivateWages_10 0 0.0 289s PrivateWages_11 0 0.0 289s PrivateWages_12 0 0.0 289s PrivateWages_13 0 0.0 289s PrivateWages_14 0 0.0 289s PrivateWages_15 0 0.0 289s PrivateWages_16 0 0.0 289s PrivateWages_17 0 0.0 289s PrivateWages_18 0 0.0 289s PrivateWages_19 0 0.0 289s PrivateWages_20 0 0.0 289s PrivateWages_21 0 0.0 289s PrivateWages_22 0 0.0 289s Consumption_corpProfLag Consumption_wages 289s Consumption_2 12.7 28.2 289s Consumption_3 12.4 32.2 289s Consumption_4 16.9 37.0 289s Consumption_5 18.4 37.0 289s Consumption_6 19.4 38.6 289s Consumption_7 20.1 40.7 289s Consumption_8 19.6 41.5 289s Consumption_9 19.8 42.9 289s Consumption_10 21.1 45.3 289s Consumption_11 21.7 42.1 289s Consumption_12 15.6 39.3 289s Consumption_13 11.4 34.3 289s Consumption_14 7.0 34.1 289s Consumption_15 11.2 36.6 289s Consumption_16 12.3 39.3 289s Consumption_17 14.0 44.2 289s Consumption_18 17.6 47.7 289s Consumption_19 17.3 45.9 289s Consumption_20 15.3 49.4 289s Consumption_21 19.0 53.0 289s Consumption_22 21.1 61.8 289s Investment_2 0.0 0.0 289s Investment_3 0.0 0.0 289s Investment_4 0.0 0.0 289s Investment_5 0.0 0.0 289s Investment_6 0.0 0.0 289s Investment_7 0.0 0.0 289s Investment_8 0.0 0.0 289s Investment_9 0.0 0.0 289s Investment_10 0.0 0.0 289s Investment_11 0.0 0.0 289s Investment_12 0.0 0.0 289s Investment_13 0.0 0.0 289s Investment_14 0.0 0.0 289s Investment_15 0.0 0.0 289s Investment_16 0.0 0.0 289s Investment_17 0.0 0.0 289s Investment_18 0.0 0.0 289s Investment_19 0.0 0.0 289s Investment_20 0.0 0.0 289s Investment_21 0.0 0.0 289s Investment_22 0.0 0.0 289s PrivateWages_2 0.0 0.0 289s PrivateWages_3 0.0 0.0 289s PrivateWages_4 0.0 0.0 289s PrivateWages_5 0.0 0.0 289s PrivateWages_6 0.0 0.0 289s PrivateWages_7 0.0 0.0 289s PrivateWages_8 0.0 0.0 289s PrivateWages_9 0.0 0.0 289s PrivateWages_10 0.0 0.0 289s PrivateWages_11 0.0 0.0 289s PrivateWages_12 0.0 0.0 289s PrivateWages_13 0.0 0.0 289s PrivateWages_14 0.0 0.0 289s PrivateWages_15 0.0 0.0 289s PrivateWages_16 0.0 0.0 289s PrivateWages_17 0.0 0.0 289s PrivateWages_18 0.0 0.0 289s PrivateWages_19 0.0 0.0 289s PrivateWages_20 0.0 0.0 289s PrivateWages_21 0.0 0.0 289s PrivateWages_22 0.0 0.0 289s Investment_(Intercept) Investment_corpProf 289s Consumption_2 0 0.0 289s Consumption_3 0 0.0 289s Consumption_4 0 0.0 289s Consumption_5 0 0.0 289s Consumption_6 0 0.0 289s Consumption_7 0 0.0 289s Consumption_8 0 0.0 289s Consumption_9 0 0.0 289s Consumption_10 0 0.0 289s Consumption_11 0 0.0 289s Consumption_12 0 0.0 289s Consumption_13 0 0.0 289s Consumption_14 0 0.0 289s Consumption_15 0 0.0 289s Consumption_16 0 0.0 289s Consumption_17 0 0.0 289s Consumption_18 0 0.0 289s Consumption_19 0 0.0 289s Consumption_20 0 0.0 289s Consumption_21 0 0.0 289s Consumption_22 0 0.0 289s Investment_2 1 12.4 289s Investment_3 1 16.9 289s Investment_4 1 18.4 289s Investment_5 1 19.4 289s Investment_6 1 20.1 289s Investment_7 1 19.6 289s Investment_8 1 19.8 289s Investment_9 1 21.1 289s Investment_10 1 21.7 289s Investment_11 1 15.6 289s Investment_12 1 11.4 289s Investment_13 1 7.0 289s Investment_14 1 11.2 289s Investment_15 1 12.3 289s Investment_16 1 14.0 289s Investment_17 1 17.6 289s Investment_18 1 17.3 289s Investment_19 1 15.3 289s Investment_20 1 19.0 289s Investment_21 1 21.1 289s Investment_22 1 23.5 289s PrivateWages_2 0 0.0 289s PrivateWages_3 0 0.0 289s PrivateWages_4 0 0.0 289s PrivateWages_5 0 0.0 289s PrivateWages_6 0 0.0 289s PrivateWages_7 0 0.0 289s PrivateWages_8 0 0.0 289s PrivateWages_9 0 0.0 289s PrivateWages_10 0 0.0 289s PrivateWages_11 0 0.0 289s PrivateWages_12 0 0.0 289s PrivateWages_13 0 0.0 289s PrivateWages_14 0 0.0 289s PrivateWages_15 0 0.0 289s PrivateWages_16 0 0.0 289s PrivateWages_17 0 0.0 289s PrivateWages_18 0 0.0 289s PrivateWages_19 0 0.0 289s PrivateWages_20 0 0.0 289s PrivateWages_21 0 0.0 289s PrivateWages_22 0 0.0 289s Investment_corpProfLag Investment_capitalLag 289s Consumption_2 0.0 0 289s Consumption_3 0.0 0 289s Consumption_4 0.0 0 289s Consumption_5 0.0 0 289s Consumption_6 0.0 0 289s Consumption_7 0.0 0 289s Consumption_8 0.0 0 289s Consumption_9 0.0 0 289s Consumption_10 0.0 0 289s Consumption_11 0.0 0 289s Consumption_12 0.0 0 289s Consumption_13 0.0 0 289s Consumption_14 0.0 0 289s Consumption_15 0.0 0 289s Consumption_16 0.0 0 289s Consumption_17 0.0 0 289s Consumption_18 0.0 0 289s Consumption_19 0.0 0 289s Consumption_20 0.0 0 289s Consumption_21 0.0 0 289s Consumption_22 0.0 0 289s Investment_2 12.7 183 289s Investment_3 12.4 183 289s Investment_4 16.9 184 289s Investment_5 18.4 190 289s Investment_6 19.4 193 289s Investment_7 20.1 198 289s Investment_8 19.6 203 289s Investment_9 19.8 208 289s Investment_10 21.1 211 289s Investment_11 21.7 216 289s Investment_12 15.6 217 289s Investment_13 11.4 213 289s Investment_14 7.0 207 289s Investment_15 11.2 202 289s Investment_16 12.3 199 289s Investment_17 14.0 198 289s Investment_18 17.6 200 289s Investment_19 17.3 202 289s Investment_20 15.3 200 289s Investment_21 19.0 201 289s Investment_22 21.1 204 289s PrivateWages_2 0.0 0 289s PrivateWages_3 0.0 0 289s PrivateWages_4 0.0 0 289s PrivateWages_5 0.0 0 289s PrivateWages_6 0.0 0 289s PrivateWages_7 0.0 0 289s PrivateWages_8 0.0 0 289s PrivateWages_9 0.0 0 289s PrivateWages_10 0.0 0 289s PrivateWages_11 0.0 0 289s PrivateWages_12 0.0 0 289s PrivateWages_13 0.0 0 289s PrivateWages_14 0.0 0 289s PrivateWages_15 0.0 0 289s PrivateWages_16 0.0 0 289s PrivateWages_17 0.0 0 289s PrivateWages_18 0.0 0 289s PrivateWages_19 0.0 0 289s PrivateWages_20 0.0 0 289s PrivateWages_21 0.0 0 289s PrivateWages_22 0.0 0 289s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 289s Consumption_2 0 0.0 0.0 289s Consumption_3 0 0.0 0.0 289s Consumption_4 0 0.0 0.0 289s Consumption_5 0 0.0 0.0 289s Consumption_6 0 0.0 0.0 289s Consumption_7 0 0.0 0.0 289s Consumption_8 0 0.0 0.0 289s Consumption_9 0 0.0 0.0 289s Consumption_10 0 0.0 0.0 289s Consumption_11 0 0.0 0.0 289s Consumption_12 0 0.0 0.0 289s Consumption_13 0 0.0 0.0 289s Consumption_14 0 0.0 0.0 289s Consumption_15 0 0.0 0.0 289s Consumption_16 0 0.0 0.0 289s Consumption_17 0 0.0 0.0 289s Consumption_18 0 0.0 0.0 289s Consumption_19 0 0.0 0.0 289s Consumption_20 0 0.0 0.0 289s Consumption_21 0 0.0 0.0 289s Consumption_22 0 0.0 0.0 289s Investment_2 0 0.0 0.0 289s Investment_3 0 0.0 0.0 289s Investment_4 0 0.0 0.0 289s Investment_5 0 0.0 0.0 289s Investment_6 0 0.0 0.0 289s Investment_7 0 0.0 0.0 289s Investment_8 0 0.0 0.0 289s Investment_9 0 0.0 0.0 289s Investment_10 0 0.0 0.0 289s Investment_11 0 0.0 0.0 289s Investment_12 0 0.0 0.0 289s Investment_13 0 0.0 0.0 289s Investment_14 0 0.0 0.0 289s Investment_15 0 0.0 0.0 289s Investment_16 0 0.0 0.0 289s Investment_17 0 0.0 0.0 289s Investment_18 0 0.0 0.0 289s Investment_19 0 0.0 0.0 289s Investment_20 0 0.0 0.0 289s Investment_21 0 0.0 0.0 289s Investment_22 0 0.0 0.0 289s PrivateWages_2 1 45.6 44.9 289s PrivateWages_3 1 50.1 45.6 289s PrivateWages_4 1 57.2 50.1 289s PrivateWages_5 1 57.1 57.2 289s PrivateWages_6 1 61.0 57.1 289s PrivateWages_7 1 64.0 61.0 289s PrivateWages_8 1 64.4 64.0 289s PrivateWages_9 1 64.5 64.4 289s PrivateWages_10 1 67.0 64.5 289s PrivateWages_11 1 61.2 67.0 289s PrivateWages_12 1 53.4 61.2 289s PrivateWages_13 1 44.3 53.4 289s PrivateWages_14 1 45.1 44.3 289s PrivateWages_15 1 49.7 45.1 289s PrivateWages_16 1 54.4 49.7 289s PrivateWages_17 1 62.7 54.4 289s PrivateWages_18 1 65.0 62.7 289s PrivateWages_19 1 60.9 65.0 289s PrivateWages_20 1 69.5 60.9 289s PrivateWages_21 1 75.7 69.5 289s PrivateWages_22 1 88.4 75.7 289s PrivateWages_trend 289s Consumption_2 0 289s Consumption_3 0 289s Consumption_4 0 289s Consumption_5 0 289s Consumption_6 0 289s Consumption_7 0 289s Consumption_8 0 289s Consumption_9 0 289s Consumption_10 0 289s Consumption_11 0 289s Consumption_12 0 289s Consumption_13 0 289s Consumption_14 0 289s Consumption_15 0 289s Consumption_16 0 289s Consumption_17 0 289s Consumption_18 0 289s Consumption_19 0 289s Consumption_20 0 289s Consumption_21 0 289s Consumption_22 0 289s Investment_2 0 289s Investment_3 0 289s Investment_4 0 289s Investment_5 0 289s Investment_6 0 289s Investment_7 0 289s Investment_8 0 289s Investment_9 0 289s Investment_10 0 289s Investment_11 0 289s Investment_12 0 289s Investment_13 0 289s Investment_14 0 289s Investment_15 0 289s Investment_16 0 289s Investment_17 0 289s Investment_18 0 289s Investment_19 0 289s Investment_20 0 289s Investment_21 0 289s Investment_22 0 289s PrivateWages_2 -10 289s PrivateWages_3 -9 289s PrivateWages_4 -8 289s PrivateWages_5 -7 289s PrivateWages_6 -6 289s PrivateWages_7 -5 289s PrivateWages_8 -4 289s PrivateWages_9 -3 289s PrivateWages_10 -2 289s PrivateWages_11 -1 289s PrivateWages_12 0 289s PrivateWages_13 1 289s PrivateWages_14 2 289s PrivateWages_15 3 289s PrivateWages_16 4 289s PrivateWages_17 5 289s PrivateWages_18 6 289s PrivateWages_19 7 289s PrivateWages_20 8 289s PrivateWages_21 9 289s PrivateWages_22 10 289s > nobs 289s [1] 63 289s > linearHypothesis 289s Linear hypothesis test (Theil's F test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df F Pr(>F) 289s 1 52 289s 2 51 1 0.82 0.37 289s Linear hypothesis test (F statistic of a Wald test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df F Pr(>F) 289s 1 52 289s 2 51 1 0.73 0.4 289s Linear hypothesis test (Chi^2 statistic of a Wald test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df Chisq Pr(>Chisq) 289s 1 52 289s 2 51 1 0.73 0.39 289s Linear hypothesis test (Theil's F test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s Consumption_corpProfLag - PrivateWages_trend = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df F Pr(>F) 289s 1 53 289s 2 51 2 0.42 0.66 289s Linear hypothesis test (F statistic of a Wald test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s Consumption_corpProfLag - PrivateWages_trend = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df F Pr(>F) 289s 1 53 289s 2 51 2 0.37 0.69 289s Linear hypothesis test (Chi^2 statistic of a Wald test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s Consumption_corpProfLag - PrivateWages_trend = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df Chisq Pr(>Chisq) 289s 1 53 289s 2 51 2 0.74 0.69 289s > logLik 289s 'log Lik.' -72.3 (df=13) 289s 'log Lik.' -77.9 (df=13) 289s compare log likelihood value with single-equation OLS 289s [1] TRUE 289s Estimating function 289s Consumption_(Intercept) Consumption_corpProf 289s Consumption_2 -0.32389 -4.016 289s Consumption_3 -1.25001 -21.125 289s Consumption_4 -1.56574 -28.810 289s Consumption_5 -0.49350 -9.574 289s Consumption_6 0.00761 0.153 289s Consumption_7 0.86910 17.034 289s Consumption_8 1.33848 26.502 289s Consumption_9 1.05498 22.260 289s Consumption_10 -0.58856 -12.772 289s Consumption_11 0.28231 4.404 289s Consumption_12 -0.22965 -2.618 289s Consumption_13 -0.32213 -2.255 289s Consumption_14 0.32228 3.610 289s Consumption_15 -0.05801 -0.714 289s Consumption_16 -0.03466 -0.485 289s Consumption_17 1.61650 28.450 289s Consumption_18 -0.43597 -7.542 289s Consumption_19 0.21005 3.214 289s Consumption_20 0.98920 18.795 289s Consumption_21 0.78508 16.565 289s Consumption_22 -2.17345 -51.076 289s Investment_2 0.00000 0.000 289s Investment_3 0.00000 0.000 289s Investment_4 0.00000 0.000 289s Investment_5 0.00000 0.000 289s Investment_6 0.00000 0.000 289s Investment_7 0.00000 0.000 289s Investment_8 0.00000 0.000 289s Investment_9 0.00000 0.000 289s Investment_10 0.00000 0.000 289s Investment_11 0.00000 0.000 289s Investment_12 0.00000 0.000 289s Investment_13 0.00000 0.000 289s Investment_14 0.00000 0.000 289s Investment_15 0.00000 0.000 289s Investment_16 0.00000 0.000 289s Investment_17 0.00000 0.000 289s Investment_18 0.00000 0.000 289s Investment_19 0.00000 0.000 289s Investment_20 0.00000 0.000 289s Investment_21 0.00000 0.000 289s Investment_22 0.00000 0.000 289s PrivateWages_2 0.00000 0.000 289s PrivateWages_3 0.00000 0.000 289s PrivateWages_4 0.00000 0.000 289s PrivateWages_5 0.00000 0.000 289s PrivateWages_6 0.00000 0.000 289s PrivateWages_7 0.00000 0.000 289s PrivateWages_8 0.00000 0.000 289s PrivateWages_9 0.00000 0.000 289s PrivateWages_10 0.00000 0.000 289s PrivateWages_11 0.00000 0.000 289s PrivateWages_12 0.00000 0.000 289s PrivateWages_13 0.00000 0.000 289s PrivateWages_14 0.00000 0.000 289s PrivateWages_15 0.00000 0.000 289s PrivateWages_16 0.00000 0.000 289s PrivateWages_17 0.00000 0.000 289s PrivateWages_18 0.00000 0.000 289s PrivateWages_19 0.00000 0.000 289s PrivateWages_20 0.00000 0.000 289s PrivateWages_21 0.00000 0.000 289s PrivateWages_22 0.00000 0.000 289s Consumption_corpProfLag Consumption_wages 289s Consumption_2 -4.113 -9.134 289s Consumption_3 -15.500 -40.250 289s Consumption_4 -26.461 -57.932 289s Consumption_5 -9.080 -18.260 289s Consumption_6 0.148 0.294 289s Consumption_7 17.469 35.372 289s Consumption_8 26.234 55.547 289s Consumption_9 20.889 45.259 289s Consumption_10 -12.419 -26.662 289s Consumption_11 6.126 11.885 289s Consumption_12 -3.583 -9.025 289s Consumption_13 -3.672 -11.049 289s Consumption_14 2.256 10.990 289s Consumption_15 -0.650 -2.123 289s Consumption_16 -0.426 -1.362 289s Consumption_17 22.631 71.449 289s Consumption_18 -7.673 -20.796 289s Consumption_19 3.634 9.641 289s Consumption_20 15.135 48.867 289s Consumption_21 14.916 41.609 289s Consumption_22 -45.860 -134.319 289s Investment_2 0.000 0.000 289s Investment_3 0.000 0.000 289s Investment_4 0.000 0.000 289s Investment_5 0.000 0.000 289s Investment_6 0.000 0.000 289s Investment_7 0.000 0.000 289s Investment_8 0.000 0.000 289s Investment_9 0.000 0.000 289s Investment_10 0.000 0.000 289s Investment_11 0.000 0.000 289s Investment_12 0.000 0.000 289s Investment_13 0.000 0.000 289s Investment_14 0.000 0.000 289s Investment_15 0.000 0.000 289s Investment_16 0.000 0.000 289s Investment_17 0.000 0.000 289s Investment_18 0.000 0.000 289s Investment_19 0.000 0.000 289s Investment_20 0.000 0.000 289s Investment_21 0.000 0.000 289s Investment_22 0.000 0.000 289s PrivateWages_2 0.000 0.000 289s PrivateWages_3 0.000 0.000 289s PrivateWages_4 0.000 0.000 289s PrivateWages_5 0.000 0.000 289s PrivateWages_6 0.000 0.000 289s PrivateWages_7 0.000 0.000 289s PrivateWages_8 0.000 0.000 289s PrivateWages_9 0.000 0.000 289s PrivateWages_10 0.000 0.000 289s PrivateWages_11 0.000 0.000 289s PrivateWages_12 0.000 0.000 289s PrivateWages_13 0.000 0.000 289s PrivateWages_14 0.000 0.000 289s PrivateWages_15 0.000 0.000 289s PrivateWages_16 0.000 0.000 289s PrivateWages_17 0.000 0.000 289s PrivateWages_18 0.000 0.000 289s PrivateWages_19 0.000 0.000 289s PrivateWages_20 0.000 0.000 289s PrivateWages_21 0.000 0.000 289s PrivateWages_22 0.000 0.000 289s Investment_(Intercept) Investment_corpProf 289s Consumption_2 0.0000 0.000 289s Consumption_3 0.0000 0.000 289s Consumption_4 0.0000 0.000 289s Consumption_5 0.0000 0.000 289s Consumption_6 0.0000 0.000 289s Consumption_7 0.0000 0.000 289s Consumption_8 0.0000 0.000 289s Consumption_9 0.0000 0.000 289s Consumption_10 0.0000 0.000 289s Consumption_11 0.0000 0.000 289s Consumption_12 0.0000 0.000 289s Consumption_13 0.0000 0.000 289s Consumption_14 0.0000 0.000 289s Consumption_15 0.0000 0.000 289s Consumption_16 0.0000 0.000 289s Consumption_17 0.0000 0.000 289s Consumption_18 0.0000 0.000 289s Consumption_19 0.0000 0.000 289s Consumption_20 0.0000 0.000 289s Consumption_21 0.0000 0.000 289s Consumption_22 0.0000 0.000 289s Investment_2 -0.0668 -0.828 289s Investment_3 -0.0476 -0.804 289s Investment_4 1.2467 22.939 289s Investment_5 -1.3512 -26.213 289s Investment_6 0.4154 8.350 289s Investment_7 1.4923 29.248 289s Investment_8 0.7889 15.620 289s Investment_9 -0.6317 -13.329 289s Investment_10 1.0830 23.500 289s Investment_11 0.2791 4.353 289s Investment_12 0.0369 0.420 289s Investment_13 0.3659 2.561 289s Investment_14 0.2237 2.505 289s Investment_15 -0.1728 -2.126 289s Investment_16 0.0101 0.141 289s Investment_17 0.9719 17.105 289s Investment_18 0.0516 0.893 289s Investment_19 -2.5656 -39.254 289s Investment_20 -0.6866 -13.045 289s Investment_21 -0.7807 -16.474 289s Investment_22 -0.6623 -15.565 289s PrivateWages_2 0.0000 0.000 289s PrivateWages_3 0.0000 0.000 289s PrivateWages_4 0.0000 0.000 289s PrivateWages_5 0.0000 0.000 289s PrivateWages_6 0.0000 0.000 289s PrivateWages_7 0.0000 0.000 289s PrivateWages_8 0.0000 0.000 289s PrivateWages_9 0.0000 0.000 289s PrivateWages_10 0.0000 0.000 289s PrivateWages_11 0.0000 0.000 289s PrivateWages_12 0.0000 0.000 289s PrivateWages_13 0.0000 0.000 289s PrivateWages_14 0.0000 0.000 289s PrivateWages_15 0.0000 0.000 289s PrivateWages_16 0.0000 0.000 289s PrivateWages_17 0.0000 0.000 289s PrivateWages_18 0.0000 0.000 289s PrivateWages_19 0.0000 0.000 289s PrivateWages_20 0.0000 0.000 289s PrivateWages_21 0.0000 0.000 289s PrivateWages_22 0.0000 0.000 289s Investment_corpProfLag Investment_capitalLag 289s Consumption_2 0.000 0.00 289s Consumption_3 0.000 0.00 289s Consumption_4 0.000 0.00 289s Consumption_5 0.000 0.00 289s Consumption_6 0.000 0.00 289s Consumption_7 0.000 0.00 289s Consumption_8 0.000 0.00 289s Consumption_9 0.000 0.00 289s Consumption_10 0.000 0.00 289s Consumption_11 0.000 0.00 289s Consumption_12 0.000 0.00 289s Consumption_13 0.000 0.00 289s Consumption_14 0.000 0.00 289s Consumption_15 0.000 0.00 289s Consumption_16 0.000 0.00 289s Consumption_17 0.000 0.00 289s Consumption_18 0.000 0.00 289s Consumption_19 0.000 0.00 289s Consumption_20 0.000 0.00 289s Consumption_21 0.000 0.00 289s Consumption_22 0.000 0.00 289s Investment_2 -0.848 -12.21 289s Investment_3 -0.590 -8.69 289s Investment_4 21.069 230.01 289s Investment_5 -24.862 -256.32 289s Investment_6 8.059 80.05 289s Investment_7 29.994 295.17 289s Investment_8 15.463 160.46 289s Investment_9 -12.507 -131.14 289s Investment_10 22.850 228.07 289s Investment_11 6.056 60.20 289s Investment_12 0.575 7.99 289s Investment_13 4.172 78.05 289s Investment_14 1.566 46.33 289s Investment_15 -1.936 -34.91 289s Investment_16 0.124 2.01 289s Investment_17 13.606 192.14 289s Investment_18 0.908 10.31 289s Investment_19 -44.385 -517.74 289s Investment_20 -10.505 -137.25 289s Investment_21 -14.834 -157.09 289s Investment_22 -13.975 -135.45 289s PrivateWages_2 0.000 0.00 289s PrivateWages_3 0.000 0.00 289s PrivateWages_4 0.000 0.00 289s PrivateWages_5 0.000 0.00 289s PrivateWages_6 0.000 0.00 289s PrivateWages_7 0.000 0.00 289s PrivateWages_8 0.000 0.00 289s PrivateWages_9 0.000 0.00 289s PrivateWages_10 0.000 0.00 289s PrivateWages_11 0.000 0.00 289s PrivateWages_12 0.000 0.00 289s PrivateWages_13 0.000 0.00 289s PrivateWages_14 0.000 0.00 289s PrivateWages_15 0.000 0.00 289s PrivateWages_16 0.000 0.00 289s PrivateWages_17 0.000 0.00 289s PrivateWages_18 0.000 0.00 289s PrivateWages_19 0.000 0.00 289s PrivateWages_20 0.000 0.00 289s PrivateWages_21 0.000 0.00 289s PrivateWages_22 0.000 0.00 289s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 289s Consumption_2 0.0000 0.000 0.000 289s Consumption_3 0.0000 0.000 0.000 289s Consumption_4 0.0000 0.000 0.000 289s Consumption_5 0.0000 0.000 0.000 289s Consumption_6 0.0000 0.000 0.000 289s Consumption_7 0.0000 0.000 0.000 289s Consumption_8 0.0000 0.000 0.000 289s Consumption_9 0.0000 0.000 0.000 289s Consumption_10 0.0000 0.000 0.000 289s Consumption_11 0.0000 0.000 0.000 289s Consumption_12 0.0000 0.000 0.000 289s Consumption_13 0.0000 0.000 0.000 289s Consumption_14 0.0000 0.000 0.000 289s Consumption_15 0.0000 0.000 0.000 289s Consumption_16 0.0000 0.000 0.000 289s Consumption_17 0.0000 0.000 0.000 289s Consumption_18 0.0000 0.000 0.000 289s Consumption_19 0.0000 0.000 0.000 289s Consumption_20 0.0000 0.000 0.000 289s Consumption_21 0.0000 0.000 0.000 289s Consumption_22 0.0000 0.000 0.000 289s Investment_2 0.0000 0.000 0.000 289s Investment_3 0.0000 0.000 0.000 289s Investment_4 0.0000 0.000 0.000 289s Investment_5 0.0000 0.000 0.000 289s Investment_6 0.0000 0.000 0.000 289s Investment_7 0.0000 0.000 0.000 289s Investment_8 0.0000 0.000 0.000 289s Investment_9 0.0000 0.000 0.000 289s Investment_10 0.0000 0.000 0.000 289s Investment_11 0.0000 0.000 0.000 289s Investment_12 0.0000 0.000 0.000 289s Investment_13 0.0000 0.000 0.000 289s Investment_14 0.0000 0.000 0.000 289s Investment_15 0.0000 0.000 0.000 289s Investment_16 0.0000 0.000 0.000 289s Investment_17 0.0000 0.000 0.000 289s Investment_18 0.0000 0.000 0.000 289s Investment_19 0.0000 0.000 0.000 289s Investment_20 0.0000 0.000 0.000 289s Investment_21 0.0000 0.000 0.000 289s Investment_22 0.0000 0.000 0.000 289s PrivateWages_2 -1.2942 -59.015 -58.109 289s PrivateWages_3 0.2957 14.813 13.482 289s PrivateWages_4 1.1877 67.938 59.505 289s PrivateWages_5 -0.1358 -7.755 -7.768 289s PrivateWages_6 -0.4654 -28.390 -26.575 289s PrivateWages_7 -0.4838 -30.965 -29.514 289s PrivateWages_8 -0.7281 -46.892 -46.601 289s PrivateWages_9 0.3392 21.881 21.847 289s PrivateWages_10 1.1957 80.111 77.122 289s PrivateWages_11 -0.1508 -9.230 -10.105 289s PrivateWages_12 0.5942 31.729 36.364 289s PrivateWages_13 0.1027 4.549 5.483 289s PrivateWages_14 0.4503 20.307 19.947 289s PrivateWages_15 0.2816 13.993 12.698 289s PrivateWages_16 0.0138 0.748 0.684 289s PrivateWages_17 -0.8508 -53.343 -46.282 289s PrivateWages_18 0.9956 64.717 62.427 289s PrivateWages_19 -0.4688 -28.547 -30.469 289s PrivateWages_20 -0.3795 -26.378 -23.114 289s PrivateWages_21 -1.0909 -82.582 -75.818 289s PrivateWages_22 0.5917 52.309 44.794 289s PrivateWages_trend 289s Consumption_2 0.000 289s Consumption_3 0.000 289s Consumption_4 0.000 289s Consumption_5 0.000 289s Consumption_6 0.000 289s Consumption_7 0.000 289s Consumption_8 0.000 289s Consumption_9 0.000 289s Consumption_10 0.000 289s Consumption_11 0.000 289s Consumption_12 0.000 289s Consumption_13 0.000 289s Consumption_14 0.000 289s Consumption_15 0.000 289s Consumption_16 0.000 289s Consumption_17 0.000 289s Consumption_18 0.000 289s Consumption_19 0.000 289s Consumption_20 0.000 289s Consumption_21 0.000 289s Consumption_22 0.000 289s Investment_2 0.000 289s Investment_3 0.000 289s Investment_4 0.000 289s Investment_5 0.000 289s Investment_6 0.000 289s Investment_7 0.000 289s Investment_8 0.000 289s Investment_9 0.000 289s Investment_10 0.000 289s Investment_11 0.000 289s Investment_12 0.000 289s Investment_13 0.000 289s Investment_14 0.000 289s Investment_15 0.000 289s Investment_16 0.000 289s Investment_17 0.000 289s Investment_18 0.000 289s Investment_19 0.000 289s Investment_20 0.000 289s Investment_21 0.000 289s Investment_22 0.000 289s PrivateWages_2 12.942 289s PrivateWages_3 -2.661 289s PrivateWages_4 -9.502 289s PrivateWages_5 0.951 289s PrivateWages_6 2.792 289s PrivateWages_7 2.419 289s PrivateWages_8 2.913 289s PrivateWages_9 -1.018 289s PrivateWages_10 -2.391 289s PrivateWages_11 0.151 289s PrivateWages_12 0.000 289s PrivateWages_13 0.103 289s PrivateWages_14 0.901 289s PrivateWages_15 0.845 289s PrivateWages_16 0.055 289s PrivateWages_17 -4.254 289s PrivateWages_18 5.974 289s PrivateWages_19 -3.281 289s PrivateWages_20 -3.036 289s PrivateWages_21 -9.818 289s PrivateWages_22 5.917 289s [1] TRUE 289s > Bread 289s Consumption_(Intercept) Consumption_corpProf 289s Consumption_(Intercept) 101.65 0.030 289s Consumption_corpProf 0.03 0.498 289s Consumption_corpProfLag -1.06 -0.316 289s Consumption_wages -1.97 -0.079 289s Investment_(Intercept) 0.00 0.000 289s Investment_corpProf 0.00 0.000 289s Investment_corpProfLag 0.00 0.000 289s Investment_capitalLag 0.00 0.000 289s PrivateWages_(Intercept) 0.00 0.000 289s PrivateWages_gnp 0.00 0.000 289s PrivateWages_gnpLag 0.00 0.000 289s PrivateWages_trend 0.00 0.000 289s Consumption_corpProfLag Consumption_wages 289s Consumption_(Intercept) -1.0607 -1.9718 289s Consumption_corpProf -0.3157 -0.0790 289s Consumption_corpProfLag 0.4922 -0.0402 289s Consumption_wages -0.0402 0.0956 289s Investment_(Intercept) 0.0000 0.0000 289s Investment_corpProf 0.0000 0.0000 289s Investment_corpProfLag 0.0000 0.0000 289s Investment_capitalLag 0.0000 0.0000 289s PrivateWages_(Intercept) 0.0000 0.0000 289s PrivateWages_gnp 0.0000 0.0000 289s PrivateWages_gnpLag 0.0000 0.0000 289s PrivateWages_trend 0.0000 0.0000 289s Investment_(Intercept) Investment_corpProf 289s Consumption_(Intercept) 0.00 0.0000 289s Consumption_corpProf 0.00 0.0000 289s Consumption_corpProfLag 0.00 0.0000 289s Consumption_wages 0.00 0.0000 289s Investment_(Intercept) 1846.89 -17.9709 289s Investment_corpProf -17.97 0.5831 289s Investment_corpProfLag 14.67 -0.5008 289s Investment_capitalLag -8.88 0.0814 289s PrivateWages_(Intercept) 0.00 0.0000 289s PrivateWages_gnp 0.00 0.0000 289s PrivateWages_gnpLag 0.00 0.0000 289s PrivateWages_trend 0.00 0.0000 289s Investment_corpProfLag Investment_capitalLag 289s Consumption_(Intercept) 0.0000 0.0000 289s Consumption_corpProf 0.0000 0.0000 289s Consumption_corpProfLag 0.0000 0.0000 289s Consumption_wages 0.0000 0.0000 289s Investment_(Intercept) 14.6742 -8.8813 289s Investment_corpProf -0.5008 0.0814 289s Investment_corpProfLag 0.6289 -0.0824 289s Investment_capitalLag -0.0824 0.0442 289s PrivateWages_(Intercept) 0.0000 0.0000 289s PrivateWages_gnp 0.0000 0.0000 289s PrivateWages_gnpLag 0.0000 0.0000 289s PrivateWages_trend 0.0000 0.0000 289s PrivateWages_(Intercept) PrivateWages_gnp 289s Consumption_(Intercept) 0.000 0.0000 289s Consumption_corpProf 0.000 0.0000 289s Consumption_corpProfLag 0.000 0.0000 289s Consumption_wages 0.000 0.0000 289s Investment_(Intercept) 0.000 0.0000 289s Investment_corpProf 0.000 0.0000 289s Investment_corpProfLag 0.000 0.0000 289s Investment_capitalLag 0.000 0.0000 289s PrivateWages_(Intercept) 172.668 -0.5919 289s PrivateWages_gnp -0.592 0.1124 289s PrivateWages_gnpLag -2.313 -0.1062 289s PrivateWages_trend 1.993 -0.0274 289s PrivateWages_gnpLag PrivateWages_trend 289s Consumption_(Intercept) 0.00000 0.00000 289s Consumption_corpProf 0.00000 0.00000 289s Consumption_corpProfLag 0.00000 0.00000 289s Consumption_wages 0.00000 0.00000 289s Investment_(Intercept) 0.00000 0.00000 289s Investment_corpProf 0.00000 0.00000 289s Investment_corpProfLag 0.00000 0.00000 289s Investment_capitalLag 0.00000 0.00000 289s PrivateWages_(Intercept) -2.31299 1.99284 289s PrivateWages_gnp -0.10624 -0.02738 289s PrivateWages_gnpLag 0.14992 -0.00601 289s PrivateWages_trend -0.00601 0.10900 289s > 289s > # 2SLS 289s > summary 289s 289s systemfit results 289s method: 2SLS 289s 289s N DF SSR detRCov OLS-R2 McElroy-R2 289s system 63 51 61 0.288 0.969 0.992 289s 289s N DF SSR MSE RMSE R2 Adj R2 289s Consumption 21 17 21.9 1.290 1.136 0.977 0.973 289s Investment 21 17 29.0 1.709 1.307 0.885 0.865 289s PrivateWages 21 17 10.0 0.589 0.767 0.987 0.985 289s 289s The covariance matrix of the residuals 289s Consumption Investment PrivateWages 289s Consumption 1.044 0.438 -0.385 289s Investment 0.438 1.383 0.193 289s PrivateWages -0.385 0.193 0.476 289s 289s The correlations of the residuals 289s Consumption Investment PrivateWages 289s Consumption 1.000 0.364 -0.546 289s Investment 0.364 1.000 0.237 289s PrivateWages -0.546 0.237 1.000 289s 289s 289s 2SLS estimates for 'Consumption' (equation 1) 289s Model Formula: consump ~ corpProf + corpProfLag + wages 289s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 289s gnpLag 289s 289s Estimate Std. Error t value Pr(>|t|) 289s (Intercept) 16.5548 1.3208 12.53 5.2e-10 *** 289s corpProf 0.0173 0.1180 0.15 0.89 289s corpProfLag 0.2162 0.1073 2.02 0.06 . 289s wages 0.8102 0.0402 20.13 2.7e-13 *** 289s --- 289s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 289s 289s Residual standard error: 1.136 on 17 degrees of freedom 289s Number of observations: 21 Degrees of Freedom: 17 289s SSR: 21.925 MSE: 1.29 Root MSE: 1.136 289s Multiple R-Squared: 0.977 Adjusted R-Squared: 0.973 289s 289s 289s 2SLS estimates for 'Investment' (equation 2) 289s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 289s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 289s gnpLag 289s 289s Estimate Std. Error t value Pr(>|t|) 289s (Intercept) 20.2782 7.5427 2.69 0.01555 * 289s corpProf 0.1502 0.1732 0.87 0.39792 289s corpProfLag 0.6159 0.1628 3.78 0.00148 ** 289s capitalLag -0.1578 0.0361 -4.37 0.00042 *** 289s --- 289s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 289s 289s Residual standard error: 1.307 on 17 degrees of freedom 289s Number of observations: 21 Degrees of Freedom: 17 289s SSR: 29.047 MSE: 1.709 Root MSE: 1.307 289s Multiple R-Squared: 0.885 Adjusted R-Squared: 0.865 289s 289s 289s 2SLS estimates for 'PrivateWages' (equation 3) 289s Model Formula: privWage ~ gnp + gnpLag + trend 289s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 289s gnpLag 289s 289s Estimate Std. Error t value Pr(>|t|) 289s (Intercept) 1.5003 1.1478 1.31 0.20857 289s gnp 0.4389 0.0356 12.32 6.8e-10 *** 289s gnpLag 0.1467 0.0388 3.78 0.00150 ** 289s trend 0.1304 0.0291 4.47 0.00033 *** 289s --- 289s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 289s 289s Residual standard error: 0.767 on 17 degrees of freedom 289s Number of observations: 21 Degrees of Freedom: 17 289s SSR: 10.005 MSE: 0.589 Root MSE: 0.767 289s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 289s 289s > residuals 289s Consumption Investment PrivateWages 289s 1 NA NA NA 289s 2 -0.46263 -1.320 -1.2940 289s 3 -0.61635 0.257 0.2981 289s 4 -1.30423 0.860 1.1918 289s 5 -0.24588 -1.594 -0.1361 289s 6 0.22948 0.259 -0.4634 289s 7 0.88538 1.207 -0.4824 289s 8 1.44189 0.969 -0.7284 289s 9 1.34190 0.113 0.3387 289s 10 -0.39403 1.796 1.1965 289s 11 -0.62564 -0.953 -0.1552 289s 12 -1.06543 -0.807 0.5882 289s 13 -1.33021 -0.895 0.0955 289s 14 0.61059 1.306 0.4487 289s 15 -0.14208 -0.151 0.2822 289s 16 0.00315 0.142 0.0145 289s 17 2.00337 1.749 -0.8478 289s 18 -0.60552 -0.192 0.9950 289s 19 -0.24771 -3.291 -0.4734 289s 20 1.38510 0.285 -0.3766 289s 21 1.03204 -0.104 -1.0893 289s 22 -1.89319 0.363 0.5974 289s > fitted 289s Consumption Investment PrivateWages 289s 1 NA NA NA 289s 2 42.4 1.120 26.8 289s 3 45.6 1.643 29.0 289s 4 50.5 4.340 32.9 289s 5 50.8 4.594 34.0 289s 6 52.4 4.841 35.9 289s 7 54.2 4.393 37.9 289s 8 54.8 3.231 38.6 289s 9 56.0 2.887 38.9 289s 10 58.2 3.304 40.1 289s 11 55.6 1.953 38.1 289s 12 52.0 -2.593 33.9 289s 13 46.9 -5.305 28.9 289s 14 45.9 -6.406 28.1 289s 15 48.8 -2.849 30.3 289s 16 51.3 -1.442 33.2 289s 17 55.7 0.351 37.6 289s 18 59.3 2.192 40.0 289s 19 57.7 1.391 38.7 289s 20 60.2 1.015 42.0 289s 21 64.0 3.404 46.1 289s 22 71.6 4.537 52.7 289s > predict 289s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 289s 1 NA NA NA NA 289s 2 42.4 0.471 41.4 43.4 289s 3 45.6 0.577 44.4 46.8 289s 4 50.5 0.354 49.8 51.3 289s 5 50.8 0.405 50.0 51.7 289s 6 52.4 0.404 51.5 53.2 289s 7 54.2 0.359 53.5 55.0 289s 8 54.8 0.328 54.1 55.4 289s 9 56.0 0.368 55.2 56.7 289s 10 58.2 0.377 57.4 59.0 289s 11 55.6 0.728 54.1 57.2 289s 12 52.0 0.604 50.7 53.2 289s 13 46.9 0.765 45.3 48.5 289s 14 45.9 0.615 44.6 47.2 289s 15 48.8 0.374 48.1 49.6 289s 16 51.3 0.333 50.6 52.0 289s 17 55.7 0.409 54.8 56.6 289s 18 59.3 0.326 58.6 60.0 289s 19 57.7 0.414 56.9 58.6 289s 20 60.2 0.478 59.2 61.2 289s 21 64.0 0.446 63.0 64.9 289s 22 71.6 0.689 70.1 73.0 289s Investment.pred Investment.se.fit Investment.lwr Investment.upr 289s 1 NA NA NA NA 289s 2 1.120 0.865 -0.706 2.946 289s 3 1.643 0.594 0.390 2.895 289s 4 4.340 0.545 3.190 5.490 289s 5 4.594 0.443 3.660 5.527 289s 6 4.841 0.411 3.973 5.709 289s 7 4.393 0.399 3.550 5.235 289s 8 3.231 0.348 2.497 3.965 289s 9 2.887 0.542 1.744 4.030 289s 10 3.304 0.593 2.054 4.555 289s 11 1.953 0.855 0.148 3.757 289s 12 -2.593 0.679 -4.026 -1.160 289s 13 -5.305 0.876 -7.152 -3.457 289s 14 -6.406 0.916 -8.338 -4.473 289s 15 -2.849 0.435 -3.765 -1.932 289s 16 -1.442 0.376 -2.236 -0.649 289s 17 0.351 0.510 -0.724 1.426 289s 18 2.192 0.299 1.560 2.823 289s 19 1.391 0.464 0.411 2.371 289s 20 1.015 0.576 -0.201 2.230 289s 21 3.404 0.471 2.410 4.398 289s 22 4.537 0.675 3.114 5.961 289s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 289s 1 NA NA NA NA 289s 2 26.8 0.318 26.1 27.5 289s 3 29.0 0.330 28.3 29.7 289s 4 32.9 0.346 32.2 33.6 289s 5 34.0 0.242 33.5 34.5 289s 6 35.9 0.248 35.3 36.4 289s 7 37.9 0.244 37.4 38.4 289s 8 38.6 0.246 38.1 39.1 289s 9 38.9 0.235 38.4 39.4 289s 10 40.1 0.224 39.6 40.6 289s 11 38.1 0.350 37.3 38.8 289s 12 33.9 0.382 33.1 34.7 289s 13 28.9 0.454 27.9 29.9 289s 14 28.1 0.342 27.3 28.8 289s 15 30.3 0.335 29.6 31.0 289s 16 33.2 0.280 32.6 33.8 289s 17 37.6 0.291 37.0 38.3 289s 18 40.0 0.215 39.6 40.5 289s 19 38.7 0.356 37.9 39.4 289s 20 42.0 0.304 41.3 42.6 289s 21 46.1 0.306 45.4 46.7 289s 22 52.7 0.489 51.7 53.7 289s > model.frame 289s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 289s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 289s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 289s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 289s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 289s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 289s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 289s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 61.0 289s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 289s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 289s 10 57.8 21.7 21.1 45.3 5.1 211 41.3 67.0 64.5 289s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 289s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 289s 13 45.6 7.0 11.4 34.3 -6.2 213 29.0 44.3 53.4 289s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 289s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 289s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 289s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 289s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 289s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 289s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 289s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 289s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 289s trend 289s 1 -11 289s 2 -10 289s 3 -9 289s 4 -8 289s 5 -7 289s 6 -6 289s 7 -5 289s 8 -4 289s 9 -3 289s 10 -2 289s 11 -1 289s 12 0 289s 13 1 289s 14 2 289s 15 3 289s 16 4 289s 17 5 289s 18 6 289s 19 7 289s 20 8 289s 21 9 289s 22 10 289s > Frames of instrumental variables 289s govExp taxes govWage trend capitalLag corpProfLag gnpLag 289s 1 2.4 3.4 2.2 -11 180 NA NA 289s 2 3.9 7.7 2.7 -10 183 12.7 44.9 289s 3 3.2 3.9 2.9 -9 183 12.4 45.6 289s 4 2.8 4.7 2.9 -8 184 16.9 50.1 289s 5 3.5 3.8 3.1 -7 190 18.4 57.2 289s 6 3.3 5.5 3.2 -6 193 19.4 57.1 289s 7 3.3 7.0 3.3 -5 198 20.1 61.0 289s 8 4.0 6.7 3.6 -4 203 19.6 64.0 289s 9 4.2 4.2 3.7 -3 208 19.8 64.4 289s 10 4.1 4.0 4.0 -2 211 21.1 64.5 289s 11 5.2 7.7 4.2 -1 216 21.7 67.0 289s 12 5.9 7.5 4.8 0 217 15.6 61.2 289s 13 4.9 8.3 5.3 1 213 11.4 53.4 289s 14 3.7 5.4 5.6 2 207 7.0 44.3 289s 15 4.0 6.8 6.0 3 202 11.2 45.1 289s 16 4.4 7.2 6.1 4 199 12.3 49.7 289s 17 2.9 8.3 7.4 5 198 14.0 54.4 289s 18 4.3 6.7 6.7 6 200 17.6 62.7 289s 19 5.3 7.4 7.7 7 202 17.3 65.0 289s 20 6.6 8.9 7.8 8 200 15.3 60.9 289s 21 7.4 9.6 8.0 9 201 19.0 69.5 289s 22 13.8 11.6 8.5 10 204 21.1 75.7 289s govExp taxes govWage trend capitalLag corpProfLag gnpLag 289s 1 2.4 3.4 2.2 -11 180 NA NA 289s 2 3.9 7.7 2.7 -10 183 12.7 44.9 289s 3 3.2 3.9 2.9 -9 183 12.4 45.6 289s 4 2.8 4.7 2.9 -8 184 16.9 50.1 289s 5 3.5 3.8 3.1 -7 190 18.4 57.2 289s 6 3.3 5.5 3.2 -6 193 19.4 57.1 289s 7 3.3 7.0 3.3 -5 198 20.1 61.0 289s 8 4.0 6.7 3.6 -4 203 19.6 64.0 289s 9 4.2 4.2 3.7 -3 208 19.8 64.4 289s 10 4.1 4.0 4.0 -2 211 21.1 64.5 289s 11 5.2 7.7 4.2 -1 216 21.7 67.0 289s 12 5.9 7.5 4.8 0 217 15.6 61.2 289s 13 4.9 8.3 5.3 1 213 11.4 53.4 289s 14 3.7 5.4 5.6 2 207 7.0 44.3 289s 15 4.0 6.8 6.0 3 202 11.2 45.1 289s 16 4.4 7.2 6.1 4 199 12.3 49.7 289s 17 2.9 8.3 7.4 5 198 14.0 54.4 289s 18 4.3 6.7 6.7 6 200 17.6 62.7 289s 19 5.3 7.4 7.7 7 202 17.3 65.0 289s 20 6.6 8.9 7.8 8 200 15.3 60.9 289s 21 7.4 9.6 8.0 9 201 19.0 69.5 289s 22 13.8 11.6 8.5 10 204 21.1 75.7 289s govExp taxes govWage trend capitalLag corpProfLag gnpLag 289s 1 2.4 3.4 2.2 -11 180 NA NA 289s 2 3.9 7.7 2.7 -10 183 12.7 44.9 289s 3 3.2 3.9 2.9 -9 183 12.4 45.6 289s 4 2.8 4.7 2.9 -8 184 16.9 50.1 289s 5 3.5 3.8 3.1 -7 190 18.4 57.2 289s 6 3.3 5.5 3.2 -6 193 19.4 57.1 289s 7 3.3 7.0 3.3 -5 198 20.1 61.0 289s 8 4.0 6.7 3.6 -4 203 19.6 64.0 289s 9 4.2 4.2 3.7 -3 208 19.8 64.4 289s 10 4.1 4.0 4.0 -2 211 21.1 64.5 289s 11 5.2 7.7 4.2 -1 216 21.7 67.0 289s 12 5.9 7.5 4.8 0 217 15.6 61.2 289s 13 4.9 8.3 5.3 1 213 11.4 53.4 289s 14 3.7 5.4 5.6 2 207 7.0 44.3 289s 15 4.0 6.8 6.0 3 202 11.2 45.1 289s 16 4.4 7.2 6.1 4 199 12.3 49.7 289s 17 2.9 8.3 7.4 5 198 14.0 54.4 289s 18 4.3 6.7 6.7 6 200 17.6 62.7 289s 19 5.3 7.4 7.7 7 202 17.3 65.0 289s 20 6.6 8.9 7.8 8 200 15.3 60.9 289s 21 7.4 9.6 8.0 9 201 19.0 69.5 289s 22 13.8 11.6 8.5 10 204 21.1 75.7 289s > model.matrix 289s [1] TRUE 289s > matrix of instrumental variables 289s Consumption_(Intercept) Consumption_govExp Consumption_taxes 289s Consumption_2 1 3.9 7.7 289s Consumption_3 1 3.2 3.9 289s Consumption_4 1 2.8 4.7 289s Consumption_5 1 3.5 3.8 289s Consumption_6 1 3.3 5.5 289s Consumption_7 1 3.3 7.0 289s Consumption_8 1 4.0 6.7 289s Consumption_9 1 4.2 4.2 289s Consumption_10 1 4.1 4.0 289s Consumption_11 1 5.2 7.7 289s Consumption_12 1 5.9 7.5 289s Consumption_13 1 4.9 8.3 289s Consumption_14 1 3.7 5.4 289s Consumption_15 1 4.0 6.8 289s Consumption_16 1 4.4 7.2 289s Consumption_17 1 2.9 8.3 289s Consumption_18 1 4.3 6.7 289s Consumption_19 1 5.3 7.4 289s Consumption_20 1 6.6 8.9 289s Consumption_21 1 7.4 9.6 289s Consumption_22 1 13.8 11.6 289s Investment_2 0 0.0 0.0 289s Investment_3 0 0.0 0.0 289s Investment_4 0 0.0 0.0 289s Investment_5 0 0.0 0.0 289s Investment_6 0 0.0 0.0 289s Investment_7 0 0.0 0.0 289s Investment_8 0 0.0 0.0 289s Investment_9 0 0.0 0.0 289s Investment_10 0 0.0 0.0 289s Investment_11 0 0.0 0.0 289s Investment_12 0 0.0 0.0 289s Investment_13 0 0.0 0.0 289s Investment_14 0 0.0 0.0 289s Investment_15 0 0.0 0.0 289s Investment_16 0 0.0 0.0 289s Investment_17 0 0.0 0.0 289s Investment_18 0 0.0 0.0 289s Investment_19 0 0.0 0.0 289s Investment_20 0 0.0 0.0 289s Investment_21 0 0.0 0.0 289s Investment_22 0 0.0 0.0 289s PrivateWages_2 0 0.0 0.0 289s PrivateWages_3 0 0.0 0.0 289s PrivateWages_4 0 0.0 0.0 289s PrivateWages_5 0 0.0 0.0 289s PrivateWages_6 0 0.0 0.0 289s PrivateWages_7 0 0.0 0.0 289s PrivateWages_8 0 0.0 0.0 289s PrivateWages_9 0 0.0 0.0 289s PrivateWages_10 0 0.0 0.0 289s PrivateWages_11 0 0.0 0.0 289s PrivateWages_12 0 0.0 0.0 289s PrivateWages_13 0 0.0 0.0 289s PrivateWages_14 0 0.0 0.0 289s PrivateWages_15 0 0.0 0.0 289s PrivateWages_16 0 0.0 0.0 289s PrivateWages_17 0 0.0 0.0 289s PrivateWages_18 0 0.0 0.0 289s PrivateWages_19 0 0.0 0.0 289s PrivateWages_20 0 0.0 0.0 289s PrivateWages_21 0 0.0 0.0 289s PrivateWages_22 0 0.0 0.0 289s Consumption_govWage Consumption_trend Consumption_capitalLag 289s Consumption_2 2.7 -10 183 289s Consumption_3 2.9 -9 183 289s Consumption_4 2.9 -8 184 289s Consumption_5 3.1 -7 190 289s Consumption_6 3.2 -6 193 289s Consumption_7 3.3 -5 198 289s Consumption_8 3.6 -4 203 289s Consumption_9 3.7 -3 208 289s Consumption_10 4.0 -2 211 289s Consumption_11 4.2 -1 216 289s Consumption_12 4.8 0 217 289s Consumption_13 5.3 1 213 289s Consumption_14 5.6 2 207 289s Consumption_15 6.0 3 202 289s Consumption_16 6.1 4 199 289s Consumption_17 7.4 5 198 289s Consumption_18 6.7 6 200 289s Consumption_19 7.7 7 202 289s Consumption_20 7.8 8 200 289s Consumption_21 8.0 9 201 289s Consumption_22 8.5 10 204 289s Investment_2 0.0 0 0 289s Investment_3 0.0 0 0 289s Investment_4 0.0 0 0 289s Investment_5 0.0 0 0 289s Investment_6 0.0 0 0 289s Investment_7 0.0 0 0 289s Investment_8 0.0 0 0 289s Investment_9 0.0 0 0 289s Investment_10 0.0 0 0 289s Investment_11 0.0 0 0 289s Investment_12 0.0 0 0 289s Investment_13 0.0 0 0 289s Investment_14 0.0 0 0 289s Investment_15 0.0 0 0 289s Investment_16 0.0 0 0 289s Investment_17 0.0 0 0 289s Investment_18 0.0 0 0 289s Investment_19 0.0 0 0 289s Investment_20 0.0 0 0 289s Investment_21 0.0 0 0 289s Investment_22 0.0 0 0 289s PrivateWages_2 0.0 0 0 289s PrivateWages_3 0.0 0 0 289s PrivateWages_4 0.0 0 0 289s PrivateWages_5 0.0 0 0 289s PrivateWages_6 0.0 0 0 289s PrivateWages_7 0.0 0 0 289s PrivateWages_8 0.0 0 0 289s PrivateWages_9 0.0 0 0 289s PrivateWages_10 0.0 0 0 289s PrivateWages_11 0.0 0 0 289s PrivateWages_12 0.0 0 0 289s PrivateWages_13 0.0 0 0 289s PrivateWages_14 0.0 0 0 289s PrivateWages_15 0.0 0 0 289s PrivateWages_16 0.0 0 0 289s PrivateWages_17 0.0 0 0 289s PrivateWages_18 0.0 0 0 289s PrivateWages_19 0.0 0 0 289s PrivateWages_20 0.0 0 0 289s PrivateWages_21 0.0 0 0 289s PrivateWages_22 0.0 0 0 289s Consumption_corpProfLag Consumption_gnpLag 289s Consumption_2 12.7 44.9 289s Consumption_3 12.4 45.6 289s Consumption_4 16.9 50.1 289s Consumption_5 18.4 57.2 289s Consumption_6 19.4 57.1 289s Consumption_7 20.1 61.0 289s Consumption_8 19.6 64.0 289s Consumption_9 19.8 64.4 289s Consumption_10 21.1 64.5 289s Consumption_11 21.7 67.0 289s Consumption_12 15.6 61.2 289s Consumption_13 11.4 53.4 289s Consumption_14 7.0 44.3 289s Consumption_15 11.2 45.1 289s Consumption_16 12.3 49.7 289s Consumption_17 14.0 54.4 289s Consumption_18 17.6 62.7 289s Consumption_19 17.3 65.0 289s Consumption_20 15.3 60.9 289s Consumption_21 19.0 69.5 289s Consumption_22 21.1 75.7 289s Investment_2 0.0 0.0 289s Investment_3 0.0 0.0 289s Investment_4 0.0 0.0 289s Investment_5 0.0 0.0 289s Investment_6 0.0 0.0 289s Investment_7 0.0 0.0 289s Investment_8 0.0 0.0 289s Investment_9 0.0 0.0 289s Investment_10 0.0 0.0 289s Investment_11 0.0 0.0 289s Investment_12 0.0 0.0 289s Investment_13 0.0 0.0 289s Investment_14 0.0 0.0 289s Investment_15 0.0 0.0 289s Investment_16 0.0 0.0 289s Investment_17 0.0 0.0 289s Investment_18 0.0 0.0 289s Investment_19 0.0 0.0 289s Investment_20 0.0 0.0 289s Investment_21 0.0 0.0 289s Investment_22 0.0 0.0 289s PrivateWages_2 0.0 0.0 289s PrivateWages_3 0.0 0.0 289s PrivateWages_4 0.0 0.0 289s PrivateWages_5 0.0 0.0 289s PrivateWages_6 0.0 0.0 289s PrivateWages_7 0.0 0.0 289s PrivateWages_8 0.0 0.0 289s PrivateWages_9 0.0 0.0 289s PrivateWages_10 0.0 0.0 289s PrivateWages_11 0.0 0.0 289s PrivateWages_12 0.0 0.0 289s PrivateWages_13 0.0 0.0 289s PrivateWages_14 0.0 0.0 289s PrivateWages_15 0.0 0.0 289s PrivateWages_16 0.0 0.0 289s PrivateWages_17 0.0 0.0 289s PrivateWages_18 0.0 0.0 289s PrivateWages_19 0.0 0.0 289s PrivateWages_20 0.0 0.0 289s PrivateWages_21 0.0 0.0 289s PrivateWages_22 0.0 0.0 289s Investment_(Intercept) Investment_govExp Investment_taxes 289s Consumption_2 0 0.0 0.0 289s Consumption_3 0 0.0 0.0 289s Consumption_4 0 0.0 0.0 289s Consumption_5 0 0.0 0.0 289s Consumption_6 0 0.0 0.0 289s Consumption_7 0 0.0 0.0 289s Consumption_8 0 0.0 0.0 289s Consumption_9 0 0.0 0.0 289s Consumption_10 0 0.0 0.0 289s Consumption_11 0 0.0 0.0 289s Consumption_12 0 0.0 0.0 289s Consumption_13 0 0.0 0.0 289s Consumption_14 0 0.0 0.0 289s Consumption_15 0 0.0 0.0 289s Consumption_16 0 0.0 0.0 289s Consumption_17 0 0.0 0.0 289s Consumption_18 0 0.0 0.0 289s Consumption_19 0 0.0 0.0 289s Consumption_20 0 0.0 0.0 289s Consumption_21 0 0.0 0.0 289s Consumption_22 0 0.0 0.0 289s Investment_2 1 3.9 7.7 289s Investment_3 1 3.2 3.9 289s Investment_4 1 2.8 4.7 289s Investment_5 1 3.5 3.8 289s Investment_6 1 3.3 5.5 289s Investment_7 1 3.3 7.0 289s Investment_8 1 4.0 6.7 289s Investment_9 1 4.2 4.2 289s Investment_10 1 4.1 4.0 289s Investment_11 1 5.2 7.7 289s Investment_12 1 5.9 7.5 289s Investment_13 1 4.9 8.3 289s Investment_14 1 3.7 5.4 289s Investment_15 1 4.0 6.8 289s Investment_16 1 4.4 7.2 289s Investment_17 1 2.9 8.3 289s Investment_18 1 4.3 6.7 289s Investment_19 1 5.3 7.4 289s Investment_20 1 6.6 8.9 289s Investment_21 1 7.4 9.6 289s Investment_22 1 13.8 11.6 289s PrivateWages_2 0 0.0 0.0 289s PrivateWages_3 0 0.0 0.0 289s PrivateWages_4 0 0.0 0.0 289s PrivateWages_5 0 0.0 0.0 289s PrivateWages_6 0 0.0 0.0 289s PrivateWages_7 0 0.0 0.0 289s PrivateWages_8 0 0.0 0.0 289s PrivateWages_9 0 0.0 0.0 289s PrivateWages_10 0 0.0 0.0 289s PrivateWages_11 0 0.0 0.0 289s PrivateWages_12 0 0.0 0.0 289s PrivateWages_13 0 0.0 0.0 289s PrivateWages_14 0 0.0 0.0 289s PrivateWages_15 0 0.0 0.0 289s PrivateWages_16 0 0.0 0.0 289s PrivateWages_17 0 0.0 0.0 289s PrivateWages_18 0 0.0 0.0 289s PrivateWages_19 0 0.0 0.0 289s PrivateWages_20 0 0.0 0.0 289s PrivateWages_21 0 0.0 0.0 289s PrivateWages_22 0 0.0 0.0 289s Investment_govWage Investment_trend Investment_capitalLag 289s Consumption_2 0.0 0 0 289s Consumption_3 0.0 0 0 289s Consumption_4 0.0 0 0 289s Consumption_5 0.0 0 0 289s Consumption_6 0.0 0 0 289s Consumption_7 0.0 0 0 289s Consumption_8 0.0 0 0 289s Consumption_9 0.0 0 0 289s Consumption_10 0.0 0 0 289s Consumption_11 0.0 0 0 289s Consumption_12 0.0 0 0 289s Consumption_13 0.0 0 0 289s Consumption_14 0.0 0 0 289s Consumption_15 0.0 0 0 289s Consumption_16 0.0 0 0 289s Consumption_17 0.0 0 0 289s Consumption_18 0.0 0 0 289s Consumption_19 0.0 0 0 289s Consumption_20 0.0 0 0 289s Consumption_21 0.0 0 0 289s Consumption_22 0.0 0 0 289s Investment_2 2.7 -10 183 289s Investment_3 2.9 -9 183 289s Investment_4 2.9 -8 184 289s Investment_5 3.1 -7 190 289s Investment_6 3.2 -6 193 289s Investment_7 3.3 -5 198 289s Investment_8 3.6 -4 203 289s Investment_9 3.7 -3 208 289s Investment_10 4.0 -2 211 289s Investment_11 4.2 -1 216 289s Investment_12 4.8 0 217 289s Investment_13 5.3 1 213 289s Investment_14 5.6 2 207 289s Investment_15 6.0 3 202 289s Investment_16 6.1 4 199 289s Investment_17 7.4 5 198 289s Investment_18 6.7 6 200 289s Investment_19 7.7 7 202 289s Investment_20 7.8 8 200 289s Investment_21 8.0 9 201 289s Investment_22 8.5 10 204 289s PrivateWages_2 0.0 0 0 289s PrivateWages_3 0.0 0 0 289s PrivateWages_4 0.0 0 0 289s PrivateWages_5 0.0 0 0 289s PrivateWages_6 0.0 0 0 289s PrivateWages_7 0.0 0 0 289s PrivateWages_8 0.0 0 0 289s PrivateWages_9 0.0 0 0 289s PrivateWages_10 0.0 0 0 289s PrivateWages_11 0.0 0 0 289s PrivateWages_12 0.0 0 0 289s PrivateWages_13 0.0 0 0 289s PrivateWages_14 0.0 0 0 289s PrivateWages_15 0.0 0 0 289s PrivateWages_16 0.0 0 0 289s PrivateWages_17 0.0 0 0 289s PrivateWages_18 0.0 0 0 289s PrivateWages_19 0.0 0 0 289s PrivateWages_20 0.0 0 0 289s PrivateWages_21 0.0 0 0 289s PrivateWages_22 0.0 0 0 289s Investment_corpProfLag Investment_gnpLag 289s Consumption_2 0.0 0.0 289s Consumption_3 0.0 0.0 289s Consumption_4 0.0 0.0 289s Consumption_5 0.0 0.0 289s Consumption_6 0.0 0.0 289s Consumption_7 0.0 0.0 289s Consumption_8 0.0 0.0 289s Consumption_9 0.0 0.0 289s Consumption_10 0.0 0.0 289s Consumption_11 0.0 0.0 289s Consumption_12 0.0 0.0 289s Consumption_13 0.0 0.0 289s Consumption_14 0.0 0.0 289s Consumption_15 0.0 0.0 289s Consumption_16 0.0 0.0 289s Consumption_17 0.0 0.0 289s Consumption_18 0.0 0.0 289s Consumption_19 0.0 0.0 289s Consumption_20 0.0 0.0 289s Consumption_21 0.0 0.0 289s Consumption_22 0.0 0.0 289s Investment_2 12.7 44.9 289s Investment_3 12.4 45.6 289s Investment_4 16.9 50.1 289s Investment_5 18.4 57.2 289s Investment_6 19.4 57.1 289s Investment_7 20.1 61.0 289s Investment_8 19.6 64.0 289s Investment_9 19.8 64.4 289s Investment_10 21.1 64.5 289s Investment_11 21.7 67.0 289s Investment_12 15.6 61.2 289s Investment_13 11.4 53.4 289s Investment_14 7.0 44.3 289s Investment_15 11.2 45.1 289s Investment_16 12.3 49.7 289s Investment_17 14.0 54.4 289s Investment_18 17.6 62.7 289s Investment_19 17.3 65.0 289s Investment_20 15.3 60.9 289s Investment_21 19.0 69.5 289s Investment_22 21.1 75.7 289s PrivateWages_2 0.0 0.0 289s PrivateWages_3 0.0 0.0 289s PrivateWages_4 0.0 0.0 289s PrivateWages_5 0.0 0.0 289s PrivateWages_6 0.0 0.0 289s PrivateWages_7 0.0 0.0 289s PrivateWages_8 0.0 0.0 289s PrivateWages_9 0.0 0.0 289s PrivateWages_10 0.0 0.0 289s PrivateWages_11 0.0 0.0 289s PrivateWages_12 0.0 0.0 289s PrivateWages_13 0.0 0.0 289s PrivateWages_14 0.0 0.0 289s PrivateWages_15 0.0 0.0 289s PrivateWages_16 0.0 0.0 289s PrivateWages_17 0.0 0.0 289s PrivateWages_18 0.0 0.0 289s PrivateWages_19 0.0 0.0 289s PrivateWages_20 0.0 0.0 289s PrivateWages_21 0.0 0.0 289s PrivateWages_22 0.0 0.0 289s PrivateWages_(Intercept) PrivateWages_govExp PrivateWages_taxes 289s Consumption_2 0 0.0 0.0 289s Consumption_3 0 0.0 0.0 289s Consumption_4 0 0.0 0.0 289s Consumption_5 0 0.0 0.0 289s Consumption_6 0 0.0 0.0 289s Consumption_7 0 0.0 0.0 289s Consumption_8 0 0.0 0.0 289s Consumption_9 0 0.0 0.0 289s Consumption_10 0 0.0 0.0 289s Consumption_11 0 0.0 0.0 289s Consumption_12 0 0.0 0.0 289s Consumption_13 0 0.0 0.0 289s Consumption_14 0 0.0 0.0 289s Consumption_15 0 0.0 0.0 289s Consumption_16 0 0.0 0.0 289s Consumption_17 0 0.0 0.0 289s Consumption_18 0 0.0 0.0 289s Consumption_19 0 0.0 0.0 289s Consumption_20 0 0.0 0.0 289s Consumption_21 0 0.0 0.0 289s Consumption_22 0 0.0 0.0 289s Investment_2 0 0.0 0.0 289s Investment_3 0 0.0 0.0 289s Investment_4 0 0.0 0.0 289s Investment_5 0 0.0 0.0 289s Investment_6 0 0.0 0.0 289s Investment_7 0 0.0 0.0 289s Investment_8 0 0.0 0.0 289s Investment_9 0 0.0 0.0 289s Investment_10 0 0.0 0.0 289s Investment_11 0 0.0 0.0 289s Investment_12 0 0.0 0.0 289s Investment_13 0 0.0 0.0 289s Investment_14 0 0.0 0.0 289s Investment_15 0 0.0 0.0 289s Investment_16 0 0.0 0.0 289s Investment_17 0 0.0 0.0 289s Investment_18 0 0.0 0.0 289s Investment_19 0 0.0 0.0 289s Investment_20 0 0.0 0.0 289s Investment_21 0 0.0 0.0 289s Investment_22 0 0.0 0.0 289s PrivateWages_2 1 3.9 7.7 289s PrivateWages_3 1 3.2 3.9 289s PrivateWages_4 1 2.8 4.7 289s PrivateWages_5 1 3.5 3.8 289s PrivateWages_6 1 3.3 5.5 289s PrivateWages_7 1 3.3 7.0 289s PrivateWages_8 1 4.0 6.7 289s PrivateWages_9 1 4.2 4.2 289s PrivateWages_10 1 4.1 4.0 289s PrivateWages_11 1 5.2 7.7 289s PrivateWages_12 1 5.9 7.5 289s PrivateWages_13 1 4.9 8.3 289s PrivateWages_14 1 3.7 5.4 289s PrivateWages_15 1 4.0 6.8 289s PrivateWages_16 1 4.4 7.2 289s PrivateWages_17 1 2.9 8.3 289s PrivateWages_18 1 4.3 6.7 289s PrivateWages_19 1 5.3 7.4 289s PrivateWages_20 1 6.6 8.9 289s PrivateWages_21 1 7.4 9.6 289s PrivateWages_22 1 13.8 11.6 289s PrivateWages_govWage PrivateWages_trend PrivateWages_capitalLag 289s Consumption_2 0.0 0 0 289s Consumption_3 0.0 0 0 289s Consumption_4 0.0 0 0 289s Consumption_5 0.0 0 0 289s Consumption_6 0.0 0 0 289s Consumption_7 0.0 0 0 289s Consumption_8 0.0 0 0 289s Consumption_9 0.0 0 0 289s Consumption_10 0.0 0 0 289s Consumption_11 0.0 0 0 289s Consumption_12 0.0 0 0 289s Consumption_13 0.0 0 0 289s Consumption_14 0.0 0 0 289s Consumption_15 0.0 0 0 289s Consumption_16 0.0 0 0 289s Consumption_17 0.0 0 0 289s Consumption_18 0.0 0 0 289s Consumption_19 0.0 0 0 289s Consumption_20 0.0 0 0 289s Consumption_21 0.0 0 0 289s Consumption_22 0.0 0 0 289s Investment_2 0.0 0 0 289s Investment_3 0.0 0 0 289s Investment_4 0.0 0 0 289s Investment_5 0.0 0 0 289s Investment_6 0.0 0 0 289s Investment_7 0.0 0 0 289s Investment_8 0.0 0 0 289s Investment_9 0.0 0 0 289s Investment_10 0.0 0 0 289s Investment_11 0.0 0 0 289s Investment_12 0.0 0 0 289s Investment_13 0.0 0 0 289s Investment_14 0.0 0 0 289s Investment_15 0.0 0 0 289s Investment_16 0.0 0 0 289s Investment_17 0.0 0 0 289s Investment_18 0.0 0 0 289s Investment_19 0.0 0 0 289s Investment_20 0.0 0 0 289s Investment_21 0.0 0 0 289s Investment_22 0.0 0 0 289s PrivateWages_2 2.7 -10 183 289s PrivateWages_3 2.9 -9 183 289s PrivateWages_4 2.9 -8 184 289s PrivateWages_5 3.1 -7 190 289s PrivateWages_6 3.2 -6 193 289s PrivateWages_7 3.3 -5 198 289s PrivateWages_8 3.6 -4 203 289s PrivateWages_9 3.7 -3 208 289s PrivateWages_10 4.0 -2 211 289s PrivateWages_11 4.2 -1 216 289s PrivateWages_12 4.8 0 217 289s PrivateWages_13 5.3 1 213 289s PrivateWages_14 5.6 2 207 289s PrivateWages_15 6.0 3 202 289s PrivateWages_16 6.1 4 199 289s PrivateWages_17 7.4 5 198 289s PrivateWages_18 6.7 6 200 289s PrivateWages_19 7.7 7 202 289s PrivateWages_20 7.8 8 200 289s PrivateWages_21 8.0 9 201 289s PrivateWages_22 8.5 10 204 289s PrivateWages_corpProfLag PrivateWages_gnpLag 289s Consumption_2 0.0 0.0 289s Consumption_3 0.0 0.0 289s Consumption_4 0.0 0.0 289s Consumption_5 0.0 0.0 289s Consumption_6 0.0 0.0 289s Consumption_7 0.0 0.0 289s Consumption_8 0.0 0.0 289s Consumption_9 0.0 0.0 289s Consumption_10 0.0 0.0 289s Consumption_11 0.0 0.0 289s Consumption_12 0.0 0.0 289s Consumption_13 0.0 0.0 289s Consumption_14 0.0 0.0 289s Consumption_15 0.0 0.0 289s Consumption_16 0.0 0.0 289s Consumption_17 0.0 0.0 289s Consumption_18 0.0 0.0 289s Consumption_19 0.0 0.0 289s Consumption_20 0.0 0.0 289s Consumption_21 0.0 0.0 289s Consumption_22 0.0 0.0 289s Investment_2 0.0 0.0 289s Investment_3 0.0 0.0 289s Investment_4 0.0 0.0 289s Investment_5 0.0 0.0 289s Investment_6 0.0 0.0 289s Investment_7 0.0 0.0 289s Investment_8 0.0 0.0 289s Investment_9 0.0 0.0 289s Investment_10 0.0 0.0 289s Investment_11 0.0 0.0 289s Investment_12 0.0 0.0 289s Investment_13 0.0 0.0 289s Investment_14 0.0 0.0 289s Investment_15 0.0 0.0 289s Investment_16 0.0 0.0 289s Investment_17 0.0 0.0 289s Investment_18 0.0 0.0 289s Investment_19 0.0 0.0 289s Investment_20 0.0 0.0 289s Investment_21 0.0 0.0 289s Investment_22 0.0 0.0 289s PrivateWages_2 12.7 44.9 289s PrivateWages_3 12.4 45.6 289s PrivateWages_4 16.9 50.1 289s PrivateWages_5 18.4 57.2 289s PrivateWages_6 19.4 57.1 289s PrivateWages_7 20.1 61.0 289s PrivateWages_8 19.6 64.0 289s PrivateWages_9 19.8 64.4 289s PrivateWages_10 21.1 64.5 289s PrivateWages_11 21.7 67.0 289s PrivateWages_12 15.6 61.2 289s PrivateWages_13 11.4 53.4 289s PrivateWages_14 7.0 44.3 289s PrivateWages_15 11.2 45.1 289s PrivateWages_16 12.3 49.7 289s PrivateWages_17 14.0 54.4 289s PrivateWages_18 17.6 62.7 289s PrivateWages_19 17.3 65.0 289s PrivateWages_20 15.3 60.9 289s PrivateWages_21 19.0 69.5 289s PrivateWages_22 21.1 75.7 289s > matrix of fitted regressors 289s Consumption_(Intercept) Consumption_corpProf 289s Consumption_2 1 13.26 289s Consumption_3 1 16.58 289s Consumption_4 1 19.28 289s Consumption_5 1 20.96 289s Consumption_6 1 19.77 289s Consumption_7 1 18.24 289s Consumption_8 1 17.57 289s Consumption_9 1 19.54 289s Consumption_10 1 20.38 289s Consumption_11 1 17.18 289s Consumption_12 1 12.71 289s Consumption_13 1 9.00 289s Consumption_14 1 9.05 289s Consumption_15 1 12.67 289s Consumption_16 1 14.42 289s Consumption_17 1 14.71 289s Consumption_18 1 19.80 289s Consumption_19 1 19.21 289s Consumption_20 1 17.42 289s Consumption_21 1 20.31 289s Consumption_22 1 22.66 289s Investment_2 0 0.00 289s Investment_3 0 0.00 289s Investment_4 0 0.00 289s Investment_5 0 0.00 289s Investment_6 0 0.00 289s Investment_7 0 0.00 289s Investment_8 0 0.00 289s Investment_9 0 0.00 289s Investment_10 0 0.00 289s Investment_11 0 0.00 289s Investment_12 0 0.00 289s Investment_13 0 0.00 289s Investment_14 0 0.00 289s Investment_15 0 0.00 289s Investment_16 0 0.00 289s Investment_17 0 0.00 289s Investment_18 0 0.00 289s Investment_19 0 0.00 289s Investment_20 0 0.00 289s Investment_21 0 0.00 289s Investment_22 0 0.00 289s PrivateWages_2 0 0.00 289s PrivateWages_3 0 0.00 289s PrivateWages_4 0 0.00 289s PrivateWages_5 0 0.00 289s PrivateWages_6 0 0.00 289s PrivateWages_7 0 0.00 289s PrivateWages_8 0 0.00 289s PrivateWages_9 0 0.00 289s PrivateWages_10 0 0.00 289s PrivateWages_11 0 0.00 289s PrivateWages_12 0 0.00 289s PrivateWages_13 0 0.00 289s PrivateWages_14 0 0.00 289s PrivateWages_15 0 0.00 289s PrivateWages_16 0 0.00 289s PrivateWages_17 0 0.00 289s PrivateWages_18 0 0.00 289s PrivateWages_19 0 0.00 289s PrivateWages_20 0 0.00 289s PrivateWages_21 0 0.00 289s PrivateWages_22 0 0.00 289s Consumption_corpProfLag Consumption_wages 289s Consumption_2 12.7 29.4 289s Consumption_3 12.4 31.8 289s Consumption_4 16.9 35.8 289s Consumption_5 18.4 39.1 289s Consumption_6 19.4 39.1 289s Consumption_7 20.1 39.4 289s Consumption_8 19.6 40.2 289s Consumption_9 19.8 42.3 289s Consumption_10 21.1 44.0 289s Consumption_11 21.7 43.7 289s Consumption_12 15.6 39.5 289s Consumption_13 11.4 35.1 289s Consumption_14 7.0 32.8 289s Consumption_15 11.2 37.5 289s Consumption_16 12.3 40.1 289s Consumption_17 14.0 41.7 289s Consumption_18 17.6 47.9 289s Consumption_19 17.3 49.3 289s Consumption_20 15.3 48.4 289s Consumption_21 19.0 53.4 289s Consumption_22 21.1 60.7 289s Investment_2 0.0 0.0 289s Investment_3 0.0 0.0 289s Investment_4 0.0 0.0 289s Investment_5 0.0 0.0 289s Investment_6 0.0 0.0 289s Investment_7 0.0 0.0 289s Investment_8 0.0 0.0 289s Investment_9 0.0 0.0 289s Investment_10 0.0 0.0 289s Investment_11 0.0 0.0 289s Investment_12 0.0 0.0 289s Investment_13 0.0 0.0 289s Investment_14 0.0 0.0 289s Investment_15 0.0 0.0 289s Investment_16 0.0 0.0 289s Investment_17 0.0 0.0 289s Investment_18 0.0 0.0 289s Investment_19 0.0 0.0 289s Investment_20 0.0 0.0 289s Investment_21 0.0 0.0 289s Investment_22 0.0 0.0 289s PrivateWages_2 0.0 0.0 289s PrivateWages_3 0.0 0.0 289s PrivateWages_4 0.0 0.0 289s PrivateWages_5 0.0 0.0 289s PrivateWages_6 0.0 0.0 289s PrivateWages_7 0.0 0.0 289s PrivateWages_8 0.0 0.0 289s PrivateWages_9 0.0 0.0 289s PrivateWages_10 0.0 0.0 289s PrivateWages_11 0.0 0.0 289s PrivateWages_12 0.0 0.0 289s PrivateWages_13 0.0 0.0 289s PrivateWages_14 0.0 0.0 289s PrivateWages_15 0.0 0.0 289s PrivateWages_16 0.0 0.0 289s PrivateWages_17 0.0 0.0 289s PrivateWages_18 0.0 0.0 289s PrivateWages_19 0.0 0.0 289s PrivateWages_20 0.0 0.0 289s PrivateWages_21 0.0 0.0 289s PrivateWages_22 0.0 0.0 289s Investment_(Intercept) Investment_corpProf 289s Consumption_2 0 0.00 289s Consumption_3 0 0.00 289s Consumption_4 0 0.00 289s Consumption_5 0 0.00 289s Consumption_6 0 0.00 289s Consumption_7 0 0.00 289s Consumption_8 0 0.00 289s Consumption_9 0 0.00 289s Consumption_10 0 0.00 289s Consumption_11 0 0.00 289s Consumption_12 0 0.00 289s Consumption_13 0 0.00 289s Consumption_14 0 0.00 289s Consumption_15 0 0.00 289s Consumption_16 0 0.00 289s Consumption_17 0 0.00 289s Consumption_18 0 0.00 289s Consumption_19 0 0.00 289s Consumption_20 0 0.00 289s Consumption_21 0 0.00 289s Consumption_22 0 0.00 289s Investment_2 1 13.26 289s Investment_3 1 16.58 289s Investment_4 1 19.28 289s Investment_5 1 20.96 289s Investment_6 1 19.77 289s Investment_7 1 18.24 289s Investment_8 1 17.57 289s Investment_9 1 19.54 289s Investment_10 1 20.38 289s Investment_11 1 17.18 289s Investment_12 1 12.71 289s Investment_13 1 9.00 289s Investment_14 1 9.05 289s Investment_15 1 12.67 289s Investment_16 1 14.42 289s Investment_17 1 14.71 289s Investment_18 1 19.80 289s Investment_19 1 19.21 289s Investment_20 1 17.42 289s Investment_21 1 20.31 289s Investment_22 1 22.66 289s PrivateWages_2 0 0.00 289s PrivateWages_3 0 0.00 289s PrivateWages_4 0 0.00 289s PrivateWages_5 0 0.00 289s PrivateWages_6 0 0.00 289s PrivateWages_7 0 0.00 289s PrivateWages_8 0 0.00 289s PrivateWages_9 0 0.00 289s PrivateWages_10 0 0.00 289s PrivateWages_11 0 0.00 289s PrivateWages_12 0 0.00 289s PrivateWages_13 0 0.00 289s PrivateWages_14 0 0.00 289s PrivateWages_15 0 0.00 289s PrivateWages_16 0 0.00 289s PrivateWages_17 0 0.00 289s PrivateWages_18 0 0.00 289s PrivateWages_19 0 0.00 289s PrivateWages_20 0 0.00 289s PrivateWages_21 0 0.00 289s PrivateWages_22 0 0.00 289s Investment_corpProfLag Investment_capitalLag 289s Consumption_2 0.0 0 289s Consumption_3 0.0 0 289s Consumption_4 0.0 0 289s Consumption_5 0.0 0 289s Consumption_6 0.0 0 289s Consumption_7 0.0 0 289s Consumption_8 0.0 0 289s Consumption_9 0.0 0 289s Consumption_10 0.0 0 289s Consumption_11 0.0 0 289s Consumption_12 0.0 0 289s Consumption_13 0.0 0 289s Consumption_14 0.0 0 289s Consumption_15 0.0 0 289s Consumption_16 0.0 0 289s Consumption_17 0.0 0 289s Consumption_18 0.0 0 289s Consumption_19 0.0 0 289s Consumption_20 0.0 0 289s Consumption_21 0.0 0 289s Consumption_22 0.0 0 289s Investment_2 12.7 183 289s Investment_3 12.4 183 289s Investment_4 16.9 184 289s Investment_5 18.4 190 289s Investment_6 19.4 193 289s Investment_7 20.1 198 289s Investment_8 19.6 203 289s Investment_9 19.8 208 289s Investment_10 21.1 211 289s Investment_11 21.7 216 289s Investment_12 15.6 217 289s Investment_13 11.4 213 289s Investment_14 7.0 207 289s Investment_15 11.2 202 289s Investment_16 12.3 199 289s Investment_17 14.0 198 289s Investment_18 17.6 200 289s Investment_19 17.3 202 289s Investment_20 15.3 200 289s Investment_21 19.0 201 289s Investment_22 21.1 204 289s PrivateWages_2 0.0 0 289s PrivateWages_3 0.0 0 289s PrivateWages_4 0.0 0 289s PrivateWages_5 0.0 0 289s PrivateWages_6 0.0 0 289s PrivateWages_7 0.0 0 289s PrivateWages_8 0.0 0 289s PrivateWages_9 0.0 0 289s PrivateWages_10 0.0 0 289s PrivateWages_11 0.0 0 289s PrivateWages_12 0.0 0 289s PrivateWages_13 0.0 0 289s PrivateWages_14 0.0 0 289s PrivateWages_15 0.0 0 289s PrivateWages_16 0.0 0 289s PrivateWages_17 0.0 0 289s PrivateWages_18 0.0 0 289s PrivateWages_19 0.0 0 289s PrivateWages_20 0.0 0 289s PrivateWages_21 0.0 0 289s PrivateWages_22 0.0 0 289s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 289s Consumption_2 0 0.0 0.0 289s Consumption_3 0 0.0 0.0 289s Consumption_4 0 0.0 0.0 289s Consumption_5 0 0.0 0.0 289s Consumption_6 0 0.0 0.0 289s Consumption_7 0 0.0 0.0 289s Consumption_8 0 0.0 0.0 289s Consumption_9 0 0.0 0.0 289s Consumption_10 0 0.0 0.0 289s Consumption_11 0 0.0 0.0 289s Consumption_12 0 0.0 0.0 289s Consumption_13 0 0.0 0.0 289s Consumption_14 0 0.0 0.0 289s Consumption_15 0 0.0 0.0 289s Consumption_16 0 0.0 0.0 289s Consumption_17 0 0.0 0.0 289s Consumption_18 0 0.0 0.0 289s Consumption_19 0 0.0 0.0 289s Consumption_20 0 0.0 0.0 289s Consumption_21 0 0.0 0.0 289s Consumption_22 0 0.0 0.0 289s Investment_2 0 0.0 0.0 289s Investment_3 0 0.0 0.0 289s Investment_4 0 0.0 0.0 289s Investment_5 0 0.0 0.0 289s Investment_6 0 0.0 0.0 289s Investment_7 0 0.0 0.0 289s Investment_8 0 0.0 0.0 289s Investment_9 0 0.0 0.0 289s Investment_10 0 0.0 0.0 289s Investment_11 0 0.0 0.0 289s Investment_12 0 0.0 0.0 289s Investment_13 0 0.0 0.0 289s Investment_14 0 0.0 0.0 289s Investment_15 0 0.0 0.0 289s Investment_16 0 0.0 0.0 289s Investment_17 0 0.0 0.0 289s Investment_18 0 0.0 0.0 289s Investment_19 0 0.0 0.0 289s Investment_20 0 0.0 0.0 289s Investment_21 0 0.0 0.0 289s Investment_22 0 0.0 0.0 289s PrivateWages_2 1 47.7 44.9 289s PrivateWages_3 1 49.3 45.6 289s PrivateWages_4 1 56.8 50.1 289s PrivateWages_5 1 60.7 57.2 289s PrivateWages_6 1 61.2 57.1 289s PrivateWages_7 1 61.3 61.0 289s PrivateWages_8 1 60.9 64.0 289s PrivateWages_9 1 62.4 64.4 289s PrivateWages_10 1 64.4 64.5 289s PrivateWages_11 1 64.4 67.0 289s PrivateWages_12 1 54.9 61.2 289s PrivateWages_13 1 47.1 53.4 289s PrivateWages_14 1 41.6 44.3 289s PrivateWages_15 1 51.0 45.1 289s PrivateWages_16 1 55.7 49.7 289s PrivateWages_17 1 57.3 54.4 289s PrivateWages_18 1 67.7 62.7 289s PrivateWages_19 1 68.2 65.0 289s PrivateWages_20 1 66.9 60.9 289s PrivateWages_21 1 75.3 69.5 289s PrivateWages_22 1 86.5 75.7 289s PrivateWages_trend 289s Consumption_2 0 289s Consumption_3 0 289s Consumption_4 0 289s Consumption_5 0 289s Consumption_6 0 289s Consumption_7 0 289s Consumption_8 0 289s Consumption_9 0 289s Consumption_10 0 289s Consumption_11 0 289s Consumption_12 0 289s Consumption_13 0 289s Consumption_14 0 289s Consumption_15 0 289s Consumption_16 0 289s Consumption_17 0 289s Consumption_18 0 289s Consumption_19 0 289s Consumption_20 0 289s Consumption_21 0 289s Consumption_22 0 289s Investment_2 0 289s Investment_3 0 289s Investment_4 0 289s Investment_5 0 289s Investment_6 0 289s Investment_7 0 289s Investment_8 0 289s Investment_9 0 289s Investment_10 0 289s Investment_11 0 289s Investment_12 0 289s Investment_13 0 289s Investment_14 0 289s Investment_15 0 289s Investment_16 0 289s Investment_17 0 289s Investment_18 0 289s Investment_19 0 289s Investment_20 0 289s Investment_21 0 289s Investment_22 0 289s PrivateWages_2 -10 289s PrivateWages_3 -9 289s PrivateWages_4 -8 289s PrivateWages_5 -7 289s PrivateWages_6 -6 289s PrivateWages_7 -5 289s PrivateWages_8 -4 289s PrivateWages_9 -3 289s PrivateWages_10 -2 289s PrivateWages_11 -1 289s PrivateWages_12 0 289s PrivateWages_13 1 289s PrivateWages_14 2 289s PrivateWages_15 3 289s PrivateWages_16 4 289s PrivateWages_17 5 289s PrivateWages_18 6 289s PrivateWages_19 7 289s PrivateWages_20 8 289s PrivateWages_21 9 289s PrivateWages_22 10 289s > nobs 289s [1] 63 289s > linearHypothesis 289s Linear hypothesis test (Theil's F test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df F Pr(>F) 289s 1 52 289s 2 51 1 1.08 0.3 289s Linear hypothesis test (F statistic of a Wald test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df F Pr(>F) 289s 1 52 289s 2 51 1 1.29 0.26 289s Linear hypothesis test (Chi^2 statistic of a Wald test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df Chisq Pr(>Chisq) 289s 1 52 289s 2 51 1 1.29 0.26 289s Linear hypothesis test (Theil's F test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s Consumption_corpProfLag - PrivateWages_trend = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df F Pr(>F) 289s 1 53 289s 2 51 2 0.54 0.58 289s Linear hypothesis test (F statistic of a Wald test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s Consumption_corpProfLag - PrivateWages_trend = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df F Pr(>F) 289s 1 53 289s 2 51 2 0.65 0.53 289s Linear hypothesis test (Chi^2 statistic of a Wald test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s Consumption_corpProfLag - PrivateWages_trend = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df Chisq Pr(>Chisq) 289s 1 53 289s 2 51 2 1.3 0.52 289s > logLik 289s 'log Lik.' -76.3 (df=13) 289s 'log Lik.' -85.5 (df=13) 289s Estimating function 289s Consumption_(Intercept) Consumption_corpProf 289s Consumption_2 -1.455 -19.28 289s Consumption_3 -0.246 -4.08 289s Consumption_4 -0.309 -5.96 289s Consumption_5 -1.952 -40.92 289s Consumption_6 -0.199 -3.93 289s Consumption_7 2.000 36.47 289s Consumption_8 2.547 44.76 289s Consumption_9 1.829 35.74 289s Consumption_10 0.665 13.55 289s Consumption_11 -1.947 -33.46 289s Consumption_12 -1.232 -15.65 289s Consumption_13 -2.039 -18.35 289s Consumption_14 1.714 15.52 289s Consumption_15 -0.877 -11.11 289s Consumption_16 -0.684 -9.87 289s Consumption_17 4.077 59.98 289s Consumption_18 -0.793 -15.70 289s Consumption_19 -3.072 -59.01 289s Consumption_20 2.230 38.84 289s Consumption_21 0.744 15.11 289s Consumption_22 -1.000 -22.66 289s Investment_2 0.000 0.00 289s Investment_3 0.000 0.00 289s Investment_4 0.000 0.00 289s Investment_5 0.000 0.00 289s Investment_6 0.000 0.00 289s Investment_7 0.000 0.00 289s Investment_8 0.000 0.00 289s Investment_9 0.000 0.00 289s Investment_10 0.000 0.00 289s Investment_11 0.000 0.00 289s Investment_12 0.000 0.00 289s Investment_13 0.000 0.00 289s Investment_14 0.000 0.00 289s Investment_15 0.000 0.00 289s Investment_16 0.000 0.00 289s Investment_17 0.000 0.00 289s Investment_18 0.000 0.00 289s Investment_19 0.000 0.00 289s Investment_20 0.000 0.00 289s Investment_21 0.000 0.00 289s Investment_22 0.000 0.00 289s PrivateWages_2 0.000 0.00 289s PrivateWages_3 0.000 0.00 289s PrivateWages_4 0.000 0.00 289s PrivateWages_5 0.000 0.00 289s PrivateWages_6 0.000 0.00 289s PrivateWages_7 0.000 0.00 289s PrivateWages_8 0.000 0.00 289s PrivateWages_9 0.000 0.00 289s PrivateWages_10 0.000 0.00 289s PrivateWages_11 0.000 0.00 289s PrivateWages_12 0.000 0.00 289s PrivateWages_13 0.000 0.00 289s PrivateWages_14 0.000 0.00 289s PrivateWages_15 0.000 0.00 289s PrivateWages_16 0.000 0.00 289s PrivateWages_17 0.000 0.00 289s PrivateWages_18 0.000 0.00 289s PrivateWages_19 0.000 0.00 289s PrivateWages_20 0.000 0.00 289s PrivateWages_21 0.000 0.00 289s PrivateWages_22 0.000 0.00 289s Consumption_corpProfLag Consumption_wages 289s Consumption_2 -18.47 -42.77 289s Consumption_3 -3.05 -7.82 289s Consumption_4 -5.22 -11.05 289s Consumption_5 -35.93 -76.29 289s Consumption_6 -3.85 -7.77 289s Consumption_7 40.20 78.70 289s Consumption_8 49.93 102.36 289s Consumption_9 36.21 77.42 289s Consumption_10 14.03 29.28 289s Consumption_11 -42.26 -85.10 289s Consumption_12 -19.22 -48.63 289s Consumption_13 -23.25 -71.64 289s Consumption_14 12.00 56.20 289s Consumption_15 -9.82 -32.89 289s Consumption_16 -8.42 -27.47 289s Consumption_17 57.07 170.01 289s Consumption_18 -13.96 -37.97 289s Consumption_19 -53.15 -151.48 289s Consumption_20 34.12 107.90 289s Consumption_21 14.14 39.73 289s Consumption_22 -21.10 -60.72 289s Investment_2 0.00 0.00 289s Investment_3 0.00 0.00 289s Investment_4 0.00 0.00 289s Investment_5 0.00 0.00 289s Investment_6 0.00 0.00 289s Investment_7 0.00 0.00 289s Investment_8 0.00 0.00 289s Investment_9 0.00 0.00 289s Investment_10 0.00 0.00 289s Investment_11 0.00 0.00 289s Investment_12 0.00 0.00 289s Investment_13 0.00 0.00 289s Investment_14 0.00 0.00 289s Investment_15 0.00 0.00 289s Investment_16 0.00 0.00 289s Investment_17 0.00 0.00 289s Investment_18 0.00 0.00 289s Investment_19 0.00 0.00 289s Investment_20 0.00 0.00 289s Investment_21 0.00 0.00 289s Investment_22 0.00 0.00 289s PrivateWages_2 0.00 0.00 289s PrivateWages_3 0.00 0.00 289s PrivateWages_4 0.00 0.00 289s PrivateWages_5 0.00 0.00 289s PrivateWages_6 0.00 0.00 289s PrivateWages_7 0.00 0.00 289s PrivateWages_8 0.00 0.00 289s PrivateWages_9 0.00 0.00 289s PrivateWages_10 0.00 0.00 289s PrivateWages_11 0.00 0.00 289s PrivateWages_12 0.00 0.00 289s PrivateWages_13 0.00 0.00 289s PrivateWages_14 0.00 0.00 289s PrivateWages_15 0.00 0.00 289s PrivateWages_16 0.00 0.00 289s PrivateWages_17 0.00 0.00 289s PrivateWages_18 0.00 0.00 289s PrivateWages_19 0.00 0.00 289s PrivateWages_20 0.00 0.00 289s PrivateWages_21 0.00 0.00 289s PrivateWages_22 0.00 0.00 289s Investment_(Intercept) Investment_corpProf 289s Consumption_2 0.0000 0.000 289s Consumption_3 0.0000 0.000 289s Consumption_4 0.0000 0.000 289s Consumption_5 0.0000 0.000 289s Consumption_6 0.0000 0.000 289s Consumption_7 0.0000 0.000 289s Consumption_8 0.0000 0.000 289s Consumption_9 0.0000 0.000 289s Consumption_10 0.0000 0.000 289s Consumption_11 0.0000 0.000 289s Consumption_12 0.0000 0.000 289s Consumption_13 0.0000 0.000 289s Consumption_14 0.0000 0.000 289s Consumption_15 0.0000 0.000 289s Consumption_16 0.0000 0.000 289s Consumption_17 0.0000 0.000 289s Consumption_18 0.0000 0.000 289s Consumption_19 0.0000 0.000 289s Consumption_20 0.0000 0.000 289s Consumption_21 0.0000 0.000 289s Consumption_22 0.0000 0.000 289s Investment_2 -1.4484 -19.199 289s Investment_3 0.3058 5.070 289s Investment_4 0.7275 14.029 289s Investment_5 -1.8279 -38.314 289s Investment_6 0.3088 6.104 289s Investment_7 1.4119 25.751 289s Investment_8 1.3034 22.906 289s Investment_9 0.3472 6.785 289s Investment_10 1.9947 40.642 289s Investment_11 -1.1903 -20.449 289s Investment_12 -1.0029 -12.742 289s Investment_13 -1.1958 -10.762 289s Investment_14 1.6279 14.739 289s Investment_15 -0.2072 -2.625 289s Investment_16 0.0790 1.140 289s Investment_17 2.1831 32.118 289s Investment_18 -0.5667 -11.219 289s Investment_19 -3.8778 -74.479 289s Investment_20 0.5228 9.107 289s Investment_21 0.0154 0.312 289s Investment_22 0.4893 11.087 289s PrivateWages_2 0.0000 0.000 289s PrivateWages_3 0.0000 0.000 289s PrivateWages_4 0.0000 0.000 289s PrivateWages_5 0.0000 0.000 289s PrivateWages_6 0.0000 0.000 289s PrivateWages_7 0.0000 0.000 289s PrivateWages_8 0.0000 0.000 289s PrivateWages_9 0.0000 0.000 289s PrivateWages_10 0.0000 0.000 289s PrivateWages_11 0.0000 0.000 289s PrivateWages_12 0.0000 0.000 289s PrivateWages_13 0.0000 0.000 289s PrivateWages_14 0.0000 0.000 289s PrivateWages_15 0.0000 0.000 289s PrivateWages_16 0.0000 0.000 289s PrivateWages_17 0.0000 0.000 289s PrivateWages_18 0.0000 0.000 289s PrivateWages_19 0.0000 0.000 289s PrivateWages_20 0.0000 0.000 289s PrivateWages_21 0.0000 0.000 289s PrivateWages_22 0.0000 0.000 289s Investment_corpProfLag Investment_capitalLag 289s Consumption_2 0.000 0.0 289s Consumption_3 0.000 0.0 289s Consumption_4 0.000 0.0 289s Consumption_5 0.000 0.0 289s Consumption_6 0.000 0.0 289s Consumption_7 0.000 0.0 289s Consumption_8 0.000 0.0 289s Consumption_9 0.000 0.0 289s Consumption_10 0.000 0.0 289s Consumption_11 0.000 0.0 289s Consumption_12 0.000 0.0 289s Consumption_13 0.000 0.0 289s Consumption_14 0.000 0.0 289s Consumption_15 0.000 0.0 289s Consumption_16 0.000 0.0 289s Consumption_17 0.000 0.0 289s Consumption_18 0.000 0.0 289s Consumption_19 0.000 0.0 289s Consumption_20 0.000 0.0 289s Consumption_21 0.000 0.0 289s Consumption_22 0.000 0.0 289s Investment_2 -18.395 -264.8 289s Investment_3 3.792 55.8 289s Investment_4 12.295 134.2 289s Investment_5 -33.634 -346.8 289s Investment_6 5.991 59.5 289s Investment_7 28.378 279.3 289s Investment_8 25.548 265.1 289s Investment_9 6.875 72.1 289s Investment_10 42.088 420.1 289s Investment_11 -25.829 -256.7 289s Investment_12 -15.646 -217.3 289s Investment_13 -13.632 -255.1 289s Investment_14 11.395 337.1 289s Investment_15 -2.320 -41.8 289s Investment_16 0.972 15.7 289s Investment_17 30.564 431.6 289s Investment_18 -9.974 -113.2 289s Investment_19 -67.085 -782.5 289s Investment_20 7.999 104.5 289s Investment_21 0.292 3.1 289s Investment_22 10.325 100.1 289s PrivateWages_2 0.000 0.0 289s PrivateWages_3 0.000 0.0 289s PrivateWages_4 0.000 0.0 289s PrivateWages_5 0.000 0.0 289s PrivateWages_6 0.000 0.0 289s PrivateWages_7 0.000 0.0 289s PrivateWages_8 0.000 0.0 289s PrivateWages_9 0.000 0.0 289s PrivateWages_10 0.000 0.0 289s PrivateWages_11 0.000 0.0 289s PrivateWages_12 0.000 0.0 289s PrivateWages_13 0.000 0.0 289s PrivateWages_14 0.000 0.0 289s PrivateWages_15 0.000 0.0 289s PrivateWages_16 0.000 0.0 289s PrivateWages_17 0.000 0.0 289s PrivateWages_18 0.000 0.0 289s PrivateWages_19 0.000 0.0 289s PrivateWages_20 0.000 0.0 289s PrivateWages_21 0.000 0.0 289s PrivateWages_22 0.000 0.0 289s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 289s Consumption_2 0.0000 0.00 0.00 289s Consumption_3 0.0000 0.00 0.00 289s Consumption_4 0.0000 0.00 0.00 289s Consumption_5 0.0000 0.00 0.00 289s Consumption_6 0.0000 0.00 0.00 289s Consumption_7 0.0000 0.00 0.00 289s Consumption_8 0.0000 0.00 0.00 289s Consumption_9 0.0000 0.00 0.00 289s Consumption_10 0.0000 0.00 0.00 289s Consumption_11 0.0000 0.00 0.00 289s Consumption_12 0.0000 0.00 0.00 289s Consumption_13 0.0000 0.00 0.00 289s Consumption_14 0.0000 0.00 0.00 289s Consumption_15 0.0000 0.00 0.00 289s Consumption_16 0.0000 0.00 0.00 289s Consumption_17 0.0000 0.00 0.00 289s Consumption_18 0.0000 0.00 0.00 289s Consumption_19 0.0000 0.00 0.00 289s Consumption_20 0.0000 0.00 0.00 289s Consumption_21 0.0000 0.00 0.00 289s Consumption_22 0.0000 0.00 0.00 289s Investment_2 0.0000 0.00 0.00 289s Investment_3 0.0000 0.00 0.00 289s Investment_4 0.0000 0.00 0.00 289s Investment_5 0.0000 0.00 0.00 289s Investment_6 0.0000 0.00 0.00 289s Investment_7 0.0000 0.00 0.00 289s Investment_8 0.0000 0.00 0.00 289s Investment_9 0.0000 0.00 0.00 289s Investment_10 0.0000 0.00 0.00 289s Investment_11 0.0000 0.00 0.00 289s Investment_12 0.0000 0.00 0.00 289s Investment_13 0.0000 0.00 0.00 289s Investment_14 0.0000 0.00 0.00 289s Investment_15 0.0000 0.00 0.00 289s Investment_16 0.0000 0.00 0.00 289s Investment_17 0.0000 0.00 0.00 289s Investment_18 0.0000 0.00 0.00 289s Investment_19 0.0000 0.00 0.00 289s Investment_20 0.0000 0.00 0.00 289s Investment_21 0.0000 0.00 0.00 289s Investment_22 0.0000 0.00 0.00 289s PrivateWages_2 -2.1987 -104.79 -98.72 289s PrivateWages_3 0.6372 31.43 29.06 289s PrivateWages_4 1.3519 76.84 67.73 289s PrivateWages_5 -1.7306 -105.10 -98.99 289s PrivateWages_6 -0.5521 -33.79 -31.52 289s PrivateWages_7 0.7059 43.27 43.06 289s PrivateWages_8 0.8269 50.32 52.92 289s PrivateWages_9 1.2718 79.33 81.90 289s PrivateWages_10 2.3392 150.64 150.88 289s PrivateWages_11 -1.5500 -99.78 -103.85 289s PrivateWages_12 -0.0625 -3.43 -3.82 289s PrivateWages_13 -1.1474 -54.08 -61.27 289s PrivateWages_14 1.9682 81.95 87.19 289s PrivateWages_15 -0.2753 -14.03 -12.42 289s PrivateWages_16 -0.5389 -30.00 -26.78 289s PrivateWages_17 1.5156 86.87 82.45 289s PrivateWages_18 -0.1787 -12.09 -11.21 289s PrivateWages_19 -3.6814 -251.10 -239.29 289s PrivateWages_20 0.7597 50.83 46.27 289s PrivateWages_21 -0.9040 -68.05 -62.83 289s PrivateWages_22 1.4431 124.79 109.24 289s PrivateWages_trend 289s Consumption_2 0.000 289s Consumption_3 0.000 289s Consumption_4 0.000 289s Consumption_5 0.000 289s Consumption_6 0.000 289s Consumption_7 0.000 289s Consumption_8 0.000 289s Consumption_9 0.000 289s Consumption_10 0.000 289s Consumption_11 0.000 289s Consumption_12 0.000 289s Consumption_13 0.000 289s Consumption_14 0.000 289s Consumption_15 0.000 289s Consumption_16 0.000 289s Consumption_17 0.000 289s Consumption_18 0.000 289s Consumption_19 0.000 289s Consumption_20 0.000 289s Consumption_21 0.000 289s Consumption_22 0.000 289s Investment_2 0.000 289s Investment_3 0.000 289s Investment_4 0.000 289s Investment_5 0.000 289s Investment_6 0.000 289s Investment_7 0.000 289s Investment_8 0.000 289s Investment_9 0.000 289s Investment_10 0.000 289s Investment_11 0.000 289s Investment_12 0.000 289s Investment_13 0.000 289s Investment_14 0.000 289s Investment_15 0.000 289s Investment_16 0.000 289s Investment_17 0.000 289s Investment_18 0.000 289s Investment_19 0.000 289s Investment_20 0.000 289s Investment_21 0.000 289s Investment_22 0.000 289s PrivateWages_2 21.987 289s PrivateWages_3 -5.735 289s PrivateWages_4 -10.815 289s PrivateWages_5 12.114 289s PrivateWages_6 3.312 289s PrivateWages_7 -3.529 289s PrivateWages_8 -3.307 289s PrivateWages_9 -3.815 289s PrivateWages_10 -4.678 289s PrivateWages_11 1.550 289s PrivateWages_12 0.000 289s PrivateWages_13 -1.147 289s PrivateWages_14 3.936 289s PrivateWages_15 -0.826 289s PrivateWages_16 -2.156 289s PrivateWages_17 7.578 289s PrivateWages_18 -1.072 289s PrivateWages_19 -25.769 289s PrivateWages_20 6.078 289s PrivateWages_21 -8.136 289s PrivateWages_22 14.431 289s [1] TRUE 289s > Bread 289s Consumption_(Intercept) Consumption_corpProf 289s Consumption_(Intercept) 105.265 -0.9259 289s Consumption_corpProf -0.926 0.8409 289s Consumption_corpProfLag -0.287 -0.5775 289s Consumption_wages -1.975 -0.0921 289s Investment_(Intercept) 0.000 0.0000 289s Investment_corpProf 0.000 0.0000 289s Investment_corpProfLag 0.000 0.0000 289s Investment_capitalLag 0.000 0.0000 289s PrivateWages_(Intercept) 0.000 0.0000 289s PrivateWages_gnp 0.000 0.0000 289s PrivateWages_gnpLag 0.000 0.0000 289s PrivateWages_trend 0.000 0.0000 289s Consumption_corpProfLag Consumption_wages 289s Consumption_(Intercept) -0.287 -1.9751 289s Consumption_corpProf -0.578 -0.0921 289s Consumption_corpProfLag 0.694 -0.0320 289s Consumption_wages -0.032 0.0978 289s Investment_(Intercept) 0.000 0.0000 289s Investment_corpProf 0.000 0.0000 289s Investment_corpProfLag 0.000 0.0000 289s Investment_capitalLag 0.000 0.0000 289s PrivateWages_(Intercept) 0.000 0.0000 289s PrivateWages_gnp 0.000 0.0000 289s PrivateWages_gnpLag 0.000 0.0000 289s PrivateWages_trend 0.000 0.0000 289s Investment_(Intercept) Investment_corpProf 289s Consumption_(Intercept) 0.0 0.000 289s Consumption_corpProf 0.0 0.000 289s Consumption_corpProfLag 0.0 0.000 289s Consumption_wages 0.0 0.000 289s Investment_(Intercept) 2591.3 -42.124 289s Investment_corpProf -42.1 1.367 289s Investment_corpProfLag 35.4 -1.174 289s Investment_capitalLag -12.3 0.191 289s PrivateWages_(Intercept) 0.0 0.000 289s PrivateWages_gnp 0.0 0.000 289s PrivateWages_gnpLag 0.0 0.000 289s PrivateWages_trend 0.0 0.000 289s Investment_corpProfLag Investment_capitalLag 289s Consumption_(Intercept) 0.000 0.0000 289s Consumption_corpProf 0.000 0.0000 289s Consumption_corpProfLag 0.000 0.0000 289s Consumption_wages 0.000 0.0000 289s Investment_(Intercept) 35.417 -12.2536 289s Investment_corpProf -1.174 0.1908 289s Investment_corpProfLag 1.207 -0.1763 289s Investment_capitalLag -0.176 0.0594 289s PrivateWages_(Intercept) 0.000 0.0000 289s PrivateWages_gnp 0.000 0.0000 289s PrivateWages_gnpLag 0.000 0.0000 289s PrivateWages_trend 0.000 0.0000 289s PrivateWages_(Intercept) PrivateWages_gnp 289s Consumption_(Intercept) 0.000 0.0000 289s Consumption_corpProf 0.000 0.0000 289s Consumption_corpProfLag 0.000 0.0000 289s Consumption_wages 0.000 0.0000 289s Investment_(Intercept) 0.000 0.0000 289s Investment_corpProf 0.000 0.0000 289s Investment_corpProfLag 0.000 0.0000 289s Investment_capitalLag 0.000 0.0000 289s PrivateWages_(Intercept) 174.205 -0.8839 289s PrivateWages_gnp -0.884 0.1679 289s PrivateWages_gnpLag -2.037 -0.1586 289s PrivateWages_trend 2.064 -0.0409 289s PrivateWages_gnpLag PrivateWages_trend 289s Consumption_(Intercept) 0.00000 0.00000 289s Consumption_corpProf 0.00000 0.00000 289s Consumption_corpProfLag 0.00000 0.00000 289s Consumption_wages 0.00000 0.00000 289s Investment_(Intercept) 0.00000 0.00000 289s Investment_corpProf 0.00000 0.00000 289s Investment_corpProfLag 0.00000 0.00000 289s Investment_capitalLag 0.00000 0.00000 289s PrivateWages_(Intercept) -2.03709 2.06394 289s PrivateWages_gnp -0.15864 -0.04088 289s PrivateWages_gnpLag 0.19944 0.00675 289s PrivateWages_trend 0.00675 0.11229 289s > 289s > # SUR 289s > summary 289s 289s systemfit results 289s method: SUR 289s 289s N DF SSR detRCov OLS-R2 McElroy-R2 289s system 63 51 46.5 0.158 0.977 0.993 289s 289s N DF SSR MSE RMSE R2 Adj R2 289s Consumption 21 17 18.1 1.065 1.032 0.981 0.977 289s Investment 21 17 17.6 1.036 1.018 0.930 0.918 289s PrivateWages 21 17 10.8 0.633 0.796 0.986 0.984 289s 289s The covariance matrix of the residuals used for estimation 289s Consumption Investment PrivateWages 289s Consumption 0.8514 0.0495 -0.381 289s Investment 0.0495 0.8249 0.121 289s PrivateWages -0.3808 0.1212 0.476 289s 289s The covariance matrix of the residuals 289s Consumption Investment PrivateWages 289s Consumption 0.8618 0.0766 -0.437 289s Investment 0.0766 0.8384 0.203 289s PrivateWages -0.4368 0.2027 0.513 289s 289s The correlations of the residuals 289s Consumption Investment PrivateWages 289s Consumption 1.0000 0.0901 -0.657 289s Investment 0.0901 1.0000 0.309 289s PrivateWages -0.6572 0.3092 1.000 289s 289s 289s SUR estimates for 'Consumption' (equation 1) 289s Model Formula: consump ~ corpProf + corpProfLag + wages 289s 289s Estimate Std. Error t value Pr(>|t|) 289s (Intercept) 15.9805 1.1687 13.67 1.3e-10 *** 289s corpProf 0.2302 0.0767 3.00 0.008 ** 289s corpProfLag 0.0673 0.0769 0.87 0.394 289s wages 0.7962 0.0353 22.58 4.1e-14 *** 289s --- 289s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 289s 289s Residual standard error: 1.032 on 17 degrees of freedom 289s Number of observations: 21 Degrees of Freedom: 17 289s SSR: 18.098 MSE: 1.065 Root MSE: 1.032 289s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.977 289s 289s 289s SUR estimates for 'Investment' (equation 2) 289s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 289s 289s Estimate Std. Error t value Pr(>|t|) 289s (Intercept) 12.9293 4.8014 2.69 0.01540 * 289s corpProf 0.4429 0.0861 5.15 8.1e-05 *** 289s corpProfLag 0.3655 0.0894 4.09 0.00077 *** 289s capitalLag -0.1253 0.0235 -5.34 5.4e-05 *** 289s --- 289s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 289s 289s Residual standard error: 1.018 on 17 degrees of freedom 289s Number of observations: 21 Degrees of Freedom: 17 289s SSR: 17.606 MSE: 1.036 Root MSE: 1.018 289s Multiple R-Squared: 0.93 Adjusted R-Squared: 0.918 289s 289s 289s SUR estimates for 'PrivateWages' (equation 3) 289s Model Formula: privWage ~ gnp + gnpLag + trend 289s 289s Estimate Std. Error t value Pr(>|t|) 289s (Intercept) 1.6347 1.1173 1.46 0.16 289s gnp 0.4098 0.0273 15.04 3.0e-11 *** 289s gnpLag 0.1744 0.0312 5.59 3.2e-05 *** 289s trend 0.1558 0.0276 5.65 2.9e-05 *** 289s --- 289s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 289s 289s Residual standard error: 0.796 on 17 degrees of freedom 289s Number of observations: 21 Degrees of Freedom: 17 289s SSR: 10.763 MSE: 0.633 Root MSE: 0.796 289s Multiple R-Squared: 0.986 Adjusted R-Squared: 0.984 289s 289s > residuals 289s Consumption Investment PrivateWages 289s 1 NA NA NA 289s 2 -0.24064 -0.3522 -1.0960 289s 3 -1.34080 -0.1605 0.5818 289s 4 -1.61038 1.0687 1.5313 289s 5 -0.54147 -1.4707 -0.0220 289s 6 -0.04372 0.3299 -0.2587 289s 7 0.85234 1.4346 -0.3243 289s 8 1.30302 0.8306 -0.6674 289s 9 0.97574 -0.4918 0.3660 289s 10 -0.66060 1.2434 1.2682 289s 11 0.45069 0.2647 -0.3467 289s 12 -0.04295 0.0795 0.3057 289s 13 -0.06686 0.3369 -0.2602 289s 14 0.32177 0.4080 0.3434 289s 15 -0.00441 -0.1533 0.2628 289s 16 -0.01931 0.0158 -0.0216 289s 17 1.53656 1.0372 -0.7988 289s 18 -0.42317 0.0176 0.8550 289s 19 0.29041 -2.6364 -0.8217 289s 20 0.88685 -0.5822 -0.3869 289s 21 0.68839 -0.7015 -1.1838 289s 22 -2.31147 -0.5183 0.6742 289s > fitted 289s Consumption Investment PrivateWages 289s 1 NA NA NA 289s 2 42.1 0.152 26.6 289s 3 46.3 2.060 28.7 289s 4 50.8 4.131 32.6 289s 5 51.1 4.471 33.9 289s 6 52.6 4.770 35.7 289s 7 54.2 4.165 37.7 289s 8 54.9 3.369 38.6 289s 9 56.3 3.492 38.8 289s 10 58.5 3.857 40.0 289s 11 54.5 0.735 38.2 289s 12 50.9 -3.479 34.2 289s 13 45.7 -6.537 29.3 289s 14 46.2 -5.508 28.2 289s 15 48.7 -2.847 30.3 289s 16 51.3 -1.316 33.2 289s 17 56.2 1.063 37.6 289s 18 59.1 1.982 40.1 289s 19 57.2 0.736 39.0 289s 20 60.7 1.882 42.0 289s 21 64.3 4.002 46.2 289s 22 72.0 5.418 52.6 289s > predict 289s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 289s 1 NA NA NA NA 289s 2 42.1 0.415 41.3 43.0 289s 3 46.3 0.449 45.4 47.2 289s 4 50.8 0.300 50.2 51.4 289s 5 51.1 0.348 50.4 51.8 289s 6 52.6 0.350 51.9 53.3 289s 7 54.2 0.317 53.6 54.9 289s 8 54.9 0.289 54.3 55.5 289s 9 56.3 0.309 55.7 56.9 289s 10 58.5 0.328 57.8 59.1 289s 11 54.5 0.516 53.5 55.6 289s 12 50.9 0.414 50.1 51.8 289s 13 45.7 0.544 44.6 46.8 289s 14 46.2 0.527 45.1 47.2 289s 15 48.7 0.332 48.0 49.4 289s 16 51.3 0.295 50.7 51.9 289s 17 56.2 0.319 55.5 56.8 289s 18 59.1 0.286 58.5 59.7 289s 19 57.2 0.323 56.6 57.9 289s 20 60.7 0.381 59.9 61.5 289s 21 64.3 0.381 63.5 65.1 289s 22 72.0 0.597 70.8 73.2 289s Investment.pred Investment.se.fit Investment.lwr Investment.upr 289s 1 NA NA NA NA 289s 2 0.152 0.536 -0.924 1.229 289s 3 2.060 0.446 1.166 2.955 289s 4 4.131 0.397 3.334 4.929 289s 5 4.471 0.329 3.809 5.132 289s 6 4.770 0.311 4.145 5.395 289s 7 4.165 0.294 3.575 4.756 289s 8 3.369 0.263 2.842 3.897 289s 9 3.492 0.347 2.796 4.188 289s 10 3.857 0.398 3.058 4.656 289s 11 0.735 0.539 -0.346 1.816 289s 12 -3.479 0.454 -4.390 -2.569 289s 13 -6.537 0.552 -7.646 -5.428 289s 14 -5.508 0.617 -6.747 -4.269 289s 15 -2.847 0.335 -3.519 -2.175 289s 16 -1.316 0.287 -1.892 -0.739 289s 17 1.063 0.311 0.439 1.686 289s 18 1.982 0.218 1.545 2.420 289s 19 0.736 0.279 0.176 1.296 289s 20 1.882 0.327 1.227 2.538 289s 21 4.002 0.297 3.405 4.598 289s 22 5.418 0.412 4.591 6.245 289s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 289s 1 NA NA NA NA 289s 2 26.6 0.313 26.0 27.2 289s 3 28.7 0.310 28.1 29.3 289s 4 32.6 0.305 32.0 33.2 289s 5 33.9 0.236 33.4 34.4 289s 6 35.7 0.233 35.2 36.1 289s 7 37.7 0.234 37.3 38.2 289s 8 38.6 0.239 38.1 39.0 289s 9 38.8 0.229 38.4 39.3 289s 10 40.0 0.219 39.6 40.5 289s 11 38.2 0.301 37.6 38.9 289s 12 34.2 0.308 33.6 34.8 289s 13 29.3 0.370 28.5 30.0 289s 14 28.2 0.332 27.5 28.8 289s 15 30.3 0.324 29.7 31.0 289s 16 33.2 0.271 32.7 33.8 289s 17 37.6 0.263 37.1 38.1 289s 18 40.1 0.211 39.7 40.6 289s 19 39.0 0.306 38.4 39.6 289s 20 42.0 0.280 41.4 42.5 289s 21 46.2 0.298 45.6 46.8 289s 22 52.6 0.445 51.7 53.5 289s > model.frame 289s [1] TRUE 289s > model.matrix 289s [1] TRUE 289s > nobs 289s [1] 63 289s > linearHypothesis 289s Linear hypothesis test (Theil's F test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df F Pr(>F) 289s 1 52 289s 2 51 1 1.44 0.24 289s Linear hypothesis test (F statistic of a Wald test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df F Pr(>F) 289s 1 52 289s 2 51 1 1.69 0.2 289s Linear hypothesis test (Chi^2 statistic of a Wald test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df Chisq Pr(>Chisq) 289s 1 52 289s 2 51 1 1.69 0.19 289s Linear hypothesis test (Theil's F test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s Consumption_corpProfLag - PrivateWages_trend = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df F Pr(>F) 289s 1 53 289s 2 51 2 0.77 0.47 289s Linear hypothesis test (F statistic of a Wald test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s Consumption_corpProfLag - PrivateWages_trend = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df F Pr(>F) 289s 1 53 289s 2 51 2 0.91 0.41 289s Linear hypothesis test (Chi^2 statistic of a Wald test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s Consumption_corpProfLag - PrivateWages_trend = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df Chisq Pr(>Chisq) 289s 1 53 289s 2 51 2 1.83 0.4 289s > logLik 289s 'log Lik.' -70 (df=18) 289s 'log Lik.' -79 (df=18) 289s Estimating function 289s Consumption_(Intercept) Consumption_corpProf 289s Consumption_2 -0.46275 -5.7381 289s Consumption_3 -2.57830 -43.5733 289s Consumption_4 -3.09670 -56.9792 289s Consumption_5 -1.04122 -20.1997 289s Consumption_6 -0.08406 -1.6897 289s Consumption_7 1.63901 32.1246 289s Consumption_8 2.50567 49.6122 289s Consumption_9 1.87631 39.5902 289s Consumption_10 -1.27032 -27.5659 289s Consumption_11 0.86667 13.5200 289s Consumption_12 -0.08259 -0.9415 289s Consumption_13 -0.12857 -0.9000 289s Consumption_14 0.61874 6.9299 289s Consumption_15 -0.00847 -0.1042 289s Consumption_16 -0.03714 -0.5200 289s Consumption_17 2.95475 52.0036 289s Consumption_18 -0.81375 -14.0778 289s Consumption_19 0.55845 8.5443 289s Consumption_20 1.70539 32.4023 289s Consumption_21 1.32376 27.9312 289s Consumption_22 -4.44487 -104.4543 289s Investment_2 0.12481 1.5477 289s Investment_3 0.05687 0.9611 289s Investment_4 -0.37877 -6.9693 289s Investment_5 0.52122 10.1116 289s Investment_6 -0.11690 -2.3498 289s Investment_7 -0.50845 -9.9656 289s Investment_8 -0.29439 -5.8289 289s Investment_9 0.17430 3.6777 289s Investment_10 -0.44066 -9.5623 289s Investment_11 -0.09381 -1.4634 289s Investment_12 -0.02816 -0.3210 289s Investment_13 -0.11941 -0.8359 289s Investment_14 -0.14460 -1.6195 289s Investment_15 0.05435 0.6685 289s Investment_16 -0.00559 -0.0783 289s Investment_17 -0.36761 -6.4700 289s Investment_18 -0.00622 -0.1077 289s Investment_19 0.93438 14.2960 289s Investment_20 0.20633 3.9202 289s Investment_21 0.24863 5.2460 289s Investment_22 0.18369 4.3168 289s PrivateWages_2 -1.78352 -22.1156 289s PrivateWages_3 0.94670 15.9992 289s PrivateWages_4 2.49170 45.8473 289s PrivateWages_5 -0.03583 -0.6950 289s PrivateWages_6 -0.42104 -8.4630 289s PrivateWages_7 -0.52776 -10.3441 289s PrivateWages_8 -1.08598 -21.5024 289s PrivateWages_9 0.59560 12.5672 289s PrivateWages_10 2.06359 44.7800 289s PrivateWages_11 -0.56422 -8.8019 289s PrivateWages_12 0.49749 5.6714 289s PrivateWages_13 -0.42337 -2.9636 289s PrivateWages_14 0.55874 6.2579 289s PrivateWages_15 0.42760 5.2595 289s PrivateWages_16 -0.03516 -0.4922 289s PrivateWages_17 -1.29986 -22.8775 289s PrivateWages_18 1.39131 24.0696 289s PrivateWages_19 -1.33711 -20.4578 289s PrivateWages_20 -0.62964 -11.9631 289s PrivateWages_21 -1.92625 -40.6439 289s PrivateWages_22 1.09700 25.7794 289s Consumption_corpProfLag Consumption_wages 289s Consumption_2 -5.8769 -13.049 289s Consumption_3 -31.9709 -83.021 289s Consumption_4 -52.3342 -114.578 289s Consumption_5 -19.1585 -38.525 289s Consumption_6 -1.6308 -3.245 289s Consumption_7 32.9441 66.708 289s Consumption_8 49.1110 103.985 289s Consumption_9 37.1510 80.494 289s Consumption_10 -26.8037 -57.545 289s Consumption_11 18.8066 36.487 289s Consumption_12 -1.2884 -3.246 289s Consumption_13 -1.4658 -4.410 289s Consumption_14 4.3312 21.099 289s Consumption_15 -0.0949 -0.310 289s Consumption_16 -0.4568 -1.460 289s Consumption_17 41.3665 130.600 289s Consumption_18 -14.3220 -38.816 289s Consumption_19 9.6612 25.633 289s Consumption_20 26.0924 84.246 289s Consumption_21 25.1514 70.159 289s Consumption_22 -93.7867 -274.693 289s Investment_2 1.5851 3.520 289s Investment_3 0.7052 1.831 289s Investment_4 -6.4012 -14.014 289s Investment_5 9.5904 19.285 289s Investment_6 -2.2679 -4.513 289s Investment_7 -10.2199 -20.694 289s Investment_8 -5.7700 -12.217 289s Investment_9 3.4511 7.477 289s Investment_10 -9.2979 -19.962 289s Investment_11 -2.0356 -3.949 289s Investment_12 -0.4393 -1.107 289s Investment_13 -1.3613 -4.096 289s Investment_14 -1.0122 -4.931 289s Investment_15 0.6087 1.989 289s Investment_16 -0.0688 -0.220 289s Investment_17 -5.1466 -16.248 289s Investment_18 -0.1095 -0.297 289s Investment_19 16.1648 42.888 289s Investment_20 3.1568 10.193 289s Investment_21 4.7239 13.177 289s Investment_22 3.8759 11.352 289s PrivateWages_2 -22.6507 -50.295 289s PrivateWages_3 11.7391 30.484 289s PrivateWages_4 42.1098 92.193 289s PrivateWages_5 -0.6592 -1.326 289s PrivateWages_6 -8.1683 -16.252 289s PrivateWages_7 -10.6080 -21.480 289s PrivateWages_8 -21.2852 -45.068 289s PrivateWages_9 11.7929 25.551 289s PrivateWages_10 43.5418 93.481 289s PrivateWages_11 -12.2437 -23.754 289s PrivateWages_12 7.7609 19.551 289s PrivateWages_13 -4.8264 -14.521 289s PrivateWages_14 3.9112 19.053 289s PrivateWages_15 4.7891 15.650 289s PrivateWages_16 -0.4325 -1.382 289s PrivateWages_17 -18.1980 -57.454 289s PrivateWages_18 24.4870 66.365 289s PrivateWages_19 -23.1320 -61.373 289s PrivateWages_20 -9.6335 -31.104 289s PrivateWages_21 -36.5988 -102.091 289s PrivateWages_22 23.1466 67.794 289s Investment_(Intercept) Investment_corpProf 289s Consumption_2 0.08529 1.0576 289s Consumption_3 0.47520 8.0308 289s Consumption_4 0.57074 10.5016 289s Consumption_5 0.19190 3.7229 289s Consumption_6 0.01549 0.3114 289s Consumption_7 -0.30208 -5.9207 289s Consumption_8 -0.46181 -9.1438 289s Consumption_9 -0.34582 -7.2967 289s Consumption_10 0.23413 5.0806 289s Consumption_11 -0.15973 -2.4918 289s Consumption_12 0.01522 0.1735 289s Consumption_13 0.02370 0.1659 289s Consumption_14 -0.11404 -1.2772 289s Consumption_15 0.00156 0.0192 289s Consumption_16 0.00685 0.0958 289s Consumption_17 -0.54458 -9.5846 289s Consumption_18 0.14998 2.5946 289s Consumption_19 -0.10293 -1.5748 289s Consumption_20 -0.31431 -5.9719 289s Consumption_21 -0.24398 -5.1479 289s Consumption_22 0.81921 19.2515 289s Investment_2 -0.46650 -5.7846 289s Investment_3 -0.21255 -3.5922 289s Investment_4 1.41568 26.0484 289s Investment_5 -1.94810 -37.7932 289s Investment_6 0.43694 8.7825 289s Investment_7 1.90038 37.2474 289s Investment_8 1.10030 21.7860 289s Investment_9 -0.65146 -13.7457 289s Investment_10 1.64701 35.7401 289s Investment_11 0.35062 5.4696 289s Investment_12 0.10525 1.1998 289s Investment_13 0.44632 3.1242 289s Investment_14 0.54045 6.0530 289s Investment_15 -0.20313 -2.4985 289s Investment_16 0.02090 0.2926 289s Investment_17 1.37398 24.1820 289s Investment_18 0.02326 0.4024 289s Investment_19 -3.49233 -53.4327 289s Investment_20 -0.77116 -14.6521 289s Investment_21 -0.92927 -19.6075 289s Investment_22 -0.68657 -16.1344 289s PrivateWages_2 0.67977 8.4291 289s PrivateWages_3 -0.36082 -6.0979 289s PrivateWages_4 -0.94969 -17.4742 289s PrivateWages_5 0.01365 0.2649 289s PrivateWages_6 0.16048 3.2256 289s PrivateWages_7 0.20115 3.9426 289s PrivateWages_8 0.41391 8.1954 289s PrivateWages_9 -0.22701 -4.7899 289s PrivateWages_10 -0.78652 -17.0674 289s PrivateWages_11 0.21505 3.3548 289s PrivateWages_12 -0.18961 -2.1616 289s PrivateWages_13 0.16136 1.1295 289s PrivateWages_14 -0.21296 -2.3851 289s PrivateWages_15 -0.16298 -2.0046 289s PrivateWages_16 0.01340 0.1876 289s PrivateWages_17 0.49543 8.7195 289s PrivateWages_18 -0.53028 -9.1739 289s PrivateWages_19 0.50963 7.7973 289s PrivateWages_20 0.23998 4.5596 289s PrivateWages_21 0.73417 15.4910 289s PrivateWages_22 -0.41811 -9.8256 289s Investment_corpProfLag Investment_capitalLag 289s Consumption_2 1.0831 15.590 289s Consumption_3 5.8924 86.771 289s Consumption_4 9.6455 105.301 289s Consumption_5 3.5310 36.404 289s Consumption_6 0.3006 2.986 289s Consumption_7 -6.0718 -59.751 289s Consumption_8 -9.0514 -93.932 289s Consumption_9 -6.8471 -71.791 289s Consumption_10 4.9401 49.307 289s Consumption_11 -3.4662 -34.454 289s Consumption_12 0.2375 3.299 289s Consumption_13 0.2701 5.055 289s Consumption_14 -0.7983 -23.617 289s Consumption_15 0.0175 0.315 289s Consumption_16 0.0842 1.362 289s Consumption_17 -7.6241 -107.663 289s Consumption_18 2.6396 29.966 289s Consumption_19 -1.7806 -20.770 289s Consumption_20 -4.8090 -62.831 289s Consumption_21 -4.6355 -49.088 289s Consumption_22 17.2854 167.529 289s Investment_2 -5.9246 -85.277 289s Investment_3 -2.6357 -38.812 289s Investment_4 23.9249 261.192 289s Investment_5 -35.8451 -369.555 289s Investment_6 8.4767 84.199 289s Investment_7 38.1976 375.895 289s Investment_8 21.5660 223.802 289s Investment_9 -12.8988 -135.242 289s Investment_10 34.7519 346.860 289s Investment_11 7.6084 75.628 289s Investment_12 1.6419 22.807 289s Investment_13 5.0880 95.199 289s Investment_14 3.7831 111.927 289s Investment_15 -2.2751 -41.032 289s Investment_16 0.2571 4.159 289s Investment_17 19.2357 271.636 289s Investment_18 0.4094 4.648 289s Investment_19 -60.4174 -704.753 289s Investment_20 -11.7988 -154.156 289s Investment_21 -17.6560 -186.968 289s Investment_22 -14.4866 -140.403 289s PrivateWages_2 8.6331 124.262 289s PrivateWages_3 -4.4742 -65.887 289s PrivateWages_4 -16.0497 -175.217 289s PrivateWages_5 0.2512 2.590 289s PrivateWages_6 3.1132 30.924 289s PrivateWages_7 4.0431 39.788 289s PrivateWages_8 8.1126 84.189 289s PrivateWages_9 -4.4947 -47.127 289s PrivateWages_10 -16.5955 -165.640 289s PrivateWages_11 4.6666 46.386 289s PrivateWages_12 -2.9580 -41.089 289s PrivateWages_13 1.8395 34.418 289s PrivateWages_14 -1.4907 -44.104 289s PrivateWages_15 -1.8253 -32.921 289s PrivateWages_16 0.1648 2.667 289s PrivateWages_17 6.9360 97.946 289s PrivateWages_18 -9.3330 -105.950 289s PrivateWages_19 8.8165 102.843 289s PrivateWages_20 3.6717 47.972 289s PrivateWages_21 13.9492 147.715 289s PrivateWages_22 -8.8221 -85.503 289s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 289s Consumption_2 -0.39158 -17.856 -17.582 289s Consumption_3 -2.18178 -109.307 -99.489 289s Consumption_4 -2.62045 -149.890 -131.285 289s Consumption_5 -0.88109 -50.310 -50.398 289s Consumption_6 -0.07113 -4.339 -4.062 289s Consumption_7 1.38694 88.764 84.604 289s Consumption_8 2.12032 136.548 135.700 289s Consumption_9 1.58775 102.410 102.251 289s Consumption_10 -1.07495 -72.022 -69.335 289s Consumption_11 0.73338 44.883 49.136 289s Consumption_12 -0.06989 -3.732 -4.277 289s Consumption_13 -0.10880 -4.820 -5.810 289s Consumption_14 0.52359 23.614 23.195 289s Consumption_15 -0.00717 -0.356 -0.323 289s Consumption_16 -0.03143 -1.710 -1.562 289s Consumption_17 2.50033 156.771 136.018 289s Consumption_18 -0.68860 -44.759 -43.175 289s Consumption_19 0.47257 28.779 30.717 289s Consumption_20 1.44311 100.296 87.885 289s Consumption_21 1.12017 84.797 77.852 289s Consumption_22 -3.76128 -332.497 -284.729 289s Investment_2 0.21842 9.960 9.807 289s Investment_3 0.09952 4.986 4.538 289s Investment_4 -0.66282 -37.913 -33.207 289s Investment_5 0.91210 52.081 52.172 289s Investment_6 -0.20458 -12.479 -11.681 289s Investment_7 -0.88976 -56.944 -54.275 289s Investment_8 -0.51516 -33.176 -32.970 289s Investment_9 0.30501 19.673 19.643 289s Investment_10 -0.77113 -51.666 -49.738 289s Investment_11 -0.16416 -10.047 -10.999 289s Investment_12 -0.04928 -2.631 -3.016 289s Investment_13 -0.20897 -9.257 -11.159 289s Investment_14 -0.25304 -11.412 -11.210 289s Investment_15 0.09511 4.727 4.289 289s Investment_16 -0.00978 -0.532 -0.486 289s Investment_17 -0.64330 -40.335 -34.995 289s Investment_18 -0.01089 -0.708 -0.683 289s Investment_19 1.63511 99.578 106.282 289s Investment_20 0.36106 25.094 21.989 289s Investment_21 0.43508 32.936 30.238 289s Investment_22 0.32145 28.416 24.334 289s PrivateWages_2 -3.89912 -177.800 -175.070 289s PrivateWages_3 2.06967 103.690 94.377 289s PrivateWages_4 5.44735 311.588 272.912 289s PrivateWages_5 -0.07832 -4.472 -4.480 289s PrivateWages_6 -0.92048 -56.150 -52.560 289s PrivateWages_7 -1.15379 -73.843 -70.381 289s PrivateWages_8 -2.37416 -152.896 -151.946 289s PrivateWages_9 1.30210 83.986 83.855 289s PrivateWages_10 4.51142 302.265 290.986 289s PrivateWages_11 -1.23351 -75.491 -82.645 289s PrivateWages_12 1.08762 58.079 66.562 289s PrivateWages_13 -0.92556 -41.002 -49.425 289s PrivateWages_14 1.22152 55.091 54.114 289s PrivateWages_15 0.93482 46.461 42.160 289s PrivateWages_16 -0.07687 -4.182 -3.820 289s PrivateWages_17 -2.84174 -178.177 -154.591 289s PrivateWages_18 3.04167 197.708 190.713 289s PrivateWages_19 -2.92319 -178.022 -190.007 289s PrivateWages_20 -1.37651 -95.667 -83.829 289s PrivateWages_21 -4.21116 -318.785 -292.676 289s PrivateWages_22 2.39825 212.005 181.548 289s PrivateWages_trend 289s Consumption_2 3.9158 289s Consumption_3 19.6360 289s Consumption_4 20.9636 289s Consumption_5 6.1676 289s Consumption_6 0.4268 289s Consumption_7 -6.9347 289s Consumption_8 -8.4813 289s Consumption_9 -4.7633 289s Consumption_10 2.1499 289s Consumption_11 -0.7334 289s Consumption_12 0.0000 289s Consumption_13 -0.1088 289s Consumption_14 1.0472 289s Consumption_15 -0.0215 289s Consumption_16 -0.1257 289s Consumption_17 12.5017 289s Consumption_18 -4.1316 289s Consumption_19 3.3080 289s Consumption_20 11.5449 289s Consumption_21 10.0816 289s Consumption_22 -37.6128 289s Investment_2 -2.1842 289s Investment_3 -0.8957 289s Investment_4 5.3026 289s Investment_5 -6.3847 289s Investment_6 1.2275 289s Investment_7 4.4488 289s Investment_8 2.0606 289s Investment_9 -0.9150 289s Investment_10 1.5423 289s Investment_11 0.1642 289s Investment_12 0.0000 289s Investment_13 -0.2090 289s Investment_14 -0.5061 289s Investment_15 0.2853 289s Investment_16 -0.0391 289s Investment_17 -3.2165 289s Investment_18 -0.0653 289s Investment_19 11.4458 289s Investment_20 2.8885 289s Investment_21 3.9157 289s Investment_22 3.2145 289s PrivateWages_2 38.9912 289s PrivateWages_3 -18.6270 289s PrivateWages_4 -43.5788 289s PrivateWages_5 0.5483 289s PrivateWages_6 5.5229 289s PrivateWages_7 5.7689 289s PrivateWages_8 9.4967 289s PrivateWages_9 -3.9063 289s PrivateWages_10 -9.0228 289s PrivateWages_11 1.2335 289s PrivateWages_12 0.0000 289s PrivateWages_13 -0.9256 289s PrivateWages_14 2.4431 289s PrivateWages_15 2.8045 289s PrivateWages_16 -0.3075 289s PrivateWages_17 -14.2087 289s PrivateWages_18 18.2500 289s PrivateWages_19 -20.4623 289s PrivateWages_20 -11.0121 289s PrivateWages_21 -37.9005 289s PrivateWages_22 23.9825 289s [1] TRUE 289s > Bread 289s Consumption_(Intercept) Consumption_corpProf Consumption_corpProfLag 289s [1,] 86.0484 -0.02454 -0.83573 289s [2,] -0.0245 0.37055 -0.22831 289s [3,] -0.8357 -0.22831 0.37290 289s [4,] -1.6729 -0.06016 -0.03411 289s [5,] 10.1786 -0.46129 0.72764 289s [6,] -0.1293 0.03988 -0.03792 289s [7,] -0.0505 -0.03436 0.04602 289s [8,] -0.0350 0.00175 -0.00419 289s [9,] -37.4223 0.06800 1.80971 289s [10,] 0.4074 -0.06333 0.04058 289s [11,] 0.2037 0.06442 -0.07324 289s [12,] 0.2057 0.03217 0.03109 289s Consumption_wages Investment_(Intercept) Investment_corpProf 289s [1,] -1.67e+00 10.179 -0.12933 289s [2,] -6.02e-02 -0.461 0.03988 289s [3,] -3.41e-02 0.728 -0.03792 289s [4,] 7.83e-02 -0.341 0.00185 289s [5,] -3.41e-01 1452.346 -13.96098 289s [6,] 1.85e-03 -13.961 0.46676 289s [7,] -2.96e-03 11.230 -0.39879 289s [8,] 1.79e-03 -6.973 0.06288 289s [9,] 1.32e-01 19.427 -0.13338 289s [10,] -5.46e-05 0.416 0.01516 289s [11,] -2.23e-03 -0.760 -0.01340 289s [12,] -3.03e-02 -0.736 0.00571 289s Investment_corpProfLag Investment_capitalLag PrivateWages_(Intercept) 289s [1,] -0.05046 -0.03501 -37.4223 289s [2,] -0.03436 0.00175 0.0680 289s [3,] 0.04602 -0.00419 1.8097 289s [4,] -0.00296 0.00179 0.1325 289s [5,] 11.22954 -6.97254 19.4266 289s [6,] -0.39879 0.06288 -0.1334 289s [7,] 0.50387 -0.06357 -0.5157 289s [8,] -0.06357 0.03467 -0.0417 289s [9,] -0.51574 -0.04172 78.6495 289s [10,] -0.00784 -0.00271 -0.3339 289s [11,] 0.01702 0.00353 -0.9859 289s [12,] -0.01390 0.00432 0.8712 289s PrivateWages_gnp PrivateWages_gnpLag PrivateWages_trend 289s [1,] 4.07e-01 0.20374 0.20573 289s [2,] -6.33e-02 0.06442 0.03217 289s [3,] 4.06e-02 -0.07324 0.03109 289s [4,] -5.46e-05 -0.00223 -0.03033 289s [5,] 4.16e-01 -0.75990 -0.73581 289s [6,] 1.52e-02 -0.01340 0.00571 289s [7,] -7.84e-03 0.01702 -0.01390 289s [8,] -2.71e-03 0.00353 0.00432 289s [9,] -3.34e-01 -0.98593 0.87119 289s [10,] 4.68e-02 -0.04271 -0.01162 289s [11,] -4.27e-02 0.06124 -0.00299 289s [12,] -1.16e-02 -0.00299 0.04791 289s > 289s > # 3SLS 289s > summary 289s 289s systemfit results 289s method: 3SLS 289s 289s N DF SSR detRCov OLS-R2 McElroy-R2 289s system 63 51 73.6 0.283 0.963 0.995 289s 289s N DF SSR MSE RMSE R2 Adj R2 289s Consumption 21 17 18.7 1.102 1.050 0.980 0.977 289s Investment 21 17 44.0 2.586 1.608 0.826 0.795 289s PrivateWages 21 17 10.9 0.642 0.801 0.986 0.984 289s 289s The covariance matrix of the residuals used for estimation 289s Consumption Investment PrivateWages 289s Consumption 1.044 0.438 -0.385 289s Investment 0.438 1.383 0.193 289s PrivateWages -0.385 0.193 0.476 289s 289s The covariance matrix of the residuals 289s Consumption Investment PrivateWages 289s Consumption 0.892 0.411 -0.394 289s Investment 0.411 2.093 0.403 289s PrivateWages -0.394 0.403 0.520 289s 289s The correlations of the residuals 289s Consumption Investment PrivateWages 289s Consumption 1.000 0.301 -0.578 289s Investment 0.301 1.000 0.386 289s PrivateWages -0.578 0.386 1.000 289s 289s 289s 3SLS estimates for 'Consumption' (equation 1) 289s Model Formula: consump ~ corpProf + corpProfLag + wages 289s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 289s gnpLag 289s 289s Estimate Std. Error t value Pr(>|t|) 289s (Intercept) 16.4408 1.3045 12.60 4.7e-10 *** 289s corpProf 0.1249 0.1081 1.16 0.26 289s corpProfLag 0.1631 0.1004 1.62 0.12 289s wages 0.7901 0.0379 20.83 1.5e-13 *** 289s --- 289s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 289s 289s Residual standard error: 1.05 on 17 degrees of freedom 289s Number of observations: 21 Degrees of Freedom: 17 289s SSR: 18.727 MSE: 1.102 Root MSE: 1.05 289s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.977 289s 289s 289s 3SLS estimates for 'Investment' (equation 2) 289s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 289s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 289s gnpLag 289s 289s Estimate Std. Error t value Pr(>|t|) 289s (Intercept) 28.1778 6.7938 4.15 0.00067 *** 289s corpProf -0.0131 0.1619 -0.08 0.93655 289s corpProfLag 0.7557 0.1529 4.94 0.00012 *** 289s capitalLag -0.1948 0.0325 -5.99 1.5e-05 *** 289s --- 289s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 289s 289s Residual standard error: 1.608 on 17 degrees of freedom 289s Number of observations: 21 Degrees of Freedom: 17 289s SSR: 43.954 MSE: 2.586 Root MSE: 1.608 289s Multiple R-Squared: 0.826 Adjusted R-Squared: 0.795 289s 289s 289s 3SLS estimates for 'PrivateWages' (equation 3) 289s Model Formula: privWage ~ gnp + gnpLag + trend 289s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 289s gnpLag 289s 289s Estimate Std. Error t value Pr(>|t|) 289s (Intercept) 1.7972 1.1159 1.61 0.13 289s gnp 0.4005 0.0318 12.59 4.8e-10 *** 289s gnpLag 0.1813 0.0342 5.31 5.8e-05 *** 289s trend 0.1497 0.0279 5.36 5.2e-05 *** 289s --- 289s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 289s 289s Residual standard error: 0.801 on 17 degrees of freedom 289s Number of observations: 21 Degrees of Freedom: 17 289s SSR: 10.921 MSE: 0.642 Root MSE: 0.801 289s Multiple R-Squared: 0.986 Adjusted R-Squared: 0.984 289s 289s > residuals 289s Consumption Investment PrivateWages 289s 1 NA NA NA 289s 2 -0.4416 -2.1951 -1.20287 289s 3 -1.0150 0.1515 0.51834 289s 4 -1.5289 0.4406 1.50936 289s 5 -0.4985 -1.8667 -0.08743 289s 6 -0.0132 0.0713 -0.28089 289s 7 0.7759 1.0294 -0.33908 289s 8 1.3004 1.1011 -0.69282 289s 9 1.0993 0.5853 0.34494 289s 10 -0.5839 2.2952 1.27590 289s 11 -0.1917 -1.3443 -0.40414 289s 12 -0.5598 -0.9944 0.22151 289s 13 -0.6746 -1.3404 -0.36962 289s 14 0.5767 1.9316 0.31006 289s 15 -0.0211 -0.1217 0.27309 289s 16 0.0539 0.1847 0.00716 289s 17 1.8555 2.0937 -0.71866 289s 18 -0.4596 -0.3216 0.90582 289s 19 0.0613 -3.6314 -0.81881 289s 20 1.2602 0.7582 -0.26942 289s 21 0.9500 0.2428 -1.06125 289s 22 -1.9451 0.9302 0.87883 289s > fitted 289s Consumption Investment PrivateWages 289s 1 NA NA NA 289s 2 42.3 1.99510 26.7 289s 3 46.0 1.74850 28.8 289s 4 50.7 4.75942 32.6 289s 5 51.1 4.86672 34.0 289s 6 52.6 5.02874 35.7 289s 7 54.3 4.57056 37.7 289s 8 54.9 3.09893 38.6 289s 9 56.2 2.41471 38.9 289s 10 58.4 2.80476 40.0 289s 11 55.2 2.34425 38.3 289s 12 51.5 -2.40558 34.3 289s 13 46.3 -4.85959 29.4 289s 14 45.9 -7.03164 28.2 289s 15 48.7 -2.87827 30.3 289s 16 51.2 -1.48466 33.2 289s 17 55.8 0.00629 37.5 289s 18 59.2 2.32164 40.1 289s 19 57.4 1.73138 39.0 289s 20 60.3 0.54175 41.9 289s 21 64.1 3.05716 46.1 289s 22 71.6 3.96979 52.4 289s > predict 289s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 289s 1 NA NA NA NA 289s 2 42.3 0.464 39.9 44.8 289s 3 46.0 0.541 43.5 48.5 289s 4 50.7 0.337 48.4 53.1 289s 5 51.1 0.385 48.7 53.5 289s 6 52.6 0.386 50.3 55.0 289s 7 54.3 0.349 52.0 56.7 289s 8 54.9 0.320 52.6 57.2 289s 9 56.2 0.355 53.9 58.5 289s 10 58.4 0.370 56.0 60.7 289s 11 55.2 0.682 52.6 57.8 289s 12 51.5 0.563 48.9 54.0 289s 13 46.3 0.719 43.6 49.0 289s 14 45.9 0.597 43.4 48.5 289s 15 48.7 0.370 46.4 51.1 289s 16 51.2 0.327 48.9 53.6 289s 17 55.8 0.391 53.5 58.2 289s 18 59.2 0.316 56.8 61.5 289s 19 57.4 0.389 55.1 59.8 289s 20 60.3 0.459 57.9 62.8 289s 21 64.1 0.438 61.7 66.4 289s 22 71.6 0.674 69.0 74.3 289s Investment.pred Investment.se.fit Investment.lwr Investment.upr 289s 1 NA NA NA NA 289s 2 1.99510 0.792 -1.787 5.777 289s 3 1.74850 0.585 -1.861 5.358 289s 4 4.75942 0.510 1.200 8.319 289s 5 4.86672 0.423 1.359 8.375 289s 6 5.02874 0.400 1.533 8.525 289s 7 4.57056 0.391 1.079 8.062 289s 8 3.09893 0.345 -0.371 6.568 289s 9 2.41471 0.511 -1.145 5.974 289s 10 2.80476 0.560 -0.788 6.397 289s 11 2.34425 0.839 -1.482 6.170 289s 12 -2.40558 0.673 -6.083 1.272 289s 13 -4.85959 0.862 -8.708 -1.011 289s 14 -7.03164 0.874 -10.893 -3.171 289s 15 -2.87827 0.433 -6.392 0.635 289s 16 -1.48466 0.375 -4.968 1.999 289s 17 0.00629 0.491 -3.541 3.554 289s 18 2.32164 0.294 -1.127 5.771 289s 19 1.73138 0.446 -1.789 5.252 289s 20 0.54175 0.547 -3.042 4.125 289s 21 3.05716 0.454 -0.468 6.582 289s 22 3.96979 0.642 0.317 7.623 289s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 289s 1 NA NA NA NA 289s 2 26.7 0.314 24.9 28.5 289s 3 28.8 0.318 27.0 30.6 289s 4 32.6 0.325 30.8 34.4 289s 5 34.0 0.235 32.2 35.7 289s 6 35.7 0.241 33.9 37.4 289s 7 37.7 0.238 36.0 39.5 289s 8 38.6 0.237 36.8 40.4 289s 9 38.9 0.227 37.1 40.6 289s 10 40.0 0.219 38.3 41.8 289s 11 38.3 0.317 36.5 40.1 289s 12 34.3 0.344 32.4 36.1 289s 13 29.4 0.419 27.5 31.3 289s 14 28.2 0.334 26.4 30.0 289s 15 30.3 0.320 28.5 32.1 289s 16 33.2 0.268 31.4 35.0 289s 17 37.5 0.269 35.7 39.3 289s 18 40.1 0.212 38.3 41.8 289s 19 39.0 0.331 37.2 40.8 289s 20 41.9 0.287 40.1 43.7 289s 21 46.1 0.301 44.3 47.9 289s 22 52.4 0.471 50.5 54.4 289s > model.frame 289s [1] TRUE 289s > model.matrix 289s [1] TRUE 289s > nobs 289s [1] 63 289s > linearHypothesis 289s Linear hypothesis test (Theil's F test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df F Pr(>F) 289s 1 52 289s 2 51 1 0.29 0.59 289s Linear hypothesis test (F statistic of a Wald test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df F Pr(>F) 289s 1 52 289s 2 51 1 0.39 0.54 289s Linear hypothesis test (Chi^2 statistic of a Wald test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df Chisq Pr(>Chisq) 289s 1 52 289s 2 51 1 0.39 0.53 289s Linear hypothesis test (Theil's F test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s Consumption_corpProfLag - PrivateWages_trend = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df F Pr(>F) 289s 1 53 289s 2 51 2 0.3 0.74 289s Linear hypothesis test (F statistic of a Wald test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s Consumption_corpProfLag - PrivateWages_trend = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df F Pr(>F) 289s 1 53 289s 2 51 2 0.4 0.67 289s Linear hypothesis test (Chi^2 statistic of a Wald test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s Consumption_corpProfLag - PrivateWages_trend = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df Chisq Pr(>Chisq) 289s 1 53 289s 2 51 2 0.8 0.67 289s > logLik 289s 'log Lik.' -76.1 (df=18) 289s 'log Lik.' -89.1 (df=18) 289s Estimating function 289s Consumption_(Intercept) Consumption_corpProf 289s Consumption_2 -3.2451 -43.02 289s Consumption_3 -1.3384 -22.19 289s Consumption_4 -1.4130 -27.25 289s Consumption_5 -5.0390 -105.62 289s Consumption_6 -0.8531 -16.86 289s Consumption_7 4.3438 79.23 289s Consumption_8 5.6608 99.48 289s Consumption_9 3.7666 73.61 289s Consumption_10 1.2798 26.08 289s Consumption_11 -3.5695 -61.32 289s Consumption_12 -1.8656 -23.70 289s Consumption_13 -3.4193 -30.77 289s Consumption_14 4.0738 36.88 289s Consumption_15 -1.6814 -21.31 289s Consumption_16 -1.4312 -20.64 289s Consumption_17 9.0552 133.22 289s Consumption_18 -1.9716 -39.03 289s Consumption_19 -6.7338 -129.33 289s Consumption_20 4.8735 84.89 289s Consumption_21 1.6324 33.15 289s Consumption_22 -2.1249 -48.14 289s Investment_2 2.1466 28.45 289s Investment_3 -0.1448 -2.40 289s Investment_4 -0.4444 -8.57 289s Investment_5 1.8148 38.04 289s Investment_6 -0.0658 -1.30 289s Investment_7 -0.9944 -18.14 289s Investment_8 -1.0536 -18.52 289s Investment_9 -0.5553 -10.85 289s Investment_10 -2.2390 -45.62 289s Investment_11 1.3010 22.35 289s Investment_12 0.9607 12.21 289s Investment_13 1.2918 11.63 289s Investment_14 -1.8711 -16.94 289s Investment_15 0.1149 1.46 289s Investment_16 -0.1869 -2.70 289s Investment_17 -2.0208 -29.73 289s Investment_18 0.2841 5.62 289s Investment_19 3.5191 67.59 289s Investment_20 -0.7250 -12.63 289s Investment_21 -0.2285 -4.64 289s Investment_22 -0.9035 -20.47 289s PrivateWages_2 -4.3513 -57.68 289s PrivateWages_3 1.7756 29.44 289s PrivateWages_4 3.5512 68.47 289s PrivateWages_5 -3.3088 -69.35 289s PrivateWages_6 -0.7761 -15.34 289s PrivateWages_7 1.5988 29.16 289s PrivateWages_8 1.5583 27.38 289s PrivateWages_9 2.5665 50.15 289s PrivateWages_10 4.9740 101.35 289s PrivateWages_11 -3.5972 -61.80 289s PrivateWages_12 -0.7986 -10.15 289s PrivateWages_13 -3.2258 -29.03 289s PrivateWages_14 3.6395 32.95 289s PrivateWages_15 -0.5056 -6.41 289s PrivateWages_16 -1.0680 -15.40 289s PrivateWages_17 3.0850 45.39 289s PrivateWages_18 -0.3546 -7.02 289s PrivateWages_19 -8.0362 -154.35 289s PrivateWages_20 1.6465 28.68 289s PrivateWages_21 -1.9137 -38.86 289s PrivateWages_22 3.5407 80.22 289s Consumption_corpProfLag Consumption_wages 289s Consumption_2 -41.21 -95.43 289s Consumption_3 -16.60 -42.49 289s Consumption_4 -23.88 -50.52 289s Consumption_5 -92.72 -196.89 289s Consumption_6 -16.55 -33.39 289s Consumption_7 87.31 170.95 289s Consumption_8 110.95 227.47 289s Consumption_9 74.58 159.45 289s Consumption_10 27.00 56.34 289s Consumption_11 -77.46 -155.98 289s Consumption_12 -29.10 -73.65 289s Consumption_13 -38.98 -120.13 289s Consumption_14 28.52 133.55 289s Consumption_15 -18.83 -63.05 289s Consumption_16 -17.60 -57.45 289s Consumption_17 126.77 377.63 289s Consumption_18 -34.70 -94.39 289s Consumption_19 -116.49 -332.00 289s Consumption_20 74.56 235.83 289s Consumption_21 31.02 87.12 289s Consumption_22 -44.84 -129.02 289s Investment_2 27.26 63.12 289s Investment_3 -1.80 -4.60 289s Investment_4 -7.51 -15.89 289s Investment_5 33.39 70.91 289s Investment_6 -1.28 -2.57 289s Investment_7 -19.99 -39.13 289s Investment_8 -20.65 -42.34 289s Investment_9 -10.99 -23.51 289s Investment_10 -47.24 -98.56 289s Investment_11 28.23 56.85 289s Investment_12 14.99 37.92 289s Investment_13 14.73 45.38 289s Investment_14 -13.10 -61.34 289s Investment_15 1.29 4.31 289s Investment_16 -2.30 -7.50 289s Investment_17 -28.29 -84.27 289s Investment_18 5.00 13.60 289s Investment_19 60.88 173.50 289s Investment_20 -11.09 -35.08 289s Investment_21 -4.34 -12.19 289s Investment_22 -19.06 -54.86 289s PrivateWages_2 -55.26 -127.96 289s PrivateWages_3 22.02 56.38 289s PrivateWages_4 60.01 126.96 289s PrivateWages_5 -60.88 -129.29 289s PrivateWages_6 -15.06 -30.37 289s PrivateWages_7 32.14 62.92 289s PrivateWages_8 30.54 62.62 289s PrivateWages_9 50.82 108.65 289s PrivateWages_10 104.95 218.96 289s PrivateWages_11 -78.06 -157.19 289s PrivateWages_12 -12.46 -31.53 289s PrivateWages_13 -36.77 -113.33 289s PrivateWages_14 25.48 119.32 289s PrivateWages_15 -5.66 -18.96 289s PrivateWages_16 -13.14 -42.87 289s PrivateWages_17 43.19 128.65 289s PrivateWages_18 -6.24 -16.98 289s PrivateWages_19 -139.03 -396.21 289s PrivateWages_20 25.19 79.68 289s PrivateWages_21 -36.36 -102.14 289s PrivateWages_22 74.71 214.98 289s Investment_(Intercept) Investment_corpProf 289s Consumption_2 1.4757 19.56 289s Consumption_3 0.6086 10.09 289s Consumption_4 0.6425 12.39 289s Consumption_5 2.2915 48.03 289s Consumption_6 0.3879 7.67 289s Consumption_7 -1.9753 -36.03 289s Consumption_8 -2.5742 -45.24 289s Consumption_9 -1.7128 -33.47 289s Consumption_10 -0.5820 -11.86 289s Consumption_11 1.6232 27.89 289s Consumption_12 0.8484 10.78 289s Consumption_13 1.5549 13.99 289s Consumption_14 -1.8525 -16.77 289s Consumption_15 0.7646 9.69 289s Consumption_16 0.6508 9.39 289s Consumption_17 -4.1178 -60.58 289s Consumption_18 0.8965 17.75 289s Consumption_19 3.0621 58.81 289s Consumption_20 -2.2162 -38.60 289s Consumption_21 -0.7423 -15.07 289s Consumption_22 0.9663 21.89 289s Investment_2 -2.6492 -35.12 289s Investment_3 0.1787 2.96 289s Investment_4 0.5485 10.58 289s Investment_5 -2.2397 -46.94 289s Investment_6 0.0811 1.60 289s Investment_7 1.2272 22.38 289s Investment_8 1.3003 22.85 289s Investment_9 0.6853 13.39 289s Investment_10 2.7633 56.30 289s Investment_11 -1.6056 -27.58 289s Investment_12 -1.1856 -15.06 289s Investment_13 -1.5943 -14.35 289s Investment_14 2.3092 20.91 289s Investment_15 -0.1418 -1.80 289s Investment_16 0.2307 3.33 289s Investment_17 2.4940 36.69 289s Investment_18 -0.3506 -6.94 289s Investment_19 -4.3431 -83.42 289s Investment_20 0.8947 15.59 289s Investment_21 0.2820 5.73 289s Investment_22 1.1150 25.26 289s PrivateWages_2 2.6070 34.56 289s PrivateWages_3 -1.0638 -17.64 289s PrivateWages_4 -2.1276 -41.02 289s PrivateWages_5 1.9824 41.55 289s PrivateWages_6 0.4650 9.19 289s PrivateWages_7 -0.9579 -17.47 289s PrivateWages_8 -0.9336 -16.41 289s PrivateWages_9 -1.5377 -30.05 289s PrivateWages_10 -2.9800 -60.72 289s PrivateWages_11 2.1552 37.03 289s PrivateWages_12 0.4785 6.08 289s PrivateWages_13 1.9327 17.39 289s PrivateWages_14 -2.1805 -19.74 289s PrivateWages_15 0.3029 3.84 289s PrivateWages_16 0.6398 9.23 289s PrivateWages_17 -1.8483 -27.19 289s PrivateWages_18 0.2125 4.21 289s PrivateWages_19 4.8147 92.47 289s PrivateWages_20 -0.9865 -17.18 289s PrivateWages_21 1.1466 23.28 289s PrivateWages_22 -2.1213 -48.06 289s Investment_corpProfLag Investment_capitalLag 289s Consumption_2 18.74 269.8 289s Consumption_3 7.55 111.1 289s Consumption_4 10.86 118.5 289s Consumption_5 42.16 434.7 289s Consumption_6 7.53 74.8 289s Consumption_7 -39.70 -390.7 289s Consumption_8 -50.45 -523.6 289s Consumption_9 -33.91 -355.6 289s Consumption_10 -12.28 -122.6 289s Consumption_11 35.22 350.1 289s Consumption_12 13.23 183.8 289s Consumption_13 17.73 331.7 289s Consumption_14 -12.97 -383.7 289s Consumption_15 8.56 154.5 289s Consumption_16 8.01 129.5 289s Consumption_17 -57.65 -814.1 289s Consumption_18 15.78 179.1 289s Consumption_19 52.98 617.9 289s Consumption_20 -33.91 -443.0 289s Consumption_21 -14.10 -149.4 289s Consumption_22 20.39 197.6 289s Investment_2 -33.65 -484.3 289s Investment_3 2.22 32.6 289s Investment_4 9.27 101.2 289s Investment_5 -41.21 -424.9 289s Investment_6 1.57 15.6 289s Investment_7 24.67 242.7 289s Investment_8 25.49 264.5 289s Investment_9 13.57 142.3 289s Investment_10 58.30 581.9 289s Investment_11 -34.84 -346.3 289s Investment_12 -18.50 -256.9 289s Investment_13 -18.17 -340.1 289s Investment_14 16.16 478.2 289s Investment_15 -1.59 -28.6 289s Investment_16 2.84 45.9 289s Investment_17 34.92 493.1 289s Investment_18 -6.17 -70.0 289s Investment_19 -75.14 -876.4 289s Investment_20 13.69 178.9 289s Investment_21 5.36 56.7 289s Investment_22 23.53 228.0 289s PrivateWages_2 33.11 476.6 289s PrivateWages_3 -13.19 -194.3 289s PrivateWages_4 -35.96 -392.5 289s PrivateWages_5 36.48 376.1 289s PrivateWages_6 9.02 89.6 289s PrivateWages_7 -19.25 -189.5 289s PrivateWages_8 -18.30 -189.9 289s PrivateWages_9 -30.45 -319.2 289s PrivateWages_10 -62.88 -627.6 289s PrivateWages_11 46.77 464.9 289s PrivateWages_12 7.46 103.7 289s PrivateWages_13 22.03 412.2 289s PrivateWages_14 -15.26 -451.6 289s PrivateWages_15 3.39 61.2 289s PrivateWages_16 7.87 127.3 289s PrivateWages_17 -25.88 -365.4 289s PrivateWages_18 3.74 42.5 289s PrivateWages_19 83.29 971.6 289s PrivateWages_20 -15.09 -197.2 289s PrivateWages_21 21.78 230.7 289s PrivateWages_22 -44.76 -433.8 289s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 289s Consumption_2 -3.220 -153.49 -144.60 289s Consumption_3 -1.328 -65.52 -60.57 289s Consumption_4 -1.402 -79.70 -70.25 289s Consumption_5 -5.001 -303.71 -286.05 289s Consumption_6 -0.847 -51.81 -48.34 289s Consumption_7 4.311 264.22 262.96 289s Consumption_8 5.618 341.88 359.54 289s Consumption_9 3.738 233.16 240.73 289s Consumption_10 1.270 81.79 81.92 289s Consumption_11 -3.542 -228.05 -237.34 289s Consumption_12 -1.851 -101.61 -113.31 289s Consumption_13 -3.393 -159.94 -181.21 289s Consumption_14 4.043 168.34 179.10 289s Consumption_15 -1.669 -85.05 -75.26 289s Consumption_16 -1.420 -79.06 -70.59 289s Consumption_17 8.987 515.06 488.87 289s Consumption_18 -1.957 -132.41 -122.68 289s Consumption_19 -6.683 -455.83 -434.38 289s Consumption_20 4.837 323.61 294.54 289s Consumption_21 1.620 121.95 112.59 289s Consumption_22 -2.109 -182.35 -159.64 289s Investment_2 2.807 133.77 126.02 289s Investment_3 -0.189 -9.34 -8.63 289s Investment_4 -0.581 -33.02 -29.11 289s Investment_5 2.373 144.11 135.73 289s Investment_6 -0.086 -5.26 -4.91 289s Investment_7 -1.300 -79.69 -79.31 289s Investment_8 -1.378 -83.84 -88.17 289s Investment_9 -0.726 -45.28 -46.75 289s Investment_10 -2.928 -188.52 -188.82 289s Investment_11 1.701 109.51 113.97 289s Investment_12 1.256 68.94 76.87 289s Investment_13 1.689 79.61 90.20 289s Investment_14 -2.446 -101.86 -108.38 289s Investment_15 0.150 7.66 6.77 289s Investment_16 -0.244 -13.60 -12.15 289s Investment_17 -2.642 -151.44 -143.74 289s Investment_18 0.371 25.13 23.29 289s Investment_19 4.601 313.85 299.09 289s Investment_20 -0.948 -63.43 -57.73 289s Investment_21 -0.299 -22.49 -20.76 289s Investment_22 -1.181 -102.15 -89.43 289s PrivateWages_2 -8.830 -420.86 -396.47 289s PrivateWages_3 3.603 177.74 164.31 289s PrivateWages_4 7.206 409.57 361.04 289s PrivateWages_5 -6.715 -407.80 -384.07 289s PrivateWages_6 -1.575 -96.39 -89.93 289s PrivateWages_7 3.244 198.86 197.91 289s PrivateWages_8 3.162 192.44 202.38 289s PrivateWages_9 5.208 324.85 335.40 289s PrivateWages_10 10.094 649.99 651.03 289s PrivateWages_11 -7.300 -469.94 -489.08 289s PrivateWages_12 -1.621 -88.94 -99.18 289s PrivateWages_13 -6.546 -308.53 -349.56 289s PrivateWages_14 7.386 307.52 327.18 289s PrivateWages_15 -1.026 -52.30 -46.27 289s PrivateWages_16 -2.167 -120.63 -107.71 289s PrivateWages_17 6.260 358.81 340.56 289s PrivateWages_18 -0.720 -48.70 -45.12 289s PrivateWages_19 -16.308 -1112.35 -1060.00 289s PrivateWages_20 3.341 223.57 203.48 289s PrivateWages_21 -3.883 -292.34 -269.90 289s PrivateWages_22 7.185 621.32 543.91 289s PrivateWages_trend 289s Consumption_2 32.205 289s Consumption_3 11.954 289s Consumption_4 11.218 289s Consumption_5 35.006 289s Consumption_6 5.080 289s Consumption_7 -21.554 289s Consumption_8 -22.471 289s Consumption_9 -11.214 289s Consumption_10 -2.540 289s Consumption_11 3.542 289s Consumption_12 0.000 289s Consumption_13 -3.393 289s Consumption_14 8.086 289s Consumption_15 -5.006 289s Consumption_16 -5.681 289s Consumption_17 44.933 289s Consumption_18 -11.740 289s Consumption_19 -46.779 289s Consumption_20 38.692 289s Consumption_21 14.580 289s Consumption_22 -21.088 289s Investment_2 -28.067 289s Investment_3 1.704 289s Investment_4 4.648 289s Investment_5 -16.610 289s Investment_6 0.516 289s Investment_7 6.501 289s Investment_8 5.511 289s Investment_9 2.178 289s Investment_10 5.855 289s Investment_11 -1.701 289s Investment_12 0.000 289s Investment_13 1.689 289s Investment_14 -4.893 289s Investment_15 0.451 289s Investment_16 -0.978 289s Investment_17 -13.211 289s Investment_18 2.228 289s Investment_19 32.209 289s Investment_20 -7.583 289s Investment_21 -2.689 289s Investment_22 -11.813 289s PrivateWages_2 88.301 289s PrivateWages_3 -32.429 289s PrivateWages_4 -57.650 289s PrivateWages_5 47.002 289s PrivateWages_6 9.450 289s PrivateWages_7 -16.222 289s PrivateWages_8 -12.649 289s PrivateWages_9 -15.624 289s PrivateWages_10 -20.187 289s PrivateWages_11 7.300 289s PrivateWages_12 0.000 289s PrivateWages_13 -6.546 289s PrivateWages_14 14.771 289s PrivateWages_15 -3.078 289s PrivateWages_16 -8.669 289s PrivateWages_17 31.301 289s PrivateWages_18 -4.318 289s PrivateWages_19 -114.154 289s PrivateWages_20 26.730 289s PrivateWages_21 -34.951 289s PrivateWages_22 71.851 289s [1] TRUE 289s > Bread 289s Consumption_(Intercept) Consumption_corpProf Consumption_corpProfLag 289s [1,] 1.07e+02 -1.06982 -0.3515 289s [2,] -1.07e+00 0.73659 -0.5079 289s [3,] -3.51e-01 -0.50793 0.6355 289s [4,] -1.93e+00 -0.07361 -0.0356 289s [5,] 1.24e+02 -0.98618 3.4455 289s [6,] -2.71e+00 0.38390 -0.3719 289s [7,] 9.65e-01 -0.31139 0.3992 289s [8,] -4.61e-01 -0.00199 -0.0185 289s [9,] -3.88e+01 0.05351 1.8003 289s [10,] 6.27e-01 -0.08533 0.0556 289s [11,] -5.96e-04 0.08746 -0.0887 289s [12,] 2.14e-01 0.04029 0.0279 289s Consumption_wages Investment_(Intercept) Investment_corpProf 289s [1,] -1.934840 123.765 -2.71e+00 289s [2,] -0.073613 -0.986 3.84e-01 289s [3,] -0.035606 3.445 -3.72e-01 289s [4,] 0.090675 -3.911 5.58e-02 289s [5,] -3.910682 2907.785 -4.61e+01 289s [6,] 0.055805 -46.132 1.65e+00 289s [7,] -0.054072 38.083 -1.41e+00 289s [8,] 0.019220 -13.707 2.06e-01 289s [9,] 0.174112 17.422 -1.06e-01 289s [10,] -0.002325 2.389 2.04e-03 289s [11,] -0.000594 -2.765 -2.91e-04 289s [12,] -0.032572 -2.080 3.10e-02 289s Investment_corpProfLag Investment_capitalLag PrivateWages_(Intercept) 289s [1,] 0.96474 -0.46130 -38.76422 289s [2,] -0.31139 -0.00199 0.05351 289s [3,] 0.39923 -0.01847 1.80032 289s [4,] -0.05407 0.01922 0.17411 289s [5,] 38.08346 -13.70662 17.42245 289s [6,] -1.40785 0.20597 -0.10564 289s [7,] 1.47348 -0.19170 -0.93153 289s [8,] -0.19170 0.06667 0.00097 289s [9,] -0.93153 0.00097 78.44334 289s [10,] 0.01112 -0.01300 -0.49810 289s [11,] 0.00455 0.01344 -0.81226 289s [12,] -0.04174 0.01117 0.88592 289s PrivateWages_gnp PrivateWages_gnpLag PrivateWages_trend 289s [1,] 0.62679 -0.000596 0.21374 289s [2,] -0.08533 0.087455 0.04029 289s [3,] 0.05563 -0.088660 0.02790 289s [4,] -0.00233 -0.000594 -0.03257 289s [5,] 2.38888 -2.764716 -2.07974 289s [6,] 0.00204 -0.000291 0.03105 289s [7,] 0.01112 0.004547 -0.04174 289s [8,] -0.01300 0.013443 0.01117 289s [9,] -0.49810 -0.812260 0.88592 289s [10,] 0.06376 -0.057450 -0.01781 289s [11,] -0.05745 0.073510 0.00317 289s [12,] -0.01781 0.003170 0.04916 289s > 289s > # I3SLS 289s > summary 289s 289s systemfit results 289s method: iterated 3SLS 289s 289s convergence achieved after 20 iterations 289s 289s N DF SSR detRCov OLS-R2 McElroy-R2 289s system 63 51 128 0.509 0.936 0.996 289s 289s N DF SSR MSE RMSE R2 Adj R2 289s Consumption 21 17 19.2 1.130 1.063 0.980 0.976 289s Investment 21 17 95.7 5.627 2.372 0.621 0.554 289s PrivateWages 21 17 12.7 0.748 0.865 0.984 0.981 289s 289s The covariance matrix of the residuals used for estimation 289s Consumption Investment PrivateWages 289s Consumption 0.915 0.642 -0.435 289s Investment 0.642 4.555 0.734 289s PrivateWages -0.435 0.734 0.606 289s 289s The covariance matrix of the residuals 289s Consumption Investment PrivateWages 289s Consumption 0.915 0.642 -0.435 289s Investment 0.642 4.555 0.734 289s PrivateWages -0.435 0.734 0.606 289s 289s The correlations of the residuals 289s Consumption Investment PrivateWages 289s Consumption 1.000 0.314 -0.584 289s Investment 0.314 1.000 0.442 289s PrivateWages -0.584 0.442 1.000 289s 289s 289s 3SLS estimates for 'Consumption' (equation 1) 289s Model Formula: consump ~ corpProf + corpProfLag + wages 289s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 289s gnpLag 289s 289s Estimate Std. Error t value Pr(>|t|) 289s (Intercept) 16.5590 1.2244 13.52 1.6e-10 *** 289s corpProf 0.1645 0.0962 1.71 0.105 289s corpProfLag 0.1766 0.0901 1.96 0.067 . 289s wages 0.7658 0.0348 22.03 6.1e-14 *** 289s --- 289s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 289s 289s Residual standard error: 1.063 on 17 degrees of freedom 289s Number of observations: 21 Degrees of Freedom: 17 289s SSR: 19.213 MSE: 1.13 Root MSE: 1.063 289s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.976 289s 289s 289s 3SLS estimates for 'Investment' (equation 2) 289s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 289s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 289s gnpLag 289s 289s Estimate Std. Error t value Pr(>|t|) 289s (Intercept) 42.8959 10.5937 4.05 0.00083 *** 289s corpProf -0.3565 0.2602 -1.37 0.18838 289s corpProfLag 1.0113 0.2488 4.07 0.00081 *** 289s capitalLag -0.2602 0.0509 -5.12 8.6e-05 *** 289s --- 289s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 289s 289s Residual standard error: 2.372 on 17 degrees of freedom 289s Number of observations: 21 Degrees of Freedom: 17 289s SSR: 95.661 MSE: 5.627 Root MSE: 2.372 289s Multiple R-Squared: 0.621 Adjusted R-Squared: 0.554 289s 289s 289s 3SLS estimates for 'PrivateWages' (equation 3) 289s Model Formula: privWage ~ gnp + gnpLag + trend 289s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 289s gnpLag 289s 289s Estimate Std. Error t value Pr(>|t|) 289s (Intercept) 2.6247 1.1956 2.20 0.042 * 289s gnp 0.3748 0.0311 12.05 9.4e-10 *** 289s gnpLag 0.1937 0.0324 5.98 1.5e-05 *** 289s trend 0.1679 0.0289 5.80 2.1e-05 *** 289s --- 289s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 289s 289s Residual standard error: 0.865 on 17 degrees of freedom 289s Number of observations: 21 Degrees of Freedom: 17 289s SSR: 12.719 MSE: 0.748 Root MSE: 0.865 289s Multiple R-Squared: 0.984 Adjusted R-Squared: 0.981 289s 289s > residuals 289s Consumption Investment PrivateWages 289s 1 NA NA NA 289s 2 -0.537 -3.95419 -1.2303 289s 3 -1.187 0.00151 0.5797 289s 4 -1.705 -0.22015 1.6794 289s 5 -0.734 -2.22753 -0.0260 289s 6 -0.251 -0.10866 -0.1362 289s 7 0.600 0.83218 -0.1837 289s 8 1.142 1.46624 -0.5825 289s 9 0.921 1.62030 0.4347 289s 10 -0.745 3.40013 1.4104 289s 11 -0.197 -2.15443 -0.4679 289s 12 -0.385 -1.62274 0.0106 289s 13 -0.390 -2.62869 -0.7363 289s 14 0.749 2.80517 0.0581 289s 15 0.112 -0.27710 0.1113 289s 16 0.170 0.13598 -0.1089 289s 17 1.925 2.76200 -0.6976 289s 18 -0.341 -0.53919 0.8651 289s 19 0.219 -4.32845 -1.0116 289s 20 1.383 1.71889 -0.2087 289s 21 1.028 1.06406 -0.9656 289s 22 -1.777 2.25466 1.2061 289s > fitted 289s Consumption Investment PrivateWages 289s 1 NA NA NA 289s 2 42.4 3.754 26.7 289s 3 46.2 1.898 28.7 289s 4 50.9 5.420 32.4 289s 5 51.3 5.228 33.9 289s 6 52.9 5.209 35.5 289s 7 54.5 4.768 37.6 289s 8 55.1 2.734 38.5 289s 9 56.4 1.380 38.8 289s 10 58.5 1.700 39.9 289s 11 55.2 3.154 38.4 289s 12 51.3 -1.777 34.5 289s 13 46.0 -3.571 29.7 289s 14 45.8 -7.905 28.4 289s 15 48.6 -2.723 30.5 289s 16 51.1 -1.436 33.3 289s 17 55.8 -0.662 37.5 289s 18 59.0 2.539 40.1 289s 19 57.3 2.428 39.2 289s 20 60.2 -0.419 41.8 289s 21 64.0 2.236 46.0 289s 22 71.5 2.645 52.1 289s > predict 289s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 289s 1 NA NA NA NA 289s 2 42.4 0.434 41.6 43.3 289s 3 46.2 0.491 45.2 47.2 289s 4 50.9 0.309 50.3 51.5 289s 5 51.3 0.351 50.6 52.0 289s 6 52.9 0.352 52.1 53.6 289s 7 54.5 0.320 53.9 55.1 289s 8 55.1 0.293 54.5 55.6 289s 9 56.4 0.324 55.7 57.0 289s 10 58.5 0.340 57.9 59.2 289s 11 55.2 0.613 54.0 56.4 289s 12 51.3 0.506 50.3 52.3 289s 13 46.0 0.649 44.7 47.3 289s 14 45.8 0.546 44.7 46.8 289s 15 48.6 0.341 47.9 49.3 289s 16 51.1 0.301 50.5 51.7 289s 17 55.8 0.357 55.1 56.5 289s 18 59.0 0.293 58.5 59.6 289s 19 57.3 0.353 56.6 58.0 289s 20 60.2 0.421 59.4 61.1 289s 21 64.0 0.409 63.2 64.8 289s 22 71.5 0.630 70.2 72.7 289s Investment.pred Investment.se.fit Investment.lwr Investment.upr 289s 1 NA NA NA NA 289s 2 3.754 1.263 1.218 6.2906 289s 3 1.898 1.022 -0.153 3.9503 289s 4 5.420 0.853 3.709 7.1317 289s 5 5.228 0.727 3.767 6.6877 289s 6 5.209 0.703 3.797 6.6200 289s 7 4.768 0.688 3.387 6.1487 289s 8 2.734 0.615 1.499 3.9683 289s 9 1.380 0.852 -0.330 3.0893 289s 10 1.700 0.938 -0.184 3.5836 289s 11 3.154 1.437 0.269 6.0398 289s 12 -1.777 1.173 -4.133 0.5780 289s 13 -3.571 1.494 -6.570 -0.5725 289s 14 -7.905 1.479 -10.875 -4.9350 289s 15 -2.723 0.778 -4.285 -1.1613 289s 16 -1.436 0.672 -2.784 -0.0875 289s 17 -0.662 0.832 -2.333 1.0088 289s 18 2.539 0.522 1.491 3.5875 289s 19 2.428 0.753 0.918 3.9392 289s 20 -0.419 0.907 -2.240 1.4019 289s 21 2.236 0.775 0.679 3.7928 289s 22 2.645 1.076 0.486 4.8047 289s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 289s 1 NA NA NA NA 289s 2 26.7 0.340 26.0 27.4 289s 3 28.7 0.339 28.0 29.4 289s 4 32.4 0.340 31.7 33.1 289s 5 33.9 0.250 33.4 34.4 289s 6 35.5 0.258 35.0 36.1 289s 7 37.6 0.256 37.1 38.1 289s 8 38.5 0.252 38.0 39.0 289s 9 38.8 0.241 38.3 39.2 289s 10 39.9 0.239 39.4 40.4 289s 11 38.4 0.314 37.7 39.0 289s 12 34.5 0.342 33.8 35.2 289s 13 29.7 0.430 28.9 30.6 289s 14 28.4 0.361 27.7 29.2 289s 15 30.5 0.336 29.8 31.2 289s 16 33.3 0.281 32.7 33.9 289s 17 37.5 0.270 37.0 38.0 289s 18 40.1 0.231 39.7 40.6 289s 19 39.2 0.343 38.5 39.9 289s 20 41.8 0.294 41.2 42.4 289s 21 46.0 0.326 45.3 46.6 289s 22 52.1 0.501 51.1 53.1 289s > model.frame 289s [1] TRUE 289s > model.matrix 289s [1] TRUE 289s > nobs 289s [1] 63 289s > linearHypothesis 289s Linear hypothesis test (Theil's F test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df F Pr(>F) 289s 1 52 289s 2 51 1 0.59 0.45 289s Linear hypothesis test (F statistic of a Wald test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df F Pr(>F) 289s 1 52 289s 2 51 1 0.73 0.4 289s Linear hypothesis test (Chi^2 statistic of a Wald test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df Chisq Pr(>Chisq) 289s 1 52 289s 2 51 1 0.73 0.39 289s Linear hypothesis test (Theil's F test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s Consumption_corpProfLag - PrivateWages_trend = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df F Pr(>F) 289s 1 53 289s 2 51 2 0.72 0.49 289s Linear hypothesis test (F statistic of a Wald test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s Consumption_corpProfLag - PrivateWages_trend = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df F Pr(>F) 289s 1 53 289s 2 51 2 0.88 0.42 289s Linear hypothesis test (Chi^2 statistic of a Wald test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s Consumption_corpProfLag - PrivateWages_trend = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df Chisq Pr(>Chisq) 289s 1 53 289s 2 51 2 1.77 0.41 289s > logLik 289s 'log Lik.' -82.3 (df=18) 289s 'log Lik.' -99.1 (df=18) 289s Estimating function 289s Consumption_(Intercept) Consumption_corpProf 289s Consumption_2 -6.979 -92.51 289s Consumption_3 -3.442 -57.06 289s Consumption_4 -3.899 -75.19 289s Consumption_5 -11.237 -235.54 289s Consumption_6 -2.642 -52.22 289s Consumption_7 8.084 147.44 289s Consumption_8 10.972 192.80 289s Consumption_9 7.028 137.33 289s Consumption_10 1.972 40.17 289s Consumption_11 -7.325 -125.85 289s Consumption_12 -3.206 -40.73 289s Consumption_13 -5.913 -53.22 289s Consumption_14 9.196 83.26 289s Consumption_15 -2.781 -35.23 289s Consumption_16 -2.363 -34.08 289s Consumption_17 18.799 276.57 289s Consumption_18 -3.872 -76.65 289s Consumption_19 -13.205 -253.63 289s Consumption_20 10.531 183.44 289s Consumption_21 3.807 77.30 289s Consumption_22 -3.522 -79.79 289s Investment_2 5.075 67.27 289s Investment_3 0.158 2.62 289s Investment_4 -0.131 -2.53 289s Investment_5 2.324 48.72 289s Investment_6 0.316 6.26 289s Investment_7 -0.482 -8.80 289s Investment_8 -0.935 -16.43 289s Investment_9 -1.481 -28.94 289s Investment_10 -4.072 -82.96 289s Investment_11 2.213 38.01 289s Investment_12 1.610 20.45 289s Investment_13 2.664 23.98 289s Investment_14 -2.837 -25.69 289s Investment_15 0.201 2.55 289s Investment_16 -0.398 -5.74 289s Investment_17 -2.409 -35.45 289s Investment_18 -0.488 -9.66 289s Investment_19 4.083 78.42 289s Investment_20 -1.607 -27.99 289s Investment_21 -1.086 -22.05 289s Investment_22 -2.718 -61.58 289s PrivateWages_2 -9.649 -127.90 289s PrivateWages_3 4.187 69.41 289s PrivateWages_4 8.749 168.69 289s PrivateWages_5 -6.685 -140.11 289s PrivateWages_6 -1.021 -20.18 289s PrivateWages_7 4.003 73.02 289s PrivateWages_8 3.592 63.12 289s PrivateWages_9 5.932 115.93 289s PrivateWages_10 11.495 234.22 289s PrivateWages_11 -7.992 -137.30 289s PrivateWages_12 -2.626 -33.36 289s PrivateWages_13 -8.660 -77.94 289s PrivateWages_14 6.531 59.13 289s PrivateWages_15 -1.757 -22.27 289s PrivateWages_16 -2.801 -40.40 289s PrivateWages_17 6.362 93.60 289s PrivateWages_18 -0.661 -13.09 289s PrivateWages_19 -18.070 -347.06 289s PrivateWages_20 3.670 63.92 289s PrivateWages_21 -3.889 -78.97 289s PrivateWages_22 9.289 210.47 289s Consumption_corpProfLag Consumption_wages 289s Consumption_2 -88.63 -205.23 289s Consumption_3 -42.68 -109.29 289s Consumption_4 -65.90 -139.41 289s Consumption_5 -206.77 -439.08 289s Consumption_6 -51.26 -103.40 289s Consumption_7 162.48 318.13 289s Consumption_8 215.04 440.87 289s Consumption_9 139.15 297.49 289s Consumption_10 41.60 86.79 289s Consumption_11 -158.95 -320.08 289s Consumption_12 -50.01 -126.56 289s Consumption_13 -67.41 -207.75 289s Consumption_14 64.37 301.49 289s Consumption_15 -31.14 -104.27 289s Consumption_16 -29.07 -94.86 289s Consumption_17 263.19 783.97 289s Consumption_18 -68.15 -185.39 289s Consumption_19 -228.45 -651.06 289s Consumption_20 161.12 509.58 289s Consumption_21 72.33 203.19 289s Consumption_22 -74.31 -213.82 289s Investment_2 64.45 149.24 289s Investment_3 1.96 5.01 289s Investment_4 -2.22 -4.70 289s Investment_5 42.77 90.82 289s Investment_6 6.14 12.39 289s Investment_7 -9.70 -18.98 289s Investment_8 -18.33 -37.57 289s Investment_9 -29.32 -62.69 289s Investment_10 -85.92 -179.25 289s Investment_11 48.02 96.69 289s Investment_12 25.11 63.55 289s Investment_13 30.37 93.60 289s Investment_14 -19.86 -93.02 289s Investment_15 2.25 7.55 289s Investment_16 -4.90 -15.98 289s Investment_17 -33.73 -100.47 289s Investment_18 -8.59 -23.36 289s Investment_19 70.63 201.29 289s Investment_20 -24.59 -77.76 289s Investment_21 -20.63 -57.96 289s Investment_22 -57.35 -165.02 289s PrivateWages_2 -122.54 -283.73 289s PrivateWages_3 51.92 132.94 289s PrivateWages_4 147.85 312.78 289s PrivateWages_5 -123.00 -261.19 289s PrivateWages_6 -19.80 -39.95 289s PrivateWages_7 80.47 157.55 289s PrivateWages_8 70.40 144.33 289s PrivateWages_9 117.46 251.13 289s PrivateWages_10 242.55 506.03 289s PrivateWages_11 -173.42 -349.22 289s PrivateWages_12 -40.96 -103.66 289s PrivateWages_13 -98.72 -304.24 289s PrivateWages_14 45.71 214.10 289s PrivateWages_15 -19.68 -65.90 289s PrivateWages_16 -34.45 -112.44 289s PrivateWages_17 89.07 265.31 289s PrivateWages_18 -11.64 -31.65 289s PrivateWages_19 -312.61 -890.90 289s PrivateWages_20 56.14 177.57 289s PrivateWages_21 -73.89 -207.57 289s PrivateWages_22 196.00 564.00 289s Investment_(Intercept) Investment_corpProf 289s Consumption_2 2.2268 29.52 289s Consumption_3 1.0983 18.21 289s Consumption_4 1.2442 23.99 289s Consumption_5 3.5856 75.15 289s Consumption_6 0.8430 16.66 289s Consumption_7 -2.5793 -47.04 289s Consumption_8 -3.5007 -61.52 289s Consumption_9 -2.2423 -43.82 289s Consumption_10 -0.6291 -12.82 289s Consumption_11 2.3372 40.15 289s Consumption_12 1.0229 13.00 289s Consumption_13 1.8868 16.98 289s Consumption_14 -2.9343 -26.57 289s Consumption_15 0.8872 11.24 289s Consumption_16 0.7541 10.87 289s Consumption_17 -5.9983 -88.25 289s Consumption_18 1.2355 24.46 289s Consumption_19 4.2135 80.93 289s Consumption_20 -3.3600 -58.53 289s Consumption_21 -1.2147 -24.67 289s Consumption_22 1.1237 25.46 289s Investment_2 -2.6152 -34.67 289s Investment_3 -0.0813 -1.35 289s Investment_4 0.0677 1.30 289s Investment_5 -1.1977 -25.10 289s Investment_6 -0.1631 -3.22 289s Investment_7 0.2486 4.53 289s Investment_8 0.4818 8.47 289s Investment_9 0.7630 14.91 289s Investment_10 2.0982 42.75 289s Investment_11 -1.1402 -19.59 289s Investment_12 -0.8295 -10.54 289s Investment_13 -1.3729 -12.36 289s Investment_14 1.4620 13.24 289s Investment_15 -0.1037 -1.31 289s Investment_16 0.2051 2.96 289s Investment_17 1.2415 18.26 289s Investment_18 0.2514 4.98 289s Investment_19 -2.1038 -40.41 289s Investment_20 0.8280 14.42 289s Investment_21 0.5596 11.36 289s Investment_22 1.4005 31.73 289s PrivateWages_2 3.7415 49.60 289s PrivateWages_3 -1.6237 -26.92 289s PrivateWages_4 -3.3924 -65.41 289s PrivateWages_5 2.5921 54.33 289s PrivateWages_6 0.3959 7.82 289s PrivateWages_7 -1.5524 -28.31 289s PrivateWages_8 -1.3929 -24.48 289s PrivateWages_9 -2.3004 -44.95 289s PrivateWages_10 -4.4576 -90.82 289s PrivateWages_11 3.0990 53.24 289s PrivateWages_12 1.0182 12.94 289s PrivateWages_13 3.3581 30.22 289s PrivateWages_14 -2.5324 -22.93 289s PrivateWages_15 0.6815 8.64 289s PrivateWages_16 1.0862 15.66 289s PrivateWages_17 -2.4670 -36.29 289s PrivateWages_18 0.2564 5.07 289s PrivateWages_19 7.0070 134.58 289s PrivateWages_20 -1.4230 -24.79 289s PrivateWages_21 1.5081 30.62 289s PrivateWages_22 -3.6021 -81.61 289s Investment_corpProfLag Investment_capitalLag 289s Consumption_2 28.28 407.1 289s Consumption_3 13.62 200.5 289s Consumption_4 21.03 229.5 289s Consumption_5 65.97 680.2 289s Consumption_6 16.35 162.4 289s Consumption_7 -51.84 -510.2 289s Consumption_8 -68.61 -712.1 289s Consumption_9 -44.40 -465.5 289s Consumption_10 -13.27 -132.5 289s Consumption_11 50.72 504.1 289s Consumption_12 15.96 221.7 289s Consumption_13 21.51 402.5 289s Consumption_14 -20.54 -607.7 289s Consumption_15 9.94 179.2 289s Consumption_16 9.27 150.1 289s Consumption_17 -83.98 -1185.9 289s Consumption_18 21.74 246.9 289s Consumption_19 72.89 850.3 289s Consumption_20 -51.41 -671.7 289s Consumption_21 -23.08 -244.4 289s Consumption_22 23.71 229.8 289s Investment_2 -33.21 -478.1 289s Investment_3 -1.01 -14.9 289s Investment_4 1.14 12.5 289s Investment_5 -22.04 -227.2 289s Investment_6 -3.16 -31.4 289s Investment_7 5.00 49.2 289s Investment_8 9.44 98.0 289s Investment_9 15.11 158.4 289s Investment_10 44.27 441.9 289s Investment_11 -24.74 -245.9 289s Investment_12 -12.94 -179.8 289s Investment_13 -15.65 -292.8 289s Investment_14 10.23 302.8 289s Investment_15 -1.16 -21.0 289s Investment_16 2.52 40.8 289s Investment_17 17.38 245.4 289s Investment_18 4.43 50.2 289s Investment_19 -36.40 -424.5 289s Investment_20 12.67 165.5 289s Investment_21 10.63 112.6 289s Investment_22 29.55 286.4 289s PrivateWages_2 47.52 683.9 289s PrivateWages_3 -20.13 -296.5 289s PrivateWages_4 -57.33 -625.9 289s PrivateWages_5 47.69 491.7 289s PrivateWages_6 7.68 76.3 289s PrivateWages_7 -31.20 -307.1 289s PrivateWages_8 -27.30 -283.3 289s PrivateWages_9 -45.55 -477.6 289s PrivateWages_10 -94.05 -938.8 289s PrivateWages_11 67.25 668.4 289s PrivateWages_12 15.88 220.6 289s PrivateWages_13 38.28 716.3 289s PrivateWages_14 -17.73 -524.5 289s PrivateWages_15 7.63 137.7 289s PrivateWages_16 13.36 216.2 289s PrivateWages_17 -34.54 -487.7 289s PrivateWages_18 4.51 51.2 289s PrivateWages_19 121.22 1414.0 289s PrivateWages_20 -21.77 -284.4 289s PrivateWages_21 28.65 303.4 289s PrivateWages_22 -76.00 -736.6 289s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 289s Consumption_2 -7.713 -367.6 -346.32 289s Consumption_3 -3.804 -187.6 -173.47 289s Consumption_4 -4.309 -244.9 -215.90 289s Consumption_5 -12.419 -754.3 -710.38 289s Consumption_6 -2.920 -178.7 -166.72 289s Consumption_7 8.934 547.6 544.97 289s Consumption_8 12.125 737.9 776.02 289s Consumption_9 7.767 484.4 500.17 289s Consumption_10 2.179 140.3 140.54 289s Consumption_11 -8.095 -521.2 -542.38 289s Consumption_12 -3.543 -194.5 -216.84 289s Consumption_13 -6.535 -308.0 -348.98 289s Consumption_14 10.163 423.2 450.24 289s Consumption_15 -3.073 -156.6 -138.60 289s Consumption_16 -2.612 -145.4 -129.81 289s Consumption_17 20.776 1190.8 1130.21 289s Consumption_18 -4.279 -289.6 -268.32 289s Consumption_19 -14.594 -995.5 -948.61 289s Consumption_20 11.638 778.7 708.75 289s Consumption_21 4.207 316.7 292.41 289s Consumption_22 -3.892 -336.6 -294.62 289s Investment_2 6.817 324.9 306.06 289s Investment_3 0.212 10.5 9.67 289s Investment_4 -0.176 -10.0 -8.84 289s Investment_5 3.122 189.6 178.58 289s Investment_6 0.425 26.0 24.27 289s Investment_7 -0.648 -39.7 -39.52 289s Investment_8 -1.256 -76.4 -80.37 289s Investment_9 -1.989 -124.1 -128.08 289s Investment_10 -5.469 -352.2 -352.75 289s Investment_11 2.972 191.3 199.12 289s Investment_12 2.162 118.7 132.32 289s Investment_13 3.579 168.7 191.09 289s Investment_14 -3.811 -158.7 -168.82 289s Investment_15 0.270 13.8 12.19 289s Investment_16 -0.535 -29.8 -26.57 289s Investment_17 -3.236 -185.5 -176.04 289s Investment_18 -0.655 -44.4 -41.09 289s Investment_19 5.484 374.0 356.44 289s Investment_20 -2.158 -144.4 -131.44 289s Investment_21 -1.459 -109.8 -101.37 289s Investment_22 -3.650 -315.7 -276.34 289s PrivateWages_2 -14.774 -704.2 -663.37 289s PrivateWages_3 6.412 316.3 292.37 289s PrivateWages_4 13.396 761.4 671.14 289s PrivateWages_5 -10.236 -621.6 -585.48 289s PrivateWages_6 -1.563 -95.7 -89.26 289s PrivateWages_7 6.130 375.7 373.95 289s PrivateWages_8 5.500 334.7 352.01 289s PrivateWages_9 9.084 566.6 585.00 289s PrivateWages_10 17.602 1133.5 1135.33 289s PrivateWages_11 -12.237 -787.8 -819.89 289s PrivateWages_12 -4.021 -220.7 -246.06 289s PrivateWages_13 -13.260 -625.0 -708.11 289s PrivateWages_14 10.000 416.4 443.00 289s PrivateWages_15 -2.691 -137.2 -121.37 289s PrivateWages_16 -4.289 -238.7 -213.18 289s PrivateWages_17 9.742 558.3 529.95 289s PrivateWages_18 -1.012 -68.5 -63.47 289s PrivateWages_19 -27.669 -1887.3 -1798.51 289s PrivateWages_20 5.619 376.0 342.19 289s PrivateWages_21 -5.955 -448.3 -413.89 289s PrivateWages_22 14.224 1230.0 1076.76 289s PrivateWages_trend 289s Consumption_2 77.130 289s Consumption_3 34.237 289s Consumption_4 34.475 289s Consumption_5 86.935 289s Consumption_6 17.519 289s Consumption_7 -44.670 289s Consumption_8 -48.501 289s Consumption_9 -23.300 289s Consumption_10 -4.358 289s Consumption_11 8.095 289s Consumption_12 0.000 289s Consumption_13 -6.535 289s Consumption_14 20.327 289s Consumption_15 -9.219 289s Consumption_16 -10.447 289s Consumption_17 103.880 289s Consumption_18 -25.676 289s Consumption_19 -102.158 289s Consumption_20 93.104 289s Consumption_21 37.866 289s Consumption_22 -38.920 289s Investment_2 -68.165 289s Investment_3 -1.908 289s Investment_4 1.411 289s Investment_5 -21.854 289s Investment_6 -2.550 289s Investment_7 3.240 289s Investment_8 5.023 289s Investment_9 5.967 289s Investment_10 10.938 289s Investment_11 -2.972 289s Investment_12 0.000 289s Investment_13 3.579 289s Investment_14 -7.622 289s Investment_15 0.811 289s Investment_16 -2.138 289s Investment_17 -16.180 289s Investment_18 -3.932 289s Investment_19 38.386 289s Investment_20 -17.267 289s Investment_21 -13.128 289s Investment_22 -36.504 289s PrivateWages_2 147.744 289s PrivateWages_3 -57.704 289s PrivateWages_4 -107.168 289s PrivateWages_5 71.650 289s PrivateWages_6 9.379 289s PrivateWages_7 -30.651 289s PrivateWages_8 -22.000 289s PrivateWages_9 -27.251 289s PrivateWages_10 -35.204 289s PrivateWages_11 12.237 289s PrivateWages_12 0.000 289s PrivateWages_13 -13.260 289s PrivateWages_14 20.000 289s PrivateWages_15 -8.073 289s PrivateWages_16 -17.157 289s PrivateWages_17 48.709 289s PrivateWages_18 -6.074 289s PrivateWages_19 -193.685 289s PrivateWages_20 44.952 289s PrivateWages_21 -53.597 289s PrivateWages_22 142.240 289s [1] TRUE 289s > Bread 289s Consumption_(Intercept) Consumption_corpProf Consumption_corpProfLag 289s [1,] 94.44678 -0.9198 -0.3009 289s [2,] -0.91977 0.5830 -0.4036 289s [3,] -0.30085 -0.4036 0.5114 289s [4,] -1.71741 -0.0559 -0.0303 289s [5,] 169.11432 -7.0463 6.8731 289s [6,] -3.78719 0.8222 -0.7139 289s [7,] 1.24504 -0.6799 0.7545 289s [8,] -0.61653 0.0214 -0.0358 289s [9,] -43.93927 0.0941 1.6110 289s [10,] 0.70520 -0.0665 0.0417 289s [11,] 0.00487 0.0673 -0.0710 289s [12,] 0.27782 0.0450 0.0254 289s Consumption_wages Investment_(Intercept) Investment_corpProf 289s [1,] -1.71741 169.11 -3.79e+00 289s [2,] -0.05588 -7.05 8.22e-01 289s [3,] -0.03031 6.87 -7.14e-01 289s [4,] 0.07612 -3.87 3.83e-02 289s [5,] -3.87475 7070.32 -1.04e+02 289s [6,] 0.03834 -104.41 4.26e+00 289s [7,] -0.05106 83.93 -3.59e+00 289s [8,] 0.02027 -33.26 4.55e-01 289s [9,] 0.35346 48.43 -5.08e-01 289s [10,] -0.00637 6.61 4.29e-03 289s [11,] 0.00050 -7.65 4.31e-03 289s [12,] -0.03505 -5.67 7.94e-02 289s Investment_corpProfLag Investment_capitalLag PrivateWages_(Intercept) 289s [1,] 1.24504 -0.6165 -43.9393 289s [2,] -0.67986 0.0214 0.0941 289s [3,] 0.75452 -0.0358 1.6110 289s [4,] -0.05106 0.0203 0.3535 289s [5,] 83.92612 -33.2552 48.4291 289s [6,] -3.59218 0.4550 -0.5077 289s [7,] 3.89889 -0.4344 -3.1131 289s [8,] -0.43443 0.1630 0.0665 289s [9,] -3.11309 0.0665 90.0495 289s [10,] 0.04234 -0.0368 -0.7131 289s [11,] 0.00984 0.0370 -0.7830 289s [12,] -0.11558 0.0310 0.9385 289s PrivateWages_gnp PrivateWages_gnpLag PrivateWages_trend 289s [1,] 0.70520 0.00487 0.27782 289s [2,] -0.06653 0.06728 0.04499 289s [3,] 0.04169 -0.07096 0.02543 289s [4,] -0.00637 0.00050 -0.03505 289s [5,] 6.61461 -7.64810 -5.66883 289s [6,] 0.00429 0.00431 0.07939 289s [7,] 0.04234 0.00984 -0.11558 289s [8,] -0.03681 0.03698 0.03103 289s [9,] -0.71315 -0.78300 0.93852 289s [10,] 0.06094 -0.05082 -0.02122 289s [11,] -0.05082 0.06614 0.00579 289s [12,] -0.02122 0.00579 0.05272 289s > 289s > # OLS 289s Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 289s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 289s > summary 289s 289s systemfit results 289s method: OLS 289s 289s N DF SSR detRCov OLS-R2 McElroy-R2 289s system 62 50 44.9 0.372 0.977 0.991 289s 289s N DF SSR MSE RMSE R2 Adj R2 289s Consumption 21 17 17.88 1.052 1.03 0.981 0.978 289s Investment 21 17 17.32 1.019 1.01 0.931 0.919 289s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 289s 289s The covariance matrix of the residuals 289s Consumption Investment PrivateWages 289s Consumption 1.0703 -0.0161 -0.463 289s Investment -0.0161 0.9435 0.199 289s PrivateWages -0.4633 0.1993 0.609 289s 289s The correlations of the residuals 289s Consumption Investment PrivateWages 289s Consumption 1.0000 -0.0201 -0.575 289s Investment -0.0201 1.0000 0.264 289s PrivateWages -0.5747 0.2639 1.000 289s 289s 289s OLS estimates for 'Consumption' (equation 1) 289s Model Formula: consump ~ corpProf + corpProfLag + wages 289s 289s Estimate Std. Error t value Pr(>|t|) 289s (Intercept) 16.2366 1.3141 12.36 6.4e-10 *** 289s corpProf 0.1929 0.0920 2.10 0.051 . 289s corpProfLag 0.0899 0.0914 0.98 0.339 289s wages 0.7962 0.0403 19.76 3.6e-13 *** 289s --- 289s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 289s 289s Residual standard error: 1.026 on 17 degrees of freedom 289s Number of observations: 21 Degrees of Freedom: 17 289s SSR: 17.879 MSE: 1.052 Root MSE: 1.026 289s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.978 289s 289s 289s OLS estimates for 'Investment' (equation 2) 289s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 289s 289s Estimate Std. Error t value Pr(>|t|) 289s (Intercept) 10.1258 5.2592 1.93 0.07108 . 289s corpProf 0.4796 0.0934 5.13 8.3e-05 *** 289s corpProfLag 0.3330 0.0971 3.43 0.00318 ** 289s capitalLag -0.1118 0.0257 -4.35 0.00044 *** 289s --- 289s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 289s 289s Residual standard error: 1.009 on 17 degrees of freedom 289s Number of observations: 21 Degrees of Freedom: 17 289s SSR: 17.323 MSE: 1.019 Root MSE: 1.009 289s Multiple R-Squared: 0.931 Adjusted R-Squared: 0.919 289s 289s 289s OLS estimates for 'PrivateWages' (equation 3) 289s Model Formula: privWage ~ gnp + gnpLag + trend 289s 289s Estimate Std. Error t value Pr(>|t|) 289s (Intercept) 1.3550 1.3093 1.03 0.3161 289s gnp 0.4417 0.0331 13.33 4.4e-10 *** 289s gnpLag 0.1466 0.0381 3.85 0.0014 ** 289s trend 0.1244 0.0336 3.70 0.0020 ** 289s --- 289s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 289s 289s Residual standard error: 0.78 on 16 degrees of freedom 289s Number of observations: 20 Degrees of Freedom: 16 289s SSR: 9.739 MSE: 0.609 Root MSE: 0.78 289s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 289s 289s compare coef with single-equation OLS 289s [1] TRUE 289s > residuals 289s Consumption Investment PrivateWages 289s 1 NA NA NA 289s 2 -0.32389 -0.0668 -1.3389 289s 3 -1.25001 -0.0476 0.2462 289s 4 -1.56574 1.2467 1.1255 289s 5 -0.49350 -1.3512 -0.1959 289s 6 0.00761 0.4154 -0.5284 289s 7 0.86910 1.4923 NA 289s 8 1.33848 0.7889 -0.7909 289s 9 1.05498 -0.6317 0.2819 289s 10 -0.58856 1.0830 1.1384 289s 11 0.28231 0.2791 -0.1904 289s 12 -0.22965 0.0369 0.5813 289s 13 -0.32213 0.3659 0.1206 289s 14 0.32228 0.2237 0.4773 289s 15 -0.05801 -0.1728 0.3035 289s 16 -0.03466 0.0101 0.0284 289s 17 1.61650 0.9719 -0.8517 289s 18 -0.43597 0.0516 0.9908 289s 19 0.21005 -2.5656 -0.4597 289s 20 0.98920 -0.6866 -0.3819 289s 21 0.78508 -0.7807 -1.1062 289s 22 -2.17345 -0.6623 0.5501 289s > fitted 289s Consumption Investment PrivateWages 289s 1 NA NA NA 289s 2 42.2 -0.133 26.8 289s 3 46.3 1.948 29.1 289s 4 50.8 3.953 33.0 289s 5 51.1 4.351 34.1 289s 6 52.6 4.685 35.9 289s 7 54.2 4.108 NA 289s 8 54.9 3.411 38.7 289s 9 56.2 3.632 38.9 289s 10 58.4 4.017 40.2 289s 11 54.7 0.721 38.1 289s 12 51.1 -3.437 33.9 289s 13 45.9 -6.566 28.9 289s 14 46.2 -5.324 28.0 289s 15 48.8 -2.827 30.3 289s 16 51.3 -1.310 33.2 289s 17 56.1 1.128 37.7 289s 18 59.1 1.948 40.0 289s 19 57.3 0.666 38.7 289s 20 60.6 1.987 42.0 289s 21 64.2 4.081 46.1 289s 22 71.9 5.562 52.7 289s > predict 289s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 289s 1 NA NA NA NA 289s 2 42.2 0.466 40.0 44.5 289s 3 46.3 0.523 43.9 48.6 289s 4 50.8 0.344 48.6 52.9 289s 5 51.1 0.399 48.9 53.3 289s 6 52.6 0.401 50.4 54.8 289s 7 54.2 0.363 52.0 56.4 289s 8 54.9 0.330 52.7 57.0 289s 9 56.2 0.354 54.1 58.4 289s 10 58.4 0.373 56.2 60.6 289s 11 54.7 0.612 52.3 57.1 289s 12 51.1 0.489 48.8 53.4 289s 13 45.9 0.634 43.5 48.3 289s 14 46.2 0.608 43.8 48.6 289s 15 48.8 0.378 46.6 51.0 289s 16 51.3 0.336 49.2 53.5 289s 17 56.1 0.369 53.9 58.3 289s 18 59.1 0.324 57.0 61.3 289s 19 57.3 0.375 55.1 59.5 289s 20 60.6 0.437 58.4 62.9 289s 21 64.2 0.429 62.0 66.4 289s 22 71.9 0.672 69.4 74.3 289s Investment.pred Investment.se.fit Investment.lwr Investment.upr 289s 1 NA NA NA NA 289s 2 -0.133 0.584 -2.476 2.209 289s 3 1.948 0.480 -0.297 4.193 289s 4 3.953 0.432 1.748 6.159 289s 5 4.351 0.357 2.201 6.502 289s 6 4.685 0.336 2.548 6.821 289s 7 4.108 0.316 1.983 6.232 289s 8 3.411 0.281 1.306 5.516 289s 9 3.632 0.374 1.469 5.794 289s 10 4.017 0.430 1.813 6.221 289s 11 0.721 0.579 -1.616 3.058 289s 12 -3.437 0.488 -5.688 -1.185 289s 13 -6.566 0.592 -8.917 -4.215 289s 14 -5.324 0.667 -7.754 -2.893 289s 15 -2.827 0.359 -4.979 -0.675 289s 16 -1.310 0.308 -3.430 0.810 289s 17 1.128 0.334 -1.008 3.264 289s 18 1.948 0.234 -0.133 4.030 289s 19 0.666 0.300 -1.450 2.781 289s 20 1.987 0.353 -0.161 4.134 289s 21 4.081 0.319 1.954 6.207 289s 22 5.562 0.444 3.348 7.777 289s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 289s 1 NA NA NA NA 289s 2 26.8 0.366 25.1 28.6 289s 3 29.1 0.369 27.3 30.8 289s 4 33.0 0.372 31.2 34.7 289s 5 34.1 0.288 32.4 35.8 289s 6 35.9 0.287 34.3 37.6 289s 7 NA NA NA NA 289s 8 38.7 0.293 37.0 40.4 289s 9 38.9 0.279 37.3 40.6 289s 10 40.2 0.266 38.5 41.8 289s 11 38.1 0.365 36.4 39.8 289s 12 33.9 0.369 32.2 35.7 289s 13 28.9 0.438 27.1 30.7 289s 14 28.0 0.385 26.3 29.8 289s 15 30.3 0.379 28.6 32.0 289s 16 33.2 0.316 31.5 34.9 289s 17 37.7 0.310 36.0 39.3 289s 18 40.0 0.243 38.4 41.7 289s 19 38.7 0.363 36.9 40.4 289s 20 42.0 0.326 40.3 43.7 289s 21 46.1 0.341 44.4 47.8 289s 22 52.7 0.514 50.9 54.6 289s > model.frame 289s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 289s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 289s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 289s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 289s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 289s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 289s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 289s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 289s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 289s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 289s 10 57.8 21.7 21.1 45.3 5.1 211 41.3 67.0 64.5 289s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 289s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 289s 13 45.6 7.0 11.4 34.3 -6.2 213 29.0 44.3 53.4 289s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 289s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 289s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 289s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 289s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 289s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 289s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 289s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 289s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 289s trend 289s 1 -11 289s 2 -10 289s 3 -9 289s 4 -8 289s 5 -7 289s 6 -6 289s 7 -5 289s 8 -4 289s 9 -3 289s 10 -2 289s 11 -1 289s 12 0 289s 13 1 289s 14 2 289s 15 3 289s 16 4 289s 17 5 289s 18 6 289s 19 7 289s 20 8 289s 21 9 289s 22 10 289s > model.matrix 289s Consumption_(Intercept) Consumption_corpProf 289s Consumption_2 1 12.4 289s Consumption_3 1 16.9 289s Consumption_4 1 18.4 289s Consumption_5 1 19.4 289s Consumption_6 1 20.1 289s Consumption_7 1 19.6 289s Consumption_8 1 19.8 289s Consumption_9 1 21.1 289s Consumption_10 1 21.7 289s Consumption_11 1 15.6 289s Consumption_12 1 11.4 289s Consumption_13 1 7.0 289s Consumption_14 1 11.2 289s Consumption_15 1 12.3 289s Consumption_16 1 14.0 289s Consumption_17 1 17.6 289s Consumption_18 1 17.3 289s Consumption_19 1 15.3 289s Consumption_20 1 19.0 289s Consumption_21 1 21.1 289s Consumption_22 1 23.5 289s Investment_2 0 0.0 289s Investment_3 0 0.0 289s Investment_4 0 0.0 289s Investment_5 0 0.0 289s Investment_6 0 0.0 289s Investment_7 0 0.0 289s Investment_8 0 0.0 289s Investment_9 0 0.0 289s Investment_10 0 0.0 289s Investment_11 0 0.0 289s Investment_12 0 0.0 289s Investment_13 0 0.0 289s Investment_14 0 0.0 289s Investment_15 0 0.0 289s Investment_16 0 0.0 289s Investment_17 0 0.0 289s Investment_18 0 0.0 289s Investment_19 0 0.0 289s Investment_20 0 0.0 289s Investment_21 0 0.0 289s Investment_22 0 0.0 289s PrivateWages_2 0 0.0 289s PrivateWages_3 0 0.0 289s PrivateWages_4 0 0.0 289s PrivateWages_5 0 0.0 289s PrivateWages_6 0 0.0 289s PrivateWages_8 0 0.0 289s PrivateWages_9 0 0.0 289s PrivateWages_10 0 0.0 289s PrivateWages_11 0 0.0 289s PrivateWages_12 0 0.0 289s PrivateWages_13 0 0.0 289s PrivateWages_14 0 0.0 289s PrivateWages_15 0 0.0 289s PrivateWages_16 0 0.0 289s PrivateWages_17 0 0.0 289s PrivateWages_18 0 0.0 289s PrivateWages_19 0 0.0 289s PrivateWages_20 0 0.0 289s PrivateWages_21 0 0.0 289s PrivateWages_22 0 0.0 289s Consumption_corpProfLag Consumption_wages 289s Consumption_2 12.7 28.2 289s Consumption_3 12.4 32.2 289s Consumption_4 16.9 37.0 289s Consumption_5 18.4 37.0 289s Consumption_6 19.4 38.6 289s Consumption_7 20.1 40.7 289s Consumption_8 19.6 41.5 289s Consumption_9 19.8 42.9 289s Consumption_10 21.1 45.3 289s Consumption_11 21.7 42.1 289s Consumption_12 15.6 39.3 289s Consumption_13 11.4 34.3 289s Consumption_14 7.0 34.1 289s Consumption_15 11.2 36.6 289s Consumption_16 12.3 39.3 289s Consumption_17 14.0 44.2 289s Consumption_18 17.6 47.7 289s Consumption_19 17.3 45.9 289s Consumption_20 15.3 49.4 289s Consumption_21 19.0 53.0 289s Consumption_22 21.1 61.8 289s Investment_2 0.0 0.0 289s Investment_3 0.0 0.0 289s Investment_4 0.0 0.0 289s Investment_5 0.0 0.0 289s Investment_6 0.0 0.0 289s Investment_7 0.0 0.0 289s Investment_8 0.0 0.0 289s Investment_9 0.0 0.0 289s Investment_10 0.0 0.0 289s Investment_11 0.0 0.0 289s Investment_12 0.0 0.0 289s Investment_13 0.0 0.0 289s Investment_14 0.0 0.0 289s Investment_15 0.0 0.0 289s Investment_16 0.0 0.0 289s Investment_17 0.0 0.0 289s Investment_18 0.0 0.0 289s Investment_19 0.0 0.0 289s Investment_20 0.0 0.0 289s Investment_21 0.0 0.0 289s Investment_22 0.0 0.0 289s PrivateWages_2 0.0 0.0 289s PrivateWages_3 0.0 0.0 289s PrivateWages_4 0.0 0.0 289s PrivateWages_5 0.0 0.0 289s PrivateWages_6 0.0 0.0 289s PrivateWages_8 0.0 0.0 289s PrivateWages_9 0.0 0.0 289s PrivateWages_10 0.0 0.0 289s PrivateWages_11 0.0 0.0 289s PrivateWages_12 0.0 0.0 289s PrivateWages_13 0.0 0.0 289s PrivateWages_14 0.0 0.0 289s PrivateWages_15 0.0 0.0 289s PrivateWages_16 0.0 0.0 289s PrivateWages_17 0.0 0.0 289s PrivateWages_18 0.0 0.0 289s PrivateWages_19 0.0 0.0 289s PrivateWages_20 0.0 0.0 289s PrivateWages_21 0.0 0.0 289s PrivateWages_22 0.0 0.0 289s Investment_(Intercept) Investment_corpProf 289s Consumption_2 0 0.0 289s Consumption_3 0 0.0 289s Consumption_4 0 0.0 289s Consumption_5 0 0.0 289s Consumption_6 0 0.0 289s Consumption_7 0 0.0 289s Consumption_8 0 0.0 289s Consumption_9 0 0.0 289s Consumption_10 0 0.0 289s Consumption_11 0 0.0 289s Consumption_12 0 0.0 289s Consumption_13 0 0.0 289s Consumption_14 0 0.0 289s Consumption_15 0 0.0 289s Consumption_16 0 0.0 289s Consumption_17 0 0.0 289s Consumption_18 0 0.0 289s Consumption_19 0 0.0 289s Consumption_20 0 0.0 289s Consumption_21 0 0.0 289s Consumption_22 0 0.0 289s Investment_2 1 12.4 289s Investment_3 1 16.9 289s Investment_4 1 18.4 289s Investment_5 1 19.4 289s Investment_6 1 20.1 289s Investment_7 1 19.6 289s Investment_8 1 19.8 289s Investment_9 1 21.1 289s Investment_10 1 21.7 289s Investment_11 1 15.6 289s Investment_12 1 11.4 289s Investment_13 1 7.0 289s Investment_14 1 11.2 289s Investment_15 1 12.3 289s Investment_16 1 14.0 289s Investment_17 1 17.6 289s Investment_18 1 17.3 289s Investment_19 1 15.3 289s Investment_20 1 19.0 289s Investment_21 1 21.1 289s Investment_22 1 23.5 289s PrivateWages_2 0 0.0 289s PrivateWages_3 0 0.0 289s PrivateWages_4 0 0.0 289s PrivateWages_5 0 0.0 289s PrivateWages_6 0 0.0 289s PrivateWages_8 0 0.0 289s PrivateWages_9 0 0.0 289s PrivateWages_10 0 0.0 289s PrivateWages_11 0 0.0 289s PrivateWages_12 0 0.0 289s PrivateWages_13 0 0.0 289s PrivateWages_14 0 0.0 289s PrivateWages_15 0 0.0 289s PrivateWages_16 0 0.0 289s PrivateWages_17 0 0.0 289s PrivateWages_18 0 0.0 289s PrivateWages_19 0 0.0 289s PrivateWages_20 0 0.0 289s PrivateWages_21 0 0.0 289s PrivateWages_22 0 0.0 289s Investment_corpProfLag Investment_capitalLag 289s Consumption_2 0.0 0 289s Consumption_3 0.0 0 289s Consumption_4 0.0 0 289s Consumption_5 0.0 0 289s Consumption_6 0.0 0 289s Consumption_7 0.0 0 289s Consumption_8 0.0 0 289s Consumption_9 0.0 0 289s Consumption_10 0.0 0 289s Consumption_11 0.0 0 289s Consumption_12 0.0 0 289s Consumption_13 0.0 0 289s Consumption_14 0.0 0 289s Consumption_15 0.0 0 289s Consumption_16 0.0 0 289s Consumption_17 0.0 0 289s Consumption_18 0.0 0 289s Consumption_19 0.0 0 289s Consumption_20 0.0 0 289s Consumption_21 0.0 0 289s Consumption_22 0.0 0 289s Investment_2 12.7 183 289s Investment_3 12.4 183 289s Investment_4 16.9 184 289s Investment_5 18.4 190 289s Investment_6 19.4 193 289s Investment_7 20.1 198 289s Investment_8 19.6 203 289s Investment_9 19.8 208 289s Investment_10 21.1 211 289s Investment_11 21.7 216 289s Investment_12 15.6 217 289s Investment_13 11.4 213 289s Investment_14 7.0 207 289s Investment_15 11.2 202 289s Investment_16 12.3 199 289s Investment_17 14.0 198 289s Investment_18 17.6 200 289s Investment_19 17.3 202 289s Investment_20 15.3 200 289s Investment_21 19.0 201 289s Investment_22 21.1 204 289s PrivateWages_2 0.0 0 289s PrivateWages_3 0.0 0 289s PrivateWages_4 0.0 0 289s PrivateWages_5 0.0 0 289s PrivateWages_6 0.0 0 289s PrivateWages_8 0.0 0 289s PrivateWages_9 0.0 0 289s PrivateWages_10 0.0 0 289s PrivateWages_11 0.0 0 289s PrivateWages_12 0.0 0 289s PrivateWages_13 0.0 0 289s PrivateWages_14 0.0 0 289s PrivateWages_15 0.0 0 289s PrivateWages_16 0.0 0 289s PrivateWages_17 0.0 0 289s PrivateWages_18 0.0 0 289s PrivateWages_19 0.0 0 289s PrivateWages_20 0.0 0 289s PrivateWages_21 0.0 0 289s PrivateWages_22 0.0 0 289s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 289s Consumption_2 0 0.0 0.0 289s Consumption_3 0 0.0 0.0 289s Consumption_4 0 0.0 0.0 289s Consumption_5 0 0.0 0.0 289s Consumption_6 0 0.0 0.0 289s Consumption_7 0 0.0 0.0 289s Consumption_8 0 0.0 0.0 289s Consumption_9 0 0.0 0.0 289s Consumption_10 0 0.0 0.0 289s Consumption_11 0 0.0 0.0 289s Consumption_12 0 0.0 0.0 289s Consumption_13 0 0.0 0.0 289s Consumption_14 0 0.0 0.0 289s Consumption_15 0 0.0 0.0 289s Consumption_16 0 0.0 0.0 289s Consumption_17 0 0.0 0.0 289s Consumption_18 0 0.0 0.0 289s Consumption_19 0 0.0 0.0 289s Consumption_20 0 0.0 0.0 289s Consumption_21 0 0.0 0.0 289s Consumption_22 0 0.0 0.0 289s Investment_2 0 0.0 0.0 289s Investment_3 0 0.0 0.0 289s Investment_4 0 0.0 0.0 289s Investment_5 0 0.0 0.0 289s Investment_6 0 0.0 0.0 289s Investment_7 0 0.0 0.0 289s Investment_8 0 0.0 0.0 289s Investment_9 0 0.0 0.0 289s Investment_10 0 0.0 0.0 289s Investment_11 0 0.0 0.0 289s Investment_12 0 0.0 0.0 289s Investment_13 0 0.0 0.0 289s Investment_14 0 0.0 0.0 289s Investment_15 0 0.0 0.0 289s Investment_16 0 0.0 0.0 289s Investment_17 0 0.0 0.0 289s Investment_18 0 0.0 0.0 289s Investment_19 0 0.0 0.0 289s Investment_20 0 0.0 0.0 289s Investment_21 0 0.0 0.0 289s Investment_22 0 0.0 0.0 289s PrivateWages_2 1 45.6 44.9 289s PrivateWages_3 1 50.1 45.6 289s PrivateWages_4 1 57.2 50.1 289s PrivateWages_5 1 57.1 57.2 289s PrivateWages_6 1 61.0 57.1 289s PrivateWages_8 1 64.4 64.0 289s PrivateWages_9 1 64.5 64.4 289s PrivateWages_10 1 67.0 64.5 289s PrivateWages_11 1 61.2 67.0 289s PrivateWages_12 1 53.4 61.2 289s PrivateWages_13 1 44.3 53.4 289s PrivateWages_14 1 45.1 44.3 289s PrivateWages_15 1 49.7 45.1 289s PrivateWages_16 1 54.4 49.7 289s PrivateWages_17 1 62.7 54.4 289s PrivateWages_18 1 65.0 62.7 289s PrivateWages_19 1 60.9 65.0 289s PrivateWages_20 1 69.5 60.9 289s PrivateWages_21 1 75.7 69.5 289s PrivateWages_22 1 88.4 75.7 289s PrivateWages_trend 289s Consumption_2 0 289s Consumption_3 0 289s Consumption_4 0 289s Consumption_5 0 289s Consumption_6 0 289s Consumption_7 0 289s Consumption_8 0 289s Consumption_9 0 289s Consumption_10 0 289s Consumption_11 0 289s Consumption_12 0 289s Consumption_13 0 289s Consumption_14 0 289s Consumption_15 0 289s Consumption_16 0 289s Consumption_17 0 289s Consumption_18 0 289s Consumption_19 0 289s Consumption_20 0 289s Consumption_21 0 289s Consumption_22 0 289s Investment_2 0 289s Investment_3 0 289s Investment_4 0 289s Investment_5 0 289s Investment_6 0 289s Investment_7 0 289s Investment_8 0 289s Investment_9 0 289s Investment_10 0 289s Investment_11 0 289s Investment_12 0 289s Investment_13 0 289s Investment_14 0 289s Investment_15 0 289s Investment_16 0 289s Investment_17 0 289s Investment_18 0 289s Investment_19 0 289s Investment_20 0 289s Investment_21 0 289s Investment_22 0 289s PrivateWages_2 -10 289s PrivateWages_3 -9 289s PrivateWages_4 -8 289s PrivateWages_5 -7 289s PrivateWages_6 -6 289s PrivateWages_8 -4 289s PrivateWages_9 -3 289s PrivateWages_10 -2 289s PrivateWages_11 -1 289s PrivateWages_12 0 289s PrivateWages_13 1 289s PrivateWages_14 2 289s PrivateWages_15 3 289s PrivateWages_16 4 289s PrivateWages_17 5 289s PrivateWages_18 6 289s PrivateWages_19 7 289s PrivateWages_20 8 289s PrivateWages_21 9 289s PrivateWages_22 10 289s > nobs 289s [1] 62 289s > linearHypothesis 289s Linear hypothesis test (Theil's F test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df F Pr(>F) 289s 1 51 289s 2 50 1 0.8 0.37 289s Linear hypothesis test (F statistic of a Wald test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df F Pr(>F) 289s 1 51 289s 2 50 1 0.72 0.4 289s Linear hypothesis test (Chi^2 statistic of a Wald test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df Chisq Pr(>Chisq) 289s 1 51 289s 2 50 1 0.72 0.4 289s Linear hypothesis test (Theil's F test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s Consumption_corpProfLag - PrivateWages_trend = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df F Pr(>F) 289s 1 52 289s 2 50 2 0.42 0.66 289s Linear hypothesis test (F statistic of a Wald test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s Consumption_corpProfLag - PrivateWages_trend = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df F Pr(>F) 289s 1 52 289s 2 50 2 0.37 0.69 289s Linear hypothesis test (Chi^2 statistic of a Wald test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s Consumption_corpProfLag - PrivateWages_trend = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df Chisq Pr(>Chisq) 289s 1 52 289s 2 50 2 0.75 0.69 289s > logLik 289s 'log Lik.' -71.9 (df=13) 289s 'log Lik.' -77.1 (df=13) 289s compare log likelihood value with single-equation OLS 289s [1] "Mean relative difference: 0.000555" 289s Estimating function 289s Consumption_(Intercept) Consumption_corpProf 289s Consumption_2 -0.32389 -4.016 289s Consumption_3 -1.25001 -21.125 289s Consumption_4 -1.56574 -28.810 289s Consumption_5 -0.49350 -9.574 289s Consumption_6 0.00761 0.153 289s Consumption_7 0.86910 17.034 289s Consumption_8 1.33848 26.502 289s Consumption_9 1.05498 22.260 289s Consumption_10 -0.58856 -12.772 289s Consumption_11 0.28231 4.404 289s Consumption_12 -0.22965 -2.618 289s Consumption_13 -0.32213 -2.255 289s Consumption_14 0.32228 3.610 289s Consumption_15 -0.05801 -0.714 289s Consumption_16 -0.03466 -0.485 289s Consumption_17 1.61650 28.450 289s Consumption_18 -0.43597 -7.542 289s Consumption_19 0.21005 3.214 289s Consumption_20 0.98920 18.795 289s Consumption_21 0.78508 16.565 289s Consumption_22 -2.17345 -51.076 289s Investment_2 0.00000 0.000 289s Investment_3 0.00000 0.000 289s Investment_4 0.00000 0.000 289s Investment_5 0.00000 0.000 289s Investment_6 0.00000 0.000 289s Investment_7 0.00000 0.000 289s Investment_8 0.00000 0.000 289s Investment_9 0.00000 0.000 289s Investment_10 0.00000 0.000 289s Investment_11 0.00000 0.000 289s Investment_12 0.00000 0.000 289s Investment_13 0.00000 0.000 289s Investment_14 0.00000 0.000 289s Investment_15 0.00000 0.000 289s Investment_16 0.00000 0.000 289s Investment_17 0.00000 0.000 289s Investment_18 0.00000 0.000 289s Investment_19 0.00000 0.000 289s Investment_20 0.00000 0.000 289s Investment_21 0.00000 0.000 289s Investment_22 0.00000 0.000 289s PrivateWages_2 0.00000 0.000 289s PrivateWages_3 0.00000 0.000 289s PrivateWages_4 0.00000 0.000 289s PrivateWages_5 0.00000 0.000 289s PrivateWages_6 0.00000 0.000 289s PrivateWages_8 0.00000 0.000 289s PrivateWages_9 0.00000 0.000 289s PrivateWages_10 0.00000 0.000 289s PrivateWages_11 0.00000 0.000 289s PrivateWages_12 0.00000 0.000 289s PrivateWages_13 0.00000 0.000 289s PrivateWages_14 0.00000 0.000 289s PrivateWages_15 0.00000 0.000 289s PrivateWages_16 0.00000 0.000 289s PrivateWages_17 0.00000 0.000 289s PrivateWages_18 0.00000 0.000 289s PrivateWages_19 0.00000 0.000 289s PrivateWages_20 0.00000 0.000 289s PrivateWages_21 0.00000 0.000 289s PrivateWages_22 0.00000 0.000 289s Consumption_corpProfLag Consumption_wages 289s Consumption_2 -4.113 -9.134 289s Consumption_3 -15.500 -40.250 289s Consumption_4 -26.461 -57.932 289s Consumption_5 -9.080 -18.260 289s Consumption_6 0.148 0.294 289s Consumption_7 17.469 35.372 289s Consumption_8 26.234 55.547 289s Consumption_9 20.889 45.259 289s Consumption_10 -12.419 -26.662 289s Consumption_11 6.126 11.885 289s Consumption_12 -3.583 -9.025 289s Consumption_13 -3.672 -11.049 289s Consumption_14 2.256 10.990 289s Consumption_15 -0.650 -2.123 289s Consumption_16 -0.426 -1.362 289s Consumption_17 22.631 71.449 289s Consumption_18 -7.673 -20.796 289s Consumption_19 3.634 9.641 289s Consumption_20 15.135 48.867 289s Consumption_21 14.916 41.609 289s Consumption_22 -45.860 -134.319 289s Investment_2 0.000 0.000 289s Investment_3 0.000 0.000 289s Investment_4 0.000 0.000 289s Investment_5 0.000 0.000 289s Investment_6 0.000 0.000 289s Investment_7 0.000 0.000 289s Investment_8 0.000 0.000 289s Investment_9 0.000 0.000 289s Investment_10 0.000 0.000 289s Investment_11 0.000 0.000 289s Investment_12 0.000 0.000 289s Investment_13 0.000 0.000 289s Investment_14 0.000 0.000 289s Investment_15 0.000 0.000 289s Investment_16 0.000 0.000 289s Investment_17 0.000 0.000 289s Investment_18 0.000 0.000 289s Investment_19 0.000 0.000 289s Investment_20 0.000 0.000 289s Investment_21 0.000 0.000 289s Investment_22 0.000 0.000 289s PrivateWages_2 0.000 0.000 289s PrivateWages_3 0.000 0.000 289s PrivateWages_4 0.000 0.000 289s PrivateWages_5 0.000 0.000 289s PrivateWages_6 0.000 0.000 289s PrivateWages_8 0.000 0.000 289s PrivateWages_9 0.000 0.000 289s PrivateWages_10 0.000 0.000 289s PrivateWages_11 0.000 0.000 289s PrivateWages_12 0.000 0.000 289s PrivateWages_13 0.000 0.000 289s PrivateWages_14 0.000 0.000 289s PrivateWages_15 0.000 0.000 289s PrivateWages_16 0.000 0.000 289s PrivateWages_17 0.000 0.000 289s PrivateWages_18 0.000 0.000 289s PrivateWages_19 0.000 0.000 289s PrivateWages_20 0.000 0.000 289s PrivateWages_21 0.000 0.000 289s PrivateWages_22 0.000 0.000 289s Investment_(Intercept) Investment_corpProf 289s Consumption_2 0.0000 0.000 289s Consumption_3 0.0000 0.000 289s Consumption_4 0.0000 0.000 289s Consumption_5 0.0000 0.000 289s Consumption_6 0.0000 0.000 289s Consumption_7 0.0000 0.000 289s Consumption_8 0.0000 0.000 289s Consumption_9 0.0000 0.000 289s Consumption_10 0.0000 0.000 289s Consumption_11 0.0000 0.000 289s Consumption_12 0.0000 0.000 289s Consumption_13 0.0000 0.000 289s Consumption_14 0.0000 0.000 289s Consumption_15 0.0000 0.000 289s Consumption_16 0.0000 0.000 289s Consumption_17 0.0000 0.000 289s Consumption_18 0.0000 0.000 289s Consumption_19 0.0000 0.000 289s Consumption_20 0.0000 0.000 289s Consumption_21 0.0000 0.000 289s Consumption_22 0.0000 0.000 289s Investment_2 -0.0668 -0.828 289s Investment_3 -0.0476 -0.804 289s Investment_4 1.2467 22.939 289s Investment_5 -1.3512 -26.213 289s Investment_6 0.4154 8.350 289s Investment_7 1.4923 29.248 289s Investment_8 0.7889 15.620 289s Investment_9 -0.6317 -13.329 289s Investment_10 1.0830 23.500 289s Investment_11 0.2791 4.353 289s Investment_12 0.0369 0.420 289s Investment_13 0.3659 2.561 289s Investment_14 0.2237 2.505 289s Investment_15 -0.1728 -2.126 289s Investment_16 0.0101 0.141 289s Investment_17 0.9719 17.105 289s Investment_18 0.0516 0.893 289s Investment_19 -2.5656 -39.254 289s Investment_20 -0.6866 -13.045 289s Investment_21 -0.7807 -16.474 289s Investment_22 -0.6623 -15.565 289s PrivateWages_2 0.0000 0.000 289s PrivateWages_3 0.0000 0.000 289s PrivateWages_4 0.0000 0.000 289s PrivateWages_5 0.0000 0.000 289s PrivateWages_6 0.0000 0.000 289s PrivateWages_8 0.0000 0.000 289s PrivateWages_9 0.0000 0.000 289s PrivateWages_10 0.0000 0.000 289s PrivateWages_11 0.0000 0.000 289s PrivateWages_12 0.0000 0.000 289s PrivateWages_13 0.0000 0.000 289s PrivateWages_14 0.0000 0.000 289s PrivateWages_15 0.0000 0.000 289s PrivateWages_16 0.0000 0.000 289s PrivateWages_17 0.0000 0.000 289s PrivateWages_18 0.0000 0.000 289s PrivateWages_19 0.0000 0.000 289s PrivateWages_20 0.0000 0.000 289s PrivateWages_21 0.0000 0.000 289s PrivateWages_22 0.0000 0.000 289s Investment_corpProfLag Investment_capitalLag 289s Consumption_2 0.000 0.00 289s Consumption_3 0.000 0.00 289s Consumption_4 0.000 0.00 289s Consumption_5 0.000 0.00 289s Consumption_6 0.000 0.00 289s Consumption_7 0.000 0.00 289s Consumption_8 0.000 0.00 289s Consumption_9 0.000 0.00 289s Consumption_10 0.000 0.00 289s Consumption_11 0.000 0.00 289s Consumption_12 0.000 0.00 289s Consumption_13 0.000 0.00 289s Consumption_14 0.000 0.00 289s Consumption_15 0.000 0.00 289s Consumption_16 0.000 0.00 289s Consumption_17 0.000 0.00 289s Consumption_18 0.000 0.00 289s Consumption_19 0.000 0.00 289s Consumption_20 0.000 0.00 289s Consumption_21 0.000 0.00 289s Consumption_22 0.000 0.00 289s Investment_2 -0.848 -12.21 289s Investment_3 -0.590 -8.69 289s Investment_4 21.069 230.01 289s Investment_5 -24.862 -256.32 289s Investment_6 8.059 80.05 289s Investment_7 29.994 295.17 289s Investment_8 15.463 160.46 289s Investment_9 -12.507 -131.14 289s Investment_10 22.850 228.07 289s Investment_11 6.056 60.20 289s Investment_12 0.575 7.99 289s Investment_13 4.172 78.05 289s Investment_14 1.566 46.33 289s Investment_15 -1.936 -34.91 289s Investment_16 0.124 2.01 289s Investment_17 13.606 192.14 289s Investment_18 0.908 10.31 289s Investment_19 -44.385 -517.74 289s Investment_20 -10.505 -137.25 289s Investment_21 -14.834 -157.09 289s Investment_22 -13.975 -135.45 289s PrivateWages_2 0.000 0.00 289s PrivateWages_3 0.000 0.00 289s PrivateWages_4 0.000 0.00 289s PrivateWages_5 0.000 0.00 289s PrivateWages_6 0.000 0.00 289s PrivateWages_8 0.000 0.00 289s PrivateWages_9 0.000 0.00 289s PrivateWages_10 0.000 0.00 289s PrivateWages_11 0.000 0.00 289s PrivateWages_12 0.000 0.00 289s PrivateWages_13 0.000 0.00 289s PrivateWages_14 0.000 0.00 289s PrivateWages_15 0.000 0.00 289s PrivateWages_16 0.000 0.00 289s PrivateWages_17 0.000 0.00 289s PrivateWages_18 0.000 0.00 289s PrivateWages_19 0.000 0.00 289s PrivateWages_20 0.000 0.00 289s PrivateWages_21 0.000 0.00 289s PrivateWages_22 0.000 0.00 289s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 289s Consumption_2 0.0000 0.00 0.00 289s Consumption_3 0.0000 0.00 0.00 289s Consumption_4 0.0000 0.00 0.00 289s Consumption_5 0.0000 0.00 0.00 289s Consumption_6 0.0000 0.00 0.00 289s Consumption_7 0.0000 0.00 0.00 289s Consumption_8 0.0000 0.00 0.00 289s Consumption_9 0.0000 0.00 0.00 289s Consumption_10 0.0000 0.00 0.00 289s Consumption_11 0.0000 0.00 0.00 289s Consumption_12 0.0000 0.00 0.00 289s Consumption_13 0.0000 0.00 0.00 289s Consumption_14 0.0000 0.00 0.00 289s Consumption_15 0.0000 0.00 0.00 289s Consumption_16 0.0000 0.00 0.00 289s Consumption_17 0.0000 0.00 0.00 289s Consumption_18 0.0000 0.00 0.00 289s Consumption_19 0.0000 0.00 0.00 289s Consumption_20 0.0000 0.00 0.00 289s Consumption_21 0.0000 0.00 0.00 289s Consumption_22 0.0000 0.00 0.00 289s Investment_2 0.0000 0.00 0.00 289s Investment_3 0.0000 0.00 0.00 289s Investment_4 0.0000 0.00 0.00 289s Investment_5 0.0000 0.00 0.00 289s Investment_6 0.0000 0.00 0.00 289s Investment_7 0.0000 0.00 0.00 289s Investment_8 0.0000 0.00 0.00 289s Investment_9 0.0000 0.00 0.00 289s Investment_10 0.0000 0.00 0.00 289s Investment_11 0.0000 0.00 0.00 289s Investment_12 0.0000 0.00 0.00 289s Investment_13 0.0000 0.00 0.00 289s Investment_14 0.0000 0.00 0.00 289s Investment_15 0.0000 0.00 0.00 289s Investment_16 0.0000 0.00 0.00 289s Investment_17 0.0000 0.00 0.00 289s Investment_18 0.0000 0.00 0.00 289s Investment_19 0.0000 0.00 0.00 289s Investment_20 0.0000 0.00 0.00 289s Investment_21 0.0000 0.00 0.00 289s Investment_22 0.0000 0.00 0.00 289s PrivateWages_2 -1.3389 -61.06 -60.12 289s PrivateWages_3 0.2462 12.33 11.23 289s PrivateWages_4 1.1255 64.38 56.39 289s PrivateWages_5 -0.1959 -11.18 -11.20 289s PrivateWages_6 -0.5284 -32.23 -30.17 289s PrivateWages_8 -0.7909 -50.94 -50.62 289s PrivateWages_9 0.2819 18.18 18.15 289s PrivateWages_10 1.1384 76.28 73.43 289s PrivateWages_11 -0.1904 -11.65 -12.76 289s PrivateWages_12 0.5813 31.04 35.58 289s PrivateWages_13 0.1206 5.34 6.44 289s PrivateWages_14 0.4773 21.53 21.14 289s PrivateWages_15 0.3035 15.09 13.69 289s PrivateWages_16 0.0284 1.55 1.41 289s PrivateWages_17 -0.8517 -53.40 -46.33 289s PrivateWages_18 0.9908 64.40 62.12 289s PrivateWages_19 -0.4597 -28.00 -29.88 289s PrivateWages_20 -0.3819 -26.54 -23.26 289s PrivateWages_21 -1.1062 -83.74 -76.88 289s PrivateWages_22 0.5501 48.63 41.64 289s PrivateWages_trend 289s Consumption_2 0.000 289s Consumption_3 0.000 289s Consumption_4 0.000 289s Consumption_5 0.000 289s Consumption_6 0.000 289s Consumption_7 0.000 289s Consumption_8 0.000 289s Consumption_9 0.000 289s Consumption_10 0.000 289s Consumption_11 0.000 289s Consumption_12 0.000 289s Consumption_13 0.000 289s Consumption_14 0.000 289s Consumption_15 0.000 289s Consumption_16 0.000 289s Consumption_17 0.000 289s Consumption_18 0.000 289s Consumption_19 0.000 289s Consumption_20 0.000 289s Consumption_21 0.000 289s Consumption_22 0.000 289s Investment_2 0.000 289s Investment_3 0.000 289s Investment_4 0.000 289s Investment_5 0.000 289s Investment_6 0.000 289s Investment_7 0.000 289s Investment_8 0.000 289s Investment_9 0.000 289s Investment_10 0.000 289s Investment_11 0.000 289s Investment_12 0.000 289s Investment_13 0.000 289s Investment_14 0.000 289s Investment_15 0.000 289s Investment_16 0.000 289s Investment_17 0.000 289s Investment_18 0.000 289s Investment_19 0.000 289s Investment_20 0.000 289s Investment_21 0.000 289s Investment_22 0.000 289s PrivateWages_2 13.389 289s PrivateWages_3 -2.216 289s PrivateWages_4 -9.004 289s PrivateWages_5 1.371 289s PrivateWages_6 3.170 289s PrivateWages_8 3.164 289s PrivateWages_9 -0.846 289s PrivateWages_10 -2.277 289s PrivateWages_11 0.190 289s PrivateWages_12 0.000 289s PrivateWages_13 0.121 289s PrivateWages_14 0.955 289s PrivateWages_15 0.911 289s PrivateWages_16 0.114 289s PrivateWages_17 -4.258 289s PrivateWages_18 5.945 289s PrivateWages_19 -3.218 289s PrivateWages_20 -3.055 289s PrivateWages_21 -9.956 289s PrivateWages_22 5.501 289s [1] TRUE 289s > Bread 289s Consumption_(Intercept) Consumption_corpProf 289s Consumption_(Intercept) 100.0401 0.0296 289s Consumption_corpProf 0.0296 0.4904 289s Consumption_corpProfLag -1.0438 -0.3107 289s Consumption_wages -1.9405 -0.0777 289s Investment_(Intercept) 0.0000 0.0000 289s Investment_corpProf 0.0000 0.0000 289s Investment_corpProfLag 0.0000 0.0000 289s Investment_capitalLag 0.0000 0.0000 289s PrivateWages_(Intercept) 0.0000 0.0000 289s PrivateWages_gnp 0.0000 0.0000 289s PrivateWages_gnpLag 0.0000 0.0000 289s PrivateWages_trend 0.0000 0.0000 289s Consumption_corpProfLag Consumption_wages 289s Consumption_(Intercept) -1.0438 -1.9405 289s Consumption_corpProf -0.3107 -0.0777 289s Consumption_corpProfLag 0.4844 -0.0396 289s Consumption_wages -0.0396 0.0941 289s Investment_(Intercept) 0.0000 0.0000 289s Investment_corpProf 0.0000 0.0000 289s Investment_corpProfLag 0.0000 0.0000 289s Investment_capitalLag 0.0000 0.0000 289s PrivateWages_(Intercept) 0.0000 0.0000 289s PrivateWages_gnp 0.0000 0.0000 289s PrivateWages_gnpLag 0.0000 0.0000 289s PrivateWages_trend 0.0000 0.0000 289s Investment_(Intercept) Investment_corpProf 289s Consumption_(Intercept) 0.00 0.0000 289s Consumption_corpProf 0.00 0.0000 289s Consumption_corpProfLag 0.00 0.0000 289s Consumption_wages 0.00 0.0000 289s Investment_(Intercept) 1817.57 -17.6857 289s Investment_corpProf -17.69 0.5738 289s Investment_corpProfLag 14.44 -0.4928 289s Investment_capitalLag -8.74 0.0801 289s PrivateWages_(Intercept) 0.00 0.0000 289s PrivateWages_gnp 0.00 0.0000 289s PrivateWages_gnpLag 0.00 0.0000 289s PrivateWages_trend 0.00 0.0000 289s Investment_corpProfLag Investment_capitalLag 289s Consumption_(Intercept) 0.0000 0.0000 289s Consumption_corpProf 0.0000 0.0000 289s Consumption_corpProfLag 0.0000 0.0000 289s Consumption_wages 0.0000 0.0000 289s Investment_(Intercept) 14.4412 -8.7403 289s Investment_corpProf -0.4928 0.0801 289s Investment_corpProfLag 0.6190 -0.0811 289s Investment_capitalLag -0.0811 0.0435 289s PrivateWages_(Intercept) 0.0000 0.0000 289s PrivateWages_gnp 0.0000 0.0000 289s PrivateWages_gnpLag 0.0000 0.0000 289s PrivateWages_trend 0.0000 0.0000 289s PrivateWages_(Intercept) PrivateWages_gnp 289s Consumption_(Intercept) 0.000 0.000 289s Consumption_corpProf 0.000 0.000 289s Consumption_corpProfLag 0.000 0.000 289s Consumption_wages 0.000 0.000 289s Investment_(Intercept) 0.000 0.000 289s Investment_corpProf 0.000 0.000 289s Investment_corpProfLag 0.000 0.000 289s Investment_capitalLag 0.000 0.000 289s PrivateWages_(Intercept) 174.627 -0.658 289s PrivateWages_gnp -0.658 0.112 289s PrivateWages_gnpLag -2.295 -0.104 289s PrivateWages_trend 2.155 -0.030 289s PrivateWages_gnpLag PrivateWages_trend 289s Consumption_(Intercept) 0.00000 0.00000 289s Consumption_corpProf 0.00000 0.00000 289s Consumption_corpProfLag 0.00000 0.00000 289s Consumption_wages 0.00000 0.00000 289s Investment_(Intercept) 0.00000 0.00000 289s Investment_corpProf 0.00000 0.00000 289s Investment_corpProfLag 0.00000 0.00000 289s Investment_capitalLag 0.00000 0.00000 289s PrivateWages_(Intercept) -2.29451 2.15506 289s PrivateWages_gnp -0.10426 -0.03004 289s PrivateWages_gnpLag 0.14761 -0.00667 289s PrivateWages_trend -0.00667 0.11527 289s > 289s > # 2SLS 289s > summary 289s 289s systemfit results 289s method: 2SLS 289s 289s N DF SSR detRCov OLS-R2 McElroy-R2 289s system 60 48 53.4 0.274 0.973 0.992 289s 289s N DF SSR MSE RMSE R2 Adj R2 289s Consumption 20 16 20.67 1.292 1.14 0.978 0.974 289s Investment 20 16 23.02 1.438 1.20 0.901 0.883 289s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 289s 289s The covariance matrix of the residuals 289s Consumption Investment PrivateWages 289s Consumption 1.034 0.309 -0.383 289s Investment 0.309 1.151 0.202 289s PrivateWages -0.383 0.202 0.487 289s 289s The correlations of the residuals 289s Consumption Investment PrivateWages 289s Consumption 1.000 0.284 -0.540 289s Investment 0.284 1.000 0.269 289s PrivateWages -0.540 0.269 1.000 289s 289s 289s 2SLS estimates for 'Consumption' (equation 1) 289s Model Formula: consump ~ corpProf + corpProfLag + wages 289s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 289s gnpLag 289s 289s Estimate Std. Error t value Pr(>|t|) 289s (Intercept) 16.5093 1.3121 12.58 1.0e-09 *** 289s corpProf 0.0219 0.1159 0.19 0.85 289s corpProfLag 0.1931 0.1071 1.80 0.09 . 289s wages 0.8174 0.0408 20.05 9.2e-13 *** 289s --- 289s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 289s 289s Residual standard error: 1.137 on 16 degrees of freedom 289s Number of observations: 20 Degrees of Freedom: 16 289s SSR: 20.671 MSE: 1.292 Root MSE: 1.137 289s Multiple R-Squared: 0.978 Adjusted R-Squared: 0.974 289s 289s 289s 2SLS estimates for 'Investment' (equation 2) 289s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 289s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 289s gnpLag 289s 289s Estimate Std. Error t value Pr(>|t|) 289s (Intercept) 17.843 6.850 2.60 0.01915 * 289s corpProf 0.217 0.155 1.40 0.18106 289s corpProfLag 0.542 0.148 3.65 0.00216 ** 289s capitalLag -0.145 0.033 -4.41 0.00044 *** 289s --- 289s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 289s 289s Residual standard error: 1.199 on 16 degrees of freedom 289s Number of observations: 20 Degrees of Freedom: 16 289s SSR: 23.016 MSE: 1.438 Root MSE: 1.199 289s Multiple R-Squared: 0.901 Adjusted R-Squared: 0.883 289s 289s 289s 2SLS estimates for 'PrivateWages' (equation 3) 289s Model Formula: privWage ~ gnp + gnpLag + trend 289s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 289s gnpLag 289s 289s Estimate Std. Error t value Pr(>|t|) 289s (Intercept) 1.3431 1.1772 1.14 0.27070 289s gnp 0.4438 0.0358 12.39 1.3e-09 *** 289s gnpLag 0.1447 0.0389 3.72 0.00185 ** 289s trend 0.1238 0.0306 4.05 0.00093 *** 289s --- 289s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 289s 289s Residual standard error: 0.78 on 16 degrees of freedom 289s Number of observations: 20 Degrees of Freedom: 16 289s SSR: 9.741 MSE: 0.609 Root MSE: 0.78 289s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 289s 289s > residuals 289s Consumption Investment PrivateWages 289s 1 NA NA NA 289s 2 -0.383 -1.0104 -1.3401 289s 3 -0.593 0.2478 0.2378 289s 4 -1.219 1.0621 1.1117 289s 5 -0.130 -1.4104 -0.1954 289s 6 0.354 0.4328 -0.5355 289s 7 NA NA NA 289s 8 1.551 1.0463 -0.7908 289s 9 1.440 0.0674 0.2831 289s 10 -0.286 1.7698 1.1353 289s 11 -0.453 -0.5912 -0.1765 289s 12 -0.994 -0.6318 0.6007 289s 13 -1.300 -0.6983 0.1443 289s 14 0.521 0.9724 0.4826 289s 15 -0.157 -0.1827 0.3016 289s 16 -0.014 0.1167 0.0261 289s 17 1.974 1.6266 -0.8614 289s 18 -0.576 -0.0525 0.9927 289s 19 -0.203 -3.0656 -0.4446 289s 20 1.342 0.1393 -0.3914 289s 21 1.039 -0.1305 -1.1115 289s 22 -1.912 0.2922 0.5312 289s > fitted 289s Consumption Investment PrivateWages 289s 1 NA NA NA 289s 2 42.3 0.810 26.8 289s 3 45.6 1.652 29.1 289s 4 50.4 4.138 33.0 289s 5 50.7 4.410 34.1 289s 6 52.2 4.667 35.9 289s 7 NA NA NA 289s 8 54.6 3.154 38.7 289s 9 55.9 2.933 38.9 289s 10 58.1 3.330 40.2 289s 11 55.5 1.591 38.1 289s 12 51.9 -2.768 33.9 289s 13 46.9 -5.502 28.9 289s 14 46.0 -6.072 28.0 289s 15 48.9 -2.817 30.3 289s 16 51.3 -1.417 33.2 289s 17 55.7 0.473 37.7 289s 18 59.3 2.053 40.0 289s 19 57.7 1.166 38.6 289s 20 60.3 1.161 42.0 289s 21 64.0 3.431 46.1 289s 22 71.6 4.608 52.8 289s > predict 289s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 289s 1 NA NA NA NA 289s 2 42.3 0.473 41.3 43.3 289s 3 45.6 0.573 44.4 46.8 289s 4 50.4 0.366 49.6 51.2 289s 5 50.7 0.423 49.8 51.6 289s 6 52.2 0.426 51.3 53.1 289s 7 NA NA NA NA 289s 8 54.6 0.347 53.9 55.4 289s 9 55.9 0.384 55.0 56.7 289s 10 58.1 0.395 57.2 58.9 289s 11 55.5 0.729 53.9 57.0 289s 12 51.9 0.594 50.6 53.2 289s 13 46.9 0.752 45.3 48.5 289s 14 46.0 0.616 44.7 47.3 289s 15 48.9 0.373 48.1 49.6 289s 16 51.3 0.331 50.6 52.0 289s 17 55.7 0.403 54.9 56.6 289s 18 59.3 0.326 58.6 60.0 289s 19 57.7 0.411 56.8 58.6 289s 20 60.3 0.472 59.3 61.3 289s 21 64.0 0.443 63.0 64.9 289s 22 71.6 0.683 70.2 73.1 289s Investment.pred Investment.se.fit Investment.lwr Investment.upr 289s 1 NA NA NA NA 289s 2 0.810 0.786 -0.8569 2.48 289s 3 1.652 0.541 0.5056 2.80 289s 4 4.138 0.511 3.0552 5.22 289s 5 4.410 0.421 3.5172 5.30 289s 6 4.667 0.395 3.8294 5.51 289s 7 NA NA NA NA 289s 8 3.154 0.327 2.4602 3.85 289s 9 2.933 0.489 1.8967 3.97 289s 10 3.330 0.537 2.1915 4.47 289s 11 1.591 0.786 -0.0748 3.26 289s 12 -2.768 0.615 -4.0716 -1.46 289s 13 -5.502 0.787 -7.1696 -3.83 289s 14 -6.072 0.842 -7.8568 -4.29 289s 15 -2.817 0.397 -3.6591 -1.98 289s 16 -1.417 0.343 -2.1436 -0.69 289s 17 0.473 0.457 -0.4954 1.44 289s 18 2.053 0.286 1.4471 2.66 289s 19 1.166 0.430 0.2549 2.08 289s 20 1.161 0.515 0.0698 2.25 289s 21 3.431 0.426 2.5282 4.33 289s 22 4.608 0.606 3.3223 5.89 289s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 289s 1 NA NA NA NA 289s 2 26.8 0.328 26.1 27.5 289s 3 29.1 0.340 28.3 29.8 289s 4 33.0 0.360 32.2 33.8 289s 5 34.1 0.258 33.5 34.6 289s 6 35.9 0.266 35.4 36.5 289s 7 NA NA NA NA 289s 8 38.7 0.262 38.1 39.2 289s 9 38.9 0.250 38.4 39.4 289s 10 40.2 0.240 39.7 40.7 289s 11 38.1 0.355 37.3 38.8 289s 12 33.9 0.382 33.1 34.7 289s 13 28.9 0.456 27.9 29.8 289s 14 28.0 0.348 27.3 28.8 289s 15 30.3 0.339 29.6 31.0 289s 16 33.2 0.284 32.6 33.8 289s 17 37.7 0.293 37.0 38.3 289s 18 40.0 0.218 39.5 40.5 289s 19 38.6 0.358 37.9 39.4 289s 20 42.0 0.307 41.3 42.6 289s 21 46.1 0.310 45.5 46.8 289s 22 52.8 0.496 51.7 53.8 289s > model.frame 289s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 289s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 289s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 289s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 289s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 289s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 289s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 289s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 289s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 289s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 289s 10 57.8 21.7 21.1 45.3 5.1 211 41.3 67.0 64.5 289s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 289s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 289s 13 45.6 7.0 11.4 34.3 -6.2 213 29.0 44.3 53.4 289s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 289s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 289s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 289s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 289s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 289s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 289s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 289s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 289s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 289s trend 289s 1 -11 289s 2 -10 289s 3 -9 289s 4 -8 289s 5 -7 289s 6 -6 289s 7 -5 289s 8 -4 289s 9 -3 289s 10 -2 289s 11 -1 289s 12 0 289s 13 1 289s 14 2 289s 15 3 289s 16 4 289s 17 5 289s 18 6 289s 19 7 289s 20 8 289s 21 9 289s 22 10 289s > Frames of instrumental variables 289s govExp taxes govWage trend capitalLag corpProfLag gnpLag 289s 1 2.4 3.4 2.2 -11 180 NA NA 289s 2 3.9 7.7 2.7 -10 183 12.7 44.9 289s 3 3.2 3.9 2.9 -9 183 12.4 45.6 289s 4 2.8 4.7 2.9 -8 184 16.9 50.1 289s 5 3.5 3.8 3.1 -7 190 18.4 57.2 289s 6 3.3 5.5 3.2 -6 193 19.4 57.1 289s 7 3.3 7.0 3.3 -5 198 20.1 NA 289s 8 4.0 6.7 3.6 -4 203 19.6 64.0 289s 9 4.2 4.2 3.7 -3 208 19.8 64.4 289s 10 4.1 4.0 4.0 -2 211 21.1 64.5 289s 11 5.2 7.7 4.2 -1 216 21.7 67.0 289s 12 5.9 7.5 4.8 0 217 15.6 61.2 289s 13 4.9 8.3 5.3 1 213 11.4 53.4 289s 14 3.7 5.4 5.6 2 207 7.0 44.3 289s 15 4.0 6.8 6.0 3 202 11.2 45.1 289s 16 4.4 7.2 6.1 4 199 12.3 49.7 289s 17 2.9 8.3 7.4 5 198 14.0 54.4 289s 18 4.3 6.7 6.7 6 200 17.6 62.7 289s 19 5.3 7.4 7.7 7 202 17.3 65.0 289s 20 6.6 8.9 7.8 8 200 15.3 60.9 289s 21 7.4 9.6 8.0 9 201 19.0 69.5 289s 22 13.8 11.6 8.5 10 204 21.1 75.7 289s govExp taxes govWage trend capitalLag corpProfLag gnpLag 289s 1 2.4 3.4 2.2 -11 180 NA NA 289s 2 3.9 7.7 2.7 -10 183 12.7 44.9 289s 3 3.2 3.9 2.9 -9 183 12.4 45.6 289s 4 2.8 4.7 2.9 -8 184 16.9 50.1 289s 5 3.5 3.8 3.1 -7 190 18.4 57.2 289s 6 3.3 5.5 3.2 -6 193 19.4 57.1 289s 7 3.3 7.0 3.3 -5 198 20.1 NA 289s 8 4.0 6.7 3.6 -4 203 19.6 64.0 289s 9 4.2 4.2 3.7 -3 208 19.8 64.4 289s 10 4.1 4.0 4.0 -2 211 21.1 64.5 289s 11 5.2 7.7 4.2 -1 216 21.7 67.0 289s 12 5.9 7.5 4.8 0 217 15.6 61.2 289s 13 4.9 8.3 5.3 1 213 11.4 53.4 289s 14 3.7 5.4 5.6 2 207 7.0 44.3 289s 15 4.0 6.8 6.0 3 202 11.2 45.1 289s 16 4.4 7.2 6.1 4 199 12.3 49.7 289s 17 2.9 8.3 7.4 5 198 14.0 54.4 289s 18 4.3 6.7 6.7 6 200 17.6 62.7 289s 19 5.3 7.4 7.7 7 202 17.3 65.0 289s 20 6.6 8.9 7.8 8 200 15.3 60.9 289s 21 7.4 9.6 8.0 9 201 19.0 69.5 289s 22 13.8 11.6 8.5 10 204 21.1 75.7 289s govExp taxes govWage trend capitalLag corpProfLag gnpLag 289s 1 2.4 3.4 2.2 -11 180 NA NA 289s 2 3.9 7.7 2.7 -10 183 12.7 44.9 289s 3 3.2 3.9 2.9 -9 183 12.4 45.6 289s 4 2.8 4.7 2.9 -8 184 16.9 50.1 289s 5 3.5 3.8 3.1 -7 190 18.4 57.2 289s 6 3.3 5.5 3.2 -6 193 19.4 57.1 289s 7 3.3 7.0 3.3 -5 198 20.1 NA 289s 8 4.0 6.7 3.6 -4 203 19.6 64.0 289s 9 4.2 4.2 3.7 -3 208 19.8 64.4 289s 10 4.1 4.0 4.0 -2 211 21.1 64.5 289s 11 5.2 7.7 4.2 -1 216 21.7 67.0 289s 12 5.9 7.5 4.8 0 217 15.6 61.2 289s 13 4.9 8.3 5.3 1 213 11.4 53.4 289s 14 3.7 5.4 5.6 2 207 7.0 44.3 289s 15 4.0 6.8 6.0 3 202 11.2 45.1 289s 16 4.4 7.2 6.1 4 199 12.3 49.7 289s 17 2.9 8.3 7.4 5 198 14.0 54.4 289s 18 4.3 6.7 6.7 6 200 17.6 62.7 289s 19 5.3 7.4 7.7 7 202 17.3 65.0 289s 20 6.6 8.9 7.8 8 200 15.3 60.9 289s 21 7.4 9.6 8.0 9 201 19.0 69.5 289s 22 13.8 11.6 8.5 10 204 21.1 75.7 289s > model.matrix 289s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0323 >" 289s [2] "Attributes: < Component “dimnames”: Component 1: 55 string mismatches >" 289s [3] "Numeric: lengths (744, 720) differ" 289s > matrix of instrumental variables 289s Consumption_(Intercept) Consumption_govExp Consumption_taxes 289s Consumption_2 1 3.9 7.7 289s Consumption_3 1 3.2 3.9 289s Consumption_4 1 2.8 4.7 289s Consumption_5 1 3.5 3.8 289s Consumption_6 1 3.3 5.5 289s Consumption_8 1 4.0 6.7 289s Consumption_9 1 4.2 4.2 289s Consumption_10 1 4.1 4.0 289s Consumption_11 1 5.2 7.7 289s Consumption_12 1 5.9 7.5 289s Consumption_13 1 4.9 8.3 289s Consumption_14 1 3.7 5.4 289s Consumption_15 1 4.0 6.8 289s Consumption_16 1 4.4 7.2 289s Consumption_17 1 2.9 8.3 289s Consumption_18 1 4.3 6.7 289s Consumption_19 1 5.3 7.4 289s Consumption_20 1 6.6 8.9 289s Consumption_21 1 7.4 9.6 289s Consumption_22 1 13.8 11.6 289s Investment_2 0 0.0 0.0 289s Investment_3 0 0.0 0.0 289s Investment_4 0 0.0 0.0 289s Investment_5 0 0.0 0.0 289s Investment_6 0 0.0 0.0 289s Investment_8 0 0.0 0.0 289s Investment_9 0 0.0 0.0 289s Investment_10 0 0.0 0.0 289s Investment_11 0 0.0 0.0 289s Investment_12 0 0.0 0.0 289s Investment_13 0 0.0 0.0 289s Investment_14 0 0.0 0.0 289s Investment_15 0 0.0 0.0 289s Investment_16 0 0.0 0.0 289s Investment_17 0 0.0 0.0 289s Investment_18 0 0.0 0.0 289s Investment_19 0 0.0 0.0 289s Investment_20 0 0.0 0.0 289s Investment_21 0 0.0 0.0 289s Investment_22 0 0.0 0.0 289s PrivateWages_2 0 0.0 0.0 289s PrivateWages_3 0 0.0 0.0 289s PrivateWages_4 0 0.0 0.0 289s PrivateWages_5 0 0.0 0.0 289s PrivateWages_6 0 0.0 0.0 289s PrivateWages_8 0 0.0 0.0 289s PrivateWages_9 0 0.0 0.0 289s PrivateWages_10 0 0.0 0.0 289s PrivateWages_11 0 0.0 0.0 289s PrivateWages_12 0 0.0 0.0 289s PrivateWages_13 0 0.0 0.0 289s PrivateWages_14 0 0.0 0.0 289s PrivateWages_15 0 0.0 0.0 289s PrivateWages_16 0 0.0 0.0 289s PrivateWages_17 0 0.0 0.0 289s PrivateWages_18 0 0.0 0.0 289s PrivateWages_19 0 0.0 0.0 289s PrivateWages_20 0 0.0 0.0 289s PrivateWages_21 0 0.0 0.0 289s PrivateWages_22 0 0.0 0.0 289s Consumption_govWage Consumption_trend Consumption_capitalLag 289s Consumption_2 2.7 -10 183 289s Consumption_3 2.9 -9 183 289s Consumption_4 2.9 -8 184 289s Consumption_5 3.1 -7 190 289s Consumption_6 3.2 -6 193 289s Consumption_8 3.6 -4 203 289s Consumption_9 3.7 -3 208 289s Consumption_10 4.0 -2 211 289s Consumption_11 4.2 -1 216 289s Consumption_12 4.8 0 217 289s Consumption_13 5.3 1 213 289s Consumption_14 5.6 2 207 289s Consumption_15 6.0 3 202 289s Consumption_16 6.1 4 199 289s Consumption_17 7.4 5 198 289s Consumption_18 6.7 6 200 289s Consumption_19 7.7 7 202 289s Consumption_20 7.8 8 200 289s Consumption_21 8.0 9 201 289s Consumption_22 8.5 10 204 289s Investment_2 0.0 0 0 289s Investment_3 0.0 0 0 289s Investment_4 0.0 0 0 289s Investment_5 0.0 0 0 289s Investment_6 0.0 0 0 289s Investment_8 0.0 0 0 289s Investment_9 0.0 0 0 289s Investment_10 0.0 0 0 289s Investment_11 0.0 0 0 289s Investment_12 0.0 0 0 289s Investment_13 0.0 0 0 289s Investment_14 0.0 0 0 289s Investment_15 0.0 0 0 289s Investment_16 0.0 0 0 289s Investment_17 0.0 0 0 289s Investment_18 0.0 0 0 289s Investment_19 0.0 0 0 289s Investment_20 0.0 0 0 289s Investment_21 0.0 0 0 289s Investment_22 0.0 0 0 289s PrivateWages_2 0.0 0 0 289s PrivateWages_3 0.0 0 0 289s PrivateWages_4 0.0 0 0 289s PrivateWages_5 0.0 0 0 289s PrivateWages_6 0.0 0 0 289s PrivateWages_8 0.0 0 0 289s PrivateWages_9 0.0 0 0 289s PrivateWages_10 0.0 0 0 289s PrivateWages_11 0.0 0 0 289s PrivateWages_12 0.0 0 0 289s PrivateWages_13 0.0 0 0 289s PrivateWages_14 0.0 0 0 289s PrivateWages_15 0.0 0 0 289s PrivateWages_16 0.0 0 0 289s PrivateWages_17 0.0 0 0 289s PrivateWages_18 0.0 0 0 289s PrivateWages_19 0.0 0 0 289s PrivateWages_20 0.0 0 0 289s PrivateWages_21 0.0 0 0 289s PrivateWages_22 0.0 0 0 289s Consumption_corpProfLag Consumption_gnpLag 289s Consumption_2 12.7 44.9 289s Consumption_3 12.4 45.6 289s Consumption_4 16.9 50.1 289s Consumption_5 18.4 57.2 289s Consumption_6 19.4 57.1 289s Consumption_8 19.6 64.0 289s Consumption_9 19.8 64.4 289s Consumption_10 21.1 64.5 289s Consumption_11 21.7 67.0 289s Consumption_12 15.6 61.2 289s Consumption_13 11.4 53.4 289s Consumption_14 7.0 44.3 289s Consumption_15 11.2 45.1 289s Consumption_16 12.3 49.7 289s Consumption_17 14.0 54.4 289s Consumption_18 17.6 62.7 289s Consumption_19 17.3 65.0 289s Consumption_20 15.3 60.9 289s Consumption_21 19.0 69.5 289s Consumption_22 21.1 75.7 289s Investment_2 0.0 0.0 289s Investment_3 0.0 0.0 289s Investment_4 0.0 0.0 289s Investment_5 0.0 0.0 289s Investment_6 0.0 0.0 289s Investment_8 0.0 0.0 289s Investment_9 0.0 0.0 289s Investment_10 0.0 0.0 289s Investment_11 0.0 0.0 289s Investment_12 0.0 0.0 289s Investment_13 0.0 0.0 289s Investment_14 0.0 0.0 289s Investment_15 0.0 0.0 289s Investment_16 0.0 0.0 289s Investment_17 0.0 0.0 289s Investment_18 0.0 0.0 289s Investment_19 0.0 0.0 289s Investment_20 0.0 0.0 289s Investment_21 0.0 0.0 289s Investment_22 0.0 0.0 289s PrivateWages_2 0.0 0.0 289s PrivateWages_3 0.0 0.0 289s PrivateWages_4 0.0 0.0 289s PrivateWages_5 0.0 0.0 289s PrivateWages_6 0.0 0.0 289s PrivateWages_8 0.0 0.0 289s PrivateWages_9 0.0 0.0 289s PrivateWages_10 0.0 0.0 289s PrivateWages_11 0.0 0.0 289s PrivateWages_12 0.0 0.0 289s PrivateWages_13 0.0 0.0 289s PrivateWages_14 0.0 0.0 289s PrivateWages_15 0.0 0.0 289s PrivateWages_16 0.0 0.0 289s PrivateWages_17 0.0 0.0 289s PrivateWages_18 0.0 0.0 289s PrivateWages_19 0.0 0.0 289s PrivateWages_20 0.0 0.0 289s PrivateWages_21 0.0 0.0 289s PrivateWages_22 0.0 0.0 289s Investment_(Intercept) Investment_govExp Investment_taxes 289s Consumption_2 0 0.0 0.0 289s Consumption_3 0 0.0 0.0 289s Consumption_4 0 0.0 0.0 289s Consumption_5 0 0.0 0.0 289s Consumption_6 0 0.0 0.0 289s Consumption_8 0 0.0 0.0 289s Consumption_9 0 0.0 0.0 289s Consumption_10 0 0.0 0.0 289s Consumption_11 0 0.0 0.0 289s Consumption_12 0 0.0 0.0 289s Consumption_13 0 0.0 0.0 289s Consumption_14 0 0.0 0.0 289s Consumption_15 0 0.0 0.0 289s Consumption_16 0 0.0 0.0 289s Consumption_17 0 0.0 0.0 289s Consumption_18 0 0.0 0.0 289s Consumption_19 0 0.0 0.0 289s Consumption_20 0 0.0 0.0 289s Consumption_21 0 0.0 0.0 289s Consumption_22 0 0.0 0.0 289s Investment_2 1 3.9 7.7 289s Investment_3 1 3.2 3.9 289s Investment_4 1 2.8 4.7 289s Investment_5 1 3.5 3.8 289s Investment_6 1 3.3 5.5 289s Investment_8 1 4.0 6.7 289s Investment_9 1 4.2 4.2 289s Investment_10 1 4.1 4.0 289s Investment_11 1 5.2 7.7 289s Investment_12 1 5.9 7.5 289s Investment_13 1 4.9 8.3 289s Investment_14 1 3.7 5.4 289s Investment_15 1 4.0 6.8 289s Investment_16 1 4.4 7.2 289s Investment_17 1 2.9 8.3 289s Investment_18 1 4.3 6.7 289s Investment_19 1 5.3 7.4 289s Investment_20 1 6.6 8.9 289s Investment_21 1 7.4 9.6 289s Investment_22 1 13.8 11.6 289s PrivateWages_2 0 0.0 0.0 289s PrivateWages_3 0 0.0 0.0 289s PrivateWages_4 0 0.0 0.0 289s PrivateWages_5 0 0.0 0.0 289s PrivateWages_6 0 0.0 0.0 289s PrivateWages_8 0 0.0 0.0 289s PrivateWages_9 0 0.0 0.0 289s PrivateWages_10 0 0.0 0.0 289s PrivateWages_11 0 0.0 0.0 289s PrivateWages_12 0 0.0 0.0 289s PrivateWages_13 0 0.0 0.0 289s PrivateWages_14 0 0.0 0.0 289s PrivateWages_15 0 0.0 0.0 289s PrivateWages_16 0 0.0 0.0 289s PrivateWages_17 0 0.0 0.0 289s PrivateWages_18 0 0.0 0.0 289s PrivateWages_19 0 0.0 0.0 289s PrivateWages_20 0 0.0 0.0 289s PrivateWages_21 0 0.0 0.0 289s PrivateWages_22 0 0.0 0.0 289s Investment_govWage Investment_trend Investment_capitalLag 289s Consumption_2 0.0 0 0 289s Consumption_3 0.0 0 0 289s Consumption_4 0.0 0 0 289s Consumption_5 0.0 0 0 289s Consumption_6 0.0 0 0 289s Consumption_8 0.0 0 0 289s Consumption_9 0.0 0 0 289s Consumption_10 0.0 0 0 289s Consumption_11 0.0 0 0 289s Consumption_12 0.0 0 0 289s Consumption_13 0.0 0 0 289s Consumption_14 0.0 0 0 289s Consumption_15 0.0 0 0 289s Consumption_16 0.0 0 0 289s Consumption_17 0.0 0 0 289s Consumption_18 0.0 0 0 289s Consumption_19 0.0 0 0 289s Consumption_20 0.0 0 0 289s Consumption_21 0.0 0 0 289s Consumption_22 0.0 0 0 289s Investment_2 2.7 -10 183 289s Investment_3 2.9 -9 183 289s Investment_4 2.9 -8 184 289s Investment_5 3.1 -7 190 289s Investment_6 3.2 -6 193 289s Investment_8 3.6 -4 203 289s Investment_9 3.7 -3 208 289s Investment_10 4.0 -2 211 289s Investment_11 4.2 -1 216 289s Investment_12 4.8 0 217 289s Investment_13 5.3 1 213 289s Investment_14 5.6 2 207 289s Investment_15 6.0 3 202 289s Investment_16 6.1 4 199 289s Investment_17 7.4 5 198 289s Investment_18 6.7 6 200 289s Investment_19 7.7 7 202 289s Investment_20 7.8 8 200 289s Investment_21 8.0 9 201 289s Investment_22 8.5 10 204 289s PrivateWages_2 0.0 0 0 289s PrivateWages_3 0.0 0 0 289s PrivateWages_4 0.0 0 0 289s PrivateWages_5 0.0 0 0 289s PrivateWages_6 0.0 0 0 289s PrivateWages_8 0.0 0 0 289s PrivateWages_9 0.0 0 0 289s PrivateWages_10 0.0 0 0 289s PrivateWages_11 0.0 0 0 289s PrivateWages_12 0.0 0 0 289s PrivateWages_13 0.0 0 0 289s PrivateWages_14 0.0 0 0 289s PrivateWages_15 0.0 0 0 289s PrivateWages_16 0.0 0 0 289s PrivateWages_17 0.0 0 0 289s PrivateWages_18 0.0 0 0 289s PrivateWages_19 0.0 0 0 289s PrivateWages_20 0.0 0 0 289s PrivateWages_21 0.0 0 0 289s PrivateWages_22 0.0 0 0 289s Investment_corpProfLag Investment_gnpLag 289s Consumption_2 0.0 0.0 289s Consumption_3 0.0 0.0 289s Consumption_4 0.0 0.0 289s Consumption_5 0.0 0.0 289s Consumption_6 0.0 0.0 289s Consumption_8 0.0 0.0 289s Consumption_9 0.0 0.0 289s Consumption_10 0.0 0.0 289s Consumption_11 0.0 0.0 289s Consumption_12 0.0 0.0 289s Consumption_13 0.0 0.0 289s Consumption_14 0.0 0.0 289s Consumption_15 0.0 0.0 289s Consumption_16 0.0 0.0 289s Consumption_17 0.0 0.0 289s Consumption_18 0.0 0.0 289s Consumption_19 0.0 0.0 289s Consumption_20 0.0 0.0 289s Consumption_21 0.0 0.0 289s Consumption_22 0.0 0.0 289s Investment_2 12.7 44.9 289s Investment_3 12.4 45.6 289s Investment_4 16.9 50.1 289s Investment_5 18.4 57.2 289s Investment_6 19.4 57.1 289s Investment_8 19.6 64.0 289s Investment_9 19.8 64.4 289s Investment_10 21.1 64.5 289s Investment_11 21.7 67.0 289s Investment_12 15.6 61.2 289s Investment_13 11.4 53.4 289s Investment_14 7.0 44.3 289s Investment_15 11.2 45.1 289s Investment_16 12.3 49.7 289s Investment_17 14.0 54.4 289s Investment_18 17.6 62.7 289s Investment_19 17.3 65.0 289s Investment_20 15.3 60.9 289s Investment_21 19.0 69.5 289s Investment_22 21.1 75.7 289s PrivateWages_2 0.0 0.0 289s PrivateWages_3 0.0 0.0 289s PrivateWages_4 0.0 0.0 289s PrivateWages_5 0.0 0.0 289s PrivateWages_6 0.0 0.0 289s PrivateWages_8 0.0 0.0 289s PrivateWages_9 0.0 0.0 289s PrivateWages_10 0.0 0.0 289s PrivateWages_11 0.0 0.0 289s PrivateWages_12 0.0 0.0 289s PrivateWages_13 0.0 0.0 289s PrivateWages_14 0.0 0.0 289s PrivateWages_15 0.0 0.0 289s PrivateWages_16 0.0 0.0 289s PrivateWages_17 0.0 0.0 289s PrivateWages_18 0.0 0.0 289s PrivateWages_19 0.0 0.0 289s PrivateWages_20 0.0 0.0 289s PrivateWages_21 0.0 0.0 289s PrivateWages_22 0.0 0.0 289s PrivateWages_(Intercept) PrivateWages_govExp PrivateWages_taxes 289s Consumption_2 0 0.0 0.0 289s Consumption_3 0 0.0 0.0 289s Consumption_4 0 0.0 0.0 289s Consumption_5 0 0.0 0.0 289s Consumption_6 0 0.0 0.0 289s Consumption_8 0 0.0 0.0 289s Consumption_9 0 0.0 0.0 289s Consumption_10 0 0.0 0.0 289s Consumption_11 0 0.0 0.0 289s Consumption_12 0 0.0 0.0 289s Consumption_13 0 0.0 0.0 289s Consumption_14 0 0.0 0.0 289s Consumption_15 0 0.0 0.0 289s Consumption_16 0 0.0 0.0 289s Consumption_17 0 0.0 0.0 289s Consumption_18 0 0.0 0.0 289s Consumption_19 0 0.0 0.0 289s Consumption_20 0 0.0 0.0 289s Consumption_21 0 0.0 0.0 289s Consumption_22 0 0.0 0.0 289s Investment_2 0 0.0 0.0 289s Investment_3 0 0.0 0.0 289s Investment_4 0 0.0 0.0 289s Investment_5 0 0.0 0.0 289s Investment_6 0 0.0 0.0 289s Investment_8 0 0.0 0.0 289s Investment_9 0 0.0 0.0 289s Investment_10 0 0.0 0.0 289s Investment_11 0 0.0 0.0 289s Investment_12 0 0.0 0.0 289s Investment_13 0 0.0 0.0 289s Investment_14 0 0.0 0.0 289s Investment_15 0 0.0 0.0 289s Investment_16 0 0.0 0.0 289s Investment_17 0 0.0 0.0 289s Investment_18 0 0.0 0.0 289s Investment_19 0 0.0 0.0 289s Investment_20 0 0.0 0.0 289s Investment_21 0 0.0 0.0 289s Investment_22 0 0.0 0.0 289s PrivateWages_2 1 3.9 7.7 289s PrivateWages_3 1 3.2 3.9 289s PrivateWages_4 1 2.8 4.7 289s PrivateWages_5 1 3.5 3.8 289s PrivateWages_6 1 3.3 5.5 289s PrivateWages_8 1 4.0 6.7 289s PrivateWages_9 1 4.2 4.2 289s PrivateWages_10 1 4.1 4.0 289s PrivateWages_11 1 5.2 7.7 289s PrivateWages_12 1 5.9 7.5 289s PrivateWages_13 1 4.9 8.3 289s PrivateWages_14 1 3.7 5.4 289s PrivateWages_15 1 4.0 6.8 289s PrivateWages_16 1 4.4 7.2 289s PrivateWages_17 1 2.9 8.3 289s PrivateWages_18 1 4.3 6.7 289s PrivateWages_19 1 5.3 7.4 289s PrivateWages_20 1 6.6 8.9 289s PrivateWages_21 1 7.4 9.6 289s PrivateWages_22 1 13.8 11.6 289s PrivateWages_govWage PrivateWages_trend PrivateWages_capitalLag 289s Consumption_2 0.0 0 0 289s Consumption_3 0.0 0 0 289s Consumption_4 0.0 0 0 289s Consumption_5 0.0 0 0 289s Consumption_6 0.0 0 0 289s Consumption_8 0.0 0 0 289s Consumption_9 0.0 0 0 289s Consumption_10 0.0 0 0 289s Consumption_11 0.0 0 0 289s Consumption_12 0.0 0 0 289s Consumption_13 0.0 0 0 289s Consumption_14 0.0 0 0 289s Consumption_15 0.0 0 0 289s Consumption_16 0.0 0 0 289s Consumption_17 0.0 0 0 289s Consumption_18 0.0 0 0 289s Consumption_19 0.0 0 0 289s Consumption_20 0.0 0 0 289s Consumption_21 0.0 0 0 289s Consumption_22 0.0 0 0 289s Investment_2 0.0 0 0 289s Investment_3 0.0 0 0 289s Investment_4 0.0 0 0 289s Investment_5 0.0 0 0 289s Investment_6 0.0 0 0 289s Investment_8 0.0 0 0 289s Investment_9 0.0 0 0 289s Investment_10 0.0 0 0 289s Investment_11 0.0 0 0 289s Investment_12 0.0 0 0 289s Investment_13 0.0 0 0 289s Investment_14 0.0 0 0 289s Investment_15 0.0 0 0 289s Investment_16 0.0 0 0 289s Investment_17 0.0 0 0 289s Investment_18 0.0 0 0 289s Investment_19 0.0 0 0 289s Investment_20 0.0 0 0 289s Investment_21 0.0 0 0 289s Investment_22 0.0 0 0 289s PrivateWages_2 2.7 -10 183 289s PrivateWages_3 2.9 -9 183 289s PrivateWages_4 2.9 -8 184 289s PrivateWages_5 3.1 -7 190 289s PrivateWages_6 3.2 -6 193 289s PrivateWages_8 3.6 -4 203 289s PrivateWages_9 3.7 -3 208 289s PrivateWages_10 4.0 -2 211 289s PrivateWages_11 4.2 -1 216 289s PrivateWages_12 4.8 0 217 289s PrivateWages_13 5.3 1 213 289s PrivateWages_14 5.6 2 207 289s PrivateWages_15 6.0 3 202 289s PrivateWages_16 6.1 4 199 289s PrivateWages_17 7.4 5 198 289s PrivateWages_18 6.7 6 200 289s PrivateWages_19 7.7 7 202 289s PrivateWages_20 7.8 8 200 289s PrivateWages_21 8.0 9 201 289s PrivateWages_22 8.5 10 204 289s PrivateWages_corpProfLag PrivateWages_gnpLag 289s Consumption_2 0.0 0.0 289s Consumption_3 0.0 0.0 289s Consumption_4 0.0 0.0 289s Consumption_5 0.0 0.0 289s Consumption_6 0.0 0.0 289s Consumption_8 0.0 0.0 289s Consumption_9 0.0 0.0 289s Consumption_10 0.0 0.0 289s Consumption_11 0.0 0.0 289s Consumption_12 0.0 0.0 289s Consumption_13 0.0 0.0 289s Consumption_14 0.0 0.0 289s Consumption_15 0.0 0.0 289s Consumption_16 0.0 0.0 289s Consumption_17 0.0 0.0 289s Consumption_18 0.0 0.0 289s Consumption_19 0.0 0.0 289s Consumption_20 0.0 0.0 289s Consumption_21 0.0 0.0 289s Consumption_22 0.0 0.0 289s Investment_2 0.0 0.0 289s Investment_3 0.0 0.0 289s Investment_4 0.0 0.0 289s Investment_5 0.0 0.0 289s Investment_6 0.0 0.0 289s Investment_8 0.0 0.0 289s Investment_9 0.0 0.0 289s Investment_10 0.0 0.0 289s Investment_11 0.0 0.0 289s Investment_12 0.0 0.0 289s Investment_13 0.0 0.0 289s Investment_14 0.0 0.0 289s Investment_15 0.0 0.0 289s Investment_16 0.0 0.0 289s Investment_17 0.0 0.0 289s Investment_18 0.0 0.0 289s Investment_19 0.0 0.0 289s Investment_20 0.0 0.0 289s Investment_21 0.0 0.0 289s Investment_22 0.0 0.0 289s PrivateWages_2 12.7 44.9 289s PrivateWages_3 12.4 45.6 289s PrivateWages_4 16.9 50.1 289s PrivateWages_5 18.4 57.2 289s PrivateWages_6 19.4 57.1 289s PrivateWages_8 19.6 64.0 289s PrivateWages_9 19.8 64.4 289s PrivateWages_10 21.1 64.5 289s PrivateWages_11 21.7 67.0 289s PrivateWages_12 15.6 61.2 289s PrivateWages_13 11.4 53.4 289s PrivateWages_14 7.0 44.3 289s PrivateWages_15 11.2 45.1 289s PrivateWages_16 12.3 49.7 289s PrivateWages_17 14.0 54.4 289s PrivateWages_18 17.6 62.7 289s PrivateWages_19 17.3 65.0 289s PrivateWages_20 15.3 60.9 289s PrivateWages_21 19.0 69.5 289s PrivateWages_22 21.1 75.7 289s > matrix of fitted regressors 289s Consumption_(Intercept) Consumption_corpProf 289s Consumption_2 1 12.96 289s Consumption_3 1 16.70 289s Consumption_4 1 19.14 289s Consumption_5 1 20.94 289s Consumption_6 1 19.47 289s Consumption_8 1 17.14 289s Consumption_9 1 19.49 289s Consumption_10 1 20.46 289s Consumption_11 1 16.85 289s Consumption_12 1 12.68 289s Consumption_13 1 8.92 289s Consumption_14 1 9.30 289s Consumption_15 1 12.79 289s Consumption_16 1 14.26 289s Consumption_17 1 14.75 289s Consumption_18 1 19.54 289s Consumption_19 1 19.36 289s Consumption_20 1 17.39 289s Consumption_21 1 20.10 289s Consumption_22 1 22.86 289s Investment_2 0 0.00 289s Investment_3 0 0.00 289s Investment_4 0 0.00 289s Investment_5 0 0.00 289s Investment_6 0 0.00 289s Investment_8 0 0.00 289s Investment_9 0 0.00 289s Investment_10 0 0.00 289s Investment_11 0 0.00 289s Investment_12 0 0.00 289s Investment_13 0 0.00 289s Investment_14 0 0.00 289s Investment_15 0 0.00 289s Investment_16 0 0.00 289s Investment_17 0 0.00 289s Investment_18 0 0.00 289s Investment_19 0 0.00 289s Investment_20 0 0.00 289s Investment_21 0 0.00 289s Investment_22 0 0.00 289s PrivateWages_2 0 0.00 289s PrivateWages_3 0 0.00 289s PrivateWages_4 0 0.00 289s PrivateWages_5 0 0.00 289s PrivateWages_6 0 0.00 289s PrivateWages_8 0 0.00 289s PrivateWages_9 0 0.00 289s PrivateWages_10 0 0.00 289s PrivateWages_11 0 0.00 289s PrivateWages_12 0 0.00 289s PrivateWages_13 0 0.00 289s PrivateWages_14 0 0.00 289s PrivateWages_15 0 0.00 289s PrivateWages_16 0 0.00 289s PrivateWages_17 0 0.00 289s PrivateWages_18 0 0.00 289s PrivateWages_19 0 0.00 289s PrivateWages_20 0 0.00 289s PrivateWages_21 0 0.00 289s PrivateWages_22 0 0.00 289s Consumption_corpProfLag Consumption_wages 289s Consumption_2 12.7 29.1 289s Consumption_3 12.4 31.9 289s Consumption_4 16.9 35.6 289s Consumption_5 18.4 39.0 289s Consumption_6 19.4 38.8 289s Consumption_8 19.6 39.8 289s Consumption_9 19.8 42.3 289s Consumption_10 21.1 44.1 289s Consumption_11 21.7 43.4 289s Consumption_12 15.6 39.5 289s Consumption_13 11.4 35.1 289s Consumption_14 7.0 33.0 289s Consumption_15 11.2 37.6 289s Consumption_16 12.3 40.0 289s Consumption_17 14.0 41.7 289s Consumption_18 17.6 47.6 289s Consumption_19 17.3 49.5 289s Consumption_20 15.3 48.4 289s Consumption_21 19.0 53.2 289s Consumption_22 21.1 60.9 289s Investment_2 0.0 0.0 289s Investment_3 0.0 0.0 289s Investment_4 0.0 0.0 289s Investment_5 0.0 0.0 289s Investment_6 0.0 0.0 289s Investment_8 0.0 0.0 289s Investment_9 0.0 0.0 289s Investment_10 0.0 0.0 289s Investment_11 0.0 0.0 289s Investment_12 0.0 0.0 289s Investment_13 0.0 0.0 289s Investment_14 0.0 0.0 289s Investment_15 0.0 0.0 289s Investment_16 0.0 0.0 289s Investment_17 0.0 0.0 289s Investment_18 0.0 0.0 289s Investment_19 0.0 0.0 289s Investment_20 0.0 0.0 289s Investment_21 0.0 0.0 289s Investment_22 0.0 0.0 289s PrivateWages_2 0.0 0.0 289s PrivateWages_3 0.0 0.0 289s PrivateWages_4 0.0 0.0 289s PrivateWages_5 0.0 0.0 289s PrivateWages_6 0.0 0.0 289s PrivateWages_8 0.0 0.0 289s PrivateWages_9 0.0 0.0 289s PrivateWages_10 0.0 0.0 289s PrivateWages_11 0.0 0.0 289s PrivateWages_12 0.0 0.0 289s PrivateWages_13 0.0 0.0 289s PrivateWages_14 0.0 0.0 289s PrivateWages_15 0.0 0.0 289s PrivateWages_16 0.0 0.0 289s PrivateWages_17 0.0 0.0 289s PrivateWages_18 0.0 0.0 289s PrivateWages_19 0.0 0.0 289s PrivateWages_20 0.0 0.0 289s PrivateWages_21 0.0 0.0 289s PrivateWages_22 0.0 0.0 289s Investment_(Intercept) Investment_corpProf 289s Consumption_2 0 0.00 289s Consumption_3 0 0.00 289s Consumption_4 0 0.00 289s Consumption_5 0 0.00 289s Consumption_6 0 0.00 289s Consumption_8 0 0.00 289s Consumption_9 0 0.00 289s Consumption_10 0 0.00 289s Consumption_11 0 0.00 289s Consumption_12 0 0.00 289s Consumption_13 0 0.00 289s Consumption_14 0 0.00 289s Consumption_15 0 0.00 289s Consumption_16 0 0.00 289s Consumption_17 0 0.00 289s Consumption_18 0 0.00 289s Consumption_19 0 0.00 289s Consumption_20 0 0.00 289s Consumption_21 0 0.00 289s Consumption_22 0 0.00 289s Investment_2 1 12.96 289s Investment_3 1 16.70 289s Investment_4 1 19.14 289s Investment_5 1 20.94 289s Investment_6 1 19.47 289s Investment_8 1 17.14 289s Investment_9 1 19.49 289s Investment_10 1 20.46 289s Investment_11 1 16.85 289s Investment_12 1 12.68 289s Investment_13 1 8.92 289s Investment_14 1 9.30 289s Investment_15 1 12.79 289s Investment_16 1 14.26 289s Investment_17 1 14.75 289s Investment_18 1 19.54 289s Investment_19 1 19.36 289s Investment_20 1 17.39 289s Investment_21 1 20.10 289s Investment_22 1 22.86 289s PrivateWages_2 0 0.00 289s PrivateWages_3 0 0.00 289s PrivateWages_4 0 0.00 289s PrivateWages_5 0 0.00 289s PrivateWages_6 0 0.00 289s PrivateWages_8 0 0.00 289s PrivateWages_9 0 0.00 289s PrivateWages_10 0 0.00 289s PrivateWages_11 0 0.00 289s PrivateWages_12 0 0.00 289s PrivateWages_13 0 0.00 289s PrivateWages_14 0 0.00 289s PrivateWages_15 0 0.00 289s PrivateWages_16 0 0.00 289s PrivateWages_17 0 0.00 289s PrivateWages_18 0 0.00 289s PrivateWages_19 0 0.00 289s PrivateWages_20 0 0.00 289s PrivateWages_21 0 0.00 289s PrivateWages_22 0 0.00 289s Investment_corpProfLag Investment_capitalLag 289s Consumption_2 0.0 0 289s Consumption_3 0.0 0 289s Consumption_4 0.0 0 289s Consumption_5 0.0 0 289s Consumption_6 0.0 0 289s Consumption_8 0.0 0 289s Consumption_9 0.0 0 289s Consumption_10 0.0 0 289s Consumption_11 0.0 0 289s Consumption_12 0.0 0 289s Consumption_13 0.0 0 289s Consumption_14 0.0 0 289s Consumption_15 0.0 0 289s Consumption_16 0.0 0 289s Consumption_17 0.0 0 289s Consumption_18 0.0 0 289s Consumption_19 0.0 0 289s Consumption_20 0.0 0 289s Consumption_21 0.0 0 289s Consumption_22 0.0 0 289s Investment_2 12.7 183 289s Investment_3 12.4 183 289s Investment_4 16.9 184 289s Investment_5 18.4 190 289s Investment_6 19.4 193 289s Investment_8 19.6 203 289s Investment_9 19.8 208 289s Investment_10 21.1 211 289s Investment_11 21.7 216 289s Investment_12 15.6 217 289s Investment_13 11.4 213 289s Investment_14 7.0 207 289s Investment_15 11.2 202 289s Investment_16 12.3 199 289s Investment_17 14.0 198 289s Investment_18 17.6 200 289s Investment_19 17.3 202 289s Investment_20 15.3 200 289s Investment_21 19.0 201 289s Investment_22 21.1 204 289s PrivateWages_2 0.0 0 289s PrivateWages_3 0.0 0 289s PrivateWages_4 0.0 0 289s PrivateWages_5 0.0 0 289s PrivateWages_6 0.0 0 289s PrivateWages_8 0.0 0 289s PrivateWages_9 0.0 0 289s PrivateWages_10 0.0 0 289s PrivateWages_11 0.0 0 289s PrivateWages_12 0.0 0 289s PrivateWages_13 0.0 0 289s PrivateWages_14 0.0 0 289s PrivateWages_15 0.0 0 289s PrivateWages_16 0.0 0 289s PrivateWages_17 0.0 0 289s PrivateWages_18 0.0 0 289s PrivateWages_19 0.0 0 289s PrivateWages_20 0.0 0 289s PrivateWages_21 0.0 0 289s PrivateWages_22 0.0 0 289s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 289s Consumption_2 0 0.0 0.0 289s Consumption_3 0 0.0 0.0 289s Consumption_4 0 0.0 0.0 289s Consumption_5 0 0.0 0.0 289s Consumption_6 0 0.0 0.0 289s Consumption_8 0 0.0 0.0 289s Consumption_9 0 0.0 0.0 289s Consumption_10 0 0.0 0.0 289s Consumption_11 0 0.0 0.0 289s Consumption_12 0 0.0 0.0 289s Consumption_13 0 0.0 0.0 289s Consumption_14 0 0.0 0.0 289s Consumption_15 0 0.0 0.0 289s Consumption_16 0 0.0 0.0 289s Consumption_17 0 0.0 0.0 289s Consumption_18 0 0.0 0.0 289s Consumption_19 0 0.0 0.0 289s Consumption_20 0 0.0 0.0 289s Consumption_21 0 0.0 0.0 289s Consumption_22 0 0.0 0.0 289s Investment_2 0 0.0 0.0 289s Investment_3 0 0.0 0.0 289s Investment_4 0 0.0 0.0 289s Investment_5 0 0.0 0.0 289s Investment_6 0 0.0 0.0 289s Investment_8 0 0.0 0.0 289s Investment_9 0 0.0 0.0 289s Investment_10 0 0.0 0.0 289s Investment_11 0 0.0 0.0 289s Investment_12 0 0.0 0.0 289s Investment_13 0 0.0 0.0 289s Investment_14 0 0.0 0.0 289s Investment_15 0 0.0 0.0 289s Investment_16 0 0.0 0.0 289s Investment_17 0 0.0 0.0 289s Investment_18 0 0.0 0.0 289s Investment_19 0 0.0 0.0 289s Investment_20 0 0.0 0.0 289s Investment_21 0 0.0 0.0 289s Investment_22 0 0.0 0.0 289s PrivateWages_2 1 47.1 44.9 289s PrivateWages_3 1 49.6 45.6 289s PrivateWages_4 1 56.5 50.1 289s PrivateWages_5 1 60.7 57.2 289s PrivateWages_6 1 60.6 57.1 289s PrivateWages_8 1 60.0 64.0 289s PrivateWages_9 1 62.3 64.4 289s PrivateWages_10 1 64.6 64.5 289s PrivateWages_11 1 63.7 67.0 289s PrivateWages_12 1 54.8 61.2 289s PrivateWages_13 1 47.0 53.4 289s PrivateWages_14 1 42.1 44.3 289s PrivateWages_15 1 51.2 45.1 289s PrivateWages_16 1 55.3 49.7 289s PrivateWages_17 1 57.4 54.4 289s PrivateWages_18 1 67.2 62.7 289s PrivateWages_19 1 68.5 65.0 289s PrivateWages_20 1 66.8 60.9 289s PrivateWages_21 1 74.9 69.5 289s PrivateWages_22 1 86.9 75.7 289s PrivateWages_trend 289s Consumption_2 0 289s Consumption_3 0 289s Consumption_4 0 289s Consumption_5 0 289s Consumption_6 0 289s Consumption_8 0 289s Consumption_9 0 289s Consumption_10 0 289s Consumption_11 0 289s Consumption_12 0 289s Consumption_13 0 289s Consumption_14 0 289s Consumption_15 0 289s Consumption_16 0 289s Consumption_17 0 289s Consumption_18 0 289s Consumption_19 0 289s Consumption_20 0 289s Consumption_21 0 289s Consumption_22 0 289s Investment_2 0 289s Investment_3 0 289s Investment_4 0 289s Investment_5 0 289s Investment_6 0 289s Investment_8 0 289s Investment_9 0 289s Investment_10 0 289s Investment_11 0 289s Investment_12 0 289s Investment_13 0 289s Investment_14 0 289s Investment_15 0 289s Investment_16 0 289s Investment_17 0 289s Investment_18 0 289s Investment_19 0 289s Investment_20 0 289s Investment_21 0 289s Investment_22 0 289s PrivateWages_2 -10 289s PrivateWages_3 -9 289s PrivateWages_4 -8 289s PrivateWages_5 -7 289s PrivateWages_6 -6 289s PrivateWages_8 -4 289s PrivateWages_9 -3 289s PrivateWages_10 -2 289s PrivateWages_11 -1 289s PrivateWages_12 0 289s PrivateWages_13 1 289s PrivateWages_14 2 289s PrivateWages_15 3 289s PrivateWages_16 4 289s PrivateWages_17 5 289s PrivateWages_18 6 289s PrivateWages_19 7 289s PrivateWages_20 8 289s PrivateWages_21 9 289s PrivateWages_22 10 289s > nobs 289s [1] 60 289s > linearHypothesis 289s Linear hypothesis test (Theil's F test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df F Pr(>F) 289s 1 49 289s 2 48 1 0.95 0.34 289s Linear hypothesis test (F statistic of a Wald test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df F Pr(>F) 289s 1 49 289s 2 48 1 1.05 0.31 289s Linear hypothesis test (Chi^2 statistic of a Wald test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df Chisq Pr(>Chisq) 289s 1 49 289s 2 48 1 1.05 0.3 289s Linear hypothesis test (Theil's F test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s Consumption_corpProfLag - PrivateWages_trend = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df F Pr(>F) 289s 1 50 289s 2 48 2 0.48 0.62 289s Linear hypothesis test (F statistic of a Wald test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s Consumption_corpProfLag - PrivateWages_trend = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df F Pr(>F) 289s 1 50 289s 2 48 2 0.53 0.59 289s Linear hypothesis test (Chi^2 statistic of a Wald test) 289s 289s Hypothesis: 289s Consumption_corpProf + Investment_capitalLag = 0 289s Consumption_corpProfLag - PrivateWages_trend = 0 289s 289s Model 1: restricted model 289s Model 2: kleinModel 289s 289s Res.Df Df Chisq Pr(>Chisq) 289s 1 50 289s 2 48 2 1.06 0.59 289s > logLik 289s 'log Lik.' -72.2 (df=13) 289s 'log Lik.' -79.7 (df=13) 289s Estimating function 289s Consumption_(Intercept) Consumption_corpProf 289s Consumption_2 -1.1407 -14.78 289s Consumption_3 -0.3242 -5.42 289s Consumption_4 -0.0963 -1.84 289s Consumption_5 -1.8392 -38.51 289s Consumption_6 0.1702 3.31 289s Consumption_8 3.0349 52.02 289s Consumption_9 1.9822 38.63 289s Consumption_10 0.7162 14.65 289s Consumption_11 -1.5151 -25.52 289s Consumption_12 -1.1471 -14.54 289s Consumption_13 -1.9595 -17.48 289s Consumption_14 1.4394 13.39 289s Consumption_15 -1.0033 -12.84 289s Consumption_16 -0.5750 -8.20 289s Consumption_17 4.0452 59.67 289s Consumption_18 -0.5669 -11.08 289s Consumption_19 -3.1962 -61.88 289s Consumption_20 2.2286 38.75 289s Consumption_21 0.9237 18.57 289s Consumption_22 -1.1770 -26.91 289s Investment_2 0.0000 0.00 289s Investment_3 0.0000 0.00 289s Investment_4 0.0000 0.00 289s Investment_5 0.0000 0.00 289s Investment_6 0.0000 0.00 289s Investment_8 0.0000 0.00 289s Investment_9 0.0000 0.00 289s Investment_10 0.0000 0.00 289s Investment_11 0.0000 0.00 289s Investment_12 0.0000 0.00 289s Investment_13 0.0000 0.00 289s Investment_14 0.0000 0.00 289s Investment_15 0.0000 0.00 289s Investment_16 0.0000 0.00 289s Investment_17 0.0000 0.00 289s Investment_18 0.0000 0.00 289s Investment_19 0.0000 0.00 289s Investment_20 0.0000 0.00 289s Investment_21 0.0000 0.00 289s Investment_22 0.0000 0.00 289s PrivateWages_2 0.0000 0.00 289s PrivateWages_3 0.0000 0.00 289s PrivateWages_4 0.0000 0.00 289s PrivateWages_5 0.0000 0.00 289s PrivateWages_6 0.0000 0.00 289s PrivateWages_8 0.0000 0.00 289s PrivateWages_9 0.0000 0.00 289s PrivateWages_10 0.0000 0.00 289s PrivateWages_11 0.0000 0.00 289s PrivateWages_12 0.0000 0.00 289s PrivateWages_13 0.0000 0.00 289s PrivateWages_14 0.0000 0.00 289s PrivateWages_15 0.0000 0.00 289s PrivateWages_16 0.0000 0.00 289s PrivateWages_17 0.0000 0.00 289s PrivateWages_18 0.0000 0.00 289s PrivateWages_19 0.0000 0.00 289s PrivateWages_20 0.0000 0.00 289s PrivateWages_21 0.0000 0.00 289s PrivateWages_22 0.0000 0.00 289s Consumption_corpProfLag Consumption_wages 289s Consumption_2 -14.49 -33.21 289s Consumption_3 -4.02 -10.33 289s Consumption_4 -1.63 -3.43 289s Consumption_5 -33.84 -71.82 289s Consumption_6 3.30 6.61 289s Consumption_8 59.48 120.65 289s Consumption_9 39.25 83.81 289s Consumption_10 15.11 31.59 289s Consumption_11 -32.88 -65.70 289s Consumption_12 -17.89 -45.25 289s Consumption_13 -22.34 -68.69 289s Consumption_14 10.08 47.54 289s Consumption_15 -11.24 -37.74 289s Consumption_16 -7.07 -22.99 289s Consumption_17 56.63 168.85 289s Consumption_18 -9.98 -27.00 289s Consumption_19 -55.29 -158.06 289s Consumption_20 34.10 107.77 289s Consumption_21 17.55 49.11 289s Consumption_22 -24.84 -71.70 289s Investment_2 0.00 0.00 289s Investment_3 0.00 0.00 289s Investment_4 0.00 0.00 289s Investment_5 0.00 0.00 289s Investment_6 0.00 0.00 289s Investment_8 0.00 0.00 289s Investment_9 0.00 0.00 289s Investment_10 0.00 0.00 289s Investment_11 0.00 0.00 289s Investment_12 0.00 0.00 289s Investment_13 0.00 0.00 289s Investment_14 0.00 0.00 289s Investment_15 0.00 0.00 289s Investment_16 0.00 0.00 289s Investment_17 0.00 0.00 289s Investment_18 0.00 0.00 289s Investment_19 0.00 0.00 289s Investment_20 0.00 0.00 289s Investment_21 0.00 0.00 289s Investment_22 0.00 0.00 289s PrivateWages_2 0.00 0.00 289s PrivateWages_3 0.00 0.00 289s PrivateWages_4 0.00 0.00 289s PrivateWages_5 0.00 0.00 289s PrivateWages_6 0.00 0.00 289s PrivateWages_8 0.00 0.00 289s PrivateWages_9 0.00 0.00 289s PrivateWages_10 0.00 0.00 289s PrivateWages_11 0.00 0.00 289s PrivateWages_12 0.00 0.00 289s PrivateWages_13 0.00 0.00 289s PrivateWages_14 0.00 0.00 289s PrivateWages_15 0.00 0.00 289s PrivateWages_16 0.00 0.00 289s PrivateWages_17 0.00 0.00 289s PrivateWages_18 0.00 0.00 289s PrivateWages_19 0.00 0.00 289s PrivateWages_20 0.00 0.00 289s PrivateWages_21 0.00 0.00 289s PrivateWages_22 0.00 0.00 289s Investment_(Intercept) Investment_corpProf 289s Consumption_2 0.0000 0.000 289s Consumption_3 0.0000 0.000 289s Consumption_4 0.0000 0.000 289s Consumption_5 0.0000 0.000 289s Consumption_6 0.0000 0.000 289s Consumption_8 0.0000 0.000 289s Consumption_9 0.0000 0.000 289s Consumption_10 0.0000 0.000 289s Consumption_11 0.0000 0.000 289s Consumption_12 0.0000 0.000 289s Consumption_13 0.0000 0.000 289s Consumption_14 0.0000 0.000 289s Consumption_15 0.0000 0.000 289s Consumption_16 0.0000 0.000 289s Consumption_17 0.0000 0.000 289s Consumption_18 0.0000 0.000 289s Consumption_19 0.0000 0.000 289s Consumption_20 0.0000 0.000 289s Consumption_21 0.0000 0.000 289s Consumption_22 0.0000 0.000 289s Investment_2 -1.1313 -14.660 289s Investment_3 0.2902 4.847 289s Investment_4 0.9027 17.274 289s Investment_5 -1.7434 -36.502 289s Investment_6 0.5695 11.088 289s Investment_8 1.6225 27.812 289s Investment_9 0.4166 8.119 289s Investment_10 2.0381 41.703 289s Investment_11 -0.8611 -14.505 289s Investment_12 -0.9091 -11.527 289s Investment_13 -1.1148 -9.946 289s Investment_14 1.3841 12.873 289s Investment_15 -0.2900 -3.710 289s Investment_16 0.0605 0.862 289s Investment_17 2.2439 33.101 289s Investment_18 -0.5390 -10.534 289s Investment_19 -3.9452 -76.375 289s Investment_20 0.4890 8.502 289s Investment_21 0.0864 1.737 289s Investment_22 0.4306 9.843 289s PrivateWages_2 0.0000 0.000 289s PrivateWages_3 0.0000 0.000 289s PrivateWages_4 0.0000 0.000 289s PrivateWages_5 0.0000 0.000 289s PrivateWages_6 0.0000 0.000 289s PrivateWages_8 0.0000 0.000 289s PrivateWages_9 0.0000 0.000 289s PrivateWages_10 0.0000 0.000 289s PrivateWages_11 0.0000 0.000 289s PrivateWages_12 0.0000 0.000 289s PrivateWages_13 0.0000 0.000 289s PrivateWages_14 0.0000 0.000 289s PrivateWages_15 0.0000 0.000 289s PrivateWages_16 0.0000 0.000 289s PrivateWages_17 0.0000 0.000 289s PrivateWages_18 0.0000 0.000 289s PrivateWages_19 0.0000 0.000 289s PrivateWages_20 0.0000 0.000 289s PrivateWages_21 0.0000 0.000 289s PrivateWages_22 0.0000 0.000 289s Investment_corpProfLag Investment_capitalLag 289s Consumption_2 0.000 0.0 289s Consumption_3 0.000 0.0 289s Consumption_4 0.000 0.0 289s Consumption_5 0.000 0.0 289s Consumption_6 0.000 0.0 289s Consumption_8 0.000 0.0 289s Consumption_9 0.000 0.0 289s Consumption_10 0.000 0.0 289s Consumption_11 0.000 0.0 289s Consumption_12 0.000 0.0 289s Consumption_13 0.000 0.0 289s Consumption_14 0.000 0.0 289s Consumption_15 0.000 0.0 289s Consumption_16 0.000 0.0 289s Consumption_17 0.000 0.0 289s Consumption_18 0.000 0.0 289s Consumption_19 0.000 0.0 289s Consumption_20 0.000 0.0 289s Consumption_21 0.000 0.0 289s Consumption_22 0.000 0.0 289s Investment_2 -14.368 -206.8 289s Investment_3 3.598 53.0 289s Investment_4 15.256 166.5 289s Investment_5 -32.079 -330.7 289s Investment_6 11.048 109.7 289s Investment_8 31.801 330.0 289s Investment_9 8.248 86.5 289s Investment_10 43.003 429.2 289s Investment_11 -18.685 -185.7 289s Investment_12 -14.182 -197.0 289s Investment_13 -12.709 -237.8 289s Investment_14 9.689 286.6 289s Investment_15 -3.247 -58.6 289s Investment_16 0.744 12.0 289s Investment_17 31.414 443.6 289s Investment_18 -9.486 -107.7 289s Investment_19 -68.252 -796.1 289s Investment_20 7.482 97.7 289s Investment_21 1.642 17.4 289s Investment_22 9.085 88.0 289s PrivateWages_2 0.000 0.0 289s PrivateWages_3 0.000 0.0 289s PrivateWages_4 0.000 0.0 289s PrivateWages_5 0.000 0.0 289s PrivateWages_6 0.000 0.0 289s PrivateWages_8 0.000 0.0 289s PrivateWages_9 0.000 0.0 289s PrivateWages_10 0.000 0.0 289s PrivateWages_11 0.000 0.0 289s PrivateWages_12 0.000 0.0 289s PrivateWages_13 0.000 0.0 289s PrivateWages_14 0.000 0.0 289s PrivateWages_15 0.000 0.0 289s PrivateWages_16 0.000 0.0 289s PrivateWages_17 0.000 0.0 289s PrivateWages_18 0.000 0.0 289s PrivateWages_19 0.000 0.0 289s PrivateWages_20 0.000 0.0 289s PrivateWages_21 0.000 0.0 289s PrivateWages_22 0.000 0.0 289s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 289s Consumption_2 0.0000 0.00 0.00 289s Consumption_3 0.0000 0.00 0.00 289s Consumption_4 0.0000 0.00 0.00 289s Consumption_5 0.0000 0.00 0.00 289s Consumption_6 0.0000 0.00 0.00 289s Consumption_8 0.0000 0.00 0.00 289s Consumption_9 0.0000 0.00 0.00 289s Consumption_10 0.0000 0.00 0.00 289s Consumption_11 0.0000 0.00 0.00 289s Consumption_12 0.0000 0.00 0.00 289s Consumption_13 0.0000 0.00 0.00 289s Consumption_14 0.0000 0.00 0.00 289s Consumption_15 0.0000 0.00 0.00 289s Consumption_16 0.0000 0.00 0.00 289s Consumption_17 0.0000 0.00 0.00 289s Consumption_18 0.0000 0.00 0.00 289s Consumption_19 0.0000 0.00 0.00 289s Consumption_20 0.0000 0.00 0.00 289s Consumption_21 0.0000 0.00 0.00 289s Consumption_22 0.0000 0.00 0.00 289s Investment_2 0.0000 0.00 0.00 289s Investment_3 0.0000 0.00 0.00 289s Investment_4 0.0000 0.00 0.00 289s Investment_5 0.0000 0.00 0.00 289s Investment_6 0.0000 0.00 0.00 289s Investment_8 0.0000 0.00 0.00 289s Investment_9 0.0000 0.00 0.00 289s Investment_10 0.0000 0.00 0.00 289s Investment_11 0.0000 0.00 0.00 289s Investment_12 0.0000 0.00 0.00 289s Investment_13 0.0000 0.00 0.00 289s Investment_14 0.0000 0.00 0.00 289s Investment_15 0.0000 0.00 0.00 289s Investment_16 0.0000 0.00 0.00 289s Investment_17 0.0000 0.00 0.00 289s Investment_18 0.0000 0.00 0.00 289s Investment_19 0.0000 0.00 0.00 289s Investment_20 0.0000 0.00 0.00 289s Investment_21 0.0000 0.00 0.00 289s Investment_22 0.0000 0.00 0.00 289s PrivateWages_2 -1.9924 -93.78 -89.46 289s PrivateWages_3 0.4683 23.22 21.35 289s PrivateWages_4 1.4034 79.35 70.31 289s PrivateWages_5 -1.7870 -108.45 -102.22 289s PrivateWages_6 -0.3627 -21.98 -20.71 289s PrivateWages_8 1.1629 69.77 74.43 289s PrivateWages_9 1.2735 79.30 82.01 289s PrivateWages_10 2.2141 142.96 142.81 289s PrivateWages_11 -1.2912 -82.26 -86.51 289s PrivateWages_12 -0.0350 -1.92 -2.14 289s PrivateWages_13 -1.0438 -49.04 -55.74 289s PrivateWages_14 1.8016 75.90 79.81 289s PrivateWages_15 -0.3714 -19.02 -16.75 289s PrivateWages_16 -0.3904 -21.61 -19.40 289s PrivateWages_17 1.4934 85.71 81.24 289s PrivateWages_18 0.0279 1.88 1.75 289s PrivateWages_19 -3.8229 -261.91 -248.49 289s PrivateWages_20 0.7870 52.61 47.93 289s PrivateWages_21 -0.7415 -55.52 -51.54 289s PrivateWages_22 1.2062 104.79 91.31 289s PrivateWages_trend 289s Consumption_2 0.000 289s Consumption_3 0.000 289s Consumption_4 0.000 289s Consumption_5 0.000 289s Consumption_6 0.000 289s Consumption_8 0.000 289s Consumption_9 0.000 289s Consumption_10 0.000 289s Consumption_11 0.000 289s Consumption_12 0.000 289s Consumption_13 0.000 289s Consumption_14 0.000 289s Consumption_15 0.000 289s Consumption_16 0.000 289s Consumption_17 0.000 289s Consumption_18 0.000 289s Consumption_19 0.000 289s Consumption_20 0.000 289s Consumption_21 0.000 289s Consumption_22 0.000 289s Investment_2 0.000 289s Investment_3 0.000 289s Investment_4 0.000 289s Investment_5 0.000 289s Investment_6 0.000 289s Investment_8 0.000 289s Investment_9 0.000 289s Investment_10 0.000 289s Investment_11 0.000 289s Investment_12 0.000 289s Investment_13 0.000 289s Investment_14 0.000 289s Investment_15 0.000 289s Investment_16 0.000 289s Investment_17 0.000 289s Investment_18 0.000 289s Investment_19 0.000 289s Investment_20 0.000 289s Investment_21 0.000 289s Investment_22 0.000 289s PrivateWages_2 19.924 289s PrivateWages_3 -4.214 289s PrivateWages_4 -11.227 289s PrivateWages_5 12.509 289s PrivateWages_6 2.176 289s PrivateWages_8 -4.652 289s PrivateWages_9 -3.820 289s PrivateWages_10 -4.428 289s PrivateWages_11 1.291 289s PrivateWages_12 0.000 289s PrivateWages_13 -1.044 289s PrivateWages_14 3.603 289s PrivateWages_15 -1.114 289s PrivateWages_16 -1.562 289s PrivateWages_17 7.467 289s PrivateWages_18 0.168 289s PrivateWages_19 -26.760 289s PrivateWages_20 6.296 289s PrivateWages_21 -6.674 289s PrivateWages_22 12.062 289s [1] TRUE 289s > Bread 289s Consumption_(Intercept) Consumption_corpProf 289s Consumption_(Intercept) 99.945 -0.7943 289s Consumption_corpProf -0.794 0.7797 289s Consumption_corpProfLag -0.325 -0.5285 289s Consumption_wages -1.888 -0.0894 289s Investment_(Intercept) 0.000 0.0000 289s Investment_corpProf 0.000 0.0000 289s Investment_corpProfLag 0.000 0.0000 289s Investment_capitalLag 0.000 0.0000 289s PrivateWages_(Intercept) 0.000 0.0000 289s PrivateWages_gnp 0.000 0.0000 289s PrivateWages_gnpLag 0.000 0.0000 289s PrivateWages_trend 0.000 0.0000 289s Consumption_corpProfLag Consumption_wages 289s Consumption_(Intercept) -0.3246 -1.8878 289s Consumption_corpProf -0.5285 -0.0894 289s Consumption_corpProfLag 0.6654 -0.0384 289s Consumption_wages -0.0384 0.0965 289s Investment_(Intercept) 0.0000 0.0000 289s Investment_corpProf 0.0000 0.0000 289s Investment_corpProfLag 0.0000 0.0000 289s Investment_capitalLag 0.0000 0.0000 289s PrivateWages_(Intercept) 0.0000 0.0000 289s PrivateWages_gnp 0.0000 0.0000 289s PrivateWages_gnpLag 0.0000 0.0000 289s PrivateWages_trend 0.0000 0.0000 289s Investment_(Intercept) Investment_corpProf 289s Consumption_(Intercept) 0.0 0.000 289s Consumption_corpProf 0.0 0.000 289s Consumption_corpProfLag 0.0 0.000 289s Consumption_wages 0.0 0.000 289s Investment_(Intercept) 2446.2 -38.918 289s Investment_corpProf -38.9 1.252 289s Investment_corpProfLag 33.4 -1.090 289s Investment_capitalLag -11.6 0.177 289s PrivateWages_(Intercept) 0.0 0.000 289s PrivateWages_gnp 0.0 0.000 289s PrivateWages_gnpLag 0.0 0.000 289s PrivateWages_trend 0.0 0.000 289s Investment_corpProfLag Investment_capitalLag 289s Consumption_(Intercept) 0.000 0.0000 289s Consumption_corpProf 0.000 0.0000 289s Consumption_corpProfLag 0.000 0.0000 289s Consumption_wages 0.000 0.0000 289s Investment_(Intercept) 33.384 -11.6216 289s Investment_corpProf -1.090 0.1774 289s Investment_corpProfLag 1.148 -0.1680 289s Investment_capitalLag -0.168 0.0567 289s PrivateWages_(Intercept) 0.000 0.0000 289s PrivateWages_gnp 0.000 0.0000 289s PrivateWages_gnpLag 0.000 0.0000 289s PrivateWages_trend 0.000 0.0000 289s PrivateWages_(Intercept) PrivateWages_gnp 289s Consumption_(Intercept) 0.000 0.0000 289s Consumption_corpProf 0.000 0.0000 289s Consumption_corpProfLag 0.000 0.0000 289s Consumption_wages 0.000 0.0000 289s Investment_(Intercept) 0.000 0.0000 289s Investment_corpProf 0.000 0.0000 289s Investment_corpProfLag 0.000 0.0000 289s Investment_capitalLag 0.000 0.0000 289s PrivateWages_(Intercept) 170.714 -0.9289 289s PrivateWages_gnp -0.929 0.1580 289s PrivateWages_gnpLag -1.948 -0.1473 289s PrivateWages_trend 2.164 -0.0424 289s PrivateWages_gnpLag PrivateWages_trend 289s Consumption_(Intercept) 0.000 0.0000 289s Consumption_corpProf 0.000 0.0000 289s Consumption_corpProfLag 0.000 0.0000 289s Consumption_wages 0.000 0.0000 289s Investment_(Intercept) 0.000 0.0000 289s Investment_corpProf 0.000 0.0000 289s Investment_corpProfLag 0.000 0.0000 289s Investment_capitalLag 0.000 0.0000 289s PrivateWages_(Intercept) -1.948 2.1641 289s PrivateWages_gnp -0.147 -0.0424 289s PrivateWages_gnpLag 0.186 0.0060 289s PrivateWages_trend 0.006 0.1151 289s > 289s > # SUR 289s Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 289s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 290s > summary 290s 290s systemfit results 290s method: SUR 290s 290s N DF SSR detRCov OLS-R2 McElroy-R2 290s system 62 50 46.2 0.154 0.977 0.993 290s 290s N DF SSR MSE RMSE R2 Adj R2 290s Consumption 21 17 18.1 1.062 1.031 0.981 0.977 290s Investment 21 17 17.5 1.030 1.015 0.931 0.918 290s PrivateWages 20 16 10.6 0.663 0.814 0.987 0.984 290s 290s The covariance matrix of the residuals used for estimation 290s Consumption Investment PrivateWages 290s Consumption 0.8562 -0.0129 -0.371 290s Investment -0.0129 0.7548 0.159 290s PrivateWages -0.3706 0.1594 0.487 290s 290s The covariance matrix of the residuals 290s Consumption Investment PrivateWages 290s Consumption 0.8684 0.0078 -0.442 290s Investment 0.0078 0.7702 0.237 290s PrivateWages -0.4416 0.2366 0.531 290s 290s The correlations of the residuals 290s Consumption Investment PrivateWages 290s Consumption 1.00000 0.00562 -0.651 290s Investment 0.00562 1.00000 0.372 290s PrivateWages -0.65109 0.37198 1.000 290s 290s 290s SUR estimates for 'Consumption' (equation 1) 290s Model Formula: consump ~ corpProf + corpProfLag + wages 290s 290s Estimate Std. Error t value Pr(>|t|) 290s (Intercept) 16.0647 1.1729 13.70 1.3e-10 *** 290s corpProf 0.2283 0.0775 2.94 0.0091 ** 290s corpProfLag 0.0723 0.0771 0.94 0.3615 290s wages 0.7930 0.0352 22.51 4.3e-14 *** 290s --- 290s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 290s 290s Residual standard error: 1.031 on 17 degrees of freedom 290s Number of observations: 21 Degrees of Freedom: 17 290s SSR: 18.06 MSE: 1.062 Root MSE: 1.031 290s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.977 290s 290s 290s SUR estimates for 'Investment' (equation 2) 290s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 290s 290s Estimate Std. Error t value Pr(>|t|) 290s (Intercept) 12.3516 4.5762 2.70 0.01520 * 290s corpProf 0.4461 0.0818 5.45 4.3e-05 *** 290s corpProfLag 0.3609 0.0849 4.25 0.00054 *** 290s capitalLag -0.1224 0.0223 -5.47 4.1e-05 *** 290s --- 290s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 290s 290s Residual standard error: 1.015 on 17 degrees of freedom 290s Number of observations: 21 Degrees of Freedom: 17 290s SSR: 17.514 MSE: 1.03 Root MSE: 1.015 290s Multiple R-Squared: 0.931 Adjusted R-Squared: 0.918 290s 290s 290s SUR estimates for 'PrivateWages' (equation 3) 290s Model Formula: privWage ~ gnp + gnpLag + trend 290s 290s Estimate Std. Error t value Pr(>|t|) 290s (Intercept) 1.5433 1.1371 1.36 0.19 290s gnp 0.4117 0.0279 14.77 9.6e-11 *** 290s gnpLag 0.1743 0.0317 5.50 4.8e-05 *** 290s trend 0.1550 0.0283 5.49 5.0e-05 *** 290s --- 290s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 290s 290s Residual standard error: 0.814 on 16 degrees of freedom 290s Number of observations: 20 Degrees of Freedom: 16 290s SSR: 10.611 MSE: 0.663 Root MSE: 0.814 290s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.984 290s 290s > residuals 290s Consumption Investment PrivateWages 290s 1 NA NA NA 290s 2 -0.27628 -0.3003 -1.0910 290s 3 -1.35400 -0.1239 0.5795 290s 4 -1.62816 1.1154 1.5172 290s 5 -0.56494 -1.4358 -0.0341 290s 6 -0.06584 0.3581 -0.2772 290s 7 0.83245 1.4526 NA 290s 8 1.28855 0.8290 -0.6896 290s 9 0.96709 -0.5092 0.3445 290s 10 -0.66705 1.2210 1.2429 290s 11 0.41992 0.2497 -0.3602 290s 12 -0.05971 0.0470 0.3068 290s 13 -0.08649 0.3096 -0.2426 290s 14 0.33124 0.3652 0.3591 290s 15 -0.00604 -0.1652 0.2710 290s 16 -0.01478 0.0124 -0.0207 290s 17 1.55472 1.0339 -0.8117 290s 18 -0.41250 0.0255 0.8398 290s 19 0.29322 -2.6293 -0.8283 290s 20 0.91756 -0.5906 -0.4091 290s 21 0.71583 -0.7036 -1.2154 290s 22 -2.26223 -0.5283 0.6207 290s > fitted 290s Consumption Investment PrivateWages 290s 1 NA NA NA 290s 2 42.2 0.100 26.6 290s 3 46.4 2.024 28.7 290s 4 50.8 4.085 32.6 290s 5 51.2 4.436 33.9 290s 6 52.7 4.742 35.7 290s 7 54.3 4.147 NA 290s 8 54.9 3.371 38.6 290s 9 56.3 3.509 38.9 290s 10 58.5 3.879 40.1 290s 11 54.6 0.750 38.3 290s 12 51.0 -3.447 34.2 290s 13 45.7 -6.510 29.2 290s 14 46.2 -5.465 28.1 290s 15 48.7 -2.835 30.3 290s 16 51.3 -1.312 33.2 290s 17 56.1 1.066 37.6 290s 18 59.1 1.974 40.2 290s 19 57.2 0.729 39.0 290s 20 60.7 1.891 42.0 290s 21 64.3 4.004 46.2 290s 22 72.0 5.428 52.7 290s > predict 290s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 290s 1 NA NA NA NA 290s 2 42.2 0.414 41.3 43.0 290s 3 46.4 0.451 45.4 47.3 290s 4 50.8 0.296 50.2 51.4 290s 5 51.2 0.342 50.5 51.9 290s 6 52.7 0.342 52.0 53.4 290s 7 54.3 0.309 53.6 54.9 290s 8 54.9 0.282 54.3 55.5 290s 9 56.3 0.303 55.7 56.9 290s 10 58.5 0.321 57.8 59.1 290s 11 54.6 0.515 53.5 55.6 290s 12 51.0 0.418 50.1 51.8 290s 13 45.7 0.548 44.6 46.8 290s 14 46.2 0.528 45.1 47.2 290s 15 48.7 0.333 48.0 49.4 290s 16 51.3 0.296 50.7 51.9 290s 17 56.1 0.321 55.5 56.8 290s 18 59.1 0.287 58.5 59.7 290s 19 57.2 0.325 56.6 57.9 290s 20 60.7 0.383 59.9 61.5 290s 21 64.3 0.382 63.5 65.1 290s 22 72.0 0.599 70.8 73.2 290s Investment.pred Investment.se.fit Investment.lwr Investment.upr 290s 1 NA NA NA NA 290s 2 0.100 0.511 -0.926 1.127 290s 3 2.024 0.425 1.170 2.878 290s 4 4.085 0.378 3.325 4.845 290s 5 4.436 0.313 3.806 5.065 290s 6 4.742 0.296 4.147 5.336 290s 7 4.147 0.279 3.586 4.709 290s 8 3.371 0.250 2.868 3.874 290s 9 3.509 0.331 2.845 4.174 290s 10 3.879 0.380 3.116 4.642 290s 11 0.750 0.512 -0.279 1.779 290s 12 -3.447 0.433 -4.316 -2.578 290s 13 -6.510 0.527 -7.568 -5.451 290s 14 -5.465 0.587 -6.645 -4.285 290s 15 -2.835 0.320 -3.477 -2.193 290s 16 -1.312 0.274 -1.863 -0.761 290s 17 1.066 0.296 0.472 1.661 290s 18 1.974 0.208 1.558 2.391 290s 19 0.729 0.265 0.197 1.262 290s 20 1.891 0.311 1.266 2.515 290s 21 4.004 0.283 3.435 4.572 290s 22 5.428 0.393 4.640 6.217 290s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 290s 1 NA NA NA NA 290s 2 26.6 0.318 26.0 27.2 290s 3 28.7 0.317 28.1 29.4 290s 4 32.6 0.315 32.0 33.2 290s 5 33.9 0.243 33.4 34.4 290s 6 35.7 0.242 35.2 36.2 290s 7 NA NA NA NA 290s 8 38.6 0.247 38.1 39.1 290s 9 38.9 0.236 38.4 39.3 290s 10 40.1 0.227 39.6 40.5 290s 11 38.3 0.306 37.6 38.9 290s 12 34.2 0.312 33.6 34.8 290s 13 29.2 0.376 28.5 30.0 290s 14 28.1 0.337 27.5 28.8 290s 15 30.3 0.328 29.7 31.0 290s 16 33.2 0.274 32.7 33.8 290s 17 37.6 0.266 37.1 38.1 290s 18 40.2 0.213 39.7 40.6 290s 19 39.0 0.310 38.4 39.7 290s 20 42.0 0.282 41.4 42.6 290s 21 46.2 0.300 45.6 46.8 290s 22 52.7 0.451 51.8 53.6 290s > model.frame 290s [1] TRUE 290s > model.matrix 290s [1] TRUE 290s > nobs 290s [1] 62 290s > linearHypothesis 290s Linear hypothesis test (Theil's F test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df F Pr(>F) 290s 1 51 290s 2 50 1 1.39 0.24 290s Linear hypothesis test (F statistic of a Wald test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df F Pr(>F) 290s 1 51 290s 2 50 1 1.7 0.2 290s Linear hypothesis test (Chi^2 statistic of a Wald test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df Chisq Pr(>Chisq) 290s 1 51 290s 2 50 1 1.7 0.19 290s Linear hypothesis test (Theil's F test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s Consumption_corpProfLag - PrivateWages_trend = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df F Pr(>F) 290s 1 52 290s 2 50 2 0.72 0.49 290s Linear hypothesis test (F statistic of a Wald test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s Consumption_corpProfLag - PrivateWages_trend = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df F Pr(>F) 290s 1 52 290s 2 50 2 0.87 0.42 290s Linear hypothesis test (Chi^2 statistic of a Wald test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s Consumption_corpProfLag - PrivateWages_trend = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df Chisq Pr(>Chisq) 290s 1 52 290s 2 50 2 1.75 0.42 290s > logLik 290s 'log Lik.' -69.4 (df=18) 290s 'log Lik.' -78.2 (df=18) 290s Estimating function 290s Consumption_(Intercept) Consumption_corpProf 290s Consumption_2 -0.49572 -6.1470 290s Consumption_3 -2.42943 -41.0573 290s Consumption_4 -2.92134 -53.7526 290s Consumption_5 -1.01365 -19.6648 290s Consumption_6 -0.11814 -2.3746 290s Consumption_7 1.49363 29.2752 290s Consumption_8 2.31199 45.7775 290s Consumption_9 1.73521 36.6129 290s Consumption_10 -1.19687 -25.9720 290s Consumption_11 0.75344 11.7537 290s Consumption_12 -0.10714 -1.2214 290s Consumption_13 -0.15519 -1.0863 290s Consumption_14 0.59434 6.6566 290s Consumption_15 -0.01083 -0.1332 290s Consumption_16 -0.02651 -0.3712 290s Consumption_17 2.78956 49.0963 290s Consumption_18 -0.74013 -12.8043 290s Consumption_19 0.52610 8.0494 290s Consumption_20 1.64635 31.2806 290s Consumption_21 1.28438 27.1004 290s Consumption_22 -4.05902 -95.3870 290s Investment_2 0.08318 1.0314 290s Investment_3 0.03433 0.5802 290s Investment_4 -0.30897 -5.6851 290s Investment_5 0.39771 7.7155 290s Investment_6 -0.09921 -1.9941 290s Investment_7 -0.40237 -7.8864 290s Investment_8 -0.22963 -4.5466 290s Investment_9 0.14106 2.9764 290s Investment_10 -0.33822 -7.3394 290s Investment_11 -0.06917 -1.0790 290s Investment_12 -0.01303 -0.1485 290s Investment_13 -0.08575 -0.6003 290s Investment_14 -0.10117 -1.1331 290s Investment_15 0.04575 0.5628 290s Investment_16 -0.00344 -0.0482 290s Investment_17 -0.28639 -5.0405 290s Investment_18 -0.00707 -0.1223 290s Investment_19 0.72832 11.1433 290s Investment_20 0.16360 3.1083 290s Investment_21 0.19490 4.1123 290s Investment_22 0.14635 3.4391 290s PrivateWages_2 -1.58896 -19.7031 290s PrivateWages_3 0.84394 14.2626 290s PrivateWages_4 2.20977 40.6598 290s PrivateWages_5 -0.04965 -0.9631 290s PrivateWages_6 -0.40373 -8.1150 290s PrivateWages_8 -1.00430 -19.8851 290s PrivateWages_9 0.50179 10.5878 290s PrivateWages_10 1.81021 39.2815 290s PrivateWages_11 -0.52455 -8.1830 290s PrivateWages_12 0.44676 5.0931 290s PrivateWages_13 -0.35330 -2.4731 290s PrivateWages_14 0.52303 5.8579 290s PrivateWages_15 0.39464 4.8541 290s PrivateWages_16 -0.03009 -0.4213 290s PrivateWages_17 -1.18225 -20.8075 290s PrivateWages_18 1.22307 21.1590 290s PrivateWages_19 -1.20633 -18.4569 290s PrivateWages_20 -0.59580 -11.3203 290s PrivateWages_21 -1.77014 -37.3499 290s PrivateWages_22 0.90407 21.2457 290s Consumption_corpProfLag Consumption_wages 290s Consumption_2 -6.2957 -13.979 290s Consumption_3 -30.1249 -78.228 290s Consumption_4 -49.3706 -108.090 290s Consumption_5 -18.6512 -37.505 290s Consumption_6 -2.2919 -4.560 290s Consumption_7 30.0220 60.791 290s Consumption_8 45.3151 95.948 290s Consumption_9 34.3571 74.440 290s Consumption_10 -25.2539 -54.218 290s Consumption_11 16.3496 31.720 290s Consumption_12 -1.6714 -4.211 290s Consumption_13 -1.7691 -5.323 290s Consumption_14 4.1604 20.267 290s Consumption_15 -0.1213 -0.396 290s Consumption_16 -0.3261 -1.042 290s Consumption_17 39.0539 123.299 290s Consumption_18 -13.0263 -35.304 290s Consumption_19 9.1016 24.148 290s Consumption_20 25.1891 81.330 290s Consumption_21 24.4032 68.072 290s Consumption_22 -85.6453 -250.847 290s Investment_2 1.0563 2.346 290s Investment_3 0.4257 1.105 290s Investment_4 -5.2216 -11.432 290s Investment_5 7.3178 14.715 290s Investment_6 -1.9246 -3.829 290s Investment_7 -8.0876 -16.376 290s Investment_8 -4.5007 -9.530 290s Investment_9 2.7930 6.052 290s Investment_10 -7.1364 -15.321 290s Investment_11 -1.5009 -2.912 290s Investment_12 -0.2033 -0.512 290s Investment_13 -0.9776 -2.941 290s Investment_14 -0.7082 -3.450 290s Investment_15 0.5124 1.675 290s Investment_16 -0.0423 -0.135 290s Investment_17 -4.0095 -12.659 290s Investment_18 -0.1244 -0.337 290s Investment_19 12.5999 33.430 290s Investment_20 2.5030 8.082 290s Investment_21 3.7031 10.330 290s Investment_22 3.0879 9.044 290s PrivateWages_2 -20.1798 -44.809 290s PrivateWages_3 10.4649 27.175 290s PrivateWages_4 37.3452 81.762 290s PrivateWages_5 -0.9135 -1.837 290s PrivateWages_6 -7.8324 -15.584 290s PrivateWages_8 -19.6842 -41.678 290s PrivateWages_9 9.9355 21.527 290s PrivateWages_10 38.1953 82.002 290s PrivateWages_11 -11.3827 -22.084 290s PrivateWages_12 6.9695 17.558 290s PrivateWages_13 -4.0277 -12.118 290s PrivateWages_14 3.6612 17.835 290s PrivateWages_15 4.4200 14.444 290s PrivateWages_16 -0.3701 -1.183 290s PrivateWages_17 -16.5515 -52.255 290s PrivateWages_18 21.5260 58.340 290s PrivateWages_19 -20.8696 -55.371 290s PrivateWages_20 -9.1158 -29.433 290s PrivateWages_21 -33.6326 -93.817 290s PrivateWages_22 19.0759 55.872 290s Investment_(Intercept) Investment_corpProf 290s Consumption_2 0.07653 0.9490 290s Consumption_3 0.37506 6.3385 290s Consumption_4 0.45100 8.2984 290s Consumption_5 0.15649 3.0359 290s Consumption_6 0.01824 0.3666 290s Consumption_7 -0.23059 -4.5195 290s Consumption_8 -0.35693 -7.0672 290s Consumption_9 -0.26788 -5.6523 290s Consumption_10 0.18477 4.0096 290s Consumption_11 -0.11632 -1.8145 290s Consumption_12 0.01654 0.1886 290s Consumption_13 0.02396 0.1677 290s Consumption_14 -0.09175 -1.0277 290s Consumption_15 0.00167 0.0206 290s Consumption_16 0.00409 0.0573 290s Consumption_17 -0.43066 -7.5796 290s Consumption_18 0.11426 1.9767 290s Consumption_19 -0.08122 -1.2427 290s Consumption_20 -0.25417 -4.8291 290s Consumption_21 -0.19828 -4.1838 290s Consumption_22 0.62664 14.7260 290s Investment_2 -0.44022 -5.4587 290s Investment_3 -0.18170 -3.0707 290s Investment_4 1.63526 30.0888 290s Investment_5 -2.10489 -40.8348 290s Investment_6 0.52506 10.5537 290s Investment_7 2.12955 41.7392 290s Investment_8 1.21532 24.0633 290s Investment_9 -0.74658 -15.7528 290s Investment_10 1.79005 38.8441 290s Investment_11 0.36607 5.7107 290s Investment_12 0.06896 0.7861 290s Investment_13 0.45385 3.1769 290s Investment_14 0.53544 5.9969 290s Investment_15 -0.24215 -2.9785 290s Investment_16 0.01822 0.2551 290s Investment_17 1.51576 26.6774 290s Investment_18 0.03741 0.6472 290s Investment_19 -3.85468 -58.9766 290s Investment_20 -0.86584 -16.4509 290s Investment_21 -1.03151 -21.7649 290s Investment_22 -0.77455 -18.2019 290s PrivateWages_2 0.75366 9.3454 290s PrivateWages_3 -0.40029 -6.7649 290s PrivateWages_4 -1.04812 -19.2855 290s PrivateWages_5 0.02355 0.4568 290s PrivateWages_6 0.19149 3.8490 290s PrivateWages_8 0.47635 9.4317 290s PrivateWages_9 -0.23801 -5.0219 290s PrivateWages_10 -0.85860 -18.6317 290s PrivateWages_11 0.24880 3.8813 290s PrivateWages_12 -0.21191 -2.4157 290s PrivateWages_13 0.16758 1.1730 290s PrivateWages_14 -0.24808 -2.7785 290s PrivateWages_15 -0.18718 -2.3024 290s PrivateWages_16 0.01427 0.1998 290s PrivateWages_17 0.56075 9.8693 290s PrivateWages_18 -0.58012 -10.0360 290s PrivateWages_19 0.57218 8.7543 290s PrivateWages_20 0.28260 5.3694 290s PrivateWages_21 0.83960 17.7155 290s PrivateWages_22 -0.42881 -10.0771 290s Investment_corpProfLag Investment_capitalLag 290s Consumption_2 0.9719 13.990 290s Consumption_3 4.6507 68.486 290s Consumption_4 7.6219 83.210 290s Consumption_5 2.8794 29.686 290s Consumption_6 0.3538 3.515 290s Consumption_7 -4.6348 -45.611 290s Consumption_8 -6.9958 -72.599 290s Consumption_9 -5.3041 -55.613 290s Consumption_10 3.8987 38.913 290s Consumption_11 -2.5241 -25.090 290s Consumption_12 0.2580 3.584 290s Consumption_13 0.2731 5.110 290s Consumption_14 -0.6423 -19.002 290s Consumption_15 0.0187 0.338 290s Consumption_16 0.0503 0.815 290s Consumption_17 -6.0292 -85.141 290s Consumption_18 2.0110 22.830 290s Consumption_19 -1.4051 -16.390 290s Consumption_20 -3.8887 -50.808 290s Consumption_21 -3.7674 -39.895 290s Consumption_22 13.2221 128.147 290s Investment_2 -5.5908 -80.472 290s Investment_3 -2.2531 -33.179 290s Investment_4 27.6359 301.706 290s Investment_5 -38.7299 -399.297 290s Investment_6 10.1862 101.179 290s Investment_7 42.8040 421.225 290s Investment_8 23.8203 247.196 290s Investment_9 -14.7822 -154.989 290s Investment_10 37.7701 376.985 290s Investment_11 7.9437 78.961 290s Investment_12 1.0757 14.943 290s Investment_13 5.1739 96.806 290s Investment_14 3.7481 110.889 290s Investment_15 -2.7121 -48.915 290s Investment_16 0.2241 3.626 290s Investment_17 21.2206 299.666 290s Investment_18 0.6585 7.475 290s Investment_19 -66.6860 -777.874 290s Investment_20 -13.2473 -173.081 290s Investment_21 -19.5987 -207.540 290s Investment_22 -16.3429 -158.395 290s PrivateWages_2 9.5715 137.769 290s PrivateWages_3 -4.9636 -73.093 290s PrivateWages_4 -17.7133 -193.379 290s PrivateWages_5 0.4333 4.467 290s PrivateWages_6 3.7150 36.901 290s PrivateWages_8 9.3365 96.890 290s PrivateWages_9 -4.7125 -49.410 290s PrivateWages_10 -18.1165 -180.822 290s PrivateWages_11 5.3990 53.666 290s PrivateWages_12 -3.3057 -45.920 290s PrivateWages_13 1.9104 35.744 290s PrivateWages_14 -1.7366 -51.377 290s PrivateWages_15 -2.0965 -37.811 290s PrivateWages_16 0.1756 2.840 290s PrivateWages_17 7.8506 110.861 290s PrivateWages_18 -10.2100 -115.907 290s PrivateWages_19 9.8987 115.466 290s PrivateWages_20 4.3237 56.491 290s PrivateWages_21 15.9524 168.927 290s PrivateWages_22 -9.0479 -87.692 290s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 290s Consumption_2 -0.40239 -18.349 -18.067 290s Consumption_3 -1.97202 -98.798 -89.924 290s Consumption_4 -2.37131 -135.639 -118.803 290s Consumption_5 -0.82280 -46.982 -47.064 290s Consumption_6 -0.09590 -5.850 -5.476 290s Consumption_7 0.00000 0.000 0.000 290s Consumption_8 1.87670 120.859 120.108 290s Consumption_9 1.40851 90.849 90.708 290s Consumption_10 -0.97152 -65.092 -62.663 290s Consumption_11 0.61158 37.429 40.976 290s Consumption_12 -0.08697 -4.644 -5.322 290s Consumption_13 -0.12597 -5.580 -6.727 290s Consumption_14 0.48244 21.758 21.372 290s Consumption_15 -0.00879 -0.437 -0.396 290s Consumption_16 -0.02152 -1.171 -1.070 290s Consumption_17 2.26435 141.975 123.181 290s Consumption_18 -0.60078 -39.051 -37.669 290s Consumption_19 0.42705 26.007 27.758 290s Consumption_20 1.33638 92.878 81.385 290s Consumption_21 1.04256 78.922 72.458 290s Consumption_22 -3.29479 -291.260 -249.416 290s Investment_2 0.20743 9.459 9.314 290s Investment_3 0.08562 4.289 3.904 290s Investment_4 -0.77054 -44.075 -38.604 290s Investment_5 0.99183 56.634 56.733 290s Investment_6 -0.24741 -15.092 -14.127 290s Investment_7 0.00000 0.000 0.000 290s Investment_8 -0.57266 -36.880 -36.650 290s Investment_9 0.35179 22.690 22.655 290s Investment_10 -0.84348 -56.513 -54.405 290s Investment_11 -0.17249 -10.557 -11.557 290s Investment_12 -0.03249 -1.735 -1.989 290s Investment_13 -0.21385 -9.474 -11.420 290s Investment_14 -0.25230 -11.379 -11.177 290s Investment_15 0.11410 5.671 5.146 290s Investment_16 -0.00859 -0.467 -0.427 290s Investment_17 -0.71423 -44.782 -38.854 290s Investment_18 -0.01763 -1.146 -1.105 290s Investment_19 1.81634 110.615 118.062 290s Investment_20 0.40799 28.355 24.846 290s Investment_21 0.48605 36.794 33.781 290s Investment_22 0.36497 32.263 27.628 290s PrivateWages_2 -3.69675 -168.572 -165.984 290s PrivateWages_3 1.96345 98.369 89.533 290s PrivateWages_4 5.14109 294.070 257.568 290s PrivateWages_5 -0.11550 -6.595 -6.607 290s PrivateWages_6 -0.93929 -57.297 -53.633 290s PrivateWages_8 -2.33652 -150.472 -149.537 290s PrivateWages_9 1.16743 75.299 75.183 290s PrivateWages_10 4.21148 282.169 271.641 290s PrivateWages_11 -1.22037 -74.687 -81.765 290s PrivateWages_12 1.03941 55.504 63.612 290s PrivateWages_13 -0.82197 -36.413 -43.893 290s PrivateWages_14 1.21684 54.880 53.906 290s PrivateWages_15 0.91815 45.632 41.409 290s PrivateWages_16 -0.07001 -3.809 -3.480 290s PrivateWages_17 -2.75052 -172.458 -149.628 290s PrivateWages_18 2.84549 184.957 178.412 290s PrivateWages_19 -2.80656 -170.920 -182.427 290s PrivateWages_20 -1.38615 -96.338 -84.417 290s PrivateWages_21 -4.11826 -311.753 -286.219 290s PrivateWages_22 2.10334 185.935 159.223 290s PrivateWages_trend 290s Consumption_2 4.0239 290s Consumption_3 17.7482 290s Consumption_4 18.9705 290s Consumption_5 5.7596 290s Consumption_6 0.5754 290s Consumption_7 0.0000 290s Consumption_8 -7.5068 290s Consumption_9 -4.2255 290s Consumption_10 1.9430 290s Consumption_11 -0.6116 290s Consumption_12 0.0000 290s Consumption_13 -0.1260 290s Consumption_14 0.9649 290s Consumption_15 -0.0264 290s Consumption_16 -0.0861 290s Consumption_17 11.3217 290s Consumption_18 -3.6047 290s Consumption_19 2.9894 290s Consumption_20 10.6910 290s Consumption_21 9.3830 290s Consumption_22 -32.9479 290s Investment_2 -2.0743 290s Investment_3 -0.7706 290s Investment_4 6.1643 290s Investment_5 -6.9428 290s Investment_6 1.4845 290s Investment_7 0.0000 290s Investment_8 2.2907 290s Investment_9 -1.0554 290s Investment_10 1.6870 290s Investment_11 0.1725 290s Investment_12 0.0000 290s Investment_13 -0.2139 290s Investment_14 -0.5046 290s Investment_15 0.3423 290s Investment_16 -0.0343 290s Investment_17 -3.5712 290s Investment_18 -0.1058 290s Investment_19 12.7144 290s Investment_20 3.2639 290s Investment_21 4.3745 290s Investment_22 3.6497 290s PrivateWages_2 36.9675 290s PrivateWages_3 -17.6711 290s PrivateWages_4 -41.1287 290s PrivateWages_5 0.8085 290s PrivateWages_6 5.6357 290s PrivateWages_8 9.3461 290s PrivateWages_9 -3.5023 290s PrivateWages_10 -8.4230 290s PrivateWages_11 1.2204 290s PrivateWages_12 0.0000 290s PrivateWages_13 -0.8220 290s PrivateWages_14 2.4337 290s PrivateWages_15 2.7544 290s PrivateWages_16 -0.2801 290s PrivateWages_17 -13.7526 290s PrivateWages_18 17.0729 290s PrivateWages_19 -19.6459 290s PrivateWages_20 -11.0892 290s PrivateWages_21 -37.0644 290s PrivateWages_22 21.0334 290s [1] TRUE 290s > Bread 290s Consumption_(Intercept) Consumption_corpProf Consumption_corpProfLag 290s [1,] 85.2889 -0.01362 -0.83841 290s [2,] -0.0136 0.37283 -0.23220 290s [3,] -0.8384 -0.23220 0.36858 290s [4,] -1.6590 -0.05994 -0.03120 290s [5,] -3.1844 -0.68255 0.70355 290s [6,] 0.0595 0.01846 -0.01774 290s [7,] -0.0239 -0.01745 0.02009 290s [8,] 0.0127 0.00329 -0.00362 290s [9,] -36.0142 0.07978 1.66083 290s [10,] 0.3888 -0.06209 0.04032 290s [11,] 0.2001 0.06287 -0.07012 290s [12,] 0.1814 0.03185 0.02619 290s Consumption_wages Investment_(Intercept) Investment_corpProf 290s [1,] -1.66e+00 -3.184 0.05950 290s [2,] -5.99e-02 -0.683 0.01846 290s [3,] -3.12e-02 0.704 -0.01774 290s [4,] 7.69e-02 0.082 -0.00204 290s [5,] 8.20e-02 1298.386 -12.39923 290s [6,] -2.04e-03 -12.399 0.41486 290s [7,] -2.16e-05 9.908 -0.35328 290s [8,] -2.54e-04 -6.230 0.05576 290s [9,] 1.50e-01 24.451 -0.18195 290s [10,] 6.53e-06 0.391 0.02158 290s [11,] -2.68e-03 -0.821 -0.01913 290s [12,] -2.78e-02 -0.890 0.00590 290s Investment_corpProfLag Investment_capitalLag PrivateWages_(Intercept) 290s [1,] -2.39e-02 0.012670 -36.0142 290s [2,] -1.75e-02 0.003286 0.0798 290s [3,] 2.01e-02 -0.003616 1.6608 290s [4,] -2.16e-05 -0.000254 0.1499 290s [5,] 9.91e+00 -6.230058 24.4513 290s [6,] -3.53e-01 0.055757 -0.1819 290s [7,] 4.47e-01 -0.056152 -0.6460 290s [8,] -5.62e-02 0.030966 -0.0512 290s [9,] -6.46e-01 -0.051180 80.1680 290s [10,] -1.22e-02 -0.002778 -0.3588 290s [11,] 2.36e-02 0.003775 -0.9890 290s [12,] -1.61e-02 0.005268 0.9201 290s PrivateWages_gnp PrivateWages_gnpLag PrivateWages_trend 290s [1,] 3.89e-01 0.20005 0.18143 290s [2,] -6.21e-02 0.06287 0.03185 290s [3,] 4.03e-02 -0.07012 0.02619 290s [4,] 6.53e-06 -0.00268 -0.02782 290s [5,] 3.91e-01 -0.82129 -0.89038 290s [6,] 2.16e-02 -0.01913 0.00590 290s [7,] -1.22e-02 0.02360 -0.01606 290s [8,] -2.78e-03 0.00377 0.00527 290s [9,] -3.59e-01 -0.98896 0.92007 290s [10,] 4.82e-02 -0.04360 -0.01308 290s [11,] -4.36e-02 0.06217 -0.00244 290s [12,] -1.31e-02 -0.00244 0.04948 290s > 290s > # 3SLS 290s > summary 290s 290s systemfit results 290s method: 3SLS 290s 290s N DF SSR detRCov OLS-R2 McElroy-R2 290s system 60 48 62.6 0.265 0.968 0.994 290s 290s N DF SSR MSE RMSE R2 Adj R2 290s Consumption 20 16 17.8 1.114 1.06 0.981 0.977 290s Investment 20 16 34.3 2.143 1.46 0.853 0.825 290s PrivateWages 20 16 10.5 0.656 0.81 0.987 0.984 290s 290s The covariance matrix of the residuals used for estimation 290s Consumption Investment PrivateWages 290s Consumption 1.034 0.309 -0.383 290s Investment 0.309 1.151 0.202 290s PrivateWages -0.383 0.202 0.487 290s 290s The covariance matrix of the residuals 290s Consumption Investment PrivateWages 290s Consumption 0.891 0.304 -0.391 290s Investment 0.304 1.715 0.388 290s PrivateWages -0.391 0.388 0.525 290s 290s The correlations of the residuals 290s Consumption Investment PrivateWages 290s Consumption 1.000 0.246 -0.571 290s Investment 0.246 1.000 0.409 290s PrivateWages -0.571 0.409 1.000 290s 290s 290s 3SLS estimates for 'Consumption' (equation 1) 290s Model Formula: consump ~ corpProf + corpProfLag + wages 290s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 290s gnpLag 290s 290s Estimate Std. Error t value Pr(>|t|) 290s (Intercept) 16.3668 1.3024 12.57 1.1e-09 *** 290s corpProf 0.1186 0.1073 1.10 0.29 290s corpProfLag 0.1448 0.1008 1.44 0.17 290s wages 0.8006 0.0391 20.47 6.7e-13 *** 290s --- 290s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 290s 290s Residual standard error: 1.056 on 16 degrees of freedom 290s Number of observations: 20 Degrees of Freedom: 16 290s SSR: 17.825 MSE: 1.114 Root MSE: 1.056 290s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.977 290s 290s 290s 3SLS estimates for 'Investment' (equation 2) 290s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 290s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 290s gnpLag 290s 290s Estimate Std. Error t value Pr(>|t|) 290s (Intercept) 24.8872 6.2956 3.95 0.00114 ** 290s corpProf 0.0702 0.1458 0.48 0.63648 290s corpProfLag 0.6688 0.1402 4.77 0.00021 *** 290s capitalLag -0.1786 0.0303 -5.90 2.3e-05 *** 290s --- 290s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 290s 290s Residual standard error: 1.464 on 16 degrees of freedom 290s Number of observations: 20 Degrees of Freedom: 16 290s SSR: 34.295 MSE: 2.143 Root MSE: 1.464 290s Multiple R-Squared: 0.853 Adjusted R-Squared: 0.825 290s 290s 290s 3SLS estimates for 'PrivateWages' (equation 3) 290s Model Formula: privWage ~ gnp + gnpLag + trend 290s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 290s gnpLag 290s 290s Estimate Std. Error t value Pr(>|t|) 290s (Intercept) 1.6387 1.1457 1.43 0.17188 290s gnp 0.4062 0.0324 12.52 1.1e-09 *** 290s gnpLag 0.1784 0.0347 5.14 1.0e-04 *** 290s trend 0.1435 0.0292 4.91 0.00016 *** 290s --- 290s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 290s 290s Residual standard error: 0.81 on 16 degrees of freedom 290s Number of observations: 20 Degrees of Freedom: 16 290s SSR: 10.497 MSE: 0.656 Root MSE: 0.81 290s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.984 290s 290s > residuals 290s Consumption Investment PrivateWages 290s 1 NA NA NA 290s 2 -0.3538 -1.795 -1.2388 290s 3 -0.9465 0.154 0.4649 290s 4 -1.4189 0.678 1.4344 290s 5 -0.3546 -1.666 -0.1354 290s 6 0.1366 0.251 -0.3452 290s 7 NA NA NA 290s 8 1.4213 1.150 -0.7445 290s 9 1.2173 0.476 0.3001 290s 10 -0.4636 2.200 1.2232 290s 11 -0.0650 -0.962 -0.4104 290s 12 -0.5422 -0.808 0.2495 290s 13 -0.7092 -1.098 -0.3057 290s 14 0.4898 1.542 0.3497 290s 15 -0.0502 -0.155 0.2949 290s 16 0.0272 0.154 0.0214 290s 17 1.8311 1.932 -0.7322 290s 18 -0.4567 -0.180 0.9090 290s 19 0.0650 -3.381 -0.7795 290s 20 1.2135 0.557 -0.2847 290s 21 0.9466 0.167 -1.0812 290s 22 -1.9877 0.784 0.8102 290s > fitted 290s Consumption Investment PrivateWages 290s 1 NA NA NA 290s 2 42.3 1.595 26.7 290s 3 45.9 1.746 28.8 290s 4 50.6 4.522 32.7 290s 5 51.0 4.666 34.0 290s 6 52.5 4.849 35.7 290s 7 NA NA NA 290s 8 54.8 3.050 38.6 290s 9 56.1 2.524 38.9 290s 10 58.3 2.900 40.1 290s 11 55.1 1.962 38.3 290s 12 51.4 -2.592 34.3 290s 13 46.3 -5.102 29.3 290s 14 46.0 -6.642 28.2 290s 15 48.8 -2.845 30.3 290s 16 51.3 -1.454 33.2 290s 17 55.9 0.168 37.5 290s 18 59.2 2.180 40.1 290s 19 57.4 1.481 39.0 290s 20 60.4 0.743 41.9 290s 21 64.1 3.133 46.1 290s 22 71.7 4.116 52.5 290s > predict 290s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 290s 1 NA NA NA NA 290s 2 42.3 0.468 39.8 44.7 290s 3 45.9 0.543 43.4 48.5 290s 4 50.6 0.352 48.3 53.0 290s 5 51.0 0.407 48.6 53.4 290s 6 52.5 0.411 50.1 54.9 290s 7 NA NA NA NA 290s 8 54.8 0.340 52.4 57.1 290s 9 56.1 0.372 53.7 58.5 290s 10 58.3 0.387 55.9 60.6 290s 11 55.1 0.687 52.4 57.7 290s 12 51.4 0.558 48.9 54.0 290s 13 46.3 0.713 43.6 49.0 290s 14 46.0 0.599 43.4 48.6 290s 15 48.8 0.368 46.4 51.1 290s 16 51.3 0.326 48.9 53.6 290s 17 55.9 0.388 53.5 58.3 290s 18 59.2 0.319 56.8 61.5 290s 19 57.4 0.391 55.0 59.8 290s 20 60.4 0.457 57.9 62.8 290s 21 64.1 0.437 61.6 66.5 290s 22 71.7 0.674 69.0 74.3 290s Investment.pred Investment.se.fit Investment.lwr Investment.upr 290s 1 NA NA NA NA 290s 2 1.595 0.731 -1.8742 5.065 290s 3 1.746 0.533 -1.5566 5.050 290s 4 4.522 0.484 1.2530 7.791 290s 5 4.666 0.406 1.4458 7.887 290s 6 4.849 0.386 1.6390 8.058 290s 7 NA NA NA NA 290s 8 3.050 0.325 -0.1296 6.229 290s 9 2.524 0.467 -0.7334 5.782 290s 10 2.900 0.515 -0.3900 6.190 290s 11 1.962 0.769 -1.5438 5.467 290s 12 -2.592 0.608 -5.9519 0.769 290s 13 -5.102 0.774 -8.6129 -1.592 290s 14 -6.642 0.807 -10.1867 -3.098 290s 15 -2.845 0.395 -6.0599 0.370 290s 16 -1.454 0.341 -4.6409 1.733 290s 17 0.168 0.442 -3.0739 3.410 290s 18 2.180 0.281 -0.9807 5.340 290s 19 1.481 0.414 -1.7440 4.706 290s 20 0.743 0.492 -2.5310 4.017 290s 21 3.133 0.414 -0.0924 6.358 290s 22 4.116 0.583 0.7756 7.457 290s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 290s 1 NA NA NA NA 290s 2 26.7 0.322 24.9 28.6 290s 3 28.8 0.328 27.0 30.7 290s 4 32.7 0.340 30.8 34.5 290s 5 34.0 0.250 32.2 35.8 290s 6 35.7 0.257 33.9 37.5 290s 7 NA NA NA NA 290s 8 38.6 0.254 36.8 40.4 290s 9 38.9 0.241 37.1 40.7 290s 10 40.1 0.235 38.3 41.9 290s 11 38.3 0.325 36.5 40.2 290s 12 34.3 0.349 32.4 36.1 290s 13 29.3 0.425 27.4 31.2 290s 14 28.2 0.340 26.3 30.0 290s 15 30.3 0.326 28.5 32.2 290s 16 33.2 0.272 31.4 35.0 290s 17 37.5 0.273 35.7 39.3 290s 18 40.1 0.214 38.3 41.9 290s 19 39.0 0.336 37.1 40.8 290s 20 41.9 0.290 40.1 43.7 290s 21 46.1 0.305 44.2 47.9 290s 22 52.5 0.479 50.5 54.5 290s > model.frame 290s [1] TRUE 290s > model.matrix 290s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0323 >" 290s [2] "Attributes: < Component “dimnames”: Component 1: 55 string mismatches >" 290s [3] "Numeric: lengths (744, 720) differ" 290s > nobs 290s [1] 60 290s > linearHypothesis 290s Linear hypothesis test (Theil's F test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df F Pr(>F) 290s 1 49 290s 2 48 1 0.22 0.64 290s Linear hypothesis test (F statistic of a Wald test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df F Pr(>F) 290s 1 49 290s 2 48 1 0.29 0.59 290s Linear hypothesis test (Chi^2 statistic of a Wald test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df Chisq Pr(>Chisq) 290s 1 49 290s 2 48 1 0.29 0.59 290s Linear hypothesis test (Theil's F test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s Consumption_corpProfLag - PrivateWages_trend = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df F Pr(>F) 290s 1 50 290s 2 48 2 0.29 0.75 290s Linear hypothesis test (F statistic of a Wald test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s Consumption_corpProfLag - PrivateWages_trend = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df F Pr(>F) 290s 1 50 290s 2 48 2 0.38 0.68 290s Linear hypothesis test (Chi^2 statistic of a Wald test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s Consumption_corpProfLag - PrivateWages_trend = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df Chisq Pr(>Chisq) 290s 1 50 290s 2 48 2 0.77 0.68 290s > logLik 290s 'log Lik.' -71.9 (df=18) 290s 'log Lik.' -82.9 (df=18) 290s Estimating function 290s Consumption_(Intercept) Consumption_corpProf 290s Consumption_2 -2.1852 -28.316 290s Consumption_3 -1.2615 -21.074 290s Consumption_4 -0.7432 -14.221 290s Consumption_5 -4.1386 -86.649 290s Consumption_6 0.0344 0.669 290s Consumption_8 5.9528 102.039 290s Consumption_9 3.6199 70.548 290s Consumption_10 1.2130 24.820 290s Consumption_11 -2.3309 -39.266 290s Consumption_12 -1.5509 -19.665 290s Consumption_13 -2.9298 -26.139 290s Consumption_14 2.9907 27.815 290s Consumption_15 -1.7611 -22.533 290s Consumption_16 -1.0403 -14.834 290s Consumption_17 7.8605 115.957 290s Consumption_18 -1.2660 -24.744 290s Consumption_19 -6.1974 -119.976 290s Consumption_20 4.2546 73.971 290s Consumption_21 1.7695 35.564 290s Consumption_22 -2.2905 -52.365 290s Investment_2 1.5294 19.818 290s Investment_3 -0.1395 -2.330 290s Investment_4 -0.5222 -9.992 290s Investment_5 1.4794 30.973 290s Investment_6 -0.2466 -4.801 290s Investment_8 -1.1148 -19.108 290s Investment_9 -0.4909 -9.566 290s Investment_10 -1.9066 -39.013 290s Investment_11 0.8748 14.736 290s Investment_12 0.7489 9.496 290s Investment_13 1.0277 9.169 290s Investment_14 -1.3972 -12.995 290s Investment_15 0.1582 2.024 290s Investment_16 -0.1132 -1.614 290s Investment_17 -1.7775 -26.221 290s Investment_18 0.2812 5.496 290s Investment_19 3.0567 59.173 290s Investment_20 -0.5590 -9.719 290s Investment_21 -0.1981 -3.981 290s Investment_22 -0.6908 -15.792 290s PrivateWages_2 -3.3803 -43.802 290s PrivateWages_3 1.2445 20.789 290s PrivateWages_4 3.1328 59.947 290s PrivateWages_5 -2.9316 -61.378 290s PrivateWages_6 -0.3443 -6.703 290s PrivateWages_8 1.9219 32.944 290s PrivateWages_9 2.2216 43.296 290s PrivateWages_10 4.0703 83.288 290s PrivateWages_11 -2.6344 -44.377 290s PrivateWages_12 -0.6120 -7.760 290s PrivateWages_13 -2.5653 -22.887 290s PrivateWages_14 2.8669 26.663 290s PrivateWages_15 -0.5912 -7.565 290s PrivateWages_16 -0.6625 -9.447 290s PrivateWages_17 2.6204 38.656 290s PrivateWages_18 0.0477 0.933 290s PrivateWages_19 -7.1288 -138.006 290s PrivateWages_20 1.4620 25.419 290s PrivateWages_21 -1.3672 -27.479 290s PrivateWages_22 2.6294 60.113 290s Consumption_corpProfLag Consumption_wages 290s Consumption_2 -27.752 -63.61 290s Consumption_3 -15.643 -40.21 290s Consumption_4 -12.560 -26.46 290s Consumption_5 -76.150 -161.61 290s Consumption_6 0.667 1.34 290s Consumption_8 116.675 236.66 290s Consumption_9 71.675 153.05 290s Consumption_10 25.593 53.50 290s Consumption_11 -50.581 -101.08 290s Consumption_12 -24.194 -61.19 290s Consumption_13 -33.399 -102.70 290s Consumption_14 20.935 98.78 290s Consumption_15 -19.724 -66.25 290s Consumption_16 -12.795 -41.59 290s Consumption_17 110.047 328.11 290s Consumption_18 -22.282 -60.30 290s Consumption_19 -107.216 -306.49 290s Consumption_20 65.095 205.74 290s Consumption_21 33.620 94.08 290s Consumption_22 -48.330 -139.53 290s Investment_2 19.424 44.52 290s Investment_3 -1.729 -4.45 290s Investment_4 -8.825 -18.59 290s Investment_5 27.221 57.77 290s Investment_6 -4.784 -9.58 290s Investment_8 -21.849 -44.32 290s Investment_9 -9.719 -20.75 290s Investment_10 -40.229 -84.09 290s Investment_11 18.983 37.94 290s Investment_12 11.683 29.55 290s Investment_13 11.716 36.03 290s Investment_14 -9.780 -46.15 290s Investment_15 1.772 5.95 290s Investment_16 -1.392 -4.53 290s Investment_17 -24.885 -74.20 290s Investment_18 4.949 13.39 290s Investment_19 52.880 151.16 290s Investment_20 -8.553 -27.03 290s Investment_21 -3.764 -10.53 290s Investment_22 -14.576 -42.08 290s PrivateWages_2 -42.929 -98.41 290s PrivateWages_3 15.432 39.67 290s PrivateWages_4 52.944 111.55 290s PrivateWages_5 -53.942 -114.48 290s PrivateWages_6 -6.679 -13.37 290s PrivateWages_8 37.670 76.41 290s PrivateWages_9 43.987 93.93 290s PrivateWages_10 85.884 179.53 290s PrivateWages_11 -57.165 -114.24 290s PrivateWages_12 -9.547 -24.14 290s PrivateWages_13 -29.244 -89.93 290s PrivateWages_14 20.068 94.68 290s PrivateWages_15 -6.622 -22.24 290s PrivateWages_16 -8.149 -26.49 290s PrivateWages_17 36.686 109.38 290s PrivateWages_18 0.840 2.27 290s PrivateWages_19 -123.329 -352.55 290s PrivateWages_20 22.369 70.70 290s PrivateWages_21 -25.977 -72.69 290s PrivateWages_22 55.481 160.18 290s Investment_(Intercept) Investment_corpProf 290s Consumption_2 0.9588 12.424 290s Consumption_3 0.5535 9.246 290s Consumption_4 0.3261 6.240 290s Consumption_5 1.8159 38.018 290s Consumption_6 -0.0151 -0.294 290s Consumption_8 -2.6118 -44.771 290s Consumption_9 -1.5883 -30.954 290s Consumption_10 -0.5322 -10.890 290s Consumption_11 1.0227 17.228 290s Consumption_12 0.6805 8.628 290s Consumption_13 1.2855 11.469 290s Consumption_14 -1.3122 -12.204 290s Consumption_15 0.7727 9.887 290s Consumption_16 0.4564 6.508 290s Consumption_17 -3.4489 -50.877 290s Consumption_18 0.5555 10.857 290s Consumption_19 2.7192 52.640 290s Consumption_20 -1.8667 -32.456 290s Consumption_21 -0.7764 -15.604 290s Consumption_22 1.0050 22.976 290s Investment_2 -2.3899 -30.969 290s Investment_3 0.2179 3.641 290s Investment_4 0.8160 15.614 290s Investment_5 -2.3118 -48.401 290s Investment_6 0.3854 7.502 290s Investment_8 1.7420 29.860 290s Investment_9 0.7670 14.948 290s Investment_10 2.9794 60.964 290s Investment_11 -1.3670 -23.027 290s Investment_12 -1.1702 -14.838 290s Investment_13 -1.6060 -14.328 290s Investment_14 2.1833 20.306 290s Investment_15 -0.2472 -3.163 290s Investment_16 0.1769 2.522 290s Investment_17 2.7776 40.974 290s Investment_18 -0.4394 -8.588 290s Investment_19 -4.7765 -92.468 290s Investment_20 0.8735 15.187 290s Investment_21 0.3095 6.221 290s Investment_22 1.0795 24.678 290s PrivateWages_2 2.1957 28.452 290s PrivateWages_3 -0.8084 -13.504 290s PrivateWages_4 -2.0349 -38.939 290s PrivateWages_5 1.9043 39.869 290s PrivateWages_6 0.2236 4.354 290s PrivateWages_8 -1.2484 -21.399 290s PrivateWages_9 -1.4431 -28.123 290s PrivateWages_10 -2.6439 -54.100 290s PrivateWages_11 1.7112 28.826 290s PrivateWages_12 0.3975 5.041 290s PrivateWages_13 1.6663 14.867 290s PrivateWages_14 -1.8622 -17.319 290s PrivateWages_15 0.3840 4.914 290s PrivateWages_16 0.4304 6.137 290s PrivateWages_17 -1.7021 -25.110 290s PrivateWages_18 -0.0310 -0.606 290s PrivateWages_19 4.6306 89.644 290s PrivateWages_20 -0.9497 -16.511 290s PrivateWages_21 0.8881 17.849 290s PrivateWages_22 -1.7080 -39.047 290s Investment_corpProfLag Investment_capitalLag 290s Consumption_2 12.176 175.26 290s Consumption_3 6.864 101.07 290s Consumption_4 5.511 60.16 290s Consumption_5 33.412 344.47 290s Consumption_6 -0.293 -2.91 290s Consumption_8 -51.192 -531.25 290s Consumption_9 -31.448 -329.73 290s Consumption_10 -11.229 -112.08 290s Consumption_11 22.193 220.60 290s Consumption_12 10.615 147.46 290s Consumption_13 14.654 274.19 290s Consumption_14 -9.185 -271.76 290s Consumption_15 8.654 156.08 290s Consumption_16 5.614 90.83 290s Consumption_17 -48.284 -681.84 290s Consumption_18 9.776 110.98 290s Consumption_19 47.042 548.73 290s Consumption_20 -28.561 -373.16 290s Consumption_21 -14.751 -156.21 290s Consumption_22 21.205 205.52 290s Investment_2 -30.352 -436.88 290s Investment_3 2.702 39.79 290s Investment_4 13.790 150.55 290s Investment_5 -42.537 -438.54 290s Investment_6 7.476 74.26 290s Investment_8 34.143 354.32 290s Investment_9 15.187 159.24 290s Investment_10 62.865 627.45 290s Investment_11 -29.663 -294.86 290s Investment_12 -18.256 -253.59 290s Investment_13 -18.308 -342.55 290s Investment_14 15.283 452.17 290s Investment_15 -2.768 -49.93 290s Investment_16 2.176 35.20 290s Investment_17 38.886 549.13 290s Investment_18 -7.734 -87.79 290s Investment_19 -82.633 -963.90 290s Investment_20 13.365 174.61 290s Investment_21 5.881 62.28 290s Investment_22 22.777 220.75 290s PrivateWages_2 27.885 401.37 290s PrivateWages_3 -10.024 -147.61 290s PrivateWages_4 -34.390 -375.44 290s PrivateWages_5 35.039 361.24 290s PrivateWages_6 4.339 43.10 290s PrivateWages_8 -24.469 -253.93 290s PrivateWages_9 -28.572 -299.58 290s PrivateWages_10 -55.787 -556.81 290s PrivateWages_11 37.132 369.10 290s PrivateWages_12 6.201 86.14 290s PrivateWages_13 18.996 355.42 290s PrivateWages_14 -13.035 -385.66 290s PrivateWages_15 4.301 77.58 290s PrivateWages_16 5.293 85.64 290s PrivateWages_17 -23.830 -336.51 290s PrivateWages_18 -0.546 -6.19 290s PrivateWages_19 80.110 934.46 290s PrivateWages_20 -14.530 -189.84 290s PrivateWages_21 16.874 178.68 290s PrivateWages_22 -36.038 -349.28 290s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 290s Consumption_2 -2.1174 -99.67 -95.07 290s Consumption_3 -1.2224 -60.61 -55.74 290s Consumption_4 -0.7201 -40.72 -36.08 290s Consumption_5 -4.0103 -243.37 -229.39 290s Consumption_6 0.0333 2.02 1.90 290s Consumption_8 5.7682 346.08 369.17 290s Consumption_9 3.5077 218.42 225.90 290s Consumption_10 1.1754 75.89 75.81 290s Consumption_11 -2.2587 -143.90 -151.33 290s Consumption_12 -1.5028 -82.40 -91.97 290s Consumption_13 -2.8389 -133.36 -151.60 290s Consumption_14 2.8980 122.09 128.38 290s Consumption_15 -1.7065 -87.40 -76.96 290s Consumption_16 -1.0080 -55.78 -50.10 290s Consumption_17 7.6168 437.16 414.35 290s Consumption_18 -1.2268 -82.41 -76.92 290s Consumption_19 -6.0053 -411.44 -390.34 290s Consumption_20 4.1227 275.58 251.07 290s Consumption_21 1.7146 128.37 119.16 290s Consumption_22 -2.2195 -192.83 -168.02 290s Investment_2 2.1940 103.27 98.51 290s Investment_3 -0.2001 -9.92 -9.12 290s Investment_4 -0.7491 -42.36 -37.53 290s Investment_5 2.1223 128.79 121.39 290s Investment_6 -0.3538 -21.44 -20.20 290s Investment_8 -1.5992 -95.95 -102.35 290s Investment_9 -0.7042 -43.85 -45.35 290s Investment_10 -2.7351 -176.60 -176.41 290s Investment_11 1.2549 79.95 84.08 290s Investment_12 1.0743 58.91 65.75 290s Investment_13 1.4743 69.26 78.73 290s Investment_14 -2.0044 -84.44 -88.79 290s Investment_15 0.2269 11.62 10.23 290s Investment_16 -0.1624 -8.99 -8.07 290s Investment_17 -2.5499 -146.35 -138.71 290s Investment_18 0.4034 27.10 25.29 290s Investment_19 4.3849 300.42 285.02 290s Investment_20 -0.8019 -53.60 -48.84 290s Investment_21 -0.2842 -21.27 -19.75 290s Investment_22 -0.9910 -86.09 -75.02 290s PrivateWages_2 -7.3399 -345.49 -329.56 290s PrivateWages_3 2.7024 133.99 123.23 290s PrivateWages_4 6.8025 384.63 340.81 290s PrivateWages_5 -6.3658 -386.31 -364.12 290s PrivateWages_6 -0.7476 -45.31 -42.69 290s PrivateWages_8 4.1733 250.39 267.09 290s PrivateWages_9 4.8240 300.38 310.66 290s PrivateWages_10 8.8383 570.68 570.07 290s PrivateWages_11 -5.7203 -364.45 -383.26 290s PrivateWages_12 -1.3289 -72.87 -81.33 290s PrivateWages_13 -5.5702 -261.67 -297.45 290s PrivateWages_14 6.2251 262.25 275.77 290s PrivateWages_15 -1.2838 -65.75 -57.90 290s PrivateWages_16 -1.4387 -79.61 -71.50 290s PrivateWages_17 5.6900 326.57 309.54 290s PrivateWages_18 0.1036 6.96 6.50 290s PrivateWages_19 -15.4796 -1060.55 -1006.17 290s PrivateWages_20 3.1746 212.21 193.34 290s PrivateWages_21 -2.9688 -222.26 -206.33 290s PrivateWages_22 5.7096 496.04 432.21 290s PrivateWages_trend 290s Consumption_2 21.174 290s Consumption_3 11.002 290s Consumption_4 5.761 290s Consumption_5 28.072 290s Consumption_6 -0.200 290s Consumption_8 -23.073 290s Consumption_9 -10.523 290s Consumption_10 -2.351 290s Consumption_11 2.259 290s Consumption_12 0.000 290s Consumption_13 -2.839 290s Consumption_14 5.796 290s Consumption_15 -5.119 290s Consumption_16 -4.032 290s Consumption_17 38.084 290s Consumption_18 -7.361 290s Consumption_19 -42.037 290s Consumption_20 32.981 290s Consumption_21 15.431 290s Consumption_22 -22.195 290s Investment_2 -21.940 290s Investment_3 1.801 290s Investment_4 5.993 290s Investment_5 -14.856 290s Investment_6 2.123 290s Investment_8 6.397 290s Investment_9 2.112 290s Investment_10 5.470 290s Investment_11 -1.255 290s Investment_12 0.000 290s Investment_13 1.474 290s Investment_14 -4.009 290s Investment_15 0.681 290s Investment_16 -0.650 290s Investment_17 -12.749 290s Investment_18 2.420 290s Investment_19 30.694 290s Investment_20 -6.415 290s Investment_21 -2.557 290s Investment_22 -9.910 290s PrivateWages_2 73.399 290s PrivateWages_3 -24.321 290s PrivateWages_4 -54.420 290s PrivateWages_5 44.560 290s PrivateWages_6 4.486 290s PrivateWages_8 -16.693 290s PrivateWages_9 -14.472 290s PrivateWages_10 -17.677 290s PrivateWages_11 5.720 290s PrivateWages_12 0.000 290s PrivateWages_13 -5.570 290s PrivateWages_14 12.450 290s PrivateWages_15 -3.851 290s PrivateWages_16 -5.755 290s PrivateWages_17 28.450 290s PrivateWages_18 0.622 290s PrivateWages_19 -108.357 290s PrivateWages_20 25.397 290s PrivateWages_21 -26.719 290s PrivateWages_22 57.096 290s [1] TRUE 290s > Bread 290s Consumption_(Intercept) Consumption_corpProf Consumption_corpProfLag 290s [1,] 101.7742 -0.858360 -0.3736 290s [2,] -0.8584 0.690973 -0.4670 290s [3,] -0.3736 -0.466994 0.6099 290s [4,] -1.8845 -0.076066 -0.0404 290s [5,] 84.1239 -0.877202 2.8173 290s [6,] -1.7843 0.267204 -0.2636 290s [7,] 0.6061 -0.218819 0.2875 290s [8,] -0.3146 -0.000285 -0.0152 290s [9,] -36.6570 0.120759 1.7724 290s [10,] 0.5673 -0.083944 0.0542 290s [11,] 0.0259 0.084615 -0.0868 290s [12,] 0.2015 0.041756 0.0283 290s Consumption_wages Investment_(Intercept) Investment_corpProf 290s [1,] -1.884465 84.124 -1.7843 290s [2,] -0.076066 -0.877 0.2672 290s [3,] -0.040367 2.817 -0.2636 290s [4,] 0.091823 -2.748 0.0379 290s [5,] -2.748307 2378.068 -36.8158 290s [6,] 0.037919 -36.816 1.2756 290s [7,] -0.038383 31.099 -1.1022 290s [8,] 0.013629 -11.271 0.1659 290s [9,] 0.115318 17.951 -0.1175 290s [10,] -0.000915 1.841 0.0121 290s [11,] -0.000905 -2.197 -0.0106 290s [12,] -0.032751 -1.985 0.0278 290s Investment_corpProfLag Investment_capitalLag PrivateWages_(Intercept) 290s [1,] 0.60609 -3.15e-01 -3.67e+01 290s [2,] -0.21882 -2.85e-04 1.21e-01 290s [3,] 0.28746 -1.52e-02 1.77e+00 290s [4,] -0.03838 1.36e-02 1.15e-01 290s [5,] 31.09923 -1.13e+01 1.80e+01 290s [6,] -1.10217 1.66e-01 -1.17e-01 290s [7,] 1.17984 -1.58e-01 -9.59e-01 290s [8,] -0.15817 5.51e-02 7.31e-04 290s [9,] -0.95890 7.31e-04 7.88e+01 290s [10,] 0.00248 -1.04e-02 -5.11e-01 290s [11,] 0.01419 1.07e-02 -8.12e-01 290s [12,] -0.04010 1.08e-02 9.53e-01 290s PrivateWages_gnp PrivateWages_gnpLag PrivateWages_trend 290s [1,] 0.567318 0.025878 0.20145 290s [2,] -0.083944 0.084615 0.04176 290s [3,] 0.054179 -0.086845 0.02834 290s [4,] -0.000915 -0.000905 -0.03275 290s [5,] 1.840734 -2.196531 -1.98486 290s [6,] 0.012109 -0.010622 0.02782 290s [7,] 0.002479 0.014187 -0.04010 290s [8,] -0.010386 0.010690 0.01081 290s [9,] -0.511083 -0.811688 0.95314 290s [10,] 0.063161 -0.056453 -0.01901 290s [11,] -0.056453 0.072451 0.00297 290s [12,] -0.019011 0.002975 0.05128 290s > 290s > # I3SLS 290s > summary 290s 290s systemfit results 290s method: iterated 3SLS 290s 290s convergence achieved after 22 iterations 290s 290s N DF SSR detRCov OLS-R2 McElroy-R2 290s system 60 48 107 0.47 0.946 0.996 290s 290s N DF SSR MSE RMSE R2 Adj R2 290s Consumption 20 16 18.1 1.13 1.063 0.981 0.977 290s Investment 20 16 76.4 4.77 2.185 0.672 0.610 290s PrivateWages 20 16 12.3 0.77 0.877 0.984 0.982 290s 290s The covariance matrix of the residuals used for estimation 290s Consumption Investment PrivateWages 290s Consumption 0.905 0.509 -0.437 290s Investment 0.509 3.819 0.709 290s PrivateWages -0.437 0.709 0.616 290s 290s The covariance matrix of the residuals 290s Consumption Investment PrivateWages 290s Consumption 0.905 0.509 -0.437 290s Investment 0.509 3.819 0.709 290s PrivateWages -0.437 0.709 0.616 290s 290s The correlations of the residuals 290s Consumption Investment PrivateWages 290s Consumption 1.000 0.274 -0.585 290s Investment 0.274 1.000 0.462 290s PrivateWages -0.585 0.462 1.000 290s 290s 290s 3SLS estimates for 'Consumption' (equation 1) 290s Model Formula: consump ~ corpProf + corpProfLag + wages 290s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 290s gnpLag 290s 290s Estimate Std. Error t value Pr(>|t|) 290s (Intercept) 16.4728 1.2187 13.52 3.6e-10 *** 290s corpProf 0.1642 0.0952 1.73 0.10 290s corpProfLag 0.1552 0.0903 1.72 0.11 290s wages 0.7756 0.0356 21.82 2.5e-13 *** 290s --- 290s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 290s 290s Residual standard error: 1.063 on 16 degrees of freedom 290s Number of observations: 20 Degrees of Freedom: 16 290s SSR: 18.095 MSE: 1.131 Root MSE: 1.063 290s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.977 290s 290s 290s 3SLS estimates for 'Investment' (equation 2) 290s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 290s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 290s gnpLag 290s 290s Estimate Std. Error t value Pr(>|t|) 290s (Intercept) 38.7938 9.7249 3.99 0.00106 ** 290s corpProf -0.2501 0.2337 -1.07 0.30036 290s corpProfLag 0.9129 0.2271 4.02 0.00099 *** 290s capitalLag -0.2409 0.0469 -5.14 9.9e-05 *** 290s --- 290s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 290s 290s Residual standard error: 2.185 on 16 degrees of freedom 290s Number of observations: 20 Degrees of Freedom: 16 290s SSR: 76.371 MSE: 4.773 Root MSE: 2.185 290s Multiple R-Squared: 0.672 Adjusted R-Squared: 0.61 290s 290s 290s 3SLS estimates for 'PrivateWages' (equation 3) 290s Model Formula: privWage ~ gnp + gnpLag + trend 290s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 290s gnpLag 290s 290s Estimate Std. Error t value Pr(>|t|) 290s (Intercept) 2.4620 1.2228 2.01 0.061 . 290s gnp 0.3776 0.0318 11.88 2.4e-09 *** 290s gnpLag 0.1937 0.0331 5.85 2.5e-05 *** 290s trend 0.1619 0.0300 5.40 5.9e-05 *** 290s --- 290s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 290s 290s Residual standard error: 0.877 on 16 degrees of freedom 290s Number of observations: 20 Degrees of Freedom: 16 290s SSR: 12.318 MSE: 0.77 Root MSE: 0.877 290s Multiple R-Squared: 0.984 Adjusted R-Squared: 0.982 290s 290s > residuals 290s Consumption Investment PrivateWages 290s 1 NA NA NA 290s 2 -0.4522 -3.4485 -1.2596 290s 3 -1.1470 0.0027 0.5437 290s 4 -1.6147 0.0274 1.6290 290s 5 -0.6117 -2.0392 -0.0707 290s 6 -0.1229 0.0457 -0.1859 290s 7 NA NA NA 290s 8 1.2461 1.4658 -0.6304 290s 9 1.0158 1.4202 0.3924 290s 10 -0.6460 3.2062 1.3671 290s 11 -0.0554 -1.7386 -0.4891 290s 12 -0.3472 -1.3793 0.0179 290s 13 -0.3947 -2.2646 -0.6968 290s 14 0.6536 2.4092 0.1021 290s 15 0.0821 -0.2787 0.1482 290s 16 0.1381 0.1196 -0.0796 290s 17 1.8826 2.5548 -0.6862 290s 18 -0.3415 -0.4009 0.8755 290s 19 0.2296 -4.0454 -0.9839 290s 20 1.3178 1.4481 -0.1989 290s 21 1.0065 0.9087 -0.9681 290s 22 -1.8388 1.9868 1.1734 290s > fitted 290s Consumption Investment PrivateWages 290s 1 NA NA NA 290s 2 42.4 3.249 26.8 290s 3 46.1 1.897 28.8 290s 4 50.8 5.173 32.5 290s 5 51.2 5.039 34.0 290s 6 52.7 5.054 35.6 290s 7 NA NA NA 290s 8 55.0 2.734 38.5 290s 9 56.3 1.580 38.8 290s 10 58.4 1.894 39.9 290s 11 55.1 2.739 38.4 290s 12 51.2 -2.021 34.5 290s 13 46.0 -3.935 29.7 290s 14 45.8 -7.509 28.4 290s 15 48.6 -2.721 30.5 290s 16 51.2 -1.420 33.3 290s 17 55.8 -0.455 37.5 290s 18 59.0 2.401 40.1 290s 19 57.3 2.145 39.2 290s 20 60.3 -0.148 41.8 290s 21 64.0 2.391 46.0 290s 22 71.5 2.913 52.1 290s > predict 290s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 290s 1 NA NA NA NA 290s 2 42.4 0.437 41.5 43.2 290s 3 46.1 0.492 45.2 47.1 290s 4 50.8 0.321 50.2 51.5 290s 5 51.2 0.369 50.5 52.0 290s 6 52.7 0.372 52.0 53.5 290s 7 NA NA NA NA 290s 8 55.0 0.310 54.3 55.6 290s 9 56.3 0.338 55.6 57.0 290s 10 58.4 0.355 57.7 59.2 290s 11 55.1 0.618 53.8 56.3 290s 12 51.2 0.501 50.2 52.3 290s 13 46.0 0.642 44.7 47.3 290s 14 45.8 0.547 44.7 46.9 290s 15 48.6 0.340 47.9 49.3 290s 16 51.2 0.300 50.6 51.8 290s 17 55.8 0.354 55.1 56.5 290s 18 59.0 0.294 58.4 59.6 290s 19 57.3 0.354 56.6 58.0 290s 20 60.3 0.418 59.4 61.1 290s 21 64.0 0.407 63.2 64.8 290s 22 71.5 0.628 70.3 72.8 290s Investment.pred Investment.se.fit Investment.lwr Investment.upr 290s 1 NA NA NA NA 290s 2 3.249 1.160 0.91672 5.580 290s 3 1.897 0.934 0.02009 3.775 290s 4 5.173 0.803 3.55865 6.787 290s 5 5.039 0.693 3.64486 6.433 290s 6 5.054 0.674 3.69840 6.410 290s 7 NA NA NA NA 290s 8 2.734 0.584 1.56002 3.908 290s 9 1.580 0.783 0.00466 3.155 290s 10 1.894 0.868 0.14846 3.639 290s 11 2.739 1.321 0.08241 5.395 290s 12 -2.021 1.064 -4.16036 0.119 290s 13 -3.935 1.349 -6.64712 -1.224 290s 14 -7.509 1.360 -10.24349 -4.775 290s 15 -2.721 0.712 -4.15288 -1.290 290s 16 -1.420 0.614 -2.65412 -0.185 290s 17 -0.455 0.751 -1.96433 1.055 290s 18 2.401 0.498 1.39939 3.402 290s 19 2.145 0.698 0.74152 3.549 290s 20 -0.148 0.816 -1.78957 1.493 290s 21 2.391 0.713 0.95855 3.824 290s 22 2.913 0.984 0.93419 4.892 290s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 290s 1 NA NA NA NA 290s 2 26.8 0.347 26.1 27.5 290s 3 28.8 0.348 28.1 29.5 290s 4 32.5 0.354 31.8 33.2 290s 5 34.0 0.263 33.4 34.5 290s 6 35.6 0.274 35.0 36.1 290s 7 NA NA NA NA 290s 8 38.5 0.268 38.0 39.1 290s 9 38.8 0.256 38.3 39.3 290s 10 39.9 0.254 39.4 40.4 290s 11 38.4 0.323 37.7 39.0 290s 12 34.5 0.347 33.8 35.2 290s 13 29.7 0.435 28.8 30.6 290s 14 28.4 0.366 27.7 29.1 290s 15 30.5 0.341 29.8 31.1 290s 16 33.3 0.285 32.7 33.9 290s 17 37.5 0.275 36.9 38.0 290s 18 40.1 0.233 39.7 40.6 290s 19 39.2 0.346 38.5 39.9 290s 20 41.8 0.298 41.2 42.4 290s 21 46.0 0.329 45.3 46.6 290s 22 52.1 0.510 51.1 53.2 290s > model.frame 290s [1] TRUE 290s > model.matrix 290s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0323 >" 290s [2] "Attributes: < Component “dimnames”: Component 1: 55 string mismatches >" 290s [3] "Numeric: lengths (744, 720) differ" 290s > nobs 290s [1] 60 290s > linearHypothesis 290s Linear hypothesis test (Theil's F test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df F Pr(>F) 290s 1 49 290s 2 48 1 0.4 0.53 290s Linear hypothesis test (F statistic of a Wald test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df F Pr(>F) 290s 1 49 290s 2 48 1 0.5 0.49 290s Linear hypothesis test (Chi^2 statistic of a Wald test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df Chisq Pr(>Chisq) 290s 1 49 290s 2 48 1 0.5 0.48 290s Linear hypothesis test (Theil's F test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s Consumption_corpProfLag - PrivateWages_trend = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df F Pr(>F) 290s 1 50 290s 2 48 2 0.66 0.52 290s Linear hypothesis test (F statistic of a Wald test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s Consumption_corpProfLag - PrivateWages_trend = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df F Pr(>F) 290s 1 50 290s 2 48 2 0.83 0.44 290s Linear hypothesis test (Chi^2 statistic of a Wald test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s Consumption_corpProfLag - PrivateWages_trend = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df Chisq Pr(>Chisq) 290s 1 50 290s 2 48 2 1.66 0.44 290s > logLik 290s 'log Lik.' -77.6 (df=18) 290s 'log Lik.' -92.7 (df=18) 290s Estimating function 290s Consumption_(Intercept) Consumption_corpProf 290s Consumption_2 -4.9216 -63.77 290s Consumption_3 -3.3974 -56.75 290s Consumption_4 -2.5781 -49.33 290s Consumption_5 -9.6538 -202.12 290s Consumption_6 -0.8124 -15.82 290s Consumption_8 11.9408 204.68 290s Consumption_9 6.9299 135.05 290s Consumption_10 1.8984 38.85 290s Consumption_11 -4.8868 -82.32 290s Consumption_12 -2.6585 -33.71 290s Consumption_13 -5.0990 -45.49 290s Consumption_14 7.0717 65.77 290s Consumption_15 -3.1138 -39.84 290s Consumption_16 -1.6973 -24.20 290s Consumption_17 16.7458 247.03 290s Consumption_18 -2.5779 -50.39 290s Consumption_19 -12.5621 -243.19 290s Consumption_20 9.4057 163.53 290s Consumption_21 4.0953 82.31 290s Consumption_22 -4.1289 -94.39 290s Investment_2 4.3863 56.84 290s Investment_3 0.0612 1.02 290s Investment_4 -0.2801 -5.36 290s Investment_5 2.1936 45.93 290s Investment_6 0.1486 2.89 290s Investment_8 -1.0616 -18.20 290s Investment_9 -1.3484 -26.28 290s Investment_10 -3.8396 -78.57 290s Investment_11 1.8918 31.87 290s Investment_12 1.4041 17.80 290s Investment_13 2.3647 21.10 290s Investment_14 -2.5638 -23.84 290s Investment_15 0.2053 2.63 290s Investment_16 -0.2445 -3.49 290s Investment_17 -2.4423 -36.03 290s Investment_18 -0.2128 -4.16 290s Investment_19 4.0168 77.76 290s Investment_20 -1.3846 -24.07 290s Investment_21 -0.8726 -17.54 290s Investment_22 -2.4220 -55.37 290s PrivateWages_2 -7.8312 -101.48 290s PrivateWages_3 3.1927 53.33 290s PrivateWages_4 8.1013 155.02 290s PrivateWages_5 -6.1495 -128.75 290s PrivateWages_6 -0.1677 -3.26 290s PrivateWages_8 4.4536 76.34 290s PrivateWages_9 5.3302 103.88 290s PrivateWages_10 9.8611 201.78 290s PrivateWages_11 -6.2042 -104.51 290s PrivateWages_12 -2.2572 -28.62 290s PrivateWages_13 -7.3701 -65.76 290s PrivateWages_14 5.2841 49.14 290s PrivateWages_15 -1.8316 -23.44 290s PrivateWages_16 -1.8732 -26.71 290s PrivateWages_17 5.6855 83.87 290s PrivateWages_18 0.2354 4.60 290s PrivateWages_19 -16.6516 -322.36 290s PrivateWages_20 3.4690 60.31 290s PrivateWages_21 -2.8192 -56.66 290s PrivateWages_22 7.5425 172.43 290s Consumption_corpProfLag Consumption_wages 290s Consumption_2 -62.504 -143.28 290s Consumption_3 -42.128 -108.30 290s Consumption_4 -43.571 -91.80 290s Consumption_5 -177.629 -376.98 290s Consumption_6 -15.760 -31.55 290s Consumption_8 234.039 474.72 290s Consumption_9 137.212 292.99 290s Consumption_10 40.056 83.73 290s Consumption_11 -106.045 -211.93 290s Consumption_12 -41.472 -104.88 290s Consumption_13 -58.128 -178.75 290s Consumption_14 49.502 233.56 290s Consumption_15 -34.874 -117.14 290s Consumption_16 -20.877 -67.86 290s Consumption_17 234.441 699.00 290s Consumption_18 -45.372 -122.79 290s Consumption_19 -217.325 -621.24 290s Consumption_20 143.908 454.84 290s Consumption_21 77.811 217.74 290s Consumption_22 -87.120 -251.52 290s Investment_2 55.705 127.69 290s Investment_3 0.759 1.95 290s Investment_4 -4.734 -9.97 290s Investment_5 40.363 85.66 290s Investment_6 2.882 5.77 290s Investment_8 -20.807 -42.21 290s Investment_9 -26.697 -57.01 290s Investment_10 -81.017 -169.36 290s Investment_11 41.052 82.04 290s Investment_12 21.904 55.40 290s Investment_13 26.957 82.89 290s Investment_14 -17.946 -84.67 290s Investment_15 2.299 7.72 290s Investment_16 -3.007 -9.77 290s Investment_17 -34.192 -101.95 290s Investment_18 -3.746 -10.14 290s Investment_19 69.491 198.65 290s Investment_20 -21.185 -66.96 290s Investment_21 -16.580 -46.40 290s Investment_22 -51.104 -147.54 290s PrivateWages_2 -99.457 -227.98 290s PrivateWages_3 39.589 101.77 290s PrivateWages_4 136.911 288.46 290s PrivateWages_5 -113.151 -240.14 290s PrivateWages_6 -3.252 -6.51 290s PrivateWages_8 87.291 177.06 290s PrivateWages_9 105.538 225.36 290s PrivateWages_10 208.070 434.95 290s PrivateWages_11 -134.631 -269.05 290s PrivateWages_12 -35.213 -89.05 290s PrivateWages_13 -84.019 -258.36 290s PrivateWages_14 36.989 174.52 290s PrivateWages_15 -20.514 -68.91 290s PrivateWages_16 -23.040 -74.89 290s PrivateWages_17 79.598 237.33 290s PrivateWages_18 4.143 11.21 290s PrivateWages_19 -288.073 -823.48 290s PrivateWages_20 53.076 167.75 290s PrivateWages_21 -53.565 -149.89 290s PrivateWages_22 159.147 459.47 290s Investment_(Intercept) Investment_corpProf 290s Consumption_2 1.6584 21.489 290s Consumption_3 1.1448 19.123 290s Consumption_4 0.8687 16.623 290s Consumption_5 3.2529 68.104 290s Consumption_6 0.2737 5.329 290s Consumption_8 -4.0235 -68.968 290s Consumption_9 -2.3351 -45.507 290s Consumption_10 -0.6397 -13.089 290s Consumption_11 1.6466 27.739 290s Consumption_12 0.8958 11.358 290s Consumption_13 1.7181 15.329 290s Consumption_14 -2.3828 -22.161 290s Consumption_15 1.0492 13.424 290s Consumption_16 0.5719 8.155 290s Consumption_17 -5.6426 -83.238 290s Consumption_18 0.8686 16.978 290s Consumption_19 4.2329 81.944 290s Consumption_20 -3.1693 -55.102 290s Consumption_21 -1.3799 -27.735 290s Consumption_22 1.3913 31.806 290s Investment_2 -2.5801 -33.433 290s Investment_3 -0.0360 -0.601 290s Investment_4 0.1648 3.153 290s Investment_5 -1.2904 -27.016 290s Investment_6 -0.0874 -1.701 290s Investment_8 0.6245 10.704 290s Investment_9 0.7931 15.457 290s Investment_10 2.2586 46.215 290s Investment_11 -1.1128 -18.746 290s Investment_12 -0.8259 -10.473 290s Investment_13 -1.3910 -12.410 290s Investment_14 1.5081 14.026 290s Investment_15 -0.1208 -1.545 290s Investment_16 0.1438 2.050 290s Investment_17 1.4366 21.193 290s Investment_18 0.1252 2.447 290s Investment_19 -2.3628 -45.741 290s Investment_20 0.8145 14.161 290s Investment_21 0.5133 10.317 290s Investment_22 1.4247 32.570 290s PrivateWages_2 3.3346 43.210 290s PrivateWages_3 -1.3594 -22.709 290s PrivateWages_4 -3.4495 -66.008 290s PrivateWages_5 2.6185 54.822 290s PrivateWages_6 0.0714 1.390 290s PrivateWages_8 -1.8964 -32.506 290s PrivateWages_9 -2.2696 -44.232 290s PrivateWages_10 -4.1989 -85.919 290s PrivateWages_11 2.6418 44.502 290s PrivateWages_12 0.9611 12.187 290s PrivateWages_13 3.1382 27.999 290s PrivateWages_14 -2.2500 -20.926 290s PrivateWages_15 0.7799 9.979 290s PrivateWages_16 0.7976 11.373 290s PrivateWages_17 -2.4209 -35.713 290s PrivateWages_18 -0.1002 -1.959 290s PrivateWages_19 7.0903 137.261 290s PrivateWages_20 -1.4771 -25.682 290s PrivateWages_21 1.2004 24.127 290s PrivateWages_22 -3.2116 -73.422 290s Investment_corpProfLag Investment_capitalLag 290s Consumption_2 21.061 303.15 290s Consumption_3 14.195 209.04 290s Consumption_4 14.681 160.28 290s Consumption_5 59.853 617.07 290s Consumption_6 5.310 52.75 290s Consumption_8 -78.860 -818.38 290s Consumption_9 -46.234 -484.76 290s Consumption_10 -13.497 -134.72 290s Consumption_11 35.732 355.18 290s Consumption_12 13.974 194.12 290s Consumption_13 19.587 366.47 290s Consumption_14 -16.680 -493.49 290s Consumption_15 11.751 211.94 290s Consumption_16 7.034 113.81 290s Consumption_17 -78.996 -1115.54 290s Consumption_18 15.288 173.56 290s Consumption_19 73.229 854.19 290s Consumption_20 -48.490 -633.54 290s Consumption_21 -26.219 -277.64 290s Consumption_22 29.355 284.51 290s Investment_2 -32.767 -471.64 290s Investment_3 -0.446 -6.57 290s Investment_4 2.785 30.40 290s Investment_5 -23.742 -244.78 290s Investment_6 -1.695 -16.84 290s Investment_8 12.239 127.02 290s Investment_9 15.704 164.66 290s Investment_10 47.656 475.66 290s Investment_11 -24.148 -240.03 290s Investment_12 -12.884 -178.98 290s Investment_13 -15.857 -296.69 290s Investment_14 10.556 312.32 290s Investment_15 -1.352 -24.39 290s Investment_16 1.769 28.62 290s Investment_17 20.113 284.02 290s Investment_18 2.203 25.01 290s Investment_19 -40.876 -476.81 290s Investment_20 12.461 162.81 290s Investment_21 9.753 103.28 290s Investment_22 30.061 291.35 290s PrivateWages_2 42.349 609.56 290s PrivateWages_3 -16.857 -248.23 290s PrivateWages_4 -58.297 -636.44 290s PrivateWages_5 48.180 496.72 290s PrivateWages_6 1.385 13.76 290s PrivateWages_8 -37.169 -385.72 290s PrivateWages_9 -44.939 -471.17 290s PrivateWages_10 -88.597 -884.29 290s PrivateWages_11 57.326 569.83 290s PrivateWages_12 14.994 208.28 290s PrivateWages_13 35.776 669.38 290s PrivateWages_14 -15.750 -465.97 290s PrivateWages_15 8.735 157.54 290s PrivateWages_16 9.810 158.72 290s PrivateWages_17 -33.893 -478.62 290s PrivateWages_18 -1.764 -20.03 290s PrivateWages_19 122.662 1430.82 290s PrivateWages_20 -22.600 -295.28 290s PrivateWages_21 22.808 241.53 290s PrivateWages_22 -67.765 -656.78 290s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 290s Consumption_2 -5.3990 -254.13 -242.42 290s Consumption_3 -3.7270 -184.79 -169.95 290s Consumption_4 -2.8282 -159.92 -141.69 290s Consumption_5 -10.5903 -642.68 -605.76 290s Consumption_6 -0.8912 -54.02 -50.89 290s Consumption_8 13.0991 785.91 838.34 290s Consumption_9 7.6022 473.37 489.58 290s Consumption_10 2.0826 134.47 134.33 290s Consumption_11 -5.3609 -341.55 -359.18 290s Consumption_12 -2.9163 -159.91 -178.48 290s Consumption_13 -5.5936 -262.77 -298.70 290s Consumption_14 7.7577 326.81 343.67 290s Consumption_15 -3.4158 -174.95 -154.05 290s Consumption_16 -1.8619 -103.04 -92.54 290s Consumption_17 18.3702 1054.34 999.34 290s Consumption_18 -2.8280 -189.97 -177.32 290s Consumption_19 -13.7808 -944.16 -895.75 290s Consumption_20 10.3182 689.71 628.38 290s Consumption_21 4.4926 336.34 312.24 290s Consumption_22 -4.5294 -393.51 -342.88 290s Investment_2 6.0805 286.21 273.02 290s Investment_3 0.0848 4.21 3.87 290s Investment_4 -0.3883 -21.96 -19.45 290s Investment_5 3.0410 184.55 173.94 290s Investment_6 0.2060 12.48 11.76 290s Investment_8 -1.4717 -88.30 -94.19 290s Investment_9 -1.8692 -116.39 -120.38 290s Investment_10 -5.3228 -343.69 -343.32 290s Investment_11 2.6225 167.09 175.71 290s Investment_12 1.9465 106.73 119.12 290s Investment_13 3.2781 154.00 175.05 290s Investment_14 -3.5541 -149.72 -157.44 290s Investment_15 0.2846 14.58 12.84 290s Investment_16 -0.3389 -18.75 -16.84 290s Investment_17 -3.3857 -194.32 -184.18 290s Investment_18 -0.2951 -19.82 -18.50 290s Investment_19 5.5684 381.50 361.95 290s Investment_20 -1.9195 -128.31 -116.90 290s Investment_21 -1.2097 -90.57 -84.07 290s Investment_22 -3.3575 -291.70 -254.16 290s PrivateWages_2 -12.3381 -580.75 -553.98 290s PrivateWages_3 5.0300 249.39 229.37 290s PrivateWages_4 12.7635 721.68 639.45 290s PrivateWages_5 -9.6885 -587.96 -554.18 290s PrivateWages_6 -0.2641 -16.01 -15.08 290s PrivateWages_8 7.0167 420.99 449.07 290s PrivateWages_9 8.3978 522.92 540.82 290s PrivateWages_10 15.5362 1003.16 1002.09 290s PrivateWages_11 -9.7747 -622.76 -654.90 290s PrivateWages_12 -3.5562 -195.00 -217.64 290s PrivateWages_13 -11.6116 -545.48 -620.06 290s PrivateWages_14 8.3251 350.72 368.80 290s PrivateWages_15 -2.8858 -147.80 -130.15 290s PrivateWages_16 -2.9512 -163.31 -146.67 290s PrivateWages_17 8.9576 514.11 487.29 290s PrivateWages_18 0.3709 24.92 23.26 290s PrivateWages_19 -26.2346 -1797.40 -1705.25 290s PrivateWages_20 5.4654 365.33 332.84 290s PrivateWages_21 -4.4417 -332.53 -308.70 290s PrivateWages_22 11.8832 1032.40 899.56 290s PrivateWages_trend 290s Consumption_2 53.990 290s Consumption_3 33.543 290s Consumption_4 22.626 290s Consumption_5 74.132 290s Consumption_6 5.347 290s Consumption_8 -52.396 290s Consumption_9 -22.806 290s Consumption_10 -4.165 290s Consumption_11 5.361 290s Consumption_12 0.000 290s Consumption_13 -5.594 290s Consumption_14 15.515 290s Consumption_15 -10.247 290s Consumption_16 -7.448 290s Consumption_17 91.851 290s Consumption_18 -16.968 290s Consumption_19 -96.465 290s Consumption_20 82.545 290s Consumption_21 40.433 290s Consumption_22 -45.294 290s Investment_2 -60.805 290s Investment_3 -0.763 290s Investment_4 3.106 290s Investment_5 -21.287 290s Investment_6 -1.236 290s Investment_8 5.887 290s Investment_9 5.608 290s Investment_10 10.646 290s Investment_11 -2.623 290s Investment_12 0.000 290s Investment_13 3.278 290s Investment_14 -7.108 290s Investment_15 0.854 290s Investment_16 -1.356 290s Investment_17 -16.928 290s Investment_18 -1.770 290s Investment_19 38.979 290s Investment_20 -15.356 290s Investment_21 -10.887 290s Investment_22 -33.575 290s PrivateWages_2 123.381 290s PrivateWages_3 -45.270 290s PrivateWages_4 -102.108 290s PrivateWages_5 67.820 290s PrivateWages_6 1.585 290s PrivateWages_8 -28.067 290s PrivateWages_9 -25.193 290s PrivateWages_10 -31.072 290s PrivateWages_11 9.775 290s PrivateWages_12 0.000 290s PrivateWages_13 -11.612 290s PrivateWages_14 16.650 290s PrivateWages_15 -8.657 290s PrivateWages_16 -11.805 290s PrivateWages_17 44.788 290s PrivateWages_18 2.225 290s PrivateWages_19 -183.642 290s PrivateWages_20 43.723 290s PrivateWages_21 -39.975 290s PrivateWages_22 118.832 290s [1] TRUE 290s > Bread 290s Consumption_(Intercept) Consumption_corpProf Consumption_corpProfLag 290s [1,] 89.117 -0.7628 -0.3161 290s [2,] -0.763 0.5437 -0.3702 290s [3,] -0.316 -0.3702 0.4897 290s [4,] -1.650 -0.0567 -0.0339 290s [5,] 127.149 -5.8142 6.0484 290s [6,] -2.757 0.6390 -0.5640 290s [7,] 0.822 -0.5332 0.6080 290s [8,] -0.462 0.0186 -0.0321 290s [9,] -41.723 0.1554 1.5996 290s [10,] 0.652 -0.0670 0.0422 290s [11,] 0.023 0.0665 -0.0715 290s [12,] 0.266 0.0460 0.0263 290s Consumption_wages Investment_(Intercept) Investment_corpProf 290s [1,] -1.649949 127.15 -2.7567 290s [2,] -0.056675 -5.81 0.6390 290s [3,] -0.033922 6.05 -0.5640 290s [4,] 0.075837 -3.04 0.0284 290s [5,] -3.037786 5674.46 -81.6232 290s [6,] 0.028439 -81.62 3.2764 290s [7,] -0.041721 66.55 -2.7837 290s [8,] 0.016133 -26.78 0.3579 290s [9,] 0.286845 49.74 -0.5482 290s [10,] -0.005120 5.39 0.0206 290s [11,] 0.000492 -6.38 -0.0122 290s [12,] -0.035219 -5.00 0.0650 290s Investment_corpProfLag Investment_capitalLag PrivateWages_(Intercept) 290s [1,] 0.8223 -0.4623 -41.7225 290s [2,] -0.5332 0.0186 0.1554 290s [3,] 0.6080 -0.0321 1.5996 290s [4,] -0.0417 0.0161 0.2868 290s [5,] 66.5535 -26.7802 49.7422 290s [6,] -2.7837 0.3579 -0.5482 290s [7,] 3.0944 -0.3490 -2.9105 290s [8,] -0.3490 0.1318 0.0433 290s [9,] -2.9105 0.0433 89.7087 290s [10,] 0.0256 -0.0306 -0.7102 290s [11,] 0.0243 0.0308 -0.7883 290s [12,] -0.1021 0.0277 0.9946 290s PrivateWages_gnp PrivateWages_gnpLag PrivateWages_trend 290s [1,] 0.65175 0.023034 0.26557 290s [2,] -0.06703 0.066494 0.04602 290s [3,] 0.04225 -0.071498 0.02630 290s [4,] -0.00512 0.000492 -0.03522 290s [5,] 5.38683 -6.377135 -4.99571 290s [6,] 0.02064 -0.012164 0.06501 290s [7,] 0.02556 0.024313 -0.10213 290s [8,] -0.03064 0.030839 0.02771 290s [9,] -0.71025 -0.788347 0.99462 290s [10,] 0.06062 -0.050369 -0.02195 290s [11,] -0.05037 0.065741 0.00529 290s [12,] -0.02195 0.005286 0.05391 290s > 290s > # OLS 290s Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 290s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 290s > summary 290s 290s systemfit results 290s method: OLS 290s 290s N DF SSR detRCov OLS-R2 McElroy-R2 290s system 61 49 44.5 0.382 0.977 0.99 290s 290s N DF SSR MSE RMSE R2 Adj R2 290s Consumption 20 16 17.48 1.093 1.04 0.981 0.978 290s Investment 21 17 17.32 1.019 1.01 0.931 0.919 290s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 290s 290s The covariance matrix of the residuals 290s Consumption Investment PrivateWages 290s Consumption 1.124 0.034 -0.442 290s Investment 0.034 0.928 0.130 290s PrivateWages -0.442 0.130 0.563 290s 290s The correlations of the residuals 290s Consumption Investment PrivateWages 290s Consumption 1.0000 0.0266 -0.563 290s Investment 0.0266 1.0000 0.169 290s PrivateWages -0.5630 0.1689 1.000 290s 290s 290s OLS estimates for 'Consumption' (equation 1) 290s Model Formula: consump ~ corpProf + corpProfLag + wages 290s 290s Estimate Std. Error t value Pr(>|t|) 290s (Intercept) 16.1357 1.3571 11.89 2.4e-09 *** 290s corpProf 0.1994 0.0949 2.10 0.052 . 290s corpProfLag 0.0969 0.0944 1.03 0.320 290s wages 0.7940 0.0415 19.16 1.9e-12 *** 290s --- 290s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 290s 290s Residual standard error: 1.045 on 16 degrees of freedom 290s Number of observations: 20 Degrees of Freedom: 16 290s SSR: 17.481 MSE: 1.093 Root MSE: 1.045 290s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.978 290s 290s 290s OLS estimates for 'Investment' (equation 2) 290s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 290s 290s Estimate Std. Error t value Pr(>|t|) 290s (Intercept) 10.1258 5.2164 1.94 0.06901 . 290s corpProf 0.4796 0.0927 5.17 7.6e-05 *** 290s corpProfLag 0.3330 0.0963 3.46 0.00299 ** 290s capitalLag -0.1118 0.0255 -4.38 0.00041 *** 290s --- 290s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 290s 290s Residual standard error: 1.009 on 17 degrees of freedom 290s Number of observations: 21 Degrees of Freedom: 17 290s SSR: 17.323 MSE: 1.019 Root MSE: 1.009 290s Multiple R-Squared: 0.931 Adjusted R-Squared: 0.919 290s 290s 290s OLS estimates for 'PrivateWages' (equation 3) 290s Model Formula: privWage ~ gnp + gnpLag + trend 290s 290s Estimate Std. Error t value Pr(>|t|) 290s (Intercept) 1.3550 1.2591 1.08 0.2978 290s gnp 0.4417 0.0319 13.86 2.5e-10 *** 290s gnpLag 0.1466 0.0366 4.01 0.0010 ** 290s trend 0.1244 0.0323 3.85 0.0014 ** 290s --- 290s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 290s 290s Residual standard error: 0.78 on 16 degrees of freedom 290s Number of observations: 20 Degrees of Freedom: 16 290s SSR: 9.739 MSE: 0.609 Root MSE: 0.78 290s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 290s 290s compare coef with single-equation OLS 290s [1] TRUE 290s > residuals 290s Consumption Investment PrivateWages 290s 1 NA NA NA 290s 2 -0.3304 -0.0668 -1.3389 290s 3 -1.2748 -0.0476 0.2462 290s 4 -1.6213 1.2467 1.1255 290s 5 -0.5661 -1.3512 -0.1959 290s 6 -0.0730 0.4154 -0.5284 290s 7 0.7915 1.4923 NA 290s 8 1.2648 0.7889 -0.7909 290s 9 0.9746 -0.6317 0.2819 290s 10 NA 1.0830 1.1384 290s 11 0.2225 0.2791 -0.1904 290s 12 -0.2256 0.0369 0.5813 290s 13 -0.2711 0.3659 0.1206 290s 14 0.3765 0.2237 0.4773 290s 15 -0.0349 -0.1728 0.3035 290s 16 -0.0243 0.0101 0.0284 290s 17 1.6023 0.9719 -0.8517 290s 18 -0.4658 0.0516 0.9908 290s 19 0.1914 -2.5656 -0.4597 290s 20 0.9683 -0.6866 -0.3819 290s 21 0.7325 -0.7807 -1.1062 290s 22 -2.2370 -0.6623 0.5501 290s > fitted 290s Consumption Investment PrivateWages 290s 1 NA NA NA 290s 2 42.2 -0.133 26.8 290s 3 46.3 1.948 29.1 290s 4 50.8 3.953 33.0 290s 5 51.2 4.351 34.1 290s 6 52.7 4.685 35.9 290s 7 54.3 4.108 NA 290s 8 54.9 3.411 38.7 290s 9 56.3 3.632 38.9 290s 10 NA 4.017 40.2 290s 11 54.8 0.721 38.1 290s 12 51.1 -3.437 33.9 290s 13 45.9 -6.566 28.9 290s 14 46.1 -5.324 28.0 290s 15 48.7 -2.827 30.3 290s 16 51.3 -1.310 33.2 290s 17 56.1 1.128 37.7 290s 18 59.2 1.948 40.0 290s 19 57.3 0.666 38.7 290s 20 60.6 1.987 42.0 290s 21 64.3 4.081 46.1 290s 22 71.9 5.562 52.7 290s > predict 290s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 290s 1 NA NA NA NA 290s 2 42.2 0.478 39.9 44.5 290s 3 46.3 0.537 43.9 48.6 290s 4 50.8 0.364 48.6 53.0 290s 5 51.2 0.427 48.9 53.4 290s 6 52.7 0.433 50.4 54.9 290s 7 54.3 0.394 52.1 56.6 290s 8 54.9 0.360 52.7 57.2 290s 9 56.3 0.387 54.1 58.6 290s 10 NA NA NA NA 290s 11 54.8 0.635 52.3 57.2 290s 12 51.1 0.501 48.8 53.5 290s 13 45.9 0.656 43.4 48.4 290s 14 46.1 0.629 43.7 48.6 290s 15 48.7 0.389 46.5 51.0 290s 16 51.3 0.345 49.1 53.5 290s 17 56.1 0.379 53.9 58.3 290s 18 59.2 0.336 57.0 61.4 290s 19 57.3 0.385 55.1 59.5 290s 20 60.6 0.450 58.3 62.9 290s 21 64.3 0.448 62.0 66.6 290s 22 71.9 0.697 69.4 74.5 290s Investment.pred Investment.se.fit Investment.lwr Investment.upr 290s 1 NA NA NA NA 290s 2 -0.133 0.579 -2.472 2.206 290s 3 1.948 0.476 -0.295 4.190 290s 4 3.953 0.428 1.750 6.157 290s 5 4.351 0.354 2.202 6.501 290s 6 4.685 0.333 2.548 6.821 290s 7 4.108 0.314 1.983 6.232 290s 8 3.411 0.279 1.306 5.516 290s 9 3.632 0.371 1.470 5.793 290s 10 4.017 0.426 1.815 6.219 290s 11 0.721 0.574 -1.613 3.054 290s 12 -3.437 0.484 -5.686 -1.188 290s 13 -6.566 0.588 -8.913 -4.219 290s 14 -5.324 0.662 -7.750 -2.898 290s 15 -2.827 0.356 -4.978 -0.676 290s 16 -1.310 0.305 -3.429 0.809 290s 17 1.128 0.332 -1.007 3.263 290s 18 1.948 0.232 -0.133 4.030 290s 19 0.666 0.298 -1.449 2.781 290s 20 1.987 0.350 -0.160 4.133 290s 21 4.081 0.317 1.955 6.207 290s 22 5.562 0.440 3.349 7.775 290s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 290s 1 NA NA NA NA 290s 2 26.8 0.352 25.1 28.6 290s 3 29.1 0.355 27.3 30.8 290s 4 33.0 0.358 31.2 34.7 290s 5 34.1 0.277 32.4 35.8 290s 6 35.9 0.276 34.3 37.6 290s 7 NA NA NA NA 290s 8 38.7 0.282 37.0 40.4 290s 9 38.9 0.268 37.3 40.6 290s 10 40.2 0.255 38.5 41.8 290s 11 38.1 0.351 36.4 39.8 290s 12 33.9 0.355 32.2 35.6 290s 13 28.9 0.421 27.1 30.7 290s 14 28.0 0.370 26.3 29.8 290s 15 30.3 0.364 28.6 32.0 290s 16 33.2 0.304 31.5 34.9 290s 17 37.7 0.298 36.0 39.3 290s 18 40.0 0.233 38.4 41.6 290s 19 38.7 0.349 36.9 40.4 290s 20 42.0 0.314 40.3 43.7 290s 21 46.1 0.328 44.4 47.8 290s 22 52.7 0.494 50.9 54.6 290s > model.frame 290s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 290s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 290s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 290s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 290s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 290s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 290s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 290s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 290s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 290s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 290s 10 57.8 21.7 21.1 NA 5.1 211 41.3 67.0 64.5 290s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 290s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 290s 13 45.6 7.0 11.4 34.3 -6.2 213 29.0 44.3 53.4 290s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 290s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 290s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 290s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 290s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 290s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 290s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 290s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 290s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 290s trend 290s 1 -11 290s 2 -10 290s 3 -9 290s 4 -8 290s 5 -7 290s 6 -6 290s 7 -5 290s 8 -4 290s 9 -3 290s 10 -2 290s 11 -1 290s 12 0 290s 13 1 290s 14 2 290s 15 3 290s 16 4 290s 17 5 290s 18 6 290s 19 7 290s 20 8 290s 21 9 290s 22 10 290s > model.matrix 290s Consumption_(Intercept) Consumption_corpProf 290s Consumption_2 1 12.4 290s Consumption_3 1 16.9 290s Consumption_4 1 18.4 290s Consumption_5 1 19.4 290s Consumption_6 1 20.1 290s Consumption_7 1 19.6 290s Consumption_8 1 19.8 290s Consumption_9 1 21.1 290s Consumption_11 1 15.6 290s Consumption_12 1 11.4 290s Consumption_13 1 7.0 290s Consumption_14 1 11.2 290s Consumption_15 1 12.3 290s Consumption_16 1 14.0 290s Consumption_17 1 17.6 290s Consumption_18 1 17.3 290s Consumption_19 1 15.3 290s Consumption_20 1 19.0 290s Consumption_21 1 21.1 290s Consumption_22 1 23.5 290s Investment_2 0 0.0 290s Investment_3 0 0.0 290s Investment_4 0 0.0 290s Investment_5 0 0.0 290s Investment_6 0 0.0 290s Investment_7 0 0.0 290s Investment_8 0 0.0 290s Investment_9 0 0.0 290s Investment_10 0 0.0 290s Investment_11 0 0.0 290s Investment_12 0 0.0 290s Investment_13 0 0.0 290s Investment_14 0 0.0 290s Investment_15 0 0.0 290s Investment_16 0 0.0 290s Investment_17 0 0.0 290s Investment_18 0 0.0 290s Investment_19 0 0.0 290s Investment_20 0 0.0 290s Investment_21 0 0.0 290s Investment_22 0 0.0 290s PrivateWages_2 0 0.0 290s PrivateWages_3 0 0.0 290s PrivateWages_4 0 0.0 290s PrivateWages_5 0 0.0 290s PrivateWages_6 0 0.0 290s PrivateWages_8 0 0.0 290s PrivateWages_9 0 0.0 290s PrivateWages_10 0 0.0 290s PrivateWages_11 0 0.0 290s PrivateWages_12 0 0.0 290s PrivateWages_13 0 0.0 290s PrivateWages_14 0 0.0 290s PrivateWages_15 0 0.0 290s PrivateWages_16 0 0.0 290s PrivateWages_17 0 0.0 290s PrivateWages_18 0 0.0 290s PrivateWages_19 0 0.0 290s PrivateWages_20 0 0.0 290s PrivateWages_21 0 0.0 290s PrivateWages_22 0 0.0 290s Consumption_corpProfLag Consumption_wages 290s Consumption_2 12.7 28.2 290s Consumption_3 12.4 32.2 290s Consumption_4 16.9 37.0 290s Consumption_5 18.4 37.0 290s Consumption_6 19.4 38.6 290s Consumption_7 20.1 40.7 290s Consumption_8 19.6 41.5 290s Consumption_9 19.8 42.9 290s Consumption_11 21.7 42.1 290s Consumption_12 15.6 39.3 290s Consumption_13 11.4 34.3 290s Consumption_14 7.0 34.1 290s Consumption_15 11.2 36.6 290s Consumption_16 12.3 39.3 290s Consumption_17 14.0 44.2 290s Consumption_18 17.6 47.7 290s Consumption_19 17.3 45.9 290s Consumption_20 15.3 49.4 290s Consumption_21 19.0 53.0 290s Consumption_22 21.1 61.8 290s Investment_2 0.0 0.0 290s Investment_3 0.0 0.0 290s Investment_4 0.0 0.0 290s Investment_5 0.0 0.0 290s Investment_6 0.0 0.0 290s Investment_7 0.0 0.0 290s Investment_8 0.0 0.0 290s Investment_9 0.0 0.0 290s Investment_10 0.0 0.0 290s Investment_11 0.0 0.0 290s Investment_12 0.0 0.0 290s Investment_13 0.0 0.0 290s Investment_14 0.0 0.0 290s Investment_15 0.0 0.0 290s Investment_16 0.0 0.0 290s Investment_17 0.0 0.0 290s Investment_18 0.0 0.0 290s Investment_19 0.0 0.0 290s Investment_20 0.0 0.0 290s Investment_21 0.0 0.0 290s Investment_22 0.0 0.0 290s PrivateWages_2 0.0 0.0 290s PrivateWages_3 0.0 0.0 290s PrivateWages_4 0.0 0.0 290s PrivateWages_5 0.0 0.0 290s PrivateWages_6 0.0 0.0 290s PrivateWages_8 0.0 0.0 290s PrivateWages_9 0.0 0.0 290s PrivateWages_10 0.0 0.0 290s PrivateWages_11 0.0 0.0 290s PrivateWages_12 0.0 0.0 290s PrivateWages_13 0.0 0.0 290s PrivateWages_14 0.0 0.0 290s PrivateWages_15 0.0 0.0 290s PrivateWages_16 0.0 0.0 290s PrivateWages_17 0.0 0.0 290s PrivateWages_18 0.0 0.0 290s PrivateWages_19 0.0 0.0 290s PrivateWages_20 0.0 0.0 290s PrivateWages_21 0.0 0.0 290s PrivateWages_22 0.0 0.0 290s Investment_(Intercept) Investment_corpProf 290s Consumption_2 0 0.0 290s Consumption_3 0 0.0 290s Consumption_4 0 0.0 290s Consumption_5 0 0.0 290s Consumption_6 0 0.0 290s Consumption_7 0 0.0 290s Consumption_8 0 0.0 290s Consumption_9 0 0.0 290s Consumption_11 0 0.0 290s Consumption_12 0 0.0 290s Consumption_13 0 0.0 290s Consumption_14 0 0.0 290s Consumption_15 0 0.0 290s Consumption_16 0 0.0 290s Consumption_17 0 0.0 290s Consumption_18 0 0.0 290s Consumption_19 0 0.0 290s Consumption_20 0 0.0 290s Consumption_21 0 0.0 290s Consumption_22 0 0.0 290s Investment_2 1 12.4 290s Investment_3 1 16.9 290s Investment_4 1 18.4 290s Investment_5 1 19.4 290s Investment_6 1 20.1 290s Investment_7 1 19.6 290s Investment_8 1 19.8 290s Investment_9 1 21.1 290s Investment_10 1 21.7 290s Investment_11 1 15.6 290s Investment_12 1 11.4 290s Investment_13 1 7.0 290s Investment_14 1 11.2 290s Investment_15 1 12.3 290s Investment_16 1 14.0 290s Investment_17 1 17.6 290s Investment_18 1 17.3 290s Investment_19 1 15.3 290s Investment_20 1 19.0 290s Investment_21 1 21.1 290s Investment_22 1 23.5 290s PrivateWages_2 0 0.0 290s PrivateWages_3 0 0.0 290s PrivateWages_4 0 0.0 290s PrivateWages_5 0 0.0 290s PrivateWages_6 0 0.0 290s PrivateWages_8 0 0.0 290s PrivateWages_9 0 0.0 290s PrivateWages_10 0 0.0 290s PrivateWages_11 0 0.0 290s PrivateWages_12 0 0.0 290s PrivateWages_13 0 0.0 290s PrivateWages_14 0 0.0 290s PrivateWages_15 0 0.0 290s PrivateWages_16 0 0.0 290s PrivateWages_17 0 0.0 290s PrivateWages_18 0 0.0 290s PrivateWages_19 0 0.0 290s PrivateWages_20 0 0.0 290s PrivateWages_21 0 0.0 290s PrivateWages_22 0 0.0 290s Investment_corpProfLag Investment_capitalLag 290s Consumption_2 0.0 0 290s Consumption_3 0.0 0 290s Consumption_4 0.0 0 290s Consumption_5 0.0 0 290s Consumption_6 0.0 0 290s Consumption_7 0.0 0 290s Consumption_8 0.0 0 290s Consumption_9 0.0 0 290s Consumption_11 0.0 0 290s Consumption_12 0.0 0 290s Consumption_13 0.0 0 290s Consumption_14 0.0 0 290s Consumption_15 0.0 0 290s Consumption_16 0.0 0 290s Consumption_17 0.0 0 290s Consumption_18 0.0 0 290s Consumption_19 0.0 0 290s Consumption_20 0.0 0 290s Consumption_21 0.0 0 290s Consumption_22 0.0 0 290s Investment_2 12.7 183 290s Investment_3 12.4 183 290s Investment_4 16.9 184 290s Investment_5 18.4 190 290s Investment_6 19.4 193 290s Investment_7 20.1 198 290s Investment_8 19.6 203 290s Investment_9 19.8 208 290s Investment_10 21.1 211 290s Investment_11 21.7 216 290s Investment_12 15.6 217 290s Investment_13 11.4 213 290s Investment_14 7.0 207 290s Investment_15 11.2 202 290s Investment_16 12.3 199 290s Investment_17 14.0 198 290s Investment_18 17.6 200 290s Investment_19 17.3 202 290s Investment_20 15.3 200 290s Investment_21 19.0 201 290s Investment_22 21.1 204 290s PrivateWages_2 0.0 0 290s PrivateWages_3 0.0 0 290s PrivateWages_4 0.0 0 290s PrivateWages_5 0.0 0 290s PrivateWages_6 0.0 0 290s PrivateWages_8 0.0 0 290s PrivateWages_9 0.0 0 290s PrivateWages_10 0.0 0 290s PrivateWages_11 0.0 0 290s PrivateWages_12 0.0 0 290s PrivateWages_13 0.0 0 290s PrivateWages_14 0.0 0 290s PrivateWages_15 0.0 0 290s PrivateWages_16 0.0 0 290s PrivateWages_17 0.0 0 290s PrivateWages_18 0.0 0 290s PrivateWages_19 0.0 0 290s PrivateWages_20 0.0 0 290s PrivateWages_21 0.0 0 290s PrivateWages_22 0.0 0 290s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 290s Consumption_2 0 0.0 0.0 290s Consumption_3 0 0.0 0.0 290s Consumption_4 0 0.0 0.0 290s Consumption_5 0 0.0 0.0 290s Consumption_6 0 0.0 0.0 290s Consumption_7 0 0.0 0.0 290s Consumption_8 0 0.0 0.0 290s Consumption_9 0 0.0 0.0 290s Consumption_11 0 0.0 0.0 290s Consumption_12 0 0.0 0.0 290s Consumption_13 0 0.0 0.0 290s Consumption_14 0 0.0 0.0 290s Consumption_15 0 0.0 0.0 290s Consumption_16 0 0.0 0.0 290s Consumption_17 0 0.0 0.0 290s Consumption_18 0 0.0 0.0 290s Consumption_19 0 0.0 0.0 290s Consumption_20 0 0.0 0.0 290s Consumption_21 0 0.0 0.0 290s Consumption_22 0 0.0 0.0 290s Investment_2 0 0.0 0.0 290s Investment_3 0 0.0 0.0 290s Investment_4 0 0.0 0.0 290s Investment_5 0 0.0 0.0 290s Investment_6 0 0.0 0.0 290s Investment_7 0 0.0 0.0 290s Investment_8 0 0.0 0.0 290s Investment_9 0 0.0 0.0 290s Investment_10 0 0.0 0.0 290s Investment_11 0 0.0 0.0 290s Investment_12 0 0.0 0.0 290s Investment_13 0 0.0 0.0 290s Investment_14 0 0.0 0.0 290s Investment_15 0 0.0 0.0 290s Investment_16 0 0.0 0.0 290s Investment_17 0 0.0 0.0 290s Investment_18 0 0.0 0.0 290s Investment_19 0 0.0 0.0 290s Investment_20 0 0.0 0.0 290s Investment_21 0 0.0 0.0 290s Investment_22 0 0.0 0.0 290s PrivateWages_2 1 45.6 44.9 290s PrivateWages_3 1 50.1 45.6 290s PrivateWages_4 1 57.2 50.1 290s PrivateWages_5 1 57.1 57.2 290s PrivateWages_6 1 61.0 57.1 290s PrivateWages_8 1 64.4 64.0 290s PrivateWages_9 1 64.5 64.4 290s PrivateWages_10 1 67.0 64.5 290s PrivateWages_11 1 61.2 67.0 290s PrivateWages_12 1 53.4 61.2 290s PrivateWages_13 1 44.3 53.4 290s PrivateWages_14 1 45.1 44.3 290s PrivateWages_15 1 49.7 45.1 290s PrivateWages_16 1 54.4 49.7 290s PrivateWages_17 1 62.7 54.4 290s PrivateWages_18 1 65.0 62.7 290s PrivateWages_19 1 60.9 65.0 290s PrivateWages_20 1 69.5 60.9 290s PrivateWages_21 1 75.7 69.5 290s PrivateWages_22 1 88.4 75.7 290s PrivateWages_trend 290s Consumption_2 0 290s Consumption_3 0 290s Consumption_4 0 290s Consumption_5 0 290s Consumption_6 0 290s Consumption_7 0 290s Consumption_8 0 290s Consumption_9 0 290s Consumption_11 0 290s Consumption_12 0 290s Consumption_13 0 290s Consumption_14 0 290s Consumption_15 0 290s Consumption_16 0 290s Consumption_17 0 290s Consumption_18 0 290s Consumption_19 0 290s Consumption_20 0 290s Consumption_21 0 290s Consumption_22 0 290s Investment_2 0 290s Investment_3 0 290s Investment_4 0 290s Investment_5 0 290s Investment_6 0 290s Investment_7 0 290s Investment_8 0 290s Investment_9 0 290s Investment_10 0 290s Investment_11 0 290s Investment_12 0 290s Investment_13 0 290s Investment_14 0 290s Investment_15 0 290s Investment_16 0 290s Investment_17 0 290s Investment_18 0 290s Investment_19 0 290s Investment_20 0 290s Investment_21 0 290s Investment_22 0 290s PrivateWages_2 -10 290s PrivateWages_3 -9 290s PrivateWages_4 -8 290s PrivateWages_5 -7 290s PrivateWages_6 -6 290s PrivateWages_8 -4 290s PrivateWages_9 -3 290s PrivateWages_10 -2 290s PrivateWages_11 -1 290s PrivateWages_12 0 290s PrivateWages_13 1 290s PrivateWages_14 2 290s PrivateWages_15 3 290s PrivateWages_16 4 290s PrivateWages_17 5 290s PrivateWages_18 6 290s PrivateWages_19 7 290s PrivateWages_20 8 290s PrivateWages_21 9 290s PrivateWages_22 10 290s > nobs 290s [1] 61 290s > linearHypothesis 290s Linear hypothesis test (Theil's F test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df F Pr(>F) 290s 1 50 290s 2 49 1 0.87 0.35 290s Linear hypothesis test (F statistic of a Wald test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df F Pr(>F) 290s 1 50 290s 2 49 1 0.8 0.38 290s Linear hypothesis test (Chi^2 statistic of a Wald test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df Chisq Pr(>Chisq) 290s 1 50 290s 2 49 1 0.8 0.37 290s Linear hypothesis test (Theil's F test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s Consumption_corpProfLag - PrivateWages_trend = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df F Pr(>F) 290s 1 51 290s 2 49 2 0.48 0.62 290s Linear hypothesis test (F statistic of a Wald test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s Consumption_corpProfLag - PrivateWages_trend = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df F Pr(>F) 290s 1 51 290s 2 49 2 0.43 0.65 290s Linear hypothesis test (Chi^2 statistic of a Wald test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s Consumption_corpProfLag - PrivateWages_trend = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df Chisq Pr(>Chisq) 290s 1 51 290s 2 49 2 0.87 0.65 290s > logLik 290s 'log Lik.' -71.7 (df=13) 290s 'log Lik.' -76.1 (df=13) 290s compare log likelihood value with single-equation OLS 290s [1] "Mean relative difference: 0.00159" 290s Estimating function 290s Consumption_(Intercept) Consumption_corpProf 290s Consumption_2 -0.3304 -4.097 290s Consumption_3 -1.2748 -21.544 290s Consumption_4 -1.6213 -29.832 290s Consumption_5 -0.5661 -10.982 290s Consumption_6 -0.0730 -1.467 290s Consumption_7 0.7915 15.513 290s Consumption_8 1.2648 25.043 290s Consumption_9 0.9746 20.563 290s Consumption_11 0.2225 3.470 290s Consumption_12 -0.2256 -2.572 290s Consumption_13 -0.2711 -1.898 290s Consumption_14 0.3765 4.217 290s Consumption_15 -0.0349 -0.429 290s Consumption_16 -0.0243 -0.341 290s Consumption_17 1.6023 28.201 290s Consumption_18 -0.4658 -8.058 290s Consumption_19 0.1914 2.928 290s Consumption_20 0.9683 18.397 290s Consumption_21 0.7325 15.456 290s Consumption_22 -2.2370 -52.569 290s Investment_2 0.0000 0.000 290s Investment_3 0.0000 0.000 290s Investment_4 0.0000 0.000 290s Investment_5 0.0000 0.000 290s Investment_6 0.0000 0.000 290s Investment_7 0.0000 0.000 290s Investment_8 0.0000 0.000 290s Investment_9 0.0000 0.000 290s Investment_10 0.0000 0.000 290s Investment_11 0.0000 0.000 290s Investment_12 0.0000 0.000 290s Investment_13 0.0000 0.000 290s Investment_14 0.0000 0.000 290s Investment_15 0.0000 0.000 290s Investment_16 0.0000 0.000 290s Investment_17 0.0000 0.000 290s Investment_18 0.0000 0.000 290s Investment_19 0.0000 0.000 290s Investment_20 0.0000 0.000 290s Investment_21 0.0000 0.000 290s Investment_22 0.0000 0.000 290s PrivateWages_2 0.0000 0.000 290s PrivateWages_3 0.0000 0.000 290s PrivateWages_4 0.0000 0.000 290s PrivateWages_5 0.0000 0.000 290s PrivateWages_6 0.0000 0.000 290s PrivateWages_8 0.0000 0.000 290s PrivateWages_9 0.0000 0.000 290s PrivateWages_10 0.0000 0.000 290s PrivateWages_11 0.0000 0.000 290s PrivateWages_12 0.0000 0.000 290s PrivateWages_13 0.0000 0.000 290s PrivateWages_14 0.0000 0.000 290s PrivateWages_15 0.0000 0.000 290s PrivateWages_16 0.0000 0.000 290s PrivateWages_17 0.0000 0.000 290s PrivateWages_18 0.0000 0.000 290s PrivateWages_19 0.0000 0.000 290s PrivateWages_20 0.0000 0.000 290s PrivateWages_21 0.0000 0.000 290s PrivateWages_22 0.0000 0.000 290s Consumption_corpProfLag Consumption_wages 290s Consumption_2 -4.196 -9.318 290s Consumption_3 -15.808 -41.049 290s Consumption_4 -27.400 -59.988 290s Consumption_5 -10.416 -20.944 290s Consumption_6 -1.416 -2.817 290s Consumption_7 15.908 32.212 290s Consumption_8 24.790 52.490 290s Consumption_9 19.296 41.809 290s Consumption_11 4.827 9.366 290s Consumption_12 -3.520 -8.867 290s Consumption_13 -3.091 -9.299 290s Consumption_14 2.636 12.839 290s Consumption_15 -0.391 -1.277 290s Consumption_16 -0.299 -0.957 290s Consumption_17 22.433 70.823 290s Consumption_18 -8.197 -22.217 290s Consumption_19 3.311 8.785 290s Consumption_20 14.815 47.833 290s Consumption_21 13.917 38.822 290s Consumption_22 -47.200 -138.245 290s Investment_2 0.000 0.000 290s Investment_3 0.000 0.000 290s Investment_4 0.000 0.000 290s Investment_5 0.000 0.000 290s Investment_6 0.000 0.000 290s Investment_7 0.000 0.000 290s Investment_8 0.000 0.000 290s Investment_9 0.000 0.000 290s Investment_10 0.000 0.000 290s Investment_11 0.000 0.000 290s Investment_12 0.000 0.000 290s Investment_13 0.000 0.000 290s Investment_14 0.000 0.000 290s Investment_15 0.000 0.000 290s Investment_16 0.000 0.000 290s Investment_17 0.000 0.000 290s Investment_18 0.000 0.000 290s Investment_19 0.000 0.000 290s Investment_20 0.000 0.000 290s Investment_21 0.000 0.000 290s Investment_22 0.000 0.000 290s PrivateWages_2 0.000 0.000 290s PrivateWages_3 0.000 0.000 290s PrivateWages_4 0.000 0.000 290s PrivateWages_5 0.000 0.000 290s PrivateWages_6 0.000 0.000 290s PrivateWages_8 0.000 0.000 290s PrivateWages_9 0.000 0.000 290s PrivateWages_10 0.000 0.000 290s PrivateWages_11 0.000 0.000 290s PrivateWages_12 0.000 0.000 290s PrivateWages_13 0.000 0.000 290s PrivateWages_14 0.000 0.000 290s PrivateWages_15 0.000 0.000 290s PrivateWages_16 0.000 0.000 290s PrivateWages_17 0.000 0.000 290s PrivateWages_18 0.000 0.000 290s PrivateWages_19 0.000 0.000 290s PrivateWages_20 0.000 0.000 290s PrivateWages_21 0.000 0.000 290s PrivateWages_22 0.000 0.000 290s Investment_(Intercept) Investment_corpProf 290s Consumption_2 0.0000 0.000 290s Consumption_3 0.0000 0.000 290s Consumption_4 0.0000 0.000 290s Consumption_5 0.0000 0.000 290s Consumption_6 0.0000 0.000 290s Consumption_7 0.0000 0.000 290s Consumption_8 0.0000 0.000 290s Consumption_9 0.0000 0.000 290s Consumption_11 0.0000 0.000 290s Consumption_12 0.0000 0.000 290s Consumption_13 0.0000 0.000 290s Consumption_14 0.0000 0.000 290s Consumption_15 0.0000 0.000 290s Consumption_16 0.0000 0.000 290s Consumption_17 0.0000 0.000 290s Consumption_18 0.0000 0.000 290s Consumption_19 0.0000 0.000 290s Consumption_20 0.0000 0.000 290s Consumption_21 0.0000 0.000 290s Consumption_22 0.0000 0.000 290s Investment_2 -0.0668 -0.828 290s Investment_3 -0.0476 -0.804 290s Investment_4 1.2467 22.939 290s Investment_5 -1.3512 -26.213 290s Investment_6 0.4154 8.350 290s Investment_7 1.4923 29.248 290s Investment_8 0.7889 15.620 290s Investment_9 -0.6317 -13.329 290s Investment_10 1.0830 23.500 290s Investment_11 0.2791 4.353 290s Investment_12 0.0369 0.420 290s Investment_13 0.3659 2.561 290s Investment_14 0.2237 2.505 290s Investment_15 -0.1728 -2.126 290s Investment_16 0.0101 0.141 290s Investment_17 0.9719 17.105 290s Investment_18 0.0516 0.893 290s Investment_19 -2.5656 -39.254 290s Investment_20 -0.6866 -13.045 290s Investment_21 -0.7807 -16.474 290s Investment_22 -0.6623 -15.565 290s PrivateWages_2 0.0000 0.000 290s PrivateWages_3 0.0000 0.000 290s PrivateWages_4 0.0000 0.000 290s PrivateWages_5 0.0000 0.000 290s PrivateWages_6 0.0000 0.000 290s PrivateWages_8 0.0000 0.000 290s PrivateWages_9 0.0000 0.000 290s PrivateWages_10 0.0000 0.000 290s PrivateWages_11 0.0000 0.000 290s PrivateWages_12 0.0000 0.000 290s PrivateWages_13 0.0000 0.000 290s PrivateWages_14 0.0000 0.000 290s PrivateWages_15 0.0000 0.000 290s PrivateWages_16 0.0000 0.000 290s PrivateWages_17 0.0000 0.000 290s PrivateWages_18 0.0000 0.000 290s PrivateWages_19 0.0000 0.000 290s PrivateWages_20 0.0000 0.000 290s PrivateWages_21 0.0000 0.000 290s PrivateWages_22 0.0000 0.000 290s Investment_corpProfLag Investment_capitalLag 290s Consumption_2 0.000 0.00 290s Consumption_3 0.000 0.00 290s Consumption_4 0.000 0.00 290s Consumption_5 0.000 0.00 290s Consumption_6 0.000 0.00 290s Consumption_7 0.000 0.00 290s Consumption_8 0.000 0.00 290s Consumption_9 0.000 0.00 290s Consumption_11 0.000 0.00 290s Consumption_12 0.000 0.00 290s Consumption_13 0.000 0.00 290s Consumption_14 0.000 0.00 290s Consumption_15 0.000 0.00 290s Consumption_16 0.000 0.00 290s Consumption_17 0.000 0.00 290s Consumption_18 0.000 0.00 290s Consumption_19 0.000 0.00 290s Consumption_20 0.000 0.00 290s Consumption_21 0.000 0.00 290s Consumption_22 0.000 0.00 290s Investment_2 -0.848 -12.21 290s Investment_3 -0.590 -8.69 290s Investment_4 21.069 230.01 290s Investment_5 -24.862 -256.32 290s Investment_6 8.059 80.05 290s Investment_7 29.994 295.17 290s Investment_8 15.463 160.46 290s Investment_9 -12.507 -131.14 290s Investment_10 22.850 228.07 290s Investment_11 6.056 60.20 290s Investment_12 0.575 7.99 290s Investment_13 4.172 78.05 290s Investment_14 1.566 46.33 290s Investment_15 -1.936 -34.91 290s Investment_16 0.124 2.01 290s Investment_17 13.606 192.14 290s Investment_18 0.908 10.31 290s Investment_19 -44.385 -517.74 290s Investment_20 -10.505 -137.25 290s Investment_21 -14.834 -157.09 290s Investment_22 -13.975 -135.45 290s PrivateWages_2 0.000 0.00 290s PrivateWages_3 0.000 0.00 290s PrivateWages_4 0.000 0.00 290s PrivateWages_5 0.000 0.00 290s PrivateWages_6 0.000 0.00 290s PrivateWages_8 0.000 0.00 290s PrivateWages_9 0.000 0.00 290s PrivateWages_10 0.000 0.00 290s PrivateWages_11 0.000 0.00 290s PrivateWages_12 0.000 0.00 290s PrivateWages_13 0.000 0.00 290s PrivateWages_14 0.000 0.00 290s PrivateWages_15 0.000 0.00 290s PrivateWages_16 0.000 0.00 290s PrivateWages_17 0.000 0.00 290s PrivateWages_18 0.000 0.00 290s PrivateWages_19 0.000 0.00 290s PrivateWages_20 0.000 0.00 290s PrivateWages_21 0.000 0.00 290s PrivateWages_22 0.000 0.00 290s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 290s Consumption_2 0.0000 0.00 0.00 290s Consumption_3 0.0000 0.00 0.00 290s Consumption_4 0.0000 0.00 0.00 290s Consumption_5 0.0000 0.00 0.00 290s Consumption_6 0.0000 0.00 0.00 290s Consumption_7 0.0000 0.00 0.00 290s Consumption_8 0.0000 0.00 0.00 290s Consumption_9 0.0000 0.00 0.00 290s Consumption_11 0.0000 0.00 0.00 290s Consumption_12 0.0000 0.00 0.00 290s Consumption_13 0.0000 0.00 0.00 290s Consumption_14 0.0000 0.00 0.00 290s Consumption_15 0.0000 0.00 0.00 290s Consumption_16 0.0000 0.00 0.00 290s Consumption_17 0.0000 0.00 0.00 290s Consumption_18 0.0000 0.00 0.00 290s Consumption_19 0.0000 0.00 0.00 290s Consumption_20 0.0000 0.00 0.00 290s Consumption_21 0.0000 0.00 0.00 290s Consumption_22 0.0000 0.00 0.00 290s Investment_2 0.0000 0.00 0.00 290s Investment_3 0.0000 0.00 0.00 290s Investment_4 0.0000 0.00 0.00 290s Investment_5 0.0000 0.00 0.00 290s Investment_6 0.0000 0.00 0.00 290s Investment_7 0.0000 0.00 0.00 290s Investment_8 0.0000 0.00 0.00 290s Investment_9 0.0000 0.00 0.00 290s Investment_10 0.0000 0.00 0.00 290s Investment_11 0.0000 0.00 0.00 290s Investment_12 0.0000 0.00 0.00 290s Investment_13 0.0000 0.00 0.00 290s Investment_14 0.0000 0.00 0.00 290s Investment_15 0.0000 0.00 0.00 290s Investment_16 0.0000 0.00 0.00 290s Investment_17 0.0000 0.00 0.00 290s Investment_18 0.0000 0.00 0.00 290s Investment_19 0.0000 0.00 0.00 290s Investment_20 0.0000 0.00 0.00 290s Investment_21 0.0000 0.00 0.00 290s Investment_22 0.0000 0.00 0.00 290s PrivateWages_2 -1.3389 -61.06 -60.12 290s PrivateWages_3 0.2462 12.33 11.23 290s PrivateWages_4 1.1255 64.38 56.39 290s PrivateWages_5 -0.1959 -11.18 -11.20 290s PrivateWages_6 -0.5284 -32.23 -30.17 290s PrivateWages_8 -0.7909 -50.94 -50.62 290s PrivateWages_9 0.2819 18.18 18.15 290s PrivateWages_10 1.1384 76.28 73.43 290s PrivateWages_11 -0.1904 -11.65 -12.76 290s PrivateWages_12 0.5813 31.04 35.58 290s PrivateWages_13 0.1206 5.34 6.44 290s PrivateWages_14 0.4773 21.53 21.14 290s PrivateWages_15 0.3035 15.09 13.69 290s PrivateWages_16 0.0284 1.55 1.41 290s PrivateWages_17 -0.8517 -53.40 -46.33 290s PrivateWages_18 0.9908 64.40 62.12 290s PrivateWages_19 -0.4597 -28.00 -29.88 290s PrivateWages_20 -0.3819 -26.54 -23.26 290s PrivateWages_21 -1.1062 -83.74 -76.88 290s PrivateWages_22 0.5501 48.63 41.64 290s PrivateWages_trend 290s Consumption_2 0.000 290s Consumption_3 0.000 290s Consumption_4 0.000 290s Consumption_5 0.000 290s Consumption_6 0.000 290s Consumption_7 0.000 290s Consumption_8 0.000 290s Consumption_9 0.000 290s Consumption_11 0.000 290s Consumption_12 0.000 290s Consumption_13 0.000 290s Consumption_14 0.000 290s Consumption_15 0.000 290s Consumption_16 0.000 290s Consumption_17 0.000 290s Consumption_18 0.000 290s Consumption_19 0.000 290s Consumption_20 0.000 290s Consumption_21 0.000 290s Consumption_22 0.000 290s Investment_2 0.000 290s Investment_3 0.000 290s Investment_4 0.000 290s Investment_5 0.000 290s Investment_6 0.000 290s Investment_7 0.000 290s Investment_8 0.000 290s Investment_9 0.000 290s Investment_10 0.000 290s Investment_11 0.000 290s Investment_12 0.000 290s Investment_13 0.000 290s Investment_14 0.000 290s Investment_15 0.000 290s Investment_16 0.000 290s Investment_17 0.000 290s Investment_18 0.000 290s Investment_19 0.000 290s Investment_20 0.000 290s Investment_21 0.000 290s Investment_22 0.000 290s PrivateWages_2 13.389 290s PrivateWages_3 -2.216 290s PrivateWages_4 -9.004 290s PrivateWages_5 1.371 290s PrivateWages_6 3.170 290s PrivateWages_8 3.164 290s PrivateWages_9 -0.846 290s PrivateWages_10 -2.277 290s PrivateWages_11 0.190 290s PrivateWages_12 0.000 290s PrivateWages_13 0.121 290s PrivateWages_14 0.955 290s PrivateWages_15 0.911 290s PrivateWages_16 0.114 290s PrivateWages_17 -4.258 290s PrivateWages_18 5.945 290s PrivateWages_19 -3.218 290s PrivateWages_20 -3.055 290s PrivateWages_21 -9.956 290s PrivateWages_22 5.501 290s [1] TRUE 290s > Bread 290s Consumption_(Intercept) Consumption_corpProf 290s Consumption_(Intercept) 99.9867 -0.0712 290s Consumption_corpProf -0.0712 0.4890 290s Consumption_corpProfLag -1.1355 -0.2987 290s Consumption_wages -1.8752 -0.0787 290s Investment_(Intercept) 0.0000 0.0000 290s Investment_corpProf 0.0000 0.0000 290s Investment_corpProfLag 0.0000 0.0000 290s Investment_capitalLag 0.0000 0.0000 290s PrivateWages_(Intercept) 0.0000 0.0000 290s PrivateWages_gnp 0.0000 0.0000 290s PrivateWages_gnpLag 0.0000 0.0000 290s PrivateWages_trend 0.0000 0.0000 290s Consumption_corpProfLag Consumption_wages 290s Consumption_(Intercept) -1.1355 -1.8752 290s Consumption_corpProf -0.2987 -0.0787 290s Consumption_corpProfLag 0.4841 -0.0413 290s Consumption_wages -0.0413 0.0933 290s Investment_(Intercept) 0.0000 0.0000 290s Investment_corpProf 0.0000 0.0000 290s Investment_corpProfLag 0.0000 0.0000 290s Investment_capitalLag 0.0000 0.0000 290s PrivateWages_(Intercept) 0.0000 0.0000 290s PrivateWages_gnp 0.0000 0.0000 290s PrivateWages_gnpLag 0.0000 0.0000 290s PrivateWages_trend 0.0000 0.0000 290s Investment_(Intercept) Investment_corpProf 290s Consumption_(Intercept) 0.0 0.0000 290s Consumption_corpProf 0.0 0.0000 290s Consumption_corpProfLag 0.0 0.0000 290s Consumption_wages 0.0 0.0000 290s Investment_(Intercept) 1788.3 -17.4004 290s Investment_corpProf -17.4 0.5646 290s Investment_corpProfLag 14.2 -0.4849 290s Investment_capitalLag -8.6 0.0788 290s PrivateWages_(Intercept) 0.0 0.0000 290s PrivateWages_gnp 0.0 0.0000 290s PrivateWages_gnpLag 0.0 0.0000 290s PrivateWages_trend 0.0 0.0000 290s Investment_corpProfLag Investment_capitalLag 290s Consumption_(Intercept) 0.0000 0.0000 290s Consumption_corpProf 0.0000 0.0000 290s Consumption_corpProfLag 0.0000 0.0000 290s Consumption_wages 0.0000 0.0000 290s Investment_(Intercept) 14.2083 -8.5994 290s Investment_corpProf -0.4849 0.0788 290s Investment_corpProfLag 0.6090 -0.0798 290s Investment_capitalLag -0.0798 0.0428 290s PrivateWages_(Intercept) 0.0000 0.0000 290s PrivateWages_gnp 0.0000 0.0000 290s PrivateWages_gnpLag 0.0000 0.0000 290s PrivateWages_trend 0.0000 0.0000 290s PrivateWages_(Intercept) PrivateWages_gnp 290s Consumption_(Intercept) 0.000 0.0000 290s Consumption_corpProf 0.000 0.0000 290s Consumption_corpProfLag 0.000 0.0000 290s Consumption_wages 0.000 0.0000 290s Investment_(Intercept) 0.000 0.0000 290s Investment_corpProf 0.000 0.0000 290s Investment_corpProfLag 0.000 0.0000 290s Investment_capitalLag 0.000 0.0000 290s PrivateWages_(Intercept) 171.811 -0.6470 290s PrivateWages_gnp -0.647 0.1100 290s PrivateWages_gnpLag -2.257 -0.1026 290s PrivateWages_trend 2.120 -0.0296 290s PrivateWages_gnpLag PrivateWages_trend 290s Consumption_(Intercept) 0.00000 0.00000 290s Consumption_corpProf 0.00000 0.00000 290s Consumption_corpProfLag 0.00000 0.00000 290s Consumption_wages 0.00000 0.00000 290s Investment_(Intercept) 0.00000 0.00000 290s Investment_corpProf 0.00000 0.00000 290s Investment_corpProfLag 0.00000 0.00000 290s Investment_capitalLag 0.00000 0.00000 290s PrivateWages_(Intercept) -2.25750 2.12030 290s PrivateWages_gnp -0.10258 -0.02955 290s PrivateWages_gnpLag 0.14523 -0.00656 290s PrivateWages_trend -0.00656 0.11341 290s > 290s > # 2SLS 290s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 290s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 290s > summary 290s 290s systemfit results 290s method: 2SLS 290s 290s N DF SSR detRCov OLS-R2 McElroy-R2 290s system 59 47 53.2 0.251 0.973 0.991 290s 290s N DF SSR MSE RMSE R2 Adj R2 290s Consumption 19 15 20.49 1.366 1.17 0.978 0.973 290s Investment 20 16 23.02 1.438 1.20 0.901 0.883 290s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 290s 290s The covariance matrix of the residuals 290s Consumption Investment PrivateWages 290s Consumption 1.079 0.354 -0.383 290s Investment 0.354 1.047 0.107 290s PrivateWages -0.383 0.107 0.445 290s 290s The correlations of the residuals 290s Consumption Investment PrivateWages 290s Consumption 1.000 0.335 -0.556 290s Investment 0.335 1.000 0.149 290s PrivateWages -0.556 0.149 1.000 290s 290s 290s 2SLS estimates for 'Consumption' (equation 1) 290s Model Formula: consump ~ corpProf + corpProfLag + wages 290s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 290s gnpLag 290s 290s Estimate Std. Error t value Pr(>|t|) 290s (Intercept) 16.4657 1.3505 12.19 3.5e-09 *** 290s corpProf 0.0243 0.1180 0.21 0.839 290s corpProfLag 0.1981 0.1087 1.82 0.088 . 290s wages 0.8159 0.0420 19.45 4.7e-12 *** 290s --- 290s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 290s 290s Residual standard error: 1.169 on 15 degrees of freedom 290s Number of observations: 19 Degrees of Freedom: 15 290s SSR: 20.493 MSE: 1.366 Root MSE: 1.169 290s Multiple R-Squared: 0.978 Adjusted R-Squared: 0.973 290s 290s 290s 2SLS estimates for 'Investment' (equation 2) 290s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 290s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 290s gnpLag 290s 290s Estimate Std. Error t value Pr(>|t|) 290s (Intercept) 17.8425 6.5319 2.73 0.01478 * 290s corpProf 0.2167 0.1478 1.47 0.16189 290s corpProfLag 0.5416 0.1415 3.83 0.00149 ** 290s capitalLag -0.1455 0.0314 -4.63 0.00028 *** 290s --- 290s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 290s 290s Residual standard error: 1.199 on 16 degrees of freedom 290s Number of observations: 20 Degrees of Freedom: 16 290s SSR: 23.016 MSE: 1.438 Root MSE: 1.199 290s Multiple R-Squared: 0.901 Adjusted R-Squared: 0.883 290s 290s 290s 2SLS estimates for 'PrivateWages' (equation 3) 290s Model Formula: privWage ~ gnp + gnpLag + trend 290s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 290s gnpLag 290s 290s Estimate Std. Error t value Pr(>|t|) 290s (Intercept) 1.3431 1.1250 1.19 0.24995 290s gnp 0.4438 0.0342 12.97 6.6e-10 *** 290s gnpLag 0.1447 0.0371 3.90 0.00128 ** 290s trend 0.1238 0.0292 4.24 0.00063 *** 290s --- 290s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 290s 290s Residual standard error: 0.78 on 16 degrees of freedom 290s Number of observations: 20 Degrees of Freedom: 16 290s SSR: 9.741 MSE: 0.609 Root MSE: 0.78 290s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 290s 290s > residuals 290s Consumption Investment PrivateWages 290s 1 NA NA NA 290s 2 -0.39161 -1.0104 -1.3401 290s 3 -0.60524 0.2478 0.2378 290s 4 -1.24952 1.0621 1.1117 290s 5 -0.17101 -1.4104 -0.1954 290s 6 0.30841 0.4328 -0.5355 290s 7 NA NA NA 290s 8 1.50999 1.0463 -0.7908 290s 9 1.39649 0.0674 0.2831 290s 10 NA 1.7698 1.1353 290s 11 -0.49339 -0.5912 -0.1765 290s 12 -0.99824 -0.6318 0.6007 290s 13 -1.27965 -0.6983 0.1443 290s 14 0.55302 0.9724 0.4826 290s 15 -0.14553 -0.1827 0.3016 290s 16 -0.00773 0.1167 0.0261 290s 17 1.97001 1.6266 -0.8614 290s 18 -0.59152 -0.0525 0.9927 290s 19 -0.21481 -3.0656 -0.4446 290s 20 1.33575 0.1393 -0.3914 290s 21 1.01443 -0.1305 -1.1115 290s 22 -1.93986 0.2922 0.5312 290s > fitted 290s Consumption Investment PrivateWages 290s 1 NA NA NA 290s 2 42.3 0.810 26.8 290s 3 45.6 1.652 29.1 290s 4 50.4 4.138 33.0 290s 5 50.8 4.410 34.1 290s 6 52.3 4.667 35.9 290s 7 NA NA NA 290s 8 54.7 3.154 38.7 290s 9 55.9 2.933 38.9 290s 10 NA 3.330 40.2 290s 11 55.5 1.591 38.1 290s 12 51.9 -2.768 33.9 290s 13 46.9 -5.502 28.9 290s 14 45.9 -6.072 28.0 290s 15 48.8 -2.817 30.3 290s 16 51.3 -1.417 33.2 290s 17 55.7 0.473 37.7 290s 18 59.3 2.053 40.0 290s 19 57.7 1.166 38.6 290s 20 60.3 1.161 42.0 290s 21 64.0 3.431 46.1 290s 22 71.6 4.608 52.8 290s > predict 290s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 290s 1 NA NA NA NA 290s 2 42.3 0.483 41.3 43.3 290s 3 45.6 0.586 44.4 46.9 290s 4 50.4 0.390 49.6 51.3 290s 5 50.8 0.456 49.8 51.7 290s 6 52.3 0.463 51.3 53.3 290s 7 NA NA NA NA 290s 8 54.7 0.382 53.9 55.5 290s 9 55.9 0.422 55.0 56.8 290s 10 NA NA NA NA 290s 11 55.5 0.742 53.9 57.1 290s 12 51.9 0.600 50.6 53.2 290s 13 46.9 0.770 45.2 48.5 290s 14 45.9 0.635 44.6 47.3 290s 15 48.8 0.383 48.0 49.7 290s 16 51.3 0.339 50.6 52.0 290s 17 55.7 0.410 54.9 56.6 290s 18 59.3 0.336 58.6 60.0 290s 19 57.7 0.418 56.8 58.6 290s 20 60.3 0.481 59.2 61.3 290s 21 64.0 0.462 63.0 65.0 290s 22 71.6 0.706 70.1 73.1 290s Investment.pred Investment.se.fit Investment.lwr Investment.upr 290s 1 NA NA NA NA 290s 2 0.810 0.750 -0.77956 2.400 290s 3 1.652 0.516 0.55883 2.746 290s 4 4.138 0.487 3.10541 5.170 290s 5 4.410 0.402 3.55860 5.262 290s 6 4.667 0.377 3.86830 5.466 290s 7 NA NA NA NA 290s 8 3.154 0.312 2.49238 3.815 290s 9 2.933 0.466 1.94478 3.920 290s 10 3.330 0.512 2.24435 4.416 290s 11 1.591 0.749 0.00249 3.180 290s 12 -2.768 0.586 -4.01111 -1.525 290s 13 -5.502 0.750 -7.09222 -3.911 290s 14 -6.072 0.803 -7.77404 -4.371 290s 15 -2.817 0.379 -3.62002 -2.015 290s 16 -1.417 0.327 -2.10985 -0.723 290s 17 0.473 0.436 -0.45046 1.397 290s 18 2.053 0.272 1.47523 2.630 290s 19 1.166 0.410 0.29710 2.034 290s 20 1.161 0.491 0.12044 2.201 290s 21 3.431 0.406 2.57004 4.291 290s 22 4.608 0.578 3.38197 5.834 290s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 290s 1 NA NA NA NA 290s 2 26.8 0.313 26.2 27.5 290s 3 29.1 0.325 28.4 29.8 290s 4 33.0 0.344 32.3 33.7 290s 5 34.1 0.246 33.6 34.6 290s 6 35.9 0.254 35.4 36.5 290s 7 NA NA NA NA 290s 8 38.7 0.251 38.2 39.2 290s 9 38.9 0.239 38.4 39.4 290s 10 40.2 0.229 39.7 40.7 290s 11 38.1 0.339 37.4 38.8 290s 12 33.9 0.365 33.1 34.7 290s 13 28.9 0.436 27.9 29.8 290s 14 28.0 0.333 27.3 28.7 290s 15 30.3 0.324 29.6 31.0 290s 16 33.2 0.271 32.6 33.7 290s 17 37.7 0.280 37.1 38.3 290s 18 40.0 0.208 39.6 40.4 290s 19 38.6 0.342 37.9 39.4 290s 20 42.0 0.293 41.4 42.6 290s 21 46.1 0.296 45.5 46.7 290s 22 52.8 0.474 51.8 53.8 290s > model.frame 290s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 290s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 290s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 290s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 290s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 290s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 290s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 290s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 290s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 290s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 290s 10 57.8 21.7 21.1 NA 5.1 211 41.3 67.0 64.5 290s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 290s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 290s 13 45.6 7.0 11.4 34.3 -6.2 213 29.0 44.3 53.4 290s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 290s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 290s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 290s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 290s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 290s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 290s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 290s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 290s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 290s trend 290s 1 -11 290s 2 -10 290s 3 -9 290s 4 -8 290s 5 -7 290s 6 -6 290s 7 -5 290s 8 -4 290s 9 -3 290s 10 -2 290s 11 -1 290s 12 0 290s 13 1 290s 14 2 290s 15 3 290s 16 4 290s 17 5 290s 18 6 290s 19 7 290s 20 8 290s 21 9 290s 22 10 290s > Frames of instrumental variables 290s govExp taxes govWage trend capitalLag corpProfLag gnpLag 290s 1 2.4 3.4 2.2 -11 180 NA NA 290s 2 3.9 7.7 2.7 -10 183 12.7 44.9 290s 3 3.2 3.9 2.9 -9 183 12.4 45.6 290s 4 2.8 4.7 2.9 -8 184 16.9 50.1 290s 5 3.5 3.8 3.1 -7 190 18.4 57.2 290s 6 3.3 5.5 3.2 -6 193 19.4 57.1 290s 7 3.3 7.0 3.3 -5 198 20.1 NA 290s 8 4.0 6.7 3.6 -4 203 19.6 64.0 290s 9 4.2 4.2 3.7 -3 208 19.8 64.4 290s 10 4.1 4.0 4.0 -2 211 21.1 64.5 290s 11 5.2 7.7 4.2 -1 216 21.7 67.0 290s 12 5.9 7.5 4.8 0 217 15.6 61.2 290s 13 4.9 8.3 5.3 1 213 11.4 53.4 290s 14 3.7 5.4 5.6 2 207 7.0 44.3 290s 15 4.0 6.8 6.0 3 202 11.2 45.1 290s 16 4.4 7.2 6.1 4 199 12.3 49.7 290s 17 2.9 8.3 7.4 5 198 14.0 54.4 290s 18 4.3 6.7 6.7 6 200 17.6 62.7 290s 19 5.3 7.4 7.7 7 202 17.3 65.0 290s 20 6.6 8.9 7.8 8 200 15.3 60.9 290s 21 7.4 9.6 8.0 9 201 19.0 69.5 290s 22 13.8 11.6 8.5 10 204 21.1 75.7 290s govExp taxes govWage trend capitalLag corpProfLag gnpLag 290s 1 2.4 3.4 2.2 -11 180 NA NA 290s 2 3.9 7.7 2.7 -10 183 12.7 44.9 290s 3 3.2 3.9 2.9 -9 183 12.4 45.6 290s 4 2.8 4.7 2.9 -8 184 16.9 50.1 290s 5 3.5 3.8 3.1 -7 190 18.4 57.2 290s 6 3.3 5.5 3.2 -6 193 19.4 57.1 290s 7 3.3 7.0 3.3 -5 198 20.1 NA 290s 8 4.0 6.7 3.6 -4 203 19.6 64.0 290s 9 4.2 4.2 3.7 -3 208 19.8 64.4 290s 10 4.1 4.0 4.0 -2 211 21.1 64.5 290s 11 5.2 7.7 4.2 -1 216 21.7 67.0 290s 12 5.9 7.5 4.8 0 217 15.6 61.2 290s 13 4.9 8.3 5.3 1 213 11.4 53.4 290s 14 3.7 5.4 5.6 2 207 7.0 44.3 290s 15 4.0 6.8 6.0 3 202 11.2 45.1 290s 16 4.4 7.2 6.1 4 199 12.3 49.7 290s 17 2.9 8.3 7.4 5 198 14.0 54.4 290s 18 4.3 6.7 6.7 6 200 17.6 62.7 290s 19 5.3 7.4 7.7 7 202 17.3 65.0 290s 20 6.6 8.9 7.8 8 200 15.3 60.9 290s 21 7.4 9.6 8.0 9 201 19.0 69.5 290s 22 13.8 11.6 8.5 10 204 21.1 75.7 290s govExp taxes govWage trend capitalLag corpProfLag gnpLag 290s 1 2.4 3.4 2.2 -11 180 NA NA 290s 2 3.9 7.7 2.7 -10 183 12.7 44.9 290s 3 3.2 3.9 2.9 -9 183 12.4 45.6 290s 4 2.8 4.7 2.9 -8 184 16.9 50.1 290s 5 3.5 3.8 3.1 -7 190 18.4 57.2 290s 6 3.3 5.5 3.2 -6 193 19.4 57.1 290s 7 3.3 7.0 3.3 -5 198 20.1 NA 290s 8 4.0 6.7 3.6 -4 203 19.6 64.0 290s 9 4.2 4.2 3.7 -3 208 19.8 64.4 290s 10 4.1 4.0 4.0 -2 211 21.1 64.5 290s 11 5.2 7.7 4.2 -1 216 21.7 67.0 290s 12 5.9 7.5 4.8 0 217 15.6 61.2 290s 13 4.9 8.3 5.3 1 213 11.4 53.4 290s 14 3.7 5.4 5.6 2 207 7.0 44.3 290s 15 4.0 6.8 6.0 3 202 11.2 45.1 290s 16 4.4 7.2 6.1 4 199 12.3 49.7 290s 17 2.9 8.3 7.4 5 198 14.0 54.4 290s 18 4.3 6.7 6.7 6 200 17.6 62.7 290s 19 5.3 7.4 7.7 7 202 17.3 65.0 290s 20 6.6 8.9 7.8 8 200 15.3 60.9 290s 21 7.4 9.6 8.0 9 201 19.0 69.5 290s 22 13.8 11.6 8.5 10 204 21.1 75.7 290s > model.matrix 290s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0328 >" 290s [2] "Attributes: < Component “dimnames”: Component 1: 54 string mismatches >" 290s [3] "Numeric: lengths (732, 708) differ" 290s > matrix of instrumental variables 290s Consumption_(Intercept) Consumption_govExp Consumption_taxes 290s Consumption_2 1 3.9 7.7 290s Consumption_3 1 3.2 3.9 290s Consumption_4 1 2.8 4.7 290s Consumption_5 1 3.5 3.8 290s Consumption_6 1 3.3 5.5 290s Consumption_8 1 4.0 6.7 290s Consumption_9 1 4.2 4.2 290s Consumption_11 1 5.2 7.7 290s Consumption_12 1 5.9 7.5 290s Consumption_13 1 4.9 8.3 290s Consumption_14 1 3.7 5.4 290s Consumption_15 1 4.0 6.8 290s Consumption_16 1 4.4 7.2 290s Consumption_17 1 2.9 8.3 290s Consumption_18 1 4.3 6.7 290s Consumption_19 1 5.3 7.4 290s Consumption_20 1 6.6 8.9 290s Consumption_21 1 7.4 9.6 290s Consumption_22 1 13.8 11.6 290s Investment_2 0 0.0 0.0 290s Investment_3 0 0.0 0.0 290s Investment_4 0 0.0 0.0 290s Investment_5 0 0.0 0.0 290s Investment_6 0 0.0 0.0 290s Investment_8 0 0.0 0.0 290s Investment_9 0 0.0 0.0 290s Investment_10 0 0.0 0.0 290s Investment_11 0 0.0 0.0 290s Investment_12 0 0.0 0.0 290s Investment_13 0 0.0 0.0 290s Investment_14 0 0.0 0.0 290s Investment_15 0 0.0 0.0 290s Investment_16 0 0.0 0.0 290s Investment_17 0 0.0 0.0 290s Investment_18 0 0.0 0.0 290s Investment_19 0 0.0 0.0 290s Investment_20 0 0.0 0.0 290s Investment_21 0 0.0 0.0 290s Investment_22 0 0.0 0.0 290s PrivateWages_2 0 0.0 0.0 290s PrivateWages_3 0 0.0 0.0 290s PrivateWages_4 0 0.0 0.0 290s PrivateWages_5 0 0.0 0.0 290s PrivateWages_6 0 0.0 0.0 290s PrivateWages_8 0 0.0 0.0 290s PrivateWages_9 0 0.0 0.0 290s PrivateWages_10 0 0.0 0.0 290s PrivateWages_11 0 0.0 0.0 290s PrivateWages_12 0 0.0 0.0 290s PrivateWages_13 0 0.0 0.0 290s PrivateWages_14 0 0.0 0.0 290s PrivateWages_15 0 0.0 0.0 290s PrivateWages_16 0 0.0 0.0 290s PrivateWages_17 0 0.0 0.0 290s PrivateWages_18 0 0.0 0.0 290s PrivateWages_19 0 0.0 0.0 290s PrivateWages_20 0 0.0 0.0 290s PrivateWages_21 0 0.0 0.0 290s PrivateWages_22 0 0.0 0.0 290s Consumption_govWage Consumption_trend Consumption_capitalLag 290s Consumption_2 2.7 -10 183 290s Consumption_3 2.9 -9 183 290s Consumption_4 2.9 -8 184 290s Consumption_5 3.1 -7 190 290s Consumption_6 3.2 -6 193 290s Consumption_8 3.6 -4 203 290s Consumption_9 3.7 -3 208 290s Consumption_11 4.2 -1 216 290s Consumption_12 4.8 0 217 290s Consumption_13 5.3 1 213 290s Consumption_14 5.6 2 207 290s Consumption_15 6.0 3 202 290s Consumption_16 6.1 4 199 290s Consumption_17 7.4 5 198 290s Consumption_18 6.7 6 200 290s Consumption_19 7.7 7 202 290s Consumption_20 7.8 8 200 290s Consumption_21 8.0 9 201 290s Consumption_22 8.5 10 204 290s Investment_2 0.0 0 0 290s Investment_3 0.0 0 0 290s Investment_4 0.0 0 0 290s Investment_5 0.0 0 0 290s Investment_6 0.0 0 0 290s Investment_8 0.0 0 0 290s Investment_9 0.0 0 0 290s Investment_10 0.0 0 0 290s Investment_11 0.0 0 0 290s Investment_12 0.0 0 0 290s Investment_13 0.0 0 0 290s Investment_14 0.0 0 0 290s Investment_15 0.0 0 0 290s Investment_16 0.0 0 0 290s Investment_17 0.0 0 0 290s Investment_18 0.0 0 0 290s Investment_19 0.0 0 0 290s Investment_20 0.0 0 0 290s Investment_21 0.0 0 0 290s Investment_22 0.0 0 0 290s PrivateWages_2 0.0 0 0 290s PrivateWages_3 0.0 0 0 290s PrivateWages_4 0.0 0 0 290s PrivateWages_5 0.0 0 0 290s PrivateWages_6 0.0 0 0 290s PrivateWages_8 0.0 0 0 290s PrivateWages_9 0.0 0 0 290s PrivateWages_10 0.0 0 0 290s PrivateWages_11 0.0 0 0 290s PrivateWages_12 0.0 0 0 290s PrivateWages_13 0.0 0 0 290s PrivateWages_14 0.0 0 0 290s PrivateWages_15 0.0 0 0 290s PrivateWages_16 0.0 0 0 290s PrivateWages_17 0.0 0 0 290s PrivateWages_18 0.0 0 0 290s PrivateWages_19 0.0 0 0 290s PrivateWages_20 0.0 0 0 290s PrivateWages_21 0.0 0 0 290s PrivateWages_22 0.0 0 0 290s Consumption_corpProfLag Consumption_gnpLag 290s Consumption_2 12.7 44.9 290s Consumption_3 12.4 45.6 290s Consumption_4 16.9 50.1 290s Consumption_5 18.4 57.2 290s Consumption_6 19.4 57.1 290s Consumption_8 19.6 64.0 290s Consumption_9 19.8 64.4 290s Consumption_11 21.7 67.0 290s Consumption_12 15.6 61.2 290s Consumption_13 11.4 53.4 290s Consumption_14 7.0 44.3 290s Consumption_15 11.2 45.1 290s Consumption_16 12.3 49.7 290s Consumption_17 14.0 54.4 290s Consumption_18 17.6 62.7 290s Consumption_19 17.3 65.0 290s Consumption_20 15.3 60.9 290s Consumption_21 19.0 69.5 290s Consumption_22 21.1 75.7 290s Investment_2 0.0 0.0 290s Investment_3 0.0 0.0 290s Investment_4 0.0 0.0 290s Investment_5 0.0 0.0 290s Investment_6 0.0 0.0 290s Investment_8 0.0 0.0 290s Investment_9 0.0 0.0 290s Investment_10 0.0 0.0 290s Investment_11 0.0 0.0 290s Investment_12 0.0 0.0 290s Investment_13 0.0 0.0 290s Investment_14 0.0 0.0 290s Investment_15 0.0 0.0 290s Investment_16 0.0 0.0 290s Investment_17 0.0 0.0 290s Investment_18 0.0 0.0 290s Investment_19 0.0 0.0 290s Investment_20 0.0 0.0 290s Investment_21 0.0 0.0 290s Investment_22 0.0 0.0 290s PrivateWages_2 0.0 0.0 290s PrivateWages_3 0.0 0.0 290s PrivateWages_4 0.0 0.0 290s PrivateWages_5 0.0 0.0 290s PrivateWages_6 0.0 0.0 290s PrivateWages_8 0.0 0.0 290s PrivateWages_9 0.0 0.0 290s PrivateWages_10 0.0 0.0 290s PrivateWages_11 0.0 0.0 290s PrivateWages_12 0.0 0.0 290s PrivateWages_13 0.0 0.0 290s PrivateWages_14 0.0 0.0 290s PrivateWages_15 0.0 0.0 290s PrivateWages_16 0.0 0.0 290s PrivateWages_17 0.0 0.0 290s PrivateWages_18 0.0 0.0 290s PrivateWages_19 0.0 0.0 290s PrivateWages_20 0.0 0.0 290s PrivateWages_21 0.0 0.0 290s PrivateWages_22 0.0 0.0 290s Investment_(Intercept) Investment_govExp Investment_taxes 290s Consumption_2 0 0.0 0.0 290s Consumption_3 0 0.0 0.0 290s Consumption_4 0 0.0 0.0 290s Consumption_5 0 0.0 0.0 290s Consumption_6 0 0.0 0.0 290s Consumption_8 0 0.0 0.0 290s Consumption_9 0 0.0 0.0 290s Consumption_11 0 0.0 0.0 290s Consumption_12 0 0.0 0.0 290s Consumption_13 0 0.0 0.0 290s Consumption_14 0 0.0 0.0 290s Consumption_15 0 0.0 0.0 290s Consumption_16 0 0.0 0.0 290s Consumption_17 0 0.0 0.0 290s Consumption_18 0 0.0 0.0 290s Consumption_19 0 0.0 0.0 290s Consumption_20 0 0.0 0.0 290s Consumption_21 0 0.0 0.0 290s Consumption_22 0 0.0 0.0 290s Investment_2 1 3.9 7.7 290s Investment_3 1 3.2 3.9 290s Investment_4 1 2.8 4.7 290s Investment_5 1 3.5 3.8 290s Investment_6 1 3.3 5.5 290s Investment_8 1 4.0 6.7 290s Investment_9 1 4.2 4.2 290s Investment_10 1 4.1 4.0 290s Investment_11 1 5.2 7.7 290s Investment_12 1 5.9 7.5 290s Investment_13 1 4.9 8.3 290s Investment_14 1 3.7 5.4 290s Investment_15 1 4.0 6.8 290s Investment_16 1 4.4 7.2 290s Investment_17 1 2.9 8.3 290s Investment_18 1 4.3 6.7 290s Investment_19 1 5.3 7.4 290s Investment_20 1 6.6 8.9 290s Investment_21 1 7.4 9.6 290s Investment_22 1 13.8 11.6 290s PrivateWages_2 0 0.0 0.0 290s PrivateWages_3 0 0.0 0.0 290s PrivateWages_4 0 0.0 0.0 290s PrivateWages_5 0 0.0 0.0 290s PrivateWages_6 0 0.0 0.0 290s PrivateWages_8 0 0.0 0.0 290s PrivateWages_9 0 0.0 0.0 290s PrivateWages_10 0 0.0 0.0 290s PrivateWages_11 0 0.0 0.0 290s PrivateWages_12 0 0.0 0.0 290s PrivateWages_13 0 0.0 0.0 290s PrivateWages_14 0 0.0 0.0 290s PrivateWages_15 0 0.0 0.0 290s PrivateWages_16 0 0.0 0.0 290s PrivateWages_17 0 0.0 0.0 290s PrivateWages_18 0 0.0 0.0 290s PrivateWages_19 0 0.0 0.0 290s PrivateWages_20 0 0.0 0.0 290s PrivateWages_21 0 0.0 0.0 290s PrivateWages_22 0 0.0 0.0 290s Investment_govWage Investment_trend Investment_capitalLag 290s Consumption_2 0.0 0 0 290s Consumption_3 0.0 0 0 290s Consumption_4 0.0 0 0 290s Consumption_5 0.0 0 0 290s Consumption_6 0.0 0 0 290s Consumption_8 0.0 0 0 290s Consumption_9 0.0 0 0 290s Consumption_11 0.0 0 0 290s Consumption_12 0.0 0 0 290s Consumption_13 0.0 0 0 290s Consumption_14 0.0 0 0 290s Consumption_15 0.0 0 0 290s Consumption_16 0.0 0 0 290s Consumption_17 0.0 0 0 290s Consumption_18 0.0 0 0 290s Consumption_19 0.0 0 0 290s Consumption_20 0.0 0 0 290s Consumption_21 0.0 0 0 290s Consumption_22 0.0 0 0 290s Investment_2 2.7 -10 183 290s Investment_3 2.9 -9 183 290s Investment_4 2.9 -8 184 290s Investment_5 3.1 -7 190 290s Investment_6 3.2 -6 193 290s Investment_8 3.6 -4 203 290s Investment_9 3.7 -3 208 290s Investment_10 4.0 -2 211 290s Investment_11 4.2 -1 216 290s Investment_12 4.8 0 217 290s Investment_13 5.3 1 213 290s Investment_14 5.6 2 207 290s Investment_15 6.0 3 202 290s Investment_16 6.1 4 199 290s Investment_17 7.4 5 198 290s Investment_18 6.7 6 200 290s Investment_19 7.7 7 202 290s Investment_20 7.8 8 200 290s Investment_21 8.0 9 201 290s Investment_22 8.5 10 204 290s PrivateWages_2 0.0 0 0 290s PrivateWages_3 0.0 0 0 290s PrivateWages_4 0.0 0 0 290s PrivateWages_5 0.0 0 0 290s PrivateWages_6 0.0 0 0 290s PrivateWages_8 0.0 0 0 290s PrivateWages_9 0.0 0 0 290s PrivateWages_10 0.0 0 0 290s PrivateWages_11 0.0 0 0 290s PrivateWages_12 0.0 0 0 290s PrivateWages_13 0.0 0 0 290s PrivateWages_14 0.0 0 0 290s PrivateWages_15 0.0 0 0 290s PrivateWages_16 0.0 0 0 290s PrivateWages_17 0.0 0 0 290s PrivateWages_18 0.0 0 0 290s PrivateWages_19 0.0 0 0 290s PrivateWages_20 0.0 0 0 290s PrivateWages_21 0.0 0 0 290s PrivateWages_22 0.0 0 0 290s Investment_corpProfLag Investment_gnpLag 290s Consumption_2 0.0 0.0 290s Consumption_3 0.0 0.0 290s Consumption_4 0.0 0.0 290s Consumption_5 0.0 0.0 290s Consumption_6 0.0 0.0 290s Consumption_8 0.0 0.0 290s Consumption_9 0.0 0.0 290s Consumption_11 0.0 0.0 290s Consumption_12 0.0 0.0 290s Consumption_13 0.0 0.0 290s Consumption_14 0.0 0.0 290s Consumption_15 0.0 0.0 290s Consumption_16 0.0 0.0 290s Consumption_17 0.0 0.0 290s Consumption_18 0.0 0.0 290s Consumption_19 0.0 0.0 290s Consumption_20 0.0 0.0 290s Consumption_21 0.0 0.0 290s Consumption_22 0.0 0.0 290s Investment_2 12.7 44.9 290s Investment_3 12.4 45.6 290s Investment_4 16.9 50.1 290s Investment_5 18.4 57.2 290s Investment_6 19.4 57.1 290s Investment_8 19.6 64.0 290s Investment_9 19.8 64.4 290s Investment_10 21.1 64.5 290s Investment_11 21.7 67.0 290s Investment_12 15.6 61.2 290s Investment_13 11.4 53.4 290s Investment_14 7.0 44.3 290s Investment_15 11.2 45.1 290s Investment_16 12.3 49.7 290s Investment_17 14.0 54.4 290s Investment_18 17.6 62.7 290s Investment_19 17.3 65.0 290s Investment_20 15.3 60.9 290s Investment_21 19.0 69.5 290s Investment_22 21.1 75.7 290s PrivateWages_2 0.0 0.0 290s PrivateWages_3 0.0 0.0 290s PrivateWages_4 0.0 0.0 290s PrivateWages_5 0.0 0.0 290s PrivateWages_6 0.0 0.0 290s PrivateWages_8 0.0 0.0 290s PrivateWages_9 0.0 0.0 290s PrivateWages_10 0.0 0.0 290s PrivateWages_11 0.0 0.0 290s PrivateWages_12 0.0 0.0 290s PrivateWages_13 0.0 0.0 290s PrivateWages_14 0.0 0.0 290s PrivateWages_15 0.0 0.0 290s PrivateWages_16 0.0 0.0 290s PrivateWages_17 0.0 0.0 290s PrivateWages_18 0.0 0.0 290s PrivateWages_19 0.0 0.0 290s PrivateWages_20 0.0 0.0 290s PrivateWages_21 0.0 0.0 290s PrivateWages_22 0.0 0.0 290s PrivateWages_(Intercept) PrivateWages_govExp PrivateWages_taxes 290s Consumption_2 0 0.0 0.0 290s Consumption_3 0 0.0 0.0 290s Consumption_4 0 0.0 0.0 290s Consumption_5 0 0.0 0.0 290s Consumption_6 0 0.0 0.0 290s Consumption_8 0 0.0 0.0 290s Consumption_9 0 0.0 0.0 290s Consumption_11 0 0.0 0.0 290s Consumption_12 0 0.0 0.0 290s Consumption_13 0 0.0 0.0 290s Consumption_14 0 0.0 0.0 290s Consumption_15 0 0.0 0.0 290s Consumption_16 0 0.0 0.0 290s Consumption_17 0 0.0 0.0 290s Consumption_18 0 0.0 0.0 290s Consumption_19 0 0.0 0.0 290s Consumption_20 0 0.0 0.0 290s Consumption_21 0 0.0 0.0 290s Consumption_22 0 0.0 0.0 290s Investment_2 0 0.0 0.0 290s Investment_3 0 0.0 0.0 290s Investment_4 0 0.0 0.0 290s Investment_5 0 0.0 0.0 290s Investment_6 0 0.0 0.0 290s Investment_8 0 0.0 0.0 290s Investment_9 0 0.0 0.0 290s Investment_10 0 0.0 0.0 290s Investment_11 0 0.0 0.0 290s Investment_12 0 0.0 0.0 290s Investment_13 0 0.0 0.0 290s Investment_14 0 0.0 0.0 290s Investment_15 0 0.0 0.0 290s Investment_16 0 0.0 0.0 290s Investment_17 0 0.0 0.0 290s Investment_18 0 0.0 0.0 290s Investment_19 0 0.0 0.0 290s Investment_20 0 0.0 0.0 290s Investment_21 0 0.0 0.0 290s Investment_22 0 0.0 0.0 290s PrivateWages_2 1 3.9 7.7 290s PrivateWages_3 1 3.2 3.9 290s PrivateWages_4 1 2.8 4.7 290s PrivateWages_5 1 3.5 3.8 290s PrivateWages_6 1 3.3 5.5 290s PrivateWages_8 1 4.0 6.7 290s PrivateWages_9 1 4.2 4.2 290s PrivateWages_10 1 4.1 4.0 290s PrivateWages_11 1 5.2 7.7 290s PrivateWages_12 1 5.9 7.5 290s PrivateWages_13 1 4.9 8.3 290s PrivateWages_14 1 3.7 5.4 290s PrivateWages_15 1 4.0 6.8 290s PrivateWages_16 1 4.4 7.2 290s PrivateWages_17 1 2.9 8.3 290s PrivateWages_18 1 4.3 6.7 290s PrivateWages_19 1 5.3 7.4 290s PrivateWages_20 1 6.6 8.9 290s PrivateWages_21 1 7.4 9.6 290s PrivateWages_22 1 13.8 11.6 290s PrivateWages_govWage PrivateWages_trend PrivateWages_capitalLag 290s Consumption_2 0.0 0 0 290s Consumption_3 0.0 0 0 290s Consumption_4 0.0 0 0 290s Consumption_5 0.0 0 0 290s Consumption_6 0.0 0 0 290s Consumption_8 0.0 0 0 290s Consumption_9 0.0 0 0 290s Consumption_11 0.0 0 0 290s Consumption_12 0.0 0 0 290s Consumption_13 0.0 0 0 290s Consumption_14 0.0 0 0 290s Consumption_15 0.0 0 0 290s Consumption_16 0.0 0 0 290s Consumption_17 0.0 0 0 290s Consumption_18 0.0 0 0 290s Consumption_19 0.0 0 0 290s Consumption_20 0.0 0 0 290s Consumption_21 0.0 0 0 290s Consumption_22 0.0 0 0 290s Investment_2 0.0 0 0 290s Investment_3 0.0 0 0 290s Investment_4 0.0 0 0 290s Investment_5 0.0 0 0 290s Investment_6 0.0 0 0 290s Investment_8 0.0 0 0 290s Investment_9 0.0 0 0 290s Investment_10 0.0 0 0 290s Investment_11 0.0 0 0 290s Investment_12 0.0 0 0 290s Investment_13 0.0 0 0 290s Investment_14 0.0 0 0 290s Investment_15 0.0 0 0 290s Investment_16 0.0 0 0 290s Investment_17 0.0 0 0 290s Investment_18 0.0 0 0 290s Investment_19 0.0 0 0 290s Investment_20 0.0 0 0 290s Investment_21 0.0 0 0 290s Investment_22 0.0 0 0 290s PrivateWages_2 2.7 -10 183 290s PrivateWages_3 2.9 -9 183 290s PrivateWages_4 2.9 -8 184 290s PrivateWages_5 3.1 -7 190 290s PrivateWages_6 3.2 -6 193 290s PrivateWages_8 3.6 -4 203 290s PrivateWages_9 3.7 -3 208 290s PrivateWages_10 4.0 -2 211 290s PrivateWages_11 4.2 -1 216 290s PrivateWages_12 4.8 0 217 290s PrivateWages_13 5.3 1 213 290s PrivateWages_14 5.6 2 207 290s PrivateWages_15 6.0 3 202 290s PrivateWages_16 6.1 4 199 290s PrivateWages_17 7.4 5 198 290s PrivateWages_18 6.7 6 200 290s PrivateWages_19 7.7 7 202 290s PrivateWages_20 7.8 8 200 290s PrivateWages_21 8.0 9 201 290s PrivateWages_22 8.5 10 204 290s PrivateWages_corpProfLag PrivateWages_gnpLag 290s Consumption_2 0.0 0.0 290s Consumption_3 0.0 0.0 290s Consumption_4 0.0 0.0 290s Consumption_5 0.0 0.0 290s Consumption_6 0.0 0.0 290s Consumption_8 0.0 0.0 290s Consumption_9 0.0 0.0 290s Consumption_11 0.0 0.0 290s Consumption_12 0.0 0.0 290s Consumption_13 0.0 0.0 290s Consumption_14 0.0 0.0 290s Consumption_15 0.0 0.0 290s Consumption_16 0.0 0.0 290s Consumption_17 0.0 0.0 290s Consumption_18 0.0 0.0 290s Consumption_19 0.0 0.0 290s Consumption_20 0.0 0.0 290s Consumption_21 0.0 0.0 290s Consumption_22 0.0 0.0 290s Investment_2 0.0 0.0 290s Investment_3 0.0 0.0 290s Investment_4 0.0 0.0 290s Investment_5 0.0 0.0 290s Investment_6 0.0 0.0 290s Investment_8 0.0 0.0 290s Investment_9 0.0 0.0 290s Investment_10 0.0 0.0 290s Investment_11 0.0 0.0 290s Investment_12 0.0 0.0 290s Investment_13 0.0 0.0 290s Investment_14 0.0 0.0 290s Investment_15 0.0 0.0 290s Investment_16 0.0 0.0 290s Investment_17 0.0 0.0 290s Investment_18 0.0 0.0 290s Investment_19 0.0 0.0 290s Investment_20 0.0 0.0 290s Investment_21 0.0 0.0 290s Investment_22 0.0 0.0 290s PrivateWages_2 12.7 44.9 290s PrivateWages_3 12.4 45.6 290s PrivateWages_4 16.9 50.1 290s PrivateWages_5 18.4 57.2 290s PrivateWages_6 19.4 57.1 290s PrivateWages_8 19.6 64.0 290s PrivateWages_9 19.8 64.4 290s PrivateWages_10 21.1 64.5 290s PrivateWages_11 21.7 67.0 290s PrivateWages_12 15.6 61.2 290s PrivateWages_13 11.4 53.4 290s PrivateWages_14 7.0 44.3 290s PrivateWages_15 11.2 45.1 290s PrivateWages_16 12.3 49.7 290s PrivateWages_17 14.0 54.4 290s PrivateWages_18 17.6 62.7 290s PrivateWages_19 17.3 65.0 290s PrivateWages_20 15.3 60.9 290s PrivateWages_21 19.0 69.5 290s PrivateWages_22 21.1 75.7 290s > matrix of fitted regressors 290s Consumption_(Intercept) Consumption_corpProf 290s Consumption_2 1 13.44 290s Consumption_3 1 16.68 290s Consumption_4 1 18.95 290s Consumption_5 1 20.63 290s Consumption_6 1 19.28 290s Consumption_8 1 17.21 290s Consumption_9 1 18.99 290s Consumption_11 1 16.43 290s Consumption_12 1 12.49 290s Consumption_13 1 9.06 290s Consumption_14 1 9.28 290s Consumption_15 1 12.49 290s Consumption_16 1 14.39 290s Consumption_17 1 14.69 290s Consumption_18 1 19.60 290s Consumption_19 1 19.15 290s Consumption_20 1 17.54 290s Consumption_21 1 20.33 290s Consumption_22 1 22.78 290s Investment_2 0 0.00 290s Investment_3 0 0.00 290s Investment_4 0 0.00 290s Investment_5 0 0.00 290s Investment_6 0 0.00 290s Investment_8 0 0.00 290s Investment_9 0 0.00 290s Investment_10 0 0.00 290s Investment_11 0 0.00 290s Investment_12 0 0.00 290s Investment_13 0 0.00 290s Investment_14 0 0.00 290s Investment_15 0 0.00 290s Investment_16 0 0.00 290s Investment_17 0 0.00 290s Investment_18 0 0.00 290s Investment_19 0 0.00 290s Investment_20 0 0.00 290s Investment_21 0 0.00 290s Investment_22 0 0.00 290s PrivateWages_2 0 0.00 290s PrivateWages_3 0 0.00 290s PrivateWages_4 0 0.00 290s PrivateWages_5 0 0.00 290s PrivateWages_6 0 0.00 290s PrivateWages_8 0 0.00 290s PrivateWages_9 0 0.00 290s PrivateWages_10 0 0.00 290s PrivateWages_11 0 0.00 290s PrivateWages_12 0 0.00 290s PrivateWages_13 0 0.00 290s PrivateWages_14 0 0.00 290s PrivateWages_15 0 0.00 290s PrivateWages_16 0 0.00 290s PrivateWages_17 0 0.00 290s PrivateWages_18 0 0.00 290s PrivateWages_19 0 0.00 290s PrivateWages_20 0 0.00 290s PrivateWages_21 0 0.00 290s PrivateWages_22 0 0.00 290s Consumption_corpProfLag Consumption_wages 290s Consumption_2 12.7 29.6 290s Consumption_3 12.4 31.9 290s Consumption_4 16.9 35.4 290s Consumption_5 18.4 38.8 290s Consumption_6 19.4 38.7 290s Consumption_8 19.6 39.8 290s Consumption_9 19.8 41.8 290s Consumption_11 21.7 43.0 290s Consumption_12 15.6 39.3 290s Consumption_13 11.4 35.2 290s Consumption_14 7.0 33.0 290s Consumption_15 11.2 37.3 290s Consumption_16 12.3 40.1 290s Consumption_17 14.0 41.7 290s Consumption_18 17.6 47.7 290s Consumption_19 17.3 49.2 290s Consumption_20 15.3 48.5 290s Consumption_21 19.0 53.4 290s Consumption_22 21.1 60.8 290s Investment_2 0.0 0.0 290s Investment_3 0.0 0.0 290s Investment_4 0.0 0.0 290s Investment_5 0.0 0.0 290s Investment_6 0.0 0.0 290s Investment_8 0.0 0.0 290s Investment_9 0.0 0.0 290s Investment_10 0.0 0.0 290s Investment_11 0.0 0.0 290s Investment_12 0.0 0.0 290s Investment_13 0.0 0.0 290s Investment_14 0.0 0.0 290s Investment_15 0.0 0.0 290s Investment_16 0.0 0.0 290s Investment_17 0.0 0.0 290s Investment_18 0.0 0.0 290s Investment_19 0.0 0.0 290s Investment_20 0.0 0.0 290s Investment_21 0.0 0.0 290s Investment_22 0.0 0.0 290s PrivateWages_2 0.0 0.0 290s PrivateWages_3 0.0 0.0 290s PrivateWages_4 0.0 0.0 290s PrivateWages_5 0.0 0.0 290s PrivateWages_6 0.0 0.0 290s PrivateWages_8 0.0 0.0 290s PrivateWages_9 0.0 0.0 290s PrivateWages_10 0.0 0.0 290s PrivateWages_11 0.0 0.0 290s PrivateWages_12 0.0 0.0 290s PrivateWages_13 0.0 0.0 290s PrivateWages_14 0.0 0.0 290s PrivateWages_15 0.0 0.0 290s PrivateWages_16 0.0 0.0 290s PrivateWages_17 0.0 0.0 290s PrivateWages_18 0.0 0.0 290s PrivateWages_19 0.0 0.0 290s PrivateWages_20 0.0 0.0 290s PrivateWages_21 0.0 0.0 290s PrivateWages_22 0.0 0.0 290s Investment_(Intercept) Investment_corpProf 290s Consumption_2 0 0.00 290s Consumption_3 0 0.00 290s Consumption_4 0 0.00 290s Consumption_5 0 0.00 290s Consumption_6 0 0.00 290s Consumption_8 0 0.00 290s Consumption_9 0 0.00 290s Consumption_11 0 0.00 290s Consumption_12 0 0.00 290s Consumption_13 0 0.00 290s Consumption_14 0 0.00 290s Consumption_15 0 0.00 290s Consumption_16 0 0.00 290s Consumption_17 0 0.00 290s Consumption_18 0 0.00 290s Consumption_19 0 0.00 290s Consumption_20 0 0.00 290s Consumption_21 0 0.00 290s Consumption_22 0 0.00 290s Investment_2 1 12.96 290s Investment_3 1 16.70 290s Investment_4 1 19.14 290s Investment_5 1 20.94 290s Investment_6 1 19.47 290s Investment_8 1 17.14 290s Investment_9 1 19.49 290s Investment_10 1 20.46 290s Investment_11 1 16.85 290s Investment_12 1 12.68 290s Investment_13 1 8.92 290s Investment_14 1 9.30 290s Investment_15 1 12.79 290s Investment_16 1 14.26 290s Investment_17 1 14.75 290s Investment_18 1 19.54 290s Investment_19 1 19.36 290s Investment_20 1 17.39 290s Investment_21 1 20.10 290s Investment_22 1 22.86 290s PrivateWages_2 0 0.00 290s PrivateWages_3 0 0.00 290s PrivateWages_4 0 0.00 290s PrivateWages_5 0 0.00 290s PrivateWages_6 0 0.00 290s PrivateWages_8 0 0.00 290s PrivateWages_9 0 0.00 290s PrivateWages_10 0 0.00 290s PrivateWages_11 0 0.00 290s PrivateWages_12 0 0.00 290s PrivateWages_13 0 0.00 290s PrivateWages_14 0 0.00 290s PrivateWages_15 0 0.00 290s PrivateWages_16 0 0.00 290s PrivateWages_17 0 0.00 290s PrivateWages_18 0 0.00 290s PrivateWages_19 0 0.00 290s PrivateWages_20 0 0.00 290s PrivateWages_21 0 0.00 290s PrivateWages_22 0 0.00 290s Investment_corpProfLag Investment_capitalLag 290s Consumption_2 0.0 0 290s Consumption_3 0.0 0 290s Consumption_4 0.0 0 290s Consumption_5 0.0 0 290s Consumption_6 0.0 0 290s Consumption_8 0.0 0 290s Consumption_9 0.0 0 290s Consumption_11 0.0 0 290s Consumption_12 0.0 0 290s Consumption_13 0.0 0 290s Consumption_14 0.0 0 290s Consumption_15 0.0 0 290s Consumption_16 0.0 0 290s Consumption_17 0.0 0 290s Consumption_18 0.0 0 290s Consumption_19 0.0 0 290s Consumption_20 0.0 0 290s Consumption_21 0.0 0 290s Consumption_22 0.0 0 290s Investment_2 12.7 183 290s Investment_3 12.4 183 290s Investment_4 16.9 184 290s Investment_5 18.4 190 290s Investment_6 19.4 193 290s Investment_8 19.6 203 290s Investment_9 19.8 208 290s Investment_10 21.1 211 290s Investment_11 21.7 216 290s Investment_12 15.6 217 290s Investment_13 11.4 213 290s Investment_14 7.0 207 290s Investment_15 11.2 202 290s Investment_16 12.3 199 290s Investment_17 14.0 198 290s Investment_18 17.6 200 290s Investment_19 17.3 202 290s Investment_20 15.3 200 290s Investment_21 19.0 201 290s Investment_22 21.1 204 290s PrivateWages_2 0.0 0 290s PrivateWages_3 0.0 0 290s PrivateWages_4 0.0 0 290s PrivateWages_5 0.0 0 290s PrivateWages_6 0.0 0 290s PrivateWages_8 0.0 0 290s PrivateWages_9 0.0 0 290s PrivateWages_10 0.0 0 290s PrivateWages_11 0.0 0 290s PrivateWages_12 0.0 0 290s PrivateWages_13 0.0 0 290s PrivateWages_14 0.0 0 290s PrivateWages_15 0.0 0 290s PrivateWages_16 0.0 0 290s PrivateWages_17 0.0 0 290s PrivateWages_18 0.0 0 290s PrivateWages_19 0.0 0 290s PrivateWages_20 0.0 0 290s PrivateWages_21 0.0 0 290s PrivateWages_22 0.0 0 290s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 290s Consumption_2 0 0.0 0.0 290s Consumption_3 0 0.0 0.0 290s Consumption_4 0 0.0 0.0 290s Consumption_5 0 0.0 0.0 290s Consumption_6 0 0.0 0.0 290s Consumption_8 0 0.0 0.0 290s Consumption_9 0 0.0 0.0 290s Consumption_11 0 0.0 0.0 290s Consumption_12 0 0.0 0.0 290s Consumption_13 0 0.0 0.0 290s Consumption_14 0 0.0 0.0 290s Consumption_15 0 0.0 0.0 290s Consumption_16 0 0.0 0.0 290s Consumption_17 0 0.0 0.0 290s Consumption_18 0 0.0 0.0 290s Consumption_19 0 0.0 0.0 290s Consumption_20 0 0.0 0.0 290s Consumption_21 0 0.0 0.0 290s Consumption_22 0 0.0 0.0 290s Investment_2 0 0.0 0.0 290s Investment_3 0 0.0 0.0 290s Investment_4 0 0.0 0.0 290s Investment_5 0 0.0 0.0 290s Investment_6 0 0.0 0.0 290s Investment_8 0 0.0 0.0 290s Investment_9 0 0.0 0.0 290s Investment_10 0 0.0 0.0 290s Investment_11 0 0.0 0.0 290s Investment_12 0 0.0 0.0 290s Investment_13 0 0.0 0.0 290s Investment_14 0 0.0 0.0 290s Investment_15 0 0.0 0.0 290s Investment_16 0 0.0 0.0 290s Investment_17 0 0.0 0.0 290s Investment_18 0 0.0 0.0 290s Investment_19 0 0.0 0.0 290s Investment_20 0 0.0 0.0 290s Investment_21 0 0.0 0.0 290s Investment_22 0 0.0 0.0 290s PrivateWages_2 1 47.1 44.9 290s PrivateWages_3 1 49.6 45.6 290s PrivateWages_4 1 56.5 50.1 290s PrivateWages_5 1 60.7 57.2 290s PrivateWages_6 1 60.6 57.1 290s PrivateWages_8 1 60.0 64.0 290s PrivateWages_9 1 62.3 64.4 290s PrivateWages_10 1 64.6 64.5 290s PrivateWages_11 1 63.7 67.0 290s PrivateWages_12 1 54.8 61.2 290s PrivateWages_13 1 47.0 53.4 290s PrivateWages_14 1 42.1 44.3 290s PrivateWages_15 1 51.2 45.1 290s PrivateWages_16 1 55.3 49.7 290s PrivateWages_17 1 57.4 54.4 290s PrivateWages_18 1 67.2 62.7 290s PrivateWages_19 1 68.5 65.0 290s PrivateWages_20 1 66.8 60.9 290s PrivateWages_21 1 74.9 69.5 290s PrivateWages_22 1 86.9 75.7 290s PrivateWages_trend 290s Consumption_2 0 290s Consumption_3 0 290s Consumption_4 0 290s Consumption_5 0 290s Consumption_6 0 290s Consumption_8 0 290s Consumption_9 0 290s Consumption_11 0 290s Consumption_12 0 290s Consumption_13 0 290s Consumption_14 0 290s Consumption_15 0 290s Consumption_16 0 290s Consumption_17 0 290s Consumption_18 0 290s Consumption_19 0 290s Consumption_20 0 290s Consumption_21 0 290s Consumption_22 0 290s Investment_2 0 290s Investment_3 0 290s Investment_4 0 290s Investment_5 0 290s Investment_6 0 290s Investment_8 0 290s Investment_9 0 290s Investment_10 0 290s Investment_11 0 290s Investment_12 0 290s Investment_13 0 290s Investment_14 0 290s Investment_15 0 290s Investment_16 0 290s Investment_17 0 290s Investment_18 0 290s Investment_19 0 290s Investment_20 0 290s Investment_21 0 290s Investment_22 0 290s PrivateWages_2 -10 290s PrivateWages_3 -9 290s PrivateWages_4 -8 290s PrivateWages_5 -7 290s PrivateWages_6 -6 290s PrivateWages_8 -4 290s PrivateWages_9 -3 290s PrivateWages_10 -2 290s PrivateWages_11 -1 290s PrivateWages_12 0 290s PrivateWages_13 1 290s PrivateWages_14 2 290s PrivateWages_15 3 290s PrivateWages_16 4 290s PrivateWages_17 5 290s PrivateWages_18 6 290s PrivateWages_19 7 290s PrivateWages_20 8 290s PrivateWages_21 9 290s PrivateWages_22 10 290s > nobs 290s [1] 59 290s > linearHypothesis 290s Linear hypothesis test (Theil's F test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df F Pr(>F) 290s 1 48 290s 2 47 1 0.87 0.36 290s Linear hypothesis test (F statistic of a Wald test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df F Pr(>F) 290s 1 48 290s 2 47 1 0.98 0.33 290s Linear hypothesis test (Chi^2 statistic of a Wald test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df Chisq Pr(>Chisq) 290s 1 48 290s 2 47 1 0.98 0.32 290s Linear hypothesis test (Theil's F test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s Consumption_corpProfLag - PrivateWages_trend = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df F Pr(>F) 290s 1 49 290s 2 47 2 0.43 0.65 290s Linear hypothesis test (F statistic of a Wald test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s Consumption_corpProfLag - PrivateWages_trend = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df F Pr(>F) 290s 1 49 290s 2 47 2 0.49 0.61 290s Linear hypothesis test (Chi^2 statistic of a Wald test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s Consumption_corpProfLag - PrivateWages_trend = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df Chisq Pr(>Chisq) 290s 1 49 290s 2 47 2 0.98 0.61 290s > logLik 290s 'log Lik.' -71.5 (df=13) 290s 'log Lik.' -78.7 (df=13) 290s Estimating function 290s Consumption_(Intercept) Consumption_corpProf 290s Consumption_2 -1.5371 -20.65 290s Consumption_3 -0.3191 -5.32 290s Consumption_4 0.0169 0.32 290s Consumption_5 -1.6346 -33.73 290s Consumption_6 0.2820 5.44 290s Consumption_8 2.9429 50.64 290s Consumption_9 2.3495 44.61 290s Consumption_11 -1.2221 -20.08 290s Consumption_12 -1.0034 -12.54 290s Consumption_13 -2.0551 -18.62 290s Consumption_14 1.4937 13.86 290s Consumption_15 -0.7418 -9.26 290s Consumption_16 -0.6703 -9.64 290s Consumption_17 4.0943 60.15 290s Consumption_18 -0.6347 -12.44 290s Consumption_19 -3.0409 -58.22 290s Consumption_20 2.1019 36.86 290s Consumption_21 0.7142 14.52 290s Consumption_22 -1.1363 -25.88 290s Investment_2 0.0000 0.00 290s Investment_3 0.0000 0.00 290s Investment_4 0.0000 0.00 290s Investment_5 0.0000 0.00 290s Investment_6 0.0000 0.00 290s Investment_8 0.0000 0.00 290s Investment_9 0.0000 0.00 290s Investment_10 0.0000 0.00 290s Investment_11 0.0000 0.00 290s Investment_12 0.0000 0.00 290s Investment_13 0.0000 0.00 290s Investment_14 0.0000 0.00 290s Investment_15 0.0000 0.00 290s Investment_16 0.0000 0.00 290s Investment_17 0.0000 0.00 290s Investment_18 0.0000 0.00 290s Investment_19 0.0000 0.00 290s Investment_20 0.0000 0.00 290s Investment_21 0.0000 0.00 290s Investment_22 0.0000 0.00 290s PrivateWages_2 0.0000 0.00 290s PrivateWages_3 0.0000 0.00 290s PrivateWages_4 0.0000 0.00 290s PrivateWages_5 0.0000 0.00 290s PrivateWages_6 0.0000 0.00 290s PrivateWages_8 0.0000 0.00 290s PrivateWages_9 0.0000 0.00 290s PrivateWages_10 0.0000 0.00 290s PrivateWages_11 0.0000 0.00 290s PrivateWages_12 0.0000 0.00 290s PrivateWages_13 0.0000 0.00 290s PrivateWages_14 0.0000 0.00 290s PrivateWages_15 0.0000 0.00 290s PrivateWages_16 0.0000 0.00 290s PrivateWages_17 0.0000 0.00 290s PrivateWages_18 0.0000 0.00 290s PrivateWages_19 0.0000 0.00 290s PrivateWages_20 0.0000 0.00 290s PrivateWages_21 0.0000 0.00 290s PrivateWages_22 0.0000 0.00 290s Consumption_corpProfLag Consumption_wages 290s Consumption_2 -19.521 -45.456 290s Consumption_3 -3.957 -10.167 290s Consumption_4 0.286 0.599 290s Consumption_5 -30.078 -63.354 290s Consumption_6 5.471 10.901 290s Consumption_8 57.681 117.190 290s Consumption_9 46.520 98.197 290s Consumption_11 -26.520 -52.512 290s Consumption_12 -15.653 -39.407 290s Consumption_13 -23.428 -72.317 290s Consumption_14 10.456 49.297 290s Consumption_15 -8.308 -27.687 290s Consumption_16 -8.244 -26.878 290s Consumption_17 57.321 170.665 290s Consumption_18 -11.170 -30.264 290s Consumption_19 -52.608 -149.761 290s Consumption_20 32.159 101.952 290s Consumption_21 13.570 38.131 290s Consumption_22 -23.976 -69.128 290s Investment_2 0.000 0.000 290s Investment_3 0.000 0.000 290s Investment_4 0.000 0.000 290s Investment_5 0.000 0.000 290s Investment_6 0.000 0.000 290s Investment_8 0.000 0.000 290s Investment_9 0.000 0.000 290s Investment_10 0.000 0.000 290s Investment_11 0.000 0.000 290s Investment_12 0.000 0.000 290s Investment_13 0.000 0.000 290s Investment_14 0.000 0.000 290s Investment_15 0.000 0.000 290s Investment_16 0.000 0.000 290s Investment_17 0.000 0.000 290s Investment_18 0.000 0.000 290s Investment_19 0.000 0.000 290s Investment_20 0.000 0.000 290s Investment_21 0.000 0.000 290s Investment_22 0.000 0.000 290s PrivateWages_2 0.000 0.000 290s PrivateWages_3 0.000 0.000 290s PrivateWages_4 0.000 0.000 290s PrivateWages_5 0.000 0.000 290s PrivateWages_6 0.000 0.000 290s PrivateWages_8 0.000 0.000 290s PrivateWages_9 0.000 0.000 290s PrivateWages_10 0.000 0.000 290s PrivateWages_11 0.000 0.000 290s PrivateWages_12 0.000 0.000 290s PrivateWages_13 0.000 0.000 290s PrivateWages_14 0.000 0.000 290s PrivateWages_15 0.000 0.000 290s PrivateWages_16 0.000 0.000 290s PrivateWages_17 0.000 0.000 290s PrivateWages_18 0.000 0.000 290s PrivateWages_19 0.000 0.000 290s PrivateWages_20 0.000 0.000 290s PrivateWages_21 0.000 0.000 290s PrivateWages_22 0.000 0.000 290s Investment_(Intercept) Investment_corpProf 290s Consumption_2 0.0000 0.000 290s Consumption_3 0.0000 0.000 290s Consumption_4 0.0000 0.000 290s Consumption_5 0.0000 0.000 290s Consumption_6 0.0000 0.000 290s Consumption_8 0.0000 0.000 290s Consumption_9 0.0000 0.000 290s Consumption_11 0.0000 0.000 290s Consumption_12 0.0000 0.000 290s Consumption_13 0.0000 0.000 290s Consumption_14 0.0000 0.000 290s Consumption_15 0.0000 0.000 290s Consumption_16 0.0000 0.000 290s Consumption_17 0.0000 0.000 290s Consumption_18 0.0000 0.000 290s Consumption_19 0.0000 0.000 290s Consumption_20 0.0000 0.000 290s Consumption_21 0.0000 0.000 290s Consumption_22 0.0000 0.000 290s Investment_2 -1.1313 -14.660 290s Investment_3 0.2902 4.847 290s Investment_4 0.9027 17.274 290s Investment_5 -1.7434 -36.502 290s Investment_6 0.5695 11.088 290s Investment_8 1.6225 27.812 290s Investment_9 0.4166 8.119 290s Investment_10 2.0381 41.703 290s Investment_11 -0.8611 -14.505 290s Investment_12 -0.9091 -11.527 290s Investment_13 -1.1148 -9.946 290s Investment_14 1.3841 12.873 290s Investment_15 -0.2900 -3.710 290s Investment_16 0.0605 0.862 290s Investment_17 2.2439 33.101 290s Investment_18 -0.5390 -10.534 290s Investment_19 -3.9452 -76.375 290s Investment_20 0.4890 8.502 290s Investment_21 0.0864 1.737 290s Investment_22 0.4306 9.843 290s PrivateWages_2 0.0000 0.000 290s PrivateWages_3 0.0000 0.000 290s PrivateWages_4 0.0000 0.000 290s PrivateWages_5 0.0000 0.000 290s PrivateWages_6 0.0000 0.000 290s PrivateWages_8 0.0000 0.000 290s PrivateWages_9 0.0000 0.000 290s PrivateWages_10 0.0000 0.000 290s PrivateWages_11 0.0000 0.000 290s PrivateWages_12 0.0000 0.000 290s PrivateWages_13 0.0000 0.000 290s PrivateWages_14 0.0000 0.000 290s PrivateWages_15 0.0000 0.000 290s PrivateWages_16 0.0000 0.000 290s PrivateWages_17 0.0000 0.000 290s PrivateWages_18 0.0000 0.000 290s PrivateWages_19 0.0000 0.000 290s PrivateWages_20 0.0000 0.000 290s PrivateWages_21 0.0000 0.000 290s PrivateWages_22 0.0000 0.000 290s Investment_corpProfLag Investment_capitalLag 290s Consumption_2 0.000 0.0 290s Consumption_3 0.000 0.0 290s Consumption_4 0.000 0.0 290s Consumption_5 0.000 0.0 290s Consumption_6 0.000 0.0 290s Consumption_8 0.000 0.0 290s Consumption_9 0.000 0.0 290s Consumption_11 0.000 0.0 290s Consumption_12 0.000 0.0 290s Consumption_13 0.000 0.0 290s Consumption_14 0.000 0.0 290s Consumption_15 0.000 0.0 290s Consumption_16 0.000 0.0 290s Consumption_17 0.000 0.0 290s Consumption_18 0.000 0.0 290s Consumption_19 0.000 0.0 290s Consumption_20 0.000 0.0 290s Consumption_21 0.000 0.0 290s Consumption_22 0.000 0.0 290s Investment_2 -14.368 -206.8 290s Investment_3 3.598 53.0 290s Investment_4 15.256 166.5 290s Investment_5 -32.079 -330.7 290s Investment_6 11.048 109.7 290s Investment_8 31.801 330.0 290s Investment_9 8.248 86.5 290s Investment_10 43.003 429.2 290s Investment_11 -18.685 -185.7 290s Investment_12 -14.182 -197.0 290s Investment_13 -12.709 -237.8 290s Investment_14 9.689 286.6 290s Investment_15 -3.247 -58.6 290s Investment_16 0.744 12.0 290s Investment_17 31.414 443.6 290s Investment_18 -9.486 -107.7 290s Investment_19 -68.252 -796.1 290s Investment_20 7.482 97.7 290s Investment_21 1.642 17.4 290s Investment_22 9.085 88.0 290s PrivateWages_2 0.000 0.0 290s PrivateWages_3 0.000 0.0 290s PrivateWages_4 0.000 0.0 290s PrivateWages_5 0.000 0.0 290s PrivateWages_6 0.000 0.0 290s PrivateWages_8 0.000 0.0 290s PrivateWages_9 0.000 0.0 290s PrivateWages_10 0.000 0.0 290s PrivateWages_11 0.000 0.0 290s PrivateWages_12 0.000 0.0 290s PrivateWages_13 0.000 0.0 290s PrivateWages_14 0.000 0.0 290s PrivateWages_15 0.000 0.0 290s PrivateWages_16 0.000 0.0 290s PrivateWages_17 0.000 0.0 290s PrivateWages_18 0.000 0.0 290s PrivateWages_19 0.000 0.0 290s PrivateWages_20 0.000 0.0 290s PrivateWages_21 0.000 0.0 290s PrivateWages_22 0.000 0.0 290s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 290s Consumption_2 0.0000 0.00 0.00 290s Consumption_3 0.0000 0.00 0.00 290s Consumption_4 0.0000 0.00 0.00 290s Consumption_5 0.0000 0.00 0.00 290s Consumption_6 0.0000 0.00 0.00 290s Consumption_8 0.0000 0.00 0.00 290s Consumption_9 0.0000 0.00 0.00 290s Consumption_11 0.0000 0.00 0.00 290s Consumption_12 0.0000 0.00 0.00 290s Consumption_13 0.0000 0.00 0.00 290s Consumption_14 0.0000 0.00 0.00 290s Consumption_15 0.0000 0.00 0.00 290s Consumption_16 0.0000 0.00 0.00 290s Consumption_17 0.0000 0.00 0.00 290s Consumption_18 0.0000 0.00 0.00 290s Consumption_19 0.0000 0.00 0.00 290s Consumption_20 0.0000 0.00 0.00 290s Consumption_21 0.0000 0.00 0.00 290s Consumption_22 0.0000 0.00 0.00 290s Investment_2 0.0000 0.00 0.00 290s Investment_3 0.0000 0.00 0.00 290s Investment_4 0.0000 0.00 0.00 290s Investment_5 0.0000 0.00 0.00 290s Investment_6 0.0000 0.00 0.00 290s Investment_8 0.0000 0.00 0.00 290s Investment_9 0.0000 0.00 0.00 290s Investment_10 0.0000 0.00 0.00 290s Investment_11 0.0000 0.00 0.00 290s Investment_12 0.0000 0.00 0.00 290s Investment_13 0.0000 0.00 0.00 290s Investment_14 0.0000 0.00 0.00 290s Investment_15 0.0000 0.00 0.00 290s Investment_16 0.0000 0.00 0.00 290s Investment_17 0.0000 0.00 0.00 290s Investment_18 0.0000 0.00 0.00 290s Investment_19 0.0000 0.00 0.00 290s Investment_20 0.0000 0.00 0.00 290s Investment_21 0.0000 0.00 0.00 290s Investment_22 0.0000 0.00 0.00 290s PrivateWages_2 -1.9924 -93.78 -89.46 290s PrivateWages_3 0.4683 23.22 21.35 290s PrivateWages_4 1.4034 79.35 70.31 290s PrivateWages_5 -1.7870 -108.45 -102.22 290s PrivateWages_6 -0.3627 -21.98 -20.71 290s PrivateWages_8 1.1629 69.77 74.43 290s PrivateWages_9 1.2735 79.30 82.01 290s PrivateWages_10 2.2141 142.96 142.81 290s PrivateWages_11 -1.2912 -82.26 -86.51 290s PrivateWages_12 -0.0350 -1.92 -2.14 290s PrivateWages_13 -1.0438 -49.04 -55.74 290s PrivateWages_14 1.8016 75.90 79.81 290s PrivateWages_15 -0.3714 -19.02 -16.75 290s PrivateWages_16 -0.3904 -21.61 -19.40 290s PrivateWages_17 1.4934 85.71 81.24 290s PrivateWages_18 0.0279 1.88 1.75 290s PrivateWages_19 -3.8229 -261.91 -248.49 290s PrivateWages_20 0.7870 52.61 47.93 290s PrivateWages_21 -0.7415 -55.52 -51.54 290s PrivateWages_22 1.2062 104.79 91.31 290s PrivateWages_trend 290s Consumption_2 0.000 290s Consumption_3 0.000 290s Consumption_4 0.000 290s Consumption_5 0.000 290s Consumption_6 0.000 290s Consumption_8 0.000 290s Consumption_9 0.000 290s Consumption_11 0.000 290s Consumption_12 0.000 290s Consumption_13 0.000 290s Consumption_14 0.000 290s Consumption_15 0.000 290s Consumption_16 0.000 290s Consumption_17 0.000 290s Consumption_18 0.000 290s Consumption_19 0.000 290s Consumption_20 0.000 290s Consumption_21 0.000 290s Consumption_22 0.000 290s Investment_2 0.000 290s Investment_3 0.000 290s Investment_4 0.000 290s Investment_5 0.000 290s Investment_6 0.000 290s Investment_8 0.000 290s Investment_9 0.000 290s Investment_10 0.000 290s Investment_11 0.000 290s Investment_12 0.000 290s Investment_13 0.000 290s Investment_14 0.000 290s Investment_15 0.000 290s Investment_16 0.000 290s Investment_17 0.000 290s Investment_18 0.000 290s Investment_19 0.000 290s Investment_20 0.000 290s Investment_21 0.000 290s Investment_22 0.000 290s PrivateWages_2 19.924 290s PrivateWages_3 -4.214 290s PrivateWages_4 -11.227 290s PrivateWages_5 12.509 290s PrivateWages_6 2.176 290s PrivateWages_8 -4.652 290s PrivateWages_9 -3.820 290s PrivateWages_10 -4.428 290s PrivateWages_11 1.291 290s PrivateWages_12 0.000 290s PrivateWages_13 -1.044 290s PrivateWages_14 3.603 290s PrivateWages_15 -1.114 290s PrivateWages_16 -1.562 290s PrivateWages_17 7.467 290s PrivateWages_18 0.168 290s PrivateWages_19 -26.760 290s PrivateWages_20 6.296 290s PrivateWages_21 -6.674 290s PrivateWages_22 12.062 290s [1] TRUE 290s > Bread 290s Consumption_(Intercept) Consumption_corpProf 290s Consumption_(Intercept) 99.763 -0.8715 290s Consumption_corpProf -0.872 0.7621 290s Consumption_corpProfLag -0.479 -0.4940 290s Consumption_wages -1.807 -0.0927 290s Investment_(Intercept) 0.000 0.0000 290s Investment_corpProf 0.000 0.0000 290s Investment_corpProfLag 0.000 0.0000 290s Investment_capitalLag 0.000 0.0000 290s PrivateWages_(Intercept) 0.000 0.0000 290s PrivateWages_gnp 0.000 0.0000 290s PrivateWages_gnpLag 0.000 0.0000 290s PrivateWages_trend 0.000 0.0000 290s Consumption_corpProfLag Consumption_wages 290s Consumption_(Intercept) -0.4786 -1.8068 290s Consumption_corpProf -0.4940 -0.0927 290s Consumption_corpProfLag 0.6462 -0.0403 290s Consumption_wages -0.0403 0.0963 290s Investment_(Intercept) 0.0000 0.0000 290s Investment_corpProf 0.0000 0.0000 290s Investment_corpProfLag 0.0000 0.0000 290s Investment_capitalLag 0.0000 0.0000 290s PrivateWages_(Intercept) 0.0000 0.0000 290s PrivateWages_gnp 0.0000 0.0000 290s PrivateWages_gnpLag 0.0000 0.0000 290s PrivateWages_trend 0.0000 0.0000 290s Investment_(Intercept) Investment_corpProf 290s Consumption_(Intercept) 0.0 0.000 290s Consumption_corpProf 0.0 0.000 290s Consumption_corpProfLag 0.0 0.000 290s Consumption_wages 0.0 0.000 290s Investment_(Intercept) 2405.5 -38.269 290s Investment_corpProf -38.3 1.231 290s Investment_corpProfLag 32.8 -1.072 290s Investment_capitalLag -11.4 0.174 290s PrivateWages_(Intercept) 0.0 0.000 290s PrivateWages_gnp 0.0 0.000 290s PrivateWages_gnpLag 0.0 0.000 290s PrivateWages_trend 0.0 0.000 290s Investment_corpProfLag Investment_capitalLag 290s Consumption_(Intercept) 0.000 0.0000 290s Consumption_corpProf 0.000 0.0000 290s Consumption_corpProfLag 0.000 0.0000 290s Consumption_wages 0.000 0.0000 290s Investment_(Intercept) 32.828 -11.4279 290s Investment_corpProf -1.072 0.1744 290s Investment_corpProfLag 1.129 -0.1652 290s Investment_capitalLag -0.165 0.0557 290s PrivateWages_(Intercept) 0.000 0.0000 290s PrivateWages_gnp 0.000 0.0000 290s PrivateWages_gnpLag 0.000 0.0000 290s PrivateWages_trend 0.000 0.0000 290s PrivateWages_(Intercept) PrivateWages_gnp 290s Consumption_(Intercept) 0.000 0.0000 290s Consumption_corpProf 0.000 0.0000 290s Consumption_corpProfLag 0.000 0.0000 290s Consumption_wages 0.000 0.0000 290s Investment_(Intercept) 0.000 0.0000 290s Investment_corpProf 0.000 0.0000 290s Investment_corpProfLag 0.000 0.0000 290s Investment_capitalLag 0.000 0.0000 290s PrivateWages_(Intercept) 167.869 -0.9135 290s PrivateWages_gnp -0.913 0.1554 290s PrivateWages_gnpLag -1.915 -0.1448 290s PrivateWages_trend 2.128 -0.0417 290s PrivateWages_gnpLag PrivateWages_trend 290s Consumption_(Intercept) 0.0000 0.0000 290s Consumption_corpProf 0.0000 0.0000 290s Consumption_corpProfLag 0.0000 0.0000 290s Consumption_wages 0.0000 0.0000 290s Investment_(Intercept) 0.0000 0.0000 290s Investment_corpProf 0.0000 0.0000 290s Investment_corpProfLag 0.0000 0.0000 290s Investment_capitalLag 0.0000 0.0000 290s PrivateWages_(Intercept) -1.9153 2.1280 290s PrivateWages_gnp -0.1448 -0.0417 290s PrivateWages_gnpLag 0.1830 0.0059 290s PrivateWages_trend 0.0059 0.1132 290s > 290s > # SUR 290s Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 290s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 290s > summary 290s 290s systemfit results 290s method: SUR 290s 290s N DF SSR detRCov OLS-R2 McElroy-R2 290s system 61 49 45.4 0.151 0.977 0.992 290s 290s N DF SSR MSE RMSE R2 Adj R2 290s Consumption 20 16 17.6 1.102 1.050 0.981 0.977 290s Investment 21 17 17.5 1.029 1.015 0.931 0.918 290s PrivateWages 20 16 10.3 0.643 0.802 0.987 0.985 290s 290s The covariance matrix of the residuals used for estimation 290s Consumption Investment PrivateWages 290s Consumption 0.8871 0.0268 -0.349 290s Investment 0.0268 0.7328 0.103 290s PrivateWages -0.3492 0.1029 0.444 290s 290s The covariance matrix of the residuals 290s Consumption Investment PrivateWages 290s Consumption 0.8852 0.0508 -0.406 290s Investment 0.0508 0.7313 0.161 290s PrivateWages -0.4063 0.1609 0.467 290s 290s The correlations of the residuals 290s Consumption Investment PrivateWages 290s Consumption 1.000 0.065 -0.635 290s Investment 0.065 1.000 0.262 290s PrivateWages -0.635 0.262 1.000 290s 290s 290s SUR estimates for 'Consumption' (equation 1) 290s Model Formula: consump ~ corpProf + corpProfLag + wages 290s 290s Estimate Std. Error t value Pr(>|t|) 290s (Intercept) 16.0876 1.2010 13.39 4.1e-10 *** 290s corpProf 0.2173 0.0799 2.72 0.015 * 290s corpProfLag 0.0694 0.0793 0.88 0.394 290s wages 0.7975 0.0360 22.15 2.0e-13 *** 290s --- 290s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 290s 290s Residual standard error: 1.05 on 16 degrees of freedom 290s Number of observations: 20 Degrees of Freedom: 16 290s SSR: 17.63 MSE: 1.102 Root MSE: 1.05 290s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.977 290s 290s 290s SUR estimates for 'Investment' (equation 2) 290s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 290s 290s Estimate Std. Error t value Pr(>|t|) 290s (Intercept) 12.3518 4.5615 2.71 0.01493 * 290s corpProf 0.4511 0.0814 5.54 3.6e-05 *** 290s corpProfLag 0.3570 0.0846 4.22 0.00058 *** 290s capitalLag -0.1225 0.0223 -5.49 4.0e-05 *** 290s --- 290s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 290s 290s Residual standard error: 1.015 on 17 degrees of freedom 290s Number of observations: 21 Degrees of Freedom: 17 290s SSR: 17.5 MSE: 1.029 Root MSE: 1.015 290s Multiple R-Squared: 0.931 Adjusted R-Squared: 0.918 290s 290s 290s SUR estimates for 'PrivateWages' (equation 3) 290s Model Formula: privWage ~ gnp + gnpLag + trend 290s 290s Estimate Std. Error t value Pr(>|t|) 290s (Intercept) 1.3964 1.0825 1.29 0.22 290s gnp 0.4177 0.0269 15.55 4.4e-11 *** 290s gnpLag 0.1709 0.0306 5.59 4.0e-05 *** 290s trend 0.1467 0.0272 5.40 5.9e-05 *** 290s --- 290s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 290s 290s Residual standard error: 0.802 on 16 degrees of freedom 290s Number of observations: 20 Degrees of Freedom: 16 290s SSR: 10.284 MSE: 0.643 Root MSE: 0.802 290s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 290s 290s > residuals 290s Consumption Investment PrivateWages 290s 1 NA NA NA 290s 2 -0.2529 -0.2920 -1.15193 290s 3 -1.2998 -0.1392 0.50193 290s 4 -1.5662 1.1106 1.42026 290s 5 -0.4876 -1.4391 -0.09801 290s 6 0.0149 0.3556 -0.35678 290s 7 0.9002 1.4558 NA 290s 8 1.3535 0.8299 -0.74964 290s 9 1.0406 -0.5136 0.29355 290s 10 NA 1.2191 1.18544 290s 11 0.4417 0.2810 -0.36558 290s 12 -0.0892 0.0754 0.33733 290s 13 -0.1541 0.3429 -0.17490 290s 14 0.2984 0.3597 0.39941 290s 15 -0.0260 -0.1602 0.29441 290s 16 -0.0250 0.0130 -0.00177 290s 17 1.5671 1.0231 -0.81891 290s 18 -0.4089 0.0306 0.85516 290s 19 0.2819 -2.6153 -0.77184 290s 20 0.9257 -0.6030 -0.41040 290s 21 0.7415 -0.7118 -1.21679 290s 22 -2.2437 -0.5398 0.57166 290s > fitted 290s Consumption Investment PrivateWages 290s 1 NA NA NA 290s 2 42.2 0.092 26.7 290s 3 46.3 2.039 28.8 290s 4 50.8 4.089 32.7 290s 5 51.1 4.439 34.0 290s 6 52.6 4.744 35.8 290s 7 54.2 4.144 NA 290s 8 54.8 3.370 38.6 290s 9 56.3 3.514 38.9 290s 10 NA 3.881 40.1 290s 11 54.6 0.719 38.3 290s 12 51.0 -3.475 34.2 290s 13 45.8 -6.543 29.2 290s 14 46.2 -5.460 28.1 290s 15 48.7 -2.840 30.3 290s 16 51.3 -1.313 33.2 290s 17 56.1 1.077 37.6 290s 18 59.1 1.969 40.1 290s 19 57.2 0.715 39.0 290s 20 60.7 1.903 42.0 290s 21 64.3 4.012 46.2 290s 22 71.9 5.440 52.7 290s > predict 290s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 290s 1 NA NA NA NA 290s 2 42.2 0.422 41.3 43.0 290s 3 46.3 0.462 45.4 47.2 290s 4 50.8 0.309 50.1 51.4 290s 5 51.1 0.359 50.4 51.8 290s 6 52.6 0.362 51.9 53.3 290s 7 54.2 0.328 53.5 54.9 290s 8 54.8 0.300 54.2 55.4 290s 9 56.3 0.323 55.6 56.9 290s 10 NA NA NA NA 290s 11 54.6 0.531 53.5 55.6 290s 12 51.0 0.427 50.1 51.8 290s 13 45.8 0.564 44.6 46.9 290s 14 46.2 0.543 45.1 47.3 290s 15 48.7 0.341 48.0 49.4 290s 16 51.3 0.302 50.7 51.9 290s 17 56.1 0.328 55.5 56.8 290s 18 59.1 0.294 58.5 59.7 290s 19 57.2 0.332 56.6 57.9 290s 20 60.7 0.392 59.9 61.5 290s 21 64.3 0.394 63.5 65.0 290s 22 71.9 0.615 70.7 73.2 290s Investment.pred Investment.se.fit Investment.lwr Investment.upr 290s 1 NA NA NA NA 290s 2 0.092 0.508 -0.929 1.113 290s 3 2.039 0.421 1.193 2.885 290s 4 4.089 0.376 3.333 4.846 290s 5 4.439 0.311 3.813 5.065 290s 6 4.744 0.294 4.154 5.335 290s 7 4.144 0.277 3.587 4.701 290s 8 3.370 0.247 2.873 3.867 290s 9 3.514 0.328 2.855 4.172 290s 10 3.881 0.376 3.126 4.636 290s 11 0.719 0.508 -0.301 1.739 290s 12 -3.475 0.428 -4.336 -2.615 290s 13 -6.543 0.521 -7.590 -5.496 290s 14 -5.460 0.583 -6.632 -4.288 290s 15 -2.840 0.316 -3.474 -2.205 290s 16 -1.313 0.271 -1.857 -0.769 290s 17 1.077 0.293 0.488 1.666 290s 18 1.969 0.205 1.557 2.382 290s 19 0.715 0.263 0.187 1.244 290s 20 1.903 0.309 1.283 2.523 290s 21 4.012 0.280 3.449 4.574 290s 22 5.440 0.389 4.659 6.221 290s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 290s 1 NA NA NA NA 290s 2 26.7 0.306 26.0 27.3 290s 3 28.8 0.305 28.2 29.4 290s 4 32.7 0.302 32.1 33.3 290s 5 34.0 0.231 33.5 34.5 290s 6 35.8 0.230 35.3 36.2 290s 7 NA NA NA NA 290s 8 38.6 0.233 38.2 39.1 290s 9 38.9 0.222 38.5 39.4 290s 10 40.1 0.213 39.7 40.5 290s 11 38.3 0.292 37.7 38.9 290s 12 34.2 0.300 33.6 34.8 290s 13 29.2 0.361 28.4 29.9 290s 14 28.1 0.322 27.5 28.7 290s 15 30.3 0.314 29.7 30.9 290s 16 33.2 0.263 32.7 33.7 290s 17 37.6 0.256 37.1 38.1 290s 18 40.1 0.204 39.7 40.6 290s 19 39.0 0.298 38.4 39.6 290s 20 42.0 0.272 41.5 42.6 290s 21 46.2 0.288 45.6 46.8 290s 22 52.7 0.431 51.9 53.6 290s > model.frame 290s [1] TRUE 290s > model.matrix 290s [1] TRUE 290s > nobs 290s [1] 61 290s > linearHypothesis 290s Linear hypothesis test (Theil's F test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df F Pr(>F) 290s 1 50 290s 2 49 1 1.01 0.32 290s Linear hypothesis test (F statistic of a Wald test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df F Pr(>F) 290s 1 50 290s 2 49 1 1.3 0.26 290s Linear hypothesis test (Chi^2 statistic of a Wald test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df Chisq Pr(>Chisq) 290s 1 50 290s 2 49 1 1.3 0.25 290s Linear hypothesis test (Theil's F test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s Consumption_corpProfLag - PrivateWages_trend = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df F Pr(>F) 290s 1 51 290s 2 49 2 0.53 0.59 290s Linear hypothesis test (F statistic of a Wald test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s Consumption_corpProfLag - PrivateWages_trend = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df F Pr(>F) 290s 1 51 290s 2 49 2 0.69 0.51 290s Linear hypothesis test (Chi^2 statistic of a Wald test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s Consumption_corpProfLag - PrivateWages_trend = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df Chisq Pr(>Chisq) 290s 1 51 290s 2 49 2 1.38 0.5 290s > logLik 290s 'log Lik.' -69.6 (df=18) 290s 'log Lik.' -76.9 (df=18) 290s Estimating function 290s Consumption_(Intercept) Consumption_corpProf 290s Consumption_2 -0.42417 -5.2597 290s Consumption_3 -2.17982 -36.8390 290s Consumption_4 -2.62648 -48.3271 290s Consumption_5 -0.81768 -15.8630 290s Consumption_6 0.02500 0.5025 290s Consumption_7 1.50966 29.5894 290s Consumption_8 2.26980 44.9421 290s Consumption_9 1.74517 36.8231 290s Consumption_11 0.74077 11.5559 290s Consumption_12 -0.14959 -1.7053 290s Consumption_13 -0.25842 -1.8090 290s Consumption_14 0.50036 5.6040 290s Consumption_15 -0.04361 -0.5363 290s Consumption_16 -0.04189 -0.5865 290s Consumption_17 2.62802 46.2532 290s Consumption_18 -0.68580 -11.8643 290s Consumption_19 0.47280 7.2339 290s Consumption_20 1.55235 29.4946 290s Consumption_21 1.24350 26.2379 290s Consumption_22 -3.76279 -88.4255 290s Investment_2 0.07441 0.9227 290s Investment_3 0.03547 0.5995 290s Investment_4 -0.28298 -5.2069 290s Investment_5 0.36669 7.1139 290s Investment_6 -0.09061 -1.8212 290s Investment_7 -0.37095 -7.2706 290s Investment_8 -0.21146 -4.1868 290s Investment_9 0.13086 2.7611 290s Investment_10 0.00000 0.0000 290s Investment_11 -0.07161 -1.1172 290s Investment_12 -0.01921 -0.2190 290s Investment_13 -0.08737 -0.6116 290s Investment_14 -0.09166 -1.0266 290s Investment_15 0.04082 0.5021 290s Investment_16 -0.00330 -0.0462 290s Investment_17 -0.26069 -4.5882 290s Investment_18 -0.00779 -0.1348 290s Investment_19 0.66639 10.1958 290s Investment_20 0.15365 2.9194 290s Investment_21 0.18136 3.8268 290s Investment_22 0.13754 3.2323 290s PrivateWages_2 -1.58616 -19.6684 290s PrivateWages_3 0.69114 11.6803 290s PrivateWages_4 1.95564 35.9837 290s PrivateWages_5 -0.13496 -2.6181 290s PrivateWages_6 -0.49127 -9.8746 290s PrivateWages_8 -1.03222 -20.4380 290s PrivateWages_9 0.40421 8.5288 290s PrivateWages_10 0.00000 0.0000 290s PrivateWages_11 -0.50339 -7.8529 290s PrivateWages_12 0.46449 5.2952 290s PrivateWages_13 -0.24083 -1.6858 290s PrivateWages_14 0.54997 6.1596 290s PrivateWages_15 0.40539 4.9863 290s PrivateWages_16 -0.00244 -0.0342 290s PrivateWages_17 -1.12761 -19.8459 290s PrivateWages_18 1.17751 20.3710 290s PrivateWages_19 -1.06279 -16.2607 290s PrivateWages_20 -0.56511 -10.7371 290s PrivateWages_21 -1.67547 -35.3524 290s PrivateWages_22 0.78715 18.4981 290s Consumption_corpProfLag Consumption_wages 290s Consumption_2 -5.3870 -11.962 290s Consumption_3 -27.0298 -70.190 290s Consumption_4 -44.3874 -97.180 290s Consumption_5 -15.0453 -30.254 290s Consumption_6 0.4850 0.965 290s Consumption_7 30.3442 61.443 290s Consumption_8 44.4881 94.197 290s Consumption_9 34.5544 74.868 290s Consumption_11 16.0746 31.186 290s Consumption_12 -2.3336 -5.879 290s Consumption_13 -2.9460 -8.864 290s Consumption_14 3.5025 17.062 290s Consumption_15 -0.4884 -1.596 290s Consumption_16 -0.5153 -1.646 290s Consumption_17 36.7923 116.159 290s Consumption_18 -12.0701 -32.713 290s Consumption_19 8.1795 21.702 290s Consumption_20 23.7509 76.686 290s Consumption_21 23.6265 65.906 290s Consumption_22 -79.3948 -232.540 290s Investment_2 0.9450 2.098 290s Investment_3 0.4399 1.142 290s Investment_4 -4.7824 -10.470 290s Investment_5 6.7472 13.568 290s Investment_6 -1.7577 -3.497 290s Investment_7 -7.4561 -15.098 290s Investment_8 -4.1445 -8.775 290s Investment_9 2.5910 5.614 290s Investment_10 0.0000 0.000 290s Investment_11 -1.5540 -3.015 290s Investment_12 -0.2997 -0.755 290s Investment_13 -0.9961 -2.997 290s Investment_14 -0.6416 -3.126 290s Investment_15 0.4572 1.494 290s Investment_16 -0.0406 -0.130 290s Investment_17 -3.6497 -11.523 290s Investment_18 -0.1371 -0.372 290s Investment_19 11.5286 30.587 290s Investment_20 2.3509 7.590 290s Investment_21 3.4459 9.612 290s Investment_22 2.9022 8.500 290s PrivateWages_2 -20.1442 -44.730 290s PrivateWages_3 8.5702 22.255 290s PrivateWages_4 33.0503 72.359 290s PrivateWages_5 -2.4832 -4.993 290s PrivateWages_6 -9.5307 -18.963 290s PrivateWages_8 -20.2315 -42.837 290s PrivateWages_9 8.0034 17.341 290s PrivateWages_10 0.0000 0.000 290s PrivateWages_11 -10.9235 -21.193 290s PrivateWages_12 7.2461 18.254 290s PrivateWages_13 -2.7454 -8.260 290s PrivateWages_14 3.8498 18.754 290s PrivateWages_15 4.5404 14.837 290s PrivateWages_16 -0.0300 -0.096 290s PrivateWages_17 -15.7865 -49.840 290s PrivateWages_18 20.7242 56.167 290s PrivateWages_19 -18.3863 -48.782 290s PrivateWages_20 -8.6462 -27.916 290s PrivateWages_21 -31.8339 -88.800 290s PrivateWages_22 16.6089 48.646 290s Investment_(Intercept) Investment_corpProf 290s Consumption_2 0.064449 0.7992 290s Consumption_3 0.331201 5.5973 290s Consumption_4 0.399066 7.3428 290s Consumption_5 0.124238 2.4102 290s Consumption_6 -0.003798 -0.0763 290s Consumption_7 -0.229378 -4.4958 290s Consumption_8 -0.344873 -6.8285 290s Consumption_9 -0.265161 -5.5949 290s Consumption_11 -0.112552 -1.7558 290s Consumption_12 0.022729 0.2591 290s Consumption_13 0.039265 0.2749 290s Consumption_14 -0.076024 -0.8515 290s Consumption_15 0.006625 0.0815 290s Consumption_16 0.006365 0.0891 290s Consumption_17 -0.399301 -7.0277 290s Consumption_18 0.104200 1.8027 290s Consumption_19 -0.071838 -1.0991 290s Consumption_20 -0.235863 -4.4814 290s Consumption_21 -0.188937 -3.9866 290s Consumption_22 0.571717 13.4353 290s Investment_2 -0.423201 -5.2477 290s Investment_3 -0.201766 -3.4098 290s Investment_4 1.609495 29.6147 290s Investment_5 -2.085613 -40.4609 290s Investment_6 0.515327 10.3581 290s Investment_7 2.109824 41.3526 290s Investment_8 1.202679 23.8131 290s Investment_9 -0.744277 -15.7042 290s Investment_10 1.766841 38.3405 290s Investment_11 0.407303 6.3539 290s Investment_12 0.109258 1.2455 290s Investment_13 0.496948 3.4786 290s Investment_14 0.521347 5.8391 290s Investment_15 -0.232156 -2.8555 290s Investment_16 0.018782 0.2630 290s Investment_17 1.482721 26.0959 290s Investment_18 0.044303 0.7664 290s Investment_19 -3.790179 -57.9897 290s Investment_20 -0.873905 -16.6042 290s Investment_21 -1.031520 -21.7651 290s Investment_22 -0.782292 -18.3839 290s PrivateWages_2 0.617327 7.6549 290s PrivateWages_3 -0.268990 -4.5459 290s PrivateWages_4 -0.761128 -14.0048 290s PrivateWages_5 0.052525 1.0190 290s PrivateWages_6 0.191202 3.8432 290s PrivateWages_8 0.401737 7.9544 290s PrivateWages_9 -0.157317 -3.3194 290s PrivateWages_10 -0.635285 -13.7857 290s PrivateWages_11 0.195917 3.0563 290s PrivateWages_12 -0.180778 -2.0609 290s PrivateWages_13 0.093729 0.6561 290s PrivateWages_14 -0.214045 -2.3973 290s PrivateWages_15 -0.157776 -1.9406 290s PrivateWages_16 0.000951 0.0133 290s PrivateWages_17 0.438862 7.7240 290s PrivateWages_18 -0.458284 -7.9283 290s PrivateWages_19 0.413636 6.3286 290s PrivateWages_20 0.219939 4.1788 290s PrivateWages_21 0.652086 13.7590 290s PrivateWages_22 -0.306358 -7.1994 290s Investment_corpProfLag Investment_capitalLag 290s Consumption_2 0.8185 11.781 290s Consumption_3 4.1069 60.477 290s Consumption_4 6.7442 73.628 290s Consumption_5 2.2860 23.568 290s Consumption_6 -0.0737 -0.732 290s Consumption_7 -4.6105 -45.371 290s Consumption_8 -6.7595 -70.147 290s Consumption_9 -5.2502 -55.047 290s Consumption_11 -2.4424 -24.277 290s Consumption_12 0.3546 4.925 290s Consumption_13 0.4476 8.375 290s Consumption_14 -0.5322 -15.745 290s Consumption_15 0.0742 1.338 290s Consumption_16 0.0783 1.267 290s Consumption_17 -5.5902 -78.942 290s Consumption_18 1.8339 20.819 290s Consumption_19 -1.2428 -14.497 290s Consumption_20 -3.6087 -47.149 290s Consumption_21 -3.5898 -38.014 290s Consumption_22 12.0632 116.916 290s Investment_2 -5.3746 -77.361 290s Investment_3 -2.5019 -36.842 290s Investment_4 27.2005 296.952 290s Investment_5 -38.3753 -395.641 290s Investment_6 9.9974 99.304 290s Investment_7 42.4075 417.323 290s Investment_8 23.5725 244.625 290s Investment_9 -14.7367 -154.512 290s Investment_10 37.2803 372.097 290s Investment_11 8.8385 87.855 290s Investment_12 1.7044 23.676 290s Investment_13 5.6652 105.999 290s Investment_14 3.6494 107.971 290s Investment_15 -2.6002 -46.896 290s Investment_16 0.2310 3.738 290s Investment_17 20.7581 293.134 290s Investment_18 0.7797 8.852 290s Investment_19 -65.5701 -764.858 290s Investment_20 -13.3707 -174.694 290s Investment_21 -19.5989 -207.542 290s Investment_22 -16.5064 -159.979 290s PrivateWages_2 7.8401 112.847 290s PrivateWages_3 -3.3355 -49.118 290s PrivateWages_4 -12.8631 -140.428 290s PrivateWages_5 0.9665 9.964 290s PrivateWages_6 3.7093 36.845 290s PrivateWages_8 7.8740 81.713 290s PrivateWages_9 -3.1149 -32.659 290s PrivateWages_10 -13.4045 -133.791 290s PrivateWages_11 4.2514 42.259 290s PrivateWages_12 -2.8201 -39.175 290s PrivateWages_13 1.0685 19.992 290s PrivateWages_14 -1.4983 -44.329 290s PrivateWages_15 -1.7671 -31.871 290s PrivateWages_16 0.0117 0.189 290s PrivateWages_17 6.1441 86.763 290s PrivateWages_18 -8.0658 -91.565 290s PrivateWages_19 7.1559 83.472 290s PrivateWages_20 3.3651 43.966 290s PrivateWages_21 12.3896 131.200 290s PrivateWages_22 -6.4641 -62.650 290s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 290s Consumption_2 -0.34828 -15.881 -15.638 290s Consumption_3 -1.78978 -89.668 -81.614 290s Consumption_4 -2.15652 -123.353 -108.042 290s Consumption_5 -0.67137 -38.335 -38.402 290s Consumption_6 0.02052 1.252 1.172 290s Consumption_7 0.00000 0.000 0.000 290s Consumption_8 1.86367 120.020 119.275 290s Consumption_9 1.43291 92.422 92.279 290s Consumption_11 0.60822 37.223 40.751 290s Consumption_12 -0.12282 -6.559 -7.517 290s Consumption_13 -0.21218 -9.400 -11.331 290s Consumption_14 0.41083 18.528 18.200 290s Consumption_15 -0.03580 -1.779 -1.615 290s Consumption_16 -0.03440 -1.871 -1.710 290s Consumption_17 2.15779 135.293 117.384 290s Consumption_18 -0.56309 -36.601 -35.306 290s Consumption_19 0.38821 23.642 25.233 290s Consumption_20 1.27458 88.584 77.622 290s Consumption_21 1.02100 77.290 70.960 290s Consumption_22 -3.08951 -273.113 -233.876 290s Investment_2 0.15649 7.136 7.027 290s Investment_3 0.07461 3.738 3.402 290s Investment_4 -0.59517 -34.043 -29.818 290s Investment_5 0.77123 44.037 44.114 290s Investment_6 -0.19056 -11.624 -10.881 290s Investment_7 0.00000 0.000 0.000 290s Investment_8 -0.44473 -28.641 -28.463 290s Investment_9 0.27522 17.752 17.724 290s Investment_10 -0.65335 -43.774 -42.141 290s Investment_11 -0.15061 -9.218 -10.091 290s Investment_12 -0.04040 -2.157 -2.473 290s Investment_13 -0.18376 -8.141 -9.813 290s Investment_14 -0.19279 -8.695 -8.540 290s Investment_15 0.08585 4.267 3.872 290s Investment_16 -0.00695 -0.378 -0.345 290s Investment_17 -0.54829 -34.378 -29.827 290s Investment_18 -0.01638 -1.065 -1.027 290s Investment_19 1.40155 85.354 91.101 290s Investment_20 0.32316 22.459 19.680 290s Investment_21 0.38144 28.875 26.510 290s Investment_22 0.28928 25.572 21.898 290s PrivateWages_2 -3.98191 -181.575 -178.788 290s PrivateWages_3 1.73505 86.926 79.118 290s PrivateWages_4 4.90946 280.821 245.964 290s PrivateWages_5 -0.33880 -19.345 -19.379 290s PrivateWages_6 -1.23330 -75.231 -70.421 290s PrivateWages_8 -2.59130 -166.880 -165.843 290s PrivateWages_9 1.01473 65.450 65.349 290s PrivateWages_10 4.09774 274.549 264.304 290s PrivateWages_11 -1.26371 -77.339 -84.669 290s PrivateWages_12 1.16606 62.268 71.363 290s PrivateWages_13 -0.60457 -26.783 -32.284 290s PrivateWages_14 1.38064 62.267 61.163 290s PrivateWages_15 1.01769 50.579 45.898 290s PrivateWages_16 -0.00613 -0.334 -0.305 290s PrivateWages_17 -2.83076 -177.489 -153.993 290s PrivateWages_18 2.95604 192.143 185.344 290s PrivateWages_19 -2.66805 -162.484 -173.423 290s PrivateWages_20 -1.41866 -98.597 -86.396 290s PrivateWages_21 -4.20611 -318.403 -292.325 290s PrivateWages_22 1.97608 174.686 149.589 290s PrivateWages_trend 290s Consumption_2 3.4828 290s Consumption_3 16.1081 290s Consumption_4 17.2522 290s Consumption_5 4.6996 290s Consumption_6 -0.1231 290s Consumption_7 0.0000 290s Consumption_8 -7.4547 290s Consumption_9 -4.2987 290s Consumption_11 -0.6082 290s Consumption_12 0.0000 290s Consumption_13 -0.2122 290s Consumption_14 0.8217 290s Consumption_15 -0.1074 290s Consumption_16 -0.1376 290s Consumption_17 10.7889 290s Consumption_18 -3.3785 290s Consumption_19 2.7174 290s Consumption_20 10.1967 290s Consumption_21 9.1890 290s Consumption_22 -30.8951 290s Investment_2 -1.5649 290s Investment_3 -0.6715 290s Investment_4 4.7613 290s Investment_5 -5.3986 290s Investment_6 1.1434 290s Investment_7 0.0000 290s Investment_8 1.7789 290s Investment_9 -0.8257 290s Investment_10 1.3067 290s Investment_11 0.1506 290s Investment_12 0.0000 290s Investment_13 -0.1838 290s Investment_14 -0.3856 290s Investment_15 0.2575 290s Investment_16 -0.0278 290s Investment_17 -2.7414 290s Investment_18 -0.0983 290s Investment_19 9.8108 290s Investment_20 2.5853 290s Investment_21 3.4330 290s Investment_22 2.8928 290s PrivateWages_2 39.8191 290s PrivateWages_3 -15.6154 290s PrivateWages_4 -39.2757 290s PrivateWages_5 2.3716 290s PrivateWages_6 7.3998 290s PrivateWages_8 10.3652 290s PrivateWages_9 -3.0442 290s PrivateWages_10 -8.1955 290s PrivateWages_11 1.2637 290s PrivateWages_12 0.0000 290s PrivateWages_13 -0.6046 290s PrivateWages_14 2.7613 290s PrivateWages_15 3.0531 290s PrivateWages_16 -0.0245 290s PrivateWages_17 -14.1538 290s PrivateWages_18 17.7363 290s PrivateWages_19 -18.6764 290s PrivateWages_20 -11.3493 290s PrivateWages_21 -37.8550 290s PrivateWages_22 19.7608 290s [1] TRUE 290s > Bread 290s Consumption_(Intercept) Consumption_corpProf Consumption_corpProfLag 290s [1,] 87.9904 -0.088084 -0.91416 290s [2,] -0.0881 0.389639 -0.23612 290s [3,] -0.9142 -0.236125 0.38341 290s [4,] -1.6692 -0.062952 -0.03326 290s [5,] 2.6851 -0.188961 0.72342 290s [6,] -0.0355 0.023370 -0.02643 290s [7,] -0.0563 -0.020038 0.03196 290s [8,] -0.0054 0.000618 -0.00397 290s [9,] -33.1687 0.063156 1.54217 290s [10,] 0.3665 -0.059172 0.03813 290s [11,] 0.1741 0.060188 -0.06574 290s [12,] 0.1831 0.029476 0.02425 290s Consumption_wages Investment_(Intercept) Investment_corpProf 290s [1,] -1.669236 2.685 -0.03549 290s [2,] -0.062952 -0.189 0.02337 290s [3,] -0.033257 0.723 -0.02643 290s [4,] 0.079061 -0.248 0.00151 290s [5,] -0.248317 1269.247 -12.23080 290s [6,] 0.001506 -12.231 0.40462 290s [7,] -0.002778 9.884 -0.34614 290s [8,] 0.001327 -6.097 0.05519 290s [9,] 0.134743 17.903 -0.13872 290s [10,] 0.000196 0.262 0.01397 290s [11,] -0.002616 -0.581 -0.01197 290s [12,] -0.026193 -0.551 0.00355 290s Investment_corpProfLag Investment_capitalLag PrivateWages_(Intercept) 290s [1,] -0.05628 -0.005396 -33.1687 290s [2,] -0.02004 0.000618 0.0632 290s [3,] 0.03196 -0.003967 1.5422 290s [4,] -0.00278 0.001327 0.1347 290s [5,] 9.88435 -6.096982 17.9032 290s [6,] -0.34614 0.055190 -0.1387 290s [7,] 0.43632 -0.055785 -0.4000 290s [8,] -0.05578 0.030317 -0.0433 290s [9,] -0.40000 -0.043343 71.4840 290s [10,] -0.00786 -0.001844 -0.3085 290s [11,] 0.01493 0.002686 -0.8909 290s [12,] -0.01033 0.003295 0.8146 290s PrivateWages_gnp PrivateWages_gnpLag PrivateWages_trend 290s [1,] 0.366465 0.17405 0.18311 290s [2,] -0.059172 0.06019 0.02948 290s [3,] 0.038129 -0.06574 0.02425 290s [4,] 0.000196 -0.00262 -0.02619 290s [5,] 0.262390 -0.58123 -0.55064 290s [6,] 0.013966 -0.01197 0.00355 290s [7,] -0.007857 0.01493 -0.01033 290s [8,] -0.001844 0.00269 0.00330 290s [9,] -0.308484 -0.89087 0.81461 290s [10,] 0.044017 -0.04022 -0.01158 290s [11,] -0.040216 0.05696 -0.00212 290s [12,] -0.011575 -0.00212 0.04506 290s > 290s > # 3SLS 290s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 290s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 290s > summary 290s 290s systemfit results 290s method: 3SLS 290s 290s N DF SSR detRCov OLS-R2 McElroy-R2 290s system 59 47 59.5 0.241 0.97 0.994 290s 290s N DF SSR MSE RMSE R2 Adj R2 290s Consumption 19 15 18.1 1.203 1.097 0.980 0.977 290s Investment 20 16 31.1 1.945 1.395 0.866 0.841 290s PrivateWages 20 16 10.3 0.645 0.803 0.987 0.985 290s 290s The covariance matrix of the residuals used for estimation 290s Consumption Investment PrivateWages 290s Consumption 1.079 0.354 -0.383 290s Investment 0.354 1.047 0.107 290s PrivateWages -0.383 0.107 0.445 290s 290s The covariance matrix of the residuals 290s Consumption Investment PrivateWages 290s Consumption 0.950 0.324 -0.395 290s Investment 0.324 1.385 0.242 290s PrivateWages -0.395 0.242 0.475 290s 290s The correlations of the residuals 290s Consumption Investment PrivateWages 290s Consumption 1.000 0.293 -0.582 290s Investment 0.293 1.000 0.292 290s PrivateWages -0.582 0.292 1.000 290s 290s 290s 3SLS estimates for 'Consumption' (equation 1) 290s Model Formula: consump ~ corpProf + corpProfLag + wages 290s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 290s gnpLag 290s 290s Estimate Std. Error t value Pr(>|t|) 290s (Intercept) 16.5606 1.3295 12.46 2.6e-09 *** 290s corpProf 0.1100 0.1098 1.00 0.33 290s corpProfLag 0.1155 0.1007 1.15 0.27 290s wages 0.8086 0.0401 20.18 2.8e-12 *** 290s --- 290s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 290s 290s Residual standard error: 1.097 on 15 degrees of freedom 290s Number of observations: 19 Degrees of Freedom: 15 290s SSR: 18.051 MSE: 1.203 Root MSE: 1.097 290s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.977 290s 290s 290s 3SLS estimates for 'Investment' (equation 2) 290s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 290s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 290s gnpLag 290s 290s Estimate Std. Error t value Pr(>|t|) 290s (Intercept) 23.6871 6.1159 3.87 0.00135 ** 290s corpProf 0.1072 0.1414 0.76 0.45918 290s corpProfLag 0.6278 0.1361 4.61 0.00029 *** 290s capitalLag -0.1726 0.0295 -5.85 2.5e-05 *** 290s --- 290s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 290s 290s Residual standard error: 1.395 on 16 degrees of freedom 290s Number of observations: 20 Degrees of Freedom: 16 290s SSR: 31.126 MSE: 1.945 Root MSE: 1.395 290s Multiple R-Squared: 0.866 Adjusted R-Squared: 0.841 290s 290s 290s 3SLS estimates for 'PrivateWages' (equation 3) 290s Model Formula: privWage ~ gnp + gnpLag + trend 290s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 290s gnpLag 290s 290s Estimate Std. Error t value Pr(>|t|) 290s (Intercept) 1.3603 1.0927 1.24 0.23109 290s gnp 0.4117 0.0315 13.06 6.0e-10 *** 290s gnpLag 0.1782 0.0336 5.31 7.1e-05 *** 290s trend 0.1370 0.0280 4.89 0.00016 *** 290s --- 290s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 290s 290s Residual standard error: 0.803 on 16 degrees of freedom 290s Number of observations: 20 Degrees of Freedom: 16 290s SSR: 10.318 MSE: 0.645 Root MSE: 0.803 290s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 290s 290s > residuals 290s Consumption Investment PrivateWages 290s 1 NA NA NA 290s 2 -0.29542 -1.636 -1.2658 290s 3 -0.89033 0.135 0.4198 290s 4 -1.25669 0.777 1.3578 290s 5 -0.14000 -1.574 -0.2036 290s 6 0.37365 0.341 -0.4283 290s 7 NA NA NA 290s 8 1.63850 1.194 -0.8319 290s 9 1.44030 0.454 0.2186 290s 10 NA 2.192 1.1346 290s 11 0.17274 -0.750 -0.4603 290s 12 -0.49629 -0.698 0.2476 290s 13 -0.78384 -0.976 -0.2528 290s 14 0.32420 1.365 0.4028 290s 15 -0.10364 -0.170 0.3295 290s 16 -0.00105 0.140 0.0377 290s 17 1.84421 1.862 -0.7540 290s 18 -0.36893 -0.103 0.8827 290s 19 0.14129 -3.255 -0.7764 290s 20 1.23511 0.475 -0.3230 290s 21 1.06553 0.152 -1.1453 290s 22 -1.85709 0.746 0.6843 290s > fitted 290s Consumption Investment PrivateWages 290s 1 NA NA NA 290s 2 42.2 1.436 26.8 290s 3 45.9 1.765 28.9 290s 4 50.5 4.423 32.7 290s 5 50.7 4.574 34.1 290s 6 52.2 4.759 35.8 290s 7 NA NA NA 290s 8 54.6 3.006 38.7 290s 9 55.9 2.546 39.0 290s 10 NA 2.908 40.2 290s 11 54.8 1.750 38.4 290s 12 51.4 -2.702 34.3 290s 13 46.4 -5.224 29.3 290s 14 46.2 -6.465 28.1 290s 15 48.8 -2.830 30.3 290s 16 51.3 -1.440 33.2 290s 17 55.9 0.238 37.6 290s 18 59.1 2.103 40.1 290s 19 57.4 1.355 39.0 290s 20 60.4 0.825 41.9 290s 21 63.9 3.148 46.1 290s 22 71.6 4.154 52.6 290s > predict 290s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 290s 1 NA NA NA NA 290s 2 42.2 0.475 39.6 44.7 290s 3 45.9 0.557 43.3 48.5 290s 4 50.5 0.372 48.0 52.9 290s 5 50.7 0.433 48.2 53.3 290s 6 52.2 0.438 49.7 54.7 290s 7 NA NA NA NA 290s 8 54.6 0.362 52.1 57.0 290s 9 55.9 0.401 53.4 58.3 290s 10 NA NA NA NA 290s 11 54.8 0.684 52.1 57.6 290s 12 51.4 0.563 48.8 54.0 290s 13 46.4 0.733 43.6 49.2 290s 14 46.2 0.612 43.5 48.9 290s 15 48.8 0.379 46.3 51.3 290s 16 51.3 0.334 48.9 53.7 290s 17 55.9 0.394 53.4 58.3 290s 18 59.1 0.322 56.6 61.5 290s 19 57.4 0.392 54.9 59.8 290s 20 60.4 0.462 57.8 62.9 290s 21 63.9 0.448 61.4 66.5 290s 22 71.6 0.686 68.8 74.3 290s Investment.pred Investment.se.fit Investment.lwr Investment.upr 290s 1 NA NA NA NA 290s 2 1.436 0.709 -1.8811 4.754 290s 3 1.765 0.512 -1.3848 4.915 290s 4 4.423 0.470 1.3027 7.543 290s 5 4.574 0.392 1.5029 7.645 290s 6 4.759 0.370 1.7000 7.818 290s 7 NA NA NA NA 290s 8 3.006 0.306 -0.0214 6.033 290s 9 2.546 0.444 -0.5575 5.649 290s 10 2.908 0.488 -0.2245 6.041 290s 11 1.750 0.738 -1.5953 5.096 290s 12 -2.702 0.583 -5.9068 0.503 290s 13 -5.224 0.743 -8.5738 -1.874 290s 14 -6.465 0.780 -9.8530 -3.077 290s 15 -2.830 0.378 -5.8936 0.233 290s 16 -1.440 0.326 -4.4762 1.597 290s 17 0.238 0.426 -2.8533 3.329 290s 18 2.103 0.268 -0.9077 5.114 290s 19 1.355 0.399 -1.7201 4.431 290s 20 0.825 0.474 -2.2981 3.947 290s 21 3.148 0.393 0.0761 6.220 290s 22 4.154 0.555 0.9719 7.336 290s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 290s 1 NA NA NA NA 290s 2 26.8 0.309 24.9 28.6 290s 3 28.9 0.315 27.1 30.7 290s 4 32.7 0.326 30.9 34.6 290s 5 34.1 0.236 32.3 35.9 290s 6 35.8 0.244 34.0 37.6 290s 7 NA NA NA NA 290s 8 38.7 0.237 37.0 40.5 290s 9 39.0 0.225 37.2 40.7 290s 10 40.2 0.219 38.4 41.9 290s 11 38.4 0.309 36.5 40.2 290s 12 34.3 0.336 32.4 36.1 290s 13 29.3 0.411 27.3 31.2 290s 14 28.1 0.326 26.3 29.9 290s 15 30.3 0.313 28.4 32.1 290s 16 33.2 0.262 31.4 35.0 290s 17 37.6 0.265 35.8 39.3 290s 18 40.1 0.205 38.4 41.9 290s 19 39.0 0.323 37.1 40.8 290s 20 41.9 0.282 40.1 43.7 290s 21 46.1 0.293 44.3 48.0 290s 22 52.6 0.463 50.7 54.6 290s > model.frame 290s [1] TRUE 290s > model.matrix 290s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0328 >" 290s [2] "Attributes: < Component “dimnames”: Component 1: 54 string mismatches >" 290s [3] "Numeric: lengths (732, 708) differ" 290s > nobs 290s [1] 59 290s > linearHypothesis 290s Linear hypothesis test (Theil's F test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df F Pr(>F) 290s 1 48 290s 2 47 1 0.23 0.64 290s Linear hypothesis test (F statistic of a Wald test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df F Pr(>F) 290s 1 48 290s 2 47 1 0.31 0.58 290s Linear hypothesis test (Chi^2 statistic of a Wald test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df Chisq Pr(>Chisq) 290s 1 48 290s 2 47 1 0.31 0.58 290s Linear hypothesis test (Theil's F test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s Consumption_corpProfLag - PrivateWages_trend = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df F Pr(>F) 290s 1 49 290s 2 47 2 0.5 0.61 290s Linear hypothesis test (F statistic of a Wald test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s Consumption_corpProfLag - PrivateWages_trend = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df F Pr(>F) 290s 1 49 290s 2 47 2 0.68 0.51 290s Linear hypothesis test (Chi^2 statistic of a Wald test) 290s 290s Hypothesis: 290s Consumption_corpProf + Investment_capitalLag = 0 290s Consumption_corpProfLag - PrivateWages_trend = 0 290s 290s Model 1: restricted model 290s Model 2: kleinModel 290s 290s Res.Df Df Chisq Pr(>Chisq) 290s 1 49 290s 2 47 2 1.37 0.5 290s > logLik 290s 'log Lik.' -71 (df=18) 290s 'log Lik.' -81.1 (df=18) 290s Estimating function 290s Consumption_(Intercept) Consumption_corpProf 290s Consumption_2 -2.7455 -36.891 290s Consumption_3 -1.0626 -17.729 290s Consumption_4 -0.0885 -1.678 290s Consumption_5 -3.0649 -63.238 290s Consumption_6 0.7553 14.561 290s Consumption_8 5.9278 102.010 290s Consumption_9 4.6365 88.027 290s Consumption_11 -1.1219 -18.435 290s Consumption_12 -1.0756 -13.439 290s Consumption_13 -3.1243 -28.309 290s Consumption_14 2.5683 23.826 290s Consumption_15 -1.2839 -16.033 290s Consumption_16 -1.2479 -17.951 290s Consumption_17 7.5868 111.454 290s Consumption_18 -1.1010 -21.581 290s Consumption_19 -5.4018 -103.426 290s Consumption_20 3.8300 67.171 290s Consumption_21 1.5068 30.633 290s Consumption_22 -1.8041 -41.092 290s Investment_2 1.3384 17.984 290s Investment_3 -0.1231 -2.053 290s Investment_4 -0.5511 -10.444 290s Investment_5 1.3722 28.313 290s Investment_6 -0.3224 -6.215 290s Investment_8 -1.1676 -20.092 290s Investment_9 -0.4950 -9.397 290s Investment_10 0.0000 0.000 290s Investment_11 0.6975 11.462 290s Investment_12 0.6591 8.235 290s Investment_13 0.9331 8.455 290s Investment_14 -1.2380 -11.485 290s Investment_15 0.1758 2.195 290s Investment_16 -0.0882 -1.269 290s Investment_17 -1.7103 -25.126 290s Investment_18 0.2715 5.322 290s Investment_19 2.9123 55.761 290s Investment_20 -0.5118 -8.975 290s Investment_21 -0.2046 -4.160 290s Investment_22 -0.6426 -14.637 290s PrivateWages_2 -3.2663 -43.888 290s PrivateWages_3 1.1062 18.456 290s PrivateWages_4 2.8429 53.880 290s PrivateWages_5 -2.9330 -60.515 290s PrivateWages_6 -0.4678 -9.018 290s PrivateWages_8 1.7117 29.456 290s PrivateWages_9 1.9856 37.698 290s PrivateWages_10 0.0000 0.000 290s PrivateWages_11 -2.6089 -42.870 290s PrivateWages_12 -0.5972 -7.462 290s PrivateWages_13 -2.3655 -21.434 290s PrivateWages_14 2.8394 26.341 290s PrivateWages_15 -0.5146 -6.427 290s PrivateWages_16 -0.6088 -8.757 290s PrivateWages_17 2.4972 36.686 290s PrivateWages_18 -0.0214 -0.419 290s PrivateWages_19 -6.8265 -130.705 290s PrivateWages_20 1.3447 23.584 290s PrivateWages_21 -1.4002 -28.468 290s PrivateWages_22 2.2878 52.110 290s Consumption_corpProfLag Consumption_wages 290s Consumption_2 -34.868 -81.19 290s Consumption_3 -13.177 -33.85 290s Consumption_4 -1.496 -3.14 290s Consumption_5 -56.394 -118.79 290s Consumption_6 14.654 29.20 290s Consumption_8 116.186 236.05 290s Consumption_9 91.802 193.78 290s Consumption_11 -24.345 -48.21 290s Consumption_12 -16.779 -42.24 290s Consumption_13 -35.617 -109.94 290s Consumption_14 17.978 84.77 290s Consumption_15 -14.380 -47.92 290s Consumption_16 -15.349 -50.04 290s Consumption_17 106.215 316.24 290s Consumption_18 -19.377 -52.50 290s Consumption_19 -93.451 -266.03 290s Consumption_20 58.598 185.77 290s Consumption_21 28.629 80.45 290s Consumption_22 -38.066 -109.75 290s Investment_2 16.998 39.58 290s Investment_3 -1.526 -3.92 290s Investment_4 -9.313 -19.52 290s Investment_5 25.249 53.18 290s Investment_6 -6.254 -12.46 290s Investment_8 -22.884 -46.49 290s Investment_9 -9.800 -20.69 290s Investment_10 0.000 0.00 290s Investment_11 15.136 29.97 290s Investment_12 10.282 25.88 290s Investment_13 10.638 32.84 290s Investment_14 -8.666 -40.86 290s Investment_15 1.969 6.56 290s Investment_16 -1.085 -3.54 290s Investment_17 -23.945 -71.29 290s Investment_18 4.779 12.95 290s Investment_19 50.383 143.43 290s Investment_20 -7.830 -24.82 290s Investment_21 -3.888 -10.92 290s Investment_22 -13.559 -39.09 290s PrivateWages_2 -41.482 -96.59 290s PrivateWages_3 13.717 35.24 290s PrivateWages_4 48.044 100.73 290s PrivateWages_5 -53.966 -113.67 290s PrivateWages_6 -9.075 -18.08 290s PrivateWages_8 33.550 68.16 290s PrivateWages_9 39.314 82.99 290s PrivateWages_10 0.000 0.00 290s PrivateWages_11 -56.613 -112.10 290s PrivateWages_12 -9.317 -23.46 290s PrivateWages_13 -26.967 -83.24 290s PrivateWages_14 19.876 93.71 290s PrivateWages_15 -5.764 -19.21 290s PrivateWages_16 -7.488 -24.41 290s PrivateWages_17 34.961 104.09 290s PrivateWages_18 -0.376 -1.02 290s PrivateWages_19 -118.099 -336.20 290s PrivateWages_20 20.574 65.22 290s PrivateWages_21 -26.605 -74.76 290s PrivateWages_22 48.272 139.18 290s Investment_(Intercept) Investment_corpProf 290s Consumption_2 1.1993 15.540 290s Consumption_3 0.4642 7.754 290s Consumption_4 0.0387 0.740 290s Consumption_5 1.3388 28.029 290s Consumption_6 -0.3299 -6.424 290s Consumption_8 -2.5893 -44.384 290s Consumption_9 -2.0252 -39.469 290s Consumption_11 0.4900 8.255 290s Consumption_12 0.4698 5.957 290s Consumption_13 1.3647 12.176 290s Consumption_14 -1.1219 -10.434 290s Consumption_15 0.5608 7.176 290s Consumption_16 0.5451 7.773 290s Consumption_17 -3.3140 -48.887 290s Consumption_18 0.4809 9.399 290s Consumption_19 2.3595 45.678 290s Consumption_20 -1.6729 -29.086 290s Consumption_21 -0.6582 -13.228 290s Consumption_22 0.7880 18.015 290s Investment_2 -2.2459 -29.102 290s Investment_3 0.2065 3.450 290s Investment_4 0.9247 17.694 290s Investment_5 -2.3026 -48.209 290s Investment_6 0.5410 10.532 290s Investment_8 1.9592 33.583 290s Investment_9 0.8306 16.187 290s Investment_10 3.0781 62.986 290s Investment_11 -1.1704 -19.716 290s Investment_12 -1.1059 -14.023 290s Investment_13 -1.5658 -13.970 290s Investment_14 2.0775 19.321 290s Investment_15 -0.2950 -3.775 290s Investment_16 0.1480 2.111 290s Investment_17 2.8700 42.338 290s Investment_18 -0.4556 -8.905 290s Investment_19 -4.8870 -94.607 290s Investment_20 0.8587 14.930 290s Investment_21 0.3434 6.901 290s Investment_22 1.0783 24.652 290s PrivateWages_2 1.8660 24.179 290s PrivateWages_3 -0.6320 -10.557 290s PrivateWages_4 -1.6241 -31.077 290s PrivateWages_5 1.6755 35.080 290s PrivateWages_6 0.2672 5.203 290s PrivateWages_8 -0.9779 -16.762 290s PrivateWages_9 -1.1343 -22.106 290s PrivateWages_10 -2.1296 -43.576 290s PrivateWages_11 1.4904 25.106 290s PrivateWages_12 0.3412 4.326 290s PrivateWages_13 1.3514 12.057 290s PrivateWages_14 -1.6221 -15.086 290s PrivateWages_15 0.2940 3.762 290s PrivateWages_16 0.3478 4.959 290s PrivateWages_17 -1.4266 -21.045 290s PrivateWages_18 0.0122 0.239 290s PrivateWages_19 3.8998 75.496 290s PrivateWages_20 -0.7682 -13.356 290s PrivateWages_21 0.7999 16.078 290s PrivateWages_22 -1.3070 -29.879 290s Investment_corpProfLag Investment_capitalLag 290s Consumption_2 15.231 219.22 290s Consumption_3 5.756 84.76 290s Consumption_4 0.654 7.13 290s Consumption_5 24.633 253.96 290s Consumption_6 -6.401 -63.58 290s Consumption_8 -50.751 -526.67 290s Consumption_9 -40.100 -420.44 290s Consumption_11 10.634 105.70 290s Consumption_12 7.329 101.81 290s Consumption_13 15.558 291.09 290s Consumption_14 -7.853 -232.34 290s Consumption_15 6.281 113.29 290s Consumption_16 6.705 108.47 290s Consumption_17 -46.395 -655.17 290s Consumption_18 8.464 96.09 290s Consumption_19 40.820 476.15 290s Consumption_20 -25.596 -334.42 290s Consumption_21 -12.505 -132.42 290s Consumption_22 16.627 161.15 290s Investment_2 -28.522 -410.54 290s Investment_3 2.561 37.71 290s Investment_4 15.627 170.61 290s Investment_5 -42.368 -436.81 290s Investment_6 10.495 104.25 290s Investment_8 38.400 398.50 290s Investment_9 16.445 172.43 290s Investment_10 64.949 648.26 290s Investment_11 -25.398 -252.46 290s Investment_12 -17.253 -239.66 290s Investment_13 -17.850 -333.99 290s Investment_14 14.542 430.24 290s Investment_15 -3.304 -59.59 290s Investment_16 1.821 29.46 290s Investment_17 40.180 567.40 290s Investment_18 -8.019 -91.03 290s Investment_19 -84.545 -986.19 290s Investment_20 13.139 171.66 290s Investment_21 6.524 69.08 290s Investment_22 22.753 220.52 290s PrivateWages_2 23.698 341.10 290s PrivateWages_3 -7.836 -115.39 290s PrivateWages_4 -27.446 -299.64 290s PrivateWages_5 30.830 317.85 290s PrivateWages_6 5.185 51.50 290s PrivateWages_8 -19.166 -198.90 290s PrivateWages_9 -22.459 -235.48 290s PrivateWages_10 -44.934 -448.49 290s PrivateWages_11 32.341 321.48 290s PrivateWages_12 5.323 73.94 290s PrivateWages_13 15.406 288.25 290s PrivateWages_14 -11.355 -335.93 290s PrivateWages_15 3.293 59.39 290s PrivateWages_16 4.278 69.21 290s PrivateWages_17 -19.973 -282.04 290s PrivateWages_18 0.215 2.44 290s PrivateWages_19 67.467 786.98 290s PrivateWages_20 -11.753 -153.56 290s PrivateWages_21 15.199 160.94 290s PrivateWages_22 -27.577 -267.27 290s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 290s Consumption_2 -2.6531 -124.88 -119.13 290s Consumption_3 -1.0269 -50.91 -46.83 290s Consumption_4 -0.0856 -4.84 -4.29 290s Consumption_5 -2.9618 -179.74 -169.41 290s Consumption_6 0.7299 44.24 41.68 290s Consumption_8 5.7284 343.69 366.62 290s Consumption_9 4.4804 278.99 288.54 290s Consumption_11 -1.0841 -69.07 -72.64 290s Consumption_12 -1.0394 -56.99 -63.61 290s Consumption_13 -3.0192 -141.83 -161.22 290s Consumption_14 2.4819 104.56 109.95 290s Consumption_15 -1.2407 -63.55 -55.96 290s Consumption_16 -1.2059 -66.73 -59.93 290s Consumption_17 7.3315 420.78 398.83 290s Consumption_18 -1.0639 -71.47 -66.71 290s Consumption_19 -5.2200 -357.64 -339.30 290s Consumption_20 3.7011 247.40 225.40 290s Consumption_21 1.4561 109.01 101.20 290s Consumption_22 -1.7434 -151.46 -131.97 290s Investment_2 1.6915 79.62 75.95 290s Investment_3 -0.1555 -7.71 -7.09 290s Investment_4 -0.6965 -39.38 -34.89 290s Investment_5 1.7343 105.25 99.20 290s Investment_6 -0.4074 -24.70 -23.26 290s Investment_8 -1.4756 -88.53 -94.44 290s Investment_9 -0.6256 -38.95 -40.29 290s Investment_10 -2.3184 -149.69 -149.53 290s Investment_11 0.8815 56.16 59.06 290s Investment_12 0.8330 45.67 50.98 290s Investment_13 1.1793 55.40 62.98 290s Investment_14 -1.5647 -65.92 -69.32 290s Investment_15 0.2222 11.38 10.02 290s Investment_16 -0.1115 -6.17 -5.54 290s Investment_17 -2.1616 -124.06 -117.59 290s Investment_18 0.3432 23.05 21.52 290s Investment_19 3.6807 252.18 239.25 290s Investment_20 -0.6468 -43.23 -39.39 290s Investment_21 -0.2586 -19.36 -17.97 290s Investment_22 -0.8122 -70.56 -61.48 290s PrivateWages_2 -7.4676 -351.50 -335.29 290s PrivateWages_3 2.5291 125.39 115.33 290s PrivateWages_4 6.4995 367.50 325.62 290s PrivateWages_5 -6.7054 -406.93 -383.55 290s PrivateWages_6 -1.0695 -64.82 -61.07 290s PrivateWages_8 3.9134 234.79 250.46 290s PrivateWages_9 4.5395 282.67 292.34 290s PrivateWages_10 8.5226 550.30 549.71 290s PrivateWages_11 -5.9646 -380.01 -399.63 290s PrivateWages_12 -1.3654 -74.87 -83.57 290s PrivateWages_13 -5.4082 -254.06 -288.80 290s PrivateWages_14 6.4916 273.48 287.58 290s PrivateWages_15 -1.1766 -60.26 -53.06 290s PrivateWages_16 -1.3918 -77.02 -69.17 290s PrivateWages_17 5.7093 327.68 310.59 290s PrivateWages_18 -0.0489 -3.28 -3.07 290s PrivateWages_19 -15.6071 -1069.28 -1014.46 290s PrivateWages_20 3.0743 205.50 187.22 290s PrivateWages_21 -3.2013 -239.67 -222.49 290s PrivateWages_22 5.2304 454.42 395.94 290s PrivateWages_trend 290s Consumption_2 26.531 290s Consumption_3 9.242 290s Consumption_4 0.684 290s Consumption_5 20.732 290s Consumption_6 -4.380 290s Consumption_8 -22.913 290s Consumption_9 -13.441 290s Consumption_11 1.084 290s Consumption_12 0.000 290s Consumption_13 -3.019 290s Consumption_14 4.964 290s Consumption_15 -3.722 290s Consumption_16 -4.824 290s Consumption_17 36.658 290s Consumption_18 -6.384 290s Consumption_19 -36.540 290s Consumption_20 29.609 290s Consumption_21 13.105 290s Consumption_22 -17.434 290s Investment_2 -16.915 290s Investment_3 1.400 290s Investment_4 5.572 290s Investment_5 -12.140 290s Investment_6 2.445 290s Investment_8 5.902 290s Investment_9 1.877 290s Investment_10 4.637 290s Investment_11 -0.882 290s Investment_12 0.000 290s Investment_13 1.179 290s Investment_14 -3.129 290s Investment_15 0.667 290s Investment_16 -0.446 290s Investment_17 -10.808 290s Investment_18 2.059 290s Investment_19 25.765 290s Investment_20 -5.174 290s Investment_21 -2.327 290s Investment_22 -8.122 290s PrivateWages_2 74.676 290s PrivateWages_3 -22.762 290s PrivateWages_4 -51.996 290s PrivateWages_5 46.938 290s PrivateWages_6 6.417 290s PrivateWages_8 -15.654 290s PrivateWages_9 -13.618 290s PrivateWages_10 -17.045 290s PrivateWages_11 5.965 290s PrivateWages_12 0.000 290s PrivateWages_13 -5.408 290s PrivateWages_14 12.983 290s PrivateWages_15 -3.530 290s PrivateWages_16 -5.567 290s PrivateWages_17 28.547 290s PrivateWages_18 -0.293 290s PrivateWages_19 -109.250 290s PrivateWages_20 24.594 290s PrivateWages_21 -28.812 290s PrivateWages_22 52.304 290s [1] TRUE 290s > Bread 290s Consumption_(Intercept) Consumption_corpProf Consumption_corpProfLag 290s [1,] 104.28657 -1.0082 -0.4696 290s [2,] -1.00824 0.7107 -0.4494 290s [3,] -0.46959 -0.4494 0.5979 290s [4,] -1.85053 -0.0857 -0.0409 290s [5,] 80.53000 1.3241 3.0428 290s [6,] -1.81359 0.2334 -0.2583 290s [7,] 0.54047 -0.1847 0.2826 290s [8,] -0.28778 -0.0112 -0.0165 290s [9,] -35.77159 0.2050 1.7044 290s [10,] 0.58031 -0.0870 0.0510 290s [11,] -0.00461 0.0862 -0.0821 290s [12,] 0.19369 0.0416 0.0268 290s Consumption_wages Investment_(Intercept) Investment_corpProf 290s [1,] -1.850529 80.530 -1.81359 290s [2,] -0.085701 1.324 0.23344 290s [3,] -0.040883 3.043 -0.25828 290s [4,] 0.094773 -3.542 0.04931 290s [5,] -3.542001 2206.842 -34.41529 290s [6,] 0.049311 -34.415 1.17951 290s [7,] -0.048133 29.517 -1.02562 290s [8,] 0.017421 -10.487 0.15573 290s [9,] 0.083728 18.025 -0.14810 290s [10,] 0.000958 1.156 0.00386 290s [11,] -0.002304 -1.519 -0.00126 290s [12,] -0.031989 -0.955 0.01443 290s Investment_corpProfLag Investment_capitalLag PrivateWages_(Intercept) 290s [1,] 0.54047 -0.28778 -35.7716 290s [2,] -0.18475 -0.01117 0.2050 290s [3,] 0.28258 -0.01647 1.7044 290s [4,] -0.04813 0.01742 0.0837 290s [5,] 29.51706 -10.48672 18.0248 290s [6,] -1.02562 0.15573 -0.1481 290s [7,] 1.09362 -0.14971 -0.4803 290s [8,] -0.14971 0.05132 -0.0381 290s [9,] -0.48030 -0.03806 70.4425 290s [10,] 0.00353 -0.00637 -0.4681 290s [11,] 0.00471 0.00732 -0.7110 290s [12,] -0.02247 0.00534 0.8424 290s PrivateWages_gnp PrivateWages_gnpLag PrivateWages_trend 290s [1,] 0.580315 -0.00461 0.19369 290s [2,] -0.086985 0.08623 0.04160 290s [3,] 0.051027 -0.08213 0.02678 290s [4,] 0.000958 -0.00230 -0.03199 290s [5,] 1.156385 -1.51874 -0.95497 290s [6,] 0.003856 -0.00126 0.01443 290s [7,] 0.003528 0.00471 -0.02247 290s [8,] -0.006374 0.00732 0.00534 290s [9,] -0.468096 -0.71104 0.84245 290s [10,] 0.058634 -0.05251 -0.01709 290s [11,] -0.052508 0.06655 0.00301 290s [12,] -0.017087 0.00301 0.04635 290s > 290s > # I3SLS 290s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 290s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 291s > summary 291s 291s systemfit results 291s method: iterated 3SLS 291s 291s convergence achieved after 15 iterations 291s 291s N DF SSR detRCov OLS-R2 McElroy-R2 291s system 59 47 81.3 0.349 0.958 0.995 291s 291s N DF SSR MSE RMSE R2 Adj R2 291s Consumption 19 15 18.1 1.209 1.100 0.980 0.976 291s Investment 20 16 52.0 3.250 1.803 0.776 0.735 291s PrivateWages 20 16 11.2 0.699 0.836 0.986 0.983 291s 291s The covariance matrix of the residuals used for estimation 291s Consumption Investment PrivateWages 291s Consumption 0.955 0.456 -0.421 291s Investment 0.456 2.294 0.375 291s PrivateWages -0.421 0.375 0.522 291s 291s The covariance matrix of the residuals 291s Consumption Investment PrivateWages 291s Consumption 0.955 0.456 -0.421 291s Investment 0.456 2.294 0.375 291s PrivateWages -0.421 0.375 0.522 291s 291s The correlations of the residuals 291s Consumption Investment PrivateWages 291s Consumption 1.000 0.322 -0.582 291s Investment 0.322 1.000 0.341 291s PrivateWages -0.582 0.341 1.000 291s 291s 291s 3SLS estimates for 'Consumption' (equation 1) 291s Model Formula: consump ~ corpProf + corpProfLag + wages 291s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 291s gnpLag 291s 291s Estimate Std. Error t value Pr(>|t|) 291s (Intercept) 16.8311 1.2489 13.48 8.7e-10 *** 291s corpProf 0.1468 0.0991 1.48 0.16 291s corpProfLag 0.0924 0.0906 1.02 0.32 291s wages 0.7945 0.0371 21.43 1.2e-12 *** 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s 291s Residual standard error: 1.1 on 15 degrees of freedom 291s Number of observations: 19 Degrees of Freedom: 15 291s SSR: 18.14 MSE: 1.209 Root MSE: 1.1 291s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.976 291s 291s 291s 3SLS estimates for 'Investment' (equation 2) 291s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 291s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 291s gnpLag 291s 291s Estimate Std. Error t value Pr(>|t|) 291s (Intercept) 32.4128 8.2695 3.92 0.00122 ** 291s corpProf -0.0799 0.1934 -0.41 0.68498 291s corpProfLag 0.7607 0.1878 4.05 0.00093 *** 291s capitalLag -0.2114 0.0400 -5.29 7.4e-05 *** 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s 291s Residual standard error: 1.803 on 16 degrees of freedom 291s Number of observations: 20 Degrees of Freedom: 16 291s SSR: 51.999 MSE: 3.25 Root MSE: 1.803 291s Multiple R-Squared: 0.776 Adjusted R-Squared: 0.735 291s 291s 291s 3SLS estimates for 'PrivateWages' (equation 3) 291s Model Formula: privWage ~ gnp + gnpLag + trend 291s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 291s gnpLag 291s 291s Estimate Std. Error t value Pr(>|t|) 291s (Intercept) 1.5421 1.1496 1.34 0.19852 291s gnp 0.3936 0.0313 12.57 1.0e-09 *** 291s gnpLag 0.1945 0.0328 5.93 2.1e-05 *** 291s trend 0.1416 0.0286 4.95 0.00014 *** 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s 291s Residual standard error: 0.836 on 16 degrees of freedom 291s Number of observations: 20 Degrees of Freedom: 16 291s SSR: 11.181 MSE: 0.699 Root MSE: 0.836 291s Multiple R-Squared: 0.986 Adjusted R-Squared: 0.983 291s 291s > residuals 291s Consumption Investment PrivateWages 291s 1 NA NA NA 291s 2 -0.3309 -2.6308 -1.3061 291s 3 -1.0419 0.0146 0.4450 291s 4 -1.2918 0.4128 1.4338 291s 5 -0.1772 -1.7488 -0.2494 291s 6 0.3563 0.2807 -0.4066 291s 7 NA NA NA 291s 8 1.6778 1.4671 -0.8700 291s 9 1.4561 1.1068 0.1712 291s 10 NA 2.9002 1.1262 291s 11 0.4237 -1.0652 -0.6189 291s 12 -0.2711 -0.9488 0.0375 291s 13 -0.5643 -1.6241 -0.5055 291s 14 0.2845 1.8477 0.3080 291s 15 -0.0514 -0.2379 0.3003 291s 16 0.0521 0.1268 0.0141 291s 17 1.8733 2.2462 -0.7083 291s 18 -0.1962 -0.1724 0.8305 291s 19 0.3553 -3.5810 -0.9448 291s 20 1.3161 1.0343 -0.2738 291s 21 1.2055 0.6622 -1.1283 291s 22 -1.6327 1.5541 0.8257 291s > fitted 291s Consumption Investment PrivateWages 291s 1 NA NA NA 291s 2 42.2 2.431 26.8 291s 3 46.0 1.885 28.9 291s 4 50.5 4.787 32.7 291s 5 50.8 4.749 34.1 291s 6 52.2 4.819 35.8 291s 7 NA NA NA 291s 8 54.5 2.733 38.8 291s 9 55.8 1.893 39.0 291s 10 NA 2.200 40.2 291s 11 54.6 2.065 38.5 291s 12 51.2 -2.451 34.5 291s 13 46.2 -4.576 29.5 291s 14 46.2 -6.948 28.2 291s 15 48.8 -2.762 30.3 291s 16 51.2 -1.427 33.2 291s 17 55.8 -0.146 37.5 291s 18 58.9 2.172 40.2 291s 19 57.1 1.681 39.1 291s 20 60.3 0.266 41.9 291s 21 63.8 2.638 46.1 291s 22 71.3 3.346 52.5 291s > predict 291s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 291s 1 NA NA NA NA 291s 2 42.2 0.446 41.3 43.1 291s 3 46.0 0.511 45.0 47.1 291s 4 50.5 0.340 49.8 51.2 291s 5 50.8 0.393 50.0 51.6 291s 6 52.2 0.396 51.4 53.0 291s 7 NA NA NA NA 291s 8 54.5 0.326 53.9 55.2 291s 9 55.8 0.362 55.1 56.6 291s 10 NA NA NA NA 291s 11 54.6 0.612 53.3 55.8 291s 12 51.2 0.511 50.1 52.2 291s 13 46.2 0.671 44.8 47.5 291s 14 46.2 0.563 45.1 47.3 291s 15 48.8 0.354 48.0 49.5 291s 16 51.2 0.311 50.6 51.9 291s 17 55.8 0.362 55.1 56.6 291s 18 58.9 0.297 58.3 59.5 291s 19 57.1 0.357 56.4 57.9 291s 20 60.3 0.427 59.4 61.1 291s 21 63.8 0.416 63.0 64.6 291s 22 71.3 0.640 70.0 72.6 291s Investment.pred Investment.se.fit Investment.lwr Investment.upr 291s 1 NA NA NA NA 291s 2 2.431 0.970 0.4798 4.382 291s 3 1.885 0.745 0.3859 3.385 291s 4 4.787 0.664 3.4506 6.124 291s 5 4.749 0.562 3.6174 5.880 291s 6 4.819 0.537 3.7391 5.900 291s 7 NA NA NA NA 291s 8 2.733 0.446 1.8351 3.631 291s 9 1.893 0.620 0.6455 3.141 291s 10 2.200 0.684 0.8232 3.576 291s 11 2.065 1.055 -0.0569 4.187 291s 12 -2.451 0.845 -4.1517 -0.751 291s 13 -4.576 1.070 -6.7293 -2.423 291s 14 -6.948 1.103 -9.1676 -4.728 291s 15 -2.762 0.556 -3.8806 -1.644 291s 16 -1.427 0.480 -2.3919 -0.462 291s 17 -0.146 0.603 -1.3588 1.066 291s 18 2.172 0.390 1.3869 2.958 291s 19 1.681 0.563 0.5476 2.815 291s 20 0.266 0.661 -1.0634 1.595 291s 21 2.638 0.558 1.5144 3.761 291s 22 3.346 0.778 1.7808 4.911 291s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 291s 1 NA NA NA NA 291s 2 26.8 0.326 26.2 27.5 291s 3 28.9 0.328 28.2 29.5 291s 4 32.7 0.334 32.0 33.3 291s 5 34.1 0.242 33.7 34.6 291s 6 35.8 0.252 35.3 36.3 291s 7 NA NA NA NA 291s 8 38.8 0.244 38.3 39.3 291s 9 39.0 0.232 38.6 39.5 291s 10 40.2 0.230 39.7 40.6 291s 11 38.5 0.308 37.9 39.1 291s 12 34.5 0.336 33.8 35.1 291s 13 29.5 0.420 28.7 30.4 291s 14 28.2 0.345 27.5 28.9 291s 15 30.3 0.325 29.6 31.0 291s 16 33.2 0.271 32.6 33.7 291s 17 37.5 0.267 37.0 38.0 291s 18 40.2 0.218 39.7 40.6 291s 19 39.1 0.331 38.5 39.8 291s 20 41.9 0.289 41.3 42.5 291s 21 46.1 0.311 45.5 46.8 291s 22 52.5 0.485 51.5 53.5 291s > model.frame 291s [1] TRUE 291s > model.matrix 291s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0328 >" 291s [2] "Attributes: < Component “dimnames”: Component 1: 54 string mismatches >" 291s [3] "Numeric: lengths (732, 708) differ" 291s > nobs 291s [1] 59 291s > linearHypothesis 291s Linear hypothesis test (Theil's F test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df F Pr(>F) 291s 1 48 291s 2 47 1 0.28 0.6 291s Linear hypothesis test (F statistic of a Wald test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df F Pr(>F) 291s 1 48 291s 2 47 1 0.37 0.55 291s Linear hypothesis test (Chi^2 statistic of a Wald test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df Chisq Pr(>Chisq) 291s 1 48 291s 2 47 1 0.37 0.54 291s Linear hypothesis test (Theil's F test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s Consumption_corpProfLag - PrivateWages_trend = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df F Pr(>F) 291s 1 49 291s 2 47 2 1.25 0.3 291s Linear hypothesis test (F statistic of a Wald test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s Consumption_corpProfLag - PrivateWages_trend = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df F Pr(>F) 291s 1 49 291s 2 47 2 1.64 0.21 291s Linear hypothesis test (Chi^2 statistic of a Wald test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s Consumption_corpProfLag - PrivateWages_trend = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df Chisq Pr(>Chisq) 291s 1 49 291s 2 47 2 3.28 0.19 291s > logLik 291s 'log Lik.' -74.5 (df=18) 291s 'log Lik.' -87.1 (df=18) 291s Estimating function 291s Consumption_(Intercept) Consumption_corpProf 291s Consumption_2 -4.75944 -63.951 291s Consumption_3 -2.22772 -37.167 291s Consumption_4 -0.38275 -7.254 291s Consumption_5 -5.30482 -109.454 291s Consumption_6 1.30597 25.176 291s Consumption_8 10.25777 176.523 291s Consumption_9 7.99665 151.823 291s Consumption_11 -1.17443 -19.299 291s Consumption_12 -1.24242 -15.523 291s Consumption_13 -4.75716 -43.103 291s Consumption_14 4.34635 40.320 291s Consumption_15 -1.98107 -24.739 291s Consumption_16 -1.93670 -27.859 291s Consumption_17 13.00314 191.023 291s Consumption_18 -1.57749 -30.922 291s Consumption_19 -8.67959 -166.185 291s Consumption_20 6.77999 118.909 291s Consumption_21 3.04771 61.962 291s Consumption_22 -2.30170 -52.427 291s Investment_2 2.92832 39.347 291s Investment_3 0.00114 0.019 291s Investment_4 -0.53396 -10.120 291s Investment_5 1.84118 37.989 291s Investment_6 -0.26074 -5.026 291s Investment_8 -1.42063 -24.447 291s Investment_9 -1.10750 -21.027 291s Investment_10 0.00000 0.000 291s Investment_11 1.09344 17.968 291s Investment_12 0.95848 11.975 291s Investment_13 1.66503 15.086 291s Investment_14 -1.92032 -17.814 291s Investment_15 0.22458 2.804 291s Investment_16 -0.16698 -2.402 291s Investment_17 -2.28568 -33.578 291s Investment_18 -0.00785 -0.154 291s Investment_19 3.68757 70.604 291s Investment_20 -1.02511 -17.979 291s Investment_21 -0.65919 -13.402 291s Investment_22 -1.70192 -38.765 291s PrivateWages_2 -6.13297 -82.407 291s PrivateWages_3 2.11354 35.262 291s PrivateWages_4 5.50774 104.386 291s PrivateWages_5 -5.40526 -111.526 291s PrivateWages_6 -0.82424 -15.889 291s PrivateWages_8 2.80754 48.314 291s PrivateWages_9 3.41557 64.847 291s PrivateWages_10 0.00000 0.000 291s PrivateWages_11 -5.23135 -85.964 291s PrivateWages_12 -1.71264 -21.398 291s PrivateWages_13 -5.07393 -45.974 291s PrivateWages_14 4.80915 44.613 291s PrivateWages_15 -0.96519 -12.053 291s PrivateWages_16 -1.15621 -16.632 291s PrivateWages_17 4.49108 65.976 291s PrivateWages_18 -0.08188 -1.605 291s PrivateWages_19 -12.82495 -245.555 291s PrivateWages_20 2.51036 44.027 291s PrivateWages_21 -2.60385 -52.938 291s PrivateWages_22 4.63537 105.582 291s Consumption_corpProfLag Consumption_wages 291s Consumption_2 -60.4449 -140.7509 291s Consumption_3 -27.6237 -70.9657 291s Consumption_4 -6.4685 -13.5614 291s Consumption_5 -97.6087 -205.5997 291s Consumption_6 25.3358 50.4846 291s Consumption_8 201.0522 408.4748 291s Consumption_9 158.3336 334.2197 291s Consumption_11 -25.4852 -50.4634 291s Consumption_12 -19.3817 -48.7944 291s Consumption_13 -54.2317 -167.3998 291s Consumption_14 30.4244 143.4489 291s Consumption_15 -22.1880 -73.9440 291s Consumption_16 -23.8214 -77.6627 291s Consumption_17 182.0440 542.0110 291s Consumption_18 -27.7639 -75.2217 291s Consumption_19 -150.1568 -427.4616 291s Consumption_20 103.7339 328.8605 291s Consumption_21 57.9064 162.7199 291s Consumption_22 -48.5659 -140.0278 291s Investment_2 37.1896 86.5991 291s Investment_3 0.0141 0.0362 291s Investment_4 -9.0240 -18.9190 291s Investment_5 33.8777 71.3589 291s Investment_6 -5.0583 -10.0793 291s Investment_8 -27.8443 -56.5709 291s Investment_9 -21.9285 -46.2880 291s Investment_10 0.0000 0.0000 291s Investment_11 23.7276 46.9832 291s Investment_12 14.9524 37.6432 291s Investment_13 18.9813 58.5907 291s Investment_14 -13.4423 -63.3793 291s Investment_15 2.5153 8.3824 291s Investment_16 -2.0538 -6.6959 291s Investment_17 -31.9996 -95.2743 291s Investment_18 -0.1382 -0.3745 291s Investment_19 63.7949 181.6093 291s Investment_20 -15.6841 -49.7224 291s Investment_21 -12.5246 -35.1949 291s Investment_22 -35.9105 -103.5390 291s PrivateWages_2 -77.8887 -181.3703 291s PrivateWages_3 26.2079 67.3285 291s PrivateWages_4 93.0807 195.1464 291s PrivateWages_5 -99.4568 -209.4924 291s PrivateWages_6 -15.9902 -31.8624 291s PrivateWages_8 55.0278 111.7991 291s PrivateWages_9 67.6282 142.7536 291s PrivateWages_10 0.0000 0.0000 291s PrivateWages_11 -113.5202 -224.7822 291s PrivateWages_12 -26.7172 -67.2617 291s PrivateWages_13 -57.8428 -178.5466 291s PrivateWages_14 33.6641 158.7235 291s PrivateWages_15 -10.8101 -36.0260 291s PrivateWages_16 -14.2214 -46.3646 291s PrivateWages_17 62.8751 187.2021 291s PrivateWages_18 -1.4410 -3.9043 291s PrivateWages_19 -221.8716 -631.6170 291s PrivateWages_20 38.4085 121.7638 291s PrivateWages_21 -49.4732 -139.0222 291s PrivateWages_22 97.8064 282.0006 291s Investment_(Intercept) Investment_corpProf 291s Consumption_2 1.782157 23.0934 291s Consumption_3 0.834162 13.9344 291s Consumption_4 0.143320 2.7425 291s Consumption_5 1.986375 41.5880 291s Consumption_6 -0.489016 -9.5207 291s Consumption_8 -3.840991 -65.8399 291s Consumption_9 -2.994321 -58.3554 291s Consumption_11 0.439763 7.4080 291s Consumption_12 0.465220 5.8989 291s Consumption_13 1.781306 15.8927 291s Consumption_14 -1.627477 -15.1363 291s Consumption_15 0.741807 9.4914 291s Consumption_16 0.725191 10.3407 291s Consumption_17 -4.868989 -71.8262 291s Consumption_18 0.590688 11.5449 291s Consumption_19 3.250046 62.9174 291s Consumption_20 -2.538748 -44.1394 291s Consumption_21 -1.141204 -22.9368 291s Consumption_22 0.861865 19.7035 291s Investment_2 -2.373514 -30.7562 291s Investment_3 -0.000921 -0.0154 291s Investment_4 0.432798 8.2817 291s Investment_5 -1.492349 -31.2447 291s Investment_6 0.211337 4.1146 291s Investment_8 1.151475 19.7379 291s Investment_9 0.897673 17.4945 291s Investment_10 2.570865 52.6054 291s Investment_11 -0.886274 -14.9297 291s Investment_12 -0.776889 -9.8508 291s Investment_13 -1.349570 -12.0408 291s Investment_14 1.556498 14.4761 291s Investment_15 -0.182029 -2.3291 291s Investment_16 0.135342 1.9299 291s Investment_17 1.852635 27.3297 291s Investment_18 0.006366 0.1244 291s Investment_19 -2.988917 -57.8622 291s Investment_20 0.830890 14.4461 291s Investment_21 0.534301 10.7388 291s Investment_22 1.379471 31.5367 291s PrivateWages_2 2.964495 38.4142 291s PrivateWages_3 -1.021623 -17.0659 291s PrivateWages_4 -2.662277 -50.9436 291s PrivateWages_5 2.612743 54.7020 291s PrivateWages_6 0.398411 7.7567 291s PrivateWages_8 -1.357082 -23.2623 291s PrivateWages_9 -1.650985 -32.1755 291s PrivateWages_10 -3.276467 -67.0436 291s PrivateWages_11 2.528678 42.5968 291s PrivateWages_12 0.827840 10.4968 291s PrivateWages_13 2.452590 21.8819 291s PrivateWages_14 -2.324602 -21.6199 291s PrivateWages_15 0.466545 5.9694 291s PrivateWages_16 0.558877 7.9692 291s PrivateWages_17 -2.170857 -32.0240 291s PrivateWages_18 0.039577 0.7735 291s PrivateWages_19 6.199203 120.0098 291s PrivateWages_20 -1.213433 -21.0971 291s PrivateWages_21 1.258626 25.2969 291s PrivateWages_22 -2.240603 -51.2233 291s Investment_corpProfLag Investment_capitalLag 291s Consumption_2 22.6334 325.778 291s Consumption_3 10.3436 152.318 291s Consumption_4 2.4221 26.443 291s Consumption_5 36.5493 376.815 291s Consumption_6 -9.4869 -94.233 291s Consumption_8 -75.2834 -781.258 291s Consumption_9 -59.2876 -621.621 291s Consumption_11 9.5429 94.857 291s Consumption_12 7.2574 100.813 291s Consumption_13 20.3069 379.952 291s Consumption_14 -11.3923 -337.050 291s Consumption_15 8.3082 149.845 291s Consumption_16 8.9199 144.313 291s Consumption_17 -68.1658 -962.599 291s Consumption_18 10.3961 118.019 291s Consumption_19 56.2258 655.859 291s Consumption_20 -38.8428 -507.496 291s Consumption_21 -21.6829 -229.610 291s Consumption_22 18.1854 176.251 291s Investment_2 -30.1436 -433.878 291s Investment_3 -0.0114 -0.168 291s Investment_4 7.3143 79.851 291s Investment_5 -27.4592 -283.099 291s Investment_6 4.0999 40.725 291s Investment_8 22.5689 234.210 291s Investment_9 17.7739 186.357 291s Investment_10 54.2453 541.424 291s Investment_11 -19.2321 -191.169 291s Investment_12 -12.1195 -168.352 291s Investment_13 -15.3851 -287.863 291s Investment_14 10.8955 322.351 291s Investment_15 -2.0387 -36.770 291s Investment_16 1.6647 26.933 291s Investment_17 25.9369 366.266 291s Investment_18 0.1120 1.272 291s Investment_19 -51.7083 -603.163 291s Investment_20 12.7126 166.095 291s Investment_21 10.1517 107.501 291s Investment_22 29.1068 282.102 291s PrivateWages_2 37.6491 541.910 291s PrivateWages_3 -12.6681 -186.548 291s PrivateWages_4 -44.9925 -491.190 291s PrivateWages_5 48.0745 495.637 291s PrivateWages_6 7.7292 76.774 291s PrivateWages_8 -26.5988 -276.031 291s PrivateWages_9 -32.6895 -342.744 291s PrivateWages_10 -69.1335 -690.024 291s PrivateWages_11 54.8723 545.436 291s PrivateWages_12 12.9143 179.393 291s PrivateWages_13 27.9595 523.137 291s PrivateWages_14 -16.2722 -481.425 291s PrivateWages_15 5.2253 94.242 291s PrivateWages_16 6.8742 111.217 291s PrivateWages_17 -30.3920 -429.178 291s PrivateWages_18 0.6966 7.908 291s PrivateWages_19 107.2462 1250.999 291s PrivateWages_20 -18.5655 -242.565 291s PrivateWages_21 23.9139 253.236 291s PrivateWages_22 -47.2767 -458.203 291s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 291s Consumption_2 -5.12212 -2.41e+02 -229.983 291s Consumption_3 -2.39748 -1.19e+02 -109.325 291s Consumption_4 -0.41192 -2.33e+01 -20.637 291s Consumption_5 -5.70906 -3.46e+02 -326.558 291s Consumption_6 1.40549 8.52e+01 80.253 291s Consumption_8 11.03944 6.62e+02 706.524 291s Consumption_9 8.60601 5.36e+02 554.227 291s Consumption_11 -1.26393 -8.05e+01 -84.683 291s Consumption_12 -1.33709 -7.33e+01 -81.830 291s Consumption_13 -5.11967 -2.41e+02 -273.390 291s Consumption_14 4.67755 1.97e+02 207.216 291s Consumption_15 -2.13204 -1.09e+02 -96.155 291s Consumption_16 -2.08428 -1.15e+02 -103.589 291s Consumption_17 13.99402 8.03e+02 761.275 291s Consumption_18 -1.69770 -1.14e+02 -106.446 291s Consumption_19 -9.34099 -6.40e+02 -607.165 291s Consumption_20 7.29665 4.88e+02 444.366 291s Consumption_21 3.27995 2.46e+02 227.957 291s Consumption_22 -2.47710 -2.15e+02 -187.516 291s Investment_2 4.06820 1.91e+02 182.662 291s Investment_3 0.00158 7.83e-02 0.072 291s Investment_4 -0.74181 -4.19e+01 -37.165 291s Investment_5 2.55788 1.55e+02 146.311 291s Investment_6 -0.36223 -2.20e+01 -20.683 291s Investment_8 -1.97362 -1.18e+02 -126.312 291s Investment_9 -1.53861 -9.58e+01 -99.086 291s Investment_10 -4.40645 -2.85e+02 -284.216 291s Investment_11 1.51907 9.68e+01 101.778 291s Investment_12 1.33159 7.30e+01 81.493 291s Investment_13 2.31316 1.09e+02 123.523 291s Investment_14 -2.66783 -1.12e+02 -118.185 291s Investment_15 0.31200 1.60e+01 14.071 291s Investment_16 -0.23198 -1.28e+01 -11.529 291s Investment_17 -3.17541 -1.82e+02 -172.742 291s Investment_18 -0.01091 -7.33e-01 -0.684 291s Investment_19 5.12299 3.51e+02 332.995 291s Investment_20 -1.42414 -9.52e+01 -86.730 291s Investment_21 -0.91579 -6.86e+01 -63.647 291s Investment_22 -2.36441 -2.05e+02 -178.986 291s PrivateWages_2 -10.69229 -5.03e+02 -480.084 291s PrivateWages_3 3.68477 1.83e+02 168.026 291s PrivateWages_4 9.60226 5.43e+02 481.073 291s PrivateWages_5 -9.42360 -5.72e+02 -539.030 291s PrivateWages_6 -1.43698 -8.71e+01 -82.052 291s PrivateWages_8 4.89470 2.94e+02 313.261 291s PrivateWages_9 5.95474 3.71e+02 383.486 291s PrivateWages_10 11.81751 7.63e+02 762.229 291s PrivateWages_11 -9.12040 -5.81e+02 -611.067 291s PrivateWages_12 -2.98584 -1.64e+02 -182.733 291s PrivateWages_13 -8.84596 -4.16e+02 -472.374 291s PrivateWages_14 8.38434 3.53e+02 371.426 291s PrivateWages_15 -1.68273 -8.62e+01 -75.891 291s PrivateWages_16 -2.01575 -1.12e+02 -100.183 291s PrivateWages_17 7.82981 4.49e+02 425.942 291s PrivateWages_18 -0.14275 -9.59e+00 -8.950 291s PrivateWages_19 -22.35918 -1.53e+03 -1453.347 291s PrivateWages_20 4.37659 2.93e+02 266.534 291s PrivateWages_21 -4.53959 -3.40e+02 -315.502 291s PrivateWages_22 8.08137 7.02e+02 611.760 291s PrivateWages_trend 291s Consumption_2 51.2212 291s Consumption_3 21.5773 291s Consumption_4 3.2953 291s Consumption_5 39.9635 291s Consumption_6 -8.4329 291s Consumption_8 -44.1578 291s Consumption_9 -25.8180 291s Consumption_11 1.2639 291s Consumption_12 0.0000 291s Consumption_13 -5.1197 291s Consumption_14 9.3551 291s Consumption_15 -6.3961 291s Consumption_16 -8.3371 291s Consumption_17 69.9701 291s Consumption_18 -10.1862 291s Consumption_19 -65.3870 291s Consumption_20 58.3732 291s Consumption_21 29.5195 291s Consumption_22 -24.7710 291s Investment_2 -40.6819 291s Investment_3 -0.0142 291s Investment_4 5.9345 291s Investment_5 -17.9052 291s Investment_6 2.1734 291s Investment_8 7.8945 291s Investment_9 4.6158 291s Investment_10 8.8129 291s Investment_11 -1.5191 291s Investment_12 0.0000 291s Investment_13 2.3132 291s Investment_14 -5.3357 291s Investment_15 0.9360 291s Investment_16 -0.9279 291s Investment_17 -15.8771 291s Investment_18 -0.0655 291s Investment_19 35.8610 291s Investment_20 -11.3931 291s Investment_21 -8.2421 291s Investment_22 -23.6441 291s PrivateWages_2 106.9229 291s PrivateWages_3 -33.1629 291s PrivateWages_4 -76.8181 291s PrivateWages_5 65.9652 291s PrivateWages_6 8.6219 291s PrivateWages_8 -19.5788 291s PrivateWages_9 -17.8642 291s PrivateWages_10 -23.6350 291s PrivateWages_11 9.1204 291s PrivateWages_12 0.0000 291s PrivateWages_13 -8.8460 291s PrivateWages_14 16.7687 291s PrivateWages_15 -5.0482 291s PrivateWages_16 -8.0630 291s PrivateWages_17 39.1491 291s PrivateWages_18 -0.8565 291s PrivateWages_19 -156.5143 291s PrivateWages_20 35.0127 291s PrivateWages_21 -40.8563 291s PrivateWages_22 80.8137 291s [1] TRUE 291s > Bread 291s Consumption_(Intercept) Consumption_corpProf Consumption_corpProfLag 291s [1,] 92.02523 -0.8883 -0.3567 291s [2,] -0.88834 0.5799 -0.3635 291s [3,] -0.35667 -0.3635 0.4840 291s [4,] -1.65059 -0.0695 -0.0345 291s [5,] 87.30345 -0.4940 5.6093 291s [6,] -2.09669 0.4100 -0.4129 291s [7,] 0.52353 -0.3352 0.4397 291s [8,] -0.29441 -0.0047 -0.0291 291s [9,] -39.25694 0.2930 1.5879 291s [10,] 0.63395 -0.0766 0.0444 291s [11,] -0.00377 0.0739 -0.0730 291s [12,] 0.26412 0.0450 0.0239 291s Consumption_wages Investment_(Intercept) Investment_corpProf 291s [1,] -1.650593 87.303 -2.09669 291s [2,] -0.069509 -0.494 0.41001 291s [3,] -0.034488 5.609 -0.41285 291s [4,] 0.081060 -3.868 0.04419 291s [5,] -3.867758 4034.682 -59.45928 291s [6,] 0.044186 -59.459 2.20583 291s [7,] -0.048017 50.679 -1.90719 291s [8,] 0.019469 -19.184 0.26586 291s [9,] 0.172081 52.203 -0.49762 291s [10,] -0.001839 2.943 0.01728 291s [11,] -0.000946 -3.971 -0.00883 291s [12,] -0.034168 -2.641 0.03741 291s Investment_corpProfLag Investment_capitalLag PrivateWages_(Intercept) 291s [1,] 0.52353 -0.2944 -39.2569 291s [2,] -0.33517 -0.0047 0.2930 291s [3,] 0.43972 -0.0291 1.5879 291s [4,] -0.04802 0.0195 0.1721 291s [5,] 50.67914 -19.1839 52.2027 291s [6,] -1.90719 0.2659 -0.4976 291s [7,] 2.08136 -0.2612 -1.5286 291s [8,] -0.26125 0.0944 -0.0914 291s [9,] -1.52864 -0.0914 77.9751 291s [10,] 0.00872 -0.0168 -0.5909 291s [11,] 0.01756 0.0191 -0.7086 291s [12,] -0.06267 0.0150 0.8675 291s PrivateWages_gnp PrivateWages_gnpLag PrivateWages_trend 291s [1,] 0.63395 -0.003771 0.26412 291s [2,] -0.07661 0.073937 0.04500 291s [3,] 0.04435 -0.072979 0.02395 291s [4,] -0.00184 -0.000946 -0.03417 291s [5,] 2.94321 -3.971150 -2.64074 291s [6,] 0.01728 -0.008829 0.03741 291s [7,] 0.00872 0.017559 -0.06267 291s [8,] -0.01682 0.019146 0.01504 291s [9,] -0.59094 -0.708614 0.86750 291s [10,] 0.05781 -0.049542 -0.01891 291s [11,] -0.04954 0.063408 0.00453 291s [12,] -0.01891 0.004534 0.04825 291s > 291s > # OLS 291s Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 291s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 291s > summary 291s 291s systemfit results 291s method: OLS 291s 291s N DF SSR detRCov OLS-R2 McElroy-R2 291s system 59 47 44.2 0.453 0.976 0.99 291s 291s N DF SSR MSE RMSE R2 Adj R2 291s Consumption 19 15 17.36 1.157 1.08 0.980 0.976 291s Investment 20 16 17.11 1.069 1.03 0.912 0.895 291s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 291s 291s The covariance matrix of the residuals 291s Consumption Investment PrivateWages 291s Consumption 1.1939 0.0559 -0.474 291s Investment 0.0559 0.9839 0.140 291s PrivateWages -0.4745 0.1403 0.602 291s 291s The correlations of the residuals 291s Consumption Investment PrivateWages 291s Consumption 1.0000 0.0447 -0.568 291s Investment 0.0447 1.0000 0.169 291s PrivateWages -0.5680 0.1689 1.000 291s 291s 291s OLS estimates for 'Consumption' (equation 1) 291s Model Formula: consump ~ corpProf + corpProfLag + wages 291s 291s Estimate Std. Error t value Pr(>|t|) 291s (Intercept) 16.2957 1.4879 10.95 1.5e-08 *** 291s corpProf 0.1796 0.1162 1.55 0.14 291s corpProfLag 0.1032 0.0994 1.04 0.32 291s wages 0.7962 0.0433 18.39 1.1e-11 *** 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s 291s Residual standard error: 1.076 on 15 degrees of freedom 291s Number of observations: 19 Degrees of Freedom: 15 291s SSR: 17.362 MSE: 1.157 Root MSE: 1.076 291s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.976 291s 291s 291s OLS estimates for 'Investment' (equation 2) 291s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 291s 291s Estimate Std. Error t value Pr(>|t|) 291s (Intercept) 10.1813 5.3720 1.90 0.07627 . 291s corpProf 0.5003 0.1052 4.75 0.00022 *** 291s corpProfLag 0.3259 0.1003 3.25 0.00502 ** 291s capitalLag -0.1134 0.0265 -4.28 0.00057 *** 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s 291s Residual standard error: 1.034 on 16 degrees of freedom 291s Number of observations: 20 Degrees of Freedom: 16 291s SSR: 17.109 MSE: 1.069 Root MSE: 1.034 291s Multiple R-Squared: 0.912 Adjusted R-Squared: 0.895 291s 291s 291s OLS estimates for 'PrivateWages' (equation 3) 291s Model Formula: privWage ~ gnp + gnpLag + trend 291s 291s Estimate Std. Error t value Pr(>|t|) 291s (Intercept) 1.3550 1.3021 1.04 0.3135 291s gnp 0.4417 0.0330 13.40 4.1e-10 *** 291s gnpLag 0.1466 0.0379 3.87 0.0013 ** 291s trend 0.1244 0.0335 3.72 0.0019 ** 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s 291s Residual standard error: 0.78 on 16 degrees of freedom 291s Number of observations: 20 Degrees of Freedom: 16 291s SSR: 9.739 MSE: 0.609 Root MSE: 0.78 291s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 291s 291s compare coef with single-equation OLS 291s [1] TRUE 291s > residuals 291s Consumption Investment PrivateWages 291s 1 NA NA NA 291s 2 -0.3863 -0.000301 -1.3389 291s 3 -1.2484 -0.076489 0.2462 291s 4 -1.6040 1.221792 1.1255 291s 5 -0.5384 -1.377872 -0.1959 291s 6 -0.0413 0.386104 -0.5284 291s 7 0.8043 1.486279 NA 291s 8 1.2830 0.784055 -0.7909 291s 9 1.0142 -0.655354 0.2819 291s 10 NA 1.060871 1.1384 291s 11 0.1429 0.395249 -0.1904 291s 12 -0.3439 0.198005 0.5813 291s 13 NA NA 0.1206 291s 14 0.3199 0.312725 0.4773 291s 15 -0.1016 -0.084685 0.3035 291s 16 -0.0702 0.066194 0.0284 291s 17 1.6064 0.963697 -0.8517 291s 18 -0.4980 0.078506 0.9908 291s 19 0.1253 -2.496401 -0.4597 291s 20 0.9805 -0.711004 -0.3819 291s 21 0.7551 -0.820172 -1.1062 291s 22 -2.1992 -0.731199 0.5501 291s > fitted 291s Consumption Investment PrivateWages 291s 1 NA NA NA 291s 2 42.3 -0.200 26.8 291s 3 46.2 1.976 29.1 291s 4 50.8 3.978 33.0 291s 5 51.1 4.378 34.1 291s 6 52.6 4.714 35.9 291s 7 54.3 4.114 NA 291s 8 54.9 3.416 38.7 291s 9 56.3 3.655 38.9 291s 10 NA 4.039 40.2 291s 11 54.9 0.605 38.1 291s 12 51.2 -3.598 33.9 291s 13 NA NA 28.9 291s 14 46.2 -5.413 28.0 291s 15 48.8 -2.915 30.3 291s 16 51.4 -1.366 33.2 291s 17 56.1 1.136 37.7 291s 18 59.2 1.921 40.0 291s 19 57.4 0.596 38.7 291s 20 60.6 2.011 42.0 291s 21 64.2 4.120 46.1 291s 22 71.9 5.631 52.7 291s > predict 291s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 291s 1 NA NA NA NA 291s 2 42.3 0.523 39.9 44.7 291s 3 46.2 0.560 43.8 48.7 291s 4 50.8 0.379 48.5 53.1 291s 5 51.1 0.448 48.8 53.5 291s 6 52.6 0.457 50.3 55.0 291s 7 54.3 0.408 52.0 56.6 291s 8 54.9 0.375 52.6 57.2 291s 9 56.3 0.418 54.0 58.6 291s 10 NA NA NA NA 291s 11 54.9 0.701 52.3 57.4 291s 12 51.2 0.638 48.7 53.8 291s 13 NA NA NA NA 291s 14 46.2 0.673 43.6 48.7 291s 15 48.8 0.453 46.5 51.2 291s 16 51.4 0.384 49.1 53.7 291s 17 56.1 0.391 53.8 58.4 291s 18 59.2 0.361 56.9 61.5 291s 19 57.4 0.449 55.0 59.7 291s 20 60.6 0.465 58.3 63.0 291s 21 64.2 0.468 61.9 66.6 291s 22 71.9 0.728 69.3 74.5 291s Investment.pred Investment.se.fit Investment.lwr Investment.upr 291s 1 NA NA NA NA 291s 2 -0.200 0.613 -2.618 2.219 291s 3 1.976 0.494 -0.329 4.282 291s 4 3.978 0.444 1.714 6.242 291s 5 4.378 0.369 2.169 6.587 291s 6 4.714 0.349 2.519 6.909 291s 7 4.114 0.323 1.934 6.293 291s 8 3.416 0.287 1.257 5.575 291s 9 3.655 0.386 1.435 5.876 291s 10 4.039 0.441 1.777 6.301 291s 11 0.605 0.641 -1.843 3.053 291s 12 -3.598 0.606 -6.010 -1.186 291s 13 NA NA NA NA 291s 14 -5.413 0.708 -7.934 -2.892 291s 15 -2.915 0.412 -5.155 -0.676 291s 16 -1.366 0.336 -3.554 0.821 291s 17 1.136 0.342 -1.055 3.327 291s 18 1.921 0.246 -0.217 4.060 291s 19 0.596 0.341 -1.594 2.787 291s 20 2.011 0.364 -0.194 4.216 291s 21 4.120 0.337 1.932 6.308 291s 22 5.631 0.477 3.341 7.922 291s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 291s 1 NA NA NA NA 291s 2 26.8 0.364 25.1 28.6 291s 3 29.1 0.367 27.3 30.8 291s 4 33.0 0.370 31.2 34.7 291s 5 34.1 0.286 32.4 35.8 291s 6 35.9 0.285 34.3 37.6 291s 7 NA NA NA NA 291s 8 38.7 0.292 37.0 40.4 291s 9 38.9 0.277 37.3 40.6 291s 10 40.2 0.264 38.5 41.8 291s 11 38.1 0.363 36.4 39.8 291s 12 33.9 0.367 32.2 35.7 291s 13 28.9 0.435 27.1 30.7 291s 14 28.0 0.383 26.3 29.8 291s 15 30.3 0.377 28.6 32.0 291s 16 33.2 0.315 31.5 34.9 291s 17 37.7 0.308 36.0 39.3 291s 18 40.0 0.241 38.4 41.7 291s 19 38.7 0.361 36.9 40.4 291s 20 42.0 0.324 40.3 43.7 291s 21 46.1 0.339 44.4 47.8 291s 22 52.7 0.511 50.9 54.6 291s > model.frame 291s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 291s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 291s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 291s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 291s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 291s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 291s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 291s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 291s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 291s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 291s 10 57.8 21.7 21.1 NA 5.1 211 41.3 67.0 64.5 291s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 291s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 291s 13 45.6 NA 11.4 34.3 -6.2 213 29.0 44.3 53.4 291s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 291s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 291s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 291s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 291s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 291s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 291s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 291s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 291s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 291s trend 291s 1 -11 291s 2 -10 291s 3 -9 291s 4 -8 291s 5 -7 291s 6 -6 291s 7 -5 291s 8 -4 291s 9 -3 291s 10 -2 291s 11 -1 291s 12 0 291s 13 1 291s 14 2 291s 15 3 291s 16 4 291s 17 5 291s 18 6 291s 19 7 291s 20 8 291s 21 9 291s 22 10 291s > model.matrix 291s Consumption_(Intercept) Consumption_corpProf 291s Consumption_2 1 12.4 291s Consumption_3 1 16.9 291s Consumption_4 1 18.4 291s Consumption_5 1 19.4 291s Consumption_6 1 20.1 291s Consumption_7 1 19.6 291s Consumption_8 1 19.8 291s Consumption_9 1 21.1 291s Consumption_11 1 15.6 291s Consumption_12 1 11.4 291s Consumption_14 1 11.2 291s Consumption_15 1 12.3 291s Consumption_16 1 14.0 291s Consumption_17 1 17.6 291s Consumption_18 1 17.3 291s Consumption_19 1 15.3 291s Consumption_20 1 19.0 291s Consumption_21 1 21.1 291s Consumption_22 1 23.5 291s Investment_2 0 0.0 291s Investment_3 0 0.0 291s Investment_4 0 0.0 291s Investment_5 0 0.0 291s Investment_6 0 0.0 291s Investment_7 0 0.0 291s Investment_8 0 0.0 291s Investment_9 0 0.0 291s Investment_10 0 0.0 291s Investment_11 0 0.0 291s Investment_12 0 0.0 291s Investment_14 0 0.0 291s Investment_15 0 0.0 291s Investment_16 0 0.0 291s Investment_17 0 0.0 291s Investment_18 0 0.0 291s Investment_19 0 0.0 291s Investment_20 0 0.0 291s Investment_21 0 0.0 291s Investment_22 0 0.0 291s PrivateWages_2 0 0.0 291s PrivateWages_3 0 0.0 291s PrivateWages_4 0 0.0 291s PrivateWages_5 0 0.0 291s PrivateWages_6 0 0.0 291s PrivateWages_8 0 0.0 291s PrivateWages_9 0 0.0 291s PrivateWages_10 0 0.0 291s PrivateWages_11 0 0.0 291s PrivateWages_12 0 0.0 291s PrivateWages_13 0 0.0 291s PrivateWages_14 0 0.0 291s PrivateWages_15 0 0.0 291s PrivateWages_16 0 0.0 291s PrivateWages_17 0 0.0 291s PrivateWages_18 0 0.0 291s PrivateWages_19 0 0.0 291s PrivateWages_20 0 0.0 291s PrivateWages_21 0 0.0 291s PrivateWages_22 0 0.0 291s Consumption_corpProfLag Consumption_wages 291s Consumption_2 12.7 28.2 291s Consumption_3 12.4 32.2 291s Consumption_4 16.9 37.0 291s Consumption_5 18.4 37.0 291s Consumption_6 19.4 38.6 291s Consumption_7 20.1 40.7 291s Consumption_8 19.6 41.5 291s Consumption_9 19.8 42.9 291s Consumption_11 21.7 42.1 291s Consumption_12 15.6 39.3 291s Consumption_14 7.0 34.1 291s Consumption_15 11.2 36.6 291s Consumption_16 12.3 39.3 291s Consumption_17 14.0 44.2 291s Consumption_18 17.6 47.7 291s Consumption_19 17.3 45.9 291s Consumption_20 15.3 49.4 291s Consumption_21 19.0 53.0 291s Consumption_22 21.1 61.8 291s Investment_2 0.0 0.0 291s Investment_3 0.0 0.0 291s Investment_4 0.0 0.0 291s Investment_5 0.0 0.0 291s Investment_6 0.0 0.0 291s Investment_7 0.0 0.0 291s Investment_8 0.0 0.0 291s Investment_9 0.0 0.0 291s Investment_10 0.0 0.0 291s Investment_11 0.0 0.0 291s Investment_12 0.0 0.0 291s Investment_14 0.0 0.0 291s Investment_15 0.0 0.0 291s Investment_16 0.0 0.0 291s Investment_17 0.0 0.0 291s Investment_18 0.0 0.0 291s Investment_19 0.0 0.0 291s Investment_20 0.0 0.0 291s Investment_21 0.0 0.0 291s Investment_22 0.0 0.0 291s PrivateWages_2 0.0 0.0 291s PrivateWages_3 0.0 0.0 291s PrivateWages_4 0.0 0.0 291s PrivateWages_5 0.0 0.0 291s PrivateWages_6 0.0 0.0 291s PrivateWages_8 0.0 0.0 291s PrivateWages_9 0.0 0.0 291s PrivateWages_10 0.0 0.0 291s PrivateWages_11 0.0 0.0 291s PrivateWages_12 0.0 0.0 291s PrivateWages_13 0.0 0.0 291s PrivateWages_14 0.0 0.0 291s PrivateWages_15 0.0 0.0 291s PrivateWages_16 0.0 0.0 291s PrivateWages_17 0.0 0.0 291s PrivateWages_18 0.0 0.0 291s PrivateWages_19 0.0 0.0 291s PrivateWages_20 0.0 0.0 291s PrivateWages_21 0.0 0.0 291s PrivateWages_22 0.0 0.0 291s Investment_(Intercept) Investment_corpProf 291s Consumption_2 0 0.0 291s Consumption_3 0 0.0 291s Consumption_4 0 0.0 291s Consumption_5 0 0.0 291s Consumption_6 0 0.0 291s Consumption_7 0 0.0 291s Consumption_8 0 0.0 291s Consumption_9 0 0.0 291s Consumption_11 0 0.0 291s Consumption_12 0 0.0 291s Consumption_14 0 0.0 291s Consumption_15 0 0.0 291s Consumption_16 0 0.0 291s Consumption_17 0 0.0 291s Consumption_18 0 0.0 291s Consumption_19 0 0.0 291s Consumption_20 0 0.0 291s Consumption_21 0 0.0 291s Consumption_22 0 0.0 291s Investment_2 1 12.4 291s Investment_3 1 16.9 291s Investment_4 1 18.4 291s Investment_5 1 19.4 291s Investment_6 1 20.1 291s Investment_7 1 19.6 291s Investment_8 1 19.8 291s Investment_9 1 21.1 291s Investment_10 1 21.7 291s Investment_11 1 15.6 291s Investment_12 1 11.4 291s Investment_14 1 11.2 291s Investment_15 1 12.3 291s Investment_16 1 14.0 291s Investment_17 1 17.6 291s Investment_18 1 17.3 291s Investment_19 1 15.3 291s Investment_20 1 19.0 291s Investment_21 1 21.1 291s Investment_22 1 23.5 291s PrivateWages_2 0 0.0 291s PrivateWages_3 0 0.0 291s PrivateWages_4 0 0.0 291s PrivateWages_5 0 0.0 291s PrivateWages_6 0 0.0 291s PrivateWages_8 0 0.0 291s PrivateWages_9 0 0.0 291s PrivateWages_10 0 0.0 291s PrivateWages_11 0 0.0 291s PrivateWages_12 0 0.0 291s PrivateWages_13 0 0.0 291s PrivateWages_14 0 0.0 291s PrivateWages_15 0 0.0 291s PrivateWages_16 0 0.0 291s PrivateWages_17 0 0.0 291s PrivateWages_18 0 0.0 291s PrivateWages_19 0 0.0 291s PrivateWages_20 0 0.0 291s PrivateWages_21 0 0.0 291s PrivateWages_22 0 0.0 291s Investment_corpProfLag Investment_capitalLag 291s Consumption_2 0.0 0 291s Consumption_3 0.0 0 291s Consumption_4 0.0 0 291s Consumption_5 0.0 0 291s Consumption_6 0.0 0 291s Consumption_7 0.0 0 291s Consumption_8 0.0 0 291s Consumption_9 0.0 0 291s Consumption_11 0.0 0 291s Consumption_12 0.0 0 291s Consumption_14 0.0 0 291s Consumption_15 0.0 0 291s Consumption_16 0.0 0 291s Consumption_17 0.0 0 291s Consumption_18 0.0 0 291s Consumption_19 0.0 0 291s Consumption_20 0.0 0 291s Consumption_21 0.0 0 291s Consumption_22 0.0 0 291s Investment_2 12.7 183 291s Investment_3 12.4 183 291s Investment_4 16.9 184 291s Investment_5 18.4 190 291s Investment_6 19.4 193 291s Investment_7 20.1 198 291s Investment_8 19.6 203 291s Investment_9 19.8 208 291s Investment_10 21.1 211 291s Investment_11 21.7 216 291s Investment_12 15.6 217 291s Investment_14 7.0 207 291s Investment_15 11.2 202 291s Investment_16 12.3 199 291s Investment_17 14.0 198 291s Investment_18 17.6 200 291s Investment_19 17.3 202 291s Investment_20 15.3 200 291s Investment_21 19.0 201 291s Investment_22 21.1 204 291s PrivateWages_2 0.0 0 291s PrivateWages_3 0.0 0 291s PrivateWages_4 0.0 0 291s PrivateWages_5 0.0 0 291s PrivateWages_6 0.0 0 291s PrivateWages_8 0.0 0 291s PrivateWages_9 0.0 0 291s PrivateWages_10 0.0 0 291s PrivateWages_11 0.0 0 291s PrivateWages_12 0.0 0 291s PrivateWages_13 0.0 0 291s PrivateWages_14 0.0 0 291s PrivateWages_15 0.0 0 291s PrivateWages_16 0.0 0 291s PrivateWages_17 0.0 0 291s PrivateWages_18 0.0 0 291s PrivateWages_19 0.0 0 291s PrivateWages_20 0.0 0 291s PrivateWages_21 0.0 0 291s PrivateWages_22 0.0 0 291s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 291s Consumption_2 0 0.0 0.0 291s Consumption_3 0 0.0 0.0 291s Consumption_4 0 0.0 0.0 291s Consumption_5 0 0.0 0.0 291s Consumption_6 0 0.0 0.0 291s Consumption_7 0 0.0 0.0 291s Consumption_8 0 0.0 0.0 291s Consumption_9 0 0.0 0.0 291s Consumption_11 0 0.0 0.0 291s Consumption_12 0 0.0 0.0 291s Consumption_14 0 0.0 0.0 291s Consumption_15 0 0.0 0.0 291s Consumption_16 0 0.0 0.0 291s Consumption_17 0 0.0 0.0 291s Consumption_18 0 0.0 0.0 291s Consumption_19 0 0.0 0.0 291s Consumption_20 0 0.0 0.0 291s Consumption_21 0 0.0 0.0 291s Consumption_22 0 0.0 0.0 291s Investment_2 0 0.0 0.0 291s Investment_3 0 0.0 0.0 291s Investment_4 0 0.0 0.0 291s Investment_5 0 0.0 0.0 291s Investment_6 0 0.0 0.0 291s Investment_7 0 0.0 0.0 291s Investment_8 0 0.0 0.0 291s Investment_9 0 0.0 0.0 291s Investment_10 0 0.0 0.0 291s Investment_11 0 0.0 0.0 291s Investment_12 0 0.0 0.0 291s Investment_14 0 0.0 0.0 291s Investment_15 0 0.0 0.0 291s Investment_16 0 0.0 0.0 291s Investment_17 0 0.0 0.0 291s Investment_18 0 0.0 0.0 291s Investment_19 0 0.0 0.0 291s Investment_20 0 0.0 0.0 291s Investment_21 0 0.0 0.0 291s Investment_22 0 0.0 0.0 291s PrivateWages_2 1 45.6 44.9 291s PrivateWages_3 1 50.1 45.6 291s PrivateWages_4 1 57.2 50.1 291s PrivateWages_5 1 57.1 57.2 291s PrivateWages_6 1 61.0 57.1 291s PrivateWages_8 1 64.4 64.0 291s PrivateWages_9 1 64.5 64.4 291s PrivateWages_10 1 67.0 64.5 291s PrivateWages_11 1 61.2 67.0 291s PrivateWages_12 1 53.4 61.2 291s PrivateWages_13 1 44.3 53.4 291s PrivateWages_14 1 45.1 44.3 291s PrivateWages_15 1 49.7 45.1 291s PrivateWages_16 1 54.4 49.7 291s PrivateWages_17 1 62.7 54.4 291s PrivateWages_18 1 65.0 62.7 291s PrivateWages_19 1 60.9 65.0 291s PrivateWages_20 1 69.5 60.9 291s PrivateWages_21 1 75.7 69.5 291s PrivateWages_22 1 88.4 75.7 291s PrivateWages_trend 291s Consumption_2 0 291s Consumption_3 0 291s Consumption_4 0 291s Consumption_5 0 291s Consumption_6 0 291s Consumption_7 0 291s Consumption_8 0 291s Consumption_9 0 291s Consumption_11 0 291s Consumption_12 0 291s Consumption_14 0 291s Consumption_15 0 291s Consumption_16 0 291s Consumption_17 0 291s Consumption_18 0 291s Consumption_19 0 291s Consumption_20 0 291s Consumption_21 0 291s Consumption_22 0 291s Investment_2 0 291s Investment_3 0 291s Investment_4 0 291s Investment_5 0 291s Investment_6 0 291s Investment_7 0 291s Investment_8 0 291s Investment_9 0 291s Investment_10 0 291s Investment_11 0 291s Investment_12 0 291s Investment_14 0 291s Investment_15 0 291s Investment_16 0 291s Investment_17 0 291s Investment_18 0 291s Investment_19 0 291s Investment_20 0 291s Investment_21 0 291s Investment_22 0 291s PrivateWages_2 -10 291s PrivateWages_3 -9 291s PrivateWages_4 -8 291s PrivateWages_5 -7 291s PrivateWages_6 -6 291s PrivateWages_8 -4 291s PrivateWages_9 -3 291s PrivateWages_10 -2 291s PrivateWages_11 -1 291s PrivateWages_12 0 291s PrivateWages_13 1 291s PrivateWages_14 2 291s PrivateWages_15 3 291s PrivateWages_16 4 291s PrivateWages_17 5 291s PrivateWages_18 6 291s PrivateWages_19 7 291s PrivateWages_20 8 291s PrivateWages_21 9 291s PrivateWages_22 10 291s > nobs 291s [1] 59 291s > linearHypothesis 291s Linear hypothesis test (Theil's F test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df F Pr(>F) 291s 1 48 291s 2 47 1 0.33 0.57 291s Linear hypothesis test (F statistic of a Wald test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df F Pr(>F) 291s 1 48 291s 2 47 1 0.31 0.58 291s Linear hypothesis test (Chi^2 statistic of a Wald test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df Chisq Pr(>Chisq) 291s 1 48 291s 2 47 1 0.31 0.58 291s Linear hypothesis test (Theil's F test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s Consumption_corpProfLag - PrivateWages_trend = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df F Pr(>F) 291s 1 49 291s 2 47 2 0.17 0.84 291s Linear hypothesis test (F statistic of a Wald test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s Consumption_corpProfLag - PrivateWages_trend = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df F Pr(>F) 291s 1 49 291s 2 47 2 0.16 0.85 291s Linear hypothesis test (Chi^2 statistic of a Wald test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s Consumption_corpProfLag - PrivateWages_trend = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df Chisq Pr(>Chisq) 291s 1 49 291s 2 47 2 0.33 0.85 291s > logLik 291s 'log Lik.' -69.6 (df=13) 291s 'log Lik.' -74.2 (df=13) 291s compare log likelihood value with single-equation OLS 291s [1] "Mean relative difference: 0.00099" 291s Estimating function 291s Consumption_(Intercept) Consumption_corpProf 291s Consumption_2 -0.3863 -4.791 291s Consumption_3 -1.2484 -21.098 291s Consumption_4 -1.6040 -29.514 291s Consumption_5 -0.5384 -10.446 291s Consumption_6 -0.0413 -0.830 291s Consumption_7 0.8043 15.763 291s Consumption_8 1.2830 25.403 291s Consumption_9 1.0142 21.399 291s Consumption_11 0.1429 2.229 291s Consumption_12 -0.3439 -3.920 291s Consumption_14 0.3199 3.583 291s Consumption_15 -0.1016 -1.250 291s Consumption_16 -0.0702 -0.983 291s Consumption_17 1.6064 28.272 291s Consumption_18 -0.4980 -8.616 291s Consumption_19 0.1253 1.917 291s Consumption_20 0.9805 18.629 291s Consumption_21 0.7551 15.933 291s Consumption_22 -2.1992 -51.681 291s Investment_2 0.0000 0.000 291s Investment_3 0.0000 0.000 291s Investment_4 0.0000 0.000 291s Investment_5 0.0000 0.000 291s Investment_6 0.0000 0.000 291s Investment_7 0.0000 0.000 291s Investment_8 0.0000 0.000 291s Investment_9 0.0000 0.000 291s Investment_10 0.0000 0.000 291s Investment_11 0.0000 0.000 291s Investment_12 0.0000 0.000 291s Investment_14 0.0000 0.000 291s Investment_15 0.0000 0.000 291s Investment_16 0.0000 0.000 291s Investment_17 0.0000 0.000 291s Investment_18 0.0000 0.000 291s Investment_19 0.0000 0.000 291s Investment_20 0.0000 0.000 291s Investment_21 0.0000 0.000 291s Investment_22 0.0000 0.000 291s PrivateWages_2 0.0000 0.000 291s PrivateWages_3 0.0000 0.000 291s PrivateWages_4 0.0000 0.000 291s PrivateWages_5 0.0000 0.000 291s PrivateWages_6 0.0000 0.000 291s PrivateWages_8 0.0000 0.000 291s PrivateWages_9 0.0000 0.000 291s PrivateWages_10 0.0000 0.000 291s PrivateWages_11 0.0000 0.000 291s PrivateWages_12 0.0000 0.000 291s PrivateWages_13 0.0000 0.000 291s PrivateWages_14 0.0000 0.000 291s PrivateWages_15 0.0000 0.000 291s PrivateWages_16 0.0000 0.000 291s PrivateWages_17 0.0000 0.000 291s PrivateWages_18 0.0000 0.000 291s PrivateWages_19 0.0000 0.000 291s PrivateWages_20 0.0000 0.000 291s PrivateWages_21 0.0000 0.000 291s PrivateWages_22 0.0000 0.000 291s Consumption_corpProfLag Consumption_wages 291s Consumption_2 -4.907 -10.90 291s Consumption_3 -15.480 -40.20 291s Consumption_4 -27.108 -59.35 291s Consumption_5 -9.907 -19.92 291s Consumption_6 -0.801 -1.59 291s Consumption_7 16.166 32.73 291s Consumption_8 25.146 53.24 291s Consumption_9 20.081 43.51 291s Consumption_11 3.100 6.01 291s Consumption_12 -5.364 -13.51 291s Consumption_14 2.239 10.91 291s Consumption_15 -1.138 -3.72 291s Consumption_16 -0.864 -2.76 291s Consumption_17 22.489 71.00 291s Consumption_18 -8.765 -23.76 291s Consumption_19 2.168 5.75 291s Consumption_20 15.002 48.44 291s Consumption_21 14.348 40.02 291s Consumption_22 -46.403 -135.91 291s Investment_2 0.000 0.00 291s Investment_3 0.000 0.00 291s Investment_4 0.000 0.00 291s Investment_5 0.000 0.00 291s Investment_6 0.000 0.00 291s Investment_7 0.000 0.00 291s Investment_8 0.000 0.00 291s Investment_9 0.000 0.00 291s Investment_10 0.000 0.00 291s Investment_11 0.000 0.00 291s Investment_12 0.000 0.00 291s Investment_14 0.000 0.00 291s Investment_15 0.000 0.00 291s Investment_16 0.000 0.00 291s Investment_17 0.000 0.00 291s Investment_18 0.000 0.00 291s Investment_19 0.000 0.00 291s Investment_20 0.000 0.00 291s Investment_21 0.000 0.00 291s Investment_22 0.000 0.00 291s PrivateWages_2 0.000 0.00 291s PrivateWages_3 0.000 0.00 291s PrivateWages_4 0.000 0.00 291s PrivateWages_5 0.000 0.00 291s PrivateWages_6 0.000 0.00 291s PrivateWages_8 0.000 0.00 291s PrivateWages_9 0.000 0.00 291s PrivateWages_10 0.000 0.00 291s PrivateWages_11 0.000 0.00 291s PrivateWages_12 0.000 0.00 291s PrivateWages_13 0.000 0.00 291s PrivateWages_14 0.000 0.00 291s PrivateWages_15 0.000 0.00 291s PrivateWages_16 0.000 0.00 291s PrivateWages_17 0.000 0.00 291s PrivateWages_18 0.000 0.00 291s PrivateWages_19 0.000 0.00 291s PrivateWages_20 0.000 0.00 291s PrivateWages_21 0.000 0.00 291s PrivateWages_22 0.000 0.00 291s Investment_(Intercept) Investment_corpProf 291s Consumption_2 0.000000 0.00000 291s Consumption_3 0.000000 0.00000 291s Consumption_4 0.000000 0.00000 291s Consumption_5 0.000000 0.00000 291s Consumption_6 0.000000 0.00000 291s Consumption_7 0.000000 0.00000 291s Consumption_8 0.000000 0.00000 291s Consumption_9 0.000000 0.00000 291s Consumption_11 0.000000 0.00000 291s Consumption_12 0.000000 0.00000 291s Consumption_14 0.000000 0.00000 291s Consumption_15 0.000000 0.00000 291s Consumption_16 0.000000 0.00000 291s Consumption_17 0.000000 0.00000 291s Consumption_18 0.000000 0.00000 291s Consumption_19 0.000000 0.00000 291s Consumption_20 0.000000 0.00000 291s Consumption_21 0.000000 0.00000 291s Consumption_22 0.000000 0.00000 291s Investment_2 -0.000301 -0.00373 291s Investment_3 -0.076489 -1.29266 291s Investment_4 1.221792 22.48097 291s Investment_5 -1.377872 -26.73071 291s Investment_6 0.386104 7.76068 291s Investment_7 1.486279 29.13107 291s Investment_8 0.784055 15.52429 291s Investment_9 -0.655354 -13.82796 291s Investment_10 1.060871 23.02091 291s Investment_11 0.395249 6.16588 291s Investment_12 0.198005 2.25726 291s Investment_14 0.312725 3.50252 291s Investment_15 -0.084685 -1.04163 291s Investment_16 0.066194 0.92672 291s Investment_17 0.963697 16.96106 291s Investment_18 0.078506 1.35816 291s Investment_19 -2.496401 -38.19494 291s Investment_20 -0.711004 -13.50907 291s Investment_21 -0.820172 -17.30564 291s Investment_22 -0.731199 -17.18317 291s PrivateWages_2 0.000000 0.00000 291s PrivateWages_3 0.000000 0.00000 291s PrivateWages_4 0.000000 0.00000 291s PrivateWages_5 0.000000 0.00000 291s PrivateWages_6 0.000000 0.00000 291s PrivateWages_8 0.000000 0.00000 291s PrivateWages_9 0.000000 0.00000 291s PrivateWages_10 0.000000 0.00000 291s PrivateWages_11 0.000000 0.00000 291s PrivateWages_12 0.000000 0.00000 291s PrivateWages_13 0.000000 0.00000 291s PrivateWages_14 0.000000 0.00000 291s PrivateWages_15 0.000000 0.00000 291s PrivateWages_16 0.000000 0.00000 291s PrivateWages_17 0.000000 0.00000 291s PrivateWages_18 0.000000 0.00000 291s PrivateWages_19 0.000000 0.00000 291s PrivateWages_20 0.000000 0.00000 291s PrivateWages_21 0.000000 0.00000 291s PrivateWages_22 0.000000 0.00000 291s Investment_corpProfLag Investment_capitalLag 291s Consumption_2 0.00000 0.000 291s Consumption_3 0.00000 0.000 291s Consumption_4 0.00000 0.000 291s Consumption_5 0.00000 0.000 291s Consumption_6 0.00000 0.000 291s Consumption_7 0.00000 0.000 291s Consumption_8 0.00000 0.000 291s Consumption_9 0.00000 0.000 291s Consumption_11 0.00000 0.000 291s Consumption_12 0.00000 0.000 291s Consumption_14 0.00000 0.000 291s Consumption_15 0.00000 0.000 291s Consumption_16 0.00000 0.000 291s Consumption_17 0.00000 0.000 291s Consumption_18 0.00000 0.000 291s Consumption_19 0.00000 0.000 291s Consumption_20 0.00000 0.000 291s Consumption_21 0.00000 0.000 291s Consumption_22 0.00000 0.000 291s Investment_2 -0.00382 -0.055 291s Investment_3 -0.94846 -13.967 291s Investment_4 20.64828 225.421 291s Investment_5 -25.35284 -261.382 291s Investment_6 7.49041 74.402 291s Investment_7 29.87421 293.986 291s Investment_8 15.36748 159.477 291s Investment_9 -12.97600 -136.051 291s Investment_10 22.38438 223.419 291s Investment_11 8.57690 85.255 291s Investment_12 3.08888 42.908 291s Investment_14 2.18907 64.765 291s Investment_15 -0.94848 -17.106 291s Investment_16 0.81419 13.173 291s Investment_17 13.49175 190.523 291s Investment_18 1.38171 15.686 291s Investment_19 -43.18774 -503.774 291s Investment_20 -10.87836 -142.130 291s Investment_21 -15.58327 -165.019 291s Investment_22 -15.42829 -149.530 291s PrivateWages_2 0.00000 0.000 291s PrivateWages_3 0.00000 0.000 291s PrivateWages_4 0.00000 0.000 291s PrivateWages_5 0.00000 0.000 291s PrivateWages_6 0.00000 0.000 291s PrivateWages_8 0.00000 0.000 291s PrivateWages_9 0.00000 0.000 291s PrivateWages_10 0.00000 0.000 291s PrivateWages_11 0.00000 0.000 291s PrivateWages_12 0.00000 0.000 291s PrivateWages_13 0.00000 0.000 291s PrivateWages_14 0.00000 0.000 291s PrivateWages_15 0.00000 0.000 291s PrivateWages_16 0.00000 0.000 291s PrivateWages_17 0.00000 0.000 291s PrivateWages_18 0.00000 0.000 291s PrivateWages_19 0.00000 0.000 291s PrivateWages_20 0.00000 0.000 291s PrivateWages_21 0.00000 0.000 291s PrivateWages_22 0.00000 0.000 291s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 291s Consumption_2 0.0000 0.00 0.00 291s Consumption_3 0.0000 0.00 0.00 291s Consumption_4 0.0000 0.00 0.00 291s Consumption_5 0.0000 0.00 0.00 291s Consumption_6 0.0000 0.00 0.00 291s Consumption_7 0.0000 0.00 0.00 291s Consumption_8 0.0000 0.00 0.00 291s Consumption_9 0.0000 0.00 0.00 291s Consumption_11 0.0000 0.00 0.00 291s Consumption_12 0.0000 0.00 0.00 291s Consumption_14 0.0000 0.00 0.00 291s Consumption_15 0.0000 0.00 0.00 291s Consumption_16 0.0000 0.00 0.00 291s Consumption_17 0.0000 0.00 0.00 291s Consumption_18 0.0000 0.00 0.00 291s Consumption_19 0.0000 0.00 0.00 291s Consumption_20 0.0000 0.00 0.00 291s Consumption_21 0.0000 0.00 0.00 291s Consumption_22 0.0000 0.00 0.00 291s Investment_2 0.0000 0.00 0.00 291s Investment_3 0.0000 0.00 0.00 291s Investment_4 0.0000 0.00 0.00 291s Investment_5 0.0000 0.00 0.00 291s Investment_6 0.0000 0.00 0.00 291s Investment_7 0.0000 0.00 0.00 291s Investment_8 0.0000 0.00 0.00 291s Investment_9 0.0000 0.00 0.00 291s Investment_10 0.0000 0.00 0.00 291s Investment_11 0.0000 0.00 0.00 291s Investment_12 0.0000 0.00 0.00 291s Investment_14 0.0000 0.00 0.00 291s Investment_15 0.0000 0.00 0.00 291s Investment_16 0.0000 0.00 0.00 291s Investment_17 0.0000 0.00 0.00 291s Investment_18 0.0000 0.00 0.00 291s Investment_19 0.0000 0.00 0.00 291s Investment_20 0.0000 0.00 0.00 291s Investment_21 0.0000 0.00 0.00 291s Investment_22 0.0000 0.00 0.00 291s PrivateWages_2 -1.3389 -61.06 -60.12 291s PrivateWages_3 0.2462 12.33 11.23 291s PrivateWages_4 1.1255 64.38 56.39 291s PrivateWages_5 -0.1959 -11.18 -11.20 291s PrivateWages_6 -0.5284 -32.23 -30.17 291s PrivateWages_8 -0.7909 -50.94 -50.62 291s PrivateWages_9 0.2819 18.18 18.15 291s PrivateWages_10 1.1384 76.28 73.43 291s PrivateWages_11 -0.1904 -11.65 -12.76 291s PrivateWages_12 0.5813 31.04 35.58 291s PrivateWages_13 0.1206 5.34 6.44 291s PrivateWages_14 0.4773 21.53 21.14 291s PrivateWages_15 0.3035 15.09 13.69 291s PrivateWages_16 0.0284 1.55 1.41 291s PrivateWages_17 -0.8517 -53.40 -46.33 291s PrivateWages_18 0.9908 64.40 62.12 291s PrivateWages_19 -0.4597 -28.00 -29.88 291s PrivateWages_20 -0.3819 -26.54 -23.26 291s PrivateWages_21 -1.1062 -83.74 -76.88 291s PrivateWages_22 0.5501 48.63 41.64 291s PrivateWages_trend 291s Consumption_2 0.000 291s Consumption_3 0.000 291s Consumption_4 0.000 291s Consumption_5 0.000 291s Consumption_6 0.000 291s Consumption_7 0.000 291s Consumption_8 0.000 291s Consumption_9 0.000 291s Consumption_11 0.000 291s Consumption_12 0.000 291s Consumption_14 0.000 291s Consumption_15 0.000 291s Consumption_16 0.000 291s Consumption_17 0.000 291s Consumption_18 0.000 291s Consumption_19 0.000 291s Consumption_20 0.000 291s Consumption_21 0.000 291s Consumption_22 0.000 291s Investment_2 0.000 291s Investment_3 0.000 291s Investment_4 0.000 291s Investment_5 0.000 291s Investment_6 0.000 291s Investment_7 0.000 291s Investment_8 0.000 291s Investment_9 0.000 291s Investment_10 0.000 291s Investment_11 0.000 291s Investment_12 0.000 291s Investment_14 0.000 291s Investment_15 0.000 291s Investment_16 0.000 291s Investment_17 0.000 291s Investment_18 0.000 291s Investment_19 0.000 291s Investment_20 0.000 291s Investment_21 0.000 291s Investment_22 0.000 291s PrivateWages_2 13.389 291s PrivateWages_3 -2.216 291s PrivateWages_4 -9.004 291s PrivateWages_5 1.371 291s PrivateWages_6 3.170 291s PrivateWages_8 3.164 291s PrivateWages_9 -0.846 291s PrivateWages_10 -2.277 291s PrivateWages_11 0.190 291s PrivateWages_12 0.000 291s PrivateWages_13 0.121 291s PrivateWages_14 0.955 291s PrivateWages_15 0.911 291s PrivateWages_16 0.114 291s PrivateWages_17 -4.258 291s PrivateWages_18 5.945 291s PrivateWages_19 -3.218 291s PrivateWages_20 -3.055 291s PrivateWages_21 -9.956 291s PrivateWages_22 5.501 291s [1] TRUE 291s > Bread 291s Consumption_(Intercept) Consumption_corpProf 291s Consumption_(Intercept) 109.396 -1.6401 291s Consumption_corpProf -1.640 0.6675 291s Consumption_corpProfLag -0.598 -0.3509 291s Consumption_wages -1.641 -0.0975 291s Investment_(Intercept) 0.000 0.0000 291s Investment_corpProf 0.000 0.0000 291s Investment_corpProfLag 0.000 0.0000 291s Investment_capitalLag 0.000 0.0000 291s PrivateWages_(Intercept) 0.000 0.0000 291s PrivateWages_gnp 0.000 0.0000 291s PrivateWages_gnpLag 0.000 0.0000 291s PrivateWages_trend 0.000 0.0000 291s Consumption_corpProfLag Consumption_wages 291s Consumption_(Intercept) -0.5979 -1.6408 291s Consumption_corpProf -0.3509 -0.0975 291s Consumption_corpProfLag 0.4880 -0.0331 291s Consumption_wages -0.0331 0.0926 291s Investment_(Intercept) 0.0000 0.0000 291s Investment_corpProf 0.0000 0.0000 291s Investment_corpProfLag 0.0000 0.0000 291s Investment_capitalLag 0.0000 0.0000 291s PrivateWages_(Intercept) 0.0000 0.0000 291s PrivateWages_gnp 0.0000 0.0000 291s PrivateWages_gnpLag 0.0000 0.0000 291s PrivateWages_trend 0.0000 0.0000 291s Investment_(Intercept) Investment_corpProf 291s Consumption_(Intercept) 0.00 0.0000 291s Consumption_corpProf 0.00 0.0000 291s Consumption_corpProfLag 0.00 0.0000 291s Consumption_wages 0.00 0.0000 291s Investment_(Intercept) 1730.48 -16.5126 291s Investment_corpProf -16.51 0.6641 291s Investment_corpProfLag 13.63 -0.5096 291s Investment_capitalLag -8.34 0.0672 291s PrivateWages_(Intercept) 0.00 0.0000 291s PrivateWages_gnp 0.00 0.0000 291s PrivateWages_gnpLag 0.00 0.0000 291s PrivateWages_trend 0.00 0.0000 291s Investment_corpProfLag Investment_capitalLag 291s Consumption_(Intercept) 0.000 0.0000 291s Consumption_corpProf 0.000 0.0000 291s Consumption_corpProfLag 0.000 0.0000 291s Consumption_wages 0.000 0.0000 291s Investment_(Intercept) 13.633 -8.3416 291s Investment_corpProf -0.510 0.0672 291s Investment_corpProfLag 0.603 -0.0740 291s Investment_capitalLag -0.074 0.0420 291s PrivateWages_(Intercept) 0.000 0.0000 291s PrivateWages_gnp 0.000 0.0000 291s PrivateWages_gnpLag 0.000 0.0000 291s PrivateWages_trend 0.000 0.0000 291s PrivateWages_(Intercept) PrivateWages_gnp 291s Consumption_(Intercept) 0.000 0.0000 291s Consumption_corpProf 0.000 0.0000 291s Consumption_corpProfLag 0.000 0.0000 291s Consumption_wages 0.000 0.0000 291s Investment_(Intercept) 0.000 0.0000 291s Investment_corpProf 0.000 0.0000 291s Investment_corpProfLag 0.000 0.0000 291s Investment_capitalLag 0.000 0.0000 291s PrivateWages_(Intercept) 166.178 -0.6258 291s PrivateWages_gnp -0.626 0.1064 291s PrivateWages_gnpLag -2.183 -0.0992 291s PrivateWages_trend 2.051 -0.0286 291s PrivateWages_gnpLag PrivateWages_trend 291s Consumption_(Intercept) 0.00000 0.00000 291s Consumption_corpProf 0.00000 0.00000 291s Consumption_corpProfLag 0.00000 0.00000 291s Consumption_wages 0.00000 0.00000 291s Investment_(Intercept) 0.00000 0.00000 291s Investment_corpProf 0.00000 0.00000 291s Investment_corpProfLag 0.00000 0.00000 291s Investment_capitalLag 0.00000 0.00000 291s PrivateWages_(Intercept) -2.18348 2.05079 291s PrivateWages_gnp -0.09921 -0.02859 291s PrivateWages_gnpLag 0.14047 -0.00635 291s PrivateWages_trend -0.00635 0.10969 291s > 291s > # 2SLS 291s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 291s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 291s > summary 291s 291s systemfit results 291s method: 2SLS 291s 291s N DF SSR detRCov OLS-R2 McElroy-R2 291s system 57 45 58.2 0.333 0.968 0.991 291s 291s N DF SSR MSE RMSE R2 Adj R2 291s Consumption 18 14 22.27 1.591 1.26 0.974 0.968 291s Investment 19 15 26.21 1.748 1.32 0.852 0.823 291s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 291s 291s The covariance matrix of the residuals 291s Consumption Investment PrivateWages 291s Consumption 1.237 0.518 -0.408 291s Investment 0.518 1.263 0.113 291s PrivateWages -0.408 0.113 0.468 291s 291s The correlations of the residuals 291s Consumption Investment PrivateWages 291s Consumption 1.000 0.416 -0.538 291s Investment 0.416 1.000 0.139 291s PrivateWages -0.538 0.139 1.000 291s 291s 291s 2SLS estimates for 'Consumption' (equation 1) 291s Model Formula: consump ~ corpProf + corpProfLag + wages 291s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 291s gnpLag 291s 291s Estimate Std. Error t value Pr(>|t|) 291s (Intercept) 17.2849 1.6018 10.79 3.6e-08 *** 291s corpProf -0.0770 0.1637 -0.47 0.645 291s corpProfLag 0.2327 0.1242 1.87 0.082 . 291s wages 0.8259 0.0459 17.98 4.5e-11 *** 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s 291s Residual standard error: 1.261 on 14 degrees of freedom 291s Number of observations: 18 Degrees of Freedom: 14 291s SSR: 22.269 MSE: 1.591 Root MSE: 1.261 291s Multiple R-Squared: 0.974 Adjusted R-Squared: 0.968 291s 291s 291s 2SLS estimates for 'Investment' (equation 2) 291s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 291s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 291s gnpLag 291s 291s Estimate Std. Error t value Pr(>|t|) 291s (Intercept) 18.4005 7.1627 2.57 0.02138 * 291s corpProf 0.1507 0.1905 0.79 0.44118 291s corpProfLag 0.5757 0.1634 3.52 0.00307 ** 291s capitalLag -0.1452 0.0339 -4.28 0.00065 *** 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s 291s Residual standard error: 1.322 on 15 degrees of freedom 291s Number of observations: 19 Degrees of Freedom: 15 291s SSR: 26.213 MSE: 1.748 Root MSE: 1.322 291s Multiple R-Squared: 0.852 Adjusted R-Squared: 0.823 291s 291s 291s 2SLS estimates for 'PrivateWages' (equation 3) 291s Model Formula: privWage ~ gnp + gnpLag + trend 291s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 291s gnpLag 291s 291s Estimate Std. Error t value Pr(>|t|) 291s (Intercept) 1.3431 1.1544 1.16 0.26172 291s gnp 0.4438 0.0351 12.64 9.7e-10 *** 291s gnpLag 0.1447 0.0381 3.80 0.00158 ** 291s trend 0.1238 0.0300 4.13 0.00078 *** 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s 291s Residual standard error: 0.78 on 16 degrees of freedom 291s Number of observations: 20 Degrees of Freedom: 16 291s SSR: 9.741 MSE: 0.609 Root MSE: 0.78 291s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 291s 291s > residuals 291s Consumption Investment PrivateWages 291s 1 NA NA NA 291s 2 -0.6754 -1.23599 -1.3401 291s 3 -0.4627 0.32957 0.2378 291s 4 -1.1585 1.08894 1.1117 291s 5 -0.0305 -1.37017 -0.1954 291s 6 0.4693 0.48431 -0.5355 291s 7 NA NA NA 291s 8 1.6045 1.06811 -0.7908 291s 9 1.6018 0.16695 0.2831 291s 10 NA 1.86380 1.1353 291s 11 -0.9031 -0.92183 -0.1765 291s 12 -1.5948 -1.03217 0.6007 291s 13 NA NA 0.1443 291s 14 0.2854 0.85468 0.4826 291s 15 -0.4718 -0.36943 0.3016 291s 16 -0.2268 0.00554 0.0261 291s 17 2.0079 1.69566 -0.8614 291s 18 -0.7434 -0.12659 0.9927 291s 19 -0.5410 -3.26209 -0.4446 291s 20 1.4186 0.25579 -0.3914 291s 21 1.1462 -0.00185 -1.1115 291s 22 -1.7256 0.50679 0.5312 291s > fitted 291s Consumption Investment PrivateWages 291s 1 NA NA NA 291s 2 42.6 1.036 26.8 291s 3 45.5 1.570 29.1 291s 4 50.4 4.111 33.0 291s 5 50.6 4.370 34.1 291s 6 52.1 4.616 35.9 291s 7 NA NA NA 291s 8 54.6 3.132 38.7 291s 9 55.7 2.833 38.9 291s 10 NA 3.236 40.2 291s 11 55.9 1.922 38.1 291s 12 52.5 -2.368 33.9 291s 13 NA NA 28.9 291s 14 46.2 -5.955 28.0 291s 15 49.2 -2.631 30.3 291s 16 51.5 -1.306 33.2 291s 17 55.7 0.404 37.7 291s 18 59.4 2.127 40.0 291s 19 58.0 1.362 38.6 291s 20 60.2 1.044 42.0 291s 21 63.9 3.302 46.1 291s 22 71.4 4.393 52.8 291s > predict 291s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 291s 1 NA NA NA NA 291s 2 42.6 0.571 41.4 43.8 291s 3 45.5 0.656 44.1 46.9 291s 4 50.4 0.431 49.4 51.3 291s 5 50.6 0.510 49.5 51.7 291s 6 52.1 0.521 51.0 53.2 291s 7 NA NA NA NA 291s 8 54.6 0.419 53.7 55.5 291s 9 55.7 0.496 54.6 56.8 291s 10 NA NA NA NA 291s 11 55.9 0.910 54.0 57.9 291s 12 52.5 0.869 50.6 54.4 291s 13 NA NA NA NA 291s 14 46.2 0.694 44.7 47.7 291s 15 49.2 0.487 48.1 50.2 291s 16 51.5 0.396 50.7 52.4 291s 17 55.7 0.445 54.7 56.6 291s 18 59.4 0.386 58.6 60.3 291s 19 58.0 0.548 56.9 59.2 291s 20 60.2 0.528 59.0 61.3 291s 21 63.9 0.515 62.8 65.0 291s 22 71.4 0.786 69.7 73.1 291s Investment.pred Investment.se.fit Investment.lwr Investment.upr 291s 1 NA NA NA NA 291s 2 1.036 0.892 -0.865 2.937 291s 3 1.570 0.579 0.335 2.805 291s 4 4.111 0.531 2.979 5.243 291s 5 4.370 0.440 3.432 5.308 291s 6 4.616 0.416 3.729 5.502 291s 7 NA NA NA NA 291s 8 3.132 0.344 2.398 3.866 291s 9 2.833 0.533 1.696 3.970 291s 10 3.236 0.580 2.000 4.473 291s 11 1.922 0.959 -0.122 3.966 291s 12 -2.368 0.860 -4.201 -0.534 291s 13 NA NA NA NA 291s 14 -5.955 0.865 -7.799 -4.110 291s 15 -2.631 0.479 -3.652 -1.610 291s 16 -1.306 0.382 -2.120 -0.491 291s 17 0.404 0.487 -0.635 1.443 291s 18 2.127 0.319 1.447 2.806 291s 19 1.362 0.537 0.218 2.506 291s 20 1.044 0.566 -0.162 2.250 291s 21 3.302 0.486 2.265 4.339 291s 22 4.393 0.713 2.874 5.912 291s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 291s 1 NA NA NA NA 291s 2 26.8 0.321 26.2 27.5 291s 3 29.1 0.334 28.4 29.8 291s 4 33.0 0.353 32.2 33.7 291s 5 34.1 0.253 33.6 34.6 291s 6 35.9 0.261 35.4 36.5 291s 7 NA NA NA NA 291s 8 38.7 0.257 38.1 39.2 291s 9 38.9 0.245 38.4 39.4 291s 10 40.2 0.235 39.7 40.7 291s 11 38.1 0.348 37.3 38.8 291s 12 33.9 0.374 33.1 34.7 291s 13 28.9 0.447 27.9 29.8 291s 14 28.0 0.341 27.3 28.7 291s 15 30.3 0.333 29.6 31.0 291s 16 33.2 0.278 32.6 33.8 291s 17 37.7 0.288 37.1 38.3 291s 18 40.0 0.214 39.6 40.5 291s 19 38.6 0.351 37.9 39.4 291s 20 42.0 0.301 41.4 42.6 291s 21 46.1 0.304 45.5 46.8 291s 22 52.8 0.486 51.7 53.8 291s > model.frame 291s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 291s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 291s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 291s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 291s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 291s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 291s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 291s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 291s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 291s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 291s 10 57.8 21.7 21.1 NA 5.1 211 41.3 67.0 64.5 291s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 291s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 291s 13 45.6 NA 11.4 34.3 -6.2 213 29.0 44.3 53.4 291s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 291s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 291s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 291s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 291s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 291s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 291s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 291s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 291s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 291s trend 291s 1 -11 291s 2 -10 291s 3 -9 291s 4 -8 291s 5 -7 291s 6 -6 291s 7 -5 291s 8 -4 291s 9 -3 291s 10 -2 291s 11 -1 291s 12 0 291s 13 1 291s 14 2 291s 15 3 291s 16 4 291s 17 5 291s 18 6 291s 19 7 291s 20 8 291s 21 9 291s 22 10 291s > Frames of instrumental variables 291s govExp taxes govWage trend capitalLag corpProfLag gnpLag 291s 1 2.4 3.4 2.2 -11 180 NA NA 291s 2 3.9 7.7 2.7 -10 183 12.7 44.9 291s 3 3.2 3.9 2.9 -9 183 12.4 45.6 291s 4 2.8 4.7 2.9 -8 184 16.9 50.1 291s 5 3.5 3.8 3.1 -7 190 18.4 57.2 291s 6 3.3 5.5 3.2 -6 193 19.4 57.1 291s 7 3.3 7.0 3.3 -5 198 20.1 NA 291s 8 4.0 6.7 3.6 -4 203 19.6 64.0 291s 9 4.2 4.2 3.7 -3 208 19.8 64.4 291s 10 4.1 4.0 4.0 -2 211 21.1 64.5 291s 11 5.2 7.7 4.2 -1 216 21.7 67.0 291s 12 5.9 7.5 4.8 0 217 15.6 61.2 291s 13 4.9 8.3 5.3 1 213 11.4 53.4 291s 14 3.7 5.4 5.6 2 207 7.0 44.3 291s 15 4.0 6.8 6.0 3 202 11.2 45.1 291s 16 4.4 7.2 6.1 4 199 12.3 49.7 291s 17 2.9 8.3 7.4 5 198 14.0 54.4 291s 18 4.3 6.7 6.7 6 200 17.6 62.7 291s 19 5.3 7.4 7.7 7 202 17.3 65.0 291s 20 6.6 8.9 7.8 8 200 15.3 60.9 291s 21 7.4 9.6 8.0 9 201 19.0 69.5 291s 22 13.8 11.6 8.5 10 204 21.1 75.7 291s govExp taxes govWage trend capitalLag corpProfLag gnpLag 291s 1 2.4 3.4 2.2 -11 180 NA NA 291s 2 3.9 7.7 2.7 -10 183 12.7 44.9 291s 3 3.2 3.9 2.9 -9 183 12.4 45.6 291s 4 2.8 4.7 2.9 -8 184 16.9 50.1 291s 5 3.5 3.8 3.1 -7 190 18.4 57.2 291s 6 3.3 5.5 3.2 -6 193 19.4 57.1 291s 7 3.3 7.0 3.3 -5 198 20.1 NA 291s 8 4.0 6.7 3.6 -4 203 19.6 64.0 291s 9 4.2 4.2 3.7 -3 208 19.8 64.4 291s 10 4.1 4.0 4.0 -2 211 21.1 64.5 291s 11 5.2 7.7 4.2 -1 216 21.7 67.0 291s 12 5.9 7.5 4.8 0 217 15.6 61.2 291s 13 4.9 8.3 5.3 1 213 11.4 53.4 291s 14 3.7 5.4 5.6 2 207 7.0 44.3 291s 15 4.0 6.8 6.0 3 202 11.2 45.1 291s 16 4.4 7.2 6.1 4 199 12.3 49.7 291s 17 2.9 8.3 7.4 5 198 14.0 54.4 291s 18 4.3 6.7 6.7 6 200 17.6 62.7 291s 19 5.3 7.4 7.7 7 202 17.3 65.0 291s 20 6.6 8.9 7.8 8 200 15.3 60.9 291s 21 7.4 9.6 8.0 9 201 19.0 69.5 291s 22 13.8 11.6 8.5 10 204 21.1 75.7 291s govExp taxes govWage trend capitalLag corpProfLag gnpLag 291s 1 2.4 3.4 2.2 -11 180 NA NA 291s 2 3.9 7.7 2.7 -10 183 12.7 44.9 291s 3 3.2 3.9 2.9 -9 183 12.4 45.6 291s 4 2.8 4.7 2.9 -8 184 16.9 50.1 291s 5 3.5 3.8 3.1 -7 190 18.4 57.2 291s 6 3.3 5.5 3.2 -6 193 19.4 57.1 291s 7 3.3 7.0 3.3 -5 198 20.1 NA 291s 8 4.0 6.7 3.6 -4 203 19.6 64.0 291s 9 4.2 4.2 3.7 -3 208 19.8 64.4 291s 10 4.1 4.0 4.0 -2 211 21.1 64.5 291s 11 5.2 7.7 4.2 -1 216 21.7 67.0 291s 12 5.9 7.5 4.8 0 217 15.6 61.2 291s 13 4.9 8.3 5.3 1 213 11.4 53.4 291s 14 3.7 5.4 5.6 2 207 7.0 44.3 291s 15 4.0 6.8 6.0 3 202 11.2 45.1 291s 16 4.4 7.2 6.1 4 199 12.3 49.7 291s 17 2.9 8.3 7.4 5 198 14.0 54.4 291s 18 4.3 6.7 6.7 6 200 17.6 62.7 291s 19 5.3 7.4 7.7 7 202 17.3 65.0 291s 20 6.6 8.9 7.8 8 200 15.3 60.9 291s 21 7.4 9.6 8.0 9 201 19.0 69.5 291s 22 13.8 11.6 8.5 10 204 21.1 75.7 291s > model.matrix 291s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0339 >" 291s [2] "Attributes: < Component “dimnames”: Component 1: 52 string mismatches >" 291s [3] "Numeric: lengths (708, 684) differ" 291s > matrix of instrumental variables 291s Consumption_(Intercept) Consumption_govExp Consumption_taxes 291s Consumption_2 1 3.9 7.7 291s Consumption_3 1 3.2 3.9 291s Consumption_4 1 2.8 4.7 291s Consumption_5 1 3.5 3.8 291s Consumption_6 1 3.3 5.5 291s Consumption_8 1 4.0 6.7 291s Consumption_9 1 4.2 4.2 291s Consumption_11 1 5.2 7.7 291s Consumption_12 1 5.9 7.5 291s Consumption_14 1 3.7 5.4 291s Consumption_15 1 4.0 6.8 291s Consumption_16 1 4.4 7.2 291s Consumption_17 1 2.9 8.3 291s Consumption_18 1 4.3 6.7 291s Consumption_19 1 5.3 7.4 291s Consumption_20 1 6.6 8.9 291s Consumption_21 1 7.4 9.6 291s Consumption_22 1 13.8 11.6 291s Investment_2 0 0.0 0.0 291s Investment_3 0 0.0 0.0 291s Investment_4 0 0.0 0.0 291s Investment_5 0 0.0 0.0 291s Investment_6 0 0.0 0.0 291s Investment_8 0 0.0 0.0 291s Investment_9 0 0.0 0.0 291s Investment_10 0 0.0 0.0 291s Investment_11 0 0.0 0.0 291s Investment_12 0 0.0 0.0 291s Investment_14 0 0.0 0.0 291s Investment_15 0 0.0 0.0 291s Investment_16 0 0.0 0.0 291s Investment_17 0 0.0 0.0 291s Investment_18 0 0.0 0.0 291s Investment_19 0 0.0 0.0 291s Investment_20 0 0.0 0.0 291s Investment_21 0 0.0 0.0 291s Investment_22 0 0.0 0.0 291s PrivateWages_2 0 0.0 0.0 291s PrivateWages_3 0 0.0 0.0 291s PrivateWages_4 0 0.0 0.0 291s PrivateWages_5 0 0.0 0.0 291s PrivateWages_6 0 0.0 0.0 291s PrivateWages_8 0 0.0 0.0 291s PrivateWages_9 0 0.0 0.0 291s PrivateWages_10 0 0.0 0.0 291s PrivateWages_11 0 0.0 0.0 291s PrivateWages_12 0 0.0 0.0 291s PrivateWages_13 0 0.0 0.0 291s PrivateWages_14 0 0.0 0.0 291s PrivateWages_15 0 0.0 0.0 291s PrivateWages_16 0 0.0 0.0 291s PrivateWages_17 0 0.0 0.0 291s PrivateWages_18 0 0.0 0.0 291s PrivateWages_19 0 0.0 0.0 291s PrivateWages_20 0 0.0 0.0 291s PrivateWages_21 0 0.0 0.0 291s PrivateWages_22 0 0.0 0.0 291s Consumption_govWage Consumption_trend Consumption_capitalLag 291s Consumption_2 2.7 -10 183 291s Consumption_3 2.9 -9 183 291s Consumption_4 2.9 -8 184 291s Consumption_5 3.1 -7 190 291s Consumption_6 3.2 -6 193 291s Consumption_8 3.6 -4 203 291s Consumption_9 3.7 -3 208 291s Consumption_11 4.2 -1 216 291s Consumption_12 4.8 0 217 291s Consumption_14 5.6 2 207 291s Consumption_15 6.0 3 202 291s Consumption_16 6.1 4 199 291s Consumption_17 7.4 5 198 291s Consumption_18 6.7 6 200 291s Consumption_19 7.7 7 202 291s Consumption_20 7.8 8 200 291s Consumption_21 8.0 9 201 291s Consumption_22 8.5 10 204 291s Investment_2 0.0 0 0 291s Investment_3 0.0 0 0 291s Investment_4 0.0 0 0 291s Investment_5 0.0 0 0 291s Investment_6 0.0 0 0 291s Investment_8 0.0 0 0 291s Investment_9 0.0 0 0 291s Investment_10 0.0 0 0 291s Investment_11 0.0 0 0 291s Investment_12 0.0 0 0 291s Investment_14 0.0 0 0 291s Investment_15 0.0 0 0 291s Investment_16 0.0 0 0 291s Investment_17 0.0 0 0 291s Investment_18 0.0 0 0 291s Investment_19 0.0 0 0 291s Investment_20 0.0 0 0 291s Investment_21 0.0 0 0 291s Investment_22 0.0 0 0 291s PrivateWages_2 0.0 0 0 291s PrivateWages_3 0.0 0 0 291s PrivateWages_4 0.0 0 0 291s PrivateWages_5 0.0 0 0 291s PrivateWages_6 0.0 0 0 291s PrivateWages_8 0.0 0 0 291s PrivateWages_9 0.0 0 0 291s PrivateWages_10 0.0 0 0 291s PrivateWages_11 0.0 0 0 291s PrivateWages_12 0.0 0 0 291s PrivateWages_13 0.0 0 0 291s PrivateWages_14 0.0 0 0 291s PrivateWages_15 0.0 0 0 291s PrivateWages_16 0.0 0 0 291s PrivateWages_17 0.0 0 0 291s PrivateWages_18 0.0 0 0 291s PrivateWages_19 0.0 0 0 291s PrivateWages_20 0.0 0 0 291s PrivateWages_21 0.0 0 0 291s PrivateWages_22 0.0 0 0 291s Consumption_corpProfLag Consumption_gnpLag 291s Consumption_2 12.7 44.9 291s Consumption_3 12.4 45.6 291s Consumption_4 16.9 50.1 291s Consumption_5 18.4 57.2 291s Consumption_6 19.4 57.1 291s Consumption_8 19.6 64.0 291s Consumption_9 19.8 64.4 291s Consumption_11 21.7 67.0 291s Consumption_12 15.6 61.2 291s Consumption_14 7.0 44.3 291s Consumption_15 11.2 45.1 291s Consumption_16 12.3 49.7 291s Consumption_17 14.0 54.4 291s Consumption_18 17.6 62.7 291s Consumption_19 17.3 65.0 291s Consumption_20 15.3 60.9 291s Consumption_21 19.0 69.5 291s Consumption_22 21.1 75.7 291s Investment_2 0.0 0.0 291s Investment_3 0.0 0.0 291s Investment_4 0.0 0.0 291s Investment_5 0.0 0.0 291s Investment_6 0.0 0.0 291s Investment_8 0.0 0.0 291s Investment_9 0.0 0.0 291s Investment_10 0.0 0.0 291s Investment_11 0.0 0.0 291s Investment_12 0.0 0.0 291s Investment_14 0.0 0.0 291s Investment_15 0.0 0.0 291s Investment_16 0.0 0.0 291s Investment_17 0.0 0.0 291s Investment_18 0.0 0.0 291s Investment_19 0.0 0.0 291s Investment_20 0.0 0.0 291s Investment_21 0.0 0.0 291s Investment_22 0.0 0.0 291s PrivateWages_2 0.0 0.0 291s PrivateWages_3 0.0 0.0 291s PrivateWages_4 0.0 0.0 291s PrivateWages_5 0.0 0.0 291s PrivateWages_6 0.0 0.0 291s PrivateWages_8 0.0 0.0 291s PrivateWages_9 0.0 0.0 291s PrivateWages_10 0.0 0.0 291s PrivateWages_11 0.0 0.0 291s PrivateWages_12 0.0 0.0 291s PrivateWages_13 0.0 0.0 291s PrivateWages_14 0.0 0.0 291s PrivateWages_15 0.0 0.0 291s PrivateWages_16 0.0 0.0 291s PrivateWages_17 0.0 0.0 291s PrivateWages_18 0.0 0.0 291s PrivateWages_19 0.0 0.0 291s PrivateWages_20 0.0 0.0 291s PrivateWages_21 0.0 0.0 291s PrivateWages_22 0.0 0.0 291s Investment_(Intercept) Investment_govExp Investment_taxes 291s Consumption_2 0 0.0 0.0 291s Consumption_3 0 0.0 0.0 291s Consumption_4 0 0.0 0.0 291s Consumption_5 0 0.0 0.0 291s Consumption_6 0 0.0 0.0 291s Consumption_8 0 0.0 0.0 291s Consumption_9 0 0.0 0.0 291s Consumption_11 0 0.0 0.0 291s Consumption_12 0 0.0 0.0 291s Consumption_14 0 0.0 0.0 291s Consumption_15 0 0.0 0.0 291s Consumption_16 0 0.0 0.0 291s Consumption_17 0 0.0 0.0 291s Consumption_18 0 0.0 0.0 291s Consumption_19 0 0.0 0.0 291s Consumption_20 0 0.0 0.0 291s Consumption_21 0 0.0 0.0 291s Consumption_22 0 0.0 0.0 291s Investment_2 1 3.9 7.7 291s Investment_3 1 3.2 3.9 291s Investment_4 1 2.8 4.7 291s Investment_5 1 3.5 3.8 291s Investment_6 1 3.3 5.5 291s Investment_8 1 4.0 6.7 291s Investment_9 1 4.2 4.2 291s Investment_10 1 4.1 4.0 291s Investment_11 1 5.2 7.7 291s Investment_12 1 5.9 7.5 291s Investment_14 1 3.7 5.4 291s Investment_15 1 4.0 6.8 291s Investment_16 1 4.4 7.2 291s Investment_17 1 2.9 8.3 291s Investment_18 1 4.3 6.7 291s Investment_19 1 5.3 7.4 291s Investment_20 1 6.6 8.9 291s Investment_21 1 7.4 9.6 291s Investment_22 1 13.8 11.6 291s PrivateWages_2 0 0.0 0.0 291s PrivateWages_3 0 0.0 0.0 291s PrivateWages_4 0 0.0 0.0 291s PrivateWages_5 0 0.0 0.0 291s PrivateWages_6 0 0.0 0.0 291s PrivateWages_8 0 0.0 0.0 291s PrivateWages_9 0 0.0 0.0 291s PrivateWages_10 0 0.0 0.0 291s PrivateWages_11 0 0.0 0.0 291s PrivateWages_12 0 0.0 0.0 291s PrivateWages_13 0 0.0 0.0 291s PrivateWages_14 0 0.0 0.0 291s PrivateWages_15 0 0.0 0.0 291s PrivateWages_16 0 0.0 0.0 291s PrivateWages_17 0 0.0 0.0 291s PrivateWages_18 0 0.0 0.0 291s PrivateWages_19 0 0.0 0.0 291s PrivateWages_20 0 0.0 0.0 291s PrivateWages_21 0 0.0 0.0 291s PrivateWages_22 0 0.0 0.0 291s Investment_govWage Investment_trend Investment_capitalLag 291s Consumption_2 0.0 0 0 291s Consumption_3 0.0 0 0 291s Consumption_4 0.0 0 0 291s Consumption_5 0.0 0 0 291s Consumption_6 0.0 0 0 291s Consumption_8 0.0 0 0 291s Consumption_9 0.0 0 0 291s Consumption_11 0.0 0 0 291s Consumption_12 0.0 0 0 291s Consumption_14 0.0 0 0 291s Consumption_15 0.0 0 0 291s Consumption_16 0.0 0 0 291s Consumption_17 0.0 0 0 291s Consumption_18 0.0 0 0 291s Consumption_19 0.0 0 0 291s Consumption_20 0.0 0 0 291s Consumption_21 0.0 0 0 291s Consumption_22 0.0 0 0 291s Investment_2 2.7 -10 183 291s Investment_3 2.9 -9 183 291s Investment_4 2.9 -8 184 291s Investment_5 3.1 -7 190 291s Investment_6 3.2 -6 193 291s Investment_8 3.6 -4 203 291s Investment_9 3.7 -3 208 291s Investment_10 4.0 -2 211 291s Investment_11 4.2 -1 216 291s Investment_12 4.8 0 217 291s Investment_14 5.6 2 207 291s Investment_15 6.0 3 202 291s Investment_16 6.1 4 199 291s Investment_17 7.4 5 198 291s Investment_18 6.7 6 200 291s Investment_19 7.7 7 202 291s Investment_20 7.8 8 200 291s Investment_21 8.0 9 201 291s Investment_22 8.5 10 204 291s PrivateWages_2 0.0 0 0 291s PrivateWages_3 0.0 0 0 291s PrivateWages_4 0.0 0 0 291s PrivateWages_5 0.0 0 0 291s PrivateWages_6 0.0 0 0 291s PrivateWages_8 0.0 0 0 291s PrivateWages_9 0.0 0 0 291s PrivateWages_10 0.0 0 0 291s PrivateWages_11 0.0 0 0 291s PrivateWages_12 0.0 0 0 291s PrivateWages_13 0.0 0 0 291s PrivateWages_14 0.0 0 0 291s PrivateWages_15 0.0 0 0 291s PrivateWages_16 0.0 0 0 291s PrivateWages_17 0.0 0 0 291s PrivateWages_18 0.0 0 0 291s PrivateWages_19 0.0 0 0 291s PrivateWages_20 0.0 0 0 291s PrivateWages_21 0.0 0 0 291s PrivateWages_22 0.0 0 0 291s Investment_corpProfLag Investment_gnpLag 291s Consumption_2 0.0 0.0 291s Consumption_3 0.0 0.0 291s Consumption_4 0.0 0.0 291s Consumption_5 0.0 0.0 291s Consumption_6 0.0 0.0 291s Consumption_8 0.0 0.0 291s Consumption_9 0.0 0.0 291s Consumption_11 0.0 0.0 291s Consumption_12 0.0 0.0 291s Consumption_14 0.0 0.0 291s Consumption_15 0.0 0.0 291s Consumption_16 0.0 0.0 291s Consumption_17 0.0 0.0 291s Consumption_18 0.0 0.0 291s Consumption_19 0.0 0.0 291s Consumption_20 0.0 0.0 291s Consumption_21 0.0 0.0 291s Consumption_22 0.0 0.0 291s Investment_2 12.7 44.9 291s Investment_3 12.4 45.6 291s Investment_4 16.9 50.1 291s Investment_5 18.4 57.2 291s Investment_6 19.4 57.1 291s Investment_8 19.6 64.0 291s Investment_9 19.8 64.4 291s Investment_10 21.1 64.5 291s Investment_11 21.7 67.0 291s Investment_12 15.6 61.2 291s Investment_14 7.0 44.3 291s Investment_15 11.2 45.1 291s Investment_16 12.3 49.7 291s Investment_17 14.0 54.4 291s Investment_18 17.6 62.7 291s Investment_19 17.3 65.0 291s Investment_20 15.3 60.9 291s Investment_21 19.0 69.5 291s Investment_22 21.1 75.7 291s PrivateWages_2 0.0 0.0 291s PrivateWages_3 0.0 0.0 291s PrivateWages_4 0.0 0.0 291s PrivateWages_5 0.0 0.0 291s PrivateWages_6 0.0 0.0 291s PrivateWages_8 0.0 0.0 291s PrivateWages_9 0.0 0.0 291s PrivateWages_10 0.0 0.0 291s PrivateWages_11 0.0 0.0 291s PrivateWages_12 0.0 0.0 291s PrivateWages_13 0.0 0.0 291s PrivateWages_14 0.0 0.0 291s PrivateWages_15 0.0 0.0 291s PrivateWages_16 0.0 0.0 291s PrivateWages_17 0.0 0.0 291s PrivateWages_18 0.0 0.0 291s PrivateWages_19 0.0 0.0 291s PrivateWages_20 0.0 0.0 291s PrivateWages_21 0.0 0.0 291s PrivateWages_22 0.0 0.0 291s PrivateWages_(Intercept) PrivateWages_govExp PrivateWages_taxes 291s Consumption_2 0 0.0 0.0 291s Consumption_3 0 0.0 0.0 291s Consumption_4 0 0.0 0.0 291s Consumption_5 0 0.0 0.0 291s Consumption_6 0 0.0 0.0 291s Consumption_8 0 0.0 0.0 291s Consumption_9 0 0.0 0.0 291s Consumption_11 0 0.0 0.0 291s Consumption_12 0 0.0 0.0 291s Consumption_14 0 0.0 0.0 291s Consumption_15 0 0.0 0.0 291s Consumption_16 0 0.0 0.0 291s Consumption_17 0 0.0 0.0 291s Consumption_18 0 0.0 0.0 291s Consumption_19 0 0.0 0.0 291s Consumption_20 0 0.0 0.0 291s Consumption_21 0 0.0 0.0 291s Consumption_22 0 0.0 0.0 291s Investment_2 0 0.0 0.0 291s Investment_3 0 0.0 0.0 291s Investment_4 0 0.0 0.0 291s Investment_5 0 0.0 0.0 291s Investment_6 0 0.0 0.0 291s Investment_8 0 0.0 0.0 291s Investment_9 0 0.0 0.0 291s Investment_10 0 0.0 0.0 291s Investment_11 0 0.0 0.0 291s Investment_12 0 0.0 0.0 291s Investment_14 0 0.0 0.0 291s Investment_15 0 0.0 0.0 291s Investment_16 0 0.0 0.0 291s Investment_17 0 0.0 0.0 291s Investment_18 0 0.0 0.0 291s Investment_19 0 0.0 0.0 291s Investment_20 0 0.0 0.0 291s Investment_21 0 0.0 0.0 291s Investment_22 0 0.0 0.0 291s PrivateWages_2 1 3.9 7.7 291s PrivateWages_3 1 3.2 3.9 291s PrivateWages_4 1 2.8 4.7 291s PrivateWages_5 1 3.5 3.8 291s PrivateWages_6 1 3.3 5.5 291s PrivateWages_8 1 4.0 6.7 291s PrivateWages_9 1 4.2 4.2 291s PrivateWages_10 1 4.1 4.0 291s PrivateWages_11 1 5.2 7.7 291s PrivateWages_12 1 5.9 7.5 291s PrivateWages_13 1 4.9 8.3 291s PrivateWages_14 1 3.7 5.4 291s PrivateWages_15 1 4.0 6.8 291s PrivateWages_16 1 4.4 7.2 291s PrivateWages_17 1 2.9 8.3 291s PrivateWages_18 1 4.3 6.7 291s PrivateWages_19 1 5.3 7.4 291s PrivateWages_20 1 6.6 8.9 291s PrivateWages_21 1 7.4 9.6 291s PrivateWages_22 1 13.8 11.6 291s PrivateWages_govWage PrivateWages_trend PrivateWages_capitalLag 291s Consumption_2 0.0 0 0 291s Consumption_3 0.0 0 0 291s Consumption_4 0.0 0 0 291s Consumption_5 0.0 0 0 291s Consumption_6 0.0 0 0 291s Consumption_8 0.0 0 0 291s Consumption_9 0.0 0 0 291s Consumption_11 0.0 0 0 291s Consumption_12 0.0 0 0 291s Consumption_14 0.0 0 0 291s Consumption_15 0.0 0 0 291s Consumption_16 0.0 0 0 291s Consumption_17 0.0 0 0 291s Consumption_18 0.0 0 0 291s Consumption_19 0.0 0 0 291s Consumption_20 0.0 0 0 291s Consumption_21 0.0 0 0 291s Consumption_22 0.0 0 0 291s Investment_2 0.0 0 0 291s Investment_3 0.0 0 0 291s Investment_4 0.0 0 0 291s Investment_5 0.0 0 0 291s Investment_6 0.0 0 0 291s Investment_8 0.0 0 0 291s Investment_9 0.0 0 0 291s Investment_10 0.0 0 0 291s Investment_11 0.0 0 0 291s Investment_12 0.0 0 0 291s Investment_14 0.0 0 0 291s Investment_15 0.0 0 0 291s Investment_16 0.0 0 0 291s Investment_17 0.0 0 0 291s Investment_18 0.0 0 0 291s Investment_19 0.0 0 0 291s Investment_20 0.0 0 0 291s Investment_21 0.0 0 0 291s Investment_22 0.0 0 0 291s PrivateWages_2 2.7 -10 183 291s PrivateWages_3 2.9 -9 183 291s PrivateWages_4 2.9 -8 184 291s PrivateWages_5 3.1 -7 190 291s PrivateWages_6 3.2 -6 193 291s PrivateWages_8 3.6 -4 203 291s PrivateWages_9 3.7 -3 208 291s PrivateWages_10 4.0 -2 211 291s PrivateWages_11 4.2 -1 216 291s PrivateWages_12 4.8 0 217 291s PrivateWages_13 5.3 1 213 291s PrivateWages_14 5.6 2 207 291s PrivateWages_15 6.0 3 202 291s PrivateWages_16 6.1 4 199 291s PrivateWages_17 7.4 5 198 291s PrivateWages_18 6.7 6 200 291s PrivateWages_19 7.7 7 202 291s PrivateWages_20 7.8 8 200 291s PrivateWages_21 8.0 9 201 291s PrivateWages_22 8.5 10 204 291s PrivateWages_corpProfLag PrivateWages_gnpLag 291s Consumption_2 0.0 0.0 291s Consumption_3 0.0 0.0 291s Consumption_4 0.0 0.0 291s Consumption_5 0.0 0.0 291s Consumption_6 0.0 0.0 291s Consumption_8 0.0 0.0 291s Consumption_9 0.0 0.0 291s Consumption_11 0.0 0.0 291s Consumption_12 0.0 0.0 291s Consumption_14 0.0 0.0 291s Consumption_15 0.0 0.0 291s Consumption_16 0.0 0.0 291s Consumption_17 0.0 0.0 291s Consumption_18 0.0 0.0 291s Consumption_19 0.0 0.0 291s Consumption_20 0.0 0.0 291s Consumption_21 0.0 0.0 291s Consumption_22 0.0 0.0 291s Investment_2 0.0 0.0 291s Investment_3 0.0 0.0 291s Investment_4 0.0 0.0 291s Investment_5 0.0 0.0 291s Investment_6 0.0 0.0 291s Investment_8 0.0 0.0 291s Investment_9 0.0 0.0 291s Investment_10 0.0 0.0 291s Investment_11 0.0 0.0 291s Investment_12 0.0 0.0 291s Investment_14 0.0 0.0 291s Investment_15 0.0 0.0 291s Investment_16 0.0 0.0 291s Investment_17 0.0 0.0 291s Investment_18 0.0 0.0 291s Investment_19 0.0 0.0 291s Investment_20 0.0 0.0 291s Investment_21 0.0 0.0 291s Investment_22 0.0 0.0 291s PrivateWages_2 12.7 44.9 291s PrivateWages_3 12.4 45.6 291s PrivateWages_4 16.9 50.1 291s PrivateWages_5 18.4 57.2 291s PrivateWages_6 19.4 57.1 291s PrivateWages_8 19.6 64.0 291s PrivateWages_9 19.8 64.4 291s PrivateWages_10 21.1 64.5 291s PrivateWages_11 21.7 67.0 291s PrivateWages_12 15.6 61.2 291s PrivateWages_13 11.4 53.4 291s PrivateWages_14 7.0 44.3 291s PrivateWages_15 11.2 45.1 291s PrivateWages_16 12.3 49.7 291s PrivateWages_17 14.0 54.4 291s PrivateWages_18 17.6 62.7 291s PrivateWages_19 17.3 65.0 291s PrivateWages_20 15.3 60.9 291s PrivateWages_21 19.0 69.5 291s PrivateWages_22 21.1 75.7 291s > matrix of fitted regressors 291s Consumption_(Intercept) Consumption_corpProf 291s Consumption_2 1 14.0 291s Consumption_3 1 16.7 291s Consumption_4 1 18.5 291s Consumption_5 1 20.3 291s Consumption_6 1 19.0 291s Consumption_8 1 17.6 291s Consumption_9 1 18.9 291s Consumption_11 1 16.7 291s Consumption_12 1 13.4 291s Consumption_14 1 10.0 291s Consumption_15 1 12.5 291s Consumption_16 1 14.5 291s Consumption_17 1 14.9 291s Consumption_18 1 19.4 291s Consumption_19 1 19.1 291s Consumption_20 1 17.7 291s Consumption_21 1 20.4 291s Consumption_22 1 22.7 291s Investment_2 0 0.0 291s Investment_3 0 0.0 291s Investment_4 0 0.0 291s Investment_5 0 0.0 291s Investment_6 0 0.0 291s Investment_8 0 0.0 291s Investment_9 0 0.0 291s Investment_10 0 0.0 291s Investment_11 0 0.0 291s Investment_12 0 0.0 291s Investment_14 0 0.0 291s Investment_15 0 0.0 291s Investment_16 0 0.0 291s Investment_17 0 0.0 291s Investment_18 0 0.0 291s Investment_19 0 0.0 291s Investment_20 0 0.0 291s Investment_21 0 0.0 291s Investment_22 0 0.0 291s PrivateWages_2 0 0.0 291s PrivateWages_3 0 0.0 291s PrivateWages_4 0 0.0 291s PrivateWages_5 0 0.0 291s PrivateWages_6 0 0.0 291s PrivateWages_8 0 0.0 291s PrivateWages_9 0 0.0 291s PrivateWages_10 0 0.0 291s PrivateWages_11 0 0.0 291s PrivateWages_12 0 0.0 291s PrivateWages_13 0 0.0 291s PrivateWages_14 0 0.0 291s PrivateWages_15 0 0.0 291s PrivateWages_16 0 0.0 291s PrivateWages_17 0 0.0 291s PrivateWages_18 0 0.0 291s PrivateWages_19 0 0.0 291s PrivateWages_20 0 0.0 291s PrivateWages_21 0 0.0 291s PrivateWages_22 0 0.0 291s Consumption_corpProfLag Consumption_wages 291s Consumption_2 12.7 29.8 291s Consumption_3 12.4 31.8 291s Consumption_4 16.9 35.3 291s Consumption_5 18.4 38.6 291s Consumption_6 19.4 38.5 291s Consumption_8 19.6 40.0 291s Consumption_9 19.8 41.8 291s Consumption_11 21.7 43.1 291s Consumption_12 15.6 39.7 291s Consumption_14 7.0 33.3 291s Consumption_15 11.2 37.3 291s Consumption_16 12.3 40.1 291s Consumption_17 14.0 41.8 291s Consumption_18 17.6 47.6 291s Consumption_19 17.3 49.2 291s Consumption_20 15.3 48.6 291s Consumption_21 19.0 53.4 291s Consumption_22 21.1 60.8 291s Investment_2 0.0 0.0 291s Investment_3 0.0 0.0 291s Investment_4 0.0 0.0 291s Investment_5 0.0 0.0 291s Investment_6 0.0 0.0 291s Investment_8 0.0 0.0 291s Investment_9 0.0 0.0 291s Investment_10 0.0 0.0 291s Investment_11 0.0 0.0 291s Investment_12 0.0 0.0 291s Investment_14 0.0 0.0 291s Investment_15 0.0 0.0 291s Investment_16 0.0 0.0 291s Investment_17 0.0 0.0 291s Investment_18 0.0 0.0 291s Investment_19 0.0 0.0 291s Investment_20 0.0 0.0 291s Investment_21 0.0 0.0 291s Investment_22 0.0 0.0 291s PrivateWages_2 0.0 0.0 291s PrivateWages_3 0.0 0.0 291s PrivateWages_4 0.0 0.0 291s PrivateWages_5 0.0 0.0 291s PrivateWages_6 0.0 0.0 291s PrivateWages_8 0.0 0.0 291s PrivateWages_9 0.0 0.0 291s PrivateWages_10 0.0 0.0 291s PrivateWages_11 0.0 0.0 291s PrivateWages_12 0.0 0.0 291s PrivateWages_13 0.0 0.0 291s PrivateWages_14 0.0 0.0 291s PrivateWages_15 0.0 0.0 291s PrivateWages_16 0.0 0.0 291s PrivateWages_17 0.0 0.0 291s PrivateWages_18 0.0 0.0 291s PrivateWages_19 0.0 0.0 291s PrivateWages_20 0.0 0.0 291s PrivateWages_21 0.0 0.0 291s PrivateWages_22 0.0 0.0 291s Investment_(Intercept) Investment_corpProf 291s Consumption_2 0 0.00 291s Consumption_3 0 0.00 291s Consumption_4 0 0.00 291s Consumption_5 0 0.00 291s Consumption_6 0 0.00 291s Consumption_8 0 0.00 291s Consumption_9 0 0.00 291s Consumption_11 0 0.00 291s Consumption_12 0 0.00 291s Consumption_14 0 0.00 291s Consumption_15 0 0.00 291s Consumption_16 0 0.00 291s Consumption_17 0 0.00 291s Consumption_18 0 0.00 291s Consumption_19 0 0.00 291s Consumption_20 0 0.00 291s Consumption_21 0 0.00 291s Consumption_22 0 0.00 291s Investment_2 1 13.41 291s Investment_3 1 16.69 291s Investment_4 1 18.79 291s Investment_5 1 20.65 291s Investment_6 1 19.26 291s Investment_8 1 17.53 291s Investment_9 1 19.53 291s Investment_10 1 20.27 291s Investment_11 1 17.19 291s Investment_12 1 13.52 291s Investment_14 1 9.99 291s Investment_15 1 12.86 291s Investment_16 1 14.33 291s Investment_17 1 14.97 291s Investment_18 1 19.37 291s Investment_19 1 19.36 291s Investment_20 1 17.47 291s Investment_21 1 20.12 291s Investment_22 1 22.78 291s PrivateWages_2 0 0.00 291s PrivateWages_3 0 0.00 291s PrivateWages_4 0 0.00 291s PrivateWages_5 0 0.00 291s PrivateWages_6 0 0.00 291s PrivateWages_8 0 0.00 291s PrivateWages_9 0 0.00 291s PrivateWages_10 0 0.00 291s PrivateWages_11 0 0.00 291s PrivateWages_12 0 0.00 291s PrivateWages_13 0 0.00 291s PrivateWages_14 0 0.00 291s PrivateWages_15 0 0.00 291s PrivateWages_16 0 0.00 291s PrivateWages_17 0 0.00 291s PrivateWages_18 0 0.00 291s PrivateWages_19 0 0.00 291s PrivateWages_20 0 0.00 291s PrivateWages_21 0 0.00 291s PrivateWages_22 0 0.00 291s Investment_corpProfLag Investment_capitalLag 291s Consumption_2 0.0 0 291s Consumption_3 0.0 0 291s Consumption_4 0.0 0 291s Consumption_5 0.0 0 291s Consumption_6 0.0 0 291s Consumption_8 0.0 0 291s Consumption_9 0.0 0 291s Consumption_11 0.0 0 291s Consumption_12 0.0 0 291s Consumption_14 0.0 0 291s Consumption_15 0.0 0 291s Consumption_16 0.0 0 291s Consumption_17 0.0 0 291s Consumption_18 0.0 0 291s Consumption_19 0.0 0 291s Consumption_20 0.0 0 291s Consumption_21 0.0 0 291s Consumption_22 0.0 0 291s Investment_2 12.7 183 291s Investment_3 12.4 183 291s Investment_4 16.9 184 291s Investment_5 18.4 190 291s Investment_6 19.4 193 291s Investment_8 19.6 203 291s Investment_9 19.8 208 291s Investment_10 21.1 211 291s Investment_11 21.7 216 291s Investment_12 15.6 217 291s Investment_14 7.0 207 291s Investment_15 11.2 202 291s Investment_16 12.3 199 291s Investment_17 14.0 198 291s Investment_18 17.6 200 291s Investment_19 17.3 202 291s Investment_20 15.3 200 291s Investment_21 19.0 201 291s Investment_22 21.1 204 291s PrivateWages_2 0.0 0 291s PrivateWages_3 0.0 0 291s PrivateWages_4 0.0 0 291s PrivateWages_5 0.0 0 291s PrivateWages_6 0.0 0 291s PrivateWages_8 0.0 0 291s PrivateWages_9 0.0 0 291s PrivateWages_10 0.0 0 291s PrivateWages_11 0.0 0 291s PrivateWages_12 0.0 0 291s PrivateWages_13 0.0 0 291s PrivateWages_14 0.0 0 291s PrivateWages_15 0.0 0 291s PrivateWages_16 0.0 0 291s PrivateWages_17 0.0 0 291s PrivateWages_18 0.0 0 291s PrivateWages_19 0.0 0 291s PrivateWages_20 0.0 0 291s PrivateWages_21 0.0 0 291s PrivateWages_22 0.0 0 291s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 291s Consumption_2 0 0.0 0.0 291s Consumption_3 0 0.0 0.0 291s Consumption_4 0 0.0 0.0 291s Consumption_5 0 0.0 0.0 291s Consumption_6 0 0.0 0.0 291s Consumption_8 0 0.0 0.0 291s Consumption_9 0 0.0 0.0 291s Consumption_11 0 0.0 0.0 291s Consumption_12 0 0.0 0.0 291s Consumption_14 0 0.0 0.0 291s Consumption_15 0 0.0 0.0 291s Consumption_16 0 0.0 0.0 291s Consumption_17 0 0.0 0.0 291s Consumption_18 0 0.0 0.0 291s Consumption_19 0 0.0 0.0 291s Consumption_20 0 0.0 0.0 291s Consumption_21 0 0.0 0.0 291s Consumption_22 0 0.0 0.0 291s Investment_2 0 0.0 0.0 291s Investment_3 0 0.0 0.0 291s Investment_4 0 0.0 0.0 291s Investment_5 0 0.0 0.0 291s Investment_6 0 0.0 0.0 291s Investment_8 0 0.0 0.0 291s Investment_9 0 0.0 0.0 291s Investment_10 0 0.0 0.0 291s Investment_11 0 0.0 0.0 291s Investment_12 0 0.0 0.0 291s Investment_14 0 0.0 0.0 291s Investment_15 0 0.0 0.0 291s Investment_16 0 0.0 0.0 291s Investment_17 0 0.0 0.0 291s Investment_18 0 0.0 0.0 291s Investment_19 0 0.0 0.0 291s Investment_20 0 0.0 0.0 291s Investment_21 0 0.0 0.0 291s Investment_22 0 0.0 0.0 291s PrivateWages_2 1 47.1 44.9 291s PrivateWages_3 1 49.6 45.6 291s PrivateWages_4 1 56.5 50.1 291s PrivateWages_5 1 60.7 57.2 291s PrivateWages_6 1 60.6 57.1 291s PrivateWages_8 1 60.0 64.0 291s PrivateWages_9 1 62.3 64.4 291s PrivateWages_10 1 64.6 64.5 291s PrivateWages_11 1 63.7 67.0 291s PrivateWages_12 1 54.8 61.2 291s PrivateWages_13 1 47.0 53.4 291s PrivateWages_14 1 42.1 44.3 291s PrivateWages_15 1 51.2 45.1 291s PrivateWages_16 1 55.3 49.7 291s PrivateWages_17 1 57.4 54.4 291s PrivateWages_18 1 67.2 62.7 291s PrivateWages_19 1 68.5 65.0 291s PrivateWages_20 1 66.8 60.9 291s PrivateWages_21 1 74.9 69.5 291s PrivateWages_22 1 86.9 75.7 291s PrivateWages_trend 291s Consumption_2 0 291s Consumption_3 0 291s Consumption_4 0 291s Consumption_5 0 291s Consumption_6 0 291s Consumption_8 0 291s Consumption_9 0 291s Consumption_11 0 291s Consumption_12 0 291s Consumption_14 0 291s Consumption_15 0 291s Consumption_16 0 291s Consumption_17 0 291s Consumption_18 0 291s Consumption_19 0 291s Consumption_20 0 291s Consumption_21 0 291s Consumption_22 0 291s Investment_2 0 291s Investment_3 0 291s Investment_4 0 291s Investment_5 0 291s Investment_6 0 291s Investment_8 0 291s Investment_9 0 291s Investment_10 0 291s Investment_11 0 291s Investment_12 0 291s Investment_14 0 291s Investment_15 0 291s Investment_16 0 291s Investment_17 0 291s Investment_18 0 291s Investment_19 0 291s Investment_20 0 291s Investment_21 0 291s Investment_22 0 291s PrivateWages_2 -10 291s PrivateWages_3 -9 291s PrivateWages_4 -8 291s PrivateWages_5 -7 291s PrivateWages_6 -6 291s PrivateWages_8 -4 291s PrivateWages_9 -3 291s PrivateWages_10 -2 291s PrivateWages_11 -1 291s PrivateWages_12 0 291s PrivateWages_13 1 291s PrivateWages_14 2 291s PrivateWages_15 3 291s PrivateWages_16 4 291s PrivateWages_17 5 291s PrivateWages_18 6 291s PrivateWages_19 7 291s PrivateWages_20 8 291s PrivateWages_21 9 291s PrivateWages_22 10 291s > nobs 291s [1] 57 291s > linearHypothesis 291s Linear hypothesis test (Theil's F test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df F Pr(>F) 291s 1 46 291s 2 45 1 1.37 0.25 291s Linear hypothesis test (F statistic of a Wald test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df F Pr(>F) 291s 1 46 291s 2 45 1 1.77 0.19 291s Linear hypothesis test (Chi^2 statistic of a Wald test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df Chisq Pr(>Chisq) 291s 1 46 291s 2 45 1 1.77 0.18 291s Linear hypothesis test (Theil's F test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s Consumption_corpProfLag - PrivateWages_trend = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df F Pr(>F) 291s 1 47 291s 2 45 2 0.69 0.51 291s Linear hypothesis test (F statistic of a Wald test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s Consumption_corpProfLag - PrivateWages_trend = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df F Pr(>F) 291s 1 47 291s 2 45 2 0.89 0.42 291s Linear hypothesis test (Chi^2 statistic of a Wald test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s Consumption_corpProfLag - PrivateWages_trend = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df Chisq Pr(>Chisq) 291s 1 47 291s 2 45 2 1.78 0.41 291s > logLik 291s 'log Lik.' -70.6 (df=13) 291s 'log Lik.' -78.7 (df=13) 291s Estimating function 291s Consumption_(Intercept) Consumption_corpProf 291s Consumption_2 -1.891 -26.49 291s Consumption_3 -0.190 -3.16 291s Consumption_4 0.294 5.45 291s Consumption_5 -1.285 -26.05 291s Consumption_6 0.431 8.19 291s Consumption_8 2.670 47.11 291s Consumption_9 2.363 44.77 291s Consumption_11 -1.642 -27.49 291s Consumption_12 -1.735 -23.21 291s Consumption_14 0.834 8.35 291s Consumption_15 -1.061 -13.27 291s Consumption_16 -0.885 -12.82 291s Consumption_17 3.801 56.68 291s Consumption_18 -0.502 -9.76 291s Consumption_19 -3.000 -57.33 291s Consumption_20 2.012 35.52 291s Consumption_21 0.746 15.21 291s Consumption_22 -0.957 -21.70 291s Investment_2 0.000 0.00 291s Investment_3 0.000 0.00 291s Investment_4 0.000 0.00 291s Investment_5 0.000 0.00 291s Investment_6 0.000 0.00 291s Investment_8 0.000 0.00 291s Investment_9 0.000 0.00 291s Investment_10 0.000 0.00 291s Investment_11 0.000 0.00 291s Investment_12 0.000 0.00 291s Investment_14 0.000 0.00 291s Investment_15 0.000 0.00 291s Investment_16 0.000 0.00 291s Investment_17 0.000 0.00 291s Investment_18 0.000 0.00 291s Investment_19 0.000 0.00 291s Investment_20 0.000 0.00 291s Investment_21 0.000 0.00 291s Investment_22 0.000 0.00 291s PrivateWages_2 0.000 0.00 291s PrivateWages_3 0.000 0.00 291s PrivateWages_4 0.000 0.00 291s PrivateWages_5 0.000 0.00 291s PrivateWages_6 0.000 0.00 291s PrivateWages_8 0.000 0.00 291s PrivateWages_9 0.000 0.00 291s PrivateWages_10 0.000 0.00 291s PrivateWages_11 0.000 0.00 291s PrivateWages_12 0.000 0.00 291s PrivateWages_13 0.000 0.00 291s PrivateWages_14 0.000 0.00 291s PrivateWages_15 0.000 0.00 291s PrivateWages_16 0.000 0.00 291s PrivateWages_17 0.000 0.00 291s PrivateWages_18 0.000 0.00 291s PrivateWages_19 0.000 0.00 291s PrivateWages_20 0.000 0.00 291s PrivateWages_21 0.000 0.00 291s PrivateWages_22 0.000 0.00 291s Consumption_corpProfLag Consumption_wages 291s Consumption_2 -24.01 -56.38 291s Consumption_3 -2.35 -6.04 291s Consumption_4 4.96 10.35 291s Consumption_5 -23.65 -49.61 291s Consumption_6 8.35 16.60 291s Consumption_8 52.33 106.81 291s Consumption_9 46.80 98.74 291s Consumption_11 -35.64 -70.78 291s Consumption_12 -27.07 -68.81 291s Consumption_14 5.83 27.78 291s Consumption_15 -11.88 -39.61 291s Consumption_16 -10.89 -35.54 291s Consumption_17 53.21 158.79 291s Consumption_18 -8.84 -23.92 291s Consumption_19 -51.90 -147.70 291s Consumption_20 30.78 97.67 291s Consumption_21 14.17 39.83 291s Consumption_22 -20.20 -58.19 291s Investment_2 0.00 0.00 291s Investment_3 0.00 0.00 291s Investment_4 0.00 0.00 291s Investment_5 0.00 0.00 291s Investment_6 0.00 0.00 291s Investment_8 0.00 0.00 291s Investment_9 0.00 0.00 291s Investment_10 0.00 0.00 291s Investment_11 0.00 0.00 291s Investment_12 0.00 0.00 291s Investment_14 0.00 0.00 291s Investment_15 0.00 0.00 291s Investment_16 0.00 0.00 291s Investment_17 0.00 0.00 291s Investment_18 0.00 0.00 291s Investment_19 0.00 0.00 291s Investment_20 0.00 0.00 291s Investment_21 0.00 0.00 291s Investment_22 0.00 0.00 291s PrivateWages_2 0.00 0.00 291s PrivateWages_3 0.00 0.00 291s PrivateWages_4 0.00 0.00 291s PrivateWages_5 0.00 0.00 291s PrivateWages_6 0.00 0.00 291s PrivateWages_8 0.00 0.00 291s PrivateWages_9 0.00 0.00 291s PrivateWages_10 0.00 0.00 291s PrivateWages_11 0.00 0.00 291s PrivateWages_12 0.00 0.00 291s PrivateWages_13 0.00 0.00 291s PrivateWages_14 0.00 0.00 291s PrivateWages_15 0.00 0.00 291s PrivateWages_16 0.00 0.00 291s PrivateWages_17 0.00 0.00 291s PrivateWages_18 0.00 0.00 291s PrivateWages_19 0.00 0.00 291s PrivateWages_20 0.00 0.00 291s PrivateWages_21 0.00 0.00 291s PrivateWages_22 0.00 0.00 291s Investment_(Intercept) Investment_corpProf 291s Consumption_2 0.000 0.000 291s Consumption_3 0.000 0.000 291s Consumption_4 0.000 0.000 291s Consumption_5 0.000 0.000 291s Consumption_6 0.000 0.000 291s Consumption_8 0.000 0.000 291s Consumption_9 0.000 0.000 291s Consumption_11 0.000 0.000 291s Consumption_12 0.000 0.000 291s Consumption_14 0.000 0.000 291s Consumption_15 0.000 0.000 291s Consumption_16 0.000 0.000 291s Consumption_17 0.000 0.000 291s Consumption_18 0.000 0.000 291s Consumption_19 0.000 0.000 291s Consumption_20 0.000 0.000 291s Consumption_21 0.000 0.000 291s Consumption_22 0.000 0.000 291s Investment_2 -1.389 -18.632 291s Investment_3 0.361 6.028 291s Investment_4 1.031 19.362 291s Investment_5 -1.558 -32.177 291s Investment_6 0.610 11.759 291s Investment_8 1.410 24.716 291s Investment_9 0.404 7.885 291s Investment_10 2.080 42.149 291s Investment_11 -1.162 -19.982 291s Investment_12 -1.352 -18.282 291s Investment_14 1.037 10.359 291s Investment_15 -0.454 -5.832 291s Investment_16 -0.044 -0.631 291s Investment_17 2.093 31.318 291s Investment_18 -0.438 -8.488 291s Investment_19 -3.873 -74.977 291s Investment_20 0.486 8.486 291s Investment_21 0.145 2.925 291s Investment_22 0.615 14.015 291s PrivateWages_2 0.000 0.000 291s PrivateWages_3 0.000 0.000 291s PrivateWages_4 0.000 0.000 291s PrivateWages_5 0.000 0.000 291s PrivateWages_6 0.000 0.000 291s PrivateWages_8 0.000 0.000 291s PrivateWages_9 0.000 0.000 291s PrivateWages_10 0.000 0.000 291s PrivateWages_11 0.000 0.000 291s PrivateWages_12 0.000 0.000 291s PrivateWages_13 0.000 0.000 291s PrivateWages_14 0.000 0.000 291s PrivateWages_15 0.000 0.000 291s PrivateWages_16 0.000 0.000 291s PrivateWages_17 0.000 0.000 291s PrivateWages_18 0.000 0.000 291s PrivateWages_19 0.000 0.000 291s PrivateWages_20 0.000 0.000 291s PrivateWages_21 0.000 0.000 291s PrivateWages_22 0.000 0.000 291s Investment_corpProfLag Investment_capitalLag 291s Consumption_2 0.000 0.00 291s Consumption_3 0.000 0.00 291s Consumption_4 0.000 0.00 291s Consumption_5 0.000 0.00 291s Consumption_6 0.000 0.00 291s Consumption_8 0.000 0.00 291s Consumption_9 0.000 0.00 291s Consumption_11 0.000 0.00 291s Consumption_12 0.000 0.00 291s Consumption_14 0.000 0.00 291s Consumption_15 0.000 0.00 291s Consumption_16 0.000 0.00 291s Consumption_17 0.000 0.00 291s Consumption_18 0.000 0.00 291s Consumption_19 0.000 0.00 291s Consumption_20 0.000 0.00 291s Consumption_21 0.000 0.00 291s Consumption_22 0.000 0.00 291s Investment_2 -17.639 -253.89 291s Investment_3 4.479 65.95 291s Investment_4 17.417 190.14 291s Investment_5 -28.673 -295.61 291s Investment_6 11.843 117.63 291s Investment_8 27.629 286.73 291s Investment_9 7.995 83.82 291s Investment_10 43.878 437.95 291s Investment_11 -25.218 -250.67 291s Investment_12 -21.091 -292.97 291s Investment_14 7.256 214.68 291s Investment_15 -5.080 -91.62 291s Investment_16 -0.541 -8.76 291s Investment_17 29.296 413.70 291s Investment_18 -7.713 -87.56 291s Investment_19 -67.010 -781.66 291s Investment_20 7.430 97.07 291s Investment_21 2.762 29.24 291s Investment_22 12.981 125.81 291s PrivateWages_2 0.000 0.00 291s PrivateWages_3 0.000 0.00 291s PrivateWages_4 0.000 0.00 291s PrivateWages_5 0.000 0.00 291s PrivateWages_6 0.000 0.00 291s PrivateWages_8 0.000 0.00 291s PrivateWages_9 0.000 0.00 291s PrivateWages_10 0.000 0.00 291s PrivateWages_11 0.000 0.00 291s PrivateWages_12 0.000 0.00 291s PrivateWages_13 0.000 0.00 291s PrivateWages_14 0.000 0.00 291s PrivateWages_15 0.000 0.00 291s PrivateWages_16 0.000 0.00 291s PrivateWages_17 0.000 0.00 291s PrivateWages_18 0.000 0.00 291s PrivateWages_19 0.000 0.00 291s PrivateWages_20 0.000 0.00 291s PrivateWages_21 0.000 0.00 291s PrivateWages_22 0.000 0.00 291s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 291s Consumption_2 0.0000 0.00 0.00 291s Consumption_3 0.0000 0.00 0.00 291s Consumption_4 0.0000 0.00 0.00 291s Consumption_5 0.0000 0.00 0.00 291s Consumption_6 0.0000 0.00 0.00 291s Consumption_8 0.0000 0.00 0.00 291s Consumption_9 0.0000 0.00 0.00 291s Consumption_11 0.0000 0.00 0.00 291s Consumption_12 0.0000 0.00 0.00 291s Consumption_14 0.0000 0.00 0.00 291s Consumption_15 0.0000 0.00 0.00 291s Consumption_16 0.0000 0.00 0.00 291s Consumption_17 0.0000 0.00 0.00 291s Consumption_18 0.0000 0.00 0.00 291s Consumption_19 0.0000 0.00 0.00 291s Consumption_20 0.0000 0.00 0.00 291s Consumption_21 0.0000 0.00 0.00 291s Consumption_22 0.0000 0.00 0.00 291s Investment_2 0.0000 0.00 0.00 291s Investment_3 0.0000 0.00 0.00 291s Investment_4 0.0000 0.00 0.00 291s Investment_5 0.0000 0.00 0.00 291s Investment_6 0.0000 0.00 0.00 291s Investment_8 0.0000 0.00 0.00 291s Investment_9 0.0000 0.00 0.00 291s Investment_10 0.0000 0.00 0.00 291s Investment_11 0.0000 0.00 0.00 291s Investment_12 0.0000 0.00 0.00 291s Investment_14 0.0000 0.00 0.00 291s Investment_15 0.0000 0.00 0.00 291s Investment_16 0.0000 0.00 0.00 291s Investment_17 0.0000 0.00 0.00 291s Investment_18 0.0000 0.00 0.00 291s Investment_19 0.0000 0.00 0.00 291s Investment_20 0.0000 0.00 0.00 291s Investment_21 0.0000 0.00 0.00 291s Investment_22 0.0000 0.00 0.00 291s PrivateWages_2 -1.9924 -93.78 -89.46 291s PrivateWages_3 0.4683 23.22 21.35 291s PrivateWages_4 1.4034 79.35 70.31 291s PrivateWages_5 -1.7870 -108.45 -102.22 291s PrivateWages_6 -0.3627 -21.98 -20.71 291s PrivateWages_8 1.1629 69.77 74.43 291s PrivateWages_9 1.2735 79.30 82.01 291s PrivateWages_10 2.2141 142.96 142.81 291s PrivateWages_11 -1.2912 -82.26 -86.51 291s PrivateWages_12 -0.0350 -1.92 -2.14 291s PrivateWages_13 -1.0438 -49.04 -55.74 291s PrivateWages_14 1.8016 75.90 79.81 291s PrivateWages_15 -0.3714 -19.02 -16.75 291s PrivateWages_16 -0.3904 -21.61 -19.40 291s PrivateWages_17 1.4934 85.71 81.24 291s PrivateWages_18 0.0279 1.88 1.75 291s PrivateWages_19 -3.8229 -261.91 -248.49 291s PrivateWages_20 0.7870 52.61 47.93 291s PrivateWages_21 -0.7415 -55.52 -51.54 291s PrivateWages_22 1.2062 104.79 91.31 291s PrivateWages_trend 291s Consumption_2 0.000 291s Consumption_3 0.000 291s Consumption_4 0.000 291s Consumption_5 0.000 291s Consumption_6 0.000 291s Consumption_8 0.000 291s Consumption_9 0.000 291s Consumption_11 0.000 291s Consumption_12 0.000 291s Consumption_14 0.000 291s Consumption_15 0.000 291s Consumption_16 0.000 291s Consumption_17 0.000 291s Consumption_18 0.000 291s Consumption_19 0.000 291s Consumption_20 0.000 291s Consumption_21 0.000 291s Consumption_22 0.000 291s Investment_2 0.000 291s Investment_3 0.000 291s Investment_4 0.000 291s Investment_5 0.000 291s Investment_6 0.000 291s Investment_8 0.000 291s Investment_9 0.000 291s Investment_10 0.000 291s Investment_11 0.000 291s Investment_12 0.000 291s Investment_14 0.000 291s Investment_15 0.000 291s Investment_16 0.000 291s Investment_17 0.000 291s Investment_18 0.000 291s Investment_19 0.000 291s Investment_20 0.000 291s Investment_21 0.000 291s Investment_22 0.000 291s PrivateWages_2 19.924 291s PrivateWages_3 -4.214 291s PrivateWages_4 -11.227 291s PrivateWages_5 12.509 291s PrivateWages_6 2.176 291s PrivateWages_8 -4.652 291s PrivateWages_9 -3.820 291s PrivateWages_10 -4.428 291s PrivateWages_11 1.291 291s PrivateWages_12 0.000 291s PrivateWages_13 -1.044 291s PrivateWages_14 3.603 291s PrivateWages_15 -1.114 291s PrivateWages_16 -1.562 291s PrivateWages_17 7.467 291s PrivateWages_18 0.168 291s PrivateWages_19 -26.760 291s PrivateWages_20 6.296 291s PrivateWages_21 -6.674 291s PrivateWages_22 12.062 291s [1] TRUE 291s > Bread 291s Consumption_(Intercept) Consumption_corpProf 291s Consumption_(Intercept) 118.21 -4.213 291s Consumption_corpProf -4.21 1.235 291s Consumption_corpProfLag 1.03 -0.689 291s Consumption_wages -1.44 -0.136 291s Investment_(Intercept) 0.00 0.000 291s Investment_corpProf 0.00 0.000 291s Investment_corpProfLag 0.00 0.000 291s Investment_capitalLag 0.00 0.000 291s PrivateWages_(Intercept) 0.00 0.000 291s PrivateWages_gnp 0.00 0.000 291s PrivateWages_gnpLag 0.00 0.000 291s PrivateWages_trend 0.00 0.000 291s Consumption_corpProfLag Consumption_wages 291s Consumption_(Intercept) 1.0298 -1.4384 291s Consumption_corpProf -0.6891 -0.1356 291s Consumption_corpProfLag 0.7104 -0.0191 291s Consumption_wages -0.0191 0.0972 291s Investment_(Intercept) 0.0000 0.0000 291s Investment_corpProf 0.0000 0.0000 291s Investment_corpProfLag 0.0000 0.0000 291s Investment_capitalLag 0.0000 0.0000 291s PrivateWages_(Intercept) 0.0000 0.0000 291s PrivateWages_gnp 0.0000 0.0000 291s PrivateWages_gnpLag 0.0000 0.0000 291s PrivateWages_trend 0.0000 0.0000 291s Investment_(Intercept) Investment_corpProf 291s Consumption_(Intercept) 0.0 0.000 291s Consumption_corpProf 0.0 0.000 291s Consumption_corpProfLag 0.0 0.000 291s Consumption_wages 0.0 0.000 291s Investment_(Intercept) 2314.8 -41.107 291s Investment_corpProf -41.1 1.637 291s Investment_corpProfLag 33.2 -1.272 291s Investment_capitalLag -10.7 0.169 291s PrivateWages_(Intercept) 0.0 0.000 291s PrivateWages_gnp 0.0 0.000 291s PrivateWages_gnpLag 0.0 0.000 291s PrivateWages_trend 0.0 0.000 291s Investment_corpProfLag Investment_capitalLag 291s Consumption_(Intercept) 0.000 0.0000 291s Consumption_corpProf 0.000 0.0000 291s Consumption_corpProfLag 0.000 0.0000 291s Consumption_wages 0.000 0.0000 291s Investment_(Intercept) 33.159 -10.7377 291s Investment_corpProf -1.272 0.1688 291s Investment_corpProfLag 1.204 -0.1550 291s Investment_capitalLag -0.155 0.0519 291s PrivateWages_(Intercept) 0.000 0.0000 291s PrivateWages_gnp 0.000 0.0000 291s PrivateWages_gnpLag 0.000 0.0000 291s PrivateWages_trend 0.000 0.0000 291s PrivateWages_(Intercept) PrivateWages_gnp 291s Consumption_(Intercept) 0.000 0.0000 291s Consumption_corpProf 0.000 0.0000 291s Consumption_corpProfLag 0.000 0.0000 291s Consumption_wages 0.000 0.0000 291s Investment_(Intercept) 0.000 0.0000 291s Investment_corpProf 0.000 0.0000 291s Investment_corpProfLag 0.000 0.0000 291s Investment_capitalLag 0.000 0.0000 291s PrivateWages_(Intercept) 162.179 -0.8825 291s PrivateWages_gnp -0.882 0.1501 291s PrivateWages_gnpLag -1.850 -0.1399 291s PrivateWages_trend 2.056 -0.0403 291s PrivateWages_gnpLag PrivateWages_trend 291s Consumption_(Intercept) 0.0000 0.0000 291s Consumption_corpProf 0.0000 0.0000 291s Consumption_corpProfLag 0.0000 0.0000 291s Consumption_wages 0.0000 0.0000 291s Investment_(Intercept) 0.0000 0.0000 291s Investment_corpProf 0.0000 0.0000 291s Investment_corpProfLag 0.0000 0.0000 291s Investment_capitalLag 0.0000 0.0000 291s PrivateWages_(Intercept) -1.8504 2.0559 291s PrivateWages_gnp -0.1399 -0.0403 291s PrivateWages_gnpLag 0.1768 0.0057 291s PrivateWages_trend 0.0057 0.1094 291s > 291s > # SUR 291s Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 291s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 291s > summary 291s 291s systemfit results 291s method: SUR 291s 291s N DF SSR detRCov OLS-R2 McElroy-R2 291s system 59 47 45.1 0.168 0.976 0.992 291s 291s N DF SSR MSE RMSE R2 Adj R2 291s Consumption 19 15 17.5 1.167 1.080 0.980 0.975 291s Investment 20 16 17.3 1.083 1.041 0.911 0.894 291s PrivateWages 20 16 10.3 0.642 0.801 0.987 0.985 291s 291s The covariance matrix of the residuals used for estimation 291s Consumption Investment PrivateWages 291s Consumption 0.9286 0.0435 -0.369 291s Investment 0.0435 0.7653 0.109 291s PrivateWages -0.3690 0.1091 0.468 291s 291s The covariance matrix of the residuals 291s Consumption Investment PrivateWages 291s Consumption 0.9251 0.0748 -0.427 291s Investment 0.0748 0.7653 0.171 291s PrivateWages -0.4268 0.1706 0.492 291s 291s The correlations of the residuals 291s Consumption Investment PrivateWages 291s Consumption 1.0000 0.0888 -0.636 291s Investment 0.0888 1.0000 0.268 291s PrivateWages -0.6364 0.2678 1.000 291s 291s 291s SUR estimates for 'Consumption' (equation 1) 291s Model Formula: consump ~ corpProf + corpProfLag + wages 291s 291s Estimate Std. Error t value Pr(>|t|) 291s (Intercept) 16.2684 1.2781 12.73 1.9e-09 *** 291s corpProf 0.1942 0.0927 2.10 0.054 . 291s corpProfLag 0.0746 0.0819 0.91 0.377 291s wages 0.8011 0.0372 21.53 1.1e-12 *** 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s 291s Residual standard error: 1.08 on 15 degrees of freedom 291s Number of observations: 19 Degrees of Freedom: 15 291s SSR: 17.501 MSE: 1.167 Root MSE: 1.08 291s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.975 291s 291s 291s SUR estimates for 'Investment' (equation 2) 291s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 291s 291s Estimate Std. Error t value Pr(>|t|) 291s (Intercept) 12.6462 4.6500 2.72 0.01515 * 291s corpProf 0.4707 0.0916 5.14 9.9e-05 *** 291s corpProfLag 0.3519 0.0874 4.03 0.00097 *** 291s capitalLag -0.1253 0.0229 -5.47 5.1e-05 *** 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s 291s Residual standard error: 1.041 on 16 degrees of freedom 291s Number of observations: 20 Degrees of Freedom: 16 291s SSR: 17.325 MSE: 1.083 Root MSE: 1.041 291s Multiple R-Squared: 0.911 Adjusted R-Squared: 0.894 291s 291s 291s SUR estimates for 'PrivateWages' (equation 3) 291s Model Formula: privWage ~ gnp + gnpLag + trend 291s 291s Estimate Std. Error t value Pr(>|t|) 291s (Intercept) 1.3245 1.0946 1.21 0.24 291s gnp 0.4184 0.0260 16.08 2.7e-11 *** 291s gnpLag 0.1714 0.0307 5.59 4.1e-05 *** 291s trend 0.1455 0.0276 5.27 7.6e-05 *** 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s 291s Residual standard error: 0.801 on 16 degrees of freedom 291s Number of observations: 20 Degrees of Freedom: 16 291s SSR: 10.265 MSE: 0.642 Root MSE: 0.801 291s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 291s 291s > residuals 291s Consumption Investment PrivateWages 291s 1 NA NA NA 291s 2 -0.3146 -0.2419 -1.1439 291s 3 -1.2707 -0.1795 0.5080 291s 4 -1.5428 1.0691 1.4208 291s 5 -0.4489 -1.4778 -0.1000 291s 6 0.0588 0.3168 -0.3599 291s 7 0.9215 1.4450 NA 291s 8 1.3791 0.8287 -0.7561 291s 9 1.0901 -0.5272 0.2880 291s 10 NA 1.2089 1.1795 291s 11 0.3577 0.4081 -0.3681 291s 12 -0.2286 0.2569 0.3439 291s 13 NA NA -0.1574 291s 14 0.2172 0.4743 0.4225 291s 15 -0.1124 -0.0607 0.3154 291s 16 -0.0876 0.0761 0.0151 291s 17 1.5611 1.0205 -0.8084 291s 18 -0.4529 0.0580 0.8611 291s 19 0.1999 -2.5444 -0.7635 291s 20 0.9266 -0.6202 -0.4039 291s 21 0.7589 -0.7478 -1.2175 291s 22 -2.2135 -0.6029 0.5611 291s > fitted 291s Consumption Investment PrivateWages 291s 1 NA NA NA 291s 2 42.2 0.0419 26.6 291s 3 46.3 2.0795 28.8 291s 4 50.7 4.1309 32.7 291s 5 51.0 4.4778 34.0 291s 6 52.5 4.7832 35.8 291s 7 54.2 4.1550 NA 291s 8 54.8 3.3713 38.7 291s 9 56.2 3.5272 38.9 291s 10 NA 3.8911 40.1 291s 11 54.6 0.5919 38.3 291s 12 51.1 -3.6569 34.2 291s 13 NA NA 29.2 291s 14 46.3 -5.5743 28.1 291s 15 48.8 -2.9393 30.3 291s 16 51.4 -1.3761 33.2 291s 17 56.1 1.0795 37.6 291s 18 59.2 1.9420 40.1 291s 19 57.3 0.6444 39.0 291s 20 60.7 1.9202 42.0 291s 21 64.2 4.0478 46.2 291s 22 71.9 5.5029 52.7 291s > predict 291s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 291s 1 NA NA NA NA 291s 2 42.2 0.448 41.3 43.1 291s 3 46.3 0.476 45.3 47.2 291s 4 50.7 0.318 50.1 51.4 291s 5 51.0 0.373 50.3 51.8 291s 6 52.5 0.378 51.8 53.3 291s 7 54.2 0.337 53.5 54.9 291s 8 54.8 0.310 54.2 55.4 291s 9 56.2 0.343 55.5 56.9 291s 10 NA NA NA NA 291s 11 54.6 0.567 53.5 55.8 291s 12 51.1 0.509 50.1 52.2 291s 13 NA NA NA NA 291s 14 46.3 0.573 45.1 47.4 291s 15 48.8 0.382 48.0 49.6 291s 16 51.4 0.328 50.7 52.0 291s 17 56.1 0.336 55.5 56.8 291s 18 59.2 0.309 58.5 59.8 291s 19 57.3 0.370 56.6 58.0 291s 20 60.7 0.401 59.9 61.5 291s 21 64.2 0.405 63.4 65.1 291s 22 71.9 0.633 70.6 73.2 291s Investment.pred Investment.se.fit Investment.lwr Investment.upr 291s 1 NA NA NA NA 291s 2 0.0419 0.533 -1.0309 1.115 291s 3 2.0795 0.433 1.2082 2.951 291s 4 4.1309 0.387 3.3532 4.909 291s 5 4.4778 0.322 3.8307 5.125 291s 6 4.7832 0.305 4.1700 5.396 291s 7 4.1550 0.283 3.5852 4.725 291s 8 3.3713 0.253 2.8630 3.880 291s 9 3.5272 0.337 2.8488 4.206 291s 10 3.8911 0.386 3.1149 4.667 291s 11 0.5919 0.561 -0.5376 1.722 291s 12 -3.6569 0.530 -4.7223 -2.591 291s 13 NA NA NA NA 291s 14 -5.5743 0.618 -6.8176 -4.331 291s 15 -2.9393 0.362 -3.6671 -2.212 291s 16 -1.3761 0.296 -1.9710 -0.781 291s 17 1.0795 0.300 0.4763 1.683 291s 18 1.9420 0.216 1.5081 2.376 291s 19 0.6444 0.298 0.0451 1.244 291s 20 1.9202 0.318 1.2798 2.561 291s 21 4.0478 0.295 3.4537 4.642 291s 22 5.5029 0.417 4.6638 6.342 291s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 291s 1 NA NA NA NA 291s 2 26.6 0.312 26.0 27.3 291s 3 28.8 0.312 28.2 29.4 291s 4 32.7 0.307 32.1 33.3 291s 5 34.0 0.237 33.5 34.5 291s 6 35.8 0.235 35.3 36.2 291s 7 NA NA NA NA 291s 8 38.7 0.239 38.2 39.1 291s 9 38.9 0.228 38.5 39.4 291s 10 40.1 0.218 39.7 40.6 291s 11 38.3 0.293 37.7 38.9 291s 12 34.2 0.290 33.6 34.7 291s 13 29.2 0.343 28.5 29.8 291s 14 28.1 0.321 27.4 28.7 291s 15 30.3 0.320 29.6 30.9 291s 16 33.2 0.268 32.6 33.7 291s 17 37.6 0.263 37.1 38.1 291s 18 40.1 0.207 39.7 40.6 291s 19 39.0 0.293 38.4 39.6 291s 20 42.0 0.279 41.4 42.6 291s 21 46.2 0.295 45.6 46.8 291s 22 52.7 0.435 51.9 53.6 291s > model.frame 291s [1] TRUE 291s > model.matrix 291s [1] TRUE 291s > nobs 291s [1] 59 291s > linearHypothesis 291s Linear hypothesis test (Theil's F test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df F Pr(>F) 291s 1 48 291s 2 47 1 0.41 0.52 291s Linear hypothesis test (F statistic of a Wald test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df F Pr(>F) 291s 1 48 291s 2 47 1 0.52 0.47 291s Linear hypothesis test (Chi^2 statistic of a Wald test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df Chisq Pr(>Chisq) 291s 1 48 291s 2 47 1 0.52 0.47 291s Linear hypothesis test (Theil's F test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s Consumption_corpProfLag - PrivateWages_trend = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df F Pr(>F) 291s 1 49 291s 2 47 2 0.31 0.73 291s Linear hypothesis test (F statistic of a Wald test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s Consumption_corpProfLag - PrivateWages_trend = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df F Pr(>F) 291s 1 49 291s 2 47 2 0.4 0.67 291s Linear hypothesis test (Chi^2 statistic of a Wald test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s Consumption_corpProfLag - PrivateWages_trend = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df Chisq Pr(>Chisq) 291s 1 49 291s 2 47 2 0.79 0.67 291s > logLik 291s 'log Lik.' -67.3 (df=18) 291s 'log Lik.' -74.9 (df=18) 291s Estimating function 291s Consumption_(Intercept) Consumption_corpProf 291s Consumption_2 -0.5115 -6.342 291s Consumption_3 -2.0659 -34.913 291s Consumption_4 -2.5083 -46.152 291s Consumption_5 -0.7298 -14.158 291s Consumption_6 0.0957 1.923 291s Consumption_7 1.4982 29.364 291s Consumption_8 2.2421 44.394 291s Consumption_9 1.7723 37.396 291s Consumption_11 0.5815 9.072 291s Consumption_12 -0.3716 -4.237 291s Consumption_14 0.3531 3.954 291s Consumption_15 -0.1827 -2.248 291s Consumption_16 -0.1424 -1.993 291s Consumption_17 2.5380 44.669 291s Consumption_18 -0.7363 -12.738 291s Consumption_19 0.3251 4.973 291s Consumption_20 1.5064 28.622 291s Consumption_21 1.2337 26.032 291s Consumption_22 -3.5987 -84.568 291s Investment_2 0.0688 0.854 291s Investment_3 0.0511 0.863 291s Investment_4 -0.3043 -5.599 291s Investment_5 0.4206 8.160 291s Investment_6 -0.0902 -1.813 291s Investment_7 -0.4113 -8.061 291s Investment_8 -0.2359 -4.670 291s Investment_9 0.1501 3.166 291s Investment_10 0.0000 0.000 291s Investment_11 -0.1161 -1.812 291s Investment_12 -0.0731 -0.834 291s Investment_14 -0.1350 -1.512 291s Investment_15 0.0173 0.212 291s Investment_16 -0.0217 -0.303 291s Investment_17 -0.2904 -5.112 291s Investment_18 -0.0165 -0.286 291s Investment_19 0.7242 11.080 291s Investment_20 0.1765 3.354 291s Investment_21 0.2128 4.491 291s Investment_22 0.1716 4.032 291s PrivateWages_2 -1.5418 -19.118 291s PrivateWages_3 0.6847 11.571 291s PrivateWages_4 1.9149 35.234 291s PrivateWages_5 -0.1348 -2.615 291s PrivateWages_6 -0.4851 -9.750 291s PrivateWages_8 -1.0191 -20.178 291s PrivateWages_9 0.3882 8.190 291s PrivateWages_10 0.0000 0.000 291s PrivateWages_11 -0.4961 -7.739 291s PrivateWages_12 0.4635 5.284 291s PrivateWages_13 0.0000 0.000 291s PrivateWages_14 0.5694 6.377 291s PrivateWages_15 0.4251 5.229 291s PrivateWages_16 0.0204 0.286 291s PrivateWages_17 -1.0895 -19.175 291s PrivateWages_18 1.1605 20.077 291s PrivateWages_19 -1.0290 -15.743 291s PrivateWages_20 -0.5443 -10.343 291s PrivateWages_21 -1.6408 -34.622 291s PrivateWages_22 0.7563 17.772 291s Consumption_corpProfLag Consumption_wages 291s Consumption_2 -6.496 -14.423 291s Consumption_3 -25.617 -66.521 291s Consumption_4 -42.390 -92.806 291s Consumption_5 -13.428 -27.003 291s Consumption_6 1.856 3.693 291s Consumption_7 30.114 60.976 291s Consumption_8 43.945 93.047 291s Consumption_9 35.092 76.033 291s Consumption_11 12.619 24.482 291s Consumption_12 -5.798 -14.606 291s Consumption_14 2.471 12.039 291s Consumption_15 -2.047 -6.688 291s Consumption_16 -1.751 -5.595 291s Consumption_17 35.532 112.180 291s Consumption_18 -12.959 -35.121 291s Consumption_19 5.624 14.920 291s Consumption_20 23.048 74.417 291s Consumption_21 23.441 65.389 291s Consumption_22 -75.932 -222.397 291s Investment_2 0.874 1.941 291s Investment_3 0.633 1.645 291s Investment_4 -5.142 -11.258 291s Investment_5 7.739 15.562 291s Investment_6 -1.749 -3.481 291s Investment_7 -8.267 -16.739 291s Investment_8 -4.623 -9.788 291s Investment_9 2.971 6.437 291s Investment_10 0.000 0.000 291s Investment_11 -2.520 -4.889 291s Investment_12 -1.141 -2.873 291s Investment_14 -0.945 -4.603 291s Investment_15 0.193 0.632 291s Investment_16 -0.266 -0.851 291s Investment_17 -4.066 -12.838 291s Investment_18 -0.291 -0.787 291s Investment_19 12.528 33.240 291s Investment_20 2.701 8.720 291s Investment_21 4.044 11.280 291s Investment_22 3.620 10.604 291s PrivateWages_2 -19.580 -43.478 291s PrivateWages_3 8.490 22.046 291s PrivateWages_4 32.362 70.851 291s PrivateWages_5 -2.480 -4.987 291s PrivateWages_6 -9.410 -18.724 291s PrivateWages_8 -19.974 -42.291 291s PrivateWages_9 7.686 16.652 291s PrivateWages_10 0.000 0.000 291s PrivateWages_11 -10.765 -20.886 291s PrivateWages_12 7.230 18.215 291s PrivateWages_13 0.000 0.000 291s PrivateWages_14 3.986 19.417 291s PrivateWages_15 4.762 15.560 291s PrivateWages_16 0.251 0.802 291s PrivateWages_17 -15.253 -48.156 291s PrivateWages_18 20.425 55.356 291s PrivateWages_19 -17.801 -47.230 291s PrivateWages_20 -8.329 -26.891 291s PrivateWages_21 -31.176 -86.965 291s PrivateWages_22 15.957 46.737 291s Investment_(Intercept) Investment_corpProf 291s Consumption_2 0.08954 1.110 291s Consumption_3 0.36165 6.112 291s Consumption_4 0.43910 8.079 291s Consumption_5 0.12776 2.479 291s Consumption_6 -0.01675 -0.337 291s Consumption_7 -0.26227 -5.141 291s Consumption_8 -0.39250 -7.772 291s Consumption_9 -0.31026 -6.547 291s Consumption_11 -0.10180 -1.588 291s Consumption_12 0.06506 0.742 291s Consumption_14 -0.06181 -0.692 291s Consumption_15 0.03199 0.393 291s Consumption_16 0.02492 0.349 291s Consumption_17 -0.44431 -7.820 291s Consumption_18 0.12890 2.230 291s Consumption_19 -0.05691 -0.871 291s Consumption_20 -0.26372 -5.011 291s Consumption_21 -0.21598 -4.557 291s Consumption_22 0.62998 14.805 291s Investment_2 -0.33900 -4.204 291s Investment_3 -0.25149 -4.250 291s Investment_4 1.49825 27.568 291s Investment_5 -2.07104 -40.178 291s Investment_6 0.44402 8.925 291s Investment_7 2.02512 39.692 291s Investment_8 1.16134 22.995 291s Investment_9 -0.73888 -15.590 291s Investment_10 1.69419 36.764 291s Investment_11 0.57188 8.921 291s Investment_12 0.36002 4.104 291s Investment_14 0.66469 7.445 291s Investment_15 -0.08500 -1.046 291s Investment_16 0.10666 1.493 291s Investment_17 1.43016 25.171 291s Investment_18 0.08129 1.406 291s Investment_19 -3.56588 -54.558 291s Investment_20 -0.86923 -16.515 291s Investment_21 -1.04801 -22.113 291s Investment_22 -0.84488 -19.855 291s PrivateWages_2 0.63026 7.815 291s PrivateWages_3 -0.27988 -4.730 291s PrivateWages_4 -0.78278 -14.403 291s PrivateWages_5 0.05510 1.069 291s PrivateWages_6 0.19829 3.986 291s PrivateWages_8 0.41658 8.248 291s PrivateWages_9 -0.15868 -3.348 291s PrivateWages_10 -0.64985 -14.102 291s PrivateWages_11 0.20280 3.164 291s PrivateWages_12 -0.18947 -2.160 291s PrivateWages_13 0.00000 0.000 291s PrivateWages_14 -0.23276 -2.607 291s PrivateWages_15 -0.17379 -2.138 291s PrivateWages_16 -0.00834 -0.117 291s PrivateWages_17 0.44538 7.839 291s PrivateWages_18 -0.47440 -8.207 291s PrivateWages_19 0.42063 6.436 291s PrivateWages_20 0.22252 4.228 291s PrivateWages_21 0.67076 14.153 291s PrivateWages_22 -0.30915 -7.265 291s Investment_corpProfLag Investment_capitalLag 291s Consumption_2 1.137 16.37 291s Consumption_3 4.484 66.04 291s Consumption_4 7.421 81.01 291s Consumption_5 2.351 24.24 291s Consumption_6 -0.325 -3.23 291s Consumption_7 -5.272 -51.88 291s Consumption_8 -7.693 -79.84 291s Consumption_9 -6.143 -64.41 291s Consumption_11 -2.209 -21.96 291s Consumption_12 1.015 14.10 291s Consumption_14 -0.433 -12.80 291s Consumption_15 0.358 6.46 291s Consumption_16 0.307 4.96 291s Consumption_17 -6.220 -87.84 291s Consumption_18 2.269 25.75 291s Consumption_19 -0.984 -11.48 291s Consumption_20 -4.035 -52.72 291s Consumption_21 -4.104 -43.46 291s Consumption_22 13.293 128.83 291s Investment_2 -4.305 -61.97 291s Investment_3 -3.118 -45.92 291s Investment_4 25.320 276.43 291s Investment_5 -38.107 -392.88 291s Investment_6 8.614 85.56 291s Investment_7 40.705 400.57 291s Investment_8 22.762 236.22 291s Investment_9 -14.630 -153.39 291s Investment_10 35.747 356.80 291s Investment_11 12.410 123.35 291s Investment_12 5.616 78.02 291s Investment_14 4.653 137.66 291s Investment_15 -0.952 -17.17 291s Investment_16 1.312 21.22 291s Investment_17 20.022 282.74 291s Investment_18 1.431 16.24 291s Investment_19 -61.690 -719.59 291s Investment_20 -13.299 -173.76 291s Investment_21 -19.912 -210.86 291s Investment_22 -17.827 -172.78 291s PrivateWages_2 8.004 115.21 291s PrivateWages_3 -3.471 -51.11 291s PrivateWages_4 -13.229 -144.42 291s PrivateWages_5 1.014 10.45 291s PrivateWages_6 3.847 38.21 291s PrivateWages_8 8.165 84.73 291s PrivateWages_9 -3.142 -32.94 291s PrivateWages_10 -13.712 -136.86 291s PrivateWages_11 4.401 43.74 291s PrivateWages_12 -2.956 -41.06 291s PrivateWages_13 0.000 0.00 291s PrivateWages_14 -1.629 -48.21 291s PrivateWages_15 -1.946 -35.11 291s PrivateWages_16 -0.103 -1.66 291s PrivateWages_17 6.235 88.05 291s PrivateWages_18 -8.349 -94.78 291s PrivateWages_19 7.277 84.88 291s PrivateWages_20 3.405 44.48 291s PrivateWages_21 12.744 134.96 291s PrivateWages_22 -6.523 -63.22 291s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 291s Consumption_2 -0.4240 -19.33 -19.04 291s Consumption_3 -1.7126 -85.80 -78.09 291s Consumption_4 -2.0793 -118.94 -104.17 291s Consumption_5 -0.6050 -34.54 -34.61 291s Consumption_6 0.0793 4.84 4.53 291s Consumption_7 0.0000 0.00 0.00 291s Consumption_8 1.8587 119.70 118.95 291s Consumption_9 1.4692 94.76 94.62 291s Consumption_11 0.4821 29.50 32.30 291s Consumption_12 -0.3081 -16.45 -18.85 291s Consumption_14 0.2927 13.20 12.97 291s Consumption_15 -0.1515 -7.53 -6.83 291s Consumption_16 -0.1180 -6.42 -5.87 291s Consumption_17 2.1040 131.92 114.46 291s Consumption_18 -0.6104 -39.67 -38.27 291s Consumption_19 0.2695 16.41 17.52 291s Consumption_20 1.2488 86.79 76.05 291s Consumption_21 1.0228 77.42 71.08 291s Consumption_22 -2.9832 -263.72 -225.83 291s Investment_2 0.1333 6.08 5.98 291s Investment_3 0.0989 4.95 4.51 291s Investment_4 -0.5890 -33.69 -29.51 291s Investment_5 0.8142 46.49 46.57 291s Investment_6 -0.1746 -10.65 -9.97 291s Investment_7 0.0000 0.00 0.00 291s Investment_8 -0.4566 -29.40 -29.22 291s Investment_9 0.2905 18.74 18.71 291s Investment_10 -0.6660 -44.62 -42.96 291s Investment_11 -0.2248 -13.76 -15.06 291s Investment_12 -0.1415 -7.56 -8.66 291s Investment_14 -0.2613 -11.79 -11.58 291s Investment_15 0.0334 1.66 1.51 291s Investment_16 -0.0419 -2.28 -2.08 291s Investment_17 -0.5622 -35.25 -30.59 291s Investment_18 -0.0320 -2.08 -2.00 291s Investment_19 1.4018 85.37 91.12 291s Investment_20 0.3417 23.75 20.81 291s Investment_21 0.4120 31.19 28.63 291s Investment_22 0.3321 29.36 25.14 291s PrivateWages_2 -3.8052 -173.52 -170.85 291s PrivateWages_3 1.6898 84.66 77.06 291s PrivateWages_4 4.7261 270.33 236.78 291s PrivateWages_5 -0.3327 -19.00 -19.03 291s PrivateWages_6 -1.1972 -73.03 -68.36 291s PrivateWages_8 -2.5152 -161.98 -160.97 291s PrivateWages_9 0.9580 61.79 61.70 291s PrivateWages_10 3.9235 262.88 253.07 291s PrivateWages_11 -1.2244 -74.93 -82.04 291s PrivateWages_12 1.1439 61.09 70.01 291s PrivateWages_13 -0.5236 -23.19 -27.96 291s PrivateWages_14 1.4053 63.38 62.26 291s PrivateWages_15 1.0493 52.15 47.32 291s PrivateWages_16 0.0503 2.74 2.50 291s PrivateWages_17 -2.6890 -168.60 -146.28 291s PrivateWages_18 2.8642 186.17 179.59 291s PrivateWages_19 -2.5396 -154.66 -165.07 291s PrivateWages_20 -1.3435 -93.37 -81.82 291s PrivateWages_21 -4.0497 -306.57 -281.46 291s PrivateWages_22 1.8665 165.00 141.30 291s PrivateWages_trend 291s Consumption_2 4.240 291s Consumption_3 15.413 291s Consumption_4 16.634 291s Consumption_5 4.235 291s Consumption_6 -0.476 291s Consumption_7 0.000 291s Consumption_8 -7.435 291s Consumption_9 -4.408 291s Consumption_11 -0.482 291s Consumption_12 0.000 291s Consumption_14 0.585 291s Consumption_15 -0.454 291s Consumption_16 -0.472 291s Consumption_17 10.520 291s Consumption_18 -3.662 291s Consumption_19 1.886 291s Consumption_20 9.990 291s Consumption_21 9.205 291s Consumption_22 -29.832 291s Investment_2 -1.333 291s Investment_3 -0.890 291s Investment_4 4.712 291s Investment_5 -5.699 291s Investment_6 1.047 291s Investment_7 0.000 291s Investment_8 1.826 291s Investment_9 -0.871 291s Investment_10 1.332 291s Investment_11 0.225 291s Investment_12 0.000 291s Investment_14 -0.523 291s Investment_15 0.100 291s Investment_16 -0.168 291s Investment_17 -2.811 291s Investment_18 -0.192 291s Investment_19 9.813 291s Investment_20 2.734 291s Investment_21 3.708 291s Investment_22 3.321 291s PrivateWages_2 38.052 291s PrivateWages_3 -15.208 291s PrivateWages_4 -37.809 291s PrivateWages_5 2.329 291s PrivateWages_6 7.183 291s PrivateWages_8 10.061 291s PrivateWages_9 -2.874 291s PrivateWages_10 -7.847 291s PrivateWages_11 1.224 291s PrivateWages_12 0.000 291s PrivateWages_13 -0.524 291s PrivateWages_14 2.811 291s PrivateWages_15 3.148 291s PrivateWages_16 0.201 291s PrivateWages_17 -13.445 291s PrivateWages_18 17.185 291s PrivateWages_19 -17.777 291s PrivateWages_20 -10.748 291s PrivateWages_21 -36.448 291s PrivateWages_22 18.665 291s [1] TRUE 291s > Bread 291s Consumption_(Intercept) Consumption_corpProf Consumption_corpProfLag 291s [1,] 9.64e+01 -1.01207 -0.67760 291s [2,] -1.01e+00 0.50717 -0.26912 291s [3,] -6.78e-01 -0.26912 0.39547 291s [4,] -1.57e+00 -0.07816 -0.02960 291s [5,] 4.72e+00 -0.06998 0.78589 291s [6,] -2.60e-01 0.05062 -0.04147 291s [7,] 5.84e-03 -0.03341 0.04369 291s [8,] -2.63e-04 -0.00132 -0.00391 291s [9,] -3.35e+01 0.06371 1.58512 291s [10,] 2.97e-01 -0.05279 0.03618 291s [11,] 2.54e-01 0.05334 -0.06435 291s [12,] 1.92e-01 0.03084 0.02478 291s Consumption_wages Investment_(Intercept) Investment_corpProf 291s [1,] -1.566759 4.725 -0.25994 291s [2,] -0.078160 -0.070 0.05062 291s [3,] -0.029602 0.786 -0.04147 291s [4,] 0.081697 -0.368 0.00116 291s [5,] -0.368191 1275.706 -12.07893 291s [6,] 0.001158 -12.079 0.49514 291s [7,] -0.003210 9.845 -0.37888 291s [8,] 0.001998 -6.140 0.04890 291s [9,] 0.126305 19.264 -0.14904 291s [10,] -0.000206 0.266 0.01283 291s [11,] -0.002055 -0.608 -0.01053 291s [12,] -0.027162 -0.549 0.00394 291s Investment_corpProfLag Investment_capitalLag PrivateWages_(Intercept) 291s [1,] 0.00584 -0.000263 -33.5037 291s [2,] -0.03341 -0.001318 0.0637 291s [3,] 0.04369 -0.003914 1.5851 291s [4,] -0.00321 0.001998 0.1263 291s [5,] 9.84516 -6.139910 19.2637 291s [6,] -0.37888 0.048897 -0.1490 291s [7,] 0.45026 -0.053769 -0.4040 291s [8,] -0.05377 0.030940 -0.0490 291s [9,] -0.40395 -0.049007 70.6849 291s [10,] -0.00755 -0.001777 -0.2111 291s [11,] 0.01465 0.002709 -0.9817 291s [12,] -0.01065 0.003278 0.7839 291s PrivateWages_gnp PrivateWages_gnpLag PrivateWages_trend 291s [1,] 0.297134 0.25379 0.19157 291s [2,] -0.052789 0.05334 0.03084 291s [3,] 0.036177 -0.06435 0.02478 291s [4,] -0.000206 -0.00206 -0.02716 291s [5,] 0.265808 -0.60808 -0.54935 291s [6,] 0.012829 -0.01053 0.00394 291s [7,] -0.007548 0.01465 -0.01065 291s [8,] -0.001777 0.00271 0.00328 291s [9,] -0.211061 -0.98166 0.78387 291s [10,] 0.039911 -0.03744 -0.00955 291s [11,] -0.037441 0.05550 -0.00377 291s [12,] -0.009553 -0.00377 0.04488 291s > 291s > # 3SLS 291s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 291s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 291s > summary 291s 291s systemfit results 291s method: 3SLS 291s 291s N DF SSR detRCov OLS-R2 McElroy-R2 291s system 57 45 66.8 0.361 0.963 0.993 291s 291s N DF SSR MSE RMSE R2 Adj R2 291s Consumption 18 14 22.6 1.616 1.271 0.974 0.968 291s Investment 19 15 34.1 2.277 1.509 0.807 0.769 291s PrivateWages 20 16 10.1 0.628 0.793 0.987 0.985 291s 291s The covariance matrix of the residuals used for estimation 291s Consumption Investment PrivateWages 291s Consumption 1.237 0.518 -0.408 291s Investment 0.518 1.263 0.113 291s PrivateWages -0.408 0.113 0.468 291s 291s The covariance matrix of the residuals 291s Consumption Investment PrivateWages 291s Consumption 1.257 0.601 -0.421 291s Investment 0.601 1.601 0.214 291s PrivateWages -0.421 0.214 0.491 291s 291s The correlations of the residuals 291s Consumption Investment PrivateWages 291s Consumption 1.000 0.425 -0.537 291s Investment 0.425 1.000 0.239 291s PrivateWages -0.537 0.239 1.000 291s 291s 291s 3SLS estimates for 'Consumption' (equation 1) 291s Model Formula: consump ~ corpProf + corpProfLag + wages 291s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 291s gnpLag 291s 291s Estimate Std. Error t value Pr(>|t|) 291s (Intercept) 18.2100 1.5273 11.92 1e-08 *** 291s corpProf -0.0639 0.1461 -0.44 0.67 291s corpProfLag 0.1687 0.1125 1.50 0.16 291s wages 0.8230 0.0431 19.07 2e-11 *** 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s 291s Residual standard error: 1.271 on 14 degrees of freedom 291s Number of observations: 18 Degrees of Freedom: 14 291s SSR: 22.626 MSE: 1.616 Root MSE: 1.271 291s Multiple R-Squared: 0.974 Adjusted R-Squared: 0.968 291s 291s 291s 3SLS estimates for 'Investment' (equation 2) 291s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 291s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 291s gnpLag 291s 291s Estimate Std. Error t value Pr(>|t|) 291s (Intercept) 24.7534 6.5548 3.78 0.00183 ** 291s corpProf 0.0524 0.1807 0.29 0.77600 291s corpProfLag 0.6584 0.1551 4.24 0.00071 *** 291s capitalLag -0.1756 0.0311 -5.64 4.7e-05 *** 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s 291s Residual standard error: 1.509 on 15 degrees of freedom 291s Number of observations: 19 Degrees of Freedom: 15 291s SSR: 34.149 MSE: 2.277 Root MSE: 1.509 291s Multiple R-Squared: 0.807 Adjusted R-Squared: 0.769 291s 291s 291s 3SLS estimates for 'PrivateWages' (equation 3) 291s Model Formula: privWage ~ gnp + gnpLag + trend 291s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 291s gnpLag 291s 291s Estimate Std. Error t value Pr(>|t|) 291s (Intercept) 0.8154 1.0961 0.74 0.46772 291s gnp 0.4250 0.0299 14.19 1.7e-10 *** 291s gnpLag 0.1731 0.0331 5.23 8.3e-05 *** 291s trend 0.1255 0.0283 4.43 0.00042 *** 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s 291s Residual standard error: 0.793 on 16 degrees of freedom 291s Number of observations: 20 Degrees of Freedom: 16 291s SSR: 10.054 MSE: 0.628 Root MSE: 0.793 291s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 291s 291s > residuals 291s Consumption Investment PrivateWages 291s 1 NA NA NA 291s 2 -0.8680 -1.857 -1.21010 291s 3 -0.7217 0.170 0.43075 291s 4 -1.1353 0.762 1.30899 291s 5 0.0755 -1.565 -0.20270 291s 6 0.6348 0.367 -0.46842 291s 7 NA NA NA 291s 8 1.7953 1.230 -0.85853 291s 9 1.7924 0.568 0.20422 291s 10 NA 2.308 1.09889 291s 11 -0.5211 -0.972 -0.39427 291s 12 -1.5560 -0.960 0.39889 291s 13 NA NA -0.00934 291s 14 -0.2384 1.327 0.59990 291s 15 -0.7342 -0.292 0.48094 291s 16 -0.4331 0.068 0.16188 291s 17 1.8775 1.932 -0.70448 291s 18 -0.6294 -0.154 0.95616 291s 19 -0.4252 -3.400 -0.62489 291s 20 1.3682 0.589 -0.29589 291s 21 1.3155 0.271 -1.14466 291s 22 -1.4276 0.942 0.55941 291s > fitted 291s Consumption Investment PrivateWages 291s 1 NA NA NA 291s 2 42.8 1.657 26.7 291s 3 45.7 1.730 28.9 291s 4 50.3 4.438 32.8 291s 5 50.5 4.565 34.1 291s 6 52.0 4.733 35.9 291s 7 NA NA NA 291s 8 54.4 2.970 38.8 291s 9 55.5 2.432 39.0 291s 10 NA 2.792 40.2 291s 11 55.5 1.972 38.3 291s 12 52.5 -2.440 34.1 291s 13 NA NA 29.0 291s 14 46.7 -6.427 27.9 291s 15 49.4 -2.708 30.1 291s 16 51.7 -1.368 33.0 291s 17 55.8 0.168 37.5 291s 18 59.3 2.154 40.0 291s 19 57.9 1.500 38.8 291s 20 60.2 0.711 41.9 291s 21 63.7 3.029 46.1 291s 22 71.1 3.958 52.7 291s > predict 291s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 291s 1 NA NA NA NA 291s 2 42.8 0.542 39.8 45.7 291s 3 45.7 0.612 42.7 48.7 291s 4 50.3 0.407 47.5 53.2 291s 5 50.5 0.478 47.6 53.4 291s 6 52.0 0.488 49.0 54.9 291s 7 NA NA NA NA 291s 8 54.4 0.394 51.5 57.3 291s 9 55.5 0.464 52.6 58.4 291s 10 NA NA NA NA 291s 11 55.5 0.811 52.3 58.8 291s 12 52.5 0.773 49.3 55.6 291s 13 NA NA NA NA 291s 14 46.7 0.666 43.7 49.8 291s 15 49.4 0.463 46.5 52.3 291s 16 51.7 0.381 48.9 54.6 291s 17 55.8 0.424 52.9 58.7 291s 18 59.3 0.359 56.5 62.2 291s 19 57.9 0.492 55.0 60.8 291s 20 60.2 0.501 57.3 63.2 291s 21 63.7 0.491 60.8 66.6 291s 22 71.1 0.749 68.0 74.3 291s Investment.pred Investment.se.fit Investment.lwr Investment.upr 291s 1 NA NA NA NA 291s 2 1.657 0.831 -2.015 5.329 291s 3 1.730 0.574 -1.711 5.171 291s 4 4.438 0.507 1.045 7.831 291s 5 4.565 0.426 1.223 7.907 291s 6 4.733 0.406 1.402 8.064 291s 7 NA NA NA NA 291s 8 2.970 0.334 -0.324 6.263 291s 9 2.432 0.501 -0.957 5.820 291s 10 2.792 0.544 -0.627 6.211 291s 11 1.972 0.937 -1.814 5.757 291s 12 -2.440 0.849 -6.131 1.250 291s 13 NA NA NA NA 291s 14 -6.427 0.836 -10.104 -2.750 291s 15 -2.708 0.477 -6.081 0.665 291s 16 -1.368 0.381 -4.685 1.949 291s 17 0.168 0.473 -3.202 3.538 291s 18 2.154 0.311 -1.130 5.438 291s 19 1.500 0.518 -1.900 4.900 291s 20 0.711 0.541 -2.705 4.127 291s 21 3.029 0.467 -0.338 6.395 291s 22 3.958 0.677 0.432 7.483 291s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 291s 1 NA NA NA NA 291s 2 26.7 0.315 24.9 28.5 291s 3 28.9 0.322 27.1 30.7 291s 4 32.8 0.330 31.0 34.6 291s 5 34.1 0.241 32.3 35.9 291s 6 35.9 0.249 34.1 37.6 291s 7 NA NA NA NA 291s 8 38.8 0.243 37.0 40.5 291s 9 39.0 0.231 37.2 40.7 291s 10 40.2 0.225 38.5 41.9 291s 11 38.3 0.305 36.5 40.1 291s 12 34.1 0.317 32.3 35.9 291s 13 29.0 0.382 27.1 30.9 291s 14 27.9 0.321 26.1 29.7 291s 15 30.1 0.316 28.3 31.9 291s 16 33.0 0.265 31.3 34.8 291s 17 37.5 0.270 35.7 39.3 291s 18 40.0 0.207 38.3 41.8 291s 19 38.8 0.311 37.0 40.6 291s 20 41.9 0.287 40.1 43.7 291s 21 46.1 0.300 44.3 47.9 291s 22 52.7 0.463 50.8 54.7 291s > model.frame 291s [1] TRUE 291s > model.matrix 291s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0339 >" 291s [2] "Attributes: < Component “dimnames”: Component 1: 52 string mismatches >" 291s [3] "Numeric: lengths (708, 684) differ" 291s > nobs 291s [1] 57 291s > linearHypothesis 291s Linear hypothesis test (Theil's F test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df F Pr(>F) 291s 1 46 291s 2 45 1 1.95 0.17 291s Linear hypothesis test (F statistic of a Wald test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df F Pr(>F) 291s 1 46 291s 2 45 1 2.71 0.11 291s Linear hypothesis test (Chi^2 statistic of a Wald test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df Chisq Pr(>Chisq) 291s 1 46 291s 2 45 1 2.71 0.1 . 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s Linear hypothesis test (Theil's F test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s Consumption_corpProfLag - PrivateWages_trend = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df F Pr(>F) 291s 1 47 291s 2 45 2 1.78 0.18 291s Linear hypothesis test (F statistic of a Wald test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s Consumption_corpProfLag - PrivateWages_trend = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df F Pr(>F) 291s 1 47 291s 2 45 2 2.48 0.095 . 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s Linear hypothesis test (Chi^2 statistic of a Wald test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s Consumption_corpProfLag - PrivateWages_trend = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df Chisq Pr(>Chisq) 291s 1 47 291s 2 45 2 4.95 0.084 . 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s > logLik 291s 'log Lik.' -71.2 (df=18) 291s 'log Lik.' -81.7 (df=18) 291s Estimating function 291s Consumption_(Intercept) Consumption_corpProf 291s Consumption_2 -3.6474 -51.112 291s Consumption_3 -0.7759 -12.930 291s Consumption_4 0.5383 9.982 291s Consumption_5 -2.0601 -41.756 291s Consumption_6 1.0597 20.157 291s Consumption_8 5.0108 88.416 291s Consumption_9 4.4804 84.874 291s Consumption_11 -2.2103 -37.003 291s Consumption_12 -2.9903 -39.999 291s Consumption_14 0.5609 5.622 291s Consumption_15 -2.2997 -28.756 291s Consumption_16 -1.9032 -27.562 291s Consumption_17 6.4249 95.811 291s Consumption_18 -0.7235 -14.050 291s Consumption_19 -5.0805 -97.079 291s Consumption_20 3.4333 60.632 291s Consumption_21 1.6077 32.791 291s Consumption_22 -1.1313 -25.654 291s Investment_2 1.6537 23.174 291s Investment_3 -0.1564 -2.607 291s Investment_4 -0.6420 -11.906 291s Investment_5 1.4113 28.605 291s Investment_6 -0.3557 -6.767 291s Investment_8 -1.1680 -20.610 291s Investment_9 -0.5634 -10.672 291s Investment_10 0.0000 0.000 291s Investment_11 0.9137 15.295 291s Investment_12 0.9272 12.402 291s Investment_14 -1.2036 -12.064 291s Investment_15 0.2779 3.475 291s Investment_16 -0.0439 -0.636 291s Investment_17 -1.7918 -26.720 291s Investment_18 0.2271 4.411 291s Investment_19 3.1278 59.767 291s Investment_20 -0.5790 -10.225 291s Investment_21 -0.2789 -5.690 291s Investment_22 -0.8484 -19.238 291s PrivateWages_2 -3.1568 -44.237 291s PrivateWages_3 1.1209 18.679 291s PrivateWages_4 2.7328 50.677 291s PrivateWages_5 -2.9712 -60.223 291s PrivateWages_6 -0.5212 -9.913 291s PrivateWages_8 1.7420 30.738 291s PrivateWages_9 1.9832 37.569 291s PrivateWages_10 0.0000 0.000 291s PrivateWages_11 -2.5151 -42.105 291s PrivateWages_12 -0.3611 -4.830 291s PrivateWages_13 0.0000 0.000 291s PrivateWages_14 3.2055 32.130 291s PrivateWages_15 -0.2814 -3.519 291s PrivateWages_16 -0.4078 -5.906 291s PrivateWages_17 2.6678 39.784 291s PrivateWages_18 0.0554 1.076 291s PrivateWages_19 -6.6416 -126.909 291s PrivateWages_20 1.4327 25.301 291s PrivateWages_21 -1.3598 -27.735 291s PrivateWages_22 2.0747 47.044 291s Consumption_corpProfLag Consumption_wages 291s Consumption_2 -46.322 -108.77 291s Consumption_3 -9.621 -24.71 291s Consumption_4 9.097 18.98 291s Consumption_5 -37.905 -79.52 291s Consumption_6 20.558 40.85 291s Consumption_8 98.211 200.48 291s Consumption_9 88.711 187.18 291s Consumption_11 -47.964 -95.27 291s Consumption_12 -46.648 -118.58 291s Consumption_14 3.926 18.69 291s Consumption_15 -25.757 -85.85 291s Consumption_16 -23.410 -76.40 291s Consumption_17 89.949 268.43 291s Consumption_18 -12.733 -34.44 291s Consumption_19 -87.892 -250.13 291s Consumption_20 52.529 166.71 291s Consumption_21 30.546 85.88 291s Consumption_22 -23.871 -68.78 291s Investment_2 21.002 49.32 291s Investment_3 -1.940 -4.98 291s Investment_4 -10.851 -22.64 291s Investment_5 25.967 54.47 291s Investment_6 -6.901 -13.71 291s Investment_8 -22.893 -46.73 291s Investment_9 -11.154 -23.53 291s Investment_10 0.000 0.00 291s Investment_11 19.827 39.38 291s Investment_12 14.464 36.77 291s Investment_14 -8.425 -40.11 291s Investment_15 3.113 10.38 291s Investment_16 -0.540 -1.76 291s Investment_17 -25.085 -74.86 291s Investment_18 3.997 10.81 291s Investment_19 54.111 153.99 291s Investment_20 -8.858 -28.11 291s Investment_21 -5.300 -14.90 291s Investment_22 -17.901 -51.58 291s PrivateWages_2 -40.091 -94.14 291s PrivateWages_3 13.899 35.70 291s PrivateWages_4 46.184 96.34 291s PrivateWages_5 -54.670 -114.69 291s PrivateWages_6 -10.110 -20.09 291s PrivateWages_8 34.144 69.70 291s PrivateWages_9 39.267 82.85 291s PrivateWages_10 0.000 0.00 291s PrivateWages_11 -54.578 -108.40 291s PrivateWages_12 -5.633 -14.32 291s PrivateWages_13 0.000 0.00 291s PrivateWages_14 22.438 106.83 291s PrivateWages_15 -3.152 -10.51 291s PrivateWages_16 -5.016 -16.37 291s PrivateWages_17 37.350 111.46 291s PrivateWages_18 0.975 2.64 291s PrivateWages_19 -114.899 -326.98 291s PrivateWages_20 21.920 69.57 291s PrivateWages_21 -25.836 -72.64 291s PrivateWages_22 43.775 126.12 291s Investment_(Intercept) Investment_corpProf 291s Consumption_2 1.8176 24.384 291s Consumption_3 0.3867 6.453 291s Consumption_4 -0.2682 -5.040 291s Consumption_5 1.0266 21.198 291s Consumption_6 -0.5281 -10.172 291s Consumption_8 -2.4970 -43.782 291s Consumption_9 -2.2327 -43.602 291s Consumption_11 1.1015 18.940 291s Consumption_12 1.4902 20.151 291s Consumption_14 -0.2795 -2.793 291s Consumption_15 1.1460 14.736 291s Consumption_16 0.9485 13.590 291s Consumption_17 -3.2018 -47.918 291s Consumption_18 0.3605 6.983 291s Consumption_19 2.5318 49.008 291s Consumption_20 -1.7109 -29.898 291s Consumption_21 -0.8012 -16.122 291s Consumption_22 0.5638 12.844 291s Investment_2 -2.3696 -31.787 291s Investment_3 0.2241 3.741 291s Investment_4 0.9200 17.284 291s Investment_5 -2.0221 -41.754 291s Investment_6 0.5097 9.819 291s Investment_8 1.6736 29.344 291s Investment_9 0.8072 15.764 291s Investment_10 2.9560 59.913 291s Investment_11 -1.3092 -22.510 291s Investment_12 -1.3285 -17.964 291s Investment_14 1.7246 17.233 291s Investment_15 -0.3982 -5.120 291s Investment_16 0.0630 0.902 291s Investment_17 2.5674 38.424 291s Investment_18 -0.3254 -6.303 291s Investment_19 -4.4817 -86.752 291s Investment_20 0.8296 14.497 291s Investment_21 0.3997 8.043 291s Investment_22 1.2156 27.693 291s PrivateWages_2 1.9315 25.910 291s PrivateWages_3 -0.6858 -11.446 291s PrivateWages_4 -1.6720 -31.413 291s PrivateWages_5 1.8179 37.537 291s PrivateWages_6 0.3189 6.142 291s PrivateWages_8 -1.0659 -18.688 291s PrivateWages_9 -1.2134 -23.696 291s PrivateWages_10 -2.2443 -45.488 291s PrivateWages_11 1.5389 26.460 291s PrivateWages_12 0.2209 2.988 291s PrivateWages_13 0.0000 0.000 291s PrivateWages_14 -1.9613 -19.598 291s PrivateWages_15 0.1722 2.214 291s PrivateWages_16 0.2495 3.576 291s PrivateWages_17 -1.6323 -24.429 291s PrivateWages_18 -0.0339 -0.657 291s PrivateWages_19 4.0636 78.659 291s PrivateWages_20 -0.8766 -15.318 291s PrivateWages_21 0.8320 16.742 291s PrivateWages_22 -1.2694 -28.917 291s Investment_corpProfLag Investment_capitalLag 291s Consumption_2 23.084 332.27 291s Consumption_3 4.795 70.60 291s Consumption_4 -4.533 -49.49 291s Consumption_5 18.890 194.75 291s Consumption_6 -10.245 -101.76 291s Consumption_8 -48.942 -507.90 291s Consumption_9 -44.208 -463.52 291s Consumption_11 23.902 237.59 291s Consumption_12 23.247 322.92 291s Consumption_14 -1.957 -57.89 291s Consumption_15 12.836 231.50 291s Consumption_16 11.666 188.74 291s Consumption_17 -44.825 -632.99 291s Consumption_18 6.345 72.04 291s Consumption_19 43.800 510.92 291s Consumption_20 -26.177 -342.01 291s Consumption_21 -15.222 -161.20 291s Consumption_22 11.896 115.30 291s Investment_2 -30.093 -433.16 291s Investment_3 2.779 40.93 291s Investment_4 15.547 169.73 291s Investment_5 -37.208 -383.60 291s Investment_6 9.888 98.22 291s Investment_8 32.803 340.41 291s Investment_9 15.983 167.58 291s Investment_10 62.371 622.53 291s Investment_11 -28.409 -282.39 291s Investment_12 -20.724 -287.88 291s Investment_14 12.072 357.16 291s Investment_15 -4.460 -80.44 291s Investment_16 0.774 12.53 291s Investment_17 35.944 507.58 291s Investment_18 -5.727 -65.02 291s Investment_19 -77.534 -904.41 291s Investment_20 12.693 165.84 291s Investment_21 7.594 80.42 291s Investment_22 25.650 248.60 291s PrivateWages_2 24.530 353.07 291s PrivateWages_3 -8.504 -125.23 291s PrivateWages_4 -28.257 -308.49 291s PrivateWages_5 33.450 344.86 291s PrivateWages_6 6.186 61.45 291s PrivateWages_8 -20.891 -216.79 291s PrivateWages_9 -24.025 -251.90 291s PrivateWages_10 -47.355 -472.65 291s PrivateWages_11 33.393 331.93 291s PrivateWages_12 3.447 47.88 291s PrivateWages_13 0.000 0.00 291s PrivateWages_14 -13.729 -406.18 291s PrivateWages_15 1.929 34.78 291s PrivateWages_16 3.069 49.66 291s PrivateWages_17 -22.852 -322.71 291s PrivateWages_18 -0.597 -6.77 291s PrivateWages_19 70.300 820.04 291s PrivateWages_20 -13.412 -175.23 291s PrivateWages_21 15.807 167.39 291s PrivateWages_22 -26.784 -259.59 291s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 291s Consumption_2 -3.6123 -170.03 -162.19 291s Consumption_3 -0.7684 -38.10 -35.04 291s Consumption_4 0.5331 30.14 26.71 291s Consumption_5 -2.0403 -123.82 -116.70 291s Consumption_6 1.0495 63.61 59.93 291s Consumption_8 4.9625 297.74 317.60 291s Consumption_9 4.4373 276.30 285.76 291s Consumption_11 -2.1891 -139.47 -146.67 291s Consumption_12 -2.9615 -162.39 -181.24 291s Consumption_14 0.5555 23.40 24.61 291s Consumption_15 -2.2776 -116.65 -102.72 291s Consumption_16 -1.8849 -104.31 -93.68 291s Consumption_17 6.3631 365.20 346.15 291s Consumption_18 -0.7165 -48.13 -44.93 291s Consumption_19 -5.0316 -344.73 -327.05 291s Consumption_20 3.4002 227.29 207.07 291s Consumption_21 1.5922 119.20 110.66 291s Consumption_22 -1.1205 -97.34 -84.82 291s Investment_2 2.0108 94.65 90.29 291s Investment_3 -0.1902 -9.43 -8.67 291s Investment_4 -0.7807 -44.14 -39.11 291s Investment_5 1.7160 104.14 98.16 291s Investment_6 -0.4326 -26.22 -24.70 291s Investment_8 -1.4203 -85.21 -90.90 291s Investment_9 -0.6850 -42.65 -44.11 291s Investment_10 -2.5085 -161.97 -161.80 291s Investment_11 1.1110 70.78 74.44 291s Investment_12 1.1274 61.82 69.00 291s Investment_14 -1.4635 -61.65 -64.83 291s Investment_15 0.3379 17.31 15.24 291s Investment_16 -0.0534 -2.96 -2.66 291s Investment_17 -2.1788 -125.05 -118.52 291s Investment_18 0.2762 18.55 17.32 291s Investment_19 3.8033 260.57 247.21 291s Investment_20 -0.7040 -47.06 -42.87 291s Investment_21 -0.3392 -25.39 -23.57 291s Investment_22 -1.0316 -89.62 -78.09 291s PrivateWages_2 -7.1301 -335.61 -320.14 291s PrivateWages_3 2.5317 125.52 115.44 291s PrivateWages_4 6.1723 349.00 309.23 291s PrivateWages_5 -6.7109 -407.26 -383.86 291s PrivateWages_6 -1.1771 -71.34 -67.21 291s PrivateWages_8 3.9346 236.07 251.82 291s PrivateWages_9 4.4793 278.92 288.47 291s PrivateWages_10 8.2849 534.95 534.38 291s PrivateWages_11 -5.6807 -361.93 -380.61 291s PrivateWages_12 -0.8156 -44.72 -49.92 291s PrivateWages_13 -4.4579 -209.42 -238.05 291s PrivateWages_14 7.2401 305.01 320.74 291s PrivateWages_15 -0.6357 -32.56 -28.67 291s PrivateWages_16 -0.9212 -50.98 -45.78 291s PrivateWages_17 6.0257 345.84 327.80 291s PrivateWages_18 0.1252 8.41 7.85 291s PrivateWages_19 -15.0009 -1027.75 -975.06 291s PrivateWages_20 3.2360 216.31 197.07 291s PrivateWages_21 -3.0713 -229.93 -213.45 291s PrivateWages_22 4.6859 407.11 354.72 291s PrivateWages_trend 291s Consumption_2 36.123 291s Consumption_3 6.916 291s Consumption_4 -4.265 291s Consumption_5 14.282 291s Consumption_6 -6.297 291s Consumption_8 -19.850 291s Consumption_9 -13.312 291s Consumption_11 2.189 291s Consumption_12 0.000 291s Consumption_14 1.111 291s Consumption_15 -6.833 291s Consumption_16 -7.540 291s Consumption_17 31.815 291s Consumption_18 -4.299 291s Consumption_19 -35.221 291s Consumption_20 27.202 291s Consumption_21 14.330 291s Consumption_22 -11.205 291s Investment_2 -20.108 291s Investment_3 1.712 291s Investment_4 6.246 291s Investment_5 -12.012 291s Investment_6 2.595 291s Investment_8 5.681 291s Investment_9 2.055 291s Investment_10 5.017 291s Investment_11 -1.111 291s Investment_12 0.000 291s Investment_14 -2.927 291s Investment_15 1.014 291s Investment_16 -0.214 291s Investment_17 -10.894 291s Investment_18 1.657 291s Investment_19 26.623 291s Investment_20 -5.632 291s Investment_21 -3.053 291s Investment_22 -10.316 291s PrivateWages_2 71.301 291s PrivateWages_3 -22.785 291s PrivateWages_4 -49.379 291s PrivateWages_5 46.976 291s PrivateWages_6 7.063 291s PrivateWages_8 -15.738 291s PrivateWages_9 -13.438 291s PrivateWages_10 -16.570 291s PrivateWages_11 5.681 291s PrivateWages_12 0.000 291s PrivateWages_13 -4.458 291s PrivateWages_14 14.480 291s PrivateWages_15 -1.907 291s PrivateWages_16 -3.685 291s PrivateWages_17 30.129 291s PrivateWages_18 0.751 291s PrivateWages_19 -105.007 291s PrivateWages_20 25.888 291s PrivateWages_21 -27.641 291s PrivateWages_22 46.859 291s [1] TRUE 291s > Bread 291s Consumption_(Intercept) Consumption_corpProf Consumption_corpProfLag 291s [1,] 132.9647 -4.1876 0.7762 291s [2,] -4.1876 1.2160 -0.6687 291s [3,] 0.7762 -0.6687 0.7219 291s [4,] -1.6897 -0.1344 -0.0278 291s [5,] 101.6483 3.2473 3.4997 291s [6,] -4.3150 0.5140 -0.4474 291s [7,] 1.5566 -0.3374 0.4240 291s [8,] -0.2539 -0.0329 -0.0138 291s [9,] -35.7522 0.3296 1.6708 291s [10,] 0.5355 -0.0797 0.0478 291s [11,] 0.0459 0.0759 -0.0780 291s [12,] 0.1973 0.0481 0.0250 291s Consumption_wages Investment_(Intercept) Investment_corpProf 291s [1,] -1.689687 101.65 -4.32e+00 291s [2,] -0.134421 3.25 5.14e-01 291s [3,] -0.027837 3.50 -4.47e-01 291s [4,] 0.106098 -5.00 6.63e-02 291s [5,] -4.996393 2449.02 -4.26e+01 291s [6,] 0.066338 -42.57 1.86e+00 291s [7,] -0.064579 34.21 -1.44e+00 291s [8,] 0.024569 -11.36 1.70e-01 291s [9,] 0.047220 27.91 -2.66e-01 291s [10,] 0.000172 1.31 3.12e-04 291s [11,] -0.000827 -1.84 4.41e-03 291s [12,] -0.034079 -0.80 1.58e-02 291s Investment_corpProfLag Investment_capitalLag PrivateWages_(Intercept) 291s [1,] 1.55659 -0.25392 -35.7522 291s [2,] -0.33742 -0.03292 0.3296 291s [3,] 0.42396 -0.01383 1.6708 291s [4,] -0.06458 0.02457 0.0472 291s [5,] 34.20897 -11.35519 27.9136 291s [6,] -1.43523 0.17002 -0.2656 291s [7,] 1.37137 -0.15991 -0.3976 291s [8,] -0.15991 0.05521 -0.0847 291s [9,] -0.39759 -0.08475 68.4821 291s [10,] 0.00601 -0.00701 -0.3279 291s [11,] 0.00088 0.00875 -0.8283 291s [12,] -0.02279 0.00445 0.7887 291s PrivateWages_gnp PrivateWages_gnpLag PrivateWages_trend 291s [1,] 0.535460 0.045866 0.197271 291s [2,] -0.079666 0.075947 0.048142 291s [3,] 0.047829 -0.078006 0.025001 291s [4,] 0.000172 -0.000827 -0.034079 291s [5,] 1.306914 -1.841775 -0.800037 291s [6,] 0.000312 0.004408 0.015824 291s [7,] 0.006007 0.000880 -0.022790 291s [8,] -0.007006 0.008751 0.004448 291s [9,] -0.327909 -0.828330 0.788744 291s [10,] 0.051096 -0.046839 -0.013933 291s [11,] -0.046839 0.062505 0.000532 291s [12,] -0.013933 0.000532 0.045663 291s > 291s > # I3SLS 291s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 291s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 291s > summary 291s 291s systemfit results 291s method: iterated 3SLS 291s 291s convergence achieved after 9 iterations 291s 291s N DF SSR detRCov OLS-R2 McElroy-R2 291s system 57 45 75 0.422 0.959 0.993 291s 291s N DF SSR MSE RMSE R2 Adj R2 291s Consumption 18 14 22.7 1.622 1.273 0.973 0.968 291s Investment 19 15 42.1 2.809 1.676 0.762 0.715 291s PrivateWages 20 16 10.2 0.638 0.799 0.987 0.985 291s 291s The covariance matrix of the residuals used for estimation 291s Consumption Investment PrivateWages 291s Consumption 1.261 0.675 -0.439 291s Investment 0.675 1.949 0.237 291s PrivateWages -0.439 0.237 0.503 291s 291s The covariance matrix of the residuals 291s Consumption Investment PrivateWages 291s Consumption 1.261 0.675 -0.439 291s Investment 0.675 1.949 0.237 291s PrivateWages -0.439 0.237 0.503 291s 291s The correlations of the residuals 291s Consumption Investment PrivateWages 291s Consumption 1.000 0.431 -0.550 291s Investment 0.431 1.000 0.239 291s PrivateWages -0.550 0.239 1.000 291s 291s 291s 3SLS estimates for 'Consumption' (equation 1) 291s Model Formula: consump ~ corpProf + corpProfLag + wages 291s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 291s gnpLag 291s 291s Estimate Std. Error t value Pr(>|t|) 291s (Intercept) 18.5887 1.5250 12.19 7.6e-09 *** 291s corpProf -0.0438 0.1441 -0.30 0.77 291s corpProfLag 0.1456 0.1109 1.31 0.21 291s wages 0.8141 0.0428 19.01 2.1e-11 *** 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s 291s Residual standard error: 1.273 on 14 degrees of freedom 291s Number of observations: 18 Degrees of Freedom: 14 291s SSR: 22.704 MSE: 1.622 Root MSE: 1.273 291s Multiple R-Squared: 0.973 Adjusted R-Squared: 0.968 291s 291s 291s 3SLS estimates for 'Investment' (equation 2) 291s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 291s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 291s gnpLag 291s 291s Estimate Std. Error t value Pr(>|t|) 291s (Intercept) 29.4725 7.6857 3.83 0.0016 ** 291s corpProf -0.0183 0.2154 -0.09 0.9333 291s corpProfLag 0.7195 0.1850 3.89 0.0015 ** 291s capitalLag -0.1985 0.0366 -5.43 6.9e-05 *** 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s 291s Residual standard error: 1.676 on 15 degrees of freedom 291s Number of observations: 19 Degrees of Freedom: 15 291s SSR: 42.136 MSE: 2.809 Root MSE: 1.676 291s Multiple R-Squared: 0.762 Adjusted R-Squared: 0.715 291s 291s 291s 3SLS estimates for 'PrivateWages' (equation 3) 291s Model Formula: privWage ~ gnp + gnpLag + trend 291s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 291s gnpLag 291s 291s Estimate Std. Error t value Pr(>|t|) 291s (Intercept) 0.5385 1.1055 0.49 0.63277 291s gnp 0.4251 0.0287 14.80 9.3e-11 *** 291s gnpLag 0.1776 0.0322 5.51 4.7e-05 *** 291s trend 0.1211 0.0283 4.28 0.00057 *** 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s 291s Residual standard error: 0.799 on 16 degrees of freedom 291s Number of observations: 20 Degrees of Freedom: 16 291s SSR: 10.204 MSE: 0.638 Root MSE: 0.799 291s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 291s 291s > residuals 291s Consumption Investment PrivateWages 291s 1 NA NA NA 291s 2 -0.9524 -2.2888 -1.1837 291s 3 -0.8681 0.0698 0.4581 291s 4 -1.1653 0.5368 1.3199 291s 5 0.0601 -1.6917 -0.2194 291s 6 0.6426 0.2972 -0.4805 291s 7 NA NA NA 291s 8 1.8394 1.3723 -0.8931 291s 9 1.8275 0.8861 0.1723 291s 10 NA 2.6574 1.0707 291s 11 -0.3387 -0.9736 -0.4288 291s 12 -1.4550 -0.8630 0.3956 291s 13 NA NA 0.0277 291s 14 -0.3782 1.7151 0.6823 291s 15 -0.7768 -0.1993 0.5638 291s 16 -0.4606 0.1448 0.2281 291s 17 1.8605 2.1295 -0.6557 291s 18 -0.5262 -0.1493 0.9718 291s 19 -0.3047 -3.4730 -0.6148 291s 20 1.3992 0.8566 -0.2636 291s 21 1.4216 0.4910 -1.1472 291s 22 -1.2431 1.2792 0.5323 291s > fitted 291s Consumption Investment PrivateWages 291s 1 NA NA NA 291s 2 42.9 2.0888 26.7 291s 3 45.9 1.8302 28.8 291s 4 50.4 4.6632 32.8 291s 5 50.5 4.6917 34.1 291s 6 52.0 4.8028 35.9 291s 7 NA NA NA 291s 8 54.4 2.8277 38.8 291s 9 55.5 2.1139 39.0 291s 10 NA 2.4426 40.2 291s 11 55.3 1.9736 38.3 291s 12 52.4 -2.5370 34.1 291s 13 NA NA 29.0 291s 14 46.9 -6.8151 27.8 291s 15 49.5 -2.8007 30.0 291s 16 51.8 -1.4448 33.0 291s 17 55.8 -0.0295 37.5 291s 18 59.2 2.1493 40.0 291s 19 57.8 1.5730 38.8 291s 20 60.2 0.4434 41.9 291s 21 63.6 2.8090 46.1 291s 22 70.9 3.6208 52.8 291s > predict 291s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 291s 1 NA NA NA NA 291s 2 42.9 0.541 41.8 43.9 291s 3 45.9 0.608 44.6 47.1 291s 4 50.4 0.403 49.6 51.2 291s 5 50.5 0.472 49.6 51.5 291s 6 52.0 0.481 51.0 52.9 291s 7 NA NA NA NA 291s 8 54.4 0.388 53.6 55.1 291s 9 55.5 0.458 54.6 56.4 291s 10 NA NA NA NA 291s 11 55.3 0.795 53.7 56.9 291s 12 52.4 0.762 50.8 53.9 291s 13 NA NA NA NA 291s 14 46.9 0.663 45.5 48.2 291s 15 49.5 0.462 48.5 50.4 291s 16 51.8 0.381 51.0 52.5 291s 17 55.8 0.423 55.0 56.7 291s 18 59.2 0.355 58.5 59.9 291s 19 57.8 0.484 56.8 58.8 291s 20 60.2 0.500 59.2 61.2 291s 21 63.6 0.490 62.6 64.6 291s 22 70.9 0.747 69.4 72.4 291s Investment.pred Investment.se.fit Investment.lwr Investment.upr 291s 1 NA NA NA NA 291s 2 2.0888 0.985 0.105 4.072 291s 3 1.8302 0.708 0.404 3.257 291s 4 4.6632 0.612 3.430 5.897 291s 5 4.6917 0.519 3.645 5.738 291s 6 4.8028 0.498 3.800 5.806 291s 7 NA NA NA NA 291s 8 2.8277 0.410 2.003 3.653 291s 9 2.1139 0.599 0.908 3.320 291s 10 2.4426 0.651 1.131 3.754 291s 11 1.9736 1.138 -0.320 4.267 291s 12 -2.5370 1.038 -4.627 -0.447 291s 13 NA NA NA NA 291s 14 -6.8151 1.011 -8.851 -4.779 291s 15 -2.8007 0.587 -3.984 -1.617 291s 16 -1.4448 0.470 -2.392 -0.498 291s 17 -0.0295 0.573 -1.183 1.124 291s 18 2.1493 0.380 1.384 2.915 291s 19 1.5730 0.624 0.315 2.831 291s 20 0.4434 0.649 -0.864 1.751 291s 21 2.8090 0.565 1.671 3.947 291s 22 3.6208 0.814 1.982 5.260 291s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 291s 1 NA NA NA NA 291s 2 26.7 0.322 26.0 27.3 291s 3 28.8 0.328 28.2 29.5 291s 4 32.8 0.332 32.1 33.4 291s 5 34.1 0.244 33.6 34.6 291s 6 35.9 0.252 35.4 36.4 291s 7 NA NA NA NA 291s 8 38.8 0.246 38.3 39.3 291s 9 39.0 0.234 38.6 39.5 291s 10 40.2 0.230 39.8 40.7 291s 11 38.3 0.299 37.7 38.9 291s 12 34.1 0.304 33.5 34.7 291s 13 29.0 0.366 28.2 29.7 291s 14 27.8 0.321 27.2 28.5 291s 15 30.0 0.317 29.4 30.7 291s 16 33.0 0.266 32.4 33.5 291s 17 37.5 0.270 36.9 38.0 291s 18 40.0 0.211 39.6 40.5 291s 19 38.8 0.305 38.2 39.4 291s 20 41.9 0.290 41.3 42.4 291s 21 46.1 0.309 45.5 46.8 291s 22 52.8 0.468 51.8 53.7 291s > model.frame 291s [1] TRUE 291s > model.matrix 291s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0339 >" 291s [2] "Attributes: < Component “dimnames”: Component 1: 52 string mismatches >" 291s [3] "Numeric: lengths (708, 684) differ" 291s > nobs 291s [1] 57 291s > linearHypothesis 291s Linear hypothesis test (Theil's F test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df F Pr(>F) 291s 1 46 291s 2 45 1 2.17 0.15 291s Linear hypothesis test (F statistic of a Wald test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df F Pr(>F) 291s 1 46 291s 2 45 1 2.84 0.099 . 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s Linear hypothesis test (Chi^2 statistic of a Wald test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df Chisq Pr(>Chisq) 291s 1 46 291s 2 45 1 2.84 0.092 . 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s Linear hypothesis test (Theil's F test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s Consumption_corpProfLag - PrivateWages_trend = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df F Pr(>F) 291s 1 47 291s 2 45 2 2.45 0.098 . 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s Linear hypothesis test (F statistic of a Wald test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s Consumption_corpProfLag - PrivateWages_trend = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df F Pr(>F) 291s 1 47 291s 2 45 2 3.2 0.05 . 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s Linear hypothesis test (Chi^2 statistic of a Wald test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s Consumption_corpProfLag - PrivateWages_trend = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df Chisq Pr(>Chisq) 291s 1 47 291s 2 45 2 6.4 0.041 * 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s > logLik 291s 'log Lik.' -72.7 (df=18) 291s 'log Lik.' -83.9 (df=18) 291s Estimating function 291s Consumption_(Intercept) Consumption_corpProf 291s Consumption_2 -4.8293 -67.67 291s Consumption_3 -1.2969 -21.61 291s Consumption_4 0.5735 10.64 291s Consumption_5 -2.6416 -53.54 291s Consumption_6 1.4014 26.66 291s Consumption_8 6.4885 114.49 291s Consumption_9 5.8062 109.99 291s Consumption_11 -2.4210 -40.53 291s Consumption_12 -3.6335 -48.60 291s Consumption_14 0.4385 4.39 291s Consumption_15 -2.9914 -37.40 291s Consumption_16 -2.4677 -35.74 291s Consumption_17 8.1448 121.46 291s Consumption_18 -0.7823 -15.19 291s Consumption_19 -6.2524 -119.47 291s Consumption_20 4.4447 78.49 291s Consumption_21 2.3016 46.94 291s Consumption_22 -1.0069 -22.83 291s Investment_2 2.3888 33.48 291s Investment_3 -0.0694 -1.16 291s Investment_4 -0.5723 -10.61 291s Investment_5 1.7561 35.59 291s Investment_6 -0.2966 -5.64 291s Investment_8 -1.4003 -24.71 291s Investment_9 -0.9021 -17.09 291s Investment_10 0.0000 0.00 291s Investment_11 0.9937 16.63 291s Investment_12 0.8671 11.60 291s Investment_14 -1.7814 -17.86 291s Investment_15 0.1989 2.49 291s Investment_16 -0.1587 -2.30 291s Investment_17 -2.1900 -32.66 291s Investment_18 0.1172 2.28 291s Investment_19 3.5762 68.34 291s Investment_20 -0.8719 -15.40 291s Investment_21 -0.4978 -10.15 291s Investment_22 -1.3322 -30.21 291s PrivateWages_2 -4.3522 -60.99 291s PrivateWages_3 1.6337 27.22 291s PrivateWages_4 3.8487 71.37 291s PrivateWages_5 -4.1966 -85.06 291s PrivateWages_6 -0.7579 -14.42 291s PrivateWages_8 2.3542 41.54 291s PrivateWages_9 2.6975 51.10 291s PrivateWages_10 0.0000 0.00 291s PrivateWages_11 -3.6015 -60.29 291s PrivateWages_12 -0.5133 -6.87 291s PrivateWages_13 0.0000 0.00 291s PrivateWages_14 4.6825 46.94 291s PrivateWages_15 -0.1944 -2.43 291s PrivateWages_16 -0.4112 -5.96 291s PrivateWages_17 3.8500 57.41 291s PrivateWages_18 0.1148 2.23 291s PrivateWages_19 -9.2669 -177.08 291s PrivateWages_20 2.0821 36.77 291s PrivateWages_21 -1.9079 -38.91 291s PrivateWages_22 2.8370 64.33 291s Consumption_corpProfLag Consumption_wages 291s Consumption_2 -61.332 -144.02 291s Consumption_3 -16.082 -41.30 291s Consumption_4 9.693 20.22 291s Consumption_5 -48.605 -101.97 291s Consumption_6 27.187 54.02 291s Consumption_8 127.174 259.60 291s Consumption_9 114.963 242.56 291s Consumption_11 -52.537 -104.35 291s Consumption_12 -56.683 -144.08 291s Consumption_14 3.069 14.61 291s Consumption_15 -33.504 -111.68 291s Consumption_16 -30.352 -99.06 291s Consumption_17 114.027 340.28 291s Consumption_18 -13.768 -37.24 291s Consumption_19 -108.167 -307.82 291s Consumption_20 68.004 215.82 291s Consumption_21 43.729 122.95 291s Consumption_22 -21.245 -61.21 291s Investment_2 30.338 71.24 291s Investment_3 -0.861 -2.21 291s Investment_4 -9.672 -20.18 291s Investment_5 32.311 67.78 291s Investment_6 -5.754 -11.43 291s Investment_8 -27.445 -56.02 291s Investment_9 -17.861 -37.69 291s Investment_10 0.000 0.00 291s Investment_11 21.563 42.83 291s Investment_12 13.527 34.39 291s Investment_14 -12.470 -59.37 291s Investment_15 2.228 7.43 291s Investment_16 -1.952 -6.37 291s Investment_17 -30.659 -91.49 291s Investment_18 2.063 5.58 291s Investment_19 61.869 176.07 291s Investment_20 -13.340 -42.34 291s Investment_21 -9.458 -26.59 291s Investment_22 -28.109 -80.99 291s PrivateWages_2 -55.273 -129.79 291s PrivateWages_3 20.257 52.03 291s PrivateWages_4 65.044 135.69 291s PrivateWages_5 -77.218 -161.99 291s PrivateWages_6 -14.704 -29.21 291s PrivateWages_8 46.143 94.19 291s PrivateWages_9 53.410 112.69 291s PrivateWages_10 0.000 0.00 291s PrivateWages_11 -78.152 -155.23 291s PrivateWages_12 -8.008 -20.36 291s PrivateWages_13 0.000 0.00 291s PrivateWages_14 32.778 156.05 291s PrivateWages_15 -2.178 -7.26 291s PrivateWages_16 -5.058 -16.51 291s PrivateWages_17 53.901 160.85 291s PrivateWages_18 2.020 5.46 291s PrivateWages_19 -160.318 -456.23 291s PrivateWages_20 31.857 101.10 291s PrivateWages_21 -36.250 -101.92 291s PrivateWages_22 59.861 172.47 291s Investment_(Intercept) Investment_corpProf 291s Consumption_2 2.3171 31.08 291s Consumption_3 0.6223 10.39 291s Consumption_4 -0.2752 -5.17 291s Consumption_5 1.2675 26.17 291s Consumption_6 -0.6724 -12.95 291s Consumption_8 -3.1132 -54.59 291s Consumption_9 -2.7858 -54.40 291s Consumption_11 1.1616 19.97 291s Consumption_12 1.7434 23.57 291s Consumption_14 -0.2104 -2.10 291s Consumption_15 1.4353 18.46 291s Consumption_16 1.1840 16.97 291s Consumption_17 -3.9079 -58.49 291s Consumption_18 0.3753 7.27 291s Consumption_19 2.9999 58.07 291s Consumption_20 -2.1326 -37.27 291s Consumption_21 -1.1043 -22.22 291s Consumption_22 0.4831 11.01 291s Investment_2 -2.3817 -31.95 291s Investment_3 0.0692 1.16 291s Investment_4 0.5706 10.72 291s Investment_5 -1.7509 -36.15 291s Investment_6 0.2957 5.70 291s Investment_8 1.3961 24.48 291s Investment_9 0.8994 17.56 291s Investment_10 2.7604 55.95 291s Investment_11 -0.9907 -17.04 291s Investment_12 -0.8646 -11.69 291s Investment_14 1.7761 17.75 291s Investment_15 -0.1983 -2.55 291s Investment_16 0.1582 2.27 291s Investment_17 2.1835 32.68 291s Investment_18 -0.1169 -2.26 291s Investment_19 -3.5657 -69.02 291s Investment_20 0.8693 15.19 291s Investment_21 0.4963 9.99 291s Investment_22 1.3282 30.26 291s PrivateWages_2 2.5510 34.22 291s PrivateWages_3 -0.9575 -15.98 291s PrivateWages_4 -2.2559 -42.38 291s PrivateWages_5 2.4598 50.79 291s PrivateWages_6 0.4442 8.56 291s PrivateWages_8 -1.3799 -24.19 291s PrivateWages_9 -1.5811 -30.88 291s PrivateWages_10 -2.9678 -60.15 291s PrivateWages_11 2.1109 36.30 291s PrivateWages_12 0.3009 4.07 291s PrivateWages_13 0.0000 0.00 291s PrivateWages_14 -2.7446 -27.43 291s PrivateWages_15 0.1140 1.47 291s PrivateWages_16 0.2410 3.45 291s PrivateWages_17 -2.2567 -33.77 291s PrivateWages_18 -0.0673 -1.30 291s PrivateWages_19 5.4317 105.14 291s PrivateWages_20 -1.2204 -21.33 291s PrivateWages_21 1.1183 22.50 291s PrivateWages_22 -1.6629 -37.88 291s Investment_corpProfLag Investment_capitalLag 291s Consumption_2 29.428 423.6 291s Consumption_3 7.716 113.6 291s Consumption_4 -4.651 -50.8 291s Consumption_5 23.321 240.4 291s Consumption_6 -13.045 -129.6 291s Consumption_8 -61.019 -633.2 291s Consumption_9 -55.160 -578.3 291s Consumption_11 25.207 250.6 291s Consumption_12 27.197 377.8 291s Consumption_14 -1.473 -43.6 291s Consumption_15 16.075 289.9 291s Consumption_16 14.563 235.6 291s Consumption_17 -54.711 -772.6 291s Consumption_18 6.606 75.0 291s Consumption_19 51.899 605.4 291s Consumption_20 -32.629 -426.3 291s Consumption_21 -20.982 -222.2 291s Consumption_22 10.194 98.8 291s Investment_2 -30.248 -435.4 291s Investment_3 0.858 12.6 291s Investment_4 9.643 105.3 291s Investment_5 -32.216 -332.1 291s Investment_6 5.737 57.0 291s Investment_8 27.364 284.0 291s Investment_9 17.808 186.7 291s Investment_10 58.244 581.3 291s Investment_11 -21.499 -213.7 291s Investment_12 -13.487 -187.4 291s Investment_14 12.433 367.8 291s Investment_15 -2.221 -40.1 291s Investment_16 1.946 31.5 291s Investment_17 30.569 431.7 291s Investment_18 -2.057 -23.4 291s Investment_19 -61.686 -719.5 291s Investment_20 13.301 173.8 291s Investment_21 9.430 99.9 291s Investment_22 28.026 271.6 291s PrivateWages_2 32.397 466.3 291s PrivateWages_3 -11.874 -174.8 291s PrivateWages_4 -38.124 -416.2 291s PrivateWages_5 45.260 466.6 291s PrivateWages_6 8.618 85.6 291s PrivateWages_8 -27.046 -280.7 291s PrivateWages_9 -31.306 -328.2 291s PrivateWages_10 -62.621 -625.0 291s PrivateWages_11 45.808 455.3 291s PrivateWages_12 4.694 65.2 291s PrivateWages_13 0.000 0.0 291s PrivateWages_14 -19.212 -568.4 291s PrivateWages_15 1.276 23.0 291s PrivateWages_16 2.965 48.0 291s PrivateWages_17 -31.593 -446.1 291s PrivateWages_18 -1.184 -13.4 291s PrivateWages_19 93.968 1096.1 291s PrivateWages_20 -18.672 -244.0 291s PrivateWages_21 21.247 225.0 291s PrivateWages_22 -35.087 -340.1 291s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 291s Consumption_2 -5.2993 -249.44 -237.94 291s Consumption_3 -1.4232 -70.56 -64.90 291s Consumption_4 0.6293 35.58 31.53 291s Consumption_5 -2.8987 -175.91 -165.80 291s Consumption_6 1.5378 93.21 87.81 291s Consumption_8 7.1199 427.18 455.67 291s Consumption_9 6.3712 396.73 410.31 291s Consumption_11 -2.6567 -169.26 -178.00 291s Consumption_12 -3.9871 -218.62 -244.01 291s Consumption_14 0.4811 20.27 21.31 291s Consumption_15 -3.2826 -168.12 -148.04 291s Consumption_16 -2.7078 -149.85 -134.58 291s Consumption_17 8.9374 512.95 486.20 291s Consumption_18 -0.8584 -57.66 -53.82 291s Consumption_19 -6.8609 -470.06 -445.96 291s Consumption_20 4.8772 326.02 297.02 291s Consumption_21 2.5255 189.08 175.52 291s Consumption_22 -1.1049 -95.99 -83.64 291s Investment_2 3.2022 150.73 143.78 291s Investment_3 -0.0931 -4.61 -4.24 291s Investment_4 -0.7671 -43.38 -38.43 291s Investment_5 2.3540 142.85 134.65 291s Investment_6 -0.3976 -24.10 -22.70 291s Investment_8 -1.8770 -112.62 -120.13 291s Investment_9 -1.2092 -75.30 -77.87 291s Investment_10 -3.7113 -239.64 -239.38 291s Investment_11 1.3320 84.87 89.25 291s Investment_12 1.1624 63.74 71.14 291s Investment_14 -2.3880 -100.60 -105.79 291s Investment_15 0.2667 13.66 12.03 291s Investment_16 -0.2127 -11.77 -10.57 291s Investment_17 -2.9356 -168.49 -159.70 291s Investment_18 0.1571 10.56 9.85 291s Investment_19 4.7939 328.45 311.61 291s Investment_20 -1.1688 -78.13 -71.18 291s Investment_21 -0.6673 -49.96 -46.38 291s Investment_22 -1.7858 -155.15 -135.18 291s PrivateWages_2 -8.5877 -404.22 -385.59 291s PrivateWages_3 3.2235 159.82 146.99 291s PrivateWages_4 7.5943 429.40 380.48 291s PrivateWages_5 -8.2808 -502.53 -473.66 291s PrivateWages_6 -1.4955 -90.64 -85.39 291s PrivateWages_8 4.6454 278.71 297.31 291s PrivateWages_9 5.3226 331.43 342.78 291s PrivateWages_10 9.9910 645.11 644.42 291s PrivateWages_11 -7.1064 -452.76 -476.13 291s PrivateWages_12 -1.0129 -55.54 -61.99 291s PrivateWages_13 -5.2725 -247.69 -281.55 291s PrivateWages_14 9.2395 389.24 409.31 291s PrivateWages_15 -0.3837 -19.65 -17.30 291s PrivateWages_16 -0.8115 -44.91 -40.33 291s PrivateWages_17 7.5969 436.02 413.27 291s PrivateWages_18 0.2264 15.21 14.20 291s PrivateWages_19 -18.2855 -1252.79 -1188.56 291s PrivateWages_20 4.1085 274.63 250.21 291s PrivateWages_21 -3.7647 -281.85 -261.64 291s PrivateWages_22 5.5980 486.35 423.77 291s PrivateWages_trend 291s Consumption_2 52.993 291s Consumption_3 12.808 291s Consumption_4 -5.035 291s Consumption_5 20.291 291s Consumption_6 -9.227 291s Consumption_8 -28.480 291s Consumption_9 -19.114 291s Consumption_11 2.657 291s Consumption_12 0.000 291s Consumption_14 0.962 291s Consumption_15 -9.848 291s Consumption_16 -10.831 291s Consumption_17 44.687 291s Consumption_18 -5.151 291s Consumption_19 -48.026 291s Consumption_20 39.018 291s Consumption_21 22.730 291s Consumption_22 -11.049 291s Investment_2 -32.022 291s Investment_3 0.838 291s Investment_4 6.137 291s Investment_5 -16.478 291s Investment_6 2.386 291s Investment_8 7.508 291s Investment_9 3.628 291s Investment_10 7.423 291s Investment_11 -1.332 291s Investment_12 0.000 291s Investment_14 -4.776 291s Investment_15 0.800 291s Investment_16 -0.851 291s Investment_17 -14.678 291s Investment_18 0.943 291s Investment_19 33.558 291s Investment_20 -9.351 291s Investment_21 -6.006 291s Investment_22 -17.858 291s PrivateWages_2 85.877 291s PrivateWages_3 -29.012 291s PrivateWages_4 -60.755 291s PrivateWages_5 57.966 291s PrivateWages_6 8.973 291s PrivateWages_8 -18.582 291s PrivateWages_9 -15.968 291s PrivateWages_10 -19.982 291s PrivateWages_11 7.106 291s PrivateWages_12 0.000 291s PrivateWages_13 -5.272 291s PrivateWages_14 18.479 291s PrivateWages_15 -1.151 291s PrivateWages_16 -3.246 291s PrivateWages_17 37.985 291s PrivateWages_18 1.359 291s PrivateWages_19 -127.998 291s PrivateWages_20 32.868 291s PrivateWages_21 -33.882 291s PrivateWages_22 55.980 291s [1] TRUE 291s > Bread 291s Consumption_(Intercept) Consumption_corpProf Consumption_corpProfLag 291s [1,] 132.5589 -4.1405 0.7711 291s [2,] -4.1405 1.1839 -0.6491 291s [3,] 0.7711 -0.6491 0.7009 291s [4,] -1.6944 -0.1297 -0.0283 291s [5,] 114.8656 3.1837 5.1587 291s [6,] -5.5704 0.7491 -0.6223 291s [7,] 1.9218 -0.4973 0.5817 291s [8,] -0.2370 -0.0398 -0.0201 291s [9,] -36.8131 0.3292 1.6643 291s [10,] 0.5110 -0.0698 0.0440 291s [11,] 0.0898 0.0655 -0.0737 291s [12,] 0.2835 0.0505 0.0244 291s Consumption_wages Investment_(Intercept) Investment_corpProf 291s [1,] -1.694379 114.87 -5.57043 291s [2,] -0.129702 3.18 0.74914 291s [3,] -0.028262 5.16 -0.62232 291s [4,] 0.104489 -5.87 0.06772 291s [5,] -5.874854 3366.95 -56.98587 291s [6,] 0.067720 -56.99 2.64551 291s [7,] -0.069795 45.44 -2.02544 291s [8,] 0.029271 -15.60 0.22292 291s [9,] 0.075832 53.51 -0.48750 291s [10,] -0.001892 2.12 0.00442 291s [11,] 0.000817 -3.12 0.00410 291s [12,] -0.036920 -1.40 0.02820 291s Investment_corpProfLag Investment_capitalLag PrivateWages_(Intercept) 291s [1,] 1.92185 -0.23700 -36.8131 291s [2,] -0.49725 -0.03983 0.3292 291s [3,] 0.58170 -0.02007 1.6643 291s [4,] -0.06979 0.02927 0.0758 291s [5,] 45.44092 -15.60143 53.5110 291s [6,] -2.02544 0.22292 -0.4875 291s [7,] 1.95029 -0.21271 -0.7904 291s [8,] -0.21271 0.07616 -0.1618 291s [9,] -0.79038 -0.16180 69.6580 291s [10,] 0.00806 -0.01150 -0.3039 291s [11,] 0.00580 0.01472 -0.8753 291s [12,] -0.04133 0.00782 0.7539 291s PrivateWages_gnp PrivateWages_gnpLag PrivateWages_trend 291s [1,] 0.51104 0.089786 0.283482 291s [2,] -0.06979 0.065456 0.050508 291s [3,] 0.04399 -0.073692 0.024378 291s [4,] -0.00189 0.000817 -0.036920 291s [5,] 2.11576 -3.117775 -1.396100 291s [6,] 0.00442 0.004099 0.028202 291s [7,] 0.00806 0.005798 -0.041335 291s [8,] -0.01150 0.014719 0.007824 291s [9,] -0.30387 -0.875279 0.753905 291s [10,] 0.04699 -0.042862 -0.013049 291s [11,] -0.04286 0.059096 0.000172 291s [12,] -0.01305 0.000172 0.045631 291s > 291s > # OLS 291s Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 291s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 291s > summary 291s 291s systemfit results 291s method: OLS 291s 291s N DF SSR detRCov OLS-R2 McElroy-R2 291s system 58 46 44.2 0.565 0.976 0.991 291s 291s N DF SSR MSE RMSE R2 Adj R2 291s Consumption 19 15 17.36 1.157 1.08 0.980 0.976 291s Investment 19 15 17.11 1.140 1.07 0.907 0.889 291s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 291s 291s The covariance matrix of the residuals 291s Consumption Investment PrivateWages 291s Consumption 1.285 0.061 -0.511 291s Investment 0.061 1.059 0.151 291s PrivateWages -0.511 0.151 0.648 291s 291s The correlations of the residuals 291s Consumption Investment PrivateWages 291s Consumption 1.0000 0.0457 -0.568 291s Investment 0.0457 1.0000 0.168 291s PrivateWages -0.5681 0.1676 1.000 291s 291s 291s OLS estimates for 'Consumption' (equation 1) 291s Model Formula: consump ~ corpProf + corpProfLag + wages 291s 291s Estimate Std. Error t value Pr(>|t|) 291s (Intercept) 16.2957 1.5438 10.56 2.4e-08 *** 291s corpProf 0.1796 0.1206 1.49 0.16 291s corpProfLag 0.1032 0.1031 1.00 0.33 291s wages 0.7962 0.0449 17.73 1.8e-11 *** 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s 291s Residual standard error: 1.076 on 15 degrees of freedom 291s Number of observations: 19 Degrees of Freedom: 15 291s SSR: 17.362 MSE: 1.157 Root MSE: 1.076 291s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.976 291s 291s 291s OLS estimates for 'Investment' (equation 2) 291s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 291s 291s Estimate Std. Error t value Pr(>|t|) 291s (Intercept) 10.1724 5.5758 1.82 0.08808 . 291s corpProf 0.5004 0.1092 4.58 0.00036 *** 291s corpProfLag 0.3270 0.1052 3.11 0.00718 ** 291s capitalLag -0.1134 0.0275 -4.13 0.00090 *** 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s 291s Residual standard error: 1.068 on 15 degrees of freedom 291s Number of observations: 19 Degrees of Freedom: 15 291s SSR: 17.105 MSE: 1.14 Root MSE: 1.068 291s Multiple R-Squared: 0.907 Adjusted R-Squared: 0.889 291s 291s 291s OLS estimates for 'PrivateWages' (equation 3) 291s Model Formula: privWage ~ gnp + gnpLag + trend 291s 291s Estimate Std. Error t value Pr(>|t|) 291s (Intercept) 1.3550 1.3512 1.00 0.3309 291s gnp 0.4417 0.0342 12.92 7e-10 *** 291s gnpLag 0.1466 0.0393 3.73 0.0018 ** 291s trend 0.1244 0.0347 3.58 0.0025 ** 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s 291s Residual standard error: 0.78 on 16 degrees of freedom 291s Number of observations: 20 Degrees of Freedom: 16 291s SSR: 9.739 MSE: 0.609 Root MSE: 0.78 291s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 291s 291s compare coef with single-equation OLS 291s [1] TRUE 291s > residuals 291s Consumption Investment PrivateWages 291s 1 NA NA NA 291s 2 -0.3863 0.00693 -1.3389 291s 3 -1.2484 -0.06954 0.2462 291s 4 -1.6040 1.22401 1.1255 291s 5 -0.5384 -1.37697 -0.1959 291s 6 -0.0413 0.38610 -0.5284 291s 7 0.8043 1.48598 NA 291s 8 1.2830 0.78465 -0.7909 291s 9 1.0142 -0.65483 0.2819 291s 10 NA 1.06018 1.1384 291s 11 0.1429 0.39508 -0.1904 291s 12 -0.3439 0.20479 0.5813 291s 13 NA NA 0.1206 291s 14 0.3199 0.32778 0.4773 291s 15 -0.1016 -0.07450 0.3035 291s 16 -0.0702 NA 0.0284 291s 17 1.6064 0.96998 -0.8517 291s 18 -0.4980 0.08124 0.9908 291s 19 0.1253 -2.49295 -0.4597 291s 20 0.9805 -0.70609 -0.3819 291s 21 0.7551 -0.81928 -1.1062 291s 22 -2.1992 -0.73256 0.5501 291s > fitted 291s Consumption Investment PrivateWages 291s 1 NA NA NA 291s 2 42.3 -0.207 26.8 291s 3 46.2 1.970 29.1 291s 4 50.8 3.976 33.0 291s 5 51.1 4.377 34.1 291s 6 52.6 4.714 35.9 291s 7 54.3 4.114 NA 291s 8 54.9 3.415 38.7 291s 9 56.3 3.655 38.9 291s 10 NA 4.040 40.2 291s 11 54.9 0.605 38.1 291s 12 51.2 -3.605 33.9 291s 13 NA NA 28.9 291s 14 46.2 -5.428 28.0 291s 15 48.8 -2.926 30.3 291s 16 51.4 NA 33.2 291s 17 56.1 1.130 37.7 291s 18 59.2 1.919 40.0 291s 19 57.4 0.593 38.7 291s 20 60.6 2.006 42.0 291s 21 64.2 4.119 46.1 291s 22 71.9 5.633 52.7 291s > predict 291s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 291s 1 NA NA NA NA 291s 2 42.3 0.543 39.9 44.7 291s 3 46.2 0.581 43.8 48.7 291s 4 50.8 0.394 48.5 53.1 291s 5 51.1 0.465 48.8 53.5 291s 6 52.6 0.474 50.3 55.0 291s 7 54.3 0.423 52.0 56.6 291s 8 54.9 0.389 52.6 57.2 291s 9 56.3 0.434 54.0 58.6 291s 10 NA NA NA NA 291s 11 54.9 0.727 52.2 57.5 291s 12 51.2 0.662 48.7 53.8 291s 13 NA NA NA NA 291s 14 46.2 0.698 43.6 48.8 291s 15 48.8 0.470 46.4 51.2 291s 16 51.4 0.398 49.1 53.7 291s 17 56.1 0.405 53.8 58.4 291s 18 59.2 0.375 56.9 61.5 291s 19 57.4 0.466 55.0 59.7 291s 20 60.6 0.482 58.2 63.0 291s 21 64.2 0.485 61.9 66.6 291s 22 71.9 0.755 69.3 74.5 291s Investment.pred Investment.se.fit Investment.lwr Investment.upr 291s 1 NA NA NA NA 291s 2 -0.207 0.645 -2.718 2.30 291s 3 1.970 0.523 -0.423 4.36 291s 4 3.976 0.462 1.634 6.32 291s 5 4.377 0.383 2.094 6.66 291s 6 4.714 0.362 2.444 6.98 291s 7 4.114 0.336 1.861 6.37 291s 8 3.415 0.298 1.184 5.65 291s 9 3.655 0.400 1.359 5.95 291s 10 4.040 0.458 1.701 6.38 291s 11 0.605 0.666 -1.928 3.14 291s 12 -3.605 0.637 -6.108 -1.10 291s 13 NA NA NA NA 291s 14 -5.428 0.767 -8.074 -2.78 291s 15 -2.926 0.453 -5.261 -0.59 291s 16 NA NA NA NA 291s 17 1.130 0.366 -1.142 3.40 291s 18 1.919 0.258 -0.293 4.13 291s 19 0.593 0.357 -1.674 2.86 291s 20 2.006 0.384 -0.278 4.29 291s 21 4.119 0.350 1.858 6.38 291s 22 5.633 0.495 3.263 8.00 291s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 291s 1 NA NA NA NA 291s 2 26.8 0.378 25.1 28.6 291s 3 29.1 0.381 27.3 30.8 291s 4 33.0 0.384 31.2 34.7 291s 5 34.1 0.297 32.4 35.8 291s 6 35.9 0.296 34.2 37.6 291s 7 NA NA NA NA 291s 8 38.7 0.303 37.0 40.4 291s 9 38.9 0.288 37.2 40.6 291s 10 40.2 0.274 38.5 41.8 291s 11 38.1 0.377 36.3 39.8 291s 12 33.9 0.381 32.2 35.7 291s 13 28.9 0.452 27.1 30.7 291s 14 28.0 0.397 26.3 29.8 291s 15 30.3 0.391 28.5 32.1 291s 16 33.2 0.327 31.5 34.9 291s 17 37.7 0.320 36.0 39.3 291s 18 40.0 0.250 38.4 41.7 291s 19 38.7 0.375 36.9 40.4 291s 20 42.0 0.337 40.3 43.7 291s 21 46.1 0.352 44.4 47.8 291s 22 52.7 0.530 50.9 54.6 291s > model.frame 291s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 291s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 291s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 291s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 291s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 291s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 291s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 291s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 291s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 291s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 291s 10 57.8 21.7 21.1 NA 5.1 211 41.3 67.0 64.5 291s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 291s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 291s 13 45.6 NA 11.4 34.3 -6.2 213 29.0 44.3 53.4 291s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 291s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 291s 16 51.3 14.0 12.3 39.3 NA 199 33.2 54.4 49.7 291s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 291s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 291s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 291s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 291s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 291s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 291s trend 291s 1 -11 291s 2 -10 291s 3 -9 291s 4 -8 291s 5 -7 291s 6 -6 291s 7 -5 291s 8 -4 291s 9 -3 291s 10 -2 291s 11 -1 291s 12 0 291s 13 1 291s 14 2 291s 15 3 291s 16 4 291s 17 5 291s 18 6 291s 19 7 291s 20 8 291s 21 9 291s 22 10 291s > model.matrix 291s Consumption_(Intercept) Consumption_corpProf 291s Consumption_2 1 12.4 291s Consumption_3 1 16.9 291s Consumption_4 1 18.4 291s Consumption_5 1 19.4 291s Consumption_6 1 20.1 291s Consumption_7 1 19.6 291s Consumption_8 1 19.8 291s Consumption_9 1 21.1 291s Consumption_11 1 15.6 291s Consumption_12 1 11.4 291s Consumption_14 1 11.2 291s Consumption_15 1 12.3 291s Consumption_16 1 14.0 291s Consumption_17 1 17.6 291s Consumption_18 1 17.3 291s Consumption_19 1 15.3 291s Consumption_20 1 19.0 291s Consumption_21 1 21.1 291s Consumption_22 1 23.5 291s Investment_2 0 0.0 291s Investment_3 0 0.0 291s Investment_4 0 0.0 291s Investment_5 0 0.0 291s Investment_6 0 0.0 291s Investment_7 0 0.0 291s Investment_8 0 0.0 291s Investment_9 0 0.0 291s Investment_10 0 0.0 291s Investment_11 0 0.0 291s Investment_12 0 0.0 291s Investment_14 0 0.0 291s Investment_15 0 0.0 291s Investment_17 0 0.0 291s Investment_18 0 0.0 291s Investment_19 0 0.0 291s Investment_20 0 0.0 291s Investment_21 0 0.0 291s Investment_22 0 0.0 291s PrivateWages_2 0 0.0 291s PrivateWages_3 0 0.0 291s PrivateWages_4 0 0.0 291s PrivateWages_5 0 0.0 291s PrivateWages_6 0 0.0 291s PrivateWages_8 0 0.0 291s PrivateWages_9 0 0.0 291s PrivateWages_10 0 0.0 291s PrivateWages_11 0 0.0 291s PrivateWages_12 0 0.0 291s PrivateWages_13 0 0.0 291s PrivateWages_14 0 0.0 291s PrivateWages_15 0 0.0 291s PrivateWages_16 0 0.0 291s PrivateWages_17 0 0.0 291s PrivateWages_18 0 0.0 291s PrivateWages_19 0 0.0 291s PrivateWages_20 0 0.0 291s PrivateWages_21 0 0.0 291s PrivateWages_22 0 0.0 291s Consumption_corpProfLag Consumption_wages 291s Consumption_2 12.7 28.2 291s Consumption_3 12.4 32.2 291s Consumption_4 16.9 37.0 291s Consumption_5 18.4 37.0 291s Consumption_6 19.4 38.6 291s Consumption_7 20.1 40.7 291s Consumption_8 19.6 41.5 291s Consumption_9 19.8 42.9 291s Consumption_11 21.7 42.1 291s Consumption_12 15.6 39.3 291s Consumption_14 7.0 34.1 291s Consumption_15 11.2 36.6 291s Consumption_16 12.3 39.3 291s Consumption_17 14.0 44.2 291s Consumption_18 17.6 47.7 291s Consumption_19 17.3 45.9 291s Consumption_20 15.3 49.4 291s Consumption_21 19.0 53.0 291s Consumption_22 21.1 61.8 291s Investment_2 0.0 0.0 291s Investment_3 0.0 0.0 291s Investment_4 0.0 0.0 291s Investment_5 0.0 0.0 291s Investment_6 0.0 0.0 291s Investment_7 0.0 0.0 291s Investment_8 0.0 0.0 291s Investment_9 0.0 0.0 291s Investment_10 0.0 0.0 291s Investment_11 0.0 0.0 291s Investment_12 0.0 0.0 291s Investment_14 0.0 0.0 291s Investment_15 0.0 0.0 291s Investment_17 0.0 0.0 291s Investment_18 0.0 0.0 291s Investment_19 0.0 0.0 291s Investment_20 0.0 0.0 291s Investment_21 0.0 0.0 291s Investment_22 0.0 0.0 291s PrivateWages_2 0.0 0.0 291s PrivateWages_3 0.0 0.0 291s PrivateWages_4 0.0 0.0 291s PrivateWages_5 0.0 0.0 291s PrivateWages_6 0.0 0.0 291s PrivateWages_8 0.0 0.0 291s PrivateWages_9 0.0 0.0 291s PrivateWages_10 0.0 0.0 291s PrivateWages_11 0.0 0.0 291s PrivateWages_12 0.0 0.0 291s PrivateWages_13 0.0 0.0 291s PrivateWages_14 0.0 0.0 291s PrivateWages_15 0.0 0.0 291s PrivateWages_16 0.0 0.0 291s PrivateWages_17 0.0 0.0 291s PrivateWages_18 0.0 0.0 291s PrivateWages_19 0.0 0.0 291s PrivateWages_20 0.0 0.0 291s PrivateWages_21 0.0 0.0 291s PrivateWages_22 0.0 0.0 291s Investment_(Intercept) Investment_corpProf 291s Consumption_2 0 0.0 291s Consumption_3 0 0.0 291s Consumption_4 0 0.0 291s Consumption_5 0 0.0 291s Consumption_6 0 0.0 291s Consumption_7 0 0.0 291s Consumption_8 0 0.0 291s Consumption_9 0 0.0 291s Consumption_11 0 0.0 291s Consumption_12 0 0.0 291s Consumption_14 0 0.0 291s Consumption_15 0 0.0 291s Consumption_16 0 0.0 291s Consumption_17 0 0.0 291s Consumption_18 0 0.0 291s Consumption_19 0 0.0 291s Consumption_20 0 0.0 291s Consumption_21 0 0.0 291s Consumption_22 0 0.0 291s Investment_2 1 12.4 291s Investment_3 1 16.9 291s Investment_4 1 18.4 291s Investment_5 1 19.4 291s Investment_6 1 20.1 291s Investment_7 1 19.6 291s Investment_8 1 19.8 291s Investment_9 1 21.1 291s Investment_10 1 21.7 291s Investment_11 1 15.6 291s Investment_12 1 11.4 291s Investment_14 1 11.2 291s Investment_15 1 12.3 291s Investment_17 1 17.6 291s Investment_18 1 17.3 291s Investment_19 1 15.3 291s Investment_20 1 19.0 291s Investment_21 1 21.1 291s Investment_22 1 23.5 291s PrivateWages_2 0 0.0 291s PrivateWages_3 0 0.0 291s PrivateWages_4 0 0.0 291s PrivateWages_5 0 0.0 291s PrivateWages_6 0 0.0 291s PrivateWages_8 0 0.0 291s PrivateWages_9 0 0.0 291s PrivateWages_10 0 0.0 291s PrivateWages_11 0 0.0 291s PrivateWages_12 0 0.0 291s PrivateWages_13 0 0.0 291s PrivateWages_14 0 0.0 291s PrivateWages_15 0 0.0 291s PrivateWages_16 0 0.0 291s PrivateWages_17 0 0.0 291s PrivateWages_18 0 0.0 291s PrivateWages_19 0 0.0 291s PrivateWages_20 0 0.0 291s PrivateWages_21 0 0.0 291s PrivateWages_22 0 0.0 291s Investment_corpProfLag Investment_capitalLag 291s Consumption_2 0.0 0 291s Consumption_3 0.0 0 291s Consumption_4 0.0 0 291s Consumption_5 0.0 0 291s Consumption_6 0.0 0 291s Consumption_7 0.0 0 291s Consumption_8 0.0 0 291s Consumption_9 0.0 0 291s Consumption_11 0.0 0 291s Consumption_12 0.0 0 291s Consumption_14 0.0 0 291s Consumption_15 0.0 0 291s Consumption_16 0.0 0 291s Consumption_17 0.0 0 291s Consumption_18 0.0 0 291s Consumption_19 0.0 0 291s Consumption_20 0.0 0 291s Consumption_21 0.0 0 291s Consumption_22 0.0 0 291s Investment_2 12.7 183 291s Investment_3 12.4 183 291s Investment_4 16.9 184 291s Investment_5 18.4 190 291s Investment_6 19.4 193 291s Investment_7 20.1 198 291s Investment_8 19.6 203 291s Investment_9 19.8 208 291s Investment_10 21.1 211 291s Investment_11 21.7 216 291s Investment_12 15.6 217 291s Investment_14 7.0 207 291s Investment_15 11.2 202 291s Investment_17 14.0 198 291s Investment_18 17.6 200 291s Investment_19 17.3 202 291s Investment_20 15.3 200 291s Investment_21 19.0 201 291s Investment_22 21.1 204 291s PrivateWages_2 0.0 0 291s PrivateWages_3 0.0 0 291s PrivateWages_4 0.0 0 291s PrivateWages_5 0.0 0 291s PrivateWages_6 0.0 0 291s PrivateWages_8 0.0 0 291s PrivateWages_9 0.0 0 291s PrivateWages_10 0.0 0 291s PrivateWages_11 0.0 0 291s PrivateWages_12 0.0 0 291s PrivateWages_13 0.0 0 291s PrivateWages_14 0.0 0 291s PrivateWages_15 0.0 0 291s PrivateWages_16 0.0 0 291s PrivateWages_17 0.0 0 291s PrivateWages_18 0.0 0 291s PrivateWages_19 0.0 0 291s PrivateWages_20 0.0 0 291s PrivateWages_21 0.0 0 291s PrivateWages_22 0.0 0 291s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 291s Consumption_2 0 0.0 0.0 291s Consumption_3 0 0.0 0.0 291s Consumption_4 0 0.0 0.0 291s Consumption_5 0 0.0 0.0 291s Consumption_6 0 0.0 0.0 291s Consumption_7 0 0.0 0.0 291s Consumption_8 0 0.0 0.0 291s Consumption_9 0 0.0 0.0 291s Consumption_11 0 0.0 0.0 291s Consumption_12 0 0.0 0.0 291s Consumption_14 0 0.0 0.0 291s Consumption_15 0 0.0 0.0 291s Consumption_16 0 0.0 0.0 291s Consumption_17 0 0.0 0.0 291s Consumption_18 0 0.0 0.0 291s Consumption_19 0 0.0 0.0 291s Consumption_20 0 0.0 0.0 291s Consumption_21 0 0.0 0.0 291s Consumption_22 0 0.0 0.0 291s Investment_2 0 0.0 0.0 291s Investment_3 0 0.0 0.0 291s Investment_4 0 0.0 0.0 291s Investment_5 0 0.0 0.0 291s Investment_6 0 0.0 0.0 291s Investment_7 0 0.0 0.0 291s Investment_8 0 0.0 0.0 291s Investment_9 0 0.0 0.0 291s Investment_10 0 0.0 0.0 291s Investment_11 0 0.0 0.0 291s Investment_12 0 0.0 0.0 291s Investment_14 0 0.0 0.0 291s Investment_15 0 0.0 0.0 291s Investment_17 0 0.0 0.0 291s Investment_18 0 0.0 0.0 291s Investment_19 0 0.0 0.0 291s Investment_20 0 0.0 0.0 291s Investment_21 0 0.0 0.0 291s Investment_22 0 0.0 0.0 291s PrivateWages_2 1 45.6 44.9 291s PrivateWages_3 1 50.1 45.6 291s PrivateWages_4 1 57.2 50.1 291s PrivateWages_5 1 57.1 57.2 291s PrivateWages_6 1 61.0 57.1 291s PrivateWages_8 1 64.4 64.0 291s PrivateWages_9 1 64.5 64.4 291s PrivateWages_10 1 67.0 64.5 291s PrivateWages_11 1 61.2 67.0 291s PrivateWages_12 1 53.4 61.2 291s PrivateWages_13 1 44.3 53.4 291s PrivateWages_14 1 45.1 44.3 291s PrivateWages_15 1 49.7 45.1 291s PrivateWages_16 1 54.4 49.7 291s PrivateWages_17 1 62.7 54.4 291s PrivateWages_18 1 65.0 62.7 291s PrivateWages_19 1 60.9 65.0 291s PrivateWages_20 1 69.5 60.9 291s PrivateWages_21 1 75.7 69.5 291s PrivateWages_22 1 88.4 75.7 291s PrivateWages_trend 291s Consumption_2 0 291s Consumption_3 0 291s Consumption_4 0 291s Consumption_5 0 291s Consumption_6 0 291s Consumption_7 0 291s Consumption_8 0 291s Consumption_9 0 291s Consumption_11 0 291s Consumption_12 0 291s Consumption_14 0 291s Consumption_15 0 291s Consumption_16 0 291s Consumption_17 0 291s Consumption_18 0 291s Consumption_19 0 291s Consumption_20 0 291s Consumption_21 0 291s Consumption_22 0 291s Investment_2 0 291s Investment_3 0 291s Investment_4 0 291s Investment_5 0 291s Investment_6 0 291s Investment_7 0 291s Investment_8 0 291s Investment_9 0 291s Investment_10 0 291s Investment_11 0 291s Investment_12 0 291s Investment_14 0 291s Investment_15 0 291s Investment_17 0 291s Investment_18 0 291s Investment_19 0 291s Investment_20 0 291s Investment_21 0 291s Investment_22 0 291s PrivateWages_2 -10 291s PrivateWages_3 -9 291s PrivateWages_4 -8 291s PrivateWages_5 -7 291s PrivateWages_6 -6 291s PrivateWages_8 -4 291s PrivateWages_9 -3 291s PrivateWages_10 -2 291s PrivateWages_11 -1 291s PrivateWages_12 0 291s PrivateWages_13 1 291s PrivateWages_14 2 291s PrivateWages_15 3 291s PrivateWages_16 4 291s PrivateWages_17 5 291s PrivateWages_18 6 291s PrivateWages_19 7 291s PrivateWages_20 8 291s PrivateWages_21 9 291s PrivateWages_22 10 291s > nobs 291s [1] 58 291s > linearHypothesis 291s Linear hypothesis test (Theil's F test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df F Pr(>F) 291s 1 47 291s 2 46 1 0.3 0.59 291s Linear hypothesis test (F statistic of a Wald test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df F Pr(>F) 291s 1 47 291s 2 46 1 0.29 0.6 291s Linear hypothesis test (Chi^2 statistic of a Wald test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df Chisq Pr(>Chisq) 291s 1 47 291s 2 46 1 0.29 0.59 291s Linear hypothesis test (Theil's F test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s Consumption_corpProfLag - PrivateWages_trend = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df F Pr(>F) 291s 1 48 291s 2 46 2 0.16 0.85 291s Linear hypothesis test (F statistic of a Wald test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s Consumption_corpProfLag - PrivateWages_trend = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df F Pr(>F) 291s 1 48 291s 2 46 2 0.15 0.86 291s Linear hypothesis test (Chi^2 statistic of a Wald test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s Consumption_corpProfLag - PrivateWages_trend = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df Chisq Pr(>Chisq) 291s 1 48 291s 2 46 2 0.3 0.86 291s > logLik 291s 'log Lik.' -68.8 (df=13) 291s 'log Lik.' -73.3 (df=13) 291s compare log likelihood value with single-equation OLS 291s [1] "Mean relative difference: 0.0011" 291s Estimating function 291s Consumption_(Intercept) Consumption_corpProf 291s Consumption_2 -0.3863 -4.791 291s Consumption_3 -1.2484 -21.098 291s Consumption_4 -1.6040 -29.514 291s Consumption_5 -0.5384 -10.446 291s Consumption_6 -0.0413 -0.830 291s Consumption_7 0.8043 15.763 291s Consumption_8 1.2830 25.403 291s Consumption_9 1.0142 21.399 291s Consumption_11 0.1429 2.229 291s Consumption_12 -0.3439 -3.920 291s Consumption_14 0.3199 3.583 291s Consumption_15 -0.1016 -1.250 291s Consumption_16 -0.0702 -0.983 291s Consumption_17 1.6064 28.272 291s Consumption_18 -0.4980 -8.616 291s Consumption_19 0.1253 1.917 291s Consumption_20 0.9805 18.629 291s Consumption_21 0.7551 15.933 291s Consumption_22 -2.1992 -51.681 291s Investment_2 0.0000 0.000 291s Investment_3 0.0000 0.000 291s Investment_4 0.0000 0.000 291s Investment_5 0.0000 0.000 291s Investment_6 0.0000 0.000 291s Investment_7 0.0000 0.000 291s Investment_8 0.0000 0.000 291s Investment_9 0.0000 0.000 291s Investment_10 0.0000 0.000 291s Investment_11 0.0000 0.000 291s Investment_12 0.0000 0.000 291s Investment_14 0.0000 0.000 291s Investment_15 0.0000 0.000 291s Investment_17 0.0000 0.000 291s Investment_18 0.0000 0.000 291s Investment_19 0.0000 0.000 291s Investment_20 0.0000 0.000 291s Investment_21 0.0000 0.000 291s Investment_22 0.0000 0.000 291s PrivateWages_2 0.0000 0.000 291s PrivateWages_3 0.0000 0.000 291s PrivateWages_4 0.0000 0.000 291s PrivateWages_5 0.0000 0.000 291s PrivateWages_6 0.0000 0.000 291s PrivateWages_8 0.0000 0.000 291s PrivateWages_9 0.0000 0.000 291s PrivateWages_10 0.0000 0.000 291s PrivateWages_11 0.0000 0.000 291s PrivateWages_12 0.0000 0.000 291s PrivateWages_13 0.0000 0.000 291s PrivateWages_14 0.0000 0.000 291s PrivateWages_15 0.0000 0.000 291s PrivateWages_16 0.0000 0.000 291s PrivateWages_17 0.0000 0.000 291s PrivateWages_18 0.0000 0.000 291s PrivateWages_19 0.0000 0.000 291s PrivateWages_20 0.0000 0.000 291s PrivateWages_21 0.0000 0.000 291s PrivateWages_22 0.0000 0.000 291s Consumption_corpProfLag Consumption_wages 291s Consumption_2 -4.907 -10.90 291s Consumption_3 -15.480 -40.20 291s Consumption_4 -27.108 -59.35 291s Consumption_5 -9.907 -19.92 291s Consumption_6 -0.801 -1.59 291s Consumption_7 16.166 32.73 291s Consumption_8 25.146 53.24 291s Consumption_9 20.081 43.51 291s Consumption_11 3.100 6.01 291s Consumption_12 -5.364 -13.51 291s Consumption_14 2.239 10.91 291s Consumption_15 -1.138 -3.72 291s Consumption_16 -0.864 -2.76 291s Consumption_17 22.489 71.00 291s Consumption_18 -8.765 -23.76 291s Consumption_19 2.168 5.75 291s Consumption_20 15.002 48.44 291s Consumption_21 14.348 40.02 291s Consumption_22 -46.403 -135.91 291s Investment_2 0.000 0.00 291s Investment_3 0.000 0.00 291s Investment_4 0.000 0.00 291s Investment_5 0.000 0.00 291s Investment_6 0.000 0.00 291s Investment_7 0.000 0.00 291s Investment_8 0.000 0.00 291s Investment_9 0.000 0.00 291s Investment_10 0.000 0.00 291s Investment_11 0.000 0.00 291s Investment_12 0.000 0.00 291s Investment_14 0.000 0.00 291s Investment_15 0.000 0.00 291s Investment_17 0.000 0.00 291s Investment_18 0.000 0.00 291s Investment_19 0.000 0.00 291s Investment_20 0.000 0.00 291s Investment_21 0.000 0.00 291s Investment_22 0.000 0.00 291s PrivateWages_2 0.000 0.00 291s PrivateWages_3 0.000 0.00 291s PrivateWages_4 0.000 0.00 291s PrivateWages_5 0.000 0.00 291s PrivateWages_6 0.000 0.00 291s PrivateWages_8 0.000 0.00 291s PrivateWages_9 0.000 0.00 291s PrivateWages_10 0.000 0.00 291s PrivateWages_11 0.000 0.00 291s PrivateWages_12 0.000 0.00 291s PrivateWages_13 0.000 0.00 291s PrivateWages_14 0.000 0.00 291s PrivateWages_15 0.000 0.00 291s PrivateWages_16 0.000 0.00 291s PrivateWages_17 0.000 0.00 291s PrivateWages_18 0.000 0.00 291s PrivateWages_19 0.000 0.00 291s PrivateWages_20 0.000 0.00 291s PrivateWages_21 0.000 0.00 291s PrivateWages_22 0.000 0.00 291s Investment_(Intercept) Investment_corpProf 291s Consumption_2 0.00000 0.000 291s Consumption_3 0.00000 0.000 291s Consumption_4 0.00000 0.000 291s Consumption_5 0.00000 0.000 291s Consumption_6 0.00000 0.000 291s Consumption_7 0.00000 0.000 291s Consumption_8 0.00000 0.000 291s Consumption_9 0.00000 0.000 291s Consumption_11 0.00000 0.000 291s Consumption_12 0.00000 0.000 291s Consumption_14 0.00000 0.000 291s Consumption_15 0.00000 0.000 291s Consumption_16 0.00000 0.000 291s Consumption_17 0.00000 0.000 291s Consumption_18 0.00000 0.000 291s Consumption_19 0.00000 0.000 291s Consumption_20 0.00000 0.000 291s Consumption_21 0.00000 0.000 291s Consumption_22 0.00000 0.000 291s Investment_2 0.00693 0.086 291s Investment_3 -0.06954 -1.175 291s Investment_4 1.22401 22.522 291s Investment_5 -1.37696 -26.713 291s Investment_6 0.38610 7.761 291s Investment_7 1.48598 29.125 291s Investment_8 0.78465 15.536 291s Investment_9 -0.65483 -13.817 291s Investment_10 1.06018 23.006 291s Investment_11 0.39508 6.163 291s Investment_12 0.20479 2.335 291s Investment_14 0.32778 3.671 291s Investment_15 -0.07450 -0.916 291s Investment_17 0.96998 17.072 291s Investment_18 0.08124 1.405 291s Investment_19 -2.49295 -38.142 291s Investment_20 -0.70609 -13.416 291s Investment_21 -0.81928 -17.287 291s Investment_22 -0.73256 -17.215 291s PrivateWages_2 0.00000 0.000 291s PrivateWages_3 0.00000 0.000 291s PrivateWages_4 0.00000 0.000 291s PrivateWages_5 0.00000 0.000 291s PrivateWages_6 0.00000 0.000 291s PrivateWages_8 0.00000 0.000 291s PrivateWages_9 0.00000 0.000 291s PrivateWages_10 0.00000 0.000 291s PrivateWages_11 0.00000 0.000 291s PrivateWages_12 0.00000 0.000 291s PrivateWages_13 0.00000 0.000 291s PrivateWages_14 0.00000 0.000 291s PrivateWages_15 0.00000 0.000 291s PrivateWages_16 0.00000 0.000 291s PrivateWages_17 0.00000 0.000 291s PrivateWages_18 0.00000 0.000 291s PrivateWages_19 0.00000 0.000 291s PrivateWages_20 0.00000 0.000 291s PrivateWages_21 0.00000 0.000 291s PrivateWages_22 0.00000 0.000 291s Investment_corpProfLag Investment_capitalLag 291s Consumption_2 0.0000 0.00 291s Consumption_3 0.0000 0.00 291s Consumption_4 0.0000 0.00 291s Consumption_5 0.0000 0.00 291s Consumption_6 0.0000 0.00 291s Consumption_7 0.0000 0.00 291s Consumption_8 0.0000 0.00 291s Consumption_9 0.0000 0.00 291s Consumption_11 0.0000 0.00 291s Consumption_12 0.0000 0.00 291s Consumption_14 0.0000 0.00 291s Consumption_15 0.0000 0.00 291s Consumption_16 0.0000 0.00 291s Consumption_17 0.0000 0.00 291s Consumption_18 0.0000 0.00 291s Consumption_19 0.0000 0.00 291s Consumption_20 0.0000 0.00 291s Consumption_21 0.0000 0.00 291s Consumption_22 0.0000 0.00 291s Investment_2 0.0881 1.27 291s Investment_3 -0.8622 -12.70 291s Investment_4 20.6858 225.83 291s Investment_5 -25.3362 -261.21 291s Investment_6 7.4903 74.40 291s Investment_7 29.8681 293.93 291s Investment_8 15.3791 159.60 291s Investment_9 -12.9657 -135.94 291s Investment_10 22.3698 223.27 291s Investment_11 8.5733 85.22 291s Investment_12 3.1947 44.38 291s Investment_14 2.2945 67.88 291s Investment_15 -0.8344 -15.05 291s Investment_17 13.5797 191.77 291s Investment_18 1.4298 16.23 291s Investment_19 -43.1281 -503.08 291s Investment_20 -10.8032 -141.15 291s Investment_21 -15.5663 -164.84 291s Investment_22 -15.4570 -149.81 291s PrivateWages_2 0.0000 0.00 291s PrivateWages_3 0.0000 0.00 291s PrivateWages_4 0.0000 0.00 291s PrivateWages_5 0.0000 0.00 291s PrivateWages_6 0.0000 0.00 291s PrivateWages_8 0.0000 0.00 291s PrivateWages_9 0.0000 0.00 291s PrivateWages_10 0.0000 0.00 291s PrivateWages_11 0.0000 0.00 291s PrivateWages_12 0.0000 0.00 291s PrivateWages_13 0.0000 0.00 291s PrivateWages_14 0.0000 0.00 291s PrivateWages_15 0.0000 0.00 291s PrivateWages_16 0.0000 0.00 291s PrivateWages_17 0.0000 0.00 291s PrivateWages_18 0.0000 0.00 291s PrivateWages_19 0.0000 0.00 291s PrivateWages_20 0.0000 0.00 291s PrivateWages_21 0.0000 0.00 291s PrivateWages_22 0.0000 0.00 291s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 291s Consumption_2 0.0000 0.00 0.00 291s Consumption_3 0.0000 0.00 0.00 291s Consumption_4 0.0000 0.00 0.00 291s Consumption_5 0.0000 0.00 0.00 291s Consumption_6 0.0000 0.00 0.00 291s Consumption_7 0.0000 0.00 0.00 291s Consumption_8 0.0000 0.00 0.00 291s Consumption_9 0.0000 0.00 0.00 291s Consumption_11 0.0000 0.00 0.00 291s Consumption_12 0.0000 0.00 0.00 291s Consumption_14 0.0000 0.00 0.00 291s Consumption_15 0.0000 0.00 0.00 291s Consumption_16 0.0000 0.00 0.00 291s Consumption_17 0.0000 0.00 0.00 291s Consumption_18 0.0000 0.00 0.00 291s Consumption_19 0.0000 0.00 0.00 291s Consumption_20 0.0000 0.00 0.00 291s Consumption_21 0.0000 0.00 0.00 291s Consumption_22 0.0000 0.00 0.00 291s Investment_2 0.0000 0.00 0.00 291s Investment_3 0.0000 0.00 0.00 291s Investment_4 0.0000 0.00 0.00 291s Investment_5 0.0000 0.00 0.00 291s Investment_6 0.0000 0.00 0.00 291s Investment_7 0.0000 0.00 0.00 291s Investment_8 0.0000 0.00 0.00 291s Investment_9 0.0000 0.00 0.00 291s Investment_10 0.0000 0.00 0.00 291s Investment_11 0.0000 0.00 0.00 291s Investment_12 0.0000 0.00 0.00 291s Investment_14 0.0000 0.00 0.00 291s Investment_15 0.0000 0.00 0.00 291s Investment_17 0.0000 0.00 0.00 291s Investment_18 0.0000 0.00 0.00 291s Investment_19 0.0000 0.00 0.00 291s Investment_20 0.0000 0.00 0.00 291s Investment_21 0.0000 0.00 0.00 291s Investment_22 0.0000 0.00 0.00 291s PrivateWages_2 -1.3389 -61.06 -60.12 291s PrivateWages_3 0.2462 12.33 11.23 291s PrivateWages_4 1.1255 64.38 56.39 291s PrivateWages_5 -0.1959 -11.18 -11.20 291s PrivateWages_6 -0.5284 -32.23 -30.17 291s PrivateWages_8 -0.7909 -50.94 -50.62 291s PrivateWages_9 0.2819 18.18 18.15 291s PrivateWages_10 1.1384 76.28 73.43 291s PrivateWages_11 -0.1904 -11.65 -12.76 291s PrivateWages_12 0.5813 31.04 35.58 291s PrivateWages_13 0.1206 5.34 6.44 291s PrivateWages_14 0.4773 21.53 21.14 291s PrivateWages_15 0.3035 15.09 13.69 291s PrivateWages_16 0.0284 1.55 1.41 291s PrivateWages_17 -0.8517 -53.40 -46.33 291s PrivateWages_18 0.9908 64.40 62.12 291s PrivateWages_19 -0.4597 -28.00 -29.88 291s PrivateWages_20 -0.3819 -26.54 -23.26 291s PrivateWages_21 -1.1062 -83.74 -76.88 291s PrivateWages_22 0.5501 48.63 41.64 291s PrivateWages_trend 291s Consumption_2 0.000 291s Consumption_3 0.000 291s Consumption_4 0.000 291s Consumption_5 0.000 291s Consumption_6 0.000 291s Consumption_7 0.000 291s Consumption_8 0.000 291s Consumption_9 0.000 291s Consumption_11 0.000 291s Consumption_12 0.000 291s Consumption_14 0.000 291s Consumption_15 0.000 291s Consumption_16 0.000 291s Consumption_17 0.000 291s Consumption_18 0.000 291s Consumption_19 0.000 291s Consumption_20 0.000 291s Consumption_21 0.000 291s Consumption_22 0.000 291s Investment_2 0.000 291s Investment_3 0.000 291s Investment_4 0.000 291s Investment_5 0.000 291s Investment_6 0.000 291s Investment_7 0.000 291s Investment_8 0.000 291s Investment_9 0.000 291s Investment_10 0.000 291s Investment_11 0.000 291s Investment_12 0.000 291s Investment_14 0.000 291s Investment_15 0.000 291s Investment_17 0.000 291s Investment_18 0.000 291s Investment_19 0.000 291s Investment_20 0.000 291s Investment_21 0.000 291s Investment_22 0.000 291s PrivateWages_2 13.389 291s PrivateWages_3 -2.216 291s PrivateWages_4 -9.004 291s PrivateWages_5 1.371 291s PrivateWages_6 3.170 291s PrivateWages_8 3.164 291s PrivateWages_9 -0.846 291s PrivateWages_10 -2.277 291s PrivateWages_11 0.190 291s PrivateWages_12 0.000 291s PrivateWages_13 0.121 291s PrivateWages_14 0.955 291s PrivateWages_15 0.911 291s PrivateWages_16 0.114 291s PrivateWages_17 -4.258 291s PrivateWages_18 5.945 291s PrivateWages_19 -3.218 291s PrivateWages_20 -3.055 291s PrivateWages_21 -9.956 291s PrivateWages_22 5.501 291s [1] TRUE 291s > Bread 291s Consumption_(Intercept) Consumption_corpProf 291s Consumption_(Intercept) 107.542 -1.6123 291s Consumption_corpProf -1.612 0.6562 291s Consumption_corpProfLag -0.588 -0.3449 291s Consumption_wages -1.613 -0.0959 291s Investment_(Intercept) 0.000 0.0000 291s Investment_corpProf 0.000 0.0000 291s Investment_corpProfLag 0.000 0.0000 291s Investment_capitalLag 0.000 0.0000 291s PrivateWages_(Intercept) 0.000 0.0000 291s PrivateWages_gnp 0.000 0.0000 291s PrivateWages_gnpLag 0.000 0.0000 291s PrivateWages_trend 0.000 0.0000 291s Consumption_corpProfLag Consumption_wages 291s Consumption_(Intercept) -0.5878 -1.6130 291s Consumption_corpProf -0.3449 -0.0959 291s Consumption_corpProfLag 0.4797 -0.0326 291s Consumption_wages -0.0326 0.0910 291s Investment_(Intercept) 0.0000 0.0000 291s Investment_corpProf 0.0000 0.0000 291s Investment_corpProfLag 0.0000 0.0000 291s Investment_capitalLag 0.0000 0.0000 291s PrivateWages_(Intercept) 0.0000 0.0000 291s PrivateWages_gnp 0.0000 0.0000 291s PrivateWages_gnpLag 0.0000 0.0000 291s PrivateWages_trend 0.0000 0.0000 291s Investment_(Intercept) Investment_corpProf 291s Consumption_(Intercept) 0.00 0.000 291s Consumption_corpProf 0.00 0.000 291s Consumption_corpProfLag 0.00 0.000 291s Consumption_wages 0.00 0.000 291s Investment_(Intercept) 1702.08 -16.246 291s Investment_corpProf -16.25 0.653 291s Investment_corpProfLag 13.29 -0.499 291s Investment_capitalLag -8.19 0.066 291s PrivateWages_(Intercept) 0.00 0.000 291s PrivateWages_gnp 0.00 0.000 291s PrivateWages_gnpLag 0.00 0.000 291s PrivateWages_trend 0.00 0.000 291s Investment_corpProfLag Investment_capitalLag 291s Consumption_(Intercept) 0.0000 0.0000 291s Consumption_corpProf 0.0000 0.0000 291s Consumption_corpProfLag 0.0000 0.0000 291s Consumption_wages 0.0000 0.0000 291s Investment_(Intercept) 13.2940 -8.1927 291s Investment_corpProf -0.4994 0.0660 291s Investment_corpProfLag 0.6054 -0.0737 291s Investment_capitalLag -0.0737 0.0414 291s PrivateWages_(Intercept) 0.0000 0.0000 291s PrivateWages_gnp 0.0000 0.0000 291s PrivateWages_gnpLag 0.0000 0.0000 291s PrivateWages_trend 0.0000 0.0000 291s PrivateWages_(Intercept) PrivateWages_gnp 291s Consumption_(Intercept) 0.000 0.0000 291s Consumption_corpProf 0.000 0.0000 291s Consumption_corpProfLag 0.000 0.0000 291s Consumption_wages 0.000 0.0000 291s Investment_(Intercept) 0.000 0.0000 291s Investment_corpProf 0.000 0.0000 291s Investment_corpProfLag 0.000 0.0000 291s Investment_capitalLag 0.000 0.0000 291s PrivateWages_(Intercept) 163.361 -0.6152 291s PrivateWages_gnp -0.615 0.1046 291s PrivateWages_gnpLag -2.146 -0.0975 291s PrivateWages_trend 2.016 -0.0281 291s PrivateWages_gnpLag PrivateWages_trend 291s Consumption_(Intercept) 0.00000 0.00000 291s Consumption_corpProf 0.00000 0.00000 291s Consumption_corpProfLag 0.00000 0.00000 291s Consumption_wages 0.00000 0.00000 291s Investment_(Intercept) 0.00000 0.00000 291s Investment_corpProf 0.00000 0.00000 291s Investment_corpProfLag 0.00000 0.00000 291s Investment_capitalLag 0.00000 0.00000 291s PrivateWages_(Intercept) -2.14647 2.01603 291s PrivateWages_gnp -0.09753 -0.02810 291s PrivateWages_gnpLag 0.13809 -0.00624 291s PrivateWages_trend -0.00624 0.10783 291s > 291s > # 2SLS 291s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 291s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 291s > summary 291s 291s systemfit results 291s method: 2SLS 291s 291s N DF SSR detRCov OLS-R2 McElroy-R2 291s system 56 44 57.9 0.391 0.968 0.992 291s 291s N DF SSR MSE RMSE R2 Adj R2 291s Consumption 18 14 22.27 1.591 1.26 0.974 0.968 291s Investment 18 14 25.85 1.847 1.36 0.847 0.815 291s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 291s 291s The covariance matrix of the residuals 291s Consumption Investment PrivateWages 291s Consumption 1.307 0.540 -0.431 291s Investment 0.540 1.319 0.119 291s PrivateWages -0.431 0.119 0.496 291s 291s The correlations of the residuals 291s Consumption Investment PrivateWages 291s Consumption 1.000 0.414 -0.538 291s Investment 0.414 1.000 0.139 291s PrivateWages -0.538 0.139 1.000 291s 291s 291s 2SLS estimates for 'Consumption' (equation 1) 291s Model Formula: consump ~ corpProf + corpProfLag + wages 291s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 291s gnpLag 291s 291s Estimate Std. Error t value Pr(>|t|) 291s (Intercept) 17.2849 1.6463 10.50 5.1e-08 *** 291s corpProf -0.0770 0.1683 -0.46 0.65 291s corpProfLag 0.2327 0.1276 1.82 0.09 . 291s wages 0.8259 0.0472 17.49 6.6e-11 *** 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s 291s Residual standard error: 1.261 on 14 degrees of freedom 291s Number of observations: 18 Degrees of Freedom: 14 291s SSR: 22.269 MSE: 1.591 Root MSE: 1.261 291s Multiple R-Squared: 0.974 Adjusted R-Squared: 0.968 291s 291s 291s 2SLS estimates for 'Investment' (equation 2) 291s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 291s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 291s gnpLag 291s 291s Estimate Std. Error t value Pr(>|t|) 291s (Intercept) 18.2571 7.3132 2.50 0.02564 * 291s corpProf 0.1564 0.1942 0.81 0.43408 291s corpProfLag 0.5714 0.1672 3.42 0.00417 ** 291s capitalLag -0.1446 0.0346 -4.18 0.00093 *** 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s 291s Residual standard error: 1.359 on 14 degrees of freedom 291s Number of observations: 18 Degrees of Freedom: 14 291s SSR: 25.852 MSE: 1.847 Root MSE: 1.359 291s Multiple R-Squared: 0.847 Adjusted R-Squared: 0.815 291s 291s 291s 2SLS estimates for 'PrivateWages' (equation 3) 291s Model Formula: privWage ~ gnp + gnpLag + trend 291s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 291s gnpLag 291s 291s Estimate Std. Error t value Pr(>|t|) 291s (Intercept) 1.3431 1.1879 1.13 0.275 291s gnp 0.4438 0.0361 12.28 1.5e-09 *** 291s gnpLag 0.1447 0.0392 3.69 0.002 ** 291s trend 0.1238 0.0308 4.01 0.001 ** 291s --- 291s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 291s 291s Residual standard error: 0.78 on 16 degrees of freedom 291s Number of observations: 20 Degrees of Freedom: 16 291s SSR: 9.741 MSE: 0.609 Root MSE: 0.78 291s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 291s 291s > residuals 291s Consumption Investment PrivateWages 291s 1 NA NA NA 291s 2 -0.6754 -1.214 -1.3401 291s 3 -0.4627 0.325 0.2378 291s 4 -1.1585 1.094 1.1117 291s 5 -0.0305 -1.368 -0.1954 291s 6 0.4693 0.486 -0.5355 291s 7 NA NA NA 291s 8 1.6045 1.066 -0.7908 291s 9 1.6018 0.156 0.2831 291s 10 NA 1.853 1.1353 291s 11 -0.9031 -0.898 -0.1765 291s 12 -1.5948 -1.012 0.6007 291s 13 NA NA 0.1443 291s 14 0.2854 0.845 0.4826 291s 15 -0.4718 -0.365 0.3016 291s 16 -0.2268 NA 0.0261 291s 17 2.0079 1.685 -0.8614 291s 18 -0.7434 -0.121 0.9927 291s 19 -0.5410 -3.248 -0.4446 291s 20 1.4186 0.241 -0.3914 291s 21 1.1462 -0.013 -1.1115 291s 22 -1.7256 0.489 0.5312 291s > fitted 291s Consumption Investment PrivateWages 291s 1 NA NA NA 291s 2 42.6 1.014 26.8 291s 3 45.5 1.575 29.1 291s 4 50.4 4.106 33.0 291s 5 50.6 4.368 34.1 291s 6 52.1 4.614 35.9 291s 7 NA NA NA 291s 8 54.6 3.134 38.7 291s 9 55.7 2.844 38.9 291s 10 NA 3.247 40.2 291s 11 55.9 1.898 38.1 291s 12 52.5 -2.388 33.9 291s 13 NA NA 28.9 291s 14 46.2 -5.945 28.0 291s 15 49.2 -2.635 30.3 291s 16 51.5 NA 33.2 291s 17 55.7 0.415 37.7 291s 18 59.4 2.121 40.0 291s 19 58.0 1.348 38.6 291s 20 60.2 1.059 42.0 291s 21 63.9 3.313 46.1 291s 22 71.4 4.411 52.8 291s > predict 291s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 291s 1 NA NA NA NA 291s 2 42.6 0.586 41.3 43.8 291s 3 45.5 0.674 44.0 46.9 291s 4 50.4 0.443 49.4 51.3 291s 5 50.6 0.524 49.5 51.8 291s 6 52.1 0.535 51.0 53.3 291s 7 NA NA NA NA 291s 8 54.6 0.431 53.7 55.5 291s 9 55.7 0.510 54.6 56.8 291s 10 NA NA NA NA 291s 11 55.9 0.936 53.9 57.9 291s 12 52.5 0.893 50.6 54.4 291s 13 NA NA NA NA 291s 14 46.2 0.713 44.7 47.7 291s 15 49.2 0.501 48.1 50.2 291s 16 51.5 0.407 50.7 52.4 291s 17 55.7 0.457 54.7 56.7 291s 18 59.4 0.397 58.6 60.3 291s 19 58.0 0.564 56.8 59.2 291s 20 60.2 0.543 59.0 61.3 291s 21 63.9 0.529 62.7 65.0 291s 22 71.4 0.808 69.7 73.2 291s Investment.pred Investment.se.fit Investment.lwr Investment.upr 291s 1 NA NA NA NA 291s 2 1.014 0.919 -0.957 2.985 291s 3 1.575 0.602 0.284 2.867 291s 4 4.106 0.544 2.940 5.272 291s 5 4.368 0.450 3.402 5.333 291s 6 4.614 0.425 3.703 5.526 291s 7 NA NA NA NA 291s 8 3.134 0.352 2.380 3.889 291s 9 2.844 0.544 1.677 4.012 291s 10 3.247 0.592 1.976 4.518 291s 11 1.898 0.978 -0.200 3.996 291s 12 -2.388 0.886 -4.289 -0.488 291s 13 NA NA NA NA 291s 14 -5.945 0.916 -7.909 -3.980 291s 15 -2.635 0.518 -3.745 -1.525 291s 16 NA NA NA NA 291s 17 0.415 0.507 -0.671 1.501 291s 18 2.121 0.329 1.416 2.826 291s 19 1.348 0.551 0.166 2.529 291s 20 1.059 0.582 -0.189 2.306 291s 21 3.313 0.496 2.248 4.377 291s 22 4.411 0.728 2.850 5.971 291s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 291s 1 NA NA NA NA 291s 2 26.8 0.330 26.1 27.5 291s 3 29.1 0.344 28.3 29.8 291s 4 33.0 0.363 32.2 33.8 291s 5 34.1 0.260 33.5 34.6 291s 6 35.9 0.268 35.4 36.5 291s 7 NA NA NA NA 291s 8 38.7 0.265 38.1 39.3 291s 9 38.9 0.252 38.4 39.5 291s 10 40.2 0.242 39.7 40.7 291s 11 38.1 0.358 37.3 38.8 291s 12 33.9 0.385 33.1 34.7 291s 13 28.9 0.460 27.9 29.8 291s 14 28.0 0.351 27.3 28.8 291s 15 30.3 0.343 29.6 31.0 291s 16 33.2 0.287 32.6 33.8 291s 17 37.7 0.296 37.0 38.3 291s 18 40.0 0.220 39.5 40.5 291s 19 38.6 0.361 37.9 39.4 291s 20 42.0 0.309 41.3 42.6 291s 21 46.1 0.312 45.4 46.8 291s 22 52.8 0.501 51.7 53.8 291s > model.frame 291s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 291s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 291s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 291s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 291s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 291s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 291s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 291s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 291s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 291s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 291s 10 57.8 21.7 21.1 NA 5.1 211 41.3 67.0 64.5 291s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 291s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 291s 13 45.6 NA 11.4 34.3 -6.2 213 29.0 44.3 53.4 291s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 291s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 291s 16 51.3 14.0 12.3 39.3 NA 199 33.2 54.4 49.7 291s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 291s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 291s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 291s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 291s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 291s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 291s trend 291s 1 -11 291s 2 -10 291s 3 -9 291s 4 -8 291s 5 -7 291s 6 -6 291s 7 -5 291s 8 -4 291s 9 -3 291s 10 -2 291s 11 -1 291s 12 0 291s 13 1 291s 14 2 291s 15 3 291s 16 4 291s 17 5 291s 18 6 291s 19 7 291s 20 8 291s 21 9 291s 22 10 291s > Frames of instrumental variables 291s govExp taxes govWage trend capitalLag corpProfLag gnpLag 291s 1 2.4 3.4 2.2 -11 180 NA NA 291s 2 3.9 7.7 2.7 -10 183 12.7 44.9 291s 3 3.2 3.9 2.9 -9 183 12.4 45.6 291s 4 2.8 4.7 2.9 -8 184 16.9 50.1 291s 5 3.5 3.8 3.1 -7 190 18.4 57.2 291s 6 3.3 5.5 3.2 -6 193 19.4 57.1 291s 7 3.3 7.0 3.3 -5 198 20.1 NA 291s 8 4.0 6.7 3.6 -4 203 19.6 64.0 291s 9 4.2 4.2 3.7 -3 208 19.8 64.4 291s 10 4.1 4.0 4.0 -2 211 21.1 64.5 291s 11 5.2 7.7 4.2 -1 216 21.7 67.0 291s 12 5.9 7.5 4.8 0 217 15.6 61.2 291s 13 4.9 8.3 5.3 1 213 11.4 53.4 291s 14 3.7 5.4 5.6 2 207 7.0 44.3 291s 15 4.0 6.8 6.0 3 202 11.2 45.1 291s 16 4.4 7.2 6.1 4 199 12.3 49.7 291s 17 2.9 8.3 7.4 5 198 14.0 54.4 291s 18 4.3 6.7 6.7 6 200 17.6 62.7 291s 19 5.3 7.4 7.7 7 202 17.3 65.0 291s 20 6.6 8.9 7.8 8 200 15.3 60.9 291s 21 7.4 9.6 8.0 9 201 19.0 69.5 291s 22 13.8 11.6 8.5 10 204 21.1 75.7 291s govExp taxes govWage trend capitalLag corpProfLag gnpLag 291s 1 2.4 3.4 2.2 -11 180 NA NA 291s 2 3.9 7.7 2.7 -10 183 12.7 44.9 291s 3 3.2 3.9 2.9 -9 183 12.4 45.6 291s 4 2.8 4.7 2.9 -8 184 16.9 50.1 291s 5 3.5 3.8 3.1 -7 190 18.4 57.2 291s 6 3.3 5.5 3.2 -6 193 19.4 57.1 291s 7 3.3 7.0 3.3 -5 198 20.1 NA 291s 8 4.0 6.7 3.6 -4 203 19.6 64.0 291s 9 4.2 4.2 3.7 -3 208 19.8 64.4 291s 10 4.1 4.0 4.0 -2 211 21.1 64.5 291s 11 5.2 7.7 4.2 -1 216 21.7 67.0 291s 12 5.9 7.5 4.8 0 217 15.6 61.2 291s 13 4.9 8.3 5.3 1 213 11.4 53.4 291s 14 3.7 5.4 5.6 2 207 7.0 44.3 291s 15 4.0 6.8 6.0 3 202 11.2 45.1 291s 16 4.4 7.2 6.1 4 199 12.3 49.7 291s 17 2.9 8.3 7.4 5 198 14.0 54.4 291s 18 4.3 6.7 6.7 6 200 17.6 62.7 291s 19 5.3 7.4 7.7 7 202 17.3 65.0 291s 20 6.6 8.9 7.8 8 200 15.3 60.9 291s 21 7.4 9.6 8.0 9 201 19.0 69.5 291s 22 13.8 11.6 8.5 10 204 21.1 75.7 291s govExp taxes govWage trend capitalLag corpProfLag gnpLag 291s 1 2.4 3.4 2.2 -11 180 NA NA 291s 2 3.9 7.7 2.7 -10 183 12.7 44.9 291s 3 3.2 3.9 2.9 -9 183 12.4 45.6 291s 4 2.8 4.7 2.9 -8 184 16.9 50.1 291s 5 3.5 3.8 3.1 -7 190 18.4 57.2 291s 6 3.3 5.5 3.2 -6 193 19.4 57.1 291s 7 3.3 7.0 3.3 -5 198 20.1 NA 291s 8 4.0 6.7 3.6 -4 203 19.6 64.0 291s 9 4.2 4.2 3.7 -3 208 19.8 64.4 291s 10 4.1 4.0 4.0 -2 211 21.1 64.5 291s 11 5.2 7.7 4.2 -1 216 21.7 67.0 291s 12 5.9 7.5 4.8 0 217 15.6 61.2 291s 13 4.9 8.3 5.3 1 213 11.4 53.4 291s 14 3.7 5.4 5.6 2 207 7.0 44.3 291s 15 4.0 6.8 6.0 3 202 11.2 45.1 291s 16 4.4 7.2 6.1 4 199 12.3 49.7 291s 17 2.9 8.3 7.4 5 198 14.0 54.4 291s 18 4.3 6.7 6.7 6 200 17.6 62.7 291s 19 5.3 7.4 7.7 7 202 17.3 65.0 291s 20 6.6 8.9 7.8 8 200 15.3 60.9 291s 21 7.4 9.6 8.0 9 201 19.0 69.5 291s 22 13.8 11.6 8.5 10 204 21.1 75.7 291s > model.matrix 291s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0345 >" 291s [2] "Attributes: < Component “dimnames”: Component 1: 51 string mismatches >" 291s [3] "Numeric: lengths (696, 672) differ" 291s > matrix of instrumental variables 291s Consumption_(Intercept) Consumption_govExp Consumption_taxes 291s Consumption_2 1 3.9 7.7 291s Consumption_3 1 3.2 3.9 291s Consumption_4 1 2.8 4.7 291s Consumption_5 1 3.5 3.8 291s Consumption_6 1 3.3 5.5 291s Consumption_8 1 4.0 6.7 291s Consumption_9 1 4.2 4.2 291s Consumption_11 1 5.2 7.7 291s Consumption_12 1 5.9 7.5 291s Consumption_14 1 3.7 5.4 291s Consumption_15 1 4.0 6.8 291s Consumption_16 1 4.4 7.2 291s Consumption_17 1 2.9 8.3 291s Consumption_18 1 4.3 6.7 291s Consumption_19 1 5.3 7.4 291s Consumption_20 1 6.6 8.9 291s Consumption_21 1 7.4 9.6 291s Consumption_22 1 13.8 11.6 291s Investment_2 0 0.0 0.0 291s Investment_3 0 0.0 0.0 291s Investment_4 0 0.0 0.0 291s Investment_5 0 0.0 0.0 291s Investment_6 0 0.0 0.0 291s Investment_8 0 0.0 0.0 291s Investment_9 0 0.0 0.0 291s Investment_10 0 0.0 0.0 291s Investment_11 0 0.0 0.0 291s Investment_12 0 0.0 0.0 291s Investment_14 0 0.0 0.0 291s Investment_15 0 0.0 0.0 291s Investment_17 0 0.0 0.0 291s Investment_18 0 0.0 0.0 291s Investment_19 0 0.0 0.0 291s Investment_20 0 0.0 0.0 291s Investment_21 0 0.0 0.0 291s Investment_22 0 0.0 0.0 291s PrivateWages_2 0 0.0 0.0 291s PrivateWages_3 0 0.0 0.0 291s PrivateWages_4 0 0.0 0.0 291s PrivateWages_5 0 0.0 0.0 291s PrivateWages_6 0 0.0 0.0 291s PrivateWages_8 0 0.0 0.0 291s PrivateWages_9 0 0.0 0.0 291s PrivateWages_10 0 0.0 0.0 291s PrivateWages_11 0 0.0 0.0 291s PrivateWages_12 0 0.0 0.0 291s PrivateWages_13 0 0.0 0.0 291s PrivateWages_14 0 0.0 0.0 291s PrivateWages_15 0 0.0 0.0 291s PrivateWages_16 0 0.0 0.0 291s PrivateWages_17 0 0.0 0.0 291s PrivateWages_18 0 0.0 0.0 291s PrivateWages_19 0 0.0 0.0 291s PrivateWages_20 0 0.0 0.0 291s PrivateWages_21 0 0.0 0.0 291s PrivateWages_22 0 0.0 0.0 291s Consumption_govWage Consumption_trend Consumption_capitalLag 291s Consumption_2 2.7 -10 183 291s Consumption_3 2.9 -9 183 291s Consumption_4 2.9 -8 184 291s Consumption_5 3.1 -7 190 291s Consumption_6 3.2 -6 193 291s Consumption_8 3.6 -4 203 291s Consumption_9 3.7 -3 208 291s Consumption_11 4.2 -1 216 291s Consumption_12 4.8 0 217 291s Consumption_14 5.6 2 207 291s Consumption_15 6.0 3 202 291s Consumption_16 6.1 4 199 291s Consumption_17 7.4 5 198 291s Consumption_18 6.7 6 200 291s Consumption_19 7.7 7 202 291s Consumption_20 7.8 8 200 291s Consumption_21 8.0 9 201 291s Consumption_22 8.5 10 204 291s Investment_2 0.0 0 0 291s Investment_3 0.0 0 0 291s Investment_4 0.0 0 0 291s Investment_5 0.0 0 0 291s Investment_6 0.0 0 0 291s Investment_8 0.0 0 0 291s Investment_9 0.0 0 0 291s Investment_10 0.0 0 0 291s Investment_11 0.0 0 0 291s Investment_12 0.0 0 0 291s Investment_14 0.0 0 0 291s Investment_15 0.0 0 0 291s Investment_17 0.0 0 0 291s Investment_18 0.0 0 0 291s Investment_19 0.0 0 0 291s Investment_20 0.0 0 0 291s Investment_21 0.0 0 0 291s Investment_22 0.0 0 0 291s PrivateWages_2 0.0 0 0 291s PrivateWages_3 0.0 0 0 291s PrivateWages_4 0.0 0 0 291s PrivateWages_5 0.0 0 0 291s PrivateWages_6 0.0 0 0 291s PrivateWages_8 0.0 0 0 291s PrivateWages_9 0.0 0 0 291s PrivateWages_10 0.0 0 0 291s PrivateWages_11 0.0 0 0 291s PrivateWages_12 0.0 0 0 291s PrivateWages_13 0.0 0 0 291s PrivateWages_14 0.0 0 0 291s PrivateWages_15 0.0 0 0 291s PrivateWages_16 0.0 0 0 291s PrivateWages_17 0.0 0 0 291s PrivateWages_18 0.0 0 0 291s PrivateWages_19 0.0 0 0 291s PrivateWages_20 0.0 0 0 291s PrivateWages_21 0.0 0 0 291s PrivateWages_22 0.0 0 0 291s Consumption_corpProfLag Consumption_gnpLag 291s Consumption_2 12.7 44.9 291s Consumption_3 12.4 45.6 291s Consumption_4 16.9 50.1 291s Consumption_5 18.4 57.2 291s Consumption_6 19.4 57.1 291s Consumption_8 19.6 64.0 291s Consumption_9 19.8 64.4 291s Consumption_11 21.7 67.0 291s Consumption_12 15.6 61.2 291s Consumption_14 7.0 44.3 291s Consumption_15 11.2 45.1 291s Consumption_16 12.3 49.7 291s Consumption_17 14.0 54.4 291s Consumption_18 17.6 62.7 291s Consumption_19 17.3 65.0 291s Consumption_20 15.3 60.9 291s Consumption_21 19.0 69.5 291s Consumption_22 21.1 75.7 291s Investment_2 0.0 0.0 291s Investment_3 0.0 0.0 291s Investment_4 0.0 0.0 291s Investment_5 0.0 0.0 291s Investment_6 0.0 0.0 291s Investment_8 0.0 0.0 291s Investment_9 0.0 0.0 291s Investment_10 0.0 0.0 291s Investment_11 0.0 0.0 291s Investment_12 0.0 0.0 291s Investment_14 0.0 0.0 291s Investment_15 0.0 0.0 291s Investment_17 0.0 0.0 291s Investment_18 0.0 0.0 291s Investment_19 0.0 0.0 291s Investment_20 0.0 0.0 291s Investment_21 0.0 0.0 291s Investment_22 0.0 0.0 291s PrivateWages_2 0.0 0.0 291s PrivateWages_3 0.0 0.0 291s PrivateWages_4 0.0 0.0 291s PrivateWages_5 0.0 0.0 291s PrivateWages_6 0.0 0.0 291s PrivateWages_8 0.0 0.0 291s PrivateWages_9 0.0 0.0 291s PrivateWages_10 0.0 0.0 291s PrivateWages_11 0.0 0.0 291s PrivateWages_12 0.0 0.0 291s PrivateWages_13 0.0 0.0 291s PrivateWages_14 0.0 0.0 291s PrivateWages_15 0.0 0.0 291s PrivateWages_16 0.0 0.0 291s PrivateWages_17 0.0 0.0 291s PrivateWages_18 0.0 0.0 291s PrivateWages_19 0.0 0.0 291s PrivateWages_20 0.0 0.0 291s PrivateWages_21 0.0 0.0 291s PrivateWages_22 0.0 0.0 291s Investment_(Intercept) Investment_govExp Investment_taxes 291s Consumption_2 0 0.0 0.0 291s Consumption_3 0 0.0 0.0 291s Consumption_4 0 0.0 0.0 291s Consumption_5 0 0.0 0.0 291s Consumption_6 0 0.0 0.0 291s Consumption_8 0 0.0 0.0 291s Consumption_9 0 0.0 0.0 291s Consumption_11 0 0.0 0.0 291s Consumption_12 0 0.0 0.0 291s Consumption_14 0 0.0 0.0 291s Consumption_15 0 0.0 0.0 291s Consumption_16 0 0.0 0.0 291s Consumption_17 0 0.0 0.0 291s Consumption_18 0 0.0 0.0 291s Consumption_19 0 0.0 0.0 291s Consumption_20 0 0.0 0.0 291s Consumption_21 0 0.0 0.0 291s Consumption_22 0 0.0 0.0 291s Investment_2 1 3.9 7.7 291s Investment_3 1 3.2 3.9 291s Investment_4 1 2.8 4.7 291s Investment_5 1 3.5 3.8 291s Investment_6 1 3.3 5.5 291s Investment_8 1 4.0 6.7 291s Investment_9 1 4.2 4.2 291s Investment_10 1 4.1 4.0 291s Investment_11 1 5.2 7.7 291s Investment_12 1 5.9 7.5 291s Investment_14 1 3.7 5.4 291s Investment_15 1 4.0 6.8 291s Investment_17 1 2.9 8.3 291s Investment_18 1 4.3 6.7 291s Investment_19 1 5.3 7.4 291s Investment_20 1 6.6 8.9 291s Investment_21 1 7.4 9.6 291s Investment_22 1 13.8 11.6 291s PrivateWages_2 0 0.0 0.0 291s PrivateWages_3 0 0.0 0.0 291s PrivateWages_4 0 0.0 0.0 291s PrivateWages_5 0 0.0 0.0 291s PrivateWages_6 0 0.0 0.0 291s PrivateWages_8 0 0.0 0.0 291s PrivateWages_9 0 0.0 0.0 291s PrivateWages_10 0 0.0 0.0 291s PrivateWages_11 0 0.0 0.0 291s PrivateWages_12 0 0.0 0.0 291s PrivateWages_13 0 0.0 0.0 291s PrivateWages_14 0 0.0 0.0 291s PrivateWages_15 0 0.0 0.0 291s PrivateWages_16 0 0.0 0.0 291s PrivateWages_17 0 0.0 0.0 291s PrivateWages_18 0 0.0 0.0 291s PrivateWages_19 0 0.0 0.0 291s PrivateWages_20 0 0.0 0.0 291s PrivateWages_21 0 0.0 0.0 291s PrivateWages_22 0 0.0 0.0 291s Investment_govWage Investment_trend Investment_capitalLag 291s Consumption_2 0.0 0 0 291s Consumption_3 0.0 0 0 291s Consumption_4 0.0 0 0 291s Consumption_5 0.0 0 0 291s Consumption_6 0.0 0 0 291s Consumption_8 0.0 0 0 291s Consumption_9 0.0 0 0 291s Consumption_11 0.0 0 0 291s Consumption_12 0.0 0 0 291s Consumption_14 0.0 0 0 291s Consumption_15 0.0 0 0 291s Consumption_16 0.0 0 0 291s Consumption_17 0.0 0 0 291s Consumption_18 0.0 0 0 291s Consumption_19 0.0 0 0 291s Consumption_20 0.0 0 0 291s Consumption_21 0.0 0 0 291s Consumption_22 0.0 0 0 291s Investment_2 2.7 -10 183 291s Investment_3 2.9 -9 183 291s Investment_4 2.9 -8 184 291s Investment_5 3.1 -7 190 291s Investment_6 3.2 -6 193 291s Investment_8 3.6 -4 203 291s Investment_9 3.7 -3 208 291s Investment_10 4.0 -2 211 291s Investment_11 4.2 -1 216 291s Investment_12 4.8 0 217 291s Investment_14 5.6 2 207 291s Investment_15 6.0 3 202 291s Investment_17 7.4 5 198 291s Investment_18 6.7 6 200 291s Investment_19 7.7 7 202 291s Investment_20 7.8 8 200 291s Investment_21 8.0 9 201 291s Investment_22 8.5 10 204 291s PrivateWages_2 0.0 0 0 291s PrivateWages_3 0.0 0 0 291s PrivateWages_4 0.0 0 0 291s PrivateWages_5 0.0 0 0 291s PrivateWages_6 0.0 0 0 291s PrivateWages_8 0.0 0 0 291s PrivateWages_9 0.0 0 0 291s PrivateWages_10 0.0 0 0 291s PrivateWages_11 0.0 0 0 291s PrivateWages_12 0.0 0 0 291s PrivateWages_13 0.0 0 0 291s PrivateWages_14 0.0 0 0 291s PrivateWages_15 0.0 0 0 291s PrivateWages_16 0.0 0 0 291s PrivateWages_17 0.0 0 0 291s PrivateWages_18 0.0 0 0 291s PrivateWages_19 0.0 0 0 291s PrivateWages_20 0.0 0 0 291s PrivateWages_21 0.0 0 0 291s PrivateWages_22 0.0 0 0 291s Investment_corpProfLag Investment_gnpLag 291s Consumption_2 0.0 0.0 291s Consumption_3 0.0 0.0 291s Consumption_4 0.0 0.0 291s Consumption_5 0.0 0.0 291s Consumption_6 0.0 0.0 291s Consumption_8 0.0 0.0 291s Consumption_9 0.0 0.0 291s Consumption_11 0.0 0.0 291s Consumption_12 0.0 0.0 291s Consumption_14 0.0 0.0 291s Consumption_15 0.0 0.0 291s Consumption_16 0.0 0.0 291s Consumption_17 0.0 0.0 291s Consumption_18 0.0 0.0 291s Consumption_19 0.0 0.0 291s Consumption_20 0.0 0.0 291s Consumption_21 0.0 0.0 291s Consumption_22 0.0 0.0 291s Investment_2 12.7 44.9 291s Investment_3 12.4 45.6 291s Investment_4 16.9 50.1 291s Investment_5 18.4 57.2 291s Investment_6 19.4 57.1 291s Investment_8 19.6 64.0 291s Investment_9 19.8 64.4 291s Investment_10 21.1 64.5 291s Investment_11 21.7 67.0 291s Investment_12 15.6 61.2 291s Investment_14 7.0 44.3 291s Investment_15 11.2 45.1 291s Investment_17 14.0 54.4 291s Investment_18 17.6 62.7 291s Investment_19 17.3 65.0 291s Investment_20 15.3 60.9 291s Investment_21 19.0 69.5 291s Investment_22 21.1 75.7 291s PrivateWages_2 0.0 0.0 291s PrivateWages_3 0.0 0.0 291s PrivateWages_4 0.0 0.0 291s PrivateWages_5 0.0 0.0 291s PrivateWages_6 0.0 0.0 291s PrivateWages_8 0.0 0.0 291s PrivateWages_9 0.0 0.0 291s PrivateWages_10 0.0 0.0 291s PrivateWages_11 0.0 0.0 291s PrivateWages_12 0.0 0.0 291s PrivateWages_13 0.0 0.0 291s PrivateWages_14 0.0 0.0 291s PrivateWages_15 0.0 0.0 291s PrivateWages_16 0.0 0.0 291s PrivateWages_17 0.0 0.0 291s PrivateWages_18 0.0 0.0 291s PrivateWages_19 0.0 0.0 291s PrivateWages_20 0.0 0.0 291s PrivateWages_21 0.0 0.0 291s PrivateWages_22 0.0 0.0 291s PrivateWages_(Intercept) PrivateWages_govExp PrivateWages_taxes 291s Consumption_2 0 0.0 0.0 291s Consumption_3 0 0.0 0.0 291s Consumption_4 0 0.0 0.0 291s Consumption_5 0 0.0 0.0 291s Consumption_6 0 0.0 0.0 291s Consumption_8 0 0.0 0.0 291s Consumption_9 0 0.0 0.0 291s Consumption_11 0 0.0 0.0 291s Consumption_12 0 0.0 0.0 291s Consumption_14 0 0.0 0.0 291s Consumption_15 0 0.0 0.0 291s Consumption_16 0 0.0 0.0 291s Consumption_17 0 0.0 0.0 291s Consumption_18 0 0.0 0.0 291s Consumption_19 0 0.0 0.0 291s Consumption_20 0 0.0 0.0 291s Consumption_21 0 0.0 0.0 291s Consumption_22 0 0.0 0.0 291s Investment_2 0 0.0 0.0 291s Investment_3 0 0.0 0.0 291s Investment_4 0 0.0 0.0 291s Investment_5 0 0.0 0.0 291s Investment_6 0 0.0 0.0 291s Investment_8 0 0.0 0.0 291s Investment_9 0 0.0 0.0 291s Investment_10 0 0.0 0.0 291s Investment_11 0 0.0 0.0 291s Investment_12 0 0.0 0.0 291s Investment_14 0 0.0 0.0 291s Investment_15 0 0.0 0.0 291s Investment_17 0 0.0 0.0 291s Investment_18 0 0.0 0.0 291s Investment_19 0 0.0 0.0 291s Investment_20 0 0.0 0.0 291s Investment_21 0 0.0 0.0 291s Investment_22 0 0.0 0.0 291s PrivateWages_2 1 3.9 7.7 291s PrivateWages_3 1 3.2 3.9 291s PrivateWages_4 1 2.8 4.7 291s PrivateWages_5 1 3.5 3.8 291s PrivateWages_6 1 3.3 5.5 291s PrivateWages_8 1 4.0 6.7 291s PrivateWages_9 1 4.2 4.2 291s PrivateWages_10 1 4.1 4.0 291s PrivateWages_11 1 5.2 7.7 291s PrivateWages_12 1 5.9 7.5 291s PrivateWages_13 1 4.9 8.3 291s PrivateWages_14 1 3.7 5.4 291s PrivateWages_15 1 4.0 6.8 291s PrivateWages_16 1 4.4 7.2 291s PrivateWages_17 1 2.9 8.3 291s PrivateWages_18 1 4.3 6.7 291s PrivateWages_19 1 5.3 7.4 291s PrivateWages_20 1 6.6 8.9 291s PrivateWages_21 1 7.4 9.6 291s PrivateWages_22 1 13.8 11.6 291s PrivateWages_govWage PrivateWages_trend PrivateWages_capitalLag 291s Consumption_2 0.0 0 0 291s Consumption_3 0.0 0 0 291s Consumption_4 0.0 0 0 291s Consumption_5 0.0 0 0 291s Consumption_6 0.0 0 0 291s Consumption_8 0.0 0 0 291s Consumption_9 0.0 0 0 291s Consumption_11 0.0 0 0 291s Consumption_12 0.0 0 0 291s Consumption_14 0.0 0 0 291s Consumption_15 0.0 0 0 291s Consumption_16 0.0 0 0 291s Consumption_17 0.0 0 0 291s Consumption_18 0.0 0 0 291s Consumption_19 0.0 0 0 291s Consumption_20 0.0 0 0 291s Consumption_21 0.0 0 0 291s Consumption_22 0.0 0 0 291s Investment_2 0.0 0 0 291s Investment_3 0.0 0 0 291s Investment_4 0.0 0 0 291s Investment_5 0.0 0 0 291s Investment_6 0.0 0 0 291s Investment_8 0.0 0 0 291s Investment_9 0.0 0 0 291s Investment_10 0.0 0 0 291s Investment_11 0.0 0 0 291s Investment_12 0.0 0 0 291s Investment_14 0.0 0 0 291s Investment_15 0.0 0 0 291s Investment_17 0.0 0 0 291s Investment_18 0.0 0 0 291s Investment_19 0.0 0 0 291s Investment_20 0.0 0 0 291s Investment_21 0.0 0 0 291s Investment_22 0.0 0 0 291s PrivateWages_2 2.7 -10 183 291s PrivateWages_3 2.9 -9 183 291s PrivateWages_4 2.9 -8 184 291s PrivateWages_5 3.1 -7 190 291s PrivateWages_6 3.2 -6 193 291s PrivateWages_8 3.6 -4 203 291s PrivateWages_9 3.7 -3 208 291s PrivateWages_10 4.0 -2 211 291s PrivateWages_11 4.2 -1 216 291s PrivateWages_12 4.8 0 217 291s PrivateWages_13 5.3 1 213 291s PrivateWages_14 5.6 2 207 291s PrivateWages_15 6.0 3 202 291s PrivateWages_16 6.1 4 199 291s PrivateWages_17 7.4 5 198 291s PrivateWages_18 6.7 6 200 291s PrivateWages_19 7.7 7 202 291s PrivateWages_20 7.8 8 200 291s PrivateWages_21 8.0 9 201 291s PrivateWages_22 8.5 10 204 291s PrivateWages_corpProfLag PrivateWages_gnpLag 291s Consumption_2 0.0 0.0 291s Consumption_3 0.0 0.0 291s Consumption_4 0.0 0.0 291s Consumption_5 0.0 0.0 291s Consumption_6 0.0 0.0 291s Consumption_8 0.0 0.0 291s Consumption_9 0.0 0.0 291s Consumption_11 0.0 0.0 291s Consumption_12 0.0 0.0 291s Consumption_14 0.0 0.0 291s Consumption_15 0.0 0.0 291s Consumption_16 0.0 0.0 291s Consumption_17 0.0 0.0 291s Consumption_18 0.0 0.0 291s Consumption_19 0.0 0.0 291s Consumption_20 0.0 0.0 291s Consumption_21 0.0 0.0 291s Consumption_22 0.0 0.0 291s Investment_2 0.0 0.0 291s Investment_3 0.0 0.0 291s Investment_4 0.0 0.0 291s Investment_5 0.0 0.0 291s Investment_6 0.0 0.0 291s Investment_8 0.0 0.0 291s Investment_9 0.0 0.0 291s Investment_10 0.0 0.0 291s Investment_11 0.0 0.0 291s Investment_12 0.0 0.0 291s Investment_14 0.0 0.0 291s Investment_15 0.0 0.0 291s Investment_17 0.0 0.0 291s Investment_18 0.0 0.0 291s Investment_19 0.0 0.0 291s Investment_20 0.0 0.0 291s Investment_21 0.0 0.0 291s Investment_22 0.0 0.0 291s PrivateWages_2 12.7 44.9 291s PrivateWages_3 12.4 45.6 291s PrivateWages_4 16.9 50.1 291s PrivateWages_5 18.4 57.2 291s PrivateWages_6 19.4 57.1 291s PrivateWages_8 19.6 64.0 291s PrivateWages_9 19.8 64.4 291s PrivateWages_10 21.1 64.5 291s PrivateWages_11 21.7 67.0 291s PrivateWages_12 15.6 61.2 291s PrivateWages_13 11.4 53.4 291s PrivateWages_14 7.0 44.3 291s PrivateWages_15 11.2 45.1 291s PrivateWages_16 12.3 49.7 291s PrivateWages_17 14.0 54.4 291s PrivateWages_18 17.6 62.7 291s PrivateWages_19 17.3 65.0 291s PrivateWages_20 15.3 60.9 291s PrivateWages_21 19.0 69.5 291s PrivateWages_22 21.1 75.7 291s > matrix of fitted regressors 291s Consumption_(Intercept) Consumption_corpProf 291s Consumption_2 1 14.0 291s Consumption_3 1 16.7 291s Consumption_4 1 18.5 291s Consumption_5 1 20.3 291s Consumption_6 1 19.0 291s Consumption_8 1 17.6 291s Consumption_9 1 18.9 291s Consumption_11 1 16.7 291s Consumption_12 1 13.4 291s Consumption_14 1 10.0 291s Consumption_15 1 12.5 291s Consumption_16 1 14.5 291s Consumption_17 1 14.9 291s Consumption_18 1 19.4 291s Consumption_19 1 19.1 291s Consumption_20 1 17.7 291s Consumption_21 1 20.4 291s Consumption_22 1 22.7 291s Investment_2 0 0.0 291s Investment_3 0 0.0 291s Investment_4 0 0.0 291s Investment_5 0 0.0 291s Investment_6 0 0.0 291s Investment_8 0 0.0 291s Investment_9 0 0.0 291s Investment_10 0 0.0 291s Investment_11 0 0.0 291s Investment_12 0 0.0 291s Investment_14 0 0.0 291s Investment_15 0 0.0 291s Investment_17 0 0.0 291s Investment_18 0 0.0 291s Investment_19 0 0.0 291s Investment_20 0 0.0 291s Investment_21 0 0.0 291s Investment_22 0 0.0 291s PrivateWages_2 0 0.0 291s PrivateWages_3 0 0.0 291s PrivateWages_4 0 0.0 291s PrivateWages_5 0 0.0 291s PrivateWages_6 0 0.0 291s PrivateWages_8 0 0.0 291s PrivateWages_9 0 0.0 291s PrivateWages_10 0 0.0 291s PrivateWages_11 0 0.0 291s PrivateWages_12 0 0.0 291s PrivateWages_13 0 0.0 291s PrivateWages_14 0 0.0 291s PrivateWages_15 0 0.0 291s PrivateWages_16 0 0.0 291s PrivateWages_17 0 0.0 291s PrivateWages_18 0 0.0 291s PrivateWages_19 0 0.0 291s PrivateWages_20 0 0.0 291s PrivateWages_21 0 0.0 291s PrivateWages_22 0 0.0 291s Consumption_corpProfLag Consumption_wages 291s Consumption_2 12.7 29.8 291s Consumption_3 12.4 31.8 291s Consumption_4 16.9 35.3 291s Consumption_5 18.4 38.6 291s Consumption_6 19.4 38.5 291s Consumption_8 19.6 40.0 291s Consumption_9 19.8 41.8 291s Consumption_11 21.7 43.1 291s Consumption_12 15.6 39.7 291s Consumption_14 7.0 33.3 291s Consumption_15 11.2 37.3 291s Consumption_16 12.3 40.1 291s Consumption_17 14.0 41.8 291s Consumption_18 17.6 47.6 291s Consumption_19 17.3 49.2 291s Consumption_20 15.3 48.6 291s Consumption_21 19.0 53.4 291s Consumption_22 21.1 60.8 291s Investment_2 0.0 0.0 291s Investment_3 0.0 0.0 291s Investment_4 0.0 0.0 291s Investment_5 0.0 0.0 291s Investment_6 0.0 0.0 291s Investment_8 0.0 0.0 291s Investment_9 0.0 0.0 291s Investment_10 0.0 0.0 291s Investment_11 0.0 0.0 291s Investment_12 0.0 0.0 291s Investment_14 0.0 0.0 291s Investment_15 0.0 0.0 291s Investment_17 0.0 0.0 291s Investment_18 0.0 0.0 291s Investment_19 0.0 0.0 291s Investment_20 0.0 0.0 291s Investment_21 0.0 0.0 291s Investment_22 0.0 0.0 291s PrivateWages_2 0.0 0.0 291s PrivateWages_3 0.0 0.0 291s PrivateWages_4 0.0 0.0 291s PrivateWages_5 0.0 0.0 291s PrivateWages_6 0.0 0.0 291s PrivateWages_8 0.0 0.0 291s PrivateWages_9 0.0 0.0 291s PrivateWages_10 0.0 0.0 291s PrivateWages_11 0.0 0.0 291s PrivateWages_12 0.0 0.0 291s PrivateWages_13 0.0 0.0 291s PrivateWages_14 0.0 0.0 291s PrivateWages_15 0.0 0.0 291s PrivateWages_16 0.0 0.0 291s PrivateWages_17 0.0 0.0 291s PrivateWages_18 0.0 0.0 291s PrivateWages_19 0.0 0.0 291s PrivateWages_20 0.0 0.0 291s PrivateWages_21 0.0 0.0 291s PrivateWages_22 0.0 0.0 291s Investment_(Intercept) Investment_corpProf 291s Consumption_2 0 0.0 291s Consumption_3 0 0.0 291s Consumption_4 0 0.0 291s Consumption_5 0 0.0 291s Consumption_6 0 0.0 291s Consumption_8 0 0.0 291s Consumption_9 0 0.0 291s Consumption_11 0 0.0 291s Consumption_12 0 0.0 291s Consumption_14 0 0.0 291s Consumption_15 0 0.0 291s Consumption_16 0 0.0 291s Consumption_17 0 0.0 291s Consumption_18 0 0.0 291s Consumption_19 0 0.0 291s Consumption_20 0 0.0 291s Consumption_21 0 0.0 291s Consumption_22 0 0.0 291s Investment_2 1 13.4 291s Investment_3 1 16.7 291s Investment_4 1 18.8 291s Investment_5 1 20.6 291s Investment_6 1 19.3 291s Investment_8 1 17.5 291s Investment_9 1 19.5 291s Investment_10 1 20.2 291s Investment_11 1 17.2 291s Investment_12 1 13.5 291s Investment_14 1 10.1 291s Investment_15 1 13.0 291s Investment_17 1 14.9 291s Investment_18 1 19.5 291s Investment_19 1 19.3 291s Investment_20 1 17.5 291s Investment_21 1 20.2 291s Investment_22 1 22.8 291s PrivateWages_2 0 0.0 291s PrivateWages_3 0 0.0 291s PrivateWages_4 0 0.0 291s PrivateWages_5 0 0.0 291s PrivateWages_6 0 0.0 291s PrivateWages_8 0 0.0 291s PrivateWages_9 0 0.0 291s PrivateWages_10 0 0.0 291s PrivateWages_11 0 0.0 291s PrivateWages_12 0 0.0 291s PrivateWages_13 0 0.0 291s PrivateWages_14 0 0.0 291s PrivateWages_15 0 0.0 291s PrivateWages_16 0 0.0 291s PrivateWages_17 0 0.0 291s PrivateWages_18 0 0.0 291s PrivateWages_19 0 0.0 291s PrivateWages_20 0 0.0 291s PrivateWages_21 0 0.0 291s PrivateWages_22 0 0.0 291s Investment_corpProfLag Investment_capitalLag 291s Consumption_2 0.0 0 291s Consumption_3 0.0 0 291s Consumption_4 0.0 0 291s Consumption_5 0.0 0 291s Consumption_6 0.0 0 291s Consumption_8 0.0 0 291s Consumption_9 0.0 0 291s Consumption_11 0.0 0 291s Consumption_12 0.0 0 291s Consumption_14 0.0 0 291s Consumption_15 0.0 0 291s Consumption_16 0.0 0 291s Consumption_17 0.0 0 291s Consumption_18 0.0 0 291s Consumption_19 0.0 0 291s Consumption_20 0.0 0 291s Consumption_21 0.0 0 291s Consumption_22 0.0 0 291s Investment_2 12.7 183 291s Investment_3 12.4 183 291s Investment_4 16.9 184 291s Investment_5 18.4 190 291s Investment_6 19.4 193 291s Investment_8 19.6 203 291s Investment_9 19.8 208 291s Investment_10 21.1 211 291s Investment_11 21.7 216 291s Investment_12 15.6 217 291s Investment_14 7.0 207 291s Investment_15 11.2 202 291s Investment_17 14.0 198 291s Investment_18 17.6 200 291s Investment_19 17.3 202 291s Investment_20 15.3 200 291s Investment_21 19.0 201 291s Investment_22 21.1 204 291s PrivateWages_2 0.0 0 291s PrivateWages_3 0.0 0 291s PrivateWages_4 0.0 0 291s PrivateWages_5 0.0 0 291s PrivateWages_6 0.0 0 291s PrivateWages_8 0.0 0 291s PrivateWages_9 0.0 0 291s PrivateWages_10 0.0 0 291s PrivateWages_11 0.0 0 291s PrivateWages_12 0.0 0 291s PrivateWages_13 0.0 0 291s PrivateWages_14 0.0 0 291s PrivateWages_15 0.0 0 291s PrivateWages_16 0.0 0 291s PrivateWages_17 0.0 0 291s PrivateWages_18 0.0 0 291s PrivateWages_19 0.0 0 291s PrivateWages_20 0.0 0 291s PrivateWages_21 0.0 0 291s PrivateWages_22 0.0 0 291s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 291s Consumption_2 0 0.0 0.0 291s Consumption_3 0 0.0 0.0 291s Consumption_4 0 0.0 0.0 291s Consumption_5 0 0.0 0.0 291s Consumption_6 0 0.0 0.0 291s Consumption_8 0 0.0 0.0 291s Consumption_9 0 0.0 0.0 291s Consumption_11 0 0.0 0.0 291s Consumption_12 0 0.0 0.0 291s Consumption_14 0 0.0 0.0 291s Consumption_15 0 0.0 0.0 291s Consumption_16 0 0.0 0.0 291s Consumption_17 0 0.0 0.0 291s Consumption_18 0 0.0 0.0 291s Consumption_19 0 0.0 0.0 291s Consumption_20 0 0.0 0.0 291s Consumption_21 0 0.0 0.0 291s Consumption_22 0 0.0 0.0 291s Investment_2 0 0.0 0.0 291s Investment_3 0 0.0 0.0 291s Investment_4 0 0.0 0.0 291s Investment_5 0 0.0 0.0 291s Investment_6 0 0.0 0.0 291s Investment_8 0 0.0 0.0 291s Investment_9 0 0.0 0.0 291s Investment_10 0 0.0 0.0 291s Investment_11 0 0.0 0.0 291s Investment_12 0 0.0 0.0 291s Investment_14 0 0.0 0.0 291s Investment_15 0 0.0 0.0 291s Investment_17 0 0.0 0.0 291s Investment_18 0 0.0 0.0 291s Investment_19 0 0.0 0.0 291s Investment_20 0 0.0 0.0 291s Investment_21 0 0.0 0.0 291s Investment_22 0 0.0 0.0 291s PrivateWages_2 1 47.1 44.9 291s PrivateWages_3 1 49.6 45.6 291s PrivateWages_4 1 56.5 50.1 291s PrivateWages_5 1 60.7 57.2 291s PrivateWages_6 1 60.6 57.1 291s PrivateWages_8 1 60.0 64.0 291s PrivateWages_9 1 62.3 64.4 291s PrivateWages_10 1 64.6 64.5 291s PrivateWages_11 1 63.7 67.0 291s PrivateWages_12 1 54.8 61.2 291s PrivateWages_13 1 47.0 53.4 291s PrivateWages_14 1 42.1 44.3 291s PrivateWages_15 1 51.2 45.1 291s PrivateWages_16 1 55.3 49.7 291s PrivateWages_17 1 57.4 54.4 291s PrivateWages_18 1 67.2 62.7 291s PrivateWages_19 1 68.5 65.0 291s PrivateWages_20 1 66.8 60.9 291s PrivateWages_21 1 74.9 69.5 291s PrivateWages_22 1 86.9 75.7 291s PrivateWages_trend 291s Consumption_2 0 291s Consumption_3 0 291s Consumption_4 0 291s Consumption_5 0 291s Consumption_6 0 291s Consumption_8 0 291s Consumption_9 0 291s Consumption_11 0 291s Consumption_12 0 291s Consumption_14 0 291s Consumption_15 0 291s Consumption_16 0 291s Consumption_17 0 291s Consumption_18 0 291s Consumption_19 0 291s Consumption_20 0 291s Consumption_21 0 291s Consumption_22 0 291s Investment_2 0 291s Investment_3 0 291s Investment_4 0 291s Investment_5 0 291s Investment_6 0 291s Investment_8 0 291s Investment_9 0 291s Investment_10 0 291s Investment_11 0 291s Investment_12 0 291s Investment_14 0 291s Investment_15 0 291s Investment_17 0 291s Investment_18 0 291s Investment_19 0 291s Investment_20 0 291s Investment_21 0 291s Investment_22 0 291s PrivateWages_2 -10 291s PrivateWages_3 -9 291s PrivateWages_4 -8 291s PrivateWages_5 -7 291s PrivateWages_6 -6 291s PrivateWages_8 -4 291s PrivateWages_9 -3 291s PrivateWages_10 -2 291s PrivateWages_11 -1 291s PrivateWages_12 0 291s PrivateWages_13 1 291s PrivateWages_14 2 291s PrivateWages_15 3 291s PrivateWages_16 4 291s PrivateWages_17 5 291s PrivateWages_18 6 291s PrivateWages_19 7 291s PrivateWages_20 8 291s PrivateWages_21 9 291s PrivateWages_22 10 291s > nobs 291s [1] 56 291s > linearHypothesis 291s Linear hypothesis test (Theil's F test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df F Pr(>F) 291s 1 45 291s 2 44 1 1.27 0.27 291s Linear hypothesis test (F statistic of a Wald test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df F Pr(>F) 291s 1 45 291s 2 44 1 1.66 0.2 291s Linear hypothesis test (Chi^2 statistic of a Wald test) 291s 291s Hypothesis: 291s Consumption_corpProf + Investment_capitalLag = 0 291s 291s Model 1: restricted model 291s Model 2: kleinModel 291s 291s Res.Df Df Chisq Pr(>Chisq) 291s 1 45 291s 2 44 1 1.66 0.2 292s Linear hypothesis test (Theil's F test) 292s 292s Hypothesis: 292s Consumption_corpProf + Investment_capitalLag = 0 292s Consumption_corpProfLag - PrivateWages_trend = 0 292s 292s Model 1: restricted model 292s Model 2: kleinModel 292s 292s Res.Df Df F Pr(>F) 292s 1 46 292s 2 44 2 0.64 0.53 292s Linear hypothesis test (F statistic of a Wald test) 292s 292s Hypothesis: 292s Consumption_corpProf + Investment_capitalLag = 0 292s Consumption_corpProfLag - PrivateWages_trend = 0 292s 292s Model 1: restricted model 292s Model 2: kleinModel 292s 292s Res.Df Df F Pr(>F) 292s 1 46 292s 2 44 2 0.84 0.44 292s Linear hypothesis test (Chi^2 statistic of a Wald test) 292s 292s Hypothesis: 292s Consumption_corpProf + Investment_capitalLag = 0 292s Consumption_corpProfLag - PrivateWages_trend = 0 292s 292s Model 1: restricted model 292s Model 2: kleinModel 292s 292s Res.Df Df Chisq Pr(>Chisq) 292s 1 46 292s 2 44 2 1.68 0.43 292s > logLik 292s 'log Lik.' -69.5 (df=13) 292s 'log Lik.' -77.5 (df=13) 292s Estimating function 292s Consumption_(Intercept) Consumption_corpProf 292s Consumption_2 -1.891 -26.49 292s Consumption_3 -0.190 -3.16 292s Consumption_4 0.294 5.45 292s Consumption_5 -1.285 -26.05 292s Consumption_6 0.431 8.19 292s Consumption_8 2.670 47.11 292s Consumption_9 2.363 44.77 292s Consumption_11 -1.642 -27.49 292s Consumption_12 -1.735 -23.21 292s Consumption_14 0.834 8.35 292s Consumption_15 -1.061 -13.27 292s Consumption_16 -0.885 -12.82 292s Consumption_17 3.801 56.68 292s Consumption_18 -0.502 -9.76 292s Consumption_19 -3.000 -57.33 292s Consumption_20 2.012 35.52 292s Consumption_21 0.746 15.21 292s Consumption_22 -0.957 -21.70 292s Investment_2 0.000 0.00 292s Investment_3 0.000 0.00 292s Investment_4 0.000 0.00 292s Investment_5 0.000 0.00 292s Investment_6 0.000 0.00 292s Investment_8 0.000 0.00 292s Investment_9 0.000 0.00 292s Investment_10 0.000 0.00 292s Investment_11 0.000 0.00 292s Investment_12 0.000 0.00 292s Investment_14 0.000 0.00 292s Investment_15 0.000 0.00 292s Investment_17 0.000 0.00 292s Investment_18 0.000 0.00 292s Investment_19 0.000 0.00 292s Investment_20 0.000 0.00 292s Investment_21 0.000 0.00 292s Investment_22 0.000 0.00 292s PrivateWages_2 0.000 0.00 292s PrivateWages_3 0.000 0.00 292s PrivateWages_4 0.000 0.00 292s PrivateWages_5 0.000 0.00 292s PrivateWages_6 0.000 0.00 292s PrivateWages_8 0.000 0.00 292s PrivateWages_9 0.000 0.00 292s PrivateWages_10 0.000 0.00 292s PrivateWages_11 0.000 0.00 292s PrivateWages_12 0.000 0.00 292s PrivateWages_13 0.000 0.00 292s PrivateWages_14 0.000 0.00 292s PrivateWages_15 0.000 0.00 292s PrivateWages_16 0.000 0.00 292s PrivateWages_17 0.000 0.00 292s PrivateWages_18 0.000 0.00 292s PrivateWages_19 0.000 0.00 292s PrivateWages_20 0.000 0.00 292s PrivateWages_21 0.000 0.00 292s PrivateWages_22 0.000 0.00 292s Consumption_corpProfLag Consumption_wages 292s Consumption_2 -24.01 -56.38 292s Consumption_3 -2.35 -6.04 292s Consumption_4 4.96 10.35 292s Consumption_5 -23.65 -49.61 292s Consumption_6 8.35 16.60 292s Consumption_8 52.33 106.81 292s Consumption_9 46.80 98.74 292s Consumption_11 -35.64 -70.78 292s Consumption_12 -27.07 -68.81 292s Consumption_14 5.83 27.78 292s Consumption_15 -11.88 -39.61 292s Consumption_16 -10.89 -35.54 292s Consumption_17 53.21 158.79 292s Consumption_18 -8.84 -23.92 292s Consumption_19 -51.90 -147.70 292s Consumption_20 30.78 97.67 292s Consumption_21 14.17 39.83 292s Consumption_22 -20.20 -58.19 292s Investment_2 0.00 0.00 292s Investment_3 0.00 0.00 292s Investment_4 0.00 0.00 292s Investment_5 0.00 0.00 292s Investment_6 0.00 0.00 292s Investment_8 0.00 0.00 292s Investment_9 0.00 0.00 292s Investment_10 0.00 0.00 292s Investment_11 0.00 0.00 292s Investment_12 0.00 0.00 292s Investment_14 0.00 0.00 292s Investment_15 0.00 0.00 292s Investment_17 0.00 0.00 292s Investment_18 0.00 0.00 292s Investment_19 0.00 0.00 292s Investment_20 0.00 0.00 292s Investment_21 0.00 0.00 292s Investment_22 0.00 0.00 292s PrivateWages_2 0.00 0.00 292s PrivateWages_3 0.00 0.00 292s PrivateWages_4 0.00 0.00 292s PrivateWages_5 0.00 0.00 292s PrivateWages_6 0.00 0.00 292s PrivateWages_8 0.00 0.00 292s PrivateWages_9 0.00 0.00 292s PrivateWages_10 0.00 0.00 292s PrivateWages_11 0.00 0.00 292s PrivateWages_12 0.00 0.00 292s PrivateWages_13 0.00 0.00 292s PrivateWages_14 0.00 0.00 292s PrivateWages_15 0.00 0.00 292s PrivateWages_16 0.00 0.00 292s PrivateWages_17 0.00 0.00 292s PrivateWages_18 0.00 0.00 292s PrivateWages_19 0.00 0.00 292s PrivateWages_20 0.00 0.00 292s PrivateWages_21 0.00 0.00 292s PrivateWages_22 0.00 0.00 292s Investment_(Intercept) Investment_corpProf 292s Consumption_2 0.000 0.00 292s Consumption_3 0.000 0.00 292s Consumption_4 0.000 0.00 292s Consumption_5 0.000 0.00 292s Consumption_6 0.000 0.00 292s Consumption_8 0.000 0.00 292s Consumption_9 0.000 0.00 292s Consumption_11 0.000 0.00 292s Consumption_12 0.000 0.00 292s Consumption_14 0.000 0.00 292s Consumption_15 0.000 0.00 292s Consumption_16 0.000 0.00 292s Consumption_17 0.000 0.00 292s Consumption_18 0.000 0.00 292s Consumption_19 0.000 0.00 292s Consumption_20 0.000 0.00 292s Consumption_21 0.000 0.00 292s Consumption_22 0.000 0.00 292s Investment_2 -1.375 -18.47 292s Investment_3 0.361 6.02 292s Investment_4 1.027 19.33 292s Investment_5 -1.558 -32.12 292s Investment_6 0.610 11.77 292s Investment_8 1.420 24.90 292s Investment_9 0.404 7.88 292s Investment_10 2.082 42.13 292s Investment_11 -1.150 -19.79 292s Investment_12 -1.339 -18.06 292s Investment_14 1.019 10.28 292s Investment_15 -0.475 -6.17 292s Investment_17 2.105 31.39 292s Investment_18 -0.465 -9.06 292s Investment_19 -3.871 -74.65 292s Investment_20 0.469 8.23 292s Investment_21 0.132 2.65 292s Investment_22 0.603 13.74 292s PrivateWages_2 0.000 0.00 292s PrivateWages_3 0.000 0.00 292s PrivateWages_4 0.000 0.00 292s PrivateWages_5 0.000 0.00 292s PrivateWages_6 0.000 0.00 292s PrivateWages_8 0.000 0.00 292s PrivateWages_9 0.000 0.00 292s PrivateWages_10 0.000 0.00 292s PrivateWages_11 0.000 0.00 292s PrivateWages_12 0.000 0.00 292s PrivateWages_13 0.000 0.00 292s PrivateWages_14 0.000 0.00 292s PrivateWages_15 0.000 0.00 292s PrivateWages_16 0.000 0.00 292s PrivateWages_17 0.000 0.00 292s PrivateWages_18 0.000 0.00 292s PrivateWages_19 0.000 0.00 292s PrivateWages_20 0.000 0.00 292s PrivateWages_21 0.000 0.00 292s PrivateWages_22 0.000 0.00 292s Investment_corpProfLag Investment_capitalLag 292s Consumption_2 0.00 0.0 292s Consumption_3 0.00 0.0 292s Consumption_4 0.00 0.0 292s Consumption_5 0.00 0.0 292s Consumption_6 0.00 0.0 292s Consumption_8 0.00 0.0 292s Consumption_9 0.00 0.0 292s Consumption_11 0.00 0.0 292s Consumption_12 0.00 0.0 292s Consumption_14 0.00 0.0 292s Consumption_15 0.00 0.0 292s Consumption_16 0.00 0.0 292s Consumption_17 0.00 0.0 292s Consumption_18 0.00 0.0 292s Consumption_19 0.00 0.0 292s Consumption_20 0.00 0.0 292s Consumption_21 0.00 0.0 292s Consumption_22 0.00 0.0 292s Investment_2 -17.46 -251.4 292s Investment_3 4.48 65.9 292s Investment_4 17.35 189.4 292s Investment_5 -28.67 -295.5 292s Investment_6 11.83 117.5 292s Investment_8 27.83 288.8 292s Investment_9 8.00 83.9 292s Investment_10 43.93 438.5 292s Investment_11 -24.96 -248.1 292s Investment_12 -20.88 -290.1 292s Investment_14 7.14 211.1 292s Investment_15 -5.32 -95.9 292s Investment_17 29.48 416.3 292s Investment_18 -8.18 -92.9 292s Investment_19 -66.97 -781.2 292s Investment_20 7.18 93.8 292s Investment_21 2.50 26.5 292s Investment_22 12.73 123.4 292s PrivateWages_2 0.00 0.0 292s PrivateWages_3 0.00 0.0 292s PrivateWages_4 0.00 0.0 292s PrivateWages_5 0.00 0.0 292s PrivateWages_6 0.00 0.0 292s PrivateWages_8 0.00 0.0 292s PrivateWages_9 0.00 0.0 292s PrivateWages_10 0.00 0.0 292s PrivateWages_11 0.00 0.0 292s PrivateWages_12 0.00 0.0 292s PrivateWages_13 0.00 0.0 292s PrivateWages_14 0.00 0.0 292s PrivateWages_15 0.00 0.0 292s PrivateWages_16 0.00 0.0 292s PrivateWages_17 0.00 0.0 292s PrivateWages_18 0.00 0.0 292s PrivateWages_19 0.00 0.0 292s PrivateWages_20 0.00 0.0 292s PrivateWages_21 0.00 0.0 292s PrivateWages_22 0.00 0.0 292s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 292s Consumption_2 0.0000 0.00 0.00 292s Consumption_3 0.0000 0.00 0.00 292s Consumption_4 0.0000 0.00 0.00 292s Consumption_5 0.0000 0.00 0.00 292s Consumption_6 0.0000 0.00 0.00 292s Consumption_8 0.0000 0.00 0.00 292s Consumption_9 0.0000 0.00 0.00 292s Consumption_11 0.0000 0.00 0.00 292s Consumption_12 0.0000 0.00 0.00 292s Consumption_14 0.0000 0.00 0.00 292s Consumption_15 0.0000 0.00 0.00 292s Consumption_16 0.0000 0.00 0.00 292s Consumption_17 0.0000 0.00 0.00 292s Consumption_18 0.0000 0.00 0.00 292s Consumption_19 0.0000 0.00 0.00 292s Consumption_20 0.0000 0.00 0.00 292s Consumption_21 0.0000 0.00 0.00 292s Consumption_22 0.0000 0.00 0.00 292s Investment_2 0.0000 0.00 0.00 292s Investment_3 0.0000 0.00 0.00 292s Investment_4 0.0000 0.00 0.00 292s Investment_5 0.0000 0.00 0.00 292s Investment_6 0.0000 0.00 0.00 292s Investment_8 0.0000 0.00 0.00 292s Investment_9 0.0000 0.00 0.00 292s Investment_10 0.0000 0.00 0.00 292s Investment_11 0.0000 0.00 0.00 292s Investment_12 0.0000 0.00 0.00 292s Investment_14 0.0000 0.00 0.00 292s Investment_15 0.0000 0.00 0.00 292s Investment_17 0.0000 0.00 0.00 292s Investment_18 0.0000 0.00 0.00 292s Investment_19 0.0000 0.00 0.00 292s Investment_20 0.0000 0.00 0.00 292s Investment_21 0.0000 0.00 0.00 292s Investment_22 0.0000 0.00 0.00 292s PrivateWages_2 -1.9924 -93.78 -89.46 292s PrivateWages_3 0.4683 23.22 21.35 292s PrivateWages_4 1.4034 79.35 70.31 292s PrivateWages_5 -1.7870 -108.45 -102.22 292s PrivateWages_6 -0.3627 -21.98 -20.71 292s PrivateWages_8 1.1629 69.77 74.43 292s PrivateWages_9 1.2735 79.30 82.01 292s PrivateWages_10 2.2141 142.96 142.81 292s PrivateWages_11 -1.2912 -82.26 -86.51 292s PrivateWages_12 -0.0350 -1.92 -2.14 292s PrivateWages_13 -1.0438 -49.04 -55.74 292s PrivateWages_14 1.8016 75.90 79.81 292s PrivateWages_15 -0.3714 -19.02 -16.75 292s PrivateWages_16 -0.3904 -21.61 -19.40 292s PrivateWages_17 1.4934 85.71 81.24 292s PrivateWages_18 0.0279 1.88 1.75 292s PrivateWages_19 -3.8229 -261.91 -248.49 292s PrivateWages_20 0.7870 52.61 47.93 292s PrivateWages_21 -0.7415 -55.52 -51.54 292s PrivateWages_22 1.2062 104.79 91.31 292s PrivateWages_trend 292s Consumption_2 0.000 292s Consumption_3 0.000 292s Consumption_4 0.000 292s Consumption_5 0.000 292s Consumption_6 0.000 292s Consumption_8 0.000 292s Consumption_9 0.000 292s Consumption_11 0.000 292s Consumption_12 0.000 292s Consumption_14 0.000 292s Consumption_15 0.000 292s Consumption_16 0.000 292s Consumption_17 0.000 292s Consumption_18 0.000 292s Consumption_19 0.000 292s Consumption_20 0.000 292s Consumption_21 0.000 292s Consumption_22 0.000 292s Investment_2 0.000 292s Investment_3 0.000 292s Investment_4 0.000 292s Investment_5 0.000 292s Investment_6 0.000 292s Investment_8 0.000 292s Investment_9 0.000 292s Investment_10 0.000 292s Investment_11 0.000 292s Investment_12 0.000 292s Investment_14 0.000 292s Investment_15 0.000 292s Investment_17 0.000 292s Investment_18 0.000 292s Investment_19 0.000 292s Investment_20 0.000 292s Investment_21 0.000 292s Investment_22 0.000 292s PrivateWages_2 19.924 292s PrivateWages_3 -4.214 292s PrivateWages_4 -11.227 292s PrivateWages_5 12.509 292s PrivateWages_6 2.176 292s PrivateWages_8 -4.652 292s PrivateWages_9 -3.820 292s PrivateWages_10 -4.428 292s PrivateWages_11 1.291 292s PrivateWages_12 0.000 292s PrivateWages_13 -1.044 292s PrivateWages_14 3.603 292s PrivateWages_15 -1.114 292s PrivateWages_16 -1.562 292s PrivateWages_17 7.467 292s PrivateWages_18 0.168 292s PrivateWages_19 -26.760 292s PrivateWages_20 6.296 292s PrivateWages_21 -6.674 292s PrivateWages_22 12.062 292s [1] TRUE 292s > Bread 292s Consumption_(Intercept) Consumption_corpProf 292s Consumption_(Intercept) 116.13 -4.139 292s Consumption_corpProf -4.14 1.213 292s Consumption_corpProfLag 1.01 -0.677 292s Consumption_wages -1.41 -0.133 292s Investment_(Intercept) 0.00 0.000 292s Investment_corpProf 0.00 0.000 292s Investment_corpProfLag 0.00 0.000 292s Investment_capitalLag 0.00 0.000 292s PrivateWages_(Intercept) 0.00 0.000 292s PrivateWages_gnp 0.00 0.000 292s PrivateWages_gnpLag 0.00 0.000 292s PrivateWages_trend 0.00 0.000 292s Consumption_corpProfLag Consumption_wages 292s Consumption_(Intercept) 1.0117 -1.4132 292s Consumption_corpProf -0.6770 -0.1333 292s Consumption_corpProfLag 0.6979 -0.0188 292s Consumption_wages -0.0188 0.0955 292s Investment_(Intercept) 0.0000 0.0000 292s Investment_corpProf 0.0000 0.0000 292s Investment_corpProfLag 0.0000 0.0000 292s Investment_capitalLag 0.0000 0.0000 292s PrivateWages_(Intercept) 0.0000 0.0000 292s PrivateWages_gnp 0.0000 0.0000 292s PrivateWages_gnpLag 0.0000 0.0000 292s PrivateWages_trend 0.0000 0.0000 292s Investment_(Intercept) Investment_corpProf 292s Consumption_(Intercept) 0.0 0.000 292s Consumption_corpProf 0.0 0.000 292s Consumption_corpProfLag 0.0 0.000 292s Consumption_wages 0.0 0.000 292s Investment_(Intercept) 2271.1 -40.229 292s Investment_corpProf -40.2 1.601 292s Investment_corpProfLag 32.3 -1.240 292s Investment_capitalLag -10.5 0.165 292s PrivateWages_(Intercept) 0.0 0.000 292s PrivateWages_gnp 0.0 0.000 292s PrivateWages_gnpLag 0.0 0.000 292s PrivateWages_trend 0.0 0.000 292s Investment_corpProfLag Investment_capitalLag 292s Consumption_(Intercept) 0.000 0.0000 292s Consumption_corpProf 0.000 0.0000 292s Consumption_corpProfLag 0.000 0.0000 292s Consumption_wages 0.000 0.0000 292s Investment_(Intercept) 32.280 -10.5200 292s Investment_corpProf -1.240 0.1648 292s Investment_corpProfLag 1.187 -0.1522 292s Investment_capitalLag -0.152 0.0509 292s PrivateWages_(Intercept) 0.000 0.0000 292s PrivateWages_gnp 0.000 0.0000 292s PrivateWages_gnpLag 0.000 0.0000 292s PrivateWages_trend 0.000 0.0000 292s PrivateWages_(Intercept) PrivateWages_gnp 292s Consumption_(Intercept) 0.000 0.0000 292s Consumption_corpProf 0.000 0.0000 292s Consumption_corpProfLag 0.000 0.0000 292s Consumption_wages 0.000 0.0000 292s Investment_(Intercept) 0.000 0.0000 292s Investment_corpProf 0.000 0.0000 292s Investment_corpProfLag 0.000 0.0000 292s Investment_capitalLag 0.000 0.0000 292s PrivateWages_(Intercept) 159.333 -0.8670 292s PrivateWages_gnp -0.867 0.1475 292s PrivateWages_gnpLag -1.818 -0.1375 292s PrivateWages_trend 2.020 -0.0396 292s PrivateWages_gnpLag PrivateWages_trend 292s Consumption_(Intercept) 0.0000 0.0000 292s Consumption_corpProf 0.0000 0.0000 292s Consumption_corpProfLag 0.0000 0.0000 292s Consumption_wages 0.0000 0.0000 292s Investment_(Intercept) 0.0000 0.0000 292s Investment_corpProf 0.0000 0.0000 292s Investment_corpProfLag 0.0000 0.0000 292s Investment_capitalLag 0.0000 0.0000 292s PrivateWages_(Intercept) -1.8179 2.0198 292s PrivateWages_gnp -0.1375 -0.0396 292s PrivateWages_gnpLag 0.1737 0.0056 292s PrivateWages_trend 0.0056 0.1075 292s > 292s > # SUR 292s Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 292s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 292s > summary 292s 292s systemfit results 292s method: SUR 292s 292s N DF SSR detRCov OLS-R2 McElroy-R2 292s system 58 46 45.1 0.199 0.975 0.993 292s 292s N DF SSR MSE RMSE R2 Adj R2 292s Consumption 19 15 17.5 1.167 1.080 0.980 0.975 292s Investment 19 15 17.3 1.155 1.075 0.906 0.887 292s PrivateWages 20 16 10.3 0.642 0.801 0.987 0.985 292s 292s The covariance matrix of the residuals used for estimation 292s Consumption Investment PrivateWages 292s Consumption 0.9830 0.0466 -0.391 292s Investment 0.0466 0.8101 0.115 292s PrivateWages -0.3906 0.1155 0.496 292s 292s The covariance matrix of the residuals 292s Consumption Investment PrivateWages 292s Consumption 0.979 0.080 -0.452 292s Investment 0.080 0.810 0.181 292s PrivateWages -0.452 0.181 0.521 292s 292s The correlations of the residuals 292s Consumption Investment PrivateWages 292s Consumption 1.0000 0.0907 -0.636 292s Investment 0.0907 1.0000 0.267 292s PrivateWages -0.6362 0.2671 1.000 292s 292s 292s SUR estimates for 'Consumption' (equation 1) 292s Model Formula: consump ~ corpProf + corpProfLag + wages 292s 292s Estimate Std. Error t value Pr(>|t|) 292s (Intercept) 16.2670 1.3148 12.37 2.8e-09 *** 292s corpProf 0.1942 0.0954 2.04 0.06 . 292s corpProfLag 0.0747 0.0842 0.89 0.39 292s wages 0.8011 0.0383 20.93 1.6e-12 *** 292s --- 292s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 292s 292s Residual standard error: 1.08 on 15 degrees of freedom 292s Number of observations: 19 Degrees of Freedom: 15 292s SSR: 17.501 MSE: 1.167 Root MSE: 1.08 292s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.975 292s 292s 292s SUR estimates for 'Investment' (equation 2) 292s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 292s 292s Estimate Std. Error t value Pr(>|t|) 292s (Intercept) 12.6390 4.7856 2.64 0.01852 * 292s corpProf 0.4708 0.0943 4.99 0.00016 *** 292s corpProfLag 0.3533 0.0907 3.89 0.00144 ** 292s capitalLag -0.1254 0.0236 -5.32 8.6e-05 *** 292s --- 292s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 292s 292s Residual standard error: 1.075 on 15 degrees of freedom 292s Number of observations: 19 Degrees of Freedom: 15 292s SSR: 17.321 MSE: 1.155 Root MSE: 1.075 292s Multiple R-Squared: 0.906 Adjusted R-Squared: 0.887 292s 292s 292s SUR estimates for 'PrivateWages' (equation 3) 292s Model Formula: privWage ~ gnp + gnpLag + trend 292s 292s Estimate Std. Error t value Pr(>|t|) 292s (Intercept) 1.3264 1.1240 1.18 0.2552 292s gnp 0.4184 0.0268 15.63 4.1e-11 *** 292s gnpLag 0.1714 0.0315 5.43 5.5e-05 *** 292s trend 0.1456 0.0284 5.13 0.0001 *** 292s --- 292s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 292s 292s Residual standard error: 0.801 on 16 degrees of freedom 292s Number of observations: 20 Degrees of Freedom: 16 292s SSR: 10.266 MSE: 0.642 Root MSE: 0.801 292s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 292s 292s > residuals 292s Consumption Investment PrivateWages 292s 1 NA NA NA 292s 2 -0.3143 -0.2326 -1.1434 292s 3 -1.2700 -0.1705 0.5084 292s 4 -1.5426 1.0718 1.4211 292s 5 -0.4489 -1.4767 -0.0992 292s 6 0.0588 0.3167 -0.3594 292s 7 0.9213 1.4446 NA 292s 8 1.3789 0.8296 -0.7554 292s 9 1.0900 -0.5263 0.2887 292s 10 NA 1.2083 1.1800 292s 11 0.3569 0.4082 -0.3673 292s 12 -0.2288 0.2663 0.3445 292s 13 NA NA -0.1571 292s 14 0.2181 0.4946 0.4220 292s 15 -0.1120 -0.0470 0.3147 292s 16 -0.0872 NA 0.0145 292s 17 1.5615 1.0289 -0.8091 292s 18 -0.4530 0.0617 0.8608 292s 19 0.1997 -2.5397 -0.7635 292s 20 0.9268 -0.6136 -0.4046 292s 21 0.7588 -0.7465 -1.2179 292s 22 -2.2137 -0.6044 0.5606 292s > fitted 292s Consumption Investment PrivateWages 292s 1 NA NA NA 292s 2 42.2 0.0326 26.6 292s 3 46.3 2.0705 28.8 292s 4 50.7 4.1282 32.7 292s 5 51.0 4.4767 34.0 292s 6 52.5 4.7833 35.8 292s 7 54.2 4.1554 NA 292s 8 54.8 3.3704 38.7 292s 9 56.2 3.5263 38.9 292s 10 NA 3.8917 40.1 292s 11 54.6 0.5918 38.3 292s 12 51.1 -3.6663 34.2 292s 13 NA NA 29.2 292s 14 46.3 -5.5946 28.1 292s 15 48.8 -2.9530 30.3 292s 16 51.4 NA 33.2 292s 17 56.1 1.0711 37.6 292s 18 59.2 1.9383 40.1 292s 19 57.3 0.6397 39.0 292s 20 60.7 1.9136 42.0 292s 21 64.2 4.0465 46.2 292s 22 71.9 5.5044 52.7 292s > predict 292s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 292s 1 NA NA NA NA 292s 2 42.2 0.460 41.3 43.1 292s 3 46.3 0.489 45.3 47.3 292s 4 50.7 0.328 50.1 51.4 292s 5 51.0 0.384 50.3 51.8 292s 6 52.5 0.389 51.8 53.3 292s 7 54.2 0.347 53.5 54.9 292s 8 54.8 0.319 54.2 55.5 292s 9 56.2 0.353 55.5 56.9 292s 10 NA NA NA NA 292s 11 54.6 0.583 53.5 55.8 292s 12 51.1 0.524 50.1 52.2 292s 13 NA NA NA NA 292s 14 46.3 0.589 45.1 47.5 292s 15 48.8 0.393 48.0 49.6 292s 16 51.4 0.337 50.7 52.1 292s 17 56.1 0.345 55.4 56.8 292s 18 59.2 0.318 58.5 59.8 292s 19 57.3 0.381 56.5 58.1 292s 20 60.7 0.413 59.8 61.5 292s 21 64.2 0.417 63.4 65.1 292s 22 71.9 0.651 70.6 73.2 292s Investment.pred Investment.se.fit Investment.lwr Investment.upr 292s 1 NA NA NA NA 292s 2 0.0326 0.556 -1.0866 1.15 292s 3 2.0705 0.454 1.1575 2.98 292s 4 4.1282 0.399 3.3256 4.93 292s 5 4.4767 0.331 3.8101 5.14 292s 6 4.7833 0.314 4.1520 5.41 292s 7 4.1554 0.291 3.5687 4.74 292s 8 3.3704 0.260 2.8469 3.89 292s 9 3.5263 0.347 2.8278 4.22 292s 10 3.8917 0.397 3.0924 4.69 292s 11 0.5918 0.578 -0.5711 1.75 292s 12 -3.6663 0.551 -4.7762 -2.56 292s 13 NA NA NA NA 292s 14 -5.5946 0.661 -6.9261 -4.26 292s 15 -2.9530 0.392 -3.7430 -2.16 292s 16 NA NA NA NA 292s 17 1.0711 0.318 0.4315 1.71 292s 18 1.9383 0.225 1.4863 2.39 292s 19 0.6397 0.310 0.0165 1.26 292s 20 1.9136 0.333 1.2436 2.58 292s 21 4.0465 0.304 3.4345 4.66 292s 22 5.5044 0.429 4.6400 6.37 292s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 292s 1 NA NA NA NA 292s 2 26.6 0.321 26.0 27.3 292s 3 28.8 0.321 28.1 29.4 292s 4 32.7 0.316 32.0 33.3 292s 5 34.0 0.244 33.5 34.5 292s 6 35.8 0.242 35.3 36.2 292s 7 NA NA NA NA 292s 8 38.7 0.246 38.2 39.2 292s 9 38.9 0.234 38.4 39.4 292s 10 40.1 0.225 39.7 40.6 292s 11 38.3 0.301 37.7 38.9 292s 12 34.2 0.298 33.6 34.8 292s 13 29.2 0.353 28.4 29.9 292s 14 28.1 0.330 27.4 28.7 292s 15 30.3 0.328 29.6 30.9 292s 16 33.2 0.275 32.6 33.7 292s 17 37.6 0.270 37.1 38.2 292s 18 40.1 0.213 39.7 40.6 292s 19 39.0 0.301 38.4 39.6 292s 20 42.0 0.287 41.4 42.6 292s 21 46.2 0.304 45.6 46.8 292s 22 52.7 0.448 51.8 53.6 292s > model.frame 292s [1] TRUE 292s > model.matrix 292s [1] TRUE 292s > nobs 292s [1] 58 292s > linearHypothesis 292s Linear hypothesis test (Theil's F test) 292s 292s Hypothesis: 292s Consumption_corpProf + Investment_capitalLag = 0 292s 292s Model 1: restricted model 292s Model 2: kleinModel 292s 292s Res.Df Df F Pr(>F) 292s 1 47 292s 2 46 1 0.4 0.53 292s Linear hypothesis test (F statistic of a Wald test) 292s 292s Hypothesis: 292s Consumption_corpProf + Investment_capitalLag = 0 292s 292s Model 1: restricted model 292s Model 2: kleinModel 292s 292s Res.Df Df F Pr(>F) 292s 1 47 292s 2 46 1 0.49 0.49 292s Linear hypothesis test (Chi^2 statistic of a Wald test) 292s 292s Hypothesis: 292s Consumption_corpProf + Investment_capitalLag = 0 292s 292s Model 1: restricted model 292s Model 2: kleinModel 292s 292s Res.Df Df Chisq Pr(>Chisq) 292s 1 47 292s 2 46 1 0.49 0.48 292s Linear hypothesis test (Theil's F test) 292s 292s Hypothesis: 292s Consumption_corpProf + Investment_capitalLag = 0 292s Consumption_corpProfLag - PrivateWages_trend = 0 292s 292s Model 1: restricted model 292s Model 2: kleinModel 292s 292s Res.Df Df F Pr(>F) 292s 1 48 292s 2 46 2 0.31 0.74 292s Linear hypothesis test (F statistic of a Wald test) 292s 292s Hypothesis: 292s Consumption_corpProf + Investment_capitalLag = 0 292s Consumption_corpProfLag - PrivateWages_trend = 0 292s 292s Model 1: restricted model 292s Model 2: kleinModel 292s 292s Res.Df Df F Pr(>F) 292s 1 48 292s 2 46 2 0.37 0.69 292s Linear hypothesis test (Chi^2 statistic of a Wald test) 292s 292s Hypothesis: 292s Consumption_corpProf + Investment_capitalLag = 0 292s Consumption_corpProfLag - PrivateWages_trend = 0 292s 292s Model 1: restricted model 292s Model 2: kleinModel 292s 292s Res.Df Df Chisq Pr(>Chisq) 292s 1 48 292s 2 46 2 0.75 0.69 292s > logLik 292s 'log Lik.' -66.4 (df=18) 292s 'log Lik.' -74.1 (df=18) 292s Estimating function 292s Consumption_(Intercept) Consumption_corpProf 292s Consumption_2 -0.4828 -5.986 292s Consumption_3 -1.9510 -32.972 292s Consumption_4 -2.3698 -43.605 292s Consumption_5 -0.6896 -13.377 292s Consumption_6 0.0903 1.814 292s Consumption_7 1.4152 27.739 292s Consumption_8 2.1183 41.942 292s Consumption_9 1.6745 35.332 292s Consumption_11 0.5483 8.553 292s Consumption_12 -0.3515 -4.008 292s Consumption_14 0.3350 3.752 292s Consumption_15 -0.1720 -2.116 292s Consumption_16 -0.1339 -1.875 292s Consumption_17 2.3987 42.218 292s Consumption_18 -0.6959 -12.040 292s Consumption_19 0.3068 4.694 292s Consumption_20 1.4238 27.052 292s Consumption_21 1.1656 24.594 292s Consumption_22 -3.4008 -79.918 292s Investment_2 0.0628 0.779 292s Investment_3 0.0460 0.778 292s Investment_4 -0.2893 -5.322 292s Investment_5 0.3986 7.732 292s Investment_6 -0.0855 -1.718 292s Investment_7 -0.3899 -7.642 292s Investment_8 -0.2239 -4.433 292s Investment_9 0.1420 2.997 292s Investment_10 0.0000 0.000 292s Investment_11 -0.1102 -1.719 292s Investment_12 -0.0719 -0.819 292s Investment_14 -0.1335 -1.495 292s Investment_15 0.0127 0.156 292s Investment_17 -0.2777 -4.887 292s Investment_18 -0.0167 -0.288 292s Investment_19 0.6855 10.488 292s Investment_20 0.1656 3.146 292s Investment_21 0.2015 4.251 292s Investment_22 0.1631 3.834 292s PrivateWages_2 -1.4560 -18.055 292s PrivateWages_3 0.6473 10.940 292s PrivateWages_4 1.8097 33.298 292s PrivateWages_5 -0.1264 -2.452 292s PrivateWages_6 -0.4576 -9.199 292s PrivateWages_8 -0.9619 -19.046 292s PrivateWages_9 0.3676 7.757 292s PrivateWages_10 0.0000 0.000 292s PrivateWages_11 -0.4677 -7.296 292s PrivateWages_12 0.4387 5.001 292s PrivateWages_13 0.0000 0.000 292s PrivateWages_14 0.5373 6.018 292s PrivateWages_15 0.4008 4.929 292s PrivateWages_16 0.0184 0.258 292s PrivateWages_17 -1.0303 -18.134 292s PrivateWages_18 1.0961 18.963 292s PrivateWages_19 -0.9722 -14.875 292s PrivateWages_20 -0.5153 -9.790 292s PrivateWages_21 -1.5509 -32.724 292s PrivateWages_22 0.7139 16.776 292s Consumption_corpProfLag Consumption_wages 292s Consumption_2 -6.131 -13.614 292s Consumption_3 -24.192 -62.822 292s Consumption_4 -40.050 -87.684 292s Consumption_5 -12.688 -25.514 292s Consumption_6 1.751 3.484 292s Consumption_7 28.447 57.601 292s Consumption_8 41.518 87.909 292s Consumption_9 33.155 71.835 292s Consumption_11 11.898 23.083 292s Consumption_12 -5.484 -13.816 292s Consumption_14 2.345 11.425 292s Consumption_15 -1.926 -6.295 292s Consumption_16 -1.647 -5.263 292s Consumption_17 33.582 106.024 292s Consumption_18 -12.249 -33.196 292s Consumption_19 5.307 14.081 292s Consumption_20 21.784 70.336 292s Consumption_21 22.146 61.777 292s Consumption_22 -71.756 -210.167 292s Investment_2 0.797 1.770 292s Investment_3 0.571 1.482 292s Investment_4 -4.889 -10.703 292s Investment_5 7.333 14.747 292s Investment_6 -1.658 -3.300 292s Investment_7 -7.837 -15.869 292s Investment_8 -4.389 -9.292 292s Investment_9 2.812 6.093 292s Investment_10 0.000 0.000 292s Investment_11 -2.391 -4.638 292s Investment_12 -1.121 -2.825 292s Investment_14 -0.934 -4.552 292s Investment_15 0.142 0.464 292s Investment_17 -3.888 -12.274 292s Investment_18 -0.293 -0.794 292s Investment_19 11.859 31.463 292s Investment_20 2.534 8.181 292s Investment_21 3.828 10.678 292s Investment_22 3.442 10.082 292s PrivateWages_2 -18.491 -41.059 292s PrivateWages_3 8.027 20.845 292s PrivateWages_4 30.584 66.958 292s PrivateWages_5 -2.325 -4.676 292s PrivateWages_6 -8.878 -17.665 292s PrivateWages_8 -18.854 -39.920 292s PrivateWages_9 7.279 15.770 292s PrivateWages_10 0.000 0.000 292s PrivateWages_11 -10.149 -19.690 292s PrivateWages_12 6.843 17.240 292s PrivateWages_13 0.000 0.000 292s PrivateWages_14 3.761 18.323 292s PrivateWages_15 4.489 14.668 292s PrivateWages_16 0.227 0.725 292s PrivateWages_17 -14.424 -45.540 292s PrivateWages_18 19.292 52.286 292s PrivateWages_19 -16.820 -44.626 292s PrivateWages_20 -7.884 -25.455 292s PrivateWages_21 -29.467 -82.197 292s PrivateWages_22 15.062 44.116 292s Investment_(Intercept) Investment_corpProf 292s Consumption_2 0.0848 1.052 292s Consumption_3 0.3428 5.793 292s Consumption_4 0.4164 7.661 292s Consumption_5 0.1211 2.350 292s Consumption_6 -0.0159 -0.319 292s Consumption_7 -0.2486 -4.873 292s Consumption_8 -0.3722 -7.369 292s Consumption_9 -0.2942 -6.207 292s Consumption_11 -0.0963 -1.503 292s Consumption_12 0.0618 0.704 292s Consumption_14 -0.0589 -0.659 292s Consumption_15 0.0302 0.372 292s Consumption_16 0.0000 0.000 292s Consumption_17 -0.4214 -7.417 292s Consumption_18 0.1223 2.115 292s Consumption_19 -0.0539 -0.825 292s Consumption_20 -0.2501 -4.753 292s Consumption_21 -0.2048 -4.321 292s Consumption_22 0.5975 14.041 292s Investment_2 -0.3080 -3.820 292s Investment_3 -0.2258 -3.815 292s Investment_4 1.4192 26.112 292s Investment_5 -1.9554 -37.935 292s Investment_6 0.4194 8.430 292s Investment_7 1.9129 37.493 292s Investment_8 1.0985 21.751 292s Investment_9 -0.6968 -14.703 292s Investment_10 1.6000 34.719 292s Investment_11 0.5405 8.432 292s Investment_12 0.3526 4.020 292s Investment_14 0.6549 7.335 292s Investment_15 -0.0622 -0.766 292s Investment_17 1.3624 23.978 292s Investment_18 0.0817 1.413 292s Investment_19 -3.3630 -51.454 292s Investment_20 -0.8125 -15.437 292s Investment_21 -0.9884 -20.856 292s Investment_22 -0.8004 -18.809 292s PrivateWages_2 0.5958 7.388 292s PrivateWages_3 -0.2649 -4.477 292s PrivateWages_4 -0.7405 -13.626 292s PrivateWages_5 0.0517 1.003 292s PrivateWages_6 0.1873 3.764 292s PrivateWages_8 0.3936 7.794 292s PrivateWages_9 -0.1504 -3.174 292s PrivateWages_10 -0.6149 -13.343 292s PrivateWages_11 0.1914 2.986 292s PrivateWages_12 -0.1795 -2.046 292s PrivateWages_13 0.0000 0.000 292s PrivateWages_14 -0.2199 -2.463 292s PrivateWages_15 -0.1640 -2.017 292s PrivateWages_16 0.0000 0.000 292s PrivateWages_17 0.4216 7.420 292s PrivateWages_18 -0.4485 -7.760 292s PrivateWages_19 0.3978 6.087 292s PrivateWages_20 0.2109 4.006 292s PrivateWages_21 0.6346 13.391 292s PrivateWages_22 -0.2921 -6.865 292s Investment_corpProfLag Investment_capitalLag 292s Consumption_2 1.077 15.50 292s Consumption_3 4.250 62.59 292s Consumption_4 7.036 76.82 292s Consumption_5 2.229 22.98 292s Consumption_6 -0.308 -3.06 292s Consumption_7 -4.998 -49.18 292s Consumption_8 -7.294 -75.70 292s Consumption_9 -5.825 -61.07 292s Consumption_11 -2.090 -20.78 292s Consumption_12 0.963 13.38 292s Consumption_14 -0.412 -12.19 292s Consumption_15 0.338 6.10 292s Consumption_16 0.000 0.00 292s Consumption_17 -5.900 -83.32 292s Consumption_18 2.152 24.43 292s Consumption_19 -0.932 -10.88 292s Consumption_20 -3.827 -50.00 292s Consumption_21 -3.891 -41.20 292s Consumption_22 12.607 122.18 292s Investment_2 -3.912 -56.31 292s Investment_3 -2.799 -41.22 292s Investment_4 23.984 261.83 292s Investment_5 -35.979 -370.94 292s Investment_6 8.137 80.82 292s Investment_7 38.449 378.37 292s Investment_8 21.531 223.44 292s Investment_9 -13.797 -144.66 292s Investment_10 33.759 336.95 292s Investment_11 11.729 116.59 292s Investment_12 5.501 76.41 292s Investment_14 4.584 135.62 292s Investment_15 -0.697 -12.57 292s Investment_17 19.074 269.35 292s Investment_18 1.438 16.32 292s Investment_19 -58.180 -678.65 292s Investment_20 -12.431 -162.42 292s Investment_21 -18.780 -198.88 292s Investment_22 -16.888 -163.68 292s PrivateWages_2 7.567 108.91 292s PrivateWages_3 -3.285 -48.37 292s PrivateWages_4 -12.515 -136.63 292s PrivateWages_5 0.951 9.81 292s PrivateWages_6 3.633 36.09 292s PrivateWages_8 7.715 80.06 292s PrivateWages_9 -2.978 -31.23 292s PrivateWages_10 -12.974 -129.50 292s PrivateWages_11 4.153 41.28 292s PrivateWages_12 -2.800 -38.90 292s PrivateWages_13 0.000 0.00 292s PrivateWages_14 -1.539 -45.54 292s PrivateWages_15 -1.837 -33.13 292s PrivateWages_16 0.000 0.00 292s PrivateWages_17 5.903 83.35 292s PrivateWages_18 -7.894 -89.62 292s PrivateWages_19 6.883 80.29 292s PrivateWages_20 3.226 42.15 292s PrivateWages_21 12.058 127.69 292s PrivateWages_22 -6.164 -59.74 292s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 292s Consumption_2 -0.4002 -18.25 -17.97 292s Consumption_3 -1.6172 -81.02 -73.75 292s Consumption_4 -1.9644 -112.37 -98.42 292s Consumption_5 -0.5716 -32.64 -32.70 292s Consumption_6 0.0748 4.56 4.27 292s Consumption_7 0.0000 0.00 0.00 292s Consumption_8 1.7559 113.08 112.38 292s Consumption_9 1.3880 89.53 89.39 292s Consumption_11 0.4545 27.81 30.45 292s Consumption_12 -0.2914 -15.56 -17.83 292s Consumption_14 0.2777 12.53 12.30 292s Consumption_15 -0.1426 -7.09 -6.43 292s Consumption_16 -0.1110 -6.04 -5.52 292s Consumption_17 1.9884 124.67 108.17 292s Consumption_18 -0.5769 -37.50 -36.17 292s Consumption_19 0.2543 15.49 16.53 292s Consumption_20 1.1803 82.03 71.88 292s Consumption_21 0.9662 73.14 67.15 292s Consumption_22 -2.8190 -249.20 -213.40 292s Investment_2 0.1212 5.53 5.44 292s Investment_3 0.0888 4.45 4.05 292s Investment_4 -0.5585 -31.95 -27.98 292s Investment_5 0.7695 43.94 44.02 292s Investment_6 -0.1651 -10.07 -9.42 292s Investment_7 0.0000 0.00 0.00 292s Investment_8 -0.4323 -27.84 -27.67 292s Investment_9 0.2742 17.69 17.66 292s Investment_10 -0.6296 -42.19 -40.61 292s Investment_11 -0.2127 -13.02 -14.25 292s Investment_12 -0.1388 -7.41 -8.49 292s Investment_14 -0.2577 -11.62 -11.42 292s Investment_15 0.0245 1.22 1.10 292s Investment_17 -0.5361 -33.62 -29.17 292s Investment_18 -0.0322 -2.09 -2.02 292s Investment_19 1.3234 80.60 86.02 292s Investment_20 0.3197 22.22 19.47 292s Investment_21 0.3890 29.45 27.03 292s Investment_22 0.3150 27.84 23.84 292s PrivateWages_2 -3.5926 -163.82 -161.31 292s PrivateWages_3 1.5973 80.02 72.84 292s PrivateWages_4 4.4653 255.42 223.71 292s PrivateWages_5 -0.3118 -17.80 -17.84 292s PrivateWages_6 -1.1292 -68.88 -64.48 292s PrivateWages_8 -2.3735 -152.85 -151.90 292s PrivateWages_9 0.9071 58.50 58.41 292s PrivateWages_10 3.7077 248.42 239.15 292s PrivateWages_11 -1.1540 -70.63 -77.32 292s PrivateWages_12 1.0824 57.80 66.24 292s PrivateWages_13 -0.4937 -21.87 -26.36 292s PrivateWages_14 1.3258 59.79 58.73 292s PrivateWages_15 0.9889 49.15 44.60 292s PrivateWages_16 0.0455 2.48 2.26 292s PrivateWages_17 -2.5423 -159.40 -138.30 292s PrivateWages_18 2.7047 175.80 169.58 292s PrivateWages_19 -2.3990 -146.10 -155.93 292s PrivateWages_20 -1.2714 -88.36 -77.43 292s PrivateWages_21 -3.8267 -289.68 -265.96 292s PrivateWages_22 1.7614 155.71 133.34 292s PrivateWages_trend 292s Consumption_2 4.0019 292s Consumption_3 14.5552 292s Consumption_4 15.7155 292s Consumption_5 4.0012 292s Consumption_6 -0.4490 292s Consumption_7 0.0000 292s Consumption_8 -7.0237 292s Consumption_9 -4.1641 292s Consumption_11 -0.4545 292s Consumption_12 0.0000 292s Consumption_14 0.5555 292s Consumption_15 -0.4277 292s Consumption_16 -0.4440 292s Consumption_17 9.9420 292s Consumption_18 -3.4614 292s Consumption_19 1.7801 292s Consumption_20 9.4420 292s Consumption_21 8.6959 292s Consumption_22 -28.1902 292s Investment_2 -1.2122 292s Investment_3 -0.7996 292s Investment_4 4.4678 292s Investment_5 -5.3865 292s Investment_6 0.9903 292s Investment_7 0.0000 292s Investment_8 1.7292 292s Investment_9 -0.8227 292s Investment_10 1.2593 292s Investment_11 0.2127 292s Investment_12 0.0000 292s Investment_14 -0.5154 292s Investment_15 0.0735 292s Investment_17 -2.6807 292s Investment_18 -0.1929 292s Investment_19 9.2640 292s Investment_20 2.5579 292s Investment_21 3.5008 292s Investment_22 3.1497 292s PrivateWages_2 35.9264 292s PrivateWages_3 -14.3757 292s PrivateWages_4 -35.7225 292s PrivateWages_5 2.1827 292s PrivateWages_6 6.7753 292s PrivateWages_8 9.4940 292s PrivateWages_9 -2.7212 292s PrivateWages_10 -7.4154 292s PrivateWages_11 1.1540 292s PrivateWages_12 0.0000 292s PrivateWages_13 -0.4937 292s PrivateWages_14 2.6517 292s PrivateWages_15 2.9666 292s PrivateWages_16 0.1820 292s PrivateWages_17 -12.7113 292s PrivateWages_18 16.2281 292s PrivateWages_19 -16.7928 292s PrivateWages_20 -10.1714 292s PrivateWages_21 -34.4407 292s PrivateWages_22 17.6141 292s [1] TRUE 292s > Bread 292s Consumption_(Intercept) Consumption_corpProf Consumption_corpProfLag 292s [1,] 1.00e+02 -1.05144 -0.70595 292s [2,] -1.05e+00 0.52767 -0.28007 292s [3,] -7.06e-01 -0.28007 0.41162 292s [4,] -1.63e+00 -0.08132 -0.03081 292s [5,] 5.03e+00 -0.06375 0.80965 292s [6,] -2.73e-01 0.05286 -0.04323 292s [7,] 4.77e-03 -0.03564 0.04677 292s [8,] -4.66e-04 -0.00135 -0.00415 292s [9,] -3.50e+01 0.07154 1.64913 292s [10,] 3.09e-01 -0.05491 0.03767 292s [11,] 2.66e-01 0.05541 -0.06699 292s [12,] 1.98e-01 0.03217 0.02582 292s Consumption_wages Investment_(Intercept) Investment_corpProf 292s [1,] -1.63020 5.0343 -0.27333 292s [2,] -0.08132 -0.0638 0.05286 292s [3,] -0.03081 0.8097 -0.04323 292s [4,] 0.08501 -0.3863 0.00122 292s [5,] -0.38629 1328.3034 -12.58281 292s [6,] 0.00122 -12.5828 0.51550 292s [7,] -0.00347 10.1576 -0.39286 292s [8,] 0.00211 -6.3831 0.05078 292s [9,] 0.13121 19.8408 -0.15336 292s [10,] -0.00022 0.2731 0.01339 292s [11,] -0.00213 -0.6257 -0.01103 292s [12,] -0.02827 -0.5788 0.00418 292s Investment_corpProfLag Investment_capitalLag PrivateWages_(Intercept) 292s [1,] 0.00477 -0.000466 -34.9530 292s [2,] -0.03564 -0.001347 0.0715 292s [3,] 0.04677 -0.004153 1.6491 292s [4,] -0.00347 0.002105 0.1312 292s [5,] 10.15755 -6.383136 19.8408 292s [6,] -0.39286 0.050784 -0.1534 292s [7,] 0.47726 -0.056526 -0.3957 292s [8,] -0.05653 0.032233 -0.0526 292s [9,] -0.39566 -0.052599 73.2779 292s [10,] -0.00743 -0.001878 -0.2209 292s [11,] 0.01439 0.002876 -1.0159 292s [12,] -0.01026 0.003357 0.8108 292s PrivateWages_gnp PrivateWages_gnpLag PrivateWages_trend 292s [1,] 0.30855 0.26619 0.19754 292s [2,] -0.05491 0.05541 0.03217 292s [3,] 0.03767 -0.06699 0.02582 292s [4,] -0.00022 -0.00213 -0.02827 292s [5,] 0.27312 -0.62569 -0.57877 292s [6,] 0.01339 -0.01103 0.00418 292s [7,] -0.00743 0.01439 -0.01026 292s [8,] -0.00188 0.00288 0.00336 292s [9,] -0.22091 -1.01587 0.81082 292s [10,] 0.04154 -0.03895 -0.00995 292s [11,] -0.03895 0.05766 -0.00383 292s [12,] -0.00995 -0.00383 0.04664 292s > 292s > # 3SLS 292s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 292s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 292s > summary 292s 292s systemfit results 292s method: 3SLS 292s 292s N DF SSR detRCov OLS-R2 McElroy-R2 292s system 56 44 67.5 0.436 0.963 0.993 292s 292s N DF SSR MSE RMSE R2 Adj R2 292s Consumption 18 14 22.4 1.598 1.264 0.974 0.968 292s Investment 18 14 35.0 2.503 1.582 0.793 0.749 292s PrivateWages 20 16 10.1 0.629 0.793 0.987 0.985 292s 292s The covariance matrix of the residuals used for estimation 292s Consumption Investment PrivateWages 292s Consumption 1.307 0.540 -0.431 292s Investment 0.540 1.319 0.119 292s PrivateWages -0.431 0.119 0.496 292s 292s The covariance matrix of the residuals 292s Consumption Investment PrivateWages 292s Consumption 1.309 0.638 -0.440 292s Investment 0.638 1.749 0.233 292s PrivateWages -0.440 0.233 0.519 292s 292s The correlations of the residuals 292s Consumption Investment PrivateWages 292s Consumption 1.000 0.422 -0.532 292s Investment 0.422 1.000 0.247 292s PrivateWages -0.532 0.247 1.000 292s 292s 292s 3SLS estimates for 'Consumption' (equation 1) 292s Model Formula: consump ~ corpProf + corpProfLag + wages 292s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 292s gnpLag 292s 292s Estimate Std. Error t value Pr(>|t|) 292s (Intercept) 18.0338 1.5648 11.52 1.6e-08 *** 292s corpProf -0.0632 0.1500 -0.42 0.68 292s corpProfLag 0.1784 0.1154 1.55 0.14 292s wages 0.8224 0.0444 18.54 3.0e-11 *** 292s --- 292s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 292s 292s Residual standard error: 1.264 on 14 degrees of freedom 292s Number of observations: 18 Degrees of Freedom: 14 292s SSR: 22.377 MSE: 1.598 Root MSE: 1.264 292s Multiple R-Squared: 0.974 Adjusted R-Squared: 0.968 292s 292s 292s 3SLS estimates for 'Investment' (equation 2) 292s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 292s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 292s gnpLag 292s 292s Estimate Std. Error t value Pr(>|t|) 292s (Intercept) 24.6766 6.7008 3.68 0.00246 ** 292s corpProf 0.0472 0.1843 0.26 0.80149 292s corpProfLag 0.6874 0.1577 4.36 0.00065 *** 292s capitalLag -0.1776 0.0318 -5.59 6.7e-05 *** 292s --- 292s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 292s 292s Residual standard error: 1.582 on 14 degrees of freedom 292s Number of observations: 18 Degrees of Freedom: 14 292s SSR: 35.037 MSE: 2.503 Root MSE: 1.582 292s Multiple R-Squared: 0.793 Adjusted R-Squared: 0.749 292s 292s 292s 3SLS estimates for 'PrivateWages' (equation 3) 292s Model Formula: privWage ~ gnp + gnpLag + trend 292s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 292s gnpLag 292s 292s Estimate Std. Error t value Pr(>|t|) 292s (Intercept) 0.7823 1.1254 0.70 0.49695 292s gnp 0.4257 0.0308 13.80 2.6e-10 *** 292s gnpLag 0.1728 0.0341 5.07 0.00011 *** 292s trend 0.1252 0.0291 4.30 0.00055 *** 292s --- 292s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 292s 292s Residual standard error: 0.793 on 16 degrees of freedom 292s Number of observations: 20 Degrees of Freedom: 16 292s SSR: 10.057 MSE: 0.629 Root MSE: 0.793 292s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 292s 292s > residuals 292s Consumption Investment PrivateWages 292s 1 NA NA NA 292s 2 -0.8058 -1.721 -1.20135 292s 3 -0.6573 0.337 0.43696 292s 4 -1.1124 0.810 1.31177 292s 5 0.0833 -1.544 -0.19794 292s 6 0.6334 0.368 -0.46596 292s 7 NA NA NA 292s 8 1.7939 1.245 -0.85614 292s 9 1.7891 0.593 0.20698 292s 10 NA 2.303 1.10034 292s 11 -0.5397 -1.015 -0.38801 292s 12 -1.5147 -0.846 0.40949 292s 13 NA NA 0.00602 292s 14 -0.1171 1.670 0.61306 292s 15 -0.6526 -0.075 0.49152 292s 16 -0.3617 NA 0.17066 292s 17 1.9331 2.086 -0.69991 292s 18 -0.6063 -0.101 0.96136 292s 19 -0.3990 -3.345 -0.61606 292s 20 1.4134 0.717 -0.29343 292s 21 1.3257 0.306 -1.14412 292s 22 -1.4340 0.935 0.55310 292s > fitted 292s Consumption Investment PrivateWages 292s 1 NA NA NA 292s 2 42.7 1.5213 26.7 292s 3 45.7 1.5632 28.9 292s 4 50.3 4.3898 32.8 292s 5 50.5 4.5444 34.1 292s 6 52.0 4.7320 35.9 292s 7 NA NA NA 292s 8 54.4 2.9547 38.8 292s 9 55.5 2.4075 39.0 292s 10 NA 2.7965 40.2 292s 11 55.5 2.0150 38.3 292s 12 52.4 -2.5541 34.1 292s 13 NA NA 29.0 292s 14 46.6 -6.7699 27.9 292s 15 49.4 -2.9250 30.1 292s 16 51.7 NA 33.0 292s 17 55.8 0.0139 37.5 292s 18 59.3 2.1013 40.0 292s 19 57.9 1.4453 38.8 292s 20 60.2 0.5828 41.9 292s 21 63.7 2.9944 46.1 292s 22 71.1 3.9651 52.7 292s > predict 292s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 292s 1 NA NA NA NA 292s 2 42.7 0.555 39.7 45.7 292s 3 45.7 0.628 42.6 48.7 292s 4 50.3 0.418 47.5 53.2 292s 5 50.5 0.492 47.6 53.4 292s 6 52.0 0.501 49.0 54.9 292s 7 NA NA NA NA 292s 8 54.4 0.405 51.6 57.3 292s 9 55.5 0.477 52.6 58.4 292s 10 NA NA NA NA 292s 11 55.5 0.832 52.3 58.8 292s 12 52.4 0.792 49.2 55.6 292s 13 NA NA NA NA 292s 14 46.6 0.676 43.5 49.7 292s 15 49.4 0.470 46.5 52.2 292s 16 51.7 0.386 48.8 54.5 292s 17 55.8 0.433 52.9 58.6 292s 18 59.3 0.368 56.5 62.1 292s 19 57.9 0.504 55.0 60.8 292s 20 60.2 0.513 57.3 63.1 292s 21 63.7 0.505 60.8 66.6 292s 22 71.1 0.771 68.0 74.3 292s Investment.pred Investment.se.fit Investment.lwr Investment.upr 292s 1 NA NA NA NA 292s 2 1.5213 0.857 -2.337 5.380 292s 3 1.5632 0.589 -2.058 5.184 292s 4 4.3898 0.519 0.819 7.961 292s 5 4.5444 0.436 1.025 8.064 292s 6 4.7320 0.415 1.224 8.240 292s 7 NA NA NA NA 292s 8 2.9547 0.342 -0.517 6.426 292s 9 2.4075 0.511 -1.158 5.973 292s 10 2.7965 0.556 -0.800 6.393 292s 11 2.0150 0.955 -1.948 5.978 292s 12 -2.5541 0.874 -6.431 1.323 292s 13 NA NA NA NA 292s 14 -6.7699 0.865 -10.637 -2.903 292s 15 -2.9250 0.503 -6.485 0.635 292s 16 NA NA NA NA 292s 17 0.0139 0.483 -3.534 3.561 292s 18 2.1013 0.320 -1.361 5.563 292s 19 1.4453 0.532 -2.134 5.025 292s 20 0.5828 0.550 -3.010 4.175 292s 21 2.9944 0.476 -0.549 6.538 292s 22 3.9651 0.692 0.261 7.669 292s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 292s 1 NA NA NA NA 292s 2 26.7 0.324 24.9 28.5 292s 3 28.9 0.331 27.0 30.7 292s 4 32.8 0.339 31.0 34.6 292s 5 34.1 0.248 32.3 35.9 292s 6 35.9 0.256 34.1 37.6 292s 7 NA NA NA NA 292s 8 38.8 0.251 37.0 40.5 292s 9 39.0 0.238 37.2 40.7 292s 10 40.2 0.232 38.4 42.0 292s 11 38.3 0.314 36.5 40.1 292s 12 34.1 0.327 32.3 35.9 292s 13 29.0 0.393 27.1 30.9 292s 14 27.9 0.329 26.1 29.7 292s 15 30.1 0.324 28.3 31.9 292s 16 33.0 0.271 31.3 34.8 292s 17 37.5 0.277 35.7 39.3 292s 18 40.0 0.213 38.3 41.8 292s 19 38.8 0.320 37.0 40.6 292s 20 41.9 0.295 40.1 43.7 292s 21 46.1 0.309 44.3 47.9 292s 22 52.7 0.476 50.8 54.7 292s > model.frame 292s [1] TRUE 292s > model.matrix 292s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0345 >" 292s [2] "Attributes: < Component “dimnames”: Component 1: 51 string mismatches >" 292s [3] "Numeric: lengths (696, 672) differ" 292s > nobs 292s [1] 56 292s > linearHypothesis 292s Linear hypothesis test (Theil's F test) 292s 292s Hypothesis: 292s Consumption_corpProf + Investment_capitalLag = 0 292s 292s Model 1: restricted model 292s Model 2: kleinModel 292s 292s Res.Df Df F Pr(>F) 292s 1 45 292s 2 44 1 1.91 0.17 292s Linear hypothesis test (F statistic of a Wald test) 292s 292s Hypothesis: 292s Consumption_corpProf + Investment_capitalLag = 0 292s 292s Model 1: restricted model 292s Model 2: kleinModel 292s 292s Res.Df Df F Pr(>F) 292s 1 45 292s 2 44 1 2.6 0.11 292s Linear hypothesis test (Chi^2 statistic of a Wald test) 292s 292s Hypothesis: 292s Consumption_corpProf + Investment_capitalLag = 0 292s 292s Model 1: restricted model 292s Model 2: kleinModel 292s 292s Res.Df Df Chisq Pr(>Chisq) 292s 1 45 292s 2 44 1 2.6 0.11 292s Linear hypothesis test (Theil's F test) 292s 292s Hypothesis: 292s Consumption_corpProf + Investment_capitalLag = 0 292s Consumption_corpProfLag - PrivateWages_trend = 0 292s 292s Model 1: restricted model 292s Model 2: kleinModel 292s 292s Res.Df Df F Pr(>F) 292s 1 46 292s 2 44 2 1.62 0.21 292s Linear hypothesis test (F statistic of a Wald test) 292s 292s Hypothesis: 292s Consumption_corpProf + Investment_capitalLag = 0 292s Consumption_corpProfLag - PrivateWages_trend = 0 292s 292s Model 1: restricted model 292s Model 2: kleinModel 292s 292s Res.Df Df F Pr(>F) 292s 1 46 292s 2 44 2 2.2 0.12 292s Linear hypothesis test (Chi^2 statistic of a Wald test) 292s 292s Hypothesis: 292s Consumption_corpProf + Investment_capitalLag = 0 292s Consumption_corpProfLag - PrivateWages_trend = 0 292s 292s Model 1: restricted model 292s Model 2: kleinModel 292s 292s Res.Df Df Chisq Pr(>Chisq) 292s 1 46 292s 2 44 2 4.41 0.11 292s > logLik 292s 'log Lik.' -70.1 (df=18) 292s 'log Lik.' -80.6 (df=18) 292s Estimating function 292s Consumption_(Intercept) Consumption_corpProf 292s Consumption_2 -3.3369 -46.76 292s Consumption_3 -0.6260 -10.43 292s Consumption_4 0.5431 10.07 292s Consumption_5 -1.9287 -39.09 292s Consumption_6 0.9979 18.98 292s Consumption_8 4.7224 83.33 292s Consumption_9 4.2195 79.93 292s Consumption_11 -2.1144 -35.40 292s Consumption_12 -2.7531 -36.83 292s Consumption_14 0.7280 7.30 292s Consumption_15 -2.0340 -25.43 292s Consumption_16 -1.6770 -24.29 292s Consumption_17 6.1486 91.69 292s Consumption_18 -0.6466 -12.56 292s Consumption_19 -4.7474 -90.72 292s Consumption_20 3.3112 58.48 292s Consumption_21 1.5335 31.28 292s Consumption_22 -1.0772 -24.43 292s Investment_2 1.4470 20.28 292s Investment_3 -0.2844 -4.74 292s Investment_4 -0.6458 -11.98 292s Investment_5 1.3096 26.54 292s Investment_6 -0.3315 -6.31 292s Investment_8 -1.1056 -19.51 292s Investment_9 -0.5457 -10.34 292s Investment_10 0.0000 0.00 292s Investment_11 0.8919 14.93 292s Investment_12 0.7723 10.33 292s Investment_14 -1.4083 -14.12 292s Investment_15 0.0885 1.11 292s Investment_17 -1.8093 -26.98 292s Investment_18 0.1676 3.25 292s Investment_19 2.8888 55.20 292s Investment_20 -0.6425 -11.35 292s Investment_21 -0.2855 -5.82 292s Investment_22 -0.7925 -17.97 292s PrivateWages_2 -2.9611 -41.49 292s PrivateWages_3 1.0665 17.77 292s PrivateWages_4 2.5794 47.83 292s PrivateWages_5 -2.7951 -56.65 292s PrivateWages_6 -0.4865 -9.25 292s PrivateWages_8 1.6497 29.11 292s PrivateWages_9 1.8751 35.52 292s PrivateWages_10 0.0000 0.00 292s PrivateWages_11 -2.3618 -39.54 292s PrivateWages_12 -0.3246 -4.34 292s PrivateWages_13 0.0000 0.00 292s PrivateWages_14 3.0441 30.51 292s PrivateWages_15 -0.2496 -3.12 292s PrivateWages_16 -0.3710 -5.37 292s PrivateWages_17 2.5263 37.67 292s PrivateWages_18 0.0583 1.13 292s PrivateWages_19 -6.2503 -119.43 292s PrivateWages_20 1.3565 23.96 292s PrivateWages_21 -1.2791 -26.09 292s PrivateWages_22 1.9457 44.12 292s Consumption_corpProfLag Consumption_wages 292s Consumption_2 -42.379 -99.51 292s Consumption_3 -7.762 -19.94 292s Consumption_4 9.179 19.15 292s Consumption_5 -35.489 -74.45 292s Consumption_6 19.359 38.46 292s Consumption_8 92.559 188.94 292s Consumption_9 83.547 176.28 292s Consumption_11 -45.883 -91.13 292s Consumption_12 -42.949 -109.17 292s Consumption_14 5.096 24.26 292s Consumption_15 -22.780 -75.93 292s Consumption_16 -20.627 -67.32 292s Consumption_17 86.080 256.88 292s Consumption_18 -11.379 -30.78 292s Consumption_19 -82.131 -233.73 292s Consumption_20 50.662 160.78 292s Consumption_21 29.137 81.92 292s Consumption_22 -22.729 -65.49 292s Investment_2 18.377 43.15 292s Investment_3 -3.526 -9.06 292s Investment_4 -10.914 -22.77 292s Investment_5 24.097 50.55 292s Investment_6 -6.431 -12.78 292s Investment_8 -21.669 -44.23 292s Investment_9 -10.805 -22.80 292s Investment_10 0.000 0.00 292s Investment_11 19.355 38.44 292s Investment_12 12.047 30.62 292s Investment_14 -9.858 -46.93 292s Investment_15 0.992 3.31 292s Investment_17 -25.331 -75.59 292s Investment_18 2.950 7.98 292s Investment_19 49.976 142.22 292s Investment_20 -9.831 -31.20 292s Investment_21 -5.425 -15.25 292s Investment_22 -16.723 -48.18 292s PrivateWages_2 -37.606 -88.31 292s PrivateWages_3 13.225 33.97 292s PrivateWages_4 43.593 90.94 292s PrivateWages_5 -51.429 -107.89 292s PrivateWages_6 -9.438 -18.75 292s PrivateWages_8 32.333 66.00 292s PrivateWages_9 37.126 78.33 292s PrivateWages_10 0.000 0.00 292s PrivateWages_11 -51.251 -101.80 292s PrivateWages_12 -5.063 -12.87 292s PrivateWages_13 0.000 0.00 292s PrivateWages_14 21.309 101.45 292s PrivateWages_15 -2.796 -9.32 292s PrivateWages_16 -4.563 -14.89 292s PrivateWages_17 35.368 105.55 292s PrivateWages_18 1.025 2.77 292s PrivateWages_19 -108.130 -307.72 292s PrivateWages_20 20.754 65.87 292s PrivateWages_21 -24.303 -68.33 292s PrivateWages_22 41.055 118.29 292s Investment_(Intercept) Investment_corpProf 292s Consumption_2 1.6657 22.369 292s Consumption_3 0.3125 5.208 292s Consumption_4 -0.2711 -5.105 292s Consumption_5 0.9628 19.850 292s Consumption_6 -0.4981 -9.617 292s Consumption_8 -2.3573 -41.335 292s Consumption_9 -2.1063 -41.098 292s Consumption_11 1.0555 18.165 292s Consumption_12 1.3743 18.540 292s Consumption_14 -0.3634 -3.664 292s Consumption_15 1.0153 13.204 292s Consumption_16 0.0000 0.000 292s Consumption_17 -3.0693 -45.765 292s Consumption_18 0.3228 6.293 292s Consumption_19 2.3698 45.702 292s Consumption_20 -1.6529 -29.000 292s Consumption_21 -0.7655 -15.445 292s Consumption_22 0.5377 12.243 292s Investment_2 -2.0943 -28.124 292s Investment_3 0.4116 6.860 292s Investment_4 0.9347 17.600 292s Investment_5 -1.8955 -39.080 292s Investment_6 0.4798 9.263 292s Investment_8 1.6002 28.058 292s Investment_9 0.7899 15.412 292s Investment_10 2.8075 56.810 292s Investment_11 -1.2910 -22.218 292s Investment_12 -1.1178 -15.079 292s Investment_14 2.0383 20.552 292s Investment_15 -0.1282 -1.667 292s Investment_17 2.6188 39.047 292s Investment_18 -0.2426 -4.730 292s Investment_19 -4.1811 -80.631 292s Investment_20 0.9300 16.316 292s Investment_21 0.4133 8.338 292s Investment_22 1.1471 26.118 292s PrivateWages_2 1.8190 24.427 292s PrivateWages_3 -0.6551 -10.919 292s PrivateWages_4 -1.5845 -29.835 292s PrivateWages_5 1.7170 35.400 292s PrivateWages_6 0.2989 5.770 292s PrivateWages_8 -1.0134 -17.769 292s PrivateWages_9 -1.1518 -22.474 292s PrivateWages_10 -2.1257 -43.013 292s PrivateWages_11 1.4508 24.969 292s PrivateWages_12 0.1994 2.690 292s PrivateWages_13 0.0000 0.000 292s PrivateWages_14 -1.8700 -18.855 292s PrivateWages_15 0.1533 1.994 292s PrivateWages_16 0.0000 0.000 292s PrivateWages_17 -1.5519 -23.140 292s PrivateWages_18 -0.0358 -0.698 292s PrivateWages_19 3.8395 74.045 292s PrivateWages_20 -0.8333 -14.620 292s PrivateWages_21 0.7858 15.853 292s PrivateWages_22 -1.1953 -27.215 292s Investment_corpProfLag Investment_capitalLag 292s Consumption_2 21.15 304.50 292s Consumption_3 3.87 57.06 292s Consumption_4 -4.58 -50.02 292s Consumption_5 17.72 182.64 292s Consumption_6 -9.66 -95.99 292s Consumption_8 -46.20 -479.48 292s Consumption_9 -41.70 -437.27 292s Consumption_11 22.90 227.67 292s Consumption_12 21.44 297.81 292s Consumption_14 -2.54 -75.26 292s Consumption_15 11.37 205.09 292s Consumption_16 0.00 0.00 292s Consumption_17 -42.97 -606.79 292s Consumption_18 5.68 64.49 292s Consumption_19 41.00 478.23 292s Consumption_20 -25.29 -330.42 292s Consumption_21 -14.54 -154.02 292s Consumption_22 11.35 109.96 292s Investment_2 -26.60 -382.84 292s Investment_3 5.10 75.16 292s Investment_4 15.80 172.46 292s Investment_5 -34.88 -359.58 292s Investment_6 9.31 92.46 292s Investment_8 31.36 325.47 292s Investment_9 15.64 163.98 292s Investment_10 59.24 591.25 292s Investment_11 -28.01 -278.46 292s Investment_12 -17.44 -242.22 292s Investment_14 14.27 422.14 292s Investment_15 -1.44 -25.89 292s Investment_17 36.66 517.73 292s Investment_18 -4.27 -48.47 292s Investment_19 -72.33 -843.75 292s Investment_20 14.23 185.90 292s Investment_21 7.85 83.15 292s Investment_22 24.20 234.58 292s PrivateWages_2 23.10 332.51 292s PrivateWages_3 -8.12 -119.63 292s PrivateWages_4 -26.78 -292.35 292s PrivateWages_5 31.59 325.71 292s PrivateWages_6 5.80 57.59 292s PrivateWages_8 -19.86 -206.12 292s PrivateWages_9 -22.81 -239.12 292s PrivateWages_10 -44.85 -447.66 292s PrivateWages_11 31.48 312.95 292s PrivateWages_12 3.11 43.21 292s PrivateWages_13 0.00 0.00 292s PrivateWages_14 -13.09 -387.28 292s PrivateWages_15 1.72 30.97 292s PrivateWages_16 0.00 0.00 292s PrivateWages_17 -21.73 -306.81 292s PrivateWages_18 -0.63 -7.15 292s PrivateWages_19 66.42 774.82 292s PrivateWages_20 -12.75 -166.57 292s PrivateWages_21 14.93 158.09 292s PrivateWages_22 -25.22 -244.43 292s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 292s Consumption_2 -3.302 -155.43 -148.27 292s Consumption_3 -0.619 -30.71 -28.25 292s Consumption_4 0.537 30.39 26.93 292s Consumption_5 -1.909 -115.83 -109.18 292s Consumption_6 0.987 59.85 56.39 292s Consumption_8 4.673 280.38 299.09 292s Consumption_9 4.176 260.01 268.91 292s Consumption_11 -2.092 -133.31 -140.19 292s Consumption_12 -2.724 -149.39 -166.74 292s Consumption_14 0.720 30.35 31.91 292s Consumption_15 -2.013 -103.09 -90.78 292s Consumption_16 -1.660 -91.84 -82.48 292s Consumption_17 6.085 349.22 331.00 292s Consumption_18 -0.640 -42.98 -40.12 292s Consumption_19 -4.698 -321.88 -305.37 292s Consumption_20 3.277 219.03 199.56 292s Consumption_21 1.518 113.62 105.47 292s Consumption_22 -1.066 -92.61 -80.70 292s Investment_2 1.762 82.94 79.12 292s Investment_3 -0.346 -17.17 -15.79 292s Investment_4 -0.786 -44.47 -39.40 292s Investment_5 1.595 96.79 91.23 292s Investment_6 -0.404 -24.47 -23.05 292s Investment_8 -1.346 -80.78 -86.17 292s Investment_9 -0.665 -41.38 -42.80 292s Investment_10 -2.362 -152.52 -152.36 292s Investment_11 1.086 69.20 72.78 292s Investment_12 0.940 51.57 57.56 292s Investment_14 -1.715 -72.25 -75.98 292s Investment_15 0.108 5.52 4.86 292s Investment_17 -2.203 -126.46 -119.87 292s Investment_18 0.204 13.71 12.80 292s Investment_19 3.518 241.02 228.67 292s Investment_20 -0.782 -52.30 -47.65 292s Investment_21 -0.348 -26.03 -24.17 292s Investment_22 -0.965 -83.85 -73.06 292s PrivateWages_2 -6.697 -315.21 -300.67 292s PrivateWages_3 2.412 119.58 109.98 292s PrivateWages_4 5.833 329.84 292.25 292s PrivateWages_5 -6.321 -383.60 -361.56 292s PrivateWages_6 -1.100 -66.69 -62.82 292s PrivateWages_8 3.731 223.83 238.77 292s PrivateWages_9 4.240 264.05 273.09 292s PrivateWages_10 7.826 505.29 504.75 292s PrivateWages_11 -5.341 -340.30 -357.86 292s PrivateWages_12 -0.734 -40.25 -44.92 292s PrivateWages_13 -4.155 -195.19 -221.87 292s PrivateWages_14 6.884 290.02 304.97 292s PrivateWages_15 -0.565 -28.91 -25.46 292s PrivateWages_16 -0.839 -46.43 -41.70 292s PrivateWages_17 5.713 327.90 310.80 292s PrivateWages_18 0.132 8.85 8.26 292s PrivateWages_19 -14.135 -968.43 -918.78 292s PrivateWages_20 3.068 205.06 186.82 292s PrivateWages_21 -2.893 -216.57 -201.04 292s PrivateWages_22 4.400 382.29 333.10 292s PrivateWages_trend 292s Consumption_2 33.022 292s Consumption_3 5.575 292s Consumption_4 -4.300 292s Consumption_5 13.361 292s Consumption_6 -5.925 292s Consumption_8 -18.693 292s Consumption_9 -12.527 292s Consumption_11 2.092 292s Consumption_12 0.000 292s Consumption_14 1.441 292s Consumption_15 -6.038 292s Consumption_16 -6.638 292s Consumption_17 30.423 292s Consumption_18 -3.839 292s Consumption_19 -32.886 292s Consumption_20 26.214 292s Consumption_21 13.658 292s Consumption_22 -10.660 292s Investment_2 -17.621 292s Investment_3 3.117 292s Investment_4 6.292 292s Investment_5 -11.164 292s Investment_6 2.422 292s Investment_8 5.385 292s Investment_9 1.994 292s Investment_10 4.724 292s Investment_11 -1.086 292s Investment_12 0.000 292s Investment_14 -3.430 292s Investment_15 0.323 292s Investment_17 -11.017 292s Investment_18 1.225 292s Investment_19 24.626 292s Investment_20 -6.260 292s Investment_21 -3.129 292s Investment_22 -9.652 292s PrivateWages_2 66.965 292s PrivateWages_3 -21.707 292s PrivateWages_4 -46.667 292s PrivateWages_5 44.247 292s PrivateWages_6 6.602 292s PrivateWages_8 -14.923 292s PrivateWages_9 -12.721 292s PrivateWages_10 -15.651 292s PrivateWages_11 5.341 292s PrivateWages_12 0.000 292s PrivateWages_13 -4.155 292s PrivateWages_14 13.769 292s PrivateWages_15 -1.694 292s PrivateWages_16 -3.356 292s PrivateWages_17 28.566 292s PrivateWages_18 0.791 292s PrivateWages_19 -98.946 292s PrivateWages_20 24.542 292s PrivateWages_21 -26.035 292s PrivateWages_22 44.003 292s [1] TRUE 292s > Bread 292s Consumption_(Intercept) Consumption_corpProf Consumption_corpProfLag 292s [1,] 137.1267 -4.2997 0.8463 292s [2,] -4.2997 1.2597 -0.6942 292s [3,] 0.8463 -0.6942 0.7454 292s [4,] -1.7733 -0.1394 -0.0281 292s [5,] 105.0265 3.4241 3.4807 292s [6,] -4.4721 0.5244 -0.4530 292s [7,] 1.6442 -0.3454 0.4268 292s [8,] -0.2644 -0.0340 -0.0134 292s [9,] -38.0151 0.3680 1.7655 292s [10,] 0.5379 -0.0825 0.0502 292s [11,] 0.0809 0.0782 -0.0821 292s [12,] 0.1895 0.0505 0.0265 292s Consumption_wages Investment_(Intercept) Investment_corpProf 292s [1,] -1.773256 105.03 -4.47211 292s [2,] -0.139424 3.42 0.52437 292s [3,] -0.028067 3.48 -0.45300 292s [4,] 0.110155 -5.14 0.06784 292s [5,] -5.138461 2514.46 -43.59967 292s [6,] 0.067843 -43.60 1.90216 292s [7,] -0.064178 34.75 -1.45456 292s [8,] 0.025084 -11.63 0.17310 292s [9,] 0.044238 27.92 -0.25822 292s [10,] 0.000203 1.31 0.00136 292s [11,] -0.000811 -1.85 0.00316 292s [12,] -0.035488 -0.85 0.01679 292s Investment_corpProfLag Investment_capitalLag PrivateWages_(Intercept) 292s [1,] 1.64420 -0.26436 -38.0151 292s [2,] -0.34536 -0.03402 0.3680 292s [3,] 0.42680 -0.01343 1.7655 292s [4,] -0.06418 0.02508 0.0442 292s [5,] 34.75055 -11.63252 27.9186 292s [6,] -1.45456 0.17310 -0.2582 292s [7,] 1.39257 -0.16270 -0.3518 292s [8,] -0.16270 0.05655 -0.0905 292s [9,] -0.35175 -0.09046 70.9283 292s [10,] 0.00769 -0.00730 -0.3444 292s [11,] -0.00156 0.00915 -0.8533 292s [12,] -0.02239 0.00456 0.8163 292s PrivateWages_gnp PrivateWages_gnpLag PrivateWages_trend 292s [1,] 0.537909 0.080946 0.189459 292s [2,] -0.082456 0.078164 0.050460 292s [3,] 0.050248 -0.082092 0.026511 292s [4,] 0.000203 -0.000811 -0.035488 292s [5,] 1.312267 -1.847095 -0.850461 292s [6,] 0.001362 0.003160 0.016792 292s [7,] 0.007689 -0.001565 -0.022388 292s [8,] -0.007301 0.009148 0.004555 292s [9,] -0.344428 -0.853347 0.816265 292s [10,] 0.053258 -0.048785 -0.014522 292s [11,] -0.048785 0.064956 0.000648 292s [12,] -0.014522 0.000648 0.047452 292s > 292s > # I3SLS 292s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 292s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 292s > summary 292s 292s systemfit results 292s method: iterated 3SLS 292s 292s convergence achieved after 10 iterations 292s 292s N DF SSR detRCov OLS-R2 McElroy-R2 292s system 56 44 79.4 0.55 0.956 0.994 292s 292s N DF SSR MSE RMSE R2 Adj R2 292s Consumption 18 14 22.3 1.595 1.263 0.974 0.968 292s Investment 18 14 46.8 3.346 1.829 0.724 0.664 292s PrivateWages 20 16 10.2 0.639 0.799 0.987 0.985 292s 292s The covariance matrix of the residuals used for estimation 292s Consumption Investment PrivateWages 292s Consumption 1.307 0.750 -0.452 292s Investment 0.750 2.318 0.272 292s PrivateWages -0.452 0.272 0.530 292s 292s The covariance matrix of the residuals 292s Consumption Investment PrivateWages 292s Consumption 1.307 0.750 -0.452 292s Investment 0.750 2.318 0.272 292s PrivateWages -0.452 0.272 0.530 292s 292s The correlations of the residuals 292s Consumption Investment PrivateWages 292s Consumption 1.000 0.424 -0.542 292s Investment 0.424 1.000 0.254 292s PrivateWages -0.542 0.254 1.000 292s 292s 292s 3SLS estimates for 'Consumption' (equation 1) 292s Model Formula: consump ~ corpProf + corpProfLag + wages 292s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 292s gnpLag 292s 292s Estimate Std. Error t value Pr(>|t|) 292s (Intercept) 18.3252 1.5452 11.86 1.1e-08 *** 292s corpProf -0.0436 0.1470 -0.30 0.77 292s corpProfLag 0.1614 0.1127 1.43 0.17 292s wages 0.8127 0.0436 18.65 2.8e-11 *** 292s --- 292s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 292s 292s Residual standard error: 1.263 on 14 degrees of freedom 292s Number of observations: 18 Degrees of Freedom: 14 292s SSR: 22.337 MSE: 1.595 Root MSE: 1.263 292s Multiple R-Squared: 0.974 Adjusted R-Squared: 0.968 292s 292s 292s 3SLS estimates for 'Investment' (equation 2) 292s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 292s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 292s gnpLag 292s 292s Estimate Std. Error t value Pr(>|t|) 292s (Intercept) 30.2418 8.3674 3.61 0.00282 ** 292s corpProf -0.0437 0.2341 -0.19 0.85457 292s corpProfLag 0.7856 0.1993 3.94 0.00147 ** 292s capitalLag -0.2065 0.0397 -5.20 0.00014 *** 292s --- 292s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 292s 292s Residual standard error: 1.829 on 14 degrees of freedom 292s Number of observations: 18 Degrees of Freedom: 14 292s SSR: 46.838 MSE: 3.346 Root MSE: 1.829 292s Multiple R-Squared: 0.724 Adjusted R-Squared: 0.664 292s 292s 292s 3SLS estimates for 'PrivateWages' (equation 3) 292s Model Formula: privWage ~ gnp + gnpLag + trend 292s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 292s gnpLag 292s 292s Estimate Std. Error t value Pr(>|t|) 292s (Intercept) 0.4741 1.1280 0.42 0.67983 292s gnp 0.4268 0.0296 14.44 1.4e-10 *** 292s gnpLag 0.1767 0.0330 5.35 6.5e-05 *** 292s trend 0.1201 0.0290 4.14 0.00076 *** 292s --- 292s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 292s 292s Residual standard error: 0.799 on 16 degrees of freedom 292s Number of observations: 20 Degrees of Freedom: 16 292s SSR: 10.218 MSE: 0.639 Root MSE: 0.799 292s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 292s 292s > residuals 292s Consumption Investment PrivateWages 292s 1 NA NA NA 292s 2 -0.8546 -2.1226 -1.1687 292s 3 -0.7611 0.3684 0.4670 292s 4 -1.1233 0.5912 1.3216 292s 5 0.0781 -1.6694 -0.2108 292s 6 0.6467 0.2952 -0.4776 292s 7 NA NA NA 292s 8 1.8444 1.4348 -0.8884 292s 9 1.8309 1.0020 0.1781 292s 10 NA 2.7265 1.0734 292s 11 -0.3652 -1.0581 -0.4134 292s 12 -1.3877 -0.6431 0.4203 292s 13 NA NA 0.0623 292s 14 -0.1818 2.4214 0.7091 292s 15 -0.6438 0.2168 0.5845 292s 16 -0.3417 NA 0.2455 292s 17 1.9583 2.4607 -0.6474 292s 18 -0.4806 -0.0468 0.9840 292s 19 -0.2563 -3.3855 -0.5930 292s 20 1.4832 1.1550 -0.2586 292s 21 1.4514 0.6086 -1.1446 292s 22 -1.2351 1.3453 0.5196 292s > fitted 292s Consumption Investment PrivateWages 292s 1 NA NA NA 292s 2 42.8 1.923 26.7 292s 3 45.8 1.532 28.8 292s 4 50.3 4.609 32.8 292s 5 50.5 4.669 34.1 292s 6 52.0 4.805 35.9 292s 7 NA NA NA 292s 8 54.4 2.765 38.8 292s 9 55.5 1.998 39.0 292s 10 NA 2.373 40.2 292s 11 55.4 2.058 38.3 292s 12 52.3 -2.757 34.1 292s 13 NA NA 28.9 292s 14 46.7 -7.521 27.8 292s 15 49.3 -3.217 30.0 292s 16 51.6 NA 33.0 292s 17 55.7 -0.361 37.4 292s 18 59.2 2.047 40.0 292s 19 57.8 1.485 38.8 292s 20 60.1 0.145 41.9 292s 21 63.5 2.691 46.1 292s 22 70.9 3.555 52.8 292s > predict 292s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 292s 1 NA NA NA NA 292s 2 42.8 0.548 41.7 43.9 292s 3 45.8 0.618 44.5 47.0 292s 4 50.3 0.411 49.5 51.2 292s 5 50.5 0.481 49.6 51.5 292s 6 52.0 0.490 51.0 52.9 292s 7 NA NA NA NA 292s 8 54.4 0.396 53.6 55.2 292s 9 55.5 0.467 54.5 56.4 292s 10 NA NA NA NA 292s 11 55.4 0.811 53.7 57.0 292s 12 52.3 0.775 50.7 53.8 292s 13 NA NA NA NA 292s 14 46.7 0.665 45.3 48.0 292s 15 49.3 0.463 48.4 50.3 292s 16 51.6 0.381 50.9 52.4 292s 17 55.7 0.428 54.9 56.6 292s 18 59.2 0.360 58.5 59.9 292s 19 57.8 0.492 56.8 58.7 292s 20 60.1 0.508 59.1 61.1 292s 21 63.5 0.499 62.5 64.6 292s 22 70.9 0.761 69.4 72.5 292s Investment.pred Investment.se.fit Investment.lwr Investment.upr 292s 1 NA NA NA NA 292s 2 1.923 1.079 -0.2526 4.098 292s 3 1.532 0.766 -0.0119 3.075 292s 4 4.609 0.668 3.2632 5.954 292s 5 4.669 0.566 3.5280 5.811 292s 6 4.805 0.543 3.7104 5.899 292s 7 NA NA NA NA 292s 8 2.765 0.447 1.8648 3.665 292s 9 1.998 0.651 0.6860 3.310 292s 10 2.373 0.710 0.9434 3.804 292s 11 2.058 1.237 -0.4350 4.551 292s 12 -2.757 1.139 -5.0532 -0.461 292s 13 NA NA NA NA 292s 14 -7.521 1.094 -9.7261 -5.317 292s 15 -3.217 0.648 -4.5217 -1.912 292s 16 NA NA NA NA 292s 17 -0.361 0.615 -1.6007 0.879 292s 18 2.047 0.417 1.2060 2.888 292s 19 1.485 0.684 0.1062 2.865 292s 20 0.145 0.699 -1.2632 1.553 292s 21 2.691 0.614 1.4548 3.928 292s 22 3.555 0.887 1.7674 5.342 292s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 292s 1 NA NA NA NA 292s 2 26.7 0.330 26.0 27.3 292s 3 28.8 0.336 28.2 29.5 292s 4 32.8 0.340 32.1 33.5 292s 5 34.1 0.251 33.6 34.6 292s 6 35.9 0.259 35.4 36.4 292s 7 NA NA NA NA 292s 8 38.8 0.253 38.3 39.3 292s 9 39.0 0.240 38.5 39.5 292s 10 40.2 0.236 39.8 40.7 292s 11 38.3 0.307 37.7 38.9 292s 12 34.1 0.313 33.4 34.7 292s 13 28.9 0.376 28.2 29.7 292s 14 27.8 0.327 27.1 28.4 292s 15 30.0 0.322 29.4 30.7 292s 16 33.0 0.270 32.4 33.5 292s 17 37.4 0.275 36.9 38.0 292s 18 40.0 0.216 39.6 40.5 292s 19 38.8 0.314 38.2 39.4 292s 20 41.9 0.296 41.3 42.5 292s 21 46.1 0.317 45.5 46.8 292s 22 52.8 0.480 51.8 53.7 292s > model.frame 292s [1] TRUE 292s > model.matrix 292s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0345 >" 292s [2] "Attributes: < Component “dimnames”: Component 1: 51 string mismatches >" 292s [3] "Numeric: lengths (696, 672) differ" 292s > nobs 292s [1] 56 292s > linearHypothesis 292s Linear hypothesis test (Theil's F test) 292s 292s Hypothesis: 292s Consumption_corpProf + Investment_capitalLag = 0 292s 292s Model 1: restricted model 292s Model 2: kleinModel 292s 292s Res.Df Df F Pr(>F) 292s 1 45 292s 2 44 1 2.29 0.14 292s Linear hypothesis test (F statistic of a Wald test) 292s 292s Hypothesis: 292s Consumption_corpProf + Investment_capitalLag = 0 292s 292s Model 1: restricted model 292s Model 2: kleinModel 292s 292s Res.Df Df F Pr(>F) 292s 1 45 292s 2 44 1 2.89 0.096 . 292s --- 292s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 292s Linear hypothesis test (Chi^2 statistic of a Wald test) 292s 292s Hypothesis: 292s Consumption_corpProf + Investment_capitalLag = 0 292s 292s Model 1: restricted model 292s Model 2: kleinModel 292s 292s Res.Df Df Chisq Pr(>Chisq) 292s 1 45 292s 2 44 1 2.89 0.089 . 292s --- 292s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 292s Linear hypothesis test (Theil's F test) 292s 292s Hypothesis: 292s Consumption_corpProf + Investment_capitalLag = 0 292s Consumption_corpProfLag - PrivateWages_trend = 0 292s 292s Model 1: restricted model 292s Model 2: kleinModel 292s 292s Res.Df Df F Pr(>F) 292s 1 46 292s 2 44 2 2.3 0.11 292s Linear hypothesis test (F statistic of a Wald test) 292s 292s Hypothesis: 292s Consumption_corpProf + Investment_capitalLag = 0 292s Consumption_corpProfLag - PrivateWages_trend = 0 292s 292s Model 1: restricted model 292s Model 2: kleinModel 292s 292s Res.Df Df F Pr(>F) 292s 1 46 292s 2 44 2 2.9 0.066 . 292s --- 292s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 292s Linear hypothesis test (Chi^2 statistic of a Wald test) 292s 292s Hypothesis: 292s Consumption_corpProf + Investment_capitalLag = 0 292s Consumption_corpProfLag - PrivateWages_trend = 0 292s 292s Model 1: restricted model 292s Model 2: kleinModel 292s 292s Res.Df Df Chisq Pr(>Chisq) 292s 1 46 292s 2 44 2 5.79 0.055 . 292s --- 292s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 292s > logLik 292s 'log Lik.' -72.2 (df=18) 292s 'log Lik.' -83.4 (df=18) 292s Estimating function 292s Consumption_(Intercept) Consumption_corpProf 292s Consumption_2 -4.4102 -61.801 292s Consumption_3 -1.0169 -16.947 292s Consumption_4 0.6316 11.712 292s Consumption_5 -2.4849 -50.366 292s Consumption_6 1.3496 25.671 292s Consumption_8 6.2136 109.641 292s Consumption_9 5.5588 105.303 292s Consumption_11 -2.3690 -39.659 292s Consumption_12 -3.3344 -44.601 292s Consumption_14 0.8298 8.317 292s Consumption_15 -2.5803 -32.264 292s Consumption_16 -2.1088 -30.539 292s Consumption_17 7.9903 119.154 292s Consumption_18 -0.6538 -12.697 292s Consumption_19 -5.8714 -112.192 292s Consumption_20 4.4259 78.161 292s Consumption_21 2.2655 46.209 292s Consumption_22 -0.9489 -21.517 292s Investment_2 1.9674 27.570 292s Investment_3 -0.3392 -5.652 292s Investment_4 -0.5776 -10.712 292s Investment_5 1.5305 31.021 292s Investment_6 -0.2467 -4.692 292s Investment_8 -1.2650 -22.320 292s Investment_9 -0.8831 -16.728 292s Investment_10 0.0000 0.000 292s Investment_11 0.9353 15.658 292s Investment_12 0.5224 6.988 292s Investment_14 -2.2467 -22.520 292s Investment_15 -0.2344 -2.931 292s Investment_17 -2.2188 -33.088 292s Investment_18 -0.0466 -0.905 292s Investment_19 3.0409 58.107 292s Investment_20 -1.0335 -18.251 292s Investment_21 -0.5381 -10.975 292s Investment_22 -1.2437 -28.202 292s PrivateWages_2 -4.0943 -57.374 292s PrivateWages_3 1.5700 26.162 292s PrivateWages_4 3.6522 67.727 292s PrivateWages_5 -3.9696 -80.460 292s PrivateWages_6 -0.7099 -13.503 292s PrivateWages_8 2.2578 39.840 292s PrivateWages_9 2.5772 48.821 292s PrivateWages_10 0.0000 0.000 292s PrivateWages_11 -3.3861 -56.686 292s PrivateWages_12 -0.4354 -5.824 292s PrivateWages_13 0.0000 0.000 292s PrivateWages_14 4.5081 45.187 292s PrivateWages_15 -0.1430 -1.788 292s PrivateWages_16 -0.3534 -5.118 292s PrivateWages_17 3.6864 54.972 292s PrivateWages_18 0.1281 2.488 292s PrivateWages_19 -8.7578 -167.347 292s PrivateWages_20 1.9940 35.215 292s PrivateWages_21 -1.7982 -36.678 292s PrivateWages_22 2.6643 60.414 292s Consumption_corpProfLag Consumption_wages 292s Consumption_2 -56.01 -131.52 292s Consumption_3 -12.61 -32.39 292s Consumption_4 10.67 22.27 292s Consumption_5 -45.72 -95.92 292s Consumption_6 26.18 52.02 292s Consumption_8 121.79 248.60 292s Consumption_9 110.06 232.23 292s Consumption_11 -51.41 -102.11 292s Consumption_12 -52.02 -132.22 292s Consumption_14 5.81 27.65 292s Consumption_15 -28.90 -96.33 292s Consumption_16 -25.94 -84.65 292s Consumption_17 111.86 333.82 292s Consumption_18 -11.51 -31.13 292s Consumption_19 -101.57 -289.06 292s Consumption_20 67.72 214.91 292s Consumption_21 43.05 121.02 292s Consumption_22 -20.02 -57.69 292s Investment_2 24.99 58.67 292s Investment_3 -4.21 -10.80 292s Investment_4 -9.76 -20.36 292s Investment_5 28.16 59.08 292s Investment_6 -4.79 -9.51 292s Investment_8 -24.79 -50.61 292s Investment_9 -17.48 -36.89 292s Investment_10 0.00 0.00 292s Investment_11 20.30 40.31 292s Investment_12 8.15 20.72 292s Investment_14 -15.73 -74.88 292s Investment_15 -2.63 -8.75 292s Investment_17 -31.06 -92.70 292s Investment_18 -0.82 -2.22 292s Investment_19 52.61 149.71 292s Investment_20 -15.81 -50.18 292s Investment_21 -10.22 -28.74 292s Investment_22 -26.24 -75.61 292s PrivateWages_2 -52.00 -122.10 292s PrivateWages_3 19.47 50.00 292s PrivateWages_4 61.72 128.76 292s PrivateWages_5 -73.04 -153.23 292s PrivateWages_6 -13.77 -27.36 292s PrivateWages_8 44.25 90.33 292s PrivateWages_9 51.03 107.67 292s PrivateWages_10 0.00 0.00 292s PrivateWages_11 -73.48 -145.95 292s PrivateWages_12 -6.79 -17.27 292s PrivateWages_13 0.00 0.00 292s PrivateWages_14 31.56 150.24 292s PrivateWages_15 -1.60 -5.34 292s PrivateWages_16 -4.35 -14.19 292s PrivateWages_17 51.61 154.01 292s PrivateWages_18 2.25 6.10 292s PrivateWages_19 -151.51 -431.17 292s PrivateWages_20 30.51 96.82 292s PrivateWages_21 -34.17 -96.06 292s PrivateWages_22 56.22 161.97 292s Investment_(Intercept) Investment_corpProf 292s Consumption_2 1.9908 26.734 292s Consumption_3 0.4591 7.651 292s Consumption_4 -0.2851 -5.368 292s Consumption_5 1.1217 23.127 292s Consumption_6 -0.6092 -11.762 292s Consumption_8 -2.8049 -49.183 292s Consumption_9 -2.5093 -48.961 292s Consumption_11 1.0694 18.405 292s Consumption_12 1.5052 20.306 292s Consumption_14 -0.3746 -3.777 292s Consumption_15 1.1648 15.147 292s Consumption_16 0.0000 0.000 292s Consumption_17 -3.6069 -53.782 292s Consumption_18 0.2951 5.754 292s Consumption_19 2.6504 51.112 292s Consumption_20 -1.9979 -35.052 292s Consumption_21 -1.0227 -20.634 292s Consumption_22 0.4283 9.753 292s Investment_2 -1.8422 -24.739 292s Investment_3 0.3176 5.293 292s Investment_4 0.5409 10.184 292s Investment_5 -1.4331 -29.546 292s Investment_6 0.2310 4.459 292s Investment_8 1.1844 20.769 292s Investment_9 0.8269 16.134 292s Investment_10 2.3608 47.771 292s Investment_11 -0.8758 -15.072 292s Investment_12 -0.4892 -6.600 292s Investment_14 2.1037 21.212 292s Investment_15 0.2195 2.854 292s Investment_17 2.0776 30.979 292s Investment_18 0.0436 0.851 292s Investment_19 -2.8474 -54.911 292s Investment_20 0.9677 16.978 292s Investment_21 0.5038 10.165 292s Investment_22 1.1646 26.516 292s PrivateWages_2 2.2726 30.518 292s PrivateWages_3 -0.8714 -14.524 292s PrivateWages_4 -2.0272 -38.170 292s PrivateWages_5 2.2034 45.428 292s PrivateWages_6 0.3940 7.607 292s PrivateWages_8 -1.2532 -21.975 292s PrivateWages_9 -1.4305 -27.911 292s PrivateWages_10 -2.6709 -54.046 292s PrivateWages_11 1.8795 32.347 292s PrivateWages_12 0.2417 3.260 292s PrivateWages_13 0.0000 0.000 292s PrivateWages_14 -2.5023 -25.230 292s PrivateWages_15 0.0794 1.032 292s PrivateWages_16 0.0000 0.000 292s PrivateWages_17 -2.0461 -30.509 292s PrivateWages_18 -0.0711 -1.386 292s PrivateWages_19 4.8611 93.745 292s PrivateWages_20 -1.1068 -19.419 292s PrivateWages_21 0.9981 20.138 292s PrivateWages_22 -1.4788 -33.672 292s Investment_corpProfLag Investment_capitalLag 292s Consumption_2 25.283 363.92 292s Consumption_3 5.692 83.82 292s Consumption_4 -4.818 -52.60 292s Consumption_5 20.639 212.79 292s Consumption_6 -11.819 -117.39 292s Consumption_8 -54.976 -570.52 292s Consumption_9 -49.684 -520.93 292s Consumption_11 23.206 230.67 292s Consumption_12 23.481 326.17 292s Consumption_14 -2.622 -77.57 292s Consumption_15 13.045 235.28 292s Consumption_16 0.000 0.00 292s Consumption_17 -50.497 -713.09 292s Consumption_18 5.194 58.97 292s Consumption_19 45.852 534.85 292s Consumption_20 -30.568 -399.38 292s Consumption_21 -19.431 -205.77 292s Consumption_22 9.038 87.60 292s Investment_2 -23.396 -336.76 292s Investment_3 3.938 57.99 292s Investment_4 9.141 99.79 292s Investment_5 -26.369 -271.86 292s Investment_6 4.481 44.51 292s Investment_8 23.215 240.92 292s Investment_9 16.372 171.66 292s Investment_10 49.812 497.18 292s Investment_11 -19.004 -188.91 292s Investment_12 -7.631 -106.01 292s Investment_14 14.726 435.68 292s Investment_15 2.458 44.34 292s Investment_17 29.086 410.74 292s Investment_18 0.768 8.72 292s Investment_19 -49.260 -574.60 292s Investment_20 14.806 193.44 292s Investment_21 9.573 101.37 292s Investment_22 24.572 238.15 292s PrivateWages_2 28.862 415.43 292s PrivateWages_3 -10.806 -159.12 292s PrivateWages_4 -34.259 -374.01 292s PrivateWages_5 40.542 417.98 292s PrivateWages_6 7.644 75.93 292s PrivateWages_8 -24.563 -254.91 292s PrivateWages_9 -28.324 -296.97 292s PrivateWages_10 -56.356 -562.49 292s PrivateWages_11 40.785 405.41 292s PrivateWages_12 3.770 52.37 292s PrivateWages_13 0.000 0.00 292s PrivateWages_14 -17.516 -518.22 292s PrivateWages_15 0.889 16.03 292s PrivateWages_16 0.000 0.00 292s PrivateWages_17 -28.646 -404.52 292s PrivateWages_18 -1.251 -14.21 292s PrivateWages_19 84.097 980.97 292s PrivateWages_20 -16.934 -221.25 292s PrivateWages_21 18.964 200.82 292s PrivateWages_22 -31.204 -302.42 292s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 292s Consumption_2 -4.7927 -225.59 -215.2 292s Consumption_3 -1.1051 -54.79 -50.4 292s Consumption_4 0.6863 38.81 34.4 292s Consumption_5 -2.7004 -163.88 -154.5 292s Consumption_6 1.4666 88.89 83.7 292s Consumption_8 6.7526 405.14 432.2 292s Consumption_9 6.0409 376.16 389.0 292s Consumption_11 -2.5745 -164.03 -172.5 292s Consumption_12 -3.6236 -198.69 -221.8 292s Consumption_14 0.9017 37.99 39.9 292s Consumption_15 -2.8041 -143.62 -126.5 292s Consumption_16 -2.2917 -126.82 -113.9 292s Consumption_17 8.6833 498.37 472.4 292s Consumption_18 -0.7105 -47.73 -44.5 292s Consumption_19 -6.3806 -437.15 -414.7 292s Consumption_20 4.8097 321.50 292.9 292s Consumption_21 2.4620 184.32 171.1 292s Consumption_22 -1.0312 -89.59 -78.1 292s Investment_2 2.6290 123.75 118.0 292s Investment_3 -0.4532 -22.47 -20.7 292s Investment_4 -0.7719 -43.64 -38.7 292s Investment_5 2.0451 124.11 117.0 292s Investment_6 -0.3296 -19.98 -18.8 292s Investment_8 -1.6903 -101.41 -108.2 292s Investment_9 -1.1800 -73.48 -76.0 292s Investment_10 -3.3690 -217.54 -217.3 292s Investment_11 1.2498 79.63 83.7 292s Investment_12 0.6981 38.28 42.7 292s Investment_14 -3.0022 -126.47 -133.0 292s Investment_15 -0.3132 -16.04 -14.1 292s Investment_17 -2.9649 -170.17 -161.3 292s Investment_18 -0.0623 -4.18 -3.9 292s Investment_19 4.0635 278.40 264.1 292s Investment_20 -1.3810 -92.31 -84.1 292s Investment_21 -0.7190 -53.83 -50.0 292s Investment_22 -1.6619 -144.39 -125.8 292s PrivateWages_2 -8.0595 -379.36 -361.9 292s PrivateWages_3 3.0904 153.23 140.9 292s PrivateWages_4 7.1892 406.50 360.2 292s PrivateWages_5 -7.8142 -474.21 -447.0 292s PrivateWages_6 -1.3974 -84.70 -79.8 292s PrivateWages_8 4.4445 266.66 284.4 292s PrivateWages_9 5.0731 315.90 326.7 292s PrivateWages_10 9.4721 611.61 611.0 292s PrivateWages_11 -6.6655 -424.67 -446.6 292s PrivateWages_12 -0.8571 -46.99 -52.5 292s PrivateWages_13 -4.8476 -227.73 -258.9 292s PrivateWages_14 8.8741 373.85 393.1 292s PrivateWages_15 -0.2815 -14.42 -12.7 292s PrivateWages_16 -0.6957 -38.50 -34.6 292s PrivateWages_17 7.2565 416.48 394.8 292s PrivateWages_18 0.2522 16.94 15.8 292s PrivateWages_19 -17.2396 -1181.13 -1120.6 292s PrivateWages_20 3.9252 262.38 239.0 292s PrivateWages_21 -3.5398 -265.01 -246.0 292s PrivateWages_22 5.2446 455.65 397.0 292s PrivateWages_trend 292s Consumption_2 47.927 292s Consumption_3 9.946 292s Consumption_4 -5.491 292s Consumption_5 18.903 292s Consumption_6 -8.800 292s Consumption_8 -27.010 292s Consumption_9 -18.123 292s Consumption_11 2.574 292s Consumption_12 0.000 292s Consumption_14 1.803 292s Consumption_15 -8.412 292s Consumption_16 -9.167 292s Consumption_17 43.417 292s Consumption_18 -4.263 292s Consumption_19 -44.664 292s Consumption_20 38.478 292s Consumption_21 22.158 292s Consumption_22 -10.312 292s Investment_2 -26.290 292s Investment_3 4.079 292s Investment_4 6.175 292s Investment_5 -14.316 292s Investment_6 1.978 292s Investment_8 6.761 292s Investment_9 3.540 292s Investment_10 6.738 292s Investment_11 -1.250 292s Investment_12 0.000 292s Investment_14 -6.004 292s Investment_15 -0.940 292s Investment_17 -14.825 292s Investment_18 -0.374 292s Investment_19 28.444 292s Investment_20 -11.048 292s Investment_21 -6.471 292s Investment_22 -16.619 292s PrivateWages_2 80.595 292s PrivateWages_3 -27.814 292s PrivateWages_4 -57.514 292s PrivateWages_5 54.699 292s PrivateWages_6 8.384 292s PrivateWages_8 -17.778 292s PrivateWages_9 -15.219 292s PrivateWages_10 -18.944 292s PrivateWages_11 6.666 292s PrivateWages_12 0.000 292s PrivateWages_13 -4.848 292s PrivateWages_14 17.748 292s PrivateWages_15 -0.844 292s PrivateWages_16 -2.783 292s PrivateWages_17 36.283 292s PrivateWages_18 1.513 292s PrivateWages_19 -120.677 292s PrivateWages_20 31.402 292s PrivateWages_21 -31.858 292s PrivateWages_22 52.446 292s [1] TRUE 292s > Bread 292s Consumption_(Intercept) Consumption_corpProf Consumption_corpProfLag 292s [1,] 133.708 -4.1980 0.8576 292s [2,] -4.198 1.2100 -0.6653 292s [3,] 0.858 -0.6653 0.7119 292s [4,] -1.738 -0.1324 -0.0277 292s [5,] 125.235 3.6584 5.4171 292s [6,] -6.184 0.8150 -0.6677 292s [7,] 2.270 -0.5431 0.6187 292s [8,] -0.265 -0.0441 -0.0204 292s [9,] -39.027 0.3871 1.7425 292s [10,] 0.490 -0.0701 0.0456 292s [11,] 0.147 0.0648 -0.0766 292s [12,] 0.260 0.0523 0.0256 292s Consumption_wages Investment_(Intercept) Investment_corpProf 292s [1,] -1.73822 125.23 -6.18369 292s [2,] -0.13241 3.66 0.81500 292s [3,] -0.02768 5.42 -0.66769 292s [4,] 0.10634 -6.40 0.07260 292s [5,] -6.40260 3920.72 -66.16832 292s [6,] 0.07260 -66.17 3.06783 292s [7,] -0.07286 52.35 -2.32206 292s [8,] 0.03170 -18.13 0.25629 292s [9,] 0.06731 57.07 -0.51824 292s [10,] -0.00202 2.27 0.00785 292s [11,] 0.00109 -3.34 0.00101 292s [12,] -0.03773 -1.63 0.03241 292s Investment_corpProfLag Investment_capitalLag PrivateWages_(Intercept) 292s [1,] 2.27003 -0.26469 -39.0267 292s [2,] -0.54312 -0.04408 0.3871 292s [3,] 0.61867 -0.02038 1.7425 292s [4,] -0.07286 0.03170 0.0673 292s [5,] 52.35486 -18.13066 57.0659 292s [6,] -2.32206 0.25629 -0.5182 292s [7,] 2.22379 -0.24386 -0.7311 292s [8,] -0.24386 0.08845 -0.1851 292s [9,] -0.73109 -0.18506 71.2482 292s [10,] 0.01103 -0.01288 -0.3220 292s [11,] 0.00202 0.01653 -0.8851 292s [12,] -0.04341 0.00871 0.7698 292s PrivateWages_gnp PrivateWages_gnpLag PrivateWages_trend 292s [1,] 0.49031 0.147339 0.260437 292s [2,] -0.07008 0.064790 0.052347 292s [3,] 0.04558 -0.076595 0.025629 292s [4,] -0.00202 0.001086 -0.037728 292s [5,] 2.27149 -3.339873 -1.627913 292s [6,] 0.00785 0.001013 0.032414 292s [7,] 0.01103 0.002018 -0.043407 292s [8,] -0.01288 0.016530 0.008714 292s [9,] -0.32199 -0.885080 0.769761 292s [10,] 0.04892 -0.044549 -0.013616 292s [11,] -0.04455 0.061046 0.000449 292s [12,] -0.01362 0.000449 0.047057 292s > 292s BEGIN TEST KleinI_noMat.R 292s 292s R version 4.3.3 (2024-02-29) -- "Angel Food Cake" 292s Copyright (C) 2024 The R Foundation for Statistical Computing 292s Platform: arm-unknown-linux-gnueabihf (32-bit) 292s 292s R is free software and comes with ABSOLUTELY NO WARRANTY. 292s You are welcome to redistribute it under certain conditions. 292s Type 'license()' or 'licence()' for distribution details. 292s 292s R is a collaborative project with many contributors. 292s Type 'contributors()' for more information and 292s 'citation()' on how to cite R or R packages in publications. 292s 292s Type 'demo()' for some demos, 'help()' for on-line help, or 292s 'help.start()' for an HTML browser interface to help. 292s Type 'q()' to quit R. 292s 292s > library( "systemfit" ) 292s Loading required package: Matrix 293s Loading required package: car 293s Loading required package: carData 293s Loading required package: lmtest 293s Loading required package: zoo 293s 293s Attaching package: ‘zoo’ 293s 293s The following objects are masked from ‘package:base’: 293s 293s as.Date, as.Date.numeric 293s 293s 293s Please cite the 'systemfit' package as: 293s Arne Henningsen and Jeff D. Hamann (2007). systemfit: A Package for Estimating Systems of Simultaneous Equations in R. Journal of Statistical Software 23(4), 1-40. http://www.jstatsoft.org/v23/i04/. 293s 293s If you have questions, suggestions, or comments regarding the 'systemfit' package, please use a forum or 'tracker' at systemfit's R-Forge site: 293s https://r-forge.r-project.org/projects/systemfit/ 293s > options( warn = 1 ) 293s > options( digits = 3 ) 293s > 293s > data( "KleinI" ) 293s > eqConsump <- consump ~ corpProf + corpProfLag + wages 293s > eqInvest <- invest ~ corpProf + corpProfLag + capitalLag 293s > eqPrivWage <- privWage ~ gnp + gnpLag + trend 293s > inst <- ~ govExp + taxes + govWage + trend + capitalLag + corpProfLag + gnpLag 293s > system <- list( Consumption = eqConsump, Investment = eqInvest, 293s + PrivateWages = eqPrivWage ) 293s > restrict <- c( "Consumption_corpProf + Investment_capitalLag = 0" ) 293s > restrict2 <- c( restrict, "Consumption_corpProfLag - PrivateWages_trend = 0" ) 293s > 293s > for( dataNo in 1:5 ) { 293s + # set some values of some variables to NA 293s + if( dataNo == 2 ) { 293s + KleinI$gnpLag[ 7 ] <- NA 293s + } else if( dataNo == 3 ) { 293s + KleinI$wages[ 10 ] <- NA 293s + } else if( dataNo == 4 ) { 293s + KleinI$corpProf[ 13 ] <- NA 293s + } else if( dataNo == 5 ) { 293s + KleinI$invest[ 16 ] <- NA 293s + } 293s + 293s + # single-equation OLS 293s + lmConsump <- lm( eqConsump, data = KleinI ) 293s + lmInvest <- lm( eqInvest, data = KleinI ) 293s + lmPrivWage <- lm( eqPrivWage, data = KleinI ) 293s + 293s + for( methodNo in 1:5 ) { 293s + method <- c( "OLS", "2SLS", "SUR", "3SLS", "3SLS" )[ methodNo ] 293s + maxit <- ifelse( methodNo == 5, 500, 1 ) 293s + 293s + cat( "> \n> # ", ifelse( maxit == 1, "", "I" ), method, "\n", sep = "" ) 293s + if( method %in% c( "OLS", "WLS", "SUR" ) ) { 293s + kleinModel <- systemfit( system, method = method, data = KleinI, 293s + methodResidCov = ifelse( method == "OLS", "geomean", "noDfCor" ), 293s + maxit = maxit, useMatrix = FALSE ) 293s + } else { 293s + kleinModel <- systemfit( system, method = method, data = KleinI, 293s + inst = inst, methodResidCov = "noDfCor", maxit = maxit, 293s + useMatrix = FALSE ) 293s + } 293s + cat( "> summary\n" ) 293s + print( summary( kleinModel ) ) 293s + if( method == "OLS" ) { 293s + cat( "compare coef with single-equation OLS\n" ) 293s + print( all.equal( coef( kleinModel ), 293s + c( coef( lmConsump ), coef( lmInvest ), coef( lmPrivWage ) ), 293s + check.attributes = FALSE ) ) 293s + } 293s + cat( "> residuals\n" ) 293s + print( residuals( kleinModel ) ) 293s + cat( "> fitted\n" ) 293s + print( fitted( kleinModel ) ) 293s + cat( "> predict\n" ) 293s + print( predict( kleinModel, se.fit = TRUE, 293s + interval = ifelse( methodNo %in% c( 1, 4 ), "prediction", "confidence" ), 293s + useDfSys = methodNo %in% c( 1, 3, 5 ) ) ) 293s + cat( "> model.frame\n" ) 293s + if( methodNo == 1 ) { 293s + mfOls <- model.frame( kleinModel ) 293s + print( mfOls ) 293s + } else if( methodNo == 2 ) { 293s + mf2sls <- model.frame( kleinModel ) 293s + print( mf2sls ) 293s + } else if( methodNo == 3 ) { 293s + print( all.equal( mfOls, model.frame( kleinModel ) ) ) 293s + } else { 293s + print( all.equal( mf2sls, model.frame( kleinModel ) ) ) 293s + } 293s + cat( "> model.matrix\n" ) 293s + if( methodNo == 1 ) { 293s + mmOls <- model.matrix( kleinModel ) 293s + print( mmOls ) 293s + } else { 293s + print( all.equal( mmOls, model.matrix( kleinModel ) ) ) 293s + } 293s + cat( "> nobs\n" ) 293s + print( nobs( kleinModel ) ) 293s + cat( "> linearHypothesis\n" ) 293s + print( linearHypothesis( kleinModel, restrict ) ) 293s + print( linearHypothesis( kleinModel, restrict, test = "F" ) ) 293s + print( linearHypothesis( kleinModel, restrict, test = "Chisq" ) ) 293s + print( linearHypothesis( kleinModel, restrict2 ) ) 293s + print( linearHypothesis( kleinModel, restrict2, test = "F" ) ) 293s + print( linearHypothesis( kleinModel, restrict2, test = "Chisq" ) ) 293s + cat( "> logLik\n" ) 293s + print( logLik( kleinModel ) ) 293s + print( logLik( kleinModel, residCovDiag = TRUE ) ) 293s + if( method == "OLS" ) { 293s + cat( "compare log likelihood value with single-equation OLS\n" ) 293s + print( all.equal( logLik( kleinModel, residCovDiag = TRUE ), 293s + logLik( lmConsump ) + logLik( lmInvest ) + logLik( lmPrivWage ), 293s + check.attributes = FALSE ) ) 293s + } 293s + } 293s + } 293s > 293s > # OLS 293s > summary 293s 293s systemfit results 293s method: OLS 293s 293s N DF SSR detRCov OLS-R2 McElroy-R2 293s system 63 51 45.2 0.371 0.977 0.991 293s 293s N DF SSR MSE RMSE R2 Adj R2 293s Consumption 21 17 17.9 1.052 1.026 0.981 0.978 293s Investment 21 17 17.3 1.019 1.009 0.931 0.919 293s PrivateWages 21 17 10.0 0.589 0.767 0.987 0.985 293s 293s The covariance matrix of the residuals 293s Consumption Investment PrivateWages 293s Consumption 1.0517 0.0611 -0.470 293s Investment 0.0611 1.0190 0.150 293s PrivateWages -0.4704 0.1497 0.589 293s 293s The correlations of the residuals 293s Consumption Investment PrivateWages 293s Consumption 1.0000 0.0591 -0.598 293s Investment 0.0591 1.0000 0.193 293s PrivateWages -0.5979 0.1933 1.000 293s 293s 293s OLS estimates for 'Consumption' (equation 1) 293s Model Formula: consump ~ corpProf + corpProfLag + wages 293s 293s Estimate Std. Error t value Pr(>|t|) 293s (Intercept) 16.2366 1.3027 12.46 5.6e-10 *** 293s corpProf 0.1929 0.0912 2.12 0.049 * 293s corpProfLag 0.0899 0.0906 0.99 0.335 293s wages 0.7962 0.0399 19.93 3.2e-13 *** 293s --- 293s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 293s 293s Residual standard error: 1.026 on 17 degrees of freedom 293s Number of observations: 21 Degrees of Freedom: 17 293s SSR: 17.879 MSE: 1.052 Root MSE: 1.026 293s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.978 293s 293s 293s OLS estimates for 'Investment' (equation 2) 293s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 293s 293s Estimate Std. Error t value Pr(>|t|) 293s (Intercept) 10.1258 5.4655 1.85 0.08137 . 293s corpProf 0.4796 0.0971 4.94 0.00012 *** 293s corpProfLag 0.3330 0.1009 3.30 0.00421 ** 293s capitalLag -0.1118 0.0267 -4.18 0.00062 *** 293s --- 293s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 293s 293s Residual standard error: 1.009 on 17 degrees of freedom 293s Number of observations: 21 Degrees of Freedom: 17 293s SSR: 17.323 MSE: 1.019 Root MSE: 1.009 293s Multiple R-Squared: 0.931 Adjusted R-Squared: 0.919 293s 293s 293s OLS estimates for 'PrivateWages' (equation 3) 293s Model Formula: privWage ~ gnp + gnpLag + trend 293s 293s Estimate Std. Error t value Pr(>|t|) 293s (Intercept) 1.4970 1.2700 1.18 0.25474 293s gnp 0.4395 0.0324 13.56 1.5e-10 *** 293s gnpLag 0.1461 0.0374 3.90 0.00114 ** 293s trend 0.1302 0.0319 4.08 0.00078 *** 293s --- 293s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 293s 293s Residual standard error: 0.767 on 17 degrees of freedom 293s Number of observations: 21 Degrees of Freedom: 17 293s SSR: 10.005 MSE: 0.589 Root MSE: 0.767 293s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 293s 293s compare coef with single-equation OLS 293s [1] TRUE 293s > residuals 293s Consumption Investment PrivateWages 293s 1 NA NA NA 293s 2 -0.32389 -0.0668 -1.2942 293s 3 -1.25001 -0.0476 0.2957 293s 4 -1.56574 1.2467 1.1877 293s 5 -0.49350 -1.3512 -0.1358 293s 6 0.00761 0.4154 -0.4654 293s 7 0.86910 1.4923 -0.4838 293s 8 1.33848 0.7889 -0.7281 293s 9 1.05498 -0.6317 0.3392 293s 10 -0.58856 1.0830 1.1957 293s 11 0.28231 0.2791 -0.1508 293s 12 -0.22965 0.0369 0.5942 293s 13 -0.32213 0.3659 0.1027 293s 14 0.32228 0.2237 0.4503 293s 15 -0.05801 -0.1728 0.2816 293s 16 -0.03466 0.0101 0.0138 293s 17 1.61650 0.9719 -0.8508 293s 18 -0.43597 0.0516 0.9956 293s 19 0.21005 -2.5656 -0.4688 293s 20 0.98920 -0.6866 -0.3795 293s 21 0.78508 -0.7807 -1.0909 293s 22 -2.17345 -0.6623 0.5917 293s > fitted 293s Consumption Investment PrivateWages 293s 1 NA NA NA 293s 2 42.2 -0.133 26.8 293s 3 46.3 1.948 29.0 293s 4 50.8 3.953 32.9 293s 5 51.1 4.351 34.0 293s 6 52.6 4.685 35.9 293s 7 54.2 4.108 37.9 293s 8 54.9 3.411 38.6 293s 9 56.2 3.632 38.9 293s 10 58.4 4.017 40.1 293s 11 54.7 0.721 38.1 293s 12 51.1 -3.437 33.9 293s 13 45.9 -6.566 28.9 293s 14 46.2 -5.324 28.0 293s 15 48.8 -2.827 30.3 293s 16 51.3 -1.310 33.2 293s 17 56.1 1.128 37.7 293s 18 59.1 1.948 40.0 293s 19 57.3 0.666 38.7 293s 20 60.6 1.987 42.0 293s 21 64.2 4.081 46.1 293s 22 71.9 5.562 52.7 293s > predict 293s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 293s 1 NA NA NA NA 293s 2 42.2 0.462 40.0 44.5 293s 3 46.3 0.518 43.9 48.6 293s 4 50.8 0.341 48.6 52.9 293s 5 51.1 0.396 48.9 53.3 293s 6 52.6 0.397 50.4 54.8 293s 7 54.2 0.359 52.0 56.4 293s 8 54.9 0.327 52.7 57.0 293s 9 56.2 0.350 54.1 58.4 293s 10 58.4 0.370 56.2 60.6 293s 11 54.7 0.606 52.3 57.1 293s 12 51.1 0.484 48.9 53.4 293s 13 45.9 0.629 43.5 48.3 293s 14 46.2 0.602 43.8 48.6 293s 15 48.8 0.374 46.6 50.9 293s 16 51.3 0.333 49.2 53.5 293s 17 56.1 0.366 53.9 58.3 293s 18 59.1 0.321 57.0 61.3 293s 19 57.3 0.371 55.1 59.5 293s 20 60.6 0.434 58.4 62.8 293s 21 64.2 0.425 62.0 66.4 293s 22 71.9 0.666 69.4 74.3 293s Investment.pred Investment.se.fit Investment.lwr Investment.upr 293s 1 NA NA NA NA 293s 2 -0.133 0.607 -2.498 2.231 293s 3 1.948 0.499 -0.313 4.208 293s 4 3.953 0.449 1.735 6.171 293s 5 4.351 0.371 2.192 6.510 293s 6 4.685 0.349 2.540 6.829 293s 7 4.108 0.329 1.976 6.239 293s 8 3.411 0.292 1.301 5.521 293s 9 3.632 0.389 1.460 5.804 293s 10 4.017 0.447 1.801 6.233 293s 11 0.721 0.601 -1.638 3.080 293s 12 -3.437 0.507 -5.704 -1.169 293s 13 -6.566 0.616 -8.940 -4.192 293s 14 -5.324 0.694 -7.783 -2.865 293s 15 -2.827 0.373 -4.988 -0.667 293s 16 -1.310 0.320 -3.436 0.816 293s 17 1.128 0.347 -1.015 3.271 293s 18 1.948 0.243 -0.136 4.033 293s 19 0.666 0.312 -1.456 2.787 293s 20 1.987 0.366 -0.169 4.143 293s 21 4.081 0.332 1.948 6.214 293s 22 5.562 0.461 3.334 7.790 293s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 293s 1 NA NA NA NA 293s 2 26.8 0.354 25.1 28.5 293s 3 29.0 0.355 27.3 30.7 293s 4 32.9 0.354 31.2 34.6 293s 5 34.0 0.269 32.4 35.7 293s 6 35.9 0.266 34.2 37.5 293s 7 37.9 0.266 36.3 39.5 293s 8 38.6 0.273 37.0 40.3 293s 9 38.9 0.261 37.2 40.5 293s 10 40.1 0.247 38.5 41.7 293s 11 38.1 0.354 36.4 39.7 293s 12 33.9 0.363 32.2 35.6 293s 13 28.9 0.429 27.1 30.7 293s 14 28.0 0.376 26.3 29.8 293s 15 30.3 0.371 28.6 32.0 293s 16 33.2 0.310 31.5 34.8 293s 17 37.7 0.305 36.0 39.3 293s 18 40.0 0.238 38.4 41.6 293s 19 38.7 0.357 37.0 40.4 293s 20 42.0 0.321 40.3 43.6 293s 21 46.1 0.335 44.4 47.8 293s 22 52.7 0.502 50.9 54.5 293s > model.frame 293s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 293s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 293s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 293s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 293s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 293s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 293s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 293s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 61.0 293s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 293s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 293s 10 57.8 21.7 21.1 45.3 5.1 211 41.3 67.0 64.5 293s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 293s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 293s 13 45.6 7.0 11.4 34.3 -6.2 213 29.0 44.3 53.4 293s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 293s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 293s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 293s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 293s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 293s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 293s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 293s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 293s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 293s trend 293s 1 -11 293s 2 -10 293s 3 -9 293s 4 -8 293s 5 -7 293s 6 -6 293s 7 -5 293s 8 -4 293s 9 -3 293s 10 -2 293s 11 -1 293s 12 0 293s 13 1 293s 14 2 293s 15 3 293s 16 4 293s 17 5 293s 18 6 293s 19 7 293s 20 8 293s 21 9 293s 22 10 293s > model.matrix 293s Consumption_(Intercept) Consumption_corpProf 293s Consumption_2 1 12.4 293s Consumption_3 1 16.9 293s Consumption_4 1 18.4 293s Consumption_5 1 19.4 293s Consumption_6 1 20.1 293s Consumption_7 1 19.6 293s Consumption_8 1 19.8 293s Consumption_9 1 21.1 293s Consumption_10 1 21.7 293s Consumption_11 1 15.6 293s Consumption_12 1 11.4 293s Consumption_13 1 7.0 293s Consumption_14 1 11.2 293s Consumption_15 1 12.3 293s Consumption_16 1 14.0 293s Consumption_17 1 17.6 293s Consumption_18 1 17.3 293s Consumption_19 1 15.3 293s Consumption_20 1 19.0 293s Consumption_21 1 21.1 293s Consumption_22 1 23.5 293s Investment_2 0 0.0 293s Investment_3 0 0.0 293s Investment_4 0 0.0 293s Investment_5 0 0.0 293s Investment_6 0 0.0 293s Investment_7 0 0.0 293s Investment_8 0 0.0 293s Investment_9 0 0.0 293s Investment_10 0 0.0 293s Investment_11 0 0.0 293s Investment_12 0 0.0 293s Investment_13 0 0.0 293s Investment_14 0 0.0 293s Investment_15 0 0.0 293s Investment_16 0 0.0 293s Investment_17 0 0.0 293s Investment_18 0 0.0 293s Investment_19 0 0.0 293s Investment_20 0 0.0 293s Investment_21 0 0.0 293s Investment_22 0 0.0 293s PrivateWages_2 0 0.0 293s PrivateWages_3 0 0.0 293s PrivateWages_4 0 0.0 293s PrivateWages_5 0 0.0 293s PrivateWages_6 0 0.0 293s PrivateWages_7 0 0.0 293s PrivateWages_8 0 0.0 293s PrivateWages_9 0 0.0 293s PrivateWages_10 0 0.0 293s PrivateWages_11 0 0.0 293s PrivateWages_12 0 0.0 293s PrivateWages_13 0 0.0 293s PrivateWages_14 0 0.0 293s PrivateWages_15 0 0.0 293s PrivateWages_16 0 0.0 293s PrivateWages_17 0 0.0 293s PrivateWages_18 0 0.0 293s PrivateWages_19 0 0.0 293s PrivateWages_20 0 0.0 293s PrivateWages_21 0 0.0 293s PrivateWages_22 0 0.0 293s Consumption_corpProfLag Consumption_wages 293s Consumption_2 12.7 28.2 293s Consumption_3 12.4 32.2 293s Consumption_4 16.9 37.0 293s Consumption_5 18.4 37.0 293s Consumption_6 19.4 38.6 293s Consumption_7 20.1 40.7 293s Consumption_8 19.6 41.5 293s Consumption_9 19.8 42.9 293s Consumption_10 21.1 45.3 293s Consumption_11 21.7 42.1 293s Consumption_12 15.6 39.3 293s Consumption_13 11.4 34.3 293s Consumption_14 7.0 34.1 293s Consumption_15 11.2 36.6 293s Consumption_16 12.3 39.3 293s Consumption_17 14.0 44.2 293s Consumption_18 17.6 47.7 293s Consumption_19 17.3 45.9 293s Consumption_20 15.3 49.4 293s Consumption_21 19.0 53.0 293s Consumption_22 21.1 61.8 293s Investment_2 0.0 0.0 293s Investment_3 0.0 0.0 293s Investment_4 0.0 0.0 293s Investment_5 0.0 0.0 293s Investment_6 0.0 0.0 293s Investment_7 0.0 0.0 293s Investment_8 0.0 0.0 293s Investment_9 0.0 0.0 293s Investment_10 0.0 0.0 293s Investment_11 0.0 0.0 293s Investment_12 0.0 0.0 293s Investment_13 0.0 0.0 293s Investment_14 0.0 0.0 293s Investment_15 0.0 0.0 293s Investment_16 0.0 0.0 293s Investment_17 0.0 0.0 293s Investment_18 0.0 0.0 293s Investment_19 0.0 0.0 293s Investment_20 0.0 0.0 293s Investment_21 0.0 0.0 293s Investment_22 0.0 0.0 293s PrivateWages_2 0.0 0.0 293s PrivateWages_3 0.0 0.0 293s PrivateWages_4 0.0 0.0 293s PrivateWages_5 0.0 0.0 293s PrivateWages_6 0.0 0.0 293s PrivateWages_7 0.0 0.0 293s PrivateWages_8 0.0 0.0 293s PrivateWages_9 0.0 0.0 293s PrivateWages_10 0.0 0.0 293s PrivateWages_11 0.0 0.0 293s PrivateWages_12 0.0 0.0 293s PrivateWages_13 0.0 0.0 293s PrivateWages_14 0.0 0.0 293s PrivateWages_15 0.0 0.0 293s PrivateWages_16 0.0 0.0 293s PrivateWages_17 0.0 0.0 293s PrivateWages_18 0.0 0.0 293s PrivateWages_19 0.0 0.0 293s PrivateWages_20 0.0 0.0 293s PrivateWages_21 0.0 0.0 293s PrivateWages_22 0.0 0.0 293s Investment_(Intercept) Investment_corpProf 293s Consumption_2 0 0.0 293s Consumption_3 0 0.0 293s Consumption_4 0 0.0 293s Consumption_5 0 0.0 293s Consumption_6 0 0.0 293s Consumption_7 0 0.0 293s Consumption_8 0 0.0 293s Consumption_9 0 0.0 293s Consumption_10 0 0.0 293s Consumption_11 0 0.0 293s Consumption_12 0 0.0 293s Consumption_13 0 0.0 293s Consumption_14 0 0.0 293s Consumption_15 0 0.0 293s Consumption_16 0 0.0 293s Consumption_17 0 0.0 293s Consumption_18 0 0.0 293s Consumption_19 0 0.0 293s Consumption_20 0 0.0 293s Consumption_21 0 0.0 293s Consumption_22 0 0.0 293s Investment_2 1 12.4 293s Investment_3 1 16.9 293s Investment_4 1 18.4 293s Investment_5 1 19.4 293s Investment_6 1 20.1 293s Investment_7 1 19.6 293s Investment_8 1 19.8 293s Investment_9 1 21.1 293s Investment_10 1 21.7 293s Investment_11 1 15.6 293s Investment_12 1 11.4 293s Investment_13 1 7.0 293s Investment_14 1 11.2 293s Investment_15 1 12.3 293s Investment_16 1 14.0 293s Investment_17 1 17.6 293s Investment_18 1 17.3 293s Investment_19 1 15.3 293s Investment_20 1 19.0 293s Investment_21 1 21.1 293s Investment_22 1 23.5 293s PrivateWages_2 0 0.0 293s PrivateWages_3 0 0.0 293s PrivateWages_4 0 0.0 293s PrivateWages_5 0 0.0 293s PrivateWages_6 0 0.0 293s PrivateWages_7 0 0.0 293s PrivateWages_8 0 0.0 293s PrivateWages_9 0 0.0 293s PrivateWages_10 0 0.0 293s PrivateWages_11 0 0.0 293s PrivateWages_12 0 0.0 293s PrivateWages_13 0 0.0 293s PrivateWages_14 0 0.0 293s PrivateWages_15 0 0.0 293s PrivateWages_16 0 0.0 293s PrivateWages_17 0 0.0 293s PrivateWages_18 0 0.0 293s PrivateWages_19 0 0.0 293s PrivateWages_20 0 0.0 293s PrivateWages_21 0 0.0 293s PrivateWages_22 0 0.0 293s Investment_corpProfLag Investment_capitalLag 293s Consumption_2 0.0 0 293s Consumption_3 0.0 0 293s Consumption_4 0.0 0 293s Consumption_5 0.0 0 293s Consumption_6 0.0 0 293s Consumption_7 0.0 0 293s Consumption_8 0.0 0 293s Consumption_9 0.0 0 293s Consumption_10 0.0 0 293s Consumption_11 0.0 0 293s Consumption_12 0.0 0 293s Consumption_13 0.0 0 293s Consumption_14 0.0 0 293s Consumption_15 0.0 0 293s Consumption_16 0.0 0 293s Consumption_17 0.0 0 293s Consumption_18 0.0 0 293s Consumption_19 0.0 0 293s Consumption_20 0.0 0 293s Consumption_21 0.0 0 293s Consumption_22 0.0 0 293s Investment_2 12.7 183 293s Investment_3 12.4 183 293s Investment_4 16.9 184 293s Investment_5 18.4 190 293s Investment_6 19.4 193 293s Investment_7 20.1 198 293s Investment_8 19.6 203 293s Investment_9 19.8 208 293s Investment_10 21.1 211 293s Investment_11 21.7 216 293s Investment_12 15.6 217 293s Investment_13 11.4 213 293s Investment_14 7.0 207 293s Investment_15 11.2 202 293s Investment_16 12.3 199 293s Investment_17 14.0 198 293s Investment_18 17.6 200 293s Investment_19 17.3 202 293s Investment_20 15.3 200 293s Investment_21 19.0 201 293s Investment_22 21.1 204 293s PrivateWages_2 0.0 0 293s PrivateWages_3 0.0 0 293s PrivateWages_4 0.0 0 293s PrivateWages_5 0.0 0 293s PrivateWages_6 0.0 0 293s PrivateWages_7 0.0 0 293s PrivateWages_8 0.0 0 293s PrivateWages_9 0.0 0 293s PrivateWages_10 0.0 0 293s PrivateWages_11 0.0 0 293s PrivateWages_12 0.0 0 293s PrivateWages_13 0.0 0 293s PrivateWages_14 0.0 0 293s PrivateWages_15 0.0 0 293s PrivateWages_16 0.0 0 293s PrivateWages_17 0.0 0 293s PrivateWages_18 0.0 0 293s PrivateWages_19 0.0 0 293s PrivateWages_20 0.0 0 293s PrivateWages_21 0.0 0 293s PrivateWages_22 0.0 0 293s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 293s Consumption_2 0 0.0 0.0 293s Consumption_3 0 0.0 0.0 293s Consumption_4 0 0.0 0.0 293s Consumption_5 0 0.0 0.0 293s Consumption_6 0 0.0 0.0 293s Consumption_7 0 0.0 0.0 293s Consumption_8 0 0.0 0.0 293s Consumption_9 0 0.0 0.0 293s Consumption_10 0 0.0 0.0 293s Consumption_11 0 0.0 0.0 293s Consumption_12 0 0.0 0.0 293s Consumption_13 0 0.0 0.0 293s Consumption_14 0 0.0 0.0 293s Consumption_15 0 0.0 0.0 293s Consumption_16 0 0.0 0.0 293s Consumption_17 0 0.0 0.0 293s Consumption_18 0 0.0 0.0 293s Consumption_19 0 0.0 0.0 293s Consumption_20 0 0.0 0.0 293s Consumption_21 0 0.0 0.0 293s Consumption_22 0 0.0 0.0 293s Investment_2 0 0.0 0.0 293s Investment_3 0 0.0 0.0 293s Investment_4 0 0.0 0.0 293s Investment_5 0 0.0 0.0 293s Investment_6 0 0.0 0.0 293s Investment_7 0 0.0 0.0 293s Investment_8 0 0.0 0.0 293s Investment_9 0 0.0 0.0 293s Investment_10 0 0.0 0.0 293s Investment_11 0 0.0 0.0 293s Investment_12 0 0.0 0.0 293s Investment_13 0 0.0 0.0 293s Investment_14 0 0.0 0.0 293s Investment_15 0 0.0 0.0 293s Investment_16 0 0.0 0.0 293s Investment_17 0 0.0 0.0 293s Investment_18 0 0.0 0.0 293s Investment_19 0 0.0 0.0 293s Investment_20 0 0.0 0.0 293s Investment_21 0 0.0 0.0 293s Investment_22 0 0.0 0.0 293s PrivateWages_2 1 45.6 44.9 293s PrivateWages_3 1 50.1 45.6 293s PrivateWages_4 1 57.2 50.1 293s PrivateWages_5 1 57.1 57.2 293s PrivateWages_6 1 61.0 57.1 293s PrivateWages_7 1 64.0 61.0 293s PrivateWages_8 1 64.4 64.0 293s PrivateWages_9 1 64.5 64.4 293s PrivateWages_10 1 67.0 64.5 293s PrivateWages_11 1 61.2 67.0 293s PrivateWages_12 1 53.4 61.2 293s PrivateWages_13 1 44.3 53.4 293s PrivateWages_14 1 45.1 44.3 293s PrivateWages_15 1 49.7 45.1 293s PrivateWages_16 1 54.4 49.7 293s PrivateWages_17 1 62.7 54.4 293s PrivateWages_18 1 65.0 62.7 293s PrivateWages_19 1 60.9 65.0 293s PrivateWages_20 1 69.5 60.9 293s PrivateWages_21 1 75.7 69.5 293s PrivateWages_22 1 88.4 75.7 293s PrivateWages_trend 293s Consumption_2 0 293s Consumption_3 0 293s Consumption_4 0 293s Consumption_5 0 293s Consumption_6 0 293s Consumption_7 0 293s Consumption_8 0 293s Consumption_9 0 293s Consumption_10 0 293s Consumption_11 0 293s Consumption_12 0 293s Consumption_13 0 293s Consumption_14 0 293s Consumption_15 0 293s Consumption_16 0 293s Consumption_17 0 293s Consumption_18 0 293s Consumption_19 0 293s Consumption_20 0 293s Consumption_21 0 293s Consumption_22 0 293s Investment_2 0 293s Investment_3 0 293s Investment_4 0 293s Investment_5 0 293s Investment_6 0 293s Investment_7 0 293s Investment_8 0 293s Investment_9 0 293s Investment_10 0 293s Investment_11 0 293s Investment_12 0 293s Investment_13 0 293s Investment_14 0 293s Investment_15 0 293s Investment_16 0 293s Investment_17 0 293s Investment_18 0 293s Investment_19 0 293s Investment_20 0 293s Investment_21 0 293s Investment_22 0 293s PrivateWages_2 -10 293s PrivateWages_3 -9 293s PrivateWages_4 -8 293s PrivateWages_5 -7 293s PrivateWages_6 -6 293s PrivateWages_7 -5 293s PrivateWages_8 -4 293s PrivateWages_9 -3 293s PrivateWages_10 -2 293s PrivateWages_11 -1 293s PrivateWages_12 0 293s PrivateWages_13 1 293s PrivateWages_14 2 293s PrivateWages_15 3 293s PrivateWages_16 4 293s PrivateWages_17 5 293s PrivateWages_18 6 293s PrivateWages_19 7 293s PrivateWages_20 8 293s PrivateWages_21 9 293s PrivateWages_22 10 293s > nobs 293s [1] 63 293s > linearHypothesis 293s Linear hypothesis test (Theil's F test) 293s 293s Hypothesis: 293s Consumption_corpProf + Investment_capitalLag = 0 293s 293s Model 1: restricted model 293s Model 2: kleinModel 293s 293s Res.Df Df F Pr(>F) 293s 1 52 293s 2 51 1 0.82 0.37 293s Linear hypothesis test (F statistic of a Wald test) 293s 293s Hypothesis: 293s Consumption_corpProf + Investment_capitalLag = 0 293s 293s Model 1: restricted model 293s Model 2: kleinModel 293s 293s Res.Df Df F Pr(>F) 293s 1 52 293s 2 51 1 0.73 0.4 293s Linear hypothesis test (Chi^2 statistic of a Wald test) 293s 293s Hypothesis: 293s Consumption_corpProf + Investment_capitalLag = 0 293s 293s Model 1: restricted model 293s Model 2: kleinModel 293s 293s Res.Df Df Chisq Pr(>Chisq) 293s 1 52 293s 2 51 1 0.73 0.39 293s Linear hypothesis test (Theil's F test) 293s 293s Hypothesis: 293s Consumption_corpProf + Investment_capitalLag = 0 293s Consumption_corpProfLag - PrivateWages_trend = 0 293s 293s Model 1: restricted model 293s Model 2: kleinModel 293s 293s Res.Df Df F Pr(>F) 293s 1 53 293s 2 51 2 0.42 0.66 293s Linear hypothesis test (F statistic of a Wald test) 293s 293s Hypothesis: 293s Consumption_corpProf + Investment_capitalLag = 0 293s Consumption_corpProfLag - PrivateWages_trend = 0 293s 293s Model 1: restricted model 293s Model 2: kleinModel 293s 293s Res.Df Df F Pr(>F) 293s 1 53 293s 2 51 2 0.37 0.69 293s Linear hypothesis test (Chi^2 statistic of a Wald test) 293s 293s Hypothesis: 293s Consumption_corpProf + Investment_capitalLag = 0 293s Consumption_corpProfLag - PrivateWages_trend = 0 293s 293s Model 1: restricted model 293s Model 2: kleinModel 293s 293s Res.Df Df Chisq Pr(>Chisq) 293s 1 53 293s 2 51 2 0.74 0.69 293s > logLik 293s 'log Lik.' -72.3 (df=13) 293s 'log Lik.' -77.9 (df=13) 293s compare log likelihood value with single-equation OLS 293s [1] TRUE 293s > 293s > # 2SLS 293s > summary 293s 293s systemfit results 293s method: 2SLS 293s 293s N DF SSR detRCov OLS-R2 McElroy-R2 293s system 63 51 61 0.288 0.969 0.992 293s 293s N DF SSR MSE RMSE R2 Adj R2 293s Consumption 21 17 21.9 1.290 1.136 0.977 0.973 293s Investment 21 17 29.0 1.709 1.307 0.885 0.865 293s PrivateWages 21 17 10.0 0.589 0.767 0.987 0.985 293s 293s The covariance matrix of the residuals 293s Consumption Investment PrivateWages 293s Consumption 1.044 0.438 -0.385 293s Investment 0.438 1.383 0.193 293s PrivateWages -0.385 0.193 0.476 293s 293s The correlations of the residuals 293s Consumption Investment PrivateWages 293s Consumption 1.000 0.364 -0.546 293s Investment 0.364 1.000 0.237 293s PrivateWages -0.546 0.237 1.000 293s 293s 293s 2SLS estimates for 'Consumption' (equation 1) 293s Model Formula: consump ~ corpProf + corpProfLag + wages 293s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 293s gnpLag 293s 293s Estimate Std. Error t value Pr(>|t|) 293s (Intercept) 16.5548 1.3208 12.53 5.2e-10 *** 293s corpProf 0.0173 0.1180 0.15 0.89 293s corpProfLag 0.2162 0.1073 2.02 0.06 . 293s wages 0.8102 0.0402 20.13 2.7e-13 *** 293s --- 293s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 293s 293s Residual standard error: 1.136 on 17 degrees of freedom 293s Number of observations: 21 Degrees of Freedom: 17 293s SSR: 21.925 MSE: 1.29 Root MSE: 1.136 293s Multiple R-Squared: 0.977 Adjusted R-Squared: 0.973 293s 293s 293s 2SLS estimates for 'Investment' (equation 2) 293s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 293s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 293s gnpLag 293s 293s Estimate Std. Error t value Pr(>|t|) 293s (Intercept) 20.2782 7.5427 2.69 0.01555 * 293s corpProf 0.1502 0.1732 0.87 0.39792 293s corpProfLag 0.6159 0.1628 3.78 0.00148 ** 293s capitalLag -0.1578 0.0361 -4.37 0.00042 *** 293s --- 293s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 293s 293s Residual standard error: 1.307 on 17 degrees of freedom 293s Number of observations: 21 Degrees of Freedom: 17 293s SSR: 29.047 MSE: 1.709 Root MSE: 1.307 293s Multiple R-Squared: 0.885 Adjusted R-Squared: 0.865 293s 293s 293s 2SLS estimates for 'PrivateWages' (equation 3) 293s Model Formula: privWage ~ gnp + gnpLag + trend 293s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 293s gnpLag 293s 293s Estimate Std. Error t value Pr(>|t|) 293s (Intercept) 1.5003 1.1478 1.31 0.20857 293s gnp 0.4389 0.0356 12.32 6.8e-10 *** 293s gnpLag 0.1467 0.0388 3.78 0.00150 ** 293s trend 0.1304 0.0291 4.47 0.00033 *** 293s --- 293s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 293s 293s Residual standard error: 0.767 on 17 degrees of freedom 293s Number of observations: 21 Degrees of Freedom: 17 293s SSR: 10.005 MSE: 0.589 Root MSE: 0.767 293s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 293s 293s > residuals 293s Consumption Investment PrivateWages 293s 1 NA NA NA 293s 2 -0.46263 -1.320 -1.2940 293s 3 -0.61635 0.257 0.2981 293s 4 -1.30423 0.860 1.1918 293s 5 -0.24588 -1.594 -0.1361 293s 6 0.22948 0.259 -0.4634 293s 7 0.88538 1.207 -0.4824 293s 8 1.44189 0.969 -0.7284 293s 9 1.34190 0.113 0.3387 293s 10 -0.39403 1.796 1.1965 293s 11 -0.62564 -0.953 -0.1552 293s 12 -1.06543 -0.807 0.5882 293s 13 -1.33021 -0.895 0.0955 293s 14 0.61059 1.306 0.4487 293s 15 -0.14208 -0.151 0.2822 293s 16 0.00315 0.142 0.0145 293s 17 2.00337 1.749 -0.8478 293s 18 -0.60552 -0.192 0.9950 293s 19 -0.24771 -3.291 -0.4734 293s 20 1.38510 0.285 -0.3766 293s 21 1.03204 -0.104 -1.0893 293s 22 -1.89319 0.363 0.5974 293s > fitted 293s Consumption Investment PrivateWages 293s 1 NA NA NA 293s 2 42.4 1.120 26.8 293s 3 45.6 1.643 29.0 293s 4 50.5 4.340 32.9 293s 5 50.8 4.594 34.0 293s 6 52.4 4.841 35.9 293s 7 54.2 4.393 37.9 293s 8 54.8 3.231 38.6 293s 9 56.0 2.887 38.9 293s 10 58.2 3.304 40.1 293s 11 55.6 1.953 38.1 293s 12 52.0 -2.593 33.9 293s 13 46.9 -5.305 28.9 293s 14 45.9 -6.406 28.1 293s 15 48.8 -2.849 30.3 293s 16 51.3 -1.442 33.2 293s 17 55.7 0.351 37.6 293s 18 59.3 2.192 40.0 293s 19 57.7 1.391 38.7 293s 20 60.2 1.015 42.0 293s 21 64.0 3.404 46.1 293s 22 71.6 4.537 52.7 293s > predict 293s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 293s 1 NA NA NA NA 293s 2 42.4 0.471 41.4 43.4 293s 3 45.6 0.577 44.4 46.8 293s 4 50.5 0.354 49.8 51.3 293s 5 50.8 0.405 50.0 51.7 293s 6 52.4 0.404 51.5 53.2 293s 7 54.2 0.359 53.5 55.0 293s 8 54.8 0.328 54.1 55.4 293s 9 56.0 0.368 55.2 56.7 293s 10 58.2 0.377 57.4 59.0 293s 11 55.6 0.728 54.1 57.2 293s 12 52.0 0.604 50.7 53.2 293s 13 46.9 0.765 45.3 48.5 293s 14 45.9 0.615 44.6 47.2 293s 15 48.8 0.374 48.1 49.6 293s 16 51.3 0.333 50.6 52.0 293s 17 55.7 0.409 54.8 56.6 293s 18 59.3 0.326 58.6 60.0 293s 19 57.7 0.414 56.9 58.6 293s 20 60.2 0.478 59.2 61.2 293s 21 64.0 0.446 63.0 64.9 293s 22 71.6 0.689 70.1 73.0 293s Investment.pred Investment.se.fit Investment.lwr Investment.upr 293s 1 NA NA NA NA 293s 2 1.120 0.865 -0.706 2.946 293s 3 1.643 0.594 0.390 2.895 293s 4 4.340 0.545 3.190 5.490 293s 5 4.594 0.443 3.660 5.527 293s 6 4.841 0.411 3.973 5.709 293s 7 4.393 0.399 3.550 5.235 293s 8 3.231 0.348 2.497 3.965 293s 9 2.887 0.542 1.744 4.030 293s 10 3.304 0.593 2.054 4.555 293s 11 1.953 0.855 0.148 3.757 293s 12 -2.593 0.679 -4.026 -1.160 293s 13 -5.305 0.876 -7.152 -3.457 293s 14 -6.406 0.916 -8.338 -4.473 293s 15 -2.849 0.435 -3.765 -1.932 293s 16 -1.442 0.376 -2.236 -0.649 293s 17 0.351 0.510 -0.724 1.426 293s 18 2.192 0.299 1.560 2.823 293s 19 1.391 0.464 0.411 2.371 293s 20 1.015 0.576 -0.201 2.230 293s 21 3.404 0.471 2.410 4.398 293s 22 4.537 0.675 3.114 5.961 293s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 293s 1 NA NA NA NA 293s 2 26.8 0.318 26.1 27.5 293s 3 29.0 0.330 28.3 29.7 293s 4 32.9 0.346 32.2 33.6 293s 5 34.0 0.242 33.5 34.5 293s 6 35.9 0.248 35.3 36.4 293s 7 37.9 0.244 37.4 38.4 293s 8 38.6 0.246 38.1 39.1 293s 9 38.9 0.235 38.4 39.4 293s 10 40.1 0.224 39.6 40.6 293s 11 38.1 0.350 37.3 38.8 293s 12 33.9 0.382 33.1 34.7 293s 13 28.9 0.454 27.9 29.9 293s 14 28.1 0.342 27.3 28.8 293s 15 30.3 0.335 29.6 31.0 293s 16 33.2 0.280 32.6 33.8 293s 17 37.6 0.291 37.0 38.3 293s 18 40.0 0.215 39.6 40.5 293s 19 38.7 0.356 37.9 39.4 293s 20 42.0 0.304 41.3 42.6 293s 21 46.1 0.306 45.4 46.7 293s 22 52.7 0.489 51.7 53.7 293s > model.frame 293s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 293s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 293s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 293s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 293s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 293s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 293s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 293s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 61.0 293s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 293s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 293s 10 57.8 21.7 21.1 45.3 5.1 211 41.3 67.0 64.5 293s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 293s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 293s 13 45.6 7.0 11.4 34.3 -6.2 213 29.0 44.3 53.4 293s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 293s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 293s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 293s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 293s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 293s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 293s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 293s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 293s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 293s trend 293s 1 -11 293s 2 -10 293s 3 -9 293s 4 -8 293s 5 -7 293s 6 -6 293s 7 -5 293s 8 -4 293s 9 -3 293s 10 -2 293s 11 -1 293s 12 0 293s 13 1 293s 14 2 293s 15 3 293s 16 4 293s 17 5 293s 18 6 293s 19 7 293s 20 8 293s 21 9 293s 22 10 293s > model.matrix 293s [1] TRUE 293s > nobs 293s [1] 63 293s > linearHypothesis 293s Linear hypothesis test (Theil's F test) 293s 293s Hypothesis: 293s Consumption_corpProf + Investment_capitalLag = 0 293s 293s Model 1: restricted model 293s Model 2: kleinModel 293s 293s Res.Df Df F Pr(>F) 293s 1 52 293s 2 51 1 1.08 0.3 293s Linear hypothesis test (F statistic of a Wald test) 293s 293s Hypothesis: 293s Consumption_corpProf + Investment_capitalLag = 0 293s 293s Model 1: restricted model 293s Model 2: kleinModel 293s 293s Res.Df Df F Pr(>F) 293s 1 52 293s 2 51 1 1.29 0.26 293s Linear hypothesis test (Chi^2 statistic of a Wald test) 293s 293s Hypothesis: 293s Consumption_corpProf + Investment_capitalLag = 0 293s 293s Model 1: restricted model 293s Model 2: kleinModel 293s 293s Res.Df Df Chisq Pr(>Chisq) 293s 1 52 293s 2 51 1 1.29 0.26 293s Linear hypothesis test (Theil's F test) 293s 293s Hypothesis: 293s Consumption_corpProf + Investment_capitalLag = 0 293s Consumption_corpProfLag - PrivateWages_trend = 0 293s 293s Model 1: restricted model 293s Model 2: kleinModel 293s 293s Res.Df Df F Pr(>F) 293s 1 53 293s 2 51 2 0.54 0.58 293s Linear hypothesis test (F statistic of a Wald test) 293s 293s Hypothesis: 293s Consumption_corpProf + Investment_capitalLag = 0 293s Consumption_corpProfLag - PrivateWages_trend = 0 293s 293s Model 1: restricted model 293s Model 2: kleinModel 293s 293s Res.Df Df F Pr(>F) 293s 1 53 293s 2 51 2 0.65 0.53 293s Linear hypothesis test (Chi^2 statistic of a Wald test) 293s 293s Hypothesis: 293s Consumption_corpProf + Investment_capitalLag = 0 293s Consumption_corpProfLag - PrivateWages_trend = 0 293s 293s Model 1: restricted model 293s Model 2: kleinModel 293s 293s Res.Df Df Chisq Pr(>Chisq) 293s 1 53 293s 2 51 2 1.3 0.52 293s > logLik 293s 'log Lik.' -76.3 (df=13) 293s 'log Lik.' -85.5 (df=13) 293s > 293s > # SUR 293s > summary 293s 293s systemfit results 293s method: SUR 293s 293s N DF SSR detRCov OLS-R2 McElroy-R2 293s system 63 51 46.5 0.158 0.977 0.993 293s 293s N DF SSR MSE RMSE R2 Adj R2 293s Consumption 21 17 18.1 1.065 1.032 0.981 0.977 293s Investment 21 17 17.6 1.036 1.018 0.930 0.918 293s PrivateWages 21 17 10.8 0.633 0.796 0.986 0.984 293s 293s The covariance matrix of the residuals used for estimation 293s Consumption Investment PrivateWages 293s Consumption 0.8514 0.0495 -0.381 293s Investment 0.0495 0.8249 0.121 293s PrivateWages -0.3808 0.1212 0.476 293s 293s The covariance matrix of the residuals 293s Consumption Investment PrivateWages 293s Consumption 0.8618 0.0766 -0.437 293s Investment 0.0766 0.8384 0.203 293s PrivateWages -0.4368 0.2027 0.513 293s 293s The correlations of the residuals 293s Consumption Investment PrivateWages 293s Consumption 1.0000 0.0901 -0.657 293s Investment 0.0901 1.0000 0.309 293s PrivateWages -0.6572 0.3092 1.000 293s 293s 293s SUR estimates for 'Consumption' (equation 1) 293s Model Formula: consump ~ corpProf + corpProfLag + wages 293s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 15.9805 1.1687 13.67 1.3e-10 *** 294s corpProf 0.2302 0.0767 3.00 0.008 ** 294s corpProfLag 0.0673 0.0769 0.87 0.394 294s wages 0.7962 0.0353 22.58 4.1e-14 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.032 on 17 degrees of freedom 294s Number of observations: 21 Degrees of Freedom: 17 294s SSR: 18.098 MSE: 1.065 Root MSE: 1.032 294s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.977 294s 294s 294s SUR estimates for 'Investment' (equation 2) 294s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 12.9293 4.8014 2.69 0.01540 * 294s corpProf 0.4429 0.0861 5.15 8.1e-05 *** 294s corpProfLag 0.3655 0.0894 4.09 0.00077 *** 294s capitalLag -0.1253 0.0235 -5.34 5.4e-05 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.018 on 17 degrees of freedom 294s Number of observations: 21 Degrees of Freedom: 17 294s SSR: 17.606 MSE: 1.036 Root MSE: 1.018 294s Multiple R-Squared: 0.93 Adjusted R-Squared: 0.918 294s 294s 294s SUR estimates for 'PrivateWages' (equation 3) 294s Model Formula: privWage ~ gnp + gnpLag + trend 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 1.6347 1.1173 1.46 0.16 294s gnp 0.4098 0.0273 15.04 3.0e-11 *** 294s gnpLag 0.1744 0.0312 5.59 3.2e-05 *** 294s trend 0.1558 0.0276 5.65 2.9e-05 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 0.796 on 17 degrees of freedom 294s Number of observations: 21 Degrees of Freedom: 17 294s SSR: 10.763 MSE: 0.633 Root MSE: 0.796 294s Multiple R-Squared: 0.986 Adjusted R-Squared: 0.984 294s 294s > residuals 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 -0.24064 -0.3522 -1.0960 294s 3 -1.34080 -0.1605 0.5818 294s 4 -1.61038 1.0687 1.5313 294s 5 -0.54147 -1.4707 -0.0220 294s 6 -0.04372 0.3299 -0.2587 294s 7 0.85234 1.4346 -0.3243 294s 8 1.30302 0.8306 -0.6674 294s 9 0.97574 -0.4918 0.3660 294s 10 -0.66060 1.2434 1.2682 294s 11 0.45069 0.2647 -0.3467 294s 12 -0.04295 0.0795 0.3057 294s 13 -0.06686 0.3369 -0.2602 294s 14 0.32177 0.4080 0.3434 294s 15 -0.00441 -0.1533 0.2628 294s 16 -0.01931 0.0158 -0.0216 294s 17 1.53656 1.0372 -0.7988 294s 18 -0.42317 0.0176 0.8550 294s 19 0.29041 -2.6364 -0.8217 294s 20 0.88685 -0.5822 -0.3869 294s 21 0.68839 -0.7015 -1.1838 294s 22 -2.31147 -0.5183 0.6742 294s > fitted 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 42.1 0.152 26.6 294s 3 46.3 2.060 28.7 294s 4 50.8 4.131 32.6 294s 5 51.1 4.471 33.9 294s 6 52.6 4.770 35.7 294s 7 54.2 4.165 37.7 294s 8 54.9 3.369 38.6 294s 9 56.3 3.492 38.8 294s 10 58.5 3.857 40.0 294s 11 54.5 0.735 38.2 294s 12 50.9 -3.479 34.2 294s 13 45.7 -6.537 29.3 294s 14 46.2 -5.508 28.2 294s 15 48.7 -2.847 30.3 294s 16 51.3 -1.316 33.2 294s 17 56.2 1.063 37.6 294s 18 59.1 1.982 40.1 294s 19 57.2 0.736 39.0 294s 20 60.7 1.882 42.0 294s 21 64.3 4.002 46.2 294s 22 72.0 5.418 52.6 294s > predict 294s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 294s 1 NA NA NA NA 294s 2 42.1 0.415 41.3 43.0 294s 3 46.3 0.449 45.4 47.2 294s 4 50.8 0.300 50.2 51.4 294s 5 51.1 0.348 50.4 51.8 294s 6 52.6 0.350 51.9 53.3 294s 7 54.2 0.317 53.6 54.9 294s 8 54.9 0.289 54.3 55.5 294s 9 56.3 0.309 55.7 56.9 294s 10 58.5 0.328 57.8 59.1 294s 11 54.5 0.516 53.5 55.6 294s 12 50.9 0.414 50.1 51.8 294s 13 45.7 0.544 44.6 46.8 294s 14 46.2 0.527 45.1 47.2 294s 15 48.7 0.332 48.0 49.4 294s 16 51.3 0.295 50.7 51.9 294s 17 56.2 0.319 55.5 56.8 294s 18 59.1 0.286 58.5 59.7 294s 19 57.2 0.323 56.6 57.9 294s 20 60.7 0.381 59.9 61.5 294s 21 64.3 0.381 63.5 65.1 294s 22 72.0 0.597 70.8 73.2 294s Investment.pred Investment.se.fit Investment.lwr Investment.upr 294s 1 NA NA NA NA 294s 2 0.152 0.536 -0.924 1.229 294s 3 2.060 0.446 1.166 2.955 294s 4 4.131 0.397 3.334 4.929 294s 5 4.471 0.329 3.809 5.132 294s 6 4.770 0.311 4.145 5.395 294s 7 4.165 0.294 3.575 4.756 294s 8 3.369 0.263 2.842 3.897 294s 9 3.492 0.347 2.796 4.188 294s 10 3.857 0.398 3.058 4.656 294s 11 0.735 0.539 -0.346 1.816 294s 12 -3.479 0.454 -4.390 -2.569 294s 13 -6.537 0.552 -7.646 -5.428 294s 14 -5.508 0.617 -6.747 -4.269 294s 15 -2.847 0.335 -3.519 -2.175 294s 16 -1.316 0.287 -1.892 -0.739 294s 17 1.063 0.311 0.439 1.686 294s 18 1.982 0.218 1.545 2.420 294s 19 0.736 0.279 0.176 1.296 294s 20 1.882 0.327 1.227 2.538 294s 21 4.002 0.297 3.405 4.598 294s 22 5.418 0.412 4.591 6.245 294s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 294s 1 NA NA NA NA 294s 2 26.6 0.313 26.0 27.2 294s 3 28.7 0.310 28.1 29.3 294s 4 32.6 0.305 32.0 33.2 294s 5 33.9 0.236 33.4 34.4 294s 6 35.7 0.233 35.2 36.1 294s 7 37.7 0.234 37.3 38.2 294s 8 38.6 0.239 38.1 39.0 294s 9 38.8 0.229 38.4 39.3 294s 10 40.0 0.219 39.6 40.5 294s 11 38.2 0.301 37.6 38.9 294s 12 34.2 0.308 33.6 34.8 294s 13 29.3 0.370 28.5 30.0 294s 14 28.2 0.332 27.5 28.8 294s 15 30.3 0.324 29.7 31.0 294s 16 33.2 0.271 32.7 33.8 294s 17 37.6 0.263 37.1 38.1 294s 18 40.1 0.211 39.7 40.6 294s 19 39.0 0.306 38.4 39.6 294s 20 42.0 0.280 41.4 42.5 294s 21 46.2 0.298 45.6 46.8 294s 22 52.6 0.445 51.7 53.5 294s > model.frame 294s [1] TRUE 294s > model.matrix 294s [1] TRUE 294s > nobs 294s [1] 63 294s > linearHypothesis 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 52 294s 2 51 1 1.44 0.24 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 52 294s 2 51 1 1.69 0.2 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 52 294s 2 51 1 1.69 0.19 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 53 294s 2 51 2 0.77 0.47 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 53 294s 2 51 2 0.91 0.41 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 53 294s 2 51 2 1.83 0.4 294s > logLik 294s 'log Lik.' -70 (df=18) 294s 'log Lik.' -79 (df=18) 294s > 294s > # 3SLS 294s > summary 294s 294s systemfit results 294s method: 3SLS 294s 294s N DF SSR detRCov OLS-R2 McElroy-R2 294s system 63 51 73.6 0.283 0.963 0.995 294s 294s N DF SSR MSE RMSE R2 Adj R2 294s Consumption 21 17 18.7 1.102 1.050 0.980 0.977 294s Investment 21 17 44.0 2.586 1.608 0.826 0.795 294s PrivateWages 21 17 10.9 0.642 0.801 0.986 0.984 294s 294s The covariance matrix of the residuals used for estimation 294s Consumption Investment PrivateWages 294s Consumption 1.044 0.438 -0.385 294s Investment 0.438 1.383 0.193 294s PrivateWages -0.385 0.193 0.476 294s 294s The covariance matrix of the residuals 294s Consumption Investment PrivateWages 294s Consumption 0.892 0.411 -0.394 294s Investment 0.411 2.093 0.403 294s PrivateWages -0.394 0.403 0.520 294s 294s The correlations of the residuals 294s Consumption Investment PrivateWages 294s Consumption 1.000 0.301 -0.578 294s Investment 0.301 1.000 0.386 294s PrivateWages -0.578 0.386 1.000 294s 294s 294s 3SLS estimates for 'Consumption' (equation 1) 294s Model Formula: consump ~ corpProf + corpProfLag + wages 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 16.4408 1.3045 12.60 4.7e-10 *** 294s corpProf 0.1249 0.1081 1.16 0.26 294s corpProfLag 0.1631 0.1004 1.62 0.12 294s wages 0.7901 0.0379 20.83 1.5e-13 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.05 on 17 degrees of freedom 294s Number of observations: 21 Degrees of Freedom: 17 294s SSR: 18.727 MSE: 1.102 Root MSE: 1.05 294s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.977 294s 294s 294s 3SLS estimates for 'Investment' (equation 2) 294s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 28.1778 6.7938 4.15 0.00067 *** 294s corpProf -0.0131 0.1619 -0.08 0.93655 294s corpProfLag 0.7557 0.1529 4.94 0.00012 *** 294s capitalLag -0.1948 0.0325 -5.99 1.5e-05 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.608 on 17 degrees of freedom 294s Number of observations: 21 Degrees of Freedom: 17 294s SSR: 43.954 MSE: 2.586 Root MSE: 1.608 294s Multiple R-Squared: 0.826 Adjusted R-Squared: 0.795 294s 294s 294s 3SLS estimates for 'PrivateWages' (equation 3) 294s Model Formula: privWage ~ gnp + gnpLag + trend 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 1.7972 1.1159 1.61 0.13 294s gnp 0.4005 0.0318 12.59 4.8e-10 *** 294s gnpLag 0.1813 0.0342 5.31 5.8e-05 *** 294s trend 0.1497 0.0279 5.36 5.2e-05 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 0.801 on 17 degrees of freedom 294s Number of observations: 21 Degrees of Freedom: 17 294s SSR: 10.921 MSE: 0.642 Root MSE: 0.801 294s Multiple R-Squared: 0.986 Adjusted R-Squared: 0.984 294s 294s > residuals 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 -0.4416 -2.1951 -1.20287 294s 3 -1.0150 0.1515 0.51834 294s 4 -1.5289 0.4406 1.50936 294s 5 -0.4985 -1.8667 -0.08743 294s 6 -0.0132 0.0713 -0.28089 294s 7 0.7759 1.0294 -0.33908 294s 8 1.3004 1.1011 -0.69282 294s 9 1.0993 0.5853 0.34494 294s 10 -0.5839 2.2952 1.27590 294s 11 -0.1917 -1.3443 -0.40414 294s 12 -0.5598 -0.9944 0.22151 294s 13 -0.6746 -1.3404 -0.36962 294s 14 0.5767 1.9316 0.31006 294s 15 -0.0211 -0.1217 0.27309 294s 16 0.0539 0.1847 0.00716 294s 17 1.8555 2.0937 -0.71866 294s 18 -0.4596 -0.3216 0.90582 294s 19 0.0613 -3.6314 -0.81881 294s 20 1.2602 0.7582 -0.26942 294s 21 0.9500 0.2428 -1.06125 294s 22 -1.9451 0.9302 0.87883 294s > fitted 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 42.3 1.99510 26.7 294s 3 46.0 1.74850 28.8 294s 4 50.7 4.75942 32.6 294s 5 51.1 4.86672 34.0 294s 6 52.6 5.02874 35.7 294s 7 54.3 4.57056 37.7 294s 8 54.9 3.09893 38.6 294s 9 56.2 2.41471 38.9 294s 10 58.4 2.80476 40.0 294s 11 55.2 2.34425 38.3 294s 12 51.5 -2.40558 34.3 294s 13 46.3 -4.85959 29.4 294s 14 45.9 -7.03164 28.2 294s 15 48.7 -2.87827 30.3 294s 16 51.2 -1.48466 33.2 294s 17 55.8 0.00629 37.5 294s 18 59.2 2.32164 40.1 294s 19 57.4 1.73138 39.0 294s 20 60.3 0.54175 41.9 294s 21 64.1 3.05716 46.1 294s 22 71.6 3.96979 52.4 294s > predict 294s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 294s 1 NA NA NA NA 294s 2 42.3 0.464 39.9 44.8 294s 3 46.0 0.541 43.5 48.5 294s 4 50.7 0.337 48.4 53.1 294s 5 51.1 0.385 48.7 53.5 294s 6 52.6 0.386 50.3 55.0 294s 7 54.3 0.349 52.0 56.7 294s 8 54.9 0.320 52.6 57.2 294s 9 56.2 0.355 53.9 58.5 294s 10 58.4 0.370 56.0 60.7 294s 11 55.2 0.682 52.6 57.8 294s 12 51.5 0.563 48.9 54.0 294s 13 46.3 0.719 43.6 49.0 294s 14 45.9 0.597 43.4 48.5 294s 15 48.7 0.370 46.4 51.1 294s 16 51.2 0.327 48.9 53.6 294s 17 55.8 0.391 53.5 58.2 294s 18 59.2 0.316 56.8 61.5 294s 19 57.4 0.389 55.1 59.8 294s 20 60.3 0.459 57.9 62.8 294s 21 64.1 0.438 61.7 66.4 294s 22 71.6 0.674 69.0 74.3 294s Investment.pred Investment.se.fit Investment.lwr Investment.upr 294s 1 NA NA NA NA 294s 2 1.99510 0.792 -1.787 5.777 294s 3 1.74850 0.585 -1.861 5.358 294s 4 4.75942 0.510 1.200 8.319 294s 5 4.86672 0.423 1.359 8.375 294s 6 5.02874 0.400 1.533 8.525 294s 7 4.57056 0.391 1.079 8.062 294s 8 3.09893 0.345 -0.371 6.568 294s 9 2.41471 0.511 -1.145 5.974 294s 10 2.80476 0.560 -0.788 6.397 294s 11 2.34425 0.839 -1.482 6.170 294s 12 -2.40558 0.673 -6.083 1.272 294s 13 -4.85959 0.862 -8.708 -1.011 294s 14 -7.03164 0.874 -10.893 -3.171 294s 15 -2.87827 0.433 -6.392 0.635 294s 16 -1.48466 0.375 -4.968 1.999 294s 17 0.00629 0.491 -3.541 3.554 294s 18 2.32164 0.294 -1.127 5.771 294s 19 1.73138 0.446 -1.789 5.252 294s 20 0.54175 0.547 -3.042 4.125 294s 21 3.05716 0.454 -0.468 6.582 294s 22 3.96979 0.642 0.317 7.623 294s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 294s 1 NA NA NA NA 294s 2 26.7 0.314 24.9 28.5 294s 3 28.8 0.318 27.0 30.6 294s 4 32.6 0.325 30.8 34.4 294s 5 34.0 0.235 32.2 35.7 294s 6 35.7 0.241 33.9 37.4 294s 7 37.7 0.238 36.0 39.5 294s 8 38.6 0.237 36.8 40.4 294s 9 38.9 0.227 37.1 40.6 294s 10 40.0 0.219 38.3 41.8 294s 11 38.3 0.317 36.5 40.1 294s 12 34.3 0.344 32.4 36.1 294s 13 29.4 0.419 27.5 31.3 294s 14 28.2 0.334 26.4 30.0 294s 15 30.3 0.320 28.5 32.1 294s 16 33.2 0.268 31.4 35.0 294s 17 37.5 0.269 35.7 39.3 294s 18 40.1 0.212 38.3 41.8 294s 19 39.0 0.331 37.2 40.8 294s 20 41.9 0.287 40.1 43.7 294s 21 46.1 0.301 44.3 47.9 294s 22 52.4 0.471 50.5 54.4 294s > model.frame 294s [1] TRUE 294s > model.matrix 294s [1] TRUE 294s > nobs 294s [1] 63 294s > linearHypothesis 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 52 294s 2 51 1 0.29 0.59 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 52 294s 2 51 1 0.39 0.54 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 52 294s 2 51 1 0.39 0.53 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 53 294s 2 51 2 0.3 0.74 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 53 294s 2 51 2 0.4 0.67 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 53 294s 2 51 2 0.8 0.67 294s > logLik 294s 'log Lik.' -76.1 (df=18) 294s 'log Lik.' -89.1 (df=18) 294s > 294s > # I3SLS 294s > summary 294s 294s systemfit results 294s method: iterated 3SLS 294s 294s convergence achieved after 20 iterations 294s 294s N DF SSR detRCov OLS-R2 McElroy-R2 294s system 63 51 128 0.509 0.936 0.996 294s 294s N DF SSR MSE RMSE R2 Adj R2 294s Consumption 21 17 19.2 1.130 1.063 0.980 0.976 294s Investment 21 17 95.7 5.627 2.372 0.621 0.554 294s PrivateWages 21 17 12.7 0.748 0.865 0.984 0.981 294s 294s The covariance matrix of the residuals used for estimation 294s Consumption Investment PrivateWages 294s Consumption 0.915 0.642 -0.435 294s Investment 0.642 4.555 0.734 294s PrivateWages -0.435 0.734 0.606 294s 294s The covariance matrix of the residuals 294s Consumption Investment PrivateWages 294s Consumption 0.915 0.642 -0.435 294s Investment 0.642 4.555 0.734 294s PrivateWages -0.435 0.734 0.606 294s 294s The correlations of the residuals 294s Consumption Investment PrivateWages 294s Consumption 1.000 0.314 -0.584 294s Investment 0.314 1.000 0.442 294s PrivateWages -0.584 0.442 1.000 294s 294s 294s 3SLS estimates for 'Consumption' (equation 1) 294s Model Formula: consump ~ corpProf + corpProfLag + wages 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 16.5590 1.2244 13.52 1.6e-10 *** 294s corpProf 0.1645 0.0962 1.71 0.105 294s corpProfLag 0.1766 0.0901 1.96 0.067 . 294s wages 0.7658 0.0348 22.03 6.1e-14 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.063 on 17 degrees of freedom 294s Number of observations: 21 Degrees of Freedom: 17 294s SSR: 19.213 MSE: 1.13 Root MSE: 1.063 294s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.976 294s 294s 294s 3SLS estimates for 'Investment' (equation 2) 294s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 42.8959 10.5937 4.05 0.00083 *** 294s corpProf -0.3565 0.2602 -1.37 0.18838 294s corpProfLag 1.0113 0.2488 4.07 0.00081 *** 294s capitalLag -0.2602 0.0509 -5.12 8.6e-05 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 2.372 on 17 degrees of freedom 294s Number of observations: 21 Degrees of Freedom: 17 294s SSR: 95.661 MSE: 5.627 Root MSE: 2.372 294s Multiple R-Squared: 0.621 Adjusted R-Squared: 0.554 294s 294s 294s 3SLS estimates for 'PrivateWages' (equation 3) 294s Model Formula: privWage ~ gnp + gnpLag + trend 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 2.6247 1.1956 2.20 0.042 * 294s gnp 0.3748 0.0311 12.05 9.4e-10 *** 294s gnpLag 0.1937 0.0324 5.98 1.5e-05 *** 294s trend 0.1679 0.0289 5.80 2.1e-05 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 0.865 on 17 degrees of freedom 294s Number of observations: 21 Degrees of Freedom: 17 294s SSR: 12.719 MSE: 0.748 Root MSE: 0.865 294s Multiple R-Squared: 0.984 Adjusted R-Squared: 0.981 294s 294s > residuals 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 -0.537 -3.95419 -1.2303 294s 3 -1.187 0.00151 0.5797 294s 4 -1.705 -0.22015 1.6794 294s 5 -0.734 -2.22753 -0.0260 294s 6 -0.251 -0.10866 -0.1362 294s 7 0.600 0.83218 -0.1837 294s 8 1.142 1.46624 -0.5825 294s 9 0.921 1.62030 0.4347 294s 10 -0.745 3.40013 1.4104 294s 11 -0.197 -2.15443 -0.4679 294s 12 -0.385 -1.62274 0.0106 294s 13 -0.390 -2.62869 -0.7363 294s 14 0.749 2.80517 0.0581 294s 15 0.112 -0.27710 0.1113 294s 16 0.170 0.13598 -0.1089 294s 17 1.925 2.76200 -0.6976 294s 18 -0.341 -0.53919 0.8651 294s 19 0.219 -4.32845 -1.0116 294s 20 1.383 1.71889 -0.2087 294s 21 1.028 1.06406 -0.9656 294s 22 -1.777 2.25466 1.2061 294s > fitted 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 42.4 3.754 26.7 294s 3 46.2 1.898 28.7 294s 4 50.9 5.420 32.4 294s 5 51.3 5.228 33.9 294s 6 52.9 5.209 35.5 294s 7 54.5 4.768 37.6 294s 8 55.1 2.734 38.5 294s 9 56.4 1.380 38.8 294s 10 58.5 1.700 39.9 294s 11 55.2 3.154 38.4 294s 12 51.3 -1.777 34.5 294s 13 46.0 -3.571 29.7 294s 14 45.8 -7.905 28.4 294s 15 48.6 -2.723 30.5 294s 16 51.1 -1.436 33.3 294s 17 55.8 -0.662 37.5 294s 18 59.0 2.539 40.1 294s 19 57.3 2.428 39.2 294s 20 60.2 -0.419 41.8 294s 21 64.0 2.236 46.0 294s 22 71.5 2.645 52.1 294s > predict 294s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 294s 1 NA NA NA NA 294s 2 42.4 0.434 41.6 43.3 294s 3 46.2 0.491 45.2 47.2 294s 4 50.9 0.309 50.3 51.5 294s 5 51.3 0.351 50.6 52.0 294s 6 52.9 0.352 52.1 53.6 294s 7 54.5 0.320 53.9 55.1 294s 8 55.1 0.293 54.5 55.6 294s 9 56.4 0.324 55.7 57.0 294s 10 58.5 0.340 57.9 59.2 294s 11 55.2 0.613 54.0 56.4 294s 12 51.3 0.506 50.3 52.3 294s 13 46.0 0.649 44.7 47.3 294s 14 45.8 0.546 44.7 46.8 294s 15 48.6 0.341 47.9 49.3 294s 16 51.1 0.301 50.5 51.7 294s 17 55.8 0.357 55.1 56.5 294s 18 59.0 0.293 58.5 59.6 294s 19 57.3 0.353 56.6 58.0 294s 20 60.2 0.421 59.4 61.1 294s 21 64.0 0.409 63.2 64.8 294s 22 71.5 0.630 70.2 72.7 294s Investment.pred Investment.se.fit Investment.lwr Investment.upr 294s 1 NA NA NA NA 294s 2 3.754 1.263 1.218 6.2906 294s 3 1.898 1.022 -0.153 3.9503 294s 4 5.420 0.853 3.709 7.1317 294s 5 5.228 0.727 3.767 6.6877 294s 6 5.209 0.703 3.797 6.6200 294s 7 4.768 0.688 3.387 6.1487 294s 8 2.734 0.615 1.499 3.9683 294s 9 1.380 0.852 -0.330 3.0893 294s 10 1.700 0.938 -0.184 3.5836 294s 11 3.154 1.437 0.269 6.0398 294s 12 -1.777 1.173 -4.133 0.5780 294s 13 -3.571 1.494 -6.570 -0.5725 294s 14 -7.905 1.479 -10.875 -4.9350 294s 15 -2.723 0.778 -4.285 -1.1613 294s 16 -1.436 0.672 -2.784 -0.0875 294s 17 -0.662 0.832 -2.333 1.0088 294s 18 2.539 0.522 1.491 3.5875 294s 19 2.428 0.753 0.918 3.9392 294s 20 -0.419 0.907 -2.240 1.4019 294s 21 2.236 0.775 0.679 3.7928 294s 22 2.645 1.076 0.486 4.8047 294s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 294s 1 NA NA NA NA 294s 2 26.7 0.340 26.0 27.4 294s 3 28.7 0.339 28.0 29.4 294s 4 32.4 0.340 31.7 33.1 294s 5 33.9 0.250 33.4 34.4 294s 6 35.5 0.258 35.0 36.1 294s 7 37.6 0.256 37.1 38.1 294s 8 38.5 0.252 38.0 39.0 294s 9 38.8 0.241 38.3 39.2 294s 10 39.9 0.239 39.4 40.4 294s 11 38.4 0.314 37.7 39.0 294s 12 34.5 0.342 33.8 35.2 294s 13 29.7 0.430 28.9 30.6 294s 14 28.4 0.361 27.7 29.2 294s 15 30.5 0.336 29.8 31.2 294s 16 33.3 0.281 32.7 33.9 294s 17 37.5 0.270 37.0 38.0 294s 18 40.1 0.231 39.7 40.6 294s 19 39.2 0.343 38.5 39.9 294s 20 41.8 0.294 41.2 42.4 294s 21 46.0 0.326 45.3 46.6 294s 22 52.1 0.501 51.1 53.1 294s > model.frame 294s [1] TRUE 294s > model.matrix 294s [1] TRUE 294s > nobs 294s [1] 63 294s > linearHypothesis 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 52 294s 2 51 1 0.59 0.45 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 52 294s 2 51 1 0.73 0.4 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 52 294s 2 51 1 0.73 0.39 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 53 294s 2 51 2 0.72 0.49 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 53 294s 2 51 2 0.88 0.42 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 53 294s 2 51 2 1.77 0.41 294s > logLik 294s 'log Lik.' -82.3 (df=18) 294s 'log Lik.' -99.1 (df=18) 294s > 294s > # OLS 294s Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 294s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 294s > summary 294s 294s systemfit results 294s method: OLS 294s 294s N DF SSR detRCov OLS-R2 McElroy-R2 294s system 62 50 44.9 0.372 0.977 0.991 294s 294s N DF SSR MSE RMSE R2 Adj R2 294s Consumption 21 17 17.88 1.052 1.03 0.981 0.978 294s Investment 21 17 17.32 1.019 1.01 0.931 0.919 294s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 294s 294s The covariance matrix of the residuals 294s Consumption Investment PrivateWages 294s Consumption 1.0703 -0.0161 -0.463 294s Investment -0.0161 0.9435 0.199 294s PrivateWages -0.4633 0.1993 0.609 294s 294s The correlations of the residuals 294s Consumption Investment PrivateWages 294s Consumption 1.0000 -0.0201 -0.575 294s Investment -0.0201 1.0000 0.264 294s PrivateWages -0.5747 0.2639 1.000 294s 294s 294s OLS estimates for 'Consumption' (equation 1) 294s Model Formula: consump ~ corpProf + corpProfLag + wages 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 16.2366 1.3141 12.36 6.4e-10 *** 294s corpProf 0.1929 0.0920 2.10 0.051 . 294s corpProfLag 0.0899 0.0914 0.98 0.339 294s wages 0.7962 0.0403 19.76 3.6e-13 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.026 on 17 degrees of freedom 294s Number of observations: 21 Degrees of Freedom: 17 294s SSR: 17.879 MSE: 1.052 Root MSE: 1.026 294s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.978 294s 294s 294s OLS estimates for 'Investment' (equation 2) 294s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 10.1258 5.2592 1.93 0.07108 . 294s corpProf 0.4796 0.0934 5.13 8.3e-05 *** 294s corpProfLag 0.3330 0.0971 3.43 0.00318 ** 294s capitalLag -0.1118 0.0257 -4.35 0.00044 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.009 on 17 degrees of freedom 294s Number of observations: 21 Degrees of Freedom: 17 294s SSR: 17.323 MSE: 1.019 Root MSE: 1.009 294s Multiple R-Squared: 0.931 Adjusted R-Squared: 0.919 294s 294s 294s OLS estimates for 'PrivateWages' (equation 3) 294s Model Formula: privWage ~ gnp + gnpLag + trend 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 1.3550 1.3093 1.03 0.3161 294s gnp 0.4417 0.0331 13.33 4.4e-10 *** 294s gnpLag 0.1466 0.0381 3.85 0.0014 ** 294s trend 0.1244 0.0336 3.70 0.0020 ** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 0.78 on 16 degrees of freedom 294s Number of observations: 20 Degrees of Freedom: 16 294s SSR: 9.739 MSE: 0.609 Root MSE: 0.78 294s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 294s 294s compare coef with single-equation OLS 294s [1] TRUE 294s > residuals 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 -0.32389 -0.0668 -1.3389 294s 3 -1.25001 -0.0476 0.2462 294s 4 -1.56574 1.2467 1.1255 294s 5 -0.49350 -1.3512 -0.1959 294s 6 0.00761 0.4154 -0.5284 294s 7 0.86910 1.4923 NA 294s 8 1.33848 0.7889 -0.7909 294s 9 1.05498 -0.6317 0.2819 294s 10 -0.58856 1.0830 1.1384 294s 11 0.28231 0.2791 -0.1904 294s 12 -0.22965 0.0369 0.5813 294s 13 -0.32213 0.3659 0.1206 294s 14 0.32228 0.2237 0.4773 294s 15 -0.05801 -0.1728 0.3035 294s 16 -0.03466 0.0101 0.0284 294s 17 1.61650 0.9719 -0.8517 294s 18 -0.43597 0.0516 0.9908 294s 19 0.21005 -2.5656 -0.4597 294s 20 0.98920 -0.6866 -0.3819 294s 21 0.78508 -0.7807 -1.1062 294s 22 -2.17345 -0.6623 0.5501 294s > fitted 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 42.2 -0.133 26.8 294s 3 46.3 1.948 29.1 294s 4 50.8 3.953 33.0 294s 5 51.1 4.351 34.1 294s 6 52.6 4.685 35.9 294s 7 54.2 4.108 NA 294s 8 54.9 3.411 38.7 294s 9 56.2 3.632 38.9 294s 10 58.4 4.017 40.2 294s 11 54.7 0.721 38.1 294s 12 51.1 -3.437 33.9 294s 13 45.9 -6.566 28.9 294s 14 46.2 -5.324 28.0 294s 15 48.8 -2.827 30.3 294s 16 51.3 -1.310 33.2 294s 17 56.1 1.128 37.7 294s 18 59.1 1.948 40.0 294s 19 57.3 0.666 38.7 294s 20 60.6 1.987 42.0 294s 21 64.2 4.081 46.1 294s 22 71.9 5.562 52.7 294s > predict 294s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 294s 1 NA NA NA NA 294s 2 42.2 0.466 40.0 44.5 294s 3 46.3 0.523 43.9 48.6 294s 4 50.8 0.344 48.6 52.9 294s 5 51.1 0.399 48.9 53.3 294s 6 52.6 0.401 50.4 54.8 294s 7 54.2 0.363 52.0 56.4 294s 8 54.9 0.330 52.7 57.0 294s 9 56.2 0.354 54.1 58.4 294s 10 58.4 0.373 56.2 60.6 294s 11 54.7 0.612 52.3 57.1 294s 12 51.1 0.489 48.8 53.4 294s 13 45.9 0.634 43.5 48.3 294s 14 46.2 0.608 43.8 48.6 294s 15 48.8 0.378 46.6 51.0 294s 16 51.3 0.336 49.2 53.5 294s 17 56.1 0.369 53.9 58.3 294s 18 59.1 0.324 57.0 61.3 294s 19 57.3 0.375 55.1 59.5 294s 20 60.6 0.437 58.4 62.9 294s 21 64.2 0.429 62.0 66.4 294s 22 71.9 0.672 69.4 74.3 294s Investment.pred Investment.se.fit Investment.lwr Investment.upr 294s 1 NA NA NA NA 294s 2 -0.133 0.584 -2.476 2.209 294s 3 1.948 0.480 -0.297 4.193 294s 4 3.953 0.432 1.748 6.159 294s 5 4.351 0.357 2.201 6.502 294s 6 4.685 0.336 2.548 6.821 294s 7 4.108 0.316 1.983 6.232 294s 8 3.411 0.281 1.306 5.516 294s 9 3.632 0.374 1.469 5.794 294s 10 4.017 0.430 1.813 6.221 294s 11 0.721 0.579 -1.616 3.058 294s 12 -3.437 0.488 -5.688 -1.185 294s 13 -6.566 0.592 -8.917 -4.215 294s 14 -5.324 0.667 -7.754 -2.893 294s 15 -2.827 0.359 -4.979 -0.675 294s 16 -1.310 0.308 -3.430 0.810 294s 17 1.128 0.334 -1.008 3.264 294s 18 1.948 0.234 -0.133 4.030 294s 19 0.666 0.300 -1.450 2.781 294s 20 1.987 0.353 -0.161 4.134 294s 21 4.081 0.319 1.954 6.207 294s 22 5.562 0.444 3.348 7.777 294s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 294s 1 NA NA NA NA 294s 2 26.8 0.366 25.1 28.6 294s 3 29.1 0.369 27.3 30.8 294s 4 33.0 0.372 31.2 34.7 294s 5 34.1 0.288 32.4 35.8 294s 6 35.9 0.287 34.3 37.6 294s 7 NA NA NA NA 294s 8 38.7 0.293 37.0 40.4 294s 9 38.9 0.279 37.3 40.6 294s 10 40.2 0.266 38.5 41.8 294s 11 38.1 0.365 36.4 39.8 294s 12 33.9 0.369 32.2 35.7 294s 13 28.9 0.438 27.1 30.7 294s 14 28.0 0.385 26.3 29.8 294s 15 30.3 0.379 28.6 32.0 294s 16 33.2 0.316 31.5 34.9 294s 17 37.7 0.310 36.0 39.3 294s 18 40.0 0.243 38.4 41.7 294s 19 38.7 0.363 36.9 40.4 294s 20 42.0 0.326 40.3 43.7 294s 21 46.1 0.341 44.4 47.8 294s 22 52.7 0.514 50.9 54.6 294s > model.frame 294s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 294s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 294s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 294s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 294s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 294s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 294s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 294s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 294s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 294s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 294s 10 57.8 21.7 21.1 45.3 5.1 211 41.3 67.0 64.5 294s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 294s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 294s 13 45.6 7.0 11.4 34.3 -6.2 213 29.0 44.3 53.4 294s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 294s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 294s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 294s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 294s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 294s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 294s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 294s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 294s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 294s trend 294s 1 -11 294s 2 -10 294s 3 -9 294s 4 -8 294s 5 -7 294s 6 -6 294s 7 -5 294s 8 -4 294s 9 -3 294s 10 -2 294s 11 -1 294s 12 0 294s 13 1 294s 14 2 294s 15 3 294s 16 4 294s 17 5 294s 18 6 294s 19 7 294s 20 8 294s 21 9 294s 22 10 294s > model.matrix 294s Consumption_(Intercept) Consumption_corpProf 294s Consumption_2 1 12.4 294s Consumption_3 1 16.9 294s Consumption_4 1 18.4 294s Consumption_5 1 19.4 294s Consumption_6 1 20.1 294s Consumption_7 1 19.6 294s Consumption_8 1 19.8 294s Consumption_9 1 21.1 294s Consumption_10 1 21.7 294s Consumption_11 1 15.6 294s Consumption_12 1 11.4 294s Consumption_13 1 7.0 294s Consumption_14 1 11.2 294s Consumption_15 1 12.3 294s Consumption_16 1 14.0 294s Consumption_17 1 17.6 294s Consumption_18 1 17.3 294s Consumption_19 1 15.3 294s Consumption_20 1 19.0 294s Consumption_21 1 21.1 294s Consumption_22 1 23.5 294s Investment_2 0 0.0 294s Investment_3 0 0.0 294s Investment_4 0 0.0 294s Investment_5 0 0.0 294s Investment_6 0 0.0 294s Investment_7 0 0.0 294s Investment_8 0 0.0 294s Investment_9 0 0.0 294s Investment_10 0 0.0 294s Investment_11 0 0.0 294s Investment_12 0 0.0 294s Investment_13 0 0.0 294s Investment_14 0 0.0 294s Investment_15 0 0.0 294s Investment_16 0 0.0 294s Investment_17 0 0.0 294s Investment_18 0 0.0 294s Investment_19 0 0.0 294s Investment_20 0 0.0 294s Investment_21 0 0.0 294s Investment_22 0 0.0 294s PrivateWages_2 0 0.0 294s PrivateWages_3 0 0.0 294s PrivateWages_4 0 0.0 294s PrivateWages_5 0 0.0 294s PrivateWages_6 0 0.0 294s PrivateWages_8 0 0.0 294s PrivateWages_9 0 0.0 294s PrivateWages_10 0 0.0 294s PrivateWages_11 0 0.0 294s PrivateWages_12 0 0.0 294s PrivateWages_13 0 0.0 294s PrivateWages_14 0 0.0 294s PrivateWages_15 0 0.0 294s PrivateWages_16 0 0.0 294s PrivateWages_17 0 0.0 294s PrivateWages_18 0 0.0 294s PrivateWages_19 0 0.0 294s PrivateWages_20 0 0.0 294s PrivateWages_21 0 0.0 294s PrivateWages_22 0 0.0 294s Consumption_corpProfLag Consumption_wages 294s Consumption_2 12.7 28.2 294s Consumption_3 12.4 32.2 294s Consumption_4 16.9 37.0 294s Consumption_5 18.4 37.0 294s Consumption_6 19.4 38.6 294s Consumption_7 20.1 40.7 294s Consumption_8 19.6 41.5 294s Consumption_9 19.8 42.9 294s Consumption_10 21.1 45.3 294s Consumption_11 21.7 42.1 294s Consumption_12 15.6 39.3 294s Consumption_13 11.4 34.3 294s Consumption_14 7.0 34.1 294s Consumption_15 11.2 36.6 294s Consumption_16 12.3 39.3 294s Consumption_17 14.0 44.2 294s Consumption_18 17.6 47.7 294s Consumption_19 17.3 45.9 294s Consumption_20 15.3 49.4 294s Consumption_21 19.0 53.0 294s Consumption_22 21.1 61.8 294s Investment_2 0.0 0.0 294s Investment_3 0.0 0.0 294s Investment_4 0.0 0.0 294s Investment_5 0.0 0.0 294s Investment_6 0.0 0.0 294s Investment_7 0.0 0.0 294s Investment_8 0.0 0.0 294s Investment_9 0.0 0.0 294s Investment_10 0.0 0.0 294s Investment_11 0.0 0.0 294s Investment_12 0.0 0.0 294s Investment_13 0.0 0.0 294s Investment_14 0.0 0.0 294s Investment_15 0.0 0.0 294s Investment_16 0.0 0.0 294s Investment_17 0.0 0.0 294s Investment_18 0.0 0.0 294s Investment_19 0.0 0.0 294s Investment_20 0.0 0.0 294s Investment_21 0.0 0.0 294s Investment_22 0.0 0.0 294s PrivateWages_2 0.0 0.0 294s PrivateWages_3 0.0 0.0 294s PrivateWages_4 0.0 0.0 294s PrivateWages_5 0.0 0.0 294s PrivateWages_6 0.0 0.0 294s PrivateWages_8 0.0 0.0 294s PrivateWages_9 0.0 0.0 294s PrivateWages_10 0.0 0.0 294s PrivateWages_11 0.0 0.0 294s PrivateWages_12 0.0 0.0 294s PrivateWages_13 0.0 0.0 294s PrivateWages_14 0.0 0.0 294s PrivateWages_15 0.0 0.0 294s PrivateWages_16 0.0 0.0 294s PrivateWages_17 0.0 0.0 294s PrivateWages_18 0.0 0.0 294s PrivateWages_19 0.0 0.0 294s PrivateWages_20 0.0 0.0 294s PrivateWages_21 0.0 0.0 294s PrivateWages_22 0.0 0.0 294s Investment_(Intercept) Investment_corpProf 294s Consumption_2 0 0.0 294s Consumption_3 0 0.0 294s Consumption_4 0 0.0 294s Consumption_5 0 0.0 294s Consumption_6 0 0.0 294s Consumption_7 0 0.0 294s Consumption_8 0 0.0 294s Consumption_9 0 0.0 294s Consumption_10 0 0.0 294s Consumption_11 0 0.0 294s Consumption_12 0 0.0 294s Consumption_13 0 0.0 294s Consumption_14 0 0.0 294s Consumption_15 0 0.0 294s Consumption_16 0 0.0 294s Consumption_17 0 0.0 294s Consumption_18 0 0.0 294s Consumption_19 0 0.0 294s Consumption_20 0 0.0 294s Consumption_21 0 0.0 294s Consumption_22 0 0.0 294s Investment_2 1 12.4 294s Investment_3 1 16.9 294s Investment_4 1 18.4 294s Investment_5 1 19.4 294s Investment_6 1 20.1 294s Investment_7 1 19.6 294s Investment_8 1 19.8 294s Investment_9 1 21.1 294s Investment_10 1 21.7 294s Investment_11 1 15.6 294s Investment_12 1 11.4 294s Investment_13 1 7.0 294s Investment_14 1 11.2 294s Investment_15 1 12.3 294s Investment_16 1 14.0 294s Investment_17 1 17.6 294s Investment_18 1 17.3 294s Investment_19 1 15.3 294s Investment_20 1 19.0 294s Investment_21 1 21.1 294s Investment_22 1 23.5 294s PrivateWages_2 0 0.0 294s PrivateWages_3 0 0.0 294s PrivateWages_4 0 0.0 294s PrivateWages_5 0 0.0 294s PrivateWages_6 0 0.0 294s PrivateWages_8 0 0.0 294s PrivateWages_9 0 0.0 294s PrivateWages_10 0 0.0 294s PrivateWages_11 0 0.0 294s PrivateWages_12 0 0.0 294s PrivateWages_13 0 0.0 294s PrivateWages_14 0 0.0 294s PrivateWages_15 0 0.0 294s PrivateWages_16 0 0.0 294s PrivateWages_17 0 0.0 294s PrivateWages_18 0 0.0 294s PrivateWages_19 0 0.0 294s PrivateWages_20 0 0.0 294s PrivateWages_21 0 0.0 294s PrivateWages_22 0 0.0 294s Investment_corpProfLag Investment_capitalLag 294s Consumption_2 0.0 0 294s Consumption_3 0.0 0 294s Consumption_4 0.0 0 294s Consumption_5 0.0 0 294s Consumption_6 0.0 0 294s Consumption_7 0.0 0 294s Consumption_8 0.0 0 294s Consumption_9 0.0 0 294s Consumption_10 0.0 0 294s Consumption_11 0.0 0 294s Consumption_12 0.0 0 294s Consumption_13 0.0 0 294s Consumption_14 0.0 0 294s Consumption_15 0.0 0 294s Consumption_16 0.0 0 294s Consumption_17 0.0 0 294s Consumption_18 0.0 0 294s Consumption_19 0.0 0 294s Consumption_20 0.0 0 294s Consumption_21 0.0 0 294s Consumption_22 0.0 0 294s Investment_2 12.7 183 294s Investment_3 12.4 183 294s Investment_4 16.9 184 294s Investment_5 18.4 190 294s Investment_6 19.4 193 294s Investment_7 20.1 198 294s Investment_8 19.6 203 294s Investment_9 19.8 208 294s Investment_10 21.1 211 294s Investment_11 21.7 216 294s Investment_12 15.6 217 294s Investment_13 11.4 213 294s Investment_14 7.0 207 294s Investment_15 11.2 202 294s Investment_16 12.3 199 294s Investment_17 14.0 198 294s Investment_18 17.6 200 294s Investment_19 17.3 202 294s Investment_20 15.3 200 294s Investment_21 19.0 201 294s Investment_22 21.1 204 294s PrivateWages_2 0.0 0 294s PrivateWages_3 0.0 0 294s PrivateWages_4 0.0 0 294s PrivateWages_5 0.0 0 294s PrivateWages_6 0.0 0 294s PrivateWages_8 0.0 0 294s PrivateWages_9 0.0 0 294s PrivateWages_10 0.0 0 294s PrivateWages_11 0.0 0 294s PrivateWages_12 0.0 0 294s PrivateWages_13 0.0 0 294s PrivateWages_14 0.0 0 294s PrivateWages_15 0.0 0 294s PrivateWages_16 0.0 0 294s PrivateWages_17 0.0 0 294s PrivateWages_18 0.0 0 294s PrivateWages_19 0.0 0 294s PrivateWages_20 0.0 0 294s PrivateWages_21 0.0 0 294s PrivateWages_22 0.0 0 294s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 294s Consumption_2 0 0.0 0.0 294s Consumption_3 0 0.0 0.0 294s Consumption_4 0 0.0 0.0 294s Consumption_5 0 0.0 0.0 294s Consumption_6 0 0.0 0.0 294s Consumption_7 0 0.0 0.0 294s Consumption_8 0 0.0 0.0 294s Consumption_9 0 0.0 0.0 294s Consumption_10 0 0.0 0.0 294s Consumption_11 0 0.0 0.0 294s Consumption_12 0 0.0 0.0 294s Consumption_13 0 0.0 0.0 294s Consumption_14 0 0.0 0.0 294s Consumption_15 0 0.0 0.0 294s Consumption_16 0 0.0 0.0 294s Consumption_17 0 0.0 0.0 294s Consumption_18 0 0.0 0.0 294s Consumption_19 0 0.0 0.0 294s Consumption_20 0 0.0 0.0 294s Consumption_21 0 0.0 0.0 294s Consumption_22 0 0.0 0.0 294s Investment_2 0 0.0 0.0 294s Investment_3 0 0.0 0.0 294s Investment_4 0 0.0 0.0 294s Investment_5 0 0.0 0.0 294s Investment_6 0 0.0 0.0 294s Investment_7 0 0.0 0.0 294s Investment_8 0 0.0 0.0 294s Investment_9 0 0.0 0.0 294s Investment_10 0 0.0 0.0 294s Investment_11 0 0.0 0.0 294s Investment_12 0 0.0 0.0 294s Investment_13 0 0.0 0.0 294s Investment_14 0 0.0 0.0 294s Investment_15 0 0.0 0.0 294s Investment_16 0 0.0 0.0 294s Investment_17 0 0.0 0.0 294s Investment_18 0 0.0 0.0 294s Investment_19 0 0.0 0.0 294s Investment_20 0 0.0 0.0 294s Investment_21 0 0.0 0.0 294s Investment_22 0 0.0 0.0 294s PrivateWages_2 1 45.6 44.9 294s PrivateWages_3 1 50.1 45.6 294s PrivateWages_4 1 57.2 50.1 294s PrivateWages_5 1 57.1 57.2 294s PrivateWages_6 1 61.0 57.1 294s PrivateWages_8 1 64.4 64.0 294s PrivateWages_9 1 64.5 64.4 294s PrivateWages_10 1 67.0 64.5 294s PrivateWages_11 1 61.2 67.0 294s PrivateWages_12 1 53.4 61.2 294s PrivateWages_13 1 44.3 53.4 294s PrivateWages_14 1 45.1 44.3 294s PrivateWages_15 1 49.7 45.1 294s PrivateWages_16 1 54.4 49.7 294s PrivateWages_17 1 62.7 54.4 294s PrivateWages_18 1 65.0 62.7 294s PrivateWages_19 1 60.9 65.0 294s PrivateWages_20 1 69.5 60.9 294s PrivateWages_21 1 75.7 69.5 294s PrivateWages_22 1 88.4 75.7 294s PrivateWages_trend 294s Consumption_2 0 294s Consumption_3 0 294s Consumption_4 0 294s Consumption_5 0 294s Consumption_6 0 294s Consumption_7 0 294s Consumption_8 0 294s Consumption_9 0 294s Consumption_10 0 294s Consumption_11 0 294s Consumption_12 0 294s Consumption_13 0 294s Consumption_14 0 294s Consumption_15 0 294s Consumption_16 0 294s Consumption_17 0 294s Consumption_18 0 294s Consumption_19 0 294s Consumption_20 0 294s Consumption_21 0 294s Consumption_22 0 294s Investment_2 0 294s Investment_3 0 294s Investment_4 0 294s Investment_5 0 294s Investment_6 0 294s Investment_7 0 294s Investment_8 0 294s Investment_9 0 294s Investment_10 0 294s Investment_11 0 294s Investment_12 0 294s Investment_13 0 294s Investment_14 0 294s Investment_15 0 294s Investment_16 0 294s Investment_17 0 294s Investment_18 0 294s Investment_19 0 294s Investment_20 0 294s Investment_21 0 294s Investment_22 0 294s PrivateWages_2 -10 294s PrivateWages_3 -9 294s PrivateWages_4 -8 294s PrivateWages_5 -7 294s PrivateWages_6 -6 294s PrivateWages_8 -4 294s PrivateWages_9 -3 294s PrivateWages_10 -2 294s PrivateWages_11 -1 294s PrivateWages_12 0 294s PrivateWages_13 1 294s PrivateWages_14 2 294s PrivateWages_15 3 294s PrivateWages_16 4 294s PrivateWages_17 5 294s PrivateWages_18 6 294s PrivateWages_19 7 294s PrivateWages_20 8 294s PrivateWages_21 9 294s PrivateWages_22 10 294s > nobs 294s [1] 62 294s > linearHypothesis 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 51 294s 2 50 1 0.8 0.37 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 51 294s 2 50 1 0.72 0.4 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 51 294s 2 50 1 0.72 0.4 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 52 294s 2 50 2 0.42 0.66 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 52 294s 2 50 2 0.37 0.69 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 52 294s 2 50 2 0.75 0.69 294s > logLik 294s 'log Lik.' -71.9 (df=13) 294s 'log Lik.' -77.1 (df=13) 294s compare log likelihood value with single-equation OLS 294s [1] "Mean relative difference: 0.000555" 294s > 294s > # 2SLS 294s > summary 294s 294s systemfit results 294s method: 2SLS 294s 294s N DF SSR detRCov OLS-R2 McElroy-R2 294s system 60 48 53.4 0.274 0.973 0.992 294s 294s N DF SSR MSE RMSE R2 Adj R2 294s Consumption 20 16 20.67 1.292 1.14 0.978 0.974 294s Investment 20 16 23.02 1.438 1.20 0.901 0.883 294s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 294s 294s The covariance matrix of the residuals 294s Consumption Investment PrivateWages 294s Consumption 1.034 0.309 -0.383 294s Investment 0.309 1.151 0.202 294s PrivateWages -0.383 0.202 0.487 294s 294s The correlations of the residuals 294s Consumption Investment PrivateWages 294s Consumption 1.000 0.284 -0.540 294s Investment 0.284 1.000 0.269 294s PrivateWages -0.540 0.269 1.000 294s 294s 294s 2SLS estimates for 'Consumption' (equation 1) 294s Model Formula: consump ~ corpProf + corpProfLag + wages 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 16.5093 1.3121 12.58 1.0e-09 *** 294s corpProf 0.0219 0.1159 0.19 0.85 294s corpProfLag 0.1931 0.1071 1.80 0.09 . 294s wages 0.8174 0.0408 20.05 9.2e-13 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.137 on 16 degrees of freedom 294s Number of observations: 20 Degrees of Freedom: 16 294s SSR: 20.671 MSE: 1.292 Root MSE: 1.137 294s Multiple R-Squared: 0.978 Adjusted R-Squared: 0.974 294s 294s 294s 2SLS estimates for 'Investment' (equation 2) 294s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 17.843 6.850 2.60 0.01915 * 294s corpProf 0.217 0.155 1.40 0.18106 294s corpProfLag 0.542 0.148 3.65 0.00216 ** 294s capitalLag -0.145 0.033 -4.41 0.00044 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.199 on 16 degrees of freedom 294s Number of observations: 20 Degrees of Freedom: 16 294s SSR: 23.016 MSE: 1.438 Root MSE: 1.199 294s Multiple R-Squared: 0.901 Adjusted R-Squared: 0.883 294s 294s 294s 2SLS estimates for 'PrivateWages' (equation 3) 294s Model Formula: privWage ~ gnp + gnpLag + trend 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 1.3431 1.1772 1.14 0.27070 294s gnp 0.4438 0.0358 12.39 1.3e-09 *** 294s gnpLag 0.1447 0.0389 3.72 0.00185 ** 294s trend 0.1238 0.0306 4.05 0.00093 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 0.78 on 16 degrees of freedom 294s Number of observations: 20 Degrees of Freedom: 16 294s SSR: 9.741 MSE: 0.609 Root MSE: 0.78 294s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 294s 294s > residuals 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 -0.383 -1.0104 -1.3401 294s 3 -0.593 0.2478 0.2378 294s 4 -1.219 1.0621 1.1117 294s 5 -0.130 -1.4104 -0.1954 294s 6 0.354 0.4328 -0.5355 294s 7 NA NA NA 294s 8 1.551 1.0463 -0.7908 294s 9 1.440 0.0674 0.2831 294s 10 -0.286 1.7698 1.1353 294s 11 -0.453 -0.5912 -0.1765 294s 12 -0.994 -0.6318 0.6007 294s 13 -1.300 -0.6983 0.1443 294s 14 0.521 0.9724 0.4826 294s 15 -0.157 -0.1827 0.3016 294s 16 -0.014 0.1167 0.0261 294s 17 1.974 1.6266 -0.8614 294s 18 -0.576 -0.0525 0.9927 294s 19 -0.203 -3.0656 -0.4446 294s 20 1.342 0.1393 -0.3914 294s 21 1.039 -0.1305 -1.1115 294s 22 -1.912 0.2922 0.5312 294s > fitted 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 42.3 0.810 26.8 294s 3 45.6 1.652 29.1 294s 4 50.4 4.138 33.0 294s 5 50.7 4.410 34.1 294s 6 52.2 4.667 35.9 294s 7 NA NA NA 294s 8 54.6 3.154 38.7 294s 9 55.9 2.933 38.9 294s 10 58.1 3.330 40.2 294s 11 55.5 1.591 38.1 294s 12 51.9 -2.768 33.9 294s 13 46.9 -5.502 28.9 294s 14 46.0 -6.072 28.0 294s 15 48.9 -2.817 30.3 294s 16 51.3 -1.417 33.2 294s 17 55.7 0.473 37.7 294s 18 59.3 2.053 40.0 294s 19 57.7 1.166 38.6 294s 20 60.3 1.161 42.0 294s 21 64.0 3.431 46.1 294s 22 71.6 4.608 52.8 294s > predict 294s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 294s 1 NA NA NA NA 294s 2 42.3 0.473 41.3 43.3 294s 3 45.6 0.573 44.4 46.8 294s 4 50.4 0.366 49.6 51.2 294s 5 50.7 0.423 49.8 51.6 294s 6 52.2 0.426 51.3 53.1 294s 7 NA NA NA NA 294s 8 54.6 0.347 53.9 55.4 294s 9 55.9 0.384 55.0 56.7 294s 10 58.1 0.395 57.2 58.9 294s 11 55.5 0.729 53.9 57.0 294s 12 51.9 0.594 50.6 53.2 294s 13 46.9 0.752 45.3 48.5 294s 14 46.0 0.616 44.7 47.3 294s 15 48.9 0.373 48.1 49.6 294s 16 51.3 0.331 50.6 52.0 294s 17 55.7 0.403 54.9 56.6 294s 18 59.3 0.326 58.6 60.0 294s 19 57.7 0.411 56.8 58.6 294s 20 60.3 0.472 59.3 61.3 294s 21 64.0 0.443 63.0 64.9 294s 22 71.6 0.683 70.2 73.1 294s Investment.pred Investment.se.fit Investment.lwr Investment.upr 294s 1 NA NA NA NA 294s 2 0.810 0.786 -0.8569 2.48 294s 3 1.652 0.541 0.5056 2.80 294s 4 4.138 0.511 3.0552 5.22 294s 5 4.410 0.421 3.5172 5.30 294s 6 4.667 0.395 3.8294 5.51 294s 7 NA NA NA NA 294s 8 3.154 0.327 2.4602 3.85 294s 9 2.933 0.489 1.8967 3.97 294s 10 3.330 0.537 2.1915 4.47 294s 11 1.591 0.786 -0.0748 3.26 294s 12 -2.768 0.615 -4.0716 -1.46 294s 13 -5.502 0.787 -7.1696 -3.83 294s 14 -6.072 0.842 -7.8568 -4.29 294s 15 -2.817 0.397 -3.6591 -1.98 294s 16 -1.417 0.343 -2.1436 -0.69 294s 17 0.473 0.457 -0.4954 1.44 294s 18 2.053 0.286 1.4471 2.66 294s 19 1.166 0.430 0.2549 2.08 294s 20 1.161 0.515 0.0698 2.25 294s 21 3.431 0.426 2.5282 4.33 294s 22 4.608 0.606 3.3223 5.89 294s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 294s 1 NA NA NA NA 294s 2 26.8 0.328 26.1 27.5 294s 3 29.1 0.340 28.3 29.8 294s 4 33.0 0.360 32.2 33.8 294s 5 34.1 0.258 33.5 34.6 294s 6 35.9 0.266 35.4 36.5 294s 7 NA NA NA NA 294s 8 38.7 0.262 38.1 39.2 294s 9 38.9 0.250 38.4 39.4 294s 10 40.2 0.240 39.7 40.7 294s 11 38.1 0.355 37.3 38.8 294s 12 33.9 0.382 33.1 34.7 294s 13 28.9 0.456 27.9 29.8 294s 14 28.0 0.348 27.3 28.8 294s 15 30.3 0.339 29.6 31.0 294s 16 33.2 0.284 32.6 33.8 294s 17 37.7 0.293 37.0 38.3 294s 18 40.0 0.218 39.5 40.5 294s 19 38.6 0.358 37.9 39.4 294s 20 42.0 0.307 41.3 42.6 294s 21 46.1 0.310 45.5 46.8 294s 22 52.8 0.496 51.7 53.8 294s > model.frame 294s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 294s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 294s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 294s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 294s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 294s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 294s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 294s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 294s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 294s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 294s 10 57.8 21.7 21.1 45.3 5.1 211 41.3 67.0 64.5 294s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 294s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 294s 13 45.6 7.0 11.4 34.3 -6.2 213 29.0 44.3 53.4 294s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 294s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 294s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 294s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 294s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 294s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 294s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 294s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 294s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 294s trend 294s 1 -11 294s 2 -10 294s 3 -9 294s 4 -8 294s 5 -7 294s 6 -6 294s 7 -5 294s 8 -4 294s 9 -3 294s 10 -2 294s 11 -1 294s 12 0 294s 13 1 294s 14 2 294s 15 3 294s 16 4 294s 17 5 294s 18 6 294s 19 7 294s 20 8 294s 21 9 294s 22 10 294s > model.matrix 294s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0323 >" 294s [2] "Attributes: < Component “dimnames”: Component 1: 55 string mismatches >" 294s [3] "Numeric: lengths (744, 720) differ" 294s > nobs 294s [1] 60 294s > linearHypothesis 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 49 294s 2 48 1 0.95 0.34 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 49 294s 2 48 1 1.05 0.31 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 49 294s 2 48 1 1.05 0.3 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 50 294s 2 48 2 0.48 0.62 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 50 294s 2 48 2 0.53 0.59 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 50 294s 2 48 2 1.06 0.59 294s > logLik 294s 'log Lik.' -72.2 (df=13) 294s 'log Lik.' -79.7 (df=13) 294s > 294s > # SUR 294s Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 294s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 294s > summary 294s 294s systemfit results 294s method: SUR 294s 294s N DF SSR detRCov OLS-R2 McElroy-R2 294s system 62 50 46.2 0.154 0.977 0.993 294s 294s N DF SSR MSE RMSE R2 Adj R2 294s Consumption 21 17 18.1 1.062 1.031 0.981 0.977 294s Investment 21 17 17.5 1.030 1.015 0.931 0.918 294s PrivateWages 20 16 10.6 0.663 0.814 0.987 0.984 294s 294s The covariance matrix of the residuals used for estimation 294s Consumption Investment PrivateWages 294s Consumption 0.8562 -0.0129 -0.371 294s Investment -0.0129 0.7548 0.159 294s PrivateWages -0.3706 0.1594 0.487 294s 294s The covariance matrix of the residuals 294s Consumption Investment PrivateWages 294s Consumption 0.8684 0.0078 -0.442 294s Investment 0.0078 0.7702 0.237 294s PrivateWages -0.4416 0.2366 0.531 294s 294s The correlations of the residuals 294s Consumption Investment PrivateWages 294s Consumption 1.00000 0.00562 -0.651 294s Investment 0.00562 1.00000 0.372 294s PrivateWages -0.65109 0.37198 1.000 294s 294s 294s SUR estimates for 'Consumption' (equation 1) 294s Model Formula: consump ~ corpProf + corpProfLag + wages 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 16.0647 1.1729 13.70 1.3e-10 *** 294s corpProf 0.2283 0.0775 2.94 0.0091 ** 294s corpProfLag 0.0723 0.0771 0.94 0.3615 294s wages 0.7930 0.0352 22.51 4.3e-14 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.031 on 17 degrees of freedom 294s Number of observations: 21 Degrees of Freedom: 17 294s SSR: 18.06 MSE: 1.062 Root MSE: 1.031 294s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.977 294s 294s 294s SUR estimates for 'Investment' (equation 2) 294s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 12.3516 4.5762 2.70 0.01520 * 294s corpProf 0.4461 0.0818 5.45 4.3e-05 *** 294s corpProfLag 0.3609 0.0849 4.25 0.00054 *** 294s capitalLag -0.1224 0.0223 -5.47 4.1e-05 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.015 on 17 degrees of freedom 294s Number of observations: 21 Degrees of Freedom: 17 294s SSR: 17.514 MSE: 1.03 Root MSE: 1.015 294s Multiple R-Squared: 0.931 Adjusted R-Squared: 0.918 294s 294s 294s SUR estimates for 'PrivateWages' (equation 3) 294s Model Formula: privWage ~ gnp + gnpLag + trend 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 1.5433 1.1371 1.36 0.19 294s gnp 0.4117 0.0279 14.77 9.6e-11 *** 294s gnpLag 0.1743 0.0317 5.50 4.8e-05 *** 294s trend 0.1550 0.0283 5.49 5.0e-05 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 0.814 on 16 degrees of freedom 294s Number of observations: 20 Degrees of Freedom: 16 294s SSR: 10.611 MSE: 0.663 Root MSE: 0.814 294s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.984 294s 294s > residuals 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 -0.27628 -0.3003 -1.0910 294s 3 -1.35400 -0.1239 0.5795 294s 4 -1.62816 1.1154 1.5172 294s 5 -0.56494 -1.4358 -0.0341 294s 6 -0.06584 0.3581 -0.2772 294s 7 0.83245 1.4526 NA 294s 8 1.28855 0.8290 -0.6896 294s 9 0.96709 -0.5092 0.3445 294s 10 -0.66705 1.2210 1.2429 294s 11 0.41992 0.2497 -0.3602 294s 12 -0.05971 0.0470 0.3068 294s 13 -0.08649 0.3096 -0.2426 294s 14 0.33124 0.3652 0.3591 294s 15 -0.00604 -0.1652 0.2710 294s 16 -0.01478 0.0124 -0.0207 294s 17 1.55472 1.0339 -0.8117 294s 18 -0.41250 0.0255 0.8398 294s 19 0.29322 -2.6293 -0.8283 294s 20 0.91756 -0.5906 -0.4091 294s 21 0.71583 -0.7036 -1.2154 294s 22 -2.26223 -0.5283 0.6207 294s > fitted 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 42.2 0.100 26.6 294s 3 46.4 2.024 28.7 294s 4 50.8 4.085 32.6 294s 5 51.2 4.436 33.9 294s 6 52.7 4.742 35.7 294s 7 54.3 4.147 NA 294s 8 54.9 3.371 38.6 294s 9 56.3 3.509 38.9 294s 10 58.5 3.879 40.1 294s 11 54.6 0.750 38.3 294s 12 51.0 -3.447 34.2 294s 13 45.7 -6.510 29.2 294s 14 46.2 -5.465 28.1 294s 15 48.7 -2.835 30.3 294s 16 51.3 -1.312 33.2 294s 17 56.1 1.066 37.6 294s 18 59.1 1.974 40.2 294s 19 57.2 0.729 39.0 294s 20 60.7 1.891 42.0 294s 21 64.3 4.004 46.2 294s 22 72.0 5.428 52.7 294s > predict 294s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 294s 1 NA NA NA NA 294s 2 42.2 0.414 41.3 43.0 294s 3 46.4 0.451 45.4 47.3 294s 4 50.8 0.296 50.2 51.4 294s 5 51.2 0.342 50.5 51.9 294s 6 52.7 0.342 52.0 53.4 294s 7 54.3 0.309 53.6 54.9 294s 8 54.9 0.282 54.3 55.5 294s 9 56.3 0.303 55.7 56.9 294s 10 58.5 0.321 57.8 59.1 294s 11 54.6 0.515 53.5 55.6 294s 12 51.0 0.418 50.1 51.8 294s 13 45.7 0.548 44.6 46.8 294s 14 46.2 0.528 45.1 47.2 294s 15 48.7 0.333 48.0 49.4 294s 16 51.3 0.296 50.7 51.9 294s 17 56.1 0.321 55.5 56.8 294s 18 59.1 0.287 58.5 59.7 294s 19 57.2 0.325 56.6 57.9 294s 20 60.7 0.383 59.9 61.5 294s 21 64.3 0.382 63.5 65.1 294s 22 72.0 0.599 70.8 73.2 294s Investment.pred Investment.se.fit Investment.lwr Investment.upr 294s 1 NA NA NA NA 294s 2 0.100 0.511 -0.926 1.127 294s 3 2.024 0.425 1.170 2.878 294s 4 4.085 0.378 3.325 4.845 294s 5 4.436 0.313 3.806 5.065 294s 6 4.742 0.296 4.147 5.336 294s 7 4.147 0.279 3.586 4.709 294s 8 3.371 0.250 2.868 3.874 294s 9 3.509 0.331 2.845 4.174 294s 10 3.879 0.380 3.116 4.642 294s 11 0.750 0.512 -0.279 1.779 294s 12 -3.447 0.433 -4.316 -2.578 294s 13 -6.510 0.527 -7.568 -5.451 294s 14 -5.465 0.587 -6.645 -4.285 294s 15 -2.835 0.320 -3.477 -2.193 294s 16 -1.312 0.274 -1.863 -0.761 294s 17 1.066 0.296 0.472 1.661 294s 18 1.974 0.208 1.558 2.391 294s 19 0.729 0.265 0.197 1.262 294s 20 1.891 0.311 1.266 2.515 294s 21 4.004 0.283 3.435 4.572 294s 22 5.428 0.393 4.640 6.217 294s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 294s 1 NA NA NA NA 294s 2 26.6 0.318 26.0 27.2 294s 3 28.7 0.317 28.1 29.4 294s 4 32.6 0.315 32.0 33.2 294s 5 33.9 0.243 33.4 34.4 294s 6 35.7 0.242 35.2 36.2 294s 7 NA NA NA NA 294s 8 38.6 0.247 38.1 39.1 294s 9 38.9 0.236 38.4 39.3 294s 10 40.1 0.227 39.6 40.5 294s 11 38.3 0.306 37.6 38.9 294s 12 34.2 0.312 33.6 34.8 294s 13 29.2 0.376 28.5 30.0 294s 14 28.1 0.337 27.5 28.8 294s 15 30.3 0.328 29.7 31.0 294s 16 33.2 0.274 32.7 33.8 294s 17 37.6 0.266 37.1 38.1 294s 18 40.2 0.213 39.7 40.6 294s 19 39.0 0.310 38.4 39.7 294s 20 42.0 0.282 41.4 42.6 294s 21 46.2 0.300 45.6 46.8 294s 22 52.7 0.451 51.8 53.6 294s > model.frame 294s [1] TRUE 294s > model.matrix 294s [1] TRUE 294s > nobs 294s [1] 62 294s > linearHypothesis 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 51 294s 2 50 1 1.39 0.24 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 51 294s 2 50 1 1.7 0.2 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 51 294s 2 50 1 1.7 0.19 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 52 294s 2 50 2 0.72 0.49 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 52 294s 2 50 2 0.87 0.42 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 52 294s 2 50 2 1.75 0.42 294s > logLik 294s 'log Lik.' -69.4 (df=18) 294s 'log Lik.' -78.2 (df=18) 294s > 294s > # 3SLS 294s > summary 294s 294s systemfit results 294s method: 3SLS 294s 294s N DF SSR detRCov OLS-R2 McElroy-R2 294s system 60 48 62.6 0.265 0.968 0.994 294s 294s N DF SSR MSE RMSE R2 Adj R2 294s Consumption 20 16 17.8 1.114 1.06 0.981 0.977 294s Investment 20 16 34.3 2.143 1.46 0.853 0.825 294s PrivateWages 20 16 10.5 0.656 0.81 0.987 0.984 294s 294s The covariance matrix of the residuals used for estimation 294s Consumption Investment PrivateWages 294s Consumption 1.034 0.309 -0.383 294s Investment 0.309 1.151 0.202 294s PrivateWages -0.383 0.202 0.487 294s 294s The covariance matrix of the residuals 294s Consumption Investment PrivateWages 294s Consumption 0.891 0.304 -0.391 294s Investment 0.304 1.715 0.388 294s PrivateWages -0.391 0.388 0.525 294s 294s The correlations of the residuals 294s Consumption Investment PrivateWages 294s Consumption 1.000 0.246 -0.571 294s Investment 0.246 1.000 0.409 294s PrivateWages -0.571 0.409 1.000 294s 294s 294s 3SLS estimates for 'Consumption' (equation 1) 294s Model Formula: consump ~ corpProf + corpProfLag + wages 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 16.3668 1.3024 12.57 1.1e-09 *** 294s corpProf 0.1186 0.1073 1.10 0.29 294s corpProfLag 0.1448 0.1008 1.44 0.17 294s wages 0.8006 0.0391 20.47 6.7e-13 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.056 on 16 degrees of freedom 294s Number of observations: 20 Degrees of Freedom: 16 294s SSR: 17.825 MSE: 1.114 Root MSE: 1.056 294s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.977 294s 294s 294s 3SLS estimates for 'Investment' (equation 2) 294s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 24.8872 6.2956 3.95 0.00114 ** 294s corpProf 0.0702 0.1458 0.48 0.63648 294s corpProfLag 0.6688 0.1402 4.77 0.00021 *** 294s capitalLag -0.1786 0.0303 -5.90 2.3e-05 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.464 on 16 degrees of freedom 294s Number of observations: 20 Degrees of Freedom: 16 294s SSR: 34.295 MSE: 2.143 Root MSE: 1.464 294s Multiple R-Squared: 0.853 Adjusted R-Squared: 0.825 294s 294s 294s 3SLS estimates for 'PrivateWages' (equation 3) 294s Model Formula: privWage ~ gnp + gnpLag + trend 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 1.6387 1.1457 1.43 0.17188 294s gnp 0.4062 0.0324 12.52 1.1e-09 *** 294s gnpLag 0.1784 0.0347 5.14 1.0e-04 *** 294s trend 0.1435 0.0292 4.91 0.00016 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 0.81 on 16 degrees of freedom 294s Number of observations: 20 Degrees of Freedom: 16 294s SSR: 10.497 MSE: 0.656 Root MSE: 0.81 294s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.984 294s 294s > residuals 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 -0.3538 -1.795 -1.2388 294s 3 -0.9465 0.154 0.4649 294s 4 -1.4189 0.678 1.4344 294s 5 -0.3546 -1.666 -0.1354 294s 6 0.1366 0.251 -0.3452 294s 7 NA NA NA 294s 8 1.4213 1.150 -0.7445 294s 9 1.2173 0.476 0.3001 294s 10 -0.4636 2.200 1.2232 294s 11 -0.0650 -0.962 -0.4104 294s 12 -0.5422 -0.808 0.2495 294s 13 -0.7092 -1.098 -0.3057 294s 14 0.4898 1.542 0.3497 294s 15 -0.0502 -0.155 0.2949 294s 16 0.0272 0.154 0.0214 294s 17 1.8311 1.932 -0.7322 294s 18 -0.4567 -0.180 0.9090 294s 19 0.0650 -3.381 -0.7795 294s 20 1.2135 0.557 -0.2847 294s 21 0.9466 0.167 -1.0812 294s 22 -1.9877 0.784 0.8102 294s > fitted 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 42.3 1.595 26.7 294s 3 45.9 1.746 28.8 294s 4 50.6 4.522 32.7 294s 5 51.0 4.666 34.0 294s 6 52.5 4.849 35.7 294s 7 NA NA NA 294s 8 54.8 3.050 38.6 294s 9 56.1 2.524 38.9 294s 10 58.3 2.900 40.1 294s 11 55.1 1.962 38.3 294s 12 51.4 -2.592 34.3 294s 13 46.3 -5.102 29.3 294s 14 46.0 -6.642 28.2 294s 15 48.8 -2.845 30.3 294s 16 51.3 -1.454 33.2 294s 17 55.9 0.168 37.5 294s 18 59.2 2.180 40.1 294s 19 57.4 1.481 39.0 294s 20 60.4 0.743 41.9 294s 21 64.1 3.133 46.1 294s 22 71.7 4.116 52.5 294s > predict 294s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 294s 1 NA NA NA NA 294s 2 42.3 0.468 39.8 44.7 294s 3 45.9 0.543 43.4 48.5 294s 4 50.6 0.352 48.3 53.0 294s 5 51.0 0.407 48.6 53.4 294s 6 52.5 0.411 50.1 54.9 294s 7 NA NA NA NA 294s 8 54.8 0.340 52.4 57.1 294s 9 56.1 0.372 53.7 58.5 294s 10 58.3 0.387 55.9 60.6 294s 11 55.1 0.687 52.4 57.7 294s 12 51.4 0.558 48.9 54.0 294s 13 46.3 0.713 43.6 49.0 294s 14 46.0 0.599 43.4 48.6 294s 15 48.8 0.368 46.4 51.1 294s 16 51.3 0.326 48.9 53.6 294s 17 55.9 0.388 53.5 58.3 294s 18 59.2 0.319 56.8 61.5 294s 19 57.4 0.391 55.0 59.8 294s 20 60.4 0.457 57.9 62.8 294s 21 64.1 0.437 61.6 66.5 294s 22 71.7 0.674 69.0 74.3 294s Investment.pred Investment.se.fit Investment.lwr Investment.upr 294s 1 NA NA NA NA 294s 2 1.595 0.731 -1.8742 5.065 294s 3 1.746 0.533 -1.5566 5.050 294s 4 4.522 0.484 1.2530 7.791 294s 5 4.666 0.406 1.4458 7.887 294s 6 4.849 0.386 1.6390 8.058 294s 7 NA NA NA NA 294s 8 3.050 0.325 -0.1296 6.229 294s 9 2.524 0.467 -0.7334 5.782 294s 10 2.900 0.515 -0.3900 6.190 294s 11 1.962 0.769 -1.5438 5.467 294s 12 -2.592 0.608 -5.9519 0.769 294s 13 -5.102 0.774 -8.6129 -1.592 294s 14 -6.642 0.807 -10.1867 -3.098 294s 15 -2.845 0.395 -6.0599 0.370 294s 16 -1.454 0.341 -4.6409 1.733 294s 17 0.168 0.442 -3.0739 3.410 294s 18 2.180 0.281 -0.9807 5.340 294s 19 1.481 0.414 -1.7440 4.706 294s 20 0.743 0.492 -2.5310 4.017 294s 21 3.133 0.414 -0.0924 6.358 294s 22 4.116 0.583 0.7756 7.457 294s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 294s 1 NA NA NA NA 294s 2 26.7 0.322 24.9 28.6 294s 3 28.8 0.328 27.0 30.7 294s 4 32.7 0.340 30.8 34.5 294s 5 34.0 0.250 32.2 35.8 294s 6 35.7 0.257 33.9 37.5 294s 7 NA NA NA NA 294s 8 38.6 0.254 36.8 40.4 294s 9 38.9 0.241 37.1 40.7 294s 10 40.1 0.235 38.3 41.9 294s 11 38.3 0.325 36.5 40.2 294s 12 34.3 0.349 32.4 36.1 294s 13 29.3 0.425 27.4 31.2 294s 14 28.2 0.340 26.3 30.0 294s 15 30.3 0.326 28.5 32.2 294s 16 33.2 0.272 31.4 35.0 294s 17 37.5 0.273 35.7 39.3 294s 18 40.1 0.214 38.3 41.9 294s 19 39.0 0.336 37.1 40.8 294s 20 41.9 0.290 40.1 43.7 294s 21 46.1 0.305 44.2 47.9 294s 22 52.5 0.479 50.5 54.5 294s > model.frame 294s [1] TRUE 294s > model.matrix 294s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0323 >" 294s [2] "Attributes: < Component “dimnames”: Component 1: 55 string mismatches >" 294s [3] "Numeric: lengths (744, 720) differ" 294s > nobs 294s [1] 60 294s > linearHypothesis 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 49 294s 2 48 1 0.22 0.64 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 49 294s 2 48 1 0.29 0.59 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 49 294s 2 48 1 0.29 0.59 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 50 294s 2 48 2 0.29 0.75 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 50 294s 2 48 2 0.38 0.68 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 50 294s 2 48 2 0.77 0.68 294s > logLik 294s 'log Lik.' -71.9 (df=18) 294s 'log Lik.' -82.9 (df=18) 294s > 294s > # I3SLS 294s > summary 294s 294s systemfit results 294s method: iterated 3SLS 294s 294s convergence achieved after 22 iterations 294s 294s N DF SSR detRCov OLS-R2 McElroy-R2 294s system 60 48 107 0.47 0.946 0.996 294s 294s N DF SSR MSE RMSE R2 Adj R2 294s Consumption 20 16 18.1 1.13 1.063 0.981 0.977 294s Investment 20 16 76.4 4.77 2.185 0.672 0.610 294s PrivateWages 20 16 12.3 0.77 0.877 0.984 0.982 294s 294s The covariance matrix of the residuals used for estimation 294s Consumption Investment PrivateWages 294s Consumption 0.905 0.509 -0.437 294s Investment 0.509 3.819 0.709 294s PrivateWages -0.437 0.709 0.616 294s 294s The covariance matrix of the residuals 294s Consumption Investment PrivateWages 294s Consumption 0.905 0.509 -0.437 294s Investment 0.509 3.819 0.709 294s PrivateWages -0.437 0.709 0.616 294s 294s The correlations of the residuals 294s Consumption Investment PrivateWages 294s Consumption 1.000 0.274 -0.585 294s Investment 0.274 1.000 0.462 294s PrivateWages -0.585 0.462 1.000 294s 294s 294s 3SLS estimates for 'Consumption' (equation 1) 294s Model Formula: consump ~ corpProf + corpProfLag + wages 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 16.4728 1.2187 13.52 3.6e-10 *** 294s corpProf 0.1642 0.0952 1.73 0.10 294s corpProfLag 0.1552 0.0903 1.72 0.11 294s wages 0.7756 0.0356 21.82 2.5e-13 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.063 on 16 degrees of freedom 294s Number of observations: 20 Degrees of Freedom: 16 294s SSR: 18.095 MSE: 1.131 Root MSE: 1.063 294s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.977 294s 294s 294s 3SLS estimates for 'Investment' (equation 2) 294s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 38.7938 9.7249 3.99 0.00106 ** 294s corpProf -0.2501 0.2337 -1.07 0.30036 294s corpProfLag 0.9129 0.2271 4.02 0.00099 *** 294s capitalLag -0.2409 0.0469 -5.14 9.9e-05 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 2.185 on 16 degrees of freedom 294s Number of observations: 20 Degrees of Freedom: 16 294s SSR: 76.371 MSE: 4.773 Root MSE: 2.185 294s Multiple R-Squared: 0.672 Adjusted R-Squared: 0.61 294s 294s 294s 3SLS estimates for 'PrivateWages' (equation 3) 294s Model Formula: privWage ~ gnp + gnpLag + trend 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 2.4620 1.2228 2.01 0.061 . 294s gnp 0.3776 0.0318 11.88 2.4e-09 *** 294s gnpLag 0.1937 0.0331 5.85 2.5e-05 *** 294s trend 0.1619 0.0300 5.40 5.9e-05 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 0.877 on 16 degrees of freedom 294s Number of observations: 20 Degrees of Freedom: 16 294s SSR: 12.318 MSE: 0.77 Root MSE: 0.877 294s Multiple R-Squared: 0.984 Adjusted R-Squared: 0.982 294s 294s > residuals 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 -0.4522 -3.4485 -1.2596 294s 3 -1.1470 0.0027 0.5437 294s 4 -1.6147 0.0274 1.6290 294s 5 -0.6117 -2.0392 -0.0707 294s 6 -0.1229 0.0457 -0.1859 294s 7 NA NA NA 294s 8 1.2461 1.4658 -0.6304 294s 9 1.0158 1.4202 0.3924 294s 10 -0.6460 3.2062 1.3671 294s 11 -0.0554 -1.7386 -0.4891 294s 12 -0.3472 -1.3793 0.0179 294s 13 -0.3947 -2.2646 -0.6968 294s 14 0.6536 2.4092 0.1021 294s 15 0.0821 -0.2787 0.1482 294s 16 0.1381 0.1196 -0.0796 294s 17 1.8826 2.5548 -0.6862 294s 18 -0.3415 -0.4009 0.8755 294s 19 0.2296 -4.0454 -0.9839 294s 20 1.3178 1.4481 -0.1989 294s 21 1.0065 0.9087 -0.9681 294s 22 -1.8388 1.9868 1.1734 294s > fitted 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 42.4 3.249 26.8 294s 3 46.1 1.897 28.8 294s 4 50.8 5.173 32.5 294s 5 51.2 5.039 34.0 294s 6 52.7 5.054 35.6 294s 7 NA NA NA 294s 8 55.0 2.734 38.5 294s 9 56.3 1.580 38.8 294s 10 58.4 1.894 39.9 294s 11 55.1 2.739 38.4 294s 12 51.2 -2.021 34.5 294s 13 46.0 -3.935 29.7 294s 14 45.8 -7.509 28.4 294s 15 48.6 -2.721 30.5 294s 16 51.2 -1.420 33.3 294s 17 55.8 -0.455 37.5 294s 18 59.0 2.401 40.1 294s 19 57.3 2.145 39.2 294s 20 60.3 -0.148 41.8 294s 21 64.0 2.391 46.0 294s 22 71.5 2.913 52.1 294s > predict 294s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 294s 1 NA NA NA NA 294s 2 42.4 0.437 41.5 43.2 294s 3 46.1 0.492 45.2 47.1 294s 4 50.8 0.321 50.2 51.5 294s 5 51.2 0.369 50.5 52.0 294s 6 52.7 0.372 52.0 53.5 294s 7 NA NA NA NA 294s 8 55.0 0.310 54.3 55.6 294s 9 56.3 0.338 55.6 57.0 294s 10 58.4 0.355 57.7 59.2 294s 11 55.1 0.618 53.8 56.3 294s 12 51.2 0.501 50.2 52.3 294s 13 46.0 0.642 44.7 47.3 294s 14 45.8 0.547 44.7 46.9 294s 15 48.6 0.340 47.9 49.3 294s 16 51.2 0.300 50.6 51.8 294s 17 55.8 0.354 55.1 56.5 294s 18 59.0 0.294 58.4 59.6 294s 19 57.3 0.354 56.6 58.0 294s 20 60.3 0.418 59.4 61.1 294s 21 64.0 0.407 63.2 64.8 294s 22 71.5 0.628 70.3 72.8 294s Investment.pred Investment.se.fit Investment.lwr Investment.upr 294s 1 NA NA NA NA 294s 2 3.249 1.160 0.91672 5.580 294s 3 1.897 0.934 0.02009 3.775 294s 4 5.173 0.803 3.55865 6.787 294s 5 5.039 0.693 3.64486 6.433 294s 6 5.054 0.674 3.69840 6.410 294s 7 NA NA NA NA 294s 8 2.734 0.584 1.56002 3.908 294s 9 1.580 0.783 0.00466 3.155 294s 10 1.894 0.868 0.14846 3.639 294s 11 2.739 1.321 0.08241 5.395 294s 12 -2.021 1.064 -4.16036 0.119 294s 13 -3.935 1.349 -6.64712 -1.224 294s 14 -7.509 1.360 -10.24349 -4.775 294s 15 -2.721 0.712 -4.15288 -1.290 294s 16 -1.420 0.614 -2.65412 -0.185 294s 17 -0.455 0.751 -1.96433 1.055 294s 18 2.401 0.498 1.39939 3.402 294s 19 2.145 0.698 0.74152 3.549 294s 20 -0.148 0.816 -1.78957 1.493 294s 21 2.391 0.713 0.95855 3.824 294s 22 2.913 0.984 0.93419 4.892 294s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 294s 1 NA NA NA NA 294s 2 26.8 0.347 26.1 27.5 294s 3 28.8 0.348 28.1 29.5 294s 4 32.5 0.354 31.8 33.2 294s 5 34.0 0.263 33.4 34.5 294s 6 35.6 0.274 35.0 36.1 294s 7 NA NA NA NA 294s 8 38.5 0.268 38.0 39.1 294s 9 38.8 0.256 38.3 39.3 294s 10 39.9 0.254 39.4 40.4 294s 11 38.4 0.323 37.7 39.0 294s 12 34.5 0.347 33.8 35.2 294s 13 29.7 0.435 28.8 30.6 294s 14 28.4 0.366 27.7 29.1 294s 15 30.5 0.341 29.8 31.1 294s 16 33.3 0.285 32.7 33.9 294s 17 37.5 0.275 36.9 38.0 294s 18 40.1 0.233 39.7 40.6 294s 19 39.2 0.346 38.5 39.9 294s 20 41.8 0.298 41.2 42.4 294s 21 46.0 0.329 45.3 46.6 294s 22 52.1 0.510 51.1 53.2 294s > model.frame 294s [1] TRUE 294s > model.matrix 294s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0323 >" 294s [2] "Attributes: < Component “dimnames”: Component 1: 55 string mismatches >" 294s [3] "Numeric: lengths (744, 720) differ" 294s > nobs 294s [1] 60 294s > linearHypothesis 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 49 294s 2 48 1 0.4 0.53 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 49 294s 2 48 1 0.5 0.49 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 49 294s 2 48 1 0.5 0.48 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 50 294s 2 48 2 0.66 0.52 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 50 294s 2 48 2 0.83 0.44 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 50 294s 2 48 2 1.66 0.44 294s > logLik 294s 'log Lik.' -77.6 (df=18) 294s 'log Lik.' -92.7 (df=18) 294s > 294s > # OLS 294s Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 294s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 294s > summary 294s 294s systemfit results 294s method: OLS 294s 294s N DF SSR detRCov OLS-R2 McElroy-R2 294s system 61 49 44.5 0.382 0.977 0.99 294s 294s N DF SSR MSE RMSE R2 Adj R2 294s Consumption 20 16 17.48 1.093 1.04 0.981 0.978 294s Investment 21 17 17.32 1.019 1.01 0.931 0.919 294s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 294s 294s The covariance matrix of the residuals 294s Consumption Investment PrivateWages 294s Consumption 1.124 0.034 -0.442 294s Investment 0.034 0.928 0.130 294s PrivateWages -0.442 0.130 0.563 294s 294s The correlations of the residuals 294s Consumption Investment PrivateWages 294s Consumption 1.0000 0.0266 -0.563 294s Investment 0.0266 1.0000 0.169 294s PrivateWages -0.5630 0.1689 1.000 294s 294s 294s OLS estimates for 'Consumption' (equation 1) 294s Model Formula: consump ~ corpProf + corpProfLag + wages 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 16.1357 1.3571 11.89 2.4e-09 *** 294s corpProf 0.1994 0.0949 2.10 0.052 . 294s corpProfLag 0.0969 0.0944 1.03 0.320 294s wages 0.7940 0.0415 19.16 1.9e-12 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.045 on 16 degrees of freedom 294s Number of observations: 20 Degrees of Freedom: 16 294s SSR: 17.481 MSE: 1.093 Root MSE: 1.045 294s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.978 294s 294s 294s OLS estimates for 'Investment' (equation 2) 294s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 10.1258 5.2164 1.94 0.06901 . 294s corpProf 0.4796 0.0927 5.17 7.6e-05 *** 294s corpProfLag 0.3330 0.0963 3.46 0.00299 ** 294s capitalLag -0.1118 0.0255 -4.38 0.00041 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.009 on 17 degrees of freedom 294s Number of observations: 21 Degrees of Freedom: 17 294s SSR: 17.323 MSE: 1.019 Root MSE: 1.009 294s Multiple R-Squared: 0.931 Adjusted R-Squared: 0.919 294s 294s 294s OLS estimates for 'PrivateWages' (equation 3) 294s Model Formula: privWage ~ gnp + gnpLag + trend 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 1.3550 1.2591 1.08 0.2978 294s gnp 0.4417 0.0319 13.86 2.5e-10 *** 294s gnpLag 0.1466 0.0366 4.01 0.0010 ** 294s trend 0.1244 0.0323 3.85 0.0014 ** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 0.78 on 16 degrees of freedom 294s Number of observations: 20 Degrees of Freedom: 16 294s SSR: 9.739 MSE: 0.609 Root MSE: 0.78 294s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 294s 294s compare coef with single-equation OLS 294s [1] TRUE 294s > residuals 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 -0.3304 -0.0668 -1.3389 294s 3 -1.2748 -0.0476 0.2462 294s 4 -1.6213 1.2467 1.1255 294s 5 -0.5661 -1.3512 -0.1959 294s 6 -0.0730 0.4154 -0.5284 294s 7 0.7915 1.4923 NA 294s 8 1.2648 0.7889 -0.7909 294s 9 0.9746 -0.6317 0.2819 294s 10 NA 1.0830 1.1384 294s 11 0.2225 0.2791 -0.1904 294s 12 -0.2256 0.0369 0.5813 294s 13 -0.2711 0.3659 0.1206 294s 14 0.3765 0.2237 0.4773 294s 15 -0.0349 -0.1728 0.3035 294s 16 -0.0243 0.0101 0.0284 294s 17 1.6023 0.9719 -0.8517 294s 18 -0.4658 0.0516 0.9908 294s 19 0.1914 -2.5656 -0.4597 294s 20 0.9683 -0.6866 -0.3819 294s 21 0.7325 -0.7807 -1.1062 294s 22 -2.2370 -0.6623 0.5501 294s > fitted 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 42.2 -0.133 26.8 294s 3 46.3 1.948 29.1 294s 4 50.8 3.953 33.0 294s 5 51.2 4.351 34.1 294s 6 52.7 4.685 35.9 294s 7 54.3 4.108 NA 294s 8 54.9 3.411 38.7 294s 9 56.3 3.632 38.9 294s 10 NA 4.017 40.2 294s 11 54.8 0.721 38.1 294s 12 51.1 -3.437 33.9 294s 13 45.9 -6.566 28.9 294s 14 46.1 -5.324 28.0 294s 15 48.7 -2.827 30.3 294s 16 51.3 -1.310 33.2 294s 17 56.1 1.128 37.7 294s 18 59.2 1.948 40.0 294s 19 57.3 0.666 38.7 294s 20 60.6 1.987 42.0 294s 21 64.3 4.081 46.1 294s 22 71.9 5.562 52.7 294s > predict 294s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 294s 1 NA NA NA NA 294s 2 42.2 0.478 39.9 44.5 294s 3 46.3 0.537 43.9 48.6 294s 4 50.8 0.364 48.6 53.0 294s 5 51.2 0.427 48.9 53.4 294s 6 52.7 0.433 50.4 54.9 294s 7 54.3 0.394 52.1 56.6 294s 8 54.9 0.360 52.7 57.2 294s 9 56.3 0.387 54.1 58.6 294s 10 NA NA NA NA 294s 11 54.8 0.635 52.3 57.2 294s 12 51.1 0.501 48.8 53.5 294s 13 45.9 0.656 43.4 48.4 294s 14 46.1 0.629 43.7 48.6 294s 15 48.7 0.389 46.5 51.0 294s 16 51.3 0.345 49.1 53.5 294s 17 56.1 0.379 53.9 58.3 294s 18 59.2 0.336 57.0 61.4 294s 19 57.3 0.385 55.1 59.5 294s 20 60.6 0.450 58.3 62.9 294s 21 64.3 0.448 62.0 66.6 294s 22 71.9 0.697 69.4 74.5 294s Investment.pred Investment.se.fit Investment.lwr Investment.upr 294s 1 NA NA NA NA 294s 2 -0.133 0.579 -2.472 2.206 294s 3 1.948 0.476 -0.295 4.190 294s 4 3.953 0.428 1.750 6.157 294s 5 4.351 0.354 2.202 6.501 294s 6 4.685 0.333 2.548 6.821 294s 7 4.108 0.314 1.983 6.232 294s 8 3.411 0.279 1.306 5.516 294s 9 3.632 0.371 1.470 5.793 294s 10 4.017 0.426 1.815 6.219 294s 11 0.721 0.574 -1.613 3.054 294s 12 -3.437 0.484 -5.686 -1.188 294s 13 -6.566 0.588 -8.913 -4.219 294s 14 -5.324 0.662 -7.750 -2.898 294s 15 -2.827 0.356 -4.978 -0.676 294s 16 -1.310 0.305 -3.429 0.809 294s 17 1.128 0.332 -1.007 3.263 294s 18 1.948 0.232 -0.133 4.030 294s 19 0.666 0.298 -1.449 2.781 294s 20 1.987 0.350 -0.160 4.133 294s 21 4.081 0.317 1.955 6.207 294s 22 5.562 0.440 3.349 7.775 294s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 294s 1 NA NA NA NA 294s 2 26.8 0.352 25.1 28.6 294s 3 29.1 0.355 27.3 30.8 294s 4 33.0 0.358 31.2 34.7 294s 5 34.1 0.277 32.4 35.8 294s 6 35.9 0.276 34.3 37.6 294s 7 NA NA NA NA 294s 8 38.7 0.282 37.0 40.4 294s 9 38.9 0.268 37.3 40.6 294s 10 40.2 0.255 38.5 41.8 294s 11 38.1 0.351 36.4 39.8 294s 12 33.9 0.355 32.2 35.6 294s 13 28.9 0.421 27.1 30.7 294s 14 28.0 0.370 26.3 29.8 294s 15 30.3 0.364 28.6 32.0 294s 16 33.2 0.304 31.5 34.9 294s 17 37.7 0.298 36.0 39.3 294s 18 40.0 0.233 38.4 41.6 294s 19 38.7 0.349 36.9 40.4 294s 20 42.0 0.314 40.3 43.7 294s 21 46.1 0.328 44.4 47.8 294s 22 52.7 0.494 50.9 54.6 294s > model.frame 294s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 294s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 294s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 294s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 294s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 294s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 294s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 294s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 294s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 294s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 294s 10 57.8 21.7 21.1 NA 5.1 211 41.3 67.0 64.5 294s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 294s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 294s 13 45.6 7.0 11.4 34.3 -6.2 213 29.0 44.3 53.4 294s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 294s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 294s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 294s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 294s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 294s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 294s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 294s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 294s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 294s trend 294s 1 -11 294s 2 -10 294s 3 -9 294s 4 -8 294s 5 -7 294s 6 -6 294s 7 -5 294s 8 -4 294s 9 -3 294s 10 -2 294s 11 -1 294s 12 0 294s 13 1 294s 14 2 294s 15 3 294s 16 4 294s 17 5 294s 18 6 294s 19 7 294s 20 8 294s 21 9 294s 22 10 294s > model.matrix 294s Consumption_(Intercept) Consumption_corpProf 294s Consumption_2 1 12.4 294s Consumption_3 1 16.9 294s Consumption_4 1 18.4 294s Consumption_5 1 19.4 294s Consumption_6 1 20.1 294s Consumption_7 1 19.6 294s Consumption_8 1 19.8 294s Consumption_9 1 21.1 294s Consumption_11 1 15.6 294s Consumption_12 1 11.4 294s Consumption_13 1 7.0 294s Consumption_14 1 11.2 294s Consumption_15 1 12.3 294s Consumption_16 1 14.0 294s Consumption_17 1 17.6 294s Consumption_18 1 17.3 294s Consumption_19 1 15.3 294s Consumption_20 1 19.0 294s Consumption_21 1 21.1 294s Consumption_22 1 23.5 294s Investment_2 0 0.0 294s Investment_3 0 0.0 294s Investment_4 0 0.0 294s Investment_5 0 0.0 294s Investment_6 0 0.0 294s Investment_7 0 0.0 294s Investment_8 0 0.0 294s Investment_9 0 0.0 294s Investment_10 0 0.0 294s Investment_11 0 0.0 294s Investment_12 0 0.0 294s Investment_13 0 0.0 294s Investment_14 0 0.0 294s Investment_15 0 0.0 294s Investment_16 0 0.0 294s Investment_17 0 0.0 294s Investment_18 0 0.0 294s Investment_19 0 0.0 294s Investment_20 0 0.0 294s Investment_21 0 0.0 294s Investment_22 0 0.0 294s PrivateWages_2 0 0.0 294s PrivateWages_3 0 0.0 294s PrivateWages_4 0 0.0 294s PrivateWages_5 0 0.0 294s PrivateWages_6 0 0.0 294s PrivateWages_8 0 0.0 294s PrivateWages_9 0 0.0 294s PrivateWages_10 0 0.0 294s PrivateWages_11 0 0.0 294s PrivateWages_12 0 0.0 294s PrivateWages_13 0 0.0 294s PrivateWages_14 0 0.0 294s PrivateWages_15 0 0.0 294s PrivateWages_16 0 0.0 294s PrivateWages_17 0 0.0 294s PrivateWages_18 0 0.0 294s PrivateWages_19 0 0.0 294s PrivateWages_20 0 0.0 294s PrivateWages_21 0 0.0 294s PrivateWages_22 0 0.0 294s Consumption_corpProfLag Consumption_wages 294s Consumption_2 12.7 28.2 294s Consumption_3 12.4 32.2 294s Consumption_4 16.9 37.0 294s Consumption_5 18.4 37.0 294s Consumption_6 19.4 38.6 294s Consumption_7 20.1 40.7 294s Consumption_8 19.6 41.5 294s Consumption_9 19.8 42.9 294s Consumption_11 21.7 42.1 294s Consumption_12 15.6 39.3 294s Consumption_13 11.4 34.3 294s Consumption_14 7.0 34.1 294s Consumption_15 11.2 36.6 294s Consumption_16 12.3 39.3 294s Consumption_17 14.0 44.2 294s Consumption_18 17.6 47.7 294s Consumption_19 17.3 45.9 294s Consumption_20 15.3 49.4 294s Consumption_21 19.0 53.0 294s Consumption_22 21.1 61.8 294s Investment_2 0.0 0.0 294s Investment_3 0.0 0.0 294s Investment_4 0.0 0.0 294s Investment_5 0.0 0.0 294s Investment_6 0.0 0.0 294s Investment_7 0.0 0.0 294s Investment_8 0.0 0.0 294s Investment_9 0.0 0.0 294s Investment_10 0.0 0.0 294s Investment_11 0.0 0.0 294s Investment_12 0.0 0.0 294s Investment_13 0.0 0.0 294s Investment_14 0.0 0.0 294s Investment_15 0.0 0.0 294s Investment_16 0.0 0.0 294s Investment_17 0.0 0.0 294s Investment_18 0.0 0.0 294s Investment_19 0.0 0.0 294s Investment_20 0.0 0.0 294s Investment_21 0.0 0.0 294s Investment_22 0.0 0.0 294s PrivateWages_2 0.0 0.0 294s PrivateWages_3 0.0 0.0 294s PrivateWages_4 0.0 0.0 294s PrivateWages_5 0.0 0.0 294s PrivateWages_6 0.0 0.0 294s PrivateWages_8 0.0 0.0 294s PrivateWages_9 0.0 0.0 294s PrivateWages_10 0.0 0.0 294s PrivateWages_11 0.0 0.0 294s PrivateWages_12 0.0 0.0 294s PrivateWages_13 0.0 0.0 294s PrivateWages_14 0.0 0.0 294s PrivateWages_15 0.0 0.0 294s PrivateWages_16 0.0 0.0 294s PrivateWages_17 0.0 0.0 294s PrivateWages_18 0.0 0.0 294s PrivateWages_19 0.0 0.0 294s PrivateWages_20 0.0 0.0 294s PrivateWages_21 0.0 0.0 294s PrivateWages_22 0.0 0.0 294s Investment_(Intercept) Investment_corpProf 294s Consumption_2 0 0.0 294s Consumption_3 0 0.0 294s Consumption_4 0 0.0 294s Consumption_5 0 0.0 294s Consumption_6 0 0.0 294s Consumption_7 0 0.0 294s Consumption_8 0 0.0 294s Consumption_9 0 0.0 294s Consumption_11 0 0.0 294s Consumption_12 0 0.0 294s Consumption_13 0 0.0 294s Consumption_14 0 0.0 294s Consumption_15 0 0.0 294s Consumption_16 0 0.0 294s Consumption_17 0 0.0 294s Consumption_18 0 0.0 294s Consumption_19 0 0.0 294s Consumption_20 0 0.0 294s Consumption_21 0 0.0 294s Consumption_22 0 0.0 294s Investment_2 1 12.4 294s Investment_3 1 16.9 294s Investment_4 1 18.4 294s Investment_5 1 19.4 294s Investment_6 1 20.1 294s Investment_7 1 19.6 294s Investment_8 1 19.8 294s Investment_9 1 21.1 294s Investment_10 1 21.7 294s Investment_11 1 15.6 294s Investment_12 1 11.4 294s Investment_13 1 7.0 294s Investment_14 1 11.2 294s Investment_15 1 12.3 294s Investment_16 1 14.0 294s Investment_17 1 17.6 294s Investment_18 1 17.3 294s Investment_19 1 15.3 294s Investment_20 1 19.0 294s Investment_21 1 21.1 294s Investment_22 1 23.5 294s PrivateWages_2 0 0.0 294s PrivateWages_3 0 0.0 294s PrivateWages_4 0 0.0 294s PrivateWages_5 0 0.0 294s PrivateWages_6 0 0.0 294s PrivateWages_8 0 0.0 294s PrivateWages_9 0 0.0 294s PrivateWages_10 0 0.0 294s PrivateWages_11 0 0.0 294s PrivateWages_12 0 0.0 294s PrivateWages_13 0 0.0 294s PrivateWages_14 0 0.0 294s PrivateWages_15 0 0.0 294s PrivateWages_16 0 0.0 294s PrivateWages_17 0 0.0 294s PrivateWages_18 0 0.0 294s PrivateWages_19 0 0.0 294s PrivateWages_20 0 0.0 294s PrivateWages_21 0 0.0 294s PrivateWages_22 0 0.0 294s Investment_corpProfLag Investment_capitalLag 294s Consumption_2 0.0 0 294s Consumption_3 0.0 0 294s Consumption_4 0.0 0 294s Consumption_5 0.0 0 294s Consumption_6 0.0 0 294s Consumption_7 0.0 0 294s Consumption_8 0.0 0 294s Consumption_9 0.0 0 294s Consumption_11 0.0 0 294s Consumption_12 0.0 0 294s Consumption_13 0.0 0 294s Consumption_14 0.0 0 294s Consumption_15 0.0 0 294s Consumption_16 0.0 0 294s Consumption_17 0.0 0 294s Consumption_18 0.0 0 294s Consumption_19 0.0 0 294s Consumption_20 0.0 0 294s Consumption_21 0.0 0 294s Consumption_22 0.0 0 294s Investment_2 12.7 183 294s Investment_3 12.4 183 294s Investment_4 16.9 184 294s Investment_5 18.4 190 294s Investment_6 19.4 193 294s Investment_7 20.1 198 294s Investment_8 19.6 203 294s Investment_9 19.8 208 294s Investment_10 21.1 211 294s Investment_11 21.7 216 294s Investment_12 15.6 217 294s Investment_13 11.4 213 294s Investment_14 7.0 207 294s Investment_15 11.2 202 294s Investment_16 12.3 199 294s Investment_17 14.0 198 294s Investment_18 17.6 200 294s Investment_19 17.3 202 294s Investment_20 15.3 200 294s Investment_21 19.0 201 294s Investment_22 21.1 204 294s PrivateWages_2 0.0 0 294s PrivateWages_3 0.0 0 294s PrivateWages_4 0.0 0 294s PrivateWages_5 0.0 0 294s PrivateWages_6 0.0 0 294s PrivateWages_8 0.0 0 294s PrivateWages_9 0.0 0 294s PrivateWages_10 0.0 0 294s PrivateWages_11 0.0 0 294s PrivateWages_12 0.0 0 294s PrivateWages_13 0.0 0 294s PrivateWages_14 0.0 0 294s PrivateWages_15 0.0 0 294s PrivateWages_16 0.0 0 294s PrivateWages_17 0.0 0 294s PrivateWages_18 0.0 0 294s PrivateWages_19 0.0 0 294s PrivateWages_20 0.0 0 294s PrivateWages_21 0.0 0 294s PrivateWages_22 0.0 0 294s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 294s Consumption_2 0 0.0 0.0 294s Consumption_3 0 0.0 0.0 294s Consumption_4 0 0.0 0.0 294s Consumption_5 0 0.0 0.0 294s Consumption_6 0 0.0 0.0 294s Consumption_7 0 0.0 0.0 294s Consumption_8 0 0.0 0.0 294s Consumption_9 0 0.0 0.0 294s Consumption_11 0 0.0 0.0 294s Consumption_12 0 0.0 0.0 294s Consumption_13 0 0.0 0.0 294s Consumption_14 0 0.0 0.0 294s Consumption_15 0 0.0 0.0 294s Consumption_16 0 0.0 0.0 294s Consumption_17 0 0.0 0.0 294s Consumption_18 0 0.0 0.0 294s Consumption_19 0 0.0 0.0 294s Consumption_20 0 0.0 0.0 294s Consumption_21 0 0.0 0.0 294s Consumption_22 0 0.0 0.0 294s Investment_2 0 0.0 0.0 294s Investment_3 0 0.0 0.0 294s Investment_4 0 0.0 0.0 294s Investment_5 0 0.0 0.0 294s Investment_6 0 0.0 0.0 294s Investment_7 0 0.0 0.0 294s Investment_8 0 0.0 0.0 294s Investment_9 0 0.0 0.0 294s Investment_10 0 0.0 0.0 294s Investment_11 0 0.0 0.0 294s Investment_12 0 0.0 0.0 294s Investment_13 0 0.0 0.0 294s Investment_14 0 0.0 0.0 294s Investment_15 0 0.0 0.0 294s Investment_16 0 0.0 0.0 294s Investment_17 0 0.0 0.0 294s Investment_18 0 0.0 0.0 294s Investment_19 0 0.0 0.0 294s Investment_20 0 0.0 0.0 294s Investment_21 0 0.0 0.0 294s Investment_22 0 0.0 0.0 294s PrivateWages_2 1 45.6 44.9 294s PrivateWages_3 1 50.1 45.6 294s PrivateWages_4 1 57.2 50.1 294s PrivateWages_5 1 57.1 57.2 294s PrivateWages_6 1 61.0 57.1 294s PrivateWages_8 1 64.4 64.0 294s PrivateWages_9 1 64.5 64.4 294s PrivateWages_10 1 67.0 64.5 294s PrivateWages_11 1 61.2 67.0 294s PrivateWages_12 1 53.4 61.2 294s PrivateWages_13 1 44.3 53.4 294s PrivateWages_14 1 45.1 44.3 294s PrivateWages_15 1 49.7 45.1 294s PrivateWages_16 1 54.4 49.7 294s PrivateWages_17 1 62.7 54.4 294s PrivateWages_18 1 65.0 62.7 294s PrivateWages_19 1 60.9 65.0 294s PrivateWages_20 1 69.5 60.9 294s PrivateWages_21 1 75.7 69.5 294s PrivateWages_22 1 88.4 75.7 294s PrivateWages_trend 294s Consumption_2 0 294s Consumption_3 0 294s Consumption_4 0 294s Consumption_5 0 294s Consumption_6 0 294s Consumption_7 0 294s Consumption_8 0 294s Consumption_9 0 294s Consumption_11 0 294s Consumption_12 0 294s Consumption_13 0 294s Consumption_14 0 294s Consumption_15 0 294s Consumption_16 0 294s Consumption_17 0 294s Consumption_18 0 294s Consumption_19 0 294s Consumption_20 0 294s Consumption_21 0 294s Consumption_22 0 294s Investment_2 0 294s Investment_3 0 294s Investment_4 0 294s Investment_5 0 294s Investment_6 0 294s Investment_7 0 294s Investment_8 0 294s Investment_9 0 294s Investment_10 0 294s Investment_11 0 294s Investment_12 0 294s Investment_13 0 294s Investment_14 0 294s Investment_15 0 294s Investment_16 0 294s Investment_17 0 294s Investment_18 0 294s Investment_19 0 294s Investment_20 0 294s Investment_21 0 294s Investment_22 0 294s PrivateWages_2 -10 294s PrivateWages_3 -9 294s PrivateWages_4 -8 294s PrivateWages_5 -7 294s PrivateWages_6 -6 294s PrivateWages_8 -4 294s PrivateWages_9 -3 294s PrivateWages_10 -2 294s PrivateWages_11 -1 294s PrivateWages_12 0 294s PrivateWages_13 1 294s PrivateWages_14 2 294s PrivateWages_15 3 294s PrivateWages_16 4 294s PrivateWages_17 5 294s PrivateWages_18 6 294s PrivateWages_19 7 294s PrivateWages_20 8 294s PrivateWages_21 9 294s PrivateWages_22 10 294s > nobs 294s [1] 61 294s > linearHypothesis 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 50 294s 2 49 1 0.87 0.35 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 50 294s 2 49 1 0.8 0.38 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 50 294s 2 49 1 0.8 0.37 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 51 294s 2 49 2 0.48 0.62 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 51 294s 2 49 2 0.43 0.65 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 51 294s 2 49 2 0.87 0.65 294s > logLik 294s 'log Lik.' -71.7 (df=13) 294s 'log Lik.' -76.1 (df=13) 294s compare log likelihood value with single-equation OLS 294s [1] "Mean relative difference: 0.00159" 294s > 294s > # 2SLS 294s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 294s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 294s > summary 294s 294s systemfit results 294s method: 2SLS 294s 294s N DF SSR detRCov OLS-R2 McElroy-R2 294s system 59 47 53.2 0.251 0.973 0.991 294s 294s N DF SSR MSE RMSE R2 Adj R2 294s Consumption 19 15 20.49 1.366 1.17 0.978 0.973 294s Investment 20 16 23.02 1.438 1.20 0.901 0.883 294s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 294s 294s The covariance matrix of the residuals 294s Consumption Investment PrivateWages 294s Consumption 1.079 0.354 -0.383 294s Investment 0.354 1.047 0.107 294s PrivateWages -0.383 0.107 0.445 294s 294s The correlations of the residuals 294s Consumption Investment PrivateWages 294s Consumption 1.000 0.335 -0.556 294s Investment 0.335 1.000 0.149 294s PrivateWages -0.556 0.149 1.000 294s 294s 294s 2SLS estimates for 'Consumption' (equation 1) 294s Model Formula: consump ~ corpProf + corpProfLag + wages 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 16.4657 1.3505 12.19 3.5e-09 *** 294s corpProf 0.0243 0.1180 0.21 0.839 294s corpProfLag 0.1981 0.1087 1.82 0.088 . 294s wages 0.8159 0.0420 19.45 4.7e-12 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.169 on 15 degrees of freedom 294s Number of observations: 19 Degrees of Freedom: 15 294s SSR: 20.493 MSE: 1.366 Root MSE: 1.169 294s Multiple R-Squared: 0.978 Adjusted R-Squared: 0.973 294s 294s 294s 2SLS estimates for 'Investment' (equation 2) 294s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 17.8425 6.5319 2.73 0.01478 * 294s corpProf 0.2167 0.1478 1.47 0.16189 294s corpProfLag 0.5416 0.1415 3.83 0.00149 ** 294s capitalLag -0.1455 0.0314 -4.63 0.00028 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.199 on 16 degrees of freedom 294s Number of observations: 20 Degrees of Freedom: 16 294s SSR: 23.016 MSE: 1.438 Root MSE: 1.199 294s Multiple R-Squared: 0.901 Adjusted R-Squared: 0.883 294s 294s 294s 2SLS estimates for 'PrivateWages' (equation 3) 294s Model Formula: privWage ~ gnp + gnpLag + trend 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 1.3431 1.1250 1.19 0.24995 294s gnp 0.4438 0.0342 12.97 6.6e-10 *** 294s gnpLag 0.1447 0.0371 3.90 0.00128 ** 294s trend 0.1238 0.0292 4.24 0.00063 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 0.78 on 16 degrees of freedom 294s Number of observations: 20 Degrees of Freedom: 16 294s SSR: 9.741 MSE: 0.609 Root MSE: 0.78 294s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 294s 294s > residuals 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 -0.39161 -1.0104 -1.3401 294s 3 -0.60524 0.2478 0.2378 294s 4 -1.24952 1.0621 1.1117 294s 5 -0.17101 -1.4104 -0.1954 294s 6 0.30841 0.4328 -0.5355 294s 7 NA NA NA 294s 8 1.50999 1.0463 -0.7908 294s 9 1.39649 0.0674 0.2831 294s 10 NA 1.7698 1.1353 294s 11 -0.49339 -0.5912 -0.1765 294s 12 -0.99824 -0.6318 0.6007 294s 13 -1.27965 -0.6983 0.1443 294s 14 0.55302 0.9724 0.4826 294s 15 -0.14553 -0.1827 0.3016 294s 16 -0.00773 0.1167 0.0261 294s 17 1.97001 1.6266 -0.8614 294s 18 -0.59152 -0.0525 0.9927 294s 19 -0.21481 -3.0656 -0.4446 294s 20 1.33575 0.1393 -0.3914 294s 21 1.01443 -0.1305 -1.1115 294s 22 -1.93986 0.2922 0.5312 294s > fitted 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 42.3 0.810 26.8 294s 3 45.6 1.652 29.1 294s 4 50.4 4.138 33.0 294s 5 50.8 4.410 34.1 294s 6 52.3 4.667 35.9 294s 7 NA NA NA 294s 8 54.7 3.154 38.7 294s 9 55.9 2.933 38.9 294s 10 NA 3.330 40.2 294s 11 55.5 1.591 38.1 294s 12 51.9 -2.768 33.9 294s 13 46.9 -5.502 28.9 294s 14 45.9 -6.072 28.0 294s 15 48.8 -2.817 30.3 294s 16 51.3 -1.417 33.2 294s 17 55.7 0.473 37.7 294s 18 59.3 2.053 40.0 294s 19 57.7 1.166 38.6 294s 20 60.3 1.161 42.0 294s 21 64.0 3.431 46.1 294s 22 71.6 4.608 52.8 294s > predict 294s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 294s 1 NA NA NA NA 294s 2 42.3 0.483 41.3 43.3 294s 3 45.6 0.586 44.4 46.9 294s 4 50.4 0.390 49.6 51.3 294s 5 50.8 0.456 49.8 51.7 294s 6 52.3 0.463 51.3 53.3 294s 7 NA NA NA NA 294s 8 54.7 0.382 53.9 55.5 294s 9 55.9 0.422 55.0 56.8 294s 10 NA NA NA NA 294s 11 55.5 0.742 53.9 57.1 294s 12 51.9 0.600 50.6 53.2 294s 13 46.9 0.770 45.2 48.5 294s 14 45.9 0.635 44.6 47.3 294s 15 48.8 0.383 48.0 49.7 294s 16 51.3 0.339 50.6 52.0 294s 17 55.7 0.410 54.9 56.6 294s 18 59.3 0.336 58.6 60.0 294s 19 57.7 0.418 56.8 58.6 294s 20 60.3 0.481 59.2 61.3 294s 21 64.0 0.462 63.0 65.0 294s 22 71.6 0.706 70.1 73.1 294s Investment.pred Investment.se.fit Investment.lwr Investment.upr 294s 1 NA NA NA NA 294s 2 0.810 0.750 -0.77956 2.400 294s 3 1.652 0.516 0.55883 2.746 294s 4 4.138 0.487 3.10541 5.170 294s 5 4.410 0.402 3.55860 5.262 294s 6 4.667 0.377 3.86830 5.466 294s 7 NA NA NA NA 294s 8 3.154 0.312 2.49238 3.815 294s 9 2.933 0.466 1.94478 3.920 294s 10 3.330 0.512 2.24435 4.416 294s 11 1.591 0.749 0.00249 3.180 294s 12 -2.768 0.586 -4.01111 -1.525 294s 13 -5.502 0.750 -7.09222 -3.911 294s 14 -6.072 0.803 -7.77404 -4.371 294s 15 -2.817 0.379 -3.62002 -2.015 294s 16 -1.417 0.327 -2.10985 -0.723 294s 17 0.473 0.436 -0.45046 1.397 294s 18 2.053 0.272 1.47523 2.630 294s 19 1.166 0.410 0.29710 2.034 294s 20 1.161 0.491 0.12044 2.201 294s 21 3.431 0.406 2.57004 4.291 294s 22 4.608 0.578 3.38197 5.834 294s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 294s 1 NA NA NA NA 294s 2 26.8 0.313 26.2 27.5 294s 3 29.1 0.325 28.4 29.8 294s 4 33.0 0.344 32.3 33.7 294s 5 34.1 0.246 33.6 34.6 294s 6 35.9 0.254 35.4 36.5 294s 7 NA NA NA NA 294s 8 38.7 0.251 38.2 39.2 294s 9 38.9 0.239 38.4 39.4 294s 10 40.2 0.229 39.7 40.7 294s 11 38.1 0.339 37.4 38.8 294s 12 33.9 0.365 33.1 34.7 294s 13 28.9 0.436 27.9 29.8 294s 14 28.0 0.333 27.3 28.7 294s 15 30.3 0.324 29.6 31.0 294s 16 33.2 0.271 32.6 33.7 294s 17 37.7 0.280 37.1 38.3 294s 18 40.0 0.208 39.6 40.4 294s 19 38.6 0.342 37.9 39.4 294s 20 42.0 0.293 41.4 42.6 294s 21 46.1 0.296 45.5 46.7 294s 22 52.8 0.474 51.8 53.8 294s > model.frame 294s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 294s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 294s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 294s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 294s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 294s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 294s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 294s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 294s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 294s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 294s 10 57.8 21.7 21.1 NA 5.1 211 41.3 67.0 64.5 294s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 294s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 294s 13 45.6 7.0 11.4 34.3 -6.2 213 29.0 44.3 53.4 294s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 294s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 294s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 294s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 294s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 294s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 294s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 294s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 294s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 294s trend 294s 1 -11 294s 2 -10 294s 3 -9 294s 4 -8 294s 5 -7 294s 6 -6 294s 7 -5 294s 8 -4 294s 9 -3 294s 10 -2 294s 11 -1 294s 12 0 294s 13 1 294s 14 2 294s 15 3 294s 16 4 294s 17 5 294s 18 6 294s 19 7 294s 20 8 294s 21 9 294s 22 10 294s > model.matrix 294s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0328 >" 294s [2] "Attributes: < Component “dimnames”: Component 1: 54 string mismatches >" 294s [3] "Numeric: lengths (732, 708) differ" 294s > nobs 294s [1] 59 294s > linearHypothesis 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 48 294s 2 47 1 0.87 0.36 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 48 294s 2 47 1 0.98 0.33 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 48 294s 2 47 1 0.98 0.32 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 49 294s 2 47 2 0.43 0.65 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 49 294s 2 47 2 0.49 0.61 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 49 294s 2 47 2 0.98 0.61 294s > logLik 294s 'log Lik.' -71.5 (df=13) 294s 'log Lik.' -78.7 (df=13) 294s > 294s > # SUR 294s > summary 294s Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 294s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 294s 294s systemfit results 294s method: SUR 294s 294s N DF SSR detRCov OLS-R2 McElroy-R2 294s system 61 49 45.4 0.151 0.977 0.992 294s 294s N DF SSR MSE RMSE R2 Adj R2 294s Consumption 20 16 17.6 1.102 1.050 0.981 0.977 294s Investment 21 17 17.5 1.029 1.015 0.931 0.918 294s PrivateWages 20 16 10.3 0.643 0.802 0.987 0.985 294s 294s The covariance matrix of the residuals used for estimation 294s Consumption Investment PrivateWages 294s Consumption 0.8871 0.0268 -0.349 294s Investment 0.0268 0.7328 0.103 294s PrivateWages -0.3492 0.1029 0.444 294s 294s The covariance matrix of the residuals 294s Consumption Investment PrivateWages 294s Consumption 0.8852 0.0508 -0.406 294s Investment 0.0508 0.7313 0.161 294s PrivateWages -0.4063 0.1609 0.467 294s 294s The correlations of the residuals 294s Consumption Investment PrivateWages 294s Consumption 1.000 0.065 -0.635 294s Investment 0.065 1.000 0.262 294s PrivateWages -0.635 0.262 1.000 294s 294s 294s SUR estimates for 'Consumption' (equation 1) 294s Model Formula: consump ~ corpProf + corpProfLag + wages 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 16.0876 1.2010 13.39 4.1e-10 *** 294s corpProf 0.2173 0.0799 2.72 0.015 * 294s corpProfLag 0.0694 0.0793 0.88 0.394 294s wages 0.7975 0.0360 22.15 2.0e-13 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.05 on 16 degrees of freedom 294s Number of observations: 20 Degrees of Freedom: 16 294s SSR: 17.63 MSE: 1.102 Root MSE: 1.05 294s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.977 294s 294s 294s SUR estimates for 'Investment' (equation 2) 294s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 12.3518 4.5615 2.71 0.01493 * 294s corpProf 0.4511 0.0814 5.54 3.6e-05 *** 294s corpProfLag 0.3570 0.0846 4.22 0.00058 *** 294s capitalLag -0.1225 0.0223 -5.49 4.0e-05 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.015 on 17 degrees of freedom 294s Number of observations: 21 Degrees of Freedom: 17 294s SSR: 17.5 MSE: 1.029 Root MSE: 1.015 294s Multiple R-Squared: 0.931 Adjusted R-Squared: 0.918 294s 294s 294s SUR estimates for 'PrivateWages' (equation 3) 294s Model Formula: privWage ~ gnp + gnpLag + trend 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 1.3964 1.0825 1.29 0.22 294s gnp 0.4177 0.0269 15.55 4.4e-11 *** 294s gnpLag 0.1709 0.0306 5.59 4.0e-05 *** 294s trend 0.1467 0.0272 5.40 5.9e-05 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 0.802 on 16 degrees of freedom 294s Number of observations: 20 Degrees of Freedom: 16 294s SSR: 10.284 MSE: 0.643 Root MSE: 0.802 294s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 294s 294s > residuals 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 -0.2529 -0.2920 -1.15193 294s 3 -1.2998 -0.1392 0.50193 294s 4 -1.5662 1.1106 1.42026 294s 5 -0.4876 -1.4391 -0.09801 294s 6 0.0149 0.3556 -0.35678 294s 7 0.9002 1.4558 NA 294s 8 1.3535 0.8299 -0.74964 294s 9 1.0406 -0.5136 0.29355 294s 10 NA 1.2191 1.18544 294s 11 0.4417 0.2810 -0.36558 294s 12 -0.0892 0.0754 0.33733 294s 13 -0.1541 0.3429 -0.17490 294s 14 0.2984 0.3597 0.39941 294s 15 -0.0260 -0.1602 0.29441 294s 16 -0.0250 0.0130 -0.00177 294s 17 1.5671 1.0231 -0.81891 294s 18 -0.4089 0.0306 0.85516 294s 19 0.2819 -2.6153 -0.77184 294s 20 0.9257 -0.6030 -0.41040 294s 21 0.7415 -0.7118 -1.21679 294s 22 -2.2437 -0.5398 0.57166 294s > fitted 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 42.2 0.092 26.7 294s 3 46.3 2.039 28.8 294s 4 50.8 4.089 32.7 294s 5 51.1 4.439 34.0 294s 6 52.6 4.744 35.8 294s 7 54.2 4.144 NA 294s 8 54.8 3.370 38.6 294s 9 56.3 3.514 38.9 294s 10 NA 3.881 40.1 294s 11 54.6 0.719 38.3 294s 12 51.0 -3.475 34.2 294s 13 45.8 -6.543 29.2 294s 14 46.2 -5.460 28.1 294s 15 48.7 -2.840 30.3 294s 16 51.3 -1.313 33.2 294s 17 56.1 1.077 37.6 294s 18 59.1 1.969 40.1 294s 19 57.2 0.715 39.0 294s 20 60.7 1.903 42.0 294s 21 64.3 4.012 46.2 294s 22 71.9 5.440 52.7 294s > predict 294s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 294s 1 NA NA NA NA 294s 2 42.2 0.422 41.3 43.0 294s 3 46.3 0.462 45.4 47.2 294s 4 50.8 0.309 50.1 51.4 294s 5 51.1 0.359 50.4 51.8 294s 6 52.6 0.362 51.9 53.3 294s 7 54.2 0.328 53.5 54.9 294s 8 54.8 0.300 54.2 55.4 294s 9 56.3 0.323 55.6 56.9 294s 10 NA NA NA NA 294s 11 54.6 0.531 53.5 55.6 294s 12 51.0 0.427 50.1 51.8 294s 13 45.8 0.564 44.6 46.9 294s 14 46.2 0.543 45.1 47.3 294s 15 48.7 0.341 48.0 49.4 294s 16 51.3 0.302 50.7 51.9 294s 17 56.1 0.328 55.5 56.8 294s 18 59.1 0.294 58.5 59.7 294s 19 57.2 0.332 56.6 57.9 294s 20 60.7 0.392 59.9 61.5 294s 21 64.3 0.394 63.5 65.0 294s 22 71.9 0.615 70.7 73.2 294s Investment.pred Investment.se.fit Investment.lwr Investment.upr 294s 1 NA NA NA NA 294s 2 0.092 0.508 -0.929 1.113 294s 3 2.039 0.421 1.193 2.885 294s 4 4.089 0.376 3.333 4.846 294s 5 4.439 0.311 3.813 5.065 294s 6 4.744 0.294 4.154 5.335 294s 7 4.144 0.277 3.587 4.701 294s 8 3.370 0.247 2.873 3.867 294s 9 3.514 0.328 2.855 4.172 294s 10 3.881 0.376 3.126 4.636 294s 11 0.719 0.508 -0.301 1.739 294s 12 -3.475 0.428 -4.336 -2.615 294s 13 -6.543 0.521 -7.590 -5.496 294s 14 -5.460 0.583 -6.632 -4.288 294s 15 -2.840 0.316 -3.474 -2.205 294s 16 -1.313 0.271 -1.857 -0.769 294s 17 1.077 0.293 0.488 1.666 294s 18 1.969 0.205 1.557 2.382 294s 19 0.715 0.263 0.187 1.244 294s 20 1.903 0.309 1.283 2.523 294s 21 4.012 0.280 3.449 4.574 294s 22 5.440 0.389 4.659 6.221 294s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 294s 1 NA NA NA NA 294s 2 26.7 0.306 26.0 27.3 294s 3 28.8 0.305 28.2 29.4 294s 4 32.7 0.302 32.1 33.3 294s 5 34.0 0.231 33.5 34.5 294s 6 35.8 0.230 35.3 36.2 294s 7 NA NA NA NA 294s 8 38.6 0.233 38.2 39.1 294s 9 38.9 0.222 38.5 39.4 294s 10 40.1 0.213 39.7 40.5 294s 11 38.3 0.292 37.7 38.9 294s 12 34.2 0.300 33.6 34.8 294s 13 29.2 0.361 28.4 29.9 294s 14 28.1 0.322 27.5 28.7 294s 15 30.3 0.314 29.7 30.9 294s 16 33.2 0.263 32.7 33.7 294s 17 37.6 0.256 37.1 38.1 294s 18 40.1 0.204 39.7 40.6 294s 19 39.0 0.298 38.4 39.6 294s 20 42.0 0.272 41.5 42.6 294s 21 46.2 0.288 45.6 46.8 294s 22 52.7 0.431 51.9 53.6 294s > model.frame 294s [1] TRUE 294s > model.matrix 294s [1] TRUE 294s > nobs 294s [1] 61 294s > linearHypothesis 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 50 294s 2 49 1 1.01 0.32 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 50 294s 2 49 1 1.3 0.26 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 50 294s 2 49 1 1.3 0.25 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 51 294s 2 49 2 0.53 0.59 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 51 294s 2 49 2 0.69 0.51 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 51 294s 2 49 2 1.38 0.5 294s > logLik 294s 'log Lik.' -69.6 (df=18) 294s 'log Lik.' -76.9 (df=18) 294s > 294s > # 3SLS 294s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 294s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 294s > summary 294s 294s systemfit results 294s method: 3SLS 294s 294s N DF SSR detRCov OLS-R2 McElroy-R2 294s system 59 47 59.5 0.241 0.97 0.994 294s 294s N DF SSR MSE RMSE R2 Adj R2 294s Consumption 19 15 18.1 1.203 1.097 0.980 0.977 294s Investment 20 16 31.1 1.945 1.395 0.866 0.841 294s PrivateWages 20 16 10.3 0.645 0.803 0.987 0.985 294s 294s The covariance matrix of the residuals used for estimation 294s Consumption Investment PrivateWages 294s Consumption 1.079 0.354 -0.383 294s Investment 0.354 1.047 0.107 294s PrivateWages -0.383 0.107 0.445 294s 294s The covariance matrix of the residuals 294s Consumption Investment PrivateWages 294s Consumption 0.950 0.324 -0.395 294s Investment 0.324 1.385 0.242 294s PrivateWages -0.395 0.242 0.475 294s 294s The correlations of the residuals 294s Consumption Investment PrivateWages 294s Consumption 1.000 0.293 -0.582 294s Investment 0.293 1.000 0.292 294s PrivateWages -0.582 0.292 1.000 294s 294s 294s 3SLS estimates for 'Consumption' (equation 1) 294s Model Formula: consump ~ corpProf + corpProfLag + wages 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 16.5606 1.3295 12.46 2.6e-09 *** 294s corpProf 0.1100 0.1098 1.00 0.33 294s corpProfLag 0.1155 0.1007 1.15 0.27 294s wages 0.8086 0.0401 20.18 2.8e-12 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.097 on 15 degrees of freedom 294s Number of observations: 19 Degrees of Freedom: 15 294s SSR: 18.051 MSE: 1.203 Root MSE: 1.097 294s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.977 294s 294s 294s 3SLS estimates for 'Investment' (equation 2) 294s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 23.6871 6.1159 3.87 0.00135 ** 294s corpProf 0.1072 0.1414 0.76 0.45918 294s corpProfLag 0.6278 0.1361 4.61 0.00029 *** 294s capitalLag -0.1726 0.0295 -5.85 2.5e-05 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.395 on 16 degrees of freedom 294s Number of observations: 20 Degrees of Freedom: 16 294s SSR: 31.126 MSE: 1.945 Root MSE: 1.395 294s Multiple R-Squared: 0.866 Adjusted R-Squared: 0.841 294s 294s 294s 3SLS estimates for 'PrivateWages' (equation 3) 294s Model Formula: privWage ~ gnp + gnpLag + trend 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 1.3603 1.0927 1.24 0.23109 294s gnp 0.4117 0.0315 13.06 6.0e-10 *** 294s gnpLag 0.1782 0.0336 5.31 7.1e-05 *** 294s trend 0.1370 0.0280 4.89 0.00016 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 0.803 on 16 degrees of freedom 294s Number of observations: 20 Degrees of Freedom: 16 294s SSR: 10.318 MSE: 0.645 Root MSE: 0.803 294s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 294s 294s > residuals 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 -0.29542 -1.636 -1.2658 294s 3 -0.89033 0.135 0.4198 294s 4 -1.25669 0.777 1.3578 294s 5 -0.14000 -1.574 -0.2036 294s 6 0.37365 0.341 -0.4283 294s 7 NA NA NA 294s 8 1.63850 1.194 -0.8319 294s 9 1.44030 0.454 0.2186 294s 10 NA 2.192 1.1346 294s 11 0.17274 -0.750 -0.4603 294s 12 -0.49629 -0.698 0.2476 294s 13 -0.78384 -0.976 -0.2528 294s 14 0.32420 1.365 0.4028 294s 15 -0.10364 -0.170 0.3295 294s 16 -0.00105 0.140 0.0377 294s 17 1.84421 1.862 -0.7540 294s 18 -0.36893 -0.103 0.8827 294s 19 0.14129 -3.255 -0.7764 294s 20 1.23511 0.475 -0.3230 294s 21 1.06553 0.152 -1.1453 294s 22 -1.85709 0.746 0.6843 294s > fitted 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 42.2 1.436 26.8 294s 3 45.9 1.765 28.9 294s 4 50.5 4.423 32.7 294s 5 50.7 4.574 34.1 294s 6 52.2 4.759 35.8 294s 7 NA NA NA 294s 8 54.6 3.006 38.7 294s 9 55.9 2.546 39.0 294s 10 NA 2.908 40.2 294s 11 54.8 1.750 38.4 294s 12 51.4 -2.702 34.3 294s 13 46.4 -5.224 29.3 294s 14 46.2 -6.465 28.1 294s 15 48.8 -2.830 30.3 294s 16 51.3 -1.440 33.2 294s 17 55.9 0.238 37.6 294s 18 59.1 2.103 40.1 294s 19 57.4 1.355 39.0 294s 20 60.4 0.825 41.9 294s 21 63.9 3.148 46.1 294s 22 71.6 4.154 52.6 294s > predict 294s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 294s 1 NA NA NA NA 294s 2 42.2 0.475 39.6 44.7 294s 3 45.9 0.557 43.3 48.5 294s 4 50.5 0.372 48.0 52.9 294s 5 50.7 0.433 48.2 53.3 294s 6 52.2 0.438 49.7 54.7 294s 7 NA NA NA NA 294s 8 54.6 0.362 52.1 57.0 294s 9 55.9 0.401 53.4 58.3 294s 10 NA NA NA NA 294s 11 54.8 0.684 52.1 57.6 294s 12 51.4 0.563 48.8 54.0 294s 13 46.4 0.733 43.6 49.2 294s 14 46.2 0.612 43.5 48.9 294s 15 48.8 0.379 46.3 51.3 294s 16 51.3 0.334 48.9 53.7 294s 17 55.9 0.394 53.4 58.3 294s 18 59.1 0.322 56.6 61.5 294s 19 57.4 0.392 54.9 59.8 294s 20 60.4 0.462 57.8 62.9 294s 21 63.9 0.448 61.4 66.5 294s 22 71.6 0.686 68.8 74.3 294s Investment.pred Investment.se.fit Investment.lwr Investment.upr 294s 1 NA NA NA NA 294s 2 1.436 0.709 -1.8811 4.754 294s 3 1.765 0.512 -1.3848 4.915 294s 4 4.423 0.470 1.3027 7.543 294s 5 4.574 0.392 1.5029 7.645 294s 6 4.759 0.370 1.7000 7.818 294s 7 NA NA NA NA 294s 8 3.006 0.306 -0.0214 6.033 294s 9 2.546 0.444 -0.5575 5.649 294s 10 2.908 0.488 -0.2245 6.041 294s 11 1.750 0.738 -1.5953 5.096 294s 12 -2.702 0.583 -5.9068 0.503 294s 13 -5.224 0.743 -8.5738 -1.874 294s 14 -6.465 0.780 -9.8530 -3.077 294s 15 -2.830 0.378 -5.8936 0.233 294s 16 -1.440 0.326 -4.4762 1.597 294s 17 0.238 0.426 -2.8533 3.329 294s 18 2.103 0.268 -0.9077 5.114 294s 19 1.355 0.399 -1.7201 4.431 294s 20 0.825 0.474 -2.2981 3.947 294s 21 3.148 0.393 0.0761 6.220 294s 22 4.154 0.555 0.9719 7.336 294s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 294s 1 NA NA NA NA 294s 2 26.8 0.309 24.9 28.6 294s 3 28.9 0.315 27.1 30.7 294s 4 32.7 0.326 30.9 34.6 294s 5 34.1 0.236 32.3 35.9 294s 6 35.8 0.244 34.0 37.6 294s 7 NA NA NA NA 294s 8 38.7 0.237 37.0 40.5 294s 9 39.0 0.225 37.2 40.7 294s 10 40.2 0.219 38.4 41.9 294s 11 38.4 0.309 36.5 40.2 294s 12 34.3 0.336 32.4 36.1 294s 13 29.3 0.411 27.3 31.2 294s 14 28.1 0.326 26.3 29.9 294s 15 30.3 0.313 28.4 32.1 294s 16 33.2 0.262 31.4 35.0 294s 17 37.6 0.265 35.8 39.3 294s 18 40.1 0.205 38.4 41.9 294s 19 39.0 0.323 37.1 40.8 294s 20 41.9 0.282 40.1 43.7 294s 21 46.1 0.293 44.3 48.0 294s 22 52.6 0.463 50.7 54.6 294s > model.frame 294s [1] TRUE 294s > model.matrix 294s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0328 >" 294s [2] "Attributes: < Component “dimnames”: Component 1: 54 string mismatches >" 294s [3] "Numeric: lengths (732, 708) differ" 294s > nobs 294s [1] 59 294s > linearHypothesis 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 48 294s 2 47 1 0.23 0.64 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 48 294s 2 47 1 0.31 0.58 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 48 294s 2 47 1 0.31 0.58 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 49 294s 2 47 2 0.5 0.61 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 49 294s 2 47 2 0.68 0.51 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 49 294s 2 47 2 1.37 0.5 294s > logLik 294s 'log Lik.' -71 (df=18) 294s 'log Lik.' -81.1 (df=18) 294s > 294s > # I3SLS 294s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 294s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 294s > summary 294s 294s systemfit results 294s method: iterated 3SLS 294s 294s convergence achieved after 15 iterations 294s 294s N DF SSR detRCov OLS-R2 McElroy-R2 294s system 59 47 81.3 0.349 0.958 0.995 294s 294s N DF SSR MSE RMSE R2 Adj R2 294s Consumption 19 15 18.1 1.209 1.100 0.980 0.976 294s Investment 20 16 52.0 3.250 1.803 0.776 0.735 294s PrivateWages 20 16 11.2 0.699 0.836 0.986 0.983 294s 294s The covariance matrix of the residuals used for estimation 294s Consumption Investment PrivateWages 294s Consumption 0.955 0.456 -0.421 294s Investment 0.456 2.294 0.375 294s PrivateWages -0.421 0.375 0.522 294s 294s The covariance matrix of the residuals 294s Consumption Investment PrivateWages 294s Consumption 0.955 0.456 -0.421 294s Investment 0.456 2.294 0.375 294s PrivateWages -0.421 0.375 0.522 294s 294s The correlations of the residuals 294s Consumption Investment PrivateWages 294s Consumption 1.000 0.322 -0.582 294s Investment 0.322 1.000 0.341 294s PrivateWages -0.582 0.341 1.000 294s 294s 294s 3SLS estimates for 'Consumption' (equation 1) 294s Model Formula: consump ~ corpProf + corpProfLag + wages 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 16.8311 1.2489 13.48 8.7e-10 *** 294s corpProf 0.1468 0.0991 1.48 0.16 294s corpProfLag 0.0924 0.0906 1.02 0.32 294s wages 0.7945 0.0371 21.43 1.2e-12 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.1 on 15 degrees of freedom 294s Number of observations: 19 Degrees of Freedom: 15 294s SSR: 18.14 MSE: 1.209 Root MSE: 1.1 294s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.976 294s 294s 294s 3SLS estimates for 'Investment' (equation 2) 294s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 32.4128 8.2695 3.92 0.00122 ** 294s corpProf -0.0799 0.1934 -0.41 0.68498 294s corpProfLag 0.7607 0.1878 4.05 0.00093 *** 294s capitalLag -0.2114 0.0400 -5.29 7.4e-05 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.803 on 16 degrees of freedom 294s Number of observations: 20 Degrees of Freedom: 16 294s SSR: 51.999 MSE: 3.25 Root MSE: 1.803 294s Multiple R-Squared: 0.776 Adjusted R-Squared: 0.735 294s 294s 294s 3SLS estimates for 'PrivateWages' (equation 3) 294s Model Formula: privWage ~ gnp + gnpLag + trend 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 1.5421 1.1496 1.34 0.19852 294s gnp 0.3936 0.0313 12.57 1.0e-09 *** 294s gnpLag 0.1945 0.0328 5.93 2.1e-05 *** 294s trend 0.1416 0.0286 4.95 0.00014 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 0.836 on 16 degrees of freedom 294s Number of observations: 20 Degrees of Freedom: 16 294s SSR: 11.181 MSE: 0.699 Root MSE: 0.836 294s Multiple R-Squared: 0.986 Adjusted R-Squared: 0.983 294s 294s > residuals 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 -0.3309 -2.6308 -1.3061 294s 3 -1.0419 0.0146 0.4450 294s 4 -1.2918 0.4128 1.4338 294s 5 -0.1772 -1.7488 -0.2494 294s 6 0.3563 0.2807 -0.4066 294s 7 NA NA NA 294s 8 1.6778 1.4671 -0.8700 294s 9 1.4561 1.1068 0.1712 294s 10 NA 2.9002 1.1262 294s 11 0.4237 -1.0652 -0.6189 294s 12 -0.2711 -0.9488 0.0375 294s 13 -0.5643 -1.6241 -0.5055 294s 14 0.2845 1.8477 0.3080 294s 15 -0.0514 -0.2379 0.3003 294s 16 0.0521 0.1268 0.0141 294s 17 1.8733 2.2462 -0.7083 294s 18 -0.1962 -0.1724 0.8305 294s 19 0.3553 -3.5810 -0.9448 294s 20 1.3161 1.0343 -0.2738 294s 21 1.2055 0.6622 -1.1283 294s 22 -1.6327 1.5541 0.8257 294s > fitted 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 42.2 2.431 26.8 294s 3 46.0 1.885 28.9 294s 4 50.5 4.787 32.7 294s 5 50.8 4.749 34.1 294s 6 52.2 4.819 35.8 294s 7 NA NA NA 294s 8 54.5 2.733 38.8 294s 9 55.8 1.893 39.0 294s 10 NA 2.200 40.2 294s 11 54.6 2.065 38.5 294s 12 51.2 -2.451 34.5 294s 13 46.2 -4.576 29.5 294s 14 46.2 -6.948 28.2 294s 15 48.8 -2.762 30.3 294s 16 51.2 -1.427 33.2 294s 17 55.8 -0.146 37.5 294s 18 58.9 2.172 40.2 294s 19 57.1 1.681 39.1 294s 20 60.3 0.266 41.9 294s 21 63.8 2.638 46.1 294s 22 71.3 3.346 52.5 294s > predict 294s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 294s 1 NA NA NA NA 294s 2 42.2 0.446 41.3 43.1 294s 3 46.0 0.511 45.0 47.1 294s 4 50.5 0.340 49.8 51.2 294s 5 50.8 0.393 50.0 51.6 294s 6 52.2 0.396 51.4 53.0 294s 7 NA NA NA NA 294s 8 54.5 0.326 53.9 55.2 294s 9 55.8 0.362 55.1 56.6 294s 10 NA NA NA NA 294s 11 54.6 0.612 53.3 55.8 294s 12 51.2 0.511 50.1 52.2 294s 13 46.2 0.671 44.8 47.5 294s 14 46.2 0.563 45.1 47.3 294s 15 48.8 0.354 48.0 49.5 294s 16 51.2 0.311 50.6 51.9 294s 17 55.8 0.362 55.1 56.6 294s 18 58.9 0.297 58.3 59.5 294s 19 57.1 0.357 56.4 57.9 294s 20 60.3 0.427 59.4 61.1 294s 21 63.8 0.416 63.0 64.6 294s 22 71.3 0.640 70.0 72.6 294s Investment.pred Investment.se.fit Investment.lwr Investment.upr 294s 1 NA NA NA NA 294s 2 2.431 0.970 0.4798 4.382 294s 3 1.885 0.745 0.3859 3.385 294s 4 4.787 0.664 3.4506 6.124 294s 5 4.749 0.562 3.6174 5.880 294s 6 4.819 0.537 3.7391 5.900 294s 7 NA NA NA NA 294s 8 2.733 0.446 1.8351 3.631 294s 9 1.893 0.620 0.6455 3.141 294s 10 2.200 0.684 0.8232 3.576 294s 11 2.065 1.055 -0.0569 4.187 294s 12 -2.451 0.845 -4.1517 -0.751 294s 13 -4.576 1.070 -6.7293 -2.423 294s 14 -6.948 1.103 -9.1676 -4.728 294s 15 -2.762 0.556 -3.8806 -1.644 294s 16 -1.427 0.480 -2.3919 -0.462 294s 17 -0.146 0.603 -1.3588 1.066 294s 18 2.172 0.390 1.3869 2.958 294s 19 1.681 0.563 0.5476 2.815 294s 20 0.266 0.661 -1.0634 1.595 294s 21 2.638 0.558 1.5144 3.761 294s 22 3.346 0.778 1.7808 4.911 294s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 294s 1 NA NA NA NA 294s 2 26.8 0.326 26.2 27.5 294s 3 28.9 0.328 28.2 29.5 294s 4 32.7 0.334 32.0 33.3 294s 5 34.1 0.242 33.7 34.6 294s 6 35.8 0.252 35.3 36.3 294s 7 NA NA NA NA 294s 8 38.8 0.244 38.3 39.3 294s 9 39.0 0.232 38.6 39.5 294s 10 40.2 0.230 39.7 40.6 294s 11 38.5 0.308 37.9 39.1 294s 12 34.5 0.336 33.8 35.1 294s 13 29.5 0.420 28.7 30.4 294s 14 28.2 0.345 27.5 28.9 294s 15 30.3 0.325 29.6 31.0 294s 16 33.2 0.271 32.6 33.7 294s 17 37.5 0.267 37.0 38.0 294s 18 40.2 0.218 39.7 40.6 294s 19 39.1 0.331 38.5 39.8 294s 20 41.9 0.289 41.3 42.5 294s 21 46.1 0.311 45.5 46.8 294s 22 52.5 0.485 51.5 53.5 294s > model.frame 294s [1] TRUE 294s > model.matrix 294s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0328 >" 294s [2] "Attributes: < Component “dimnames”: Component 1: 54 string mismatches >" 294s [3] "Numeric: lengths (732, 708) differ" 294s > nobs 294s [1] 59 294s > linearHypothesis 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 48 294s 2 47 1 0.28 0.6 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 48 294s 2 47 1 0.37 0.55 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 48 294s 2 47 1 0.37 0.54 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 49 294s 2 47 2 1.25 0.3 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 49 294s 2 47 2 1.64 0.21 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 49 294s 2 47 2 3.28 0.19 294s > logLik 294s 'log Lik.' -74.5 (df=18) 294s 'log Lik.' -87.1 (df=18) 294s > 294s > # OLS 294s > summary 294s Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 294s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 294s 294s systemfit results 294s method: OLS 294s 294s N DF SSR detRCov OLS-R2 McElroy-R2 294s system 59 47 44.2 0.453 0.976 0.99 294s 294s N DF SSR MSE RMSE R2 Adj R2 294s Consumption 19 15 17.36 1.157 1.08 0.980 0.976 294s Investment 20 16 17.11 1.069 1.03 0.912 0.895 294s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 294s 294s The covariance matrix of the residuals 294s Consumption Investment PrivateWages 294s Consumption 1.1939 0.0559 -0.474 294s Investment 0.0559 0.9839 0.140 294s PrivateWages -0.4745 0.1403 0.602 294s 294s The correlations of the residuals 294s Consumption Investment PrivateWages 294s Consumption 1.0000 0.0447 -0.568 294s Investment 0.0447 1.0000 0.169 294s PrivateWages -0.5680 0.1689 1.000 294s 294s 294s OLS estimates for 'Consumption' (equation 1) 294s Model Formula: consump ~ corpProf + corpProfLag + wages 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 16.2957 1.4879 10.95 1.5e-08 *** 294s corpProf 0.1796 0.1162 1.55 0.14 294s corpProfLag 0.1032 0.0994 1.04 0.32 294s wages 0.7962 0.0433 18.39 1.1e-11 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.076 on 15 degrees of freedom 294s Number of observations: 19 Degrees of Freedom: 15 294s SSR: 17.362 MSE: 1.157 Root MSE: 1.076 294s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.976 294s 294s 294s OLS estimates for 'Investment' (equation 2) 294s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 10.1813 5.3720 1.90 0.07627 . 294s corpProf 0.5003 0.1052 4.75 0.00022 *** 294s corpProfLag 0.3259 0.1003 3.25 0.00502 ** 294s capitalLag -0.1134 0.0265 -4.28 0.00057 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.034 on 16 degrees of freedom 294s Number of observations: 20 Degrees of Freedom: 16 294s SSR: 17.109 MSE: 1.069 Root MSE: 1.034 294s Multiple R-Squared: 0.912 Adjusted R-Squared: 0.895 294s 294s 294s OLS estimates for 'PrivateWages' (equation 3) 294s Model Formula: privWage ~ gnp + gnpLag + trend 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 1.3550 1.3021 1.04 0.3135 294s gnp 0.4417 0.0330 13.40 4.1e-10 *** 294s gnpLag 0.1466 0.0379 3.87 0.0013 ** 294s trend 0.1244 0.0335 3.72 0.0019 ** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 0.78 on 16 degrees of freedom 294s Number of observations: 20 Degrees of Freedom: 16 294s SSR: 9.739 MSE: 0.609 Root MSE: 0.78 294s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 294s 294s compare coef with single-equation OLS 294s [1] TRUE 294s > residuals 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 -0.3863 -0.000301 -1.3389 294s 3 -1.2484 -0.076489 0.2462 294s 4 -1.6040 1.221792 1.1255 294s 5 -0.5384 -1.377872 -0.1959 294s 6 -0.0413 0.386104 -0.5284 294s 7 0.8043 1.486279 NA 294s 8 1.2830 0.784055 -0.7909 294s 9 1.0142 -0.655354 0.2819 294s 10 NA 1.060871 1.1384 294s 11 0.1429 0.395249 -0.1904 294s 12 -0.3439 0.198005 0.5813 294s 13 NA NA 0.1206 294s 14 0.3199 0.312725 0.4773 294s 15 -0.1016 -0.084685 0.3035 294s 16 -0.0702 0.066194 0.0284 294s 17 1.6064 0.963697 -0.8517 294s 18 -0.4980 0.078506 0.9908 294s 19 0.1253 -2.496401 -0.4597 294s 20 0.9805 -0.711004 -0.3819 294s 21 0.7551 -0.820172 -1.1062 294s 22 -2.1992 -0.731199 0.5501 294s > fitted 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 42.3 -0.200 26.8 294s 3 46.2 1.976 29.1 294s 4 50.8 3.978 33.0 294s 5 51.1 4.378 34.1 294s 6 52.6 4.714 35.9 294s 7 54.3 4.114 NA 294s 8 54.9 3.416 38.7 294s 9 56.3 3.655 38.9 294s 10 NA 4.039 40.2 294s 11 54.9 0.605 38.1 294s 12 51.2 -3.598 33.9 294s 13 NA NA 28.9 294s 14 46.2 -5.413 28.0 294s 15 48.8 -2.915 30.3 294s 16 51.4 -1.366 33.2 294s 17 56.1 1.136 37.7 294s 18 59.2 1.921 40.0 294s 19 57.4 0.596 38.7 294s 20 60.6 2.011 42.0 294s 21 64.2 4.120 46.1 294s 22 71.9 5.631 52.7 294s > predict 294s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 294s 1 NA NA NA NA 294s 2 42.3 0.523 39.9 44.7 294s 3 46.2 0.560 43.8 48.7 294s 4 50.8 0.379 48.5 53.1 294s 5 51.1 0.448 48.8 53.5 294s 6 52.6 0.457 50.3 55.0 294s 7 54.3 0.408 52.0 56.6 294s 8 54.9 0.375 52.6 57.2 294s 9 56.3 0.418 54.0 58.6 294s 10 NA NA NA NA 294s 11 54.9 0.701 52.3 57.4 294s 12 51.2 0.638 48.7 53.8 294s 13 NA NA NA NA 294s 14 46.2 0.673 43.6 48.7 294s 15 48.8 0.453 46.5 51.2 294s 16 51.4 0.384 49.1 53.7 294s 17 56.1 0.391 53.8 58.4 294s 18 59.2 0.361 56.9 61.5 294s 19 57.4 0.449 55.0 59.7 294s 20 60.6 0.465 58.3 63.0 294s 21 64.2 0.468 61.9 66.6 294s 22 71.9 0.728 69.3 74.5 294s Investment.pred Investment.se.fit Investment.lwr Investment.upr 294s 1 NA NA NA NA 294s 2 -0.200 0.613 -2.618 2.219 294s 3 1.976 0.494 -0.329 4.282 294s 4 3.978 0.444 1.714 6.242 294s 5 4.378 0.369 2.169 6.587 294s 6 4.714 0.349 2.519 6.909 294s 7 4.114 0.323 1.934 6.293 294s 8 3.416 0.287 1.257 5.575 294s 9 3.655 0.386 1.435 5.876 294s 10 4.039 0.441 1.777 6.301 294s 11 0.605 0.641 -1.843 3.053 294s 12 -3.598 0.606 -6.010 -1.186 294s 13 NA NA NA NA 294s 14 -5.413 0.708 -7.934 -2.892 294s 15 -2.915 0.412 -5.155 -0.676 294s 16 -1.366 0.336 -3.554 0.821 294s 17 1.136 0.342 -1.055 3.327 294s 18 1.921 0.246 -0.217 4.060 294s 19 0.596 0.341 -1.594 2.787 294s 20 2.011 0.364 -0.194 4.216 294s 21 4.120 0.337 1.932 6.308 294s 22 5.631 0.477 3.341 7.922 294s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 294s 1 NA NA NA NA 294s 2 26.8 0.364 25.1 28.6 294s 3 29.1 0.367 27.3 30.8 294s 4 33.0 0.370 31.2 34.7 294s 5 34.1 0.286 32.4 35.8 294s 6 35.9 0.285 34.3 37.6 294s 7 NA NA NA NA 294s 8 38.7 0.292 37.0 40.4 294s 9 38.9 0.277 37.3 40.6 294s 10 40.2 0.264 38.5 41.8 294s 11 38.1 0.363 36.4 39.8 294s 12 33.9 0.367 32.2 35.7 294s 13 28.9 0.435 27.1 30.7 294s 14 28.0 0.383 26.3 29.8 294s 15 30.3 0.377 28.6 32.0 294s 16 33.2 0.315 31.5 34.9 294s 17 37.7 0.308 36.0 39.3 294s 18 40.0 0.241 38.4 41.7 294s 19 38.7 0.361 36.9 40.4 294s 20 42.0 0.324 40.3 43.7 294s 21 46.1 0.339 44.4 47.8 294s 22 52.7 0.511 50.9 54.6 294s > model.frame 294s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 294s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 294s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 294s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 294s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 294s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 294s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 294s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 294s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 294s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 294s 10 57.8 21.7 21.1 NA 5.1 211 41.3 67.0 64.5 294s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 294s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 294s 13 45.6 NA 11.4 34.3 -6.2 213 29.0 44.3 53.4 294s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 294s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 294s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 294s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 294s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 294s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 294s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 294s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 294s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 294s trend 294s 1 -11 294s 2 -10 294s 3 -9 294s 4 -8 294s 5 -7 294s 6 -6 294s 7 -5 294s 8 -4 294s 9 -3 294s 10 -2 294s 11 -1 294s 12 0 294s 13 1 294s 14 2 294s 15 3 294s 16 4 294s 17 5 294s 18 6 294s 19 7 294s 20 8 294s 21 9 294s 22 10 294s > model.matrix 294s Consumption_(Intercept) Consumption_corpProf 294s Consumption_2 1 12.4 294s Consumption_3 1 16.9 294s Consumption_4 1 18.4 294s Consumption_5 1 19.4 294s Consumption_6 1 20.1 294s Consumption_7 1 19.6 294s Consumption_8 1 19.8 294s Consumption_9 1 21.1 294s Consumption_11 1 15.6 294s Consumption_12 1 11.4 294s Consumption_14 1 11.2 294s Consumption_15 1 12.3 294s Consumption_16 1 14.0 294s Consumption_17 1 17.6 294s Consumption_18 1 17.3 294s Consumption_19 1 15.3 294s Consumption_20 1 19.0 294s Consumption_21 1 21.1 294s Consumption_22 1 23.5 294s Investment_2 0 0.0 294s Investment_3 0 0.0 294s Investment_4 0 0.0 294s Investment_5 0 0.0 294s Investment_6 0 0.0 294s Investment_7 0 0.0 294s Investment_8 0 0.0 294s Investment_9 0 0.0 294s Investment_10 0 0.0 294s Investment_11 0 0.0 294s Investment_12 0 0.0 294s Investment_14 0 0.0 294s Investment_15 0 0.0 294s Investment_16 0 0.0 294s Investment_17 0 0.0 294s Investment_18 0 0.0 294s Investment_19 0 0.0 294s Investment_20 0 0.0 294s Investment_21 0 0.0 294s Investment_22 0 0.0 294s PrivateWages_2 0 0.0 294s PrivateWages_3 0 0.0 294s PrivateWages_4 0 0.0 294s PrivateWages_5 0 0.0 294s PrivateWages_6 0 0.0 294s PrivateWages_8 0 0.0 294s PrivateWages_9 0 0.0 294s PrivateWages_10 0 0.0 294s PrivateWages_11 0 0.0 294s PrivateWages_12 0 0.0 294s PrivateWages_13 0 0.0 294s PrivateWages_14 0 0.0 294s PrivateWages_15 0 0.0 294s PrivateWages_16 0 0.0 294s PrivateWages_17 0 0.0 294s PrivateWages_18 0 0.0 294s PrivateWages_19 0 0.0 294s PrivateWages_20 0 0.0 294s PrivateWages_21 0 0.0 294s PrivateWages_22 0 0.0 294s Consumption_corpProfLag Consumption_wages 294s Consumption_2 12.7 28.2 294s Consumption_3 12.4 32.2 294s Consumption_4 16.9 37.0 294s Consumption_5 18.4 37.0 294s Consumption_6 19.4 38.6 294s Consumption_7 20.1 40.7 294s Consumption_8 19.6 41.5 294s Consumption_9 19.8 42.9 294s Consumption_11 21.7 42.1 294s Consumption_12 15.6 39.3 294s Consumption_14 7.0 34.1 294s Consumption_15 11.2 36.6 294s Consumption_16 12.3 39.3 294s Consumption_17 14.0 44.2 294s Consumption_18 17.6 47.7 294s Consumption_19 17.3 45.9 294s Consumption_20 15.3 49.4 294s Consumption_21 19.0 53.0 294s Consumption_22 21.1 61.8 294s Investment_2 0.0 0.0 294s Investment_3 0.0 0.0 294s Investment_4 0.0 0.0 294s Investment_5 0.0 0.0 294s Investment_6 0.0 0.0 294s Investment_7 0.0 0.0 294s Investment_8 0.0 0.0 294s Investment_9 0.0 0.0 294s Investment_10 0.0 0.0 294s Investment_11 0.0 0.0 294s Investment_12 0.0 0.0 294s Investment_14 0.0 0.0 294s Investment_15 0.0 0.0 294s Investment_16 0.0 0.0 294s Investment_17 0.0 0.0 294s Investment_18 0.0 0.0 294s Investment_19 0.0 0.0 294s Investment_20 0.0 0.0 294s Investment_21 0.0 0.0 294s Investment_22 0.0 0.0 294s PrivateWages_2 0.0 0.0 294s PrivateWages_3 0.0 0.0 294s PrivateWages_4 0.0 0.0 294s PrivateWages_5 0.0 0.0 294s PrivateWages_6 0.0 0.0 294s PrivateWages_8 0.0 0.0 294s PrivateWages_9 0.0 0.0 294s PrivateWages_10 0.0 0.0 294s PrivateWages_11 0.0 0.0 294s PrivateWages_12 0.0 0.0 294s PrivateWages_13 0.0 0.0 294s PrivateWages_14 0.0 0.0 294s PrivateWages_15 0.0 0.0 294s PrivateWages_16 0.0 0.0 294s PrivateWages_17 0.0 0.0 294s PrivateWages_18 0.0 0.0 294s PrivateWages_19 0.0 0.0 294s PrivateWages_20 0.0 0.0 294s PrivateWages_21 0.0 0.0 294s PrivateWages_22 0.0 0.0 294s Investment_(Intercept) Investment_corpProf 294s Consumption_2 0 0.0 294s Consumption_3 0 0.0 294s Consumption_4 0 0.0 294s Consumption_5 0 0.0 294s Consumption_6 0 0.0 294s Consumption_7 0 0.0 294s Consumption_8 0 0.0 294s Consumption_9 0 0.0 294s Consumption_11 0 0.0 294s Consumption_12 0 0.0 294s Consumption_14 0 0.0 294s Consumption_15 0 0.0 294s Consumption_16 0 0.0 294s Consumption_17 0 0.0 294s Consumption_18 0 0.0 294s Consumption_19 0 0.0 294s Consumption_20 0 0.0 294s Consumption_21 0 0.0 294s Consumption_22 0 0.0 294s Investment_2 1 12.4 294s Investment_3 1 16.9 294s Investment_4 1 18.4 294s Investment_5 1 19.4 294s Investment_6 1 20.1 294s Investment_7 1 19.6 294s Investment_8 1 19.8 294s Investment_9 1 21.1 294s Investment_10 1 21.7 294s Investment_11 1 15.6 294s Investment_12 1 11.4 294s Investment_14 1 11.2 294s Investment_15 1 12.3 294s Investment_16 1 14.0 294s Investment_17 1 17.6 294s Investment_18 1 17.3 294s Investment_19 1 15.3 294s Investment_20 1 19.0 294s Investment_21 1 21.1 294s Investment_22 1 23.5 294s PrivateWages_2 0 0.0 294s PrivateWages_3 0 0.0 294s PrivateWages_4 0 0.0 294s PrivateWages_5 0 0.0 294s PrivateWages_6 0 0.0 294s PrivateWages_8 0 0.0 294s PrivateWages_9 0 0.0 294s PrivateWages_10 0 0.0 294s PrivateWages_11 0 0.0 294s PrivateWages_12 0 0.0 294s PrivateWages_13 0 0.0 294s PrivateWages_14 0 0.0 294s PrivateWages_15 0 0.0 294s PrivateWages_16 0 0.0 294s PrivateWages_17 0 0.0 294s PrivateWages_18 0 0.0 294s PrivateWages_19 0 0.0 294s PrivateWages_20 0 0.0 294s PrivateWages_21 0 0.0 294s PrivateWages_22 0 0.0 294s Investment_corpProfLag Investment_capitalLag 294s Consumption_2 0.0 0 294s Consumption_3 0.0 0 294s Consumption_4 0.0 0 294s Consumption_5 0.0 0 294s Consumption_6 0.0 0 294s Consumption_7 0.0 0 294s Consumption_8 0.0 0 294s Consumption_9 0.0 0 294s Consumption_11 0.0 0 294s Consumption_12 0.0 0 294s Consumption_14 0.0 0 294s Consumption_15 0.0 0 294s Consumption_16 0.0 0 294s Consumption_17 0.0 0 294s Consumption_18 0.0 0 294s Consumption_19 0.0 0 294s Consumption_20 0.0 0 294s Consumption_21 0.0 0 294s Consumption_22 0.0 0 294s Investment_2 12.7 183 294s Investment_3 12.4 183 294s Investment_4 16.9 184 294s Investment_5 18.4 190 294s Investment_6 19.4 193 294s Investment_7 20.1 198 294s Investment_8 19.6 203 294s Investment_9 19.8 208 294s Investment_10 21.1 211 294s Investment_11 21.7 216 294s Investment_12 15.6 217 294s Investment_14 7.0 207 294s Investment_15 11.2 202 294s Investment_16 12.3 199 294s Investment_17 14.0 198 294s Investment_18 17.6 200 294s Investment_19 17.3 202 294s Investment_20 15.3 200 294s Investment_21 19.0 201 294s Investment_22 21.1 204 294s PrivateWages_2 0.0 0 294s PrivateWages_3 0.0 0 294s PrivateWages_4 0.0 0 294s PrivateWages_5 0.0 0 294s PrivateWages_6 0.0 0 294s PrivateWages_8 0.0 0 294s PrivateWages_9 0.0 0 294s PrivateWages_10 0.0 0 294s PrivateWages_11 0.0 0 294s PrivateWages_12 0.0 0 294s PrivateWages_13 0.0 0 294s PrivateWages_14 0.0 0 294s PrivateWages_15 0.0 0 294s PrivateWages_16 0.0 0 294s PrivateWages_17 0.0 0 294s PrivateWages_18 0.0 0 294s PrivateWages_19 0.0 0 294s PrivateWages_20 0.0 0 294s PrivateWages_21 0.0 0 294s PrivateWages_22 0.0 0 294s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 294s Consumption_2 0 0.0 0.0 294s Consumption_3 0 0.0 0.0 294s Consumption_4 0 0.0 0.0 294s Consumption_5 0 0.0 0.0 294s Consumption_6 0 0.0 0.0 294s Consumption_7 0 0.0 0.0 294s Consumption_8 0 0.0 0.0 294s Consumption_9 0 0.0 0.0 294s Consumption_11 0 0.0 0.0 294s Consumption_12 0 0.0 0.0 294s Consumption_14 0 0.0 0.0 294s Consumption_15 0 0.0 0.0 294s Consumption_16 0 0.0 0.0 294s Consumption_17 0 0.0 0.0 294s Consumption_18 0 0.0 0.0 294s Consumption_19 0 0.0 0.0 294s Consumption_20 0 0.0 0.0 294s Consumption_21 0 0.0 0.0 294s Consumption_22 0 0.0 0.0 294s Investment_2 0 0.0 0.0 294s Investment_3 0 0.0 0.0 294s Investment_4 0 0.0 0.0 294s Investment_5 0 0.0 0.0 294s Investment_6 0 0.0 0.0 294s Investment_7 0 0.0 0.0 294s Investment_8 0 0.0 0.0 294s Investment_9 0 0.0 0.0 294s Investment_10 0 0.0 0.0 294s Investment_11 0 0.0 0.0 294s Investment_12 0 0.0 0.0 294s Investment_14 0 0.0 0.0 294s Investment_15 0 0.0 0.0 294s Investment_16 0 0.0 0.0 294s Investment_17 0 0.0 0.0 294s Investment_18 0 0.0 0.0 294s Investment_19 0 0.0 0.0 294s Investment_20 0 0.0 0.0 294s Investment_21 0 0.0 0.0 294s Investment_22 0 0.0 0.0 294s PrivateWages_2 1 45.6 44.9 294s PrivateWages_3 1 50.1 45.6 294s PrivateWages_4 1 57.2 50.1 294s PrivateWages_5 1 57.1 57.2 294s PrivateWages_6 1 61.0 57.1 294s PrivateWages_8 1 64.4 64.0 294s PrivateWages_9 1 64.5 64.4 294s PrivateWages_10 1 67.0 64.5 294s PrivateWages_11 1 61.2 67.0 294s PrivateWages_12 1 53.4 61.2 294s PrivateWages_13 1 44.3 53.4 294s PrivateWages_14 1 45.1 44.3 294s PrivateWages_15 1 49.7 45.1 294s PrivateWages_16 1 54.4 49.7 294s PrivateWages_17 1 62.7 54.4 294s PrivateWages_18 1 65.0 62.7 294s PrivateWages_19 1 60.9 65.0 294s PrivateWages_20 1 69.5 60.9 294s PrivateWages_21 1 75.7 69.5 294s PrivateWages_22 1 88.4 75.7 294s PrivateWages_trend 294s Consumption_2 0 294s Consumption_3 0 294s Consumption_4 0 294s Consumption_5 0 294s Consumption_6 0 294s Consumption_7 0 294s Consumption_8 0 294s Consumption_9 0 294s Consumption_11 0 294s Consumption_12 0 294s Consumption_14 0 294s Consumption_15 0 294s Consumption_16 0 294s Consumption_17 0 294s Consumption_18 0 294s Consumption_19 0 294s Consumption_20 0 294s Consumption_21 0 294s Consumption_22 0 294s Investment_2 0 294s Investment_3 0 294s Investment_4 0 294s Investment_5 0 294s Investment_6 0 294s Investment_7 0 294s Investment_8 0 294s Investment_9 0 294s Investment_10 0 294s Investment_11 0 294s Investment_12 0 294s Investment_14 0 294s Investment_15 0 294s Investment_16 0 294s Investment_17 0 294s Investment_18 0 294s Investment_19 0 294s Investment_20 0 294s Investment_21 0 294s Investment_22 0 294s PrivateWages_2 -10 294s PrivateWages_3 -9 294s PrivateWages_4 -8 294s PrivateWages_5 -7 294s PrivateWages_6 -6 294s PrivateWages_8 -4 294s PrivateWages_9 -3 294s PrivateWages_10 -2 294s PrivateWages_11 -1 294s PrivateWages_12 0 294s PrivateWages_13 1 294s PrivateWages_14 2 294s PrivateWages_15 3 294s PrivateWages_16 4 294s PrivateWages_17 5 294s PrivateWages_18 6 294s PrivateWages_19 7 294s PrivateWages_20 8 294s PrivateWages_21 9 294s PrivateWages_22 10 294s > nobs 294s [1] 59 294s > linearHypothesis 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 48 294s 2 47 1 0.33 0.57 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 48 294s 2 47 1 0.31 0.58 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 48 294s 2 47 1 0.31 0.58 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 49 294s 2 47 2 0.17 0.84 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 49 294s 2 47 2 0.16 0.85 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 49 294s 2 47 2 0.33 0.85 294s > logLik 294s 'log Lik.' -69.6 (df=13) 294s 'log Lik.' -74.2 (df=13) 294s compare log likelihood value with single-equation OLS 294s [1] "Mean relative difference: 0.00099" 294s > 294s > # 2SLS 294s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 294s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 294s > summary 294s 294s systemfit results 294s method: 2SLS 294s 294s N DF SSR detRCov OLS-R2 McElroy-R2 294s system 57 45 58.2 0.333 0.968 0.991 294s 294s N DF SSR MSE RMSE R2 Adj R2 294s Consumption 18 14 22.27 1.591 1.26 0.974 0.968 294s Investment 19 15 26.21 1.748 1.32 0.852 0.823 294s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 294s 294s The covariance matrix of the residuals 294s Consumption Investment PrivateWages 294s Consumption 1.237 0.518 -0.408 294s Investment 0.518 1.263 0.113 294s PrivateWages -0.408 0.113 0.468 294s 294s The correlations of the residuals 294s Consumption Investment PrivateWages 294s Consumption 1.000 0.416 -0.538 294s Investment 0.416 1.000 0.139 294s PrivateWages -0.538 0.139 1.000 294s 294s 294s 2SLS estimates for 'Consumption' (equation 1) 294s Model Formula: consump ~ corpProf + corpProfLag + wages 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 17.2849 1.6018 10.79 3.6e-08 *** 294s corpProf -0.0770 0.1637 -0.47 0.645 294s corpProfLag 0.2327 0.1242 1.87 0.082 . 294s wages 0.8259 0.0459 17.98 4.5e-11 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.261 on 14 degrees of freedom 294s Number of observations: 18 Degrees of Freedom: 14 294s SSR: 22.269 MSE: 1.591 Root MSE: 1.261 294s Multiple R-Squared: 0.974 Adjusted R-Squared: 0.968 294s 294s 294s 2SLS estimates for 'Investment' (equation 2) 294s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 18.4005 7.1627 2.57 0.02138 * 294s corpProf 0.1507 0.1905 0.79 0.44118 294s corpProfLag 0.5757 0.1634 3.52 0.00307 ** 294s capitalLag -0.1452 0.0339 -4.28 0.00065 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.322 on 15 degrees of freedom 294s Number of observations: 19 Degrees of Freedom: 15 294s SSR: 26.213 MSE: 1.748 Root MSE: 1.322 294s Multiple R-Squared: 0.852 Adjusted R-Squared: 0.823 294s 294s 294s 2SLS estimates for 'PrivateWages' (equation 3) 294s Model Formula: privWage ~ gnp + gnpLag + trend 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 1.3431 1.1544 1.16 0.26172 294s gnp 0.4438 0.0351 12.64 9.7e-10 *** 294s gnpLag 0.1447 0.0381 3.80 0.00158 ** 294s trend 0.1238 0.0300 4.13 0.00078 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 0.78 on 16 degrees of freedom 294s Number of observations: 20 Degrees of Freedom: 16 294s SSR: 9.741 MSE: 0.609 Root MSE: 0.78 294s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 294s 294s > residuals 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 -0.6754 -1.23599 -1.3401 294s 3 -0.4627 0.32957 0.2378 294s 4 -1.1585 1.08894 1.1117 294s 5 -0.0305 -1.37017 -0.1954 294s 6 0.4693 0.48431 -0.5355 294s 7 NA NA NA 294s 8 1.6045 1.06811 -0.7908 294s 9 1.6018 0.16695 0.2831 294s 10 NA 1.86380 1.1353 294s 11 -0.9031 -0.92183 -0.1765 294s 12 -1.5948 -1.03217 0.6007 294s 13 NA NA 0.1443 294s 14 0.2854 0.85468 0.4826 294s 15 -0.4718 -0.36943 0.3016 294s 16 -0.2268 0.00554 0.0261 294s 17 2.0079 1.69566 -0.8614 294s 18 -0.7434 -0.12659 0.9927 294s 19 -0.5410 -3.26209 -0.4446 294s 20 1.4186 0.25579 -0.3914 294s 21 1.1462 -0.00185 -1.1115 294s 22 -1.7256 0.50679 0.5312 294s > fitted 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 42.6 1.036 26.8 294s 3 45.5 1.570 29.1 294s 4 50.4 4.111 33.0 294s 5 50.6 4.370 34.1 294s 6 52.1 4.616 35.9 294s 7 NA NA NA 294s 8 54.6 3.132 38.7 294s 9 55.7 2.833 38.9 294s 10 NA 3.236 40.2 294s 11 55.9 1.922 38.1 294s 12 52.5 -2.368 33.9 294s 13 NA NA 28.9 294s 14 46.2 -5.955 28.0 294s 15 49.2 -2.631 30.3 294s 16 51.5 -1.306 33.2 294s 17 55.7 0.404 37.7 294s 18 59.4 2.127 40.0 294s 19 58.0 1.362 38.6 294s 20 60.2 1.044 42.0 294s 21 63.9 3.302 46.1 294s 22 71.4 4.393 52.8 294s > predict 294s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 294s 1 NA NA NA NA 294s 2 42.6 0.571 41.4 43.8 294s 3 45.5 0.656 44.1 46.9 294s 4 50.4 0.431 49.4 51.3 294s 5 50.6 0.510 49.5 51.7 294s 6 52.1 0.521 51.0 53.2 294s 7 NA NA NA NA 294s 8 54.6 0.419 53.7 55.5 294s 9 55.7 0.496 54.6 56.8 294s 10 NA NA NA NA 294s 11 55.9 0.910 54.0 57.9 294s 12 52.5 0.869 50.6 54.4 294s 13 NA NA NA NA 294s 14 46.2 0.694 44.7 47.7 294s 15 49.2 0.487 48.1 50.2 294s 16 51.5 0.396 50.7 52.4 294s 17 55.7 0.445 54.7 56.6 294s 18 59.4 0.386 58.6 60.3 294s 19 58.0 0.548 56.9 59.2 294s 20 60.2 0.528 59.0 61.3 294s 21 63.9 0.515 62.8 65.0 294s 22 71.4 0.786 69.7 73.1 294s Investment.pred Investment.se.fit Investment.lwr Investment.upr 294s 1 NA NA NA NA 294s 2 1.036 0.892 -0.865 2.937 294s 3 1.570 0.579 0.335 2.805 294s 4 4.111 0.531 2.979 5.243 294s 5 4.370 0.440 3.432 5.308 294s 6 4.616 0.416 3.729 5.502 294s 7 NA NA NA NA 294s 8 3.132 0.344 2.398 3.866 294s 9 2.833 0.533 1.696 3.970 294s 10 3.236 0.580 2.000 4.473 294s 11 1.922 0.959 -0.122 3.966 294s 12 -2.368 0.860 -4.201 -0.534 294s 13 NA NA NA NA 294s 14 -5.955 0.865 -7.799 -4.110 294s 15 -2.631 0.479 -3.652 -1.610 294s 16 -1.306 0.382 -2.120 -0.491 294s 17 0.404 0.487 -0.635 1.443 294s 18 2.127 0.319 1.447 2.806 294s 19 1.362 0.537 0.218 2.506 294s 20 1.044 0.566 -0.162 2.250 294s 21 3.302 0.486 2.265 4.339 294s 22 4.393 0.713 2.874 5.912 294s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 294s 1 NA NA NA NA 294s 2 26.8 0.321 26.2 27.5 294s 3 29.1 0.334 28.4 29.8 294s 4 33.0 0.353 32.2 33.7 294s 5 34.1 0.253 33.6 34.6 294s 6 35.9 0.261 35.4 36.5 294s 7 NA NA NA NA 294s 8 38.7 0.257 38.1 39.2 294s 9 38.9 0.245 38.4 39.4 294s 10 40.2 0.235 39.7 40.7 294s 11 38.1 0.348 37.3 38.8 294s 12 33.9 0.374 33.1 34.7 294s 13 28.9 0.447 27.9 29.8 294s 14 28.0 0.341 27.3 28.7 294s 15 30.3 0.333 29.6 31.0 294s 16 33.2 0.278 32.6 33.8 294s 17 37.7 0.288 37.1 38.3 294s 18 40.0 0.214 39.6 40.5 294s 19 38.6 0.351 37.9 39.4 294s 20 42.0 0.301 41.4 42.6 294s 21 46.1 0.304 45.5 46.8 294s 22 52.8 0.486 51.7 53.8 294s > model.frame 294s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 294s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 294s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 294s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 294s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 294s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 294s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 294s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 294s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 294s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 294s 10 57.8 21.7 21.1 NA 5.1 211 41.3 67.0 64.5 294s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 294s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 294s 13 45.6 NA 11.4 34.3 -6.2 213 29.0 44.3 53.4 294s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 294s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 294s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 294s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 294s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 294s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 294s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 294s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 294s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 294s trend 294s 1 -11 294s 2 -10 294s 3 -9 294s 4 -8 294s 5 -7 294s 6 -6 294s 7 -5 294s 8 -4 294s 9 -3 294s 10 -2 294s 11 -1 294s 12 0 294s 13 1 294s 14 2 294s 15 3 294s 16 4 294s 17 5 294s 18 6 294s 19 7 294s 20 8 294s 21 9 294s 22 10 294s > model.matrix 294s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0339 >" 294s [2] "Attributes: < Component “dimnames”: Component 1: 52 string mismatches >" 294s [3] "Numeric: lengths (708, 684) differ" 294s > nobs 294s [1] 57 294s > linearHypothesis 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 46 294s 2 45 1 1.37 0.25 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 46 294s 2 45 1 1.77 0.19 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 46 294s 2 45 1 1.77 0.18 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 47 294s 2 45 2 0.69 0.51 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 47 294s 2 45 2 0.89 0.42 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 47 294s 2 45 2 1.78 0.41 294s > logLik 294s 'log Lik.' -70.6 (df=13) 294s 'log Lik.' -78.7 (df=13) 294s > 294s > # SUR 294s Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 294s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 294s > summary 294s 294s systemfit results 294s method: SUR 294s 294s N DF SSR detRCov OLS-R2 McElroy-R2 294s system 59 47 45.1 0.168 0.976 0.992 294s 294s N DF SSR MSE RMSE R2 Adj R2 294s Consumption 19 15 17.5 1.167 1.080 0.980 0.975 294s Investment 20 16 17.3 1.083 1.041 0.911 0.894 294s PrivateWages 20 16 10.3 0.642 0.801 0.987 0.985 294s 294s The covariance matrix of the residuals used for estimation 294s Consumption Investment PrivateWages 294s Consumption 0.9286 0.0435 -0.369 294s Investment 0.0435 0.7653 0.109 294s PrivateWages -0.3690 0.1091 0.468 294s 294s The covariance matrix of the residuals 294s Consumption Investment PrivateWages 294s Consumption 0.9251 0.0748 -0.427 294s Investment 0.0748 0.7653 0.171 294s PrivateWages -0.4268 0.1706 0.492 294s 294s The correlations of the residuals 294s Consumption Investment PrivateWages 294s Consumption 1.0000 0.0888 -0.636 294s Investment 0.0888 1.0000 0.268 294s PrivateWages -0.6364 0.2678 1.000 294s 294s 294s SUR estimates for 'Consumption' (equation 1) 294s Model Formula: consump ~ corpProf + corpProfLag + wages 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 16.2684 1.2781 12.73 1.9e-09 *** 294s corpProf 0.1942 0.0927 2.10 0.054 . 294s corpProfLag 0.0746 0.0819 0.91 0.377 294s wages 0.8011 0.0372 21.53 1.1e-12 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.08 on 15 degrees of freedom 294s Number of observations: 19 Degrees of Freedom: 15 294s SSR: 17.501 MSE: 1.167 Root MSE: 1.08 294s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.975 294s 294s 294s SUR estimates for 'Investment' (equation 2) 294s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 12.6462 4.6500 2.72 0.01515 * 294s corpProf 0.4707 0.0916 5.14 9.9e-05 *** 294s corpProfLag 0.3519 0.0874 4.03 0.00097 *** 294s capitalLag -0.1253 0.0229 -5.47 5.1e-05 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.041 on 16 degrees of freedom 294s Number of observations: 20 Degrees of Freedom: 16 294s SSR: 17.325 MSE: 1.083 Root MSE: 1.041 294s Multiple R-Squared: 0.911 Adjusted R-Squared: 0.894 294s 294s 294s SUR estimates for 'PrivateWages' (equation 3) 294s Model Formula: privWage ~ gnp + gnpLag + trend 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 1.3245 1.0946 1.21 0.24 294s gnp 0.4184 0.0260 16.08 2.7e-11 *** 294s gnpLag 0.1714 0.0307 5.59 4.1e-05 *** 294s trend 0.1455 0.0276 5.27 7.6e-05 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 0.801 on 16 degrees of freedom 294s Number of observations: 20 Degrees of Freedom: 16 294s SSR: 10.265 MSE: 0.642 Root MSE: 0.801 294s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 294s 294s > residuals 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 -0.3146 -0.2419 -1.1439 294s 3 -1.2707 -0.1795 0.5080 294s 4 -1.5428 1.0691 1.4208 294s 5 -0.4489 -1.4778 -0.1000 294s 6 0.0588 0.3168 -0.3599 294s 7 0.9215 1.4450 NA 294s 8 1.3791 0.8287 -0.7561 294s 9 1.0901 -0.5272 0.2880 294s 10 NA 1.2089 1.1795 294s 11 0.3577 0.4081 -0.3681 294s 12 -0.2286 0.2569 0.3439 294s 13 NA NA -0.1574 294s 14 0.2172 0.4743 0.4225 294s 15 -0.1124 -0.0607 0.3154 294s 16 -0.0876 0.0761 0.0151 294s 17 1.5611 1.0205 -0.8084 294s 18 -0.4529 0.0580 0.8611 294s 19 0.1999 -2.5444 -0.7635 294s 20 0.9266 -0.6202 -0.4039 294s 21 0.7589 -0.7478 -1.2175 294s 22 -2.2135 -0.6029 0.5611 294s > fitted 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 42.2 0.0419 26.6 294s 3 46.3 2.0795 28.8 294s 4 50.7 4.1309 32.7 294s 5 51.0 4.4778 34.0 294s 6 52.5 4.7832 35.8 294s 7 54.2 4.1550 NA 294s 8 54.8 3.3713 38.7 294s 9 56.2 3.5272 38.9 294s 10 NA 3.8911 40.1 294s 11 54.6 0.5919 38.3 294s 12 51.1 -3.6569 34.2 294s 13 NA NA 29.2 294s 14 46.3 -5.5743 28.1 294s 15 48.8 -2.9393 30.3 294s 16 51.4 -1.3761 33.2 294s 17 56.1 1.0795 37.6 294s 18 59.2 1.9420 40.1 294s 19 57.3 0.6444 39.0 294s 20 60.7 1.9202 42.0 294s 21 64.2 4.0478 46.2 294s 22 71.9 5.5029 52.7 294s > predict 294s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 294s 1 NA NA NA NA 294s 2 42.2 0.448 41.3 43.1 294s 3 46.3 0.476 45.3 47.2 294s 4 50.7 0.318 50.1 51.4 294s 5 51.0 0.373 50.3 51.8 294s 6 52.5 0.378 51.8 53.3 294s 7 54.2 0.337 53.5 54.9 294s 8 54.8 0.310 54.2 55.4 294s 9 56.2 0.343 55.5 56.9 294s 10 NA NA NA NA 294s 11 54.6 0.567 53.5 55.8 294s 12 51.1 0.509 50.1 52.2 294s 13 NA NA NA NA 294s 14 46.3 0.573 45.1 47.4 294s 15 48.8 0.382 48.0 49.6 294s 16 51.4 0.328 50.7 52.0 294s 17 56.1 0.336 55.5 56.8 294s 18 59.2 0.309 58.5 59.8 294s 19 57.3 0.370 56.6 58.0 294s 20 60.7 0.401 59.9 61.5 294s 21 64.2 0.405 63.4 65.1 294s 22 71.9 0.633 70.6 73.2 294s Investment.pred Investment.se.fit Investment.lwr Investment.upr 294s 1 NA NA NA NA 294s 2 0.0419 0.533 -1.0309 1.115 294s 3 2.0795 0.433 1.2082 2.951 294s 4 4.1309 0.387 3.3532 4.909 294s 5 4.4778 0.322 3.8307 5.125 294s 6 4.7832 0.305 4.1700 5.396 294s 7 4.1550 0.283 3.5852 4.725 294s 8 3.3713 0.253 2.8630 3.880 294s 9 3.5272 0.337 2.8488 4.206 294s 10 3.8911 0.386 3.1149 4.667 294s 11 0.5919 0.561 -0.5376 1.722 294s 12 -3.6569 0.530 -4.7223 -2.591 294s 13 NA NA NA NA 294s 14 -5.5743 0.618 -6.8176 -4.331 294s 15 -2.9393 0.362 -3.6671 -2.212 294s 16 -1.3761 0.296 -1.9710 -0.781 294s 17 1.0795 0.300 0.4763 1.683 294s 18 1.9420 0.216 1.5081 2.376 294s 19 0.6444 0.298 0.0451 1.244 294s 20 1.9202 0.318 1.2798 2.561 294s 21 4.0478 0.295 3.4537 4.642 294s 22 5.5029 0.417 4.6638 6.342 294s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 294s 1 NA NA NA NA 294s 2 26.6 0.312 26.0 27.3 294s 3 28.8 0.312 28.2 29.4 294s 4 32.7 0.307 32.1 33.3 294s 5 34.0 0.237 33.5 34.5 294s 6 35.8 0.235 35.3 36.2 294s 7 NA NA NA NA 294s 8 38.7 0.239 38.2 39.1 294s 9 38.9 0.228 38.5 39.4 294s 10 40.1 0.218 39.7 40.6 294s 11 38.3 0.293 37.7 38.9 294s 12 34.2 0.290 33.6 34.7 294s 13 29.2 0.343 28.5 29.8 294s 14 28.1 0.321 27.4 28.7 294s 15 30.3 0.320 29.6 30.9 294s 16 33.2 0.268 32.6 33.7 294s 17 37.6 0.263 37.1 38.1 294s 18 40.1 0.207 39.7 40.6 294s 19 39.0 0.293 38.4 39.6 294s 20 42.0 0.279 41.4 42.6 294s 21 46.2 0.295 45.6 46.8 294s 22 52.7 0.435 51.9 53.6 294s > model.frame 294s [1] TRUE 294s > model.matrix 294s [1] TRUE 294s > nobs 294s [1] 59 294s > linearHypothesis 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 48 294s 2 47 1 0.41 0.52 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 48 294s 2 47 1 0.52 0.47 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 48 294s 2 47 1 0.52 0.47 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 49 294s 2 47 2 0.31 0.73 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 49 294s 2 47 2 0.4 0.67 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 49 294s 2 47 2 0.79 0.67 294s > logLik 294s 'log Lik.' -67.3 (df=18) 294s 'log Lik.' -74.9 (df=18) 294s > 294s > # 3SLS 294s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 294s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 294s > summary 294s 294s systemfit results 294s method: 3SLS 294s 294s N DF SSR detRCov OLS-R2 McElroy-R2 294s system 57 45 66.8 0.361 0.963 0.993 294s 294s N DF SSR MSE RMSE R2 Adj R2 294s Consumption 18 14 22.6 1.616 1.271 0.974 0.968 294s Investment 19 15 34.1 2.277 1.509 0.807 0.769 294s PrivateWages 20 16 10.1 0.628 0.793 0.987 0.985 294s 294s The covariance matrix of the residuals used for estimation 294s Consumption Investment PrivateWages 294s Consumption 1.237 0.518 -0.408 294s Investment 0.518 1.263 0.113 294s PrivateWages -0.408 0.113 0.468 294s 294s The covariance matrix of the residuals 294s Consumption Investment PrivateWages 294s Consumption 1.257 0.601 -0.421 294s Investment 0.601 1.601 0.214 294s PrivateWages -0.421 0.214 0.491 294s 294s The correlations of the residuals 294s Consumption Investment PrivateWages 294s Consumption 1.000 0.425 -0.537 294s Investment 0.425 1.000 0.239 294s PrivateWages -0.537 0.239 1.000 294s 294s 294s 3SLS estimates for 'Consumption' (equation 1) 294s Model Formula: consump ~ corpProf + corpProfLag + wages 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 18.2100 1.5273 11.92 1e-08 *** 294s corpProf -0.0639 0.1461 -0.44 0.67 294s corpProfLag 0.1687 0.1125 1.50 0.16 294s wages 0.8230 0.0431 19.07 2e-11 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.271 on 14 degrees of freedom 294s Number of observations: 18 Degrees of Freedom: 14 294s SSR: 22.626 MSE: 1.616 Root MSE: 1.271 294s Multiple R-Squared: 0.974 Adjusted R-Squared: 0.968 294s 294s 294s 3SLS estimates for 'Investment' (equation 2) 294s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 24.7534 6.5548 3.78 0.00183 ** 294s corpProf 0.0524 0.1807 0.29 0.77600 294s corpProfLag 0.6584 0.1551 4.24 0.00071 *** 294s capitalLag -0.1756 0.0311 -5.64 4.7e-05 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.509 on 15 degrees of freedom 294s Number of observations: 19 Degrees of Freedom: 15 294s SSR: 34.149 MSE: 2.277 Root MSE: 1.509 294s Multiple R-Squared: 0.807 Adjusted R-Squared: 0.769 294s 294s 294s 3SLS estimates for 'PrivateWages' (equation 3) 294s Model Formula: privWage ~ gnp + gnpLag + trend 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 0.8154 1.0961 0.74 0.46772 294s gnp 0.4250 0.0299 14.19 1.7e-10 *** 294s gnpLag 0.1731 0.0331 5.23 8.3e-05 *** 294s trend 0.1255 0.0283 4.43 0.00042 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 0.793 on 16 degrees of freedom 294s Number of observations: 20 Degrees of Freedom: 16 294s SSR: 10.054 MSE: 0.628 Root MSE: 0.793 294s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 294s 294s > residuals 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 -0.8680 -1.857 -1.21010 294s 3 -0.7217 0.170 0.43075 294s 4 -1.1353 0.762 1.30899 294s 5 0.0755 -1.565 -0.20270 294s 6 0.6348 0.367 -0.46842 294s 7 NA NA NA 294s 8 1.7953 1.230 -0.85853 294s 9 1.7924 0.568 0.20422 294s 10 NA 2.308 1.09889 294s 11 -0.5211 -0.972 -0.39427 294s 12 -1.5560 -0.960 0.39889 294s 13 NA NA -0.00934 294s 14 -0.2384 1.327 0.59990 294s 15 -0.7342 -0.292 0.48094 294s 16 -0.4331 0.068 0.16188 294s 17 1.8775 1.932 -0.70448 294s 18 -0.6294 -0.154 0.95616 294s 19 -0.4252 -3.400 -0.62489 294s 20 1.3682 0.589 -0.29589 294s 21 1.3155 0.271 -1.14466 294s 22 -1.4276 0.942 0.55941 294s > fitted 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 42.8 1.657 26.7 294s 3 45.7 1.730 28.9 294s 4 50.3 4.438 32.8 294s 5 50.5 4.565 34.1 294s 6 52.0 4.733 35.9 294s 7 NA NA NA 294s 8 54.4 2.970 38.8 294s 9 55.5 2.432 39.0 294s 10 NA 2.792 40.2 294s 11 55.5 1.972 38.3 294s 12 52.5 -2.440 34.1 294s 13 NA NA 29.0 294s 14 46.7 -6.427 27.9 294s 15 49.4 -2.708 30.1 294s 16 51.7 -1.368 33.0 294s 17 55.8 0.168 37.5 294s 18 59.3 2.154 40.0 294s 19 57.9 1.500 38.8 294s 20 60.2 0.711 41.9 294s 21 63.7 3.029 46.1 294s 22 71.1 3.958 52.7 294s > predict 294s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 294s 1 NA NA NA NA 294s 2 42.8 0.542 39.8 45.7 294s 3 45.7 0.612 42.7 48.7 294s 4 50.3 0.407 47.5 53.2 294s 5 50.5 0.478 47.6 53.4 294s 6 52.0 0.488 49.0 54.9 294s 7 NA NA NA NA 294s 8 54.4 0.394 51.5 57.3 294s 9 55.5 0.464 52.6 58.4 294s 10 NA NA NA NA 294s 11 55.5 0.811 52.3 58.8 294s 12 52.5 0.773 49.3 55.6 294s 13 NA NA NA NA 294s 14 46.7 0.666 43.7 49.8 294s 15 49.4 0.463 46.5 52.3 294s 16 51.7 0.381 48.9 54.6 294s 17 55.8 0.424 52.9 58.7 294s 18 59.3 0.359 56.5 62.2 294s 19 57.9 0.492 55.0 60.8 294s 20 60.2 0.501 57.3 63.2 294s 21 63.7 0.491 60.8 66.6 294s 22 71.1 0.749 68.0 74.3 294s Investment.pred Investment.se.fit Investment.lwr Investment.upr 294s 1 NA NA NA NA 294s 2 1.657 0.831 -2.015 5.329 294s 3 1.730 0.574 -1.711 5.171 294s 4 4.438 0.507 1.045 7.831 294s 5 4.565 0.426 1.223 7.907 294s 6 4.733 0.406 1.402 8.064 294s 7 NA NA NA NA 294s 8 2.970 0.334 -0.324 6.263 294s 9 2.432 0.501 -0.957 5.820 294s 10 2.792 0.544 -0.627 6.211 294s 11 1.972 0.937 -1.814 5.757 294s 12 -2.440 0.849 -6.131 1.250 294s 13 NA NA NA NA 294s 14 -6.427 0.836 -10.104 -2.750 294s 15 -2.708 0.477 -6.081 0.665 294s 16 -1.368 0.381 -4.685 1.949 294s 17 0.168 0.473 -3.202 3.538 294s 18 2.154 0.311 -1.130 5.438 294s 19 1.500 0.518 -1.900 4.900 294s 20 0.711 0.541 -2.705 4.127 294s 21 3.029 0.467 -0.338 6.395 294s 22 3.958 0.677 0.432 7.483 294s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 294s 1 NA NA NA NA 294s 2 26.7 0.315 24.9 28.5 294s 3 28.9 0.322 27.1 30.7 294s 4 32.8 0.330 31.0 34.6 294s 5 34.1 0.241 32.3 35.9 294s 6 35.9 0.249 34.1 37.6 294s 7 NA NA NA NA 294s 8 38.8 0.243 37.0 40.5 294s 9 39.0 0.231 37.2 40.7 294s 10 40.2 0.225 38.5 41.9 294s 11 38.3 0.305 36.5 40.1 294s 12 34.1 0.317 32.3 35.9 294s 13 29.0 0.382 27.1 30.9 294s 14 27.9 0.321 26.1 29.7 294s 15 30.1 0.316 28.3 31.9 294s 16 33.0 0.265 31.3 34.8 294s 17 37.5 0.270 35.7 39.3 294s 18 40.0 0.207 38.3 41.8 294s 19 38.8 0.311 37.0 40.6 294s 20 41.9 0.287 40.1 43.7 294s 21 46.1 0.300 44.3 47.9 294s 22 52.7 0.463 50.8 54.7 294s > model.frame 294s [1] TRUE 294s > model.matrix 294s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0339 >" 294s [2] "Attributes: < Component “dimnames”: Component 1: 52 string mismatches >" 294s [3] "Numeric: lengths (708, 684) differ" 294s > nobs 294s [1] 57 294s > linearHypothesis 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 46 294s 2 45 1 1.95 0.17 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 46 294s 2 45 1 2.71 0.11 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 46 294s 2 45 1 2.71 0.1 . 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 47 294s 2 45 2 1.78 0.18 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 47 294s 2 45 2 2.48 0.095 . 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 47 294s 2 45 2 4.95 0.084 . 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s > logLik 294s 'log Lik.' -71.2 (df=18) 294s 'log Lik.' -81.7 (df=18) 294s > 294s > # I3SLS 294s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 294s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 294s > summary 294s 294s systemfit results 294s method: iterated 3SLS 294s 294s convergence achieved after 9 iterations 294s 294s N DF SSR detRCov OLS-R2 McElroy-R2 294s system 57 45 75 0.422 0.959 0.993 294s 294s N DF SSR MSE RMSE R2 Adj R2 294s Consumption 18 14 22.7 1.622 1.273 0.973 0.968 294s Investment 19 15 42.1 2.809 1.676 0.762 0.715 294s PrivateWages 20 16 10.2 0.638 0.799 0.987 0.985 294s 294s The covariance matrix of the residuals used for estimation 294s Consumption Investment PrivateWages 294s Consumption 1.261 0.675 -0.439 294s Investment 0.675 1.949 0.237 294s PrivateWages -0.439 0.237 0.503 294s 294s The covariance matrix of the residuals 294s Consumption Investment PrivateWages 294s Consumption 1.261 0.675 -0.439 294s Investment 0.675 1.949 0.237 294s PrivateWages -0.439 0.237 0.503 294s 294s The correlations of the residuals 294s Consumption Investment PrivateWages 294s Consumption 1.000 0.431 -0.550 294s Investment 0.431 1.000 0.239 294s PrivateWages -0.550 0.239 1.000 294s 294s 294s 3SLS estimates for 'Consumption' (equation 1) 294s Model Formula: consump ~ corpProf + corpProfLag + wages 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 18.5887 1.5250 12.19 7.6e-09 *** 294s corpProf -0.0438 0.1441 -0.30 0.77 294s corpProfLag 0.1456 0.1109 1.31 0.21 294s wages 0.8141 0.0428 19.01 2.1e-11 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.273 on 14 degrees of freedom 294s Number of observations: 18 Degrees of Freedom: 14 294s SSR: 22.704 MSE: 1.622 Root MSE: 1.273 294s Multiple R-Squared: 0.973 Adjusted R-Squared: 0.968 294s 294s 294s 3SLS estimates for 'Investment' (equation 2) 294s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 29.4725 7.6857 3.83 0.0016 ** 294s corpProf -0.0183 0.2154 -0.09 0.9333 294s corpProfLag 0.7195 0.1850 3.89 0.0015 ** 294s capitalLag -0.1985 0.0366 -5.43 6.9e-05 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.676 on 15 degrees of freedom 294s Number of observations: 19 Degrees of Freedom: 15 294s SSR: 42.136 MSE: 2.809 Root MSE: 1.676 294s Multiple R-Squared: 0.762 Adjusted R-Squared: 0.715 294s 294s 294s 3SLS estimates for 'PrivateWages' (equation 3) 294s Model Formula: privWage ~ gnp + gnpLag + trend 294s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 294s gnpLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 0.5385 1.1055 0.49 0.63277 294s gnp 0.4251 0.0287 14.80 9.3e-11 *** 294s gnpLag 0.1776 0.0322 5.51 4.7e-05 *** 294s trend 0.1211 0.0283 4.28 0.00057 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 0.799 on 16 degrees of freedom 294s Number of observations: 20 Degrees of Freedom: 16 294s SSR: 10.204 MSE: 0.638 Root MSE: 0.799 294s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 294s 294s > residuals 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 -0.9524 -2.2888 -1.1837 294s 3 -0.8681 0.0698 0.4581 294s 4 -1.1653 0.5368 1.3199 294s 5 0.0601 -1.6917 -0.2194 294s 6 0.6426 0.2972 -0.4805 294s 7 NA NA NA 294s 8 1.8394 1.3723 -0.8931 294s 9 1.8275 0.8861 0.1723 294s 10 NA 2.6574 1.0707 294s 11 -0.3387 -0.9736 -0.4288 294s 12 -1.4550 -0.8630 0.3956 294s 13 NA NA 0.0277 294s 14 -0.3782 1.7151 0.6823 294s 15 -0.7768 -0.1993 0.5638 294s 16 -0.4606 0.1448 0.2281 294s 17 1.8605 2.1295 -0.6557 294s 18 -0.5262 -0.1493 0.9718 294s 19 -0.3047 -3.4730 -0.6148 294s 20 1.3992 0.8566 -0.2636 294s 21 1.4216 0.4910 -1.1472 294s 22 -1.2431 1.2792 0.5323 294s > fitted 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 42.9 2.0888 26.7 294s 3 45.9 1.8302 28.8 294s 4 50.4 4.6632 32.8 294s 5 50.5 4.6917 34.1 294s 6 52.0 4.8028 35.9 294s 7 NA NA NA 294s 8 54.4 2.8277 38.8 294s 9 55.5 2.1139 39.0 294s 10 NA 2.4426 40.2 294s 11 55.3 1.9736 38.3 294s 12 52.4 -2.5370 34.1 294s 13 NA NA 29.0 294s 14 46.9 -6.8151 27.8 294s 15 49.5 -2.8007 30.0 294s 16 51.8 -1.4448 33.0 294s 17 55.8 -0.0295 37.5 294s 18 59.2 2.1493 40.0 294s 19 57.8 1.5730 38.8 294s 20 60.2 0.4434 41.9 294s 21 63.6 2.8090 46.1 294s 22 70.9 3.6208 52.8 294s > predict 294s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 294s 1 NA NA NA NA 294s 2 42.9 0.541 41.8 43.9 294s 3 45.9 0.608 44.6 47.1 294s 4 50.4 0.403 49.6 51.2 294s 5 50.5 0.472 49.6 51.5 294s 6 52.0 0.481 51.0 52.9 294s 7 NA NA NA NA 294s 8 54.4 0.388 53.6 55.1 294s 9 55.5 0.458 54.6 56.4 294s 10 NA NA NA NA 294s 11 55.3 0.795 53.7 56.9 294s 12 52.4 0.762 50.8 53.9 294s 13 NA NA NA NA 294s 14 46.9 0.663 45.5 48.2 294s 15 49.5 0.462 48.5 50.4 294s 16 51.8 0.381 51.0 52.5 294s 17 55.8 0.423 55.0 56.7 294s 18 59.2 0.355 58.5 59.9 294s 19 57.8 0.484 56.8 58.8 294s 20 60.2 0.500 59.2 61.2 294s 21 63.6 0.490 62.6 64.6 294s 22 70.9 0.747 69.4 72.4 294s Investment.pred Investment.se.fit Investment.lwr Investment.upr 294s 1 NA NA NA NA 294s 2 2.0888 0.985 0.105 4.072 294s 3 1.8302 0.708 0.404 3.257 294s 4 4.6632 0.612 3.430 5.897 294s 5 4.6917 0.519 3.645 5.738 294s 6 4.8028 0.498 3.800 5.806 294s 7 NA NA NA NA 294s 8 2.8277 0.410 2.003 3.653 294s 9 2.1139 0.599 0.908 3.320 294s 10 2.4426 0.651 1.131 3.754 294s 11 1.9736 1.138 -0.320 4.267 294s 12 -2.5370 1.038 -4.627 -0.447 294s 13 NA NA NA NA 294s 14 -6.8151 1.011 -8.851 -4.779 294s 15 -2.8007 0.587 -3.984 -1.617 294s 16 -1.4448 0.470 -2.392 -0.498 294s 17 -0.0295 0.573 -1.183 1.124 294s 18 2.1493 0.380 1.384 2.915 294s 19 1.5730 0.624 0.315 2.831 294s 20 0.4434 0.649 -0.864 1.751 294s 21 2.8090 0.565 1.671 3.947 294s 22 3.6208 0.814 1.982 5.260 294s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 294s 1 NA NA NA NA 294s 2 26.7 0.322 26.0 27.3 294s 3 28.8 0.328 28.2 29.5 294s 4 32.8 0.332 32.1 33.4 294s 5 34.1 0.244 33.6 34.6 294s 6 35.9 0.252 35.4 36.4 294s 7 NA NA NA NA 294s 8 38.8 0.246 38.3 39.3 294s 9 39.0 0.234 38.6 39.5 294s 10 40.2 0.230 39.8 40.7 294s 11 38.3 0.299 37.7 38.9 294s 12 34.1 0.304 33.5 34.7 294s 13 29.0 0.366 28.2 29.7 294s 14 27.8 0.321 27.2 28.5 294s 15 30.0 0.317 29.4 30.7 294s 16 33.0 0.266 32.4 33.5 294s 17 37.5 0.270 36.9 38.0 294s 18 40.0 0.211 39.6 40.5 294s 19 38.8 0.305 38.2 39.4 294s 20 41.9 0.290 41.3 42.4 294s 21 46.1 0.309 45.5 46.8 294s 22 52.8 0.468 51.8 53.7 294s > model.frame 294s [1] TRUE 294s > model.matrix 294s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0339 >" 294s [2] "Attributes: < Component “dimnames”: Component 1: 52 string mismatches >" 294s [3] "Numeric: lengths (708, 684) differ" 294s > nobs 294s [1] 57 294s > linearHypothesis 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 46 294s 2 45 1 2.17 0.15 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 46 294s 2 45 1 2.84 0.099 . 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 46 294s 2 45 1 2.84 0.092 . 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 47 294s 2 45 2 2.45 0.098 . 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 47 294s 2 45 2 3.2 0.05 . 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 47 294s 2 45 2 6.4 0.041 * 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s > logLik 294s 'log Lik.' -72.7 (df=18) 294s 'log Lik.' -83.9 (df=18) 294s > 294s > # OLS 294s Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 294s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 294s > summary 294s 294s systemfit results 294s method: OLS 294s 294s N DF SSR detRCov OLS-R2 McElroy-R2 294s system 58 46 44.2 0.565 0.976 0.991 294s 294s N DF SSR MSE RMSE R2 Adj R2 294s Consumption 19 15 17.36 1.157 1.08 0.980 0.976 294s Investment 19 15 17.11 1.140 1.07 0.907 0.889 294s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 294s 294s The covariance matrix of the residuals 294s Consumption Investment PrivateWages 294s Consumption 1.285 0.061 -0.511 294s Investment 0.061 1.059 0.151 294s PrivateWages -0.511 0.151 0.648 294s 294s The correlations of the residuals 294s Consumption Investment PrivateWages 294s Consumption 1.0000 0.0457 -0.568 294s Investment 0.0457 1.0000 0.168 294s PrivateWages -0.5681 0.1676 1.000 294s 294s 294s OLS estimates for 'Consumption' (equation 1) 294s Model Formula: consump ~ corpProf + corpProfLag + wages 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 16.2957 1.5438 10.56 2.4e-08 *** 294s corpProf 0.1796 0.1206 1.49 0.16 294s corpProfLag 0.1032 0.1031 1.00 0.33 294s wages 0.7962 0.0449 17.73 1.8e-11 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.076 on 15 degrees of freedom 294s Number of observations: 19 Degrees of Freedom: 15 294s SSR: 17.362 MSE: 1.157 Root MSE: 1.076 294s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.976 294s 294s 294s OLS estimates for 'Investment' (equation 2) 294s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 10.1724 5.5758 1.82 0.08808 . 294s corpProf 0.5004 0.1092 4.58 0.00036 *** 294s corpProfLag 0.3270 0.1052 3.11 0.00718 ** 294s capitalLag -0.1134 0.0275 -4.13 0.00090 *** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 1.068 on 15 degrees of freedom 294s Number of observations: 19 Degrees of Freedom: 15 294s SSR: 17.105 MSE: 1.14 Root MSE: 1.068 294s Multiple R-Squared: 0.907 Adjusted R-Squared: 0.889 294s 294s 294s OLS estimates for 'PrivateWages' (equation 3) 294s Model Formula: privWage ~ gnp + gnpLag + trend 294s 294s Estimate Std. Error t value Pr(>|t|) 294s (Intercept) 1.3550 1.3512 1.00 0.3309 294s gnp 0.4417 0.0342 12.92 7e-10 *** 294s gnpLag 0.1466 0.0393 3.73 0.0018 ** 294s trend 0.1244 0.0347 3.58 0.0025 ** 294s --- 294s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 294s 294s Residual standard error: 0.78 on 16 degrees of freedom 294s Number of observations: 20 Degrees of Freedom: 16 294s SSR: 9.739 MSE: 0.609 Root MSE: 0.78 294s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 294s 294s compare coef with single-equation OLS 294s [1] TRUE 294s > residuals 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 -0.3863 0.00693 -1.3389 294s 3 -1.2484 -0.06954 0.2462 294s 4 -1.6040 1.22401 1.1255 294s 5 -0.5384 -1.37697 -0.1959 294s 6 -0.0413 0.38610 -0.5284 294s 7 0.8043 1.48598 NA 294s 8 1.2830 0.78465 -0.7909 294s 9 1.0142 -0.65483 0.2819 294s 10 NA 1.06018 1.1384 294s 11 0.1429 0.39508 -0.1904 294s 12 -0.3439 0.20479 0.5813 294s 13 NA NA 0.1206 294s 14 0.3199 0.32778 0.4773 294s 15 -0.1016 -0.07450 0.3035 294s 16 -0.0702 NA 0.0284 294s 17 1.6064 0.96998 -0.8517 294s 18 -0.4980 0.08124 0.9908 294s 19 0.1253 -2.49295 -0.4597 294s 20 0.9805 -0.70609 -0.3819 294s 21 0.7551 -0.81928 -1.1062 294s 22 -2.1992 -0.73256 0.5501 294s > fitted 294s Consumption Investment PrivateWages 294s 1 NA NA NA 294s 2 42.3 -0.207 26.8 294s 3 46.2 1.970 29.1 294s 4 50.8 3.976 33.0 294s 5 51.1 4.377 34.1 294s 6 52.6 4.714 35.9 294s 7 54.3 4.114 NA 294s 8 54.9 3.415 38.7 294s 9 56.3 3.655 38.9 294s 10 NA 4.040 40.2 294s 11 54.9 0.605 38.1 294s 12 51.2 -3.605 33.9 294s 13 NA NA 28.9 294s 14 46.2 -5.428 28.0 294s 15 48.8 -2.926 30.3 294s 16 51.4 NA 33.2 294s 17 56.1 1.130 37.7 294s 18 59.2 1.919 40.0 294s 19 57.4 0.593 38.7 294s 20 60.6 2.006 42.0 294s 21 64.2 4.119 46.1 294s 22 71.9 5.633 52.7 294s > predict 294s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 294s 1 NA NA NA NA 294s 2 42.3 0.543 39.9 44.7 294s 3 46.2 0.581 43.8 48.7 294s 4 50.8 0.394 48.5 53.1 294s 5 51.1 0.465 48.8 53.5 294s 6 52.6 0.474 50.3 55.0 294s 7 54.3 0.423 52.0 56.6 294s 8 54.9 0.389 52.6 57.2 294s 9 56.3 0.434 54.0 58.6 294s 10 NA NA NA NA 294s 11 54.9 0.727 52.2 57.5 294s 12 51.2 0.662 48.7 53.8 294s 13 NA NA NA NA 294s 14 46.2 0.698 43.6 48.8 294s 15 48.8 0.470 46.4 51.2 294s 16 51.4 0.398 49.1 53.7 294s 17 56.1 0.405 53.8 58.4 294s 18 59.2 0.375 56.9 61.5 294s 19 57.4 0.466 55.0 59.7 294s 20 60.6 0.482 58.2 63.0 294s 21 64.2 0.485 61.9 66.6 294s 22 71.9 0.755 69.3 74.5 294s Investment.pred Investment.se.fit Investment.lwr Investment.upr 294s 1 NA NA NA NA 294s 2 -0.207 0.645 -2.718 2.30 294s 3 1.970 0.523 -0.423 4.36 294s 4 3.976 0.462 1.634 6.32 294s 5 4.377 0.383 2.094 6.66 294s 6 4.714 0.362 2.444 6.98 294s 7 4.114 0.336 1.861 6.37 294s 8 3.415 0.298 1.184 5.65 294s 9 3.655 0.400 1.359 5.95 294s 10 4.040 0.458 1.701 6.38 294s 11 0.605 0.666 -1.928 3.14 294s 12 -3.605 0.637 -6.108 -1.10 294s 13 NA NA NA NA 294s 14 -5.428 0.767 -8.074 -2.78 294s 15 -2.926 0.453 -5.261 -0.59 294s 16 NA NA NA NA 294s 17 1.130 0.366 -1.142 3.40 294s 18 1.919 0.258 -0.293 4.13 294s 19 0.593 0.357 -1.674 2.86 294s 20 2.006 0.384 -0.278 4.29 294s 21 4.119 0.350 1.858 6.38 294s 22 5.633 0.495 3.263 8.00 294s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 294s 1 NA NA NA NA 294s 2 26.8 0.378 25.1 28.6 294s 3 29.1 0.381 27.3 30.8 294s 4 33.0 0.384 31.2 34.7 294s 5 34.1 0.297 32.4 35.8 294s 6 35.9 0.296 34.2 37.6 294s 7 NA NA NA NA 294s 8 38.7 0.303 37.0 40.4 294s 9 38.9 0.288 37.2 40.6 294s 10 40.2 0.274 38.5 41.8 294s 11 38.1 0.377 36.3 39.8 294s 12 33.9 0.381 32.2 35.7 294s 13 28.9 0.452 27.1 30.7 294s 14 28.0 0.397 26.3 29.8 294s 15 30.3 0.391 28.5 32.1 294s 16 33.2 0.327 31.5 34.9 294s 17 37.7 0.320 36.0 39.3 294s 18 40.0 0.250 38.4 41.7 294s 19 38.7 0.375 36.9 40.4 294s 20 42.0 0.337 40.3 43.7 294s 21 46.1 0.352 44.4 47.8 294s 22 52.7 0.530 50.9 54.6 294s > model.frame 294s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 294s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 294s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 294s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 294s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 294s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 294s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 294s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 294s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 294s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 294s 10 57.8 21.7 21.1 NA 5.1 211 41.3 67.0 64.5 294s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 294s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 294s 13 45.6 NA 11.4 34.3 -6.2 213 29.0 44.3 53.4 294s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 294s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 294s 16 51.3 14.0 12.3 39.3 NA 199 33.2 54.4 49.7 294s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 294s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 294s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 294s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 294s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 294s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 294s trend 294s 1 -11 294s 2 -10 294s 3 -9 294s 4 -8 294s 5 -7 294s 6 -6 294s 7 -5 294s 8 -4 294s 9 -3 294s 10 -2 294s 11 -1 294s 12 0 294s 13 1 294s 14 2 294s 15 3 294s 16 4 294s 17 5 294s 18 6 294s 19 7 294s 20 8 294s 21 9 294s 22 10 294s > model.matrix 294s Consumption_(Intercept) Consumption_corpProf 294s Consumption_2 1 12.4 294s Consumption_3 1 16.9 294s Consumption_4 1 18.4 294s Consumption_5 1 19.4 294s Consumption_6 1 20.1 294s Consumption_7 1 19.6 294s Consumption_8 1 19.8 294s Consumption_9 1 21.1 294s Consumption_11 1 15.6 294s Consumption_12 1 11.4 294s Consumption_14 1 11.2 294s Consumption_15 1 12.3 294s Consumption_16 1 14.0 294s Consumption_17 1 17.6 294s Consumption_18 1 17.3 294s Consumption_19 1 15.3 294s Consumption_20 1 19.0 294s Consumption_21 1 21.1 294s Consumption_22 1 23.5 294s Investment_2 0 0.0 294s Investment_3 0 0.0 294s Investment_4 0 0.0 294s Investment_5 0 0.0 294s Investment_6 0 0.0 294s Investment_7 0 0.0 294s Investment_8 0 0.0 294s Investment_9 0 0.0 294s Investment_10 0 0.0 294s Investment_11 0 0.0 294s Investment_12 0 0.0 294s Investment_14 0 0.0 294s Investment_15 0 0.0 294s Investment_17 0 0.0 294s Investment_18 0 0.0 294s Investment_19 0 0.0 294s Investment_20 0 0.0 294s Investment_21 0 0.0 294s Investment_22 0 0.0 294s PrivateWages_2 0 0.0 294s PrivateWages_3 0 0.0 294s PrivateWages_4 0 0.0 294s PrivateWages_5 0 0.0 294s PrivateWages_6 0 0.0 294s PrivateWages_8 0 0.0 294s PrivateWages_9 0 0.0 294s PrivateWages_10 0 0.0 294s PrivateWages_11 0 0.0 294s PrivateWages_12 0 0.0 294s PrivateWages_13 0 0.0 294s PrivateWages_14 0 0.0 294s PrivateWages_15 0 0.0 294s PrivateWages_16 0 0.0 294s PrivateWages_17 0 0.0 294s PrivateWages_18 0 0.0 294s PrivateWages_19 0 0.0 294s PrivateWages_20 0 0.0 294s PrivateWages_21 0 0.0 294s PrivateWages_22 0 0.0 294s Consumption_corpProfLag Consumption_wages 294s Consumption_2 12.7 28.2 294s Consumption_3 12.4 32.2 294s Consumption_4 16.9 37.0 294s Consumption_5 18.4 37.0 294s Consumption_6 19.4 38.6 294s Consumption_7 20.1 40.7 294s Consumption_8 19.6 41.5 294s Consumption_9 19.8 42.9 294s Consumption_11 21.7 42.1 294s Consumption_12 15.6 39.3 294s Consumption_14 7.0 34.1 294s Consumption_15 11.2 36.6 294s Consumption_16 12.3 39.3 294s Consumption_17 14.0 44.2 294s Consumption_18 17.6 47.7 294s Consumption_19 17.3 45.9 294s Consumption_20 15.3 49.4 294s Consumption_21 19.0 53.0 294s Consumption_22 21.1 61.8 294s Investment_2 0.0 0.0 294s Investment_3 0.0 0.0 294s Investment_4 0.0 0.0 294s Investment_5 0.0 0.0 294s Investment_6 0.0 0.0 294s Investment_7 0.0 0.0 294s Investment_8 0.0 0.0 294s Investment_9 0.0 0.0 294s Investment_10 0.0 0.0 294s Investment_11 0.0 0.0 294s Investment_12 0.0 0.0 294s Investment_14 0.0 0.0 294s Investment_15 0.0 0.0 294s Investment_17 0.0 0.0 294s Investment_18 0.0 0.0 294s Investment_19 0.0 0.0 294s Investment_20 0.0 0.0 294s Investment_21 0.0 0.0 294s Investment_22 0.0 0.0 294s PrivateWages_2 0.0 0.0 294s PrivateWages_3 0.0 0.0 294s PrivateWages_4 0.0 0.0 294s PrivateWages_5 0.0 0.0 294s PrivateWages_6 0.0 0.0 294s PrivateWages_8 0.0 0.0 294s PrivateWages_9 0.0 0.0 294s PrivateWages_10 0.0 0.0 294s PrivateWages_11 0.0 0.0 294s PrivateWages_12 0.0 0.0 294s PrivateWages_13 0.0 0.0 294s PrivateWages_14 0.0 0.0 294s PrivateWages_15 0.0 0.0 294s PrivateWages_16 0.0 0.0 294s PrivateWages_17 0.0 0.0 294s PrivateWages_18 0.0 0.0 294s PrivateWages_19 0.0 0.0 294s PrivateWages_20 0.0 0.0 294s PrivateWages_21 0.0 0.0 294s PrivateWages_22 0.0 0.0 294s Investment_(Intercept) Investment_corpProf 294s Consumption_2 0 0.0 294s Consumption_3 0 0.0 294s Consumption_4 0 0.0 294s Consumption_5 0 0.0 294s Consumption_6 0 0.0 294s Consumption_7 0 0.0 294s Consumption_8 0 0.0 294s Consumption_9 0 0.0 294s Consumption_11 0 0.0 294s Consumption_12 0 0.0 294s Consumption_14 0 0.0 294s Consumption_15 0 0.0 294s Consumption_16 0 0.0 294s Consumption_17 0 0.0 294s Consumption_18 0 0.0 294s Consumption_19 0 0.0 294s Consumption_20 0 0.0 294s Consumption_21 0 0.0 294s Consumption_22 0 0.0 294s Investment_2 1 12.4 294s Investment_3 1 16.9 294s Investment_4 1 18.4 294s Investment_5 1 19.4 294s Investment_6 1 20.1 294s Investment_7 1 19.6 294s Investment_8 1 19.8 294s Investment_9 1 21.1 294s Investment_10 1 21.7 294s Investment_11 1 15.6 294s Investment_12 1 11.4 294s Investment_14 1 11.2 294s Investment_15 1 12.3 294s Investment_17 1 17.6 294s Investment_18 1 17.3 294s Investment_19 1 15.3 294s Investment_20 1 19.0 294s Investment_21 1 21.1 294s Investment_22 1 23.5 294s PrivateWages_2 0 0.0 294s PrivateWages_3 0 0.0 294s PrivateWages_4 0 0.0 294s PrivateWages_5 0 0.0 294s PrivateWages_6 0 0.0 294s PrivateWages_8 0 0.0 294s PrivateWages_9 0 0.0 294s PrivateWages_10 0 0.0 294s PrivateWages_11 0 0.0 294s PrivateWages_12 0 0.0 294s PrivateWages_13 0 0.0 294s PrivateWages_14 0 0.0 294s PrivateWages_15 0 0.0 294s PrivateWages_16 0 0.0 294s PrivateWages_17 0 0.0 294s PrivateWages_18 0 0.0 294s PrivateWages_19 0 0.0 294s PrivateWages_20 0 0.0 294s PrivateWages_21 0 0.0 294s PrivateWages_22 0 0.0 294s Investment_corpProfLag Investment_capitalLag 294s Consumption_2 0.0 0 294s Consumption_3 0.0 0 294s Consumption_4 0.0 0 294s Consumption_5 0.0 0 294s Consumption_6 0.0 0 294s Consumption_7 0.0 0 294s Consumption_8 0.0 0 294s Consumption_9 0.0 0 294s Consumption_11 0.0 0 294s Consumption_12 0.0 0 294s Consumption_14 0.0 0 294s Consumption_15 0.0 0 294s Consumption_16 0.0 0 294s Consumption_17 0.0 0 294s Consumption_18 0.0 0 294s Consumption_19 0.0 0 294s Consumption_20 0.0 0 294s Consumption_21 0.0 0 294s Consumption_22 0.0 0 294s Investment_2 12.7 183 294s Investment_3 12.4 183 294s Investment_4 16.9 184 294s Investment_5 18.4 190 294s Investment_6 19.4 193 294s Investment_7 20.1 198 294s Investment_8 19.6 203 294s Investment_9 19.8 208 294s Investment_10 21.1 211 294s Investment_11 21.7 216 294s Investment_12 15.6 217 294s Investment_14 7.0 207 294s Investment_15 11.2 202 294s Investment_17 14.0 198 294s Investment_18 17.6 200 294s Investment_19 17.3 202 294s Investment_20 15.3 200 294s Investment_21 19.0 201 294s Investment_22 21.1 204 294s PrivateWages_2 0.0 0 294s PrivateWages_3 0.0 0 294s PrivateWages_4 0.0 0 294s PrivateWages_5 0.0 0 294s PrivateWages_6 0.0 0 294s PrivateWages_8 0.0 0 294s PrivateWages_9 0.0 0 294s PrivateWages_10 0.0 0 294s PrivateWages_11 0.0 0 294s PrivateWages_12 0.0 0 294s PrivateWages_13 0.0 0 294s PrivateWages_14 0.0 0 294s PrivateWages_15 0.0 0 294s PrivateWages_16 0.0 0 294s PrivateWages_17 0.0 0 294s PrivateWages_18 0.0 0 294s PrivateWages_19 0.0 0 294s PrivateWages_20 0.0 0 294s PrivateWages_21 0.0 0 294s PrivateWages_22 0.0 0 294s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 294s Consumption_2 0 0.0 0.0 294s Consumption_3 0 0.0 0.0 294s Consumption_4 0 0.0 0.0 294s Consumption_5 0 0.0 0.0 294s Consumption_6 0 0.0 0.0 294s Consumption_7 0 0.0 0.0 294s Consumption_8 0 0.0 0.0 294s Consumption_9 0 0.0 0.0 294s Consumption_11 0 0.0 0.0 294s Consumption_12 0 0.0 0.0 294s Consumption_14 0 0.0 0.0 294s Consumption_15 0 0.0 0.0 294s Consumption_16 0 0.0 0.0 294s Consumption_17 0 0.0 0.0 294s Consumption_18 0 0.0 0.0 294s Consumption_19 0 0.0 0.0 294s Consumption_20 0 0.0 0.0 294s Consumption_21 0 0.0 0.0 294s Consumption_22 0 0.0 0.0 294s Investment_2 0 0.0 0.0 294s Investment_3 0 0.0 0.0 294s Investment_4 0 0.0 0.0 294s Investment_5 0 0.0 0.0 294s Investment_6 0 0.0 0.0 294s Investment_7 0 0.0 0.0 294s Investment_8 0 0.0 0.0 294s Investment_9 0 0.0 0.0 294s Investment_10 0 0.0 0.0 294s Investment_11 0 0.0 0.0 294s Investment_12 0 0.0 0.0 294s Investment_14 0 0.0 0.0 294s Investment_15 0 0.0 0.0 294s Investment_17 0 0.0 0.0 294s Investment_18 0 0.0 0.0 294s Investment_19 0 0.0 0.0 294s Investment_20 0 0.0 0.0 294s Investment_21 0 0.0 0.0 294s Investment_22 0 0.0 0.0 294s PrivateWages_2 1 45.6 44.9 294s PrivateWages_3 1 50.1 45.6 294s PrivateWages_4 1 57.2 50.1 294s PrivateWages_5 1 57.1 57.2 294s PrivateWages_6 1 61.0 57.1 294s PrivateWages_8 1 64.4 64.0 294s PrivateWages_9 1 64.5 64.4 294s PrivateWages_10 1 67.0 64.5 294s PrivateWages_11 1 61.2 67.0 294s PrivateWages_12 1 53.4 61.2 294s PrivateWages_13 1 44.3 53.4 294s PrivateWages_14 1 45.1 44.3 294s PrivateWages_15 1 49.7 45.1 294s PrivateWages_16 1 54.4 49.7 294s PrivateWages_17 1 62.7 54.4 294s PrivateWages_18 1 65.0 62.7 294s PrivateWages_19 1 60.9 65.0 294s PrivateWages_20 1 69.5 60.9 294s PrivateWages_21 1 75.7 69.5 294s PrivateWages_22 1 88.4 75.7 294s PrivateWages_trend 294s Consumption_2 0 294s Consumption_3 0 294s Consumption_4 0 294s Consumption_5 0 294s Consumption_6 0 294s Consumption_7 0 294s Consumption_8 0 294s Consumption_9 0 294s Consumption_11 0 294s Consumption_12 0 294s Consumption_14 0 294s Consumption_15 0 294s Consumption_16 0 294s Consumption_17 0 294s Consumption_18 0 294s Consumption_19 0 294s Consumption_20 0 294s Consumption_21 0 294s Consumption_22 0 294s Investment_2 0 294s Investment_3 0 294s Investment_4 0 294s Investment_5 0 294s Investment_6 0 294s Investment_7 0 294s Investment_8 0 294s Investment_9 0 294s Investment_10 0 294s Investment_11 0 294s Investment_12 0 294s Investment_14 0 294s Investment_15 0 294s Investment_17 0 294s Investment_18 0 294s Investment_19 0 294s Investment_20 0 294s Investment_21 0 294s Investment_22 0 294s PrivateWages_2 -10 294s PrivateWages_3 -9 294s PrivateWages_4 -8 294s PrivateWages_5 -7 294s PrivateWages_6 -6 294s PrivateWages_8 -4 294s PrivateWages_9 -3 294s PrivateWages_10 -2 294s PrivateWages_11 -1 294s PrivateWages_12 0 294s PrivateWages_13 1 294s PrivateWages_14 2 294s PrivateWages_15 3 294s PrivateWages_16 4 294s PrivateWages_17 5 294s PrivateWages_18 6 294s PrivateWages_19 7 294s PrivateWages_20 8 294s PrivateWages_21 9 294s PrivateWages_22 10 294s > nobs 294s [1] 58 294s > linearHypothesis 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 47 294s 2 46 1 0.3 0.59 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 47 294s 2 46 1 0.29 0.6 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 47 294s 2 46 1 0.29 0.59 294s Linear hypothesis test (Theil's F test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 48 294s 2 46 2 0.16 0.85 294s Linear hypothesis test (F statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df F Pr(>F) 294s 1 48 294s 2 46 2 0.15 0.86 294s Linear hypothesis test (Chi^2 statistic of a Wald test) 294s 294s Hypothesis: 294s Consumption_corpProf + Investment_capitalLag = 0 294s Consumption_corpProfLag - PrivateWages_trend = 0 294s 294s Model 1: restricted model 294s Model 2: kleinModel 294s 294s Res.Df Df Chisq Pr(>Chisq) 294s 1 48 294s 2 46 2 0.3 0.86 294s > logLik 294s 'log Lik.' -68.8 (df=13) 294s 'log Lik.' -73.3 (df=13) 294s compare log likelihood value with single-equation OLS 294s [1] "Mean relative difference: 0.0011" 294s > 294s > # 2SLS 294s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 294s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 294s > summary 295s 295s systemfit results 295s method: 2SLS 295s 295s N DF SSR detRCov OLS-R2 McElroy-R2 295s system 56 44 57.9 0.391 0.968 0.992 295s 295s N DF SSR MSE RMSE R2 Adj R2 295s Consumption 18 14 22.27 1.591 1.26 0.974 0.968 295s Investment 18 14 25.85 1.847 1.36 0.847 0.815 295s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 295s 295s The covariance matrix of the residuals 295s Consumption Investment PrivateWages 295s Consumption 1.307 0.540 -0.431 295s Investment 0.540 1.319 0.119 295s PrivateWages -0.431 0.119 0.496 295s 295s The correlations of the residuals 295s Consumption Investment PrivateWages 295s Consumption 1.000 0.414 -0.538 295s Investment 0.414 1.000 0.139 295s PrivateWages -0.538 0.139 1.000 295s 295s 295s 2SLS estimates for 'Consumption' (equation 1) 295s Model Formula: consump ~ corpProf + corpProfLag + wages 295s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 295s gnpLag 295s 295s Estimate Std. Error t value Pr(>|t|) 295s (Intercept) 17.2849 1.6463 10.50 5.1e-08 *** 295s corpProf -0.0770 0.1683 -0.46 0.65 295s corpProfLag 0.2327 0.1276 1.82 0.09 . 295s wages 0.8259 0.0472 17.49 6.6e-11 *** 295s --- 295s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 295s 295s Residual standard error: 1.261 on 14 degrees of freedom 295s Number of observations: 18 Degrees of Freedom: 14 295s SSR: 22.269 MSE: 1.591 Root MSE: 1.261 295s Multiple R-Squared: 0.974 Adjusted R-Squared: 0.968 295s 295s 295s 2SLS estimates for 'Investment' (equation 2) 295s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 295s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 295s gnpLag 295s 295s Estimate Std. Error t value Pr(>|t|) 295s (Intercept) 18.2571 7.3132 2.50 0.02564 * 295s corpProf 0.1564 0.1942 0.81 0.43408 295s corpProfLag 0.5714 0.1672 3.42 0.00417 ** 295s capitalLag -0.1446 0.0346 -4.18 0.00093 *** 295s --- 295s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 295s 295s Residual standard error: 1.359 on 14 degrees of freedom 295s Number of observations: 18 Degrees of Freedom: 14 295s SSR: 25.852 MSE: 1.847 Root MSE: 1.359 295s Multiple R-Squared: 0.847 Adjusted R-Squared: 0.815 295s 295s 295s 2SLS estimates for 'PrivateWages' (equation 3) 295s Model Formula: privWage ~ gnp + gnpLag + trend 295s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 295s gnpLag 295s 295s Estimate Std. Error t value Pr(>|t|) 295s (Intercept) 1.3431 1.1879 1.13 0.275 295s gnp 0.4438 0.0361 12.28 1.5e-09 *** 295s gnpLag 0.1447 0.0392 3.69 0.002 ** 295s trend 0.1238 0.0308 4.01 0.001 ** 295s --- 295s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 295s 295s Residual standard error: 0.78 on 16 degrees of freedom 295s Number of observations: 20 Degrees of Freedom: 16 295s SSR: 9.741 MSE: 0.609 Root MSE: 0.78 295s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 295s 295s > residuals 295s Consumption Investment PrivateWages 295s 1 NA NA NA 295s 2 -0.6754 -1.214 -1.3401 295s 3 -0.4627 0.325 0.2378 295s 4 -1.1585 1.094 1.1117 295s 5 -0.0305 -1.368 -0.1954 295s 6 0.4693 0.486 -0.5355 295s 7 NA NA NA 295s 8 1.6045 1.066 -0.7908 295s 9 1.6018 0.156 0.2831 295s 10 NA 1.853 1.1353 295s 11 -0.9031 -0.898 -0.1765 295s 12 -1.5948 -1.012 0.6007 295s 13 NA NA 0.1443 295s 14 0.2854 0.845 0.4826 295s 15 -0.4718 -0.365 0.3016 295s 16 -0.2268 NA 0.0261 295s 17 2.0079 1.685 -0.8614 295s 18 -0.7434 -0.121 0.9927 295s 19 -0.5410 -3.248 -0.4446 295s 20 1.4186 0.241 -0.3914 295s 21 1.1462 -0.013 -1.1115 295s 22 -1.7256 0.489 0.5312 295s > fitted 295s Consumption Investment PrivateWages 295s 1 NA NA NA 295s 2 42.6 1.014 26.8 295s 3 45.5 1.575 29.1 295s 4 50.4 4.106 33.0 295s 5 50.6 4.368 34.1 295s 6 52.1 4.614 35.9 295s 7 NA NA NA 295s 8 54.6 3.134 38.7 295s 9 55.7 2.844 38.9 295s 10 NA 3.247 40.2 295s 11 55.9 1.898 38.1 295s 12 52.5 -2.388 33.9 295s 13 NA NA 28.9 295s 14 46.2 -5.945 28.0 295s 15 49.2 -2.635 30.3 295s 16 51.5 NA 33.2 295s 17 55.7 0.415 37.7 295s 18 59.4 2.121 40.0 295s 19 58.0 1.348 38.6 295s 20 60.2 1.059 42.0 295s 21 63.9 3.313 46.1 295s 22 71.4 4.411 52.8 295s > predict 295s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 295s 1 NA NA NA NA 295s 2 42.6 0.586 41.3 43.8 295s 3 45.5 0.674 44.0 46.9 295s 4 50.4 0.443 49.4 51.3 295s 5 50.6 0.524 49.5 51.8 295s 6 52.1 0.535 51.0 53.3 295s 7 NA NA NA NA 295s 8 54.6 0.431 53.7 55.5 295s 9 55.7 0.510 54.6 56.8 295s 10 NA NA NA NA 295s 11 55.9 0.936 53.9 57.9 295s 12 52.5 0.893 50.6 54.4 295s 13 NA NA NA NA 295s 14 46.2 0.713 44.7 47.7 295s 15 49.2 0.501 48.1 50.2 295s 16 51.5 0.407 50.7 52.4 295s 17 55.7 0.457 54.7 56.7 295s 18 59.4 0.397 58.6 60.3 295s 19 58.0 0.564 56.8 59.2 295s 20 60.2 0.543 59.0 61.3 295s 21 63.9 0.529 62.7 65.0 295s 22 71.4 0.808 69.7 73.2 295s Investment.pred Investment.se.fit Investment.lwr Investment.upr 295s 1 NA NA NA NA 295s 2 1.014 0.919 -0.957 2.985 295s 3 1.575 0.602 0.284 2.867 295s 4 4.106 0.544 2.940 5.272 295s 5 4.368 0.450 3.402 5.333 295s 6 4.614 0.425 3.703 5.526 295s 7 NA NA NA NA 295s 8 3.134 0.352 2.380 3.889 295s 9 2.844 0.544 1.677 4.012 295s 10 3.247 0.592 1.976 4.518 295s 11 1.898 0.978 -0.200 3.996 295s 12 -2.388 0.886 -4.289 -0.488 295s 13 NA NA NA NA 295s 14 -5.945 0.916 -7.909 -3.980 295s 15 -2.635 0.518 -3.745 -1.525 295s 16 NA NA NA NA 295s 17 0.415 0.507 -0.671 1.501 295s 18 2.121 0.329 1.416 2.826 295s 19 1.348 0.551 0.166 2.529 295s 20 1.059 0.582 -0.189 2.306 295s 21 3.313 0.496 2.248 4.377 295s 22 4.411 0.728 2.850 5.971 295s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 295s 1 NA NA NA NA 295s 2 26.8 0.330 26.1 27.5 295s 3 29.1 0.344 28.3 29.8 295s 4 33.0 0.363 32.2 33.8 295s 5 34.1 0.260 33.5 34.6 295s 6 35.9 0.268 35.4 36.5 295s 7 NA NA NA NA 295s 8 38.7 0.265 38.1 39.3 295s 9 38.9 0.252 38.4 39.5 295s 10 40.2 0.242 39.7 40.7 295s 11 38.1 0.358 37.3 38.8 295s 12 33.9 0.385 33.1 34.7 295s 13 28.9 0.460 27.9 29.8 295s 14 28.0 0.351 27.3 28.8 295s 15 30.3 0.343 29.6 31.0 295s 16 33.2 0.287 32.6 33.8 295s 17 37.7 0.296 37.0 38.3 295s 18 40.0 0.220 39.5 40.5 295s 19 38.6 0.361 37.9 39.4 295s 20 42.0 0.309 41.3 42.6 295s 21 46.1 0.312 45.4 46.8 295s 22 52.8 0.501 51.7 53.8 295s > model.frame 295s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 295s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 295s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 295s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 295s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 295s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 295s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 295s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 295s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 295s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 295s 10 57.8 21.7 21.1 NA 5.1 211 41.3 67.0 64.5 295s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 295s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 295s 13 45.6 NA 11.4 34.3 -6.2 213 29.0 44.3 53.4 295s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 295s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 295s 16 51.3 14.0 12.3 39.3 NA 199 33.2 54.4 49.7 295s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 295s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 295s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 295s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 295s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 295s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 295s trend 295s 1 -11 295s 2 -10 295s 3 -9 295s 4 -8 295s 5 -7 295s 6 -6 295s 7 -5 295s 8 -4 295s 9 -3 295s 10 -2 295s 11 -1 295s 12 0 295s 13 1 295s 14 2 295s 15 3 295s 16 4 295s 17 5 295s 18 6 295s 19 7 295s 20 8 295s 21 9 295s 22 10 295s > model.matrix 295s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0345 >" 295s [2] "Attributes: < Component “dimnames”: Component 1: 51 string mismatches >" 295s [3] "Numeric: lengths (696, 672) differ" 295s > nobs 295s [1] 56 295s > linearHypothesis 295s Linear hypothesis test (Theil's F test) 295s 295s Hypothesis: 295s Consumption_corpProf + Investment_capitalLag = 0 295s 295s Model 1: restricted model 295s Model 2: kleinModel 295s 295s Res.Df Df F Pr(>F) 295s 1 45 295s 2 44 1 1.27 0.27 295s Linear hypothesis test (F statistic of a Wald test) 295s 295s Hypothesis: 295s Consumption_corpProf + Investment_capitalLag = 0 295s 295s Model 1: restricted model 295s Model 2: kleinModel 295s 295s Res.Df Df F Pr(>F) 295s 1 45 295s 2 44 1 1.66 0.2 295s Linear hypothesis test (Chi^2 statistic of a Wald test) 295s 295s Hypothesis: 295s Consumption_corpProf + Investment_capitalLag = 0 295s 295s Model 1: restricted model 295s Model 2: kleinModel 295s 295s Res.Df Df Chisq Pr(>Chisq) 295s 1 45 295s 2 44 1 1.66 0.2 295s Linear hypothesis test (Theil's F test) 295s 295s Hypothesis: 295s Consumption_corpProf + Investment_capitalLag = 0 295s Consumption_corpProfLag - PrivateWages_trend = 0 295s 295s Model 1: restricted model 295s Model 2: kleinModel 295s 295s Res.Df Df F Pr(>F) 295s 1 46 295s 2 44 2 0.64 0.53 295s Linear hypothesis test (F statistic of a Wald test) 295s 295s Hypothesis: 295s Consumption_corpProf + Investment_capitalLag = 0 295s Consumption_corpProfLag - PrivateWages_trend = 0 295s 295s Model 1: restricted model 295s Model 2: kleinModel 295s 295s Res.Df Df F Pr(>F) 295s 1 46 295s 2 44 2 0.84 0.44 295s Linear hypothesis test (Chi^2 statistic of a Wald test) 295s 295s Hypothesis: 295s Consumption_corpProf + Investment_capitalLag = 0 295s Consumption_corpProfLag - PrivateWages_trend = 0 295s 295s Model 1: restricted model 295s Model 2: kleinModel 295s 295s Res.Df Df Chisq Pr(>Chisq) 295s 1 46 295s 2 44 2 1.68 0.43 295s > logLik 295s 'log Lik.' -69.5 (df=13) 295s 'log Lik.' -77.5 (df=13) 295s > 295s > # SUR 295s Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 295s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 295s > summary 295s 295s systemfit results 295s method: SUR 295s 295s N DF SSR detRCov OLS-R2 McElroy-R2 295s system 58 46 45.1 0.199 0.975 0.993 295s 295s N DF SSR MSE RMSE R2 Adj R2 295s Consumption 19 15 17.5 1.167 1.080 0.980 0.975 295s Investment 19 15 17.3 1.155 1.075 0.906 0.887 295s PrivateWages 20 16 10.3 0.642 0.801 0.987 0.985 295s 295s The covariance matrix of the residuals used for estimation 295s Consumption Investment PrivateWages 295s Consumption 0.9830 0.0466 -0.391 295s Investment 0.0466 0.8101 0.115 295s PrivateWages -0.3906 0.1155 0.496 295s 295s The covariance matrix of the residuals 295s Consumption Investment PrivateWages 295s Consumption 0.979 0.080 -0.452 295s Investment 0.080 0.810 0.181 295s PrivateWages -0.452 0.181 0.521 295s 295s The correlations of the residuals 295s Consumption Investment PrivateWages 295s Consumption 1.0000 0.0907 -0.636 295s Investment 0.0907 1.0000 0.267 295s PrivateWages -0.6362 0.2671 1.000 295s 295s 295s SUR estimates for 'Consumption' (equation 1) 295s Model Formula: consump ~ corpProf + corpProfLag + wages 295s 295s Estimate Std. Error t value Pr(>|t|) 295s (Intercept) 16.2670 1.3148 12.37 2.8e-09 *** 295s corpProf 0.1942 0.0954 2.04 0.06 . 295s corpProfLag 0.0747 0.0842 0.89 0.39 295s wages 0.8011 0.0383 20.93 1.6e-12 *** 295s --- 295s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 295s 295s Residual standard error: 1.08 on 15 degrees of freedom 295s Number of observations: 19 Degrees of Freedom: 15 295s SSR: 17.501 MSE: 1.167 Root MSE: 1.08 295s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.975 295s 295s 295s SUR estimates for 'Investment' (equation 2) 295s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 295s 295s Estimate Std. Error t value Pr(>|t|) 295s (Intercept) 12.6390 4.7856 2.64 0.01852 * 295s corpProf 0.4708 0.0943 4.99 0.00016 *** 295s corpProfLag 0.3533 0.0907 3.89 0.00144 ** 295s capitalLag -0.1254 0.0236 -5.32 8.6e-05 *** 295s --- 295s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 295s 295s Residual standard error: 1.075 on 15 degrees of freedom 295s Number of observations: 19 Degrees of Freedom: 15 295s SSR: 17.321 MSE: 1.155 Root MSE: 1.075 295s Multiple R-Squared: 0.906 Adjusted R-Squared: 0.887 295s 295s 295s SUR estimates for 'PrivateWages' (equation 3) 295s Model Formula: privWage ~ gnp + gnpLag + trend 295s 295s Estimate Std. Error t value Pr(>|t|) 295s (Intercept) 1.3264 1.1240 1.18 0.2552 295s gnp 0.4184 0.0268 15.63 4.1e-11 *** 295s gnpLag 0.1714 0.0315 5.43 5.5e-05 *** 295s trend 0.1456 0.0284 5.13 0.0001 *** 295s --- 295s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 295s 295s Residual standard error: 0.801 on 16 degrees of freedom 295s Number of observations: 20 Degrees of Freedom: 16 295s SSR: 10.266 MSE: 0.642 Root MSE: 0.801 295s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 295s 295s > residuals 295s Consumption Investment PrivateWages 295s 1 NA NA NA 295s 2 -0.3143 -0.2326 -1.1434 295s 3 -1.2700 -0.1705 0.5084 295s 4 -1.5426 1.0718 1.4211 295s 5 -0.4489 -1.4767 -0.0992 295s 6 0.0588 0.3167 -0.3594 295s 7 0.9213 1.4446 NA 295s 8 1.3789 0.8296 -0.7554 295s 9 1.0900 -0.5263 0.2887 295s 10 NA 1.2083 1.1800 295s 11 0.3569 0.4082 -0.3673 295s 12 -0.2288 0.2663 0.3445 295s 13 NA NA -0.1571 295s 14 0.2181 0.4946 0.4220 295s 15 -0.1120 -0.0470 0.3147 295s 16 -0.0872 NA 0.0145 295s 17 1.5615 1.0289 -0.8091 295s 18 -0.4530 0.0617 0.8608 295s 19 0.1997 -2.5397 -0.7635 295s 20 0.9268 -0.6136 -0.4046 295s 21 0.7588 -0.7465 -1.2179 295s 22 -2.2137 -0.6044 0.5606 295s > fitted 295s Consumption Investment PrivateWages 295s 1 NA NA NA 295s 2 42.2 0.0326 26.6 295s 3 46.3 2.0705 28.8 295s 4 50.7 4.1282 32.7 295s 5 51.0 4.4767 34.0 295s 6 52.5 4.7833 35.8 295s 7 54.2 4.1554 NA 295s 8 54.8 3.3704 38.7 295s 9 56.2 3.5263 38.9 295s 10 NA 3.8917 40.1 295s 11 54.6 0.5918 38.3 295s 12 51.1 -3.6663 34.2 295s 13 NA NA 29.2 295s 14 46.3 -5.5946 28.1 295s 15 48.8 -2.9530 30.3 295s 16 51.4 NA 33.2 295s 17 56.1 1.0711 37.6 295s 18 59.2 1.9383 40.1 295s 19 57.3 0.6397 39.0 295s 20 60.7 1.9136 42.0 295s 21 64.2 4.0465 46.2 295s 22 71.9 5.5044 52.7 295s > predict 295s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 295s 1 NA NA NA NA 295s 2 42.2 0.460 41.3 43.1 295s 3 46.3 0.489 45.3 47.3 295s 4 50.7 0.328 50.1 51.4 295s 5 51.0 0.384 50.3 51.8 295s 6 52.5 0.389 51.8 53.3 295s 7 54.2 0.347 53.5 54.9 295s 8 54.8 0.319 54.2 55.5 295s 9 56.2 0.353 55.5 56.9 295s 10 NA NA NA NA 295s 11 54.6 0.583 53.5 55.8 295s 12 51.1 0.524 50.1 52.2 295s 13 NA NA NA NA 295s 14 46.3 0.589 45.1 47.5 295s 15 48.8 0.393 48.0 49.6 295s 16 51.4 0.337 50.7 52.1 295s 17 56.1 0.345 55.4 56.8 295s 18 59.2 0.318 58.5 59.8 295s 19 57.3 0.381 56.5 58.1 295s 20 60.7 0.413 59.8 61.5 295s 21 64.2 0.417 63.4 65.1 295s 22 71.9 0.651 70.6 73.2 295s Investment.pred Investment.se.fit Investment.lwr Investment.upr 295s 1 NA NA NA NA 295s 2 0.0326 0.556 -1.0866 1.15 295s 3 2.0705 0.454 1.1575 2.98 295s 4 4.1282 0.399 3.3256 4.93 295s 5 4.4767 0.331 3.8101 5.14 295s 6 4.7833 0.314 4.1520 5.41 295s 7 4.1554 0.291 3.5687 4.74 295s 8 3.3704 0.260 2.8469 3.89 295s 9 3.5263 0.347 2.8278 4.22 295s 10 3.8917 0.397 3.0924 4.69 295s 11 0.5918 0.578 -0.5711 1.75 295s 12 -3.6663 0.551 -4.7762 -2.56 295s 13 NA NA NA NA 295s 14 -5.5946 0.661 -6.9261 -4.26 295s 15 -2.9530 0.392 -3.7430 -2.16 295s 16 NA NA NA NA 295s 17 1.0711 0.318 0.4315 1.71 295s 18 1.9383 0.225 1.4863 2.39 295s 19 0.6397 0.310 0.0165 1.26 295s 20 1.9136 0.333 1.2436 2.58 295s 21 4.0465 0.304 3.4345 4.66 295s 22 5.5044 0.429 4.6400 6.37 295s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 295s 1 NA NA NA NA 295s 2 26.6 0.321 26.0 27.3 295s 3 28.8 0.321 28.1 29.4 295s 4 32.7 0.316 32.0 33.3 295s 5 34.0 0.244 33.5 34.5 295s 6 35.8 0.242 35.3 36.2 295s 7 NA NA NA NA 295s 8 38.7 0.246 38.2 39.2 295s 9 38.9 0.234 38.4 39.4 295s 10 40.1 0.225 39.7 40.6 295s 11 38.3 0.301 37.7 38.9 295s 12 34.2 0.298 33.6 34.8 295s 13 29.2 0.353 28.4 29.9 295s 14 28.1 0.330 27.4 28.7 295s 15 30.3 0.328 29.6 30.9 295s 16 33.2 0.275 32.6 33.7 295s 17 37.6 0.270 37.1 38.2 295s 18 40.1 0.213 39.7 40.6 295s 19 39.0 0.301 38.4 39.6 295s 20 42.0 0.287 41.4 42.6 295s 21 46.2 0.304 45.6 46.8 295s 22 52.7 0.448 51.8 53.6 295s > model.frame 295s [1] TRUE 295s > model.matrix 295s [1] TRUE 295s > nobs 295s [1] 58 295s > linearHypothesis 295s Linear hypothesis test (Theil's F test) 295s 295s Hypothesis: 295s Consumption_corpProf + Investment_capitalLag = 0 295s 295s Model 1: restricted model 295s Model 2: kleinModel 295s 295s Res.Df Df F Pr(>F) 295s 1 47 295s 2 46 1 0.4 0.53 295s Linear hypothesis test (F statistic of a Wald test) 295s 295s Hypothesis: 295s Consumption_corpProf + Investment_capitalLag = 0 295s 295s Model 1: restricted model 295s Model 2: kleinModel 295s 295s Res.Df Df F Pr(>F) 295s 1 47 295s 2 46 1 0.49 0.49 295s Linear hypothesis test (Chi^2 statistic of a Wald test) 295s 295s Hypothesis: 295s Consumption_corpProf + Investment_capitalLag = 0 295s 295s Model 1: restricted model 295s Model 2: kleinModel 295s 295s Res.Df Df Chisq Pr(>Chisq) 295s 1 47 295s 2 46 1 0.49 0.48 295s Linear hypothesis test (Theil's F test) 295s 295s Hypothesis: 295s Consumption_corpProf + Investment_capitalLag = 0 295s Consumption_corpProfLag - PrivateWages_trend = 0 295s 295s Model 1: restricted model 295s Model 2: kleinModel 295s 295s Res.Df Df F Pr(>F) 295s 1 48 295s 2 46 2 0.31 0.74 295s Linear hypothesis test (F statistic of a Wald test) 295s 295s Hypothesis: 295s Consumption_corpProf + Investment_capitalLag = 0 295s Consumption_corpProfLag - PrivateWages_trend = 0 295s 295s Model 1: restricted model 295s Model 2: kleinModel 295s 295s Res.Df Df F Pr(>F) 295s 1 48 295s 2 46 2 0.37 0.69 295s Linear hypothesis test (Chi^2 statistic of a Wald test) 295s 295s Hypothesis: 295s Consumption_corpProf + Investment_capitalLag = 0 295s Consumption_corpProfLag - PrivateWages_trend = 0 295s 295s Model 1: restricted model 295s Model 2: kleinModel 295s 295s Res.Df Df Chisq Pr(>Chisq) 295s 1 48 295s 2 46 2 0.75 0.69 295s > logLik 295s 'log Lik.' -66.4 (df=18) 295s 'log Lik.' -74.1 (df=18) 295s > 295s > # 3SLS 295s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 295s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 295s > summary 295s 295s systemfit results 295s method: 3SLS 295s 295s N DF SSR detRCov OLS-R2 McElroy-R2 295s system 56 44 67.5 0.436 0.963 0.993 295s 295s N DF SSR MSE RMSE R2 Adj R2 295s Consumption 18 14 22.4 1.598 1.264 0.974 0.968 295s Investment 18 14 35.0 2.503 1.582 0.793 0.749 295s PrivateWages 20 16 10.1 0.629 0.793 0.987 0.985 295s 295s The covariance matrix of the residuals used for estimation 295s Consumption Investment PrivateWages 295s Consumption 1.307 0.540 -0.431 295s Investment 0.540 1.319 0.119 295s PrivateWages -0.431 0.119 0.496 295s 295s The covariance matrix of the residuals 295s Consumption Investment PrivateWages 295s Consumption 1.309 0.638 -0.440 295s Investment 0.638 1.749 0.233 295s PrivateWages -0.440 0.233 0.519 295s 295s The correlations of the residuals 295s Consumption Investment PrivateWages 295s Consumption 1.000 0.422 -0.532 295s Investment 0.422 1.000 0.247 295s PrivateWages -0.532 0.247 1.000 295s 295s 295s 3SLS estimates for 'Consumption' (equation 1) 295s Model Formula: consump ~ corpProf + corpProfLag + wages 295s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 295s gnpLag 295s 295s Estimate Std. Error t value Pr(>|t|) 295s (Intercept) 18.0338 1.5648 11.52 1.6e-08 *** 295s corpProf -0.0632 0.1500 -0.42 0.68 295s corpProfLag 0.1784 0.1154 1.55 0.14 295s wages 0.8224 0.0444 18.54 3.0e-11 *** 295s --- 295s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 295s 295s Residual standard error: 1.264 on 14 degrees of freedom 295s Number of observations: 18 Degrees of Freedom: 14 295s SSR: 22.377 MSE: 1.598 Root MSE: 1.264 295s Multiple R-Squared: 0.974 Adjusted R-Squared: 0.968 295s 295s 295s 3SLS estimates for 'Investment' (equation 2) 295s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 295s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 295s gnpLag 295s 295s Estimate Std. Error t value Pr(>|t|) 295s (Intercept) 24.6766 6.7008 3.68 0.00246 ** 295s corpProf 0.0472 0.1843 0.26 0.80149 295s corpProfLag 0.6874 0.1577 4.36 0.00065 *** 295s capitalLag -0.1776 0.0318 -5.59 6.7e-05 *** 295s --- 295s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 295s 295s Residual standard error: 1.582 on 14 degrees of freedom 295s Number of observations: 18 Degrees of Freedom: 14 295s SSR: 35.037 MSE: 2.503 Root MSE: 1.582 295s Multiple R-Squared: 0.793 Adjusted R-Squared: 0.749 295s 295s 295s 3SLS estimates for 'PrivateWages' (equation 3) 295s Model Formula: privWage ~ gnp + gnpLag + trend 295s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 295s gnpLag 295s 295s Estimate Std. Error t value Pr(>|t|) 295s (Intercept) 0.7823 1.1254 0.70 0.49695 295s gnp 0.4257 0.0308 13.80 2.6e-10 *** 295s gnpLag 0.1728 0.0341 5.07 0.00011 *** 295s trend 0.1252 0.0291 4.30 0.00055 *** 295s --- 295s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 295s 295s Residual standard error: 0.793 on 16 degrees of freedom 295s Number of observations: 20 Degrees of Freedom: 16 295s SSR: 10.057 MSE: 0.629 Root MSE: 0.793 295s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 295s 295s > residuals 295s Consumption Investment PrivateWages 295s 1 NA NA NA 295s 2 -0.8058 -1.721 -1.20135 295s 3 -0.6573 0.337 0.43696 295s 4 -1.1124 0.810 1.31177 295s 5 0.0833 -1.544 -0.19794 295s 6 0.6334 0.368 -0.46596 295s 7 NA NA NA 295s 8 1.7939 1.245 -0.85614 295s 9 1.7891 0.593 0.20698 295s 10 NA 2.303 1.10034 295s 11 -0.5397 -1.015 -0.38801 295s 12 -1.5147 -0.846 0.40949 295s 13 NA NA 0.00602 295s 14 -0.1171 1.670 0.61306 295s 15 -0.6526 -0.075 0.49152 295s 16 -0.3617 NA 0.17066 295s 17 1.9331 2.086 -0.69991 295s 18 -0.6063 -0.101 0.96136 295s 19 -0.3990 -3.345 -0.61606 295s 20 1.4134 0.717 -0.29343 295s 21 1.3257 0.306 -1.14412 295s 22 -1.4340 0.935 0.55310 295s > fitted 295s Consumption Investment PrivateWages 295s 1 NA NA NA 295s 2 42.7 1.5213 26.7 295s 3 45.7 1.5632 28.9 295s 4 50.3 4.3898 32.8 295s 5 50.5 4.5444 34.1 295s 6 52.0 4.7320 35.9 295s 7 NA NA NA 295s 8 54.4 2.9547 38.8 295s 9 55.5 2.4075 39.0 295s 10 NA 2.7965 40.2 295s 11 55.5 2.0150 38.3 295s 12 52.4 -2.5541 34.1 295s 13 NA NA 29.0 295s 14 46.6 -6.7699 27.9 295s 15 49.4 -2.9250 30.1 295s 16 51.7 NA 33.0 295s 17 55.8 0.0139 37.5 295s 18 59.3 2.1013 40.0 295s 19 57.9 1.4453 38.8 295s 20 60.2 0.5828 41.9 295s 21 63.7 2.9944 46.1 295s 22 71.1 3.9651 52.7 295s > predict 295s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 295s 1 NA NA NA NA 295s 2 42.7 0.555 39.7 45.7 295s 3 45.7 0.628 42.6 48.7 295s 4 50.3 0.418 47.5 53.2 295s 5 50.5 0.492 47.6 53.4 295s 6 52.0 0.501 49.0 54.9 295s 7 NA NA NA NA 295s 8 54.4 0.405 51.6 57.3 295s 9 55.5 0.477 52.6 58.4 295s 10 NA NA NA NA 295s 11 55.5 0.832 52.3 58.8 295s 12 52.4 0.792 49.2 55.6 295s 13 NA NA NA NA 295s 14 46.6 0.676 43.5 49.7 295s 15 49.4 0.470 46.5 52.2 295s 16 51.7 0.386 48.8 54.5 295s 17 55.8 0.433 52.9 58.6 295s 18 59.3 0.368 56.5 62.1 295s 19 57.9 0.504 55.0 60.8 295s 20 60.2 0.513 57.3 63.1 295s 21 63.7 0.505 60.8 66.6 295s 22 71.1 0.771 68.0 74.3 295s Investment.pred Investment.se.fit Investment.lwr Investment.upr 295s 1 NA NA NA NA 295s 2 1.5213 0.857 -2.337 5.380 295s 3 1.5632 0.589 -2.058 5.184 295s 4 4.3898 0.519 0.819 7.961 295s 5 4.5444 0.436 1.025 8.064 295s 6 4.7320 0.415 1.224 8.240 295s 7 NA NA NA NA 295s 8 2.9547 0.342 -0.517 6.426 295s 9 2.4075 0.511 -1.158 5.973 295s 10 2.7965 0.556 -0.800 6.393 295s 11 2.0150 0.955 -1.948 5.978 295s 12 -2.5541 0.874 -6.431 1.323 295s 13 NA NA NA NA 295s 14 -6.7699 0.865 -10.637 -2.903 295s 15 -2.9250 0.503 -6.485 0.635 295s 16 NA NA NA NA 295s 17 0.0139 0.483 -3.534 3.561 295s 18 2.1013 0.320 -1.361 5.563 295s 19 1.4453 0.532 -2.134 5.025 295s 20 0.5828 0.550 -3.010 4.175 295s 21 2.9944 0.476 -0.549 6.538 295s 22 3.9651 0.692 0.261 7.669 295s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 295s 1 NA NA NA NA 295s 2 26.7 0.324 24.9 28.5 295s 3 28.9 0.331 27.0 30.7 295s 4 32.8 0.339 31.0 34.6 295s 5 34.1 0.248 32.3 35.9 295s 6 35.9 0.256 34.1 37.6 295s 7 NA NA NA NA 295s 8 38.8 0.251 37.0 40.5 295s 9 39.0 0.238 37.2 40.7 295s 10 40.2 0.232 38.4 42.0 295s 11 38.3 0.314 36.5 40.1 295s 12 34.1 0.327 32.3 35.9 295s 13 29.0 0.393 27.1 30.9 295s 14 27.9 0.329 26.1 29.7 295s 15 30.1 0.324 28.3 31.9 295s 16 33.0 0.271 31.3 34.8 295s 17 37.5 0.277 35.7 39.3 295s 18 40.0 0.213 38.3 41.8 295s 19 38.8 0.320 37.0 40.6 295s 20 41.9 0.295 40.1 43.7 295s 21 46.1 0.309 44.3 47.9 295s 22 52.7 0.476 50.8 54.7 295s > model.frame 295s [1] TRUE 295s > model.matrix 295s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0345 >" 295s [2] "Attributes: < Component “dimnames”: Component 1: 51 string mismatches >" 295s [3] "Numeric: lengths (696, 672) differ" 295s > nobs 295s [1] 56 295s > linearHypothesis 295s Linear hypothesis test (Theil's F test) 295s 295s Hypothesis: 295s Consumption_corpProf + Investment_capitalLag = 0 295s 295s Model 1: restricted model 295s Model 2: kleinModel 295s 295s Res.Df Df F Pr(>F) 295s 1 45 295s 2 44 1 1.91 0.17 295s Linear hypothesis test (F statistic of a Wald test) 295s 295s Hypothesis: 295s Consumption_corpProf + Investment_capitalLag = 0 295s 295s Model 1: restricted model 295s Model 2: kleinModel 295s 295s Res.Df Df F Pr(>F) 295s 1 45 295s 2 44 1 2.6 0.11 295s Linear hypothesis test (Chi^2 statistic of a Wald test) 295s 295s Hypothesis: 295s Consumption_corpProf + Investment_capitalLag = 0 295s 295s Model 1: restricted model 295s Model 2: kleinModel 295s 295s Res.Df Df Chisq Pr(>Chisq) 295s 1 45 295s 2 44 1 2.6 0.11 295s Linear hypothesis test (Theil's F test) 295s 295s Hypothesis: 295s Consumption_corpProf + Investment_capitalLag = 0 295s Consumption_corpProfLag - PrivateWages_trend = 0 295s 295s Model 1: restricted model 295s Model 2: kleinModel 295s 295s Res.Df Df F Pr(>F) 295s 1 46 295s 2 44 2 1.62 0.21 295s Linear hypothesis test (F statistic of a Wald test) 295s 295s Hypothesis: 295s Consumption_corpProf + Investment_capitalLag = 0 295s Consumption_corpProfLag - PrivateWages_trend = 0 295s 295s Model 1: restricted model 295s Model 2: kleinModel 295s 295s Res.Df Df F Pr(>F) 295s 1 46 295s 2 44 2 2.2 0.12 295s Linear hypothesis test (Chi^2 statistic of a Wald test) 295s 295s Hypothesis: 295s Consumption_corpProf + Investment_capitalLag = 0 295s Consumption_corpProfLag - PrivateWages_trend = 0 295s 295s Model 1: restricted model 295s Model 2: kleinModel 295s 295s Res.Df Df Chisq Pr(>Chisq) 295s 1 46 295s 2 44 2 4.41 0.11 295s > logLik 295s 'log Lik.' -70.1 (df=18) 295s 'log Lik.' -80.6 (df=18) 295s > 295s > # I3SLS 295s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 295s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 295s > summary 295s 295s systemfit results 295s method: iterated 3SLS 295s 295s convergence achieved after 10 iterations 295s 295s N DF SSR detRCov OLS-R2 McElroy-R2 295s system 56 44 79.4 0.55 0.956 0.994 295s 295s N DF SSR MSE RMSE R2 Adj R2 295s Consumption 18 14 22.3 1.595 1.263 0.974 0.968 295s Investment 18 14 46.8 3.346 1.829 0.724 0.664 295s PrivateWages 20 16 10.2 0.639 0.799 0.987 0.985 295s 295s The covariance matrix of the residuals used for estimation 295s Consumption Investment PrivateWages 295s Consumption 1.307 0.750 -0.452 295s Investment 0.750 2.318 0.272 295s PrivateWages -0.452 0.272 0.530 295s 295s The covariance matrix of the residuals 295s Consumption Investment PrivateWages 295s Consumption 1.307 0.750 -0.452 295s Investment 0.750 2.318 0.272 295s PrivateWages -0.452 0.272 0.530 295s 295s The correlations of the residuals 295s Consumption Investment PrivateWages 295s Consumption 1.000 0.424 -0.542 295s Investment 0.424 1.000 0.254 295s PrivateWages -0.542 0.254 1.000 295s 295s 295s 3SLS estimates for 'Consumption' (equation 1) 295s Model Formula: consump ~ corpProf + corpProfLag + wages 295s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 295s gnpLag 295s 295s Estimate Std. Error t value Pr(>|t|) 295s (Intercept) 18.3252 1.5452 11.86 1.1e-08 *** 295s corpProf -0.0436 0.1470 -0.30 0.77 295s corpProfLag 0.1614 0.1127 1.43 0.17 295s wages 0.8127 0.0436 18.65 2.8e-11 *** 295s --- 295s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 295s 295s Residual standard error: 1.263 on 14 degrees of freedom 295s Number of observations: 18 Degrees of Freedom: 14 295s SSR: 22.337 MSE: 1.595 Root MSE: 1.263 295s Multiple R-Squared: 0.974 Adjusted R-Squared: 0.968 295s 295s 295s 3SLS estimates for 'Investment' (equation 2) 295s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 295s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 295s gnpLag 295s 295s Estimate Std. Error t value Pr(>|t|) 295s (Intercept) 30.2418 8.3674 3.61 0.00282 ** 295s corpProf -0.0437 0.2341 -0.19 0.85457 295s corpProfLag 0.7856 0.1993 3.94 0.00147 ** 295s capitalLag -0.2065 0.0397 -5.20 0.00014 *** 295s --- 295s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 295s 295s Residual standard error: 1.829 on 14 degrees of freedom 295s Number of observations: 18 Degrees of Freedom: 14 295s SSR: 46.838 MSE: 3.346 Root MSE: 1.829 295s Multiple R-Squared: 0.724 Adjusted R-Squared: 0.664 295s 295s 295s 3SLS estimates for 'PrivateWages' (equation 3) 295s Model Formula: privWage ~ gnp + gnpLag + trend 295s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 295s gnpLag 295s 295s Estimate Std. Error t value Pr(>|t|) 295s (Intercept) 0.4741 1.1280 0.42 0.67983 295s gnp 0.4268 0.0296 14.44 1.4e-10 *** 295s gnpLag 0.1767 0.0330 5.35 6.5e-05 *** 295s trend 0.1201 0.0290 4.14 0.00076 *** 295s --- 295s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 295s 295s Residual standard error: 0.799 on 16 degrees of freedom 295s Number of observations: 20 Degrees of Freedom: 16 295s SSR: 10.218 MSE: 0.639 Root MSE: 0.799 295s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 295s 295s > residuals 295s Consumption Investment PrivateWages 295s 1 NA NA NA 295s 2 -0.8546 -2.1226 -1.1687 295s 3 -0.7611 0.3684 0.4670 295s 4 -1.1233 0.5912 1.3216 295s 5 0.0781 -1.6694 -0.2108 295s 6 0.6467 0.2952 -0.4776 295s 7 NA NA NA 295s 8 1.8444 1.4348 -0.8884 295s 9 1.8309 1.0020 0.1781 295s 10 NA 2.7265 1.0734 295s 11 -0.3652 -1.0581 -0.4134 295s 12 -1.3877 -0.6431 0.4203 295s 13 NA NA 0.0623 295s 14 -0.1818 2.4214 0.7091 295s 15 -0.6438 0.2168 0.5845 295s 16 -0.3417 NA 0.2455 295s 17 1.9583 2.4607 -0.6474 295s 18 -0.4806 -0.0468 0.9840 295s 19 -0.2563 -3.3855 -0.5930 295s 20 1.4832 1.1550 -0.2586 295s 21 1.4514 0.6086 -1.1446 295s 22 -1.2351 1.3453 0.5196 295s > fitted 295s Consumption Investment PrivateWages 295s 1 NA NA NA 295s 2 42.8 1.923 26.7 295s 3 45.8 1.532 28.8 295s 4 50.3 4.609 32.8 295s 5 50.5 4.669 34.1 295s 6 52.0 4.805 35.9 295s 7 NA NA NA 295s 8 54.4 2.765 38.8 295s 9 55.5 1.998 39.0 295s 10 NA 2.373 40.2 295s 11 55.4 2.058 38.3 295s 12 52.3 -2.757 34.1 295s 13 NA NA 28.9 295s 14 46.7 -7.521 27.8 295s 15 49.3 -3.217 30.0 295s 16 51.6 NA 33.0 295s 17 55.7 -0.361 37.4 295s 18 59.2 2.047 40.0 295s 19 57.8 1.485 38.8 295s 20 60.1 0.145 41.9 295s 21 63.5 2.691 46.1 295s 22 70.9 3.555 52.8 295s > predict 295s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 295s 1 NA NA NA NA 295s 2 42.8 0.548 41.7 43.9 295s 3 45.8 0.618 44.5 47.0 295s 4 50.3 0.411 49.5 51.2 295s 5 50.5 0.481 49.6 51.5 295s 6 52.0 0.490 51.0 52.9 295s 7 NA NA NA NA 295s 8 54.4 0.396 53.6 55.2 295s 9 55.5 0.467 54.5 56.4 295s 10 NA NA NA NA 295s 11 55.4 0.811 53.7 57.0 295s 12 52.3 0.775 50.7 53.8 295s 13 NA NA NA NA 295s 14 46.7 0.665 45.3 48.0 295s 15 49.3 0.463 48.4 50.3 295s 16 51.6 0.381 50.9 52.4 295s 17 55.7 0.428 54.9 56.6 295s 18 59.2 0.360 58.5 59.9 295s 19 57.8 0.492 56.8 58.7 295s 20 60.1 0.508 59.1 61.1 295s 21 63.5 0.499 62.5 64.6 295s 22 70.9 0.761 69.4 72.5 295s Investment.pred Investment.se.fit Investment.lwr Investment.upr 295s 1 NA NA NA NA 295s 2 1.923 1.079 -0.2526 4.098 295s 3 1.532 0.766 -0.0119 3.075 295s 4 4.609 0.668 3.2632 5.954 295s 5 4.669 0.566 3.5280 5.811 295s 6 4.805 0.543 3.7104 5.899 295s 7 NA NA NA NA 295s 8 2.765 0.447 1.8648 3.665 295s 9 1.998 0.651 0.6860 3.310 295s 10 2.373 0.710 0.9434 3.804 295s 11 2.058 1.237 -0.4350 4.551 295s 12 -2.757 1.139 -5.0532 -0.461 295s 13 NA NA NA NA 295s 14 -7.521 1.094 -9.7261 -5.317 295s 15 -3.217 0.648 -4.5217 -1.912 295s 16 NA NA NA NA 295s 17 -0.361 0.615 -1.6007 0.879 295s 18 2.047 0.417 1.2060 2.888 295s 19 1.485 0.684 0.1062 2.865 295s 20 0.145 0.699 -1.2632 1.553 295s 21 2.691 0.614 1.4548 3.928 295s 22 3.555 0.887 1.7674 5.342 295s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 295s 1 NA NA NA NA 295s 2 26.7 0.330 26.0 27.3 295s 3 28.8 0.336 28.2 29.5 295s 4 32.8 0.340 32.1 33.5 295s 5 34.1 0.251 33.6 34.6 295s 6 35.9 0.259 35.4 36.4 295s 7 NA NA NA NA 295s 8 38.8 0.253 38.3 39.3 295s 9 39.0 0.240 38.5 39.5 295s 10 40.2 0.236 39.8 40.7 295s 11 38.3 0.307 37.7 38.9 295s 12 34.1 0.313 33.4 34.7 295s 13 28.9 0.376 28.2 29.7 295s 14 27.8 0.327 27.1 28.4 295s 15 30.0 0.322 29.4 30.7 295s 16 33.0 0.270 32.4 33.5 295s 17 37.4 0.275 36.9 38.0 295s 18 40.0 0.216 39.6 40.5 295s 19 38.8 0.314 38.2 39.4 295s 20 41.9 0.296 41.3 42.5 295s 21 46.1 0.317 45.5 46.8 295s 22 52.8 0.480 51.8 53.7 295s > model.frame 295s [1] TRUE 295s > model.matrix 295s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0345 >" 295s [2] "Attributes: < Component “dimnames”: Component 1: 51 string mismatches >" 295s [3] "Numeric: lengths (696, 672) differ" 295s > nobs 295s [1] 56 295s > linearHypothesis 295s Linear hypothesis test (Theil's F test) 295s 295s Hypothesis: 295s Consumption_corpProf + Investment_capitalLag = 0 295s 295s Model 1: restricted model 295s Model 2: kleinModel 295s 295s Res.Df Df F Pr(>F) 295s 1 45 295s 2 44 1 2.29 0.14 295s Linear hypothesis test (F statistic of a Wald test) 295s 295s Hypothesis: 295s Consumption_corpProf + Investment_capitalLag = 0 295s 295s Model 1: restricted model 295s Model 2: kleinModel 295s 295s Res.Df Df F Pr(>F) 295s 1 45 295s 2 44 1 2.89 0.096 . 295s --- 295s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 295s Linear hypothesis test (Chi^2 statistic of a Wald test) 295s 295s Hypothesis: 295s Consumption_corpProf + Investment_capitalLag = 0 295s 295s Model 1: restricted model 295s Model 2: kleinModel 295s 295s Res.Df Df Chisq Pr(>Chisq) 295s 1 45 295s 2 44 1 2.89 0.089 . 295s --- 295s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 295s Linear hypothesis test (Theil's F test) 295s 295s Hypothesis: 295s Consumption_corpProf + Investment_capitalLag = 0 295s Consumption_corpProfLag - PrivateWages_trend = 0 295s 295s Model 1: restricted model 295s Model 2: kleinModel 295s 295s Res.Df Df F Pr(>F) 295s 1 46 295s 2 44 2 2.3 0.11 295s Linear hypothesis test (F statistic of a Wald test) 295s 295s Hypothesis: 295s Consumption_corpProf + Investment_capitalLag = 0 295s Consumption_corpProfLag - PrivateWages_trend = 0 295s 295s Model 1: restricted model 295s Model 2: kleinModel 295s 295s Res.Df Df F Pr(>F) 295s 1 46 295s 2 44 2 2.9 0.066 . 295s --- 295s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 295s Linear hypothesis test (Chi^2 statistic of a Wald test) 295s 295s Hypothesis: 295s Consumption_corpProf + Investment_capitalLag = 0 295s Consumption_corpProfLag - PrivateWages_trend = 0 295s 295s Model 1: restricted model 295s Model 2: kleinModel 295s 295s Res.Df Df Chisq Pr(>Chisq) 295s 1 46 295s 2 44 2 5.79 0.055 . 295s --- 295s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 295s > logLik 295s 'log Lik.' -72.2 (df=18) 295s 'log Lik.' -83.4 (df=18) 295s > 295s BEGIN TEST test_2sls.R 295s 295s R version 4.3.3 (2024-02-29) -- "Angel Food Cake" 295s Copyright (C) 2024 The R Foundation for Statistical Computing 295s Platform: arm-unknown-linux-gnueabihf (32-bit) 295s 295s R is free software and comes with ABSOLUTELY NO WARRANTY. 295s You are welcome to redistribute it under certain conditions. 295s Type 'license()' or 'licence()' for distribution details. 295s 295s R is a collaborative project with many contributors. 295s Type 'contributors()' for more information and 295s 'citation()' on how to cite R or R packages in publications. 295s 295s Type 'demo()' for some demos, 'help()' for on-line help, or 295s 'help.start()' for an HTML browser interface to help. 295s Type 'q()' to quit R. 295s 295s > library( systemfit ) 295s Loading required package: Matrix 296s Loading required package: car 296s Loading required package: carData 296s Loading required package: lmtest 296s Loading required package: zoo 296s 296s Attaching package: ‘zoo’ 296s 296s The following objects are masked from ‘package:base’: 296s 296s as.Date, as.Date.numeric 296s 296s 296s Please cite the 'systemfit' package as: 296s Arne Henningsen and Jeff D. Hamann (2007). systemfit: A Package for Estimating Systems of Simultaneous Equations in R. Journal of Statistical Software 23(4), 1-40. http://www.jstatsoft.org/v23/i04/. 296s 296s If you have questions, suggestions, or comments regarding the 'systemfit' package, please use a forum or 'tracker' at systemfit's R-Forge site: 296s https://r-forge.r-project.org/projects/systemfit/ 296s > options( digits = 3 ) 296s > 296s > data( "Kmenta" ) 296s > useMatrix <- FALSE 296s > 296s > demand <- consump ~ price + income 296s > supply <- consump ~ price + farmPrice + trend 296s > inst <- ~ income + farmPrice + trend 296s > inst1 <- ~ income + farmPrice 296s > instlist <- list( inst1, inst ) 296s > system <- list( demand = demand, supply = supply ) 296s > restrm <- matrix(0,1,7) # restriction matrix "R" 296s > restrm[1,3] <- 1 296s > restrm[1,7] <- -1 296s > restrict <- "demand_income - supply_trend = 0" 296s > restr2m <- matrix(0,2,7) # restriction matrix "R" 2 296s > restr2m[1,3] <- 1 296s > restr2m[1,7] <- -1 296s > restr2m[2,2] <- -1 296s > restr2m[2,5] <- 1 296s > restr2q <- c( 0, 0.5 ) # restriction vector "q" 2 296s > restrict2 <- c( "demand_income - supply_trend = 0", 296s + "- demand_price + supply_price = 0.5" ) 296s > tc <- matrix(0,7,6) 296s > tc[1,1] <- 1 296s > tc[2,2] <- 1 296s > tc[3,3] <- 1 296s > tc[4,4] <- 1 296s > tc[5,5] <- 1 296s > tc[6,6] <- 1 296s > tc[7,3] <- 1 296s > restr3m <- matrix(0,1,6) # restriction matrix "R" 2 296s > restr3m[1,2] <- -1 296s > restr3m[1,5] <- 1 296s > restr3q <- c( 0.5 ) # restriction vector "q" 2 296s > restrict3 <- "- C2 + C5 = 0.5" 296s > 296s > # It is not possible to estimate 2SLS with systemfit exactly 296s > # as EViews does, because EViews uses 296s > # methodResidCov == "geomean" for the coefficient covariance matrix and 296s > # methodResidCov == "noDfCor" for the residual covariance matrix. 296s > # systemfit uses always the same formulas for both calculations. 296s > 296s > ## *************** 2SLS estimation ************************ 296s > ## ************ 2SLS estimation (default)********************* 296s > fit2sls1 <- systemfit( system, "2SLS", data = Kmenta, inst = inst, 296s + x = TRUE, useMatrix = useMatrix ) 296s > print( summary( fit2sls1 ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 33 162 4.36 0.697 0.548 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 65.7 3.87 1.97 0.755 0.726 296s supply 20 16 96.6 6.04 2.46 0.640 0.572 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.87 4.36 296s supply 4.36 6.04 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.902 296s supply 0.902 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 94.6333 7.9208 11.95 1.1e-09 *** 296s price -0.2436 0.0965 -2.52 0.022 * 296s income 0.3140 0.0469 6.69 3.8e-06 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.966 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 296s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 49.5324 12.0105 4.12 0.0008 *** 296s price 0.2401 0.0999 2.40 0.0288 * 296s farmPrice 0.2556 0.0473 5.41 5.8e-05 *** 296s trend 0.2529 0.0997 2.54 0.0219 * 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.458 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 296s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 296s 296s > nobs( fit2sls1 ) 296s [1] 40 296s > 296s > ## *************** 2SLS estimation (singleEqSigma=F)******************* 296s > fit2sls1s <- systemfit( system, "2SLS", data = Kmenta, inst = inst, 296s + singleEqSigma = FALSE, useMatrix = useMatrix ) 296s > print( summary( fit2sls1s ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 33 162 4.36 0.697 0.548 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 65.7 3.87 1.97 0.755 0.726 296s supply 20 16 96.6 6.04 2.46 0.640 0.572 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.87 4.36 296s supply 4.36 6.04 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.902 296s supply 0.902 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 94.633 8.935 10.59 6.6e-09 *** 296s price -0.244 0.109 -2.24 0.039 * 296s income 0.314 0.053 5.93 1.6e-05 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.966 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 296s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 49.5324 10.8404 4.57 0.00032 *** 296s price 0.2401 0.0902 2.66 0.01706 * 296s farmPrice 0.2556 0.0426 5.99 1.9e-05 *** 296s trend 0.2529 0.0899 2.81 0.01253 * 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.458 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 296s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 296s 296s > nobs( fit2sls1s ) 296s [1] 40 296s > 296s > ## ********************* 2SLS (useDfSys = TRUE) ***************** 296s > print( summary( fit2sls1, useDfSys = TRUE ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 33 162 4.36 0.697 0.548 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 65.7 3.87 1.97 0.755 0.726 296s supply 20 16 96.6 6.04 2.46 0.640 0.572 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.87 4.36 296s supply 4.36 6.04 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.902 296s supply 0.902 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 296s price -0.2436 0.0965 -2.52 0.017 * 296s income 0.3140 0.0469 6.69 1.3e-07 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.966 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 296s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 49.5324 12.0105 4.12 0.00024 *** 296s price 0.2401 0.0999 2.40 0.02208 * 296s farmPrice 0.2556 0.0473 5.41 5.5e-06 *** 296s trend 0.2529 0.0997 2.54 0.01605 * 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.458 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 296s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 296s 296s > nobs( fit2sls1 ) 296s [1] 40 296s > 296s > ## ********************* 2SLS (methodResidCov = "noDfCor" ) ***************** 296s > fit2sls1r <- systemfit( system, "2SLS", data = Kmenta, inst = inst, 296s + methodResidCov = "noDfCor", useMatrix = useMatrix ) 296s > print( summary( fit2sls1r ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 33 162 2.97 0.697 0.525 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 65.7 3.87 1.97 0.755 0.726 296s supply 20 16 96.6 6.04 2.46 0.640 0.572 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.29 3.59 296s supply 3.59 4.83 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.902 296s supply 0.902 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 94.6333 7.3027 12.96 3.1e-10 *** 296s price -0.2436 0.0890 -2.74 0.014 * 296s income 0.3140 0.0433 7.25 1.3e-06 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.966 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 296s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 49.5324 10.7425 4.61 0.00029 *** 296s price 0.2401 0.0894 2.69 0.01623 * 296s farmPrice 0.2556 0.0423 6.05 1.7e-05 *** 296s trend 0.2529 0.0891 2.84 0.01188 * 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.458 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 296s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 296s 296s > nobs( fit2sls1r ) 296s [1] 40 296s > 296s > ## *************** 2SLS (methodResidCov="noDfCor", singleEqSigma=F) ************* 296s > fit2sls1rs <- systemfit( system, "2SLS", data = Kmenta, inst = inst, 296s + methodResidCov = "noDfCor", singleEqSigma = FALSE, useMatrix = useMatrix ) 296s > print( summary( fit2sls1rs ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 33 162 2.97 0.697 0.525 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 65.7 3.87 1.97 0.755 0.726 296s supply 20 16 96.6 6.04 2.46 0.640 0.572 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.29 3.59 296s supply 3.59 4.83 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.902 296s supply 0.902 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 94.6333 8.1158 11.66 1.6e-09 *** 296s price -0.2436 0.0989 -2.46 0.025 * 296s income 0.3140 0.0481 6.53 5.2e-06 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.966 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 296s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 49.5324 9.8463 5.03 0.00012 *** 296s price 0.2401 0.0819 2.93 0.00980 ** 296s farmPrice 0.2556 0.0387 6.60 6.1e-06 *** 296s trend 0.2529 0.0817 3.10 0.00694 ** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.458 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 296s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 296s 296s > nobs( fit2sls1rs ) 296s [1] 40 296s > 296s > ## ********************* 2SLS with restriction ******************** 296s > ## **************** 2SLS with restriction (default)******************** 296s > fit2sls2 <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restrm, 296s + inst = inst, useMatrix = useMatrix ) 296s > print( summary( fit2sls2 ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 34 166 3.6 0.691 0.553 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 67.4 3.97 1.99 0.749 0.719 296s supply 20 16 98.2 6.13 2.48 0.634 0.565 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.97 4.55 296s supply 4.55 6.13 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.923 296s supply 0.923 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 94.2816 8.8693 10.63 2.4e-12 *** 296s price -0.2247 0.1034 -2.17 0.037 * 296s income 0.2983 0.0454 6.57 1.6e-07 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.991 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 67.418 MSE: 3.966 Root MSE: 1.991 296s Multiple R-Squared: 0.749 Adjusted R-Squared: 0.719 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 48.1843 10.5384 4.57 6.1e-05 *** 296s price 0.2427 0.0896 2.71 0.011 * 296s farmPrice 0.2619 0.0411 6.38 2.8e-07 *** 296s trend 0.2983 0.0454 6.57 1.6e-07 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.477 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 98.155 MSE: 6.135 Root MSE: 2.477 296s Multiple R-Squared: 0.634 Adjusted R-Squared: 0.565 296s 296s > nobs( fit2sls2 ) 296s [1] 40 296s > # the same with symbolically specified restrictions 296s > fit2sls2Sym <- systemfit( system, "2SLS", data = Kmenta, 296s + restrict.matrix = restrict, inst = inst, useMatrix = useMatrix ) 296s > all.equal( fit2sls2, fit2sls2Sym ) 296s [1] "Component “call”: target, current do not match when deparsed" 296s > nobs( fit2sls2Sym ) 296s [1] 40 296s > 296s > ## ************* 2SLS with restriction (singleEqSigma=T) ***************** 296s > fit2sls2s <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restrm, 296s + inst = inst, singleEqSigma = TRUE, x = TRUE, 296s + useMatrix = useMatrix ) 296s > print( summary( fit2sls2s ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 34 166 3.6 0.691 0.553 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 67.4 3.97 1.99 0.749 0.719 296s supply 20 16 98.2 6.13 2.48 0.634 0.565 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.97 4.55 296s supply 4.55 6.13 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.923 296s supply 0.923 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 94.2816 8.0090 11.77 1.5e-13 *** 296s price -0.2247 0.0946 -2.37 0.023 * 296s income 0.2983 0.0430 6.94 5.3e-08 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.991 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 67.418 MSE: 3.966 Root MSE: 1.991 296s Multiple R-Squared: 0.749 Adjusted R-Squared: 0.719 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 48.1843 11.8001 4.08 0.00025 *** 296s price 0.2427 0.1006 2.41 0.02135 * 296s farmPrice 0.2619 0.0459 5.70 2.1e-06 *** 296s trend 0.2983 0.0430 6.94 5.3e-08 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.477 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 98.155 MSE: 6.135 Root MSE: 2.477 296s Multiple R-Squared: 0.634 Adjusted R-Squared: 0.565 296s 296s > nobs( fit2sls2s ) 296s [1] 40 296s > 296s > ## ********************* 2SLS with restriction (useDfSys=T) ************** 296s > print( summary( fit2sls2, useDfSys = TRUE ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 34 166 3.6 0.691 0.553 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 67.4 3.97 1.99 0.749 0.719 296s supply 20 16 98.2 6.13 2.48 0.634 0.565 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.97 4.55 296s supply 4.55 6.13 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.923 296s supply 0.923 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 94.2816 8.8693 10.63 2.4e-12 *** 296s price -0.2247 0.1034 -2.17 0.037 * 296s income 0.2983 0.0454 6.57 1.6e-07 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.991 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 67.418 MSE: 3.966 Root MSE: 1.991 296s Multiple R-Squared: 0.749 Adjusted R-Squared: 0.719 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 48.1843 10.5384 4.57 6.1e-05 *** 296s price 0.2427 0.0896 2.71 0.011 * 296s farmPrice 0.2619 0.0411 6.38 2.8e-07 *** 296s trend 0.2983 0.0454 6.57 1.6e-07 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.477 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 98.155 MSE: 6.135 Root MSE: 2.477 296s Multiple R-Squared: 0.634 Adjusted R-Squared: 0.565 296s 296s > nobs( fit2sls2 ) 296s [1] 40 296s > 296s > ## ********************* 2SLS with restriction (methodResidCov = "noDfCor") ************** 296s > fit2sls2r <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restrm, 296s + inst = inst, methodResidCov = "noDfCor", useMatrix = useMatrix ) 296s > print( summary( fit2sls2r ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 34 166 2.45 0.691 0.526 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 67.4 3.97 1.99 0.749 0.719 296s supply 20 16 98.2 6.13 2.48 0.634 0.565 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.37 3.75 296s supply 3.75 4.91 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.923 296s supply 0.923 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 94.2816 8.1771 11.53 2.7e-13 *** 296s price -0.2247 0.0954 -2.36 0.024 * 296s income 0.2983 0.0419 7.13 3.1e-08 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.991 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 67.418 MSE: 3.966 Root MSE: 1.991 296s Multiple R-Squared: 0.749 Adjusted R-Squared: 0.719 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 48.1843 9.7159 4.96 1.9e-05 *** 296s price 0.2427 0.0826 2.94 0.0059 ** 296s farmPrice 0.2619 0.0379 6.92 5.7e-08 *** 296s trend 0.2983 0.0419 7.13 3.1e-08 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.477 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 98.155 MSE: 6.135 Root MSE: 2.477 296s Multiple R-Squared: 0.634 Adjusted R-Squared: 0.565 296s 296s > nobs( fit2sls2r ) 296s [1] 40 296s > 296s > ## ******** 2SLS with restriction (methodResidCov="noDfCor", singleEqSigma=TRUE) ********* 296s > fit2sls2rs <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restrm, 296s + inst = inst, methodResidCov = "noDfCor", singleEqSigma = TRUE, 296s + useMatrix = useMatrix ) 296s > print( summary( fit2sls2rs ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 34 166 2.45 0.691 0.526 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 67.4 3.97 1.99 0.749 0.719 296s supply 20 16 98.2 6.13 2.48 0.634 0.565 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.37 3.75 296s supply 3.75 4.91 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.923 296s supply 0.923 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 94.2816 7.3834 12.77 1.6e-14 *** 296s price -0.2247 0.0871 -2.58 0.014 * 296s income 0.2983 0.0394 7.57 8.5e-09 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.991 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 67.418 MSE: 3.966 Root MSE: 1.991 296s Multiple R-Squared: 0.749 Adjusted R-Squared: 0.719 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 48.1843 10.5574 4.56 6.3e-05 *** 296s price 0.2427 0.0900 2.70 0.011 * 296s farmPrice 0.2619 0.0411 6.37 2.8e-07 *** 296s trend 0.2983 0.0394 7.57 8.5e-09 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.477 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 98.155 MSE: 6.135 Root MSE: 2.477 296s Multiple R-Squared: 0.634 Adjusted R-Squared: 0.565 296s 296s > nobs( fit2sls2rs ) 296s [1] 40 296s > 296s > ## ********************* 2SLS with restriction via restrict.regMat ****************** 296s > ## *************** 2SLS with restriction via restrict.regMat (default )*************** 296s > fit2sls3 <- systemfit( system, "2SLS", data = Kmenta, restrict.regMat = tc, 296s + inst = inst, methodResidCov = "noDfCor", useMatrix = useMatrix ) 296s > print( summary( fit2sls3, useDfSys = TRUE ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 34 166 2.45 0.691 0.526 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 67.4 3.97 1.99 0.749 0.719 296s supply 20 16 98.2 6.13 2.48 0.634 0.565 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.37 3.75 296s supply 3.75 4.91 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.923 296s supply 0.923 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 94.2816 8.1771 11.53 2.7e-13 *** 296s price -0.2247 0.0954 -2.36 0.024 * 296s income 0.2983 0.0419 7.13 3.1e-08 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.991 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 67.418 MSE: 3.966 Root MSE: 1.991 296s Multiple R-Squared: 0.749 Adjusted R-Squared: 0.719 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 48.1843 9.7159 4.96 1.9e-05 *** 296s price 0.2427 0.0826 2.94 0.0059 ** 296s farmPrice 0.2619 0.0379 6.92 5.7e-08 *** 296s trend 0.2983 0.0419 7.13 3.1e-08 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.477 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 98.155 MSE: 6.135 Root MSE: 2.477 296s Multiple R-Squared: 0.634 Adjusted R-Squared: 0.565 296s 296s > nobs( fit2sls3 ) 296s [1] 40 296s > 296s > 296s > ## ***************** 2SLS with 2 restrictions ******************* 296s > ## ************** 2SLS with 2 restrictions (default) ************** 296s > fit2sls4 <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restr2m, 296s + restrict.rhs = restr2q, inst = inst, useMatrix = useMatrix ) 296s > print( summary( fit2sls4 ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 35 166 3.78 0.69 0.568 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 66.1 3.89 1.97 0.754 0.725 296s supply 20 16 100.0 6.25 2.50 0.627 0.557 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.89 4.53 296s supply 4.53 6.25 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.919 296s supply 0.919 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 95.7059 6.4189 14.91 < 2e-16 *** 296s price -0.2433 0.0663 -3.67 0.00081 *** 296s income 0.3027 0.0408 7.42 1.1e-08 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.972 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 66.081 MSE: 3.887 Root MSE: 1.972 296s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 46.5637 7.8941 5.90 1.1e-06 *** 296s price 0.2567 0.0663 3.87 0.00045 *** 296s farmPrice 0.2637 0.0398 6.62 1.2e-07 *** 296s trend 0.3027 0.0408 7.42 1.1e-08 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.5 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 99.971 MSE: 6.248 Root MSE: 2.5 296s Multiple R-Squared: 0.627 Adjusted R-Squared: 0.557 296s 296s > nobs( fit2sls4 ) 296s [1] 40 296s > # the same with symbolically specified restrictions 296s > fit2sls4Sym <- systemfit( system, "2SLS", data = Kmenta, 296s + restrict.matrix = restrict2, inst = inst, useMatrix = useMatrix ) 296s > all.equal( fit2sls4, fit2sls4Sym ) 296s [1] "Component “call”: target, current do not match when deparsed" 296s > nobs( fit2sls4Sym ) 296s [1] 40 296s > 296s > ## ************ 2SLS with 2 restrictions (singleEqSigma=T) ************** 296s > fit2sls4s <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restr2m, 296s + restrict.rhs = restr2q, inst = inst, singleEqSigma = TRUE, 296s + useMatrix = useMatrix ) 296s > print( summary( fit2sls4s ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 35 166 3.78 0.69 0.568 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 66.1 3.89 1.97 0.754 0.725 296s supply 20 16 100.0 6.25 2.50 0.627 0.557 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.89 4.53 296s supply 4.53 6.25 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.919 296s supply 0.919 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 95.7059 6.3056 15.18 < 2e-16 *** 296s price -0.2433 0.0684 -3.56 0.0011 ** 296s income 0.3027 0.0394 7.69 5.1e-09 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.972 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 66.081 MSE: 3.887 Root MSE: 1.972 296s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 46.5637 8.3296 5.59 2.7e-06 *** 296s price 0.2567 0.0684 3.75 0.00064 *** 296s farmPrice 0.2637 0.0455 5.79 1.5e-06 *** 296s trend 0.3027 0.0394 7.69 5.1e-09 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.5 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 99.971 MSE: 6.248 Root MSE: 2.5 296s Multiple R-Squared: 0.627 Adjusted R-Squared: 0.557 296s 296s > nobs( fit2sls4s ) 296s [1] 40 296s > 296s > ## ***************** 2SLS with 2 restrictions (useDfSys=T) ************** 296s > print( summary( fit2sls4, useDfSys = TRUE ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 35 166 3.78 0.69 0.568 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 66.1 3.89 1.97 0.754 0.725 296s supply 20 16 100.0 6.25 2.50 0.627 0.557 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.89 4.53 296s supply 4.53 6.25 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.919 296s supply 0.919 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 95.7059 6.4189 14.91 < 2e-16 *** 296s price -0.2433 0.0663 -3.67 0.00081 *** 296s income 0.3027 0.0408 7.42 1.1e-08 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.972 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 66.081 MSE: 3.887 Root MSE: 1.972 296s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 46.5637 7.8941 5.90 1.1e-06 *** 296s price 0.2567 0.0663 3.87 0.00045 *** 296s farmPrice 0.2637 0.0398 6.62 1.2e-07 *** 296s trend 0.3027 0.0408 7.42 1.1e-08 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.5 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 99.971 MSE: 6.248 Root MSE: 2.5 296s Multiple R-Squared: 0.627 Adjusted R-Squared: 0.557 296s 296s > nobs( fit2sls4 ) 296s [1] 40 296s > 296s > ## ***************** 2SLS with 2 restrictions (methodResidCov="noDfCor") ************** 296s > fit2sls4r <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restr2m, 296s + restrict.rhs = restr2q, inst = inst, methodResidCov = "noDfCor", 296s + x = TRUE, useMatrix = useMatrix ) 296s > print( summary( fit2sls4r ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 35 166 2.57 0.69 0.54 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 66.1 3.89 1.97 0.754 0.725 296s supply 20 16 100.0 6.25 2.50 0.627 0.557 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.30 3.73 296s supply 3.73 5.00 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.919 296s supply 0.919 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 95.7059 6.0044 15.94 < 2e-16 *** 296s price -0.2433 0.0621 -3.92 0.00039 *** 296s income 0.3027 0.0382 7.93 2.5e-09 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.972 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 66.081 MSE: 3.887 Root MSE: 1.972 296s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 46.5637 7.3842 6.31 3.1e-07 *** 296s price 0.2567 0.0621 4.14 0.00021 *** 296s farmPrice 0.2637 0.0373 7.08 3.0e-08 *** 296s trend 0.3027 0.0382 7.93 2.5e-09 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.5 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 99.971 MSE: 6.248 Root MSE: 2.5 296s Multiple R-Squared: 0.627 Adjusted R-Squared: 0.557 296s 296s > nobs( fit2sls4r ) 296s [1] 40 296s > 296s > ## ***** 2SLS with 2 restrictions (methodResidCov="noDfCor", singleEqSigma=T) ******* 296s > fit2sls4rs <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restr2m, 296s + restrict.rhs = restr2q, inst = inst, methodResidCov = "noDfCor", 296s + singleEqSigma = TRUE, useMatrix = useMatrix ) 296s > print( summary( fit2sls4rs ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 35 166 2.57 0.69 0.54 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 66.1 3.89 1.97 0.754 0.725 296s supply 20 16 100.0 6.25 2.50 0.627 0.557 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.30 3.73 296s supply 3.73 5.00 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.919 296s supply 0.919 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 95.7059 5.7579 16.62 < 2e-16 *** 296s price -0.2433 0.0621 -3.92 4e-04 *** 296s income 0.3027 0.0360 8.40 6.6e-10 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.972 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 66.081 MSE: 3.887 Root MSE: 1.972 296s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 46.5637 7.5360 6.18 4.5e-07 *** 296s price 0.2567 0.0621 4.13 0.00021 *** 296s farmPrice 0.2637 0.0407 6.47 1.8e-07 *** 296s trend 0.3027 0.0360 8.40 6.6e-10 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.5 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 99.971 MSE: 6.248 Root MSE: 2.5 296s Multiple R-Squared: 0.627 Adjusted R-Squared: 0.557 296s 296s > nobs( fit2sls4rs ) 296s [1] 40 296s > 296s > ## ************* 2SLS with 2 restrictions via R and restrict.regMat ****************** 296s > ## ******** 2SLS with 2 restrictions via R and restrict.regMat (default) ************* 296s > fit2sls5 <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restr3m, 296s + restrict.rhs = restr3q, restrict.regMat = tc, inst = inst, 296s + useMatrix = useMatrix ) 296s > print( summary( fit2sls5 ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 35 166 3.78 0.69 0.568 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 66.1 3.89 1.97 0.754 0.725 296s supply 20 16 100.0 6.25 2.50 0.627 0.557 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.89 4.53 296s supply 4.53 6.25 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.919 296s supply 0.919 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 95.7059 6.4189 14.91 < 2e-16 *** 296s price -0.2433 0.0663 -3.67 0.00081 *** 296s income 0.3027 0.0408 7.42 1.1e-08 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.972 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 66.081 MSE: 3.887 Root MSE: 1.972 296s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 46.5637 7.8941 5.90 1.1e-06 *** 296s price 0.2567 0.0663 3.87 0.00045 *** 296s farmPrice 0.2637 0.0398 6.62 1.2e-07 *** 296s trend 0.3027 0.0408 7.42 1.1e-08 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.5 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 99.971 MSE: 6.248 Root MSE: 2.5 296s Multiple R-Squared: 0.627 Adjusted R-Squared: 0.557 296s 296s > nobs( fit2sls5 ) 296s [1] 40 296s > # the same with symbolically specified restrictions 296s > fit2sls5Sym <- systemfit( system, "2SLS", data = Kmenta, 296s + restrict.matrix = restrict3, restrict.regMat = tc, inst = inst, 296s + useMatrix = useMatrix ) 296s > all.equal( fit2sls5, fit2sls5Sym ) 296s [1] "Component “call”: target, current do not match when deparsed" 296s > nobs( fit2sls5Sym ) 296s [1] 40 296s > 296s > ## ******* 2SLS with 2 restrictions via R and restrict.regMat (singleEqSigma=T) ****** 296s > fit2sls5s <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restr3m, 296s + restrict.rhs = restr3q, restrict.regMat = tc, inst = inst, 296s + singleEqSigma = TRUE, useMatrix = useMatrix ) 296s > print( summary( fit2sls5s ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 35 166 3.78 0.69 0.568 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 66.1 3.89 1.97 0.754 0.725 296s supply 20 16 100.0 6.25 2.50 0.627 0.557 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.89 4.53 296s supply 4.53 6.25 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.919 296s supply 0.919 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 95.7059 6.3056 15.18 < 2e-16 *** 296s price -0.2433 0.0684 -3.56 0.0011 ** 296s income 0.3027 0.0394 7.69 5.1e-09 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.972 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 66.081 MSE: 3.887 Root MSE: 1.972 296s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 46.5637 8.3296 5.59 2.7e-06 *** 296s price 0.2567 0.0684 3.75 0.00064 *** 296s farmPrice 0.2637 0.0455 5.79 1.5e-06 *** 296s trend 0.3027 0.0394 7.69 5.1e-09 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.5 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 99.971 MSE: 6.248 Root MSE: 2.5 296s Multiple R-Squared: 0.627 Adjusted R-Squared: 0.557 296s 296s > nobs( fit2sls5s ) 296s [1] 40 296s > 296s > ## ********** 2SLS with 2 restrictions via R and restrict.regMat (useDfSys=T) ******* 296s > print( summary( fit2sls5, useDfSys = TRUE ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 35 166 3.78 0.69 0.568 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 66.1 3.89 1.97 0.754 0.725 296s supply 20 16 100.0 6.25 2.50 0.627 0.557 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.89 4.53 296s supply 4.53 6.25 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.919 296s supply 0.919 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 95.7059 6.4189 14.91 < 2e-16 *** 296s price -0.2433 0.0663 -3.67 0.00081 *** 296s income 0.3027 0.0408 7.42 1.1e-08 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.972 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 66.081 MSE: 3.887 Root MSE: 1.972 296s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 46.5637 7.8941 5.90 1.1e-06 *** 296s price 0.2567 0.0663 3.87 0.00045 *** 296s farmPrice 0.2637 0.0398 6.62 1.2e-07 *** 296s trend 0.3027 0.0408 7.42 1.1e-08 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.5 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 99.971 MSE: 6.248 Root MSE: 2.5 296s Multiple R-Squared: 0.627 Adjusted R-Squared: 0.557 296s 296s > nobs( fit2sls5 ) 296s [1] 40 296s > 296s > ## ************* 2SLS with 2 restrictions via R and restrict.regMat (methodResidCov="noDfCor") ********* 296s > fit2sls5r <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restr3m, 296s + restrict.rhs = restr3q, restrict.regMat = tc, inst = inst, 296s + methodResidCov = "noDfCor", useMatrix = useMatrix ) 296s > print( summary( fit2sls5r ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 35 166 2.57 0.69 0.54 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 66.1 3.89 1.97 0.754 0.725 296s supply 20 16 100.0 6.25 2.50 0.627 0.557 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.30 3.73 296s supply 3.73 5.00 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.919 296s supply 0.919 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 95.7059 6.0044 15.94 < 2e-16 *** 296s price -0.2433 0.0621 -3.92 0.00039 *** 296s income 0.3027 0.0382 7.93 2.5e-09 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.972 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 66.081 MSE: 3.887 Root MSE: 1.972 296s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 46.5637 7.3842 6.31 3.1e-07 *** 296s price 0.2567 0.0621 4.14 0.00021 *** 296s farmPrice 0.2637 0.0373 7.08 3.0e-08 *** 296s trend 0.3027 0.0382 7.93 2.5e-09 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.5 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 99.971 MSE: 6.248 Root MSE: 2.5 296s Multiple R-Squared: 0.627 Adjusted R-Squared: 0.557 296s 296s > nobs( fit2sls5r ) 296s [1] 40 296s > 296s > ## ** 2SLS with 2 restrictions via R and restrict.regMat (methodResidCov="noDfCor", singleEqSigma=T) ** 296s > fit2sls5rs <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restr3m, 296s + restrict.rhs = restr3q, restrict.regMat = tc, inst = inst, 296s + methodResidCov = "noDfCor", singleEqSigma = TRUE, 296s + x = TRUE, useMatrix = useMatrix ) 296s > print( summary( fit2sls5rs ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 35 166 2.57 0.69 0.54 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 66.1 3.89 1.97 0.754 0.725 296s supply 20 16 100.0 6.25 2.50 0.627 0.557 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.30 3.73 296s supply 3.73 5.00 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.919 296s supply 0.919 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 95.7059 5.7579 16.62 < 2e-16 *** 296s price -0.2433 0.0621 -3.92 4e-04 *** 296s income 0.3027 0.0360 8.40 6.6e-10 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.972 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 66.081 MSE: 3.887 Root MSE: 1.972 296s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 46.5637 7.5360 6.18 4.5e-07 *** 296s price 0.2567 0.0621 4.13 0.00021 *** 296s farmPrice 0.2637 0.0407 6.47 1.8e-07 *** 296s trend 0.3027 0.0360 8.40 6.6e-10 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.5 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 99.971 MSE: 6.248 Root MSE: 2.5 296s Multiple R-Squared: 0.627 Adjusted R-Squared: 0.557 296s 296s > nobs( fit2sls5rs ) 296s [1] 40 296s > 296s > ## *********** 2SLS estimation with different instruments ************** 296s > ## ******* 2SLS estimation with different instruments (default) ********* 296s > fit2slsd1 <- systemfit( system, "2SLS", data = Kmenta, inst = instlist, 296s + useMatrix = useMatrix ) 296s > print( summary( fit2slsd1 ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 33 164 9.25 0.694 0.512 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 67.4 3.97 1.99 0.748 0.719 296s supply 20 16 96.6 6.04 2.46 0.640 0.572 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.97 3.84 296s supply 3.84 6.04 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.784 296s supply 0.784 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 106.7894 11.1435 9.58 2.9e-08 *** 296s price -0.4116 0.1448 -2.84 0.011 * 296s income 0.3617 0.0564 6.41 6.4e-06 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.992 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 296s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 49.5324 12.0105 4.12 0.0008 *** 296s price 0.2401 0.0999 2.40 0.0288 * 296s farmPrice 0.2556 0.0473 5.41 5.8e-05 *** 296s trend 0.2529 0.0997 2.54 0.0219 * 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.458 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 296s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 296s 296s > nobs( fit2slsd1 ) 296s [1] 40 296s > 296s > ## *********** 2SLS estimation with different instruments (singleEqSigma=F)***** 296s > fit2slsd1s <- systemfit( system, "2SLS", data = Kmenta, inst = instlist, 296s + singleEqSigma = FALSE, useMatrix = useMatrix ) 296s > print( summary( fit2slsd1s ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 33 164 9.25 0.694 0.512 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 67.4 3.97 1.99 0.748 0.719 296s supply 20 16 96.6 6.04 2.46 0.640 0.572 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.97 3.84 296s supply 3.84 6.04 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.784 296s supply 0.784 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 106.7894 12.4749 8.56 1.4e-07 *** 296s price -0.4116 0.1622 -2.54 0.021 * 296s income 0.3617 0.0631 5.73 2.5e-05 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.992 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 296s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 49.5324 10.8976 4.55 0.00033 *** 296s price 0.2401 0.0907 2.65 0.01755 * 296s farmPrice 0.2556 0.0429 5.96 2e-05 *** 296s trend 0.2529 0.0904 2.80 0.01292 * 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.458 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 296s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 296s 296s > nobs( fit2slsd1s ) 296s [1] 40 296s > 296s > ## ********* 2SLS estimation with different instruments (useDfSys=T) ******* 296s > print( summary( fit2slsd1, useDfSys = TRUE ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 33 164 9.25 0.694 0.512 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 67.4 3.97 1.99 0.748 0.719 296s supply 20 16 96.6 6.04 2.46 0.640 0.572 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.97 3.84 296s supply 3.84 6.04 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.784 296s supply 0.784 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 106.7894 11.1435 9.58 4.7e-11 *** 296s price -0.4116 0.1448 -2.84 0.0076 ** 296s income 0.3617 0.0564 6.41 2.9e-07 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.992 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 296s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 49.5324 12.0105 4.12 0.00024 *** 296s price 0.2401 0.0999 2.40 0.02208 * 296s farmPrice 0.2556 0.0473 5.41 5.5e-06 *** 296s trend 0.2529 0.0997 2.54 0.01605 * 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.458 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 296s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 296s 296s > nobs( fit2slsd1 ) 296s [1] 40 296s > 296s > ## ********* 2SLS estimation with different instruments (methodResidCov="noDfCor") ****** 296s > fit2slsd1r <- systemfit( system, "2SLS", data = Kmenta, inst = instlist, 296s + methodResidCov = "noDfCor", useMatrix = useMatrix ) 296s > print( summary( fit2slsd1r ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 33 164 6.29 0.694 0.5 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 67.4 3.97 1.99 0.748 0.719 296s supply 20 16 96.6 6.04 2.46 0.640 0.572 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.37 3.16 296s supply 3.16 4.83 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.784 296s supply 0.784 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 106.789 10.274 10.39 8.8e-09 *** 296s price -0.412 0.134 -3.08 0.0068 ** 296s income 0.362 0.052 6.95 2.3e-06 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.992 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 296s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 49.5324 10.7425 4.61 0.00029 *** 296s price 0.2401 0.0894 2.69 0.01623 * 296s farmPrice 0.2556 0.0423 6.05 1.7e-05 *** 296s trend 0.2529 0.0891 2.84 0.01188 * 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.458 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 296s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 296s 296s > nobs( fit2slsd1r ) 296s [1] 40 296s > 296s > ## 2SLS estimation with different instruments (methodResidCov="noDfCor",singleEqSigma=F) 296s > fit2slsd1r <- systemfit( system, "2SLS", data = Kmenta, inst = instlist, 296s + methodResidCov = "noDfCor", singleEqSigma = FALSE, 296s + useMatrix = useMatrix ) 296s > print( summary( fit2slsd1r ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 33 164 6.29 0.694 0.5 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 67.4 3.97 1.99 0.748 0.719 296s supply 20 16 96.6 6.04 2.46 0.640 0.572 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.37 3.16 296s supply 3.16 4.83 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.784 296s supply 0.784 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 106.7894 11.3309 9.42 3.7e-08 *** 296s price -0.4116 0.1473 -2.79 0.012 * 296s income 0.3617 0.0574 6.31 7.9e-06 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.992 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 296s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 49.5324 9.8982 5.00 0.00013 *** 296s price 0.2401 0.0824 2.92 0.01012 * 296s farmPrice 0.2556 0.0389 6.56 6.5e-06 *** 296s trend 0.2529 0.0821 3.08 0.00718 ** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.458 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 296s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 296s 296s > nobs( fit2slsd1r ) 296s [1] 40 296s > 296s > ## **** 2SLS estimation with different instruments and restriction ******* 296s > ## ** 2SLS estimation with different instruments and restriction (default) **** 296s > fit2slsd2 <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restrm, 296s + inst = instlist, useMatrix = useMatrix ) 296s > print( summary( fit2slsd2 ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 34 165 4.89 0.693 0.56 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 64.4 3.79 1.95 0.760 0.731 296s supply 20 16 100.3 6.27 2.50 0.626 0.556 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.79 4.35 296s supply 4.35 6.27 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.891 296s supply 0.891 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 103.5936 11.8930 8.71 3.5e-10 *** 296s price -0.3449 0.1455 -2.37 0.024 * 296s income 0.3260 0.0511 6.38 2.8e-07 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.947 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 64.445 MSE: 3.791 Root MSE: 1.947 296s Multiple R-Squared: 0.76 Adjusted R-Squared: 0.731 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 47.3592 10.5362 4.49 7.7e-05 *** 296s price 0.2443 0.0894 2.73 0.0099 ** 296s farmPrice 0.2657 0.0411 6.47 2.1e-07 *** 296s trend 0.3260 0.0511 6.38 2.8e-07 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.504 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 100.321 MSE: 6.27 Root MSE: 2.504 296s Multiple R-Squared: 0.626 Adjusted R-Squared: 0.556 296s 296s > nobs( fit2slsd2 ) 296s [1] 40 296s > 296s > ## 2SLS estimation with different instruments and restriction (singleEqSigma=T) 296s > fit2slsd2s <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restrm, 296s + inst = instlist, singleEqSigma = TRUE, useMatrix = useMatrix ) 296s > print( summary( fit2slsd2s ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 34 165 4.89 0.693 0.56 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 64.4 3.79 1.95 0.760 0.731 296s supply 20 16 100.3 6.27 2.50 0.626 0.556 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.79 4.35 296s supply 4.35 6.27 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.891 296s supply 0.891 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 103.5936 10.6344 9.74 2.3e-11 *** 296s price -0.3449 0.1327 -2.60 0.014 * 296s income 0.3260 0.0485 6.73 9.9e-08 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.947 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 64.445 MSE: 3.791 Root MSE: 1.947 296s Multiple R-Squared: 0.76 Adjusted R-Squared: 0.731 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 47.3592 11.9466 3.96 0.00036 *** 296s price 0.2443 0.1017 2.40 0.02188 * 296s farmPrice 0.2657 0.0465 5.71 2.0e-06 *** 296s trend 0.3260 0.0485 6.73 9.9e-08 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.504 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 100.321 MSE: 6.27 Root MSE: 2.504 296s Multiple R-Squared: 0.626 Adjusted R-Squared: 0.556 296s 296s > nobs( fit2slsd2s ) 296s [1] 40 296s > 296s > ## **** 2SLS estimation with different instruments and restriction (useDfSys=F) 296s > print( summary( fit2slsd2, useDfSys = FALSE ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 34 165 4.89 0.693 0.56 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 64.4 3.79 1.95 0.760 0.731 296s supply 20 16 100.3 6.27 2.50 0.626 0.556 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.79 4.35 296s supply 4.35 6.27 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.891 296s supply 0.891 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 103.5936 11.8930 8.71 1.1e-07 *** 296s price -0.3449 0.1455 -2.37 0.03 * 296s income 0.3260 0.0511 6.38 6.9e-06 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.947 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 64.445 MSE: 3.791 Root MSE: 1.947 296s Multiple R-Squared: 0.76 Adjusted R-Squared: 0.731 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 47.3592 10.5362 4.49 0.00037 *** 296s price 0.2443 0.0894 2.73 0.01475 * 296s farmPrice 0.2657 0.0411 6.47 7.8e-06 *** 296s trend 0.3260 0.0511 6.38 9.1e-06 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.504 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 100.321 MSE: 6.27 Root MSE: 2.504 296s Multiple R-Squared: 0.626 Adjusted R-Squared: 0.556 296s 296s > nobs( fit2slsd2 ) 296s [1] 40 296s > 296s > ## **** 2SLS estimation with different instruments and restriction (methodResidCov="noDfCor") 296s > fit2slsd2r <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restrm, 296s + inst = instlist, methodResidCov = "noDfCor", useMatrix = useMatrix ) 296s > print( summary( fit2slsd2r ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 34 165 3.32 0.693 0.537 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 64.4 3.79 1.95 0.760 0.731 296s supply 20 16 100.3 6.27 2.50 0.626 0.556 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.22 3.58 296s supply 3.58 5.02 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.891 296s supply 0.891 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 103.5936 10.9648 9.45 4.9e-11 *** 296s price -0.3449 0.1341 -2.57 0.015 * 296s income 0.3260 0.0471 6.92 5.7e-08 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.947 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 64.445 MSE: 3.791 Root MSE: 1.947 296s Multiple R-Squared: 0.76 Adjusted R-Squared: 0.731 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 47.3592 9.7139 4.88 2.5e-05 *** 296s price 0.2443 0.0824 2.96 0.0055 ** 296s farmPrice 0.2657 0.0379 7.01 4.3e-08 *** 296s trend 0.3260 0.0471 6.92 5.7e-08 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.504 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 100.321 MSE: 6.27 Root MSE: 2.504 296s Multiple R-Squared: 0.626 Adjusted R-Squared: 0.556 296s 296s > nobs( fit2slsd2r ) 296s [1] 40 296s > 296s > ## 2SLS estimation with different instr. and restr. (methodResidCov="noDfCor", singleEqSigma=T) 296s > fit2slsd2rs <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restrm, 296s + inst = instlist, methodResidCov = "noDfCor", singleEqSigma = TRUE, 296s + useMatrix = useMatrix ) 296s > print( summary( fit2slsd2rs ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 34 165 3.32 0.693 0.537 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 64.4 3.79 1.95 0.760 0.731 296s supply 20 16 100.3 6.27 2.50 0.626 0.556 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.22 3.58 296s supply 3.58 5.02 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.891 296s supply 0.891 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 103.5936 9.7929 10.58 2.7e-12 *** 296s price -0.3449 0.1220 -2.83 0.0078 ** 296s income 0.3260 0.0444 7.35 1.6e-08 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.947 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 64.445 MSE: 3.791 Root MSE: 1.947 296s Multiple R-Squared: 0.76 Adjusted R-Squared: 0.731 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 47.3592 10.6890 4.43 9.3e-05 *** 296s price 0.2443 0.0910 2.69 0.011 * 296s farmPrice 0.2657 0.0416 6.38 2.8e-07 *** 296s trend 0.3260 0.0444 7.35 1.6e-08 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.504 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 100.321 MSE: 6.27 Root MSE: 2.504 296s Multiple R-Squared: 0.626 Adjusted R-Squared: 0.556 296s 296s > nobs( fit2slsd2rs ) 296s [1] 40 296s > 296s > ## **** 2SLS estimation with different instruments and restriction via restrict.regMat * 296s > ## 2SLS estimation with different instruments and restriction via restrict.regMat (default) 296s > fit2slsd3 <- systemfit( system, "2SLS", data = Kmenta, restrict.regMat = tc, 296s + inst = instlist, useMatrix = useMatrix ) 296s > print( summary( fit2slsd3 ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 34 165 4.89 0.693 0.56 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 64.4 3.79 1.95 0.760 0.731 296s supply 20 16 100.3 6.27 2.50 0.626 0.556 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.79 4.35 296s supply 4.35 6.27 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.891 296s supply 0.891 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 103.5936 11.8930 8.71 3.5e-10 *** 296s price -0.3449 0.1455 -2.37 0.024 * 296s income 0.3260 0.0511 6.38 2.8e-07 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.947 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 64.445 MSE: 3.791 Root MSE: 1.947 296s Multiple R-Squared: 0.76 Adjusted R-Squared: 0.731 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 47.3592 10.5362 4.49 7.7e-05 *** 296s price 0.2443 0.0894 2.73 0.0099 ** 296s farmPrice 0.2657 0.0411 6.47 2.1e-07 *** 296s trend 0.3260 0.0511 6.38 2.8e-07 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.504 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 100.321 MSE: 6.27 Root MSE: 2.504 296s Multiple R-Squared: 0.626 Adjusted R-Squared: 0.556 296s 296s > nobs( fit2slsd3 ) 296s [1] 40 296s > 296s > ## **** 2SLS estimation with different instr. and restr. via restrict.regMat (methodResidCov="noDfCor") 296s > fit2slsd3r <- systemfit( system, "2SLS", data = Kmenta, restrict.regMat = tc, 296s + inst = instlist, methodResidCov = "noDfCor", useMatrix = useMatrix ) 296s > print( summary( fit2slsd3r ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 34 165 3.32 0.693 0.537 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 64.4 3.79 1.95 0.760 0.731 296s supply 20 16 100.3 6.27 2.50 0.626 0.556 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.22 3.58 296s supply 3.58 5.02 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.891 296s supply 0.891 1.000 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 103.5936 10.9648 9.45 4.9e-11 *** 296s price -0.3449 0.1341 -2.57 0.015 * 296s income 0.3260 0.0471 6.92 5.7e-08 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.947 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 64.445 MSE: 3.791 Root MSE: 1.947 296s Multiple R-Squared: 0.76 Adjusted R-Squared: 0.731 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 47.3592 9.7139 4.88 2.5e-05 *** 296s price 0.2443 0.0824 2.96 0.0055 ** 296s farmPrice 0.2657 0.0379 7.01 4.3e-08 *** 296s trend 0.3260 0.0471 6.92 5.7e-08 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.504 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 100.321 MSE: 6.27 Root MSE: 2.504 296s Multiple R-Squared: 0.626 Adjusted R-Squared: 0.556 296s 296s > nobs( fit2slsd3r ) 296s [1] 40 296s > 296s > 296s > ## *********** estimations with a single regressor ************ 296s > fit2slsS1 <- systemfit( 296s + list( consump ~ price - 1, price ~ consump + trend ), "2SLS", 296s + data = Kmenta, inst = ~ farmPrice + trend + income, useMatrix = useMatrix ) 296s > print( summary( fit2slsS1 ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 36 1544 179 -0.65 0.852 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s eq1 20 19 861 45.3 6.73 -2.213 -2.213 296s eq2 20 17 682 40.1 6.33 -0.022 -0.143 296s 296s The covariance matrix of the residuals 296s eq1 eq2 296s eq1 45.3 -40.5 296s eq2 -40.5 40.1 296s 296s The correlations of the residuals 296s eq1 eq2 296s eq1 1.00 -0.95 296s eq2 -0.95 1.00 296s 296s 296s 2SLS estimates for 'eq1' (equation 1) 296s Model Formula: consump ~ price - 1 296s Instruments: ~farmPrice + trend + income 296s 296s Estimate Std. Error t value Pr(>|t|) 296s price 1.006 0.015 66.9 <2e-16 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 6.734 on 19 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 19 296s SSR: 861.48 MSE: 45.341 Root MSE: 6.734 296s Multiple R-Squared: -2.213 Adjusted R-Squared: -2.213 296s 296s 296s 2SLS estimates for 'eq2' (equation 2) 296s Model Formula: price ~ consump + trend 296s Instruments: ~farmPrice + trend + income 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 55.5365 46.2668 1.20 0.25 296s consump 0.4453 0.4622 0.96 0.35 296s trend -0.0426 0.2496 -0.17 0.87 296s 296s Residual standard error: 6.335 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 682.257 MSE: 40.133 Root MSE: 6.335 296s Multiple R-Squared: -0.022 Adjusted R-Squared: -0.143 296s 296s > nobs( fit2slsS1 ) 296s [1] 40 296s > fit2slsS2 <- systemfit( 296s + list( consump ~ price - 1, consump ~ trend - 1 ), "2SLS", 296s + data = Kmenta, inst = ~ farmPrice + price + income, useMatrix = useMatrix ) 296s > print( summary( fit2slsS2 ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 38 47456 111148 -87.5 -5.28 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s eq1 20 19 861 45.3 6.73 -2.21 -2.21 296s eq2 20 19 46595 2452.3 49.52 -172.79 -172.79 296s 296s The covariance matrix of the residuals 296s eq1 eq2 296s eq1 45.34 -6.33 296s eq2 -6.33 2452.34 296s 296s The correlations of the residuals 296s eq1 eq2 296s eq1 1.0000 -0.0448 296s eq2 -0.0448 1.0000 296s 296s 296s 2SLS estimates for 'eq1' (equation 1) 296s Model Formula: consump ~ price - 1 296s Instruments: ~farmPrice + price + income 296s 296s Estimate Std. Error t value Pr(>|t|) 296s price 1.006 0.015 66.9 <2e-16 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 6.733 on 19 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 19 296s SSR: 861.449 MSE: 45.339 Root MSE: 6.733 296s Multiple R-Squared: -2.213 Adjusted R-Squared: -2.213 296s 296s 296s 2SLS estimates for 'eq2' (equation 2) 296s Model Formula: consump ~ trend - 1 296s Instruments: ~farmPrice + price + income 296s 296s Estimate Std. Error t value Pr(>|t|) 296s trend 7.578 0.934 8.11 1.4e-07 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 49.521 on 19 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 19 296s SSR: 46594.549 MSE: 2452.345 Root MSE: 49.521 296s Multiple R-Squared: -172.786 Adjusted R-Squared: -172.786 296s 296s > nobs( fit2slsS2 ) 296s [1] 40 296s > fit2slsS3 <- systemfit( 296s + list( consump ~ trend - 1, price ~ trend - 1 ), "2SLS", 296s + data = Kmenta, inst = instlist, useMatrix = useMatrix ) 296s > print( summary( fit2slsS3 ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 38 97978 687515 -104 -10.6 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s eq1 20 19 50950 2682 51.8 -189.0 -189.0 296s eq2 20 19 47028 2475 49.8 -69.5 -69.5 296s 296s The covariance matrix of the residuals 296s eq1 eq2 296s eq1 2682 2439 296s eq2 2439 2475 296s 296s The correlations of the residuals 296s eq1 eq2 296s eq1 1.000 0.989 296s eq2 0.989 1.000 296s 296s 296s 2SLS estimates for 'eq1' (equation 1) 296s Model Formula: consump ~ trend - 1 296s Instruments: ~income + farmPrice 296s 296s Estimate Std. Error t value Pr(>|t|) 296s trend 8.65 1.05 8.27 1e-07 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 51.784 on 19 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 19 296s SSR: 50949.985 MSE: 2681.578 Root MSE: 51.784 296s Multiple R-Squared: -189.031 Adjusted R-Squared: -189.031 296s 296s 296s 2SLS estimates for 'eq2' (equation 2) 296s Model Formula: price ~ trend - 1 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s trend 7.318 0.929 7.88 2.1e-07 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 49.751 on 19 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 19 296s SSR: 47028.107 MSE: 2475.164 Root MSE: 49.751 296s Multiple R-Squared: -69.48 Adjusted R-Squared: -69.48 296s 296s > nobs( fit2slsS3 ) 296s [1] 40 296s > fit2slsS4 <- systemfit( 296s + list( consump ~ trend - 1, price ~ trend - 1 ), "2SLS", 296s + data = Kmenta, inst = ~ farmPrice + trend + income, 296s + restrict.matrix = matrix( c( 1, -1 ), nrow = 1 ), useMatrix = useMatrix ) 296s > print( summary( fit2slsS4 ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 39 93548 111736 -99 -1.03 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s eq1 20 19 46514 2448 49.5 -172.5 -172.5 296s eq2 20 19 47033 2475 49.8 -69.5 -69.5 296s 296s The covariance matrix of the residuals 296s eq1 eq2 296s eq1 2448 2439 296s eq2 2439 2475 296s 296s The correlations of the residuals 296s eq1 eq2 296s eq1 1.000 0.988 296s eq2 0.988 1.000 296s 296s 296s 2SLS estimates for 'eq1' (equation 1) 296s Model Formula: consump ~ trend - 1 296s Instruments: ~farmPrice + trend + income 296s 296s Estimate Std. Error t value Pr(>|t|) 296s trend 7.362 0.646 11.4 5.7e-14 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 49.478 on 19 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 19 296s SSR: 46514.283 MSE: 2448.12 Root MSE: 49.478 296s Multiple R-Squared: -172.487 Adjusted R-Squared: -172.487 296s 296s 296s 2SLS estimates for 'eq2' (equation 2) 296s Model Formula: price ~ trend - 1 296s Instruments: ~farmPrice + trend + income 296s 296s Estimate Std. Error t value Pr(>|t|) 296s trend 7.362 0.646 11.4 5.7e-14 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 49.754 on 19 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 19 296s SSR: 47033.469 MSE: 2475.446 Root MSE: 49.754 296s Multiple R-Squared: -69.488 Adjusted R-Squared: -69.488 296s 296s > nobs( fit2slsS4 ) 296s [1] 40 296s > fit2slsS5 <- systemfit( 296s + list( consump ~ 1, price ~ 1 ), "2SLS", 296s + data = Kmenta, inst = ~ farmPrice, useMatrix = useMatrix ) 296s > print( summary( fit2slsS1 ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 36 1544 179 -0.65 0.852 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s eq1 20 19 861 45.3 6.73 -2.213 -2.213 296s eq2 20 17 682 40.1 6.33 -0.022 -0.143 296s 296s The covariance matrix of the residuals 296s eq1 eq2 296s eq1 45.3 -40.5 296s eq2 -40.5 40.1 296s 296s The correlations of the residuals 296s eq1 eq2 296s eq1 1.00 -0.95 296s eq2 -0.95 1.00 296s 296s 296s 2SLS estimates for 'eq1' (equation 1) 296s Model Formula: consump ~ price - 1 296s Instruments: ~farmPrice + trend + income 296s 296s Estimate Std. Error t value Pr(>|t|) 296s price 1.006 0.015 66.9 <2e-16 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 6.734 on 19 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 19 296s SSR: 861.48 MSE: 45.341 Root MSE: 6.734 296s Multiple R-Squared: -2.213 Adjusted R-Squared: -2.213 296s 296s 296s 2SLS estimates for 'eq2' (equation 2) 296s Model Formula: price ~ consump + trend 296s Instruments: ~farmPrice + trend + income 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 55.5365 46.2668 1.20 0.25 296s consump 0.4453 0.4622 0.96 0.35 296s trend -0.0426 0.2496 -0.17 0.87 296s 296s Residual standard error: 6.335 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 682.257 MSE: 40.133 Root MSE: 6.335 296s Multiple R-Squared: -0.022 Adjusted R-Squared: -0.143 296s 296s > 296s > 296s > ## **************** shorter summaries ********************** 296s > print( summary( fit2sls1, useDfSys = TRUE, residCov = FALSE ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 33 162 4.36 0.697 0.548 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 65.7 3.87 1.97 0.755 0.726 296s supply 20 16 96.6 6.04 2.46 0.640 0.572 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 296s price -0.2436 0.0965 -2.52 0.017 * 296s income 0.3140 0.0469 6.69 1.3e-07 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.966 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 296s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 49.5324 12.0105 4.12 0.00024 *** 296s price 0.2401 0.0999 2.40 0.02208 * 296s farmPrice 0.2556 0.0473 5.41 5.5e-06 *** 296s trend 0.2529 0.0997 2.54 0.01605 * 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.458 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 296s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 296s 296s > 296s > print( summary( fit2sls1, equations = FALSE ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 33 162 4.36 0.697 0.548 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 65.7 3.87 1.97 0.755 0.726 296s supply 20 16 96.6 6.04 2.46 0.640 0.572 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.87 4.36 296s supply 4.36 6.04 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.902 296s supply 0.902 1.000 296s 296s 296s Coefficients: 296s Estimate Std. Error t value Pr(>|t|) 296s demand_(Intercept) 94.6333 7.9208 11.95 1.1e-09 *** 296s demand_price -0.2436 0.0965 -2.52 0.0218 * 296s demand_income 0.3140 0.0469 6.69 3.8e-06 *** 296s supply_(Intercept) 49.5324 12.0105 4.12 0.0008 *** 296s supply_price 0.2401 0.0999 2.40 0.0288 * 296s supply_farmPrice 0.2556 0.0473 5.41 5.8e-05 *** 296s supply_trend 0.2529 0.0997 2.54 0.0219 * 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s > 296s > print( summary( fit2sls1rs, residCov = FALSE, equations = FALSE ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 33 162 2.97 0.697 0.525 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 65.7 3.87 1.97 0.755 0.726 296s supply 20 16 96.6 6.04 2.46 0.640 0.572 296s 296s 296s Coefficients: 296s Estimate Std. Error t value Pr(>|t|) 296s demand_(Intercept) 94.6333 8.1158 11.66 1.6e-09 *** 296s demand_price -0.2436 0.0989 -2.46 0.02471 * 296s demand_income 0.3140 0.0481 6.53 5.2e-06 *** 296s supply_(Intercept) 49.5324 9.8463 5.03 0.00012 *** 296s supply_price 0.2401 0.0819 2.93 0.00980 ** 296s supply_farmPrice 0.2556 0.0387 6.60 6.1e-06 *** 296s supply_trend 0.2529 0.0817 3.10 0.00694 ** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s > 296s > print( summary( fit2sls2Sym, useDfSys = FALSE ), equations = FALSE ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 34 166 3.6 0.691 0.553 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 67.4 3.97 1.99 0.749 0.719 296s supply 20 16 98.2 6.13 2.48 0.634 0.565 296s 296s The covariance matrix of the residuals 296s demand supply 296s demand 3.97 4.55 296s supply 4.55 6.13 296s 296s The correlations of the residuals 296s demand supply 296s demand 1.000 0.923 296s supply 0.923 1.000 296s 296s 296s Coefficients: 296s Estimate Std. Error t value Pr(>|t|) 296s demand_(Intercept) 94.2816 8.8693 10.63 6.3e-09 *** 296s demand_price -0.2247 0.1034 -2.17 0.04425 * 296s demand_income 0.2983 0.0454 6.57 4.8e-06 *** 296s supply_(Intercept) 48.1843 10.5384 4.57 0.00031 *** 296s supply_price 0.2427 0.0896 2.71 0.01551 * 296s supply_farmPrice 0.2619 0.0411 6.38 9.1e-06 *** 296s supply_trend 0.2983 0.0454 6.57 6.4e-06 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s > 296s > print( summary( fit2sls2 ), residCov = FALSE ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 34 166 3.6 0.691 0.553 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 67.4 3.97 1.99 0.749 0.719 296s supply 20 16 98.2 6.13 2.48 0.634 0.565 296s 296s 296s 2SLS estimates for 'demand' (equation 1) 296s Model Formula: consump ~ price + income 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 94.2816 8.8693 10.63 2.4e-12 *** 296s price -0.2247 0.1034 -2.17 0.037 * 296s income 0.2983 0.0454 6.57 1.6e-07 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 1.991 on 17 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 17 296s SSR: 67.418 MSE: 3.966 Root MSE: 1.991 296s Multiple R-Squared: 0.749 Adjusted R-Squared: 0.719 296s 296s 296s 2SLS estimates for 'supply' (equation 2) 296s Model Formula: consump ~ price + farmPrice + trend 296s Instruments: ~income + farmPrice + trend 296s 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 48.1843 10.5384 4.57 6.1e-05 *** 296s price 0.2427 0.0896 2.71 0.011 * 296s farmPrice 0.2619 0.0411 6.38 2.8e-07 *** 296s trend 0.2983 0.0454 6.57 1.6e-07 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s 296s Residual standard error: 2.477 on 16 degrees of freedom 296s Number of observations: 20 Degrees of Freedom: 16 296s SSR: 98.155 MSE: 6.135 Root MSE: 2.477 296s Multiple R-Squared: 0.634 Adjusted R-Squared: 0.565 296s 296s > 296s > print( summary( fit2sls3, useDfSys = FALSE, residCov = FALSE, 296s + equations = FALSE ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 34 166 2.45 0.691 0.526 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 67.4 3.97 1.99 0.749 0.719 296s supply 20 16 98.2 6.13 2.48 0.634 0.565 296s 296s 296s Coefficients: 296s Estimate Std. Error t value Pr(>|t|) 296s demand_(Intercept) 94.2816 8.1771 11.53 1.8e-09 *** 296s demand_price -0.2247 0.0954 -2.36 0.03071 * 296s demand_income 0.2983 0.0419 7.13 1.7e-06 *** 296s supply_(Intercept) 48.1843 9.7159 4.96 0.00014 *** 296s supply_price 0.2427 0.0826 2.94 0.00966 ** 296s supply_farmPrice 0.2619 0.0379 6.92 3.5e-06 *** 296s supply_trend 0.2983 0.0419 7.13 2.4e-06 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s > 296s > print( summary( fit2sls4s ), equations = FALSE, residCov = FALSE ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 35 166 3.78 0.69 0.568 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 66.1 3.89 1.97 0.754 0.725 296s supply 20 16 100.0 6.25 2.50 0.627 0.557 296s 296s 296s Coefficients: 296s Estimate Std. Error t value Pr(>|t|) 296s demand_(Intercept) 95.7059 6.3056 15.18 < 2e-16 *** 296s demand_price -0.2433 0.0684 -3.56 0.00110 ** 296s demand_income 0.3027 0.0394 7.69 5.1e-09 *** 296s supply_(Intercept) 46.5637 8.3296 5.59 2.7e-06 *** 296s supply_price 0.2567 0.0684 3.75 0.00064 *** 296s supply_farmPrice 0.2637 0.0455 5.79 1.5e-06 *** 296s supply_trend 0.3027 0.0394 7.69 5.1e-09 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s > 296s > print( summary( fit2sls5r, equations = FALSE, residCov = FALSE ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 35 166 2.57 0.69 0.54 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 66.1 3.89 1.97 0.754 0.725 296s supply 20 16 100.0 6.25 2.50 0.627 0.557 296s 296s 296s Coefficients: 296s Estimate Std. Error t value Pr(>|t|) 296s demand_(Intercept) 95.7059 6.0044 15.94 < 2e-16 *** 296s demand_price -0.2433 0.0621 -3.92 0.00039 *** 296s demand_income 0.3027 0.0382 7.93 2.5e-09 *** 296s supply_(Intercept) 46.5637 7.3842 6.31 3.1e-07 *** 296s supply_price 0.2567 0.0621 4.14 0.00021 *** 296s supply_farmPrice 0.2637 0.0373 7.08 3.0e-08 *** 296s supply_trend 0.3027 0.0382 7.93 2.5e-09 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s > 296s > print( summary( fit2slsd1s ), residCov = FALSE, equations = FALSE ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 33 164 9.25 0.694 0.512 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 67.4 3.97 1.99 0.748 0.719 296s supply 20 16 96.6 6.04 2.46 0.640 0.572 296s 296s 296s Coefficients: 296s Estimate Std. Error t value Pr(>|t|) 296s demand_(Intercept) 106.7894 12.4749 8.56 1.4e-07 *** 296s demand_price -0.4116 0.1622 -2.54 0.02121 * 296s demand_income 0.3617 0.0631 5.73 2.5e-05 *** 296s supply_(Intercept) 49.5324 10.8976 4.55 0.00033 *** 296s supply_price 0.2401 0.0907 2.65 0.01755 * 296s supply_farmPrice 0.2556 0.0429 5.96 2.0e-05 *** 296s supply_trend 0.2529 0.0904 2.80 0.01292 * 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s > 296s > print( summary( fit2slsd2, residCov = FALSE, equations = FALSE ) ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 34 165 4.89 0.693 0.56 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 64.4 3.79 1.95 0.760 0.731 296s supply 20 16 100.3 6.27 2.50 0.626 0.556 296s 296s 296s Coefficients: 296s Estimate Std. Error t value Pr(>|t|) 296s demand_(Intercept) 103.5936 11.8930 8.71 3.5e-10 *** 296s demand_price -0.3449 0.1455 -2.37 0.0236 * 296s demand_income 0.3260 0.0511 6.38 2.8e-07 *** 296s supply_(Intercept) 47.3592 10.5362 4.49 7.7e-05 *** 296s supply_price 0.2443 0.0894 2.73 0.0099 ** 296s supply_farmPrice 0.2657 0.0411 6.47 2.1e-07 *** 296s supply_trend 0.3260 0.0511 6.38 2.8e-07 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s > 296s > print( summary( fit2slsd3r ), residCov = FALSE, equations = FALSE ) 296s 296s systemfit results 296s method: 2SLS 296s 296s N DF SSR detRCov OLS-R2 McElroy-R2 296s system 40 34 165 3.32 0.693 0.537 296s 296s N DF SSR MSE RMSE R2 Adj R2 296s demand 20 17 64.4 3.79 1.95 0.760 0.731 296s supply 20 16 100.3 6.27 2.50 0.626 0.556 296s 296s 296s Coefficients: 296s Estimate Std. Error t value Pr(>|t|) 296s demand_(Intercept) 103.5936 10.9648 9.45 4.9e-11 *** 296s demand_price -0.3449 0.1341 -2.57 0.0147 * 296s demand_income 0.3260 0.0471 6.92 5.7e-08 *** 296s supply_(Intercept) 47.3592 9.7139 4.88 2.5e-05 *** 296s supply_price 0.2443 0.0824 2.96 0.0055 ** 296s supply_farmPrice 0.2657 0.0379 7.01 4.3e-08 *** 296s supply_trend 0.3260 0.0471 6.92 5.7e-08 *** 296s --- 296s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 296s > 296s > 296s > ## ****************** residuals ************************** 296s > print( residuals( fit2sls1 ) ) 296s demand supply 296s 1 0.843 -0.4348 296s 2 -0.698 -1.2131 296s 3 2.359 1.7090 296s 4 1.490 0.7956 296s 5 2.139 1.5942 296s 6 1.277 0.6595 296s 7 1.571 1.4346 296s 8 -3.066 -4.8724 296s 9 -1.125 -2.3975 296s 10 2.492 3.1427 296s 11 -0.108 0.0689 296s 12 -2.292 -1.3978 296s 13 -1.598 -1.1136 296s 14 -0.271 1.1684 296s 15 1.958 3.4865 296s 16 -3.430 -3.8285 296s 17 -0.313 0.6793 296s 18 -2.151 -2.7713 296s 19 1.592 2.6668 296s 20 -0.668 0.6235 296s > print( residuals( fit2sls1$eq[[ 1 ]] ) ) 296s 1 2 3 4 5 6 7 8 9 10 11 296s 0.843 -0.698 2.359 1.490 2.139 1.277 1.571 -3.066 -1.125 2.492 -0.108 296s 12 13 14 15 16 17 18 19 20 296s -2.292 -1.598 -0.271 1.958 -3.430 -0.313 -2.151 1.592 -0.668 296s > 296s > print( residuals( fit2sls2s ) ) 296s demand supply 296s 1 0.678 -0.0135 296s 2 -0.777 -0.8544 296s 3 2.281 2.0245 296s 4 1.416 1.0692 296s 5 2.213 1.7598 296s 6 1.334 0.7923 296s 7 1.640 1.5342 296s 8 -2.994 -4.8544 296s 9 -1.072 -2.3959 296s 10 2.522 3.1637 296s 11 -0.330 0.1628 296s 12 -2.593 -1.2864 296s 13 -1.856 -1.0729 296s 14 -0.356 1.1087 296s 15 2.138 3.2597 296s 16 -3.282 -4.1265 296s 17 -0.076 0.3331 296s 18 -2.119 -3.0961 296s 19 1.690 2.3122 296s 20 -0.458 0.1799 296s > print( residuals( fit2sls2s$eq[[ 2 ]] ) ) 296s 1 2 3 4 5 6 7 8 9 10 296s -0.0135 -0.8544 2.0245 1.0692 1.7598 0.7923 1.5342 -4.8544 -2.3959 3.1637 296s 11 12 13 14 15 16 17 18 19 20 296s 0.1628 -1.2864 -1.0729 1.1087 3.2597 -4.1265 0.3331 -3.0961 2.3122 0.1799 296s > 296s > print( residuals( fit2sls3 ) ) 296s demand supply 296s 1 0.678 -0.0135 296s 2 -0.777 -0.8544 296s 3 2.281 2.0245 296s 4 1.416 1.0692 296s 5 2.213 1.7598 296s 6 1.334 0.7923 296s 7 1.640 1.5342 296s 8 -2.994 -4.8544 296s 9 -1.072 -2.3959 296s 10 2.522 3.1637 296s 11 -0.330 0.1628 296s 12 -2.593 -1.2864 296s 13 -1.856 -1.0729 296s 14 -0.356 1.1087 296s 15 2.138 3.2597 296s 16 -3.282 -4.1265 296s 17 -0.076 0.3331 296s 18 -2.119 -3.0961 296s 19 1.690 2.3122 296s 20 -0.458 0.1799 296s > print( residuals( fit2sls3$eq[[ 1 ]] ) ) 296s 1 2 3 4 5 6 7 8 9 10 11 296s 0.678 -0.777 2.281 1.416 2.213 1.334 1.640 -2.994 -1.072 2.522 -0.330 296s 12 13 14 15 16 17 18 19 20 296s -2.593 -1.856 -0.356 2.138 -3.282 -0.076 -2.119 1.690 -0.458 296s > 296s > print( residuals( fit2sls4r ) ) 296s demand supply 296s 1 0.729 0.0219 296s 2 -0.698 -0.8806 296s 3 2.349 2.0055 296s 4 1.496 1.0326 296s 5 2.165 1.7870 296s 6 1.310 0.7993 296s 7 1.635 1.5189 296s 8 -2.951 -4.9334 296s 9 -1.134 -2.3609 296s 10 2.397 3.2818 296s 11 -0.359 0.2857 296s 12 -2.524 -1.2257 296s 13 -1.745 -1.0782 296s 14 -0.349 1.1382 296s 15 2.022 3.2981 296s 16 -3.345 -4.1440 296s 17 -0.322 0.4686 296s 18 -2.075 -3.1779 296s 19 1.738 2.2072 296s 20 -0.339 -0.0444 296s > print( residuals( fit2sls4r$eq[[ 2 ]] ) ) 296s 1 2 3 4 5 6 7 8 9 10 296s 0.0219 -0.8806 2.0055 1.0326 1.7870 0.7993 1.5189 -4.9334 -2.3609 3.2818 296s 11 12 13 14 15 16 17 18 19 20 296s 0.2857 -1.2257 -1.0782 1.1382 3.2981 -4.1440 0.4686 -3.1779 2.2072 -0.0444 296s > 296s > print( residuals( fit2sls5rs ) ) 296s demand supply 296s 1 0.729 0.0219 296s 2 -0.698 -0.8806 296s 3 2.349 2.0055 296s 4 1.496 1.0326 296s 5 2.165 1.7870 296s 6 1.310 0.7993 296s 7 1.635 1.5189 296s 8 -2.951 -4.9334 296s 9 -1.134 -2.3609 296s 10 2.397 3.2818 296s 11 -0.359 0.2857 296s 12 -2.524 -1.2257 296s 13 -1.745 -1.0782 296s 14 -0.349 1.1382 296s 15 2.022 3.2981 296s 16 -3.345 -4.1440 296s 17 -0.322 0.4686 296s 18 -2.075 -3.1779 296s 19 1.738 2.2072 296s 20 -0.339 -0.0444 296s > print( residuals( fit2sls5rs$eq[[ 1 ]] ) ) 296s 1 2 3 4 5 6 7 8 9 10 11 296s 0.729 -0.698 2.349 1.496 2.165 1.310 1.635 -2.951 -1.134 2.397 -0.359 296s 12 13 14 15 16 17 18 19 20 296s -2.524 -1.745 -0.349 2.022 -3.345 -0.322 -2.075 1.738 -0.339 296s > 296s > print( residuals( fit2slsd1 ) ) 296s demand supply 296s 1 1.3775 -0.4348 296s 2 0.0125 -1.2131 296s 3 2.9728 1.7090 296s 4 2.2121 0.7956 296s 5 1.6920 1.5942 296s 6 1.0407 0.6595 296s 7 1.4768 1.4346 296s 8 -2.7583 -4.8724 296s 9 -1.6807 -2.3975 296s 10 1.4265 3.1427 296s 11 -0.2029 0.0689 296s 12 -1.5123 -1.3978 296s 13 -0.4958 -1.1136 296s 14 -0.1528 1.1684 296s 15 0.8692 3.4865 296s 16 -4.0547 -3.8285 296s 17 -2.5309 0.6793 296s 18 -1.8070 -2.7713 296s 19 1.9299 2.6668 296s 20 0.1853 0.6235 296s > print( residuals( fit2slsd1$eq[[ 2 ]] ) ) 296s 1 2 3 4 5 6 7 8 9 10 296s -0.4348 -1.2131 1.7090 0.7956 1.5942 0.6595 1.4346 -4.8724 -2.3975 3.1427 296s 11 12 13 14 15 16 17 18 19 20 296s 0.0689 -1.3978 -1.1136 1.1684 3.4865 -3.8285 0.6793 -2.7713 2.6668 0.6235 296s > 296s > print( residuals( fit2slsd2r ) ) 296s demand supply 296s 1 0.996 0.2444 296s 2 -0.268 -0.6349 296s 3 2.715 2.2177 296s 4 1.936 1.2367 296s 5 1.907 1.8612 296s 6 1.184 0.8736 296s 7 1.609 1.5951 296s 8 -2.709 -4.8434 296s 9 -1.476 -2.3949 296s 10 1.705 3.1765 296s 11 -0.540 0.2202 296s 12 -2.167 -1.2182 296s 13 -1.150 -1.0480 296s 14 -0.316 1.0722 296s 15 1.395 3.1209 296s 16 -3.680 -4.3088 296s 17 -1.669 0.1212 296s 18 -1.829 -3.2948 296s 19 2.016 2.0952 296s 20 0.341 -0.0916 296s > print( residuals( fit2slsd2r$eq[[ 1 ]] ) ) 296s 1 2 3 4 5 6 7 8 9 10 11 296s 0.996 -0.268 2.715 1.936 1.907 1.184 1.609 -2.709 -1.476 1.705 -0.540 296s 12 13 14 15 16 17 18 19 20 296s -2.167 -1.150 -0.316 1.395 -3.680 -1.669 -1.829 2.016 0.341 296s > 296s > 296s > ## *************** coefficients ********************* 296s > print( round( coef( fit2sls1s ), digits = 6 ) ) 296s demand_(Intercept) demand_price demand_income supply_(Intercept) 296s 94.633 -0.244 0.314 49.532 296s supply_price supply_farmPrice supply_trend 296s 0.240 0.256 0.253 296s > print( round( coef( fit2sls1s$eq[[ 1 ]] ), digits = 6 ) ) 296s (Intercept) price income 296s 94.633 -0.244 0.314 296s > 296s > print( round( coef( fit2sls2 ), digits = 6 ) ) 296s demand_(Intercept) demand_price demand_income supply_(Intercept) 296s 94.282 -0.225 0.298 48.184 296s supply_price supply_farmPrice supply_trend 296s 0.243 0.262 0.298 296s > print( round( coef( fit2sls2$eq[[ 2 ]] ), digits = 6 ) ) 296s (Intercept) price farmPrice trend 296s 48.184 0.243 0.262 0.298 296s > 296s > print( round( coef( fit2sls3 ), digits = 6 ) ) 296s demand_(Intercept) demand_price demand_income supply_(Intercept) 296s 94.282 -0.225 0.298 48.184 296s supply_price supply_farmPrice supply_trend 296s 0.243 0.262 0.298 296s > print( round( coef( fit2sls3, modified.regMat = TRUE ), digits = 6 ) ) 296s C1 C2 C3 C4 C5 C6 296s 94.282 -0.225 0.298 48.184 0.243 0.262 296s > print( round( coef( fit2sls3$eq[[ 1 ]] ), digits = 6 ) ) 296s (Intercept) price income 296s 94.282 -0.225 0.298 296s > 296s > print( round( coef( fit2sls4s ), digits = 6 ) ) 296s demand_(Intercept) demand_price demand_income supply_(Intercept) 296s 95.706 -0.243 0.303 46.564 296s supply_price supply_farmPrice supply_trend 296s 0.257 0.264 0.303 296s > print( round( coef( fit2sls4s$eq[[ 2 ]] ), digits = 6 ) ) 296s (Intercept) price farmPrice trend 296s 46.564 0.257 0.264 0.303 296s > 296s > print( round( coef( fit2sls5r ), digits = 6 ) ) 296s demand_(Intercept) demand_price demand_income supply_(Intercept) 296s 95.706 -0.243 0.303 46.564 296s supply_price supply_farmPrice supply_trend 296s 0.257 0.264 0.303 296s > print( round( coef( fit2sls5r, modified.regMat = TRUE ), digits = 6 ) ) 296s C1 C2 C3 C4 C5 C6 296s 95.706 -0.243 0.303 46.564 0.257 0.264 296s > print( round( coef( fit2sls5r$eq[[ 2 ]] ), digits = 6 ) ) 296s (Intercept) price farmPrice trend 296s 46.564 0.257 0.264 0.303 296s > 296s > 296s > ## *************** coefficients with stats ********************* 296s > print( round( coef( summary( fit2sls1s ) ), digits = 6 ) ) 296s Estimate Std. Error t value Pr(>|t|) 296s demand_(Intercept) 94.633 8.9352 10.59 0.000000 296s demand_price -0.244 0.1088 -2.24 0.038916 296s demand_income 0.314 0.0530 5.93 0.000016 296s supply_(Intercept) 49.532 10.8404 4.57 0.000315 296s supply_price 0.240 0.0902 2.66 0.017058 296s supply_farmPrice 0.256 0.0426 5.99 0.000019 296s supply_trend 0.253 0.0899 2.81 0.012528 296s > print( round( coef( summary( fit2sls1s$eq[[ 1 ]] ) ), digits = 6 ) ) 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 94.633 8.935 10.59 0.000000 296s price -0.244 0.109 -2.24 0.038916 296s income 0.314 0.053 5.93 0.000016 296s > 296s > print( round( coef( summary( fit2sls2, useDfSys = FALSE ) ), digits = 6 ) ) 296s Estimate Std. Error t value Pr(>|t|) 296s demand_(Intercept) 94.282 8.8693 10.63 0.000000 296s demand_price -0.225 0.1034 -2.17 0.044246 296s demand_income 0.298 0.0454 6.57 0.000005 296s supply_(Intercept) 48.184 10.5384 4.57 0.000313 296s supply_price 0.243 0.0896 2.71 0.015508 296s supply_farmPrice 0.262 0.0411 6.38 0.000009 296s supply_trend 0.298 0.0454 6.57 0.000006 296s > print( round( coef( summary( fit2sls2$eq[[ 2 ]], useDfSys = FALSE ) ), 296s + digits = 6 ) ) 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 48.184 10.5384 4.57 0.000313 296s price 0.243 0.0896 2.71 0.015508 296s farmPrice 0.262 0.0411 6.38 0.000009 296s trend 0.298 0.0454 6.57 0.000006 296s > 296s > print( round( coef( summary( fit2sls3 ) ), digits = 6 ) ) 296s Estimate Std. Error t value Pr(>|t|) 296s demand_(Intercept) 94.282 8.1771 11.53 0.000000 296s demand_price -0.225 0.0954 -2.36 0.024352 296s demand_income 0.298 0.0419 7.13 0.000000 296s supply_(Intercept) 48.184 9.7159 4.96 0.000019 296s supply_price 0.243 0.0826 2.94 0.005903 296s supply_farmPrice 0.262 0.0379 6.92 0.000000 296s supply_trend 0.298 0.0419 7.13 0.000000 296s > print( round( coef( summary( fit2sls3 ), modified.regMat = TRUE ), digits = 6 ) ) 296s Estimate Std. Error t value Pr(>|t|) 296s C1 94.282 8.1771 11.53 0.000000 296s C2 -0.225 0.0954 -2.36 0.024352 296s C3 0.298 0.0419 7.13 0.000000 296s C4 48.184 9.7159 4.96 0.000019 296s C5 0.243 0.0826 2.94 0.005903 296s C6 0.262 0.0379 6.92 0.000000 296s > print( round( coef( summary( fit2sls3$eq[[ 1 ]] ) ), digits = 6 ) ) 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 94.282 8.1771 11.53 0.0000 296s price -0.225 0.0954 -2.36 0.0244 296s income 0.298 0.0419 7.13 0.0000 296s > 296s > print( round( coef( summary( fit2sls4s ) ), digits = 6 ) ) 296s Estimate Std. Error t value Pr(>|t|) 296s demand_(Intercept) 95.706 6.3056 15.18 0.000000 296s demand_price -0.243 0.0684 -3.56 0.001104 296s demand_income 0.303 0.0394 7.69 0.000000 296s supply_(Intercept) 46.564 8.3296 5.59 0.000003 296s supply_price 0.257 0.0684 3.75 0.000635 296s supply_farmPrice 0.264 0.0455 5.79 0.000001 296s supply_trend 0.303 0.0394 7.69 0.000000 296s > print( round( coef( summary( fit2sls4s$eq[[ 2 ]] ) ), digits = 6 ) ) 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 46.564 8.3296 5.59 0.000003 296s price 0.257 0.0684 3.75 0.000635 296s farmPrice 0.264 0.0455 5.79 0.000001 296s trend 0.303 0.0394 7.69 0.000000 296s > 296s > print( round( coef( summary( fit2sls5r, useDfSys = FALSE ) ), digits = 6 ) ) 296s Estimate Std. Error t value Pr(>|t|) 296s demand_(Intercept) 95.706 6.0044 15.94 0.000000 296s demand_price -0.243 0.0621 -3.92 0.001102 296s demand_income 0.303 0.0382 7.93 0.000000 296s supply_(Intercept) 46.564 7.3842 6.31 0.000010 296s supply_price 0.257 0.0621 4.14 0.000774 296s supply_farmPrice 0.264 0.0373 7.08 0.000003 296s supply_trend 0.303 0.0382 7.93 0.000001 296s > print( round( coef( summary( fit2sls5r, useDfSys = FALSE ), 296s + modified.regMat = TRUE ), digits = 6 ) ) 296s Estimate Std. Error t value Pr(>|t|) 296s C1 95.706 6.0044 15.94 NA 296s C2 -0.243 0.0621 -3.92 NA 296s C3 0.303 0.0382 7.93 NA 296s C4 46.564 7.3842 6.31 NA 296s C5 0.257 0.0621 4.14 NA 296s C6 0.264 0.0373 7.08 NA 296s > print( round( coef( summary( fit2sls5r$eq[[ 2 ]], useDfSys = FALSE ) ), 296s + digits = 6 ) ) 296s Estimate Std. Error t value Pr(>|t|) 296s (Intercept) 46.564 7.3842 6.31 0.000010 296s price 0.257 0.0621 4.14 0.000774 296s farmPrice 0.264 0.0373 7.08 0.000003 296s trend 0.303 0.0382 7.93 0.000001 296s > 296s > 296s > ## *********** variance covariance matrix of the coefficients ******* 296s > print( round( vcov( fit2sls1s ), digits = 6 ) ) 296s demand_(Intercept) demand_price demand_income 296s demand_(Intercept) 79.8371 -0.85694 0.06274 296s demand_price -0.8569 0.01185 -0.00336 296s demand_income 0.0627 -0.00336 0.00280 296s supply_(Intercept) 0.0000 0.00000 0.00000 296s supply_price 0.0000 0.00000 0.00000 296s supply_farmPrice 0.0000 0.00000 0.00000 296s supply_trend 0.0000 0.00000 0.00000 296s supply_(Intercept) supply_price supply_farmPrice 296s demand_(Intercept) 0.000 0.000000 0.000000 296s demand_price 0.000 0.000000 0.000000 296s demand_income 0.000 0.000000 0.000000 296s supply_(Intercept) 117.514 -0.892363 -0.263795 296s supply_price -0.892 0.008136 0.000763 296s supply_farmPrice -0.264 0.000763 0.001819 296s supply_trend -0.241 0.000472 0.001122 296s supply_trend 296s demand_(Intercept) 0.000000 296s demand_price 0.000000 296s demand_income 0.000000 296s supply_(Intercept) -0.240505 296s supply_price 0.000472 296s supply_farmPrice 0.001122 296s supply_trend 0.008090 296s > print( round( vcov( fit2sls1s$eq[[ 1 ]] ), digits = 6 ) ) 296s (Intercept) price income 296s (Intercept) 79.8371 -0.85694 0.06274 296s price -0.8569 0.01185 -0.00336 296s income 0.0627 -0.00336 0.00280 296s > 296s > print( round( vcov( fit2sls1r ), digits = 6 ) ) 296s demand_(Intercept) demand_price demand_income 296s demand_(Intercept) 53.3287 -0.57241 0.04191 296s demand_price -0.5724 0.00791 -0.00225 296s demand_income 0.0419 -0.00225 0.00187 296s supply_(Intercept) 0.0000 0.00000 0.00000 296s supply_price 0.0000 0.00000 0.00000 296s supply_farmPrice 0.0000 0.00000 0.00000 296s supply_trend 0.0000 0.00000 0.00000 296s supply_(Intercept) supply_price supply_farmPrice 296s demand_(Intercept) 0.000 0.000000 0.000000 296s demand_price 0.000 0.000000 0.000000 296s demand_income 0.000 0.000000 0.000000 296s supply_(Intercept) 115.402 -0.876328 -0.259055 296s supply_price -0.876 0.007989 0.000749 296s supply_farmPrice -0.259 0.000749 0.001786 296s supply_trend -0.236 0.000463 0.001101 296s supply_trend 296s demand_(Intercept) 0.000000 296s demand_price 0.000000 296s demand_income 0.000000 296s supply_(Intercept) -0.236183 296s supply_price 0.000463 296s supply_farmPrice 0.001101 296s supply_trend 0.007945 296s > print( round( vcov( fit2sls1r$eq[[ 2 ]] ), digits = 6 ) ) 296s (Intercept) price farmPrice trend 296s (Intercept) 115.402 -0.876328 -0.259055 -0.236183 296s price -0.876 0.007989 0.000749 0.000463 296s farmPrice -0.259 0.000749 0.001786 0.001101 296s trend -0.236 0.000463 0.001101 0.007945 296s > 296s > print( round( vcov( fit2sls2 ), digits = 6 ) ) 296s demand_(Intercept) demand_price demand_income 296s demand_(Intercept) 78.66379 -0.829021 0.046112 296s demand_price -0.82902 0.010698 -0.002471 296s demand_income 0.04611 -0.002471 0.002061 296s supply_(Intercept) -1.37081 0.073457 -0.061273 296s supply_price 0.00269 -0.000144 0.000120 296s supply_farmPrice 0.00639 -0.000343 0.000286 296s supply_trend 0.04611 -0.002471 0.002061 296s supply_(Intercept) supply_price supply_farmPrice 296s demand_(Intercept) -1.3708 0.002689 0.006393 296s demand_price 0.0735 -0.000144 -0.000343 296s demand_income -0.0613 0.000120 0.000286 296s supply_(Intercept) 111.0580 -0.872938 -0.236592 296s supply_price -0.8729 0.008032 0.000707 296s supply_farmPrice -0.2366 0.000707 0.001686 296s supply_trend -0.0613 0.000120 0.000286 296s supply_trend 296s demand_(Intercept) 0.046112 296s demand_price -0.002471 296s demand_income 0.002061 296s supply_(Intercept) -0.061273 296s supply_price 0.000120 296s supply_farmPrice 0.000286 296s supply_trend 0.002061 296s > print( round( vcov( fit2sls2$eq[[ 1 ]] ), digits = 6 ) ) 296s (Intercept) price income 296s (Intercept) 78.6638 -0.82902 0.04611 296s price -0.8290 0.01070 -0.00247 296s income 0.0461 -0.00247 0.00206 296s > 296s > print( round( vcov( fit2sls3 ), digits = 6 ) ) 296s demand_(Intercept) demand_price demand_income 296s demand_(Intercept) 66.86423 -0.704668 0.039196 296s demand_price -0.70467 0.009094 -0.002100 296s demand_income 0.03920 -0.002100 0.001752 296s supply_(Intercept) -1.16519 0.062438 -0.052082 296s supply_price 0.00229 -0.000122 0.000102 296s supply_farmPrice 0.00543 -0.000291 0.000243 296s supply_trend 0.03920 -0.002100 0.001752 296s supply_(Intercept) supply_price supply_farmPrice 296s demand_(Intercept) -1.1652 0.002285 0.005434 296s demand_price 0.0624 -0.000122 -0.000291 296s demand_income -0.0521 0.000102 0.000243 296s supply_(Intercept) 94.3993 -0.741997 -0.201104 296s supply_price -0.7420 0.006827 0.000601 296s supply_farmPrice -0.2011 0.000601 0.001433 296s supply_trend -0.0521 0.000102 0.000243 296s supply_trend 296s demand_(Intercept) 0.039196 296s demand_price -0.002100 296s demand_income 0.001752 296s supply_(Intercept) -0.052082 296s supply_price 0.000102 296s supply_farmPrice 0.000243 296s supply_trend 0.001752 296s > print( round( vcov( fit2sls3, modified.regMat = TRUE ), digits = 6 ) ) 296s C1 C2 C3 C4 C5 C6 296s C1 66.86423 -0.704668 0.039196 -1.1652 0.002285 0.005434 296s C2 -0.70467 0.009094 -0.002100 0.0624 -0.000122 -0.000291 296s C3 0.03920 -0.002100 0.001752 -0.0521 0.000102 0.000243 296s C4 -1.16519 0.062438 -0.052082 94.3993 -0.741997 -0.201104 296s C5 0.00229 -0.000122 0.000102 -0.7420 0.006827 0.000601 296s C6 0.00543 -0.000291 0.000243 -0.2011 0.000601 0.001433 296s > print( round( vcov( fit2sls3$eq[[ 2 ]] ), digits = 6 ) ) 296s (Intercept) price farmPrice trend 296s (Intercept) 94.3993 -0.741997 -0.201104 -0.052082 296s price -0.7420 0.006827 0.000601 0.000102 296s farmPrice -0.2011 0.000601 0.001433 0.000243 296s trend -0.0521 0.000102 0.000243 0.001752 296s > 296s > print( round( vcov( fit2sls4s ), digits = 6 ) ) 296s demand_(Intercept) demand_price demand_income 296s demand_(Intercept) 39.7610 -0.358128 -0.03842 296s demand_price -0.3581 0.004681 -0.00113 296s demand_income -0.0384 -0.001129 0.00155 296s supply_(Intercept) 39.6949 -0.480685 0.08595 296s supply_price -0.3581 0.004681 -0.00113 296s supply_farmPrice -0.0359 0.000252 0.00011 296s supply_trend -0.0384 -0.001129 0.00155 296s supply_(Intercept) supply_price supply_farmPrice 296s demand_(Intercept) 39.6949 -0.358128 -0.035932 296s demand_price -0.4807 0.004681 0.000252 296s demand_income 0.0859 -0.001129 0.000110 296s supply_(Intercept) 69.3817 -0.480685 -0.226588 296s supply_price -0.4807 0.004681 0.000252 296s supply_farmPrice -0.2266 0.000252 0.002072 296s supply_trend 0.0859 -0.001129 0.000110 296s supply_trend 296s demand_(Intercept) -0.03842 296s demand_price -0.00113 296s demand_income 0.00155 296s supply_(Intercept) 0.08595 296s supply_price -0.00113 296s supply_farmPrice 0.00011 296s supply_trend 0.00155 296s > print( round( vcov( fit2sls4s$eq[[ 1 ]] ), digits = 6 ) ) 296s (Intercept) price income 296s (Intercept) 39.7610 -0.35813 -0.03842 296s price -0.3581 0.00468 -0.00113 296s income -0.0384 -0.00113 0.00155 296s > 296s > print( round( vcov( fit2sls5r ), digits = 6 ) ) 296s demand_(Intercept) demand_price demand_income 296s demand_(Intercept) 36.0523 -0.302514 -0.057288 296s demand_price -0.3025 0.003851 -0.000847 296s demand_income -0.0573 -0.000847 0.001456 296s supply_(Intercept) 34.1121 -0.397307 0.057684 296s supply_price -0.3025 0.003851 -0.000847 296s supply_farmPrice -0.0337 0.000218 0.000122 296s supply_trend -0.0573 -0.000847 0.001456 296s supply_(Intercept) supply_price supply_farmPrice 296s demand_(Intercept) 34.1121 -0.302514 -0.033671 296s demand_price -0.3973 0.003851 0.000218 296s demand_income 0.0577 -0.000847 0.000122 296s supply_(Intercept) 54.5267 -0.397307 -0.157170 296s supply_price -0.3973 0.003851 0.000218 296s supply_farmPrice -0.1572 0.000218 0.001388 296s supply_trend 0.0577 -0.000847 0.000122 296s supply_trend 296s demand_(Intercept) -0.057288 296s demand_price -0.000847 296s demand_income 0.001456 296s supply_(Intercept) 0.057684 296s supply_price -0.000847 296s supply_farmPrice 0.000122 296s supply_trend 0.001456 296s > print( round( vcov( fit2sls5r, modified.regMat = TRUE ), digits = 6 ) ) 296s C1 C2 C3 C4 C5 C6 296s C1 36.0523 -0.302514 -0.057288 34.1121 -0.302514 -0.033671 296s C2 -0.3025 0.003851 -0.000847 -0.3973 0.003851 0.000218 296s C3 -0.0573 -0.000847 0.001456 0.0577 -0.000847 0.000122 296s C4 34.1121 -0.397307 0.057684 54.5267 -0.397307 -0.157170 296s C5 -0.3025 0.003851 -0.000847 -0.3973 0.003851 0.000218 296s C6 -0.0337 0.000218 0.000122 -0.1572 0.000218 0.001388 296s > print( round( vcov( fit2sls5r$eq[[ 2 ]] ), digits = 6 ) ) 296s (Intercept) price farmPrice trend 296s (Intercept) 54.5267 -0.397307 -0.157170 0.057684 296s price -0.3973 0.003851 0.000218 -0.000847 296s farmPrice -0.1572 0.000218 0.001388 0.000122 296s trend 0.0577 -0.000847 0.000122 0.001456 296s > 296s > print( round( vcov( fit2slsd1 ), digits = 6 ) ) 296s demand_(Intercept) demand_price demand_income 296s demand_(Intercept) 124.179 -1.51767 0.28519 296s demand_price -1.518 0.02098 -0.00595 296s demand_income 0.285 -0.00595 0.00318 296s supply_(Intercept) 0.000 0.00000 0.00000 296s supply_price 0.000 0.00000 0.00000 296s supply_farmPrice 0.000 0.00000 0.00000 296s supply_trend 0.000 0.00000 0.00000 296s supply_(Intercept) supply_price supply_farmPrice 296s demand_(Intercept) 0.000 0.000000 0.000000 296s demand_price 0.000 0.000000 0.000000 296s demand_income 0.000 0.000000 0.000000 296s supply_(Intercept) 144.253 -1.095410 -0.323818 296s supply_price -1.095 0.009987 0.000936 296s supply_farmPrice -0.324 0.000936 0.002233 296s supply_trend -0.295 0.000579 0.001377 296s supply_trend 296s demand_(Intercept) 0.000000 296s demand_price 0.000000 296s demand_income 0.000000 296s supply_(Intercept) -0.295229 296s supply_price 0.000579 296s supply_farmPrice 0.001377 296s supply_trend 0.009931 296s > print( round( vcov( fit2slsd1$eq[[ 1 ]] ), digits = 6 ) ) 296s (Intercept) price income 296s (Intercept) 124.179 -1.51767 0.28519 296s price -1.518 0.02098 -0.00595 296s income 0.285 -0.00595 0.00318 296s > 296s > print( round( vcov( fit2slsd2rs ), digits = 6 ) ) 296s demand_(Intercept) demand_price demand_income 296s demand_(Intercept) 95.9017 -1.129212 0.176368 296s demand_price -1.1292 0.014881 -0.003682 296s demand_income 0.1764 -0.003682 0.001968 296s supply_(Intercept) -5.2430 0.109460 -0.058492 296s supply_price 0.0103 -0.000215 0.000115 296s supply_farmPrice 0.0245 -0.000510 0.000273 296s supply_trend 0.1764 -0.003682 0.001968 296s supply_(Intercept) supply_price supply_farmPrice 296s demand_(Intercept) -5.2430 0.010284 0.024451 296s demand_price 0.1095 -0.000215 -0.000510 296s demand_income -0.0585 0.000115 0.000273 296s supply_(Intercept) 114.2555 -0.898881 -0.243056 296s supply_price -0.8989 0.008273 0.000727 296s supply_farmPrice -0.2431 0.000727 0.001733 296s supply_trend -0.0585 0.000115 0.000273 296s supply_trend 296s demand_(Intercept) 0.176368 296s demand_price -0.003682 296s demand_income 0.001968 296s supply_(Intercept) -0.058492 296s supply_price 0.000115 296s supply_farmPrice 0.000273 296s supply_trend 0.001968 296s > print( round( vcov( fit2slsd2rs$eq[[ 2 ]] ), digits = 6 ) ) 296s (Intercept) price farmPrice trend 296s (Intercept) 114.2555 -0.898881 -0.243056 -0.058492 296s price -0.8989 0.008273 0.000727 0.000115 296s farmPrice -0.2431 0.000727 0.001733 0.000273 296s trend -0.0585 0.000115 0.000273 0.001968 296s > 296s > print( round( vcov( fit2slsd3 ), digits = 6 ) ) 296s demand_(Intercept) demand_price demand_income 296s demand_(Intercept) 141.4425 -1.640068 0.234151 296s demand_price -1.6401 0.021165 -0.004888 296s demand_income 0.2342 -0.004888 0.002612 296s supply_(Intercept) -6.9607 0.145321 -0.077656 296s supply_price 0.0137 -0.000285 0.000152 296s supply_farmPrice 0.0325 -0.000678 0.000362 296s supply_trend 0.2342 -0.004888 0.002612 296s supply_(Intercept) supply_price supply_farmPrice 296s demand_(Intercept) -6.9607 0.013653 0.032462 296s demand_price 0.1453 -0.000285 -0.000678 296s demand_income -0.0777 0.000152 0.000362 296s supply_(Intercept) 111.0123 -0.869653 -0.237751 296s supply_price -0.8697 0.007995 0.000708 296s supply_farmPrice -0.2378 0.000708 0.001688 296s supply_trend -0.0777 0.000152 0.000362 296s supply_trend 296s demand_(Intercept) 0.234151 296s demand_price -0.004888 296s demand_income 0.002612 296s supply_(Intercept) -0.077656 296s supply_price 0.000152 296s supply_farmPrice 0.000362 296s supply_trend 0.002612 296s > print( round( vcov( fit2slsd3, modified.regMat = TRUE ), digits = 6 ) ) 296s C1 C2 C3 C4 C5 C6 296s C1 141.4425 -1.640068 0.234151 -6.9607 0.013653 0.032462 296s C2 -1.6401 0.021165 -0.004888 0.1453 -0.000285 -0.000678 296s C3 0.2342 -0.004888 0.002612 -0.0777 0.000152 0.000362 296s C4 -6.9607 0.145321 -0.077656 111.0123 -0.869653 -0.237751 296s C5 0.0137 -0.000285 0.000152 -0.8697 0.007995 0.000708 296s C6 0.0325 -0.000678 0.000362 -0.2378 0.000708 0.001688 296s > print( round( vcov( fit2slsd3$eq[[ 1 ]] ), digits = 6 ) ) 296s (Intercept) price income 296s (Intercept) 141.442 -1.64007 0.23415 296s price -1.640 0.02116 -0.00489 296s income 0.234 -0.00489 0.00261 296s > 296s > 296s > ## *********** confidence intervals of coefficients ************* 296s > print( confint( fit2sls1 ) ) 296s 2.5 % 97.5 % 296s demand_(Intercept) 77.922 111.345 296s demand_price -0.447 -0.040 296s demand_income 0.215 0.413 296s supply_(Intercept) 24.071 74.994 296s supply_price 0.028 0.452 296s supply_farmPrice 0.155 0.356 296s supply_trend 0.042 0.464 296s > print( confint( fit2sls1$eq[[ 1 ]], level = 0.9 ) ) 296s 5 % 95 % 296s (Intercept) 80.854 108.412 296s price -0.411 -0.076 296s income 0.232 0.396 296s > 296s > print( confint( fit2sls2s, level = 0.9 ) ) 296s 5 % 95 % 296s demand_(Intercept) 78.005 110.558 296s demand_price -0.417 -0.032 296s demand_income 0.211 0.386 296s supply_(Intercept) 24.204 72.165 296s supply_price 0.038 0.447 296s supply_farmPrice 0.169 0.355 296s supply_trend 0.211 0.386 296s > print( confint( fit2sls2s$eq[[ 2 ]], level = 0.99 ) ) 296s 0.5 % 99.5 % 296s (Intercept) 15.989 80.380 296s price -0.032 0.517 296s farmPrice 0.137 0.387 296s trend 0.181 0.416 296s > 296s > print( confint( fit2sls3, level = 0.99, useDfSys = TRUE ) ) 296s 0.5 % 99.5 % 296s demand_(Intercept) 77.664 110.899 296s demand_price -0.419 -0.031 296s demand_income 0.213 0.383 296s supply_(Intercept) 28.439 67.929 296s supply_price 0.075 0.411 296s supply_farmPrice 0.185 0.339 296s supply_trend 0.213 0.383 296s > print( confint( fit2sls3$eq[[ 1 ]], level = 0.5, useDfSys = TRUE ) ) 296s 25 % 75 % 296s (Intercept) 88.71 99.857 296s price -0.29 -0.160 296s income 0.27 0.327 296s > 296s > print( confint( fit2sls4r, level = 0.5 ) ) 296s 25 % 75 % 296s demand_(Intercept) 83.516 107.895 296s demand_price -0.369 -0.117 296s demand_income 0.225 0.380 296s supply_(Intercept) 31.573 61.554 296s supply_price 0.131 0.383 296s supply_farmPrice 0.188 0.339 296s supply_trend 0.225 0.380 296s > print( confint( fit2sls4r$eq[[ 2 ]], level = 0.25 ) ) 296s 37.5 % 62.5 % 296s (Intercept) 44.192 48.935 296s price 0.237 0.277 296s farmPrice 0.252 0.276 296s trend 0.290 0.315 296s > 296s > print( confint( fit2sls5rs, level = 0.25 ) ) 296s 37.5 % 62.5 % 296s demand_(Intercept) 84.017 107.395 296s demand_price -0.369 -0.117 296s demand_income 0.230 0.376 296s supply_(Intercept) 31.265 61.863 296s supply_price 0.131 0.383 296s supply_farmPrice 0.181 0.346 296s supply_trend 0.230 0.376 296s > print( confint( fit2sls5rs$eq[[ 1 ]], level = 0.975 ) ) 296s 1.3 % 98.8 % 296s (Intercept) 82.221 109.191 296s price -0.389 -0.098 296s income 0.218 0.387 296s > 296s > print( confint( fit2slsd1, level = 0.975, useDfSys = TRUE ) ) 296s 1.3 % 98.8 % 296s demand_(Intercept) 84.118 129.461 296s demand_price -0.706 -0.117 296s demand_income 0.247 0.476 296s supply_(Intercept) 25.097 73.968 296s supply_price 0.037 0.443 296s supply_farmPrice 0.159 0.352 296s supply_trend 0.050 0.456 296s > print( confint( fit2slsd1$eq[[ 2 ]], level = 0.999, useDfSys = TRUE ) ) 296s 0.1 % 100 % 296s (Intercept) 6.163 92.901 296s price -0.121 0.601 296s farmPrice 0.085 0.426 296s trend -0.107 0.613 296s > 296s > print( confint( fit2slsd2r, level = 0.999 ) ) 296s 0.1 % 100 % 296s demand_(Intercept) 81.311 125.877 296s demand_price -0.617 -0.072 296s demand_income 0.230 0.422 296s supply_(Intercept) 27.618 67.100 296s supply_price 0.077 0.412 296s supply_farmPrice 0.189 0.343 296s supply_trend 0.230 0.422 296s > print( confint( fit2slsd2r$eq[[ 1 ]] ) ) 296s 2.5 % 97.5 % 296s (Intercept) 81.311 125.877 296s price -0.617 -0.072 296s income 0.230 0.422 296s > 296s > 296s > ## *********** fitted values ************* 296s > print( fitted( fit2sls1, se.fit = TRUE, interval = "prediction" ) ) 296s demand supply 296s 1 97.6 98.9 296s 2 99.9 100.4 296s 3 99.8 100.5 296s 4 100.0 100.7 296s 5 102.1 102.6 296s 6 102.0 102.6 296s 7 102.4 102.6 296s 8 103.0 104.8 296s 9 101.5 102.7 296s 10 100.3 99.7 296s 11 95.5 95.4 296s 12 94.7 93.8 296s 13 96.1 95.6 296s 14 99.0 97.6 296s 15 103.8 102.3 296s 16 103.7 104.1 296s 17 103.8 102.8 296s 18 102.1 102.7 296s 19 103.6 102.6 296s 20 106.9 105.6 296s > print( fitted( fit2sls1$eq[[ 1 ]] ) ) 296s 1 2 3 4 5 6 7 8 9 10 11 12 13 296s 97.6 99.9 99.8 100.0 102.1 102.0 102.4 103.0 101.5 100.3 95.5 94.7 96.1 296s 14 15 16 17 18 19 20 296s 99.0 103.8 103.7 103.8 102.1 103.6 106.9 296s > 296s > print( fitted( fit2sls2s ) ) 296s demand supply 296s 1 97.8 98.5 296s 2 100.0 100.0 296s 3 99.9 100.1 296s 4 100.1 100.4 296s 5 102.0 102.5 296s 6 101.9 102.5 296s 7 102.4 102.5 296s 8 102.9 104.8 296s 9 101.4 102.7 296s 10 100.3 99.7 296s 11 95.8 95.3 296s 12 95.0 93.7 296s 13 96.4 95.6 296s 14 99.1 97.6 296s 15 103.7 102.5 296s 16 103.5 104.4 296s 17 103.6 103.2 296s 18 102.0 103.0 296s 19 103.5 102.9 296s 20 106.7 106.1 296s > print( fitted( fit2sls2s$eq[[ 2 ]] ) ) 296s 1 2 3 4 5 6 7 8 9 10 11 12 13 296s 98.5 100.0 100.1 100.4 102.5 102.5 102.5 104.8 102.7 99.7 95.3 93.7 95.6 296s 14 15 16 17 18 19 20 296s 97.6 102.5 104.4 103.2 103.0 102.9 106.1 296s > 296s > print( fitted( fit2sls3 ) ) 296s demand supply 296s 1 97.8 98.5 296s 2 100.0 100.0 296s 3 99.9 100.1 296s 4 100.1 100.4 296s 5 102.0 102.5 296s 6 101.9 102.5 296s 7 102.4 102.5 296s 8 102.9 104.8 296s 9 101.4 102.7 296s 10 100.3 99.7 296s 11 95.8 95.3 296s 12 95.0 93.7 296s 13 96.4 95.6 296s 14 99.1 97.6 296s 15 103.7 102.5 296s 16 103.5 104.4 296s 17 103.6 103.2 296s 18 102.0 103.0 296s 19 103.5 102.9 296s 20 106.7 106.1 296s > print( fitted( fit2sls3$eq[[ 1 ]] ) ) 296s 1 2 3 4 5 6 7 8 9 10 11 12 13 296s 97.8 100.0 99.9 100.1 102.0 101.9 102.4 102.9 101.4 100.3 95.8 95.0 96.4 296s 14 15 16 17 18 19 20 296s 99.1 103.7 103.5 103.6 102.0 103.5 106.7 296s > 296s > print( fitted( fit2sls4r ) ) 296s demand supply 296s 1 97.8 98.5 296s 2 99.9 100.1 296s 3 99.8 100.2 296s 4 100.0 100.5 296s 5 102.1 102.5 296s 6 101.9 102.4 296s 7 102.4 102.5 296s 8 102.9 104.8 296s 9 101.5 102.7 296s 10 100.4 99.5 296s 11 95.8 95.1 296s 12 94.9 93.6 296s 13 96.3 95.6 296s 14 99.1 97.6 296s 15 103.8 102.5 296s 16 103.6 104.4 296s 17 103.8 103.1 296s 18 102.0 103.1 296s 19 103.5 103.0 296s 20 106.6 106.3 296s > print( fitted( fit2sls4r$eq[[ 2 ]] ) ) 296s 1 2 3 4 5 6 7 8 9 10 11 12 13 296s 98.5 100.1 100.2 100.5 102.5 102.4 102.5 104.8 102.7 99.5 95.1 93.6 95.6 296s 14 15 16 17 18 19 20 296s 97.6 102.5 104.4 103.1 103.1 103.0 106.3 296s > 296s > print( fitted( fit2sls5rs ) ) 296s demand supply 296s 1 97.8 98.5 296s 2 99.9 100.1 296s 3 99.8 100.2 296s 4 100.0 100.5 296s 5 102.1 102.5 296s 6 101.9 102.4 296s 7 102.4 102.5 296s 8 102.9 104.8 296s 9 101.5 102.7 296s 10 100.4 99.5 296s 11 95.8 95.1 296s 12 94.9 93.6 296s 13 96.3 95.6 296s 14 99.1 97.6 296s 15 103.8 102.5 296s 16 103.6 104.4 296s 17 103.8 103.1 296s 18 102.0 103.1 296s 19 103.5 103.0 296s 20 106.6 106.3 296s > print( fitted( fit2sls5rs$eq[[ 1 ]] ) ) 296s 1 2 3 4 5 6 7 8 9 10 11 12 13 296s 97.8 99.9 99.8 100.0 102.1 101.9 102.4 102.9 101.5 100.4 95.8 94.9 96.3 296s 14 15 16 17 18 19 20 296s 99.1 103.8 103.6 103.8 102.0 103.5 106.6 296s > 296s > print( fitted( fit2slsd1 ) ) 296s demand supply 296s 1 97.1 98.9 296s 2 99.2 100.4 296s 3 99.2 100.5 296s 4 99.3 100.7 296s 5 102.5 102.6 296s 6 102.2 102.6 296s 7 102.5 102.6 296s 8 102.7 104.8 296s 9 102.0 102.7 296s 10 101.4 99.7 296s 11 95.6 95.4 296s 12 93.9 93.8 296s 13 95.0 95.6 296s 14 98.9 97.6 296s 15 104.9 102.3 296s 16 104.3 104.1 296s 17 106.1 102.8 296s 18 101.7 102.7 296s 19 103.3 102.6 296s 20 106.0 105.6 296s > print( fitted( fit2slsd1$eq[[ 2 ]] ) ) 296s 1 2 3 4 5 6 7 8 9 10 11 12 13 296s 98.9 100.4 100.5 100.7 102.6 102.6 102.6 104.8 102.7 99.7 95.4 93.8 95.6 296s 14 15 16 17 18 19 20 296s 97.6 102.3 104.1 102.8 102.7 102.6 105.6 296s > 296s > print( fitted( fit2slsd2r ) ) 296s demand supply 296s 1 97.5 98.2 296s 2 99.5 99.8 296s 3 99.4 99.9 296s 4 99.6 100.3 296s 5 102.3 102.4 296s 6 102.1 102.4 296s 7 102.4 102.4 296s 8 102.6 104.7 296s 9 101.8 102.7 296s 10 101.1 99.6 296s 11 96.0 95.2 296s 12 94.6 93.6 296s 13 95.7 95.6 296s 14 99.1 97.7 296s 15 104.4 102.7 296s 16 103.9 104.5 296s 17 105.2 103.4 296s 18 101.8 103.2 296s 19 103.2 103.1 296s 20 105.9 106.3 296s > print( fitted( fit2slsd2r$eq[[ 1 ]] ) ) 296s 1 2 3 4 5 6 7 8 9 10 11 12 13 296s 97.5 99.5 99.4 99.6 102.3 102.1 102.4 102.6 101.8 101.1 96.0 94.6 95.7 296s 14 15 16 17 18 19 20 296s 99.1 104.4 103.9 105.2 101.8 103.2 105.9 296s > 296s > 296s > ## *********** predicted values ************* 296s > predictData <- Kmenta 296s > predictData$consump <- NULL 296s > predictData$price <- Kmenta$price * 0.9 296s > predictData$income <- Kmenta$income * 1.1 296s > 296s > print( predict( fit2sls1, se.fit = TRUE, interval = "prediction" ) ) 296s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 296s 1 97.6 0.661 93.3 102.0 98.9 1.079 296s 2 99.9 0.600 95.5 104.2 100.4 1.064 296s 3 99.8 0.564 95.5 104.1 100.5 0.962 296s 4 100.0 0.605 95.7 104.4 100.7 0.938 296s 5 102.1 0.516 97.8 106.4 102.6 0.914 296s 6 102.0 0.474 97.7 106.2 102.6 0.808 296s 7 102.4 0.493 98.1 106.7 102.6 0.736 296s 8 103.0 0.615 98.6 107.3 104.8 0.994 296s 9 101.5 0.544 97.2 105.8 102.7 0.808 296s 10 100.3 0.822 95.8 104.8 99.7 1.023 296s 11 95.5 0.963 90.9 100.2 95.4 1.228 296s 12 94.7 1.006 90.1 99.4 93.8 1.428 296s 13 96.1 0.915 91.6 100.7 95.6 1.272 296s 14 99.0 0.518 94.7 103.3 97.6 0.917 296s 15 103.8 0.793 99.4 108.3 102.3 0.899 296s 16 103.7 0.636 99.3 108.0 104.1 0.936 296s 17 103.8 1.348 98.8 108.9 102.8 1.665 296s 18 102.1 0.549 97.8 106.4 102.7 0.988 296s 19 103.6 0.695 99.2 108.0 102.6 1.129 296s 20 106.9 1.306 101.9 111.9 105.6 1.733 296s supply.lwr supply.upr 296s 1 93.2 104.6 296s 2 94.7 106.1 296s 3 94.9 106.0 296s 4 95.1 106.3 296s 5 97.1 108.2 296s 6 97.1 108.1 296s 7 97.1 108.0 296s 8 99.2 110.4 296s 9 97.3 108.2 296s 10 94.0 105.3 296s 11 89.5 101.2 296s 12 87.8 99.8 296s 13 89.8 101.5 296s 14 92.0 103.1 296s 15 96.8 107.9 296s 16 98.5 109.6 296s 17 96.5 109.1 296s 18 97.1 108.3 296s 19 96.8 108.3 296s 20 99.2 112.0 296s > print( predict( fit2sls1$eq[[ 1 ]], se.fit = TRUE, interval = "prediction" ) ) 296s fit se.fit lwr upr 296s 1 97.6 0.661 93.3 102.0 296s 2 99.9 0.600 95.5 104.2 296s 3 99.8 0.564 95.5 104.1 296s 4 100.0 0.605 95.7 104.4 296s 5 102.1 0.516 97.8 106.4 296s 6 102.0 0.474 97.7 106.2 296s 7 102.4 0.493 98.1 106.7 296s 8 103.0 0.615 98.6 107.3 296s 9 101.5 0.544 97.2 105.8 296s 10 100.3 0.822 95.8 104.8 296s 11 95.5 0.963 90.9 100.2 296s 12 94.7 1.006 90.1 99.4 296s 13 96.1 0.915 91.6 100.7 296s 14 99.0 0.518 94.7 103.3 296s 15 103.8 0.793 99.4 108.3 296s 16 103.7 0.636 99.3 108.0 296s 17 103.8 1.348 98.8 108.9 296s 18 102.1 0.549 97.8 106.4 296s 19 103.6 0.695 99.2 108.0 296s 20 106.9 1.306 101.9 111.9 296s > 296s > print( predict( fit2sls2s, se.pred = TRUE, interval = "confidence", 296s + level = 0.999, newdata = predictData ) ) 296s demand.pred demand.se.pred demand.lwr demand.upr supply.pred supply.se.pred 296s 1 102.7 2.23 99.1 106 96.1 2.75 296s 2 105.2 2.23 101.6 109 97.5 2.64 296s 3 105.1 2.24 101.4 109 97.6 2.65 296s 4 105.4 2.23 101.8 109 97.9 2.62 296s 5 107.2 2.52 101.7 113 100.1 2.83 296s 6 107.1 2.46 101.9 112 100.0 2.77 296s 7 107.7 2.45 102.6 113 100.0 2.70 296s 8 108.5 2.41 103.6 113 102.2 2.65 296s 9 106.5 2.53 100.9 112 100.4 2.87 296s 10 105.0 2.66 98.7 111 97.4 3.10 296s 11 100.1 2.42 95.1 105 93.0 3.17 296s 12 99.5 2.22 96.0 103 91.3 3.14 296s 13 101.2 2.13 98.5 104 93.1 2.95 296s 14 104.0 2.32 99.7 108 95.3 2.91 296s 15 108.9 2.74 102.1 116 100.2 2.92 296s 16 108.9 2.62 102.7 115 102.0 2.79 296s 17 108.4 3.09 99.9 117 101.1 3.37 296s 18 107.5 2.36 102.9 112 100.5 2.65 296s 19 109.2 2.44 104.1 114 100.3 2.64 296s 20 113.0 2.67 106.6 119 103.3 2.58 296s supply.lwr supply.upr 296s 1 91.8 100.4 296s 2 94.3 100.8 296s 3 94.2 101.0 296s 4 94.8 101.0 296s 5 95.2 105.0 296s 6 95.6 104.5 296s 7 96.1 103.9 296s 8 98.9 105.6 296s 9 95.2 105.6 296s 10 90.7 104.1 296s 11 85.9 100.2 296s 12 84.4 98.3 296s 13 87.3 98.9 296s 14 89.7 100.8 296s 15 94.7 105.8 296s 16 97.3 106.6 296s 17 92.9 109.3 296s 18 97.1 103.9 296s 19 97.1 103.6 296s 20 100.7 105.9 296s > print( predict( fit2sls2s$eq[[ 2 ]], se.pred = TRUE, interval = "confidence", 296s + level = 0.999, newdata = predictData ) ) 296s fit se.pred lwr upr 296s 1 96.1 2.75 91.8 100.4 296s 2 97.5 2.64 94.3 100.8 296s 3 97.6 2.65 94.2 101.0 296s 4 97.9 2.62 94.8 101.0 296s 5 100.1 2.83 95.2 105.0 296s 6 100.0 2.77 95.6 104.5 296s 7 100.0 2.70 96.1 103.9 296s 8 102.2 2.65 98.9 105.6 296s 9 100.4 2.87 95.2 105.6 296s 10 97.4 3.10 90.7 104.1 296s 11 93.0 3.17 85.9 100.2 296s 12 91.3 3.14 84.4 98.3 296s 13 93.1 2.95 87.3 98.9 296s 14 95.3 2.91 89.7 100.8 296s 15 100.2 2.92 94.7 105.8 296s 16 102.0 2.79 97.3 106.6 296s 17 101.1 3.37 92.9 109.3 296s 18 100.5 2.65 97.1 103.9 296s 19 100.3 2.64 97.1 103.6 296s 20 103.3 2.58 100.7 105.9 296s > 296s > print( predict( fit2sls3, se.pred = TRUE, interval = "prediction", 296s + level = 0.975, useDfSys = TRUE ) ) 296s demand.pred demand.se.pred demand.lwr demand.upr supply.pred supply.se.pred 296s 1 97.8 2.09 92.9 103 98.5 2.55 296s 2 100.0 2.08 95.1 105 100.0 2.57 296s 3 99.9 2.07 95.0 105 100.1 2.55 296s 4 100.1 2.08 95.2 105 100.4 2.56 296s 5 102.0 2.06 97.2 107 102.5 2.58 296s 6 101.9 2.05 97.1 107 102.5 2.56 296s 7 102.4 2.05 97.5 107 102.5 2.55 296s 8 102.9 2.09 98.0 108 104.8 2.61 296s 9 101.4 2.07 96.6 106 102.7 2.57 296s 10 100.3 2.17 95.2 105 99.7 2.62 296s 11 95.8 2.20 90.6 101 95.3 2.67 296s 12 95.0 2.20 89.9 100 93.7 2.74 296s 13 96.4 2.17 91.3 101 95.6 2.69 296s 14 99.1 2.06 94.3 104 97.6 2.59 296s 15 103.7 2.14 98.7 109 102.5 2.56 296s 16 103.5 2.08 98.6 108 104.4 2.55 296s 17 103.6 2.40 98.0 109 103.2 2.78 296s 18 102.0 2.07 97.2 107 103.0 2.56 296s 19 103.5 2.11 98.6 108 102.9 2.59 296s 20 106.7 2.38 101.1 112 106.1 2.78 296s supply.lwr supply.upr 296s 1 92.5 104 296s 2 94.0 106 296s 3 94.1 106 296s 4 94.4 106 296s 5 96.4 109 296s 6 96.5 108 296s 7 96.5 108 296s 8 98.6 111 296s 9 96.7 109 296s 10 93.5 106 296s 11 89.0 102 296s 12 87.3 100 296s 13 89.3 102 296s 14 91.6 104 296s 15 96.5 109 296s 16 98.4 110 296s 17 96.7 110 296s 18 97.0 109 296s 19 96.8 109 296s 20 99.5 113 296s > print( predict( fit2sls3$eq[[ 1 ]], se.pred = TRUE, interval = "prediction", 296s + level = 0.975, useDfSys = TRUE ) ) 296s fit se.pred lwr upr 296s 1 97.8 2.09 92.9 103 296s 2 100.0 2.08 95.1 105 296s 3 99.9 2.07 95.0 105 296s 4 100.1 2.08 95.2 105 296s 5 102.0 2.06 97.2 107 296s 6 101.9 2.05 97.1 107 296s 7 102.4 2.05 97.5 107 296s 8 102.9 2.09 98.0 108 296s 9 101.4 2.07 96.6 106 296s 10 100.3 2.17 95.2 105 296s 11 95.8 2.20 90.6 101 296s 12 95.0 2.20 89.9 100 296s 13 96.4 2.17 91.3 101 296s 14 99.1 2.06 94.3 104 296s 15 103.7 2.14 98.7 109 296s 16 103.5 2.08 98.6 108 296s 17 103.6 2.40 98.0 109 296s 18 102.0 2.07 97.2 107 296s 19 103.5 2.11 98.6 108 296s 20 106.7 2.38 101.1 112 296s > 296s > print( predict( fit2sls4r, se.fit = TRUE, interval = "confidence", 296s + level = 0.25 ) ) 296s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 296s 1 97.8 0.602 97.6 97.9 98.5 0.586 296s 2 99.9 0.526 99.7 100.1 100.1 0.672 296s 3 99.8 0.508 99.7 100.0 100.2 0.621 296s 4 100.0 0.530 99.8 100.2 100.5 0.632 296s 5 102.1 0.488 101.9 102.2 102.5 0.704 296s 6 101.9 0.474 101.8 102.1 102.4 0.636 296s 7 102.4 0.498 102.2 102.5 102.5 0.587 296s 8 102.9 0.604 102.7 103.0 104.8 0.764 296s 9 101.5 0.502 101.3 101.6 102.7 0.656 296s 10 100.4 0.696 100.2 100.6 99.5 0.710 296s 11 95.8 0.928 95.5 96.1 95.1 0.885 296s 12 94.9 0.889 94.7 95.2 93.6 1.146 296s 13 96.3 0.739 96.0 96.5 95.6 1.052 296s 14 99.1 0.519 98.9 99.3 97.6 0.746 296s 15 103.8 0.626 103.6 104.0 102.5 0.637 296s 16 103.6 0.566 103.4 103.8 104.4 0.615 296s 17 103.8 0.942 103.5 104.1 103.1 1.153 296s 18 102.0 0.540 101.8 102.2 103.1 0.556 296s 19 103.5 0.677 103.3 103.7 103.0 0.631 296s 20 106.6 1.226 106.2 107.0 106.3 0.900 296s supply.lwr supply.upr 296s 1 98.3 98.7 296s 2 99.9 100.3 296s 3 100.0 100.4 296s 4 100.3 100.7 296s 5 102.2 102.7 296s 6 102.2 102.6 296s 7 102.3 102.7 296s 8 104.6 105.1 296s 9 102.5 102.9 296s 10 99.3 99.8 296s 11 94.9 95.4 296s 12 93.3 94.0 296s 13 95.3 96.0 296s 14 97.4 97.9 296s 15 102.3 102.7 296s 16 104.2 104.6 296s 17 102.7 103.4 296s 18 102.9 103.3 296s 19 102.8 103.2 296s 20 106.0 106.6 296s > print( predict( fit2sls4r$eq[[ 2 ]], se.fit = TRUE, interval = "confidence", 296s + level = 0.25 ) ) 296s fit se.fit lwr upr 296s 1 98.5 0.586 98.3 98.7 296s 2 100.1 0.672 99.9 100.3 296s 3 100.2 0.621 100.0 100.4 296s 4 100.5 0.632 100.3 100.7 296s 5 102.5 0.704 102.2 102.7 296s 6 102.4 0.636 102.2 102.6 296s 7 102.5 0.587 102.3 102.7 296s 8 104.8 0.764 104.6 105.1 296s 9 102.7 0.656 102.5 102.9 296s 10 99.5 0.710 99.3 99.8 296s 11 95.1 0.885 94.9 95.4 296s 12 93.6 1.146 93.3 94.0 296s 13 95.6 1.052 95.3 96.0 296s 14 97.6 0.746 97.4 97.9 296s 15 102.5 0.637 102.3 102.7 296s 16 104.4 0.615 104.2 104.6 296s 17 103.1 1.153 102.7 103.4 296s 18 103.1 0.556 102.9 103.3 296s 19 103.0 0.631 102.8 103.2 296s 20 106.3 0.900 106.0 106.6 296s > 296s > print( predict( fit2sls5rs, se.fit = TRUE, se.pred = TRUE, 296s + interval = "prediction", level = 0.5, newdata = predictData ) ) 296s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 296s 1 102.8 0.713 2.10 101.4 104 95.9 296s 2 105.4 0.742 2.11 103.9 107 97.4 296s 3 105.3 0.751 2.11 103.8 107 97.5 296s 4 105.5 0.749 2.11 104.1 107 97.8 296s 5 107.5 1.080 2.25 105.9 109 99.9 296s 6 107.4 1.031 2.23 105.9 109 99.9 296s 7 107.9 1.040 2.23 106.4 109 99.9 296s 8 108.7 1.044 2.23 107.1 110 102.1 296s 9 106.8 1.073 2.24 105.2 108 100.2 296s 10 105.3 1.188 2.30 103.8 107 97.2 296s 11 100.3 1.013 2.22 98.8 102 92.8 296s 12 99.7 0.770 2.12 98.2 101 91.1 296s 13 101.3 0.584 2.06 99.9 103 93.0 296s 14 104.3 0.833 2.14 102.8 106 95.1 296s 15 109.2 1.310 2.37 107.6 111 100.1 296s 16 109.1 1.214 2.32 107.6 111 101.8 296s 17 108.9 1.582 2.53 107.1 111 100.8 296s 18 107.7 0.958 2.19 106.2 109 100.4 296s 19 109.4 1.111 2.26 107.9 111 100.3 296s 20 113.2 1.529 2.50 111.5 115 103.4 296s supply.se.fit supply.se.pred supply.lwr supply.upr 296s 1 0.746 2.61 94.1 97.7 296s 2 0.628 2.58 95.6 99.1 296s 3 0.642 2.58 95.7 99.3 296s 4 0.607 2.57 96.0 99.5 296s 5 0.978 2.68 98.1 101.8 296s 6 0.881 2.65 98.1 101.7 296s 7 0.786 2.62 98.1 101.7 296s 8 0.780 2.62 100.4 103.9 296s 9 1.031 2.70 98.4 102.1 296s 10 1.212 2.78 95.3 99.1 296s 11 1.339 2.84 90.8 94.7 296s 12 1.478 2.90 89.1 93.1 296s 13 1.292 2.81 91.1 94.9 296s 14 1.123 2.74 93.2 97.0 296s 15 1.105 2.73 98.2 101.9 296s 16 0.996 2.69 100.0 103.7 296s 17 1.636 2.99 98.8 102.9 296s 18 0.777 2.62 98.7 102.2 296s 19 0.775 2.62 98.5 102.1 296s 20 0.600 2.57 101.6 105.1 296s > print( predict( fit2sls5rs$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 296s + interval = "prediction", level = 0.5, newdata = predictData ) ) 296s fit se.fit se.pred lwr upr 296s 1 102.8 0.713 2.10 101.4 104 296s 2 105.4 0.742 2.11 103.9 107 296s 3 105.3 0.751 2.11 103.8 107 296s 4 105.5 0.749 2.11 104.1 107 296s 5 107.5 1.080 2.25 105.9 109 296s 6 107.4 1.031 2.23 105.9 109 296s 7 107.9 1.040 2.23 106.4 109 296s 8 108.7 1.044 2.23 107.1 110 296s 9 106.8 1.073 2.24 105.2 108 296s 10 105.3 1.188 2.30 103.8 107 296s 11 100.3 1.013 2.22 98.8 102 296s 12 99.7 0.770 2.12 98.2 101 296s 13 101.3 0.584 2.06 99.9 103 296s 14 104.3 0.833 2.14 102.8 106 296s 15 109.2 1.310 2.37 107.6 111 296s 16 109.1 1.214 2.32 107.6 111 296s 17 108.9 1.582 2.53 107.1 111 296s 18 107.7 0.958 2.19 106.2 109 296s 19 109.4 1.111 2.26 107.9 111 296s 20 113.2 1.529 2.50 111.5 115 296s > 296s > print( predict( fit2slsd1, se.fit = TRUE, se.pred = TRUE, 296s + interval = "confidence", level = 0.99, useDfSys = TRUE ) ) 296s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 296s 1 97.1 0.751 2.13 95.1 99.2 98.9 296s 2 99.2 0.757 2.13 97.1 101.2 100.4 296s 3 99.2 0.692 2.11 97.3 101.1 100.5 296s 4 99.3 0.766 2.13 97.2 101.4 100.7 296s 5 102.5 0.595 2.08 100.9 104.2 102.6 296s 6 102.2 0.503 2.05 100.8 103.6 102.6 296s 7 102.5 0.503 2.05 101.1 103.9 102.6 296s 8 102.7 0.653 2.10 100.9 104.4 104.8 296s 9 102.0 0.655 2.10 100.2 103.8 102.7 296s 10 101.4 1.074 2.26 98.5 104.3 99.7 296s 11 95.6 0.978 2.22 93.0 98.3 95.4 296s 12 93.9 1.134 2.29 90.8 97.0 93.8 296s 13 95.0 1.162 2.31 91.9 98.2 95.6 296s 14 98.9 0.530 2.06 97.5 100.4 97.6 296s 15 104.9 1.061 2.26 102.0 107.8 102.3 296s 16 104.3 0.757 2.13 102.2 106.3 104.1 296s 17 106.1 1.963 2.80 100.7 111.4 102.8 296s 18 101.7 0.597 2.08 100.1 103.4 102.7 296s 19 103.3 0.736 2.12 101.3 105.3 102.6 296s 20 106.0 1.430 2.45 102.1 110.0 105.6 296s supply.se.fit supply.se.pred supply.lwr supply.upr 296s 1 1.079 2.68 96.0 101.9 296s 2 1.064 2.68 97.5 103.3 296s 3 0.962 2.64 97.8 103.1 296s 4 0.938 2.63 98.1 103.3 296s 5 0.914 2.62 100.1 105.1 296s 6 0.808 2.59 100.4 104.8 296s 7 0.736 2.57 100.5 104.6 296s 8 0.994 2.65 102.1 107.5 296s 9 0.808 2.59 100.5 105.0 296s 10 1.023 2.66 96.9 102.5 296s 11 1.228 2.75 92.0 98.7 296s 12 1.428 2.84 89.9 97.7 296s 13 1.272 2.77 92.2 99.1 296s 14 0.917 2.62 95.1 100.1 296s 15 0.899 2.62 99.9 104.8 296s 16 0.936 2.63 101.5 106.6 296s 17 1.665 2.97 98.3 107.4 296s 18 0.988 2.65 100.0 105.4 296s 19 1.129 2.70 99.5 105.6 296s 20 1.733 3.01 100.9 110.3 296s > print( predict( fit2slsd1$eq[[ 2 ]], se.fit = TRUE, se.pred = TRUE, 296s + interval = "confidence", level = 0.99, useDfSys = TRUE ) ) 296s fit se.fit se.pred lwr upr 296s 1 98.9 1.079 2.68 96.0 101.9 296s 2 100.4 1.064 2.68 97.5 103.3 296s 3 100.5 0.962 2.64 97.8 103.1 296s 4 100.7 0.938 2.63 98.1 103.3 296s 5 102.6 0.914 2.62 100.1 105.1 296s 6 102.6 0.808 2.59 100.4 104.8 296s 7 102.6 0.736 2.57 100.5 104.6 296s 8 104.8 0.994 2.65 102.1 107.5 296s 9 102.7 0.808 2.59 100.5 105.0 296s 10 99.7 1.023 2.66 96.9 102.5 296s 11 95.4 1.228 2.75 92.0 98.7 296s 12 93.8 1.428 2.84 89.9 97.7 296s 13 95.6 1.272 2.77 92.2 99.1 296s 14 97.6 0.917 2.62 95.1 100.1 296s 15 102.3 0.899 2.62 99.9 104.8 296s 16 104.1 0.936 2.63 101.5 106.6 296s 17 102.8 1.665 2.97 98.3 107.4 296s 18 102.7 0.988 2.65 100.0 105.4 296s 19 102.6 1.129 2.70 99.5 105.6 296s 20 105.6 1.733 3.01 100.9 110.3 296s > 296s > print( predict( fit2slsd2r, se.fit = TRUE, interval = "prediction", 296s + level = 0.9, newdata = predictData ) ) 296s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 296s 1 104 1.34 99.8 108 95.8 1.026 296s 2 106 1.27 102.3 110 97.3 0.786 296s 3 106 1.32 102.2 110 97.4 0.804 296s 4 106 1.27 102.4 110 97.7 0.734 296s 5 109 2.06 104.2 114 100.0 1.130 296s 6 109 1.92 104.1 113 99.9 1.014 296s 7 109 1.86 104.7 114 99.9 0.893 296s 8 110 1.67 105.4 114 102.2 0.765 296s 9 108 2.12 103.4 113 100.4 1.187 296s 10 107 2.45 101.9 112 97.4 1.525 296s 11 102 1.85 97.1 106 92.9 1.627 296s 12 101 1.26 96.6 104 91.2 1.587 296s 13 102 0.98 98.3 106 93.1 1.314 296s 14 105 1.63 101.1 110 95.3 1.253 296s 15 111 2.53 105.6 116 100.4 1.269 296s 16 111 2.23 105.7 116 102.1 1.075 296s 17 111 3.28 104.9 118 101.3 1.888 296s 18 109 1.59 104.5 113 100.7 0.796 296s 19 110 1.70 106.1 115 100.5 0.772 296s 20 114 1.87 109.4 119 103.6 0.656 296s supply.lwr supply.upr 296s 1 91.2 100.4 296s 2 92.8 101.7 296s 3 93.0 101.9 296s 4 93.3 102.1 296s 5 95.3 104.6 296s 6 95.4 104.5 296s 7 95.4 104.4 296s 8 97.8 106.6 296s 9 95.7 105.1 296s 10 92.5 102.4 296s 11 87.9 98.0 296s 12 86.2 96.2 296s 13 88.3 97.9 296s 14 90.5 100.0 296s 15 95.6 105.1 296s 16 97.5 106.7 296s 17 96.0 106.6 296s 18 96.2 105.1 296s 19 96.1 105.0 296s 20 99.2 107.9 296s > print( predict( fit2slsd2r$eq[[ 1 ]], se.fit = TRUE, interval = "prediction", 296s + level = 0.9, newdata = predictData ) ) 296s fit se.fit lwr upr 296s 1 104 1.34 99.8 108 296s 2 106 1.27 102.3 110 296s 3 106 1.32 102.2 110 296s 4 106 1.27 102.4 110 296s 5 109 2.06 104.2 114 296s 6 109 1.92 104.1 113 296s 7 109 1.86 104.7 114 296s 8 110 1.67 105.4 114 296s 9 108 2.12 103.4 113 296s 10 107 2.45 101.9 112 296s 11 102 1.85 97.1 106 296s 12 101 1.26 96.6 104 296s 13 102 0.98 98.3 106 296s 14 105 1.63 101.1 110 296s 15 111 2.53 105.6 116 296s 16 111 2.23 105.7 116 296s 17 111 3.28 104.9 118 296s 18 109 1.59 104.5 113 296s 19 110 1.70 106.1 115 296s 20 114 1.87 109.4 119 296s > 296s > # predict just one observation 296s > smallData <- data.frame( price = 130, income = 150, farmPrice = 120, 296s + trend = 25 ) 296s > 296s > print( predict( fit2sls1rs, newdata = smallData ) ) 296s demand.pred supply.pred 296s 1 110 118 296s > print( predict( fit2sls1rs$eq[[ 1 ]], newdata = smallData ) ) 296s fit 296s 1 110 296s > 296s > print( predict( fit2sls2, se.fit = TRUE, level = 0.9, 296s + newdata = smallData ) ) 296s demand.pred demand.se.fit supply.pred supply.se.fit 296s 1 110 2.79 119 3.18 296s > print( predict( fit2sls2$eq[[ 1 ]], se.pred = TRUE, level = 0.99, 296s + newdata = smallData ) ) 296s fit se.pred 296s 1 110 3.42 296s > 296s > print( predict( fit2sls3, interval = "prediction", level = 0.975, 296s + newdata = smallData ) ) 296s demand.pred demand.lwr demand.upr supply.pred supply.lwr supply.upr 296s 1 110 102 117 119 110 128 296s > print( predict( fit2sls3$eq[[ 1 ]], interval = "confidence", level = 0.8, 296s + newdata = smallData ) ) 296s fit lwr upr 296s 1 110 106 113 296s > 296s > print( predict( fit2sls4r, se.fit = TRUE, interval = "confidence", 296s + level = 0.999, newdata = smallData ) ) 296s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 296s 1 109 2.24 101 118 119 2.09 296s supply.lwr supply.upr 296s 1 112 127 296s > print( predict( fit2sls4r$eq[[ 2 ]], se.pred = TRUE, interval = "prediction", 296s + level = 0.75, newdata = smallData ) ) 296s fit se.pred lwr upr 296s 1 119 3.26 115 123 296s > 296s > print( predict( fit2sls5s, se.fit = TRUE, interval = "prediction", 296s + newdata = smallData ) ) 296s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 296s 1 109 2.26 103 116 119 2.33 296s supply.lwr supply.upr 296s 1 112 126 296s > print( predict( fit2sls5s$eq[[ 1 ]], se.pred = TRUE, interval = "confidence", 296s + newdata = smallData ) ) 296s fit se.pred lwr upr 296s 1 109 3 105 114 296s > 296s > print( predict( fit2slsd3, se.fit = TRUE, se.pred = TRUE, 296s + interval = "prediction", level = 0.5, newdata = smallData ) ) 296s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 296s 1 108 3.33 3.86 105 110 119 296s supply.se.fit supply.se.pred supply.lwr supply.upr 296s 1 3.2 4.07 116 122 296s > print( predict( fit2slsd3$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 296s + interval = "confidence", level = 0.25, newdata = smallData ) ) 296s fit se.fit se.pred lwr upr 296s 1 108 3.33 3.86 107 109 296s > 296s > 296s > ## ************ correlation of predicted values *************** 296s > print( correlation.systemfit( fit2sls1, 1, 2 ) ) 296s [,1] 296s [1,] 0 296s [2,] 0 296s [3,] 0 296s [4,] 0 296s [5,] 0 296s [6,] 0 296s [7,] 0 296s [8,] 0 296s [9,] 0 296s [10,] 0 296s [11,] 0 296s [12,] 0 296s [13,] 0 296s [14,] 0 296s [15,] 0 296s [16,] 0 296s [17,] 0 296s [18,] 0 296s [19,] 0 296s [20,] 0 296s > 296s > print( correlation.systemfit( fit2sls2s, 2, 1 ) ) 296s [,1] 296s [1,] 0.413453 296s [2,] 0.153759 296s [3,] 0.152962 296s [4,] 0.112671 296s [5,] -0.071442 296s [6,] -0.053943 296s [7,] -0.050961 296s [8,] -0.005442 296s [9,] -0.000476 296s [10,] -0.001894 296s [11,] 0.047351 296s [12,] 0.064973 296s [13,] 0.024591 296s [14,] -0.028036 296s [15,] 0.175326 296s [16,] 0.254878 296s [17,] 0.104540 296s [18,] 0.065579 296s [19,] 0.147008 296s [20,] 0.124593 296s > 296s > print( correlation.systemfit( fit2sls3, 1, 2 ) ) 296s [,1] 296s [1,] 0.44877 296s [2,] 0.16875 296s [3,] 0.16850 296s [4,] 0.12519 296s [5,] -0.08079 296s [6,] -0.06096 296s [7,] -0.05780 296s [8,] -0.00618 296s [9,] -0.00054 296s [10,] -0.00214 296s [11,] 0.05454 296s [12,] 0.07607 296s [13,] 0.02868 296s [14,] -0.03197 296s [15,] 0.19899 296s [16,] 0.28551 296s [17,] 0.11838 296s [18,] 0.07184 296s [19,] 0.16271 296s [20,] 0.13995 296s > 296s > print( correlation.systemfit( fit2sls4r, 2, 1 ) ) 296s [,1] 296s [1,] 0.4078 296s [2,] 0.2866 296s [3,] 0.2528 296s [4,] 0.2836 296s [5,] -0.0300 296s [6,] -0.0537 296s [7,] -0.0627 296s [8,] 0.1044 296s [9,] 0.1003 296s [10,] 0.4530 296s [11,] 0.1293 296s [12,] 0.0184 296s [13,] 0.0449 296s [14,] -0.0409 296s [15,] 0.4229 296s [16,] 0.2649 296s [17,] 0.6554 296s [18,] 0.2693 296s [19,] 0.3831 296s [20,] 0.5784 296s > 296s > print( correlation.systemfit( fit2sls5rs, 1, 2 ) ) 296s [,1] 296s [1,] 0.38438 296s [2,] 0.30697 296s [3,] 0.26690 296s [4,] 0.30163 296s [5,] -0.02768 296s [6,] -0.05086 296s [7,] -0.05895 296s [8,] 0.10102 296s [9,] 0.10072 296s [10,] 0.45547 296s [11,] 0.10817 296s [12,] 0.00552 296s [13,] 0.04219 296s [14,] -0.04054 296s [15,] 0.42100 296s [16,] 0.24974 296s [17,] 0.65722 296s [18,] 0.24286 296s [19,] 0.34336 296s [20,] 0.54717 296s > 296s > print( correlation.systemfit( fit2slsd1, 2, 1 ) ) 296s [,1] 296s [1,] 0 296s [2,] 0 296s [3,] 0 296s [4,] 0 296s [5,] 0 296s [6,] 0 296s [7,] 0 296s [8,] 0 296s [9,] 0 296s [10,] 0 296s [11,] 0 296s [12,] 0 296s [13,] 0 296s [14,] 0 296s [15,] 0 296s [16,] 0 296s [17,] 0 296s [18,] 0 296s [19,] 0 296s [20,] 0 296s > 296s > print( correlation.systemfit( fit2slsd2r, 1, 2 ) ) 296s [,1] 296s [1,] 0.51320 296s [2,] 0.27263 296s [3,] 0.26221 296s [4,] 0.21307 296s [5,] -0.11973 296s [6,] -0.08282 296s [7,] -0.06158 296s [8,] -0.00225 296s [9,] -0.00103 296s [10,] -0.00892 296s [11,] 0.04576 296s [12,] 0.08710 296s [13,] 0.03423 296s [14,] -0.03425 296s [15,] 0.25625 296s [16,] 0.35070 296s [17,] 0.17505 296s [18,] -0.02443 296s [19,] 0.07277 296s [20,] 0.05142 296s > 296s > 296s > ## ************ Log-Likelihood values *************** 296s > print( logLik( fit2sls1 ) ) 296s 'log Lik.' -67.6 (df=8) 296s > print( logLik( fit2sls1, residCovDiag = TRUE ) ) 296s 'log Lik.' -84.4 (df=8) 296s > 296s > print( logLik( fit2sls2s ) ) 296s 'log Lik.' -65.7 (df=7) 296s > print( logLik( fit2sls2s, residCovDiag = TRUE ) ) 296s 'log Lik.' -84.8 (df=7) 296s > 296s > print( logLik( fit2sls3 ) ) 296s 'log Lik.' -65.7 (df=7) 296s > print( logLik( fit2sls3, residCovDiag = TRUE ) ) 296s 'log Lik.' -84.8 (df=7) 296s > 296s > print( logLik( fit2sls4r ) ) 296s 'log Lik.' -66.2 (df=6) 296s > print( logLik( fit2sls4r, residCovDiag = TRUE ) ) 296s 'log Lik.' -84.8 (df=6) 296s > 296s > print( logLik( fit2sls5rs ) ) 296s 'log Lik.' -66.2 (df=6) 296s > print( logLik( fit2sls5rs, residCovDiag = TRUE ) ) 296s 'log Lik.' -84.8 (df=6) 296s > 296s > print( logLik( fit2slsd1 ) ) 296s 'log Lik.' -75.1 (df=8) 296s > print( logLik( fit2slsd1, residCovDiag = TRUE ) ) 296s 'log Lik.' -84.7 (df=8) 296s > 296s > print( logLik( fit2slsd2r ) ) 296s 'log Lik.' -68.8 (df=7) 296s > print( logLik( fit2slsd2r, residCovDiag = TRUE ) ) 296s 'log Lik.' -84.6 (df=7) 296s > 296s > 296s > ## ************** F tests **************** 296s > # testing first restriction 296s > print( linearHypothesis( fit2sls1, restrm ) ) 296s Linear hypothesis test (Theil's F test) 296s 296s Hypothesis: 296s demand_income - supply_trend = 0 296s 296s Model 1: restricted model 296s Model 2: fit2sls1 296s 296s Res.Df Df F Pr(>F) 296s 1 34 296s 2 33 1 0.06 0.8 296s > linearHypothesis( fit2sls1, restrict ) 296s Linear hypothesis test (Theil's F test) 296s 296s Hypothesis: 296s demand_income - supply_trend = 0 296s 296s Model 1: restricted model 296s Model 2: fit2sls1 296s 296s Res.Df Df F Pr(>F) 296s 1 34 296s 2 33 1 0.06 0.8 296s > 296s > print( linearHypothesis( fit2sls1s, restrm ) ) 296s Linear hypothesis test (Theil's F test) 296s 296s Hypothesis: 296s demand_income - supply_trend = 0 296s 296s Model 1: restricted model 296s Model 2: fit2sls1s 296s 296s Res.Df Df F Pr(>F) 296s 1 34 296s 2 33 1 0.07 0.79 296s > linearHypothesis( fit2sls1s, restrict ) 296s Linear hypothesis test (Theil's F test) 296s 296s Hypothesis: 296s demand_income - supply_trend = 0 296s 296s Model 1: restricted model 296s Model 2: fit2sls1s 296s 296s Res.Df Df F Pr(>F) 296s 1 34 296s 2 33 1 0.07 0.79 296s > 296s > print( linearHypothesis( fit2sls1, restrm ) ) 296s Linear hypothesis test (Theil's F test) 296s 296s Hypothesis: 296s demand_income - supply_trend = 0 296s 296s Model 1: restricted model 296s Model 2: fit2sls1 296s 296s Res.Df Df F Pr(>F) 296s 1 34 296s 2 33 1 0.06 0.8 296s > linearHypothesis( fit2sls1, restrict ) 296s Linear hypothesis test (Theil's F test) 296s 296s Hypothesis: 296s demand_income - supply_trend = 0 296s 296s Model 1: restricted model 296s Model 2: fit2sls1 296s 296s Res.Df Df F Pr(>F) 296s 1 34 296s 2 33 1 0.06 0.8 296s > 296s > print( linearHypothesis( fit2sls1r, restrm ) ) 296s Linear hypothesis test (Theil's F test) 296s 296s Hypothesis: 296s demand_income - supply_trend = 0 296s 296s Model 1: restricted model 296s Model 2: fit2sls1r 296s 296s Res.Df Df F Pr(>F) 296s 1 34 296s 2 33 1 0.08 0.78 296s > linearHypothesis( fit2sls1r, restrict ) 296s Linear hypothesis test (Theil's F test) 296s 296s Hypothesis: 296s demand_income - supply_trend = 0 296s 296s Model 1: restricted model 296s Model 2: fit2sls1r 296s 296s Res.Df Df F Pr(>F) 296s 1 34 296s 2 33 1 0.08 0.78 296s > 296s > # testing second restriction 296s > restrOnly2m <- matrix(0,1,7) 296s > restrOnly2q <- 0.5 296s > restrOnly2m[1,2] <- -1 296s > restrOnly2m[1,5] <- 1 296s > restrictOnly2 <- "- demand_price + supply_price = 0.5" 296s > # first restriction not imposed 296s > print( linearHypothesis( fit2sls1, restrOnly2m, restrOnly2q ) ) 296s Linear hypothesis test (Theil's F test) 296s 296s Hypothesis: 296s - demand_price + supply_price = 0.5 296s 296s Model 1: restricted model 296s Model 2: fit2sls1 296s 296s Res.Df Df F Pr(>F) 296s 1 34 296s 2 33 1 0 0.96 296s > linearHypothesis( fit2sls1, restrictOnly2 ) 296s Linear hypothesis test (Theil's F test) 296s 296s Hypothesis: 296s - demand_price + supply_price = 0.5 296s 296s Model 1: restricted model 296s Model 2: fit2sls1 296s 296s Res.Df Df F Pr(>F) 296s 1 34 296s 2 33 1 0 0.96 296s > 296s > # first restriction imposed 296s > print( linearHypothesis( fit2sls2, restrOnly2m, restrOnly2q ) ) 296s Linear hypothesis test (Theil's F test) 296s 296s Hypothesis: 296s - demand_price + supply_price = 0.5 296s 296s Model 1: restricted model 296s Model 2: fit2sls2 296s 296s Res.Df Df F Pr(>F) 296s 1 35 296s 2 34 1 0.01 0.92 296s > linearHypothesis( fit2sls2, restrictOnly2 ) 296s Linear hypothesis test (Theil's F test) 296s 296s Hypothesis: 296s - demand_price + supply_price = 0.5 296s 296s Model 1: restricted model 296s Model 2: fit2sls2 296s 296s Res.Df Df F Pr(>F) 296s 1 35 296s 2 34 1 0.01 0.92 296s > 296s > print( linearHypothesis( fit2sls2r, restrOnly2m, restrOnly2q ) ) 296s Linear hypothesis test (Theil's F test) 296s 296s Hypothesis: 296s - demand_price + supply_price = 0.5 296s 296s Model 1: restricted model 296s Model 2: fit2sls2r 296s 296s Res.Df Df F Pr(>F) 296s 1 35 296s 2 34 1 0.01 0.91 296s > linearHypothesis( fit2sls2r, restrictOnly2 ) 296s Linear hypothesis test (Theil's F test) 296s 296s Hypothesis: 296s - demand_price + supply_price = 0.5 296s 296s Model 1: restricted model 296s Model 2: fit2sls2r 296s 296s Res.Df Df F Pr(>F) 296s 1 35 296s 2 34 1 0.01 0.91 296s > 296s > print( linearHypothesis( fit2sls3, restrOnly2m, restrOnly2q ) ) 297s Linear hypothesis test (Theil's F test) 297s 297s Hypothesis: 297s - demand_price + supply_price = 0.5 297s 297s Model 1: restricted model 297s Model 2: fit2sls3 297s 297s Res.Df Df F Pr(>F) 297s 1 35 297s 2 34 1 0.01 0.91 297s > linearHypothesis( fit2sls3, restrictOnly2 ) 297s Linear hypothesis test (Theil's F test) 297s 297s Hypothesis: 297s - demand_price + supply_price = 0.5 297s 297s Model 1: restricted model 297s Model 2: fit2sls3 297s 297s Res.Df Df F Pr(>F) 297s 1 35 297s 2 34 1 0.01 0.91 297s > 297s > # testing both of the restrictions 297s > print( linearHypothesis( fit2sls1, restr2m, restr2q ) ) 297s Linear hypothesis test (Theil's F test) 297s 297s Hypothesis: 297s demand_income - supply_trend = 0 297s - demand_price + supply_price = 0.5 297s 297s Model 1: restricted model 297s Model 2: fit2sls1 297s 297s Res.Df Df F Pr(>F) 297s 1 35 297s 2 33 2 0.04 0.97 297s > linearHypothesis( fit2sls1, restrict2 ) 297s Linear hypothesis test (Theil's F test) 297s 297s Hypothesis: 297s demand_income - supply_trend = 0 297s - demand_price + supply_price = 0.5 297s 297s Model 1: restricted model 297s Model 2: fit2sls1 297s 297s Res.Df Df F Pr(>F) 297s 1 35 297s 2 33 2 0.04 0.97 297s > 297s > 297s > ## ************** Wald tests **************** 297s > # testing first restriction 297s > print( linearHypothesis( fit2sls1, restrm, test = "Chisq" ) ) 297s Linear hypothesis test (Chi^2 statistic of a Wald test) 297s 297s Hypothesis: 297s demand_income - supply_trend = 0 297s 297s Model 1: restricted model 297s Model 2: fit2sls1 297s 297s Res.Df Df Chisq Pr(>Chisq) 297s 1 34 297s 2 33 1 0.31 0.58 297s > linearHypothesis( fit2sls1, restrict, test = "Chisq" ) 297s Linear hypothesis test (Chi^2 statistic of a Wald test) 297s 297s Hypothesis: 297s demand_income - supply_trend = 0 297s 297s Model 1: restricted model 297s Model 2: fit2sls1 297s 297s Res.Df Df Chisq Pr(>Chisq) 297s 1 34 297s 2 33 1 0.31 0.58 297s > 297s > print( linearHypothesis( fit2sls1s, restrm, test = "Chisq" ) ) 297s Linear hypothesis test (Chi^2 statistic of a Wald test) 297s 297s Hypothesis: 297s demand_income - supply_trend = 0 297s 297s Model 1: restricted model 297s Model 2: fit2sls1s 297s 297s Res.Df Df Chisq Pr(>Chisq) 297s 1 34 297s 2 33 1 0.34 0.56 297s > linearHypothesis( fit2sls1s, restrict, test = "Chisq" ) 297s Linear hypothesis test (Chi^2 statistic of a Wald test) 297s 297s Hypothesis: 297s demand_income - supply_trend = 0 297s 297s Model 1: restricted model 297s Model 2: fit2sls1s 297s 297s Res.Df Df Chisq Pr(>Chisq) 297s 1 34 297s 2 33 1 0.34 0.56 297s > 297s > print( linearHypothesis( fit2sls1, restrm, test = "Chisq" ) ) 297s Linear hypothesis test (Chi^2 statistic of a Wald test) 297s 297s Hypothesis: 297s demand_income - supply_trend = 0 297s 297s Model 1: restricted model 297s Model 2: fit2sls1 297s 297s Res.Df Df Chisq Pr(>Chisq) 297s 1 34 297s 2 33 1 0.31 0.58 297s > linearHypothesis( fit2sls1, restrict, test = "Chisq" ) 297s Linear hypothesis test (Chi^2 statistic of a Wald test) 297s 297s Hypothesis: 297s demand_income - supply_trend = 0 297s 297s Model 1: restricted model 297s Model 2: fit2sls1 297s 297s Res.Df Df Chisq Pr(>Chisq) 297s 1 34 297s 2 33 1 0.31 0.58 297s > 297s > print( linearHypothesis( fit2sls1r, restrm, test = "Chisq" ) ) 297s Linear hypothesis test (Chi^2 statistic of a Wald test) 297s 297s Hypothesis: 297s demand_income - supply_trend = 0 297s 297s Model 1: restricted model 297s Model 2: fit2sls1r 297s 297s Res.Df Df Chisq Pr(>Chisq) 297s 1 34 297s 2 33 1 0.38 0.54 297s > linearHypothesis( fit2sls1r, restrict, test = "Chisq" ) 297s Linear hypothesis test (Chi^2 statistic of a Wald test) 297s 297s Hypothesis: 297s demand_income - supply_trend = 0 297s 297s Model 1: restricted model 297s Model 2: fit2sls1r 297s 297s Res.Df Df Chisq Pr(>Chisq) 297s 1 34 297s 2 33 1 0.38 0.54 297s > 297s > # testing second restriction 297s > # first restriction not imposed 297s > print( linearHypothesis( fit2sls1, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 297s Linear hypothesis test (Chi^2 statistic of a Wald test) 297s 297s Hypothesis: 297s - demand_price + supply_price = 0.5 297s 297s Model 1: restricted model 297s Model 2: fit2sls1 297s 297s Res.Df Df Chisq Pr(>Chisq) 297s 1 34 297s 2 33 1 0.01 0.91 297s > linearHypothesis( fit2sls1, restrictOnly2, test = "Chisq" ) 297s Linear hypothesis test (Chi^2 statistic of a Wald test) 297s 297s Hypothesis: 297s - demand_price + supply_price = 0.5 297s 297s Model 1: restricted model 297s Model 2: fit2sls1 297s 297s Res.Df Df Chisq Pr(>Chisq) 297s 1 34 297s 2 33 1 0.01 0.91 297s > # first restriction imposed 297s > print( linearHypothesis( fit2sls2, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 297s Linear hypothesis test (Chi^2 statistic of a Wald test) 297s 297s Hypothesis: 297s - demand_price + supply_price = 0.5 297s 297s Model 1: restricted model 297s Model 2: fit2sls2 297s 297s Res.Df Df Chisq Pr(>Chisq) 297s 1 35 297s 2 34 1 0.06 0.81 297s > linearHypothesis( fit2sls2, restrictOnly2, test = "Chisq" ) 297s Linear hypothesis test (Chi^2 statistic of a Wald test) 297s 297s Hypothesis: 297s - demand_price + supply_price = 0.5 297s 297s Model 1: restricted model 297s Model 2: fit2sls2 297s 297s Res.Df Df Chisq Pr(>Chisq) 297s 1 35 297s 2 34 1 0.06 0.81 297s > 297s > print( linearHypothesis( fit2sls2r, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 297s Linear hypothesis test (Chi^2 statistic of a Wald test) 297s 297s Hypothesis: 297s - demand_price + supply_price = 0.5 297s 297s Model 1: restricted model 297s Model 2: fit2sls2r 297s 297s Res.Df Df Chisq Pr(>Chisq) 297s 1 35 297s 2 34 1 0.07 0.8 297s > linearHypothesis( fit2sls2r, restrictOnly2, test = "Chisq" ) 297s Linear hypothesis test (Chi^2 statistic of a Wald test) 297s 297s Hypothesis: 297s - demand_price + supply_price = 0.5 297s 297s Model 1: restricted model 297s Model 2: fit2sls2r 297s 297s Res.Df Df Chisq Pr(>Chisq) 297s 1 35 297s 2 34 1 0.07 0.8 297s > 297s > print( linearHypothesis( fit2sls3, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 297s Linear hypothesis test (Chi^2 statistic of a Wald test) 297s 297s Hypothesis: 297s - demand_price + supply_price = 0.5 297s 297s Model 1: restricted model 297s Model 2: fit2sls3 297s 297s Res.Df Df Chisq Pr(>Chisq) 297s 1 35 297s 2 34 1 0.07 0.8 297s > linearHypothesis( fit2sls3, restrictOnly2, test = "Chisq" ) 297s Linear hypothesis test (Chi^2 statistic of a Wald test) 297s 297s Hypothesis: 297s - demand_price + supply_price = 0.5 297s 297s Model 1: restricted model 297s Model 2: fit2sls3 297s 297s Res.Df Df Chisq Pr(>Chisq) 297s 1 35 297s 2 34 1 0.07 0.8 297s > 297s > # testing both of the restrictions 297s > print( linearHypothesis( fit2sls1, restr2m, restr2q, test = "Chisq" ) ) 297s Linear hypothesis test (Chi^2 statistic of a Wald test) 297s 297s Hypothesis: 297s demand_income - supply_trend = 0 297s - demand_price + supply_price = 0.5 297s 297s Model 1: restricted model 297s Model 2: fit2sls1 297s 297s Res.Df Df Chisq Pr(>Chisq) 297s 1 35 297s 2 33 2 0.35 0.84 297s > linearHypothesis( fit2sls1, restrict2, test = "Chisq" ) 297s Linear hypothesis test (Chi^2 statistic of a Wald test) 297s 297s Hypothesis: 297s demand_income - supply_trend = 0 297s - demand_price + supply_price = 0.5 297s 297s Model 1: restricted model 297s Model 2: fit2sls1 297s 297s Res.Df Df Chisq Pr(>Chisq) 297s 1 35 297s 2 33 2 0.35 0.84 297s > 297s > 297s > ## **************** model frame ************************ 297s > print( mf <- model.frame( fit2sls1 ) ) 297s consump price income farmPrice trend 297s 1 98.5 100.3 87.4 98.0 1 297s 2 99.2 104.3 97.6 99.1 2 297s 3 102.2 103.4 96.7 99.1 3 297s 4 101.5 104.5 98.2 98.1 4 297s 5 104.2 98.0 99.8 110.8 5 297s 6 103.2 99.5 100.5 108.2 6 297s 7 104.0 101.1 103.2 105.6 7 297s 8 99.9 104.8 107.8 109.8 8 297s 9 100.3 96.4 96.6 108.7 9 297s 10 102.8 91.2 88.9 100.6 10 297s 11 95.4 93.1 75.1 81.0 11 297s 12 92.4 98.8 76.9 68.6 12 297s 13 94.5 102.9 84.6 70.9 13 297s 14 98.8 98.8 90.6 81.4 14 297s 15 105.8 95.1 103.1 102.3 15 297s 16 100.2 98.5 105.1 105.0 16 297s 17 103.5 86.5 96.4 110.5 17 297s 18 99.9 104.0 104.4 92.5 18 297s 19 105.2 105.8 110.7 89.3 19 297s 20 106.2 113.5 127.1 93.0 20 297s > print( mf1 <- model.frame( fit2sls1$eq[[ 1 ]] ) ) 297s consump price income 297s 1 98.5 100.3 87.4 297s 2 99.2 104.3 97.6 297s 3 102.2 103.4 96.7 297s 4 101.5 104.5 98.2 297s 5 104.2 98.0 99.8 297s 6 103.2 99.5 100.5 297s 7 104.0 101.1 103.2 297s 8 99.9 104.8 107.8 297s 9 100.3 96.4 96.6 297s 10 102.8 91.2 88.9 297s 11 95.4 93.1 75.1 297s 12 92.4 98.8 76.9 297s 13 94.5 102.9 84.6 297s 14 98.8 98.8 90.6 297s 15 105.8 95.1 103.1 297s 16 100.2 98.5 105.1 297s 17 103.5 86.5 96.4 297s 18 99.9 104.0 104.4 297s 19 105.2 105.8 110.7 297s 20 106.2 113.5 127.1 297s > print( attributes( mf1 )$terms ) 297s consump ~ price + income 297s attr(,"variables") 297s list(consump, price, income) 297s attr(,"factors") 297s price income 297s consump 0 0 297s price 1 0 297s income 0 1 297s attr(,"term.labels") 297s [1] "price" "income" 297s attr(,"order") 297s [1] 1 1 297s attr(,"intercept") 297s [1] 1 297s attr(,"response") 297s [1] 1 297s attr(,".Environment") 297s 297s attr(,"predvars") 297s list(consump, price, income) 297s attr(,"dataClasses") 297s consump price income 297s "numeric" "numeric" "numeric" 297s > print( mf2 <- model.frame( fit2sls1$eq[[ 2 ]] ) ) 297s consump price farmPrice trend 297s 1 98.5 100.3 98.0 1 297s 2 99.2 104.3 99.1 2 297s 3 102.2 103.4 99.1 3 297s 4 101.5 104.5 98.1 4 297s 5 104.2 98.0 110.8 5 297s 6 103.2 99.5 108.2 6 297s 7 104.0 101.1 105.6 7 297s 8 99.9 104.8 109.8 8 297s 9 100.3 96.4 108.7 9 297s 10 102.8 91.2 100.6 10 297s 11 95.4 93.1 81.0 11 297s 12 92.4 98.8 68.6 12 297s 13 94.5 102.9 70.9 13 297s 14 98.8 98.8 81.4 14 297s 15 105.8 95.1 102.3 15 297s 16 100.2 98.5 105.0 16 297s 17 103.5 86.5 110.5 17 297s 18 99.9 104.0 92.5 18 297s 19 105.2 105.8 89.3 19 297s 20 106.2 113.5 93.0 20 297s > print( attributes( mf2 )$terms ) 297s consump ~ price + farmPrice + trend 297s attr(,"variables") 297s list(consump, price, farmPrice, trend) 297s attr(,"factors") 297s price farmPrice trend 297s consump 0 0 0 297s price 1 0 0 297s farmPrice 0 1 0 297s trend 0 0 1 297s attr(,"term.labels") 297s [1] "price" "farmPrice" "trend" 297s attr(,"order") 297s [1] 1 1 1 297s attr(,"intercept") 297s [1] 1 297s attr(,"response") 297s [1] 1 297s attr(,".Environment") 297s 297s attr(,"predvars") 297s list(consump, price, farmPrice, trend) 297s attr(,"dataClasses") 297s consump price farmPrice trend 297s "numeric" "numeric" "numeric" "numeric" 297s > 297s > print( all.equal( mf, model.frame( fit2sls2s ) ) ) 297s [1] TRUE 297s > print( all.equal( mf2, model.frame( fit2sls2s$eq[[ 2 ]] ) ) ) 297s [1] TRUE 297s > 297s > print( all.equal( mf, model.frame( fit2sls3 ) ) ) 297s [1] TRUE 297s > print( all.equal( mf1, model.frame( fit2sls3$eq[[ 1 ]] ) ) ) 297s [1] TRUE 297s > 297s > print( all.equal( mf, model.frame( fit2sls4r ) ) ) 297s [1] TRUE 297s > print( all.equal( mf2, model.frame( fit2sls4r$eq[[ 2 ]] ) ) ) 297s [1] TRUE 297s > 297s > print( all.equal( mf, model.frame( fit2sls5rs ) ) ) 297s [1] TRUE 297s > print( all.equal( mf1, model.frame( fit2sls5rs$eq[[ 1 ]] ) ) ) 297s [1] TRUE 297s > 297s > fit2sls1$eq[[ 1 ]]$modelInst 297s income farmPrice trend 297s 1 87.4 98.0 1 297s 2 97.6 99.1 2 297s 3 96.7 99.1 3 297s 4 98.2 98.1 4 297s 5 99.8 110.8 5 297s 6 100.5 108.2 6 297s 7 103.2 105.6 7 297s 8 107.8 109.8 8 297s 9 96.6 108.7 9 297s 10 88.9 100.6 10 297s 11 75.1 81.0 11 297s 12 76.9 68.6 12 297s 13 84.6 70.9 13 297s 14 90.6 81.4 14 297s 15 103.1 102.3 15 297s 16 105.1 105.0 16 297s 17 96.4 110.5 17 297s 18 104.4 92.5 18 297s 19 110.7 89.3 19 297s 20 127.1 93.0 20 297s > fit2sls1$eq[[ 2 ]]$modelInst 297s income farmPrice trend 297s 1 87.4 98.0 1 297s 2 97.6 99.1 2 297s 3 96.7 99.1 3 297s 4 98.2 98.1 4 297s 5 99.8 110.8 5 297s 6 100.5 108.2 6 297s 7 103.2 105.6 7 297s 8 107.8 109.8 8 297s 9 96.6 108.7 9 297s 10 88.9 100.6 10 297s 11 75.1 81.0 11 297s 12 76.9 68.6 12 297s 13 84.6 70.9 13 297s 14 90.6 81.4 14 297s 15 103.1 102.3 15 297s 16 105.1 105.0 16 297s 17 96.4 110.5 17 297s 18 104.4 92.5 18 297s 19 110.7 89.3 19 297s 20 127.1 93.0 20 297s > 297s > fit2sls2s$eq[[ 1 ]]$modelInst 297s income farmPrice trend 297s 1 87.4 98.0 1 297s 2 97.6 99.1 2 297s 3 96.7 99.1 3 297s 4 98.2 98.1 4 297s 5 99.8 110.8 5 297s 6 100.5 108.2 6 297s 7 103.2 105.6 7 297s 8 107.8 109.8 8 297s 9 96.6 108.7 9 297s 10 88.9 100.6 10 297s 11 75.1 81.0 11 297s 12 76.9 68.6 12 297s 13 84.6 70.9 13 297s 14 90.6 81.4 14 297s 15 103.1 102.3 15 297s 16 105.1 105.0 16 297s 17 96.4 110.5 17 297s 18 104.4 92.5 18 297s 19 110.7 89.3 19 297s 20 127.1 93.0 20 297s > fit2sls2s$eq[[ 2 ]]$modelInst 297s income farmPrice trend 297s 1 87.4 98.0 1 297s 2 97.6 99.1 2 297s 3 96.7 99.1 3 297s 4 98.2 98.1 4 297s 5 99.8 110.8 5 297s 6 100.5 108.2 6 297s 7 103.2 105.6 7 297s 8 107.8 109.8 8 297s 9 96.6 108.7 9 297s 10 88.9 100.6 10 297s 11 75.1 81.0 11 297s 12 76.9 68.6 12 297s 13 84.6 70.9 13 297s 14 90.6 81.4 14 297s 15 103.1 102.3 15 297s 16 105.1 105.0 16 297s 17 96.4 110.5 17 297s 18 104.4 92.5 18 297s 19 110.7 89.3 19 297s 20 127.1 93.0 20 297s > 297s > fit2sls5rs$eq[[ 1 ]]$modelInst 297s income farmPrice trend 297s 1 87.4 98.0 1 297s 2 97.6 99.1 2 297s 3 96.7 99.1 3 297s 4 98.2 98.1 4 297s 5 99.8 110.8 5 297s 6 100.5 108.2 6 297s 7 103.2 105.6 7 297s 8 107.8 109.8 8 297s 9 96.6 108.7 9 297s 10 88.9 100.6 10 297s 11 75.1 81.0 11 297s 12 76.9 68.6 12 297s 13 84.6 70.9 13 297s 14 90.6 81.4 14 297s 15 103.1 102.3 15 297s 16 105.1 105.0 16 297s 17 96.4 110.5 17 297s 18 104.4 92.5 18 297s 19 110.7 89.3 19 297s 20 127.1 93.0 20 297s > fit2sls5rs$eq[[ 2 ]]$modelInst 297s income farmPrice trend 297s 1 87.4 98.0 1 297s 2 97.6 99.1 2 297s 3 96.7 99.1 3 297s 4 98.2 98.1 4 297s 5 99.8 110.8 5 297s 6 100.5 108.2 6 297s 7 103.2 105.6 7 297s 8 107.8 109.8 8 297s 9 96.6 108.7 9 297s 10 88.9 100.6 10 297s 11 75.1 81.0 11 297s 12 76.9 68.6 12 297s 13 84.6 70.9 13 297s 14 90.6 81.4 14 297s 15 103.1 102.3 15 297s 16 105.1 105.0 16 297s 17 96.4 110.5 17 297s 18 104.4 92.5 18 297s 19 110.7 89.3 19 297s 20 127.1 93.0 20 297s > 297s > 297s > ## **************** model matrix ************************ 297s > # with x (returnModelMatrix) = TRUE 297s > print( !is.null( fit2sls1$eq[[ 1 ]]$x ) ) 297s [1] TRUE 297s > print( mm <- model.matrix( fit2sls1 ) ) 297s demand_(Intercept) demand_price demand_income supply_(Intercept) 297s demand_1 1 100.3 87.4 0 297s demand_2 1 104.3 97.6 0 297s demand_3 1 103.4 96.7 0 297s demand_4 1 104.5 98.2 0 297s demand_5 1 98.0 99.8 0 297s demand_6 1 99.5 100.5 0 297s demand_7 1 101.1 103.2 0 297s demand_8 1 104.8 107.8 0 297s demand_9 1 96.4 96.6 0 297s demand_10 1 91.2 88.9 0 297s demand_11 1 93.1 75.1 0 297s demand_12 1 98.8 76.9 0 297s demand_13 1 102.9 84.6 0 297s demand_14 1 98.8 90.6 0 297s demand_15 1 95.1 103.1 0 297s demand_16 1 98.5 105.1 0 297s demand_17 1 86.5 96.4 0 297s demand_18 1 104.0 104.4 0 297s demand_19 1 105.8 110.7 0 297s demand_20 1 113.5 127.1 0 297s supply_1 0 0.0 0.0 1 297s supply_2 0 0.0 0.0 1 297s supply_3 0 0.0 0.0 1 297s supply_4 0 0.0 0.0 1 297s supply_5 0 0.0 0.0 1 297s supply_6 0 0.0 0.0 1 297s supply_7 0 0.0 0.0 1 297s supply_8 0 0.0 0.0 1 297s supply_9 0 0.0 0.0 1 297s supply_10 0 0.0 0.0 1 297s supply_11 0 0.0 0.0 1 297s supply_12 0 0.0 0.0 1 297s supply_13 0 0.0 0.0 1 297s supply_14 0 0.0 0.0 1 297s supply_15 0 0.0 0.0 1 297s supply_16 0 0.0 0.0 1 297s supply_17 0 0.0 0.0 1 297s supply_18 0 0.0 0.0 1 297s supply_19 0 0.0 0.0 1 297s supply_20 0 0.0 0.0 1 297s supply_price supply_farmPrice supply_trend 297s demand_1 0.0 0.0 0 297s demand_2 0.0 0.0 0 297s demand_3 0.0 0.0 0 297s demand_4 0.0 0.0 0 297s demand_5 0.0 0.0 0 297s demand_6 0.0 0.0 0 297s demand_7 0.0 0.0 0 297s demand_8 0.0 0.0 0 297s demand_9 0.0 0.0 0 297s demand_10 0.0 0.0 0 297s demand_11 0.0 0.0 0 297s demand_12 0.0 0.0 0 297s demand_13 0.0 0.0 0 297s demand_14 0.0 0.0 0 297s demand_15 0.0 0.0 0 297s demand_16 0.0 0.0 0 297s demand_17 0.0 0.0 0 297s demand_18 0.0 0.0 0 297s demand_19 0.0 0.0 0 297s demand_20 0.0 0.0 0 297s supply_1 100.3 98.0 1 297s supply_2 104.3 99.1 2 297s supply_3 103.4 99.1 3 297s supply_4 104.5 98.1 4 297s supply_5 98.0 110.8 5 297s supply_6 99.5 108.2 6 297s supply_7 101.1 105.6 7 297s supply_8 104.8 109.8 8 297s supply_9 96.4 108.7 9 297s supply_10 91.2 100.6 10 297s supply_11 93.1 81.0 11 297s supply_12 98.8 68.6 12 297s supply_13 102.9 70.9 13 297s supply_14 98.8 81.4 14 297s supply_15 95.1 102.3 15 297s supply_16 98.5 105.0 16 297s supply_17 86.5 110.5 17 297s supply_18 104.0 92.5 18 297s supply_19 105.8 89.3 19 297s supply_20 113.5 93.0 20 297s > print( mm1 <- model.matrix( fit2sls1$eq[[ 1 ]] ) ) 297s (Intercept) price income 297s 1 1 100.3 87.4 297s 2 1 104.3 97.6 297s 3 1 103.4 96.7 297s 4 1 104.5 98.2 297s 5 1 98.0 99.8 297s 6 1 99.5 100.5 297s 7 1 101.1 103.2 297s 8 1 104.8 107.8 297s 9 1 96.4 96.6 297s 10 1 91.2 88.9 297s 11 1 93.1 75.1 297s 12 1 98.8 76.9 297s 13 1 102.9 84.6 297s 14 1 98.8 90.6 297s 15 1 95.1 103.1 297s 16 1 98.5 105.1 297s 17 1 86.5 96.4 297s 18 1 104.0 104.4 297s 19 1 105.8 110.7 297s 20 1 113.5 127.1 297s attr(,"assign") 297s [1] 0 1 2 297s > print( mm2 <- model.matrix( fit2sls1$eq[[ 2 ]] ) ) 297s (Intercept) price farmPrice trend 297s 1 1 100.3 98.0 1 297s 2 1 104.3 99.1 2 297s 3 1 103.4 99.1 3 297s 4 1 104.5 98.1 4 297s 5 1 98.0 110.8 5 297s 6 1 99.5 108.2 6 297s 7 1 101.1 105.6 7 297s 8 1 104.8 109.8 8 297s 9 1 96.4 108.7 9 297s 10 1 91.2 100.6 10 297s 11 1 93.1 81.0 11 297s 12 1 98.8 68.6 12 297s 13 1 102.9 70.9 13 297s 14 1 98.8 81.4 14 297s 15 1 95.1 102.3 15 297s 16 1 98.5 105.0 16 297s 17 1 86.5 110.5 17 297s 18 1 104.0 92.5 18 297s 19 1 105.8 89.3 19 297s 20 1 113.5 93.0 20 297s attr(,"assign") 297s [1] 0 1 2 3 297s > 297s > # with x (returnModelMatrix) = FALSE 297s > print( all.equal( mm, model.matrix( fit2sls1s ) ) ) 297s [1] TRUE 297s > print( all.equal( mm1, model.matrix( fit2sls1s$eq[[ 1 ]] ) ) ) 297s [1] TRUE 297s > print( all.equal( mm2, model.matrix( fit2sls1s$eq[[ 2 ]] ) ) ) 297s [1] TRUE 297s > print( !is.null( fit2sls1s$eq[[ 1 ]]$x ) ) 297s [1] FALSE 297s > 297s > # with x (returnModelMatrix) = TRUE 297s > print( !is.null( fit2sls2s$eq[[ 1 ]]$x ) ) 297s [1] TRUE 297s > print( all.equal( mm, model.matrix( fit2sls2s ) ) ) 297s [1] TRUE 297s > print( all.equal( mm1, model.matrix( fit2sls2s$eq[[ 1 ]] ) ) ) 297s [1] TRUE 297s > print( all.equal( mm2, model.matrix( fit2sls2s$eq[[ 2 ]] ) ) ) 297s [1] TRUE 297s > 297s > # with x (returnModelMatrix) = FALSE 297s > print( all.equal( mm, model.matrix( fit2sls2Sym ) ) ) 297s [1] TRUE 297s > print( all.equal( mm1, model.matrix( fit2sls2Sym$eq[[ 1 ]] ) ) ) 297s [1] TRUE 297s > print( all.equal( mm2, model.matrix( fit2sls2Sym$eq[[ 2 ]] ) ) ) 297s [1] TRUE 297s > print( !is.null( fit2sls2Sym$eq[[ 1 ]]$x ) ) 297s [1] FALSE 297s > 297s > # with x (returnModelMatrix) = FALSE 297s > print( all.equal( mm, model.matrix( fit2sls3 ) ) ) 297s [1] TRUE 297s > print( all.equal( mm1, model.matrix( fit2sls3$eq[[ 1 ]] ) ) ) 297s [1] TRUE 297s > print( all.equal( mm2, model.matrix( fit2sls3$eq[[ 2 ]] ) ) ) 297s [1] TRUE 297s > print( !is.null( fit2sls3$eq[[ 1 ]]$x ) ) 297s [1] FALSE 297s > 297s > # with x (returnModelMatrix) = TRUE 297s > print( !is.null( fit2sls4r$eq[[ 1 ]]$x ) ) 297s [1] TRUE 297s > print( all.equal( mm, model.matrix( fit2sls4r ) ) ) 297s [1] TRUE 297s > print( all.equal( mm1, model.matrix( fit2sls4r$eq[[ 1 ]] ) ) ) 297s [1] TRUE 297s > print( all.equal( mm2, model.matrix( fit2sls4r$eq[[ 2 ]] ) ) ) 297s [1] TRUE 297s > 297s > # with x (returnModelMatrix) = FALSE 297s > print( all.equal( mm, model.matrix( fit2sls4s ) ) ) 297s [1] TRUE 297s > print( all.equal( mm1, model.matrix( fit2sls4s$eq[[ 1 ]] ) ) ) 297s [1] TRUE 297s > print( all.equal( mm2, model.matrix( fit2sls4s$eq[[ 2 ]] ) ) ) 297s [1] TRUE 297s > print( !is.null( fit2sls4s$eq[[ 1 ]]$x ) ) 297s [1] FALSE 297s > 297s > # with x (returnModelMatrix) = TRUE 297s > print( !is.null( fit2sls5rs$eq[[ 1 ]]$x ) ) 297s [1] TRUE 297s > print( all.equal( mm, model.matrix( fit2sls5rs ) ) ) 297s [1] TRUE 297s > print( all.equal( mm1, model.matrix( fit2sls5rs$eq[[ 1 ]] ) ) ) 297s [1] TRUE 297s > print( all.equal( mm2, model.matrix( fit2sls5rs$eq[[ 2 ]] ) ) ) 297s [1] TRUE 297s > 297s > # with x (returnModelMatrix) = FALSE 297s > print( all.equal( mm, model.matrix( fit2sls5r ) ) ) 297s [1] TRUE 297s > print( all.equal( mm1, model.matrix( fit2sls5r$eq[[ 1 ]] ) ) ) 297s [1] TRUE 297s > print( all.equal( mm2, model.matrix( fit2sls5r$eq[[ 2 ]] ) ) ) 297s [1] TRUE 297s > print( !is.null( fit2sls5r$eq[[ 1 ]]$x ) ) 297s [1] FALSE 297s > 297s > # matrices of instrumental variables 297s > model.matrix( fit2sls1, which = "z" ) 297s demand_(Intercept) demand_income demand_farmPrice demand_trend 297s demand_1 1 87.4 98.0 1 297s demand_2 1 97.6 99.1 2 297s demand_3 1 96.7 99.1 3 297s demand_4 1 98.2 98.1 4 297s demand_5 1 99.8 110.8 5 297s demand_6 1 100.5 108.2 6 297s demand_7 1 103.2 105.6 7 297s demand_8 1 107.8 109.8 8 297s demand_9 1 96.6 108.7 9 297s demand_10 1 88.9 100.6 10 297s demand_11 1 75.1 81.0 11 297s demand_12 1 76.9 68.6 12 297s demand_13 1 84.6 70.9 13 297s demand_14 1 90.6 81.4 14 297s demand_15 1 103.1 102.3 15 297s demand_16 1 105.1 105.0 16 297s demand_17 1 96.4 110.5 17 297s demand_18 1 104.4 92.5 18 297s demand_19 1 110.7 89.3 19 297s demand_20 1 127.1 93.0 20 297s supply_1 0 0.0 0.0 0 297s supply_2 0 0.0 0.0 0 297s supply_3 0 0.0 0.0 0 297s supply_4 0 0.0 0.0 0 297s supply_5 0 0.0 0.0 0 297s supply_6 0 0.0 0.0 0 297s supply_7 0 0.0 0.0 0 297s supply_8 0 0.0 0.0 0 297s supply_9 0 0.0 0.0 0 297s supply_10 0 0.0 0.0 0 297s supply_11 0 0.0 0.0 0 297s supply_12 0 0.0 0.0 0 297s supply_13 0 0.0 0.0 0 297s supply_14 0 0.0 0.0 0 297s supply_15 0 0.0 0.0 0 297s supply_16 0 0.0 0.0 0 297s supply_17 0 0.0 0.0 0 297s supply_18 0 0.0 0.0 0 297s supply_19 0 0.0 0.0 0 297s supply_20 0 0.0 0.0 0 297s supply_(Intercept) supply_income supply_farmPrice supply_trend 297s demand_1 0 0.0 0.0 0 297s demand_2 0 0.0 0.0 0 297s demand_3 0 0.0 0.0 0 297s demand_4 0 0.0 0.0 0 297s demand_5 0 0.0 0.0 0 297s demand_6 0 0.0 0.0 0 297s demand_7 0 0.0 0.0 0 297s demand_8 0 0.0 0.0 0 297s demand_9 0 0.0 0.0 0 297s demand_10 0 0.0 0.0 0 297s demand_11 0 0.0 0.0 0 297s demand_12 0 0.0 0.0 0 297s demand_13 0 0.0 0.0 0 297s demand_14 0 0.0 0.0 0 297s demand_15 0 0.0 0.0 0 297s demand_16 0 0.0 0.0 0 297s demand_17 0 0.0 0.0 0 297s demand_18 0 0.0 0.0 0 297s demand_19 0 0.0 0.0 0 297s demand_20 0 0.0 0.0 0 297s supply_1 1 87.4 98.0 1 297s supply_2 1 97.6 99.1 2 297s supply_3 1 96.7 99.1 3 297s supply_4 1 98.2 98.1 4 297s supply_5 1 99.8 110.8 5 297s supply_6 1 100.5 108.2 6 297s supply_7 1 103.2 105.6 7 297s supply_8 1 107.8 109.8 8 297s supply_9 1 96.6 108.7 9 297s supply_10 1 88.9 100.6 10 297s supply_11 1 75.1 81.0 11 297s supply_12 1 76.9 68.6 12 297s supply_13 1 84.6 70.9 13 297s supply_14 1 90.6 81.4 14 297s supply_15 1 103.1 102.3 15 297s supply_16 1 105.1 105.0 16 297s supply_17 1 96.4 110.5 17 297s supply_18 1 104.4 92.5 18 297s supply_19 1 110.7 89.3 19 297s supply_20 1 127.1 93.0 20 297s > model.matrix( fit2sls1$eq[[ 1 ]], which = "z" ) 297s (Intercept) income farmPrice trend 297s 1 1 87.4 98.0 1 297s 2 1 97.6 99.1 2 297s 3 1 96.7 99.1 3 297s 4 1 98.2 98.1 4 297s 5 1 99.8 110.8 5 297s 6 1 100.5 108.2 6 297s 7 1 103.2 105.6 7 297s 8 1 107.8 109.8 8 297s 9 1 96.6 108.7 9 297s 10 1 88.9 100.6 10 297s 11 1 75.1 81.0 11 297s 12 1 76.9 68.6 12 297s 13 1 84.6 70.9 13 297s 14 1 90.6 81.4 14 297s 15 1 103.1 102.3 15 297s 16 1 105.1 105.0 16 297s 17 1 96.4 110.5 17 297s 18 1 104.4 92.5 18 297s 19 1 110.7 89.3 19 297s 20 1 127.1 93.0 20 297s attr(,"assign") 297s [1] 0 1 2 3 297s > model.matrix( fit2sls1$eq[[ 2 ]], which = "z" ) 297s (Intercept) income farmPrice trend 297s 1 1 87.4 98.0 1 297s 2 1 97.6 99.1 2 297s 3 1 96.7 99.1 3 297s 4 1 98.2 98.1 4 297s 5 1 99.8 110.8 5 297s 6 1 100.5 108.2 6 297s 7 1 103.2 105.6 7 297s 8 1 107.8 109.8 8 297s 9 1 96.6 108.7 9 297s 10 1 88.9 100.6 10 297s 11 1 75.1 81.0 11 297s 12 1 76.9 68.6 12 297s 13 1 84.6 70.9 13 297s 14 1 90.6 81.4 14 297s 15 1 103.1 102.3 15 297s 16 1 105.1 105.0 16 297s 17 1 96.4 110.5 17 297s 18 1 104.4 92.5 18 297s 19 1 110.7 89.3 19 297s 20 1 127.1 93.0 20 297s attr(,"assign") 297s [1] 0 1 2 3 297s > 297s > # matrices of fitted regressors 297s > model.matrix( fit2sls5r, which = "xHat" ) 297s demand_(Intercept) demand_price demand_income supply_(Intercept) 297s demand_1 1 99.6 87.4 0 297s demand_2 1 105.1 97.6 0 297s demand_3 1 103.8 96.7 0 297s demand_4 1 104.5 98.2 0 297s demand_5 1 98.7 99.8 0 297s demand_6 1 99.6 100.5 0 297s demand_7 1 102.0 103.2 0 297s demand_8 1 102.2 107.8 0 297s demand_9 1 94.6 96.6 0 297s demand_10 1 92.7 88.9 0 297s demand_11 1 92.4 75.1 0 297s demand_12 1 98.9 76.9 0 297s demand_13 1 102.2 84.6 0 297s demand_14 1 100.3 90.6 0 297s demand_15 1 97.6 103.1 0 297s demand_16 1 96.9 105.1 0 297s demand_17 1 87.7 96.4 0 297s demand_18 1 101.1 104.4 0 297s demand_19 1 106.1 110.7 0 297s demand_20 1 114.4 127.1 0 297s supply_1 0 0.0 0.0 1 297s supply_2 0 0.0 0.0 1 297s supply_3 0 0.0 0.0 1 297s supply_4 0 0.0 0.0 1 297s supply_5 0 0.0 0.0 1 297s supply_6 0 0.0 0.0 1 297s supply_7 0 0.0 0.0 1 297s supply_8 0 0.0 0.0 1 297s supply_9 0 0.0 0.0 1 297s supply_10 0 0.0 0.0 1 297s supply_11 0 0.0 0.0 1 297s supply_12 0 0.0 0.0 1 297s supply_13 0 0.0 0.0 1 297s supply_14 0 0.0 0.0 1 297s supply_15 0 0.0 0.0 1 297s supply_16 0 0.0 0.0 1 297s supply_17 0 0.0 0.0 1 297s supply_18 0 0.0 0.0 1 297s supply_19 0 0.0 0.0 1 297s supply_20 0 0.0 0.0 1 297s supply_price supply_farmPrice supply_trend 297s demand_1 0.0 0.0 0 297s demand_2 0.0 0.0 0 297s demand_3 0.0 0.0 0 297s demand_4 0.0 0.0 0 297s demand_5 0.0 0.0 0 297s demand_6 0.0 0.0 0 297s demand_7 0.0 0.0 0 297s demand_8 0.0 0.0 0 297s demand_9 0.0 0.0 0 297s demand_10 0.0 0.0 0 297s demand_11 0.0 0.0 0 297s demand_12 0.0 0.0 0 297s demand_13 0.0 0.0 0 297s demand_14 0.0 0.0 0 297s demand_15 0.0 0.0 0 297s demand_16 0.0 0.0 0 297s demand_17 0.0 0.0 0 297s demand_18 0.0 0.0 0 297s demand_19 0.0 0.0 0 297s demand_20 0.0 0.0 0 297s supply_1 99.6 98.0 1 297s supply_2 105.1 99.1 2 297s supply_3 103.8 99.1 3 297s supply_4 104.5 98.1 4 297s supply_5 98.7 110.8 5 297s supply_6 99.6 108.2 6 297s supply_7 102.0 105.6 7 297s supply_8 102.2 109.8 8 297s supply_9 94.6 108.7 9 297s supply_10 92.7 100.6 10 297s supply_11 92.4 81.0 11 297s supply_12 98.9 68.6 12 297s supply_13 102.2 70.9 13 297s supply_14 100.3 81.4 14 297s supply_15 97.6 102.3 15 297s supply_16 96.9 105.0 16 297s supply_17 87.7 110.5 17 297s supply_18 101.1 92.5 18 297s supply_19 106.1 89.3 19 297s supply_20 114.4 93.0 20 297s > model.matrix( fit2sls5r$eq[[ 1 ]], which = "xHat" ) 297s (Intercept) price income 297s 1 1 99.6 87.4 297s 2 1 105.1 97.6 297s 3 1 103.8 96.7 297s 4 1 104.5 98.2 297s 5 1 98.7 99.8 297s 6 1 99.6 100.5 297s 7 1 102.0 103.2 297s 8 1 102.2 107.8 297s 9 1 94.6 96.6 297s 10 1 92.7 88.9 297s 11 1 92.4 75.1 297s 12 1 98.9 76.9 297s 13 1 102.2 84.6 297s 14 1 100.3 90.6 297s 15 1 97.6 103.1 297s 16 1 96.9 105.1 297s 17 1 87.7 96.4 297s 18 1 101.1 104.4 297s 19 1 106.1 110.7 297s 20 1 114.4 127.1 297s > model.matrix( fit2sls5r$eq[[ 2 ]], which = "xHat" ) 297s (Intercept) price farmPrice trend 297s 1 1 99.6 98.0 1 297s 2 1 105.1 99.1 2 297s 3 1 103.8 99.1 3 297s 4 1 104.5 98.1 4 297s 5 1 98.7 110.8 5 297s 6 1 99.6 108.2 6 297s 7 1 102.0 105.6 7 297s 8 1 102.2 109.8 8 297s 9 1 94.6 108.7 9 297s 10 1 92.7 100.6 10 297s 11 1 92.4 81.0 11 297s 12 1 98.9 68.6 12 297s 13 1 102.2 70.9 13 297s 14 1 100.3 81.4 14 297s 15 1 97.6 102.3 15 297s 16 1 96.9 105.0 16 297s 17 1 87.7 110.5 17 297s 18 1 101.1 92.5 18 297s 19 1 106.1 89.3 19 297s 20 1 114.4 93.0 20 297s > 297s > 297s > ## **************** formulas ************************ 297s > formula( fit2sls1 ) 297s $demand 297s consump ~ price + income 297s 297s $supply 297s consump ~ price + farmPrice + trend 297s 297s > formula( fit2sls1$eq[[ 1 ]] ) 297s consump ~ price + income 297s > 297s > formula( fit2sls2s ) 297s $demand 297s consump ~ price + income 297s 297s $supply 297s consump ~ price + farmPrice + trend 297s 297s > formula( fit2sls2s$eq[[ 2 ]] ) 297s consump ~ price + farmPrice + trend 297s > 297s > formula( fit2sls3 ) 297s $demand 297s consump ~ price + income 297s 297s $supply 297s consump ~ price + farmPrice + trend 297s 297s > formula( fit2sls3$eq[[ 1 ]] ) 297s consump ~ price + income 297s > 297s > formula( fit2sls4r ) 297s $demand 297s consump ~ price + income 297s 297s $supply 297s consump ~ price + farmPrice + trend 297s 297s > formula( fit2sls4r$eq[[ 2 ]] ) 297s consump ~ price + farmPrice + trend 297s > 297s > formula( fit2sls5rs ) 297s $demand 297s consump ~ price + income 297s 297s $supply 297s consump ~ price + farmPrice + trend 297s 297s > formula( fit2sls5rs$eq[[ 1 ]] ) 297s consump ~ price + income 297s > 297s > formula( fit2slsd1 ) 297s $demand 297s consump ~ price + income 297s 297s $supply 297s consump ~ price + farmPrice + trend 297s 297s > formula( fit2slsd1$eq[[ 2 ]] ) 297s consump ~ price + farmPrice + trend 297s > 297s > formula( fit2slsd2r ) 297s $demand 297s consump ~ price + income 297s 297s $supply 297s consump ~ price + farmPrice + trend 297s 297s > formula( fit2slsd2r$eq[[ 1 ]] ) 297s consump ~ price + income 297s > 297s > 297s > ## **************** model terms ******************* 297s > terms( fit2sls1 ) 297s $demand 297s consump ~ price + income 297s attr(,"variables") 297s list(consump, price, income) 297s attr(,"factors") 297s price income 297s consump 0 0 297s price 1 0 297s income 0 1 297s attr(,"term.labels") 297s [1] "price" "income" 297s attr(,"order") 297s [1] 1 1 297s attr(,"intercept") 297s [1] 1 297s attr(,"response") 297s [1] 1 297s attr(,".Environment") 297s 297s attr(,"predvars") 297s list(consump, price, income) 297s attr(,"dataClasses") 297s consump price income 297s "numeric" "numeric" "numeric" 297s 297s $supply 297s consump ~ price + farmPrice + trend 297s attr(,"variables") 297s list(consump, price, farmPrice, trend) 297s attr(,"factors") 297s price farmPrice trend 297s consump 0 0 0 297s price 1 0 0 297s farmPrice 0 1 0 297s trend 0 0 1 297s attr(,"term.labels") 297s [1] "price" "farmPrice" "trend" 297s attr(,"order") 297s [1] 1 1 1 297s attr(,"intercept") 297s [1] 1 297s attr(,"response") 297s [1] 1 297s attr(,".Environment") 297s 297s attr(,"predvars") 297s list(consump, price, farmPrice, trend) 297s attr(,"dataClasses") 297s consump price farmPrice trend 297s "numeric" "numeric" "numeric" "numeric" 297s 297s > terms( fit2sls1$eq[[ 1 ]] ) 297s consump ~ price + income 297s attr(,"variables") 297s list(consump, price, income) 297s attr(,"factors") 297s price income 297s consump 0 0 297s price 1 0 297s income 0 1 297s attr(,"term.labels") 297s [1] "price" "income" 297s attr(,"order") 297s [1] 1 1 297s attr(,"intercept") 297s [1] 1 297s attr(,"response") 297s [1] 1 297s attr(,".Environment") 297s 297s attr(,"predvars") 297s list(consump, price, income) 297s attr(,"dataClasses") 297s consump price income 297s "numeric" "numeric" "numeric" 297s > 297s > terms( fit2sls2s ) 297s $demand 297s consump ~ price + income 297s attr(,"variables") 297s list(consump, price, income) 297s attr(,"factors") 297s price income 297s consump 0 0 297s price 1 0 297s income 0 1 297s attr(,"term.labels") 297s [1] "price" "income" 297s attr(,"order") 297s [1] 1 1 297s attr(,"intercept") 297s [1] 1 297s attr(,"response") 297s [1] 1 297s attr(,".Environment") 297s 297s attr(,"predvars") 297s list(consump, price, income) 297s attr(,"dataClasses") 297s consump price income 297s "numeric" "numeric" "numeric" 297s 297s $supply 297s consump ~ price + farmPrice + trend 297s attr(,"variables") 297s list(consump, price, farmPrice, trend) 297s attr(,"factors") 297s price farmPrice trend 297s consump 0 0 0 297s price 1 0 0 297s farmPrice 0 1 0 297s trend 0 0 1 297s attr(,"term.labels") 297s [1] "price" "farmPrice" "trend" 297s attr(,"order") 297s [1] 1 1 1 297s attr(,"intercept") 297s [1] 1 297s attr(,"response") 297s [1] 1 297s attr(,".Environment") 297s 297s attr(,"predvars") 297s list(consump, price, farmPrice, trend) 297s attr(,"dataClasses") 297s consump price farmPrice trend 297s "numeric" "numeric" "numeric" "numeric" 297s 297s > terms( fit2sls2s$eq[[ 2 ]] ) 297s consump ~ price + farmPrice + trend 297s attr(,"variables") 297s list(consump, price, farmPrice, trend) 297s attr(,"factors") 297s price farmPrice trend 297s consump 0 0 0 297s price 1 0 0 297s farmPrice 0 1 0 297s trend 0 0 1 297s attr(,"term.labels") 297s [1] "price" "farmPrice" "trend" 297s attr(,"order") 297s [1] 1 1 1 297s attr(,"intercept") 297s [1] 1 297s attr(,"response") 297s [1] 1 297s attr(,".Environment") 297s 297s attr(,"predvars") 297s list(consump, price, farmPrice, trend) 297s attr(,"dataClasses") 297s consump price farmPrice trend 297s "numeric" "numeric" "numeric" "numeric" 297s > 297s > terms( fit2sls3 ) 297s $demand 297s consump ~ price + income 297s attr(,"variables") 297s list(consump, price, income) 297s attr(,"factors") 297s price income 297s consump 0 0 297s price 1 0 297s income 0 1 297s attr(,"term.labels") 297s [1] "price" "income" 297s attr(,"order") 297s [1] 1 1 297s attr(,"intercept") 297s [1] 1 297s attr(,"response") 297s [1] 1 297s attr(,".Environment") 297s 297s attr(,"predvars") 297s list(consump, price, income) 297s attr(,"dataClasses") 297s consump price income 297s "numeric" "numeric" "numeric" 297s 297s $supply 297s consump ~ price + farmPrice + trend 297s attr(,"variables") 297s list(consump, price, farmPrice, trend) 297s attr(,"factors") 297s price farmPrice trend 297s consump 0 0 0 297s price 1 0 0 297s farmPrice 0 1 0 297s trend 0 0 1 297s attr(,"term.labels") 297s [1] "price" "farmPrice" "trend" 297s attr(,"order") 297s [1] 1 1 1 297s attr(,"intercept") 297s [1] 1 297s attr(,"response") 297s [1] 1 297s attr(,".Environment") 297s 297s attr(,"predvars") 297s list(consump, price, farmPrice, trend) 297s attr(,"dataClasses") 297s consump price farmPrice trend 297s "numeric" "numeric" "numeric" "numeric" 297s 297s > terms( fit2sls3$eq[[ 1 ]] ) 297s consump ~ price + income 297s attr(,"variables") 297s list(consump, price, income) 297s attr(,"factors") 297s price income 297s consump 0 0 297s price 1 0 297s income 0 1 297s attr(,"term.labels") 297s [1] "price" "income" 297s attr(,"order") 297s [1] 1 1 297s attr(,"intercept") 297s [1] 1 297s attr(,"response") 297s [1] 1 297s attr(,".Environment") 297s 297s attr(,"predvars") 297s list(consump, price, income) 297s attr(,"dataClasses") 297s consump price income 297s "numeric" "numeric" "numeric" 297s > 297s > terms( fit2sls4r ) 297s $demand 297s consump ~ price + income 297s attr(,"variables") 297s list(consump, price, income) 297s attr(,"factors") 297s price income 297s consump 0 0 297s price 1 0 297s income 0 1 297s attr(,"term.labels") 297s [1] "price" "income" 297s attr(,"order") 297s [1] 1 1 297s attr(,"intercept") 297s [1] 1 297s attr(,"response") 297s [1] 1 297s attr(,".Environment") 297s 297s attr(,"predvars") 297s list(consump, price, income) 297s attr(,"dataClasses") 297s consump price income 297s "numeric" "numeric" "numeric" 297s 297s $supply 297s consump ~ price + farmPrice + trend 297s attr(,"variables") 297s list(consump, price, farmPrice, trend) 297s attr(,"factors") 297s price farmPrice trend 297s consump 0 0 0 297s price 1 0 0 297s farmPrice 0 1 0 297s trend 0 0 1 297s attr(,"term.labels") 297s [1] "price" "farmPrice" "trend" 297s attr(,"order") 297s [1] 1 1 1 297s attr(,"intercept") 297s [1] 1 297s attr(,"response") 297s [1] 1 297s attr(,".Environment") 297s 297s attr(,"predvars") 297s list(consump, price, farmPrice, trend) 297s attr(,"dataClasses") 297s consump price farmPrice trend 297s "numeric" "numeric" "numeric" "numeric" 297s 297s > terms( fit2sls4r$eq[[ 2 ]] ) 297s consump ~ price + farmPrice + trend 297s attr(,"variables") 297s list(consump, price, farmPrice, trend) 297s attr(,"factors") 297s price farmPrice trend 297s consump 0 0 0 297s price 1 0 0 297s farmPrice 0 1 0 297s trend 0 0 1 297s attr(,"term.labels") 297s [1] "price" "farmPrice" "trend" 297s attr(,"order") 297s [1] 1 1 1 297s attr(,"intercept") 297s [1] 1 297s attr(,"response") 297s [1] 1 297s attr(,".Environment") 297s 297s attr(,"predvars") 297s list(consump, price, farmPrice, trend) 297s attr(,"dataClasses") 297s consump price farmPrice trend 297s "numeric" "numeric" "numeric" "numeric" 297s > 297s > terms( fit2sls5rs ) 297s $demand 297s consump ~ price + income 297s attr(,"variables") 297s list(consump, price, income) 297s attr(,"factors") 297s price income 297s consump 0 0 297s price 1 0 297s income 0 1 297s attr(,"term.labels") 297s [1] "price" "income" 297s attr(,"order") 297s [1] 1 1 297s attr(,"intercept") 297s [1] 1 297s attr(,"response") 297s [1] 1 297s attr(,".Environment") 297s 297s attr(,"predvars") 297s list(consump, price, income) 297s attr(,"dataClasses") 297s consump price income 297s "numeric" "numeric" "numeric" 297s 297s $supply 297s consump ~ price + farmPrice + trend 297s attr(,"variables") 297s list(consump, price, farmPrice, trend) 297s attr(,"factors") 297s price farmPrice trend 297s consump 0 0 0 297s price 1 0 0 297s farmPrice 0 1 0 297s trend 0 0 1 297s attr(,"term.labels") 297s [1] "price" "farmPrice" "trend" 297s attr(,"order") 297s [1] 1 1 1 297s attr(,"intercept") 297s [1] 1 297s attr(,"response") 297s [1] 1 297s attr(,".Environment") 297s 297s attr(,"predvars") 297s list(consump, price, farmPrice, trend) 297s attr(,"dataClasses") 297s consump price farmPrice trend 297s "numeric" "numeric" "numeric" "numeric" 297s 297s > terms( fit2sls5rs$eq[[ 1 ]] ) 297s consump ~ price + income 297s attr(,"variables") 297s list(consump, price, income) 297s attr(,"factors") 297s price income 297s consump 0 0 297s price 1 0 297s income 0 1 297s attr(,"term.labels") 297s [1] "price" "income" 297s attr(,"order") 297s [1] 1 1 297s attr(,"intercept") 297s [1] 1 297s attr(,"response") 297s [1] 1 297s attr(,".Environment") 297s 297s attr(,"predvars") 297s list(consump, price, income) 297s attr(,"dataClasses") 297s consump price income 297s "numeric" "numeric" "numeric" 297s > 297s > terms( fit2slsd1 ) 297s $demand 297s consump ~ price + income 297s attr(,"variables") 297s list(consump, price, income) 297s attr(,"factors") 297s price income 297s consump 0 0 297s price 1 0 297s income 0 1 297s attr(,"term.labels") 297s [1] "price" "income" 297s attr(,"order") 297s [1] 1 1 297s attr(,"intercept") 297s [1] 1 297s attr(,"response") 297s [1] 1 297s attr(,".Environment") 297s 297s attr(,"predvars") 297s list(consump, price, income) 297s attr(,"dataClasses") 297s consump price income 297s "numeric" "numeric" "numeric" 297s 297s $supply 297s consump ~ price + farmPrice + trend 297s attr(,"variables") 297s list(consump, price, farmPrice, trend) 297s attr(,"factors") 297s price farmPrice trend 297s consump 0 0 0 297s price 1 0 0 297s farmPrice 0 1 0 297s trend 0 0 1 297s attr(,"term.labels") 297s [1] "price" "farmPrice" "trend" 297s attr(,"order") 297s [1] 1 1 1 297s attr(,"intercept") 297s [1] 1 297s attr(,"response") 297s [1] 1 297s attr(,".Environment") 297s 297s attr(,"predvars") 297s list(consump, price, farmPrice, trend) 297s attr(,"dataClasses") 297s consump price farmPrice trend 297s "numeric" "numeric" "numeric" "numeric" 297s 297s > terms( fit2slsd1$eq[[ 2 ]] ) 297s consump ~ price + farmPrice + trend 297s attr(,"variables") 297s list(consump, price, farmPrice, trend) 297s attr(,"factors") 297s price farmPrice trend 297s consump 0 0 0 297s price 1 0 0 297s farmPrice 0 1 0 297s trend 0 0 1 297s attr(,"term.labels") 297s [1] "price" "farmPrice" "trend" 297s attr(,"order") 297s [1] 1 1 1 297s attr(,"intercept") 297s [1] 1 297s attr(,"response") 297s [1] 1 297s attr(,".Environment") 297s 297s attr(,"predvars") 297s list(consump, price, farmPrice, trend) 297s attr(,"dataClasses") 297s consump price farmPrice trend 297s "numeric" "numeric" "numeric" "numeric" 297s > 297s > terms( fit2slsd2r ) 297s $demand 297s consump ~ price + income 297s attr(,"variables") 297s list(consump, price, income) 297s attr(,"factors") 297s price income 297s consump 0 0 297s price 1 0 297s income 0 1 297s attr(,"term.labels") 297s [1] "price" "income" 297s attr(,"order") 297s [1] 1 1 297s attr(,"intercept") 297s [1] 1 297s attr(,"response") 297s [1] 1 297s attr(,".Environment") 297s 297s attr(,"predvars") 297s list(consump, price, income) 297s attr(,"dataClasses") 297s consump price income 297s "numeric" "numeric" "numeric" 297s 297s $supply 297s consump ~ price + farmPrice + trend 297s attr(,"variables") 297s list(consump, price, farmPrice, trend) 297s attr(,"factors") 297s price farmPrice trend 297s consump 0 0 0 297s price 1 0 0 297s farmPrice 0 1 0 297s trend 0 0 1 297s attr(,"term.labels") 297s [1] "price" "farmPrice" "trend" 297s attr(,"order") 297s [1] 1 1 1 297s attr(,"intercept") 297s [1] 1 297s attr(,"response") 297s [1] 1 297s attr(,".Environment") 297s 297s attr(,"predvars") 297s list(consump, price, farmPrice, trend) 297s attr(,"dataClasses") 297s consump price farmPrice trend 297s "numeric" "numeric" "numeric" "numeric" 297s 297s > terms( fit2slsd2r$eq[[ 1 ]] ) 297s consump ~ price + income 297s attr(,"variables") 297s list(consump, price, income) 297s attr(,"factors") 297s price income 297s consump 0 0 297s price 1 0 297s income 0 1 297s attr(,"term.labels") 297s [1] "price" "income" 297s attr(,"order") 297s [1] 1 1 297s attr(,"intercept") 297s [1] 1 297s attr(,"response") 297s [1] 1 297s attr(,".Environment") 297s 297s attr(,"predvars") 297s list(consump, price, income) 297s attr(,"dataClasses") 297s consump price income 297s "numeric" "numeric" "numeric" 297s > 297s > 297s > ## **************** terms of instruments ******************* 297s > fit2sls1$eq[[ 1 ]]$termsInst 297s ~income + farmPrice + trend 297s attr(,"variables") 297s list(income, farmPrice, trend) 297s attr(,"factors") 297s income farmPrice trend 297s income 1 0 0 297s farmPrice 0 1 0 297s trend 0 0 1 297s attr(,"term.labels") 297s [1] "income" "farmPrice" "trend" 297s attr(,"order") 297s [1] 1 1 1 297s attr(,"intercept") 297s [1] 1 297s attr(,"response") 297s [1] 0 297s attr(,".Environment") 297s 297s attr(,"predvars") 297s list(income, farmPrice, trend) 297s attr(,"dataClasses") 297s income farmPrice trend 297s "numeric" "numeric" "numeric" 297s > 297s > fit2sls2s$eq[[ 2 ]]$termsInst 297s ~income + farmPrice + trend 297s attr(,"variables") 297s list(income, farmPrice, trend) 297s attr(,"factors") 297s income farmPrice trend 297s income 1 0 0 297s farmPrice 0 1 0 297s trend 0 0 1 297s attr(,"term.labels") 297s [1] "income" "farmPrice" "trend" 297s attr(,"order") 297s [1] 1 1 1 297s attr(,"intercept") 297s [1] 1 297s attr(,"response") 297s [1] 0 297s attr(,".Environment") 297s 297s attr(,"predvars") 297s list(income, farmPrice, trend) 297s attr(,"dataClasses") 297s income farmPrice trend 297s "numeric" "numeric" "numeric" 297s > 297s > fit2sls3$eq[[ 1 ]]$termsInst 297s ~income + farmPrice + trend 297s attr(,"variables") 297s list(income, farmPrice, trend) 297s attr(,"factors") 297s income farmPrice trend 297s income 1 0 0 297s farmPrice 0 1 0 297s trend 0 0 1 297s attr(,"term.labels") 297s [1] "income" "farmPrice" "trend" 297s attr(,"order") 297s [1] 1 1 1 297s attr(,"intercept") 297s [1] 1 297s attr(,"response") 297s [1] 0 297s attr(,".Environment") 297s 297s attr(,"predvars") 297s list(income, farmPrice, trend) 297s attr(,"dataClasses") 297s income farmPrice trend 297s "numeric" "numeric" "numeric" 297s > 297s > fit2sls4r$eq[[ 2 ]]$termsInst 297s ~income + farmPrice + trend 297s attr(,"variables") 297s list(income, farmPrice, trend) 297s attr(,"factors") 297s income farmPrice trend 297s income 1 0 0 297s farmPrice 0 1 0 297s trend 0 0 1 297s attr(,"term.labels") 297s [1] "income" "farmPrice" "trend" 297s attr(,"order") 297s [1] 1 1 1 297s attr(,"intercept") 297s [1] 1 297s attr(,"response") 297s [1] 0 297s attr(,".Environment") 297s 297s attr(,"predvars") 297s list(income, farmPrice, trend) 297s attr(,"dataClasses") 297s income farmPrice trend 297s "numeric" "numeric" "numeric" 297s > 297s > fit2sls5rs$eq[[ 1 ]]$termsInst 297s ~income + farmPrice + trend 297s attr(,"variables") 297s list(income, farmPrice, trend) 297s attr(,"factors") 297s income farmPrice trend 297s income 1 0 0 297s farmPrice 0 1 0 297s trend 0 0 1 297s attr(,"term.labels") 297s [1] "income" "farmPrice" "trend" 297s attr(,"order") 297s [1] 1 1 1 297s attr(,"intercept") 297s [1] 1 297s attr(,"response") 297s [1] 0 297s attr(,".Environment") 297s 297s attr(,"predvars") 297s list(income, farmPrice, trend) 297s attr(,"dataClasses") 297s income farmPrice trend 297s "numeric" "numeric" "numeric" 297s > 297s > fit2slsd1$eq[[ 2 ]]$termsInst 297s ~income + farmPrice + trend 297s attr(,"variables") 297s list(income, farmPrice, trend) 297s attr(,"factors") 297s income farmPrice trend 297s income 1 0 0 297s farmPrice 0 1 0 297s trend 0 0 1 297s attr(,"term.labels") 297s [1] "income" "farmPrice" "trend" 297s attr(,"order") 297s [1] 1 1 1 297s attr(,"intercept") 297s [1] 1 297s attr(,"response") 297s [1] 0 297s attr(,".Environment") 297s 297s attr(,"predvars") 297s list(income, farmPrice, trend) 297s attr(,"dataClasses") 297s income farmPrice trend 297s "numeric" "numeric" "numeric" 297s > 297s > fit2slsd2r$eq[[ 1 ]]$termsInst 297s ~income + farmPrice 297s attr(,"variables") 297s list(income, farmPrice) 297s attr(,"factors") 297s income farmPrice 297s income 1 0 297s farmPrice 0 1 297s attr(,"term.labels") 297s [1] "income" "farmPrice" 297s attr(,"order") 297s [1] 1 1 297s attr(,"intercept") 297s [1] 1 297s attr(,"response") 297s [1] 0 297s attr(,".Environment") 297s 297s attr(,"predvars") 297s list(income, farmPrice) 297s attr(,"dataClasses") 297s income farmPrice 297s "numeric" "numeric" 297s > 297s > 297s > ## **************** estfun ************************ 297s > library( "sandwich" ) 297s > 297s > estfun( fit2sls1 ) 297s demand_(Intercept) demand_price demand_income supply_(Intercept) 297s demand_1 0.6738 67.13 58.89 0.000 297s demand_2 -0.4897 -51.48 -47.80 0.000 297s demand_3 2.4440 253.65 236.33 0.000 297s demand_4 1.4958 156.35 146.88 0.000 297s demand_5 2.2975 226.65 229.29 0.000 297s demand_6 1.3235 131.89 133.02 0.000 297s demand_7 1.7917 182.70 184.90 0.000 297s demand_8 -3.6818 -376.41 -396.90 0.000 297s demand_9 -1.5729 -148.80 -151.94 0.000 297s demand_10 2.8552 264.73 253.83 0.000 297s demand_11 -0.2736 -25.29 -20.55 0.000 297s demand_12 -2.2634 -223.89 -174.06 0.000 297s demand_13 -1.7795 -181.80 -150.55 0.000 297s demand_14 0.0991 9.93 8.98 0.000 297s demand_15 2.5674 250.64 264.70 0.000 297s demand_16 -3.8102 -369.18 -400.45 0.000 297s demand_17 -0.0206 -1.81 -1.99 0.000 297s demand_18 -2.8715 -290.19 -299.78 0.000 297s demand_19 1.6632 176.41 184.12 0.000 297s demand_20 -0.4478 -51.23 -56.92 0.000 297s supply_1 0.0000 0.00 0.00 -0.268 297s supply_2 0.0000 0.00 0.00 -1.418 297s supply_3 0.0000 0.00 0.00 1.625 297s supply_4 0.0000 0.00 0.00 0.790 297s supply_5 0.0000 0.00 0.00 1.438 297s supply_6 0.0000 0.00 0.00 0.613 297s supply_7 0.0000 0.00 0.00 1.217 297s supply_8 0.0000 0.00 0.00 -4.265 297s supply_9 0.0000 0.00 0.00 -1.956 297s supply_10 0.0000 0.00 0.00 2.785 297s supply_11 0.0000 0.00 0.00 0.233 297s supply_12 0.0000 0.00 0.00 -1.426 297s supply_13 0.0000 0.00 0.00 -0.935 297s supply_14 0.0000 0.00 0.00 0.803 297s supply_15 0.0000 0.00 0.00 2.886 297s supply_16 0.0000 0.00 0.00 -3.454 297s supply_17 0.0000 0.00 0.00 0.391 297s supply_18 0.0000 0.00 0.00 -2.061 297s supply_19 0.0000 0.00 0.00 2.596 297s supply_20 0.0000 0.00 0.00 0.406 297s supply_price supply_farmPrice supply_trend 297s demand_1 0.0 0.0 0.000 297s demand_2 0.0 0.0 0.000 297s demand_3 0.0 0.0 0.000 297s demand_4 0.0 0.0 0.000 297s demand_5 0.0 0.0 0.000 297s demand_6 0.0 0.0 0.000 297s demand_7 0.0 0.0 0.000 297s demand_8 0.0 0.0 0.000 297s demand_9 0.0 0.0 0.000 297s demand_10 0.0 0.0 0.000 297s demand_11 0.0 0.0 0.000 297s demand_12 0.0 0.0 0.000 297s demand_13 0.0 0.0 0.000 297s demand_14 0.0 0.0 0.000 297s demand_15 0.0 0.0 0.000 297s demand_16 0.0 0.0 0.000 297s demand_17 0.0 0.0 0.000 297s demand_18 0.0 0.0 0.000 297s demand_19 0.0 0.0 0.000 297s demand_20 0.0 0.0 0.000 297s supply_1 -26.7 -26.3 -0.268 297s supply_2 -149.1 -140.5 -2.836 297s supply_3 168.7 161.1 4.876 297s supply_4 82.6 77.5 3.159 297s supply_5 141.9 159.3 7.190 297s supply_6 61.1 66.4 3.680 297s supply_7 124.1 128.5 8.520 297s supply_8 -436.1 -468.3 -34.122 297s supply_9 -185.0 -212.6 -17.602 297s supply_10 258.2 280.1 27.848 297s supply_11 21.5 18.8 2.558 297s supply_12 -141.0 -97.8 -17.107 297s supply_13 -95.5 -66.3 -12.152 297s supply_14 80.6 65.4 11.246 297s supply_15 281.7 295.2 43.286 297s supply_16 -334.7 -362.7 -55.267 297s supply_17 34.3 43.2 6.650 297s supply_18 -208.3 -190.7 -37.106 297s supply_19 275.4 231.8 49.327 297s supply_20 46.5 37.8 8.122 297s > round( colSums( estfun( fit2sls1 ) ), digits = 7 ) 297s demand_(Intercept) demand_price demand_income supply_(Intercept) 297s 0 0 0 0 297s supply_price supply_farmPrice supply_trend 297s 0 0 0 297s > 297s > estfun( fit2sls1s ) 297s demand_(Intercept) demand_price demand_income supply_(Intercept) 297s demand_1 0.6738 67.13 58.89 0.000 297s demand_2 -0.4897 -51.48 -47.80 0.000 297s demand_3 2.4440 253.65 236.33 0.000 297s demand_4 1.4958 156.35 146.88 0.000 297s demand_5 2.2975 226.65 229.29 0.000 297s demand_6 1.3235 131.89 133.02 0.000 297s demand_7 1.7917 182.70 184.90 0.000 297s demand_8 -3.6818 -376.41 -396.90 0.000 297s demand_9 -1.5729 -148.80 -151.94 0.000 297s demand_10 2.8552 264.73 253.83 0.000 297s demand_11 -0.2736 -25.29 -20.55 0.000 297s demand_12 -2.2634 -223.89 -174.06 0.000 297s demand_13 -1.7795 -181.80 -150.55 0.000 297s demand_14 0.0991 9.93 8.98 0.000 297s demand_15 2.5674 250.64 264.70 0.000 297s demand_16 -3.8102 -369.18 -400.45 0.000 297s demand_17 -0.0206 -1.81 -1.99 0.000 297s demand_18 -2.8715 -290.19 -299.78 0.000 297s demand_19 1.6632 176.41 184.12 0.000 297s demand_20 -0.4478 -51.23 -56.92 0.000 297s supply_1 0.0000 0.00 0.00 -0.268 297s supply_2 0.0000 0.00 0.00 -1.418 297s supply_3 0.0000 0.00 0.00 1.625 297s supply_4 0.0000 0.00 0.00 0.790 297s supply_5 0.0000 0.00 0.00 1.438 297s supply_6 0.0000 0.00 0.00 0.613 297s supply_7 0.0000 0.00 0.00 1.217 297s supply_8 0.0000 0.00 0.00 -4.265 297s supply_9 0.0000 0.00 0.00 -1.956 297s supply_10 0.0000 0.00 0.00 2.785 297s supply_11 0.0000 0.00 0.00 0.233 297s supply_12 0.0000 0.00 0.00 -1.426 297s supply_13 0.0000 0.00 0.00 -0.935 297s supply_14 0.0000 0.00 0.00 0.803 297s supply_15 0.0000 0.00 0.00 2.886 297s supply_16 0.0000 0.00 0.00 -3.454 297s supply_17 0.0000 0.00 0.00 0.391 297s supply_18 0.0000 0.00 0.00 -2.061 297s supply_19 0.0000 0.00 0.00 2.596 297s supply_20 0.0000 0.00 0.00 0.406 297s supply_price supply_farmPrice supply_trend 297s demand_1 0.0 0.0 0.000 297s demand_2 0.0 0.0 0.000 297s demand_3 0.0 0.0 0.000 297s demand_4 0.0 0.0 0.000 297s demand_5 0.0 0.0 0.000 297s demand_6 0.0 0.0 0.000 297s demand_7 0.0 0.0 0.000 297s demand_8 0.0 0.0 0.000 297s demand_9 0.0 0.0 0.000 297s demand_10 0.0 0.0 0.000 297s demand_11 0.0 0.0 0.000 297s demand_12 0.0 0.0 0.000 297s demand_13 0.0 0.0 0.000 297s demand_14 0.0 0.0 0.000 297s demand_15 0.0 0.0 0.000 297s demand_16 0.0 0.0 0.000 297s demand_17 0.0 0.0 0.000 297s demand_18 0.0 0.0 0.000 297s demand_19 0.0 0.0 0.000 297s demand_20 0.0 0.0 0.000 297s supply_1 -26.7 -26.3 -0.268 297s supply_2 -149.1 -140.5 -2.836 297s supply_3 168.7 161.1 4.876 297s supply_4 82.6 77.5 3.159 297s supply_5 141.9 159.3 7.190 297s supply_6 61.1 66.4 3.680 297s supply_7 124.1 128.5 8.520 297s supply_8 -436.1 -468.3 -34.122 297s supply_9 -185.0 -212.6 -17.602 297s supply_10 258.2 280.1 27.848 297s supply_11 21.5 18.8 2.558 297s supply_12 -141.0 -97.8 -17.107 297s supply_13 -95.5 -66.3 -12.152 297s supply_14 80.6 65.4 11.246 297s supply_15 281.7 295.2 43.286 297s supply_16 -334.7 -362.7 -55.267 297s supply_17 34.3 43.2 6.650 297s supply_18 -208.3 -190.7 -37.106 297s supply_19 275.4 231.8 49.327 297s supply_20 46.5 37.8 8.122 297s > round( colSums( estfun( fit2sls1s ) ), digits = 7 ) 297s demand_(Intercept) demand_price demand_income supply_(Intercept) 297s 0 0 0 0 297s supply_price supply_farmPrice supply_trend 297s 0 0 0 297s > 297s > estfun( fit2sls1r ) 297s demand_(Intercept) demand_price demand_income supply_(Intercept) 297s demand_1 0.6738 67.13 58.89 0.000 297s demand_2 -0.4897 -51.48 -47.80 0.000 297s demand_3 2.4440 253.65 236.33 0.000 297s demand_4 1.4958 156.35 146.88 0.000 297s demand_5 2.2975 226.65 229.29 0.000 297s demand_6 1.3235 131.89 133.02 0.000 297s demand_7 1.7917 182.70 184.90 0.000 297s demand_8 -3.6818 -376.41 -396.90 0.000 297s demand_9 -1.5729 -148.80 -151.94 0.000 297s demand_10 2.8552 264.73 253.83 0.000 297s demand_11 -0.2736 -25.29 -20.55 0.000 297s demand_12 -2.2634 -223.89 -174.06 0.000 297s demand_13 -1.7795 -181.80 -150.55 0.000 297s demand_14 0.0991 9.93 8.98 0.000 297s demand_15 2.5674 250.64 264.70 0.000 297s demand_16 -3.8102 -369.18 -400.45 0.000 297s demand_17 -0.0206 -1.81 -1.99 0.000 297s demand_18 -2.8715 -290.19 -299.78 0.000 297s demand_19 1.6632 176.41 184.12 0.000 297s demand_20 -0.4478 -51.23 -56.92 0.000 297s supply_1 0.0000 0.00 0.00 -0.268 297s supply_2 0.0000 0.00 0.00 -1.418 297s supply_3 0.0000 0.00 0.00 1.625 297s supply_4 0.0000 0.00 0.00 0.790 297s supply_5 0.0000 0.00 0.00 1.438 297s supply_6 0.0000 0.00 0.00 0.613 297s supply_7 0.0000 0.00 0.00 1.217 297s supply_8 0.0000 0.00 0.00 -4.265 297s supply_9 0.0000 0.00 0.00 -1.956 297s supply_10 0.0000 0.00 0.00 2.785 297s supply_11 0.0000 0.00 0.00 0.233 297s supply_12 0.0000 0.00 0.00 -1.426 297s supply_13 0.0000 0.00 0.00 -0.935 297s supply_14 0.0000 0.00 0.00 0.803 297s supply_15 0.0000 0.00 0.00 2.886 297s supply_16 0.0000 0.00 0.00 -3.454 297s supply_17 0.0000 0.00 0.00 0.391 297s supply_18 0.0000 0.00 0.00 -2.061 297s supply_19 0.0000 0.00 0.00 2.596 297s supply_20 0.0000 0.00 0.00 0.406 297s supply_price supply_farmPrice supply_trend 297s demand_1 0.0 0.0 0.000 297s demand_2 0.0 0.0 0.000 297s demand_3 0.0 0.0 0.000 297s demand_4 0.0 0.0 0.000 297s demand_5 0.0 0.0 0.000 297s demand_6 0.0 0.0 0.000 297s demand_7 0.0 0.0 0.000 297s demand_8 0.0 0.0 0.000 297s demand_9 0.0 0.0 0.000 297s demand_10 0.0 0.0 0.000 297s demand_11 0.0 0.0 0.000 297s demand_12 0.0 0.0 0.000 297s demand_13 0.0 0.0 0.000 297s demand_14 0.0 0.0 0.000 297s demand_15 0.0 0.0 0.000 297s demand_16 0.0 0.0 0.000 297s demand_17 0.0 0.0 0.000 297s demand_18 0.0 0.0 0.000 297s demand_19 0.0 0.0 0.000 297s demand_20 0.0 0.0 0.000 297s supply_1 -26.7 -26.3 -0.268 297s supply_2 -149.1 -140.5 -2.836 297s supply_3 168.7 161.1 4.876 297s supply_4 82.6 77.5 3.159 297s supply_5 141.9 159.3 7.190 297s supply_6 61.1 66.4 3.680 297s supply_7 124.1 128.5 8.520 297s supply_8 -436.1 -468.3 -34.122 297s supply_9 -185.0 -212.6 -17.602 297s supply_10 258.2 280.1 27.848 297s supply_11 21.5 18.8 2.558 297s supply_12 -141.0 -97.8 -17.107 297s supply_13 -95.5 -66.3 -12.152 297s supply_14 80.6 65.4 11.246 297s supply_15 281.7 295.2 43.286 297s supply_16 -334.7 -362.7 -55.267 297s supply_17 34.3 43.2 6.650 297s supply_18 -208.3 -190.7 -37.106 297s supply_19 275.4 231.8 49.327 297s supply_20 46.5 37.8 8.122 297s > round( colSums( estfun( fit2sls1r ) ), digits = 7 ) 297s demand_(Intercept) demand_price demand_income supply_(Intercept) 297s 0 0 0 0 297s supply_price supply_farmPrice supply_trend 297s 0 0 0 297s > 297s > 297s > ## **************** bread ************************ 297s > bread( fit2sls1 ) 297s demand_(Intercept) demand_price demand_income 297s demand_(Intercept) 649.07 -6.9669 0.5100 297s demand_price -6.97 0.0963 -0.0273 297s demand_income 0.51 -0.0273 0.0228 297s supply_(Intercept) 0.00 0.0000 0.0000 297s supply_price 0.00 0.0000 0.0000 297s supply_farmPrice 0.00 0.0000 0.0000 297s supply_trend 0.00 0.0000 0.0000 297s supply_(Intercept) supply_price supply_farmPrice 297s demand_(Intercept) 0.00 0.00000 0.00000 297s demand_price 0.00 0.00000 0.00000 297s demand_income 0.00 0.00000 0.00000 297s supply_(Intercept) 955.38 -7.25488 -2.14464 297s supply_price -7.25 0.06614 0.00620 297s supply_farmPrice -2.14 0.00620 0.01479 297s supply_trend -1.96 0.00384 0.00912 297s supply_trend 297s demand_(Intercept) 0.00000 297s demand_price 0.00000 297s demand_income 0.00000 297s supply_(Intercept) -1.95529 297s supply_price 0.00384 297s supply_farmPrice 0.00912 297s supply_trend 0.06577 297s > 297s > bread( fit2sls1s ) 297s demand_(Intercept) demand_price demand_income 297s demand_(Intercept) 649.07 -6.9669 0.5100 297s demand_price -6.97 0.0963 -0.0273 297s demand_income 0.51 -0.0273 0.0228 297s supply_(Intercept) 0.00 0.0000 0.0000 297s supply_price 0.00 0.0000 0.0000 297s supply_farmPrice 0.00 0.0000 0.0000 297s supply_trend 0.00 0.0000 0.0000 297s supply_(Intercept) supply_price supply_farmPrice 297s demand_(Intercept) 0.00 0.00000 0.00000 297s demand_price 0.00 0.00000 0.00000 297s demand_income 0.00 0.00000 0.00000 297s supply_(Intercept) 955.38 -7.25488 -2.14464 297s supply_price -7.25 0.06614 0.00620 297s supply_farmPrice -2.14 0.00620 0.01479 297s supply_trend -1.96 0.00384 0.00912 297s supply_trend 297s demand_(Intercept) 0.00000 297s demand_price 0.00000 297s demand_income 0.00000 297s supply_(Intercept) -1.95529 297s supply_price 0.00384 297s supply_farmPrice 0.00912 297s supply_trend 0.06577 297s > 297s > bread( fit2sls1r ) 297s demand_(Intercept) demand_price demand_income 297s demand_(Intercept) 649.07 -6.9669 0.5100 297s demand_price -6.97 0.0963 -0.0273 297s demand_income 0.51 -0.0273 0.0228 297s supply_(Intercept) 0.00 0.0000 0.0000 297s supply_price 0.00 0.0000 0.0000 297s supply_farmPrice 0.00 0.0000 0.0000 297s supply_trend 0.00 0.0000 0.0000 297s supply_(Intercept) supply_price supply_farmPrice 297s demand_(Intercept) 0.00 0.00000 0.00000 297s demand_price 0.00 0.00000 0.00000 297s demand_income 0.00 0.00000 0.00000 297s supply_(Intercept) 955.38 -7.25488 -2.14464 297s supply_price -7.25 0.06614 0.00620 297s supply_farmPrice -2.14 0.00620 0.01479 297s supply_trend -1.96 0.00384 0.00912 297s supply_trend 297s demand_(Intercept) 0.00000 297s demand_price 0.00000 297s demand_income 0.00000 297s supply_(Intercept) -1.95529 297s supply_price 0.00384 297s supply_farmPrice 0.00912 297s supply_trend 0.06577 297s > 297s BEGIN TEST test_3sls.R 297s 297s R version 4.3.3 (2024-02-29) -- "Angel Food Cake" 297s Copyright (C) 2024 The R Foundation for Statistical Computing 297s Platform: arm-unknown-linux-gnueabihf (32-bit) 297s 297s R is free software and comes with ABSOLUTELY NO WARRANTY. 297s You are welcome to redistribute it under certain conditions. 297s Type 'license()' or 'licence()' for distribution details. 297s 297s R is a collaborative project with many contributors. 297s Type 'contributors()' for more information and 297s 'citation()' on how to cite R or R packages in publications. 297s 297s Type 'demo()' for some demos, 'help()' for on-line help, or 297s 'help.start()' for an HTML browser interface to help. 297s Type 'q()' to quit R. 297s 297s > library( systemfit ) 297s Loading required package: Matrix 298s Loading required package: car 298s Loading required package: carData 298s Loading required package: lmtest 298s Loading required package: zoo 298s 298s Attaching package: ‘zoo’ 298s 298s The following objects are masked from ‘package:base’: 298s 298s as.Date, as.Date.numeric 298s 298s 298s Please cite the 'systemfit' package as: 298s Arne Henningsen and Jeff D. Hamann (2007). systemfit: A Package for Estimating Systems of Simultaneous Equations in R. Journal of Statistical Software 23(4), 1-40. http://www.jstatsoft.org/v23/i04/. 298s 298s If you have questions, suggestions, or comments regarding the 'systemfit' package, please use a forum or 'tracker' at systemfit's R-Forge site: 298s https://r-forge.r-project.org/projects/systemfit/ 298s > options( digits = 3 ) 298s > 298s > data( "Kmenta" ) 298s > useMatrix <- FALSE 298s > 298s > demand <- consump ~ price + income 298s > supply <- consump ~ price + farmPrice + trend 298s > inst <- ~ income + farmPrice + trend 298s > inst1 <- ~ income + farmPrice 298s > instlist <- list( inst1, inst ) 298s > system <- list( demand = demand, supply = supply ) 298s > restrm <- matrix(0,1,7) # restriction matrix "R" 298s > restrm[1,3] <- 1 298s > restrm[1,7] <- -1 298s > restrict <- "demand_income - supply_trend = 0" 298s > restr2m <- matrix(0,2,7) # restriction matrix "R" 2 298s > restr2m[1,3] <- 1 298s > restr2m[1,7] <- -1 298s > restr2m[2,2] <- -1 298s > restr2m[2,5] <- 1 298s > restr2q <- c( 0, 0.5 ) # restriction vector "q" 2 298s > restrict2 <- c( "demand_income - supply_trend = 0", 298s + "- demand_price + supply_price = 0.5" ) 298s > tc <- matrix(0,7,6) 298s > tc[1,1] <- 1 298s > tc[2,2] <- 1 298s > tc[3,3] <- 1 298s > tc[4,4] <- 1 298s > tc[5,5] <- 1 298s > tc[6,6] <- 1 298s > tc[7,3] <- 1 298s > restr3m <- matrix(0,1,6) # restriction matrix "R" 2 298s > restr3m[1,2] <- -1 298s > restr3m[1,5] <- 1 298s > restr3q <- c( 0.5 ) # restriction vector "q" 2 298s > restrict3 <- "- C2 + C5 = 0.5" 298s > 298s > 298s > ## *************** 3SLS estimation ************************ 298s > fit3sls <- list() 298s > formulas <- c( "GLS", "IV", "Schmidt", "GMM", "EViews" ) 298s > for( i in seq( along = formulas ) ) { 298s + fit3sls[[ i ]] <- list() 298s + 298s + print( "***************************************************" ) 298s + print( paste( "3SLS formula:", formulas[ i ] ) ) 298s + print( "************* 3SLS *********************************" ) 298s + fit3sls[[ i ]]$e1 <- systemfit( system, "3SLS", data = Kmenta, 298s + inst = inst, method3sls = formulas[ i ], useMatrix = useMatrix ) 298s + print( summary( fit3sls[[ i ]]$e1 ) ) 298s + 298s + print( "********************* 3SLS EViews-like *****************" ) 298s + fit3sls[[ i ]]$e1e <- systemfit( system, "3SLS", data = Kmenta, 298s + inst = inst, methodResidCov = "noDfCor", method3sls = formulas[ i ], 298s + useMatrix = useMatrix ) 298s + print( summary( fit3sls[[ i ]]$e1e, useDfSys = TRUE ) ) 298s + 298s + print( "********************* 3SLS with methodResidCov = Theil *****************" ) 298s + fit3sls[[ i ]]$e1c <- systemfit( system, "3SLS", data = Kmenta, 298s + inst = inst, methodResidCov = "Theil", method3sls = formulas[ i ], 298s + x = TRUE, useMatrix = useMatrix ) 298s + print( summary( fit3sls[[ i ]]$e1c, useDfSys = TRUE ) ) 298s + 298s + print( "*************** W3SLS with methodResidCov = Theil *****************" ) 298s + fit3sls[[ i ]]$e1wc <- systemfit( system, "3SLS", data = Kmenta, 298s + inst = inst, methodResidCov = "Theil", method3sls = formulas[ i ], 298s + residCovWeighted = TRUE, useMatrix = useMatrix ) 298s + print( summary( fit3sls[[ i ]]$e1wc, useDfSys = TRUE ) ) 298s + 298s + 298s + print( "*************** 3SLS with restriction *****************" ) 298s + fit3sls[[ i ]]$e2 <- systemfit( system, "3SLS", data = Kmenta, 298s + inst = inst, restrict.matrix = restrm, method3sls = formulas[ i ], 298s + x = TRUE, useMatrix = useMatrix ) 298s + print( summary( fit3sls[[ i ]]$e2 ) ) 298s + # the same with symbolically specified restrictions 298s + fit3sls[[ i ]]$e2Sym <- systemfit( system, "3SLS", data = Kmenta, 298s + inst = inst, restrict.matrix = restrict, method3sls = formulas[ i ], 298s + x = TRUE, useMatrix = useMatrix ) 298s + print( all.equal( fit3sls[[ i ]]$e2, fit3sls[[ i ]]$e2Sym ) ) 298s + 298s + print( "************** 3SLS with restriction (EViews-like) *****************" ) 298s + fit3sls[[ i ]]$e2e <- systemfit( system, "3SLS", data = Kmenta, 298s + inst = inst, methodResidCov = "noDfCor", restrict.matrix = restrm, 298s + method3sls = formulas[ i ], useMatrix = useMatrix ) 298s + print( summary( fit3sls[[ i ]]$e2e, useDfSys = TRUE ) ) 298s + print( nobs( fit3sls[[i]]$e2e )) 298s + 298s + print( "*************** W3SLS with restriction *****************" ) 298s + fit3sls[[ i ]]$e2w <- systemfit( system, "3SLS", data = Kmenta, 298s + inst = inst, restrict.matrix = restrm, method3sls = formulas[ i ], 298s + residCovWeighted = TRUE, useMatrix = useMatrix ) 298s + print( summary( fit3sls[[ i ]]$e2w ) ) 298s + 298s + 298s + print( "*************** 3SLS with restriction via restrict.regMat ********************" ) 298s + fit3sls[[ i ]]$e3 <- systemfit( system, "3SLS", data = Kmenta, 298s + inst = inst, restrict.regMat = tc, method3sls = formulas[ i ], 298s + useMatrix = useMatrix ) 298s + print( summary( fit3sls[[ i ]]$e3 ) ) 298s + 298s + print( "*************** 3SLS with restriction via restrict.regMat (EViews-like) *******" ) 298s + fit3sls[[ i ]]$e3e <- systemfit( system, "3SLS", data = Kmenta, 298s + inst = inst, methodResidCov = "noDfCor", restrict.regMat = tc, 298s + method3sls = formulas[ i ], x = TRUE, 298s + useMatrix = useMatrix ) 298s + print( summary( fit3sls[[ i ]]$e3e, useDfSys = TRUE ) ) 298s + 298s + print( "**** W3SLS with restriction via restrict.regMat (EViews-like) ****" ) 298s + fit3sls[[ i ]]$e3we <- systemfit( system, "3SLS", data = Kmenta, 298s + inst = inst, methodResidCov = "noDfCor", restrict.regMat = tc, 298s + method3sls = formulas[ i ], residCovWeighted = TRUE, 298s + useMatrix = useMatrix ) 298s + print( summary( fit3sls[[ i ]]$e3we, useDfSys = TRUE ) ) 298s + 298s + 298s + print( "*************** 3SLS with 2 restrictions **********************" ) 298s + fit3sls[[ i ]]$e4 <- systemfit( system, "3SLS", data = Kmenta, 298s + inst = inst, restrict.matrix = restr2m, restrict.rhs = restr2q, 298s + method3sls = formulas[ i ], x = TRUE, 298s + useMatrix = useMatrix ) 298s + print( summary( fit3sls[[ i ]]$e4 ) ) 298s + # the same with symbolically specified restrictions 298s + fit3sls[[ i ]]$e4Sym <- systemfit( system, "3SLS", data = Kmenta, 298s + inst = inst, restrict.matrix = restrict2, method3sls = formulas[ i ], 298s + x = TRUE, useMatrix = useMatrix ) 298s + print( all.equal( fit3sls[[ i ]]$e4, fit3sls[[ i ]]$e4Sym ) ) 298s + 298s + print( "*************** 3SLS with 2 restrictions (EViews-like) ************" ) 298s + fit3sls[[ i ]]$e4e <- systemfit( system, "3SLS", data = Kmenta, 298s + inst = inst, methodResidCov = "noDfCor", restrict.matrix = restr2m, 298s + restrict.rhs = restr2q, method3sls = formulas[ i ], 298s + useMatrix = useMatrix ) 298s + print( summary( fit3sls[[ i ]]$e4e, useDfSys = TRUE ) ) 298s + 298s + print( "********** W3SLS with 2 (symbolic) restrictions ***************" ) 298s + fit3sls[[ i ]]$e4wSym <- systemfit( system, "3SLS", data = Kmenta, 298s + inst = inst, restrict.matrix = restrict2, method3sls = formulas[ i ], 298s + residCovWeighted = TRUE, useMatrix = useMatrix ) 298s + print( summary( fit3sls[[ i ]]$e4wSym ) ) 298s + 298s + 298s + print( "*************** 3SLS with 2 restrictions via R and restrict.regMat **********" ) 298s + fit3sls[[ i ]]$e5 <- systemfit( system, "3SLS", data = Kmenta, 298s + inst = inst, restrict.regMat = tc, restrict.matrix = restr3m, 298s + restrict.rhs = restr3q, method3sls = formulas[ i ], 298s + useMatrix = useMatrix ) 298s + print( summary( fit3sls[[ i ]]$e5 ) ) 298s + # the same with symbolically specified restrictions 298s + fit3sls[[ i ]]$e5Sym <- systemfit( system, "3SLS", data = Kmenta, 298s + inst = inst, restrict.regMat = tc, restrict.matrix = restrict3, 298s + method3sls = formulas[ i ], useMatrix = useMatrix ) 298s + print( all.equal( fit3sls[[ i ]]$e5, fit3sls[[ i ]]$e5Sym ) ) 298s + 298s + print( "******** 3SLS with 2 restrictions via R and restrict.regMat (EViews-like)*****" ) 298s + fit3sls[[ i ]]$e5e <- systemfit( system, "3SLS", data = Kmenta, 298s + inst = inst, restrict.regMat = tc, methodResidCov = "noDfCor", 298s + restrict.matrix = restr3m, restrict.rhs = restr3q, 298s + method3sls = formulas[ i ], x = TRUE, 298s + useMatrix = useMatrix ) 298s + print( summary( fit3sls[[ i ]]$e5e, useDfSys = TRUE ) ) 298s + 298s + print( "*** W3SLS with 2 restrictions via R and restrict.regMat (EViews-like) ***" ) 298s + fit3sls[[ i ]]$e5we <- systemfit( system, "3SLS", data = Kmenta, 298s + inst = inst, restrict.regMat = tc, methodResidCov = "noDfCor", 298s + restrict.matrix = restr3m, restrict.rhs = restr3q, method3sls = formulas[ i ], 298s + residCovWeighted = TRUE, useMatrix = useMatrix ) 298s + print( summary( fit3sls[[ i ]]$e5we, useDfSys = TRUE ) ) 298s + 298s + ## *********** estimations with a single regressor ************ 298s + fit3sls[[ i ]]$S1 <- systemfit( 298s + list( farmPrice ~ consump - 1, price ~ consump + trend ), "3SLS", 298s + data = Kmenta, inst = ~ trend + income, useMatrix = useMatrix ) 298s + print( summary( fit3sls[[ i ]]$S1 ) ) 298s + fit3sls[[ i ]]$S2 <- systemfit( 298s + list( consump ~ farmPrice - 1, consump ~ trend - 1 ), "3SLS", 298s + data = Kmenta, inst = ~ price + income, useMatrix = useMatrix ) 298s + print( summary( fit3sls[[ i ]]$S2 ) ) 298s + fit3sls[[ i ]]$S3 <- systemfit( 298s + list( consump ~ trend - 1, farmPrice ~ trend - 1 ), "3SLS", 298s + data = Kmenta, inst = instlist, useMatrix = useMatrix ) 298s + print( summary( fit3sls[[ i ]]$S3 ) ) 298s + fit3sls[[ i ]]$S4 <- systemfit( 298s + list( consump ~ farmPrice - 1, price ~ trend - 1 ), "3SLS", 298s + data = Kmenta, inst = ~ farmPrice + trend + income, 298s + restrict.matrix = matrix( c( 1, -1 ), nrow = 1 ), useMatrix = useMatrix ) 298s + print( summary( fit3sls[[ i ]]$S4 ) ) 298s + fit3sls[[ i ]]$S5 <- systemfit( 298s + list( consump ~ 1, price ~ 1 ), "3SLS", 298s + data = Kmenta, inst = ~ income, useMatrix = useMatrix ) 298s + print( summary( fit3sls[[ i ]]$S5 ) ) 298s + } 298s [1] "***************************************************" 298s [1] "3SLS formula: GLS" 298s [1] "************* 3SLS *********************************" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 33 174 1.03 0.676 0.786 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 65.7 3.87 1.97 0.755 0.726 298s supply 20 16 107.9 6.75 2.60 0.598 0.522 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.87 4.36 298s supply 4.36 6.04 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 3.87 5.00 298s supply 5.00 6.74 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.00 0.98 298s supply 0.98 1.00 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 94.6333 7.9208 11.95 1.1e-09 *** 298s price -0.2436 0.0965 -2.52 0.022 * 298s income 0.3140 0.0469 6.69 3.8e-06 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 1.966 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 298s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 52.1972 11.8934 4.39 0.00046 *** 298s price 0.2286 0.0997 2.29 0.03571 * 298s farmPrice 0.2282 0.0440 5.19 9e-05 *** 298s trend 0.3611 0.0729 4.95 0.00014 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.597 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 107.914 MSE: 6.745 Root MSE: 2.597 298s Multiple R-Squared: 0.598 Adjusted R-Squared: 0.522 298s 298s [1] "********************* 3SLS EViews-like *****************" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 33 173 0.719 0.677 0.748 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 65.7 3.87 1.97 0.755 0.726 298s supply 20 16 107.2 6.70 2.59 0.600 0.525 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.29 3.59 298s supply 3.59 4.83 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 3.29 4.11 298s supply 4.11 5.36 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.000 0.979 298s supply 0.979 1.000 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 298s price -0.2436 0.0890 -2.74 0.0099 ** 298s income 0.3140 0.0433 7.25 2.5e-08 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 1.966 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 298s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 52.1176 10.6378 4.90 2.5e-05 *** 298s price 0.2289 0.0892 2.57 0.015 * 298s farmPrice 0.2290 0.0393 5.82 1.6e-06 *** 298s trend 0.3579 0.0652 5.49 4.3e-06 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.589 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 107.216 MSE: 6.701 Root MSE: 2.589 298s Multiple R-Squared: 0.6 Adjusted R-Squared: 0.525 298s 298s [1] "********************* 3SLS with methodResidCov = Theil *****************" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 33 174 -0.718 0.675 0.922 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 65.7 3.87 1.97 0.755 0.726 298s supply 20 16 108.7 6.79 2.61 0.594 0.518 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.87 4.50 298s supply 4.50 6.04 298s 298s warning: this covariance matrix is NOT positive semidefinit! 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 3.87 5.2 298s supply 5.20 6.8 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.000 0.981 298s supply 0.981 1.000 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 298s price -0.2436 0.0965 -2.52 0.017 * 298s income 0.3140 0.0469 6.69 1.3e-07 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 1.966 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 298s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 52.2869 11.8853 4.40 0.00011 *** 298s price 0.2282 0.0997 2.29 0.02855 * 298s farmPrice 0.2272 0.0438 5.19 1.0e-05 *** 298s trend 0.3648 0.0707 5.16 1.1e-05 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.607 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 108.727 MSE: 6.795 Root MSE: 2.607 298s Multiple R-Squared: 0.594 Adjusted R-Squared: 0.518 298s 298s [1] "*************** W3SLS with methodResidCov = Theil *****************" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 33 174 -0.718 0.675 0.922 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 65.7 3.87 1.97 0.755 0.726 298s supply 20 16 108.7 6.79 2.61 0.594 0.518 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.87 4.50 298s supply 4.50 6.04 298s 298s warning: this covariance matrix is NOT positive semidefinit! 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 3.87 5.2 298s supply 5.20 6.8 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.000 0.981 298s supply 0.981 1.000 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 298s price -0.2436 0.0965 -2.52 0.017 * 298s income 0.3140 0.0469 6.69 1.3e-07 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 1.966 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 298s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 52.2869 11.8853 4.40 0.00011 *** 298s price 0.2282 0.0997 2.29 0.02855 * 298s farmPrice 0.2272 0.0438 5.19 1.0e-05 *** 298s trend 0.3648 0.0707 5.16 1.1e-05 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.607 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 108.727 MSE: 6.795 Root MSE: 2.607 298s Multiple R-Squared: 0.594 Adjusted R-Squared: 0.518 298s 298s [1] "*************** 3SLS with restriction *****************" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 34 173 1.27 0.678 0.722 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 67.8 3.99 2.00 0.747 0.717 298s supply 20 16 104.8 6.55 2.56 0.609 0.536 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.97 4.55 298s supply 4.55 6.13 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 3.99 4.98 298s supply 4.98 6.55 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.000 0.975 298s supply 0.975 1.000 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 94.222 8.015 11.76 1.6e-13 *** 298s price -0.222 0.096 -2.31 0.027 * 298s income 0.296 0.045 6.57 1.6e-07 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 1.997 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 67.796 MSE: 3.988 Root MSE: 1.997 298s Multiple R-Squared: 0.747 Adjusted R-Squared: 0.717 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 55.9604 11.5777 4.83 2.8e-05 *** 298s price 0.2193 0.1002 2.19 0.036 * 298s farmPrice 0.2060 0.0403 5.11 1.3e-05 *** 298s trend 0.2956 0.0450 6.57 1.6e-07 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.559 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 104.753 MSE: 6.547 Root MSE: 2.559 298s Multiple R-Squared: 0.609 Adjusted R-Squared: 0.536 298s 298s [1] "Component “call”: target, current do not match when deparsed" 298s [1] "************** 3SLS with restriction (EViews-like) *****************" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 34 171 0.887 0.68 0.678 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 67.5 3.97 1.99 0.748 0.719 298s supply 20 16 104.0 6.50 2.55 0.612 0.539 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.37 3.75 298s supply 3.75 4.91 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 3.37 4.08 298s supply 4.08 5.20 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.000 0.974 298s supply 0.974 1.000 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 94.2737 7.3905 12.76 1.6e-14 *** 298s price -0.2243 0.0888 -2.53 0.016 * 298s income 0.2979 0.0420 7.10 3.4e-08 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 1.992 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 67.467 MSE: 3.969 Root MSE: 1.992 298s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 55.4521 10.3994 5.33 6.4e-06 *** 298s price 0.2207 0.0896 2.46 0.019 * 298s farmPrice 0.2095 0.0366 5.73 1.9e-06 *** 298s trend 0.2979 0.0420 7.10 3.4e-08 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.55 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 104.013 MSE: 6.501 Root MSE: 2.55 298s Multiple R-Squared: 0.612 Adjusted R-Squared: 0.539 298s 298s [1] 40 298s [1] "*************** W3SLS with restriction *****************" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 34 173 1.24 0.677 0.725 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 68.1 4.00 2.00 0.746 0.716 298s supply 20 16 105.2 6.57 2.56 0.608 0.534 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.93 4.56 298s supply 4.56 6.15 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 4.00 5.01 298s supply 5.01 6.57 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.000 0.976 298s supply 0.976 1.000 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 94.1823 7.9793 11.8 1.4e-13 *** 298s price -0.2194 0.0954 -2.3 0.028 * 298s income 0.2938 0.0445 6.6 1.4e-07 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.001 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 68.057 MSE: 4.003 Root MSE: 2.001 298s Multiple R-Squared: 0.746 Adjusted R-Squared: 0.716 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 56.2541 11.5687 4.86 2.6e-05 *** 298s price 0.2184 0.1003 2.18 0.036 * 298s farmPrice 0.2040 0.0401 5.09 1.3e-05 *** 298s trend 0.2938 0.0445 6.60 1.4e-07 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.564 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 105.161 MSE: 6.573 Root MSE: 2.564 298s Multiple R-Squared: 0.608 Adjusted R-Squared: 0.534 298s 298s [1] "*************** 3SLS with restriction via restrict.regMat ********************" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 34 173 1.27 0.678 0.722 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 67.8 3.99 2.00 0.747 0.717 298s supply 20 16 104.8 6.55 2.56 0.609 0.536 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.97 4.55 298s supply 4.55 6.13 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 3.99 4.98 298s supply 4.98 6.55 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.000 0.975 298s supply 0.975 1.000 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 94.222 8.015 11.76 1.6e-13 *** 298s price -0.222 0.096 -2.31 0.027 * 298s income 0.296 0.045 6.57 1.6e-07 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 1.997 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 67.796 MSE: 3.988 Root MSE: 1.997 298s Multiple R-Squared: 0.747 Adjusted R-Squared: 0.717 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 55.9604 11.5777 4.83 2.8e-05 *** 298s price 0.2193 0.1002 2.19 0.036 * 298s farmPrice 0.2060 0.0403 5.11 1.3e-05 *** 298s trend 0.2956 0.0450 6.57 1.6e-07 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.559 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 104.753 MSE: 6.547 Root MSE: 2.559 298s Multiple R-Squared: 0.609 Adjusted R-Squared: 0.536 298s 298s [1] "*************** 3SLS with restriction via restrict.regMat (EViews-like) *******" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 34 171 0.887 0.68 0.678 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 67.5 3.97 1.99 0.748 0.719 298s supply 20 16 104.0 6.50 2.55 0.612 0.539 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.37 3.75 298s supply 3.75 4.91 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 3.37 4.08 298s supply 4.08 5.20 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.000 0.974 298s supply 0.974 1.000 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 94.2737 7.3905 12.76 1.6e-14 *** 298s price -0.2243 0.0888 -2.53 0.016 * 298s income 0.2979 0.0420 7.10 3.4e-08 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 1.992 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 67.467 MSE: 3.969 Root MSE: 1.992 298s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 55.4521 10.3994 5.33 6.4e-06 *** 298s price 0.2207 0.0896 2.46 0.019 * 298s farmPrice 0.2095 0.0366 5.73 1.9e-06 *** 298s trend 0.2979 0.0420 7.10 3.4e-08 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.55 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 104.013 MSE: 6.501 Root MSE: 2.55 298s Multiple R-Squared: 0.612 Adjusted R-Squared: 0.539 298s 298s [1] "**** W3SLS with restriction via restrict.regMat (EViews-like) ****" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 34 172 0.873 0.679 0.681 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 67.7 3.98 2.00 0.748 0.718 298s supply 20 16 104.3 6.52 2.55 0.611 0.538 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.35 3.76 298s supply 3.76 4.92 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 3.38 4.10 298s supply 4.10 5.22 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.000 0.975 298s supply 0.975 1.000 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 94.2409 7.3617 12.80 1.5e-14 *** 298s price -0.2225 0.0883 -2.52 0.017 * 298s income 0.2964 0.0416 7.13 3.1e-08 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 1.995 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 67.672 MSE: 3.981 Root MSE: 1.995 298s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.718 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 55.6925 10.3937 5.36 5.9e-06 *** 298s price 0.2201 0.0897 2.45 0.019 * 298s farmPrice 0.2078 0.0364 5.71 2.0e-06 *** 298s trend 0.2964 0.0416 7.13 3.1e-08 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.553 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 104.312 MSE: 6.519 Root MSE: 2.553 298s Multiple R-Squared: 0.611 Adjusted R-Squared: 0.538 298s 298s [1] "*************** 3SLS with 2 restrictions **********************" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 35 171 1.74 0.681 0.696 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 65.8 3.87 1.97 0.755 0.726 298s supply 20 16 105.4 6.59 2.57 0.607 0.533 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.89 4.53 298s supply 4.53 6.25 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 3.87 4.87 298s supply 4.87 6.59 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.000 0.965 298s supply 0.965 1.000 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 93.9070 7.9234 11.85 8.3e-14 *** 298s price -0.2457 0.0891 -2.76 0.0092 ** 298s income 0.3236 0.0233 13.91 8.9e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 1.967 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 65.807 MSE: 3.871 Root MSE: 1.967 298s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 49.9049 8.1797 6.10 5.7e-07 *** 298s price 0.2543 0.0891 2.85 0.0072 ** 298s farmPrice 0.2293 0.0241 9.52 3.1e-11 *** 298s trend 0.3236 0.0233 13.91 8.9e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.566 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 105.389 MSE: 6.587 Root MSE: 2.566 298s Multiple R-Squared: 0.607 Adjusted R-Squared: 0.533 298s 298s [1] "Component “call”: target, current do not match when deparsed" 298s [1] "*************** 3SLS with 2 restrictions (EViews-like) ************" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 35 170 1.19 0.683 0.658 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 65.6 3.86 1.96 0.755 0.727 298s supply 20 16 104.6 6.54 2.56 0.610 0.537 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.30 3.73 298s supply 3.73 5.00 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 3.28 4.00 298s supply 4.00 5.23 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.000 0.965 298s supply 0.965 1.000 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 94.1703 7.3154 12.87 7.8e-15 *** 298s price -0.2494 0.0812 -3.07 0.0041 ** 298s income 0.3248 0.0209 15.57 < 2e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 1.964 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 65.577 MSE: 3.857 Root MSE: 1.964 298s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.727 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 50.0853 7.5503 6.63 1.1e-07 *** 298s price 0.2506 0.0812 3.09 0.0039 ** 298s farmPrice 0.2312 0.0212 10.88 8.9e-13 *** 298s trend 0.3248 0.0209 15.57 < 2e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.557 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 104.607 MSE: 6.538 Root MSE: 2.557 298s Multiple R-Squared: 0.61 Adjusted R-Squared: 0.537 298s 298s [1] "********** W3SLS with 2 (symbolic) restrictions ***************" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 35 172 1.74 0.68 0.697 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 65.9 3.88 1.97 0.754 0.725 298s supply 20 16 105.7 6.60 2.57 0.606 0.532 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.88 4.55 298s supply 4.55 6.27 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 3.88 4.88 298s supply 4.88 6.60 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.000 0.965 298s supply 0.965 1.000 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 93.7870 7.9088 11.86 8.2e-14 *** 298s price -0.2443 0.0892 -2.74 0.0096 ** 298s income 0.3234 0.0229 14.14 4.4e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 1.969 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 65.907 MSE: 3.877 Root MSE: 1.969 298s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 49.8093 8.1522 6.11 5.5e-07 *** 298s price 0.2557 0.0892 2.87 0.0069 ** 298s farmPrice 0.2289 0.0237 9.67 2.0e-11 *** 298s trend 0.3234 0.0229 14.14 4.4e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.57 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 105.659 MSE: 6.604 Root MSE: 2.57 298s Multiple R-Squared: 0.606 Adjusted R-Squared: 0.532 298s 298s [1] "*************** 3SLS with 2 restrictions via R and restrict.regMat **********" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 35 171 1.74 0.681 0.696 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 65.8 3.87 1.97 0.755 0.726 298s supply 20 16 105.4 6.59 2.57 0.607 0.533 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.89 4.53 298s supply 4.53 6.25 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 3.87 4.87 298s supply 4.87 6.59 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.000 0.965 298s supply 0.965 1.000 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 93.9070 7.9234 11.85 8.3e-14 *** 298s price -0.2457 0.0891 -2.76 0.0092 ** 298s income 0.3236 0.0233 13.91 8.9e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 1.967 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 65.807 MSE: 3.871 Root MSE: 1.967 298s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 49.9049 8.1797 6.10 5.7e-07 *** 298s price 0.2543 0.0891 2.85 0.0072 ** 298s farmPrice 0.2293 0.0241 9.52 3.1e-11 *** 298s trend 0.3236 0.0233 13.91 8.9e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.566 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 105.389 MSE: 6.587 Root MSE: 2.566 298s Multiple R-Squared: 0.607 Adjusted R-Squared: 0.533 298s 298s [1] "Component “call”: target, current do not match when deparsed" 298s [1] "******** 3SLS with 2 restrictions via R and restrict.regMat (EViews-like)*****" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 35 170 1.19 0.683 0.658 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 65.6 3.86 1.96 0.755 0.727 298s supply 20 16 104.6 6.54 2.56 0.610 0.537 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.30 3.73 298s supply 3.73 5.00 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 3.28 4.00 298s supply 4.00 5.23 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.000 0.965 298s supply 0.965 1.000 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 94.1703 7.3154 12.87 7.8e-15 *** 298s price -0.2494 0.0812 -3.07 0.0041 ** 298s income 0.3248 0.0209 15.57 < 2e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 1.964 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 65.577 MSE: 3.857 Root MSE: 1.964 298s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.727 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 50.0853 7.5503 6.63 1.1e-07 *** 298s price 0.2506 0.0812 3.09 0.0039 ** 298s farmPrice 0.2312 0.0212 10.88 8.9e-13 *** 298s trend 0.3248 0.0209 15.57 < 2e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.557 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 104.607 MSE: 6.538 Root MSE: 2.557 298s Multiple R-Squared: 0.61 Adjusted R-Squared: 0.537 298s 298s [1] "*** W3SLS with 2 restrictions via R and restrict.regMat (EViews-like) ***" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 35 170 1.19 0.682 0.659 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 65.6 3.86 1.97 0.755 0.726 298s supply 20 16 104.8 6.55 2.56 0.609 0.536 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.30 3.75 298s supply 3.75 5.01 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 3.28 4.00 298s supply 4.00 5.24 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.000 0.965 298s supply 0.965 1.000 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 94.0830 7.3058 12.88 7.5e-15 *** 298s price -0.2484 0.0812 -3.06 0.0042 ** 298s income 0.3246 0.0205 15.81 < 2e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 1.965 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 65.646 MSE: 3.862 Root MSE: 1.965 298s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 50.0190 7.5314 6.64 1.1e-07 *** 298s price 0.2516 0.0812 3.10 0.0038 ** 298s farmPrice 0.2309 0.0209 11.05 5.9e-13 *** 298s trend 0.3246 0.0205 15.81 < 2e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.559 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 104.795 MSE: 6.55 Root MSE: 2.559 298s Multiple R-Squared: 0.609 Adjusted R-Squared: 0.536 298s 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 36 3690 5613 0.012 0.368 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s eq1 20 19 2132 112.2 10.59 0.305 0.305 298s eq2 20 17 1558 91.7 9.57 -1.335 -1.610 298s 298s The covariance matrix of the residuals used for estimation 298s eq1 eq2 298s eq1 112.2 -44.8 298s eq2 -44.8 56.8 298s 298s The covariance matrix of the residuals 298s eq1 eq2 298s eq1 112.2 -68.3 298s eq2 -68.3 91.7 298s 298s The correlations of the residuals 298s eq1 eq2 298s eq1 1.000 -0.674 298s eq2 -0.674 1.000 298s 298s 298s 3SLS estimates for 'eq1' (equation 1) 298s Model Formula: farmPrice ~ consump - 1 298s Instruments: ~trend + income 298s 298s Estimate Std. Error t value Pr(>|t|) 298s consump 0.9588 0.0235 40.9 <2e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 10.592 on 19 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 19 298s SSR: 2131.725 MSE: 112.196 Root MSE: 10.592 298s Multiple R-Squared: 0.305 Adjusted R-Squared: 0.305 298s 298s 298s 3SLS estimates for 'eq2' (equation 2) 298s Model Formula: price ~ consump + trend 298s Instruments: ~trend + income 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) -92.192 49.896 -1.85 0.0821 . 298s consump 1.953 0.499 3.92 0.0011 ** 298s trend -0.469 0.247 -1.90 0.0743 . 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 9.574 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 1558.311 MSE: 91.665 Root MSE: 9.574 298s Multiple R-Squared: -1.335 Adjusted R-Squared: -1.61 298s 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 38 56326 283068 -104 -10.6 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s eq1 20 19 2313 122 11.0 -7.63 -7.63 298s eq2 20 19 54013 2843 53.3 -200.46 -200.46 298s 298s The covariance matrix of the residuals used for estimation 298s eq1 eq2 298s eq1 121 -255 298s eq2 -255 2953 298s 298s The covariance matrix of the residuals 298s eq1 eq2 298s eq1 122 -251 298s eq2 -251 2843 298s 298s The correlations of the residuals 298s eq1 eq2 298s eq1 1.000 -0.433 298s eq2 -0.433 1.000 298s 298s 298s 3SLS estimates for 'eq1' (equation 1) 298s Model Formula: consump ~ farmPrice - 1 298s Instruments: ~price + income 298s 298s Estimate Std. Error t value Pr(>|t|) 298s farmPrice 1.0417 0.0253 41.2 <2e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 11.034 on 19 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 19 298s SSR: 2313.073 MSE: 121.741 Root MSE: 11.034 298s Multiple R-Squared: -7.627 Adjusted R-Squared: -7.627 298s 298s 298s 3SLS estimates for 'eq2' (equation 2) 298s Model Formula: consump ~ trend - 1 298s Instruments: ~price + income 298s 298s Estimate Std. Error t value Pr(>|t|) 298s trend 9.02 1.13 8 1.7e-07 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 53.318 on 19 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 19 298s SSR: 54013.367 MSE: 2842.809 Root MSE: 53.318 298s Multiple R-Squared: -200.456 Adjusted R-Squared: -200.456 298s 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 38 167069 397886 -49.1 -0.82 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s eq1 20 19 76692 4036 63.5 -285.0 -285.0 298s eq2 20 19 90377 4757 69.0 -28.5 -28.5 298s 298s The covariance matrix of the residuals used for estimation 298s eq1 eq2 298s eq1 2682 2547 298s eq2 2547 2741 298s 298s The covariance matrix of the residuals 298s eq1 eq2 298s eq1 4036 4336 298s eq2 4336 4757 298s 298s The correlations of the residuals 298s eq1 eq2 298s eq1 1.000 0.928 298s eq2 0.928 1.000 298s 298s 298s 3SLS estimates for 'eq1' (equation 1) 298s Model Formula: consump ~ trend - 1 298s Instruments: ~income + farmPrice 298s 298s Estimate Std. Error t value Pr(>|t|) 298s trend 4.162 0.723 5.75 1.5e-05 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 63.533 on 19 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 19 298s SSR: 76691.636 MSE: 4036.402 Root MSE: 63.533 298s Multiple R-Squared: -285.041 Adjusted R-Squared: -285.041 298s 298s 298s 3SLS estimates for 'eq2' (equation 2) 298s Model Formula: farmPrice ~ trend - 1 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s trend 3.274 0.676 4.84 0.00011 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 68.969 on 19 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 19 298s SSR: 90377.499 MSE: 4756.71 Root MSE: 68.969 298s Multiple R-Squared: -28.451 Adjusted R-Squared: -28.451 298s 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 39 161126 1162329 -171 -17.4 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s eq1 20 19 3553 187 13.7 -12.3 -12.3 298s eq2 20 19 157573 8293 91.1 -235.2 -235.2 298s 298s The covariance matrix of the residuals used for estimation 298s eq1 eq2 298s eq1 208 -731 298s eq2 -731 8271 298s 298s The covariance matrix of the residuals 298s eq1 eq2 298s eq1 187 -623 298s eq2 -623 8293 298s 298s The correlations of the residuals 298s eq1 eq2 298s eq1 1.000 -0.121 298s eq2 -0.121 1.000 298s 298s 298s 3SLS estimates for 'eq1' (equation 1) 298s Model Formula: consump ~ farmPrice - 1 298s Instruments: ~farmPrice + trend + income 298s 298s Estimate Std. Error t value Pr(>|t|) 298s farmPrice 1.1122 0.0272 40.8 <2e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 13.675 on 19 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 19 298s SSR: 3553.118 MSE: 187.006 Root MSE: 13.675 298s Multiple R-Squared: -12.252 Adjusted R-Squared: -12.252 298s 298s 298s 3SLS estimates for 'eq2' (equation 2) 298s Model Formula: price ~ trend - 1 298s Instruments: ~farmPrice + trend + income 298s 298s Estimate Std. Error t value Pr(>|t|) 298s trend 1.1122 0.0272 40.8 <2e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 91.068 on 19 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 19 298s SSR: 157573.328 MSE: 8293.333 Root MSE: 91.068 298s Multiple R-Squared: -235.153 Adjusted R-Squared: -235.153 298s 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 38 935 491 0 0 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s eq1 20 19 268 14.1 3.76 0 0 298s eq2 20 19 667 35.1 5.93 0 0 298s 298s The covariance matrix of the residuals used for estimation 298s eq1 eq2 298s eq1 14.11 2.18 298s eq2 2.18 35.12 298s 298s The covariance matrix of the residuals 298s eq1 eq2 298s eq1 14.11 2.18 298s eq2 2.18 35.12 298s 298s The correlations of the residuals 298s eq1 eq2 298s eq1 1.0000 0.0981 298s eq2 0.0981 1.0000 298s 298s 298s 3SLS estimates for 'eq1' (equation 1) 298s Model Formula: consump ~ 1 298s Instruments: ~income 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 100.90 0.84 120 <2e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 3.756 on 19 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 19 298s SSR: 268.114 MSE: 14.111 Root MSE: 3.756 298s Multiple R-Squared: 0 Adjusted R-Squared: 0 298s 298s 298s 3SLS estimates for 'eq2' (equation 2) 298s Model Formula: price ~ 1 298s Instruments: ~income 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 100.02 1.33 75.5 <2e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 5.926 on 19 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 19 298s SSR: 667.251 MSE: 35.118 Root MSE: 5.926 298s Multiple R-Squared: 0 Adjusted R-Squared: 0 298s 298s [1] "***************************************************" 298s [1] "3SLS formula: IV" 298s [1] "************* 3SLS *********************************" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 33 174 1.03 0.676 0.786 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 65.7 3.87 1.97 0.755 0.726 298s supply 20 16 107.9 6.75 2.60 0.598 0.522 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.87 4.36 298s supply 4.36 6.04 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 3.87 5.00 298s supply 5.00 6.74 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.00 0.98 298s supply 0.98 1.00 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 94.6333 7.9208 11.95 1.1e-09 *** 298s price -0.2436 0.0965 -2.52 0.022 * 298s income 0.3140 0.0469 6.69 3.8e-06 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 1.966 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 298s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 52.1972 11.8934 4.39 0.00046 *** 298s price 0.2286 0.0997 2.29 0.03571 * 298s farmPrice 0.2282 0.0440 5.19 9e-05 *** 298s trend 0.3611 0.0729 4.95 0.00014 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.597 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 107.914 MSE: 6.745 Root MSE: 2.597 298s Multiple R-Squared: 0.598 Adjusted R-Squared: 0.522 298s 298s [1] "********************* 3SLS EViews-like *****************" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 33 173 0.719 0.677 0.748 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 65.7 3.87 1.97 0.755 0.726 298s supply 20 16 107.2 6.70 2.59 0.600 0.525 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.29 3.59 298s supply 3.59 4.83 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 3.29 4.11 298s supply 4.11 5.36 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.000 0.979 298s supply 0.979 1.000 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 298s price -0.2436 0.0890 -2.74 0.0099 ** 298s income 0.3140 0.0433 7.25 2.5e-08 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 1.966 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 298s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 52.1176 10.6378 4.90 2.5e-05 *** 298s price 0.2289 0.0892 2.57 0.015 * 298s farmPrice 0.2290 0.0393 5.82 1.6e-06 *** 298s trend 0.3579 0.0652 5.49 4.3e-06 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.589 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 107.216 MSE: 6.701 Root MSE: 2.589 298s Multiple R-Squared: 0.6 Adjusted R-Squared: 0.525 298s 298s [1] "********************* 3SLS with methodResidCov = Theil *****************" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 33 174 -0.718 0.675 0.922 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 65.7 3.87 1.97 0.755 0.726 298s supply 20 16 108.7 6.79 2.61 0.594 0.518 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.87 4.50 298s supply 4.50 6.04 298s 298s warning: this covariance matrix is NOT positive semidefinit! 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 3.87 5.2 298s supply 5.20 6.8 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.000 0.981 298s supply 0.981 1.000 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 298s price -0.2436 0.0965 -2.52 0.017 * 298s income 0.3140 0.0469 6.69 1.3e-07 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 1.966 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 298s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 52.2869 11.8853 4.40 0.00011 *** 298s price 0.2282 0.0997 2.29 0.02855 * 298s farmPrice 0.2272 0.0438 5.19 1.0e-05 *** 298s trend 0.3648 0.0707 5.16 1.1e-05 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.607 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 108.727 MSE: 6.795 Root MSE: 2.607 298s Multiple R-Squared: 0.594 Adjusted R-Squared: 0.518 298s 298s [1] "*************** W3SLS with methodResidCov = Theil *****************" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 33 174 -0.718 0.675 0.922 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 65.7 3.87 1.97 0.755 0.726 298s supply 20 16 108.7 6.79 2.61 0.594 0.518 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.87 4.50 298s supply 4.50 6.04 298s 298s warning: this covariance matrix is NOT positive semidefinit! 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 3.87 5.2 298s supply 5.20 6.8 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.000 0.981 298s supply 0.981 1.000 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 298s price -0.2436 0.0965 -2.52 0.017 * 298s income 0.3140 0.0469 6.69 1.3e-07 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 1.966 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 298s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 52.2869 11.8853 4.40 0.00011 *** 298s price 0.2282 0.0997 2.29 0.02855 * 298s farmPrice 0.2272 0.0438 5.19 1.0e-05 *** 298s trend 0.3648 0.0707 5.16 1.1e-05 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.607 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 108.727 MSE: 6.795 Root MSE: 2.607 298s Multiple R-Squared: 0.594 Adjusted R-Squared: 0.518 298s 298s [1] "*************** 3SLS with restriction *****************" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 34 173 1.27 0.678 0.722 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 67.8 3.99 2.00 0.747 0.717 298s supply 20 16 104.8 6.55 2.56 0.609 0.536 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.97 4.55 298s supply 4.55 6.13 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 3.99 4.98 298s supply 4.98 6.55 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.000 0.975 298s supply 0.975 1.000 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 94.222 8.015 11.76 1.6e-13 *** 298s price -0.222 0.096 -2.31 0.027 * 298s income 0.296 0.045 6.57 1.6e-07 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 1.997 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 67.796 MSE: 3.988 Root MSE: 1.997 298s Multiple R-Squared: 0.747 Adjusted R-Squared: 0.717 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 55.9604 11.5777 4.83 2.8e-05 *** 298s price 0.2193 0.1002 2.19 0.036 * 298s farmPrice 0.2060 0.0403 5.11 1.3e-05 *** 298s trend 0.2956 0.0450 6.57 1.6e-07 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.559 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 104.753 MSE: 6.547 Root MSE: 2.559 298s Multiple R-Squared: 0.609 Adjusted R-Squared: 0.536 298s 298s [1] "Component “call”: target, current do not match when deparsed" 298s [1] "************** 3SLS with restriction (EViews-like) *****************" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 34 171 0.887 0.68 0.678 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 67.5 3.97 1.99 0.748 0.719 298s supply 20 16 104.0 6.50 2.55 0.612 0.539 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.37 3.75 298s supply 3.75 4.91 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 3.37 4.08 298s supply 4.08 5.20 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.000 0.974 298s supply 0.974 1.000 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 94.2737 7.3905 12.76 1.6e-14 *** 298s price -0.2243 0.0888 -2.53 0.016 * 298s income 0.2979 0.0420 7.10 3.4e-08 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 1.992 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 67.467 MSE: 3.969 Root MSE: 1.992 298s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 55.4521 10.3994 5.33 6.4e-06 *** 298s price 0.2207 0.0896 2.46 0.019 * 298s farmPrice 0.2095 0.0366 5.73 1.9e-06 *** 298s trend 0.2979 0.0420 7.10 3.4e-08 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.55 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 104.013 MSE: 6.501 Root MSE: 2.55 298s Multiple R-Squared: 0.612 Adjusted R-Squared: 0.539 298s 298s [1] 40 298s [1] "*************** W3SLS with restriction *****************" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 34 173 1.24 0.677 0.725 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 68.1 4.00 2.00 0.746 0.716 298s supply 20 16 105.2 6.57 2.56 0.608 0.534 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.93 4.56 298s supply 4.56 6.15 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 4.00 5.01 298s supply 5.01 6.57 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.000 0.976 298s supply 0.976 1.000 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 94.1823 7.9793 11.8 1.4e-13 *** 298s price -0.2194 0.0954 -2.3 0.028 * 298s income 0.2938 0.0445 6.6 1.4e-07 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.001 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 68.057 MSE: 4.003 Root MSE: 2.001 298s Multiple R-Squared: 0.746 Adjusted R-Squared: 0.716 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 56.2541 11.5687 4.86 2.6e-05 *** 298s price 0.2184 0.1003 2.18 0.036 * 298s farmPrice 0.2040 0.0401 5.09 1.3e-05 *** 298s trend 0.2938 0.0445 6.60 1.4e-07 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.564 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 105.161 MSE: 6.573 Root MSE: 2.564 298s Multiple R-Squared: 0.608 Adjusted R-Squared: 0.534 298s 298s [1] "*************** 3SLS with restriction via restrict.regMat ********************" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 34 173 1.27 0.678 0.722 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 67.8 3.99 2.00 0.747 0.717 298s supply 20 16 104.8 6.55 2.56 0.609 0.536 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.97 4.55 298s supply 4.55 6.13 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 3.99 4.98 298s supply 4.98 6.55 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.000 0.975 298s supply 0.975 1.000 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 94.222 8.015 11.76 1.6e-13 *** 298s price -0.222 0.096 -2.31 0.027 * 298s income 0.296 0.045 6.57 1.6e-07 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 1.997 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 67.796 MSE: 3.988 Root MSE: 1.997 298s Multiple R-Squared: 0.747 Adjusted R-Squared: 0.717 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 55.9604 11.5777 4.83 2.8e-05 *** 298s price 0.2193 0.1002 2.19 0.036 * 298s farmPrice 0.2060 0.0403 5.11 1.3e-05 *** 298s trend 0.2956 0.0450 6.57 1.6e-07 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.559 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 104.753 MSE: 6.547 Root MSE: 2.559 298s Multiple R-Squared: 0.609 Adjusted R-Squared: 0.536 298s 298s [1] "*************** 3SLS with restriction via restrict.regMat (EViews-like) *******" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 34 171 0.887 0.68 0.678 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 67.5 3.97 1.99 0.748 0.719 298s supply 20 16 104.0 6.50 2.55 0.612 0.539 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.37 3.75 298s supply 3.75 4.91 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 3.37 4.08 298s supply 4.08 5.20 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.000 0.974 298s supply 0.974 1.000 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 94.2737 7.3905 12.76 1.6e-14 *** 298s price -0.2243 0.0888 -2.53 0.016 * 298s income 0.2979 0.0420 7.10 3.4e-08 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 1.992 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 67.467 MSE: 3.969 Root MSE: 1.992 298s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 55.4521 10.3994 5.33 6.4e-06 *** 298s price 0.2207 0.0896 2.46 0.019 * 298s farmPrice 0.2095 0.0366 5.73 1.9e-06 *** 298s trend 0.2979 0.0420 7.10 3.4e-08 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.55 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 104.013 MSE: 6.501 Root MSE: 2.55 298s Multiple R-Squared: 0.612 Adjusted R-Squared: 0.539 298s 298s [1] "**** W3SLS with restriction via restrict.regMat (EViews-like) ****" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 34 172 0.873 0.679 0.681 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 67.7 3.98 2.00 0.748 0.718 298s supply 20 16 104.3 6.52 2.55 0.611 0.538 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.35 3.76 298s supply 3.76 4.92 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 3.38 4.10 298s supply 4.10 5.22 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.000 0.975 298s supply 0.975 1.000 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 94.2409 7.3617 12.80 1.5e-14 *** 298s price -0.2225 0.0883 -2.52 0.017 * 298s income 0.2964 0.0416 7.13 3.1e-08 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 1.995 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 67.672 MSE: 3.981 Root MSE: 1.995 298s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.718 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 55.6925 10.3937 5.36 5.9e-06 *** 298s price 0.2201 0.0897 2.45 0.019 * 298s farmPrice 0.2078 0.0364 5.71 2.0e-06 *** 298s trend 0.2964 0.0416 7.13 3.1e-08 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.553 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 104.312 MSE: 6.519 Root MSE: 2.553 298s Multiple R-Squared: 0.611 Adjusted R-Squared: 0.538 298s 298s [1] "*************** 3SLS with 2 restrictions **********************" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 35 171 1.74 0.681 0.696 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 65.8 3.87 1.97 0.755 0.726 298s supply 20 16 105.4 6.59 2.57 0.607 0.533 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.89 4.53 298s supply 4.53 6.25 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 3.87 4.87 298s supply 4.87 6.59 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.000 0.965 298s supply 0.965 1.000 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 93.9070 7.9234 11.85 8.3e-14 *** 298s price -0.2457 0.0891 -2.76 0.0092 ** 298s income 0.3236 0.0233 13.91 8.9e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 1.967 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 65.807 MSE: 3.871 Root MSE: 1.967 298s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 49.9049 8.1797 6.10 5.7e-07 *** 298s price 0.2543 0.0891 2.85 0.0072 ** 298s farmPrice 0.2293 0.0241 9.52 3.1e-11 *** 298s trend 0.3236 0.0233 13.91 8.9e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.566 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 105.389 MSE: 6.587 Root MSE: 2.566 298s Multiple R-Squared: 0.607 Adjusted R-Squared: 0.533 298s 298s [1] "Component “call”: target, current do not match when deparsed" 298s [1] "*************** 3SLS with 2 restrictions (EViews-like) ************" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 35 170 1.19 0.683 0.658 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 65.6 3.86 1.96 0.755 0.727 298s supply 20 16 104.6 6.54 2.56 0.610 0.537 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.30 3.73 298s supply 3.73 5.00 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 3.28 4.00 298s supply 4.00 5.23 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.000 0.965 298s supply 0.965 1.000 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 94.1703 7.3154 12.87 7.8e-15 *** 298s price -0.2494 0.0812 -3.07 0.0041 ** 298s income 0.3248 0.0209 15.57 < 2e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 1.964 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 65.577 MSE: 3.857 Root MSE: 1.964 298s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.727 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 50.0853 7.5503 6.63 1.1e-07 *** 298s price 0.2506 0.0812 3.09 0.0039 ** 298s farmPrice 0.2312 0.0212 10.88 8.9e-13 *** 298s trend 0.3248 0.0209 15.57 < 2e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.557 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 104.607 MSE: 6.538 Root MSE: 2.557 298s Multiple R-Squared: 0.61 Adjusted R-Squared: 0.537 298s 298s [1] "********** W3SLS with 2 (symbolic) restrictions ***************" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 35 172 1.74 0.68 0.697 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 65.9 3.88 1.97 0.754 0.725 298s supply 20 16 105.7 6.60 2.57 0.606 0.532 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.88 4.55 298s supply 4.55 6.27 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 3.88 4.88 298s supply 4.88 6.60 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.000 0.965 298s supply 0.965 1.000 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 93.7870 7.9088 11.86 8.2e-14 *** 298s price -0.2443 0.0892 -2.74 0.0096 ** 298s income 0.3234 0.0229 14.14 4.4e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 1.969 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 65.907 MSE: 3.877 Root MSE: 1.969 298s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 49.8093 8.1522 6.11 5.5e-07 *** 298s price 0.2557 0.0892 2.87 0.0069 ** 298s farmPrice 0.2289 0.0237 9.67 2.0e-11 *** 298s trend 0.3234 0.0229 14.14 4.4e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.57 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 105.659 MSE: 6.604 Root MSE: 2.57 298s Multiple R-Squared: 0.606 Adjusted R-Squared: 0.532 298s 298s [1] "*************** 3SLS with 2 restrictions via R and restrict.regMat **********" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 35 171 1.74 0.681 0.696 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 65.8 3.87 1.97 0.755 0.726 298s supply 20 16 105.4 6.59 2.57 0.607 0.533 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.89 4.53 298s supply 4.53 6.25 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 3.87 4.87 298s supply 4.87 6.59 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.000 0.965 298s supply 0.965 1.000 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 93.9070 7.9234 11.85 8.3e-14 *** 298s price -0.2457 0.0891 -2.76 0.0092 ** 298s income 0.3236 0.0233 13.91 8.9e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 1.967 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 65.807 MSE: 3.871 Root MSE: 1.967 298s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 49.9049 8.1797 6.10 5.7e-07 *** 298s price 0.2543 0.0891 2.85 0.0072 ** 298s farmPrice 0.2293 0.0241 9.52 3.1e-11 *** 298s trend 0.3236 0.0233 13.91 8.9e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.566 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 105.389 MSE: 6.587 Root MSE: 2.566 298s Multiple R-Squared: 0.607 Adjusted R-Squared: 0.533 298s 298s [1] "Component “call”: target, current do not match when deparsed" 298s [1] "******** 3SLS with 2 restrictions via R and restrict.regMat (EViews-like)*****" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 35 170 1.19 0.683 0.658 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 65.6 3.86 1.96 0.755 0.727 298s supply 20 16 104.6 6.54 2.56 0.610 0.537 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.30 3.73 298s supply 3.73 5.00 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 3.28 4.00 298s supply 4.00 5.23 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.000 0.965 298s supply 0.965 1.000 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 94.1703 7.3154 12.87 7.8e-15 *** 298s price -0.2494 0.0812 -3.07 0.0041 ** 298s income 0.3248 0.0209 15.57 < 2e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 1.964 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 65.577 MSE: 3.857 Root MSE: 1.964 298s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.727 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 50.0853 7.5503 6.63 1.1e-07 *** 298s price 0.2506 0.0812 3.09 0.0039 ** 298s farmPrice 0.2312 0.0212 10.88 8.9e-13 *** 298s trend 0.3248 0.0209 15.57 < 2e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.557 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 104.607 MSE: 6.538 Root MSE: 2.557 298s Multiple R-Squared: 0.61 Adjusted R-Squared: 0.537 298s 298s [1] "*** W3SLS with 2 restrictions via R and restrict.regMat (EViews-like) ***" 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 35 170 1.19 0.682 0.659 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s demand 20 17 65.6 3.86 1.97 0.755 0.726 298s supply 20 16 104.8 6.55 2.56 0.609 0.536 298s 298s The covariance matrix of the residuals used for estimation 298s demand supply 298s demand 3.30 3.75 298s supply 3.75 5.01 298s 298s The covariance matrix of the residuals 298s demand supply 298s demand 3.28 4.00 298s supply 4.00 5.24 298s 298s The correlations of the residuals 298s demand supply 298s demand 1.000 0.965 298s supply 0.965 1.000 298s 298s 298s 3SLS estimates for 'demand' (equation 1) 298s Model Formula: consump ~ price + income 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 94.0830 7.3058 12.88 7.5e-15 *** 298s price -0.2484 0.0812 -3.06 0.0042 ** 298s income 0.3246 0.0205 15.81 < 2e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 1.965 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 65.646 MSE: 3.862 Root MSE: 1.965 298s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 298s 298s 298s 3SLS estimates for 'supply' (equation 2) 298s Model Formula: consump ~ price + farmPrice + trend 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 50.0190 7.5314 6.64 1.1e-07 *** 298s price 0.2516 0.0812 3.10 0.0038 ** 298s farmPrice 0.2309 0.0209 11.05 5.9e-13 *** 298s trend 0.3246 0.0205 15.81 < 2e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 2.559 on 16 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 16 298s SSR: 104.795 MSE: 6.55 Root MSE: 2.559 298s Multiple R-Squared: 0.609 Adjusted R-Squared: 0.536 298s 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 36 3690 5613 0.012 0.368 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s eq1 20 19 2132 112.2 10.59 0.305 0.305 298s eq2 20 17 1558 91.7 9.57 -1.335 -1.610 298s 298s The covariance matrix of the residuals used for estimation 298s eq1 eq2 298s eq1 112.2 -44.8 298s eq2 -44.8 56.8 298s 298s The covariance matrix of the residuals 298s eq1 eq2 298s eq1 112.2 -68.3 298s eq2 -68.3 91.7 298s 298s The correlations of the residuals 298s eq1 eq2 298s eq1 1.000 -0.674 298s eq2 -0.674 1.000 298s 298s 298s 3SLS estimates for 'eq1' (equation 1) 298s Model Formula: farmPrice ~ consump - 1 298s Instruments: ~trend + income 298s 298s Estimate Std. Error t value Pr(>|t|) 298s consump 0.9588 0.0235 40.9 <2e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 10.592 on 19 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 19 298s SSR: 2131.725 MSE: 112.196 Root MSE: 10.592 298s Multiple R-Squared: 0.305 Adjusted R-Squared: 0.305 298s 298s 298s 3SLS estimates for 'eq2' (equation 2) 298s Model Formula: price ~ consump + trend 298s Instruments: ~trend + income 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) -92.192 49.896 -1.85 0.0821 . 298s consump 1.953 0.499 3.92 0.0011 ** 298s trend -0.469 0.247 -1.90 0.0743 . 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 9.574 on 17 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 17 298s SSR: 1558.311 MSE: 91.665 Root MSE: 9.574 298s Multiple R-Squared: -1.335 Adjusted R-Squared: -1.61 298s 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 38 56326 283068 -104 -10.6 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s eq1 20 19 2313 122 11.0 -7.63 -7.63 298s eq2 20 19 54013 2843 53.3 -200.46 -200.46 298s 298s The covariance matrix of the residuals used for estimation 298s eq1 eq2 298s eq1 121 -255 298s eq2 -255 2953 298s 298s The covariance matrix of the residuals 298s eq1 eq2 298s eq1 122 -251 298s eq2 -251 2843 298s 298s The correlations of the residuals 298s eq1 eq2 298s eq1 1.000 -0.433 298s eq2 -0.433 1.000 298s 298s 298s 3SLS estimates for 'eq1' (equation 1) 298s Model Formula: consump ~ farmPrice - 1 298s Instruments: ~price + income 298s 298s Estimate Std. Error t value Pr(>|t|) 298s farmPrice 1.0417 0.0253 41.2 <2e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 11.034 on 19 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 19 298s SSR: 2313.073 MSE: 121.741 Root MSE: 11.034 298s Multiple R-Squared: -7.627 Adjusted R-Squared: -7.627 298s 298s 298s 3SLS estimates for 'eq2' (equation 2) 298s Model Formula: consump ~ trend - 1 298s Instruments: ~price + income 298s 298s Estimate Std. Error t value Pr(>|t|) 298s trend 9.02 1.13 8 1.7e-07 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 53.318 on 19 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 19 298s SSR: 54013.367 MSE: 2842.809 Root MSE: 53.318 298s Multiple R-Squared: -200.456 Adjusted R-Squared: -200.456 298s 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 38 167069 397886 -49.1 -0.82 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s eq1 20 19 76692 4036 63.5 -285.0 -285.0 298s eq2 20 19 90377 4757 69.0 -28.5 -28.5 298s 298s The covariance matrix of the residuals used for estimation 298s eq1 eq2 298s eq1 2682 2547 298s eq2 2547 2741 298s 298s The covariance matrix of the residuals 298s eq1 eq2 298s eq1 4036 4336 298s eq2 4336 4757 298s 298s The correlations of the residuals 298s eq1 eq2 298s eq1 1.000 0.928 298s eq2 0.928 1.000 298s 298s 298s 3SLS estimates for 'eq1' (equation 1) 298s Model Formula: consump ~ trend - 1 298s Instruments: ~income + farmPrice 298s 298s Estimate Std. Error t value Pr(>|t|) 298s trend 4.162 0.723 5.75 1.5e-05 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 63.533 on 19 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 19 298s SSR: 76691.636 MSE: 4036.402 Root MSE: 63.533 298s Multiple R-Squared: -285.041 Adjusted R-Squared: -285.041 298s 298s 298s 3SLS estimates for 'eq2' (equation 2) 298s Model Formula: farmPrice ~ trend - 1 298s Instruments: ~income + farmPrice + trend 298s 298s Estimate Std. Error t value Pr(>|t|) 298s trend 3.274 0.676 4.84 0.00011 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 68.969 on 19 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 19 298s SSR: 90377.499 MSE: 4756.71 Root MSE: 68.969 298s Multiple R-Squared: -28.451 Adjusted R-Squared: -28.451 298s 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 39 161126 1162329 -171 -17.4 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s eq1 20 19 3553 187 13.7 -12.3 -12.3 298s eq2 20 19 157573 8293 91.1 -235.2 -235.2 298s 298s The covariance matrix of the residuals used for estimation 298s eq1 eq2 298s eq1 208 -731 298s eq2 -731 8271 298s 298s The covariance matrix of the residuals 298s eq1 eq2 298s eq1 187 -623 298s eq2 -623 8293 298s 298s The correlations of the residuals 298s eq1 eq2 298s eq1 1.000 -0.121 298s eq2 -0.121 1.000 298s 298s 298s 3SLS estimates for 'eq1' (equation 1) 298s Model Formula: consump ~ farmPrice - 1 298s Instruments: ~farmPrice + trend + income 298s 298s Estimate Std. Error t value Pr(>|t|) 298s farmPrice 1.1122 0.0272 40.8 <2e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 13.675 on 19 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 19 298s SSR: 3553.118 MSE: 187.006 Root MSE: 13.675 298s Multiple R-Squared: -12.252 Adjusted R-Squared: -12.252 298s 298s 298s 3SLS estimates for 'eq2' (equation 2) 298s Model Formula: price ~ trend - 1 298s Instruments: ~farmPrice + trend + income 298s 298s Estimate Std. Error t value Pr(>|t|) 298s trend 1.1122 0.0272 40.8 <2e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 91.068 on 19 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 19 298s SSR: 157573.328 MSE: 8293.333 Root MSE: 91.068 298s Multiple R-Squared: -235.153 Adjusted R-Squared: -235.153 298s 298s 298s systemfit results 298s method: 3SLS 298s 298s N DF SSR detRCov OLS-R2 McElroy-R2 298s system 40 38 935 491 0 0 298s 298s N DF SSR MSE RMSE R2 Adj R2 298s eq1 20 19 268 14.1 3.76 0 0 298s eq2 20 19 667 35.1 5.93 0 0 298s 298s The covariance matrix of the residuals used for estimation 298s eq1 eq2 298s eq1 14.11 2.18 298s eq2 2.18 35.12 298s 298s The covariance matrix of the residuals 298s eq1 eq2 298s eq1 14.11 2.18 298s eq2 2.18 35.12 298s 298s The correlations of the residuals 298s eq1 eq2 298s eq1 1.0000 0.0981 298s eq2 0.0981 1.0000 298s 298s 298s 3SLS estimates for 'eq1' (equation 1) 298s Model Formula: consump ~ 1 298s Instruments: ~income 298s 298s Estimate Std. Error t value Pr(>|t|) 298s (Intercept) 100.90 0.84 120 <2e-16 *** 298s --- 298s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 298s 298s Residual standard error: 3.756 on 19 degrees of freedom 298s Number of observations: 20 Degrees of Freedom: 19 298s SSR: 268.114 MSE: 14.111 Root MSE: 3.756 298s Multiple R-Squared: 0 Adjusted R-Squared: 0 299s 299s 299s 3SLS estimates for 'eq2' (equation 2) 299s Model Formula: price ~ 1 299s Instruments: ~income 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 100.02 1.33 75.5 <2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 5.926 on 19 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 19 299s SSR: 667.251 MSE: 35.118 Root MSE: 5.926 299s Multiple R-Squared: 0 Adjusted R-Squared: 0 299s 299s [1] "***************************************************" 299s [1] "3SLS formula: Schmidt" 299s [1] "************* 3SLS *********************************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 33 174 1.03 0.676 0.786 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 65.7 3.87 1.97 0.755 0.726 299s supply 20 16 107.9 6.75 2.60 0.598 0.522 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.87 4.36 299s supply 4.36 6.04 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.87 5.00 299s supply 5.00 6.74 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.00 0.98 299s supply 0.98 1.00 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.6333 7.9208 11.95 1.1e-09 *** 299s price -0.2436 0.0965 -2.52 0.022 * 299s income 0.3140 0.0469 6.69 3.8e-06 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.966 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 299s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 52.1972 11.8934 4.39 0.00046 *** 299s price 0.2286 0.0997 2.29 0.03571 * 299s farmPrice 0.2282 0.0440 5.19 9e-05 *** 299s trend 0.3611 0.0729 4.95 0.00014 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.597 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 107.914 MSE: 6.745 Root MSE: 2.597 299s Multiple R-Squared: 0.598 Adjusted R-Squared: 0.522 299s 299s [1] "********************* 3SLS EViews-like *****************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 33 173 0.719 0.677 0.748 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 65.7 3.87 1.97 0.755 0.726 299s supply 20 16 107.2 6.70 2.59 0.600 0.525 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.29 3.59 299s supply 3.59 4.83 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.29 4.11 299s supply 4.11 5.36 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.979 299s supply 0.979 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 299s price -0.2436 0.0890 -2.74 0.0099 ** 299s income 0.3140 0.0433 7.25 2.5e-08 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.966 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 299s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 52.1176 10.6378 4.90 2.5e-05 *** 299s price 0.2289 0.0892 2.57 0.015 * 299s farmPrice 0.2290 0.0393 5.82 1.6e-06 *** 299s trend 0.3579 0.0652 5.49 4.3e-06 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.589 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 107.216 MSE: 6.701 Root MSE: 2.589 299s Multiple R-Squared: 0.6 Adjusted R-Squared: 0.525 299s 299s [1] "********************* 3SLS with methodResidCov = Theil *****************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 33 174 -0.718 0.675 0.922 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 65.7 3.87 1.97 0.755 0.726 299s supply 20 16 108.7 6.79 2.61 0.594 0.518 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.87 4.50 299s supply 4.50 6.04 299s 299s warning: this covariance matrix is NOT positive semidefinit! 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.87 5.2 299s supply 5.20 6.8 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.981 299s supply 0.981 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 299s price -0.2436 0.0965 -2.52 0.017 * 299s income 0.3140 0.0469 6.69 1.3e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.966 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 299s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 52.2869 11.8853 4.40 0.00011 *** 299s price 0.2282 0.0997 2.29 0.02855 * 299s farmPrice 0.2272 0.0438 5.19 1.0e-05 *** 299s trend 0.3648 0.0707 5.16 1.1e-05 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.607 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 108.727 MSE: 6.795 Root MSE: 2.607 299s Multiple R-Squared: 0.594 Adjusted R-Squared: 0.518 299s 299s [1] "*************** W3SLS with methodResidCov = Theil *****************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 33 174 -0.718 0.675 0.922 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 65.7 3.87 1.97 0.755 0.726 299s supply 20 16 108.7 6.79 2.61 0.594 0.518 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.87 4.50 299s supply 4.50 6.04 299s 299s warning: this covariance matrix is NOT positive semidefinit! 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.87 5.2 299s supply 5.20 6.8 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.981 299s supply 0.981 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 299s price -0.2436 0.0965 -2.52 0.017 * 299s income 0.3140 0.0469 6.69 1.3e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.966 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 299s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 52.2869 11.8853 4.40 0.00011 *** 299s price 0.2282 0.0997 2.29 0.02855 * 299s farmPrice 0.2272 0.0438 5.19 1.0e-05 *** 299s trend 0.3648 0.0707 5.16 1.1e-05 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.607 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 108.727 MSE: 6.795 Root MSE: 2.607 299s Multiple R-Squared: 0.594 Adjusted R-Squared: 0.518 299s 299s [1] "*************** 3SLS with restriction *****************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 34 173 1.27 0.678 0.722 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 67.8 3.99 2.00 0.747 0.717 299s supply 20 16 104.8 6.55 2.56 0.609 0.536 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.97 4.55 299s supply 4.55 6.13 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.99 4.98 299s supply 4.98 6.55 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.975 299s supply 0.975 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.222 8.015 11.76 1.6e-13 *** 299s price -0.222 0.096 -2.31 0.027 * 299s income 0.296 0.045 6.57 1.6e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.997 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 67.796 MSE: 3.988 Root MSE: 1.997 299s Multiple R-Squared: 0.747 Adjusted R-Squared: 0.717 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 55.9604 11.5777 4.83 2.8e-05 *** 299s price 0.2193 0.1002 2.19 0.036 * 299s farmPrice 0.2060 0.0403 5.11 1.3e-05 *** 299s trend 0.2956 0.0450 6.57 1.6e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.559 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 104.753 MSE: 6.547 Root MSE: 2.559 299s Multiple R-Squared: 0.609 Adjusted R-Squared: 0.536 299s 299s [1] "Component “call”: target, current do not match when deparsed" 299s [1] "************** 3SLS with restriction (EViews-like) *****************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 34 171 0.887 0.68 0.678 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 67.5 3.97 1.99 0.748 0.719 299s supply 20 16 104.0 6.50 2.55 0.612 0.539 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.37 3.75 299s supply 3.75 4.91 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.37 4.08 299s supply 4.08 5.20 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.974 299s supply 0.974 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.2737 7.3905 12.76 1.6e-14 *** 299s price -0.2243 0.0888 -2.53 0.016 * 299s income 0.2979 0.0420 7.10 3.4e-08 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.992 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 67.467 MSE: 3.969 Root MSE: 1.992 299s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 55.4521 10.3994 5.33 6.4e-06 *** 299s price 0.2207 0.0896 2.46 0.019 * 299s farmPrice 0.2095 0.0366 5.73 1.9e-06 *** 299s trend 0.2979 0.0420 7.10 3.4e-08 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.55 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 104.013 MSE: 6.501 Root MSE: 2.55 299s Multiple R-Squared: 0.612 Adjusted R-Squared: 0.539 299s 299s [1] 40 299s [1] "*************** W3SLS with restriction *****************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 34 173 1.24 0.677 0.725 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 68.1 4.00 2.00 0.746 0.716 299s supply 20 16 105.2 6.57 2.56 0.608 0.534 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.93 4.56 299s supply 4.56 6.15 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 4.00 5.01 299s supply 5.01 6.57 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.976 299s supply 0.976 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.1823 7.9793 11.8 1.4e-13 *** 299s price -0.2194 0.0954 -2.3 0.028 * 299s income 0.2938 0.0445 6.6 1.4e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.001 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 68.057 MSE: 4.003 Root MSE: 2.001 299s Multiple R-Squared: 0.746 Adjusted R-Squared: 0.716 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 56.2541 11.5687 4.86 2.6e-05 *** 299s price 0.2184 0.1003 2.18 0.036 * 299s farmPrice 0.2040 0.0401 5.09 1.3e-05 *** 299s trend 0.2938 0.0445 6.60 1.4e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.564 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 105.161 MSE: 6.573 Root MSE: 2.564 299s Multiple R-Squared: 0.608 Adjusted R-Squared: 0.534 299s 299s [1] "*************** 3SLS with restriction via restrict.regMat ********************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 34 173 1.27 0.678 0.722 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 67.8 3.99 2.00 0.747 0.717 299s supply 20 16 104.8 6.55 2.56 0.609 0.536 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.97 4.55 299s supply 4.55 6.13 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.99 4.98 299s supply 4.98 6.55 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.975 299s supply 0.975 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.222 8.015 11.76 1.6e-13 *** 299s price -0.222 0.096 -2.31 0.027 * 299s income 0.296 0.045 6.57 1.6e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.997 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 67.796 MSE: 3.988 Root MSE: 1.997 299s Multiple R-Squared: 0.747 Adjusted R-Squared: 0.717 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 55.9604 11.5777 4.83 2.8e-05 *** 299s price 0.2193 0.1002 2.19 0.036 * 299s farmPrice 0.2060 0.0403 5.11 1.3e-05 *** 299s trend 0.2956 0.0450 6.57 1.6e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.559 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 104.753 MSE: 6.547 Root MSE: 2.559 299s Multiple R-Squared: 0.609 Adjusted R-Squared: 0.536 299s 299s [1] "*************** 3SLS with restriction via restrict.regMat (EViews-like) *******" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 34 171 0.887 0.68 0.678 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 67.5 3.97 1.99 0.748 0.719 299s supply 20 16 104.0 6.50 2.55 0.612 0.539 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.37 3.75 299s supply 3.75 4.91 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.37 4.08 299s supply 4.08 5.20 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.974 299s supply 0.974 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.2737 7.3905 12.76 1.6e-14 *** 299s price -0.2243 0.0888 -2.53 0.016 * 299s income 0.2979 0.0420 7.10 3.4e-08 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.992 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 67.467 MSE: 3.969 Root MSE: 1.992 299s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 55.4521 10.3994 5.33 6.4e-06 *** 299s price 0.2207 0.0896 2.46 0.019 * 299s farmPrice 0.2095 0.0366 5.73 1.9e-06 *** 299s trend 0.2979 0.0420 7.10 3.4e-08 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.55 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 104.013 MSE: 6.501 Root MSE: 2.55 299s Multiple R-Squared: 0.612 Adjusted R-Squared: 0.539 299s 299s [1] "**** W3SLS with restriction via restrict.regMat (EViews-like) ****" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 34 172 0.873 0.679 0.681 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 67.7 3.98 2.00 0.748 0.718 299s supply 20 16 104.3 6.52 2.55 0.611 0.538 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.35 3.76 299s supply 3.76 4.92 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.38 4.10 299s supply 4.10 5.22 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.975 299s supply 0.975 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.2409 7.3617 12.80 1.5e-14 *** 299s price -0.2225 0.0883 -2.52 0.017 * 299s income 0.2964 0.0416 7.13 3.1e-08 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.995 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 67.672 MSE: 3.981 Root MSE: 1.995 299s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.718 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 55.6925 10.3937 5.36 5.9e-06 *** 299s price 0.2201 0.0897 2.45 0.019 * 299s farmPrice 0.2078 0.0364 5.71 2.0e-06 *** 299s trend 0.2964 0.0416 7.13 3.1e-08 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.553 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 104.312 MSE: 6.519 Root MSE: 2.553 299s Multiple R-Squared: 0.611 Adjusted R-Squared: 0.538 299s 299s [1] "*************** 3SLS with 2 restrictions **********************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 35 171 1.74 0.681 0.696 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 65.8 3.87 1.97 0.755 0.726 299s supply 20 16 105.4 6.59 2.57 0.607 0.533 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.89 4.53 299s supply 4.53 6.25 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.87 4.87 299s supply 4.87 6.59 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.965 299s supply 0.965 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 93.9070 7.9234 11.85 8.3e-14 *** 299s price -0.2457 0.0891 -2.76 0.0092 ** 299s income 0.3236 0.0233 13.91 8.9e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.967 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 65.807 MSE: 3.871 Root MSE: 1.967 299s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 49.9049 8.1797 6.10 5.7e-07 *** 299s price 0.2543 0.0891 2.85 0.0072 ** 299s farmPrice 0.2293 0.0241 9.52 3.1e-11 *** 299s trend 0.3236 0.0233 13.91 8.9e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.566 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 105.389 MSE: 6.587 Root MSE: 2.566 299s Multiple R-Squared: 0.607 Adjusted R-Squared: 0.533 299s 299s [1] "Component “call”: target, current do not match when deparsed" 299s [1] "*************** 3SLS with 2 restrictions (EViews-like) ************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 35 170 1.19 0.683 0.658 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 65.6 3.86 1.96 0.755 0.727 299s supply 20 16 104.6 6.54 2.56 0.610 0.537 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.30 3.73 299s supply 3.73 5.00 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.28 4.00 299s supply 4.00 5.23 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.965 299s supply 0.965 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.1703 7.3154 12.87 7.8e-15 *** 299s price -0.2494 0.0812 -3.07 0.0041 ** 299s income 0.3248 0.0209 15.57 < 2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.964 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 65.577 MSE: 3.857 Root MSE: 1.964 299s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.727 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 50.0853 7.5503 6.63 1.1e-07 *** 299s price 0.2506 0.0812 3.09 0.0039 ** 299s farmPrice 0.2312 0.0212 10.88 8.9e-13 *** 299s trend 0.3248 0.0209 15.57 < 2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.557 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 104.607 MSE: 6.538 Root MSE: 2.557 299s Multiple R-Squared: 0.61 Adjusted R-Squared: 0.537 299s 299s [1] "********** W3SLS with 2 (symbolic) restrictions ***************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 35 172 1.74 0.68 0.697 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 65.9 3.88 1.97 0.754 0.725 299s supply 20 16 105.7 6.60 2.57 0.606 0.532 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.88 4.55 299s supply 4.55 6.27 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.88 4.88 299s supply 4.88 6.60 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.965 299s supply 0.965 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 93.7870 7.9088 11.86 8.2e-14 *** 299s price -0.2443 0.0892 -2.74 0.0096 ** 299s income 0.3234 0.0229 14.14 4.4e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.969 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 65.907 MSE: 3.877 Root MSE: 1.969 299s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 49.8093 8.1522 6.11 5.5e-07 *** 299s price 0.2557 0.0892 2.87 0.0069 ** 299s farmPrice 0.2289 0.0237 9.67 2.0e-11 *** 299s trend 0.3234 0.0229 14.14 4.4e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.57 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 105.659 MSE: 6.604 Root MSE: 2.57 299s Multiple R-Squared: 0.606 Adjusted R-Squared: 0.532 299s 299s [1] "*************** 3SLS with 2 restrictions via R and restrict.regMat **********" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 35 171 1.74 0.681 0.696 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 65.8 3.87 1.97 0.755 0.726 299s supply 20 16 105.4 6.59 2.57 0.607 0.533 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.89 4.53 299s supply 4.53 6.25 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.87 4.87 299s supply 4.87 6.59 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.965 299s supply 0.965 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 93.9070 7.9234 11.85 8.3e-14 *** 299s price -0.2457 0.0891 -2.76 0.0092 ** 299s income 0.3236 0.0233 13.91 8.9e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.967 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 65.807 MSE: 3.871 Root MSE: 1.967 299s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 49.9049 8.1797 6.10 5.7e-07 *** 299s price 0.2543 0.0891 2.85 0.0072 ** 299s farmPrice 0.2293 0.0241 9.52 3.1e-11 *** 299s trend 0.3236 0.0233 13.91 8.9e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.566 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 105.389 MSE: 6.587 Root MSE: 2.566 299s Multiple R-Squared: 0.607 Adjusted R-Squared: 0.533 299s 299s [1] "Component “call”: target, current do not match when deparsed" 299s [1] "******** 3SLS with 2 restrictions via R and restrict.regMat (EViews-like)*****" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 35 170 1.19 0.683 0.658 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 65.6 3.86 1.96 0.755 0.727 299s supply 20 16 104.6 6.54 2.56 0.610 0.537 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.30 3.73 299s supply 3.73 5.00 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.28 4.00 299s supply 4.00 5.23 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.965 299s supply 0.965 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.1703 7.3154 12.87 7.8e-15 *** 299s price -0.2494 0.0812 -3.07 0.0041 ** 299s income 0.3248 0.0209 15.57 < 2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.964 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 65.577 MSE: 3.857 Root MSE: 1.964 299s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.727 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 50.0853 7.5503 6.63 1.1e-07 *** 299s price 0.2506 0.0812 3.09 0.0039 ** 299s farmPrice 0.2312 0.0212 10.88 8.9e-13 *** 299s trend 0.3248 0.0209 15.57 < 2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.557 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 104.607 MSE: 6.538 Root MSE: 2.557 299s Multiple R-Squared: 0.61 Adjusted R-Squared: 0.537 299s 299s [1] "*** W3SLS with 2 restrictions via R and restrict.regMat (EViews-like) ***" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 35 170 1.19 0.682 0.659 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 65.6 3.86 1.97 0.755 0.726 299s supply 20 16 104.8 6.55 2.56 0.609 0.536 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.30 3.75 299s supply 3.75 5.01 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.28 4.00 299s supply 4.00 5.24 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.965 299s supply 0.965 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.0830 7.3058 12.88 7.5e-15 *** 299s price -0.2484 0.0812 -3.06 0.0042 ** 299s income 0.3246 0.0205 15.81 < 2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.965 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 65.646 MSE: 3.862 Root MSE: 1.965 299s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 50.0190 7.5314 6.64 1.1e-07 *** 299s price 0.2516 0.0812 3.10 0.0038 ** 299s farmPrice 0.2309 0.0209 11.05 5.9e-13 *** 299s trend 0.3246 0.0205 15.81 < 2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.559 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 104.795 MSE: 6.55 Root MSE: 2.559 299s Multiple R-Squared: 0.609 Adjusted R-Squared: 0.536 299s 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 36 3690 5613 0.012 0.368 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s eq1 20 19 2132 112.2 10.59 0.305 0.305 299s eq2 20 17 1558 91.7 9.57 -1.335 -1.610 299s 299s The covariance matrix of the residuals used for estimation 299s eq1 eq2 299s eq1 112.2 -44.8 299s eq2 -44.8 56.8 299s 299s The covariance matrix of the residuals 299s eq1 eq2 299s eq1 112.2 -68.3 299s eq2 -68.3 91.7 299s 299s The correlations of the residuals 299s eq1 eq2 299s eq1 1.000 -0.674 299s eq2 -0.674 1.000 299s 299s 299s 3SLS estimates for 'eq1' (equation 1) 299s Model Formula: farmPrice ~ consump - 1 299s Instruments: ~trend + income 299s 299s Estimate Std. Error t value Pr(>|t|) 299s consump 0.9588 0.0235 40.9 <2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 10.592 on 19 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 19 299s SSR: 2131.725 MSE: 112.196 Root MSE: 10.592 299s Multiple R-Squared: 0.305 Adjusted R-Squared: 0.305 299s 299s 299s 3SLS estimates for 'eq2' (equation 2) 299s Model Formula: price ~ consump + trend 299s Instruments: ~trend + income 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) -92.192 49.896 -1.85 0.0821 . 299s consump 1.953 0.499 3.92 0.0011 ** 299s trend -0.469 0.247 -1.90 0.0743 . 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 9.574 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 1558.311 MSE: 91.665 Root MSE: 9.574 299s Multiple R-Squared: -1.335 Adjusted R-Squared: -1.61 299s 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 38 56326 283068 -104 -10.6 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s eq1 20 19 2313 122 11.0 -7.63 -7.63 299s eq2 20 19 54013 2843 53.3 -200.46 -200.46 299s 299s The covariance matrix of the residuals used for estimation 299s eq1 eq2 299s eq1 121 -255 299s eq2 -255 2953 299s 299s The covariance matrix of the residuals 299s eq1 eq2 299s eq1 122 -251 299s eq2 -251 2843 299s 299s The correlations of the residuals 299s eq1 eq2 299s eq1 1.000 -0.433 299s eq2 -0.433 1.000 299s 299s 299s 3SLS estimates for 'eq1' (equation 1) 299s Model Formula: consump ~ farmPrice - 1 299s Instruments: ~price + income 299s 299s Estimate Std. Error t value Pr(>|t|) 299s farmPrice 1.0417 0.0253 41.2 <2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 11.034 on 19 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 19 299s SSR: 2313.073 MSE: 121.741 Root MSE: 11.034 299s Multiple R-Squared: -7.627 Adjusted R-Squared: -7.627 299s 299s 299s 3SLS estimates for 'eq2' (equation 2) 299s Model Formula: consump ~ trend - 1 299s Instruments: ~price + income 299s 299s Estimate Std. Error t value Pr(>|t|) 299s trend 9.02 1.13 8 1.7e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 53.318 on 19 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 19 299s SSR: 54013.367 MSE: 2842.809 Root MSE: 53.318 299s Multiple R-Squared: -200.456 Adjusted R-Squared: -200.456 299s 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 38 167069 397886 -49.1 -0.82 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s eq1 20 19 76692 4036 63.5 -285.0 -285.0 299s eq2 20 19 90377 4757 69.0 -28.5 -28.5 299s 299s The covariance matrix of the residuals used for estimation 299s eq1 eq2 299s eq1 2682 2547 299s eq2 2547 2741 299s 299s The covariance matrix of the residuals 299s eq1 eq2 299s eq1 4036 4336 299s eq2 4336 4757 299s 299s The correlations of the residuals 299s eq1 eq2 299s eq1 1.000 0.928 299s eq2 0.928 1.000 299s 299s 299s 3SLS estimates for 'eq1' (equation 1) 299s Model Formula: consump ~ trend - 1 299s Instruments: ~income + farmPrice 299s 299s Estimate Std. Error t value Pr(>|t|) 299s trend 4.162 0.723 5.75 1.5e-05 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 63.533 on 19 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 19 299s SSR: 76691.636 MSE: 4036.402 Root MSE: 63.533 299s Multiple R-Squared: -285.041 Adjusted R-Squared: -285.041 299s 299s 299s 3SLS estimates for 'eq2' (equation 2) 299s Model Formula: farmPrice ~ trend - 1 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s trend 3.274 0.676 4.84 0.00011 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 68.969 on 19 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 19 299s SSR: 90377.499 MSE: 4756.71 Root MSE: 68.969 299s Multiple R-Squared: -28.451 Adjusted R-Squared: -28.451 299s 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 39 161126 1162329 -171 -17.4 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s eq1 20 19 3553 187 13.7 -12.3 -12.3 299s eq2 20 19 157573 8293 91.1 -235.2 -235.2 299s 299s The covariance matrix of the residuals used for estimation 299s eq1 eq2 299s eq1 208 -731 299s eq2 -731 8271 299s 299s The covariance matrix of the residuals 299s eq1 eq2 299s eq1 187 -623 299s eq2 -623 8293 299s 299s The correlations of the residuals 299s eq1 eq2 299s eq1 1.000 -0.121 299s eq2 -0.121 1.000 299s 299s 299s 3SLS estimates for 'eq1' (equation 1) 299s Model Formula: consump ~ farmPrice - 1 299s Instruments: ~farmPrice + trend + income 299s 299s Estimate Std. Error t value Pr(>|t|) 299s farmPrice 1.1122 0.0272 40.8 <2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 13.675 on 19 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 19 299s SSR: 3553.118 MSE: 187.006 Root MSE: 13.675 299s Multiple R-Squared: -12.252 Adjusted R-Squared: -12.252 299s 299s 299s 3SLS estimates for 'eq2' (equation 2) 299s Model Formula: price ~ trend - 1 299s Instruments: ~farmPrice + trend + income 299s 299s Estimate Std. Error t value Pr(>|t|) 299s trend 1.1122 0.0272 40.8 <2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 91.068 on 19 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 19 299s SSR: 157573.328 MSE: 8293.333 Root MSE: 91.068 299s Multiple R-Squared: -235.153 Adjusted R-Squared: -235.153 299s 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 38 935 491 0 0 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s eq1 20 19 268 14.1 3.76 0 0 299s eq2 20 19 667 35.1 5.93 0 0 299s 299s The covariance matrix of the residuals used for estimation 299s eq1 eq2 299s eq1 14.11 2.18 299s eq2 2.18 35.12 299s 299s The covariance matrix of the residuals 299s eq1 eq2 299s eq1 14.11 2.18 299s eq2 2.18 35.12 299s 299s The correlations of the residuals 299s eq1 eq2 299s eq1 1.0000 0.0981 299s eq2 0.0981 1.0000 299s 299s 299s 3SLS estimates for 'eq1' (equation 1) 299s Model Formula: consump ~ 1 299s Instruments: ~income 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 100.90 0.84 120 <2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 3.756 on 19 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 19 299s SSR: 268.114 MSE: 14.111 Root MSE: 3.756 299s Multiple R-Squared: 0 Adjusted R-Squared: 0 299s 299s 299s 3SLS estimates for 'eq2' (equation 2) 299s Model Formula: price ~ 1 299s Instruments: ~income 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 100.02 1.33 75.5 <2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 5.926 on 19 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 19 299s SSR: 667.251 MSE: 35.118 Root MSE: 5.926 299s Multiple R-Squared: 0 Adjusted R-Squared: 0 299s 299s [1] "***************************************************" 299s [1] "3SLS formula: GMM" 299s [1] "************* 3SLS *********************************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 33 174 1.03 0.676 0.786 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 65.7 3.87 1.97 0.755 0.726 299s supply 20 16 107.9 6.75 2.60 0.598 0.522 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.87 4.36 299s supply 4.36 6.04 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.87 5.00 299s supply 5.00 6.74 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.00 0.98 299s supply 0.98 1.00 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.6333 7.9208 11.95 1.1e-09 *** 299s price -0.2436 0.0965 -2.52 0.022 * 299s income 0.3140 0.0469 6.69 3.8e-06 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.966 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 299s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 52.1972 11.8934 4.39 0.00046 *** 299s price 0.2286 0.0997 2.29 0.03571 * 299s farmPrice 0.2282 0.0440 5.19 9e-05 *** 299s trend 0.3611 0.0729 4.95 0.00014 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.597 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 107.914 MSE: 6.745 Root MSE: 2.597 299s Multiple R-Squared: 0.598 Adjusted R-Squared: 0.522 299s 299s [1] "********************* 3SLS EViews-like *****************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 33 173 0.719 0.677 0.748 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 65.7 3.87 1.97 0.755 0.726 299s supply 20 16 107.2 6.70 2.59 0.600 0.525 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.29 3.59 299s supply 3.59 4.83 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.29 4.11 299s supply 4.11 5.36 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.979 299s supply 0.979 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 299s price -0.2436 0.0890 -2.74 0.0099 ** 299s income 0.3140 0.0433 7.25 2.5e-08 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.966 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 299s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 52.1176 10.6378 4.90 2.5e-05 *** 299s price 0.2289 0.0892 2.57 0.015 * 299s farmPrice 0.2290 0.0393 5.82 1.6e-06 *** 299s trend 0.3579 0.0652 5.49 4.3e-06 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.589 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 107.216 MSE: 6.701 Root MSE: 2.589 299s Multiple R-Squared: 0.6 Adjusted R-Squared: 0.525 299s 299s [1] "********************* 3SLS with methodResidCov = Theil *****************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 33 174 -0.718 0.675 0.922 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 65.7 3.87 1.97 0.755 0.726 299s supply 20 16 108.7 6.79 2.61 0.594 0.518 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.87 4.50 299s supply 4.50 6.04 299s 299s warning: this covariance matrix is NOT positive semidefinit! 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.87 5.2 299s supply 5.20 6.8 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.981 299s supply 0.981 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 299s price -0.2436 0.0965 -2.52 0.017 * 299s income 0.3140 0.0469 6.69 1.3e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.966 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 299s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 52.2869 11.8853 4.40 0.00011 *** 299s price 0.2282 0.0997 2.29 0.02855 * 299s farmPrice 0.2272 0.0438 5.19 1.0e-05 *** 299s trend 0.3648 0.0707 5.16 1.1e-05 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.607 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 108.727 MSE: 6.795 Root MSE: 2.607 299s Multiple R-Squared: 0.594 Adjusted R-Squared: 0.518 299s 299s [1] "*************** W3SLS with methodResidCov = Theil *****************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 33 174 -0.718 0.675 0.922 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 65.7 3.87 1.97 0.755 0.726 299s supply 20 16 108.7 6.79 2.61 0.594 0.518 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.87 4.50 299s supply 4.50 6.04 299s 299s warning: this covariance matrix is NOT positive semidefinit! 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.87 5.2 299s supply 5.20 6.8 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.981 299s supply 0.981 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 299s price -0.2436 0.0965 -2.52 0.017 * 299s income 0.3140 0.0469 6.69 1.3e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.966 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 299s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 52.2869 11.8853 4.40 0.00011 *** 299s price 0.2282 0.0997 2.29 0.02855 * 299s farmPrice 0.2272 0.0438 5.19 1.0e-05 *** 299s trend 0.3648 0.0707 5.16 1.1e-05 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.607 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 108.727 MSE: 6.795 Root MSE: 2.607 299s Multiple R-Squared: 0.594 Adjusted R-Squared: 0.518 299s 299s [1] "*************** 3SLS with restriction *****************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 34 173 1.27 0.678 0.722 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 67.8 3.99 2.00 0.747 0.717 299s supply 20 16 104.8 6.55 2.56 0.609 0.536 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.97 4.55 299s supply 4.55 6.13 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.99 4.98 299s supply 4.98 6.55 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.975 299s supply 0.975 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.222 8.015 11.76 1.6e-13 *** 299s price -0.222 0.096 -2.31 0.027 * 299s income 0.296 0.045 6.57 1.6e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.997 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 67.796 MSE: 3.988 Root MSE: 1.997 299s Multiple R-Squared: 0.747 Adjusted R-Squared: 0.717 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 55.9604 11.5777 4.83 2.8e-05 *** 299s price 0.2193 0.1002 2.19 0.036 * 299s farmPrice 0.2060 0.0403 5.11 1.3e-05 *** 299s trend 0.2956 0.0450 6.57 1.6e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.559 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 104.753 MSE: 6.547 Root MSE: 2.559 299s Multiple R-Squared: 0.609 Adjusted R-Squared: 0.536 299s 299s [1] "Component “call”: target, current do not match when deparsed" 299s [1] "************** 3SLS with restriction (EViews-like) *****************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 34 171 0.887 0.68 0.678 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 67.5 3.97 1.99 0.748 0.719 299s supply 20 16 104.0 6.50 2.55 0.612 0.539 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.37 3.75 299s supply 3.75 4.91 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.37 4.08 299s supply 4.08 5.20 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.974 299s supply 0.974 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.2737 7.3905 12.76 1.6e-14 *** 299s price -0.2243 0.0888 -2.53 0.016 * 299s income 0.2979 0.0420 7.10 3.4e-08 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.992 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 67.467 MSE: 3.969 Root MSE: 1.992 299s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 55.4521 10.3994 5.33 6.4e-06 *** 299s price 0.2207 0.0896 2.46 0.019 * 299s farmPrice 0.2095 0.0366 5.73 1.9e-06 *** 299s trend 0.2979 0.0420 7.10 3.4e-08 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.55 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 104.013 MSE: 6.501 Root MSE: 2.55 299s Multiple R-Squared: 0.612 Adjusted R-Squared: 0.539 299s 299s [1] 40 299s [1] "*************** W3SLS with restriction *****************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 34 173 1.24 0.677 0.725 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 68.1 4.00 2.00 0.746 0.716 299s supply 20 16 105.2 6.57 2.56 0.608 0.534 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.93 4.56 299s supply 4.56 6.15 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 4.00 5.01 299s supply 5.01 6.57 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.976 299s supply 0.976 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.1823 7.9793 11.8 1.4e-13 *** 299s price -0.2194 0.0954 -2.3 0.028 * 299s income 0.2938 0.0445 6.6 1.4e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.001 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 68.057 MSE: 4.003 Root MSE: 2.001 299s Multiple R-Squared: 0.746 Adjusted R-Squared: 0.716 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 56.2541 11.5687 4.86 2.6e-05 *** 299s price 0.2184 0.1003 2.18 0.036 * 299s farmPrice 0.2040 0.0401 5.09 1.3e-05 *** 299s trend 0.2938 0.0445 6.60 1.4e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.564 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 105.161 MSE: 6.573 Root MSE: 2.564 299s Multiple R-Squared: 0.608 Adjusted R-Squared: 0.534 299s 299s [1] "*************** 3SLS with restriction via restrict.regMat ********************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 34 173 1.27 0.678 0.722 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 67.8 3.99 2.00 0.747 0.717 299s supply 20 16 104.8 6.55 2.56 0.609 0.536 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.97 4.55 299s supply 4.55 6.13 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.99 4.98 299s supply 4.98 6.55 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.975 299s supply 0.975 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.222 8.015 11.76 1.6e-13 *** 299s price -0.222 0.096 -2.31 0.027 * 299s income 0.296 0.045 6.57 1.6e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.997 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 67.796 MSE: 3.988 Root MSE: 1.997 299s Multiple R-Squared: 0.747 Adjusted R-Squared: 0.717 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 55.9604 11.5777 4.83 2.8e-05 *** 299s price 0.2193 0.1002 2.19 0.036 * 299s farmPrice 0.2060 0.0403 5.11 1.3e-05 *** 299s trend 0.2956 0.0450 6.57 1.6e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.559 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 104.753 MSE: 6.547 Root MSE: 2.559 299s Multiple R-Squared: 0.609 Adjusted R-Squared: 0.536 299s 299s [1] "*************** 3SLS with restriction via restrict.regMat (EViews-like) *******" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 34 171 0.887 0.68 0.678 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 67.5 3.97 1.99 0.748 0.719 299s supply 20 16 104.0 6.50 2.55 0.612 0.539 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.37 3.75 299s supply 3.75 4.91 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.37 4.08 299s supply 4.08 5.20 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.974 299s supply 0.974 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.2737 7.3905 12.76 1.6e-14 *** 299s price -0.2243 0.0888 -2.53 0.016 * 299s income 0.2979 0.0420 7.10 3.4e-08 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.992 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 67.467 MSE: 3.969 Root MSE: 1.992 299s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 55.4521 10.3994 5.33 6.4e-06 *** 299s price 0.2207 0.0896 2.46 0.019 * 299s farmPrice 0.2095 0.0366 5.73 1.9e-06 *** 299s trend 0.2979 0.0420 7.10 3.4e-08 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.55 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 104.013 MSE: 6.501 Root MSE: 2.55 299s Multiple R-Squared: 0.612 Adjusted R-Squared: 0.539 299s 299s [1] "**** W3SLS with restriction via restrict.regMat (EViews-like) ****" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 34 172 0.873 0.679 0.681 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 67.7 3.98 2.00 0.748 0.718 299s supply 20 16 104.3 6.52 2.55 0.611 0.538 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.35 3.76 299s supply 3.76 4.92 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.38 4.10 299s supply 4.10 5.22 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.975 299s supply 0.975 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.2409 7.3617 12.80 1.5e-14 *** 299s price -0.2225 0.0883 -2.52 0.017 * 299s income 0.2964 0.0416 7.13 3.1e-08 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.995 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 67.672 MSE: 3.981 Root MSE: 1.995 299s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.718 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 55.6925 10.3937 5.36 5.9e-06 *** 299s price 0.2201 0.0897 2.45 0.019 * 299s farmPrice 0.2078 0.0364 5.71 2.0e-06 *** 299s trend 0.2964 0.0416 7.13 3.1e-08 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.553 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 104.312 MSE: 6.519 Root MSE: 2.553 299s Multiple R-Squared: 0.611 Adjusted R-Squared: 0.538 299s 299s [1] "*************** 3SLS with 2 restrictions **********************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 35 171 1.74 0.681 0.696 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 65.8 3.87 1.97 0.755 0.726 299s supply 20 16 105.4 6.59 2.57 0.607 0.533 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.89 4.53 299s supply 4.53 6.25 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.87 4.87 299s supply 4.87 6.59 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.965 299s supply 0.965 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 93.9070 7.9234 11.85 8.3e-14 *** 299s price -0.2457 0.0891 -2.76 0.0092 ** 299s income 0.3236 0.0233 13.91 8.9e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.967 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 65.807 MSE: 3.871 Root MSE: 1.967 299s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 49.9049 8.1797 6.10 5.7e-07 *** 299s price 0.2543 0.0891 2.85 0.0072 ** 299s farmPrice 0.2293 0.0241 9.52 3.1e-11 *** 299s trend 0.3236 0.0233 13.91 8.9e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.566 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 105.389 MSE: 6.587 Root MSE: 2.566 299s Multiple R-Squared: 0.607 Adjusted R-Squared: 0.533 299s 299s [1] "Component “call”: target, current do not match when deparsed" 299s [1] "*************** 3SLS with 2 restrictions (EViews-like) ************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 35 170 1.19 0.683 0.658 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 65.6 3.86 1.96 0.755 0.727 299s supply 20 16 104.6 6.54 2.56 0.610 0.537 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.30 3.73 299s supply 3.73 5.00 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.28 4.00 299s supply 4.00 5.23 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.965 299s supply 0.965 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.1703 7.3154 12.87 7.8e-15 *** 299s price -0.2494 0.0812 -3.07 0.0041 ** 299s income 0.3248 0.0209 15.57 < 2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.964 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 65.577 MSE: 3.857 Root MSE: 1.964 299s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.727 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 50.0853 7.5503 6.63 1.1e-07 *** 299s price 0.2506 0.0812 3.09 0.0039 ** 299s farmPrice 0.2312 0.0212 10.88 8.9e-13 *** 299s trend 0.3248 0.0209 15.57 < 2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.557 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 104.607 MSE: 6.538 Root MSE: 2.557 299s Multiple R-Squared: 0.61 Adjusted R-Squared: 0.537 299s 299s [1] "********** W3SLS with 2 (symbolic) restrictions ***************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 35 172 1.74 0.68 0.697 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 65.9 3.88 1.97 0.754 0.725 299s supply 20 16 105.7 6.60 2.57 0.606 0.532 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.88 4.55 299s supply 4.55 6.27 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.88 4.88 299s supply 4.88 6.60 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.965 299s supply 0.965 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 93.7870 7.9088 11.86 8.2e-14 *** 299s price -0.2443 0.0892 -2.74 0.0096 ** 299s income 0.3234 0.0229 14.14 4.4e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.969 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 65.907 MSE: 3.877 Root MSE: 1.969 299s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 49.8093 8.1522 6.11 5.5e-07 *** 299s price 0.2557 0.0892 2.87 0.0069 ** 299s farmPrice 0.2289 0.0237 9.67 2.0e-11 *** 299s trend 0.3234 0.0229 14.14 4.4e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.57 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 105.659 MSE: 6.604 Root MSE: 2.57 299s Multiple R-Squared: 0.606 Adjusted R-Squared: 0.532 299s 299s [1] "*************** 3SLS with 2 restrictions via R and restrict.regMat **********" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 35 171 1.74 0.681 0.696 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 65.8 3.87 1.97 0.755 0.726 299s supply 20 16 105.4 6.59 2.57 0.607 0.533 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.89 4.53 299s supply 4.53 6.25 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.87 4.87 299s supply 4.87 6.59 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.965 299s supply 0.965 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 93.9070 7.9234 11.85 8.3e-14 *** 299s price -0.2457 0.0891 -2.76 0.0092 ** 299s income 0.3236 0.0233 13.91 8.9e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.967 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 65.807 MSE: 3.871 Root MSE: 1.967 299s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 49.9049 8.1797 6.10 5.7e-07 *** 299s price 0.2543 0.0891 2.85 0.0072 ** 299s farmPrice 0.2293 0.0241 9.52 3.1e-11 *** 299s trend 0.3236 0.0233 13.91 8.9e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.566 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 105.389 MSE: 6.587 Root MSE: 2.566 299s Multiple R-Squared: 0.607 Adjusted R-Squared: 0.533 299s 299s [1] "Component “call”: target, current do not match when deparsed" 299s [1] "******** 3SLS with 2 restrictions via R and restrict.regMat (EViews-like)*****" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 35 170 1.19 0.683 0.658 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 65.6 3.86 1.96 0.755 0.727 299s supply 20 16 104.6 6.54 2.56 0.610 0.537 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.30 3.73 299s supply 3.73 5.00 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.28 4.00 299s supply 4.00 5.23 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.965 299s supply 0.965 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.1703 7.3154 12.87 7.8e-15 *** 299s price -0.2494 0.0812 -3.07 0.0041 ** 299s income 0.3248 0.0209 15.57 < 2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.964 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 65.577 MSE: 3.857 Root MSE: 1.964 299s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.727 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 50.0853 7.5503 6.63 1.1e-07 *** 299s price 0.2506 0.0812 3.09 0.0039 ** 299s farmPrice 0.2312 0.0212 10.88 8.9e-13 *** 299s trend 0.3248 0.0209 15.57 < 2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.557 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 104.607 MSE: 6.538 Root MSE: 2.557 299s Multiple R-Squared: 0.61 Adjusted R-Squared: 0.537 299s 299s [1] "*** W3SLS with 2 restrictions via R and restrict.regMat (EViews-like) ***" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 35 170 1.19 0.682 0.659 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 65.6 3.86 1.97 0.755 0.726 299s supply 20 16 104.8 6.55 2.56 0.609 0.536 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.30 3.75 299s supply 3.75 5.01 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.28 4.00 299s supply 4.00 5.24 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.965 299s supply 0.965 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.0830 7.3058 12.88 7.5e-15 *** 299s price -0.2484 0.0812 -3.06 0.0042 ** 299s income 0.3246 0.0205 15.81 < 2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.965 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 65.646 MSE: 3.862 Root MSE: 1.965 299s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 50.0190 7.5314 6.64 1.1e-07 *** 299s price 0.2516 0.0812 3.10 0.0038 ** 299s farmPrice 0.2309 0.0209 11.05 5.9e-13 *** 299s trend 0.3246 0.0205 15.81 < 2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.559 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 104.795 MSE: 6.55 Root MSE: 2.559 299s Multiple R-Squared: 0.609 Adjusted R-Squared: 0.536 299s 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 36 3690 5613 0.012 0.368 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s eq1 20 19 2132 112.2 10.59 0.305 0.305 299s eq2 20 17 1558 91.7 9.57 -1.335 -1.610 299s 299s The covariance matrix of the residuals used for estimation 299s eq1 eq2 299s eq1 112.2 -44.8 299s eq2 -44.8 56.8 299s 299s The covariance matrix of the residuals 299s eq1 eq2 299s eq1 112.2 -68.3 299s eq2 -68.3 91.7 299s 299s The correlations of the residuals 299s eq1 eq2 299s eq1 1.000 -0.674 299s eq2 -0.674 1.000 299s 299s 299s 3SLS estimates for 'eq1' (equation 1) 299s Model Formula: farmPrice ~ consump - 1 299s Instruments: ~trend + income 299s 299s Estimate Std. Error t value Pr(>|t|) 299s consump 0.9588 0.0235 40.9 <2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 10.592 on 19 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 19 299s SSR: 2131.725 MSE: 112.196 Root MSE: 10.592 299s Multiple R-Squared: 0.305 Adjusted R-Squared: 0.305 299s 299s 299s 3SLS estimates for 'eq2' (equation 2) 299s Model Formula: price ~ consump + trend 299s Instruments: ~trend + income 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) -92.192 49.896 -1.85 0.0821 . 299s consump 1.953 0.499 3.92 0.0011 ** 299s trend -0.469 0.247 -1.90 0.0743 . 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 9.574 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 1558.311 MSE: 91.665 Root MSE: 9.574 299s Multiple R-Squared: -1.335 Adjusted R-Squared: -1.61 299s 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 38 56326 283068 -104 -10.6 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s eq1 20 19 2313 122 11.0 -7.63 -7.63 299s eq2 20 19 54013 2843 53.3 -200.46 -200.46 299s 299s The covariance matrix of the residuals used for estimation 299s eq1 eq2 299s eq1 121 -255 299s eq2 -255 2953 299s 299s The covariance matrix of the residuals 299s eq1 eq2 299s eq1 122 -251 299s eq2 -251 2843 299s 299s The correlations of the residuals 299s eq1 eq2 299s eq1 1.000 -0.433 299s eq2 -0.433 1.000 299s 299s 299s 3SLS estimates for 'eq1' (equation 1) 299s Model Formula: consump ~ farmPrice - 1 299s Instruments: ~price + income 299s 299s Estimate Std. Error t value Pr(>|t|) 299s farmPrice 1.0417 0.0253 41.2 <2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 11.034 on 19 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 19 299s SSR: 2313.073 MSE: 121.741 Root MSE: 11.034 299s Multiple R-Squared: -7.627 Adjusted R-Squared: -7.627 299s 299s 299s 3SLS estimates for 'eq2' (equation 2) 299s Model Formula: consump ~ trend - 1 299s Instruments: ~price + income 299s 299s Estimate Std. Error t value Pr(>|t|) 299s trend 9.02 1.13 8 1.7e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 53.318 on 19 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 19 299s SSR: 54013.367 MSE: 2842.809 Root MSE: 53.318 299s Multiple R-Squared: -200.456 Adjusted R-Squared: -200.456 299s 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 38 167069 397886 -49.1 -0.82 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s eq1 20 19 76692 4036 63.5 -285.0 -285.0 299s eq2 20 19 90377 4757 69.0 -28.5 -28.5 299s 299s The covariance matrix of the residuals used for estimation 299s eq1 eq2 299s eq1 2682 2547 299s eq2 2547 2741 299s 299s The covariance matrix of the residuals 299s eq1 eq2 299s eq1 4036 4336 299s eq2 4336 4757 299s 299s The correlations of the residuals 299s eq1 eq2 299s eq1 1.000 0.928 299s eq2 0.928 1.000 299s 299s 299s 3SLS estimates for 'eq1' (equation 1) 299s Model Formula: consump ~ trend - 1 299s Instruments: ~income + farmPrice 299s 299s Estimate Std. Error t value Pr(>|t|) 299s trend 4.162 0.723 5.75 1.5e-05 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 63.533 on 19 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 19 299s SSR: 76691.636 MSE: 4036.402 Root MSE: 63.533 299s Multiple R-Squared: -285.041 Adjusted R-Squared: -285.041 299s 299s 299s 3SLS estimates for 'eq2' (equation 2) 299s Model Formula: farmPrice ~ trend - 1 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s trend 3.274 0.676 4.84 0.00011 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 68.969 on 19 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 19 299s SSR: 90377.499 MSE: 4756.71 Root MSE: 68.969 299s Multiple R-Squared: -28.451 Adjusted R-Squared: -28.451 299s 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 39 161126 1162329 -171 -17.4 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s eq1 20 19 3553 187 13.7 -12.3 -12.3 299s eq2 20 19 157573 8293 91.1 -235.2 -235.2 299s 299s The covariance matrix of the residuals used for estimation 299s eq1 eq2 299s eq1 208 -731 299s eq2 -731 8271 299s 299s The covariance matrix of the residuals 299s eq1 eq2 299s eq1 187 -623 299s eq2 -623 8293 299s 299s The correlations of the residuals 299s eq1 eq2 299s eq1 1.000 -0.121 299s eq2 -0.121 1.000 299s 299s 299s 3SLS estimates for 'eq1' (equation 1) 299s Model Formula: consump ~ farmPrice - 1 299s Instruments: ~farmPrice + trend + income 299s 299s Estimate Std. Error t value Pr(>|t|) 299s farmPrice 1.1122 0.0272 40.8 <2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 13.675 on 19 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 19 299s SSR: 3553.118 MSE: 187.006 Root MSE: 13.675 299s Multiple R-Squared: -12.252 Adjusted R-Squared: -12.252 299s 299s 299s 3SLS estimates for 'eq2' (equation 2) 299s Model Formula: price ~ trend - 1 299s Instruments: ~farmPrice + trend + income 299s 299s Estimate Std. Error t value Pr(>|t|) 299s trend 1.1122 0.0272 40.8 <2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 91.068 on 19 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 19 299s SSR: 157573.328 MSE: 8293.333 Root MSE: 91.068 299s Multiple R-Squared: -235.153 Adjusted R-Squared: -235.153 299s 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 38 935 491 0 0 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s eq1 20 19 268 14.1 3.76 0 0 299s eq2 20 19 667 35.1 5.93 0 0 299s 299s The covariance matrix of the residuals used for estimation 299s eq1 eq2 299s eq1 14.11 2.18 299s eq2 2.18 35.12 299s 299s The covariance matrix of the residuals 299s eq1 eq2 299s eq1 14.11 2.18 299s eq2 2.18 35.12 299s 299s The correlations of the residuals 299s eq1 eq2 299s eq1 1.0000 0.0981 299s eq2 0.0981 1.0000 299s 299s 299s 3SLS estimates for 'eq1' (equation 1) 299s Model Formula: consump ~ 1 299s Instruments: ~income 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 100.90 0.84 120 <2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 3.756 on 19 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 19 299s SSR: 268.114 MSE: 14.111 Root MSE: 3.756 299s Multiple R-Squared: 0 Adjusted R-Squared: 0 299s 299s 299s 3SLS estimates for 'eq2' (equation 2) 299s Model Formula: price ~ 1 299s Instruments: ~income 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 100.02 1.33 75.5 <2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 5.926 on 19 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 19 299s SSR: 667.251 MSE: 35.118 Root MSE: 5.926 299s Multiple R-Squared: 0 Adjusted R-Squared: 0 299s 299s [1] "***************************************************" 299s [1] "3SLS formula: EViews" 299s [1] "************* 3SLS *********************************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 33 174 1.03 0.676 0.786 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 65.7 3.87 1.97 0.755 0.726 299s supply 20 16 107.9 6.75 2.60 0.598 0.522 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.87 4.36 299s supply 4.36 6.04 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.87 5.00 299s supply 5.00 6.74 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.00 0.98 299s supply 0.98 1.00 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.6333 7.9208 11.95 1.1e-09 *** 299s price -0.2436 0.0965 -2.52 0.022 * 299s income 0.3140 0.0469 6.69 3.8e-06 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.966 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 299s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 52.1972 11.8934 4.39 0.00046 *** 299s price 0.2286 0.0997 2.29 0.03571 * 299s farmPrice 0.2282 0.0440 5.19 9e-05 *** 299s trend 0.3611 0.0729 4.95 0.00014 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.597 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 107.914 MSE: 6.745 Root MSE: 2.597 299s Multiple R-Squared: 0.598 Adjusted R-Squared: 0.522 299s 299s [1] "********************* 3SLS EViews-like *****************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 33 173 0.719 0.677 0.748 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 65.7 3.87 1.97 0.755 0.726 299s supply 20 16 107.2 6.70 2.59 0.600 0.525 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.29 3.59 299s supply 3.59 4.83 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.29 4.11 299s supply 4.11 5.36 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.979 299s supply 0.979 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 299s price -0.2436 0.0890 -2.74 0.0099 ** 299s income 0.3140 0.0433 7.25 2.5e-08 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.966 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 299s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 52.1176 10.6378 4.90 2.5e-05 *** 299s price 0.2289 0.0892 2.57 0.015 * 299s farmPrice 0.2290 0.0393 5.82 1.6e-06 *** 299s trend 0.3579 0.0652 5.49 4.3e-06 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.589 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 107.216 MSE: 6.701 Root MSE: 2.589 299s Multiple R-Squared: 0.6 Adjusted R-Squared: 0.525 299s 299s [1] "********************* 3SLS with methodResidCov = Theil *****************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 33 174 -0.718 0.675 0.922 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 65.7 3.87 1.97 0.755 0.726 299s supply 20 16 108.7 6.79 2.61 0.594 0.518 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.87 4.50 299s supply 4.50 6.04 299s 299s warning: this covariance matrix is NOT positive semidefinit! 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.87 5.2 299s supply 5.20 6.8 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.981 299s supply 0.981 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 299s price -0.2436 0.0965 -2.52 0.017 * 299s income 0.3140 0.0469 6.69 1.3e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.966 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 299s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 52.2869 11.8853 4.40 0.00011 *** 299s price 0.2282 0.0997 2.29 0.02855 * 299s farmPrice 0.2272 0.0438 5.19 1.0e-05 *** 299s trend 0.3648 0.0707 5.16 1.1e-05 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.607 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 108.727 MSE: 6.795 Root MSE: 2.607 299s Multiple R-Squared: 0.594 Adjusted R-Squared: 0.518 299s 299s [1] "*************** W3SLS with methodResidCov = Theil *****************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 33 174 -0.718 0.675 0.922 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 65.7 3.87 1.97 0.755 0.726 299s supply 20 16 108.7 6.79 2.61 0.594 0.518 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.87 4.50 299s supply 4.50 6.04 299s 299s warning: this covariance matrix is NOT positive semidefinit! 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.87 5.2 299s supply 5.20 6.8 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.981 299s supply 0.981 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 299s price -0.2436 0.0965 -2.52 0.017 * 299s income 0.3140 0.0469 6.69 1.3e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.966 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 299s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 52.2869 11.8853 4.40 0.00011 *** 299s price 0.2282 0.0997 2.29 0.02855 * 299s farmPrice 0.2272 0.0438 5.19 1.0e-05 *** 299s trend 0.3648 0.0707 5.16 1.1e-05 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.607 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 108.727 MSE: 6.795 Root MSE: 2.607 299s Multiple R-Squared: 0.594 Adjusted R-Squared: 0.518 299s 299s [1] "*************** 3SLS with restriction *****************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 34 173 1.27 0.678 0.722 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 67.8 3.99 2.00 0.747 0.717 299s supply 20 16 104.8 6.55 2.56 0.609 0.536 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.97 4.55 299s supply 4.55 6.13 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.99 4.98 299s supply 4.98 6.55 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.975 299s supply 0.975 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.222 8.015 11.76 1.6e-13 *** 299s price -0.222 0.096 -2.31 0.027 * 299s income 0.296 0.045 6.57 1.6e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.997 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 67.796 MSE: 3.988 Root MSE: 1.997 299s Multiple R-Squared: 0.747 Adjusted R-Squared: 0.717 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 55.9604 11.5777 4.83 2.8e-05 *** 299s price 0.2193 0.1002 2.19 0.036 * 299s farmPrice 0.2060 0.0403 5.11 1.3e-05 *** 299s trend 0.2956 0.0450 6.57 1.6e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.559 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 104.753 MSE: 6.547 Root MSE: 2.559 299s Multiple R-Squared: 0.609 Adjusted R-Squared: 0.536 299s 299s [1] "Component “call”: target, current do not match when deparsed" 299s [1] "************** 3SLS with restriction (EViews-like) *****************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 34 171 0.887 0.68 0.678 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 67.5 3.97 1.99 0.748 0.719 299s supply 20 16 104.0 6.50 2.55 0.612 0.539 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.37 3.75 299s supply 3.75 4.91 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.37 4.08 299s supply 4.08 5.20 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.974 299s supply 0.974 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.2737 7.3905 12.76 1.6e-14 *** 299s price -0.2243 0.0888 -2.53 0.016 * 299s income 0.2979 0.0420 7.10 3.4e-08 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.992 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 67.467 MSE: 3.969 Root MSE: 1.992 299s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 55.4521 10.3994 5.33 6.4e-06 *** 299s price 0.2207 0.0896 2.46 0.019 * 299s farmPrice 0.2095 0.0366 5.73 1.9e-06 *** 299s trend 0.2979 0.0420 7.10 3.4e-08 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.55 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 104.013 MSE: 6.501 Root MSE: 2.55 299s Multiple R-Squared: 0.612 Adjusted R-Squared: 0.539 299s 299s [1] 40 299s [1] "*************** W3SLS with restriction *****************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 34 173 1.24 0.677 0.725 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 68.1 4.00 2.00 0.746 0.716 299s supply 20 16 105.2 6.57 2.56 0.608 0.534 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.93 4.56 299s supply 4.56 6.15 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 4.00 5.01 299s supply 5.01 6.57 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.976 299s supply 0.976 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.1823 7.9793 11.8 1.4e-13 *** 299s price -0.2194 0.0954 -2.3 0.028 * 299s income 0.2938 0.0445 6.6 1.4e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.001 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 68.057 MSE: 4.003 Root MSE: 2.001 299s Multiple R-Squared: 0.746 Adjusted R-Squared: 0.716 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 56.2541 11.5687 4.86 2.6e-05 *** 299s price 0.2184 0.1003 2.18 0.036 * 299s farmPrice 0.2040 0.0401 5.09 1.3e-05 *** 299s trend 0.2938 0.0445 6.60 1.4e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.564 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 105.161 MSE: 6.573 Root MSE: 2.564 299s Multiple R-Squared: 0.608 Adjusted R-Squared: 0.534 299s 299s [1] "*************** 3SLS with restriction via restrict.regMat ********************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 34 173 1.27 0.678 0.722 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 67.8 3.99 2.00 0.747 0.717 299s supply 20 16 104.8 6.55 2.56 0.609 0.536 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.97 4.55 299s supply 4.55 6.13 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.99 4.98 299s supply 4.98 6.55 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.975 299s supply 0.975 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.222 8.015 11.76 1.6e-13 *** 299s price -0.222 0.096 -2.31 0.027 * 299s income 0.296 0.045 6.57 1.6e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.997 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 67.796 MSE: 3.988 Root MSE: 1.997 299s Multiple R-Squared: 0.747 Adjusted R-Squared: 0.717 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 55.9604 11.5777 4.83 2.8e-05 *** 299s price 0.2193 0.1002 2.19 0.036 * 299s farmPrice 0.2060 0.0403 5.11 1.3e-05 *** 299s trend 0.2956 0.0450 6.57 1.6e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.559 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 104.753 MSE: 6.547 Root MSE: 2.559 299s Multiple R-Squared: 0.609 Adjusted R-Squared: 0.536 299s 299s [1] "*************** 3SLS with restriction via restrict.regMat (EViews-like) *******" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 34 171 0.887 0.68 0.678 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 67.5 3.97 1.99 0.748 0.719 299s supply 20 16 104.0 6.50 2.55 0.612 0.539 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.37 3.75 299s supply 3.75 4.91 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.37 4.08 299s supply 4.08 5.20 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.974 299s supply 0.974 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.2737 7.3905 12.76 1.6e-14 *** 299s price -0.2243 0.0888 -2.53 0.016 * 299s income 0.2979 0.0420 7.10 3.4e-08 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.992 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 67.467 MSE: 3.969 Root MSE: 1.992 299s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 55.4521 10.3994 5.33 6.4e-06 *** 299s price 0.2207 0.0896 2.46 0.019 * 299s farmPrice 0.2095 0.0366 5.73 1.9e-06 *** 299s trend 0.2979 0.0420 7.10 3.4e-08 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.55 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 104.013 MSE: 6.501 Root MSE: 2.55 299s Multiple R-Squared: 0.612 Adjusted R-Squared: 0.539 299s 299s [1] "**** W3SLS with restriction via restrict.regMat (EViews-like) ****" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 34 172 0.873 0.679 0.681 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 67.7 3.98 2.00 0.748 0.718 299s supply 20 16 104.3 6.52 2.55 0.611 0.538 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.35 3.76 299s supply 3.76 4.92 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.38 4.10 299s supply 4.10 5.22 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.975 299s supply 0.975 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.2409 7.3617 12.80 1.5e-14 *** 299s price -0.2225 0.0883 -2.52 0.017 * 299s income 0.2964 0.0416 7.13 3.1e-08 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.995 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 67.672 MSE: 3.981 Root MSE: 1.995 299s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.718 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 55.6925 10.3937 5.36 5.9e-06 *** 299s price 0.2201 0.0897 2.45 0.019 * 299s farmPrice 0.2078 0.0364 5.71 2.0e-06 *** 299s trend 0.2964 0.0416 7.13 3.1e-08 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.553 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 104.312 MSE: 6.519 Root MSE: 2.553 299s Multiple R-Squared: 0.611 Adjusted R-Squared: 0.538 299s 299s [1] "*************** 3SLS with 2 restrictions **********************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 35 442 31.1 0.176 -0.052 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 164 9.66 3.11 0.388 0.316 299s supply 20 16 278 17.36 4.17 -0.036 -0.230 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.89 4.53 299s supply 4.53 6.25 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 9.66 11.7 299s supply 11.69 17.4 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.903 299s supply 0.903 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 91.2986 7.9234 11.52 1.8e-13 *** 299s price -0.4494 0.0891 -5.04 1.4e-05 *** 299s income 0.5592 0.0233 24.04 < 2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 3.108 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 164.177 MSE: 9.657 Root MSE: 3.108 299s Multiple R-Squared: 0.388 Adjusted R-Squared: 0.316 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) -1.8394 8.1797 -0.22 0.82 299s price 0.5506 0.0891 6.18 4.5e-07 *** 299s farmPrice 0.4325 0.0241 17.95 < 2e-16 *** 299s trend 0.5592 0.0233 24.04 < 2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 4.167 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 277.77 MSE: 17.361 Root MSE: 4.167 299s Multiple R-Squared: -0.036 Adjusted R-Squared: -0.23 299s 299s [1] "Component “call”: target, current do not match when deparsed" 299s [1] "*************** 3SLS with 2 restrictions (EViews-like) ************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 35 439 21.3 0.18 -0.18 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 169 9.93 3.15 0.370 0.296 299s supply 20 16 271 16.91 4.11 -0.009 -0.198 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.30 3.73 299s supply 3.73 5.00 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 8.44 9.64 299s supply 9.64 13.53 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.902 299s supply 0.902 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 93.2926 7.3154 12.75 1.0e-14 *** 299s price -0.4781 0.0812 -5.89 1.1e-06 *** 299s income 0.5683 0.0209 27.24 < 2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 3.152 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 168.882 MSE: 9.934 Root MSE: 3.152 299s Multiple R-Squared: 0.37 Adjusted R-Squared: 0.296 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 0.6559 7.5503 0.09 0.93 299s price 0.5219 0.0812 6.43 2.1e-07 *** 299s farmPrice 0.4355 0.0212 20.49 < 2e-16 *** 299s trend 0.5683 0.0209 27.24 < 2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 4.112 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 270.595 MSE: 16.912 Root MSE: 4.112 299s Multiple R-Squared: -0.009 Adjusted R-Squared: -0.198 299s 299s [1] "********** W3SLS with 2 (symbolic) restrictions ***************" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 35 448 31.2 0.165 -0.057 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 166 9.77 3.13 0.38 0.307 299s supply 20 16 281 17.59 4.19 -0.05 -0.246 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.88 4.55 299s supply 4.55 6.27 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 9.77 11.9 299s supply 11.86 17.6 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.905 299s supply 0.905 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 90.6391 7.9088 11.46 2.1e-13 *** 299s price -0.4438 0.0892 -4.98 1.7e-05 *** 299s income 0.5603 0.0229 24.50 < 2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 3.126 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 166.148 MSE: 9.773 Root MSE: 3.126 299s Multiple R-Squared: 0.38 Adjusted R-Squared: 0.307 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) -2.5480 8.1522 -0.31 0.76 299s price 0.5562 0.0892 6.24 3.7e-07 *** 299s farmPrice 0.4340 0.0237 18.33 < 2e-16 *** 299s trend 0.5603 0.0229 24.50 < 2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 4.194 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 281.4 MSE: 17.587 Root MSE: 4.194 299s Multiple R-Squared: -0.05 Adjusted R-Squared: -0.246 299s 299s [1] "*************** 3SLS with 2 restrictions via R and restrict.regMat **********" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 35 442 31.1 0.176 -0.052 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 164 9.66 3.11 0.388 0.316 299s supply 20 16 278 17.36 4.17 -0.036 -0.230 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.89 4.53 299s supply 4.53 6.25 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 9.66 11.7 299s supply 11.69 17.4 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.903 299s supply 0.903 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 91.2986 7.9234 11.52 1.8e-13 *** 299s price -0.4494 0.0891 -5.04 1.4e-05 *** 299s income 0.5592 0.0233 24.04 < 2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 3.108 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 164.177 MSE: 9.657 Root MSE: 3.108 299s Multiple R-Squared: 0.388 Adjusted R-Squared: 0.316 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) -1.8394 8.1797 -0.22 0.82 299s price 0.5506 0.0891 6.18 4.5e-07 *** 299s farmPrice 0.4325 0.0241 17.95 < 2e-16 *** 299s trend 0.5592 0.0233 24.04 < 2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 4.167 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 277.77 MSE: 17.361 Root MSE: 4.167 299s Multiple R-Squared: -0.036 Adjusted R-Squared: -0.23 299s 299s [1] "Component “call”: target, current do not match when deparsed" 299s [1] "******** 3SLS with 2 restrictions via R and restrict.regMat (EViews-like)*****" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 35 439 21.3 0.18 -0.18 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 169 9.93 3.15 0.370 0.296 299s supply 20 16 271 16.91 4.11 -0.009 -0.198 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.30 3.73 299s supply 3.73 5.00 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 8.44 9.64 299s supply 9.64 13.53 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.902 299s supply 0.902 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 93.2926 7.3154 12.75 1.0e-14 *** 299s price -0.4781 0.0812 -5.89 1.1e-06 *** 299s income 0.5683 0.0209 27.24 < 2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 3.152 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 168.882 MSE: 9.934 Root MSE: 3.152 299s Multiple R-Squared: 0.37 Adjusted R-Squared: 0.296 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 0.6559 7.5503 0.09 0.93 299s price 0.5219 0.0812 6.43 2.1e-07 *** 299s farmPrice 0.4355 0.0212 20.49 < 2e-16 *** 299s trend 0.5683 0.0209 27.24 < 2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 4.112 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 270.595 MSE: 16.912 Root MSE: 4.112 299s Multiple R-Squared: -0.009 Adjusted R-Squared: -0.198 299s 299s [1] "*** W3SLS with 2 restrictions via R and restrict.regMat (EViews-like) ***" 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 35 444 21.3 0.172 -0.188 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 171 10.0 3.17 0.363 0.289 299s supply 20 16 274 17.1 4.13 -0.020 -0.212 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.30 3.75 299s supply 3.75 5.01 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 8.53 9.77 299s supply 9.77 13.68 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.904 299s supply 0.904 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 92.7628 7.3058 12.70 1.2e-14 *** 299s price -0.4740 0.0812 -5.84 1.3e-06 *** 299s income 0.5694 0.0205 27.74 < 2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 3.168 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 170.659 MSE: 10.039 Root MSE: 3.168 299s Multiple R-Squared: 0.363 Adjusted R-Squared: 0.289 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 0.0845 7.5314 0.01 0.99 299s price 0.5260 0.0812 6.48 1.8e-07 *** 299s farmPrice 0.4370 0.0209 20.91 < 2e-16 *** 299s trend 0.5694 0.0205 27.74 < 2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 4.135 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 273.568 MSE: 17.098 Root MSE: 4.135 299s Multiple R-Squared: -0.02 Adjusted R-Squared: -0.212 299s 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 36 3690 5613 0.012 0.368 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s eq1 20 19 2132 112.2 10.59 0.305 0.305 299s eq2 20 17 1558 91.7 9.57 -1.335 -1.610 299s 299s The covariance matrix of the residuals used for estimation 299s eq1 eq2 299s eq1 112.2 -44.8 299s eq2 -44.8 56.8 299s 299s The covariance matrix of the residuals 299s eq1 eq2 299s eq1 112.2 -68.3 299s eq2 -68.3 91.7 299s 299s The correlations of the residuals 299s eq1 eq2 299s eq1 1.000 -0.674 299s eq2 -0.674 1.000 299s 299s 299s 3SLS estimates for 'eq1' (equation 1) 299s Model Formula: farmPrice ~ consump - 1 299s Instruments: ~trend + income 299s 299s Estimate Std. Error t value Pr(>|t|) 299s consump 0.9588 0.0235 40.9 <2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 10.592 on 19 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 19 299s SSR: 2131.725 MSE: 112.196 Root MSE: 10.592 299s Multiple R-Squared: 0.305 Adjusted R-Squared: 0.305 299s 299s 299s 3SLS estimates for 'eq2' (equation 2) 299s Model Formula: price ~ consump + trend 299s Instruments: ~trend + income 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) -92.192 49.896 -1.85 0.0821 . 299s consump 1.953 0.499 3.92 0.0011 ** 299s trend -0.469 0.247 -1.90 0.0743 . 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 9.574 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 1558.311 MSE: 91.665 Root MSE: 9.574 299s Multiple R-Squared: -1.335 Adjusted R-Squared: -1.61 299s 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 38 56326 283068 -104 -10.6 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s eq1 20 19 2313 122 11.0 -7.63 -7.63 299s eq2 20 19 54013 2843 53.3 -200.46 -200.46 299s 299s The covariance matrix of the residuals used for estimation 299s eq1 eq2 299s eq1 121 -255 299s eq2 -255 2953 299s 299s The covariance matrix of the residuals 299s eq1 eq2 299s eq1 122 -251 299s eq2 -251 2843 299s 299s The correlations of the residuals 299s eq1 eq2 299s eq1 1.000 -0.433 299s eq2 -0.433 1.000 299s 299s 299s 3SLS estimates for 'eq1' (equation 1) 299s Model Formula: consump ~ farmPrice - 1 299s Instruments: ~price + income 299s 299s Estimate Std. Error t value Pr(>|t|) 299s farmPrice 1.0417 0.0253 41.2 <2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 11.034 on 19 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 19 299s SSR: 2313.073 MSE: 121.741 Root MSE: 11.034 299s Multiple R-Squared: -7.627 Adjusted R-Squared: -7.627 299s 299s 299s 3SLS estimates for 'eq2' (equation 2) 299s Model Formula: consump ~ trend - 1 299s Instruments: ~price + income 299s 299s Estimate Std. Error t value Pr(>|t|) 299s trend 9.02 1.13 8 1.7e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 53.318 on 19 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 19 299s SSR: 54013.367 MSE: 2842.809 Root MSE: 53.318 299s Multiple R-Squared: -200.456 Adjusted R-Squared: -200.456 299s 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 38 167069 397886 -49.1 -0.82 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s eq1 20 19 76692 4036 63.5 -285.0 -285.0 299s eq2 20 19 90377 4757 69.0 -28.5 -28.5 299s 299s The covariance matrix of the residuals used for estimation 299s eq1 eq2 299s eq1 2682 2547 299s eq2 2547 2741 299s 299s The covariance matrix of the residuals 299s eq1 eq2 299s eq1 4036 4336 299s eq2 4336 4757 299s 299s The correlations of the residuals 299s eq1 eq2 299s eq1 1.000 0.928 299s eq2 0.928 1.000 299s 299s 299s 3SLS estimates for 'eq1' (equation 1) 299s Model Formula: consump ~ trend - 1 299s Instruments: ~income + farmPrice 299s 299s Estimate Std. Error t value Pr(>|t|) 299s trend 4.162 0.723 5.75 1.5e-05 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 63.533 on 19 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 19 299s SSR: 76691.636 MSE: 4036.402 Root MSE: 63.533 299s Multiple R-Squared: -285.041 Adjusted R-Squared: -285.041 299s 299s 299s 3SLS estimates for 'eq2' (equation 2) 299s Model Formula: farmPrice ~ trend - 1 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s trend 3.274 0.676 4.84 0.00011 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 68.969 on 19 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 19 299s SSR: 90377.499 MSE: 4756.71 Root MSE: 68.969 299s Multiple R-Squared: -28.451 Adjusted R-Squared: -28.451 299s 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 39 161126 1162329 -171 -17.4 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s eq1 20 19 3553 187 13.7 -12.3 -12.3 299s eq2 20 19 157573 8293 91.1 -235.2 -235.2 299s 299s The covariance matrix of the residuals used for estimation 299s eq1 eq2 299s eq1 208 -731 299s eq2 -731 8271 299s 299s The covariance matrix of the residuals 299s eq1 eq2 299s eq1 187 -623 299s eq2 -623 8293 299s 299s The correlations of the residuals 299s eq1 eq2 299s eq1 1.000 -0.121 299s eq2 -0.121 1.000 299s 299s 299s 3SLS estimates for 'eq1' (equation 1) 299s Model Formula: consump ~ farmPrice - 1 299s Instruments: ~farmPrice + trend + income 299s 299s Estimate Std. Error t value Pr(>|t|) 299s farmPrice 1.1122 0.0272 40.8 <2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 13.675 on 19 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 19 299s SSR: 3553.118 MSE: 187.006 Root MSE: 13.675 299s Multiple R-Squared: -12.252 Adjusted R-Squared: -12.252 299s 299s 299s 3SLS estimates for 'eq2' (equation 2) 299s Model Formula: price ~ trend - 1 299s Instruments: ~farmPrice + trend + income 299s 299s Estimate Std. Error t value Pr(>|t|) 299s trend 1.1122 0.0272 40.8 <2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 91.068 on 19 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 19 299s SSR: 157573.328 MSE: 8293.333 Root MSE: 91.068 299s Multiple R-Squared: -235.153 Adjusted R-Squared: -235.153 299s 299s 299s systemfit results 299s method: 3SLS 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 38 935 491 0 0 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s eq1 20 19 268 14.1 3.76 0 0 299s eq2 20 19 667 35.1 5.93 0 0 299s 299s The covariance matrix of the residuals used for estimation 299s eq1 eq2 299s eq1 14.11 2.18 299s eq2 2.18 35.12 299s 299s The covariance matrix of the residuals 299s eq1 eq2 299s eq1 14.11 2.18 299s eq2 2.18 35.12 299s 299s The correlations of the residuals 299s eq1 eq2 299s eq1 1.0000 0.0981 299s eq2 0.0981 1.0000 299s 299s 299s 3SLS estimates for 'eq1' (equation 1) 299s Model Formula: consump ~ 1 299s Instruments: ~income 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 100.90 0.84 120 <2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 3.756 on 19 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 19 299s SSR: 268.114 MSE: 14.111 Root MSE: 3.756 299s Multiple R-Squared: 0 Adjusted R-Squared: 0 299s 299s 299s 3SLS estimates for 'eq2' (equation 2) 299s Model Formula: price ~ 1 299s Instruments: ~income 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 100.02 1.33 75.5 <2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 5.926 on 19 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 19 299s SSR: 667.251 MSE: 35.118 Root MSE: 5.926 299s Multiple R-Squared: 0 Adjusted R-Squared: 0 299s 299s > 299s > ## ******************** iterated 3SLS ********************** 299s > fit3slsi <- list() 299s > formulas <- c( "GLS", "IV", "Schmidt", "GMM", "EViews" ) 299s > for( i in seq( along = formulas ) ) { 299s + fit3slsi[[ i ]] <- list() 299s + 299s + print( "***************************************************" ) 299s + print( paste( "3SLS formula:", formulas[ i ] ) ) 299s + print( "************* 3SLS *********************************" ) 299s + fit3slsi[[ i ]]$e1 <- systemfit( system, "3SLS", data = Kmenta, 299s + inst = inst, method3sls = formulas[ i ], maxiter = 100, 299s + useMatrix = useMatrix ) 299s + print( summary( fit3slsi[[ i ]]$e1 ) ) 299s + 299s + print( "********************* iterated 3SLS EViews-like ****************" ) 299s + fit3slsi[[ i ]]$e1e <- systemfit( system, "3SLS", data = Kmenta, 299s + inst = inst, methodResidCov = "noDfCor", method3sls = formulas[ i ], 299s + maxiter = 100, useMatrix = useMatrix ) 299s + print( summary( fit3slsi[[ i ]]$e1e, useDfSys = TRUE ) ) 299s + 299s + print( "************** iterated 3SLS with methodResidCov = Theil **************" ) 299s + fit3slsi[[ i ]]$e1c <- systemfit( system, "3SLS", data = Kmenta, 299s + inst = inst, methodResidCov = "Theil", method3sls = formulas[ i ], 299s + maxiter = 100, x = TRUE, useMatrix = useMatrix ) 299s + print( summary( fit3slsi[[ i ]]$e1c, useDfSys = TRUE ) ) 299s + 299s + print( "**************** iterated W3SLS EViews-like ****************" ) 299s + fit3slsi[[ i ]]$e1we <- systemfit( system, "3SLS", data = Kmenta, 299s + inst = inst, methodResidCov = "noDfCor", method3sls = formulas[ i ], 299s + maxiter = 100, residCovWeighted = TRUE, useMatrix = useMatrix ) 299s + print( summary( fit3slsi[[ i ]]$e1we, useDfSys = TRUE ) ) 299s + 299s + 299s + print( "******* iterated 3SLS with restriction *****************" ) 299s + fit3slsi[[ i ]]$e2 <- systemfit( system, "3SLS", data = Kmenta, 299s + inst = inst, restrict.matrix = restrm, method3sls = formulas[ i ], 299s + maxiter = 100, x = TRUE, useMatrix = useMatrix ) 299s + print( summary( fit3slsi[[ i ]]$e2 ) ) 299s + 299s + print( "********* iterated 3SLS with restriction (EViews-like) *********" ) 299s + fit3slsi[[ i ]]$e2e <- systemfit( system, "3SLS", data = Kmenta, 299s + inst = inst, methodResidCov = "noDfCor", restrict.matrix = restrm, 299s + method3sls = formulas[ i ], maxiter = 100, useMatrix = useMatrix ) 299s + print( summary( fit3slsi[[ i ]]$e2e, useDfSys = TRUE ) ) 299s + 299s + print( "******** iterated W3SLS with restriction (EViews-like) *********" ) 299s + fit3slsi[[ i ]]$e2we <- systemfit( system, "3SLS", data = Kmenta, 299s + inst = inst, methodResidCov = "noDfCor", restrict.matrix = restrm, 299s + method3sls = formulas[ i ], maxiter = 100, residCovWeighted = TRUE, 299s + useMatrix = useMatrix ) 299s + print( summary( fit3slsi[[ i ]]$e2we, useDfSys = TRUE ) ) 299s + 299s + 299s + print( "********* iterated 3SLS with restriction via restrict.regMat *****************" ) 299s + fit3slsi[[ i ]]$e3 <- systemfit( system, "3SLS", data = Kmenta, 299s + inst = inst, restrict.regMat = tc, method3sls = formulas[ i ], 299s + maxiter = 100, useMatrix = useMatrix ) 299s + print( summary( fit3slsi[[ i ]]$e3 ) ) 299s + 299s + print( "********* iterated 3SLS with restriction via restrict.regMat (EViews-like) ***" ) 299s + fit3slsi[[ i ]]$e3e <- systemfit( system, "3SLS", data = Kmenta, 299s + inst = inst, methodResidCov = "noDfCor", restrict.regMat = tc, 299s + method3sls = formulas[ i ], maxiter = 100, x = TRUE, 299s + useMatrix = useMatrix ) 299s + print( summary( fit3slsi[[ i ]]$e3e, useDfSys = TRUE ) ) 299s + 299s + print( "***** iterated W3SLS with restriction via restrict.regMat ********" ) 299s + fit3slsi[[ i ]]$e3w <- systemfit( system, "3SLS", data = Kmenta, 299s + inst = inst, restrict.regMat = tc, method3sls = formulas[ i ], maxiter = 100, 299s + residCovWeighted = TRUE, x = TRUE, useMatrix = useMatrix ) 299s + print( summary( fit3slsi[[ i ]]$e3w ) ) 299s + 299s + 299s + print( "******** iterated 3SLS with 2 restrictions *********************" ) 299s + fit3slsi[[ i ]]$e4 <- systemfit( system, "3SLS", data = Kmenta, 299s + inst = inst, restrict.matrix = restr2m, restrict.rhs = restr2q, 299s + method3sls = formulas[ i ], maxiter = 100, x = TRUE, 299s + useMatrix = useMatrix ) 299s + print( summary( fit3slsi[[ i ]]$e4 ) ) 299s + 299s + print( "********* iterated 3SLS with 2 restrictions (EViews-like) *******" ) 299s + fit3slsi[[ i ]]$e4e <- systemfit( system, "3SLS", data = Kmenta, 299s + inst = inst, methodResidCov = "noDfCor", restrict.matrix = restr2m, 299s + restrict.rhs = restr2q, method3sls = formulas[ i ], maxiter = 100, 299s + useMatrix = useMatrix ) 299s + print( summary( fit3slsi[[ i ]]$e4e, useDfSys = TRUE ) ) 299s + 299s + print( "******** iterated W3SLS with 2 restrictions (EViews-like) *******" ) 299s + fit3slsi[[ i ]]$e4we <- systemfit( system, "3SLS", data = Kmenta, 299s + inst = inst, methodResidCov = "noDfCor", restrict.matrix = restr2m, 299s + restrict.rhs = restr2q, method3sls = formulas[ i ], maxiter = 100, 299s + residCovWeighted = TRUE, useMatrix = useMatrix ) 299s + print( summary( fit3slsi[[ i ]]$e4we, useDfSys = TRUE ) ) 299s + 299s + 299s + print( "******** iterated 3SLS with 2 restrictions via R and restrict.regMat *********" ) 299s + fit3slsi[[ i ]]$e5 <- systemfit( system, "3SLS", data = Kmenta, 299s + inst = inst, restrict.regMat = tc, restrict.matrix = restr3m, 299s + restrict.rhs = restr3q, method3sls = formulas[ i ], maxiter = 100, 299s + useMatrix = useMatrix ) 299s + print( summary( fit3slsi[[ i ]]$e5 ) ) 299s + 299s + print( "*** iterated 3SLS with 2 restrictions via R and restrict.regMat (EViews-like)**" ) 299s + fit3slsi[[ i ]]$e5e <- systemfit( system, "3SLS", data = Kmenta, 299s + inst = inst, restrict.regMat = tc, methodResidCov = "noDfCor", 299s + restrict.matrix = restr3m, restrict.rhs = restr3q, 299s + method3sls = formulas[ i ], maxiter = 100, useMatrix = useMatrix ) 299s + print( summary( fit3slsi[[ i ]]$e5e, useDfSys = TRUE ) ) 299s + 299s + print( "** iterated W3SLS with 2 restrictions via R and restrict.regMat ***" ) 299s + fit3slsi[[ i ]]$e5w <- systemfit( system, "3SLS", data = Kmenta, 299s + inst = inst, restrict.regMat = tc, restrict.matrix = restr3m, 299s + restrict.rhs = restr3q, method3sls = formulas[ i ], maxiter = 100, 299s + residCovWeighted = TRUE, x = TRUE, 299s + useMatrix = useMatrix ) 299s + print( summary( fit3slsi[[ i ]]$e5w ) ) 299s + } 299s [1] "***************************************************" 299s [1] "3SLS formula: GLS" 299s [1] "************* 3SLS *********************************" 299s 299s systemfit results 299s method: iterated 3SLS 299s 299s convergence achieved after 6 iterations 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 33 178 0.983 0.668 0.814 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 65.7 3.87 1.97 0.755 0.726 299s supply 20 16 112.4 7.03 2.65 0.581 0.502 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.87 5.12 299s supply 5.12 7.03 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.87 5.12 299s supply 5.12 7.03 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.982 299s supply 0.982 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.6333 7.9208 11.95 1.1e-09 *** 299s price -0.2436 0.0965 -2.52 0.022 * 299s income 0.3140 0.0469 6.69 3.8e-06 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.966 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 299s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 52.6618 12.8051 4.11 0.00081 *** 299s price 0.2266 0.1075 2.11 0.05110 . 299s farmPrice 0.2234 0.0468 4.78 0.00021 *** 299s trend 0.3800 0.0720 5.28 7.5e-05 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.651 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 112.431 MSE: 7.027 Root MSE: 2.651 299s Multiple R-Squared: 0.581 Adjusted R-Squared: 0.502 299s 299s [1] "********************* iterated 3SLS EViews-like ****************" 299s 299s systemfit results 299s method: iterated 3SLS 299s 299s convergence achieved after 6 iterations 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 33 177 0.667 0.67 0.782 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 65.7 3.87 1.97 0.755 0.726 299s supply 20 16 111.3 6.96 2.64 0.585 0.507 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.29 4.20 299s supply 4.20 5.57 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.29 4.20 299s supply 4.20 5.57 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.982 299s supply 0.982 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 299s price -0.2436 0.0890 -2.74 0.0099 ** 299s income 0.3140 0.0433 7.25 2.5e-08 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.966 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 299s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 52.5527 11.3956 4.61 5.8e-05 *** 299s price 0.2271 0.0956 2.37 0.024 * 299s farmPrice 0.2245 0.0416 5.39 5.8e-06 *** 299s trend 0.3756 0.0641 5.86 1.5e-06 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.637 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 111.302 MSE: 6.956 Root MSE: 2.637 299s Multiple R-Squared: 0.585 Adjusted R-Squared: 0.507 299s 299s [1] "************** iterated 3SLS with methodResidCov = Theil **************" 299s 299s systemfit results 299s method: iterated 3SLS 299s 299s convergence achieved after 6 iterations 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 33 179 -0.818 0.665 0.957 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 65.7 3.87 1.97 0.755 0.726 299s supply 20 16 113.8 7.11 2.67 0.576 0.496 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.87 5.32 299s supply 5.32 7.11 299s 299s warning: this covariance matrix is NOT positive semidefinit! 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.87 5.32 299s supply 5.32 7.11 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.982 299s supply 0.982 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 299s price -0.2436 0.0965 -2.52 0.017 * 299s income 0.3140 0.0469 6.69 1.3e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.966 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 299s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 52.7863 12.8707 4.10 0.00025 *** 299s price 0.2261 0.1081 2.09 0.04425 * 299s farmPrice 0.2221 0.0467 4.75 3.8e-05 *** 299s trend 0.3851 0.0693 5.55 3.6e-06 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.667 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 113.77 MSE: 7.111 Root MSE: 2.667 299s Multiple R-Squared: 0.576 Adjusted R-Squared: 0.496 299s 299s [1] "**************** iterated W3SLS EViews-like ****************" 299s 299s systemfit results 299s method: iterated 3SLS 299s 299s convergence achieved after 6 iterations 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 33 177 0.667 0.67 0.782 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 65.7 3.87 1.97 0.755 0.726 299s supply 20 16 111.3 6.96 2.64 0.585 0.507 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 3.29 4.20 299s supply 4.20 5.57 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 3.29 4.20 299s supply 4.20 5.57 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.982 299s supply 0.982 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 299s price -0.2436 0.0890 -2.74 0.0099 ** 299s income 0.3140 0.0433 7.25 2.5e-08 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 1.966 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 299s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 52.5527 11.3956 4.61 5.8e-05 *** 299s price 0.2271 0.0956 2.37 0.024 * 299s farmPrice 0.2245 0.0416 5.39 5.8e-06 *** 299s trend 0.3756 0.0641 5.86 1.5e-06 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.637 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 111.302 MSE: 6.956 Root MSE: 2.637 299s Multiple R-Squared: 0.585 Adjusted R-Squared: 0.507 299s 299s [1] "******* iterated 3SLS with restriction *****************" 299s 299s systemfit results 299s method: iterated 3SLS 299s 299s convergence achieved after 17 iterations 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 34 240 0.56 0.553 0.819 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 98.4 5.79 2.41 0.633 0.590 299s supply 20 16 141.1 8.82 2.97 0.474 0.375 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 5.79 7.11 299s supply 7.11 8.82 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 5.79 7.11 299s supply 7.11 8.82 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.995 299s supply 0.995 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 92.0742 9.6303 9.56 3.6e-11 *** 299s price -0.1064 0.1023 -1.04 0.31 299s income 0.1996 0.0297 6.73 9.9e-08 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.406 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 98.435 MSE: 5.79 Root MSE: 2.406 299s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.59 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 68.8551 12.4839 5.52 3.7e-06 *** 299s price 0.1833 0.1189 1.54 0.13 299s farmPrice 0.1202 0.0260 4.63 5.1e-05 *** 299s trend 0.1996 0.0297 6.73 9.9e-08 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.97 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 141.147 MSE: 8.822 Root MSE: 2.97 299s Multiple R-Squared: 0.474 Adjusted R-Squared: 0.375 299s 299s [1] "********* iterated 3SLS with restriction (EViews-like) *********" 299s 299s systemfit results 299s method: iterated 3SLS 299s 299s convergence achieved after 20 iterations 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 34 237 0.364 0.557 0.755 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 99.3 5.84 2.42 0.630 0.586 299s supply 20 16 138.1 8.63 2.94 0.485 0.388 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 4.96 5.82 299s supply 5.82 6.90 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 4.96 5.82 299s supply 5.82 6.90 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.995 299s supply 0.995 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 299s price -0.1043 0.0958 -1.09 0.28 299s income 0.1979 0.0299 6.61 1.4e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.417 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 99.297 MSE: 5.841 Root MSE: 2.417 299s Multiple R-Squared: 0.63 Adjusted R-Squared: 0.586 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 299s price 0.1851 0.1053 1.76 0.088 . 299s farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 299s trend 0.1979 0.0299 6.61 1.4e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.938 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 138.088 MSE: 8.63 Root MSE: 2.938 299s Multiple R-Squared: 0.485 Adjusted R-Squared: 0.388 299s 299s [1] "******** iterated W3SLS with restriction (EViews-like) *********" 299s 299s systemfit results 299s method: iterated 3SLS 299s 299s convergence achieved after 20 iterations 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 34 237 0.364 0.557 0.755 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 99.3 5.84 2.42 0.630 0.586 299s supply 20 16 138.1 8.63 2.94 0.485 0.388 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 4.96 5.82 299s supply 5.82 6.90 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 4.96 5.82 299s supply 5.82 6.90 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.995 299s supply 0.995 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 299s price -0.1043 0.0958 -1.09 0.28 299s income 0.1979 0.0299 6.61 1.4e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.417 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 99.297 MSE: 5.841 Root MSE: 2.417 299s Multiple R-Squared: 0.63 Adjusted R-Squared: 0.586 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 299s price 0.1851 0.1053 1.76 0.088 . 299s farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 299s trend 0.1979 0.0299 6.61 1.4e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.938 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 138.088 MSE: 8.63 Root MSE: 2.938 299s Multiple R-Squared: 0.485 Adjusted R-Squared: 0.388 299s 299s [1] "********* iterated 3SLS with restriction via restrict.regMat *****************" 299s 299s systemfit results 299s method: iterated 3SLS 299s 299s convergence achieved after 17 iterations 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 34 240 0.56 0.553 0.819 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 98.4 5.79 2.41 0.633 0.590 299s supply 20 16 141.1 8.82 2.97 0.474 0.375 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 5.79 7.11 299s supply 7.11 8.82 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 5.79 7.11 299s supply 7.11 8.82 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.995 299s supply 0.995 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 92.0742 9.6303 9.56 3.6e-11 *** 299s price -0.1064 0.1023 -1.04 0.31 299s income 0.1996 0.0297 6.73 9.9e-08 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.406 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 98.435 MSE: 5.79 Root MSE: 2.406 299s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.59 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 68.8551 12.4839 5.52 3.7e-06 *** 299s price 0.1833 0.1189 1.54 0.13 299s farmPrice 0.1202 0.0260 4.63 5.1e-05 *** 299s trend 0.1996 0.0297 6.73 9.9e-08 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.97 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 141.147 MSE: 8.822 Root MSE: 2.97 299s Multiple R-Squared: 0.474 Adjusted R-Squared: 0.375 299s 299s [1] "********* iterated 3SLS with restriction via restrict.regMat (EViews-like) ***" 299s 299s systemfit results 299s method: iterated 3SLS 299s 299s convergence achieved after 20 iterations 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 34 237 0.364 0.557 0.755 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 99.3 5.84 2.42 0.630 0.586 299s supply 20 16 138.1 8.63 2.94 0.485 0.388 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 4.96 5.82 299s supply 5.82 6.90 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 4.96 5.82 299s supply 5.82 6.90 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.995 299s supply 0.995 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 299s price -0.1043 0.0958 -1.09 0.28 299s income 0.1979 0.0299 6.61 1.4e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.417 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 99.297 MSE: 5.841 Root MSE: 2.417 299s Multiple R-Squared: 0.63 Adjusted R-Squared: 0.586 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 299s price 0.1851 0.1053 1.76 0.088 . 299s farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 299s trend 0.1979 0.0299 6.61 1.4e-07 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.938 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 138.088 MSE: 8.63 Root MSE: 2.938 299s Multiple R-Squared: 0.485 Adjusted R-Squared: 0.388 299s 299s [1] "***** iterated W3SLS with restriction via restrict.regMat ********" 299s 299s systemfit results 299s method: iterated 3SLS 299s 299s convergence achieved after 17 iterations 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 34 240 0.56 0.553 0.819 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 98.4 5.79 2.41 0.633 0.590 299s supply 20 16 141.1 8.82 2.97 0.474 0.375 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 5.79 7.11 299s supply 7.11 8.82 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 5.79 7.11 299s supply 7.11 8.82 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.000 0.995 299s supply 0.995 1.000 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 92.0742 9.6303 9.56 3.6e-11 *** 299s price -0.1064 0.1023 -1.04 0.31 299s income 0.1996 0.0297 6.73 9.9e-08 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.406 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 98.435 MSE: 5.79 Root MSE: 2.406 299s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.59 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 68.8551 12.4839 5.52 3.7e-06 *** 299s price 0.1833 0.1189 1.54 0.13 299s farmPrice 0.1202 0.0260 4.63 5.1e-05 *** 299s trend 0.1996 0.0297 6.73 9.9e-08 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.97 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 141.147 MSE: 8.822 Root MSE: 2.97 299s Multiple R-Squared: 0.474 Adjusted R-Squared: 0.375 299s 299s [1] "******** iterated 3SLS with 2 restrictions *********************" 299s 299s systemfit results 299s method: iterated 3SLS 299s 299s convergence achieved after 9 iterations 299s 299s N DF SSR detRCov OLS-R2 McElroy-R2 299s system 40 35 185 1.76 0.655 0.71 299s 299s N DF SSR MSE RMSE R2 Adj R2 299s demand 20 17 69.9 4.11 2.03 0.739 0.709 299s supply 20 16 114.8 7.18 2.68 0.572 0.491 299s 299s The covariance matrix of the residuals used for estimation 299s demand supply 299s demand 4.11 5.27 299s supply 5.27 7.18 299s 299s The covariance matrix of the residuals 299s demand supply 299s demand 4.11 5.27 299s supply 5.27 7.18 299s 299s The correlations of the residuals 299s demand supply 299s demand 1.00 0.97 299s supply 0.97 1.00 299s 299s 299s 3SLS estimates for 'demand' (equation 1) 299s Model Formula: consump ~ price + income 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 90.1569 7.9174 11.39 2.5e-13 *** 299s price -0.2007 0.0920 -2.18 0.036 * 299s income 0.3159 0.0192 16.42 < 2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.028 on 17 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 17 299s SSR: 69.917 MSE: 4.113 Root MSE: 2.028 299s Multiple R-Squared: 0.739 Adjusted R-Squared: 0.709 299s 299s 299s 3SLS estimates for 'supply' (equation 2) 299s Model Formula: consump ~ price + farmPrice + trend 299s Instruments: ~income + farmPrice + trend 299s 299s Estimate Std. Error t value Pr(>|t|) 299s (Intercept) 46.4810 8.0104 5.80 1.4e-06 *** 299s price 0.2993 0.0920 3.25 0.0025 ** 299s farmPrice 0.2190 0.0196 11.20 4.0e-13 *** 299s trend 0.3159 0.0192 16.42 < 2e-16 *** 299s --- 299s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 299s 299s Residual standard error: 2.679 on 16 degrees of freedom 299s Number of observations: 20 Degrees of Freedom: 16 299s SSR: 114.83 MSE: 7.177 Root MSE: 2.679 299s Multiple R-Squared: 0.572 Adjusted R-Squared: 0.491 299s 299s [1] "********* iterated 3SLS with 2 restrictions (EViews-like) *******" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 8 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 35 179 1.19 0.666 0.668 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 68.3 4.02 2.00 0.745 0.715 300s supply 20 16 110.8 6.92 2.63 0.587 0.509 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 3.41 4.21 300s supply 4.21 5.54 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 3.41 4.21 300s supply 4.21 5.54 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.968 300s supply 0.968 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 91.3901 7.3161 12.5 1.9e-14 *** 300s price -0.2168 0.0835 -2.6 0.014 * 300s income 0.3199 0.0168 19.1 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.004 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 68.293 MSE: 4.017 Root MSE: 2.004 300s Multiple R-Squared: 0.745 Adjusted R-Squared: 0.715 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 47.5787 7.4268 6.41 2.3e-07 *** 300s price 0.2832 0.0835 3.39 0.0017 ** 300s farmPrice 0.2240 0.0168 13.36 2.7e-15 *** 300s trend 0.3199 0.0168 19.07 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.631 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 110.791 MSE: 6.924 Root MSE: 2.631 300s Multiple R-Squared: 0.587 Adjusted R-Squared: 0.509 300s 300s [1] "******** iterated W3SLS with 2 restrictions (EViews-like) *******" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 8 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 35 179 1.19 0.666 0.668 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 68.3 4.02 2.00 0.745 0.715 300s supply 20 16 110.8 6.92 2.63 0.587 0.509 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 3.41 4.21 300s supply 4.21 5.54 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 3.41 4.21 300s supply 4.21 5.54 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.968 300s supply 0.968 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 91.3901 7.3161 12.5 1.9e-14 *** 300s price -0.2168 0.0835 -2.6 0.014 * 300s income 0.3199 0.0168 19.1 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.004 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 68.293 MSE: 4.017 Root MSE: 2.004 300s Multiple R-Squared: 0.745 Adjusted R-Squared: 0.715 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 47.5787 7.4268 6.41 2.3e-07 *** 300s price 0.2832 0.0835 3.39 0.0017 ** 300s farmPrice 0.2240 0.0168 13.36 2.7e-15 *** 300s trend 0.3199 0.0168 19.07 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.631 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 110.791 MSE: 6.924 Root MSE: 2.631 300s Multiple R-Squared: 0.587 Adjusted R-Squared: 0.509 300s 300s [1] "******** iterated 3SLS with 2 restrictions via R and restrict.regMat *********" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 9 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 35 185 1.76 0.655 0.71 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 69.9 4.11 2.03 0.739 0.709 300s supply 20 16 114.8 7.18 2.68 0.572 0.491 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 4.11 5.27 300s supply 5.27 7.18 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 4.11 5.27 300s supply 5.27 7.18 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.00 0.97 300s supply 0.97 1.00 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 90.1569 7.9174 11.39 2.5e-13 *** 300s price -0.2007 0.0920 -2.18 0.036 * 300s income 0.3159 0.0192 16.42 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.028 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 69.917 MSE: 4.113 Root MSE: 2.028 300s Multiple R-Squared: 0.739 Adjusted R-Squared: 0.709 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 46.4810 8.0104 5.80 1.4e-06 *** 300s price 0.2993 0.0920 3.25 0.0025 ** 300s farmPrice 0.2190 0.0196 11.20 4.0e-13 *** 300s trend 0.3159 0.0192 16.42 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.679 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 114.83 MSE: 7.177 Root MSE: 2.679 300s Multiple R-Squared: 0.572 Adjusted R-Squared: 0.491 300s 300s [1] "*** iterated 3SLS with 2 restrictions via R and restrict.regMat (EViews-like)**" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 8 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 35 179 1.19 0.666 0.668 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 68.3 4.02 2.00 0.745 0.715 300s supply 20 16 110.8 6.92 2.63 0.587 0.509 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 3.41 4.21 300s supply 4.21 5.54 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 3.41 4.21 300s supply 4.21 5.54 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.968 300s supply 0.968 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 91.3901 7.3161 12.5 1.9e-14 *** 300s price -0.2168 0.0835 -2.6 0.014 * 300s income 0.3199 0.0168 19.1 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.004 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 68.293 MSE: 4.017 Root MSE: 2.004 300s Multiple R-Squared: 0.745 Adjusted R-Squared: 0.715 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 47.5787 7.4268 6.41 2.3e-07 *** 300s price 0.2832 0.0835 3.39 0.0017 ** 300s farmPrice 0.2240 0.0168 13.36 2.7e-15 *** 300s trend 0.3199 0.0168 19.07 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.631 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 110.791 MSE: 6.924 Root MSE: 2.631 300s Multiple R-Squared: 0.587 Adjusted R-Squared: 0.509 300s 300s [1] "** iterated W3SLS with 2 restrictions via R and restrict.regMat ***" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 9 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 35 185 1.76 0.655 0.71 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 69.9 4.11 2.03 0.739 0.709 300s supply 20 16 114.8 7.18 2.68 0.572 0.491 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 4.11 5.27 300s supply 5.27 7.18 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 4.11 5.27 300s supply 5.27 7.18 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.00 0.97 300s supply 0.97 1.00 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 90.1569 7.9174 11.39 2.5e-13 *** 300s price -0.2007 0.0920 -2.18 0.036 * 300s income 0.3159 0.0192 16.42 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.028 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 69.917 MSE: 4.113 Root MSE: 2.028 300s Multiple R-Squared: 0.739 Adjusted R-Squared: 0.709 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 46.4810 8.0104 5.80 1.4e-06 *** 300s price 0.2993 0.0920 3.25 0.0025 ** 300s farmPrice 0.2190 0.0196 11.20 4.0e-13 *** 300s trend 0.3159 0.0192 16.42 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.679 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 114.83 MSE: 7.177 Root MSE: 2.679 300s Multiple R-Squared: 0.572 Adjusted R-Squared: 0.491 300s 300s [1] "***************************************************" 300s [1] "3SLS formula: IV" 300s [1] "************* 3SLS *********************************" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 6 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 33 178 0.983 0.668 0.814 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 65.7 3.87 1.97 0.755 0.726 300s supply 20 16 112.4 7.03 2.65 0.581 0.502 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 3.87 5.12 300s supply 5.12 7.03 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 3.87 5.12 300s supply 5.12 7.03 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.982 300s supply 0.982 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 94.6333 7.9208 11.95 1.1e-09 *** 300s price -0.2436 0.0965 -2.52 0.022 * 300s income 0.3140 0.0469 6.69 3.8e-06 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 1.966 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 300s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 52.6618 12.8051 4.11 0.00081 *** 300s price 0.2266 0.1075 2.11 0.05110 . 300s farmPrice 0.2234 0.0468 4.78 0.00021 *** 300s trend 0.3800 0.0720 5.28 7.5e-05 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.651 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 112.431 MSE: 7.027 Root MSE: 2.651 300s Multiple R-Squared: 0.581 Adjusted R-Squared: 0.502 300s 300s [1] "********************* iterated 3SLS EViews-like ****************" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 6 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 33 177 0.667 0.67 0.782 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 65.7 3.87 1.97 0.755 0.726 300s supply 20 16 111.3 6.96 2.64 0.585 0.507 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 3.29 4.20 300s supply 4.20 5.57 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 3.29 4.20 300s supply 4.20 5.57 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.982 300s supply 0.982 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 300s price -0.2436 0.0890 -2.74 0.0099 ** 300s income 0.3140 0.0433 7.25 2.5e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 1.966 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 300s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 52.5527 11.3956 4.61 5.8e-05 *** 300s price 0.2271 0.0956 2.37 0.024 * 300s farmPrice 0.2245 0.0416 5.39 5.8e-06 *** 300s trend 0.3756 0.0641 5.86 1.5e-06 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.637 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 111.302 MSE: 6.956 Root MSE: 2.637 300s Multiple R-Squared: 0.585 Adjusted R-Squared: 0.507 300s 300s [1] "************** iterated 3SLS with methodResidCov = Theil **************" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 6 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 33 179 -0.818 0.665 0.957 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 65.7 3.87 1.97 0.755 0.726 300s supply 20 16 113.8 7.11 2.67 0.576 0.496 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 3.87 5.32 300s supply 5.32 7.11 300s 300s warning: this covariance matrix is NOT positive semidefinit! 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 3.87 5.32 300s supply 5.32 7.11 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.982 300s supply 0.982 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 300s price -0.2436 0.0965 -2.52 0.017 * 300s income 0.3140 0.0469 6.69 1.3e-07 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 1.966 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 300s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 52.7863 12.8707 4.10 0.00025 *** 300s price 0.2261 0.1081 2.09 0.04425 * 300s farmPrice 0.2221 0.0467 4.75 3.8e-05 *** 300s trend 0.3851 0.0693 5.55 3.6e-06 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.667 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 113.77 MSE: 7.111 Root MSE: 2.667 300s Multiple R-Squared: 0.576 Adjusted R-Squared: 0.496 300s 300s [1] "**************** iterated W3SLS EViews-like ****************" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 6 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 33 177 0.667 0.67 0.782 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 65.7 3.87 1.97 0.755 0.726 300s supply 20 16 111.3 6.96 2.64 0.585 0.507 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 3.29 4.20 300s supply 4.20 5.57 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 3.29 4.20 300s supply 4.20 5.57 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.982 300s supply 0.982 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 300s price -0.2436 0.0890 -2.74 0.0099 ** 300s income 0.3140 0.0433 7.25 2.5e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 1.966 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 300s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 52.5527 11.3956 4.61 5.8e-05 *** 300s price 0.2271 0.0956 2.37 0.024 * 300s farmPrice 0.2245 0.0416 5.39 5.8e-06 *** 300s trend 0.3756 0.0641 5.86 1.5e-06 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.637 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 111.302 MSE: 6.956 Root MSE: 2.637 300s Multiple R-Squared: 0.585 Adjusted R-Squared: 0.507 300s 300s [1] "******* iterated 3SLS with restriction *****************" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 17 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 34 240 0.56 0.553 0.819 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 98.4 5.79 2.41 0.633 0.590 300s supply 20 16 141.1 8.82 2.97 0.474 0.375 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 5.79 7.11 300s supply 7.11 8.82 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 5.79 7.11 300s supply 7.11 8.82 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.995 300s supply 0.995 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 92.0742 9.6303 9.56 3.6e-11 *** 300s price -0.1064 0.1023 -1.04 0.31 300s income 0.1996 0.0297 6.73 9.9e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.406 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 98.435 MSE: 5.79 Root MSE: 2.406 300s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.59 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 68.8551 12.4839 5.52 3.7e-06 *** 300s price 0.1833 0.1189 1.54 0.13 300s farmPrice 0.1202 0.0260 4.63 5.1e-05 *** 300s trend 0.1996 0.0297 6.73 9.9e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.97 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 141.147 MSE: 8.822 Root MSE: 2.97 300s Multiple R-Squared: 0.474 Adjusted R-Squared: 0.375 300s 300s [1] "********* iterated 3SLS with restriction (EViews-like) *********" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 20 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 34 237 0.364 0.557 0.755 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 99.3 5.84 2.42 0.630 0.586 300s supply 20 16 138.1 8.63 2.94 0.485 0.388 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 4.96 5.82 300s supply 5.82 6.90 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 4.96 5.82 300s supply 5.82 6.90 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.995 300s supply 0.995 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 300s price -0.1043 0.0958 -1.09 0.28 300s income 0.1979 0.0299 6.61 1.4e-07 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.417 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 99.297 MSE: 5.841 Root MSE: 2.417 300s Multiple R-Squared: 0.63 Adjusted R-Squared: 0.586 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 300s price 0.1851 0.1053 1.76 0.088 . 300s farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 300s trend 0.1979 0.0299 6.61 1.4e-07 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.938 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 138.088 MSE: 8.63 Root MSE: 2.938 300s Multiple R-Squared: 0.485 Adjusted R-Squared: 0.388 300s 300s [1] "******** iterated W3SLS with restriction (EViews-like) *********" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 20 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 34 237 0.364 0.557 0.755 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 99.3 5.84 2.42 0.630 0.586 300s supply 20 16 138.1 8.63 2.94 0.485 0.388 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 4.96 5.82 300s supply 5.82 6.90 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 4.96 5.82 300s supply 5.82 6.90 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.995 300s supply 0.995 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 300s price -0.1043 0.0958 -1.09 0.28 300s income 0.1979 0.0299 6.61 1.4e-07 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.417 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 99.297 MSE: 5.841 Root MSE: 2.417 300s Multiple R-Squared: 0.63 Adjusted R-Squared: 0.586 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 300s price 0.1851 0.1053 1.76 0.088 . 300s farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 300s trend 0.1979 0.0299 6.61 1.4e-07 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.938 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 138.088 MSE: 8.63 Root MSE: 2.938 300s Multiple R-Squared: 0.485 Adjusted R-Squared: 0.388 300s 300s [1] "********* iterated 3SLS with restriction via restrict.regMat *****************" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 17 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 34 240 0.56 0.553 0.819 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 98.4 5.79 2.41 0.633 0.590 300s supply 20 16 141.1 8.82 2.97 0.474 0.375 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 5.79 7.11 300s supply 7.11 8.82 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 5.79 7.11 300s supply 7.11 8.82 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.995 300s supply 0.995 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 92.0742 9.6303 9.56 3.6e-11 *** 300s price -0.1064 0.1023 -1.04 0.31 300s income 0.1996 0.0297 6.73 9.9e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.406 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 98.435 MSE: 5.79 Root MSE: 2.406 300s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.59 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 68.8551 12.4839 5.52 3.7e-06 *** 300s price 0.1833 0.1189 1.54 0.13 300s farmPrice 0.1202 0.0260 4.63 5.1e-05 *** 300s trend 0.1996 0.0297 6.73 9.9e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.97 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 141.147 MSE: 8.822 Root MSE: 2.97 300s Multiple R-Squared: 0.474 Adjusted R-Squared: 0.375 300s 300s [1] "********* iterated 3SLS with restriction via restrict.regMat (EViews-like) ***" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 20 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 34 237 0.364 0.557 0.755 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 99.3 5.84 2.42 0.630 0.586 300s supply 20 16 138.1 8.63 2.94 0.485 0.388 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 4.96 5.82 300s supply 5.82 6.90 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 4.96 5.82 300s supply 5.82 6.90 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.995 300s supply 0.995 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 300s price -0.1043 0.0958 -1.09 0.28 300s income 0.1979 0.0299 6.61 1.4e-07 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.417 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 99.297 MSE: 5.841 Root MSE: 2.417 300s Multiple R-Squared: 0.63 Adjusted R-Squared: 0.586 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 300s price 0.1851 0.1053 1.76 0.088 . 300s farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 300s trend 0.1979 0.0299 6.61 1.4e-07 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.938 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 138.088 MSE: 8.63 Root MSE: 2.938 300s Multiple R-Squared: 0.485 Adjusted R-Squared: 0.388 300s 300s [1] "***** iterated W3SLS with restriction via restrict.regMat ********" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 17 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 34 240 0.56 0.553 0.819 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 98.4 5.79 2.41 0.633 0.590 300s supply 20 16 141.1 8.82 2.97 0.474 0.375 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 5.79 7.11 300s supply 7.11 8.82 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 5.79 7.11 300s supply 7.11 8.82 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.995 300s supply 0.995 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 92.0742 9.6303 9.56 3.6e-11 *** 300s price -0.1064 0.1023 -1.04 0.31 300s income 0.1996 0.0297 6.73 9.9e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.406 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 98.435 MSE: 5.79 Root MSE: 2.406 300s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.59 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 68.8551 12.4839 5.52 3.7e-06 *** 300s price 0.1833 0.1189 1.54 0.13 300s farmPrice 0.1202 0.0260 4.63 5.1e-05 *** 300s trend 0.1996 0.0297 6.73 9.9e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.97 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 141.147 MSE: 8.822 Root MSE: 2.97 300s Multiple R-Squared: 0.474 Adjusted R-Squared: 0.375 300s 300s [1] "******** iterated 3SLS with 2 restrictions *********************" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 9 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 35 185 1.76 0.655 0.71 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 69.9 4.11 2.03 0.739 0.709 300s supply 20 16 114.8 7.18 2.68 0.572 0.491 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 4.11 5.27 300s supply 5.27 7.18 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 4.11 5.27 300s supply 5.27 7.18 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.00 0.97 300s supply 0.97 1.00 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 90.1569 7.9174 11.39 2.5e-13 *** 300s price -0.2007 0.0920 -2.18 0.036 * 300s income 0.3159 0.0192 16.42 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.028 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 69.917 MSE: 4.113 Root MSE: 2.028 300s Multiple R-Squared: 0.739 Adjusted R-Squared: 0.709 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 46.4810 8.0104 5.80 1.4e-06 *** 300s price 0.2993 0.0920 3.25 0.0025 ** 300s farmPrice 0.2190 0.0196 11.20 4.0e-13 *** 300s trend 0.3159 0.0192 16.42 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.679 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 114.83 MSE: 7.177 Root MSE: 2.679 300s Multiple R-Squared: 0.572 Adjusted R-Squared: 0.491 300s 300s [1] "********* iterated 3SLS with 2 restrictions (EViews-like) *******" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 8 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 35 179 1.19 0.666 0.668 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 68.3 4.02 2.00 0.745 0.715 300s supply 20 16 110.8 6.92 2.63 0.587 0.509 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 3.41 4.21 300s supply 4.21 5.54 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 3.41 4.21 300s supply 4.21 5.54 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.968 300s supply 0.968 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 91.3901 7.3161 12.5 1.9e-14 *** 300s price -0.2168 0.0835 -2.6 0.014 * 300s income 0.3199 0.0168 19.1 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.004 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 68.293 MSE: 4.017 Root MSE: 2.004 300s Multiple R-Squared: 0.745 Adjusted R-Squared: 0.715 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 47.5787 7.4268 6.41 2.3e-07 *** 300s price 0.2832 0.0835 3.39 0.0017 ** 300s farmPrice 0.2240 0.0168 13.36 2.7e-15 *** 300s trend 0.3199 0.0168 19.07 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.631 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 110.791 MSE: 6.924 Root MSE: 2.631 300s Multiple R-Squared: 0.587 Adjusted R-Squared: 0.509 300s 300s [1] "******** iterated W3SLS with 2 restrictions (EViews-like) *******" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 8 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 35 179 1.19 0.666 0.668 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 68.3 4.02 2.00 0.745 0.715 300s supply 20 16 110.8 6.92 2.63 0.587 0.509 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 3.41 4.21 300s supply 4.21 5.54 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 3.41 4.21 300s supply 4.21 5.54 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.968 300s supply 0.968 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 91.3901 7.3161 12.5 1.9e-14 *** 300s price -0.2168 0.0835 -2.6 0.014 * 300s income 0.3199 0.0168 19.1 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.004 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 68.293 MSE: 4.017 Root MSE: 2.004 300s Multiple R-Squared: 0.745 Adjusted R-Squared: 0.715 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 47.5787 7.4268 6.41 2.3e-07 *** 300s price 0.2832 0.0835 3.39 0.0017 ** 300s farmPrice 0.2240 0.0168 13.36 2.7e-15 *** 300s trend 0.3199 0.0168 19.07 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.631 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 110.791 MSE: 6.924 Root MSE: 2.631 300s Multiple R-Squared: 0.587 Adjusted R-Squared: 0.509 300s 300s [1] "******** iterated 3SLS with 2 restrictions via R and restrict.regMat *********" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 9 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 35 185 1.76 0.655 0.71 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 69.9 4.11 2.03 0.739 0.709 300s supply 20 16 114.8 7.18 2.68 0.572 0.491 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 4.11 5.27 300s supply 5.27 7.18 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 4.11 5.27 300s supply 5.27 7.18 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.00 0.97 300s supply 0.97 1.00 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 90.1569 7.9174 11.39 2.5e-13 *** 300s price -0.2007 0.0920 -2.18 0.036 * 300s income 0.3159 0.0192 16.42 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.028 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 69.917 MSE: 4.113 Root MSE: 2.028 300s Multiple R-Squared: 0.739 Adjusted R-Squared: 0.709 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 46.4810 8.0104 5.80 1.4e-06 *** 300s price 0.2993 0.0920 3.25 0.0025 ** 300s farmPrice 0.2190 0.0196 11.20 4.0e-13 *** 300s trend 0.3159 0.0192 16.42 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.679 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 114.83 MSE: 7.177 Root MSE: 2.679 300s Multiple R-Squared: 0.572 Adjusted R-Squared: 0.491 300s 300s [1] "*** iterated 3SLS with 2 restrictions via R and restrict.regMat (EViews-like)**" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 8 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 35 179 1.19 0.666 0.668 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 68.3 4.02 2.00 0.745 0.715 300s supply 20 16 110.8 6.92 2.63 0.587 0.509 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 3.41 4.21 300s supply 4.21 5.54 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 3.41 4.21 300s supply 4.21 5.54 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.968 300s supply 0.968 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 91.3901 7.3161 12.5 1.9e-14 *** 300s price -0.2168 0.0835 -2.6 0.014 * 300s income 0.3199 0.0168 19.1 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.004 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 68.293 MSE: 4.017 Root MSE: 2.004 300s Multiple R-Squared: 0.745 Adjusted R-Squared: 0.715 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 47.5787 7.4268 6.41 2.3e-07 *** 300s price 0.2832 0.0835 3.39 0.0017 ** 300s farmPrice 0.2240 0.0168 13.36 2.7e-15 *** 300s trend 0.3199 0.0168 19.07 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.631 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 110.791 MSE: 6.924 Root MSE: 2.631 300s Multiple R-Squared: 0.587 Adjusted R-Squared: 0.509 300s 300s [1] "** iterated W3SLS with 2 restrictions via R and restrict.regMat ***" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 9 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 35 185 1.76 0.655 0.71 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 69.9 4.11 2.03 0.739 0.709 300s supply 20 16 114.8 7.18 2.68 0.572 0.491 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 4.11 5.27 300s supply 5.27 7.18 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 4.11 5.27 300s supply 5.27 7.18 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.00 0.97 300s supply 0.97 1.00 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 90.1569 7.9174 11.39 2.5e-13 *** 300s price -0.2007 0.0920 -2.18 0.036 * 300s income 0.3159 0.0192 16.42 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.028 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 69.917 MSE: 4.113 Root MSE: 2.028 300s Multiple R-Squared: 0.739 Adjusted R-Squared: 0.709 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 46.4810 8.0104 5.80 1.4e-06 *** 300s price 0.2993 0.0920 3.25 0.0025 ** 300s farmPrice 0.2190 0.0196 11.20 4.0e-13 *** 300s trend 0.3159 0.0192 16.42 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.679 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 114.83 MSE: 7.177 Root MSE: 2.679 300s Multiple R-Squared: 0.572 Adjusted R-Squared: 0.491 300s 300s [1] "***************************************************" 300s [1] "3SLS formula: Schmidt" 300s [1] "************* 3SLS *********************************" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 6 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 33 178 0.983 0.668 0.814 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 65.7 3.87 1.97 0.755 0.726 300s supply 20 16 112.4 7.03 2.65 0.581 0.502 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 3.87 5.12 300s supply 5.12 7.03 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 3.87 5.12 300s supply 5.12 7.03 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.982 300s supply 0.982 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 94.6333 7.9208 11.95 1.1e-09 *** 300s price -0.2436 0.0965 -2.52 0.022 * 300s income 0.3140 0.0469 6.69 3.8e-06 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 1.966 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 300s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 52.6618 12.8051 4.11 0.00081 *** 300s price 0.2266 0.1075 2.11 0.05110 . 300s farmPrice 0.2234 0.0468 4.78 0.00021 *** 300s trend 0.3800 0.0720 5.28 7.5e-05 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.651 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 112.431 MSE: 7.027 Root MSE: 2.651 300s Multiple R-Squared: 0.581 Adjusted R-Squared: 0.502 300s 300s [1] "********************* iterated 3SLS EViews-like ****************" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 6 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 33 177 0.667 0.67 0.782 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 65.7 3.87 1.97 0.755 0.726 300s supply 20 16 111.3 6.96 2.64 0.585 0.507 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 3.29 4.20 300s supply 4.20 5.57 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 3.29 4.20 300s supply 4.20 5.57 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.982 300s supply 0.982 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 300s price -0.2436 0.0890 -2.74 0.0099 ** 300s income 0.3140 0.0433 7.25 2.5e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 1.966 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 300s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 52.5527 11.3956 4.61 5.8e-05 *** 300s price 0.2271 0.0956 2.37 0.024 * 300s farmPrice 0.2245 0.0416 5.39 5.8e-06 *** 300s trend 0.3756 0.0641 5.86 1.5e-06 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.637 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 111.302 MSE: 6.956 Root MSE: 2.637 300s Multiple R-Squared: 0.585 Adjusted R-Squared: 0.507 300s 300s [1] "************** iterated 3SLS with methodResidCov = Theil **************" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 6 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 33 179 -0.818 0.665 0.957 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 65.7 3.87 1.97 0.755 0.726 300s supply 20 16 113.8 7.11 2.67 0.576 0.496 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 3.87 5.32 300s supply 5.32 7.11 300s 300s warning: this covariance matrix is NOT positive semidefinit! 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 3.87 5.32 300s supply 5.32 7.11 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.982 300s supply 0.982 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 300s price -0.2436 0.0965 -2.52 0.017 * 300s income 0.3140 0.0469 6.69 1.3e-07 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 1.966 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 300s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 52.7863 12.8707 4.10 0.00025 *** 300s price 0.2261 0.1081 2.09 0.04425 * 300s farmPrice 0.2221 0.0467 4.75 3.8e-05 *** 300s trend 0.3851 0.0693 5.55 3.6e-06 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.667 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 113.77 MSE: 7.111 Root MSE: 2.667 300s Multiple R-Squared: 0.576 Adjusted R-Squared: 0.496 300s 300s [1] "**************** iterated W3SLS EViews-like ****************" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 6 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 33 177 0.667 0.67 0.782 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 65.7 3.87 1.97 0.755 0.726 300s supply 20 16 111.3 6.96 2.64 0.585 0.507 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 3.29 4.20 300s supply 4.20 5.57 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 3.29 4.20 300s supply 4.20 5.57 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.982 300s supply 0.982 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 300s price -0.2436 0.0890 -2.74 0.0099 ** 300s income 0.3140 0.0433 7.25 2.5e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 1.966 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 300s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 52.5527 11.3956 4.61 5.8e-05 *** 300s price 0.2271 0.0956 2.37 0.024 * 300s farmPrice 0.2245 0.0416 5.39 5.8e-06 *** 300s trend 0.3756 0.0641 5.86 1.5e-06 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.637 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 111.302 MSE: 6.956 Root MSE: 2.637 300s Multiple R-Squared: 0.585 Adjusted R-Squared: 0.507 300s 300s [1] "******* iterated 3SLS with restriction *****************" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 17 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 34 240 0.56 0.553 0.819 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 98.4 5.79 2.41 0.633 0.590 300s supply 20 16 141.1 8.82 2.97 0.474 0.375 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 5.79 7.11 300s supply 7.11 8.82 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 5.79 7.11 300s supply 7.11 8.82 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.995 300s supply 0.995 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 92.0742 9.6303 9.56 3.6e-11 *** 300s price -0.1064 0.1023 -1.04 0.31 300s income 0.1996 0.0297 6.73 9.9e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.406 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 98.435 MSE: 5.79 Root MSE: 2.406 300s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.59 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 68.8551 12.4839 5.52 3.7e-06 *** 300s price 0.1833 0.1189 1.54 0.13 300s farmPrice 0.1202 0.0260 4.63 5.1e-05 *** 300s trend 0.1996 0.0297 6.73 9.9e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.97 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 141.147 MSE: 8.822 Root MSE: 2.97 300s Multiple R-Squared: 0.474 Adjusted R-Squared: 0.375 300s 300s [1] "********* iterated 3SLS with restriction (EViews-like) *********" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 20 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 34 237 0.364 0.557 0.755 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 99.3 5.84 2.42 0.630 0.586 300s supply 20 16 138.1 8.63 2.94 0.485 0.388 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 4.96 5.82 300s supply 5.82 6.90 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 4.96 5.82 300s supply 5.82 6.90 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.995 300s supply 0.995 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 300s price -0.1043 0.0958 -1.09 0.28 300s income 0.1979 0.0299 6.61 1.4e-07 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.417 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 99.297 MSE: 5.841 Root MSE: 2.417 300s Multiple R-Squared: 0.63 Adjusted R-Squared: 0.586 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 300s price 0.1851 0.1053 1.76 0.088 . 300s farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 300s trend 0.1979 0.0299 6.61 1.4e-07 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.938 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 138.088 MSE: 8.63 Root MSE: 2.938 300s Multiple R-Squared: 0.485 Adjusted R-Squared: 0.388 300s 300s [1] "******** iterated W3SLS with restriction (EViews-like) *********" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 20 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 34 237 0.364 0.557 0.755 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 99.3 5.84 2.42 0.630 0.586 300s supply 20 16 138.1 8.63 2.94 0.485 0.388 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 4.96 5.82 300s supply 5.82 6.90 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 4.96 5.82 300s supply 5.82 6.90 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.995 300s supply 0.995 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 300s price -0.1043 0.0958 -1.09 0.28 300s income 0.1979 0.0299 6.61 1.4e-07 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.417 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 99.297 MSE: 5.841 Root MSE: 2.417 300s Multiple R-Squared: 0.63 Adjusted R-Squared: 0.586 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 300s price 0.1851 0.1053 1.76 0.088 . 300s farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 300s trend 0.1979 0.0299 6.61 1.4e-07 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.938 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 138.088 MSE: 8.63 Root MSE: 2.938 300s Multiple R-Squared: 0.485 Adjusted R-Squared: 0.388 300s 300s [1] "********* iterated 3SLS with restriction via restrict.regMat *****************" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 17 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 34 240 0.56 0.553 0.819 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 98.4 5.79 2.41 0.633 0.590 300s supply 20 16 141.1 8.82 2.97 0.474 0.375 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 5.79 7.11 300s supply 7.11 8.82 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 5.79 7.11 300s supply 7.11 8.82 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.995 300s supply 0.995 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 92.0742 9.6303 9.56 3.6e-11 *** 300s price -0.1064 0.1023 -1.04 0.31 300s income 0.1996 0.0297 6.73 9.9e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.406 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 98.435 MSE: 5.79 Root MSE: 2.406 300s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.59 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 68.8551 12.4839 5.52 3.7e-06 *** 300s price 0.1833 0.1189 1.54 0.13 300s farmPrice 0.1202 0.0260 4.63 5.1e-05 *** 300s trend 0.1996 0.0297 6.73 9.9e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.97 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 141.147 MSE: 8.822 Root MSE: 2.97 300s Multiple R-Squared: 0.474 Adjusted R-Squared: 0.375 300s 300s [1] "********* iterated 3SLS with restriction via restrict.regMat (EViews-like) ***" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 20 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 34 237 0.364 0.557 0.755 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 99.3 5.84 2.42 0.630 0.586 300s supply 20 16 138.1 8.63 2.94 0.485 0.388 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 4.96 5.82 300s supply 5.82 6.90 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 4.96 5.82 300s supply 5.82 6.90 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.995 300s supply 0.995 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 300s price -0.1043 0.0958 -1.09 0.28 300s income 0.1979 0.0299 6.61 1.4e-07 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.417 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 99.297 MSE: 5.841 Root MSE: 2.417 300s Multiple R-Squared: 0.63 Adjusted R-Squared: 0.586 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 300s price 0.1851 0.1053 1.76 0.088 . 300s farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 300s trend 0.1979 0.0299 6.61 1.4e-07 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.938 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 138.088 MSE: 8.63 Root MSE: 2.938 300s Multiple R-Squared: 0.485 Adjusted R-Squared: 0.388 300s 300s [1] "***** iterated W3SLS with restriction via restrict.regMat ********" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 17 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 34 240 0.56 0.553 0.819 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 98.4 5.79 2.41 0.633 0.590 300s supply 20 16 141.1 8.82 2.97 0.474 0.375 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 5.79 7.11 300s supply 7.11 8.82 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 5.79 7.11 300s supply 7.11 8.82 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.995 300s supply 0.995 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 92.0742 9.6303 9.56 3.6e-11 *** 300s price -0.1064 0.1023 -1.04 0.31 300s income 0.1996 0.0297 6.73 9.9e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.406 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 98.435 MSE: 5.79 Root MSE: 2.406 300s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.59 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 68.8551 12.4839 5.52 3.7e-06 *** 300s price 0.1833 0.1189 1.54 0.13 300s farmPrice 0.1202 0.0260 4.63 5.1e-05 *** 300s trend 0.1996 0.0297 6.73 9.9e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.97 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 141.147 MSE: 8.822 Root MSE: 2.97 300s Multiple R-Squared: 0.474 Adjusted R-Squared: 0.375 300s 300s [1] "******** iterated 3SLS with 2 restrictions *********************" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 9 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 35 185 1.76 0.655 0.71 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 69.9 4.11 2.03 0.739 0.709 300s supply 20 16 114.8 7.18 2.68 0.572 0.491 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 4.11 5.27 300s supply 5.27 7.18 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 4.11 5.27 300s supply 5.27 7.18 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.00 0.97 300s supply 0.97 1.00 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 90.1569 7.9174 11.39 2.5e-13 *** 300s price -0.2007 0.0920 -2.18 0.036 * 300s income 0.3159 0.0192 16.42 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.028 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 69.917 MSE: 4.113 Root MSE: 2.028 300s Multiple R-Squared: 0.739 Adjusted R-Squared: 0.709 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 46.4810 8.0104 5.80 1.4e-06 *** 300s price 0.2993 0.0920 3.25 0.0025 ** 300s farmPrice 0.2190 0.0196 11.20 4.0e-13 *** 300s trend 0.3159 0.0192 16.42 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.679 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 114.83 MSE: 7.177 Root MSE: 2.679 300s Multiple R-Squared: 0.572 Adjusted R-Squared: 0.491 300s 300s [1] "********* iterated 3SLS with 2 restrictions (EViews-like) *******" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 8 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 35 179 1.19 0.666 0.668 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 68.3 4.02 2.00 0.745 0.715 300s supply 20 16 110.8 6.92 2.63 0.587 0.509 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 3.41 4.21 300s supply 4.21 5.54 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 3.41 4.21 300s supply 4.21 5.54 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.968 300s supply 0.968 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 91.3901 7.3161 12.5 1.9e-14 *** 300s price -0.2168 0.0835 -2.6 0.014 * 300s income 0.3199 0.0168 19.1 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.004 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 68.293 MSE: 4.017 Root MSE: 2.004 300s Multiple R-Squared: 0.745 Adjusted R-Squared: 0.715 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 47.5787 7.4268 6.41 2.3e-07 *** 300s price 0.2832 0.0835 3.39 0.0017 ** 300s farmPrice 0.2240 0.0168 13.36 2.7e-15 *** 300s trend 0.3199 0.0168 19.07 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.631 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 110.791 MSE: 6.924 Root MSE: 2.631 300s Multiple R-Squared: 0.587 Adjusted R-Squared: 0.509 300s 300s [1] "******** iterated W3SLS with 2 restrictions (EViews-like) *******" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 8 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 35 179 1.19 0.666 0.668 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 68.3 4.02 2.00 0.745 0.715 300s supply 20 16 110.8 6.92 2.63 0.587 0.509 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 3.41 4.21 300s supply 4.21 5.54 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 3.41 4.21 300s supply 4.21 5.54 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.968 300s supply 0.968 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 91.3901 7.3161 12.5 1.9e-14 *** 300s price -0.2168 0.0835 -2.6 0.014 * 300s income 0.3199 0.0168 19.1 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.004 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 68.293 MSE: 4.017 Root MSE: 2.004 300s Multiple R-Squared: 0.745 Adjusted R-Squared: 0.715 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 47.5787 7.4268 6.41 2.3e-07 *** 300s price 0.2832 0.0835 3.39 0.0017 ** 300s farmPrice 0.2240 0.0168 13.36 2.7e-15 *** 300s trend 0.3199 0.0168 19.07 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.631 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 110.791 MSE: 6.924 Root MSE: 2.631 300s Multiple R-Squared: 0.587 Adjusted R-Squared: 0.509 300s 300s [1] "******** iterated 3SLS with 2 restrictions via R and restrict.regMat *********" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 9 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 35 185 1.76 0.655 0.71 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 69.9 4.11 2.03 0.739 0.709 300s supply 20 16 114.8 7.18 2.68 0.572 0.491 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 4.11 5.27 300s supply 5.27 7.18 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 4.11 5.27 300s supply 5.27 7.18 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.00 0.97 300s supply 0.97 1.00 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 90.1569 7.9174 11.39 2.5e-13 *** 300s price -0.2007 0.0920 -2.18 0.036 * 300s income 0.3159 0.0192 16.42 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.028 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 69.917 MSE: 4.113 Root MSE: 2.028 300s Multiple R-Squared: 0.739 Adjusted R-Squared: 0.709 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 46.4810 8.0104 5.80 1.4e-06 *** 300s price 0.2993 0.0920 3.25 0.0025 ** 300s farmPrice 0.2190 0.0196 11.20 4.0e-13 *** 300s trend 0.3159 0.0192 16.42 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.679 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 114.83 MSE: 7.177 Root MSE: 2.679 300s Multiple R-Squared: 0.572 Adjusted R-Squared: 0.491 300s 300s [1] "*** iterated 3SLS with 2 restrictions via R and restrict.regMat (EViews-like)**" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 8 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 35 179 1.19 0.666 0.668 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 68.3 4.02 2.00 0.745 0.715 300s supply 20 16 110.8 6.92 2.63 0.587 0.509 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 3.41 4.21 300s supply 4.21 5.54 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 3.41 4.21 300s supply 4.21 5.54 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.968 300s supply 0.968 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 91.3901 7.3161 12.5 1.9e-14 *** 300s price -0.2168 0.0835 -2.6 0.014 * 300s income 0.3199 0.0168 19.1 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.004 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 68.293 MSE: 4.017 Root MSE: 2.004 300s Multiple R-Squared: 0.745 Adjusted R-Squared: 0.715 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 47.5787 7.4268 6.41 2.3e-07 *** 300s price 0.2832 0.0835 3.39 0.0017 ** 300s farmPrice 0.2240 0.0168 13.36 2.7e-15 *** 300s trend 0.3199 0.0168 19.07 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.631 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 110.791 MSE: 6.924 Root MSE: 2.631 300s Multiple R-Squared: 0.587 Adjusted R-Squared: 0.509 300s 300s [1] "** iterated W3SLS with 2 restrictions via R and restrict.regMat ***" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 9 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 35 185 1.76 0.655 0.71 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 69.9 4.11 2.03 0.739 0.709 300s supply 20 16 114.8 7.18 2.68 0.572 0.491 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 4.11 5.27 300s supply 5.27 7.18 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 4.11 5.27 300s supply 5.27 7.18 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.00 0.97 300s supply 0.97 1.00 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 90.1569 7.9174 11.39 2.5e-13 *** 300s price -0.2007 0.0920 -2.18 0.036 * 300s income 0.3159 0.0192 16.42 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.028 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 69.917 MSE: 4.113 Root MSE: 2.028 300s Multiple R-Squared: 0.739 Adjusted R-Squared: 0.709 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 46.4810 8.0104 5.80 1.4e-06 *** 300s price 0.2993 0.0920 3.25 0.0025 ** 300s farmPrice 0.2190 0.0196 11.20 4.0e-13 *** 300s trend 0.3159 0.0192 16.42 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.679 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 114.83 MSE: 7.177 Root MSE: 2.679 300s Multiple R-Squared: 0.572 Adjusted R-Squared: 0.491 300s 300s [1] "***************************************************" 300s [1] "3SLS formula: GMM" 300s [1] "************* 3SLS *********************************" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 6 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 33 178 0.983 0.668 0.814 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 65.7 3.87 1.97 0.755 0.726 300s supply 20 16 112.4 7.03 2.65 0.581 0.502 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 3.87 5.12 300s supply 5.12 7.03 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 3.87 5.12 300s supply 5.12 7.03 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.982 300s supply 0.982 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 94.6333 7.9208 11.95 1.1e-09 *** 300s price -0.2436 0.0965 -2.52 0.022 * 300s income 0.3140 0.0469 6.69 3.8e-06 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 1.966 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 300s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 52.6618 12.8051 4.11 0.00081 *** 300s price 0.2266 0.1075 2.11 0.05110 . 300s farmPrice 0.2234 0.0468 4.78 0.00021 *** 300s trend 0.3800 0.0720 5.28 7.5e-05 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.651 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 112.431 MSE: 7.027 Root MSE: 2.651 300s Multiple R-Squared: 0.581 Adjusted R-Squared: 0.502 300s 300s [1] "********************* iterated 3SLS EViews-like ****************" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 6 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 33 177 0.667 0.67 0.782 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 65.7 3.87 1.97 0.755 0.726 300s supply 20 16 111.3 6.96 2.64 0.585 0.507 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 3.29 4.20 300s supply 4.20 5.57 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 3.29 4.20 300s supply 4.20 5.57 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.982 300s supply 0.982 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 300s price -0.2436 0.0890 -2.74 0.0099 ** 300s income 0.3140 0.0433 7.25 2.5e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 1.966 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 300s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 52.5527 11.3956 4.61 5.8e-05 *** 300s price 0.2271 0.0956 2.37 0.024 * 300s farmPrice 0.2245 0.0416 5.39 5.8e-06 *** 300s trend 0.3756 0.0641 5.86 1.5e-06 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.637 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 111.302 MSE: 6.956 Root MSE: 2.637 300s Multiple R-Squared: 0.585 Adjusted R-Squared: 0.507 300s 300s [1] "************** iterated 3SLS with methodResidCov = Theil **************" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 6 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 33 179 -0.818 0.665 0.957 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 65.7 3.87 1.97 0.755 0.726 300s supply 20 16 113.8 7.11 2.67 0.576 0.496 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 3.87 5.32 300s supply 5.32 7.11 300s 300s warning: this covariance matrix is NOT positive semidefinit! 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 3.87 5.32 300s supply 5.32 7.11 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.982 300s supply 0.982 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 300s price -0.2436 0.0965 -2.52 0.017 * 300s income 0.3140 0.0469 6.69 1.3e-07 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 1.966 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 300s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 52.7863 12.8707 4.10 0.00025 *** 300s price 0.2261 0.1081 2.09 0.04425 * 300s farmPrice 0.2221 0.0467 4.75 3.8e-05 *** 300s trend 0.3851 0.0693 5.55 3.6e-06 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.667 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 113.77 MSE: 7.111 Root MSE: 2.667 300s Multiple R-Squared: 0.576 Adjusted R-Squared: 0.496 300s 300s [1] "**************** iterated W3SLS EViews-like ****************" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 6 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 33 177 0.667 0.67 0.782 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 65.7 3.87 1.97 0.755 0.726 300s supply 20 16 111.3 6.96 2.64 0.585 0.507 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 3.29 4.20 300s supply 4.20 5.57 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 3.29 4.20 300s supply 4.20 5.57 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.982 300s supply 0.982 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 300s price -0.2436 0.0890 -2.74 0.0099 ** 300s income 0.3140 0.0433 7.25 2.5e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 1.966 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 300s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 52.5527 11.3956 4.61 5.8e-05 *** 300s price 0.2271 0.0956 2.37 0.024 * 300s farmPrice 0.2245 0.0416 5.39 5.8e-06 *** 300s trend 0.3756 0.0641 5.86 1.5e-06 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.637 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 111.302 MSE: 6.956 Root MSE: 2.637 300s Multiple R-Squared: 0.585 Adjusted R-Squared: 0.507 300s 300s [1] "******* iterated 3SLS with restriction *****************" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 17 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 34 240 0.56 0.553 0.819 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 98.4 5.79 2.41 0.633 0.590 300s supply 20 16 141.1 8.82 2.97 0.474 0.375 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 5.79 7.11 300s supply 7.11 8.82 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 5.79 7.11 300s supply 7.11 8.82 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.995 300s supply 0.995 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 92.0742 9.6303 9.56 3.6e-11 *** 300s price -0.1064 0.1023 -1.04 0.31 300s income 0.1996 0.0297 6.73 9.9e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.406 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 98.435 MSE: 5.79 Root MSE: 2.406 300s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.59 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 68.8551 12.4839 5.52 3.7e-06 *** 300s price 0.1833 0.1189 1.54 0.13 300s farmPrice 0.1202 0.0260 4.63 5.1e-05 *** 300s trend 0.1996 0.0297 6.73 9.9e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.97 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 141.147 MSE: 8.822 Root MSE: 2.97 300s Multiple R-Squared: 0.474 Adjusted R-Squared: 0.375 300s 300s [1] "********* iterated 3SLS with restriction (EViews-like) *********" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 20 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 34 237 0.364 0.557 0.755 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 99.3 5.84 2.42 0.630 0.586 300s supply 20 16 138.1 8.63 2.94 0.485 0.388 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 4.96 5.82 300s supply 5.82 6.90 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 4.96 5.82 300s supply 5.82 6.90 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.995 300s supply 0.995 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 300s price -0.1043 0.0958 -1.09 0.28 300s income 0.1979 0.0299 6.61 1.4e-07 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.417 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 99.297 MSE: 5.841 Root MSE: 2.417 300s Multiple R-Squared: 0.63 Adjusted R-Squared: 0.586 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 300s price 0.1851 0.1053 1.76 0.088 . 300s farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 300s trend 0.1979 0.0299 6.61 1.4e-07 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.938 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 138.088 MSE: 8.63 Root MSE: 2.938 300s Multiple R-Squared: 0.485 Adjusted R-Squared: 0.388 300s 300s [1] "******** iterated W3SLS with restriction (EViews-like) *********" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 20 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 34 237 0.364 0.557 0.755 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 99.3 5.84 2.42 0.630 0.586 300s supply 20 16 138.1 8.63 2.94 0.485 0.388 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 4.96 5.82 300s supply 5.82 6.90 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 4.96 5.82 300s supply 5.82 6.90 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.995 300s supply 0.995 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 300s price -0.1043 0.0958 -1.09 0.28 300s income 0.1979 0.0299 6.61 1.4e-07 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.417 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 99.297 MSE: 5.841 Root MSE: 2.417 300s Multiple R-Squared: 0.63 Adjusted R-Squared: 0.586 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 300s price 0.1851 0.1053 1.76 0.088 . 300s farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 300s trend 0.1979 0.0299 6.61 1.4e-07 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.938 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 138.088 MSE: 8.63 Root MSE: 2.938 300s Multiple R-Squared: 0.485 Adjusted R-Squared: 0.388 300s 300s [1] "********* iterated 3SLS with restriction via restrict.regMat *****************" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 17 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 34 240 0.56 0.553 0.819 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 98.4 5.79 2.41 0.633 0.590 300s supply 20 16 141.1 8.82 2.97 0.474 0.375 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 5.79 7.11 300s supply 7.11 8.82 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 5.79 7.11 300s supply 7.11 8.82 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.995 300s supply 0.995 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 92.0742 9.6303 9.56 3.6e-11 *** 300s price -0.1064 0.1023 -1.04 0.31 300s income 0.1996 0.0297 6.73 9.9e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.406 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 98.435 MSE: 5.79 Root MSE: 2.406 300s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.59 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 68.8551 12.4839 5.52 3.7e-06 *** 300s price 0.1833 0.1189 1.54 0.13 300s farmPrice 0.1202 0.0260 4.63 5.1e-05 *** 300s trend 0.1996 0.0297 6.73 9.9e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.97 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 141.147 MSE: 8.822 Root MSE: 2.97 300s Multiple R-Squared: 0.474 Adjusted R-Squared: 0.375 300s 300s [1] "********* iterated 3SLS with restriction via restrict.regMat (EViews-like) ***" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 20 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 34 237 0.364 0.557 0.755 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 99.3 5.84 2.42 0.630 0.586 300s supply 20 16 138.1 8.63 2.94 0.485 0.388 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 4.96 5.82 300s supply 5.82 6.90 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 4.96 5.82 300s supply 5.82 6.90 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.995 300s supply 0.995 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 300s price -0.1043 0.0958 -1.09 0.28 300s income 0.1979 0.0299 6.61 1.4e-07 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.417 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 99.297 MSE: 5.841 Root MSE: 2.417 300s Multiple R-Squared: 0.63 Adjusted R-Squared: 0.586 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 300s price 0.1851 0.1053 1.76 0.088 . 300s farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 300s trend 0.1979 0.0299 6.61 1.4e-07 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.938 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 138.088 MSE: 8.63 Root MSE: 2.938 300s Multiple R-Squared: 0.485 Adjusted R-Squared: 0.388 300s 300s [1] "***** iterated W3SLS with restriction via restrict.regMat ********" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 17 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 34 240 0.56 0.553 0.819 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 98.4 5.79 2.41 0.633 0.590 300s supply 20 16 141.1 8.82 2.97 0.474 0.375 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 5.79 7.11 300s supply 7.11 8.82 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 5.79 7.11 300s supply 7.11 8.82 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.995 300s supply 0.995 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 92.0742 9.6303 9.56 3.6e-11 *** 300s price -0.1064 0.1023 -1.04 0.31 300s income 0.1996 0.0297 6.73 9.9e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.406 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 98.435 MSE: 5.79 Root MSE: 2.406 300s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.59 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 68.8551 12.4839 5.52 3.7e-06 *** 300s price 0.1833 0.1189 1.54 0.13 300s farmPrice 0.1202 0.0260 4.63 5.1e-05 *** 300s trend 0.1996 0.0297 6.73 9.9e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.97 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 141.147 MSE: 8.822 Root MSE: 2.97 300s Multiple R-Squared: 0.474 Adjusted R-Squared: 0.375 300s 300s [1] "******** iterated 3SLS with 2 restrictions *********************" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 9 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 35 185 1.76 0.655 0.71 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 69.9 4.11 2.03 0.739 0.709 300s supply 20 16 114.8 7.18 2.68 0.572 0.491 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 4.11 5.27 300s supply 5.27 7.18 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 4.11 5.27 300s supply 5.27 7.18 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.00 0.97 300s supply 0.97 1.00 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 90.1569 7.9174 11.39 2.5e-13 *** 300s price -0.2007 0.0920 -2.18 0.036 * 300s income 0.3159 0.0192 16.42 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.028 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 69.917 MSE: 4.113 Root MSE: 2.028 300s Multiple R-Squared: 0.739 Adjusted R-Squared: 0.709 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 46.4810 8.0104 5.80 1.4e-06 *** 300s price 0.2993 0.0920 3.25 0.0025 ** 300s farmPrice 0.2190 0.0196 11.20 4.0e-13 *** 300s trend 0.3159 0.0192 16.42 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.679 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 114.83 MSE: 7.177 Root MSE: 2.679 300s Multiple R-Squared: 0.572 Adjusted R-Squared: 0.491 300s 300s [1] "********* iterated 3SLS with 2 restrictions (EViews-like) *******" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 8 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 35 179 1.19 0.666 0.668 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 68.3 4.02 2.00 0.745 0.715 300s supply 20 16 110.8 6.92 2.63 0.587 0.509 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 3.41 4.21 300s supply 4.21 5.54 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 3.41 4.21 300s supply 4.21 5.54 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.968 300s supply 0.968 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 91.3901 7.3161 12.5 1.9e-14 *** 300s price -0.2168 0.0835 -2.6 0.014 * 300s income 0.3199 0.0168 19.1 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.004 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 68.293 MSE: 4.017 Root MSE: 2.004 300s Multiple R-Squared: 0.745 Adjusted R-Squared: 0.715 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 47.5787 7.4268 6.41 2.3e-07 *** 300s price 0.2832 0.0835 3.39 0.0017 ** 300s farmPrice 0.2240 0.0168 13.36 2.7e-15 *** 300s trend 0.3199 0.0168 19.07 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.631 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 110.791 MSE: 6.924 Root MSE: 2.631 300s Multiple R-Squared: 0.587 Adjusted R-Squared: 0.509 300s 300s [1] "******** iterated W3SLS with 2 restrictions (EViews-like) *******" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 8 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 35 179 1.19 0.666 0.668 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 68.3 4.02 2.00 0.745 0.715 300s supply 20 16 110.8 6.92 2.63 0.587 0.509 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 3.41 4.21 300s supply 4.21 5.54 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 3.41 4.21 300s supply 4.21 5.54 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.968 300s supply 0.968 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 91.3901 7.3161 12.5 1.9e-14 *** 300s price -0.2168 0.0835 -2.6 0.014 * 300s income 0.3199 0.0168 19.1 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.004 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 68.293 MSE: 4.017 Root MSE: 2.004 300s Multiple R-Squared: 0.745 Adjusted R-Squared: 0.715 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 47.5787 7.4268 6.41 2.3e-07 *** 300s price 0.2832 0.0835 3.39 0.0017 ** 300s farmPrice 0.2240 0.0168 13.36 2.7e-15 *** 300s trend 0.3199 0.0168 19.07 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.631 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 110.791 MSE: 6.924 Root MSE: 2.631 300s Multiple R-Squared: 0.587 Adjusted R-Squared: 0.509 300s 300s [1] "******** iterated 3SLS with 2 restrictions via R and restrict.regMat *********" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 9 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 35 185 1.76 0.655 0.71 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 69.9 4.11 2.03 0.739 0.709 300s supply 20 16 114.8 7.18 2.68 0.572 0.491 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 4.11 5.27 300s supply 5.27 7.18 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 4.11 5.27 300s supply 5.27 7.18 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.00 0.97 300s supply 0.97 1.00 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 90.1569 7.9174 11.39 2.5e-13 *** 300s price -0.2007 0.0920 -2.18 0.036 * 300s income 0.3159 0.0192 16.42 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.028 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 69.917 MSE: 4.113 Root MSE: 2.028 300s Multiple R-Squared: 0.739 Adjusted R-Squared: 0.709 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 46.4810 8.0104 5.80 1.4e-06 *** 300s price 0.2993 0.0920 3.25 0.0025 ** 300s farmPrice 0.2190 0.0196 11.20 4.0e-13 *** 300s trend 0.3159 0.0192 16.42 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.679 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 114.83 MSE: 7.177 Root MSE: 2.679 300s Multiple R-Squared: 0.572 Adjusted R-Squared: 0.491 300s 300s [1] "*** iterated 3SLS with 2 restrictions via R and restrict.regMat (EViews-like)**" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 8 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 35 179 1.19 0.666 0.668 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 68.3 4.02 2.00 0.745 0.715 300s supply 20 16 110.8 6.92 2.63 0.587 0.509 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 3.41 4.21 300s supply 4.21 5.54 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 3.41 4.21 300s supply 4.21 5.54 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.968 300s supply 0.968 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 91.3901 7.3161 12.5 1.9e-14 *** 300s price -0.2168 0.0835 -2.6 0.014 * 300s income 0.3199 0.0168 19.1 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.004 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 68.293 MSE: 4.017 Root MSE: 2.004 300s Multiple R-Squared: 0.745 Adjusted R-Squared: 0.715 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 47.5787 7.4268 6.41 2.3e-07 *** 300s price 0.2832 0.0835 3.39 0.0017 ** 300s farmPrice 0.2240 0.0168 13.36 2.7e-15 *** 300s trend 0.3199 0.0168 19.07 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.631 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 110.791 MSE: 6.924 Root MSE: 2.631 300s Multiple R-Squared: 0.587 Adjusted R-Squared: 0.509 300s 300s [1] "** iterated W3SLS with 2 restrictions via R and restrict.regMat ***" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 9 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 35 185 1.76 0.655 0.71 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 69.9 4.11 2.03 0.739 0.709 300s supply 20 16 114.8 7.18 2.68 0.572 0.491 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 4.11 5.27 300s supply 5.27 7.18 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 4.11 5.27 300s supply 5.27 7.18 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.00 0.97 300s supply 0.97 1.00 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 90.1569 7.9174 11.39 2.5e-13 *** 300s price -0.2007 0.0920 -2.18 0.036 * 300s income 0.3159 0.0192 16.42 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.028 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 69.917 MSE: 4.113 Root MSE: 2.028 300s Multiple R-Squared: 0.739 Adjusted R-Squared: 0.709 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 46.4810 8.0104 5.80 1.4e-06 *** 300s price 0.2993 0.0920 3.25 0.0025 ** 300s farmPrice 0.2190 0.0196 11.20 4.0e-13 *** 300s trend 0.3159 0.0192 16.42 < 2e-16 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.679 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 114.83 MSE: 7.177 Root MSE: 2.679 300s Multiple R-Squared: 0.572 Adjusted R-Squared: 0.491 300s 300s [1] "***************************************************" 300s [1] "3SLS formula: EViews" 300s [1] "************* 3SLS *********************************" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 6 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 33 178 0.983 0.668 0.814 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 65.7 3.87 1.97 0.755 0.726 300s supply 20 16 112.4 7.03 2.65 0.581 0.502 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 3.87 5.12 300s supply 5.12 7.03 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 3.87 5.12 300s supply 5.12 7.03 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.982 300s supply 0.982 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 94.6333 7.9208 11.95 1.1e-09 *** 300s price -0.2436 0.0965 -2.52 0.022 * 300s income 0.3140 0.0469 6.69 3.8e-06 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 1.966 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 300s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 52.6618 12.8051 4.11 0.00081 *** 300s price 0.2266 0.1075 2.11 0.05110 . 300s farmPrice 0.2234 0.0468 4.78 0.00021 *** 300s trend 0.3800 0.0720 5.28 7.5e-05 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.651 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 112.431 MSE: 7.027 Root MSE: 2.651 300s Multiple R-Squared: 0.581 Adjusted R-Squared: 0.502 300s 300s [1] "********************* iterated 3SLS EViews-like ****************" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 6 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 33 177 0.667 0.67 0.782 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 65.7 3.87 1.97 0.755 0.726 300s supply 20 16 111.3 6.96 2.64 0.585 0.507 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 3.29 4.20 300s supply 4.20 5.57 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 3.29 4.20 300s supply 4.20 5.57 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.982 300s supply 0.982 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 300s price -0.2436 0.0890 -2.74 0.0099 ** 300s income 0.3140 0.0433 7.25 2.5e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 1.966 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 300s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 52.5527 11.3956 4.61 5.8e-05 *** 300s price 0.2271 0.0956 2.37 0.024 * 300s farmPrice 0.2245 0.0416 5.39 5.8e-06 *** 300s trend 0.3756 0.0641 5.86 1.5e-06 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.637 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 111.302 MSE: 6.956 Root MSE: 2.637 300s Multiple R-Squared: 0.585 Adjusted R-Squared: 0.507 300s 300s [1] "************** iterated 3SLS with methodResidCov = Theil **************" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 6 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 33 179 -0.818 0.665 0.957 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 65.7 3.87 1.97 0.755 0.726 300s supply 20 16 113.8 7.11 2.67 0.576 0.496 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 3.87 5.32 300s supply 5.32 7.11 300s 300s warning: this covariance matrix is NOT positive semidefinit! 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 3.87 5.32 300s supply 5.32 7.11 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.982 300s supply 0.982 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 300s price -0.2436 0.0965 -2.52 0.017 * 300s income 0.3140 0.0469 6.69 1.3e-07 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 1.966 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 300s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 52.7863 12.8707 4.10 0.00025 *** 300s price 0.2261 0.1081 2.09 0.04425 * 300s farmPrice 0.2221 0.0467 4.75 3.8e-05 *** 300s trend 0.3851 0.0693 5.55 3.6e-06 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.667 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 113.77 MSE: 7.111 Root MSE: 2.667 300s Multiple R-Squared: 0.576 Adjusted R-Squared: 0.496 300s 300s [1] "**************** iterated W3SLS EViews-like ****************" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 6 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 33 177 0.667 0.67 0.782 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 65.7 3.87 1.97 0.755 0.726 300s supply 20 16 111.3 6.96 2.64 0.585 0.507 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 3.29 4.20 300s supply 4.20 5.57 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 3.29 4.20 300s supply 4.20 5.57 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.982 300s supply 0.982 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 300s price -0.2436 0.0890 -2.74 0.0099 ** 300s income 0.3140 0.0433 7.25 2.5e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 1.966 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 300s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 52.5527 11.3956 4.61 5.8e-05 *** 300s price 0.2271 0.0956 2.37 0.024 * 300s farmPrice 0.2245 0.0416 5.39 5.8e-06 *** 300s trend 0.3756 0.0641 5.86 1.5e-06 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.637 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 111.302 MSE: 6.956 Root MSE: 2.637 300s Multiple R-Squared: 0.585 Adjusted R-Squared: 0.507 300s 300s [1] "******* iterated 3SLS with restriction *****************" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 17 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 34 240 0.56 0.553 0.819 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 98.4 5.79 2.41 0.633 0.590 300s supply 20 16 141.1 8.82 2.97 0.474 0.375 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 5.79 7.11 300s supply 7.11 8.82 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 5.79 7.11 300s supply 7.11 8.82 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.995 300s supply 0.995 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 92.0742 9.6303 9.56 3.6e-11 *** 300s price -0.1064 0.1023 -1.04 0.31 300s income 0.1996 0.0297 6.73 9.9e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.406 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 98.435 MSE: 5.79 Root MSE: 2.406 300s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.59 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 68.8551 12.4839 5.52 3.7e-06 *** 300s price 0.1833 0.1189 1.54 0.13 300s farmPrice 0.1202 0.0260 4.63 5.1e-05 *** 300s trend 0.1996 0.0297 6.73 9.9e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.97 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 141.147 MSE: 8.822 Root MSE: 2.97 300s Multiple R-Squared: 0.474 Adjusted R-Squared: 0.375 300s 300s [1] "********* iterated 3SLS with restriction (EViews-like) *********" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 20 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 34 237 0.364 0.557 0.755 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 99.3 5.84 2.42 0.630 0.586 300s supply 20 16 138.1 8.63 2.94 0.485 0.388 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 4.96 5.82 300s supply 5.82 6.90 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 4.96 5.82 300s supply 5.82 6.90 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.995 300s supply 0.995 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 300s price -0.1043 0.0958 -1.09 0.28 300s income 0.1979 0.0299 6.61 1.4e-07 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.417 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 99.297 MSE: 5.841 Root MSE: 2.417 300s Multiple R-Squared: 0.63 Adjusted R-Squared: 0.586 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 300s price 0.1851 0.1053 1.76 0.088 . 300s farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 300s trend 0.1979 0.0299 6.61 1.4e-07 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.938 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 138.088 MSE: 8.63 Root MSE: 2.938 300s Multiple R-Squared: 0.485 Adjusted R-Squared: 0.388 300s 300s [1] "******** iterated W3SLS with restriction (EViews-like) *********" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 20 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 34 237 0.364 0.557 0.755 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 99.3 5.84 2.42 0.630 0.586 300s supply 20 16 138.1 8.63 2.94 0.485 0.388 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 4.96 5.82 300s supply 5.82 6.90 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 4.96 5.82 300s supply 5.82 6.90 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.995 300s supply 0.995 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 300s price -0.1043 0.0958 -1.09 0.28 300s income 0.1979 0.0299 6.61 1.4e-07 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.417 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 99.297 MSE: 5.841 Root MSE: 2.417 300s Multiple R-Squared: 0.63 Adjusted R-Squared: 0.586 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 300s price 0.1851 0.1053 1.76 0.088 . 300s farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 300s trend 0.1979 0.0299 6.61 1.4e-07 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.938 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 138.088 MSE: 8.63 Root MSE: 2.938 300s Multiple R-Squared: 0.485 Adjusted R-Squared: 0.388 300s 300s [1] "********* iterated 3SLS with restriction via restrict.regMat *****************" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 17 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 34 240 0.56 0.553 0.819 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 98.4 5.79 2.41 0.633 0.590 300s supply 20 16 141.1 8.82 2.97 0.474 0.375 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 5.79 7.11 300s supply 7.11 8.82 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 5.79 7.11 300s supply 7.11 8.82 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.995 300s supply 0.995 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 92.0742 9.6303 9.56 3.6e-11 *** 300s price -0.1064 0.1023 -1.04 0.31 300s income 0.1996 0.0297 6.73 9.9e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.406 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 98.435 MSE: 5.79 Root MSE: 2.406 300s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.59 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 68.8551 12.4839 5.52 3.7e-06 *** 300s price 0.1833 0.1189 1.54 0.13 300s farmPrice 0.1202 0.0260 4.63 5.1e-05 *** 300s trend 0.1996 0.0297 6.73 9.9e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.97 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 141.147 MSE: 8.822 Root MSE: 2.97 300s Multiple R-Squared: 0.474 Adjusted R-Squared: 0.375 300s 300s [1] "********* iterated 3SLS with restriction via restrict.regMat (EViews-like) ***" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 20 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 34 237 0.364 0.557 0.755 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 99.3 5.84 2.42 0.630 0.586 300s supply 20 16 138.1 8.63 2.94 0.485 0.388 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 4.96 5.82 300s supply 5.82 6.90 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 4.96 5.82 300s supply 5.82 6.90 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.995 300s supply 0.995 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 300s price -0.1043 0.0958 -1.09 0.28 300s income 0.1979 0.0299 6.61 1.4e-07 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.417 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 99.297 MSE: 5.841 Root MSE: 2.417 300s Multiple R-Squared: 0.63 Adjusted R-Squared: 0.586 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 300s price 0.1851 0.1053 1.76 0.088 . 300s farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 300s trend 0.1979 0.0299 6.61 1.4e-07 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.938 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 138.088 MSE: 8.63 Root MSE: 2.938 300s Multiple R-Squared: 0.485 Adjusted R-Squared: 0.388 300s 300s [1] "***** iterated W3SLS with restriction via restrict.regMat ********" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s convergence achieved after 17 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 34 240 0.56 0.553 0.819 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 98.4 5.79 2.41 0.633 0.590 300s supply 20 16 141.1 8.82 2.97 0.474 0.375 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 5.79 7.11 300s supply 7.11 8.82 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 5.79 7.11 300s supply 7.11 8.82 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.995 300s supply 0.995 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 92.0742 9.6303 9.56 3.6e-11 *** 300s price -0.1064 0.1023 -1.04 0.31 300s income 0.1996 0.0297 6.73 9.9e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.406 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 98.435 MSE: 5.79 Root MSE: 2.406 300s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.59 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 68.8551 12.4839 5.52 3.7e-06 *** 300s price 0.1833 0.1189 1.54 0.13 300s farmPrice 0.1202 0.0260 4.63 5.1e-05 *** 300s trend 0.1996 0.0297 6.73 9.9e-08 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 2.97 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 141.147 MSE: 8.822 Root MSE: 2.97 300s Multiple R-Squared: 0.474 Adjusted R-Squared: 0.375 300s 300s [1] "******** iterated 3SLS with 2 restrictions *********************" 300s 300s systemfit results 300s method: iterated 3SLS 300s 300s warning: convergence not achieved after 100 iterations 300s 300s N DF SSR detRCov OLS-R2 McElroy-R2 300s system 40 35 1194 34.7 -1.23 0.688 300s 300s N DF SSR MSE RMSE R2 Adj R2 300s demand 20 17 274 16.1 4.02 -0.024 -0.144 300s supply 20 16 920 57.5 7.58 -2.431 -3.074 300s 300s The covariance matrix of the residuals used for estimation 300s demand supply 300s demand 16.1 29.9 300s supply 29.9 57.5 300s 300s The covariance matrix of the residuals 300s demand supply 300s demand 16.1 29.9 300s supply 29.9 57.5 300s 300s The correlations of the residuals 300s demand supply 300s demand 1.000 0.981 300s supply 0.981 1.000 300s 300s 300s 3SLS estimates for 'demand' (equation 1) 300s Model Formula: consump ~ price + income 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) 43.5261 10.3602 4.20 0.00017 *** 300s price 0.2553 0.1380 1.85 0.07275 . 300s income 0.3264 0.0424 7.71 4.8e-09 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 4.018 on 17 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 17 300s SSR: 274.43 MSE: 16.143 Root MSE: 4.018 300s Multiple R-Squared: -0.024 Adjusted R-Squared: -0.144 300s 300s 300s 3SLS estimates for 'supply' (equation 2) 300s Model Formula: consump ~ price + farmPrice + trend 300s Instruments: ~income + farmPrice + trend 300s 300s Estimate Std. Error t value Pr(>|t|) 300s (Intercept) -49.0143 9.6115 -5.10 1.2e-05 *** 300s price 1.2553 0.1380 9.10 9.5e-11 *** 300s farmPrice 0.2166 0.0573 3.78 0.00058 *** 300s trend 0.3264 0.0424 7.71 4.8e-09 *** 300s --- 300s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 300s 300s Residual standard error: 7.582 on 16 degrees of freedom 300s Number of observations: 20 Degrees of Freedom: 16 300s SSR: 919.812 MSE: 57.488 Root MSE: 7.582 300s Multiple R-Squared: -2.431 Adjusted R-Squared: -3.074 300s 300s [1] "********* iterated 3SLS with 2 restrictions (EViews-like) *******" 301s 301s systemfit results 301s method: iterated 3SLS 301s 301s convergence achieved after 66 iterations 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 35 615 20.5 -0.147 0.48 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 151 8.87 2.98 0.437 0.371 301s supply 20 16 464 29.00 5.38 -0.731 -1.055 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 7.54 12.4 301s supply 12.43 23.2 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 7.54 12.4 301s supply 12.43 23.2 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.939 301s supply 0.939 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 68.3925 9.6792 7.07 3.1e-08 *** 301s price -0.0907 0.1236 -0.73 0.47 301s income 0.4263 0.0385 11.08 5.4e-13 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.979 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 150.821 MSE: 8.872 Root MSE: 2.979 301s Multiple R-Squared: 0.437 Adjusted R-Squared: 0.371 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) -27.3424 9.5498 -2.86 0.007 ** 301s price 0.9093 0.1236 7.36 1.3e-08 *** 301s farmPrice 0.3396 0.0498 6.82 6.5e-08 *** 301s trend 0.4263 0.0385 11.08 5.4e-13 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 5.385 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 464.036 MSE: 29.002 Root MSE: 5.385 301s Multiple R-Squared: -0.731 Adjusted R-Squared: -1.055 301s 301s [1] "******** iterated W3SLS with 2 restrictions (EViews-like) *******" 301s 301s systemfit results 301s method: iterated 3SLS 301s 301s convergence achieved after 66 iterations 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 35 615 20.5 -0.147 0.48 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 151 8.87 2.98 0.437 0.371 301s supply 20 16 464 29.00 5.38 -0.731 -1.055 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 7.54 12.4 301s supply 12.43 23.2 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 7.54 12.4 301s supply 12.43 23.2 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.939 301s supply 0.939 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 68.3925 9.6792 7.07 3.1e-08 *** 301s price -0.0907 0.1236 -0.73 0.47 301s income 0.4263 0.0385 11.08 5.4e-13 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.979 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 150.821 MSE: 8.872 Root MSE: 2.979 301s Multiple R-Squared: 0.437 Adjusted R-Squared: 0.371 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) -27.3423 9.5498 -2.86 0.007 ** 301s price 0.9093 0.1236 7.36 1.3e-08 *** 301s farmPrice 0.3396 0.0498 6.82 6.5e-08 *** 301s trend 0.4263 0.0385 11.08 5.4e-13 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 5.385 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 464.036 MSE: 29.002 Root MSE: 5.385 301s Multiple R-Squared: -0.731 Adjusted R-Squared: -1.055 301s 301s [1] "******** iterated 3SLS with 2 restrictions via R and restrict.regMat *********" 301s 301s systemfit results 301s method: iterated 3SLS 301s 301s warning: convergence not achieved after 100 iterations 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 35 1194 34.7 -1.23 0.688 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 274 16.1 4.02 -0.024 -0.144 301s supply 20 16 920 57.5 7.58 -2.431 -3.074 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 16.1 29.9 301s supply 29.9 57.5 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 16.1 29.9 301s supply 29.9 57.5 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.981 301s supply 0.981 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 43.5261 10.3602 4.20 0.00017 *** 301s price 0.2553 0.1380 1.85 0.07275 . 301s income 0.3264 0.0424 7.71 4.8e-09 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 4.018 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 274.43 MSE: 16.143 Root MSE: 4.018 301s Multiple R-Squared: -0.024 Adjusted R-Squared: -0.144 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) -49.0143 9.6115 -5.10 1.2e-05 *** 301s price 1.2553 0.1380 9.10 9.5e-11 *** 301s farmPrice 0.2166 0.0573 3.78 0.00058 *** 301s trend 0.3264 0.0424 7.71 4.8e-09 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 7.582 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 919.812 MSE: 57.488 Root MSE: 7.582 301s Multiple R-Squared: -2.431 Adjusted R-Squared: -3.074 301s 301s [1] "*** iterated 3SLS with 2 restrictions via R and restrict.regMat (EViews-like)**" 301s 301s systemfit results 301s method: iterated 3SLS 301s 301s convergence achieved after 66 iterations 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 35 615 20.5 -0.147 0.48 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 151 8.87 2.98 0.437 0.371 301s supply 20 16 464 29.00 5.38 -0.731 -1.055 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 7.54 12.4 301s supply 12.43 23.2 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 7.54 12.4 301s supply 12.43 23.2 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.939 301s supply 0.939 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 68.3925 9.6792 7.07 3.1e-08 *** 301s price -0.0907 0.1236 -0.73 0.47 301s income 0.4263 0.0385 11.08 5.4e-13 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.979 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 150.821 MSE: 8.872 Root MSE: 2.979 301s Multiple R-Squared: 0.437 Adjusted R-Squared: 0.371 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) -27.3424 9.5498 -2.86 0.007 ** 301s price 0.9093 0.1236 7.36 1.3e-08 *** 301s farmPrice 0.3396 0.0498 6.82 6.5e-08 *** 301s trend 0.4263 0.0385 11.08 5.4e-13 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 5.385 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 464.036 MSE: 29.002 Root MSE: 5.385 301s Multiple R-Squared: -0.731 Adjusted R-Squared: -1.055 301s 301s [1] "** iterated W3SLS with 2 restrictions via R and restrict.regMat ***" 301s 301s systemfit results 301s method: iterated 3SLS 301s 301s warning: convergence not achieved after 100 iterations 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 35 1194 34.7 -1.23 0.688 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 274 16.1 4.02 -0.024 -0.144 301s supply 20 16 920 57.5 7.58 -2.431 -3.074 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 16.1 29.9 301s supply 29.9 57.5 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 16.1 29.9 301s supply 29.9 57.5 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.981 301s supply 0.981 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 43.5261 10.3602 4.20 0.00017 *** 301s price 0.2553 0.1380 1.85 0.07275 . 301s income 0.3264 0.0424 7.71 4.8e-09 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 4.018 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 274.43 MSE: 16.143 Root MSE: 4.018 301s Multiple R-Squared: -0.024 Adjusted R-Squared: -0.144 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) -49.0142 9.6115 -5.10 1.2e-05 *** 301s price 1.2553 0.1380 9.10 9.5e-11 *** 301s farmPrice 0.2166 0.0573 3.78 0.00058 *** 301s trend 0.3264 0.0424 7.71 4.8e-09 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 7.582 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 919.811 MSE: 57.488 Root MSE: 7.582 301s Multiple R-Squared: -2.431 Adjusted R-Squared: -3.074 301s 301s > 301s > ## **************** 3SLS with different instruments ************* 301s > fit3slsd <- list() 301s > formulas <- c( "GLS", "IV", "Schmidt", "GMM", "EViews" ) 301s > for( i in seq( along = formulas ) ) { 301s + fit3slsd[[ i ]] <- list() 301s + 301s + print( "***************************************************" ) 301s + print( paste( "3SLS formula:", formulas[ i ] ) ) 301s + print( "************* 3SLS with different instruments **************" ) 301s + fit3slsd[[ i ]]$e1 <- systemfit( system, "3SLS", data = Kmenta, 301s + inst = instlist, method3sls = formulas[ i ], useMatrix = useMatrix ) 301s + print( summary( fit3slsd[[ i ]]$e1 ) ) 301s + 301s + print( "******* 3SLS with different instruments (EViews-like) **********" ) 301s + fit3slsd[[ i ]]$e1e <- systemfit( system, "3SLS", data = Kmenta, 301s + inst = instlist, methodResidCov = "noDfCor", method3sls = formulas[ i ], 301s + useMatrix = useMatrix ) 301s + print( summary( fit3slsd[[ i ]]$e1e, useDfSys = TRUE ) ) 301s + 301s + print( "**** 3SLS with different instruments and methodResidCov = Theil ***" ) 301s + fit3slsd[[ i ]]$e1c <- systemfit( system, "3SLS", data = Kmenta, 301s + inst = instlist, methodResidCov = "Theil", method3sls = formulas[ i ], 301s + x = TRUE, useMatrix = useMatrix ) 301s + print( summary( fit3slsd[[ i ]]$e1c, useDfSys = TRUE ) ) 301s + 301s + print( "************* W3SLS with different instruments **************" ) 301s + fit3slsd[[ i ]]$e1w <- systemfit( system, "3SLS", data = Kmenta, 301s + inst = instlist, method3sls = formulas[ i ], residCovWeighted = TRUE, 301s + useMatrix = useMatrix ) 301s + print( summary( fit3slsd[[ i ]]$e1w ) ) 301s + 301s + 301s + print( "******* 3SLS with different instruments and restriction ********" ) 301s + fit3slsd[[ i ]]$e2 <- systemfit( system, "3SLS", data = Kmenta, 301s + inst = instlist, restrict.matrix = restrm, method3sls = formulas[ i ], 301s + x = TRUE, useMatrix = useMatrix ) 301s + print( summary( fit3slsd[[ i ]]$e2 ) ) 301s + 301s + print( "** 3SLS with different instruments and restriction (EViews-like) *" ) 301s + fit3slsd[[ i ]]$e2e <- systemfit( system, "3SLS", data = Kmenta, 301s + inst = instlist, methodResidCov = "noDfCor", restrict.matrix = restrm, 301s + method3sls = formulas[ i ], useMatrix = useMatrix ) 301s + print( summary( fit3slsd[[ i ]]$e2e, useDfSys = TRUE ) ) 301s + 301s + print( "** W3SLS with different instruments and restriction (EViews-like) *" ) 301s + fit3slsd[[ i ]]$e2we <- systemfit( system, "3SLS", data = Kmenta, 301s + inst = instlist, methodResidCov = "noDfCor", restrict.matrix = restrm, 301s + method3sls = formulas[ i ], residCovWeighted = TRUE, 301s + useMatrix = useMatrix ) 301s + print( summary( fit3slsd[[ i ]]$e2we, useDfSys = TRUE ) ) 301s + 301s + 301s + print( "** 3SLS with different instruments and restriction via restrict.regMat *******" ) 301s + fit3slsd[[ i ]]$e3 <- systemfit( system, "3SLS", data = Kmenta, 301s + inst = instlist, restrict.regMat = tc, method3sls = formulas[ i ], 301s + useMatrix = useMatrix ) 301s + print( summary( fit3slsd[[ i ]]$e3 ) ) 301s + 301s + print( "3SLS with different instruments with restriction via restrict.regMat (EViews-like)" ) 301s + fit3slsd[[ i ]]$e3e <- systemfit( system, "3SLS", data = Kmenta, 301s + inst = instlist, methodResidCov = "noDfCor", restrict.regMat = tc, 301s + method3sls = formulas[ i ], x = TRUE, 301s + useMatrix = useMatrix ) 301s + print( summary( fit3slsd[[ i ]]$e3e, useDfSys = TRUE ) ) 301s + 301s + print( "** W3SLS with different instr. and restr. via restrict.regMat ****" ) 301s + fit3slsd[[ i ]]$e3w <- systemfit( system, "3SLS", data = Kmenta, 301s + inst = instlist, restrict.regMat = tc, method3sls = formulas[ i ], 301s + residCovWeighted = TRUE, x = TRUE, 301s + useMatrix = useMatrix ) 301s + print( summary( fit3slsd[[ i ]]$e3w ) ) 301s + 301s + 301s + print( "****** 3SLS with different instruments and 2 restrictions *********" ) 301s + fit3slsd[[ i ]]$e4 <- systemfit( system, "3SLS", data = Kmenta, 301s + inst = instlist, restrict.matrix = restr2m, restrict.rhs = restr2q, 301s + method3sls = formulas[ i ], x = TRUE, 301s + useMatrix = useMatrix ) 301s + print( summary( fit3slsd[[ i ]]$e4 ) ) 301s + 301s + print( "** 3SLS with different instruments and 2 restrictions (EViews-like) *" ) 301s + fit3slsd[[ i ]]$e4e <- systemfit( system, "3SLS", data = Kmenta, 301s + inst = instlist, methodResidCov = "noDfCor", restrict.matrix = restr2m, 301s + restrict.rhs = restr2q, method3sls = formulas[ i ], 301s + useMatrix = useMatrix ) 301s + print( summary( fit3slsd[[ i ]]$e4e, useDfSys = TRUE ) ) 301s + 301s + print( "**** W3SLS with different instruments and 2 restrictions *********" ) 301s + fit3slsd[[ i ]]$e4w <- systemfit( system, "3SLS", data = Kmenta, 301s + inst = instlist, restrict.matrix = restr2m, restrict.rhs = restr2q, 301s + method3sls = formulas[ i ], residCovWeighted = TRUE, 301s + useMatrix = useMatrix ) 301s + print( summary( fit3slsd[[ i ]]$e4w ) ) 301s + 301s + 301s + print( " 3SLS with different instruments with 2 restrictions via R and restrict.regMat" ) 301s + fit3slsd[[ i ]]$e5 <- systemfit( system, "3SLS", data = Kmenta, 301s + inst = instlist, restrict.regMat = tc, restrict.matrix = restr3m, 301s + restrict.rhs = restr3q, method3sls = formulas[ i ], 301s + useMatrix = useMatrix ) 301s + print( summary( fit3slsd[[ i ]]$e5 ) ) 301s + 301s + print( "3SLS with diff. instruments and 2 restr. via R and restrict.regMat (EViews-like)" ) 301s + fit3slsd[[ i ]]$e5e <- systemfit( system, "3SLS", data = Kmenta, 301s + inst = instlist, restrict.regMat = tc, methodResidCov = "noDfCor", 301s + restrict.matrix = restr3m, restrict.rhs = restr3q, 301s + method3sls = formulas[ i ], x = TRUE, 301s + useMatrix = useMatrix ) 301s + print( summary( fit3slsd[[ i ]]$e5e, useDfSys = TRUE ) ) 301s + 301s + print( "W3SLS with diff. instr. and 2 restr. via R and restrict.regMat (EViews-like)" ) 301s + fit3slsd[[ i ]]$e5we <- systemfit( system, "3SLS", data = Kmenta, 301s + inst = instlist, restrict.regMat = tc, methodResidCov = "noDfCor", 301s + restrict.matrix = restr3m, restrict.rhs = restr3q, method3sls = formulas[ i ], 301s + residCovWeighted = TRUE, useMatrix = useMatrix ) 301s + print( summary( fit3slsd[[ i ]]$e5we, useDfSys = TRUE ) ) 301s + } 301s [1] "***************************************************" 301s [1] "3SLS formula: GLS" 301s [1] "************* 3SLS with different instruments **************" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 33 170 13.4 0.683 0.52 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 67.4 3.97 1.99 0.748 0.719 301s supply 20 16 102.4 6.40 2.53 0.618 0.546 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.97 3.84 301s supply 3.84 6.04 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.97 3.47 301s supply 3.47 6.40 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.688 301s supply 0.688 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 106.7894 11.1435 9.58 2.9e-08 *** 301s price -0.4116 0.1448 -2.84 0.011 * 301s income 0.3617 0.0564 6.41 6.4e-06 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.992 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 301s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 46.9385 11.5390 4.07 0.0009 *** 301s price 0.2744 0.0897 3.06 0.0075 ** 301s farmPrice 0.2521 0.0470 5.36 6.4e-05 *** 301s trend 0.2048 0.0781 2.62 0.0185 * 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.53 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 102.443 MSE: 6.403 Root MSE: 2.53 301s Multiple R-Squared: 0.618 Adjusted R-Squared: 0.546 301s 301s [1] "******* 3SLS with different instruments (EViews-like) **********" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 33 170 9 0.684 0.511 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 67.4 3.97 1.99 0.748 0.719 301s supply 20 16 102.2 6.39 2.53 0.619 0.547 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.37 3.16 301s supply 3.16 4.83 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.37 2.87 301s supply 2.87 5.11 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.691 301s supply 0.691 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 106.789 10.274 10.39 6.1e-12 *** 301s price -0.412 0.134 -3.08 0.0041 ** 301s income 0.362 0.052 6.95 6.0e-08 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.992 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 301s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 47.0160 10.3208 4.56 6.8e-05 *** 301s price 0.2734 0.0802 3.41 0.0017 ** 301s farmPrice 0.2522 0.0421 6.00 9.8e-07 *** 301s trend 0.2062 0.0699 2.95 0.0058 ** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.527 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 102.203 MSE: 6.388 Root MSE: 2.527 301s Multiple R-Squared: 0.619 Adjusted R-Squared: 0.547 301s 301s [1] "**** 3SLS with different instruments and methodResidCov = Theil ***" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 33 170 12.7 0.683 0.502 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 67.4 3.97 1.99 0.748 0.719 301s supply 20 16 102.7 6.42 2.53 0.617 0.545 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.97 3.96 301s supply 3.96 6.04 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.97 3.57 301s supply 3.57 6.42 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.685 301s supply 0.685 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 106.7894 11.1435 9.58 4.7e-11 *** 301s price -0.4116 0.1448 -2.84 0.0076 ** 301s income 0.3617 0.0564 6.41 2.9e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.992 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 301s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 46.8512 11.5060 4.07 0.00027 *** 301s price 0.2756 0.0889 3.10 0.00395 ** 301s farmPrice 0.2520 0.0470 5.36 6.4e-06 *** 301s trend 0.2032 0.0765 2.66 0.01204 * 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.534 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 102.718 MSE: 6.42 Root MSE: 2.534 301s Multiple R-Squared: 0.617 Adjusted R-Squared: 0.545 301s 301s [1] "************* W3SLS with different instruments **************" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 33 170 13.4 0.683 0.52 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 67.4 3.97 1.99 0.748 0.719 301s supply 20 16 102.4 6.40 2.53 0.618 0.546 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.97 3.84 301s supply 3.84 6.04 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.97 3.47 301s supply 3.47 6.40 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.688 301s supply 0.688 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 106.7894 11.1435 9.58 2.9e-08 *** 301s price -0.4116 0.1448 -2.84 0.011 * 301s income 0.3617 0.0564 6.41 6.4e-06 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.992 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 301s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 46.9385 11.5390 4.07 0.0009 *** 301s price 0.2744 0.0897 3.06 0.0075 ** 301s farmPrice 0.2521 0.0470 5.36 6.4e-05 *** 301s trend 0.2048 0.0781 2.62 0.0185 * 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.53 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 102.443 MSE: 6.403 Root MSE: 2.53 301s Multiple R-Squared: 0.618 Adjusted R-Squared: 0.546 301s 301s [1] "******* 3SLS with different instruments and restriction ********" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 34 201 2.72 0.626 0.685 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 72.3 4.25 2.06 0.730 0.699 301s supply 20 16 128.3 8.02 2.83 0.521 0.432 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.79 4.35 301s supply 4.35 6.27 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 4.25 5.60 301s supply 5.60 8.02 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.959 301s supply 0.959 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 88.9456 6.3475 14.01 1.1e-15 *** 301s price -0.1778 0.0812 -2.19 0.036 * 301s income 0.3049 0.0474 6.43 2.4e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.062 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 72.262 MSE: 4.251 Root MSE: 2.062 301s Multiple R-Squared: 0.73 Adjusted R-Squared: 0.699 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 40.2918 11.2022 3.60 0.001 ** 301s price 0.3613 0.0785 4.60 5.6e-05 *** 301s farmPrice 0.2201 0.0453 4.86 2.6e-05 *** 301s trend 0.3049 0.0474 6.43 2.4e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.832 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 128.304 MSE: 8.019 Root MSE: 2.832 301s Multiple R-Squared: 0.521 Adjusted R-Squared: 0.432 301s 301s [1] "** 3SLS with different instruments and restriction (EViews-like) *" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 34 200 1.75 0.627 0.651 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 72.7 4.28 2.07 0.729 0.697 301s supply 20 16 127.0 7.94 2.82 0.526 0.437 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.22 3.58 301s supply 3.58 5.02 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.64 4.62 301s supply 4.62 6.35 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.961 301s supply 0.961 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 88.7634 5.8428 15.19 < 2e-16 *** 301s price -0.1738 0.0737 -2.36 0.024 * 301s income 0.3027 0.0432 7.00 4.5e-08 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.068 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 72.717 MSE: 4.277 Root MSE: 2.068 301s Multiple R-Squared: 0.729 Adjusted R-Squared: 0.697 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 40.8177 10.0564 4.06 0.00027 *** 301s price 0.3569 0.0705 5.06 1.4e-05 *** 301s farmPrice 0.2195 0.0403 5.45 4.4e-06 *** 301s trend 0.3027 0.0432 7.00 4.5e-08 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.818 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 127.044 MSE: 7.94 Root MSE: 2.818 301s Multiple R-Squared: 0.526 Adjusted R-Squared: 0.437 301s 301s [1] "** W3SLS with different instruments and restriction (EViews-like) *" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 34 199 1.77 0.629 0.65 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 72.4 4.26 2.06 0.730 0.698 301s supply 20 16 126.7 7.92 2.81 0.527 0.439 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.24 3.60 301s supply 3.60 5.06 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.62 4.60 301s supply 4.60 6.34 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.961 301s supply 0.961 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 88.9298 5.9083 15.05 < 2e-16 *** 301s price -0.1760 0.0746 -2.36 0.024 * 301s income 0.3032 0.0434 6.99 4.6e-08 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.064 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 72.435 MSE: 4.261 Root MSE: 2.064 301s Multiple R-Squared: 0.73 Adjusted R-Squared: 0.698 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 40.8325 10.1094 4.04 0.00029 *** 301s price 0.3562 0.0711 5.01 1.7e-05 *** 301s farmPrice 0.2200 0.0405 5.43 4.8e-06 *** 301s trend 0.3032 0.0434 6.99 4.6e-08 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.814 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 126.74 MSE: 7.921 Root MSE: 2.814 301s Multiple R-Squared: 0.527 Adjusted R-Squared: 0.439 301s 301s [1] "** 3SLS with different instruments and restriction via restrict.regMat *******" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 34 201 2.72 0.626 0.685 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 72.3 4.25 2.06 0.730 0.699 301s supply 20 16 128.3 8.02 2.83 0.521 0.432 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.79 4.35 301s supply 4.35 6.27 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 4.25 5.60 301s supply 5.60 8.02 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.959 301s supply 0.959 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 88.9456 6.3475 14.01 1.1e-15 *** 301s price -0.1778 0.0812 -2.19 0.036 * 301s income 0.3049 0.0474 6.43 2.4e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.062 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 72.262 MSE: 4.251 Root MSE: 2.062 301s Multiple R-Squared: 0.73 Adjusted R-Squared: 0.699 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 40.2918 11.2022 3.60 0.001 ** 301s price 0.3613 0.0785 4.60 5.6e-05 *** 301s farmPrice 0.2201 0.0453 4.86 2.6e-05 *** 301s trend 0.3049 0.0474 6.43 2.4e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.832 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 128.304 MSE: 8.019 Root MSE: 2.832 301s Multiple R-Squared: 0.521 Adjusted R-Squared: 0.432 301s 301s [1] "3SLS with different instruments with restriction via restrict.regMat (EViews-like)" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 34 200 1.75 0.627 0.651 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 72.7 4.28 2.07 0.729 0.697 301s supply 20 16 127.0 7.94 2.82 0.526 0.437 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.22 3.58 301s supply 3.58 5.02 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.64 4.62 301s supply 4.62 6.35 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.961 301s supply 0.961 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 88.7634 5.8428 15.19 < 2e-16 *** 301s price -0.1738 0.0737 -2.36 0.024 * 301s income 0.3027 0.0432 7.00 4.5e-08 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.068 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 72.717 MSE: 4.277 Root MSE: 2.068 301s Multiple R-Squared: 0.729 Adjusted R-Squared: 0.697 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 40.8177 10.0564 4.06 0.00027 *** 301s price 0.3569 0.0705 5.06 1.4e-05 *** 301s farmPrice 0.2195 0.0403 5.45 4.4e-06 *** 301s trend 0.3027 0.0432 7.00 4.5e-08 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.818 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 127.044 MSE: 7.94 Root MSE: 2.818 301s Multiple R-Squared: 0.526 Adjusted R-Squared: 0.437 301s 301s [1] "** W3SLS with different instr. and restr. via restrict.regMat ****" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 34 200 2.75 0.627 0.684 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 71.9 4.23 2.06 0.732 0.700 301s supply 20 16 127.9 8.00 2.83 0.523 0.433 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.81 4.36 301s supply 4.36 6.34 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 4.23 5.58 301s supply 5.58 7.99 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.958 301s supply 0.958 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 89.1391 6.4318 13.86 1.6e-15 *** 301s price -0.1803 0.0823 -2.19 0.035 * 301s income 0.3055 0.0476 6.42 2.5e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.057 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 71.945 MSE: 4.232 Root MSE: 2.057 301s Multiple R-Squared: 0.732 Adjusted R-Squared: 0.7 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 40.3187 11.2699 3.58 0.0011 ** 301s price 0.3604 0.0792 4.55 6.5e-05 *** 301s farmPrice 0.2207 0.0456 4.84 2.8e-05 *** 301s trend 0.3055 0.0476 6.42 2.5e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.828 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 127.918 MSE: 7.995 Root MSE: 2.828 301s Multiple R-Squared: 0.523 Adjusted R-Squared: 0.433 301s 301s [1] "****** 3SLS with different instruments and 2 restrictions *********" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 35 211 2.1 0.606 0.71 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 77.9 4.58 2.14 0.709 0.675 301s supply 20 16 133.2 8.32 2.88 0.503 0.410 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.79 4.45 301s supply 4.45 6.06 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 4.58 6.01 301s supply 6.01 8.32 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.972 301s supply 0.972 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 86.4443 5.3770 16.08 <2e-16 *** 301s price -0.1371 0.0504 -2.72 0.01 * 301s income 0.2888 0.0182 15.89 <2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.141 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 77.945 MSE: 4.585 Root MSE: 2.141 301s Multiple R-Squared: 0.709 Adjusted R-Squared: 0.675 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 41.8618 5.4316 7.71 4.8e-09 *** 301s price 0.3629 0.0504 7.20 2.1e-08 *** 301s farmPrice 0.2040 0.0205 9.96 9.4e-12 *** 301s trend 0.2888 0.0182 15.89 < 2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.885 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 133.177 MSE: 8.324 Root MSE: 2.885 301s Multiple R-Squared: 0.503 Adjusted R-Squared: 0.41 301s 301s [1] "** 3SLS with different instruments and 2 restrictions (EViews-like) *" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 35 210 1.42 0.609 0.668 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 77.9 4.58 2.14 0.709 0.675 301s supply 20 16 132.0 8.25 2.87 0.508 0.415 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.22 3.67 301s supply 3.67 4.85 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.90 4.93 301s supply 4.93 6.60 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.972 301s supply 0.972 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 86.3521 4.9704 17.4 <2e-16 *** 301s price -0.1376 0.0458 -3.0 0.0049 ** 301s income 0.2902 0.0168 17.3 <2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.141 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 77.912 MSE: 4.583 Root MSE: 2.141 301s Multiple R-Squared: 0.709 Adjusted R-Squared: 0.675 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 41.6089 4.9950 8.33 8.0e-10 *** 301s price 0.3624 0.0458 7.91 2.6e-09 *** 301s farmPrice 0.2069 0.0184 11.27 3.4e-13 *** 301s trend 0.2902 0.0168 17.27 < 2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.872 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 131.997 MSE: 8.25 Root MSE: 2.872 301s Multiple R-Squared: 0.508 Adjusted R-Squared: 0.415 301s 301s [1] "**** W3SLS with different instruments and 2 restrictions *********" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 35 214 2.1 0.601 0.713 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 78.9 4.64 2.15 0.706 0.671 301s supply 20 16 135.2 8.45 2.91 0.496 0.401 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.75 4.46 301s supply 4.46 6.04 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 4.64 6.09 301s supply 6.09 8.45 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.973 301s supply 0.973 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 85.9516 5.1136 16.81 <2e-16 *** 301s price -0.1318 0.0479 -2.75 0.0093 ** 301s income 0.2884 0.0171 16.86 <2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.154 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 78.853 MSE: 4.638 Root MSE: 2.154 301s Multiple R-Squared: 0.706 Adjusted R-Squared: 0.671 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 41.4498 5.1591 8.03 1.9e-09 *** 301s price 0.3682 0.0479 7.69 5.0e-09 *** 301s farmPrice 0.2028 0.0193 10.50 2.3e-12 *** 301s trend 0.2884 0.0171 16.86 < 2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.907 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 135.215 MSE: 8.451 Root MSE: 2.907 301s Multiple R-Squared: 0.496 Adjusted R-Squared: 0.401 301s 301s [1] " 3SLS with different instruments with 2 restrictions via R and restrict.regMat" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 35 211 2.1 0.606 0.71 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 77.9 4.58 2.14 0.709 0.675 301s supply 20 16 133.2 8.32 2.88 0.503 0.410 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.79 4.45 301s supply 4.45 6.06 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 4.58 6.01 301s supply 6.01 8.32 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.972 301s supply 0.972 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 86.4443 5.3770 16.08 <2e-16 *** 301s price -0.1371 0.0504 -2.72 0.01 * 301s income 0.2888 0.0182 15.89 <2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.141 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 77.945 MSE: 4.585 Root MSE: 2.141 301s Multiple R-Squared: 0.709 Adjusted R-Squared: 0.675 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 41.8618 5.4316 7.71 4.8e-09 *** 301s price 0.3629 0.0504 7.20 2.1e-08 *** 301s farmPrice 0.2040 0.0205 9.96 9.4e-12 *** 301s trend 0.2888 0.0182 15.89 < 2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.885 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 133.177 MSE: 8.324 Root MSE: 2.885 301s Multiple R-Squared: 0.503 Adjusted R-Squared: 0.41 301s 301s [1] "3SLS with diff. instruments and 2 restr. via R and restrict.regMat (EViews-like)" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 35 210 1.42 0.609 0.668 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 77.9 4.58 2.14 0.709 0.675 301s supply 20 16 132.0 8.25 2.87 0.508 0.415 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.22 3.67 301s supply 3.67 4.85 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.90 4.93 301s supply 4.93 6.60 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.972 301s supply 0.972 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 86.3521 4.9704 17.4 <2e-16 *** 301s price -0.1376 0.0458 -3.0 0.0049 ** 301s income 0.2902 0.0168 17.3 <2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.141 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 77.912 MSE: 4.583 Root MSE: 2.141 301s Multiple R-Squared: 0.709 Adjusted R-Squared: 0.675 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 41.6089 4.9950 8.33 8.0e-10 *** 301s price 0.3624 0.0458 7.91 2.6e-09 *** 301s farmPrice 0.2069 0.0184 11.27 3.4e-13 *** 301s trend 0.2902 0.0168 17.27 < 2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.872 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 131.997 MSE: 8.25 Root MSE: 2.872 301s Multiple R-Squared: 0.508 Adjusted R-Squared: 0.415 301s 301s [1] "W3SLS with diff. instr. and 2 restr. via R and restrict.regMat (EViews-like)" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 35 212 1.42 0.604 0.671 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 78.7 4.63 2.15 0.706 0.672 301s supply 20 16 133.7 8.36 2.89 0.501 0.408 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.19 3.68 301s supply 3.68 4.83 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.94 4.99 301s supply 4.99 6.69 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.973 301s supply 0.973 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 85.9108 4.7598 18.05 <2e-16 *** 301s price -0.1329 0.0438 -3.03 0.0045 ** 301s income 0.2900 0.0159 18.18 <2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.152 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 78.713 MSE: 4.63 Root MSE: 2.152 301s Multiple R-Squared: 0.706 Adjusted R-Squared: 0.672 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 41.2362 4.7784 8.63 3.5e-10 *** 301s price 0.3671 0.0438 8.38 7.0e-10 *** 301s farmPrice 0.2060 0.0174 11.81 9.1e-14 *** 301s trend 0.2900 0.0159 18.18 < 2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.891 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 133.715 MSE: 8.357 Root MSE: 2.891 301s Multiple R-Squared: 0.501 Adjusted R-Squared: 0.408 301s 301s [1] "***************************************************" 301s [1] "3SLS formula: IV" 301s [1] "************* 3SLS with different instruments **************" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 33 174 2.12 0.675 0.659 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 67.4 3.97 1.99 0.748 0.719 301s supply 20 16 106.6 6.66 2.58 0.602 0.528 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.97 3.84 301s supply 3.84 6.04 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.97 4.93 301s supply 4.93 6.66 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.959 301s supply 0.959 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 106.7894 11.1435 9.58 2.9e-08 *** 301s price -0.4116 0.1448 -2.84 0.011 * 301s income 0.3617 0.0564 6.41 6.4e-06 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.992 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 301s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 57.2953 11.7078 4.89 0.00016 *** 301s price 0.1373 0.0979 1.40 0.17978 301s farmPrice 0.2660 0.0483 5.51 4.8e-05 *** 301s trend 0.3970 0.0672 5.91 2.2e-05 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.582 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 106.628 MSE: 6.664 Root MSE: 2.582 301s Multiple R-Squared: 0.602 Adjusted R-Squared: 0.528 301s 301s [1] "******* 3SLS with different instruments (EViews-like) **********" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 33 173 1.51 0.677 0.612 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 67.4 3.97 1.99 0.748 0.719 301s supply 20 16 105.7 6.61 2.57 0.606 0.532 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.37 3.16 301s supply 3.16 4.83 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.37 4.04 301s supply 4.04 5.29 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.957 301s supply 0.957 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 106.789 10.274 10.39 6.1e-12 *** 301s price -0.412 0.134 -3.08 0.0041 ** 301s income 0.362 0.052 6.95 6.0e-08 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.992 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 301s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 57.0636 10.4717 5.45 4.9e-06 *** 301s price 0.1403 0.0875 1.60 0.12 301s farmPrice 0.2657 0.0432 6.15 6.2e-07 *** 301s trend 0.3927 0.0601 6.53 2.0e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.571 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 105.735 MSE: 6.608 Root MSE: 2.571 301s Multiple R-Squared: 0.606 Adjusted R-Squared: 0.532 301s 301s [1] "**** 3SLS with different instruments and methodResidCov = Theil ***" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 33 175 0.321 0.673 0.655 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 67.4 3.97 1.99 0.748 0.719 301s supply 20 16 107.7 6.73 2.59 0.598 0.523 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.97 3.96 301s supply 3.96 6.04 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.97 5.14 301s supply 5.14 6.73 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.962 301s supply 0.962 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 106.7894 11.1435 9.58 4.7e-11 *** 301s price -0.4116 0.1448 -2.84 0.0076 ** 301s income 0.3617 0.0564 6.41 2.9e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.992 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 301s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 57.5567 11.6867 4.92 2.3e-05 *** 301s price 0.1338 0.0977 1.37 0.18 301s farmPrice 0.2664 0.0484 5.51 4.1e-06 *** 301s trend 0.4018 0.0644 6.24 4.8e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.594 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 107.679 MSE: 6.73 Root MSE: 2.594 301s Multiple R-Squared: 0.598 Adjusted R-Squared: 0.523 301s 301s [1] "************* W3SLS with different instruments **************" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 33 174 2.12 0.675 0.659 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 67.4 3.97 1.99 0.748 0.719 301s supply 20 16 106.6 6.66 2.58 0.602 0.528 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.97 3.84 301s supply 3.84 6.04 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.97 4.93 301s supply 4.93 6.66 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.959 301s supply 0.959 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 106.7894 11.1435 9.58 2.9e-08 *** 301s price -0.4116 0.1448 -2.84 0.011 * 301s income 0.3617 0.0564 6.41 6.4e-06 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.992 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 301s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 57.2953 11.7078 4.89 0.00016 *** 301s price 0.1373 0.0979 1.40 0.17978 301s farmPrice 0.2660 0.0483 5.51 4.8e-05 *** 301s trend 0.3970 0.0672 5.91 2.2e-05 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.582 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 106.628 MSE: 6.664 Root MSE: 2.582 301s Multiple R-Squared: 0.602 Adjusted R-Squared: 0.528 301s 301s [1] "******* 3SLS with different instruments and restriction ********" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 34 397 11.4 0.26 -0.128 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 175 10.3 3.20 0.349 0.273 301s supply 20 16 223 13.9 3.73 0.170 0.014 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.79 4.35 301s supply 4.35 6.27 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 10.3 11.5 301s supply 11.5 13.9 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.959 301s supply 0.959 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 137.2061 12.4591 11.01 9.3e-13 *** 301s price -0.8101 0.1734 -4.67 4.5e-05 *** 301s income 0.4585 0.0659 6.96 5.0e-08 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 3.204 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 174.513 MSE: 10.265 Root MSE: 3.204 301s Multiple R-Squared: 0.349 Adjusted R-Squared: 0.273 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 81.1339 9.1968 8.82 2.6e-10 *** 301s price -0.1765 0.0892 -1.98 0.056 . 301s farmPrice 0.3374 0.0591 5.71 2.1e-06 *** 301s trend 0.4585 0.0659 6.96 5.0e-08 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 3.73 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 222.562 MSE: 13.91 Root MSE: 3.73 301s Multiple R-Squared: 0.17 Adjusted R-Squared: 0.014 301s 301s [1] "** 3SLS with different instruments and restriction (EViews-like) *" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 34 365 7.14 0.319 -0.166 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 163 9.57 3.09 0.393 0.322 301s supply 20 16 202 12.65 3.56 0.245 0.104 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.22 3.58 301s supply 3.58 5.02 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 8.13 8.67 301s supply 8.67 10.12 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.956 301s supply 0.956 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 134.9751 11.3086 11.94 1.0e-13 *** 301s price -0.7834 0.1565 -5.01 1.7e-05 *** 301s income 0.4539 0.0598 7.60 8.0e-09 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 3.093 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 162.635 MSE: 9.567 Root MSE: 3.093 301s Multiple R-Squared: 0.393 Adjusted R-Squared: 0.322 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 78.1824 8.5029 9.19 9.6e-11 *** 301s price -0.1415 0.0807 -1.75 0.089 . 301s farmPrice 0.3322 0.0524 6.34 3.1e-07 *** 301s trend 0.4539 0.0598 7.60 8.0e-09 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 3.557 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 202.39 MSE: 12.649 Root MSE: 3.557 301s Multiple R-Squared: 0.245 Adjusted R-Squared: 0.104 301s 301s [1] "** W3SLS with different instruments and restriction (EViews-like) *" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 34 351 6.72 0.345 -0.118 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 156 9.18 3.03 0.418 0.349 301s supply 20 16 195 12.20 3.49 0.272 0.135 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.24 3.60 301s supply 3.60 5.06 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 7.81 8.34 301s supply 8.34 9.76 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.955 301s supply 0.955 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 133.7954 11.2810 11.86 1.2e-13 *** 301s price -0.7678 0.1558 -4.93 2.1e-05 *** 301s income 0.4501 0.0595 7.56 8.8e-09 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 3.031 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 156.133 MSE: 9.184 Root MSE: 3.031 301s Multiple R-Squared: 0.418 Adjusted R-Squared: 0.349 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 77.4097 8.6219 8.98 1.7e-10 *** 301s price -0.1304 0.0814 -1.60 0.12 301s farmPrice 0.3292 0.0523 6.29 3.6e-07 *** 301s trend 0.4501 0.0595 7.56 8.8e-09 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 3.493 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 195.256 MSE: 12.204 Root MSE: 3.493 301s Multiple R-Squared: 0.272 Adjusted R-Squared: 0.135 301s 301s [1] "** 3SLS with different instruments and restriction via restrict.regMat *******" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 34 397 11.4 0.26 -0.128 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 175 10.3 3.20 0.349 0.273 301s supply 20 16 223 13.9 3.73 0.170 0.014 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.79 4.35 301s supply 4.35 6.27 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 10.3 11.5 301s supply 11.5 13.9 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.959 301s supply 0.959 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 137.2061 12.4591 11.01 9.3e-13 *** 301s price -0.8101 0.1734 -4.67 4.5e-05 *** 301s income 0.4585 0.0659 6.96 5.0e-08 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 3.204 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 174.513 MSE: 10.265 Root MSE: 3.204 301s Multiple R-Squared: 0.349 Adjusted R-Squared: 0.273 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 81.1339 9.1968 8.82 2.6e-10 *** 301s price -0.1765 0.0892 -1.98 0.056 . 301s farmPrice 0.3374 0.0591 5.71 2.1e-06 *** 301s trend 0.4585 0.0659 6.96 5.0e-08 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 3.73 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 222.562 MSE: 13.91 Root MSE: 3.73 301s Multiple R-Squared: 0.17 Adjusted R-Squared: 0.014 301s 301s [1] "3SLS with different instruments with restriction via restrict.regMat (EViews-like)" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 34 365 7.14 0.319 -0.166 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 163 9.57 3.09 0.393 0.322 301s supply 20 16 202 12.65 3.56 0.245 0.104 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.22 3.58 301s supply 3.58 5.02 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 8.13 8.67 301s supply 8.67 10.12 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.956 301s supply 0.956 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 134.9751 11.3086 11.94 1.0e-13 *** 301s price -0.7834 0.1565 -5.01 1.7e-05 *** 301s income 0.4539 0.0598 7.60 8.0e-09 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 3.093 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 162.635 MSE: 9.567 Root MSE: 3.093 301s Multiple R-Squared: 0.393 Adjusted R-Squared: 0.322 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 78.1824 8.5029 9.19 9.6e-11 *** 301s price -0.1415 0.0807 -1.75 0.089 . 301s farmPrice 0.3322 0.0524 6.34 3.1e-07 *** 301s trend 0.4539 0.0598 7.60 8.0e-09 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 3.557 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 202.39 MSE: 12.649 Root MSE: 3.557 301s Multiple R-Squared: 0.245 Adjusted R-Squared: 0.104 301s 301s [1] "** W3SLS with different instr. and restr. via restrict.regMat ****" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 34 378 10.5 0.295 -0.071 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 166 9.74 3.12 0.382 0.309 301s supply 20 16 212 13.26 3.64 0.209 0.060 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.81 4.36 301s supply 4.36 6.34 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 9.75 10.9 301s supply 10.89 13.3 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.958 301s supply 0.958 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 135.6740 12.4146 10.93 1.1e-12 *** 301s price -0.7901 0.1723 -4.59 5.9e-05 *** 301s income 0.4537 0.0655 6.92 5.6e-08 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 3.122 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 165.668 MSE: 9.745 Root MSE: 3.122 301s Multiple R-Squared: 0.382 Adjusted R-Squared: 0.309 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 80.0613 9.3724 8.54 5.6e-10 *** 301s price -0.1614 0.0902 -1.79 0.082 . 301s farmPrice 0.3335 0.0590 5.65 2.4e-06 *** 301s trend 0.4537 0.0655 6.92 5.6e-08 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 3.642 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 212.177 MSE: 13.261 Root MSE: 3.642 301s Multiple R-Squared: 0.209 Adjusted R-Squared: 0.06 301s 301s [1] "****** 3SLS with different instruments and 2 restrictions *********" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 35 362 6.33 0.325 0.259 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 149 8.79 2.96 0.443 0.377 301s supply 20 16 213 13.30 3.65 0.206 0.058 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.79 4.45 301s supply 4.45 6.06 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 8.79 10.5 301s supply 10.51 13.3 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.973 301s supply 0.973 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 135.467 10.955 12.37 2.5e-14 *** 301s price -0.727 0.116 -6.27 3.4e-07 *** 301s income 0.391 0.018 21.77 < 2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.964 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 149.387 MSE: 8.787 Root MSE: 2.964 301s Multiple R-Squared: 0.443 Adjusted R-Squared: 0.377 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 92.2897 11.0352 8.36 7.3e-10 *** 301s price -0.2272 0.1160 -1.96 0.058 . 301s farmPrice 0.2817 0.0209 13.47 2.0e-15 *** 301s trend 0.3913 0.0180 21.77 < 2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 3.647 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 212.786 MSE: 13.299 Root MSE: 3.647 301s Multiple R-Squared: 0.206 Adjusted R-Squared: 0.058 301s 301s [1] "** 3SLS with different instruments and 2 restrictions (EViews-like) *" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 35 306 3.37 0.43 0.248 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 127 7.5 2.74 0.525 0.469 301s supply 20 16 178 11.2 3.34 0.334 0.210 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.22 3.67 301s supply 3.67 4.85 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 6.37 7.31 301s supply 7.31 8.92 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.00 0.97 301s supply 0.97 1.00 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 130.7296 9.6847 13.50 2.0e-15 *** 301s price -0.6671 0.1009 -6.61 1.2e-07 *** 301s income 0.3782 0.0159 23.74 < 2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.738 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 127.413 MSE: 7.495 Root MSE: 2.738 301s Multiple R-Squared: 0.525 Adjusted R-Squared: 0.469 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 87.4510 9.7547 8.96 1.4e-10 *** 301s price -0.1671 0.1009 -1.66 0.11 301s farmPrice 0.2710 0.0183 14.81 < 2e-16 *** 301s trend 0.3782 0.0159 23.74 < 2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 3.34 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 178.456 MSE: 11.154 Root MSE: 3.34 301s Multiple R-Squared: 0.334 Adjusted R-Squared: 0.21 301s 301s [1] "**** W3SLS with different instruments and 2 restrictions *********" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 35 467 8.98 0.128 0.113 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 193 11.3 3.37 0.282 0.197 301s supply 20 16 275 17.2 4.14 -0.025 -0.217 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.75 4.46 301s supply 4.46 6.04 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 11.3 13.6 301s supply 13.6 17.2 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.977 301s supply 0.977 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 143.4678 11.2566 12.75 1.0e-14 *** 301s price -0.8203 0.1194 -6.87 5.6e-08 *** 301s income 0.4047 0.0168 24.13 < 2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 3.366 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 192.561 MSE: 11.327 Root MSE: 3.366 301s Multiple R-Squared: 0.282 Adjusted R-Squared: 0.197 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 100.3734 11.3093 8.88 1.7e-10 *** 301s price -0.3203 0.1194 -2.68 0.011 * 301s farmPrice 0.2930 0.0198 14.79 < 2e-16 *** 301s trend 0.4047 0.0168 24.13 < 2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 4.144 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 274.775 MSE: 17.173 Root MSE: 4.144 301s Multiple R-Squared: -0.025 Adjusted R-Squared: -0.217 301s 301s [1] " 3SLS with different instruments with 2 restrictions via R and restrict.regMat" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 35 362 6.33 0.325 0.259 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 149 8.79 2.96 0.443 0.377 301s supply 20 16 213 13.30 3.65 0.206 0.058 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.79 4.45 301s supply 4.45 6.06 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 8.79 10.5 301s supply 10.51 13.3 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.973 301s supply 0.973 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 135.467 10.955 12.37 2.5e-14 *** 301s price -0.727 0.116 -6.27 3.4e-07 *** 301s income 0.391 0.018 21.77 < 2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.964 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 149.387 MSE: 8.787 Root MSE: 2.964 301s Multiple R-Squared: 0.443 Adjusted R-Squared: 0.377 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 92.2897 11.0352 8.36 7.3e-10 *** 301s price -0.2272 0.1160 -1.96 0.058 . 301s farmPrice 0.2817 0.0209 13.47 2.0e-15 *** 301s trend 0.3913 0.0180 21.77 < 2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 3.647 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 212.786 MSE: 13.299 Root MSE: 3.647 301s Multiple R-Squared: 0.206 Adjusted R-Squared: 0.058 301s 301s [1] "3SLS with diff. instruments and 2 restr. via R and restrict.regMat (EViews-like)" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 35 306 3.37 0.43 0.248 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 127 7.5 2.74 0.525 0.469 301s supply 20 16 178 11.2 3.34 0.334 0.210 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.22 3.67 301s supply 3.67 4.85 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 6.37 7.31 301s supply 7.31 8.92 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.00 0.97 301s supply 0.97 1.00 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 130.7296 9.6847 13.50 2.0e-15 *** 301s price -0.6671 0.1009 -6.61 1.2e-07 *** 301s income 0.3782 0.0159 23.74 < 2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.738 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 127.413 MSE: 7.495 Root MSE: 2.738 301s Multiple R-Squared: 0.525 Adjusted R-Squared: 0.469 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 87.4510 9.7547 8.96 1.4e-10 *** 301s price -0.1671 0.1009 -1.66 0.11 301s farmPrice 0.2710 0.0183 14.81 < 2e-16 *** 301s trend 0.3782 0.0159 23.74 < 2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 3.34 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 178.456 MSE: 11.154 Root MSE: 3.34 301s Multiple R-Squared: 0.334 Adjusted R-Squared: 0.21 301s 301s [1] "W3SLS with diff. instr. and 2 restr. via R and restrict.regMat (EViews-like)" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 35 365 4.27 0.319 0.127 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 153 8.97 3.00 0.431 0.364 301s supply 20 16 213 13.29 3.65 0.207 0.058 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.19 3.68 301s supply 3.68 4.83 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 7.63 8.77 301s supply 8.77 10.64 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.973 301s supply 0.973 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 136.2729 9.8523 13.83 8.9e-16 *** 301s price -0.7306 0.1027 -7.11 2.7e-08 *** 301s income 0.3865 0.0149 25.95 < 2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.996 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 152.579 MSE: 8.975 Root MSE: 2.996 301s Multiple R-Squared: 0.431 Adjusted R-Squared: 0.364 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 93.0701 9.9030 9.40 4.2e-11 *** 301s price -0.2306 0.1027 -2.24 0.031 * 301s farmPrice 0.2777 0.0174 15.99 < 2e-16 *** 301s trend 0.3865 0.0149 25.95 < 2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 3.646 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 212.723 MSE: 13.295 Root MSE: 3.646 301s Multiple R-Squared: 0.207 Adjusted R-Squared: 0.058 301s 301s [1] "***************************************************" 301s [1] "3SLS formula: Schmidt" 301s [1] "************* 3SLS with different instruments **************" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 33 164 9.25 0.694 0.512 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 67.4 3.97 1.99 0.748 0.719 301s supply 20 16 96.6 6.04 2.46 0.640 0.572 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.97 3.84 301s supply 3.84 6.04 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.97 3.84 301s supply 3.84 6.04 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.784 301s supply 0.784 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 106.7894 11.1435 9.58 2.9e-08 *** 301s price -0.4116 0.1448 -2.84 0.011 * 301s income 0.3617 0.0564 6.41 6.4e-06 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.992 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 301s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 49.5324 12.0105 4.12 0.0008 *** 301s price 0.2401 0.0999 2.40 0.0288 * 301s farmPrice 0.2556 0.0473 5.41 5.8e-05 *** 301s trend 0.2529 0.0997 2.54 0.0219 * 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.458 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 301s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 301s 301s [1] "******* 3SLS with different instruments (EViews-like) **********" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 33 164 6.29 0.694 0.5 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 67.4 3.97 1.99 0.748 0.719 301s supply 20 16 96.6 6.04 2.46 0.640 0.572 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.37 3.16 301s supply 3.16 4.83 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.37 3.16 301s supply 3.16 4.83 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.784 301s supply 0.784 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 106.789 10.274 10.39 6.1e-12 *** 301s price -0.412 0.134 -3.08 0.0041 ** 301s income 0.362 0.052 6.95 6.0e-08 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.992 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 301s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 49.5324 10.7425 4.61 5.8e-05 *** 301s price 0.2401 0.0894 2.69 0.0112 * 301s farmPrice 0.2556 0.0423 6.05 8.4e-07 *** 301s trend 0.2529 0.0891 2.84 0.0077 ** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.458 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 301s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 301s 301s [1] "**** 3SLS with different instruments and methodResidCov = Theil ***" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 33 164 8.24 0.694 0.481 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 67.4 3.97 1.99 0.748 0.719 301s supply 20 16 96.6 6.04 2.46 0.640 0.572 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.97 3.96 301s supply 3.96 6.04 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.97 3.96 301s supply 3.96 6.04 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.784 301s supply 0.784 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 106.7894 11.1435 9.58 4.7e-11 *** 301s price -0.4116 0.1448 -2.84 0.0076 ** 301s income 0.3617 0.0564 6.41 2.9e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.992 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 301s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 49.5324 12.0105 4.12 0.00024 *** 301s price 0.2401 0.0999 2.40 0.02208 * 301s farmPrice 0.2556 0.0473 5.41 5.5e-06 *** 301s trend 0.2529 0.0997 2.54 0.01605 * 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.458 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 301s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 301s 301s [1] "************* W3SLS with different instruments **************" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 33 164 9.25 0.694 0.512 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 67.4 3.97 1.99 0.748 0.719 301s supply 20 16 96.6 6.04 2.46 0.640 0.572 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.97 3.84 301s supply 3.84 6.04 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.97 3.84 301s supply 3.84 6.04 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.784 301s supply 0.784 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 106.7894 11.1435 9.58 2.9e-08 *** 301s price -0.4116 0.1448 -2.84 0.011 * 301s income 0.3617 0.0564 6.41 6.4e-06 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.992 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 301s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 49.5324 12.0105 4.12 0.0008 *** 301s price 0.2401 0.0999 2.40 0.0288 * 301s farmPrice 0.2556 0.0473 5.41 5.8e-05 *** 301s trend 0.2529 0.0997 2.54 0.0219 * 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.458 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 301s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 301s 301s [1] "******* 3SLS with different instruments and restriction ********" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 34 175 2.68 0.673 0.665 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 65 3.82 1.96 0.758 0.729 301s supply 20 16 110 6.90 2.63 0.588 0.511 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.79 4.35 301s supply 4.35 6.27 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.82 4.87 301s supply 4.87 6.90 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.948 301s supply 0.948 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 95.0869 9.9882 9.52 4.0e-11 *** 301s price -0.2583 0.1296 -1.99 0.054 . 301s income 0.3244 0.0534 6.08 6.8e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.955 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 64.961 MSE: 3.821 Root MSE: 1.955 301s Multiple R-Squared: 0.758 Adjusted R-Squared: 0.729 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 45.4891 12.9647 3.51 0.0013 ** 301s price 0.2929 0.1164 2.52 0.0167 * 301s farmPrice 0.2350 0.0490 4.80 3.1e-05 *** 301s trend 0.3244 0.0534 6.08 6.8e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.627 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 110.382 MSE: 6.899 Root MSE: 2.627 301s Multiple R-Squared: 0.588 Adjusted R-Squared: 0.511 301s 301s [1] "** 3SLS with different instruments and restriction (EViews-like) *" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 34 175 1.75 0.673 0.636 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 65.2 3.83 1.96 0.757 0.728 301s supply 20 16 110.0 6.88 2.62 0.590 0.513 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.22 3.58 301s supply 3.58 5.02 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.26 4.02 301s supply 4.02 5.50 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.00 0.95 301s supply 0.95 1.00 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 94.845 9.149 10.37 4.6e-12 *** 301s price -0.254 0.119 -2.14 0.039 * 301s income 0.323 0.049 6.58 1.5e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.958 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 65.171 MSE: 3.834 Root MSE: 1.958 301s Multiple R-Squared: 0.757 Adjusted R-Squared: 0.728 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 45.7348 11.5558 3.96 0.00037 *** 301s price 0.2913 0.1036 2.81 0.00814 ** 301s farmPrice 0.2343 0.0438 5.35 6.0e-06 *** 301s trend 0.3226 0.0490 6.58 1.5e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.622 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 110.035 MSE: 6.877 Root MSE: 2.622 301s Multiple R-Squared: 0.59 Adjusted R-Squared: 0.513 301s 301s [1] "** W3SLS with different instruments and restriction (EViews-like) *" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 34 175 1.76 0.674 0.635 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 65.1 3.83 1.96 0.757 0.729 301s supply 20 16 109.9 6.87 2.62 0.590 0.513 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.24 3.60 301s supply 3.60 5.06 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.25 4.02 301s supply 4.02 5.50 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.949 301s supply 0.949 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 94.9533 9.1511 10.38 4.5e-12 *** 301s price -0.2555 0.1186 -2.15 0.038 * 301s income 0.3229 0.0491 6.58 1.5e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.957 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 65.09 MSE: 3.829 Root MSE: 1.957 301s Multiple R-Squared: 0.757 Adjusted R-Squared: 0.729 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 45.7433 11.6043 3.94 0.00038 *** 301s price 0.2908 0.1039 2.80 0.00839 ** 301s farmPrice 0.2347 0.0440 5.34 6.2e-06 *** 301s trend 0.3229 0.0491 6.58 1.5e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.621 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 109.922 MSE: 6.87 Root MSE: 2.621 301s Multiple R-Squared: 0.59 Adjusted R-Squared: 0.513 301s 301s [1] "** 3SLS with different instruments and restriction via restrict.regMat *******" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 34 175 2.68 0.673 0.665 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 65 3.82 1.96 0.758 0.729 301s supply 20 16 110 6.90 2.63 0.588 0.511 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.79 4.35 301s supply 4.35 6.27 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.82 4.87 301s supply 4.87 6.90 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.948 301s supply 0.948 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 95.0869 9.9882 9.52 4.0e-11 *** 301s price -0.2583 0.1296 -1.99 0.054 . 301s income 0.3244 0.0534 6.08 6.8e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.955 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 64.961 MSE: 3.821 Root MSE: 1.955 301s Multiple R-Squared: 0.758 Adjusted R-Squared: 0.729 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 45.4891 12.9647 3.51 0.0013 ** 301s price 0.2929 0.1164 2.52 0.0167 * 301s farmPrice 0.2350 0.0490 4.80 3.1e-05 *** 301s trend 0.3244 0.0534 6.08 6.8e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.627 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 110.382 MSE: 6.899 Root MSE: 2.627 301s Multiple R-Squared: 0.588 Adjusted R-Squared: 0.511 301s 301s [1] "3SLS with different instruments with restriction via restrict.regMat (EViews-like)" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 34 175 1.75 0.673 0.636 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 65.2 3.83 1.96 0.757 0.728 301s supply 20 16 110.0 6.88 2.62 0.590 0.513 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.22 3.58 301s supply 3.58 5.02 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.26 4.02 301s supply 4.02 5.50 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.00 0.95 301s supply 0.95 1.00 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 94.845 9.149 10.37 4.6e-12 *** 301s price -0.254 0.119 -2.14 0.039 * 301s income 0.323 0.049 6.58 1.5e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.958 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 65.171 MSE: 3.834 Root MSE: 1.958 301s Multiple R-Squared: 0.757 Adjusted R-Squared: 0.728 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 45.7348 11.5558 3.96 0.00037 *** 301s price 0.2913 0.1036 2.81 0.00814 ** 301s farmPrice 0.2343 0.0438 5.35 6.0e-06 *** 301s trend 0.3226 0.0490 6.58 1.5e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.622 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 110.035 MSE: 6.877 Root MSE: 2.622 301s Multiple R-Squared: 0.59 Adjusted R-Squared: 0.513 301s 301s [1] "** W3SLS with different instr. and restr. via restrict.regMat ****" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 34 175 2.7 0.673 0.664 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 64.9 3.82 1.95 0.758 0.730 301s supply 20 16 110.2 6.89 2.62 0.589 0.512 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.81 4.36 301s supply 4.36 6.34 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.82 4.86 301s supply 4.86 6.89 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.947 301s supply 0.947 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 95.2108 9.9899 9.53 3.9e-11 *** 301s price -0.2599 0.1296 -2.00 0.053 . 301s income 0.3248 0.0535 6.08 6.9e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.954 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 64.876 MSE: 3.816 Root MSE: 1.954 301s Multiple R-Squared: 0.758 Adjusted R-Squared: 0.73 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 45.5042 13.0242 3.49 0.0013 ** 301s price 0.2923 0.1167 2.50 0.0172 * 301s farmPrice 0.2354 0.0492 4.78 3.3e-05 *** 301s trend 0.3248 0.0535 6.08 6.9e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.625 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 110.241 MSE: 6.89 Root MSE: 2.625 301s Multiple R-Squared: 0.589 Adjusted R-Squared: 0.512 301s 301s [1] "****** 3SLS with different instruments and 2 restrictions *********" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 35 178 1.92 0.667 0.696 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 67.5 3.97 1.99 0.748 0.719 301s supply 20 16 110.9 6.93 2.63 0.586 0.509 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.79 4.45 301s supply 4.45 6.06 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.97 5.06 301s supply 5.06 6.93 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.964 301s supply 0.964 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 93.3937 10.2477 9.11 9.1e-11 *** 301s price -0.2208 0.1165 -1.90 0.066 . 301s income 0.3033 0.0257 11.78 9.9e-14 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.993 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 67.513 MSE: 3.971 Root MSE: 1.993 301s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 49.0104 10.4895 4.67 4.3e-05 *** 301s price 0.2792 0.1165 2.40 0.022 * 301s farmPrice 0.2150 0.0247 8.70 2.8e-10 *** 301s trend 0.3033 0.0257 11.78 9.9e-14 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.633 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 110.934 MSE: 6.933 Root MSE: 2.633 301s Multiple R-Squared: 0.586 Adjusted R-Squared: 0.509 301s 301s [1] "** 3SLS with different instruments and 2 restrictions (EViews-like) *" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 35 178 1.3 0.668 0.659 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 67.6 3.98 1.99 0.748 0.718 301s supply 20 16 110.7 6.92 2.63 0.587 0.510 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.22 3.67 301s supply 3.67 4.85 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.38 4.17 301s supply 4.17 5.53 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.965 301s supply 0.965 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 93.210 9.365 9.95 9.6e-12 *** 301s price -0.219 0.105 -2.09 0.044 * 301s income 0.304 0.023 13.19 3.8e-15 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.994 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 67.616 MSE: 3.977 Root MSE: 1.994 301s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.718 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 48.6930 9.6005 5.07 1.3e-05 *** 301s price 0.2806 0.1052 2.67 0.011 * 301s farmPrice 0.2168 0.0216 10.02 8.1e-12 *** 301s trend 0.3038 0.0230 13.19 3.8e-15 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.63 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 110.672 MSE: 6.917 Root MSE: 2.63 301s Multiple R-Squared: 0.587 Adjusted R-Squared: 0.51 301s 301s [1] "**** W3SLS with different instruments and 2 restrictions *********" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 35 179 1.92 0.666 0.698 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 67.7 3.98 2.00 0.747 0.718 301s supply 20 16 111.6 6.98 2.64 0.584 0.506 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.75 4.46 301s supply 4.46 6.04 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.98 5.09 301s supply 5.09 6.98 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.965 301s supply 0.965 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 93.180 10.378 8.98 1.3e-10 *** 301s price -0.218 0.118 -1.85 0.073 . 301s income 0.303 0.025 12.11 4.5e-14 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.996 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 67.719 MSE: 3.983 Root MSE: 1.996 301s Multiple R-Squared: 0.747 Adjusted R-Squared: 0.718 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 48.8549 10.5929 4.61 5.1e-05 *** 301s price 0.2817 0.1182 2.38 0.023 * 301s farmPrice 0.2141 0.0239 8.94 1.5e-10 *** 301s trend 0.3030 0.0250 12.11 4.5e-14 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.641 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 111.614 MSE: 6.976 Root MSE: 2.641 301s Multiple R-Squared: 0.584 Adjusted R-Squared: 0.506 301s 301s [1] " 3SLS with different instruments with 2 restrictions via R and restrict.regMat" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 35 178 1.92 0.667 0.696 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 67.5 3.97 1.99 0.748 0.719 301s supply 20 16 110.9 6.93 2.63 0.586 0.509 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.79 4.45 301s supply 4.45 6.06 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.97 5.06 301s supply 5.06 6.93 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.964 301s supply 0.964 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 93.3937 10.2477 9.11 9.1e-11 *** 301s price -0.2208 0.1165 -1.90 0.066 . 301s income 0.3033 0.0257 11.78 9.9e-14 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.993 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 67.513 MSE: 3.971 Root MSE: 1.993 301s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 49.0104 10.4895 4.67 4.3e-05 *** 301s price 0.2792 0.1165 2.40 0.022 * 301s farmPrice 0.2150 0.0247 8.70 2.8e-10 *** 301s trend 0.3033 0.0257 11.78 9.9e-14 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.633 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 110.934 MSE: 6.933 Root MSE: 2.633 301s Multiple R-Squared: 0.586 Adjusted R-Squared: 0.509 301s 301s [1] "3SLS with diff. instruments and 2 restr. via R and restrict.regMat (EViews-like)" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 35 178 1.3 0.668 0.659 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 67.6 3.98 1.99 0.748 0.718 301s supply 20 16 110.7 6.92 2.63 0.587 0.510 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.22 3.67 301s supply 3.67 4.85 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.38 4.17 301s supply 4.17 5.53 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.965 301s supply 0.965 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 93.210 9.365 9.95 9.6e-12 *** 301s price -0.219 0.105 -2.09 0.044 * 301s income 0.304 0.023 13.19 3.8e-15 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.994 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 67.616 MSE: 3.977 Root MSE: 1.994 301s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.718 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 48.6930 9.6005 5.07 1.3e-05 *** 301s price 0.2806 0.1052 2.67 0.011 * 301s farmPrice 0.2168 0.0216 10.02 8.1e-12 *** 301s trend 0.3038 0.0230 13.19 3.8e-15 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.63 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 110.672 MSE: 6.917 Root MSE: 2.63 301s Multiple R-Squared: 0.587 Adjusted R-Squared: 0.51 301s 301s [1] "W3SLS with diff. instr. and 2 restr. via R and restrict.regMat (EViews-like)" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 35 179 1.3 0.666 0.661 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 67.8 3.99 2.00 0.747 0.717 301s supply 20 16 111.2 6.95 2.64 0.585 0.507 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.19 3.68 301s supply 3.68 4.83 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.39 4.19 301s supply 4.19 5.56 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.965 301s supply 0.965 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 93.0165 9.4718 9.82 1.4e-11 *** 301s price -0.2172 0.1066 -2.04 0.049 * 301s income 0.3036 0.0224 13.56 1.8e-15 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.997 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 67.8 MSE: 3.988 Root MSE: 1.997 301s Multiple R-Squared: 0.747 Adjusted R-Squared: 0.717 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 48.5496 9.6886 5.01 1.6e-05 *** 301s price 0.2828 0.1066 2.65 0.012 * 301s farmPrice 0.2161 0.0210 10.30 3.9e-12 *** 301s trend 0.3036 0.0224 13.56 1.8e-15 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.637 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 111.249 MSE: 6.953 Root MSE: 2.637 301s Multiple R-Squared: 0.585 Adjusted R-Squared: 0.507 301s 301s [1] "***************************************************" 301s [1] "3SLS formula: GMM" 301s [1] "************* 3SLS with different instruments **************" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 33 164 9.25 0.694 0.512 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 67.4 3.97 1.99 0.748 0.719 301s supply 20 16 96.6 6.04 2.46 0.640 0.572 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.97 3.84 301s supply 3.84 6.04 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.97 3.84 301s supply 3.84 6.04 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.784 301s supply 0.784 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 106.7894 11.1435 9.58 2.9e-08 *** 301s price -0.4116 0.1448 -2.84 0.011 * 301s income 0.3617 0.0564 6.41 6.4e-06 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.992 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 301s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 49.5324 12.0105 4.12 0.0008 *** 301s price 0.2401 0.0999 2.40 0.0288 * 301s farmPrice 0.2556 0.0473 5.41 5.8e-05 *** 301s trend 0.2529 0.0997 2.54 0.0219 * 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.458 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 301s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 301s 301s [1] "******* 3SLS with different instruments (EViews-like) **********" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 33 164 6.29 0.694 0.5 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 67.4 3.97 1.99 0.748 0.719 301s supply 20 16 96.6 6.04 2.46 0.640 0.572 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.37 3.16 301s supply 3.16 4.83 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.37 3.16 301s supply 3.16 4.83 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.784 301s supply 0.784 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 106.789 10.274 10.39 6.1e-12 *** 301s price -0.412 0.134 -3.08 0.0041 ** 301s income 0.362 0.052 6.95 6.0e-08 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.992 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 301s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 49.5324 10.7425 4.61 5.8e-05 *** 301s price 0.2401 0.0894 2.69 0.0112 * 301s farmPrice 0.2556 0.0423 6.05 8.4e-07 *** 301s trend 0.2529 0.0891 2.84 0.0077 ** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.458 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 301s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 301s 301s [1] "**** 3SLS with different instruments and methodResidCov = Theil ***" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 33 164 8.24 0.694 0.481 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 67.4 3.97 1.99 0.748 0.719 301s supply 20 16 96.6 6.04 2.46 0.640 0.572 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.97 3.96 301s supply 3.96 6.04 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.97 3.96 301s supply 3.96 6.04 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.784 301s supply 0.784 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 106.7894 11.1435 9.58 4.7e-11 *** 301s price -0.4116 0.1448 -2.84 0.0076 ** 301s income 0.3617 0.0564 6.41 2.9e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.992 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 301s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 49.5324 12.0105 4.12 0.00024 *** 301s price 0.2401 0.0999 2.40 0.02208 * 301s farmPrice 0.2556 0.0473 5.41 5.5e-06 *** 301s trend 0.2529 0.0997 2.54 0.01605 * 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.458 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 301s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 301s 301s [1] "************* W3SLS with different instruments **************" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 33 164 9.25 0.694 0.512 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 67.4 3.97 1.99 0.748 0.719 301s supply 20 16 96.6 6.04 2.46 0.640 0.572 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.97 3.84 301s supply 3.84 6.04 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.97 3.84 301s supply 3.84 6.04 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.784 301s supply 0.784 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 106.7894 11.1435 9.58 2.9e-08 *** 301s price -0.4116 0.1448 -2.84 0.011 * 301s income 0.3617 0.0564 6.41 6.4e-06 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.992 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 301s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 49.5324 12.0105 4.12 0.0008 *** 301s price 0.2401 0.0999 2.40 0.0288 * 301s farmPrice 0.2556 0.0473 5.41 5.8e-05 *** 301s trend 0.2529 0.0997 2.54 0.0219 * 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.458 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 301s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 301s 301s [1] "******* 3SLS with different instruments and restriction ********" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 34 166 2.78 0.691 0.636 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 63.4 3.73 1.93 0.764 0.736 301s supply 20 16 102.2 6.39 2.53 0.619 0.547 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.79 4.35 301s supply 4.35 6.27 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.73 4.59 301s supply 4.59 6.39 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.00 0.94 301s supply 0.94 1.00 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 100.1363 8.6083 11.63 2.1e-13 *** 301s price -0.3244 0.1114 -2.91 0.0063 ** 301s income 0.3405 0.0509 6.69 1.1e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.931 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 63.395 MSE: 3.729 Root MSE: 1.931 301s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 49.7623 12.2354 4.07 0.00027 *** 301s price 0.2366 0.1018 2.33 0.02617 * 301s farmPrice 0.2473 0.0474 5.22 9.0e-06 *** 301s trend 0.3405 0.0509 6.69 1.1e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.527 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 102.181 MSE: 6.386 Root MSE: 2.527 301s Multiple R-Squared: 0.619 Adjusted R-Squared: 0.547 301s 301s [1] "** 3SLS with different instruments and restriction (EViews-like) *" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 34 165 1.84 0.691 0.608 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 63.4 3.73 1.93 0.764 0.736 301s supply 20 16 102.1 6.38 2.53 0.619 0.548 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.22 3.58 301s supply 3.58 5.02 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.17 3.79 301s supply 3.79 5.10 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.941 301s supply 0.941 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 99.9363 7.9106 12.63 2.1e-14 *** 301s price -0.3212 0.1019 -3.15 0.0034 ** 301s income 0.3393 0.0466 7.28 2.0e-08 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.931 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 63.37 MSE: 3.728 Root MSE: 1.931 301s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 49.8516 10.9418 4.56 6.4e-05 *** 301s price 0.2364 0.0910 2.60 0.014 * 301s farmPrice 0.2467 0.0423 5.83 1.4e-06 *** 301s trend 0.3393 0.0466 7.28 2.0e-08 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.526 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 102.07 MSE: 6.379 Root MSE: 2.526 301s Multiple R-Squared: 0.619 Adjusted R-Squared: 0.548 301s 301s [1] "** W3SLS with different instruments and restriction (EViews-like) *" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 34 165 1.85 0.691 0.608 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 63.4 3.73 1.93 0.764 0.736 301s supply 20 16 102.1 6.38 2.53 0.619 0.548 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.24 3.60 301s supply 3.60 5.06 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.17 3.78 301s supply 3.78 5.10 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.941 301s supply 0.941 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 99.9706 7.9399 12.59 2.4e-14 *** 301s price -0.3217 0.1023 -3.15 0.0034 ** 301s income 0.3394 0.0467 7.26 2.1e-08 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.931 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 63.372 MSE: 3.728 Root MSE: 1.931 301s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 49.8336 10.9955 4.53 6.9e-05 *** 301s price 0.2364 0.0915 2.59 0.014 * 301s farmPrice 0.2469 0.0425 5.80 1.6e-06 *** 301s trend 0.3394 0.0467 7.26 2.1e-08 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.526 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 102.073 MSE: 6.38 Root MSE: 2.526 301s Multiple R-Squared: 0.619 Adjusted R-Squared: 0.548 301s 301s [1] "** 3SLS with different instruments and restriction via restrict.regMat *******" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 34 166 2.78 0.691 0.636 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 63.4 3.73 1.93 0.764 0.736 301s supply 20 16 102.2 6.39 2.53 0.619 0.547 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.79 4.35 301s supply 4.35 6.27 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.73 4.59 301s supply 4.59 6.39 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.00 0.94 301s supply 0.94 1.00 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 100.1363 8.6083 11.63 2.1e-13 *** 301s price -0.3244 0.1114 -2.91 0.0063 ** 301s income 0.3405 0.0509 6.69 1.1e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.931 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 63.395 MSE: 3.729 Root MSE: 1.931 301s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 49.7623 12.2354 4.07 0.00027 *** 301s price 0.2366 0.1018 2.33 0.02617 * 301s farmPrice 0.2473 0.0474 5.22 9.0e-06 *** 301s trend 0.3405 0.0509 6.69 1.1e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.527 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 102.181 MSE: 6.386 Root MSE: 2.527 301s Multiple R-Squared: 0.619 Adjusted R-Squared: 0.547 301s 301s [1] "3SLS with different instruments with restriction via restrict.regMat (EViews-like)" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 34 165 1.84 0.691 0.608 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 63.4 3.73 1.93 0.764 0.736 301s supply 20 16 102.1 6.38 2.53 0.619 0.548 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.22 3.58 301s supply 3.58 5.02 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.17 3.79 301s supply 3.79 5.10 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.941 301s supply 0.941 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 99.9363 7.9106 12.63 2.1e-14 *** 301s price -0.3212 0.1019 -3.15 0.0034 ** 301s income 0.3393 0.0466 7.28 2.0e-08 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.931 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 63.37 MSE: 3.728 Root MSE: 1.931 301s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 49.8516 10.9418 4.56 6.4e-05 *** 301s price 0.2364 0.0910 2.60 0.014 * 301s farmPrice 0.2467 0.0423 5.83 1.4e-06 *** 301s trend 0.3393 0.0466 7.28 2.0e-08 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.526 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 102.07 MSE: 6.379 Root MSE: 2.526 301s Multiple R-Squared: 0.619 Adjusted R-Squared: 0.548 301s 301s [1] "** W3SLS with different instr. and restr. via restrict.regMat ****" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 34 166 2.79 0.691 0.635 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 63.4 3.73 1.93 0.764 0.736 301s supply 20 16 102.2 6.39 2.53 0.619 0.547 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.81 4.36 301s supply 4.36 6.34 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.73 4.59 301s supply 4.59 6.39 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.00 0.94 301s supply 0.94 1.00 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 100.174 8.646 11.59 2.4e-13 *** 301s price -0.325 0.112 -2.91 0.0064 ** 301s income 0.341 0.051 6.67 1.2e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.931 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 63.398 MSE: 3.729 Root MSE: 1.931 301s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 49.7425 12.3029 4.04 0.00029 *** 301s price 0.2367 0.1023 2.31 0.02691 * 301s farmPrice 0.2474 0.0477 5.19 9.8e-06 *** 301s trend 0.3406 0.0510 6.67 1.2e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.527 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 102.183 MSE: 6.386 Root MSE: 2.527 301s Multiple R-Squared: 0.619 Adjusted R-Squared: 0.547 301s 301s [1] "****** 3SLS with different instruments and 2 restrictions *********" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 35 165 1.89 0.692 0.677 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 64.1 3.77 1.94 0.761 0.733 301s supply 20 16 101.2 6.32 2.52 0.623 0.552 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.79 4.45 301s supply 4.45 6.06 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.77 4.68 301s supply 4.68 6.32 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.00 0.96 301s supply 0.96 1.00 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 98.8949 8.2696 11.96 6.4e-14 *** 301s price -0.2870 0.0909 -3.16 0.0033 ** 301s income 0.3148 0.0224 14.04 4.4e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.941 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 64.072 MSE: 3.769 Root MSE: 1.941 301s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 54.6693 8.4422 6.48 1.8e-07 *** 301s price 0.2130 0.0909 2.34 0.025 * 301s farmPrice 0.2237 0.0228 9.82 1.3e-11 *** 301s trend 0.3148 0.0224 14.04 4.4e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.515 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 101.181 MSE: 6.324 Root MSE: 2.515 301s Multiple R-Squared: 0.623 Adjusted R-Squared: 0.552 301s 301s [1] "** 3SLS with different instruments and 2 restrictions (EViews-like) *" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 35 165 1.28 0.692 0.642 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 64.1 3.77 1.94 0.761 0.733 301s supply 20 16 101.1 6.32 2.51 0.623 0.552 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.22 3.67 301s supply 3.67 4.85 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.21 3.86 301s supply 3.86 5.06 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.00 0.96 301s supply 0.96 1.00 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 98.6650 7.5755 13.02 5.6e-15 *** 301s price -0.2845 0.0822 -3.46 0.0014 ** 301s income 0.3146 0.0203 15.52 < 2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.942 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 64.111 MSE: 3.771 Root MSE: 1.942 301s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 54.3281 7.7347 7.02 3.6e-08 *** 301s price 0.2155 0.0822 2.62 0.013 * 301s farmPrice 0.2247 0.0201 11.16 4.4e-13 *** 301s trend 0.3146 0.0203 15.52 < 2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.514 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 101.149 MSE: 6.322 Root MSE: 2.514 301s Multiple R-Squared: 0.623 Adjusted R-Squared: 0.552 301s 301s [1] "**** W3SLS with different instruments and 2 restrictions *********" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 35 165 1.89 0.692 0.677 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 64.1 3.77 1.94 0.761 0.733 301s supply 20 16 101.3 6.33 2.52 0.622 0.551 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.75 4.46 301s supply 4.46 6.04 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.77 4.69 301s supply 4.69 6.33 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.00 0.96 301s supply 0.96 1.00 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 98.9360 8.2215 12.03 5.4e-14 *** 301s price -0.2872 0.0907 -3.17 0.0032 ** 301s income 0.3147 0.0215 14.64 2.2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.941 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 64.08 MSE: 3.769 Root MSE: 1.941 301s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 54.7520 8.3733 6.54 1.5e-07 *** 301s price 0.2128 0.0907 2.35 0.025 * 301s farmPrice 0.2231 0.0218 10.24 4.5e-12 *** 301s trend 0.3147 0.0215 14.64 2.2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.516 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 101.278 MSE: 6.33 Root MSE: 2.516 301s Multiple R-Squared: 0.622 Adjusted R-Squared: 0.551 301s 301s [1] " 3SLS with different instruments with 2 restrictions via R and restrict.regMat" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 35 165 1.89 0.692 0.677 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 64.1 3.77 1.94 0.761 0.733 301s supply 20 16 101.2 6.32 2.52 0.623 0.552 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.79 4.45 301s supply 4.45 6.06 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.77 4.68 301s supply 4.68 6.32 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.00 0.96 301s supply 0.96 1.00 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 98.8949 8.2696 11.96 6.4e-14 *** 301s price -0.2870 0.0909 -3.16 0.0033 ** 301s income 0.3148 0.0224 14.04 4.4e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.941 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 64.072 MSE: 3.769 Root MSE: 1.941 301s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 54.6693 8.4422 6.48 1.8e-07 *** 301s price 0.2130 0.0909 2.34 0.025 * 301s farmPrice 0.2237 0.0228 9.82 1.3e-11 *** 301s trend 0.3148 0.0224 14.04 4.4e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.515 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 101.181 MSE: 6.324 Root MSE: 2.515 301s Multiple R-Squared: 0.623 Adjusted R-Squared: 0.552 301s 301s [1] "3SLS with diff. instruments and 2 restr. via R and restrict.regMat (EViews-like)" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 35 165 1.28 0.692 0.642 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 64.1 3.77 1.94 0.761 0.733 301s supply 20 16 101.1 6.32 2.51 0.623 0.552 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.22 3.67 301s supply 3.67 4.85 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.21 3.86 301s supply 3.86 5.06 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.00 0.96 301s supply 0.96 1.00 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 98.6650 7.5755 13.02 5.6e-15 *** 301s price -0.2845 0.0822 -3.46 0.0014 ** 301s income 0.3146 0.0203 15.52 < 2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.942 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 64.111 MSE: 3.771 Root MSE: 1.942 301s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 54.3281 7.7347 7.02 3.6e-08 *** 301s price 0.2155 0.0822 2.62 0.013 * 301s farmPrice 0.2247 0.0201 11.16 4.4e-13 *** 301s trend 0.3146 0.0203 15.52 < 2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.514 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 101.149 MSE: 6.322 Root MSE: 2.514 301s Multiple R-Squared: 0.623 Adjusted R-Squared: 0.552 301s 301s [1] "W3SLS with diff. instr. and 2 restr. via R and restrict.regMat (EViews-like)" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 35 165 1.28 0.692 0.643 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 64.1 3.77 1.94 0.761 0.733 301s supply 20 16 101.2 6.33 2.52 0.622 0.552 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.19 3.68 301s supply 3.68 4.83 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.21 3.87 301s supply 3.87 5.06 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.00 0.96 301s supply 0.96 1.00 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 98.6980 7.5376 13.09 4.9e-15 *** 301s price -0.2847 0.0820 -3.47 0.0014 ** 301s income 0.3145 0.0195 16.13 < 2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.942 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 64.117 MSE: 3.772 Root MSE: 1.942 301s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 54.3972 7.6824 7.08 3.0e-08 *** 301s price 0.2153 0.0820 2.62 0.013 * 301s farmPrice 0.2242 0.0193 11.60 1.5e-13 *** 301s trend 0.3145 0.0195 16.13 < 2e-16 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.515 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 101.231 MSE: 6.327 Root MSE: 2.515 301s Multiple R-Squared: 0.622 Adjusted R-Squared: 0.552 301s 301s [1] "***************************************************" 301s [1] "3SLS formula: EViews" 301s [1] "************* 3SLS with different instruments **************" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 33 174 2.12 0.675 0.659 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 67.4 3.97 1.99 0.748 0.719 301s supply 20 16 106.6 6.66 2.58 0.602 0.528 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.97 3.84 301s supply 3.84 6.04 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.97 4.93 301s supply 4.93 6.66 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.959 301s supply 0.959 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 106.7894 11.1435 9.58 2.9e-08 *** 301s price -0.4116 0.1448 -2.84 0.011 * 301s income 0.3617 0.0564 6.41 6.4e-06 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.992 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 301s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 57.2953 11.5390 4.97 0.00014 *** 301s price 0.1373 0.0897 1.53 0.14529 301s farmPrice 0.2660 0.0470 5.66 3.6e-05 *** 301s trend 0.3970 0.0781 5.08 0.00011 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.582 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 106.628 MSE: 6.664 Root MSE: 2.582 301s Multiple R-Squared: 0.602 Adjusted R-Squared: 0.528 301s 301s [1] "******* 3SLS with different instruments (EViews-like) **********" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 33 173 1.51 0.677 0.612 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 67.4 3.97 1.99 0.748 0.719 301s supply 20 16 105.7 6.61 2.57 0.606 0.532 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.37 3.16 301s supply 3.16 4.83 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.37 4.04 301s supply 4.04 5.29 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.957 301s supply 0.957 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 106.789 10.274 10.39 6.1e-12 *** 301s price -0.412 0.134 -3.08 0.0041 ** 301s income 0.362 0.052 6.95 6.0e-08 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.992 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 301s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 57.0636 10.3208 5.53 3.9e-06 *** 301s price 0.1403 0.0802 1.75 0.089 . 301s farmPrice 0.2657 0.0421 6.32 3.8e-07 *** 301s trend 0.3927 0.0699 5.62 3.0e-06 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.571 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 105.735 MSE: 6.608 Root MSE: 2.571 301s Multiple R-Squared: 0.606 Adjusted R-Squared: 0.532 301s 301s [1] "**** 3SLS with different instruments and methodResidCov = Theil ***" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 33 175 0.321 0.673 0.655 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 67.4 3.97 1.99 0.748 0.719 301s supply 20 16 107.7 6.73 2.59 0.598 0.523 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.97 3.96 301s supply 3.96 6.04 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.97 5.14 301s supply 5.14 6.73 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.962 301s supply 0.962 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 106.7894 11.1435 9.58 4.7e-11 *** 301s price -0.4116 0.1448 -2.84 0.0076 ** 301s income 0.3617 0.0564 6.41 2.9e-07 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.992 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 301s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 57.5567 11.5060 5.00 1.8e-05 *** 301s price 0.1338 0.0889 1.50 0.14 301s farmPrice 0.2664 0.0470 5.66 2.6e-06 *** 301s trend 0.4018 0.0765 5.26 8.7e-06 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.594 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 107.679 MSE: 6.73 Root MSE: 2.594 301s Multiple R-Squared: 0.598 Adjusted R-Squared: 0.523 301s 301s [1] "************* W3SLS with different instruments **************" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 33 174 2.12 0.675 0.659 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 67.4 3.97 1.99 0.748 0.719 301s supply 20 16 106.6 6.66 2.58 0.602 0.528 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.97 3.84 301s supply 3.84 6.04 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.97 4.93 301s supply 4.93 6.66 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.959 301s supply 0.959 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 106.7894 11.1435 9.58 2.9e-08 *** 301s price -0.4116 0.1448 -2.84 0.011 * 301s income 0.3617 0.0564 6.41 6.4e-06 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 1.992 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 301s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 57.2953 11.5390 4.97 0.00014 *** 301s price 0.1373 0.0897 1.53 0.14529 301s farmPrice 0.2660 0.0470 5.66 3.6e-05 *** 301s trend 0.3970 0.0781 5.08 0.00011 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.582 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 106.628 MSE: 6.664 Root MSE: 2.582 301s Multiple R-Squared: 0.602 Adjusted R-Squared: 0.528 301s 301s [1] "******* 3SLS with different instruments and restriction ********" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 34 174 3.39 0.676 0.542 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 71.1 4.18 2.04 0.735 0.704 301s supply 20 16 102.6 6.41 2.53 0.617 0.546 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.79 4.35 301s supply 4.35 6.27 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 4.18 4.84 301s supply 4.84 6.41 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.935 301s supply 0.935 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 109.4916 6.3475 17.25 < 2e-16 *** 301s price -0.4470 0.0812 -5.50 3.8e-06 *** 301s income 0.3703 0.0474 7.81 4.3e-09 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.045 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 71.077 MSE: 4.181 Root MSE: 2.045 301s Multiple R-Squared: 0.735 Adjusted R-Squared: 0.704 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 57.6795 11.2022 5.15 1.1e-05 *** 301s price 0.1324 0.0785 1.69 0.1 301s farmPrice 0.2700 0.0453 5.97 9.5e-07 *** 301s trend 0.3703 0.0474 7.81 4.3e-09 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.532 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 102.574 MSE: 6.411 Root MSE: 2.532 301s Multiple R-Squared: 0.617 Adjusted R-Squared: 0.546 301s 301s [1] "** 3SLS with different instruments and restriction (EViews-like) *" 301s 301s systemfit results 301s method: 3SLS 301s 301s N DF SSR detRCov OLS-R2 McElroy-R2 301s system 40 34 173 2.29 0.678 0.515 301s 301s N DF SSR MSE RMSE R2 Adj R2 301s demand 20 17 70.5 4.15 2.04 0.737 0.706 301s supply 20 16 102.2 6.38 2.53 0.619 0.548 301s 301s The covariance matrix of the residuals used for estimation 301s demand supply 301s demand 3.22 3.58 301s supply 3.58 5.02 301s 301s The covariance matrix of the residuals 301s demand supply 301s demand 3.53 3.96 301s supply 3.96 5.11 301s 301s The correlations of the residuals 301s demand supply 301s demand 1.000 0.934 301s supply 0.934 1.000 301s 301s 301s 3SLS estimates for 'demand' (equation 1) 301s Model Formula: consump ~ price + income 301s Instruments: ~income + farmPrice 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 109.1085 5.8428 18.67 < 2e-16 *** 301s price -0.4422 0.0737 -6.00 8.6e-07 *** 301s income 0.3693 0.0432 8.54 5.6e-10 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.037 on 17 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 17 301s SSR: 70.515 MSE: 4.148 Root MSE: 2.037 301s Multiple R-Squared: 0.737 Adjusted R-Squared: 0.706 301s 301s 301s 3SLS estimates for 'supply' (equation 2) 301s Model Formula: consump ~ price + farmPrice + trend 301s Instruments: ~income + farmPrice + trend 301s 301s Estimate Std. Error t value Pr(>|t|) 301s (Intercept) 57.2679 10.0564 5.69 2.1e-06 *** 301s price 0.1375 0.0705 1.95 0.06 . 301s farmPrice 0.2691 0.0403 6.68 1.1e-07 *** 301s trend 0.3693 0.0432 8.54 5.6e-10 *** 301s --- 301s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 301s 301s Residual standard error: 2.527 on 16 degrees of freedom 301s Number of observations: 20 Degrees of Freedom: 16 301s SSR: 102.156 MSE: 6.385 Root MSE: 2.527 301s Multiple R-Squared: 0.619 Adjusted R-Squared: 0.548 301s 301s [1] "** W3SLS with different instruments and restriction (EViews-like) *" 302s 302s systemfit results 302s method: 3SLS 302s 302s N DF SSR detRCov OLS-R2 McElroy-R2 302s system 40 34 173 2.29 0.678 0.515 302s 302s N DF SSR MSE RMSE R2 Adj R2 302s demand 20 17 70.5 4.15 2.04 0.737 0.706 302s supply 20 16 102.1 6.38 2.53 0.619 0.548 302s 302s The covariance matrix of the residuals used for estimation 302s demand supply 302s demand 3.24 3.60 302s supply 3.60 5.06 302s 302s The covariance matrix of the residuals 302s demand supply 302s demand 3.52 3.96 302s supply 3.96 5.11 302s 302s The correlations of the residuals 302s demand supply 302s demand 1.000 0.934 302s supply 0.934 1.000 302s 302s 302s 3SLS estimates for 'demand' (equation 1) 302s Model Formula: consump ~ price + income 302s Instruments: ~income + farmPrice 302s 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 109.0818 5.9083 18.46 < 2e-16 *** 302s price -0.4418 0.0746 -5.92 1.1e-06 *** 302s income 0.3692 0.0434 8.51 6.2e-10 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s 302s Residual standard error: 2.036 on 17 degrees of freedom 302s Number of observations: 20 Degrees of Freedom: 17 302s SSR: 70.475 MSE: 4.146 Root MSE: 2.036 302s Multiple R-Squared: 0.737 Adjusted R-Squared: 0.706 302s 302s 302s 3SLS estimates for 'supply' (equation 2) 302s Model Formula: consump ~ price + farmPrice + trend 302s Instruments: ~income + farmPrice + trend 302s 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 57.2616 10.1094 5.66 2.4e-06 *** 302s price 0.1376 0.0711 1.94 0.061 . 302s farmPrice 0.2690 0.0405 6.64 1.3e-07 *** 302s trend 0.3692 0.0434 8.51 6.2e-10 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s 302s Residual standard error: 2.527 on 16 degrees of freedom 302s Number of observations: 20 Degrees of Freedom: 16 302s SSR: 102.135 MSE: 6.383 Root MSE: 2.527 302s Multiple R-Squared: 0.619 Adjusted R-Squared: 0.548 302s 302s [1] "** 3SLS with different instruments and restriction via restrict.regMat *******" 302s 302s systemfit results 302s method: 3SLS 302s 302s N DF SSR detRCov OLS-R2 McElroy-R2 302s system 40 34 174 3.39 0.676 0.542 302s 302s N DF SSR MSE RMSE R2 Adj R2 302s demand 20 17 71.1 4.18 2.04 0.735 0.704 302s supply 20 16 102.6 6.41 2.53 0.617 0.546 302s 302s The covariance matrix of the residuals used for estimation 302s demand supply 302s demand 3.79 4.35 302s supply 4.35 6.27 302s 302s The covariance matrix of the residuals 302s demand supply 302s demand 4.18 4.84 302s supply 4.84 6.41 302s 302s The correlations of the residuals 302s demand supply 302s demand 1.000 0.935 302s supply 0.935 1.000 302s 302s 302s 3SLS estimates for 'demand' (equation 1) 302s Model Formula: consump ~ price + income 302s Instruments: ~income + farmPrice 302s 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 109.4916 6.3475 17.25 < 2e-16 *** 302s price -0.4470 0.0812 -5.50 3.8e-06 *** 302s income 0.3703 0.0474 7.81 4.3e-09 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s 302s Residual standard error: 2.045 on 17 degrees of freedom 302s Number of observations: 20 Degrees of Freedom: 17 302s SSR: 71.077 MSE: 4.181 Root MSE: 2.045 302s Multiple R-Squared: 0.735 Adjusted R-Squared: 0.704 302s 302s 302s 3SLS estimates for 'supply' (equation 2) 302s Model Formula: consump ~ price + farmPrice + trend 302s Instruments: ~income + farmPrice + trend 302s 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 57.6795 11.2022 5.15 1.1e-05 *** 302s price 0.1324 0.0785 1.69 0.1 302s farmPrice 0.2700 0.0453 5.97 9.5e-07 *** 302s trend 0.3703 0.0474 7.81 4.3e-09 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s 302s Residual standard error: 2.532 on 16 degrees of freedom 302s Number of observations: 20 Degrees of Freedom: 16 302s SSR: 102.574 MSE: 6.411 Root MSE: 2.532 302s Multiple R-Squared: 0.617 Adjusted R-Squared: 0.546 302s 302s [1] "3SLS with different instruments with restriction via restrict.regMat (EViews-like)" 302s 302s systemfit results 302s method: 3SLS 302s 302s N DF SSR detRCov OLS-R2 McElroy-R2 302s system 40 34 173 2.29 0.678 0.515 302s 302s N DF SSR MSE RMSE R2 Adj R2 302s demand 20 17 70.5 4.15 2.04 0.737 0.706 302s supply 20 16 102.2 6.38 2.53 0.619 0.548 302s 302s The covariance matrix of the residuals used for estimation 302s demand supply 302s demand 3.22 3.58 302s supply 3.58 5.02 302s 302s The covariance matrix of the residuals 302s demand supply 302s demand 3.53 3.96 302s supply 3.96 5.11 302s 302s The correlations of the residuals 302s demand supply 302s demand 1.000 0.934 302s supply 0.934 1.000 302s 302s 302s 3SLS estimates for 'demand' (equation 1) 302s Model Formula: consump ~ price + income 302s Instruments: ~income + farmPrice 302s 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 109.1085 5.8428 18.67 < 2e-16 *** 302s price -0.4422 0.0737 -6.00 8.6e-07 *** 302s income 0.3693 0.0432 8.54 5.6e-10 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s 302s Residual standard error: 2.037 on 17 degrees of freedom 302s Number of observations: 20 Degrees of Freedom: 17 302s SSR: 70.515 MSE: 4.148 Root MSE: 2.037 302s Multiple R-Squared: 0.737 Adjusted R-Squared: 0.706 302s 302s 302s 3SLS estimates for 'supply' (equation 2) 302s Model Formula: consump ~ price + farmPrice + trend 302s Instruments: ~income + farmPrice + trend 302s 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 57.2679 10.0564 5.69 2.1e-06 *** 302s price 0.1375 0.0705 1.95 0.06 . 302s farmPrice 0.2691 0.0403 6.68 1.1e-07 *** 302s trend 0.3693 0.0432 8.54 5.6e-10 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s 302s Residual standard error: 2.527 on 16 degrees of freedom 302s Number of observations: 20 Degrees of Freedom: 16 302s SSR: 102.156 MSE: 6.385 Root MSE: 2.527 302s Multiple R-Squared: 0.619 Adjusted R-Squared: 0.548 302s 302s [1] "** W3SLS with different instr. and restr. via restrict.regMat ****" 302s 302s systemfit results 302s method: 3SLS 302s 302s N DF SSR detRCov OLS-R2 McElroy-R2 302s system 40 34 174 3.38 0.676 0.543 302s 302s N DF SSR MSE RMSE R2 Adj R2 302s demand 20 17 71 4.18 2.04 0.735 0.704 302s supply 20 16 103 6.41 2.53 0.618 0.546 302s 302s The covariance matrix of the residuals used for estimation 302s demand supply 302s demand 3.81 4.36 302s supply 4.36 6.34 302s 302s The covariance matrix of the residuals 302s demand supply 302s demand 4.18 4.84 302s supply 4.84 6.41 302s 302s The correlations of the residuals 302s demand supply 302s demand 1.000 0.935 302s supply 0.935 1.000 302s 302s 302s 3SLS estimates for 'demand' (equation 1) 302s Model Formula: consump ~ price + income 302s Instruments: ~income + farmPrice 302s 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 109.4522 6.4318 17.02 < 2e-16 *** 302s price -0.4465 0.0823 -5.42 4.8e-06 *** 302s income 0.3702 0.0476 7.78 4.8e-09 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s 302s Residual standard error: 2.044 on 17 degrees of freedom 302s Number of observations: 20 Degrees of Freedom: 17 302s SSR: 71.017 MSE: 4.177 Root MSE: 2.044 302s Multiple R-Squared: 0.735 Adjusted R-Squared: 0.704 302s 302s 302s 3SLS estimates for 'supply' (equation 2) 302s Model Formula: consump ~ price + farmPrice + trend 302s Instruments: ~income + farmPrice + trend 302s 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 57.6669 11.2699 5.12 1.2e-05 *** 302s price 0.1326 0.0792 1.67 0.1 302s farmPrice 0.2699 0.0456 5.92 1.1e-06 *** 302s trend 0.3702 0.0476 7.78 4.8e-09 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s 302s Residual standard error: 2.532 on 16 degrees of freedom 302s Number of observations: 20 Degrees of Freedom: 16 302s SSR: 102.539 MSE: 6.409 Root MSE: 2.532 302s Multiple R-Squared: 0.618 Adjusted R-Squared: 0.546 302s 302s [1] "****** 3SLS with different instruments and 2 restrictions *********" 302s 302s systemfit results 302s method: 3SLS 302s 302s N DF SSR detRCov OLS-R2 McElroy-R2 302s system 40 35 358 32.4 0.333 -0.013 302s 302s N DF SSR MSE RMSE R2 Adj R2 302s demand 20 17 141 8.32 2.88 0.472 0.410 302s supply 20 16 216 13.53 3.68 0.193 0.042 302s 302s The covariance matrix of the residuals used for estimation 302s demand supply 302s demand 3.79 4.45 302s supply 4.45 6.06 302s 302s The covariance matrix of the residuals 302s demand supply 302s demand 8.32 8.95 302s supply 8.95 13.53 302s 302s The correlations of the residuals 302s demand supply 302s demand 1.000 0.844 302s supply 0.844 1.000 302s 302s 302s 3SLS estimates for 'demand' (equation 1) 302s Model Formula: consump ~ price + income 302s Instruments: ~income + farmPrice 302s 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 108.5837 5.3770 20.2 < 2e-16 *** 302s price -0.6034 0.0504 -12.0 6.2e-14 *** 302s income 0.5399 0.0182 29.7 < 2e-16 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s 302s Residual standard error: 2.884 on 17 degrees of freedom 302s Number of observations: 20 Degrees of Freedom: 17 302s SSR: 141.436 MSE: 8.32 Root MSE: 2.884 302s Multiple R-Squared: 0.472 Adjusted R-Squared: 0.41 302s 302s 302s 3SLS estimates for 'supply' (equation 2) 302s Model Formula: consump ~ price + farmPrice + trend 302s Instruments: ~income + farmPrice + trend 302s 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 14.7043 5.4316 2.71 0.01 * 302s price 0.3966 0.0504 7.87 3e-09 *** 302s farmPrice 0.4228 0.0205 20.65 <2e-16 *** 302s trend 0.5399 0.0182 29.71 <2e-16 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s 302s Residual standard error: 3.678 on 16 degrees of freedom 302s Number of observations: 20 Degrees of Freedom: 16 302s SSR: 216.4 MSE: 13.525 Root MSE: 3.678 302s Multiple R-Squared: 0.193 Adjusted R-Squared: 0.042 302s 302s [1] "** 3SLS with different instruments and 2 restrictions (EViews-like) *" 302s 302s systemfit results 302s method: 3SLS 302s 302s N DF SSR detRCov OLS-R2 McElroy-R2 302s system 40 35 359 21.9 0.331 -0.059 302s 302s N DF SSR MSE RMSE R2 Adj R2 302s demand 20 17 143 8.38 2.90 0.468 0.406 302s supply 20 16 216 13.52 3.68 0.193 0.042 302s 302s The covariance matrix of the residuals used for estimation 302s demand supply 302s demand 3.22 3.67 302s supply 3.67 4.85 302s 302s The covariance matrix of the residuals 302s demand supply 302s demand 7.13 7.43 302s supply 7.43 10.82 302s 302s The correlations of the residuals 302s demand supply 302s demand 1.000 0.846 302s supply 0.846 1.000 302s 302s 302s 3SLS estimates for 'demand' (equation 1) 302s Model Formula: consump ~ price + income 302s Instruments: ~income + farmPrice 302s 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 107.9852 4.9704 21.7 < 2e-16 *** 302s price -0.5994 0.0458 -13.1 4.9e-15 *** 302s income 0.5420 0.0168 32.2 < 2e-16 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s 302s Residual standard error: 2.896 on 17 degrees of freedom 302s Number of observations: 20 Degrees of Freedom: 17 302s SSR: 142.542 MSE: 8.385 Root MSE: 2.896 302s Multiple R-Squared: 0.468 Adjusted R-Squared: 0.406 302s 302s 302s 3SLS estimates for 'supply' (equation 2) 302s Model Formula: consump ~ price + farmPrice + trend 302s Instruments: ~income + farmPrice + trend 302s 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 14.4922 4.9950 2.90 0.0064 ** 302s price 0.4006 0.0458 8.75 2.5e-10 *** 302s farmPrice 0.4207 0.0184 22.92 < 2e-16 *** 302s trend 0.5420 0.0168 32.25 < 2e-16 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s 302s Residual standard error: 3.677 on 16 degrees of freedom 302s Number of observations: 20 Degrees of Freedom: 16 302s SSR: 216.315 MSE: 13.52 Root MSE: 3.677 302s Multiple R-Squared: 0.193 Adjusted R-Squared: 0.042 302s 302s [1] "**** W3SLS with different instruments and 2 restrictions *********" 302s 302s systemfit results 302s method: 3SLS 302s 302s N DF SSR detRCov OLS-R2 McElroy-R2 302s system 40 35 364 32.3 0.322 -0.022 302s 302s N DF SSR MSE RMSE R2 Adj R2 302s demand 20 17 143 8.43 2.90 0.466 0.403 302s supply 20 16 220 13.78 3.71 0.178 0.024 302s 302s The covariance matrix of the residuals used for estimation 302s demand supply 302s demand 3.75 4.46 302s supply 4.46 6.04 302s 302s The covariance matrix of the residuals 302s demand supply 302s demand 8.43 9.15 302s supply 9.15 13.78 302s 302s The correlations of the residuals 302s demand supply 302s demand 1.00 0.85 302s supply 0.85 1.00 302s 302s 302s 3SLS estimates for 'demand' (equation 1) 302s Model Formula: consump ~ price + income 302s Instruments: ~income + farmPrice 302s 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 107.9125 5.1136 21.1 < 2e-16 *** 302s price -0.5996 0.0479 -12.5 1.7e-14 *** 302s income 0.5430 0.0171 31.7 < 2e-16 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s 302s Residual standard error: 2.903 on 17 degrees of freedom 302s Number of observations: 20 Degrees of Freedom: 17 302s SSR: 143.236 MSE: 8.426 Root MSE: 2.903 302s Multiple R-Squared: 0.466 Adjusted R-Squared: 0.403 302s 302s 302s 3SLS estimates for 'supply' (equation 2) 302s Model Formula: consump ~ price + farmPrice + trend 302s Instruments: ~income + farmPrice + trend 302s 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 13.9658 5.1591 2.71 0.01 * 302s price 0.4004 0.0479 8.36 7.3e-10 *** 302s farmPrice 0.4263 0.0193 22.08 < 2e-16 *** 302s trend 0.5430 0.0171 31.74 < 2e-16 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s 302s Residual standard error: 3.712 on 16 degrees of freedom 302s Number of observations: 20 Degrees of Freedom: 16 302s SSR: 220.468 MSE: 13.779 Root MSE: 3.712 302s Multiple R-Squared: 0.178 Adjusted R-Squared: 0.024 302s 302s [1] " 3SLS with different instruments with 2 restrictions via R and restrict.regMat" 302s 302s systemfit results 302s method: 3SLS 302s 302s N DF SSR detRCov OLS-R2 McElroy-R2 302s system 40 35 358 32.4 0.333 -0.013 302s 302s N DF SSR MSE RMSE R2 Adj R2 302s demand 20 17 141 8.32 2.88 0.472 0.410 302s supply 20 16 216 13.53 3.68 0.193 0.042 302s 302s The covariance matrix of the residuals used for estimation 302s demand supply 302s demand 3.79 4.45 302s supply 4.45 6.06 302s 302s The covariance matrix of the residuals 302s demand supply 302s demand 8.32 8.95 302s supply 8.95 13.53 302s 302s The correlations of the residuals 302s demand supply 302s demand 1.000 0.844 302s supply 0.844 1.000 302s 302s 302s 3SLS estimates for 'demand' (equation 1) 302s Model Formula: consump ~ price + income 302s Instruments: ~income + farmPrice 302s 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 108.5837 5.3770 20.2 < 2e-16 *** 302s price -0.6034 0.0504 -12.0 6.2e-14 *** 302s income 0.5399 0.0182 29.7 < 2e-16 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s 302s Residual standard error: 2.884 on 17 degrees of freedom 302s Number of observations: 20 Degrees of Freedom: 17 302s SSR: 141.436 MSE: 8.32 Root MSE: 2.884 302s Multiple R-Squared: 0.472 Adjusted R-Squared: 0.41 302s 302s 302s 3SLS estimates for 'supply' (equation 2) 302s Model Formula: consump ~ price + farmPrice + trend 302s Instruments: ~income + farmPrice + trend 302s 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 14.7043 5.4316 2.71 0.01 * 302s price 0.3966 0.0504 7.87 3e-09 *** 302s farmPrice 0.4228 0.0205 20.65 <2e-16 *** 302s trend 0.5399 0.0182 29.71 <2e-16 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s 302s Residual standard error: 3.678 on 16 degrees of freedom 302s Number of observations: 20 Degrees of Freedom: 16 302s SSR: 216.4 MSE: 13.525 Root MSE: 3.678 302s Multiple R-Squared: 0.193 Adjusted R-Squared: 0.042 302s 302s [1] "3SLS with diff. instruments and 2 restr. via R and restrict.regMat (EViews-like)" 302s 302s systemfit results 302s method: 3SLS 302s 302s N DF SSR detRCov OLS-R2 McElroy-R2 302s system 40 35 359 21.9 0.331 -0.059 302s 302s N DF SSR MSE RMSE R2 Adj R2 302s demand 20 17 143 8.38 2.90 0.468 0.406 302s supply 20 16 216 13.52 3.68 0.193 0.042 302s 302s The covariance matrix of the residuals used for estimation 302s demand supply 302s demand 3.22 3.67 302s supply 3.67 4.85 302s 302s The covariance matrix of the residuals 302s demand supply 302s demand 7.13 7.43 302s supply 7.43 10.82 302s 302s The correlations of the residuals 302s demand supply 302s demand 1.000 0.846 302s supply 0.846 1.000 302s 302s 302s 3SLS estimates for 'demand' (equation 1) 302s Model Formula: consump ~ price + income 302s Instruments: ~income + farmPrice 302s 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 107.9852 4.9704 21.7 < 2e-16 *** 302s price -0.5994 0.0458 -13.1 4.9e-15 *** 302s income 0.5420 0.0168 32.2 < 2e-16 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s 302s Residual standard error: 2.896 on 17 degrees of freedom 302s Number of observations: 20 Degrees of Freedom: 17 302s SSR: 142.542 MSE: 8.385 Root MSE: 2.896 302s Multiple R-Squared: 0.468 Adjusted R-Squared: 0.406 302s 302s 302s 3SLS estimates for 'supply' (equation 2) 302s Model Formula: consump ~ price + farmPrice + trend 302s Instruments: ~income + farmPrice + trend 302s 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 14.4922 4.9950 2.90 0.0064 ** 302s price 0.4006 0.0458 8.75 2.5e-10 *** 302s farmPrice 0.4207 0.0184 22.92 < 2e-16 *** 302s trend 0.5420 0.0168 32.25 < 2e-16 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s 302s Residual standard error: 3.677 on 16 degrees of freedom 302s Number of observations: 20 Degrees of Freedom: 16 302s SSR: 216.315 MSE: 13.52 Root MSE: 3.677 302s Multiple R-Squared: 0.193 Adjusted R-Squared: 0.042 302s 302s [1] "W3SLS with diff. instr. and 2 restr. via R and restrict.regMat (EViews-like)" 302s 302s systemfit results 302s method: 3SLS 302s 302s N DF SSR detRCov OLS-R2 McElroy-R2 302s system 40 35 364 21.8 0.321 -0.069 302s 302s N DF SSR MSE RMSE R2 Adj R2 302s demand 20 17 144 8.49 2.91 0.462 0.399 302s supply 20 16 220 13.76 3.71 0.179 0.025 302s 302s The covariance matrix of the residuals used for estimation 302s demand supply 302s demand 3.19 3.68 302s supply 3.68 4.83 302s 302s The covariance matrix of the residuals 302s demand supply 302s demand 7.21 7.59 302s supply 7.59 11.00 302s 302s The correlations of the residuals 302s demand supply 302s demand 1.000 0.852 302s supply 0.852 1.000 302s 302s 302s 3SLS estimates for 'demand' (equation 1) 302s Model Formula: consump ~ price + income 302s Instruments: ~income + farmPrice 302s 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 107.3179 4.7598 22.6 < 2e-16 *** 302s price -0.5955 0.0438 -13.6 1.6e-15 *** 302s income 0.5449 0.0159 34.2 < 2e-16 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s 302s Residual standard error: 2.913 on 17 degrees of freedom 302s Number of observations: 20 Degrees of Freedom: 17 302s SSR: 144.274 MSE: 8.487 Root MSE: 2.913 302s Multiple R-Squared: 0.462 Adjusted R-Squared: 0.399 302s 302s 302s 3SLS estimates for 'supply' (equation 2) 302s Model Formula: consump ~ price + farmPrice + trend 302s Instruments: ~income + farmPrice + trend 302s 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 13.7761 4.7784 2.88 0.0067 ** 302s price 0.4045 0.0438 9.23 6.6e-11 *** 302s farmPrice 0.4237 0.0174 24.30 < 2e-16 *** 302s trend 0.5449 0.0159 34.17 < 2e-16 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s 302s Residual standard error: 3.709 on 16 degrees of freedom 302s Number of observations: 20 Degrees of Freedom: 16 302s SSR: 220.081 MSE: 13.755 Root MSE: 3.709 302s Multiple R-Squared: 0.179 Adjusted R-Squared: 0.025 302s 302s > 302s > 302s > ## **************** shorter summaries ********************** 302s > print( summary( fit3sls[[ 2 ]]$e1c, equations = FALSE ) ) 302s 302s systemfit results 302s method: 3SLS 302s 302s N DF SSR detRCov OLS-R2 McElroy-R2 302s system 40 33 174 -0.718 0.675 0.922 302s 302s N DF SSR MSE RMSE R2 Adj R2 302s demand 20 17 65.7 3.87 1.97 0.755 0.726 302s supply 20 16 108.7 6.79 2.61 0.594 0.518 302s 302s The covariance matrix of the residuals used for estimation 302s demand supply 302s demand 3.87 4.50 302s supply 4.50 6.04 302s 302s warning: this covariance matrix is NOT positive semidefinit! 302s 302s The covariance matrix of the residuals 302s demand supply 302s demand 3.87 5.2 302s supply 5.20 6.8 302s 302s The correlations of the residuals 302s demand supply 302s demand 1.000 0.981 302s supply 0.981 1.000 302s 302s 302s Coefficients: 302s Estimate Std. Error t value Pr(>|t|) 302s demand_(Intercept) 94.6333 7.9208 11.95 1.1e-09 *** 302s demand_price -0.2436 0.0965 -2.52 0.02183 * 302s demand_income 0.3140 0.0469 6.69 3.8e-06 *** 302s supply_(Intercept) 52.2869 11.8853 4.40 0.00045 *** 302s supply_price 0.2282 0.0997 2.29 0.03595 * 302s supply_farmPrice 0.2272 0.0438 5.19 8.9e-05 *** 302s supply_trend 0.3648 0.0707 5.16 9.5e-05 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s > 302s > print( summary( fit3sls[[ 3 ]]$e2e ), residCov = FALSE ) 302s 302s systemfit results 302s method: 3SLS 302s 302s N DF SSR detRCov OLS-R2 McElroy-R2 302s system 40 34 171 0.887 0.68 0.678 302s 302s N DF SSR MSE RMSE R2 Adj R2 302s demand 20 17 67.5 3.97 1.99 0.748 0.719 302s supply 20 16 104.0 6.50 2.55 0.612 0.539 302s 302s 302s 3SLS estimates for 'demand' (equation 1) 302s Model Formula: consump ~ price + income 302s Instruments: ~income + farmPrice + trend 302s 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 94.2737 7.3905 12.76 1.6e-14 *** 302s price -0.2243 0.0888 -2.53 0.016 * 302s income 0.2979 0.0420 7.10 3.4e-08 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s 302s Residual standard error: 1.992 on 17 degrees of freedom 302s Number of observations: 20 Degrees of Freedom: 17 302s SSR: 67.467 MSE: 3.969 Root MSE: 1.992 302s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 302s 302s 302s 3SLS estimates for 'supply' (equation 2) 302s Model Formula: consump ~ price + farmPrice + trend 302s Instruments: ~income + farmPrice + trend 302s 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 55.4521 10.3994 5.33 6.4e-06 *** 302s price 0.2207 0.0896 2.46 0.019 * 302s farmPrice 0.2095 0.0366 5.73 1.9e-06 *** 302s trend 0.2979 0.0420 7.10 3.4e-08 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s 302s Residual standard error: 2.55 on 16 degrees of freedom 302s Number of observations: 20 Degrees of Freedom: 16 302s SSR: 104.013 MSE: 6.501 Root MSE: 2.55 302s Multiple R-Squared: 0.612 Adjusted R-Squared: 0.539 302s 302s > 302s > print( summary( fit3sls[[ 4 ]]$e3, useDfSys = FALSE ), residCov = FALSE ) 302s 302s systemfit results 302s method: 3SLS 302s 302s N DF SSR detRCov OLS-R2 McElroy-R2 302s system 40 34 173 1.27 0.678 0.722 302s 302s N DF SSR MSE RMSE R2 Adj R2 302s demand 20 17 67.8 3.99 2.00 0.747 0.717 302s supply 20 16 104.8 6.55 2.56 0.609 0.536 302s 302s 302s 3SLS estimates for 'demand' (equation 1) 302s Model Formula: consump ~ price + income 302s Instruments: ~income + farmPrice + trend 302s 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 94.222 8.015 11.76 1.4e-09 *** 302s price -0.222 0.096 -2.31 0.034 * 302s income 0.296 0.045 6.57 4.8e-06 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s 302s Residual standard error: 1.997 on 17 degrees of freedom 302s Number of observations: 20 Degrees of Freedom: 17 302s SSR: 67.796 MSE: 3.988 Root MSE: 1.997 302s Multiple R-Squared: 0.747 Adjusted R-Squared: 0.717 302s 302s 302s 3SLS estimates for 'supply' (equation 2) 302s Model Formula: consump ~ price + farmPrice + trend 302s Instruments: ~income + farmPrice + trend 302s 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 55.9604 11.5777 4.83 0.00018 *** 302s price 0.2193 0.1002 2.19 0.04374 * 302s farmPrice 0.2060 0.0403 5.11 0.00011 *** 302s trend 0.2956 0.0450 6.57 6.5e-06 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s 302s Residual standard error: 2.559 on 16 degrees of freedom 302s Number of observations: 20 Degrees of Freedom: 16 302s SSR: 104.753 MSE: 6.547 Root MSE: 2.559 302s Multiple R-Squared: 0.609 Adjusted R-Squared: 0.536 302s 302s > 302s > print( summary( fit3sls[[ 5 ]]$e4e, equations = FALSE ), 302s + equations = FALSE ) 302s 302s systemfit results 302s method: 3SLS 302s 302s N DF SSR detRCov OLS-R2 McElroy-R2 302s system 40 35 439 21.3 0.18 -0.18 302s 302s N DF SSR MSE RMSE R2 Adj R2 302s demand 20 17 169 9.93 3.15 0.370 0.296 302s supply 20 16 271 16.91 4.11 -0.009 -0.198 302s 302s The covariance matrix of the residuals used for estimation 302s demand supply 302s demand 3.30 3.73 302s supply 3.73 5.00 302s 302s The covariance matrix of the residuals 302s demand supply 302s demand 8.44 9.64 302s supply 9.64 13.53 302s 302s The correlations of the residuals 302s demand supply 302s demand 1.000 0.902 302s supply 0.902 1.000 302s 302s 302s Coefficients: 302s Estimate Std. Error t value Pr(>|t|) 302s demand_(Intercept) 93.2926 7.3154 12.75 1.0e-14 *** 302s demand_price -0.4781 0.0812 -5.89 1.1e-06 *** 302s demand_income 0.5683 0.0209 27.24 < 2e-16 *** 302s supply_(Intercept) 0.6559 7.5503 0.09 0.93 302s supply_price 0.5219 0.0812 6.43 2.1e-07 *** 302s supply_farmPrice 0.4355 0.0212 20.49 < 2e-16 *** 302s supply_trend 0.5683 0.0209 27.24 < 2e-16 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s > 302s > print( summary( fit3sls[[ 1 ]]$e4wSym, residCov = FALSE ), 302s + equations = FALSE ) 302s 302s systemfit results 302s method: 3SLS 302s 302s N DF SSR detRCov OLS-R2 McElroy-R2 302s system 40 35 172 1.74 0.68 0.697 302s 302s N DF SSR MSE RMSE R2 Adj R2 302s demand 20 17 65.9 3.88 1.97 0.754 0.725 302s supply 20 16 105.7 6.60 2.57 0.606 0.532 302s 302s 302s Coefficients: 302s Estimate Std. Error t value Pr(>|t|) 302s demand_(Intercept) 93.7870 7.9088 11.86 8.2e-14 *** 302s demand_price -0.2443 0.0892 -2.74 0.0096 ** 302s demand_income 0.3234 0.0229 14.14 4.4e-16 *** 302s supply_(Intercept) 49.8093 8.1522 6.11 5.5e-07 *** 302s supply_price 0.2557 0.0892 2.87 0.0069 ** 302s supply_farmPrice 0.2289 0.0237 9.67 2.0e-11 *** 302s supply_trend 0.3234 0.0229 14.14 4.4e-16 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s > 302s > print( summary( fit3sls[[ 2 ]]$e5, residCov = FALSE ), residCov = TRUE ) 302s 302s systemfit results 302s method: 3SLS 302s 302s N DF SSR detRCov OLS-R2 McElroy-R2 302s system 40 35 171 1.74 0.681 0.696 302s 302s N DF SSR MSE RMSE R2 Adj R2 302s demand 20 17 65.8 3.87 1.97 0.755 0.726 302s supply 20 16 105.4 6.59 2.57 0.607 0.533 302s 302s The covariance matrix of the residuals used for estimation 302s demand supply 302s demand 3.89 4.53 302s supply 4.53 6.25 302s 302s The covariance matrix of the residuals 302s demand supply 302s demand 3.87 4.87 302s supply 4.87 6.59 302s 302s The correlations of the residuals 302s demand supply 302s demand 1.000 0.965 302s supply 0.965 1.000 302s 302s 302s 3SLS estimates for 'demand' (equation 1) 302s Model Formula: consump ~ price + income 302s Instruments: ~income + farmPrice + trend 302s 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 93.9070 7.9234 11.85 8.3e-14 *** 302s price -0.2457 0.0891 -2.76 0.0092 ** 302s income 0.3236 0.0233 13.91 8.9e-16 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s 302s Residual standard error: 1.967 on 17 degrees of freedom 302s Number of observations: 20 Degrees of Freedom: 17 302s SSR: 65.807 MSE: 3.871 Root MSE: 1.967 302s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 302s 302s 302s 3SLS estimates for 'supply' (equation 2) 302s Model Formula: consump ~ price + farmPrice + trend 302s Instruments: ~income + farmPrice + trend 302s 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 49.9049 8.1797 6.10 5.7e-07 *** 302s price 0.2543 0.0891 2.85 0.0072 ** 302s farmPrice 0.2293 0.0241 9.52 3.1e-11 *** 302s trend 0.3236 0.0233 13.91 8.9e-16 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s 302s Residual standard error: 2.566 on 16 degrees of freedom 302s Number of observations: 20 Degrees of Freedom: 16 302s SSR: 105.389 MSE: 6.587 Root MSE: 2.566 302s Multiple R-Squared: 0.607 Adjusted R-Squared: 0.533 302s 302s > 302s > print( summary( fit3slsi[[ 3 ]]$e3e, residCov = FALSE, 302s + equations = FALSE ) ) 302s 302s systemfit results 302s method: iterated 3SLS 302s 302s convergence achieved after 20 iterations 302s 302s N DF SSR detRCov OLS-R2 McElroy-R2 302s system 40 34 237 0.364 0.557 0.755 302s 302s N DF SSR MSE RMSE R2 Adj R2 302s demand 20 17 99.3 5.84 2.42 0.630 0.586 302s supply 20 16 138.1 8.63 2.94 0.485 0.388 302s 302s 302s Coefficients: 302s Estimate Std. Error t value Pr(>|t|) 302s demand_(Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 302s demand_price -0.1043 0.0958 -1.09 0.284 302s demand_income 0.1979 0.0299 6.61 1.4e-07 *** 302s supply_(Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 302s supply_price 0.1851 0.1053 1.76 0.088 . 302s supply_farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 302s supply_trend 0.1979 0.0299 6.61 1.4e-07 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s > 302s > print( summary( fit3slsi[[ 4 ]]$e1we ), equations = FALSE, residCov = FALSE ) 302s 302s systemfit results 302s method: iterated 3SLS 302s 302s convergence achieved after 6 iterations 302s 302s N DF SSR detRCov OLS-R2 McElroy-R2 302s system 40 33 177 0.667 0.67 0.782 302s 302s N DF SSR MSE RMSE R2 Adj R2 302s demand 20 17 65.7 3.87 1.97 0.755 0.726 302s supply 20 16 111.3 6.96 2.64 0.585 0.507 302s 302s 302s Coefficients: 302s Estimate Std. Error t value Pr(>|t|) 302s demand_(Intercept) 94.6333 7.3027 12.96 3.1e-10 *** 302s demand_price -0.2436 0.0890 -2.74 0.01402 * 302s demand_income 0.3140 0.0433 7.25 1.3e-06 *** 302s supply_(Intercept) 52.5527 11.3956 4.61 0.00029 *** 302s supply_price 0.2271 0.0956 2.37 0.03043 * 302s supply_farmPrice 0.2245 0.0416 5.39 6.0e-05 *** 302s supply_trend 0.3756 0.0641 5.86 2.4e-05 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s > 302s > print( summary( fit3slsd[[ 5 ]]$e4, residCov = FALSE ) ) 302s 302s systemfit results 302s method: 3SLS 302s 302s N DF SSR detRCov OLS-R2 McElroy-R2 302s system 40 35 358 32.4 0.333 -0.013 302s 302s N DF SSR MSE RMSE R2 Adj R2 302s demand 20 17 141 8.32 2.88 0.472 0.410 302s supply 20 16 216 13.53 3.68 0.193 0.042 302s 302s 302s 3SLS estimates for 'demand' (equation 1) 302s Model Formula: consump ~ price + income 302s Instruments: ~income + farmPrice 302s 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 108.5837 5.3770 20.2 < 2e-16 *** 302s price -0.6034 0.0504 -12.0 6.2e-14 *** 302s income 0.5399 0.0182 29.7 < 2e-16 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s 302s Residual standard error: 2.884 on 17 degrees of freedom 302s Number of observations: 20 Degrees of Freedom: 17 302s SSR: 141.436 MSE: 8.32 Root MSE: 2.884 302s Multiple R-Squared: 0.472 Adjusted R-Squared: 0.41 302s 302s 302s 3SLS estimates for 'supply' (equation 2) 302s Model Formula: consump ~ price + farmPrice + trend 302s Instruments: ~income + farmPrice + trend 302s 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 14.7043 5.4316 2.71 0.01 * 302s price 0.3966 0.0504 7.87 3e-09 *** 302s farmPrice 0.4228 0.0205 20.65 <2e-16 *** 302s trend 0.5399 0.0182 29.71 <2e-16 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s 302s Residual standard error: 3.678 on 16 degrees of freedom 302s Number of observations: 20 Degrees of Freedom: 16 302s SSR: 216.4 MSE: 13.525 Root MSE: 3.678 302s Multiple R-Squared: 0.193 Adjusted R-Squared: 0.042 302s 302s > 302s > print( summary( fit3slsd[[ 1 ]]$e2we, equations = FALSE ) ) 302s 302s systemfit results 302s method: 3SLS 302s 302s N DF SSR detRCov OLS-R2 McElroy-R2 302s system 40 34 199 1.77 0.629 0.65 302s 302s N DF SSR MSE RMSE R2 Adj R2 302s demand 20 17 72.4 4.26 2.06 0.730 0.698 302s supply 20 16 126.7 7.92 2.81 0.527 0.439 302s 302s The covariance matrix of the residuals used for estimation 302s demand supply 302s demand 3.24 3.60 302s supply 3.60 5.06 302s 302s The covariance matrix of the residuals 302s demand supply 302s demand 3.62 4.60 302s supply 4.60 6.34 302s 302s The correlations of the residuals 302s demand supply 302s demand 1.000 0.961 302s supply 0.961 1.000 302s 302s 302s Coefficients: 302s Estimate Std. Error t value Pr(>|t|) 302s demand_(Intercept) 88.9298 5.9083 15.05 < 2e-16 *** 302s demand_price -0.1760 0.0746 -2.36 0.02415 * 302s demand_income 0.3032 0.0434 6.99 4.6e-08 *** 302s supply_(Intercept) 40.8325 10.1094 4.04 0.00029 *** 302s supply_price 0.3562 0.0711 5.01 1.7e-05 *** 302s supply_farmPrice 0.2200 0.0405 5.43 4.8e-06 *** 302s supply_trend 0.3032 0.0434 6.99 4.6e-08 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s > 302s > 302s > ## ****************** residuals ************************** 302s > print( residuals( fit3sls[[ 1 ]]$e1c ) ) 302s demand supply 302s 1 0.843 0.670 302s 2 -0.698 -0.142 302s 3 2.359 2.659 302s 4 1.490 1.618 302s 5 2.139 2.588 302s 6 1.277 1.485 302s 7 1.571 2.093 302s 8 -3.066 -4.163 302s 9 -1.125 -1.929 302s 10 2.492 3.207 302s 11 -0.108 -0.513 302s 12 -2.292 -2.375 302s 13 -1.598 -2.089 302s 14 -0.271 0.330 302s 15 1.958 3.086 302s 16 -3.430 -4.225 302s 17 -0.313 0.185 302s 18 -2.151 -3.680 302s 19 1.592 1.576 302s 20 -0.668 -0.382 302s > print( residuals( fit3sls[[ 1 ]]$e1c$eq[[ 1 ]] ) ) 302s 1 2 3 4 5 6 7 8 9 10 11 302s 0.843 -0.698 2.359 1.490 2.139 1.277 1.571 -3.066 -1.125 2.492 -0.108 302s 12 13 14 15 16 17 18 19 20 302s -2.292 -1.598 -0.271 1.958 -3.430 -0.313 -2.151 1.592 -0.668 302s > 302s > print( residuals( fit3sls[[ 4 ]]$e1wc ) ) 302s demand supply 302s 1 0.843 0.670 302s 2 -0.698 -0.142 302s 3 2.359 2.659 302s 4 1.490 1.618 302s 5 2.139 2.588 302s 6 1.277 1.485 302s 7 1.571 2.093 302s 8 -3.066 -4.163 302s 9 -1.125 -1.929 302s 10 2.492 3.207 302s 11 -0.108 -0.513 302s 12 -2.292 -2.375 302s 13 -1.598 -2.089 302s 14 -0.271 0.330 302s 15 1.958 3.086 302s 16 -3.430 -4.225 302s 17 -0.313 0.185 302s 18 -2.151 -3.680 302s 19 1.592 1.576 302s 20 -0.668 -0.382 302s > print( residuals( fit3sls[[ 4 ]]$e1wc$eq[[ 1 ]] ) ) 302s 1 2 3 4 5 6 7 8 9 10 11 302s 0.843 -0.698 2.359 1.490 2.139 1.277 1.571 -3.066 -1.125 2.492 -0.108 302s 12 13 14 15 16 17 18 19 20 302s -2.292 -1.598 -0.271 1.958 -3.430 -0.313 -2.151 1.592 -0.668 302s > 302s > print( residuals( fit3sls[[ 2 ]]$e2e ) ) 302s demand supply 302s 1 0.6744 0.0619 302s 2 -0.7785 -0.6344 302s 3 2.2797 2.2267 302s 4 1.4140 1.2428 302s 5 2.2144 2.4566 302s 6 1.3352 1.3851 302s 7 1.6419 2.0264 302s 8 -2.9923 -4.0603 302s 9 -1.0710 -1.8419 302s 10 2.5226 3.1787 302s 11 -0.3346 -0.8086 302s 12 -2.5999 -2.7819 302s 13 -1.8617 -2.3572 302s 14 -0.3584 0.2840 302s 15 2.1419 3.4511 302s 16 -3.2786 -3.7199 302s 17 -0.0706 0.7656 302s 18 -2.1179 -3.2218 302s 19 1.6924 2.0576 302s 20 -0.4528 0.2893 302s > print( residuals( fit3sls[[ 2 ]]$e2e$eq[[ 2 ]] ) ) 302s 1 2 3 4 5 6 7 8 9 10 302s 0.0619 -0.6344 2.2267 1.2428 2.4566 1.3851 2.0264 -4.0603 -1.8419 3.1787 302s 11 12 13 14 15 16 17 18 19 20 302s -0.8086 -2.7819 -2.3572 0.2840 3.4511 -3.7199 0.7656 -3.2218 2.0576 0.2893 302s > 302s > print( residuals( fit3sls[[ 3 ]]$e3 ) ) 302s demand supply 302s 1 0.6499 0.045 302s 2 -0.7902 -0.639 302s 3 2.2682 2.223 302s 4 1.4031 1.239 302s 5 2.2253 2.490 302s 6 1.3437 1.414 302s 7 1.6522 2.051 302s 8 -2.9817 -4.013 302s 9 -1.0632 -1.808 302s 10 2.5270 3.179 302s 11 -0.3675 -0.872 302s 12 -2.6445 -2.878 302s 13 -1.8999 -2.437 302s 14 -0.3711 0.237 302s 15 2.1685 3.474 302s 16 -3.2566 -3.680 302s 17 -0.0355 0.809 302s 18 -2.1131 -3.213 302s 19 1.7070 2.060 302s 20 -0.4215 0.319 302s > print( residuals( fit3sls[[ 3 ]]$e3$eq[[ 1 ]] ) ) 302s 1 2 3 4 5 6 7 8 9 10 302s 0.6499 -0.7902 2.2682 1.4031 2.2253 1.3437 1.6522 -2.9817 -1.0632 2.5270 302s 11 12 13 14 15 16 17 18 19 20 302s -0.3675 -2.6445 -1.8999 -0.3711 2.1685 -3.2566 -0.0355 -2.1131 1.7070 -0.4215 302s > 302s > print( residuals( fit3sls[[ 4 ]]$e4e ) ) 302s demand supply 302s 1 0.9543 0.278 302s 2 -0.6734 -0.586 302s 3 2.3881 2.272 302s 4 1.5091 1.252 302s 5 2.1028 2.356 302s 6 1.2414 1.271 302s 7 1.5161 1.894 302s 8 -3.1487 -4.421 302s 9 -1.1358 -1.958 302s 10 2.5334 3.368 302s 11 0.0936 -0.275 302s 12 -2.0762 -2.176 302s 13 -1.4415 -1.951 302s 14 -0.2039 0.559 302s 15 1.8691 3.353 302s 16 -3.5213 -4.003 302s 17 -0.3804 0.692 302s 18 -2.2018 -3.453 302s 19 1.4834 1.817 302s 20 -0.9080 -0.289 302s > print( residuals( fit3sls[[ 4 ]]$e4e$eq[[ 2 ]] ) ) 302s 1 2 3 4 5 6 7 8 9 10 11 302s 0.278 -0.586 2.272 1.252 2.356 1.271 1.894 -4.421 -1.958 3.368 -0.275 302s 12 13 14 15 16 17 18 19 20 302s -2.176 -1.951 0.559 3.353 -4.003 0.692 -3.453 1.817 -0.289 302s > 302s > print( residuals( fit3sls[[ 5 ]]$e5 ) ) 302s demand supply 302s 1 3.391 2.137 302s 2 0.160 -0.366 302s 3 3.267 2.508 302s 4 2.250 1.132 302s 5 1.168 1.398 302s 6 0.434 0.165 302s 7 0.397 0.594 302s 8 -4.607 -7.911 302s 9 -1.631 -2.964 302s 10 2.800 5.323 302s 11 3.967 4.833 302s 12 2.518 3.479 302s 13 2.169 1.774 302s 14 1.169 3.182 302s 15 -0.415 2.626 302s 16 -5.608 -6.508 302s 17 -2.817 0.433 302s 18 -3.012 -5.580 302s 19 -0.454 -0.427 302s 20 -5.146 -5.829 302s > print( residuals( fit3sls[[ 5 ]]$e5$eq[[ 1 ]] ) ) 302s 1 2 3 4 5 6 7 8 9 10 11 302s 3.391 0.160 3.267 2.250 1.168 0.434 0.397 -4.607 -1.631 2.800 3.967 302s 12 13 14 15 16 17 18 19 20 302s 2.518 2.169 1.169 -0.415 -5.608 -2.817 -3.012 -0.454 -5.146 302s > 302s > print( residuals( fit3slsi[[ 2 ]]$e3e ) ) 302s demand supply 302s 1 -0.376 -0.761 302s 2 -1.281 -1.123 302s 3 1.786 1.809 302s 4 0.942 0.878 302s 5 2.683 3.039 302s 6 1.699 1.899 302s 7 2.083 2.477 302s 8 -2.534 -3.021 302s 9 -0.736 -1.093 302s 10 2.713 3.153 302s 11 -1.748 -2.334 302s 12 -4.518 -5.058 302s 13 -3.502 -4.191 302s 14 -0.901 -0.705 302s 15 3.286 4.209 302s 16 -2.334 -2.514 302s 17 1.438 2.113 302s 18 -1.911 -2.680 302s 19 2.320 2.490 302s 20 0.889 1.412 302s > print( residuals( fit3slsi[[ 2 ]]$e3e$eq[[ 1 ]] ) ) 302s 1 2 3 4 5 6 7 8 9 10 11 302s -0.376 -1.281 1.786 0.942 2.683 1.699 2.083 -2.534 -0.736 2.713 -1.748 302s 12 13 14 15 16 17 18 19 20 302s -4.518 -3.502 -0.901 3.286 -2.334 1.438 -1.911 2.320 0.889 302s > 302s > print( residuals( fit3slsi[[ 1 ]]$e2we ) ) 302s demand supply 302s 1 -0.376 -0.761 302s 2 -1.281 -1.123 302s 3 1.786 1.809 302s 4 0.942 0.878 302s 5 2.683 3.039 302s 6 1.699 1.899 302s 7 2.083 2.477 302s 8 -2.534 -3.021 302s 9 -0.736 -1.093 302s 10 2.713 3.153 302s 11 -1.748 -2.334 302s 12 -4.518 -5.058 302s 13 -3.502 -4.191 302s 14 -0.901 -0.705 302s 15 3.286 4.209 302s 16 -2.334 -2.514 302s 17 1.438 2.113 302s 18 -1.911 -2.680 302s 19 2.320 2.490 302s 20 0.889 1.412 302s > print( residuals( fit3slsi[[ 1 ]]$e2we$eq[[ 1 ]] ) ) 302s 1 2 3 4 5 6 7 8 9 10 11 302s -0.376 -1.281 1.786 0.942 2.683 1.699 2.083 -2.534 -0.736 2.713 -1.748 302s 12 13 14 15 16 17 18 19 20 302s -4.518 -3.502 -0.901 3.286 -2.334 1.438 -1.911 2.320 0.889 302s > 302s > print( residuals( fit3slsd[[ 3 ]]$e4 ) ) 302s demand supply 302s 1 0.7282 0.088 302s 2 -0.7938 -0.850 302s 3 2.2722 2.054 302s 4 1.3947 1.007 302s 5 2.2092 2.526 302s 6 1.3211 1.378 302s 7 1.6076 1.935 302s 8 -3.0646 -4.397 302s 9 -1.0534 -1.692 302s 10 2.6003 3.674 302s 11 -0.1888 -0.319 302s 12 -2.4839 -2.564 302s 13 -1.8018 -2.397 302s 14 -0.3164 0.423 302s 15 2.1290 3.682 302s 16 -3.3141 -3.704 302s 17 -0.0169 1.445 302s 18 -2.1692 -3.473 302s 19 1.6008 1.716 302s 20 -0.6603 -0.530 302s > print( residuals( fit3slsd[[ 3 ]]$e4$eq[[ 2 ]] ) ) 302s 1 2 3 4 5 6 7 8 9 10 11 302s 0.088 -0.850 2.054 1.007 2.526 1.378 1.935 -4.397 -1.692 3.674 -0.319 302s 12 13 14 15 16 17 18 19 20 302s -2.564 -2.397 0.423 3.682 -3.704 1.445 -3.473 1.716 -0.530 302s > 302s > print( residuals( fit3slsd[[ 5 ]]$e5we ) ) 302s demand supply 302s 1 3.290 2.057 302s 2 0.781 0.154 302s 3 3.754 2.921 302s 4 2.915 1.707 302s 5 0.906 1.148 302s 6 0.394 0.120 302s 7 0.632 0.775 302s 8 -3.766 -7.138 302s 9 -2.167 -3.402 302s 10 1.391 4.066 302s 11 2.631 3.690 302s 12 2.043 3.077 302s 13 2.405 2.007 302s 14 0.885 2.914 302s 15 -1.051 2.024 302s 16 -5.729 -6.584 302s 17 -4.810 -1.328 302s 18 -2.329 -4.924 302s 19 0.576 0.472 302s 20 -2.753 -3.755 302s > print( residuals( fit3slsd[[ 5 ]]$e5we$eq[[ 2 ]] ) ) 302s 1 2 3 4 5 6 7 8 9 10 11 302s 2.057 0.154 2.921 1.707 1.148 0.120 0.775 -7.138 -3.402 4.066 3.690 302s 12 13 14 15 16 17 18 19 20 302s 3.077 2.007 2.914 2.024 -6.584 -1.328 -4.924 0.472 -3.755 302s > 302s > 302s > ## *************** coefficients ********************* 302s > print( round( coef( fit3sls[[ 3 ]]$e1c ), digits = 6 ) ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 94.633 -0.244 0.314 52.287 302s supply_price supply_farmPrice supply_trend 302s 0.228 0.227 0.365 302s > print( round( coef( fit3sls[[ 4 ]]$e1c$eq[[ 2 ]] ), digits = 6 ) ) 302s (Intercept) price farmPrice trend 302s 52.287 0.228 0.227 0.365 302s > 302s > print( round( coef( fit3slsi[[ 4 ]]$e2 ), digits = 6 ) ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 92.074 -0.106 0.200 68.855 302s supply_price supply_farmPrice supply_trend 302s 0.183 0.120 0.200 302s > print( round( coef( fit3slsi[[ 5 ]]$e2$eq[[ 1 ]] ), digits = 6 ) ) 302s (Intercept) price income 302s 92.074 -0.106 0.200 302s > 302s > print( round( coef( fit3sls[[ 2 ]]$e2w ), digits = 6 ) ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 94.182 -0.219 0.294 56.254 302s supply_price supply_farmPrice supply_trend 302s 0.218 0.204 0.294 302s > print( round( coef( fit3sls[[ 3 ]]$e2w$eq[[ 1 ]] ), digits = 6 ) ) 302s (Intercept) price income 302s 94.182 -0.219 0.294 302s > 302s > print( round( coef( fit3slsd[[ 5 ]]$e3e ), digits = 6 ) ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 109.109 -0.442 0.369 57.268 302s supply_price supply_farmPrice supply_trend 302s 0.137 0.269 0.369 302s > print( round( coef( fit3slsd[[ 5 ]]$e3e, modified.regMat = TRUE ), digits = 6 ) ) 302s C1 C2 C3 C4 C5 C6 302s 109.109 -0.442 0.369 57.268 0.137 0.269 302s > print( round( coef( fit3slsd[[ 1 ]]$e3e$eq[[ 2 ]] ), digits = 6 ) ) 302s (Intercept) price farmPrice trend 302s 40.818 0.357 0.219 0.303 302s > 302s > print( round( coef( fit3slsd[[ 4 ]]$e3w ), digits = 6 ) ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 100.174 -0.325 0.341 49.743 302s supply_price supply_farmPrice supply_trend 302s 0.237 0.247 0.341 302s > print( round( coef( fit3slsd[[ 4 ]]$e3w, modified.regMat = TRUE ), digits = 6 ) ) 302s C1 C2 C3 C4 C5 C6 302s 100.174 -0.325 0.341 49.743 0.237 0.247 302s > print( round( coef( fit3slsd[[ 5 ]]$e3w$eq[[ 2 ]] ), digits = 6 ) ) 302s (Intercept) price farmPrice trend 302s 57.667 0.133 0.270 0.370 302s > 302s > print( round( coef( fit3sls[[ 1 ]]$e4 ), digits = 6 ) ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 93.907 -0.246 0.324 49.905 302s supply_price supply_farmPrice supply_trend 302s 0.254 0.229 0.324 302s > print( round( coef( fit3sls[[ 2 ]]$e4$eq[[ 1 ]] ), digits = 6 ) ) 302s (Intercept) price income 302s 93.907 -0.246 0.324 302s > 302s > print( round( coef( fit3slsi[[ 2 ]]$e4we ), digits = 6 ) ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 91.390 -0.217 0.320 47.579 302s supply_price supply_farmPrice supply_trend 302s 0.283 0.224 0.320 302s > print( round( coef( fit3slsi[[ 1 ]]$e4we$eq[[ 1 ]] ), digits = 6 ) ) 302s (Intercept) price income 302s 91.390 -0.217 0.320 302s > 302s > print( round( coef( fit3slsi[[ 2 ]]$e5e ), digits = 6 ) ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 91.390 -0.217 0.320 47.579 302s supply_price supply_farmPrice supply_trend 302s 0.283 0.224 0.320 302s > print( round( coef( fit3slsi[[ 2 ]]$e5e, modified.regMat = TRUE ), digits = 6 ) ) 302s C1 C2 C3 C4 C5 C6 302s 91.390 -0.217 0.320 47.579 0.283 0.224 302s > print( round( coef( fit3slsi[[ 3 ]]$e5e$eq[[ 2 ]] ), digits = 6 ) ) 302s (Intercept) price farmPrice trend 302s 47.579 0.283 0.224 0.320 302s > 302s > 302s > ## *************** coefficients with stats ********************* 302s > print( round( coef( summary( fit3sls[[ 3 ]]$e1c, useDfSys = FALSE ) ), 302s + digits = 6 ) ) 302s Estimate Std. Error t value Pr(>|t|) 302s demand_(Intercept) 94.633 7.9208 11.95 0.000000 302s demand_price -0.244 0.0965 -2.52 0.021832 302s demand_income 0.314 0.0469 6.69 0.000004 302s supply_(Intercept) 52.287 11.8853 4.40 0.000448 302s supply_price 0.228 0.0997 2.29 0.035951 302s supply_farmPrice 0.227 0.0438 5.19 0.000089 302s supply_trend 0.365 0.0707 5.16 0.000095 302s > print( round( coef( summary( fit3sls[[ 4 ]]$e1c$eq[[ 2 ]], useDfSys = FALSE ) ), 302s + digits = 6 ) ) 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 52.287 11.8853 4.40 0.000448 302s price 0.228 0.0997 2.29 0.035951 302s farmPrice 0.227 0.0438 5.19 0.000089 302s trend 0.365 0.0707 5.16 0.000095 302s > 302s > print( round( coef( summary( fit3slsd[[ 2 ]]$e1w, useDfSys = FALSE ) ), 302s + digits = 6 ) ) 302s Estimate Std. Error t value Pr(>|t|) 302s demand_(Intercept) 106.789 11.1435 9.58 0.000000 302s demand_price -0.412 0.1448 -2.84 0.011271 302s demand_income 0.362 0.0564 6.41 0.000006 302s supply_(Intercept) 57.295 11.7078 4.89 0.000162 302s supply_price 0.137 0.0979 1.40 0.179781 302s supply_farmPrice 0.266 0.0483 5.51 0.000048 302s supply_trend 0.397 0.0672 5.91 0.000022 302s > print( round( coef( summary( fit3slsd[[ 3 ]]$e1w$eq[[ 2 ]], useDfSys = FALSE ) ), 302s + digits = 3 ) ) 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 49.532 12.011 4.12 0.001 302s price 0.240 0.100 2.40 0.029 302s farmPrice 0.256 0.047 5.41 0.000 302s trend 0.253 0.100 2.54 0.022 302s > 302s > print( round( coef( summary( fit3slsi[[ 4 ]]$e2 ) ), digits = 6 ) ) 302s Estimate Std. Error t value Pr(>|t|) 302s demand_(Intercept) 92.074 9.6303 9.56 0.000000 302s demand_price -0.106 0.1023 -1.04 0.305469 302s demand_income 0.200 0.0297 6.73 0.000000 302s supply_(Intercept) 68.855 12.4839 5.52 0.000004 302s supply_price 0.183 0.1189 1.54 0.132354 302s supply_farmPrice 0.120 0.0260 4.63 0.000051 302s supply_trend 0.200 0.0297 6.73 0.000000 302s > print( round( coef( summary( fit3slsi[[ 5 ]]$e2$eq[[ 1 ]] ) ), digits = 6 ) ) 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 92.074 9.6303 9.56 0.000 302s price -0.106 0.1023 -1.04 0.305 302s income 0.200 0.0297 6.73 0.000 302s > 302s > print( round( coef( summary( fit3slsd[[ 5 ]]$e3e, useDfSys = FALSE ) ), 302s + digits = 6 ) ) 302s Estimate Std. Error t value Pr(>|t|) 302s demand_(Intercept) 109.109 5.8428 18.67 0.000000 302s demand_price -0.442 0.0737 -6.00 0.000014 302s demand_income 0.369 0.0432 8.54 0.000000 302s supply_(Intercept) 57.268 10.0564 5.69 0.000033 302s supply_price 0.137 0.0705 1.95 0.069081 302s supply_farmPrice 0.269 0.0403 6.68 0.000005 302s supply_trend 0.369 0.0432 8.54 0.000000 302s > print( round( coef( summary( fit3slsd[[ 5 ]]$e3e, useDfSys = FALSE ), 302s + modified.regMat = TRUE ), digits = 6 ) ) 302s Estimate Std. Error t value Pr(>|t|) 302s C1 109.109 5.8428 18.67 NA 302s C2 -0.442 0.0737 -6.00 NA 302s C3 0.369 0.0432 8.54 NA 302s C4 57.268 10.0564 5.69 NA 302s C5 0.137 0.0705 1.95 NA 302s C6 0.269 0.0403 6.68 NA 302s > print( round( coef( summary( fit3slsd[[ 1 ]]$e3e$eq[[ 2 ]], useDfSys = FALSE ) ), 302s + digits = 6 ) ) 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 40.818 10.0564 4.06 0.000912 302s price 0.357 0.0705 5.06 0.000116 302s farmPrice 0.219 0.0403 5.45 0.000053 302s trend 0.303 0.0432 7.00 0.000003 302s > 302s > print( round( coef( summary( fit3slsi[[ 4 ]]$e3w, useDfSys = FALSE ) ), 302s + digits = 6 ) ) 302s Estimate Std. Error t value Pr(>|t|) 302s demand_(Intercept) 92.074 9.6303 9.56 0.000000 302s demand_price -0.106 0.1023 -1.04 0.312700 302s demand_income 0.200 0.0297 6.73 0.000004 302s supply_(Intercept) 68.855 12.4839 5.52 0.000047 302s supply_price 0.183 0.1189 1.54 0.142642 302s supply_farmPrice 0.120 0.0260 4.63 0.000278 302s supply_trend 0.200 0.0297 6.73 0.000005 302s > print( round( coef( summary( fit3slsi[[ 4 ]]$e3w, useDfSys = FALSE ), 302s + modified.regMat = TRUE ), digits = 6 ) ) 302s Estimate Std. Error t value Pr(>|t|) 302s C1 92.074 9.6303 9.56 NA 302s C2 -0.106 0.1023 -1.04 NA 302s C3 0.200 0.0297 6.73 NA 302s C4 68.855 12.4839 5.52 NA 302s C5 0.183 0.1189 1.54 NA 302s C6 0.120 0.0260 4.63 NA 302s > print( round( coef( summary( fit3slsi[[ 5 ]]$e3w$eq[[ 2 ]], useDfSys = FALSE ) ), 302s + digits = 6 ) ) 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 68.855 12.4839 5.52 0.000047 302s price 0.183 0.1189 1.54 0.142642 302s farmPrice 0.120 0.0260 4.63 0.000278 302s trend 0.200 0.0297 6.73 0.000005 302s > 302s > print( round( coef( summary( fit3sls[[ 1 ]]$e4 ) ), digits = 6 ) ) 302s Estimate Std. Error t value Pr(>|t|) 302s demand_(Intercept) 93.907 7.9234 11.85 0.000000 302s demand_price -0.246 0.0891 -2.76 0.009212 302s demand_income 0.324 0.0233 13.91 0.000000 302s supply_(Intercept) 49.905 8.1797 6.10 0.000001 302s supply_price 0.254 0.0891 2.85 0.007217 302s supply_farmPrice 0.229 0.0241 9.52 0.000000 302s supply_trend 0.324 0.0233 13.91 0.000000 302s > print( round( coef( summary( fit3sls[[ 2 ]]$e4$eq[[ 1 ]] ) ), digits = 6 ) ) 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 93.907 7.9234 11.85 0.00000 302s price -0.246 0.0891 -2.76 0.00921 302s income 0.324 0.0233 13.91 0.00000 302s > 302s > print( round( coef( summary( fit3slsi[[ 2 ]]$e5e ) ), digits = 6 ) ) 302s Estimate Std. Error t value Pr(>|t|) 302s demand_(Intercept) 91.390 7.3161 12.49 0.00000 302s demand_price -0.217 0.0835 -2.60 0.01365 302s demand_income 0.320 0.0168 19.07 0.00000 302s supply_(Intercept) 47.579 7.4268 6.41 0.00000 302s supply_price 0.283 0.0835 3.39 0.00174 302s supply_farmPrice 0.224 0.0168 13.36 0.00000 302s supply_trend 0.320 0.0168 19.07 0.00000 302s > print( round( coef( summary( fit3slsi[[ 2 ]]$e5e ), modified.regMat = TRUE ), 302s + digits = 6 ) ) 302s Estimate Std. Error t value Pr(>|t|) 302s C1 91.390 7.3161 12.49 0.00000 302s C2 -0.217 0.0835 -2.60 0.01365 302s C3 0.320 0.0168 19.07 0.00000 302s C4 47.579 7.4268 6.41 0.00000 302s C5 0.283 0.0835 3.39 0.00174 302s C6 0.224 0.0168 13.36 0.00000 302s > print( round( coef( summary( fit3slsi[[ 3 ]]$e5e$eq[[ 2 ]] ) ), digits = 6 ) ) 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 47.579 7.4268 6.41 0.00000 302s price 0.283 0.0835 3.39 0.00174 302s farmPrice 0.224 0.0168 13.36 0.00000 302s trend 0.320 0.0168 19.07 0.00000 302s > 302s > print( round( coef( summary( fit3sls[[ 2 ]]$e5we ) ), digits = 6 ) ) 302s Estimate Std. Error t value Pr(>|t|) 302s demand_(Intercept) 94.083 7.3058 12.88 0.00000 302s demand_price -0.248 0.0812 -3.06 0.00424 302s demand_income 0.325 0.0205 15.81 0.00000 302s supply_(Intercept) 50.019 7.5314 6.64 0.00000 302s supply_price 0.252 0.0812 3.10 0.00383 302s supply_farmPrice 0.231 0.0209 11.05 0.00000 302s supply_trend 0.325 0.0205 15.81 0.00000 302s > print( round( coef( summary( fit3sls[[ 2 ]]$e5we ), modified.regMat = TRUE ), 302s + digits = 6 ) ) 302s Estimate Std. Error t value Pr(>|t|) 302s C1 94.083 7.3058 12.88 0.00000 302s C2 -0.248 0.0812 -3.06 0.00424 302s C3 0.325 0.0205 15.81 0.00000 302s C4 50.019 7.5314 6.64 0.00000 302s C5 0.252 0.0812 3.10 0.00383 302s C6 0.231 0.0209 11.05 0.00000 302s > print( round( coef( summary( fit3sls[[ 3 ]]$e5we$eq[[ 2 ]] ) ), digits = 6 ) ) 302s Estimate Std. Error t value Pr(>|t|) 302s (Intercept) 50.019 7.5314 6.64 0.00000 302s price 0.252 0.0812 3.10 0.00383 302s farmPrice 0.231 0.0209 11.05 0.00000 302s trend 0.325 0.0205 15.81 0.00000 302s > 302s > 302s > ## *********** variance covariance matrix of the coefficients ******* 302s > print( round( vcov( fit3sls[[ 3 ]]$e1c ), digits = 5 ) ) 302s demand_(Intercept) demand_price demand_income 302s demand_(Intercept) 62.7397 -0.67342 0.04930 302s demand_price -0.6734 0.00931 -0.00264 302s demand_income 0.0493 -0.00264 0.00220 302s supply_(Intercept) 65.2708 -0.36561 -0.29198 302s supply_price -0.6979 0.00620 0.00079 302s supply_farmPrice 0.0423 -0.00227 0.00189 302s supply_trend 0.0638 -0.00342 0.00285 302s supply_(Intercept) supply_price supply_farmPrice 302s demand_(Intercept) 65.271 -0.69790 0.04230 302s demand_price -0.366 0.00620 -0.00227 302s demand_income -0.292 0.00079 0.00189 302s supply_(Intercept) 141.261 -1.08251 -0.29300 302s supply_price -1.083 0.00993 0.00080 302s supply_farmPrice -0.293 0.00080 0.00192 302s supply_trend -0.417 0.00110 0.00263 302s supply_trend 302s demand_(Intercept) 0.06383 302s demand_price -0.00342 302s demand_income 0.00285 302s supply_(Intercept) -0.41674 302s supply_price 0.00110 302s supply_farmPrice 0.00263 302s supply_trend 0.00500 302s > print( round( vcov( fit3sls[[ 4 ]]$e1c$eq[[ 2 ]] ), digits = 5 ) ) 302s (Intercept) price farmPrice trend 302s (Intercept) 141.261 -1.08251 -0.29300 -0.41674 302s price -1.083 0.00993 0.00080 0.00110 302s farmPrice -0.293 0.00080 0.00192 0.00263 302s trend -0.417 0.00110 0.00263 0.00500 302s > 302s > print( round( vcov( fit3sls[[ 4 ]]$e2 ), digits = 5 ) ) 302s demand_(Intercept) demand_price demand_income 302s demand_(Intercept) 64.2351 -0.68447 0.04535 302s demand_price -0.6845 0.00921 -0.00243 302s demand_income 0.0454 -0.00243 0.00203 302s supply_(Intercept) 67.0281 -0.42600 -0.24804 302s supply_price -0.7080 0.00641 0.00069 302s supply_farmPrice 0.0366 -0.00196 0.00164 302s supply_trend 0.0454 -0.00243 0.00203 302s supply_(Intercept) supply_price supply_farmPrice 302s demand_(Intercept) 67.028 -0.70800 0.03661 302s demand_price -0.426 0.00641 -0.00196 302s demand_income -0.248 0.00069 0.00164 302s supply_(Intercept) 134.043 -1.07653 -0.24277 302s supply_price -1.077 0.01003 0.00068 302s supply_farmPrice -0.243 0.00068 0.00163 302s supply_trend -0.248 0.00069 0.00164 302s supply_trend 302s demand_(Intercept) 0.04535 302s demand_price -0.00243 302s demand_income 0.00203 302s supply_(Intercept) -0.24804 302s supply_price 0.00069 302s supply_farmPrice 0.00164 302s supply_trend 0.00203 302s > print( round( vcov( fit3sls[[ 5 ]]$e2$eq[[ 1 ]] ), digits = 5 ) ) 302s (Intercept) price income 302s (Intercept) 64.2351 -0.68447 0.04535 302s price -0.6845 0.00921 -0.00243 302s income 0.0454 -0.00243 0.00203 302s > 302s > print( round( vcov( fit3sls[[ 5 ]]$e3e ), digits = 5 ) ) 302s demand_(Intercept) demand_price demand_income 302s demand_(Intercept) 54.6190 -0.58283 0.03940 302s demand_price -0.5828 0.00789 -0.00211 302s demand_income 0.0394 -0.00211 0.00176 302s supply_(Intercept) 55.1360 -0.34396 -0.21065 302s supply_price -0.5835 0.00527 0.00058 302s supply_farmPrice 0.0310 -0.00166 0.00139 302s supply_trend 0.0394 -0.00211 0.00176 302s supply_(Intercept) supply_price supply_farmPrice 302s demand_(Intercept) 55.136 -0.58348 0.03102 302s demand_price -0.344 0.00527 -0.00166 302s demand_income -0.211 0.00058 0.00139 302s supply_(Intercept) 108.147 -0.86360 -0.19987 302s supply_price -0.864 0.00803 0.00056 302s supply_farmPrice -0.200 0.00056 0.00134 302s supply_trend -0.211 0.00058 0.00139 302s supply_trend 302s demand_(Intercept) 0.03940 302s demand_price -0.00211 302s demand_income 0.00176 302s supply_(Intercept) -0.21065 302s supply_price 0.00058 302s supply_farmPrice 0.00139 302s supply_trend 0.00176 302s > print( round( vcov( fit3sls[[ 5 ]]$e3e, modified.regMat = TRUE ), digits = 5 ) ) 302s C1 C2 C3 C4 C5 C6 302s C1 54.6190 -0.58283 0.03940 55.136 -0.58348 0.03102 302s C2 -0.5828 0.00789 -0.00211 -0.344 0.00527 -0.00166 302s C3 0.0394 -0.00211 0.00176 -0.211 0.00058 0.00139 302s C4 55.1360 -0.34396 -0.21065 108.147 -0.86360 -0.19987 302s C5 -0.5835 0.00527 0.00058 -0.864 0.00803 0.00056 302s C6 0.0310 -0.00166 0.00139 -0.200 0.00056 0.00134 302s > print( round( vcov( fit3sls[[ 1 ]]$e3e$eq[[ 2 ]] ), digits = 5 ) ) 302s (Intercept) price farmPrice trend 302s (Intercept) 108.147 -0.86360 -0.19987 -0.21065 302s price -0.864 0.00803 0.00056 0.00058 302s farmPrice -0.200 0.00056 0.00134 0.00139 302s trend -0.211 0.00058 0.00139 0.00176 302s > 302s > print( round( vcov( fit3sls[[ 1 ]]$e4 ), digits = 5 ) ) 302s demand_(Intercept) demand_price demand_income 302s demand_(Intercept) 62.7805 -0.68439 0.06014 302s demand_price -0.6844 0.00794 -0.00113 302s demand_income 0.0601 -0.00113 0.00054 302s supply_(Intercept) 63.2287 -0.69892 0.07078 302s supply_price -0.6844 0.00794 -0.00113 302s supply_farmPrice 0.0499 -0.00087 0.00038 302s supply_trend 0.0601 -0.00113 0.00054 302s supply_(Intercept) supply_price supply_farmPrice 302s demand_(Intercept) 63.2287 -0.68439 0.04986 302s demand_price -0.6989 0.00794 -0.00087 302s demand_income 0.0708 -0.00113 0.00038 302s supply_(Intercept) 66.9073 -0.69892 0.02657 302s supply_price -0.6989 0.00794 -0.00087 302s supply_farmPrice 0.0266 -0.00087 0.00058 302s supply_trend 0.0708 -0.00113 0.00038 302s supply_trend 302s demand_(Intercept) 0.06014 302s demand_price -0.00113 302s demand_income 0.00054 302s supply_(Intercept) 0.07078 302s supply_price -0.00113 302s supply_farmPrice 0.00038 302s supply_trend 0.00054 302s > print( round( vcov( fit3sls[[ 2 ]]$e4$eq[[ 1 ]] ), digits = 5 ) ) 302s (Intercept) price income 302s (Intercept) 62.7805 -0.68439 0.06014 302s price -0.6844 0.00794 -0.00113 302s income 0.0601 -0.00113 0.00054 302s > 302s > print( round( vcov( fit3sls[[ 3 ]]$e4wSym ), digits = 5 ) ) 302s demand_(Intercept) demand_price demand_income 302s demand_(Intercept) 62.5490 -0.68436 0.06248 302s demand_price -0.6844 0.00795 -0.00113 302s demand_income 0.0625 -0.00113 0.00052 302s supply_(Intercept) 62.9766 -0.69799 0.07241 302s supply_price -0.6844 0.00795 -0.00113 302s supply_farmPrice 0.0522 -0.00088 0.00037 302s supply_trend 0.0625 -0.00113 0.00052 302s supply_(Intercept) supply_price supply_farmPrice 302s demand_(Intercept) 62.9766 -0.68436 0.05220 302s demand_price -0.6980 0.00795 -0.00088 302s demand_income 0.0724 -0.00113 0.00037 302s supply_(Intercept) 66.4588 -0.69799 0.03007 302s supply_price -0.6980 0.00795 -0.00088 302s supply_farmPrice 0.0301 -0.00088 0.00056 302s supply_trend 0.0724 -0.00113 0.00037 302s supply_trend 302s demand_(Intercept) 0.06248 302s demand_price -0.00113 302s demand_income 0.00052 302s supply_(Intercept) 0.07241 302s supply_price -0.00113 302s supply_farmPrice 0.00037 302s supply_trend 0.00052 302s > print( round( vcov( fit3sls[[ 4 ]]$e4wSym$eq[[ 1 ]] ), digits = 5 ) ) 302s (Intercept) price income 302s (Intercept) 62.5490 -0.68436 0.06248 302s price -0.6844 0.00795 -0.00113 302s income 0.0625 -0.00113 0.00052 302s > 302s > print( round( vcov( fit3sls[[ 2 ]]$e5e ), digits = 5 ) ) 302s demand_(Intercept) demand_price demand_income 302s demand_(Intercept) 53.5147 -0.57537 0.04304 302s demand_price -0.5754 0.00659 -0.00085 302s demand_income 0.0430 -0.00085 0.00044 302s supply_(Intercept) 53.9493 -0.58881 0.05259 302s supply_price -0.5754 0.00659 -0.00085 302s supply_farmPrice 0.0345 -0.00063 0.00029 302s supply_trend 0.0430 -0.00085 0.00044 302s supply_(Intercept) supply_price supply_farmPrice 302s demand_(Intercept) 53.9493 -0.57537 0.03449 302s demand_price -0.5888 0.00659 -0.00063 302s demand_income 0.0526 -0.00085 0.00029 302s supply_(Intercept) 57.0063 -0.58881 0.01639 302s supply_price -0.5888 0.00659 -0.00063 302s supply_farmPrice 0.0164 -0.00063 0.00045 302s supply_trend 0.0526 -0.00085 0.00029 302s supply_trend 302s demand_(Intercept) 0.04304 302s demand_price -0.00085 302s demand_income 0.00044 302s supply_(Intercept) 0.05259 302s supply_price -0.00085 302s supply_farmPrice 0.00029 302s supply_trend 0.00044 302s > print( round( vcov( fit3sls[[ 2 ]]$e5e, modified.regMat = TRUE ), digits = 5 ) ) 302s C1 C2 C3 C4 C5 C6 302s C1 53.5147 -0.57537 0.04304 53.9493 -0.57537 0.03449 302s C2 -0.5754 0.00659 -0.00085 -0.5888 0.00659 -0.00063 302s C3 0.0430 -0.00085 0.00044 0.0526 -0.00085 0.00029 302s C4 53.9493 -0.58881 0.05259 57.0063 -0.58881 0.01639 302s C5 -0.5754 0.00659 -0.00085 -0.5888 0.00659 -0.00063 302s C6 0.0345 -0.00063 0.00029 0.0164 -0.00063 0.00045 302s > print( round( vcov( fit3sls[[ 3 ]]$e5e$eq[[ 2 ]] ), digits = 5 ) ) 302s (Intercept) price farmPrice trend 302s (Intercept) 57.0063 -0.58881 0.01639 0.05259 302s price -0.5888 0.00659 -0.00063 -0.00085 302s farmPrice 0.0164 -0.00063 0.00045 0.00029 302s trend 0.0526 -0.00085 0.00029 0.00044 302s > 302s > print( round( vcov( fit3slsi[[ 4 ]]$e1e ), digits = 5 ) ) 302s demand_(Intercept) demand_price demand_income 302s demand_(Intercept) 53.3287 -0.57241 0.04191 302s demand_price -0.5724 0.00791 -0.00225 302s demand_income 0.0419 -0.00225 0.00187 302s supply_(Intercept) 60.8329 -0.34075 -0.27213 302s supply_price -0.6504 0.00578 0.00074 302s supply_farmPrice 0.0394 -0.00211 0.00176 302s supply_trend 0.0595 -0.00319 0.00266 302s supply_(Intercept) supply_price supply_farmPrice 302s demand_(Intercept) 60.833 -0.65044 0.03942 302s demand_price -0.341 0.00578 -0.00211 302s demand_income -0.272 0.00074 0.00176 302s supply_(Intercept) 129.860 -0.99616 -0.26688 302s supply_price -0.996 0.00915 0.00073 302s supply_farmPrice -0.267 0.00073 0.00173 302s supply_trend -0.396 0.00107 0.00255 302s supply_trend 302s demand_(Intercept) 0.05949 302s demand_price -0.00319 302s demand_income 0.00266 302s supply_(Intercept) -0.39621 302s supply_price 0.00107 302s supply_farmPrice 0.00255 302s supply_trend 0.00411 302s > print( round( vcov( fit3slsi[[ 3 ]]$e1e$eq[[ 1 ]] ), digits = 5 ) ) 302s (Intercept) price income 302s (Intercept) 53.3287 -0.57241 0.04191 302s price -0.5724 0.00791 -0.00225 302s income 0.0419 -0.00225 0.00187 302s > 302s > print( round( vcov( fit3slsi[[ 5 ]]$e1we ), digits = 5 ) ) 302s demand_(Intercept) demand_price demand_income 302s demand_(Intercept) 53.3287 -0.57241 0.04191 302s demand_price -0.5724 0.00791 -0.00225 302s demand_income 0.0419 -0.00225 0.00187 302s supply_(Intercept) 60.8329 -0.34075 -0.27213 302s supply_price -0.6504 0.00578 0.00074 302s supply_farmPrice 0.0394 -0.00211 0.00176 302s supply_trend 0.0595 -0.00319 0.00266 302s supply_(Intercept) supply_price supply_farmPrice 302s demand_(Intercept) 60.833 -0.65044 0.03942 302s demand_price -0.341 0.00578 -0.00211 302s demand_income -0.272 0.00074 0.00176 302s supply_(Intercept) 129.860 -0.99616 -0.26688 302s supply_price -0.996 0.00915 0.00073 302s supply_farmPrice -0.267 0.00073 0.00173 302s supply_trend -0.396 0.00107 0.00255 302s supply_trend 302s demand_(Intercept) 0.05949 302s demand_price -0.00319 302s demand_income 0.00266 302s supply_(Intercept) -0.39621 302s supply_price 0.00107 302s supply_farmPrice 0.00255 302s supply_trend 0.00411 302s > print( round( vcov( fit3slsi[[ 1 ]]$e1we$eq[[ 2 ]] ), digits = 5 ) ) 302s (Intercept) price farmPrice trend 302s (Intercept) 129.860 -0.99616 -0.26688 -0.39621 302s price -0.996 0.00915 0.00073 0.00107 302s farmPrice -0.267 0.00073 0.00173 0.00255 302s trend -0.396 0.00107 0.00255 0.00411 302s > 302s > print( round( vcov( fit3slsi[[ 5 ]]$e2e ), digits = 5 ) ) 302s demand_(Intercept) demand_price demand_income 302s demand_(Intercept) 79.5917 -0.81281 0.02003 302s demand_price -0.8128 0.00917 -0.00107 302s demand_income 0.0200 -0.00107 0.00090 302s supply_(Intercept) 90.3437 -0.79178 -0.11134 302s supply_price -0.9184 0.00888 0.00031 302s supply_farmPrice 0.0165 -0.00088 0.00074 302s supply_trend 0.0200 -0.00107 0.00090 302s supply_(Intercept) supply_price supply_farmPrice 302s demand_(Intercept) 90.3437 -0.91836 0.01646 302s demand_price -0.7918 0.00888 -0.00088 302s demand_income -0.1113 0.00031 0.00074 302s supply_(Intercept) 124.3894 -1.13680 -0.09494 302s supply_price -1.1368 0.01108 0.00026 302s supply_farmPrice -0.0949 0.00026 0.00063 302s supply_trend -0.1113 0.00031 0.00074 302s supply_trend 302s demand_(Intercept) 0.02003 302s demand_price -0.00107 302s demand_income 0.00090 302s supply_(Intercept) -0.11134 302s supply_price 0.00031 302s supply_farmPrice 0.00074 302s supply_trend 0.00090 302s > print( round( vcov( fit3slsi[[ 4 ]]$e2e$eq[[ 2 ]] ), digits = 5 ) ) 302s (Intercept) price farmPrice trend 302s (Intercept) 124.3894 -1.13680 -0.09494 -0.11134 302s price -1.1368 0.01108 0.00026 0.00031 302s farmPrice -0.0949 0.00026 0.00063 0.00074 302s trend -0.1113 0.00031 0.00074 0.00090 302s > 302s > print( round( vcov( fit3slsi[[ 1 ]]$e3 ), digits = 5 ) ) 302s demand_(Intercept) demand_price demand_income 302s demand_(Intercept) 92.7431 -0.94355 0.01968 302s demand_price -0.9435 0.01046 -0.00105 302s demand_income 0.0197 -0.00105 0.00088 302s supply_(Intercept) 110.7701 -0.99345 -0.11331 302s supply_price -1.1222 0.01091 0.00031 302s supply_farmPrice 0.0168 -0.00090 0.00075 302s supply_trend 0.0197 -0.00105 0.00088 302s supply_(Intercept) supply_price supply_farmPrice 302s demand_(Intercept) 110.770 -1.12223 0.01680 302s demand_price -0.993 0.01091 -0.00090 302s demand_income -0.113 0.00031 0.00075 302s supply_(Intercept) 155.849 -1.44407 -0.10125 302s supply_price -1.444 0.01413 0.00028 302s supply_farmPrice -0.101 0.00028 0.00067 302s supply_trend -0.113 0.00031 0.00075 302s supply_trend 302s demand_(Intercept) 0.01968 302s demand_price -0.00105 302s demand_income 0.00088 302s supply_(Intercept) -0.11331 302s supply_price 0.00031 302s supply_farmPrice 0.00075 302s supply_trend 0.00088 302s > print( round( vcov( fit3slsi[[ 1 ]]$e3, modified.regMat = TRUE ), digits = 5 ) ) 302s C1 C2 C3 C4 C5 C6 302s C1 92.7431 -0.94355 0.01968 110.770 -1.12223 0.01680 302s C2 -0.9435 0.01046 -0.00105 -0.993 0.01091 -0.00090 302s C3 0.0197 -0.00105 0.00088 -0.113 0.00031 0.00075 302s C4 110.7701 -0.99345 -0.11331 155.849 -1.44407 -0.10125 302s C5 -1.1222 0.01091 0.00031 -1.444 0.01413 0.00028 302s C6 0.0168 -0.00090 0.00075 -0.101 0.00028 0.00067 302s > print( round( vcov( fit3slsi[[ 5 ]]$e3$eq[[ 1 ]] ), digits = 5 ) ) 302s (Intercept) price income 302s (Intercept) 92.7431 -0.94355 0.01968 302s price -0.9435 0.01046 -0.00105 302s income 0.0197 -0.00105 0.00088 302s > 302s > print( round( vcov( fit3slsi[[ 2 ]]$e4e ), digits = 5 ) ) 302s demand_(Intercept) demand_price demand_income 302s demand_(Intercept) 53.5249 -0.60193 0.07023 302s demand_price -0.6019 0.00697 -0.00098 302s demand_income 0.0702 -0.00098 0.00028 302s supply_(Intercept) 53.7695 -0.60749 0.07383 302s supply_price -0.6019 0.00697 -0.00098 302s supply_farmPrice 0.0611 -0.00082 0.00022 302s supply_trend 0.0702 -0.00098 0.00028 302s supply_(Intercept) supply_price supply_farmPrice 302s demand_(Intercept) 53.7695 -0.60193 0.06114 302s demand_price -0.6075 0.00697 -0.00082 302s demand_income 0.0738 -0.00098 0.00022 302s supply_(Intercept) 55.1575 -0.60749 0.05283 302s supply_price -0.6075 0.00697 -0.00082 302s supply_farmPrice 0.0528 -0.00082 0.00028 302s supply_trend 0.0738 -0.00098 0.00022 302s supply_trend 302s demand_(Intercept) 0.07023 302s demand_price -0.00098 302s demand_income 0.00028 302s supply_(Intercept) 0.07383 302s supply_price -0.00098 302s supply_farmPrice 0.00022 302s supply_trend 0.00028 302s > print( round( vcov( fit3slsi[[ 1 ]]$e4e$eq[[ 2 ]] ), digits = 5 ) ) 302s (Intercept) price farmPrice trend 302s (Intercept) 55.1575 -0.60749 0.05283 0.07383 302s price -0.6075 0.00697 -0.00082 -0.00098 302s farmPrice 0.0528 -0.00082 0.00028 0.00022 302s trend 0.0738 -0.00098 0.00022 0.00028 302s > 302s > print( round( vcov( fit3slsi[[ 3 ]]$e5 ), digits = 5 ) ) 302s demand_(Intercept) demand_price demand_income 302s demand_(Intercept) 62.6857 -0.71803 0.09573 302s demand_price -0.7180 0.00846 -0.00132 302s demand_income 0.0957 -0.00132 0.00037 302s supply_(Intercept) 62.7317 -0.72119 0.09909 302s supply_price -0.7180 0.00846 -0.00132 302s supply_farmPrice 0.0863 -0.00115 0.00030 302s supply_trend 0.0957 -0.00132 0.00037 302s supply_(Intercept) supply_price supply_farmPrice 302s demand_(Intercept) 62.7317 -0.71803 0.08635 302s demand_price -0.7212 0.00846 -0.00115 302s demand_income 0.0991 -0.00132 0.00030 302s supply_(Intercept) 64.1668 -0.72119 0.07539 302s supply_price -0.7212 0.00846 -0.00115 302s supply_farmPrice 0.0754 -0.00115 0.00038 302s supply_trend 0.0991 -0.00132 0.00030 302s supply_trend 302s demand_(Intercept) 0.09573 302s demand_price -0.00132 302s demand_income 0.00037 302s supply_(Intercept) 0.09909 302s supply_price -0.00132 302s supply_farmPrice 0.00030 302s supply_trend 0.00037 302s > print( round( vcov( fit3slsi[[ 3 ]]$e5, modified.regMat = TRUE ), digits = 5 ) ) 302s C1 C2 C3 C4 C5 C6 302s C1 62.6857 -0.71803 0.09573 62.7317 -0.71803 0.08635 302s C2 -0.7180 0.00846 -0.00132 -0.7212 0.00846 -0.00115 302s C3 0.0957 -0.00132 0.00037 0.0991 -0.00132 0.00030 302s C4 62.7317 -0.72119 0.09909 64.1668 -0.72119 0.07539 302s C5 -0.7180 0.00846 -0.00132 -0.7212 0.00846 -0.00115 302s C6 0.0863 -0.00115 0.00030 0.0754 -0.00115 0.00038 302s > print( round( vcov( fit3slsi[[ 2 ]]$e5$eq[[ 1 ]] ), digits = 5 ) ) 302s (Intercept) price income 302s (Intercept) 62.6857 -0.71803 0.09573 302s price -0.7180 0.00846 -0.00132 302s income 0.0957 -0.00132 0.00037 302s > 302s > print( round( vcov( fit3slsi[[ 5 ]]$e5w ), digits = 5 ) ) 302s demand_(Intercept) demand_price demand_income 302s demand_(Intercept) 107.334 -1.39936 0.34281 302s demand_price -1.399 0.01904 -0.00518 302s demand_income 0.343 -0.00518 0.00179 302s supply_(Intercept) 95.422 -1.22389 0.29205 302s supply_price -1.399 0.01904 -0.00518 302s supply_farmPrice 0.439 -0.00648 0.00214 302s supply_trend 0.343 -0.00518 0.00179 302s supply_(Intercept) supply_price supply_farmPrice 302s demand_(Intercept) 95.422 -1.39936 0.43918 302s demand_price -1.224 0.01904 -0.00648 302s demand_income 0.292 -0.00518 0.00214 302s supply_(Intercept) 92.381 -1.22389 0.30881 302s supply_price -1.224 0.01904 -0.00648 302s supply_farmPrice 0.309 -0.00648 0.00328 302s supply_trend 0.292 -0.00518 0.00214 302s supply_trend 302s demand_(Intercept) 0.34281 302s demand_price -0.00518 302s demand_income 0.00179 302s supply_(Intercept) 0.29205 302s supply_price -0.00518 302s supply_farmPrice 0.00214 302s supply_trend 0.00179 302s > print( round( vcov( fit3slsi[[ 5 ]]$e5w, modified.regMat = TRUE ), digits = 5 ) ) 302s C1 C2 C3 C4 C5 C6 302s C1 107.334 -1.39936 0.34281 95.422 -1.39936 0.43918 302s C2 -1.399 0.01904 -0.00518 -1.224 0.01904 -0.00648 302s C3 0.343 -0.00518 0.00179 0.292 -0.00518 0.00214 302s C4 95.422 -1.22389 0.29205 92.381 -1.22389 0.30881 302s C5 -1.399 0.01904 -0.00518 -1.224 0.01904 -0.00648 302s C6 0.439 -0.00648 0.00214 0.309 -0.00648 0.00328 302s > print( round( vcov( fit3slsi[[ 4 ]]$e5w$eq[[ 1 ]] ), digits = 5 ) ) 302s (Intercept) price income 302s (Intercept) 62.6858 -0.71803 0.09573 302s price -0.7180 0.00846 -0.00132 302s income 0.0957 -0.00132 0.00037 302s > 302s > print( round( vcov( fit3slsd[[ 5 ]]$e1c ), digits = 5 ) ) 302s demand_(Intercept) demand_price demand_income 302s demand_(Intercept) 124.179 -1.51767 0.28519 302s demand_price -1.518 0.02098 -0.00595 302s demand_income 0.285 -0.00595 0.00318 302s supply_(Intercept) 45.831 -0.16114 -0.30261 302s supply_price -0.564 0.00477 0.00089 302s supply_farmPrice 0.157 -0.00365 0.00213 302s supply_trend -0.416 0.00351 0.00066 302s supply_(Intercept) supply_price supply_farmPrice 302s demand_(Intercept) 45.831 -0.56422 0.15696 302s demand_price -0.161 0.00477 -0.00365 302s demand_income -0.303 0.00089 0.00213 302s supply_(Intercept) 132.389 -0.93831 -0.33973 302s supply_price -0.938 0.00791 0.00115 302s supply_farmPrice -0.340 0.00115 0.00221 302s supply_trend -0.515 0.00349 0.00108 302s supply_trend 302s demand_(Intercept) -0.41585 302s demand_price 0.00351 302s demand_income 0.00066 302s supply_(Intercept) -0.51541 302s supply_price 0.00349 302s supply_farmPrice 0.00108 302s supply_trend 0.00585 302s > print( round( vcov( fit3slsd[[ 2 ]]$e1c$eq[[ 2 ]] ), digits = 5 ) ) 302s (Intercept) price farmPrice trend 302s (Intercept) 136.580 -1.06234 -0.24479 -0.60682 302s price -0.994 0.00955 -0.00011 0.00471 302s farmPrice -0.334 0.00098 0.00234 0.00096 302s trend -0.438 0.00119 0.00284 0.00415 302s > 302s > print( round( vcov( fit3slsd[[ 1 ]]$e2 ), digits = 5 ) ) 302s demand_(Intercept) demand_price demand_income 302s demand_(Intercept) 40.2908 -0.42351 0.02315 302s demand_price -0.4235 0.00660 -0.00242 302s demand_income 0.0232 -0.00242 0.00225 302s supply_(Intercept) 23.1539 0.17811 -0.41781 302s supply_price -0.2648 0.00059 0.00211 302s supply_farmPrice 0.0342 -0.00220 0.00190 302s supply_trend 0.0232 -0.00242 0.00225 302s supply_(Intercept) supply_price supply_farmPrice 302s demand_(Intercept) 23.154 -0.26482 0.03423 302s demand_price 0.178 0.00059 -0.00220 302s demand_income -0.418 0.00211 0.00190 302s supply_(Intercept) 125.488 -0.81757 -0.40378 302s supply_price -0.818 0.00616 0.00186 302s supply_farmPrice -0.404 0.00186 0.00205 302s supply_trend -0.418 0.00211 0.00190 302s supply_trend 302s demand_(Intercept) 0.02315 302s demand_price -0.00242 302s demand_income 0.00225 302s supply_(Intercept) -0.41781 302s supply_price 0.00211 302s supply_farmPrice 0.00190 302s supply_trend 0.00225 302s > print( round( vcov( fit3slsd[[ 3 ]]$e2$eq[[ 1 ]] ), digits = 5 ) ) 302s (Intercept) price income 302s (Intercept) 99.763 -1.2027 0.21239 302s price -1.203 0.0168 -0.00490 302s income 0.212 -0.0049 0.00285 302s > 302s > print( round( vcov( fit3slsd[[ 5 ]]$e2we ), digits = 5 ) ) 302s demand_(Intercept) demand_price demand_income 302s demand_(Intercept) 34.9080 -0.36232 0.01530 302s demand_price -0.3623 0.00556 -0.00199 302s demand_income 0.0153 -0.00199 0.00188 302s supply_(Intercept) 20.3293 0.13409 -0.34409 302s supply_price -0.2272 0.00057 0.00174 302s supply_farmPrice 0.0249 -0.00176 0.00155 302s supply_trend 0.0153 -0.00199 0.00188 302s supply_(Intercept) supply_price supply_farmPrice 302s demand_(Intercept) 20.329 -0.22716 0.02494 302s demand_price 0.134 0.00057 -0.00176 302s demand_income -0.344 0.00174 0.00155 302s supply_(Intercept) 102.201 -0.66897 -0.32522 302s supply_price -0.669 0.00505 0.00150 302s supply_farmPrice -0.325 0.00150 0.00164 302s supply_trend -0.344 0.00174 0.00155 302s supply_trend 302s demand_(Intercept) 0.01530 302s demand_price -0.00199 302s demand_income 0.00188 302s supply_(Intercept) -0.34409 302s supply_price 0.00174 302s supply_farmPrice 0.00155 302s supply_trend 0.00188 302s > print( round( vcov( fit3slsd[[ 3 ]]$e2we$eq[[ 1 ]] ), digits = 5 ) ) 302s (Intercept) price income 302s (Intercept) 83.743 -1.0065 0.17519 302s price -1.006 0.0141 -0.00410 302s income 0.175 -0.0041 0.00241 302s > 302s > print( round( vcov( fit3slsd[[ 2 ]]$e3 ), digits = 5 ) ) 302s demand_(Intercept) demand_price demand_income 302s demand_(Intercept) 155.228 -2.21373 0.68055 302s demand_price -1.929 0.03005 -0.01103 302s demand_income 0.389 -0.00812 0.00434 302s supply_(Intercept) 120.424 -1.33693 0.13854 302s supply_price -1.546 0.02054 -0.00522 302s supply_farmPrice 0.314 -0.00655 0.00350 302s supply_trend 0.389 -0.00812 0.00434 302s supply_(Intercept) supply_price supply_farmPrice 302s demand_(Intercept) -25.183 -0.42614 0.63002 302s demand_price 0.811 0.00271 -0.01000 302s demand_income -0.572 0.00159 0.00380 302s supply_(Intercept) 84.582 -0.95409 0.10043 302s supply_price -0.279 0.00796 -0.00478 302s supply_farmPrice -0.521 0.00147 0.00350 302s supply_trend -0.572 0.00159 0.00380 302s supply_trend 302s demand_(Intercept) 0.68055 302s demand_price -0.01103 302s demand_income 0.00434 302s supply_(Intercept) 0.13854 302s supply_price -0.00522 302s supply_farmPrice 0.00350 302s supply_trend 0.00434 302s > print( round( vcov( fit3slsd[[ 2 ]]$e3, modified.regMat = TRUE ), digits = 5 ) ) 302s C1 C2 C3 C4 C5 C6 302s C1 155.228 -2.21373 0.68055 -25.183 -0.42614 0.63002 302s C2 -1.929 0.03005 -0.01103 0.811 0.00271 -0.01000 302s C3 0.389 -0.00812 0.00434 -0.572 0.00159 0.00380 302s C4 120.424 -1.33693 0.13854 84.582 -0.95409 0.10043 302s C5 -1.546 0.02054 -0.00522 -0.279 0.00796 -0.00478 302s C6 0.314 -0.00655 0.00350 -0.521 0.00147 0.00350 302s > print( round( vcov( fit3slsd[[ 4 ]]$e3$eq[[ 2 ]] ), digits = 5 ) ) 302s (Intercept) price farmPrice trend 302s (Intercept) 149.704 -1.13641 -0.33425 -0.32676 302s price -1.136 0.01036 0.00094 0.00091 302s farmPrice -0.334 0.00094 0.00225 0.00216 302s trend -0.327 0.00091 0.00216 0.00259 302s > 302s > print( round( vcov( fit3slsd[[ 3 ]]$e4 ), digits = 5 ) ) 302s demand_(Intercept) demand_price demand_income 302s demand_(Intercept) 105.016 -1.17085 0.12591 302s demand_price -1.171 0.01356 -0.00191 302s demand_income 0.126 -0.00191 0.00066 302s supply_(Intercept) 106.127 -1.19320 0.13778 302s supply_price -1.171 0.01356 -0.00191 302s supply_farmPrice 0.102 -0.00148 0.00047 302s supply_trend 0.126 -0.00191 0.00066 302s supply_(Intercept) supply_price supply_farmPrice 302s demand_(Intercept) 106.1266 -1.17085 0.10227 302s demand_price -1.1932 0.01356 -0.00148 302s demand_income 0.1378 -0.00191 0.00047 302s supply_(Intercept) 110.0305 -1.19320 0.08453 302s supply_price -1.1932 0.01356 -0.00148 302s supply_farmPrice 0.0845 -0.00148 0.00061 302s supply_trend 0.1378 -0.00191 0.00047 302s supply_trend 302s demand_(Intercept) 0.12591 302s demand_price -0.00191 302s demand_income 0.00066 302s supply_(Intercept) 0.13778 302s supply_price -0.00191 302s supply_farmPrice 0.00047 302s supply_trend 0.00066 302s > print( round( vcov( fit3slsd[[ 5 ]]$e4$eq[[ 1 ]] ), digits = 5 ) ) 302s (Intercept) price income 302s (Intercept) 28.9118 -0.25481 -0.03319 302s price -0.2548 0.00254 0.00001 302s income -0.0332 0.00001 0.00033 302s > 302s > print( round( vcov( fit3slsd[[ 4 ]]$e5e ), digits = 5 ) ) 302s demand_(Intercept) demand_price demand_income 302s demand_(Intercept) 57.3878 -0.60414 0.03280 302s demand_price -0.6041 0.00675 -0.00073 302s demand_income 0.0328 -0.00073 0.00041 302s supply_(Intercept) 57.4828 -0.61352 0.04167 302s supply_price -0.6041 0.00675 -0.00073 302s supply_farmPrice 0.0288 -0.00056 0.00028 302s supply_trend 0.0328 -0.00073 0.00041 302s supply_(Intercept) supply_price supply_farmPrice 302s demand_(Intercept) 57.4828 -0.60414 0.02879 302s demand_price -0.6135 0.00675 -0.00056 302s demand_income 0.0417 -0.00073 0.00028 302s supply_(Intercept) 59.8263 -0.61352 0.01389 302s supply_price -0.6135 0.00675 -0.00056 302s supply_farmPrice 0.0139 -0.00056 0.00041 302s supply_trend 0.0417 -0.00073 0.00028 302s supply_trend 302s demand_(Intercept) 0.03280 302s demand_price -0.00073 302s demand_income 0.00041 302s supply_(Intercept) 0.04167 302s supply_price -0.00073 302s supply_farmPrice 0.00028 302s supply_trend 0.00041 302s > print( round( vcov( fit3slsd[[ 4 ]]$e5e, modified.regMat = TRUE ), digits = 5 ) ) 302s C1 C2 C3 C4 C5 C6 302s C1 57.3878 -0.60414 0.03280 57.4828 -0.60414 0.02879 302s C2 -0.6041 0.00675 -0.00073 -0.6135 0.00675 -0.00056 302s C3 0.0328 -0.00073 0.00041 0.0417 -0.00073 0.00028 302s C4 57.4828 -0.61352 0.04167 59.8263 -0.61352 0.01389 302s C5 -0.6041 0.00675 -0.00073 -0.6135 0.00675 -0.00056 302s C6 0.0288 -0.00056 0.00028 0.0139 -0.00056 0.00041 302s > print( round( vcov( fit3slsd[[ 1 ]]$e5e$eq[[ 2 ]] ), digits = 5 ) ) 302s (Intercept) price farmPrice trend 302s (Intercept) 24.9502 -0.21066 -0.03490 -0.02530 302s price -0.2107 0.00210 0.00000 0.00004 302s farmPrice -0.0349 0.00000 0.00034 0.00018 302s trend -0.0253 0.00004 0.00018 0.00028 302s > 302s > 302s > ## *********** confidence intervals of coefficients ************* 302s > print( confint( fit3sls[[ 1 ]]$e1c, useDfSys = TRUE ) ) 302s 2.5 % 97.5 % 302s demand_(Intercept) 78.518 110.748 302s demand_price -0.440 -0.047 302s demand_income 0.218 0.409 302s supply_(Intercept) 28.106 76.468 302s supply_price 0.025 0.431 302s supply_farmPrice 0.138 0.316 302s supply_trend 0.221 0.509 302s > print( confint( fit3sls[[ 1 ]]$e1c$eq[[ 1 ]], level = 0.9, useDfSys = TRUE ) ) 302s 5 % 95 % 302s (Intercept) 81.228 108.038 302s price -0.407 -0.080 302s income 0.235 0.393 302s > 302s > print( confint( fit3sls[[ 2 ]]$e2e, level = 0.9, useDfSys = TRUE ) ) 302s 5 % 95 % 302s demand_(Intercept) 79.254 109.293 302s demand_price -0.405 -0.044 302s demand_income 0.213 0.383 302s supply_(Intercept) 34.318 76.586 302s supply_price 0.039 0.403 302s supply_farmPrice 0.135 0.284 302s supply_trend 0.213 0.383 302s > print( confint( fit3sls[[ 2 ]]$e2e$eq[[ 2 ]], level = 0.99, useDfSys = TRUE ) ) 302s 0.5 % 99.5 % 302s (Intercept) 27.079 83.826 302s price -0.024 0.465 302s farmPrice 0.110 0.309 302s trend 0.183 0.412 302s > 302s > print( confint( fit3sls[[ 3 ]]$e3, level = 0.99 ) ) 302s 0.5 % 99.5 % 302s demand_(Intercept) 77.934 110.509 302s demand_price -0.417 -0.026 302s demand_income 0.204 0.387 302s supply_(Intercept) 32.432 79.489 302s supply_price 0.016 0.423 302s supply_farmPrice 0.124 0.288 302s supply_trend 0.204 0.387 302s > print( confint( fit3sls[[ 3 ]]$e3$eq[[ 1 ]], level = 0.5 ) ) 302s 25 % 75 % 302s (Intercept) 88.757 99.686 302s price -0.287 -0.156 302s income 0.265 0.326 302s > 302s > print( confint( fit3sls[[ 5 ]]$e3we, level = 0.99 ) ) 302s 0.5 % 99.5 % 302s demand_(Intercept) 79.280 109.202 302s demand_price -0.402 -0.043 302s demand_income 0.212 0.381 302s supply_(Intercept) 34.570 76.815 302s supply_price 0.038 0.402 302s supply_farmPrice 0.134 0.282 302s supply_trend 0.212 0.381 302s > print( confint( fit3sls[[ 5 ]]$e3we$eq[[ 1 ]], level = 0.5 ) ) 302s 25 % 75 % 302s (Intercept) 89.222 99.260 302s price -0.283 -0.162 302s income 0.268 0.325 302s > 302s > print( confint( fit3sls[[ 4 ]]$e4e, level = 0.5, useDfSys = TRUE ) ) 302s 25 % 75 % 302s demand_(Intercept) 79.319 109.021 302s demand_price -0.414 -0.085 302s demand_income 0.282 0.367 302s supply_(Intercept) 34.758 65.413 302s supply_price 0.086 0.415 302s supply_farmPrice 0.188 0.274 302s supply_trend 0.282 0.367 302s > print( confint( fit3sls[[ 4 ]]$e4e$eq[[ 2 ]], level = 0.25, useDfSys = TRUE ) ) 302s 37.5 % 62.5 % 302s (Intercept) 47.661 52.510 302s price 0.224 0.277 302s farmPrice 0.224 0.238 302s trend 0.318 0.331 302s > 302s > print( confint( fit3sls[[ 5 ]]$e5, level = 0.25 ) ) 302s 37.5 % 62.5 % 302s demand_(Intercept) 75.213 107.384 302s demand_price -0.630 -0.268 302s demand_income 0.512 0.606 302s supply_(Intercept) -18.445 14.766 302s supply_price 0.370 0.732 302s supply_farmPrice 0.384 0.481 302s supply_trend 0.512 0.606 302s > print( confint( fit3sls[[ 5 ]]$e5$eq[[ 1 ]], level = 0.975 ) ) 302s 1.3 % 98.8 % 302s (Intercept) 72.742 109.855 302s price -0.658 -0.241 302s income 0.505 0.614 302s > 302s > print( confint( fit3slsi[[ 2 ]]$e3e, level = 0.975, useDfSys = TRUE ) ) 302s 1.3 % 98.8 % 302s demand_(Intercept) 73.905 110.166 302s demand_price -0.299 0.090 302s demand_income 0.137 0.259 302s supply_(Intercept) 45.617 90.949 302s supply_price -0.029 0.399 302s supply_farmPrice 0.073 0.175 302s supply_trend 0.137 0.259 302s > print( confint( fit3slsi[[ 2 ]]$e3e$eq[[ 1 ]], level = 0.999, useDfSys = TRUE ) ) 302s 0.1 % 100 % 302s (Intercept) 59.912 124.159 302s price -0.449 0.241 302s income 0.090 0.306 302s > 302s > print( confint( fit3slsi[[ 1 ]]$e5w, level = 0.975, useDfSys = TRUE ) ) 302s 1.3 % 98.8 % 302s demand_(Intercept) 74.084 106.230 302s demand_price -0.387 -0.014 302s demand_income 0.277 0.355 302s supply_(Intercept) 30.219 62.743 302s supply_price 0.113 0.486 302s supply_farmPrice 0.179 0.259 302s supply_trend 0.277 0.355 302s > print( confint( fit3slsi[[ 1 ]]$e5w$eq[[ 1 ]], level = 0.999, useDfSys = TRUE ) ) 302s 0.1 % 100 % 302s (Intercept) 61.724 118.590 302s price -0.531 0.130 302s income 0.247 0.385 302s > 302s > print( confint( fit3slsd[[ 3 ]]$e4, level = 0.999 ) ) 302s 0.1 % 100 % 302s demand_(Intercept) 72.590 114.198 302s demand_price -0.457 0.016 302s demand_income 0.251 0.356 302s supply_(Intercept) 27.716 70.305 302s supply_price 0.043 0.516 302s supply_farmPrice 0.165 0.265 302s supply_trend 0.251 0.356 302s > print( confint( fit3slsd[[ 3 ]]$e4$eq[[ 2 ]] ) ) 302s 2.5 % 97.5 % 302s (Intercept) 27.716 70.305 302s price 0.043 0.516 302s farmPrice 0.165 0.265 302s trend 0.251 0.356 302s > 302s > print( confint( fit3slsd[[ 2 ]]$e4w, level = 0.999 ) ) 302s 0.1 % 100 % 302s demand_(Intercept) 120.616 166.320 302s demand_price -1.063 -0.578 302s demand_income 0.371 0.439 302s supply_(Intercept) 77.414 123.333 302s supply_price -0.563 -0.078 302s supply_farmPrice 0.253 0.333 302s supply_trend 0.371 0.439 302s > print( confint( fit3slsd[[ 2 ]]$e4w$eq[[ 2 ]] ) ) 302s 2.5 % 97.5 % 302s (Intercept) 77.414 123.333 302s price -0.563 -0.078 302s farmPrice 0.253 0.333 302s trend 0.371 0.439 302s > 302s > 302s > ## *********** fitted values ************* 302s > print( fitted( fit3sls[[ 2 ]]$e1c ) ) 302s demand supply 302s 1 97.6 97.8 302s 2 99.9 99.3 302s 3 99.8 99.5 302s 4 100.0 99.9 302s 5 102.1 101.7 302s 6 102.0 101.8 302s 7 102.4 101.9 302s 8 103.0 104.1 302s 9 101.5 102.3 302s 10 100.3 99.6 302s 11 95.5 95.9 302s 12 94.7 94.8 302s 13 96.1 96.6 302s 14 99.0 98.4 302s 15 103.8 102.7 302s 16 103.7 104.4 302s 17 103.8 103.3 302s 18 102.1 103.6 302s 19 103.6 103.6 302s 20 106.9 106.6 302s > print( fitted( fit3sls[[ 2 ]]$e1c$eq[[ 1 ]] ) ) 302s 1 2 3 4 5 6 7 8 9 10 11 12 13 302s 97.6 99.9 99.8 100.0 102.1 102.0 102.4 103.0 101.5 100.3 95.5 94.7 96.1 302s 14 15 16 17 18 19 20 302s 99.0 103.8 103.7 103.8 102.1 103.6 106.9 302s > 302s > print( fitted( fit3sls[[ 1 ]]$e1wc ) ) 302s demand supply 302s 1 97.6 97.8 302s 2 99.9 99.3 302s 3 99.8 99.5 302s 4 100.0 99.9 302s 5 102.1 101.7 302s 6 102.0 101.8 302s 7 102.4 101.9 302s 8 103.0 104.1 302s 9 101.5 102.3 302s 10 100.3 99.6 302s 11 95.5 95.9 302s 12 94.7 94.8 302s 13 96.1 96.6 302s 14 99.0 98.4 302s 15 103.8 102.7 302s 16 103.7 104.4 302s 17 103.8 103.3 302s 18 102.1 103.6 302s 19 103.6 103.6 302s 20 106.9 106.6 302s > print( fitted( fit3sls[[ 1 ]]$e1wc$eq[[ 1 ]] ) ) 302s 1 2 3 4 5 6 7 8 9 10 11 12 13 302s 97.6 99.9 99.8 100.0 102.1 102.0 102.4 103.0 101.5 100.3 95.5 94.7 96.1 302s 14 15 16 17 18 19 20 302s 99.0 103.8 103.7 103.8 102.1 103.6 106.9 302s > 302s > print( fitted( fit3sls[[ 3 ]]$e2e ) ) 302s demand supply 302s 1 97.8 98.4 302s 2 100.0 99.8 302s 3 99.9 99.9 302s 4 100.1 100.3 302s 5 102.0 101.8 302s 6 101.9 101.9 302s 7 102.4 102.0 302s 8 102.9 104.0 302s 9 101.4 102.2 302s 10 100.3 99.6 302s 11 95.8 96.2 302s 12 95.0 95.2 302s 13 96.4 96.9 302s 14 99.1 98.5 302s 15 103.7 102.3 302s 16 103.5 103.9 302s 17 103.6 102.8 302s 18 102.0 103.2 302s 19 103.5 103.2 302s 20 106.7 105.9 302s > print( fitted( fit3sls[[ 3 ]]$e2e$eq[[ 2 ]] ) ) 302s 1 2 3 4 5 6 7 8 9 10 11 12 13 302s 98.4 99.8 99.9 100.3 101.8 101.9 102.0 104.0 102.2 99.6 96.2 95.2 96.9 302s 14 15 16 17 18 19 20 302s 98.5 102.3 103.9 102.8 103.2 103.2 105.9 302s > 302s > print( fitted( fit3sls[[ 4 ]]$e3 ) ) 302s demand supply 302s 1 97.8 98.4 302s 2 100.0 99.8 302s 3 99.9 99.9 302s 4 100.1 100.3 302s 5 102.0 101.7 302s 6 101.9 101.8 302s 7 102.3 101.9 302s 8 102.9 103.9 302s 9 101.4 102.2 302s 10 100.3 99.6 302s 11 95.8 96.3 302s 12 95.1 95.3 302s 13 96.4 97.0 302s 14 99.1 98.5 302s 15 103.6 102.3 302s 16 103.5 103.9 302s 17 103.6 102.7 302s 18 102.0 103.1 302s 19 103.5 103.2 302s 20 106.7 105.9 302s > print( fitted( fit3sls[[ 4 ]]$e3$eq[[ 1 ]] ) ) 302s 1 2 3 4 5 6 7 8 9 10 11 12 13 302s 97.8 100.0 99.9 100.1 102.0 101.9 102.3 102.9 101.4 100.3 95.8 95.1 96.4 302s 14 15 16 17 18 19 20 302s 99.1 103.6 103.5 103.6 102.0 103.5 106.7 302s > 302s > print( fitted( fit3sls[[ 5 ]]$e4e ) ) 302s demand supply 302s 1 95.0 96.3 302s 2 98.9 99.4 302s 3 98.8 99.5 302s 4 99.1 100.2 302s 5 103.2 102.9 302s 6 102.9 103.1 302s 7 103.6 103.4 302s 8 104.5 107.7 302s 9 102.1 103.4 302s 10 100.2 97.8 302s 11 91.5 90.8 302s 12 89.8 88.9 302s 13 92.2 92.6 302s 14 97.6 95.6 302s 15 106.4 103.4 302s 16 105.9 106.9 302s 17 106.7 103.6 302s 18 102.9 105.4 302s 19 105.6 105.5 302s 20 111.3 111.7 302s > print( fitted( fit3sls[[ 5 ]]$e4e$eq[[ 2 ]] ) ) 302s 1 2 3 4 5 6 7 8 9 10 11 12 13 302s 96.3 99.4 99.5 100.2 102.9 103.1 103.4 107.7 103.4 97.8 90.8 88.9 92.6 302s 14 15 16 17 18 19 20 302s 95.6 103.4 106.9 103.6 105.4 105.5 111.7 302s > 302s > print( fitted( fit3sls[[ 1 ]]$e5 ) ) 302s demand supply 302s 1 97.5 98.2 302s 2 99.9 99.8 302s 3 99.8 99.9 302s 4 100.0 100.3 302s 5 102.1 101.9 302s 6 102.0 102.0 302s 7 102.5 102.1 302s 8 103.1 104.3 302s 9 101.5 102.3 302s 10 100.3 99.4 302s 11 95.3 95.7 302s 12 94.5 94.6 302s 13 96.0 96.5 302s 14 99.0 98.2 302s 15 103.9 102.4 302s 16 103.7 104.2 302s 17 103.9 102.7 302s 18 102.1 103.4 302s 19 103.7 103.4 302s 20 107.2 106.6 302s > print( fitted( fit3sls[[ 1 ]]$e5$eq[[ 1 ]] ) ) 302s 1 2 3 4 5 6 7 8 9 10 11 12 13 302s 97.5 99.9 99.8 100.0 102.1 102.0 102.5 103.1 101.5 100.3 95.3 94.5 96.0 302s 14 15 16 17 18 19 20 302s 99.0 103.9 103.7 103.9 102.1 103.7 107.2 302s > 302s > print( fitted( fit3slsi[[ 3 ]]$e3e ) ) 302s demand supply 302s 1 98.9 99.2 302s 2 100.5 100.3 302s 3 100.4 100.4 302s 4 100.6 100.6 302s 5 101.6 101.2 302s 6 101.5 101.3 302s 7 101.9 101.5 302s 8 102.4 102.9 302s 9 101.1 101.4 302s 10 100.1 99.7 302s 11 97.2 97.8 302s 12 96.9 97.5 302s 13 98.0 98.7 302s 14 99.7 99.5 302s 15 102.5 101.6 302s 16 102.6 102.7 302s 17 102.1 101.4 302s 18 101.8 102.6 302s 19 102.9 102.7 302s 20 105.3 104.8 302s > print( fitted( fit3slsi[[ 3 ]]$e3e$eq[[ 1 ]] ) ) 302s 1 2 3 4 5 6 7 8 9 10 11 12 13 302s 98.9 100.5 100.4 100.6 101.6 101.5 101.9 102.4 101.1 100.1 97.2 96.9 98.0 302s 14 15 16 17 18 19 20 302s 99.7 102.5 102.6 102.1 101.8 102.9 105.3 302s > 302s > print( fitted( fit3slsd[[ 4 ]]$e4 ) ) 302s demand supply 302s 1 97.6 98.3 302s 2 99.7 99.7 302s 3 99.7 99.8 302s 4 99.8 100.1 302s 5 102.2 101.9 302s 6 102.0 102.0 302s 7 102.4 102.0 302s 8 102.8 104.1 302s 9 101.6 102.4 302s 10 100.7 99.8 302s 11 95.8 96.1 302s 12 94.8 94.8 302s 13 96.0 96.5 302s 14 99.1 98.3 302s 15 104.1 102.5 302s 16 103.7 104.2 302s 17 104.4 103.2 302s 18 101.9 103.2 302s 19 103.4 103.2 302s 20 106.3 105.9 302s > print( fitted( fit3slsd[[ 4 ]]$e4$eq[[ 2 ]] ) ) 302s 1 2 3 4 5 6 7 8 9 10 11 12 13 302s 98.3 99.7 99.8 100.1 101.9 102.0 102.0 104.1 102.4 99.8 96.1 94.8 96.5 302s 14 15 16 17 18 19 20 302s 98.3 102.5 104.2 103.2 103.2 103.2 105.9 302s > 302s > print( fitted( fit3slsd[[ 2 ]]$e3w ) ) 302s demand supply 302s 1 96.1 97.0 302s 2 97.6 97.2 302s 3 97.8 97.8 302s 4 97.7 97.7 302s 5 103.5 103.5 302s 6 102.7 102.8 302s 7 102.6 102.1 302s 8 101.8 103.4 302s 9 103.3 104.8 302s 10 103.9 103.4 302s 11 96.2 97.0 302s 12 92.5 92.4 302s 13 92.7 93.0 302s 14 98.8 97.6 302s 15 107.3 105.6 302s 16 105.6 106.4 302s 17 111.1 110.7 302s 18 100.9 102.3 302s 19 102.3 101.4 302s 20 103.7 101.8 302s > print( fitted( fit3slsd[[ 2 ]]$e3w$eq[[ 2 ]] ) ) 302s 1 2 3 4 5 6 7 8 9 10 11 12 13 302s 97.0 97.2 97.8 97.7 103.5 102.8 102.1 103.4 104.8 103.4 97.0 92.4 93.0 302s 14 15 16 17 18 19 20 302s 97.6 105.6 106.4 110.7 102.3 101.4 101.8 302s > 302s > 302s > ## *********** predicted values ************* 302s > predictData <- Kmenta 302s > predictData$consump <- NULL 302s > predictData$price <- Kmenta$price * 0.9 302s > predictData$income <- Kmenta$income * 1.1 302s > 302s > print( predict( fit3sls[[ 2 ]]$e1c, se.fit = TRUE, interval = "prediction", 302s + useDfSys = TRUE ) ) 302s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 302s 1 97.6 0.661 93.4 101.9 97.8 0.826 302s 2 99.9 0.600 95.7 104.1 99.3 0.825 302s 3 99.8 0.564 95.6 104.0 99.5 0.755 302s 4 100.0 0.605 95.8 104.2 99.9 0.783 302s 5 102.1 0.516 98.0 106.2 101.7 0.669 302s 6 102.0 0.474 97.9 106.1 101.8 0.620 302s 7 102.4 0.493 98.3 106.5 101.9 0.608 302s 8 103.0 0.615 98.8 107.2 104.1 0.889 302s 9 101.5 0.544 97.3 105.6 102.3 0.753 302s 10 100.3 0.822 96.0 104.7 99.6 1.022 302s 11 95.5 0.963 91.1 100.0 95.9 1.172 302s 12 94.7 1.006 90.2 99.2 94.8 1.289 302s 13 96.1 0.915 91.7 100.5 96.6 1.114 302s 14 99.0 0.518 94.9 103.2 98.4 0.751 302s 15 103.8 0.793 99.5 108.2 102.7 0.863 302s 16 103.7 0.636 99.5 107.9 104.4 0.902 302s 17 103.8 1.348 99.0 108.7 103.3 1.636 302s 18 102.1 0.549 97.9 106.2 103.6 0.807 302s 19 103.6 0.695 99.4 107.9 103.6 0.898 302s 20 106.9 1.306 102.1 111.7 106.6 1.613 302s supply.lwr supply.upr 302s 1 92.3 103 302s 2 93.8 105 302s 3 94.0 105 302s 4 94.3 105 302s 5 96.2 107 302s 6 96.3 107 302s 7 96.5 107 302s 8 98.5 110 302s 9 96.8 108 302s 10 93.9 105 302s 11 90.1 102 302s 12 88.9 101 302s 13 90.9 102 302s 14 92.9 104 302s 15 97.1 108 302s 16 98.8 110 302s 17 97.1 110 302s 18 98.1 109 302s 19 98.0 109 302s 20 100.4 113 302s > print( predict( fit3sls[[ 2 ]]$e1c$eq[[ 1 ]], se.fit = TRUE, interval = "prediction", 302s + useDfSys = TRUE ) ) 302s fit se.fit lwr upr 302s 1 97.6 0.661 93.4 101.9 302s 2 99.9 0.600 95.7 104.1 302s 3 99.8 0.564 95.6 104.0 302s 4 100.0 0.605 95.8 104.2 302s 5 102.1 0.516 98.0 106.2 302s 6 102.0 0.474 97.9 106.1 302s 7 102.4 0.493 98.3 106.5 302s 8 103.0 0.615 98.8 107.2 302s 9 101.5 0.544 97.3 105.6 302s 10 100.3 0.822 96.0 104.7 302s 11 95.5 0.963 91.1 100.0 302s 12 94.7 1.006 90.2 99.2 302s 13 96.1 0.915 91.7 100.5 302s 14 99.0 0.518 94.9 103.2 302s 15 103.8 0.793 99.5 108.2 302s 16 103.7 0.636 99.5 107.9 302s 17 103.8 1.348 99.0 108.7 302s 18 102.1 0.549 97.9 106.2 302s 19 103.6 0.695 99.4 107.9 302s 20 106.9 1.306 102.1 111.7 302s > 302s > print( predict( fit3sls[[ 3 ]]$e2e, se.pred = TRUE, interval = "confidence", 302s + level = 0.999, newdata = predictData, useDfSys = TRUE ) ) 302s demand.pred demand.se.pred demand.lwr demand.upr supply.pred supply.se.pred 302s 1 102.7 2.20 99.3 106 96.2 2.78 302s 2 105.2 2.21 101.8 109 97.5 2.68 302s 3 105.1 2.22 101.6 109 97.7 2.69 302s 4 105.4 2.21 101.9 109 98.0 2.67 302s 5 107.2 2.47 101.9 112 99.6 2.80 302s 6 107.1 2.43 102.1 112 99.7 2.76 302s 7 107.7 2.42 102.8 113 99.7 2.72 302s 8 108.5 2.38 103.7 113 101.6 2.66 302s 9 106.5 2.48 101.2 112 100.1 2.85 302s 10 105.0 2.59 99.1 111 97.6 3.04 302s 11 100.1 2.36 95.5 105 94.2 3.07 302s 12 99.5 2.19 96.3 103 93.0 3.00 302s 13 101.2 2.11 98.7 104 94.6 2.85 302s 14 104.0 2.29 100.0 108 96.3 2.84 302s 15 108.9 2.68 102.4 115 100.2 2.90 302s 16 108.8 2.57 103.0 115 101.8 2.81 302s 17 108.4 2.99 100.4 116 100.8 3.28 302s 18 107.5 2.34 103.1 112 100.9 2.66 302s 19 109.2 2.42 104.3 114 100.8 2.64 302s 20 113.0 2.63 106.8 119 103.4 2.62 302s supply.lwr supply.upr 302s 1 92.2 100.2 302s 2 94.6 100.5 302s 3 94.6 100.7 302s 4 95.1 100.8 302s 5 95.4 103.8 302s 6 95.8 103.5 302s 7 96.3 103.1 302s 8 98.9 104.4 302s 9 95.4 104.7 302s 10 91.6 103.6 302s 11 88.0 100.4 302s 12 87.3 98.7 302s 13 90.1 99.2 302s 14 91.8 100.8 302s 15 95.3 105.2 302s 16 97.5 106.0 302s 17 93.4 108.3 302s 18 98.1 103.6 302s 19 98.4 103.2 302s 20 101.2 105.6 302s > print( predict( fit3sls[[ 3 ]]$e2e$eq[[ 2 ]], se.pred = TRUE, interval = "confidence", 302s + level = 0.999, newdata = predictData, useDfSys = TRUE ) ) 302s fit se.pred lwr upr 302s 1 96.2 2.78 92.2 100.2 302s 2 97.5 2.68 94.6 100.5 302s 3 97.7 2.69 94.6 100.7 302s 4 98.0 2.67 95.1 100.8 302s 5 99.6 2.80 95.4 103.8 302s 6 99.7 2.76 95.8 103.5 302s 7 99.7 2.72 96.3 103.1 302s 8 101.6 2.66 98.9 104.4 302s 9 100.1 2.85 95.4 104.7 302s 10 97.6 3.04 91.6 103.6 302s 11 94.2 3.07 88.0 100.4 302s 12 93.0 3.00 87.3 98.7 302s 13 94.6 2.85 90.1 99.2 302s 14 96.3 2.84 91.8 100.8 302s 15 100.2 2.90 95.3 105.2 302s 16 101.8 2.81 97.5 106.0 302s 17 100.8 3.28 93.4 108.3 302s 18 100.9 2.66 98.1 103.6 302s 19 100.8 2.64 98.4 103.2 302s 20 103.4 2.62 101.2 105.6 302s > 302s > print( predict( fit3sls[[ 5 ]]$e2w, se.pred = TRUE, interval = "confidence", 302s + level = 0.999, newdata = predictData, useDfSys = TRUE ) ) 302s demand.pred demand.se.pred demand.lwr demand.upr supply.pred supply.se.pred 302s 1 102.6 2.24 99.0 106 96.3 2.84 302s 2 105.1 2.24 101.5 109 97.6 2.72 302s 3 105.0 2.25 101.3 109 97.7 2.73 302s 4 105.3 2.24 101.6 109 98.0 2.71 302s 5 107.1 2.54 101.5 113 99.6 2.88 302s 6 107.0 2.49 101.7 112 99.6 2.82 302s 7 107.6 2.48 102.3 113 99.7 2.77 302s 8 108.3 2.44 103.3 113 101.6 2.70 302s 9 106.4 2.55 100.7 112 100.0 2.94 302s 10 104.9 2.67 98.5 111 97.6 3.17 302s 11 100.1 2.43 95.1 105 94.3 3.20 302s 12 99.5 2.23 96.0 103 93.2 3.11 302s 13 101.2 2.14 98.5 104 94.8 2.92 302s 14 104.0 2.33 99.6 108 96.4 2.92 302s 15 108.7 2.77 101.8 116 100.2 2.99 302s 16 108.7 2.65 102.5 115 101.7 2.88 302s 17 108.3 3.12 99.7 117 100.8 3.45 302s 18 107.4 2.39 102.7 112 100.9 2.70 302s 19 109.1 2.48 103.8 114 100.8 2.67 302s 20 112.9 2.71 106.3 119 103.4 2.65 302s supply.lwr supply.upr 302s 1 91.8 100.7 302s 2 94.3 100.8 302s 3 94.3 101.1 302s 4 94.8 101.1 302s 5 94.9 104.3 302s 6 95.4 103.9 302s 7 95.9 103.5 302s 8 98.5 104.7 302s 9 94.9 105.2 302s 10 90.9 104.4 302s 11 87.4 101.2 302s 12 86.9 99.5 302s 13 89.7 99.8 302s 14 91.4 101.4 302s 15 94.7 105.8 302s 16 97.0 106.5 302s 17 92.5 109.1 302s 18 97.8 103.9 302s 19 98.1 103.5 302s 20 101.0 105.9 302s > print( predict( fit3sls[[ 5 ]]$e2w$eq[[ 2 ]], se.pred = TRUE, interval = "confidence", 302s + level = 0.999, newdata = predictData, useDfSys = TRUE ) ) 302s fit se.pred lwr upr 302s 1 96.3 2.84 91.8 100.7 302s 2 97.6 2.72 94.3 100.8 302s 3 97.7 2.73 94.3 101.1 302s 4 98.0 2.71 94.8 101.1 302s 5 99.6 2.88 94.9 104.3 302s 6 99.6 2.82 95.4 103.9 302s 7 99.7 2.77 95.9 103.5 302s 8 101.6 2.70 98.5 104.7 302s 9 100.0 2.94 94.9 105.2 302s 10 97.6 3.17 90.9 104.4 302s 11 94.3 3.20 87.4 101.2 302s 12 93.2 3.11 86.9 99.5 302s 13 94.8 2.92 89.7 99.8 302s 14 96.4 2.92 91.4 101.4 302s 15 100.2 2.99 94.7 105.8 302s 16 101.7 2.88 97.0 106.5 302s 17 100.8 3.45 92.5 109.1 302s 18 100.9 2.70 97.8 103.9 302s 19 100.8 2.67 98.1 103.5 302s 20 103.4 2.65 101.0 105.9 302s > 302s > print( predict( fit3sls[[ 4 ]]$e3, se.pred = TRUE, interval = "prediction", 302s + level = 0.975 ) ) 302s demand.pred demand.se.pred demand.lwr demand.upr supply.pred supply.se.pred 302s 1 97.8 2.10 92.9 103 98.4 2.64 302s 2 100.0 2.09 95.1 105 99.8 2.66 302s 3 99.9 2.08 95.0 105 99.9 2.65 302s 4 100.1 2.09 95.2 105 100.3 2.66 302s 5 102.0 2.06 97.2 107 101.7 2.65 302s 6 101.9 2.05 97.1 107 101.8 2.63 302s 7 102.3 2.06 97.5 107 101.9 2.63 302s 8 102.9 2.09 98.0 108 103.9 2.71 302s 9 101.4 2.07 96.6 106 102.2 2.67 302s 10 100.3 2.16 95.2 105 99.6 2.76 302s 11 95.8 2.21 90.6 101 96.3 2.80 302s 12 95.1 2.22 89.9 100 95.3 2.84 302s 13 96.4 2.19 91.3 102 97.0 2.78 302s 14 99.1 2.06 94.3 104 98.5 2.67 302s 15 103.6 2.15 98.6 109 102.3 2.68 302s 16 103.5 2.09 98.6 108 103.9 2.68 302s 17 103.6 2.41 97.9 109 102.7 3.00 302s 18 102.0 2.07 97.2 107 103.1 2.66 302s 19 103.5 2.12 98.6 108 103.2 2.69 302s 20 106.7 2.39 101.1 112 105.9 2.98 302s supply.lwr supply.upr 302s 1 92.2 105 302s 2 93.6 106 302s 3 93.7 106 302s 4 94.0 107 302s 5 95.5 108 302s 6 95.7 108 302s 7 95.8 108 302s 8 97.6 110 302s 9 95.9 108 302s 10 93.2 106 302s 11 89.7 103 302s 12 88.6 102 302s 13 90.5 103 302s 14 92.3 105 302s 15 96.0 109 302s 16 97.6 110 302s 17 95.7 110 302s 18 96.9 109 302s 19 96.9 109 302s 20 98.9 113 302s > print( predict( fit3sls[[ 4 ]]$e3$eq[[ 1 ]], se.pred = TRUE, interval = "prediction", 302s + level = 0.975 ) ) 302s fit se.pred lwr upr 302s 1 97.8 2.10 92.9 103 302s 2 100.0 2.09 95.1 105 302s 3 99.9 2.08 95.0 105 302s 4 100.1 2.09 95.2 105 302s 5 102.0 2.06 97.2 107 302s 6 101.9 2.05 97.1 107 302s 7 102.3 2.06 97.5 107 302s 8 102.9 2.09 98.0 108 302s 9 101.4 2.07 96.6 106 302s 10 100.3 2.16 95.2 105 302s 11 95.8 2.21 90.6 101 302s 12 95.1 2.22 89.9 100 302s 13 96.4 2.19 91.3 102 302s 14 99.1 2.06 94.3 104 302s 15 103.6 2.15 98.6 109 302s 16 103.5 2.09 98.6 108 302s 17 103.6 2.41 97.9 109 302s 18 102.0 2.07 97.2 107 302s 19 103.5 2.12 98.6 108 302s 20 106.7 2.39 101.1 112 302s > 302s > print( predict( fit3sls[[ 5 ]]$e4e, se.fit = TRUE, interval = "confidence", 302s + level = 0.25, useDfSys = TRUE ) ) 302s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 302s 1 95.0 0.465 94.8 95.1 96.3 0.536 302s 2 98.9 0.532 98.7 99.1 99.4 0.663 302s 3 98.8 0.497 98.6 99.0 99.5 0.613 302s 4 99.1 0.541 99.0 99.3 100.2 0.662 302s 5 103.2 0.450 103.0 103.3 102.9 0.593 302s 6 102.9 0.417 102.7 103.0 103.1 0.543 302s 7 103.6 0.420 103.5 103.8 103.4 0.524 302s 8 104.5 0.525 104.3 104.6 107.7 0.634 302s 9 102.1 0.494 101.9 102.2 103.4 0.660 302s 10 100.2 0.760 100.0 100.4 97.8 0.895 302s 11 91.5 0.660 91.3 91.7 90.8 0.736 302s 12 89.8 0.563 89.6 89.9 88.9 0.742 302s 13 92.2 0.597 92.0 92.4 92.6 0.806 302s 14 97.6 0.426 97.4 97.7 95.6 0.568 302s 15 106.4 0.619 106.2 106.6 103.4 0.721 302s 16 105.9 0.476 105.8 106.1 106.9 0.608 302s 17 106.7 1.159 106.3 107.1 103.6 1.414 302s 18 102.9 0.494 102.7 103.0 105.4 0.582 302s 19 105.6 0.574 105.4 105.8 105.5 0.676 302s 20 111.3 1.030 110.9 111.6 111.7 1.146 302s supply.lwr supply.upr 302s 1 96.1 96.4 302s 2 99.1 99.6 302s 3 99.3 99.7 302s 4 100.0 100.4 302s 5 102.7 103.1 302s 6 102.9 103.3 302s 7 103.2 103.5 302s 8 107.5 107.9 302s 9 103.2 103.7 302s 10 97.5 98.0 302s 11 90.5 91.0 302s 12 88.7 89.1 302s 13 92.4 92.9 302s 14 95.4 95.8 302s 15 103.1 103.6 302s 16 106.7 107.0 302s 17 103.1 104.0 302s 18 105.3 105.6 302s 19 105.3 105.8 302s 20 111.4 112.1 302s > print( predict( fit3sls[[ 5 ]]$e4e$eq[[ 2 ]], se.fit = TRUE, interval = "confidence", 302s + level = 0.25, useDfSys = TRUE ) ) 302s fit se.fit lwr upr 302s 1 96.3 0.536 96.1 96.4 302s 2 99.4 0.663 99.1 99.6 302s 3 99.5 0.613 99.3 99.7 302s 4 100.2 0.662 100.0 100.4 302s 5 102.9 0.593 102.7 103.1 302s 6 103.1 0.543 102.9 103.3 302s 7 103.4 0.524 103.2 103.5 302s 8 107.7 0.634 107.5 107.9 302s 9 103.4 0.660 103.2 103.7 302s 10 97.8 0.895 97.5 98.0 302s 11 90.8 0.736 90.5 91.0 302s 12 88.9 0.742 88.7 89.1 302s 13 92.6 0.806 92.4 92.9 302s 14 95.6 0.568 95.4 95.8 302s 15 103.4 0.721 103.1 103.6 302s 16 106.9 0.608 106.7 107.0 302s 17 103.6 1.414 103.1 104.0 302s 18 105.4 0.582 105.3 105.6 302s 19 105.5 0.676 105.3 105.8 302s 20 111.7 1.146 111.4 112.1 302s > 302s > print( predict( fit3sls[[ 1 ]]$e5, se.fit = TRUE, se.pred = TRUE, 302s + interval = "prediction", level = 0.5, newdata = predictData ) ) 302s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 302s 1 102.8 0.957 2.19 101.3 104 95.7 302s 2 105.6 0.829 2.13 104.1 107 97.1 302s 3 105.5 0.869 2.15 104.0 107 97.3 302s 4 105.8 0.823 2.13 104.3 107 97.6 302s 5 107.8 1.308 2.36 106.2 109 99.4 302s 6 107.7 1.213 2.31 106.1 109 99.4 302s 7 108.3 1.145 2.28 106.7 110 99.5 302s 8 109.1 0.984 2.20 107.6 111 101.7 302s 9 107.0 1.372 2.40 105.3 109 99.8 302s 10 105.4 1.659 2.57 103.6 107 97.1 302s 11 100.1 1.365 2.39 98.4 102 93.3 302s 12 99.4 0.969 2.19 97.9 101 92.1 302s 13 101.3 0.752 2.11 99.8 103 93.9 302s 14 104.3 1.112 2.26 102.8 106 95.7 302s 15 109.6 1.580 2.52 107.9 111 100.0 302s 16 109.6 1.368 2.40 107.9 111 101.7 302s 17 109.1 2.136 2.90 107.1 111 100.5 302s 18 108.1 0.966 2.19 106.6 110 100.8 302s 19 109.9 0.980 2.20 108.4 111 100.7 302s 20 114.1 0.997 2.21 112.6 116 103.7 302s supply.se.fit supply.se.pred supply.lwr supply.upr 302s 1 0.959 2.74 93.8 97.5 302s 2 0.742 2.67 95.3 99.0 302s 3 0.791 2.69 95.4 99.1 302s 4 0.735 2.67 95.8 99.4 302s 5 1.280 2.87 97.4 101.3 302s 6 1.159 2.82 97.5 101.3 302s 7 1.031 2.77 97.6 101.4 302s 8 0.867 2.71 99.8 103.5 302s 9 1.416 2.93 97.8 101.8 302s 10 1.724 3.09 95.0 99.2 302s 11 1.457 2.95 91.3 95.4 302s 12 1.102 2.79 90.2 94.0 302s 13 0.894 2.72 92.1 95.8 302s 14 1.092 2.79 93.8 97.6 302s 15 1.516 2.98 98.0 102.0 302s 16 1.321 2.89 99.7 103.7 302s 17 2.297 3.44 98.2 102.9 302s 18 0.847 2.70 98.9 102.6 302s 19 0.743 2.67 98.9 102.6 302s 20 0.589 2.63 101.9 105.5 302s > print( predict( fit3sls[[ 1 ]]$e5$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 302s + interval = "prediction", level = 0.5, newdata = predictData ) ) 302s fit se.fit se.pred lwr upr 302s 1 102.8 0.957 2.19 101.3 104 302s 2 105.6 0.829 2.13 104.1 107 302s 3 105.5 0.869 2.15 104.0 107 302s 4 105.8 0.823 2.13 104.3 107 302s 5 107.8 1.308 2.36 106.2 109 302s 6 107.7 1.213 2.31 106.1 109 302s 7 108.3 1.145 2.28 106.7 110 302s 8 109.1 0.984 2.20 107.6 111 302s 9 107.0 1.372 2.40 105.3 109 302s 10 105.4 1.659 2.57 103.6 107 302s 11 100.1 1.365 2.39 98.4 102 302s 12 99.4 0.969 2.19 97.9 101 302s 13 101.3 0.752 2.11 99.8 103 302s 14 104.3 1.112 2.26 102.8 106 302s 15 109.6 1.580 2.52 107.9 111 302s 16 109.6 1.368 2.40 107.9 111 302s 17 109.1 2.136 2.90 107.1 111 302s 18 108.1 0.966 2.19 106.6 110 302s 19 109.9 0.980 2.20 108.4 111 302s 20 114.1 0.997 2.21 112.6 116 302s > 302s > print( predict( fit3slsi[[ 3 ]]$e3e, se.fit = TRUE, se.pred = TRUE, 302s + interval = "confidence", level = 0.99, useDfSys = TRUE ) ) 302s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 302s 1 98.9 0.590 2.49 97.3 100.5 99.2 302s 2 100.5 0.643 2.50 98.7 102.2 100.3 302s 3 100.4 0.602 2.49 98.7 102.0 100.4 302s 4 100.6 0.653 2.50 98.8 102.3 100.6 302s 5 101.6 0.548 2.48 100.1 103.1 101.2 302s 6 101.5 0.512 2.47 100.1 102.9 101.3 302s 7 101.9 0.524 2.47 100.5 103.3 101.5 302s 8 102.4 0.667 2.51 100.6 104.3 102.9 302s 9 101.1 0.599 2.49 99.5 102.7 101.4 302s 10 100.1 0.928 2.59 97.6 102.6 99.7 302s 11 97.2 0.898 2.58 94.7 99.6 97.8 302s 12 96.9 0.767 2.54 94.8 99.0 97.5 302s 13 98.0 0.745 2.53 96.0 100.1 98.7 302s 14 99.7 0.536 2.48 98.2 101.1 99.5 302s 15 102.5 0.745 2.53 100.5 104.5 101.6 302s 16 102.6 0.589 2.49 101.0 104.2 102.7 302s 17 102.1 1.376 2.78 98.3 105.8 101.4 302s 18 101.8 0.615 2.49 100.2 103.5 102.6 302s 19 102.9 0.738 2.53 100.9 104.9 102.7 302s 20 105.3 1.357 2.77 101.6 109.0 104.8 302s supply.se.fit supply.se.pred supply.lwr supply.upr 302s 1 0.638 3.01 97.5 101.0 302s 2 0.752 3.03 98.3 102.4 302s 3 0.700 3.02 98.4 102.3 302s 4 0.761 3.03 98.6 102.7 302s 5 0.649 3.01 99.4 103.0 302s 6 0.610 3.00 99.7 103.0 302s 7 0.613 3.00 99.8 103.2 302s 8 0.829 3.05 100.7 105.2 302s 9 0.731 3.03 99.4 103.4 302s 10 1.092 3.13 96.7 102.6 302s 11 1.037 3.12 94.9 100.6 302s 12 0.902 3.07 95.0 99.9 302s 13 0.855 3.06 96.4 101.1 302s 14 0.670 3.01 97.6 101.3 302s 15 0.812 3.05 99.4 103.8 302s 16 0.707 3.02 100.8 104.7 302s 17 1.584 3.34 97.1 105.7 302s 18 0.740 3.03 100.6 104.6 302s 19 0.852 3.06 100.4 105.1 302s 20 1.564 3.33 100.6 109.1 302s > print( predict( fit3slsi[[ 3 ]]$e3e$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 302s + interval = "confidence", level = 0.99, useDfSys = TRUE ) ) 302s fit se.fit se.pred lwr upr 302s 1 98.9 0.590 2.49 97.3 100.5 302s 2 100.5 0.643 2.50 98.7 102.2 302s 3 100.4 0.602 2.49 98.7 102.0 302s 4 100.6 0.653 2.50 98.8 102.3 302s 5 101.6 0.548 2.48 100.1 103.1 302s 6 101.5 0.512 2.47 100.1 102.9 302s 7 101.9 0.524 2.47 100.5 103.3 302s 8 102.4 0.667 2.51 100.6 104.3 302s 9 101.1 0.599 2.49 99.5 102.7 302s 10 100.1 0.928 2.59 97.6 102.6 302s 11 97.2 0.898 2.58 94.7 99.6 302s 12 96.9 0.767 2.54 94.8 99.0 302s 13 98.0 0.745 2.53 96.0 100.1 302s 14 99.7 0.536 2.48 98.2 101.1 302s 15 102.5 0.745 2.53 100.5 104.5 302s 16 102.6 0.589 2.49 101.0 104.2 302s 17 102.1 1.376 2.78 98.3 105.8 302s 18 101.8 0.615 2.49 100.2 103.5 302s 19 102.9 0.738 2.53 100.9 104.9 302s 20 105.3 1.357 2.77 101.6 109.0 302s > 302s > print( predict( fit3slsi[[ 1 ]]$e5w, se.fit = TRUE, se.pred = TRUE, 302s + interval = "prediction", level = 0.5, newdata = predictData ) ) 302s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 302s 1 102.4 0.986 2.25 100.9 104 95.3 302s 2 105.2 0.851 2.20 103.7 107 96.9 302s 3 105.1 0.896 2.22 103.6 107 97.0 302s 4 105.4 0.844 2.20 103.9 107 97.4 302s 5 107.1 1.351 2.44 105.5 109 98.7 302s 6 107.1 1.250 2.38 105.5 109 98.9 302s 7 107.8 1.173 2.34 106.2 109 99.0 302s 8 108.7 0.983 2.25 107.2 110 101.3 302s 9 106.3 1.420 2.48 104.6 108 99.1 302s 10 104.6 1.713 2.65 102.8 106 96.2 302s 11 99.4 1.372 2.45 97.8 101 92.8 302s 12 99.0 0.965 2.25 97.5 101 91.9 302s 13 101.0 0.768 2.17 99.5 102 93.8 302s 14 103.8 1.149 2.33 102.2 105 95.3 302s 15 108.8 1.631 2.60 107.0 111 99.2 302s 16 108.9 1.405 2.47 107.2 111 101.1 302s 17 108.0 2.211 3.00 106.0 110 99.4 302s 18 107.7 0.978 2.25 106.1 109 100.4 302s 19 109.5 0.964 2.25 108.0 111 100.5 302s 20 113.8 0.818 2.19 112.3 115 103.7 302s supply.se.fit supply.se.pred supply.lwr supply.upr 302s 1 0.987 2.85 93.3 97.2 302s 2 0.772 2.79 95.0 98.8 302s 3 0.824 2.80 95.1 98.9 302s 4 0.767 2.79 95.5 99.3 302s 5 1.341 3.00 96.7 100.8 302s 6 1.215 2.94 96.9 100.9 302s 7 1.084 2.89 97.1 101.0 302s 8 0.907 2.83 99.4 103.2 302s 9 1.483 3.06 97.0 101.2 302s 10 1.795 3.22 94.1 98.4 302s 11 1.455 3.05 90.7 94.8 302s 12 1.002 2.86 90.0 93.9 302s 13 0.805 2.80 91.9 95.7 302s 14 1.087 2.89 93.4 97.3 302s 15 1.585 3.11 97.1 101.4 302s 16 1.383 3.01 99.0 103.1 302s 17 2.399 3.60 96.9 101.8 302s 18 0.883 2.82 98.5 102.4 302s 19 0.770 2.79 98.6 102.4 302s 20 0.616 2.75 101.9 105.6 302s > print( predict( fit3slsi[[ 1 ]]$e5w$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 302s + interval = "prediction", level = 0.5, newdata = predictData ) ) 302s fit se.fit se.pred lwr upr 302s 1 102.4 0.986 2.25 100.9 104 302s 2 105.2 0.851 2.20 103.7 107 302s 3 105.1 0.896 2.22 103.6 107 302s 4 105.4 0.844 2.20 103.9 107 302s 5 107.1 1.351 2.44 105.5 109 302s 6 107.1 1.250 2.38 105.5 109 302s 7 107.8 1.173 2.34 106.2 109 302s 8 108.7 0.983 2.25 107.2 110 302s 9 106.3 1.420 2.48 104.6 108 302s 10 104.6 1.713 2.65 102.8 106 302s 11 99.4 1.372 2.45 97.8 101 302s 12 99.0 0.965 2.25 97.5 101 302s 13 101.0 0.768 2.17 99.5 102 302s 14 103.8 1.149 2.33 102.2 105 302s 15 108.8 1.631 2.60 107.0 111 302s 16 108.9 1.405 2.47 107.2 111 302s 17 108.0 2.211 3.00 106.0 110 302s 18 107.7 0.978 2.25 106.1 109 302s 19 109.5 0.964 2.25 108.0 111 302s 20 113.8 0.818 2.19 112.3 115 302s > 302s > print( predict( fit3slsd[[ 4 ]]$e4, se.fit = TRUE, interval = "prediction", 302s + level = 0.9, newdata = predictData ) ) 302s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 302s 1 103 0.972 99.6 107 96.1 0.980 302s 2 106 0.820 102.2 109 97.5 0.751 302s 3 106 0.863 102.1 109 97.6 0.801 302s 4 106 0.813 102.4 109 97.9 0.741 302s 5 108 1.305 104.2 112 99.8 1.287 302s 6 108 1.206 104.1 112 99.8 1.164 302s 7 109 1.132 104.7 112 99.9 1.035 302s 8 109 0.960 105.5 113 101.8 0.857 302s 9 107 1.377 103.4 111 100.3 1.422 302s 10 106 1.688 101.8 110 97.8 1.748 302s 11 101 1.415 96.8 105 94.1 1.490 302s 12 100 1.004 96.3 104 92.7 1.115 302s 13 102 0.766 98.1 105 94.4 0.891 302s 14 105 1.124 101.0 109 96.2 1.107 302s 15 110 1.575 105.8 114 100.5 1.523 302s 16 110 1.355 105.9 114 102.1 1.318 302s 17 110 2.158 105.0 115 101.3 2.305 302s 18 108 0.947 104.5 112 101.0 0.843 302s 19 110 0.953 106.3 114 100.9 0.735 302s 20 114 0.974 109.9 117 103.5 0.583 302s supply.lwr supply.upr 302s 1 91.6 100.7 302s 2 93.0 101.9 302s 3 93.2 102.1 302s 4 93.5 102.3 302s 5 95.0 104.6 302s 6 95.2 104.5 302s 7 95.3 104.5 302s 8 97.3 106.3 302s 9 95.4 105.2 302s 10 92.6 103.0 302s 11 89.2 99.0 302s 12 88.1 97.4 302s 13 89.8 98.9 302s 14 91.6 100.9 302s 15 95.5 105.5 302s 16 97.3 106.9 302s 17 95.6 107.1 302s 18 96.5 105.5 302s 19 96.5 105.3 302s 20 99.2 107.9 302s > print( predict( fit3slsd[[ 4 ]]$e4$eq[[ 2 ]], se.fit = TRUE, interval = "prediction", 302s + level = 0.9, newdata = predictData ) ) 302s fit se.fit lwr upr 302s 1 96.1 0.980 91.6 100.7 302s 2 97.5 0.751 93.0 101.9 302s 3 97.6 0.801 93.2 102.1 302s 4 97.9 0.741 93.5 102.3 302s 5 99.8 1.287 95.0 104.6 302s 6 99.8 1.164 95.2 104.5 302s 7 99.9 1.035 95.3 104.5 302s 8 101.8 0.857 97.3 106.3 302s 9 100.3 1.422 95.4 105.2 302s 10 97.8 1.748 92.6 103.0 302s 11 94.1 1.490 89.2 99.0 302s 12 92.7 1.115 88.1 97.4 302s 13 94.4 0.891 89.8 98.9 302s 14 96.2 1.107 91.6 100.9 302s 15 100.5 1.523 95.5 105.5 302s 16 102.1 1.318 97.3 106.9 302s 17 101.3 2.305 95.6 107.1 302s 18 101.0 0.843 96.5 105.5 302s 19 100.9 0.735 96.5 105.3 302s 20 103.5 0.583 99.2 107.9 302s > 302s > print( predict( fit3slsd[[ 2 ]]$e3w, se.fit = TRUE, se.pred = TRUE, 302s + interval = "confidence", level = 0.99, useDfSys = TRUE ) ) 302s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 302s 1 96.1 0.832 3.23 93.8 98.3 97.0 302s 2 97.6 0.849 3.24 95.3 99.9 97.2 302s 3 97.8 0.771 3.22 95.7 99.9 97.8 302s 4 97.7 0.857 3.24 95.3 100.0 97.7 302s 5 103.5 0.648 3.19 101.8 105.3 103.5 302s 6 102.7 0.519 3.16 101.3 104.1 102.8 302s 7 102.6 0.499 3.16 101.3 104.0 102.1 302s 8 101.8 0.627 3.18 100.1 103.5 103.4 302s 9 103.3 0.714 3.20 101.3 105.2 104.8 302s 10 103.9 1.172 3.33 100.7 107.1 103.4 302s 11 96.2 0.920 3.25 93.7 98.7 97.0 302s 12 92.5 1.261 3.37 89.1 95.9 92.4 302s 13 92.7 1.364 3.41 89.0 96.5 93.0 302s 14 98.8 0.528 3.17 97.3 100.2 97.6 302s 15 107.3 1.245 3.36 103.9 110.7 105.6 302s 16 105.6 0.856 3.24 103.2 107.9 106.4 302s 17 111.1 2.310 3.88 104.8 117.4 110.7 302s 18 100.9 0.592 3.18 99.2 102.5 102.3 302s 19 102.3 0.700 3.20 100.4 104.2 101.4 302s 20 103.7 1.350 3.40 100.0 107.4 101.8 302s supply.se.fit supply.se.pred supply.lwr supply.upr 302s 1 0.791 3.73 94.8 99.2 302s 2 0.857 3.74 94.8 99.5 302s 3 0.776 3.72 95.7 99.9 302s 4 0.825 3.73 95.5 100.0 302s 5 0.817 3.73 101.2 105.7 302s 6 0.713 3.71 100.9 104.8 302s 7 0.644 3.70 100.4 103.9 302s 8 0.858 3.74 101.0 105.7 302s 9 0.962 3.77 102.2 107.4 302s 10 1.040 3.79 100.6 106.3 302s 11 1.083 3.80 94.1 100.0 302s 12 1.633 3.99 88.0 96.9 302s 13 1.568 3.96 88.7 97.3 302s 14 0.871 3.74 95.2 100.0 302s 15 1.029 3.78 102.8 108.4 302s 16 1.056 3.79 103.6 109.3 302s 17 2.050 4.18 105.1 116.2 302s 18 0.687 3.71 100.4 104.2 302s 19 0.773 3.72 99.3 103.5 302s 20 1.300 3.87 98.3 105.4 302s > print( predict( fit3slsd[[ 2 ]]$e3w$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 302s + interval = "confidence", level = 0.99, useDfSys = TRUE ) ) 302s fit se.fit se.pred lwr upr 302s 1 96.1 0.832 3.23 93.8 98.3 302s 2 97.6 0.849 3.24 95.3 99.9 302s 3 97.8 0.771 3.22 95.7 99.9 302s 4 97.7 0.857 3.24 95.3 100.0 302s 5 103.5 0.648 3.19 101.8 105.3 302s 6 102.7 0.519 3.16 101.3 104.1 302s 7 102.6 0.499 3.16 101.3 104.0 302s 8 101.8 0.627 3.18 100.1 103.5 302s 9 103.3 0.714 3.20 101.3 105.2 302s 10 103.9 1.172 3.33 100.7 107.1 302s 11 96.2 0.920 3.25 93.7 98.7 302s 12 92.5 1.261 3.37 89.1 95.9 302s 13 92.7 1.364 3.41 89.0 96.5 302s 14 98.8 0.528 3.17 97.3 100.2 302s 15 107.3 1.245 3.36 103.9 110.7 302s 16 105.6 0.856 3.24 103.2 107.9 302s 17 111.1 2.310 3.88 104.8 117.4 302s 18 100.9 0.592 3.18 99.2 102.5 302s 19 102.3 0.700 3.20 100.4 104.2 302s 20 103.7 1.350 3.40 100.0 107.4 302s > 302s > 302s > # predict just one observation 302s > smallData <- data.frame( price = 130, income = 150, farmPrice = 120, 302s + trend = 25 ) 302s > 302s > print( predict( fit3sls[[ 3 ]]$e1c, newdata = smallData ) ) 302s demand.pred supply.pred 302s 1 110 118 302s > print( predict( fit3sls[[ 3 ]]$e1c$eq[[ 1 ]], newdata = smallData ) ) 302s fit 302s 1 110 302s > 302s > print( predict( fit3sls[[ 4 ]]$e2e, se.fit = TRUE, level = 0.9, 302s + newdata = smallData ) ) 302s demand.pred demand.se.fit supply.pred supply.se.fit 302s 1 110 2.34 117 3.29 302s > print( predict( fit3sls[[ 5 ]]$e2e$eq[[ 1 ]], se.pred = TRUE, level = 0.99, 302s + newdata = smallData ) ) 302s fit se.pred 302s 1 110 3.07 302s > 302s > print( predict( fit3sls[[ 1]]$e3, interval = "prediction", level = 0.975, 302s + newdata = smallData ) ) 302s demand.pred demand.lwr demand.upr supply.pred supply.lwr supply.upr 302s 1 110 102 117 117 106 127 302s > print( predict( fit3sls[[ 1 ]]$e3$eq[[ 1 ]], interval = "confidence", level = 0.8, 302s + newdata = smallData ) ) 302s fit lwr upr 302s 1 110 106 113 302s > 302s > print( predict( fit3sls[[ 4]]$e3we, interval = "prediction", level = 0.975, 302s + newdata = smallData ) ) 302s demand.pred demand.lwr demand.upr supply.pred supply.lwr supply.upr 302s 1 110 103 117 117 107 126 302s > print( predict( fit3sls[[ 4 ]]$e3we$eq[[ 1 ]], interval = "confidence", level = 0.8, 302s + newdata = smallData ) ) 302s fit lwr upr 302s 1 110 107 113 302s > 302s > print( predict( fit3sls[[ 2 ]]$e4e, se.fit = TRUE, interval = "confidence", 302s + level = 0.999, newdata = smallData ) ) 302s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 302s 1 110 2.14 103 118 119 2.25 302s supply.lwr supply.upr 302s 1 110 127 302s > print( predict( fit3sls[[ 2 ]]$e4e$eq[[ 2 ]], se.pred = TRUE, interval = "prediction", 302s + level = 0.75, newdata = smallData ) ) 302s fit se.pred lwr upr 302s 1 119 3.41 115 123 302s > 302s > print( predict( fit3sls[[ 3 ]]$e5, se.fit = TRUE, interval = "prediction", 302s + newdata = smallData ) ) 302s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 302s 1 111 2.3 104 117 119 2.44 302s supply.lwr supply.upr 302s 1 111 126 302s > print( predict( fit3sls[[ 3 ]]$e5$eq[[ 1 ]], se.pred = TRUE, interval = "confidence", 302s + newdata = smallData ) ) 302s fit se.pred lwr upr 302s 1 111 3.02 106 115 302s > 302s > print( predict( fit3slsi[[ 4 ]]$e3e, se.fit = TRUE, se.pred = TRUE, 302s + interval = "prediction", level = 0.5, newdata = smallData ) ) 302s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 302s 1 108 2.75 3.66 106 111 112 302s supply.se.fit supply.se.pred supply.lwr supply.upr 302s 1 3.46 4.54 109 115 302s > print( predict( fit3slsd[[ 5 ]]$e4$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 302s + interval = "confidence", level = 0.25, newdata = smallData ) ) 302s fit se.fit se.pred lwr upr 302s 1 111 1.85 3.42 111 112 302s > 302s > print( predict( fit3slsd[[ 2 ]]$e2we, se.fit = TRUE, se.pred = TRUE, 302s + interval = "prediction", level = 0.5, newdata = smallData ) ) 302s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 302s 1 101 2.76 4.1 98.7 104 111 302s supply.se.fit supply.se.pred supply.lwr supply.upr 302s 1 2.79 4.47 108 114 302s > print( predict( fit3slsi[[ 3 ]]$e4we$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 302s + interval = "confidence", level = 0.25, newdata = smallData ) ) 302s fit se.fit se.pred lwr upr 302s 1 111 2.03 2.86 111 112 302s > 302s > 302s > ## ************ correlation of predicted values *************** 302s > print( correlation.systemfit( fit3sls[[ 1 ]]$e1c, 2, 1 ) ) 302s [,1] 302s [1,] 0.880 302s [2,] 0.881 302s [3,] 0.886 302s [4,] 0.901 302s [5,] 0.866 302s [6,] 0.881 302s [7,] 0.892 302s [8,] 0.887 302s [9,] 0.901 302s [10,] 0.924 302s [11,] 0.925 302s [12,] 0.916 302s [13,] 0.910 302s [14,] 0.885 302s [15,] 0.909 302s [16,] 0.921 302s [17,] 0.928 302s [18,] 0.845 302s [19,] 0.890 302s [20,] 0.920 302s > 302s > print( correlation.systemfit( fit3sls[[ 2 ]]$e2e, 1, 2 ) ) 302s [,1] 302s [1,] 0.935 302s [2,] 0.927 302s [3,] 0.923 302s [4,] 0.921 302s [5,] 0.876 302s [6,] 0.884 302s [7,] 0.894 302s [8,] 0.875 302s [9,] 0.890 302s [10,] 0.917 302s [11,] 0.911 302s [12,] 0.898 302s [13,] 0.892 302s [14,] 0.871 302s [15,] 0.905 302s [16,] 0.945 302s [17,] 0.926 302s [18,] 0.908 302s [19,] 0.915 302s [20,] 0.926 302s > 302s > print( correlation.systemfit( fit3sls[[ 5 ]]$e2w, 2, 1 ) ) 302s [,1] 302s [1,] 0.932 302s [2,] 0.928 302s [3,] 0.925 302s [4,] 0.923 302s [5,] 0.882 302s [6,] 0.890 302s [7,] 0.899 302s [8,] 0.880 302s [9,] 0.895 302s [10,] 0.921 302s [11,] 0.914 302s [12,] 0.900 302s [13,] 0.895 302s [14,] 0.876 302s [15,] 0.905 302s [16,] 0.947 302s [17,] 0.928 302s [18,] 0.915 302s [19,] 0.916 302s [20,] 0.928 302s > 302s > print( correlation.systemfit( fit3sls[[ 3 ]]$e3, 2, 1 ) ) 302s [,1] 302s [1,] 0.931 302s [2,] 0.925 302s [3,] 0.922 302s [4,] 0.920 302s [5,] 0.877 302s [6,] 0.884 302s [7,] 0.894 302s [8,] 0.875 302s [9,] 0.890 302s [10,] 0.917 302s [11,] 0.910 302s [12,] 0.896 302s [13,] 0.891 302s [14,] 0.871 302s [15,] 0.903 302s [16,] 0.944 302s [17,] 0.925 302s [18,] 0.911 302s [19,] 0.913 302s [20,] 0.925 302s > 302s > print( correlation.systemfit( fit3sls[[ 4 ]]$e4e, 1, 2 ) ) 302s [,1] 302s [1,] 0.924 302s [2,] 0.933 302s [3,] 0.933 302s [4,] 0.938 302s [5,] 0.862 302s [6,] 0.868 302s [7,] 0.874 302s [8,] 0.879 302s [9,] 0.883 302s [10,] 0.943 302s [11,] 0.830 302s [12,] 0.744 302s [13,] 0.826 302s [14,] 0.834 302s [15,] 0.952 302s [16,] 0.918 302s [17,] 0.954 302s [18,] 0.930 302s [19,] 0.890 302s [20,] 0.893 302s > 302s > print( correlation.systemfit( fit3sls[[ 5 ]]$e5, 2, 1 ) ) 302s [,1] 302s [1,] 0.922 302s [2,] 0.935 302s [3,] 0.934 302s [4,] 0.939 302s [5,] 0.863 302s [6,] 0.868 302s [7,] 0.874 302s [8,] 0.876 302s [9,] 0.884 302s [10,] 0.942 302s [11,] 0.824 302s [12,] 0.747 302s [13,] 0.830 302s [14,] 0.833 302s [15,] 0.952 302s [16,] 0.919 302s [17,] 0.955 302s [18,] 0.928 302s [19,] 0.886 302s [20,] 0.888 302s > 302s > print( correlation.systemfit( fit3slsi[[ 2 ]]$e3e, 1, 2 ) ) 302s [,1] 302s [1,] 0.982 302s [2,] 0.994 302s [3,] 0.993 302s [4,] 0.992 302s [5,] 0.990 302s [6,] 0.990 302s [7,] 0.991 302s [8,] 0.978 302s [9,] 0.984 302s [10,] 0.992 302s [11,] 0.991 302s [12,] 0.985 302s [13,] 0.986 302s [14,] 0.980 302s [15,] 0.976 302s [16,] 0.994 302s [17,] 0.992 302s [18,] 0.987 302s [19,] 0.990 302s [20,] 0.991 302s > 302s > print( correlation.systemfit( fit3slsi[[ 4 ]]$e5w, 1, 2 ) ) 302s [,1] 302s [1,] 0.962 302s [2,] 0.975 302s [3,] 0.974 302s [4,] 0.976 302s [5,] 0.946 302s [6,] 0.948 302s [7,] 0.951 302s [8,] 0.944 302s [9,] 0.952 302s [10,] 0.976 302s [11,] 0.912 302s [12,] 0.871 302s [13,] 0.926 302s [14,] 0.927 302s [15,] 0.979 302s [16,] 0.968 302s [17,] 0.981 302s [18,] 0.970 302s [19,] 0.947 302s [20,] 0.943 302s > 302s > print( correlation.systemfit( fit3slsd[[ 3 ]]$e4, 2, 1 ) ) 302s [,1] 302s [1,] 0.932 302s [2,] 0.954 302s [3,] 0.952 302s [4,] 0.957 302s [5,] 0.892 302s [6,] 0.887 302s [7,] 0.887 302s [8,] 0.905 302s [9,] 0.914 302s [10,] 0.963 302s [11,] 0.860 302s [12,] 0.779 302s [13,] 0.878 302s [14,] 0.852 302s [15,] 0.968 302s [16,] 0.938 302s [17,] 0.973 302s [18,] 0.946 302s [19,] 0.913 302s [20,] 0.921 302s > 302s > 302s > ## ************ Log-Likelihood values *************** 302s > print( logLik( fit3sls[[ 1 ]]$e1c ) ) 302s 'log Lik.' -53 (df=10) 302s > print( logLik( fit3sls[[ 1 ]]$e1c, residCovDiag = TRUE ) ) 302s 'log Lik.' -85.6 (df=10) 302s > 302s > print( logLik( fit3sls[[ 2 ]]$e2e ) ) 302s 'log Lik.' -55.6 (df=9) 302s > print( logLik( fit3sls[[ 2 ]]$e2e, residCovDiag = TRUE ) ) 302s 'log Lik.' -85.4 (df=9) 302s > 302s > print( logLik( fit3sls[[ 3 ]]$e3 ) ) 302s 'log Lik.' -55.3 (df=9) 302s > print( logLik( fit3sls[[ 3 ]]$e3, residCovDiag = TRUE ) ) 302s 'log Lik.' -85.5 (df=9) 302s > 302s > print( logLik( fit3sls[[ 4 ]]$e4e ) ) 302s 'log Lik.' -58.5 (df=8) 302s > print( logLik( fit3sls[[ 4 ]]$e4e, residCovDiag = TRUE ) ) 302s 'log Lik.' -85.2 (df=8) 302s > 302s > print( logLik( fit3sls[[ 2 ]]$e4wSym ) ) 302s 'log Lik.' -58.5 (df=8) 302s > print( logLik( fit3sls[[ 2 ]]$e4wSym, residCovDiag = TRUE ) ) 302s 'log Lik.' -85.3 (df=8) 302s > 302s > print( logLik( fit3sls[[ 5 ]]$e5 ) ) 302s 'log Lik.' -87.3 (df=8) 302s > print( logLik( fit3sls[[ 5 ]]$e5, residCovDiag = TRUE ) ) 302s 'log Lik.' -104 (df=8) 302s > 302s > print( logLik( fit3slsi[[ 2 ]]$e3e ) ) 302s 'log Lik.' -46.7 (df=9) 302s > print( logLik( fit3slsi[[ 2 ]]$e3e, residCovDiag = TRUE ) ) 302s 'log Lik.' -92.1 (df=9) 302s > 302s > print( logLik( fit3slsi[[ 1 ]]$e1we ) ) 302s 'log Lik.' -52.7 (df=10) 302s > print( logLik( fit3slsi[[ 1 ]]$e1we, residCovDiag = TRUE ) ) 302s 'log Lik.' -85.8 (df=10) 302s > 302s > print( logLik( fit3slsd[[ 3 ]]$e4 ) ) 302s 'log Lik.' -59.4 (df=8) 302s > print( logLik( fit3slsd[[ 3 ]]$e4, residCovDiag = TRUE ) ) 302s 'log Lik.' -86.1 (df=8) 302s > 302s > print( logLik( fit3slsd[[ 5 ]]$e2we ) ) 302s 'log Lik.' -65 (df=9) 302s > print( logLik( fit3slsd[[ 5 ]]$e2we, residCovDiag = TRUE ) ) 302s 'log Lik.' -85.7 (df=9) 302s > 302s > 302s > ## ************** F tests **************** 302s > # testing first restriction 302s > print( linearHypothesis( fit3sls[[ 1 ]]$e1, restrm ) ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[1]]$e1 302s 302s Res.Df Df F Pr(>F) 302s 1 34 302s 2 33 1 1.69 0.2 302s > linearHypothesis( fit3sls[[ 1 ]]$e1, restrict ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[1]]$e1 302s 302s Res.Df Df F Pr(>F) 302s 1 34 302s 2 33 1 1.69 0.2 302s > 302s > print( linearHypothesis( fit3sls[[ 2 ]]$e1e, restrm ) ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[2]]$e1e 302s 302s Res.Df Df F Pr(>F) 302s 1 34 302s 2 33 1 1.52 0.23 302s > linearHypothesis( fit3sls[[ 2 ]]$e1e, restrict ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[2]]$e1e 302s 302s Res.Df Df F Pr(>F) 302s 1 34 302s 2 33 1 1.52 0.23 302s > 302s > print( linearHypothesis( fit3sls[[ 3 ]]$e1c, restrm ) ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[3]]$e1c 302s 302s Res.Df Df F Pr(>F) 302s 1 34 302s 2 33 1 2.47 0.13 302s > linearHypothesis( fit3sls[[ 3 ]]$e1c, restrict ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[3]]$e1c 302s 302s Res.Df Df F Pr(>F) 302s 1 34 302s 2 33 1 2.47 0.13 302s > 302s > print( linearHypothesis( fit3slsi[[ 4 ]]$e1, restrm ) ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s 302s Model 1: restricted model 302s Model 2: fit3slsi[[4]]$e1 302s 302s Res.Df Df F Pr(>F) 302s 1 34 302s 2 33 1 4.75 0.037 * 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s > linearHypothesis( fit3slsi[[ 4 ]]$e1, restrict ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s 302s Model 1: restricted model 302s Model 2: fit3slsi[[4]]$e1 302s 302s Res.Df Df F Pr(>F) 302s 1 34 302s 2 33 1 4.75 0.037 * 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s > 302s > print( linearHypothesis( fit3slsd[[ 5 ]]$e1e, restrm ) ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s 302s Model 1: restricted model 302s Model 2: fit3slsd[[5]]$e1e 302s 302s Res.Df Df F Pr(>F) 302s 1 34 302s 2 33 1 0.18 0.68 302s > linearHypothesis( fit3slsd[[ 5 ]]$e1e, restrict ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s 302s Model 1: restricted model 302s Model 2: fit3slsd[[5]]$e1e 302s 302s Res.Df Df F Pr(>F) 302s 1 34 302s 2 33 1 0.18 0.68 302s > 302s > print( linearHypothesis( fit3slsd[[ 2 ]]$e1w, restrm ) ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s 302s Model 1: restricted model 302s Model 2: fit3slsd[[2]]$e1w 302s 302s Res.Df Df F Pr(>F) 302s 1 34 302s 2 33 1 0.51 0.48 302s > linearHypothesis( fit3slsd[[ 2 ]]$e1w, restrict ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s 302s Model 1: restricted model 302s Model 2: fit3slsd[[2]]$e1w 302s 302s Res.Df Df F Pr(>F) 302s 1 34 302s 2 33 1 0.51 0.48 302s > 302s > # testing second restriction 302s > restrOnly2m <- matrix(0,1,7) 302s > restrOnly2q <- 0.5 302s > restrOnly2m[1,2] <- -1 302s > restrOnly2m[1,5] <- 1 302s > restrictOnly2 <- "- demand_price + supply_price = 0.5" 302s > # first restriction not imposed 302s > print( linearHypothesis( fit3sls[[ 5 ]]$e1c, restrOnly2m, restrOnly2q ) ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[5]]$e1c 302s 302s Res.Df Df F Pr(>F) 302s 1 34 302s 2 33 1 0.17 0.69 302s > linearHypothesis( fit3sls[[ 5 ]]$e1c, restrictOnly2 ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[5]]$e1c 302s 302s Res.Df Df F Pr(>F) 302s 1 34 302s 2 33 1 0.17 0.69 302s > 302s > print( linearHypothesis( fit3slsi[[ 1 ]]$e1e, restrOnly2m, restrOnly2q ) ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsi[[1]]$e1e 302s 302s Res.Df Df F Pr(>F) 302s 1 34 302s 2 33 1 0.13 0.72 302s > linearHypothesis( fit3slsi[[ 1 ]]$e1e, restrictOnly2 ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsi[[1]]$e1e 302s 302s Res.Df Df F Pr(>F) 302s 1 34 302s 2 33 1 0.13 0.72 302s > 302s > print( linearHypothesis( fit3slsi[[ 3 ]]$e1we, restrOnly2m, restrOnly2q ) ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsi[[3]]$e1we 302s 302s Res.Df Df F Pr(>F) 302s 1 34 302s 2 33 1 0.13 0.72 302s > linearHypothesis( fit3slsi[[ 3 ]]$e1we, restrictOnly2 ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsi[[3]]$e1we 302s 302s Res.Df Df F Pr(>F) 302s 1 34 302s 2 33 1 0.13 0.72 302s > 302s > print( linearHypothesis( fit3slsd[[ 2 ]]$e1, restrOnly2m, restrOnly2q ) ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsd[[2]]$e1 302s 302s Res.Df Df F Pr(>F) 302s 1 34 302s 2 33 1 0.25 0.62 302s > linearHypothesis( fit3slsd[[ 2 ]]$e1, restrictOnly2 ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsd[[2]]$e1 302s 302s Res.Df Df F Pr(>F) 302s 1 34 302s 2 33 1 0.25 0.62 302s > 302s > # first restriction imposed 302s > print( linearHypothesis( fit3sls[[ 4 ]]$e2, restrOnly2m, restrOnly2q ) ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[4]]$e2 302s 302s Res.Df Df F Pr(>F) 302s 1 35 302s 2 34 1 0.81 0.38 302s > linearHypothesis( fit3sls[[ 4 ]]$e2, restrictOnly2 ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[4]]$e2 302s 302s Res.Df Df F Pr(>F) 302s 1 35 302s 2 34 1 0.81 0.38 302s > 302s > print( linearHypothesis( fit3sls[[ 4 ]]$e3, restrOnly2m, restrOnly2q ) ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[4]]$e3 302s 302s Res.Df Df F Pr(>F) 302s 1 35 302s 2 34 1 0.81 0.38 302s > linearHypothesis( fit3sls[[ 4 ]]$e3, restrictOnly2 ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[4]]$e3 302s 302s Res.Df Df F Pr(>F) 302s 1 35 302s 2 34 1 0.81 0.38 302s > 302s > print( linearHypothesis( fit3sls[[ 1 ]]$e2w, restrOnly2m, restrOnly2q ) ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[1]]$e2w 302s 302s Res.Df Df F Pr(>F) 302s 1 35 302s 2 34 1 0.9 0.35 302s > linearHypothesis( fit3sls[[ 1 ]]$e2w, restrictOnly2 ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[1]]$e2w 302s 302s Res.Df Df F Pr(>F) 302s 1 35 302s 2 34 1 0.9 0.35 302s > 302s > print( linearHypothesis( fit3sls[[ 1 ]]$e3we, restrOnly2m, restrOnly2q ) ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[1]]$e3we 302s 302s Res.Df Df F Pr(>F) 302s 1 35 302s 2 34 1 0.75 0.39 302s > linearHypothesis( fit3sls[[ 1 ]]$e3we, restrictOnly2 ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[1]]$e3we 302s 302s Res.Df Df F Pr(>F) 302s 1 35 302s 2 34 1 0.75 0.39 302s > 302s > print( linearHypothesis( fit3slsi[[ 5 ]]$e2e, restrOnly2m, restrOnly2q ) ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsi[[5]]$e2e 302s 302s Res.Df Df F Pr(>F) 302s 1 35 302s 2 34 1 15.1 0.00044 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s > linearHypothesis( fit3slsi[[ 5 ]]$e2e, restrictOnly2 ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsi[[5]]$e2e 302s 302s Res.Df Df F Pr(>F) 302s 1 35 302s 2 34 1 15.1 0.00044 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s > 302s > print( linearHypothesis( fit3slsi[[ 5 ]]$e3e, restrOnly2m, restrOnly2q ) ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsi[[5]]$e3e 302s 302s Res.Df Df F Pr(>F) 302s 1 35 302s 2 34 1 15.1 0.00044 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s > linearHypothesis( fit3slsi[[ 5 ]]$e3e, restrictOnly2 ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsi[[5]]$e3e 302s 302s Res.Df Df F Pr(>F) 302s 1 35 302s 2 34 1 15.1 0.00044 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s > 302s > print( linearHypothesis( fit3slsd[[ 1 ]]$e2, restrOnly2m, restrOnly2q ) ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsd[[1]]$e2 302s 302s Res.Df Df F Pr(>F) 302s 1 35 302s 2 34 1 0.16 0.69 302s > linearHypothesis( fit3slsd[[ 1 ]]$e2, restrictOnly2 ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsd[[1]]$e2 302s 302s Res.Df Df F Pr(>F) 302s 1 35 302s 2 34 1 0.16 0.69 302s > 302s > print( linearHypothesis( fit3slsd[[ 1 ]]$e3, restrOnly2m, restrOnly2q ) ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsd[[1]]$e3 302s 302s Res.Df Df F Pr(>F) 302s 1 35 302s 2 34 1 0.16 0.69 302s > linearHypothesis( fit3slsd[[ 1 ]]$e3, restrictOnly2 ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsd[[1]]$e3 302s 302s Res.Df Df F Pr(>F) 302s 1 35 302s 2 34 1 0.16 0.69 302s > 302s > # testing both of the restrictions 302s > print( linearHypothesis( fit3sls[[ 2 ]]$e1e, restr2m, restr2q ) ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[2]]$e1e 302s 302s Res.Df Df F Pr(>F) 302s 1 35 302s 2 33 2 1 0.38 302s > linearHypothesis( fit3sls[[ 2 ]]$e1e, restrict2 ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[2]]$e1e 302s 302s Res.Df Df F Pr(>F) 302s 1 35 302s 2 33 2 1 0.38 302s > 302s > print( linearHypothesis( fit3slsi[[ 3 ]]$e1, restr2m, restr2q ) ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsi[[3]]$e1 302s 302s Res.Df Df F Pr(>F) 302s 1 35 302s 2 33 2 5.59 0.0081 ** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s > linearHypothesis( fit3slsi[[ 3 ]]$e1, restrict2 ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsi[[3]]$e1 302s 302s Res.Df Df F Pr(>F) 302s 1 35 302s 2 33 2 5.59 0.0081 ** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s > 302s > print( linearHypothesis( fit3slsd[[ 4 ]]$e1e, restr2m, restr2q ) ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsd[[4]]$e1e 302s 302s Res.Df Df F Pr(>F) 302s 1 35 302s 2 33 2 0.64 0.53 302s > linearHypothesis( fit3slsd[[ 4 ]]$e1e, restrict2 ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsd[[4]]$e1e 302s 302s Res.Df Df F Pr(>F) 302s 1 35 302s 2 33 2 0.64 0.53 302s > 302s > print( linearHypothesis( fit3slsd[[ 5 ]]$e1w, restr2m, restr2q ) ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsd[[5]]$e1w 302s 302s Res.Df Df F Pr(>F) 302s 1 35 302s 2 33 2 0.45 0.64 302s > linearHypothesis( fit3slsd[[ 5 ]]$e1w, restrict2 ) 302s Linear hypothesis test (Theil's F test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsd[[5]]$e1w 302s 302s Res.Df Df F Pr(>F) 302s 1 35 302s 2 33 2 0.45 0.64 302s > 302s > 302s > ## ************** Wald tests **************** 302s > # testing first restriction 302s > print( linearHypothesis( fit3sls[[ 1 ]]$e1, restrm, test = "Chisq" ) ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[1]]$e1 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 34 302s 2 33 1 1.11 0.29 302s > linearHypothesis( fit3sls[[ 1 ]]$e1, restrict, test = "Chisq" ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[1]]$e1 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 34 302s 2 33 1 1.11 0.29 302s > 302s > print( linearHypothesis( fit3sls[[ 2 ]]$e1e, restrm, test = "Chisq" ) ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[2]]$e1e 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 34 302s 2 33 1 1.23 0.27 302s > linearHypothesis( fit3sls[[ 2 ]]$e1e, restrict, test = "Chisq" ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[2]]$e1e 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 34 302s 2 33 1 1.23 0.27 302s > 302s > print( linearHypothesis( fit3sls[[ 3 ]]$e1c, restrm, test = "Chisq" ) ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[3]]$e1c 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 34 302s 2 33 1 1.73 0.19 302s > linearHypothesis( fit3sls[[ 3 ]]$e1c, restrict, test = "Chisq" ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[3]]$e1c 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 34 302s 2 33 1 1.73 0.19 302s > 302s > print( linearHypothesis( fit3slsi[[ 4 ]]$e1, restrm, test = "Chisq" ) ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s 302s Model 1: restricted model 302s Model 2: fit3slsi[[4]]$e1 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 34 302s 2 33 1 4.81 0.028 * 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s > linearHypothesis( fit3slsi[[ 4 ]]$e1, restrict, test = "Chisq" ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s 302s Model 1: restricted model 302s Model 2: fit3slsi[[4]]$e1 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 34 302s 2 33 1 4.81 0.028 * 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s > 302s > print( linearHypothesis( fit3slsi[[ 2 ]]$e1we, restrm, test = "Chisq" ) ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s 302s Model 1: restricted model 302s Model 2: fit3slsi[[2]]$e1we 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 34 302s 2 33 1 5.72 0.017 * 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s > linearHypothesis( fit3slsi[[ 2 ]]$e1we, restrict, test = "Chisq" ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s 302s Model 1: restricted model 302s Model 2: fit3slsi[[2]]$e1we 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 34 302s 2 33 1 5.72 0.017 * 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s > 302s > print( linearHypothesis( fit3slsd[[ 5 ]]$e1e, restrm, test = "Chisq" ) ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s 302s Model 1: restricted model 302s Model 2: fit3slsd[[5]]$e1e 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 34 302s 2 33 1 0.15 0.7 302s > linearHypothesis( fit3slsd[[ 5 ]]$e1e, restrict, test = "Chisq" ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s 302s Model 1: restricted model 302s Model 2: fit3slsd[[5]]$e1e 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 34 302s 2 33 1 0.15 0.7 302s > 302s > # testing second restriction 302s > # first restriction not imposed 302s > print( linearHypothesis( fit3sls[[ 5 ]]$e1c, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[5]]$e1c 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 34 302s 2 33 1 0.12 0.73 302s > linearHypothesis( fit3sls[[ 5 ]]$e1c, restrictOnly2, test = "Chisq" ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[5]]$e1c 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 34 302s 2 33 1 0.12 0.73 302s > 302s > print( linearHypothesis( fit3sls[[ 3 ]]$e1wc, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[3]]$e1wc 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 34 302s 2 33 1 0.12 0.73 302s > linearHypothesis( fit3sls[[ 3 ]]$e1wc, restrictOnly2, test = "Chisq" ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[3]]$e1wc 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 34 302s 2 33 1 0.12 0.73 302s > 302s > print( linearHypothesis( fit3slsi[[ 1 ]]$e1e, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsi[[1]]$e1e 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 34 302s 2 33 1 0.16 0.69 302s > linearHypothesis( fit3slsi[[ 1 ]]$e1e, restrictOnly2, test = "Chisq" ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsi[[1]]$e1e 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 34 302s 2 33 1 0.16 0.69 302s > 302s > print( linearHypothesis( fit3slsd[[ 2 ]]$e1, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsd[[2]]$e1 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 34 302s 2 33 1 0.17 0.68 302s > linearHypothesis( fit3slsd[[ 2 ]]$e1, restrictOnly2, test = "Chisq" ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsd[[2]]$e1 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 34 302s 2 33 1 0.17 0.68 302s > 302s > # first restriction imposed 302s > print( linearHypothesis( fit3sls[[ 4 ]]$e2, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[4]]$e2 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 35 302s 2 34 1 0.55 0.46 302s > linearHypothesis( fit3sls[[ 4 ]]$e2, restrictOnly2, test = "Chisq" ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[4]]$e2 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 35 302s 2 34 1 0.55 0.46 302s > 302s > print( linearHypothesis( fit3sls[[ 4 ]]$e3, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[4]]$e3 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 35 302s 2 34 1 0.55 0.46 302s > linearHypothesis( fit3sls[[ 4 ]]$e3, restrictOnly2, test = "Chisq" ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[4]]$e3 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 35 302s 2 34 1 0.55 0.46 302s > 302s > print( linearHypothesis( fit3slsi[[ 5 ]]$e2e, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsi[[5]]$e2e 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 35 302s 2 34 1 17.8 2.4e-05 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s > linearHypothesis( fit3slsi[[ 5 ]]$e2e, restrictOnly2, test = "Chisq" ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsi[[5]]$e2e 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 35 302s 2 34 1 17.8 2.4e-05 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s > 302s > print( linearHypothesis( fit3slsi[[ 5 ]]$e3e, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsi[[5]]$e3e 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 35 302s 2 34 1 17.8 2.4e-05 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s > linearHypothesis( fit3slsi[[ 5 ]]$e3e, restrictOnly2, test = "Chisq" ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsi[[5]]$e3e 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 35 302s 2 34 1 17.8 2.4e-05 *** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s > 302s > print( linearHypothesis( fit3slsd[[ 1 ]]$e2, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsd[[1]]$e2 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 35 302s 2 34 1 0.13 0.72 302s > linearHypothesis( fit3slsd[[ 1 ]]$e2, restrictOnly2, test = "Chisq" ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsd[[1]]$e2 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 35 302s 2 34 1 0.13 0.72 302s > 302s > print( linearHypothesis( fit3slsd[[ 1 ]]$e3, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsd[[1]]$e3 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 35 302s 2 34 1 0.13 0.72 302s > linearHypothesis( fit3slsd[[ 1 ]]$e3, restrictOnly2, test = "Chisq" ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsd[[1]]$e3 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 35 302s 2 34 1 0.13 0.72 302s > 302s > print( linearHypothesis( fit3slsd[[ 2 ]]$e2we, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsd[[2]]$e2we 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 35 302s 2 34 1 1.52 0.22 302s > linearHypothesis( fit3slsd[[ 2 ]]$e2we, restrictOnly2, test = "Chisq" ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsd[[2]]$e2we 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 35 302s 2 34 1 1.52 0.22 302s > 302s > print( linearHypothesis( fit3slsd[[ 3 ]]$e3w, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsd[[3]]$e3w 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 35 302s 2 34 1 0.23 0.63 302s > linearHypothesis( fit3slsd[[ 3 ]]$e3w, restrictOnly2, test = "Chisq" ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsd[[3]]$e3w 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 35 302s 2 34 1 0.23 0.63 302s > 302s > # testing both of the restrictions 302s > print( linearHypothesis( fit3sls[[ 2 ]]$e1e, restr2m, restr2q, test = "Chisq" ) ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[2]]$e1e 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 35 302s 2 33 2 1.62 0.44 302s > linearHypothesis( fit3sls[[ 2 ]]$e1e, restrict2, test = "Chisq" ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[2]]$e1e 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 35 302s 2 33 2 1.62 0.44 302s > 302s > print( linearHypothesis( fit3sls[[ 5 ]]$e1wc, restr2m, restr2q, test = "Chisq" ) ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[5]]$e1wc 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 35 302s 2 33 2 2.43 0.3 302s > linearHypothesis( fit3sls[[ 5 ]]$e1wc, restrict2, test = "Chisq" ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3sls[[5]]$e1wc 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 35 302s 2 33 2 2.43 0.3 302s > 302s > print( linearHypothesis( fit3slsi[[ 3 ]]$e1, restr2m, restr2q, test = "Chisq" ) ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsi[[3]]$e1 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 35 302s 2 33 2 11.3 0.0035 ** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s > linearHypothesis( fit3slsi[[ 3 ]]$e1, restrict2, test = "Chisq" ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsi[[3]]$e1 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 35 302s 2 33 2 11.3 0.0035 ** 302s --- 302s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 302s > 302s > print( linearHypothesis( fit3slsd[[ 4 ]]$e1e, restr2m, restr2q, test = "Chisq" ) ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsd[[4]]$e1e 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 35 302s 2 33 2 1.55 0.46 302s > linearHypothesis( fit3slsd[[ 4 ]]$e1e, restrict2, test = "Chisq" ) 302s Linear hypothesis test (Chi^2 statistic of a Wald test) 302s 302s Hypothesis: 302s demand_income - supply_trend = 0 302s - demand_price + supply_price = 0.5 302s 302s Model 1: restricted model 302s Model 2: fit3slsd[[4]]$e1e 302s 302s Res.Df Df Chisq Pr(>Chisq) 302s 1 35 302s 2 33 2 1.55 0.46 302s > 302s > 302s > ## *********** model frame ************* 302s > print( mf <- model.frame( fit3sls[[ 3 ]]$e1c ) ) 302s consump price income farmPrice trend 302s 1 98.5 100.3 87.4 98.0 1 302s 2 99.2 104.3 97.6 99.1 2 302s 3 102.2 103.4 96.7 99.1 3 302s 4 101.5 104.5 98.2 98.1 4 302s 5 104.2 98.0 99.8 110.8 5 302s 6 103.2 99.5 100.5 108.2 6 302s 7 104.0 101.1 103.2 105.6 7 302s 8 99.9 104.8 107.8 109.8 8 302s 9 100.3 96.4 96.6 108.7 9 302s 10 102.8 91.2 88.9 100.6 10 302s 11 95.4 93.1 75.1 81.0 11 302s 12 92.4 98.8 76.9 68.6 12 302s 13 94.5 102.9 84.6 70.9 13 302s 14 98.8 98.8 90.6 81.4 14 302s 15 105.8 95.1 103.1 102.3 15 302s 16 100.2 98.5 105.1 105.0 16 302s 17 103.5 86.5 96.4 110.5 17 302s 18 99.9 104.0 104.4 92.5 18 302s 19 105.2 105.8 110.7 89.3 19 302s 20 106.2 113.5 127.1 93.0 20 302s > print( mf1 <- model.frame( fit3sls[[ 3 ]]$e1c$eq[[ 1 ]] ) ) 302s consump price income 302s 1 98.5 100.3 87.4 302s 2 99.2 104.3 97.6 302s 3 102.2 103.4 96.7 302s 4 101.5 104.5 98.2 302s 5 104.2 98.0 99.8 302s 6 103.2 99.5 100.5 302s 7 104.0 101.1 103.2 302s 8 99.9 104.8 107.8 302s 9 100.3 96.4 96.6 302s 10 102.8 91.2 88.9 302s 11 95.4 93.1 75.1 302s 12 92.4 98.8 76.9 302s 13 94.5 102.9 84.6 302s 14 98.8 98.8 90.6 302s 15 105.8 95.1 103.1 302s 16 100.2 98.5 105.1 302s 17 103.5 86.5 96.4 302s 18 99.9 104.0 104.4 302s 19 105.2 105.8 110.7 302s 20 106.2 113.5 127.1 302s > print( attributes( mf1 )$terms ) 302s consump ~ price + income 302s attr(,"variables") 302s list(consump, price, income) 302s attr(,"factors") 302s price income 302s consump 0 0 302s price 1 0 302s income 0 1 302s attr(,"term.labels") 302s [1] "price" "income" 302s attr(,"order") 302s [1] 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 1 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(consump, price, income) 302s attr(,"dataClasses") 302s consump price income 302s "numeric" "numeric" "numeric" 302s > print( mf2 <- model.frame( fit3sls[[ 3 ]]$e1c$eq[[ 2 ]] ) ) 302s consump price farmPrice trend 302s 1 98.5 100.3 98.0 1 302s 2 99.2 104.3 99.1 2 302s 3 102.2 103.4 99.1 3 302s 4 101.5 104.5 98.1 4 302s 5 104.2 98.0 110.8 5 302s 6 103.2 99.5 108.2 6 302s 7 104.0 101.1 105.6 7 302s 8 99.9 104.8 109.8 8 302s 9 100.3 96.4 108.7 9 302s 10 102.8 91.2 100.6 10 302s 11 95.4 93.1 81.0 11 302s 12 92.4 98.8 68.6 12 302s 13 94.5 102.9 70.9 13 302s 14 98.8 98.8 81.4 14 302s 15 105.8 95.1 102.3 15 302s 16 100.2 98.5 105.0 16 302s 17 103.5 86.5 110.5 17 302s 18 99.9 104.0 92.5 18 302s 19 105.2 105.8 89.3 19 302s 20 106.2 113.5 93.0 20 302s > print( attributes( mf2 )$terms ) 302s consump ~ price + farmPrice + trend 302s attr(,"variables") 302s list(consump, price, farmPrice, trend) 302s attr(,"factors") 302s price farmPrice trend 302s consump 0 0 0 302s price 1 0 0 302s farmPrice 0 1 0 302s trend 0 0 1 302s attr(,"term.labels") 302s [1] "price" "farmPrice" "trend" 302s attr(,"order") 302s [1] 1 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 1 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(consump, price, farmPrice, trend) 302s attr(,"dataClasses") 302s consump price farmPrice trend 302s "numeric" "numeric" "numeric" "numeric" 302s > 302s > print( all.equal( mf, model.frame( fit3sls[[ 3 ]]$e1wc ) ) ) 302s [1] TRUE 302s > print( all.equal( mf2, model.frame( fit3sls[[ 3 ]]$e1wc$eq[[ 2 ]] ) ) ) 302s [1] TRUE 302s > 302s > print( all.equal( mf, model.frame( fit3sls[[ 4 ]]$e2e ) ) ) 302s [1] TRUE 302s > print( all.equal( mf2, model.frame( fit3sls[[ 4 ]]$e2e$eq[[ 2 ]] ) ) ) 302s [1] TRUE 302s > 302s > print( all.equal( mf, model.frame( fit3sls[[ 5 ]]$e3 ) ) ) 302s [1] TRUE 302s > print( all.equal( mf1, model.frame( fit3sls[[ 5 ]]$e3$eq[[ 1 ]] ) ) ) 302s [1] TRUE 302s > 302s > print( all.equal( mf, model.frame( fit3sls[[ 1 ]]$e4e ) ) ) 302s [1] TRUE 302s > print( all.equal( mf2, model.frame( fit3sls[[ 1 ]]$e4e$eq[[ 2 ]] ) ) ) 302s [1] TRUE 302s > 302s > print( all.equal( mf, model.frame( fit3sls[[ 2 ]]$e5 ) ) ) 302s [1] TRUE 302s > print( all.equal( mf1, model.frame( fit3sls[[ 3 ]]$e5$eq[[ 1 ]] ) ) ) 302s [1] TRUE 302s > 302s > print( all.equal( mf, model.frame( fit3slsi[[ 4 ]]$e3e ) ) ) 302s [1] TRUE 302s > print( all.equal( mf1, model.frame( fit3slsi[[ 4 ]]$e3e$eq[[ 1 ]] ) ) ) 302s [1] TRUE 302s > 302s > print( all.equal( mf, model.frame( fit3slsd[[ 5 ]]$e4 ) ) ) 302s [1] TRUE 302s > print( all.equal( mf2, model.frame( fit3slsd[[ 5 ]]$e4$eq[[ 2 ]] ) ) ) 302s [1] TRUE 302s > 302s > fit3sls[[ 3 ]]$e1c$eq[[ 1 ]]$modelInst 302s income farmPrice trend 302s 1 87.4 98.0 1 302s 2 97.6 99.1 2 302s 3 96.7 99.1 3 302s 4 98.2 98.1 4 302s 5 99.8 110.8 5 302s 6 100.5 108.2 6 302s 7 103.2 105.6 7 302s 8 107.8 109.8 8 302s 9 96.6 108.7 9 302s 10 88.9 100.6 10 302s 11 75.1 81.0 11 302s 12 76.9 68.6 12 302s 13 84.6 70.9 13 302s 14 90.6 81.4 14 302s 15 103.1 102.3 15 302s 16 105.1 105.0 16 302s 17 96.4 110.5 17 302s 18 104.4 92.5 18 302s 19 110.7 89.3 19 302s 20 127.1 93.0 20 302s > fit3sls[[ 3 ]]$e1c$eq[[ 2 ]]$modelInst 302s income farmPrice trend 302s 1 87.4 98.0 1 302s 2 97.6 99.1 2 302s 3 96.7 99.1 3 302s 4 98.2 98.1 4 302s 5 99.8 110.8 5 302s 6 100.5 108.2 6 302s 7 103.2 105.6 7 302s 8 107.8 109.8 8 302s 9 96.6 108.7 9 302s 10 88.9 100.6 10 302s 11 75.1 81.0 11 302s 12 76.9 68.6 12 302s 13 84.6 70.9 13 302s 14 90.6 81.4 14 302s 15 103.1 102.3 15 302s 16 105.1 105.0 16 302s 17 96.4 110.5 17 302s 18 104.4 92.5 18 302s 19 110.7 89.3 19 302s 20 127.1 93.0 20 302s > 302s > fit3sls[[ 1 ]]$e3$eq[[ 1 ]]$modelInst 302s income farmPrice trend 302s 1 87.4 98.0 1 302s 2 97.6 99.1 2 302s 3 96.7 99.1 3 302s 4 98.2 98.1 4 302s 5 99.8 110.8 5 302s 6 100.5 108.2 6 302s 7 103.2 105.6 7 302s 8 107.8 109.8 8 302s 9 96.6 108.7 9 302s 10 88.9 100.6 10 302s 11 75.1 81.0 11 302s 12 76.9 68.6 12 302s 13 84.6 70.9 13 302s 14 90.6 81.4 14 302s 15 103.1 102.3 15 302s 16 105.1 105.0 16 302s 17 96.4 110.5 17 302s 18 104.4 92.5 18 302s 19 110.7 89.3 19 302s 20 127.1 93.0 20 302s > fit3sls[[ 1 ]]$e3$eq[[ 2 ]]$modelInst 302s income farmPrice trend 302s 1 87.4 98.0 1 302s 2 97.6 99.1 2 302s 3 96.7 99.1 3 302s 4 98.2 98.1 4 302s 5 99.8 110.8 5 302s 6 100.5 108.2 6 302s 7 103.2 105.6 7 302s 8 107.8 109.8 8 302s 9 96.6 108.7 9 302s 10 88.9 100.6 10 302s 11 75.1 81.0 11 302s 12 76.9 68.6 12 302s 13 84.6 70.9 13 302s 14 90.6 81.4 14 302s 15 103.1 102.3 15 302s 16 105.1 105.0 16 302s 17 96.4 110.5 17 302s 18 104.4 92.5 18 302s 19 110.7 89.3 19 302s 20 127.1 93.0 20 302s > 302s > fit3slsd[[ 5 ]]$e4$eq[[ 1 ]]$modelInst 302s income farmPrice 302s 1 87.4 98.0 302s 2 97.6 99.1 302s 3 96.7 99.1 302s 4 98.2 98.1 302s 5 99.8 110.8 302s 6 100.5 108.2 302s 7 103.2 105.6 302s 8 107.8 109.8 302s 9 96.6 108.7 302s 10 88.9 100.6 302s 11 75.1 81.0 302s 12 76.9 68.6 302s 13 84.6 70.9 302s 14 90.6 81.4 302s 15 103.1 102.3 302s 16 105.1 105.0 302s 17 96.4 110.5 302s 18 104.4 92.5 302s 19 110.7 89.3 302s 20 127.1 93.0 302s > fit3slsd[[ 5 ]]$e4$eq[[ 2 ]]$modelInst 302s income farmPrice trend 302s 1 87.4 98.0 1 302s 2 97.6 99.1 2 302s 3 96.7 99.1 3 302s 4 98.2 98.1 4 302s 5 99.8 110.8 5 302s 6 100.5 108.2 6 302s 7 103.2 105.6 7 302s 8 107.8 109.8 8 302s 9 96.6 108.7 9 302s 10 88.9 100.6 10 302s 11 75.1 81.0 11 302s 12 76.9 68.6 12 302s 13 84.6 70.9 13 302s 14 90.6 81.4 14 302s 15 103.1 102.3 15 302s 16 105.1 105.0 16 302s 17 96.4 110.5 17 302s 18 104.4 92.5 18 302s 19 110.7 89.3 19 302s 20 127.1 93.0 20 302s > 302s > 302s > ## **************** model matrix ************************ 302s > # with x (returnModelMatrix) = TRUE 302s > print( !is.null( fit3sls[[ 4 ]]$e1c$eq[[ 1 ]]$x ) ) 302s [1] TRUE 302s > print( mm <- model.matrix( fit3sls[[ 4 ]]$e1c ) ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s demand_1 1 100.3 87.4 0 302s demand_2 1 104.3 97.6 0 302s demand_3 1 103.4 96.7 0 302s demand_4 1 104.5 98.2 0 302s demand_5 1 98.0 99.8 0 302s demand_6 1 99.5 100.5 0 302s demand_7 1 101.1 103.2 0 302s demand_8 1 104.8 107.8 0 302s demand_9 1 96.4 96.6 0 302s demand_10 1 91.2 88.9 0 302s demand_11 1 93.1 75.1 0 302s demand_12 1 98.8 76.9 0 302s demand_13 1 102.9 84.6 0 302s demand_14 1 98.8 90.6 0 302s demand_15 1 95.1 103.1 0 302s demand_16 1 98.5 105.1 0 302s demand_17 1 86.5 96.4 0 302s demand_18 1 104.0 104.4 0 302s demand_19 1 105.8 110.7 0 302s demand_20 1 113.5 127.1 0 302s supply_1 0 0.0 0.0 1 302s supply_2 0 0.0 0.0 1 302s supply_3 0 0.0 0.0 1 302s supply_4 0 0.0 0.0 1 302s supply_5 0 0.0 0.0 1 302s supply_6 0 0.0 0.0 1 302s supply_7 0 0.0 0.0 1 302s supply_8 0 0.0 0.0 1 302s supply_9 0 0.0 0.0 1 302s supply_10 0 0.0 0.0 1 302s supply_11 0 0.0 0.0 1 302s supply_12 0 0.0 0.0 1 302s supply_13 0 0.0 0.0 1 302s supply_14 0 0.0 0.0 1 302s supply_15 0 0.0 0.0 1 302s supply_16 0 0.0 0.0 1 302s supply_17 0 0.0 0.0 1 302s supply_18 0 0.0 0.0 1 302s supply_19 0 0.0 0.0 1 302s supply_20 0 0.0 0.0 1 302s supply_price supply_farmPrice supply_trend 302s demand_1 0.0 0.0 0 302s demand_2 0.0 0.0 0 302s demand_3 0.0 0.0 0 302s demand_4 0.0 0.0 0 302s demand_5 0.0 0.0 0 302s demand_6 0.0 0.0 0 302s demand_7 0.0 0.0 0 302s demand_8 0.0 0.0 0 302s demand_9 0.0 0.0 0 302s demand_10 0.0 0.0 0 302s demand_11 0.0 0.0 0 302s demand_12 0.0 0.0 0 302s demand_13 0.0 0.0 0 302s demand_14 0.0 0.0 0 302s demand_15 0.0 0.0 0 302s demand_16 0.0 0.0 0 302s demand_17 0.0 0.0 0 302s demand_18 0.0 0.0 0 302s demand_19 0.0 0.0 0 302s demand_20 0.0 0.0 0 302s supply_1 100.3 98.0 1 302s supply_2 104.3 99.1 2 302s supply_3 103.4 99.1 3 302s supply_4 104.5 98.1 4 302s supply_5 98.0 110.8 5 302s supply_6 99.5 108.2 6 302s supply_7 101.1 105.6 7 302s supply_8 104.8 109.8 8 302s supply_9 96.4 108.7 9 302s supply_10 91.2 100.6 10 302s supply_11 93.1 81.0 11 302s supply_12 98.8 68.6 12 302s supply_13 102.9 70.9 13 302s supply_14 98.8 81.4 14 302s supply_15 95.1 102.3 15 302s supply_16 98.5 105.0 16 302s supply_17 86.5 110.5 17 302s supply_18 104.0 92.5 18 302s supply_19 105.8 89.3 19 302s supply_20 113.5 93.0 20 302s > print( mm1 <- model.matrix( fit3sls[[ 4 ]]$e1c$eq[[ 1 ]] ) ) 302s (Intercept) price income 302s 1 1 100.3 87.4 302s 2 1 104.3 97.6 302s 3 1 103.4 96.7 302s 4 1 104.5 98.2 302s 5 1 98.0 99.8 302s 6 1 99.5 100.5 302s 7 1 101.1 103.2 302s 8 1 104.8 107.8 302s 9 1 96.4 96.6 302s 10 1 91.2 88.9 302s 11 1 93.1 75.1 302s 12 1 98.8 76.9 302s 13 1 102.9 84.6 302s 14 1 98.8 90.6 302s 15 1 95.1 103.1 302s 16 1 98.5 105.1 302s 17 1 86.5 96.4 302s 18 1 104.0 104.4 302s 19 1 105.8 110.7 302s 20 1 113.5 127.1 302s attr(,"assign") 302s [1] 0 1 2 302s > print( mm2 <- model.matrix( fit3sls[[ 4 ]]$e1c$eq[[ 2 ]] ) ) 302s (Intercept) price farmPrice trend 302s 1 1 100.3 98.0 1 302s 2 1 104.3 99.1 2 302s 3 1 103.4 99.1 3 302s 4 1 104.5 98.1 4 302s 5 1 98.0 110.8 5 302s 6 1 99.5 108.2 6 302s 7 1 101.1 105.6 7 302s 8 1 104.8 109.8 8 302s 9 1 96.4 108.7 9 302s 10 1 91.2 100.6 10 302s 11 1 93.1 81.0 11 302s 12 1 98.8 68.6 12 302s 13 1 102.9 70.9 13 302s 14 1 98.8 81.4 14 302s 15 1 95.1 102.3 15 302s 16 1 98.5 105.0 16 302s 17 1 86.5 110.5 17 302s 18 1 104.0 92.5 18 302s 19 1 105.8 89.3 19 302s 20 1 113.5 93.0 20 302s attr(,"assign") 302s [1] 0 1 2 3 302s > 302s > # with x (returnModelMatrix) = FALSE 302s > print( all.equal( mm, model.matrix( fit3sls[[ 4 ]]$e1wc ) ) ) 302s [1] TRUE 302s > print( all.equal( mm1, model.matrix( fit3sls[[ 4 ]]$e1wc$eq[[ 1 ]] ) ) ) 302s [1] TRUE 302s > print( all.equal( mm2, model.matrix( fit3sls[[ 4 ]]$e1wc$eq[[ 2 ]] ) ) ) 302s [1] TRUE 302s > print( !is.null( fit3sls[[ 4 ]]$e1wc$eq[[ 1 ]]$x ) ) 302s [1] FALSE 302s > 302s > # with x (returnModelMatrix) = TRUE 302s > print( !is.null( fit3sls[[ 5 ]]$e2$eq[[ 1 ]]$x ) ) 302s [1] TRUE 302s > print( all.equal( mm, model.matrix( fit3sls[[ 5 ]]$e2 ) ) ) 302s [1] TRUE 302s > print( all.equal( mm1, model.matrix( fit3sls[[ 5 ]]$e2$eq[[ 1 ]] ) ) ) 302s [1] TRUE 302s > print( all.equal( mm2, model.matrix( fit3sls[[ 5 ]]$e2$eq[[ 2 ]] ) ) ) 302s [1] TRUE 302s > 302s > # with x (returnModelMatrix) = FALSE 302s > print( all.equal( mm, model.matrix( fit3sls[[ 5 ]]$e2e ) ) ) 302s [1] TRUE 302s > print( all.equal( mm1, model.matrix( fit3sls[[ 5 ]]$e2e$eq[[ 1 ]] ) ) ) 302s [1] TRUE 302s > print( all.equal( mm2, model.matrix( fit3sls[[ 5 ]]$e2e$eq[[ 2 ]] ) ) ) 302s [1] TRUE 302s > print( !is.null( fit3sls[[ 5 ]]$e1wc$e2e[[ 1 ]]$x ) ) 302s [1] FALSE 302s > 302s > # with x (returnModelMatrix) = TRUE 302s > print( !is.null( fit3sls[[ 1 ]]$e3e$eq[[ 1 ]]$x ) ) 302s [1] TRUE 302s > print( all.equal( mm, model.matrix( fit3sls[[ 1 ]]$e3e ) ) ) 302s [1] TRUE 302s > print( all.equal( mm1, model.matrix( fit3sls[[ 1 ]]$e3e$eq[[ 1 ]] ) ) ) 302s [1] TRUE 302s > print( all.equal( mm2, model.matrix( fit3sls[[ 1 ]]$e3e$eq[[ 2 ]] ) ) ) 302s [1] TRUE 302s > 302s > # with x (returnModelMatrix) = FALSE 302s > print( all.equal( mm, model.matrix( fit3sls[[ 1 ]]$e3 ) ) ) 302s [1] TRUE 302s > print( all.equal( mm1, model.matrix( fit3sls[[ 1 ]]$e3$eq[[ 1 ]] ) ) ) 302s [1] TRUE 302s > print( all.equal( mm2, model.matrix( fit3sls[[ 1 ]]$e3$eq[[ 2 ]] ) ) ) 302s [1] TRUE 302s > print( !is.null( fit3sls[[ 1 ]]$e3$eq[[ 1 ]]$x ) ) 302s [1] FALSE 302s > 302s > # with x (returnModelMatrix) = TRUE 302s > print( !is.null( fit3slsi[[ 2 ]]$e4$eq[[ 1 ]]$x ) ) 302s [1] TRUE 302s > print( all.equal( mm, model.matrix( fit3slsi[[ 2 ]]$e4 ) ) ) 302s [1] TRUE 302s > print( all.equal( mm1, model.matrix( fit3slsi[[ 2 ]]$e4$eq[[ 1 ]] ) ) ) 302s [1] TRUE 302s > print( all.equal( mm2, model.matrix( fit3slsi[[ 2 ]]$e4$eq[[ 2 ]] ) ) ) 302s [1] TRUE 302s > 302s > # with x (returnModelMatrix) = FALSE 302s > print( all.equal( mm, model.matrix( fit3slsi[[ 2 ]]$e4we ) ) ) 302s [1] TRUE 302s > print( all.equal( mm1, model.matrix( fit3slsi[[ 2 ]]$e4we$eq[[ 1 ]] ) ) ) 302s [1] TRUE 302s > print( all.equal( mm2, model.matrix( fit3slsi[[ 2 ]]$e4we$eq[[ 2 ]] ) ) ) 302s [1] TRUE 302s > print( !is.null( fit3slsi[[ 2 ]]$e1wc$e4we[[ 1 ]]$x ) ) 302s [1] FALSE 302s > 302s > # with x (returnModelMatrix) = TRUE 302s > print( !is.null( fit3slsi[[ 5 ]]$e5w$eq[[ 1 ]]$x ) ) 302s [1] TRUE 302s > print( all.equal( mm, model.matrix( fit3slsi[[ 5 ]]$e5w ) ) ) 302s [1] TRUE 302s > print( all.equal( mm1, model.matrix( fit3slsi[[ 5 ]]$e5w$eq[[ 1 ]] ) ) ) 302s [1] TRUE 302s > print( all.equal( mm2, model.matrix( fit3slsi[[ 5 ]]$e5w$eq[[ 2 ]] ) ) ) 302s [1] TRUE 302s > 302s > # with x (returnModelMatrix) = FALSE 302s > print( all.equal( mm, model.matrix( fit3slsi[[ 5 ]]$e5 ) ) ) 302s [1] TRUE 302s > print( all.equal( mm1, model.matrix( fit3slsi[[ 5 ]]$e5$eq[[ 1 ]] ) ) ) 302s [1] TRUE 302s > print( all.equal( mm2, model.matrix( fit3slsi[[ 5 ]]$e5$eq[[ 2 ]] ) ) ) 302s [1] TRUE 302s > print( !is.null( fit3slsi[[ 5 ]]$e5$eq[[ 1 ]]$x ) ) 302s [1] FALSE 302s > 302s > # with x (returnModelMatrix) = TRUE 302s > print( !is.null( fit3slsd[[ 3 ]]$e5e$eq[[ 1 ]]$x ) ) 302s [1] TRUE 302s > print( all.equal( mm, model.matrix( fit3slsd[[ 3 ]]$e5e ) ) ) 302s [1] TRUE 302s > print( all.equal( mm1, model.matrix( fit3slsd[[ 3 ]]$e5e$eq[[ 1 ]] ) ) ) 302s [1] TRUE 302s > print( all.equal( mm2, model.matrix( fit3slsd[[ 3 ]]$e5e$eq[[ 2 ]] ) ) ) 302s [1] TRUE 302s > 302s > # with x (returnModelMatrix) = FALSE 302s > print( all.equal( mm, model.matrix( fit3slsd[[ 3 ]]$e5we ) ) ) 302s [1] TRUE 302s > print( all.equal( mm1, model.matrix( fit3slsd[[ 3 ]]$e5we$eq[[ 1 ]] ) ) ) 302s [1] TRUE 302s > print( all.equal( mm2, model.matrix( fit3slsd[[ 3 ]]$e5we$eq[[ 2 ]] ) ) ) 302s [1] TRUE 302s > print( !is.null( fit3sls[[ 3 ]]$e5we$eq[[ 1 ]]$x ) ) 302s [1] FALSE 302s > 302s > # with x (returnModelMatrix) = TRUE 302s > print( !is.null( fit3slsd[[ 2 ]]$e3w$eq[[ 1 ]]$x ) ) 302s [1] TRUE 302s > print( all.equal( mm, model.matrix( fit3slsd[[ 2 ]]$e3w ) ) ) 302s [1] TRUE 302s > print( all.equal( mm1, model.matrix( fit3slsd[[ 2 ]]$e3w$eq[[ 1 ]] ) ) ) 302s [1] TRUE 302s > print( all.equal( mm2, model.matrix( fit3slsd[[ 2 ]]$e3w$eq[[ 2 ]] ) ) ) 302s [1] TRUE 302s > 302s > # with x (returnModelMatrix) = FALSE 302s > print( all.equal( mm, model.matrix( fit3slsd[[ 2 ]]$e3 ) ) ) 302s [1] TRUE 302s > print( all.equal( mm1, model.matrix( fit3slsd[[ 2 ]]$e3$eq[[ 1 ]] ) ) ) 302s [1] TRUE 302s > print( all.equal( mm2, model.matrix( fit3slsd[[ 2 ]]$e3$eq[[ 2 ]] ) ) ) 302s [1] TRUE 302s > print( !is.null( fit3slsd[[ 2 ]]$e3$eq[[ 1 ]]$x ) ) 302s [1] FALSE 302s > 302s > # matrices of instrumental variables 302s > model.matrix( fit3sls[[ 1 ]]$e1c, which = "z" ) 302s demand_(Intercept) demand_income demand_farmPrice demand_trend 302s demand_1 1 87.4 98.0 1 302s demand_2 1 97.6 99.1 2 302s demand_3 1 96.7 99.1 3 302s demand_4 1 98.2 98.1 4 302s demand_5 1 99.8 110.8 5 302s demand_6 1 100.5 108.2 6 302s demand_7 1 103.2 105.6 7 302s demand_8 1 107.8 109.8 8 302s demand_9 1 96.6 108.7 9 302s demand_10 1 88.9 100.6 10 302s demand_11 1 75.1 81.0 11 302s demand_12 1 76.9 68.6 12 302s demand_13 1 84.6 70.9 13 302s demand_14 1 90.6 81.4 14 302s demand_15 1 103.1 102.3 15 302s demand_16 1 105.1 105.0 16 302s demand_17 1 96.4 110.5 17 302s demand_18 1 104.4 92.5 18 302s demand_19 1 110.7 89.3 19 302s demand_20 1 127.1 93.0 20 302s supply_1 0 0.0 0.0 0 302s supply_2 0 0.0 0.0 0 302s supply_3 0 0.0 0.0 0 302s supply_4 0 0.0 0.0 0 302s supply_5 0 0.0 0.0 0 302s supply_6 0 0.0 0.0 0 302s supply_7 0 0.0 0.0 0 302s supply_8 0 0.0 0.0 0 302s supply_9 0 0.0 0.0 0 302s supply_10 0 0.0 0.0 0 302s supply_11 0 0.0 0.0 0 302s supply_12 0 0.0 0.0 0 302s supply_13 0 0.0 0.0 0 302s supply_14 0 0.0 0.0 0 302s supply_15 0 0.0 0.0 0 302s supply_16 0 0.0 0.0 0 302s supply_17 0 0.0 0.0 0 302s supply_18 0 0.0 0.0 0 302s supply_19 0 0.0 0.0 0 302s supply_20 0 0.0 0.0 0 302s supply_(Intercept) supply_income supply_farmPrice supply_trend 302s demand_1 0 0.0 0.0 0 302s demand_2 0 0.0 0.0 0 302s demand_3 0 0.0 0.0 0 302s demand_4 0 0.0 0.0 0 302s demand_5 0 0.0 0.0 0 302s demand_6 0 0.0 0.0 0 302s demand_7 0 0.0 0.0 0 302s demand_8 0 0.0 0.0 0 302s demand_9 0 0.0 0.0 0 302s demand_10 0 0.0 0.0 0 302s demand_11 0 0.0 0.0 0 302s demand_12 0 0.0 0.0 0 302s demand_13 0 0.0 0.0 0 302s demand_14 0 0.0 0.0 0 302s demand_15 0 0.0 0.0 0 302s demand_16 0 0.0 0.0 0 302s demand_17 0 0.0 0.0 0 302s demand_18 0 0.0 0.0 0 302s demand_19 0 0.0 0.0 0 302s demand_20 0 0.0 0.0 0 302s supply_1 1 87.4 98.0 1 302s supply_2 1 97.6 99.1 2 302s supply_3 1 96.7 99.1 3 302s supply_4 1 98.2 98.1 4 302s supply_5 1 99.8 110.8 5 302s supply_6 1 100.5 108.2 6 302s supply_7 1 103.2 105.6 7 302s supply_8 1 107.8 109.8 8 302s supply_9 1 96.6 108.7 9 302s supply_10 1 88.9 100.6 10 302s supply_11 1 75.1 81.0 11 302s supply_12 1 76.9 68.6 12 302s supply_13 1 84.6 70.9 13 302s supply_14 1 90.6 81.4 14 302s supply_15 1 103.1 102.3 15 302s supply_16 1 105.1 105.0 16 302s supply_17 1 96.4 110.5 17 302s supply_18 1 104.4 92.5 18 302s supply_19 1 110.7 89.3 19 302s supply_20 1 127.1 93.0 20 302s > model.matrix( fit3sls[[ 3 ]]$e1c$eq[[ 1 ]], which = "z" ) 302s (Intercept) income farmPrice trend 302s 1 1 87.4 98.0 1 302s 2 1 97.6 99.1 2 302s 3 1 96.7 99.1 3 302s 4 1 98.2 98.1 4 302s 5 1 99.8 110.8 5 302s 6 1 100.5 108.2 6 302s 7 1 103.2 105.6 7 302s 8 1 107.8 109.8 8 302s 9 1 96.6 108.7 9 302s 10 1 88.9 100.6 10 302s 11 1 75.1 81.0 11 302s 12 1 76.9 68.6 12 302s 13 1 84.6 70.9 13 302s 14 1 90.6 81.4 14 302s 15 1 103.1 102.3 15 302s 16 1 105.1 105.0 16 302s 17 1 96.4 110.5 17 302s 18 1 104.4 92.5 18 302s 19 1 110.7 89.3 19 302s 20 1 127.1 93.0 20 302s attr(,"assign") 302s [1] 0 1 2 3 302s > model.matrix( fit3sls[[ 4 ]]$e1c$eq[[ 2 ]], which = "z" ) 302s (Intercept) income farmPrice trend 302s 1 1 87.4 98.0 1 302s 2 1 97.6 99.1 2 302s 3 1 96.7 99.1 3 302s 4 1 98.2 98.1 4 302s 5 1 99.8 110.8 5 302s 6 1 100.5 108.2 6 302s 7 1 103.2 105.6 7 302s 8 1 107.8 109.8 8 302s 9 1 96.6 108.7 9 302s 10 1 88.9 100.6 10 302s 11 1 75.1 81.0 11 302s 12 1 76.9 68.6 12 302s 13 1 84.6 70.9 13 302s 14 1 90.6 81.4 14 302s 15 1 103.1 102.3 15 302s 16 1 105.1 105.0 16 302s 17 1 96.4 110.5 17 302s 18 1 104.4 92.5 18 302s 19 1 110.7 89.3 19 302s 20 1 127.1 93.0 20 302s attr(,"assign") 302s [1] 0 1 2 3 302s > 302s > # matrices of fitted regressors 302s > model.matrix( fit3slsd[[ 1 ]]$e3w, which = "xHat" ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s demand_1 1 95.2 87.4 0 302s demand_2 1 99.3 97.6 0 302s demand_3 1 99.0 96.7 0 302s demand_4 1 99.9 98.2 0 302s demand_5 1 97.0 99.8 0 302s demand_6 1 98.0 100.5 0 302s demand_7 1 99.9 103.2 0 302s demand_8 1 100.7 107.8 0 302s demand_9 1 96.2 96.6 0 302s demand_10 1 95.1 88.9 0 302s demand_11 1 94.7 75.1 0 302s demand_12 1 99.0 76.9 0 302s demand_13 1 101.7 84.6 0 302s demand_14 1 101.3 90.6 0 302s demand_15 1 100.8 103.1 0 302s demand_16 1 100.9 105.1 0 302s demand_17 1 95.6 96.4 0 302s demand_18 1 104.2 104.4 0 302s demand_19 1 107.8 110.7 0 302s demand_20 1 113.9 127.1 0 302s supply_1 0 0.0 0.0 1 302s supply_2 0 0.0 0.0 1 302s supply_3 0 0.0 0.0 1 302s supply_4 0 0.0 0.0 1 302s supply_5 0 0.0 0.0 1 302s supply_6 0 0.0 0.0 1 302s supply_7 0 0.0 0.0 1 302s supply_8 0 0.0 0.0 1 302s supply_9 0 0.0 0.0 1 302s supply_10 0 0.0 0.0 1 302s supply_11 0 0.0 0.0 1 302s supply_12 0 0.0 0.0 1 302s supply_13 0 0.0 0.0 1 302s supply_14 0 0.0 0.0 1 302s supply_15 0 0.0 0.0 1 302s supply_16 0 0.0 0.0 1 302s supply_17 0 0.0 0.0 1 302s supply_18 0 0.0 0.0 1 302s supply_19 0 0.0 0.0 1 302s supply_20 0 0.0 0.0 1 302s supply_price supply_farmPrice supply_trend 302s demand_1 0.0 0.0 0 302s demand_2 0.0 0.0 0 302s demand_3 0.0 0.0 0 302s demand_4 0.0 0.0 0 302s demand_5 0.0 0.0 0 302s demand_6 0.0 0.0 0 302s demand_7 0.0 0.0 0 302s demand_8 0.0 0.0 0 302s demand_9 0.0 0.0 0 302s demand_10 0.0 0.0 0 302s demand_11 0.0 0.0 0 302s demand_12 0.0 0.0 0 302s demand_13 0.0 0.0 0 302s demand_14 0.0 0.0 0 302s demand_15 0.0 0.0 0 302s demand_16 0.0 0.0 0 302s demand_17 0.0 0.0 0 302s demand_18 0.0 0.0 0 302s demand_19 0.0 0.0 0 302s demand_20 0.0 0.0 0 302s supply_1 99.6 98.0 1 302s supply_2 105.1 99.1 2 302s supply_3 103.8 99.1 3 302s supply_4 104.5 98.1 4 302s supply_5 98.7 110.8 5 302s supply_6 99.6 108.2 6 302s supply_7 102.0 105.6 7 302s supply_8 102.2 109.8 8 302s supply_9 94.6 108.7 9 302s supply_10 92.7 100.6 10 302s supply_11 92.4 81.0 11 302s supply_12 98.9 68.6 12 302s supply_13 102.2 70.9 13 302s supply_14 100.3 81.4 14 302s supply_15 97.6 102.3 15 302s supply_16 96.9 105.0 16 302s supply_17 87.7 110.5 17 302s supply_18 101.1 92.5 18 302s supply_19 106.1 89.3 19 302s supply_20 114.4 93.0 20 302s > model.matrix( fit3slsd[[ 3 ]]$e3w$eq[[ 1 ]], which = "xHat" ) 302s (Intercept) price income 302s 1 1 95.2 87.4 302s 2 1 99.3 97.6 302s 3 1 99.0 96.7 302s 4 1 99.9 98.2 302s 5 1 97.0 99.8 302s 6 1 98.0 100.5 302s 7 1 99.9 103.2 302s 8 1 100.7 107.8 302s 9 1 96.2 96.6 302s 10 1 95.1 88.9 302s 11 1 94.7 75.1 302s 12 1 99.0 76.9 302s 13 1 101.7 84.6 302s 14 1 101.3 90.6 302s 15 1 100.8 103.1 302s 16 1 100.9 105.1 302s 17 1 95.6 96.4 302s 18 1 104.2 104.4 302s 19 1 107.8 110.7 302s 20 1 113.9 127.1 302s > model.matrix( fit3slsd[[ 4 ]]$e3w$eq[[ 2 ]], which = "xHat" ) 302s (Intercept) price farmPrice trend 302s 1 1 99.6 98.0 1 302s 2 1 105.1 99.1 2 302s 3 1 103.8 99.1 3 302s 4 1 104.5 98.1 4 302s 5 1 98.7 110.8 5 302s 6 1 99.6 108.2 6 302s 7 1 102.0 105.6 7 302s 8 1 102.2 109.8 8 302s 9 1 94.6 108.7 9 302s 10 1 92.7 100.6 10 302s 11 1 92.4 81.0 11 302s 12 1 98.9 68.6 12 302s 13 1 102.2 70.9 13 302s 14 1 100.3 81.4 14 302s 15 1 97.6 102.3 15 302s 16 1 96.9 105.0 16 302s 17 1 87.7 110.5 17 302s 18 1 101.1 92.5 18 302s 19 1 106.1 89.3 19 302s 20 1 114.4 93.0 20 302s > 302s > 302s > ## **************** formulas ************************ 302s > formula( fit3sls[[ 2 ]]$e1c ) 302s $demand 302s consump ~ price + income 302s 302s $supply 302s consump ~ price + farmPrice + trend 302s 302s > formula( fit3sls[[ 2 ]]$e1c$eq[[ 1 ]] ) 302s consump ~ price + income 302s > 302s > formula( fit3sls[[ 3 ]]$e2e ) 302s $demand 302s consump ~ price + income 302s 302s $supply 302s consump ~ price + farmPrice + trend 302s 302s > formula( fit3sls[[ 3 ]]$e2e$eq[[ 2 ]] ) 302s consump ~ price + farmPrice + trend 302s > 302s > formula( fit3sls[[ 4 ]]$e3 ) 302s $demand 302s consump ~ price + income 302s 302s $supply 302s consump ~ price + farmPrice + trend 302s 302s > formula( fit3sls[[ 4 ]]$e3$eq[[ 1 ]] ) 302s consump ~ price + income 302s > 302s > formula( fit3sls[[ 5 ]]$e4e ) 302s $demand 302s consump ~ price + income 302s 302s $supply 302s consump ~ price + farmPrice + trend 302s 302s > formula( fit3sls[[ 5 ]]$e4e$eq[[ 2 ]] ) 302s consump ~ price + farmPrice + trend 302s > 302s > formula( fit3sls[[ 1 ]]$e5 ) 302s $demand 302s consump ~ price + income 302s 302s $supply 302s consump ~ price + farmPrice + trend 302s 302s > formula( fit3sls[[ 1 ]]$e5$eq[[ 1 ]] ) 302s consump ~ price + income 302s > 302s > formula( fit3slsi[[ 3 ]]$e3e ) 302s $demand 302s consump ~ price + income 302s 302s $supply 302s consump ~ price + farmPrice + trend 302s 302s > formula( fit3slsi[[ 3 ]]$e3e$eq[[ 1 ]] ) 302s consump ~ price + income 302s > 302s > formula( fit3slsd[[ 4 ]]$e4 ) 302s $demand 302s consump ~ price + income 302s 302s $supply 302s consump ~ price + farmPrice + trend 302s 302s > formula( fit3slsd[[ 4 ]]$e4$eq[[ 2 ]] ) 302s consump ~ price + farmPrice + trend 302s > 302s > formula( fit3slsd[[ 2 ]]$e1w ) 302s $demand 302s consump ~ price + income 302s 302s $supply 302s consump ~ price + farmPrice + trend 302s 302s > formula( fit3slsd[[ 2 ]]$e1w$eq[[ 1 ]] ) 302s consump ~ price + income 302s > 302s > 302s > ## **************** model terms ******************* 302s > terms( fit3sls[[ 2 ]]$e1c ) 302s $demand 302s consump ~ price + income 302s attr(,"variables") 302s list(consump, price, income) 302s attr(,"factors") 302s price income 302s consump 0 0 302s price 1 0 302s income 0 1 302s attr(,"term.labels") 302s [1] "price" "income" 302s attr(,"order") 302s [1] 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 1 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(consump, price, income) 302s attr(,"dataClasses") 302s consump price income 302s "numeric" "numeric" "numeric" 302s 302s $supply 302s consump ~ price + farmPrice + trend 302s attr(,"variables") 302s list(consump, price, farmPrice, trend) 302s attr(,"factors") 302s price farmPrice trend 302s consump 0 0 0 302s price 1 0 0 302s farmPrice 0 1 0 302s trend 0 0 1 302s attr(,"term.labels") 302s [1] "price" "farmPrice" "trend" 302s attr(,"order") 302s [1] 1 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 1 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(consump, price, farmPrice, trend) 302s attr(,"dataClasses") 302s consump price farmPrice trend 302s "numeric" "numeric" "numeric" "numeric" 302s 302s > terms( fit3sls[[ 2 ]]$e1c$eq[[ 1 ]] ) 302s consump ~ price + income 302s attr(,"variables") 302s list(consump, price, income) 302s attr(,"factors") 302s price income 302s consump 0 0 302s price 1 0 302s income 0 1 302s attr(,"term.labels") 302s [1] "price" "income" 302s attr(,"order") 302s [1] 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 1 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(consump, price, income) 302s attr(,"dataClasses") 302s consump price income 302s "numeric" "numeric" "numeric" 302s > 302s > terms( fit3sls[[ 3 ]]$e2e ) 302s $demand 302s consump ~ price + income 302s attr(,"variables") 302s list(consump, price, income) 302s attr(,"factors") 302s price income 302s consump 0 0 302s price 1 0 302s income 0 1 302s attr(,"term.labels") 302s [1] "price" "income" 302s attr(,"order") 302s [1] 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 1 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(consump, price, income) 302s attr(,"dataClasses") 302s consump price income 302s "numeric" "numeric" "numeric" 302s 302s $supply 302s consump ~ price + farmPrice + trend 302s attr(,"variables") 302s list(consump, price, farmPrice, trend) 302s attr(,"factors") 302s price farmPrice trend 302s consump 0 0 0 302s price 1 0 0 302s farmPrice 0 1 0 302s trend 0 0 1 302s attr(,"term.labels") 302s [1] "price" "farmPrice" "trend" 302s attr(,"order") 302s [1] 1 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 1 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(consump, price, farmPrice, trend) 302s attr(,"dataClasses") 302s consump price farmPrice trend 302s "numeric" "numeric" "numeric" "numeric" 302s 302s > terms( fit3sls[[ 3 ]]$e2e$eq[[ 2 ]] ) 302s consump ~ price + farmPrice + trend 302s attr(,"variables") 302s list(consump, price, farmPrice, trend) 302s attr(,"factors") 302s price farmPrice trend 302s consump 0 0 0 302s price 1 0 0 302s farmPrice 0 1 0 302s trend 0 0 1 302s attr(,"term.labels") 302s [1] "price" "farmPrice" "trend" 302s attr(,"order") 302s [1] 1 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 1 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(consump, price, farmPrice, trend) 302s attr(,"dataClasses") 302s consump price farmPrice trend 302s "numeric" "numeric" "numeric" "numeric" 302s > 302s > terms( fit3sls[[ 4 ]]$e3 ) 302s $demand 302s consump ~ price + income 302s attr(,"variables") 302s list(consump, price, income) 302s attr(,"factors") 302s price income 302s consump 0 0 302s price 1 0 302s income 0 1 302s attr(,"term.labels") 302s [1] "price" "income" 302s attr(,"order") 302s [1] 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 1 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(consump, price, income) 302s attr(,"dataClasses") 302s consump price income 302s "numeric" "numeric" "numeric" 302s 302s $supply 302s consump ~ price + farmPrice + trend 302s attr(,"variables") 302s list(consump, price, farmPrice, trend) 302s attr(,"factors") 302s price farmPrice trend 302s consump 0 0 0 302s price 1 0 0 302s farmPrice 0 1 0 302s trend 0 0 1 302s attr(,"term.labels") 302s [1] "price" "farmPrice" "trend" 302s attr(,"order") 302s [1] 1 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 1 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(consump, price, farmPrice, trend) 302s attr(,"dataClasses") 302s consump price farmPrice trend 302s "numeric" "numeric" "numeric" "numeric" 302s 302s > terms( fit3sls[[ 4 ]]$e3$eq[[ 1 ]] ) 302s consump ~ price + income 302s attr(,"variables") 302s list(consump, price, income) 302s attr(,"factors") 302s price income 302s consump 0 0 302s price 1 0 302s income 0 1 302s attr(,"term.labels") 302s [1] "price" "income" 302s attr(,"order") 302s [1] 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 1 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(consump, price, income) 302s attr(,"dataClasses") 302s consump price income 302s "numeric" "numeric" "numeric" 302s > 302s > terms( fit3sls[[ 5 ]]$e4e ) 302s $demand 302s consump ~ price + income 302s attr(,"variables") 302s list(consump, price, income) 302s attr(,"factors") 302s price income 302s consump 0 0 302s price 1 0 302s income 0 1 302s attr(,"term.labels") 302s [1] "price" "income" 302s attr(,"order") 302s [1] 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 1 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(consump, price, income) 302s attr(,"dataClasses") 302s consump price income 302s "numeric" "numeric" "numeric" 302s 302s $supply 302s consump ~ price + farmPrice + trend 302s attr(,"variables") 302s list(consump, price, farmPrice, trend) 302s attr(,"factors") 302s price farmPrice trend 302s consump 0 0 0 302s price 1 0 0 302s farmPrice 0 1 0 302s trend 0 0 1 302s attr(,"term.labels") 302s [1] "price" "farmPrice" "trend" 302s attr(,"order") 302s [1] 1 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 1 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(consump, price, farmPrice, trend) 302s attr(,"dataClasses") 302s consump price farmPrice trend 302s "numeric" "numeric" "numeric" "numeric" 302s 302s > terms( fit3sls[[ 5 ]]$e4e$eq[[ 2 ]] ) 302s consump ~ price + farmPrice + trend 302s attr(,"variables") 302s list(consump, price, farmPrice, trend) 302s attr(,"factors") 302s price farmPrice trend 302s consump 0 0 0 302s price 1 0 0 302s farmPrice 0 1 0 302s trend 0 0 1 302s attr(,"term.labels") 302s [1] "price" "farmPrice" "trend" 302s attr(,"order") 302s [1] 1 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 1 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(consump, price, farmPrice, trend) 302s attr(,"dataClasses") 302s consump price farmPrice trend 302s "numeric" "numeric" "numeric" "numeric" 302s > 302s > terms( fit3sls[[ 1 ]]$e5 ) 302s $demand 302s consump ~ price + income 302s attr(,"variables") 302s list(consump, price, income) 302s attr(,"factors") 302s price income 302s consump 0 0 302s price 1 0 302s income 0 1 302s attr(,"term.labels") 302s [1] "price" "income" 302s attr(,"order") 302s [1] 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 1 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(consump, price, income) 302s attr(,"dataClasses") 302s consump price income 302s "numeric" "numeric" "numeric" 302s 302s $supply 302s consump ~ price + farmPrice + trend 302s attr(,"variables") 302s list(consump, price, farmPrice, trend) 302s attr(,"factors") 302s price farmPrice trend 302s consump 0 0 0 302s price 1 0 0 302s farmPrice 0 1 0 302s trend 0 0 1 302s attr(,"term.labels") 302s [1] "price" "farmPrice" "trend" 302s attr(,"order") 302s [1] 1 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 1 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(consump, price, farmPrice, trend) 302s attr(,"dataClasses") 302s consump price farmPrice trend 302s "numeric" "numeric" "numeric" "numeric" 302s 302s > terms( fit3sls[[ 1 ]]$e5$eq[[ 1 ]] ) 302s consump ~ price + income 302s attr(,"variables") 302s list(consump, price, income) 302s attr(,"factors") 302s price income 302s consump 0 0 302s price 1 0 302s income 0 1 302s attr(,"term.labels") 302s [1] "price" "income" 302s attr(,"order") 302s [1] 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 1 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(consump, price, income) 302s attr(,"dataClasses") 302s consump price income 302s "numeric" "numeric" "numeric" 302s > 302s > terms( fit3sls[[ 2 ]]$e4wSym ) 302s $demand 302s consump ~ price + income 302s attr(,"variables") 302s list(consump, price, income) 302s attr(,"factors") 302s price income 302s consump 0 0 302s price 1 0 302s income 0 1 302s attr(,"term.labels") 302s [1] "price" "income" 302s attr(,"order") 302s [1] 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 1 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(consump, price, income) 302s attr(,"dataClasses") 302s consump price income 302s "numeric" "numeric" "numeric" 302s 302s $supply 302s consump ~ price + farmPrice + trend 302s attr(,"variables") 302s list(consump, price, farmPrice, trend) 302s attr(,"factors") 302s price farmPrice trend 302s consump 0 0 0 302s price 1 0 0 302s farmPrice 0 1 0 302s trend 0 0 1 302s attr(,"term.labels") 302s [1] "price" "farmPrice" "trend" 302s attr(,"order") 302s [1] 1 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 1 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(consump, price, farmPrice, trend) 302s attr(,"dataClasses") 302s consump price farmPrice trend 302s "numeric" "numeric" "numeric" "numeric" 302s 302s > terms( fit3sls[[ 2 ]]$e4wSym$eq[[ 1 ]] ) 302s consump ~ price + income 302s attr(,"variables") 302s list(consump, price, income) 302s attr(,"factors") 302s price income 302s consump 0 0 302s price 1 0 302s income 0 1 302s attr(,"term.labels") 302s [1] "price" "income" 302s attr(,"order") 302s [1] 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 1 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(consump, price, income) 302s attr(,"dataClasses") 302s consump price income 302s "numeric" "numeric" "numeric" 302s > 302s > terms( fit3slsi[[ 3 ]]$e3e ) 302s $demand 302s consump ~ price + income 302s attr(,"variables") 302s list(consump, price, income) 302s attr(,"factors") 302s price income 302s consump 0 0 302s price 1 0 302s income 0 1 302s attr(,"term.labels") 302s [1] "price" "income" 302s attr(,"order") 302s [1] 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 1 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(consump, price, income) 302s attr(,"dataClasses") 302s consump price income 302s "numeric" "numeric" "numeric" 302s 302s $supply 302s consump ~ price + farmPrice + trend 302s attr(,"variables") 302s list(consump, price, farmPrice, trend) 302s attr(,"factors") 302s price farmPrice trend 302s consump 0 0 0 302s price 1 0 0 302s farmPrice 0 1 0 302s trend 0 0 1 302s attr(,"term.labels") 302s [1] "price" "farmPrice" "trend" 302s attr(,"order") 302s [1] 1 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 1 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(consump, price, farmPrice, trend) 302s attr(,"dataClasses") 302s consump price farmPrice trend 302s "numeric" "numeric" "numeric" "numeric" 302s 302s > terms( fit3slsi[[ 3 ]]$e3e$eq[[ 1 ]] ) 302s consump ~ price + income 302s attr(,"variables") 302s list(consump, price, income) 302s attr(,"factors") 302s price income 302s consump 0 0 302s price 1 0 302s income 0 1 302s attr(,"term.labels") 302s [1] "price" "income" 302s attr(,"order") 302s [1] 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 1 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(consump, price, income) 302s attr(,"dataClasses") 302s consump price income 302s "numeric" "numeric" "numeric" 302s > 302s > terms( fit3slsd[[ 4 ]]$e4 ) 302s $demand 302s consump ~ price + income 302s attr(,"variables") 302s list(consump, price, income) 302s attr(,"factors") 302s price income 302s consump 0 0 302s price 1 0 302s income 0 1 302s attr(,"term.labels") 302s [1] "price" "income" 302s attr(,"order") 302s [1] 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 1 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(consump, price, income) 302s attr(,"dataClasses") 302s consump price income 302s "numeric" "numeric" "numeric" 302s 302s $supply 302s consump ~ price + farmPrice + trend 302s attr(,"variables") 302s list(consump, price, farmPrice, trend) 302s attr(,"factors") 302s price farmPrice trend 302s consump 0 0 0 302s price 1 0 0 302s farmPrice 0 1 0 302s trend 0 0 1 302s attr(,"term.labels") 302s [1] "price" "farmPrice" "trend" 302s attr(,"order") 302s [1] 1 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 1 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(consump, price, farmPrice, trend) 302s attr(,"dataClasses") 302s consump price farmPrice trend 302s "numeric" "numeric" "numeric" "numeric" 302s 302s > terms( fit3slsd[[ 4 ]]$e4$eq[[ 2 ]] ) 302s consump ~ price + farmPrice + trend 302s attr(,"variables") 302s list(consump, price, farmPrice, trend) 302s attr(,"factors") 302s price farmPrice trend 302s consump 0 0 0 302s price 1 0 0 302s farmPrice 0 1 0 302s trend 0 0 1 302s attr(,"term.labels") 302s [1] "price" "farmPrice" "trend" 302s attr(,"order") 302s [1] 1 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 1 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(consump, price, farmPrice, trend) 302s attr(,"dataClasses") 302s consump price farmPrice trend 302s "numeric" "numeric" "numeric" "numeric" 302s > 302s > terms( fit3slsd[[ 5 ]]$e5we ) 302s $demand 302s consump ~ price + income 302s attr(,"variables") 302s list(consump, price, income) 302s attr(,"factors") 302s price income 302s consump 0 0 302s price 1 0 302s income 0 1 302s attr(,"term.labels") 302s [1] "price" "income" 302s attr(,"order") 302s [1] 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 1 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(consump, price, income) 302s attr(,"dataClasses") 302s consump price income 302s "numeric" "numeric" "numeric" 302s 302s $supply 302s consump ~ price + farmPrice + trend 302s attr(,"variables") 302s list(consump, price, farmPrice, trend) 302s attr(,"factors") 302s price farmPrice trend 302s consump 0 0 0 302s price 1 0 0 302s farmPrice 0 1 0 302s trend 0 0 1 302s attr(,"term.labels") 302s [1] "price" "farmPrice" "trend" 302s attr(,"order") 302s [1] 1 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 1 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(consump, price, farmPrice, trend) 302s attr(,"dataClasses") 302s consump price farmPrice trend 302s "numeric" "numeric" "numeric" "numeric" 302s 302s > terms( fit3slsd[[ 5 ]]$e5we$eq[[ 2 ]] ) 302s consump ~ price + farmPrice + trend 302s attr(,"variables") 302s list(consump, price, farmPrice, trend) 302s attr(,"factors") 302s price farmPrice trend 302s consump 0 0 0 302s price 1 0 0 302s farmPrice 0 1 0 302s trend 0 0 1 302s attr(,"term.labels") 302s [1] "price" "farmPrice" "trend" 302s attr(,"order") 302s [1] 1 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 1 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(consump, price, farmPrice, trend) 302s attr(,"dataClasses") 302s consump price farmPrice trend 302s "numeric" "numeric" "numeric" "numeric" 302s > 302s > 302s > ## **************** terms of instruments ******************* 302s > fit3sls[[ 2 ]]$e1c$eq[[ 1 ]]$termsInst 302s ~income + farmPrice + trend 302s attr(,"variables") 302s list(income, farmPrice, trend) 302s attr(,"factors") 302s income farmPrice trend 302s income 1 0 0 302s farmPrice 0 1 0 302s trend 0 0 1 302s attr(,"term.labels") 302s [1] "income" "farmPrice" "trend" 302s attr(,"order") 302s [1] 1 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 0 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(income, farmPrice, trend) 302s attr(,"dataClasses") 302s income farmPrice trend 302s "numeric" "numeric" "numeric" 302s > 302s > fit3sls[[ 3 ]]$e2e$eq[[ 2 ]]$termsInst 302s ~income + farmPrice + trend 302s attr(,"variables") 302s list(income, farmPrice, trend) 302s attr(,"factors") 302s income farmPrice trend 302s income 1 0 0 302s farmPrice 0 1 0 302s trend 0 0 1 302s attr(,"term.labels") 302s [1] "income" "farmPrice" "trend" 302s attr(,"order") 302s [1] 1 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 0 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(income, farmPrice, trend) 302s attr(,"dataClasses") 302s income farmPrice trend 302s "numeric" "numeric" "numeric" 302s > 302s > fit3sls[[ 4 ]]$e3$eq[[ 1 ]]$termsInst 302s ~income + farmPrice + trend 302s attr(,"variables") 302s list(income, farmPrice, trend) 302s attr(,"factors") 302s income farmPrice trend 302s income 1 0 0 302s farmPrice 0 1 0 302s trend 0 0 1 302s attr(,"term.labels") 302s [1] "income" "farmPrice" "trend" 302s attr(,"order") 302s [1] 1 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 0 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(income, farmPrice, trend) 302s attr(,"dataClasses") 302s income farmPrice trend 302s "numeric" "numeric" "numeric" 302s > 302s > fit3sls[[ 5 ]]$e4e$eq[[ 2 ]]$termsInst 302s ~income + farmPrice + trend 302s attr(,"variables") 302s list(income, farmPrice, trend) 302s attr(,"factors") 302s income farmPrice trend 302s income 1 0 0 302s farmPrice 0 1 0 302s trend 0 0 1 302s attr(,"term.labels") 302s [1] "income" "farmPrice" "trend" 302s attr(,"order") 302s [1] 1 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 0 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(income, farmPrice, trend) 302s attr(,"dataClasses") 302s income farmPrice trend 302s "numeric" "numeric" "numeric" 302s > 302s > fit3sls[[ 1 ]]$e5$eq[[ 1 ]]$termsInst 302s ~income + farmPrice + trend 302s attr(,"variables") 302s list(income, farmPrice, trend) 302s attr(,"factors") 302s income farmPrice trend 302s income 1 0 0 302s farmPrice 0 1 0 302s trend 0 0 1 302s attr(,"term.labels") 302s [1] "income" "farmPrice" "trend" 302s attr(,"order") 302s [1] 1 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 0 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(income, farmPrice, trend) 302s attr(,"dataClasses") 302s income farmPrice trend 302s "numeric" "numeric" "numeric" 302s > 302s > fit3sls[[ 2 ]]$e4wSym$eq[[ 1 ]]$termsInst 302s ~income + farmPrice + trend 302s attr(,"variables") 302s list(income, farmPrice, trend) 302s attr(,"factors") 302s income farmPrice trend 302s income 1 0 0 302s farmPrice 0 1 0 302s trend 0 0 1 302s attr(,"term.labels") 302s [1] "income" "farmPrice" "trend" 302s attr(,"order") 302s [1] 1 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 0 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(income, farmPrice, trend) 302s attr(,"dataClasses") 302s income farmPrice trend 302s "numeric" "numeric" "numeric" 302s > 302s > fit3slsi[[ 3 ]]$e3e$eq[[ 1 ]]$termsInst 302s ~income + farmPrice + trend 302s attr(,"variables") 302s list(income, farmPrice, trend) 302s attr(,"factors") 302s income farmPrice trend 302s income 1 0 0 302s farmPrice 0 1 0 302s trend 0 0 1 302s attr(,"term.labels") 302s [1] "income" "farmPrice" "trend" 302s attr(,"order") 302s [1] 1 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 0 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(income, farmPrice, trend) 302s attr(,"dataClasses") 302s income farmPrice trend 302s "numeric" "numeric" "numeric" 302s > 302s > fit3slsd[[ 4 ]]$e4$eq[[ 2 ]]$termsInst 302s ~income + farmPrice + trend 302s attr(,"variables") 302s list(income, farmPrice, trend) 302s attr(,"factors") 302s income farmPrice trend 302s income 1 0 0 302s farmPrice 0 1 0 302s trend 0 0 1 302s attr(,"term.labels") 302s [1] "income" "farmPrice" "trend" 302s attr(,"order") 302s [1] 1 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 0 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(income, farmPrice, trend) 302s attr(,"dataClasses") 302s income farmPrice trend 302s "numeric" "numeric" "numeric" 302s > 302s > fit3slsd[[ 5 ]]$e5we$eq[[ 2 ]]$termsInst 302s ~income + farmPrice + trend 302s attr(,"variables") 302s list(income, farmPrice, trend) 302s attr(,"factors") 302s income farmPrice trend 302s income 1 0 0 302s farmPrice 0 1 0 302s trend 0 0 1 302s attr(,"term.labels") 302s [1] "income" "farmPrice" "trend" 302s attr(,"order") 302s [1] 1 1 1 302s attr(,"intercept") 302s [1] 1 302s attr(,"response") 302s [1] 0 302s attr(,".Environment") 302s 302s attr(,"predvars") 302s list(income, farmPrice, trend) 302s attr(,"dataClasses") 302s income farmPrice trend 302s "numeric" "numeric" "numeric" 302s > 302s > 302s > ## **************** estfun ************************ 302s > library( "sandwich" ) 302s > 302s > estfun( fit3sls[[ 1 ]]$e1 ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s demand_1 0.93243 92.895 81.494 -0.67273 302s demand_2 -0.67769 -71.238 -66.143 0.48894 302s demand_3 3.38220 351.019 327.058 -2.44019 302s demand_4 2.06995 216.373 203.269 -1.49343 302s demand_5 3.17940 313.652 317.304 -2.29388 302s demand_6 1.83161 182.517 184.077 -1.32147 302s demand_7 2.47947 252.837 255.881 -1.78889 302s demand_8 -5.09517 -520.901 -549.259 3.67607 302s demand_9 -2.17668 -205.928 -210.267 1.57043 302s demand_10 3.95122 366.354 351.263 -2.85073 302s demand_11 -0.37870 -34.993 -28.440 0.27322 302s demand_12 -3.13231 -309.838 -240.875 2.25990 302s demand_13 -2.46263 -251.590 -208.339 1.77674 302s demand_14 0.13711 13.748 12.422 -0.09892 302s demand_15 3.55301 346.849 366.315 -2.56343 302s demand_16 -5.27287 -510.898 -554.179 3.80428 302s demand_17 -0.02852 -2.502 -2.750 0.02058 302s demand_18 -3.97374 -401.582 -414.859 2.86698 302s demand_19 2.30169 244.124 254.797 -1.66062 302s demand_20 -0.61976 -70.898 -78.771 0.44714 302s supply_1 -0.79213 -78.918 -69.232 0.70287 302s supply_2 0.37122 39.022 36.231 -0.32939 302s supply_3 -2.54401 -264.028 -246.006 2.25734 302s supply_4 -1.58295 -165.467 -155.446 1.40458 302s supply_5 -2.40285 -237.044 -239.804 2.13208 302s supply_6 -1.41153 -140.656 -141.858 1.25247 302s supply_7 -1.86174 -189.846 -192.132 1.65195 302s supply_8 3.60208 368.256 388.304 -3.19618 302s supply_9 1.52187 143.979 147.013 -1.35038 302s supply_10 -2.85966 -265.145 -254.224 2.53741 302s supply_11 0.33741 31.177 25.339 -0.29938 302s supply_12 2.36613 234.051 181.956 -2.09950 302s supply_13 1.88385 192.460 159.374 -1.67157 302s supply_14 -0.00962 -0.965 -0.872 0.00854 302s supply_15 -2.52306 -246.304 -260.128 2.23875 302s supply_16 3.84942 372.977 404.574 -3.41564 302s supply_17 0.07279 6.384 7.017 -0.06459 302s supply_18 2.96969 300.114 310.035 -2.63504 302s supply_19 -1.54232 -163.584 -170.735 1.36853 302s supply_20 0.55542 63.538 70.594 -0.49283 302s supply_price supply_farmPrice supply_trend 302s demand_1 -67.022 -65.927 -0.673 302s demand_2 51.397 48.454 0.978 302s demand_3 -253.253 -241.823 -7.321 302s demand_4 -156.109 -146.505 -5.974 302s demand_5 -226.294 -254.162 -11.469 302s demand_6 -131.682 -142.983 -7.929 302s demand_7 -182.417 -188.907 -12.522 302s demand_8 375.820 403.632 29.409 302s demand_9 148.573 170.706 14.134 302s demand_10 -264.317 -286.783 -28.507 302s demand_11 25.247 22.131 3.005 302s demand_12 223.542 155.029 27.119 302s demand_13 181.517 125.971 23.098 302s demand_14 -9.919 -8.052 -1.385 302s demand_15 -250.245 -262.238 -38.451 302s demand_16 368.603 399.449 60.868 302s demand_17 1.805 2.274 0.350 302s demand_18 289.734 265.195 51.606 302s demand_19 -176.131 -148.294 -31.552 302s demand_20 51.151 41.584 8.943 302s supply_1 70.025 68.881 0.703 302s supply_2 -34.625 -32.642 -0.659 302s supply_3 234.276 223.702 6.772 302s supply_4 146.821 137.789 5.618 302s supply_5 210.332 236.235 10.660 302s supply_6 124.806 135.517 7.515 302s supply_7 168.453 174.446 11.564 302s supply_8 -326.759 -350.940 -25.569 302s supply_9 -127.755 -146.786 -12.153 302s supply_10 235.267 255.264 25.374 302s supply_11 -27.664 -24.250 -3.293 302s supply_12 -207.676 -144.026 -25.194 302s supply_13 -170.773 -118.514 -21.730 302s supply_14 0.856 0.695 0.120 302s supply_15 218.549 229.024 33.581 302s supply_16 -330.948 -358.642 -54.650 302s supply_17 -5.665 -7.137 -1.098 302s supply_18 -266.295 -243.742 -47.431 302s supply_19 145.150 122.209 26.002 302s supply_20 -56.378 -45.834 -9.857 302s > round( colSums( estfun( fit3sls[[ 1 ]]$e1 ) ), digits = 7 ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 0 0 0 0 302s supply_price supply_farmPrice supply_trend 302s 0 0 0 302s > 302s > estfun( fit3sls[[ 2 ]]$e1e ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s demand_1 1.0970 109.29 95.88 -0.8158 302s demand_2 -0.7973 -83.81 -77.82 0.5929 302s demand_3 3.9791 412.96 384.77 -2.9592 302s demand_4 2.4352 254.56 239.14 -1.8110 302s demand_5 3.7405 369.00 373.30 -2.7817 302s demand_6 2.1548 214.73 216.56 -1.6025 302s demand_7 2.9170 297.45 301.04 -2.1693 302s demand_8 -5.9943 -612.82 -646.19 4.4579 302s demand_9 -2.5608 -242.27 -247.37 1.9044 302s demand_10 4.6485 431.00 413.25 -3.4570 302s demand_11 -0.4455 -41.17 -33.46 0.3313 302s demand_12 -3.6851 -364.52 -283.38 2.7405 302s demand_13 -2.8972 -295.99 -245.10 2.1546 302s demand_14 0.1613 16.17 14.61 -0.1200 302s demand_15 4.1800 408.06 430.96 -3.1086 302s demand_16 -6.2034 -601.06 -651.98 4.6134 302s demand_17 -0.0336 -2.94 -3.24 0.0250 302s demand_18 -4.6750 -472.45 -488.07 3.4767 302s demand_19 2.7079 287.21 299.76 -2.0138 302s demand_20 -0.7291 -83.41 -92.67 0.5422 302s supply_1 -0.9222 -91.88 -80.60 0.8435 302s supply_2 0.4880 51.30 47.63 -0.4463 302s supply_3 -3.0517 -316.72 -295.10 2.7912 302s supply_4 -1.8908 -197.65 -185.68 1.7294 302s supply_5 -2.8789 -284.00 -287.31 2.6331 302s supply_6 -1.6828 -167.69 -169.12 1.5391 302s supply_7 -2.2343 -227.83 -230.58 2.0435 302s supply_8 4.3919 449.01 473.45 -4.0170 302s supply_9 1.8611 176.08 179.79 -1.7022 302s supply_10 -3.4650 -321.27 -308.04 3.1691 302s supply_11 0.3885 35.90 29.18 -0.3554 302s supply_12 2.8352 280.45 218.03 -2.5932 302s supply_13 2.2501 229.88 190.36 -2.0580 302s supply_14 -0.0404 -4.05 -3.66 0.0369 302s supply_15 -3.0726 -299.95 -316.79 2.8103 302s supply_16 4.6536 450.90 489.09 -4.2563 302s supply_17 0.0715 6.27 6.89 -0.0654 302s supply_18 3.5683 360.61 372.53 -3.2636 302s supply_19 -1.9084 -202.41 -211.25 1.7454 302s supply_20 0.6388 73.07 81.19 -0.5842 302s supply_price supply_farmPrice supply_trend 302s demand_1 -81.28 -79.95 -0.816 302s demand_2 62.33 58.76 1.186 302s demand_3 -307.11 -293.25 -8.877 302s demand_4 -189.31 -177.66 -7.244 302s demand_5 -274.42 -308.22 -13.909 302s demand_6 -159.69 -173.39 -9.615 302s demand_7 -221.21 -229.08 -15.185 302s demand_8 455.75 489.48 35.663 302s demand_9 180.17 207.01 17.140 302s demand_10 -320.53 -347.78 -34.570 302s demand_11 30.62 26.84 3.645 302s demand_12 271.08 188.00 32.886 302s demand_13 220.12 152.76 28.010 302s demand_14 -12.03 -9.76 -1.679 302s demand_15 -303.47 -318.01 -46.629 302s demand_16 447.00 484.40 73.814 302s demand_17 2.19 2.76 0.424 302s demand_18 351.35 321.60 62.581 302s demand_19 -213.59 -179.83 -38.262 302s demand_20 62.03 50.43 10.845 302s supply_1 84.04 82.66 0.843 302s supply_2 -46.92 -44.23 -0.893 302s supply_3 289.68 276.60 8.373 302s supply_4 180.78 169.66 6.918 302s supply_5 259.76 291.74 13.165 302s supply_6 153.37 166.53 9.235 302s supply_7 208.38 215.80 14.305 302s supply_8 -410.67 -441.06 -32.136 302s supply_9 -161.04 -185.03 -15.320 302s supply_10 293.84 318.82 31.691 302s supply_11 -32.84 -28.78 -3.909 302s supply_12 -256.51 -177.89 -31.118 302s supply_13 -210.25 -145.91 -26.754 302s supply_14 3.70 3.00 0.517 302s supply_15 274.34 287.49 42.154 302s supply_16 -412.40 -446.91 -68.101 302s supply_17 -5.73 -7.23 -1.112 302s supply_18 -329.82 -301.88 -58.745 302s supply_19 185.13 155.87 33.163 302s supply_20 -66.83 -54.33 -11.684 302s > round( colSums( estfun( fit3sls[[ 2 ]]$e1e ) ), digits = 7 ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 0 0 0 0 302s supply_price supply_farmPrice supply_trend 302s 0 0 0 302s > 302s > estfun( fit3sls[[ 3 ]]$e1c ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s demand_1 1.3280 132.31 116.07 -0.9904 302s demand_2 -0.9652 -101.46 -94.20 0.7198 302s demand_3 4.8171 499.94 465.81 -3.5924 302s demand_4 2.9481 308.17 289.50 -2.1986 302s demand_5 4.5282 446.72 451.92 -3.3770 302s demand_6 2.6087 259.95 262.17 -1.9455 302s demand_7 3.5314 360.10 364.44 -2.6336 302s demand_8 -7.2568 -741.89 -782.28 5.4119 302s demand_9 -3.1001 -293.29 -299.47 2.3120 302s demand_10 5.6275 521.78 500.28 -4.1968 302s demand_11 -0.5394 -49.84 -40.51 0.4022 302s demand_12 -4.4612 -441.28 -343.06 3.3270 302s demand_13 -3.5074 -358.33 -296.72 2.6157 302s demand_14 0.1953 19.58 17.69 -0.1456 302s demand_15 5.0603 494.00 521.72 -3.7739 302s demand_16 -7.5098 -727.64 -789.29 5.6006 302s demand_17 -0.0406 -3.56 -3.92 0.0303 302s demand_18 -5.6596 -571.95 -590.86 4.2207 302s demand_19 3.2782 347.69 362.89 -2.4448 302s demand_20 -0.8827 -100.98 -112.19 0.6583 302s supply_1 -1.2187 -121.42 -106.51 1.0461 302s supply_2 0.4947 52.00 48.29 -0.4247 302s supply_3 -3.7909 -393.44 -366.58 3.2542 302s supply_4 -2.3698 -247.71 -232.71 2.0343 302s supply_5 -3.5854 -353.70 -357.82 3.0777 302s supply_6 -2.1176 -211.02 -212.82 1.8178 302s supply_7 -2.7729 -282.76 -286.16 2.3803 302s supply_8 5.2704 538.82 568.15 -4.5242 302s supply_9 2.2191 209.94 214.37 -1.9049 302s supply_10 -4.2139 -390.71 -374.62 3.6173 302s supply_11 0.5250 48.51 39.42 -0.4506 302s supply_12 3.5301 349.19 271.47 -3.0303 302s supply_13 2.8205 288.15 238.61 -2.4212 302s supply_14 0.0251 2.52 2.28 -0.0216 302s supply_15 -3.6967 -360.87 -381.13 3.1733 302s supply_16 5.6869 551.02 597.70 -4.8817 302s supply_17 0.1301 11.41 12.54 -0.1117 302s supply_18 4.4171 446.39 461.15 -3.7917 302s supply_19 -2.2186 -235.31 -245.60 1.9044 302s supply_20 0.8653 98.99 109.98 -0.7428 302s supply_price supply_farmPrice supply_trend 302s demand_1 -98.67 -97.06 -0.990 302s demand_2 75.67 71.33 1.440 302s demand_3 -372.84 -356.01 -10.777 302s demand_4 -229.82 -215.68 -8.794 302s demand_5 -333.15 -374.17 -16.885 302s demand_6 -193.86 -210.50 -11.673 302s demand_7 -268.55 -278.11 -18.435 302s demand_8 553.28 594.22 43.295 302s demand_9 218.73 251.31 20.808 302s demand_10 -389.13 -422.20 -41.968 302s demand_11 37.17 32.58 4.425 302s demand_12 329.10 228.23 39.924 302s demand_13 267.23 185.45 34.004 302s demand_14 -14.60 -11.85 -2.039 302s demand_15 -368.41 -386.07 -56.608 302s demand_16 542.65 588.07 89.610 302s demand_17 2.66 3.35 0.515 302s demand_18 426.54 390.42 75.973 302s demand_19 -259.30 -218.32 -46.450 302s demand_20 75.30 61.22 13.166 302s supply_1 104.22 102.52 1.046 302s supply_2 -44.64 -42.09 -0.849 302s supply_3 337.73 322.49 9.763 302s supply_4 212.64 199.56 8.137 302s supply_5 303.62 341.01 15.389 302s supply_6 181.14 196.69 10.907 302s supply_7 242.72 251.36 16.662 302s supply_8 -462.53 -496.76 -36.194 302s supply_9 -180.22 -207.07 -17.144 302s supply_10 335.39 363.90 36.173 302s supply_11 -41.64 -36.50 -4.957 302s supply_12 -299.75 -207.88 -36.364 302s supply_13 -247.35 -171.66 -31.475 302s supply_14 -2.16 -1.75 -0.302 302s supply_15 309.78 324.63 47.599 302s supply_16 -473.00 -512.58 -78.108 302s supply_17 -9.80 -12.34 -1.899 302s supply_18 -383.19 -350.73 -68.251 302s supply_19 201.99 170.07 36.184 302s supply_20 -84.97 -69.08 -14.856 302s > round( colSums( estfun( fit3sls[[ 3 ]]$e1c ) ), digits = 7 ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 0 0 0 0 302s supply_price supply_farmPrice supply_trend 302s 0 0 0 302s > 302s > estfun( fit3sls[[ 4 ]]$e1wc ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s demand_1 1.3280 132.31 116.07 -0.9904 302s demand_2 -0.9652 -101.46 -94.20 0.7198 302s demand_3 4.8171 499.94 465.81 -3.5924 302s demand_4 2.9481 308.17 289.50 -2.1986 302s demand_5 4.5282 446.72 451.92 -3.3770 302s demand_6 2.6087 259.95 262.17 -1.9455 302s demand_7 3.5314 360.10 364.44 -2.6336 302s demand_8 -7.2568 -741.89 -782.28 5.4119 302s demand_9 -3.1001 -293.29 -299.47 2.3120 302s demand_10 5.6275 521.78 500.28 -4.1968 302s demand_11 -0.5394 -49.84 -40.51 0.4022 302s demand_12 -4.4612 -441.28 -343.06 3.3270 302s demand_13 -3.5074 -358.33 -296.72 2.6157 302s demand_14 0.1953 19.58 17.69 -0.1456 302s demand_15 5.0603 494.00 521.72 -3.7739 302s demand_16 -7.5098 -727.64 -789.29 5.6006 302s demand_17 -0.0406 -3.56 -3.92 0.0303 302s demand_18 -5.6596 -571.95 -590.86 4.2207 302s demand_19 3.2782 347.69 362.89 -2.4448 302s demand_20 -0.8827 -100.98 -112.19 0.6583 302s supply_1 -1.2187 -121.42 -106.51 1.0461 302s supply_2 0.4947 52.00 48.29 -0.4247 302s supply_3 -3.7909 -393.44 -366.58 3.2542 302s supply_4 -2.3698 -247.71 -232.71 2.0343 302s supply_5 -3.5854 -353.70 -357.82 3.0777 302s supply_6 -2.1176 -211.02 -212.82 1.8178 302s supply_7 -2.7729 -282.76 -286.16 2.3803 302s supply_8 5.2704 538.82 568.15 -4.5242 302s supply_9 2.2191 209.94 214.37 -1.9049 302s supply_10 -4.2139 -390.71 -374.62 3.6173 302s supply_11 0.5250 48.51 39.42 -0.4506 302s supply_12 3.5301 349.19 271.47 -3.0303 302s supply_13 2.8205 288.15 238.61 -2.4212 302s supply_14 0.0251 2.52 2.28 -0.0216 302s supply_15 -3.6967 -360.87 -381.13 3.1733 302s supply_16 5.6869 551.02 597.70 -4.8817 302s supply_17 0.1301 11.41 12.54 -0.1117 302s supply_18 4.4171 446.39 461.15 -3.7917 302s supply_19 -2.2186 -235.31 -245.60 1.9044 302s supply_20 0.8653 98.99 109.98 -0.7428 302s supply_price supply_farmPrice supply_trend 302s demand_1 -98.67 -97.06 -0.990 302s demand_2 75.67 71.33 1.440 302s demand_3 -372.84 -356.01 -10.777 302s demand_4 -229.82 -215.68 -8.794 302s demand_5 -333.15 -374.17 -16.885 302s demand_6 -193.86 -210.50 -11.673 302s demand_7 -268.55 -278.11 -18.435 302s demand_8 553.28 594.22 43.295 302s demand_9 218.73 251.31 20.808 302s demand_10 -389.13 -422.20 -41.968 302s demand_11 37.17 32.58 4.425 302s demand_12 329.10 228.23 39.924 302s demand_13 267.23 185.45 34.004 302s demand_14 -14.60 -11.85 -2.039 302s demand_15 -368.41 -386.07 -56.608 302s demand_16 542.65 588.07 89.610 302s demand_17 2.66 3.35 0.515 302s demand_18 426.54 390.42 75.973 302s demand_19 -259.30 -218.32 -46.450 302s demand_20 75.30 61.22 13.166 302s supply_1 104.22 102.52 1.046 302s supply_2 -44.64 -42.09 -0.849 302s supply_3 337.73 322.49 9.763 302s supply_4 212.64 199.56 8.137 302s supply_5 303.62 341.01 15.389 302s supply_6 181.14 196.69 10.907 302s supply_7 242.72 251.36 16.662 302s supply_8 -462.53 -496.76 -36.194 302s supply_9 -180.22 -207.07 -17.144 302s supply_10 335.39 363.90 36.173 302s supply_11 -41.64 -36.50 -4.957 302s supply_12 -299.75 -207.88 -36.364 302s supply_13 -247.35 -171.66 -31.475 302s supply_14 -2.16 -1.75 -0.302 302s supply_15 309.78 324.63 47.599 302s supply_16 -473.00 -512.58 -78.108 302s supply_17 -9.80 -12.34 -1.899 302s supply_18 -383.19 -350.73 -68.251 302s supply_19 201.99 170.07 36.184 302s supply_20 -84.97 -69.08 -14.856 302s > 302s > round( colSums( estfun( fit3sls[[ 5 ]]$e1wc ) ), digits = 7 ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 0 0 0 0 302s supply_price supply_farmPrice supply_trend 302s 0 0 0 302s > round( colSums( estfun( fit3sls[[ 5 ]]$e1wc, residFit = FALSE ) ), digits = 7 ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 0 0 0 0 302s supply_price supply_farmPrice supply_trend 302s 0 0 0 302s > 302s > round( colSums( estfun( fit3sls[[ 4 ]]$e1wc ) ), digits = 7 ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 0 0 0 0 302s supply_price supply_farmPrice supply_trend 302s 0 0 0 302s > round( colSums( estfun( fit3sls[[ 4 ]]$e1wc, residFit = FALSE ) ), digits = 7 ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 0 0 0 0 302s supply_price supply_farmPrice supply_trend 302s 0 0 0 302s > 302s > round( colSums( estfun( fit3sls[[ 3 ]]$e1wc ) ), digits = 7 ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 0 0 0 0 302s supply_price supply_farmPrice supply_trend 302s 0 0 0 302s > round( colSums( estfun( fit3sls[[ 3 ]]$e1wc, residFit = FALSE ) ), digits = 7 ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 0 0 0 0 302s supply_price supply_farmPrice supply_trend 302s 0 0 0 302s > 302s > round( colSums( estfun( fit3sls[[ 2 ]]$e1wc ) ), digits = 7 ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 0 0 0 0 302s supply_price supply_farmPrice supply_trend 302s 0 0 0 302s > round( colSums( estfun( fit3sls[[ 2 ]]$e1wc, residFit = FALSE ) ), digits = 7 ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 0 0 0 0 302s supply_price supply_farmPrice supply_trend 302s 0 0 0 302s > 302s > round( colSums( estfun( fit3sls[[ 1 ]]$e1wc ) ), digits = 7 ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 0 0 0 0 302s supply_price supply_farmPrice supply_trend 302s 0 0 0 302s > round( colSums( estfun( fit3sls[[ 1 ]]$e1wc, residFit = FALSE ) ), digits = 7 ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 0 0 0 0 302s supply_price supply_farmPrice supply_trend 302s 0 0 0 302s > 302s > estfun( fit3slsd[[ 5 ]]$e1w ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s demand_1 -0.471 -44.9 -41.2 0.299 302s demand_2 -1.315 -130.6 -128.3 0.835 302s demand_3 0.736 72.8 71.2 -0.467 302s demand_4 0.203 20.3 19.9 -0.129 302s demand_5 0.825 80.0 82.4 -0.524 302s demand_6 0.290 28.4 29.1 -0.184 302s demand_7 0.657 65.6 67.8 -0.417 302s demand_8 -2.887 -290.8 -311.2 1.833 302s demand_9 -1.172 -112.7 -113.2 0.744Warning message: 302s In estfun.systemfit(fit3slsd[[5]]$e1w) : 302s the columns of the returned estimating function do not all sum up to zero, which indicates that the wrong estimating function is returned 302s 302s demand_10 1.981 188.4 176.1 -1.258 302s demand_11 0.308 29.2 23.1 -0.196 302s demand_12 -0.922 -91.4 -70.9 0.586 302s demand_13 -0.639 -65.0 -54.1 0.406 302s demand_14 0.597 60.5 54.0 -0.379 302s demand_15 2.100 211.7 216.5 -1.333 302s demand_16 -1.984 -200.3 -208.6 1.260 302s demand_17 0.785 75.0 75.7 -0.499 302s demand_18 -1.136 -118.3 -118.6 0.721 302s demand_19 1.814 195.6 200.8 -1.152 302s demand_20 0.232 26.4 29.5 -0.147 302s supply_1 -0.434 -41.3 -37.9 0.449 302s supply_2 -0.126 -12.6 -12.3 0.131 302s supply_3 -1.272 -125.8 -123.0 1.316 302s supply_4 -0.902 -90.1 -88.6 0.933 302s supply_5 -0.805 -78.1 -80.4 0.833 302s supply_6 -0.457 -44.8 -46.0 0.473 302s supply_7 -0.758 -75.8 -78.3 0.784 302s supply_8 1.582 159.3 170.5 -1.636 302s supply_9 1.004 96.6 97.0 -1.039 302s supply_10 -0.856 -81.5 -76.1 0.886 302s supply_11 0.191 18.1 14.3 -0.197 302s supply_12 0.607 60.1 46.7 -0.628 302s supply_13 0.335 34.0 28.3 -0.346 302s supply_14 -0.201 -20.3 -18.2 0.208 302s supply_15 -0.801 -80.8 -82.6 0.829 302s supply_16 1.930 194.8 202.9 -1.997 302s supply_17 0.811 77.5 78.2 -0.839 302s supply_18 1.241 129.3 129.5 -1.283 302s supply_19 -0.858 -92.5 -95.0 0.888 302s supply_20 -0.229 -26.1 -29.1 0.237 302s supply_price supply_farmPrice supply_trend 302s demand_1 29.8 29.3 0.299 302s demand_2 87.8 82.7 1.670 302s demand_3 -48.5 -46.3 -1.402 302s demand_4 -13.5 -12.7 -0.516 302s demand_5 -51.7 -58.1 -2.620 302s demand_6 -18.3 -19.9 -1.105 302s demand_7 -42.5 -44.0 -2.919 302s demand_8 187.4 201.3 14.667 302s demand_9 70.4 80.9 6.698 302s demand_10 -116.6 -126.5 -12.579 302s demand_11 -18.1 -15.8 -2.152 302s demand_12 57.9 40.2 7.029 302s demand_13 41.5 28.8 5.278 302s demand_14 -38.0 -30.8 -5.304 302s demand_15 -130.2 -136.4 -20.000 302s demand_16 122.1 132.3 20.164 302s demand_17 -43.7 -55.1 -8.477 302s demand_18 72.9 66.7 12.986 302s demand_19 -122.2 -102.9 -21.890 302s demand_20 -16.9 -13.7 -2.947 302s supply_1 44.7 44.0 0.449 302s supply_2 13.7 13.0 0.262 302s supply_3 136.5 130.4 3.947 302s supply_4 97.5 91.5 3.731 302s supply_5 82.2 92.3 4.165 302s supply_6 47.1 51.2 2.839 302s supply_7 80.0 82.8 5.491 302s supply_8 -167.3 -179.7 -13.089 302s supply_9 -98.3 -112.9 -9.349 302s supply_10 82.1 89.1 8.857 302s supply_11 -18.2 -16.0 -2.169 302s supply_12 -62.1 -43.1 -7.532 302s supply_13 -35.4 -24.5 -4.499 302s supply_14 20.8 16.9 2.907 302s supply_15 80.9 84.8 12.430 302s supply_16 -193.5 -209.7 -31.948 302s supply_17 -73.6 -92.7 -14.264 302s supply_18 -129.7 -118.7 -23.101 302s supply_19 94.1 79.3 16.863 302s supply_20 27.1 22.1 4.744 302s > estfun( fit3slsd[[ 5 ]]$e1w, residFit = FALSE ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s demand_1 0.89947 85.649 78.613 -0.57123 302s demand_2 0.00817 0.811 0.797 -0.00519 302s demand_3 1.94109 192.071 187.703 -1.23275 302s demand_4 1.44439 144.277 141.839 -0.91731 302s demand_5 1.10477 107.119 110.256 -0.70162 302s demand_6 0.67950 66.596 68.290 -0.43154 302s demand_7 0.96428 96.352 99.513 -0.61239 302s demand_8 -1.80100 -181.402 -194.148 1.14378 302s demand_9 -1.09741 -105.536 -106.009 0.69694 302s demand_10 0.93145 88.611 82.806 -0.59155 302s demand_11 -0.13250 -12.551 -9.951 0.08415 302s demand_12 -0.98743 -97.798 -75.933 0.62710 302s demand_13 -0.32371 -32.932 -27.386 0.20558 302s demand_14 -0.09978 -10.112 -9.040 0.06337 302s demand_15 0.56754 57.219 58.513 -0.36043 302s demand_16 -2.64753 -267.185 -278.255 1.68140 302s demand_17 -1.65258 -157.934 -159.308 1.04952 302s demand_18 -1.17988 -122.919 -123.179 0.74932 302s demand_19 1.26015 135.883 139.499 -0.80030 302s demand_20 0.12101 13.783 15.380 -0.07685 302s supply_1 -0.39424 -37.540 -34.456 0.40779 302s supply_2 -0.17503 -17.388 -17.083 0.18104 302s supply_3 -1.29167 -127.811 -124.905 1.33607 302s supply_4 -0.90312 -90.210 -88.686 0.93416 302s supply_5 -0.84242 -81.682 -84.074 0.87137 302s supply_6 -0.46834 -45.901 -47.069 0.48444 302s supply_7 -0.80988 -80.925 -83.580 0.83772 302s supply_8 1.72577 173.825 186.038 -1.78508 302s supply_9 1.10899 106.650 107.128 -1.14710 302s supply_10 -0.94120 -89.538 -83.673 0.97355 302s supply_11 0.22943 21.733 17.231 -0.23732 302s supply_12 0.60019 59.445 46.155 -0.62082 302s supply_13 0.37695 38.348 31.890 -0.38990 302s supply_14 -0.28729 -29.116 -26.029 0.29717 302s supply_15 -0.94355 -95.128 -97.280 0.97597 302s supply_16 2.01917 203.771 212.215 -2.08856 302s supply_17 0.74286 70.994 71.612 -0.76839 302s supply_18 1.40908 146.797 147.108 -1.45750 302s supply_19 -0.87479 -94.329 -96.840 0.90486 302s supply_20 -0.28090 -31.995 -35.702 0.29055 302s supply_price supply_farmPrice supply_trend 302s demand_1 -56.911 -55.981 -0.5712 302s demand_2 -0.545 -0.514 -0.0104 302s demand_3 -127.940 -122.166 -3.6983 302s demand_4 -95.886 -89.988 -3.6692 302s demand_5 -69.215 -77.739 -3.5081 302s demand_6 -43.002 -46.692 -2.5892 302s demand_7 -62.447 -64.669 -4.2868 302s demand_8 116.934 125.587 9.1502 302s demand_9 65.935 75.758 6.2725 302s demand_10 -54.848 -59.510 -5.9155 302s demand_11 7.776 6.816 0.9257 302s demand_12 62.030 43.019 7.5252 302s demand_13 21.003 14.576 2.6726 302s demand_14 6.354 5.158 0.8871 302s demand_15 -35.186 -36.872 -5.4065 302s demand_16 162.914 176.547 26.9023 302s demand_17 92.041 115.972 17.8418 302s demand_18 75.726 69.312 13.4878 302s demand_19 -84.882 -71.467 -15.2057 302s demand_20 -8.791 -7.147 -1.5370 302s supply_1 40.627 39.963 0.4078 302s supply_2 19.031 17.941 0.3621 302s supply_3 138.662 132.404 4.0082 302s supply_4 97.648 91.641 3.7366 302s supply_5 85.962 96.548 4.3569 302s supply_6 48.274 52.416 2.9066 302s supply_7 85.424 88.463 5.8640 302s supply_8 -182.496 -196.002 -14.2806 302s supply_9 -108.523 -124.690 -10.3239 302s supply_10 90.266 97.939 9.7355 302s supply_11 -21.929 -19.223 -2.6105 302s supply_12 -61.410 -42.588 -7.4498 302s supply_13 -39.834 -27.644 -5.0687 302s supply_14 29.799 24.189 4.1603 302s supply_15 95.276 99.842 14.6396 302s supply_16 -202.365 -219.299 -33.4170 302s supply_17 -67.387 -84.908 -13.0627 302s supply_18 -147.294 -134.819 -26.2351 302s supply_19 95.972 80.804 17.1923 302s supply_20 33.238 27.021 5.8111 302s > 302s > round( colSums( estfun( fit3slsd[[ 5 ]]$e1w ) ), digits = 7 ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 0.0 0.0 0.0 0.0 302s supply_price supply_farmPrice supply_trend 302s 38.6 0.0 -52.4 302s > round( colSums( estfun( fit3slsd[[ 5 ]]$e1w, residFit = FALSE ) ), digits = 7 ) 302s Warning message: 302s In estfun.systemfit(fit3slsd[[5]]$e1w) : 302s the columns of the returned estimating function do not all sum up to zero, which indicates that the wrong estimating function is returned 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 0 0 0 0 302s supply_price supply_farmPrice supply_trend 302s 0 0 0 302s > 302s > round( colSums( estfun( fit3slsd[[ 4 ]]$e1w ) ), digits = 7 ) 302s Warning message: 302s In estfun.systemfit(fit3slsd[[4]]$e1w) : 302s the columns of the returned estimating function do not all sum up to zero, which indicates that the wrong estimating function is returned 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 0.00 0.00 0.00 0.00 302s supply_price supply_farmPrice supply_trend 302s 9.67 0.00 -13.12 302s > round( colSums( estfun( fit3slsd[[ 4 ]]$e1w, residFit = FALSE ) ), digits = 7 ) 302s Warning message: 302s In estfun.systemfit(fit3slsd[[4]]$e1w, residFit = FALSE) : 302s the columns of the returned estimating function do not all sum up to zero, which indicates that the wrong estimating function is returned 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 0.0 0.0 0.0 0.0 302s supply_price supply_farmPrice supply_trend 302s -28.9 0.0 39.3 302s > 302s > round( colSums( estfun( fit3slsd[[ 3 ]]$e1w ) ), digits = 7 ) 302s Warning message: 302s In estfun.systemfit(fit3slsd[[3]]$e1w) : 302s the columns of the returned estimating function do not all sum up to zero, which indicates that the wrong estimating function is returned 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 0.00 0.00 0.00 0.00 302s supply_price supply_farmPrice supply_trend 302s 9.67 0.00 -13.12 302s > round( colSums( estfun( fit3slsd[[ 3 ]]$e1w, residFit = FALSE ) ), digits = 7 ) 302s Warning message: 302s In estfun.systemfit(fit3slsd[[3]]$e1w, residFit = FALSE) : 302s the columns of the returned estimating function do not all sum up to zero, which indicates that the wrong estimating function is returned 302s Warning message: 302s In estfun.systemfit(fit3slsd[[2]]$e1w) : 302s the columns of the returned estimating function do not all sum up to zero, which indicates that the wrong estimating function is returned 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 0.0 0.0 0.0 0.0 302s supply_price supply_farmPrice supply_trend 302s -28.9 0.0 39.3 302s > 302s > round( colSums( estfun( fit3slsd[[ 2 ]]$e1w ) ), digits = 7 ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 0.0 0.0 0.0 0.0 302s supply_price supply_farmPrice supply_trend 302s 38.6 0.0 -52.4 302s > round( colSums( estfun( fit3slsd[[ 2 ]]$e1w, residFit = FALSE ) ), digits = 7 ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 0 0 0 0 302s supply_price supply_farmPrice supply_trend 302s 0 0 0 302s > 302s > round( colSums( estfun( fit3slsd[[ 1 ]]$e1w ) ), digits = 7 ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 0 0 0 0 302s supply_price supply_farmPrice supply_trend 302s 0 0 0 302s > round( colSums( estfun( fit3slsd[[ 1 ]]$e1w, residFit = FALSE ) ), digits = 7 ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s 0.0 0.0 0.0 0.0 302s supply_price supply_farmPrice supply_trend 302s -38.6 0.0 52.4 302s > 302s > 302s > ## **************** bread ************************ 302s > bread( fit3sls[[ 1 ]]$e1 ) 302s Warning message: 302s In estfun.systemfit(fit3slsd[[1]]$e1w, residFit = FALSE) : 302s the columns of the returned estimating function do not all sum up to zero, which indicates that the wrong estimating function is returned 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s [1,] 2509.59 -26.9369 1.9721 2525.8 302s [2,] -26.94 0.3724 -0.1057 -14.1 302s [3,] 1.97 -0.1057 0.0881 -11.3 302s [4,] 2525.80 -14.1479 -11.2987 5658.1 302s [5,] -27.01 0.2401 0.0307 -43.3 302s [6,] 1.64 -0.0877 0.0732 -11.8 302s [7,] 2.47 -0.1324 0.1104 -16.4 302s supply_price supply_farmPrice supply_trend 302s [1,] -27.0066 1.6369 2.4699 302s [2,] 0.2401 -0.0877 -0.1324 302s [3,] 0.0307 0.0732 0.1104 302s [4,] -43.3336 -11.7989 -16.3581 302s [5,] 0.3974 0.0325 0.0428 302s [6,] 0.0325 0.0774 0.1019 302s [7,] 0.0428 0.1019 0.2125 302s > 302s > bread( fit3sls[[ 2 ]]$e1e ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s [1,] 2133.15 -22.8963 1.6763 2082.83 302s [2,] -22.90 0.3165 -0.0898 -11.67 302s [3,] 1.68 -0.0898 0.0749 -9.32 302s [4,] 2082.83 -11.6667 -9.3172 4526.47 302s [5,] -22.27 0.1980 0.0253 -34.67 302s [6,] 1.35 -0.0723 0.0603 -9.44 302s [7,] 2.04 -0.1091 0.0910 -13.09 302s supply_price supply_farmPrice supply_trend 302s [1,] -22.2702 1.3498 2.0367 302s [2,] 0.1980 -0.0723 -0.1091 302s [3,] 0.0253 0.0603 0.0910 302s [4,] -34.6668 -9.4391 -13.0865 302s [5,] 0.3179 0.0260 0.0342 302s [6,] 0.0260 0.0619 0.0815 302s [7,] 0.0342 0.0815 0.1700 302s > 302s > bread( fit3sls[[ 3 ]]$e1c ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s [1,] 2509.59 -26.9369 1.9721 2610.8 302s [2,] -26.94 0.3724 -0.1057 -14.6 302s [3,] 1.97 -0.1057 0.0881 -11.7 302s [4,] 2610.83 -14.6243 -11.6791 5650.4 302s [5,] -27.92 0.2482 0.0317 -43.3 302s [6,] 1.69 -0.0907 0.0756 -11.7 302s [7,] 2.55 -0.1368 0.1141 -16.7 302s supply_price supply_farmPrice supply_trend 302s [1,] -27.9159 1.6920 2.5531 302s [2,] 0.2482 -0.0907 -0.1368 302s [3,] 0.0317 0.0756 0.1141 302s [4,] -43.3005 -11.7199 -16.6696 302s [5,] 0.3972 0.0321 0.0441 302s [6,] 0.0321 0.0766 0.1051 302s [7,] 0.0441 0.1051 0.1999 302s > 302s > bread( fit3sls[[ 4 ]]$e1wc ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s [1,] 2509.59 -26.9369 1.9721 2610.8 302s [2,] -26.94 0.3724 -0.1057 -14.6 302s [3,] 1.97 -0.1057 0.0881 -11.7 302s [4,] 2610.83 -14.6243 -11.6791 5650.4 302s [5,] -27.92 0.2482 0.0317 -43.3 302s [6,] 1.69 -0.0907 0.0756 -11.7 302s [7,] 2.55 -0.1368 0.1141 -16.7 302s supply_price supply_farmPrice supply_trend 302s [1,] -27.9159 1.6920 2.5531 302s [2,] 0.2482 -0.0907 -0.1368 302s [3,] 0.0317 0.0756 0.1141 302s [4,] -43.3005 -11.7199 -16.6696 302s [5,] 0.3972 0.0321 0.0441 302s [6,] 0.0321 0.0766 0.1051 302s [7,] 0.0441 0.1051 0.1999 302s > 302s > bread( fit3slsd[[ 5 ]]$e1w ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s [1,] 4967.14 -60.707 11.4076 1773.52 302s [2,] -60.71 0.839 -0.2382 -6.24 302s [3,] 11.41 -0.238 0.1273 -11.71 302s [4,] 1773.52 -6.236 -11.7103 5325.96 302s [5,] -21.83 0.185 0.0346 -37.94 302s [6,] 6.07 -0.141 0.0826 -13.55 302s [7,] -16.09 0.136 0.0255 -20.05 302s supply_price supply_farmPrice supply_trend 302s [1,] -21.8336 6.0740 -16.0922 302s [2,] 0.1845 -0.1413 0.1360 302s [3,] 0.0346 0.0826 0.0255 302s [4,] -37.9350 -13.5483 -20.0519 302s [5,] 0.3216 0.0453 0.1323 302s [6,] 0.0453 0.0885 0.0440 302s [7,] 0.1323 0.0440 0.2443 302s > 302s > bread( fit3slsd[[ 4 ]]$e1w ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s [1,] 4967.14 -60.707 11.4076 1773.52 302s [2,] -60.71 0.839 -0.2382 -6.24 302s [3,] 11.41 -0.238 0.1273 -11.71 302s [4,] 1773.52 -6.236 -11.7103 5325.96 302s [5,] -21.83 0.185 0.0346 -37.94 302s [6,] 6.07 -0.141 0.0826 -13.55 302s [7,] -16.09 0.136 0.0255 -20.05 302s supply_price supply_farmPrice supply_trend 302s [1,] -21.8336 6.0740 -16.0922 302s [2,] 0.1845 -0.1413 0.1360 302s [3,] 0.0346 0.0826 0.0255 302s [4,] -37.9350 -13.5483 -20.0519 302s [5,] 0.3216 0.0453 0.1323 302s [6,] 0.0453 0.0885 0.0440 302s [7,] 0.1323 0.0440 0.2443 302s > 302s > bread( fit3slsd[[ 3 ]]$e1w ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s [1,] 4967.14 -60.707 11.4076 1773.52 302s [2,] -60.71 0.839 -0.2382 -6.24 302s [3,] 11.41 -0.238 0.1273 -11.71 302s [4,] 1773.52 -6.236 -11.7103 5325.96 302s [5,] -21.83 0.185 0.0346 -37.94 302s [6,] 6.07 -0.141 0.0826 -13.55 302s [7,] -16.09 0.136 0.0255 -20.05 302s supply_price supply_farmPrice supply_trend 302s [1,] -21.8336 6.0740 -16.0922 302s [2,] 0.1845 -0.1413 0.1360 302s [3,] 0.0346 0.0826 0.0255 302s [4,] -37.9350 -13.5483 -20.0519 302s [5,] 0.3216 0.0453 0.1323 302s [6,] 0.0453 0.0885 0.0440 302s [7,] 0.1323 0.0440 0.2443 302s > 302s > bread( fit3slsd[[ 2 ]]$e1w ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s [1,] 4967.14 -60.707 11.4076 1773.52 302s [2,] -60.71 0.839 -0.2382 -6.24 302s [3,] 11.41 -0.238 0.1273 -11.71 302s [4,] 1773.52 -6.236 -11.7103 5325.96 302s [5,] -21.83 0.185 0.0346 -37.94 302s [6,] 6.07 -0.141 0.0826 -13.55 302s [7,] -16.09 0.136 0.0255 -20.05 302s supply_price supply_farmPrice supply_trend 302s [1,] -21.8336 6.0740 -16.0922 302s [2,] 0.1845 -0.1413 0.1360 302s [3,] 0.0346 0.0826 0.0255 302s [4,] -37.9350 -13.5483 -20.0519 302s [5,] 0.3216 0.0453 0.1323 302s [6,] 0.0453 0.0885 0.0440 302s [7,] 0.1323 0.0440 0.2443 302s > 302s > bread( fit3slsd[[ 1 ]]$e1w ) 302s demand_(Intercept) demand_price demand_income supply_(Intercept) 302s [1,] 4967.14 -60.707 11.4076 1773.52 302s [2,] -60.71 0.839 -0.2382 -6.24 302s [3,] 11.41 -0.238 0.1273 -11.71 302s [4,] 1773.52 -6.236 -11.7103 5325.96 302s [5,] -21.83 0.185 0.0346 -37.94 302s [6,] 6.07 -0.141 0.0826 -13.55 302s [7,] -16.09 0.136 0.0255 -20.05 302s supply_price supply_farmPrice supply_trend 302s [1,] -21.8336 6.0740 -16.0922 302s [2,] 0.1845 -0.1413 0.1360 302s [3,] 0.0346 0.0826 0.0255 302s [4,] -37.9350 -13.5483 -20.0519 302s [5,] 0.3216 0.0453 0.1323 302s [6,] 0.0453 0.0885 0.0440 302s [7,] 0.1323 0.0440 0.2443 302s > 302s BEGIN TEST test_hausman.R 302s 302s R version 4.3.3 (2024-02-29) -- "Angel Food Cake" 302s Copyright (C) 2024 The R Foundation for Statistical Computing 302s Platform: arm-unknown-linux-gnueabihf (32-bit) 302s 302s R is free software and comes with ABSOLUTELY NO WARRANTY. 302s You are welcome to redistribute it under certain conditions. 302s Type 'license()' or 'licence()' for distribution details. 302s 302s R is a collaborative project with many contributors. 302s Type 'contributors()' for more information and 302s 'citation()' on how to cite R or R packages in publications. 302s 302s Type 'demo()' for some demos, 'help()' for on-line help, or 302s 'help.start()' for an HTML browser interface to help. 302s Type 'q()' to quit R. 302s 302s > library( "systemfit" ) 302s Loading required package: Matrix 303s Loading required package: car 303s Loading required package: carData 303s Loading required package: lmtest 303s Loading required package: zoo 303s 303s Attaching package: ‘zoo’ 303s 303s The following objects are masked from ‘package:base’: 303s 303s as.Date, as.Date.numeric 303s 304s 304s Please cite the 'systemfit' package as: 304s Arne Henningsen and Jeff D. Hamann (2007). systemfit: A Package for Estimating Systems of Simultaneous Equations in R. Journal of Statistical Software 23(4), 1-40. http://www.jstatsoft.org/v23/i04/. 304s 304s If you have questions, suggestions, or comments regarding the 'systemfit' package, please use a forum or 'tracker' at systemfit's R-Forge site: 304s https://r-forge.r-project.org/projects/systemfit/ 304s > options( digits = 5 ) 304s > 304s > data( "Kmenta" ) 304s > useMatrix <- FALSE 304s > 304s > eqDemand <- consump ~ price + income 304s > eqSupply <- consump ~ price + farmPrice + trend 304s > inst <- ~ income + farmPrice + trend 304s > eqSystem <- list( demand = eqDemand, supply = eqSupply ) 304s > restrm <- matrix(0,1,7) # restriction matrix "R" 304s > restrm[1,3] <- 1 304s > restrm[1,7] <- -1 304s > restr2m <- matrix(0,2,7) # restriction matrix "R" 2 304s > restr2m[1,3] <- 1 304s > restr2m[1,7] <- -1 304s > restr2m[2,2] <- -1 304s > restr2m[2,5] <- 1 304s > restr2q <- c( 0, 0.5 ) # restriction vector "q" 2 304s > tc <- matrix(0,7,6) 304s > tc[1,1] <- 1 304s > tc[2,2] <- 1 304s > tc[3,3] <- 1 304s > tc[4,4] <- 1 304s > tc[5,5] <- 1 304s > tc[6,6] <- 1 304s > tc[7,3] <- 1 304s > restr3m <- matrix(0,1,6) # restriction matrix "R" 2 304s > restr3m[1,2] <- -1 304s > restr3m[1,5] <- 1 304s > restr3q <- c( 0.5 ) # restriction vector "q" 2 304s > 304s > 304s > ## ******************* unrestricted estimation ***************** 304s > ## ******************** default estimation ********************* 304s > fit2sls1 <- systemfit( eqSystem, "2SLS", data = Kmenta, inst = inst, 304s + useMatrix = useMatrix ) 304s > fit3sls1 <- systemfit( eqSystem, "3SLS", data = Kmenta, inst = inst, 304s + useMatrix = useMatrix ) 304s > print( hausman.systemfit( fit2sls1, fit3sls1 ) ) 304s 304s Hausman specification test for consistency of the 3SLS estimation 304s 304s data: Kmenta 304s Hausman = 2.54, df = 7, p-value = 0.92 304s 304s > 304s > ## ************** 2SLS estimation with singleEqSigma = FALSE ***************** 304s > fit2sls1s <- systemfit( eqSystem, "2SLS", data = Kmenta, inst = inst, 304s + singleEqSigma = FALSE, useMatrix = useMatrix ) 304s > print( hausman.systemfit( fit2sls1s, fit3sls1 ) ) 304s 304s Hausman specification test for consistency of the 3SLS estimation 304s 304s data: Kmenta 304s Hausman = 3.28, df = 7, p-value = 0.86 304s 304s > 304s > ## ******************* estimations with methodResidCov = 0 ***************** 304s > fit2sls1r <- systemfit( eqSystem, "2SLS", data = Kmenta, inst = inst, 304s + methodResidCov = "noDfCor", useMatrix = useMatrix ) 304s > fit3sls1r <- systemfit( eqSystem, "3SLS", data = Kmenta, inst = inst, 304s + methodResidCov = "noDfCor", useMatrix = useMatrix ) 304s > print( hausman.systemfit( fit2sls1r, fit3sls1r ) ) 304s 304s Hausman specification test for consistency of the 3SLS estimation 304s 304s data: Kmenta 304s Hausman = 2.98, df = 7, p-value = 0.89 304s 304s > 304s > 304s > ## ********************* estimation with restriction ******************** 304s > ## *********************** default estimation *********************** 304s > fit2sls2 <- systemfit( eqSystem, "2SLS", data = Kmenta, restrict.matrix = restrm, 304s + inst = inst, useMatrix = useMatrix ) 304s > fit3sls2 <- systemfit( eqSystem, "3SLS", data = Kmenta, restrict.matrix = restrm, 304s + inst = inst, useMatrix = useMatrix ) 304s > # print( hausman.systemfit( fit2sls2, fit3sls2 ) ) 304s > 304s > ## ************* 2SLS estimation with singleEqSigma = TRUE ***************** 304s > fit2sls2s <- systemfit( eqSystem, "2SLS", data = Kmenta, restrict.matrix = restrm, 304s + inst = inst, singleEqSigma = TRUE, useMatrix = useMatrix ) 304s > # print( hausman.systemfit( fit2sls2s, fit3sls2 ) ) 304s > 304s > ## ********************* estimations with methodResidCov = 0 ************** 304s > fit2sls2r <- systemfit( eqSystem, "2SLS", data = Kmenta, restrict.matrix = restrm, 304s + inst = inst, methodResidCov = "noDfCor", useMatrix = useMatrix ) 304s > fit3sls2r <- systemfit( eqSystem, "3SLS", data = Kmenta, restrict.matrix = restrm, 304s + inst = inst, methodResidCov = "noDfCor", useMatrix = useMatrix ) 304s > # print( hausman.systemfit( fit2sls2r, fit3sls2r ) ) 304s > 304s > 304s > ## ****************** estimation with restriction via restrict.regMat ****************** 304s > ## ********************** default estimation ******************** 304s > fit2sls3 <- systemfit( eqSystem, "2SLS", data = Kmenta, restrict.regMat = tc, 304s + inst = inst, useMatrix = useMatrix ) 304s > fit3sls3 <- systemfit( eqSystem, "3SLS", data = Kmenta, restrict.regMat = tc, 304s + inst = inst, useMatrix = useMatrix ) 304s > print( hausman.systemfit( fit2sls3, fit3sls3 ) ) 304s 304s Hausman specification test for consistency of the 3SLS estimation 304s 304s data: Kmenta 304s Hausman = -0.281, df = 6, p-value = 1 304s 304s > 304s > ## ******************* estimations with methodResidCov = 0 ******* 304s > fit2sls3r <- systemfit( eqSystem, "2SLS", data = Kmenta, restrict.regMat = tc, 304s + inst = inst, methodResidCov = "noDfCor", useMatrix = useMatrix ) 304s > fit3sls3r <- systemfit( eqSystem, "3SLS", data = Kmenta, restrict.regMat = tc, 304s + inst = inst, methodResidCov = "noDfCor", useMatrix = useMatrix ) 304s > print( hausman.systemfit( fit2sls3r, fit3sls3r ) ) 304s 304s Hausman specification test for consistency of the 3SLS estimation 304s 304s data: Kmenta 304s Hausman = -0.0132, df = 6, p-value = 1 304s 304s > 304s > 304s > ## ***************** estimations with 2 restrictions ******************* 304s > ## *********************** default estimations ************** 304s > fit2sls4 <- systemfit( eqSystem, "2SLS", data = Kmenta, restrict.matrix = restr2m, 304s + restrict.rhs = restr2q, inst = inst, useMatrix = useMatrix ) 304s > fit3sls4 <- systemfit( eqSystem, "3SLS", data = Kmenta, restrict.matrix = restr2m, 304s + restrict.rhs = restr2q, inst = inst, useMatrix = useMatrix ) 304s > # print( hausman.systemfit( fit2sls4, fit3sls4 ) ) 304s > 304s > ## ***************** estimations with methodResidCov = 0 ************** 304s > fit2sls4r <- systemfit( eqSystem, "2SLS", data = Kmenta, restrict.matrix = restr2m, 304s + restrict.rhs = restr2q, inst = inst, methodResidCov = "noDfCor", 304s + useMatrix = useMatrix ) 304s > fit3sls4r <- systemfit( eqSystem, "3SLS", data = Kmenta, restrict.matrix = restr2m, 304s + restrict.rhs = restr2q, inst = inst, methodResidCov = "noDfCor", 304s + useMatrix = useMatrix ) 304s > # print( hausman.systemfit( fit2sls4r, fit3sls4r ) ) 304s > 304s > 304s > ## *********** estimations with 2 restrictions via R and restrict.regMat *************** 304s > ## ***************** default estimations ******************* 304s > fit2sls5 <- systemfit( eqSystem, "2SLS", data = Kmenta, restrict.matrix = restr3m, 304s + restrict.rhs = restr3q, restrict.regMat = tc, inst = inst, 304s + useMatrix = useMatrix ) 304s > fit3sls5 <- systemfit( eqSystem, "3SLS", data = Kmenta, restrict.matrix = restr3m, 304s + restrict.rhs = restr3q, restrict.regMat = tc, inst = inst, 304s + useMatrix = useMatrix ) 304s > # print( hausman.systemfit( fit2sls5, fit3sls5 ) ) 304s > 304s > ## ************* estimations with methodResidCov = 0 ********* 304s > fit2sls5r <- systemfit( eqSystem, "2SLS", data = Kmenta, restrict.matrix = restr3m, 304s + restrict.rhs = restr3q, restrict.regMat = tc, inst = inst, 304s + methodResidCov = "noDfCor", useMatrix = useMatrix ) 304s > fit3sls5r <- systemfit( eqSystem, "3SLS", data = Kmenta, restrict.matrix = restr3m, 304s + restrict.rhs = restr3q, restrict.regMat = tc, inst = inst, 304s + methodResidCov = "noDfCor", useMatrix = useMatrix ) 304s > # print( hausman.systemfit( fit2sls5r, fit3sls5r ) ) 304s > 304s BEGIN TEST test_ols.R 304s 304s R version 4.3.3 (2024-02-29) -- "Angel Food Cake" 304s Copyright (C) 2024 The R Foundation for Statistical Computing 304s Platform: arm-unknown-linux-gnueabihf (32-bit) 304s 304s R is free software and comes with ABSOLUTELY NO WARRANTY. 304s You are welcome to redistribute it under certain conditions. 304s Type 'license()' or 'licence()' for distribution details. 304s 304s R is a collaborative project with many contributors. 304s Type 'contributors()' for more information and 304s 'citation()' on how to cite R or R packages in publications. 304s 304s Type 'demo()' for some demos, 'help()' for on-line help, or 304s 'help.start()' for an HTML browser interface to help. 304s Type 'q()' to quit R. 304s 304s > library( systemfit ) 304s Loading required package: Matrix 305s Loading required package: car 305s Loading required package: carData 305s Loading required package: lmtest 305s Loading required package: zoo 305s 305s Attaching package: ‘zoo’ 305s 305s The following objects are masked from ‘package:base’: 305s 305s as.Date, as.Date.numeric 305s 305s 305s Please cite the 'systemfit' package as: 305s Arne Henningsen and Jeff D. Hamann (2007). systemfit: A Package for Estimating Systems of Simultaneous Equations in R. Journal of Statistical Software 23(4), 1-40. http://www.jstatsoft.org/v23/i04/. 305s 305s If you have questions, suggestions, or comments regarding the 'systemfit' package, please use a forum or 'tracker' at systemfit's R-Forge site: 305s https://r-forge.r-project.org/projects/systemfit/ 305s > options( digits = 3 ) 305s > 305s > data( "Kmenta" ) 305s > useMatrix <- FALSE 305s > 305s > demand <- consump ~ price + income 305s > supply <- consump ~ price + farmPrice + trend 305s > system <- list( demand = demand, supply = supply ) 305s > restrm <- matrix(0,1,7) # restriction matrix "R" 305s > restrm[1,3] <- 1 305s > restrm[1,7] <- -1 305s > restrict <- "demand_income - supply_trend = 0" 305s > restr2m <- matrix(0,2,7) # restriction matrix "R" 2 305s > restr2m[1,3] <- 1 305s > restr2m[1,7] <- -1 305s > restr2m[2,2] <- -1 305s > restr2m[2,5] <- 1 305s > restr2q <- c( 0, 0.5 ) # restriction vector "q" 2 305s > restrict2 <- c( "demand_income - supply_trend = 0", 305s + "- demand_price + supply_price = 0.5" ) 305s > tc <- matrix(0,7,6) 305s > tc[1,1] <- 1 305s > tc[2,2] <- 1 305s > tc[3,3] <- 1 305s > tc[4,4] <- 1 305s > tc[5,5] <- 1 305s > tc[6,6] <- 1 305s > tc[7,3] <- 1 305s > restr3m <- matrix(0,1,6) # restriction matrix "R" 2 305s > restr3m[1,2] <- -1 305s > restr3m[1,5] <- 1 305s > restr3q <- c( 0.5 ) # restriction vector "q" 2 305s > restrict3 <- "- C2 + C5 = 0.5" 305s > 305s > # It is not possible to estimate OLS with systemfit 305s > # exactly as EViews does, because EViews uses 305s > # methodResidCov == "geomean" for the coefficient covariance matrix and 305s > # methodResidCov == "noDfCor" for the residual covariance matrix, while 305s > # systemfit uses always the same formulas for both calculations. 305s > 305s > ## ******* single-equation OLS estimations ********************* 305s > lmDemand <- lm( demand, data = Kmenta ) 305s > lmSupply <- lm( supply, data = Kmenta ) 305s > 305s > ## *************** OLS estimation ************************ 305s > ## ********** OLS estimation (default) ******************** 305s > fitols1 <- systemfit( system, "OLS", data = Kmenta, useMatrix = useMatrix ) 305s > print( summary( fitols1 ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 33 156 4.43 0.709 0.558 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 63.3 3.73 1.93 0.764 0.736 305s supply 20 16 92.6 5.78 2.40 0.655 0.590 305s 305s The covariance matrix of the residuals 305s demand supply 305s demand 3.73 4.14 305s supply 4.14 5.78 305s 305s The correlations of the residuals 305s demand supply 305s demand 1.000 0.891 305s supply 0.891 1.000 305s 305s 305s OLS estimates for 'demand' (equation 1) 305s Model Formula: consump ~ price + income 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 99.8954 7.5194 13.29 2.1e-10 *** 305s price -0.3163 0.0907 -3.49 0.0028 ** 305s income 0.3346 0.0454 7.37 1.1e-06 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 1.93 on 17 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 17 305s SSR: 63.332 MSE: 3.725 Root MSE: 1.93 305s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 305s 305s 305s OLS estimates for 'supply' (equation 2) 305s Model Formula: consump ~ price + farmPrice + trend 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 58.2754 11.4629 5.08 0.00011 *** 305s price 0.1604 0.0949 1.69 0.11039 305s farmPrice 0.2481 0.0462 5.37 6.2e-05 *** 305s trend 0.2483 0.0975 2.55 0.02157 * 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 2.405 on 16 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 16 305s SSR: 92.551 MSE: 5.784 Root MSE: 2.405 305s Multiple R-Squared: 0.655 Adjusted R-Squared: 0.59 305s 305s > nobs( fitols1 ) 305s [1] 40 305s > all.equal( coef( fitols1 ), c( coef( lmDemand ), coef( lmSupply ) ), 305s + check.attributes = FALSE ) 305s [1] TRUE 305s > all.equal( coef( summary( fitols1 ) ), 305s + rbind( coef( summary( lmDemand ) ), coef( summary( lmSupply ) ) ), 305s + check.attributes = FALSE ) 305s [1] TRUE 305s > all.equal( vcov( fitols1 ), 305s + as.matrix( bdiag( vcov( lmDemand ), vcov( lmSupply ) ) ), 305s + check.attributes = FALSE ) 305s [1] TRUE 305s > 305s > ## ********** OLS estimation (no singleEqSigma=F) ****************** 305s > fitols1s <- systemfit( system, "OLS", data = Kmenta, 305s + singleEqSigma = FALSE, useMatrix = useMatrix ) 305s > print( summary( fitols1s ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 33 156 4.43 0.709 0.558 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 63.3 3.73 1.93 0.764 0.736 305s supply 20 16 92.6 5.78 2.40 0.655 0.590 305s 305s The covariance matrix of the residuals 305s demand supply 305s demand 3.73 4.14 305s supply 4.14 5.78 305s 305s The correlations of the residuals 305s demand supply 305s demand 1.000 0.891 305s supply 0.891 1.000 305s 305s 305s OLS estimates for 'demand' (equation 1) 305s Model Formula: consump ~ price + income 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 99.8954 8.4671 11.80 1.3e-09 *** 305s price -0.3163 0.1021 -3.10 0.0065 ** 305s income 0.3346 0.0511 6.54 5.0e-06 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 1.93 on 17 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 17 305s SSR: 63.332 MSE: 3.725 Root MSE: 1.93 305s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 305s 305s 305s OLS estimates for 'supply' (equation 2) 305s Model Formula: consump ~ price + farmPrice + trend 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 58.2754 10.3587 5.63 3.8e-05 *** 305s price 0.1604 0.0857 1.87 0.080 . 305s farmPrice 0.2481 0.0417 5.94 2.1e-05 *** 305s trend 0.2483 0.0881 2.82 0.012 * 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 2.405 on 16 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 16 305s SSR: 92.551 MSE: 5.784 Root MSE: 2.405 305s Multiple R-Squared: 0.655 Adjusted R-Squared: 0.59 305s 305s > all.equal( coef( fitols1s ), c( coef( lmDemand ), coef( lmSupply ) ), 305s + check.attributes = FALSE ) 305s [1] TRUE 305s > 305s > ## **************** OLS (useDfSys=T) *********************** 305s > print( summary( fitols1, useDfSys = TRUE ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 33 156 4.43 0.709 0.558 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 63.3 3.73 1.93 0.764 0.736 305s supply 20 16 92.6 5.78 2.40 0.655 0.590 305s 305s The covariance matrix of the residuals 305s demand supply 305s demand 3.73 4.14 305s supply 4.14 5.78 305s 305s The correlations of the residuals 305s demand supply 305s demand 1.000 0.891 305s supply 0.891 1.000 305s 305s 305s OLS estimates for 'demand' (equation 1) 305s Model Formula: consump ~ price + income 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 99.8954 7.5194 13.29 8.4e-15 *** 305s price -0.3163 0.0907 -3.49 0.0014 ** 305s income 0.3346 0.0454 7.37 1.8e-08 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 1.93 on 17 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 17 305s SSR: 63.332 MSE: 3.725 Root MSE: 1.93 305s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 305s 305s 305s OLS estimates for 'supply' (equation 2) 305s Model Formula: consump ~ price + farmPrice + trend 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 58.2754 11.4629 5.08 1.4e-05 *** 305s price 0.1604 0.0949 1.69 0.100 305s farmPrice 0.2481 0.0462 5.37 6.1e-06 *** 305s trend 0.2483 0.0975 2.55 0.016 * 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 2.405 on 16 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 16 305s SSR: 92.551 MSE: 5.784 Root MSE: 2.405 305s Multiple R-Squared: 0.655 Adjusted R-Squared: 0.59 305s 305s > 305s > ## **************** OLS (methodResidCov="noDfCor") *********************** 305s > fitols1r <- systemfit( system, "OLS", data = Kmenta, 305s + methodResidCov = "noDfCor", x = TRUE, 305s + useMatrix = useMatrix ) 305s > print( summary( fitols1r ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 33 156 3.02 0.709 0.537 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 63.3 3.73 1.93 0.764 0.736 305s supply 20 16 92.6 5.78 2.40 0.655 0.590 305s 305s The covariance matrix of the residuals 305s demand supply 305s demand 3.17 3.41 305s supply 3.41 4.63 305s 305s The correlations of the residuals 305s demand supply 305s demand 1.000 0.891 305s supply 0.891 1.000 305s 305s 305s OLS estimates for 'demand' (equation 1) 305s Model Formula: consump ~ price + income 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 99.8954 6.9325 14.41 5.8e-11 *** 305s price -0.3163 0.0836 -3.78 0.0015 ** 305s income 0.3346 0.0419 7.99 3.7e-07 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 1.93 on 17 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 17 305s SSR: 63.332 MSE: 3.725 Root MSE: 1.93 305s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 305s 305s 305s OLS estimates for 'supply' (equation 2) 305s Model Formula: consump ~ price + farmPrice + trend 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 58.2754 10.2527 5.68 3.4e-05 *** 305s price 0.1604 0.0849 1.89 0.077 . 305s farmPrice 0.2481 0.0413 6.01 1.8e-05 *** 305s trend 0.2483 0.0872 2.85 0.012 * 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 2.405 on 16 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 16 305s SSR: 92.551 MSE: 5.784 Root MSE: 2.405 305s Multiple R-Squared: 0.655 Adjusted R-Squared: 0.59 305s 305s > all.equal( coef( fitols1r ), c( coef( lmDemand ), coef( lmSupply ) ), 305s + check.attributes = FALSE ) 305s [1] TRUE 305s > 305s > ## ******** OLS (methodResidCov="noDfCor", singleEqSigma=F) *********** 305s > fitols1rs <- systemfit( system, "OLS", data = Kmenta, 305s + methodResidCov = "noDfCor", singleEqSigma = FALSE, 305s + useMatrix = useMatrix ) 305s > print( summary( fitols1rs ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 33 156 3.02 0.709 0.537 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 63.3 3.73 1.93 0.764 0.736 305s supply 20 16 92.6 5.78 2.40 0.655 0.590 305s 305s The covariance matrix of the residuals 305s demand supply 305s demand 3.17 3.41 305s supply 3.41 4.63 305s 305s The correlations of the residuals 305s demand supply 305s demand 1.000 0.891 305s supply 0.891 1.000 305s 305s 305s OLS estimates for 'demand' (equation 1) 305s Model Formula: consump ~ price + income 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 99.8954 7.6907 12.99 3.0e-10 *** 305s price -0.3163 0.0927 -3.41 0.0033 ** 305s income 0.3346 0.0465 7.20 1.5e-06 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 1.93 on 17 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 17 305s SSR: 63.332 MSE: 3.725 Root MSE: 1.93 305s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 305s 305s 305s OLS estimates for 'supply' (equation 2) 305s Model Formula: consump ~ price + farmPrice + trend 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 58.2754 9.4088 6.19 1.3e-05 *** 305s price 0.1604 0.0779 2.06 0.0561 . 305s farmPrice 0.2481 0.0379 6.55 6.7e-06 *** 305s trend 0.2483 0.0800 3.10 0.0068 ** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 2.405 on 16 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 16 305s SSR: 92.551 MSE: 5.784 Root MSE: 2.405 305s Multiple R-Squared: 0.655 Adjusted R-Squared: 0.59 305s 305s > all.equal( coef( fitols1rs ), c( coef( lmDemand ), coef( lmSupply ) ), 305s + check.attributes = FALSE ) 305s [1] TRUE 305s > 305s > ## **************** OLS (methodResidCov="Theil" ) *********************** 305s > fitols1r <- systemfit( system, "OLS", data = Kmenta, 305s + methodResidCov = "Theil", x = TRUE, 305s + useMatrix = useMatrix ) 305s > print( summary( fitols1r ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 33 156 3.26 0.709 0.503 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 63.3 3.73 1.93 0.764 0.736 305s supply 20 16 92.6 5.78 2.40 0.655 0.590 305s 305s The covariance matrix of the residuals 305s demand supply 305s demand 3.73 4.28 305s supply 4.28 5.78 305s 305s The correlations of the residuals 305s demand supply 305s demand 1.000 0.891 305s supply 0.891 1.000 305s 305s 305s OLS estimates for 'demand' (equation 1) 305s Model Formula: consump ~ price + income 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 99.8954 7.5194 13.29 2.1e-10 *** 305s price -0.3163 0.0907 -3.49 0.0028 ** 305s income 0.3346 0.0454 7.37 1.1e-06 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 1.93 on 17 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 17 305s SSR: 63.332 MSE: 3.725 Root MSE: 1.93 305s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 305s 305s 305s OLS estimates for 'supply' (equation 2) 305s Model Formula: consump ~ price + farmPrice + trend 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 58.2754 11.4629 5.08 0.00011 *** 305s price 0.1604 0.0949 1.69 0.11039 305s farmPrice 0.2481 0.0462 5.37 6.2e-05 *** 305s trend 0.2483 0.0975 2.55 0.02157 * 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 2.405 on 16 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 16 305s SSR: 92.551 MSE: 5.784 Root MSE: 2.405 305s Multiple R-Squared: 0.655 Adjusted R-Squared: 0.59 305s 305s > all.equal( coef( fitols1r ), c( coef( lmDemand ), coef( lmSupply ) ), 305s + check.attributes = FALSE ) 305s [1] TRUE 305s > 305s > ## **************** OLS (methodResidCov="max") *********************** 305s > fitols1r <- systemfit( system, "OLS", data = Kmenta, 305s + methodResidCov = "max", x = TRUE, 305s + useMatrix = useMatrix ) 305s > print( summary( fitols1r ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 33 156 3.37 0.709 0.509 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 63.3 3.73 1.93 0.764 0.736 305s supply 20 16 92.6 5.78 2.40 0.655 0.590 305s 305s The covariance matrix of the residuals 305s demand supply 305s demand 3.73 4.26 305s supply 4.26 5.78 305s 305s The correlations of the residuals 305s demand supply 305s demand 1.000 0.891 305s supply 0.891 1.000 305s 305s 305s OLS estimates for 'demand' (equation 1) 305s Model Formula: consump ~ price + income 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 99.8954 7.5194 13.29 2.1e-10 *** 305s price -0.3163 0.0907 -3.49 0.0028 ** 305s income 0.3346 0.0454 7.37 1.1e-06 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 1.93 on 17 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 17 305s SSR: 63.332 MSE: 3.725 Root MSE: 1.93 305s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 305s 305s 305s OLS estimates for 'supply' (equation 2) 305s Model Formula: consump ~ price + farmPrice + trend 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 58.2754 11.4629 5.08 0.00011 *** 305s price 0.1604 0.0949 1.69 0.11039 305s farmPrice 0.2481 0.0462 5.37 6.2e-05 *** 305s trend 0.2483 0.0975 2.55 0.02157 * 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 2.405 on 16 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 16 305s SSR: 92.551 MSE: 5.784 Root MSE: 2.405 305s Multiple R-Squared: 0.655 Adjusted R-Squared: 0.59 305s 305s > 305s > ## ******** OLS (methodResidCov="max", singleEqSigma=F) *********** 305s > fitols1rs <- systemfit( system, "OLS", data = Kmenta, 305s + methodResidCov = "max", singleEqSigma = FALSE, 305s + useMatrix = useMatrix ) 305s > print( summary( fitols1rs ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 33 156 3.37 0.709 0.509 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 63.3 3.73 1.93 0.764 0.736 305s supply 20 16 92.6 5.78 2.40 0.655 0.590 305s 305s The covariance matrix of the residuals 305s demand supply 305s demand 3.73 4.26 305s supply 4.26 5.78 305s 305s The correlations of the residuals 305s demand supply 305s demand 1.000 0.891 305s supply 0.891 1.000 305s 305s 305s OLS estimates for 'demand' (equation 1) 305s Model Formula: consump ~ price + income 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 99.8954 8.4671 11.80 1.3e-09 *** 305s price -0.3163 0.1021 -3.10 0.0065 ** 305s income 0.3346 0.0511 6.54 5.0e-06 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 1.93 on 17 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 17 305s SSR: 63.332 MSE: 3.725 Root MSE: 1.93 305s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 305s 305s 305s OLS estimates for 'supply' (equation 2) 305s Model Formula: consump ~ price + farmPrice + trend 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 58.2754 10.3587 5.63 3.8e-05 *** 305s price 0.1604 0.0857 1.87 0.080 . 305s farmPrice 0.2481 0.0417 5.94 2.1e-05 *** 305s trend 0.2483 0.0881 2.82 0.012 * 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 2.405 on 16 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 16 305s SSR: 92.551 MSE: 5.784 Root MSE: 2.405 305s Multiple R-Squared: 0.655 Adjusted R-Squared: 0.59 305s 305s > 305s > 305s > ## ********* OLS with cross-equation restriction ************ 305s > ## ****** OLS with cross-equation restriction (default) ********* 305s > fitols2 <- systemfit( system, "OLS", data = Kmenta, 305s + restrict.matrix = restrm, useMatrix = useMatrix ) 305s > print( summary( fitols2 ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 34 159 2.5 0.703 0.608 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 64.2 3.78 1.94 0.761 0.732 305s supply 20 16 95.1 5.94 2.44 0.645 0.579 305s 305s The covariance matrix of the residuals 305s demand supply 305s demand 3.78 4.47 305s supply 4.47 5.94 305s 305s The correlations of the residuals 305s demand supply 305s demand 1.000 0.943 305s supply 0.943 1.000 305s 305s 305s OLS estimates for 'demand' (equation 1) 305s Model Formula: consump ~ price + income 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 99.5563 8.4225 11.82 1.4e-13 *** 305s price -0.2917 0.0975 -2.99 0.0051 ** 305s income 0.3129 0.0441 7.10 3.3e-08 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 1.943 on 17 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 17 305s SSR: 64.186 MSE: 3.776 Root MSE: 1.943 305s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.732 305s 305s 305s OLS estimates for 'supply' (equation 2) 305s Model Formula: consump ~ price + farmPrice + trend 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 56.3795 10.0721 5.60 2.9e-06 *** 305s price 0.1639 0.0853 1.92 0.063 . 305s farmPrice 0.2571 0.0402 6.39 2.7e-07 *** 305s trend 0.3129 0.0441 7.10 3.3e-08 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 2.438 on 16 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 16 305s SSR: 95.088 MSE: 5.943 Root MSE: 2.438 305s Multiple R-Squared: 0.645 Adjusted R-Squared: 0.579 305s 305s > # the same with symbolically specified restrictions 305s > fitols2Sym <- systemfit( system, "OLS", data = Kmenta, 305s + restrict.matrix = restrict, useMatrix = useMatrix ) 305s > all.equal( fitols2, fitols2Sym ) 305s [1] "Component “call”: target, current do not match when deparsed" 305s > 305s > ## ****** OLS with cross-equation restriction (singleEqSigma=T) ******* 305s > fitols2s <- systemfit( system, "OLS", data = Kmenta, 305s + restrict.matrix = restrm, singleEqSigma = TRUE, 305s + useMatrix = useMatrix ) 305s > print( summary( fitols2s ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 34 159 2.5 0.703 0.608 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 64.2 3.78 1.94 0.761 0.732 305s supply 20 16 95.1 5.94 2.44 0.645 0.579 305s 305s The covariance matrix of the residuals 305s demand supply 305s demand 3.78 4.47 305s supply 4.47 5.94 305s 305s The correlations of the residuals 305s demand supply 305s demand 1.000 0.943 305s supply 0.943 1.000 305s 305s 305s OLS estimates for 'demand' (equation 1) 305s Model Formula: consump ~ price + income 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 99.5563 7.5640 13.16 6.7e-15 *** 305s price -0.2917 0.0887 -3.29 0.0023 ** 305s income 0.3129 0.0415 7.54 9.4e-09 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 1.943 on 17 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 17 305s SSR: 64.186 MSE: 3.776 Root MSE: 1.943 305s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.732 305s 305s 305s OLS estimates for 'supply' (equation 2) 305s Model Formula: consump ~ price + farmPrice + trend 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 56.3795 11.3165 4.98 1.8e-05 *** 305s price 0.1639 0.0960 1.71 0.097 . 305s farmPrice 0.2571 0.0451 5.69 2.1e-06 *** 305s trend 0.3129 0.0415 7.54 9.4e-09 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 2.438 on 16 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 16 305s SSR: 95.088 MSE: 5.943 Root MSE: 2.438 305s Multiple R-Squared: 0.645 Adjusted R-Squared: 0.579 305s 305s > 305s > ## ****** OLS with cross-equation restriction (useDfSys=F) ******* 305s > print( summary( fitols2, useDfSys = FALSE ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 34 159 2.5 0.703 0.608 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 64.2 3.78 1.94 0.761 0.732 305s supply 20 16 95.1 5.94 2.44 0.645 0.579 305s 305s The covariance matrix of the residuals 305s demand supply 305s demand 3.78 4.47 305s supply 4.47 5.94 305s 305s The correlations of the residuals 305s demand supply 305s demand 1.000 0.943 305s supply 0.943 1.000 305s 305s 305s OLS estimates for 'demand' (equation 1) 305s Model Formula: consump ~ price + income 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 99.5563 8.4225 11.82 1.3e-09 *** 305s price -0.2917 0.0975 -2.99 0.0082 ** 305s income 0.3129 0.0441 7.10 1.8e-06 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 1.943 on 17 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 17 305s SSR: 64.186 MSE: 3.776 Root MSE: 1.943 305s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.732 305s 305s 305s OLS estimates for 'supply' (equation 2) 305s Model Formula: consump ~ price + farmPrice + trend 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 56.3795 10.0721 5.60 4.0e-05 *** 305s price 0.1639 0.0853 1.92 0.073 . 305s farmPrice 0.2571 0.0402 6.39 8.9e-06 *** 305s trend 0.3129 0.0441 7.10 2.5e-06 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 2.438 on 16 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 16 305s SSR: 95.088 MSE: 5.943 Root MSE: 2.438 305s Multiple R-Squared: 0.645 Adjusted R-Squared: 0.579 305s 305s > 305s > ## ****** OLS with cross-equation restriction (methodResidCov="noDfCor") ******* 305s > fitols2r <- systemfit( system, "OLS", data = Kmenta, 305s + restrict.matrix = restrm, methodResidCov = "noDfCor", 305s + useMatrix = useMatrix ) 305s > print( summary( fitols2r ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 34 159 1.7 0.703 0.577 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 64.2 3.78 1.94 0.761 0.732 305s supply 20 16 95.1 5.94 2.44 0.645 0.579 305s 305s The covariance matrix of the residuals 305s demand supply 305s demand 3.21 3.68 305s supply 3.68 4.75 305s 305s The correlations of the residuals 305s demand supply 305s demand 1.000 0.943 305s supply 0.943 1.000 305s 305s 305s OLS estimates for 'demand' (equation 1) 305s Model Formula: consump ~ price + income 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 99.5563 7.7652 12.82 1.4e-14 *** 305s price -0.2917 0.0899 -3.25 0.0026 ** 305s income 0.3129 0.0406 7.70 5.9e-09 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 1.943 on 17 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 17 305s SSR: 64.186 MSE: 3.776 Root MSE: 1.943 305s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.732 305s 305s 305s OLS estimates for 'supply' (equation 2) 305s Model Formula: consump ~ price + farmPrice + trend 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 56.3795 9.2860 6.07 7.0e-07 *** 305s price 0.1639 0.0786 2.08 0.045 * 305s farmPrice 0.2571 0.0371 6.93 5.4e-08 *** 305s trend 0.3129 0.0406 7.70 5.9e-09 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 2.438 on 16 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 16 305s SSR: 95.088 MSE: 5.943 Root MSE: 2.438 305s Multiple R-Squared: 0.645 Adjusted R-Squared: 0.579 305s 305s > 305s > ## ** OLS with cross-equation restriction (methodResidCov="noDfCor",singleEqSigma=T) *** 305s > fitols2rs <- systemfit( system, "OLS", data = Kmenta, 305s + restrict.matrix = restrm, methodResidCov = "noDfCor", 305s + x = TRUE, useMatrix = useMatrix ) 305s > print( summary( fitols2rs ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 34 159 1.7 0.703 0.577 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 64.2 3.78 1.94 0.761 0.732 305s supply 20 16 95.1 5.94 2.44 0.645 0.579 305s 305s The covariance matrix of the residuals 305s demand supply 305s demand 3.21 3.68 305s supply 3.68 4.75 305s 305s The correlations of the residuals 305s demand supply 305s demand 1.000 0.943 305s supply 0.943 1.000 305s 305s 305s OLS estimates for 'demand' (equation 1) 305s Model Formula: consump ~ price + income 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 99.5563 7.7652 12.82 1.4e-14 *** 305s price -0.2917 0.0899 -3.25 0.0026 ** 305s income 0.3129 0.0406 7.70 5.9e-09 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 1.943 on 17 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 17 305s SSR: 64.186 MSE: 3.776 Root MSE: 1.943 305s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.732 305s 305s 305s OLS estimates for 'supply' (equation 2) 305s Model Formula: consump ~ price + farmPrice + trend 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 56.3795 9.2860 6.07 7.0e-07 *** 305s price 0.1639 0.0786 2.08 0.045 * 305s farmPrice 0.2571 0.0371 6.93 5.4e-08 *** 305s trend 0.3129 0.0406 7.70 5.9e-09 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 2.438 on 16 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 16 305s SSR: 95.088 MSE: 5.943 Root MSE: 2.438 305s Multiple R-Squared: 0.645 Adjusted R-Squared: 0.579 305s 305s > 305s > ## *** OLS with cross-equation restriction via restrict.regMat *** 305s > ## *** OLS with cross-equation restriction via restrict.regMat (default) *** 305s > fitols3 <- systemfit( system, "OLS", data = Kmenta, restrict.regMat = tc, 305s + x = TRUE, useMatrix = useMatrix ) 305s > print( summary( fitols3 ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 34 159 2.5 0.703 0.608 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 64.2 3.78 1.94 0.761 0.732 305s supply 20 16 95.1 5.94 2.44 0.645 0.579 305s 305s The covariance matrix of the residuals 305s demand supply 305s demand 3.78 4.47 305s supply 4.47 5.94 305s 305s The correlations of the residuals 305s demand supply 305s demand 1.000 0.943 305s supply 0.943 1.000 305s 305s 305s OLS estimates for 'demand' (equation 1) 305s Model Formula: consump ~ price + income 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 99.5563 8.4225 11.82 1.4e-13 *** 305s price -0.2917 0.0975 -2.99 0.0051 ** 305s income 0.3129 0.0441 7.10 3.3e-08 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 1.943 on 17 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 17 305s SSR: 64.186 MSE: 3.776 Root MSE: 1.943 305s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.732 305s 305s 305s OLS estimates for 'supply' (equation 2) 305s Model Formula: consump ~ price + farmPrice + trend 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 56.3795 10.0721 5.60 2.9e-06 *** 305s price 0.1639 0.0853 1.92 0.063 . 305s farmPrice 0.2571 0.0402 6.39 2.7e-07 *** 305s trend 0.3129 0.0441 7.10 3.3e-08 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 2.438 on 16 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 16 305s SSR: 95.088 MSE: 5.943 Root MSE: 2.438 305s Multiple R-Squared: 0.645 Adjusted R-Squared: 0.579 305s 305s > 305s > ## *** OLS with cross-equation restriction via restrict.regMat (singleEqSigma=T) *** 305s > fitols3s <- systemfit( system, "OLS", data = Kmenta, 305s + restrict.regMat = tc, singleEqSigma = TRUE, useMatrix = useMatrix ) 305s > print( summary( fitols3s ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 34 159 2.5 0.703 0.608 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 64.2 3.78 1.94 0.761 0.732 305s supply 20 16 95.1 5.94 2.44 0.645 0.579 305s 305s The covariance matrix of the residuals 305s demand supply 305s demand 3.78 4.47 305s supply 4.47 5.94 305s 305s The correlations of the residuals 305s demand supply 305s demand 1.000 0.943 305s supply 0.943 1.000 305s 305s 305s OLS estimates for 'demand' (equation 1) 305s Model Formula: consump ~ price + income 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 99.5563 7.5640 13.16 6.7e-15 *** 305s price -0.2917 0.0887 -3.29 0.0023 ** 305s income 0.3129 0.0415 7.54 9.4e-09 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 1.943 on 17 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 17 305s SSR: 64.186 MSE: 3.776 Root MSE: 1.943 305s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.732 305s 305s 305s OLS estimates for 'supply' (equation 2) 305s Model Formula: consump ~ price + farmPrice + trend 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 56.3795 11.3165 4.98 1.8e-05 *** 305s price 0.1639 0.0960 1.71 0.097 . 305s farmPrice 0.2571 0.0451 5.69 2.1e-06 *** 305s trend 0.3129 0.0415 7.54 9.4e-09 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 2.438 on 16 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 16 305s SSR: 95.088 MSE: 5.943 Root MSE: 2.438 305s Multiple R-Squared: 0.645 Adjusted R-Squared: 0.579 305s 305s > 305s > ## *** OLS with cross-equation restriction via restrict.regMat (useDfSys=F) *** 305s > print( summary( fitols3, useDfSys = FALSE ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 34 159 2.5 0.703 0.608 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 64.2 3.78 1.94 0.761 0.732 305s supply 20 16 95.1 5.94 2.44 0.645 0.579 305s 305s The covariance matrix of the residuals 305s demand supply 305s demand 3.78 4.47 305s supply 4.47 5.94 305s 305s The correlations of the residuals 305s demand supply 305s demand 1.000 0.943 305s supply 0.943 1.000 305s 305s 305s OLS estimates for 'demand' (equation 1) 305s Model Formula: consump ~ price + income 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 99.5563 8.4225 11.82 1.3e-09 *** 305s price -0.2917 0.0975 -2.99 0.0082 ** 305s income 0.3129 0.0441 7.10 1.8e-06 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 1.943 on 17 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 17 305s SSR: 64.186 MSE: 3.776 Root MSE: 1.943 305s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.732 305s 305s 305s OLS estimates for 'supply' (equation 2) 305s Model Formula: consump ~ price + farmPrice + trend 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 56.3795 10.0721 5.60 4.0e-05 *** 305s price 0.1639 0.0853 1.92 0.073 . 305s farmPrice 0.2571 0.0402 6.39 8.9e-06 *** 305s trend 0.3129 0.0441 7.10 2.5e-06 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 2.438 on 16 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 16 305s SSR: 95.088 MSE: 5.943 Root MSE: 2.438 305s Multiple R-Squared: 0.645 Adjusted R-Squared: 0.579 305s 305s > 305s > ## *** OLS with cross-equation restriction via restrict.regMat (methodResidCov="noDfCor") *** 305s > fitols3r <- systemfit( system, "OLS", data = Kmenta, 305s + restrict.regMat = tc, methodResidCov = "noDfCor", 305s + useMatrix = useMatrix ) 305s > print( summary( fitols3r ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 34 159 1.7 0.703 0.577 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 64.2 3.78 1.94 0.761 0.732 305s supply 20 16 95.1 5.94 2.44 0.645 0.579 305s 305s The covariance matrix of the residuals 305s demand supply 305s demand 3.21 3.68 305s supply 3.68 4.75 305s 305s The correlations of the residuals 305s demand supply 305s demand 1.000 0.943 305s supply 0.943 1.000 305s 305s 305s OLS estimates for 'demand' (equation 1) 305s Model Formula: consump ~ price + income 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 99.5563 7.7652 12.82 1.4e-14 *** 305s price -0.2917 0.0899 -3.25 0.0026 ** 305s income 0.3129 0.0406 7.70 5.9e-09 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 1.943 on 17 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 17 305s SSR: 64.186 MSE: 3.776 Root MSE: 1.943 305s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.732 305s 305s 305s OLS estimates for 'supply' (equation 2) 305s Model Formula: consump ~ price + farmPrice + trend 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 56.3795 9.2860 6.07 7.0e-07 *** 305s price 0.1639 0.0786 2.08 0.045 * 305s farmPrice 0.2571 0.0371 6.93 5.4e-08 *** 305s trend 0.3129 0.0406 7.70 5.9e-09 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 2.438 on 16 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 16 305s SSR: 95.088 MSE: 5.943 Root MSE: 2.438 305s Multiple R-Squared: 0.645 Adjusted R-Squared: 0.579 305s 305s > 305s > ## OLS with cross-equation restriction via restrict.regMat (methodResidCov="noDfCor",singleEqSigma=T) 305s > fitols3rs <- systemfit( system, "OLS", data = Kmenta, 305s + restrict.regMat = tc, methodResidCov = "noDfCor", singleEqSigma = TRUE, 305s + useMatrix = useMatrix ) 305s > print( summary( fitols3rs ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 34 159 1.7 0.703 0.577 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 64.2 3.78 1.94 0.761 0.732 305s supply 20 16 95.1 5.94 2.44 0.645 0.579 305s 305s The covariance matrix of the residuals 305s demand supply 305s demand 3.21 3.68 305s supply 3.68 4.75 305s 305s The correlations of the residuals 305s demand supply 305s demand 1.000 0.943 305s supply 0.943 1.000 305s 305s 305s OLS estimates for 'demand' (equation 1) 305s Model Formula: consump ~ price + income 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 99.5563 6.9734 14.28 6.7e-16 *** 305s price -0.2917 0.0816 -3.57 0.0011 ** 305s income 0.3129 0.0381 8.22 1.4e-09 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 1.943 on 17 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 17 305s SSR: 64.186 MSE: 3.776 Root MSE: 1.943 305s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.732 305s 305s 305s OLS estimates for 'supply' (equation 2) 305s Model Formula: consump ~ price + farmPrice + trend 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 56.3795 10.1248 5.57 3.1e-06 *** 305s price 0.1639 0.0859 1.91 0.065 . 305s farmPrice 0.2571 0.0404 6.36 2.9e-07 *** 305s trend 0.3129 0.0381 8.22 1.4e-09 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 2.438 on 16 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 16 305s SSR: 95.088 MSE: 5.943 Root MSE: 2.438 305s Multiple R-Squared: 0.645 Adjusted R-Squared: 0.579 305s 305s > 305s > ## ********* OLS with 2 cross-equation restrictions *********** 305s > ## ********* OLS with 2 cross-equation restrictions (default) *********** 305s > fitols4 <- systemfit( system, "OLS", data = Kmenta, restrict.matrix = restr2m, 305s + restrict.rhs = restr2q, useMatrix = useMatrix ) 305s > print( summary( fitols4 ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 35 160 2.69 0.702 0.605 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 64.0 3.77 1.94 0.761 0.733 305s supply 20 16 95.8 5.99 2.45 0.643 0.576 305s 305s The covariance matrix of the residuals 305s demand supply 305s demand 3.76 4.46 305s supply 4.46 5.99 305s 305s The correlations of the residuals 305s demand supply 305s demand 1.000 0.938 305s supply 0.938 1.000 305s 305s 305s OLS estimates for 'demand' (equation 1) 305s Model Formula: consump ~ price + income 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 101.4817 6.1599 16.47 < 2e-16 *** 305s price -0.3168 0.0629 -5.04 1.4e-05 *** 305s income 0.3189 0.0399 8.00 2.0e-09 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 1.94 on 17 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 17 305s SSR: 64.003 MSE: 3.765 Root MSE: 1.94 305s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 305s 305s 305s OLS estimates for 'supply' (equation 2) 305s Model Formula: consump ~ price + farmPrice + trend 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 54.1494 7.5515 7.17 2.3e-08 *** 305s price 0.1832 0.0629 2.91 0.0062 ** 305s farmPrice 0.2595 0.0391 6.64 1.1e-07 *** 305s trend 0.3189 0.0399 8.00 2.0e-09 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 2.447 on 16 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 16 305s SSR: 95.813 MSE: 5.988 Root MSE: 2.447 305s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.576 305s 305s > # the same with symbolically specified restrictions 305s > fitols4Sym <- systemfit( system, "OLS", data = Kmenta, 305s + restrict.matrix = restrict2, useMatrix = useMatrix ) 305s > all.equal( fitols4, fitols4Sym ) 305s [1] "Component “call”: target, current do not match when deparsed" 305s > 305s > ## ****** OLS with 2 cross-equation restrictions (singleEqSigma=T) ******* 305s > fitols4s <- systemfit( system, "OLS", data = Kmenta, restrict.matrix = restr2m, 305s + restrict.rhs = restr2q, singleEqSigma = TRUE, x = TRUE, 305s + useMatrix = useMatrix ) 305s > print( summary( fitols4s ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 35 160 2.69 0.702 0.605 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 64.0 3.77 1.94 0.761 0.733 305s supply 20 16 95.8 5.99 2.45 0.643 0.576 305s 305s The covariance matrix of the residuals 305s demand supply 305s demand 3.76 4.46 305s supply 4.46 5.99 305s 305s The correlations of the residuals 305s demand supply 305s demand 1.000 0.938 305s supply 0.938 1.000 305s 305s 305s OLS estimates for 'demand' (equation 1) 305s Model Formula: consump ~ price + income 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 101.4817 6.0474 16.78 < 2e-16 *** 305s price -0.3168 0.0648 -4.89 2.3e-05 *** 305s income 0.3189 0.0385 8.29 9.1e-10 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 1.94 on 17 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 17 305s SSR: 64.003 MSE: 3.765 Root MSE: 1.94 305s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 305s 305s 305s OLS estimates for 'supply' (equation 2) 305s Model Formula: consump ~ price + farmPrice + trend 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 54.1494 7.9687 6.80 7.0e-08 *** 305s price 0.1832 0.0648 2.83 0.0077 ** 305s farmPrice 0.2595 0.0446 5.82 1.3e-06 *** 305s trend 0.3189 0.0385 8.29 9.1e-10 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 2.447 on 16 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 16 305s SSR: 95.813 MSE: 5.988 Root MSE: 2.447 305s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.576 305s 305s > 305s > ## ****** OLS with 2 cross-equation restrictions (useDfSys=F) ******* 305s > print( summary( fitols4, useDfSys = FALSE ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 35 160 2.69 0.702 0.605 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 64.0 3.77 1.94 0.761 0.733 305s supply 20 16 95.8 5.99 2.45 0.643 0.576 305s 305s The covariance matrix of the residuals 305s demand supply 305s demand 3.76 4.46 305s supply 4.46 5.99 305s 305s The correlations of the residuals 305s demand supply 305s demand 1.000 0.938 305s supply 0.938 1.000 305s 305s 305s OLS estimates for 'demand' (equation 1) 305s Model Formula: consump ~ price + income 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 101.4817 6.1599 16.47 6.9e-12 *** 305s price -0.3168 0.0629 -5.04 1e-04 *** 305s income 0.3189 0.0399 8.00 3.6e-07 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 1.94 on 17 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 17 305s SSR: 64.003 MSE: 3.765 Root MSE: 1.94 305s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 305s 305s 305s OLS estimates for 'supply' (equation 2) 305s Model Formula: consump ~ price + farmPrice + trend 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 54.1494 7.5515 7.17 2.2e-06 *** 305s price 0.1832 0.0629 2.91 0.01 * 305s farmPrice 0.2595 0.0391 6.64 5.6e-06 *** 305s trend 0.3189 0.0399 8.00 5.5e-07 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 2.447 on 16 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 16 305s SSR: 95.813 MSE: 5.988 Root MSE: 2.447 305s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.576 305s 305s > 305s > ## ****** OLS with 2 cross-equation restrictions (methodResidCov="noDfCor") ******* 305s > fitols4r <- systemfit( system, "OLS", data = Kmenta, restrict.matrix = restr2m, 305s + restrict.rhs = restr2q, methodResidCov = "noDfCor", 305s + useMatrix = useMatrix ) 305s > print( summary( fitols4r ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 35 160 1.83 0.702 0.575 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 64.0 3.77 1.94 0.761 0.733 305s supply 20 16 95.8 5.99 2.45 0.643 0.576 305s 305s The covariance matrix of the residuals 305s demand supply 305s demand 3.20 3.67 305s supply 3.67 4.79 305s 305s The correlations of the residuals 305s demand supply 305s demand 1.000 0.938 305s supply 0.938 1.000 305s 305s 305s OLS estimates for 'demand' (equation 1) 305s Model Formula: consump ~ price + income 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 101.4817 5.7621 17.61 < 2e-16 *** 305s price -0.3168 0.0589 -5.38 5.0e-06 *** 305s income 0.3189 0.0373 8.55 4.3e-10 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 1.94 on 17 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 17 305s SSR: 64.003 MSE: 3.765 Root MSE: 1.94 305s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 305s 305s 305s OLS estimates for 'supply' (equation 2) 305s Model Formula: consump ~ price + farmPrice + trend 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 54.1494 7.0638 7.67 5.4e-09 *** 305s price 0.1832 0.0589 3.11 0.0037 ** 305s farmPrice 0.2595 0.0365 7.10 2.8e-08 *** 305s trend 0.3189 0.0373 8.55 4.3e-10 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 2.447 on 16 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 16 305s SSR: 95.813 MSE: 5.988 Root MSE: 2.447 305s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.576 305s 305s > 305s > ## OLS with 2 cross-equation restrictions (methodResidCov="noDfCor", singleEqSigma=T) * 305s > fitols4rs <- systemfit( system, "OLS", data = Kmenta, restrict.matrix = restr2m, 305s + restrict.rhs = restr2q, methodResidCov = "noDfCor", 305s + singleEqSigma = TRUE, useMatrix = useMatrix ) 305s > print( summary( fitols4rs ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 35 160 1.83 0.702 0.575 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 64.0 3.77 1.94 0.761 0.733 305s supply 20 16 95.8 5.99 2.45 0.643 0.576 305s 305s The covariance matrix of the residuals 305s demand supply 305s demand 3.20 3.67 305s supply 3.67 4.79 305s 305s The correlations of the residuals 305s demand supply 305s demand 1.000 0.938 305s supply 0.938 1.000 305s 305s 305s OLS estimates for 'demand' (equation 1) 305s Model Formula: consump ~ price + income 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 101.4817 5.5234 18.37 < 2e-16 *** 305s price -0.3168 0.0589 -5.38 5.0e-06 *** 305s income 0.3189 0.0352 9.05 1.1e-10 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 1.94 on 17 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 17 305s SSR: 64.003 MSE: 3.765 Root MSE: 1.94 305s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 305s 305s 305s OLS estimates for 'supply' (equation 2) 305s Model Formula: consump ~ price + farmPrice + trend 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 54.1494 7.2089 7.51 8.5e-09 *** 305s price 0.1832 0.0589 3.11 0.0037 ** 305s farmPrice 0.2595 0.0399 6.51 1.7e-07 *** 305s trend 0.3189 0.0352 9.05 1.1e-10 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 2.447 on 16 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 16 305s SSR: 95.813 MSE: 5.988 Root MSE: 2.447 305s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.576 305s 305s > 305s > ## ***** OLS with 2 cross-equation restrictions via R and restrict.regMat **** 305s > ## ***** OLS with 2 cross-equation restrictions via R and restrict.regMat (default) **** 305s > fitols5 <- systemfit( system, "OLS", data = Kmenta, restrict.matrix = restr3m, 305s + restrict.rhs = restr3q, restrict.regMat = tc, methodResidCov = "noDfCor", 305s + useMatrix = useMatrix ) 305s > print( summary( fitols5 ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 35 160 1.83 0.702 0.575 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 64.0 3.77 1.94 0.761 0.733 305s supply 20 16 95.8 5.99 2.45 0.643 0.576 305s 305s The covariance matrix of the residuals 305s demand supply 305s demand 3.20 3.67 305s supply 3.67 4.79 305s 305s The correlations of the residuals 305s demand supply 305s demand 1.000 0.938 305s supply 0.938 1.000 305s 305s 305s OLS estimates for 'demand' (equation 1) 305s Model Formula: consump ~ price + income 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 101.4817 5.7621 17.61 < 2e-16 *** 305s price -0.3168 0.0589 -5.38 5.0e-06 *** 305s income 0.3189 0.0373 8.55 4.3e-10 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 1.94 on 17 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 17 305s SSR: 64.003 MSE: 3.765 Root MSE: 1.94 305s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 305s 305s 305s OLS estimates for 'supply' (equation 2) 305s Model Formula: consump ~ price + farmPrice + trend 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 54.1494 7.0638 7.67 5.4e-09 *** 305s price 0.1832 0.0589 3.11 0.0037 ** 305s farmPrice 0.2595 0.0365 7.10 2.8e-08 *** 305s trend 0.3189 0.0373 8.55 4.3e-10 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 2.447 on 16 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 16 305s SSR: 95.813 MSE: 5.988 Root MSE: 2.447 305s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.576 305s 305s > # the same with symbolically specified restrictions 305s > fitols5Sym <- systemfit( system, "OLS", data = Kmenta, 305s + restrict.matrix = restrict3, restrict.regMat = tc, 305s + methodResidCov = "noDfCor", useMatrix = useMatrix ) 305s > all.equal( fitols5, fitols5Sym ) 305s [1] "Component “call”: target, current do not match when deparsed" 305s > 305s > ## ***** OLS with 2 cross-equation restrictions via R and restrict.regMat (singleEqSigma=T) **** 305s > fitols5s <- systemfit( system, "OLS", data = Kmenta,restrict.matrix = restr3m, 305s + restrict.rhs = restr3q, restrict.regMat = tc, singleEqSigma = T, 305s + x = TRUE, useMatrix = useMatrix ) 305s > print( summary( fitols5s ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 35 160 2.69 0.702 0.605 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 64.0 3.77 1.94 0.761 0.733 305s supply 20 16 95.8 5.99 2.45 0.643 0.576 305s 305s The covariance matrix of the residuals 305s demand supply 305s demand 3.76 4.46 305s supply 4.46 5.99 305s 305s The correlations of the residuals 305s demand supply 305s demand 1.000 0.938 305s supply 0.938 1.000 305s 305s 305s OLS estimates for 'demand' (equation 1) 305s Model Formula: consump ~ price + income 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 101.4817 6.0474 16.78 < 2e-16 *** 305s price -0.3168 0.0648 -4.89 2.3e-05 *** 305s income 0.3189 0.0385 8.29 9.1e-10 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 1.94 on 17 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 17 305s SSR: 64.003 MSE: 3.765 Root MSE: 1.94 305s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 305s 305s 305s OLS estimates for 'supply' (equation 2) 305s Model Formula: consump ~ price + farmPrice + trend 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 54.1494 7.9687 6.80 7.0e-08 *** 305s price 0.1832 0.0648 2.83 0.0077 ** 305s farmPrice 0.2595 0.0446 5.82 1.3e-06 *** 305s trend 0.3189 0.0385 8.29 9.1e-10 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 2.447 on 16 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 16 305s SSR: 95.813 MSE: 5.988 Root MSE: 2.447 305s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.576 305s 305s > 305s > ## ***** OLS with 2 cross-equation restrictions via R and restrict.regMat (useDfSys=F) **** 305s > fitols5o <- systemfit( system, "OLS", data = Kmenta,restrict.matrix = restr3m, 305s + restrict.rhs = restr3q, restrict.regMat = tc, useMatrix = useMatrix ) 305s > print( summary( fitols5o, useDfSys = FALSE ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 35 160 2.69 0.702 0.605 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 64.0 3.77 1.94 0.761 0.733 305s supply 20 16 95.8 5.99 2.45 0.643 0.576 305s 305s The covariance matrix of the residuals 305s demand supply 305s demand 3.76 4.46 305s supply 4.46 5.99 305s 305s The correlations of the residuals 305s demand supply 305s demand 1.000 0.938 305s supply 0.938 1.000 305s 305s 305s OLS estimates for 'demand' (equation 1) 305s Model Formula: consump ~ price + income 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 101.4817 6.1599 16.47 6.9e-12 *** 305s price -0.3168 0.0629 -5.04 1e-04 *** 305s income 0.3189 0.0399 8.00 3.6e-07 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 1.94 on 17 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 17 305s SSR: 64.003 MSE: 3.765 Root MSE: 1.94 305s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 305s 305s 305s OLS estimates for 'supply' (equation 2) 305s Model Formula: consump ~ price + farmPrice + trend 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 54.1494 7.5515 7.17 2.2e-06 *** 305s price 0.1832 0.0629 2.91 0.01 * 305s farmPrice 0.2595 0.0391 6.64 5.6e-06 *** 305s trend 0.3189 0.0399 8.00 5.5e-07 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 2.447 on 16 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 16 305s SSR: 95.813 MSE: 5.988 Root MSE: 2.447 305s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.576 305s 305s > 305s > ## OLS with 2 cross-equation restr. via R and restrict.regMat (methodResidCov="noDfCor",singleEqSigma=T) 305s > fitols5rs <- systemfit( system, "OLS", data = Kmenta,restrict.matrix = restr3m, 305s + restrict.rhs = restr3q, restrict.regMat = tc, methodResidCov = "noDfCor", 305s + singleEqSigma = TRUE, useMatrix = useMatrix ) 305s > print( summary( fitols5rs ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 35 160 1.83 0.702 0.575 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 64.0 3.77 1.94 0.761 0.733 305s supply 20 16 95.8 5.99 2.45 0.643 0.576 305s 305s The covariance matrix of the residuals 305s demand supply 305s demand 3.20 3.67 305s supply 3.67 4.79 305s 305s The correlations of the residuals 305s demand supply 305s demand 1.000 0.938 305s supply 0.938 1.000 305s 305s 305s OLS estimates for 'demand' (equation 1) 305s Model Formula: consump ~ price + income 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 101.4817 5.5234 18.37 < 2e-16 *** 305s price -0.3168 0.0589 -5.38 5.0e-06 *** 305s income 0.3189 0.0352 9.05 1.1e-10 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 1.94 on 17 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 17 305s SSR: 64.003 MSE: 3.765 Root MSE: 1.94 305s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 305s 305s 305s OLS estimates for 'supply' (equation 2) 305s Model Formula: consump ~ price + farmPrice + trend 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 54.1494 7.2089 7.51 8.5e-09 *** 305s price 0.1832 0.0589 3.11 0.0037 ** 305s farmPrice 0.2595 0.0399 6.51 1.7e-07 *** 305s trend 0.3189 0.0352 9.05 1.1e-10 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 2.447 on 16 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 16 305s SSR: 95.813 MSE: 5.988 Root MSE: 2.447 305s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.576 305s 305s > 305s > 305s > ## *********** estimations with a single regressor ************ 305s > fitolsS1 <- systemfit( 305s + list( consump ~ price - 1, consump ~ price + trend ), "OLS", 305s + data = Kmenta, useMatrix = useMatrix ) 305s > print( summary( fitolsS1 ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 36 1121 484 -1.09 -1.05 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s eq1 20 19 861 45.3 6.73 -2.213 -2.213 305s eq2 20 17 259 15.3 3.91 0.032 -0.082 305s 305s The covariance matrix of the residuals 305s eq1 eq2 305s eq1 45.3 14.4 305s eq2 14.4 15.3 305s 305s The correlations of the residuals 305s eq1 eq2 305s eq1 1.000 0.549 305s eq2 0.549 1.000 305s 305s 305s OLS estimates for 'eq1' (equation 1) 305s Model Formula: consump ~ price - 1 305s 305s Estimate Std. Error t value Pr(>|t|) 305s price 1.006 0.015 66.9 <2e-16 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 6.733 on 19 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 19 305s SSR: 861.449 MSE: 45.339 Root MSE: 6.733 305s Multiple R-Squared: -2.213 Adjusted R-Squared: -2.213 305s 305s 305s OLS estimates for 'eq2' (equation 2) 305s Model Formula: consump ~ price + trend 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 93.6767 15.2367 6.15 1.1e-05 *** 305s price 0.0622 0.1513 0.41 0.69 305s trend 0.0953 0.1515 0.63 0.54 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 3.907 on 17 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 17 305s SSR: 259.497 MSE: 15.265 Root MSE: 3.907 305s Multiple R-Squared: 0.032 Adjusted R-Squared: -0.082 305s 305s > fitolsS2 <- systemfit( 305s + list( consump ~ price - 1, consump ~ trend - 1 ), "OLS", 305s + data = Kmenta, useMatrix = useMatrix ) 305s > print( summary( fitolsS2 ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 38 47370 110957 -87.3 -5.28 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s eq1 20 19 861 45.3 6.73 -2.21 -2.21 305s eq2 20 19 46509 2447.8 49.48 -172.47 -172.47 305s 305s The covariance matrix of the residuals 305s eq1 eq2 305s eq1 45.34 -5.15 305s eq2 -5.15 2447.84 305s 305s The correlations of the residuals 305s eq1 eq2 305s eq1 1.0000 -0.0439 305s eq2 -0.0439 1.0000 305s 305s 305s OLS estimates for 'eq1' (equation 1) 305s Model Formula: consump ~ price - 1 305s 305s Estimate Std. Error t value Pr(>|t|) 305s price 1.006 0.015 66.9 <2e-16 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 6.733 on 19 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 19 305s SSR: 861.449 MSE: 45.339 Root MSE: 6.733 305s Multiple R-Squared: -2.213 Adjusted R-Squared: -2.213 305s 305s 305s OLS estimates for 'eq2' (equation 2) 305s Model Formula: consump ~ trend - 1 305s 305s Estimate Std. Error t value Pr(>|t|) 305s trend 7.405 0.924 8.02 1.6e-07 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 49.476 on 19 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 19 305s SSR: 46508.922 MSE: 2447.838 Root MSE: 49.476 305s Multiple R-Squared: -172.467 Adjusted R-Squared: -172.467 305s 305s > fitolsS3 <- systemfit( 305s + list( consump ~ trend - 1, price ~ trend - 1 ), "OLS", 305s + data = Kmenta, useMatrix = useMatrix ) 305s > print( summary( fitolsS3 ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 38 93537 108970 -99 -0.977 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s eq1 20 19 46509 2448 49.5 -172.5 -172.5 305s eq2 20 19 47028 2475 49.8 -69.5 -69.5 305s 305s The covariance matrix of the residuals 305s eq1 eq2 305s eq1 2448 2439 305s eq2 2439 2475 305s 305s The correlations of the residuals 305s eq1 eq2 305s eq1 1.000 0.988 305s eq2 0.988 1.000 305s 305s 305s OLS estimates for 'eq1' (equation 1) 305s Model Formula: consump ~ trend - 1 305s 305s Estimate Std. Error t value Pr(>|t|) 305s trend 7.405 0.924 8.02 1.6e-07 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 49.476 on 19 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 19 305s SSR: 46508.922 MSE: 2447.838 Root MSE: 49.476 305s Multiple R-Squared: -172.467 Adjusted R-Squared: -172.467 305s 305s 305s OLS estimates for 'eq2' (equation 2) 305s Model Formula: price ~ trend - 1 305s 305s Estimate Std. Error t value Pr(>|t|) 305s trend 7.318 0.929 7.88 2.1e-07 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 49.751 on 19 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 19 305s SSR: 47028.107 MSE: 2475.164 Root MSE: 49.751 305s Multiple R-Squared: -69.48 Adjusted R-Squared: -69.48 305s 305s > fitolsS4 <- systemfit( 305s + list( consump ~ trend - 1, price ~ trend - 1 ), "OLS", 305s + data = Kmenta, restrict.matrix = matrix( c( 1, -1 ), nrow = 1 ), 305s + useMatrix = useMatrix ) 305s > print( summary( fitolsS4 ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 39 93548 111736 -99 -1.03 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s eq1 20 19 46514 2448 49.5 -172.5 -172.5 305s eq2 20 19 47033 2475 49.8 -69.5 -69.5 305s 305s The covariance matrix of the residuals 305s eq1 eq2 305s eq1 2448 2439 305s eq2 2439 2475 305s 305s The correlations of the residuals 305s eq1 eq2 305s eq1 1.000 0.988 305s eq2 0.988 1.000 305s 305s 305s OLS estimates for 'eq1' (equation 1) 305s Model Formula: consump ~ trend - 1 305s 305s Estimate Std. Error t value Pr(>|t|) 305s trend 7.362 0.646 11.4 5.7e-14 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 49.478 on 19 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 19 305s SSR: 46514.283 MSE: 2448.12 Root MSE: 49.478 305s Multiple R-Squared: -172.487 Adjusted R-Squared: -172.487 305s 305s 305s OLS estimates for 'eq2' (equation 2) 305s Model Formula: price ~ trend - 1 305s 305s Estimate Std. Error t value Pr(>|t|) 305s trend 7.362 0.646 11.4 5.7e-14 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 49.754 on 19 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 19 305s SSR: 47033.469 MSE: 2475.446 Root MSE: 49.754 305s Multiple R-Squared: -69.488 Adjusted R-Squared: -69.488 305s 305s > fitolsS5 <- systemfit( 305s + list( consump ~ 1, farmPrice ~ 1 ), "OLS", 305s + data = Kmenta, useMatrix = useMatrix ) 305s > print( summary( fitolsS5 ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 38 3337 1224 0 0 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s eq1 20 19 268 14.1 3.76 0 0 305s eq2 20 19 3069 161.5 12.71 0 0 305s 305s The covariance matrix of the residuals 305s eq1 eq2 305s eq1 14.1 32.5 305s eq2 32.5 161.5 305s 305s The correlations of the residuals 305s eq1 eq2 305s eq1 1.000 0.681 305s eq2 0.681 1.000 305s 305s 305s OLS estimates for 'eq1' (equation 1) 305s Model Formula: consump ~ 1 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 100.90 0.84 120 <2e-16 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 3.756 on 19 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 19 305s SSR: 268.114 MSE: 14.111 Root MSE: 3.756 305s Multiple R-Squared: 0 Adjusted R-Squared: 0 305s 305s 305s OLS estimates for 'eq2' (equation 2) 305s Model Formula: farmPrice ~ 1 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 96.62 2.84 34 <2e-16 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 12.709 on 19 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 19 305s SSR: 3068.757 MSE: 161.514 Root MSE: 12.709 305s Multiple R-Squared: 0 Adjusted R-Squared: 0 305s 305s > 305s > 305s > ## **************** shorter summaries ********************** 305s > print( summary( fitols1, useDfSys = TRUE, equations = FALSE ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 33 156 4.43 0.709 0.558 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 63.3 3.73 1.93 0.764 0.736 305s supply 20 16 92.6 5.78 2.40 0.655 0.590 305s 305s The covariance matrix of the residuals 305s demand supply 305s demand 3.73 4.14 305s supply 4.14 5.78 305s 305s The correlations of the residuals 305s demand supply 305s demand 1.000 0.891 305s supply 0.891 1.000 305s 305s 305s Coefficients: 305s Estimate Std. Error t value Pr(>|t|) 305s demand_(Intercept) 99.8954 7.5194 13.29 8.4e-15 *** 305s demand_price -0.3163 0.0907 -3.49 0.0014 ** 305s demand_income 0.3346 0.0454 7.37 1.8e-08 *** 305s supply_(Intercept) 58.2754 11.4629 5.08 1.4e-05 *** 305s supply_price 0.1604 0.0949 1.69 0.1004 305s supply_farmPrice 0.2481 0.0462 5.37 6.1e-06 *** 305s supply_trend 0.2483 0.0975 2.55 0.0157 * 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s > 305s > print( summary( fitols2r ), residCov = FALSE, equations = FALSE ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 34 159 1.7 0.703 0.577 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 64.2 3.78 1.94 0.761 0.732 305s supply 20 16 95.1 5.94 2.44 0.645 0.579 305s 305s 305s Coefficients: 305s Estimate Std. Error t value Pr(>|t|) 305s demand_(Intercept) 99.5563 7.7652 12.82 1.4e-14 *** 305s demand_price -0.2917 0.0899 -3.25 0.0026 ** 305s demand_income 0.3129 0.0406 7.70 5.9e-09 *** 305s supply_(Intercept) 56.3795 9.2860 6.07 7.0e-07 *** 305s supply_price 0.1639 0.0786 2.08 0.0447 * 305s supply_farmPrice 0.2571 0.0371 6.93 5.4e-08 *** 305s supply_trend 0.3129 0.0406 7.70 5.9e-09 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s > 305s > print( summary( fitols3s, useDfSys = FALSE ), residCov = TRUE ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 34 159 2.5 0.703 0.608 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 64.2 3.78 1.94 0.761 0.732 305s supply 20 16 95.1 5.94 2.44 0.645 0.579 305s 305s The covariance matrix of the residuals 305s demand supply 305s demand 3.78 4.47 305s supply 4.47 5.94 305s 305s The correlations of the residuals 305s demand supply 305s demand 1.000 0.943 305s supply 0.943 1.000 305s 305s 305s OLS estimates for 'demand' (equation 1) 305s Model Formula: consump ~ price + income 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 99.5563 7.5640 13.16 2.4e-10 *** 305s price -0.2917 0.0887 -3.29 0.0043 ** 305s income 0.3129 0.0415 7.54 8.1e-07 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 1.943 on 17 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 17 305s SSR: 64.186 MSE: 3.776 Root MSE: 1.943 305s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.732 305s 305s 305s OLS estimates for 'supply' (equation 2) 305s Model Formula: consump ~ price + farmPrice + trend 305s 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 56.3795 11.3165 4.98 0.00014 *** 305s price 0.1639 0.0960 1.71 0.10724 305s farmPrice 0.2571 0.0451 5.69 3.3e-05 *** 305s trend 0.3129 0.0415 7.54 1.2e-06 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s 305s Residual standard error: 2.438 on 16 degrees of freedom 305s Number of observations: 20 Degrees of Freedom: 16 305s SSR: 95.088 MSE: 5.943 Root MSE: 2.438 305s Multiple R-Squared: 0.645 Adjusted R-Squared: 0.579 305s 305s > 305s > print( summary( fitols4rs, residCov = FALSE, equations = FALSE ) ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 35 160 1.83 0.702 0.575 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 64.0 3.77 1.94 0.761 0.733 305s supply 20 16 95.8 5.99 2.45 0.643 0.576 305s 305s 305s Coefficients: 305s Estimate Std. Error t value Pr(>|t|) 305s demand_(Intercept) 101.4817 5.5234 18.37 < 2e-16 *** 305s demand_price -0.3168 0.0589 -5.38 5.0e-06 *** 305s demand_income 0.3189 0.0352 9.05 1.1e-10 *** 305s supply_(Intercept) 54.1494 7.2089 7.51 8.5e-09 *** 305s supply_price 0.1832 0.0589 3.11 0.0037 ** 305s supply_farmPrice 0.2595 0.0399 6.51 1.7e-07 *** 305s supply_trend 0.3189 0.0352 9.05 1.1e-10 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s > 305s > print( summary( fitols5, equations = FALSE ), residCov = FALSE ) 305s 305s systemfit results 305s method: OLS 305s 305s N DF SSR detRCov OLS-R2 McElroy-R2 305s system 40 35 160 1.83 0.702 0.575 305s 305s N DF SSR MSE RMSE R2 Adj R2 305s demand 20 17 64.0 3.77 1.94 0.761 0.733 305s supply 20 16 95.8 5.99 2.45 0.643 0.576 305s 305s 305s Coefficients: 305s Estimate Std. Error t value Pr(>|t|) 305s demand_(Intercept) 101.4817 5.7621 17.61 < 2e-16 *** 305s demand_price -0.3168 0.0589 -5.38 5.0e-06 *** 305s demand_income 0.3189 0.0373 8.55 4.3e-10 *** 305s supply_(Intercept) 54.1494 7.0638 7.67 5.4e-09 *** 305s supply_price 0.1832 0.0589 3.11 0.0037 ** 305s supply_farmPrice 0.2595 0.0365 7.10 2.8e-08 *** 305s supply_trend 0.3189 0.0373 8.55 4.3e-10 *** 305s --- 305s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 305s > 305s > 305s > ## ****************** residuals ************************** 305s > print( residuals( fitols1 ) ) 305s demand supply 305s 1 1.074 -0.444 305s 2 -0.390 -0.896 305s 3 2.625 1.965 305s 4 1.802 1.134 305s 5 1.946 1.514 305s 6 1.175 0.680 305s 7 1.530 1.569 305s 8 -2.933 -4.407 305s 9 -1.365 -2.599 305s 10 2.031 2.469 305s 11 -0.149 -0.598 305s 12 -1.954 -1.697 305s 13 -1.121 -1.064 305s 14 -0.220 0.970 305s 15 1.487 3.159 305s 16 -3.701 -3.866 305s 17 -1.273 -0.265 305s 18 -2.002 -2.449 305s 19 1.738 3.110 305s 20 -0.299 1.714 305s > print( residuals( fitols1$eq[[ 2 ]] ) ) 305s 1 2 3 4 5 6 7 8 9 10 11 305s -0.444 -0.896 1.965 1.134 1.514 0.680 1.569 -4.407 -2.599 2.469 -0.598 305s 12 13 14 15 16 17 18 19 20 305s -1.697 -1.064 0.970 3.159 -3.866 -0.265 -2.449 3.110 1.714 305s > 305s > print( residuals( fitols2r ) ) 305s demand supply 305s 1 0.8465 0.156 305s 2 -0.4933 -0.384 305s 3 2.5225 2.415 305s 4 1.7066 1.525 305s 5 2.0445 1.750 305s 6 1.2529 0.870 305s 7 1.6277 1.711 305s 8 -2.8261 -4.380 305s 9 -1.2979 -2.597 305s 10 2.0592 2.497 305s 11 -0.4663 -0.466 305s 12 -2.3732 -1.540 305s 13 -1.4734 -1.006 305s 14 -0.3398 0.885 305s 15 1.7283 2.835 305s 16 -3.4975 -4.290 305s 17 -0.9651 -0.760 305s 18 -1.9512 -2.911 305s 19 1.8829 2.606 305s 20 0.0129 1.085 305s > print( residuals( fitols2r$eq[[ 1 ]] ) ) 305s 1 2 3 4 5 6 7 8 9 10 305s 0.8465 -0.4933 2.5225 1.7066 2.0445 1.2529 1.6277 -2.8261 -1.2979 2.0592 305s 11 12 13 14 15 16 17 18 19 20 305s -0.4663 -2.3732 -1.4734 -0.3398 1.7283 -3.4975 -0.9651 -1.9512 1.8829 0.0129 305s > 305s > print( residuals( fitols3s ) ) 305s demand supply 305s 1 0.8465 0.156 305s 2 -0.4933 -0.384 305s 3 2.5225 2.415 305s 4 1.7066 1.525 305s 5 2.0445 1.750 305s 6 1.2529 0.870 305s 7 1.6277 1.711 305s 8 -2.8261 -4.380 305s 9 -1.2979 -2.597 305s 10 2.0592 2.497 305s 11 -0.4663 -0.466 305s 12 -2.3732 -1.540 305s 13 -1.4734 -1.006 305s 14 -0.3398 0.885 305s 15 1.7283 2.835 305s 16 -3.4975 -4.290 305s 17 -0.9651 -0.760 305s 18 -1.9512 -2.911 305s 19 1.8829 2.606 305s 20 0.0129 1.085 305s > print( residuals( fitols3s$eq[[ 2 ]] ) ) 305s 1 2 3 4 5 6 7 8 9 10 11 305s 0.156 -0.384 2.415 1.525 1.750 0.870 1.711 -4.380 -2.597 2.497 -0.466 305s 12 13 14 15 16 17 18 19 20 305s -1.540 -1.006 0.885 2.835 -4.290 -0.760 -2.911 2.606 1.085 305s > 305s > print( residuals( fitols4rs ) ) 305s demand supply 305s 1 0.915 0.204 305s 2 -0.387 -0.421 305s 3 2.613 2.388 305s 4 1.815 1.474 305s 5 1.980 1.787 305s 6 1.221 0.879 305s 7 1.620 1.690 305s 8 -2.769 -4.489 305s 9 -1.382 -2.549 305s 10 1.890 2.660 305s 11 -0.506 -0.297 305s 12 -2.280 -1.456 305s 13 -1.323 -1.013 305s 14 -0.330 0.925 305s 15 1.572 2.889 305s 16 -3.582 -4.313 305s 17 -1.298 -0.573 305s 18 -1.892 -3.023 305s 19 1.948 2.462 305s 20 0.174 0.777 305s > print( residuals( fitols4rs$eq[[ 1 ]] ) ) 305s 1 2 3 4 5 6 7 8 9 10 11 305s 0.915 -0.387 2.613 1.815 1.980 1.221 1.620 -2.769 -1.382 1.890 -0.506 305s 12 13 14 15 16 17 18 19 20 305s -2.280 -1.323 -0.330 1.572 -3.582 -1.298 -1.892 1.948 0.174 305s > 305s > print( residuals( fitols5 ) ) 305s demand supply 305s 1 0.915 0.204 305s 2 -0.387 -0.421 305s 3 2.613 2.388 305s 4 1.815 1.474 305s 5 1.980 1.787 305s 6 1.221 0.879 305s 7 1.620 1.690 305s 8 -2.769 -4.489 305s 9 -1.382 -2.549 305s 10 1.890 2.660 305s 11 -0.506 -0.297 305s 12 -2.280 -1.456 305s 13 -1.323 -1.013 305s 14 -0.330 0.925 305s 15 1.572 2.889 305s 16 -3.582 -4.313 305s 17 -1.298 -0.573 305s 18 -1.892 -3.023 305s 19 1.948 2.462 305s 20 0.174 0.777 305s > print( residuals( fitols5$eq[[ 2 ]] ) ) 305s 1 2 3 4 5 6 7 8 9 10 11 305s 0.204 -0.421 2.388 1.474 1.787 0.879 1.690 -4.489 -2.549 2.660 -0.297 305s 12 13 14 15 16 17 18 19 20 305s -1.456 -1.013 0.925 2.889 -4.313 -0.573 -3.023 2.462 0.777 305s > 305s > 305s > ## *************** coefficients ********************* 305s > print( round( coef( fitols1rs ), digits = 6 ) ) 305s demand_(Intercept) demand_price demand_income supply_(Intercept) 305s 99.895 -0.316 0.335 58.275 305s supply_price supply_farmPrice supply_trend 305s 0.160 0.248 0.248 305s > print( round( coef( fitols1rs$eq[[ 2 ]] ), digits = 6 ) ) 305s (Intercept) price farmPrice trend 305s 58.275 0.160 0.248 0.248 305s > 305s > print( round( coef( fitols2s ), digits = 6 ) ) 305s demand_(Intercept) demand_price demand_income supply_(Intercept) 305s 99.556 -0.292 0.313 56.380 305s supply_price supply_farmPrice supply_trend 305s 0.164 0.257 0.313 305s > print( round( coef( fitols2s$eq[[ 1 ]] ), digits = 6 ) ) 305s (Intercept) price income 305s 99.556 -0.292 0.313 305s > 305s > print( round( coef( fitols3 ), digits = 6 ) ) 305s demand_(Intercept) demand_price demand_income supply_(Intercept) 305s 99.556 -0.292 0.313 56.380 305s supply_price supply_farmPrice supply_trend 305s 0.164 0.257 0.313 305s > print( round( coef( fitols3, modified.regMat = TRUE ), digits = 6 ) ) 305s C1 C2 C3 C4 C5 C6 305s 99.556 -0.292 0.313 56.380 0.164 0.257 305s > print( round( coef( fitols3$eq[[ 2 ]] ), digits = 6 ) ) 305s (Intercept) price farmPrice trend 305s 56.380 0.164 0.257 0.313 305s > 305s > print( round( coef( fitols4r ), digits = 6 ) ) 305s demand_(Intercept) demand_price demand_income supply_(Intercept) 305s 101.482 -0.317 0.319 54.149 305s supply_price supply_farmPrice supply_trend 305s 0.183 0.260 0.319 305s > print( round( coef( fitols4r$eq[[ 1 ]] ), digits = 6 ) ) 305s (Intercept) price income 305s 101.482 -0.317 0.319 305s > 305s > print( round( coef( fitols5 ), digits = 6 ) ) 305s demand_(Intercept) demand_price demand_income supply_(Intercept) 305s 101.482 -0.317 0.319 54.149 305s supply_price supply_farmPrice supply_trend 305s 0.183 0.260 0.319 305s > print( round( coef( fitols5, modified.regMat = TRUE ), digits = 6 ) ) 305s C1 C2 C3 C4 C5 C6 305s 101.482 -0.317 0.319 54.149 0.183 0.260 305s > print( round( coef( fitols5$eq[[ 2 ]] ), digits = 6 ) ) 305s (Intercept) price farmPrice trend 305s 54.149 0.183 0.260 0.319 305s > 305s > 305s > ## *************** coefficients with stats ********************* 305s > print( round( coef( summary( fitols1rs, useDfSys = FALSE ) ), digits = 6 ) ) 305s Estimate Std. Error t value Pr(>|t|) 305s demand_(Intercept) 99.895 8.4671 11.80 0.000000 305s demand_price -0.316 0.1021 -3.10 0.006536 305s demand_income 0.335 0.0511 6.54 0.000005 305s supply_(Intercept) 58.275 10.3587 5.63 0.000038 305s supply_price 0.160 0.0857 1.87 0.079851 305s supply_farmPrice 0.248 0.0417 5.94 0.000021 305s supply_trend 0.248 0.0881 2.82 0.012382 305s > print( round( coef( summary( fitols1rs$eq[[ 2 ]], useDfSys = FALSE ) ), 305s + digits = 6 ) ) 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 58.275 10.3587 5.63 0.000038 305s price 0.160 0.0857 1.87 0.079851 305s farmPrice 0.248 0.0417 5.94 0.000021 305s trend 0.248 0.0881 2.82 0.012382 305s > 305s > print( round( coef( summary( fitols2s ) ), digits = 6 ) ) 305s Estimate Std. Error t value Pr(>|t|) 305s demand_(Intercept) 99.556 7.5640 13.16 0.000000 305s demand_price -0.292 0.0887 -3.29 0.002340 305s demand_income 0.313 0.0415 7.54 0.000000 305s supply_(Intercept) 56.380 11.3165 4.98 0.000018 305s supply_price 0.164 0.0960 1.71 0.097028 305s supply_farmPrice 0.257 0.0451 5.69 0.000002 305s supply_trend 0.313 0.0415 7.54 0.000000 305s > print( round( coef( summary( fitols2s$eq[[ 1 ]] ) ), digits = 6 ) ) 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 99.556 7.5640 13.16 0.00000 305s price -0.292 0.0887 -3.29 0.00234 305s income 0.313 0.0415 7.54 0.00000 305s > 305s > print( round( coef( summary( fitols3, useDfSys = FALSE ) ), digits = 6 ) ) 305s Estimate Std. Error t value Pr(>|t|) 305s demand_(Intercept) 99.556 8.4225 11.82 0.000000 305s demand_price -0.292 0.0975 -2.99 0.008189 305s demand_income 0.313 0.0441 7.10 0.000002 305s supply_(Intercept) 56.380 10.0721 5.60 0.000040 305s supply_price 0.164 0.0853 1.92 0.072611 305s supply_farmPrice 0.257 0.0402 6.39 0.000009 305s supply_trend 0.313 0.0441 7.10 0.000003 305s > print( round( coef( summary( fitols3, useDfSys = FALSE ), modified.regMat = TRUE ), 305s + digits = 6 ) ) 305s Estimate Std. Error t value Pr(>|t|) 305s C1 99.556 8.4225 11.82 NA 305s C2 -0.292 0.0975 -2.99 NA 305s C3 0.313 0.0441 7.10 NA 305s C4 56.380 10.0721 5.60 NA 305s C5 0.164 0.0853 1.92 NA 305s C6 0.257 0.0402 6.39 NA 305s > print( round( coef( summary( fitols3$eq[[ 2 ]], useDfSys = FALSE ) ), 305s + digits = 6 ) ) 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 56.380 10.0721 5.60 0.000040 305s price 0.164 0.0853 1.92 0.072611 305s farmPrice 0.257 0.0402 6.39 0.000009 305s trend 0.313 0.0441 7.10 0.000003 305s > 305s > print( round( coef( summary( fitols4r, useDfSys = FALSE ) ), digits = 6 ) ) 305s Estimate Std. Error t value Pr(>|t|) 305s demand_(Intercept) 101.482 5.7621 17.61 0.0e+00 305s demand_price -0.317 0.0589 -5.38 5.0e-05 305s demand_income 0.319 0.0373 8.55 0.0e+00 305s supply_(Intercept) 54.149 7.0638 7.67 1.0e-06 305s supply_price 0.183 0.0589 3.11 6.7e-03 305s supply_farmPrice 0.260 0.0365 7.10 3.0e-06 305s supply_trend 0.319 0.0373 8.55 0.0e+00 305s > print( round( coef( summary( fitols4r$eq[[ 1 ]], useDfSys = FALSE ) ), 305s + digits = 6 ) ) 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 101.482 5.7621 17.61 0e+00 305s price -0.317 0.0589 -5.38 5e-05 305s income 0.319 0.0373 8.55 0e+00 305s > 305s > print( round( coef( summary( fitols5 ) ), digits = 6 ) ) 305s Estimate Std. Error t value Pr(>|t|) 305s demand_(Intercept) 101.482 5.7621 17.61 0.000000 305s demand_price -0.317 0.0589 -5.38 0.000005 305s demand_income 0.319 0.0373 8.55 0.000000 305s supply_(Intercept) 54.149 7.0638 7.67 0.000000 305s supply_price 0.183 0.0589 3.11 0.003680 305s supply_farmPrice 0.260 0.0365 7.10 0.000000 305s supply_trend 0.319 0.0373 8.55 0.000000 305s > print( round( coef( summary( fitols5 ), modified.regMat = TRUE ), digits = 6 ) ) 305s Estimate Std. Error t value Pr(>|t|) 305s C1 101.482 5.7621 17.61 0.000000 305s C2 -0.317 0.0589 -5.38 0.000005 305s C3 0.319 0.0373 8.55 0.000000 305s C4 54.149 7.0638 7.67 0.000000 305s C5 0.183 0.0589 3.11 0.003680 305s C6 0.260 0.0365 7.10 0.000000 305s > print( round( coef( summary( fitols5$eq[[ 2 ]] ) ), digits = 6 ) ) 305s Estimate Std. Error t value Pr(>|t|) 305s (Intercept) 54.149 7.0638 7.67 0.00000 305s price 0.183 0.0589 3.11 0.00368 305s farmPrice 0.260 0.0365 7.10 0.00000 305s trend 0.319 0.0373 8.55 0.00000 305s > 305s > 305s > ## *********** variance covariance matrix of the coefficients ******* 305s > print( round( vcov( fitols1rs ), digits = 6 ) ) 305s demand_(Intercept) demand_price demand_income 305s demand_(Intercept) 71.6926 -0.75420 0.04078 305s demand_price -0.7542 0.01043 -0.00296 305s demand_income 0.0408 -0.00296 0.00262 305s supply_(Intercept) 0.0000 0.00000 0.00000 305s supply_price 0.0000 0.00000 0.00000 305s supply_farmPrice 0.0000 0.00000 0.00000 305s supply_trend 0.0000 0.00000 0.00000 305s supply_(Intercept) supply_price supply_farmPrice 305s demand_(Intercept) 0.000 0.000000 0.000000 305s demand_price 0.000 0.000000 0.000000 305s demand_income 0.000 0.000000 0.000000 305s supply_(Intercept) 107.303 -0.806417 -0.248549 305s supply_price -0.806 0.007352 0.000689 305s supply_farmPrice -0.249 0.000689 0.001742 305s supply_trend -0.228 0.000426 0.001074 305s supply_trend 305s demand_(Intercept) 0.000000 305s demand_price 0.000000 305s demand_income 0.000000 305s supply_(Intercept) -0.227988 305s supply_price 0.000426 305s supply_farmPrice 0.001074 305s supply_trend 0.007766 305s > print( round( vcov( fitols1rs$eq[[ 2 ]] ), digits = 6 ) ) 305s (Intercept) price farmPrice trend 305s (Intercept) 107.303 -0.806417 -0.248549 -0.227988 305s price -0.806 0.007352 0.000689 0.000426 305s farmPrice -0.249 0.000689 0.001742 0.001074 305s trend -0.228 0.000426 0.001074 0.007766 305s > 305s > print( round( vcov( fitols2s ), digits = 6 ) ) 305s demand_(Intercept) demand_price demand_income 305s demand_(Intercept) 57.21413 -0.596328 0.026850 305s demand_price -0.59633 0.007862 -0.001948 305s demand_income 0.02685 -0.001948 0.001722 305s supply_(Intercept) -0.78825 0.057190 -0.050565 305s supply_price 0.00147 -0.000107 0.000095 305s supply_farmPrice 0.00371 -0.000269 0.000238 305s supply_trend 0.02685 -0.001948 0.001722 305s supply_(Intercept) supply_price supply_farmPrice 305s demand_(Intercept) -0.7883 0.001474 0.003714 305s demand_price 0.0572 -0.000107 -0.000269 305s demand_income -0.0506 0.000095 0.000238 305s supply_(Intercept) 128.0635 -1.001596 -0.280017 305s supply_price -1.0016 0.009225 0.000806 305s supply_farmPrice -0.2800 0.000806 0.002038 305s supply_trend -0.0506 0.000095 0.000238 305s supply_trend 305s demand_(Intercept) 0.026850 305s demand_price -0.001948 305s demand_income 0.001722 305s supply_(Intercept) -0.050565 305s supply_price 0.000095 305s supply_farmPrice 0.000238 305s supply_trend 0.001722 305s > print( round( vcov( fitols2s$eq[[ 1 ]] ), digits = 6 ) ) 305s (Intercept) price income 305s (Intercept) 57.2141 -0.59633 0.02685 305s price -0.5963 0.00786 -0.00195 305s income 0.0268 -0.00195 0.00172 305s > 305s > print( round( vcov( fitols3 ), digits = 6 ) ) 305s demand_(Intercept) demand_price demand_income 305s demand_(Intercept) 70.93892 -0.736413 0.030252 305s demand_price -0.73641 0.009503 -0.002195 305s demand_income 0.03025 -0.002195 0.001941 305s supply_(Intercept) -0.88813 0.064436 -0.056972 305s supply_price 0.00166 -0.000120 0.000107 305s supply_farmPrice 0.00419 -0.000304 0.000268 305s supply_trend 0.03025 -0.002195 0.001941 305s supply_(Intercept) supply_price supply_farmPrice 305s demand_(Intercept) -0.8881 0.001661 0.004185 305s demand_price 0.0644 -0.000120 -0.000304 305s demand_income -0.0570 0.000107 0.000268 305s supply_(Intercept) 101.4478 -0.790443 -0.223090 305s supply_price -0.7904 0.007274 0.000640 305s supply_farmPrice -0.2231 0.000640 0.001617 305s supply_trend -0.0570 0.000107 0.000268 305s supply_trend 305s demand_(Intercept) 0.030252 305s demand_price -0.002195 305s demand_income 0.001941 305s supply_(Intercept) -0.056972 305s supply_price 0.000107 305s supply_farmPrice 0.000268 305s supply_trend 0.001941 305s > print( round( vcov( fitols3, modified.regMat = TRUE ), digits = 6 ) ) 305s C1 C2 C3 C4 C5 C6 305s C1 70.93892 -0.736413 0.030252 -0.8881 0.001661 0.004185 305s C2 -0.73641 0.009503 -0.002195 0.0644 -0.000120 -0.000304 305s C3 0.03025 -0.002195 0.001941 -0.0570 0.000107 0.000268 305s C4 -0.88813 0.064436 -0.056972 101.4478 -0.790443 -0.223090 305s C5 0.00166 -0.000120 0.000107 -0.7904 0.007274 0.000640 305s C6 0.00419 -0.000304 0.000268 -0.2231 0.000640 0.001617 305s > print( round( vcov( fitols3$eq[[ 2 ]] ), digits = 6 ) ) 305s (Intercept) price farmPrice trend 305s (Intercept) 101.448 -0.790443 -0.223090 -0.056972 305s price -0.790 0.007274 0.000640 0.000107 305s farmPrice -0.223 0.000640 0.001617 0.000268 305s trend -0.057 0.000107 0.000268 0.001941 305s > 305s > print( round( vcov( fitols4r ), digits = 6 ) ) 305s demand_(Intercept) demand_price demand_income 305s demand_(Intercept) 33.2016 -0.272100 -0.059329 305s demand_price -0.2721 0.003464 -0.000762 305s demand_income -0.0593 -0.000762 0.001390 305s supply_(Intercept) 30.8652 -0.357363 0.050012 305s supply_price -0.2721 0.003464 -0.000762 305s supply_farmPrice -0.0313 0.000196 0.000120 305s supply_trend -0.0593 -0.000762 0.001390 305s supply_(Intercept) supply_price supply_farmPrice 305s demand_(Intercept) 30.865 -0.272100 -0.031328 305s demand_price -0.357 0.003464 0.000196 305s demand_income 0.050 -0.000762 0.000120 305s supply_(Intercept) 49.897 -0.357363 -0.149852 305s supply_price -0.357 0.003464 0.000196 305s supply_farmPrice -0.150 0.000196 0.001335 305s supply_trend 0.050 -0.000762 0.000120 305s supply_trend 305s demand_(Intercept) -0.059329 305s demand_price -0.000762 305s demand_income 0.001390 305s supply_(Intercept) 0.050012 305s supply_price -0.000762 305s supply_farmPrice 0.000120 305s supply_trend 0.001390 305s > print( round( vcov( fitols4r$eq[[ 1 ]] ), digits = 6 ) ) 305s (Intercept) price income 305s (Intercept) 33.2016 -0.272100 -0.059329 305s price -0.2721 0.003464 -0.000762 305s income -0.0593 -0.000762 0.001390 305s > 305s > print( round( vcov( fitols5 ), digits = 6 ) ) 305s demand_(Intercept) demand_price demand_income 305s demand_(Intercept) 33.2016 -0.272100 -0.059329 305s demand_price -0.2721 0.003464 -0.000762 305s demand_income -0.0593 -0.000762 0.001390 305s supply_(Intercept) 30.8652 -0.357363 0.050012 305s supply_price -0.2721 0.003464 -0.000762 305s supply_farmPrice -0.0313 0.000196 0.000120 305s supply_trend -0.0593 -0.000762 0.001390 305s supply_(Intercept) supply_price supply_farmPrice 305s demand_(Intercept) 30.865 -0.272100 -0.031328 305s demand_price -0.357 0.003464 0.000196 305s demand_income 0.050 -0.000762 0.000120 305s supply_(Intercept) 49.897 -0.357363 -0.149852 305s supply_price -0.357 0.003464 0.000196 305s supply_farmPrice -0.150 0.000196 0.001335 305s supply_trend 0.050 -0.000762 0.000120 305s supply_trend 305s demand_(Intercept) -0.059329 305s demand_price -0.000762 305s demand_income 0.001390 305s supply_(Intercept) 0.050012 305s supply_price -0.000762 305s supply_farmPrice 0.000120 305s supply_trend 0.001390 305s > print( round( vcov( fitols5, modified.regMat = TRUE ), digits = 6 ) ) 305s C1 C2 C3 C4 C5 C6 305s C1 33.2016 -0.272100 -0.059329 30.865 -0.272100 -0.031328 305s C2 -0.2721 0.003464 -0.000762 -0.357 0.003464 0.000196 305s C3 -0.0593 -0.000762 0.001390 0.050 -0.000762 0.000120 305s C4 30.8652 -0.357363 0.050012 49.897 -0.357363 -0.149852 305s C5 -0.2721 0.003464 -0.000762 -0.357 0.003464 0.000196 305s C6 -0.0313 0.000196 0.000120 -0.150 0.000196 0.001335 305s > print( round( vcov( fitols5$eq[[ 2 ]] ), digits = 6 ) ) 305s (Intercept) price farmPrice trend 305s (Intercept) 49.897 -0.357363 -0.149852 0.050012 305s price -0.357 0.003464 0.000196 -0.000762 305s farmPrice -0.150 0.000196 0.001335 0.000120 305s trend 0.050 -0.000762 0.000120 0.001390 305s > 305s > 305s > ## *********** confidence intervals of coefficients ************* 305s > print( confint( fitols1, useDfSys = TRUE ) ) 305s 2.5 % 97.5 % 305s demand_(Intercept) 84.597 115.194 305s demand_price -0.501 -0.132 305s demand_income 0.242 0.427 305s supply_(Intercept) 34.954 81.597 305s supply_price -0.033 0.353 305s supply_farmPrice 0.154 0.342 305s supply_trend 0.050 0.447 305s > print( confint( fitols1$eq[[ 2 ]], level = 0.9, useDfSys = TRUE ) ) 305s 5 % 95 % 305s (Intercept) 38.876 77.675 305s price 0.000 0.321 305s farmPrice 0.170 0.326 305s trend 0.083 0.413 305s > 305s > print( confint( fitols2r, level = 0.9 ) ) 305s 5 % 95 % 305s demand_(Intercept) 83.776 115.337 305s demand_price -0.474 -0.109 305s demand_income 0.230 0.395 305s supply_(Intercept) 37.508 75.251 305s supply_price 0.004 0.324 305s supply_farmPrice 0.182 0.332 305s supply_trend 0.230 0.395 305s > print( confint( fitols2r$eq[[ 1 ]], level = 0.99 ) ) 305s 0.5 % 99.5 % 305s (Intercept) 78.370 120.743 305s price -0.537 -0.046 305s income 0.202 0.424 305s > 305s > print( confint( fitols3s, level = 0.99 ) ) 305s 0.5 % 99.5 % 305s demand_(Intercept) 84.184 114.928 305s demand_price -0.472 -0.112 305s demand_income 0.229 0.397 305s supply_(Intercept) 33.382 79.377 305s supply_price -0.031 0.359 305s supply_farmPrice 0.165 0.349 305s supply_trend 0.229 0.397 305s > print( confint( fitols3s$eq[[ 2 ]], level = 0.5 ) ) 305s 25 % 75 % 305s (Intercept) 48.664 64.095 305s price 0.098 0.229 305s farmPrice 0.226 0.288 305s trend 0.285 0.341 305s > 305s > print( confint( fitols4rs, level = 0.5 ) ) 305s 25 % 75 % 305s demand_(Intercept) 90.269 112.695 305s demand_price -0.436 -0.197 305s demand_income 0.247 0.390 305s supply_(Intercept) 39.515 68.784 305s supply_price 0.064 0.303 305s supply_farmPrice 0.179 0.340 305s supply_trend 0.247 0.390 305s > print( confint( fitols4rs$eq[[ 1 ]], level = 0.25 ) ) 305s 37.5 % 62.5 % 305s (Intercept) 99.708 103.256 305s price -0.336 -0.298 305s income 0.308 0.330 305s > 305s > print( confint( fitols5, level = 0.25 ) ) 305s 37.5 % 62.5 % 305s demand_(Intercept) 89.784 113.179 305s demand_price -0.436 -0.197 305s demand_income 0.243 0.395 305s supply_(Intercept) 39.809 68.490 305s supply_price 0.064 0.303 305s supply_farmPrice 0.185 0.334 305s supply_trend 0.243 0.395 305s > print( confint( fitols5$eq[[ 2 ]], level = 0.999 ) ) 305s 0.1 % 100 % 305s (Intercept) 28.782 79.517 305s price -0.028 0.395 305s farmPrice 0.128 0.391 305s trend 0.185 0.453 305s > 305s > print( confint( fitols3, level = 0.999, useDfSys = FALSE ) ) 305s 0.1 % 100 % 305s demand_(Intercept) 81.786 117.326 305s demand_price -0.497 -0.086 305s demand_income 0.220 0.406 305s supply_(Intercept) 35.028 77.731 305s supply_price -0.017 0.345 305s supply_farmPrice 0.172 0.342 305s supply_trend 0.219 0.406 305s > print( confint( fitols3$eq[[ 1 ]], useDfSys = FALSE ) ) 305s 2.5 % 97.5 % 305s (Intercept) 81.786 117.326 305s price -0.497 -0.086 305s income 0.220 0.406 305s > 305s > 305s > ## *********** fitted values ************* 305s > print( fitted( fitols1 ) ) 305s demand supply 305s 1 97.4 98.9 305s 2 99.6 100.1 305s 3 99.5 100.2 305s 4 99.7 100.4 305s 5 102.3 102.7 305s 6 102.1 102.6 305s 7 102.5 102.4 305s 8 102.8 104.3 305s 9 101.7 102.9 305s 10 100.8 100.4 305s 11 95.6 96.0 305s 12 94.4 94.1 305s 13 95.7 95.6 305s 14 99.0 97.8 305s 15 104.3 102.6 305s 16 103.9 104.1 305s 17 104.8 103.8 305s 18 101.9 102.4 305s 19 103.5 102.1 305s 20 106.5 104.5 305s > print( fitted( fitols1$eq[[ 2 ]] ) ) 305s 1 2 3 4 5 6 7 8 9 10 11 12 13 305s 98.9 100.1 100.2 100.4 102.7 102.6 102.4 104.3 102.9 100.4 96.0 94.1 95.6 305s 14 15 16 17 18 19 20 305s 97.8 102.6 104.1 103.8 102.4 102.1 104.5 305s > 305s > print( fitted( fitols2r ) ) 305s demand supply 305s 1 97.6 98.3 305s 2 99.7 99.6 305s 3 99.6 99.7 305s 4 99.8 100.0 305s 5 102.2 102.5 305s 6 102.0 102.4 305s 7 102.4 102.3 305s 8 102.7 104.3 305s 9 101.6 102.9 305s 10 100.8 100.3 305s 11 95.9 95.9 305s 12 94.8 94.0 305s 13 96.0 95.5 305s 14 99.1 97.9 305s 15 104.1 103.0 305s 16 103.7 104.5 305s 17 104.5 104.3 305s 18 101.9 102.8 305s 19 103.3 102.6 305s 20 106.2 105.1 305s > print( fitted( fitols2r$eq[[ 1 ]] ) ) 305s 1 2 3 4 5 6 7 8 9 10 11 12 13 305s 97.6 99.7 99.6 99.8 102.2 102.0 102.4 102.7 101.6 100.8 95.9 94.8 96.0 305s 14 15 16 17 18 19 20 305s 99.1 104.1 103.7 104.5 101.9 103.3 106.2 305s > 305s > print( fitted( fitols3s ) ) 305s demand supply 305s 1 97.6 98.3 305s 2 99.7 99.6 305s 3 99.6 99.7 305s 4 99.8 100.0 305s 5 102.2 102.5 305s 6 102.0 102.4 305s 7 102.4 102.3 305s 8 102.7 104.3 305s 9 101.6 102.9 305s 10 100.8 100.3 305s 11 95.9 95.9 305s 12 94.8 94.0 305s 13 96.0 95.5 305s 14 99.1 97.9 305s 15 104.1 103.0 305s 16 103.7 104.5 305s 17 104.5 104.3 305s 18 101.9 102.8 305s 19 103.3 102.6 305s 20 106.2 105.1 305s > print( fitted( fitols3s$eq[[ 2 ]] ) ) 305s 1 2 3 4 5 6 7 8 9 10 11 12 13 305s 98.3 99.6 99.7 100.0 102.5 102.4 102.3 104.3 102.9 100.3 95.9 94.0 95.5 305s 14 15 16 17 18 19 20 305s 97.9 103.0 104.5 104.3 102.8 102.6 105.1 305s > 305s > print( fitted( fitols4rs ) ) 305s demand supply 305s 1 97.6 98.3 305s 2 99.6 99.6 305s 3 99.5 99.8 305s 4 99.7 100.0 305s 5 102.3 102.5 305s 6 102.0 102.4 305s 7 102.4 102.3 305s 8 102.7 104.4 305s 9 101.7 102.9 305s 10 100.9 100.2 305s 11 95.9 95.7 305s 12 94.7 93.9 305s 13 95.9 95.5 305s 14 99.1 97.8 305s 15 104.2 102.9 305s 16 103.8 104.5 305s 17 104.8 104.1 305s 18 101.8 103.0 305s 19 103.3 102.8 305s 20 106.1 105.5 305s > print( fitted( fitols4rs$eq[[ 1 ]] ) ) 305s 1 2 3 4 5 6 7 8 9 10 11 12 13 305s 97.6 99.6 99.5 99.7 102.3 102.0 102.4 102.7 101.7 100.9 95.9 94.7 95.9 305s 14 15 16 17 18 19 20 305s 99.1 104.2 103.8 104.8 101.8 103.3 106.1 305s > 305s > print( fitted( fitols5 ) ) 305s demand supply 305s 1 97.6 98.3 305s 2 99.6 99.6 305s 3 99.5 99.8 305s 4 99.7 100.0 305s 5 102.3 102.5 305s 6 102.0 102.4 305s 7 102.4 102.3 305s 8 102.7 104.4 305s 9 101.7 102.9 305s 10 100.9 100.2 305s 11 95.9 95.7 305s 12 94.7 93.9 305s 13 95.9 95.5 305s 14 99.1 97.8 305s 15 104.2 102.9 305s 16 103.8 104.5 305s 17 104.8 104.1 305s 18 101.8 103.0 305s 19 103.3 102.8 305s 20 106.1 105.5 305s > print( fitted( fitols5$eq[[ 2 ]] ) ) 305s 1 2 3 4 5 6 7 8 9 10 11 12 13 305s 98.3 99.6 99.8 100.0 102.5 102.4 102.3 104.4 102.9 100.2 95.7 93.9 95.5 305s 14 15 16 17 18 19 20 305s 97.8 102.9 104.5 104.1 103.0 102.8 105.5 305s > 305s > 305s > ## *********** predicted values ************* 305s > predictData <- Kmenta 305s > predictData$consump <- NULL 305s > predictData$price <- Kmenta$price * 0.9 305s > predictData$income <- Kmenta$income * 1.1 305s > 305s > print( predict( fitols1, se.fit = TRUE, interval = "prediction", 305s + useDfSys = TRUE ) ) 305s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 305s 1 97.4 0.643 93.3 101.5 98.9 1.056 305s 2 99.6 0.577 95.5 103.7 100.1 1.037 305s 3 99.5 0.545 95.5 103.6 100.2 0.939 305s 4 99.7 0.582 95.6 103.8 100.4 0.912 305s 5 102.3 0.502 98.2 106.4 102.7 0.895 305s 6 102.1 0.463 98.0 106.1 102.6 0.791 305s 7 102.5 0.484 98.4 106.5 102.4 0.719 305s 8 102.8 0.601 98.7 106.9 104.3 0.963 305s 9 101.7 0.527 97.6 105.8 102.9 0.788 305s 10 100.8 0.788 96.5 105.0 100.4 0.981 305s 11 95.6 0.946 91.2 100.0 96.0 1.185 305s 12 94.4 0.980 90.0 98.8 94.1 1.394 305s 13 95.7 0.880 91.3 100.0 95.6 1.244 305s 14 99.0 0.508 94.9 103.0 97.8 0.896 305s 15 104.3 0.758 100.1 108.5 102.6 0.874 305s 16 103.9 0.616 99.8 108.0 104.1 0.916 305s 17 104.8 1.273 100.1 109.5 103.8 1.605 305s 18 101.9 0.536 97.9 106.0 102.4 0.962 305s 19 103.5 0.680 99.3 107.6 102.1 1.098 305s 20 106.5 1.274 101.8 111.2 104.5 1.664 305s supply.lwr supply.upr 305s 1 93.6 104.3 305s 2 94.8 105.4 305s 3 94.9 105.5 305s 4 95.1 105.6 305s 5 97.5 107.9 305s 6 97.4 107.7 305s 7 97.3 107.5 305s 8 99.0 109.6 305s 9 97.8 108.1 305s 10 95.1 105.6 305s 11 90.6 101.5 305s 12 88.5 99.8 305s 13 90.1 101.1 305s 14 92.6 103.0 305s 15 97.4 107.8 305s 16 98.9 109.3 305s 17 97.9 109.7 305s 18 97.1 107.6 305s 19 96.7 107.5 305s 20 98.6 110.5 305s > print( predict( fitols1$eq[[ 2 ]], se.fit = TRUE, interval = "prediction", 305s + useDfSys = TRUE ) ) 305s fit se.fit lwr upr 305s 1 98.9 1.056 93.6 104.3 305s 2 100.1 1.037 94.8 105.4 305s 3 100.2 0.939 94.9 105.5 305s 4 100.4 0.912 95.1 105.6 305s 5 102.7 0.895 97.5 107.9 305s 6 102.6 0.791 97.4 107.7 305s 7 102.4 0.719 97.3 107.5 305s 8 104.3 0.963 99.0 109.6 305s 9 102.9 0.788 97.8 108.1 305s 10 100.4 0.981 95.1 105.6 305s 11 96.0 1.185 90.6 101.5 305s 12 94.1 1.394 88.5 99.8 305s 13 95.6 1.244 90.1 101.1 305s 14 97.8 0.896 92.6 103.0 305s 15 102.6 0.874 97.4 107.8 305s 16 104.1 0.916 98.9 109.3 305s 17 103.8 1.605 97.9 109.7 305s 18 102.4 0.962 97.1 107.6 305s 19 102.1 1.098 96.7 107.5 305s 20 104.5 1.664 98.6 110.5 305s > 305s > print( predict( fitols2r, se.pred = TRUE, interval = "confidence", 305s + level = 0.999, newdata = predictData ) ) 305s demand.pred demand.se.pred demand.lwr demand.upr supply.pred supply.se.pred 305s 1 103 2.17 99.9 107 96.7 2.62 305s 2 106 2.16 102.4 109 97.9 2.55 305s 3 106 2.17 102.2 109 98.1 2.55 305s 4 106 2.16 102.5 109 98.3 2.54 305s 5 108 2.43 102.9 113 100.9 2.67 305s 6 108 2.38 103.1 113 100.7 2.63 305s 7 109 2.37 103.7 113 100.6 2.59 305s 8 109 2.33 104.5 114 102.6 2.55 305s 9 107 2.44 102.2 113 101.4 2.69 305s 10 106 2.57 100.2 112 98.8 2.84 305s 11 101 2.36 96.1 106 94.4 2.89 305s 12 100 2.17 96.6 104 92.3 2.88 305s 13 102 2.08 99.0 104 93.9 2.75 305s 14 105 2.25 100.7 109 96.3 2.72 305s 15 110 2.63 103.7 116 101.4 2.72 305s 16 110 2.52 104.1 116 102.9 2.65 305s 17 110 2.96 102.0 118 102.9 3.03 305s 18 108 2.28 103.9 112 101.1 2.55 305s 19 110 2.36 105.1 115 100.9 2.55 305s 20 114 2.57 107.4 120 103.3 2.51 305s supply.lwr supply.upr 305s 1 93.2 100.2 305s 2 95.2 100.5 305s 3 95.3 100.8 305s 4 95.8 100.8 305s 5 97.0 104.8 305s 6 97.2 104.3 305s 7 97.5 103.7 305s 8 99.9 105.2 305s 9 97.3 105.5 305s 10 93.6 104.1 305s 11 88.8 100.0 305s 12 86.8 97.9 305s 13 89.3 98.5 305s 14 91.9 100.6 305s 15 97.0 105.8 305s 16 99.2 106.6 305s 17 96.4 109.4 305s 18 98.4 103.9 305s 19 98.2 103.5 305s 20 101.1 105.5 305s > print( predict( fitols2r$eq[[ 1 ]], se.pred = TRUE, interval = "confidence", 305s + level = 0.999, newdata = predictData ) ) 305s fit se.pred lwr upr 305s 1 103 2.17 99.9 107 305s 2 106 2.16 102.4 109 305s 3 106 2.17 102.2 109 305s 4 106 2.16 102.5 109 305s 5 108 2.43 102.9 113 305s 6 108 2.38 103.1 113 305s 7 109 2.37 103.7 113 305s 8 109 2.33 104.5 114 305s 9 107 2.44 102.2 113 305s 10 106 2.57 100.2 112 305s 11 101 2.36 96.1 106 305s 12 100 2.17 96.6 104 305s 13 102 2.08 99.0 104 305s 14 105 2.25 100.7 109 305s 15 110 2.63 103.7 116 305s 16 110 2.52 104.1 116 305s 17 110 2.96 102.0 118 305s 18 108 2.28 103.9 112 305s 19 110 2.36 105.1 115 305s 20 114 2.57 107.4 120 305s > 305s > print( predict( fitols3s, se.fit = TRUE, se.pred = TRUE, 305s + interval = "prediction", level = 0.5, newdata = predictData ) ) 305s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 305s 1 103 0.940 2.16 101.8 105 96.7 305s 2 106 0.944 2.16 104.3 107 97.9 305s 3 106 0.969 2.17 104.2 107 98.1 305s 4 106 0.949 2.16 104.4 107 98.3 305s 5 108 1.452 2.43 106.5 110 100.9 305s 6 108 1.372 2.38 106.4 110 100.7 305s 7 109 1.356 2.37 106.9 110 100.6 305s 8 109 1.296 2.34 107.6 111 102.6 305s 9 107 1.464 2.43 105.8 109 101.4 305s 10 106 1.652 2.55 104.5 108 98.8 305s 11 101 1.305 2.34 99.4 103 94.4 305s 12 100 0.941 2.16 98.6 102 92.3 305s 13 102 0.725 2.07 100.2 103 93.9 305s 14 105 1.124 2.24 103.3 106 96.3 305s 15 110 1.774 2.63 108.3 112 101.4 305s 16 110 1.606 2.52 108.2 112 102.9 305s 17 110 2.216 2.95 108.0 112 102.9 305s 18 108 1.208 2.29 106.6 110 101.1 305s 19 110 1.356 2.37 108.3 112 100.9 305s 20 114 1.718 2.59 111.7 115 103.3 305s supply.se.fit supply.se.pred supply.lwr supply.upr 305s 1 1.149 2.69 94.8 98.5 305s 2 0.873 2.59 96.1 99.6 305s 3 0.907 2.60 96.3 99.8 305s 4 0.831 2.58 96.5 100.0 305s 5 1.324 2.77 99.0 102.8 305s 6 1.188 2.71 98.9 102.6 305s 7 1.049 2.65 98.8 102.4 305s 8 0.911 2.60 100.8 104.3 305s 9 1.396 2.81 99.5 103.3 305s 10 1.782 3.02 96.8 100.9 305s 11 1.906 3.09 92.3 96.5 305s 12 1.875 3.08 90.2 94.4 305s 13 1.560 2.89 91.9 95.8 305s 14 1.475 2.85 94.3 98.2 305s 15 1.477 2.85 99.5 103.3 305s 16 1.245 2.74 101.0 104.8 305s 17 2.195 3.28 100.6 105.1 305s 18 0.909 2.60 99.4 102.9 305s 19 0.875 2.59 99.1 102.6 305s 20 0.704 2.54 101.6 105.0 305s > print( predict( fitols3s$eq[[ 2 ]], se.fit = TRUE, se.pred = TRUE, 305s + interval = "prediction", level = 0.5, newdata = predictData ) ) 305s fit se.fit se.pred lwr upr 305s 1 96.7 1.149 2.69 94.8 98.5 305s 2 97.9 0.873 2.59 96.1 99.6 305s 3 98.1 0.907 2.60 96.3 99.8 305s 4 98.3 0.831 2.58 96.5 100.0 305s 5 100.9 1.324 2.77 99.0 102.8 305s 6 100.7 1.188 2.71 98.9 102.6 305s 7 100.6 1.049 2.65 98.8 102.4 305s 8 102.6 0.911 2.60 100.8 104.3 305s 9 101.4 1.396 2.81 99.5 103.3 305s 10 98.8 1.782 3.02 96.8 100.9 305s 11 94.4 1.906 3.09 92.3 96.5 305s 12 92.3 1.875 3.08 90.2 94.4 305s 13 93.9 1.560 2.89 91.9 95.8 305s 14 96.3 1.475 2.85 94.3 98.2 305s 15 101.4 1.477 2.85 99.5 103.3 305s 16 102.9 1.245 2.74 101.0 104.8 305s 17 102.9 2.195 3.28 100.6 105.1 305s 18 101.1 0.909 2.60 99.4 102.9 305s 19 100.9 0.875 2.59 99.1 102.6 305s 20 103.3 0.704 2.54 101.6 105.0 305s > 305s > print( predict( fitols4rs, se.fit = TRUE, se.pred = TRUE, 305s + interval = "confidence", level = 0.99 ) ) 305s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 305s 1 97.6 0.541 2.01 96.1 99.0 98.3 305s 2 99.6 0.471 2.00 98.3 100.9 99.6 305s 3 99.5 0.454 1.99 98.3 100.8 99.8 305s 4 99.7 0.475 2.00 98.4 101.0 100.0 305s 5 102.3 0.434 1.99 101.1 103.4 102.5 305s 6 102.0 0.418 1.98 100.9 103.2 102.4 305s 7 102.4 0.440 1.99 101.2 103.6 102.3 305s 8 102.7 0.537 2.01 101.2 104.1 104.4 305s 9 101.7 0.447 1.99 100.5 102.9 102.9 305s 10 100.9 0.628 2.04 99.2 102.6 100.2 305s 11 95.9 0.833 2.11 93.7 98.2 95.7 305s 12 94.7 0.807 2.10 92.5 96.9 93.9 305s 13 95.9 0.677 2.06 94.0 97.7 95.5 305s 14 99.1 0.459 1.99 97.8 100.3 97.8 305s 15 104.2 0.572 2.02 102.7 105.8 102.9 305s 16 103.8 0.509 2.01 102.4 105.2 104.5 305s 17 104.8 0.877 2.13 102.4 107.2 104.1 305s 18 101.8 0.478 2.00 100.5 103.1 103.0 305s 19 103.3 0.604 2.03 101.6 104.9 102.8 305s 20 106.1 1.102 2.23 103.1 109.1 105.5 305s supply.se.fit supply.se.pred supply.lwr supply.upr 305s 1 0.598 2.52 96.7 99.9 305s 2 0.679 2.54 97.8 101.5 305s 3 0.634 2.53 98.0 101.5 305s 4 0.643 2.53 98.3 101.8 305s 5 0.753 2.56 100.4 104.5 305s 6 0.680 2.54 100.5 104.2 305s 7 0.625 2.53 100.6 104.0 305s 8 0.799 2.57 102.2 106.6 305s 9 0.700 2.55 101.0 104.8 305s 10 0.716 2.55 98.2 102.1 305s 11 0.916 2.61 93.2 98.2 305s 12 1.226 2.74 90.5 97.2 305s 13 1.130 2.70 92.5 98.6 305s 14 0.796 2.57 95.7 100.0 305s 15 0.656 2.53 101.1 104.7 305s 16 0.644 2.53 102.8 106.3 305s 17 1.150 2.70 101.0 107.2 305s 18 0.575 2.51 101.4 104.5 305s 19 0.649 2.53 101.0 104.5 305s 20 0.875 2.60 103.1 107.8 305s > print( predict( fitols4rs$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 305s + interval = "confidence", level = 0.99 ) ) 305s fit se.fit se.pred lwr upr 305s 1 97.6 0.541 2.01 96.1 99.0 305s 2 99.6 0.471 2.00 98.3 100.9 305s 3 99.5 0.454 1.99 98.3 100.8 305s 4 99.7 0.475 2.00 98.4 101.0 305s 5 102.3 0.434 1.99 101.1 103.4 305s 6 102.0 0.418 1.98 100.9 103.2 305s 7 102.4 0.440 1.99 101.2 103.6 305s 8 102.7 0.537 2.01 101.2 104.1 305s 9 101.7 0.447 1.99 100.5 102.9 305s 10 100.9 0.628 2.04 99.2 102.6 305s 11 95.9 0.833 2.11 93.7 98.2 305s 12 94.7 0.807 2.10 92.5 96.9 305s 13 95.9 0.677 2.06 94.0 97.7 305s 14 99.1 0.459 1.99 97.8 100.3 305s 15 104.2 0.572 2.02 102.7 105.8 305s 16 103.8 0.509 2.01 102.4 105.2 305s 17 104.8 0.877 2.13 102.4 107.2 305s 18 101.8 0.478 2.00 100.5 103.1 305s 19 103.3 0.604 2.03 101.6 104.9 305s 20 106.1 1.102 2.23 103.1 109.1 305s > 305s > print( predict( fitols5, se.fit = TRUE, interval = "prediction", 305s + level = 0.9, newdata = predictData ) ) 305s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 305s 1 104 0.714 100.0 107 96.4 0.712 305s 2 106 0.748 102.5 110 97.7 0.591 305s 3 106 0.753 102.4 109 97.9 0.602 305s 4 106 0.756 102.6 110 98.1 0.565 305s 5 109 1.055 104.8 112 100.7 0.900 305s 6 108 1.013 104.7 112 100.5 0.811 305s 7 109 1.029 105.2 113 100.5 0.722 305s 8 109 1.055 105.7 113 102.5 0.703 305s 9 108 1.042 104.1 112 101.1 0.952 305s 10 107 1.148 102.8 110 98.5 1.136 305s 11 101 1.026 97.6 105 94.0 1.245 305s 12 100 0.800 96.7 104 92.1 1.347 305s 13 102 0.606 98.4 105 93.7 1.170 305s 14 105 0.820 101.5 109 96.0 1.034 305s 15 111 1.272 106.6 114 101.2 1.031 305s 16 110 1.191 106.4 114 102.7 0.925 305s 17 111 1.513 106.5 115 102.5 1.529 305s 18 108 0.963 104.8 112 101.0 0.720 305s 19 110 1.129 106.4 114 100.8 0.717 305s 20 114 1.601 109.5 118 103.4 0.562 305s supply.lwr supply.upr 305s 1 92.1 100.7 305s 2 93.4 102.0 305s 3 93.6 102.1 305s 4 93.9 102.4 305s 5 96.3 105.1 305s 6 96.2 104.9 305s 7 96.1 104.8 305s 8 98.2 106.8 305s 9 96.7 105.6 305s 10 93.9 103.0 305s 11 89.4 98.7 305s 12 87.4 96.8 305s 13 89.1 98.2 305s 14 91.5 100.5 305s 15 96.7 105.7 305s 16 98.3 107.2 305s 17 97.6 107.4 305s 18 96.7 105.4 305s 19 96.5 105.1 305s 20 99.1 107.6 305s > print( predict( fitols5$eq[[ 2 ]], se.fit = TRUE, interval = "prediction", 305s + level = 0.9, newdata = predictData ) ) 305s fit se.fit lwr upr 305s 1 96.4 0.712 92.1 100.7 305s 2 97.7 0.591 93.4 102.0 305s 3 97.9 0.602 93.6 102.1 305s 4 98.1 0.565 93.9 102.4 305s 5 100.7 0.900 96.3 105.1 305s 6 100.5 0.811 96.2 104.9 305s 7 100.5 0.722 96.1 104.8 305s 8 102.5 0.703 98.2 106.8 305s 9 101.1 0.952 96.7 105.6 305s 10 98.5 1.136 93.9 103.0 305s 11 94.0 1.245 89.4 98.7 305s 12 92.1 1.347 87.4 96.8 305s 13 93.7 1.170 89.1 98.2 305s 14 96.0 1.034 91.5 100.5 305s 15 101.2 1.031 96.7 105.7 305s 16 102.7 0.925 98.3 107.2 305s 17 102.5 1.529 97.6 107.4 305s 18 101.0 0.720 96.7 105.4 305s 19 100.8 0.717 96.5 105.1 305s 20 103.4 0.562 99.1 107.6 305s > 305s > # predict just one observation 305s > smallData <- data.frame( price = 130, income = 150, farmPrice = 120, 305s + trend = 25 ) 305s > 305s > print( predict( fitols1, newdata = smallData ) ) 305s demand.pred supply.pred 305s 1 109 115 305s > print( predict( fitols1$eq[[ 1 ]], newdata = smallData ) ) 305s fit 305s 1 109 305s > 305s > print( predict( fitols2r, se.fit = TRUE, level = 0.9, 305s + newdata = smallData ) ) 305s demand.pred demand.se.fit supply.pred supply.se.fit 305s 1 109 2.48 116 2.8 305s > print( predict( fitols2r$eq[[ 1 ]], se.pred = TRUE, level = 0.99, 305s + newdata = smallData ) ) 305s fit se.pred 305s 1 109 3.15 305s > 305s > print( predict( fitols3s, interval = "prediction", level = 0.975, 305s + newdata = smallData ) ) 305s demand.pred demand.lwr demand.upr supply.pred supply.lwr supply.upr 305s 1 109 101 116 116 107 126 305s > print( predict( fitols3s$eq[[ 1 ]], interval = "confidence", level = 0.8, 305s + newdata = smallData ) ) 305s fit lwr upr 305s 1 109 105 112 305s > 305s > print( predict( fitols4rs, se.fit = TRUE, interval = "confidence", 305s + level = 0.999, newdata = smallData ) ) 305s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 305s 1 108 2.02 101 115 117 2.02 305s supply.lwr supply.upr 305s 1 110 124 305s > print( predict( fitols4rs$eq[[ 2 ]], se.pred = TRUE, interval = "prediction", 305s + level = 0.75, newdata = smallData ) ) 305s fit se.pred lwr upr 305s 1 117 3.18 113 121 305s > 305s > print( predict( fitols5, se.fit = TRUE, interval = "prediction", 305s + newdata = smallData ) ) 305s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 305s 1 108 2.18 102 114 117 2.01 305s supply.lwr supply.upr 305s 1 111 124 305s > print( predict( fitols5$eq[[ 1 ]], se.pred = TRUE, interval = "confidence", 305s + newdata = smallData ) ) 305s fit se.pred lwr upr 305s 1 108 2.92 104 113 305s > 305s > print( predict( fitols5rs, se.fit = TRUE, se.pred = TRUE, 305s + interval = "prediction", level = 0.5, newdata = smallData ) ) 305s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 305s 1 108 2.02 2.8 106 110 117 305s supply.se.fit supply.se.pred supply.lwr supply.upr 305s 1 2.02 3.18 115 119 305s > print( predict( fitols5rs$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 305s + interval = "confidence", level = 0.25, newdata = smallData ) ) 305s fit se.fit se.pred lwr upr 305s 1 108 2.02 2.8 107 109 305s > 305s > 305s > ## ************ correlation of predicted values *************** 305s > print( correlation.systemfit( fitols1, 1, 2 ) ) 305s [,1] 305s [1,] 0 305s [2,] 0 305s [3,] 0 305s [4,] 0 305s [5,] 0 305s [6,] 0 305s [7,] 0 305s [8,] 0 305s [9,] 0 305s [10,] 0 305s [11,] 0 305s [12,] 0 305s [13,] 0 305s [14,] 0 305s [15,] 0 305s [16,] 0 305s [17,] 0 305s [18,] 0 305s [19,] 0 305s [20,] 0 305s > 305s > print( correlation.systemfit( fitols2r, 2, 1 ) ) 305s [,1] 305s [1,] 0.443122 305s [2,] 0.160426 305s [3,] 0.161091 305s [4,] 0.118312 305s [5,] -0.077411 305s [6,] -0.059235 305s [7,] -0.057777 305s [8,] -0.006908 305s [9,] -0.000372 305s [10,] -0.001410 305s [11,] 0.055233 305s [12,] 0.074936 305s [13,] 0.028274 305s [14,] -0.032082 305s [15,] 0.196029 305s [16,] 0.279921 305s [17,] 0.115570 305s [18,] 0.080620 305s [19,] 0.171681 305s [20,] 0.150544 305s > 305s > print( correlation.systemfit( fitols3s, 1, 2 ) ) 305s [,1] 305s [1,] 0.405901 305s [2,] 0.145364 305s [3,] 0.145375 305s [4,] 0.105835 305s [5,] -0.067958 305s [6,] -0.052026 305s [7,] -0.050543 305s [8,] -0.006031 305s [9,] -0.000326 305s [10,] -0.001237 305s [11,] 0.047534 305s [12,] 0.063493 305s [13,] 0.024060 305s [14,] -0.027910 305s [15,] 0.171580 305s [16,] 0.248212 305s [17,] 0.101409 305s [18,] 0.073084 305s [19,] 0.153950 305s [20,] 0.132944 305s > 305s > print( correlation.systemfit( fitols4rs, 2, 1 ) ) 305s [,1] 305s [1,] 0.38162 305s [2,] 0.29173 305s [3,] 0.25421 305s [4,] 0.28598 305s [5,] -0.02775 305s [6,] -0.04974 305s [7,] -0.05850 305s [8,] 0.09388 305s [9,] 0.09469 305s [10,] 0.43814 305s [11,] 0.10559 305s [12,] 0.00876 305s [13,] 0.04090 305s [14,] -0.03984 305s [15,] 0.40767 305s [16,] 0.24571 305s [17,] 0.64160 305s [18,] 0.24037 305s [19,] 0.34075 305s [20,] 0.54270 305s > 305s > print( correlation.systemfit( fitols5, 1, 2 ) ) 305s [,1] 305s [1,] 0.4051 305s [2,] 0.2729 305s [3,] 0.2415 305s [4,] 0.2693 305s [5,] -0.0301 305s [6,] -0.0527 305s [7,] -0.0624 305s [8,] 0.0971 305s [9,] 0.0945 305s [10,] 0.4365 305s [11,] 0.1258 305s [12,] 0.0210 305s [13,] 0.0436 305s [14,] -0.0405 305s [15,] 0.4102 305s [16,] 0.2610 305s [17,] 0.6400 305s [18,] 0.2661 305s [19,] 0.3796 305s [20,] 0.5742 305s > 305s > 305s > ## ************ Log-Likelihood values *************** 305s > print( logLik( fitols1 ) ) 305s 'log Lik.' -67.8 (df=8) 305s > print( logLik( fitols1, residCovDiag = TRUE ) ) 305s 'log Lik.' -83.6 (df=8) 305s > all.equal( logLik( fitols1, residCovDiag = TRUE ), 305s + logLik( lmDemand ) + logLik( lmSupply ), 305s + check.attributes = FALSE ) 305s [1] TRUE 305s > 305s > print( logLik( fitols2r ) ) 305s 'log Lik.' -62 (df=7) 305s > print( logLik( fitols2r, residCovDiag = TRUE ) ) 305s 'log Lik.' -84 (df=7) 305s > 305s > print( logLik( fitols3s ) ) 305s 'log Lik.' -62 (df=7) 305s > print( logLik( fitols3s, residCovDiag = TRUE ) ) 305s 'log Lik.' -84 (df=7) 305s > 305s > print( logLik( fitols4rs ) ) 305s 'log Lik.' -62.8 (df=6) 305s > print( logLik( fitols4rs, residCovDiag = TRUE ) ) 305s 'log Lik.' -84.1 (df=6) 305s > 305s > print( logLik( fitols5 ) ) 305s 'log Lik.' -62.8 (df=6) 305s > print( logLik( fitols5, residCovDiag = TRUE ) ) 305s 'log Lik.' -84.1 (df=6) 305s > 305s > 305s > ## ************** F tests **************** 305s > # testing first restriction 305s > print( linearHypothesis( fitols1, restrm ) ) 305s Linear hypothesis test (Theil's F test) 305s 305s Hypothesis: 305s demand_income - supply_trend = 0 305s 305s Model 1: restricted model 305s Model 2: fitols1 305s 305s Res.Df Df F Pr(>F) 305s 1 34 305s 2 33 1 0.14 0.71 305s > linearHypothesis( fitols1, restrict ) 305s Linear hypothesis test (Theil's F test) 305s 305s Hypothesis: 305s demand_income - supply_trend = 0 305s 305s Model 1: restricted model 305s Model 2: fitols1 305s 305s Res.Df Df F Pr(>F) 305s 1 34 305s 2 33 1 0.14 0.71 305s > 305s > print( linearHypothesis( fitols1s, restrm ) ) 305s Linear hypothesis test (Theil's F test) 305s 305s Hypothesis: 305s demand_income - supply_trend = 0 305s 305s Model 1: restricted model 305s Model 2: fitols1s 305s 305s Res.Df Df F Pr(>F) 305s 1 34 305s 2 33 1 0.15 0.7 305s > linearHypothesis( fitols1s, restrict ) 305s Linear hypothesis test (Theil's F test) 305s 305s Hypothesis: 305s demand_income - supply_trend = 0 305s 305s Model 1: restricted model 305s Model 2: fitols1s 305s 305s Res.Df Df F Pr(>F) 305s 1 34 305s 2 33 1 0.15 0.7 305s > 305s > print( linearHypothesis( fitols1, restrm ) ) 305s Linear hypothesis test (Theil's F test) 305s 305s Hypothesis: 305s demand_income - supply_trend = 0 305s 305s Model 1: restricted model 305s Model 2: fitols1 305s 305s Res.Df Df F Pr(>F) 305s 1 34 305s 2 33 1 0.14 0.71 305s > linearHypothesis( fitols1, restrict ) 305s Linear hypothesis test (Theil's F test) 305s 305s Hypothesis: 305s demand_income - supply_trend = 0 305s 305s Model 1: restricted model 305s Model 2: fitols1 305s 305s Res.Df Df F Pr(>F) 305s 1 34 305s 2 33 1 0.14 0.71 305s > 305s > print( linearHypothesis( fitols1r, restrm ) ) 305s Linear hypothesis test (Theil's F test) 305s 305s Hypothesis: 305s demand_income - supply_trend = 0 305s 305s Model 1: restricted model 305s Model 2: fitols1r 305s 305s Res.Df Df F Pr(>F) 305s 1 34 305s 2 33 1 0.14 0.71 305s > linearHypothesis( fitols1r, restrict ) 305s Linear hypothesis test (Theil's F test) 305s 305s Hypothesis: 305s demand_income - supply_trend = 0 305s 305s Model 1: restricted model 305s Model 2: fitols1r 305s 305s Res.Df Df F Pr(>F) 305s 1 34 305s 2 33 1 0.14 0.71 305s > 305s > # testing second restriction 305s > restrOnly2m <- matrix(0,1,7) 305s > restrOnly2q <- 0.5 305s > restrOnly2m[1,2] <- -1 305s > restrOnly2m[1,5] <- 1 305s > restrictOnly2 <- "- demand_price + supply_price = 0.5" 305s > # first restriction not imposed 305s > print( linearHypothesis( fitols1, restrOnly2m, restrOnly2q ) ) 305s Linear hypothesis test (Theil's F test) 305s 305s Hypothesis: 305s - demand_price + supply_price = 0.5 305s 305s Model 1: restricted model 305s Model 2: fitols1 305s 305s Res.Df Df F Pr(>F) 305s 1 34 305s 2 33 1 0.01 0.94 305s > linearHypothesis( fitols1, restrictOnly2 ) 305s Linear hypothesis test (Theil's F test) 305s 305s Hypothesis: 305s - demand_price + supply_price = 0.5 305s 305s Model 1: restricted model 305s Model 2: fitols1 305s 305s Res.Df Df F Pr(>F) 305s 1 34 305s 2 33 1 0.01 0.94 305s > 305s > # first restriction imposed 305s > print( linearHypothesis( fitols2, restrOnly2m, restrOnly2q ) ) 305s Linear hypothesis test (Theil's F test) 305s 305s Hypothesis: 305s - demand_price + supply_price = 0.5 305s 305s Model 1: restricted model 305s Model 2: fitols2 305s 305s Res.Df Df F Pr(>F) 305s 1 35 305s 2 34 1 0.02 0.88 305s > linearHypothesis( fitols2, restrictOnly2 ) 305s Linear hypothesis test (Theil's F test) 305s 305s Hypothesis: 305s - demand_price + supply_price = 0.5 305s 305s Model 1: restricted model 305s Model 2: fitols2 305s 305s Res.Df Df F Pr(>F) 305s 1 35 305s 2 34 1 0.02 0.88 305s > 305s > print( linearHypothesis( fitols3, restrOnly2m, restrOnly2q ) ) 305s Linear hypothesis test (Theil's F test) 305s 305s Hypothesis: 305s - demand_price + supply_price = 0.5 305s 305s Model 1: restricted model 305s Model 2: fitols3 305s 305s Res.Df Df F Pr(>F) 305s 1 35 305s 2 34 1 0.02 0.88 305s > linearHypothesis( fitols3, restrictOnly2 ) 305s Linear hypothesis test (Theil's F test) 305s 305s Hypothesis: 305s - demand_price + supply_price = 0.5 305s 305s Model 1: restricted model 305s Model 2: fitols3 305s 305s Res.Df Df F Pr(>F) 305s 1 35 305s 2 34 1 0.02 0.88 305s > 305s > # testing both of the restrictions 305s > print( linearHypothesis( fitols1, restr2m, restr2q ) ) 305s Linear hypothesis test (Theil's F test) 305s 305s Hypothesis: 305s demand_income - supply_trend = 0 305s - demand_price + supply_price = 0.5 305s 305s Model 1: restricted model 305s Model 2: fitols1 305s 305s Res.Df Df F Pr(>F) 305s 1 35 305s 2 33 2 0.08 0.93 305s > linearHypothesis( fitols1, restrict2 ) 305s Linear hypothesis test (Theil's F test) 305s 305s Hypothesis: 305s demand_income - supply_trend = 0 305s - demand_price + supply_price = 0.5 305s 305s Model 1: restricted model 305s Model 2: fitols1 305s 305s Res.Df Df F Pr(>F) 305s 1 35 305s 2 33 2 0.08 0.93 305s > 305s > 305s > ## ************** Wald tests **************** 305s > # testing first restriction 305s > print( linearHypothesis( fitols1, restrm, test = "Chisq" ) ) 305s Linear hypothesis test (Chi^2 statistic of a Wald test) 305s 305s Hypothesis: 305s demand_income - supply_trend = 0 305s 305s Model 1: restricted model 305s Model 2: fitols1 305s 305s Res.Df Df Chisq Pr(>Chisq) 305s 1 34 305s 2 33 1 0.64 0.42 305s > linearHypothesis( fitols1, restrict, test = "Chisq" ) 305s Linear hypothesis test (Chi^2 statistic of a Wald test) 305s 305s Hypothesis: 305s demand_income - supply_trend = 0 305s 305s Model 1: restricted model 305s Model 2: fitols1 305s 305s Res.Df Df Chisq Pr(>Chisq) 305s 1 34 305s 2 33 1 0.64 0.42 305s > 305s > print( linearHypothesis( fitols1s, restrm, test = "Chisq" ) ) 305s Linear hypothesis test (Chi^2 statistic of a Wald test) 305s 305s Hypothesis: 305s demand_income - supply_trend = 0 305s 305s Model 1: restricted model 305s Model 2: fitols1s 305s 305s Res.Df Df Chisq Pr(>Chisq) 305s 1 34 305s 2 33 1 0.72 0.4 305s > linearHypothesis( fitols1s, restrict, test = "Chisq" ) 305s Linear hypothesis test (Chi^2 statistic of a Wald test) 305s 305s Hypothesis: 305s demand_income - supply_trend = 0 305s 305s Model 1: restricted model 305s Model 2: fitols1s 305s 305s Res.Df Df Chisq Pr(>Chisq) 305s 1 34 305s 2 33 1 0.72 0.4 305s > 305s > print( linearHypothesis( fitols1, restrm, test = "Chisq" ) ) 305s Linear hypothesis test (Chi^2 statistic of a Wald test) 305s 305s Hypothesis: 305s demand_income - supply_trend = 0 305s 305s Model 1: restricted model 305s Model 2: fitols1 305s 305s Res.Df Df Chisq Pr(>Chisq) 305s 1 34 305s 2 33 1 0.64 0.42 305s > linearHypothesis( fitols1, restrict, test = "Chisq" ) 305s Linear hypothesis test (Chi^2 statistic of a Wald test) 305s 305s Hypothesis: 305s demand_income - supply_trend = 0 305s 305s Model 1: restricted model 305s Model 2: fitols1 305s 305s Res.Df Df Chisq Pr(>Chisq) 305s 1 34 305s 2 33 1 0.64 0.42 305s > 305s > print( linearHypothesis( fitols1r, restrm, test = "Chisq" ) ) 305s Linear hypothesis test (Chi^2 statistic of a Wald test) 305s 305s Hypothesis: 305s demand_income - supply_trend = 0 305s 305s Model 1: restricted model 305s Model 2: fitols1r 305s 305s Res.Df Df Chisq Pr(>Chisq) 305s 1 34 305s 2 33 1 0.64 0.42 305s > linearHypothesis( fitols1r, restrict, test = "Chisq" ) 305s Linear hypothesis test (Chi^2 statistic of a Wald test) 305s 305s Hypothesis: 305s demand_income - supply_trend = 0 305s 305s Model 1: restricted model 305s Model 2: fitols1r 305s 305s Res.Df Df Chisq Pr(>Chisq) 305s 1 34 305s 2 33 1 0.64 0.42 305s > 305s > # testing second restriction 305s > # first restriction not imposed 305s > print( linearHypothesis( fitols1, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 305s Linear hypothesis test (Chi^2 statistic of a Wald test) 305s 305s Hypothesis: 305s - demand_price + supply_price = 0.5 305s 305s Model 1: restricted model 305s Model 2: fitols1 305s 305s Res.Df Df Chisq Pr(>Chisq) 305s 1 34 305s 2 33 1 0.03 0.86 305s > linearHypothesis( fitols1, restrictOnly2, test = "Chisq" ) 305s Linear hypothesis test (Chi^2 statistic of a Wald test) 305s 305s Hypothesis: 305s - demand_price + supply_price = 0.5 305s 305s Model 1: restricted model 305s Model 2: fitols1 305s 305s Res.Df Df Chisq Pr(>Chisq) 305s 1 34 305s 2 33 1 0.03 0.86 305s > # first restriction imposed 305s > print( linearHypothesis( fitols2, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 305s Linear hypothesis test (Chi^2 statistic of a Wald test) 305s 305s Hypothesis: 305s - demand_price + supply_price = 0.5 305s 305s Model 1: restricted model 305s Model 2: fitols2 305s 305s Res.Df Df Chisq Pr(>Chisq) 305s 1 35 305s 2 34 1 0.12 0.73 305s > linearHypothesis( fitols2, restrictOnly2, test = "Chisq" ) 305s Linear hypothesis test (Chi^2 statistic of a Wald test) 305s 305s Hypothesis: 305s - demand_price + supply_price = 0.5 305s 305s Model 1: restricted model 305s Model 2: fitols2 305s 305s Res.Df Df Chisq Pr(>Chisq) 305s 1 35 305s 2 34 1 0.12 0.73 305s > 305s > print( linearHypothesis( fitols3, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 305s Linear hypothesis test (Chi^2 statistic of a Wald test) 305s 305s Hypothesis: 305s - demand_price + supply_price = 0.5 305s 305s Model 1: restricted model 305s Model 2: fitols3 305s 305s Res.Df Df Chisq Pr(>Chisq) 305s 1 35 305s 2 34 1 0.12 0.73 305s > linearHypothesis( fitols3, restrictOnly2, test = "Chisq" ) 305s Linear hypothesis test (Chi^2 statistic of a Wald test) 305s 305s Hypothesis: 305s - demand_price + supply_price = 0.5 305s 305s Model 1: restricted model 305s Model 2: fitols3 305s 305s Res.Df Df Chisq Pr(>Chisq) 305s 1 35 305s 2 34 1 0.12 0.73 305s > 305s > # testing both of the restrictions 305s > print( linearHypothesis( fitols1, restr2m, restr2q, test = "Chisq" ) ) 305s Linear hypothesis test (Chi^2 statistic of a Wald test) 305s 305s Hypothesis: 305s demand_income - supply_trend = 0 305s - demand_price + supply_price = 0.5 305s 305s Model 1: restricted model 305s Model 2: fitols1 305s 305s Res.Df Df Chisq Pr(>Chisq) 305s 1 35 305s 2 33 2 0.72 0.7 305s > linearHypothesis( fitols1, restrict2, test = "Chisq" ) 305s Linear hypothesis test (Chi^2 statistic of a Wald test) 305s 305s Hypothesis: 305s demand_income - supply_trend = 0 305s - demand_price + supply_price = 0.5 305s 305s Model 1: restricted model 305s Model 2: fitols1 305s 305s Res.Df Df Chisq Pr(>Chisq) 305s 1 35 305s 2 33 2 0.72 0.7 305s > 305s > 305s > ## ****************** model frame ************************** 305s > print( mf <- model.frame( fitols1 ) ) 305s consump price income farmPrice trend 305s 1 98.5 100.3 87.4 98.0 1 305s 2 99.2 104.3 97.6 99.1 2 305s 3 102.2 103.4 96.7 99.1 3 305s 4 101.5 104.5 98.2 98.1 4 305s 5 104.2 98.0 99.8 110.8 5 305s 6 103.2 99.5 100.5 108.2 6 305s 7 104.0 101.1 103.2 105.6 7 305s 8 99.9 104.8 107.8 109.8 8 305s 9 100.3 96.4 96.6 108.7 9 305s 10 102.8 91.2 88.9 100.6 10 305s 11 95.4 93.1 75.1 81.0 11 305s 12 92.4 98.8 76.9 68.6 12 305s 13 94.5 102.9 84.6 70.9 13 305s 14 98.8 98.8 90.6 81.4 14 305s 15 105.8 95.1 103.1 102.3 15 305s 16 100.2 98.5 105.1 105.0 16 305s 17 103.5 86.5 96.4 110.5 17 305s 18 99.9 104.0 104.4 92.5 18 305s 19 105.2 105.8 110.7 89.3 19 305s 20 106.2 113.5 127.1 93.0 20 305s > print( mf1 <- model.frame( fitols1$eq[[ 1 ]] ) ) 305s consump price income 305s 1 98.5 100.3 87.4 305s 2 99.2 104.3 97.6 305s 3 102.2 103.4 96.7 305s 4 101.5 104.5 98.2 305s 5 104.2 98.0 99.8 305s 6 103.2 99.5 100.5 305s 7 104.0 101.1 103.2 305s 8 99.9 104.8 107.8 305s 9 100.3 96.4 96.6 305s 10 102.8 91.2 88.9 305s 11 95.4 93.1 75.1 305s 12 92.4 98.8 76.9 305s 13 94.5 102.9 84.6 305s 14 98.8 98.8 90.6 305s 15 105.8 95.1 103.1 305s 16 100.2 98.5 105.1 305s 17 103.5 86.5 96.4 305s 18 99.9 104.0 104.4 305s 19 105.2 105.8 110.7 305s 20 106.2 113.5 127.1 305s > print( attributes( mf1 )$terms ) 305s consump ~ price + income 305s attr(,"variables") 305s list(consump, price, income) 305s attr(,"factors") 305s price income 305s consump 0 0 305s price 1 0 305s income 0 1 305s attr(,"term.labels") 305s [1] "price" "income" 305s attr(,"order") 305s [1] 1 1 305s attr(,"intercept") 305s [1] 1 305s attr(,"response") 305s [1] 1 305s attr(,".Environment") 305s 305s attr(,"predvars") 305s list(consump, price, income) 305s attr(,"dataClasses") 305s consump price income 305s "numeric" "numeric" "numeric" 305s > print( mf2 <- model.frame( fitols1$eq[[ 2 ]] ) ) 305s consump price farmPrice trend 305s 1 98.5 100.3 98.0 1 305s 2 99.2 104.3 99.1 2 305s 3 102.2 103.4 99.1 3 305s 4 101.5 104.5 98.1 4 305s 5 104.2 98.0 110.8 5 305s 6 103.2 99.5 108.2 6 305s 7 104.0 101.1 105.6 7 305s 8 99.9 104.8 109.8 8 305s 9 100.3 96.4 108.7 9 305s 10 102.8 91.2 100.6 10 305s 11 95.4 93.1 81.0 11 305s 12 92.4 98.8 68.6 12 305s 13 94.5 102.9 70.9 13 305s 14 98.8 98.8 81.4 14 305s 15 105.8 95.1 102.3 15 305s 16 100.2 98.5 105.0 16 305s 17 103.5 86.5 110.5 17 305s 18 99.9 104.0 92.5 18 305s 19 105.2 105.8 89.3 19 305s 20 106.2 113.5 93.0 20 305s > print( attributes( mf2 )$terms ) 305s consump ~ price + farmPrice + trend 305s attr(,"variables") 305s list(consump, price, farmPrice, trend) 305s attr(,"factors") 305s price farmPrice trend 305s consump 0 0 0 305s price 1 0 0 305s farmPrice 0 1 0 305s trend 0 0 1 305s attr(,"term.labels") 305s [1] "price" "farmPrice" "trend" 305s attr(,"order") 305s [1] 1 1 1 305s attr(,"intercept") 305s [1] 1 305s attr(,"response") 305s [1] 1 305s attr(,".Environment") 305s 305s attr(,"predvars") 305s list(consump, price, farmPrice, trend) 305s attr(,"dataClasses") 305s consump price farmPrice trend 305s "numeric" "numeric" "numeric" "numeric" 305s > 305s > print( all.equal( mf, model.frame( fitols2r ) ) ) 305s [1] TRUE 305s > print( all.equal( mf1, model.frame( fitols2r$eq[[ 1 ]] ) ) ) 305s [1] TRUE 305s > 305s > print( all.equal( mf, model.frame( fitols3s ) ) ) 305s [1] TRUE 305s > print( all.equal( mf2, model.frame( fitols3s$eq[[ 2 ]] ) ) ) 305s [1] TRUE 305s > 305s > print( all.equal( mf, model.frame( fitols4rs ) ) ) 305s [1] TRUE 305s > print( all.equal( mf1, model.frame( fitols4rs$eq[[ 1 ]] ) ) ) 305s [1] TRUE 305s > 305s > print( all.equal( mf, model.frame( fitols5 ) ) ) 305s [1] TRUE 305s > print( all.equal( mf2, model.frame( fitols5$eq[[ 2 ]] ) ) ) 305s [1] TRUE 305s > 305s > 305s > ## **************** model matrix ************************ 305s > # with x (returnModelMatrix) = TRUE 305s > print( !is.null( fitols1r$eq[[ 1 ]]$x ) ) 305s [1] TRUE 305s > print( mm <- model.matrix( fitols1r ) ) 305s demand_(Intercept) demand_price demand_income supply_(Intercept) 305s demand_1 1 100.3 87.4 0 305s demand_2 1 104.3 97.6 0 305s demand_3 1 103.4 96.7 0 305s demand_4 1 104.5 98.2 0 305s demand_5 1 98.0 99.8 0 305s demand_6 1 99.5 100.5 0 305s demand_7 1 101.1 103.2 0 305s demand_8 1 104.8 107.8 0 305s demand_9 1 96.4 96.6 0 305s demand_10 1 91.2 88.9 0 305s demand_11 1 93.1 75.1 0 305s demand_12 1 98.8 76.9 0 305s demand_13 1 102.9 84.6 0 305s demand_14 1 98.8 90.6 0 305s demand_15 1 95.1 103.1 0 305s demand_16 1 98.5 105.1 0 305s demand_17 1 86.5 96.4 0 305s demand_18 1 104.0 104.4 0 305s demand_19 1 105.8 110.7 0 305s demand_20 1 113.5 127.1 0 305s supply_1 0 0.0 0.0 1 305s supply_2 0 0.0 0.0 1 305s supply_3 0 0.0 0.0 1 305s supply_4 0 0.0 0.0 1 305s supply_5 0 0.0 0.0 1 305s supply_6 0 0.0 0.0 1 305s supply_7 0 0.0 0.0 1 305s supply_8 0 0.0 0.0 1 305s supply_9 0 0.0 0.0 1 305s supply_10 0 0.0 0.0 1 305s supply_11 0 0.0 0.0 1 305s supply_12 0 0.0 0.0 1 305s supply_13 0 0.0 0.0 1 305s supply_14 0 0.0 0.0 1 305s supply_15 0 0.0 0.0 1 305s supply_16 0 0.0 0.0 1 305s supply_17 0 0.0 0.0 1 305s supply_18 0 0.0 0.0 1 305s supply_19 0 0.0 0.0 1 305s supply_20 0 0.0 0.0 1 305s supply_price supply_farmPrice supply_trend 305s demand_1 0.0 0.0 0 305s demand_2 0.0 0.0 0 305s demand_3 0.0 0.0 0 305s demand_4 0.0 0.0 0 305s demand_5 0.0 0.0 0 305s demand_6 0.0 0.0 0 305s demand_7 0.0 0.0 0 305s demand_8 0.0 0.0 0 305s demand_9 0.0 0.0 0 305s demand_10 0.0 0.0 0 305s demand_11 0.0 0.0 0 305s demand_12 0.0 0.0 0 305s demand_13 0.0 0.0 0 305s demand_14 0.0 0.0 0 305s demand_15 0.0 0.0 0 305s demand_16 0.0 0.0 0 305s demand_17 0.0 0.0 0 305s demand_18 0.0 0.0 0 305s demand_19 0.0 0.0 0 305s demand_20 0.0 0.0 0 305s supply_1 100.3 98.0 1 305s supply_2 104.3 99.1 2 305s supply_3 103.4 99.1 3 305s supply_4 104.5 98.1 4 305s supply_5 98.0 110.8 5 305s supply_6 99.5 108.2 6 305s supply_7 101.1 105.6 7 305s supply_8 104.8 109.8 8 305s supply_9 96.4 108.7 9 305s supply_10 91.2 100.6 10 305s supply_11 93.1 81.0 11 305s supply_12 98.8 68.6 12 305s supply_13 102.9 70.9 13 305s supply_14 98.8 81.4 14 305s supply_15 95.1 102.3 15 305s supply_16 98.5 105.0 16 305s supply_17 86.5 110.5 17 305s supply_18 104.0 92.5 18 305s supply_19 105.8 89.3 19 305s supply_20 113.5 93.0 20 305s > print( mm1 <- model.matrix( fitols1r$eq[[ 1 ]] ) ) 305s (Intercept) price income 305s 1 1 100.3 87.4 305s 2 1 104.3 97.6 305s 3 1 103.4 96.7 305s 4 1 104.5 98.2 305s 5 1 98.0 99.8 305s 6 1 99.5 100.5 305s 7 1 101.1 103.2 305s 8 1 104.8 107.8 305s 9 1 96.4 96.6 305s 10 1 91.2 88.9 305s 11 1 93.1 75.1 305s 12 1 98.8 76.9 305s 13 1 102.9 84.6 305s 14 1 98.8 90.6 305s 15 1 95.1 103.1 305s 16 1 98.5 105.1 305s 17 1 86.5 96.4 305s 18 1 104.0 104.4 305s 19 1 105.8 110.7 305s 20 1 113.5 127.1 305s attr(,"assign") 305s [1] 0 1 2 305s > print( mm2 <- model.matrix( fitols1r$eq[[ 2 ]] ) ) 305s (Intercept) price farmPrice trend 305s 1 1 100.3 98.0 1 305s 2 1 104.3 99.1 2 305s 3 1 103.4 99.1 3 305s 4 1 104.5 98.1 4 305s 5 1 98.0 110.8 5 305s 6 1 99.5 108.2 6 305s 7 1 101.1 105.6 7 305s 8 1 104.8 109.8 8 305s 9 1 96.4 108.7 9 305s 10 1 91.2 100.6 10 305s 11 1 93.1 81.0 11 305s 12 1 98.8 68.6 12 305s 13 1 102.9 70.9 13 305s 14 1 98.8 81.4 14 305s 15 1 95.1 102.3 15 305s 16 1 98.5 105.0 16 305s 17 1 86.5 110.5 17 305s 18 1 104.0 92.5 18 305s 19 1 105.8 89.3 19 305s 20 1 113.5 93.0 20 305s attr(,"assign") 305s [1] 0 1 2 3 305s > 305s > # with x (returnModelMatrix) = FALSE 305s > print( all.equal( mm, model.matrix( fitols1rs ) ) ) 305s [1] TRUE 305s > print( all.equal( mm1, model.matrix( fitols1rs$eq[[ 1 ]] ) ) ) 305s [1] TRUE 305s > print( all.equal( mm2, model.matrix( fitols1rs$eq[[ 2 ]] ) ) ) 305s [1] TRUE 305s > print( !is.null( fitols1rs$eq[[ 1 ]]$x ) ) 305s [1] FALSE 305s > 305s > # with x (returnModelMatrix) = TRUE 305s > print( !is.null( fitols2rs$eq[[ 1 ]]$x ) ) 305s [1] TRUE 305s > print( all.equal( mm, model.matrix( fitols2rs ) ) ) 305s [1] TRUE 305s > print( all.equal( mm1, model.matrix( fitols2rs$eq[[ 1 ]] ) ) ) 305s [1] TRUE 305s > print( all.equal( mm2, model.matrix( fitols2rs$eq[[ 2 ]] ) ) ) 305s [1] TRUE 305s > 305s > # with x (returnModelMatrix) = FALSE 305s > print( all.equal( mm, model.matrix( fitols2 ) ) ) 305s [1] TRUE 305s > print( all.equal( mm1, model.matrix( fitols2$eq[[ 1 ]] ) ) ) 305s [1] TRUE 305s > print( all.equal( mm2, model.matrix( fitols2$eq[[ 2 ]] ) ) ) 305s [1] TRUE 305s > print( !is.null( fitols2$eq[[ 1 ]]$x ) ) 305s [1] FALSE 305s > 305s > # with x (returnModelMatrix) = TRUE 305s > print( !is.null( fitols3$eq[[ 1 ]]$x ) ) 305s [1] TRUE 305s > print( all.equal( mm, model.matrix( fitols3 ) ) ) 305s [1] TRUE 305s > print( all.equal( mm1, model.matrix( fitols3$eq[[ 1 ]] ) ) ) 305s [1] TRUE 305s > print( all.equal( mm2, model.matrix( fitols3$eq[[ 2 ]] ) ) ) 305s [1] TRUE 305s > 305s > # with x (returnModelMatrix) = FALSE 305s > print( all.equal( mm, model.matrix( fitols3r ) ) ) 305s [1] TRUE 305s > print( all.equal( mm1, model.matrix( fitols3r$eq[[ 1 ]] ) ) ) 305s [1] TRUE 305s > print( all.equal( mm2, model.matrix( fitols3r$eq[[ 2 ]] ) ) ) 305s [1] TRUE 305s > print( !is.null( fitols3r$eq[[ 1 ]]$x ) ) 305s [1] FALSE 305s > 305s > # with x (returnModelMatrix) = TRUE 305s > print( !is.null( fitols4s$eq[[ 1 ]]$x ) ) 305s [1] TRUE 305s > print( all.equal( mm, model.matrix( fitols4s ) ) ) 305s [1] TRUE 305s > print( all.equal( mm1, model.matrix( fitols4s$eq[[ 1 ]] ) ) ) 305s [1] TRUE 305s > print( all.equal( mm2, model.matrix( fitols4s$eq[[ 2 ]] ) ) ) 305s [1] TRUE 305s > 305s > # with x (returnModelMatrix) = FALSE 305s > print( all.equal( mm, model.matrix( fitols4Sym ) ) ) 305s [1] TRUE 305s > print( all.equal( mm1, model.matrix( fitols4Sym$eq[[ 1 ]] ) ) ) 305s [1] TRUE 305s > print( all.equal( mm2, model.matrix( fitols4Sym$eq[[ 2 ]] ) ) ) 305s [1] TRUE 305s > print( !is.null( fitols4Sym$eq[[ 1 ]]$x ) ) 305s [1] FALSE 305s > 305s > # with x (returnModelMatrix) = TRUE 305s > print( !is.null( fitols5s$eq[[ 1 ]]$x ) ) 305s [1] TRUE 305s > print( all.equal( mm, model.matrix( fitols5s ) ) ) 305s [1] TRUE 305s > print( all.equal( mm1, model.matrix( fitols5s$eq[[ 1 ]] ) ) ) 305s [1] TRUE 305s > print( all.equal( mm2, model.matrix( fitols5s$eq[[ 2 ]] ) ) ) 305s [1] TRUE 305s > 305s > # with x (returnModelMatrix) = FALSE 305s > print( all.equal( mm, model.matrix( fitols5 ) ) ) 305s [1] TRUE 305s > print( all.equal( mm1, model.matrix( fitols5$eq[[ 1 ]] ) ) ) 305s [1] TRUE 305s > print( all.equal( mm2, model.matrix( fitols5$eq[[ 2 ]] ) ) ) 305s [1] TRUE 305s > print( !is.null( fitols5$eq[[ 1 ]]$x ) ) 305s [1] FALSE 305s > 305s > try( model.matrix( fitols1, which = "z" ) ) 305s > 305s > 305s > ## **************** formulas ************************ 305s > formula( fitols1 ) 305s $demand 305s consump ~ price + income 305s 305s $supply 305s consump ~ price + farmPrice + trend 305s 305s > formula( fitols1$eq[[ 2 ]] ) 305s consump ~ price + farmPrice + trend 305s > 305s > formula( fitols2r ) 305s $demand 305s consump ~ price + income 305s 305s $supply 305s consump ~ price + farmPrice + trend 305s 305s > formula( fitols2r$eq[[ 1 ]] ) 305s consump ~ price + income 305s > 305s > formula( fitols3s ) 305s $demand 305s consump ~ price + income 305s 305s $supply 305s consump ~ price + farmPrice + trend 305s 305s > formula( fitols3s$eq[[ 2 ]] ) 305s consump ~ price + farmPrice + trend 305s > 305s > formula( fitols4rs ) 305s $demand 305s consump ~ price + income 305s 305s $supply 305s consump ~ price + farmPrice + trend 305s 305s > formula( fitols4rs$eq[[ 1 ]] ) 305s consump ~ price + income 305s > 305s > formula( fitols5 ) 305s $demand 305s consump ~ price + income 305s 305s $supply 305s consump ~ price + farmPrice + trend 305s 305s > formula( fitols5$eq[[ 2 ]] ) 305s consump ~ price + farmPrice + trend 305s > 305s > 305s > ## **************** model terms ******************* 305s > terms( fitols1 ) 305s $demand 305s consump ~ price + income 305s attr(,"variables") 305s list(consump, price, income) 305s attr(,"factors") 305s price income 305s consump 0 0 305s price 1 0 305s income 0 1 305s attr(,"term.labels") 305s [1] "price" "income" 305s attr(,"order") 305s [1] 1 1 305s attr(,"intercept") 305s [1] 1 305s attr(,"response") 305s [1] 1 305s attr(,".Environment") 305s 305s attr(,"predvars") 305s list(consump, price, income) 305s attr(,"dataClasses") 305s consump price income 305s "numeric" "numeric" "numeric" 305s 305s $supply 305s consump ~ price + farmPrice + trend 305s attr(,"variables") 305s list(consump, price, farmPrice, trend) 305s attr(,"factors") 305s price farmPrice trend 305s consump 0 0 0 305s price 1 0 0 305s farmPrice 0 1 0 305s trend 0 0 1 305s attr(,"term.labels") 305s [1] "price" "farmPrice" "trend" 305s attr(,"order") 305s [1] 1 1 1 305s attr(,"intercept") 305s [1] 1 305s attr(,"response") 305s [1] 1 305s attr(,".Environment") 305s 305s attr(,"predvars") 305s list(consump, price, farmPrice, trend) 305s attr(,"dataClasses") 305s consump price farmPrice trend 305s "numeric" "numeric" "numeric" "numeric" 305s 305s > terms( fitols1$eq[[ 2 ]] ) 305s consump ~ price + farmPrice + trend 305s attr(,"variables") 305s list(consump, price, farmPrice, trend) 305s attr(,"factors") 305s price farmPrice trend 305s consump 0 0 0 305s price 1 0 0 305s farmPrice 0 1 0 305s trend 0 0 1 305s attr(,"term.labels") 305s [1] "price" "farmPrice" "trend" 305s attr(,"order") 305s [1] 1 1 1 305s attr(,"intercept") 305s [1] 1 305s attr(,"response") 305s [1] 1 305s attr(,".Environment") 305s 305s attr(,"predvars") 305s list(consump, price, farmPrice, trend) 305s attr(,"dataClasses") 305s consump price farmPrice trend 305s "numeric" "numeric" "numeric" "numeric" 305s > 305s > terms( fitols2r ) 305s $demand 305s consump ~ price + income 305s attr(,"variables") 305s list(consump, price, income) 305s attr(,"factors") 305s price income 305s consump 0 0 305s price 1 0 305s income 0 1 305s attr(,"term.labels") 305s [1] "price" "income" 305s attr(,"order") 305s [1] 1 1 305s attr(,"intercept") 305s [1] 1 305s attr(,"response") 305s [1] 1 305s attr(,".Environment") 305s 305s attr(,"predvars") 305s list(consump, price, income) 305s attr(,"dataClasses") 305s consump price income 305s "numeric" "numeric" "numeric" 305s 305s $supply 305s consump ~ price + farmPrice + trend 305s attr(,"variables") 305s list(consump, price, farmPrice, trend) 305s attr(,"factors") 305s price farmPrice trend 305s consump 0 0 0 305s price 1 0 0 305s farmPrice 0 1 0 305s trend 0 0 1 305s attr(,"term.labels") 305s [1] "price" "farmPrice" "trend" 305s attr(,"order") 305s [1] 1 1 1 305s attr(,"intercept") 305s [1] 1 305s attr(,"response") 305s [1] 1 305s attr(,".Environment") 305s 305s attr(,"predvars") 305s list(consump, price, farmPrice, trend) 305s attr(,"dataClasses") 305s consump price farmPrice trend 305s "numeric" "numeric" "numeric" "numeric" 305s 305s > terms( fitols2r$eq[[ 1 ]] ) 305s consump ~ price + income 305s attr(,"variables") 305s list(consump, price, income) 305s attr(,"factors") 305s price income 305s consump 0 0 305s price 1 0 305s income 0 1 305s attr(,"term.labels") 305s [1] "price" "income" 305s attr(,"order") 305s [1] 1 1 305s attr(,"intercept") 305s [1] 1 305s attr(,"response") 305s [1] 1 305s attr(,".Environment") 305s 305s attr(,"predvars") 305s list(consump, price, income) 305s attr(,"dataClasses") 305s consump price income 305s "numeric" "numeric" "numeric" 305s > 305s > terms( fitols3s ) 305s $demand 305s consump ~ price + income 305s attr(,"variables") 305s list(consump, price, income) 305s attr(,"factors") 305s price income 305s consump 0 0 305s price 1 0 305s income 0 1 305s attr(,"term.labels") 305s [1] "price" "income" 305s attr(,"order") 305s [1] 1 1 305s attr(,"intercept") 305s [1] 1 305s attr(,"response") 305s [1] 1 305s attr(,".Environment") 305s 305s attr(,"predvars") 305s list(consump, price, income) 305s attr(,"dataClasses") 305s consump price income 305s "numeric" "numeric" "numeric" 305s 305s $supply 305s consump ~ price + farmPrice + trend 305s attr(,"variables") 305s list(consump, price, farmPrice, trend) 305s attr(,"factors") 305s price farmPrice trend 305s consump 0 0 0 305s price 1 0 0 305s farmPrice 0 1 0 305s trend 0 0 1Error in model.matrix.systemfit.equation(object$eq[[i]], which = which) : 305s argument 'which' can only be set to "xHat" or "z" if instruments were used 305s 305s attr(,"term.labels") 305s [1] "price" "farmPrice" "trend" 305s attr(,"order") 305s [1] 1 1 1 305s attr(,"intercept") 305s [1] 1 305s attr(,"response") 305s [1] 1 305s attr(,".Environment") 305s 305s attr(,"predvars") 305s list(consump, price, farmPrice, trend) 305s attr(,"dataClasses") 305s consump price farmPrice trend 305s "numeric" "numeric" "numeric" "numeric" 305s 305s > terms( fitols3s$eq[[ 2 ]] ) 305s consump ~ price + farmPrice + trend 305s attr(,"variables") 305s list(consump, price, farmPrice, trend) 305s attr(,"factors") 305s price farmPrice trend 305s consump 0 0 0 305s price 1 0 0 305s farmPrice 0 1 0 305s trend 0 0 1 305s attr(,"term.labels") 305s [1] "price" "farmPrice" "trend" 305s attr(,"order") 305s [1] 1 1 1 305s attr(,"intercept") 305s [1] 1 305s attr(,"response") 305s [1] 1 305s attr(,".Environment") 305s 305s attr(,"predvars") 305s list(consump, price, farmPrice, trend) 305s attr(,"dataClasses") 305s consump price farmPrice trend 305s "numeric" "numeric" "numeric" "numeric" 305s > 305s > terms( fitols4rs ) 305s $demand 305s consump ~ price + income 305s attr(,"variables") 305s list(consump, price, income) 305s attr(,"factors") 305s price income 305s consump 0 0 305s price 1 0 305s income 0 1 305s attr(,"term.labels") 305s [1] "price" "income" 305s attr(,"order") 305s [1] 1 1 305s attr(,"intercept") 305s [1] 1 305s attr(,"response") 305s [1] 1 305s attr(,".Environment") 305s 305s attr(,"predvars") 305s list(consump, price, income) 305s attr(,"dataClasses") 305s consump price income 305s "numeric" "numeric" "numeric" 305s 305s $supply 305s consump ~ price + farmPrice + trend 305s attr(,"variables") 305s list(consump, price, farmPrice, trend) 305s attr(,"factors") 305s price farmPrice trend 305s consump 0 0 0 305s price 1 0 0 305s farmPrice 0 1 0 305s trend 0 0 1 305s attr(,"term.labels") 305s [1] "price" "farmPrice" "trend" 305s attr(,"order") 305s [1] 1 1 1 305s attr(,"intercept") 305s [1] 1 305s attr(,"response") 305s [1] 1 305s attr(,".Environment") 305s 305s attr(,"predvars") 305s list(consump, price, farmPrice, trend) 305s attr(,"dataClasses") 305s consump price farmPrice trend 305s "numeric" "numeric" "numeric" "numeric" 305s 305s > terms( fitols4rs$eq[[ 1 ]] ) 305s consump ~ price + income 305s attr(,"variables") 305s list(consump, price, income) 305s attr(,"factors") 305s price income 305s consump 0 0 305s price 1 0 305s income 0 1 305s attr(,"term.labels") 305s [1] "price" "income" 305s attr(,"order") 305s [1] 1 1 305s attr(,"intercept") 305s [1] 1 305s attr(,"response") 305s [1] 1 305s attr(,".Environment") 305s 305s attr(,"predvars") 305s list(consump, price, income) 305s attr(,"dataClasses") 305s consump price income 305s "numeric" "numeric" "numeric" 305s > 305s > terms( fitols5 ) 305s $demand 305s consump ~ price + income 305s attr(,"variables") 305s list(consump, price, income) 305s attr(,"factors") 305s price income 305s consump 0 0 305s price 1 0 305s income 0 1 305s attr(,"term.labels") 305s [1] "price" "income" 305s attr(,"order") 305s [1] 1 1 305s attr(,"intercept") 305s [1] 1 305s attr(,"response") 305s [1] 1 305s attr(,".Environment") 305s 305s attr(,"predvars") 305s list(consump, price, income) 305s attr(,"dataClasses") 305s consump price income 305s "numeric" "numeric" "numeric" 305s 305s $supply 305s consump ~ price + farmPrice + trend 305s attr(,"variables") 305s list(consump, price, farmPrice, trend) 305s attr(,"factors") 305s price farmPrice trend 305s consump 0 0 0 305s price 1 0 0 305s farmPrice 0 1 0 305s trend 0 0 1 305s attr(,"term.labels") 305s [1] "price" "farmPrice" "trend" 305s attr(,"order") 305s [1] 1 1 1 305s attr(,"intercept") 305s [1] 1 305s attr(,"response") 305s [1] 1 305s attr(,".Environment") 305s 305s attr(,"predvars") 305s list(consump, price, farmPrice, trend) 305s attr(,"dataClasses") 305s consump price farmPrice trend 305s "numeric" "numeric" "numeric" "numeric" 305s 305s > terms( fitols5$eq[[ 2 ]] ) 305s consump ~ price + farmPrice + trend 305s attr(,"variables") 305s list(consump, price, farmPrice, trend) 305s attr(,"factors") 305s price farmPrice trend 305s consump 0 0 0 305s price 1 0 0 305s farmPrice 0 1 0 305s trend 0 0 1 305s attr(,"term.labels") 305s [1] "price" "farmPrice" "trend" 305s attr(,"order") 305s [1] 1 1 1 305s attr(,"intercept") 305s [1] 1 305s attr(,"response") 305s [1] 1 305s attr(,".Environment") 305s 305s attr(,"predvars") 305s list(consump, price, farmPrice, trend) 305s attr(,"dataClasses") 305s consump price farmPrice trend 305s "numeric" "numeric" "numeric" "numeric" 305s > 305s > 305s > ## **************** estfun ************************ 305s > library( "sandwich" ) 305s > 305s > estfun( fitols1 ) 305s demand_(Intercept) demand_price demand_income supply_(Intercept) 305s demand_1 1.074 107.8 93.9 0.000 305s demand_2 -0.390 -40.7 -38.1 0.000 305s demand_3 2.625 271.5 253.8 0.000 305s demand_4 1.802 188.4 177.0 0.000 305s demand_5 1.946 190.7 194.2 0.000 305s demand_6 1.175 116.8 118.0 0.000 305s demand_7 1.530 154.7 157.9 0.000 305s demand_8 -2.933 -307.2 -316.1 0.000 305s demand_9 -1.365 -131.7 -131.9 0.000 305s demand_10 2.031 185.3 180.5 0.000 305s demand_11 -0.149 -13.9 -11.2 0.000 305s demand_12 -1.954 -193.1 -150.3 0.000 305s demand_13 -1.121 -115.4 -94.8 0.000 305s demand_14 -0.220 -21.7 -19.9 0.000 305s demand_15 1.487 141.4 153.3 0.000 305s demand_16 -3.701 -364.3 -388.9 0.000 305s demand_17 -1.273 -110.1 -122.7 0.000 305s demand_18 -2.002 -208.3 -209.0 0.000 305s demand_19 1.738 183.8 192.4 0.000 305s demand_20 -0.299 -33.9 -38.0 0.000 305s supply_1 0.000 0.0 0.0 -0.444 305s supply_2 0.000 0.0 0.0 -0.896 305s supply_3 0.000 0.0 0.0 1.965 305s supply_4 0.000 0.0 0.0 1.134 305s supply_5 0.000 0.0 0.0 1.514 305s supply_6 0.000 0.0 0.0 0.680 305s supply_7 0.000 0.0 0.0 1.569 305s supply_8 0.000 0.0 0.0 -4.407 305s supply_9 0.000 0.0 0.0 -2.599 305s supply_10 0.000 0.0 0.0 2.469 305s supply_11 0.000 0.0 0.0 -0.598 305s supply_12 0.000 0.0 0.0 -1.697 305s supply_13 0.000 0.0 0.0 -1.064 305s supply_14 0.000 0.0 0.0 0.970 305s supply_15 0.000 0.0 0.0 3.159 305s supply_16 0.000 0.0 0.0 -3.866 305s supply_17 0.000 0.0 0.0 -0.265 305s supply_18 0.000 0.0 0.0 -2.449 305s supply_19 0.000 0.0 0.0 3.110 305s supply_20 0.000 0.0 0.0 1.714 305s supply_price supply_farmPrice supply_trend 305s demand_1 0.0 0.0 0.000 305s demand_2 0.0 0.0 0.000 305s demand_3 0.0 0.0 0.000 305s demand_4 0.0 0.0 0.000 305s demand_5 0.0 0.0 0.000 305s demand_6 0.0 0.0 0.000 305s demand_7 0.0 0.0 0.000 305s demand_8 0.0 0.0 0.000 305s demand_9 0.0 0.0 0.000 305s demand_10 0.0 0.0 0.000 305s demand_11 0.0 0.0 0.000 305s demand_12 0.0 0.0 0.000 305s demand_13 0.0 0.0 0.000 305s demand_14 0.0 0.0 0.000 305s demand_15 0.0 0.0 0.000 305s demand_16 0.0 0.0 0.000 305s demand_17 0.0 0.0 0.000 305s demand_18 0.0 0.0 0.000 305s demand_19 0.0 0.0 0.000 305s demand_20 0.0 0.0 0.000 305s supply_1 -44.6 -43.5 -0.444 305s supply_2 -93.4 -88.7 -1.791 305s supply_3 203.3 194.7 5.895 305s supply_4 118.5 111.3 4.537 305s supply_5 148.4 167.7 7.569 305s supply_6 67.7 73.6 4.082 305s supply_7 158.6 165.7 10.983 305s supply_8 -461.7 -483.9 -35.259 305s supply_9 -250.7 -282.5 -23.391 305s supply_10 225.3 248.4 24.694 305s supply_11 -55.7 -48.5 -6.581 305s supply_12 -167.7 -116.4 -20.369 305s supply_13 -109.5 -75.4 -13.832 305s supply_14 95.8 79.0 13.582 305s supply_15 300.5 323.2 47.386 305s supply_16 -380.6 -405.9 -61.848 305s supply_17 -22.9 -29.2 -4.500 305s supply_18 -254.7 -226.5 -44.080 305s supply_19 328.9 277.7 59.084 305s supply_20 194.5 159.4 34.282 305s > round( colSums( estfun( fitols1 ) ), digits = 7 ) 305s demand_(Intercept) demand_price demand_income supply_(Intercept) 305s 0 0 0 0 305s supply_price supply_farmPrice supply_trend 305s 0 0 0 305s > 305s > estfun( fitols1s ) 305s demand_(Intercept) demand_price demand_income supply_(Intercept) 305s demand_1 1.074 107.8 93.9 0.000 305s demand_2 -0.390 -40.7 -38.1 0.000 305s demand_3 2.625 271.5 253.8 0.000 305s demand_4 1.802 188.4 177.0 0.000 305s demand_5 1.946 190.7 194.2 0.000 305s demand_6 1.175 116.8 118.0 0.000 305s demand_7 1.530 154.7 157.9 0.000 305s demand_8 -2.933 -307.2 -316.1 0.000 305s demand_9 -1.365 -131.7 -131.9 0.000 305s demand_10 2.031 185.3 180.5 0.000 305s demand_11 -0.149 -13.9 -11.2 0.000 305s demand_12 -1.954 -193.1 -150.3 0.000 305s demand_13 -1.121 -115.4 -94.8 0.000 305s demand_14 -0.220 -21.7 -19.9 0.000 305s demand_15 1.487 141.4 153.3 0.000 305s demand_16 -3.701 -364.3 -388.9 0.000 305s demand_17 -1.273 -110.1 -122.7 0.000 305s demand_18 -2.002 -208.3 -209.0 0.000 305s demand_19 1.738 183.8 192.4 0.000 305s demand_20 -0.299 -33.9 -38.0 0.000 305s supply_1 0.000 0.0 0.0 -0.444 305s supply_2 0.000 0.0 0.0 -0.896 305s supply_3 0.000 0.0 0.0 1.965 305s supply_4 0.000 0.0 0.0 1.134 305s supply_5 0.000 0.0 0.0 1.514 305s supply_6 0.000 0.0 0.0 0.680 305s supply_7 0.000 0.0 0.0 1.569 305s supply_8 0.000 0.0 0.0 -4.407 305s supply_9 0.000 0.0 0.0 -2.599 305s supply_10 0.000 0.0 0.0 2.469 305s supply_11 0.000 0.0 0.0 -0.598 305s supply_12 0.000 0.0 0.0 -1.697 305s supply_13 0.000 0.0 0.0 -1.064 305s supply_14 0.000 0.0 0.0 0.970 305s supply_15 0.000 0.0 0.0 3.159 305s supply_16 0.000 0.0 0.0 -3.866 305s supply_17 0.000 0.0 0.0 -0.265 305s supply_18 0.000 0.0 0.0 -2.449 305s supply_19 0.000 0.0 0.0 3.110 305s supply_20 0.000 0.0 0.0 1.714 305s supply_price supply_farmPrice supply_trend 305s demand_1 0.0 0.0 0.000 305s demand_2 0.0 0.0 0.000 305s demand_3 0.0 0.0 0.000 305s demand_4 0.0 0.0 0.000 305s demand_5 0.0 0.0 0.000 305s demand_6 0.0 0.0 0.000 305s demand_7 0.0 0.0 0.000 305s demand_8 0.0 0.0 0.000 305s demand_9 0.0 0.0 0.000 305s demand_10 0.0 0.0 0.000 305s demand_11 0.0 0.0 0.000 305s demand_12 0.0 0.0 0.000 306s demand_13 0.0 0.0 0.000 306s demand_14 0.0 0.0 0.000 306s demand_15 0.0 0.0 0.000 306s demand_16 0.0 0.0 0.000 306s demand_17 0.0 0.0 0.000 306s demand_18 0.0 0.0 0.000 306s demand_19 0.0 0.0 0.000 306s demand_20 0.0 0.0 0.000 306s supply_1 -44.6 -43.5 -0.444 306s supply_2 -93.4 -88.7 -1.791 306s supply_3 203.3 194.7 5.895 306s supply_4 118.5 111.3 4.537 306s supply_5 148.4 167.7 7.569 306s supply_6 67.7 73.6 4.082 306s supply_7 158.6 165.7 10.983 306s supply_8 -461.7 -483.9 -35.259 306s supply_9 -250.7 -282.5 -23.391 306s supply_10 225.3 248.4 24.694 306s supply_11 -55.7 -48.5 -6.581 306s supply_12 -167.7 -116.4 -20.369 306s supply_13 -109.5 -75.4 -13.832 306s supply_14 95.8 79.0 13.582 306s supply_15 300.5 323.2 47.386 306s supply_16 -380.6 -405.9 -61.848 306s supply_17 -22.9 -29.2 -4.500 306s supply_18 -254.7 -226.5 -44.080 306s supply_19 328.9 277.7 59.084 306s supply_20 194.5 159.4 34.282 306s > round( colSums( estfun( fitols1s ) ), digits = 7 ) 306s demand_(Intercept) demand_price demand_income supply_(Intercept) 306s 0 0 0 0 306s supply_price supply_farmPrice supply_trend 306s 0 0 0 306s > 306s > estfun( fitols1r ) 306s demand_(Intercept) demand_price demand_income supply_(Intercept) 306s demand_1 1.074 107.8 93.9 0.000 306s demand_2 -0.390 -40.7 -38.1 0.000 306s demand_3 2.625 271.5 253.8 0.000 306s demand_4 1.802 188.4 177.0 0.000 306s demand_5 1.946 190.7 194.2 0.000 306s demand_6 1.175 116.8 118.0 0.000 306s demand_7 1.530 154.7 157.9 0.000 306s demand_8 -2.933 -307.2 -316.1 0.000 306s demand_9 -1.365 -131.7 -131.9 0.000 306s demand_10 2.031 185.3 180.5 0.000 306s demand_11 -0.149 -13.9 -11.2 0.000 306s demand_12 -1.954 -193.1 -150.3 0.000 306s demand_13 -1.121 -115.4 -94.8 0.000 306s demand_14 -0.220 -21.7 -19.9 0.000 306s demand_15 1.487 141.4 153.3 0.000 306s demand_16 -3.701 -364.3 -388.9 0.000 306s demand_17 -1.273 -110.1 -122.7 0.000 306s demand_18 -2.002 -208.3 -209.0 0.000 306s demand_19 1.738 183.8 192.4 0.000 306s demand_20 -0.299 -33.9 -38.0 0.000 306s supply_1 0.000 0.0 0.0 -0.444 306s supply_2 0.000 0.0 0.0 -0.896 306s supply_3 0.000 0.0 0.0 1.965 306s supply_4 0.000 0.0 0.0 1.134 306s supply_5 0.000 0.0 0.0 1.514 306s supply_6 0.000 0.0 0.0 0.680 306s supply_7 0.000 0.0 0.0 1.569 306s supply_8 0.000 0.0 0.0 -4.407 306s supply_9 0.000 0.0 0.0 -2.599 306s supply_10 0.000 0.0 0.0 2.469 306s supply_11 0.000 0.0 0.0 -0.598 306s supply_12 0.000 0.0 0.0 -1.697 306s supply_13 0.000 0.0 0.0 -1.064 306s supply_14 0.000 0.0 0.0 0.970 306s supply_15 0.000 0.0 0.0 3.159 306s supply_16 0.000 0.0 0.0 -3.866 306s supply_17 0.000 0.0 0.0 -0.265 306s supply_18 0.000 0.0 0.0 -2.449 306s supply_19 0.000 0.0 0.0 3.110 306s supply_20 0.000 0.0 0.0 1.714 306s supply_price supply_farmPrice supply_trend 306s demand_1 0.0 0.0 0.000 306s demand_2 0.0 0.0 0.000 306s demand_3 0.0 0.0 0.000 306s demand_4 0.0 0.0 0.000 306s demand_5 0.0 0.0 0.000 306s demand_6 0.0 0.0 0.000 306s demand_7 0.0 0.0 0.000 306s demand_8 0.0 0.0 0.000 306s demand_9 0.0 0.0 0.000 306s demand_10 0.0 0.0 0.000 306s demand_11 0.0 0.0 0.000 306s demand_12 0.0 0.0 0.000 306s demand_13 0.0 0.0 0.000 306s demand_14 0.0 0.0 0.000 306s demand_15 0.0 0.0 0.000 306s demand_16 0.0 0.0 0.000 306s demand_17 0.0 0.0 0.000 306s demand_18 0.0 0.0 0.000 306s demand_19 0.0 0.0 0.000 306s demand_20 0.0 0.0 0.000 306s supply_1 -44.6 -43.5 -0.444 306s supply_2 -93.4 -88.7 -1.791 306s supply_3 203.3 194.7 5.895 306s supply_4 118.5 111.3 4.537 306s supply_5 148.4 167.7 7.569 306s supply_6 67.7 73.6 4.082 306s supply_7 158.6 165.7 10.983 306s supply_8 -461.7 -483.9 -35.259 306s supply_9 -250.7 -282.5 -23.391 306s supply_10 225.3 248.4 24.694 306s supply_11 -55.7 -48.5 -6.581 306s supply_12 -167.7 -116.4 -20.369 306s supply_13 -109.5 -75.4 -13.832 306s supply_14 95.8 79.0 13.582 306s supply_15 300.5 323.2 47.386 306s supply_16 -380.6 -405.9 -61.848 306s supply_17 -22.9 -29.2 -4.500 306s supply_18 -254.7 -226.5 -44.080 306s supply_19 328.9 277.7 59.084 306s supply_20 194.5 159.4 34.282 306s > round( colSums( estfun( fitols1r ) ), digits = 7 ) 306s demand_(Intercept) demand_price demand_income supply_(Intercept) 306s 0 0 0 0 306s supply_price supply_farmPrice supply_trend 306s 0 0 0 306s > 306s > try( estfun( fitols2 ) ) 306s > 306s > try( estfun( fitols2Sym ) ) 306s > 306s > try( estfun( fitols3s ) ) 306s > 306s > try( estfun( fitols4r ) ) 306s > 306s > try( estfun( fitols4Sym ) ) 306s > 306s > try( estfun( fitols5 ) ) 306s Error in estfun.systemfit(fitols2) : 306s returning the estimation function for models with restrictions has not yet been implemented. 306s Error in estfun.systemfit(fitols2Sym) : 306s returning the estimation function for models with restrictions has not yet been implemented. 306s Error in estfun.systemfit(fitols3s) : 306s returning the estimation function for models with restrictions has not yet been implemented. 306s Error in estfun.systemfit(fitols4r) : 306s returning the estimation function for models with restrictions has not yet been implemented. 306s Error in estfun.systemfit(fitols4Sym) : 306s returning the estimation function for models with restrictions has not yet been implemented. 306s Error in estfun.systemfit(fitols5) : 306s returning the estimation function for models with restrictions has not yet been implemented. 306s > 306s > try( estfun( fitols5Sym ) ) 306s > 306s > 306s > ## **************** bread ************************ 306s > bread( fitols1 ) 306s Error in estfun.systemfit(fitols5Sym) : 306s returning the estimation function for models with restrictions has not yet been implemented. 306s demand_(Intercept) demand_price demand_income 306s demand_(Intercept) 607.086 -6.3865 0.3453 306s demand_price -6.386 0.0883 -0.0251 306s demand_income 0.345 -0.0251 0.0222 306s supply_(Intercept) 0.000 0.0000 0.0000 306s supply_price 0.000 0.0000 0.0000 306s supply_farmPrice 0.000 0.0000 0.0000 306s supply_trend 0.000 0.0000 0.0000 306s supply_(Intercept) supply_price supply_farmPrice 306s demand_(Intercept) 0.00 0.00000 0.00000 306s demand_price 0.00 0.00000 0.00000 306s demand_income 0.00 0.00000 0.00000 306s supply_(Intercept) 908.63 -6.82866 -2.10469 306s supply_price -6.83 0.06226 0.00584 306s supply_farmPrice -2.10 0.00584 0.01475 306s supply_trend -1.93 0.00361 0.00910 306s supply_trend 306s demand_(Intercept) 0.00000 306s demand_price 0.00000 306s demand_income 0.00000 306s supply_(Intercept) -1.93058 306s supply_price 0.00361 306s supply_farmPrice 0.00910 306s supply_trend 0.06576 306s > 306s > bread( fitols1s ) 306s demand_(Intercept) demand_price demand_income 306s demand_(Intercept) 607.086 -6.3865 0.3453 306s demand_price -6.386 0.0883 -0.0251 306s demand_income 0.345 -0.0251 0.0222 306s supply_(Intercept) 0.000 0.0000 0.0000 306s supply_price 0.000 0.0000 0.0000 306s supply_farmPrice 0.000 0.0000 0.0000 306s supply_trend 0.000 0.0000 0.0000 306s supply_(Intercept) supply_price supply_farmPrice 306s demand_(Intercept) 0.00 0.00000 0.00000 306s demand_price 0.00 0.00000 0.00000 306s demand_income 0.00 0.00000 0.00000 306s supply_(Intercept) 908.63 -6.82866 -2.10469 306s supply_price -6.83 0.06226 0.00584 306s supply_farmPrice -2.10 0.00584 0.01475 306s supply_trend -1.93 0.00361 0.00910 306s supply_trend 306s demand_(Intercept) 0.00000 306s demand_price 0.00000 306s demand_income 0.00000 306s supply_(Intercept) -1.93058 306s supply_price 0.00361 306s supply_farmPrice 0.00910 306s supply_trend 0.06576 306s > 306s > bread( fitols1r ) 306s demand_(Intercept) demand_price demand_income 306s demand_(Intercept) 607.086 -6.3865 0.3453 306s demand_price -6.386 0.0883 -0.0251 306s demand_income 0.345 -0.0251 0.0222 306s supply_(Intercept) 0.000 0.0000 0.0000 306s supply_price 0.000 0.0000 0.0000 306s supply_farmPrice 0.000 0.0000 0.0000 306s supply_trend 0.000 0.0000 0.0000 306s supply_(Intercept) supply_price supply_farmPrice 306s demand_(Intercept) 0.00 0.00000 0.00000 306s demand_price 0.00 0.00000 0.00000 306s demand_income 0.00 0.00000 0.00000 306s supply_(Intercept) 908.63 -6.82866 -2.10469 306s supply_price -6.83 0.06226 0.00584 306s supply_farmPrice -2.10 0.00584 0.01475 306s supply_trend -1.93 0.00361 0.00910 306s supply_trend 306s demand_(Intercept) 0.00000 306s demand_price 0.00000 306s demand_income 0.00000 306s supply_(Intercept) -1.93058 306s supply_price 0.00361 306s supply_farmPrice 0.00910 306s supply_trend 0.06576 306s > 306s > try( bread( fitols2 ) ) 306s > 306s Error in bread.systemfit(fitols2) : 306s returning the 'bread' for models with restrictions has not yet been implemented. 306s BEGIN TEST test_panel.R 306s 306s R version 4.3.3 (2024-02-29) -- "Angel Food Cake" 306s Copyright (C) 2024 The R Foundation for Statistical Computing 306s Platform: arm-unknown-linux-gnueabihf (32-bit) 306s 306s R is free software and comes with ABSOLUTELY NO WARRANTY. 306s You are welcome to redistribute it under certain conditions. 306s Type 'license()' or 'licence()' for distribution details. 306s 306s R is a collaborative project with many contributors. 306s Type 'contributors()' for more information and 306s 'citation()' on how to cite R or R packages in publications. 306s 306s Type 'demo()' for some demos, 'help()' for on-line help, or 306s 'help.start()' for an HTML browser interface to help. 306s Type 'q()' to quit R. 306s 306s > library( systemfit ) 306s Loading required package: Matrix 307s Loading required package: car 307s Loading required package: carData 307s Loading required package: lmtest 307s Loading required package: zoo 307s 307s Attaching package: ‘zoo’ 307s 307s The following objects are masked from ‘package:base’: 307s 307s as.Date, as.Date.numeric 307s 307s 307s Please cite the 'systemfit' package as: 307s Arne Henningsen and Jeff D. Hamann (2007). systemfit: A Package for Estimating Systems of Simultaneous Equations in R. Journal of Statistical Software 23(4), 1-40. http://www.jstatsoft.org/v23/i04/. 307s 307s If you have questions, suggestions, or comments regarding the 'systemfit' package, please use a forum or 'tracker' at systemfit's R-Forge site: 307s https://r-forge.r-project.org/projects/systemfit/ 307s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 307s + library( plm ) 307s + options( digits = 3 ) 307s + useMatrix <- FALSE 307s + } 307s > 307s > ## Repeating the OLS and SUR estimations in Theil (1971, pp. 295, 300) 307s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 307s + data( "GrunfeldGreene" ) 307s + GrunfeldTheil <- subset( GrunfeldGreene, 307s + firm %in% c( "General Electric", "Westinghouse" ) ) 307s + GrunfeldTheil <- pdata.frame( GrunfeldTheil, c( "firm", "year" ) ) 307s + formulaGrunfeld <- invest ~ value + capital 307s + } 307s > 307s > # OLS 307s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 307s + theilOls <- systemfit( formulaGrunfeld, "OLS", 307s + data = GrunfeldTheil, useMatrix = useMatrix ) 307s + print( theilOls ) 307s + print( summary( theilOls ) ) 307s + print( summary( theilOls, useDfSys = TRUE, residCov = FALSE, 307s + equations = FALSE ) ) 307s + print( summary( theilOls, equations = FALSE ) ) 307s + print( coef( theilOls ) ) 307s + print( coef( summary(theilOls ) ) ) 307s + print( vcov( theilOls ) ) 307s + print( residuals( theilOls ) ) 307s + print( confint( theilOls ) ) 307s + print( fitted(theilOls ) ) 307s + print( logLik( theilOls ) ) 307s + print( logLik( theilOls, residCovDiag = TRUE ) ) 307s + print( nobs( theilOls ) ) 307s + print( model.frame( theilOls ) ) 307s + print( model.matrix( theilOls ) ) 307s + print( formula( theilOls ) ) 307s + print( formula( theilOls$eq[[ 1 ]] ) ) 307s + print( terms( theilOls ) ) 307s + print( terms( theilOls$eq[[ 1 ]] ) ) 307s + } 307s 307s systemfit results 307s method: OLS 307s 307s Coefficients: 307s General.Electric_(Intercept) General.Electric_value 307s -9.9563 0.0266 307s General.Electric_capital Westinghouse_(Intercept) 307s 0.1517 -0.5094 307s Westinghouse_value Westinghouse_capital 307s 0.0529 0.0924 307s 307s systemfit results 307s method: OLS 307s 307s N DF SSR detRCov OLS-R2 McElroy-R2 307s system 40 34 14990 38001 0.711 0.618 307s 307s N DF SSR MSE RMSE R2 Adj R2 307s General.Electric 20 17 13217 777 27.9 0.705 0.671 307s Westinghouse 20 17 1773 104 10.2 0.744 0.714 307s 307s The covariance matrix of the residuals 307s General.Electric Westinghouse 307s General.Electric 777 208 307s Westinghouse 208 104 307s 307s The correlations of the residuals 307s General.Electric Westinghouse 307s General.Electric 1.000 0.729 307s Westinghouse 0.729 1.000 307s 307s 307s OLS estimates for 'General.Electric' (equation 1) 307s Model Formula: General.Electric_invest ~ General.Electric_value + General.Electric_capital 307s 307s 307s Estimate Std. Error t value Pr(>|t|) 307s (Intercept) -9.9563 31.3742 -0.32 0.75 307s value 0.0266 0.0156 1.71 0.11 307s capital 0.1517 0.0257 5.90 1.7e-05 *** 307s --- 307s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 307s 307s Residual standard error: 27.883 on 17 degrees of freedom 307s Number of observations: 20 Degrees of Freedom: 17 307s SSR: 13216.588 MSE: 777.446 Root MSE: 27.883 307s Multiple R-Squared: 0.705 Adjusted R-Squared: 0.671 307s 307s 307s OLS estimates for 'Westinghouse' (equation 2) 307s Model Formula: Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 307s 307s 307s Estimate Std. Error t value Pr(>|t|) 307s (Intercept) -0.5094 8.0153 -0.06 0.9501 307s value 0.0529 0.0157 3.37 0.0037 ** 307s capital 0.0924 0.0561 1.65 0.1179 307s --- 307s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 307s 307s Residual standard error: 10.213 on 17 degrees of freedom 307s Number of observations: 20 Degrees of Freedom: 17 307s SSR: 1773.234 MSE: 104.308 Root MSE: 10.213 307s Multiple R-Squared: 0.744 Adjusted R-Squared: 0.714 307s 307s 307s systemfit results 307s method: OLS 307s 307s N DF SSR detRCov OLS-R2 McElroy-R2 307s system 40 34 14990 38001 0.711 0.618 307s 307s N DF SSR MSE RMSE R2 Adj R2 307s General.Electric 20 17 13217 777 27.9 0.705 0.671 307s Westinghouse 20 17 1773 104 10.2 0.744 0.714 307s 307s 307s Coefficients: 307s Estimate Std. Error t value Pr(>|t|) 307s General.Electric_(Intercept) -9.9563 31.3742 -0.32 0.7529 307s General.Electric_value 0.0266 0.0156 1.71 0.0972 . 307s General.Electric_capital 0.1517 0.0257 5.90 1.2e-06 *** 307s Westinghouse_(Intercept) -0.5094 8.0153 -0.06 0.9497 307s Westinghouse_value 0.0529 0.0157 3.37 0.0019 ** 307s Westinghouse_capital 0.0924 0.0561 1.65 0.1087 307s --- 307s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 307s 307s systemfit results 307s method: OLS 307s 307s N DF SSR detRCov OLS-R2 McElroy-R2 307s system 40 34 14990 38001 0.711 0.618 307s 307s N DF SSR MSE RMSE R2 Adj R2 307s General.Electric 20 17 13217 777 27.9 0.705 0.671 307s Westinghouse 20 17 1773 104 10.2 0.744 0.714 307s 307s The covariance matrix of the residuals 307s General.Electric Westinghouse 307s General.Electric 777 208 307s Westinghouse 208 104 307s 307s The correlations of the residuals 307s General.Electric Westinghouse 307s General.Electric 1.000 0.729 307s Westinghouse 0.729 1.000 307s 307s 307s Coefficients: 307s Estimate Std. Error t value Pr(>|t|) 307s General.Electric_(Intercept) -9.9563 31.3742 -0.32 0.7548 307s General.Electric_value 0.0266 0.0156 1.71 0.1063 307s General.Electric_capital 0.1517 0.0257 5.90 1.7e-05 *** 307s Westinghouse_(Intercept) -0.5094 8.0153 -0.06 0.9501 307s Westinghouse_value 0.0529 0.0157 3.37 0.0037 ** 307s Westinghouse_capital 0.0924 0.0561 1.65 0.1179 307s --- 307s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 307s General.Electric_(Intercept) General.Electric_value 307s -9.9563 0.0266 307s General.Electric_capital Westinghouse_(Intercept) 307s 0.1517 -0.5094 307s Westinghouse_value Westinghouse_capital 307s 0.0529 0.0924 307s Estimate Std. Error t value Pr(>|t|) 307s General.Electric_(Intercept) -9.9563 31.3742 -0.3173 7.55e-01 307s General.Electric_value 0.0266 0.0156 1.7057 1.06e-01 307s General.Electric_capital 0.1517 0.0257 5.9015 1.74e-05 307s Westinghouse_(Intercept) -0.5094 8.0153 -0.0636 9.50e-01 307s Westinghouse_value 0.0529 0.0157 3.3677 3.65e-03 307s Westinghouse_capital 0.0924 0.0561 1.6472 1.18e-01 307s General.Electric_(Intercept) 307s General.Electric_(Intercept) 984.344 307s General.Electric_value -0.451 307s General.Electric_capital -0.173 307s Westinghouse_(Intercept) 0.000 307s Westinghouse_value 0.000 307s Westinghouse_capital 0.000 307s General.Electric_value General.Electric_capital 307s General.Electric_(Intercept) -4.51e-01 -1.73e-01 307s General.Electric_value 2.42e-04 -4.73e-05 307s General.Electric_capital -4.73e-05 6.61e-04 307s Westinghouse_(Intercept) 0.00e+00 0.00e+00 307s Westinghouse_value 0.00e+00 0.00e+00 307s Westinghouse_capital 0.00e+00 0.00e+00 307s Westinghouse_(Intercept) Westinghouse_value 307s General.Electric_(Intercept) 0.000 0.000000 307s General.Electric_value 0.000 0.000000 307s General.Electric_capital 0.000 0.000000 307s Westinghouse_(Intercept) 64.245 -0.109545 307s Westinghouse_value -0.110 0.000247 307s Westinghouse_capital 0.169 -0.000653 307s Westinghouse_capital 307s General.Electric_(Intercept) 0.000000 307s General.Electric_value 0.000000 307s General.Electric_capital 0.000000 307s Westinghouse_(Intercept) 0.168911 307s Westinghouse_value -0.000653 307s Westinghouse_capital 0.003147 307s General.Electric Westinghouse 307s X1935 -2.860 3.144 307s X1936 -14.402 -0.958 307s X1937 -5.175 -3.684 307s X1938 -23.295 -7.915 307s X1939 -28.031 -10.322 307s X1940 -0.562 -6.613 307s X1941 40.750 17.265 307s X1942 16.036 8.547 307s X1943 -23.719 -2.916 307s X1944 -26.780 -3.257 307s X1945 1.768 -7.753 307s X1946 58.737 5.796 307s X1947 43.936 15.050 307s X1948 31.227 2.969 307s X1949 -23.552 -11.433 307s X1950 -37.511 -13.481 307s X1951 -4.983 4.619 307s X1952 1.893 13.138 307s X1953 5.087 11.308 307s X1954 -8.563 -13.505 307s 2.5 % 97.5 % 307s General.Electric_(Intercept) -76.150 56.238 307s General.Electric_value -0.006 0.059 307s General.Electric_capital 0.097 0.206 307s Westinghouse_(Intercept) -17.420 16.401 307s Westinghouse_value 0.020 0.086 307s Westinghouse_capital -0.026 0.211 307s General.Electric Westinghouse 307s X1935 36.0 9.79 307s X1936 59.4 26.86 307s X1937 82.4 38.73 307s X1938 67.9 30.81 307s X1939 76.1 29.16 307s X1940 75.0 35.18 307s X1941 72.3 31.25 307s X1942 75.9 34.79 307s X1943 85.0 39.94 307s X1944 83.6 41.07 307s X1945 91.8 47.02 307s X1946 101.2 47.66 307s X1947 103.3 40.51 307s X1948 115.1 46.59 307s X1949 121.9 43.47 307s X1950 131.0 45.72 307s X1951 140.2 49.76 307s X1952 155.4 58.64 307s X1953 174.4 78.77 307s X1954 198.2 82.11 307s 'log Lik.' -159 (df=7) 307s 'log Lik.' -167 (df=7) 307s [1] 40 307s General.Electric_invest General.Electric_value General.Electric_capital 307s X1935 33.1 1171 97.8 307s X1936 45.0 2016 104.4 307s X1937 77.2 2803 118.0 307s X1938 44.6 2040 156.2 307s X1939 48.1 2256 172.6 307s X1940 74.4 2132 186.6 307s X1941 113.0 1834 220.9 307s X1942 91.9 1588 287.8 307s X1943 61.3 1749 319.9 307s X1944 56.8 1687 321.3 307s X1945 93.6 2008 319.6 307s X1946 159.9 2208 346.0 307s X1947 147.2 1657 456.4 307s X1948 146.3 1604 543.4 307s X1949 98.3 1432 618.3 307s X1950 93.5 1610 647.4 307s X1951 135.2 1819 671.3 307s X1952 157.3 2080 726.1 307s X1953 179.5 2372 800.3 307s X1954 189.6 2760 888.9 307s Westinghouse_invest Westinghouse_value Westinghouse_capital 307s X1935 12.9 192 1.8 307s X1936 25.9 516 0.8 307s X1937 35.0 729 7.4 307s X1938 22.9 560 18.1 307s X1939 18.8 520 23.5 307s X1940 28.6 628 26.5 307s X1941 48.5 537 36.2 307s X1942 43.3 561 60.8 307s X1943 37.0 617 84.4 307s X1944 37.8 627 91.2 307s X1945 39.3 737 92.4 307s X1946 53.5 760 86.0 307s X1947 55.6 581 111.1 307s X1948 49.6 662 130.6 307s X1949 32.0 584 141.8 307s X1950 32.2 635 136.7 307s X1951 54.4 724 129.7 307s X1952 71.8 864 145.5 307s X1953 90.1 1194 174.8 307s X1954 68.6 1189 213.5 307s General.Electric_(Intercept) General.Electric_value 307s General.Electric_X1935 1 1171 307s General.Electric_X1936 1 2016 307s General.Electric_X1937 1 2803 307s General.Electric_X1938 1 2040 307s General.Electric_X1939 1 2256 307s General.Electric_X1940 1 2132 307s General.Electric_X1941 1 1834 307s General.Electric_X1942 1 1588 307s General.Electric_X1943 1 1749 307s General.Electric_X1944 1 1687 307s General.Electric_X1945 1 2008 307s General.Electric_X1946 1 2208 307s General.Electric_X1947 1 1657 307s General.Electric_X1948 1 1604 307s General.Electric_X1949 1 1432 307s General.Electric_X1950 1 1610 307s General.Electric_X1951 1 1819 307s General.Electric_X1952 1 2080 307s General.Electric_X1953 1 2372 307s General.Electric_X1954 1 2760 307s Westinghouse_X1935 0 0 307s Westinghouse_X1936 0 0 307s Westinghouse_X1937 0 0 307s Westinghouse_X1938 0 0 307s Westinghouse_X1939 0 0 307s Westinghouse_X1940 0 0 307s Westinghouse_X1941 0 0 307s Westinghouse_X1942 0 0 307s Westinghouse_X1943 0 0 307s Westinghouse_X1944 0 0 307s Westinghouse_X1945 0 0 307s Westinghouse_X1946 0 0 307s Westinghouse_X1947 0 0 307s Westinghouse_X1948 0 0 307s Westinghouse_X1949 0 0 307s Westinghouse_X1950 0 0 307s Westinghouse_X1951 0 0 307s Westinghouse_X1952 0 0 307s Westinghouse_X1953 0 0 307s Westinghouse_X1954 0 0 307s General.Electric_capital Westinghouse_(Intercept) 307s General.Electric_X1935 97.8 0 307s General.Electric_X1936 104.4 0 307s General.Electric_X1937 118.0 0 307s General.Electric_X1938 156.2 0 307s General.Electric_X1939 172.6 0 307s General.Electric_X1940 186.6 0 307s General.Electric_X1941 220.9 0 307s General.Electric_X1942 287.8 0 307s General.Electric_X1943 319.9 0 307s General.Electric_X1944 321.3 0 307s General.Electric_X1945 319.6 0 307s General.Electric_X1946 346.0 0 307s General.Electric_X1947 456.4 0 307s General.Electric_X1948 543.4 0 307s General.Electric_X1949 618.3 0 307s General.Electric_X1950 647.4 0 307s General.Electric_X1951 671.3 0 307s General.Electric_X1952 726.1 0 307s General.Electric_X1953 800.3 0 307s General.Electric_X1954 888.9 0 307s Westinghouse_X1935 0.0 1 307s Westinghouse_X1936 0.0 1 307s Westinghouse_X1937 0.0 1 307s Westinghouse_X1938 0.0 1 307s Westinghouse_X1939 0.0 1 307s Westinghouse_X1940 0.0 1 307s Westinghouse_X1941 0.0 1 307s Westinghouse_X1942 0.0 1 307s Westinghouse_X1943 0.0 1 307s Westinghouse_X1944 0.0 1 307s Westinghouse_X1945 0.0 1 307s Westinghouse_X1946 0.0 1 307s Westinghouse_X1947 0.0 1 307s Westinghouse_X1948 0.0 1 307s Westinghouse_X1949 0.0 1 307s Westinghouse_X1950 0.0 1 307s Westinghouse_X1951 0.0 1 307s Westinghouse_X1952 0.0 1 307s Westinghouse_X1953 0.0 1 307s Westinghouse_X1954 0.0 1 307s Westinghouse_value Westinghouse_capital 307s General.Electric_X1935 0 0.0 307s General.Electric_X1936 0 0.0 307s General.Electric_X1937 0 0.0 307s General.Electric_X1938 0 0.0 307s General.Electric_X1939 0 0.0 307s General.Electric_X1940 0 0.0 307s General.Electric_X1941 0 0.0 307s General.Electric_X1942 0 0.0 307s General.Electric_X1943 0 0.0 307s General.Electric_X1944 0 0.0 307s General.Electric_X1945 0 0.0 307s General.Electric_X1946 0 0.0 307s General.Electric_X1947 0 0.0 307s General.Electric_X1948 0 0.0 307s General.Electric_X1949 0 0.0 307s General.Electric_X1950 0 0.0 307s General.Electric_X1951 0 0.0 307s General.Electric_X1952 0 0.0 307s General.Electric_X1953 0 0.0 307s General.Electric_X1954 0 0.0 307s Westinghouse_X1935 192 1.8 307s Westinghouse_X1936 516 0.8 307s Westinghouse_X1937 729 7.4 307s Westinghouse_X1938 560 18.1 307s Westinghouse_X1939 520 23.5 307s Westinghouse_X1940 628 26.5 307s Westinghouse_X1941 537 36.2 307s Westinghouse_X1942 561 60.8 307s Westinghouse_X1943 617 84.4 307s Westinghouse_X1944 627 91.2 307s Westinghouse_X1945 737 92.4 307s Westinghouse_X1946 760 86.0 307s Westinghouse_X1947 581 111.1 307s Westinghouse_X1948 662 130.6 307s Westinghouse_X1949 584 141.8 307s Westinghouse_X1950 635 136.7 307s Westinghouse_X1951 724 129.7 307s Westinghouse_X1952 864 145.5 307s Westinghouse_X1953 1194 174.8 307s Westinghouse_X1954 1189 213.5 307s $General.Electric 307s General.Electric_invest ~ General.Electric_value + General.Electric_capital 307s 307s 307s $Westinghouse 307s Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 307s 307s 307s General.Electric_invest ~ General.Electric_value + General.Electric_capital 307s 307s $General.Electric 307s General.Electric_invest ~ General.Electric_value + General.Electric_capital 307s attr(,"variables") 307s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 307s attr(,"factors") 307s General.Electric_value General.Electric_capital 307s General.Electric_invest 0 0 307s General.Electric_value 1 0 307s General.Electric_capital 0 1 307s attr(,"term.labels") 307s [1] "General.Electric_value" "General.Electric_capital" 307s attr(,"order") 307s [1] 1 1 307s attr(,"intercept") 307s [1] 1 307s attr(,"response") 307s [1] 1 307s attr(,".Environment") 307s 307s attr(,"predvars") 307s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 307s attr(,"dataClasses") 307s General.Electric_invest General.Electric_value General.Electric_capital 307s "numeric" "numeric" "numeric" 307s 307s $Westinghouse 307s Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 307s attr(,"variables") 307s list(Westinghouse_invest, Westinghouse_value, Westinghouse_capital) 307s attr(,"factors") 307s Westinghouse_value Westinghouse_capital 307s Westinghouse_invest 0 0 307s Westinghouse_value 1 0 307s Westinghouse_capital 0 1 307s attr(,"term.labels") 307s [1] "Westinghouse_value" "Westinghouse_capital" 307s attr(,"order") 307s [1] 1 1 307s attr(,"intercept") 307s [1] 1 307s attr(,"response") 307s [1] 1 307s attr(,".Environment") 307s 307s attr(,"predvars") 307s list(Westinghouse_invest, Westinghouse_value, Westinghouse_capital) 307s attr(,"dataClasses") 307s Westinghouse_invest Westinghouse_value Westinghouse_capital 307s "numeric" "numeric" "numeric" 307s 307s General.Electric_invest ~ General.Electric_value + General.Electric_capital 307s attr(,"variables") 307s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 307s attr(,"factors") 307s General.Electric_value General.Electric_capital 307s General.Electric_invest 0 0 307s General.Electric_value 1 0 307s General.Electric_capital 0 1 307s attr(,"term.labels") 307s [1] "General.Electric_value" "General.Electric_capital" 307s attr(,"order") 307s [1] 1 1 307s attr(,"intercept") 307s [1] 1 307s attr(,"response") 307s [1] 1 307s attr(,".Environment") 307s 307s attr(,"predvars") 307s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 307s attr(,"dataClasses") 307s General.Electric_invest General.Electric_value General.Electric_capital 307s "numeric" "numeric" "numeric" 307s > 307s > # SUR 307s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 307s + theilSur <- systemfit( formulaGrunfeld, "SUR", 307s + data = GrunfeldTheil, methodResidCov = "noDfCor", useMatrix = useMatrix ) 307s + print( theilSur ) 307s + print( summary( theilSur ) ) 307s + print( summary( theilSur, useDfSys = TRUE, equations = FALSE ) ) 307s + print( summary( theilSur, residCov = FALSE, equations = FALSE ) ) 307s + print( coef( theilSur ) ) 307s + print( coef( summary( theilSur ) ) ) 307s + print( vcov( theilSur ) ) 307s + print( residuals( theilSur ) ) 307s + print( confint( theilSur ) ) 307s + print( fitted( theilSur ) ) 307s + print( logLik( theilSur ) ) 307s + print( logLik( theilSur, residCovDiag = TRUE ) ) 307s + print( nobs( theilSur ) ) 307s + print( model.frame( theilSur ) ) 307s + print( model.matrix( theilSur ) ) 307s + print( formula( theilSur ) ) 307s + print( formula( theilSur$eq[[ 2 ]] ) ) 307s + print( terms( theilSur ) ) 307s + print( terms( theilSur$eq[[ 2 ]] ) ) 307s + } 307s 307s systemfit results 307s method: SUR 307s 307s Coefficients: 307s General.Electric_(Intercept) General.Electric_value 307s -27.7193 0.0383 307s General.Electric_capital Westinghouse_(Intercept) 307s 0.1390 -1.2520 307s Westinghouse_value Westinghouse_capital 307s 0.0576 0.0640 307s 307s systemfit results 307s method: SUR 307s 307s N DF SSR detRCov OLS-R2 McElroy-R2 307s system 40 34 15590 25750 0.699 0.615 307s 307s N DF SSR MSE RMSE R2 Adj R2 307s General.Electric 20 17 13788 811 28.5 0.693 0.656 307s Westinghouse 20 17 1801 106 10.3 0.740 0.710 307s 307s The covariance matrix of the residuals used for estimation 307s General.Electric Westinghouse 307s General.Electric 661 176.4 307s Westinghouse 176 88.7 307s 307s The covariance matrix of the residuals 307s General.Electric Westinghouse 307s General.Electric 689 190.6 307s Westinghouse 191 90.1 307s 307s The correlations of the residuals 307s General.Electric Westinghouse 307s General.Electric 1.000 0.765 307s Westinghouse 0.765 1.000 307s 307s 307s SUR estimates for 'General.Electric' (equation 1) 307s Model Formula: General.Electric_invest ~ General.Electric_value + General.Electric_capital 307s 307s 307s Estimate Std. Error t value Pr(>|t|) 307s (Intercept) -27.7193 27.0328 -1.03 0.32 307s value 0.0383 0.0133 2.88 0.01 * 307s capital 0.1390 0.0230 6.04 1.3e-05 *** 307s --- 307s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 307s 307s Residual standard error: 28.479 on 17 degrees of freedom 307s Number of observations: 20 Degrees of Freedom: 17 307s SSR: 13788.376 MSE: 811.081 Root MSE: 28.479 307s Multiple R-Squared: 0.693 Adjusted R-Squared: 0.656 307s 307s 307s SUR estimates for 'Westinghouse' (equation 2) 307s Model Formula: Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 307s 307s 307s Estimate Std. Error t value Pr(>|t|) 307s (Intercept) -1.2520 6.9563 -0.18 0.85930 307s value 0.0576 0.0134 4.30 0.00049 *** 307s capital 0.0640 0.0489 1.31 0.20818 307s --- 307s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 307s 307s Residual standard error: 10.294 on 17 degrees of freedom 307s Number of observations: 20 Degrees of Freedom: 17 307s SSR: 1801.301 MSE: 105.959 Root MSE: 10.294 307s Multiple R-Squared: 0.74 Adjusted R-Squared: 0.71 307s 307s 307s systemfit results 307s method: SUR 307s 307s N DF SSR detRCov OLS-R2 McElroy-R2 307s system 40 34 15590 25750 0.699 0.615 307s 307s N DF SSR MSE RMSE R2 Adj R2 307s General.Electric 20 17 13788 811 28.5 0.693 0.656 307s Westinghouse 20 17 1801 106 10.3 0.740 0.710 307s 307s The covariance matrix of the residuals used for estimation 307s General.Electric Westinghouse 307s General.Electric 661 176.4 307s Westinghouse 176 88.7 307s 307s The covariance matrix of the residuals 307s General.Electric Westinghouse 307s General.Electric 689 190.6 307s Westinghouse 191 90.1 307s 307s The correlations of the residuals 307s General.Electric Westinghouse 307s General.Electric 1.000 0.765 307s Westinghouse 0.765 1.000 307s 307s 307s Coefficients: 307s Estimate Std. Error t value Pr(>|t|) 307s General.Electric_(Intercept) -27.7193 27.0328 -1.03 0.31242 307s General.Electric_value 0.0383 0.0133 2.88 0.00679 ** 307s General.Electric_capital 0.1390 0.0230 6.04 7.7e-07 *** 307s Westinghouse_(Intercept) -1.2520 6.9563 -0.18 0.85824 307s Westinghouse_value 0.0576 0.0134 4.30 0.00014 *** 307s Westinghouse_capital 0.0640 0.0489 1.31 0.19954 307s --- 307s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 307s 307s systemfit results 307s method: SUR 307s 307s N DF SSR detRCov OLS-R2 McElroy-R2 307s system 40 34 15590 25750 0.699 0.615 307s 307s N DF SSR MSE RMSE R2 Adj R2 307s General.Electric 20 17 13788 811 28.5 0.693 0.656 307s Westinghouse 20 17 1801 106 10.3 0.740 0.710 307s 307s 307s Coefficients: 307s Estimate Std. Error t value Pr(>|t|) 307s General.Electric_(Intercept) -27.7193 27.0328 -1.03 0.31955 307s General.Electric_value 0.0383 0.0133 2.88 0.01034 * 307s General.Electric_capital 0.1390 0.0230 6.04 1.3e-05 *** 307s Westinghouse_(Intercept) -1.2520 6.9563 -0.18 0.85930 307s Westinghouse_value 0.0576 0.0134 4.30 0.00049 *** 307s Westinghouse_capital 0.0640 0.0489 1.31 0.20818 307s --- 307s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 307s General.Electric_(Intercept) General.Electric_value 307s -27.7193 0.0383 307s General.Electric_capital Westinghouse_(Intercept) 307s 0.1390 -1.2520 307s Westinghouse_value Westinghouse_capital 307s 0.0576 0.0640 307s Estimate Std. Error t value Pr(>|t|) 307s General.Electric_(Intercept) -27.7193 27.0328 -1.03 3.20e-01 307s General.Electric_value 0.0383 0.0133 2.88 1.03e-02 307s General.Electric_capital 0.1390 0.0230 6.04 1.34e-05 307s Westinghouse_(Intercept) -1.2520 6.9563 -0.18 8.59e-01 307s Westinghouse_value 0.0576 0.0134 4.30 4.88e-04 307s Westinghouse_capital 0.0640 0.0489 1.31 2.08e-01 307s General.Electric_(Intercept) 307s General.Electric_(Intercept) 730.774 307s General.Electric_value -0.329 307s General.Electric_capital -0.146 307s Westinghouse_(Intercept) 126.963 307s Westinghouse_value -0.226 307s Westinghouse_capital 0.393 307s General.Electric_value General.Electric_capital 307s General.Electric_(Intercept) -0.329266 -1.46e-01 307s General.Electric_value 0.000177 -3.40e-05 307s General.Electric_capital -0.000034 5.31e-04 307s Westinghouse_(Intercept) -0.052688 -3.96e-02 307s Westinghouse_value 0.000120 -1.69e-05 307s Westinghouse_capital -0.000325 5.95e-04 307s Westinghouse_(Intercept) Westinghouse_value 307s General.Electric_(Intercept) 126.9626 -2.26e-01 307s General.Electric_value -0.0527 1.20e-04 307s General.Electric_capital -0.0396 -1.69e-05 307s Westinghouse_(Intercept) 48.3908 -8.00e-02 307s Westinghouse_value -0.0800 1.80e-04 307s Westinghouse_capital 0.1136 -4.75e-04 307s Westinghouse_capital 307s General.Electric_(Intercept) 0.392515 307s General.Electric_value -0.000325 307s General.Electric_capital 0.000595 307s Westinghouse_(Intercept) 0.113618 307s Westinghouse_value -0.000475 307s Westinghouse_capital 0.002391 307s General.Electric Westinghouse 307s X1935 2.3756 3.03 307s X1936 -19.0218 -2.64 307s X1937 -18.8820 -6.18 307s X1938 -27.5395 -9.31 307s X1939 -34.6138 -11.37 307s X1940 -5.5099 -8.09 307s X1941 39.7415 16.49 307s X1942 18.7681 8.36 307s X1943 -22.4783 -2.70 307s X1944 -24.7900 -2.89 307s X1945 -0.0321 -7.87 307s X1946 54.9123 5.38 307s X1947 47.9946 16.20 307s X1948 37.0021 4.29 307s X1949 -14.7994 -9.42 307s X1950 -30.4914 -11.86 307s X1951 -0.1173 5.62 307s X1952 4.3913 13.93 307s X1953 5.0921 11.37 307s X1954 -12.0024 -12.32 307s 2.5 % 97.5 % 307s General.Electric_(Intercept) -84.754 29.315 307s General.Electric_value 0.010 0.066 307s General.Electric_capital 0.090 0.188 307s Westinghouse_(Intercept) -15.929 13.425 307s Westinghouse_value 0.029 0.086 307s Westinghouse_capital -0.039 0.167 307s General.Electric Westinghouse 307s X1935 30.7 9.9 307s X1936 64.0 28.5 307s X1937 96.1 41.2 307s X1938 72.1 32.2 307s X1939 82.7 30.2 307s X1940 79.9 36.7 307s X1941 73.3 32.0 307s X1942 73.1 35.0 307s X1943 83.8 39.7 307s X1944 81.6 40.7 307s X1945 93.6 47.1 307s X1946 105.0 48.1 307s X1947 99.2 39.4 307s X1948 109.3 45.3 307s X1949 113.1 41.5 307s X1950 124.0 44.1 307s X1951 135.3 48.8 307s X1952 152.9 57.9 307s X1953 174.4 78.7 307s X1954 201.6 80.9 307s 'log Lik.' -158 (df=9) 307s 'log Lik.' -167 (df=9) 307s [1] 40 307s General.Electric_invest General.Electric_value General.Electric_capital 307s X1935 33.1 1171 97.8 307s X1936 45.0 2016 104.4 307s X1937 77.2 2803 118.0 307s X1938 44.6 2040 156.2 307s X1939 48.1 2256 172.6 307s X1940 74.4 2132 186.6 307s X1941 113.0 1834 220.9 307s X1942 91.9 1588 287.8 307s X1943 61.3 1749 319.9 307s X1944 56.8 1687 321.3 307s X1945 93.6 2008 319.6 307s X1946 159.9 2208 346.0 307s X1947 147.2 1657 456.4 307s X1948 146.3 1604 543.4 307s X1949 98.3 1432 618.3 307s X1950 93.5 1610 647.4 307s X1951 135.2 1819 671.3 307s X1952 157.3 2080 726.1 307s X1953 179.5 2372 800.3 307s X1954 189.6 2760 888.9 307s Westinghouse_invest Westinghouse_value Westinghouse_capital 307s X1935 12.9 192 1.8 307s X1936 25.9 516 0.8 307s X1937 35.0 729 7.4 307s X1938 22.9 560 18.1 307s X1939 18.8 520 23.5 307s X1940 28.6 628 26.5 307s X1941 48.5 537 36.2 307s X1942 43.3 561 60.8 307s X1943 37.0 617 84.4 307s X1944 37.8 627 91.2 307s X1945 39.3 737 92.4 307s X1946 53.5 760 86.0 307s X1947 55.6 581 111.1 307s X1948 49.6 662 130.6 307s X1949 32.0 584 141.8 307s X1950 32.2 635 136.7 307s X1951 54.4 724 129.7 307s X1952 71.8 864 145.5 307s X1953 90.1 1194 174.8 307s X1954 68.6 1189 213.5 307s General.Electric_(Intercept) General.Electric_value 307s General.Electric_X1935 1 1171 307s General.Electric_X1936 1 2016 307s General.Electric_X1937 1 2803 307s General.Electric_X1938 1 2040 307s General.Electric_X1939 1 2256 307s General.Electric_X1940 1 2132 307s General.Electric_X1941 1 1834 307s General.Electric_X1942 1 1588 307s General.Electric_X1943 1 1749 307s General.Electric_X1944 1 1687 307s General.Electric_X1945 1 2008 307s General.Electric_X1946 1 2208 307s General.Electric_X1947 1 1657 307s General.Electric_X1948 1 1604 307s General.Electric_X1949 1 1432 307s General.Electric_X1950 1 1610 307s General.Electric_X1951 1 1819 307s General.Electric_X1952 1 2080 307s General.Electric_X1953 1 2372 307s General.Electric_X1954 1 2760 307s Westinghouse_X1935 0 0 307s Westinghouse_X1936 0 0 307s Westinghouse_X1937 0 0 307s Westinghouse_X1938 0 0 307s Westinghouse_X1939 0 0 307s Westinghouse_X1940 0 0 307s Westinghouse_X1941 0 0 307s Westinghouse_X1942 0 0 307s Westinghouse_X1943 0 0 307s Westinghouse_X1944 0 0 307s Westinghouse_X1945 0 0 307s Westinghouse_X1946 0 0 307s Westinghouse_X1947 0 0 307s Westinghouse_X1948 0 0 307s Westinghouse_X1949 0 0 307s Westinghouse_X1950 0 0 307s Westinghouse_X1951 0 0 307s Westinghouse_X1952 0 0 307s Westinghouse_X1953 0 0 307s Westinghouse_X1954 0 0 307s General.Electric_capital Westinghouse_(Intercept) 307s General.Electric_X1935 97.8 0 307s General.Electric_X1936 104.4 0 307s General.Electric_X1937 118.0 0 307s General.Electric_X1938 156.2 0 307s General.Electric_X1939 172.6 0 307s General.Electric_X1940 186.6 0 307s General.Electric_X1941 220.9 0 307s General.Electric_X1942 287.8 0 307s General.Electric_X1943 319.9 0 307s General.Electric_X1944 321.3 0 307s General.Electric_X1945 319.6 0 307s General.Electric_X1946 346.0 0 307s General.Electric_X1947 456.4 0 307s General.Electric_X1948 543.4 0 307s General.Electric_X1949 618.3 0 307s General.Electric_X1950 647.4 0 307s General.Electric_X1951 671.3 0 307s General.Electric_X1952 726.1 0 307s General.Electric_X1953 800.3 0 307s General.Electric_X1954 888.9 0 307s Westinghouse_X1935 0.0 1 307s Westinghouse_X1936 0.0 1 307s Westinghouse_X1937 0.0 1 307s Westinghouse_X1938 0.0 1 307s Westinghouse_X1939 0.0 1 307s Westinghouse_X1940 0.0 1 307s Westinghouse_X1941 0.0 1 307s Westinghouse_X1942 0.0 1 307s Westinghouse_X1943 0.0 1 307s Westinghouse_X1944 0.0 1 307s Westinghouse_X1945 0.0 1 307s Westinghouse_X1946 0.0 1 307s Westinghouse_X1947 0.0 1 307s Westinghouse_X1948 0.0 1 307s Westinghouse_X1949 0.0 1 307s Westinghouse_X1950 0.0 1 307s Westinghouse_X1951 0.0 1 307s Westinghouse_X1952 0.0 1 307s Westinghouse_X1953 0.0 1 307s Westinghouse_X1954 0.0 1 307s Westinghouse_value Westinghouse_capital 307s General.Electric_X1935 0 0.0 307s General.Electric_X1936 0 0.0 307s General.Electric_X1937 0 0.0 307s General.Electric_X1938 0 0.0 307s General.Electric_X1939 0 0.0 307s General.Electric_X1940 0 0.0 307s General.Electric_X1941 0 0.0 307s General.Electric_X1942 0 0.0 307s General.Electric_X1943 0 0.0 307s General.Electric_X1944 0 0.0 307s General.Electric_X1945 0 0.0 307s General.Electric_X1946 0 0.0 307s General.Electric_X1947 0 0.0 307s General.Electric_X1948 0 0.0 307s General.Electric_X1949 0 0.0 307s General.Electric_X1950 0 0.0 307s General.Electric_X1951 0 0.0 307s General.Electric_X1952 0 0.0 307s General.Electric_X1953 0 0.0 307s General.Electric_X1954 0 0.0 307s Westinghouse_X1935 192 1.8 307s Westinghouse_X1936 516 0.8 307s Westinghouse_X1937 729 7.4 307s Westinghouse_X1938 560 18.1 307s Westinghouse_X1939 520 23.5 307s Westinghouse_X1940 628 26.5 307s Westinghouse_X1941 537 36.2 307s Westinghouse_X1942 561 60.8 307s Westinghouse_X1943 617 84.4 307s Westinghouse_X1944 627 91.2 307s Westinghouse_X1945 737 92.4 307s Westinghouse_X1946 760 86.0 307s Westinghouse_X1947 581 111.1 307s Westinghouse_X1948 662 130.6 307s Westinghouse_X1949 584 141.8 307s Westinghouse_X1950 635 136.7 307s Westinghouse_X1951 724 129.7 307s Westinghouse_X1952 864 145.5 307s Westinghouse_X1953 1194 174.8 307s Westinghouse_X1954 1189 213.5 307s $General.Electric 307s General.Electric_invest ~ General.Electric_value + General.Electric_capital 307s 307s 307s $Westinghouse 307s Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 307s 307s 307s Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 307s 307s $General.Electric 307s General.Electric_invest ~ General.Electric_value + General.Electric_capital 307s attr(,"variables") 307s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 307s attr(,"factors") 307s General.Electric_value General.Electric_capital 307s General.Electric_invest 0 0 307s General.Electric_value 1 0 307s General.Electric_capital 0 1 307s attr(,"term.labels") 307s [1] "General.Electric_value" "General.Electric_capital" 307s attr(,"order") 307s [1] 1 1 307s attr(,"intercept") 307s [1] 1 307s attr(,"response") 307s [1] 1 307s attr(,".Environment") 307s 307s attr(,"predvars") 307s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 307s attr(,"dataClasses") 307s General.Electric_invest General.Electric_value General.Electric_capital 307s "numeric" "numeric" "numeric" 307s 307s $Westinghouse 307s Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 307s attr(,"variables") 307s list(Westinghouse_invest, Westinghouse_value, Westinghouse_capital) 307s attr(,"factors") 307s Westinghouse_value Westinghouse_capital 307s Westinghouse_invest 0 0 307s Westinghouse_value 1 0 307s Westinghouse_capital 0 1 307s attr(,"term.labels") 307s [1] "Westinghouse_value" "Westinghouse_capital" 307s attr(,"order") 307s [1] 1 1 307s attr(,"intercept") 307s [1] 1 307s attr(,"response") 307s [1] 1 307s attr(,".Environment") 307s 307s attr(,"predvars") 307s list(Westinghouse_invest, Westinghouse_value, Westinghouse_capital) 307s attr(,"dataClasses") 307s Westinghouse_invest Westinghouse_value Westinghouse_capital 307s "numeric" "numeric" "numeric" 307s 307s Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 307s attr(,"variables") 307s list(Westinghouse_invest, Westinghouse_value, Westinghouse_capital) 307s attr(,"factors") 307s Westinghouse_value Westinghouse_capital 307s Westinghouse_invest 0 0 307s Westinghouse_value 1 0 307s Westinghouse_capital 0 1 307s attr(,"term.labels") 307s [1] "Westinghouse_value" "Westinghouse_capital" 307s attr(,"order") 307s [1] 1 1 307s attr(,"intercept") 307s [1] 1 307s attr(,"response") 307s [1] 1 307s attr(,".Environment") 307s 307s attr(,"predvars") 307s list(Westinghouse_invest, Westinghouse_value, Westinghouse_capital) 307s attr(,"dataClasses") 307s Westinghouse_invest Westinghouse_value Westinghouse_capital 307s "numeric" "numeric" "numeric" 307s > 307s > ## Repeating the OLS and SUR estimations in Greene (2003, pp. 351) 307s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 307s + GrunfeldGreene <- pdata.frame( GrunfeldGreene, c( "firm", "year" ) ) 307s + formulaGrunfeld <- invest ~ value + capital 307s + } 307s > 307s > # OLS 307s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 307s + greeneOls <- systemfit( formulaGrunfeld, "OLS", 307s + data = GrunfeldGreene, useMatrix = useMatrix ) 307s + print( greeneOls ) 307s + print( summary( greeneOls ) ) 307s + print( summary( greeneOls, useDfSys = TRUE, equations = FALSE ) ) 307s + print( summary( greeneOls, residCov = FALSE ) ) 307s + print( sapply( greeneOls$eq, function(x){return(summary(x)$ssr/20)} ) ) # sigma^2 307s + print( coef( greeneOls ) ) 307s + print( coef( summary( greeneOls ) ) ) 307s + print( vcov( greeneOls ) ) 307s + print( residuals( greeneOls ) ) 307s + print( confint(greeneOls ) ) 307s + print( fitted( greeneOls ) ) 307s + print( logLik( greeneOls ) ) 307s + print( logLik( greeneOls, residCovDiag = TRUE ) ) 307s + print( nobs( greeneOls ) ) 307s + print( model.frame( greeneOls ) ) 307s + print( model.matrix( greeneOls ) ) 307s + print( formula( greeneOls ) ) 307s + print( formula( greeneOls$eq[[ 2 ]] ) ) 307s + print( terms( greeneOls ) ) 307s + print( terms( greeneOls$eq[[ 2 ]] ) ) 307s + } 307s 307s systemfit results 307s method: OLS 307s 307s Coefficients: 307s Chrysler_(Intercept) Chrysler_value 307s -6.1900 0.0779 307s Chrysler_capital General.Electric_(Intercept) 307s 0.3157 -9.9563 307s General.Electric_value General.Electric_capital 307s 0.0266 0.1517 307s General.Motors_(Intercept) General.Motors_value 307s -149.7825 0.1193 307s General.Motors_capital US.Steel_(Intercept) 307s 0.3714 -30.3685 307s US.Steel_value US.Steel_capital 307s 0.1566 0.4239 307s Westinghouse_(Intercept) Westinghouse_value 307s -0.5094 0.0529 307s Westinghouse_capital 307s 0.0924 307s 307s systemfit results 307s method: OLS 307s 307s N DF SSR detRCov OLS-R2 McElroy-R2 307s system 100 85 339121 2.09e+14 0.848 0.862 307s 307s N DF SSR MSE RMSE R2 Adj R2 307s Chrysler 20 17 2997 176 13.3 0.914 0.903 307s General.Electric 20 17 13217 777 27.9 0.705 0.671 307s General.Motors 20 17 143206 8424 91.8 0.921 0.912 307s US.Steel 20 17 177928 10466 102.3 0.440 0.374 307s Westinghouse 20 17 1773 104 10.2 0.744 0.714 307s 307s The covariance matrix of the residuals 307s Chrysler General.Electric General.Motors US.Steel Westinghouse 307s Chrysler 176.3 -25.1 -333 492 15.7 307s General.Electric -25.1 777.4 715 1065 207.6 307s General.Motors -332.7 714.7 8424 -2614 148.4 307s US.Steel 491.9 1064.6 -2614 10466 642.6 307s Westinghouse 15.7 207.6 148 643 104.3 307s 307s The correlations of the residuals 307s Chrysler General.Electric General.Motors US.Steel Westinghouse 307s Chrysler 1.0000 -0.0679 -0.273 0.362 0.115 307s General.Electric -0.0679 1.0000 0.279 0.373 0.729 307s General.Motors -0.2730 0.2793 1.000 -0.278 0.158 307s US.Steel 0.3621 0.3732 -0.278 1.000 0.615 307s Westinghouse 0.1154 0.7290 0.158 0.615 1.000 307s 307s 307s OLS estimates for 'Chrysler' (equation 1) 307s Model Formula: Chrysler_invest ~ Chrysler_value + Chrysler_capital 307s 307s 307s Estimate Std. Error t value Pr(>|t|) 307s (Intercept) -6.1900 13.5065 -0.46 0.6525 307s value 0.0779 0.0200 3.90 0.0011 ** 307s capital 0.3157 0.0288 10.96 4e-09 *** 307s --- 307s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 307s 307s Residual standard error: 13.279 on 17 degrees of freedom 307s Number of observations: 20 Degrees of Freedom: 17 307s SSR: 2997.444 MSE: 176.32 Root MSE: 13.279 307s Multiple R-Squared: 0.914 Adjusted R-Squared: 0.903 307s 307s 307s OLS estimates for 'General.Electric' (equation 2) 307s Model Formula: General.Electric_invest ~ General.Electric_value + General.Electric_capital 307s 307s 307s Estimate Std. Error t value Pr(>|t|) 307s (Intercept) -9.9563 31.3742 -0.32 0.75 307s value 0.0266 0.0156 1.71 0.11 307s capital 0.1517 0.0257 5.90 1.7e-05 *** 307s --- 307s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 307s 307s Residual standard error: 27.883 on 17 degrees of freedom 307s Number of observations: 20 Degrees of Freedom: 17 307s SSR: 13216.588 MSE: 777.446 Root MSE: 27.883 307s Multiple R-Squared: 0.705 Adjusted R-Squared: 0.671 307s 307s 307s OLS estimates for 'General.Motors' (equation 3) 307s Model Formula: General.Motors_invest ~ General.Motors_value + General.Motors_capital 307s 307s 307s Estimate Std. Error t value Pr(>|t|) 307s (Intercept) -149.7825 105.8421 -1.42 0.17508 307s value 0.1193 0.0258 4.62 0.00025 *** 307s capital 0.3714 0.0371 10.02 1.5e-08 *** 307s --- 307s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 307s 307s Residual standard error: 91.782 on 17 degrees of freedom 307s Number of observations: 20 Degrees of Freedom: 17 307s SSR: 143205.877 MSE: 8423.875 Root MSE: 91.782 307s Multiple R-Squared: 0.921 Adjusted R-Squared: 0.912 307s 307s 307s OLS estimates for 'US.Steel' (equation 4) 307s Model Formula: US.Steel_invest ~ US.Steel_value + US.Steel_capital 307s 307s 307s Estimate Std. Error t value Pr(>|t|) 307s (Intercept) -30.3685 157.0477 -0.19 0.849 307s value 0.1566 0.0789 1.98 0.064 . 307s capital 0.4239 0.1552 2.73 0.014 * 307s --- 307s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 307s 307s Residual standard error: 102.305 on 17 degrees of freedom 307s Number of observations: 20 Degrees of Freedom: 17 307s SSR: 177928.314 MSE: 10466.371 Root MSE: 102.305 307s Multiple R-Squared: 0.44 Adjusted R-Squared: 0.374 307s 307s 307s OLS estimates for 'Westinghouse' (equation 5) 307s Model Formula: Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 307s 307s 307s Estimate Std. Error t value Pr(>|t|) 307s (Intercept) -0.5094 8.0153 -0.06 0.9501 307s value 0.0529 0.0157 3.37 0.0037 ** 307s capital 0.0924 0.0561 1.65 0.1179 307s --- 307s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 307s 307s Residual standard error: 10.213 on 17 degrees of freedom 307s Number of observations: 20 Degrees of Freedom: 17 307s SSR: 1773.234 MSE: 104.308 Root MSE: 10.213 307s Multiple R-Squared: 0.744 Adjusted R-Squared: 0.714 307s 307s 307s systemfit results 307s method: OLS 307s 307s N DF SSR detRCov OLS-R2 McElroy-R2 307s system 100 85 339121 2.09e+14 0.848 0.862 307s 307s N DF SSR MSE RMSE R2 Adj R2 307s Chrysler 20 17 2997 176 13.3 0.914 0.903 307s General.Electric 20 17 13217 777 27.9 0.705 0.671 307s General.Motors 20 17 143206 8424 91.8 0.921 0.912 307s US.Steel 20 17 177928 10466 102.3 0.440 0.374 307s Westinghouse 20 17 1773 104 10.2 0.744 0.714 307s 307s The covariance matrix of the residuals 307s Chrysler General.Electric General.Motors US.Steel Westinghouse 307s Chrysler 176.3 -25.1 -333 492 15.7 307s General.Electric -25.1 777.4 715 1065 207.6 307s General.Motors -332.7 714.7 8424 -2614 148.4 307s US.Steel 491.9 1064.6 -2614 10466 642.6 307s Westinghouse 15.7 207.6 148 643 104.3 307s 307s The correlations of the residuals 307s Chrysler General.Electric General.Motors US.Steel Westinghouse 307s Chrysler 1.0000 -0.0679 -0.273 0.362 0.115 307s General.Electric -0.0679 1.0000 0.279 0.373 0.729 307s General.Motors -0.2730 0.2793 1.000 -0.278 0.158 307s US.Steel 0.3621 0.3732 -0.278 1.000 0.615 307s Westinghouse 0.1154 0.7290 0.158 0.615 1.000 307s 307s 307s Coefficients: 307s Estimate Std. Error t value Pr(>|t|) 307s Chrysler_(Intercept) -6.1900 13.5065 -0.46 0.64791 307s Chrysler_value 0.0779 0.0200 3.90 0.00019 *** 307s Chrysler_capital 0.3157 0.0288 10.96 < 2e-16 *** 307s General.Electric_(Intercept) -9.9563 31.3742 -0.32 0.75176 307s General.Electric_value 0.0266 0.0156 1.71 0.09171 . 307s General.Electric_capital 0.1517 0.0257 5.90 7.2e-08 *** 307s General.Motors_(Intercept) -149.7825 105.8421 -1.42 0.16068 307s General.Motors_value 0.1193 0.0258 4.62 1.4e-05 *** 307s General.Motors_capital 0.3714 0.0371 10.02 4.4e-16 *** 307s US.Steel_(Intercept) -30.3685 157.0477 -0.19 0.84713 307s US.Steel_value 0.1566 0.0789 1.98 0.05039 . 307s US.Steel_capital 0.4239 0.1552 2.73 0.00768 ** 307s Westinghouse_(Intercept) -0.5094 8.0153 -0.06 0.94948 307s Westinghouse_value 0.0529 0.0157 3.37 0.00114 ** 307s Westinghouse_capital 0.0924 0.0561 1.65 0.10321 307s --- 307s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 307s 307s systemfit results 307s method: OLS 307s 307s N DF SSR detRCov OLS-R2 McElroy-R2 307s system 100 85 339121 2.09e+14 0.848 0.862 307s 307s N DF SSR MSE RMSE R2 Adj R2 307s Chrysler 20 17 2997 176 13.3 0.914 0.903 307s General.Electric 20 17 13217 777 27.9 0.705 0.671 307s General.Motors 20 17 143206 8424 91.8 0.921 0.912 307s US.Steel 20 17 177928 10466 102.3 0.440 0.374 307s Westinghouse 20 17 1773 104 10.2 0.744 0.714 307s 307s 307s OLS estimates for 'Chrysler' (equation 1) 307s Model Formula: Chrysler_invest ~ Chrysler_value + Chrysler_capital 307s 307s 307s Estimate Std. Error t value Pr(>|t|) 307s (Intercept) -6.1900 13.5065 -0.46 0.6525 307s value 0.0779 0.0200 3.90 0.0011 ** 307s capital 0.3157 0.0288 10.96 4e-09 *** 307s --- 307s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 307s 307s Residual standard error: 13.279 on 17 degrees of freedom 307s Number of observations: 20 Degrees of Freedom: 17 307s SSR: 2997.444 MSE: 176.32 Root MSE: 13.279 307s Multiple R-Squared: 0.914 Adjusted R-Squared: 0.903 307s 307s 307s OLS estimates for 'General.Electric' (equation 2) 307s Model Formula: General.Electric_invest ~ General.Electric_value + General.Electric_capital 307s 307s 307s Estimate Std. Error t value Pr(>|t|) 307s (Intercept) -9.9563 31.3742 -0.32 0.75 307s value 0.0266 0.0156 1.71 0.11 307s capital 0.1517 0.0257 5.90 1.7e-05 *** 307s --- 307s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 307s 307s Residual standard error: 27.883 on 17 degrees of freedom 307s Number of observations: 20 Degrees of Freedom: 17 307s SSR: 13216.588 MSE: 777.446 Root MSE: 27.883 307s Multiple R-Squared: 0.705 Adjusted R-Squared: 0.671 307s 307s 307s OLS estimates for 'General.Motors' (equation 3) 307s Model Formula: General.Motors_invest ~ General.Motors_value + General.Motors_capital 307s 307s 307s Estimate Std. Error t value Pr(>|t|) 307s (Intercept) -149.7825 105.8421 -1.42 0.17508 307s value 0.1193 0.0258 4.62 0.00025 *** 307s capital 0.3714 0.0371 10.02 1.5e-08 *** 307s --- 307s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 307s 307s Residual standard error: 91.782 on 17 degrees of freedom 307s Number of observations: 20 Degrees of Freedom: 17 307s SSR: 143205.877 MSE: 8423.875 Root MSE: 91.782 307s Multiple R-Squared: 0.921 Adjusted R-Squared: 0.912 307s 307s 307s OLS estimates for 'US.Steel' (equation 4) 307s Model Formula: US.Steel_invest ~ US.Steel_value + US.Steel_capital 307s 307s 307s Estimate Std. Error t value Pr(>|t|) 307s (Intercept) -30.3685 157.0477 -0.19 0.849 307s value 0.1566 0.0789 1.98 0.064 . 307s capital 0.4239 0.1552 2.73 0.014 * 307s --- 307s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 307s 307s Residual standard error: 102.305 on 17 degrees of freedom 307s Number of observations: 20 Degrees of Freedom: 17 307s SSR: 177928.314 MSE: 10466.371 Root MSE: 102.305 307s Multiple R-Squared: 0.44 Adjusted R-Squared: 0.374 307s 307s 307s OLS estimates for 'Westinghouse' (equation 5) 307s Model Formula: Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 307s 307s 307s Estimate Std. Error t value Pr(>|t|) 307s (Intercept) -0.5094 8.0153 -0.06 0.9501 307s value 0.0529 0.0157 3.37 0.0037 ** 307s capital 0.0924 0.0561 1.65 0.1179 307s --- 307s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 307s 307s Residual standard error: 10.213 on 17 degrees of freedom 307s Number of observations: 20 Degrees of Freedom: 17 307s SSR: 1773.234 MSE: 104.308 Root MSE: 10.213 307s Multiple R-Squared: 0.744 Adjusted R-Squared: 0.714 307s 307s [1] 149.9 660.8 7160.3 8896.4 88.7 307s Chrysler_(Intercept) Chrysler_value 307s -6.1900 0.0779 307s Chrysler_capital General.Electric_(Intercept) 307s 0.3157 -9.9563 307s General.Electric_value General.Electric_capital 307s 0.0266 0.1517 307s General.Motors_(Intercept) General.Motors_value 307s -149.7825 0.1193 307s General.Motors_capital US.Steel_(Intercept) 307s 0.3714 -30.3685 307s US.Steel_value US.Steel_capital 307s 0.1566 0.4239 307s Westinghouse_(Intercept) Westinghouse_value 307s -0.5094 0.0529 307s Westinghouse_capital 307s 0.0924 307s Estimate Std. Error t value Pr(>|t|) 307s Chrysler_(Intercept) -6.1900 13.5065 -0.4583 6.53e-01 307s Chrysler_value 0.0779 0.0200 3.9026 1.15e-03 307s Chrysler_capital 0.3157 0.0288 10.9574 3.99e-09 307s General.Electric_(Intercept) -9.9563 31.3742 -0.3173 7.55e-01 307s General.Electric_value 0.0266 0.0156 1.7057 1.06e-01 307s General.Electric_capital 0.1517 0.0257 5.9015 1.74e-05 307s General.Motors_(Intercept) -149.7825 105.8421 -1.4151 1.75e-01 307s General.Motors_value 0.1193 0.0258 4.6172 2.46e-04 307s General.Motors_capital 0.3714 0.0371 10.0193 1.51e-08 307s US.Steel_(Intercept) -30.3685 157.0477 -0.1934 8.49e-01 307s US.Steel_value 0.1566 0.0789 1.9848 6.35e-02 307s US.Steel_capital 0.4239 0.1552 2.7308 1.42e-02 307s Westinghouse_(Intercept) -0.5094 8.0153 -0.0636 9.50e-01 307s Westinghouse_value 0.0529 0.0157 3.3677 3.65e-03 307s Westinghouse_capital 0.0924 0.0561 1.6472 1.18e-01 307s Chrysler_(Intercept) Chrysler_value 307s Chrysler_(Intercept) 182.4250 -0.254690 307s Chrysler_value -0.2547 0.000399 307s Chrysler_capital 0.0243 -0.000180 307s General.Electric_(Intercept) 0.0000 0.000000 307s General.Electric_value 0.0000 0.000000 307s General.Electric_capital 0.0000 0.000000 307s General.Motors_(Intercept) 0.0000 0.000000 307s General.Motors_value 0.0000 0.000000 307s General.Motors_capital 0.0000 0.000000 307s US.Steel_(Intercept) 0.0000 0.000000 307s US.Steel_value 0.0000 0.000000 307s US.Steel_capital 0.0000 0.000000 307s Westinghouse_(Intercept) 0.0000 0.000000 307s Westinghouse_value 0.0000 0.000000 307s Westinghouse_capital 0.0000 0.000000 307s Chrysler_capital General.Electric_(Intercept) 307s Chrysler_(Intercept) 0.02429 0.000 307s Chrysler_value -0.00018 0.000 307s Chrysler_capital 0.00083 0.000 307s General.Electric_(Intercept) 0.00000 984.344 307s General.Electric_value 0.00000 -0.451 307s General.Electric_capital 0.00000 -0.173 307s General.Motors_(Intercept) 0.00000 0.000 307s General.Motors_value 0.00000 0.000 307s General.Motors_capital 0.00000 0.000 307s US.Steel_(Intercept) 0.00000 0.000 307s US.Steel_value 0.00000 0.000 307s US.Steel_capital 0.00000 0.000 307s Westinghouse_(Intercept) 0.00000 0.000 307s Westinghouse_value 0.00000 0.000 307s Westinghouse_capital 0.00000 0.000 307s General.Electric_value General.Electric_capital 307s Chrysler_(Intercept) 0.00e+00 0.00e+00 307s Chrysler_value 0.00e+00 0.00e+00 307s Chrysler_capital 0.00e+00 0.00e+00 307s General.Electric_(Intercept) -4.51e-01 -1.73e-01 307s General.Electric_value 2.42e-04 -4.73e-05 307s General.Electric_capital -4.73e-05 6.61e-04 307s General.Motors_(Intercept) 0.00e+00 0.00e+00 307s General.Motors_value 0.00e+00 0.00e+00 307s General.Motors_capital 0.00e+00 0.00e+00 307s US.Steel_(Intercept) 0.00e+00 0.00e+00 307s US.Steel_value 0.00e+00 0.00e+00 307s US.Steel_capital 0.00e+00 0.00e+00 307s Westinghouse_(Intercept) 0.00e+00 0.00e+00 307s Westinghouse_value 0.00e+00 0.00e+00 307s Westinghouse_capital 0.00e+00 0.00e+00 307s General.Motors_(Intercept) General.Motors_value 307s Chrysler_(Intercept) 0.000 0.000000 307s Chrysler_value 0.000 0.000000 307s Chrysler_capital 0.000 0.000000 307s General.Electric_(Intercept) 0.000 0.000000 307s General.Electric_value 0.000 0.000000 307s General.Electric_capital 0.000 0.000000 307s General.Motors_(Intercept) 11202.555 -2.623398 307s General.Motors_value -2.623 0.000667 307s General.Motors_capital 0.907 -0.000415 307s US.Steel_(Intercept) 0.000 0.000000 307s US.Steel_value 0.000 0.000000 307s US.Steel_capital 0.000 0.000000 307s Westinghouse_(Intercept) 0.000 0.000000 307s Westinghouse_value 0.000 0.000000 307s Westinghouse_capital 0.000 0.000000 307s General.Motors_capital US.Steel_(Intercept) 307s Chrysler_(Intercept) 0.000000 0.00 307s Chrysler_value 0.000000 0.00 307s Chrysler_capital 0.000000 0.00 307s General.Electric_(Intercept) 0.000000 0.00 307s General.Electric_value 0.000000 0.00 307s General.Electric_capital 0.000000 0.00 307s General.Motors_(Intercept) 0.906860 0.00 307s General.Motors_value -0.000415 0.00 307s General.Motors_capital 0.001374 0.00 307s US.Steel_(Intercept) 0.000000 24663.98 307s US.Steel_value 0.000000 -11.71 307s US.Steel_capital 0.000000 -3.52 307s Westinghouse_(Intercept) 0.000000 0.00 307s Westinghouse_value 0.000000 0.00 307s Westinghouse_capital 0.000000 0.00 307s US.Steel_value US.Steel_capital 307s Chrysler_(Intercept) 0.00000 0.00000 307s Chrysler_value 0.00000 0.00000 307s Chrysler_capital 0.00000 0.00000 307s General.Electric_(Intercept) 0.00000 0.00000 307s General.Electric_value 0.00000 0.00000 307s General.Electric_capital 0.00000 0.00000 307s General.Motors_(Intercept) 0.00000 0.00000 307s General.Motors_value 0.00000 0.00000 307s General.Motors_capital 0.00000 0.00000 307s US.Steel_(Intercept) -11.70740 -3.52078 307s US.Steel_value 0.00622 -0.00188 307s US.Steel_capital -0.00188 0.02409 307s Westinghouse_(Intercept) 0.00000 0.00000 307s Westinghouse_value 0.00000 0.00000 307s Westinghouse_capital 0.00000 0.00000 307s Westinghouse_(Intercept) Westinghouse_value 307s Chrysler_(Intercept) 0.000 0.000000 307s Chrysler_value 0.000 0.000000 307s Chrysler_capital 0.000 0.000000 307s General.Electric_(Intercept) 0.000 0.000000 307s General.Electric_value 0.000 0.000000 307s General.Electric_capital 0.000 0.000000 307s General.Motors_(Intercept) 0.000 0.000000 307s General.Motors_value 0.000 0.000000 307s General.Motors_capital 0.000 0.000000 307s US.Steel_(Intercept) 0.000 0.000000 307s US.Steel_value 0.000 0.000000 307s US.Steel_capital 0.000 0.000000 307s Westinghouse_(Intercept) 64.245 -0.109545 307s Westinghouse_value -0.110 0.000247 307s Westinghouse_capital 0.169 -0.000653 307s Westinghouse_capital 307s Chrysler_(Intercept) 0.000000 307s Chrysler_value 0.000000 307s Chrysler_capital 0.000000 307s General.Electric_(Intercept) 0.000000 307s General.Electric_value 0.000000 307s General.Electric_capital 0.000000 307s General.Motors_(Intercept) 0.000000 307s General.Motors_value 0.000000 307s General.Motors_capital 0.000000 307s US.Steel_(Intercept) 0.000000 307s US.Steel_value 0.000000 307s US.Steel_capital 0.000000 307s Westinghouse_(Intercept) 0.168911 307s Westinghouse_value -0.000653 307s Westinghouse_capital 0.003147 307s Chrysler General.Electric General.Motors US.Steel Westinghouse 307s X1935 10.622 -2.860 99.14 4.15 3.144 307s X1936 10.425 -14.402 -34.01 81.32 -0.958 307s X1937 -7.404 -5.175 -140.48 31.18 -3.684 307s X1938 7.302 -23.295 -3.28 -99.75 -7.915 307s X1939 -14.682 -28.031 -109.45 -178.23 -10.322 307s X1940 -2.315 -0.562 -19.91 -160.69 -6.613 307s X1941 0.631 40.750 24.12 19.65 17.265 307s X1942 -1.581 16.036 98.02 9.82 8.547 307s X1943 -13.459 -23.719 67.76 -46.76 -2.916 307s X1944 -7.780 -26.780 100.03 -83.74 -3.257 307s X1945 11.757 1.768 35.12 -91.24 -7.753 307s X1946 -16.133 58.737 103.90 28.34 5.796 307s X1947 -6.823 43.936 15.18 57.32 15.050 307s X1948 6.615 31.227 -51.86 140.23 2.969 307s X1949 -7.379 -23.552 -115.39 25.65 -11.433 307s X1950 1.268 -37.511 -63.51 34.88 -13.481 307s X1951 39.502 -4.983 -119.40 115.10 4.619 307s X1952 2.774 1.893 -77.82 149.19 13.138 307s X1953 -6.215 5.087 49.50 89.00 11.308 307s X1954 -7.124 -8.563 142.33 -125.42 -13.505 307s 2.5 % 97.5 % 307s Chrysler_(Intercept) -34.686 22.306 307s Chrysler_value 0.036 0.120 307s Chrysler_capital 0.255 0.377 307s General.Electric_(Intercept) -76.150 56.238 307s General.Electric_value -0.006 0.059 307s General.Electric_capital 0.097 0.206 307s General.Motors_(Intercept) -373.090 73.525 307s General.Motors_value 0.065 0.174 307s General.Motors_capital 0.293 0.450 307s US.Steel_(Intercept) -361.710 300.973 307s US.Steel_value -0.010 0.323 307s US.Steel_capital 0.096 0.751 307s Westinghouse_(Intercept) -17.420 16.401 307s Westinghouse_value 0.020 0.086 307s Westinghouse_capital -0.026 0.211 307s Chrysler General.Electric General.Motors US.Steel Westinghouse 307s X1935 29.7 36.0 218 206 9.79 307s X1936 62.3 59.4 426 274 26.86 307s X1937 73.7 82.4 551 439 38.73 307s X1938 44.3 67.9 261 362 30.81 307s X1939 67.1 76.1 440 409 29.16 307s X1940 71.7 75.0 481 422 35.18 307s X1941 67.7 72.3 488 453 31.25 307s X1942 48.4 75.9 350 436 34.79 307s X1943 60.9 85.0 432 408 39.94 307s X1944 67.3 83.6 447 372 41.07 307s X1945 77.0 91.8 526 350 47.02 307s X1946 90.3 101.2 584 392 47.66 307s X1947 69.5 103.3 554 363 40.51 307s X1948 82.7 115.1 581 354 46.59 307s X1949 86.4 121.9 670 379 43.47 307s X1950 99.4 131.0 706 384 45.72 307s X1951 121.1 140.2 875 473 49.76 307s X1952 142.2 155.4 969 496 58.64 307s X1953 181.1 174.4 1255 552 78.77 307s X1954 179.6 198.2 1344 585 82.11 307s 'log Lik.' -464 (df=16) 307s 'log Lik.' -481 (df=16) 307s [1] 100 307s Chrysler_invest Chrysler_value Chrysler_capital General.Electric_invest 307s X1935 40.3 418 10.5 33.1 307s X1936 72.8 838 10.2 45.0 307s X1937 66.3 884 34.7 77.2 307s X1938 51.6 438 51.8 44.6 307s X1939 52.4 680 64.3 48.1 307s X1940 69.4 728 67.1 74.4 307s X1941 68.3 644 75.2 113.0 307s X1942 46.8 411 71.4 91.9 307s X1943 47.4 588 67.1 61.3 307s X1944 59.6 698 60.5 56.8 307s X1945 88.8 846 54.6 93.6 307s X1946 74.1 894 84.8 159.9 307s X1947 62.7 579 96.8 147.2 307s X1948 89.4 695 110.2 146.3 307s X1949 79.0 590 147.4 98.3 307s X1950 100.7 694 163.2 93.5 307s X1951 160.6 809 203.5 135.2 307s X1952 145.0 727 290.6 157.3 307s X1953 174.9 1002 346.1 179.5 307s X1954 172.5 703 414.9 189.6 307s General.Electric_value General.Electric_capital General.Motors_invest 307s X1935 1171 97.8 318 307s X1936 2016 104.4 392 307s X1937 2803 118.0 411 307s X1938 2040 156.2 258 307s X1939 2256 172.6 331 307s X1940 2132 186.6 461 307s X1941 1834 220.9 512 307s X1942 1588 287.8 448 307s X1943 1749 319.9 500 307s X1944 1687 321.3 548 307s X1945 2008 319.6 561 307s X1946 2208 346.0 688 307s X1947 1657 456.4 569 307s X1948 1604 543.4 529 307s X1949 1432 618.3 555 307s X1950 1610 647.4 643 307s X1951 1819 671.3 756 307s X1952 2080 726.1 891 307s X1953 2372 800.3 1304 307s X1954 2760 888.9 1487 307s General.Motors_value General.Motors_capital US.Steel_invest 307s X1935 3078 2.8 210 307s X1936 4662 52.6 355 307s X1937 5387 156.9 470 307s X1938 2792 209.2 262 307s X1939 4313 203.4 230 307s X1940 4644 207.2 262 307s X1941 4551 255.2 473 307s X1942 3244 303.7 446 307s X1943 4054 264.1 362 307s X1944 4379 201.6 288 307s X1945 4841 265.0 259 307s X1946 4901 402.2 420 307s X1947 3526 761.5 420 307s X1948 3255 922.4 494 307s X1949 3700 1020.1 405 307s X1950 3756 1099.0 419 307s X1951 4833 1207.7 588 307s X1952 4925 1430.5 645 307s X1953 6242 1777.3 641 307s X1954 5594 2226.3 459 307s US.Steel_value US.Steel_capital Westinghouse_invest Westinghouse_value 307s X1935 1362 53.8 12.9 192 307s X1936 1807 50.5 25.9 516 307s X1937 2676 118.1 35.0 729 307s X1938 1802 260.2 22.9 560 307s X1939 1957 312.7 18.8 520 307s X1940 2203 254.2 28.6 628 307s X1941 2380 261.4 48.5 537 307s X1942 2169 298.7 43.3 561 307s X1943 1985 301.8 37.0 617 307s X1944 1814 279.1 37.8 627 307s X1945 1850 213.8 39.3 737 307s X1946 2068 232.6 53.5 760 307s X1947 1797 264.8 55.6 581 307s X1948 1626 306.9 49.6 662 307s X1949 1667 351.1 32.0 584 307s X1950 1677 357.8 32.2 635 307s X1951 2290 342.1 54.4 724 307s X1952 2159 444.2 71.8 864 307s X1953 2031 623.6 90.1 1194 307s X1954 2116 669.7 68.6 1189 307s Westinghouse_capital 307s X1935 1.8 307s X1936 0.8 307s X1937 7.4 307s X1938 18.1 307s X1939 23.5 307s X1940 26.5 307s X1941 36.2 307s X1942 60.8 307s X1943 84.4 307s X1944 91.2 307s X1945 92.4 307s X1946 86.0 307s X1947 111.1 307s X1948 130.6 307s X1949 141.8 307s X1950 136.7 307s X1951 129.7 307s X1952 145.5 307s X1953 174.8 307s X1954 213.5 307s Chrysler_(Intercept) Chrysler_value Chrysler_capital 307s Chrysler_X1935 1 418 10.5 307s Chrysler_X1936 1 838 10.2 307s Chrysler_X1937 1 884 34.7 307s Chrysler_X1938 1 438 51.8 307s Chrysler_X1939 1 680 64.3 307s Chrysler_X1940 1 728 67.1 307s Chrysler_X1941 1 644 75.2 307s Chrysler_X1942 1 411 71.4 307s Chrysler_X1943 1 588 67.1 307s Chrysler_X1944 1 698 60.5 307s Chrysler_X1945 1 846 54.6 307s Chrysler_X1946 1 894 84.8 307s Chrysler_X1947 1 579 96.8 307s Chrysler_X1948 1 695 110.2 307s Chrysler_X1949 1 590 147.4 307s Chrysler_X1950 1 694 163.2 307s Chrysler_X1951 1 809 203.5 307s Chrysler_X1952 1 727 290.6 307s Chrysler_X1953 1 1002 346.1 307s Chrysler_X1954 1 703 414.9 307s General.Electric_X1935 0 0 0.0 307s General.Electric_X1936 0 0 0.0 307s General.Electric_X1937 0 0 0.0 307s General.Electric_X1938 0 0 0.0 307s General.Electric_X1939 0 0 0.0 307s General.Electric_X1940 0 0 0.0 307s General.Electric_X1941 0 0 0.0 307s General.Electric_X1942 0 0 0.0 307s General.Electric_X1943 0 0 0.0 307s General.Electric_X1944 0 0 0.0 307s General.Electric_X1945 0 0 0.0 307s General.Electric_X1946 0 0 0.0 307s General.Electric_X1947 0 0 0.0 307s General.Electric_X1948 0 0 0.0 307s General.Electric_X1949 0 0 0.0 307s General.Electric_X1950 0 0 0.0 307s General.Electric_X1951 0 0 0.0 307s General.Electric_X1952 0 0 0.0 307s General.Electric_X1953 0 0 0.0 307s General.Electric_X1954 0 0 0.0 307s General.Motors_X1935 0 0 0.0 307s General.Motors_X1936 0 0 0.0 307s General.Motors_X1937 0 0 0.0 307s General.Motors_X1938 0 0 0.0 307s General.Motors_X1939 0 0 0.0 307s General.Motors_X1940 0 0 0.0 307s General.Motors_X1941 0 0 0.0 307s General.Motors_X1942 0 0 0.0 307s General.Motors_X1943 0 0 0.0 307s General.Motors_X1944 0 0 0.0 307s General.Motors_X1945 0 0 0.0 307s General.Motors_X1946 0 0 0.0 307s General.Motors_X1947 0 0 0.0 307s General.Motors_X1948 0 0 0.0 307s General.Motors_X1949 0 0 0.0 307s General.Motors_X1950 0 0 0.0 307s General.Motors_X1951 0 0 0.0 307s General.Motors_X1952 0 0 0.0 307s General.Motors_X1953 0 0 0.0 307s General.Motors_X1954 0 0 0.0 307s US.Steel_X1935 0 0 0.0 307s US.Steel_X1936 0 0 0.0 307s US.Steel_X1937 0 0 0.0 307s US.Steel_X1938 0 0 0.0 307s US.Steel_X1939 0 0 0.0 307s US.Steel_X1940 0 0 0.0 307s US.Steel_X1941 0 0 0.0 307s US.Steel_X1942 0 0 0.0 307s US.Steel_X1943 0 0 0.0 307s US.Steel_X1944 0 0 0.0 307s US.Steel_X1945 0 0 0.0 307s US.Steel_X1946 0 0 0.0 307s US.Steel_X1947 0 0 0.0 307s US.Steel_X1948 0 0 0.0 307s US.Steel_X1949 0 0 0.0 307s US.Steel_X1950 0 0 0.0 307s US.Steel_X1951 0 0 0.0 307s US.Steel_X1952 0 0 0.0 307s US.Steel_X1953 0 0 0.0 307s US.Steel_X1954 0 0 0.0 307s Westinghouse_X1935 0 0 0.0 307s Westinghouse_X1936 0 0 0.0 307s Westinghouse_X1937 0 0 0.0 307s Westinghouse_X1938 0 0 0.0 307s Westinghouse_X1939 0 0 0.0 307s Westinghouse_X1940 0 0 0.0 307s Westinghouse_X1941 0 0 0.0 307s Westinghouse_X1942 0 0 0.0 307s Westinghouse_X1943 0 0 0.0 307s Westinghouse_X1944 0 0 0.0 307s Westinghouse_X1945 0 0 0.0 307s Westinghouse_X1946 0 0 0.0 307s Westinghouse_X1947 0 0 0.0 307s Westinghouse_X1948 0 0 0.0 307s Westinghouse_X1949 0 0 0.0 307s Westinghouse_X1950 0 0 0.0 307s Westinghouse_X1951 0 0 0.0 307s Westinghouse_X1952 0 0 0.0 307s Westinghouse_X1953 0 0 0.0 307s Westinghouse_X1954 0 0 0.0 307s General.Electric_(Intercept) General.Electric_value 307s Chrysler_X1935 0 0 307s Chrysler_X1936 0 0 307s Chrysler_X1937 0 0 307s Chrysler_X1938 0 0 307s Chrysler_X1939 0 0 307s Chrysler_X1940 0 0 307s Chrysler_X1941 0 0 307s Chrysler_X1942 0 0 307s Chrysler_X1943 0 0 307s Chrysler_X1944 0 0 307s Chrysler_X1945 0 0 307s Chrysler_X1946 0 0 307s Chrysler_X1947 0 0 307s Chrysler_X1948 0 0 307s Chrysler_X1949 0 0 307s Chrysler_X1950 0 0 307s Chrysler_X1951 0 0 307s Chrysler_X1952 0 0 307s Chrysler_X1953 0 0 307s Chrysler_X1954 0 0 307s General.Electric_X1935 1 1171 307s General.Electric_X1936 1 2016 307s General.Electric_X1937 1 2803 307s General.Electric_X1938 1 2040 307s General.Electric_X1939 1 2256 307s General.Electric_X1940 1 2132 307s General.Electric_X1941 1 1834 307s General.Electric_X1942 1 1588 307s General.Electric_X1943 1 1749 307s General.Electric_X1944 1 1687 307s General.Electric_X1945 1 2008 307s General.Electric_X1946 1 2208 307s General.Electric_X1947 1 1657 307s General.Electric_X1948 1 1604 307s General.Electric_X1949 1 1432 307s General.Electric_X1950 1 1610 307s General.Electric_X1951 1 1819 307s General.Electric_X1952 1 2080 307s General.Electric_X1953 1 2372 307s General.Electric_X1954 1 2760 307s General.Motors_X1935 0 0 307s General.Motors_X1936 0 0 307s General.Motors_X1937 0 0 307s General.Motors_X1938 0 0 307s General.Motors_X1939 0 0 307s General.Motors_X1940 0 0 307s General.Motors_X1941 0 0 307s General.Motors_X1942 0 0 307s General.Motors_X1943 0 0 307s General.Motors_X1944 0 0 307s General.Motors_X1945 0 0 307s General.Motors_X1946 0 0 307s General.Motors_X1947 0 0 307s General.Motors_X1948 0 0 307s General.Motors_X1949 0 0 307s General.Motors_X1950 0 0 307s General.Motors_X1951 0 0 307s General.Motors_X1952 0 0 307s General.Motors_X1953 0 0 307s General.Motors_X1954 0 0 307s US.Steel_X1935 0 0 307s US.Steel_X1936 0 0 307s US.Steel_X1937 0 0 307s US.Steel_X1938 0 0 307s US.Steel_X1939 0 0 307s US.Steel_X1940 0 0 307s US.Steel_X1941 0 0 307s US.Steel_X1942 0 0 307s US.Steel_X1943 0 0 307s US.Steel_X1944 0 0 307s US.Steel_X1945 0 0 307s US.Steel_X1946 0 0 307s US.Steel_X1947 0 0 307s US.Steel_X1948 0 0 307s US.Steel_X1949 0 0 307s US.Steel_X1950 0 0 307s US.Steel_X1951 0 0 307s US.Steel_X1952 0 0 307s US.Steel_X1953 0 0 307s US.Steel_X1954 0 0 307s Westinghouse_X1935 0 0 307s Westinghouse_X1936 0 0 307s Westinghouse_X1937 0 0 307s Westinghouse_X1938 0 0 307s Westinghouse_X1939 0 0 307s Westinghouse_X1940 0 0 307s Westinghouse_X1941 0 0 307s Westinghouse_X1942 0 0 307s Westinghouse_X1943 0 0 307s Westinghouse_X1944 0 0 307s Westinghouse_X1945 0 0 307s Westinghouse_X1946 0 0 307s Westinghouse_X1947 0 0 307s Westinghouse_X1948 0 0 307s Westinghouse_X1949 0 0 307s Westinghouse_X1950 0 0 307s Westinghouse_X1951 0 0 307s Westinghouse_X1952 0 0 307s Westinghouse_X1953 0 0 307s Westinghouse_X1954 0 0 307s General.Electric_capital General.Motors_(Intercept) 307s Chrysler_X1935 0.0 0 307s Chrysler_X1936 0.0 0 307s Chrysler_X1937 0.0 0 307s Chrysler_X1938 0.0 0 307s Chrysler_X1939 0.0 0 307s Chrysler_X1940 0.0 0 307s Chrysler_X1941 0.0 0 307s Chrysler_X1942 0.0 0 307s Chrysler_X1943 0.0 0 307s Chrysler_X1944 0.0 0 307s Chrysler_X1945 0.0 0 307s Chrysler_X1946 0.0 0 307s Chrysler_X1947 0.0 0 307s Chrysler_X1948 0.0 0 307s Chrysler_X1949 0.0 0 307s Chrysler_X1950 0.0 0 307s Chrysler_X1951 0.0 0 307s Chrysler_X1952 0.0 0 307s Chrysler_X1953 0.0 0 307s Chrysler_X1954 0.0 0 307s General.Electric_X1935 97.8 0 307s General.Electric_X1936 104.4 0 307s General.Electric_X1937 118.0 0 307s General.Electric_X1938 156.2 0 307s General.Electric_X1939 172.6 0 307s General.Electric_X1940 186.6 0 307s General.Electric_X1941 220.9 0 307s General.Electric_X1942 287.8 0 307s General.Electric_X1943 319.9 0 307s General.Electric_X1944 321.3 0 307s General.Electric_X1945 319.6 0 307s General.Electric_X1946 346.0 0 307s General.Electric_X1947 456.4 0 307s General.Electric_X1948 543.4 0 307s General.Electric_X1949 618.3 0 307s General.Electric_X1950 647.4 0 307s General.Electric_X1951 671.3 0 307s General.Electric_X1952 726.1 0 307s General.Electric_X1953 800.3 0 307s General.Electric_X1954 888.9 0 307s General.Motors_X1935 0.0 1 307s General.Motors_X1936 0.0 1 307s General.Motors_X1937 0.0 1 307s General.Motors_X1938 0.0 1 307s General.Motors_X1939 0.0 1 307s General.Motors_X1940 0.0 1 307s General.Motors_X1941 0.0 1 307s General.Motors_X1942 0.0 1 307s General.Motors_X1943 0.0 1 307s General.Motors_X1944 0.0 1 307s General.Motors_X1945 0.0 1 307s General.Motors_X1946 0.0 1 307s General.Motors_X1947 0.0 1 307s General.Motors_X1948 0.0 1 307s General.Motors_X1949 0.0 1 307s General.Motors_X1950 0.0 1 307s General.Motors_X1951 0.0 1 307s General.Motors_X1952 0.0 1 307s General.Motors_X1953 0.0 1 307s General.Motors_X1954 0.0 1 307s US.Steel_X1935 0.0 0 307s US.Steel_X1936 0.0 0 307s US.Steel_X1937 0.0 0 307s US.Steel_X1938 0.0 0 307s US.Steel_X1939 0.0 0 307s US.Steel_X1940 0.0 0 307s US.Steel_X1941 0.0 0 307s US.Steel_X1942 0.0 0 307s US.Steel_X1943 0.0 0 307s US.Steel_X1944 0.0 0 307s US.Steel_X1945 0.0 0 307s US.Steel_X1946 0.0 0 307s US.Steel_X1947 0.0 0 307s US.Steel_X1948 0.0 0 307s US.Steel_X1949 0.0 0 307s US.Steel_X1950 0.0 0 307s US.Steel_X1951 0.0 0 307s US.Steel_X1952 0.0 0 307s US.Steel_X1953 0.0 0 307s US.Steel_X1954 0.0 0 307s Westinghouse_X1935 0.0 0 307s Westinghouse_X1936 0.0 0 307s Westinghouse_X1937 0.0 0 307s Westinghouse_X1938 0.0 0 307s Westinghouse_X1939 0.0 0 307s Westinghouse_X1940 0.0 0 307s Westinghouse_X1941 0.0 0 307s Westinghouse_X1942 0.0 0 307s Westinghouse_X1943 0.0 0 307s Westinghouse_X1944 0.0 0 307s Westinghouse_X1945 0.0 0 307s Westinghouse_X1946 0.0 0 307s Westinghouse_X1947 0.0 0 307s Westinghouse_X1948 0.0 0 307s Westinghouse_X1949 0.0 0 307s Westinghouse_X1950 0.0 0 307s Westinghouse_X1951 0.0 0 307s Westinghouse_X1952 0.0 0 307s Westinghouse_X1953 0.0 0 307s Westinghouse_X1954 0.0 0 307s General.Motors_value General.Motors_capital 307s Chrysler_X1935 0 0.0 307s Chrysler_X1936 0 0.0 307s Chrysler_X1937 0 0.0 307s Chrysler_X1938 0 0.0 307s Chrysler_X1939 0 0.0 307s Chrysler_X1940 0 0.0 307s Chrysler_X1941 0 0.0 307s Chrysler_X1942 0 0.0 307s Chrysler_X1943 0 0.0 307s Chrysler_X1944 0 0.0 307s Chrysler_X1945 0 0.0 307s Chrysler_X1946 0 0.0 307s Chrysler_X1947 0 0.0 307s Chrysler_X1948 0 0.0 307s Chrysler_X1949 0 0.0 307s Chrysler_X1950 0 0.0 307s Chrysler_X1951 0 0.0 307s Chrysler_X1952 0 0.0 307s Chrysler_X1953 0 0.0 307s Chrysler_X1954 0 0.0 307s General.Electric_X1935 0 0.0 307s General.Electric_X1936 0 0.0 307s General.Electric_X1937 0 0.0 307s General.Electric_X1938 0 0.0 307s General.Electric_X1939 0 0.0 307s General.Electric_X1940 0 0.0 307s General.Electric_X1941 0 0.0 307s General.Electric_X1942 0 0.0 307s General.Electric_X1943 0 0.0 307s General.Electric_X1944 0 0.0 307s General.Electric_X1945 0 0.0 307s General.Electric_X1946 0 0.0 307s General.Electric_X1947 0 0.0 307s General.Electric_X1948 0 0.0 307s General.Electric_X1949 0 0.0 307s General.Electric_X1950 0 0.0 307s General.Electric_X1951 0 0.0 307s General.Electric_X1952 0 0.0 307s General.Electric_X1953 0 0.0 307s General.Electric_X1954 0 0.0 307s General.Motors_X1935 3078 2.8 307s General.Motors_X1936 4662 52.6 307s General.Motors_X1937 5387 156.9 307s General.Motors_X1938 2792 209.2 307s General.Motors_X1939 4313 203.4 307s General.Motors_X1940 4644 207.2 307s General.Motors_X1941 4551 255.2 307s General.Motors_X1942 3244 303.7 307s General.Motors_X1943 4054 264.1 307s General.Motors_X1944 4379 201.6 307s General.Motors_X1945 4841 265.0 307s General.Motors_X1946 4901 402.2 307s General.Motors_X1947 3526 761.5 307s General.Motors_X1948 3255 922.4 307s General.Motors_X1949 3700 1020.1 307s General.Motors_X1950 3756 1099.0 307s General.Motors_X1951 4833 1207.7 307s General.Motors_X1952 4925 1430.5 307s General.Motors_X1953 6242 1777.3 307s General.Motors_X1954 5594 2226.3 307s US.Steel_X1935 0 0.0 307s US.Steel_X1936 0 0.0 307s US.Steel_X1937 0 0.0 307s US.Steel_X1938 0 0.0 307s US.Steel_X1939 0 0.0 307s US.Steel_X1940 0 0.0 307s US.Steel_X1941 0 0.0 307s US.Steel_X1942 0 0.0 307s US.Steel_X1943 0 0.0 307s US.Steel_X1944 0 0.0 307s US.Steel_X1945 0 0.0 307s US.Steel_X1946 0 0.0 307s US.Steel_X1947 0 0.0 307s US.Steel_X1948 0 0.0 307s US.Steel_X1949 0 0.0 307s US.Steel_X1950 0 0.0 307s US.Steel_X1951 0 0.0 307s US.Steel_X1952 0 0.0 307s US.Steel_X1953 0 0.0 307s US.Steel_X1954 0 0.0 307s Westinghouse_X1935 0 0.0 307s Westinghouse_X1936 0 0.0 307s Westinghouse_X1937 0 0.0 307s Westinghouse_X1938 0 0.0 307s Westinghouse_X1939 0 0.0 307s Westinghouse_X1940 0 0.0 307s Westinghouse_X1941 0 0.0 307s Westinghouse_X1942 0 0.0 307s Westinghouse_X1943 0 0.0 307s Westinghouse_X1944 0 0.0 307s Westinghouse_X1945 0 0.0 307s Westinghouse_X1946 0 0.0 307s Westinghouse_X1947 0 0.0 307s Westinghouse_X1948 0 0.0 307s Westinghouse_X1949 0 0.0 307s Westinghouse_X1950 0 0.0 307s Westinghouse_X1951 0 0.0 307s Westinghouse_X1952 0 0.0 307s Westinghouse_X1953 0 0.0 307s Westinghouse_X1954 0 0.0 307s US.Steel_(Intercept) US.Steel_value US.Steel_capital 307s Chrysler_X1935 0 0 0.0 307s Chrysler_X1936 0 0 0.0 307s Chrysler_X1937 0 0 0.0 307s Chrysler_X1938 0 0 0.0 307s Chrysler_X1939 0 0 0.0 307s Chrysler_X1940 0 0 0.0 307s Chrysler_X1941 0 0 0.0 307s Chrysler_X1942 0 0 0.0 307s Chrysler_X1943 0 0 0.0 307s Chrysler_X1944 0 0 0.0 307s Chrysler_X1945 0 0 0.0 307s Chrysler_X1946 0 0 0.0 307s Chrysler_X1947 0 0 0.0 307s Chrysler_X1948 0 0 0.0 307s Chrysler_X1949 0 0 0.0 307s Chrysler_X1950 0 0 0.0 307s Chrysler_X1951 0 0 0.0 307s Chrysler_X1952 0 0 0.0 307s Chrysler_X1953 0 0 0.0 307s Chrysler_X1954 0 0 0.0 307s General.Electric_X1935 0 0 0.0 307s General.Electric_X1936 0 0 0.0 307s General.Electric_X1937 0 0 0.0 307s General.Electric_X1938 0 0 0.0 307s General.Electric_X1939 0 0 0.0 307s General.Electric_X1940 0 0 0.0 307s General.Electric_X1941 0 0 0.0 307s General.Electric_X1942 0 0 0.0 307s General.Electric_X1943 0 0 0.0 307s General.Electric_X1944 0 0 0.0 307s General.Electric_X1945 0 0 0.0 307s General.Electric_X1946 0 0 0.0 307s General.Electric_X1947 0 0 0.0 307s General.Electric_X1948 0 0 0.0 307s General.Electric_X1949 0 0 0.0 307s General.Electric_X1950 0 0 0.0 307s General.Electric_X1951 0 0 0.0 307s General.Electric_X1952 0 0 0.0 307s General.Electric_X1953 0 0 0.0 307s General.Electric_X1954 0 0 0.0 307s General.Motors_X1935 0 0 0.0 307s General.Motors_X1936 0 0 0.0 307s General.Motors_X1937 0 0 0.0 307s General.Motors_X1938 0 0 0.0 307s General.Motors_X1939 0 0 0.0 307s General.Motors_X1940 0 0 0.0 307s General.Motors_X1941 0 0 0.0 307s General.Motors_X1942 0 0 0.0 307s General.Motors_X1943 0 0 0.0 307s General.Motors_X1944 0 0 0.0 307s General.Motors_X1945 0 0 0.0 307s General.Motors_X1946 0 0 0.0 307s General.Motors_X1947 0 0 0.0 307s General.Motors_X1948 0 0 0.0 307s General.Motors_X1949 0 0 0.0 307s General.Motors_X1950 0 0 0.0 307s General.Motors_X1951 0 0 0.0 307s General.Motors_X1952 0 0 0.0 307s General.Motors_X1953 0 0 0.0 307s General.Motors_X1954 0 0 0.0 307s US.Steel_X1935 1 1362 53.8 307s US.Steel_X1936 1 1807 50.5 307s US.Steel_X1937 1 2676 118.1 307s US.Steel_X1938 1 1802 260.2 307s US.Steel_X1939 1 1957 312.7 307s US.Steel_X1940 1 2203 254.2 307s US.Steel_X1941 1 2380 261.4 307s US.Steel_X1942 1 2169 298.7 307s US.Steel_X1943 1 1985 301.8 307s US.Steel_X1944 1 1814 279.1 307s US.Steel_X1945 1 1850 213.8 307s US.Steel_X1946 1 2068 232.6 307s US.Steel_X1947 1 1797 264.8 307s US.Steel_X1948 1 1626 306.9 307s US.Steel_X1949 1 1667 351.1 307s US.Steel_X1950 1 1677 357.8 307s US.Steel_X1951 1 2290 342.1 307s US.Steel_X1952 1 2159 444.2 307s US.Steel_X1953 1 2031 623.6 307s US.Steel_X1954 1 2116 669.7 307s Westinghouse_X1935 0 0 0.0 307s Westinghouse_X1936 0 0 0.0 307s Westinghouse_X1937 0 0 0.0 307s Westinghouse_X1938 0 0 0.0 307s Westinghouse_X1939 0 0 0.0 307s Westinghouse_X1940 0 0 0.0 307s Westinghouse_X1941 0 0 0.0 307s Westinghouse_X1942 0 0 0.0 307s Westinghouse_X1943 0 0 0.0 307s Westinghouse_X1944 0 0 0.0 307s Westinghouse_X1945 0 0 0.0 307s Westinghouse_X1946 0 0 0.0 307s Westinghouse_X1947 0 0 0.0 307s Westinghouse_X1948 0 0 0.0 307s Westinghouse_X1949 0 0 0.0 307s Westinghouse_X1950 0 0 0.0 307s Westinghouse_X1951 0 0 0.0 307s Westinghouse_X1952 0 0 0.0 307s Westinghouse_X1953 0 0 0.0 307s Westinghouse_X1954 0 0 0.0 307s Westinghouse_(Intercept) Westinghouse_value 307s Chrysler_X1935 0 0 307s Chrysler_X1936 0 0 307s Chrysler_X1937 0 0 307s Chrysler_X1938 0 0 307s Chrysler_X1939 0 0 307s Chrysler_X1940 0 0 307s Chrysler_X1941 0 0 307s Chrysler_X1942 0 0 307s Chrysler_X1943 0 0 307s Chrysler_X1944 0 0 307s Chrysler_X1945 0 0 307s Chrysler_X1946 0 0 307s Chrysler_X1947 0 0 307s Chrysler_X1948 0 0 307s Chrysler_X1949 0 0 307s Chrysler_X1950 0 0 307s Chrysler_X1951 0 0 307s Chrysler_X1952 0 0 307s Chrysler_X1953 0 0 307s Chrysler_X1954 0 0 307s General.Electric_X1935 0 0 307s General.Electric_X1936 0 0 307s General.Electric_X1937 0 0 307s General.Electric_X1938 0 0 307s General.Electric_X1939 0 0 307s General.Electric_X1940 0 0 307s General.Electric_X1941 0 0 307s General.Electric_X1942 0 0 307s General.Electric_X1943 0 0 307s General.Electric_X1944 0 0 307s General.Electric_X1945 0 0 307s General.Electric_X1946 0 0 307s General.Electric_X1947 0 0 307s General.Electric_X1948 0 0 307s General.Electric_X1949 0 0 307s General.Electric_X1950 0 0 307s General.Electric_X1951 0 0 307s General.Electric_X1952 0 0 307s General.Electric_X1953 0 0 307s General.Electric_X1954 0 0 307s General.Motors_X1935 0 0 307s General.Motors_X1936 0 0 307s General.Motors_X1937 0 0 307s General.Motors_X1938 0 0 307s General.Motors_X1939 0 0 307s General.Motors_X1940 0 0 307s General.Motors_X1941 0 0 307s General.Motors_X1942 0 0 307s General.Motors_X1943 0 0 307s General.Motors_X1944 0 0 307s General.Motors_X1945 0 0 307s General.Motors_X1946 0 0 307s General.Motors_X1947 0 0 307s General.Motors_X1948 0 0 307s General.Motors_X1949 0 0 307s General.Motors_X1950 0 0 307s General.Motors_X1951 0 0 307s General.Motors_X1952 0 0 307s General.Motors_X1953 0 0 307s General.Motors_X1954 0 0 307s US.Steel_X1935 0 0 307s US.Steel_X1936 0 0 307s US.Steel_X1937 0 0 307s US.Steel_X1938 0 0 307s US.Steel_X1939 0 0 307s US.Steel_X1940 0 0 307s US.Steel_X1941 0 0 307s US.Steel_X1942 0 0 307s US.Steel_X1943 0 0 307s US.Steel_X1944 0 0 307s US.Steel_X1945 0 0 307s US.Steel_X1946 0 0 307s US.Steel_X1947 0 0 307s US.Steel_X1948 0 0 307s US.Steel_X1949 0 0 307s US.Steel_X1950 0 0 307s US.Steel_X1951 0 0 307s US.Steel_X1952 0 0 307s US.Steel_X1953 0 0 307s US.Steel_X1954 0 0 307s Westinghouse_X1935 1 192 307s Westinghouse_X1936 1 516 307s Westinghouse_X1937 1 729 307s Westinghouse_X1938 1 560 307s Westinghouse_X1939 1 520 307s Westinghouse_X1940 1 628 307s Westinghouse_X1941 1 537 307s Westinghouse_X1942 1 561 307s Westinghouse_X1943 1 617 307s Westinghouse_X1944 1 627 307s Westinghouse_X1945 1 737 307s Westinghouse_X1946 1 760 307s Westinghouse_X1947 1 581 307s Westinghouse_X1948 1 662 307s Westinghouse_X1949 1 584 307s Westinghouse_X1950 1 635 307s Westinghouse_X1951 1 724 307s Westinghouse_X1952 1 864 307s Westinghouse_X1953 1 1194 307s Westinghouse_X1954 1 1189 307s Westinghouse_capital 307s Chrysler_X1935 0.0 307s Chrysler_X1936 0.0 307s Chrysler_X1937 0.0 307s Chrysler_X1938 0.0 307s Chrysler_X1939 0.0 307s Chrysler_X1940 0.0 307s Chrysler_X1941 0.0 307s Chrysler_X1942 0.0 307s Chrysler_X1943 0.0 307s Chrysler_X1944 0.0 307s Chrysler_X1945 0.0 307s Chrysler_X1946 0.0 307s Chrysler_X1947 0.0 307s Chrysler_X1948 0.0 307s Chrysler_X1949 0.0 307s Chrysler_X1950 0.0 307s Chrysler_X1951 0.0 307s Chrysler_X1952 0.0 307s Chrysler_X1953 0.0 307s Chrysler_X1954 0.0 307s General.Electric_X1935 0.0 307s General.Electric_X1936 0.0 307s General.Electric_X1937 0.0 307s General.Electric_X1938 0.0 307s General.Electric_X1939 0.0 307s General.Electric_X1940 0.0 307s General.Electric_X1941 0.0 307s General.Electric_X1942 0.0 307s General.Electric_X1943 0.0 307s General.Electric_X1944 0.0 307s General.Electric_X1945 0.0 307s General.Electric_X1946 0.0 307s General.Electric_X1947 0.0 307s General.Electric_X1948 0.0 307s General.Electric_X1949 0.0 307s General.Electric_X1950 0.0 307s General.Electric_X1951 0.0 307s General.Electric_X1952 0.0 307s General.Electric_X1953 0.0 307s General.Electric_X1954 0.0 307s General.Motors_X1935 0.0 307s General.Motors_X1936 0.0 307s General.Motors_X1937 0.0 307s General.Motors_X1938 0.0 307s General.Motors_X1939 0.0 307s General.Motors_X1940 0.0 307s General.Motors_X1941 0.0 307s General.Motors_X1942 0.0 307s General.Motors_X1943 0.0 307s General.Motors_X1944 0.0 307s General.Motors_X1945 0.0 307s General.Motors_X1946 0.0 307s General.Motors_X1947 0.0 307s General.Motors_X1948 0.0 307s General.Motors_X1949 0.0 307s General.Motors_X1950 0.0 307s General.Motors_X1951 0.0 307s General.Motors_X1952 0.0 307s General.Motors_X1953 0.0 307s General.Motors_X1954 0.0 307s US.Steel_X1935 0.0 307s US.Steel_X1936 0.0 307s US.Steel_X1937 0.0 307s US.Steel_X1938 0.0 307s US.Steel_X1939 0.0 307s US.Steel_X1940 0.0 307s US.Steel_X1941 0.0 307s US.Steel_X1942 0.0 307s US.Steel_X1943 0.0 307s US.Steel_X1944 0.0 307s US.Steel_X1945 0.0 307s US.Steel_X1946 0.0 307s US.Steel_X1947 0.0 307s US.Steel_X1948 0.0 307s US.Steel_X1949 0.0 307s US.Steel_X1950 0.0 307s US.Steel_X1951 0.0 307s US.Steel_X1952 0.0 307s US.Steel_X1953 0.0 307s US.Steel_X1954 0.0 307s Westinghouse_X1935 1.8 307s Westinghouse_X1936 0.8 307s Westinghouse_X1937 7.4 307s Westinghouse_X1938 18.1 307s Westinghouse_X1939 23.5 307s Westinghouse_X1940 26.5 307s Westinghouse_X1941 36.2 307s Westinghouse_X1942 60.8 307s Westinghouse_X1943 84.4 307s Westinghouse_X1944 91.2 307s Westinghouse_X1945 92.4 307s Westinghouse_X1946 86.0 307s Westinghouse_X1947 111.1 307s Westinghouse_X1948 130.6 307s Westinghouse_X1949 141.8 307s Westinghouse_X1950 136.7 307s Westinghouse_X1951 129.7 307s Westinghouse_X1952 145.5 307s Westinghouse_X1953 174.8 307s Westinghouse_X1954 213.5 307s $Chrysler 307s Chrysler_invest ~ Chrysler_value + Chrysler_capital 307s 307s 307s $General.Electric 307s General.Electric_invest ~ General.Electric_value + General.Electric_capital 307s 307s 307s $General.Motors 307s General.Motors_invest ~ General.Motors_value + General.Motors_capital 307s 307s 307s $US.Steel 307s US.Steel_invest ~ US.Steel_value + US.Steel_capital 307s 307s 307s $Westinghouse 307s Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 307s 307s 307s General.Electric_invest ~ General.Electric_value + General.Electric_capital 307s 307s $Chrysler 307s Chrysler_invest ~ Chrysler_value + Chrysler_capital 307s attr(,"variables") 307s list(Chrysler_invest, Chrysler_value, Chrysler_capital) 307s attr(,"factors") 307s Chrysler_value Chrysler_capital 307s Chrysler_invest 0 0 307s Chrysler_value 1 0 307s Chrysler_capital 0 1 307s attr(,"term.labels") 307s [1] "Chrysler_value" "Chrysler_capital" 307s attr(,"order") 307s [1] 1 1 307s attr(,"intercept") 307s [1] 1 307s attr(,"response") 307s [1] 1 307s attr(,".Environment") 307s 307s attr(,"predvars") 307s list(Chrysler_invest, Chrysler_value, Chrysler_capital) 307s attr(,"dataClasses") 307s Chrysler_invest Chrysler_value Chrysler_capital 307s "numeric" "numeric" "numeric" 307s 307s $General.Electric 307s General.Electric_invest ~ General.Electric_value + General.Electric_capital 307s attr(,"variables") 307s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 307s attr(,"factors") 307s General.Electric_value General.Electric_capital 307s General.Electric_invest 0 0 307s General.Electric_value 1 0 307s General.Electric_capital 0 1 307s attr(,"term.labels") 307s [1] "General.Electric_value" "General.Electric_capital" 307s attr(,"order") 307s [1] 1 1 307s attr(,"intercept") 307s [1] 1 307s attr(,"response") 307s [1] 1 307s attr(,".Environment") 307s 307s attr(,"predvars") 307s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 307s attr(,"dataClasses") 307s General.Electric_invest General.Electric_value General.Electric_capital 307s "numeric" "numeric" "numeric" 307s 307s $General.Motors 307s General.Motors_invest ~ General.Motors_value + General.Motors_capital 307s attr(,"variables") 307s list(General.Motors_invest, General.Motors_value, General.Motors_capital) 307s attr(,"factors") 307s General.Motors_value General.Motors_capital 307s General.Motors_invest 0 0 307s General.Motors_value 1 0 307s General.Motors_capital 0 1 307s attr(,"term.labels") 307s [1] "General.Motors_value" "General.Motors_capital" 307s attr(,"order") 307s [1] 1 1 307s attr(,"intercept") 307s [1] 1 307s attr(,"response") 307s [1] 1 307s attr(,".Environment") 307s 307s attr(,"predvars") 307s list(General.Motors_invest, General.Motors_value, General.Motors_capital) 307s attr(,"dataClasses") 307s General.Motors_invest General.Motors_value General.Motors_capital 307s "numeric" "numeric" "numeric" 307s 307s $US.Steel 307s US.Steel_invest ~ US.Steel_value + US.Steel_capital 307s attr(,"variables") 307s list(US.Steel_invest, US.Steel_value, US.Steel_capital) 307s attr(,"factors") 307s US.Steel_value US.Steel_capital 307s US.Steel_invest 0 0 307s US.Steel_value 1 0 307s US.Steel_capital 0 1 307s attr(,"term.labels") 307s [1] "US.Steel_value" "US.Steel_capital" 307s attr(,"order") 307s [1] 1 1 307s attr(,"intercept") 307s [1] 1 307s attr(,"response") 307s [1] 1 307s attr(,".Environment") 307s 307s attr(,"predvars") 307s list(US.Steel_invest, US.Steel_value, US.Steel_capital) 307s attr(,"dataClasses") 307s US.Steel_invest US.Steel_value US.Steel_capital 307s "numeric" "numeric" "numeric" 307s 307s $Westinghouse 307s Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 307s attr(,"variables") 307s list(Westinghouse_invest, Westinghouse_value, Westinghouse_capital) 307s attr(,"factors") 307s Westinghouse_value Westinghouse_capital 307s Westinghouse_invest 0 0 307s Westinghouse_value 1 0 307s Westinghouse_capital 0 1 307s attr(,"term.labels") 307s [1] "Westinghouse_value" "Westinghouse_capital" 307s attr(,"order") 307s [1] 1 1 307s attr(,"intercept") 307s [1] 1 307s attr(,"response") 307s [1] 1 307s attr(,".Environment") 307s 307s attr(,"predvars") 307s list(Westinghouse_invest, Westinghouse_value, Westinghouse_capital) 307s attr(,"dataClasses") 307s Westinghouse_invest Westinghouse_value Westinghouse_capital 307s "numeric" "numeric" "numeric" 307s 307s General.Electric_invest ~ General.Electric_value + General.Electric_capital 307s attr(,"variables") 307s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 307s attr(,"factors") 307s General.Electric_value General.Electric_capital 307s General.Electric_invest 0 0 307s General.Electric_value 1 0 307s General.Electric_capital 0 1 307s attr(,"term.labels") 307s [1] "General.Electric_value" "General.Electric_capital" 307s attr(,"order") 307s [1] 1 1 307s attr(,"intercept") 307s [1] 1 307s attr(,"response") 307s [1] 1 307s attr(,".Environment") 307s 307s attr(,"predvars") 307s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 307s attr(,"dataClasses") 307s General.Electric_invest General.Electric_value General.Electric_capital 307s "numeric" "numeric" "numeric" 307s > 307s > # OLS Pooled 307s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 307s + greeneOlsPooled <- systemfit( formulaGrunfeld, "OLS", 307s + data = GrunfeldGreene, pooled = TRUE, useMatrix = useMatrix ) 307s + print( greeneOlsPooled ) 307s + print( summary( greeneOlsPooled ) ) 307s + print( summary( greeneOlsPooled, useDfSys = FALSE, residCov = FALSE ) ) 307s + print( summary( greeneOlsPooled, residCov = FALSE, equations = FALSE ) ) 307s + print( sum( sapply( greeneOlsPooled$eq, function(x){return(summary(x)$ssr)}) )/97 ) # sigma^2 307s + print( coef( greeneOlsPooled ) ) 307s + print( coef( greeneOlsPooled, modified.regMat = TRUE ) ) 307s + print( coef( summary( greeneOlsPooled ) ) ) 307s + print( coef( summary( greeneOlsPooled ), modified.regMat = TRUE ) ) 307s + print( vcov( greeneOlsPooled ) ) 307s + print( vcov( greeneOlsPooled, modified.regMat = TRUE ) ) 307s + print( residuals( greeneOlsPooled ) ) 307s + print( confint( greeneOlsPooled ) ) 307s + print( fitted( greeneOlsPooled ) ) 307s + print( logLik( greeneOlsPooled ) ) 307s + print( logLik( greeneOlsPooled, residCovDiag = TRUE ) ) 307s + print( nobs( greeneOlsPooled ) ) 307s + print( model.frame( greeneOlsPooled ) ) 307s + print( model.matrix( greeneOlsPooled ) ) 307s + print( formula( greeneOlsPooled ) ) 307s + print( formula( greeneOlsPooled$eq[[ 1 ]] ) ) 307s + print( terms( greeneOlsPooled ) ) 307s + print( terms( greeneOlsPooled$eq[[ 1 ]] ) ) 307s + } 307s 307s systemfit results 307s method: OLS 307s 307s Coefficients: 307s Chrysler_(Intercept) Chrysler_value 307s -48.030 0.105 307s Chrysler_capital General.Electric_(Intercept) 307s 0.305 -48.030 307s General.Electric_value General.Electric_capital 307s 0.105 0.305 307s General.Motors_(Intercept) General.Motors_value 307s -48.030 0.105 307s General.Motors_capital US.Steel_(Intercept) 307s 0.305 -48.030 307s US.Steel_value US.Steel_capital 307s 0.105 0.305 307s Westinghouse_(Intercept) Westinghouse_value 307s -48.030 0.105 307s Westinghouse_capital 307s 0.305 307s 307s systemfit results 307s method: OLS 307s 307s N DF SSR detRCov OLS-R2 McElroy-R2 307s system 100 97 1570884 4.2e+17 0.294 0.812 307s 307s N DF SSR MSE RMSE R2 Adj R2 307s Chrysler 20 17 15117 889 29.8 0.564 0.513 307s General.Electric 20 17 685770 40339 200.8 -14.291 -16.090 307s General.Motors 20 17 188218 11072 105.2 0.897 0.884 307s US.Steel 20 17 669110 39359 198.4 -1.105 -1.352 307s Westinghouse 20 17 12668 745 27.3 -0.826 -1.041 307s 307s The covariance matrix of the residuals 307s Chrysler General.Electric General.Motors US.Steel Westinghouse 307s Chrysler 889.2 -4898 -198 4748 -94.6 307s General.Electric -4898.1 40339 -2254 -32821 2658.0 307s General.Motors -197.7 -2254 11072 304 -1328.6 307s US.Steel 4748.1 -32821 304 39359 -1377.3 307s Westinghouse -94.6 2658 -1329 -1377 745.2 307s 307s The correlations of the residuals 307s Chrysler General.Electric General.Motors US.Steel Westinghouse 307s Chrysler 1.000 0.144 -0.1852 0.2218 0.186 307s General.Electric 0.144 1.000 -0.2592 -0.1216 0.881 307s General.Motors -0.185 -0.259 1.0000 -0.0155 -0.469 307s US.Steel 0.222 -0.122 -0.0155 1.0000 -0.119 307s Westinghouse 0.186 0.881 -0.4689 -0.1186 1.000 307s 307s 307s OLS estimates for 'Chrysler' (equation 1) 307s Model Formula: Chrysler_invest ~ Chrysler_value + Chrysler_capital 307s 307s 307s Estimate Std. Error t value Pr(>|t|) 307s (Intercept) -48.0297 21.4802 -2.24 0.028 * 307s value 0.1051 0.0114 9.24 6.0e-15 *** 307s capital 0.3054 0.0435 7.02 3.1e-10 *** 307s --- 307s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 307s 307s Residual standard error: 29.82 on 17 degrees of freedom 307s Number of observations: 20 Degrees of Freedom: 17 307s SSR: 15117.016 MSE: 889.236 Root MSE: 29.82 307s Multiple R-Squared: 0.564 Adjusted R-Squared: 0.513 307s 307s 307s OLS estimates for 'General.Electric' (equation 2) 307s Model Formula: General.Electric_invest ~ General.Electric_value + General.Electric_capital 307s 307s 307s Estimate Std. Error t value Pr(>|t|) 307s (Intercept) -48.0297 21.4802 -2.24 0.028 * 307s value 0.1051 0.0114 9.24 6.0e-15 *** 307s capital 0.3054 0.0435 7.02 3.1e-10 *** 307s --- 307s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 307s 307s Residual standard error: 200.847 on 17 degrees of freedom 307s Number of observations: 20 Degrees of Freedom: 17 307s SSR: 685769.815 MSE: 40339.401 Root MSE: 200.847 307s Multiple R-Squared: -14.291 Adjusted R-Squared: -16.09 307s 307s 307s OLS estimates for 'General.Motors' (equation 3) 307s Model Formula: General.Motors_invest ~ General.Motors_value + General.Motors_capital 307s 307s 307s Estimate Std. Error t value Pr(>|t|) 307s (Intercept) -48.0297 21.4802 -2.24 0.028 * 307s value 0.1051 0.0114 9.24 6.0e-15 *** 307s capital 0.3054 0.0435 7.02 3.1e-10 *** 307s --- 307s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 307s 307s Residual standard error: 105.222 on 17 degrees of freedom 307s Number of observations: 20 Degrees of Freedom: 17 307s SSR: 188218.158 MSE: 11071.656 Root MSE: 105.222 307s Multiple R-Squared: 0.897 Adjusted R-Squared: 0.884 307s 307s 307s OLS estimates for 'US.Steel' (equation 4) 307s Model Formula: US.Steel_invest ~ US.Steel_value + US.Steel_capital 307s 307s 307s Estimate Std. Error t value Pr(>|t|) 307s (Intercept) -48.0297 21.4802 -2.24 0.028 * 307s value 0.1051 0.0114 9.24 6.0e-15 *** 307s capital 0.3054 0.0435 7.02 3.1e-10 *** 307s --- 307s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 307s 307s Residual standard error: 198.392 on 17 degrees of freedom 307s Number of observations: 20 Degrees of Freedom: 17 307s SSR: 669110.225 MSE: 39359.425 Root MSE: 198.392 307s Multiple R-Squared: -1.105 Adjusted R-Squared: -1.352 307s 307s 307s OLS estimates for 'Westinghouse' (equation 5) 307s Model Formula: Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 307s 307s 307s Estimate Std. Error t value Pr(>|t|) 307s (Intercept) -48.0297 21.4802 -2.24 0.028 * 307s value 0.1051 0.0114 9.24 6.0e-15 *** 307s capital 0.3054 0.0435 7.02 3.1e-10 *** 307s --- 307s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 307s 307s Residual standard error: 27.298 on 17 degrees of freedom 307s Number of observations: 20 Degrees of Freedom: 17 307s SSR: 12668.473 MSE: 745.204 Root MSE: 27.298 307s Multiple R-Squared: -0.826 Adjusted R-Squared: -1.041 307s 307s 307s systemfit results 307s method: OLS 307s 307s N DF SSR detRCov OLS-R2 McElroy-R2 307s system 100 97 1570884 4.2e+17 0.294 0.812 307s 307s N DF SSR MSE RMSE R2 Adj R2 307s Chrysler 20 17 15117 889 29.8 0.564 0.513 307s General.Electric 20 17 685770 40339 200.8 -14.291 -16.090 307s General.Motors 20 17 188218 11072 105.2 0.897 0.884 307s US.Steel 20 17 669110 39359 198.4 -1.105 -1.352 307s Westinghouse 20 17 12668 745 27.3 -0.826 -1.041 307s 307s 307s OLS estimates for 'Chrysler' (equation 1) 307s Model Formula: Chrysler_invest ~ Chrysler_value + Chrysler_capital 307s 307s 307s Estimate Std. Error t value Pr(>|t|) 307s (Intercept) -48.0297 21.4802 -2.24 0.039 * 307s value 0.1051 0.0114 9.24 4.9e-08 *** 307s capital 0.3054 0.0435 7.02 2.1e-06 *** 307s --- 307s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 307s 307s Residual standard error: 29.82 on 17 degrees of freedom 307s Number of observations: 20 Degrees of Freedom: 17 307s SSR: 15117.016 MSE: 889.236 Root MSE: 29.82 307s Multiple R-Squared: 0.564 Adjusted R-Squared: 0.513 307s 307s 307s OLS estimates for 'General.Electric' (equation 2) 307s Model Formula: General.Electric_invest ~ General.Electric_value + General.Electric_capital 307s 307s 307s Estimate Std. Error t value Pr(>|t|) 307s (Intercept) -48.0297 21.4802 -2.24 0.039 * 307s value 0.1051 0.0114 9.24 4.9e-08 *** 307s capital 0.3054 0.0435 7.02 2.1e-06 *** 307s --- 307s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 307s 307s Residual standard error: 200.847 on 17 degrees of freedom 307s Number of observations: 20 Degrees of Freedom: 17 307s SSR: 685769.815 MSE: 40339.401 Root MSE: 200.847 307s Multiple R-Squared: -14.291 Adjusted R-Squared: -16.09 307s 307s 307s OLS estimates for 'General.Motors' (equation 3) 307s Model Formula: General.Motors_invest ~ General.Motors_value + General.Motors_capital 307s 307s 307s Estimate Std. Error t value Pr(>|t|) 307s (Intercept) -48.0297 21.4802 -2.24 0.039 * 307s value 0.1051 0.0114 9.24 4.9e-08 *** 307s capital 0.3054 0.0435 7.02 2.1e-06 *** 307s --- 307s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 307s 307s Residual standard error: 105.222 on 17 degrees of freedom 307s Number of observations: 20 Degrees of Freedom: 17 307s SSR: 188218.158 MSE: 11071.656 Root MSE: 105.222 307s Multiple R-Squared: 0.897 Adjusted R-Squared: 0.884 307s 307s 307s OLS estimates for 'US.Steel' (equation 4) 307s Model Formula: US.Steel_invest ~ US.Steel_value + US.Steel_capital 307s 307s 307s Estimate Std. Error t value Pr(>|t|) 307s (Intercept) -48.0297 21.4802 -2.24 0.039 * 307s value 0.1051 0.0114 9.24 4.9e-08 *** 307s capital 0.3054 0.0435 7.02 2.1e-06 *** 307s --- 307s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 307s 307s Residual standard error: 198.392 on 17 degrees of freedom 307s Number of observations: 20 Degrees of Freedom: 17 307s SSR: 669110.225 MSE: 39359.425 Root MSE: 198.392 307s Multiple R-Squared: -1.105 Adjusted R-Squared: -1.352 307s 307s 307s OLS estimates for 'Westinghouse' (equation 5) 307s Model Formula: Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 307s 307s 307s Estimate Std. Error t value Pr(>|t|) 307s (Intercept) -48.0297 21.4802 -2.24 0.039 * 307s value 0.1051 0.0114 9.24 4.9e-08 *** 307s capital 0.3054 0.0435 7.02 2.1e-06 *** 307s --- 307s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 307s 307s Residual standard error: 27.298 on 17 degrees of freedom 307s Number of observations: 20 Degrees of Freedom: 17 307s SSR: 12668.473 MSE: 745.204 Root MSE: 27.298 307s Multiple R-Squared: -0.826 Adjusted R-Squared: -1.041 307s 307s 307s systemfit results 307s method: OLS 307s 307s N DF SSR detRCov OLS-R2 McElroy-R2 307s system 100 97 1570884 4.2e+17 0.294 0.812 307s 307s N DF SSR MSE RMSE R2 Adj R2 307s Chrysler 20 17 15117 889 29.8 0.564 0.513 307s General.Electric 20 17 685770 40339 200.8 -14.291 -16.090 307s General.Motors 20 17 188218 11072 105.2 0.897 0.884 307s US.Steel 20 17 669110 39359 198.4 -1.105 -1.352 307s Westinghouse 20 17 12668 745 27.3 -0.826 -1.041 307s 307s 307s Coefficients: 307s Estimate Std. Error t value Pr(>|t|) 307s Chrysler_(Intercept) -48.0297 21.4802 -2.24 0.028 * 307s Chrysler_value 0.1051 0.0114 9.24 6.0e-15 *** 307s Chrysler_capital 0.3054 0.0435 7.02 3.1e-10 *** 307s General.Electric_(Intercept) -48.0297 21.4802 -2.24 0.028 * 307s General.Electric_value 0.1051 0.0114 9.24 6.0e-15 *** 307s General.Electric_capital 0.3054 0.0435 7.02 3.1e-10 *** 307s General.Motors_(Intercept) -48.0297 21.4802 -2.24 0.028 * 307s General.Motors_value 0.1051 0.0114 9.24 6.0e-15 *** 307s General.Motors_capital 0.3054 0.0435 7.02 3.1e-10 *** 307s US.Steel_(Intercept) -48.0297 21.4802 -2.24 0.028 * 307s US.Steel_value 0.1051 0.0114 9.24 6.0e-15 *** 307s US.Steel_capital 0.3054 0.0435 7.02 3.1e-10 *** 307s Westinghouse_(Intercept) -48.0297 21.4802 -2.24 0.028 * 307s Westinghouse_value 0.1051 0.0114 9.24 6.0e-15 *** 307s Westinghouse_capital 0.3054 0.0435 7.02 3.1e-10 *** 307s --- 307s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 307s [1] 16195 307s Chrysler_(Intercept) Chrysler_value 307s -48.030 0.105 307s Chrysler_capital General.Electric_(Intercept) 307s 0.305 -48.030 307s General.Electric_value General.Electric_capital 307s 0.105 0.305 307s General.Motors_(Intercept) General.Motors_value 307s -48.030 0.105 307s General.Motors_capital US.Steel_(Intercept) 307s 0.305 -48.030 307s US.Steel_value US.Steel_capital 307s 0.105 0.305 307s Westinghouse_(Intercept) Westinghouse_value 307s -48.030 0.105 307s Westinghouse_capital 307s 0.305 307s C1 C2 C3 307s -48.030 0.105 0.305 307s Estimate Std. Error t value Pr(>|t|) 307s Chrysler_(Intercept) -48.030 21.4802 -2.24 2.76e-02 307s Chrysler_value 0.105 0.0114 9.24 6.00e-15 307s Chrysler_capital 0.305 0.0435 7.02 3.06e-10 307s General.Electric_(Intercept) -48.030 21.4802 -2.24 2.76e-02 307s General.Electric_value 0.105 0.0114 9.24 6.00e-15 307s General.Electric_capital 0.305 0.0435 7.02 3.06e-10 307s General.Motors_(Intercept) -48.030 21.4802 -2.24 2.76e-02 307s General.Motors_value 0.105 0.0114 9.24 6.00e-15 307s General.Motors_capital 0.305 0.0435 7.02 3.06e-10 307s US.Steel_(Intercept) -48.030 21.4802 -2.24 2.76e-02 307s US.Steel_value 0.105 0.0114 9.24 6.00e-15 307s US.Steel_capital 0.305 0.0435 7.02 3.06e-10 307s Westinghouse_(Intercept) -48.030 21.4802 -2.24 2.76e-02 307s Westinghouse_value 0.105 0.0114 9.24 6.00e-15 307s Westinghouse_capital 0.305 0.0435 7.02 3.06e-10 307s Estimate Std. Error t value Pr(>|t|) 307s C1 -48.030 21.4802 -2.24 2.76e-02 307s C2 0.105 0.0114 9.24 6.00e-15 307s C3 0.305 0.0435 7.02 3.06e-10 307s Chrysler_(Intercept) Chrysler_value 307s Chrysler_(Intercept) 461.39750 -0.154668 307s Chrysler_value -0.15467 0.000129 307s Chrysler_capital -0.00689 -0.000303 307s General.Electric_(Intercept) 461.39750 -0.154668 307s General.Electric_value -0.15467 0.000129 307s General.Electric_capital -0.00689 -0.000303 307s General.Motors_(Intercept) 461.39750 -0.154668 307s General.Motors_value -0.15467 0.000129 307s General.Motors_capital -0.00689 -0.000303 307s US.Steel_(Intercept) 461.39750 -0.154668 307s US.Steel_value -0.15467 0.000129 307s US.Steel_capital -0.00689 -0.000303 307s Westinghouse_(Intercept) 461.39750 -0.154668 307s Westinghouse_value -0.15467 0.000129 307s Westinghouse_capital -0.00689 -0.000303 307s Chrysler_capital General.Electric_(Intercept) 307s Chrysler_(Intercept) -0.006891 461.39750 307s Chrysler_value -0.000303 -0.15467 307s Chrysler_capital 0.001893 -0.00689 307s General.Electric_(Intercept) -0.006891 461.39750 307s General.Electric_value -0.000303 -0.15467 307s General.Electric_capital 0.001893 -0.00689 307s General.Motors_(Intercept) -0.006891 461.39750 307s General.Motors_value -0.000303 -0.15467 307s General.Motors_capital 0.001893 -0.00689 307s US.Steel_(Intercept) -0.006891 461.39750 307s US.Steel_value -0.000303 -0.15467 307s US.Steel_capital 0.001893 -0.00689 307s Westinghouse_(Intercept) -0.006891 461.39750 307s Westinghouse_value -0.000303 -0.15467 307s Westinghouse_capital 0.001893 -0.00689 307s General.Electric_value General.Electric_capital 307s Chrysler_(Intercept) -0.154668 -0.006891 307s Chrysler_value 0.000129 -0.000303 307s Chrysler_capital -0.000303 0.001893 307s General.Electric_(Intercept) -0.154668 -0.006891 307s General.Electric_value 0.000129 -0.000303 307s General.Electric_capital -0.000303 0.001893 307s General.Motors_(Intercept) -0.154668 -0.006891 307s General.Motors_value 0.000129 -0.000303 307s General.Motors_capital -0.000303 0.001893 307s US.Steel_(Intercept) -0.154668 -0.006891 307s US.Steel_value 0.000129 -0.000303 307s US.Steel_capital -0.000303 0.001893 307s Westinghouse_(Intercept) -0.154668 -0.006891 307s Westinghouse_value 0.000129 -0.000303 307s Westinghouse_capital -0.000303 0.001893 307s General.Motors_(Intercept) General.Motors_value 307s Chrysler_(Intercept) 461.39750 -0.154668 307s Chrysler_value -0.15467 0.000129 307s Chrysler_capital -0.00689 -0.000303 307s General.Electric_(Intercept) 461.39750 -0.154668 307s General.Electric_value -0.15467 0.000129 307s General.Electric_capital -0.00689 -0.000303 307s General.Motors_(Intercept) 461.39750 -0.154668 307s General.Motors_value -0.15467 0.000129 307s General.Motors_capital -0.00689 -0.000303 307s US.Steel_(Intercept) 461.39750 -0.154668 307s US.Steel_value -0.15467 0.000129 307s US.Steel_capital -0.00689 -0.000303 307s Westinghouse_(Intercept) 461.39750 -0.154668 307s Westinghouse_value -0.15467 0.000129 307s Westinghouse_capital -0.00689 -0.000303 307s General.Motors_capital US.Steel_(Intercept) 307s Chrysler_(Intercept) -0.006891 461.39750 307s Chrysler_value -0.000303 -0.15467 307s Chrysler_capital 0.001893 -0.00689 307s General.Electric_(Intercept) -0.006891 461.39750 307s General.Electric_value -0.000303 -0.15467 307s General.Electric_capital 0.001893 -0.00689 307s General.Motors_(Intercept) -0.006891 461.39750 307s General.Motors_value -0.000303 -0.15467 307s General.Motors_capital 0.001893 -0.00689 307s US.Steel_(Intercept) -0.006891 461.39750 307s US.Steel_value -0.000303 -0.15467 307s US.Steel_capital 0.001893 -0.00689 307s Westinghouse_(Intercept) -0.006891 461.39750 307s Westinghouse_value -0.000303 -0.15467 307s Westinghouse_capital 0.001893 -0.00689 307s US.Steel_value US.Steel_capital 307s Chrysler_(Intercept) -0.154668 -0.006891 307s Chrysler_value 0.000129 -0.000303 307s Chrysler_capital -0.000303 0.001893 307s General.Electric_(Intercept) -0.154668 -0.006891 307s General.Electric_value 0.000129 -0.000303 307s General.Electric_capital -0.000303 0.001893 307s General.Motors_(Intercept) -0.154668 -0.006891 307s General.Motors_value 0.000129 -0.000303 307s General.Motors_capital -0.000303 0.001893 307s US.Steel_(Intercept) -0.154668 -0.006891 307s US.Steel_value 0.000129 -0.000303 307s US.Steel_capital -0.000303 0.001893 307s Westinghouse_(Intercept) -0.154668 -0.006891 307s Westinghouse_value 0.000129 -0.000303 307s Westinghouse_capital -0.000303 0.001893 307s Westinghouse_(Intercept) Westinghouse_value 307s Chrysler_(Intercept) 461.39750 -0.154668 307s Chrysler_value -0.15467 0.000129 307s Chrysler_capital -0.00689 -0.000303 307s General.Electric_(Intercept) 461.39750 -0.154668 307s General.Electric_value -0.15467 0.000129 307s General.Electric_capital -0.00689 -0.000303 307s General.Motors_(Intercept) 461.39750 -0.154668 307s General.Motors_value -0.15467 0.000129 307s General.Motors_capital -0.00689 -0.000303 307s US.Steel_(Intercept) 461.39750 -0.154668 307s US.Steel_value -0.15467 0.000129 307s US.Steel_capital -0.00689 -0.000303 307s Westinghouse_(Intercept) 461.39750 -0.154668 307s Westinghouse_value -0.15467 0.000129 307s Westinghouse_capital -0.00689 -0.000303 307s Westinghouse_capital 307s Chrysler_(Intercept) -0.006891 307s Chrysler_value -0.000303 307s Chrysler_capital 0.001893 307s General.Electric_(Intercept) -0.006891 307s General.Electric_value -0.000303 307s General.Electric_capital 0.001893 307s General.Motors_(Intercept) -0.006891 307s General.Motors_value -0.000303 307s General.Motors_capital 0.001893 307s US.Steel_(Intercept) -0.006891 307s US.Steel_value -0.000303 307s US.Steel_capital 0.001893 307s Westinghouse_(Intercept) -0.006891 307s Westinghouse_value -0.000303 307s Westinghouse_capital 0.001893 307s C1 C2 C3 307s C1 461.39750 -0.154668 -0.006891 307s C2 -0.15467 0.000129 -0.000303 307s C3 -0.00689 -0.000303 0.001893 307s Chrysler General.Electric General.Motors US.Steel Westinghouse 307s X1935 41.24 -71.7 41.27 98.333 40.29 307s X1936 29.63 -150.7 -66.11 198.009 19.46 307s X1937 10.81 -205.4 -155.39 200.626 4.21 307s X1938 37.79 -169.4 -51.57 41.520 6.50 307s X1939 9.38 -193.7 -136.54 -22.742 5.06 307s X1940 20.47 -158.6 -42.05 0.513 2.46 307s X1941 25.78 -99.2 3.84 190.851 29.04 307s X1942 29.85 -114.8 62.38 174.529 13.83 307s X1943 13.11 -172.2 41.00 108.865 -5.58 307s X1944 15.73 -170.6 73.77 60.388 -7.87 307s X1945 31.19 -166.9 19.60 47.014 -18.39 307s X1946 2.33 -129.8 98.30 180.017 -4.69 307s X1947 20.31 -118.2 13.81 198.862 8.57 307s X1948 30.75 -140.2 -46.46 277.965 -11.89 307s X1949 19.97 -192.9 -97.21 170.739 -24.58 307s X1950 25.98 -225.4 -39.33 181.300 -28.22 307s X1951 61.49 -213.0 -72.74 291.171 -13.26 307s X1952 27.89 -234.9 -15.13 330.665 -15.43 307s X1953 12.03 -266.1 153.79 285.144 -40.69 307s X1954 19.93 -323.8 267.09 80.518 -73.50 307s 2.5 % 97.5 % 307s Chrysler_(Intercept) -90.662 -5.398 307s Chrysler_value 0.083 0.128 307s Chrysler_capital 0.219 0.392 307s General.Electric_(Intercept) -90.662 -5.398 307s General.Electric_value 0.083 0.128 307s General.Electric_capital 0.219 0.392 307s General.Motors_(Intercept) -90.662 -5.398 307s General.Motors_value 0.083 0.128 307s General.Motors_capital 0.219 0.392 307s US.Steel_(Intercept) -90.662 -5.398 307s US.Steel_value 0.083 0.128 307s US.Steel_capital 0.219 0.392 307s Westinghouse_(Intercept) -90.662 -5.398 307s Westinghouse_value 0.083 0.128 307s Westinghouse_capital 0.219 0.392 307s Chrysler General.Electric General.Motors US.Steel Westinghouse 307s X1935 -0.95 105 276 112 -27.36 307s X1936 43.13 196 458 157 6.44 307s X1937 55.45 283 566 269 30.84 307s X1938 13.81 214 309 221 16.39 307s X1939 43.03 242 467 253 13.78 307s X1940 48.94 233 503 261 26.11 307s X1941 42.57 212 508 282 19.47 307s X1942 16.95 207 386 271 29.51 307s X1943 34.29 233 459 253 42.60 307s X1944 43.84 227 474 228 45.68 307s X1945 57.59 261 542 212 57.66 307s X1946 71.79 290 590 240 58.15 307s X1947 42.37 265 555 222 46.99 307s X1948 58.61 287 576 217 61.45 307s X1949 59.01 291 652 234 56.62 307s X1950 74.68 319 682 238 60.46 307s X1951 99.13 348 829 297 67.64 307s X1952 117.11 392 906 315 87.21 307s X1953 162.90 446 1151 356 130.77 307s X1954 152.56 513 1220 379 142.10 307s 'log Lik.' -540 (df=4) 307s 'log Lik.' -573 (df=4) 307s [1] 100 307s Chrysler_invest Chrysler_value Chrysler_capital General.Electric_invest 307s X1935 40.3 418 10.5 33.1 307s X1936 72.8 838 10.2 45.0 307s X1937 66.3 884 34.7 77.2 307s X1938 51.6 438 51.8 44.6 307s X1939 52.4 680 64.3 48.1 307s X1940 69.4 728 67.1 74.4 307s X1941 68.3 644 75.2 113.0 307s X1942 46.8 411 71.4 91.9 307s X1943 47.4 588 67.1 61.3 307s X1944 59.6 698 60.5 56.8 307s X1945 88.8 846 54.6 93.6 307s X1946 74.1 894 84.8 159.9 307s X1947 62.7 579 96.8 147.2 307s X1948 89.4 695 110.2 146.3 307s X1949 79.0 590 147.4 98.3 307s X1950 100.7 694 163.2 93.5 307s X1951 160.6 809 203.5 135.2 307s X1952 145.0 727 290.6 157.3 307s X1953 174.9 1002 346.1 179.5 307s X1954 172.5 703 414.9 189.6 307s General.Electric_value General.Electric_capital General.Motors_invest 307s X1935 1171 97.8 318 307s X1936 2016 104.4 392 307s X1937 2803 118.0 411 307s X1938 2040 156.2 258 307s X1939 2256 172.6 331 307s X1940 2132 186.6 461 307s X1941 1834 220.9 512 307s X1942 1588 287.8 448 307s X1943 1749 319.9 500 307s X1944 1687 321.3 548 307s X1945 2008 319.6 561 307s X1946 2208 346.0 688 307s X1947 1657 456.4 569 307s X1948 1604 543.4 529 307s X1949 1432 618.3 555 307s X1950 1610 647.4 643 307s X1951 1819 671.3 756 307s X1952 2080 726.1 891 307s X1953 2372 800.3 1304 307s X1954 2760 888.9 1487 307s General.Motors_value General.Motors_capital US.Steel_invest 307s X1935 3078 2.8 210 307s X1936 4662 52.6 355 307s X1937 5387 156.9 470 307s X1938 2792 209.2 262 307s X1939 4313 203.4 230 307s X1940 4644 207.2 262 307s X1941 4551 255.2 473 307s X1942 3244 303.7 446 307s X1943 4054 264.1 362 307s X1944 4379 201.6 288 307s X1945 4841 265.0 259 307s X1946 4901 402.2 420 307s X1947 3526 761.5 420 307s X1948 3255 922.4 494 307s X1949 3700 1020.1 405 307s X1950 3756 1099.0 419 307s X1951 4833 1207.7 588 307s X1952 4925 1430.5 645 307s X1953 6242 1777.3 641 307s X1954 5594 2226.3 459 307s US.Steel_value US.Steel_capital Westinghouse_invest Westinghouse_value 307s X1935 1362 53.8 12.9 192 307s X1936 1807 50.5 25.9 516 307s X1937 2676 118.1 35.0 729 307s X1938 1802 260.2 22.9 560 307s X1939 1957 312.7 18.8 520 307s X1940 2203 254.2 28.6 628 307s X1941 2380 261.4 48.5 537 307s X1942 2169 298.7 43.3 561 307s X1943 1985 301.8 37.0 617 307s X1944 1814 279.1 37.8 627 307s X1945 1850 213.8 39.3 737 307s X1946 2068 232.6 53.5 760 307s X1947 1797 264.8 55.6 581 307s X1948 1626 306.9 49.6 662 307s X1949 1667 351.1 32.0 584 307s X1950 1677 357.8 32.2 635 307s X1951 2290 342.1 54.4 724 307s X1952 2159 444.2 71.8 864 307s X1953 2031 623.6 90.1 1194 307s X1954 2116 669.7 68.6 1189 307s Westinghouse_capital 307s X1935 1.8 307s X1936 0.8 307s X1937 7.4 307s X1938 18.1 307s X1939 23.5 307s X1940 26.5 307s X1941 36.2 307s X1942 60.8 307s X1943 84.4 307s X1944 91.2 307s X1945 92.4 307s X1946 86.0 307s X1947 111.1 307s X1948 130.6 307s X1949 141.8 307s X1950 136.7 307s X1951 129.7 307s X1952 145.5 307s X1953 174.8 307s X1954 213.5 307s Chrysler_(Intercept) Chrysler_value Chrysler_capital 307s Chrysler_X1935 1 418 10.5 307s Chrysler_X1936 1 838 10.2 307s Chrysler_X1937 1 884 34.7 307s Chrysler_X1938 1 438 51.8 307s Chrysler_X1939 1 680 64.3 307s Chrysler_X1940 1 728 67.1 307s Chrysler_X1941 1 644 75.2 307s Chrysler_X1942 1 411 71.4 307s Chrysler_X1943 1 588 67.1 307s Chrysler_X1944 1 698 60.5 307s Chrysler_X1945 1 846 54.6 307s Chrysler_X1946 1 894 84.8 307s Chrysler_X1947 1 579 96.8 307s Chrysler_X1948 1 695 110.2 307s Chrysler_X1949 1 590 147.4 307s Chrysler_X1950 1 694 163.2 307s Chrysler_X1951 1 809 203.5 307s Chrysler_X1952 1 727 290.6 307s Chrysler_X1953 1 1002 346.1 307s Chrysler_X1954 1 703 414.9 307s General.Electric_X1935 0 0 0.0 307s General.Electric_X1936 0 0 0.0 307s General.Electric_X1937 0 0 0.0 307s General.Electric_X1938 0 0 0.0 307s General.Electric_X1939 0 0 0.0 307s General.Electric_X1940 0 0 0.0 307s General.Electric_X1941 0 0 0.0 307s General.Electric_X1942 0 0 0.0 307s General.Electric_X1943 0 0 0.0 307s General.Electric_X1944 0 0 0.0 307s General.Electric_X1945 0 0 0.0 307s General.Electric_X1946 0 0 0.0 307s General.Electric_X1947 0 0 0.0 307s General.Electric_X1948 0 0 0.0 307s General.Electric_X1949 0 0 0.0 307s General.Electric_X1950 0 0 0.0 307s General.Electric_X1951 0 0 0.0 307s General.Electric_X1952 0 0 0.0 307s General.Electric_X1953 0 0 0.0 307s General.Electric_X1954 0 0 0.0 307s General.Motors_X1935 0 0 0.0 307s General.Motors_X1936 0 0 0.0 307s General.Motors_X1937 0 0 0.0 307s General.Motors_X1938 0 0 0.0 307s General.Motors_X1939 0 0 0.0 307s General.Motors_X1940 0 0 0.0 307s General.Motors_X1941 0 0 0.0 307s General.Motors_X1942 0 0 0.0 307s General.Motors_X1943 0 0 0.0 307s General.Motors_X1944 0 0 0.0 307s General.Motors_X1945 0 0 0.0 307s General.Motors_X1946 0 0 0.0 307s General.Motors_X1947 0 0 0.0 307s General.Motors_X1948 0 0 0.0 307s General.Motors_X1949 0 0 0.0 307s General.Motors_X1950 0 0 0.0 307s General.Motors_X1951 0 0 0.0 307s General.Motors_X1952 0 0 0.0 307s General.Motors_X1953 0 0 0.0 307s General.Motors_X1954 0 0 0.0 307s US.Steel_X1935 0 0 0.0 307s US.Steel_X1936 0 0 0.0 307s US.Steel_X1937 0 0 0.0 307s US.Steel_X1938 0 0 0.0 307s US.Steel_X1939 0 0 0.0 307s US.Steel_X1940 0 0 0.0 307s US.Steel_X1941 0 0 0.0 307s US.Steel_X1942 0 0 0.0 307s US.Steel_X1943 0 0 0.0 307s US.Steel_X1944 0 0 0.0 307s US.Steel_X1945 0 0 0.0 307s US.Steel_X1946 0 0 0.0 307s US.Steel_X1947 0 0 0.0 307s US.Steel_X1948 0 0 0.0 307s US.Steel_X1949 0 0 0.0 307s US.Steel_X1950 0 0 0.0 307s US.Steel_X1951 0 0 0.0 307s US.Steel_X1952 0 0 0.0 307s US.Steel_X1953 0 0 0.0 307s US.Steel_X1954 0 0 0.0 307s Westinghouse_X1935 0 0 0.0 307s Westinghouse_X1936 0 0 0.0 307s Westinghouse_X1937 0 0 0.0 307s Westinghouse_X1938 0 0 0.0 307s Westinghouse_X1939 0 0 0.0 307s Westinghouse_X1940 0 0 0.0 307s Westinghouse_X1941 0 0 0.0 307s Westinghouse_X1942 0 0 0.0 307s Westinghouse_X1943 0 0 0.0 307s Westinghouse_X1944 0 0 0.0 307s Westinghouse_X1945 0 0 0.0 307s Westinghouse_X1946 0 0 0.0 307s Westinghouse_X1947 0 0 0.0 307s Westinghouse_X1948 0 0 0.0 307s Westinghouse_X1949 0 0 0.0 307s Westinghouse_X1950 0 0 0.0 307s Westinghouse_X1951 0 0 0.0 307s Westinghouse_X1952 0 0 0.0 307s Westinghouse_X1953 0 0 0.0 307s Westinghouse_X1954 0 0 0.0 307s General.Electric_(Intercept) General.Electric_value 307s Chrysler_X1935 0 0 307s Chrysler_X1936 0 0 307s Chrysler_X1937 0 0 307s Chrysler_X1938 0 0 307s Chrysler_X1939 0 0 307s Chrysler_X1940 0 0 307s Chrysler_X1941 0 0 307s Chrysler_X1942 0 0 307s Chrysler_X1943 0 0 307s Chrysler_X1944 0 0 307s Chrysler_X1945 0 0 307s Chrysler_X1946 0 0 307s Chrysler_X1947 0 0 307s Chrysler_X1948 0 0 307s Chrysler_X1949 0 0 307s Chrysler_X1950 0 0 307s Chrysler_X1951 0 0 307s Chrysler_X1952 0 0 307s Chrysler_X1953 0 0 307s Chrysler_X1954 0 0 307s General.Electric_X1935 1 1171 307s General.Electric_X1936 1 2016 307s General.Electric_X1937 1 2803 307s General.Electric_X1938 1 2040 307s General.Electric_X1939 1 2256 307s General.Electric_X1940 1 2132 307s General.Electric_X1941 1 1834 307s General.Electric_X1942 1 1588 307s General.Electric_X1943 1 1749 307s General.Electric_X1944 1 1687 307s General.Electric_X1945 1 2008 307s General.Electric_X1946 1 2208 307s General.Electric_X1947 1 1657 307s General.Electric_X1948 1 1604 307s General.Electric_X1949 1 1432 307s General.Electric_X1950 1 1610 307s General.Electric_X1951 1 1819 307s General.Electric_X1952 1 2080 307s General.Electric_X1953 1 2372 307s General.Electric_X1954 1 2760 307s General.Motors_X1935 0 0 307s General.Motors_X1936 0 0 307s General.Motors_X1937 0 0 307s General.Motors_X1938 0 0 307s General.Motors_X1939 0 0 307s General.Motors_X1940 0 0 307s General.Motors_X1941 0 0 307s General.Motors_X1942 0 0 307s General.Motors_X1943 0 0 307s General.Motors_X1944 0 0 307s General.Motors_X1945 0 0 307s General.Motors_X1946 0 0 307s General.Motors_X1947 0 0 307s General.Motors_X1948 0 0 307s General.Motors_X1949 0 0 307s General.Motors_X1950 0 0 307s General.Motors_X1951 0 0 307s General.Motors_X1952 0 0 307s General.Motors_X1953 0 0 307s General.Motors_X1954 0 0 307s US.Steel_X1935 0 0 307s US.Steel_X1936 0 0 307s US.Steel_X1937 0 0 307s US.Steel_X1938 0 0 307s US.Steel_X1939 0 0 307s US.Steel_X1940 0 0 307s US.Steel_X1941 0 0 307s US.Steel_X1942 0 0 307s US.Steel_X1943 0 0 307s US.Steel_X1944 0 0 307s US.Steel_X1945 0 0 307s US.Steel_X1946 0 0 307s US.Steel_X1947 0 0 307s US.Steel_X1948 0 0 307s US.Steel_X1949 0 0 307s US.Steel_X1950 0 0 307s US.Steel_X1951 0 0 307s US.Steel_X1952 0 0 307s US.Steel_X1953 0 0 307s US.Steel_X1954 0 0 307s Westinghouse_X1935 0 0 307s Westinghouse_X1936 0 0 307s Westinghouse_X1937 0 0 307s Westinghouse_X1938 0 0 307s Westinghouse_X1939 0 0 307s Westinghouse_X1940 0 0 307s Westinghouse_X1941 0 0 307s Westinghouse_X1942 0 0 307s Westinghouse_X1943 0 0 307s Westinghouse_X1944 0 0 307s Westinghouse_X1945 0 0 307s Westinghouse_X1946 0 0 307s Westinghouse_X1947 0 0 307s Westinghouse_X1948 0 0 307s Westinghouse_X1949 0 0 307s Westinghouse_X1950 0 0 307s Westinghouse_X1951 0 0 307s Westinghouse_X1952 0 0 307s Westinghouse_X1953 0 0 307s Westinghouse_X1954 0 0 307s General.Electric_capital General.Motors_(Intercept) 307s Chrysler_X1935 0.0 0 307s Chrysler_X1936 0.0 0 307s Chrysler_X1937 0.0 0 307s Chrysler_X1938 0.0 0 307s Chrysler_X1939 0.0 0 307s Chrysler_X1940 0.0 0 307s Chrysler_X1941 0.0 0 307s Chrysler_X1942 0.0 0 307s Chrysler_X1943 0.0 0 307s Chrysler_X1944 0.0 0 307s Chrysler_X1945 0.0 0 307s Chrysler_X1946 0.0 0 307s Chrysler_X1947 0.0 0 307s Chrysler_X1948 0.0 0 307s Chrysler_X1949 0.0 0 307s Chrysler_X1950 0.0 0 307s Chrysler_X1951 0.0 0 307s Chrysler_X1952 0.0 0 307s Chrysler_X1953 0.0 0 307s Chrysler_X1954 0.0 0 307s General.Electric_X1935 97.8 0 307s General.Electric_X1936 104.4 0 307s General.Electric_X1937 118.0 0 307s General.Electric_X1938 156.2 0 307s General.Electric_X1939 172.6 0 307s General.Electric_X1940 186.6 0 307s General.Electric_X1941 220.9 0 307s General.Electric_X1942 287.8 0 307s General.Electric_X1943 319.9 0 307s General.Electric_X1944 321.3 0 307s General.Electric_X1945 319.6 0 307s General.Electric_X1946 346.0 0 307s General.Electric_X1947 456.4 0 307s General.Electric_X1948 543.4 0 307s General.Electric_X1949 618.3 0 307s General.Electric_X1950 647.4 0 307s General.Electric_X1951 671.3 0 307s General.Electric_X1952 726.1 0 307s General.Electric_X1953 800.3 0 307s General.Electric_X1954 888.9 0 307s General.Motors_X1935 0.0 1 307s General.Motors_X1936 0.0 1 307s General.Motors_X1937 0.0 1 307s General.Motors_X1938 0.0 1 307s General.Motors_X1939 0.0 1 307s General.Motors_X1940 0.0 1 307s General.Motors_X1941 0.0 1 307s General.Motors_X1942 0.0 1 307s General.Motors_X1943 0.0 1 307s General.Motors_X1944 0.0 1 307s General.Motors_X1945 0.0 1 307s General.Motors_X1946 0.0 1 307s General.Motors_X1947 0.0 1 307s General.Motors_X1948 0.0 1 307s General.Motors_X1949 0.0 1 307s General.Motors_X1950 0.0 1 307s General.Motors_X1951 0.0 1 307s General.Motors_X1952 0.0 1 307s General.Motors_X1953 0.0 1 307s General.Motors_X1954 0.0 1 307s US.Steel_X1935 0.0 0 307s US.Steel_X1936 0.0 0 307s US.Steel_X1937 0.0 0 307s US.Steel_X1938 0.0 0 307s US.Steel_X1939 0.0 0 307s US.Steel_X1940 0.0 0 307s US.Steel_X1941 0.0 0 307s US.Steel_X1942 0.0 0 307s US.Steel_X1943 0.0 0 307s US.Steel_X1944 0.0 0 307s US.Steel_X1945 0.0 0 307s US.Steel_X1946 0.0 0 307s US.Steel_X1947 0.0 0 307s US.Steel_X1948 0.0 0 307s US.Steel_X1949 0.0 0 307s US.Steel_X1950 0.0 0 307s US.Steel_X1951 0.0 0 307s US.Steel_X1952 0.0 0 307s US.Steel_X1953 0.0 0 307s US.Steel_X1954 0.0 0 307s Westinghouse_X1935 0.0 0 307s Westinghouse_X1936 0.0 0 307s Westinghouse_X1937 0.0 0 307s Westinghouse_X1938 0.0 0 307s Westinghouse_X1939 0.0 0 307s Westinghouse_X1940 0.0 0 307s Westinghouse_X1941 0.0 0 307s Westinghouse_X1942 0.0 0 307s Westinghouse_X1943 0.0 0 307s Westinghouse_X1944 0.0 0 307s Westinghouse_X1945 0.0 0 307s Westinghouse_X1946 0.0 0 307s Westinghouse_X1947 0.0 0 307s Westinghouse_X1948 0.0 0 307s Westinghouse_X1949 0.0 0 307s Westinghouse_X1950 0.0 0 307s Westinghouse_X1951 0.0 0 307s Westinghouse_X1952 0.0 0 307s Westinghouse_X1953 0.0 0 307s Westinghouse_X1954 0.0 0 307s General.Motors_value General.Motors_capital 307s Chrysler_X1935 0 0.0 307s Chrysler_X1936 0 0.0 307s Chrysler_X1937 0 0.0 307s Chrysler_X1938 0 0.0 307s Chrysler_X1939 0 0.0 307s Chrysler_X1940 0 0.0 307s Chrysler_X1941 0 0.0 307s Chrysler_X1942 0 0.0 307s Chrysler_X1943 0 0.0 307s Chrysler_X1944 0 0.0 307s Chrysler_X1945 0 0.0 307s Chrysler_X1946 0 0.0 307s Chrysler_X1947 0 0.0 307s Chrysler_X1948 0 0.0 307s Chrysler_X1949 0 0.0 307s Chrysler_X1950 0 0.0 307s Chrysler_X1951 0 0.0 307s Chrysler_X1952 0 0.0 307s Chrysler_X1953 0 0.0 307s Chrysler_X1954 0 0.0 307s General.Electric_X1935 0 0.0 307s General.Electric_X1936 0 0.0 307s General.Electric_X1937 0 0.0 307s General.Electric_X1938 0 0.0 307s General.Electric_X1939 0 0.0 307s General.Electric_X1940 0 0.0 307s General.Electric_X1941 0 0.0 307s General.Electric_X1942 0 0.0 307s General.Electric_X1943 0 0.0 307s General.Electric_X1944 0 0.0 307s General.Electric_X1945 0 0.0 307s General.Electric_X1946 0 0.0 307s General.Electric_X1947 0 0.0 307s General.Electric_X1948 0 0.0 307s General.Electric_X1949 0 0.0 307s General.Electric_X1950 0 0.0 307s General.Electric_X1951 0 0.0 307s General.Electric_X1952 0 0.0 307s General.Electric_X1953 0 0.0 307s General.Electric_X1954 0 0.0 307s General.Motors_X1935 3078 2.8 307s General.Motors_X1936 4662 52.6 307s General.Motors_X1937 5387 156.9 307s General.Motors_X1938 2792 209.2 307s General.Motors_X1939 4313 203.4 307s General.Motors_X1940 4644 207.2 307s General.Motors_X1941 4551 255.2 307s General.Motors_X1942 3244 303.7 307s General.Motors_X1943 4054 264.1 307s General.Motors_X1944 4379 201.6 307s General.Motors_X1945 4841 265.0 307s General.Motors_X1946 4901 402.2 307s General.Motors_X1947 3526 761.5 307s General.Motors_X1948 3255 922.4 307s General.Motors_X1949 3700 1020.1 307s General.Motors_X1950 3756 1099.0 307s General.Motors_X1951 4833 1207.7 307s General.Motors_X1952 4925 1430.5 307s General.Motors_X1953 6242 1777.3 307s General.Motors_X1954 5594 2226.3 307s US.Steel_X1935 0 0.0 307s US.Steel_X1936 0 0.0 307s US.Steel_X1937 0 0.0 307s US.Steel_X1938 0 0.0 307s US.Steel_X1939 0 0.0 307s US.Steel_X1940 0 0.0 307s US.Steel_X1941 0 0.0 307s US.Steel_X1942 0 0.0 307s US.Steel_X1943 0 0.0 307s US.Steel_X1944 0 0.0 307s US.Steel_X1945 0 0.0 307s US.Steel_X1946 0 0.0 307s US.Steel_X1947 0 0.0 307s US.Steel_X1948 0 0.0 307s US.Steel_X1949 0 0.0 307s US.Steel_X1950 0 0.0 307s US.Steel_X1951 0 0.0 307s US.Steel_X1952 0 0.0 307s US.Steel_X1953 0 0.0 307s US.Steel_X1954 0 0.0 307s Westinghouse_X1935 0 0.0 307s Westinghouse_X1936 0 0.0 307s Westinghouse_X1937 0 0.0 307s Westinghouse_X1938 0 0.0 307s Westinghouse_X1939 0 0.0 307s Westinghouse_X1940 0 0.0 307s Westinghouse_X1941 0 0.0 307s Westinghouse_X1942 0 0.0 307s Westinghouse_X1943 0 0.0 307s Westinghouse_X1944 0 0.0 307s Westinghouse_X1945 0 0.0 307s Westinghouse_X1946 0 0.0 307s Westinghouse_X1947 0 0.0 307s Westinghouse_X1948 0 0.0 307s Westinghouse_X1949 0 0.0 307s Westinghouse_X1950 0 0.0 307s Westinghouse_X1951 0 0.0 307s Westinghouse_X1952 0 0.0 307s Westinghouse_X1953 0 0.0 307s Westinghouse_X1954 0 0.0 307s US.Steel_(Intercept) US.Steel_value US.Steel_capital 307s Chrysler_X1935 0 0 0.0 307s Chrysler_X1936 0 0 0.0 307s Chrysler_X1937 0 0 0.0 307s Chrysler_X1938 0 0 0.0 307s Chrysler_X1939 0 0 0.0 307s Chrysler_X1940 0 0 0.0 307s Chrysler_X1941 0 0 0.0 307s Chrysler_X1942 0 0 0.0 307s Chrysler_X1943 0 0 0.0 307s Chrysler_X1944 0 0 0.0 307s Chrysler_X1945 0 0 0.0 307s Chrysler_X1946 0 0 0.0 307s Chrysler_X1947 0 0 0.0 307s Chrysler_X1948 0 0 0.0 307s Chrysler_X1949 0 0 0.0 307s Chrysler_X1950 0 0 0.0 307s Chrysler_X1951 0 0 0.0 307s Chrysler_X1952 0 0 0.0 307s Chrysler_X1953 0 0 0.0 307s Chrysler_X1954 0 0 0.0 307s General.Electric_X1935 0 0 0.0 307s General.Electric_X1936 0 0 0.0 307s General.Electric_X1937 0 0 0.0 307s General.Electric_X1938 0 0 0.0 307s General.Electric_X1939 0 0 0.0 307s General.Electric_X1940 0 0 0.0 307s General.Electric_X1941 0 0 0.0 307s General.Electric_X1942 0 0 0.0 307s General.Electric_X1943 0 0 0.0 307s General.Electric_X1944 0 0 0.0 307s General.Electric_X1945 0 0 0.0 307s General.Electric_X1946 0 0 0.0 307s General.Electric_X1947 0 0 0.0 307s General.Electric_X1948 0 0 0.0 307s General.Electric_X1949 0 0 0.0 307s General.Electric_X1950 0 0 0.0 307s General.Electric_X1951 0 0 0.0 307s General.Electric_X1952 0 0 0.0 307s General.Electric_X1953 0 0 0.0 307s General.Electric_X1954 0 0 0.0 307s General.Motors_X1935 0 0 0.0 307s General.Motors_X1936 0 0 0.0 307s General.Motors_X1937 0 0 0.0 307s General.Motors_X1938 0 0 0.0 307s General.Motors_X1939 0 0 0.0 307s General.Motors_X1940 0 0 0.0 307s General.Motors_X1941 0 0 0.0 307s General.Motors_X1942 0 0 0.0 307s General.Motors_X1943 0 0 0.0 307s General.Motors_X1944 0 0 0.0 307s General.Motors_X1945 0 0 0.0 307s General.Motors_X1946 0 0 0.0 307s General.Motors_X1947 0 0 0.0 307s General.Motors_X1948 0 0 0.0 307s General.Motors_X1949 0 0 0.0 307s General.Motors_X1950 0 0 0.0 307s General.Motors_X1951 0 0 0.0 307s General.Motors_X1952 0 0 0.0 307s General.Motors_X1953 0 0 0.0 307s General.Motors_X1954 0 0 0.0 307s US.Steel_X1935 1 1362 53.8 307s US.Steel_X1936 1 1807 50.5 307s US.Steel_X1937 1 2676 118.1 307s US.Steel_X1938 1 1802 260.2 307s US.Steel_X1939 1 1957 312.7 307s US.Steel_X1940 1 2203 254.2 307s US.Steel_X1941 1 2380 261.4 307s US.Steel_X1942 1 2169 298.7 307s US.Steel_X1943 1 1985 301.8 307s US.Steel_X1944 1 1814 279.1 307s US.Steel_X1945 1 1850 213.8 307s US.Steel_X1946 1 2068 232.6 307s US.Steel_X1947 1 1797 264.8 307s US.Steel_X1948 1 1626 306.9 307s US.Steel_X1949 1 1667 351.1 307s US.Steel_X1950 1 1677 357.8 307s US.Steel_X1951 1 2290 342.1 307s US.Steel_X1952 1 2159 444.2 307s US.Steel_X1953 1 2031 623.6 307s US.Steel_X1954 1 2116 669.7 307s Westinghouse_X1935 0 0 0.0 307s Westinghouse_X1936 0 0 0.0 307s Westinghouse_X1937 0 0 0.0 307s Westinghouse_X1938 0 0 0.0 307s Westinghouse_X1939 0 0 0.0 307s Westinghouse_X1940 0 0 0.0 307s Westinghouse_X1941 0 0 0.0 307s Westinghouse_X1942 0 0 0.0 307s Westinghouse_X1943 0 0 0.0 307s Westinghouse_X1944 0 0 0.0 307s Westinghouse_X1945 0 0 0.0 307s Westinghouse_X1946 0 0 0.0 307s Westinghouse_X1947 0 0 0.0 307s Westinghouse_X1948 0 0 0.0 307s Westinghouse_X1949 0 0 0.0 307s Westinghouse_X1950 0 0 0.0 307s Westinghouse_X1951 0 0 0.0 307s Westinghouse_X1952 0 0 0.0 307s Westinghouse_X1953 0 0 0.0 307s Westinghouse_X1954 0 0 0.0 307s Westinghouse_(Intercept) Westinghouse_value 307s Chrysler_X1935 0 0 307s Chrysler_X1936 0 0 307s Chrysler_X1937 0 0 307s Chrysler_X1938 0 0 307s Chrysler_X1939 0 0 307s Chrysler_X1940 0 0 307s Chrysler_X1941 0 0 307s Chrysler_X1942 0 0 307s Chrysler_X1943 0 0 307s Chrysler_X1944 0 0 307s Chrysler_X1945 0 0 307s Chrysler_X1946 0 0 307s Chrysler_X1947 0 0 307s Chrysler_X1948 0 0 307s Chrysler_X1949 0 0 307s Chrysler_X1950 0 0 307s Chrysler_X1951 0 0 307s Chrysler_X1952 0 0 307s Chrysler_X1953 0 0 307s Chrysler_X1954 0 0 307s General.Electric_X1935 0 0 307s General.Electric_X1936 0 0 307s General.Electric_X1937 0 0 307s General.Electric_X1938 0 0 307s General.Electric_X1939 0 0 307s General.Electric_X1940 0 0 307s General.Electric_X1941 0 0 307s General.Electric_X1942 0 0 307s General.Electric_X1943 0 0 307s General.Electric_X1944 0 0 307s General.Electric_X1945 0 0 307s General.Electric_X1946 0 0 307s General.Electric_X1947 0 0 307s General.Electric_X1948 0 0 307s General.Electric_X1949 0 0 307s General.Electric_X1950 0 0 307s General.Electric_X1951 0 0 307s General.Electric_X1952 0 0 307s General.Electric_X1953 0 0 307s General.Electric_X1954 0 0 307s General.Motors_X1935 0 0 307s General.Motors_X1936 0 0 307s General.Motors_X1937 0 0 307s General.Motors_X1938 0 0 307s General.Motors_X1939 0 0 307s General.Motors_X1940 0 0 307s General.Motors_X1941 0 0 307s General.Motors_X1942 0 0 307s General.Motors_X1943 0 0 307s General.Motors_X1944 0 0 307s General.Motors_X1945 0 0 307s General.Motors_X1946 0 0 307s General.Motors_X1947 0 0 307s General.Motors_X1948 0 0 307s General.Motors_X1949 0 0 307s General.Motors_X1950 0 0 307s General.Motors_X1951 0 0 307s General.Motors_X1952 0 0 307s General.Motors_X1953 0 0 307s General.Motors_X1954 0 0 307s US.Steel_X1935 0 0 307s US.Steel_X1936 0 0 307s US.Steel_X1937 0 0 307s US.Steel_X1938 0 0 307s US.Steel_X1939 0 0 307s US.Steel_X1940 0 0 307s US.Steel_X1941 0 0 307s US.Steel_X1942 0 0 307s US.Steel_X1943 0 0 307s US.Steel_X1944 0 0 307s US.Steel_X1945 0 0 307s US.Steel_X1946 0 0 307s US.Steel_X1947 0 0 307s US.Steel_X1948 0 0 307s US.Steel_X1949 0 0 307s US.Steel_X1950 0 0 307s US.Steel_X1951 0 0 307s US.Steel_X1952 0 0 307s US.Steel_X1953 0 0 307s US.Steel_X1954 0 0 307s Westinghouse_X1935 1 192 307s Westinghouse_X1936 1 516 307s Westinghouse_X1937 1 729 307s Westinghouse_X1938 1 560 307s Westinghouse_X1939 1 520 307s Westinghouse_X1940 1 628 307s Westinghouse_X1941 1 537 307s Westinghouse_X1942 1 561 307s Westinghouse_X1943 1 617 307s Westinghouse_X1944 1 627 307s Westinghouse_X1945 1 737 307s Westinghouse_X1946 1 760 307s Westinghouse_X1947 1 581 307s Westinghouse_X1948 1 662 307s Westinghouse_X1949 1 584 307s Westinghouse_X1950 1 635 307s Westinghouse_X1951 1 724 307s Westinghouse_X1952 1 864 307s Westinghouse_X1953 1 1194 307s Westinghouse_X1954 1 1189 307s Westinghouse_capital 307s Chrysler_X1935 0.0 307s Chrysler_X1936 0.0 307s Chrysler_X1937 0.0 307s Chrysler_X1938 0.0 307s Chrysler_X1939 0.0 307s Chrysler_X1940 0.0 307s Chrysler_X1941 0.0 307s Chrysler_X1942 0.0 307s Chrysler_X1943 0.0 307s Chrysler_X1944 0.0 307s Chrysler_X1945 0.0 307s Chrysler_X1946 0.0 307s Chrysler_X1947 0.0 307s Chrysler_X1948 0.0 307s Chrysler_X1949 0.0 307s Chrysler_X1950 0.0 307s Chrysler_X1951 0.0 307s Chrysler_X1952 0.0 307s Chrysler_X1953 0.0 307s Chrysler_X1954 0.0 307s General.Electric_X1935 0.0 307s General.Electric_X1936 0.0 307s General.Electric_X1937 0.0 307s General.Electric_X1938 0.0 307s General.Electric_X1939 0.0 307s General.Electric_X1940 0.0 307s General.Electric_X1941 0.0 307s General.Electric_X1942 0.0 307s General.Electric_X1943 0.0 307s General.Electric_X1944 0.0 307s General.Electric_X1945 0.0 307s General.Electric_X1946 0.0 307s General.Electric_X1947 0.0 307s General.Electric_X1948 0.0 307s General.Electric_X1949 0.0 307s General.Electric_X1950 0.0 307s General.Electric_X1951 0.0 307s General.Electric_X1952 0.0 307s General.Electric_X1953 0.0 307s General.Electric_X1954 0.0 307s General.Motors_X1935 0.0 307s General.Motors_X1936 0.0 307s General.Motors_X1937 0.0 307s General.Motors_X1938 0.0 307s General.Motors_X1939 0.0 307s General.Motors_X1940 0.0 307s General.Motors_X1941 0.0 307s General.Motors_X1942 0.0 307s General.Motors_X1943 0.0 307s General.Motors_X1944 0.0 307s General.Motors_X1945 0.0 307s General.Motors_X1946 0.0 307s General.Motors_X1947 0.0 307s General.Motors_X1948 0.0 307s General.Motors_X1949 0.0 307s General.Motors_X1950 0.0 307s General.Motors_X1951 0.0 307s General.Motors_X1952 0.0 307s General.Motors_X1953 0.0 307s General.Motors_X1954 0.0 307s US.Steel_X1935 0.0 307s US.Steel_X1936 0.0 307s US.Steel_X1937 0.0 307s US.Steel_X1938 0.0 307s US.Steel_X1939 0.0 307s US.Steel_X1940 0.0 307s US.Steel_X1941 0.0 307s US.Steel_X1942 0.0 307s US.Steel_X1943 0.0 307s US.Steel_X1944 0.0 307s US.Steel_X1945 0.0 307s US.Steel_X1946 0.0 307s US.Steel_X1947 0.0 307s US.Steel_X1948 0.0 307s US.Steel_X1949 0.0 307s US.Steel_X1950 0.0 307s US.Steel_X1951 0.0 307s US.Steel_X1952 0.0 307s US.Steel_X1953 0.0 307s US.Steel_X1954 0.0 307s Westinghouse_X1935 1.8 307s Westinghouse_X1936 0.8 307s Westinghouse_X1937 7.4 307s Westinghouse_X1938 18.1 307s Westinghouse_X1939 23.5 307s Westinghouse_X1940 26.5 307s Westinghouse_X1941 36.2 307s Westinghouse_X1942 60.8 307s Westinghouse_X1943 84.4 307s Westinghouse_X1944 91.2 307s Westinghouse_X1945 92.4 307s Westinghouse_X1946 86.0 307s Westinghouse_X1947 111.1 307s Westinghouse_X1948 130.6 307s Westinghouse_X1949 141.8 307s Westinghouse_X1950 136.7 307s Westinghouse_X1951 129.7 307s Westinghouse_X1952 145.5 307s Westinghouse_X1953 174.8 307s Westinghouse_X1954 213.5 307s $Chrysler 307s Chrysler_invest ~ Chrysler_value + Chrysler_capital 307s 307s 307s $General.Electric 307s General.Electric_invest ~ General.Electric_value + General.Electric_capital 307s 307s 307s $General.Motors 307s General.Motors_invest ~ General.Motors_value + General.Motors_capital 307s 307s 307s $US.Steel 307s US.Steel_invest ~ US.Steel_value + US.Steel_capital 307s 307s 307s $Westinghouse 307s Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 307s 307s 307s Chrysler_invest ~ Chrysler_value + Chrysler_capital 307s 307s $Chrysler 307s Chrysler_invest ~ Chrysler_value + Chrysler_capital 307s attr(,"variables") 307s list(Chrysler_invest, Chrysler_value, Chrysler_capital) 307s attr(,"factors") 307s Chrysler_value Chrysler_capital 307s Chrysler_invest 0 0 307s Chrysler_value 1 0 307s Chrysler_capital 0 1 307s attr(,"term.labels") 307s [1] "Chrysler_value" "Chrysler_capital" 307s attr(,"order") 307s [1] 1 1 307s attr(,"intercept") 307s [1] 1 307s attr(,"response") 307s [1] 1 307s attr(,".Environment") 307s 307s attr(,"predvars") 307s list(Chrysler_invest, Chrysler_value, Chrysler_capital) 307s attr(,"dataClasses") 307s Chrysler_invest Chrysler_value Chrysler_capital 307s "numeric" "numeric" "numeric" 307s 307s $General.Electric 307s General.Electric_invest ~ General.Electric_value + General.Electric_capital 307s attr(,"variables") 307s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 307s attr(,"factors") 307s General.Electric_value General.Electric_capital 307s General.Electric_invest 0 0 307s General.Electric_value 1 0 307s General.Electric_capital 0 1 307s attr(,"term.labels") 307s [1] "General.Electric_value" "General.Electric_capital" 307s attr(,"order") 307s [1] 1 1 307s attr(,"intercept") 307s [1] 1 307s attr(,"response") 307s [1] 1 307s attr(,".Environment") 307s 307s attr(,"predvars") 307s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 307s attr(,"dataClasses") 307s General.Electric_invest General.Electric_value General.Electric_capital 307s "numeric" "numeric" "numeric" 307s 307s $General.Motors 307s General.Motors_invest ~ General.Motors_value + General.Motors_capital 307s attr(,"variables") 307s list(General.Motors_invest, General.Motors_value, General.Motors_capital) 307s attr(,"factors") 307s General.Motors_value General.Motors_capital 307s General.Motors_invest 0 0 307s General.Motors_value 1 0 307s General.Motors_capital 0 1 307s attr(,"term.labels") 307s [1] "General.Motors_value" "General.Motors_capital" 307s attr(,"order") 307s [1] 1 1 307s attr(,"intercept") 307s [1] 1 307s attr(,"response") 307s [1] 1 307s attr(,".Environment") 307s 307s attr(,"predvars") 307s list(General.Motors_invest, General.Motors_value, General.Motors_capital) 307s attr(,"dataClasses") 307s General.Motors_invest General.Motors_value General.Motors_capital 307s "numeric" "numeric" "numeric" 307s 307s $US.Steel 307s US.Steel_invest ~ US.Steel_value + US.Steel_capital 307s attr(,"variables") 307s list(US.Steel_invest, US.Steel_value, US.Steel_capital) 307s attr(,"factors") 307s US.Steel_value US.Steel_capital 307s US.Steel_invest 0 0 307s US.Steel_value 1 0 307s US.Steel_capital 0 1 307s attr(,"term.labels") 307s [1] "US.Steel_value" "US.Steel_capital" 307s attr(,"order") 307s [1] 1 1 307s attr(,"intercept") 307s [1] 1 307s attr(,"response") 307s [1] 1 307s attr(,".Environment") 307s 307s attr(,"predvars") 307s list(US.Steel_invest, US.Steel_value, US.Steel_capital) 307s attr(,"dataClasses") 307s US.Steel_invest US.Steel_value US.Steel_capital 307s "numeric" "numeric" "numeric" 307s 307s $Westinghouse 307s Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 307s attr(,"variables") 307s list(Westinghouse_invest, Westinghouse_value, Westinghouse_capital) 307s attr(,"factors") 307s Westinghouse_value Westinghouse_capital 307s Westinghouse_invest 0 0 307s Westinghouse_value 1 0 307s Westinghouse_capital 0 1 307s attr(,"term.labels") 307s [1] "Westinghouse_value" "Westinghouse_capital" 307s attr(,"order") 307s [1] 1 1 307s attr(,"intercept") 307s [1] 1 307s attr(,"response") 307s [1] 1 307s attr(,".Environment") 307s 307s attr(,"predvars") 307s list(Westinghouse_invest, Westinghouse_value, Westinghouse_capital) 307s attr(,"dataClasses") 307s Westinghouse_invest Westinghouse_value Westinghouse_capital 307s "numeric" "numeric" "numeric" 307s 307s Chrysler_invest ~ Chrysler_value + Chrysler_capital 307s attr(,"variables") 307s list(Chrysler_invest, Chrysler_value, Chrysler_capital) 307s attr(,"factors") 307s Chrysler_value Chrysler_capital 307s Chrysler_invest 0 0 307s Chrysler_value 1 0 307s Chrysler_capital 0 1 307s attr(,"term.labels") 307s [1] "Chrysler_value" "Chrysler_capital" 307s attr(,"order") 307s [1] 1 1 307s attr(,"intercept") 307s [1] 1 307s attr(,"response") 307s [1] 1 307s attr(,".Environment") 307s 307s attr(,"predvars") 307s list(Chrysler_invest, Chrysler_value, Chrysler_capital) 307s attr(,"dataClasses") 307s Chrysler_invest Chrysler_value Chrysler_capital 307s "numeric" "numeric" "numeric" 307s > 307s > # SUR 307s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 307s + greeneSur <- systemfit( formulaGrunfeld, "SUR", 307s + data = GrunfeldGreene, methodResidCov = "noDfCor", useMatrix = useMatrix ) 307s + print( greeneSur ) 307s + print( summary( greeneSur ) ) 307s + print( summary( greeneSur, useDfSys = TRUE, residCov = FALSE ) ) 307s + print( summary( greeneSur, equations = FALSE ) ) 307s + print( coef( greeneSur ) ) 307s + print( coef( summary( greeneSur ) ) ) 307s + print( vcov( greeneSur ) ) 307s + print( residuals( greeneSur ) ) 307s + print( confint( greeneSur ) ) 307s + print( fitted( greeneSur ) ) 307s + print( logLik( greeneSur ) ) 307s + print( logLik( greeneSur, residCovDiag = TRUE ) ) 307s + print( nobs( greeneSur ) ) 307s + print( model.frame( greeneSur ) ) 307s + print( model.matrix( greeneSur ) ) 307s + print( formula( greeneSur ) ) 307s + print( formula( greeneSur$eq[[ 1 ]] ) ) 307s + print( terms( greeneSur ) ) 307s + print( terms( greeneSur$eq[[ 1 ]] ) ) 307s + } 308s 308s systemfit results 308s method: SUR 308s 308s Coefficients: 308s Chrysler_(Intercept) Chrysler_value 308s 0.5043 0.0695 308s Chrysler_capital General.Electric_(Intercept) 308s 0.3085 -22.4389 308s General.Electric_value General.Electric_capital 308s 0.0373 0.1308 308s General.Motors_(Intercept) General.Motors_value 308s -162.3641 0.1205 308s General.Motors_capital US.Steel_(Intercept) 308s 0.3827 85.4233 308s US.Steel_value US.Steel_capital 308s 0.1015 0.4000 308s Westinghouse_(Intercept) Westinghouse_value 308s 1.0889 0.0570 308s Westinghouse_capital 308s 0.0415 308s 308s systemfit results 308s method: SUR 308s 308s N DF SSR detRCov OLS-R2 McElroy-R2 308s system 100 85 347048 6.18e+13 0.844 0.869 308s 308s N DF SSR MSE RMSE R2 Adj R2 308s Chrysler 20 17 3057 180 13.4 0.912 0.901 308s General.Electric 20 17 14009 824 28.7 0.688 0.651 308s General.Motors 20 17 144321 8489 92.1 0.921 0.911 308s US.Steel 20 17 183763 10810 104.0 0.422 0.354 308s Westinghouse 20 17 1898 112 10.6 0.726 0.694 308s 308s The covariance matrix of the residuals used for estimation 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 149.9 -21.4 -283 418 13.3 308s General.Electric -21.4 660.8 608 905 176.4 308s General.Motors -282.8 607.5 7160 -2222 126.2 308s US.Steel 418.1 905.0 -2222 8896 546.2 308s Westinghouse 13.3 176.4 126 546 88.7 308s 308s The covariance matrix of the residuals 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 152.85 2.05 -314 455 16.7 308s General.Electric 2.05 700.46 605 1224 200.3 308s General.Motors -313.70 605.34 7216 -2687 129.9 308s US.Steel 455.09 1224.41 -2687 9188 652.7 308s Westinghouse 16.66 200.32 130 653 94.9 308s 308s The correlations of the residuals 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 1.00000 0.00626 -0.299 0.384 0.138 308s General.Electric 0.00626 1.00000 0.269 0.483 0.777 308s General.Motors -0.29870 0.26925 1.000 -0.330 0.157 308s US.Steel 0.38402 0.48264 -0.330 1.000 0.699 308s Westinghouse 0.13832 0.77690 0.157 0.699 1.000 308s 308s 308s SUR estimates for 'Chrysler' (equation 1) 308s Model Formula: Chrysler_invest ~ Chrysler_value + Chrysler_capital 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) 0.5043 11.5128 0.04 0.96557 308s value 0.0695 0.0169 4.12 0.00072 *** 308s capital 0.3085 0.0259 11.93 1.1e-09 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 13.41 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 3056.985 MSE: 179.823 Root MSE: 13.41 308s Multiple R-Squared: 0.912 Adjusted R-Squared: 0.901 308s 308s 308s SUR estimates for 'General.Electric' (equation 2) 308s Model Formula: General.Electric_invest ~ General.Electric_value + General.Electric_capital 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -22.4389 25.5186 -0.88 0.3915 308s value 0.0373 0.0123 3.04 0.0074 ** 308s capital 0.1308 0.0220 5.93 1.6e-05 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 28.707 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 14009.115 MSE: 824.066 Root MSE: 28.707 308s Multiple R-Squared: 0.688 Adjusted R-Squared: 0.651 308s 308s 308s SUR estimates for 'General.Motors' (equation 3) 308s Model Formula: General.Motors_invest ~ General.Motors_value + General.Motors_capital 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -162.3641 89.4592 -1.81 0.087 . 308s value 0.1205 0.0216 5.57 3.4e-05 *** 308s capital 0.3827 0.0328 11.68 1.5e-09 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 92.138 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 144320.876 MSE: 8489.463 Root MSE: 92.138 308s Multiple R-Squared: 0.921 Adjusted R-Squared: 0.911 308s 308s 308s SUR estimates for 'US.Steel' (equation 4) 308s Model Formula: US.Steel_invest ~ US.Steel_value + US.Steel_capital 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) 85.4233 111.8774 0.76 0.4556 308s value 0.1015 0.0548 1.85 0.0814 . 308s capital 0.4000 0.1278 3.13 0.0061 ** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 103.969 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 183763.011 MSE: 10809.589 Root MSE: 103.969 308s Multiple R-Squared: 0.422 Adjusted R-Squared: 0.354 308s 308s 308s SUR estimates for 'Westinghouse' (equation 5) 308s Model Formula: Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) 1.0889 6.2588 0.17 0.86394 308s value 0.0570 0.0114 5.02 0.00011 *** 308s capital 0.0415 0.0412 1.01 0.32787 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 10.567 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 1898.249 MSE: 111.662 Root MSE: 10.567 308s Multiple R-Squared: 0.726 Adjusted R-Squared: 0.694 308s 308s 308s systemfit results 308s method: SUR 308s 308s N DF SSR detRCov OLS-R2 McElroy-R2 308s system 100 85 347048 6.18e+13 0.844 0.869 308s 308s N DF SSR MSE RMSE R2 Adj R2 308s Chrysler 20 17 3057 180 13.4 0.912 0.901 308s General.Electric 20 17 14009 824 28.7 0.688 0.651 308s General.Motors 20 17 144321 8489 92.1 0.921 0.911 308s US.Steel 20 17 183763 10810 104.0 0.422 0.354 308s Westinghouse 20 17 1898 112 10.6 0.726 0.694 308s 308s 308s SUR estimates for 'Chrysler' (equation 1) 308s Model Formula: Chrysler_invest ~ Chrysler_value + Chrysler_capital 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) 0.5043 11.5128 0.04 0.97 308s value 0.0695 0.0169 4.12 8.9e-05 *** 308s capital 0.3085 0.0259 11.93 < 2e-16 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 13.41 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 3056.985 MSE: 179.823 Root MSE: 13.41 308s Multiple R-Squared: 0.912 Adjusted R-Squared: 0.901 308s 308s 308s SUR estimates for 'General.Electric' (equation 2) 308s Model Formula: General.Electric_invest ~ General.Electric_value + General.Electric_capital 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -22.4389 25.5186 -0.88 0.3817 308s value 0.0373 0.0123 3.04 0.0031 ** 308s capital 0.1308 0.0220 5.93 6.3e-08 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 28.707 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 14009.115 MSE: 824.066 Root MSE: 28.707 308s Multiple R-Squared: 0.688 Adjusted R-Squared: 0.651 308s 308s 308s SUR estimates for 'General.Motors' (equation 3) 308s Model Formula: General.Motors_invest ~ General.Motors_value + General.Motors_capital 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -162.3641 89.4592 -1.81 0.073 . 308s value 0.1205 0.0216 5.57 2.9e-07 *** 308s capital 0.3827 0.0328 11.68 < 2e-16 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 92.138 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 144320.876 MSE: 8489.463 Root MSE: 92.138 308s Multiple R-Squared: 0.921 Adjusted R-Squared: 0.911 308s 308s 308s SUR estimates for 'US.Steel' (equation 4) 308s Model Formula: US.Steel_invest ~ US.Steel_value + US.Steel_capital 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) 85.4233 111.8774 0.76 0.4473 308s value 0.1015 0.0548 1.85 0.0674 . 308s capital 0.4000 0.1278 3.13 0.0024 ** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 103.969 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 183763.011 MSE: 10809.589 Root MSE: 103.969 308s Multiple R-Squared: 0.422 Adjusted R-Squared: 0.354 308s 308s 308s SUR estimates for 'Westinghouse' (equation 5) 308s Model Formula: Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) 1.0889 6.2588 0.17 0.86 308s value 0.0570 0.0114 5.02 2.8e-06 *** 308s capital 0.0415 0.0412 1.01 0.32 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 10.567 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 1898.249 MSE: 111.662 Root MSE: 10.567 308s Multiple R-Squared: 0.726 Adjusted R-Squared: 0.694 308s 308s 308s systemfit results 308s method: SUR 308s 308s N DF SSR detRCov OLS-R2 McElroy-R2 308s system 100 85 347048 6.18e+13 0.844 0.869 308s 308s N DF SSR MSE RMSE R2 Adj R2 308s Chrysler 20 17 3057 180 13.4 0.912 0.901 308s General.Electric 20 17 14009 824 28.7 0.688 0.651 308s General.Motors 20 17 144321 8489 92.1 0.921 0.911 308s US.Steel 20 17 183763 10810 104.0 0.422 0.354 308s Westinghouse 20 17 1898 112 10.6 0.726 0.694 308s 308s The covariance matrix of the residuals used for estimation 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 149.9 -21.4 -283 418 13.3 308s General.Electric -21.4 660.8 608 905 176.4 308s General.Motors -282.8 607.5 7160 -2222 126.2 308s US.Steel 418.1 905.0 -2222 8896 546.2 308s Westinghouse 13.3 176.4 126 546 88.7 308s 308s The covariance matrix of the residuals 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 152.85 2.05 -314 455 16.7 308s General.Electric 2.05 700.46 605 1224 200.3 308s General.Motors -313.70 605.34 7216 -2687 129.9 308s US.Steel 455.09 1224.41 -2687 9188 652.7 308s Westinghouse 16.66 200.32 130 653 94.9 308s 308s The correlations of the residuals 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 1.00000 0.00626 -0.299 0.384 0.138 308s General.Electric 0.00626 1.00000 0.269 0.483 0.777 308s General.Motors -0.29870 0.26925 1.000 -0.330 0.157 308s US.Steel 0.38402 0.48264 -0.330 1.000 0.699 308s Westinghouse 0.13832 0.77690 0.157 0.699 1.000 308s 308s 308s Coefficients: 308s Estimate Std. Error t value Pr(>|t|) 308s Chrysler_(Intercept) 0.5043 11.5128 0.04 0.96557 308s Chrysler_value 0.0695 0.0169 4.12 0.00072 *** 308s Chrysler_capital 0.3085 0.0259 11.93 1.1e-09 *** 308s General.Electric_(Intercept) -22.4389 25.5186 -0.88 0.39149 308s General.Electric_value 0.0373 0.0123 3.04 0.00738 ** 308s General.Electric_capital 0.1308 0.0220 5.93 1.6e-05 *** 308s General.Motors_(Intercept) -162.3641 89.4592 -1.81 0.08722 . 308s General.Motors_value 0.1205 0.0216 5.57 3.4e-05 *** 308s General.Motors_capital 0.3827 0.0328 11.68 1.5e-09 *** 308s US.Steel_(Intercept) 85.4233 111.8774 0.76 0.45561 308s US.Steel_value 0.1015 0.0548 1.85 0.08142 . 308s US.Steel_capital 0.4000 0.1278 3.13 0.00610 ** 308s Westinghouse_(Intercept) 1.0889 6.2588 0.17 0.86394 308s Westinghouse_value 0.0570 0.0114 5.02 0.00011 *** 308s Westinghouse_capital 0.0415 0.0412 1.01 0.32787 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s Chrysler_(Intercept) Chrysler_value 308s 0.5043 0.0695 308s Chrysler_capital General.Electric_(Intercept) 308s 0.3085 -22.4389 308s General.Electric_value General.Electric_capital 308s 0.0373 0.1308 308s General.Motors_(Intercept) General.Motors_value 308s -162.3641 0.1205 308s General.Motors_capital US.Steel_(Intercept) 308s 0.3827 85.4233 308s US.Steel_value US.Steel_capital 308s 0.1015 0.4000 308s Westinghouse_(Intercept) Westinghouse_value 308s 1.0889 0.0570 308s Westinghouse_capital 308s 0.0415 308s Estimate Std. Error t value Pr(>|t|) 308s Chrysler_(Intercept) 0.5043 11.5128 0.0438 9.66e-01 308s Chrysler_value 0.0695 0.0169 4.1157 7.22e-04 308s Chrysler_capital 0.3085 0.0259 11.9297 1.10e-09 308s General.Electric_(Intercept) -22.4389 25.5186 -0.8793 3.91e-01 308s General.Electric_value 0.0373 0.0123 3.0409 7.38e-03 308s General.Electric_capital 0.1308 0.0220 5.9313 1.64e-05 308s General.Motors_(Intercept) -162.3641 89.4592 -1.8150 8.72e-02 308s General.Motors_value 0.1205 0.0216 5.5709 3.38e-05 308s General.Motors_capital 0.3827 0.0328 11.6805 1.52e-09 308s US.Steel_(Intercept) 85.4233 111.8774 0.7635 4.56e-01 308s US.Steel_value 0.1015 0.0548 1.8523 8.14e-02 308s US.Steel_capital 0.4000 0.1278 3.1300 6.10e-03 308s Westinghouse_(Intercept) 1.0889 6.2588 0.1740 8.64e-01 308s Westinghouse_value 0.0570 0.0114 5.0174 1.06e-04 308s Westinghouse_capital 0.0415 0.0412 1.0074 3.28e-01 308s Chrysler_(Intercept) Chrysler_value 308s Chrysler_(Intercept) 1.33e+02 -1.82e-01 308s Chrysler_value -1.82e-01 2.86e-04 308s Chrysler_capital 9.57e-03 -1.31e-04 308s General.Electric_(Intercept) -2.94e+01 3.74e-02 308s General.Electric_value 1.28e-02 -1.86e-05 308s General.Electric_capital 8.80e-03 -2.96e-06 308s General.Motors_(Intercept) -1.56e+02 1.91e-01 308s General.Motors_value 3.28e-02 -4.91e-05 308s General.Motors_capital -8.18e-04 3.42e-05 308s US.Steel_(Intercept) 1.80e+02 -1.87e-01 308s US.Steel_value -7.46e-02 1.13e-04 308s US.Steel_capital -4.03e-02 -1.22e-04 308s Westinghouse_(Intercept) -3.04e-01 3.03e-03 308s Westinghouse_value 1.14e-03 -3.70e-06 308s Westinghouse_capital 2.42e-03 -6.41e-06 308s Chrysler_capital General.Electric_(Intercept) 308s Chrysler_(Intercept) 9.57e-03 -29.3642 308s Chrysler_value -1.31e-04 0.0374 308s Chrysler_capital 6.69e-04 0.0198 308s General.Electric_(Intercept) 1.98e-02 651.1982 308s General.Electric_value 1.28e-06 -0.2851 308s General.Electric_capital -5.56e-05 -0.1615 308s General.Motors_(Intercept) 7.79e-02 571.3402 308s General.Motors_value 1.03e-05 -0.1196 308s General.Motors_capital -1.89e-04 -0.0352 308s US.Steel_(Intercept) -2.45e-01 644.2920 308s US.Steel_value -3.26e-05 -0.2201 308s US.Steel_capital 1.03e-03 -0.5505 308s Westinghouse_(Intercept) -9.35e-03 102.8679 308s Westinghouse_value 1.18e-05 -0.1700 308s Westinghouse_capital 1.67e-05 0.2338 308s General.Electric_value General.Electric_capital 308s Chrysler_(Intercept) 1.28e-02 8.80e-03 308s Chrysler_value -1.86e-05 -2.96e-06 308s Chrysler_capital 1.28e-06 -5.56e-05 308s General.Electric_(Intercept) -2.85e-01 -1.61e-01 308s General.Electric_value 1.50e-04 -1.70e-05 308s General.Electric_capital -1.70e-05 4.86e-04 308s General.Motors_(Intercept) -2.61e-01 -8.74e-02 308s General.Motors_value 6.35e-05 -9.49e-06 308s General.Motors_capital -2.27e-05 1.98e-04 308s US.Steel_(Intercept) -3.04e-01 -2.30e-02 308s US.Steel_value 1.35e-04 -1.07e-04 308s US.Steel_capital 1.23e-04 7.77e-04 308s Westinghouse_(Intercept) -4.02e-02 -4.02e-02 308s Westinghouse_value 8.74e-05 1.04e-06 308s Westinghouse_capital -2.16e-04 4.61e-04 308s General.Motors_(Intercept) General.Motors_value 308s Chrysler_(Intercept) -1.56e+02 3.28e-02 308s Chrysler_value 1.91e-01 -4.91e-05 308s Chrysler_capital 7.79e-02 1.03e-05 308s General.Electric_(Intercept) 5.71e+02 -1.20e-01 308s General.Electric_value -2.61e-01 6.35e-05 308s General.Electric_capital -8.74e-02 -9.49e-06 308s General.Motors_(Intercept) 8.00e+03 -1.84e+00 308s General.Motors_value -1.84e+00 4.68e-04 308s General.Motors_capital 5.32e-01 -2.83e-04 308s US.Steel_(Intercept) -1.75e+03 3.73e-01 308s US.Steel_value 8.02e-01 -2.06e-04 308s US.Steel_capital 2.01e-01 1.09e-04 308s Westinghouse_(Intercept) 1.10e+02 -2.33e-02 308s Westinghouse_value -2.06e-01 5.10e-05 308s Westinghouse_capital 3.98e-01 -1.28e-04 308s General.Motors_capital US.Steel_(Intercept) 308s Chrysler_(Intercept) -8.18e-04 1.80e+02 308s Chrysler_value 3.42e-05 -1.87e-01 308s Chrysler_capital -1.89e-04 -2.45e-01 308s General.Electric_(Intercept) -3.52e-02 6.44e+02 308s General.Electric_value -2.27e-05 -3.04e-01 308s General.Electric_capital 1.98e-04 -2.30e-02 308s General.Motors_(Intercept) 5.32e-01 -1.75e+03 308s General.Motors_value -2.83e-04 3.73e-01 308s General.Motors_capital 1.07e-03 3.74e-02 308s US.Steel_(Intercept) 3.74e-02 1.25e+04 308s US.Steel_value 1.39e-04 -5.65e+00 308s US.Steel_capital -1.04e-03 -3.12e+00 308s Westinghouse_(Intercept) -4.87e-03 2.74e+02 308s Westinghouse_value -2.38e-05 -5.09e-01 308s Westinghouse_capital 2.43e-04 1.10e+00 308s US.Steel_value US.Steel_capital 308s Chrysler_(Intercept) -7.46e-02 -0.040281 308s Chrysler_value 1.13e-04 -0.000122 308s Chrysler_capital -3.26e-05 0.001031 308s General.Electric_(Intercept) -2.20e-01 -0.550482 308s General.Electric_value 1.35e-04 0.000123 308s General.Electric_capital -1.07e-04 0.000777 308s General.Motors_(Intercept) 8.02e-01 0.200945 308s General.Motors_value -2.06e-04 0.000109 308s General.Motors_capital 1.39e-04 -0.001036 308s US.Steel_(Intercept) -5.65e+00 -3.119830 308s US.Steel_value 3.00e-03 -0.000901 308s US.Steel_capital -9.01e-04 0.016331 308s Westinghouse_(Intercept) -8.35e-02 -0.275101 308s Westinghouse_value 2.23e-04 0.000229 308s Westinghouse_capital -7.74e-04 0.001422 308s Westinghouse_(Intercept) Westinghouse_value 308s Chrysler_(Intercept) -0.30387 1.14e-03 308s Chrysler_value 0.00303 -3.70e-06 308s Chrysler_capital -0.00935 1.18e-05 308s General.Electric_(Intercept) 102.86790 -1.70e-01 308s General.Electric_value -0.04016 8.74e-05 308s General.Electric_capital -0.04021 1.04e-06 308s General.Motors_(Intercept) 110.26166 -2.06e-01 308s General.Motors_value -0.02326 5.10e-05 308s General.Motors_capital -0.00487 -2.38e-05 308s US.Steel_(Intercept) 274.40848 -5.09e-01 308s US.Steel_value -0.08348 2.23e-04 308s US.Steel_capital -0.27510 2.29e-04 308s Westinghouse_(Intercept) 39.17263 -5.99e-02 308s Westinghouse_value -0.05992 1.29e-04 308s Westinghouse_capital 0.06376 -3.12e-04 308s Westinghouse_capital 308s Chrysler_(Intercept) 2.42e-03 308s Chrysler_value -6.41e-06 308s Chrysler_capital 1.67e-05 308s General.Electric_(Intercept) 2.34e-01 308s General.Electric_value -2.16e-04 308s General.Electric_capital 4.61e-04 308s General.Motors_(Intercept) 3.98e-01 308s General.Motors_value -1.28e-04 308s General.Motors_capital 2.43e-04 308s US.Steel_(Intercept) 1.10e+00 308s US.Steel_value -7.74e-04 308s US.Steel_capital 1.42e-03 308s Westinghouse_(Intercept) 6.38e-02 308s Westinghouse_value -3.12e-04 308s Westinghouse_capital 1.70e-03 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s X1935 7.511 -0.905 107.95 -35.3 0.849 308s X1936 10.843 -21.387 -27.67 66.3 -4.639 308s X1937 -6.422 -20.333 -136.20 65.7 -7.906 308s X1938 4.659 -29.453 3.55 -110.1 -10.898 308s X1939 -15.204 -36.171 -104.40 -178.7 -12.863 308s X1940 -2.413 -7.078 -15.30 -149.0 -9.449 308s X1941 -0.116 38.153 28.30 41.3 15.299 308s X1942 -4.311 17.481 103.23 20.6 7.734 308s X1943 -14.728 -23.336 72.44 -46.0 -2.758 308s X1944 -8.172 -25.700 105.03 -92.9 -2.792 308s X1945 12.566 -0.629 38.84 -100.0 -7.681 308s X1946 -14.709 54.737 106.00 32.0 5.446 308s X1947 -7.958 48.169 14.88 46.8 16.715 308s X1948 6.548 37.841 -53.65 121.3 5.293 308s X1949 -8.057 -13.518 -118.82 10.1 -8.216 308s X1950 1.571 -28.788 -67.90 20.0 -10.735 308s X1951 41.064 1.996 -126.32 133.6 6.645 308s X1952 4.273 7.222 -87.37 163.0 15.390 308s X1953 -2.011 8.833 34.43 100.0 13.695 308s X1954 -4.934 -7.135 122.97 -108.7 -9.129 308s 2.5 % 97.5 % 308s Chrysler_(Intercept) -23.786 24.794 308s Chrysler_value 0.034 0.105 308s Chrysler_capital 0.254 0.363 308s General.Electric_(Intercept) -76.278 31.401 308s General.Electric_value 0.011 0.063 308s General.Electric_capital 0.084 0.177 308s General.Motors_(Intercept) -351.107 26.378 308s General.Motors_value 0.075 0.166 308s General.Motors_capital 0.314 0.452 308s US.Steel_(Intercept) -150.617 321.464 308s US.Steel_value -0.014 0.217 308s US.Steel_capital 0.130 0.670 308s Westinghouse_(Intercept) -12.116 14.294 308s Westinghouse_value 0.033 0.081 308s Westinghouse_capital -0.045 0.128 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s X1935 32.8 34.0 210 245 12.1 308s X1936 61.9 66.4 419 289 30.5 308s X1937 72.7 97.5 547 404 43.0 308s X1938 46.9 74.1 254 372 33.8 308s X1939 67.6 84.3 435 409 31.7 308s X1940 71.8 81.5 476 411 38.0 308s X1941 68.5 74.8 484 432 33.2 308s X1942 51.1 74.4 345 425 35.6 308s X1943 62.1 84.6 427 408 39.8 308s X1944 67.7 82.5 442 381 40.6 308s X1945 76.2 94.2 522 359 47.0 308s X1946 88.8 105.2 582 388 48.0 308s X1947 70.6 99.0 554 374 38.8 308s X1948 82.8 108.5 583 373 44.3 308s X1949 87.0 111.8 674 395 40.3 308s X1950 99.1 122.3 711 399 43.0 308s X1951 119.6 133.2 882 455 47.7 308s X1952 140.7 150.1 979 482 56.4 308s X1953 176.9 170.7 1270 541 76.4 308s X1954 177.4 196.7 1364 568 77.7 308s 'log Lik.' -459 (df=30) 308s 'log Lik.' -483 (df=30) 308s [1] 100 308s Chrysler_invest Chrysler_value Chrysler_capital General.Electric_invest 308s X1935 40.3 418 10.5 33.1 308s X1936 72.8 838 10.2 45.0 308s X1937 66.3 884 34.7 77.2 308s X1938 51.6 438 51.8 44.6 308s X1939 52.4 680 64.3 48.1 308s X1940 69.4 728 67.1 74.4 308s X1941 68.3 644 75.2 113.0 308s X1942 46.8 411 71.4 91.9 308s X1943 47.4 588 67.1 61.3 308s X1944 59.6 698 60.5 56.8 308s X1945 88.8 846 54.6 93.6 308s X1946 74.1 894 84.8 159.9 308s X1947 62.7 579 96.8 147.2 308s X1948 89.4 695 110.2 146.3 308s X1949 79.0 590 147.4 98.3 308s X1950 100.7 694 163.2 93.5 308s X1951 160.6 809 203.5 135.2 308s X1952 145.0 727 290.6 157.3 308s X1953 174.9 1002 346.1 179.5 308s X1954 172.5 703 414.9 189.6 308s General.Electric_value General.Electric_capital General.Motors_invest 308s X1935 1171 97.8 318 308s X1936 2016 104.4 392 308s X1937 2803 118.0 411 308s X1938 2040 156.2 258 308s X1939 2256 172.6 331 308s X1940 2132 186.6 461 308s X1941 1834 220.9 512 308s X1942 1588 287.8 448 308s X1943 1749 319.9 500 308s X1944 1687 321.3 548 308s X1945 2008 319.6 561 308s X1946 2208 346.0 688 308s X1947 1657 456.4 569 308s X1948 1604 543.4 529 308s X1949 1432 618.3 555 308s X1950 1610 647.4 643 308s X1951 1819 671.3 756 308s X1952 2080 726.1 891 308s X1953 2372 800.3 1304 308s X1954 2760 888.9 1487 308s General.Motors_value General.Motors_capital US.Steel_invest 308s X1935 3078 2.8 210 308s X1936 4662 52.6 355 308s X1937 5387 156.9 470 308s X1938 2792 209.2 262 308s X1939 4313 203.4 230 308s X1940 4644 207.2 262 308s X1941 4551 255.2 473 308s X1942 3244 303.7 446 308s X1943 4054 264.1 362 308s X1944 4379 201.6 288 308s X1945 4841 265.0 259 308s X1946 4901 402.2 420 308s X1947 3526 761.5 420 308s X1948 3255 922.4 494 308s X1949 3700 1020.1 405 308s X1950 3756 1099.0 419 308s X1951 4833 1207.7 588 308s X1952 4925 1430.5 645 308s X1953 6242 1777.3 641 308s X1954 5594 2226.3 459 308s US.Steel_value US.Steel_capital Westinghouse_invest Westinghouse_value 308s X1935 1362 53.8 12.9 192 308s X1936 1807 50.5 25.9 516 308s X1937 2676 118.1 35.0 729 308s X1938 1802 260.2 22.9 560 308s X1939 1957 312.7 18.8 520 308s X1940 2203 254.2 28.6 628 308s X1941 2380 261.4 48.5 537 308s X1942 2169 298.7 43.3 561 308s X1943 1985 301.8 37.0 617 308s X1944 1814 279.1 37.8 627 308s X1945 1850 213.8 39.3 737 308s X1946 2068 232.6 53.5 760 308s X1947 1797 264.8 55.6 581 308s X1948 1626 306.9 49.6 662 308s X1949 1667 351.1 32.0 584 308s X1950 1677 357.8 32.2 635 308s X1951 2290 342.1 54.4 724 308s X1952 2159 444.2 71.8 864 308s X1953 2031 623.6 90.1 1194 308s X1954 2116 669.7 68.6 1189 308s Westinghouse_capital 308s X1935 1.8 308s X1936 0.8 308s X1937 7.4 308s X1938 18.1 308s X1939 23.5 308s X1940 26.5 308s X1941 36.2 308s X1942 60.8 308s X1943 84.4 308s X1944 91.2 308s X1945 92.4 308s X1946 86.0 308s X1947 111.1 308s X1948 130.6 308s X1949 141.8 308s X1950 136.7 308s X1951 129.7 308s X1952 145.5 308s X1953 174.8 308s X1954 213.5 308s Chrysler_(Intercept) Chrysler_value Chrysler_capital 308s Chrysler_X1935 1 418 10.5 308s Chrysler_X1936 1 838 10.2 308s Chrysler_X1937 1 884 34.7 308s Chrysler_X1938 1 438 51.8 308s Chrysler_X1939 1 680 64.3 308s Chrysler_X1940 1 728 67.1 308s Chrysler_X1941 1 644 75.2 308s Chrysler_X1942 1 411 71.4 308s Chrysler_X1943 1 588 67.1 308s Chrysler_X1944 1 698 60.5 308s Chrysler_X1945 1 846 54.6 308s Chrysler_X1946 1 894 84.8 308s Chrysler_X1947 1 579 96.8 308s Chrysler_X1948 1 695 110.2 308s Chrysler_X1949 1 590 147.4 308s Chrysler_X1950 1 694 163.2 308s Chrysler_X1951 1 809 203.5 308s Chrysler_X1952 1 727 290.6 308s Chrysler_X1953 1 1002 346.1 308s Chrysler_X1954 1 703 414.9 308s General.Electric_X1935 0 0 0.0 308s General.Electric_X1936 0 0 0.0 308s General.Electric_X1937 0 0 0.0 308s General.Electric_X1938 0 0 0.0 308s General.Electric_X1939 0 0 0.0 308s General.Electric_X1940 0 0 0.0 308s General.Electric_X1941 0 0 0.0 308s General.Electric_X1942 0 0 0.0 308s General.Electric_X1943 0 0 0.0 308s General.Electric_X1944 0 0 0.0 308s General.Electric_X1945 0 0 0.0 308s General.Electric_X1946 0 0 0.0 308s General.Electric_X1947 0 0 0.0 308s General.Electric_X1948 0 0 0.0 308s General.Electric_X1949 0 0 0.0 308s General.Electric_X1950 0 0 0.0 308s General.Electric_X1951 0 0 0.0 308s General.Electric_X1952 0 0 0.0 308s General.Electric_X1953 0 0 0.0 308s General.Electric_X1954 0 0 0.0 308s General.Motors_X1935 0 0 0.0 308s General.Motors_X1936 0 0 0.0 308s General.Motors_X1937 0 0 0.0 308s General.Motors_X1938 0 0 0.0 308s General.Motors_X1939 0 0 0.0 308s General.Motors_X1940 0 0 0.0 308s General.Motors_X1941 0 0 0.0 308s General.Motors_X1942 0 0 0.0 308s General.Motors_X1943 0 0 0.0 308s General.Motors_X1944 0 0 0.0 308s General.Motors_X1945 0 0 0.0 308s General.Motors_X1946 0 0 0.0 308s General.Motors_X1947 0 0 0.0 308s General.Motors_X1948 0 0 0.0 308s General.Motors_X1949 0 0 0.0 308s General.Motors_X1950 0 0 0.0 308s General.Motors_X1951 0 0 0.0 308s General.Motors_X1952 0 0 0.0 308s General.Motors_X1953 0 0 0.0 308s General.Motors_X1954 0 0 0.0 308s US.Steel_X1935 0 0 0.0 308s US.Steel_X1936 0 0 0.0 308s US.Steel_X1937 0 0 0.0 308s US.Steel_X1938 0 0 0.0 308s US.Steel_X1939 0 0 0.0 308s US.Steel_X1940 0 0 0.0 308s US.Steel_X1941 0 0 0.0 308s US.Steel_X1942 0 0 0.0 308s US.Steel_X1943 0 0 0.0 308s US.Steel_X1944 0 0 0.0 308s US.Steel_X1945 0 0 0.0 308s US.Steel_X1946 0 0 0.0 308s US.Steel_X1947 0 0 0.0 308s US.Steel_X1948 0 0 0.0 308s US.Steel_X1949 0 0 0.0 308s US.Steel_X1950 0 0 0.0 308s US.Steel_X1951 0 0 0.0 308s US.Steel_X1952 0 0 0.0 308s US.Steel_X1953 0 0 0.0 308s US.Steel_X1954 0 0 0.0 308s Westinghouse_X1935 0 0 0.0 308s Westinghouse_X1936 0 0 0.0 308s Westinghouse_X1937 0 0 0.0 308s Westinghouse_X1938 0 0 0.0 308s Westinghouse_X1939 0 0 0.0 308s Westinghouse_X1940 0 0 0.0 308s Westinghouse_X1941 0 0 0.0 308s Westinghouse_X1942 0 0 0.0 308s Westinghouse_X1943 0 0 0.0 308s Westinghouse_X1944 0 0 0.0 308s Westinghouse_X1945 0 0 0.0 308s Westinghouse_X1946 0 0 0.0 308s Westinghouse_X1947 0 0 0.0 308s Westinghouse_X1948 0 0 0.0 308s Westinghouse_X1949 0 0 0.0 308s Westinghouse_X1950 0 0 0.0 308s Westinghouse_X1951 0 0 0.0 308s Westinghouse_X1952 0 0 0.0 308s Westinghouse_X1953 0 0 0.0 308s Westinghouse_X1954 0 0 0.0 308s General.Electric_(Intercept) General.Electric_value 308s Chrysler_X1935 0 0 308s Chrysler_X1936 0 0 308s Chrysler_X1937 0 0 308s Chrysler_X1938 0 0 308s Chrysler_X1939 0 0 308s Chrysler_X1940 0 0 308s Chrysler_X1941 0 0 308s Chrysler_X1942 0 0 308s Chrysler_X1943 0 0 308s Chrysler_X1944 0 0 308s Chrysler_X1945 0 0 308s Chrysler_X1946 0 0 308s Chrysler_X1947 0 0 308s Chrysler_X1948 0 0 308s Chrysler_X1949 0 0 308s Chrysler_X1950 0 0 308s Chrysler_X1951 0 0 308s Chrysler_X1952 0 0 308s Chrysler_X1953 0 0 308s Chrysler_X1954 0 0 308s General.Electric_X1935 1 1171 308s General.Electric_X1936 1 2016 308s General.Electric_X1937 1 2803 308s General.Electric_X1938 1 2040 308s General.Electric_X1939 1 2256 308s General.Electric_X1940 1 2132 308s General.Electric_X1941 1 1834 308s General.Electric_X1942 1 1588 308s General.Electric_X1943 1 1749 308s General.Electric_X1944 1 1687 308s General.Electric_X1945 1 2008 308s General.Electric_X1946 1 2208 308s General.Electric_X1947 1 1657 308s General.Electric_X1948 1 1604 308s General.Electric_X1949 1 1432 308s General.Electric_X1950 1 1610 308s General.Electric_X1951 1 1819 308s General.Electric_X1952 1 2080 308s General.Electric_X1953 1 2372 308s General.Electric_X1954 1 2760 308s General.Motors_X1935 0 0 308s General.Motors_X1936 0 0 308s General.Motors_X1937 0 0 308s General.Motors_X1938 0 0 308s General.Motors_X1939 0 0 308s General.Motors_X1940 0 0 308s General.Motors_X1941 0 0 308s General.Motors_X1942 0 0 308s General.Motors_X1943 0 0 308s General.Motors_X1944 0 0 308s General.Motors_X1945 0 0 308s General.Motors_X1946 0 0 308s General.Motors_X1947 0 0 308s General.Motors_X1948 0 0 308s General.Motors_X1949 0 0 308s General.Motors_X1950 0 0 308s General.Motors_X1951 0 0 308s General.Motors_X1952 0 0 308s General.Motors_X1953 0 0 308s General.Motors_X1954 0 0 308s US.Steel_X1935 0 0 308s US.Steel_X1936 0 0 308s US.Steel_X1937 0 0 308s US.Steel_X1938 0 0 308s US.Steel_X1939 0 0 308s US.Steel_X1940 0 0 308s US.Steel_X1941 0 0 308s US.Steel_X1942 0 0 308s US.Steel_X1943 0 0 308s US.Steel_X1944 0 0 308s US.Steel_X1945 0 0 308s US.Steel_X1946 0 0 308s US.Steel_X1947 0 0 308s US.Steel_X1948 0 0 308s US.Steel_X1949 0 0 308s US.Steel_X1950 0 0 308s US.Steel_X1951 0 0 308s US.Steel_X1952 0 0 308s US.Steel_X1953 0 0 308s US.Steel_X1954 0 0 308s Westinghouse_X1935 0 0 308s Westinghouse_X1936 0 0 308s Westinghouse_X1937 0 0 308s Westinghouse_X1938 0 0 308s Westinghouse_X1939 0 0 308s Westinghouse_X1940 0 0 308s Westinghouse_X1941 0 0 308s Westinghouse_X1942 0 0 308s Westinghouse_X1943 0 0 308s Westinghouse_X1944 0 0 308s Westinghouse_X1945 0 0 308s Westinghouse_X1946 0 0 308s Westinghouse_X1947 0 0 308s Westinghouse_X1948 0 0 308s Westinghouse_X1949 0 0 308s Westinghouse_X1950 0 0 308s Westinghouse_X1951 0 0 308s Westinghouse_X1952 0 0 308s Westinghouse_X1953 0 0 308s Westinghouse_X1954 0 0 308s General.Electric_capital General.Motors_(Intercept) 308s Chrysler_X1935 0.0 0 308s Chrysler_X1936 0.0 0 308s Chrysler_X1937 0.0 0 308s Chrysler_X1938 0.0 0 308s Chrysler_X1939 0.0 0 308s Chrysler_X1940 0.0 0 308s Chrysler_X1941 0.0 0 308s Chrysler_X1942 0.0 0 308s Chrysler_X1943 0.0 0 308s Chrysler_X1944 0.0 0 308s Chrysler_X1945 0.0 0 308s Chrysler_X1946 0.0 0 308s Chrysler_X1947 0.0 0 308s Chrysler_X1948 0.0 0 308s Chrysler_X1949 0.0 0 308s Chrysler_X1950 0.0 0 308s Chrysler_X1951 0.0 0 308s Chrysler_X1952 0.0 0 308s Chrysler_X1953 0.0 0 308s Chrysler_X1954 0.0 0 308s General.Electric_X1935 97.8 0 308s General.Electric_X1936 104.4 0 308s General.Electric_X1937 118.0 0 308s General.Electric_X1938 156.2 0 308s General.Electric_X1939 172.6 0 308s General.Electric_X1940 186.6 0 308s General.Electric_X1941 220.9 0 308s General.Electric_X1942 287.8 0 308s General.Electric_X1943 319.9 0 308s General.Electric_X1944 321.3 0 308s General.Electric_X1945 319.6 0 308s General.Electric_X1946 346.0 0 308s General.Electric_X1947 456.4 0 308s General.Electric_X1948 543.4 0 308s General.Electric_X1949 618.3 0 308s General.Electric_X1950 647.4 0 308s General.Electric_X1951 671.3 0 308s General.Electric_X1952 726.1 0 308s General.Electric_X1953 800.3 0 308s General.Electric_X1954 888.9 0 308s General.Motors_X1935 0.0 1 308s General.Motors_X1936 0.0 1 308s General.Motors_X1937 0.0 1 308s General.Motors_X1938 0.0 1 308s General.Motors_X1939 0.0 1 308s General.Motors_X1940 0.0 1 308s General.Motors_X1941 0.0 1 308s General.Motors_X1942 0.0 1 308s General.Motors_X1943 0.0 1 308s General.Motors_X1944 0.0 1 308s General.Motors_X1945 0.0 1 308s General.Motors_X1946 0.0 1 308s General.Motors_X1947 0.0 1 308s General.Motors_X1948 0.0 1 308s General.Motors_X1949 0.0 1 308s General.Motors_X1950 0.0 1 308s General.Motors_X1951 0.0 1 308s General.Motors_X1952 0.0 1 308s General.Motors_X1953 0.0 1 308s General.Motors_X1954 0.0 1 308s US.Steel_X1935 0.0 0 308s US.Steel_X1936 0.0 0 308s US.Steel_X1937 0.0 0 308s US.Steel_X1938 0.0 0 308s US.Steel_X1939 0.0 0 308s US.Steel_X1940 0.0 0 308s US.Steel_X1941 0.0 0 308s US.Steel_X1942 0.0 0 308s US.Steel_X1943 0.0 0 308s US.Steel_X1944 0.0 0 308s US.Steel_X1945 0.0 0 308s US.Steel_X1946 0.0 0 308s US.Steel_X1947 0.0 0 308s US.Steel_X1948 0.0 0 308s US.Steel_X1949 0.0 0 308s US.Steel_X1950 0.0 0 308s US.Steel_X1951 0.0 0 308s US.Steel_X1952 0.0 0 308s US.Steel_X1953 0.0 0 308s US.Steel_X1954 0.0 0 308s Westinghouse_X1935 0.0 0 308s Westinghouse_X1936 0.0 0 308s Westinghouse_X1937 0.0 0 308s Westinghouse_X1938 0.0 0 308s Westinghouse_X1939 0.0 0 308s Westinghouse_X1940 0.0 0 308s Westinghouse_X1941 0.0 0 308s Westinghouse_X1942 0.0 0 308s Westinghouse_X1943 0.0 0 308s Westinghouse_X1944 0.0 0 308s Westinghouse_X1945 0.0 0 308s Westinghouse_X1946 0.0 0 308s Westinghouse_X1947 0.0 0 308s Westinghouse_X1948 0.0 0 308s Westinghouse_X1949 0.0 0 308s Westinghouse_X1950 0.0 0 308s Westinghouse_X1951 0.0 0 308s Westinghouse_X1952 0.0 0 308s Westinghouse_X1953 0.0 0 308s Westinghouse_X1954 0.0 0 308s General.Motors_value General.Motors_capital 308s Chrysler_X1935 0 0.0 308s Chrysler_X1936 0 0.0 308s Chrysler_X1937 0 0.0 308s Chrysler_X1938 0 0.0 308s Chrysler_X1939 0 0.0 308s Chrysler_X1940 0 0.0 308s Chrysler_X1941 0 0.0 308s Chrysler_X1942 0 0.0 308s Chrysler_X1943 0 0.0 308s Chrysler_X1944 0 0.0 308s Chrysler_X1945 0 0.0 308s Chrysler_X1946 0 0.0 308s Chrysler_X1947 0 0.0 308s Chrysler_X1948 0 0.0 308s Chrysler_X1949 0 0.0 308s Chrysler_X1950 0 0.0 308s Chrysler_X1951 0 0.0 308s Chrysler_X1952 0 0.0 308s Chrysler_X1953 0 0.0 308s Chrysler_X1954 0 0.0 308s General.Electric_X1935 0 0.0 308s General.Electric_X1936 0 0.0 308s General.Electric_X1937 0 0.0 308s General.Electric_X1938 0 0.0 308s General.Electric_X1939 0 0.0 308s General.Electric_X1940 0 0.0 308s General.Electric_X1941 0 0.0 308s General.Electric_X1942 0 0.0 308s General.Electric_X1943 0 0.0 308s General.Electric_X1944 0 0.0 308s General.Electric_X1945 0 0.0 308s General.Electric_X1946 0 0.0 308s General.Electric_X1947 0 0.0 308s General.Electric_X1948 0 0.0 308s General.Electric_X1949 0 0.0 308s General.Electric_X1950 0 0.0 308s General.Electric_X1951 0 0.0 308s General.Electric_X1952 0 0.0 308s General.Electric_X1953 0 0.0 308s General.Electric_X1954 0 0.0 308s General.Motors_X1935 3078 2.8 308s General.Motors_X1936 4662 52.6 308s General.Motors_X1937 5387 156.9 308s General.Motors_X1938 2792 209.2 308s General.Motors_X1939 4313 203.4 308s General.Motors_X1940 4644 207.2 308s General.Motors_X1941 4551 255.2 308s General.Motors_X1942 3244 303.7 308s General.Motors_X1943 4054 264.1 308s General.Motors_X1944 4379 201.6 308s General.Motors_X1945 4841 265.0 308s General.Motors_X1946 4901 402.2 308s General.Motors_X1947 3526 761.5 308s General.Motors_X1948 3255 922.4 308s General.Motors_X1949 3700 1020.1 308s General.Motors_X1950 3756 1099.0 308s General.Motors_X1951 4833 1207.7 308s General.Motors_X1952 4925 1430.5 308s General.Motors_X1953 6242 1777.3 308s General.Motors_X1954 5594 2226.3 308s US.Steel_X1935 0 0.0 308s US.Steel_X1936 0 0.0 308s US.Steel_X1937 0 0.0 308s US.Steel_X1938 0 0.0 308s US.Steel_X1939 0 0.0 308s US.Steel_X1940 0 0.0 308s US.Steel_X1941 0 0.0 308s US.Steel_X1942 0 0.0 308s US.Steel_X1943 0 0.0 308s US.Steel_X1944 0 0.0 308s US.Steel_X1945 0 0.0 308s US.Steel_X1946 0 0.0 308s US.Steel_X1947 0 0.0 308s US.Steel_X1948 0 0.0 308s US.Steel_X1949 0 0.0 308s US.Steel_X1950 0 0.0 308s US.Steel_X1951 0 0.0 308s US.Steel_X1952 0 0.0 308s US.Steel_X1953 0 0.0 308s US.Steel_X1954 0 0.0 308s Westinghouse_X1935 0 0.0 308s Westinghouse_X1936 0 0.0 308s Westinghouse_X1937 0 0.0 308s Westinghouse_X1938 0 0.0 308s Westinghouse_X1939 0 0.0 308s Westinghouse_X1940 0 0.0 308s Westinghouse_X1941 0 0.0 308s Westinghouse_X1942 0 0.0 308s Westinghouse_X1943 0 0.0 308s Westinghouse_X1944 0 0.0 308s Westinghouse_X1945 0 0.0 308s Westinghouse_X1946 0 0.0 308s Westinghouse_X1947 0 0.0 308s Westinghouse_X1948 0 0.0 308s Westinghouse_X1949 0 0.0 308s Westinghouse_X1950 0 0.0 308s Westinghouse_X1951 0 0.0 308s Westinghouse_X1952 0 0.0 308s Westinghouse_X1953 0 0.0 308s Westinghouse_X1954 0 0.0 308s US.Steel_(Intercept) US.Steel_value US.Steel_capital 308s Chrysler_X1935 0 0 0.0 308s Chrysler_X1936 0 0 0.0 308s Chrysler_X1937 0 0 0.0 308s Chrysler_X1938 0 0 0.0 308s Chrysler_X1939 0 0 0.0 308s Chrysler_X1940 0 0 0.0 308s Chrysler_X1941 0 0 0.0 308s Chrysler_X1942 0 0 0.0 308s Chrysler_X1943 0 0 0.0 308s Chrysler_X1944 0 0 0.0 308s Chrysler_X1945 0 0 0.0 308s Chrysler_X1946 0 0 0.0 308s Chrysler_X1947 0 0 0.0 308s Chrysler_X1948 0 0 0.0 308s Chrysler_X1949 0 0 0.0 308s Chrysler_X1950 0 0 0.0 308s Chrysler_X1951 0 0 0.0 308s Chrysler_X1952 0 0 0.0 308s Chrysler_X1953 0 0 0.0 308s Chrysler_X1954 0 0 0.0 308s General.Electric_X1935 0 0 0.0 308s General.Electric_X1936 0 0 0.0 308s General.Electric_X1937 0 0 0.0 308s General.Electric_X1938 0 0 0.0 308s General.Electric_X1939 0 0 0.0 308s General.Electric_X1940 0 0 0.0 308s General.Electric_X1941 0 0 0.0 308s General.Electric_X1942 0 0 0.0 308s General.Electric_X1943 0 0 0.0 308s General.Electric_X1944 0 0 0.0 308s General.Electric_X1945 0 0 0.0 308s General.Electric_X1946 0 0 0.0 308s General.Electric_X1947 0 0 0.0 308s General.Electric_X1948 0 0 0.0 308s General.Electric_X1949 0 0 0.0 308s General.Electric_X1950 0 0 0.0 308s General.Electric_X1951 0 0 0.0 308s General.Electric_X1952 0 0 0.0 308s General.Electric_X1953 0 0 0.0 308s General.Electric_X1954 0 0 0.0 308s General.Motors_X1935 0 0 0.0 308s General.Motors_X1936 0 0 0.0 308s General.Motors_X1937 0 0 0.0 308s General.Motors_X1938 0 0 0.0 308s General.Motors_X1939 0 0 0.0 308s General.Motors_X1940 0 0 0.0 308s General.Motors_X1941 0 0 0.0 308s General.Motors_X1942 0 0 0.0 308s General.Motors_X1943 0 0 0.0 308s General.Motors_X1944 0 0 0.0 308s General.Motors_X1945 0 0 0.0 308s General.Motors_X1946 0 0 0.0 308s General.Motors_X1947 0 0 0.0 308s General.Motors_X1948 0 0 0.0 308s General.Motors_X1949 0 0 0.0 308s General.Motors_X1950 0 0 0.0 308s General.Motors_X1951 0 0 0.0 308s General.Motors_X1952 0 0 0.0 308s General.Motors_X1953 0 0 0.0 308s General.Motors_X1954 0 0 0.0 308s US.Steel_X1935 1 1362 53.8 308s US.Steel_X1936 1 1807 50.5 308s US.Steel_X1937 1 2676 118.1 308s US.Steel_X1938 1 1802 260.2 308s US.Steel_X1939 1 1957 312.7 308s US.Steel_X1940 1 2203 254.2 308s US.Steel_X1941 1 2380 261.4 308s US.Steel_X1942 1 2169 298.7 308s US.Steel_X1943 1 1985 301.8 308s US.Steel_X1944 1 1814 279.1 308s US.Steel_X1945 1 1850 213.8 308s US.Steel_X1946 1 2068 232.6 308s US.Steel_X1947 1 1797 264.8 308s US.Steel_X1948 1 1626 306.9 308s US.Steel_X1949 1 1667 351.1 308s US.Steel_X1950 1 1677 357.8 308s US.Steel_X1951 1 2290 342.1 308s US.Steel_X1952 1 2159 444.2 308s US.Steel_X1953 1 2031 623.6 308s US.Steel_X1954 1 2116 669.7 308s Westinghouse_X1935 0 0 0.0 308s Westinghouse_X1936 0 0 0.0 308s Westinghouse_X1937 0 0 0.0 308s Westinghouse_X1938 0 0 0.0 308s Westinghouse_X1939 0 0 0.0 308s Westinghouse_X1940 0 0 0.0 308s Westinghouse_X1941 0 0 0.0 308s Westinghouse_X1942 0 0 0.0 308s Westinghouse_X1943 0 0 0.0 308s Westinghouse_X1944 0 0 0.0 308s Westinghouse_X1945 0 0 0.0 308s Westinghouse_X1946 0 0 0.0 308s Westinghouse_X1947 0 0 0.0 308s Westinghouse_X1948 0 0 0.0 308s Westinghouse_X1949 0 0 0.0 308s Westinghouse_X1950 0 0 0.0 308s Westinghouse_X1951 0 0 0.0 308s Westinghouse_X1952 0 0 0.0 308s Westinghouse_X1953 0 0 0.0 308s Westinghouse_X1954 0 0 0.0 308s Westinghouse_(Intercept) Westinghouse_value 308s Chrysler_X1935 0 0 308s Chrysler_X1936 0 0 308s Chrysler_X1937 0 0 308s Chrysler_X1938 0 0 308s Chrysler_X1939 0 0 308s Chrysler_X1940 0 0 308s Chrysler_X1941 0 0 308s Chrysler_X1942 0 0 308s Chrysler_X1943 0 0 308s Chrysler_X1944 0 0 308s Chrysler_X1945 0 0 308s Chrysler_X1946 0 0 308s Chrysler_X1947 0 0 308s Chrysler_X1948 0 0 308s Chrysler_X1949 0 0 308s Chrysler_X1950 0 0 308s Chrysler_X1951 0 0 308s Chrysler_X1952 0 0 308s Chrysler_X1953 0 0 308s Chrysler_X1954 0 0 308s General.Electric_X1935 0 0 308s General.Electric_X1936 0 0 308s General.Electric_X1937 0 0 308s General.Electric_X1938 0 0 308s General.Electric_X1939 0 0 308s General.Electric_X1940 0 0 308s General.Electric_X1941 0 0 308s General.Electric_X1942 0 0 308s General.Electric_X1943 0 0 308s General.Electric_X1944 0 0 308s General.Electric_X1945 0 0 308s General.Electric_X1946 0 0 308s General.Electric_X1947 0 0 308s General.Electric_X1948 0 0 308s General.Electric_X1949 0 0 308s General.Electric_X1950 0 0 308s General.Electric_X1951 0 0 308s General.Electric_X1952 0 0 308s General.Electric_X1953 0 0 308s General.Electric_X1954 0 0 308s General.Motors_X1935 0 0 308s General.Motors_X1936 0 0 308s General.Motors_X1937 0 0 308s General.Motors_X1938 0 0 308s General.Motors_X1939 0 0 308s General.Motors_X1940 0 0 308s General.Motors_X1941 0 0 308s General.Motors_X1942 0 0 308s General.Motors_X1943 0 0 308s General.Motors_X1944 0 0 308s General.Motors_X1945 0 0 308s General.Motors_X1946 0 0 308s General.Motors_X1947 0 0 308s General.Motors_X1948 0 0 308s General.Motors_X1949 0 0 308s General.Motors_X1950 0 0 308s General.Motors_X1951 0 0 308s General.Motors_X1952 0 0 308s General.Motors_X1953 0 0 308s General.Motors_X1954 0 0 308s US.Steel_X1935 0 0 308s US.Steel_X1936 0 0 308s US.Steel_X1937 0 0 308s US.Steel_X1938 0 0 308s US.Steel_X1939 0 0 308s US.Steel_X1940 0 0 308s US.Steel_X1941 0 0 308s US.Steel_X1942 0 0 308s US.Steel_X1943 0 0 308s US.Steel_X1944 0 0 308s US.Steel_X1945 0 0 308s US.Steel_X1946 0 0 308s US.Steel_X1947 0 0 308s US.Steel_X1948 0 0 308s US.Steel_X1949 0 0 308s US.Steel_X1950 0 0 308s US.Steel_X1951 0 0 308s US.Steel_X1952 0 0 308s US.Steel_X1953 0 0 308s US.Steel_X1954 0 0 308s Westinghouse_X1935 1 192 308s Westinghouse_X1936 1 516 308s Westinghouse_X1937 1 729 308s Westinghouse_X1938 1 560 308s Westinghouse_X1939 1 520 308s Westinghouse_X1940 1 628 308s Westinghouse_X1941 1 537 308s Westinghouse_X1942 1 561 308s Westinghouse_X1943 1 617 308s Westinghouse_X1944 1 627 308s Westinghouse_X1945 1 737 308s Westinghouse_X1946 1 760 308s Westinghouse_X1947 1 581 308s Westinghouse_X1948 1 662 308s Westinghouse_X1949 1 584 308s Westinghouse_X1950 1 635 308s Westinghouse_X1951 1 724 308s Westinghouse_X1952 1 864 308s Westinghouse_X1953 1 1194 308s Westinghouse_X1954 1 1189 308s Westinghouse_capital 308s Chrysler_X1935 0.0 308s Chrysler_X1936 0.0 308s Chrysler_X1937 0.0 308s Chrysler_X1938 0.0 308s Chrysler_X1939 0.0 308s Chrysler_X1940 0.0 308s Chrysler_X1941 0.0 308s Chrysler_X1942 0.0 308s Chrysler_X1943 0.0 308s Chrysler_X1944 0.0 308s Chrysler_X1945 0.0 308s Chrysler_X1946 0.0 308s Chrysler_X1947 0.0 308s Chrysler_X1948 0.0 308s Chrysler_X1949 0.0 308s Chrysler_X1950 0.0 308s Chrysler_X1951 0.0 308s Chrysler_X1952 0.0 308s Chrysler_X1953 0.0 308s Chrysler_X1954 0.0 308s General.Electric_X1935 0.0 308s General.Electric_X1936 0.0 308s General.Electric_X1937 0.0 308s General.Electric_X1938 0.0 308s General.Electric_X1939 0.0 308s General.Electric_X1940 0.0 308s General.Electric_X1941 0.0 308s General.Electric_X1942 0.0 308s General.Electric_X1943 0.0 308s General.Electric_X1944 0.0 308s General.Electric_X1945 0.0 308s General.Electric_X1946 0.0 308s General.Electric_X1947 0.0 308s General.Electric_X1948 0.0 308s General.Electric_X1949 0.0 308s General.Electric_X1950 0.0 308s General.Electric_X1951 0.0 308s General.Electric_X1952 0.0 308s General.Electric_X1953 0.0 308s General.Electric_X1954 0.0 308s General.Motors_X1935 0.0 308s General.Motors_X1936 0.0 308s General.Motors_X1937 0.0 308s General.Motors_X1938 0.0 308s General.Motors_X1939 0.0 308s General.Motors_X1940 0.0 308s General.Motors_X1941 0.0 308s General.Motors_X1942 0.0 308s General.Motors_X1943 0.0 308s General.Motors_X1944 0.0 308s General.Motors_X1945 0.0 308s General.Motors_X1946 0.0 308s General.Motors_X1947 0.0 308s General.Motors_X1948 0.0 308s General.Motors_X1949 0.0 308s General.Motors_X1950 0.0 308s General.Motors_X1951 0.0 308s General.Motors_X1952 0.0 308s General.Motors_X1953 0.0 308s General.Motors_X1954 0.0 308s US.Steel_X1935 0.0 308s US.Steel_X1936 0.0 308s US.Steel_X1937 0.0 308s US.Steel_X1938 0.0 308s US.Steel_X1939 0.0 308s US.Steel_X1940 0.0 308s US.Steel_X1941 0.0 308s US.Steel_X1942 0.0 308s US.Steel_X1943 0.0 308s US.Steel_X1944 0.0 308s US.Steel_X1945 0.0 308s US.Steel_X1946 0.0 308s US.Steel_X1947 0.0 308s US.Steel_X1948 0.0 308s US.Steel_X1949 0.0 308s US.Steel_X1950 0.0 308s US.Steel_X1951 0.0 308s US.Steel_X1952 0.0 308s US.Steel_X1953 0.0 308s US.Steel_X1954 0.0 308s Westinghouse_X1935 1.8 308s Westinghouse_X1936 0.8 308s Westinghouse_X1937 7.4 308s Westinghouse_X1938 18.1 308s Westinghouse_X1939 23.5 308s Westinghouse_X1940 26.5 308s Westinghouse_X1941 36.2 308s Westinghouse_X1942 60.8 308s Westinghouse_X1943 84.4 308s Westinghouse_X1944 91.2 308s Westinghouse_X1945 92.4 308s Westinghouse_X1946 86.0 308s Westinghouse_X1947 111.1 308s Westinghouse_X1948 130.6 308s Westinghouse_X1949 141.8 308s Westinghouse_X1950 136.7 308s Westinghouse_X1951 129.7 308s Westinghouse_X1952 145.5 308s Westinghouse_X1953 174.8 308s Westinghouse_X1954 213.5 308s $Chrysler 308s Chrysler_invest ~ Chrysler_value + Chrysler_capital 308s 308s 308s $General.Electric 308s General.Electric_invest ~ General.Electric_value + General.Electric_capital 308s 308s 308s $General.Motors 308s General.Motors_invest ~ General.Motors_value + General.Motors_capital 308s 308s 308s $US.Steel 308s US.Steel_invest ~ US.Steel_value + US.Steel_capital 308s 308s 308s $Westinghouse 308s Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 308s 308s 308s Chrysler_invest ~ Chrysler_value + Chrysler_capital 308s 308s $Chrysler 308s Chrysler_invest ~ Chrysler_value + Chrysler_capital 308s attr(,"variables") 308s list(Chrysler_invest, Chrysler_value, Chrysler_capital) 308s attr(,"factors") 308s Chrysler_value Chrysler_capital 308s Chrysler_invest 0 0 308s Chrysler_value 1 0 308s Chrysler_capital 0 1 308s attr(,"term.labels") 308s [1] "Chrysler_value" "Chrysler_capital" 308s attr(,"order") 308s [1] 1 1 308s attr(,"intercept") 308s [1] 1 308s attr(,"response") 308s [1] 1 308s attr(,".Environment") 308s 308s attr(,"predvars") 308s list(Chrysler_invest, Chrysler_value, Chrysler_capital) 308s attr(,"dataClasses") 308s Chrysler_invest Chrysler_value Chrysler_capital 308s "numeric" "numeric" "numeric" 308s 308s $General.Electric 308s General.Electric_invest ~ General.Electric_value + General.Electric_capital 308s attr(,"variables") 308s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 308s attr(,"factors") 308s General.Electric_value General.Electric_capital 308s General.Electric_invest 0 0 308s General.Electric_value 1 0 308s General.Electric_capital 0 1 308s attr(,"term.labels") 308s [1] "General.Electric_value" "General.Electric_capital" 308s attr(,"order") 308s [1] 1 1 308s attr(,"intercept") 308s [1] 1 308s attr(,"response") 308s [1] 1 308s attr(,".Environment") 308s 308s attr(,"predvars") 308s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 308s attr(,"dataClasses") 308s General.Electric_invest General.Electric_value General.Electric_capital 308s "numeric" "numeric" "numeric" 308s 308s $General.Motors 308s General.Motors_invest ~ General.Motors_value + General.Motors_capital 308s attr(,"variables") 308s list(General.Motors_invest, General.Motors_value, General.Motors_capital) 308s attr(,"factors") 308s General.Motors_value General.Motors_capital 308s General.Motors_invest 0 0 308s General.Motors_value 1 0 308s General.Motors_capital 0 1 308s attr(,"term.labels") 308s [1] "General.Motors_value" "General.Motors_capital" 308s attr(,"order") 308s [1] 1 1 308s attr(,"intercept") 308s [1] 1 308s attr(,"response") 308s [1] 1 308s attr(,".Environment") 308s 308s attr(,"predvars") 308s list(General.Motors_invest, General.Motors_value, General.Motors_capital) 308s attr(,"dataClasses") 308s General.Motors_invest General.Motors_value General.Motors_capital 308s "numeric" "numeric" "numeric" 308s 308s $US.Steel 308s US.Steel_invest ~ US.Steel_value + US.Steel_capital 308s attr(,"variables") 308s list(US.Steel_invest, US.Steel_value, US.Steel_capital) 308s attr(,"factors") 308s US.Steel_value US.Steel_capital 308s US.Steel_invest 0 0 308s US.Steel_value 1 0 308s US.Steel_capital 0 1 308s attr(,"term.labels") 308s [1] "US.Steel_value" "US.Steel_capital" 308s attr(,"order") 308s [1] 1 1 308s attr(,"intercept") 308s [1] 1 308s attr(,"response") 308s [1] 1 308s attr(,".Environment") 308s 308s attr(,"predvars") 308s list(US.Steel_invest, US.Steel_value, US.Steel_capital) 308s attr(,"dataClasses") 308s US.Steel_invest US.Steel_value US.Steel_capital 308s "numeric" "numeric" "numeric" 308s 308s $Westinghouse 308s Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 308s attr(,"variables") 308s list(Westinghouse_invest, Westinghouse_value, Westinghouse_capital) 308s attr(,"factors") 308s Westinghouse_value Westinghouse_capital 308s Westinghouse_invest 0 0 308s Westinghouse_value 1 0 308s Westinghouse_capital 0 1 308s attr(,"term.labels") 308s [1] "Westinghouse_value" "Westinghouse_capital" 308s attr(,"order") 308s [1] 1 1 308s attr(,"intercept") 308s [1] 1 308s attr(,"response") 308s [1] 1 308s attr(,".Environment") 308s 308s attr(,"predvars") 308s list(Westinghouse_invest, Westinghouse_value, Westinghouse_capital) 308s attr(,"dataClasses") 308s Westinghouse_invest Westinghouse_value Westinghouse_capital 308s "numeric" "numeric" "numeric" 308s 308s Chrysler_invest ~ Chrysler_value + Chrysler_capital 308s attr(,"variables") 308s list(Chrysler_invest, Chrysler_value, Chrysler_capital) 308s attr(,"factors") 308s Chrysler_value Chrysler_capital 308s Chrysler_invest 0 0 308s Chrysler_value 1 0 308s Chrysler_capital 0 1 308s attr(,"term.labels") 308s [1] "Chrysler_value" "Chrysler_capital" 308s attr(,"order") 308s [1] 1 1 308s attr(,"intercept") 308s [1] 1 308s attr(,"response") 308s [1] 1 308s attr(,".Environment") 308s 308s attr(,"predvars") 308s list(Chrysler_invest, Chrysler_value, Chrysler_capital) 308s attr(,"dataClasses") 308s Chrysler_invest Chrysler_value Chrysler_capital 308s "numeric" "numeric" "numeric" 308s > 308s > # SUR Pooled 308s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 308s + greeneSurPooled <- systemfit( formulaGrunfeld, "SUR", 308s + data = GrunfeldGreene, pooled = TRUE, methodResidCov = "noDfCor", 308s + residCovWeighted = TRUE, useMatrix = useMatrix ) 308s + print( greeneSurPooled ) 308s + print( summary( greeneSurPooled ) ) 308s + print( summary( greeneSurPooled, useDfSys = FALSE, equations = FALSE ) ) 308s + print( summary( greeneSurPooled, residCov = FALSE, equations = FALSE ) ) 308s + print( coef( greeneSurPooled ) ) 308s + print( coef( greeneSurPooled, modified.regMat = TRUE ) ) 308s + print( coef( summary( greeneSurPooled ) ) ) 308s + print( coef( summary( greeneSurPooled ), modified.regMat = TRUE ) ) 308s + print( vcov( greeneSurPooled ) ) 308s + print( vcov( greeneSurPooled, modified.regMat = TRUE ) ) 308s + print( residuals( greeneSurPooled ) ) 308s + print( confint( greeneSurPooled ) ) 308s + print( fitted( greeneSurPooled ) ) 308s + print( logLik( greeneSurPooled ) ) 308s + print( logLik( greeneSurPooled, residCovDiag = TRUE ) ) 308s + print( nobs( greeneSurPooled ) ) 308s + print( model.frame( greeneSurPooled ) ) 308s + print( model.matrix( greeneSurPooled ) ) 308s + print( formula( greeneSurPooled ) ) 308s + print( formula( greeneSurPooled$eq[[ 1 ]] ) ) 308s + print( terms( greeneSurPooled ) ) 308s + print( terms( greeneSurPooled$eq[[ 1 ]] ) ) 308s + } 308s 308s systemfit results 308s method: SUR 308s 308s Coefficients: 308s Chrysler_(Intercept) Chrysler_value 308s -28.2467 0.0891 308s Chrysler_capital General.Electric_(Intercept) 308s 0.3340 -28.2467 308s General.Electric_value General.Electric_capital 308s 0.0891 0.3340 308s General.Motors_(Intercept) General.Motors_value 308s -28.2467 0.0891 308s General.Motors_capital US.Steel_(Intercept) 308s 0.3340 -28.2467 308s US.Steel_value US.Steel_capital 308s 0.0891 0.3340 308s Westinghouse_(Intercept) Westinghouse_value 308s -28.2467 0.0891 308s Westinghouse_capital 308s 0.3340 308s 308s systemfit results 308s method: SUR 308s 308s N DF SSR detRCov OLS-R2 McElroy-R2 308s system 100 97 1604301 9.95e+16 0.279 0.844 308s 308s N DF SSR MSE RMSE R2 Adj R2 308s Chrysler 20 17 6112 360 19.0 0.824 0.803 308s General.Electric 20 17 691132 40655 201.6 -14.410 -16.223 308s General.Motors 20 17 201010 11824 108.7 0.890 0.877 308s US.Steel 20 17 689380 40552 201.4 -1.168 -1.424 308s Westinghouse 20 17 16667 980 31.3 -1.402 -1.685 308s 308s The covariance matrix of the residuals used for estimation 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 409 -2594 -197 2594 -102 308s General.Electric -2594 36563 -3480 -28623 3797 308s General.Motors -197 -3480 8612 996 -971 308s US.Steel 2594 -28623 996 32903 -2272 308s Westinghouse -102 3797 -971 -2272 778 308s 308s The covariance matrix of the residuals 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 305.61 -1967 -4.81 2159 -124 308s General.Electric -1966.65 34557 -7160.67 -28722 4274 308s General.Motors -4.81 -7161 10050.52 4440 -1401 308s US.Steel 2158.60 -28722 4439.99 34469 -2894 308s Westinghouse -123.92 4274 -1400.75 -2894 833 308s 308s The correlations of the residuals 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 1.000 0.220 -0.3447 0.2008 0.2907 308s General.Electric 0.220 1.000 -0.2233 -0.1587 0.8973 308s General.Motors -0.345 -0.223 1.0000 -0.0924 -0.3760 308s US.Steel 0.201 -0.159 -0.0924 1.0000 -0.0757 308s Westinghouse 0.291 0.897 -0.3760 -0.0757 1.0000 308s 308s 308s SUR estimates for 'Chrysler' (equation 1) 308s Model Formula: Chrysler_invest ~ Chrysler_value + Chrysler_capital 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -28.24669 4.88824 -5.78 9.1e-08 *** 308s value 0.08910 0.00507 17.57 < 2e-16 *** 308s capital 0.33402 0.01671 19.99 < 2e-16 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 18.962 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 6112.2 MSE: 359.541 Root MSE: 18.962 308s Multiple R-Squared: 0.824 Adjusted R-Squared: 0.803 308s 308s 308s SUR estimates for 'General.Electric' (equation 2) 308s Model Formula: General.Electric_invest ~ General.Electric_value + General.Electric_capital 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -28.24669 4.88824 -5.78 9.1e-08 *** 308s value 0.08910 0.00507 17.57 < 2e-16 *** 308s capital 0.33402 0.01671 19.99 < 2e-16 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 201.63 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 691132.056 MSE: 40654.827 Root MSE: 201.63 308s Multiple R-Squared: -14.41 Adjusted R-Squared: -16.223 308s 308s 308s SUR estimates for 'General.Motors' (equation 3) 308s Model Formula: General.Motors_invest ~ General.Motors_value + General.Motors_capital 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -28.24669 4.88824 -5.78 9.1e-08 *** 308s value 0.08910 0.00507 17.57 < 2e-16 *** 308s capital 0.33402 0.01671 19.99 < 2e-16 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 108.739 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 201010.497 MSE: 11824.147 Root MSE: 108.739 308s Multiple R-Squared: 0.89 Adjusted R-Squared: 0.877 308s 308s 308s SUR estimates for 'US.Steel' (equation 4) 308s Model Formula: US.Steel_invest ~ US.Steel_value + US.Steel_capital 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -28.24669 4.88824 -5.78 9.1e-08 *** 308s value 0.08910 0.00507 17.57 < 2e-16 *** 308s capital 0.33402 0.01671 19.99 < 2e-16 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 201.375 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 689379.52 MSE: 40551.736 Root MSE: 201.375 308s Multiple R-Squared: -1.168 Adjusted R-Squared: -1.424 308s 308s 308s SUR estimates for 'Westinghouse' (equation 5) 308s Model Formula: Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -28.24669 4.88824 -5.78 9.1e-08 *** 308s value 0.08910 0.00507 17.57 < 2e-16 *** 308s capital 0.33402 0.01671 19.99 < 2e-16 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 31.312 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 16667.149 MSE: 980.421 Root MSE: 31.312 308s Multiple R-Squared: -1.402 Adjusted R-Squared: -1.685 308s 308s 308s systemfit results 308s method: SUR 308s 308s N DF SSR detRCov OLS-R2 McElroy-R2 308s system 100 97 1604301 9.95e+16 0.279 0.844 308s 308s N DF SSR MSE RMSE R2 Adj R2 308s Chrysler 20 17 6112 360 19.0 0.824 0.803 308s General.Electric 20 17 691132 40655 201.6 -14.410 -16.223 308s General.Motors 20 17 201010 11824 108.7 0.890 0.877 308s US.Steel 20 17 689380 40552 201.4 -1.168 -1.424 308s Westinghouse 20 17 16667 980 31.3 -1.402 -1.685 308s 308s The covariance matrix of the residuals used for estimation 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 409 -2594 -197 2594 -102 308s General.Electric -2594 36563 -3480 -28623 3797 308s General.Motors -197 -3480 8612 996 -971 308s US.Steel 2594 -28623 996 32903 -2272 308s Westinghouse -102 3797 -971 -2272 778 308s 308s The covariance matrix of the residuals 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 305.61 -1967 -4.81 2159 -124 308s General.Electric -1966.65 34557 -7160.67 -28722 4274 308s General.Motors -4.81 -7161 10050.52 4440 -1401 308s US.Steel 2158.60 -28722 4439.99 34469 -2894 308s Westinghouse -123.92 4274 -1400.75 -2894 833 308s 308s The correlations of the residuals 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 1.000 0.220 -0.3447 0.2008 0.2907 308s General.Electric 0.220 1.000 -0.2233 -0.1587 0.8973 308s General.Motors -0.345 -0.223 1.0000 -0.0924 -0.3760 308s US.Steel 0.201 -0.159 -0.0924 1.0000 -0.0757 308s Westinghouse 0.291 0.897 -0.3760 -0.0757 1.0000 308s 308s 308s Coefficients: 308s Estimate Std. Error t value Pr(>|t|) 308s Chrysler_(Intercept) -28.24669 4.88824 -5.78 2.2e-05 *** 308s Chrysler_value 0.08910 0.00507 17.57 2.5e-12 *** 308s Chrysler_capital 0.33402 0.01671 19.99 3.0e-13 *** 308s General.Electric_(Intercept) -28.24669 4.88824 -5.78 2.2e-05 *** 308s General.Electric_value 0.08910 0.00507 17.57 2.5e-12 *** 308s General.Electric_capital 0.33402 0.01671 19.99 3.0e-13 *** 308s General.Motors_(Intercept) -28.24669 4.88824 -5.78 2.2e-05 *** 308s General.Motors_value 0.08910 0.00507 17.57 2.5e-12 *** 308s General.Motors_capital 0.33402 0.01671 19.99 3.0e-13 *** 308s US.Steel_(Intercept) -28.24669 4.88824 -5.78 2.2e-05 *** 308s US.Steel_value 0.08910 0.00507 17.57 2.5e-12 *** 308s US.Steel_capital 0.33402 0.01671 19.99 3.0e-13 *** 308s Westinghouse_(Intercept) -28.24669 4.88824 -5.78 2.2e-05 *** 308s Westinghouse_value 0.08910 0.00507 17.57 2.5e-12 *** 308s Westinghouse_capital 0.33402 0.01671 19.99 3.0e-13 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s systemfit results 308s method: SUR 308s 308s N DF SSR detRCov OLS-R2 McElroy-R2 308s system 100 97 1604301 9.95e+16 0.279 0.844 308s 308s N DF SSR MSE RMSE R2 Adj R2 308s Chrysler 20 17 6112 360 19.0 0.824 0.803 308s General.Electric 20 17 691132 40655 201.6 -14.410 -16.223 308s General.Motors 20 17 201010 11824 108.7 0.890 0.877 308s US.Steel 20 17 689380 40552 201.4 -1.168 -1.424 308s Westinghouse 20 17 16667 980 31.3 -1.402 -1.685 308s 308s 308s Coefficients: 308s Estimate Std. Error t value Pr(>|t|) 308s Chrysler_(Intercept) -28.24669 4.88824 -5.78 9.1e-08 *** 308s Chrysler_value 0.08910 0.00507 17.57 < 2e-16 *** 308s Chrysler_capital 0.33402 0.01671 19.99 < 2e-16 *** 308s General.Electric_(Intercept) -28.24669 4.88824 -5.78 9.1e-08 *** 308s General.Electric_value 0.08910 0.00507 17.57 < 2e-16 *** 308s General.Electric_capital 0.33402 0.01671 19.99 < 2e-16 *** 308s General.Motors_(Intercept) -28.24669 4.88824 -5.78 9.1e-08 *** 308s General.Motors_value 0.08910 0.00507 17.57 < 2e-16 *** 308s General.Motors_capital 0.33402 0.01671 19.99 < 2e-16 *** 308s US.Steel_(Intercept) -28.24669 4.88824 -5.78 9.1e-08 *** 308s US.Steel_value 0.08910 0.00507 17.57 < 2e-16 *** 308s US.Steel_capital 0.33402 0.01671 19.99 < 2e-16 *** 308s Westinghouse_(Intercept) -28.24669 4.88824 -5.78 9.1e-08 *** 308s Westinghouse_value 0.08910 0.00507 17.57 < 2e-16 *** 308s Westinghouse_capital 0.33402 0.01671 19.99 < 2e-16 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s Chrysler_(Intercept) Chrysler_value 308s -28.2467 0.0891 308s Chrysler_capital General.Electric_(Intercept) 308s 0.3340 -28.2467 308s General.Electric_value General.Electric_capital 308s 0.0891 0.3340 308s General.Motors_(Intercept) General.Motors_value 308s -28.2467 0.0891 308s General.Motors_capital US.Steel_(Intercept) 308s 0.3340 -28.2467 308s US.Steel_value US.Steel_capital 308s 0.0891 0.3340 308s Westinghouse_(Intercept) Westinghouse_value 308s -28.2467 0.0891 308s Westinghouse_capital 308s 0.3340 308s C1 C2 C3 308s -28.2467 0.0891 0.3340 308s Estimate Std. Error t value Pr(>|t|) 308s Chrysler_(Intercept) -28.2467 4.88824 -5.78 9.12e-08 308s Chrysler_value 0.0891 0.00507 17.57 0.00e+00 308s Chrysler_capital 0.3340 0.01671 19.99 0.00e+00 308s General.Electric_(Intercept) -28.2467 4.88824 -5.78 9.12e-08 308s General.Electric_value 0.0891 0.00507 17.57 0.00e+00 308s General.Electric_capital 0.3340 0.01671 19.99 0.00e+00 308s General.Motors_(Intercept) -28.2467 4.88824 -5.78 9.12e-08 308s General.Motors_value 0.0891 0.00507 17.57 0.00e+00 308s General.Motors_capital 0.3340 0.01671 19.99 0.00e+00 308s US.Steel_(Intercept) -28.2467 4.88824 -5.78 9.12e-08 308s US.Steel_value 0.0891 0.00507 17.57 0.00e+00 308s US.Steel_capital 0.3340 0.01671 19.99 0.00e+00 308s Westinghouse_(Intercept) -28.2467 4.88824 -5.78 9.12e-08 308s Westinghouse_value 0.0891 0.00507 17.57 0.00e+00 308s Westinghouse_capital 0.3340 0.01671 19.99 0.00e+00 308s Estimate Std. Error t value Pr(>|t|) 308s C1 -28.2467 4.88824 -5.78 9.12e-08 308s C2 0.0891 0.00507 17.57 0.00e+00 308s C3 0.3340 0.01671 19.99 0.00e+00 308s Chrysler_(Intercept) Chrysler_value 308s Chrysler_(Intercept) 23.89487 -1.73e-02 308s Chrysler_value -0.01729 2.57e-05 308s Chrysler_capital 0.00114 -4.74e-05 308s General.Electric_(Intercept) 23.89487 -1.73e-02 308s General.Electric_value -0.01729 2.57e-05 308s General.Electric_capital 0.00114 -4.74e-05 308s General.Motors_(Intercept) 23.89487 -1.73e-02 308s General.Motors_value -0.01729 2.57e-05 308s General.Motors_capital 0.00114 -4.74e-05 308s US.Steel_(Intercept) 23.89487 -1.73e-02 308s US.Steel_value -0.01729 2.57e-05 308s US.Steel_capital 0.00114 -4.74e-05 308s Westinghouse_(Intercept) 23.89487 -1.73e-02 308s Westinghouse_value -0.01729 2.57e-05 308s Westinghouse_capital 0.00114 -4.74e-05 308s Chrysler_capital General.Electric_(Intercept) 308s Chrysler_(Intercept) 1.14e-03 23.89487 308s Chrysler_value -4.74e-05 -0.01729 308s Chrysler_capital 2.79e-04 0.00114 308s General.Electric_(Intercept) 1.14e-03 23.89487 308s General.Electric_value -4.74e-05 -0.01729 308s General.Electric_capital 2.79e-04 0.00114 308s General.Motors_(Intercept) 1.14e-03 23.89487 308s General.Motors_value -4.74e-05 -0.01729 308s General.Motors_capital 2.79e-04 0.00114 308s US.Steel_(Intercept) 1.14e-03 23.89487 308s US.Steel_value -4.74e-05 -0.01729 308s US.Steel_capital 2.79e-04 0.00114 308s Westinghouse_(Intercept) 1.14e-03 23.89487 308s Westinghouse_value -4.74e-05 -0.01729 308s Westinghouse_capital 2.79e-04 0.00114 308s General.Electric_value General.Electric_capital 308s Chrysler_(Intercept) -1.73e-02 1.14e-03 308s Chrysler_value 2.57e-05 -4.74e-05 308s Chrysler_capital -4.74e-05 2.79e-04 308s General.Electric_(Intercept) -1.73e-02 1.14e-03 308s General.Electric_value 2.57e-05 -4.74e-05 308s General.Electric_capital -4.74e-05 2.79e-04 308s General.Motors_(Intercept) -1.73e-02 1.14e-03 308s General.Motors_value 2.57e-05 -4.74e-05 308s General.Motors_capital -4.74e-05 2.79e-04 308s US.Steel_(Intercept) -1.73e-02 1.14e-03 308s US.Steel_value 2.57e-05 -4.74e-05 308s US.Steel_capital -4.74e-05 2.79e-04 308s Westinghouse_(Intercept) -1.73e-02 1.14e-03 308s Westinghouse_value 2.57e-05 -4.74e-05 308s Westinghouse_capital -4.74e-05 2.79e-04 308s General.Motors_(Intercept) General.Motors_value 308s Chrysler_(Intercept) 23.89487 -1.73e-02 308s Chrysler_value -0.01729 2.57e-05 308s Chrysler_capital 0.00114 -4.74e-05 308s General.Electric_(Intercept) 23.89487 -1.73e-02 308s General.Electric_value -0.01729 2.57e-05 308s General.Electric_capital 0.00114 -4.74e-05 308s General.Motors_(Intercept) 23.89487 -1.73e-02 308s General.Motors_value -0.01729 2.57e-05 308s General.Motors_capital 0.00114 -4.74e-05 308s US.Steel_(Intercept) 23.89487 -1.73e-02 308s US.Steel_value -0.01729 2.57e-05 308s US.Steel_capital 0.00114 -4.74e-05 308s Westinghouse_(Intercept) 23.89487 -1.73e-02 308s Westinghouse_value -0.01729 2.57e-05 308s Westinghouse_capital 0.00114 -4.74e-05 308s General.Motors_capital US.Steel_(Intercept) 308s Chrysler_(Intercept) 1.14e-03 23.89487 308s Chrysler_value -4.74e-05 -0.01729 308s Chrysler_capital 2.79e-04 0.00114 308s General.Electric_(Intercept) 1.14e-03 23.89487 308s General.Electric_value -4.74e-05 -0.01729 308s General.Electric_capital 2.79e-04 0.00114 308s General.Motors_(Intercept) 1.14e-03 23.89487 308s General.Motors_value -4.74e-05 -0.01729 308s General.Motors_capital 2.79e-04 0.00114 308s US.Steel_(Intercept) 1.14e-03 23.89487 308s US.Steel_value -4.74e-05 -0.01729 308s US.Steel_capital 2.79e-04 0.00114 308s Westinghouse_(Intercept) 1.14e-03 23.89487 308s Westinghouse_value -4.74e-05 -0.01729 308s Westinghouse_capital 2.79e-04 0.00114 308s US.Steel_value US.Steel_capital 308s Chrysler_(Intercept) -1.73e-02 1.14e-03 308s Chrysler_value 2.57e-05 -4.74e-05 308s Chrysler_capital -4.74e-05 2.79e-04 308s General.Electric_(Intercept) -1.73e-02 1.14e-03 308s General.Electric_value 2.57e-05 -4.74e-05 308s General.Electric_capital -4.74e-05 2.79e-04 308s General.Motors_(Intercept) -1.73e-02 1.14e-03 308s General.Motors_value 2.57e-05 -4.74e-05 308s General.Motors_capital -4.74e-05 2.79e-04 308s US.Steel_(Intercept) -1.73e-02 1.14e-03 308s US.Steel_value 2.57e-05 -4.74e-05 308s US.Steel_capital -4.74e-05 2.79e-04 308s Westinghouse_(Intercept) -1.73e-02 1.14e-03 308s Westinghouse_value 2.57e-05 -4.74e-05 308s Westinghouse_capital -4.74e-05 2.79e-04 308s Westinghouse_(Intercept) Westinghouse_value 308s Chrysler_(Intercept) 23.89487 -1.73e-02 308s Chrysler_value -0.01729 2.57e-05 308s Chrysler_capital 0.00114 -4.74e-05 308s General.Electric_(Intercept) 23.89487 -1.73e-02 308s General.Electric_value -0.01729 2.57e-05 308s General.Electric_capital 0.00114 -4.74e-05 308s General.Motors_(Intercept) 23.89487 -1.73e-02 308s General.Motors_value -0.01729 2.57e-05 308s General.Motors_capital 0.00114 -4.74e-05 308s US.Steel_(Intercept) 23.89487 -1.73e-02 308s US.Steel_value -0.01729 2.57e-05 308s US.Steel_capital 0.00114 -4.74e-05 308s Westinghouse_(Intercept) 23.89487 -1.73e-02 308s Westinghouse_value -0.01729 2.57e-05 308s Westinghouse_capital 0.00114 -4.74e-05 308s Westinghouse_capital 308s Chrysler_(Intercept) 1.14e-03 308s Chrysler_value -4.74e-05 308s Chrysler_capital 2.79e-04 308s General.Electric_(Intercept) 1.14e-03 308s General.Electric_value -4.74e-05 308s General.Electric_capital 2.79e-04 308s General.Motors_(Intercept) 1.14e-03 308s General.Motors_value -4.74e-05 308s General.Motors_capital 2.79e-04 308s US.Steel_(Intercept) 1.14e-03 308s US.Steel_value -4.74e-05 308s US.Steel_capital 2.79e-04 308s Westinghouse_(Intercept) 1.14e-03 308s Westinghouse_value -4.74e-05 308s Westinghouse_capital 2.79e-04 308s C1 C2 C3 308s C1 23.89487 -1.73e-02 1.14e-03 308s C2 -0.01729 2.57e-05 -4.74e-05 308s C3 0.00114 -4.74e-05 2.79e-04 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s X1935 27.830 -75.6 70.61 98.79 23.51 308s X1936 22.951 -141.2 -12.88 205.66 7.90 308s X1937 4.160 -183.7 -93.56 220.24 -4.13 308s X1938 23.527 -161.1 -32.72 43.09 -4.84 308s X1939 -1.382 -182.3 -93.20 -20.20 -7.09 308s X1940 10.397 -149.7 6.46 8.66 -8.03 308s X1941 14.133 -96.0 49.49 201.63 16.81 308s X1942 14.586 -117.5 85.75 180.85 1.28 308s X1943 0.807 -173.2 78.44 112.17 -17.92 308s X1944 5.381 -172.6 118.21 61.60 -20.25 308s X1945 23.374 -163.8 69.60 50.68 -29.03 308s X1946 -5.596 -124.2 145.33 186.62 -14.78 308s X1947 7.005 -124.6 28.58 200.21 -5.11 308s X1948 18.909 -149.9 -40.65 275.38 -24.83 308s X1949 5.397 -207.5 -87.07 167.54 -39.09 308s X1950 12.604 -238.0 -30.56 178.08 -41.77 308s X1951 48.812 -222.9 -49.87 298.18 -25.19 308s X1952 11.406 -242.3 2.83 332.67 -25.56 308s X1953 -1.660 -270.9 182.86 279.96 -46.40 308s X1954 -0.502 -325.0 272.93 75.36 -80.40 308s 2.5 % 97.5 % 308s Chrysler_(Intercept) -37.948 -18.545 308s Chrysler_value 0.079 0.099 308s Chrysler_capital 0.301 0.367 308s General.Electric_(Intercept) -37.948 -18.545 308s General.Electric_value 0.079 0.099 308s General.Electric_capital 0.301 0.367 308s General.Motors_(Intercept) -37.948 -18.545 308s General.Motors_value 0.079 0.099 308s General.Motors_capital 0.301 0.367 308s US.Steel_(Intercept) -37.948 -18.545 308s US.Steel_value 0.079 0.099 308s US.Steel_capital 0.301 0.367 308s Westinghouse_(Intercept) -37.948 -18.545 308s Westinghouse_value 0.079 0.099 308s Westinghouse_capital 0.301 0.367 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s X1935 12.5 109 247 111 -10.6 308s X1936 49.8 186 405 150 18.0 308s X1937 62.1 261 504 250 39.2 308s X1938 28.1 206 290 219 27.7 308s X1939 53.8 230 424 251 25.9 308s X1940 59.0 224 455 253 36.6 308s X1941 54.2 209 463 271 31.7 308s X1942 32.2 209 362 265 42.1 308s X1943 46.6 234 421 249 54.9 308s X1944 54.2 229 429 227 58.1 308s X1945 65.4 257 492 208 68.3 308s X1946 79.7 284 543 234 68.2 308s X1947 55.7 272 540 220 60.7 308s X1948 70.5 296 570 219 74.4 308s X1949 73.6 306 642 238 71.1 308s X1950 88.1 331 673 241 74.0 308s X1951 111.8 358 806 290 79.6 308s X1952 133.6 400 888 313 97.3 308s X1953 176.6 450 1122 361 136.5 308s X1954 173.0 515 1214 384 149.0 308s 'log Lik.' -533 (df=18) 308s 'log Lik.' -568 (df=18) 308s [1] 100 308s Chrysler_invest Chrysler_value Chrysler_capital General.Electric_invest 308s X1935 40.3 418 10.5 33.1 308s X1936 72.8 838 10.2 45.0 308s X1937 66.3 884 34.7 77.2 308s X1938 51.6 438 51.8 44.6 308s X1939 52.4 680 64.3 48.1 308s X1940 69.4 728 67.1 74.4 308s X1941 68.3 644 75.2 113.0 308s X1942 46.8 411 71.4 91.9 308s X1943 47.4 588 67.1 61.3 308s X1944 59.6 698 60.5 56.8 308s X1945 88.8 846 54.6 93.6 308s X1946 74.1 894 84.8 159.9 308s X1947 62.7 579 96.8 147.2 308s X1948 89.4 695 110.2 146.3 308s X1949 79.0 590 147.4 98.3 308s X1950 100.7 694 163.2 93.5 308s X1951 160.6 809 203.5 135.2 308s X1952 145.0 727 290.6 157.3 308s X1953 174.9 1002 346.1 179.5 308s X1954 172.5 703 414.9 189.6 308s General.Electric_value General.Electric_capital General.Motors_invest 308s X1935 1171 97.8 318 308s X1936 2016 104.4 392 308s X1937 2803 118.0 411 308s X1938 2040 156.2 258 308s X1939 2256 172.6 331 308s X1940 2132 186.6 461 308s X1941 1834 220.9 512 308s X1942 1588 287.8 448 308s X1943 1749 319.9 500 308s X1944 1687 321.3 548 308s X1945 2008 319.6 561 308s X1946 2208 346.0 688 308s X1947 1657 456.4 569 308s X1948 1604 543.4 529 308s X1949 1432 618.3 555 308s X1950 1610 647.4 643 308s X1951 1819 671.3 756 308s X1952 2080 726.1 891 308s X1953 2372 800.3 1304 308s X1954 2760 888.9 1487 308s General.Motors_value General.Motors_capital US.Steel_invest 308s X1935 3078 2.8 210 308s X1936 4662 52.6 355 308s X1937 5387 156.9 470 308s X1938 2792 209.2 262 308s X1939 4313 203.4 230 308s X1940 4644 207.2 262 308s X1941 4551 255.2 473 308s X1942 3244 303.7 446 308s X1943 4054 264.1 362 308s X1944 4379 201.6 288 308s X1945 4841 265.0 259 308s X1946 4901 402.2 420 308s X1947 3526 761.5 420 308s X1948 3255 922.4 494 308s X1949 3700 1020.1 405 308s X1950 3756 1099.0 419 308s X1951 4833 1207.7 588 308s X1952 4925 1430.5 645 308s X1953 6242 1777.3 641 308s X1954 5594 2226.3 459 308s US.Steel_value US.Steel_capital Westinghouse_invest Westinghouse_value 308s X1935 1362 53.8 12.9 192 308s X1936 1807 50.5 25.9 516 308s X1937 2676 118.1 35.0 729 308s X1938 1802 260.2 22.9 560 308s X1939 1957 312.7 18.8 520 308s X1940 2203 254.2 28.6 628 308s X1941 2380 261.4 48.5 537 308s X1942 2169 298.7 43.3 561 308s X1943 1985 301.8 37.0 617 308s X1944 1814 279.1 37.8 627 308s X1945 1850 213.8 39.3 737 308s X1946 2068 232.6 53.5 760 308s X1947 1797 264.8 55.6 581 308s X1948 1626 306.9 49.6 662 308s X1949 1667 351.1 32.0 584 308s X1950 1677 357.8 32.2 635 308s X1951 2290 342.1 54.4 724 308s X1952 2159 444.2 71.8 864 308s X1953 2031 623.6 90.1 1194 308s X1954 2116 669.7 68.6 1189 308s Westinghouse_capital 308s X1935 1.8 308s X1936 0.8 308s X1937 7.4 308s X1938 18.1 308s X1939 23.5 308s X1940 26.5 308s X1941 36.2 308s X1942 60.8 308s X1943 84.4 308s X1944 91.2 308s X1945 92.4 308s X1946 86.0 308s X1947 111.1 308s X1948 130.6 308s X1949 141.8 308s X1950 136.7 308s X1951 129.7 308s X1952 145.5 308s X1953 174.8 308s X1954 213.5 308s Chrysler_(Intercept) Chrysler_value Chrysler_capital 308s Chrysler_X1935 1 418 10.5 308s Chrysler_X1936 1 838 10.2 308s Chrysler_X1937 1 884 34.7 308s Chrysler_X1938 1 438 51.8 308s Chrysler_X1939 1 680 64.3 308s Chrysler_X1940 1 728 67.1 308s Chrysler_X1941 1 644 75.2 308s Chrysler_X1942 1 411 71.4 308s Chrysler_X1943 1 588 67.1 308s Chrysler_X1944 1 698 60.5 308s Chrysler_X1945 1 846 54.6 308s Chrysler_X1946 1 894 84.8 308s Chrysler_X1947 1 579 96.8 308s Chrysler_X1948 1 695 110.2 308s Chrysler_X1949 1 590 147.4 308s Chrysler_X1950 1 694 163.2 308s Chrysler_X1951 1 809 203.5 308s Chrysler_X1952 1 727 290.6 308s Chrysler_X1953 1 1002 346.1 308s Chrysler_X1954 1 703 414.9 308s General.Electric_X1935 0 0 0.0 308s General.Electric_X1936 0 0 0.0 308s General.Electric_X1937 0 0 0.0 308s General.Electric_X1938 0 0 0.0 308s General.Electric_X1939 0 0 0.0 308s General.Electric_X1940 0 0 0.0 308s General.Electric_X1941 0 0 0.0 308s General.Electric_X1942 0 0 0.0 308s General.Electric_X1943 0 0 0.0 308s General.Electric_X1944 0 0 0.0 308s General.Electric_X1945 0 0 0.0 308s General.Electric_X1946 0 0 0.0 308s General.Electric_X1947 0 0 0.0 308s General.Electric_X1948 0 0 0.0 308s General.Electric_X1949 0 0 0.0 308s General.Electric_X1950 0 0 0.0 308s General.Electric_X1951 0 0 0.0 308s General.Electric_X1952 0 0 0.0 308s General.Electric_X1953 0 0 0.0 308s General.Electric_X1954 0 0 0.0 308s General.Motors_X1935 0 0 0.0 308s General.Motors_X1936 0 0 0.0 308s General.Motors_X1937 0 0 0.0 308s General.Motors_X1938 0 0 0.0 308s General.Motors_X1939 0 0 0.0 308s General.Motors_X1940 0 0 0.0 308s General.Motors_X1941 0 0 0.0 308s General.Motors_X1942 0 0 0.0 308s General.Motors_X1943 0 0 0.0 308s General.Motors_X1944 0 0 0.0 308s General.Motors_X1945 0 0 0.0 308s General.Motors_X1946 0 0 0.0 308s General.Motors_X1947 0 0 0.0 308s General.Motors_X1948 0 0 0.0 308s General.Motors_X1949 0 0 0.0 308s General.Motors_X1950 0 0 0.0 308s General.Motors_X1951 0 0 0.0 308s General.Motors_X1952 0 0 0.0 308s General.Motors_X1953 0 0 0.0 308s General.Motors_X1954 0 0 0.0 308s US.Steel_X1935 0 0 0.0 308s US.Steel_X1936 0 0 0.0 308s US.Steel_X1937 0 0 0.0 308s US.Steel_X1938 0 0 0.0 308s US.Steel_X1939 0 0 0.0 308s US.Steel_X1940 0 0 0.0 308s US.Steel_X1941 0 0 0.0 308s US.Steel_X1942 0 0 0.0 308s US.Steel_X1943 0 0 0.0 308s US.Steel_X1944 0 0 0.0 308s US.Steel_X1945 0 0 0.0 308s US.Steel_X1946 0 0 0.0 308s US.Steel_X1947 0 0 0.0 308s US.Steel_X1948 0 0 0.0 308s US.Steel_X1949 0 0 0.0 308s US.Steel_X1950 0 0 0.0 308s US.Steel_X1951 0 0 0.0 308s US.Steel_X1952 0 0 0.0 308s US.Steel_X1953 0 0 0.0 308s US.Steel_X1954 0 0 0.0 308s Westinghouse_X1935 0 0 0.0 308s Westinghouse_X1936 0 0 0.0 308s Westinghouse_X1937 0 0 0.0 308s Westinghouse_X1938 0 0 0.0 308s Westinghouse_X1939 0 0 0.0 308s Westinghouse_X1940 0 0 0.0 308s Westinghouse_X1941 0 0 0.0 308s Westinghouse_X1942 0 0 0.0 308s Westinghouse_X1943 0 0 0.0 308s Westinghouse_X1944 0 0 0.0 308s Westinghouse_X1945 0 0 0.0 308s Westinghouse_X1946 0 0 0.0 308s Westinghouse_X1947 0 0 0.0 308s Westinghouse_X1948 0 0 0.0 308s Westinghouse_X1949 0 0 0.0 308s Westinghouse_X1950 0 0 0.0 308s Westinghouse_X1951 0 0 0.0 308s Westinghouse_X1952 0 0 0.0 308s Westinghouse_X1953 0 0 0.0 308s Westinghouse_X1954 0 0 0.0 308s General.Electric_(Intercept) General.Electric_value 308s Chrysler_X1935 0 0 308s Chrysler_X1936 0 0 308s Chrysler_X1937 0 0 308s Chrysler_X1938 0 0 308s Chrysler_X1939 0 0 308s Chrysler_X1940 0 0 308s Chrysler_X1941 0 0 308s Chrysler_X1942 0 0 308s Chrysler_X1943 0 0 308s Chrysler_X1944 0 0 308s Chrysler_X1945 0 0 308s Chrysler_X1946 0 0 308s Chrysler_X1947 0 0 308s Chrysler_X1948 0 0 308s Chrysler_X1949 0 0 308s Chrysler_X1950 0 0 308s Chrysler_X1951 0 0 308s Chrysler_X1952 0 0 308s Chrysler_X1953 0 0 308s Chrysler_X1954 0 0 308s General.Electric_X1935 1 1171 308s General.Electric_X1936 1 2016 308s General.Electric_X1937 1 2803 308s General.Electric_X1938 1 2040 308s General.Electric_X1939 1 2256 308s General.Electric_X1940 1 2132 308s General.Electric_X1941 1 1834 308s General.Electric_X1942 1 1588 308s General.Electric_X1943 1 1749 308s General.Electric_X1944 1 1687 308s General.Electric_X1945 1 2008 308s General.Electric_X1946 1 2208 308s General.Electric_X1947 1 1657 308s General.Electric_X1948 1 1604 308s General.Electric_X1949 1 1432 308s General.Electric_X1950 1 1610 308s General.Electric_X1951 1 1819 308s General.Electric_X1952 1 2080 308s General.Electric_X1953 1 2372 308s General.Electric_X1954 1 2760 308s General.Motors_X1935 0 0 308s General.Motors_X1936 0 0 308s General.Motors_X1937 0 0 308s General.Motors_X1938 0 0 308s General.Motors_X1939 0 0 308s General.Motors_X1940 0 0 308s General.Motors_X1941 0 0 308s General.Motors_X1942 0 0 308s General.Motors_X1943 0 0 308s General.Motors_X1944 0 0 308s General.Motors_X1945 0 0 308s General.Motors_X1946 0 0 308s General.Motors_X1947 0 0 308s General.Motors_X1948 0 0 308s General.Motors_X1949 0 0 308s General.Motors_X1950 0 0 308s General.Motors_X1951 0 0 308s General.Motors_X1952 0 0 308s General.Motors_X1953 0 0 308s General.Motors_X1954 0 0 308s US.Steel_X1935 0 0 308s US.Steel_X1936 0 0 308s US.Steel_X1937 0 0 308s US.Steel_X1938 0 0 308s US.Steel_X1939 0 0 308s US.Steel_X1940 0 0 308s US.Steel_X1941 0 0 308s US.Steel_X1942 0 0 308s US.Steel_X1943 0 0 308s US.Steel_X1944 0 0 308s US.Steel_X1945 0 0 308s US.Steel_X1946 0 0 308s US.Steel_X1947 0 0 308s US.Steel_X1948 0 0 308s US.Steel_X1949 0 0 308s US.Steel_X1950 0 0 308s US.Steel_X1951 0 0 308s US.Steel_X1952 0 0 308s US.Steel_X1953 0 0 308s US.Steel_X1954 0 0 308s Westinghouse_X1935 0 0 308s Westinghouse_X1936 0 0 308s Westinghouse_X1937 0 0 308s Westinghouse_X1938 0 0 308s Westinghouse_X1939 0 0 308s Westinghouse_X1940 0 0 308s Westinghouse_X1941 0 0 308s Westinghouse_X1942 0 0 308s Westinghouse_X1943 0 0 308s Westinghouse_X1944 0 0 308s Westinghouse_X1945 0 0 308s Westinghouse_X1946 0 0 308s Westinghouse_X1947 0 0 308s Westinghouse_X1948 0 0 308s Westinghouse_X1949 0 0 308s Westinghouse_X1950 0 0 308s Westinghouse_X1951 0 0 308s Westinghouse_X1952 0 0 308s Westinghouse_X1953 0 0 308s Westinghouse_X1954 0 0 308s General.Electric_capital General.Motors_(Intercept) 308s Chrysler_X1935 0.0 0 308s Chrysler_X1936 0.0 0 308s Chrysler_X1937 0.0 0 308s Chrysler_X1938 0.0 0 308s Chrysler_X1939 0.0 0 308s Chrysler_X1940 0.0 0 308s Chrysler_X1941 0.0 0 308s Chrysler_X1942 0.0 0 308s Chrysler_X1943 0.0 0 308s Chrysler_X1944 0.0 0 308s Chrysler_X1945 0.0 0 308s Chrysler_X1946 0.0 0 308s Chrysler_X1947 0.0 0 308s Chrysler_X1948 0.0 0 308s Chrysler_X1949 0.0 0 308s Chrysler_X1950 0.0 0 308s Chrysler_X1951 0.0 0 308s Chrysler_X1952 0.0 0 308s Chrysler_X1953 0.0 0 308s Chrysler_X1954 0.0 0 308s General.Electric_X1935 97.8 0 308s General.Electric_X1936 104.4 0 308s General.Electric_X1937 118.0 0 308s General.Electric_X1938 156.2 0 308s General.Electric_X1939 172.6 0 308s General.Electric_X1940 186.6 0 308s General.Electric_X1941 220.9 0 308s General.Electric_X1942 287.8 0 308s General.Electric_X1943 319.9 0 308s General.Electric_X1944 321.3 0 308s General.Electric_X1945 319.6 0 308s General.Electric_X1946 346.0 0 308s General.Electric_X1947 456.4 0 308s General.Electric_X1948 543.4 0 308s General.Electric_X1949 618.3 0 308s General.Electric_X1950 647.4 0 308s General.Electric_X1951 671.3 0 308s General.Electric_X1952 726.1 0 308s General.Electric_X1953 800.3 0 308s General.Electric_X1954 888.9 0 308s General.Motors_X1935 0.0 1 308s General.Motors_X1936 0.0 1 308s General.Motors_X1937 0.0 1 308s General.Motors_X1938 0.0 1 308s General.Motors_X1939 0.0 1 308s General.Motors_X1940 0.0 1 308s General.Motors_X1941 0.0 1 308s General.Motors_X1942 0.0 1 308s General.Motors_X1943 0.0 1 308s General.Motors_X1944 0.0 1 308s General.Motors_X1945 0.0 1 308s General.Motors_X1946 0.0 1 308s General.Motors_X1947 0.0 1 308s General.Motors_X1948 0.0 1 308s General.Motors_X1949 0.0 1 308s General.Motors_X1950 0.0 1 308s General.Motors_X1951 0.0 1 308s General.Motors_X1952 0.0 1 308s General.Motors_X1953 0.0 1 308s General.Motors_X1954 0.0 1 308s US.Steel_X1935 0.0 0 308s US.Steel_X1936 0.0 0 308s US.Steel_X1937 0.0 0 308s US.Steel_X1938 0.0 0 308s US.Steel_X1939 0.0 0 308s US.Steel_X1940 0.0 0 308s US.Steel_X1941 0.0 0 308s US.Steel_X1942 0.0 0 308s US.Steel_X1943 0.0 0 308s US.Steel_X1944 0.0 0 308s US.Steel_X1945 0.0 0 308s US.Steel_X1946 0.0 0 308s US.Steel_X1947 0.0 0 308s US.Steel_X1948 0.0 0 308s US.Steel_X1949 0.0 0 308s US.Steel_X1950 0.0 0 308s US.Steel_X1951 0.0 0 308s US.Steel_X1952 0.0 0 308s US.Steel_X1953 0.0 0 308s US.Steel_X1954 0.0 0 308s Westinghouse_X1935 0.0 0 308s Westinghouse_X1936 0.0 0 308s Westinghouse_X1937 0.0 0 308s Westinghouse_X1938 0.0 0 308s Westinghouse_X1939 0.0 0 308s Westinghouse_X1940 0.0 0 308s Westinghouse_X1941 0.0 0 308s Westinghouse_X1942 0.0 0 308s Westinghouse_X1943 0.0 0 308s Westinghouse_X1944 0.0 0 308s Westinghouse_X1945 0.0 0 308s Westinghouse_X1946 0.0 0 308s Westinghouse_X1947 0.0 0 308s Westinghouse_X1948 0.0 0 308s Westinghouse_X1949 0.0 0 308s Westinghouse_X1950 0.0 0 308s Westinghouse_X1951 0.0 0 308s Westinghouse_X1952 0.0 0 308s Westinghouse_X1953 0.0 0 308s Westinghouse_X1954 0.0 0 308s General.Motors_value General.Motors_capital 308s Chrysler_X1935 0 0.0 308s Chrysler_X1936 0 0.0 308s Chrysler_X1937 0 0.0 308s Chrysler_X1938 0 0.0 308s Chrysler_X1939 0 0.0 308s Chrysler_X1940 0 0.0 308s Chrysler_X1941 0 0.0 308s Chrysler_X1942 0 0.0 308s Chrysler_X1943 0 0.0 308s Chrysler_X1944 0 0.0 308s Chrysler_X1945 0 0.0 308s Chrysler_X1946 0 0.0 308s Chrysler_X1947 0 0.0 308s Chrysler_X1948 0 0.0 308s Chrysler_X1949 0 0.0 308s Chrysler_X1950 0 0.0 308s Chrysler_X1951 0 0.0 308s Chrysler_X1952 0 0.0 308s Chrysler_X1953 0 0.0 308s Chrysler_X1954 0 0.0 308s General.Electric_X1935 0 0.0 308s General.Electric_X1936 0 0.0 308s General.Electric_X1937 0 0.0 308s General.Electric_X1938 0 0.0 308s General.Electric_X1939 0 0.0 308s General.Electric_X1940 0 0.0 308s General.Electric_X1941 0 0.0 308s General.Electric_X1942 0 0.0 308s General.Electric_X1943 0 0.0 308s General.Electric_X1944 0 0.0 308s General.Electric_X1945 0 0.0 308s General.Electric_X1946 0 0.0 308s General.Electric_X1947 0 0.0 308s General.Electric_X1948 0 0.0 308s General.Electric_X1949 0 0.0 308s General.Electric_X1950 0 0.0 308s General.Electric_X1951 0 0.0 308s General.Electric_X1952 0 0.0 308s General.Electric_X1953 0 0.0 308s General.Electric_X1954 0 0.0 308s General.Motors_X1935 3078 2.8 308s General.Motors_X1936 4662 52.6 308s General.Motors_X1937 5387 156.9 308s General.Motors_X1938 2792 209.2 308s General.Motors_X1939 4313 203.4 308s General.Motors_X1940 4644 207.2 308s General.Motors_X1941 4551 255.2 308s General.Motors_X1942 3244 303.7 308s General.Motors_X1943 4054 264.1 308s General.Motors_X1944 4379 201.6 308s General.Motors_X1945 4841 265.0 308s General.Motors_X1946 4901 402.2 308s General.Motors_X1947 3526 761.5 308s General.Motors_X1948 3255 922.4 308s General.Motors_X1949 3700 1020.1 308s General.Motors_X1950 3756 1099.0 308s General.Motors_X1951 4833 1207.7 308s General.Motors_X1952 4925 1430.5 308s General.Motors_X1953 6242 1777.3 308s General.Motors_X1954 5594 2226.3 308s US.Steel_X1935 0 0.0 308s US.Steel_X1936 0 0.0 308s US.Steel_X1937 0 0.0 308s US.Steel_X1938 0 0.0 308s US.Steel_X1939 0 0.0 308s US.Steel_X1940 0 0.0 308s US.Steel_X1941 0 0.0 308s US.Steel_X1942 0 0.0 308s US.Steel_X1943 0 0.0 308s US.Steel_X1944 0 0.0 308s US.Steel_X1945 0 0.0 308s US.Steel_X1946 0 0.0 308s US.Steel_X1947 0 0.0 308s US.Steel_X1948 0 0.0 308s US.Steel_X1949 0 0.0 308s US.Steel_X1950 0 0.0 308s US.Steel_X1951 0 0.0 308s US.Steel_X1952 0 0.0 308s US.Steel_X1953 0 0.0 308s US.Steel_X1954 0 0.0 308s Westinghouse_X1935 0 0.0 308s Westinghouse_X1936 0 0.0 308s Westinghouse_X1937 0 0.0 308s Westinghouse_X1938 0 0.0 308s Westinghouse_X1939 0 0.0 308s Westinghouse_X1940 0 0.0 308s Westinghouse_X1941 0 0.0 308s Westinghouse_X1942 0 0.0 308s Westinghouse_X1943 0 0.0 308s Westinghouse_X1944 0 0.0 308s Westinghouse_X1945 0 0.0 308s Westinghouse_X1946 0 0.0 308s Westinghouse_X1947 0 0.0 308s Westinghouse_X1948 0 0.0 308s Westinghouse_X1949 0 0.0 308s Westinghouse_X1950 0 0.0 308s Westinghouse_X1951 0 0.0 308s Westinghouse_X1952 0 0.0 308s Westinghouse_X1953 0 0.0 308s Westinghouse_X1954 0 0.0 308s US.Steel_(Intercept) US.Steel_value US.Steel_capital 308s Chrysler_X1935 0 0 0.0 308s Chrysler_X1936 0 0 0.0 308s Chrysler_X1937 0 0 0.0 308s Chrysler_X1938 0 0 0.0 308s Chrysler_X1939 0 0 0.0 308s Chrysler_X1940 0 0 0.0 308s Chrysler_X1941 0 0 0.0 308s Chrysler_X1942 0 0 0.0 308s Chrysler_X1943 0 0 0.0 308s Chrysler_X1944 0 0 0.0 308s Chrysler_X1945 0 0 0.0 308s Chrysler_X1946 0 0 0.0 308s Chrysler_X1947 0 0 0.0 308s Chrysler_X1948 0 0 0.0 308s Chrysler_X1949 0 0 0.0 308s Chrysler_X1950 0 0 0.0 308s Chrysler_X1951 0 0 0.0 308s Chrysler_X1952 0 0 0.0 308s Chrysler_X1953 0 0 0.0 308s Chrysler_X1954 0 0 0.0 308s General.Electric_X1935 0 0 0.0 308s General.Electric_X1936 0 0 0.0 308s General.Electric_X1937 0 0 0.0 308s General.Electric_X1938 0 0 0.0 308s General.Electric_X1939 0 0 0.0 308s General.Electric_X1940 0 0 0.0 308s General.Electric_X1941 0 0 0.0 308s General.Electric_X1942 0 0 0.0 308s General.Electric_X1943 0 0 0.0 308s General.Electric_X1944 0 0 0.0 308s General.Electric_X1945 0 0 0.0 308s General.Electric_X1946 0 0 0.0 308s General.Electric_X1947 0 0 0.0 308s General.Electric_X1948 0 0 0.0 308s General.Electric_X1949 0 0 0.0 308s General.Electric_X1950 0 0 0.0 308s General.Electric_X1951 0 0 0.0 308s General.Electric_X1952 0 0 0.0 308s General.Electric_X1953 0 0 0.0 308s General.Electric_X1954 0 0 0.0 308s General.Motors_X1935 0 0 0.0 308s General.Motors_X1936 0 0 0.0 308s General.Motors_X1937 0 0 0.0 308s General.Motors_X1938 0 0 0.0 308s General.Motors_X1939 0 0 0.0 308s General.Motors_X1940 0 0 0.0 308s General.Motors_X1941 0 0 0.0 308s General.Motors_X1942 0 0 0.0 308s General.Motors_X1943 0 0 0.0 308s General.Motors_X1944 0 0 0.0 308s General.Motors_X1945 0 0 0.0 308s General.Motors_X1946 0 0 0.0 308s General.Motors_X1947 0 0 0.0 308s General.Motors_X1948 0 0 0.0 308s General.Motors_X1949 0 0 0.0 308s General.Motors_X1950 0 0 0.0 308s General.Motors_X1951 0 0 0.0 308s General.Motors_X1952 0 0 0.0 308s General.Motors_X1953 0 0 0.0 308s General.Motors_X1954 0 0 0.0 308s US.Steel_X1935 1 1362 53.8 308s US.Steel_X1936 1 1807 50.5 308s US.Steel_X1937 1 2676 118.1 308s US.Steel_X1938 1 1802 260.2 308s US.Steel_X1939 1 1957 312.7 308s US.Steel_X1940 1 2203 254.2 308s US.Steel_X1941 1 2380 261.4 308s US.Steel_X1942 1 2169 298.7 308s US.Steel_X1943 1 1985 301.8 308s US.Steel_X1944 1 1814 279.1 308s US.Steel_X1945 1 1850 213.8 308s US.Steel_X1946 1 2068 232.6 308s US.Steel_X1947 1 1797 264.8 308s US.Steel_X1948 1 1626 306.9 308s US.Steel_X1949 1 1667 351.1 308s US.Steel_X1950 1 1677 357.8 308s US.Steel_X1951 1 2290 342.1 308s US.Steel_X1952 1 2159 444.2 308s US.Steel_X1953 1 2031 623.6 308s US.Steel_X1954 1 2116 669.7 308s Westinghouse_X1935 0 0 0.0 308s Westinghouse_X1936 0 0 0.0 308s Westinghouse_X1937 0 0 0.0 308s Westinghouse_X1938 0 0 0.0 308s Westinghouse_X1939 0 0 0.0 308s Westinghouse_X1940 0 0 0.0 308s Westinghouse_X1941 0 0 0.0 308s Westinghouse_X1942 0 0 0.0 308s Westinghouse_X1943 0 0 0.0 308s Westinghouse_X1944 0 0 0.0 308s Westinghouse_X1945 0 0 0.0 308s Westinghouse_X1946 0 0 0.0 308s Westinghouse_X1947 0 0 0.0 308s Westinghouse_X1948 0 0 0.0 308s Westinghouse_X1949 0 0 0.0 308s Westinghouse_X1950 0 0 0.0 308s Westinghouse_X1951 0 0 0.0 308s Westinghouse_X1952 0 0 0.0 308s Westinghouse_X1953 0 0 0.0 308s Westinghouse_X1954 0 0 0.0 308s Westinghouse_(Intercept) Westinghouse_value 308s Chrysler_X1935 0 0 308s Chrysler_X1936 0 0 308s Chrysler_X1937 0 0 308s Chrysler_X1938 0 0 308s Chrysler_X1939 0 0 308s Chrysler_X1940 0 0 308s Chrysler_X1941 0 0 308s Chrysler_X1942 0 0 308s Chrysler_X1943 0 0 308s Chrysler_X1944 0 0 308s Chrysler_X1945 0 0 308s Chrysler_X1946 0 0 308s Chrysler_X1947 0 0 308s Chrysler_X1948 0 0 308s Chrysler_X1949 0 0 308s Chrysler_X1950 0 0 308s Chrysler_X1951 0 0 308s Chrysler_X1952 0 0 308s Chrysler_X1953 0 0 308s Chrysler_X1954 0 0 308s General.Electric_X1935 0 0 308s General.Electric_X1936 0 0 308s General.Electric_X1937 0 0 308s General.Electric_X1938 0 0 308s General.Electric_X1939 0 0 308s General.Electric_X1940 0 0 308s General.Electric_X1941 0 0 308s General.Electric_X1942 0 0 308s General.Electric_X1943 0 0 308s General.Electric_X1944 0 0 308s General.Electric_X1945 0 0 308s General.Electric_X1946 0 0 308s General.Electric_X1947 0 0 308s General.Electric_X1948 0 0 308s General.Electric_X1949 0 0 308s General.Electric_X1950 0 0 308s General.Electric_X1951 0 0 308s General.Electric_X1952 0 0 308s General.Electric_X1953 0 0 308s General.Electric_X1954 0 0 308s General.Motors_X1935 0 0 308s General.Motors_X1936 0 0 308s General.Motors_X1937 0 0 308s General.Motors_X1938 0 0 308s General.Motors_X1939 0 0 308s General.Motors_X1940 0 0 308s General.Motors_X1941 0 0 308s General.Motors_X1942 0 0 308s General.Motors_X1943 0 0 308s General.Motors_X1944 0 0 308s General.Motors_X1945 0 0 308s General.Motors_X1946 0 0 308s General.Motors_X1947 0 0 308s General.Motors_X1948 0 0 308s General.Motors_X1949 0 0 308s General.Motors_X1950 0 0 308s General.Motors_X1951 0 0 308s General.Motors_X1952 0 0 308s General.Motors_X1953 0 0 308s General.Motors_X1954 0 0 308s US.Steel_X1935 0 0 308s US.Steel_X1936 0 0 308s US.Steel_X1937 0 0 308s US.Steel_X1938 0 0 308s US.Steel_X1939 0 0 308s US.Steel_X1940 0 0 308s US.Steel_X1941 0 0 308s US.Steel_X1942 0 0 308s US.Steel_X1943 0 0 308s US.Steel_X1944 0 0 308s US.Steel_X1945 0 0 308s US.Steel_X1946 0 0 308s US.Steel_X1947 0 0 308s US.Steel_X1948 0 0 308s US.Steel_X1949 0 0 308s US.Steel_X1950 0 0 308s US.Steel_X1951 0 0 308s US.Steel_X1952 0 0 308s US.Steel_X1953 0 0 308s US.Steel_X1954 0 0 308s Westinghouse_X1935 1 192 308s Westinghouse_X1936 1 516 308s Westinghouse_X1937 1 729 308s Westinghouse_X1938 1 560 308s Westinghouse_X1939 1 520 308s Westinghouse_X1940 1 628 308s Westinghouse_X1941 1 537 308s Westinghouse_X1942 1 561 308s Westinghouse_X1943 1 617 308s Westinghouse_X1944 1 627 308s Westinghouse_X1945 1 737 308s Westinghouse_X1946 1 760 308s Westinghouse_X1947 1 581 308s Westinghouse_X1948 1 662 308s Westinghouse_X1949 1 584 308s Westinghouse_X1950 1 635 308s Westinghouse_X1951 1 724 308s Westinghouse_X1952 1 864 308s Westinghouse_X1953 1 1194 308s Westinghouse_X1954 1 1189 308s Westinghouse_capital 308s Chrysler_X1935 0.0 308s Chrysler_X1936 0.0 308s Chrysler_X1937 0.0 308s Chrysler_X1938 0.0 308s Chrysler_X1939 0.0 308s Chrysler_X1940 0.0 308s Chrysler_X1941 0.0 308s Chrysler_X1942 0.0 308s Chrysler_X1943 0.0 308s Chrysler_X1944 0.0 308s Chrysler_X1945 0.0 308s Chrysler_X1946 0.0 308s Chrysler_X1947 0.0 308s Chrysler_X1948 0.0 308s Chrysler_X1949 0.0 308s Chrysler_X1950 0.0 308s Chrysler_X1951 0.0 308s Chrysler_X1952 0.0 308s Chrysler_X1953 0.0 308s Chrysler_X1954 0.0 308s General.Electric_X1935 0.0 308s General.Electric_X1936 0.0 308s General.Electric_X1937 0.0 308s General.Electric_X1938 0.0 308s General.Electric_X1939 0.0 308s General.Electric_X1940 0.0 308s General.Electric_X1941 0.0 308s General.Electric_X1942 0.0 308s General.Electric_X1943 0.0 308s General.Electric_X1944 0.0 308s General.Electric_X1945 0.0 308s General.Electric_X1946 0.0 308s General.Electric_X1947 0.0 308s General.Electric_X1948 0.0 308s General.Electric_X1949 0.0 308s General.Electric_X1950 0.0 308s General.Electric_X1951 0.0 308s General.Electric_X1952 0.0 308s General.Electric_X1953 0.0 308s General.Electric_X1954 0.0 308s General.Motors_X1935 0.0 308s General.Motors_X1936 0.0 308s General.Motors_X1937 0.0 308s General.Motors_X1938 0.0 308s General.Motors_X1939 0.0 308s General.Motors_X1940 0.0 308s General.Motors_X1941 0.0 308s General.Motors_X1942 0.0 308s General.Motors_X1943 0.0 308s General.Motors_X1944 0.0 308s General.Motors_X1945 0.0 308s General.Motors_X1946 0.0 308s General.Motors_X1947 0.0 308s General.Motors_X1948 0.0 308s General.Motors_X1949 0.0 308s General.Motors_X1950 0.0 308s General.Motors_X1951 0.0 308s General.Motors_X1952 0.0 308s General.Motors_X1953 0.0 308s General.Motors_X1954 0.0 308s US.Steel_X1935 0.0 308s US.Steel_X1936 0.0 308s US.Steel_X1937 0.0 308s US.Steel_X1938 0.0 308s US.Steel_X1939 0.0 308s US.Steel_X1940 0.0 308s US.Steel_X1941 0.0 308s US.Steel_X1942 0.0 308s US.Steel_X1943 0.0 308s US.Steel_X1944 0.0 308s US.Steel_X1945 0.0 308s US.Steel_X1946 0.0 308s US.Steel_X1947 0.0 308s US.Steel_X1948 0.0 308s US.Steel_X1949 0.0 308s US.Steel_X1950 0.0 308s US.Steel_X1951 0.0 308s US.Steel_X1952 0.0 308s US.Steel_X1953 0.0 308s US.Steel_X1954 0.0 308s Westinghouse_X1935 1.8 308s Westinghouse_X1936 0.8 308s Westinghouse_X1937 7.4 308s Westinghouse_X1938 18.1 308s Westinghouse_X1939 23.5 308s Westinghouse_X1940 26.5 308s Westinghouse_X1941 36.2 308s Westinghouse_X1942 60.8 308s Westinghouse_X1943 84.4 308s Westinghouse_X1944 91.2 308s Westinghouse_X1945 92.4 308s Westinghouse_X1946 86.0 308s Westinghouse_X1947 111.1 308s Westinghouse_X1948 130.6 308s Westinghouse_X1949 141.8 308s Westinghouse_X1950 136.7 308s Westinghouse_X1951 129.7 308s Westinghouse_X1952 145.5 308s Westinghouse_X1953 174.8 308s Westinghouse_X1954 213.5 308s $Chrysler 308s Chrysler_invest ~ Chrysler_value + Chrysler_capital 308s 308s 308s $General.Electric 308s General.Electric_invest ~ General.Electric_value + General.Electric_capital 308s 308s 308s $General.Motors 308s General.Motors_invest ~ General.Motors_value + General.Motors_capital 308s 308s 308s $US.Steel 308s US.Steel_invest ~ US.Steel_value + US.Steel_capital 308s 308s 308s $Westinghouse 308s Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 308s 308s 308s Chrysler_invest ~ Chrysler_value + Chrysler_capital 308s 308s $Chrysler 308s Chrysler_invest ~ Chrysler_value + Chrysler_capital 308s attr(,"variables") 308s list(Chrysler_invest, Chrysler_value, Chrysler_capital) 308s attr(,"factors") 308s Chrysler_value Chrysler_capital 308s Chrysler_invest 0 0 308s Chrysler_value 1 0 308s Chrysler_capital 0 1 308s attr(,"term.labels") 308s [1] "Chrysler_value" "Chrysler_capital" 308s attr(,"order") 308s [1] 1 1 308s attr(,"intercept") 308s [1] 1 308s attr(,"response") 308s [1] 1 308s attr(,".Environment") 308s 308s attr(,"predvars") 308s list(Chrysler_invest, Chrysler_value, Chrysler_capital) 308s attr(,"dataClasses") 308s Chrysler_invest Chrysler_value Chrysler_capital 308s "numeric" "numeric" "numeric" 308s 308s $General.Electric 308s General.Electric_invest ~ General.Electric_value + General.Electric_capital 308s attr(,"variables") 308s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 308s attr(,"factors") 308s General.Electric_value General.Electric_capital 308s General.Electric_invest 0 0 308s General.Electric_value 1 0 308s General.Electric_capital 0 1 308s attr(,"term.labels") 308s [1] "General.Electric_value" "General.Electric_capital" 308s attr(,"order") 308s [1] 1 1 308s attr(,"intercept") 308s [1] 1 308s attr(,"response") 308s [1] 1 308s attr(,".Environment") 308s 308s attr(,"predvars") 308s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 308s attr(,"dataClasses") 308s General.Electric_invest General.Electric_value General.Electric_capital 308s "numeric" "numeric" "numeric" 308s 308s $General.Motors 308s General.Motors_invest ~ General.Motors_value + General.Motors_capital 308s attr(,"variables") 308s list(General.Motors_invest, General.Motors_value, General.Motors_capital) 308s attr(,"factors") 308s General.Motors_value General.Motors_capital 308s General.Motors_invest 0 0 308s General.Motors_value 1 0 308s General.Motors_capital 0 1 308s attr(,"term.labels") 308s [1] "General.Motors_value" "General.Motors_capital" 308s attr(,"order") 308s [1] 1 1 308s attr(,"intercept") 308s [1] 1 308s attr(,"response") 308s [1] 1 308s attr(,".Environment") 308s 308s attr(,"predvars") 308s list(General.Motors_invest, General.Motors_value, General.Motors_capital) 308s attr(,"dataClasses") 308s General.Motors_invest General.Motors_value General.Motors_capital 308s "numeric" "numeric" "numeric" 308s 308s $US.Steel 308s US.Steel_invest ~ US.Steel_value + US.Steel_capital 308s attr(,"variables") 308s list(US.Steel_invest, US.Steel_value, US.Steel_capital) 308s attr(,"factors") 308s US.Steel_value US.Steel_capital 308s US.Steel_invest 0 0 308s US.Steel_value 1 0 308s US.Steel_capital 0 1 308s attr(,"term.labels") 308s [1] "US.Steel_value" "US.Steel_capital" 308s attr(,"order") 308s [1] 1 1 308s attr(,"intercept") 308s [1] 1 308s attr(,"response") 308s [1] 1 308s attr(,".Environment") 308s 308s attr(,"predvars") 308s list(US.Steel_invest, US.Steel_value, US.Steel_capital) 308s attr(,"dataClasses") 308s US.Steel_invest US.Steel_value US.Steel_capital 308s "numeric" "numeric" "numeric" 308s 308s $Westinghouse 308s Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 308s attr(,"variables") 308s list(Westinghouse_invest, Westinghouse_value, Westinghouse_capital) 308s attr(,"factors") 308s Westinghouse_value Westinghouse_capital 308s Westinghouse_invest 0 0 308s Westinghouse_value 1 0 308s Westinghouse_capital 0 1 308s attr(,"term.labels") 308s [1] "Westinghouse_value" "Westinghouse_capital" 308s attr(,"order") 308s [1] 1 1 308s attr(,"intercept") 308s [1] 1 308s attr(,"response") 308s [1] 1 308s attr(,".Environment") 308s 308s attr(,"predvars") 308s list(Westinghouse_invest, Westinghouse_value, Westinghouse_capital) 308s attr(,"dataClasses") 308s Westinghouse_invest Westinghouse_value Westinghouse_capital 308s "numeric" "numeric" "numeric" 308s 308s Chrysler_invest ~ Chrysler_value + Chrysler_capital 308s attr(,"variables") 308s list(Chrysler_invest, Chrysler_value, Chrysler_capital) 308s attr(,"factors") 308s Chrysler_value Chrysler_capital 308s Chrysler_invest 0 0 308s Chrysler_value 1 0 308s Chrysler_capital 0 1 308s attr(,"term.labels") 308s [1] "Chrysler_value" "Chrysler_capital" 308s attr(,"order") 308s [1] 1 1 308s attr(,"intercept") 308s [1] 1 308s attr(,"response") 308s [1] 1 308s attr(,".Environment") 308s 308s attr(,"predvars") 308s list(Chrysler_invest, Chrysler_value, Chrysler_capital) 308s attr(,"dataClasses") 308s Chrysler_invest Chrysler_value Chrysler_capital 308s "numeric" "numeric" "numeric" 308s > 308s > 308s > ######### IV estimation ####################### 308s > ### 2SLS ### 308s > # instruments = explanatory variables -> 2SLS estimates = OLS estimates 308s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 308s + greene2sls <- systemfit( formulaGrunfeld, inst = ~ value + capital, "2SLS", 308s + data = GrunfeldGreene, useMatrix = useMatrix ) 308s + print( greene2sls ) 308s + print( summary( greene2sls ) ) 308s + print( all.equal( coef( summary( greene2sls ) ), coef( summary( greeneOls ) ) ) ) 308s + print( all.equal( greene2sls[ -c(1,2,6) ], greeneOls[ -c(1,2,6) ] ) ) 308s + for( i in 1:length( greene2sls$eq ) ) { 308s + print( all.equal( greene2sls$eq[[i]][ -c(3,15:17) ], 308s + greeneOls$eq[[i]][-3] ) ) 308s + } 308s + } 308s 308s systemfit results 308s method: 2SLS 308s 308s Coefficients: 308s Chrysler_(Intercept) Chrysler_value 308s -6.1900 0.0779 308s Chrysler_capital General.Electric_(Intercept) 308s 0.3157 -9.9563 308s General.Electric_value General.Electric_capital 308s 0.0266 0.1517 308s General.Motors_(Intercept) General.Motors_value 308s -149.7825 0.1193 308s General.Motors_capital US.Steel_(Intercept) 308s 0.3714 -30.3685 308s US.Steel_value US.Steel_capital 308s 0.1566 0.4239 308s Westinghouse_(Intercept) Westinghouse_value 308s -0.5094 0.0529 308s Westinghouse_capital 308s 0.0924 308s 308s systemfit results 308s method: 2SLS 308s 308s N DF SSR detRCov OLS-R2 McElroy-R2 308s system 100 85 339121 2.09e+14 0.848 0.862 308s 308s N DF SSR MSE RMSE R2 Adj R2 308s Chrysler 20 17 2997 176 13.3 0.914 0.903 308s General.Electric 20 17 13217 777 27.9 0.705 0.671 308s General.Motors 20 17 143206 8424 91.8 0.921 0.912 308s US.Steel 20 17 177928 10466 102.3 0.440 0.374 308s Westinghouse 20 17 1773 104 10.2 0.744 0.714 308s 308s The covariance matrix of the residuals 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 176.3 -25.1 -333 492 15.7 308s General.Electric -25.1 777.4 715 1065 207.6 308s General.Motors -332.7 714.7 8424 -2614 148.4 308s US.Steel 491.9 1064.6 -2614 10466 642.6 308s Westinghouse 15.7 207.6 148 643 104.3 308s 308s The correlations of the residuals 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 1.0000 -0.0679 -0.273 0.362 0.115 308s General.Electric -0.0679 1.0000 0.279 0.373 0.729 308s General.Motors -0.2730 0.2793 1.000 -0.278 0.158 308s US.Steel 0.3621 0.3732 -0.278 1.000 0.615 308s Westinghouse 0.1154 0.7290 0.158 0.615 1.000 308s 308s 308s 2SLS estimates for 'Chrysler' (equation 1) 308s Model Formula: Chrysler_invest ~ Chrysler_value + Chrysler_capital 308s 308s Instruments: ~Chrysler_value + Chrysler_capital 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -6.1900 13.5065 -0.46 0.6525 308s value 0.0779 0.0200 3.90 0.0011 ** 308s capital 0.3157 0.0288 10.96 4e-09 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 13.279 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 2997.444 MSE: 176.32 Root MSE: 13.279 308s Multiple R-Squared: 0.914 Adjusted R-Squared: 0.903 308s 308s 308s 2SLS estimates for 'General.Electric' (equation 2) 308s Model Formula: General.Electric_invest ~ General.Electric_value + General.Electric_capital 308s 308s Instruments: ~General.Electric_value + General.Electric_capital 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -9.9563 31.3742 -0.32 0.75 308s value 0.0266 0.0156 1.71 0.11 308s capital 0.1517 0.0257 5.90 1.7e-05 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 27.883 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 13216.588 MSE: 777.446 Root MSE: 27.883 308s Multiple R-Squared: 0.705 Adjusted R-Squared: 0.671 308s 308s 308s 2SLS estimates for 'General.Motors' (equation 3) 308s Model Formula: General.Motors_invest ~ General.Motors_value + General.Motors_capital 308s 308s Instruments: ~General.Motors_value + General.Motors_capital 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -149.7825 105.8421 -1.42 0.17508 308s value 0.1193 0.0258 4.62 0.00025 *** 308s capital 0.3714 0.0371 10.02 1.5e-08 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 91.782 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 143205.877 MSE: 8423.875 Root MSE: 91.782 308s Multiple R-Squared: 0.921 Adjusted R-Squared: 0.912 308s 308s 308s 2SLS estimates for 'US.Steel' (equation 4) 308s Model Formula: US.Steel_invest ~ US.Steel_value + US.Steel_capital 308s 308s Instruments: ~US.Steel_value + US.Steel_capital 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -30.3685 157.0477 -0.19 0.849 308s value 0.1566 0.0789 1.98 0.064 . 308s capital 0.4239 0.1552 2.73 0.014 * 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 102.305 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 177928.314 MSE: 10466.371 Root MSE: 102.305 308s Multiple R-Squared: 0.44 Adjusted R-Squared: 0.374 308s 308s 308s 2SLS estimates for 'Westinghouse' (equation 5) 308s Model Formula: Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 308s 308s Instruments: ~Westinghouse_value + Westinghouse_capital 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -0.5094 8.0153 -0.06 0.9501 308s value 0.0529 0.0157 3.37 0.0037 ** 308s capital 0.0924 0.0561 1.65 0.1179 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 10.213 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 1773.234 MSE: 104.308 Root MSE: 10.213 308s Multiple R-Squared: 0.744 Adjusted R-Squared: 0.714 308s 308s [1] TRUE 308s [1] TRUE 308s [1] TRUE 308s [1] TRUE 308s [1] TRUE 308s [1] TRUE 308s [1] TRUE 308s > # 'real' IV/2SLS estimation 308s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 308s + greene2slsR <- systemfit( invest ~ capital, inst = ~ value, "2SLS", 308s + data = GrunfeldGreene, useMatrix = useMatrix ) 308s + print( greene2slsR ) 308s + print( summary( greene2slsR ) ) 308s + } 308s 308s systemfit results 308s method: 2SLS 308s 308s Coefficients: 308s Chrysler_(Intercept) Chrysler_capital 308s 4.314 0.675 308s General.Electric_(Intercept) General.Electric_capital 308s -106.788 0.522 308s General.Motors_(Intercept) General.Motors_capital 308s 110.940 0.767 308s US.Steel_(Intercept) US.Steel_capital 308s -323.878 2.432 308s Westinghouse_(Intercept) Westinghouse_capital 308s 13.163 0.347 308s 308s systemfit results 308s method: 2SLS 308s 308s N DF SSR detRCov OLS-R2 McElroy-R2 308s system 100 90 3239824 2.75e+17 -0.456 0.476 308s 308s N DF SSR MSE RMSE R2 Adj R2 308s Chrysler 20 18 30374 1687 41.1 0.124 0.076 308s General.Electric 20 18 174998 9722 98.6 -2.902 -3.119 308s General.Motors 20 18 1100181 61121 247.2 0.396 0.362 308s US.Steel 20 18 1930347 107242 327.5 -5.072 -5.409 308s Westinghouse 20 18 3924 218 14.8 0.434 0.403 308s 308s The covariance matrix of the residuals 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 1687 3089 6820 11741 179 308s General.Electric 3089 9722 20780 23319 886 308s General.Motors 6820 20780 61121 44203 1908 308s US.Steel 11741 23319 44203 107242 1977 308s Westinghouse 179 886 1908 1977 218 308s 308s The correlations of the residuals 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 1.000 0.763 0.672 0.873 0.295 308s General.Electric 0.763 1.000 0.852 0.722 0.608 308s General.Motors 0.672 0.852 1.000 0.546 0.523 308s US.Steel 0.873 0.722 0.546 1.000 0.409 308s Westinghouse 0.295 0.608 0.523 0.409 1.000 308s 308s 308s 2SLS estimates for 'Chrysler' (equation 1) 308s Model Formula: Chrysler_invest ~ Chrysler_capital 308s 308s Instruments: ~Chrysler_value 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) 4.314 34.033 0.13 0.901 308s capital 0.675 0.270 2.50 0.022 * 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 41.078 on 18 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 18 308s SSR: 30373.531 MSE: 1687.418 Root MSE: 41.078 308s Multiple R-Squared: 0.124 Adjusted R-Squared: 0.076 308s 308s 308s 2SLS estimates for 'General.Electric' (equation 2) 308s Model Formula: General.Electric_invest ~ General.Electric_capital 308s 308s Instruments: ~General.Electric_value 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -106.788 306.251 -0.35 0.73 308s capital 0.522 0.763 0.68 0.50 308s 308s Residual standard error: 98.601 on 18 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 18 308s SSR: 174998.166 MSE: 9722.12 Root MSE: 98.601 308s Multiple R-Squared: -2.902 Adjusted R-Squared: -3.119 308s 308s 308s 2SLS estimates for 'General.Motors' (equation 3) 308s Model Formula: General.Motors_invest ~ General.Motors_capital 308s 308s Instruments: ~General.Motors_value 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) 110.940 145.626 0.76 0.4560 308s capital 0.767 0.208 3.69 0.0017 ** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 247.227 on 18 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 18 308s SSR: 1100180.666 MSE: 61121.148 Root MSE: 247.227 308s Multiple R-Squared: 0.396 Adjusted R-Squared: 0.362 308s 308s 308s 2SLS estimates for 'US.Steel' (equation 4) 308s Model Formula: US.Steel_invest ~ US.Steel_capital 308s 308s Instruments: ~US.Steel_value 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -323.88 962.57 -0.34 0.74 308s capital 2.43 3.20 0.76 0.46 308s 308s Residual standard error: 327.478 on 18 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 18 308s SSR: 1930347.395 MSE: 107241.522 Root MSE: 327.478 308s Multiple R-Squared: -5.072 Adjusted R-Squared: -5.409 308s 308s 308s 2SLS estimates for 'Westinghouse' (equation 5) 308s Model Formula: Westinghouse_invest ~ Westinghouse_capital 308s 308s Instruments: ~Westinghouse_value 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) 13.1626 7.0965 1.85 0.08008 . 308s capital 0.3471 0.0734 4.73 0.00017 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 14.765 on 18 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 18 308s SSR: 3923.899 MSE: 217.994 Root MSE: 14.765 308s Multiple R-Squared: 0.434 Adjusted R-Squared: 0.403 308s 308s > 308s > ### 2SLS, pooled ### 308s > # instruments = explanatory variables -> 2SLS estimates = OLS estimates 308s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 308s + greene2slsPooled <- systemfit( formulaGrunfeld, inst = ~ value + capital, "2SLS", 308s + data = GrunfeldGreene, pooled = TRUE, useMatrix = useMatrix ) 308s + print( greene2slsPooled ) 308s + print( summary( greene2slsPooled ) ) 308s + print( all.equal( coef( summary( greene2slsPooled ) ), 308s + coef( summary( greeneOlsPooled ) ) ) ) 308s + print( all.equal( greene2slsPooled[ -c(1,2,6) ], greeneOlsPooled[ -c(1,2,6) ] ) ) 308s + for( i in 1:length( greene2slsPooled$eq ) ) { 308s + print( all.equal( greene2slsPooled$eq[[i]][ -c(3,15:17) ], 308s + greeneOlsPooled$eq[[i]][-3] ) ) 308s + } 308s + } 308s 308s systemfit results 308s method: 2SLS 308s 308s Coefficients: 308s Chrysler_(Intercept) Chrysler_value 308s -48.030 0.105 308s Chrysler_capital General.Electric_(Intercept) 308s 0.305 -48.030 308s General.Electric_value General.Electric_capital 308s 0.105 0.305 308s General.Motors_(Intercept) General.Motors_value 308s -48.030 0.105 308s General.Motors_capital US.Steel_(Intercept) 308s 0.305 -48.030 308s US.Steel_value US.Steel_capital 308s 0.105 0.305 308s Westinghouse_(Intercept) Westinghouse_value 308s -48.030 0.105 308s Westinghouse_capital 308s 0.305 308s 308s systemfit results 308s method: 2SLS 308s 308s N DF SSR detRCov OLS-R2 McElroy-R2 308s system 100 97 1570884 4.2e+17 0.294 0.812 308s 308s N DF SSR MSE RMSE R2 Adj R2 308s Chrysler 20 17 15117 889 29.8 0.564 0.513 308s General.Electric 20 17 685770 40339 200.8 -14.291 -16.090 308s General.Motors 20 17 188218 11072 105.2 0.897 0.884 308s US.Steel 20 17 669110 39359 198.4 -1.105 -1.352 308s Westinghouse 20 17 12668 745 27.3 -0.826 -1.041 308s 308s The covariance matrix of the residuals 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 889.2 -4898 -198 4748 -94.6 308s General.Electric -4898.1 40339 -2254 -32821 2658.0 308s General.Motors -197.7 -2254 11072 304 -1328.6 308s US.Steel 4748.1 -32821 304 39359 -1377.3 308s Westinghouse -94.6 2658 -1329 -1377 745.2 308s 308s The correlations of the residuals 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 1.000 0.144 -0.1852 0.2218 0.186 308s General.Electric 0.144 1.000 -0.2592 -0.1216 0.881 308s General.Motors -0.185 -0.259 1.0000 -0.0155 -0.469 308s US.Steel 0.222 -0.122 -0.0155 1.0000 -0.119 308s Westinghouse 0.186 0.881 -0.4689 -0.1186 1.000 308s 308s 308s 2SLS estimates for 'Chrysler' (equation 1) 308s Model Formula: Chrysler_invest ~ Chrysler_value + Chrysler_capital 308s 308s Instruments: ~Chrysler_value + Chrysler_capital 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -48.0297 21.4802 -2.24 0.028 * 308s value 0.1051 0.0114 9.24 6.0e-15 *** 308s capital 0.3054 0.0435 7.02 3.1e-10 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 29.82 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 15117.016 MSE: 889.236 Root MSE: 29.82 308s Multiple R-Squared: 0.564 Adjusted R-Squared: 0.513 308s 308s 308s 2SLS estimates for 'General.Electric' (equation 2) 308s Model Formula: General.Electric_invest ~ General.Electric_value + General.Electric_capital 308s 308s Instruments: ~General.Electric_value + General.Electric_capital 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -48.0297 21.4802 -2.24 0.028 * 308s value 0.1051 0.0114 9.24 6.0e-15 *** 308s capital 0.3054 0.0435 7.02 3.1e-10 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 200.847 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 685769.815 MSE: 40339.401 Root MSE: 200.847 308s Multiple R-Squared: -14.291 Adjusted R-Squared: -16.09 308s 308s 308s 2SLS estimates for 'General.Motors' (equation 3) 308s Model Formula: General.Motors_invest ~ General.Motors_value + General.Motors_capital 308s 308s Instruments: ~General.Motors_value + General.Motors_capital 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -48.0297 21.4802 -2.24 0.028 * 308s value 0.1051 0.0114 9.24 6.0e-15 *** 308s capital 0.3054 0.0435 7.02 3.1e-10 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 105.222 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 188218.158 MSE: 11071.656 Root MSE: 105.222 308s Multiple R-Squared: 0.897 Adjusted R-Squared: 0.884 308s 308s 308s 2SLS estimates for 'US.Steel' (equation 4) 308s Model Formula: US.Steel_invest ~ US.Steel_value + US.Steel_capital 308s 308s Instruments: ~US.Steel_value + US.Steel_capital 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -48.0297 21.4802 -2.24 0.028 * 308s value 0.1051 0.0114 9.24 6.0e-15 *** 308s capital 0.3054 0.0435 7.02 3.1e-10 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 198.392 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 669110.225 MSE: 39359.425 Root MSE: 198.392 308s Multiple R-Squared: -1.105 Adjusted R-Squared: -1.352 308s 308s 308s 2SLS estimates for 'Westinghouse' (equation 5) 308s Model Formula: Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 308s 308s Instruments: ~Westinghouse_value + Westinghouse_capital 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -48.0297 21.4802 -2.24 0.028 * 308s value 0.1051 0.0114 9.24 6.0e-15 *** 308s capital 0.3054 0.0435 7.02 3.1e-10 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 27.298 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 12668.473 MSE: 745.204 Root MSE: 27.298 308s Multiple R-Squared: -0.826 Adjusted R-Squared: -1.041 308s 308s [1] TRUE 308s [1] TRUE 308s [1] TRUE 308s [1] TRUE 308s [1] TRUE 308s [1] TRUE 308s [1] TRUE 308s > # 'real' IV/2SLS estimation 308s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 308s + greene2slsRPooled <- systemfit( invest ~ capital, inst = ~ value, "2SLS", 308s + data = GrunfeldGreene, pooled = TRUE, useMatrix = useMatrix ) 308s + print( greene2slsRPooled ) 308s + print( summary( greene2slsRPooled ) ) 308s + } 308s 308s systemfit results 308s method: 2SLS 308s 308s Coefficients: 308s Chrysler_(Intercept) Chrysler_capital 308s -15.105 0.849 308s General.Electric_(Intercept) General.Electric_capital 308s -15.105 0.849 308s General.Motors_(Intercept) General.Motors_capital 308s -15.105 0.849 308s US.Steel_(Intercept) US.Steel_capital 308s -15.105 0.849 308s Westinghouse_(Intercept) Westinghouse_capital 308s -15.105 0.849 308s 308s systemfit results 308s method: 2SLS 308s 308s N DF SSR detRCov OLS-R2 McElroy-R2 308s system 100 98 4164182 2.53e+19 -0.871 -0.832 308s 308s N DF SSR MSE RMSE R2 Adj R2 308s Chrysler 20 18 64130 3563 59.7 -0.849 -0.952 308s General.Electric 20 18 1575287 87516 295.8 -34.125 -36.076 308s General.Motors 20 18 1655592 91977 303.3 0.091 0.040 308s US.Steel 20 18 833908 46328 215.2 -1.623 -1.769 308s Westinghouse 20 18 35264 1959 44.3 -4.082 -4.365 308s 308s The covariance matrix of the residuals 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 3563 9506 13222 2659 1862 308s General.Electric 9506 87516 29381 -35898 10615 308s General.Motors 13222 29381 91977 17584 8562 308s US.Steel 2659 -35898 17584 46328 -762 308s Westinghouse 1862 10615 8562 -762 1959 308s 308s The correlations of the residuals 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 1.000 0.843 0.763 0.397 0.742 308s General.Electric 0.843 1.000 0.893 0.226 0.933 308s General.Motors 0.763 0.893 1.000 0.114 0.801 308s US.Steel 0.397 0.226 0.114 1.000 0.375 308s Westinghouse 0.742 0.933 0.801 0.375 1.000 308s 308s 308s 2SLS estimates for 'Chrysler' (equation 1) 308s Model Formula: Chrysler_invest ~ Chrysler_capital 308s 308s Instruments: ~Chrysler_value 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -15.1045 33.8915 -0.45 0.66 308s capital 0.8489 0.0865 9.82 4.4e-16 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 59.689 on 18 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 18 308s SSR: 64130.003 MSE: 3562.778 Root MSE: 59.689 308s Multiple R-Squared: -0.849 Adjusted R-Squared: -0.952 308s 308s 308s 2SLS estimates for 'General.Electric' (equation 2) 308s Model Formula: General.Electric_invest ~ General.Electric_capital 308s 308s Instruments: ~General.Electric_value 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -15.1045 33.8915 -0.45 0.66 308s capital 0.8489 0.0865 9.82 4.4e-16 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 295.831 on 18 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 18 308s SSR: 1575287.29 MSE: 87515.961 Root MSE: 295.831 308s Multiple R-Squared: -34.125 Adjusted R-Squared: -36.076 308s 308s 308s 2SLS estimates for 'General.Motors' (equation 3) 308s Model Formula: General.Motors_invest ~ General.Motors_capital 308s 308s Instruments: ~General.Motors_value 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -15.1045 33.8915 -0.45 0.66 308s capital 0.8489 0.0865 9.82 4.4e-16 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 303.278 on 18 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 18 308s SSR: 1655591.854 MSE: 91977.325 Root MSE: 303.278 308s Multiple R-Squared: 0.091 Adjusted R-Squared: 0.04 308s 308s 308s 2SLS estimates for 'US.Steel' (equation 4) 308s Model Formula: US.Steel_invest ~ US.Steel_capital 308s 308s Instruments: ~US.Steel_value 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -15.1045 33.8915 -0.45 0.66 308s capital 0.8489 0.0865 9.82 4.4e-16 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 215.24 on 18 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 18 308s SSR: 833908.389 MSE: 46328.244 Root MSE: 215.24 308s Multiple R-Squared: -1.623 Adjusted R-Squared: -1.769 308s 308s 308s 2SLS estimates for 'Westinghouse' (equation 5) 308s Model Formula: Westinghouse_invest ~ Westinghouse_capital 308s 308s Instruments: ~Westinghouse_value 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -15.1045 33.8915 -0.45 0.66 308s capital 0.8489 0.0865 9.82 4.4e-16 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 44.262 on 18 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 18 308s SSR: 35264.462 MSE: 1959.137 Root MSE: 44.262 308s Multiple R-Squared: -4.082 Adjusted R-Squared: -4.365 308s 308s > 308s > ### 3SLS ### 308s > # instruments = explanatory variables -> 3SLS estimates = SUR estimates 308s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 308s + greene3sls <- systemfit( formulaGrunfeld, inst = ~ value + capital, "3SLS", 308s + data = GrunfeldGreene, useMatrix = useMatrix, methodResidCov = "noDfCor" ) 308s + print( greene3sls ) 308s + print( summary( greene3sls ) ) 308s + print( all.equal( coef( summary( greene3sls ) ), coef( summary( greeneSur ) ) ) ) 308s + print( all.equal( greene3sls[ -c(1,2,7) ], greeneSur[ -c(1,2,7) ] ) ) 308s + for( i in 1:length( greene3sls$eq ) ) { 308s + print( all.equal( greene3sls$eq[[i]][ -c(3,15:17) ], 308s + greeneSur$eq[[i]][-3] ) ) 308s + } 308s + } 308s 308s systemfit results 308s method: 3SLS 308s 308s Coefficients: 308s Chrysler_(Intercept) Chrysler_value 308s 0.5043 0.0695 308s Chrysler_capital General.Electric_(Intercept) 308s 0.3085 -22.4389 308s General.Electric_value General.Electric_capital 308s 0.0373 0.1308 308s General.Motors_(Intercept) General.Motors_value 308s -162.3641 0.1205 308s General.Motors_capital US.Steel_(Intercept) 308s 0.3827 85.4233 308s US.Steel_value US.Steel_capital 308s 0.1015 0.4000 308s Westinghouse_(Intercept) Westinghouse_value 308s 1.0889 0.0570 308s Westinghouse_capital 308s 0.0415 308s 308s systemfit results 308s method: 3SLS 308s 308s N DF SSR detRCov OLS-R2 McElroy-R2 308s system 100 85 347048 6.18e+13 0.844 0.869 308s 308s N DF SSR MSE RMSE R2 Adj R2 308s Chrysler 20 17 3057 180 13.4 0.912 0.901 308s General.Electric 20 17 14009 824 28.7 0.688 0.651 308s General.Motors 20 17 144321 8489 92.1 0.921 0.911 308s US.Steel 20 17 183763 10810 104.0 0.422 0.354 308s Westinghouse 20 17 1898 112 10.6 0.726 0.694 308s 308s The covariance matrix of the residuals used for estimation 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 149.9 -21.4 -283 418 13.3 308s General.Electric -21.4 660.8 608 905 176.4 308s General.Motors -282.8 607.5 7160 -2222 126.2 308s US.Steel 418.1 905.0 -2222 8896 546.2 308s Westinghouse 13.3 176.4 126 546 88.7 308s 308s The covariance matrix of the residuals 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 152.85 2.05 -314 455 16.7 308s General.Electric 2.05 700.46 605 1224 200.3 308s General.Motors -313.70 605.34 7216 -2687 129.9 308s US.Steel 455.09 1224.41 -2687 9188 652.7 308s Westinghouse 16.66 200.32 130 653 94.9 308s 308s The correlations of the residuals 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 1.00000 0.00626 -0.299 0.384 0.138 308s General.Electric 0.00626 1.00000 0.269 0.483 0.777 308s General.Motors -0.29870 0.26925 1.000 -0.330 0.157 308s US.Steel 0.38402 0.48264 -0.330 1.000 0.699 308s Westinghouse 0.13832 0.77690 0.157 0.699 1.000 308s 308s 308s 3SLS estimates for 'Chrysler' (equation 1) 308s Model Formula: Chrysler_invest ~ Chrysler_value + Chrysler_capital 308s 308s Instruments: ~Chrysler_value + Chrysler_capital 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) 0.5043 11.5128 0.04 0.96557 308s value 0.0695 0.0169 4.12 0.00072 *** 308s capital 0.3085 0.0259 11.93 1.1e-09 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 13.41 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 3056.985 MSE: 179.823 Root MSE: 13.41 308s Multiple R-Squared: 0.912 Adjusted R-Squared: 0.901 308s 308s 308s 3SLS estimates for 'General.Electric' (equation 2) 308s Model Formula: General.Electric_invest ~ General.Electric_value + General.Electric_capital 308s 308s Instruments: ~General.Electric_value + General.Electric_capital 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -22.4389 25.5186 -0.88 0.3915 308s value 0.0373 0.0123 3.04 0.0074 ** 308s capital 0.1308 0.0220 5.93 1.6e-05 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 28.707 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 14009.115 MSE: 824.066 Root MSE: 28.707 308s Multiple R-Squared: 0.688 Adjusted R-Squared: 0.651 308s 308s 308s 3SLS estimates for 'General.Motors' (equation 3) 308s Model Formula: General.Motors_invest ~ General.Motors_value + General.Motors_capital 308s 308s Instruments: ~General.Motors_value + General.Motors_capital 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -162.3641 89.4592 -1.81 0.087 . 308s value 0.1205 0.0216 5.57 3.4e-05 *** 308s capital 0.3827 0.0328 11.68 1.5e-09 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 92.138 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 144320.876 MSE: 8489.463 Root MSE: 92.138 308s Multiple R-Squared: 0.921 Adjusted R-Squared: 0.911 308s 308s 308s 3SLS estimates for 'US.Steel' (equation 4) 308s Model Formula: US.Steel_invest ~ US.Steel_value + US.Steel_capital 308s 308s Instruments: ~US.Steel_value + US.Steel_capital 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) 85.4233 111.8774 0.76 0.4556 308s value 0.1015 0.0548 1.85 0.0814 . 308s capital 0.4000 0.1278 3.13 0.0061 ** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 103.969 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 183763.011 MSE: 10809.589 Root MSE: 103.969 308s Multiple R-Squared: 0.422 Adjusted R-Squared: 0.354 308s 308s 308s 3SLS estimates for 'Westinghouse' (equation 5) 308s Model Formula: Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 308s 308s Instruments: ~Westinghouse_value + Westinghouse_capital 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) 1.0889 6.2588 0.17 0.86394 308s value 0.0570 0.0114 5.02 0.00011 *** 308s capital 0.0415 0.0412 1.01 0.32787 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 10.567 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 1898.249 MSE: 111.662 Root MSE: 10.567 308s Multiple R-Squared: 0.726 Adjusted R-Squared: 0.694 308s 308s [1] TRUE 308s [1] TRUE 308s [1] TRUE 308s [1] TRUE 308s [1] TRUE 308s [1] TRUE 308s [1] TRUE 308s > # 'real' IV/3SLS estimation 308s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 308s + greene3slsR <- systemfit( invest ~ capital, inst = ~ value, "3SLS", 308s + data = GrunfeldGreene, useMatrix = useMatrix ) 308s + print( greene3slsR ) 308s + print( summary( greene3slsR ) ) 308s + } 308s 308s systemfit results 308s method: 3SLS 308s 308s Coefficients: 308s Chrysler_(Intercept) Chrysler_capital 308s 23.499 0.517 308s General.Electric_(Intercept) General.Electric_capital 308s -108.596 0.527 308s General.Motors_(Intercept) General.Motors_capital 308s 199.856 0.629 308s US.Steel_(Intercept) US.Steel_capital 308s 181.691 0.746 308s Westinghouse_(Intercept) Westinghouse_capital 308s 11.668 0.365 308s 308s systemfit results 308s method: 3SLS 308s 308s N DF SSR detRCov OLS-R2 McElroy-R2 308s system 100 90 1026043 4.46e+16 0.539 0.539 308s 308s N DF SSR MSE RMSE R2 Adj R2 308s Chrysler 20 18 12139 674 26.0 0.650 0.631 308s General.Electric 20 18 178965 9942 99.7 -2.990 -3.212 308s General.Motors 20 18 577860 32103 179.2 0.683 0.665 308s US.Steel 20 18 252838 14047 118.5 0.205 0.160 308s Westinghouse 20 18 4241 236 15.3 0.389 0.355 308s 308s The covariance matrix of the residuals used for estimation 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 1687 3089 6820 11741 179 308s General.Electric 3089 9722 20780 23319 886 308s General.Motors 6820 20780 61121 44203 1908 308s US.Steel 11741 23319 44203 107242 1977 308s Westinghouse 179 886 1908 1977 218 308s 308s The covariance matrix of the residuals 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 674 1587 1944 1371 137 308s General.Electric 1587 9942 13003 2009 996 308s General.Motors 1944 13003 32103 -908 1571 308s US.Steel 1371 2009 -908 14047 888 308s Westinghouse 137 996 1571 888 236 308s 308s The correlations of the residuals 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 1.000 0.613 0.4178 0.4454 0.343 308s General.Electric 0.613 1.000 0.7278 0.1700 0.651 308s General.Motors 0.418 0.728 1.0000 -0.0428 0.571 308s US.Steel 0.445 0.170 -0.0428 1.0000 0.488 308s Westinghouse 0.343 0.651 0.5713 0.4880 1.000 308s 308s 308s 3SLS estimates for 'Chrysler' (equation 1) 308s Model Formula: Chrysler_invest ~ Chrysler_capital 308s 308s Instruments: ~Chrysler_value 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) 23.499 17.165 1.37 0.18784 308s capital 0.517 0.120 4.32 0.00041 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 25.969 on 18 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 18 308s SSR: 12138.974 MSE: 674.387 Root MSE: 25.969 308s Multiple R-Squared: 0.65 Adjusted R-Squared: 0.631 308s 308s 308s 3SLS estimates for 'General.Electric' (equation 2) 308s Model Formula: General.Electric_invest ~ General.Electric_capital 308s 308s Instruments: ~General.Electric_value 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -108.596 152.939 -0.71 0.49 308s capital 0.527 0.378 1.39 0.18 308s 308s Residual standard error: 99.712 on 18 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 18 308s SSR: 178964.956 MSE: 9942.498 Root MSE: 99.712 308s Multiple R-Squared: -2.99 Adjusted R-Squared: -3.212 308s 308s 308s 3SLS estimates for 'General.Motors' (equation 3) 308s Model Formula: General.Motors_invest ~ General.Motors_capital 308s 308s Instruments: ~General.Motors_value 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) 199.856 98.953 2.02 0.059 . 308s capital 0.629 0.127 4.97 9.8e-05 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 179.174 on 18 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 18 308s SSR: 577859.714 MSE: 32103.317 Root MSE: 179.174 308s Multiple R-Squared: 0.683 Adjusted R-Squared: 0.665 308s 308s 308s 3SLS estimates for 'US.Steel' (equation 4) 308s Model Formula: US.Steel_invest ~ US.Steel_capital 308s 308s Instruments: ~US.Steel_value 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) 181.691 448.797 0.40 0.69 308s capital 0.746 1.477 0.51 0.62 308s 308s Residual standard error: 118.518 on 18 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 18 308s SSR: 252838.286 MSE: 14046.571 Root MSE: 118.518 308s Multiple R-Squared: 0.205 Adjusted R-Squared: 0.16 308s 308s 308s 3SLS estimates for 'Westinghouse' (equation 5) 308s Model Formula: Westinghouse_invest ~ Westinghouse_capital 308s 308s Instruments: ~Westinghouse_value 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) 11.6685 5.9043 1.98 0.064 . 308s capital 0.3646 0.0572 6.38 5.2e-06 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 15.349 on 18 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 18 308s SSR: 4240.92 MSE: 235.607 Root MSE: 15.349 308s Multiple R-Squared: 0.389 Adjusted R-Squared: 0.355 308s 308s > 308s > ### 3SLS, Pooled ### 308s > # instruments = explanatory variables -> 3SLS estimates = SUR estimates 308s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 308s + greene3slsPooled <- systemfit( formulaGrunfeld, inst = ~ capital + value, "3SLS", 308s + data = GrunfeldGreene, pooled = TRUE, useMatrix = useMatrix, 308s + residCovWeighted = TRUE, methodResidCov = "noDfCor" ) 308s + print( greene3slsPooled ) 308s + print( summary( greene3slsPooled ) ) 308s + print( all.equal( coef( summary( greene3slsPooled ) ), 308s + coef( summary( greeneSurPooled ) ) ) ) 308s + print( all.equal( greene3slsPooled[ -c(1,2,7) ], greeneSurPooled[ -c(1,2,7) ] ) ) 308s + for( i in 1:length( greene3slsPooled$eq ) ) { 308s + print( all.equal( greene3slsPooled$eq[[i]][ -c(3,15:17) ], 308s + greeneSurPooled$eq[[i]][-3] ) ) 308s + } 308s + } 308s 308s systemfit results 308s method: 3SLS 308s 308s Coefficients: 308s Chrysler_(Intercept) Chrysler_value 308s -28.2467 0.0891 308s Chrysler_capital General.Electric_(Intercept) 308s 0.3340 -28.2467 308s General.Electric_value General.Electric_capital 308s 0.0891 0.3340 308s General.Motors_(Intercept) General.Motors_value 308s -28.2467 0.0891 308s General.Motors_capital US.Steel_(Intercept) 308s 0.3340 -28.2467 308s US.Steel_value US.Steel_capital 308s 0.0891 0.3340 308s Westinghouse_(Intercept) Westinghouse_value 308s -28.2467 0.0891 308s Westinghouse_capital 308s 0.3340 308s 308s systemfit results 308s method: 3SLS 308s 308s N DF SSR detRCov OLS-R2 McElroy-R2 308s system 100 97 1604301 9.95e+16 0.279 0.844 308s 308s N DF SSR MSE RMSE R2 Adj R2 308s Chrysler 20 17 6112 360 19.0 0.824 0.803 308s General.Electric 20 17 691132 40655 201.6 -14.410 -16.223 308s General.Motors 20 17 201010 11824 108.7 0.890 0.877 308s US.Steel 20 17 689380 40552 201.4 -1.168 -1.424 308s Westinghouse 20 17 16667 980 31.3 -1.402 -1.685 308s 308s The covariance matrix of the residuals used for estimation 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 409 -2594 -197 2594 -102 308s General.Electric -2594 36563 -3480 -28623 3797 308s General.Motors -197 -3480 8612 996 -971 308s US.Steel 2594 -28623 996 32903 -2272 308s Westinghouse -102 3797 -971 -2272 778 308s 308s The covariance matrix of the residuals 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 305.61 -1967 -4.81 2159 -124 308s General.Electric -1966.65 34557 -7160.67 -28722 4274 308s General.Motors -4.81 -7161 10050.52 4440 -1401 308s US.Steel 2158.60 -28722 4439.99 34469 -2894 308s Westinghouse -123.92 4274 -1400.75 -2894 833 308s 308s The correlations of the residuals 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 1.000 0.220 -0.3447 0.2008 0.2907 308s General.Electric 0.220 1.000 -0.2233 -0.1587 0.8973 308s General.Motors -0.345 -0.223 1.0000 -0.0924 -0.3760 308s US.Steel 0.201 -0.159 -0.0924 1.0000 -0.0757 308s Westinghouse 0.291 0.897 -0.3760 -0.0757 1.0000 308s 308s 308s 3SLS estimates for 'Chrysler' (equation 1) 308s Model Formula: Chrysler_invest ~ Chrysler_value + Chrysler_capital 308s 308s Instruments: ~Chrysler_capital + Chrysler_value 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -28.24669 4.88824 -5.78 9.1e-08 *** 308s value 0.08910 0.00507 17.57 < 2e-16 *** 308s capital 0.33402 0.01671 19.99 < 2e-16 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 18.962 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 6112.2 MSE: 359.541 Root MSE: 18.962 308s Multiple R-Squared: 0.824 Adjusted R-Squared: 0.803 308s 308s 308s 3SLS estimates for 'General.Electric' (equation 2) 308s Model Formula: General.Electric_invest ~ General.Electric_value + General.Electric_capital 308s 308s Instruments: ~General.Electric_capital + General.Electric_value 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -28.24669 4.88824 -5.78 9.1e-08 *** 308s value 0.08910 0.00507 17.57 < 2e-16 *** 308s capital 0.33402 0.01671 19.99 < 2e-16 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 201.63 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 691132.056 MSE: 40654.827 Root MSE: 201.63 308s Multiple R-Squared: -14.41 Adjusted R-Squared: -16.223 308s 308s 308s 3SLS estimates for 'General.Motors' (equation 3) 308s Model Formula: General.Motors_invest ~ General.Motors_value + General.Motors_capital 308s 308s Instruments: ~General.Motors_capital + General.Motors_value 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -28.24669 4.88824 -5.78 9.1e-08 *** 308s value 0.08910 0.00507 17.57 < 2e-16 *** 308s capital 0.33402 0.01671 19.99 < 2e-16 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 108.739 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 201010.497 MSE: 11824.147 Root MSE: 108.739 308s Multiple R-Squared: 0.89 Adjusted R-Squared: 0.877 308s 308s 308s 3SLS estimates for 'US.Steel' (equation 4) 308s Model Formula: US.Steel_invest ~ US.Steel_value + US.Steel_capital 308s 308s Instruments: ~US.Steel_capital + US.Steel_value 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -28.24669 4.88824 -5.78 9.1e-08 *** 308s value 0.08910 0.00507 17.57 < 2e-16 *** 308s capital 0.33402 0.01671 19.99 < 2e-16 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 201.375 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 689379.52 MSE: 40551.736 Root MSE: 201.375 308s Multiple R-Squared: -1.168 Adjusted R-Squared: -1.424 308s 308s 308s 3SLS estimates for 'Westinghouse' (equation 5) 308s Model Formula: Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 308s 308s Instruments: ~Westinghouse_capital + Westinghouse_value 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -28.24669 4.88824 -5.78 9.1e-08 *** 308s value 0.08910 0.00507 17.57 < 2e-16 *** 308s capital 0.33402 0.01671 19.99 < 2e-16 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 31.312 on 17 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 17 308s SSR: 16667.149 MSE: 980.421 Root MSE: 31.312 308s Multiple R-Squared: -1.402 Adjusted R-Squared: -1.685 308s 308s [1] TRUE 308s [1] TRUE 308s [1] TRUE 308s [1] TRUE 308s [1] TRUE 308s [1] TRUE 308s [1] TRUE 308s > # 'real' IV/3SLS estimation 308s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 308s + greene3slsRPooled <- systemfit( invest ~ capital, inst = ~ value, "3SLS", 308s + data = GrunfeldGreene, useMatrix = useMatrix ) 308s + print( greene3slsRPooled ) 308s + print( summary( greene3slsRPooled ) ) 308s + } 308s 308s systemfit results 308s method: 3SLS 308s 308s Coefficients: 308s Chrysler_(Intercept) Chrysler_capital 308s 23.499 0.517 308s General.Electric_(Intercept) General.Electric_capital 308s -108.596 0.527 308s General.Motors_(Intercept) General.Motors_capital 308s 199.856 0.629 308s US.Steel_(Intercept) US.Steel_capital 308s 181.691 0.746 308s Westinghouse_(Intercept) Westinghouse_capital 308s 11.668 0.365 308s 308s systemfit results 308s method: 3SLS 308s 308s N DF SSR detRCov OLS-R2 McElroy-R2 308s system 100 90 1026043 4.46e+16 0.539 0.539 308s 308s N DF SSR MSE RMSE R2 Adj R2 308s Chrysler 20 18 12139 674 26.0 0.650 0.631 308s General.Electric 20 18 178965 9942 99.7 -2.990 -3.212 308s General.Motors 20 18 577860 32103 179.2 0.683 0.665 308s US.Steel 20 18 252838 14047 118.5 0.205 0.160 308s Westinghouse 20 18 4241 236 15.3 0.389 0.355 308s 308s The covariance matrix of the residuals used for estimation 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 1687 3089 6820 11741 179 308s General.Electric 3089 9722 20780 23319 886 308s General.Motors 6820 20780 61121 44203 1908 308s US.Steel 11741 23319 44203 107242 1977 308s Westinghouse 179 886 1908 1977 218 308s 308s The covariance matrix of the residuals 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 674 1587 1944 1371 137 308s General.Electric 1587 9942 13003 2009 996 308s General.Motors 1944 13003 32103 -908 1571 308s US.Steel 1371 2009 -908 14047 888 308s Westinghouse 137 996 1571 888 236 308s 308s The correlations of the residuals 308s Chrysler General.Electric General.Motors US.Steel Westinghouse 308s Chrysler 1.000 0.613 0.4178 0.4454 0.343 308s General.Electric 0.613 1.000 0.7278 0.1700 0.651 308s General.Motors 0.418 0.728 1.0000 -0.0428 0.571 308s US.Steel 0.445 0.170 -0.0428 1.0000 0.488 308s Westinghouse 0.343 0.651 0.5713 0.4880 1.000 308s 308s 308s 3SLS estimates for 'Chrysler' (equation 1) 308s Model Formula: Chrysler_invest ~ Chrysler_capital 308s 308s Instruments: ~Chrysler_value 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) 23.499 17.165 1.37 0.18784 308s capital 0.517 0.120 4.32 0.00041 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 25.969 on 18 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 18 308s SSR: 12138.974 MSE: 674.387 Root MSE: 25.969 308s Multiple R-Squared: 0.65 Adjusted R-Squared: 0.631 308s 308s 308s 3SLS estimates for 'General.Electric' (equation 2) 308s Model Formula: General.Electric_invest ~ General.Electric_capital 308s 308s Instruments: ~General.Electric_value 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) -108.596 152.939 -0.71 0.49 308s capital 0.527 0.378 1.39 0.18 308s 308s Residual standard error: 99.712 on 18 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 18 308s SSR: 178964.956 MSE: 9942.498 Root MSE: 99.712 308s Multiple R-Squared: -2.99 Adjusted R-Squared: -3.212 308s 308s 308s 3SLS estimates for 'General.Motors' (equation 3) 308s Model Formula: General.Motors_invest ~ General.Motors_capital 308s 308s Instruments: ~General.Motors_value 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) 199.856 98.953 2.02 0.059 . 308s capital 0.629 0.127 4.97 9.8e-05 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 179.174 on 18 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 18 308s SSR: 577859.714 MSE: 32103.317 Root MSE: 179.174 308s Multiple R-Squared: 0.683 Adjusted R-Squared: 0.665 308s 308s 308s 3SLS estimates for 'US.Steel' (equation 4) 308s Model Formula: US.Steel_invest ~ US.Steel_capital 308s 308s Instruments: ~US.Steel_value 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) 181.691 448.797 0.40 0.69 308s capital 0.746 1.477 0.51 0.62 308s 308s Residual standard error: 118.518 on 18 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 18 308s SSR: 252838.286 MSE: 14046.571 Root MSE: 118.518 308s Multiple R-Squared: 0.205 Adjusted R-Squared: 0.16 308s 308s 308s 3SLS estimates for 'Westinghouse' (equation 5) 308s Model Formula: Westinghouse_invest ~ Westinghouse_capital 308s 308s Instruments: ~Westinghouse_value 308s 308s 308s Estimate Std. Error t value Pr(>|t|) 308s (Intercept) 11.6685 5.9043 1.98 0.064 . 308s capital 0.3646 0.0572 6.38 5.2e-06 *** 308s --- 308s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 308s 308s Residual standard error: 15.349 on 18 degrees of freedom 308s Number of observations: 20 Degrees of Freedom: 18 308s SSR: 4240.92 MSE: 235.607 Root MSE: 15.349 308s Multiple R-Squared: 0.389 Adjusted R-Squared: 0.355 308s 308s > 308s > 308s > ## **************** estfun ************************ 308s > library( "sandwich" ) 308s > 308s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 308s + print( estfun( theilOls ) ) 308s + print( round( colSums( estfun( theilOls ) ), digits = 7 ) ) 308s + 308s + print( estfun( theilSur ) ) 308s + print( round( colSums( estfun( theilSur ) ), digits = 7 ) ) 308s + 308s + print( estfun( greeneOls ) ) 308s + print( round( colSums( estfun( greeneOls ) ), digits = 7 ) ) 308s + 308s + print( try( estfun( greeneOlsPooled ) ) ) 308s + 308s + print( estfun( greeneSur ) ) 308s + print( round( colSums( estfun( greeneSur ) ), digits = 7 ) ) 308s + 308s + print( try( estfun( greeneSurPooled ) ) ) 308s + } 308s General.Electric_(Intercept) General.Electric_value 308s General.Electric_X1935 -2.860 -3348 308s General.Electric_X1936 -14.402 -29032 308s General.Electric_X1937 -5.175 -14506 308s General.Electric_X1938 -23.295 -47514 308s General.Electric_X1939 -28.031 -63243 308s General.Electric_X1940 -0.562 -1199 308s General.Electric_X1941 40.750 74739 308s General.Electric_X1942 16.036 25464 308s General.Electric_X1943 -23.719 -41494 308s General.Electric_X1944 -26.780 -45183 308s General.Electric_X1945 1.768 3550 308s General.Electric_X1946 58.737 129709 308s General.Electric_X1947 43.936 72789 308s General.Electric_X1948 31.227 50101 308s General.Electric_X1949 -23.552 -33722 308s General.Electric_X1950 -37.511 -60411 308s General.Electric_X1951 -4.983 -9066 308s General.Electric_X1952 1.893 3937 308s General.Electric_X1953 5.087 12064 308s General.Electric_X1954 -8.563 -23633 308s Westinghouse_X1935 0.000 0 308s Westinghouse_X1936 0.000 0 308s Westinghouse_X1937 0.000 0 308s Westinghouse_X1938 0.000 0 308s Westinghouse_X1939 0.000 0 308s Westinghouse_X1940 0.000 0 308s Westinghouse_X1941 0.000 0 308s Westinghouse_X1942 0.000 0 308s Westinghouse_X1943 0.000 0 308s Westinghouse_X1944 0.000 0 308s Westinghouse_X1945 0.000 0 308s Westinghouse_X1946 0.000 0 308s Westinghouse_X1947 0.000 0 308s Westinghouse_X1948 0.000 0 308s Westinghouse_X1949 0.000 0 308s Westinghouse_X1950 0.000 0 308s Westinghouse_X1951 0.000 0 308s Westinghouse_X1952 0.000 0 308s Westinghouse_X1953 0.000 0 308s Westinghouse_X1954 0.000 0 308s General.Electric_capital Westinghouse_(Intercept) 308s General.Electric_X1935 -280 0.000 308s General.Electric_X1936 -1504 0.000 308s General.Electric_X1937 -611 0.000 308s General.Electric_X1938 -3639 0.000 308s General.Electric_X1939 -4838 0.000 308s General.Electric_X1940 -105 0.000 308s General.Electric_X1941 9002 0.000 308s General.Electric_X1942 4615 0.000 308s General.Electric_X1943 -7588 0.000 308s General.Electric_X1944 -8604 0.000 308s General.Electric_X1945 565 0.000 308s General.Electric_X1946 20323 0.000 308s General.Electric_X1947 20052 0.000 308s General.Electric_X1948 16969 0.000 308s General.Electric_X1949 -14562 0.000 308s General.Electric_X1950 -24285 0.000 308s General.Electric_X1951 -3345 0.000 308s General.Electric_X1952 1374 0.000 308s General.Electric_X1953 4071 0.000 308s General.Electric_X1954 -7612 0.000 308s Westinghouse_X1935 0 3.144 308s Westinghouse_X1936 0 -0.958 308s Westinghouse_X1937 0 -3.684 308s Westinghouse_X1938 0 -7.915 308s Westinghouse_X1939 0 -10.322 308s Westinghouse_X1940 0 -6.613 308s Westinghouse_X1941 0 17.265 308s Westinghouse_X1942 0 8.547 308s Westinghouse_X1943 0 -2.916 308s Westinghouse_X1944 0 -3.257 308s Westinghouse_X1945 0 -7.753 308s Westinghouse_X1946 0 5.796 308s Westinghouse_X1947 0 15.050 308s Westinghouse_X1948 0 2.969 308s Westinghouse_X1949 0 -11.433 308s Westinghouse_X1950 0 -13.481 308s Westinghouse_X1951 0 4.619 308s Westinghouse_X1952 0 13.138 308s Westinghouse_X1953 0 11.308 308s Westinghouse_X1954 0 -13.505 308s Westinghouse_value Westinghouse_capital 308s General.Electric_X1935 0 0.000 308s General.Electric_X1936 0 0.000 308s General.Electric_X1937 0 0.000 308s General.Electric_X1938 0 0.000 308s General.Electric_X1939 0 0.000 308s General.Electric_X1940 0 0.000 308s General.Electric_X1941 0 0.000 308s General.Electric_X1942 0 0.000 308s General.Electric_X1943 0 0.000 308s General.Electric_X1944 0 0.000 308s General.Electric_X1945 0 0.000 308s General.Electric_X1946 0 0.000 308s General.Electric_X1947 0 0.000 308s General.Electric_X1948 0 0.000 308s General.Electric_X1949 0 0.000 308s General.Electric_X1950 0 0.000 308s General.Electric_X1951 0 0.000 308s General.Electric_X1952 0 0.000 308s General.Electric_X1953 0 0.000 308s General.Electric_X1954 0 0.000 308s Westinghouse_X1935 602 5.659 308s Westinghouse_X1936 -494 -0.766 308s Westinghouse_X1937 -2686 -27.263 308s Westinghouse_X1938 -4436 -143.262 308s Westinghouse_X1939 -5366 -242.563 308s Westinghouse_X1940 -4156 -175.254 308s Westinghouse_X1941 9273 624.987 308s Westinghouse_X1942 4797 519.651 308s Westinghouse_X1943 -1800 -246.108 308s Westinghouse_X1944 -2041 -297.023 308s Westinghouse_X1945 -5715 -716.333 308s Westinghouse_X1946 4408 498.495 308s Westinghouse_X1947 8750 1672.098 308s Westinghouse_X1948 1967 387.794 308s Westinghouse_X1949 -6675 -1621.262 308s Westinghouse_X1950 -8563 -1842.843 308s Westinghouse_X1951 3344 599.149 308s Westinghouse_X1952 11353 1911.642 308s Westinghouse_X1953 13496 1976.568 308s Westinghouse_X1954 -16056 -2883.365 308s General.Electric_(Intercept) General.Electric_value 308s 0 0 308s General.Electric_capital Westinghouse_(Intercept) 308s 0 0 308s Westinghouse_value Westinghouse_capital 308s 0 0 308s General.Electric_(Intercept) General.Electric_value 308s General.Electric_X1935 0.007671 8.980 308s General.Electric_X1936 -0.061426 -123.822 308s General.Electric_X1937 -0.060974 -170.929 308s General.Electric_X1938 -0.088931 -181.393 308s General.Electric_X1939 -0.111776 -252.189 308s General.Electric_X1940 -0.017793 -37.937 308s General.Electric_X1941 0.128334 235.378 308s General.Electric_X1942 0.060606 96.243 308s General.Electric_X1943 -0.072587 -126.985 308s General.Electric_X1944 -0.080053 -135.065 308s General.Electric_X1945 -0.000104 -0.208 308s General.Electric_X1946 0.177325 391.586 308s General.Electric_X1947 0.154986 256.765 308s General.Electric_X1948 0.119488 191.707 308s General.Electric_X1949 -0.047791 -68.427 308s General.Electric_X1950 -0.098464 -158.576 308s General.Electric_X1951 -0.000379 -0.689 308s General.Electric_X1952 0.014181 29.492 308s General.Electric_X1953 0.016444 38.998 308s General.Electric_X1954 -0.038758 -106.969 308s Westinghouse_X1935 -0.019477 -22.800 308s Westinghouse_X1936 0.016942 34.151 308s Westinghouse_X1937 0.039739 111.402 308s Westinghouse_X1938 0.059843 122.062 308s Westinghouse_X1939 0.073091 164.909 308s Westinghouse_X1940 0.052015 110.907 308s Westinghouse_X1941 -0.105994 -194.404 308s Westinghouse_X1942 -0.053728 -85.321 308s Westinghouse_X1943 0.017332 30.320 308s Westinghouse_X1944 0.018569 31.330 308s Westinghouse_X1945 0.050605 101.599 308s Westinghouse_X1946 -0.034591 -76.387 308s Westinghouse_X1947 -0.104099 -172.460 308s Westinghouse_X1948 -0.027559 -44.215 308s Westinghouse_X1949 0.060567 86.720 308s Westinghouse_X1950 0.076221 122.754 308s Westinghouse_X1951 -0.036128 -65.731 308s Westinghouse_X1952 -0.089492 -186.117 308s Westinghouse_X1953 -0.073054 -173.256 308s Westinghouse_X1954 0.079198 218.578 308s General.Electric_capital Westinghouse_(Intercept) 308s General.Electric_X1935 0.7503 -0.015267 308s General.Electric_X1936 -6.4128 0.122246 308s General.Electric_X1937 -7.1950 0.121347 308s General.Electric_X1938 -13.8911 0.176986 308s General.Electric_X1939 -19.2925 0.222450 308s General.Electric_X1940 -3.3201 0.035410 308s General.Electric_X1941 28.3490 -0.255403 308s General.Electric_X1942 17.4425 -0.120615 308s General.Electric_X1943 -23.2207 0.144459 308s General.Electric_X1944 -25.7209 0.159316 308s General.Electric_X1945 -0.0331 0.000206 308s General.Electric_X1946 61.3543 -0.352901 308s General.Electric_X1947 70.7355 -0.308443 308s General.Electric_X1948 64.9300 -0.237798 308s General.Electric_X1949 -29.5489 0.095110 308s General.Electric_X1950 -63.7453 0.195956 308s General.Electric_X1951 -0.2543 0.000754 308s General.Electric_X1952 10.2966 -0.028221 308s General.Electric_X1953 13.1598 -0.032725 308s General.Electric_X1954 -34.4523 0.077135 308s Westinghouse_X1935 -1.9049 0.072945 308s Westinghouse_X1936 1.7687 -0.063449 308s Westinghouse_X1937 4.6893 -0.148830 308s Westinghouse_X1938 9.3475 -0.224122 308s Westinghouse_X1939 12.6156 -0.273739 308s Westinghouse_X1940 9.7061 -0.194806 308s Westinghouse_X1941 -23.4141 0.396965 308s Westinghouse_X1942 -15.4630 0.201221 308s Westinghouse_X1943 5.5444 -0.064910 308s Westinghouse_X1944 5.9663 -0.069544 308s Westinghouse_X1945 16.1733 -0.189523 308s Westinghouse_X1946 -11.9684 0.129548 308s Westinghouse_X1947 -47.5107 0.389866 308s Westinghouse_X1948 -14.9755 0.103212 308s Westinghouse_X1949 37.4485 -0.226832 308s Westinghouse_X1950 49.3457 -0.285461 308s Westinghouse_X1951 -24.2526 0.135304 308s Westinghouse_X1952 -64.9804 0.335163 308s Westinghouse_X1953 -58.4654 0.273600 308s Westinghouse_X1954 70.3989 -0.296608 308s Westinghouse_value Westinghouse_capital 308s General.Electric_X1935 -2.924 -0.0275 308s General.Electric_X1936 63.079 0.0978 308s General.Electric_X1937 88.462 0.8980 308s General.Electric_X1938 99.183 3.2034 308s General.Electric_X1939 115.652 5.2276 308s General.Electric_X1940 22.255 0.9384 308s General.Electric_X1941 -137.177 -9.2456 308s General.Electric_X1942 -67.689 -7.3334 308s General.Electric_X1943 89.160 12.1924 308s General.Electric_X1944 99.843 14.5296 308s General.Electric_X1945 0.152 0.0190 308s General.Electric_X1946 -268.381 -30.3494 308s General.Electric_X1947 -179.329 -34.2680 308s General.Electric_X1948 -157.494 -31.0565 308s General.Electric_X1949 55.525 13.4866 308s General.Electric_X1950 124.471 26.7872 308s General.Electric_X1951 0.546 0.0978 308s General.Electric_X1952 -24.386 -4.1062 308s General.Electric_X1953 -39.057 -5.7203 308s General.Electric_X1954 91.705 16.4682 308s Westinghouse_X1935 13.969 0.1313 308s Westinghouse_X1936 -32.740 -0.0508 308s Westinghouse_X1937 -108.497 -1.1013 308s Westinghouse_X1938 -125.598 -4.0566 308s Westinghouse_X1939 -142.317 -6.4329 308s Westinghouse_X1940 -122.436 -5.1624 308s Westinghouse_X1941 213.210 14.3701 308s Westinghouse_X1942 112.925 12.2342 308s Westinghouse_X1943 -40.063 -5.4784 308s Westinghouse_X1944 -43.583 -6.3424 308s Westinghouse_X1945 -139.717 -17.5120 308s Westinghouse_X1946 98.521 11.1411 308s Westinghouse_X1947 226.668 43.3141 308s Westinghouse_X1948 68.357 13.4795 308s Westinghouse_X1949 -132.425 -32.1648 308s Westinghouse_X1950 -181.325 -39.0225 308s Westinghouse_X1951 97.933 17.5490 308s Westinghouse_X1952 289.614 48.7662 308s Westinghouse_X1953 326.541 47.8252 308s Westinghouse_X1954 -352.637 -63.3258 308s General.Electric_(Intercept) General.Electric_value 308s 0 0 308s General.Electric_capital Westinghouse_(Intercept) 308s 0 0 308s Westinghouse_value Westinghouse_capital 308s 0 0 308s Chrysler_(Intercept) Chrysler_value Chrysler_capital 308s Chrysler_X1935 10.622 4435 111.5 308s Chrysler_X1936 10.425 8734 106.3 308s Chrysler_X1937 -7.404 -6544 -256.9 308s Chrysler_X1938 7.302 3198 378.3 308s Chrysler_X1939 -14.682 -9979 -944.0 308s Chrysler_X1940 -2.315 -1685 -155.3 308s Chrysler_X1941 0.631 406 47.4 308s Chrysler_X1942 -1.581 -650 -112.9 308s Chrysler_X1943 -13.459 -7919 -903.1 308s Chrysler_X1944 -7.780 -5433 -470.7 308s Chrysler_X1945 11.757 9951 641.9 308s Chrysler_X1946 -16.133 -14419 -1368.1 308s Chrysler_X1947 -6.823 -3951 -660.5 308s Chrysler_X1948 6.615 4595 729.0 308s Chrysler_X1949 -7.379 -4356 -1087.7 308s Chrysler_X1950 1.268 879 206.9 308s Chrysler_X1951 39.502 31957 8038.6 308s Chrysler_X1952 2.774 2017 806.2 308s Chrysler_X1953 -6.215 -6224 -2151.0 308s Chrysler_X1954 -7.124 -5010 -2955.9 308s General.Electric_X1935 0.000 0 0.0 308s General.Electric_X1936 0.000 0 0.0 308s General.Electric_X1937 0.000 0 0.0 308s General.Electric_X1938 0.000 0 0.0 308s General.Electric_X1939 0.000 0 0.0 308s General.Electric_X1940 0.000 0 0.0 308s General.Electric_X1941 0.000 0 0.0 308s General.Electric_X1942 0.000 0 0.0 308s General.Electric_X1943 0.000 0 0.0 308s General.Electric_X1944 0.000 0 0.0 308s General.Electric_X1945 0.000 0 0.0 308s General.Electric_X1946 0.000 0 0.0 308s General.Electric_X1947 0.000 0 0.0 308s General.Electric_X1948 0.000 0 0.0 308s General.Electric_X1949 0.000 0 0.0 308s General.Electric_X1950 0.000 0 0.0 308s General.Electric_X1951 0.000 0 0.0 308s General.Electric_X1952 0.000 0 0.0 308s General.Electric_X1953 0.000 0 0.0 308s General.Electric_X1954 0.000 0 0.0 308s General.Motors_X1935 0.000 0 0.0 308s General.Motors_X1936 0.000 0 0.0 308s General.Motors_X1937 0.000 0 0.0 308s General.Motors_X1938 0.000 0 0.0 308s General.Motors_X1939 0.000 0 0.0 308s General.Motors_X1940 0.000 0 0.0 308s General.Motors_X1941 0.000 0 0.0 308s General.Motors_X1942 0.000 0 0.0 308s General.Motors_X1943 0.000 0 0.0 308s General.Motors_X1944 0.000 0 0.0 308s General.Motors_X1945 0.000 0 0.0 308s General.Motors_X1946 0.000 0 0.0 308s General.Motors_X1947 0.000 0 0.0 308s General.Motors_X1948 0.000 0 0.0 308s General.Motors_X1949 0.000 0 0.0 308s General.Motors_X1950 0.000 0 0.0 308s General.Motors_X1951 0.000 0 0.0 308s General.Motors_X1952 0.000 0 0.0 308s General.Motors_X1953 0.000 0 0.0 308s General.Motors_X1954 0.000 0 0.0 308s US.Steel_X1935 0.000 0 0.0 308s US.Steel_X1936 0.000 0 0.0 308s US.Steel_X1937 0.000 0 0.0 308s US.Steel_X1938 0.000 0 0.0 308s US.Steel_X1939 0.000 0 0.0 308s US.Steel_X1940 0.000 0 0.0 308s US.Steel_X1941 0.000 0 0.0 308s US.Steel_X1942 0.000 0 0.0 308s US.Steel_X1943 0.000 0 0.0 308s US.Steel_X1944 0.000 0 0.0 308s US.Steel_X1945 0.000 0 0.0 308s US.Steel_X1946 0.000 0 0.0 308s US.Steel_X1947 0.000 0 0.0 308s US.Steel_X1948 0.000 0 0.0 308s US.Steel_X1949 0.000 0 0.0 308s US.Steel_X1950 0.000 0 0.0 308s US.Steel_X1951 0.000 0 0.0 308s US.Steel_X1952 0.000 0 0.0 308s US.Steel_X1953 0.000 0 0.0 308s US.Steel_X1954 0.000 0 0.0 308s Westinghouse_X1935 0.000 0 0.0 308s Westinghouse_X1936 0.000 0 0.0 308s Westinghouse_X1937 0.000 0 0.0 308s Westinghouse_X1938 0.000 0 0.0 308s Westinghouse_X1939 0.000 0 0.0 308s Westinghouse_X1940 0.000 0 0.0 308s Westinghouse_X1941 0.000 0 0.0 308s Westinghouse_X1942 0.000 0 0.0 308s Westinghouse_X1943 0.000 0 0.0 308s Westinghouse_X1944 0.000 0 0.0 308s Westinghouse_X1945 0.000 0 0.0 308s Westinghouse_X1946 0.000 0 0.0 308s Westinghouse_X1947 0.000 0 0.0 308s Westinghouse_X1948 0.000 0 0.0 308s Westinghouse_X1949 0.000 0 0.0 308s Westinghouse_X1950 0.000 0 0.0 308s Westinghouse_X1951 0.000 0 0.0 308s Westinghouse_X1952 0.000 0 0.0 308s Westinghouse_X1953 0.000 0 0.0 308s Westinghouse_X1954 0.000 0 0.0 308s General.Electric_(Intercept) General.Electric_value 308s Chrysler_X1935 0.000 0 308s Chrysler_X1936 0.000 0 308s Chrysler_X1937 0.000 0 308s Chrysler_X1938 0.000 0 308s Chrysler_X1939 0.000 0 308s Chrysler_X1940 0.000 0 308s Chrysler_X1941 0.000 0 308s Chrysler_X1942 0.000 0 308s Chrysler_X1943 0.000 0 308s Chrysler_X1944 0.000 0 308s Chrysler_X1945 0.000 0 308s Chrysler_X1946 0.000 0 308s Chrysler_X1947 0.000 0 308s Chrysler_X1948 0.000 0 308s Chrysler_X1949 0.000 0 308s Chrysler_X1950 0.000 0 308s Chrysler_X1951 0.000 0 308s Chrysler_X1952 0.000 0 308s Chrysler_X1953 0.000 0 308s Chrysler_X1954 0.000 0 308s General.Electric_X1935 -2.860 -3348 308s General.Electric_X1936 -14.402 -29032 308s General.Electric_X1937 -5.175 -14506 308s General.Electric_X1938 -23.295 -47514 308s General.Electric_X1939 -28.031 -63243 308s General.Electric_X1940 -0.562 -1199 308s General.Electric_X1941 40.750 74739 308s General.Electric_X1942 16.036 25464 308s General.Electric_X1943 -23.719 -41494 308s General.Electric_X1944 -26.780 -45183 308s General.Electric_X1945 1.768 3550 308s General.Electric_X1946 58.737 129709 308s General.Electric_X1947 43.936 72789 308s General.Electric_X1948 31.227 50101 308s General.Electric_X1949 -23.552 -33722 308s General.Electric_X1950 -37.511 -60411 308s General.Electric_X1951 -4.983 -9066 308s General.Electric_X1952 1.893 3937 308s General.Electric_X1953 5.087 12064 308s General.Electric_X1954 -8.563 -23633 308s General.Motors_X1935 0.000 0 308s General.Motors_X1936 0.000 0 308s General.Motors_X1937 0.000 0 308s General.Motors_X1938 0.000 0 308s General.Motors_X1939 0.000 0 308s General.Motors_X1940 0.000 0 308s General.Motors_X1941 0.000 0 308s General.Motors_X1942 0.000 0 308s General.Motors_X1943 0.000 0 308s General.Motors_X1944 0.000 0 308s General.Motors_X1945 0.000 0 308s General.Motors_X1946 0.000 0 308s General.Motors_X1947 0.000 0 308s General.Motors_X1948 0.000 0 308s General.Motors_X1949 0.000 0 308s General.Motors_X1950 0.000 0 308s General.Motors_X1951 0.000 0 308s General.Motors_X1952 0.000 0 308s General.Motors_X1953 0.000 0 308s General.Motors_X1954 0.000 0 308s US.Steel_X1935 0.000 0 308s US.Steel_X1936 0.000 0 308s US.Steel_X1937 0.000 0 308s US.Steel_X1938 0.000 0 308s US.Steel_X1939 0.000 0 308s US.Steel_X1940 0.000 0 308s US.Steel_X1941 0.000 0 308s US.Steel_X1942 0.000 0 308s US.Steel_X1943 0.000 0 308s US.Steel_X1944 0.000 0 308s US.Steel_X1945 0.000 0 308s US.Steel_X1946 0.000 0 308s US.Steel_X1947 0.000 0 308s US.Steel_X1948 0.000 0 308s US.Steel_X1949 0.000 0 308s US.Steel_X1950 0.000 0 308s US.Steel_X1951 0.000 0 308s US.Steel_X1952 0.000 0 308s US.Steel_X1953 0.000 0 308s US.Steel_X1954 0.000 0 308s Westinghouse_X1935 0.000 0 308s Westinghouse_X1936 0.000 0 308s Westinghouse_X1937 0.000 0 308s Westinghouse_X1938 0.000 0 308s Westinghouse_X1939 0.000 0 308s Westinghouse_X1940 0.000 0 308s Westinghouse_X1941 0.000 0 308s Westinghouse_X1942 0.000 0 308s Westinghouse_X1943 0.000 0 308s Westinghouse_X1944 0.000 0 308s Westinghouse_X1945 0.000 0 308s Westinghouse_X1946 0.000 0 308s Westinghouse_X1947 0.000 0 308s Westinghouse_X1948 0.000 0 308s Westinghouse_X1949 0.000 0 308s Westinghouse_X1950 0.000 0 308s Westinghouse_X1951 0.000 0 308s Westinghouse_X1952 0.000 0 308s Westinghouse_X1953 0.000 0 308s Westinghouse_X1954 0.000 0 308s General.Electric_capital General.Motors_(Intercept) 308s Chrysler_X1935 0 0.00 308s Chrysler_X1936 0 0.00 308s Chrysler_X1937 0 0.00 308s Chrysler_X1938 0 0.00 308s Chrysler_X1939 0 0.00 308s Chrysler_X1940 0 0.00 308s Chrysler_X1941 0 0.00 308s Chrysler_X1942 0 0.00 308s Chrysler_X1943 0 0.00 308s Chrysler_X1944 0 0.00 308s Chrysler_X1945 0 0.00 308s Chrysler_X1946 0 0.00 308s Chrysler_X1947 0 0.00 308s Chrysler_X1948 0 0.00 308s Chrysler_X1949 0 0.00 308s Chrysler_X1950 0 0.00 308s Chrysler_X1951 0 0.00 308s Chrysler_X1952 0 0.00 308s Chrysler_X1953 0 0.00 308s Chrysler_X1954 0 0.00 308s General.Electric_X1935 -280 0.00 308s General.Electric_X1936 -1504 0.00 308s General.Electric_X1937 -611 0.00 308s General.Electric_X1938 -3639 0.00 308s General.Electric_X1939 -4838 0.00 308s General.Electric_X1940 -105 0.00 308s General.Electric_X1941 9002 0.00 308s General.Electric_X1942 4615 0.00 308s General.Electric_X1943 -7588 0.00 308s General.Electric_X1944 -8604 0.00 308s General.Electric_X1945 565 0.00 308s General.Electric_X1946 20323 0.00 308s General.Electric_X1947 20052 0.00 308s General.Electric_X1948 16969 0.00 308s General.Electric_X1949 -14562 0.00 308s General.Electric_X1950 -24285 0.00 308s General.Electric_X1951 -3345 0.00 308s General.Electric_X1952 1374 0.00 308s General.Electric_X1953 4071 0.00 308s General.Electric_X1954 -7612 0.00 308s General.Motors_X1935 0 99.14 308s General.Motors_X1936 0 -34.01 308s General.Motors_X1937 0 -140.48 308s General.Motors_X1938 0 -3.28 308s General.Motors_X1939 0 -109.45 308s General.Motors_X1940 0 -19.91 308s General.Motors_X1941 0 24.12 308s General.Motors_X1942 0 98.02 308s General.Motors_X1943 0 67.76 308s General.Motors_X1944 0 100.03 308s General.Motors_X1945 0 35.12 308s General.Motors_X1946 0 103.90 308s General.Motors_X1947 0 15.18 308s General.Motors_X1948 0 -51.86 308s General.Motors_X1949 0 -115.39 308s General.Motors_X1950 0 -63.51 308s General.Motors_X1951 0 -119.40 308s General.Motors_X1952 0 -77.82 308s General.Motors_X1953 0 49.50 308s General.Motors_X1954 0 142.33 308s US.Steel_X1935 0 0.00 308s US.Steel_X1936 0 0.00 308s US.Steel_X1937 0 0.00 308s US.Steel_X1938 0 0.00 308s US.Steel_X1939 0 0.00 308s US.Steel_X1940 0 0.00 308s US.Steel_X1941 0 0.00 308s US.Steel_X1942 0 0.00 308s US.Steel_X1943 0 0.00 308s US.Steel_X1944 0 0.00 308s US.Steel_X1945 0 0.00 308s US.Steel_X1946 0 0.00 308s US.Steel_X1947 0 0.00 308s US.Steel_X1948 0 0.00 308s US.Steel_X1949 0 0.00 308s US.Steel_X1950 0 0.00 308s US.Steel_X1951 0 0.00 308s US.Steel_X1952 0 0.00 308s US.Steel_X1953 0 0.00 308s US.Steel_X1954 0 0.00 308s Westinghouse_X1935 0 0.00 308s Westinghouse_X1936 0 0.00 308s Westinghouse_X1937 0 0.00 308s Westinghouse_X1938 0 0.00 308s Westinghouse_X1939 0 0.00 308s Westinghouse_X1940 0 0.00 308s Westinghouse_X1941 0 0.00 308s Westinghouse_X1942 0 0.00 308s Westinghouse_X1943 0 0.00 308s Westinghouse_X1944 0 0.00 308s Westinghouse_X1945 0 0.00 308s Westinghouse_X1946 0 0.00 308s Westinghouse_X1947 0 0.00 308s Westinghouse_X1948 0 0.00 308s Westinghouse_X1949 0 0.00 308s Westinghouse_X1950 0 0.00 308s Westinghouse_X1951 0 0.00 308s Westinghouse_X1952 0 0.00 308s Westinghouse_X1953 0 0.00 308s Westinghouse_X1954 0 0.00 308s General.Motors_value General.Motors_capital 308s Chrysler_X1935 0 0 308s Chrysler_X1936 0 0 308s Chrysler_X1937 0 0 308s Chrysler_X1938 0 0 308s Chrysler_X1939 0 0 308s Chrysler_X1940 0 0 308s Chrysler_X1941 0 0 308s Chrysler_X1942 0 0 308s Chrysler_X1943 0 0 308s Chrysler_X1944 0 0 308s Chrysler_X1945 0 0 308s Chrysler_X1946 0 0 308s Chrysler_X1947 0 0 308s Chrysler_X1948 0 0 308s Chrysler_X1949 0 0 308s Chrysler_X1950 0 0 308s Chrysler_X1951 0 0 308s Chrysler_X1952 0 0 308s Chrysler_X1953 0 0 308s Chrysler_X1954 0 0 308s General.Electric_X1935 0 0 308s General.Electric_X1936 0 0 308s General.Electric_X1937 0 0 308s General.Electric_X1938 0 0 308s General.Electric_X1939 0 0 308s General.Electric_X1940 0 0 308s General.Electric_X1941 0 0 308s General.Electric_X1942 0 0 308s General.Electric_X1943 0 0 308s General.Electric_X1944 0 0 308s General.Electric_X1945 0 0 308s General.Electric_X1946 0 0 308s General.Electric_X1947 0 0 308s General.Electric_X1948 0 0 308s General.Electric_X1949 0 0 308s General.Electric_X1950 0 0 308s General.Electric_X1951 0 0 308s General.Electric_X1952 0 0 308s General.Electric_X1953 0 0 308s General.Electric_X1954 0 0 308s General.Motors_X1935 305191 278 308s General.Motors_X1936 -158530 -1789 308s General.Motors_X1937 -756753 -22041 308s General.Motors_X1938 -9158 -686 308s General.Motors_X1939 -472086 -22262 308s General.Motors_X1940 -92456 -4125 308s General.Motors_X1941 109770 6155 308s General.Motors_X1942 317973 29767 308s General.Motors_X1943 274659 17894 308s General.Motors_X1944 438073 20167 308s General.Motors_X1945 170027 9308 308s General.Motors_X1946 509223 41790 308s General.Motors_X1947 53544 11562 308s General.Motors_X1948 -168794 -47837 308s General.Motors_X1949 -426971 -117711 308s General.Motors_X1950 -238505 -69794 308s General.Motors_X1951 -577039 -144194 308s General.Motors_X1952 -383234 -111315 308s General.Motors_X1953 308954 87974 308s General.Motors_X1954 796113 316860 308s US.Steel_X1935 0 0 308s US.Steel_X1936 0 0 308s US.Steel_X1937 0 0 308s US.Steel_X1938 0 0 308s US.Steel_X1939 0 0 308s US.Steel_X1940 0 0 308s US.Steel_X1941 0 0 308s US.Steel_X1942 0 0 308s US.Steel_X1943 0 0 308s US.Steel_X1944 0 0 308s US.Steel_X1945 0 0 308s US.Steel_X1946 0 0 308s US.Steel_X1947 0 0 308s US.Steel_X1948 0 0 308s US.Steel_X1949 0 0 308s US.Steel_X1950 0 0 308s US.Steel_X1951 0 0 308s US.Steel_X1952 0 0 308s US.Steel_X1953 0 0 308s US.Steel_X1954 0 0 308s Westinghouse_X1935 0 0 308s Westinghouse_X1936 0 0 308s Westinghouse_X1937 0 0 308s Westinghouse_X1938 0 0 308s Westinghouse_X1939 0 0 308s Westinghouse_X1940 0 0 308s Westinghouse_X1941 0 0 308s Westinghouse_X1942 0 0 308s Westinghouse_X1943 0 0 308s Westinghouse_X1944 0 0 308s Westinghouse_X1945 0 0 308s Westinghouse_X1946 0 0 308s Westinghouse_X1947 0 0 308s Westinghouse_X1948 0 0 308s Westinghouse_X1949 0 0 308s Westinghouse_X1950 0 0 308s Westinghouse_X1951 0 0 308s Westinghouse_X1952 0 0 308s Westinghouse_X1953 0 0 308s Westinghouse_X1954 0 0 308s US.Steel_(Intercept) US.Steel_value US.Steel_capital 308s Chrysler_X1935 0.00 0 0 308s Chrysler_X1936 0.00 0 0 308s Chrysler_X1937 0.00 0 0 308s Chrysler_X1938 0.00 0 0 308s Chrysler_X1939 0.00 0 0 308s Chrysler_X1940 0.00 0 0 308s Chrysler_X1941 0.00 0 0 308s Chrysler_X1942 0.00 0 0 308s Chrysler_X1943 0.00 0 0 308s Chrysler_X1944 0.00 0 0 308s Chrysler_X1945 0.00 0 0 308s Chrysler_X1946 0.00 0 0 308s Chrysler_X1947 0.00 0 0 308s Chrysler_X1948 0.00 0 0 308s Chrysler_X1949 0.00 0 0 308s Chrysler_X1950 0.00 0 0 308s Chrysler_X1951 0.00 0 0 308s Chrysler_X1952 0.00 0 0 308s Chrysler_X1953 0.00 0 0 308s Chrysler_X1954 0.00 0 0 308s General.Electric_X1935 0.00 0 0 308s General.Electric_X1936 0.00 0 0 308s General.Electric_X1937 0.00 0 0 308s General.Electric_X1938 0.00 0 0 308s General.Electric_X1939 0.00 0 0 308s General.Electric_X1940 0.00 0 0 308s General.Electric_X1941 0.00 0 0 308s General.Electric_X1942 0.00 0 0 308s General.Electric_X1943 0.00 0 0 308s General.Electric_X1944 0.00 0 0 308s General.Electric_X1945 0.00 0 0 308s General.Electric_X1946 0.00 0 0 308s General.Electric_X1947 0.00 0 0 308s General.Electric_X1948 0.00 0 0 308s General.Electric_X1949 0.00 0 0 308s General.Electric_X1950 0.00 0 0 308s General.Electric_X1951 0.00 0 0 308s General.Electric_X1952 0.00 0 0 308s General.Electric_X1953 0.00 0 0 308s General.Electric_X1954 0.00 0 0 308s General.Motors_X1935 0.00 0 0 308s General.Motors_X1936 0.00 0 0 308s General.Motors_X1937 0.00 0 0 308s General.Motors_X1938 0.00 0 0 308s General.Motors_X1939 0.00 0 0 308s General.Motors_X1940 0.00 0 0 308s General.Motors_X1941 0.00 0 0 308s General.Motors_X1942 0.00 0 0 308s General.Motors_X1943 0.00 0 0 308s General.Motors_X1944 0.00 0 0 308s General.Motors_X1945 0.00 0 0 308s General.Motors_X1946 0.00 0 0 308s General.Motors_X1947 0.00 0 0 308s General.Motors_X1948 0.00 0 0 308s General.Motors_X1949 0.00 0 0 308s General.Motors_X1950 0.00 0 0 308s General.Motors_X1951 0.00 0 0 308s General.Motors_X1952 0.00 0 0 308s General.Motors_X1953 0.00 0 0 308s General.Motors_X1954 0.00 0 0 308s US.Steel_X1935 4.15 5657 223 308s US.Steel_X1936 81.32 146961 4107 308s US.Steel_X1937 31.18 83446 3682 308s US.Steel_X1938 -99.75 -179733 -25954 308s US.Steel_X1939 -178.23 -348850 -55733 308s US.Steel_X1940 -160.69 -353980 -40847 308s US.Steel_X1941 19.65 46784 5137 308s US.Steel_X1942 9.82 21296 2933 308s US.Steel_X1943 -46.76 -92829 -14113 308s US.Steel_X1944 -83.74 -151889 -23371 308s US.Steel_X1945 -91.24 -168815 -19507 308s US.Steel_X1946 28.34 58590 6591 308s US.Steel_X1947 57.32 102983 15178 308s US.Steel_X1948 140.23 227988 43037 308s US.Steel_X1949 25.65 42751 9004 308s US.Steel_X1950 34.88 58503 12479 308s US.Steel_X1951 115.10 263510 39374 308s US.Steel_X1952 149.19 322157 66269 308s US.Steel_X1953 89.00 180793 55503 308s US.Steel_X1954 -125.42 -265326 -83994 308s Westinghouse_X1935 0.00 0 0 308s Westinghouse_X1936 0.00 0 0 308s Westinghouse_X1937 0.00 0 0 308s Westinghouse_X1938 0.00 0 0 308s Westinghouse_X1939 0.00 0 0 308s Westinghouse_X1940 0.00 0 0 308s Westinghouse_X1941 0.00 0 0 308s Westinghouse_X1942 0.00 0 0 308s Westinghouse_X1943 0.00 0 0 308s Westinghouse_X1944 0.00 0 0 308s Westinghouse_X1945 0.00 0 0 308s Westinghouse_X1946 0.00 0 0 308s Westinghouse_X1947 0.00 0 0 308s Westinghouse_X1948 0.00 0 0 308s Westinghouse_X1949 0.00 0 0 308s Westinghouse_X1950 0.00 0 0 308s Westinghouse_X1951 0.00 0 0 308s Westinghouse_X1952 0.00 0 0 308s Westinghouse_X1953 0.00 0 0 308s Westinghouse_X1954 0.00 0 0 308s Westinghouse_(Intercept) Westinghouse_value 308s Chrysler_X1935 0.000 0 308s Chrysler_X1936 0.000 0 308s Chrysler_X1937 0.000 0 308s Chrysler_X1938 0.000 0 308s Chrysler_X1939 0.000 0 308s Chrysler_X1940 0.000 0 308s Chrysler_X1941 0.000 0 308s Chrysler_X1942 0.000 0 308s Chrysler_X1943 0.000 0 308s Chrysler_X1944 0.000 0 308s Chrysler_X1945 0.000 0 308s Chrysler_X1946 0.000 0 308s Chrysler_X1947 0.000 0 308s Chrysler_X1948 0.000 0 308s Chrysler_X1949 0.000 0 308s Chrysler_X1950 0.000 0 308s Chrysler_X1951 0.000 0 308s Chrysler_X1952 0.000 0 308s Chrysler_X1953 0.000 0 308s Chrysler_X1954 0.000 0 308s General.Electric_X1935 0.000 0 308s General.Electric_X1936 0.000 0 308s General.Electric_X1937 0.000 0 308s General.Electric_X1938 0.000 0 308s General.Electric_X1939 0.000 0 308s General.Electric_X1940 0.000 0 308s General.Electric_X1941 0.000 0 308s General.Electric_X1942 0.000 0 308s General.Electric_X1943 0.000 0 308s General.Electric_X1944 0.000 0 308s General.Electric_X1945 0.000 0 308s General.Electric_X1946 0.000 0 308s General.Electric_X1947 0.000 0 308s General.Electric_X1948 0.000 0 308s General.Electric_X1949 0.000 0 308s General.Electric_X1950 0.000 0 308s General.Electric_X1951 0.000 0 308s General.Electric_X1952 0.000 0 308s General.Electric_X1953 0.000 0 308s General.Electric_X1954 0.000 0 308s General.Motors_X1935 0.000 0 308s General.Motors_X1936 0.000 0 308s General.Motors_X1937 0.000 0 308s General.Motors_X1938 0.000 0 308s General.Motors_X1939 0.000 0 308s General.Motors_X1940 0.000 0 308s General.Motors_X1941 0.000 0 308s General.Motors_X1942 0.000 0 308s General.Motors_X1943 0.000 0 308s General.Motors_X1944 0.000 0 308s General.Motors_X1945 0.000 0 308s General.Motors_X1946 0.000 0 308s General.Motors_X1947 0.000 0 308s General.Motors_X1948 0.000 0 308s General.Motors_X1949 0.000 0 308s General.Motors_X1950 0.000 0 308s General.Motors_X1951 0.000 0 308s General.Motors_X1952 0.000 0 308s General.Motors_X1953 0.000 0 308s General.Motors_X1954 0.000 0 308s US.Steel_X1935 0.000 0 308s US.Steel_X1936 0.000 0 308s US.Steel_X1937 0.000 0 308s US.Steel_X1938 0.000 0 308s US.Steel_X1939 0.000 0 308s US.Steel_X1940 0.000 0 308s US.Steel_X1941 0.000 0 308s US.Steel_X1942 0.000 0 308s US.Steel_X1943 0.000 0 308s US.Steel_X1944 0.000 0 308s US.Steel_X1945 0.000 0 308s US.Steel_X1946 0.000 0 308s US.Steel_X1947 0.000 0 308s US.Steel_X1948 0.000 0 308s US.Steel_X1949 0.000 0 308s US.Steel_X1950 0.000 0 308s US.Steel_X1951 0.000 0 308s US.Steel_X1952 0.000 0 308s US.Steel_X1953 0.000 0 308s US.Steel_X1954 0.000 0 308s Westinghouse_X1935 3.144 602 308s Westinghouse_X1936 -0.958 -494 308s Westinghouse_X1937 -3.684 -2686 308s Westinghouse_X1938 -7.915 -4436 308s Westinghouse_X1939 -10.322 -5366 308s Westinghouse_X1940 -6.613 -4156 308s Westinghouse_X1941 17.265 9273 308s Westinghouse_X1942 8.547 4797 308s Westinghouse_X1943 -2.916 -1800 308s Westinghouse_X1944 -3.257 -2041 308s Westinghouse_X1945 -7.753 -5715 308s Westinghouse_X1946 5.796 4408 308s Westinghouse_X1947 15.050 8750 308s Westinghouse_X1948 2.969 1967 308s Westinghouse_X1949 -11.433 -6675 308s Westinghouse_X1950 -13.481 -8563 308s Westinghouse_X1951 4.619 3344 308s Westinghouse_X1952 13.138 11353 308s Westinghouse_X1953 11.308 13496 308s Westinghouse_X1954 -13.505 -16056 308s Westinghouse_capital 308s Chrysler_X1935 0.000 308s Chrysler_X1936 0.000 308s Chrysler_X1937 0.000 308s Chrysler_X1938 0.000 308s Chrysler_X1939 0.000 308s Chrysler_X1940 0.000 308s Chrysler_X1941 0.000 308s Chrysler_X1942 0.000 308s Chrysler_X1943 0.000 308s Chrysler_X1944 0.000 308s Chrysler_X1945 0.000 308s Chrysler_X1946 0.000 308s Chrysler_X1947 0.000 308s Chrysler_X1948 0.000 308s Chrysler_X1949 0.000 308s Chrysler_X1950 0.000 308s Chrysler_X1951 0.000 308s Chrysler_X1952 0.000 308s Chrysler_X1953 0.000 308s Chrysler_X1954 0.000 308s General.Electric_X1935 0.000 308s General.Electric_X1936 0.000 308s General.Electric_X1937 0.000 308s General.Electric_X1938 0.000 308s General.Electric_X1939 0.000 308s General.Electric_X1940 0.000 308s General.Electric_X1941 0.000 308s General.Electric_X1942 0.000 308s General.Electric_X1943 0.000 308s General.Electric_X1944 0.000 308s General.Electric_X1945 0.000 308s General.Electric_X1946 0.000 308s General.Electric_X1947 0.000 308s General.Electric_X1948 0.000 308s General.Electric_X1949 0.000 308s General.Electric_X1950 0.000 308s General.Electric_X1951 0.000 308s General.Electric_X1952 0.000 308s General.Electric_X1953 0.000 308s General.Electric_X1954 0.000 308s General.Motors_X1935 0.000 308s General.Motors_X1936 0.000 308s General.Motors_X1937 0.000 308s General.Motors_X1938 0.000 308s General.Motors_X1939 0.000 308s General.Motors_X1940 0.000 308s General.Motors_X1941 0.000 308s General.Motors_X1942 0.000 308s General.Motors_X1943 0.000 308s General.Motors_X1944 0.000 308s General.Motors_X1945 0.000 308s General.Motors_X1946 0.000 308s General.Motors_X1947 0.000 308s General.Motors_X1948 0.000 308s General.Motors_X1949 0.000 308s General.Motors_X1950 0.000 308s General.Motors_X1951 0.000 308s General.Motors_X1952 0.000 308s General.Motors_X1953 0.000 308s General.Motors_X1954 0.000 308s US.Steel_X1935 0.000 308s US.Steel_X1936 0.000 308s US.Steel_X1937 0.000 308s US.Steel_X1938 0.000 308s US.Steel_X1939 0.000 308s US.Steel_X1940 0.000 308s US.Steel_X1941 0.000 308s US.Steel_X1942 0.000 308s US.Steel_X1943 0.000 308s US.Steel_X1944 0.000 308s US.Steel_X1945 0.000 308s US.Steel_X1946 0.000 308s US.Steel_X1947 0.000 308s US.Steel_X1948 0.000 308s US.Steel_X1949 0.000 308s US.Steel_X1950 0.000 308s US.Steel_X1951 0.000 308s US.Steel_X1952 0.000 308s US.Steel_X1953 0.000 308s US.Steel_X1954 0.000 308s Westinghouse_X1935 5.659 308s Westinghouse_X1936 -0.766 308s Westinghouse_X1937 -27.263 308s Westinghouse_X1938 -143.262 308s Westinghouse_X1939 -242.563 308s Westinghouse_X1940 -175.254 308s Westinghouse_X1941 624.987 308s Westinghouse_X1942 519.651 308s Westinghouse_X1943 -246.108 308s Westinghouse_X1944 -297.023 308s Westinghouse_X1945 -716.333 308s Westinghouse_X1946 498.495 308s Westinghouse_X1947 1672.098 308s Westinghouse_X1948 387.794 308s Westinghouse_X1949 -1621.262 308s Westinghouse_X1950 -1842.843 308s Westinghouse_X1951 599.149 308s Westinghouse_X1952 1911.642 308s Westinghouse_X1953 1976.568 308s Westinghouse_X1954 -2883.365 308s Chrysler_(Intercept) Chrysler_value 308s 0 0 308s Chrysler_capital General.Electric_(Intercept) 308s 0 0 308s General.Electric_value General.Electric_capital 308s 0 0 308s General.Motors_(Intercept) General.Motors_value 308s 0 0 308s General.Motors_capital US.Steel_(Intercept) 308s 0 0 308s US.Steel_value US.Steel_capital 308s 0 0 308s Westinghouse_(Intercept) Westinghouse_value 308s 0 0 308s Westinghouse_capital 308s 0 308s [1] "Error in estfun.systemfit(greeneOlsPooled) : \n returning the estimation function for models with restrictions has not yet been implemented.\n" 308s attr(,"class") 308s [1] "try-error" 308s attr(,"condition") 308s 308s Error in estfun.systemfit(greeneOlsPooled) : 308s returning the estimation function for models with restrictions has not yet been implemented. 308s Chrysler_(Intercept) Chrysler_value Chrysler_capital 308s Chrysler_X1935 0.061827 25.813 0.64918 308s Chrysler_X1936 0.089260 74.782 0.91045 308s Chrysler_X1937 -0.052866 -46.729 -1.83447 308s Chrysler_X1938 0.038353 16.795 1.98668 308s Chrysler_X1939 -0.125156 -85.069 -8.04755 308s Chrysler_X1940 -0.019863 -14.456 -1.33281 308s Chrysler_X1941 -0.000958 -0.617 -0.07206 308s Chrysler_X1942 -0.035485 -14.581 -2.53362 308s Chrysler_X1943 -0.121241 -71.338 -8.13529 308s Chrysler_X1944 -0.067270 -46.981 -4.06984 308s Chrysler_X1945 0.103440 87.551 5.64781 308s Chrysler_X1946 -0.121081 -108.222 -10.26763 308s Chrysler_X1947 -0.065512 -37.931 -6.34155 308s Chrysler_X1948 0.053900 37.439 5.93977 308s Chrysler_X1949 -0.066320 -39.149 -9.77563 308s Chrysler_X1950 0.012935 8.971 2.11101 308s Chrysler_X1951 0.338038 273.472 68.79064 308s Chrysler_X1952 0.035175 25.572 10.22178 308s Chrysler_X1953 -0.016558 -16.583 -5.73086 308s Chrysler_X1954 -0.040615 -28.561 -16.85128 308s General.Electric_X1935 -0.000794 -0.332 -0.00834 308s General.Electric_X1936 -0.018766 -15.722 -0.19142 308s General.Electric_X1937 -0.017841 -15.770 -0.61909 308s General.Electric_X1938 -0.025844 -11.317 -1.33872 308s General.Electric_X1939 -0.031739 -21.573 -2.04083 308s General.Electric_X1940 -0.006211 -4.520 -0.41674 308s General.Electric_X1941 0.033478 21.546 2.51754 308s General.Electric_X1942 0.015339 6.303 1.09520 308s General.Electric_X1943 -0.020477 -12.049 -1.37400 308s General.Electric_X1944 -0.022551 -15.749 -1.36432 308s General.Electric_X1945 -0.000552 -0.467 -0.03015 308s General.Electric_X1946 0.048030 42.930 4.07298 308s General.Electric_X1947 0.042267 24.472 4.09142 308s General.Electric_X1948 0.033204 23.064 3.65913 308s General.Electric_X1949 -0.011862 -7.002 -1.74842 308s General.Electric_X1950 -0.025261 -17.518 -4.12252 308s General.Electric_X1951 0.001752 1.417 0.35646 308s General.Electric_X1952 0.006337 4.607 1.84166 308s General.Electric_X1953 0.007751 7.762 2.68249 308s General.Electric_X1954 -0.006261 -4.402 -2.59748 308s General.Motors_X1935 0.015266 6.374 0.16030 308s General.Motors_X1936 -0.003913 -3.278 -0.03991 308s General.Motors_X1937 -0.019260 -17.024 -0.66833 308s General.Motors_X1938 0.000502 0.220 0.02603 308s General.Motors_X1939 -0.014763 -10.035 -0.94928 308s General.Motors_X1940 -0.002163 -1.575 -0.14517 308s General.Motors_X1941 0.004002 2.576 0.30095 308s General.Motors_X1942 0.014599 5.999 1.04234 308s General.Motors_X1943 0.010244 6.027 0.68736 308s General.Motors_X1944 0.014852 10.373 0.89857 308s General.Motors_X1945 0.005493 4.649 0.29991 308s General.Motors_X1946 0.014990 13.398 1.27114 308s General.Motors_X1947 0.002105 1.219 0.20375 308s General.Motors_X1948 -0.007587 -5.270 -0.83607 308s General.Motors_X1949 -0.016803 -9.919 -2.47682 308s General.Motors_X1950 -0.009602 -6.659 -1.56700 308s General.Motors_X1951 -0.017864 -14.452 -3.63526 308s General.Motors_X1952 -0.012355 -8.982 -3.59050 308s General.Motors_X1953 0.004869 4.876 1.68503 308s General.Motors_X1954 0.017389 12.228 7.21481 308s US.Steel_X1935 0.013928 5.815 0.14625 308s US.Steel_X1936 -0.026161 -21.918 -0.26684 308s US.Steel_X1937 -0.025907 -22.899 -0.89897 308s US.Steel_X1938 0.043429 19.017 2.24961 308s US.Steel_X1939 0.070526 47.937 4.53484 308s US.Steel_X1940 0.058816 42.806 3.94653 308s US.Steel_X1941 -0.016278 -10.476 -1.22408 308s US.Steel_X1942 -0.008142 -3.346 -0.58136 308s US.Steel_X1943 0.018146 10.677 1.21761 308s US.Steel_X1944 0.036672 25.612 2.21866 308s US.Steel_X1945 0.039460 33.399 2.15450 308s US.Steel_X1946 -0.012632 -11.291 -1.07122 308s US.Steel_X1947 -0.018481 -10.700 -1.78894 308s US.Steel_X1948 -0.047880 -33.258 -5.27643 308s US.Steel_X1949 -0.003976 -2.347 -0.58605 308s US.Steel_X1950 -0.007908 -5.484 -1.29060 308s US.Steel_X1951 -0.052722 -42.652 -10.72894 308s US.Steel_X1952 -0.064309 -46.753 -18.68822 308s US.Steel_X1953 -0.039465 -39.524 -13.65875 308s US.Steel_X1954 0.042884 30.156 17.79265 308s Westinghouse_X1935 -0.000639 -0.267 -0.00671 308s Westinghouse_X1936 0.003489 2.923 0.03559 308s Westinghouse_X1937 0.005946 5.256 0.20632 308s Westinghouse_X1938 0.008196 3.589 0.42458 308s Westinghouse_X1939 0.009675 6.576 0.62207 308s Westinghouse_X1940 0.007107 5.172 0.47686 308s Westinghouse_X1941 -0.011506 -7.406 -0.86528 308s Westinghouse_X1942 -0.005817 -2.390 -0.41532 308s Westinghouse_X1943 0.002074 1.221 0.13919 308s Westinghouse_X1944 0.002100 1.466 0.12704 308s Westinghouse_X1945 0.005777 4.890 0.31543 308s Westinghouse_X1946 -0.004096 -3.661 -0.34734 308s Westinghouse_X1947 -0.012571 -7.279 -1.21688 308s Westinghouse_X1948 -0.003981 -2.765 -0.43871 308s Westinghouse_X1949 0.006180 3.648 0.91087 308s Westinghouse_X1950 0.008074 5.599 1.31765 308s Westinghouse_X1951 -0.004997 -4.043 -1.01696 308s Westinghouse_X1952 -0.011575 -8.415 -3.36372 308s Westinghouse_X1953 -0.010300 -10.316 -3.56494 308s Westinghouse_X1954 0.006866 4.828 2.84858 308s General.Electric_(Intercept) General.Electric_value 308s Chrysler_X1935 0.006590 7.715 308s Chrysler_X1936 0.009515 19.180 308s Chrysler_X1937 -0.005635 -15.797 308s Chrysler_X1938 0.004088 8.339 308s Chrysler_X1939 -0.013341 -30.100 308s Chrysler_X1940 -0.002117 -4.514 308s Chrysler_X1941 -0.000102 -0.187 308s Chrysler_X1942 -0.003782 -6.007 308s Chrysler_X1943 -0.012924 -22.609 308s Chrysler_X1944 -0.007171 -12.098 308s Chrysler_X1945 0.011026 22.137 308s Chrysler_X1946 -0.012907 -28.501 308s Chrysler_X1947 -0.006983 -11.569 308s Chrysler_X1948 0.005745 9.218 308s Chrysler_X1949 -0.007069 -10.122 308s Chrysler_X1950 0.001379 2.221 308s Chrysler_X1951 0.036033 65.558 308s Chrysler_X1952 0.003749 7.798 308s Chrysler_X1953 -0.001765 -4.186 308s Chrysler_X1954 -0.004329 -11.949 308s General.Electric_X1935 -0.003192 -3.736 308s General.Electric_X1936 -0.075425 -152.042 308s General.Electric_X1937 -0.071707 -201.016 308s General.Electric_X1938 -0.103871 -211.866 308s General.Electric_X1939 -0.127565 -287.812 308s General.Electric_X1940 -0.024962 -53.224 308s General.Electric_X1941 0.134553 246.784 308s General.Electric_X1942 0.061649 97.899 308s General.Electric_X1943 -0.082300 -143.975 308s General.Electric_X1944 -0.090635 -152.920 308s General.Electric_X1945 -0.002219 -4.456 308s General.Electric_X1946 0.193042 426.295 308s General.Electric_X1947 0.169877 281.435 308s General.Electric_X1948 0.133454 214.114 308s General.Electric_X1949 -0.047674 -68.260 308s General.Electric_X1950 -0.101526 -163.508 308s General.Electric_X1951 0.007040 12.809 308s General.Electric_X1952 0.025471 52.972 308s General.Electric_X1953 0.031151 73.878 308s General.Electric_X1954 -0.025162 -69.445 308s General.Motors_X1935 -0.016212 -18.978 308s General.Motors_X1936 0.004155 8.376 308s General.Motors_X1937 0.020453 57.337 308s General.Motors_X1938 -0.000534 -1.088 308s General.Motors_X1939 0.015678 35.372 308s General.Motors_X1940 0.002297 4.899 308s General.Motors_X1941 -0.004250 -7.795 308s General.Motors_X1942 -0.015503 -24.619 308s General.Motors_X1943 -0.010878 -19.031 308s General.Motors_X1944 -0.015772 -26.611 308s General.Motors_X1945 -0.005833 -11.711 308s General.Motors_X1946 -0.015918 -35.152 308s General.Motors_X1947 -0.002235 -3.703 308s General.Motors_X1948 0.008057 12.926 308s General.Motors_X1949 0.017844 25.549 308s General.Motors_X1950 0.010196 16.421 308s General.Motors_X1951 0.018970 34.514 308s General.Motors_X1952 0.013121 27.287 308s General.Motors_X1953 -0.005170 -12.262 308s General.Motors_X1954 -0.018466 -50.965 308s US.Steel_X1935 0.000660 0.772 308s US.Steel_X1936 -0.001239 -2.497 308s US.Steel_X1937 -0.001227 -3.439 308s US.Steel_X1938 0.002057 4.195 308s US.Steel_X1939 0.003340 7.535 308s US.Steel_X1940 0.002785 5.939 308s US.Steel_X1941 -0.000771 -1.414 308s US.Steel_X1942 -0.000386 -0.612 308s US.Steel_X1943 0.000859 1.503 308s US.Steel_X1944 0.001737 2.930 308s US.Steel_X1945 0.001869 3.752 308s US.Steel_X1946 -0.000598 -1.321 308s US.Steel_X1947 -0.000875 -1.450 308s US.Steel_X1948 -0.002267 -3.638 308s US.Steel_X1949 -0.000188 -0.270 308s US.Steel_X1950 -0.000374 -0.603 308s US.Steel_X1951 -0.002497 -4.542 308s US.Steel_X1952 -0.003045 -6.333 308s US.Steel_X1953 -0.001869 -4.432 308s US.Steel_X1954 0.002031 5.605 308s Westinghouse_X1935 -0.005793 -6.781 308s Westinghouse_X1936 0.031644 63.787 308s Westinghouse_X1937 0.053929 151.178 308s Westinghouse_X1938 0.074341 151.634 308s Westinghouse_X1939 0.087747 197.975 308s Westinghouse_X1940 0.064457 137.434 308s Westinghouse_X1941 -0.104362 -191.410 308s Westinghouse_X1942 -0.052757 -83.779 308s Westinghouse_X1943 0.018814 32.913 308s Westinghouse_X1944 0.019045 32.133 308s Westinghouse_X1945 0.052397 105.198 308s Westinghouse_X1946 -0.037151 -82.040 308s Westinghouse_X1947 -0.114019 -188.895 308s Westinghouse_X1948 -0.036108 -57.931 308s Westinghouse_X1949 0.056048 80.250 308s Westinghouse_X1950 0.073229 117.935 308s Westinghouse_X1951 -0.045325 -82.465 308s Westinghouse_X1952 -0.104985 -218.337 308s Westinghouse_X1953 -0.093423 -221.562 308s Westinghouse_X1954 0.062271 171.863 308s General.Electric_capital General.Motors_(Intercept) 308s Chrysler_X1935 0.6445 1.06e-03 308s Chrysler_X1936 0.9933 1.53e-03 308s Chrysler_X1937 -0.6650 -9.08e-04 308s Chrysler_X1938 0.6386 6.59e-04 308s Chrysler_X1939 -2.3026 -2.15e-03 308s Chrysler_X1940 -0.3951 -3.41e-04 308s Chrysler_X1941 -0.0226 -1.65e-05 308s Chrysler_X1942 -1.0886 -6.10e-04 308s Chrysler_X1943 -4.1343 -2.08e-03 308s Chrysler_X1944 -2.3039 -1.16e-03 308s Chrysler_X1945 3.5239 1.78e-03 308s Chrysler_X1946 -4.4657 -2.08e-03 308s Chrysler_X1947 -3.1871 -1.13e-03 308s Chrysler_X1948 3.1221 9.26e-04 308s Chrysler_X1949 -4.3710 -1.14e-03 308s Chrysler_X1950 0.8926 2.22e-04 308s Chrysler_X1951 24.1889 5.81e-03 308s Chrysler_X1952 2.7225 6.04e-04 308s Chrysler_X1953 -1.4126 -2.84e-04 308s Chrysler_X1954 -3.8484 -6.98e-04 308s General.Electric_X1935 -0.3121 1.36e-04 308s General.Electric_X1936 -7.8744 3.21e-03 308s General.Electric_X1937 -8.4614 3.05e-03 308s General.Electric_X1938 -16.2246 4.42e-03 308s General.Electric_X1939 -22.0177 5.43e-03 308s General.Electric_X1940 -4.6579 1.06e-03 308s General.Electric_X1941 29.7228 -5.73e-03 308s General.Electric_X1942 17.7427 -2.63e-03 308s General.Electric_X1943 -26.3277 3.50e-03 308s General.Electric_X1944 -29.1212 3.86e-03 308s General.Electric_X1945 -0.7094 9.45e-05 308s General.Electric_X1946 66.7926 -8.22e-03 308s General.Electric_X1947 77.5319 -7.23e-03 308s General.Electric_X1948 72.5190 -5.68e-03 308s General.Electric_X1949 -29.4770 2.03e-03 308s General.Electric_X1950 -65.7280 4.32e-03 308s General.Electric_X1951 4.7261 -3.00e-04 308s General.Electric_X1952 18.4946 -1.08e-03 308s General.Electric_X1953 24.9302 -1.33e-03 308s General.Electric_X1954 -22.3665 1.07e-03 308s General.Motors_X1935 -1.5855 2.13e-02 308s General.Motors_X1936 0.4338 -5.46e-03 308s General.Motors_X1937 2.4135 -2.69e-02 308s General.Motors_X1938 -0.0833 7.00e-04 308s General.Motors_X1939 2.7060 -2.06e-02 308s General.Motors_X1940 0.4287 -3.02e-03 308s General.Motors_X1941 -0.9388 5.58e-03 308s General.Motors_X1942 -4.4617 2.04e-02 308s General.Motors_X1943 -3.4800 1.43e-02 308s General.Motors_X1944 -5.0677 2.07e-02 308s General.Motors_X1945 -1.8642 7.66e-03 308s General.Motors_X1946 -5.5077 2.09e-02 308s General.Motors_X1947 -1.0202 2.93e-03 308s General.Motors_X1948 4.3781 -1.06e-02 308s General.Motors_X1949 11.0331 -2.34e-02 308s General.Motors_X1950 6.6012 -1.34e-02 308s General.Motors_X1951 12.7347 -2.49e-02 308s General.Motors_X1952 9.5270 -1.72e-02 308s General.Motors_X1953 -4.1377 6.79e-03 308s General.Motors_X1954 -16.4148 2.42e-02 308s US.Steel_X1935 0.0645 -3.30e-03 308s US.Steel_X1936 -0.1293 6.19e-03 308s US.Steel_X1937 -0.1448 6.13e-03 308s US.Steel_X1938 0.3212 -1.03e-02 308s US.Steel_X1939 0.5764 -1.67e-02 308s US.Steel_X1940 0.5197 -1.39e-02 308s US.Steel_X1941 -0.1703 3.85e-03 308s US.Steel_X1942 -0.1110 1.93e-03 308s US.Steel_X1943 0.2749 -4.29e-03 308s US.Steel_X1944 0.5580 -8.68e-03 308s US.Steel_X1945 0.5972 -9.34e-03 308s US.Steel_X1946 -0.2070 2.99e-03 308s US.Steel_X1947 -0.3994 4.37e-03 308s US.Steel_X1948 -1.2321 1.13e-02 308s US.Steel_X1949 -0.1164 9.41e-04 308s US.Steel_X1950 -0.2424 1.87e-03 308s US.Steel_X1951 -1.6760 1.25e-02 308s US.Steel_X1952 -2.2112 1.52e-02 308s US.Steel_X1953 -1.4956 9.34e-03 308s US.Steel_X1954 1.8051 -1.01e-02 308s Westinghouse_X1935 -0.5665 -4.91e-04 308s Westinghouse_X1936 3.3036 2.68e-03 308s Westinghouse_X1937 6.3636 4.57e-03 308s Westinghouse_X1938 11.6121 6.30e-03 308s Westinghouse_X1939 15.1452 7.44e-03 308s Westinghouse_X1940 12.0276 5.46e-03 308s Westinghouse_X1941 -23.0535 -8.84e-03 308s Westinghouse_X1942 -15.1836 -4.47e-03 308s Westinghouse_X1943 6.0186 1.59e-03 308s Westinghouse_X1944 6.1191 1.61e-03 308s Westinghouse_X1945 16.7462 4.44e-03 308s Westinghouse_X1946 -12.8541 -3.15e-03 308s Westinghouse_X1947 -52.0382 -9.66e-03 308s Westinghouse_X1948 -19.6209 -3.06e-03 308s Westinghouse_X1949 34.6547 4.75e-03 308s Westinghouse_X1950 47.4084 6.21e-03 308s Westinghouse_X1951 -30.4270 -3.84e-03 308s Westinghouse_X1952 -76.2296 -8.90e-03 308s Westinghouse_X1953 -74.7663 -7.92e-03 308s Westinghouse_X1954 55.3529 5.28e-03 308s General.Motors_value General.Motors_capital 308s Chrysler_X1935 3.2697 2.97e-03 308s Chrysler_X1936 7.1482 8.07e-02 308s Chrysler_X1937 -4.8925 -1.42e-01 308s Chrysler_X1938 1.8397 1.38e-01 308s Chrysler_X1939 -9.2736 -4.37e-01 308s Chrysler_X1940 -1.5846 -7.07e-02 308s Chrysler_X1941 -0.0749 -4.20e-03 308s Chrysler_X1942 -1.9776 -1.85e-01 308s Chrysler_X1943 -8.4430 -5.50e-01 308s Chrysler_X1944 -5.0608 -2.33e-01 308s Chrysler_X1945 8.6022 4.71e-01 308s Chrysler_X1946 -10.1940 -8.37e-01 308s Chrysler_X1947 -3.9688 -8.57e-01 308s Chrysler_X1948 3.0137 8.54e-01 308s Chrysler_X1949 -4.2157 -1.16e+00 308s Chrysler_X1950 0.8345 2.44e-01 308s Chrysler_X1951 28.0658 7.01e+00 308s Chrysler_X1952 2.9759 8.64e-01 308s Chrysler_X1953 -1.7755 -5.06e-01 308s Chrysler_X1954 -3.9028 -1.55e+00 308s General.Electric_X1935 0.4184 3.81e-04 308s General.Electric_X1936 14.9723 1.69e-01 308s General.Electric_X1937 16.4491 4.79e-01 308s General.Electric_X1938 12.3500 9.25e-01 308s General.Electric_X1939 23.4292 1.10e+00 308s General.Electric_X1940 4.9361 2.20e-01 308s General.Electric_X1941 -26.0763 -1.46e+00 308s General.Electric_X1942 -8.5163 -7.97e-01 308s General.Electric_X1943 14.2062 9.26e-01 308s General.Electric_X1944 16.9016 7.78e-01 308s General.Electric_X1945 0.4575 2.50e-02 308s General.Electric_X1946 -40.2860 -3.31e+00 308s General.Electric_X1947 -25.5097 -5.51e+00 308s General.Electric_X1948 -18.4956 -5.24e+00 308s General.Electric_X1949 7.5116 2.07e+00 308s General.Electric_X1950 16.2362 4.75e+00 308s General.Electric_X1951 -1.4489 -3.62e-01 308s General.Electric_X1952 -5.3416 -1.55e+00 308s General.Electric_X1953 -8.2795 -2.36e+00 308s General.Electric_X1954 5.9933 2.39e+00 308s General.Motors_X1935 65.5183 5.96e-02 308s General.Motors_X1936 -25.4300 -2.87e-01 308s General.Motors_X1937 -144.6452 -4.21e+00 308s General.Motors_X1938 1.9558 1.47e-01 308s General.Motors_X1939 -88.7707 -4.19e+00 308s General.Motors_X1940 -14.0060 -6.25e-01 308s General.Motors_X1941 25.3914 1.42e+00 308s General.Motors_X1942 66.0227 6.18e+00 308s General.Motors_X1943 57.8898 3.77e+00 308s General.Motors_X1944 90.6754 4.17e+00 308s General.Motors_X1945 37.0686 2.03e+00 308s General.Motors_X1946 102.4144 8.40e+00 308s General.Motors_X1947 10.3479 2.23e+00 308s General.Motors_X1948 -34.4239 -9.76e+00 308s General.Motors_X1949 -86.6782 -2.39e+01 308s General.Motors_X1950 -50.2708 -1.47e+01 308s General.Motors_X1951 -120.3581 -3.01e+01 308s General.Motors_X1952 -84.8289 -2.46e+01 308s General.Motors_X1953 42.3640 1.21e+01 308s General.Motors_X1954 135.6002 5.40e+01 308s US.Steel_X1935 -10.1444 -9.23e-03 308s US.Steel_X1936 28.8526 3.26e-01 308s US.Steel_X1937 33.0183 9.62e-01 308s US.Steel_X1938 -28.6886 -2.15e+00 308s US.Steel_X1939 -71.9676 -3.39e+00 308s US.Steel_X1940 -64.6193 -2.88e+00 308s US.Steel_X1941 17.5269 9.83e-01 308s US.Steel_X1942 6.2492 5.85e-01 308s US.Steel_X1943 -17.4030 -1.13e+00 308s US.Steel_X1944 -37.9949 -1.75e+00 308s US.Steel_X1945 -45.1924 -2.47e+00 308s US.Steel_X1946 14.6469 1.20e+00 308s US.Steel_X1947 15.4188 3.33e+00 308s US.Steel_X1948 36.8685 1.04e+01 308s US.Steel_X1949 3.4806 9.60e-01 308s US.Steel_X1950 7.0265 2.06e+00 308s US.Steel_X1951 60.2830 1.51e+01 308s US.Steel_X1952 74.9299 2.18e+01 308s US.Steel_X1953 58.2771 1.66e+01 308s US.Steel_X1954 -56.7511 -2.26e+01 308s Westinghouse_X1935 -1.5111 -1.37e-03 308s Westinghouse_X1936 12.4999 1.41e-01 308s Westinghouse_X1937 24.6178 7.17e-01 308s Westinghouse_X1938 17.5894 1.32e+00 308s Westinghouse_X1939 32.0707 1.51e+00 308s Westinghouse_X1940 25.3645 1.13e+00 308s Westinghouse_X1941 -40.2479 -2.26e+00 308s Westinghouse_X1942 -14.5028 -1.36e+00 308s Westinghouse_X1943 6.4627 4.21e-01 308s Westinghouse_X1944 7.0674 3.25e-01 308s Westinghouse_X1945 21.4937 1.18e+00 308s Westinghouse_X1946 -15.4283 -1.27e+00 308s Westinghouse_X1947 -34.0718 -7.36e+00 308s Westinghouse_X1948 -9.9583 -2.82e+00 308s Westinghouse_X1949 17.5737 4.84e+00 308s Westinghouse_X1950 23.3044 6.82e+00 308s Westinghouse_X1951 -18.5624 -4.64e+00 308s Westinghouse_X1952 -43.8127 -1.27e+01 308s Westinghouse_X1953 -49.4119 -1.41e+01 308s Westinghouse_X1954 29.5158 1.17e+01 308s US.Steel_(Intercept) US.Steel_value US.Steel_capital 308s Chrysler_X1935 -2.96e-03 -4.0379 -0.15945 308s Chrysler_X1936 -4.28e-03 -7.7323 -0.21608 308s Chrysler_X1937 2.53e-03 6.7824 0.29930 308s Chrysler_X1938 -1.84e-03 -3.3128 -0.47838 308s Chrysler_X1939 6.00e-03 11.7430 1.87608 308s Chrysler_X1940 9.52e-04 2.0975 0.24204 308s Chrysler_X1941 4.59e-05 0.1094 0.01201 308s Chrysler_X1942 1.70e-03 3.6889 0.50810 308s Chrysler_X1943 5.81e-03 11.5373 1.75404 308s Chrysler_X1944 3.22e-03 5.8493 0.90002 308s Chrysler_X1945 -4.96e-03 -9.1744 -1.06014 308s Chrysler_X1946 5.80e-03 12.0014 1.35006 308s Chrysler_X1947 3.14e-03 5.6424 0.83159 308s Chrysler_X1948 -2.58e-03 -4.2007 -0.79297 308s Chrysler_X1949 3.18e-03 5.2997 1.11622 308s Chrysler_X1950 -6.20e-04 -1.0401 -0.22186 308s Chrysler_X1951 -1.62e-02 -37.1002 -5.54355 308s Chrysler_X1952 -1.69e-03 -3.6411 -0.74900 308s Chrysler_X1953 7.94e-04 1.6124 0.49499 308s Chrysler_X1954 1.95e-03 4.1188 1.30389 308s General.Electric_X1935 1.69e-05 0.0230 0.00091 308s General.Electric_X1936 4.00e-04 0.7222 0.02018 308s General.Electric_X1937 3.80e-04 1.0168 0.04487 308s General.Electric_X1938 5.50e-04 0.9917 0.14321 308s General.Electric_X1939 6.76e-04 1.3230 0.21136 308s General.Electric_X1940 1.32e-04 0.2914 0.03362 308s General.Electric_X1941 -7.13e-04 -1.6972 -0.18636 308s General.Electric_X1942 -3.27e-04 -0.7084 -0.09757 308s General.Electric_X1943 4.36e-04 0.8656 0.13161 308s General.Electric_X1944 4.80e-04 0.8711 0.13403 308s General.Electric_X1945 1.18e-05 0.0218 0.00251 308s General.Electric_X1946 -1.02e-03 -2.1149 -0.23791 308s General.Electric_X1947 -9.00e-04 -1.6172 -0.23835 308s General.Electric_X1948 -7.07e-04 -1.1496 -0.21701 308s General.Electric_X1949 2.53e-04 0.4211 0.08869 308s General.Electric_X1950 5.38e-04 0.9023 0.19248 308s General.Electric_X1951 -3.73e-05 -0.0854 -0.01276 308s General.Electric_X1952 -1.35e-04 -0.2914 -0.05995 308s General.Electric_X1953 -1.65e-04 -0.3353 -0.10293 308s General.Electric_X1954 1.33e-04 0.2820 0.08929 308s General.Motors_X1935 1.01e-02 13.7309 0.54222 308s General.Motors_X1936 -2.58e-03 -4.6683 -0.13046 308s General.Motors_X1937 -1.27e-02 -34.0295 -1.50166 308s General.Motors_X1938 3.32e-04 0.5977 0.08631 308s General.Motors_X1939 -9.75e-03 -19.0765 -3.04769 308s General.Motors_X1940 -1.43e-03 -3.1463 -0.36306 308s General.Motors_X1941 2.64e-03 6.2893 0.69062 308s General.Motors_X1942 9.64e-03 20.9002 2.87877 308s General.Motors_X1943 6.76e-03 13.4247 2.04099 308s General.Motors_X1944 9.81e-03 17.7857 2.73663 308s General.Motors_X1945 3.63e-03 6.7092 0.77528 308s General.Motors_X1946 9.90e-03 20.4619 2.30180 308s General.Motors_X1947 1.39e-03 2.4966 0.36796 308s General.Motors_X1948 -5.01e-03 -8.1431 -1.53716 308s General.Motors_X1949 -1.11e-02 -18.4924 -3.89482 308s General.Motors_X1950 -6.34e-03 -10.6327 -2.26803 308s General.Motors_X1951 -1.18e-02 -27.0005 -4.03445 308s General.Motors_X1952 -8.16e-03 -17.6138 -3.62324 308s General.Motors_X1953 3.21e-03 6.5289 2.00435 308s General.Motors_X1954 1.15e-02 24.2859 7.68815 308s US.Steel_X1935 -8.99e-03 -12.2508 -0.48377 308s US.Steel_X1936 1.69e-02 30.5206 0.85291 308s US.Steel_X1937 1.67e-02 44.7615 1.97524 308s US.Steel_X1938 -2.80e-02 -50.5201 -7.29526 308s US.Steel_X1939 -4.55e-02 -89.1179 -14.23756 308s US.Steel_X1940 -3.80e-02 -83.6458 -9.65217 308s US.Steel_X1941 1.05e-02 25.0160 2.74698 308s US.Steel_X1942 5.26e-03 11.3993 1.57013 308s US.Steel_X1943 -1.17e-02 -23.2554 -3.53559 308s US.Steel_X1944 -2.37e-02 -42.9442 -6.60771 308s US.Steel_X1945 -2.55e-02 -47.1333 -5.44650 308s US.Steel_X1946 8.16e-03 16.8627 1.89692 308s US.Steel_X1947 1.19e-02 21.4365 3.15933 308s US.Steel_X1948 3.09e-02 50.2553 9.48663 308s US.Steel_X1949 2.57e-03 4.2789 0.90121 308s US.Steel_X1950 5.11e-03 8.5638 1.82670 308s US.Steel_X1951 3.40e-02 77.9272 11.64398 308s US.Steel_X1952 4.15e-02 89.6523 18.44196 308s US.Steel_X1953 2.55e-02 51.7535 15.88809 308s US.Steel_X1954 -2.77e-02 -58.5688 -18.54102 308s Westinghouse_X1935 -1.36e-03 -1.8578 -0.07336 308s Westinghouse_X1936 7.45e-03 13.4613 0.37618 308s Westinghouse_X1937 1.27e-02 33.9762 1.49930 308s Westinghouse_X1938 1.75e-02 31.5341 4.55362 308s Westinghouse_X1939 2.07e-02 40.4306 6.45923 308s Westinghouse_X1940 1.52e-02 33.4258 3.85712 308s Westinghouse_X1941 -2.46e-02 -58.4830 -6.42196 308s Westinghouse_X1942 -1.24e-02 -26.9329 -3.70970 308s Westinghouse_X1943 4.43e-03 8.7920 1.33667 308s Westinghouse_X1944 4.48e-03 8.1323 1.25129 308s Westinghouse_X1945 1.23e-02 22.8217 2.63717 308s Westinghouse_X1946 -8.75e-03 -18.0831 -2.03421 308s Westinghouse_X1947 -2.68e-02 -48.2250 -7.10746 308s Westinghouse_X1948 -8.50e-03 -13.8193 -2.60865 308s Westinghouse_X1949 1.32e-02 21.9947 4.63248 308s Westinghouse_X1950 1.72e-02 28.9161 6.16798 308s Westinghouse_X1951 -1.07e-02 -24.4289 -3.65019 308s Westinghouse_X1952 -2.47e-02 -53.3679 -10.97807 308s Westinghouse_X1953 -2.20e-02 -44.6732 -13.71448 308s Westinghouse_X1954 1.47e-02 31.0114 9.81721 308s Westinghouse_(Intercept) Westinghouse_value 308s Chrysler_X1935 -5.65e-03 -1.082 308s Chrysler_X1936 -8.16e-03 -4.208 308s Chrysler_X1937 4.83e-03 3.521 308s Chrysler_X1938 -3.50e-03 -1.964 308s Chrysler_X1939 1.14e-02 5.945 308s Chrysler_X1940 1.81e-03 1.141 308s Chrysler_X1941 8.76e-05 0.047 308s Chrysler_X1942 3.24e-03 1.819 308s Chrysler_X1943 1.11e-02 6.837 308s Chrysler_X1944 6.15e-03 3.852 308s Chrysler_X1945 -9.45e-03 -6.967 308s Chrysler_X1946 1.11e-02 8.413 308s Chrysler_X1947 5.99e-03 3.480 308s Chrysler_X1948 -4.92e-03 -3.262 308s Chrysler_X1949 6.06e-03 3.537 308s Chrysler_X1950 -1.18e-03 -0.751 308s Chrysler_X1951 -3.09e-02 -22.354 308s Chrysler_X1952 -3.21e-03 -2.777 308s Chrysler_X1953 1.51e-03 1.806 308s Chrysler_X1954 3.71e-03 4.412 308s General.Electric_X1935 6.17e-03 1.182 308s General.Electric_X1936 1.46e-01 75.280 308s General.Electric_X1937 1.39e-01 101.111 308s General.Electric_X1938 2.01e-01 112.591 308s General.Electric_X1939 2.47e-01 128.281 308s General.Electric_X1940 4.83e-02 30.346 308s General.Electric_X1941 -2.60e-01 -139.785 308s General.Electric_X1942 -1.19e-01 -66.920 308s General.Electric_X1943 1.59e-01 98.251 308s General.Electric_X1944 1.75e-01 109.867 308s General.Electric_X1945 4.29e-03 3.165 308s General.Electric_X1946 -3.73e-01 -283.963 308s General.Electric_X1947 -3.29e-01 -191.038 308s General.Electric_X1948 -2.58e-01 -170.961 308s General.Electric_X1949 9.22e-02 53.834 308s General.Electric_X1950 1.96e-01 124.738 308s General.Electric_X1951 -1.36e-02 -9.856 308s General.Electric_X1952 -4.93e-02 -42.572 308s General.Electric_X1953 -6.03e-02 -71.913 308s General.Electric_X1954 4.87e-02 57.863 308s General.Motors_X1935 -6.24e-02 -11.950 308s General.Motors_X1936 1.60e-02 8.253 308s General.Motors_X1937 7.87e-02 57.392 308s General.Motors_X1938 -2.05e-03 -1.151 308s General.Motors_X1939 6.03e-02 31.373 308s General.Motors_X1940 8.84e-03 5.558 308s General.Motors_X1941 -1.64e-02 -8.786 308s General.Motors_X1942 -5.97e-02 -33.488 308s General.Motors_X1943 -4.19e-02 -25.843 308s General.Motors_X1944 -6.07e-02 -38.047 308s General.Motors_X1945 -2.25e-02 -16.552 308s General.Motors_X1946 -6.13e-02 -46.597 308s General.Motors_X1947 -8.60e-03 -5.002 308s General.Motors_X1948 3.10e-02 20.539 308s General.Motors_X1949 6.87e-02 40.098 308s General.Motors_X1950 3.92e-02 24.930 308s General.Motors_X1951 7.30e-02 52.851 308s General.Motors_X1952 5.05e-02 43.640 308s General.Motors_X1953 -1.99e-02 -23.751 308s General.Motors_X1954 -7.11e-02 -84.506 308s US.Steel_X1935 5.67e-02 10.854 308s US.Steel_X1936 -1.06e-01 -54.933 308s US.Steel_X1937 -1.05e-01 -76.855 308s US.Steel_X1938 1.77e-01 99.039 308s US.Steel_X1939 2.87e-01 149.211 308s US.Steel_X1940 2.39e-01 150.428 308s US.Steel_X1941 -6.62e-02 -35.578 308s US.Steel_X1942 -3.31e-02 -18.595 308s US.Steel_X1943 7.38e-02 45.577 308s US.Steel_X1944 1.49e-01 93.525 308s US.Steel_X1945 1.61e-01 118.378 308s US.Steel_X1946 -5.14e-02 -39.094 308s US.Steel_X1947 -7.52e-02 -43.725 308s US.Steel_X1948 -1.95e-01 -129.046 308s US.Steel_X1949 -1.62e-02 -9.446 308s US.Steel_X1950 -3.22e-02 -20.441 308s US.Steel_X1951 -2.15e-01 -155.289 308s US.Steel_X1952 -2.62e-01 -226.135 308s US.Steel_X1953 -1.61e-01 -191.674 308s US.Steel_X1954 1.75e-01 207.479 308s Westinghouse_X1935 3.03e-02 5.802 308s Westinghouse_X1936 -1.66e-01 -85.410 308s Westinghouse_X1937 -2.82e-01 -205.647 308s Westinghouse_X1938 -3.89e-01 -217.923 308s Westinghouse_X1939 -4.59e-01 -238.632 308s Westinghouse_X1940 -3.37e-01 -211.909 308s Westinghouse_X1941 5.46e-01 293.206 308s Westinghouse_X1942 2.76e-01 154.873 308s Westinghouse_X1943 -9.84e-02 -60.742 308s Westinghouse_X1944 -9.96e-02 -62.433 308s Westinghouse_X1945 -2.74e-01 -202.055 308s Westinghouse_X1946 1.94e-01 147.788 308s Westinghouse_X1947 5.96e-01 346.758 308s Westinghouse_X1948 1.89e-01 125.092 308s Westinghouse_X1949 -2.93e-01 -171.160 308s Westinghouse_X1950 -3.83e-01 -243.315 308s Westinghouse_X1951 2.37e-01 171.608 308s Westinghouse_X1952 5.49e-01 474.533 308s Westinghouse_X1953 4.89e-01 583.245 308s Westinghouse_X1954 -3.26e-01 -387.265 308s Westinghouse_capital 308s Chrysler_X1935 -0.01017 308s Chrysler_X1936 -0.00652 308s Chrysler_X1937 0.03574 308s Chrysler_X1938 -0.06342 308s Chrysler_X1939 0.26872 308s Chrysler_X1940 0.04809 308s Chrysler_X1941 0.00317 308s Chrysler_X1942 0.19712 308s Chrysler_X1943 0.93491 308s Chrysler_X1944 0.56053 308s Chrysler_X1945 -0.87325 308s Chrysler_X1946 0.95137 308s Chrysler_X1947 0.66499 308s Chrysler_X1948 -0.64315 308s Chrysler_X1949 0.85922 308s Chrysler_X1950 -0.16155 308s Chrysler_X1951 -4.00575 308s Chrysler_X1952 -0.46760 308s Chrysler_X1953 0.26445 308s Chrysler_X1954 0.79226 308s General.Electric_X1935 0.01111 308s General.Electric_X1936 0.11671 308s General.Electric_X1937 1.02637 308s General.Electric_X1938 3.63650 308s General.Electric_X1939 5.79842 308s General.Electric_X1940 1.27948 308s General.Electric_X1941 -9.42136 308s General.Electric_X1942 -7.25009 308s General.Electric_X1943 13.43544 308s General.Electric_X1944 15.98834 308s General.Electric_X1945 0.39668 308s General.Electric_X1946 -32.11157 308s General.Electric_X1947 -36.50562 308s General.Electric_X1948 -33.71211 308s General.Electric_X1949 13.07588 308s General.Electric_X1950 26.84462 308s General.Electric_X1951 -1.76618 308s General.Electric_X1952 -7.16842 308s General.Electric_X1953 -10.53235 308s General.Electric_X1954 10.39092 308s General.Motors_X1935 -0.11232 308s General.Motors_X1936 0.01280 308s General.Motors_X1937 0.58258 308s General.Motors_X1938 -0.03717 308s General.Motors_X1939 1.41811 308s General.Motors_X1940 0.23434 308s General.Motors_X1941 -0.59216 308s General.Motors_X1942 -3.62807 308s General.Motors_X1943 -3.53399 308s General.Motors_X1944 -5.53672 308s General.Motors_X1945 -2.07456 308s General.Motors_X1946 -5.26934 308s General.Motors_X1947 -0.95586 308s General.Motors_X1948 4.05009 308s General.Motors_X1949 9.73943 308s General.Motors_X1950 5.36510 308s General.Motors_X1951 9.47046 308s General.Motors_X1952 7.34822 308s General.Motors_X1953 -3.47863 308s General.Motors_X1954 -15.17538 308s US.Steel_X1935 0.10202 308s US.Steel_X1936 -0.08517 308s US.Steel_X1937 -0.78015 308s US.Steel_X1938 3.19880 308s US.Steel_X1939 6.74450 308s US.Steel_X1940 6.34264 308s US.Steel_X1941 -2.39791 308s US.Steel_X1942 -2.01455 308s US.Steel_X1943 6.23245 308s US.Steel_X1944 13.61008 308s US.Steel_X1945 14.83734 308s US.Steel_X1946 -4.42093 308s US.Steel_X1947 -8.35538 308s US.Steel_X1948 -25.44676 308s US.Steel_X1949 -2.29427 308s US.Steel_X1950 -4.39917 308s US.Steel_X1951 -27.82678 308s US.Steel_X1952 -38.07729 308s US.Steel_X1953 -28.07253 308s US.Steel_X1954 37.25854 308s Westinghouse_X1935 0.05454 308s Westinghouse_X1936 -0.13242 308s Westinghouse_X1937 -2.08750 308s Westinghouse_X1938 -7.03855 308s Westinghouse_X1939 -10.78640 308s Westinghouse_X1940 -8.93489 308s Westinghouse_X1941 19.76178 308s Westinghouse_X1942 16.77886 308s Westinghouse_X1943 -8.30621 308s Westinghouse_X1944 -9.08553 308s Westinghouse_X1945 -25.32546 308s Westinghouse_X1946 16.71244 308s Westinghouse_X1947 66.26222 308s Westinghouse_X1948 24.66709 308s Westinghouse_X1949 -41.57334 308s Westinghouse_X1950 -52.36326 308s Westinghouse_X1951 30.75091 308s Westinghouse_X1952 79.90351 308s Westinghouse_X1953 85.42211 308s Westinghouse_X1954 -69.54427 308s Chrysler_(Intercept) Chrysler_value 308s 0 0 308s Chrysler_capital General.Electric_(Intercept) 308s 0 0 308s General.Electric_value General.Electric_capital 308s 0 0 308s General.Motors_(Intercept) General.Motors_value 308s 0 0 308s General.Motors_capital US.Steel_(Intercept) 308s 0 0 308s US.Steel_value US.Steel_capital 308s 0 0 308s Westinghouse_(Intercept) Westinghouse_value 308s 0 0 308s Westinghouse_capital 308s 0 308s [1] "Error in estfun.systemfit(greeneSurPooled) : \n returning the estimation function for models with restrictions has not yet been implemented.\n" 308s attr(,"class") 308s [1] "try-error" 308s attr(,"condition") 308s 308s > 308s > ## **************** bread ************************ 308s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 308s + print( bread( theilOls ) ) 308s + 308s + print( bread( theilSur ) ) 308s + 308s + print( bread( greeneOls ) ) 308s + 308s + print( try( bread( greeneOlsPooled ) ) ) 308s + 308s + print( bread( greeneSur ) ) 308s + 308s + print( try( bread( greeneSurPooled ) ) ) 308s + } 308s General.Electric_(Intercept) 308s General.Electric_(Intercept) 50.64496 308s General.Electric_value -0.02323 308s General.Electric_capital -0.00888 308s Westinghouse_(Intercept) 0.00000 308s Westinghouse_value 0.00000 308s Westinghouse_capital 0.00000 308s General.Electric_value General.Electric_capital 308s General.Electric_(Intercept) -2.32e-02 -8.88e-03 308s General.Electric_value 1.25e-05 -2.43e-06 308s General.Electric_capital -2.43e-06 3.40e-05 308s Westinghouse_(Intercept) 0.00e+00 0.00e+00 308s Westinghouse_value 0.00e+00 0.00e+00 308s Westinghouse_capital 0.00e+00 0.00e+00 308s Westinghouse_(Intercept) Westinghouse_value 308s General.Electric_(Intercept) 0.0000 0.00e+00 308s General.Electric_value 0.0000 0.00e+00 308s General.Electric_capital 0.0000 0.00e+00 308s Westinghouse_(Intercept) 24.6366 -4.20e-02 308s Westinghouse_value -0.0420 9.46e-05 308s Westinghouse_capital 0.0648 -2.51e-04 308s Westinghouse_capital 308s General.Electric_(Intercept) 0.000000 308s General.Electric_value 0.000000 308s General.Electric_capital 0.000000 308s Westinghouse_(Intercept) 0.064774 308s Westinghouse_value -0.000251 308s Westinghouse_capital 0.001207 308s General.Electric_(Intercept) General.Electric_value 308s [1,] 29230.95 -13.17064 308s [2,] -13.17 0.00707 308s [3,] -5.85 -0.00136 308s [4,] 5078.50 -2.10754 308s [5,] -9.05 0.00480 308s [6,] 15.70 -0.01299 308s General.Electric_capital Westinghouse_(Intercept) Westinghouse_value 308s [1,] -5.849668 5078.50 -9.047719 308s [2,] -0.001362 -2.11 0.004800 308s [3,] 0.021226 -1.58 -0.000675 308s [4,] -1.584851 1935.63 -3.200900 308s [5,] -0.000675 -3.20 0.007194 308s [6,] 0.023793 4.54 -0.018984 308s Westinghouse_capital 308s [1,] 15.7006 308s [2,] -0.0130 308s [3,] 0.0238 308s [4,] 4.5447 308s [5,] -0.0190 308s [6,] 0.0957 308s Chrysler_(Intercept) Chrysler_value 308s Chrysler_(Intercept) 103.4623 -0.144448 308s Chrysler_value -0.1444 0.000226 308s Chrysler_capital 0.0138 -0.000102 308s General.Electric_(Intercept) 0.0000 0.000000 308s General.Electric_value 0.0000 0.000000 308s General.Electric_capital 0.0000 0.000000 308s General.Motors_(Intercept) 0.0000 0.000000 308s General.Motors_value 0.0000 0.000000 308s General.Motors_capital 0.0000 0.000000 308s US.Steel_(Intercept) 0.0000 0.000000 308s US.Steel_value 0.0000 0.000000 308s US.Steel_capital 0.0000 0.000000 308s Westinghouse_(Intercept) 0.0000 0.000000 308s Westinghouse_value 0.0000 0.000000 308s Westinghouse_capital 0.0000 0.000000 308s Chrysler_capital General.Electric_(Intercept) 308s Chrysler_(Intercept) 0.013776 0.0000 308s Chrysler_value -0.000102 0.0000 308s Chrysler_capital 0.000471 0.0000 308s General.Electric_(Intercept) 0.000000 126.6124 308s General.Electric_value 0.000000 -0.0581 308s General.Electric_capital 0.000000 -0.0222 308s General.Motors_(Intercept) 0.000000 0.0000 308s General.Motors_value 0.000000 0.0000 308s General.Motors_capital 0.000000 0.0000 308s US.Steel_(Intercept) 0.000000 0.0000 308s US.Steel_value 0.000000 0.0000 308s US.Steel_capital 0.000000 0.0000 308s Westinghouse_(Intercept) 0.000000 0.0000 308s Westinghouse_value 0.000000 0.0000 308s Westinghouse_capital 0.000000 0.0000 308s General.Electric_value General.Electric_capital 308s Chrysler_(Intercept) 0.00e+00 0.00e+00 308s Chrysler_value 0.00e+00 0.00e+00 308s Chrysler_capital 0.00e+00 0.00e+00 308s General.Electric_(Intercept) -5.81e-02 -2.22e-02 308s General.Electric_value 3.12e-05 -6.09e-06 308s General.Electric_capital -6.09e-06 8.50e-05 308s General.Motors_(Intercept) 0.00e+00 0.00e+00 308s General.Motors_value 0.00e+00 0.00e+00 308s General.Motors_capital 0.00e+00 0.00e+00 308s US.Steel_(Intercept) 0.00e+00 0.00e+00 308s US.Steel_value 0.00e+00 0.00e+00 308s US.Steel_capital 0.00e+00 0.00e+00 308s Westinghouse_(Intercept) 0.00e+00 0.00e+00 308s Westinghouse_value 0.00e+00 0.00e+00 308s Westinghouse_capital 0.00e+00 0.00e+00 308s General.Motors_(Intercept) General.Motors_value 308s Chrysler_(Intercept) 0.0000 0.00e+00 308s Chrysler_value 0.0000 0.00e+00 308s Chrysler_capital 0.0000 0.00e+00 308s General.Electric_(Intercept) 0.0000 0.00e+00 308s General.Electric_value 0.0000 0.00e+00 308s General.Electric_capital 0.0000 0.00e+00 308s General.Motors_(Intercept) 132.9858 -3.11e-02 308s General.Motors_value -0.0311 7.92e-06 308s General.Motors_capital 0.0108 -4.93e-06 308s US.Steel_(Intercept) 0.0000 0.00e+00 308s US.Steel_value 0.0000 0.00e+00 308s US.Steel_capital 0.0000 0.00e+00 308s Westinghouse_(Intercept) 0.0000 0.00e+00 308s Westinghouse_value 0.0000 0.00e+00 308s Westinghouse_capital 0.0000 0.00e+00 308s General.Motors_capital US.Steel_(Intercept) 308s Chrysler_(Intercept) 0.00e+00 0.0000 308s Chrysler_value 0.00e+00 0.0000 308s Chrysler_capital 0.00e+00 0.0000 308s General.Electric_(Intercept) 0.00e+00 0.0000 308s General.Electric_value 0.00e+00 0.0000 308s General.Electric_capital 0.00e+00 0.0000 308s General.Motors_(Intercept) 1.08e-02 0.0000 308s General.Motors_value -4.93e-06 0.0000 308s General.Motors_capital 1.63e-05 0.0000 308s US.Steel_(Intercept) 0.00e+00 235.6498 308s US.Steel_value 0.00e+00 -0.1119 308s US.Steel_capital 0.00e+00 -0.0336 308s Westinghouse_(Intercept) 0.00e+00 0.0000 308s Westinghouse_value 0.00e+00 0.0000 308s Westinghouse_capital 0.00e+00 0.0000 308s US.Steel_value US.Steel_capital 308s Chrysler_(Intercept) 0.00e+00 0.00e+00 308s Chrysler_value 0.00e+00 0.00e+00 308s Chrysler_capital 0.00e+00 0.00e+00 308s General.Electric_(Intercept) 0.00e+00 0.00e+00 308s General.Electric_value 0.00e+00 0.00e+00 308s General.Electric_capital 0.00e+00 0.00e+00 308s General.Motors_(Intercept) 0.00e+00 0.00e+00 308s General.Motors_value 0.00e+00 0.00e+00 308s General.Motors_capital 0.00e+00 0.00e+00 308s US.Steel_(Intercept) -1.12e-01 -3.36e-02 308s US.Steel_value 5.95e-05 -1.79e-05 308s US.Steel_capital -1.79e-05 2.30e-04 308s Westinghouse_(Intercept) 0.00e+00 0.00e+00 308s Westinghouse_value 0.00e+00 0.00e+00 308s Westinghouse_capital 0.00e+00 0.00e+00 308s Westinghouse_(Intercept) Westinghouse_value 308s Chrysler_(Intercept) 0.000 0.000000 308s Chrysler_value 0.000 0.000000 308s Chrysler_capital 0.000 0.000000 308s General.Electric_(Intercept) 0.000 0.000000 308s General.Electric_value 0.000 0.000000 308s General.Electric_capital 0.000 0.000000 308s General.Motors_(Intercept) 0.000 0.000000 308s General.Motors_value 0.000 0.000000 308s General.Motors_capital 0.000 0.000000 308s US.Steel_(Intercept) 0.000 0.000000 308s US.Steel_value 0.000 0.000000 308s US.Steel_capital 0.000 0.000000 308s Westinghouse_(Intercept) 61.592 -0.105021 308s Westinghouse_value -0.105 0.000237 308s Westinghouse_capital 0.162 -0.000626 308s Westinghouse_capital 308s Chrysler_(Intercept) 0.000000 308s Chrysler_value 0.000000 308s Chrysler_capital 0.000000 308s General.Electric_(Intercept) 0.000000 308s General.Electric_value 0.000000 308s General.Electric_capital 0.000000 308s General.Motors_(Intercept) 0.000000 308s General.Motors_value 0.000000 308s General.Motors_capital 0.000000 308s US.Steel_(Intercept) 0.000000 308s US.Steel_value 0.000000 308s US.Steel_capital 0.000000 308s Westinghouse_(Intercept) 0.161935 308s Westinghouse_value -0.000626 308s Westinghouse_capital 0.003017 308s [1] "Error in bread.systemfit(greeneOlsPooled) : \n returning the 'bread' for models with restrictions has not yet been implemented.\n" 308s attr(,"class") 308s [1] "try-error" 308s attr(,"condition") 308s 308s Error in estfun.systemfit(greeneSurPooled) : 308s returning the estimation function for models with restrictions has not yet been implemented. 308s Error in bread.systemfit(greeneOlsPooled) : 308s returning the 'bread' for models with restrictions has not yet been implemented. 308s Chrysler_(Intercept) Chrysler_value Chrysler_capital 308s [1,] 1.33e+04 -1.82e+01 9.57e-01 308s [2,] -1.82e+01 2.86e-02 -1.31e-02 308s [3,] 9.57e-01 -1.31e-02 6.69e-02 308s [4,] -2.94e+03 3.74e+00 1.98e+00 308s [5,] 1.28e+00 -1.86e-03 1.28e-04 308s [6,] 8.80e-01 -2.96e-04 -5.56e-03 308s [7,] -1.56e+04 1.91e+01 7.79e+00 308s [8,] 3.28e+00 -4.91e-03 1.03e-03 308s [9,] -8.18e-02 3.42e-03 -1.89e-02 308s [10,] 1.80e+04 -1.87e+01 -2.45e+01 308s [11,] -7.46e+00 1.13e-02 -3.26e-03 308s [12,] -4.03e+00 -1.22e-02 1.03e-01 308s [13,] -3.04e+01 3.03e-01 -9.35e-01 308s [14,] 1.14e-01 -3.70e-04 1.18e-03 308s [15,] 2.42e-01 -6.41e-04 1.67e-03 308s General.Electric_(Intercept) General.Electric_value 308s [1,] -2936.42 1.28e+00 308s [2,] 3.74 -1.86e-03 308s [3,] 1.98 1.28e-04 308s [4,] 65119.82 -2.85e+01 308s [5,] -28.51 1.50e-02 308s [6,] -16.15 -1.70e-03 308s [7,] 57134.02 -2.61e+01 308s [8,] -11.96 6.35e-03 308s [9,] -3.52 -2.27e-03 308s [10,] 64429.20 -3.04e+01 308s [11,] -22.01 1.35e-02 308s [12,] -55.05 1.23e-02 308s [13,] 10286.79 -4.02e+00 308s [14,] -17.00 8.74e-03 308s [15,] 23.38 -2.16e-02 308s General.Electric_capital General.Motors_(Intercept) General.Motors_value 308s [1,] 8.80e-01 -1.56e+04 3.28e+00 308s [2,] -2.96e-04 1.91e+01 -4.91e-03 308s [3,] -5.56e-03 7.79e+00 1.03e-03 308s [4,] -1.61e+01 5.71e+04 -1.20e+01 308s [5,] -1.70e-03 -2.61e+01 6.35e-03 308s [6,] 4.86e-02 -8.74e+00 -9.49e-04 308s [7,] -8.74e+00 8.00e+05 -1.84e+02 308s [8,] -9.49e-04 -1.84e+02 4.68e-02 308s [9,] 1.98e-02 5.32e+01 -2.83e-02 308s [10,] -2.30e+00 -1.75e+05 3.73e+01 308s [11,] -1.07e-02 8.02e+01 -2.06e-02 308s [12,] 7.77e-02 2.01e+01 1.09e-02 308s [13,] -4.02e+00 1.10e+04 -2.33e+00 308s [14,] 1.04e-04 -2.06e+01 5.10e-03 308s [15,] 4.61e-02 3.98e+01 -1.28e-02 308s General.Motors_capital US.Steel_(Intercept) US.Steel_value 308s [1,] -0.08183 1.80e+04 -7.46e+00 308s [2,] 0.00342 -1.87e+01 1.13e-02 308s [3,] -0.01889 -2.45e+01 -3.26e-03 308s [4,] -3.51957 6.44e+04 -2.20e+01 308s [5,] -0.00227 -3.04e+01 1.35e-02 308s [6,] 0.01982 -2.30e+00 -1.07e-02 308s [7,] 53.22544 -1.75e+05 8.02e+01 308s [8,] -0.02835 3.73e+01 -2.06e-02 308s [9,] 0.10737 3.74e+00 1.39e-02 308s [10,] 3.74276 1.25e+06 -5.65e+02 308s [11,] 0.01386 -5.65e+02 3.00e-01 308s [12,] -0.10360 -3.12e+02 -9.01e-02 308s [13,] -0.48733 2.74e+04 -8.35e+00 308s [14,] -0.00238 -5.09e+01 2.23e-02 308s [15,] 0.02432 1.10e+02 -7.74e-02 308s US.Steel_capital Westinghouse_(Intercept) Westinghouse_value 308s [1,] -4.0281 -30.387 1.14e-01 308s [2,] -0.0122 0.303 -3.70e-04 308s [3,] 0.1031 -0.935 1.18e-03 308s [4,] -55.0482 10286.790 -1.70e+01 308s [5,] 0.0123 -4.016 8.74e-03 308s [6,] 0.0777 -4.021 1.04e-04 308s [7,] 20.0945 11026.166 -2.06e+01 308s [8,] 0.0109 -2.326 5.10e-03 308s [9,] -0.1036 -0.487 -2.38e-03 308s [10,] -311.9830 27440.848 -5.09e+01 308s [11,] -0.0901 -8.348 2.23e-02 308s [12,] 1.6331 -27.510 2.29e-02 308s [13,] -27.5101 3917.263 -5.99e+00 308s [14,] 0.0229 -5.992 1.29e-02 308s [15,] 0.1422 6.376 -3.12e-02 308s Westinghouse_capital 308s [1,] 2.42e-01 308s [2,] -6.41e-04 308s [3,] 1.67e-03 308s [4,] 2.34e+01 308s [5,] -2.16e-02 308s [6,] 4.61e-02 308s [7,] 3.98e+01 308s [8,] -1.28e-02 308s [9,] 2.43e-02 308s [10,] 1.10e+02 308s [11,] -7.74e-02 308s [12,] 1.42e-01 308s [13,] 6.38e+00 308s [14,] -3.12e-02 308s [15,] 1.70e-01 308s [1] "Error in bread.systemfit(greeneSurPooled) : \n returning the 'bread' for models with restrictions has not yet been implemented.\n" 308s attr(,"class") 308s [1] "try-error" 308s attr(,"condition") 308s 308s > 308s Error in bread.systemfit(greeneSurPooled) : 308s returning the 'bread' for models with restrictions has not yet been implemented. 308s BEGIN TEST test_sur.R 308s 308s R version 4.3.3 (2024-02-29) -- "Angel Food Cake" 308s Copyright (C) 2024 The R Foundation for Statistical Computing 308s Platform: arm-unknown-linux-gnueabihf (32-bit) 308s 308s R is free software and comes with ABSOLUTELY NO WARRANTY. 308s You are welcome to redistribute it under certain conditions. 308s Type 'license()' or 'licence()' for distribution details. 308s 308s R is a collaborative project with many contributors. 308s Type 'contributors()' for more information and 308s 'citation()' on how to cite R or R packages in publications. 308s 308s Type 'demo()' for some demos, 'help()' for on-line help, or 308s 'help.start()' for an HTML browser interface to help. 308s Type 'q()' to quit R. 308s 308s > library( systemfit ) 308s Loading required package: Matrix 309s Loading required package: car 309s Loading required package: carData 309s Loading required package: lmtest 309s Loading required package: zoo 309s 309s Attaching package: ‘zoo’ 309s 309s The following objects are masked from ‘package:base’: 309s 309s as.Date, as.Date.numeric 309s 309s 309s Please cite the 'systemfit' package as: 309s Arne Henningsen and Jeff D. Hamann (2007). systemfit: A Package for Estimating Systems of Simultaneous Equations in R. Journal of Statistical Software 23(4), 1-40. http://www.jstatsoft.org/v23/i04/. 309s 309s If you have questions, suggestions, or comments regarding the 'systemfit' package, please use a forum or 'tracker' at systemfit's R-Forge site: 309s https://r-forge.r-project.org/projects/systemfit/ 309s > options( digits = 3 ) 309s > 309s > data( "Kmenta" ) 309s > useMatrix <- FALSE 309s > 309s > demand <- consump ~ price + income 309s > supply <- consump ~ price + farmPrice + trend 309s > system <- list( demand = demand, supply = supply ) 309s > restrm <- matrix(0,1,7) # restriction matrix "R" 309s > restrm[1,3] <- 1 309s > restrm[1,7] <- -1 309s > restrict <- "demand_income - supply_trend = 0" 309s > restr2m <- matrix(0,2,7) # restriction matrix "R" 2 309s > restr2m[1,3] <- 1 309s > restr2m[1,7] <- -1 309s > restr2m[2,2] <- -1 309s > restr2m[2,5] <- 1 309s > restr2q <- c( 0, 0.5 ) # restriction vector "q" 2 309s > restrict2 <- c( "demand_income - supply_trend = 0", 309s + "- demand_price + supply_price = 0.5" ) 309s > restrict2i <- c( "demand_income - supply_trend = 0", 309s + "- demand_price + supply_income = 0.5" ) 309s > tc <- matrix(0,7,6) 309s > tc[1,1] <- 1 309s > tc[2,2] <- 1 309s > tc[3,3] <- 1 309s > tc[4,4] <- 1 309s > tc[5,5] <- 1 309s > tc[6,6] <- 1 309s > tc[7,3] <- 1 309s > restr3m <- matrix(0,1,6) # restriction matrix "R" 2 309s > restr3m[1,2] <- -1 309s > restr3m[1,5] <- 1 309s > restr3q <- c( 0.5 ) # restriction vector "q" 2 309s > restrict3 <- "- C2 + C5 = 0.5" 309s > 309s > # the standard equations do not converge and lead to a singular weighting matrix 309s > # both in R and in EViews, since both equations have the same endogenous variable 309s > supply2 <- price ~ income + farmPrice + trend 309s > system2 <- list( demand = demand, supply = supply2 ) 309s > 309s > 309s > ## *************** SUR estimation ************************ 309s > fitsur1 <- systemfit( system, "SUR", data = Kmenta, useMatrix = useMatrix ) 309s > print( summary( fitsur1 ) ) 309s 309s systemfit results 309s method: SUR 309s 309s N DF SSR detRCov OLS-R2 McElroy-R2 309s system 40 33 170 0.879 0.683 0.789 309s 309s N DF SSR MSE RMSE R2 Adj R2 309s demand 20 17 65.7 3.86 1.97 0.755 0.726 309s supply 20 16 104.1 6.50 2.55 0.612 0.539 309s 309s The covariance matrix of the residuals used for estimation 309s demand supply 309s demand 3.73 4.14 309s supply 4.14 5.78 309s 309s The covariance matrix of the residuals 309s demand supply 309s demand 3.86 4.92 309s supply 4.92 6.50 309s 309s The correlations of the residuals 309s demand supply 309s demand 1.000 0.982 309s supply 0.982 1.000 309s 309s 309s SUR estimates for 'demand' (equation 1) 309s Model Formula: consump ~ price + income 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 99.3329 7.5145 13.22 2.3e-10 *** 309s price -0.2755 0.0885 -3.11 0.0063 ** 309s income 0.2986 0.0419 7.12 1.7e-06 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 1.966 on 17 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 17 309s SSR: 65.683 MSE: 3.864 Root MSE: 1.966 309s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 309s 309s 309s SUR estimates for 'supply' (equation 2) 309s Model Formula: consump ~ price + farmPrice + trend 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 61.9662 11.0808 5.59 4.0e-05 *** 309s price 0.1469 0.0944 1.56 0.13941 309s farmPrice 0.2140 0.0399 5.37 6.3e-05 *** 309s trend 0.3393 0.0679 5.00 0.00013 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 2.55 on 16 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 16 309s SSR: 104.058 MSE: 6.504 Root MSE: 2.55 309s Multiple R-Squared: 0.612 Adjusted R-Squared: 0.539 309s 309s > nobs( fitsur1 ) 309s [1] 40 309s > 309s > ## ********************* SUR (EViews-like) ***************** 309s > fitsur1e <- systemfit( system, "SUR", data = Kmenta, methodResidCov = "noDfCor", 309s + useMatrix = useMatrix ) 309s > print( summary( fitsur1e, useDfSys = TRUE ) ) 309s 309s systemfit results 309s method: SUR 309s 309s N DF SSR detRCov OLS-R2 McElroy-R2 309s system 40 33 170 0.598 0.683 0.748 309s 309s N DF SSR MSE RMSE R2 Adj R2 309s demand 20 17 66.2 3.89 1.97 0.753 0.724 309s supply 20 16 103.5 6.47 2.54 0.614 0.541 309s 309s The covariance matrix of the residuals used for estimation 309s demand supply 309s demand 3.17 3.41 309s supply 3.41 4.63 309s 309s The covariance matrix of the residuals 309s demand supply 309s demand 3.31 4.07 309s supply 4.07 5.18 309s 309s The correlations of the residuals 309s demand supply 309s demand 1.000 0.982 309s supply 0.982 1.000 309s 309s 309s SUR estimates for 'demand' (equation 1) 309s Model Formula: consump ~ price + income 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 99.2757 6.9280 14.33 8.9e-16 *** 309s price -0.2713 0.0816 -3.33 0.0022 ** 309s income 0.2949 0.0387 7.63 8.9e-09 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 1.973 on 17 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 17 309s SSR: 66.186 MSE: 3.893 Root MSE: 1.973 309s Multiple R-Squared: 0.753 Adjusted R-Squared: 0.724 309s 309s 309s SUR estimates for 'supply' (equation 2) 309s Model Formula: consump ~ price + farmPrice + trend 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 62.2942 9.9110 6.29 4.2e-07 *** 309s price 0.1461 0.0845 1.73 0.093 . 309s farmPrice 0.2121 0.0357 5.95 1.1e-06 *** 309s trend 0.3322 0.0607 5.47 4.6e-06 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 2.544 on 16 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 16 309s SSR: 103.55 MSE: 6.472 Root MSE: 2.544 309s Multiple R-Squared: 0.614 Adjusted R-Squared: 0.541 309s 309s > nobs( fitsur1e ) 309s [1] 40 309s > 309s > ## ********************* SUR (methodResidCov="Theil") ***************** 309s > fitsur1r2 <- systemfit( system, "SUR", data = Kmenta, methodResidCov = "Theil", 309s + useMatrix = useMatrix ) 309s > print( summary( fitsur1r2 ) ) 309s 309s systemfit results 309s method: SUR 309s 309s N DF SSR detRCov OLS-R2 McElroy-R2 309s system 40 33 172 -0.896 0.679 1.01 309s 309s N DF SSR MSE RMSE R2 Adj R2 309s demand 20 17 66.8 3.93 1.98 0.751 0.722 309s supply 20 16 105.3 6.58 2.57 0.607 0.534 309s 309s The covariance matrix of the residuals used for estimation 309s demand supply 309s demand 3.73 4.28 309s supply 4.28 5.78 309s 309s warning: this covariance matrix is NOT positive semidefinit! 309s 309s The covariance matrix of the residuals 309s demand supply 309s demand 3.93 5.17 309s supply 5.17 6.58 309s 309s The correlations of the residuals 309s demand supply 309s demand 1.000 0.984 309s supply 0.984 1.000 309s 309s 309s SUR estimates for 'demand' (equation 1) 309s Model Formula: consump ~ price + income 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 99.2120 7.5127 13.21 2.3e-10 *** 309s price -0.2667 0.0877 -3.04 0.0074 ** 309s income 0.2908 0.0406 7.16 1.6e-06 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 1.982 on 17 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 17 309s SSR: 66.802 MSE: 3.93 Root MSE: 1.982 309s Multiple R-Squared: 0.751 Adjusted R-Squared: 0.722 309s 309s 309s SUR estimates for 'supply' (equation 2) 309s Model Formula: consump ~ price + farmPrice + trend 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 63.0768 10.9735 5.75 3.0e-05 *** 309s price 0.1439 0.0943 1.52 0.15 309s farmPrice 0.2064 0.0384 5.37 6.2e-05 *** 309s trend 0.3325 0.0640 5.19 8.9e-05 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 2.566 on 16 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 16 309s SSR: 105.322 MSE: 6.583 Root MSE: 2.566 309s Multiple R-Squared: 0.607 Adjusted R-Squared: 0.534 309s 309s > 309s > ## *************** SUR (methodResidCov="Theil", useDfSys = TRUE ) *************** 309s > fitsur1e2 <- systemfit( system, "SUR", data = Kmenta, methodResidCov = "Theil", 309s + x = TRUE, useMatrix = useMatrix ) 309s > print( summary( fitsur1e2, useDfSys = TRUE ) ) 309s 309s systemfit results 309s method: SUR 309s 309s N DF SSR detRCov OLS-R2 McElroy-R2 309s system 40 33 172 -0.896 0.679 1.01 309s 309s N DF SSR MSE RMSE R2 Adj R2 309s demand 20 17 66.8 3.93 1.98 0.751 0.722 309s supply 20 16 105.3 6.58 2.57 0.607 0.534 309s 309s The covariance matrix of the residuals used for estimation 309s demand supply 309s demand 3.73 4.28 309s supply 4.28 5.78 309s 309s warning: this covariance matrix is NOT positive semidefinit! 309s 309s The covariance matrix of the residuals 309s demand supply 309s demand 3.93 5.17 309s supply 5.17 6.58 309s 309s The correlations of the residuals 309s demand supply 309s demand 1.000 0.984 309s supply 0.984 1.000 309s 309s 309s SUR estimates for 'demand' (equation 1) 309s Model Formula: consump ~ price + income 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 99.2120 7.5127 13.21 1.0e-14 *** 309s price -0.2667 0.0877 -3.04 0.0046 ** 309s income 0.2908 0.0406 7.16 3.3e-08 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 1.982 on 17 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 17 309s SSR: 66.802 MSE: 3.93 Root MSE: 1.982 309s Multiple R-Squared: 0.751 Adjusted R-Squared: 0.722 309s 309s 309s SUR estimates for 'supply' (equation 2) 309s Model Formula: consump ~ price + farmPrice + trend 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 63.0768 10.9735 5.75 2.0e-06 *** 309s price 0.1439 0.0943 1.52 0.14 309s farmPrice 0.2064 0.0384 5.37 6.1e-06 *** 309s trend 0.3325 0.0640 5.19 1.0e-05 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 2.566 on 16 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 16 309s SSR: 105.322 MSE: 6.583 Root MSE: 2.566 309s Multiple R-Squared: 0.607 Adjusted R-Squared: 0.534 309s 309s > 309s > ## ********************* SUR (methodResidCov="max") ***************** 309s > fitsur1r3 <- systemfit( system, "SUR", data = Kmenta, methodResidCov = "max", 309s + useMatrix = useMatrix ) 309s > print( summary( fitsur1r3 ) ) 309s 309s systemfit results 309s method: SUR 309s 309s N DF SSR detRCov OLS-R2 McElroy-R2 309s system 40 33 172 -0.735 0.68 0.957 309s 309s N DF SSR MSE RMSE R2 Adj R2 309s demand 20 17 66.7 3.92 1.98 0.751 0.722 309s supply 20 16 105.2 6.57 2.56 0.608 0.534 309s 309s The covariance matrix of the residuals used for estimation 309s demand supply 309s demand 3.73 4.26 309s supply 4.26 5.78 309s 309s warning: this covariance matrix is NOT positive semidefinit! 309s 309s The covariance matrix of the residuals 309s demand supply 309s demand 3.92 5.15 309s supply 5.15 6.57 309s 309s The correlations of the residuals 309s demand supply 309s demand 1.000 0.984 309s supply 0.984 1.000 309s 309s 309s SUR estimates for 'demand' (equation 1) 309s Model Formula: consump ~ price + income 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 99.2250 7.5129 13.21 2.3e-10 *** 309s price -0.2677 0.0878 -3.05 0.0073 ** 309s income 0.2916 0.0408 7.15 1.6e-06 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 1.98 on 17 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 17 309s SSR: 66.671 MSE: 3.922 Root MSE: 1.98 309s Multiple R-Squared: 0.751 Adjusted R-Squared: 0.722 309s 309s 309s SUR estimates for 'supply' (equation 2) 309s Model Formula: consump ~ price + farmPrice + trend 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 62.9575 10.9850 5.73 3.1e-05 *** 309s price 0.1442 0.0944 1.53 0.15 309s farmPrice 0.2072 0.0386 5.37 6.2e-05 *** 309s trend 0.3333 0.0644 5.18 9.2e-05 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 2.564 on 16 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 16 309s SSR: 105.187 MSE: 6.574 Root MSE: 2.564 309s Multiple R-Squared: 0.608 Adjusted R-Squared: 0.534 309s 309s > 309s > ## *************** WSUR estimation ************************ 309s > fitsur1w <- systemfit( system, "SUR", data = Kmenta, residCovWeighted = TRUE, 309s + useMatrix = useMatrix ) 309s > summary( fitsur1w ) 309s 309s systemfit results 309s method: SUR 309s 309s N DF SSR detRCov OLS-R2 McElroy-R2 309s system 40 33 170 0.879 0.683 0.789 309s 309s N DF SSR MSE RMSE R2 Adj R2 309s demand 20 17 65.7 3.86 1.97 0.755 0.726 309s supply 20 16 104.1 6.50 2.55 0.612 0.539 309s 309s The covariance matrix of the residuals used for estimation 309s demand supply 309s demand 3.73 4.14 309s supply 4.14 5.78 309s 309s The covariance matrix of the residuals 309s demand supply 309s demand 3.86 4.92 309s supply 4.92 6.50 309s 309s The correlations of the residuals 309s demand supply 309s demand 1.000 0.982 309s supply 0.982 1.000 309s 309s 309s SUR estimates for 'demand' (equation 1) 309s Model Formula: consump ~ price + income 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 99.3329 7.5145 13.22 2.3e-10 *** 309s price -0.2755 0.0885 -3.11 0.0063 ** 309s income 0.2986 0.0419 7.12 1.7e-06 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 1.966 on 17 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 17 309s SSR: 65.683 MSE: 3.864 Root MSE: 1.966 309s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 309s 309s 309s SUR estimates for 'supply' (equation 2) 309s Model Formula: consump ~ price + farmPrice + trend 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 61.9662 11.0808 5.59 4.0e-05 *** 309s price 0.1469 0.0944 1.56 0.13941 309s farmPrice 0.2140 0.0399 5.37 6.3e-05 *** 309s trend 0.3393 0.0679 5.00 0.00013 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 2.55 on 16 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 16 309s SSR: 104.058 MSE: 6.504 Root MSE: 2.55 309s Multiple R-Squared: 0.612 Adjusted R-Squared: 0.539 309s 309s > nobs( fitsur1w ) 309s [1] 40 309s > 309s > ## *************** WSUR (methodResidCov="Theil", useDfSys = TRUE ) *************** 309s > fitsur1we2 <- systemfit( system, "SUR", data = Kmenta, methodResidCov = "Theil", 309s + residCovWeighted = TRUE, useMatrix = useMatrix ) 309s > summary( fitsur1we2, useDfSys = TRUE ) 309s 309s systemfit results 309s method: SUR 309s 309s N DF SSR detRCov OLS-R2 McElroy-R2 309s system 40 33 172 -0.896 0.679 1.01 309s 309s N DF SSR MSE RMSE R2 Adj R2 309s demand 20 17 66.8 3.93 1.98 0.751 0.722 309s supply 20 16 105.3 6.58 2.57 0.607 0.534 309s 309s The covariance matrix of the residuals used for estimation 309s demand supply 309s demand 3.73 4.28 309s supply 4.28 5.78 309s 309s warning: this covariance matrix is NOT positive semidefinit! 309s 309s The covariance matrix of the residuals 309s demand supply 309s demand 3.93 5.17 309s supply 5.17 6.58 309s 309s The correlations of the residuals 309s demand supply 309s demand 1.000 0.984 309s supply 0.984 1.000 309s 309s 309s SUR estimates for 'demand' (equation 1) 309s Model Formula: consump ~ price + income 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 99.2120 7.5127 13.21 1.0e-14 *** 309s price -0.2667 0.0877 -3.04 0.0046 ** 309s income 0.2908 0.0406 7.16 3.3e-08 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 1.982 on 17 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 17 309s SSR: 66.802 MSE: 3.93 Root MSE: 1.982 309s Multiple R-Squared: 0.751 Adjusted R-Squared: 0.722 309s 309s 309s SUR estimates for 'supply' (equation 2) 309s Model Formula: consump ~ price + farmPrice + trend 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 63.0768 10.9735 5.75 2.0e-06 *** 309s price 0.1439 0.0943 1.52 0.14 309s farmPrice 0.2064 0.0384 5.37 6.1e-06 *** 309s trend 0.3325 0.0640 5.19 1.0e-05 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 2.566 on 16 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 16 309s SSR: 105.322 MSE: 6.583 Root MSE: 2.566 309s Multiple R-Squared: 0.607 Adjusted R-Squared: 0.534 309s 309s > 309s > 309s > ## *************** SUR with cross-equation restriction ************** 309s > fitsur2 <- systemfit( system, "SUR", data = Kmenta, restrict.matrix = restrm, 309s + useMatrix = useMatrix ) 309s > print( summary( fitsur2 ) ) 309s 309s systemfit results 309s method: SUR 309s 309s N DF SSR detRCov OLS-R2 McElroy-R2 309s system 40 34 179 0.933 0.665 0.753 309s 309s N DF SSR MSE RMSE R2 Adj R2 309s demand 20 17 71.6 4.21 2.05 0.733 0.702 309s supply 20 16 107.8 6.74 2.60 0.598 0.523 309s 309s The covariance matrix of the residuals used for estimation 309s demand supply 309s demand 3.78 4.47 309s supply 4.47 5.94 309s 309s The covariance matrix of the residuals 309s demand supply 309s demand 4.21 5.24 309s supply 5.24 6.74 309s 309s The correlations of the residuals 309s demand supply 309s demand 1.000 0.983 309s supply 0.983 1.000 309s 309s 309s SUR estimates for 'demand' (equation 1) 309s Model Formula: consump ~ price + income 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 98.8408 7.5581 13.08 8.0e-15 *** 309s price -0.2398 0.0860 -2.79 0.0086 ** 309s income 0.2670 0.0368 7.25 2.2e-08 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 2.052 on 17 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 17 309s SSR: 71.597 MSE: 4.212 Root MSE: 2.052 309s Multiple R-Squared: 0.733 Adjusted R-Squared: 0.702 309s 309s 309s SUR estimates for 'supply' (equation 2) 309s Model Formula: consump ~ price + farmPrice + trend 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 67.4283 10.6647 6.32 3.3e-07 *** 309s price 0.1332 0.0953 1.40 0.17 309s farmPrice 0.1795 0.0337 5.33 6.3e-06 *** 309s trend 0.2670 0.0368 7.25 2.2e-08 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 2.596 on 16 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 16 309s SSR: 107.806 MSE: 6.738 Root MSE: 2.596 309s Multiple R-Squared: 0.598 Adjusted R-Squared: 0.523 309s 309s > nobs( fitsur2 ) 309s [1] 40 309s > # the same with symbolically specified restrictions 309s > fitsur2Sym <- systemfit( system, "SUR", data = Kmenta, 309s + restrict.matrix = restrict, useMatrix = useMatrix ) 309s > all.equal( fitsur2, fitsur2Sym ) 309s [1] "Component “call”: target, current do not match when deparsed" 309s > nobs( fitsur2Sym ) 309s [1] 40 309s > 309s > ## *************** SUR with cross-equation restriction (EViews-like) ** 309s > fitsur2e <- systemfit( system, "SUR", data = Kmenta, restrict.matrix = restrm, 309s + methodResidCov = "noDfCor", x = TRUE, 309s + useMatrix = useMatrix ) 309s > print( summary( fitsur2e ) ) 309s 309s systemfit results 309s method: SUR 309s 309s N DF SSR detRCov OLS-R2 McElroy-R2 309s system 40 34 180 0.62 0.663 0.707 309s 309s N DF SSR MSE RMSE R2 Adj R2 309s demand 20 17 72.6 4.27 2.07 0.729 0.697 309s supply 20 16 107.9 6.75 2.60 0.597 0.522 309s 309s The covariance matrix of the residuals used for estimation 309s demand supply 309s demand 3.21 3.68 309s supply 3.68 4.75 309s 309s The covariance matrix of the residuals 309s demand supply 309s demand 3.63 4.35 309s supply 4.35 5.40 309s 309s The correlations of the residuals 309s demand supply 309s demand 1.000 0.984 309s supply 0.984 1.000 309s 309s 309s SUR estimates for 'demand' (equation 1) 309s Model Formula: consump ~ price + income 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 98.7799 6.9687 14.17 8.9e-16 *** 309s price -0.2354 0.0795 -2.96 0.0056 ** 309s income 0.2631 0.0344 7.66 6.7e-09 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 2.066 on 17 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 17 309s SSR: 72.577 MSE: 4.269 Root MSE: 2.066 309s Multiple R-Squared: 0.729 Adjusted R-Squared: 0.697 309s 309s 309s SUR estimates for 'supply' (equation 2) 309s Model Formula: consump ~ price + farmPrice + trend 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 67.6039 9.5712 7.06 3.7e-08 *** 309s price 0.1328 0.0853 1.56 0.13 309s farmPrice 0.1785 0.0305 5.85 1.3e-06 *** 309s trend 0.2631 0.0344 7.66 6.7e-09 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 2.597 on 16 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 16 309s SSR: 107.917 MSE: 6.745 Root MSE: 2.597 309s Multiple R-Squared: 0.597 Adjusted R-Squared: 0.522 309s 309s > 309s > ## *************** WSUR with cross-equation restriction (EViews-like) ** 309s > fitsur2we <- systemfit( system, "SUR", data = Kmenta, restrict.matrix = restrm, 309s + methodResidCov = "noDfCor", residCovWeighted = TRUE, 309s + x = TRUE, useMatrix = useMatrix ) 309s > summary( fitsur2we ) 309s 309s systemfit results 309s method: SUR 309s 309s N DF SSR detRCov OLS-R2 McElroy-R2 309s system 40 34 182 0.609 0.661 0.711 309s 309s N DF SSR MSE RMSE R2 Adj R2 309s demand 20 17 73 4.29 2.07 0.728 0.696 309s supply 20 16 109 6.79 2.61 0.595 0.519 309s 309s The covariance matrix of the residuals used for estimation 309s demand supply 309s demand 3.19 3.69 309s supply 3.69 4.78 309s 309s The covariance matrix of the residuals 309s demand supply 309s demand 3.65 4.38 309s supply 4.38 5.43 309s 309s The correlations of the residuals 309s demand supply 309s demand 1.000 0.985 309s supply 0.985 1.000 309s 309s 309s SUR estimates for 'demand' (equation 1) 309s Model Formula: consump ~ price + income 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 98.7542 6.9468 14.22 6.7e-16 *** 309s price -0.2335 0.0790 -2.96 0.0056 ** 309s income 0.2614 0.0338 7.74 5.3e-09 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 2.072 on 17 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 17 309s SSR: 73.009 MSE: 4.295 Root MSE: 2.072 309s Multiple R-Squared: 0.728 Adjusted R-Squared: 0.696 309s 309s 309s SUR estimates for 'supply' (equation 2) 309s Model Formula: consump ~ price + farmPrice + trend 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 67.8882 9.5640 7.10 3.4e-08 *** 309s price 0.1320 0.0855 1.55 0.13 309s farmPrice 0.1765 0.0301 5.86 1.3e-06 *** 309s trend 0.2614 0.0338 7.74 5.3e-09 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 2.606 on 16 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 16 309s SSR: 108.634 MSE: 6.79 Root MSE: 2.606 309s Multiple R-Squared: 0.595 Adjusted R-Squared: 0.519 309s 309s > 309s > 309s > ## *************** SUR with restriction via restrict.regMat ******************* 309s > fitsur3 <- systemfit( system, "SUR", data = Kmenta, restrict.regMat = tc, 309s + useMatrix = useMatrix ) 309s > print( summary( fitsur3 ) ) 309s 309s systemfit results 309s method: SUR 309s 309s N DF SSR detRCov OLS-R2 McElroy-R2 309s system 40 34 179 0.933 0.665 0.753 309s 309s N DF SSR MSE RMSE R2 Adj R2 309s demand 20 17 71.6 4.21 2.05 0.733 0.702 309s supply 20 16 107.8 6.74 2.60 0.598 0.523 309s 309s The covariance matrix of the residuals used for estimation 309s demand supply 309s demand 3.78 4.47 309s supply 4.47 5.94 309s 309s The covariance matrix of the residuals 309s demand supply 309s demand 4.21 5.24 309s supply 5.24 6.74 309s 309s The correlations of the residuals 309s demand supply 309s demand 1.000 0.983 309s supply 0.983 1.000 309s 309s 309s SUR estimates for 'demand' (equation 1) 309s Model Formula: consump ~ price + income 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 98.8408 7.5581 13.08 8.0e-15 *** 309s price -0.2398 0.0860 -2.79 0.0086 ** 309s income 0.2670 0.0368 7.25 2.2e-08 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 2.052 on 17 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 17 309s SSR: 71.597 MSE: 4.212 Root MSE: 2.052 309s Multiple R-Squared: 0.733 Adjusted R-Squared: 0.702 309s 309s 309s SUR estimates for 'supply' (equation 2) 309s Model Formula: consump ~ price + farmPrice + trend 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 67.4283 10.6647 6.32 3.3e-07 *** 309s price 0.1332 0.0953 1.40 0.17 309s farmPrice 0.1795 0.0337 5.33 6.3e-06 *** 309s trend 0.2670 0.0368 7.25 2.2e-08 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 2.596 on 16 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 16 309s SSR: 107.806 MSE: 6.738 Root MSE: 2.596 309s Multiple R-Squared: 0.598 Adjusted R-Squared: 0.523 309s 309s > nobs( fitsur3 ) 309s [1] 40 309s > 309s > ## *************** SUR with restriction via restrict.regMat (EViews-like) ************** 309s > fitsur3e <- systemfit( system, "SUR", data = Kmenta, restrict.regMat = tc, 309s + methodResidCov = "noDfCor", x = TRUE, 309s + useMatrix = useMatrix ) 309s > print( summary( fitsur3e ) ) 309s 309s systemfit results 309s method: SUR 309s 309s N DF SSR detRCov OLS-R2 McElroy-R2 309s system 40 34 180 0.62 0.663 0.707 309s 309s N DF SSR MSE RMSE R2 Adj R2 309s demand 20 17 72.6 4.27 2.07 0.729 0.697 309s supply 20 16 107.9 6.75 2.60 0.597 0.522 309s 309s The covariance matrix of the residuals used for estimation 309s demand supply 309s demand 3.21 3.68 309s supply 3.68 4.75 309s 309s The covariance matrix of the residuals 309s demand supply 309s demand 3.63 4.35 309s supply 4.35 5.40 309s 309s The correlations of the residuals 309s demand supply 309s demand 1.000 0.984 309s supply 0.984 1.000 309s 309s 309s SUR estimates for 'demand' (equation 1) 309s Model Formula: consump ~ price + income 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 98.7799 6.9687 14.17 8.9e-16 *** 309s price -0.2354 0.0795 -2.96 0.0056 ** 309s income 0.2631 0.0344 7.66 6.7e-09 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 2.066 on 17 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 17 309s SSR: 72.577 MSE: 4.269 Root MSE: 2.066 309s Multiple R-Squared: 0.729 Adjusted R-Squared: 0.697 309s 309s 309s SUR estimates for 'supply' (equation 2) 309s Model Formula: consump ~ price + farmPrice + trend 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 67.6039 9.5712 7.06 3.7e-08 *** 309s price 0.1328 0.0853 1.56 0.13 309s farmPrice 0.1785 0.0305 5.85 1.3e-06 *** 309s trend 0.2631 0.0344 7.66 6.7e-09 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 2.597 on 16 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 16 309s SSR: 107.917 MSE: 6.745 Root MSE: 2.597 309s Multiple R-Squared: 0.597 Adjusted R-Squared: 0.522 309s 309s > 309s > ## *************** WSUR with restriction via restrict.regMat ******************* 309s > fitsur3w <- systemfit( system, "SUR", data = Kmenta, restrict.regMat = tc, 309s + residCovWeighted = TRUE, x = TRUE, useMatrix = useMatrix ) 309s > summary( fitsur3w ) 309s 309s systemfit results 309s method: SUR 309s 309s N DF SSR detRCov OLS-R2 McElroy-R2 309s system 40 34 181 0.919 0.663 0.757 309s 309s N DF SSR MSE RMSE R2 Adj R2 309s demand 20 17 72 4.24 2.06 0.731 0.700 309s supply 20 16 109 6.79 2.60 0.595 0.519 309s 309s The covariance matrix of the residuals used for estimation 309s demand supply 309s demand 3.75 4.48 309s supply 4.48 5.98 309s 309s The covariance matrix of the residuals 309s demand supply 309s demand 4.24 5.28 309s supply 5.28 6.79 309s 309s The correlations of the residuals 309s demand supply 309s demand 1.000 0.984 309s supply 0.984 1.000 309s 309s 309s SUR estimates for 'demand' (equation 1) 309s Model Formula: consump ~ price + income 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 98.8139 7.5317 13.12 7.3e-15 *** 309s price -0.2378 0.0854 -2.79 0.0087 ** 309s income 0.2653 0.0361 7.34 1.7e-08 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 2.058 on 17 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 17 309s SSR: 72.023 MSE: 4.237 Root MSE: 2.058 309s Multiple R-Squared: 0.731 Adjusted R-Squared: 0.7 309s 309s 309s SUR estimates for 'supply' (equation 2) 309s Model Formula: consump ~ price + farmPrice + trend 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 67.7366 10.6556 6.36 3.0e-07 *** 309s price 0.1324 0.0955 1.39 0.17 309s farmPrice 0.1774 0.0332 5.35 6.1e-06 *** 309s trend 0.2653 0.0361 7.34 1.7e-08 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 2.605 on 16 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 16 309s SSR: 108.579 MSE: 6.786 Root MSE: 2.605 309s Multiple R-Squared: 0.595 Adjusted R-Squared: 0.519 309s 309s > 309s > 309s > ## *************** SUR with 2 restrictions *************************** 309s > fitsur4 <- systemfit( system, "SUR", data = Kmenta, restrict.matrix = restr2m, 309s + restrict.rhs = restr2q, useMatrix = useMatrix ) 309s > print( summary( fitsur4 ) ) 309s 309s systemfit results 309s method: SUR 309s 309s N DF SSR detRCov OLS-R2 McElroy-R2 309s system 40 35 165 1.76 0.691 0.69 309s 309s N DF SSR MSE RMSE R2 Adj R2 309s demand 20 17 64 3.76 1.94 0.761 0.733 309s supply 20 16 101 6.34 2.52 0.622 0.551 309s 309s The covariance matrix of the residuals used for estimation 309s demand supply 309s demand 3.76 4.46 309s supply 4.46 5.99 309s 309s The covariance matrix of the residuals 309s demand supply 309s demand 3.76 4.70 309s supply 4.70 6.34 309s 309s The correlations of the residuals 309s demand supply 309s demand 1.000 0.962 309s supply 0.962 1.000 309s 309s 309s SUR estimates for 'demand' (equation 1) 309s Model Formula: consump ~ price + income 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 96.8275 7.4665 12.97 6.2e-15 *** 309s price -0.2798 0.0840 -3.33 0.002 ** 309s income 0.3286 0.0206 15.93 < 2e-16 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 1.94 on 17 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 17 309s SSR: 63.987 MSE: 3.764 Root MSE: 1.94 309s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 309s 309s 309s SUR estimates for 'supply' (equation 2) 309s Model Formula: consump ~ price + farmPrice + trend 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 52.9386 7.6655 6.91 5.1e-08 *** 309s price 0.2202 0.0840 2.62 0.013 * 309s farmPrice 0.2327 0.0212 10.97 7.2e-13 *** 309s trend 0.3286 0.0206 15.93 < 2e-16 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 2.518 on 16 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 16 309s SSR: 101.473 MSE: 6.342 Root MSE: 2.518 309s Multiple R-Squared: 0.622 Adjusted R-Squared: 0.551 309s 309s > nobs( fitsur4 ) 309s [1] 40 309s > # the same with symbolically specified restrictions 309s > fitsur4Sym <- systemfit( system, "SUR", data = Kmenta, 309s + restrict.matrix = restrict2, useMatrix = useMatrix ) 309s > all.equal( fitsur4, fitsur4Sym ) 309s [1] "Component “call”: target, current do not match when deparsed" 309s > 309s > ## *************** SUR with 2 restrictions (EViews-like) ************** 309s > fitsur4e <- systemfit( system, "SUR", data = Kmenta, methodResidCov = "noDfCor", 309s + restrict.matrix = restr2m, restrict.rhs = restr2q, useMatrix = useMatrix ) 309s > print( summary( fitsur4e ) ) 309s 309s systemfit results 309s method: SUR 309s 309s N DF SSR detRCov OLS-R2 McElroy-R2 309s system 40 35 165 1.2 0.693 0.653 309s 309s N DF SSR MSE RMSE R2 Adj R2 309s demand 20 17 63.8 3.75 1.94 0.762 0.734 309s supply 20 16 100.8 6.30 2.51 0.624 0.553 309s 309s The covariance matrix of the residuals used for estimation 309s demand supply 309s demand 3.20 3.67 309s supply 3.67 4.79 309s 309s The covariance matrix of the residuals 309s demand supply 309s demand 3.19 3.86 309s supply 3.86 5.04 309s 309s The correlations of the residuals 309s demand supply 309s demand 1.000 0.962 309s supply 0.962 1.000 309s 309s 309s SUR estimates for 'demand' (equation 1) 309s Model Formula: consump ~ price + income 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 97.2678 6.9200 14.06 4.4e-16 *** 309s price -0.2851 0.0767 -3.72 7e-04 *** 309s income 0.3296 0.0184 17.86 < 2e-16 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 1.937 on 17 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 17 309s SSR: 63.811 MSE: 3.754 Root MSE: 1.937 309s Multiple R-Squared: 0.762 Adjusted R-Squared: 0.734 309s 309s 309s SUR estimates for 'supply' (equation 2) 309s Model Formula: consump ~ price + farmPrice + trend 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 53.3040 7.1045 7.5 8.7e-09 *** 309s price 0.2149 0.0767 2.8 0.0082 ** 309s farmPrice 0.2343 0.0187 12.6 1.6e-14 *** 309s trend 0.3296 0.0184 17.9 < 2e-16 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 2.51 on 16 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 16 309s SSR: 100.835 MSE: 6.302 Root MSE: 2.51 309s Multiple R-Squared: 0.624 Adjusted R-Squared: 0.553 309s 309s > 309s > ## *************** SUR with 2 restrictions (methodResidCov = "Theil") ************** 309s > fitsur4r2 <- systemfit( system, "SUR", data = Kmenta, methodResidCov = "Theil", 309s + restrict.matrix = restr2m, restrict.rhs = restr2q, useMatrix = useMatrix ) 309s > print( summary( fitsur4r2 ) ) 309s 309s systemfit results 309s method: SUR 309s 309s N DF SSR detRCov OLS-R2 McElroy-R2 309s system 40 35 175 0.034 0.673 0.708 309s 309s N DF SSR MSE RMSE R2 Adj R2 309s demand 20 17 67 3.94 1.99 0.750 0.721 309s supply 20 16 108 6.76 2.60 0.596 0.521 309s 309s The covariance matrix of the residuals used for estimation 309s demand supply 309s demand 3.76 4.61 309s supply 4.61 5.99 309s 309s The covariance matrix of the residuals 309s demand supply 309s demand 3.94 5.16 309s supply 5.16 6.76 309s 309s The correlations of the residuals 309s demand supply 309s demand 1.000 0.967 309s supply 0.967 1.000 309s 309s 309s SUR estimates for 'demand' (equation 1) 309s Model Formula: consump ~ price + income 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 92.5266 7.2896 12.69 1.2e-14 *** 309s price -0.2304 0.0827 -2.79 0.0086 ** 309s income 0.3221 0.0166 19.37 < 2e-16 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 1.986 on 17 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 17 309s SSR: 67.048 MSE: 3.944 Root MSE: 1.986 309s Multiple R-Squared: 0.75 Adjusted R-Squared: 0.721 309s 309s 309s SUR estimates for 'supply' (equation 2) 309s Model Formula: consump ~ price + farmPrice + trend 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 48.7011 7.4034 6.58 1.3e-07 *** 309s price 0.2696 0.0827 3.26 0.0025 ** 309s farmPrice 0.2261 0.0166 13.62 1.6e-15 *** 309s trend 0.3221 0.0166 19.37 < 2e-16 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 2.601 on 16 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 16 309s SSR: 108.217 MSE: 6.764 Root MSE: 2.601 309s Multiple R-Squared: 0.596 Adjusted R-Squared: 0.521 309s 309s > 309s > ## *************** SUR with 2 restrictions (methodResidCov = "max") ************** 309s > fitsur4r3 <- systemfit( system, "SUR", data = Kmenta, methodResidCov = "max", 309s + restrict.matrix = restr2m, restrict.rhs = restr2q, 309s + x = TRUE, useMatrix = useMatrix ) 309s > print( summary( fitsur4r3 ) ) 309s 309s systemfit results 309s method: SUR 309s 309s N DF SSR detRCov OLS-R2 McElroy-R2 309s system 40 35 173 0.217 0.677 0.702 309s 309s N DF SSR MSE RMSE R2 Adj R2 309s demand 20 17 66.4 3.91 1.98 0.752 0.723 309s supply 20 16 106.9 6.68 2.58 0.601 0.526 309s 309s The covariance matrix of the residuals used for estimation 309s demand supply 309s demand 3.76 4.59 309s supply 4.59 5.99 309s 309s The covariance matrix of the residuals 309s demand supply 309s demand 3.91 5.09 309s supply 5.09 6.68 309s 309s The correlations of the residuals 309s demand supply 309s demand 1.000 0.966 309s supply 0.966 1.000 309s 309s 309s SUR estimates for 'demand' (equation 1) 309s Model Formula: consump ~ price + income 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 93.1978 7.3168 12.74 1.1e-14 *** 309s price -0.2381 0.0829 -2.87 0.0069 ** 309s income 0.3231 0.0170 18.96 < 2e-16 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 1.976 on 17 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 17 309s SSR: 66.405 MSE: 3.906 Root MSE: 1.976 309s Multiple R-Squared: 0.752 Adjusted R-Squared: 0.723 309s 309s 309s SUR estimates for 'supply' (equation 2) 309s Model Formula: consump ~ price + farmPrice + trend 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 49.3676 7.4381 6.64 1.1e-07 *** 309s price 0.2619 0.0829 3.16 0.0033 ** 309s farmPrice 0.2271 0.0171 13.29 3.1e-15 *** 309s trend 0.3231 0.0170 18.96 < 2e-16 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 2.585 on 16 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 16 309s SSR: 106.924 MSE: 6.683 Root MSE: 2.585 309s Multiple R-Squared: 0.601 Adjusted R-Squared: 0.526 309s 309s > 309s > ## *************** WSUR with 2 restrictions (EViews-like) ************** 309s > fitsur4we <- systemfit( system, "SUR", data = Kmenta, methodResidCov = "noDfCor", 309s + restrict.matrix = restr2m, restrict.rhs = restr2q, residCovWeighted = TRUE, 309s + useMatrix = useMatrix ) 309s > summary( fitsur4we ) 309s 309s systemfit results 309s method: SUR 309s 309s N DF SSR detRCov OLS-R2 McElroy-R2 309s system 40 35 165 1.2 0.692 0.654 309s 309s N DF SSR MSE RMSE R2 Adj R2 309s demand 20 17 63.9 3.76 1.94 0.762 0.733 309s supply 20 16 101.2 6.33 2.52 0.623 0.552 309s 309s The covariance matrix of the residuals used for estimation 309s demand supply 309s demand 3.18 3.69 309s supply 3.69 4.81 309s 309s The covariance matrix of the residuals 309s demand supply 309s demand 3.20 3.87 309s supply 3.87 5.06 309s 309s The correlations of the residuals 309s demand supply 309s demand 1.000 0.962 309s supply 0.962 1.000 309s 309s 309s SUR estimates for 'demand' (equation 1) 309s Model Formula: consump ~ price + income 309s 309s Estimate Std. Error t value Pr(>|t|) 309s (Intercept) 96.9414 6.8894 14.07 4.4e-16 *** 309s price -0.2814 0.0766 -3.67 8e-04 *** 309s income 0.3291 0.0181 18.18 < 2e-16 *** 309s --- 309s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 309s 309s Residual standard error: 1.939 on 17 degrees of freedom 309s Number of observations: 20 Degrees of Freedom: 17 309s SSR: 63.936 MSE: 3.761 Root MSE: 1.939 309s Multiple R-Squared: 0.762 Adjusted R-Squared: 0.733 309s 309s 309s SUR estimates for 'supply' (equation 2) 309s Model Formula: consump ~ price + farmPrice + trend 309s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 52.9963 7.0652 7.50 8.7e-09 *** 310s price 0.2186 0.0766 2.85 0.0072 ** 310s farmPrice 0.2337 0.0183 12.76 1.0e-14 *** 310s trend 0.3291 0.0181 18.18 < 2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 2.515 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 101.201 MSE: 6.325 Root MSE: 2.515 310s Multiple R-Squared: 0.623 Adjusted R-Squared: 0.552 310s 310s > 310s > 310s > ## *************** SUR with 2 restrictions via R and restrict.regMat **************** 310s > fitsur5 <- systemfit( system, "SUR", data = Kmenta, restrict.matrix = restr3m, 310s + restrict.rhs = restr3q, restrict.regMat = tc, 310s + x = TRUE, useMatrix = useMatrix ) 310s > print( summary( fitsur5 ) ) 310s 310s systemfit results 310s method: SUR 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 35 165 1.76 0.691 0.69 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 64 3.76 1.94 0.761 0.733 310s supply 20 16 101 6.34 2.52 0.622 0.551 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 3.76 4.46 310s supply 4.46 5.99 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 3.76 4.70 310s supply 4.70 6.34 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.000 0.962 310s supply 0.962 1.000 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 96.8275 7.4665 12.97 6.2e-15 *** 310s price -0.2798 0.0840 -3.33 0.002 ** 310s income 0.3286 0.0206 15.93 < 2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.94 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 63.987 MSE: 3.764 Root MSE: 1.94 310s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: consump ~ price + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 52.9386 7.6655 6.91 5.1e-08 *** 310s price 0.2202 0.0840 2.62 0.013 * 310s farmPrice 0.2327 0.0212 10.97 7.2e-13 *** 310s trend 0.3286 0.0206 15.93 < 2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 2.518 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 101.473 MSE: 6.342 Root MSE: 2.518 310s Multiple R-Squared: 0.622 Adjusted R-Squared: 0.551 310s 310s > nobs( fitsur5 ) 310s [1] 40 310s > # the same with symbolically specified restrictions 310s > fitsur5Sym <- systemfit( system, "SUR", data = Kmenta, 310s + restrict.matrix = restrict3, restrict.regMat = tc, 310s + x = TRUE, useMatrix = useMatrix ) 310s > all.equal( fitsur5, fitsur5Sym ) 310s [1] "Component “call”: target, current do not match when deparsed" 310s > 310s > ## *************** SUR with 2 restrictions via R and restrict.regMat (EViews-like) ************** 310s > fitsur5e <- systemfit( system, "SUR", data = Kmenta, methodResidCov = "noDfCor", 310s + restrict.matrix = restr3m, restrict.rhs = restr3q, restrict.regMat = tc, 310s + useMatrix = useMatrix ) 310s > print( summary( fitsur5e ) ) 310s 310s systemfit results 310s method: SUR 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 35 165 1.2 0.693 0.653 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 63.8 3.75 1.94 0.762 0.734 310s supply 20 16 100.8 6.30 2.51 0.624 0.553 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 3.20 3.67 310s supply 3.67 4.79 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 3.19 3.86 310s supply 3.86 5.04 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.000 0.962 310s supply 0.962 1.000 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 97.2678 6.9200 14.06 4.4e-16 *** 310s price -0.2851 0.0767 -3.72 7e-04 *** 310s income 0.3296 0.0184 17.86 < 2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.937 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 63.811 MSE: 3.754 Root MSE: 1.937 310s Multiple R-Squared: 0.762 Adjusted R-Squared: 0.734 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: consump ~ price + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 53.3040 7.1045 7.5 8.7e-09 *** 310s price 0.2149 0.0767 2.8 0.0082 ** 310s farmPrice 0.2343 0.0187 12.6 1.6e-14 *** 310s trend 0.3296 0.0184 17.9 < 2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 2.51 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 100.835 MSE: 6.302 Root MSE: 2.51 310s Multiple R-Squared: 0.624 Adjusted R-Squared: 0.553 310s 310s > 310s > ## ************ WSUR with 2 restrictions via R and restrict.regMat ************ 310s > fitsur5w <- systemfit( system, "SUR", data = Kmenta, restrict.matrix = restr3m, 310s + restrict.rhs = restr3q, restrict.regMat = tc, residCovWeighted = TRUE, 310s + useMatrix = useMatrix ) 310s > summary( fitsur5w ) 310s 310s systemfit results 310s method: SUR 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 35 166 1.75 0.69 0.691 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 64.2 3.77 1.94 0.761 0.733 310s supply 20 16 102.0 6.37 2.52 0.620 0.548 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 3.74 4.47 310s supply 4.47 6.02 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 3.77 4.72 310s supply 4.72 6.37 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.000 0.963 310s supply 0.963 1.000 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 96.4421 7.4234 12.99 6e-15 *** 310s price -0.2753 0.0838 -3.29 0.0023 ** 310s income 0.3280 0.0202 16.21 <2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.943 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 64.16 MSE: 3.774 Root MSE: 1.943 310s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: consump ~ price + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 52.5761 7.6099 6.91 5.0e-08 *** 310s price 0.2247 0.0838 2.68 0.011 * 310s farmPrice 0.2318 0.0208 11.14 4.7e-13 *** 310s trend 0.3280 0.0202 16.21 < 2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 2.524 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 101.967 MSE: 6.373 Root MSE: 2.524 310s Multiple R-Squared: 0.62 Adjusted R-Squared: 0.548 310s 310s > 310s > 310s > ## ************** iterated SUR **************************** 310s > fitsuri1 <- systemfit( system2, "SUR", data = Kmenta, maxit = 100, 310s + useMatrix = useMatrix ) 310s > print( summary( fitsuri1 ) ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 6 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 33 108 4.42 0.885 0.958 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 66.3 3.90 1.98 0.753 0.724 310s supply 20 16 41.4 2.59 1.61 0.938 0.926 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 3.90 -2.38 310s supply -2.38 2.59 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 3.90 -2.38 310s supply -2.38 2.59 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.000 -0.749 310s supply -0.749 1.000 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 94.0537 7.4051 12.70 4.2e-10 *** 310s price -0.2355 0.0882 -2.67 0.016 * 310s income 0.3117 0.0457 6.81 3.0e-06 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.975 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 66.286 MSE: 3.899 Root MSE: 1.975 310s Multiple R-Squared: 0.753 Adjusted R-Squared: 0.724 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: price ~ income + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 89.2982 3.3822 26.4 1.3e-14 *** 310s income 0.6655 0.0423 15.7 3.7e-11 *** 310s farmPrice -0.4742 0.0372 -12.7 8.7e-10 *** 310s trend -0.7966 0.0656 -12.2 1.7e-09 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.609 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 41.411 MSE: 2.588 Root MSE: 1.609 310s Multiple R-Squared: 0.938 Adjusted R-Squared: 0.926 310s 310s > nobs( fitsuri1 ) 310s [1] 40 310s > 310s > ## ************** iterated SUR (EViews-like) ***************** 310s > fitsuri1e <- systemfit( system2, "SUR", data = Kmenta, methodResidCov = "noDfCor", 310s + maxit = 100, useMatrix = useMatrix ) 310s > print( summary( fitsuri1e, useDfSys = TRUE ) ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 7 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 33 108 3.01 0.885 0.959 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 66.7 3.93 1.98 0.751 0.722 310s supply 20 16 41.2 2.57 1.60 0.938 0.927 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 3.34 -1.97 310s supply -1.97 2.06 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 3.34 -1.97 310s supply -1.97 2.06 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.00 -0.75 310s supply -0.75 1.00 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 93.6193 6.8499 13.67 4.0e-15 *** 310s price -0.2295 0.0816 -2.81 0.0082 ** 310s income 0.3100 0.0423 7.33 2.1e-08 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.981 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 66.742 MSE: 3.926 Root MSE: 1.981 310s Multiple R-Squared: 0.751 Adjusted R-Squared: 0.722 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: price ~ income + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 89.2690 3.0165 29.6 < 2e-16 *** 310s income 0.6641 0.0377 17.6 < 2e-16 *** 310s farmPrice -0.4730 0.0332 -14.2 1.3e-15 *** 310s trend -0.7919 0.0585 -13.6 4.9e-15 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.604 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 41.176 MSE: 2.573 Root MSE: 1.604 310s Multiple R-Squared: 0.938 Adjusted R-Squared: 0.927 310s 310s > 310s > ## ************** iterated SUR (methodResidCov = "Theil") **************************** 310s > fitsuri1r2 <- systemfit( system2, "SUR", data = Kmenta, maxit = 100, 310s + methodResidCov = "Theil", useMatrix = useMatrix ) 310s > print( summary( fitsuri1r2 ) ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 7 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 33 109 4 0.884 0.961 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 66.9 3.94 1.98 0.750 0.721 310s supply 20 16 41.8 2.61 1.62 0.937 0.926 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 3.94 -2.51 310s supply -2.51 2.61 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 3.94 -2.51 310s supply -2.51 2.61 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.000 -0.754 310s supply -0.754 1.000 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 93.4405 7.3821 12.66 4.4e-10 *** 310s price -0.2271 0.0877 -2.59 0.019 * 310s income 0.3093 0.0458 6.75 3.4e-06 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.984 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 66.939 MSE: 3.938 Root MSE: 1.984 310s Multiple R-Squared: 0.75 Adjusted R-Squared: 0.721 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: price ~ income + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 89.1602 3.3868 26.3 1.3e-14 *** 310s income 0.6635 0.0423 15.7 3.9e-11 *** 310s farmPrice -0.4710 0.0369 -12.8 8.5e-10 *** 310s trend -0.7952 0.0643 -12.4 1.3e-09 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.616 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 41.764 MSE: 2.61 Root MSE: 1.616 310s Multiple R-Squared: 0.937 Adjusted R-Squared: 0.926 310s 310s > 310s > ## ************** iterated SUR (methodResidCov="Theil", useDfSys=TRUE) ***************** 310s > fitsuri1e2 <- systemfit( system2, "SUR", data = Kmenta, methodResidCov = "Theil", 310s + maxit = 100, x = TRUE, useMatrix = useMatrix ) 310s > print( summary( fitsuri1e2, useDfSys = TRUE ) ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 7 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 33 109 4 0.884 0.961 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 66.9 3.94 1.98 0.750 0.721 310s supply 20 16 41.8 2.61 1.62 0.937 0.926 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 3.94 -2.51 310s supply -2.51 2.61 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 3.94 -2.51 310s supply -2.51 2.61 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.000 -0.754 310s supply -0.754 1.000 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 93.4405 7.3821 12.66 3.3e-14 *** 310s price -0.2271 0.0877 -2.59 0.014 * 310s income 0.3093 0.0458 6.75 1.1e-07 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.984 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 66.939 MSE: 3.938 Root MSE: 1.984 310s Multiple R-Squared: 0.75 Adjusted R-Squared: 0.721 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: price ~ income + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 89.1602 3.3868 26.3 < 2e-16 *** 310s income 0.6635 0.0423 15.7 < 2e-16 *** 310s farmPrice -0.4710 0.0369 -12.8 2.7e-14 *** 310s trend -0.7952 0.0643 -12.4 6.0e-14 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.616 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 41.764 MSE: 2.61 Root MSE: 1.616 310s Multiple R-Squared: 0.937 Adjusted R-Squared: 0.926 310s 310s > 310s > ## ************** iterated SUR (methodResidCov = "max") **************************** 310s > fitsuri1r3 <- systemfit( system2, "SUR", data = Kmenta, maxit = 100, 310s + methodResidCov = "max", useMatrix = useMatrix ) 310s > print( summary( fitsuri1r3 ) ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 7 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 33 109 4.06 0.884 0.96 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 66.8 3.93 1.98 0.751 0.721 310s supply 20 16 41.7 2.61 1.61 0.937 0.926 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 3.93 -2.49 310s supply -2.49 2.61 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 3.93 -2.49 310s supply -2.49 2.61 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.000 -0.754 310s supply -0.754 1.000 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 93.5427 7.3858 12.67 4.4e-10 *** 310s price -0.2285 0.0877 -2.60 0.019 * 310s income 0.3097 0.0458 6.76 3.3e-06 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.983 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 66.826 MSE: 3.931 Root MSE: 1.983 310s Multiple R-Squared: 0.751 Adjusted R-Squared: 0.721 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: price ~ income + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 89.1830 3.3861 26.3 1.3e-14 *** 310s income 0.6639 0.0423 15.7 3.8e-11 *** 310s farmPrice -0.4715 0.0370 -12.8 8.5e-10 *** 310s trend -0.7955 0.0645 -12.3 1.4e-09 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.615 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 41.708 MSE: 2.607 Root MSE: 1.615 310s Multiple R-Squared: 0.937 Adjusted R-Squared: 0.926 310s 310s > 310s > ## ************** iterated WSUR (methodResidCov = "max") **************************** 310s > fitsuri1wr3 <- systemfit( system2, "SUR", data = Kmenta, maxit = 100, 310s + methodResidCov = "max", residCovWeighted = TRUE, useMatrix = useMatrix ) 310s > summary( fitsuri1wr3 ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 7 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 33 109 4.06 0.884 0.96 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 66.8 3.93 1.98 0.751 0.721 310s supply 20 16 41.7 2.61 1.61 0.937 0.926 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 3.93 -2.49 310s supply -2.49 2.61 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 3.93 -2.49 310s supply -2.49 2.61 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.000 -0.754 310s supply -0.754 1.000 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 93.5427 7.3858 12.67 4.4e-10 *** 310s price -0.2285 0.0877 -2.60 0.019 * 310s income 0.3097 0.0458 6.76 3.3e-06 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.983 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 66.826 MSE: 3.931 Root MSE: 1.983 310s Multiple R-Squared: 0.751 Adjusted R-Squared: 0.721 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: price ~ income + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 89.1830 3.3861 26.3 1.3e-14 *** 310s income 0.6639 0.0423 15.7 3.8e-11 *** 310s farmPrice -0.4715 0.0370 -12.8 8.5e-10 *** 310s trend -0.7955 0.0645 -12.3 1.4e-09 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.615 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 41.708 MSE: 2.607 Root MSE: 1.615 310s Multiple R-Squared: 0.937 Adjusted R-Squared: 0.926 310s 310s > 310s > 310s > ## *********** iterated SUR with restriction ******************* 310s > fitsuri2 <- systemfit( system2, "SUR", data = Kmenta, restrict.matrix = restrm, 310s + maxit = 100, useMatrix = useMatrix ) 310s > print( summary( fitsuri2 ) ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 21 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 34 587 110 0.372 0.669 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 67 3.94 1.99 0.75 0.721 310s supply 20 16 520 32.52 5.70 0.22 0.074 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 3.94 4.24 310s supply 4.24 32.52 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 3.94 4.24 310s supply 4.24 32.52 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.000 0.375 310s supply 0.375 1.000 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 107.3678 7.4986 14.32 4.4e-16 *** 310s price -0.3945 0.0912 -4.33 0.00013 *** 310s income 0.3382 0.0466 7.25 2.1e-08 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.986 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 67.024 MSE: 3.943 Root MSE: 1.986 310s Multiple R-Squared: 0.75 Adjusted R-Squared: 0.721 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: price ~ income + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 85.0448 12.1069 7.02 4.2e-08 *** 310s income 0.3125 0.1233 2.53 0.016 * 310s farmPrice -0.1972 0.1157 -1.70 0.097 . 310s trend 0.3382 0.0466 7.25 2.1e-08 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 5.703 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 520.329 MSE: 32.521 Root MSE: 5.703 310s Multiple R-Squared: 0.22 Adjusted R-Squared: 0.074 310s 310s > 310s > ## *********** iterated SUR with restriction (EViews-like) *************** 310s > fitsuri2e <- systemfit( system2, "SUR", data = Kmenta, restrict.matrix = restrm, 310s + methodResidCov = "noDfCor", maxit = 100, x = TRUE, 310s + useMatrix = useMatrix ) 310s > print( summary( fitsuri2e ) ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 22 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 34 588 74.9 0.372 0.664 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 67.5 3.97 1.99 0.748 0.719 310s supply 20 16 520.2 32.51 5.70 0.220 0.074 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 3.37 3.58 310s supply 3.58 26.01 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 3.37 3.58 310s supply 3.58 26.01 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.000 0.382 310s supply 0.382 1.000 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 107.8051 6.9270 15.56 < 2e-16 *** 310s price -0.3986 0.0843 -4.73 3.8e-05 *** 310s income 0.3379 0.0431 7.84 4.0e-09 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.992 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 67.47 MSE: 3.969 Root MSE: 1.992 310s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: price ~ income + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 85.1071 10.8287 7.86 3.8e-09 *** 310s income 0.3106 0.1101 2.82 0.0079 ** 310s farmPrice -0.1960 0.1034 -1.89 0.0667 . 310s trend 0.3379 0.0431 7.84 4.0e-09 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 5.702 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 520.205 MSE: 32.513 Root MSE: 5.702 310s Multiple R-Squared: 0.22 Adjusted R-Squared: 0.074 310s 310s > 310s > ## *********** iterated WSUR with restriction ******************* 310s > fitsuri2w <- systemfit( system2, "SUR", data = Kmenta, restrict.matrix = restrm, 310s + maxit = 100, residCovWeighted = TRUE, useMatrix = useMatrix ) 310s > summary( fitsuri2w ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 18 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 34 587 110 0.372 0.669 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 67 3.94 1.99 0.75 0.721 310s supply 20 16 520 32.52 5.70 0.22 0.074 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 3.94 4.24 310s supply 4.24 32.52 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 3.94 4.24 310s supply 4.24 32.52 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.000 0.375 310s supply 0.375 1.000 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 107.3672 7.4986 14.32 4.4e-16 *** 310s price -0.3945 0.0912 -4.33 0.00013 *** 310s income 0.3382 0.0466 7.25 2.1e-08 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.986 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 67.023 MSE: 3.943 Root MSE: 1.986 310s Multiple R-Squared: 0.75 Adjusted R-Squared: 0.721 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: price ~ income + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 85.0448 12.1069 7.02 4.2e-08 *** 310s income 0.3125 0.1233 2.53 0.016 * 310s farmPrice -0.1972 0.1157 -1.70 0.097 . 310s trend 0.3382 0.0466 7.25 2.1e-08 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 5.703 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 520.327 MSE: 32.52 Root MSE: 5.703 310s Multiple R-Squared: 0.22 Adjusted R-Squared: 0.074 310s 310s > 310s > 310s > ## *********** iterated SUR with restriction via restrict.regMat ******************** 310s > fitsuri3 <- systemfit( system2, "SUR", data = Kmenta, restrict.regMat = tc, 310s + maxit = 100, useMatrix = useMatrix ) 310s > print( summary( fitsuri3 ) ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 21 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 34 587 110 0.372 0.669 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 67 3.94 1.99 0.75 0.721 310s supply 20 16 520 32.52 5.70 0.22 0.074 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 3.94 4.24 310s supply 4.24 32.52 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 3.94 4.24 310s supply 4.24 32.52 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.000 0.375 310s supply 0.375 1.000 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 107.3678 7.4986 14.32 4.4e-16 *** 310s price -0.3945 0.0912 -4.33 0.00013 *** 310s income 0.3382 0.0466 7.25 2.1e-08 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.986 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 67.024 MSE: 3.943 Root MSE: 1.986 310s Multiple R-Squared: 0.75 Adjusted R-Squared: 0.721 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: price ~ income + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 85.0448 12.1069 7.02 4.2e-08 *** 310s income 0.3125 0.1233 2.53 0.016 * 310s farmPrice -0.1972 0.1157 -1.70 0.097 . 310s trend 0.3382 0.0466 7.25 2.1e-08 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 5.703 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 520.329 MSE: 32.521 Root MSE: 5.703 310s Multiple R-Squared: 0.22 Adjusted R-Squared: 0.074 310s 310s > 310s > ## *********** iterated SUR with restriction via restrict.regMat (EViews-like) *************** 310s > fitsuri3e <- systemfit( system2, "SUR", data = Kmenta, restrict.regMat = tc, 310s + methodResidCov = "noDfCor", maxit = 100, x = TRUE, 310s + useMatrix = useMatrix ) 310s > print( summary( fitsuri3e ) ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 22 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 34 588 74.9 0.372 0.664 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 67.5 3.97 1.99 0.748 0.719 310s supply 20 16 520.2 32.51 5.70 0.220 0.074 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 3.37 3.58 310s supply 3.58 26.01 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 3.37 3.58 310s supply 3.58 26.01 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.000 0.382 310s supply 0.382 1.000 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 107.8051 6.9270 15.56 < 2e-16 *** 310s price -0.3986 0.0843 -4.73 3.8e-05 *** 310s income 0.3379 0.0431 7.84 4.0e-09 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.992 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 67.47 MSE: 3.969 Root MSE: 1.992 310s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: price ~ income + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 85.1071 10.8287 7.86 3.8e-09 *** 310s income 0.3106 0.1101 2.82 0.0079 ** 310s farmPrice -0.1960 0.1034 -1.89 0.0667 . 310s trend 0.3379 0.0431 7.84 4.0e-09 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 5.702 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 520.205 MSE: 32.513 Root MSE: 5.702 310s Multiple R-Squared: 0.22 Adjusted R-Squared: 0.074 310s 310s > 310s > ## *********** iterated WSUR with restriction via restrict.regMat (EViews-like) *************** 310s > fitsuri3we <- systemfit( system2, "SUR", data = Kmenta, restrict.regMat = tc, 310s + methodResidCov = "noDfCor", maxit = 100, residCovWeighted = TRUE, 310s + useMatrix = useMatrix ) 310s > summary( fitsuri3we ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 20 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 34 588 74.9 0.372 0.664 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 67.5 3.97 1.99 0.748 0.719 310s supply 20 16 520.2 32.51 5.70 0.220 0.074 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 3.37 3.58 310s supply 3.58 26.01 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 3.37 3.58 310s supply 3.58 26.01 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.000 0.382 310s supply 0.382 1.000 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 107.8055 6.9270 15.56 < 2e-16 *** 310s price -0.3986 0.0843 -4.73 3.8e-05 *** 310s income 0.3379 0.0431 7.84 4.0e-09 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.992 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 67.471 MSE: 3.969 Root MSE: 1.992 310s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: price ~ income + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 85.1071 10.8288 7.86 3.8e-09 *** 310s income 0.3106 0.1101 2.82 0.008 ** 310s farmPrice -0.1960 0.1034 -1.89 0.067 . 310s trend 0.3379 0.0431 7.84 4.0e-09 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 5.702 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 520.206 MSE: 32.513 Root MSE: 5.702 310s Multiple R-Squared: 0.22 Adjusted R-Squared: 0.074 310s 310s > 310s > 310s > ## *************** iterated SUR with 2 restrictions *************************** 310s > fitsurio4 <- systemfit( system, "SUR", data = Kmenta, restrict.matrix = restr2m, 310s + restrict.rhs = restr2q, maxit = 100, useMatrix = useMatrix ) 310s > print( summary( fitsurio4 ) ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 10 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 35 176 1.74 0.671 0.705 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 67.2 3.95 1.99 0.749 0.720 310s supply 20 16 109.2 6.83 2.61 0.593 0.516 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 3.95 5.02 310s supply 5.02 6.83 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 3.95 5.02 310s supply 5.02 6.83 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.000 0.967 310s supply 0.967 1.000 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 92.4262 7.3543 12.57 1.6e-14 *** 310s price -0.2276 0.0850 -2.68 0.011 * 310s income 0.3203 0.0185 17.32 < 2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.988 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 67.206 MSE: 3.953 Root MSE: 1.988 310s Multiple R-Squared: 0.749 Adjusted R-Squared: 0.72 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: consump ~ price + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 48.7295 7.4587 6.53 1.5e-07 *** 310s price 0.2724 0.0850 3.20 0.0029 ** 310s farmPrice 0.2232 0.0190 11.76 1.0e-13 *** 310s trend 0.3203 0.0185 17.32 < 2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 2.613 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 109.234 MSE: 6.827 Root MSE: 2.613 310s Multiple R-Squared: 0.593 Adjusted R-Squared: 0.516 310s 310s > fitsuri4 <- systemfit( system2, "SUR", data = Kmenta, restrict.matrix = restr2m, 310s + restrict.rhs = restr2q, maxit = 100, useMatrix = useMatrix ) 310s > print( summary( fitsuri4 ) ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 19 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 35 575 121 0.385 0.637 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 65.5 3.85 1.96 0.756 0.727 310s supply 20 16 509.3 31.83 5.64 0.237 0.094 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 3.85 1.23 310s supply 1.23 31.83 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 3.85 1.23 310s supply 1.23 31.83 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.000 0.111 310s supply 0.111 1.000 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 98.0356 6.7437 14.54 2.2e-16 *** 310s price -0.2646 0.0777 -3.40 0.0017 ** 310s income 0.3007 0.0436 6.89 5.3e-08 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.963 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 65.532 MSE: 3.855 Root MSE: 1.963 310s Multiple R-Squared: 0.756 Adjusted R-Squared: 0.727 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: price ~ income + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 90.0046 10.4367 8.62 3.5e-10 *** 310s income 0.2354 0.0777 3.03 0.0046 ** 310s farmPrice -0.1667 0.1108 -1.50 0.1416 310s trend 0.3007 0.0436 6.89 5.3e-08 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 5.642 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 509.345 MSE: 31.834 Root MSE: 5.642 310s Multiple R-Squared: 0.237 Adjusted R-Squared: 0.094 310s 310s > 310s > ## *************** iterated SUR with 2 restrictions (EViews-like) ************** 310s > fitsurio4e <- systemfit( system, "SUR", data = Kmenta, methodResidCov = "noDfCor", 310s + restrict.matrix = restr2m, restrict.rhs = restr2q, maxit = 100, 310s + useMatrix = useMatrix ) 310s > print( summary( fitsurio4e ) ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 9 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 35 173 1.18 0.677 0.665 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 66.3 3.90 1.97 0.753 0.724 310s supply 20 16 106.7 6.67 2.58 0.602 0.527 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 3.31 4.06 310s supply 4.06 5.34 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 3.31 4.06 310s supply 4.06 5.34 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.000 0.966 310s supply 0.966 1.000 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 93.3596 6.8576 13.61 1.6e-15 *** 310s price -0.2398 0.0779 -3.08 0.0041 ** 310s income 0.3232 0.0163 19.81 < 2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.974 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 66.265 MSE: 3.898 Root MSE: 1.974 310s Multiple R-Squared: 0.753 Adjusted R-Squared: 0.724 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: consump ~ price + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 49.5456 6.9727 7.11 2.8e-08 *** 310s price 0.2602 0.0779 3.34 0.002 ** 310s farmPrice 0.2270 0.0164 13.81 8.9e-16 *** 310s trend 0.3232 0.0163 19.81 < 2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 2.583 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 106.722 MSE: 6.67 Root MSE: 2.583 310s Multiple R-Squared: 0.602 Adjusted R-Squared: 0.527 310s 310s > fitsuri4e <- systemfit( system2, "SUR", data = Kmenta, methodResidCov = "noDfCor", 310s + restrict.matrix = restr2m, restrict.rhs = restr2q, maxit = 100, 310s + useMatrix = useMatrix ) 310s > print( summary( fitsuri4e ) ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 20 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 35 570 82.4 0.391 0.629 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 66 3.88 1.97 0.754 0.725 310s supply 20 16 504 31.50 5.61 0.245 0.103 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 3.300 0.876 310s supply 0.876 25.203 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 3.300 0.876 310s supply 0.876 25.203 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.0000 0.0961 310s supply 0.0961 1.0000 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 97.6297 6.1560 15.86 < 2e-16 *** 310s price -0.2576 0.0709 -3.63 0.00089 *** 310s income 0.2976 0.0403 7.38 1.2e-08 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.97 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 65.995 MSE: 3.882 Root MSE: 1.97 310s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: price ~ income + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 89.5437 9.3372 9.59 2.5e-11 *** 310s income 0.2424 0.0709 3.42 0.0016 ** 310s farmPrice -0.1687 0.0988 -1.71 0.0967 . 310s trend 0.2976 0.0403 7.38 1.2e-08 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 5.613 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 504.066 MSE: 31.504 Root MSE: 5.613 310s Multiple R-Squared: 0.245 Adjusted R-Squared: 0.103 310s 310s > 310s > ## *************** iterated WSUR with 2 restrictions *************************** 310s > fitsurio4w <- systemfit( system, "SUR", data = Kmenta, restrict.matrix = restr2m, 310s + restrict.rhs = restr2q, maxit = 100, residCovWeighted = TRUE, 310s + useMatrix = useMatrix ) 310s > summary( fitsurio4w ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 10 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 35 176 1.74 0.671 0.705 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 67.2 3.95 1.99 0.749 0.720 310s supply 20 16 109.2 6.83 2.61 0.593 0.516 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 3.95 5.02 310s supply 5.02 6.83 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 3.95 5.02 310s supply 5.02 6.83 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.000 0.967 310s supply 0.967 1.000 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 92.4262 7.3543 12.57 1.6e-14 *** 310s price -0.2276 0.0850 -2.68 0.011 * 310s income 0.3203 0.0185 17.32 < 2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.988 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 67.206 MSE: 3.953 Root MSE: 1.988 310s Multiple R-Squared: 0.749 Adjusted R-Squared: 0.72 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: consump ~ price + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 48.7294 7.4587 6.53 1.5e-07 *** 310s price 0.2724 0.0850 3.20 0.0029 ** 310s farmPrice 0.2232 0.0190 11.76 1.0e-13 *** 310s trend 0.3203 0.0185 17.32 < 2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 2.613 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 109.234 MSE: 6.827 Root MSE: 2.613 310s Multiple R-Squared: 0.593 Adjusted R-Squared: 0.516 310s 310s > fitsuri4w <- systemfit( system2, "SUR", data = Kmenta, restrict.matrix = restr2m, 310s + restrict.rhs = restr2q, maxit = 100, residCovWeighted = TRUE, 310s + useMatrix = useMatrix ) 310s > summary( fitsuri4w ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 18 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 35 575 121 0.385 0.637 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 65.5 3.85 1.96 0.756 0.727 310s supply 20 16 509.3 31.83 5.64 0.237 0.094 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 3.85 1.23 310s supply 1.23 31.83 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 3.85 1.23 310s supply 1.23 31.83 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.000 0.111 310s supply 0.111 1.000 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 98.0361 6.7437 14.54 2.2e-16 *** 310s price -0.2646 0.0777 -3.40 0.0017 ** 310s income 0.3007 0.0436 6.89 5.3e-08 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.963 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 65.531 MSE: 3.855 Root MSE: 1.963 310s Multiple R-Squared: 0.756 Adjusted R-Squared: 0.727 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: price ~ income + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 90.0052 10.4368 8.62 3.5e-10 *** 310s income 0.2354 0.0777 3.03 0.0046 ** 310s farmPrice -0.1667 0.1108 -1.50 0.1416 310s trend 0.3007 0.0436 6.89 5.3e-08 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 5.642 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 509.349 MSE: 31.834 Root MSE: 5.642 310s Multiple R-Squared: 0.237 Adjusted R-Squared: 0.094 310s 310s > 310s > 310s > ## *************** iterated SUR with 2 restrictions via R and restrict.regMat **************** 310s > fitsurio5 <- systemfit( system, "SUR", data = Kmenta, restrict.matrix = restr3m, 310s + restrict.rhs = restr3q, restrict.regMat = tc, maxit = 100, 310s + useMatrix = useMatrix ) 310s > print( summary( fitsurio5 ) ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 10 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 35 176 1.74 0.671 0.705 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 67.2 3.95 1.99 0.749 0.720 310s supply 20 16 109.2 6.83 2.61 0.593 0.516 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 3.95 5.02 310s supply 5.02 6.83 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 3.95 5.02 310s supply 5.02 6.83 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.000 0.967 310s supply 0.967 1.000 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 92.4262 7.3543 12.57 1.6e-14 *** 310s price -0.2276 0.0850 -2.68 0.011 * 310s income 0.3203 0.0185 17.32 < 2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.988 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 67.206 MSE: 3.953 Root MSE: 1.988 310s Multiple R-Squared: 0.749 Adjusted R-Squared: 0.72 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: consump ~ price + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 48.7295 7.4587 6.53 1.5e-07 *** 310s price 0.2724 0.0850 3.20 0.0029 ** 310s farmPrice 0.2232 0.0190 11.76 1.0e-13 *** 310s trend 0.3203 0.0185 17.32 < 2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 2.613 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 109.234 MSE: 6.827 Root MSE: 2.613 310s Multiple R-Squared: 0.593 Adjusted R-Squared: 0.516 310s 310s > fitsuri5 <- systemfit( system2, "SUR", data = Kmenta, restrict.matrix = restr3m, 310s + restrict.rhs = restr3q, restrict.regMat = tc, maxit = 100, 310s + useMatrix = useMatrix ) 310s > print( summary( fitsuri5 ) ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 19 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 35 575 121 0.385 0.637 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 65.5 3.85 1.96 0.756 0.727 310s supply 20 16 509.3 31.83 5.64 0.237 0.094 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 3.85 1.23 310s supply 1.23 31.83 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 3.85 1.23 310s supply 1.23 31.83 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.000 0.111 310s supply 0.111 1.000 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 98.0356 6.7437 14.54 2.2e-16 *** 310s price -0.2646 0.0777 -3.40 0.0017 ** 310s income 0.3007 0.0436 6.89 5.3e-08 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.963 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 65.532 MSE: 3.855 Root MSE: 1.963 310s Multiple R-Squared: 0.756 Adjusted R-Squared: 0.727 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: price ~ income + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 90.0046 10.4367 8.62 3.5e-10 *** 310s income 0.2354 0.0777 3.03 0.0046 ** 310s farmPrice -0.1667 0.1108 -1.50 0.1416 310s trend 0.3007 0.0436 6.89 5.3e-08 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 5.642 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 509.345 MSE: 31.834 Root MSE: 5.642 310s Multiple R-Squared: 0.237 Adjusted R-Squared: 0.094 310s 310s > 310s > ## ********* iterated SUR with 2 restrictions via R and restrict.regMat (EViews-like) ********** 310s > fitsurio5e <- systemfit( system, "SUR", data = Kmenta, methodResidCov = "noDfCor", 310s + restrict.matrix = restr3m, restrict.rhs = restr3q, restrict.regMat = tc, 310s + maxit = 100, useMatrix = useMatrix ) 310s > print( summary( fitsurio5e ) ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 9 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 35 173 1.18 0.677 0.665 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 66.3 3.90 1.97 0.753 0.724 310s supply 20 16 106.7 6.67 2.58 0.602 0.527 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 3.31 4.06 310s supply 4.06 5.34 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 3.31 4.06 310s supply 4.06 5.34 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.000 0.966 310s supply 0.966 1.000 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 93.3596 6.8576 13.61 1.6e-15 *** 310s price -0.2398 0.0779 -3.08 0.0041 ** 310s income 0.3232 0.0163 19.81 < 2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.974 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 66.265 MSE: 3.898 Root MSE: 1.974 310s Multiple R-Squared: 0.753 Adjusted R-Squared: 0.724 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: consump ~ price + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 49.5456 6.9727 7.11 2.8e-08 *** 310s price 0.2602 0.0779 3.34 0.002 ** 310s farmPrice 0.2270 0.0164 13.81 8.9e-16 *** 310s trend 0.3232 0.0163 19.81 < 2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 2.583 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 106.722 MSE: 6.67 Root MSE: 2.583 310s Multiple R-Squared: 0.602 Adjusted R-Squared: 0.527 310s 310s > fitsuri5e <- systemfit( system2, "SUR", data = Kmenta, methodResidCov = "noDfCor", 310s + restrict.matrix = restr3m, restrict.rhs = restr3q, restrict.regMat = tc, 310s + maxit = 100, useMatrix = useMatrix ) 310s > print( summary( fitsuri5e ) ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 20 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 35 570 82.4 0.391 0.629 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 66 3.88 1.97 0.754 0.725 310s supply 20 16 504 31.50 5.61 0.245 0.103 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 3.300 0.876 310s supply 0.876 25.203 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 3.300 0.876 310s supply 0.876 25.203 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.0000 0.0961 310s supply 0.0961 1.0000 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 97.6297 6.1560 15.86 < 2e-16 *** 310s price -0.2576 0.0709 -3.63 0.00089 *** 310s income 0.2976 0.0403 7.38 1.2e-08 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.97 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 65.995 MSE: 3.882 Root MSE: 1.97 310s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: price ~ income + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 89.5437 9.3372 9.59 2.5e-11 *** 310s income 0.2424 0.0709 3.42 0.0016 ** 310s farmPrice -0.1687 0.0988 -1.71 0.0967 . 310s trend 0.2976 0.0403 7.38 1.2e-08 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 5.613 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 504.066 MSE: 31.504 Root MSE: 5.613 310s Multiple R-Squared: 0.245 Adjusted R-Squared: 0.103 310s 310s > nobs( fitsuri5e ) 310s [1] 40 310s > 310s > ## ********* iterated SUR with 2 restrictions via R and restrict.regMat (methodResidCov="Theil") ********** 310s > fitsurio5r2 <- systemfit( system, "SUR", data = Kmenta, methodResidCov = "Theil", 310s + restrict.matrix = restr3m, restrict.rhs = restr3q, restrict.regMat = tc, 310s + maxit = 100, x = TRUE, useMatrix = useMatrix ) 310s > print( summary( fitsurio5r2 ) ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s warning: convergence not achieved after 100 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 35 253 -1.67 0.527 0.927 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 95.8 5.63 2.37 0.643 0.601 310s supply 20 16 157.7 9.86 3.14 0.412 0.301 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 4.26 5.29 310s supply 5.29 6.69 310s 310s warning: this covariance matrix is NOT positive semidefinit! 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 5.63 7.56 310s supply 7.56 9.86 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.000 0.982 310s supply 0.982 1.000 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 78.0342 7.1638 10.89 8.6e-13 *** 310s price -0.0647 0.0815 -0.79 0.43 310s income 0.3007 0.0131 23.01 < 2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 2.373 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 95.76 MSE: 5.633 Root MSE: 2.373 310s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.601 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: consump ~ price + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 34.1958 7.2257 4.73 3.6e-05 *** 310s price 0.4353 0.0815 5.34 5.7e-06 *** 310s farmPrice 0.2070 0.0124 16.68 < 2e-16 *** 310s trend 0.3007 0.0131 23.01 < 2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 3.14 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 157.737 MSE: 9.859 Root MSE: 3.14 310s Multiple R-Squared: 0.412 Adjusted R-Squared: 0.301 310s 310s > fitsuri5r2 <- systemfit( system2, "SUR", data = Kmenta, methodResidCov = "Theil", 310s + restrict.matrix = restr3m, restrict.rhs = restr3q, restrict.regMat = tc, 310s + maxit = 100, x = TRUE, useMatrix = useMatrix ) 310s > print( summary( fitsuri5r2 ) ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 21 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 35 576 121 0.384 0.637 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 65.4 3.85 1.96 0.756 0.727 310s supply 20 16 510.8 31.92 5.65 0.235 0.091 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 3.85 1.34 310s supply 1.34 31.92 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 3.85 1.34 310s supply 1.34 31.92 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.000 0.117 310s supply 0.117 1.000 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 98.2200 6.7593 14.53 2.2e-16 *** 310s price -0.2669 0.0778 -3.43 0.0016 ** 310s income 0.3011 0.0435 6.92 4.9e-08 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.962 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 65.447 MSE: 3.85 Root MSE: 1.962 310s Multiple R-Squared: 0.756 Adjusted R-Squared: 0.727 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: price ~ income + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 90.2167 10.4342 8.65 3.3e-10 *** 310s income 0.2331 0.0778 3.00 0.005 ** 310s farmPrice -0.1666 0.1111 -1.50 0.143 310s trend 0.3011 0.0435 6.92 4.9e-08 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 5.65 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 510.75 MSE: 31.922 Root MSE: 5.65 310s Multiple R-Squared: 0.235 Adjusted R-Squared: 0.091 310s 310s > 310s > ## ********* iterated SUR with 2 restrictions via R and restrict.regMat (methodResidCov="max") ********** 310s > # fitsuri5e <- systemfit( system, "SUR", data = Kmenta, methodResidCov = "max", 310s > # restrict.matrix = restr3m, restrict.rhs = restr3q, restrict.regMat = tc, 310s > # maxit = 100, useMatrix = useMatrix ) 310s > # print( summary( fitsuri5e ) ) 310s > # print( round( vcov( fitsuri5e ), digits = 6 ) ) 310s > # disabled, because the estimation does not converge 310s > 310s > ## ********* iterated WSUR with 2 restrictions via R and restrict.regMat (methodResidCov="Theil") ********** 310s > fitsurio5wr2 <- systemfit( system, "SUR", data = Kmenta, methodResidCov = "Theil", 310s + restrict.matrix = restr3m, restrict.rhs = restr3q, restrict.regMat = tc, 310s + maxit = 100, residCovWeighted = TRUE, useMatrix = useMatrix ) 310s > summary( fitsurio5wr2 ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s warning: convergence not achieved after 100 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 35 253 -1.67 0.527 0.927 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 95.8 5.63 2.37 0.643 0.601 310s supply 20 16 157.7 9.86 3.14 0.412 0.301 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 4.26 5.29 310s supply 5.29 6.69 310s 310s warning: this covariance matrix is NOT positive semidefinit! 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 5.63 7.56 310s supply 7.56 9.86 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.000 0.982 310s supply 0.982 1.000 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 78.0342 7.1638 10.89 8.6e-13 *** 310s price -0.0647 0.0815 -0.79 0.43 310s income 0.3007 0.0131 23.01 < 2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 2.373 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 95.76 MSE: 5.633 Root MSE: 2.373 310s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.601 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: consump ~ price + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 34.1958 7.2257 4.73 3.6e-05 *** 310s price 0.4353 0.0815 5.34 5.7e-06 *** 310s farmPrice 0.2070 0.0124 16.68 < 2e-16 *** 310s trend 0.3007 0.0131 23.01 < 2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 3.14 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 157.737 MSE: 9.859 Root MSE: 3.14 310s Multiple R-Squared: 0.412 Adjusted R-Squared: 0.301 310s 310s > fitsuri5wr2 <- systemfit( system2, "SUR", data = Kmenta, methodResidCov = "Theil", 310s + restrict.matrix = restr3m, restrict.rhs = restr3q, restrict.regMat = tc, 310s + maxit = 100, residCovWeighted = TRUE, useMatrix = useMatrix ) 310s > summary( fitsuri5wr2 ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 19 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 35 576 121 0.384 0.637 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 65.4 3.85 1.96 0.756 0.727 310s supply 20 16 510.8 31.92 5.65 0.235 0.091 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 3.85 1.34 310s supply 1.34 31.92 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 3.85 1.34 310s supply 1.34 31.92 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.000 0.117 310s supply 0.117 1.000 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 98.2200 6.7593 14.53 2.2e-16 *** 310s price -0.2669 0.0778 -3.43 0.0016 ** 310s income 0.3011 0.0435 6.92 4.9e-08 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.962 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 65.447 MSE: 3.85 Root MSE: 1.962 310s Multiple R-Squared: 0.756 Adjusted R-Squared: 0.727 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: price ~ income + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 90.2168 10.4342 8.65 3.3e-10 *** 310s income 0.2331 0.0778 3.00 0.005 ** 310s farmPrice -0.1666 0.1111 -1.50 0.143 310s trend 0.3011 0.0435 6.92 4.9e-08 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 5.65 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 510.75 MSE: 31.922 Root MSE: 5.65 310s Multiple R-Squared: 0.235 Adjusted R-Squared: 0.091 310s 310s > 310s > 310s > ## *********** estimations with a single regressor ************ 310s > fitsurS1 <- systemfit( 310s + list( price ~ consump - 1, farmPrice ~ consump + trend ), "SUR", 310s + data = Kmenta, useMatrix = useMatrix ) 310s > print( summary( fitsurS1 ) ) 310s 310s systemfit results 310s method: SUR 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 36 2060 2543 0.449 0.465 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s eq1 20 19 848 44.6 6.68 -0.271 -0.271 310s eq2 20 17 1211 71.3 8.44 0.605 0.559 310s 310s The covariance matrix of the residuals used for estimation 310s eq1 eq2 310s eq1 44.6 -20.5 310s eq2 -20.5 68.9 310s 310s The covariance matrix of the residuals 310s eq1 eq2 310s eq1 44.6 -25.3 310s eq2 -25.3 71.3 310s 310s The correlations of the residuals 310s eq1 eq2 310s eq1 1.000 -0.448 310s eq2 -0.448 1.000 310s 310s 310s SUR estimates for 'eq1' (equation 1) 310s Model Formula: price ~ consump - 1 310s 310s Estimate Std. Error t value Pr(>|t|) 310s consump 0.9902 0.0148 66.9 <2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 6.682 on 19 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 19 310s SSR: 848.208 MSE: 44.643 Root MSE: 6.682 310s Multiple R-Squared: -0.271 Adjusted R-Squared: -0.271 310s 310s 310s SUR estimates for 'eq2' (equation 2) 310s Model Formula: farmPrice ~ consump + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) -108.487 47.754 -2.27 0.03638 * 310s consump 2.123 0.477 4.45 0.00035 *** 310s trend -0.862 0.303 -2.85 0.01111 * 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 8.441 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 1211.393 MSE: 71.258 Root MSE: 8.441 310s Multiple R-Squared: 0.605 Adjusted R-Squared: 0.559 310s 310s > nobs( fitsurS1 ) 310s [1] 40 310s > fitsurS2 <- systemfit( 310s + list( consump ~ price - 1, consump ~ trend - 1 ), "SUR", 310s + data = Kmenta, useMatrix = useMatrix ) 310s > print( summary( fitsurS2 ) ) 310s 310s systemfit results 310s method: SUR 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 38 47370 110949 -87.3 -5.28 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s eq1 20 19 861 45.3 6.73 -2.21 -2.21 310s eq2 20 19 46509 2447.8 49.48 -172.47 -172.47 310s 310s The covariance matrix of the residuals used for estimation 310s eq1 eq2 310s eq1 45.34 -5.15 310s eq2 -5.15 2447.84 310s 310s The covariance matrix of the residuals 310s eq1 eq2 310s eq1 45.34 -6.37 310s eq2 -6.37 2447.84 310s 310s The correlations of the residuals 310s eq1 eq2 310s eq1 1.0000 -0.0439 310s eq2 -0.0439 1.0000 310s 310s 310s SUR estimates for 'eq1' (equation 1) 310s Model Formula: consump ~ price - 1 310s 310s Estimate Std. Error t value Pr(>|t|) 310s price 1.006 0.015 67 <2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 6.734 on 19 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 19 310s SSR: 861.496 MSE: 45.342 Root MSE: 6.734 310s Multiple R-Squared: -2.213 Adjusted R-Squared: -2.213 310s 310s 310s SUR estimates for 'eq2' (equation 2) 310s Model Formula: consump ~ trend - 1 310s 310s Estimate Std. Error t value Pr(>|t|) 310s trend 7.410 0.924 8.02 1.6e-07 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 49.476 on 19 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 19 310s SSR: 46508.986 MSE: 2447.841 Root MSE: 49.476 310s Multiple R-Squared: -172.467 Adjusted R-Squared: -172.467 310s 310s > nobs( fitsurS2 ) 310s [1] 40 310s > fitsurS3 <- systemfit( 310s + list( consump ~ trend - 1, price ~ trend - 1 ), "SUR", 310s + data = Kmenta, useMatrix = useMatrix ) 310s > nobs( fitsurS3 ) 310s [1] 40 310s > print( summary( fitsurS3 ) ) 310s 310s systemfit results 310s method: SUR 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 38 93537 108970 -99 -0.977 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s eq1 20 19 46509 2448 49.5 -172.5 -172.5 310s eq2 20 19 47028 2475 49.8 -69.5 -69.5 310s 310s The covariance matrix of the residuals used for estimation 310s eq1 eq2 310s eq1 2448 2439 310s eq2 2439 2475 310s 310s The covariance matrix of the residuals 310s eq1 eq2 310s eq1 2448 2439 310s eq2 2439 2475 310s 310s The correlations of the residuals 310s eq1 eq2 310s eq1 1.000 0.988 310s eq2 0.988 1.000 310s 310s 310s SUR estimates for 'eq1' (equation 1) 310s Model Formula: consump ~ trend - 1 310s 310s Estimate Std. Error t value Pr(>|t|) 310s trend 7.405 0.924 8.02 1.6e-07 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 49.476 on 19 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 19 310s SSR: 46508.922 MSE: 2447.838 Root MSE: 49.476 310s Multiple R-Squared: -172.467 Adjusted R-Squared: -172.467 310s 310s 310s SUR estimates for 'eq2' (equation 2) 310s Model Formula: price ~ trend - 1 310s 310s Estimate Std. Error t value Pr(>|t|) 310s trend 7.318 0.929 7.88 2.1e-07 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 49.751 on 19 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 19 310s SSR: 47028.107 MSE: 2475.164 Root MSE: 49.751 310s Multiple R-Squared: -69.48 Adjusted R-Squared: -69.48 310s 310s > fitsurS4 <- systemfit( 310s + list( consump ~ trend - 1, price ~ trend - 1 ), "SUR", 310s + data = Kmenta, restrict.matrix = matrix( c( 1, -1 ), nrow = 1 ), 310s + useMatrix = useMatrix ) 310s > print( summary( fitsurS4 ) ) 310s 310s systemfit results 310s method: SUR 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 39 93552 111731 -99 -1.03 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s eq1 20 19 46510 2448 49.5 -172.5 -172.5 310s eq2 20 19 47042 2476 49.8 -69.5 -69.5 310s 310s The covariance matrix of the residuals used for estimation 310s eq1 eq2 310s eq1 2448 2439 310s eq2 2439 2475 310s 310s The covariance matrix of the residuals 310s eq1 eq2 310s eq1 2448 2439 310s eq2 2439 2476 310s 310s The correlations of the residuals 310s eq1 eq2 310s eq1 1.000 0.988 310s eq2 0.988 1.000 310s 310s 310s SUR estimates for 'eq1' (equation 1) 310s Model Formula: consump ~ trend - 1 310s 310s Estimate Std. Error t value Pr(>|t|) 310s trend 7.388 0.923 8 9.4e-10 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 49.476 on 19 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 19 310s SSR: 46509.787 MSE: 2447.884 Root MSE: 49.476 310s Multiple R-Squared: -172.47 Adjusted R-Squared: -172.47 310s 310s 310s SUR estimates for 'eq2' (equation 2) 310s Model Formula: price ~ trend - 1 310s 310s Estimate Std. Error t value Pr(>|t|) 310s trend 7.388 0.923 8 9.4e-10 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 49.758 on 19 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 19 310s SSR: 47041.803 MSE: 2475.884 Root MSE: 49.758 310s Multiple R-Squared: -69.501 Adjusted R-Squared: -69.501 310s 310s > nobs( fitsurS4 ) 310s [1] 40 310s > fitsurS5 <- systemfit( 310s + list( consump ~ 1, price ~ 1 ), "SUR", 310s + data = Kmenta, useMatrix = useMatrix ) 310s > print( summary( fitsurS5 ) ) 310s 310s systemfit results 310s method: SUR 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 38 935 491 0 0 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s eq1 20 19 268 14.1 3.76 0 0 310s eq2 20 19 667 35.1 5.93 0 0 310s 310s The covariance matrix of the residuals used for estimation 310s eq1 eq2 310s eq1 14.11 2.18 310s eq2 2.18 35.12 310s 310s The covariance matrix of the residuals 310s eq1 eq2 310s eq1 14.11 2.18 310s eq2 2.18 35.12 310s 310s The correlations of the residuals 310s eq1 eq2 310s eq1 1.0000 0.0981 310s eq2 0.0981 1.0000 310s 310s 310s SUR estimates for 'eq1' (equation 1) 310s Model Formula: consump ~ 1 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 100.90 0.84 120 <2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 3.756 on 19 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 19 310s SSR: 268.114 MSE: 14.111 Root MSE: 3.756 310s Multiple R-Squared: 0 Adjusted R-Squared: 0 310s 310s 310s SUR estimates for 'eq2' (equation 2) 310s Model Formula: price ~ 1 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 100.02 1.33 75.5 <2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 5.926 on 19 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 19 310s SSR: 667.251 MSE: 35.118 Root MSE: 5.926 310s Multiple R-Squared: 0 Adjusted R-Squared: 0 310s 310s > nobs( fitsurS5 ) 310s [1] 40 310s > 310s > 310s > ## **************** shorter summaries ********************** 310s > print( summary( fitsur1e2, useDfSys = TRUE, equations = FALSE ) ) 310s 310s systemfit results 310s method: SUR 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 33 172 -0.896 0.679 1.01 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 66.8 3.93 1.98 0.751 0.722 310s supply 20 16 105.3 6.58 2.57 0.607 0.534 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 3.73 4.28 310s supply 4.28 5.78 310s 310s warning: this covariance matrix is NOT positive semidefinit! 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 3.93 5.17 310s supply 5.17 6.58 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.000 0.984 310s supply 0.984 1.000 310s 310s 310s Coefficients: 310s Estimate Std. Error t value Pr(>|t|) 310s demand_(Intercept) 99.2120 7.5127 13.21 1.0e-14 *** 310s demand_price -0.2667 0.0877 -3.04 0.0046 ** 310s demand_income 0.2908 0.0406 7.16 3.3e-08 *** 310s supply_(Intercept) 63.0768 10.9735 5.75 2.0e-06 *** 310s supply_price 0.1439 0.0943 1.52 0.1368 310s supply_farmPrice 0.2064 0.0384 5.37 6.1e-06 *** 310s supply_trend 0.3325 0.0640 5.19 1.0e-05 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s > 310s > print( summary( fitsur2e, useDfSys = FALSE, residCov = FALSE ) ) 310s 310s systemfit results 310s method: SUR 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 34 180 0.62 0.663 0.707 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 72.6 4.27 2.07 0.729 0.697 310s supply 20 16 107.9 6.75 2.60 0.597 0.522 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 98.7799 6.9687 14.17 7.6e-11 *** 310s price -0.2354 0.0795 -2.96 0.0088 ** 310s income 0.2631 0.0344 7.66 6.6e-07 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 2.066 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 72.577 MSE: 4.269 Root MSE: 2.066 310s Multiple R-Squared: 0.729 Adjusted R-Squared: 0.697 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: consump ~ price + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 67.6039 9.5712 7.06 2.7e-06 *** 310s price 0.1328 0.0853 1.56 0.14 310s farmPrice 0.1785 0.0305 5.85 2.5e-05 *** 310s trend 0.2631 0.0344 7.66 9.7e-07 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 2.597 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 107.917 MSE: 6.745 Root MSE: 2.597 310s Multiple R-Squared: 0.597 Adjusted R-Squared: 0.522 310s 310s > 310s > print( summary( fitsur3 ), equations = FALSE ) 310s 310s systemfit results 310s method: SUR 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 34 179 0.933 0.665 0.753 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 71.6 4.21 2.05 0.733 0.702 310s supply 20 16 107.8 6.74 2.60 0.598 0.523 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 3.78 4.47 310s supply 4.47 5.94 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 4.21 5.24 310s supply 5.24 6.74 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.000 0.983 310s supply 0.983 1.000 310s 310s 310s Coefficients: 310s Estimate Std. Error t value Pr(>|t|) 310s demand_(Intercept) 98.8408 7.5581 13.08 8.0e-15 *** 310s demand_price -0.2398 0.0860 -2.79 0.0086 ** 310s demand_income 0.2670 0.0368 7.25 2.2e-08 *** 310s supply_(Intercept) 67.4283 10.6647 6.32 3.3e-07 *** 310s supply_price 0.1332 0.0953 1.40 0.1713 310s supply_farmPrice 0.1795 0.0337 5.33 6.3e-06 *** 310s supply_trend 0.2670 0.0368 7.25 2.2e-08 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s > 310s > print( summary( fitsur4r3 ), residCov = FALSE, equations = FALSE ) 310s 310s systemfit results 310s method: SUR 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 35 173 0.217 0.677 0.702 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 66.4 3.91 1.98 0.752 0.723 310s supply 20 16 106.9 6.68 2.58 0.601 0.526 310s 310s 310s Coefficients: 310s Estimate Std. Error t value Pr(>|t|) 310s demand_(Intercept) 93.1978 7.3168 12.74 1.1e-14 *** 310s demand_price -0.2381 0.0829 -2.87 0.0069 ** 310s demand_income 0.3231 0.0170 18.96 < 2e-16 *** 310s supply_(Intercept) 49.3676 7.4381 6.64 1.1e-07 *** 310s supply_price 0.2619 0.0829 3.16 0.0033 ** 310s supply_farmPrice 0.2271 0.0171 13.29 3.1e-15 *** 310s supply_trend 0.3231 0.0170 18.96 < 2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s > 310s > print( summary( fitsur5, residCov = FALSE ), equations = FALSE ) 310s 310s systemfit results 310s method: SUR 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 35 165 1.76 0.691 0.69 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 64 3.76 1.94 0.761 0.733 310s supply 20 16 101 6.34 2.52 0.622 0.551 310s 310s 310s Coefficients: 310s Estimate Std. Error t value Pr(>|t|) 310s demand_(Intercept) 96.8275 7.4665 12.97 6.2e-15 *** 310s demand_price -0.2798 0.0840 -3.33 0.002 ** 310s demand_income 0.3286 0.0206 15.93 < 2e-16 *** 310s supply_(Intercept) 52.9386 7.6655 6.91 5.1e-08 *** 310s supply_price 0.2202 0.0840 2.62 0.013 * 310s supply_farmPrice 0.2327 0.0212 10.97 7.2e-13 *** 310s supply_trend 0.3286 0.0206 15.93 < 2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s > 310s > print( summary( fitsur5w, equations = FALSE, residCov = FALSE ), 310s + equations = TRUE ) 310s 310s systemfit results 310s method: SUR 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 35 166 1.75 0.69 0.691 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 64.2 3.77 1.94 0.761 0.733 310s supply 20 16 102.0 6.37 2.52 0.620 0.548 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 96.4421 7.4234 12.99 6e-15 *** 310s price -0.2753 0.0838 -3.29 0.0023 ** 310s income 0.3280 0.0202 16.21 <2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.943 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 64.16 MSE: 3.774 Root MSE: 1.943 310s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: consump ~ price + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 52.5761 7.6099 6.91 5.0e-08 *** 310s price 0.2247 0.0838 2.68 0.011 * 310s farmPrice 0.2318 0.0208 11.14 4.7e-13 *** 310s trend 0.3280 0.0202 16.21 < 2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 2.524 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 101.967 MSE: 6.373 Root MSE: 2.524 310s Multiple R-Squared: 0.62 Adjusted R-Squared: 0.548 310s 310s > 310s > print( summary( fitsuri1r3, useDfSys = FALSE ), residCov = FALSE ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 7 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 33 109 4.06 0.884 0.96 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 66.8 3.93 1.98 0.751 0.721 310s supply 20 16 41.7 2.61 1.61 0.937 0.926 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 93.5427 7.3858 12.67 4.4e-10 *** 310s price -0.2285 0.0877 -2.60 0.019 * 310s income 0.3097 0.0458 6.76 3.3e-06 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.983 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 66.826 MSE: 3.931 Root MSE: 1.983 310s Multiple R-Squared: 0.751 Adjusted R-Squared: 0.721 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: price ~ income + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 89.1830 3.3861 26.3 1.3e-14 *** 310s income 0.6639 0.0423 15.7 3.8e-11 *** 310s farmPrice -0.4715 0.0370 -12.8 8.5e-10 *** 310s trend -0.7955 0.0645 -12.3 1.4e-09 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.615 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 41.708 MSE: 2.607 Root MSE: 1.615 310s Multiple R-Squared: 0.937 Adjusted R-Squared: 0.926 310s 310s > 310s > print( summary( fitsuri2 ), residCov = FALSE ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 21 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 34 587 110 0.372 0.669 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 67 3.94 1.99 0.75 0.721 310s supply 20 16 520 32.52 5.70 0.22 0.074 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 107.3678 7.4986 14.32 4.4e-16 *** 310s price -0.3945 0.0912 -4.33 0.00013 *** 310s income 0.3382 0.0466 7.25 2.1e-08 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.986 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 67.024 MSE: 3.943 Root MSE: 1.986 310s Multiple R-Squared: 0.75 Adjusted R-Squared: 0.721 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: price ~ income + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 85.0448 12.1069 7.02 4.2e-08 *** 310s income 0.3125 0.1233 2.53 0.016 * 310s farmPrice -0.1972 0.1157 -1.70 0.097 . 310s trend 0.3382 0.0466 7.25 2.1e-08 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 5.703 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 520.329 MSE: 32.521 Root MSE: 5.703 310s Multiple R-Squared: 0.22 Adjusted R-Squared: 0.074 310s 310s > 310s > print( summary( fitsuri3e, residCov = FALSE, equations = FALSE ) ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 22 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 34 588 74.9 0.372 0.664 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 67.5 3.97 1.99 0.748 0.719 310s supply 20 16 520.2 32.51 5.70 0.220 0.074 310s 310s 310s Coefficients: 310s Estimate Std. Error t value Pr(>|t|) 310s demand_(Intercept) 107.8051 6.9270 15.56 < 2e-16 *** 310s demand_price -0.3986 0.0843 -4.73 3.8e-05 *** 310s demand_income 0.3379 0.0431 7.84 4.0e-09 *** 310s supply_(Intercept) 85.1071 10.8287 7.86 3.8e-09 *** 310s supply_income 0.3106 0.1101 2.82 0.0079 ** 310s supply_farmPrice -0.1960 0.1034 -1.89 0.0667 . 310s supply_trend 0.3379 0.0431 7.84 4.0e-09 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s > 310s > print( summary( fitsurio4, residCov = FALSE ), equations = FALSE ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 10 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 35 176 1.74 0.671 0.705 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 67.2 3.95 1.99 0.749 0.720 310s supply 20 16 109.2 6.83 2.61 0.593 0.516 310s 310s 310s Coefficients: 310s Estimate Std. Error t value Pr(>|t|) 310s demand_(Intercept) 92.4262 7.3543 12.57 1.6e-14 *** 310s demand_price -0.2276 0.0850 -2.68 0.0112 * 310s demand_income 0.3203 0.0185 17.32 < 2e-16 *** 310s supply_(Intercept) 48.7295 7.4587 6.53 1.5e-07 *** 310s supply_price 0.2724 0.0850 3.20 0.0029 ** 310s supply_farmPrice 0.2232 0.0190 11.76 1.0e-13 *** 310s supply_trend 0.3203 0.0185 17.32 < 2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s > print( summary( fitsuri4, equations = FALSE ), residCov = FALSE ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 19 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 35 575 121 0.385 0.637 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 65.5 3.85 1.96 0.756 0.727 310s supply 20 16 509.3 31.83 5.64 0.237 0.094 310s 310s 310s Coefficients: 310s Estimate Std. Error t value Pr(>|t|) 310s demand_(Intercept) 98.0356 6.7437 14.54 2.2e-16 *** 310s demand_price -0.2646 0.0777 -3.40 0.0017 ** 310s demand_income 0.3007 0.0436 6.89 5.3e-08 *** 310s supply_(Intercept) 90.0046 10.4367 8.62 3.5e-10 *** 310s supply_income 0.2354 0.0777 3.03 0.0046 ** 310s supply_farmPrice -0.1667 0.1108 -1.50 0.1416 310s supply_trend 0.3007 0.0436 6.89 5.3e-08 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s > 310s > print( summary( fitsuri4w, useDfSys = FALSE, equations = FALSE ) ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 18 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 35 575 121 0.385 0.637 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 65.5 3.85 1.96 0.756 0.727 310s supply 20 16 509.3 31.83 5.64 0.237 0.094 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 3.85 1.23 310s supply 1.23 31.83 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 3.85 1.23 310s supply 1.23 31.83 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.000 0.111 310s supply 0.111 1.000 310s 310s 310s Coefficients: 310s Estimate Std. Error t value Pr(>|t|) 310s demand_(Intercept) 98.0361 6.7437 14.54 5.1e-11 *** 310s demand_price -0.2646 0.0777 -3.40 0.0034 ** 310s demand_income 0.3007 0.0436 6.89 2.6e-06 *** 310s supply_(Intercept) 90.0052 10.4368 8.62 2.1e-07 *** 310s supply_income 0.2354 0.0777 3.03 0.0080 ** 310s supply_farmPrice -0.1667 0.1108 -1.50 0.1521 310s supply_trend 0.3007 0.0436 6.89 3.6e-06 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s > 310s > print( summary( fitsurio5r2, equations = FALSE ) ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s warning: convergence not achieved after 100 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 35 253 -1.67 0.527 0.927 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 95.8 5.63 2.37 0.643 0.601 310s supply 20 16 157.7 9.86 3.14 0.412 0.301 310s 310s The covariance matrix of the residuals used for estimation 310s demand supply 310s demand 4.26 5.29 310s supply 5.29 6.69 310s 310s warning: this covariance matrix is NOT positive semidefinit! 310s 310s The covariance matrix of the residuals 310s demand supply 310s demand 5.63 7.56 310s supply 7.56 9.86 310s 310s The correlations of the residuals 310s demand supply 310s demand 1.000 0.982 310s supply 0.982 1.000 310s 310s 310s Coefficients: 310s Estimate Std. Error t value Pr(>|t|) 310s demand_(Intercept) 78.0342 7.1638 10.89 8.6e-13 *** 310s demand_price -0.0647 0.0815 -0.79 0.43 310s demand_income 0.3007 0.0131 23.01 < 2e-16 *** 310s supply_(Intercept) 34.1958 7.2257 4.73 3.6e-05 *** 310s supply_price 0.4353 0.0815 5.34 5.7e-06 *** 310s supply_farmPrice 0.2070 0.0124 16.68 < 2e-16 *** 310s supply_trend 0.3007 0.0131 23.01 < 2e-16 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s > print( summary( fitsuri5r2 ), residCov = FALSE ) 310s 310s systemfit results 310s method: iterated SUR 310s 310s convergence achieved after 21 iterations 310s 310s N DF SSR detRCov OLS-R2 McElroy-R2 310s system 40 35 576 121 0.384 0.637 310s 310s N DF SSR MSE RMSE R2 Adj R2 310s demand 20 17 65.4 3.85 1.96 0.756 0.727 310s supply 20 16 510.8 31.92 5.65 0.235 0.091 310s 310s 310s SUR estimates for 'demand' (equation 1) 310s Model Formula: consump ~ price + income 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 98.2200 6.7593 14.53 2.2e-16 *** 310s price -0.2669 0.0778 -3.43 0.0016 ** 310s income 0.3011 0.0435 6.92 4.9e-08 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 1.962 on 17 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 17 310s SSR: 65.447 MSE: 3.85 Root MSE: 1.962 310s Multiple R-Squared: 0.756 Adjusted R-Squared: 0.727 310s 310s 310s SUR estimates for 'supply' (equation 2) 310s Model Formula: price ~ income + farmPrice + trend 310s 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 90.2167 10.4342 8.65 3.3e-10 *** 310s income 0.2331 0.0778 3.00 0.005 ** 310s farmPrice -0.1666 0.1111 -1.50 0.143 310s trend 0.3011 0.0435 6.92 4.9e-08 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s 310s Residual standard error: 5.65 on 16 degrees of freedom 310s Number of observations: 20 Degrees of Freedom: 16 310s SSR: 510.75 MSE: 31.922 Root MSE: 5.65 310s Multiple R-Squared: 0.235 Adjusted R-Squared: 0.091 310s 310s > 310s > 310s > ## ****************** residuals ************************** 310s > print( residuals( fitsur1e2 ) ) 310s demand supply 310s 1 0.615 0.41825 310s 2 -0.598 -0.00625 310s 3 2.419 2.75649 310s 4 1.609 1.81727 310s 5 2.145 2.53566 310s 6 1.332 1.53338 310s 7 1.727 2.25581 310s 8 -2.718 -3.56834 310s 9 -1.229 -2.02733 310s 10 2.088 2.53245 310s 11 -0.789 -1.40733 310s 12 -2.799 -3.01416 310s 13 -1.831 -2.30119 310s 14 -0.461 0.01871 310s 15 1.974 2.93624 310s 16 -3.291 -4.00484 310s 17 -0.652 -0.45580 310s 18 -1.899 -3.18683 310s 19 2.030 2.18284 310s 20 0.329 0.98497 310s > print( residuals( fitsur1e2$eq[[ 2 ]] ) ) 310s 1 2 3 4 5 6 7 8 310s 0.41825 -0.00625 2.75649 1.81727 2.53566 1.53338 2.25581 -3.56834 310s 9 10 11 12 13 14 15 16 310s -2.02733 2.53245 -1.40733 -3.01416 -2.30119 0.01871 2.93624 -4.00484 310s 17 18 19 20 310s -0.45580 -3.18683 2.18284 0.98497 310s > 310s > print( residuals( fitsur1w ) ) 310s demand supply 310s 1 0.696 0.4713 310s 2 -0.561 0.0197 310s 3 2.455 2.7782 310s 4 1.643 1.8366 310s 5 2.110 2.4709 310s 6 1.304 1.4773 310s 7 1.692 2.2079 310s 8 -2.756 -3.6663 310s 9 -1.253 -2.0985 310s 10 2.078 2.5321 310s 11 -0.675 -1.2705 310s 12 -2.649 -2.8068 310s 13 -1.706 -2.1305 310s 14 -0.419 0.1150 310s 15 1.887 2.8772 310s 16 -3.364 -4.1013 310s 17 -0.762 -0.5650 310s 18 -1.918 -3.2183 310s 19 1.978 2.1637 310s 20 0.218 0.9075 310s > print( residuals( fitsur1w$eq[[ 2 ]] ) ) 310s 1 2 3 4 5 6 7 8 9 10 310s 0.4713 0.0197 2.7782 1.8366 2.4709 1.4773 2.2079 -3.6663 -2.0985 2.5321 310s 11 12 13 14 15 16 17 18 19 20 310s -1.2705 -2.8068 -2.1305 0.1150 2.8772 -4.1013 -0.5650 -3.2183 2.1637 0.9075 310s > 310s > print( residuals( fitsur2e ) ) 310s demand supply 310s 1 0.325 -0.200 310s 2 -0.729 -0.481 310s 3 2.288 2.342 310s 4 1.487 1.457 310s 5 2.271 2.527 310s 6 1.432 1.537 310s 7 1.851 2.275 310s 8 -2.582 -3.322 310s 9 -1.143 -1.834 310s 10 2.124 2.512 310s 11 -1.193 -1.885 310s 12 -3.332 -3.705 310s 13 -2.280 -2.813 310s 14 -0.614 -0.177 310s 15 2.281 3.353 310s 16 -3.032 -3.407 310s 17 -0.260 0.233 310s 18 -1.834 -2.737 310s 19 2.215 2.632 310s 20 0.726 1.692 310s > print( residuals( fitsur2e$eq[[ 1 ]] ) ) 310s 1 2 3 4 5 6 7 8 9 10 11 310s 0.325 -0.729 2.288 1.487 2.271 1.432 1.851 -2.582 -1.143 2.124 -1.193 310s 12 13 14 15 16 17 18 19 20 310s -3.332 -2.280 -0.614 2.281 -3.032 -0.260 -1.834 2.215 0.726 310s > 310s > print( residuals( fitsur3 ) ) 310s demand supply 310s 1 0.366 -0.164 310s 2 -0.711 -0.452 310s 3 2.307 2.368 310s 4 1.504 1.479 310s 5 2.253 2.535 310s 6 1.418 1.544 310s 7 1.833 2.279 310s 8 -2.601 -3.327 310s 9 -1.155 -1.839 310s 10 2.119 2.513 310s 11 -1.136 -1.869 310s 12 -3.257 -3.682 310s 13 -2.217 -2.798 310s 14 -0.593 -0.175 310s 15 2.238 3.332 310s 16 -3.069 -3.436 310s 17 -0.315 0.199 310s 18 -1.844 -2.764 310s 19 2.189 2.604 310s 20 0.671 1.654 310s > print( residuals( fitsur3$eq[[ 2 ]] ) ) 310s 1 2 3 4 5 6 7 8 9 10 11 310s -0.164 -0.452 2.368 1.479 2.535 1.544 2.279 -3.327 -1.839 2.513 -1.869 310s 12 13 14 15 16 17 18 19 20 310s -3.682 -2.798 -0.175 3.332 -3.436 0.199 -2.764 2.604 1.654 310s > 310s > print( residuals( fitsur4r3 ) ) 310s demand supply 310s 1 0.934 0.265 310s 2 -0.721 -0.638 310s 3 2.348 2.232 310s 4 1.459 1.196 310s 5 2.129 2.428 310s 6 1.253 1.318 310s 7 1.514 1.913 310s 8 -3.185 -4.425 310s 9 -1.097 -1.870 310s 10 2.619 3.483 310s 11 0.135 -0.260 310s 12 -2.097 -2.275 310s 13 -1.496 -2.085 310s 14 -0.201 0.516 310s 15 1.934 3.439 310s 16 -3.491 -3.942 310s 17 -0.229 0.913 310s 18 -2.236 -3.503 310s 19 1.440 1.736 310s 20 -1.012 -0.441 310s > print( residuals( fitsur4r3$eq[[ 1 ]] ) ) 310s 1 2 3 4 5 6 7 8 9 10 11 310s 0.934 -0.721 2.348 1.459 2.129 1.253 1.514 -3.185 -1.097 2.619 0.135 310s 12 13 14 15 16 17 18 19 20 310s -2.097 -1.496 -0.201 1.934 -3.491 -0.229 -2.236 1.440 -1.012 310s > 310s > print( residuals( fitsur5 ) ) 310s demand supply 310s 1 1.0025 0.3219 310s 2 -0.5449 -0.4286 310s 3 2.4949 2.4014 310s 4 1.6426 1.4106 310s 5 2.0329 2.2956 310s 6 1.2129 1.2545 310s 7 1.5260 1.9262 310s 8 -3.0444 -4.2868 310s 9 -1.2406 -2.0779 310s 10 2.3001 3.0973 310s 11 -0.0303 -0.4650 310s 12 -2.0337 -2.1783 310s 13 -1.3041 -1.8356 310s 14 -0.2155 0.5292 310s 15 1.6991 3.1787 310s 16 -3.5980 -4.0840 310s 17 -0.7860 0.2371 310s 18 -2.1070 -3.3544 310s 19 1.6070 1.9694 310s 20 -0.6134 0.0885 310s > print( residuals( fitsur5$eq[[ 2 ]] ) ) 310s 1 2 3 4 5 6 7 8 9 10 310s 0.3219 -0.4286 2.4014 1.4106 2.2956 1.2545 1.9262 -4.2868 -2.0779 3.0973 310s 11 12 13 14 15 16 17 18 19 20 310s -0.4650 -2.1783 -1.8356 0.5292 3.1787 -4.0840 0.2371 -3.3544 1.9694 0.0885 310s > 310s > print( residuals( fitsuri1r3 ) ) 310s demand supply 310s 1 0.7952 0.123 310s 2 -0.7614 -1.393 310s 3 2.3039 -0.829 310s 4 1.4250 -0.430 310s 5 2.1792 -1.213 310s 6 1.2979 -0.653 310s 7 1.5795 -1.266 310s 8 -3.0935 2.153 310s 9 -1.0750 1.548 310s 10 2.5876 -1.582 310s 11 -0.0991 0.990 310s 12 -2.3616 0.460 310s 13 -1.6970 1.335 310s 14 -0.2819 -1.054 310s 15 2.0557 -2.339 310s 16 -3.3745 1.734 310s 17 -0.1140 -1.054 310s 18 -2.1822 3.461 310s 19 1.5612 0.318 310s 20 -0.7450 -0.308 310s > print( residuals( fitsuri1r3$eq[[ 1 ]] ) ) 310s 1 2 3 4 5 6 7 8 9 10 310s 0.7952 -0.7614 2.3039 1.4250 2.1792 1.2979 1.5795 -3.0935 -1.0750 2.5876 310s 11 12 13 14 15 16 17 18 19 20 310s -0.0991 -2.3616 -1.6970 -0.2819 2.0557 -3.3745 -0.1140 -2.1822 1.5612 -0.7450 310s > 310s > print( residuals( fitsuri2 ) ) 310s demand supply 310s 1 1.1341 6.955 310s 2 -0.0587 7.587 310s 3 2.8946 6.701 310s 4 2.1508 6.768 310s 5 1.7798 1.930 310s 6 1.1200 2.315 310s 7 1.5920 2.230 310s 8 -2.5983 4.980 310s 9 -1.6414 -0.392 310s 10 1.3742 -5.140 310s 11 -0.6115 -3.174 310s 12 -1.9764 -0.804 310s 13 -0.8493 1.012 310s 14 -0.2942 -3.282 310s 15 1.0840 -7.042 310s 16 -3.8500 -4.140 310s 17 -2.3259 -12.628 310s 18 -1.7141 -1.498 310s 19 2.1409 -2.683 310s 20 0.6494 0.305 310s > print( residuals( fitsuri2$eq[[ 2 ]] ) ) 310s 1 2 3 4 5 6 7 8 9 10 310s 6.955 7.587 6.701 6.768 1.930 2.315 2.230 4.980 -0.392 -5.140 310s 11 12 13 14 15 16 17 18 19 20 310s -3.174 -0.804 1.012 -3.282 -7.042 -4.140 -12.628 -1.498 -2.683 0.305 310s > 310s > print( residuals( fitsuri3e ) ) 310s demand supply 310s 1 1.1327 6.932 310s 2 -0.0412 7.582 310s 3 2.9085 6.695 310s 4 2.1695 6.766 310s 5 1.7721 1.915 310s 6 1.1185 2.305 310s 7 1.5978 2.229 310s 8 -2.5761 4.982 310s 9 -1.6564 -0.410 310s 10 1.3358 -5.161 310s 11 -0.6458 -3.196 310s 12 -1.9868 -0.807 310s 13 -0.8408 1.021 310s 14 -0.3012 -3.275 310s 15 1.0652 -7.037 310s 16 -3.8545 -4.135 310s 17 -2.3819 -12.646 310s 18 -1.6959 -1.478 310s 19 2.1679 -2.647 310s 20 0.7125 0.366 310s > print( residuals( fitsuri3e$eq[[ 1 ]] ) ) 310s 1 2 3 4 5 6 7 8 9 10 310s 1.1327 -0.0412 2.9085 2.1695 1.7721 1.1185 1.5978 -2.5761 -1.6564 1.3358 310s 11 12 13 14 15 16 17 18 19 20 310s -0.6458 -1.9868 -0.8408 -0.3012 1.0652 -3.8545 -2.3819 -1.6959 2.1679 0.7125 310s > 310s > print( residuals( fitsurio4 ) ) 310s demand supply 310s 1 0.9019 0.240 310s 2 -0.7658 -0.697 310s 3 2.3097 2.184 310s 4 1.4141 1.136 310s 5 2.1571 2.490 310s 6 1.2670 1.356 310s 7 1.5188 1.928 310s 8 -3.2060 -4.430 310s 9 -1.0620 -1.789 310s 10 2.6864 3.589 310s 11 0.1438 -0.248 310s 12 -2.1427 -2.369 310s 13 -1.5629 -2.210 310s 14 -0.2076 0.479 310s 15 2.0012 3.526 310s 16 -3.4530 -3.876 310s 17 -0.0902 1.129 310s 18 -2.2581 -3.539 310s 19 1.4172 1.671 310s 20 -1.0688 -0.569 310s > print( residuals( fitsurio4$eq[[ 2 ]] ) ) 310s 1 2 3 4 5 6 7 8 9 10 11 310s 0.240 -0.697 2.184 1.136 2.490 1.356 1.928 -4.430 -1.789 3.589 -0.248 310s 12 13 14 15 16 17 18 19 20 310s -2.369 -2.210 0.479 3.526 -3.876 1.129 -3.539 1.671 -0.569 310s > print( residuals( fitsuri4 ) ) 310s demand supply 310s 1 0.7146 5.775 310s 2 -0.6076 7.198 310s 3 2.4197 6.280 310s 4 1.5931 6.531 310s 5 2.1268 1.465 310s 6 1.3043 2.021 310s 7 1.6685 2.261 310s 8 -2.8295 5.275 310s 9 -1.2125 -0.890 310s 10 2.1921 -5.945 310s 11 -0.5521 -4.407 310s 12 -2.5920 -1.482 310s 13 -1.7095 0.895 310s 14 -0.3902 -3.220 310s 15 1.9290 -6.617 310s 16 -3.3627 -3.607 310s 17 -0.6125 -12.896 310s 18 -1.9758 -0.562 310s 19 1.8877 -1.126 310s 20 0.0085 3.051 310s > print( residuals( fitsuri4$eq[[ 2 ]] ) ) 310s 1 2 3 4 5 6 7 8 9 10 310s 5.775 7.198 6.280 6.531 1.465 2.021 2.261 5.275 -0.890 -5.945 310s 11 12 13 14 15 16 17 18 19 20 310s -4.407 -1.482 0.895 -3.220 -6.617 -3.607 -12.896 -0.562 -1.126 3.051 310s > 310s > print( residuals( fitsuri4w ) ) 310s demand supply 310s 1 0.71463 5.775 310s 2 -0.60754 7.198 310s 3 2.41972 6.280 310s 4 1.59308 6.531 310s 5 2.12679 1.465 310s 6 1.30430 2.021 310s 7 1.66846 2.262 310s 8 -2.82945 5.275 310s 9 -1.21248 -0.890 310s 10 2.19209 -5.946 310s 11 -0.55215 -4.407 310s 12 -2.59194 -1.482 310s 13 -1.70948 0.895 310s 14 -0.39018 -3.220 310s 15 1.92897 -6.617 310s 16 -3.36276 -3.607 310s 17 -0.61256 -12.896 310s 18 -1.97579 -0.562 310s 19 1.88776 -1.126 310s 20 0.00854 3.051 310s > print( residuals( fitsuri4w$eq[[ 2 ]] ) ) 310s 1 2 3 4 5 6 7 8 9 10 310s 5.775 7.198 6.280 6.531 1.465 2.021 2.262 5.275 -0.890 -5.946 310s 11 12 13 14 15 16 17 18 19 20 310s -4.407 -1.482 0.895 -3.220 -6.617 -3.607 -12.896 -0.562 -1.126 3.051 310s > 310s > print( residuals( fitsurio5r2 ) ) 310s demand supply 310s 1 0.655 0.0269 310s 2 -1.456 -1.5152 310s 3 1.737 1.5210 310s 4 0.696 0.3020 310s 5 2.530 2.9397 310s 6 1.417 1.5469 310s 7 1.459 1.8336 310s 8 -3.779 -5.0391 310s 9 -0.498 -1.0416 310s 10 3.950 5.0761 310s 11 0.836 0.6398 310s 12 -2.347 -2.5930 310s 13 -2.286 -3.0468 310s 14 -0.137 0.5081 310s 15 2.908 4.5036 310s 16 -3.050 -3.3786 310s 17 2.091 3.6824 310s 18 -2.775 -4.1107 310s 19 0.737 0.7819 310s 20 -2.686 -2.6370 310s > print( residuals( fitsurio5r2$eq[[ 1 ]] ) ) 310s 1 2 3 4 5 6 7 8 9 10 11 310s 0.655 -1.456 1.737 0.696 2.530 1.417 1.459 -3.779 -0.498 3.950 0.836 310s 12 13 14 15 16 17 18 19 20 310s -2.347 -2.286 -0.137 2.908 -3.050 2.091 -2.775 0.737 -2.686 310s > print( residuals( fitsuri5r2 ) ) 310s demand supply 310s 1 0.7199 5.756 310s 2 -0.5979 7.202 310s 3 2.4279 6.281 310s 4 1.6030 6.535 310s 5 2.1212 1.472 310s 6 1.3017 2.029 310s 7 1.6683 2.275 310s 8 -2.8233 5.299 310s 9 -1.2202 -0.892 310s 10 2.1760 -5.965 310s 11 -0.5578 -4.458 310s 12 -2.5854 -1.528 310s 13 -1.6970 0.866 310s 14 -0.3899 -3.237 310s 15 1.9153 -6.607 310s 16 -3.3698 -3.593 310s 17 -0.6429 -12.902 310s 18 -1.9698 -0.549 310s 19 1.8949 -1.099 310s 20 0.0259 3.114 310s > print( residuals( fitsuri5r2$eq[[ 1 ]] ) ) 310s 1 2 3 4 5 6 7 8 9 10 310s 0.7199 -0.5979 2.4279 1.6030 2.1212 1.3017 1.6683 -2.8233 -1.2202 2.1760 310s 11 12 13 14 15 16 17 18 19 20 310s -0.5578 -2.5854 -1.6970 -0.3899 1.9153 -3.3698 -0.6429 -1.9698 1.8949 0.0259 310s > 310s > 310s > ## *************** coefficients ********************* 310s > print( round( coef( fitsur1r3 ), digits = 6 ) ) 310s demand_(Intercept) demand_price demand_income supply_(Intercept) 310s 99.225 -0.268 0.292 62.958 310s supply_price supply_farmPrice supply_trend 310s 0.144 0.207 0.333 310s > print( round( coef( fitsur1r3$eq[[ 2 ]] ), digits = 6 ) ) 310s (Intercept) price farmPrice trend 310s 62.958 0.144 0.207 0.333 310s > 310s > print( round( coef( fitsuri2 ), digits = 6 ) ) 310s demand_(Intercept) demand_price demand_income supply_(Intercept) 310s 107.368 -0.394 0.338 85.045 310s supply_income supply_farmPrice supply_trend 310s 0.312 -0.197 0.338 310s > print( round( coef( fitsuri2$eq[[ 1 ]] ), digits = 6 ) ) 310s (Intercept) price income 310s 107.368 -0.394 0.338 310s > 310s > print( round( coef( fitsur2we ), digits = 6 ) ) 310s demand_(Intercept) demand_price demand_income supply_(Intercept) 310s 98.754 -0.234 0.261 67.888 310s supply_price supply_farmPrice supply_trend 310s 0.132 0.177 0.261 310s > print( round( coef( fitsur2we$eq[[ 1 ]] ), digits = 6 ) ) 310s (Intercept) price income 310s 98.754 -0.234 0.261 310s > 310s > print( round( coef( fitsur3 ), digits = 6 ) ) 310s demand_(Intercept) demand_price demand_income supply_(Intercept) 310s 98.841 -0.240 0.267 67.428 310s supply_price supply_farmPrice supply_trend 310s 0.133 0.179 0.267 310s > print( round( coef( fitsur3, modified.regMat = TRUE ), digits = 6 ) ) 310s C1 C2 C3 C4 C5 C6 310s 98.841 -0.240 0.267 67.428 0.133 0.179 310s > print( round( coef( fitsur3$eq[[ 2 ]] ), digits = 6 ) ) 310s (Intercept) price farmPrice trend 310s 67.428 0.133 0.179 0.267 310s > 310s > print( round( coef( fitsur4r2 ), digits = 6 ) ) 310s demand_(Intercept) demand_price demand_income supply_(Intercept) 310s 92.527 -0.230 0.322 48.701 310s supply_price supply_farmPrice supply_trend 310s 0.270 0.226 0.322 310s > print( round( coef( fitsur4r2$eq[[ 1 ]] ), digits = 6 ) ) 310s (Intercept) price income 310s 92.527 -0.230 0.322 310s > 310s > print( round( coef( fitsuri5e ), digits = 6 ) ) 310s demand_(Intercept) demand_price demand_income supply_(Intercept) 310s 97.630 -0.258 0.298 89.544 310s supply_income supply_farmPrice supply_trend 310s 0.242 -0.169 0.298 310s > print( round( coef( fitsuri5e, modified.regMat = TRUE ), digits = 6 ) ) 310s C1 C2 C3 C4 C5 C6 310s 97.630 -0.258 0.298 89.544 0.242 -0.169 310s > print( round( coef( fitsuri5e$eq[[ 2 ]] ), digits = 6 ) ) 310s (Intercept) income farmPrice trend 310s 89.544 0.242 -0.169 0.298 310s > 310s > print( round( coef( fitsur5w ), digits = 6 ) ) 310s demand_(Intercept) demand_price demand_income supply_(Intercept) 310s 96.442 -0.275 0.328 52.576 310s supply_price supply_farmPrice supply_trend 310s 0.225 0.232 0.328 310s > print( round( coef( fitsur5w, modified.regMat = TRUE ), digits = 6 ) ) 310s C1 C2 C3 C4 C5 C6 310s 96.442 -0.275 0.328 52.576 0.225 0.232 310s > print( round( coef( fitsur5w$eq[[ 1 ]] ), digits = 6 ) ) 310s (Intercept) price income 310s 96.442 -0.275 0.328 310s > 310s > 310s > ## *************** coefficients with stats ********************* 310s > print( round( coef( summary( fitsur1r3 ) ), digits = 6 ) ) 310s Estimate Std. Error t value Pr(>|t|) 310s demand_(Intercept) 99.225 7.5129 13.21 0.000000 310s demand_price -0.268 0.0878 -3.05 0.007262 310s demand_income 0.292 0.0408 7.15 0.000002 310s supply_(Intercept) 62.958 10.9850 5.73 0.000031 310s supply_price 0.144 0.0944 1.53 0.145991 310s supply_farmPrice 0.207 0.0386 5.37 0.000062 310s supply_trend 0.333 0.0644 5.18 0.000092 310s > print( round( coef( summary( fitsur1r3$eq[[ 2 ]] ) ), digits = 6 ) ) 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 62.958 10.9850 5.73 0.000031 310s price 0.144 0.0944 1.53 0.145991 310s farmPrice 0.207 0.0386 5.37 0.000062 310s trend 0.333 0.0644 5.18 0.000092 310s > 310s > print( round( coef( summary( fitsuri2, useDfSys = FALSE ) ), digits = 6 ) ) 310s Estimate Std. Error t value Pr(>|t|) 310s demand_(Intercept) 107.368 7.4986 14.32 0.000000 310s demand_price -0.394 0.0912 -4.33 0.000459 310s demand_income 0.338 0.0466 7.25 0.000001 310s supply_(Intercept) 85.045 12.1069 7.02 0.000003 310s supply_income 0.312 0.1233 2.53 0.022132 310s supply_farmPrice -0.197 0.1157 -1.70 0.107654 310s supply_trend 0.338 0.0466 7.25 0.000002 310s > print( round( coef( summary( fitsuri2$eq[[ 1 ]], useDfSys = FALSE ) ), 310s + digits = 6 ) ) 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 107.368 7.4986 14.32 0.000000 310s price -0.394 0.0912 -4.33 0.000459 310s income 0.338 0.0466 7.25 0.000001 310s > 310s > print( round( coef( summary( fitsur3 ) ), digits = 6 ) ) 310s Estimate Std. Error t value Pr(>|t|) 310s demand_(Intercept) 98.841 7.5581 13.08 0.000000 310s demand_price -0.240 0.0860 -2.79 0.008613 310s demand_income 0.267 0.0368 7.25 0.000000 310s supply_(Intercept) 67.428 10.6647 6.32 0.000000 310s supply_price 0.133 0.0953 1.40 0.171250 310s supply_farmPrice 0.179 0.0337 5.33 0.000006 310s supply_trend 0.267 0.0368 7.25 0.000000 310s > print( round( coef( summary( fitsur3 ), modified.regMat = TRUE ), digits = 6 ) ) 310s Estimate Std. Error t value Pr(>|t|) 310s C1 98.841 7.5581 13.08 0.000000 310s C2 -0.240 0.0860 -2.79 0.008613 310s C3 0.267 0.0368 7.25 0.000000 310s C4 67.428 10.6647 6.32 0.000000 310s C5 0.133 0.0953 1.40 0.171250 310s C6 0.179 0.0337 5.33 0.000006 310s > print( round( coef( summary( fitsur3$eq[[ 2 ]] ) ), digits = 6 ) ) 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 67.428 10.6647 6.32 0.000000 310s price 0.133 0.0953 1.40 0.171250 310s farmPrice 0.179 0.0337 5.33 0.000006 310s trend 0.267 0.0368 7.25 0.000000 310s > 310s > print( round( coef( summary( fitsuri3we ) ), digits = 6 ) ) 310s Estimate Std. Error t value Pr(>|t|) 310s demand_(Intercept) 107.806 6.9270 15.56 0.000000 310s demand_price -0.399 0.0843 -4.73 0.000038 310s demand_income 0.338 0.0431 7.84 0.000000 310s supply_(Intercept) 85.107 10.8288 7.86 0.000000 310s supply_income 0.311 0.1101 2.82 0.007950 310s supply_farmPrice -0.196 0.1034 -1.89 0.066671 310s supply_trend 0.338 0.0431 7.84 0.000000 310s > print( round( coef( summary( fitsuri3we ), modified.regMat = TRUE ), digits = 6 ) ) 310s Estimate Std. Error t value Pr(>|t|) 310s C1 107.806 6.9270 15.56 0.000000 310s C2 -0.399 0.0843 -4.73 0.000038 310s C3 0.338 0.0431 7.84 0.000000 310s C4 85.107 10.8288 7.86 0.000000 310s C5 0.311 0.1101 2.82 0.007950 310s C6 -0.196 0.1034 -1.89 0.066671 310s > print( round( coef( summary( fitsuri3we$eq[[ 1 ]] ) ), digits = 6 ) ) 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 107.806 6.9270 15.56 0.0e+00 310s price -0.399 0.0843 -4.73 3.8e-05 310s income 0.338 0.0431 7.84 0.0e+00 310s > 310s > print( round( coef( summary( fitsur4r2 ) ), digits = 6 ) ) 310s Estimate Std. Error t value Pr(>|t|) 310s demand_(Intercept) 92.527 7.2896 12.69 0.00000 310s demand_price -0.230 0.0827 -2.79 0.00855 310s demand_income 0.322 0.0166 19.37 0.00000 310s supply_(Intercept) 48.701 7.4034 6.58 0.00000 310s supply_price 0.270 0.0827 3.26 0.00248 310s supply_farmPrice 0.226 0.0166 13.62 0.00000 310s supply_trend 0.322 0.0166 19.37 0.00000 310s > print( round( coef( summary( fitsur4r2$eq[[ 1 ]] ) ), digits = 6 ) ) 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 92.527 7.2896 12.69 0.00000 310s price -0.230 0.0827 -2.79 0.00855 310s income 0.322 0.0166 19.37 0.00000 310s > 310s > print( round( coef( summary( fitsur4we ) ), digits = 6 ) ) 310s Estimate Std. Error t value Pr(>|t|) 310s demand_(Intercept) 96.941 6.8894 14.07 0.000000 310s demand_price -0.281 0.0766 -3.67 0.000796 310s demand_income 0.329 0.0181 18.18 0.000000 310s supply_(Intercept) 52.996 7.0652 7.50 0.000000 310s supply_price 0.219 0.0766 2.85 0.007215 310s supply_farmPrice 0.234 0.0183 12.76 0.000000 310s supply_trend 0.329 0.0181 18.18 0.000000 310s > print( round( coef( summary( fitsur4we$eq[[ 2 ]] ) ), digits = 6 ) ) 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 52.996 7.0652 7.50 0.00000 310s price 0.219 0.0766 2.85 0.00722 310s farmPrice 0.234 0.0183 12.76 0.00000 310s trend 0.329 0.0181 18.18 0.00000 310s > 310s > print( round( coef( summary( fitsuri5e, useDfSys = FALSE ) ), digits = 6 ) ) 310s Estimate Std. Error t value Pr(>|t|) 310s demand_(Intercept) 97.630 6.1560 15.86 0.000000 310s demand_price -0.258 0.0709 -3.63 0.002060 310s demand_income 0.298 0.0403 7.38 0.000001 310s supply_(Intercept) 89.544 9.3372 9.59 0.000000 310s supply_income 0.242 0.0709 3.42 0.003516 310s supply_farmPrice -0.169 0.0988 -1.71 0.107123 310s supply_trend 0.298 0.0403 7.38 0.000002 310s > print( round( coef( summary( fitsuri5e, useDfSys = FALSE ), 310s + modified.regMat = TRUE ), digits = 6 ) ) 310s Estimate Std. Error t value Pr(>|t|) 310s C1 97.630 6.1560 15.86 NA 310s C2 -0.258 0.0709 -3.63 NA 310s C3 0.298 0.0403 7.38 NA 310s C4 89.544 9.3372 9.59 NA 310s C5 0.242 0.0709 3.42 NA 310s C6 -0.169 0.0988 -1.71 NA 310s > print( round( coef( summary( fitsuri5e$eq[[ 2 ]], useDfSys = FALSE ) ), 310s + digits = 6 ) ) 310s Estimate Std. Error t value Pr(>|t|) 310s (Intercept) 89.544 9.3372 9.59 0.000000 310s income 0.242 0.0709 3.42 0.003516 310s farmPrice -0.169 0.0988 -1.71 0.107123 310s trend 0.298 0.0403 7.38 0.000002 310s > 310s > 310s > ## *********** variance covariance matrix of the coefficients ******* 310s > print( round( vcov( fitsur1e2 ), digits = 6 ) ) 310s demand_(Intercept) demand_price demand_income 310s demand_(Intercept) 56.4403 -0.58751 0.025716 310s demand_price -0.5875 0.00769 -0.001866 310s demand_income 0.0257 -0.00187 0.001650 310s supply_(Intercept) 61.0550 -0.40370 -0.209805 310s supply_price -0.6325 0.00579 0.000546 310s supply_farmPrice 0.0215 -0.00156 0.001379 310s supply_trend 0.0327 -0.00237 0.002095 310s supply_(Intercept) supply_price supply_farmPrice 310s demand_(Intercept) 61.055 -0.632489 0.021495 310s demand_price -0.404 0.005792 -0.001559 310s demand_income -0.210 0.000546 0.001379 310s supply_(Intercept) 120.418 -0.954714 -0.221454 310s supply_price -0.955 0.008900 0.000584 310s supply_farmPrice -0.221 0.000584 0.001476 310s supply_trend -0.309 0.000772 0.001950 310s supply_trend 310s demand_(Intercept) 0.032652 310s demand_price -0.002369 310s demand_income 0.002095 310s supply_(Intercept) -0.308674 310s supply_price 0.000772 310s supply_farmPrice 0.001950 310s supply_trend 0.004100 310s > print( round( vcov( fitsur1e2$eq[[ 1 ]] ), digits = 6 ) ) 310s (Intercept) price income 310s (Intercept) 56.4403 -0.58751 0.02572 310s price -0.5875 0.00769 -0.00187 310s income 0.0257 -0.00187 0.00165 310s > 310s > print( round( vcov( fitsur1r3 ), digits = 6 ) ) 310s demand_(Intercept) demand_price demand_income 310s demand_(Intercept) 56.4432 -0.58772 0.025901 310s demand_price -0.5877 0.00771 -0.001879 310s demand_income 0.0259 -0.00188 0.001662 310s supply_(Intercept) 60.8607 -0.40086 -0.210729 310s supply_price -0.6307 0.00577 0.000548 310s supply_farmPrice 0.0216 -0.00157 0.001385 310s supply_trend 0.0328 -0.00238 0.002104 310s supply_(Intercept) supply_price supply_farmPrice 310s demand_(Intercept) 60.861 -0.630659 0.021589 310s demand_price -0.401 0.005771 -0.001566 310s demand_income -0.211 0.000548 0.001385 310s supply_(Intercept) 120.671 -0.955395 -0.223176 310s supply_price -0.955 0.008902 0.000589 310s supply_farmPrice -0.223 0.000589 0.001487 310s supply_trend -0.310 0.000776 0.001959 310s supply_trend 310s demand_(Intercept) 0.032796 310s demand_price -0.002379 310s demand_income 0.002104 310s supply_(Intercept) -0.310422 310s supply_price 0.000776 310s supply_farmPrice 0.001959 310s supply_trend 0.004149 310s > print( round( vcov( fitsur1r3$eq[[ 2 ]] ), digits = 6 ) ) 310s (Intercept) price farmPrice trend 310s (Intercept) 120.671 -0.955395 -0.223176 -0.310422 310s price -0.955 0.008902 0.000589 0.000776 310s farmPrice -0.223 0.000589 0.001487 0.001959 310s trend -0.310 0.000776 0.001959 0.004149 310s > 310s > print( round( vcov( fitsur2e ), digits = 6 ) ) 310s demand_(Intercept) demand_price demand_income 310s demand_(Intercept) 48.5631 -0.50188 0.018400 310s demand_price -0.5019 0.00632 -0.001335 310s demand_income 0.0184 -0.00134 0.001180 310s supply_(Intercept) 53.2014 -0.39283 -0.140738 310s supply_price -0.5462 0.00510 0.000373 310s supply_farmPrice 0.0147 -0.00107 0.000942 310s supply_trend 0.0184 -0.00134 0.001180 310s supply_(Intercept) supply_price supply_farmPrice 310s demand_(Intercept) 53.201 -0.546194 0.014689 310s demand_price -0.393 0.005097 -0.001066 310s demand_income -0.141 0.000373 0.000942 310s supply_(Intercept) 91.607 -0.766739 -0.136644 310s supply_price -0.767 0.007271 0.000368 310s supply_farmPrice -0.137 0.000368 0.000931 310s supply_trend -0.141 0.000373 0.000942 310s supply_trend 310s demand_(Intercept) 0.018400 310s demand_price -0.001335 310s demand_income 0.001180 310s supply_(Intercept) -0.140738 310s supply_price 0.000373 310s supply_farmPrice 0.000942 310s supply_trend 0.001180 310s > print( round( vcov( fitsur2e$eq[[ 1 ]] ), digits = 6 ) ) 310s (Intercept) price income 310s (Intercept) 48.5631 -0.50188 0.01840 310s price -0.5019 0.00632 -0.00134 310s income 0.0184 -0.00134 0.00118 310s > 310s > print( round( vcov( fitsur3 ), digits = 6 ) ) 310s demand_(Intercept) demand_price demand_income 310s demand_(Intercept) 57.1254 -0.58989 0.02116 310s demand_price -0.5899 0.00739 -0.00153 310s demand_income 0.0212 -0.00153 0.00136 310s supply_(Intercept) 64.5952 -0.48211 -0.16560 310s supply_price -0.6626 0.00619 0.00044 310s supply_farmPrice 0.0173 -0.00126 0.00111 310s supply_trend 0.0212 -0.00153 0.00136 310s supply_(Intercept) supply_price supply_farmPrice 310s demand_(Intercept) 64.595 -0.662552 0.017322 310s demand_price -0.482 0.006195 -0.001257 310s demand_income -0.166 0.000440 0.001111 310s supply_(Intercept) 113.736 -0.956493 -0.165927 310s supply_price -0.956 0.009084 0.000448 310s supply_farmPrice -0.166 0.000448 0.001133 310s supply_trend -0.166 0.000440 0.001111 310s supply_trend 310s demand_(Intercept) 0.02116 310s demand_price -0.00153 310s demand_income 0.00136 310s supply_(Intercept) -0.16560 310s supply_price 0.00044 310s supply_farmPrice 0.00111 310s supply_trend 0.00136 310s > print( round( vcov( fitsur3, modified.regMat = TRUE ), digits = 6 ) ) 310s C1 C2 C3 C4 C5 C6 310s C1 57.1254 -0.58989 0.02116 64.595 -0.662552 0.017322 310s C2 -0.5899 0.00739 -0.00153 -0.482 0.006195 -0.001257 310s C3 0.0212 -0.00153 0.00136 -0.166 0.000440 0.001111 310s C4 64.5952 -0.48211 -0.16560 113.736 -0.956493 -0.165927 310s C5 -0.6626 0.00619 0.00044 -0.956 0.009084 0.000448 310s C6 0.0173 -0.00126 0.00111 -0.166 0.000448 0.001133 310s > print( round( vcov( fitsur3$eq[[ 2 ]] ), digits = 6 ) ) 310s (Intercept) price farmPrice trend 310s (Intercept) 113.736 -0.956493 -0.165927 -0.16560 310s price -0.956 0.009084 0.000448 0.00044 310s farmPrice -0.166 0.000448 0.001133 0.00111 310s trend -0.166 0.000440 0.001111 0.00136 310s > 310s > print( round( vcov( fitsur3w ), digits = 6 ) ) 310s demand_(Intercept) demand_price demand_income 310s demand_(Intercept) 56.7267 -0.58513 0.020348 310s demand_price -0.5851 0.00729 -0.001476 310s demand_income 0.0203 -0.00148 0.001305 310s supply_(Intercept) 64.8820 -0.48999 -0.160451 310s supply_price -0.6648 0.00623 0.000426 310s supply_farmPrice 0.0168 -0.00122 0.001077 310s supply_trend 0.0203 -0.00148 0.001305 310s supply_(Intercept) supply_price supply_farmPrice 310s demand_(Intercept) 64.882 -0.664819 0.016795 310s demand_price -0.490 0.006231 -0.001219 310s demand_income -0.160 0.000426 0.001077 310s supply_(Intercept) 113.543 -0.959668 -0.161181 310s supply_price -0.960 0.009129 0.000435 310s supply_farmPrice -0.161 0.000435 0.001100 310s supply_trend -0.160 0.000426 0.001077 310s supply_trend 310s demand_(Intercept) 0.020348 310s demand_price -0.001476 310s demand_income 0.001305 310s supply_(Intercept) -0.160451 310s supply_price 0.000426 310s supply_farmPrice 0.001077 310s supply_trend 0.001305 310s > print( round( vcov( fitsur3w, modified.regMat = TRUE ), digits = 6 ) ) 310s C1 C2 C3 C4 C5 C6 310s C1 56.7267 -0.58513 0.020348 64.882 -0.664819 0.016795 310s C2 -0.5851 0.00729 -0.001476 -0.490 0.006231 -0.001219 310s C3 0.0203 -0.00148 0.001305 -0.160 0.000426 0.001077 310s C4 64.8820 -0.48999 -0.160451 113.543 -0.959668 -0.161181 310s C5 -0.6648 0.00623 0.000426 -0.960 0.009129 0.000435 310s C6 0.0168 -0.00122 0.001077 -0.161 0.000435 0.001100 310s > print( round( vcov( fitsur3w$eq[[ 1 ]] ), digits = 6 ) ) 310s (Intercept) price income 310s (Intercept) 56.7267 -0.58513 0.02035 310s price -0.5851 0.00729 -0.00148 310s income 0.0203 -0.00148 0.00130 310s > 310s > print( round( vcov( fitsur4r2 ), digits = 6 ) ) 310s demand_(Intercept) demand_price demand_income 310s demand_(Intercept) 53.1384 -0.593514 0.065746 310s demand_price -0.5935 0.006838 -0.000927 310s demand_income 0.0657 -0.000927 0.000276 310s supply_(Intercept) 53.3903 -0.599312 0.069540 310s supply_price -0.5935 0.006838 -0.000927 310s supply_farmPrice 0.0570 -0.000775 0.000210 310s supply_trend 0.0657 -0.000927 0.000276 310s supply_(Intercept) supply_price supply_farmPrice 310s demand_(Intercept) 53.3903 -0.593514 0.057048 310s demand_price -0.5993 0.006838 -0.000775 310s demand_income 0.0695 -0.000927 0.000210 310s supply_(Intercept) 54.8108 -0.599312 0.048653 310s supply_price -0.5993 0.006838 -0.000775 310s supply_farmPrice 0.0487 -0.000775 0.000276 310s supply_trend 0.0695 -0.000927 0.000210 310s supply_trend 310s demand_(Intercept) 0.065746 310s demand_price -0.000927 310s demand_income 0.000276 310s supply_(Intercept) 0.069540 310s supply_price -0.000927 310s supply_farmPrice 0.000210 310s supply_trend 0.000276 310s > print( round( vcov( fitsur4r2$eq[[ 1 ]] ), digits = 6 ) ) 310s (Intercept) price income 310s (Intercept) 53.1384 -0.593514 0.065746 310s price -0.5935 0.006838 -0.000927 310s income 0.0657 -0.000927 0.000276 310s > 310s > print( round( vcov( fitsur5e ), digits = 6 ) ) 310s demand_(Intercept) demand_price demand_income 310s demand_(Intercept) 47.8867 -0.516747 0.040579 310s demand_price -0.5167 0.005886 -0.000738 310s demand_income 0.0406 -0.000738 0.000340 310s supply_(Intercept) 48.2187 -0.526670 0.047594 310s supply_price -0.5167 0.005886 -0.000738 310s supply_farmPrice 0.0334 -0.000562 0.000234 310s supply_trend 0.0406 -0.000738 0.000340 310s supply_(Intercept) supply_price supply_farmPrice 310s demand_(Intercept) 48.2187 -0.516747 0.033361 310s demand_price -0.5267 0.005886 -0.000562 310s demand_income 0.0476 -0.000738 0.000234 310s supply_(Intercept) 50.4739 -0.526670 0.020109 310s supply_price -0.5267 0.005886 -0.000562 310s supply_farmPrice 0.0201 -0.000562 0.000348 310s supply_trend 0.0476 -0.000738 0.000234 310s supply_trend 310s demand_(Intercept) 0.040579 310s demand_price -0.000738 310s demand_income 0.000340 310s supply_(Intercept) 0.047594 310s supply_price -0.000738 310s supply_farmPrice 0.000234 310s supply_trend 0.000340 310s > print( round( vcov( fitsur5e, modified.regMat = TRUE ), digits = 6 ) ) 310s C1 C2 C3 C4 C5 C6 310s C1 47.8867 -0.516747 0.040579 48.2187 -0.516747 0.033361 310s C2 -0.5167 0.005886 -0.000738 -0.5267 0.005886 -0.000562 310s C3 0.0406 -0.000738 0.000340 0.0476 -0.000738 0.000234 310s C4 48.2187 -0.526670 0.047594 50.4739 -0.526670 0.020109 310s C5 -0.5167 0.005886 -0.000738 -0.5267 0.005886 -0.000562 310s C6 0.0334 -0.000562 0.000234 0.0201 -0.000562 0.000348 310s > print( round( vcov( fitsur5e$eq[[ 2 ]] ), digits = 6 ) ) 310s (Intercept) price farmPrice trend 310s (Intercept) 50.4739 -0.526670 0.020109 0.047594 310s price -0.5267 0.005886 -0.000562 -0.000738 310s farmPrice 0.0201 -0.000562 0.000348 0.000234 310s trend 0.0476 -0.000738 0.000234 0.000340 310s > 310s > print( round( vcov( fitsuri1r3 ), digits = 6 ) ) 310s demand_(Intercept) demand_price demand_income 310s demand_(Intercept) 54.5505 -0.55698 0.013891 310s demand_price -0.5570 0.00770 -0.002185 310s demand_income 0.0139 -0.00218 0.002098 310s supply_(Intercept) -2.7032 -0.08733 0.115993 310s supply_income 0.2249 -0.00185 -0.000411 310s supply_farmPrice -0.1721 0.00238 -0.000675 310s supply_trend -0.2597 0.00359 -0.001019 310s supply_(Intercept) supply_income supply_farmPrice 310s demand_(Intercept) -2.7032 0.224902 -0.172110 310s demand_price -0.0873 -0.001848 0.002379 310s demand_income 0.1160 -0.000411 -0.000675 310s supply_(Intercept) 11.4659 -0.058750 -0.051728 310s supply_income -0.0587 0.001787 -0.001018 310s supply_farmPrice -0.0517 -0.001018 0.001368 310s supply_trend -0.0578 -0.001631 0.001794 310s supply_trend 310s demand_(Intercept) -0.25970 310s demand_price 0.00359 310s demand_income -0.00102 310s supply_(Intercept) -0.05784 310s supply_income -0.00163 310s supply_farmPrice 0.00179 310s supply_trend 0.00416 310s > print( round( vcov( fitsuri1r3$eq[[ 1 ]] ), digits = 6 ) ) 310s (Intercept) price income 310s (Intercept) 54.5505 -0.55698 0.01389 310s price -0.5570 0.00770 -0.00218 310s income 0.0139 -0.00218 0.00210 310s > 310s > print( round( vcov( fitsuri2 ), digits = 6 ) ) 310s demand_(Intercept) demand_price demand_income 310s demand_(Intercept) 56.2287 -0.59260 0.033216 310s demand_price -0.5926 0.00831 -0.002451 310s demand_income 0.0332 -0.00245 0.002173 310s supply_(Intercept) 5.9548 0.14141 -0.203885 310s supply_income -0.2516 0.00201 0.000518 310s supply_farmPrice 0.1910 -0.00323 0.001351 310s supply_trend 0.0332 -0.00245 0.002173 310s supply_(Intercept) supply_income supply_farmPrice 310s demand_(Intercept) 5.955 -0.251647 0.19097 310s demand_price 0.141 0.002011 -0.00323 310s demand_income -0.204 0.000518 0.00135 310s supply_(Intercept) 146.577 -0.828954 -0.64122 310s supply_income -0.829 0.015214 -0.00683 310s supply_farmPrice -0.641 -0.006835 0.01339 310s supply_trend -0.204 0.000518 0.00135 310s supply_trend 310s demand_(Intercept) 0.033216 310s demand_price -0.002451 310s demand_income 0.002173 310s supply_(Intercept) -0.203885 310s supply_income 0.000518 310s supply_farmPrice 0.001351 310s supply_trend 0.002173 310s > print( round( vcov( fitsuri2$eq[[ 2 ]] ), digits = 6 ) ) 310s (Intercept) income farmPrice trend 310s (Intercept) 146.577 -0.828954 -0.64122 -0.203885 310s income -0.829 0.015214 -0.00683 0.000518 310s farmPrice -0.641 -0.006835 0.01339 0.001351 310s trend -0.204 0.000518 0.00135 0.002173 310s > 310s > print( round( vcov( fitsuri3e ), digits = 6 ) ) 310s demand_(Intercept) demand_price demand_income 310s demand_(Intercept) 47.9834 -0.50592 0.028570 310s demand_price -0.5059 0.00710 -0.002098 310s demand_income 0.0286 -0.00210 0.001859 310s supply_(Intercept) 4.9860 0.11975 -0.172089 310s supply_income -0.2118 0.00170 0.000428 310s supply_farmPrice 0.1609 -0.00273 0.001147 310s supply_trend 0.0286 -0.00210 0.001859 310s supply_(Intercept) supply_income supply_farmPrice 310s demand_(Intercept) 4.986 -0.211763 0.16090 310s demand_price 0.120 0.001700 -0.00273 310s demand_income -0.172 0.000428 0.00115 310s supply_(Intercept) 117.261 -0.661134 -0.51405 310s supply_income -0.661 0.012132 -0.00545 310s supply_farmPrice -0.514 -0.005450 0.01070 310s supply_trend -0.172 0.000428 0.00115 310s supply_trend 310s demand_(Intercept) 0.028570 310s demand_price -0.002098 310s demand_income 0.001859 310s supply_(Intercept) -0.172089 310s supply_income 0.000428 310s supply_farmPrice 0.001147 310s supply_trend 0.001859 310s > print( round( vcov( fitsuri3e, modified.regMat = TRUE ), digits = 6 ) ) 310s C1 C2 C3 C4 C5 C6 310s C1 47.9834 -0.50592 0.028570 4.986 -0.211763 0.16090 310s C2 -0.5059 0.00710 -0.002098 0.120 0.001700 -0.00273 310s C3 0.0286 -0.00210 0.001859 -0.172 0.000428 0.00115 310s C4 4.9860 0.11975 -0.172089 117.261 -0.661134 -0.51405 310s C5 -0.2118 0.00170 0.000428 -0.661 0.012132 -0.00545 310s C6 0.1609 -0.00273 0.001147 -0.514 -0.005450 0.01070 310s > print( round( vcov( fitsuri3e$eq[[ 1 ]] ), digits = 6 ) ) 310s (Intercept) price income 310s (Intercept) 47.9834 -0.5059 0.02857 310s price -0.5059 0.0071 -0.00210 310s income 0.0286 -0.0021 0.00186 310s > 310s > print( round( vcov( fitsurio4e ), digits = 6 ) ) 310s demand_(Intercept) demand_price demand_income 310s demand_(Intercept) 47.0268 -0.525375 0.058300 310s demand_price -0.5254 0.006074 -0.000842 310s demand_income 0.0583 -0.000842 0.000266 310s supply_(Intercept) 47.2346 -0.530682 0.061997 310s supply_price -0.5254 0.006074 -0.000842 310s supply_farmPrice 0.0508 -0.000704 0.000201 310s supply_trend 0.0583 -0.000842 0.000266 310s supply_(Intercept) supply_price supply_farmPrice 310s demand_(Intercept) 47.2346 -0.525375 0.050751 310s demand_price -0.5307 0.006074 -0.000704 310s demand_income 0.0620 -0.000842 0.000201 310s supply_(Intercept) 48.6183 -0.530682 0.042182 310s supply_price -0.5307 0.006074 -0.000704 310s supply_farmPrice 0.0422 -0.000704 0.000270 310s supply_trend 0.0620 -0.000842 0.000201 310s supply_trend 310s demand_(Intercept) 0.058300 310s demand_price -0.000842 310s demand_income 0.000266 310s supply_(Intercept) 0.061997 310s supply_price -0.000842 310s supply_farmPrice 0.000201 310s supply_trend 0.000266 310s > print( round( vcov( fitsurio4e$eq[[ 2 ]] ), digits = 6 ) ) 310s (Intercept) price farmPrice trend 310s (Intercept) 48.6183 -0.530682 0.042182 0.061997 310s price -0.5307 0.006074 -0.000704 -0.000842 310s farmPrice 0.0422 -0.000704 0.000270 0.000201 310s trend 0.0620 -0.000842 0.000201 0.000266 310s > print( round( vcov( fitsuri4e ), digits = 6 ) ) 310s demand_(Intercept) demand_price demand_income 310s demand_(Intercept) 37.8960 -0.36274 -0.01487 310s demand_price -0.3627 0.00503 -0.00144 310s demand_income -0.0149 -0.00144 0.00163 310s supply_(Intercept) 19.0822 -0.20611 0.01617 310s supply_income -0.3627 0.00503 -0.00144 310s supply_farmPrice 0.1707 -0.00279 0.00111 310s supply_trend -0.0149 -0.00144 0.00163 310s supply_(Intercept) supply_income supply_farmPrice 310s demand_(Intercept) 19.0822 -0.36274 0.17073 310s demand_price -0.2061 0.00503 -0.00279 310s demand_income 0.0162 -0.00144 0.00111 310s supply_(Intercept) 87.1827 -0.20611 -0.68294 310s supply_income -0.2061 0.00503 -0.00279 310s supply_farmPrice -0.6829 -0.00279 0.00976 310s supply_trend 0.0162 -0.00144 0.00111 310s supply_trend 310s demand_(Intercept) -0.01487 310s demand_price -0.00144 310s demand_income 0.00163 310s supply_(Intercept) 0.01617 310s supply_income -0.00144 310s supply_farmPrice 0.00111 310s supply_trend 0.00163 310s > print( round( vcov( fitsuri4e$eq[[ 2 ]] ), digits = 6 ) ) 310s (Intercept) income farmPrice trend 310s (Intercept) 87.1827 -0.20611 -0.68294 0.01617 310s income -0.2061 0.00503 -0.00279 -0.00144 310s farmPrice -0.6829 -0.00279 0.00976 0.00111 310s trend 0.0162 -0.00144 0.00111 0.00163 310s > 310s > print( round( vcov( fitsurio5r2 ), digits = 6 ) ) 310s demand_(Intercept) demand_price demand_income 310s demand_(Intercept) 51.3196 -0.579747 0.070528 310s demand_price -0.5797 0.006646 -0.000872 310s demand_income 0.0705 -0.000872 0.000171 310s supply_(Intercept) 51.5518 -0.583025 0.072036 310s supply_price -0.5797 0.006646 -0.000872 310s supply_farmPrice 0.0617 -0.000751 0.000138 310s supply_trend 0.0705 -0.000872 0.000171 310s supply_(Intercept) supply_price supply_farmPrice 310s demand_(Intercept) 51.5518 -0.579747 0.061658 310s demand_price -0.5830 0.006646 -0.000751 310s demand_income 0.0720 -0.000872 0.000138 310s supply_(Intercept) 52.2109 -0.583025 0.058794 310s supply_price -0.5830 0.006646 -0.000751 310s supply_farmPrice 0.0588 -0.000751 0.000154 310s supply_trend 0.0720 -0.000872 0.000138 310s supply_trend 310s demand_(Intercept) 0.070528 310s demand_price -0.000872 310s demand_income 0.000171 310s supply_(Intercept) 0.072036 310s supply_price -0.000872 310s supply_farmPrice 0.000138 310s supply_trend 0.000171 310s > print( round( vcov( fitsurio5r2, modified.regMat = TRUE ), digits = 6 ) ) 310s C1 C2 C3 C4 C5 C6 310s C1 51.3196 -0.579747 0.070528 51.5518 -0.579747 0.061658 310s C2 -0.5797 0.006646 -0.000872 -0.5830 0.006646 -0.000751 310s C3 0.0705 -0.000872 0.000171 0.0720 -0.000872 0.000138 310s C4 51.5518 -0.583025 0.072036 52.2109 -0.583025 0.058794 310s C5 -0.5797 0.006646 -0.000872 -0.5830 0.006646 -0.000751 310s C6 0.0617 -0.000751 0.000138 0.0588 -0.000751 0.000154 310s > print( round( vcov( fitsurio5r2$eq[[ 1 ]] ), digits = 6 ) ) 310s (Intercept) price income 310s (Intercept) 51.3196 -0.579747 0.070528 310s price -0.5797 0.006646 -0.000872 310s income 0.0705 -0.000872 0.000171 310s > print( round( vcov( fitsuri5r2 ), digits = 6 ) ) 310s demand_(Intercept) demand_price demand_income 310s demand_(Intercept) 45.6881 -0.44008 -0.01517 310s demand_price -0.4401 0.00605 -0.00170 310s demand_income -0.0152 -0.00170 0.00190 310s supply_(Intercept) 22.8172 -0.23903 0.01186 310s supply_income -0.4401 0.00605 -0.00170 310s supply_farmPrice 0.2104 -0.00345 0.00138 310s supply_trend -0.0152 -0.00170 0.00190 310s supply_(Intercept) supply_income supply_farmPrice 310s demand_(Intercept) 22.8172 -0.44008 0.21042 310s demand_price -0.2390 0.00605 -0.00345 310s demand_income 0.0119 -0.00170 0.00138 310s supply_(Intercept) 108.8722 -0.23903 -0.87024 310s supply_income -0.2390 0.00605 -0.00345 310s supply_farmPrice -0.8702 -0.00345 0.01234 310s supply_trend 0.0119 -0.00170 0.00138 310s supply_trend 310s demand_(Intercept) -0.01517 310s demand_price -0.00170 310s demand_income 0.00190 310s supply_(Intercept) 0.01186 310s supply_income -0.00170 310s supply_farmPrice 0.00138 310s supply_trend 0.00190 310s > print( round( vcov( fitsuri5r2, modified.regMat = TRUE ), digits = 6 ) ) 310s C1 C2 C3 C4 C5 C6 310s C1 45.6881 -0.44008 -0.01517 22.8172 -0.44008 0.21042 310s C2 -0.4401 0.00605 -0.00170 -0.2390 0.00605 -0.00345 310s C3 -0.0152 -0.00170 0.00190 0.0119 -0.00170 0.00138 310s C4 22.8172 -0.23903 0.01186 108.8722 -0.23903 -0.87024 310s C5 -0.4401 0.00605 -0.00170 -0.2390 0.00605 -0.00345 310s C6 0.2104 -0.00345 0.00138 -0.8702 -0.00345 0.01234 310s > print( round( vcov( fitsuri5r2$eq[[ 1 ]] ), digits = 6 ) ) 310s (Intercept) price income 310s (Intercept) 45.6881 -0.44008 -0.0152 310s price -0.4401 0.00605 -0.0017 310s income -0.0152 -0.00170 0.0019 310s > 310s > print( round( vcov( fitsurio5wr2 ), digits = 6 ) ) 310s demand_(Intercept) demand_price demand_income 310s demand_(Intercept) 51.3196 -0.579747 0.070528 310s demand_price -0.5797 0.006646 -0.000872 310s demand_income 0.0705 -0.000872 0.000171 310s supply_(Intercept) 51.5518 -0.583025 0.072036 310s supply_price -0.5797 0.006646 -0.000872 310s supply_farmPrice 0.0617 -0.000751 0.000138 310s supply_trend 0.0705 -0.000872 0.000171 310s supply_(Intercept) supply_price supply_farmPrice 310s demand_(Intercept) 51.5518 -0.579747 0.061658 310s demand_price -0.5830 0.006646 -0.000751 310s demand_income 0.0720 -0.000872 0.000138 310s supply_(Intercept) 52.2109 -0.583025 0.058794 310s supply_price -0.5830 0.006646 -0.000751 310s supply_farmPrice 0.0588 -0.000751 0.000154 310s supply_trend 0.0720 -0.000872 0.000138 310s supply_trend 310s demand_(Intercept) 0.070528 310s demand_price -0.000872 310s demand_income 0.000171 310s supply_(Intercept) 0.072036 310s supply_price -0.000872 310s supply_farmPrice 0.000138 310s supply_trend 0.000171 310s > print( round( vcov( fitsurio5wr2, modified.regMat = TRUE ), digits = 6 ) ) 310s C1 C2 C3 C4 C5 C6 310s C1 51.3196 -0.579747 0.070528 51.5518 -0.579747 0.061658 310s C2 -0.5797 0.006646 -0.000872 -0.5830 0.006646 -0.000751 310s C3 0.0705 -0.000872 0.000171 0.0720 -0.000872 0.000138 310s C4 51.5518 -0.583025 0.072036 52.2109 -0.583025 0.058794 310s C5 -0.5797 0.006646 -0.000872 -0.5830 0.006646 -0.000751 310s C6 0.0617 -0.000751 0.000138 0.0588 -0.000751 0.000154 310s > print( round( vcov( fitsurio5wr2$eq[[ 2 ]] ), digits = 6 ) ) 310s (Intercept) price farmPrice trend 310s (Intercept) 52.2109 -0.583025 0.058794 0.072036 310s price -0.5830 0.006646 -0.000751 -0.000872 310s farmPrice 0.0588 -0.000751 0.000154 0.000138 310s trend 0.0720 -0.000872 0.000138 0.000171 310s > 310s > 310s > ## *********** confidence intervals of coefficients ************* 310s > print( confint( fitsur1e2, useDfSys = TRUE ) ) 310s 2.5 % 97.5 % 310s demand_(Intercept) 83.927 114.497 310s demand_price -0.445 -0.088 310s demand_income 0.208 0.373 310s supply_(Intercept) 40.751 85.403 310s supply_price -0.048 0.336 310s supply_farmPrice 0.128 0.285 310s supply_trend 0.202 0.463 310s > print( confint( fitsur1e2$eq[[ 2 ]], level = 0.9, useDfSys = TRUE ) ) 310s 5 % 95 % 310s (Intercept) 44.506 81.648 310s price -0.016 0.304 310s farmPrice 0.141 0.271 310s trend 0.224 0.441 310s > 310s > print( confint( fitsur1we2, useDfSys = TRUE ) ) 310s 2.5 % 97.5 % 310s demand_(Intercept) 83.927 114.497 310s demand_price -0.445 -0.088 310s demand_income 0.208 0.373 310s supply_(Intercept) 40.751 85.403 310s supply_price -0.048 0.336 310s supply_farmPrice 0.128 0.285 310s supply_trend 0.202 0.463 310s > print( confint( fitsur1we2$eq[[ 1 ]], level = 0.9, useDfSys = TRUE ) ) 310s 5 % 95 % 310s (Intercept) 86.498 111.926 310s price -0.415 -0.118 310s income 0.222 0.360 310s > 310s > print( confint( fitsur2e, level = 0.9 ) ) 310s 5 % 95 % 310s demand_(Intercept) 84.618 112.942 310s demand_price -0.397 -0.074 310s demand_income 0.193 0.333 310s supply_(Intercept) 48.153 87.055 310s supply_price -0.040 0.306 310s supply_farmPrice 0.116 0.240 310s supply_trend 0.193 0.333 310s > print( confint( fitsur2e$eq[[ 1 ]], level = 0.99 ) ) 310s 0.5 % 99.5 % 310s (Intercept) 79.767 117.793 310s price -0.452 -0.018 310s income 0.169 0.357 310s > 310s > print( confint( fitsur3, level = 0.99 ) ) 310s 0.5 % 99.5 % 310s demand_(Intercept) 83.481 114.201 310s demand_price -0.415 -0.065 310s demand_income 0.192 0.342 310s supply_(Intercept) 45.755 89.102 310s supply_price -0.060 0.327 310s supply_farmPrice 0.111 0.248 310s supply_trend 0.192 0.342 310s > print( confint( fitsur3$eq[[ 2 ]], level = 0.5 ) ) 310s 25 % 75 % 310s (Intercept) 60.157 74.699 310s price 0.068 0.198 310s farmPrice 0.157 0.202 310s trend 0.242 0.292 310s > 310s > print( confint( fitsur4r3, level = 0.5 ) ) 310s 25 % 75 % 310s demand_(Intercept) 78.344 108.052 310s demand_price -0.406 -0.070 310s demand_income 0.289 0.358 310s supply_(Intercept) 34.267 64.468 310s supply_price 0.094 0.430 310s supply_farmPrice 0.192 0.262 310s supply_trend 0.289 0.358 310s > print( confint( fitsur4r3$eq[[ 1 ]], level = 0.25 ) ) 310s 37.5 % 62.5 % 310s (Intercept) 90.848 95.548 310s price -0.265 -0.211 310s income 0.318 0.329 310s > 310s > print( confint( fitsur5, level = 0.25 ) ) 310s 37.5 % 62.5 % 310s demand_(Intercept) 81.670 111.985 310s demand_price -0.450 -0.109 310s demand_income 0.287 0.371 310s supply_(Intercept) 37.377 68.500 310s supply_price 0.050 0.391 310s supply_farmPrice 0.190 0.276 310s supply_trend 0.287 0.371 310s > print( confint( fitsur5$eq[[ 2 ]], level = 0.975 ) ) 310s 1.3 % 98.8 % 310s (Intercept) 34.986 70.891 310s price 0.024 0.417 310s farmPrice 0.183 0.282 310s trend 0.280 0.377 310s > 310s > print( confint( fitsuri1r3, level = 0.975 ) ) 310s 1.3 % 98.8 % 310s demand_(Intercept) 77.960 109.125 310s demand_price -0.414 -0.043 310s demand_income 0.213 0.406 310s supply_(Intercept) 82.005 96.361 310s supply_income 0.574 0.753 310s supply_farmPrice -0.550 -0.393 310s supply_trend -0.932 -0.659 310s > print( confint( fitsuri1r3$eq[[ 1 ]], level = 0.999 ) ) 310s 0.1 % 100 % 310s (Intercept) 64.257 122.828 310s price -0.576 0.119 310s income 0.128 0.491 310s > 310s > print( confint( fitsuri2, level = 0.999 ) ) 310s 0.1 % 100 % 310s demand_(Intercept) 92.129 122.607 310s demand_price -0.580 -0.209 310s demand_income 0.243 0.433 310s supply_(Intercept) 60.441 109.649 310s supply_income 0.062 0.563 310s supply_farmPrice -0.432 0.038 310s supply_trend 0.243 0.433 310s > print( confint( fitsuri2$eq[[ 2 ]], level = 0.1 ) ) 310s 45 % 55 % 310s (Intercept) 83.512 86.578 310s income 0.297 0.328 310s farmPrice -0.212 -0.183 310s trend 0.332 0.344 310s > 310s > print( confint( fitsuri3e, level = 0.1 ) ) 310s 45 % 55 % 310s demand_(Intercept) 93.728 121.882 310s demand_price -0.570 -0.227 310s demand_income 0.250 0.426 310s supply_(Intercept) 63.100 107.114 310s supply_income 0.087 0.534 310s supply_farmPrice -0.406 0.014 310s supply_trend 0.250 0.426 310s > print( confint( fitsuri3e$eq[[ 1 ]], level = 0.01 ) ) 310s 49.5 % 50.5 % 310s (Intercept) 107.718 107.893 310s price -0.400 -0.398 310s income 0.337 0.338 310s > 310s > print( confint( fitsurio4, level = 0.01 ) ) 310s 49.5 % 50.5 % 310s demand_(Intercept) 77.496 107.356 310s demand_price -0.400 -0.055 310s demand_income 0.283 0.358 310s supply_(Intercept) 33.588 63.871 310s supply_price 0.100 0.445 310s supply_farmPrice 0.185 0.262 310s supply_trend 0.283 0.358 310s > print( confint( fitsurio4$eq[[ 2 ]], level = 0.33 ) ) 310s 33.5 % 66.5 % 310s (Intercept) 45.524 51.935 310s price 0.236 0.309 310s farmPrice 0.215 0.231 310s trend 0.312 0.328 310s > print( confint( fitsuri4, level = 0.01 ) ) 310s 49.5 % 50.5 % 310s demand_(Intercept) 84.345 111.726 310s demand_price -0.422 -0.107 310s demand_income 0.212 0.389 310s supply_(Intercept) 68.817 111.192 310s supply_income 0.078 0.393 310s supply_farmPrice -0.392 0.058 310s supply_trend 0.212 0.389 310s > print( confint( fitsuri4$eq[[ 2 ]], level = 0.33 ) ) 310s 33.5 % 66.5 % 310s (Intercept) 85.519 94.490 310s income 0.202 0.269 310s farmPrice -0.214 -0.119 310s trend 0.282 0.319 310s > 310s > print( confint( fitsurio4w, level = 0.01 ) ) 310s 49.5 % 50.5 % 310s demand_(Intercept) 77.496 107.356 310s demand_price -0.400 -0.055 310s demand_income 0.283 0.358 310s supply_(Intercept) 33.587 63.871 310s supply_price 0.100 0.445 310s supply_farmPrice 0.185 0.262 310s supply_trend 0.283 0.358 310s > print( confint( fitsurio4w$eq[[ 1 ]], level = 0.33 ) ) 310s 33.5 % 66.5 % 310s (Intercept) 89.266 95.587 310s price -0.264 -0.191 310s income 0.312 0.328 310s > 310s > print( confint( fitsurio5r2, level = 0.33 ) ) 310s 33.5 % 66.5 % 310s demand_(Intercept) 63.491 92.577 310s demand_price -0.230 0.101 310s demand_income 0.274 0.327 310s supply_(Intercept) 19.527 48.865 310s supply_price 0.270 0.601 310s supply_farmPrice 0.182 0.232 310s supply_trend 0.274 0.327 310s > print( confint( fitsurio5r2$eq[[ 1 ]] ) ) 310s 2.5 % 97.5 % 310s (Intercept) 63.491 92.577 310s price -0.230 0.101 310s income 0.274 0.327 310s > print( confint( fitsuri5r2, level = 0.33 ) ) 310s 33.5 % 66.5 % 310s demand_(Intercept) 84.498 111.942 310s demand_price -0.425 -0.109 310s demand_income 0.213 0.390 310s supply_(Intercept) 69.034 111.399 310s supply_income 0.075 0.391 310s supply_farmPrice -0.392 0.059 310s supply_trend 0.213 0.390 310s > print( confint( fitsuri5r2$eq[[ 1 ]] ) ) 310s 2.5 % 97.5 % 310s (Intercept) 84.498 111.942 310s price -0.425 -0.109 310s income 0.213 0.390 310s > 310s > 310s > ## *********** fitted values ************* 310s > print( fitted( fitsur1e2 ) ) 310s demand supply 310s 1 97.9 98.1 310s 2 99.8 99.2 310s 3 99.7 99.4 310s 4 99.9 99.7 310s 5 102.1 101.7 310s 6 101.9 101.7 310s 7 102.3 101.7 310s 8 102.6 103.5 310s 9 101.6 102.4 310s 10 100.7 100.3 310s 11 96.2 96.8 310s 12 95.2 95.4 310s 13 96.4 96.8 310s 14 99.2 98.7 310s 15 103.8 102.9 310s 16 103.5 104.2 310s 17 104.2 104.0 310s 18 101.8 103.1 310s 19 103.2 103.0 310s 20 105.9 105.2 310s > print( fitted( fitsur1e2$eq[[ 2 ]] ) ) 310s 1 2 3 4 5 6 7 8 9 10 11 12 13 310s 98.1 99.2 99.4 99.7 101.7 101.7 101.7 103.5 102.4 100.3 96.8 95.4 96.8 310s 14 15 16 17 18 19 20 310s 98.7 102.9 104.2 104.0 103.1 103.0 105.2 310s > 310s > print( fitted( fitsur2e ) ) 310s demand supply 310s 1 98.2 98.7 310s 2 99.9 99.7 310s 3 99.9 99.8 310s 4 100.0 100.0 310s 5 102.0 101.7 310s 6 101.8 101.7 310s 7 102.1 101.7 310s 8 102.5 103.2 310s 9 101.5 102.2 310s 10 100.7 100.3 310s 11 96.6 97.3 310s 12 95.8 96.1 310s 13 96.8 97.3 310s 14 99.4 98.9 310s 15 103.5 102.4 310s 16 103.3 103.6 310s 17 103.8 103.3 310s 18 101.8 102.7 310s 19 103.0 102.6 310s 20 105.5 104.5 310s > print( fitted( fitsur2e$eq[[ 1 ]] ) ) 310s 1 2 3 4 5 6 7 8 9 10 11 12 13 310s 98.2 99.9 99.9 100.0 102.0 101.8 102.1 102.5 101.5 100.7 96.6 95.8 96.8 310s 14 15 16 17 18 19 20 310s 99.4 103.5 103.3 103.8 101.8 103.0 105.5 310s > 310s > print( fitted( fitsur2we ) ) 310s demand supply 310s 1 98.2 98.7 310s 2 99.9 99.7 310s 3 99.9 99.8 310s 4 100.0 100.1 310s 5 102.0 101.7 310s 6 101.8 101.7 310s 7 102.1 101.7 310s 8 102.5 103.2 310s 9 101.5 102.2 310s 10 100.7 100.3 310s 11 96.7 97.4 310s 12 95.8 96.2 310s 13 96.8 97.4 310s 14 99.4 99.0 310s 15 103.5 102.4 310s 16 103.2 103.6 310s 17 103.8 103.3 310s 18 101.8 102.7 310s 19 103.0 102.6 310s 20 105.5 104.5 310s > print( fitted( fitsur2we$eq[[ 2 ]] ) ) 310s 1 2 3 4 5 6 7 8 9 10 11 12 13 310s 98.7 99.7 99.8 100.1 101.7 101.7 101.7 103.2 102.2 100.3 97.4 96.2 97.4 310s 14 15 16 17 18 19 20 310s 99.0 102.4 103.6 103.3 102.7 102.6 104.5 310s > 310s > print( fitted( fitsur3 ) ) 310s demand supply 310s 1 98.1 98.6 310s 2 99.9 99.6 310s 3 99.9 99.8 310s 4 100.0 100.0 310s 5 102.0 101.7 310s 6 101.8 101.7 310s 7 102.2 101.7 310s 8 102.5 103.2 310s 9 101.5 102.2 310s 10 100.7 100.3 310s 11 96.6 97.3 310s 12 95.7 96.1 310s 13 96.8 97.3 310s 14 99.3 98.9 310s 15 103.6 102.5 310s 16 103.3 103.7 310s 17 103.8 103.3 310s 18 101.8 102.7 310s 19 103.0 102.6 310s 20 105.6 104.6 310s > print( fitted( fitsur3$eq[[ 2 ]] ) ) 310s 1 2 3 4 5 6 7 8 9 10 11 12 13 310s 98.6 99.6 99.8 100.0 101.7 101.7 101.7 103.2 102.2 100.3 97.3 96.1 97.3 310s 14 15 16 17 18 19 20 310s 98.9 102.5 103.7 103.3 102.7 102.6 104.6 310s > 310s > print( fitted( fitsur4r3 ) ) 310s demand supply 310s 1 97.6 98.2 310s 2 99.9 99.8 310s 3 99.8 99.9 310s 4 100.0 100.3 310s 5 102.1 101.8 310s 6 102.0 101.9 310s 7 102.5 102.1 310s 8 103.1 104.3 310s 9 101.4 102.2 310s 10 100.2 99.3 310s 11 95.3 95.7 310s 12 94.5 94.7 310s 13 96.0 96.6 310s 14 99.0 98.2 310s 15 103.9 102.4 310s 16 103.7 104.2 310s 17 103.8 102.6 310s 18 102.2 103.4 310s 19 103.8 103.5 310s 20 107.2 106.7 310s > print( fitted( fitsur4r3$eq[[ 1 ]] ) ) 310s 1 2 3 4 5 6 7 8 9 10 11 12 13 310s 97.6 99.9 99.8 100.0 102.1 102.0 102.5 103.1 101.4 100.2 95.3 94.5 96.0 310s 14 15 16 17 18 19 20 310s 99.0 103.9 103.7 103.8 102.2 103.8 107.2 310s > 310s > print( fitted( fitsur5 ) ) 310s demand supply 310s 1 97.5 98.2 310s 2 99.7 99.6 310s 3 99.7 99.8 310s 4 99.9 100.1 310s 5 102.2 101.9 310s 6 102.0 102.0 310s 7 102.5 102.1 310s 8 102.9 104.2 310s 9 101.6 102.4 310s 10 100.5 99.7 310s 11 95.5 95.9 310s 12 94.5 94.6 310s 13 95.8 96.4 310s 14 99.0 98.2 310s 15 104.1 102.6 310s 16 103.8 104.3 310s 17 104.3 103.3 310s 18 102.0 103.3 310s 19 103.6 103.3 310s 20 106.8 106.1 310s > print( fitted( fitsur5$eq[[ 2 ]] ) ) 310s 1 2 3 4 5 6 7 8 9 10 11 12 13 310s 98.2 99.6 99.8 100.1 101.9 102.0 102.1 104.2 102.4 99.7 95.9 94.6 96.4 310s 14 15 16 17 18 19 20 310s 98.2 102.6 104.3 103.3 103.3 103.3 106.1 310s > 310s > print( fitted( fitsuri1r3 ) ) 310s demand supply 310s 1 97.7 100.2 310s 2 99.9 105.7 310s 3 99.9 104.3 310s 4 100.1 104.9 310s 5 102.1 99.2 310s 6 101.9 100.1 310s 7 102.4 102.3 310s 8 103.0 102.6 310s 9 101.4 94.9 310s 10 100.2 92.8 310s 11 95.5 92.1 310s 12 94.8 98.3 310s 13 96.2 101.6 310s 14 99.0 99.8 310s 15 103.7 97.5 310s 16 103.6 96.7 310s 17 103.6 87.6 310s 18 102.1 100.6 310s 19 103.7 105.5 310s 20 107.0 113.8 310s > print( fitted( fitsuri1r3$eq[[ 1 ]] ) ) 310s 1 2 3 4 5 6 7 8 9 10 11 12 13 310s 97.7 99.9 99.9 100.1 102.1 101.9 102.4 103.0 101.4 100.2 95.5 94.8 96.2 310s 14 15 16 17 18 19 20 310s 99.0 103.7 103.6 103.6 102.1 103.7 107.0 310s > 310s > print( fitted( fitsuri1wr3 ) ) 310s demand supply 310s 1 97.7 100.2 310s 2 99.9 105.7 310s 3 99.9 104.3 310s 4 100.1 104.9 310s 5 102.1 99.2 310s 6 101.9 100.1 310s 7 102.4 102.3 310s 8 103.0 102.6 310s 9 101.4 94.9 310s 10 100.2 92.8 310s 11 95.5 92.1 310s 12 94.8 98.3 310s 13 96.2 101.6 310s 14 99.0 99.8 310s 15 103.7 97.5 310s 16 103.6 96.7 310s 17 103.6 87.6 310s 18 102.1 100.6 310s 19 103.7 105.5 310s 20 107.0 113.8 310s > print( fitted( fitsuri1wr3$eq[[ 2 ]] ) ) 310s 1 2 3 4 5 6 7 8 9 10 11 12 13 310s 100.2 105.7 104.3 104.9 99.2 100.1 102.3 102.6 94.9 92.8 92.1 98.3 101.6 310s 14 15 16 17 18 19 20 310s 99.8 97.5 96.7 87.6 100.6 105.5 113.8 310s > 310s > print( fitted( fitsuri2 ) ) 310s demand supply 310s 1 97.4 93.4 310s 2 99.2 96.7 310s 3 99.3 96.7 310s 4 99.4 97.7 310s 5 102.5 96.1 310s 6 102.1 97.1 310s 7 102.4 98.8 310s 8 102.5 99.8 310s 9 102.0 96.8 310s 10 101.4 96.4 310s 11 96.0 96.3 310s 12 94.4 99.6 310s 13 95.4 101.9 310s 14 99.1 102.0 310s 15 104.7 102.2 310s 16 104.1 102.6 310s 17 105.8 99.1 310s 18 101.6 105.5 310s 19 103.1 108.5 310s 20 105.6 113.2 310s > print( fitted( fitsuri2$eq[[ 2 ]] ) ) 310s 1 2 3 4 5 6 7 8 9 10 11 12 13 310s 93.4 96.7 96.7 97.7 96.1 97.1 98.8 99.8 96.8 96.4 96.3 99.6 101.9 310s 14 15 16 17 18 19 20 310s 102.0 102.2 102.6 99.1 105.5 108.5 113.2 310s > 310s > print( fitted( fitsuri3e ) ) 310s demand supply 310s 1 97.4 93.4 310s 2 99.2 96.7 310s 3 99.3 96.7 310s 4 99.3 97.7 310s 5 102.5 96.1 310s 6 102.1 97.2 310s 7 102.4 98.8 310s 8 102.5 99.8 310s 9 102.0 96.9 310s 10 101.5 96.4 310s 11 96.1 96.3 310s 12 94.4 99.6 310s 13 95.4 101.9 310s 14 99.1 102.0 310s 15 104.7 102.2 310s 16 104.1 102.6 310s 17 105.9 99.1 310s 18 101.6 105.5 310s 19 103.1 108.4 310s 20 105.5 113.1 310s > print( fitted( fitsuri3e$eq[[ 1 ]] ) ) 310s 1 2 3 4 5 6 7 8 9 10 11 12 13 310s 97.4 99.2 99.3 99.3 102.5 102.1 102.4 102.5 102.0 101.5 96.1 94.4 95.4 310s 14 15 16 17 18 19 20 310s 99.1 104.7 104.1 105.9 101.6 103.1 105.5 310s > 310s > print( fitted( fitsurio4 ) ) 310s demand supply 310s 1 97.6 98.2 310s 2 100.0 99.9 310s 3 99.9 100.0 310s 4 100.1 100.4 310s 5 102.1 101.8 310s 6 102.0 101.9 310s 7 102.5 102.1 310s 8 103.1 104.3 310s 9 101.4 102.1 310s 10 100.1 99.2 310s 11 95.3 95.7 310s 12 94.6 94.8 310s 13 96.1 96.7 310s 14 99.0 98.3 310s 15 103.8 102.3 310s 16 103.7 104.1 310s 17 103.6 102.4 310s 18 102.2 103.5 310s 19 103.8 103.6 310s 20 107.3 106.8 310s > print( fitted( fitsurio4$eq[[ 2 ]] ) ) 310s 1 2 3 4 5 6 7 8 9 10 11 12 13 310s 98.2 99.9 100.0 100.4 101.8 101.9 102.1 104.3 102.1 99.2 95.7 94.8 96.7 310s 14 15 16 17 18 19 20 310s 98.3 102.3 104.1 102.4 103.5 103.6 106.8 310s > print( fitted( fitsuri4 ) ) 310s demand supply 310s 1 97.8 94.5 310s 2 99.8 97.1 310s 3 99.7 97.2 310s 4 99.9 98.0 310s 5 102.1 96.5 310s 6 101.9 97.4 310s 7 102.3 98.8 310s 8 102.7 99.5 310s 9 101.6 97.3 310s 10 100.6 97.2 310s 11 96.0 97.5 310s 12 95.0 100.3 310s 13 96.2 102.0 310s 14 99.1 102.0 310s 15 103.9 101.7 310s 16 103.6 102.1 310s 17 104.1 99.4 310s 18 101.9 104.6 310s 19 103.3 106.9 310s 20 106.2 110.4 310s > print( fitted( fitsuri4$eq[[ 2 ]] ) ) 310s 1 2 3 4 5 6 7 8 9 10 11 12 13 310s 94.5 97.1 97.2 98.0 96.5 97.4 98.8 99.5 97.3 97.2 97.5 100.3 102.0 310s 14 15 16 17 18 19 20 310s 102.0 101.7 102.1 99.4 104.6 106.9 110.4 310s > 310s > print( fitted( fitsurio5r2 ) ) 310s demand supply 310s 1 97.8 98.5 310s 2 100.6 100.7 310s 3 100.4 100.6 310s 4 100.8 101.2 310s 5 101.7 101.3 310s 6 101.8 101.7 310s 7 102.5 102.2 310s 8 103.7 104.9 310s 9 100.8 101.4 310s 10 98.9 97.7 310s 11 94.6 94.8 310s 12 94.8 95.0 310s 13 96.8 97.6 310s 14 98.9 98.2 310s 15 102.9 101.3 310s 16 103.3 103.6 310s 17 101.4 99.8 310s 18 102.7 104.0 310s 19 104.5 104.4 310s 20 108.9 108.9 310s > print( fitted( fitsurio5r2$eq[[ 1 ]] ) ) 310s 1 2 3 4 5 6 7 8 9 10 11 12 13 310s 97.8 100.6 100.4 100.8 101.7 101.8 102.5 103.7 100.8 98.9 94.6 94.8 96.8 310s 14 15 16 17 18 19 20 310s 98.9 102.9 103.3 101.4 102.7 104.5 108.9 310s > print( fitted( fitsuri5r2 ) ) 310s demand supply 310s 1 97.8 94.6 310s 2 99.8 97.1 310s 3 99.7 97.2 310s 4 99.9 98.0 310s 5 102.1 96.5 310s 6 101.9 97.4 310s 7 102.3 98.8 310s 8 102.7 99.5 310s 9 101.6 97.3 310s 10 100.6 97.2 310s 11 96.0 97.5 310s 12 95.0 100.3 310s 13 96.2 102.0 310s 14 99.1 102.0 310s 15 103.9 101.7 310s 16 103.6 102.0 310s 17 104.2 99.4 310s 18 101.9 104.6 310s 19 103.3 106.9 310s 20 106.2 110.4 310s > print( fitted( fitsuri5r2$eq[[ 1 ]] ) ) 310s 1 2 3 4 5 6 7 8 9 10 11 12 13 310s 97.8 99.8 99.7 99.9 102.1 101.9 102.3 102.7 101.6 100.6 96.0 95.0 96.2 310s 14 15 16 17 18 19 20 310s 99.1 103.9 103.6 104.2 101.9 103.3 106.2 310s > 310s > 310s > ## *********** predicted values ************* 310s > predictData <- Kmenta 310s > predictData$consump <- NULL 310s > predictData$price <- Kmenta$price * 0.9 310s > predictData$income <- Kmenta$income * 1.1 310s > 310s > print( predict( fitsur1e2, se.fit = TRUE, interval = "prediction", 310s + useDfSys = TRUE ) ) 310s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 310s 1 97.9 0.607 93.7 102.1 98.1 0.780 310s 2 99.8 0.569 95.6 104.0 99.2 0.793 310s 3 99.7 0.537 95.6 103.9 99.4 0.728 310s 4 99.9 0.575 95.7 104.1 99.7 0.755 310s 5 102.1 0.493 97.9 106.3 101.7 0.652 310s 6 101.9 0.458 97.8 106.0 101.7 0.605 310s 7 102.3 0.475 98.1 106.4 101.7 0.592 310s 8 102.6 0.593 98.4 106.8 103.5 0.835 310s 9 101.6 0.523 97.4 105.8 102.4 0.717 310s 10 100.7 0.788 96.4 105.1 100.3 0.980 310s 11 96.2 0.898 91.8 100.7 96.8 1.081 310s 12 95.2 0.898 90.8 99.7 95.4 1.159 310s 13 96.4 0.816 92.0 100.7 96.8 1.019 310s 14 99.2 0.495 95.1 103.4 98.7 0.710 310s 15 103.8 0.724 99.5 108.1 102.9 0.816 310s 16 103.5 0.586 99.3 107.7 104.2 0.830 310s 17 104.2 1.240 99.4 108.9 104.0 1.540 310s 18 101.8 0.533 97.7 106.0 103.1 0.770 310s 19 103.2 0.666 98.9 107.4 103.0 0.862 310s 20 105.9 1.240 101.1 110.7 105.2 1.517 310s supply.lwr supply.upr 310s 1 92.6 104 310s 2 93.7 105 310s 3 94.0 105 310s 4 94.2 105 310s 5 96.3 107 310s 6 96.3 107 310s 7 96.4 107 310s 8 98.0 109 310s 9 97.0 108 310s 10 94.7 106 310s 11 91.2 103 310s 12 89.7 101 310s 13 91.2 102 310s 14 93.3 104 310s 15 97.4 108 310s 16 98.7 110 310s 17 97.9 110 310s 18 97.7 109 310s 19 97.5 109 310s 20 99.2 111 310s > print( predict( fitsur1e2$eq[[ 2 ]], se.fit = TRUE, interval = "prediction", 310s + useDfSys = TRUE ) ) 310s fit se.fit lwr upr 310s 1 98.1 0.780 92.6 104 310s 2 99.2 0.793 93.7 105 310s 3 99.4 0.728 94.0 105 310s 4 99.7 0.755 94.2 105 310s 5 101.7 0.652 96.3 107 310s 6 101.7 0.605 96.3 107 310s 7 101.7 0.592 96.4 107 310s 8 103.5 0.835 98.0 109 310s 9 102.4 0.717 97.0 108 310s 10 100.3 0.980 94.7 106 310s 11 96.8 1.081 91.2 103 310s 12 95.4 1.159 89.7 101 310s 13 96.8 1.019 91.2 102 310s 14 98.7 0.710 93.3 104 310s 15 102.9 0.816 97.4 108 310s 16 104.2 0.830 98.7 110 310s 17 104.0 1.540 97.9 110 310s 18 103.1 0.770 97.7 109 310s 19 103.0 0.862 97.5 109 310s 20 105.2 1.517 99.2 111 310s > 310s > print( predict( fitsur2e, se.pred = TRUE, interval = "confidence", 310s + level = 0.999, newdata = predictData ) ) 310s demand.pred demand.se.pred demand.lwr demand.upr supply.pred supply.se.pred 310s 1 103 2.23 99.8 106 97.4 2.80 310s 2 105 2.22 102.0 108 98.3 2.71 310s 3 105 2.23 101.8 108 98.4 2.72 310s 4 105 2.23 102.1 108 98.7 2.70 310s 5 107 2.42 102.3 111 100.4 2.83 310s 6 107 2.39 102.5 111 100.4 2.79 310s 7 107 2.37 103.0 111 100.4 2.75 310s 8 108 2.34 103.8 112 101.8 2.70 310s 9 106 2.44 101.7 111 100.9 2.87 310s 10 105 2.54 99.8 111 99.1 3.05 310s 11 101 2.39 96.5 105 96.1 3.05 310s 12 100 2.24 97.0 103 94.8 2.96 310s 13 101 2.17 99.1 104 96.0 2.83 310s 14 104 2.30 100.5 108 97.6 2.85 310s 15 108 2.58 102.9 114 101.2 2.91 310s 16 108 2.49 103.4 113 102.3 2.83 310s 17 108 2.85 101.3 115 102.1 3.26 310s 18 107 2.31 103.2 111 101.3 2.70 310s 19 108 2.36 104.3 113 101.2 2.68 310s 20 112 2.52 106.4 117 103.0 2.66 310s supply.lwr supply.upr 310s 1 93.6 101.1 310s 2 95.5 101.1 310s 3 95.5 101.3 310s 4 96.0 101.3 310s 5 96.4 104.4 310s 6 96.7 104.1 310s 7 97.1 103.7 310s 8 99.2 104.5 310s 9 96.5 105.3 310s 10 93.4 104.8 310s 11 90.3 101.8 310s 12 89.7 99.9 310s 13 91.9 100.0 310s 14 93.4 101.8 310s 15 96.4 105.9 310s 16 98.3 106.4 310s 17 95.1 109.2 310s 18 98.6 103.9 310s 19 98.9 103.5 310s 20 101.0 105.1 310s > print( predict( fitsur2e$eq[[ 1 ]], se.pred = TRUE, interval = "confidence", 310s + level = 0.999, newdata = predictData ) ) 310s fit se.pred lwr upr 310s 1 103 2.23 99.8 106 310s 2 105 2.22 102.0 108 310s 3 105 2.23 101.8 108 310s 4 105 2.23 102.1 108 310s 5 107 2.42 102.3 111 310s 6 107 2.39 102.5 111 310s 7 107 2.37 103.0 111 310s 8 108 2.34 103.8 112 310s 9 106 2.44 101.7 111 310s 10 105 2.54 99.8 111 310s 11 101 2.39 96.5 105 310s 12 100 2.24 97.0 103 310s 13 101 2.17 99.1 104 310s 14 104 2.30 100.5 108 310s 15 108 2.58 102.9 114 310s 16 108 2.49 103.4 113 310s 17 108 2.85 101.3 115 310s 18 107 2.31 103.2 111 310s 19 108 2.36 104.3 113 310s 20 112 2.52 106.4 117 310s > 310s > print( predict( fitsur3, se.pred = TRUE, interval = "prediction", 310s + level = 0.975 ) ) 310s demand.pred demand.se.pred demand.lwr demand.upr supply.pred supply.se.pred 310s 1 98.1 2.13 93.1 103 98.6 2.67 310s 2 99.9 2.13 94.9 105 99.6 2.69 310s 3 99.9 2.12 94.9 105 99.8 2.68 310s 4 100.0 2.13 95.0 105 100.0 2.69 310s 5 102.0 2.11 97.0 107 101.7 2.67 310s 6 101.8 2.10 96.9 107 101.7 2.66 310s 7 102.2 2.11 97.2 107 101.7 2.66 310s 8 102.5 2.14 97.5 108 103.2 2.72 310s 9 101.5 2.12 96.5 106 102.2 2.69 310s 10 100.7 2.20 95.5 106 100.3 2.78 310s 11 96.6 2.23 91.3 102 97.3 2.80 310s 12 95.7 2.22 90.5 101 96.1 2.81 310s 13 96.8 2.19 91.6 102 97.3 2.77 310s 14 99.3 2.11 94.4 104 98.9 2.69 310s 15 103.6 2.17 98.5 109 102.5 2.71 310s 16 103.3 2.13 98.3 108 103.7 2.69 310s 17 103.8 2.39 98.2 109 103.3 2.99 310s 18 101.8 2.12 96.8 107 102.7 2.69 310s 19 103.0 2.16 98.0 108 102.6 2.71 310s 20 105.6 2.39 100.0 111 104.6 2.97 310s supply.lwr supply.upr 310s 1 92.4 105 310s 2 93.3 106 310s 3 93.5 106 310s 4 93.7 106 310s 5 95.4 108 310s 6 95.5 108 310s 7 95.5 108 310s 8 96.8 110 310s 9 95.9 109 310s 10 93.8 107 310s 11 90.7 104 310s 12 89.5 103 310s 13 90.8 104 310s 14 92.6 105 310s 15 96.1 109 310s 16 97.3 110 310s 17 96.3 110 310s 18 96.4 109 310s 19 96.3 109 310s 20 97.6 112 310s > print( predict( fitsur3$eq[[ 2 ]], se.pred = TRUE, interval = "prediction", 310s + level = 0.975 ) ) 310s fit se.pred lwr upr 310s 1 98.6 2.67 92.4 105 310s 2 99.6 2.69 93.3 106 310s 3 99.8 2.68 93.5 106 310s 4 100.0 2.69 93.7 106 310s 5 101.7 2.67 95.4 108 310s 6 101.7 2.66 95.5 108 310s 7 101.7 2.66 95.5 108 310s 8 103.2 2.72 96.8 110 310s 9 102.2 2.69 95.9 109 310s 10 100.3 2.78 93.8 107 310s 11 97.3 2.80 90.7 104 310s 12 96.1 2.81 89.5 103 310s 13 97.3 2.77 90.8 104 310s 14 98.9 2.69 92.6 105 310s 15 102.5 2.71 96.1 109 310s 16 103.7 2.69 97.3 110 310s 17 103.3 2.99 96.3 110 310s 18 102.7 2.69 96.4 109 310s 19 102.6 2.71 96.3 109 310s 20 104.6 2.97 97.6 112 310s > 310s > print( predict( fitsur4r3, se.fit = TRUE, interval = "confidence", 310s + level = 0.25 ) ) 310s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 310s 1 97.6 0.474 97.4 97.7 98.2 0.571 310s 2 99.9 0.558 99.7 100.1 99.8 0.699 310s 3 99.8 0.523 99.6 100.0 99.9 0.651 310s 4 100.0 0.567 99.9 100.2 100.3 0.701 310s 5 102.1 0.476 102.0 102.3 101.8 0.620 310s 6 102.0 0.443 101.8 102.1 101.9 0.574 310s 7 102.5 0.440 102.3 102.6 102.1 0.559 310s 8 103.1 0.532 102.9 103.3 104.3 0.646 310s 9 101.4 0.520 101.3 101.6 102.2 0.692 310s 10 100.2 0.774 100.0 100.4 99.3 0.939 310s 11 95.3 0.612 95.1 95.5 95.7 0.732 310s 12 94.5 0.525 94.4 94.7 94.7 0.687 310s 13 96.0 0.603 95.8 96.2 96.6 0.791 310s 14 99.0 0.444 98.8 99.1 98.2 0.580 310s 15 103.9 0.643 103.7 104.1 102.4 0.759 310s 16 103.7 0.494 103.6 103.9 104.2 0.634 310s 17 103.8 1.191 103.4 104.1 102.6 1.456 310s 18 102.2 0.510 102.0 102.3 103.4 0.622 310s 19 103.8 0.570 103.6 104.0 103.5 0.714 310s 20 107.2 0.973 106.9 107.6 106.7 1.183 310s supply.lwr supply.upr 310s 1 98.0 98.4 310s 2 99.6 100.0 310s 3 99.7 100.1 310s 4 100.1 100.5 310s 5 101.6 102.0 310s 6 101.7 102.1 310s 7 101.9 102.3 310s 8 104.1 104.5 310s 9 102.0 102.4 310s 10 99.0 99.6 310s 11 95.5 95.9 310s 12 94.5 94.9 310s 13 96.4 96.9 310s 14 98.1 98.4 310s 15 102.1 102.6 310s 16 104.0 104.4 310s 17 102.1 103.1 310s 18 103.2 103.6 310s 19 103.3 103.7 310s 20 106.3 107.1 310s > print( predict( fitsur4r3$eq[[ 1 ]], se.fit = TRUE, interval = "confidence", 310s + level = 0.25 ) ) 310s fit se.fit lwr upr 310s 1 97.6 0.474 97.4 97.7 310s 2 99.9 0.558 99.7 100.1 310s 3 99.8 0.523 99.6 100.0 310s 4 100.0 0.567 99.9 100.2 310s 5 102.1 0.476 102.0 102.3 310s 6 102.0 0.443 101.8 102.1 310s 7 102.5 0.440 102.3 102.6 310s 8 103.1 0.532 102.9 103.3 310s 9 101.4 0.520 101.3 101.6 310s 10 100.2 0.774 100.0 100.4 310s 11 95.3 0.612 95.1 95.5 310s 12 94.5 0.525 94.4 94.7 310s 13 96.0 0.603 95.8 96.2 310s 14 99.0 0.444 98.8 99.1 310s 15 103.9 0.643 103.7 104.1 310s 16 103.7 0.494 103.6 103.9 310s 17 103.8 1.191 103.4 104.1 310s 18 102.2 0.510 102.0 102.3 310s 19 103.8 0.570 103.6 104.0 310s 20 107.2 0.973 106.9 107.6 310s > 310s > print( predict( fitsur4we, se.fit = TRUE, interval = "confidence", 310s + level = 0.25 ) ) 310s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 310s 1 97.5 0.445 97.3 97.6 98.2 0.519 310s 2 99.7 0.514 99.6 99.9 99.6 0.636 310s 3 99.7 0.482 99.5 99.8 99.8 0.591 310s 4 99.9 0.523 99.7 100.0 100.1 0.636 310s 5 102.2 0.438 102.1 102.4 102.0 0.568 310s 6 102.0 0.408 101.9 102.2 102.0 0.523 310s 7 102.5 0.409 102.3 102.6 102.1 0.508 310s 8 102.9 0.503 102.8 103.1 104.2 0.603 310s 9 101.6 0.479 101.4 101.7 102.4 0.631 310s 10 100.5 0.724 100.3 100.8 99.7 0.856 310s 11 95.5 0.612 95.3 95.7 95.9 0.694 310s 12 94.4 0.520 94.3 94.6 94.6 0.677 310s 13 95.8 0.565 95.6 96.0 96.3 0.748 310s 14 99.0 0.414 98.8 99.1 98.2 0.540 310s 15 104.1 0.592 103.9 104.3 102.6 0.690 310s 16 103.8 0.458 103.7 104.0 104.3 0.581 310s 17 104.3 1.100 104.0 104.7 103.3 1.334 310s 18 102.0 0.477 101.9 102.2 103.3 0.564 310s 19 103.6 0.545 103.4 103.8 103.2 0.651 310s 20 106.8 0.958 106.5 107.1 106.1 1.091 310s supply.lwr supply.upr 310s 1 98.0 98.3 310s 2 99.4 99.8 310s 3 99.6 99.9 310s 4 99.9 100.3 310s 5 101.8 102.1 310s 6 101.8 102.2 310s 7 101.9 102.2 310s 8 104.0 104.4 310s 9 102.2 102.6 310s 10 99.5 100.0 310s 11 95.7 96.1 310s 12 94.4 94.8 310s 13 96.1 96.6 310s 14 98.0 98.4 310s 15 102.4 102.9 310s 16 104.1 104.5 310s 17 102.9 103.8 310s 18 103.1 103.5 310s 19 103.0 103.5 310s 20 105.8 106.5 310s > print( predict( fitsur4we$eq[[ 2 ]], se.fit = TRUE, interval = "confidence", 310s + level = 0.25 ) ) 310s fit se.fit lwr upr 310s 1 98.2 0.519 98.0 98.3 310s 2 99.6 0.636 99.4 99.8 310s 3 99.8 0.591 99.6 99.9 310s 4 100.1 0.636 99.9 100.3 310s 5 102.0 0.568 101.8 102.1 310s 6 102.0 0.523 101.8 102.2 310s 7 102.1 0.508 101.9 102.2 310s 8 104.2 0.603 104.0 104.4 310s 9 102.4 0.631 102.2 102.6 310s 10 99.7 0.856 99.5 100.0 310s 11 95.9 0.694 95.7 96.1 310s 12 94.6 0.677 94.4 94.8 310s 13 96.3 0.748 96.1 96.6 310s 14 98.2 0.540 98.0 98.4 310s 15 102.6 0.690 102.4 102.9 310s 16 104.3 0.581 104.1 104.5 310s 17 103.3 1.334 102.9 103.8 310s 18 103.3 0.564 103.1 103.5 310s 19 103.2 0.651 103.0 103.5 310s 20 106.1 1.091 105.8 106.5 310s > 310s > print( predict( fitsur5, se.fit = TRUE, se.pred = TRUE, 310s + interval = "prediction", level = 0.5, newdata = predictData ) ) 310s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 310s 1 103.2 0.911 2.14 101.7 105 96.0 310s 2 105.9 0.786 2.09 104.4 107 97.3 310s 3 105.7 0.824 2.11 104.3 107 97.5 310s 4 106.0 0.780 2.09 104.6 107 97.8 310s 5 108.2 1.233 2.30 106.7 110 99.8 310s 6 108.1 1.143 2.25 106.6 110 99.8 310s 7 108.7 1.076 2.22 107.2 110 99.8 310s 8 109.4 0.919 2.15 108.0 111 101.9 310s 9 107.5 1.295 2.33 105.9 109 100.3 310s 10 106.0 1.568 2.49 104.3 108 97.7 310s 11 100.5 1.292 2.33 98.9 102 93.8 310s 12 99.7 0.921 2.15 98.3 101 92.4 310s 13 101.5 0.720 2.07 100.1 103 94.1 310s 14 104.7 1.054 2.21 103.2 106 96.1 310s 15 110.1 1.485 2.44 108.5 112 100.5 310s 16 110.0 1.284 2.33 108.4 112 102.1 310s 17 109.9 2.013 2.80 108.0 112 101.4 310s 18 108.4 0.906 2.14 106.9 110 101.0 310s 19 110.2 0.911 2.14 108.8 112 100.9 310s 20 114.2 0.898 2.14 112.7 116 103.6 310s supply.se.fit supply.se.pred supply.lwr supply.upr 310s 1 0.916 2.68 94.1 97.8 310s 2 0.715 2.62 95.5 99.1 310s 3 0.760 2.63 95.7 99.3 310s 4 0.708 2.62 96.0 99.6 310s 5 1.213 2.80 97.9 101.7 310s 6 1.100 2.75 97.9 101.7 310s 7 0.982 2.70 98.0 101.7 310s 8 0.825 2.65 100.1 103.7 310s 9 1.339 2.85 98.4 102.2 310s 10 1.631 3.00 95.7 99.8 310s 11 1.375 2.87 91.9 95.8 310s 12 1.025 2.72 90.6 94.3 310s 13 0.831 2.65 92.3 95.9 310s 14 1.033 2.72 94.2 97.9 310s 15 1.434 2.90 98.5 102.5 310s 16 1.249 2.81 100.2 104.1 310s 17 2.163 3.32 99.1 103.6 310s 18 0.809 2.65 99.2 102.8 310s 19 0.712 2.62 99.1 102.7 310s 20 0.572 2.58 101.9 105.4 310s > print( predict( fitsur5$eq[[ 2 ]], se.fit = TRUE, se.pred = TRUE, 310s + interval = "prediction", level = 0.5, newdata = predictData ) ) 310s fit se.fit se.pred lwr upr 310s 1 96.0 0.916 2.68 94.1 97.8 310s 2 97.3 0.715 2.62 95.5 99.1 310s 3 97.5 0.760 2.63 95.7 99.3 310s 4 97.8 0.708 2.62 96.0 99.6 310s 5 99.8 1.213 2.80 97.9 101.7 310s 6 99.8 1.100 2.75 97.9 101.7 310s 7 99.8 0.982 2.70 98.0 101.7 310s 8 101.9 0.825 2.65 100.1 103.7 310s 9 100.3 1.339 2.85 98.4 102.2 310s 10 97.7 1.631 3.00 95.7 99.8 310s 11 93.8 1.375 2.87 91.9 95.8 310s 12 92.4 1.025 2.72 90.6 94.3 310s 13 94.1 0.831 2.65 92.3 95.9 310s 14 96.1 1.033 2.72 94.2 97.9 310s 15 100.5 1.434 2.90 98.5 102.5 310s 16 102.1 1.249 2.81 100.2 104.1 310s 17 101.4 2.163 3.32 99.1 103.6 310s 18 101.0 0.809 2.65 99.2 102.8 310s 19 100.9 0.712 2.62 99.1 102.7 310s 20 103.6 0.572 2.58 101.9 105.4 310s > 310s > print( predict( fitsuri1r3, se.fit = TRUE, se.pred = TRUE, 310s + interval = "confidence", level = 0.99 ) ) 310s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 310s 1 97.7 0.653 2.09 95.8 99.6 100.2 310s 2 99.9 0.578 2.07 98.3 101.6 105.7 310s 3 99.9 0.548 2.06 98.3 101.4 104.3 310s 4 100.1 0.583 2.07 98.4 101.8 104.9 310s 5 102.1 0.509 2.05 100.6 103.5 99.2 310s 6 101.9 0.474 2.04 100.6 103.3 100.1 310s 7 102.4 0.496 2.04 101.0 103.9 102.3 310s 8 103.0 0.615 2.08 101.2 104.8 102.6 310s 9 101.4 0.531 2.05 99.9 103.0 94.9 310s 10 100.2 0.785 2.13 98.0 102.5 92.8 310s 11 95.5 0.971 2.21 92.7 98.3 92.1 310s 12 94.8 0.996 2.22 91.9 97.7 98.3 310s 13 96.2 0.880 2.17 93.7 98.8 101.6 310s 14 99.0 0.521 2.05 97.5 100.5 99.8 310s 15 103.7 0.752 2.12 101.6 105.9 97.5 310s 16 103.6 0.622 2.08 101.8 105.4 96.7 310s 17 103.6 1.241 2.34 100.0 107.2 87.6 310s 18 102.1 0.546 2.06 100.5 103.7 100.6 310s 19 103.7 0.696 2.10 101.6 105.7 105.5 310s 20 107.0 1.299 2.37 103.2 110.7 113.8 310s supply.se.fit supply.se.pred supply.lwr supply.upr 310s 1 0.599 1.72 98.4 101.9 310s 2 0.604 1.72 103.9 107.4 310s 3 0.539 1.70 102.7 105.8 310s 4 0.536 1.70 103.4 106.5 310s 5 0.486 1.69 97.8 100.6 310s 6 0.448 1.68 98.8 101.4 310s 7 0.444 1.67 101.0 103.6 310s 8 0.522 1.70 101.1 104.1 310s 9 0.542 1.70 93.3 96.5 310s 10 0.579 1.72 91.1 94.5 310s 11 0.812 1.81 89.7 94.5 310s 12 0.865 1.83 95.8 100.9 310s 13 0.747 1.78 99.4 103.8 310s 14 0.507 1.69 98.3 101.3 310s 15 0.509 1.69 96.0 98.9 310s 16 0.596 1.72 95.0 98.5 310s 17 0.975 1.89 84.7 90.4 310s 18 0.500 1.69 99.1 102.0 310s 19 0.649 1.74 103.6 107.3 310s 20 1.124 1.97 110.5 117.1 310s > print( predict( fitsuri1r3$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 310s + interval = "confidence", level = 0.99 ) ) 310s fit se.fit se.pred lwr upr 310s 1 97.7 0.653 2.09 95.8 99.6 310s 2 99.9 0.578 2.07 98.3 101.6 310s 3 99.9 0.548 2.06 98.3 101.4 310s 4 100.1 0.583 2.07 98.4 101.8 310s 5 102.1 0.509 2.05 100.6 103.5 310s 6 101.9 0.474 2.04 100.6 103.3 310s 7 102.4 0.496 2.04 101.0 103.9 310s 8 103.0 0.615 2.08 101.2 104.8 310s 9 101.4 0.531 2.05 99.9 103.0 310s 10 100.2 0.785 2.13 98.0 102.5 310s 11 95.5 0.971 2.21 92.7 98.3 310s 12 94.8 0.996 2.22 91.9 97.7 310s 13 96.2 0.880 2.17 93.7 98.8 310s 14 99.0 0.521 2.05 97.5 100.5 310s 15 103.7 0.752 2.12 101.6 105.9 310s 16 103.6 0.622 2.08 101.8 105.4 310s 17 103.6 1.241 2.34 100.0 107.2 310s 18 102.1 0.546 2.06 100.5 103.7 310s 19 103.7 0.696 2.10 101.6 105.7 310s 20 107.0 1.299 2.37 103.2 110.7 310s > 310s > print( predict( fitsuri2, se.fit = TRUE, interval = "prediction", 310s + level = 0.9, newdata = predictData ) ) 310s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 310s 1 104 0.960 100.5 108 96.1 1.37 310s 2 107 1.011 102.9 110 99.7 1.69 310s 3 107 1.032 102.8 110 99.8 1.61 310s 4 107 1.019 103.0 111 100.8 1.76 310s 5 110 1.547 105.4 114 99.2 2.00 310s 6 109 1.468 105.3 114 100.3 1.94 310s 7 110 1.465 105.7 114 102.1 2.12 310s 8 110 1.423 106.1 114 103.2 2.60 310s 9 109 1.543 104.8 113 99.9 1.80 310s 10 108 1.699 103.6 112 99.1 1.35 310s 11 102 1.299 98.2 106 98.6 2.25 310s 12 101 0.939 97.2 105 102.0 3.10 310s 13 102 0.731 98.7 106 104.5 3.01 310s 14 106 1.164 102.1 110 104.9 2.27 310s 15 112 1.896 107.3 117 105.4 2.20 310s 16 112 1.733 107.1 116 105.9 2.40 310s 17 113 2.316 107.4 118 102.1 2.02 310s 18 109 1.316 105.2 113 108.8 2.75 310s 19 111 1.497 106.8 115 111.9 3.73 310s 20 114 1.918 109.7 119 117.2 5.62 310s supply.lwr supply.upr 310s 1 86.2 106 310s 2 89.7 110 310s 3 89.7 110 310s 4 90.7 111 310s 5 89.0 109 310s 6 90.1 110 310s 7 91.8 112 310s 8 92.6 114 310s 9 89.7 110 310s 10 89.2 109 310s 11 88.2 109 310s 12 91.0 113 310s 13 93.6 115 310s 14 94.5 115 310s 15 95.0 116 310s 16 95.4 116 310s 17 91.9 112 310s 18 98.1 119 310s 19 100.4 123 310s 20 103.6 131 310s > print( predict( fitsuri2$eq[[ 2 ]], se.fit = TRUE, interval = "prediction", 310s + level = 0.9, newdata = predictData ) ) 310s fit se.fit lwr upr 310s 1 96.1 1.37 86.2 106 310s 2 99.7 1.69 89.7 110 310s 3 99.8 1.61 89.7 110 310s 4 100.8 1.76 90.7 111 310s 5 99.2 2.00 89.0 109 310s 6 100.3 1.94 90.1 110 310s 7 102.1 2.12 91.8 112 310s 8 103.2 2.60 92.6 114 310s 9 99.9 1.80 89.7 110 310s 10 99.1 1.35 89.2 109 310s 11 98.6 2.25 88.2 109 310s 12 102.0 3.10 91.0 113 310s 13 104.5 3.01 93.6 115 310s 14 104.9 2.27 94.5 115 310s 15 105.4 2.20 95.0 116 310s 16 105.9 2.40 95.4 116 310s 17 102.1 2.02 91.9 112 310s 18 108.8 2.75 98.1 119 310s 19 111.9 3.73 100.4 123 310s 20 117.2 5.62 103.6 131 310s > 310s > print( predict( fitsuri2w, se.fit = TRUE, interval = "prediction", 310s + level = 0.9, newdata = predictData ) ) 310s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 310s 1 104 0.960 100.5 108 96.1 1.37 310s 2 107 1.011 102.9 110 99.7 1.69 310s 3 107 1.032 102.8 110 99.8 1.61 310s 4 107 1.019 103.0 111 100.8 1.76 310s 5 110 1.547 105.4 114 99.2 2.00 310s 6 109 1.468 105.3 114 100.3 1.94 310s 7 110 1.465 105.7 114 102.1 2.12 310s 8 110 1.423 106.1 114 103.2 2.60 310s 9 109 1.543 104.8 113 99.9 1.80 310s 10 108 1.699 103.6 112 99.1 1.35 310s 11 102 1.299 98.2 106 98.6 2.25 310s 12 101 0.939 97.2 105 102.0 3.10 310s 13 102 0.731 98.7 106 104.5 3.01 310s 14 106 1.164 102.1 110 104.9 2.27 310s 15 112 1.896 107.3 117 105.4 2.20 310s 16 112 1.733 107.1 116 105.9 2.40 310s 17 113 2.316 107.4 118 102.1 2.02 310s 18 109 1.316 105.2 113 108.8 2.75 310s 19 111 1.497 106.8 115 111.9 3.73 310s 20 114 1.918 109.7 119 117.2 5.62 310s supply.lwr supply.upr 310s 1 86.2 106 310s 2 89.7 110 310s 3 89.7 110 310s 4 90.7 111 310s 5 89.0 109 310s 6 90.1 110 310s 7 91.8 112 310s 8 92.6 114 310s 9 89.7 110 310s 10 89.2 109 310s 11 88.2 109 310s 12 91.0 113 310s 13 93.6 115 310s 14 94.5 115 310s 15 95.0 116 310s 16 95.4 116 310s 17 91.9 112 310s 18 98.1 119 310s 19 100.4 123 310s 20 103.6 131 310s > print( predict( fitsuri2w$eq[[ 2 ]], se.fit = TRUE, interval = "prediction", 310s + level = 0.9, newdata = predictData ) ) 310s fit se.fit lwr upr 310s 1 96.1 1.37 86.2 106 310s 2 99.7 1.69 89.7 110 310s 3 99.8 1.61 89.7 110 310s 4 100.8 1.76 90.7 111 310s 5 99.2 2.00 89.0 109 310s 6 100.3 1.94 90.1 110 310s 7 102.1 2.12 91.8 112 310s 8 103.2 2.60 92.6 114 310s 9 99.9 1.80 89.7 110 310s 10 99.1 1.35 89.2 109 310s 11 98.6 2.25 88.2 109 310s 12 102.0 3.10 91.0 113 310s 13 104.5 3.01 93.6 115 310s 14 104.9 2.27 94.5 115 310s 15 105.4 2.20 95.0 116 310s 16 105.9 2.40 95.4 116 310s 17 102.1 2.02 91.9 112 310s 18 108.8 2.75 98.1 119 310s 19 111.9 3.73 100.4 123 310s 20 117.2 5.62 103.6 131 310s > 310s > print( predict( fitsuri3e, interval = "prediction", level = 0.925 ) ) 310s demand.pred demand.lwr demand.upr supply.pred supply.lwr supply.upr 310s 1 97.4 93.5 101.2 93.4 82.5 104 310s 2 99.2 95.4 103.0 96.7 86.0 107 310s 3 99.3 95.5 103.0 96.7 86.0 107 310s 4 99.3 95.5 103.1 97.7 87.0 108 310s 5 102.5 98.7 106.2 96.1 85.1 107 310s 6 102.1 98.4 105.9 97.2 86.3 108 310s 7 102.4 98.6 106.2 98.8 88.1 110 310s 8 102.5 98.7 106.3 99.8 88.9 111 310s 9 102.0 98.2 105.8 96.9 85.9 108 310s 10 101.5 97.6 105.4 96.4 85.5 107 310s 11 96.1 92.1 100.1 96.3 84.9 108 310s 12 94.4 90.4 98.4 99.6 87.9 111 310s 13 95.4 91.4 99.3 101.9 90.4 113 310s 14 99.1 95.3 102.8 102.0 91.1 113 310s 15 104.7 100.8 108.6 102.2 91.4 113 310s 16 104.1 100.3 107.9 102.6 91.8 113 310s 17 105.9 101.6 110.2 99.1 88.1 110 310s 18 101.6 97.9 105.4 105.5 94.6 116 310s 19 103.1 99.2 106.9 108.4 97.1 120 310s 20 105.5 101.3 109.8 113.1 100.7 126 310s > print( predict( fitsuri3e$eq[[ 1 ]], interval = "prediction", level = 0.925 ) ) 310s fit lwr upr 310s 1 97.4 93.5 101.2 310s 2 99.2 95.4 103.0 310s 3 99.3 95.5 103.0 310s 4 99.3 95.5 103.1 310s 5 102.5 98.7 106.2 310s 6 102.1 98.4 105.9 310s 7 102.4 98.6 106.2 310s 8 102.5 98.7 106.3 310s 9 102.0 98.2 105.8 310s 10 101.5 97.6 105.4 310s 11 96.1 92.1 100.1 310s 12 94.4 90.4 98.4 310s 13 95.4 91.4 99.3 310s 14 99.1 95.3 102.8 310s 15 104.7 100.8 108.6 310s 16 104.1 100.3 107.9 310s 17 105.9 101.6 110.2 310s 18 101.6 97.9 105.4 310s 19 103.1 99.2 106.9 310s 20 105.5 101.3 109.8 310s > 310s > print( predict( fitsurio4, interval = "confidence", newdata = predictData ) ) 310s demand.pred demand.lwr demand.upr supply.pred supply.lwr supply.upr 310s 1 102.7 100.8 105 95.5 93.6 97.4 310s 2 105.5 103.8 107 97.0 95.5 98.5 310s 3 105.3 103.6 107 97.2 95.6 98.8 310s 4 105.6 104.0 107 97.5 96.0 99.0 310s 5 107.5 105.0 110 99.1 96.5 101.6 310s 6 107.5 105.1 110 99.2 96.9 101.5 310s 7 108.1 105.9 110 99.3 97.2 101.4 310s 8 108.9 107.1 111 101.5 99.7 103.2 310s 9 106.7 104.0 109 99.5 96.7 102.3 310s 10 105.1 101.8 108 96.7 93.4 100.1 310s 11 99.8 97.2 102 93.1 90.4 95.9 310s 12 99.3 97.4 101 92.1 90.1 94.1 310s 13 101.1 99.7 103 93.9 92.3 95.5 310s 14 104.1 101.9 106 95.6 93.5 97.7 310s 15 109.3 106.2 112 99.7 96.7 102.7 310s 16 109.3 106.6 112 101.4 98.8 104.0 310s 17 108.7 104.5 113 100.0 95.5 104.5 310s 18 107.9 106.0 110 100.6 98.9 102.3 310s 19 109.8 107.9 112 100.7 99.2 102.2 310s 20 114.0 112.3 116 103.7 102.5 104.9 310s > print( predict( fitsurio4$eq[[ 2 ]], interval = "confidence", 310s + newdata = predictData ) ) 310s fit lwr upr 310s 1 95.5 93.6 97.4 310s 2 97.0 95.5 98.5 310s 3 97.2 95.6 98.8 310s 4 97.5 96.0 99.0 310s 5 99.1 96.5 101.6 310s 6 99.2 96.9 101.5 310s 7 99.3 97.2 101.4 310s 8 101.5 99.7 103.2 310s 9 99.5 96.7 102.3 310s 10 96.7 93.4 100.1 310s 11 93.1 90.4 95.9 310s 12 92.1 90.1 94.1 310s 13 93.9 92.3 95.5 310s 14 95.6 93.5 97.7 310s 15 99.7 96.7 102.7 310s 16 101.4 98.8 104.0 310s 17 100.0 95.5 104.5 310s 18 100.6 98.9 102.3 310s 19 100.7 99.2 102.2 310s 20 103.7 102.5 104.9 310s > print( predict( fitsuri4, interval = "confidence", newdata = predictData ) ) 310s demand.pred demand.lwr demand.upr supply.pred supply.lwr supply.upr 310s 1 103.1 101.3 105 96.6 93.9 99.3 310s 2 105.5 103.7 107 99.4 96.2 102.5 310s 3 105.4 103.5 107 99.4 96.4 102.5 310s 4 105.6 103.8 107 100.3 97.1 103.5 310s 5 107.7 105.0 110 98.9 94.9 102.9 310s 6 107.6 105.0 110 99.8 96.1 103.5 310s 7 108.1 105.5 111 101.2 97.6 104.9 310s 8 108.7 106.1 111 102.0 97.7 106.4 310s 9 107.0 104.3 110 99.6 96.0 103.2 310s 10 105.7 102.7 109 99.3 96.6 102.0 310s 11 100.7 98.3 103 99.3 95.0 103.5 310s 12 99.9 98.2 102 102.1 95.8 108.4 310s 13 101.5 100.2 103 104.0 97.9 110.1 310s 14 104.5 102.4 107 104.1 99.8 108.4 310s 15 109.5 106.1 113 104.2 100.8 107.5 310s 16 109.4 106.3 112 104.5 100.9 108.2 310s 17 109.3 105.3 113 101.7 97.7 105.6 310s 18 107.8 105.4 110 107.0 103.1 110.9 310s 19 109.5 106.7 112 109.5 104.4 114.6 310s 20 113.0 109.4 117 113.4 106.3 120.6 310s > print( predict( fitsuri4$eq[[ 2 ]], interval = "confidence", 310s + newdata = predictData ) ) 310s fit lwr upr 310s 1 96.6 93.9 99.3 310s 2 99.4 96.2 102.5 310s 3 99.4 96.4 102.5 310s 4 100.3 97.1 103.5 310s 5 98.9 94.9 102.9 310s 6 99.8 96.1 103.5 310s 7 101.2 97.6 104.9 310s 8 102.0 97.7 106.4 310s 9 99.6 96.0 103.2 310s 10 99.3 96.6 102.0 310s 11 99.3 95.0 103.5 310s 12 102.1 95.8 108.4 310s 13 104.0 97.9 110.1 310s 14 104.1 99.8 108.4 310s 15 104.2 100.8 107.5 310s 16 104.5 100.9 108.2 310s 17 101.7 97.7 105.6 310s 18 107.0 103.1 110.9 310s 19 109.5 104.4 114.6 310s 20 113.4 106.3 120.6 310s > 310s > print( predict( fitsurio5r2 ) ) 310s demand.pred supply.pred 310s 1 97.8 98.5 310s 2 100.6 100.7 310s 3 100.4 100.6 310s 4 100.8 101.2 310s 5 101.7 101.3 310s 6 101.8 101.7 310s 7 102.5 102.2 310s 8 103.7 104.9 310s 9 100.8 101.4 310s 10 98.9 97.7 310s 11 94.6 94.8 310s 12 94.8 95.0 310s 13 96.8 97.6 310s 14 98.9 98.2 310s 15 102.9 101.3 310s 16 103.3 103.6 310s 17 101.4 99.8 310s 18 102.7 104.0 310s 19 104.5 104.4 310s 20 108.9 108.9 310s > print( predict( fitsurio5r2$eq[[ 1 ]] ) ) 310s fit 310s 1 97.8 310s 2 100.6 310s 3 100.4 310s 4 100.8 310s 5 101.7 310s 6 101.8 310s 7 102.5 310s 8 103.7 310s 9 100.8 310s 10 98.9 310s 11 94.6 310s 12 94.8 310s 13 96.8 310s 14 98.9 310s 15 102.9 310s 16 103.3 310s 17 101.4 310s 18 102.7 310s 19 104.5 310s 20 108.9 310s > print( predict( fitsuri5r2 ) ) 310s demand.pred supply.pred 310s 1 97.8 94.6 310s 2 99.8 97.1 310s 3 99.7 97.2 310s 4 99.9 98.0 310s 5 102.1 96.5 310s 6 101.9 97.4 310s 7 102.3 98.8 310s 8 102.7 99.5 310s 9 101.6 97.3 310s 10 100.6 97.2 310s 11 96.0 97.5 310s 12 95.0 100.3 310s 13 96.2 102.0 310s 14 99.1 102.0 310s 15 103.9 101.7 310s 16 103.6 102.0 310s 17 104.2 99.4 310s 18 101.9 104.6 310s 19 103.3 106.9 310s 20 106.2 110.4 310s > print( predict( fitsuri5r2$eq[[ 1 ]] ) ) 310s fit 310s 1 97.8 310s 2 99.8 310s 3 99.7 310s 4 99.9 310s 5 102.1 310s 6 101.9 310s 7 102.3 310s 8 102.7 310s 9 101.6 310s 10 100.6 310s 11 96.0 310s 12 95.0 310s 13 96.2 310s 14 99.1 310s 15 103.9 310s 16 103.6 310s 17 104.2 310s 18 101.9 310s 19 103.3 310s 20 106.2 310s > 310s > # predict just one observation 310s > smallData <- data.frame( price = 130, income = 150, farmPrice = 120, 310s + trend = 25 ) 310s > 310s > print( predict( fitsur1e2, newdata = smallData ) ) 310s demand.pred supply.pred 310s 1 108 115 310s > print( predict( fitsur1e2$eq[[ 1 ]], newdata = smallData ) ) 310s fit 310s 1 108 310s > 310s > print( predict( fitsur2e, se.fit = TRUE, level = 0.9, 310s + newdata = smallData ) ) 310s demand.pred demand.se.fit supply.pred supply.se.fit 310s 1 108 2.21 113 3 310s > print( predict( fitsur2e$eq[[ 1 ]], se.pred = TRUE, level = 0.99, 310s + newdata = smallData ) ) 310s fit se.pred 310s 1 108 3.03 310s > 310s > print( predict( fitsur3, interval = "prediction", level = 0.975, 310s + newdata = smallData ) ) 310s demand.pred demand.lwr demand.upr supply.pred supply.lwr supply.upr 310s 1 108 100 115 113 103 123 310s > print( predict( fitsur3$eq[[ 1 ]], interval = "confidence", level = 0.8, 310s + newdata = smallData ) ) 310s fit lwr upr 310s 1 108 105 111 310s > 310s > print( predict( fitsur4r3, se.fit = TRUE, interval = "confidence", 310s + level = 0.999, newdata = smallData ) ) 310s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 310s 1 111 2.06 103 118 119 2.22 310s supply.lwr supply.upr 310s 1 111 127 310s > print( predict( fitsur4r3$eq[[ 2 ]], se.pred = TRUE, interval = "prediction", 310s + level = 0.75, newdata = smallData ) ) 310s fit se.pred lwr upr 310s 1 119 3.41 115 123 310s > 310s > print( predict( fitsur5, se.fit = TRUE, interval = "prediction", 310s + newdata = smallData ) ) 310s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 310s 1 110 2.15 104 116 118 2.29 310s supply.lwr supply.upr 310s 1 111 125 310s > print( predict( fitsur5$eq[[ 1 ]], se.pred = TRUE, interval = "confidence", 310s + newdata = smallData ) ) 310s fit se.pred lwr upr 310s 1 110 2.9 105 114 310s > 310s > print( predict( fitsurio5r2, se.fit = TRUE, se.pred = TRUE, 310s + interval = "prediction", level = 0.5, newdata = smallData ) ) 310s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 310s 1 115 1.98 3.09 113 117 123 310s supply.se.fit supply.se.pred supply.lwr supply.upr 310s 1 2.17 3.82 121 126 310s > print( predict( fitsurio5r2$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 310s + interval = "confidence", level = 0.25, newdata = smallData ) ) 310s fit se.fit se.pred lwr upr 310s 1 115 1.98 3.09 114 115 310s > print( predict( fitsuri5r2, se.fit = TRUE, se.pred = TRUE, 310s + interval = "prediction", level = 0.5, newdata = smallData ) ) 310s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 310s 1 109 2.35 3.06 107 111 113 310s supply.se.fit supply.se.pred supply.lwr supply.upr 310s 1 3.91 6.87 108 117 310s > print( predict( fitsuri5r2$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 310s + interval = "confidence", level = 0.25, newdata = smallData ) ) 310s fit se.fit se.pred lwr upr 310s 1 109 2.35 3.06 108 109 310s > 310s > print( predict( fitsuri5wr2, se.fit = TRUE, se.pred = TRUE, 310s + interval = "prediction", level = 0.5, newdata = smallData ) ) 310s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 310s 1 109 2.35 3.06 107 111 113 310s supply.se.fit supply.se.pred supply.lwr supply.upr 310s 1 3.91 6.87 108 117 310s > print( predict( fitsuri5wr2$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 310s + interval = "confidence", level = 0.25, newdata = smallData ) ) 310s fit se.fit se.pred lwr upr 310s 1 109 2.35 3.06 108 109 310s > 310s > 310s > ## ************ correlation of predicted values *************** 310s > print( correlation.systemfit( fitsur1e2, 2, 1 ) ) 310s [,1] 310s [1,] 0.849 310s [2,] 0.856 310s [3,] 0.864 310s [4,] 0.882 310s [5,] 0.844 310s [6,] 0.861 310s [7,] 0.875 310s [8,] 0.877 310s [9,] 0.884 310s [10,] 0.918 310s [11,] 0.903 310s [12,] 0.884 310s [13,] 0.880 310s [14,] 0.863 310s [15,] 0.896 310s [16,] 0.897 310s [17,] 0.914 310s [18,] 0.839 310s [19,] 0.867 310s [20,] 0.902 310s > 310s > print( correlation.systemfit( fitsur2e, 1, 2 ) ) 310s [,1] 310s [1,] 0.942 310s [2,] 0.944 310s [3,] 0.942 310s [4,] 0.941 310s [5,] 0.902 310s [6,] 0.909 310s [7,] 0.917 310s [8,] 0.903 310s [9,] 0.910 310s [10,] 0.941 310s [11,] 0.923 310s [12,] 0.902 310s [13,] 0.901 310s [14,] 0.893 310s [15,] 0.925 310s [16,] 0.952 310s [17,] 0.944 310s [18,] 0.935 310s [19,] 0.930 310s [20,] 0.938 310s > 310s > print( correlation.systemfit( fitsur3, 2, 1 ) ) 310s [,1] 310s [1,] 0.939 310s [2,] 0.943 310s [3,] 0.941 310s [4,] 0.940 310s [5,] 0.902 310s [6,] 0.909 310s [7,] 0.918 310s [8,] 0.903 310s [9,] 0.910 310s [10,] 0.941 310s [11,] 0.922 310s [12,] 0.900 310s [13,] 0.899 310s [14,] 0.892 310s [15,] 0.923 310s [16,] 0.952 310s [17,] 0.943 310s [18,] 0.936 310s [19,] 0.929 310s [20,] 0.937 310s > 310s > print( correlation.systemfit( fitsur3w, 2, 1 ) ) 310s [,1] 310s [1,] 0.940 310s [2,] 0.946 310s [3,] 0.944 310s [4,] 0.944 310s [5,] 0.908 310s [6,] 0.914 310s [7,] 0.922 310s [8,] 0.907 310s [9,] 0.914 310s [10,] 0.944 310s [11,] 0.926 310s [12,] 0.904 310s [13,] 0.903 310s [14,] 0.897 310s [15,] 0.926 310s [16,] 0.954 310s [17,] 0.946 310s [18,] 0.940 310s [19,] 0.932 310s [20,] 0.940 310s > 310s > print( correlation.systemfit( fitsur4r3, 1, 2 ) ) 310s [,1] 310s [1,] 0.963 310s [2,] 0.971 310s [3,] 0.971 310s [4,] 0.973 310s [5,] 0.940 310s [6,] 0.944 310s [7,] 0.947 310s [8,] 0.942 310s [9,] 0.947 310s [10,] 0.973 310s [11,] 0.910 310s [12,] 0.858 310s [13,] 0.914 310s [14,] 0.923 310s [15,] 0.977 310s [16,] 0.964 310s [17,] 0.978 310s [18,] 0.969 310s [19,] 0.946 310s [20,] 0.941 310s > 310s > print( correlation.systemfit( fitsur5, 2, 1 ) ) 310s [,1] 310s [1,] 0.938 310s [2,] 0.948 310s [3,] 0.948 310s [4,] 0.951 310s [5,] 0.892 310s [6,] 0.897 310s [7,] 0.903 310s [8,] 0.900 310s [9,] 0.907 310s [10,] 0.952 310s [11,] 0.853 310s [12,] 0.784 310s [13,] 0.858 310s [14,] 0.867 310s [15,] 0.961 310s [16,] 0.935 310s [17,] 0.961 310s [18,] 0.944 310s [19,] 0.907 310s [20,] 0.904 310s > 310s > print( correlation.systemfit( fitsuri1r3, 1, 2 ) ) 310s [,1] 310s [1,] -0.662 310s [2,] -0.656 310s [3,] -0.664 310s [4,] -0.689 310s [5,] -0.629 310s [6,] -0.664 310s [7,] -0.696 310s [8,] -0.675 310s [9,] -0.722 310s [10,] -0.757 310s [11,] -0.759 310s [12,] -0.732 310s [13,] -0.710 310s [14,] -0.669 310s [15,] -0.728 310s [16,] -0.737 310s [17,] -0.741 310s [18,] -0.583 310s [19,] -0.684 310s [20,] -0.746 310s > 310s > print( correlation.systemfit( fitsuri2, 2, 1 ) ) 310s [,1] 310s [1,] 0.360 310s [2,] 0.337 310s [3,] 0.337 310s [4,] 0.336 310s [5,] 0.286 310s [6,] 0.299 310s [7,] 0.317 310s [8,] 0.275 310s [9,] 0.322 310s [10,] 0.318 310s [11,] 0.334 310s [12,] 0.334 310s [13,] 0.318 310s [14,] 0.286 310s [15,] 0.358 310s [16,] 0.432 310s [17,] 0.367 310s [18,] 0.362 310s [19,] 0.333 310s [20,] 0.335 310s > 310s > print( correlation.systemfit( fitsuri2w, 1, 2 ) ) 310s [,1] 310s [1,] 0.360 310s [2,] 0.337 310s [3,] 0.337 310s [4,] 0.336 310s [5,] 0.286 310s [6,] 0.299 310s [7,] 0.317 310s [8,] 0.275 310s [9,] 0.322 310s [10,] 0.318 310s [11,] 0.334 310s [12,] 0.334 310s [13,] 0.318 310s [14,] 0.286 310s [15,] 0.358 310s [16,] 0.432 310s [17,] 0.367 310s [18,] 0.362 310s [19,] 0.333 310s [20,] 0.335 310s > 310s > print( correlation.systemfit( fitsuri3e, 1, 2 ) ) 310s [,1] 310s [1,] 0.368 310s [2,] 0.345 310s [3,] 0.344 310s [4,] 0.344 310s [5,] 0.292 310s [6,] 0.305 310s [7,] 0.323 310s [8,] 0.280 310s [9,] 0.329 310s [10,] 0.325 310s [11,] 0.340 310s [12,] 0.340 310s [13,] 0.324 310s [14,] 0.291 310s [15,] 0.366 310s [16,] 0.441 310s [17,] 0.375 310s [18,] 0.369 310s [19,] 0.340 310s [20,] 0.342 310s > 310s > print( correlation.systemfit( fitsurio4, 2, 1 ) ) 310s [,1] 310s [1,] 0.961 310s [2,] 0.971 310s [3,] 0.971 310s [4,] 0.973 310s [5,] 0.940 310s [6,] 0.944 310s [7,] 0.947 310s [8,] 0.939 310s [9,] 0.947 310s [10,] 0.972 310s [11,] 0.904 310s [12,] 0.861 310s [13,] 0.917 310s [14,] 0.922 310s [15,] 0.976 310s [16,] 0.964 310s [17,] 0.978 310s [18,] 0.967 310s [19,] 0.942 310s [20,] 0.934 310s > print( correlation.systemfit( fitsuri4, 2, 1 ) ) 310s [,1] 310s [1,] 0.0384 310s [2,] 0.1213 310s [3,] 0.0975 310s [4,] 0.1381 310s [5,] 0.1295 310s [6,] 0.0937 310s [7,] 0.0630 310s [8,] 0.1056 310s [9,] 0.2180 310s [10,] 0.4042 310s [11,] 0.1074 310s [12,] 0.0337 310s [13,] 0.0760 310s [14,] 0.0701 310s [15,] 0.0680 310s [16,] 0.1263 310s [17,] 0.3859 310s [18,] 0.2715 310s [19,] 0.2850 310s [20,] 0.3967 310s > 310s > print( correlation.systemfit( fitsurio5r2, 1, 2 ) ) 310s [,1] 310s [1,] 0.986 310s [2,] 0.991 310s [3,] 0.991 310s [4,] 0.991 310s [5,] 0.981 310s [6,] 0.983 310s [7,] 0.984 310s [8,] 0.980 310s [9,] 0.982 310s [10,] 0.991 310s [11,] 0.968 310s [12,] 0.947 310s [13,] 0.970 310s [14,] 0.975 310s [15,] 0.991 310s [16,] 0.989 310s [17,] 0.992 310s [18,] 0.990 310s [19,] 0.982 310s [20,] 0.978 310s > print( correlation.systemfit( fitsuri5r2, 1, 2 ) ) 310s [,1] 310s [1,] 0.0440 310s [2,] 0.1279 310s [3,] 0.1045 310s [4,] 0.1451 310s [5,] 0.1375 310s [6,] 0.1021 310s [7,] 0.0719 310s [8,] 0.1124 310s [9,] 0.2252 310s [10,] 0.4097 310s [11,] 0.1145 310s [12,] 0.0410 310s [13,] 0.0834 310s [14,] 0.0778 310s [15,] 0.0750 310s [16,] 0.1344 310s [17,] 0.3900 310s [18,] 0.2789 310s [19,] 0.2897 310s [20,] 0.4005 310s > 310s > 310s > ## ************ Log-Likelihood values *************** 310s > print( logLik( fitsur1e2 ) ) 310s 'log Lik.' -50.9 (df=10) 310s > print( logLik( fitsur1e2, residCovDiag = TRUE ) ) 310s 'log Lik.' -85.4 (df=10) 310s > 310s > print( logLik( fitsur2e ) ) 310s 'log Lik.' -52 (df=9) 310s > print( logLik( fitsur2e, residCovDiag = TRUE ) ) 310s 'log Lik.' -86.5 (df=9) 310s > 310s > print( logLik( fitsur3 ) ) 310s 'log Lik.' -52.2 (df=9) 310s > print( logLik( fitsur3, residCovDiag = TRUE ) ) 310s 'log Lik.' -86.4 (df=9) 310s > 310s > print( logLik( fitsur4r3 ) ) 310s 'log Lik.' -58.4 (df=8) 310s > print( logLik( fitsur4r3, residCovDiag = TRUE ) ) 310s 'log Lik.' -85.5 (df=8) 310s > 310s > print( logLik( fitsur5 ) ) 310s 'log Lik.' -58.5 (df=8) 310s > print( logLik( fitsur5, residCovDiag = TRUE ) ) 310s 'log Lik.' -84.6 (df=8) 310s > 310s > print( logLik( fitsur5w ) ) 310s 'log Lik.' -58.5 (df=8) 310s > print( logLik( fitsur5w, residCovDiag = TRUE ) ) 310s 'log Lik.' -84.7 (df=8) 310s > 310s > print( logLik( fitsuri1r3 ) ) 310s 'log Lik.' -67.8 (df=10) 310s > print( logLik( fitsuri1r3, residCovDiag = TRUE ) ) 310s 'log Lik.' -76.2 (df=10) 310s > 310s > print( logLik( fitsuri2 ) ) 310s 'log Lik.' -99.9 (df=9) 310s > print( logLik( fitsuri2, residCovDiag = TRUE ) ) 310s 'log Lik.' -101 (df=9) 310s > 310s > print( logLik( fitsuri3e ) ) 310s 'log Lik.' -99.9 (df=9) 310s > print( logLik( fitsuri3e, residCovDiag = TRUE ) ) 310s 'log Lik.' -102 (df=9) 310s > 310s > print( logLik( fitsurio4 ) ) 310s 'log Lik.' -58.5 (df=8) 310s > print( logLik( fitsurio4, residCovDiag = TRUE ) ) 310s 'log Lik.' -85.9 (df=8) 310s > 310s > print( logLik( fitsuri4 ) ) 310s 'log Lik.' -101 (df=8) 310s > print( logLik( fitsuri4, residCovDiag = TRUE ) ) 310s 'log Lik.' -101 (df=8) 310s > 310s > print( logLik( fitsuri4w ) ) 310s 'log Lik.' -101 (df=8) 310s > print( logLik( fitsuri4w, residCovDiag = TRUE ) ) 310s 'log Lik.' -101 (df=8) 310s > 310s > print( logLik( fitsurio5r2 ) ) 310s 'log Lik.' -59.8 (df=8) 310s > print( logLik( fitsurio5r2, residCovDiag = TRUE ) ) 310s 'log Lik.' -93.1 (df=8) 310s > 310s > print( logLik( fitsuri5r2 ) ) 310s 'log Lik.' -101 (df=8) 310s > print( logLik( fitsuri5r2, residCovDiag = TRUE ) ) 310s 'log Lik.' -101 (df=8) 310s > 310s > 310s > ## *********** likelihood ratio tests ************* 310s > # testing first restriction 310s > # non-iterating, methodResidCov = 1 310s > print( lrtest( fitsur2, fitsur1 ) ) 310s Likelihood ratio test 310s 310s Model 1: fitsur2 310s Model 2: fitsur1 310s #Df LogLik Df Chisq Pr(>Chisq) 310s 1 9 -52.2 310s 2 10 -51.6 1 1.19 0.28 310s > print( lrtest( fitsur3, fitsur1 ) ) 310s Likelihood ratio test 310s 310s Model 1: fitsur3 310s Model 2: fitsur1 310s #Df LogLik Df Chisq Pr(>Chisq) 310s 1 9 -52.2 310s 2 10 -51.6 1 1.19 0.28 310s > # non-iterating, methodResidCov = 0 310s > print( lrtest( fitsur2e, fitsur1e ) ) 310s Likelihood ratio test 310s 310s Model 1: fitsur2e 310s Model 2: fitsur1e 310s #Df LogLik Df Chisq Pr(>Chisq) 310s 1 9 -52.0 310s 2 10 -51.6 1 0.7 0.4 310s > print( lrtest( fitsur3e, fitsur1e ) ) 310s Likelihood ratio test 310s 310s Model 1: fitsur3e 310s Model 2: fitsur1e 310s #Df LogLik Df Chisq Pr(>Chisq) 310s 1 9 -52.0 310s 2 10 -51.6 1 0.7 0.4 310s > # iterating, methodResidCov = 1 310s > print( lrtest( fitsuri2, fitsuri1 ) ) 310s Likelihood ratio test 310s 310s Model 1: fitsuri2 310s Model 2: fitsuri1 310s #Df LogLik Df Chisq Pr(>Chisq) 310s 1 9 -99.9 310s 2 10 -67.8 1 64.3 1.1e-15 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s > print( lrtest( fitsuri3, fitsuri1 ) ) 310s Likelihood ratio test 310s 310s Model 1: fitsuri3 310s Model 2: fitsuri1 310s #Df LogLik Df Chisq Pr(>Chisq) 310s 1 9 -99.9 310s 2 10 -67.8 1 64.3 1.1e-15 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s > # iterating, methodResidCov = 0 310s > print( lrtest( fitsuri2e, fitsuri1e ) ) 310s Likelihood ratio test 310s 310s Model 1: fitsuri2e 310s Model 2: fitsuri1e 310s #Df LogLik Df Chisq Pr(>Chisq) 310s 1 9 -99.9 310s 2 10 -67.8 1 64.3 1.1e-15 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s > print( lrtest( fitsuri3e, fitsuri1e ) ) 310s Likelihood ratio test 310s 310s Model 1: fitsuri3e 310s Model 2: fitsuri1e 310s #Df LogLik Df Chisq Pr(>Chisq) 310s 1 9 -99.9 310s 2 10 -67.8 1 64.3 1.1e-15 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s > # non-iterating, methodResidCov = 1, WSUR 310s > print( lrtest( fitsur3w, fitsur1w ) ) 310s Likelihood ratio test 310s 310s Model 1: fitsur3w 310s Model 2: fitsur1w 310s #Df LogLik Df Chisq Pr(>Chisq) 310s 1 9 -52.1 310s 2 10 -51.6 1 0.87 0.35 310s > 310s > # testing second restriction 310s > # non-iterating, methodResidCov = 1 310s > print( lrtest( fitsur4, fitsur2 ) ) 310s Likelihood ratio test 310s 310s Model 1: fitsur4 310s Model 2: fitsur2 310s #Df LogLik Df Chisq Pr(>Chisq) 310s 1 8 -58.5 310s 2 9 -52.2 1 12.7 0.00037 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s > print( lrtest( fitsur4, fitsur3 ) ) 310s Likelihood ratio test 310s 310s Model 1: fitsur4 310s Model 2: fitsur3 310s #Df LogLik Df Chisq Pr(>Chisq) 310s 1 8 -58.5 310s 2 9 -52.2 1 12.7 0.00037 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s > print( lrtest( fitsur5, fitsur2 ) ) 310s Likelihood ratio test 310s 310s Model 1: fitsur5 310s Model 2: fitsur2 310s #Df LogLik Df Chisq Pr(>Chisq) 310s 1 8 -58.5 310s 2 9 -52.2 1 12.7 0.00037 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s > print( lrtest( fitsur5, fitsur3 ) ) 310s Likelihood ratio test 310s 310s Model 1: fitsur5 310s Model 2: fitsur3 310s #Df LogLik Df Chisq Pr(>Chisq) 310s 1 8 -58.5 310s 2 9 -52.2 1 12.7 0.00037 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s > # non-iterating, methodResidCov = 0 310s > print( lrtest( fitsur4e, fitsur2e ) ) 310s Likelihood ratio test 310s 310s Model 1: fitsur4e 310s Model 2: fitsur2e 310s #Df LogLik Df Chisq Pr(>Chisq) 310s 1 8 -58.6 310s 2 9 -52.0 1 13.2 0.00028 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s > print( lrtest( fitsur4e, fitsur3e ) ) 310s Likelihood ratio test 310s 310s Model 1: fitsur4e 310s Model 2: fitsur3e 310s #Df LogLik Df Chisq Pr(>Chisq) 310s 1 8 -58.6 310s 2 9 -52.0 1 13.2 0.00028 *** 310s --- 310s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 310s > print( lrtest( fitsur5e, fitsur2e ) ) 310s Likelihood ratio test 310s 310s Model 1: fitsur5e 310s Model 2: fitsur2e 310s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 8 -58.6 311s 2 9 -52.0 1 13.2 0.00028 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > print( lrtest( fitsur5e, fitsur3e ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsur5e 311s Model 2: fitsur3e 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 8 -58.6 311s 2 9 -52.0 1 13.2 0.00028 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > # iterating, methodResidCov = 1 311s > print( lrtest( fitsurio4, fitsuri2 ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsurio4 311s Model 2: fitsuri2 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 8 -58.5 311s 2 9 -99.9 1 82.9 <2e-16 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > print( lrtest( fitsurio4, fitsuri3 ) ) 311s Warning message: 311s In lrtest.systemfit(fitsurio4, fitsuri2) : 311s model '2' has a smaller log-likelihood value than the more restricted model '1' 311s Likelihood ratio test 311s 311s Model 1: fitsurio4 311s Model 2: fitsuri3 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 8 -58.5 311s 2 9 -99.9 1 82.9 <2e-16 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > print( lrtest( fitsurio5, fitsuri2 ) ) 311s Warning message: 311s In lrtest.systemfit(fitsurio4, fitsuri3) : 311s model '2' has a smaller log-likelihood value than the more restricted model '1' 311s Likelihood ratio test 311s 311s Model 1: fitsurio5 311s Model 2: fitsuri2 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 8 -58.5 311s 2 9 -99.9 1 82.9 <2e-16 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > print( lrtest( fitsurio5, fitsuri3 ) ) 311s Warning message: 311s In lrtest.systemfit(fitsurio5, fitsuri2) : 311s model '2' has a smaller log-likelihood value than the more restricted model '1' 311s Likelihood ratio test 311s 311s Model 1: fitsurio5 311s Model 2: fitsuri3 311s Warning message: 311s In lrtest.systemfit(fitsurio5, fitsuri3) : 311s model '2' has a smaller log-likelihood value than the more restricted model '1' 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 8 -58.5 311s 2 9 -99.9 1 82.9 <2e-16 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > # corrected 311s > print( lrtest( fitsuri2, fitsuri4 ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsuri2 311s Model 2: fitsuri4 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 9 -99.9 311s 2 8 -100.9 -1 1.9 0.17 311s > print( lrtest( fitsuri3, fitsuri4 ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsuri3 311s Model 2: fitsuri4 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 9 -99.9 311s 2 8 -100.9 -1 1.9 0.17 311s > print( lrtest( fitsuri2, fitsuri5 ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsuri2 311s Model 2: fitsuri5 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 9 -99.9 311s 2 8 -100.9 -1 1.9 0.17 311s > print( lrtest( fitsuri3, fitsuri5 ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsuri3 311s Model 2: fitsuri5 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 9 -99.9 311s 2 8 -100.9 -1 1.9 0.17 311s > 311s > # iterating, methodResidCov = 0 311s > print( lrtest( fitsurio4e, fitsuri2e ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsurio4e 311s Model 2: fitsuri2e 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 8 -58.4 311s 2 9 -99.9 1 83 <2e-16 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > print( lrtest( fitsurio4e, fitsuri3e ) ) 311s Warning message: 311s In lrtest.systemfit(fitsurio4e, fitsuri2e) : 311s model '2' has a smaller log-likelihood value than the more restricted model '1' 311s Likelihood ratio test 311s 311s Model 1: fitsurio4e 311s Model 2: fitsuri3e 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 8 -58.4 311s 2 9 -99.9 1 83 <2e-16 *** 311s Warning message: 311s In lrtest.systemfit(fitsurio4e, fitsuri3e) : 311s model '2' has a smaller log-likelihood value than the more restricted model '1' 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > print( lrtest( fitsurio5e, fitsuri2e ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsurio5e 311s Model 2: fitsuri2e 311s Warning message: 311s In lrtest.systemfit(fitsurio5e, fitsuri2e) : 311s model '2' has a smaller log-likelihood value than the more restricted model '1' 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 8 -58.4 311s 2 9 -99.9 1 83 <2e-16 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > print( lrtest( fitsurio5e, fitsuri3e ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsurio5e 311s Model 2: fitsuri3e 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 8 -58.4 311s 2 9 -99.9 1 83 <2e-16 *** 311s Warning message: 311s In lrtest.systemfit(fitsurio5e, fitsuri3e) : 311s model '2' has a smaller log-likelihood value than the more restricted model '1' 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > # corrected 311s > print( lrtest( fitsuri2e, fitsuri4e ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsuri2e 311s Model 2: fitsuri4e 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 9 -99.9 311s 2 8 -100.9 -1 1.9 0.17 311s > print( lrtest( fitsuri3e, fitsuri4e ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsuri3e 311s Model 2: fitsuri4e 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 9 -99.9 311s 2 8 -100.9 -1 1.9 0.17 311s > print( lrtest( fitsuri2e, fitsuri5e ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsuri2e 311s Model 2: fitsuri5e 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 9 -99.9 311s 2 8 -100.9 -1 1.9 0.17 311s > print( lrtest( fitsuri3e, fitsuri5e ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsuri3e 311s Model 2: fitsuri5e 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 9 -99.9 311s 2 8 -100.9 -1 1.9 0.17 311s > 311s > # non-iterating, methodResidCov = 0, WSUR 311s > print( lrtest( fitsur4we, fitsur2we ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsur4we 311s Model 2: fitsur2we 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 8 -58.6 311s 2 9 -51.8 1 13.5 0.00024 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > 311s > # iterating, methodResidCov = 1, WSUR 311s > print( lrtest( fitsuri2w, fitsuri4w ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsuri2w 311s Model 2: fitsuri4w 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 9 -99.9 311s 2 8 -100.9 -1 1.9 0.17 311s > 311s > # testing both of the restrictions 311s > # non-iterating, methodResidCov = 1 311s > print( lrtest( fitsur4, fitsur1 ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsur4 311s Model 2: fitsur1 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 8 -58.5 311s 2 10 -51.6 2 13.8 0.00098 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > print( lrtest( fitsur5, fitsur1 ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsur5 311s Model 2: fitsur1 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 8 -58.5 311s 2 10 -51.6 2 13.8 0.00098 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > # non-iterating, methodResidCov = 0 311s > print( lrtest( fitsur4e, fitsur1e ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsur4e 311s Model 2: fitsur1e 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 8 -58.6 311s 2 10 -51.6 2 13.9 0.00095 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > print( lrtest( fitsur5e, fitsur1e ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsur5e 311s Model 2: fitsur1e 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 8 -58.6 311s 2 10 -51.6 2 13.9 0.00095 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > # iterating, methodResidCov = 1 311s > print( lrtest( fitsurio4, fitsuri1 ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsurio4 311s Model 2: fitsuri1 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 8 -58.5 311s 2 10 -67.8 2 18.6 9e-05 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > print( lrtest( fitsurio5, fitsuri1 ) ) 311s Warning message: 311s In lrtest.systemfit(fitsurio4, fitsuri1) : 311s model '2' has a smaller log-likelihood value than the more restricted model '1' 311s Likelihood ratio test 311s 311s Model 1: fitsurio5 311s Model 2: fitsuri1 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 8 -58.5 311s 2 10 -67.8 2 18.6 9e-05 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > # corrected 311s > print( lrtest( fitsuri1, fitsuri4 ) ) 311s Warning message: 311s In lrtest.systemfit(fitsurio5, fitsuri1) : 311s model '2' has a smaller log-likelihood value than the more restricted model '1' 311s Likelihood ratio test 311s 311s Model 1: fitsuri1 311s Model 2: fitsuri4 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 10 -67.8 311s 2 8 -100.9 -2 66.2 4.2e-15 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > print( lrtest( fitsuri1, fitsuri5 ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsuri1 311s Model 2: fitsuri5 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 10 -67.8 311s 2 8 -100.9 -2 66.2 4.2e-15 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > # iterating, methodResidCov = 0 311s > print( lrtest( fitsurio4e, fitsuri1e ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsurio4e 311s Model 2: fitsuri1e 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 8 -58.4 311s 2 10 -67.8 2 18.7 8.9e-05 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > print( lrtest( fitsurio5e, fitsuri1e ) ) 311s Warning message: 311s In lrtest.systemfit(fitsurio4e, fitsuri1e) : 311s model '2' has a smaller log-likelihood value than the more restricted model '1' 311s Likelihood ratio test 311s 311s Model 1: fitsurio5e 311s Model 2: fitsuri1e 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 8 -58.4 311s 2 10 -67.8 2 18.7 8.9e-05 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > # corrected 311s > print( lrtest( fitsuri1e, fitsuri4e ) ) 311s Warning message: 311s In lrtest.systemfit(fitsurio5e, fitsuri1e) : 311s model '2' has a smaller log-likelihood value than the more restricted model '1' 311s Likelihood ratio test 311s 311s Model 1: fitsuri1e 311s Model 2: fitsuri4e 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 10 -67.8 311s 2 8 -100.9 -2 66.2 4.2e-15 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > print( lrtest( fitsuri1e, fitsuri5e ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsuri1e 311s Model 2: fitsuri5e 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 10 -67.8 311s 2 8 -100.9 -2 66.2 4.2e-15 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > # non-iterating, methodResidCov = 1, WSUR 311s > print( lrtest( fitsur5w, fitsur1w ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsur5w 311s Model 2: fitsur1w 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 8 -58.5 311s 2 10 -51.6 2 13.8 0.001 ** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > 311s > # testing the two restrictions with one call 311s > # non-iterating, methodResidCov = 1 311s > print( lrtest( fitsur4, fitsur2, fitsur1 ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsur4 311s Model 2: fitsur2 311s Model 3: fitsur1 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 8 -58.5 311s 2 9 -52.2 1 12.66 0.00037 *** 311s 3 10 -51.6 1 1.19 0.27520 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > print( lrtest( fitsur5, fitsur3, fitsur1 ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsur5 311s Model 2: fitsur3 311s Model 3: fitsur1 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 8 -58.5 311s 2 9 -52.2 1 12.66 0.00037 *** 311s 3 10 -51.6 1 1.19 0.27520 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > print( lrtest( fitsur1, fitsur3, fitsur5 ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsur1 311s Model 2: fitsur3 311s Model 3: fitsur5 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 10 -51.6 311s 2 9 -52.2 -1 1.19 0.27520 311s 3 8 -58.5 -1 12.66 0.00037 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > print( lrtest( object = fitsur5, fitsur3, fitsur1 ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsur5 311s Model 2: fitsur3 311s Model 3: fitsur1 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 8 -58.5 311s 2 9 -52.2 1 12.66 0.00037 *** 311s 3 10 -51.6 1 1.19 0.27520 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > print( lrtest( fitsur3, object = fitsur5, fitsur1 ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsur5 311s Model 2: fitsur3 311s Model 3: fitsur1 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 8 -58.5 311s 2 9 -52.2 1 12.66 0.00037 *** 311s 3 10 -51.6 1 1.19 0.27520 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > print( lrtest( fitsur3, fitsur1, object = fitsur5 ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsur5 311s Model 2: fitsur3 311s Model 3: fitsur1 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 8 -58.5 311s 2 9 -52.2 1 12.66 0.00037 *** 311s 3 10 -51.6 1 1.19 0.27520 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > # iterating, methodResidCov = 0 311s > print( lrtest( fitsuri4e, fitsuri2e, fitsuri1e ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsuri4e 311s Model 2: fitsuri2e 311s Model 3: fitsuri1e 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 8 -100.9 311s 2 9 -99.9 1 1.9 0.17 311s 3 10 -67.8 1 64.3 1.1e-15 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > print( lrtest( fitsuri5e, fitsuri3e, fitsuri1e ) ) 311s Likelihood ratio test 311s 311s Model 1: fitsuri5e 311s Model 2: fitsuri3e 311s Model 3: fitsuri1e 311s #Df LogLik Df Chisq Pr(>Chisq) 311s 1 8 -100.9 311s 2 9 -99.9 1 1.9 0.17 311s 3 10 -67.8 1 64.3 1.1e-15 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > 311s > ## ************** F tests **************** 311s > # testing first restriction 311s > print( linearHypothesis( fitsur1, restrm ) ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s 311s Model 1: restricted model 311s Model 2: fitsur1 311s 311s Res.Df Df F Pr(>F) 311s 1 34 311s 2 33 1 1.24 0.27 311s > linearHypothesis( fitsur1, restrict ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s 311s Model 1: restricted model 311s Model 2: fitsur1 311s 311s Res.Df Df F Pr(>F) 311s 1 34 311s 2 33 1 1.24 0.27 311s > 311s > print( linearHypothesis( fitsur1r2, restrm ) ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s 311s Model 1: restricted model 311s Model 2: fitsur1r2 311s 311s Res.Df Df F Pr(>F) 311s 1 34 311s 2 33 1 1.65 0.21 311s > linearHypothesis( fitsur1r2, restrict ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s 311s Model 1: restricted model 311s Model 2: fitsur1r2 311s 311s Res.Df Df F Pr(>F) 311s 1 34 311s 2 33 1 1.65 0.21 311s > 311s > print( linearHypothesis( fitsuri1e2, restrm ) ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s 311s Model 1: restricted model 311s Model 2: fitsuri1e2 311s 311s Res.Df Df F Pr(>F) 311s 1 34 311s 2 33 1 140 2.1e-13 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > linearHypothesis( fitsuri1e2, restrict ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s 311s Model 1: restricted model 311s Model 2: fitsuri1e2 311s 311s Res.Df Df F Pr(>F) 311s 1 34 311s 2 33 1 140 2.1e-13 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > 311s > print( linearHypothesis( fitsuri1r3, restrm ) ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s 311s Model 1: restricted model 311s Model 2: fitsuri1r3 311s 311s Res.Df Df F Pr(>F) 311s 1 34 311s 2 33 1 141 1.9e-13 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > linearHypothesis( fitsuri1r3, restrict ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s 311s Model 1: restricted model 311s Model 2: fitsuri1r3 311s 311s Res.Df Df F Pr(>F) 311s 1 34 311s 2 33 1 141 1.9e-13 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > 311s > print( linearHypothesis( fitsur1we2, restrm ) ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s 311s Model 1: restricted model 311s Model 2: fitsur1we2 311s 311s Res.Df Df F Pr(>F) 311s 1 34 311s 2 33 1 1.65 0.21 311s > linearHypothesis( fitsur1we2, restrict ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s 311s Model 1: restricted model 311s Model 2: fitsur1we2 311s 311s Res.Df Df F Pr(>F) 311s 1 34 311s 2 33 1 1.65 0.21 311s > 311s > print( linearHypothesis( fitsuri1wr3, restrm ) ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s 311s Model 1: restricted model 311s Model 2: fitsuri1wr3 311s 311s Res.Df Df F Pr(>F) 311s 1 34 311s 2 33 1 141 1.9e-13 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > linearHypothesis( fitsuri1wr3, restrict ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s 311s Model 1: restricted model 311s Model 2: fitsuri1wr3 311s 311s Res.Df Df F Pr(>F) 311s 1 34 311s 2 33 1 141 1.9e-13 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > 311s > # testing second restriction 311s > restrOnly2m <- matrix(0,1,7) 311s > restrOnly2q <- 0.5 311s > restrOnly2m[1,2] <- -1 311s > restrOnly2m[1,5] <- 1 311s > restrictOnly2 <- "- demand_price + supply_price = 0.5" 311s > restrictOnly2i <- "- demand_price + supply_income = 0.5" 311s > # first restriction not imposed 311s > print( linearHypothesis( fitsur1e2, restrOnly2m, restrOnly2q ) ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s - demand_price + supply_price = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsur1e2 311s 311s Res.Df Df F Pr(>F) 311s 1 34 311s 2 33 1 2.36 0.13 311s > linearHypothesis( fitsur1e2, restrictOnly2 ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s - demand_price + supply_price = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsur1e2 311s 311s Res.Df Df F Pr(>F) 311s 1 34 311s 2 33 1 2.36 0.13 311s > 311s > print( linearHypothesis( fitsuri1, restrOnly2m, restrOnly2q ) ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s - demand_price + supply_income = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsuri1 311s 311s Res.Df Df F Pr(>F) 311s 1 34 311s 2 33 1 12.2 0.0014 ** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > linearHypothesis( fitsuri1, restrictOnly2i ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s - demand_price + supply_income = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsuri1 311s 311s Res.Df Df F Pr(>F) 311s 1 34 311s 2 33 1 12.2 0.0014 ** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > 311s > # first restriction imposed 311s > print( linearHypothesis( fitsur2, restrOnly2m, restrOnly2q ) ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s - demand_price + supply_price = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsur2 311s 311s Res.Df Df F Pr(>F) 311s 1 35 311s 2 34 1 5.5 0.025 * 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > linearHypothesis( fitsur2, restrictOnly2 ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s - demand_price + supply_price = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsur2 311s 311s Res.Df Df F Pr(>F) 311s 1 35 311s 2 34 1 5.5 0.025 * 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > 311s > print( linearHypothesis( fitsur3, restrOnly2m, restrOnly2q ) ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s - demand_price + supply_price = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsur3 311s 311s Res.Df Df F Pr(>F) 311s 1 35 311s 2 34 1 5.5 0.025 * 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > linearHypothesis( fitsur3, restrictOnly2 ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s - demand_price + supply_price = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsur3 311s 311s Res.Df Df F Pr(>F) 311s 1 35 311s 2 34 1 5.5 0.025 * 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > 311s > print( linearHypothesis( fitsuri2e, restrOnly2m, restrOnly2q ) ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s - demand_price + supply_income = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsuri2e 311s 311s Res.Df Df F Pr(>F) 311s 1 35 311s 2 34 1 2.35 0.13 311s > linearHypothesis( fitsuri2e, restrictOnly2i ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s - demand_price + supply_income = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsuri2e 311s 311s Res.Df Df F Pr(>F) 311s 1 35 311s 2 34 1 2.35 0.13 311s > 311s > print( linearHypothesis( fitsuri3e, restrOnly2m, restrOnly2q ) ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s - demand_price + supply_income = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsuri3e 311s 311s Res.Df Df F Pr(>F) 311s 1 35 311s 2 34 1 2.35 0.13 311s > linearHypothesis( fitsuri3e, restrictOnly2i ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s - demand_price + supply_income = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsuri3e 311s 311s Res.Df Df F Pr(>F) 311s 1 35 311s 2 34 1 2.35 0.13 311s > 311s > print( linearHypothesis( fitsur2we, restrOnly2m, restrOnly2q ) ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s - demand_price + supply_price = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsur2we 311s 311s Res.Df Df F Pr(>F) 311s 1 35 311s 2 34 1 6.26 0.017 * 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > linearHypothesis( fitsur2we, restrictOnly2 ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s - demand_price + supply_price = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsur2we 311s 311s Res.Df Df F Pr(>F) 311s 1 35 311s 2 34 1 6.26 0.017 * 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > 311s > print( linearHypothesis( fitsuri3we, restrOnly2m, restrOnly2q ) ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s - demand_price + supply_income = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsuri3we 311s 311s Res.Df Df F Pr(>F) 311s 1 35 311s 2 34 1 2.35 0.13 311s > linearHypothesis( fitsuri3we, restrictOnly2i ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s - demand_price + supply_income = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsuri3we 311s 311s Res.Df Df F Pr(>F) 311s 1 35 311s 2 34 1 2.35 0.13 311s > 311s > # testing both of the restrictions 311s > print( linearHypothesis( fitsur1r3, restr2m, restr2q ) ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s - demand_price + supply_price = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsur1r3 311s 311s Res.Df Df F Pr(>F) 311s 1 35 311s 2 33 2 2.6 0.089 . 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > linearHypothesis( fitsur1r3, restrict2 ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s - demand_price + supply_price = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsur1r3 311s 311s Res.Df Df F Pr(>F) 311s 1 35 311s 2 33 2 2.6 0.089 . 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > 311s > print( linearHypothesis( fitsuri1e2, restr2m, restr2q ) ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s - demand_price + supply_income = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsuri1e2 311s 311s Res.Df Df F Pr(>F) 311s 1 35 311s 2 33 2 89.1 5e-14 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > linearHypothesis( fitsuri1e2, restrict2i ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s - demand_price + supply_income = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsuri1e2 311s 311s Res.Df Df F Pr(>F) 311s 1 35 311s 2 33 2 89.1 5e-14 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > 311s > print( linearHypothesis( fitsur1w, restr2m, restr2q ) ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s - demand_price + supply_price = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsur1w 311s 311s Res.Df Df F Pr(>F) 311s 1 35 311s 2 33 2 1.8 0.18 311s > linearHypothesis( fitsur1w, restrict2 ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s - demand_price + supply_price = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsur1w 311s 311s Res.Df Df F Pr(>F) 311s 1 35 311s 2 33 2 1.8 0.18 311s > 311s > print( linearHypothesis( fitsuri1wr3, restr2m, restr2q ) ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s - demand_price + supply_income = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsuri1wr3 311s 311s Res.Df Df F Pr(>F) 311s 1 35 311s 2 33 2 89.6 4.6e-14 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > linearHypothesis( fitsuri1wr3, restrict2i ) 311s Linear hypothesis test (Theil's F test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s - demand_price + supply_income = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsuri1wr3 311s 311s Res.Df Df F Pr(>F) 311s 1 35 311s 2 33 2 89.6 4.6e-14 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > 311s > 311s > ## ************** Wald tests **************** 311s > # testing first restriction 311s > print( linearHypothesis( fitsur1, restrm, test = "Chisq" ) ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s 311s Model 1: restricted model 311s Model 2: fitsur1 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 34 311s 2 33 1 0.81 0.37 311s > linearHypothesis( fitsur1, restrict, test = "Chisq" ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s 311s Model 1: restricted model 311s Model 2: fitsur1 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 34 311s 2 33 1 0.81 0.37 311s > 311s > print( linearHypothesis( fitsur1r2, restrm, test = "Chisq" ) ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s 311s Model 1: restricted model 311s Model 2: fitsur1r2 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 34 311s 2 33 1 1.12 0.29 311s > linearHypothesis( fitsur1r2, restrict, test = "Chisq" ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s 311s Model 1: restricted model 311s Model 2: fitsur1r2 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 34 311s 2 33 1 1.12 0.29 311s > 311s > print( linearHypothesis( fitsuri1e2, restrm, test = "Chisq" ) ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s 311s Model 1: restricted model 311s Model 2: fitsuri1e2 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 34 311s 2 33 1 147 <2e-16 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > linearHypothesis( fitsuri1e2, restrict, test = "Chisq" ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s 311s Model 1: restricted model 311s Model 2: fitsuri1e2 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 34 311s 2 33 1 147 <2e-16 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > 311s > print( linearHypothesis( fitsuri1r3, restrm, test = "Chisq" ) ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s 311s Model 1: restricted model 311s Model 2: fitsuri1r3 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 34 311s 2 33 1 147 <2e-16 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > linearHypothesis( fitsuri1r3, restrict, test = "Chisq" ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s 311s Model 1: restricted model 311s Model 2: fitsuri1r3 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 34 311s 2 33 1 147 <2e-16 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > 311s > print( linearHypothesis( fitsur1w, restrm, test = "Chisq" ) ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s 311s Model 1: restricted model 311s Model 2: fitsur1w 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 34 311s 2 33 1 0.81 0.37 311s > linearHypothesis( fitsur1w, restrict, test = "Chisq" ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s 311s Model 1: restricted model 311s Model 2: fitsur1w 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 34 311s 2 33 1 0.81 0.37 311s > 311s > # testing second restriction 311s > # first restriction not imposed 311s > print( linearHypothesis( fitsur1e2, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s - demand_price + supply_price = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsur1e2 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 34 311s 2 33 1 1.6 0.21 311s > linearHypothesis( fitsur1e2, restrictOnly2, test = "Chisq" ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s - demand_price + supply_price = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsur1e2 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 34 311s 2 33 1 1.6 0.21 311s > 311s > print( linearHypothesis( fitsuri1, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s - demand_price + supply_income = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsuri1 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 34 311s 2 33 1 12.2 0.00047 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > linearHypothesis( fitsuri1, restrictOnly2i, test = "Chisq" ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s - demand_price + supply_income = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsuri1 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 34 311s 2 33 1 12.2 0.00047 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > 311s > # first restriction imposed 311s > print( linearHypothesis( fitsur2, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s - demand_price + supply_price = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsur2 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 35 311s 2 34 1 3.95 0.047 * 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > linearHypothesis( fitsur2, restrictOnly2, test = "Chisq" ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s - demand_price + supply_price = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsur2 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 35 311s 2 34 1 3.95 0.047 * 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > 311s > print( linearHypothesis( fitsur3, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s - demand_price + supply_price = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsur3 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 35 311s 2 34 1 3.95 0.047 * 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > linearHypothesis( fitsur3, restrictOnly2, test = "Chisq" ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s - demand_price + supply_price = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsur3 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 35 311s 2 34 1 3.95 0.047 * 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > 311s > print( linearHypothesis( fitsuri2e, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s - demand_price + supply_income = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsuri2e 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 35 311s 2 34 1 2.76 0.096 . 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > linearHypothesis( fitsuri2e, restrictOnly2i, test = "Chisq" ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s - demand_price + supply_income = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsuri2e 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 35 311s 2 34 1 2.76 0.096 . 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > 311s > print( linearHypothesis( fitsuri3e, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s - demand_price + supply_income = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsuri3e 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 35 311s 2 34 1 2.76 0.096 . 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > linearHypothesis( fitsuri3e, restrictOnly2i, test = "Chisq" ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s - demand_price + supply_income = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsuri3e 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 35 311s 2 34 1 2.76 0.096 . 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > 311s > print( linearHypothesis( fitsuri2w, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s - demand_price + supply_income = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsuri2w 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 35 311s 2 34 1 2.2 0.14 311s > linearHypothesis( fitsuri2w, restrictOnly2i, test = "Chisq" ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s - demand_price + supply_income = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsuri2w 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 35 311s 2 34 1 2.2 0.14 311s > 311s > print( linearHypothesis( fitsur3w, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s - demand_price + supply_price = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsur3w 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 35 311s 2 34 1 4.26 0.039 * 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > linearHypothesis( fitsur3w, restrictOnly2, test = "Chisq" ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s - demand_price + supply_price = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsur3w 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 35 311s 2 34 1 4.26 0.039 * 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > 311s > 311s > # testing both of the restrictions 311s > print( linearHypothesis( fitsur1r3, restr2m, restr2q, test = "Chisq" ) ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s - demand_price + supply_price = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsur1r3 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 35 311s 2 33 2 3.51 0.17 311s > linearHypothesis( fitsur1r3, restrict2, test = "Chisq" ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s - demand_price + supply_price = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsur1r3 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 35 311s 2 33 2 3.51 0.17 311s > 311s > print( linearHypothesis( fitsuri1e2, restr2m, restr2q, test = "Chisq" ) ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s - demand_price + supply_income = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsuri1e2 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 35 311s 2 33 2 188 <2e-16 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > linearHypothesis( fitsuri1e2, restrict2i, test = "Chisq" ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s - demand_price + supply_income = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsuri1e2 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 35 311s 2 33 2 188 <2e-16 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > 311s > print( linearHypothesis( fitsur1we2, restr2m, restr2q, test = "Chisq" ) ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s - demand_price + supply_price = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsur1we2 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 35 311s 2 33 2 3.66 0.16 311s > linearHypothesis( fitsur1we2, restrict2, test = "Chisq" ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s - demand_price + supply_price = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsur1we2 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 35 311s 2 33 2 3.66 0.16 311s > 311s > print( linearHypothesis( fitsuri1wr3, restr2m, restr2q, test = "Chisq" ) ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s - demand_price + supply_income = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsuri1wr3 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 35 311s 2 33 2 187 <2e-16 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > linearHypothesis( fitsuri1wr3, restrict2i, test = "Chisq" ) 311s Linear hypothesis test (Chi^2 statistic of a Wald test) 311s 311s Hypothesis: 311s demand_income - supply_trend = 0 311s - demand_price + supply_income = 0.5 311s 311s Model 1: restricted model 311s Model 2: fitsuri1wr3 311s 311s Res.Df Df Chisq Pr(>Chisq) 311s 1 35 311s 2 33 2 187 <2e-16 *** 311s --- 311s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 311s > 311s > 311s > ## ****************** model frame ************************** 311s > print( mf <- model.frame( fitsur1e2 ) ) 311s consump price income farmPrice trend 311s 1 98.5 100.3 87.4 98.0 1 311s 2 99.2 104.3 97.6 99.1 2 311s 3 102.2 103.4 96.7 99.1 3 311s 4 101.5 104.5 98.2 98.1 4 311s 5 104.2 98.0 99.8 110.8 5 311s 6 103.2 99.5 100.5 108.2 6 311s 7 104.0 101.1 103.2 105.6 7 311s 8 99.9 104.8 107.8 109.8 8 311s 9 100.3 96.4 96.6 108.7 9 311s 10 102.8 91.2 88.9 100.6 10 311s 11 95.4 93.1 75.1 81.0 11 311s 12 92.4 98.8 76.9 68.6 12 311s 13 94.5 102.9 84.6 70.9 13 311s 14 98.8 98.8 90.6 81.4 14 311s 15 105.8 95.1 103.1 102.3 15 311s 16 100.2 98.5 105.1 105.0 16 311s 17 103.5 86.5 96.4 110.5 17 311s 18 99.9 104.0 104.4 92.5 18 311s 19 105.2 105.8 110.7 89.3 19 311s 20 106.2 113.5 127.1 93.0 20 311s > print( mf1 <- model.frame( fitsur1e2$eq[[ 1 ]] ) ) 311s consump price income 311s 1 98.5 100.3 87.4 311s 2 99.2 104.3 97.6 311s 3 102.2 103.4 96.7 311s 4 101.5 104.5 98.2 311s 5 104.2 98.0 99.8 311s 6 103.2 99.5 100.5 311s 7 104.0 101.1 103.2 311s 8 99.9 104.8 107.8 311s 9 100.3 96.4 96.6 311s 10 102.8 91.2 88.9 311s 11 95.4 93.1 75.1 311s 12 92.4 98.8 76.9 311s 13 94.5 102.9 84.6 311s 14 98.8 98.8 90.6 311s 15 105.8 95.1 103.1 311s 16 100.2 98.5 105.1 311s 17 103.5 86.5 96.4 311s 18 99.9 104.0 104.4 311s 19 105.2 105.8 110.7 311s 20 106.2 113.5 127.1 311s > print( attributes( mf1 )$terms ) 311s consump ~ price + income 311s attr(,"variables") 311s list(consump, price, income) 311s attr(,"factors") 311s price income 311s consump 0 0 311s price 1 0 311s income 0 1 311s attr(,"term.labels") 311s [1] "price" "income" 311s attr(,"order") 311s [1] 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, income) 311s attr(,"dataClasses") 311s consump price income 311s "numeric" "numeric" "numeric" 311s > print( mf2 <- model.frame( fitsur1e2$eq[[ 2 ]] ) ) 311s consump price farmPrice trend 311s 1 98.5 100.3 98.0 1 311s 2 99.2 104.3 99.1 2 311s 3 102.2 103.4 99.1 3 311s 4 101.5 104.5 98.1 4 311s 5 104.2 98.0 110.8 5 311s 6 103.2 99.5 108.2 6 311s 7 104.0 101.1 105.6 7 311s 8 99.9 104.8 109.8 8 311s 9 100.3 96.4 108.7 9 311s 10 102.8 91.2 100.6 10 311s 11 95.4 93.1 81.0 11 311s 12 92.4 98.8 68.6 12 311s 13 94.5 102.9 70.9 13 311s 14 98.8 98.8 81.4 14 311s 15 105.8 95.1 102.3 15 311s 16 100.2 98.5 105.0 16 311s 17 103.5 86.5 110.5 17 311s 18 99.9 104.0 92.5 18 311s 19 105.2 105.8 89.3 19 311s 20 106.2 113.5 93.0 20 311s > print( attributes( mf2 )$terms ) 311s consump ~ price + farmPrice + trend 311s attr(,"variables") 311s list(consump, price, farmPrice, trend) 311s attr(,"factors") 311s price farmPrice trend 311s consump 0 0 0 311s price 1 0 0 311s farmPrice 0 1 0 311s trend 0 0 1 311s attr(,"term.labels") 311s [1] "price" "farmPrice" "trend" 311s attr(,"order") 311s [1] 1 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, farmPrice, trend) 311s attr(,"dataClasses") 311s consump price farmPrice trend 311s "numeric" "numeric" "numeric" "numeric" 311s > 311s > print( all.equal( mf, model.frame( fitsur1w ) ) ) 311s [1] TRUE 311s > print( all.equal( mf1, model.frame( fitsur1w$eq[[ 1 ]] ) ) ) 311s [1] TRUE 311s > 311s > print( all.equal( mf, model.frame( fitsur2e ) ) ) 311s [1] TRUE 311s > print( all.equal( mf1, model.frame( fitsur2e$eq[[ 1 ]] ) ) ) 311s [1] TRUE 311s > 311s > print( all.equal( mf, model.frame( fitsur3 ) ) ) 311s [1] TRUE 311s > print( all.equal( mf2, model.frame( fitsur3$eq[[ 2 ]] ) ) ) 311s [1] TRUE 311s > 311s > print( all.equal( mf, model.frame( fitsur4r3 ) ) ) 311s [1] TRUE 311s > print( all.equal( mf1, model.frame( fitsur4r3$eq[[ 1 ]] ) ) ) 311s [1] TRUE 311s > 311s > print( all.equal( mf, model.frame( fitsur4we ) ) ) 311s [1] TRUE 311s > print( all.equal( mf2, model.frame( fitsur4we$eq[[ 2 ]] ) ) ) 311s [1] TRUE 311s > 311s > print( all.equal( mf, model.frame( fitsur5 ) ) ) 311s [1] TRUE 311s > print( all.equal( mf2, model.frame( fitsur5$eq[[ 2 ]] ) ) ) 311s [1] TRUE 311s > 311s > print( all.equal( mf, model.frame( fitsuri1r3 ) ) ) 311s [1] TRUE 311s > print( all.equal( mf1, model.frame( fitsuri1r3$eq[[ 1 ]] ) ) ) 311s [1] TRUE 311s > 311s > print( all.equal( mf, model.frame( fitsuri2 ) ) ) 311s [1] TRUE 311s > print( all.equal( mf1, model.frame( fitsuri2$eq[[ 1 ]] ) ) ) 311s [1] TRUE 311s > 311s > print( all.equal( mf, model.frame( fitsuri3e ) ) ) 311s [1] TRUE 311s > print( all.equal( mf1, model.frame( fitsuri3e$eq[[ 1 ]] ) ) ) 311s [1] TRUE 311s > 311s > print( all.equal( mf, model.frame( fitsurio4 ) ) ) 311s [1] TRUE 311s > print( all.equal( mf2, model.frame( fitsurio4$eq[[ 2 ]] ) ) ) 311s [1] TRUE 311s > print( all.equal( mf, model.frame( fitsuri4 ) ) ) 311s [1] TRUE 311s > print( all.equal( mf1, model.frame( fitsuri4$eq[[ 1 ]] ) ) ) 311s [1] TRUE 311s > 311s > print( all.equal( mf, model.frame( fitsurio5r2 ) ) ) 311s [1] TRUE 311s > print( all.equal( mf1, model.frame( fitsurio5r2$eq[[ 1 ]] ) ) ) 311s [1] TRUE 311s > print( all.equal( mf, model.frame( fitsuri5r2 ) ) ) 311s [1] TRUE 311s > print( all.equal( mf1, model.frame( fitsuri5r2$eq[[ 1 ]] ) ) ) 311s [1] TRUE 311s > 311s > print( all.equal( mf, model.frame( fitsuri5wr2 ) ) ) 311s [1] TRUE 311s > print( all.equal( mf1, model.frame( fitsuri5wr2$eq[[ 1 ]] ) ) ) 311s [1] TRUE 311s > 311s > 311s > ## **************** model matrix ************************ 311s > # with x (returnModelMatrix) = TRUE 311s > print( !is.null( fitsur1e2$eq[[ 1 ]]$x ) ) 311s [1] TRUE 311s > print( mm <- model.matrix( fitsur1e2 ) ) 311s demand_(Intercept) demand_price demand_income supply_(Intercept) 311s demand_1 1 100.3 87.4 0 311s demand_2 1 104.3 97.6 0 311s demand_3 1 103.4 96.7 0 311s demand_4 1 104.5 98.2 0 311s demand_5 1 98.0 99.8 0 311s demand_6 1 99.5 100.5 0 311s demand_7 1 101.1 103.2 0 311s demand_8 1 104.8 107.8 0 311s demand_9 1 96.4 96.6 0 311s demand_10 1 91.2 88.9 0 311s demand_11 1 93.1 75.1 0 311s demand_12 1 98.8 76.9 0 311s demand_13 1 102.9 84.6 0 311s demand_14 1 98.8 90.6 0 311s demand_15 1 95.1 103.1 0 311s demand_16 1 98.5 105.1 0 311s demand_17 1 86.5 96.4 0 311s demand_18 1 104.0 104.4 0 311s demand_19 1 105.8 110.7 0 311s demand_20 1 113.5 127.1 0 311s supply_1 0 0.0 0.0 1 311s supply_2 0 0.0 0.0 1 311s supply_3 0 0.0 0.0 1 311s supply_4 0 0.0 0.0 1 311s supply_5 0 0.0 0.0 1 311s supply_6 0 0.0 0.0 1 311s supply_7 0 0.0 0.0 1 311s supply_8 0 0.0 0.0 1 311s supply_9 0 0.0 0.0 1 311s supply_10 0 0.0 0.0 1 311s supply_11 0 0.0 0.0 1 311s supply_12 0 0.0 0.0 1 311s supply_13 0 0.0 0.0 1 311s supply_14 0 0.0 0.0 1 311s supply_15 0 0.0 0.0 1 311s supply_16 0 0.0 0.0 1 311s supply_17 0 0.0 0.0 1 311s supply_18 0 0.0 0.0 1 311s supply_19 0 0.0 0.0 1 311s supply_20 0 0.0 0.0 1 311s supply_price supply_farmPrice supply_trend 311s demand_1 0.0 0.0 0 311s demand_2 0.0 0.0 0 311s demand_3 0.0 0.0 0 311s demand_4 0.0 0.0 0 311s demand_5 0.0 0.0 0 311s demand_6 0.0 0.0 0 311s demand_7 0.0 0.0 0 311s demand_8 0.0 0.0 0 311s demand_9 0.0 0.0 0 311s demand_10 0.0 0.0 0 311s demand_11 0.0 0.0 0 311s demand_12 0.0 0.0 0 311s demand_13 0.0 0.0 0 311s demand_14 0.0 0.0 0 311s demand_15 0.0 0.0 0 311s demand_16 0.0 0.0 0 311s demand_17 0.0 0.0 0 311s demand_18 0.0 0.0 0 311s demand_19 0.0 0.0 0 311s demand_20 0.0 0.0 0 311s supply_1 100.3 98.0 1 311s supply_2 104.3 99.1 2 311s supply_3 103.4 99.1 3 311s supply_4 104.5 98.1 4 311s supply_5 98.0 110.8 5 311s supply_6 99.5 108.2 6 311s supply_7 101.1 105.6 7 311s supply_8 104.8 109.8 8 311s supply_9 96.4 108.7 9 311s supply_10 91.2 100.6 10 311s supply_11 93.1 81.0 11 311s supply_12 98.8 68.6 12 311s supply_13 102.9 70.9 13 311s supply_14 98.8 81.4 14 311s supply_15 95.1 102.3 15 311s supply_16 98.5 105.0 16 311s supply_17 86.5 110.5 17 311s supply_18 104.0 92.5 18 311s supply_19 105.8 89.3 19 311s supply_20 113.5 93.0 20 311s > print( mm1 <- model.matrix( fitsur1e2$eq[[ 1 ]] ) ) 311s (Intercept) price income 311s 1 1 100.3 87.4 311s 2 1 104.3 97.6 311s 3 1 103.4 96.7 311s 4 1 104.5 98.2 311s 5 1 98.0 99.8 311s 6 1 99.5 100.5 311s 7 1 101.1 103.2 311s 8 1 104.8 107.8 311s 9 1 96.4 96.6 311s 10 1 91.2 88.9 311s 11 1 93.1 75.1 311s 12 1 98.8 76.9 311s 13 1 102.9 84.6 311s 14 1 98.8 90.6 311s 15 1 95.1 103.1 311s 16 1 98.5 105.1 311s 17 1 86.5 96.4 311s 18 1 104.0 104.4 311s 19 1 105.8 110.7 311s 20 1 113.5 127.1 311s attr(,"assign") 311s [1] 0 1 2 311s > print( mm2 <- model.matrix( fitsur1e2$eq[[ 2 ]] ) ) 311s (Intercept) price farmPrice trend 311s 1 1 100.3 98.0 1 311s 2 1 104.3 99.1 2 311s 3 1 103.4 99.1 3 311s 4 1 104.5 98.1 4 311s 5 1 98.0 110.8 5 311s 6 1 99.5 108.2 6 311s 7 1 101.1 105.6 7 311s 8 1 104.8 109.8 8 311s 9 1 96.4 108.7 9 311s 10 1 91.2 100.6 10 311s 11 1 93.1 81.0 11 311s 12 1 98.8 68.6 12 311s 13 1 102.9 70.9 13 311s 14 1 98.8 81.4 14 311s 15 1 95.1 102.3 15 311s 16 1 98.5 105.0 16 311s 17 1 86.5 110.5 17 311s 18 1 104.0 92.5 18 311s 19 1 105.8 89.3 19 311s 20 1 113.5 93.0 20 311s attr(,"assign") 311s [1] 0 1 2 3 311s > 311s > # with x (returnModelMatrix) = FALSE 311s > print( all.equal( mm, model.matrix( fitsur1r2 ) ) ) 311s [1] TRUE 311s > print( all.equal( mm1, model.matrix( fitsur1r2$eq[[ 1 ]] ) ) ) 311s [1] TRUE 311s > print( all.equal( mm2, model.matrix( fitsur1r2$eq[[ 2 ]] ) ) ) 311s [1] TRUE 311s > print( !is.null( fitsur1r2$eq[[ 1 ]]$x ) ) 311s [1] FALSE 311s > 311s > # with x (returnModelMatrix) = TRUE 311s > print( !is.null( fitsur2e$eq[[ 1 ]]$x ) ) 311s [1] TRUE 311s > print( all.equal( mm, model.matrix( fitsur2e ) ) ) 311s [1] TRUE 311s > print( all.equal( mm1, model.matrix( fitsur2e$eq[[ 1 ]] ) ) ) 311s [1] TRUE 311s > print( all.equal( mm2, model.matrix( fitsur2e$eq[[ 2 ]] ) ) ) 311s [1] TRUE 311s > 311s > # with x (returnModelMatrix) = FALSE 311s > print( all.equal( mm, model.matrix( fitsur2 ) ) ) 311s [1] TRUE 311s > print( all.equal( mm1, model.matrix( fitsur2$eq[[ 1 ]] ) ) ) 311s [1] TRUE 311s > print( all.equal( mm2, model.matrix( fitsur2$eq[[ 2 ]] ) ) ) 311s [1] TRUE 311s > print( !is.null( fitsur2$eq[[ 1 ]]$x ) ) 311s [1] FALSE 311s > 311s > # with x (returnModelMatrix) = TRUE 311s > print( !is.null( fitsur2we$eq[[ 1 ]]$x ) ) 311s [1] TRUE 311s > print( all.equal( mm, model.matrix( fitsur2we ) ) ) 311s [1] TRUE 311s > print( all.equal( mm1, model.matrix( fitsur2we$eq[[ 1 ]] ) ) ) 311s [1] TRUE 311s > print( all.equal( mm2, model.matrix( fitsur2we$eq[[ 2 ]] ) ) ) 311s [1] TRUE 311s > 311s > # with x (returnModelMatrix) = FALSE 311s > print( all.equal( mm, model.matrix( fitsur2 ) ) ) 311s [1] TRUE 311s > print( all.equal( mm1, model.matrix( fitsur2$eq[[ 1 ]] ) ) ) 311s [1] TRUE 311s > print( all.equal( mm2, model.matrix( fitsur2$eq[[ 2 ]] ) ) ) 311s [1] TRUE 311s > print( !is.null( fitsuri2$eq[[ 1 ]]$x ) ) 311s [1] FALSE 311s > 311s > # with x (returnModelMatrix) = TRUE 311s > print( !is.null( fitsur3e$eq[[ 1 ]]$x ) ) 311s [1] TRUE 311s > print( all.equal( mm, model.matrix( fitsur3e ) ) ) 311s [1] TRUE 311s > print( all.equal( mm1, model.matrix( fitsur3e$eq[[ 1 ]] ) ) ) 311s [1] TRUE 311s > print( all.equal( mm2, model.matrix( fitsur3e$eq[[ 2 ]] ) ) ) 311s [1] TRUE 311s > 311s > # with x (returnModelMatrix) = FALSE 311s > print( all.equal( mm, model.matrix( fitsur3 ) ) ) 311s [1] TRUE 311s > print( all.equal( mm1, model.matrix( fitsur3$eq[[ 1 ]] ) ) ) 311s [1] TRUE 311s > print( all.equal( mm2, model.matrix( fitsur3$eq[[ 2 ]] ) ) ) 311s [1] TRUE 311s > print( !is.null( fitsur3$eq[[ 1 ]]$x ) ) 311s [1] FALSE 311s > 311s > # with x (returnModelMatrix) = TRUE 311s > print( !is.null( fitsur3w$eq[[ 1 ]]$x ) ) 311s [1] TRUE 311s > print( all.equal( mm, model.matrix( fitsur3w ) ) ) 311s [1] TRUE 311s > print( all.equal( mm1, model.matrix( fitsur3w$eq[[ 1 ]] ) ) ) 311s [1] TRUE 311s > print( all.equal( mm2, model.matrix( fitsur3w$eq[[ 2 ]] ) ) ) 311s [1] TRUE 311s > 311s > # with x (returnModelMatrix) = FALSE 311s > print( all.equal( mm, model.matrix( fitsur3 ) ) ) 311s [1] TRUE 311s > print( all.equal( mm1, model.matrix( fitsur3$eq[[ 1 ]] ) ) ) 311s [1] TRUE 311s > print( all.equal( mm2, model.matrix( fitsur3$eq[[ 2 ]] ) ) ) 311s [1] TRUE 311s > print( !is.null( fitsuri3$eq[[ 1 ]]$x ) ) 311s [1] FALSE 311s > 311s > # with x (returnModelMatrix) = TRUE 311s > print( !is.null( fitsur4r3$eq[[ 1 ]]$x ) ) 311s [1] TRUE 311s > print( all.equal( mm, model.matrix( fitsur4r3 ) ) ) 311s [1] TRUE 311s > print( all.equal( mm1, model.matrix( fitsur4r3$eq[[ 1 ]] ) ) ) 311s [1] TRUE 311s > print( all.equal( mm2, model.matrix( fitsur4r3$eq[[ 2 ]] ) ) ) 311s [1] TRUE 311s > 311s > # with x (returnModelMatrix) = FALSE 311s > print( all.equal( mm, model.matrix( fitsur4we ) ) ) 311s [1] TRUE 311s > print( all.equal( mm1, model.matrix( fitsur4we$eq[[ 1 ]] ) ) ) 311s [1] TRUE 311s > print( all.equal( mm2, model.matrix( fitsur4we$eq[[ 2 ]] ) ) ) 311s [1] TRUE 311s > print( !is.null( fitsur4we$eq[[ 1 ]]$x ) ) 311s [1] FALSE 311s > 311s > # with x (returnModelMatrix) = TRUE 311s > print( !is.null( fitsurio5r2$eq[[ 1 ]]$x ) ) 311s [1] TRUE 311s > print( !is.null( fitsur5$eq[[ 1 ]]$x ) ) 311s [1] TRUE 311s > print( all.equal( mm, model.matrix( fitsurio5r2 ) ) ) 311s [1] TRUE 311s > print( all.equal( mm1, model.matrix( fitsurio5r2$eq[[ 1 ]] ) ) ) 311s [1] TRUE 311s > print( all.equal( mm, model.matrix( fitsur5 ) ) ) 311s [1] TRUE 311s > print( all.equal( mm1, model.matrix( fitsur5$eq[[ 1 ]] ) ) ) 311s [1] TRUE 311s > #print( all.equal( mm2, model.matrix( fitsuri5r2$eq[[ 2 ]] ) ) ) 311s > 311s > # with x (returnModelMatrix) = FALSE 311s > print( all.equal( mm, model.matrix( fitsurio5 ) ) ) 311s [1] TRUE 311s > print( all.equal( mm1, model.matrix( fitsurio5$eq[[ 1 ]] ) ) ) 311s [1] TRUE 311s > 311s > # with x (returnModelMatrix) = FALSE 311s > print( all.equal( mm, model.matrix( fitsur5w ) ) ) 311s [1] TRUE 311s > print( all.equal( mm1, model.matrix( fitsur5w$eq[[ 1 ]] ) ) ) 311s [1] TRUE 311s > #print( all.equal( mm2, model.matrix( fitsuri5r2$eq[[ 1 ]] ) ) ) 311s > print( !is.null( fitsurio5$eq[[ 1 ]]$x ) ) 311s [1] FALSE 311s > print( !is.null( fitsur5w$eq[[ 1 ]]$x ) ) 311s [1] FALSE 311s > 311s > 311s > ## **************** formulas ************************ 311s > formula( fitsur1e2 ) 311s $demand 311s consump ~ price + income 311s 311s $supply 311s consump ~ price + farmPrice + trend 311s 311s > formula( fitsur1e2$eq[[ 2 ]] ) 311s consump ~ price + farmPrice + trend 311s > 311s > formula( fitsur2e ) 311s $demand 311s consump ~ price + income 311s 311s $supply 311s consump ~ price + farmPrice + trend 311s 311s > formula( fitsur2e$eq[[ 1 ]] ) 311s consump ~ price + income 311s > 311s > formula( fitsur2we ) 311s $demand 311s consump ~ price + income 311s 311s $supply 311s consump ~ price + farmPrice + trend 311s 311s > formula( fitsur2we$eq[[ 1 ]] ) 311s consump ~ price + income 311s > 311s > formula( fitsur3 ) 311s $demand 311s consump ~ price + income 311s 311s $supply 311s consump ~ price + farmPrice + trend 311s 311s > formula( fitsur3$eq[[ 2 ]] ) 311s consump ~ price + farmPrice + trend 311s > 311s > formula( fitsur4r3 ) 311s $demand 311s consump ~ price + income 311s 311s $supply 311s consump ~ price + farmPrice + trend 311s 311s > formula( fitsur4r3$eq[[ 1 ]] ) 311s consump ~ price + income 311s > 311s > formula( fitsur5 ) 311s $demand 311s consump ~ price + income 311s 311s $supply 311s consump ~ price + farmPrice + trend 311s 311s > formula( fitsur5$eq[[ 2 ]] ) 311s consump ~ price + farmPrice + trend 311s > 311s > formula( fitsuri1r3 ) 311s $demand 311s consump ~ price + income 311s 311s $supply 311s price ~ income + farmPrice + trend 311s 311s > formula( fitsuri1r3$eq[[ 1 ]] ) 311s consump ~ price + income 311s > 311s > formula( fitsuri2 ) 311s $demand 311s consump ~ price + income 311s 311s $supply 311s price ~ income + farmPrice + trend 311s 311s > formula( fitsuri2$eq[[ 2 ]] ) 311s price ~ income + farmPrice + trend 311s > 311s > formula( fitsuri3e ) 311s $demand 311s consump ~ price + income 311s 311s $supply 311s price ~ income + farmPrice + trend 311s 311s > formula( fitsuri3e$eq[[ 1 ]] ) 311s consump ~ price + income 311s > 311s > formula( fitsurio4 ) 311s $demand 311s consump ~ price + income 311s 311s $supply 311s consump ~ price + farmPrice + trend 311s 311s > formula( fitsurio4$eq[[ 2 ]] ) 311s consump ~ price + farmPrice + trend 311s > formula( fitsuri4 ) 311s $demand 311s consump ~ price + income 311s 311s $supply 311s price ~ income + farmPrice + trend 311s 311s > formula( fitsuri4$eq[[ 2 ]] ) 311s price ~ income + farmPrice + trend 311s > 311s > formula( fitsurio5r2 ) 311s $demand 311s consump ~ price + income 311s 311s $supply 311s consump ~ price + farmPrice + trend 311s 311s > formula( fitsurio5r2$eq[[ 1 ]] ) 311s consump ~ price + income 311s > formula( fitsuri5r2 ) 311s $demand 311s consump ~ price + income 311s 311s $supply 311s price ~ income + farmPrice + trend 311s 311s > formula( fitsuri5r2$eq[[ 1 ]] ) 311s consump ~ price + income 311s > 311s > formula( fitsuri5wr2 ) 311s $demand 311s consump ~ price + income 311s 311s $supply 311s price ~ income + farmPrice + trend 311s 311s > formula( fitsuri5wr2$eq[[ 1 ]] ) 311s consump ~ price + income 311s > 311s > 311s > ## **************** model terms ******************* 311s > terms( fitsur1e2 ) 311s $demand 311s consump ~ price + income 311s attr(,"variables") 311s list(consump, price, income) 311s attr(,"factors") 311s price income 311s consump 0 0 311s price 1 0 311s income 0 1 311s attr(,"term.labels") 311s [1] "price" "income" 311s attr(,"order") 311s [1] 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, income) 311s attr(,"dataClasses") 311s consump price income 311s "numeric" "numeric" "numeric" 311s 311s $supply 311s consump ~ price + farmPrice + trend 311s attr(,"variables") 311s list(consump, price, farmPrice, trend) 311s attr(,"factors") 311s price farmPrice trend 311s consump 0 0 0 311s price 1 0 0 311s farmPrice 0 1 0 311s trend 0 0 1 311s attr(,"term.labels") 311s [1] "price" "farmPrice" "trend" 311s attr(,"order") 311s [1] 1 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, farmPrice, trend) 311s attr(,"dataClasses") 311s consump price farmPrice trend 311s "numeric" "numeric" "numeric" "numeric" 311s 311s > terms( fitsur1e2$eq[[ 2 ]] ) 311s consump ~ price + farmPrice + trend 311s attr(,"variables") 311s list(consump, price, farmPrice, trend) 311s attr(,"factors") 311s price farmPrice trend 311s consump 0 0 0 311s price 1 0 0 311s farmPrice 0 1 0 311s trend 0 0 1 311s attr(,"term.labels") 311s [1] "price" "farmPrice" "trend" 311s attr(,"order") 311s [1] 1 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, farmPrice, trend) 311s attr(,"dataClasses") 311s consump price farmPrice trend 311s "numeric" "numeric" "numeric" "numeric" 311s > 311s > terms( fitsur2e ) 311s $demand 311s consump ~ price + income 311s attr(,"variables") 311s list(consump, price, income) 311s attr(,"factors") 311s price income 311s consump 0 0 311s price 1 0 311s income 0 1 311s attr(,"term.labels") 311s [1] "price" "income" 311s attr(,"order") 311s [1] 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, income) 311s attr(,"dataClasses") 311s consump price income 311s "numeric" "numeric" "numeric" 311s 311s $supply 311s consump ~ price + farmPrice + trend 311s attr(,"variables") 311s list(consump, price, farmPrice, trend) 311s attr(,"factors") 311s price farmPrice trend 311s consump 0 0 0 311s price 1 0 0 311s farmPrice 0 1 0 311s trend 0 0 1 311s attr(,"term.labels") 311s [1] "price" "farmPrice" "trend" 311s attr(,"order") 311s [1] 1 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, farmPrice, trend) 311s attr(,"dataClasses") 311s consump price farmPrice trend 311s "numeric" "numeric" "numeric" "numeric" 311s 311s > terms( fitsur2e$eq[[ 1 ]] ) 311s consump ~ price + income 311s attr(,"variables") 311s list(consump, price, income) 311s attr(,"factors") 311s price income 311s consump 0 0 311s price 1 0 311s income 0 1 311s attr(,"term.labels") 311s [1] "price" "income" 311s attr(,"order") 311s [1] 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, income) 311s attr(,"dataClasses") 311s consump price income 311s "numeric" "numeric" "numeric" 311s > 311s > terms( fitsur3 ) 311s $demand 311s consump ~ price + income 311s attr(,"variables") 311s list(consump, price, income) 311s attr(,"factors") 311s price income 311s consump 0 0 311s price 1 0 311s income 0 1 311s attr(,"term.labels") 311s [1] "price" "income" 311s attr(,"order") 311s [1] 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, income) 311s attr(,"dataClasses") 311s consump price income 311s "numeric" "numeric" "numeric" 311s 311s $supply 311s consump ~ price + farmPrice + trend 311s attr(,"variables") 311s list(consump, price, farmPrice, trend) 311s attr(,"factors") 311s price farmPrice trend 311s consump 0 0 0 311s price 1 0 0 311s farmPrice 0 1 0 311s trend 0 0 1 311s attr(,"term.labels") 311s [1] "price" "farmPrice" "trend" 311s attr(,"order") 311s [1] 1 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, farmPrice, trend) 311s attr(,"dataClasses") 311s consump price farmPrice trend 311s "numeric" "numeric" "numeric" "numeric" 311s 311s > terms( fitsur3$eq[[ 2 ]] ) 311s consump ~ price + farmPrice + trend 311s attr(,"variables") 311s list(consump, price, farmPrice, trend) 311s attr(,"factors") 311s price farmPrice trend 311s consump 0 0 0 311s price 1 0 0 311s farmPrice 0 1 0 311s trend 0 0 1 311s attr(,"term.labels") 311s [1] "price" "farmPrice" "trend" 311s attr(,"order") 311s [1] 1 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, farmPrice, trend) 311s attr(,"dataClasses") 311s consump price farmPrice trend 311s "numeric" "numeric" "numeric" "numeric" 311s > 311s > terms( fitsur3w ) 311s $demand 311s consump ~ price + income 311s attr(,"variables") 311s list(consump, price, income) 311s attr(,"factors") 311s price income 311s consump 0 0 311s price 1 0 311s income 0 1 311s attr(,"term.labels") 311s [1] "price" "income" 311s attr(,"order") 311s [1] 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, income) 311s attr(,"dataClasses") 311s consump price income 311s "numeric" "numeric" "numeric" 311s 311s $supply 311s consump ~ price + farmPrice + trend 311s attr(,"variables") 311s list(consump, price, farmPrice, trend) 311s attr(,"factors") 311s price farmPrice trend 311s consump 0 0 0 311s price 1 0 0 311s farmPrice 0 1 0 311s trend 0 0 1 311s attr(,"term.labels") 311s [1] "price" "farmPrice" "trend" 311s attr(,"order") 311s [1] 1 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, farmPrice, trend) 311s attr(,"dataClasses") 311s consump price farmPrice trend 311s "numeric" "numeric" "numeric" "numeric" 311s 311s > terms( fitsur3w$eq[[ 2 ]] ) 311s consump ~ price + farmPrice + trend 311s attr(,"variables") 311s list(consump, price, farmPrice, trend) 311s attr(,"factors") 311s price farmPrice trend 311s consump 0 0 0 311s price 1 0 0 311s farmPrice 0 1 0 311s trend 0 0 1 311s attr(,"term.labels") 311s [1] "price" "farmPrice" "trend" 311s attr(,"order") 311s [1] 1 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, farmPrice, trend) 311s attr(,"dataClasses") 311s consump price farmPrice trend 311s "numeric" "numeric" "numeric" "numeric" 311s > 311s > terms( fitsur4r3 ) 311s $demand 311s consump ~ price + income 311s attr(,"variables") 311s list(consump, price, income) 311s attr(,"factors") 311s price income 311s consump 0 0 311s price 1 0 311s income 0 1 311s attr(,"term.labels") 311s [1] "price" "income" 311s attr(,"order") 311s [1] 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, income) 311s attr(,"dataClasses") 311s consump price income 311s "numeric" "numeric" "numeric" 311s 311s $supply 311s consump ~ price + farmPrice + trend 311s attr(,"variables") 311s list(consump, price, farmPrice, trend) 311s attr(,"factors") 311s price farmPrice trend 311s consump 0 0 0 311s price 1 0 0 311s farmPrice 0 1 0 311s trend 0 0 1 311s attr(,"term.labels") 311s [1] "price" "farmPrice" "trend" 311s attr(,"order") 311s [1] 1 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, farmPrice, trend) 311s attr(,"dataClasses") 311s consump price farmPrice trend 311s "numeric" "numeric" "numeric" "numeric" 311s 311s > terms( fitsur4r3$eq[[ 1 ]] ) 311s consump ~ price + income 311s attr(,"variables") 311s list(consump, price, income) 311s attr(,"factors") 311s price income 311s consump 0 0 311s price 1 0 311s income 0 1 311s attr(,"term.labels") 311s [1] "price" "income" 311s attr(,"order") 311s [1] 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, income) 311s attr(,"dataClasses") 311s consump price income 311s "numeric" "numeric" "numeric" 311s > 311s > terms( fitsur4we ) 311s $demand 311s consump ~ price + income 311s attr(,"variables") 311s list(consump, price, income) 311s attr(,"factors") 311s price income 311s consump 0 0 311s price 1 0 311s income 0 1 311s attr(,"term.labels") 311s [1] "price" "income" 311s attr(,"order") 311s [1] 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, income) 311s attr(,"dataClasses") 311s consump price income 311s "numeric" "numeric" "numeric" 311s 311s $supply 311s consump ~ price + farmPrice + trend 311s attr(,"variables") 311s list(consump, price, farmPrice, trend) 311s attr(,"factors") 311s price farmPrice trend 311s consump 0 0 0 311s price 1 0 0 311s farmPrice 0 1 0 311s trend 0 0 1 311s attr(,"term.labels") 311s [1] "price" "farmPrice" "trend" 311s attr(,"order") 311s [1] 1 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, farmPrice, trend) 311s attr(,"dataClasses") 311s consump price farmPrice trend 311s "numeric" "numeric" "numeric" "numeric" 311s 311s > terms( fitsur4we$eq[[ 1 ]] ) 311s consump ~ price + income 311s attr(,"variables") 311s list(consump, price, income) 311s attr(,"factors") 311s price income 311s consump 0 0 311s price 1 0 311s income 0 1 311s attr(,"term.labels") 311s [1] "price" "income" 311s attr(,"order") 311s [1] 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, income) 311s attr(,"dataClasses") 311s consump price income 311s "numeric" "numeric" "numeric" 311s > 311s > terms( fitsur5 ) 311s $demand 311s consump ~ price + income 311s attr(,"variables") 311s list(consump, price, income) 311s attr(,"factors") 311s price income 311s consump 0 0 311s price 1 0 311s income 0 1 311s attr(,"term.labels") 311s [1] "price" "income" 311s attr(,"order") 311s [1] 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, income) 311s attr(,"dataClasses") 311s consump price income 311s "numeric" "numeric" "numeric" 311s 311s $supply 311s consump ~ price + farmPrice + trend 311s attr(,"variables") 311s list(consump, price, farmPrice, trend) 311s attr(,"factors") 311s price farmPrice trend 311s consump 0 0 0 311s price 1 0 0 311s farmPrice 0 1 0 311s trend 0 0 1 311s attr(,"term.labels") 311s [1] "price" "farmPrice" "trend" 311s attr(,"order") 311s [1] 1 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, farmPrice, trend) 311s attr(,"dataClasses") 311s consump price farmPrice trend 311s "numeric" "numeric" "numeric" "numeric" 311s 311s > terms( fitsur5$eq[[ 2 ]] ) 311s consump ~ price + farmPrice + trend 311s attr(,"variables") 311s list(consump, price, farmPrice, trend) 311s attr(,"factors") 311s price farmPrice trend 311s consump 0 0 0 311s price 1 0 0 311s farmPrice 0 1 0 311s trend 0 0 1 311s attr(,"term.labels") 311s [1] "price" "farmPrice" "trend" 311s attr(,"order") 311s [1] 1 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, farmPrice, trend) 311s attr(,"dataClasses") 311s consump price farmPrice trend 311s "numeric" "numeric" "numeric" "numeric" 311s > 311s > terms( fitsuri1r3 ) 311s $demand 311s consump ~ price + income 311s attr(,"variables") 311s list(consump, price, income) 311s attr(,"factors") 311s price income 311s consump 0 0 311s price 1 0 311s income 0 1 311s attr(,"term.labels") 311s [1] "price" "income" 311s attr(,"order") 311s [1] 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, income) 311s attr(,"dataClasses") 311s consump price income 311s "numeric" "numeric" "numeric" 311s 311s $supply 311s price ~ income + farmPrice + trend 311s attr(,"variables") 311s list(price, income, farmPrice, trend) 311s attr(,"factors") 311s income farmPrice trend 311s price 0 0 0 311s income 1 0 0 311s farmPrice 0 1 0 311s trend 0 0 1 311s attr(,"term.labels") 311s [1] "income" "farmPrice" "trend" 311s attr(,"order") 311s [1] 1 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(price, income, farmPrice, trend) 311s attr(,"dataClasses") 311s price income farmPrice trend 311s "numeric" "numeric" "numeric" "numeric" 311s 311s > terms( fitsuri1r3$eq[[ 1 ]] ) 311s consump ~ price + income 311s attr(,"variables") 311s list(consump, price, income) 311s attr(,"factors") 311s price income 311s consump 0 0 311s price 1 0 311s income 0 1 311s attr(,"term.labels") 311s [1] "price" "income" 311s attr(,"order") 311s [1] 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, income) 311s attr(,"dataClasses") 311s consump price income 311s "numeric" "numeric" "numeric" 311s > 311s > terms( fitsuri2 ) 311s $demand 311s consump ~ price + income 311s attr(,"variables") 311s list(consump, price, income) 311s attr(,"factors") 311s price income 311s consump 0 0 311s price 1 0 311s income 0 1 311s attr(,"term.labels") 311s [1] "price" "income" 311s attr(,"order") 311s [1] 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, income) 311s attr(,"dataClasses") 311s consump price income 311s "numeric" "numeric" "numeric" 311s 311s $supply 311s price ~ income + farmPrice + trend 311s attr(,"variables") 311s list(price, income, farmPrice, trend) 311s attr(,"factors") 311s income farmPrice trend 311s price 0 0 0 311s income 1 0 0 311s farmPrice 0 1 0 311s trend 0 0 1 311s attr(,"term.labels") 311s [1] "income" "farmPrice" "trend" 311s attr(,"order") 311s [1] 1 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(price, income, farmPrice, trend) 311s attr(,"dataClasses") 311s price income farmPrice trend 311s "numeric" "numeric" "numeric" "numeric" 311s 311s > terms( fitsuri2$eq[[ 2 ]] ) 311s price ~ income + farmPrice + trend 311s attr(,"variables") 311s list(price, income, farmPrice, trend) 311s attr(,"factors") 311s income farmPrice trend 311s price 0 0 0 311s income 1 0 0 311s farmPrice 0 1 0 311s trend 0 0 1 311s attr(,"term.labels") 311s [1] "income" "farmPrice" "trend" 311s attr(,"order") 311s [1] 1 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(price, income, farmPrice, trend) 311s attr(,"dataClasses") 311s price income farmPrice trend 311s "numeric" "numeric" "numeric" "numeric" 311s > 311s > terms( fitsuri3e ) 311s $demand 311s consump ~ price + income 311s attr(,"variables") 311s list(consump, price, income) 311s attr(,"factors") 311s price income 311s consump 0 0 311s price 1 0 311s income 0 1 311s attr(,"term.labels") 311s [1] "price" "income" 311s attr(,"order") 311s [1] 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, income) 311s attr(,"dataClasses") 311s consump price income 311s "numeric" "numeric" "numeric" 311s 311s $supply 311s price ~ income + farmPrice + trend 311s attr(,"variables") 311s list(price, income, farmPrice, trend) 311s attr(,"factors") 311s income farmPrice trend 311s price 0 0 0 311s income 1 0 0 311s farmPrice 0 1 0 311s trend 0 0 1 311s attr(,"term.labels") 311s [1] "income" "farmPrice" "trend" 311s attr(,"order") 311s [1] 1 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(price, income, farmPrice, trend) 311s attr(,"dataClasses") 311s price income farmPrice trend 311s "numeric" "numeric" "numeric" "numeric" 311s 311s > terms( fitsuri3e$eq[[ 1 ]] ) 311s consump ~ price + income 311s attr(,"variables") 311s list(consump, price, income) 311s attr(,"factors") 311s price income 311s consump 0 0 311s price 1 0 311s income 0 1 311s attr(,"term.labels") 311s [1] "price" "income" 311s attr(,"order") 311s [1] 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, income) 311s attr(,"dataClasses") 311s consump price income 311s "numeric" "numeric" "numeric" 311s > 311s > terms( fitsurio4 ) 311s $demand 311s consump ~ price + income 311s attr(,"variables") 311s list(consump, price, income) 311s attr(,"factors") 311s price income 311s consump 0 0 311s price 1 0 311s income 0 1 311s attr(,"term.labels") 311s [1] "price" "income" 311s attr(,"order") 311s [1] 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, income) 311s attr(,"dataClasses") 311s consump price income 311s "numeric" "numeric" "numeric" 311s 311s $supply 311s consump ~ price + farmPrice + trend 311s attr(,"variables") 311s list(consump, price, farmPrice, trend) 311s attr(,"factors") 311s price farmPrice trend 311s consump 0 0 0 311s price 1 0 0 311s farmPrice 0 1 0 311s trend 0 0 1 311s attr(,"term.labels") 311s [1] "price" "farmPrice" "trend" 311s attr(,"order") 311s [1] 1 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, farmPrice, trend) 311s attr(,"dataClasses") 311s consump price farmPrice trend 311s "numeric" "numeric" "numeric" "numeric" 311s 311s > terms( fitsurio4$eq[[ 2 ]] ) 311s consump ~ price + farmPrice + trend 311s attr(,"variables") 311s list(consump, price, farmPrice, trend) 311s attr(,"factors") 311s price farmPrice trend 311s consump 0 0 0 311s price 1 0 0 311s farmPrice 0 1 0 311s trend 0 0 1 311s attr(,"term.labels") 311s [1] "price" "farmPrice" "trend" 311s attr(,"order") 311s [1] 1 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, farmPrice, trend) 311s attr(,"dataClasses") 311s consump price farmPrice trend 311s "numeric" "numeric" "numeric" "numeric" 311s > terms( fitsuri4 ) 311s $demand 311s consump ~ price + income 311s attr(,"variables") 311s list(consump, price, income) 311s attr(,"factors") 311s price income 311s consump 0 0 311s price 1 0 311s income 0 1 311s attr(,"term.labels") 311s [1] "price" "income" 311s attr(,"order") 311s [1] 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, income) 311s attr(,"dataClasses") 311s consump price income 311s "numeric" "numeric" "numeric" 311s 311s $supply 311s price ~ income + farmPrice + trend 311s attr(,"variables") 311s list(price, income, farmPrice, trend) 311s attr(,"factors") 311s income farmPrice trend 311s price 0 0 0 311s income 1 0 0 311s farmPrice 0 1 0 311s trend 0 0 1 311s attr(,"term.labels") 311s [1] "income" "farmPrice" "trend" 311s attr(,"order") 311s [1] 1 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(price, income, farmPrice, trend) 311s attr(,"dataClasses") 311s price income farmPrice trend 311s "numeric" "numeric" "numeric" "numeric" 311s 311s > terms( fitsuri4$eq[[ 2 ]] ) 311s price ~ income + farmPrice + trend 311s attr(,"variables") 311s list(price, income, farmPrice, trend) 311s attr(,"factors") 311s income farmPrice trend 311s price 0 0 0 311s income 1 0 0 311s farmPrice 0 1 0 311s trend 0 0 1 311s attr(,"term.labels") 311s [1] "income" "farmPrice" "trend" 311s attr(,"order") 311s [1] 1 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(price, income, farmPrice, trend) 311s attr(,"dataClasses") 311s price income farmPrice trend 311s "numeric" "numeric" "numeric" "numeric" 311s > 311s > terms( fitsurio5r2 ) 311s $demand 311s consump ~ price + income 311s attr(,"variables") 311s list(consump, price, income) 311s attr(,"factors") 311s price income 311s consump 0 0 311s price 1 0 311s income 0 1 311s attr(,"term.labels") 311s [1] "price" "income" 311s attr(,"order") 311s [1] 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, income) 311s attr(,"dataClasses") 311s consump price income 311s "numeric" "numeric" "numeric" 311s 311s $supply 311s consump ~ price + farmPrice + trend 311s attr(,"variables") 311s list(consump, price, farmPrice, trend) 311s attr(,"factors") 311s price farmPrice trend 311s consump 0 0 0 311s price 1 0 0 311s farmPrice 0 1 0 311s trend 0 0 1 311s attr(,"term.labels") 311s [1] "price" "farmPrice" "trend" 311s attr(,"order") 311s [1] 1 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, farmPrice, trend) 311s attr(,"dataClasses") 311s consump price farmPrice trend 311s "numeric" "numeric" "numeric" "numeric" 311s 311s > terms( fitsurio5r2$eq[[ 1 ]] ) 311s consump ~ price + income 311s attr(,"variables") 311s list(consump, price, income) 311s attr(,"factors") 311s price income 311s consump 0 0 311s price 1 0 311s income 0 1 311s attr(,"term.labels") 311s [1] "price" "income" 311s attr(,"order") 311s [1] 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, income) 311s attr(,"dataClasses") 311s consump price income 311s "numeric" "numeric" "numeric" 311s > terms( fitsuri5r2 ) 311s $demand 311s consump ~ price + income 311s attr(,"variables") 311s list(consump, price, income) 311s attr(,"factors") 311s price income 311s consump 0 0 311s price 1 0 311s income 0 1 311s attr(,"term.labels") 311s [1] "price" "income" 311s attr(,"order") 311s [1] 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, income) 311s attr(,"dataClasses") 311s consump price income 311s "numeric" "numeric" "numeric" 311s 311s $supply 311s price ~ income + farmPrice + trend 311s attr(,"variables") 311s list(price, income, farmPrice, trend) 311s attr(,"factors") 311s income farmPrice trend 311s price 0 0 0 311s income 1 0 0 311s farmPrice 0 1 0 311s trend 0 0 1 311s attr(,"term.labels") 311s [1] "income" "farmPrice" "trend" 311s attr(,"order") 311s [1] 1 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(price, income, farmPrice, trend) 311s attr(,"dataClasses") 311s price income farmPrice trend 311s "numeric" "numeric" "numeric" "numeric" 311s 311s > terms( fitsuri5r2$eq[[ 1 ]] ) 311s consump ~ price + income 311s attr(,"variables") 311s list(consump, price, income) 311s attr(,"factors") 311s price income 311s consump 0 0 311s price 1 0 311s income 0 1 311s attr(,"term.labels") 311s [1] "price" "income" 311s attr(,"order") 311s [1] 1 1 311s attr(,"intercept") 311s [1] 1 311s attr(,"response") 311s [1] 1 311s attr(,".Environment") 311s 311s attr(,"predvars") 311s list(consump, price, income) 311s attr(,"dataClasses") 311s consump price income 311s "numeric" "numeric" "numeric" 311s > 311s > 311s > ## **************** estfun ************************ 311s > library( "sandwich" ) 311s > 311s > estfun( fitsur1 ) 311s demand_(Intercept) demand_price demand_income supply_(Intercept) 311s demand_1 0.9083 91.12 79.38 -0.6496 311s demand_2 -0.7320 -76.32 -71.44 0.5235 311s demand_3 3.2023 331.23 309.66 -2.2902 311s demand_4 2.1435 224.00 210.49 -1.5330 311s demand_5 2.7516 269.66 274.61 -1.9679 311s demand_6 1.7015 169.22 171.00 -1.2169 311s demand_7 2.2068 223.03 227.74 -1.5783 311s demand_8 -3.5946 -376.58 -387.50 2.5708 311s demand_9 -1.6348 -157.67 -157.92 1.1692 311s demand_10 2.7103 247.26 240.95 -1.9384 311s demand_11 -0.8810 -82.01 -66.16 0.6301 311s demand_12 -3.4554 -341.39 -265.72 2.4712 311s demand_13 -2.2246 -228.93 -188.20 1.5910 311s demand_14 -0.5461 -53.93 -49.48 0.3906 311s demand_15 2.4619 234.17 253.82 -1.7607 311s demand_16 -4.3873 -431.94 -461.11 3.1378 311s demand_17 -0.9942 -85.99 -95.84 0.7110 311s demand_18 -2.5012 -260.17 -261.13 1.7888 311s demand_19 2.5805 272.93 285.66 -1.8455 311s demand_20 0.2846 32.30 36.17 -0.2036 311s supply_1 -0.4396 -44.11 -38.42 0.3959 311s supply_2 -0.0184 -1.92 -1.79 0.0166 311s supply_3 -2.5916 -268.06 -250.60 2.3337 311s supply_4 -1.7132 -179.04 -168.24 1.5428 311s supply_5 -2.3049 -225.88 -230.03 2.0756 311s supply_6 -1.3780 -137.06 -138.49 1.2410 311s supply_7 -2.0596 -208.16 -212.55 1.8547 311s supply_8 3.4200 358.29 368.68 -3.0798 311s supply_9 1.9576 188.80 189.10 -1.7628 311s supply_10 -2.3620 -215.48 -209.98 2.1270 311s supply_11 1.1852 110.32 89.01 -1.0673 311s supply_12 2.6183 258.69 201.34 -2.3578 311s supply_13 1.9874 204.52 168.14 -1.7897 311s supply_14 -0.1072 -10.59 -9.72 0.0966 311s supply_15 -2.6839 -255.29 -276.71 2.4169 311s supply_16 3.8259 376.66 402.10 -3.4452 311s supply_17 0.5270 45.59 50.80 -0.4746 311s supply_18 3.0021 312.27 313.42 -2.7035 311s supply_19 -2.0184 -213.48 -223.44 1.8176 311s supply_20 -0.8466 -96.08 -107.60 0.7623 311s supply_price supply_farmPrice supply_trend 311s demand_1 -65.17 -63.66 -0.6496 311s demand_2 54.58 51.88 1.0470 311s demand_3 -236.89 -226.96 -6.8707 311s demand_4 -160.20 -150.38 -6.1319 311s demand_5 -192.86 -218.05 -9.8397 311s demand_6 -121.02 -131.66 -7.3012 311s demand_7 -159.51 -166.67 -11.0480 311s demand_8 269.33 282.28 20.5665 311s demand_9 112.76 127.09 10.5227 311s demand_10 -176.84 -195.00 -19.3840 311s demand_11 58.65 51.04 6.9309 311s demand_12 244.16 169.53 29.6547 311s demand_13 163.73 112.80 20.6833 311s demand_14 38.57 31.79 5.4681 311s demand_15 -167.48 -180.12 -26.4104 311s demand_16 308.92 329.47 50.2044 311s demand_17 61.50 78.57 12.0871 311s demand_18 186.07 165.47 32.1991 311s demand_19 -195.20 -164.81 -35.0650 311s demand_20 -23.10 -18.93 -4.0710 311s supply_1 39.72 38.80 0.3959 311s supply_2 1.73 1.64 0.0331 311s supply_3 241.39 231.27 7.0012 311s supply_4 161.23 151.34 6.1710 311s supply_5 203.41 229.98 10.3781 311s supply_6 123.42 134.27 7.4457 311s supply_7 187.45 195.86 12.9829 311s supply_8 -322.64 -338.16 -24.6380 311s supply_9 -170.02 -191.62 -15.8653 311s supply_10 194.04 213.98 21.2699 311s supply_11 -99.35 -86.45 -11.7402 311s supply_12 -232.95 -161.74 -28.2933 311s supply_13 -184.18 -126.89 -23.2663 311s supply_14 9.54 7.86 1.3521 311s supply_15 229.90 247.25 36.2539 311s supply_16 -339.19 -361.75 -55.1237 311s supply_17 -41.05 -52.44 -8.0678 311s supply_18 -281.20 -250.07 -48.6623 311s supply_19 192.24 162.31 34.5341 311s supply_20 86.52 70.90 15.2466 311s > round( colSums( estfun( fitsur1 ) ), digits = 7 ) 311s demand_(Intercept) demand_price demand_income supply_(Intercept) 311s 0 0 0 0 311s supply_price supply_farmPrice supply_trend 311s 0 0 0 311s > 311s > estfun( fitsur1e2 ) 311s demand_(Intercept) demand_price demand_income supply_(Intercept) 311s demand_1 1.09034 109.386 95.295 -0.80605 311s demand_2 -1.05992 -110.511 -103.448 0.78356 311s demand_3 4.28760 443.488 414.611 -3.16968 311s demand_4 2.85253 298.107 280.119 -2.10878 311s demand_5 3.80226 372.625 379.466 -2.81088 311s demand_6 2.36197 234.912 237.378 -1.74612 311s demand_7 3.06088 309.351 315.883 -2.26280 311s demand_8 -4.81806 -504.754 -519.386 3.56182 311s demand_9 -2.17915 -210.170 -210.506 1.61097 311s demand_10 3.70159 337.689 329.071 -2.73646 311s demand_11 -1.39799 -130.132 -104.989 1.03349 311s demand_12 -4.96091 -490.143 -381.494 3.66743 311s demand_13 -3.24623 -334.063 -274.631 2.39983 311s demand_14 -0.81794 -80.776 -74.105 0.60467 311s demand_15 3.49861 332.784 360.707 -2.58640 311s demand_16 -5.83443 -574.406 -613.199 4.31320 311s demand_17 -1.15650 -100.035 -111.487 0.85496 311s demand_18 -3.36717 -350.239 -351.532 2.48923 311s demand_19 3.59870 380.631 398.376 -2.66040 311s demand_20 0.58382 66.257 74.203 -0.43160 311s supply_1 -0.54811 -54.988 -47.905 0.47751 311s supply_2 0.00819 0.854 0.799 -0.00713 311s supply_3 -3.61236 -373.644 -349.315 3.14703 311s supply_4 -2.38151 -248.882 -233.865 2.07474 311s supply_5 -3.32295 -325.653 -331.631 2.89490 311s supply_6 -2.00948 -199.855 -201.953 1.75063 311s supply_7 -2.95622 -298.773 -305.081 2.57541 311s supply_8 4.67628 489.901 504.103 -4.07390 311s supply_9 2.65680 256.238 256.647 -2.31456 311s supply_10 -3.31875 -302.763 -295.037 2.89124 311s supply_11 1.84429 171.676 138.506 -1.60672 311s supply_12 3.95003 390.267 303.757 -3.44120 311s supply_13 3.01568 310.338 255.127 -2.62722 311s supply_14 -0.02452 -2.421 -2.221 0.02136 311s supply_15 -3.84791 -366.010 -396.720 3.35224 311s supply_16 5.24831 516.701 551.597 -4.57224 311s supply_17 0.59732 51.667 57.582 -0.52037 311s supply_18 4.17631 434.404 436.007 -3.63834 311s supply_19 -2.86060 -302.562 -316.668 2.49211 311s supply_20 -1.29079 -146.492 -164.060 1.12452 311s supply_price supply_farmPrice supply_trend 311s demand_1 -80.865 -78.993 -0.8060 311s demand_2 81.697 77.651 1.5671 311s demand_3 -327.856 -314.115 -9.5090 311s demand_4 -220.380 -206.871 -8.4351 311s demand_5 -275.469 -311.446 -14.0544 311s demand_6 -173.662 -188.931 -10.4767 311s demand_7 -228.692 -238.952 -15.8396 311s demand_8 373.147 391.088 28.4946 311s demand_9 155.372 175.113 14.4987 311s demand_10 -249.642 -275.288 -27.3646 311s demand_11 96.202 83.712 11.3683 311s demand_12 362.346 251.586 44.0092 311s demand_13 246.962 170.148 31.1978 311s demand_14 59.715 49.220 8.4654 311s demand_15 -246.016 -264.589 -38.7961 311s demand_16 424.638 452.886 69.0111 311s demand_17 73.953 94.473 14.5344 311s demand_18 258.920 230.254 44.8061 311s demand_19 -281.388 -237.573 -50.5475 311s demand_20 -48.982 -40.138 -8.6319 311s supply_1 47.905 46.796 0.4775 311s supply_2 -0.744 -0.707 -0.0143 311s supply_3 325.513 311.871 9.4411 311s supply_4 216.822 203.532 8.2989 311s supply_5 283.704 320.755 14.4745 311s supply_6 174.111 189.418 10.5038 311s supply_7 260.286 271.963 18.0279 311s supply_8 -426.794 -447.314 -32.5912 311s supply_9 -223.230 -251.593 -20.8310 311s supply_10 263.762 290.859 28.9124 311s supply_11 -149.561 -130.144 -17.6739 311s supply_12 -339.994 -236.066 -41.2944 311s supply_13 -270.361 -186.270 -34.1538 311s supply_14 2.109 1.739 0.2990 311s supply_15 318.862 342.934 50.2836 311s supply_16 -450.142 -480.085 -73.1559 311s supply_17 -45.011 -57.501 -8.8464 311s supply_18 -378.445 -336.546 -65.4901 311s supply_19 263.588 222.545 47.3500 311s supply_20 127.621 104.580 22.4903 311s > round( colSums( estfun( fitsur1e2 ) ), digits = 7 ) 311s demand_(Intercept) demand_price demand_income supply_(Intercept) 311s 0 0 0 0 311s supply_price supply_farmPrice supply_trend 311s 0 0 0 311s > 311s > estfun( fitsur1r3 ) 311s demand_(Intercept) demand_price demand_income supply_(Intercept) 311s demand_1 1.07229 107.575 93.718 -0.79049 311s demand_2 -1.02096 -106.450 -99.646 0.75265 311s demand_3 4.16424 430.729 402.682 -3.06988 311s demand_4 2.77231 289.723 272.240 -2.04374 311s demand_5 3.68037 360.680 367.301 -2.71316 311s demand_6 2.28513 227.270 229.656 -1.68460 311s demand_7 2.96157 299.314 305.634 -2.18327 311s demand_8 -4.67889 -490.175 -504.385 3.44927 311s demand_9 -2.11749 -204.223 -204.549 1.56101 311s demand_10 3.58740 327.271 318.920 -2.64463 311s demand_11 -1.33464 -124.235 -100.231 0.98389 311s demand_12 -4.78276 -472.541 -367.794 3.52584 311s demand_13 -3.12449 -321.535 -264.332 2.30337 311s demand_14 -0.78522 -77.545 -71.141 0.57886 311s demand_15 3.37652 321.171 348.119 -2.48917 311s demand_16 -5.67080 -558.296 -596.001 4.18051 311s demand_17 -1.14172 -98.757 -110.062 0.84168 311s demand_18 -3.26836 -339.962 -341.217 2.40943 311s demand_19 3.47995 368.071 385.231 -2.56542 311s demand_20 0.54555 61.914 69.339 -0.40218 311s supply_1 -0.53834 -54.008 -47.051 0.47031 311s supply_2 0.00335 0.349 0.327 -0.00293 311s supply_3 -3.49682 -361.694 -338.143 3.05492 311s supply_4 -2.30621 -241.013 -226.470 2.01477 311s supply_5 -3.20507 -314.100 -319.866 2.80004 311s supply_6 -1.93606 -192.553 -194.574 1.69139 311s supply_7 -2.85248 -288.289 -294.376 2.49200 311s supply_8 4.53460 475.059 488.830 -3.96155 311s supply_9 2.57840 248.676 249.073 -2.25256 311s supply_10 -3.20906 -292.756 -285.286 2.80352 311s supply_11 1.76494 164.289 132.547 -1.54190 311s supply_12 3.79168 374.622 291.580 -3.31251 311s supply_13 2.89330 297.744 244.773 -2.52766 311s supply_14 -0.03625 -3.580 -3.284 0.03167 311s supply_15 -3.71220 -353.101 -382.728 3.24307 311s supply_16 5.08854 500.972 534.805 -4.44548 311s supply_17 0.59312 51.303 57.176 -0.51816 311s supply_18 4.04346 420.584 422.137 -3.53247 311s supply_19 -2.76240 -292.176 -305.797 2.41330 311s supply_20 -1.23648 -140.329 -157.157 1.08023 311s supply_price supply_farmPrice supply_trend 311s demand_1 -79.304 -77.47 -0.79049 311s demand_2 78.475 74.59 1.50531 311s demand_3 -317.533 -304.22 -9.20963 311s demand_4 -213.583 -200.49 -8.17496 311s demand_5 -265.893 -300.62 -13.56581 311s demand_6 -167.543 -182.27 -10.10759 311s demand_7 -220.654 -230.55 -15.28289 311s demand_8 361.356 378.73 27.59420 311s demand_9 150.553 169.68 14.04907 311s demand_10 -241.264 -266.05 -26.44627 311s demand_11 91.586 79.70 10.82281 311s demand_12 348.357 241.87 42.31014 311s demand_13 237.035 163.31 29.94383 311s demand_14 57.166 47.12 8.10410 311s demand_15 -236.767 -254.64 -37.33751 311s demand_16 411.575 438.95 66.88809 311s demand_17 72.803 93.01 14.30850 311s demand_18 250.619 222.87 43.36977 311s demand_19 -271.341 -229.09 -48.74290 311s demand_20 -45.643 -37.40 -8.04353 311s supply_1 47.183 46.09 0.47031 311s supply_2 -0.305 -0.29 -0.00585 311s supply_3 315.985 302.74 9.16476 311s supply_4 210.555 197.65 8.05908 311s supply_5 274.406 310.24 14.00018 311s supply_6 168.219 183.01 10.14835 311s supply_7 251.857 263.16 17.44401 311s supply_8 -415.024 -434.98 -31.69241 311s supply_9 -217.250 -244.85 -20.27300 311s supply_10 255.760 282.03 28.03523 311s supply_11 -143.528 -124.89 -16.96088 311s supply_12 -327.279 -227.24 -39.75013 311s supply_13 -260.117 -179.21 -32.85963 311s supply_14 3.128 2.58 0.44339 311s supply_15 308.478 331.77 48.64611 311s supply_16 -437.662 -466.78 -71.12773 311s supply_17 -44.820 -57.26 -8.80876 311s supply_18 -367.434 -326.75 -63.58452 311s supply_19 255.253 215.51 45.85274 311s supply_20 122.595 100.46 21.60450 311s > round( colSums( estfun( fitsur1r3 ) ), digits = 7 ) 311s demand_(Intercept) demand_price demand_income supply_(Intercept) 311s 0 0 0 0 311s supply_price supply_farmPrice supply_trend 311s 0 0 0 311s > 311s > estfun( fitsur1w ) 311s demand_(Intercept) demand_price demand_income supply_(Intercept) 311s demand_1 0.9083 91.12 79.38 -0.6496 311s demand_2 -0.7320 -76.32 -71.44 0.5235 311s demand_3 3.2023 331.23 309.66 -2.2902 311s demand_4 2.1435 224.00 210.49 -1.5330 311s demand_5 2.7516 269.66 274.61 -1.9679 311s demand_6 1.7015 169.22 171.00 -1.2169 311s demand_7 2.2068 223.03 227.74 -1.5783 311s demand_8 -3.5946 -376.58 -387.50 2.5708 311s demand_9 -1.6348 -157.67 -157.92 1.1692 311s demand_10 2.7103 247.26 240.95 -1.9384 311s demand_11 -0.8810 -82.01 -66.16 0.6301 311s demand_12 -3.4554 -341.39 -265.72 2.4712 311s demand_13 -2.2246 -228.93 -188.20 1.5910 311s demand_14 -0.5461 -53.93 -49.48 0.3906 311s demand_15 2.4619 234.17 253.82 -1.7607 311s demand_16 -4.3873 -431.94 -461.11 3.1378 311s demand_17 -0.9942 -85.99 -95.84 0.7110 311s demand_18 -2.5012 -260.17 -261.13 1.7888 311s demand_19 2.5805 272.93 285.66 -1.8455 311s demand_20 0.2846 32.30 36.17 -0.2036 311s supply_1 -0.4396 -44.11 -38.42 0.3959 311s supply_2 -0.0184 -1.92 -1.79 0.0166 311s supply_3 -2.5916 -268.06 -250.60 2.3337 311s supply_4 -1.7132 -179.04 -168.24 1.5428 311s supply_5 -2.3049 -225.88 -230.03 2.0756 311s supply_6 -1.3780 -137.06 -138.49 1.2410 311s supply_7 -2.0596 -208.16 -212.55 1.8547 311s supply_8 3.4200 358.29 368.68 -3.0798 311s supply_9 1.9576 188.80 189.10 -1.7628 311s supply_10 -2.3620 -215.48 -209.98 2.1270 311s supply_11 1.1852 110.32 89.01 -1.0673 311s supply_12 2.6183 258.69 201.34 -2.3578 311s supply_13 1.9874 204.52 168.14 -1.7897 311s supply_14 -0.1072 -10.59 -9.72 0.0966 311s supply_15 -2.6839 -255.29 -276.71 2.4169 311s supply_16 3.8259 376.66 402.10 -3.4452 311s supply_17 0.5270 45.59 50.80 -0.4746 311s supply_18 3.0021 312.27 313.42 -2.7035 311s supply_19 -2.0184 -213.48 -223.44 1.8176 311s supply_20 -0.8466 -96.08 -107.60 0.7623 311s supply_price supply_farmPrice supply_trend 311s demand_1 -65.17 -63.66 -0.6496 311s demand_2 54.58 51.88 1.0470 311s demand_3 -236.89 -226.96 -6.8707 311s demand_4 -160.20 -150.38 -6.1319 311s demand_5 -192.86 -218.05 -9.8397 311s demand_6 -121.02 -131.66 -7.3012 311s demand_7 -159.51 -166.67 -11.0480 311s demand_8 269.33 282.28 20.5665 311s demand_9 112.76 127.09 10.5227 311s demand_10 -176.84 -195.00 -19.3840 311s demand_11 58.65 51.04 6.9309 311s demand_12 244.16 169.53 29.6547 311s demand_13 163.73 112.80 20.6833 311s demand_14 38.57 31.79 5.4681 311s demand_15 -167.48 -180.12 -26.4104 311s demand_16 308.92 329.47 50.2044 311s demand_17 61.50 78.57 12.0871 311s demand_18 186.07 165.47 32.1991 311s demand_19 -195.20 -164.81 -35.0650 311s demand_20 -23.10 -18.93 -4.0710 311s supply_1 39.72 38.80 0.3959 311s supply_2 1.73 1.64 0.0331 311s supply_3 241.39 231.27 7.0012 311s supply_4 161.23 151.34 6.1710 311s supply_5 203.41 229.98 10.3781 311s supply_6 123.42 134.27 7.4457 311s supply_7 187.45 195.86 12.9829 311s supply_8 -322.64 -338.16 -24.6380 311s supply_9 -170.02 -191.62 -15.8653 311s supply_10 194.04 213.98 21.2699 311s supply_11 -99.35 -86.45 -11.7402 311s supply_12 -232.95 -161.74 -28.2933 311s supply_13 -184.18 -126.89 -23.2663 311s supply_14 9.54 7.86 1.3521 311s supply_15 229.90 247.25 36.2539 311s supply_16 -339.19 -361.75 -55.1237 311s supply_17 -41.05 -52.44 -8.0678 311s supply_18 -281.20 -250.07 -48.6623 311s supply_19 192.24 162.31 34.5341 311s supply_20 86.52 70.90 15.2466 311s > round( colSums( estfun( fitsur1w ) ), digits = 7 ) 311s demand_(Intercept) demand_price demand_income supply_(Intercept) 311s 0 0 0 0 311s supply_price supply_farmPrice supply_trend 311s 0 0 0 311s > 311s > estfun( fitsuri1e ) 311s demand_(Intercept) demand_price demand_income supply_(Intercept) 311s demand_1 0.5467 54.84 47.78 0.5219 311s demand_2 -0.5182 -54.03 -50.58 -0.4947 311s demand_3 1.5799 163.41 152.77 1.5082 311s demand_4 0.9787 102.28 96.11 0.9343 311s demand_5 1.4899 146.02 148.70 1.4224 311s demand_6 0.8875 88.27 89.19 0.8472 311s demand_7 1.0809 109.24 111.55 1.0319 311s demand_8 -2.1165 -221.73 -228.15 -2.0205 311s demand_9 -0.7383 -71.21 -71.32 -0.7049 311s demand_10 1.7668 161.19 157.07 1.6867 311s demand_11 -0.0682 -6.35 -5.12 -0.0651 311s demand_12 -1.6133 -159.40 -124.07 -1.5402 311s demand_13 -1.1570 -119.06 -97.88 -1.1045 311s demand_14 -0.1925 -19.01 -17.44 -0.1838 311s demand_15 1.4026 133.41 144.61 1.3390 311s demand_16 -2.3128 -227.70 -243.08 -2.2080 311s demand_17 -0.0876 -7.58 -8.44 -0.0836 311s demand_18 -1.4924 -155.23 -155.81 -1.4247 311s demand_19 1.0702 113.20 118.47 1.0217 311s demand_20 -0.5064 -57.47 -64.36 -0.4834 311s supply_1 0.1054 10.57 9.21 0.1789 311s supply_2 -0.8882 -92.60 -86.68 -1.5080 311s supply_3 -0.5218 -53.97 -50.46 -0.8859 311s supply_4 -0.2644 -27.63 -25.96 -0.4489 311s supply_5 -0.7666 -75.13 -76.51 -1.3016 311s supply_6 -0.4056 -40.34 -40.77 -0.6887 311s supply_7 -0.8114 -82.00 -83.74 -1.3777 311s supply_8 1.4243 149.22 153.54 2.4183 311s supply_9 1.0270 99.05 99.21 1.7438 311s supply_10 -1.0278 -93.77 -91.37 -1.7451 311s supply_11 0.6336 58.98 47.58 1.0758 311s supply_12 0.2724 26.92 20.95 0.4626 311s supply_13 0.8434 86.79 71.35 1.4319 311s supply_14 -0.7107 -70.19 -64.39 -1.2067 311s supply_15 -1.5343 -145.94 -158.18 -2.6050 311s supply_16 1.1276 111.01 118.51 1.9145 311s supply_17 -0.6907 -59.75 -66.58 -1.1727 311s supply_18 2.2394 232.94 233.79 3.8022 311s supply_19 0.1792 18.96 19.84 0.3043 311s supply_20 -0.2309 -26.21 -29.35 -0.3921 311s supply_income supply_farmPrice supply_trend 311s demand_1 45.61 51.15 0.522 311s demand_2 -48.28 -49.03 -0.989 311s demand_3 145.85 149.47 4.525 311s demand_4 91.75 91.66 3.737 311s demand_5 141.95 157.60 7.112 311s demand_6 85.15 91.67 5.083 311s demand_7 106.49 108.97 7.223 311s demand_8 -217.81 -221.85 -16.164 311s demand_9 -68.09 -76.62 -6.344 311s demand_10 149.95 169.69 16.867 311s demand_11 -4.89 -5.28 -0.717 311s demand_12 -118.44 -105.66 -18.482 311s demand_13 -93.44 -78.31 -14.359 311s demand_14 -16.65 -14.96 -2.573 311s demand_15 138.05 136.98 20.085 311s demand_16 -232.06 -231.84 -35.327 311s demand_17 -8.06 -9.24 -1.421 311s demand_18 -148.74 -131.79 -25.645 311s demand_19 113.10 91.24 19.412 311s demand_20 -61.44 -44.96 -9.668 311s supply_1 15.64 17.53 0.179 311s supply_2 -147.18 -149.44 -3.016 311s supply_3 -85.67 -87.79 -2.658 311s supply_4 -44.08 -44.04 -1.796 311s supply_5 -129.90 -144.21 -6.508 311s supply_6 -69.22 -74.52 -4.132 311s supply_7 -142.17 -145.48 -9.644 311s supply_8 260.69 265.53 19.346 311s supply_9 168.45 189.55 15.694 311s supply_10 -155.14 -175.56 -17.451 311s supply_11 80.79 87.14 11.833 311s supply_12 35.57 31.73 5.551 311s supply_13 121.14 101.52 18.615 311s supply_14 -109.33 -98.23 -16.894 311s supply_15 -268.57 -266.49 -39.075 311s supply_16 201.22 201.03 30.633 311s supply_17 -113.05 -129.59 -19.937 311s supply_18 396.95 351.71 68.440 311s supply_19 33.69 27.18 5.782 311s supply_20 -49.83 -36.46 -7.841 311s > round( colSums( estfun( fitsuri1e ) ), digits = 7 ) 311s demand_(Intercept) demand_price demand_income supply_(Intercept) 311s 0 0 0 0 311s supply_income supply_farmPrice supply_trend 311s 0 0 0 311s > 311s > estfun( fitsuri1wr3 ) 311s demand_(Intercept) demand_price demand_income supply_(Intercept) 311s demand_1 0.5102 51.19 44.59 0.4867 311s demand_2 -0.4886 -50.94 -47.68 -0.4661 311s demand_3 1.4782 152.90 142.94 1.4102 311s demand_4 0.9143 95.55 89.79 0.8722 311s demand_5 1.3982 137.03 139.54 1.3339 311s demand_6 0.8327 82.82 83.69 0.7944 311s demand_7 1.0134 102.42 104.59 0.9668 311s demand_8 -1.9849 -207.94 -213.97 -1.8935 311s demand_9 -0.6897 -66.52 -66.63 -0.6580 311s demand_10 1.6602 151.46 147.60 1.5838 311s demand_11 -0.0636 -5.92 -4.77 -0.0606 311s demand_12 -1.5152 -149.71 -116.52 -1.4455 311s demand_13 -1.0888 -112.05 -92.11 -1.0387 311s demand_14 -0.1809 -17.86 -16.39 -0.1726 311s demand_15 1.3190 125.46 135.99 1.2583 311s demand_16 -2.1651 -213.16 -227.55 -2.0655 311s demand_17 -0.0731 -6.33 -7.05 -0.0698 311s demand_18 -1.4001 -145.63 -146.17 -1.3357 311s demand_19 1.0017 105.95 110.89 0.9556 311s demand_20 -0.4780 -54.25 -60.76 -0.4560 311s supply_1 0.0755 7.57 6.60 0.1193 311s supply_2 -0.8526 -88.90 -83.22 -1.3478 311s supply_3 -0.5074 -52.48 -49.07 -0.8021 311s supply_4 -0.2631 -27.49 -25.83 -0.4159 311s supply_5 -0.7425 -72.77 -74.10 -1.1737 311s supply_6 -0.3998 -39.77 -40.18 -0.6320 311s supply_7 -0.7750 -78.33 -79.98 -1.2251 311s supply_8 1.3178 138.06 142.06 2.0831 311s supply_9 0.9476 91.39 91.54 1.4979 311s supply_10 -0.9683 -88.34 -86.08 -1.5306 311s supply_11 0.6060 56.40 45.51 0.9578 311s supply_12 0.2813 27.79 21.63 0.4446 311s supply_13 0.8170 84.07 69.12 1.2914 311s supply_14 -0.6451 -63.71 -58.44 -1.0197 311s supply_15 -1.4315 -136.17 -147.59 -2.2629 311s supply_16 1.0615 104.50 111.56 1.6779 311s supply_17 -0.6453 -55.82 -62.21 -1.0200 311s supply_18 2.1183 220.33 221.15 3.3484 311s supply_19 0.1946 20.58 21.54 0.3076 311s supply_20 -0.1888 -21.42 -23.99 -0.2984 311s supply_income supply_farmPrice supply_trend 311s demand_1 42.54 47.70 0.487 311s demand_2 -45.49 -46.19 -0.932 311s demand_3 136.37 139.75 4.231 311s demand_4 85.65 85.57 3.489 311s demand_5 133.12 147.79 6.669 311s demand_6 79.84 85.95 4.766 311s demand_7 99.77 102.09 6.768 311s demand_8 -204.12 -207.91 -15.148 311s demand_9 -63.56 -71.52 -5.922 311s demand_10 140.80 159.34 15.838 311s demand_11 -4.55 -4.91 -0.667 311s demand_12 -111.16 -99.16 -17.346 311s demand_13 -87.88 -73.64 -13.503 311s demand_14 -15.63 -14.05 -2.416 311s demand_15 129.73 128.72 18.874 311s demand_16 -217.08 -216.88 -33.048 311s demand_17 -6.73 -7.71 -1.186 311s demand_18 -139.45 -123.55 -24.042 311s demand_19 105.78 85.33 18.156 311s demand_20 -57.96 -42.41 -9.120 311s supply_1 10.43 11.69 0.119 311s supply_2 -131.54 -133.56 -2.696 311s supply_3 -77.56 -79.49 -2.406 311s supply_4 -40.84 -40.80 -1.663 311s supply_5 -117.13 -130.04 -5.868 311s supply_6 -63.52 -68.39 -3.792 311s supply_7 -126.43 -129.37 -8.575 311s supply_8 224.56 228.72 16.665 311s supply_9 144.70 162.82 13.481 311s supply_10 -136.07 -153.98 -15.306 311s supply_11 71.93 77.58 10.536 311s supply_12 34.19 30.50 5.335 311s supply_13 109.25 91.56 16.788 311s supply_14 -92.38 -83.00 -14.276 311s supply_15 -233.30 -231.49 -33.943 311s supply_16 176.34 176.17 26.846 311s supply_17 -98.33 -112.71 -17.341 311s supply_18 349.57 309.73 60.271 311s supply_19 34.05 27.47 5.845 311s supply_20 -37.92 -27.75 -5.967 311s > round( colSums( estfun( fitsuri1wr3 ) ), digits = 7 ) 311s demand_(Intercept) demand_price demand_income supply_(Intercept) 311s 0 0 0 0 311s supply_income supply_farmPrice supply_trend 311s 0 0 0 311s > 311s > estfun( fitsurS1 ) 311s eq1_consump eq2_(Intercept) eq2_consump eq2_trend 311s eq1_1 7.162 0.02160 2.127 0.0216 311s eq1_2 15.562 0.04659 4.621 0.0932 311s eq1_3 6.026 0.01752 1.789 0.0525 311s eq1_4 10.524 0.03079 3.125 0.1232 311s eq1_5 -14.099 -0.04017 -4.187 -0.2008 311s eq1_6 -7.426 -0.02136 -2.205 -0.1282 311s eq1_7 -5.141 -0.01468 -1.527 -0.1028 311s eq1_8 15.138 0.04500 4.495 0.3600 311s eq1_9 -7.596 -0.02248 -2.256 -0.2023 311s eq1_10 -28.217 -0.08150 -8.379 -0.8150 311s eq1_11 -3.498 -0.01088 -1.039 -0.1197 311s eq1_12 17.457 0.05609 5.184 0.6731 311s eq1_13 22.800 0.07162 6.771 0.9311 311s eq1_14 2.479 0.00746 0.736 0.1044 311s eq1_15 -26.446 -0.07423 -7.853 -1.1135 311s eq1_16 -2.054 -0.00609 -0.610 -0.0974 311s eq1_17 -42.973 -0.12327 -12.761 -2.0956 311s eq1_18 13.132 0.03902 3.900 0.7024 311s eq1_19 4.307 0.01216 1.279 0.2310 311s eq1_20 22.866 0.06392 6.790 1.2784 311s eq2_1 -1.322 -0.02928 -2.884 -0.0293 311s eq2_2 -0.971 -0.02136 -2.118 -0.0427 311s eq2_3 -5.293 -0.11298 -11.542 -0.3389 311s eq2_4 -4.273 -0.09180 -9.318 -0.3672 311s eq2_5 1.836 0.03840 4.003 0.1920 311s eq2_6 2.119 0.04477 4.622 0.2686 311s eq2_7 -0.532 -0.01115 -1.160 -0.0781 311s eq2_8 10.068 0.21978 21.956 1.7582 311s eq2_9 9.192 0.19974 20.044 1.7977 311s eq2_10 -0.465 -0.00986 -1.014 -0.0986 311s eq2_11 -2.679 -0.06122 -5.843 -0.6735 311s eq2_12 -6.257 -0.14762 -13.644 -1.7715 311s eq2_13 -7.360 -0.16978 -16.050 -2.2072 311s eq2_14 -5.865 -0.12951 -12.790 -1.8131 311s eq2_15 -0.730 -0.01505 -1.593 -0.2258 311s eq2_16 11.188 0.24342 24.396 3.8947 311s eq2_17 11.047 0.23271 24.091 3.9561 311s eq2_18 3.346 0.07302 7.297 1.3144 311s eq2_19 -7.478 -0.15498 -16.307 -2.9445 311s eq2_20 -5.570 -0.11434 -12.146 -2.2868 311s > round( colSums( estfun( fitsurS1 ) ), digits = 7 ) 311s eq1_consump eq2_(Intercept) eq2_consump eq2_trend 311s 0 0 0 0 311s > 311s > estfun( fitsurS2 ) 311s eq1_price eq2_trend 311s eq1_1 -5.42871 -0.000114 311s eq1_2 -13.14782 -0.000531 311s eq1_3 -4.34907 -0.000266 311s eq1_4 -8.39779 -0.000677 311s eq1_5 12.19030 0.001310 311s eq1_6 6.97176 0.000886 311s eq1_7 5.14513 0.000750 311s eq1_8 -12.72321 -0.002046 311s eq1_9 7.04895 0.001385 311s eq1_10 22.20478 0.005126 311s eq1_11 3.65437 0.000909 311s eq1_12 -15.21951 -0.003893 311s eq1_13 -20.44077 -0.005438 311s eq1_14 -1.31641 -0.000393 311s eq1_15 21.18383 0.007035 311s eq1_16 2.54257 0.000870 311s eq1_17 31.47441 0.013026 311s eq1_18 -10.84129 -0.003951 311s eq1_19 -2.78655 -0.001054 311s eq1_20 -19.91341 -0.007390 311s eq2_1 0.42448 0.037215 311s eq2_2 0.40866 0.068949 311s eq2_3 0.38411 0.097989 311s eq2_4 0.34891 0.117463 311s eq2_5 0.30591 0.137281 311s eq2_6 0.27161 0.144126 311s eq2_7 0.24474 0.149098 311s eq2_8 0.19771 0.132796 311s eq2_9 0.15083 0.123801 311s eq2_10 0.12174 0.117373 311s eq2_11 0.06024 0.062610 311s eq2_12 0.01611 0.017205 311s eq2_13 -0.00856 -0.009507 311s eq2_14 -0.02284 -0.028474 311s eq2_15 -0.02363 -0.032773 311s eq2_16 -0.08383 -0.119831 311s eq2_17 -0.09018 -0.155889 311s eq2_18 -0.16161 -0.245985 311s eq2_19 -0.17473 -0.276076 311s eq2_20 -0.22123 -0.342915 311s > round( colSums( estfun( fitsurS2 ) ), digits = 7 ) 311s eq1_price eq2_trend 311s 0 0 311s > 311s > estfun( fitsurS3 ) 311s eq1_trend eq2_trend 311s eq1_1 2.069 -2.039 311s eq1_2 3.833 -3.777 311s eq1_3 5.448 -5.369 311s eq1_4 6.531 -6.436 311s eq1_5 7.634 -7.523 311s eq1_6 8.015 -7.899 311s eq1_7 8.293 -8.173 311s eq1_8 7.389 -7.281 311s eq1_9 6.890 -6.790 311s eq1_10 6.535 -6.440 311s eq1_11 3.493 -3.443 311s eq1_12 0.972 -0.958 311s eq1_13 -0.510 0.503 311s eq1_14 -1.562 1.539 311s eq1_15 -1.798 1.772 311s eq1_16 -6.634 6.537 311s eq1_17 -8.634 8.509 311s eq1_18 -13.639 13.441 311s eq1_19 -15.308 15.085 311s eq1_20 -19.019 18.743 311s eq2_1 -2.082 2.089 311s eq2_2 -4.012 4.027 311s eq2_3 -5.472 5.491 311s eq2_4 -6.736 6.760 311s eq2_5 -6.873 6.897 311s eq2_6 -7.460 7.486 311s eq2_7 -7.809 7.837 311s eq2_8 -8.276 8.305 311s eq2_9 -6.161 6.182 311s eq2_10 -4.039 4.053 311s eq2_11 -3.098 3.109 311s eq2_12 -2.949 2.960 311s eq2_13 -2.261 2.269 311s eq2_14 1.160 -1.164 311s eq2_15 4.921 -4.939 311s eq2_16 6.677 -6.701 311s eq2_17 14.428 -14.479 311s eq2_18 11.167 -11.207 311s eq2_19 14.155 -14.205 311s eq2_20 14.719 -14.771 311s > round( colSums( estfun( fitsurS3 ) ), digits = 7 ) 311s Error in estfun.systemfit(fitsurS4) : 311s returning the estimation function for models with restrictions has not yet been implemented. 311s eq1_trend eq2_trend 311s 0 0 311s > 311s > try( estfun( fitsurS4 ) ) 311s > 311s > estfun( fitsurS5 ) 311s eq1_(Intercept) eq2_(Intercept) 311s eq1_1 -0.17267 0.01074 311s eq1_2 -0.12244 0.00761 311s eq1_3 0.09050 -0.00563 311s eq1_4 0.04335 -0.00270 311s eq1_5 0.23912 -0.01487 311s eq1_6 0.16778 -0.01043 311s eq1_7 0.22144 -0.01377 311s eq1_8 -0.07143 0.00444 311s eq1_9 -0.03923 0.00244 311s eq1_10 0.13751 -0.00855 311s eq1_11 -0.39091 0.02431 311s eq1_12 -0.60636 0.03770 311s eq1_13 -0.45531 0.02831 311s eq1_14 -0.15321 0.00953 311s eq1_15 0.35053 -0.02180 311s eq1_16 -0.04817 0.00300 311s eq1_17 0.18774 -0.01167 311s eq1_18 -0.06935 0.00431 311s eq1_19 0.30946 -0.01924 311s eq1_20 0.38165 -0.02373 311s eq2_1 -0.00135 0.00874 311s eq2_2 -0.01889 0.12205 311s eq2_3 -0.01520 0.09821 311s eq2_4 -0.01996 0.12901 311s eq2_5 0.00898 -0.05802 311s eq2_6 0.00251 -0.01619 311s eq2_7 -0.00466 0.03010 311s eq2_8 -0.02111 0.13640 311s eq2_9 0.01590 -0.10273 311s eq2_10 0.03911 -0.25276 311s eq2_11 0.03085 -0.19937 311s eq2_12 0.00542 -0.03502 311s eq2_13 -0.01285 0.08306 311s eq2_14 0.00562 -0.03631 311s eq2_15 0.02180 -0.14088 311s eq2_16 0.00698 -0.04508 311s eq2_17 0.06016 -0.38875 311s eq2_18 -0.01778 0.11492 311s eq2_19 -0.02558 0.16532 311s eq2_20 -0.05994 0.38731 311s > round( colSums( estfun( fitsurS5 ) ), digits = 7 ) 311s eq1_(Intercept) eq2_(Intercept) 311s 0 0 311s > 311s > 311s > ## **************** bread ************************ 311s > round( bread( fitsur1 ), digits = 7 ) 311s demand_(Intercept) demand_price demand_income supply_(Intercept) 311s [1,] 2258.680 -23.5779 1.0971 2354.23 311s [2,] -23.578 0.3134 -0.0796 -15.01 311s [3,] 1.097 -0.0796 0.0704 -8.66 311s [4,] 2354.232 -15.0109 -8.6593 4911.36 311s [5,] -24.454 0.2225 0.0225 -38.45 311s [6,] 0.887 -0.0644 0.0569 -9.51 311s [7,] 1.348 -0.0978 0.0864 -12.94 311s supply_price supply_farmPrice supply_trend 311s [1,] -24.4536 0.8871 1.3477 311s [2,] 0.2225 -0.0644 -0.0978 311s [3,] 0.0225 0.0569 0.0864 311s [4,] -38.4456 -9.5077 -12.9352 311s [5,] 0.3567 0.0252 0.0320 311s [6,] 0.0252 0.0636 0.0807 311s [7,] 0.0320 0.0807 0.1845 311s > 311s > round( bread( fitsur1e2 ), digits = 7 ) 311s demand_(Intercept) demand_price demand_income supply_(Intercept) 311s [1,] 2257.61 -23.5004 1.0286 2442.20 311s [2,] -23.50 0.3077 -0.0746 -16.15 311s [3,] 1.03 -0.0746 0.0660 -8.39 311s [4,] 2442.20 -16.1480 -8.3922 4816.72 311s [5,] -25.30 0.2317 0.0218 -38.19 311s [6,] 0.86 -0.0624 0.0552 -8.86 311s [7,] 1.31 -0.0948 0.0838 -12.35 311s supply_price supply_farmPrice supply_trend 311s [1,] -25.2995 0.8598 1.3061 311s [2,] 0.2317 -0.0624 -0.0948 311s [3,] 0.0218 0.0552 0.0838 311s [4,] -38.1886 -8.8582 -12.3470 311s [5,] 0.3560 0.0234 0.0309 311s [6,] 0.0234 0.0590 0.0780 311s [7,] 0.0309 0.0780 0.1640 311s > 311s > round( bread( fitsur1r3 ), digits = 7 ) 311s demand_(Intercept) demand_price demand_income supply_(Intercept) 311s [1,] 2257.728 -23.5088 1.0361 2434.43 311s [2,] -23.509 0.3083 -0.0752 -16.03 311s [3,] 1.036 -0.0752 0.0665 -8.43 311s [4,] 2434.429 -16.0346 -8.4292 4826.83 311s [5,] -25.226 0.2308 0.0219 -38.22 311s [6,] 0.864 -0.0627 0.0554 -8.93 311s [7,] 1.312 -0.0952 0.0842 -12.42 311s supply_price supply_farmPrice supply_trend 311s [1,] -25.2264 0.8636 1.3118 311s [2,] 0.2308 -0.0627 -0.0952 311s [3,] 0.0219 0.0554 0.0842 311s [4,] -38.2158 -8.9270 -12.4169 311s [5,] 0.3561 0.0235 0.0310 311s [6,] 0.0235 0.0595 0.0784 311s [7,] 0.0310 0.0784 0.1660 311s > 311s > round( bread( fitsur1w ), digits = 7 ) 311s demand_(Intercept) demand_price demand_income supply_(Intercept) 311s [1,] 2258.680 -23.5779 1.0971 2354.23 311s [2,] -23.578 0.3134 -0.0796 -15.01 311s [3,] 1.097 -0.0796 0.0704 -8.66 311s [4,] 2354.232 -15.0109 -8.6593 4911.36 311s [5,] -24.454 0.2225 0.0225 -38.45 311s [6,] 0.887 -0.0644 0.0569 -9.51 311s [7,] 1.348 -0.0978 0.0864 -12.94 311s supply_price supply_farmPrice supply_trend 311s [1,] -24.4536 0.8871 1.3477 311s [2,] 0.2225 -0.0644 -0.0978 311s [3,] 0.0225 0.0569 0.0864 311s [4,] -38.4456 -9.5077 -12.9352 311s [5,] 0.3567 0.0252 0.0320 311s [6,] 0.0252 0.0636 0.0807 311s [7,] 0.0320 0.0807 0.1845 311s > 311s > round( bread( fitsuri1e ), digits = 7 ) 311s demand_(Intercept) demand_price demand_income supply_(Intercept) 311s [1,] 1876.862 -19.2519 0.5677 -81.89 311s [2,] -19.252 0.2661 -0.0755 -2.81 311s [3,] 0.568 -0.0755 0.0716 3.68 311s [4,] -81.887 -2.8102 3.6811 363.96 311s [5,] 7.186 -0.0595 -0.0127 -1.84 311s [6,] -5.538 0.0766 -0.0217 -1.67 311s [7,] -8.357 0.1155 -0.0328 -1.82 311s supply_income supply_farmPrice supply_trend 311s [1,] 7.1857 -5.5385 -8.3572 311s [2,] -0.0595 0.0766 0.1155 311s [3,] -0.0127 -0.0217 -0.0328 311s [4,] -1.8380 -1.6714 -1.8169 311s [5,] 0.0569 -0.0327 -0.0527 311s [6,] -0.0327 0.0441 0.0571 311s [7,] -0.0527 0.0571 0.1367 311s > 311s > round( bread( fitsuri1wr3 ), digits = 7 ) 311s demand_(Intercept) demand_price demand_income supply_(Intercept) 311s [1,] 2182.020 -22.2793 0.5557 -108.13 311s [2,] -22.279 0.3080 -0.0874 -3.49 311s [3,] 0.556 -0.0874 0.0839 4.64 311s [4,] -108.127 -3.4932 4.6397 458.64 311s [5,] 8.996 -0.0739 -0.0164 -2.35 311s [6,] -6.884 0.0952 -0.0270 -2.07 311s [7,] -10.388 0.1436 -0.0408 -2.31 311s supply_income supply_farmPrice supply_trend 311s [1,] 8.9961 -6.8844 -10.3882 311s [2,] -0.0739 0.0952 0.1436 311s [3,] -0.0164 -0.0270 -0.0408 311s [4,] -2.3500 -2.0691 -2.3134 311s [5,] 0.0715 -0.0407 -0.0653 311s [6,] -0.0407 0.0547 0.0717 311s [7,] -0.0653 0.0717 0.1662 311s > 311s > round( bread( fitsurS1 ), digits = 7 ) 311s eq1_consump eq2_(Intercept) eq2_consump eq2_trend 311s [1,] 0.00876 0.0 -4.02e-03 0.000 311s [2,] 0.00000 91218.4 -9.08e+02 48.892 311s [3,] -0.00402 -908.0 9.09e+00 -0.866 311s [4,] 0.00000 48.9 -8.66e-01 3.664 311s > 311s > round( bread( fitsurS2 ), digits = 7 ) 311s eq1_price eq2_trend 311s [1,] 0.00903 -0.00752 311s [2,] -0.00752 34.11430 311s > 311s > round( bread( fitsurS3 ), digits = 7 ) 311s eq1_trend eq2_trend 311s [1,] 34.1 34.0 311s [2,] 34.0 34.5 311s > 311s Error in bread.systemfit(fitsurS4) : 311s returning the 'bread' for models with restrictions has not yet been implemented. 311s > try( bread( fitsurS4 ) ) 311s > 311s BEGIN TEST test_w2sls.R 311s 311s R version 4.3.3 (2024-02-29) -- "Angel Food Cake" 311s Copyright (C) 2024 The R Foundation for Statistical Computing 311s Platform: arm-unknown-linux-gnueabihf (32-bit) 311s 311s R is free software and comes with ABSOLUTELY NO WARRANTY. 311s You are welcome to redistribute it under certain conditions. 311s Type 'license()' or 'licence()' for distribution details. 311s 311s R is a collaborative project with many contributors. 311s Type 'contributors()' for more information and 311s 'citation()' on how to cite R or R packages in publications. 311s 311s Type 'demo()' for some demos, 'help()' for on-line help, or 311s 'help.start()' for an HTML browser interface to help. 311s Type 'q()' to quit R. 311s 311s > library( systemfit ) 311s Loading required package: Matrix 312s Loading required package: car 312s Loading required package: carData 312s Loading required package: lmtest 312s Loading required package: zoo 312s 312s Attaching package: ‘zoo’ 312s 312s The following objects are masked from ‘package:base’: 312s 312s as.Date, as.Date.numeric 312s 312s 312s Please cite the 'systemfit' package as: 312s Arne Henningsen and Jeff D. Hamann (2007). systemfit: A Package for Estimating Systems of Simultaneous Equations in R. Journal of Statistical Software 23(4), 1-40. http://www.jstatsoft.org/v23/i04/. 312s 312s If you have questions, suggestions, or comments regarding the 'systemfit' package, please use a forum or 'tracker' at systemfit's R-Forge site: 312s https://r-forge.r-project.org/projects/systemfit/ 312s > options( digits = 3 ) 312s > 312s > data( "Kmenta" ) 312s > useMatrix <- FALSE 312s > 312s > demand <- consump ~ price + income 312s > supply <- consump ~ price + farmPrice + trend 312s > inst <- ~ income + farmPrice + trend 312s > inst1 <- ~ income + farmPrice 312s > instlist <- list( inst1, inst ) 312s > system <- list( demand = demand, supply = supply ) 312s > restrm <- matrix(0,1,7) # restriction matrix "R" 312s > restrm[1,3] <- 1 312s > restrm[1,7] <- -1 312s > restrict <- "demand_income - supply_trend = 0" 312s > restr2m <- matrix(0,2,7) # restriction matrix "R" 2 312s > restr2m[1,3] <- 1 312s > restr2m[1,7] <- -1 312s > restr2m[2,2] <- -1 312s > restr2m[2,5] <- 1 312s > restr2q <- c( 0, 0.5 ) # restriction vector "q" 2 312s > restrict2 <- c( "demand_income - supply_trend = 0", 312s + "- demand_price + supply_price = 0.5" ) 312s > tc <- matrix(0,7,6) 312s > tc[1,1] <- 1 312s > tc[2,2] <- 1 312s > tc[3,3] <- 1 312s > tc[4,4] <- 1 312s > tc[5,5] <- 1 312s > tc[6,6] <- 1 312s > tc[7,3] <- 1 312s > restr3m <- matrix(0,1,6) # restriction matrix "R" 2 312s > restr3m[1,2] <- -1 312s > restr3m[1,5] <- 1 312s > restr3q <- c( 0.5 ) # restriction vector "q" 2 312s > restrict3 <- "- C2 + C5 = 0.5" 312s > 312s > 312s > ## ********************* W2SLS ***************** 312s > fitw2sls1 <- systemfit( system, "W2SLS", data = Kmenta, inst = inst, 312s + useMatrix = useMatrix ) 312s > print( summary( fitw2sls1 ) ) 312s 312s systemfit results 312s method: W2SLS 312s 312s N DF SSR detRCov OLS-R2 McElroy-R2 312s system 40 33 162 4.36 0.697 0.548 312s 312s N DF SSR MSE RMSE R2 Adj R2 312s demand 20 17 65.7 3.87 1.97 0.755 0.726 312s supply 20 16 96.6 6.04 2.46 0.640 0.572 312s 312s The covariance matrix of the residuals used for estimation 312s demand supply 312s demand 3.87 0.00 312s supply 0.00 6.04 312s 312s The covariance matrix of the residuals 312s demand supply 312s demand 3.87 4.36 312s supply 4.36 6.04 312s 312s The correlations of the residuals 312s demand supply 312s demand 1.000 0.902 312s supply 0.902 1.000 312s 312s 312s W2SLS estimates for 'demand' (equation 1) 312s Model Formula: consump ~ price + income 312s Instruments: ~income + farmPrice + trend 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 94.6333 7.9208 11.95 1.1e-09 *** 312s price -0.2436 0.0965 -2.52 0.022 * 312s income 0.3140 0.0469 6.69 3.8e-06 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 1.966 on 17 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 17 312s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 312s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 312s 312s 312s W2SLS estimates for 'supply' (equation 2) 312s Model Formula: consump ~ price + farmPrice + trend 312s Instruments: ~income + farmPrice + trend 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 49.5324 12.0105 4.12 0.0008 *** 312s price 0.2401 0.0999 2.40 0.0288 * 312s farmPrice 0.2556 0.0473 5.41 5.8e-05 *** 312s trend 0.2529 0.0997 2.54 0.0219 * 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 2.458 on 16 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 16 312s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 312s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 312s 312s > 312s > ## ********************* W2SLS (EViews-like) ***************** 312s > fitw2sls1e <- systemfit( system, "W2SLS", data = Kmenta, inst = inst, 312s + methodResidCov = "noDfCor", x = TRUE, 312s + useMatrix = useMatrix ) 312s > print( summary( fitw2sls1e, useDfSys = TRUE ) ) 312s 312s systemfit results 312s method: W2SLS 312s 312s N DF SSR detRCov OLS-R2 McElroy-R2 312s system 40 33 162 2.97 0.697 0.525 312s 312s N DF SSR MSE RMSE R2 Adj R2 312s demand 20 17 65.7 3.87 1.97 0.755 0.726 312s supply 20 16 96.6 6.04 2.46 0.640 0.572 312s 312s The covariance matrix of the residuals used for estimation 312s demand supply 312s demand 3.29 0.00 312s supply 0.00 4.83 312s 312s The covariance matrix of the residuals 312s demand supply 312s demand 3.29 3.59 312s supply 3.59 4.83 312s 312s The correlations of the residuals 312s demand supply 312s demand 1.000 0.902 312s supply 0.902 1.000 312s 312s 312s W2SLS estimates for 'demand' (equation 1) 312s Model Formula: consump ~ price + income 312s Instruments: ~income + farmPrice + trend 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 312s price -0.2436 0.0890 -2.74 0.0099 ** 312s income 0.3140 0.0433 7.25 2.5e-08 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 1.966 on 17 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 17 312s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 312s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 312s 312s 312s W2SLS estimates for 'supply' (equation 2) 312s Model Formula: consump ~ price + farmPrice + trend 312s Instruments: ~income + farmPrice + trend 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 49.5324 10.7425 4.61 5.8e-05 *** 312s price 0.2401 0.0894 2.69 0.0112 * 312s farmPrice 0.2556 0.0423 6.05 8.4e-07 *** 312s trend 0.2529 0.0891 2.84 0.0077 ** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 2.458 on 16 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 16 312s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 312s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 312s 312s > 312s > ## ********************* W2SLS with restriction ******************* 312s > fitw2sls2 <- systemfit( system, "W2SLS", data = Kmenta, restrict.matrix = restrm, 312s + inst = inst, useMatrix = useMatrix ) 312s > print( summary( fitw2sls2 ) ) 312s 312s systemfit results 312s method: W2SLS 312s 312s N DF SSR detRCov OLS-R2 McElroy-R2 312s system 40 34 165 3.41 0.692 0.565 312s 312s N DF SSR MSE RMSE R2 Adj R2 312s demand 20 17 66.8 3.93 1.98 0.751 0.721 312s supply 20 16 98.4 6.15 2.48 0.633 0.564 312s 312s The covariance matrix of the residuals used for estimation 312s demand supply 312s demand 3.97 0.00 312s supply 0.00 6.13 312s 312s The covariance matrix of the residuals 312s demand supply 312s demand 3.93 4.56 312s supply 4.56 6.15 312s 312s The correlations of the residuals 312s demand supply 312s demand 1.000 0.927 312s supply 0.927 1.000 312s 312s 312s W2SLS estimates for 'demand' (equation 1) 312s Model Formula: consump ~ price + income 312s Instruments: ~income + farmPrice + trend 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 94.3832 8.0090 11.78 1.5e-13 *** 312s price -0.2302 0.0946 -2.43 0.02 * 312s income 0.3028 0.0430 7.05 3.9e-08 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 1.983 on 17 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 17 312s SSR: 66.838 MSE: 3.932 Root MSE: 1.983 312s Multiple R-Squared: 0.751 Adjusted R-Squared: 0.721 312s 312s 312s W2SLS estimates for 'supply' (equation 2) 312s Model Formula: consump ~ price + farmPrice + trend 312s Instruments: ~income + farmPrice + trend 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 48.0494 11.8001 4.07 0.00026 *** 312s price 0.2430 0.1006 2.42 0.02122 * 312s farmPrice 0.2625 0.0459 5.72 2.0e-06 *** 312s trend 0.3028 0.0430 7.05 3.9e-08 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 2.48 on 16 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 16 312s SSR: 98.445 MSE: 6.153 Root MSE: 2.48 312s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.564 312s 312s > # the same with symbolically specified restrictions 312s > fitw2sls2Sym <- systemfit( system, "W2SLS", data = Kmenta, 312s + restrict.matrix = restrict, inst = inst, useMatrix = useMatrix ) 312s > all.equal( fitw2sls2, fitw2sls2Sym ) 312s [1] "Component “call”: target, current do not match when deparsed" 312s > 312s > ## ********************* W2SLS with restriction (EViews-like) ************** 312s > fitw2sls2e <- systemfit( system, "W2SLS", data = Kmenta, restrict.matrix = restrm, 312s + inst = inst, methodResidCov = "noDfCor", x = TRUE, 312s + useMatrix = useMatrix ) 312s > print( summary( fitw2sls2e, useDfSys = TRUE ) ) 312s 312s systemfit results 312s method: W2SLS 312s 312s N DF SSR detRCov OLS-R2 McElroy-R2 312s system 40 34 165 2.33 0.692 0.535 312s 312s N DF SSR MSE RMSE R2 Adj R2 312s demand 20 17 66.9 3.94 1.98 0.750 0.721 312s supply 20 16 98.4 6.15 2.48 0.633 0.564 312s 312s The covariance matrix of the residuals used for estimation 312s demand supply 312s demand 3.37 0.00 312s supply 0.00 4.91 312s 312s The covariance matrix of the residuals 312s demand supply 312s demand 3.35 3.76 312s supply 3.76 4.92 312s 312s The correlations of the residuals 312s demand supply 312s demand 1.000 0.926 312s supply 0.926 1.000 312s 312s 312s W2SLS estimates for 'demand' (equation 1) 312s Model Formula: consump ~ price + income 312s Instruments: ~income + farmPrice + trend 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 94.3706 7.3834 12.78 1.6e-14 *** 312s price -0.2295 0.0871 -2.63 0.013 * 312s income 0.3022 0.0394 7.67 6.4e-09 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 1.984 on 17 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 17 312s SSR: 66.906 MSE: 3.936 Root MSE: 1.984 312s Multiple R-Squared: 0.75 Adjusted R-Squared: 0.721 312s 312s 312s W2SLS estimates for 'supply' (equation 2) 312s Model Formula: consump ~ price + farmPrice + trend 312s Instruments: ~income + farmPrice + trend 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 48.0661 10.5574 4.55 6.5e-05 *** 312s price 0.2430 0.0900 2.70 0.011 * 312s farmPrice 0.2624 0.0411 6.39 2.7e-07 *** 312s trend 0.3022 0.0394 7.67 6.4e-09 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 2.48 on 16 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 16 312s SSR: 98.408 MSE: 6.15 Root MSE: 2.48 312s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.564 312s 312s > nobs( fitw2sls2e ) 312s [1] 40 312s > 312s > ## ********************* W2SLS with restriction via restrict.regMat ******************* 312s > fitw2sls3 <- systemfit( system, "W2SLS", data = Kmenta, restrict.regMat = tc, 312s + inst = inst, x = TRUE, useMatrix = useMatrix ) 312s > print( summary( fitw2sls3 ) ) 312s 312s systemfit results 312s method: W2SLS 312s 312s N DF SSR detRCov OLS-R2 McElroy-R2 312s system 40 34 165 3.41 0.692 0.565 312s 312s N DF SSR MSE RMSE R2 Adj R2 312s demand 20 17 66.8 3.93 1.98 0.751 0.721 312s supply 20 16 98.4 6.15 2.48 0.633 0.564 312s 312s The covariance matrix of the residuals used for estimation 312s demand supply 312s demand 3.97 0.00 312s supply 0.00 6.13 312s 312s The covariance matrix of the residuals 312s demand supply 312s demand 3.93 4.56 312s supply 4.56 6.15 312s 312s The correlations of the residuals 312s demand supply 312s demand 1.000 0.927 312s supply 0.927 1.000 312s 312s 312s W2SLS estimates for 'demand' (equation 1) 312s Model Formula: consump ~ price + income 312s Instruments: ~income + farmPrice + trend 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 94.3832 8.0090 11.78 1.5e-13 *** 312s price -0.2302 0.0946 -2.43 0.02 * 312s income 0.3028 0.0430 7.05 3.9e-08 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 1.983 on 17 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 17 312s SSR: 66.838 MSE: 3.932 Root MSE: 1.983 312s Multiple R-Squared: 0.751 Adjusted R-Squared: 0.721 312s 312s 312s W2SLS estimates for 'supply' (equation 2) 312s Model Formula: consump ~ price + farmPrice + trend 312s Instruments: ~income + farmPrice + trend 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 48.0494 11.8001 4.07 0.00026 *** 312s price 0.2430 0.1006 2.42 0.02122 * 312s farmPrice 0.2625 0.0459 5.72 2.0e-06 *** 312s trend 0.3028 0.0430 7.05 3.9e-08 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 2.48 on 16 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 16 312s SSR: 98.445 MSE: 6.153 Root MSE: 2.48 312s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.564 312s 312s > 312s > ## ********************* W2SLS with restriction via restrict.regMat (EViews-like) ************** 312s > fitw2sls3e <- systemfit( system, "W2SLS", data = Kmenta, restrict.regMat = tc, 312s + inst = inst, methodResidCov = "noDfCor", useMatrix = useMatrix ) 312s > print( summary( fitw2sls3e, useDfSys = TRUE ) ) 312s 312s systemfit results 312s method: W2SLS 312s 312s N DF SSR detRCov OLS-R2 McElroy-R2 312s system 40 34 165 2.33 0.692 0.535 312s 312s N DF SSR MSE RMSE R2 Adj R2 312s demand 20 17 66.9 3.94 1.98 0.750 0.721 312s supply 20 16 98.4 6.15 2.48 0.633 0.564 312s 312s The covariance matrix of the residuals used for estimation 312s demand supply 312s demand 3.37 0.00 312s supply 0.00 4.91 312s 312s The covariance matrix of the residuals 312s demand supply 312s demand 3.35 3.76 312s supply 3.76 4.92 312s 312s The correlations of the residuals 312s demand supply 312s demand 1.000 0.926 312s supply 0.926 1.000 312s 312s 312s W2SLS estimates for 'demand' (equation 1) 312s Model Formula: consump ~ price + income 312s Instruments: ~income + farmPrice + trend 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 94.3706 7.3834 12.78 1.6e-14 *** 312s price -0.2295 0.0871 -2.63 0.013 * 312s income 0.3022 0.0394 7.67 6.4e-09 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 1.984 on 17 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 17 312s SSR: 66.906 MSE: 3.936 Root MSE: 1.984 312s Multiple R-Squared: 0.75 Adjusted R-Squared: 0.721 312s 312s 312s W2SLS estimates for 'supply' (equation 2) 312s Model Formula: consump ~ price + farmPrice + trend 312s Instruments: ~income + farmPrice + trend 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 48.0661 10.5574 4.55 6.5e-05 *** 312s price 0.2430 0.0900 2.70 0.011 * 312s farmPrice 0.2624 0.0411 6.39 2.7e-07 *** 312s trend 0.3022 0.0394 7.67 6.4e-09 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 2.48 on 16 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 16 312s SSR: 98.408 MSE: 6.15 Root MSE: 2.48 312s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.564 312s 312s > 312s > ## ***************** W2SLS with 2 restrictions ******************** 312s > fitw2sls4 <- systemfit( system, "W2SLS", data = Kmenta, restrict.matrix = restr2m, 312s + restrict.rhs = restr2q, inst = inst, x = TRUE, 312s + useMatrix = useMatrix ) 312s > print( summary( fitw2sls4 ) ) 312s 312s systemfit results 312s method: W2SLS 312s 312s N DF SSR detRCov OLS-R2 McElroy-R2 312s system 40 35 166 3.57 0.69 0.575 312s 312s N DF SSR MSE RMSE R2 Adj R2 312s demand 20 17 65.9 3.88 1.97 0.754 0.725 312s supply 20 16 100.3 6.27 2.50 0.626 0.556 312s 312s The covariance matrix of the residuals used for estimation 312s demand supply 312s demand 3.89 0.00 312s supply 0.00 6.25 312s 312s The covariance matrix of the residuals 312s demand supply 312s demand 3.88 4.55 312s supply 4.55 6.27 312s 312s The correlations of the residuals 312s demand supply 312s demand 1.000 0.924 312s supply 0.924 1.000 312s 312s 312s W2SLS estimates for 'demand' (equation 1) 312s Model Formula: consump ~ price + income 312s Instruments: ~income + farmPrice + trend 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 95.3043 6.3056 15.11 < 2e-16 *** 312s price -0.2428 0.0684 -3.55 0.0011 ** 312s income 0.3063 0.0394 7.78 3.9e-09 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 1.969 on 17 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 17 312s SSR: 65.931 MSE: 3.878 Root MSE: 1.969 312s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 312s 312s 312s W2SLS estimates for 'supply' (equation 2) 312s Model Formula: consump ~ price + farmPrice + trend 312s Instruments: ~income + farmPrice + trend 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 46.4229 8.3296 5.57 2.8e-06 *** 312s price 0.2572 0.0684 3.76 0.00062 *** 312s farmPrice 0.2642 0.0455 5.80 1.4e-06 *** 312s trend 0.3063 0.0394 7.78 3.9e-09 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 2.503 on 16 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 16 312s SSR: 100.255 MSE: 6.266 Root MSE: 2.503 312s Multiple R-Squared: 0.626 Adjusted R-Squared: 0.556 312s 312s > # the same with symbolically specified restrictions 312s > fitw2sls4Sym <- systemfit( system, "W2SLS", data = Kmenta, 312s + restrict.matrix = restrict2, inst = inst, x = TRUE, 312s + useMatrix = useMatrix ) 312s > all.equal( fitw2sls4, fitw2sls4Sym ) 312s [1] "Component “call”: target, current do not match when deparsed" 312s > 312s > ## ***************** W2SLS with 2 restrictions (EViews-like) ************** 312s > fitw2sls4e <- systemfit( system, "W2SLS", data = Kmenta, restrict.matrix = restr2m, 312s + restrict.rhs = restr2q, inst = inst, methodResidCov = "noDfCor", 312s + useMatrix = useMatrix ) 312s > print( summary( fitw2sls4e, useDfSys = TRUE ) ) 312s 312s systemfit results 312s method: W2SLS 312s 312s N DF SSR detRCov OLS-R2 McElroy-R2 312s system 40 35 166 2.44 0.69 0.546 312s 312s N DF SSR MSE RMSE R2 Adj R2 312s demand 20 17 65.9 3.88 1.97 0.754 0.725 312s supply 20 16 100.2 6.26 2.50 0.626 0.556 312s 312s The covariance matrix of the residuals used for estimation 312s demand supply 312s demand 3.3 0 312s supply 0.0 5 312s 312s The covariance matrix of the residuals 312s demand supply 312s demand 3.30 3.75 312s supply 3.75 5.01 312s 312s The correlations of the residuals 312s demand supply 312s demand 1.000 0.923 312s supply 0.923 1.000 312s 312s 312s W2SLS estimates for 'demand' (equation 1) 312s Model Formula: consump ~ price + income 312s Instruments: ~income + farmPrice + trend 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 95.3470 5.7579 16.56 < 2e-16 *** 312s price -0.2428 0.0621 -3.91 0.00041 *** 312s income 0.3059 0.0360 8.49 5.1e-10 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 1.97 on 17 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 17 312s SSR: 65.945 MSE: 3.879 Root MSE: 1.97 312s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 312s 312s 312s W2SLS estimates for 'supply' (equation 2) 312s Model Formula: consump ~ price + farmPrice + trend 312s Instruments: ~income + farmPrice + trend 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 46.4366 7.5360 6.16 4.7e-07 *** 312s price 0.2572 0.0621 4.14 0.00021 *** 312s farmPrice 0.2642 0.0407 6.48 1.8e-07 *** 312s trend 0.3059 0.0360 8.49 5.1e-10 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 2.503 on 16 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 16 312s SSR: 100.225 MSE: 6.264 Root MSE: 2.503 312s Multiple R-Squared: 0.626 Adjusted R-Squared: 0.556 312s 312s > 312s > ## ***************** W2SLS with 2 restrictions via R and restrict.regMat ****************** 312s > fitw2sls5 <- systemfit( system, "W2SLS", data = Kmenta, restrict.matrix = restr3m, 312s + restrict.rhs = restr3q, restrict.regMat = tc, inst = inst, 312s + x = TRUE, useMatrix = useMatrix ) 312s > print( summary( fitw2sls5 ) ) 312s 312s systemfit results 312s method: W2SLS 312s 312s N DF SSR detRCov OLS-R2 McElroy-R2 312s system 40 35 166 3.57 0.69 0.575 312s 312s N DF SSR MSE RMSE R2 Adj R2 312s demand 20 17 65.9 3.88 1.97 0.754 0.725 312s supply 20 16 100.3 6.27 2.50 0.626 0.556 312s 312s The covariance matrix of the residuals used for estimation 312s demand supply 312s demand 3.89 0.00 312s supply 0.00 6.25 312s 312s The covariance matrix of the residuals 312s demand supply 312s demand 3.88 4.55 312s supply 4.55 6.27 312s 312s The correlations of the residuals 312s demand supply 312s demand 1.000 0.924 312s supply 0.924 1.000 312s 312s 312s W2SLS estimates for 'demand' (equation 1) 312s Model Formula: consump ~ price + income 312s Instruments: ~income + farmPrice + trend 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 95.3043 6.3056 15.11 < 2e-16 *** 312s price -0.2428 0.0684 -3.55 0.0011 ** 312s income 0.3063 0.0394 7.78 3.9e-09 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 1.969 on 17 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 17 312s SSR: 65.931 MSE: 3.878 Root MSE: 1.969 312s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 312s 312s 312s W2SLS estimates for 'supply' (equation 2) 312s Model Formula: consump ~ price + farmPrice + trend 312s Instruments: ~income + farmPrice + trend 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 46.4229 8.3296 5.57 2.8e-06 *** 312s price 0.2572 0.0684 3.76 0.00062 *** 312s farmPrice 0.2642 0.0455 5.80 1.4e-06 *** 312s trend 0.3063 0.0394 7.78 3.9e-09 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 2.503 on 16 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 16 312s SSR: 100.255 MSE: 6.266 Root MSE: 2.503 312s Multiple R-Squared: 0.626 Adjusted R-Squared: 0.556 312s 312s > # the same with symbolically specified restrictions 312s > fitw2sls5Sym <- systemfit( system, "W2SLS", data = Kmenta, 312s + restrict.matrix = restrict3, restrict.regMat = tc, inst = inst, 312s + x = TRUE, useMatrix = useMatrix ) 312s > all.equal( fitw2sls5, fitw2sls5Sym ) 312s [1] "Component “call”: target, current do not match when deparsed" 312s > 312s > ## ***************** W2SLS with 2 restrictions via R and restrict.regMat (EViews-like) ************** 312s > fitw2sls5e <- systemfit( system, "W2SLS", data = Kmenta, restrict.matrix = restr3m, 312s + restrict.rhs = restr3q, restrict.regMat = tc, inst = inst, 312s + methodResidCov = "noDfCor", useMatrix = useMatrix ) 312s > print( summary( fitw2sls5e, useDfSys = TRUE ) ) 312s 312s systemfit results 312s method: W2SLS 312s 312s N DF SSR detRCov OLS-R2 McElroy-R2 312s system 40 35 166 2.44 0.69 0.546 312s 312s N DF SSR MSE RMSE R2 Adj R2 312s demand 20 17 65.9 3.88 1.97 0.754 0.725 312s supply 20 16 100.2 6.26 2.50 0.626 0.556 312s 312s The covariance matrix of the residuals used for estimation 312s demand supply 312s demand 3.3 0 312s supply 0.0 5 312s 312s The covariance matrix of the residuals 312s demand supply 312s demand 3.30 3.75 312s supply 3.75 5.01 312s 312s The correlations of the residuals 312s demand supply 312s demand 1.000 0.923 312s supply 0.923 1.000 312s 312s 312s W2SLS estimates for 'demand' (equation 1) 312s Model Formula: consump ~ price + income 312s Instruments: ~income + farmPrice + trend 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 95.3470 5.7579 16.56 < 2e-16 *** 312s price -0.2428 0.0621 -3.91 0.00041 *** 312s income 0.3059 0.0360 8.49 5.1e-10 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 1.97 on 17 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 17 312s SSR: 65.945 MSE: 3.879 Root MSE: 1.97 312s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 312s 312s 312s W2SLS estimates for 'supply' (equation 2) 312s Model Formula: consump ~ price + farmPrice + trend 312s Instruments: ~income + farmPrice + trend 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 46.4366 7.5360 6.16 4.7e-07 *** 312s price 0.2572 0.0621 4.14 0.00021 *** 312s farmPrice 0.2642 0.0407 6.48 1.8e-07 *** 312s trend 0.3059 0.0360 8.49 5.1e-10 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 2.503 on 16 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 16 312s SSR: 100.225 MSE: 6.264 Root MSE: 2.503 312s Multiple R-Squared: 0.626 Adjusted R-Squared: 0.556 312s 312s > 312s > ## ****** 2SLS estimation with different instruments ********************** 312s > fitw2slsd1 <- systemfit( system, "W2SLS", data = Kmenta, inst = instlist, 312s + useMatrix = useMatrix ) 312s > print( summary( fitw2slsd1 ) ) 312s 312s systemfit results 312s method: W2SLS 312s 312s N DF SSR detRCov OLS-R2 McElroy-R2 312s system 40 33 164 9.25 0.694 0.512 312s 312s N DF SSR MSE RMSE R2 Adj R2 312s demand 20 17 67.4 3.97 1.99 0.748 0.719 312s supply 20 16 96.6 6.04 2.46 0.640 0.572 312s 312s The covariance matrix of the residuals used for estimation 312s demand supply 312s demand 3.97 0.00 312s supply 0.00 6.04 312s 312s The covariance matrix of the residuals 312s demand supply 312s demand 3.97 3.84 312s supply 3.84 6.04 312s 312s The correlations of the residuals 312s demand supply 312s demand 1.000 0.784 312s supply 0.784 1.000 312s 312s 312s W2SLS estimates for 'demand' (equation 1) 312s Model Formula: consump ~ price + income 312s Instruments: ~income + farmPrice 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 106.7894 11.1435 9.58 2.9e-08 *** 312s price -0.4116 0.1448 -2.84 0.011 * 312s income 0.3617 0.0564 6.41 6.4e-06 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 1.992 on 17 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 17 312s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 312s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 312s 312s 312s W2SLS estimates for 'supply' (equation 2) 312s Model Formula: consump ~ price + farmPrice + trend 312s Instruments: ~income + farmPrice + trend 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 49.5324 12.0105 4.12 0.0008 *** 312s price 0.2401 0.0999 2.40 0.0288 * 312s farmPrice 0.2556 0.0473 5.41 5.8e-05 *** 312s trend 0.2529 0.0997 2.54 0.0219 * 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 2.458 on 16 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 16 312s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 312s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 312s 312s > 312s > ## ****** 2SLS estimation with different instruments (EViews-like)****************** 312s > fitw2slsd1e <- systemfit( system, "W2SLS", data = Kmenta, inst = instlist, 312s + methodResidCov = "noDfCor", x = TRUE, 312s + useMatrix = useMatrix ) 312s > print( summary( fitw2slsd1e, useDfSys = TRUE ) ) 312s 312s systemfit results 312s method: W2SLS 312s 312s N DF SSR detRCov OLS-R2 McElroy-R2 312s system 40 33 164 6.29 0.694 0.5 312s 312s N DF SSR MSE RMSE R2 Adj R2 312s demand 20 17 67.4 3.97 1.99 0.748 0.719 312s supply 20 16 96.6 6.04 2.46 0.640 0.572 312s 312s The covariance matrix of the residuals used for estimation 312s demand supply 312s demand 3.37 0.00 312s supply 0.00 4.83 312s 312s The covariance matrix of the residuals 312s demand supply 312s demand 3.37 3.16 312s supply 3.16 4.83 312s 312s The correlations of the residuals 312s demand supply 312s demand 1.000 0.784 312s supply 0.784 1.000 312s 312s 312s W2SLS estimates for 'demand' (equation 1) 312s Model Formula: consump ~ price + income 312s Instruments: ~income + farmPrice 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 106.789 10.274 10.39 6.1e-12 *** 312s price -0.412 0.134 -3.08 0.0041 ** 312s income 0.362 0.052 6.95 6.0e-08 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 1.992 on 17 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 17 312s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 312s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 312s 312s 312s W2SLS estimates for 'supply' (equation 2) 312s Model Formula: consump ~ price + farmPrice + trend 312s Instruments: ~income + farmPrice + trend 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 49.5324 10.7425 4.61 5.8e-05 *** 312s price 0.2401 0.0894 2.69 0.0112 * 312s farmPrice 0.2556 0.0423 6.05 8.4e-07 *** 312s trend 0.2529 0.0891 2.84 0.0077 ** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 2.458 on 16 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 16 312s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 312s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 312s 312s > 312s > ## **** W2SLS estimation with different instruments and restriction ******** 312s > fitw2slsd2 <- systemfit( system, "W2SLS", data = Kmenta, restrict.matrix = restrm, 312s + inst = instlist, useMatrix = useMatrix ) 312s > print( summary( fitw2slsd2 ) ) 312s 312s systemfit results 312s method: W2SLS 312s 312s N DF SSR detRCov OLS-R2 McElroy-R2 312s system 40 34 166 5.11 0.69 0.557 312s 312s N DF SSR MSE RMSE R2 Adj R2 312s demand 20 17 64.8 3.81 1.95 0.758 0.730 312s supply 20 16 101.4 6.34 2.52 0.622 0.551 312s 312s The covariance matrix of the residuals used for estimation 312s demand supply 312s demand 3.79 0.00 312s supply 0.00 6.27 312s 312s The covariance matrix of the residuals 312s demand supply 312s demand 3.81 4.36 312s supply 4.36 6.34 312s 312s The correlations of the residuals 312s demand supply 312s demand 1.000 0.888 312s supply 0.888 1.000 312s 312s 312s W2SLS estimates for 'demand' (equation 1) 312s Model Formula: consump ~ price + income 312s Instruments: ~income + farmPrice 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 104.5695 10.6344 9.83 1.8e-11 *** 312s price -0.3653 0.1327 -2.75 0.0094 ** 312s income 0.3369 0.0485 6.95 5.1e-08 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 1.952 on 17 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 17 312s SSR: 64.776 MSE: 3.81 Root MSE: 1.952 312s Multiple R-Squared: 0.758 Adjusted R-Squared: 0.73 312s 312s 312s W2SLS estimates for 'supply' (equation 2) 312s Model Formula: consump ~ price + farmPrice + trend 312s Instruments: ~income + farmPrice + trend 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 47.0356 11.9466 3.94 0.00039 *** 312s price 0.2450 0.1017 2.41 0.02156 * 312s farmPrice 0.2672 0.0465 5.74 1.9e-06 *** 312s trend 0.3369 0.0485 6.95 5.1e-08 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 2.518 on 16 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 16 312s SSR: 101.426 MSE: 6.339 Root MSE: 2.518 312s Multiple R-Squared: 0.622 Adjusted R-Squared: 0.551 312s 312s > 312s > ## **** W2SLS estimation with different instruments and restriction (EViews-like)* 312s > fitw2slsd2e <- systemfit( system, "W2SLS", data = Kmenta, restrict.matrix = restrm, 312s + inst = instlist, methodResidCov = "noDfCor", x = TRUE, 312s + useMatrix = useMatrix ) 312s > print( summary( fitw2slsd2e, useDfSys = TRUE ) ) 312s 312s systemfit results 312s method: W2SLS 312s 312s N DF SSR detRCov OLS-R2 McElroy-R2 312s system 40 34 166 3.45 0.69 0.535 312s 312s N DF SSR MSE RMSE R2 Adj R2 312s demand 20 17 64.7 3.81 1.95 0.759 0.730 312s supply 20 16 101.3 6.33 2.52 0.622 0.551 312s 312s The covariance matrix of the residuals used for estimation 312s demand supply 312s demand 3.22 0.00 312s supply 0.00 5.02 312s 312s The covariance matrix of the residuals 312s demand supply 312s demand 3.24 3.60 312s supply 3.60 5.06 312s 312s The correlations of the residuals 312s demand supply 312s demand 1.000 0.888 312s supply 0.888 1.000 312s 312s 312s W2SLS estimates for 'demand' (equation 1) 312s Model Formula: consump ~ price + income 312s Instruments: ~income + farmPrice 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 104.4638 9.7929 10.67 2.2e-12 *** 312s price -0.3630 0.1220 -2.98 0.0053 ** 312s income 0.3357 0.0444 7.57 8.6e-09 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 1.951 on 17 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 17 312s SSR: 64.715 MSE: 3.807 Root MSE: 1.951 312s Multiple R-Squared: 0.759 Adjusted R-Squared: 0.73 312s 312s 312s W2SLS estimates for 'supply' (equation 2) 312s Model Formula: consump ~ price + farmPrice + trend 312s Instruments: ~income + farmPrice + trend 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 47.0706 10.6890 4.40 0.0001 *** 312s price 0.2449 0.0910 2.69 0.0109 * 312s farmPrice 0.2671 0.0416 6.41 2.5e-07 *** 312s trend 0.3357 0.0444 7.57 8.6e-09 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 2.516 on 16 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 16 312s SSR: 101.299 MSE: 6.331 Root MSE: 2.516 312s Multiple R-Squared: 0.622 Adjusted R-Squared: 0.551 312s 312s > 312s > ## ** W2SLS estimation with different instruments and restriction via restrict.regMat **** 312s > fitw2slsd3 <- systemfit( system, "W2SLS", data = Kmenta, restrict.regMat = tc, 312s + inst = instlist, x = TRUE, useMatrix = useMatrix ) 312s > print( summary( fitw2slsd3 ) ) 312s 312s systemfit results 312s method: W2SLS 312s 312s N DF SSR detRCov OLS-R2 McElroy-R2 312s system 40 34 166 5.11 0.69 0.557 312s 312s N DF SSR MSE RMSE R2 Adj R2 312s demand 20 17 64.8 3.81 1.95 0.758 0.730 312s supply 20 16 101.4 6.34 2.52 0.622 0.551 312s 312s The covariance matrix of the residuals used for estimation 312s demand supply 312s demand 3.79 0.00 312s supply 0.00 6.27 312s 312s The covariance matrix of the residuals 312s demand supply 312s demand 3.81 4.36 312s supply 4.36 6.34 312s 312s The correlations of the residuals 312s demand supply 312s demand 1.000 0.888 312s supply 0.888 1.000 312s 312s 312s W2SLS estimates for 'demand' (equation 1) 312s Model Formula: consump ~ price + income 312s Instruments: ~income + farmPrice 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 104.5695 10.6344 9.83 1.8e-11 *** 312s price -0.3653 0.1327 -2.75 0.0094 ** 312s income 0.3369 0.0485 6.95 5.1e-08 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 1.952 on 17 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 17 312s SSR: 64.776 MSE: 3.81 Root MSE: 1.952 312s Multiple R-Squared: 0.758 Adjusted R-Squared: 0.73 312s 312s 312s W2SLS estimates for 'supply' (equation 2) 312s Model Formula: consump ~ price + farmPrice + trend 312s Instruments: ~income + farmPrice + trend 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 47.0356 11.9466 3.94 0.00039 *** 312s price 0.2450 0.1017 2.41 0.02156 * 312s farmPrice 0.2672 0.0465 5.74 1.9e-06 *** 312s trend 0.3369 0.0485 6.95 5.1e-08 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 2.518 on 16 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 16 312s SSR: 101.426 MSE: 6.339 Root MSE: 2.518 312s Multiple R-Squared: 0.622 Adjusted R-Squared: 0.551 312s 312s > 312s > ## W2SLS estimation with different instruments and restriction via restrict.regMat (EViews-like) 312s > fitw2slsd3e <- systemfit( system, "W2SLS", data = Kmenta, restrict.regMat = tc, 312s + inst = instlist, methodResidCov = "noDfCor", useMatrix = useMatrix ) 312s > print( summary( fitw2slsd3e, useDfSys = TRUE ) ) 312s 312s systemfit results 312s method: W2SLS 312s 312s N DF SSR detRCov OLS-R2 McElroy-R2 312s system 40 34 166 3.45 0.69 0.535 312s 312s N DF SSR MSE RMSE R2 Adj R2 312s demand 20 17 64.7 3.81 1.95 0.759 0.730 312s supply 20 16 101.3 6.33 2.52 0.622 0.551 312s 312s The covariance matrix of the residuals used for estimation 312s demand supply 312s demand 3.22 0.00 312s supply 0.00 5.02 312s 312s The covariance matrix of the residuals 312s demand supply 312s demand 3.24 3.60 312s supply 3.60 5.06 312s 312s The correlations of the residuals 312s demand supply 312s demand 1.000 0.888 312s supply 0.888 1.000 312s 312s 312s W2SLS estimates for 'demand' (equation 1) 312s Model Formula: consump ~ price + income 312s Instruments: ~income + farmPrice 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 104.4638 9.7929 10.67 2.2e-12 *** 312s price -0.3630 0.1220 -2.98 0.0053 ** 312s income 0.3357 0.0444 7.57 8.6e-09 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 1.951 on 17 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 17 312s SSR: 64.715 MSE: 3.807 Root MSE: 1.951 312s Multiple R-Squared: 0.759 Adjusted R-Squared: 0.73 312s 312s 312s W2SLS estimates for 'supply' (equation 2) 312s Model Formula: consump ~ price + farmPrice + trend 312s Instruments: ~income + farmPrice + trend 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 47.0706 10.6890 4.40 0.0001 *** 312s price 0.2449 0.0910 2.69 0.0109 * 312s farmPrice 0.2671 0.0416 6.41 2.5e-07 *** 312s trend 0.3357 0.0444 7.57 8.6e-09 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 2.516 on 16 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 16 312s SSR: 101.299 MSE: 6.331 Root MSE: 2.516 312s Multiple R-Squared: 0.622 Adjusted R-Squared: 0.551 312s 312s > 312s > 312s > ## *********** estimations with a single regressor ************ 312s > fitw2slsS1 <- systemfit( 312s + list( consump ~ price - 1, price ~ consump + trend ), "W2SLS", 312s + data = Kmenta, inst = ~ farmPrice + trend + income, useMatrix = useMatrix ) 312s > print( summary( fitw2slsS1 ) ) 312s 312s systemfit results 312s method: W2SLS 312s 312s N DF SSR detRCov OLS-R2 McElroy-R2 312s system 40 36 1544 179 -0.65 0.852 312s 312s N DF SSR MSE RMSE R2 Adj R2 312s eq1 20 19 861 45.3 6.73 -2.213 -2.213 312s eq2 20 17 682 40.1 6.33 -0.022 -0.143 312s 312s The covariance matrix of the residuals used for estimation 312s eq1 eq2 312s eq1 45.3 0.0 312s eq2 0.0 40.1 312s 312s The covariance matrix of the residuals 312s eq1 eq2 312s eq1 45.3 -40.5 312s eq2 -40.5 40.1 312s 312s The correlations of the residuals 312s eq1 eq2 312s eq1 1.00 -0.95 312s eq2 -0.95 1.00 312s 312s 312s W2SLS estimates for 'eq1' (equation 1) 312s Model Formula: consump ~ price - 1 312s Instruments: ~farmPrice + trend + income 312s 312s Estimate Std. Error t value Pr(>|t|) 312s price 1.006 0.015 66.9 <2e-16 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 6.734 on 19 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 19 312s SSR: 861.48 MSE: 45.341 Root MSE: 6.734 312s Multiple R-Squared: -2.213 Adjusted R-Squared: -2.213 312s 312s 312s W2SLS estimates for 'eq2' (equation 2) 312s Model Formula: price ~ consump + trend 312s Instruments: ~farmPrice + trend + income 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 55.5365 46.2668 1.20 0.25 312s consump 0.4453 0.4622 0.96 0.35 312s trend -0.0426 0.2496 -0.17 0.87 312s 312s Residual standard error: 6.335 on 17 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 17 312s SSR: 682.257 MSE: 40.133 Root MSE: 6.335 312s Multiple R-Squared: -0.022 Adjusted R-Squared: -0.143 312s 312s > fitw2slsS2 <- systemfit( 312s + list( consump ~ price - 1, consump ~ trend - 1 ), "W2SLS", 312s + data = Kmenta, inst = ~ farmPrice + price + income, useMatrix = useMatrix ) 312s > print( summary( fitw2slsS2 ) ) 312s 312s systemfit results 312s method: W2SLS 312s 312s N DF SSR detRCov OLS-R2 McElroy-R2 312s system 40 38 47456 111148 -87.5 -5.28 312s 312s N DF SSR MSE RMSE R2 Adj R2 312s eq1 20 19 861 45.3 6.73 -2.21 -2.21 312s eq2 20 19 46595 2452.3 49.52 -172.79 -172.79 312s 312s The covariance matrix of the residuals used for estimation 312s eq1 eq2 312s eq1 45.3 0 312s eq2 0.0 2452 312s 312s The covariance matrix of the residuals 312s eq1 eq2 312s eq1 45.34 -6.33 312s eq2 -6.33 2452.34 312s 312s The correlations of the residuals 312s eq1 eq2 312s eq1 1.0000 -0.0448 312s eq2 -0.0448 1.0000 312s 312s 312s W2SLS estimates for 'eq1' (equation 1) 312s Model Formula: consump ~ price - 1 312s Instruments: ~farmPrice + price + income 312s 312s Estimate Std. Error t value Pr(>|t|) 312s price 1.006 0.015 66.9 <2e-16 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 6.733 on 19 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 19 312s SSR: 861.449 MSE: 45.339 Root MSE: 6.733 312s Multiple R-Squared: -2.213 Adjusted R-Squared: -2.213 312s 312s 312s W2SLS estimates for 'eq2' (equation 2) 312s Model Formula: consump ~ trend - 1 312s Instruments: ~farmPrice + price + income 312s 312s Estimate Std. Error t value Pr(>|t|) 312s trend 7.578 0.934 8.11 1.4e-07 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 49.521 on 19 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 19 312s SSR: 46594.549 MSE: 2452.345 Root MSE: 49.521 312s Multiple R-Squared: -172.786 Adjusted R-Squared: -172.786 312s 312s > fitw2slsS3 <- systemfit( 312s + list( consump ~ trend - 1, price ~ trend - 1 ), "W2SLS", 312s + data = Kmenta, inst = instlist, useMatrix = useMatrix ) 312s > print( summary( fitw2slsS3 ) ) 312s 312s systemfit results 312s method: W2SLS 312s 312s N DF SSR detRCov OLS-R2 McElroy-R2 312s system 40 38 97978 687515 -104 -10.6 312s 312s N DF SSR MSE RMSE R2 Adj R2 312s eq1 20 19 50950 2682 51.8 -189.0 -189.0 312s eq2 20 19 47028 2475 49.8 -69.5 -69.5 312s 312s The covariance matrix of the residuals used for estimation 312s eq1 eq2 312s eq1 2682 0 312s eq2 0 2475 312s 312s The covariance matrix of the residuals 312s eq1 eq2 312s eq1 2682 2439 312s eq2 2439 2475 312s 312s The correlations of the residuals 312s eq1 eq2 312s eq1 1.000 0.989 312s eq2 0.989 1.000 312s 312s 312s W2SLS estimates for 'eq1' (equation 1) 312s Model Formula: consump ~ trend - 1 312s Instruments: ~income + farmPrice 312s 312s Estimate Std. Error t value Pr(>|t|) 312s trend 8.65 1.05 8.27 1e-07 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 51.784 on 19 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 19 312s SSR: 50949.985 MSE: 2681.578 Root MSE: 51.784 312s Multiple R-Squared: -189.031 Adjusted R-Squared: -189.031 312s 312s 312s W2SLS estimates for 'eq2' (equation 2) 312s Model Formula: price ~ trend - 1 312s Instruments: ~income + farmPrice + trend 312s 312s Estimate Std. Error t value Pr(>|t|) 312s trend 7.318 0.929 7.88 2.1e-07 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 49.751 on 19 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 19 312s SSR: 47028.107 MSE: 2475.164 Root MSE: 49.751 312s Multiple R-Squared: -69.48 Adjusted R-Squared: -69.48 312s 312s > fitw2slsS4 <- systemfit( 312s + list( consump ~ trend - 1, price ~ trend - 1 ), "W2SLS", 312s + data = Kmenta, inst = ~ farmPrice + trend + income, 312s + restrict.matrix = matrix( c( 1, -1 ), nrow = 1 ), useMatrix = useMatrix ) 312s > print( summary( fitw2slsS4 ) ) 312s 312s systemfit results 312s method: W2SLS 312s 312s N DF SSR detRCov OLS-R2 McElroy-R2 312s system 40 39 93548 111736 -99 -1.03 312s 312s N DF SSR MSE RMSE R2 Adj R2 312s eq1 20 19 46514 2448 49.5 -172.5 -172.5 312s eq2 20 19 47034 2475 49.8 -69.5 -69.5 312s 312s The covariance matrix of the residuals used for estimation 312s eq1 eq2 312s eq1 2448 0 312s eq2 0 2475 312s 312s The covariance matrix of the residuals 312s eq1 eq2 312s eq1 2448 2439 312s eq2 2439 2475 312s 312s The correlations of the residuals 312s eq1 eq2 312s eq1 1.000 0.988 312s eq2 0.988 1.000 312s 312s 312s W2SLS estimates for 'eq1' (equation 1) 312s Model Formula: consump ~ trend - 1 312s Instruments: ~farmPrice + trend + income 312s 312s Estimate Std. Error t value Pr(>|t|) 312s trend 7.362 0.655 11.2 8.4e-14 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 49.478 on 19 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 19 312s SSR: 46514.224 MSE: 2448.117 Root MSE: 49.478 312s Multiple R-Squared: -172.487 Adjusted R-Squared: -172.487 312s 312s 312s W2SLS estimates for 'eq2' (equation 2) 312s Model Formula: price ~ trend - 1 312s Instruments: ~farmPrice + trend + income 312s 312s Estimate Std. Error t value Pr(>|t|) 312s trend 7.362 0.655 11.2 8.4e-14 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 49.754 on 19 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 19 312s SSR: 47033.528 MSE: 2475.449 Root MSE: 49.754 312s Multiple R-Squared: -69.488 Adjusted R-Squared: -69.488 312s 312s > fitw2slsS5 <- systemfit( 312s + list( consump ~ 1, price ~ 1 ), "W2SLS", 312s + data = Kmenta, inst = instlist, useMatrix = useMatrix ) 312s > print( summary( fitw2slsS5 ) ) 312s 312s systemfit results 312s method: W2SLS 312s 312s N DF SSR detRCov OLS-R2 McElroy-R2 312s system 40 38 935 491 0 0 312s 312s N DF SSR MSE RMSE R2 Adj R2 312s eq1 20 19 268 14.1 3.76 0 0 312s eq2 20 19 667 35.1 5.93 0 0 312s 312s The covariance matrix of the residuals used for estimation 312s eq1 eq2 312s eq1 14.1 0.0 312s eq2 0.0 35.1 312s 312s The covariance matrix of the residuals 312s eq1 eq2 312s eq1 14.11 2.18 312s eq2 2.18 35.12 312s 312s The correlations of the residuals 312s eq1 eq2 312s eq1 1.0000 0.0981 312s eq2 0.0981 1.0000 312s 312s 312s W2SLS estimates for 'eq1' (equation 1) 312s Model Formula: consump ~ 1 312s Instruments: ~income + farmPrice 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 100.90 0.84 120 <2e-16 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 3.756 on 19 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 19 312s SSR: 268.114 MSE: 14.111 Root MSE: 3.756 312s Multiple R-Squared: 0 Adjusted R-Squared: 0 312s 312s 312s W2SLS estimates for 'eq2' (equation 2) 312s Model Formula: price ~ 1 312s Instruments: ~income + farmPrice + trend 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 100.02 1.33 75.5 <2e-16 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 5.926 on 19 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 19 312s SSR: 667.251 MSE: 35.118 Root MSE: 5.926 312s Multiple R-Squared: 0 Adjusted R-Squared: 0 312s 312s > 312s > 312s > ## **************** shorter summaries ********************** 312s > print( summary( fitw2sls1e, residCov = FALSE ) ) 312s 312s systemfit results 312s method: W2SLS 312s 312s N DF SSR detRCov OLS-R2 McElroy-R2 312s system 40 33 162 2.97 0.697 0.525 312s 312s N DF SSR MSE RMSE R2 Adj R2 312s demand 20 17 65.7 3.87 1.97 0.755 0.726 312s supply 20 16 96.6 6.04 2.46 0.640 0.572 312s 312s 312s W2SLS estimates for 'demand' (equation 1) 312s Model Formula: consump ~ price + income 312s Instruments: ~income + farmPrice + trend 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 94.6333 7.3027 12.96 3.1e-10 *** 312s price -0.2436 0.0890 -2.74 0.014 * 312s income 0.3140 0.0433 7.25 1.3e-06 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 1.966 on 17 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 17 312s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 312s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 312s 312s 312s W2SLS estimates for 'supply' (equation 2) 312s Model Formula: consump ~ price + farmPrice + trend 312s Instruments: ~income + farmPrice + trend 312s 312s Estimate Std. Error t value Pr(>|t|) 312s (Intercept) 49.5324 10.7425 4.61 0.00029 *** 312s price 0.2401 0.0894 2.69 0.01623 * 312s farmPrice 0.2556 0.0423 6.05 1.7e-05 *** 312s trend 0.2529 0.0891 2.84 0.01188 * 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s 312s Residual standard error: 2.458 on 16 degrees of freedom 312s Number of observations: 20 Degrees of Freedom: 16 312s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 312s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 312s 312s > 312s > print( summary( fitw2sls2, residCov = FALSE, equations = FALSE ) ) 312s 312s systemfit results 312s method: W2SLS 312s 312s N DF SSR detRCov OLS-R2 McElroy-R2 312s system 40 34 165 3.41 0.692 0.565 312s 312s N DF SSR MSE RMSE R2 Adj R2 312s demand 20 17 66.8 3.93 1.98 0.751 0.721 312s supply 20 16 98.4 6.15 2.48 0.633 0.564 312s 312s 312s Coefficients: 312s Estimate Std. Error t value Pr(>|t|) 312s demand_(Intercept) 94.3832 8.0090 11.78 1.5e-13 *** 312s demand_price -0.2302 0.0946 -2.43 0.02042 * 312s demand_income 0.3028 0.0430 7.05 3.9e-08 *** 312s supply_(Intercept) 48.0494 11.8001 4.07 0.00026 *** 312s supply_price 0.2430 0.1006 2.42 0.02122 * 312s supply_farmPrice 0.2625 0.0459 5.72 2.0e-06 *** 312s supply_trend 0.3028 0.0430 7.05 3.9e-08 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s > 312s > print( summary( fitw2sls3, useDfSys = FALSE ), equations = FALSE ) 312s 312s systemfit results 312s method: W2SLS 312s 312s N DF SSR detRCov OLS-R2 McElroy-R2 312s system 40 34 165 3.41 0.692 0.565 312s 312s N DF SSR MSE RMSE R2 Adj R2 312s demand 20 17 66.8 3.93 1.98 0.751 0.721 312s supply 20 16 98.4 6.15 2.48 0.633 0.564 312s 312s The covariance matrix of the residuals used for estimation 312s demand supply 312s demand 3.97 0.00 312s supply 0.00 6.13 312s 312s The covariance matrix of the residuals 312s demand supply 312s demand 3.93 4.56 312s supply 4.56 6.15 312s 312s The correlations of the residuals 312s demand supply 312s demand 1.000 0.927 312s supply 0.927 1.000 312s 312s 312s Coefficients: 312s Estimate Std. Error t value Pr(>|t|) 312s demand_(Intercept) 94.3832 8.0090 11.78 1.3e-09 *** 312s demand_price -0.2302 0.0946 -2.43 0.02634 * 312s demand_income 0.3028 0.0430 7.05 2.0e-06 *** 312s supply_(Intercept) 48.0494 11.8001 4.07 0.00089 *** 312s supply_price 0.2430 0.1006 2.42 0.02802 * 312s supply_farmPrice 0.2625 0.0459 5.72 3.2e-05 *** 312s supply_trend 0.3028 0.0430 7.05 2.8e-06 *** 312s --- 312s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 312s > 312s > print( summary( fitw2sls4e ), residCov = FALSE ) 313s 313s systemfit results 313s method: W2SLS 313s 313s N DF SSR detRCov OLS-R2 McElroy-R2 313s system 40 35 166 2.44 0.69 0.546 313s 313s N DF SSR MSE RMSE R2 Adj R2 313s demand 20 17 65.9 3.88 1.97 0.754 0.725 313s supply 20 16 100.2 6.26 2.50 0.626 0.556 313s 313s 313s W2SLS estimates for 'demand' (equation 1) 313s Model Formula: consump ~ price + income 313s Instruments: ~income + farmPrice + trend 313s 313s Estimate Std. Error t value Pr(>|t|) 313s (Intercept) 95.3470 5.7579 16.56 < 2e-16 *** 313s price -0.2428 0.0621 -3.91 0.00041 *** 313s income 0.3059 0.0360 8.49 5.1e-10 *** 313s --- 313s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 313s 313s Residual standard error: 1.97 on 17 degrees of freedom 313s Number of observations: 20 Degrees of Freedom: 17 313s SSR: 65.945 MSE: 3.879 Root MSE: 1.97 313s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 313s 313s 313s W2SLS estimates for 'supply' (equation 2) 313s Model Formula: consump ~ price + farmPrice + trend 313s Instruments: ~income + farmPrice + trend 313s 313s Estimate Std. Error t value Pr(>|t|) 313s (Intercept) 46.4366 7.5360 6.16 4.7e-07 *** 313s price 0.2572 0.0621 4.14 0.00021 *** 313s farmPrice 0.2642 0.0407 6.48 1.8e-07 *** 313s trend 0.3059 0.0360 8.49 5.1e-10 *** 313s --- 313s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 313s 313s Residual standard error: 2.503 on 16 degrees of freedom 313s Number of observations: 20 Degrees of Freedom: 16 313s SSR: 100.225 MSE: 6.264 Root MSE: 2.503 313s Multiple R-Squared: 0.626 Adjusted R-Squared: 0.556 313s 313s > 313s > print( summary( fitw2sls5, useDfSys = FALSE, residCov = FALSE ) ) 313s 313s systemfit results 313s method: W2SLS 313s 313s N DF SSR detRCov OLS-R2 McElroy-R2 313s system 40 35 166 3.57 0.69 0.575 313s 313s N DF SSR MSE RMSE R2 Adj R2 313s demand 20 17 65.9 3.88 1.97 0.754 0.725 313s supply 20 16 100.3 6.27 2.50 0.626 0.556 313s 313s 313s W2SLS estimates for 'demand' (equation 1) 313s Model Formula: consump ~ price + income 313s Instruments: ~income + farmPrice + trend 313s 313s Estimate Std. Error t value Pr(>|t|) 313s (Intercept) 95.3043 6.3056 15.11 2.7e-11 *** 313s price -0.2428 0.0684 -3.55 0.0025 ** 313s income 0.3063 0.0394 7.78 5.4e-07 *** 313s --- 313s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 313s 313s Residual standard error: 1.969 on 17 degrees of freedom 313s Number of observations: 20 Degrees of Freedom: 17 313s SSR: 65.931 MSE: 3.878 Root MSE: 1.969 313s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 313s 313s 313s W2SLS estimates for 'supply' (equation 2) 313s Model Formula: consump ~ price + farmPrice + trend 313s Instruments: ~income + farmPrice + trend 313s 313s Estimate Std. Error t value Pr(>|t|) 313s (Intercept) 46.4229 8.3296 5.57 4.2e-05 *** 313s price 0.2572 0.0684 3.76 0.0017 ** 313s farmPrice 0.2642 0.0455 5.80 2.7e-05 *** 313s trend 0.3063 0.0394 7.78 8.0e-07 *** 313s --- 313s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 313s 313s Residual standard error: 2.503 on 16 degrees of freedom 313s Number of observations: 20 Degrees of Freedom: 16 313s SSR: 100.255 MSE: 6.266 Root MSE: 2.503 313s Multiple R-Squared: 0.626 Adjusted R-Squared: 0.556 313s 313s > 313s > print( summary( fitw2slsd1, useDfSys = TRUE ), residCov = FALSE, 313s + equations = FALSE ) 313s 313s systemfit results 313s method: W2SLS 313s 313s N DF SSR detRCov OLS-R2 McElroy-R2 313s system 40 33 164 9.25 0.694 0.512 313s 313s N DF SSR MSE RMSE R2 Adj R2 313s demand 20 17 67.4 3.97 1.99 0.748 0.719 313s supply 20 16 96.6 6.04 2.46 0.640 0.572 313s 313s 313s Coefficients: 313s Estimate Std. Error t value Pr(>|t|) 313s demand_(Intercept) 106.7894 11.1435 9.58 4.7e-11 *** 313s demand_price -0.4116 0.1448 -2.84 0.00764 ** 313s demand_income 0.3617 0.0564 6.41 2.9e-07 *** 313s supply_(Intercept) 49.5324 12.0105 4.12 0.00024 *** 313s supply_price 0.2401 0.0999 2.40 0.02208 * 313s supply_farmPrice 0.2556 0.0473 5.41 5.5e-06 *** 313s supply_trend 0.2529 0.0997 2.54 0.01605 * 313s --- 313s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 313s > 313s > print( summary( fitw2slsd2e, equations = TRUE ), equations = FALSE ) 313s 313s systemfit results 313s method: W2SLS 313s 313s N DF SSR detRCov OLS-R2 McElroy-R2 313s system 40 34 166 3.45 0.69 0.535 313s 313s N DF SSR MSE RMSE R2 Adj R2 313s demand 20 17 64.7 3.81 1.95 0.759 0.730 313s supply 20 16 101.3 6.33 2.52 0.622 0.551 313s 313s The covariance matrix of the residuals used for estimation 313s demand supply 313s demand 3.22 0.00 313s supply 0.00 5.02 313s 313s The covariance matrix of the residuals 313s demand supply 313s demand 3.24 3.60 313s supply 3.60 5.06 313s 313s The correlations of the residuals 313s demand supply 313s demand 1.000 0.888 313s supply 0.888 1.000 313s 313s 313s Coefficients: 313s Estimate Std. Error t value Pr(>|t|) 313s demand_(Intercept) 104.4638 9.7929 10.67 2.2e-12 *** 313s demand_price -0.3630 0.1220 -2.98 0.0053 ** 313s demand_income 0.3357 0.0444 7.57 8.6e-09 *** 313s supply_(Intercept) 47.0706 10.6890 4.40 0.0001 *** 313s supply_price 0.2449 0.0910 2.69 0.0109 * 313s supply_farmPrice 0.2671 0.0416 6.41 2.5e-07 *** 313s supply_trend 0.3357 0.0444 7.57 8.6e-09 *** 313s --- 313s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 313s > 313s > print( summary( fitw2slsd3e, equations = FALSE ), residCov = FALSE ) 313s 313s systemfit results 313s method: W2SLS 313s 313s N DF SSR detRCov OLS-R2 McElroy-R2 313s system 40 34 166 3.45 0.69 0.535 313s 313s N DF SSR MSE RMSE R2 Adj R2 313s demand 20 17 64.7 3.81 1.95 0.759 0.730 313s supply 20 16 101.3 6.33 2.52 0.622 0.551 313s 313s 313s Coefficients: 313s Estimate Std. Error t value Pr(>|t|) 313s demand_(Intercept) 104.4638 9.7929 10.67 2.2e-12 *** 313s demand_price -0.3630 0.1220 -2.98 0.0053 ** 313s demand_income 0.3357 0.0444 7.57 8.6e-09 *** 313s supply_(Intercept) 47.0706 10.6890 4.40 0.0001 *** 313s supply_price 0.2449 0.0910 2.69 0.0109 * 313s supply_farmPrice 0.2671 0.0416 6.41 2.5e-07 *** 313s supply_trend 0.3357 0.0444 7.57 8.6e-09 *** 313s --- 313s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 313s > 313s > 313s > ## ****************** residuals ************************** 313s > print( residuals( fitw2sls1e ) ) 313s demand supply 313s 1 0.843 -0.4348 313s 2 -0.698 -1.2131 313s 3 2.359 1.7090 313s 4 1.490 0.7956 313s 5 2.139 1.5942 313s 6 1.277 0.6595 313s 7 1.571 1.4346 313s 8 -3.066 -4.8724 313s 9 -1.125 -2.3975 313s 10 2.492 3.1427 313s 11 -0.108 0.0689 313s 12 -2.292 -1.3978 313s 13 -1.598 -1.1136 313s 14 -0.271 1.1684 313s 15 1.958 3.4865 313s 16 -3.430 -3.8285 313s 17 -0.313 0.6793 313s 18 -2.151 -2.7713 313s 19 1.592 2.6668 313s 20 -0.668 0.6235 313s > print( residuals( fitw2sls1e$eq[[ 1 ]] ) ) 313s 1 2 3 4 5 6 7 8 9 10 11 313s 0.843 -0.698 2.359 1.490 2.139 1.277 1.571 -3.066 -1.125 2.492 -0.108 313s 12 13 14 15 16 17 18 19 20 313s -2.292 -1.598 -0.271 1.958 -3.430 -0.313 -2.151 1.592 -0.668 313s > 313s > print( residuals( fitw2sls2 ) ) 313s demand supply 313s 1 0.726 0.0287 313s 2 -0.754 -0.8185 313s 3 2.304 2.0561 313s 4 1.437 1.0966 313s 5 2.191 1.7764 313s 6 1.317 0.8056 313s 7 1.620 1.5441 313s 8 -3.015 -4.8526 313s 9 -1.087 -2.3957 313s 10 2.513 3.1658 313s 11 -0.265 0.1722 313s 12 -2.506 -1.2753 313s 13 -1.781 -1.0688 313s 14 -0.332 1.1028 313s 15 2.086 3.2370 313s 16 -3.325 -4.1563 313s 17 -0.144 0.2984 313s 18 -2.128 -3.1286 313s 19 1.662 2.2767 313s 20 -0.518 0.1355 313s > print( residuals( fitw2sls2$eq[[ 2 ]] ) ) 313s 1 2 3 4 5 6 7 8 9 10 313s 0.0287 -0.8185 2.0561 1.0966 1.7764 0.8056 1.5441 -4.8526 -2.3957 3.1658 313s 11 12 13 14 15 16 17 18 19 20 313s 0.1722 -1.2753 -1.0688 1.1028 3.2370 -4.1563 0.2984 -3.1286 2.2767 0.1355 313s > 313s > print( residuals( fitw2sls3 ) ) 313s demand supply 313s 1 0.726 0.0287 313s 2 -0.754 -0.8185 313s 3 2.304 2.0561 313s 4 1.437 1.0966 313s 5 2.191 1.7764 313s 6 1.317 0.8056 313s 7 1.620 1.5441 313s 8 -3.015 -4.8526 313s 9 -1.087 -2.3957 313s 10 2.513 3.1658 313s 11 -0.265 0.1722 313s 12 -2.506 -1.2753 313s 13 -1.781 -1.0688 313s 14 -0.332 1.1028 313s 15 2.086 3.2370 313s 16 -3.325 -4.1563 313s 17 -0.144 0.2984 313s 18 -2.128 -3.1286 313s 19 1.662 2.2767 313s 20 -0.518 0.1355 313s > print( residuals( fitw2sls3$eq[[ 1 ]] ) ) 313s 1 2 3 4 5 6 7 8 9 10 11 313s 0.726 -0.754 2.304 1.437 2.191 1.317 1.620 -3.015 -1.087 2.513 -0.265 313s 12 13 14 15 16 17 18 19 20 313s -2.506 -1.781 -0.332 2.086 -3.325 -0.144 -2.128 1.662 -0.518 313s > 313s > print( residuals( fitw2sls4e ) ) 313s demand supply 313s 1 0.761 0.0514 313s 2 -0.700 -0.8567 313s 3 2.350 2.0266 313s 4 1.492 1.0504 313s 5 2.159 1.7988 313s 6 1.301 0.8085 313s 7 1.616 1.5253 313s 8 -2.986 -4.9339 313s 9 -1.130 -2.3600 313s 10 2.429 3.2858 313s 11 -0.284 0.2948 313s 12 -2.458 -1.2168 313s 13 -1.705 -1.0756 313s 14 -0.327 1.1348 313s 15 2.007 3.2835 313s 16 -3.368 -4.1646 313s 17 -0.312 0.4480 313s 18 -2.099 -3.2018 313s 19 1.694 2.1807 313s 20 -0.439 -0.0794 313s > print( residuals( fitw2sls4e$eq[[ 2 ]] ) ) 313s 1 2 3 4 5 6 7 8 9 10 313s 0.0514 -0.8567 2.0266 1.0504 1.7988 0.8085 1.5253 -4.9339 -2.3600 3.2858 313s 11 12 13 14 15 16 17 18 19 20 313s 0.2948 -1.2168 -1.0756 1.1348 3.2835 -4.1646 0.4480 -3.2018 2.1807 -0.0794 313s > 313s > print( residuals( fitw2sls5 ) ) 313s demand supply 313s 1 0.765 0.0551 313s 2 -0.701 -0.8537 313s 3 2.350 2.0293 313s 4 1.491 1.0527 313s 5 2.158 1.8003 313s 6 1.300 0.8097 313s 7 1.614 1.5262 313s 8 -2.991 -4.9339 313s 9 -1.129 -2.3600 313s 10 2.433 3.2862 313s 11 -0.275 0.2958 313s 12 -2.450 -1.2157 313s 13 -1.700 -1.0752 313s 14 -0.324 1.1344 313s 15 2.005 3.2816 313s 16 -3.371 -4.1672 313s 17 -0.311 0.4452 313s 18 -2.102 -3.2047 313s 19 1.688 2.1776 313s 20 -0.451 -0.0835 313s > print( residuals( fitw2sls5$eq[[ 1 ]] ) ) 313s 1 2 3 4 5 6 7 8 9 10 11 313s 0.765 -0.701 2.350 1.491 2.158 1.300 1.614 -2.991 -1.129 2.433 -0.275 313s 12 13 14 15 16 17 18 19 20 313s -2.450 -1.700 -0.324 2.005 -3.371 -0.311 -2.102 1.688 -0.451 313s > 313s > print( residuals( fitw2slsd1 ) ) 313s demand supply 313s 1 1.3775 -0.4348 313s 2 0.0125 -1.2131 313s 3 2.9728 1.7090 313s 4 2.2121 0.7956 313s 5 1.6920 1.5942 313s 6 1.0407 0.6595 313s 7 1.4768 1.4346 313s 8 -2.7583 -4.8724 313s 9 -1.6807 -2.3975 313s 10 1.4265 3.1427 313s 11 -0.2029 0.0689 313s 12 -1.5123 -1.3978 313s 13 -0.4958 -1.1136 313s 14 -0.1528 1.1684 313s 15 0.8692 3.4865 313s 16 -4.0547 -3.8285 313s 17 -2.5309 0.6793 313s 18 -1.8070 -2.7713 313s 19 1.9299 2.6668 313s 20 0.1853 0.6235 313s > print( residuals( fitw2slsd1$eq[[ 2 ]] ) ) 313s 1 2 3 4 5 6 7 8 9 10 313s -0.4348 -1.2131 1.7090 0.7956 1.5942 0.6595 1.4346 -4.8724 -2.3975 3.1427 313s 11 12 13 14 15 16 17 18 19 20 313s 0.0689 -1.3978 -1.1136 1.1684 3.4865 -3.8285 0.6793 -2.7713 2.6668 0.6235 313s > 313s > print( residuals( fitw2slsd2e ) ) 313s demand supply 313s 1 1.100 0.3346 313s 2 -0.192 -0.5581 313s 3 2.785 2.2852 313s 4 2.012 1.2953 313s 5 1.849 1.8966 313s 6 1.145 0.9020 313s 7 1.573 1.6164 313s 8 -2.722 -4.8395 313s 9 -1.531 -2.3946 313s 10 1.629 3.1810 313s 11 -0.448 0.2403 313s 12 -1.988 -1.1944 313s 13 -0.972 -1.0393 313s 14 -0.271 1.0594 313s 15 1.251 3.0723 313s 16 -3.782 -4.3726 313s 17 -1.904 0.0471 313s 18 -1.823 -3.3644 313s 19 1.992 2.0193 313s 20 0.298 -0.1866 313s > print( residuals( fitw2slsd2e$eq[[ 1 ]] ) ) 313s 1 2 3 4 5 6 7 8 9 10 11 313s 1.100 -0.192 2.785 2.012 1.849 1.145 1.573 -2.722 -1.531 1.629 -0.448 313s 12 13 14 15 16 17 18 19 20 313s -1.988 -0.972 -0.271 1.251 -3.782 -1.904 -1.823 1.992 0.298 313s > 313s > print( residuals( fitw2slsd3e ) ) 313s demand supply 313s 1 1.100 0.3346 313s 2 -0.192 -0.5581 313s 3 2.785 2.2852 313s 4 2.012 1.2953 313s 5 1.849 1.8966 313s 6 1.145 0.9020 313s 7 1.573 1.6164 313s 8 -2.722 -4.8395 313s 9 -1.531 -2.3946 313s 10 1.629 3.1810 313s 11 -0.448 0.2403 313s 12 -1.988 -1.1944 313s 13 -0.972 -1.0393 313s 14 -0.271 1.0594 313s 15 1.251 3.0723 313s 16 -3.782 -4.3726 313s 17 -1.904 0.0471 313s 18 -1.823 -3.3644 313s 19 1.992 2.0193 313s 20 0.298 -0.1866 313s > print( residuals( fitw2slsd3e$eq[[ 2 ]] ) ) 313s 1 2 3 4 5 6 7 8 9 10 313s 0.3346 -0.5581 2.2852 1.2953 1.8966 0.9020 1.6164 -4.8395 -2.3946 3.1810 313s 11 12 13 14 15 16 17 18 19 20 313s 0.2403 -1.1944 -1.0393 1.0594 3.0723 -4.3726 0.0471 -3.3644 2.0193 -0.1866 313s > 313s > 313s > ## *************** coefficients ********************* 313s > print( round( coef( fitw2sls1e ), digits = 6 ) ) 313s demand_(Intercept) demand_price demand_income supply_(Intercept) 313s 94.633 -0.244 0.314 49.532 313s supply_price supply_farmPrice supply_trend 313s 0.240 0.256 0.253 313s > print( round( coef( fitw2sls1e$eq[[ 2 ]] ), digits = 6 ) ) 313s (Intercept) price farmPrice trend 313s 49.532 0.240 0.256 0.253 313s > 313s > print( round( coef( fitw2slsd2e ), digits = 6 ) ) 313s demand_(Intercept) demand_price demand_income supply_(Intercept) 313s 104.464 -0.363 0.336 47.071 313s supply_price supply_farmPrice supply_trend 313s 0.245 0.267 0.336 313s > print( round( coef( fitw2slsd2e$eq[[ 1 ]] ), digits = 6 ) ) 313s (Intercept) price income 313s 104.464 -0.363 0.336 313s > 313s > print( round( coef( fitw2slsd3e ), digits = 6 ) ) 313s demand_(Intercept) demand_price demand_income supply_(Intercept) 313s 104.464 -0.363 0.336 47.071 313s supply_price supply_farmPrice supply_trend 313s 0.245 0.267 0.336 313s > print( round( coef( fitw2slsd3e, modified.regMat = TRUE ), digits = 6 ) ) 313s C1 C2 C3 C4 C5 C6 313s 104.464 -0.363 0.336 47.071 0.245 0.267 313s > print( round( coef( fitw2slsd3e$eq[[ 2 ]] ), digits = 6 ) ) 313s (Intercept) price farmPrice trend 313s 47.071 0.245 0.267 0.336 313s > 313s > print( round( coef( fitw2sls4 ), digits = 6 ) ) 313s demand_(Intercept) demand_price demand_income supply_(Intercept) 313s 95.304 -0.243 0.306 46.423 313s supply_price supply_farmPrice supply_trend 313s 0.257 0.264 0.306 313s > print( round( coef( fitw2sls4$eq[[ 1 ]] ), digits = 6 ) ) 313s (Intercept) price income 313s 95.304 -0.243 0.306 313s > 313s > print( round( coef( fitw2sls5 ), digits = 6 ) ) 313s demand_(Intercept) demand_price demand_income supply_(Intercept) 313s 95.304 -0.243 0.306 46.423 313s supply_price supply_farmPrice supply_trend 313s 0.257 0.264 0.306 313s > print( round( coef( fitw2sls5, modified.regMat = TRUE ), digits = 6 ) ) 313s C1 C2 C3 C4 C5 C6 313s 95.304 -0.243 0.306 46.423 0.257 0.264 313s > print( round( coef( fitw2sls5$eq[[ 2 ]] ), digits = 6 ) ) 313s (Intercept) price farmPrice trend 313s 46.423 0.257 0.264 0.306 313s > 313s > 313s > ## *************** coefficients with stats ********************* 313s > print( round( coef( summary( fitw2sls1e, useDfSys = FALSE ) ), digits = 6 ) ) 313s Estimate Std. Error t value Pr(>|t|) 313s demand_(Intercept) 94.633 7.3027 12.96 0.000000 313s demand_price -0.244 0.0890 -2.74 0.014016 313s demand_income 0.314 0.0433 7.25 0.000001 313s supply_(Intercept) 49.532 10.7425 4.61 0.000289 313s supply_price 0.240 0.0894 2.69 0.016234 313s supply_farmPrice 0.256 0.0423 6.05 0.000017 313s supply_trend 0.253 0.0891 2.84 0.011883 313s > print( round( coef( summary( fitw2sls1e$eq[[ 2 ]], useDfSys = FALSE ) ), 313s + digits = 6 ) ) 313s Estimate Std. Error t value Pr(>|t|) 313s (Intercept) 49.532 10.7425 4.61 0.000289 313s price 0.240 0.0894 2.69 0.016234 313s farmPrice 0.256 0.0423 6.05 0.000017 313s trend 0.253 0.0891 2.84 0.011883 313s > 313s > print( round( coef( summary( fitw2slsd2e ) ), digits = 6 ) ) 313s Estimate Std. Error t value Pr(>|t|) 313s demand_(Intercept) 104.464 9.7929 10.67 0.00000 313s demand_price -0.363 0.1220 -2.98 0.00534 313s demand_income 0.336 0.0444 7.57 0.00000 313s supply_(Intercept) 47.071 10.6890 4.40 0.00010 313s supply_price 0.245 0.0910 2.69 0.01093 313s supply_farmPrice 0.267 0.0416 6.41 0.00000 313s supply_trend 0.336 0.0444 7.57 0.00000 313s > print( round( coef( summary( fitw2slsd2e$eq[[ 1 ]] ) ), digits = 6 ) ) 313s Estimate Std. Error t value Pr(>|t|) 313s (Intercept) 104.464 9.7929 10.67 0.00000 313s price -0.363 0.1220 -2.98 0.00534 313s income 0.336 0.0444 7.57 0.00000 313s > 313s > print( round( coef( summary( fitw2slsd3e, useDfSys = FALSE ) ), digits = 6 ) ) 313s Estimate Std. Error t value Pr(>|t|) 313s demand_(Intercept) 104.464 9.7929 10.67 0.000000 313s demand_price -0.363 0.1220 -2.98 0.008475 313s demand_income 0.336 0.0444 7.57 0.000001 313s supply_(Intercept) 47.071 10.6890 4.40 0.000444 313s supply_price 0.245 0.0910 2.69 0.016014 313s supply_farmPrice 0.267 0.0416 6.41 0.000009 313s supply_trend 0.336 0.0444 7.57 0.000001 313s > print( round( coef( summary( fitw2slsd3e, useDfSys = FALSE ), 313s + modified.regMat = TRUE ), digits = 6 ) ) 313s Estimate Std. Error t value Pr(>|t|) 313s C1 104.464 9.7929 10.67 NA 313s C2 -0.363 0.1220 -2.98 NA 313s C3 0.336 0.0444 7.57 NA 313s C4 47.071 10.6890 4.40 NA 313s C5 0.245 0.0910 2.69 NA 313s C6 0.267 0.0416 6.41 NA 313s > print( round( coef( summary( fitw2slsd3e$eq[[ 2 ]], useDfSys = FALSE ) ), 313s + digits = 6 ) ) 313s Estimate Std. Error t value Pr(>|t|) 313s (Intercept) 47.071 10.6890 4.40 0.000444 313s price 0.245 0.0910 2.69 0.016014 313s farmPrice 0.267 0.0416 6.41 0.000009 313s trend 0.336 0.0444 7.57 0.000001 313s > 313s > print( round( coef( summary( fitw2sls4 ) ), digits = 6 ) ) 313s Estimate Std. Error t value Pr(>|t|) 313s demand_(Intercept) 95.304 6.3056 15.11 0.000000 313s demand_price -0.243 0.0684 -3.55 0.001128 313s demand_income 0.306 0.0394 7.78 0.000000 313s supply_(Intercept) 46.423 8.3296 5.57 0.000003 313s supply_price 0.257 0.0684 3.76 0.000622 313s supply_farmPrice 0.264 0.0455 5.80 0.000001 313s supply_trend 0.306 0.0394 7.78 0.000000 313s > print( round( coef( summary( fitw2sls4$eq[[ 1 ]] ) ), digits = 6 ) ) 313s Estimate Std. Error t value Pr(>|t|) 313s (Intercept) 95.304 6.3056 15.11 0.00000 313s price -0.243 0.0684 -3.55 0.00113 313s income 0.306 0.0394 7.78 0.00000 313s > 313s > print( round( coef( summary( fitw2sls5 ) ), digits = 6 ) ) 313s Estimate Std. Error t value Pr(>|t|) 313s demand_(Intercept) 95.304 6.3056 15.11 0.000000 313s demand_price -0.243 0.0684 -3.55 0.001128 313s demand_income 0.306 0.0394 7.78 0.000000 313s supply_(Intercept) 46.423 8.3296 5.57 0.000003 313s supply_price 0.257 0.0684 3.76 0.000622 313s supply_farmPrice 0.264 0.0455 5.80 0.000001 313s supply_trend 0.306 0.0394 7.78 0.000000 313s > print( round( coef( summary( fitw2sls5 ), modified.regMat = TRUE ), digits = 6 ) ) 313s Estimate Std. Error t value Pr(>|t|) 313s C1 95.304 6.3056 15.11 0.000000 313s C2 -0.243 0.0684 -3.55 0.001128 313s C3 0.306 0.0394 7.78 0.000000 313s C4 46.423 8.3296 5.57 0.000003 313s C5 0.257 0.0684 3.76 0.000622 313s C6 0.264 0.0455 5.80 0.000001 313s > print( round( coef( summary( fitw2sls5$eq[[ 2 ]] ) ), digits = 6 ) ) 313s Estimate Std. Error t value Pr(>|t|) 313s (Intercept) 46.423 8.3296 5.57 0.000003 313s price 0.257 0.0684 3.76 0.000622 313s farmPrice 0.264 0.0455 5.80 0.000001 313s trend 0.306 0.0394 7.78 0.000000 313s > 313s > 313s > ## *********** variance covariance matrix of the coefficients ******* 313s > print( round( vcov( fitw2sls1e ), digits = 6 ) ) 313s demand_(Intercept) demand_price demand_income 313s demand_(Intercept) 53.3287 -0.57241 0.04191 313s demand_price -0.5724 0.00791 -0.00225 313s demand_income 0.0419 -0.00225 0.00187 313s supply_(Intercept) 0.0000 0.00000 0.00000 313s supply_price 0.0000 0.00000 0.00000 313s supply_farmPrice 0.0000 0.00000 0.00000 313s supply_trend 0.0000 0.00000 0.00000 313s supply_(Intercept) supply_price supply_farmPrice 313s demand_(Intercept) 0.000 0.000000 0.000000 313s demand_price 0.000 0.000000 0.000000 313s demand_income 0.000 0.000000 0.000000 313s supply_(Intercept) 115.402 -0.876328 -0.259055 313s supply_price -0.876 0.007989 0.000749 313s supply_farmPrice -0.259 0.000749 0.001786 313s supply_trend -0.236 0.000463 0.001101 313s supply_trend 313s demand_(Intercept) 0.000000 313s demand_price 0.000000 313s demand_income 0.000000 313s supply_(Intercept) -0.236183 313s supply_price 0.000463 313s supply_farmPrice 0.001101 313s supply_trend 0.007945 313s > print( round( vcov( fitw2sls1e$eq[[ 2 ]] ), digits = 6 ) ) 313s (Intercept) price farmPrice trend 313s (Intercept) 115.402 -0.876328 -0.259055 -0.236183 313s price -0.876 0.007989 0.000749 0.000463 313s farmPrice -0.259 0.000749 0.001786 0.001101 313s trend -0.236 0.000463 0.001101 0.007945 313s > 313s > print( round( vcov( fitw2sls2 ), digits = 6 ) ) 313s demand_(Intercept) demand_price demand_income 313s demand_(Intercept) 64.14482 -0.679629 0.041312 313s demand_price -0.67963 0.008954 -0.002214 313s demand_income 0.04131 -0.002214 0.001847 313s supply_(Intercept) -1.22810 0.065809 -0.054894 313s supply_price 0.00241 -0.000129 0.000108 313s supply_farmPrice 0.00573 -0.000307 0.000256 313s supply_trend 0.04131 -0.002214 0.001847 313s supply_(Intercept) supply_price supply_farmPrice 313s demand_(Intercept) -1.2281 0.002409 0.005727 313s demand_price 0.0658 -0.000129 -0.000307 313s demand_income -0.0549 0.000108 0.000256 313s supply_(Intercept) 139.2416 -1.098376 -0.294954 313s supply_price -1.0984 0.010116 0.000884 313s supply_farmPrice -0.2950 0.000884 0.002109 313s supply_trend -0.0549 0.000108 0.000256 313s supply_trend 313s demand_(Intercept) 0.041312 313s demand_price -0.002214 313s demand_income 0.001847 313s supply_(Intercept) -0.054894 313s supply_price 0.000108 313s supply_farmPrice 0.000256 313s supply_trend 0.001847 313s > print( round( vcov( fitw2sls2$eq[[ 1 ]] ), digits = 6 ) ) 313s (Intercept) price income 313s (Intercept) 64.1448 -0.67963 0.04131 313s price -0.6796 0.00895 -0.00221 313s income 0.0413 -0.00221 0.00185 313s > 313s > print( round( vcov( fitw2sls3e ), digits = 6 ) ) 313s demand_(Intercept) demand_price demand_income 313s demand_(Intercept) 54.51421 -0.577209 0.034718 313s demand_price -0.57721 0.007585 -0.001860 313s demand_income 0.03472 -0.001860 0.001552 313s supply_(Intercept) -1.03208 0.055305 -0.046132 313s supply_price 0.00202 -0.000108 0.000090 313s supply_farmPrice 0.00481 -0.000258 0.000215 313s supply_trend 0.03472 -0.001860 0.001552 313s supply_(Intercept) supply_price supply_farmPrice 313s demand_(Intercept) -1.0321 0.002024 0.004813 313s demand_price 0.0553 -0.000108 -0.000258 313s demand_income -0.0461 0.000090 0.000215 313s supply_(Intercept) 111.4592 -0.878830 -0.236271 313s supply_price -0.8788 0.008093 0.000708 313s supply_farmPrice -0.2363 0.000708 0.001689 313s supply_trend -0.0461 0.000090 0.000215 313s supply_trend 313s demand_(Intercept) 0.034718 313s demand_price -0.001860 313s demand_income 0.001552 313s supply_(Intercept) -0.046132 313s supply_price 0.000090 313s supply_farmPrice 0.000215 313s supply_trend 0.001552 313s > print( round( vcov( fitw2sls3e, modified.regMat = TRUE ), digits = 6 ) ) 313s C1 C2 C3 C4 C5 C6 313s C1 54.51421 -0.577209 0.034718 -1.0321 0.002024 0.004813 313s C2 -0.57721 0.007585 -0.001860 0.0553 -0.000108 -0.000258 313s C3 0.03472 -0.001860 0.001552 -0.0461 0.000090 0.000215 313s C4 -1.03208 0.055305 -0.046132 111.4592 -0.878830 -0.236271 313s C5 0.00202 -0.000108 0.000090 -0.8788 0.008093 0.000708 313s C6 0.00481 -0.000258 0.000215 -0.2363 0.000708 0.001689 313s > print( round( vcov( fitw2sls3e$eq[[ 2 ]] ), digits = 6 ) ) 313s (Intercept) price farmPrice trend 313s (Intercept) 111.4592 -0.878830 -0.236271 -0.046132 313s price -0.8788 0.008093 0.000708 0.000090 313s farmPrice -0.2363 0.000708 0.001689 0.000215 313s trend -0.0461 0.000090 0.000215 0.001552 313s > 313s > print( round( vcov( fitw2sls4 ), digits = 6 ) ) 313s demand_(Intercept) demand_price demand_income 313s demand_(Intercept) 39.7610 -0.358128 -0.03842 313s demand_price -0.3581 0.004681 -0.00113 313s demand_income -0.0384 -0.001129 0.00155 313s supply_(Intercept) 39.6949 -0.480685 0.08595 313s supply_price -0.3581 0.004681 -0.00113 313s supply_farmPrice -0.0359 0.000252 0.00011 313s supply_trend -0.0384 -0.001129 0.00155 313s supply_(Intercept) supply_price supply_farmPrice 313s demand_(Intercept) 39.6949 -0.358128 -0.035932 313s demand_price -0.4807 0.004681 0.000252 313s demand_income 0.0859 -0.001129 0.000110 313s supply_(Intercept) 69.3817 -0.480685 -0.226588 313s supply_price -0.4807 0.004681 0.000252 313s supply_farmPrice -0.2266 0.000252 0.002072 313s supply_trend 0.0859 -0.001129 0.000110 313s supply_trend 313s demand_(Intercept) -0.03842 313s demand_price -0.00113 313s demand_income 0.00155 313s supply_(Intercept) 0.08595 313s supply_price -0.00113 313s supply_farmPrice 0.00011 313s supply_trend 0.00155 313s > print( round( vcov( fitw2sls4$eq[[ 1 ]] ), digits = 6 ) ) 313s (Intercept) price income 313s (Intercept) 39.7610 -0.35813 -0.03842 313s price -0.3581 0.00468 -0.00113 313s income -0.0384 -0.00113 0.00155 313s > 313s > print( round( vcov( fitw2sls5 ), digits = 6 ) ) 313s demand_(Intercept) demand_price demand_income 313s demand_(Intercept) 39.7610 -0.358128 -0.03842 313s demand_price -0.3581 0.004681 -0.00113 313s demand_income -0.0384 -0.001129 0.00155 313s supply_(Intercept) 39.6949 -0.480685 0.08595 313s supply_price -0.3581 0.004681 -0.00113 313s supply_farmPrice -0.0359 0.000252 0.00011 313s supply_trend -0.0384 -0.001129 0.00155 313s supply_(Intercept) supply_price supply_farmPrice 313s demand_(Intercept) 39.6949 -0.358128 -0.035932 313s demand_price -0.4807 0.004681 0.000252 313s demand_income 0.0859 -0.001129 0.000110 313s supply_(Intercept) 69.3817 -0.480685 -0.226588 313s supply_price -0.4807 0.004681 0.000252 313s supply_farmPrice -0.2266 0.000252 0.002072 313s supply_trend 0.0859 -0.001129 0.000110 313s supply_trend 313s demand_(Intercept) -0.03842 313s demand_price -0.00113 313s demand_income 0.00155 313s supply_(Intercept) 0.08595 313s supply_price -0.00113 313s supply_farmPrice 0.00011 313s supply_trend 0.00155 313s > print( round( vcov( fitw2sls5, modified.regMat = TRUE ), digits = 6 ) ) 313s C1 C2 C3 C4 C5 C6 313s C1 39.7610 -0.358128 -0.03842 39.6949 -0.358128 -0.035932 313s C2 -0.3581 0.004681 -0.00113 -0.4807 0.004681 0.000252 313s C3 -0.0384 -0.001129 0.00155 0.0859 -0.001129 0.000110 313s C4 39.6949 -0.480685 0.08595 69.3817 -0.480685 -0.226588 313s C5 -0.3581 0.004681 -0.00113 -0.4807 0.004681 0.000252 313s C6 -0.0359 0.000252 0.00011 -0.2266 0.000252 0.002072 313s > print( round( vcov( fitw2sls5$eq[[ 2 ]] ), digits = 6 ) ) 313s (Intercept) price farmPrice trend 313s (Intercept) 69.3817 -0.480685 -0.226588 0.08595 313s price -0.4807 0.004681 0.000252 -0.00113 313s farmPrice -0.2266 0.000252 0.002072 0.00011 313s trend 0.0859 -0.001129 0.000110 0.00155 313s > 313s > print( round( vcov( fitw2slsd1 ), digits = 6 ) ) 313s demand_(Intercept) demand_price demand_income 313s demand_(Intercept) 124.179 -1.51767 0.28519 313s demand_price -1.518 0.02098 -0.00595 313s demand_income 0.285 -0.00595 0.00318 313s supply_(Intercept) 0.000 0.00000 0.00000 313s supply_price 0.000 0.00000 0.00000 313s supply_farmPrice 0.000 0.00000 0.00000 313s supply_trend 0.000 0.00000 0.00000 313s supply_(Intercept) supply_price supply_farmPrice 313s demand_(Intercept) 0.000 0.000000 0.000000 313s demand_price 0.000 0.000000 0.000000 313s demand_income 0.000 0.000000 0.000000 313s supply_(Intercept) 144.253 -1.095410 -0.323818 313s supply_price -1.095 0.009987 0.000936 313s supply_farmPrice -0.324 0.000936 0.002233 313s supply_trend -0.295 0.000579 0.001377 313s supply_trend 313s demand_(Intercept) 0.000000 313s demand_price 0.000000 313s demand_income 0.000000 313s supply_(Intercept) -0.295229 313s supply_price 0.000579 313s supply_farmPrice 0.001377 313s supply_trend 0.009931 313s > print( round( vcov( fitw2slsd1$eq[[ 1 ]] ), digits = 6 ) ) 313s (Intercept) price income 313s (Intercept) 124.179 -1.51767 0.28519 313s price -1.518 0.02098 -0.00595 313s income 0.285 -0.00595 0.00318 313s > 313s > print( round( vcov( fitw2slsd2e ), digits = 6 ) ) 313s demand_(Intercept) demand_price demand_income 313s demand_(Intercept) 95.9017 -1.129212 0.176368 313s demand_price -1.1292 0.014881 -0.003682 313s demand_income 0.1764 -0.003682 0.001968 313s supply_(Intercept) -5.2430 0.109460 -0.058492 313s supply_price 0.0103 -0.000215 0.000115 313s supply_farmPrice 0.0245 -0.000510 0.000273 313s supply_trend 0.1764 -0.003682 0.001968 313s supply_(Intercept) supply_price supply_farmPrice 313s demand_(Intercept) -5.2430 0.010284 0.024451 313s demand_price 0.1095 -0.000215 -0.000510 313s demand_income -0.0585 0.000115 0.000273 313s supply_(Intercept) 114.2555 -0.898881 -0.243056 313s supply_price -0.8989 0.008273 0.000727 313s supply_farmPrice -0.2431 0.000727 0.001733 313s supply_trend -0.0585 0.000115 0.000273 313s supply_trend 313s demand_(Intercept) 0.176368 313s demand_price -0.003682 313s demand_income 0.001968 313s supply_(Intercept) -0.058492 313s supply_price 0.000115 313s supply_farmPrice 0.000273 313s supply_trend 0.001968 313s > print( round( vcov( fitw2slsd2e$eq[[ 2 ]] ), digits = 6 ) ) 313s (Intercept) price farmPrice trend 313s (Intercept) 114.2555 -0.898881 -0.243056 -0.058492 313s price -0.8989 0.008273 0.000727 0.000115 313s farmPrice -0.2431 0.000727 0.001733 0.000273 313s trend -0.0585 0.000115 0.000273 0.001968 313s > 313s > print( round( vcov( fitw2slsd3 ), digits = 6 ) ) 313s demand_(Intercept) demand_price demand_income 313s demand_(Intercept) 113.0903 -1.334011 0.210445 313s demand_price -1.3340 0.017622 -0.004394 313s demand_income 0.2104 -0.004394 0.002348 313s supply_(Intercept) -6.2560 0.130609 -0.069794 313s supply_price 0.0123 -0.000256 0.000137 313s supply_farmPrice 0.0292 -0.000609 0.000325 313s supply_trend 0.2104 -0.004394 0.002348 313s supply_(Intercept) supply_price supply_farmPrice 313s demand_(Intercept) -6.2560 0.012271 0.029175 313s demand_price 0.1306 -0.000256 -0.000609 313s demand_income -0.0698 0.000137 0.000325 313s supply_(Intercept) 142.7207 -1.123408 -0.303360 313s supply_price -1.1234 0.010341 0.000908 313s supply_farmPrice -0.3034 0.000908 0.002165 313s supply_trend -0.0698 0.000137 0.000325 313s supply_trend 313s demand_(Intercept) 0.210445 313s demand_price -0.004394 313s demand_income 0.002348 313s supply_(Intercept) -0.069794 313s supply_price 0.000137 313s supply_farmPrice 0.000325 313s supply_trend 0.002348 313s > print( round( vcov( fitw2slsd3, modified.regMat = TRUE ), digits = 6 ) ) 313s C1 C2 C3 C4 C5 C6 313s C1 113.0903 -1.334011 0.210445 -6.2560 0.012271 0.029175 313s C2 -1.3340 0.017622 -0.004394 0.1306 -0.000256 -0.000609 313s C3 0.2104 -0.004394 0.002348 -0.0698 0.000137 0.000325 313s C4 -6.2560 0.130609 -0.069794 142.7207 -1.123408 -0.303360 313s C5 0.0123 -0.000256 0.000137 -1.1234 0.010341 0.000908 313s C6 0.0292 -0.000609 0.000325 -0.3034 0.000908 0.002165 313s > print( round( vcov( fitw2slsd3$eq[[ 1 ]] ), digits = 6 ) ) 313s (Intercept) price income 313s (Intercept) 113.09 -1.33401 0.21044 313s price -1.33 0.01762 -0.00439 313s income 0.21 -0.00439 0.00235 313s > 313s > 313s > ## *********** confidence intervals of coefficients ************* 313s > print( confint( fitw2sls1e, useDfSys = TRUE ) ) 313s 2.5 % 97.5 % 313s demand_(Intercept) 79.776 109.491 313s demand_price -0.425 -0.063 313s demand_income 0.226 0.402 313s supply_(Intercept) 27.677 71.388 313s supply_price 0.058 0.422 313s supply_farmPrice 0.170 0.342 313s supply_trend 0.072 0.434 313s > print( confint( fitw2sls1e$eq[[ 1 ]], level = 0.9, useDfSys = TRUE ) ) 313s 5 % 95 % 313s (Intercept) 82.275 106.992 313s price -0.394 -0.093 313s income 0.241 0.387 313s > 313s > print( confint( fitw2sls2, level = 0.9 ) ) 313s 5 % 95 % 313s demand_(Intercept) 78.107 110.660 313s demand_price -0.422 -0.038 313s demand_income 0.215 0.390 313s supply_(Intercept) 24.069 72.030 313s supply_price 0.039 0.447 313s supply_farmPrice 0.169 0.356 313s supply_trend 0.215 0.390 313s > print( confint( fitw2sls2$eq[[ 2 ]], level = 0.99 ) ) 313s 0.5 % 99.5 % 313s (Intercept) 15.854 80.245 313s price -0.031 0.517 313s farmPrice 0.137 0.388 313s trend 0.186 0.420 313s > 313s > print( confint( fitw2sls3, level = 0.99 ) ) 313s 0.5 % 99.5 % 313s demand_(Intercept) 78.107 110.660 313s demand_price -0.422 -0.038 313s demand_income 0.215 0.390 313s supply_(Intercept) 24.069 72.030 313s supply_price 0.039 0.447 313s supply_farmPrice 0.169 0.356 313s supply_trend 0.215 0.390 313s > print( confint( fitw2sls3$eq[[ 1 ]], level = 0.5 ) ) 313s 25 % 75 % 313s (Intercept) 88.923 99.844 313s price -0.295 -0.166 313s income 0.274 0.332 313s > 313s > print( confint( fitw2sls4e, level = 0.5, useDfSys = TRUE ) ) 313s 25 % 75 % 313s demand_(Intercept) 83.658 107.036 313s demand_price -0.369 -0.117 313s demand_income 0.233 0.379 313s supply_(Intercept) 31.138 61.736 313s supply_price 0.131 0.383 313s supply_farmPrice 0.181 0.347 313s supply_trend 0.233 0.379 313s > print( confint( fitw2sls4e$eq[[ 2 ]], level = 0.25, useDfSys = TRUE ) ) 313s 37.5 % 62.5 % 313s (Intercept) 44.016 48.857 313s price 0.237 0.277 313s farmPrice 0.251 0.277 313s trend 0.294 0.317 313s > 313s > print( confint( fitw2sls5, level = 0.25 ) ) 313s 37.5 % 62.5 % 313s demand_(Intercept) 82.503 108.105 313s demand_price -0.382 -0.104 313s demand_income 0.226 0.386 313s supply_(Intercept) 29.513 63.333 313s supply_price 0.118 0.396 313s supply_farmPrice 0.172 0.357 313s supply_trend 0.226 0.386 313s > print( confint( fitw2sls5$eq[[ 1 ]], level = 0.975 ) ) 313s 1.3 % 98.8 % 313s (Intercept) 80.537 110.072 313s price -0.403 -0.083 313s income 0.214 0.399 313s > 313s > print( confint( fitw2slsd1, level = 0.975 ) ) 313s 1.3 % 98.8 % 313s demand_(Intercept) 83.279 130.300 313s demand_price -0.717 -0.106 313s demand_income 0.243 0.481 313s supply_(Intercept) 24.071 74.994 313s supply_price 0.028 0.452 313s supply_farmPrice 0.155 0.356 313s supply_trend 0.042 0.464 313s > print( confint( fitw2slsd1$eq[[ 2 ]], level = 0.999 ) ) 313s 0.1 % 100 % 313s (Intercept) 1.310 97.755 313s price -0.161 0.641 313s farmPrice 0.066 0.445 313s trend -0.147 0.653 313s > 313s > print( confint( fitw2slsd2e, level = 0.999, useDfSys = TRUE ) ) 313s 0.1 % 100 % 313s demand_(Intercept) 84.562 124.365 313s demand_price -0.611 -0.115 313s demand_income 0.246 0.426 313s supply_(Intercept) 25.348 68.793 313s supply_price 0.060 0.430 313s supply_farmPrice 0.182 0.352 313s supply_trend 0.246 0.426 313s > print( confint( fitw2slsd2e$eq[[ 1 ]], level = 0.01, useDfSys = TRUE ) ) 313s 49.5 % 50.5 % 313s (Intercept) 104.340 104.587 313s price -0.365 -0.362 313s income 0.335 0.336 313s > 313s > print( confint( fitw2slsd3e, level = 0.01, useDfSys = TRUE ) ) 313s 49.5 % 50.5 % 313s demand_(Intercept) 84.562 124.365 313s demand_price -0.611 -0.115 313s demand_income 0.246 0.426 313s supply_(Intercept) 25.348 68.793 313s supply_price 0.060 0.430 313s supply_farmPrice 0.182 0.352 313s supply_trend 0.246 0.426 313s > print( confint( fitw2slsd3e$eq[[ 2 ]], useDfSys = TRUE ) ) 313s 2.5 % 97.5 % 313s (Intercept) 25.348 68.793 313s price 0.060 0.430 313s farmPrice 0.182 0.352 313s trend 0.246 0.426 313s > 313s > 313s > ## *********** fitted values ************* 313s > print( fitted( fitw2sls1e ) ) 313s demand supply 313s 1 97.6 98.9 313s 2 99.9 100.4 313s 3 99.8 100.5 313s 4 100.0 100.7 313s 5 102.1 102.6 313s 6 102.0 102.6 313s 7 102.4 102.6 313s 8 103.0 104.8 313s 9 101.5 102.7 313s 10 100.3 99.7 313s 11 95.5 95.4 313s 12 94.7 93.8 313s 13 96.1 95.6 313s 14 99.0 97.6 313s 15 103.8 102.3 313s 16 103.7 104.1 313s 17 103.8 102.8 313s 18 102.1 102.7 313s 19 103.6 102.6 313s 20 106.9 105.6 313s > print( fitted( fitw2sls1e$eq[[ 1 ]] ) ) 313s 1 2 3 4 5 6 7 8 9 10 11 12 13 313s 97.6 99.9 99.8 100.0 102.1 102.0 102.4 103.0 101.5 100.3 95.5 94.7 96.1 313s 14 15 16 17 18 19 20 313s 99.0 103.8 103.7 103.8 102.1 103.6 106.9 313s > 313s > print( fitted( fitw2sls2 ) ) 313s demand supply 313s 1 97.8 98.5 313s 2 99.9 100.0 313s 3 99.9 100.1 313s 4 100.1 100.4 313s 5 102.0 102.5 313s 6 101.9 102.4 313s 7 102.4 102.4 313s 8 102.9 104.8 313s 9 101.4 102.7 313s 10 100.3 99.7 313s 11 95.7 95.3 313s 12 94.9 93.7 313s 13 96.3 95.6 313s 14 99.1 97.7 313s 15 103.7 102.6 313s 16 103.5 104.4 313s 17 103.7 103.2 313s 18 102.1 103.1 313s 19 103.6 102.9 313s 20 106.8 106.1 313s > print( fitted( fitw2sls2$eq[[ 2 ]] ) ) 313s 1 2 3 4 5 6 7 8 9 10 11 12 13 313s 98.5 100.0 100.1 100.4 102.5 102.4 102.4 104.8 102.7 99.7 95.3 93.7 95.6 313s 14 15 16 17 18 19 20 313s 97.7 102.6 104.4 103.2 103.1 102.9 106.1 313s > 313s > print( fitted( fitw2sls3 ) ) 313s demand supply 313s 1 97.8 98.5 313s 2 99.9 100.0 313s 3 99.9 100.1 313s 4 100.1 100.4 313s 5 102.0 102.5 313s 6 101.9 102.4 313s 7 102.4 102.4 313s 8 102.9 104.8 313s 9 101.4 102.7 313s 10 100.3 99.7 313s 11 95.7 95.3 313s 12 94.9 93.7 313s 13 96.3 95.6 313s 14 99.1 97.7 313s 15 103.7 102.6 313s 16 103.5 104.4 313s 17 103.7 103.2 313s 18 102.1 103.1 313s 19 103.6 102.9 313s 20 106.8 106.1 313s > print( fitted( fitw2sls3$eq[[ 1 ]] ) ) 313s 1 2 3 4 5 6 7 8 9 10 11 12 13 313s 97.8 99.9 99.9 100.1 102.0 101.9 102.4 102.9 101.4 100.3 95.7 94.9 96.3 313s 14 15 16 17 18 19 20 313s 99.1 103.7 103.5 103.7 102.1 103.6 106.8 313s > 313s > print( fitted( fitw2sls4e ) ) 313s demand supply 313s 1 97.7 98.4 313s 2 99.9 100.0 313s 3 99.8 100.1 313s 4 100.0 100.5 313s 5 102.1 102.4 313s 6 101.9 102.4 313s 7 102.4 102.5 313s 8 102.9 104.8 313s 9 101.5 102.7 313s 10 100.4 99.5 313s 11 95.7 95.1 313s 12 94.9 93.6 313s 13 96.2 95.6 313s 14 99.1 97.6 313s 15 103.8 102.5 313s 16 103.6 104.4 313s 17 103.8 103.1 313s 18 102.0 103.1 313s 19 103.5 103.0 313s 20 106.7 106.3 313s > print( fitted( fitw2sls4e$eq[[ 2 ]] ) ) 313s 1 2 3 4 5 6 7 8 9 10 11 12 13 313s 98.4 100.0 100.1 100.5 102.4 102.4 102.5 104.8 102.7 99.5 95.1 93.6 95.6 313s 14 15 16 17 18 19 20 313s 97.6 102.5 104.4 103.1 103.1 103.0 106.3 313s > 313s > print( fitted( fitw2sls5 ) ) 313s demand supply 313s 1 97.7 98.4 313s 2 99.9 100.0 313s 3 99.8 100.1 313s 4 100.0 100.5 313s 5 102.1 102.4 313s 6 101.9 102.4 313s 7 102.4 102.5 313s 8 102.9 104.8 313s 9 101.5 102.7 313s 10 100.4 99.5 313s 11 95.7 95.1 313s 12 94.9 93.6 313s 13 96.2 95.6 313s 14 99.1 97.6 313s 15 103.8 102.5 313s 16 103.6 104.4 313s 17 103.8 103.1 313s 18 102.0 103.1 313s 19 103.5 103.0 313s 20 106.7 106.3 313s > print( fitted( fitw2sls5$eq[[ 1 ]] ) ) 313s 1 2 3 4 5 6 7 8 9 10 11 12 13 313s 97.7 99.9 99.8 100.0 102.1 101.9 102.4 102.9 101.5 100.4 95.7 94.9 96.2 313s 14 15 16 17 18 19 20 313s 99.1 103.8 103.6 103.8 102.0 103.5 106.7 313s > 313s > print( fitted( fitw2slsd1 ) ) 313s demand supply 313s 1 97.1 98.9 313s 2 99.2 100.4 313s 3 99.2 100.5 313s 4 99.3 100.7 313s 5 102.5 102.6 313s 6 102.2 102.6 313s 7 102.5 102.6 313s 8 102.7 104.8 313s 9 102.0 102.7 313s 10 101.4 99.7 313s 11 95.6 95.4 313s 12 93.9 93.8 313s 13 95.0 95.6 313s 14 98.9 97.6 313s 15 104.9 102.3 313s 16 104.3 104.1 313s 17 106.1 102.8 313s 18 101.7 102.7 313s 19 103.3 102.6 313s 20 106.0 105.6 313s > print( fitted( fitw2slsd1$eq[[ 2 ]] ) ) 313s 1 2 3 4 5 6 7 8 9 10 11 12 13 313s 98.9 100.4 100.5 100.7 102.6 102.6 102.6 104.8 102.7 99.7 95.4 93.8 95.6 313s 14 15 16 17 18 19 20 313s 97.6 102.3 104.1 102.8 102.7 102.6 105.6 313s > 313s > print( fitted( fitw2slsd2e ) ) 313s demand supply 313s 1 97.4 98.2 313s 2 99.4 99.7 313s 3 99.4 99.9 313s 4 99.5 100.2 313s 5 102.4 102.3 313s 6 102.1 102.3 313s 7 102.4 102.4 313s 8 102.6 104.7 313s 9 101.9 102.7 313s 10 101.2 99.6 313s 11 95.9 95.2 313s 12 94.4 93.6 313s 13 95.5 95.6 313s 14 99.0 97.7 313s 15 104.5 102.7 313s 16 104.0 104.6 313s 17 105.4 103.5 313s 18 101.8 103.3 313s 19 103.2 103.2 313s 20 105.9 106.4 313s > print( fitted( fitw2slsd2e$eq[[ 1 ]] ) ) 313s 1 2 3 4 5 6 7 8 9 10 11 12 13 313s 97.4 99.4 99.4 99.5 102.4 102.1 102.4 102.6 101.9 101.2 95.9 94.4 95.5 313s 14 15 16 17 18 19 20 313s 99.0 104.5 104.0 105.4 101.8 103.2 105.9 313s > 313s > print( fitted( fitw2slsd3e ) ) 313s demand supply 313s 1 97.4 98.2 313s 2 99.4 99.7 313s 3 99.4 99.9 313s 4 99.5 100.2 313s 5 102.4 102.3 313s 6 102.1 102.3 313s 7 102.4 102.4 313s 8 102.6 104.7 313s 9 101.9 102.7 313s 10 101.2 99.6 313s 11 95.9 95.2 313s 12 94.4 93.6 313s 13 95.5 95.6 313s 14 99.0 97.7 313s 15 104.5 102.7 313s 16 104.0 104.6 313s 17 105.4 103.5 313s 18 101.8 103.3 313s 19 103.2 103.2 313s 20 105.9 106.4 313s > print( fitted( fitw2slsd3e$eq[[ 2 ]] ) ) 313s 1 2 3 4 5 6 7 8 9 10 11 12 13 313s 98.2 99.7 99.9 100.2 102.3 102.3 102.4 104.7 102.7 99.6 95.2 93.6 95.6 313s 14 15 16 17 18 19 20 313s 97.7 102.7 104.6 103.5 103.3 103.2 106.4 313s > 313s > 313s > ## *********** predicted values ************* 313s > predictData <- Kmenta 313s > predictData$consump <- NULL 313s > predictData$price <- Kmenta$price * 0.9 313s > predictData$income <- Kmenta$income * 1.1 313s > 313s > print( predict( fitw2sls1e, se.fit = TRUE, interval = "prediction", 313s + useDfSys = TRUE ) ) 313s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 313s 1 97.6 0.609 93.5 101.8 98.9 0.965 313s 2 99.9 0.553 95.7 104.0 100.4 0.952 313s 3 99.8 0.520 95.7 103.9 100.5 0.861 313s 4 100.0 0.558 95.9 104.2 100.7 0.839 313s 5 102.1 0.476 98.0 106.2 102.6 0.818 313s 6 102.0 0.437 97.9 106.1 102.6 0.723 313s 7 102.4 0.454 98.3 106.5 102.6 0.658 313s 8 103.0 0.567 98.8 107.1 104.8 0.889 313s 9 101.5 0.502 97.3 105.6 102.7 0.723 313s 10 100.3 0.758 96.0 104.6 99.7 0.915 313s 11 95.5 0.888 91.2 99.9 95.4 1.098 313s 12 94.7 0.928 90.3 99.1 93.8 1.277 313s 13 96.1 0.844 91.8 100.5 95.6 1.137 313s 14 99.0 0.477 94.9 103.1 97.6 0.820 313s 15 103.8 0.731 99.6 108.1 102.3 0.804 313s 16 103.7 0.587 99.5 107.8 104.1 0.837 313s 17 103.8 1.243 99.1 108.6 102.8 1.489 313s 18 102.1 0.506 97.9 106.2 102.7 0.884 313s 19 103.6 0.641 99.4 107.8 102.6 1.010 313s 20 106.9 1.204 102.2 111.6 105.6 1.550 313s supply.lwr supply.upr 313s 1 93.5 104.3 313s 2 95.0 105.8 313s 3 95.2 105.8 313s 4 95.4 106.0 313s 5 97.4 107.9 313s 6 97.4 107.8 313s 7 97.4 107.7 313s 8 99.5 110.1 313s 9 97.5 108.0 313s 10 94.3 105.0 313s 11 89.9 100.8 313s 12 88.2 99.5 313s 13 90.1 101.2 313s 14 92.3 102.9 313s 15 97.1 107.6 313s 16 98.8 109.3 313s 17 97.0 108.7 313s 18 97.4 108.0 313s 19 97.2 108.0 313s 20 99.7 111.5 313s > print( predict( fitw2sls1e$eq[[ 1 ]], se.fit = TRUE, interval = "prediction", 313s + useDfSys = TRUE ) ) 313s fit se.fit lwr upr 313s 1 97.6 0.609 93.5 101.8 313s 2 99.9 0.553 95.7 104.0 313s 3 99.8 0.520 95.7 103.9 313s 4 100.0 0.558 95.9 104.2 313s 5 102.1 0.476 98.0 106.2 313s 6 102.0 0.437 97.9 106.1 313s 7 102.4 0.454 98.3 106.5 313s 8 103.0 0.567 98.8 107.1 313s 9 101.5 0.502 97.3 105.6 313s 10 100.3 0.758 96.0 104.6 313s 11 95.5 0.888 91.2 99.9 313s 12 94.7 0.928 90.3 99.1 313s 13 96.1 0.844 91.8 100.5 313s 14 99.0 0.477 94.9 103.1 313s 15 103.8 0.731 99.6 108.1 313s 16 103.7 0.587 99.5 107.8 313s 17 103.8 1.243 99.1 108.6 313s 18 102.1 0.506 97.9 106.2 313s 19 103.6 0.641 99.4 107.8 313s 20 106.9 1.204 102.2 111.6 313s > 313s > print( predict( fitw2sls2, se.pred = TRUE, interval = "confidence", 313s + level = 0.999, newdata = predictData ) ) 313s demand.pred demand.se.pred demand.lwr demand.upr supply.pred supply.se.pred 313s 1 102.7 2.22 99.1 106 96.0 2.75 313s 2 105.3 2.22 101.7 109 97.5 2.64 313s 3 105.2 2.23 101.5 109 97.6 2.65 313s 4 105.4 2.22 101.9 109 97.9 2.62 313s 5 107.3 2.51 101.8 113 100.1 2.83 313s 6 107.3 2.46 102.0 112 100.0 2.77 313s 7 107.8 2.44 102.7 113 100.0 2.71 313s 8 108.6 2.40 103.7 113 102.2 2.65 313s 9 106.6 2.52 101.0 112 100.4 2.87 313s 10 105.1 2.65 98.8 111 97.4 3.10 313s 11 100.1 2.41 95.2 105 93.0 3.18 313s 12 99.5 2.21 96.0 103 91.3 3.15 313s 13 101.2 2.12 98.5 104 93.1 2.95 313s 14 104.1 2.31 99.8 108 95.3 2.91 313s 15 109.0 2.73 102.3 116 100.2 2.92 313s 16 109.0 2.61 102.9 115 102.0 2.80 313s 17 108.6 3.08 100.1 117 101.1 3.37 313s 18 107.6 2.35 103.0 112 100.5 2.65 313s 19 109.3 2.44 104.2 114 100.4 2.64 313s 20 113.2 2.66 106.8 120 103.3 2.58 313s supply.lwr supply.upr 313s 1 91.7 100.3 313s 2 94.2 100.7 313s 3 94.2 101.0 313s 4 94.8 101.0 313s 5 95.1 105.0 313s 6 95.6 104.4 313s 7 96.1 103.9 313s 8 98.8 105.6 313s 9 95.2 105.6 313s 10 90.7 104.1 313s 11 85.9 100.1 313s 12 84.3 98.3 313s 13 87.3 98.9 313s 14 89.7 100.8 313s 15 94.7 105.8 313s 16 97.3 106.6 313s 17 92.9 109.4 313s 18 97.1 103.9 313s 19 97.1 103.6 313s 20 100.7 105.9 313s > print( predict( fitw2sls2$eq[[ 2 ]], se.pred = TRUE, interval = "confidence", 313s + level = 0.999, newdata = predictData ) ) 313s fit se.pred lwr upr 313s 1 96.0 2.75 91.7 100.3 313s 2 97.5 2.64 94.2 100.7 313s 3 97.6 2.65 94.2 101.0 313s 4 97.9 2.62 94.8 101.0 313s 5 100.1 2.83 95.1 105.0 313s 6 100.0 2.77 95.6 104.4 313s 7 100.0 2.71 96.1 103.9 313s 8 102.2 2.65 98.8 105.6 313s 9 100.4 2.87 95.2 105.6 313s 10 97.4 3.10 90.7 104.1 313s 11 93.0 3.18 85.9 100.1 313s 12 91.3 3.15 84.3 98.3 313s 13 93.1 2.95 87.3 98.9 313s 14 95.3 2.91 89.7 100.8 313s 15 100.2 2.92 94.7 105.8 313s 16 102.0 2.80 97.3 106.6 313s 17 101.1 3.37 92.9 109.4 313s 18 100.5 2.65 97.1 103.9 313s 19 100.4 2.64 97.1 103.6 313s 20 103.3 2.58 100.7 105.9 313s > 313s > print( predict( fitw2sls3, se.pred = TRUE, interval = "prediction", 313s + level = 0.975 ) ) 313s demand.pred demand.se.pred demand.lwr demand.upr supply.pred supply.se.pred 313s 1 97.8 2.08 92.9 103 98.5 2.57 313s 2 99.9 2.07 95.1 105 100.0 2.61 313s 3 99.9 2.06 95.0 105 100.1 2.59 313s 4 100.1 2.07 95.2 105 100.4 2.60 313s 5 102.0 2.05 97.2 107 102.5 2.63 313s 6 101.9 2.04 97.1 107 102.4 2.60 313s 7 102.4 2.04 97.6 107 102.4 2.58 313s 8 102.9 2.08 98.0 108 104.8 2.68 313s 9 101.4 2.06 96.6 106 102.7 2.61 313s 10 100.3 2.15 95.3 105 99.7 2.69 313s 11 95.7 2.19 90.6 101 95.3 2.77 313s 12 94.9 2.20 89.8 100 93.7 2.86 313s 13 96.3 2.16 91.2 101 95.6 2.79 313s 14 99.1 2.05 94.3 104 97.7 2.64 313s 15 103.7 2.13 98.7 109 102.6 2.60 313s 16 103.5 2.08 98.7 108 104.4 2.59 313s 17 103.7 2.39 98.1 109 103.2 2.91 313s 18 102.1 2.06 97.2 107 103.1 2.59 313s 19 103.6 2.10 98.6 108 102.9 2.64 313s 20 106.8 2.37 101.2 112 106.1 2.90 313s supply.lwr supply.upr 313s 1 92.4 104 313s 2 93.9 106 313s 3 94.0 106 313s 4 94.3 106 313s 5 96.3 109 313s 6 96.3 109 313s 7 96.4 109 313s 8 98.5 111 313s 9 96.6 109 313s 10 93.4 106 313s 11 88.8 102 313s 12 87.0 100 313s 13 89.1 102 313s 14 91.5 104 313s 15 96.5 109 313s 16 98.3 110 313s 17 96.4 110 313s 18 97.0 109 313s 19 96.8 109 313s 20 99.3 113 313s > print( predict( fitw2sls3$eq[[ 1 ]], se.pred = TRUE, interval = "prediction", 313s + level = 0.975 ) ) 313s fit se.pred lwr upr 313s 1 97.8 2.08 92.9 103 313s 2 99.9 2.07 95.1 105 313s 3 99.9 2.06 95.0 105 313s 4 100.1 2.07 95.2 105 313s 5 102.0 2.05 97.2 107 313s 6 101.9 2.04 97.1 107 313s 7 102.4 2.04 97.6 107 313s 8 102.9 2.08 98.0 108 313s 9 101.4 2.06 96.6 106 313s 10 100.3 2.15 95.3 105 313s 11 95.7 2.19 90.6 101 313s 12 94.9 2.20 89.8 100 313s 13 96.3 2.16 91.2 101 313s 14 99.1 2.05 94.3 104 313s 15 103.7 2.13 98.7 109 313s 16 103.5 2.08 98.7 108 313s 17 103.7 2.39 98.1 109 313s 18 102.1 2.06 97.2 107 313s 19 103.6 2.10 98.6 108 313s 20 106.8 2.37 101.2 112 313s > 313s > print( predict( fitw2sls4e, se.fit = TRUE, interval = "confidence", 313s + level = 0.25, useDfSys = TRUE ) ) 313s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 313s 1 97.7 0.552 97.5 97.9 98.4 0.611 313s 2 99.9 0.484 99.7 100.0 100.0 0.700 313s 3 99.8 0.465 99.7 100.0 100.1 0.652 313s 4 100.0 0.488 99.9 100.2 100.5 0.664 313s 5 102.1 0.443 101.9 102.2 102.4 0.769 313s 6 101.9 0.425 101.8 102.1 102.4 0.695 313s 7 102.4 0.447 102.2 102.5 102.5 0.639 313s 8 102.9 0.547 102.7 103.1 104.8 0.821 313s 9 101.5 0.458 101.3 101.6 102.7 0.716 313s 10 100.4 0.648 100.2 100.6 99.5 0.743 313s 11 95.7 0.847 95.4 96.0 95.1 0.944 313s 12 94.9 0.823 94.6 95.1 93.6 1.254 313s 13 96.2 0.695 96.0 96.5 95.6 1.154 313s 14 99.1 0.467 98.9 99.2 97.6 0.814 313s 15 103.8 0.590 103.6 104.0 102.5 0.675 313s 16 103.6 0.520 103.4 103.8 104.4 0.659 313s 17 103.8 0.919 103.5 104.1 103.1 1.196 313s 18 102.0 0.487 101.9 102.2 103.1 0.587 313s 19 103.5 0.615 103.3 103.7 103.0 0.664 313s 20 106.7 1.126 106.3 107.0 106.3 0.909 313s supply.lwr supply.upr 313s 1 98.2 98.6 313s 2 99.8 100.3 313s 3 99.9 100.3 313s 4 100.2 100.7 313s 5 102.2 102.7 313s 6 102.2 102.7 313s 7 102.3 102.7 313s 8 104.6 105.1 313s 9 102.5 102.9 313s 10 99.3 99.8 313s 11 94.8 95.4 313s 12 93.2 94.0 313s 13 95.2 96.0 313s 14 97.4 97.9 313s 15 102.3 102.7 313s 16 104.2 104.6 313s 17 102.7 103.5 313s 18 102.9 103.3 313s 19 102.8 103.3 313s 20 106.0 106.6 313s > print( predict( fitw2sls4e$eq[[ 2 ]], se.fit = TRUE, interval = "confidence", 313s + level = 0.25, useDfSys = TRUE ) ) 313s fit se.fit lwr upr 313s 1 98.4 0.611 98.2 98.6 313s 2 100.0 0.700 99.8 100.3 313s 3 100.1 0.652 99.9 100.3 313s 4 100.5 0.664 100.2 100.7 313s 5 102.4 0.769 102.2 102.7 313s 6 102.4 0.695 102.2 102.7 313s 7 102.5 0.639 102.3 102.7 313s 8 104.8 0.821 104.6 105.1 313s 9 102.7 0.716 102.5 102.9 313s 10 99.5 0.743 99.3 99.8 313s 11 95.1 0.944 94.8 95.4 313s 12 93.6 1.254 93.2 94.0 313s 13 95.6 1.154 95.2 96.0 313s 14 97.6 0.814 97.4 97.9 313s 15 102.5 0.675 102.3 102.7 313s 16 104.4 0.659 104.2 104.6 313s 17 103.1 1.196 102.7 103.5 313s 18 103.1 0.587 102.9 103.3 313s 19 103.0 0.664 102.8 103.3 313s 20 106.3 0.909 106.0 106.6 313s > 313s > print( predict( fitw2sls5, se.fit = TRUE, se.pred = TRUE, 313s + interval = "prediction", level = 0.5, newdata = predictData ) ) 313s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 313s 1 102.8 0.781 2.12 101.4 104 95.8 313s 2 105.4 0.812 2.13 104.0 107 97.4 313s 3 105.3 0.824 2.13 103.8 107 97.5 313s 4 105.6 0.820 2.13 104.1 107 97.8 313s 5 107.5 1.186 2.30 106.0 109 99.9 313s 6 107.4 1.133 2.27 105.9 109 99.9 313s 7 108.0 1.141 2.28 106.4 110 99.9 313s 8 108.7 1.143 2.28 107.2 110 102.1 313s 9 106.8 1.179 2.30 105.2 108 100.2 313s 10 105.3 1.307 2.36 103.7 107 97.2 313s 11 100.3 1.108 2.26 98.7 102 92.7 313s 12 99.6 0.841 2.14 98.2 101 91.1 313s 13 101.3 0.638 2.07 99.9 103 93.0 313s 14 104.3 0.914 2.17 102.8 106 95.1 313s 15 109.3 1.440 2.44 107.6 111 100.1 313s 16 109.2 1.333 2.38 107.6 111 101.9 313s 17 108.9 1.742 2.63 107.1 111 100.9 313s 18 107.8 1.049 2.23 106.2 109 100.5 313s 19 109.5 1.216 2.31 107.9 111 100.3 313s 20 113.3 1.669 2.58 111.6 115 103.4 313s supply.se.fit supply.se.pred supply.lwr supply.upr 313s 1 0.825 2.64 94.1 97.6 313s 2 0.696 2.60 95.6 99.1 313s 3 0.712 2.60 95.7 99.2 313s 4 0.674 2.59 96.0 99.5 313s 5 1.087 2.73 98.1 101.8 313s 6 0.979 2.69 98.0 101.7 313s 7 0.874 2.65 98.1 101.7 313s 8 0.871 2.65 100.3 103.9 313s 9 1.143 2.75 98.4 102.1 313s 10 1.338 2.84 95.3 99.1 313s 11 1.483 2.91 90.8 94.7 313s 12 1.645 3.00 89.1 93.1 313s 13 1.440 2.89 91.0 94.9 313s 14 1.247 2.80 93.2 97.0 313s 15 1.222 2.79 98.2 102.0 313s 16 1.104 2.74 100.0 103.7 313s 17 1.808 3.09 98.7 103.0 313s 18 0.861 2.65 98.7 102.3 313s 19 0.861 2.65 98.5 102.1 313s 20 0.666 2.59 101.6 105.2 313s > print( predict( fitw2sls5$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 313s + interval = "prediction", level = 0.5, newdata = predictData ) ) 313s fit se.fit se.pred lwr upr 313s 1 102.8 0.781 2.12 101.4 104 313s 2 105.4 0.812 2.13 104.0 107 313s 3 105.3 0.824 2.13 103.8 107 313s 4 105.6 0.820 2.13 104.1 107 313s 5 107.5 1.186 2.30 106.0 109 313s 6 107.4 1.133 2.27 105.9 109 313s 7 108.0 1.141 2.28 106.4 110 313s 8 108.7 1.143 2.28 107.2 110 313s 9 106.8 1.179 2.30 105.2 108 313s 10 105.3 1.307 2.36 103.7 107 313s 11 100.3 1.108 2.26 98.7 102 313s 12 99.6 0.841 2.14 98.2 101 313s 13 101.3 0.638 2.07 99.9 103 313s 14 104.3 0.914 2.17 102.8 106 313s 15 109.3 1.440 2.44 107.6 111 313s 16 109.2 1.333 2.38 107.6 111 313s 17 108.9 1.742 2.63 107.1 111 313s 18 107.8 1.049 2.23 106.2 109 313s 19 109.5 1.216 2.31 107.9 111 313s 20 113.3 1.669 2.58 111.6 115 313s > 313s > print( predict( fitw2slsd1, se.fit = TRUE, se.pred = TRUE, 313s + interval = "confidence", level = 0.99 ) ) 313s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 313s 1 97.1 0.751 2.13 94.9 99.3 98.9 313s 2 99.2 0.757 2.13 97.0 101.4 100.4 313s 3 99.2 0.692 2.11 97.2 101.2 100.5 313s 4 99.3 0.766 2.13 97.1 101.5 100.7 313s 5 102.5 0.595 2.08 100.8 104.3 102.6 313s 6 102.2 0.503 2.05 100.7 103.7 102.6 313s 7 102.5 0.503 2.05 101.1 104.0 102.6 313s 8 102.7 0.653 2.10 100.8 104.5 104.8 313s 9 102.0 0.655 2.10 100.1 103.9 102.7 313s 10 101.4 1.074 2.26 98.3 104.5 99.7 313s 11 95.6 0.978 2.22 92.8 98.5 95.4 313s 12 93.9 1.134 2.29 90.7 97.2 93.8 313s 13 95.0 1.162 2.31 91.7 98.4 95.6 313s 14 98.9 0.530 2.06 97.4 100.4 97.6 313s 15 104.9 1.061 2.26 101.9 108.0 102.3 313s 16 104.3 0.757 2.13 102.1 106.5 104.1 313s 17 106.1 1.963 2.80 100.4 111.7 102.8 313s 18 101.7 0.597 2.08 100.0 103.5 102.7 313s 19 103.3 0.736 2.12 101.2 105.4 102.6 313s 20 106.0 1.430 2.45 101.9 110.2 105.6 313s supply.se.fit supply.se.pred supply.lwr supply.upr 313s 1 1.079 2.68 95.8 102.1 313s 2 1.064 2.68 97.3 103.5 313s 3 0.962 2.64 97.6 103.3 313s 4 0.938 2.63 98.0 103.4 313s 5 0.914 2.62 100.0 105.3 313s 6 0.808 2.59 100.2 104.9 313s 7 0.736 2.57 100.4 104.7 313s 8 0.994 2.65 101.9 107.7 313s 9 0.808 2.59 100.4 105.1 313s 10 1.023 2.66 96.7 102.7 313s 11 1.228 2.75 91.8 99.0 313s 12 1.428 2.84 89.7 98.0 313s 13 1.272 2.77 91.9 99.4 313s 14 0.917 2.62 94.9 100.3 313s 15 0.899 2.62 99.7 104.9 313s 16 0.936 2.63 101.3 106.8 313s 17 1.665 2.97 98.0 107.7 313s 18 0.988 2.65 99.8 105.6 313s 19 1.129 2.70 99.3 105.9 313s 20 1.733 3.01 100.5 110.7 313s > print( predict( fitw2slsd1$eq[[ 2 ]], se.fit = TRUE, se.pred = TRUE, 313s + interval = "confidence", level = 0.99 ) ) 313s fit se.fit se.pred lwr upr 313s 1 98.9 1.079 2.68 95.8 102.1 313s 2 100.4 1.064 2.68 97.3 103.5 313s 3 100.5 0.962 2.64 97.6 103.3 313s 4 100.7 0.938 2.63 98.0 103.4 313s 5 102.6 0.914 2.62 100.0 105.3 313s 6 102.6 0.808 2.59 100.2 104.9 313s 7 102.6 0.736 2.57 100.4 104.7 313s 8 104.8 0.994 2.65 101.9 107.7 313s 9 102.7 0.808 2.59 100.4 105.1 313s 10 99.7 1.023 2.66 96.7 102.7 313s 11 95.4 1.228 2.75 91.8 99.0 313s 12 93.8 1.428 2.84 89.7 98.0 313s 13 95.6 1.272 2.77 91.9 99.4 313s 14 97.6 0.917 2.62 94.9 100.3 313s 15 102.3 0.899 2.62 99.7 104.9 313s 16 104.1 0.936 2.63 101.3 106.8 313s 17 102.8 1.665 2.97 98.0 107.7 313s 18 102.7 0.988 2.65 99.8 105.6 313s 19 102.6 1.129 2.70 99.3 105.9 313s 20 105.6 1.733 3.01 100.5 110.7 313s > 313s > print( predict( fitw2slsd2e, se.fit = TRUE, interval = "prediction", 313s + level = 0.9, newdata = predictData, useDfSys = TRUE ) ) 313s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 313s 1 104 1.214 100.1 108 95.7 1.100 313s 2 106 1.169 102.6 110 97.2 0.835 313s 3 106 1.216 102.5 110 97.3 0.864 313s 4 107 1.169 102.7 110 97.6 0.789 313s 5 109 1.897 104.7 114 99.9 1.242 313s 6 109 1.773 104.6 114 99.9 1.115 313s 7 110 1.718 105.2 114 99.9 0.983 313s 8 110 1.552 105.8 114 102.2 0.843 313s 9 109 1.939 104.0 113 100.4 1.310 313s 10 107 2.229 102.5 112 97.4 1.683 313s 11 102 1.655 97.5 106 92.9 1.794 313s 12 101 1.125 96.8 104 91.2 1.750 313s 13 102 0.879 98.5 106 93.1 1.449 313s 14 106 1.480 101.5 110 95.3 1.383 313s 15 111 2.331 106.3 117 100.4 1.395 313s 16 111 2.064 106.3 116 102.2 1.175 313s 17 112 3.001 105.7 118 101.4 2.074 313s 18 109 1.475 104.9 113 100.7 0.861 313s 19 111 1.589 106.5 115 100.6 0.829 313s 20 114 1.756 109.9 119 103.6 0.680 313s supply.lwr supply.upr 313s 1 91.1 100.3 313s 2 92.7 101.7 313s 3 92.8 101.8 313s 4 93.2 102.1 313s 5 95.2 104.7 313s 6 95.3 104.6 313s 7 95.3 104.5 313s 8 97.7 106.7 313s 9 95.6 105.2 313s 10 92.3 102.5 313s 11 87.7 98.1 313s 12 86.0 96.4 313s 13 88.1 98.0 313s 14 90.4 100.1 313s 15 95.5 105.3 313s 16 97.5 106.9 313s 17 95.8 106.9 313s 18 96.2 105.2 313s 19 96.1 105.1 313s 20 99.2 108.0 313s > print( predict( fitw2slsd2e$eq[[ 1 ]], se.fit = TRUE, interval = "prediction", 313s + level = 0.9, newdata = predictData, useDfSys = TRUE ) ) 313s fit se.fit lwr upr 313s 1 104 1.214 100.1 108 313s 2 106 1.169 102.6 110 313s 3 106 1.216 102.5 110 313s 4 107 1.169 102.7 110 313s 5 109 1.897 104.7 114 313s 6 109 1.773 104.6 114 313s 7 110 1.718 105.2 114 313s 8 110 1.552 105.8 114 313s 9 109 1.939 104.0 113 313s 10 107 2.229 102.5 112 313s 11 102 1.655 97.5 106 313s 12 101 1.125 96.8 104 313s 13 102 0.879 98.5 106 313s 14 106 1.480 101.5 110 313s 15 111 2.331 106.3 117 313s 16 111 2.064 106.3 116 313s 17 112 3.001 105.7 118 313s 18 109 1.475 104.9 113 313s 19 111 1.589 106.5 115 313s 20 114 1.756 109.9 119 313s > 313s > print( predict( fitw2slsd3e, se.fit = TRUE, se.pred = TRUE, 313s + interval = "prediction", level = 0.01, useDfSys = TRUE ) ) 313s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 313s 1 97.4 0.622 2.05 97.4 97.4 98.2 313s 2 99.4 0.654 2.06 99.4 99.4 99.7 313s 3 99.4 0.598 2.04 99.4 99.4 99.9 313s 4 99.5 0.663 2.06 99.5 99.5 100.2 313s 5 102.4 0.515 2.02 102.4 102.4 102.3 313s 6 102.1 0.442 2.00 102.1 102.1 102.3 313s 7 102.4 0.444 2.00 102.4 102.4 102.4 313s 8 102.6 0.587 2.04 102.6 102.6 104.7 313s 9 101.9 0.573 2.03 101.9 101.9 102.7 313s 10 101.2 0.948 2.17 101.2 101.2 99.6 313s 11 95.9 0.849 2.13 95.9 95.9 95.2 313s 12 94.4 0.914 2.15 94.4 94.4 93.6 313s 13 95.5 0.943 2.17 95.5 95.5 95.6 313s 14 99.0 0.464 2.01 99.0 99.1 97.7 313s 15 104.5 0.883 2.14 104.5 104.6 102.7 313s 16 104.0 0.631 2.05 104.0 104.0 104.6 313s 17 105.4 1.665 2.56 105.4 105.5 103.5 313s 18 101.8 0.538 2.02 101.7 101.8 103.3 313s 19 103.2 0.661 2.06 103.2 103.3 103.2 313s 20 105.9 1.284 2.34 105.9 106.0 106.4 313s supply.se.fit supply.se.pred supply.lwr supply.upr 313s 1 0.652 2.60 98.1 98.2 313s 2 0.740 2.62 99.7 99.8 313s 3 0.682 2.61 99.8 99.9 313s 4 0.708 2.61 100.2 100.2 313s 5 0.782 2.63 102.3 102.4 313s 6 0.699 2.61 102.3 102.4 313s 7 0.648 2.60 102.3 102.4 313s 8 0.906 2.67 104.7 104.8 313s 9 0.736 2.62 102.7 102.8 313s 10 0.931 2.68 99.6 99.7 313s 11 1.107 2.75 95.2 95.2 313s 12 1.287 2.83 93.6 93.7 313s 13 1.157 2.77 95.5 95.6 313s 14 0.829 2.65 97.7 97.7 313s 15 0.717 2.62 102.7 102.8 313s 16 0.676 2.61 104.6 104.6 313s 17 1.392 2.88 103.4 103.5 313s 18 0.699 2.61 103.3 103.3 313s 19 0.822 2.65 103.2 103.2 313s 20 1.376 2.87 106.4 106.5 313s > print( predict( fitw2slsd3e$eq[[ 2 ]], se.fit = TRUE, se.pred = TRUE, 313s + interval = "prediction", level = 0.01, useDfSys = TRUE ) ) 313s fit se.fit se.pred lwr upr 313s 1 98.2 0.652 2.60 98.1 98.2 313s 2 99.7 0.740 2.62 99.7 99.8 313s 3 99.9 0.682 2.61 99.8 99.9 313s 4 100.2 0.708 2.61 100.2 100.2 313s 5 102.3 0.782 2.63 102.3 102.4 313s 6 102.3 0.699 2.61 102.3 102.4 313s 7 102.4 0.648 2.60 102.3 102.4 313s 8 104.7 0.906 2.67 104.7 104.8 313s 9 102.7 0.736 2.62 102.7 102.8 313s 10 99.6 0.931 2.68 99.6 99.7 313s 11 95.2 1.107 2.75 95.2 95.2 313s 12 93.6 1.287 2.83 93.6 93.7 313s 13 95.6 1.157 2.77 95.5 95.6 313s 14 97.7 0.829 2.65 97.7 97.7 313s 15 102.7 0.717 2.62 102.7 102.8 313s 16 104.6 0.676 2.61 104.6 104.6 313s 17 103.5 1.392 2.88 103.4 103.5 313s 18 103.3 0.699 2.61 103.3 103.3 313s 19 103.2 0.822 2.65 103.2 103.2 313s 20 106.4 1.376 2.87 106.4 106.5 313s > 313s > # predict just one observation 313s > smallData <- data.frame( price = 130, income = 150, farmPrice = 120, 313s + trend = 25 ) 313s > 313s > print( predict( fitw2sls1e, newdata = smallData ) ) 313s demand.pred supply.pred 313s 1 110 118 313s > print( predict( fitw2sls1e$eq[[ 1 ]], newdata = smallData ) ) 313s fit 313s 1 110 313s > 313s > print( predict( fitw2sls2, se.fit = TRUE, level = 0.9, 313s + newdata = smallData ) ) 313s demand.pred demand.se.fit supply.pred supply.se.fit 313s 1 110 2.52 119 3.53 313s > print( predict( fitw2sls2$eq[[ 1 ]], se.pred = TRUE, level = 0.99, 313s + newdata = smallData ) ) 313s fit se.pred 313s 1 110 3.21 313s > 313s > print( predict( fitw2sls3, interval = "prediction", level = 0.975, 313s + newdata = smallData ) ) 313s demand.pred demand.lwr demand.upr supply.pred supply.lwr supply.upr 313s 1 110 102 117 119 109 129 313s > print( predict( fitw2sls3$eq[[ 1 ]], interval = "confidence", level = 0.8, 313s + newdata = smallData ) ) 313s fit lwr upr 313s 1 110 107 113 313s > 313s > print( predict( fitw2sls4e, se.fit = TRUE, interval = "confidence", 313s + level = 0.999, newdata = smallData ) ) 313s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 313s 1 110 2.08 102 117 119 2.11 313s supply.lwr supply.upr 313s 1 112 127 313s > print( predict( fitw2sls4e$eq[[ 2 ]], se.pred = TRUE, interval = "prediction", 313s + level = 0.75, newdata = smallData ) ) 313s fit se.pred lwr upr 313s 1 119 3.27 115 123 313s > 313s > print( predict( fitw2sls5, se.fit = TRUE, interval = "prediction", 313s + newdata = smallData ) ) 313s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 313s 1 110 2.26 104 116 119 2.33 313s supply.lwr supply.upr 313s 1 112 126 313s > print( predict( fitw2sls5$eq[[ 1 ]], se.pred = TRUE, interval = "confidence", 313s + newdata = smallData ) ) 313s fit se.pred lwr upr 313s 1 110 3 105 114 313s > 313s > print( predict( fitw2slsd2e, se.fit = TRUE, se.pred = TRUE, 313s + interval = "prediction", level = 0.5, newdata = smallData ) ) 313s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 313s 1 108 2.71 3.34 105 110 119 313s supply.se.fit supply.se.pred supply.lwr supply.upr 313s 1 3.22 4.08 117 122 313s > print( predict( fitw2slsd2e$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 313s + interval = "confidence", level = 0.25, newdata = smallData ) ) 313s fit se.fit se.pred lwr upr 313s 1 108 2.71 3.34 107 109 313s > 313s > 313s > ## ************ correlation of predicted values *************** 313s > print( correlation.systemfit( fitw2sls1e, 1, 2 ) ) 313s [,1] 313s [1,] 0 313s [2,] 0 313s [3,] 0 313s [4,] 0 313s [5,] 0 313s [6,] 0 313s [7,] 0 313s [8,] 0 313s [9,] 0 313s [10,] 0 313s [11,] 0 313s [12,] 0 313s [13,] 0 313s [14,] 0 313s [15,] 0 313s [16,] 0 313s [17,] 0 313s [18,] 0 313s [19,] 0 313s [20,] 0 313s > 313s > print( correlation.systemfit( fitw2sls2, 2, 1 ) ) 313s [,1] 313s [1,] 0.413453 313s [2,] 0.153759 313s [3,] 0.152962 313s [4,] 0.112671 313s [5,] -0.071442 313s [6,] -0.053943 313s [7,] -0.050961 313s [8,] -0.005442 313s [9,] -0.000476 313s [10,] -0.001894 313s [11,] 0.047351 313s [12,] 0.064973 313s [13,] 0.024591 313s [14,] -0.028036 313s [15,] 0.175326 313s [16,] 0.254878 313s [17,] 0.104540 313s [18,] 0.065579 313s [19,] 0.147008 313s [20,] 0.124593 313s > 313s > print( correlation.systemfit( fitw2sls3, 1, 2 ) ) 313s [,1] 313s [1,] 0.413453 313s [2,] 0.153759 313s [3,] 0.152962 313s [4,] 0.112671 313s [5,] -0.071442 313s [6,] -0.053943 313s [7,] -0.050961 313s [8,] -0.005442 313s [9,] -0.000476 313s [10,] -0.001894 313s [11,] 0.047351 313s [12,] 0.064973 313s [13,] 0.024591 313s [14,] -0.028036 313s [15,] 0.175326 313s [16,] 0.254878 313s [17,] 0.104540 313s [18,] 0.065579 313s [19,] 0.147008 313s [20,] 0.124593 313s > 313s > print( correlation.systemfit( fitw2sls4e, 2, 1 ) ) 313s [,1] 313s [1,] 0.38438 313s [2,] 0.30697 313s [3,] 0.26690 313s [4,] 0.30163 313s [5,] -0.02768 313s [6,] -0.05086 313s [7,] -0.05895 313s [8,] 0.10102 313s [9,] 0.10072 313s [10,] 0.45547 313s [11,] 0.10817 313s [12,] 0.00552 313s [13,] 0.04219 313s [14,] -0.04054 313s [15,] 0.42100 313s [16,] 0.24974 313s [17,] 0.65722 313s [18,] 0.24286 313s [19,] 0.34336 313s [20,] 0.54717 313s > 313s > print( correlation.systemfit( fitw2sls5, 1, 2 ) ) 313s [,1] 313s [1,] 0.38030 313s [2,] 0.30892 313s [3,] 0.26808 313s [4,] 0.30325 313s [5,] -0.02730 313s [6,] -0.05035 313s [7,] -0.05831 313s [8,] 0.10036 313s [9,] 0.10045 313s [10,] 0.45492 313s [11,] 0.10525 313s [12,] 0.00394 313s [13,] 0.04171 313s [14,] -0.04037 313s [15,] 0.41958 313s [16,] 0.24706 313s [17,] 0.65619 313s [18,] 0.23872 313s [19,] 0.33729 313s [20,] 0.54239 313s > 313s > print( correlation.systemfit( fitw2slsd1, 2, 1 ) ) 313s [,1] 313s [1,] 0 313s [2,] 0 313s [3,] 0 313s [4,] 0 313s [5,] 0 313s [6,] 0 313s [7,] 0 313s [8,] 0 313s [9,] 0 313s [10,] 0 313s [11,] 0 313s [12,] 0 313s [13,] 0 313s [14,] 0 313s [15,] 0 313s [16,] 0 313s [17,] 0 313s [18,] 0 313s [19,] 0 313s [20,] 0 313s > 313s > print( correlation.systemfit( fitw2slsd2e, 1, 2 ) ) 313s [,1] 313s [1,] 0.482214 313s [2,] 0.253368 313s [3,] 0.242824 313s [4,] 0.195411 313s [5,] -0.107828 313s [6,] -0.074958 313s [7,] -0.055696 313s [8,] -0.002037 313s [9,] -0.000921 313s [10,] -0.008040 313s [11,] 0.040999 313s [12,] 0.075418 313s [13,] 0.029702 313s [14,] -0.030775 313s [15,] 0.229063 313s [16,] 0.318607 313s [17,] 0.156734 313s [18,] -0.023016 313s [19,] 0.068128 313s [20,] 0.047481 313s > 313s > print( correlation.systemfit( fitw2slsd3e, 2, 1 ) ) 313s [,1] 313s [1,] 0.482214 313s [2,] 0.253368 313s [3,] 0.242824 313s [4,] 0.195411 313s [5,] -0.107828 313s [6,] -0.074958 313s [7,] -0.055696 313s [8,] -0.002037 313s [9,] -0.000921 313s [10,] -0.008040 313s [11,] 0.040999 313s [12,] 0.075418 313s [13,] 0.029702 313s [14,] -0.030775 313s [15,] 0.229063 313s [16,] 0.318607 313s [17,] 0.156734 313s [18,] -0.023016 313s [19,] 0.068128 313s [20,] 0.047481 313s > 313s > 313s > ## ************ LOG-Likelihood values *************** 313s > print( logLik( fitw2sls1e ) ) 313s 'log Lik.' -67.6 (df=9) 313s > print( logLik( fitw2sls1e, residCovDiag = TRUE ) ) 313s 'log Lik.' -84.4 (df=9) 313s > 313s > print( logLik( fitw2sls2 ) ) 313s 'log Lik.' -65.2 (df=8) 313s > print( logLik( fitw2sls2, residCovDiag = TRUE ) ) 313s 'log Lik.' -84.8 (df=8) 313s > 313s > print( logLik( fitw2sls3 ) ) 313s 'log Lik.' -65.2 (df=8) 313s > print( logLik( fitw2sls3, residCovDiag = TRUE ) ) 313s 'log Lik.' -84.8 (df=8) 313s > 313s > print( logLik( fitw2sls4e ) ) 313s 'log Lik.' -65.7 (df=7) 313s > print( logLik( fitw2sls4e, residCovDiag = TRUE ) ) 313s 'log Lik.' -84.8 (df=7) 313s > 313s > print( logLik( fitw2sls5 ) ) 313s 'log Lik.' -65.6 (df=7) 313s > print( logLik( fitw2sls5, residCovDiag = TRUE ) ) 313s 'log Lik.' -84.8 (df=7) 313s > 313s > print( logLik( fitw2slsd1 ) ) 313s 'log Lik.' -75.1 (df=9) 313s > print( logLik( fitw2slsd1, residCovDiag = TRUE ) ) 313s 'log Lik.' -84.7 (df=9) 313s > 313s > print( logLik( fitw2slsd2e ) ) 313s 'log Lik.' -69.1 (df=8) 313s > print( logLik( fitw2slsd2e, residCovDiag = TRUE ) ) 313s 'log Lik.' -84.7 (df=8) 313s > 313s > print( logLik( fitw2slsd3e ) ) 313s 'log Lik.' -69.1 (df=8) 313s > print( logLik( fitw2slsd3e, residCovDiag = TRUE ) ) 313s 'log Lik.' -84.7 (df=8) 313s > 313s > 313s > ## ************** F tests **************** 313s > # testing first restriction 313s > print( linearHypothesis( fitw2sls1, restrm ) ) 313s Linear hypothesis test (Theil's F test) 313s 313s Hypothesis: 313s demand_income - supply_trend = 0 313s 313s Model 1: restricted model 313s Model 2: fitw2sls1 313s 313s Res.Df Df F Pr(>F) 313s 1 34 313s 2 33 1 0.31 0.58 313s > linearHypothesis( fitw2sls1, restrict ) 313s Linear hypothesis test (Theil's F test) 313s 313s Hypothesis: 313s demand_income - supply_trend = 0 313s 313s Model 1: restricted model 313s Model 2: fitw2sls1 313s 313s Res.Df Df F Pr(>F) 313s 1 34 313s 2 33 1 0.31 0.58 313s > 313s > print( linearHypothesis( fitw2slsd1e, restrm ) ) 313s Linear hypothesis test (Theil's F test) 313s 313s Hypothesis: 313s demand_income - supply_trend = 0 313s 313s Model 1: restricted model 313s Model 2: fitw2slsd1e 313s 313s Res.Df Df F Pr(>F) 313s 1 34 313s 2 33 1 0.92 0.35 313s > linearHypothesis( fitw2slsd1e, restrict ) 313s Linear hypothesis test (Theil's F test) 313s 313s Hypothesis: 313s demand_income - supply_trend = 0 313s 313s Model 1: restricted model 313s Model 2: fitw2slsd1e 313s 313s Res.Df Df F Pr(>F) 313s 1 34 313s 2 33 1 0.92 0.35 313s > 313s > # testing second restriction 313s > restrOnly2m <- matrix(0,1,7) 313s > restrOnly2q <- 0.5 313s > restrOnly2m[1,2] <- -1 313s > restrOnly2m[1,5] <- 1 313s > restrictOnly2 <- "- demand_price + supply_price = 0.5" 313s > # first restriction not imposed 313s > print( linearHypothesis( fitw2sls1e, restrOnly2m, restrOnly2q ) ) 313s Linear hypothesis test (Theil's F test) 313s 313s Hypothesis: 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2sls1e 313s 313s Res.Df Df F Pr(>F) 313s 1 34 313s 2 33 1 0.01 0.91 313s > linearHypothesis( fitw2sls1e, restrictOnly2 ) 313s Linear hypothesis test (Theil's F test) 313s 313s Hypothesis: 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2sls1e 313s 313s Res.Df Df F Pr(>F) 313s 1 34 313s 2 33 1 0.01 0.91 313s > 313s > print( linearHypothesis( fitw2slsd1, restrOnly2m, restrOnly2q ) ) 313s Linear hypothesis test (Theil's F test) 313s 313s Hypothesis: 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2slsd1 313s 313s Res.Df Df F Pr(>F) 313s 1 34 313s 2 33 1 0.74 0.39 313s > linearHypothesis( fitw2slsd1, restrictOnly2 ) 313s Linear hypothesis test (Theil's F test) 313s 313s Hypothesis: 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2slsd1 313s 313s Res.Df Df F Pr(>F) 313s 1 34 313s 2 33 1 0.74 0.39 313s > 313s > # first restriction imposed 313s > print( linearHypothesis( fitw2sls2, restrOnly2m, restrOnly2q ) ) 313s Linear hypothesis test (Theil's F test) 313s 313s Hypothesis: 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2sls2 313s 313s Res.Df Df F Pr(>F) 313s 1 35 313s 2 34 1 0.04 0.85 313s > linearHypothesis( fitw2sls2, restrictOnly2 ) 313s Linear hypothesis test (Theil's F test) 313s 313s Hypothesis: 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2sls2 313s 313s Res.Df Df F Pr(>F) 313s 1 35 313s 2 34 1 0.04 0.85 313s > 313s > print( linearHypothesis( fitw2sls3, restrOnly2m, restrOnly2q ) ) 313s Linear hypothesis test (Theil's F test) 313s 313s Hypothesis: 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2sls3 313s 313s Res.Df Df F Pr(>F) 313s 1 35 313s 2 34 1 0.04 0.85 313s > linearHypothesis( fitw2sls3, restrictOnly2 ) 313s Linear hypothesis test (Theil's F test) 313s 313s Hypothesis: 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2sls3 313s 313s Res.Df Df F Pr(>F) 313s 1 35 313s 2 34 1 0.04 0.85 313s > 313s > print( linearHypothesis( fitw2slsd2e, restrOnly2m, restrOnly2q ) ) 313s Linear hypothesis test (Theil's F test) 313s 313s Hypothesis: 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2slsd2e 313s 313s Res.Df Df F Pr(>F) 313s 1 35 313s 2 34 1 0.42 0.52 313s > linearHypothesis( fitw2slsd2e, restrictOnly2 ) 313s Linear hypothesis test (Theil's F test) 313s 313s Hypothesis: 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2slsd2e 313s 313s Res.Df Df F Pr(>F) 313s 1 35 313s 2 34 1 0.42 0.52 313s > 313s > print( linearHypothesis( fitw2slsd3e, restrOnly2m, restrOnly2q ) ) 313s Linear hypothesis test (Theil's F test) 313s 313s Hypothesis: 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2slsd3e 313s 313s Res.Df Df F Pr(>F) 313s 1 35 313s 2 34 1 0.42 0.52 313s > linearHypothesis( fitw2slsd3e, restrictOnly2 ) 313s Linear hypothesis test (Theil's F test) 313s 313s Hypothesis: 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2slsd3e 313s 313s Res.Df Df F Pr(>F) 313s 1 35 313s 2 34 1 0.42 0.52 313s > 313s > # testing both of the restrictions 313s > print( linearHypothesis( fitw2sls1e, restr2m, restr2q ) ) 313s Linear hypothesis test (Theil's F test) 313s 313s Hypothesis: 313s demand_income - supply_trend = 0 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2sls1e 313s 313s Res.Df Df F Pr(>F) 313s 1 35 313s 2 33 2 0.18 0.84 313s > linearHypothesis( fitw2sls1e, restrict2 ) 313s Linear hypothesis test (Theil's F test) 313s 313s Hypothesis: 313s demand_income - supply_trend = 0 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2sls1e 313s 313s Res.Df Df F Pr(>F) 313s 1 35 313s 2 33 2 0.18 0.84 313s > 313s > print( linearHypothesis( fitw2slsd1, restr2m, restr2q ) ) 313s Linear hypothesis test (Theil's F test) 313s 313s Hypothesis: 313s demand_income - supply_trend = 0 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2slsd1 313s 313s Res.Df Df F Pr(>F) 313s 1 35 313s 2 33 2 0.65 0.53 313s > linearHypothesis( fitw2slsd1, restrict2 ) 313s Linear hypothesis test (Theil's F test) 313s 313s Hypothesis: 313s demand_income - supply_trend = 0 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2slsd1 313s 313s Res.Df Df F Pr(>F) 313s 1 35 313s 2 33 2 0.65 0.53 313s > 313s > 313s > ## ************** Wald tests **************** 313s > # testing first restriction 313s > print( linearHypothesis( fitw2sls1, restrm, test = "Chisq" ) ) 313s Linear hypothesis test (Chi^2 statistic of a Wald test) 313s 313s Hypothesis: 313s demand_income - supply_trend = 0 313s 313s Model 1: restricted model 313s Model 2: fitw2sls1 313s 313s Res.Df Df Chisq Pr(>Chisq) 313s 1 34 313s 2 33 1 0.31 0.58 313s > linearHypothesis( fitw2sls1, restrict, test = "Chisq" ) 313s Linear hypothesis test (Chi^2 statistic of a Wald test) 313s 313s Hypothesis: 313s demand_income - supply_trend = 0 313s 313s Model 1: restricted model 313s Model 2: fitw2sls1 313s 313s Res.Df Df Chisq Pr(>Chisq) 313s 1 34 313s 2 33 1 0.31 0.58 313s > 313s > print( linearHypothesis( fitw2slsd1e, restrm, test = "Chisq" ) ) 313s Linear hypothesis test (Chi^2 statistic of a Wald test) 313s 313s Hypothesis: 313s demand_income - supply_trend = 0 313s 313s Model 1: restricted model 313s Model 2: fitw2slsd1e 313s 313s Res.Df Df Chisq Pr(>Chisq) 313s 1 34 313s 2 33 1 1.11 0.29 313s > linearHypothesis( fitw2slsd1e, restrict, test = "Chisq" ) 313s Linear hypothesis test (Chi^2 statistic of a Wald test) 313s 313s Hypothesis: 313s demand_income - supply_trend = 0 313s 313s Model 1: restricted model 313s Model 2: fitw2slsd1e 313s 313s Res.Df Df Chisq Pr(>Chisq) 313s 1 34 313s 2 33 1 1.11 0.29 313s > 313s > # testing second restriction 313s > # first restriction not imposed 313s > print( linearHypothesis( fitw2sls1e, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 313s Linear hypothesis test (Chi^2 statistic of a Wald test) 313s 313s Hypothesis: 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2sls1e 313s 313s Res.Df Df Chisq Pr(>Chisq) 313s 1 34 313s 2 33 1 0.02 0.9 313s > linearHypothesis( fitw2sls1e, restrictOnly2, test = "Chisq" ) 313s Linear hypothesis test (Chi^2 statistic of a Wald test) 313s 313s Hypothesis: 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2sls1e 313s 313s Res.Df Df Chisq Pr(>Chisq) 313s 1 34 313s 2 33 1 0.02 0.9 313s > 313s > print( linearHypothesis( fitw2slsd1, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 313s Linear hypothesis test (Chi^2 statistic of a Wald test) 313s 313s Hypothesis: 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2slsd1 313s 313s Res.Df Df Chisq Pr(>Chisq) 313s 1 34 313s 2 33 1 0.74 0.39 313s > linearHypothesis( fitw2slsd1, restrictOnly2, test = "Chisq" ) 313s Linear hypothesis test (Chi^2 statistic of a Wald test) 313s 313s Hypothesis: 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2slsd1 313s 313s Res.Df Df Chisq Pr(>Chisq) 313s 1 34 313s 2 33 1 0.74 0.39 313s > # first restriction imposed 313s > print( linearHypothesis( fitw2sls2, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 313s Linear hypothesis test (Chi^2 statistic of a Wald test) 313s 313s Hypothesis: 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2sls2 313s 313s Res.Df Df Chisq Pr(>Chisq) 313s 1 35 313s 2 34 1 0.04 0.85 313s > linearHypothesis( fitw2sls2, restrictOnly2, test = "Chisq" ) 313s Linear hypothesis test (Chi^2 statistic of a Wald test) 313s 313s Hypothesis: 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2sls2 313s 313s Res.Df Df Chisq Pr(>Chisq) 313s 1 35 313s 2 34 1 0.04 0.85 313s > 313s > print( linearHypothesis( fitw2sls3, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 313s Linear hypothesis test (Chi^2 statistic of a Wald test) 313s 313s Hypothesis: 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2sls3 313s 313s Res.Df Df Chisq Pr(>Chisq) 313s 1 35 313s 2 34 1 0.04 0.85 313s > linearHypothesis( fitw2sls3, restrictOnly2, test = "Chisq" ) 313s Linear hypothesis test (Chi^2 statistic of a Wald test) 313s 313s Hypothesis: 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2sls3 313s 313s Res.Df Df Chisq Pr(>Chisq) 313s 1 35 313s 2 34 1 0.04 0.85 313s > 313s > print( linearHypothesis( fitw2slsd2e, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 313s Linear hypothesis test (Chi^2 statistic of a Wald test) 313s 313s Hypothesis: 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2slsd2e 313s 313s Res.Df Df Chisq Pr(>Chisq) 313s 1 35 313s 2 34 1 0.49 0.48 313s > linearHypothesis( fitw2slsd2e, restrictOnly2, test = "Chisq" ) 313s Linear hypothesis test (Chi^2 statistic of a Wald test) 313s 313s Hypothesis: 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2slsd2e 313s 313s Res.Df Df Chisq Pr(>Chisq) 313s 1 35 313s 2 34 1 0.49 0.48 313s > 313s > print( linearHypothesis( fitw2slsd3e, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 313s Linear hypothesis test (Chi^2 statistic of a Wald test) 313s 313s Hypothesis: 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2slsd3e 313s 313s Res.Df Df Chisq Pr(>Chisq) 313s 1 35 313s 2 34 1 0.49 0.48 313s > linearHypothesis( fitw2slsd3e, restrictOnly2, test = "Chisq" ) 313s Linear hypothesis test (Chi^2 statistic of a Wald test) 313s 313s Hypothesis: 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2slsd3e 313s 313s Res.Df Df Chisq Pr(>Chisq) 313s 1 35 313s 2 34 1 0.49 0.48 313s > 313s > # testing both of the restrictions 313s > print( linearHypothesis( fitw2sls1e, restr2m, restr2q, test = "Chisq" ) ) 313s Linear hypothesis test (Chi^2 statistic of a Wald test) 313s 313s Hypothesis: 313s demand_income - supply_trend = 0 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2sls1e 313s 313s Res.Df Df Chisq Pr(>Chisq) 313s 1 35 313s 2 33 2 0.43 0.81 313s > linearHypothesis( fitw2sls1e, restrict2, test = "Chisq" ) 313s Linear hypothesis test (Chi^2 statistic of a Wald test) 313s 313s Hypothesis: 313s demand_income - supply_trend = 0 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2sls1e 313s 313s Res.Df Df Chisq Pr(>Chisq) 313s 1 35 313s 2 33 2 0.43 0.81 313s > 313s > print( linearHypothesis( fitw2slsd1, restr2m, restr2q, test = "Chisq" ) ) 313s Linear hypothesis test (Chi^2 statistic of a Wald test) 313s 313s Hypothesis: 313s demand_income - supply_trend = 0 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2slsd1 313s 313s Res.Df Df Chisq Pr(>Chisq) 313s 1 35 313s 2 33 2 1.3 0.52 313s > linearHypothesis( fitw2slsd1, restrict2, test = "Chisq" ) 313s Linear hypothesis test (Chi^2 statistic of a Wald test) 313s 313s Hypothesis: 313s demand_income - supply_trend = 0 313s - demand_price + supply_price = 0.5 313s 313s Model 1: restricted model 313s Model 2: fitw2slsd1 313s 313s Res.Df Df Chisq Pr(>Chisq) 313s 1 35 313s 2 33 2 1.3 0.52 313s > 313s > 313s > ## ****************** model frame ************************** 313s > print( mf <- model.frame( fitw2sls1e ) ) 313s consump price income farmPrice trend 313s 1 98.5 100.3 87.4 98.0 1 313s 2 99.2 104.3 97.6 99.1 2 313s 3 102.2 103.4 96.7 99.1 3 313s 4 101.5 104.5 98.2 98.1 4 313s 5 104.2 98.0 99.8 110.8 5 313s 6 103.2 99.5 100.5 108.2 6 313s 7 104.0 101.1 103.2 105.6 7 313s 8 99.9 104.8 107.8 109.8 8 313s 9 100.3 96.4 96.6 108.7 9 313s 10 102.8 91.2 88.9 100.6 10 313s 11 95.4 93.1 75.1 81.0 11 313s 12 92.4 98.8 76.9 68.6 12 313s 13 94.5 102.9 84.6 70.9 13 313s 14 98.8 98.8 90.6 81.4 14 313s 15 105.8 95.1 103.1 102.3 15 313s 16 100.2 98.5 105.1 105.0 16 313s 17 103.5 86.5 96.4 110.5 17 313s 18 99.9 104.0 104.4 92.5 18 313s 19 105.2 105.8 110.7 89.3 19 313s 20 106.2 113.5 127.1 93.0 20 313s > print( mf1 <- model.frame( fitw2sls1e$eq[[ 1 ]] ) ) 313s consump price income 313s 1 98.5 100.3 87.4 313s 2 99.2 104.3 97.6 313s 3 102.2 103.4 96.7 313s 4 101.5 104.5 98.2 313s 5 104.2 98.0 99.8 313s 6 103.2 99.5 100.5 313s 7 104.0 101.1 103.2 313s 8 99.9 104.8 107.8 313s 9 100.3 96.4 96.6 313s 10 102.8 91.2 88.9 313s 11 95.4 93.1 75.1 313s 12 92.4 98.8 76.9 313s 13 94.5 102.9 84.6 313s 14 98.8 98.8 90.6 313s 15 105.8 95.1 103.1 313s 16 100.2 98.5 105.1 313s 17 103.5 86.5 96.4 313s 18 99.9 104.0 104.4 313s 19 105.2 105.8 110.7 313s 20 106.2 113.5 127.1 313s > print( attributes( mf1 )$terms ) 313s consump ~ price + income 313s attr(,"variables") 313s list(consump, price, income) 313s attr(,"factors") 313s price income 313s consump 0 0 313s price 1 0 313s income 0 1 313s attr(,"term.labels") 313s [1] "price" "income" 313s attr(,"order") 313s [1] 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 1 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(consump, price, income) 313s attr(,"dataClasses") 313s consump price income 313s "numeric" "numeric" "numeric" 313s > print( mf2 <- model.frame( fitw2sls1e$eq[[ 2 ]] ) ) 313s consump price farmPrice trend 313s 1 98.5 100.3 98.0 1 313s 2 99.2 104.3 99.1 2 313s 3 102.2 103.4 99.1 3 313s 4 101.5 104.5 98.1 4 313s 5 104.2 98.0 110.8 5 313s 6 103.2 99.5 108.2 6 313s 7 104.0 101.1 105.6 7 313s 8 99.9 104.8 109.8 8 313s 9 100.3 96.4 108.7 9 313s 10 102.8 91.2 100.6 10 313s 11 95.4 93.1 81.0 11 313s 12 92.4 98.8 68.6 12 313s 13 94.5 102.9 70.9 13 313s 14 98.8 98.8 81.4 14 313s 15 105.8 95.1 102.3 15 313s 16 100.2 98.5 105.0 16 313s 17 103.5 86.5 110.5 17 313s 18 99.9 104.0 92.5 18 313s 19 105.2 105.8 89.3 19 313s 20 106.2 113.5 93.0 20 313s > print( attributes( mf2 )$terms ) 313s consump ~ price + farmPrice + trend 313s attr(,"variables") 313s list(consump, price, farmPrice, trend) 313s attr(,"factors") 313s price farmPrice trend 313s consump 0 0 0 313s price 1 0 0 313s farmPrice 0 1 0 313s trend 0 0 1 313s attr(,"term.labels") 313s [1] "price" "farmPrice" "trend" 313s attr(,"order") 313s [1] 1 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 1 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(consump, price, farmPrice, trend) 313s attr(,"dataClasses") 313s consump price farmPrice trend 313s "numeric" "numeric" "numeric" "numeric" 313s > 313s > print( all.equal( mf, model.frame( fitw2sls2 ) ) ) 313s [1] TRUE 313s > print( all.equal( mf2, model.frame( fitw2sls2$eq[[ 2 ]] ) ) ) 313s [1] TRUE 313s > 313s > print( all.equal( mf, model.frame( fitw2sls3 ) ) ) 313s [1] TRUE 313s > print( all.equal( mf1, model.frame( fitw2sls3$eq[[ 1 ]] ) ) ) 313s [1] TRUE 313s > 313s > print( all.equal( mf, model.frame( fitw2sls4e ) ) ) 313s [1] TRUE 313s > print( all.equal( mf2, model.frame( fitw2sls4e$eq[[ 2 ]] ) ) ) 313s [1] TRUE 313s > 313s > print( all.equal( mf, model.frame( fitw2sls5 ) ) ) 313s [1] TRUE 313s > print( all.equal( mf1, model.frame( fitw2sls5$eq[[ 1 ]] ) ) ) 313s [1] TRUE 313s > 313s > print( all.equal( mf, model.frame( fitw2slsd1 ) ) ) 313s [1] TRUE 313s > print( all.equal( mf2, model.frame( fitw2slsd1$eq[[ 2 ]] ) ) ) 313s [1] TRUE 313s > 313s > print( all.equal( mf, model.frame( fitw2slsd2e ) ) ) 313s [1] TRUE 313s > print( all.equal( mf1, model.frame( fitw2slsd2e$eq[[ 1 ]] ) ) ) 313s [1] TRUE 313s > 313s > print( all.equal( mf, model.frame( fitw2slsd3e ) ) ) 313s [1] TRUE 313s > print( all.equal( mf2, model.frame( fitw2slsd3e$eq[[ 2 ]] ) ) ) 313s [1] TRUE 313s > 313s > fitw2sls1e$eq[[ 1 ]]$modelInst 313s income farmPrice trend 313s 1 87.4 98.0 1 313s 2 97.6 99.1 2 313s 3 96.7 99.1 3 313s 4 98.2 98.1 4 313s 5 99.8 110.8 5 313s 6 100.5 108.2 6 313s 7 103.2 105.6 7 313s 8 107.8 109.8 8 313s 9 96.6 108.7 9 313s 10 88.9 100.6 10 313s 11 75.1 81.0 11 313s 12 76.9 68.6 12 313s 13 84.6 70.9 13 313s 14 90.6 81.4 14 313s 15 103.1 102.3 15 313s 16 105.1 105.0 16 313s 17 96.4 110.5 17 313s 18 104.4 92.5 18 313s 19 110.7 89.3 19 313s 20 127.1 93.0 20 313s > fitw2sls1e$eq[[ 2 ]]$modelInst 313s income farmPrice trend 313s 1 87.4 98.0 1 313s 2 97.6 99.1 2 313s 3 96.7 99.1 3 313s 4 98.2 98.1 4 313s 5 99.8 110.8 5 313s 6 100.5 108.2 6 313s 7 103.2 105.6 7 313s 8 107.8 109.8 8 313s 9 96.6 108.7 9 313s 10 88.9 100.6 10 313s 11 75.1 81.0 11 313s 12 76.9 68.6 12 313s 13 84.6 70.9 13 313s 14 90.6 81.4 14 313s 15 103.1 102.3 15 313s 16 105.1 105.0 16 313s 17 96.4 110.5 17 313s 18 104.4 92.5 18 313s 19 110.7 89.3 19 313s 20 127.1 93.0 20 313s > 313s > fitw2sls4Sym$eq[[ 1 ]]$modelInst 313s income farmPrice trend 313s 1 87.4 98.0 1 313s 2 97.6 99.1 2 313s 3 96.7 99.1 3 313s 4 98.2 98.1 4 313s 5 99.8 110.8 5 313s 6 100.5 108.2 6 313s 7 103.2 105.6 7 313s 8 107.8 109.8 8 313s 9 96.6 108.7 9 313s 10 88.9 100.6 10 313s 11 75.1 81.0 11 313s 12 76.9 68.6 12 313s 13 84.6 70.9 13 313s 14 90.6 81.4 14 313s 15 103.1 102.3 15 313s 16 105.1 105.0 16 313s 17 96.4 110.5 17 313s 18 104.4 92.5 18 313s 19 110.7 89.3 19 313s 20 127.1 93.0 20 313s > fitw2sls4Sym$eq[[ 2 ]]$modelInst 313s income farmPrice trend 313s 1 87.4 98.0 1 313s 2 97.6 99.1 2 313s 3 96.7 99.1 3 313s 4 98.2 98.1 4 313s 5 99.8 110.8 5 313s 6 100.5 108.2 6 313s 7 103.2 105.6 7 313s 8 107.8 109.8 8 313s 9 96.6 108.7 9 313s 10 88.9 100.6 10 313s 11 75.1 81.0 11 313s 12 76.9 68.6 12 313s 13 84.6 70.9 13 313s 14 90.6 81.4 14 313s 15 103.1 102.3 15 313s 16 105.1 105.0 16 313s 17 96.4 110.5 17 313s 18 104.4 92.5 18 313s 19 110.7 89.3 19 313s 20 127.1 93.0 20 313s > 313s > fitw2sls5$eq[[ 1 ]]$modelInst 313s income farmPrice trend 313s 1 87.4 98.0 1 313s 2 97.6 99.1 2 313s 3 96.7 99.1 3 313s 4 98.2 98.1 4 313s 5 99.8 110.8 5 313s 6 100.5 108.2 6 313s 7 103.2 105.6 7 313s 8 107.8 109.8 8 313s 9 96.6 108.7 9 313s 10 88.9 100.6 10 313s 11 75.1 81.0 11 313s 12 76.9 68.6 12 313s 13 84.6 70.9 13 313s 14 90.6 81.4 14 313s 15 103.1 102.3 15 313s 16 105.1 105.0 16 313s 17 96.4 110.5 17 313s 18 104.4 92.5 18 313s 19 110.7 89.3 19 313s 20 127.1 93.0 20 313s > fitw2sls5$eq[[ 2 ]]$modelInst 313s income farmPrice trend 313s 1 87.4 98.0 1 313s 2 97.6 99.1 2 313s 3 96.7 99.1 3 313s 4 98.2 98.1 4 313s 5 99.8 110.8 5 313s 6 100.5 108.2 6 313s 7 103.2 105.6 7 313s 8 107.8 109.8 8 313s 9 96.6 108.7 9 313s 10 88.9 100.6 10 313s 11 75.1 81.0 11 313s 12 76.9 68.6 12 313s 13 84.6 70.9 13 313s 14 90.6 81.4 14 313s 15 103.1 102.3 15 313s 16 105.1 105.0 16 313s 17 96.4 110.5 17 313s 18 104.4 92.5 18 313s 19 110.7 89.3 19 313s 20 127.1 93.0 20 313s > 313s > 313s > ## **************** model matrix ************************ 313s > # with x (returnModelMatrix) = TRUE 313s > print( !is.null( fitw2sls1e$eq[[ 1 ]]$x ) ) 313s [1] TRUE 313s > print( mm <- model.matrix( fitw2sls1e ) ) 313s demand_(Intercept) demand_price demand_income supply_(Intercept) 313s demand_1 1 100.3 87.4 0 313s demand_2 1 104.3 97.6 0 313s demand_3 1 103.4 96.7 0 313s demand_4 1 104.5 98.2 0 313s demand_5 1 98.0 99.8 0 313s demand_6 1 99.5 100.5 0 313s demand_7 1 101.1 103.2 0 313s demand_8 1 104.8 107.8 0 313s demand_9 1 96.4 96.6 0 313s demand_10 1 91.2 88.9 0 313s demand_11 1 93.1 75.1 0 313s demand_12 1 98.8 76.9 0 313s demand_13 1 102.9 84.6 0 313s demand_14 1 98.8 90.6 0 313s demand_15 1 95.1 103.1 0 313s demand_16 1 98.5 105.1 0 313s demand_17 1 86.5 96.4 0 313s demand_18 1 104.0 104.4 0 313s demand_19 1 105.8 110.7 0 313s demand_20 1 113.5 127.1 0 313s supply_1 0 0.0 0.0 1 313s supply_2 0 0.0 0.0 1 313s supply_3 0 0.0 0.0 1 313s supply_4 0 0.0 0.0 1 313s supply_5 0 0.0 0.0 1 313s supply_6 0 0.0 0.0 1 313s supply_7 0 0.0 0.0 1 313s supply_8 0 0.0 0.0 1 313s supply_9 0 0.0 0.0 1 313s supply_10 0 0.0 0.0 1 313s supply_11 0 0.0 0.0 1 313s supply_12 0 0.0 0.0 1 313s supply_13 0 0.0 0.0 1 313s supply_14 0 0.0 0.0 1 313s supply_15 0 0.0 0.0 1 313s supply_16 0 0.0 0.0 1 313s supply_17 0 0.0 0.0 1 313s supply_18 0 0.0 0.0 1 313s supply_19 0 0.0 0.0 1 313s supply_20 0 0.0 0.0 1 313s supply_price supply_farmPrice supply_trend 313s demand_1 0.0 0.0 0 313s demand_2 0.0 0.0 0 313s demand_3 0.0 0.0 0 313s demand_4 0.0 0.0 0 313s demand_5 0.0 0.0 0 313s demand_6 0.0 0.0 0 313s demand_7 0.0 0.0 0 313s demand_8 0.0 0.0 0 313s demand_9 0.0 0.0 0 313s demand_10 0.0 0.0 0 313s demand_11 0.0 0.0 0 313s demand_12 0.0 0.0 0 313s demand_13 0.0 0.0 0 313s demand_14 0.0 0.0 0 313s demand_15 0.0 0.0 0 313s demand_16 0.0 0.0 0 313s demand_17 0.0 0.0 0 313s demand_18 0.0 0.0 0 313s demand_19 0.0 0.0 0 313s demand_20 0.0 0.0 0 313s supply_1 100.3 98.0 1 313s supply_2 104.3 99.1 2 313s supply_3 103.4 99.1 3 313s supply_4 104.5 98.1 4 313s supply_5 98.0 110.8 5 313s supply_6 99.5 108.2 6 313s supply_7 101.1 105.6 7 313s supply_8 104.8 109.8 8 313s supply_9 96.4 108.7 9 313s supply_10 91.2 100.6 10 313s supply_11 93.1 81.0 11 313s supply_12 98.8 68.6 12 313s supply_13 102.9 70.9 13 313s supply_14 98.8 81.4 14 313s supply_15 95.1 102.3 15 313s supply_16 98.5 105.0 16 313s supply_17 86.5 110.5 17 313s supply_18 104.0 92.5 18 313s supply_19 105.8 89.3 19 313s supply_20 113.5 93.0 20 313s > print( mm1 <- model.matrix( fitw2sls1e$eq[[ 1 ]] ) ) 313s (Intercept) price income 313s 1 1 100.3 87.4 313s 2 1 104.3 97.6 313s 3 1 103.4 96.7 313s 4 1 104.5 98.2 313s 5 1 98.0 99.8 313s 6 1 99.5 100.5 313s 7 1 101.1 103.2 313s 8 1 104.8 107.8 313s 9 1 96.4 96.6 313s 10 1 91.2 88.9 313s 11 1 93.1 75.1 313s 12 1 98.8 76.9 313s 13 1 102.9 84.6 313s 14 1 98.8 90.6 313s 15 1 95.1 103.1 313s 16 1 98.5 105.1 313s 17 1 86.5 96.4 313s 18 1 104.0 104.4 313s 19 1 105.8 110.7 313s 20 1 113.5 127.1 313s attr(,"assign") 313s [1] 0 1 2 313s > print( mm2 <- model.matrix( fitw2sls1e$eq[[ 2 ]] ) ) 313s (Intercept) price farmPrice trend 313s 1 1 100.3 98.0 1 313s 2 1 104.3 99.1 2 313s 3 1 103.4 99.1 3 313s 4 1 104.5 98.1 4 313s 5 1 98.0 110.8 5 313s 6 1 99.5 108.2 6 313s 7 1 101.1 105.6 7 313s 8 1 104.8 109.8 8 313s 9 1 96.4 108.7 9 313s 10 1 91.2 100.6 10 313s 11 1 93.1 81.0 11 313s 12 1 98.8 68.6 12 313s 13 1 102.9 70.9 13 313s 14 1 98.8 81.4 14 313s 15 1 95.1 102.3 15 313s 16 1 98.5 105.0 16 313s 17 1 86.5 110.5 17 313s 18 1 104.0 92.5 18 313s 19 1 105.8 89.3 19 313s 20 1 113.5 93.0 20 313s attr(,"assign") 313s [1] 0 1 2 3 313s > 313s > # with x (returnModelMatrix) = FALSE 313s > print( all.equal( mm, model.matrix( fitw2sls1 ) ) ) 313s [1] TRUE 313s > print( all.equal( mm1, model.matrix( fitw2sls1$eq[[ 1 ]] ) ) ) 313s [1] TRUE 313s > print( all.equal( mm2, model.matrix( fitw2sls1$eq[[ 2 ]] ) ) ) 313s [1] TRUE 313s > print( !is.null( fitw2sls1$eq[[ 1 ]]$x ) ) 313s [1] FALSE 313s > 313s > # with x (returnModelMatrix) = TRUE 313s > print( !is.null( fitw2sls2e$eq[[ 1 ]]$x ) ) 313s [1] TRUE 313s > print( all.equal( mm, model.matrix( fitw2sls2e ) ) ) 313s [1] TRUE 313s > print( all.equal( mm1, model.matrix( fitw2sls2e$eq[[ 1 ]] ) ) ) 313s [1] TRUE 313s > print( all.equal( mm2, model.matrix( fitw2sls2e$eq[[ 2 ]] ) ) ) 313s [1] TRUE 313s > 313s > # with x (returnModelMatrix) = FALSE 313s > print( all.equal( mm, model.matrix( fitw2sls2Sym ) ) ) 313s [1] TRUE 313s > print( all.equal( mm1, model.matrix( fitw2sls2Sym$eq[[ 1 ]] ) ) ) 313s [1] TRUE 313s > print( all.equal( mm2, model.matrix( fitw2sls2Sym$eq[[ 2 ]] ) ) ) 313s [1] TRUE 313s > print( !is.null( fitw2sls2Sym$eq[[ 1 ]]$x ) ) 313s [1] FALSE 313s > 313s > # with x (returnModelMatrix) = TRUE 313s > print( !is.null( fitw2slsd3$eq[[ 1 ]]$x ) ) 313s [1] TRUE 313s > print( all.equal( mm, model.matrix( fitw2slsd3 ) ) ) 313s [1] TRUE 313s > print( all.equal( mm1, model.matrix( fitw2slsd3$eq[[ 1 ]] ) ) ) 313s [1] TRUE 313s > print( all.equal( mm2, model.matrix( fitw2slsd3$eq[[ 2 ]] ) ) ) 313s [1] TRUE 313s > 313s > # with x (returnModelMatrix) = FALSE 313s > print( all.equal( mm, model.matrix( fitw2slsd3e ) ) ) 313s [1] TRUE 313s > print( all.equal( mm1, model.matrix( fitw2slsd3e$eq[[ 1 ]] ) ) ) 313s [1] TRUE 313s > print( all.equal( mm2, model.matrix( fitw2slsd3e$eq[[ 2 ]] ) ) ) 313s [1] TRUE 313s > print( !is.null( fitw2slsd3e$eq[[ 1 ]]$x ) ) 313s [1] FALSE 313s > 313s > # with x (returnModelMatrix) = TRUE 313s > print( !is.null( fitw2sls4$eq[[ 1 ]]$x ) ) 313s [1] TRUE 313s > print( all.equal( mm, model.matrix( fitw2sls4 ) ) ) 313s [1] TRUE 313s > print( all.equal( mm1, model.matrix( fitw2sls4$eq[[ 1 ]] ) ) ) 313s [1] TRUE 313s > print( all.equal( mm2, model.matrix( fitw2sls4$eq[[ 2 ]] ) ) ) 313s [1] TRUE 313s > 313s > # with x (returnModelMatrix) = FALSE 313s > print( all.equal( mm, model.matrix( fitw2sls4e ) ) ) 313s [1] TRUE 313s > print( all.equal( mm1, model.matrix( fitw2sls4e$eq[[ 1 ]] ) ) ) 313s [1] TRUE 313s > print( all.equal( mm2, model.matrix( fitw2sls4e$eq[[ 2 ]] ) ) ) 313s [1] TRUE 313s > print( !is.null( fitw2sls4e$eq[[ 1 ]]$x ) ) 313s [1] FALSE 313s > 313s > # with x (returnModelMatrix) = TRUE 313s > print( !is.null( fitw2sls5$eq[[ 1 ]]$x ) ) 313s [1] TRUE 313s > print( all.equal( mm, model.matrix( fitw2sls5 ) ) ) 313s [1] TRUE 313s > print( all.equal( mm1, model.matrix( fitw2sls5$eq[[ 1 ]] ) ) ) 313s [1] TRUE 313s > print( all.equal( mm2, model.matrix( fitw2sls5$eq[[ 2 ]] ) ) ) 313s [1] TRUE 313s > 313s > # with x (returnModelMatrix) = FALSE 313s > print( all.equal( mm, model.matrix( fitw2sls5e ) ) ) 313s [1] TRUE 313s > print( all.equal( mm1, model.matrix( fitw2sls5e$eq[[ 1 ]] ) ) ) 313s [1] TRUE 313s > print( all.equal( mm2, model.matrix( fitw2sls5e$eq[[ 2 ]] ) ) ) 313s [1] TRUE 313s > print( !is.null( fitw2sls5e$eq[[ 1 ]]$x ) ) 313s [1] FALSE 313s > 313s > # matrices of instrumental variables 313s > model.matrix( fitw2sls1, which = "z" ) 313s demand_(Intercept) demand_income demand_farmPrice demand_trend 313s demand_1 1 87.4 98.0 1 313s demand_2 1 97.6 99.1 2 313s demand_3 1 96.7 99.1 3 313s demand_4 1 98.2 98.1 4 313s demand_5 1 99.8 110.8 5 313s demand_6 1 100.5 108.2 6 313s demand_7 1 103.2 105.6 7 313s demand_8 1 107.8 109.8 8 313s demand_9 1 96.6 108.7 9 313s demand_10 1 88.9 100.6 10 313s demand_11 1 75.1 81.0 11 313s demand_12 1 76.9 68.6 12 313s demand_13 1 84.6 70.9 13 313s demand_14 1 90.6 81.4 14 313s demand_15 1 103.1 102.3 15 313s demand_16 1 105.1 105.0 16 313s demand_17 1 96.4 110.5 17 313s demand_18 1 104.4 92.5 18 313s demand_19 1 110.7 89.3 19 313s demand_20 1 127.1 93.0 20 313s supply_1 0 0.0 0.0 0 313s supply_2 0 0.0 0.0 0 313s supply_3 0 0.0 0.0 0 313s supply_4 0 0.0 0.0 0 313s supply_5 0 0.0 0.0 0 313s supply_6 0 0.0 0.0 0 313s supply_7 0 0.0 0.0 0 313s supply_8 0 0.0 0.0 0 313s supply_9 0 0.0 0.0 0 313s supply_10 0 0.0 0.0 0 313s supply_11 0 0.0 0.0 0 313s supply_12 0 0.0 0.0 0 313s supply_13 0 0.0 0.0 0 313s supply_14 0 0.0 0.0 0 313s supply_15 0 0.0 0.0 0 313s supply_16 0 0.0 0.0 0 313s supply_17 0 0.0 0.0 0 313s supply_18 0 0.0 0.0 0 313s supply_19 0 0.0 0.0 0 313s supply_20 0 0.0 0.0 0 313s supply_(Intercept) supply_income supply_farmPrice supply_trend 313s demand_1 0 0.0 0.0 0 313s demand_2 0 0.0 0.0 0 313s demand_3 0 0.0 0.0 0 313s demand_4 0 0.0 0.0 0 313s demand_5 0 0.0 0.0 0 313s demand_6 0 0.0 0.0 0 313s demand_7 0 0.0 0.0 0 313s demand_8 0 0.0 0.0 0 313s demand_9 0 0.0 0.0 0 313s demand_10 0 0.0 0.0 0 313s demand_11 0 0.0 0.0 0 313s demand_12 0 0.0 0.0 0 313s demand_13 0 0.0 0.0 0 313s demand_14 0 0.0 0.0 0 313s demand_15 0 0.0 0.0 0 313s demand_16 0 0.0 0.0 0 313s demand_17 0 0.0 0.0 0 313s demand_18 0 0.0 0.0 0 313s demand_19 0 0.0 0.0 0 313s demand_20 0 0.0 0.0 0 313s supply_1 1 87.4 98.0 1 313s supply_2 1 97.6 99.1 2 313s supply_3 1 96.7 99.1 3 313s supply_4 1 98.2 98.1 4 313s supply_5 1 99.8 110.8 5 313s supply_6 1 100.5 108.2 6 313s supply_7 1 103.2 105.6 7 313s supply_8 1 107.8 109.8 8 313s supply_9 1 96.6 108.7 9 313s supply_10 1 88.9 100.6 10 313s supply_11 1 75.1 81.0 11 313s supply_12 1 76.9 68.6 12 313s supply_13 1 84.6 70.9 13 313s supply_14 1 90.6 81.4 14 313s supply_15 1 103.1 102.3 15 313s supply_16 1 105.1 105.0 16 313s supply_17 1 96.4 110.5 17 313s supply_18 1 104.4 92.5 18 313s supply_19 1 110.7 89.3 19 313s supply_20 1 127.1 93.0 20 313s > model.matrix( fitw2sls1$eq[[ 1 ]], which = "z" ) 313s (Intercept) income farmPrice trend 313s 1 1 87.4 98.0 1 313s 2 1 97.6 99.1 2 313s 3 1 96.7 99.1 3 313s 4 1 98.2 98.1 4 313s 5 1 99.8 110.8 5 313s 6 1 100.5 108.2 6 313s 7 1 103.2 105.6 7 313s 8 1 107.8 109.8 8 313s 9 1 96.6 108.7 9 313s 10 1 88.9 100.6 10 313s 11 1 75.1 81.0 11 313s 12 1 76.9 68.6 12 313s 13 1 84.6 70.9 13 313s 14 1 90.6 81.4 14 313s 15 1 103.1 102.3 15 313s 16 1 105.1 105.0 16 313s 17 1 96.4 110.5 17 313s 18 1 104.4 92.5 18 313s 19 1 110.7 89.3 19 313s 20 1 127.1 93.0 20 313s attr(,"assign") 313s [1] 0 1 2 3 313s > model.matrix( fitw2sls1$eq[[ 2 ]], which = "z" ) 313s (Intercept) income farmPrice trend 313s 1 1 87.4 98.0 1 313s 2 1 97.6 99.1 2 313s 3 1 96.7 99.1 3 313s 4 1 98.2 98.1 4 313s 5 1 99.8 110.8 5 313s 6 1 100.5 108.2 6 313s 7 1 103.2 105.6 7 313s 8 1 107.8 109.8 8 313s 9 1 96.6 108.7 9 313s 10 1 88.9 100.6 10 313s 11 1 75.1 81.0 11 313s 12 1 76.9 68.6 12 313s 13 1 84.6 70.9 13 313s 14 1 90.6 81.4 14 313s 15 1 103.1 102.3 15 313s 16 1 105.1 105.0 16 313s 17 1 96.4 110.5 17 313s 18 1 104.4 92.5 18 313s 19 1 110.7 89.3 19 313s 20 1 127.1 93.0 20 313s attr(,"assign") 313s [1] 0 1 2 3 313s > 313s > # matrices of fitted regressors 313s > model.matrix( fitw2sls5e, which = "xHat" ) 313s demand_(Intercept) demand_price demand_income supply_(Intercept) 313s demand_1 1 99.6 87.4 0 313s demand_2 1 105.1 97.6 0 313s demand_3 1 103.8 96.7 0 313s demand_4 1 104.5 98.2 0 313s demand_5 1 98.7 99.8 0 313s demand_6 1 99.6 100.5 0 313s demand_7 1 102.0 103.2 0 313s demand_8 1 102.2 107.8 0 313s demand_9 1 94.6 96.6 0 313s demand_10 1 92.7 88.9 0 313s demand_11 1 92.4 75.1 0 313s demand_12 1 98.9 76.9 0 313s demand_13 1 102.2 84.6 0 313s demand_14 1 100.3 90.6 0 313s demand_15 1 97.6 103.1 0 313s demand_16 1 96.9 105.1 0 313s demand_17 1 87.7 96.4 0 313s demand_18 1 101.1 104.4 0 313s demand_19 1 106.1 110.7 0 313s demand_20 1 114.4 127.1 0 313s supply_1 0 0.0 0.0 1 313s supply_2 0 0.0 0.0 1 313s supply_3 0 0.0 0.0 1 313s supply_4 0 0.0 0.0 1 313s supply_5 0 0.0 0.0 1 313s supply_6 0 0.0 0.0 1 313s supply_7 0 0.0 0.0 1 313s supply_8 0 0.0 0.0 1 313s supply_9 0 0.0 0.0 1 313s supply_10 0 0.0 0.0 1 313s supply_11 0 0.0 0.0 1 313s supply_12 0 0.0 0.0 1 313s supply_13 0 0.0 0.0 1 313s supply_14 0 0.0 0.0 1 313s supply_15 0 0.0 0.0 1 313s supply_16 0 0.0 0.0 1 313s supply_17 0 0.0 0.0 1 313s supply_18 0 0.0 0.0 1 313s supply_19 0 0.0 0.0 1 313s supply_20 0 0.0 0.0 1 313s supply_price supply_farmPrice supply_trend 313s demand_1 0.0 0.0 0 313s demand_2 0.0 0.0 0 313s demand_3 0.0 0.0 0 313s demand_4 0.0 0.0 0 313s demand_5 0.0 0.0 0 313s demand_6 0.0 0.0 0 313s demand_7 0.0 0.0 0 313s demand_8 0.0 0.0 0 313s demand_9 0.0 0.0 0 313s demand_10 0.0 0.0 0 313s demand_11 0.0 0.0 0 313s demand_12 0.0 0.0 0 313s demand_13 0.0 0.0 0 313s demand_14 0.0 0.0 0 313s demand_15 0.0 0.0 0 313s demand_16 0.0 0.0 0 313s demand_17 0.0 0.0 0 313s demand_18 0.0 0.0 0 313s demand_19 0.0 0.0 0 313s demand_20 0.0 0.0 0 313s supply_1 99.6 98.0 1 313s supply_2 105.1 99.1 2 313s supply_3 103.8 99.1 3 313s supply_4 104.5 98.1 4 313s supply_5 98.7 110.8 5 313s supply_6 99.6 108.2 6 313s supply_7 102.0 105.6 7 313s supply_8 102.2 109.8 8 313s supply_9 94.6 108.7 9 313s supply_10 92.7 100.6 10 313s supply_11 92.4 81.0 11 313s supply_12 98.9 68.6 12 313s supply_13 102.2 70.9 13 313s supply_14 100.3 81.4 14 313s supply_15 97.6 102.3 15 313s supply_16 96.9 105.0 16 313s supply_17 87.7 110.5 17 313s supply_18 101.1 92.5 18 313s supply_19 106.1 89.3 19 313s supply_20 114.4 93.0 20 313s > model.matrix( fitw2sls5e$eq[[ 1 ]], which = "xHat" ) 313s (Intercept) price income 313s 1 1 99.6 87.4 313s 2 1 105.1 97.6 313s 3 1 103.8 96.7 313s 4 1 104.5 98.2 313s 5 1 98.7 99.8 313s 6 1 99.6 100.5 313s 7 1 102.0 103.2 313s 8 1 102.2 107.8 313s 9 1 94.6 96.6 313s 10 1 92.7 88.9 313s 11 1 92.4 75.1 313s 12 1 98.9 76.9 313s 13 1 102.2 84.6 313s 14 1 100.3 90.6 313s 15 1 97.6 103.1 313s 16 1 96.9 105.1 313s 17 1 87.7 96.4 313s 18 1 101.1 104.4 313s 19 1 106.1 110.7 313s 20 1 114.4 127.1 313s > model.matrix( fitw2sls5e$eq[[ 2 ]], which = "xHat" ) 313s (Intercept) price farmPrice trend 313s 1 1 99.6 98.0 1 313s 2 1 105.1 99.1 2 313s 3 1 103.8 99.1 3 313s 4 1 104.5 98.1 4 313s 5 1 98.7 110.8 5 313s 6 1 99.6 108.2 6 313s 7 1 102.0 105.6 7 313s 8 1 102.2 109.8 8 313s 9 1 94.6 108.7 9 313s 10 1 92.7 100.6 10 313s 11 1 92.4 81.0 11 313s 12 1 98.9 68.6 12 313s 13 1 102.2 70.9 13 313s 14 1 100.3 81.4 14 313s 15 1 97.6 102.3 15 313s 16 1 96.9 105.0 16 313s 17 1 87.7 110.5 17 313s 18 1 101.1 92.5 18 313s 19 1 106.1 89.3 19 313s 20 1 114.4 93.0 20 313s > 313s > 313s > ## **************** formulas ************************ 313s > formula( fitw2sls1e ) 313s $demand 313s consump ~ price + income 313s 313s $supply 313s consump ~ price + farmPrice + trend 313s 313s > formula( fitw2sls1e$eq[[ 1 ]] ) 313s consump ~ price + income 313s > 313s > formula( fitw2sls2 ) 313s $demand 313s consump ~ price + income 313s 313s $supply 313s consump ~ price + farmPrice + trend 313s 313s > formula( fitw2sls2$eq[[ 2 ]] ) 313s consump ~ price + farmPrice + trend 313s > 313s > formula( fitw2sls3 ) 313s $demand 313s consump ~ price + income 313s 313s $supply 313s consump ~ price + farmPrice + trend 313s 313s > formula( fitw2sls3$eq[[ 1 ]] ) 313s consump ~ price + income 313s > 313s > formula( fitw2sls4e ) 313s $demand 313s consump ~ price + income 313s 313s $supply 313s consump ~ price + farmPrice + trend 313s 313s > formula( fitw2sls4e$eq[[ 2 ]] ) 313s consump ~ price + farmPrice + trend 313s > 313s > formula( fitw2sls5 ) 313s $demand 313s consump ~ price + income 313s 313s $supply 313s consump ~ price + farmPrice + trend 313s 313s > formula( fitw2sls5$eq[[ 1 ]] ) 313s consump ~ price + income 313s > 313s > formula( fitw2slsd1 ) 313s $demand 313s consump ~ price + income 313s 313s $supply 313s consump ~ price + farmPrice + trend 313s 313s > formula( fitw2slsd1$eq[[ 2 ]] ) 313s consump ~ price + farmPrice + trend 313s > 313s > formula( fitw2slsd2e ) 313s $demand 313s consump ~ price + income 313s 313s $supply 313s consump ~ price + farmPrice + trend 313s 313s > formula( fitw2slsd2e$eq[[ 1 ]] ) 313s consump ~ price + income 313s > 313s > formula( fitw2slsd3e ) 313s $demand 313s consump ~ price + income 313s 313s $supply 313s consump ~ price + farmPrice + trend 313s 313s > formula( fitw2slsd3e$eq[[ 2 ]] ) 313s consump ~ price + farmPrice + trend 313s > 313s > 313s > ## **************** model terms ******************* 313s > terms( fitw2sls1e ) 313s $demand 313s consump ~ price + income 313s attr(,"variables") 313s list(consump, price, income) 313s attr(,"factors") 313s price income 313s consump 0 0 313s price 1 0 313s income 0 1 313s attr(,"term.labels") 313s [1] "price" "income" 313s attr(,"order") 313s [1] 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 1 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(consump, price, income) 313s attr(,"dataClasses") 313s consump price income 313s "numeric" "numeric" "numeric" 313s 313s $supply 313s consump ~ price + farmPrice + trend 313s attr(,"variables") 313s list(consump, price, farmPrice, trend) 313s attr(,"factors") 313s price farmPrice trend 313s consump 0 0 0 313s price 1 0 0 313s farmPrice 0 1 0 313s trend 0 0 1 313s attr(,"term.labels") 313s [1] "price" "farmPrice" "trend" 313s attr(,"order") 313s [1] 1 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 1 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(consump, price, farmPrice, trend) 313s attr(,"dataClasses") 313s consump price farmPrice trend 313s "numeric" "numeric" "numeric" "numeric" 313s 313s > terms( fitw2sls1e$eq[[ 1 ]] ) 313s consump ~ price + income 313s attr(,"variables") 313s list(consump, price, income) 313s attr(,"factors") 313s price income 313s consump 0 0 313s price 1 0 313s income 0 1 313s attr(,"term.labels") 313s [1] "price" "income" 313s attr(,"order") 313s [1] 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 1 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(consump, price, income) 313s attr(,"dataClasses") 313s consump price income 313s "numeric" "numeric" "numeric" 313s > 313s > terms( fitw2sls2 ) 313s $demand 313s consump ~ price + income 313s attr(,"variables") 313s list(consump, price, income) 313s attr(,"factors") 313s price income 313s consump 0 0 313s price 1 0 313s income 0 1 313s attr(,"term.labels") 313s [1] "price" "income" 313s attr(,"order") 313s [1] 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 1 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(consump, price, income) 313s attr(,"dataClasses") 313s consump price income 313s "numeric" "numeric" "numeric" 313s 313s $supply 313s consump ~ price + farmPrice + trend 313s attr(,"variables") 313s list(consump, price, farmPrice, trend) 313s attr(,"factors") 313s price farmPrice trend 313s consump 0 0 0 313s price 1 0 0 313s farmPrice 0 1 0 313s trend 0 0 1 313s attr(,"term.labels") 313s [1] "price" "farmPrice" "trend" 313s attr(,"order") 313s [1] 1 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 1 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(consump, price, farmPrice, trend) 313s attr(,"dataClasses") 313s consump price farmPrice trend 313s "numeric" "numeric" "numeric" "numeric" 313s 313s > terms( fitw2sls2$eq[[ 2 ]] ) 313s consump ~ price + farmPrice + trend 313s attr(,"variables") 313s list(consump, price, farmPrice, trend) 313s attr(,"factors") 313s price farmPrice trend 313s consump 0 0 0 313s price 1 0 0 313s farmPrice 0 1 0 313s trend 0 0 1 313s attr(,"term.labels") 313s [1] "price" "farmPrice" "trend" 313s attr(,"order") 313s [1] 1 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 1 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(consump, price, farmPrice, trend) 313s attr(,"dataClasses") 313s consump price farmPrice trend 313s "numeric" "numeric" "numeric" "numeric" 313s > 313s > terms( fitw2sls3 ) 313s $demand 313s consump ~ price + income 313s attr(,"variables") 313s list(consump, price, income) 313s attr(,"factors") 313s price income 313s consump 0 0 313s price 1 0 313s income 0 1 313s attr(,"term.labels") 313s [1] "price" "income" 313s attr(,"order") 313s [1] 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 1 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(consump, price, income) 313s attr(,"dataClasses") 313s consump price income 313s "numeric" "numeric" "numeric" 313s 313s $supply 313s consump ~ price + farmPrice + trend 313s attr(,"variables") 313s list(consump, price, farmPrice, trend) 313s attr(,"factors") 313s price farmPrice trend 313s consump 0 0 0 313s price 1 0 0 313s farmPrice 0 1 0 313s trend 0 0 1 313s attr(,"term.labels") 313s [1] "price" "farmPrice" "trend" 313s attr(,"order") 313s [1] 1 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 1 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(consump, price, farmPrice, trend) 313s attr(,"dataClasses") 313s consump price farmPrice trend 313s "numeric" "numeric" "numeric" "numeric" 313s 313s > terms( fitw2sls3$eq[[ 1 ]] ) 313s consump ~ price + income 313s attr(,"variables") 313s list(consump, price, income) 313s attr(,"factors") 313s price income 313s consump 0 0 313s price 1 0 313s income 0 1 313s attr(,"term.labels") 313s [1] "price" "income" 313s attr(,"order") 313s [1] 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 1 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(consump, price, income) 313s attr(,"dataClasses") 313s consump price income 313s "numeric" "numeric" "numeric" 313s > 313s > terms( fitw2sls4e ) 313s $demand 313s consump ~ price + income 313s attr(,"variables") 313s list(consump, price, income) 313s attr(,"factors") 313s price income 313s consump 0 0 313s price 1 0 313s income 0 1 313s attr(,"term.labels") 313s [1] "price" "income" 313s attr(,"order") 313s [1] 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 1 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(consump, price, income) 313s attr(,"dataClasses") 313s consump price income 313s "numeric" "numeric" "numeric" 313s 313s $supply 313s consump ~ price + farmPrice + trend 313s attr(,"variables") 313s list(consump, price, farmPrice, trend) 313s attr(,"factors") 313s price farmPrice trend 313s consump 0 0 0 313s price 1 0 0 313s farmPrice 0 1 0 313s trend 0 0 1 313s attr(,"term.labels") 313s [1] "price" "farmPrice" "trend" 313s attr(,"order") 313s [1] 1 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 1 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(consump, price, farmPrice, trend) 313s attr(,"dataClasses") 313s consump price farmPrice trend 313s "numeric" "numeric" "numeric" "numeric" 313s 313s > terms( fitw2sls4e$eq[[ 2 ]] ) 313s consump ~ price + farmPrice + trend 313s attr(,"variables") 313s list(consump, price, farmPrice, trend) 313s attr(,"factors") 313s price farmPrice trend 313s consump 0 0 0 313s price 1 0 0 313s farmPrice 0 1 0 313s trend 0 0 1 313s attr(,"term.labels") 313s [1] "price" "farmPrice" "trend" 313s attr(,"order") 313s [1] 1 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 1 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(consump, price, farmPrice, trend) 313s attr(,"dataClasses") 313s consump price farmPrice trend 313s "numeric" "numeric" "numeric" "numeric" 313s > 313s > terms( fitw2sls5 ) 313s $demand 313s consump ~ price + income 313s attr(,"variables") 313s list(consump, price, income) 313s attr(,"factors") 313s price income 313s consump 0 0 313s price 1 0 313s income 0 1 313s attr(,"term.labels") 313s [1] "price" "income" 313s attr(,"order") 313s [1] 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 1 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(consump, price, income) 313s attr(,"dataClasses") 313s consump price income 313s "numeric" "numeric" "numeric" 313s 313s $supply 313s consump ~ price + farmPrice + trend 313s attr(,"variables") 313s list(consump, price, farmPrice, trend) 313s attr(,"factors") 313s price farmPrice trend 313s consump 0 0 0 313s price 1 0 0 313s farmPrice 0 1 0 313s trend 0 0 1 313s attr(,"term.labels") 313s [1] "price" "farmPrice" "trend" 313s attr(,"order") 313s [1] 1 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 1 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(consump, price, farmPrice, trend) 313s attr(,"dataClasses") 313s consump price farmPrice trend 313s "numeric" "numeric" "numeric" "numeric" 313s 313s > terms( fitw2sls5$eq[[ 1 ]] ) 313s consump ~ price + income 313s attr(,"variables") 313s list(consump, price, income) 313s attr(,"factors") 313s price income 313s consump 0 0 313s price 1 0 313s income 0 1 313s attr(,"term.labels") 313s [1] "price" "income" 313s attr(,"order") 313s [1] 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 1 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(consump, price, income) 313s attr(,"dataClasses") 313s consump price income 313s "numeric" "numeric" "numeric" 313s > 313s > terms( fitw2slsd1 ) 313s $demand 313s consump ~ price + income 313s attr(,"variables") 313s list(consump, price, income) 313s attr(,"factors") 313s price income 313s consump 0 0 313s price 1 0 313s income 0 1 313s attr(,"term.labels") 313s [1] "price" "income" 313s attr(,"order") 313s [1] 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 1 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(consump, price, income) 313s attr(,"dataClasses") 313s consump price income 313s "numeric" "numeric" "numeric" 313s 313s $supply 313s consump ~ price + farmPrice + trend 313s attr(,"variables") 313s list(consump, price, farmPrice, trend) 313s attr(,"factors") 313s price farmPrice trend 313s consump 0 0 0 313s price 1 0 0 313s farmPrice 0 1 0 313s trend 0 0 1 313s attr(,"term.labels") 313s [1] "price" "farmPrice" "trend" 313s attr(,"order") 313s [1] 1 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 1 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(consump, price, farmPrice, trend) 313s attr(,"dataClasses") 313s consump price farmPrice trend 313s "numeric" "numeric" "numeric" "numeric" 313s 313s > terms( fitw2slsd1$eq[[ 2 ]] ) 313s consump ~ price + farmPrice + trend 313s attr(,"variables") 313s list(consump, price, farmPrice, trend) 313s attr(,"factors") 313s price farmPrice trend 313s consump 0 0 0 313s price 1 0 0 313s farmPrice 0 1 0 313s trend 0 0 1 313s attr(,"term.labels") 313s [1] "price" "farmPrice" "trend" 313s attr(,"order") 313s [1] 1 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 1 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(consump, price, farmPrice, trend) 313s attr(,"dataClasses") 313s consump price farmPrice trend 313s "numeric" "numeric" "numeric" "numeric" 313s > 313s > terms( fitw2slsd2e ) 313s $demand 313s consump ~ price + income 313s attr(,"variables") 313s list(consump, price, income) 313s attr(,"factors") 313s price income 313s consump 0 0 313s price 1 0 313s income 0 1 313s attr(,"term.labels") 313s [1] "price" "income" 313s attr(,"order") 313s [1] 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 1 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(consump, price, income) 313s attr(,"dataClasses") 313s consump price income 313s "numeric" "numeric" "numeric" 313s 313s $supply 313s consump ~ price + farmPrice + trend 313s attr(,"variables") 313s list(consump, price, farmPrice, trend) 313s attr(,"factors") 313s price farmPrice trend 313s consump 0 0 0 313s price 1 0 0 313s farmPrice 0 1 0 313s trend 0 0 1 313s attr(,"term.labels") 313s [1] "price" "farmPrice" "trend" 313s attr(,"order") 313s [1] 1 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 1 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(consump, price, farmPrice, trend) 313s attr(,"dataClasses") 313s consump price farmPrice trend 313s "numeric" "numeric" "numeric" "numeric" 313s 313s > terms( fitw2slsd2e$eq[[ 1 ]] ) 313s consump ~ price + income 313s attr(,"variables") 313s list(consump, price, income) 313s attr(,"factors") 313s price income 313s consump 0 0 313s price 1 0 313s income 0 1 313s attr(,"term.labels") 313s [1] "price" "income" 313s attr(,"order") 313s [1] 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 1 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(consump, price, income) 313s attr(,"dataClasses") 313s consump price income 313s "numeric" "numeric" "numeric" 313s > 313s > terms( fitw2slsd3e ) 313s $demand 313s consump ~ price + income 313s attr(,"variables") 313s list(consump, price, income) 313s attr(,"factors") 313s price income 313s consump 0 0 313s price 1 0 313s income 0 1 313s attr(,"term.labels") 313s [1] "price" "income" 313s attr(,"order") 313s [1] 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 1 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(consump, price, income) 313s attr(,"dataClasses") 313s consump price income 313s "numeric" "numeric" "numeric" 313s 313s $supply 313s consump ~ price + farmPrice + trend 313s attr(,"variables") 313s list(consump, price, farmPrice, trend) 313s attr(,"factors") 313s price farmPrice trend 313s consump 0 0 0 313s price 1 0 0 313s farmPrice 0 1 0 313s trend 0 0 1 313s attr(,"term.labels") 313s [1] "price" "farmPrice" "trend" 313s attr(,"order") 313s [1] 1 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 1 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(consump, price, farmPrice, trend) 313s attr(,"dataClasses") 313s consump price farmPrice trend 313s "numeric" "numeric" "numeric" "numeric" 313s 313s > terms( fitw2slsd3e$eq[[ 2 ]] ) 313s consump ~ price + farmPrice + trend 313s attr(,"variables") 313s list(consump, price, farmPrice, trend) 313s attr(,"factors") 313s price farmPrice trend 313s consump 0 0 0 313s price 1 0 0 313s farmPrice 0 1 0 313s trend 0 0 1 313s attr(,"term.labels") 313s [1] "price" "farmPrice" "trend" 313s attr(,"order") 313s [1] 1 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 1 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(consump, price, farmPrice, trend) 313s attr(,"dataClasses") 313s consump price farmPrice trend 313s "numeric" "numeric" "numeric" "numeric" 313s > 313s > 313s > ## **************** terms of instruments ******************* 313s > fitw2sls1e$eq[[ 1 ]]$termsInst 313s ~income + farmPrice + trend 313s attr(,"variables") 313s list(income, farmPrice, trend) 313s attr(,"factors") 313s income farmPrice trend 313s income 1 0 0 313s farmPrice 0 1 0 313s trend 0 0 1 313s attr(,"term.labels") 313s [1] "income" "farmPrice" "trend" 313s attr(,"order") 313s [1] 1 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 0 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(income, farmPrice, trend) 313s attr(,"dataClasses") 313s income farmPrice trend 313s "numeric" "numeric" "numeric" 313s > 313s > fitw2sls2$eq[[ 2 ]]$termsInst 313s ~income + farmPrice + trend 313s attr(,"variables") 313s list(income, farmPrice, trend) 313s attr(,"factors") 313s income farmPrice trend 313s income 1 0 0 313s farmPrice 0 1 0 313s trend 0 0 1 313s attr(,"term.labels") 313s [1] "income" "farmPrice" "trend" 313s attr(,"order") 313s [1] 1 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 0 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(income, farmPrice, trend) 313s attr(,"dataClasses") 313s income farmPrice trend 313s "numeric" "numeric" "numeric" 313s > 313s > fitw2sls3$eq[[ 1 ]]$termsInst 313s ~income + farmPrice + trend 313s attr(,"variables") 313s list(income, farmPrice, trend) 313s attr(,"factors") 313s income farmPrice trend 313s income 1 0 0 313s farmPrice 0 1 0 313s trend 0 0 1 313s attr(,"term.labels") 313s [1] "income" "farmPrice" "trend" 313s attr(,"order") 313s [1] 1 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 0 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(income, farmPrice, trend) 313s attr(,"dataClasses") 313s income farmPrice trend 313s "numeric" "numeric" "numeric" 313s > 313s > fitw2sls4e$eq[[ 2 ]]$termsInst 313s ~income + farmPrice + trend 313s attr(,"variables") 313s list(income, farmPrice, trend) 313s attr(,"factors") 313s income farmPrice trend 313s income 1 0 0 313s farmPrice 0 1 0 313s trend 0 0 1 313s attr(,"term.labels") 313s [1] "income" "farmPrice" "trend" 313s attr(,"order") 313s [1] 1 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 0 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(income, farmPrice, trend) 313s attr(,"dataClasses") 313s income farmPrice trend 313s "numeric" "numeric" "numeric" 313s > 313s > fitw2sls5$eq[[ 1 ]]$termsInst 313s ~income + farmPrice + trend 313s attr(,"variables") 313s list(income, farmPrice, trend) 313s attr(,"factors") 313s income farmPrice trend 313s income 1 0 0 313s farmPrice 0 1 0 313s trend 0 0 1 313s attr(,"term.labels") 313s [1] "income" "farmPrice" "trend" 313s attr(,"order") 313s [1] 1 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 0 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(income, farmPrice, trend) 313s attr(,"dataClasses") 313s income farmPrice trend 313s "numeric" "numeric" "numeric" 313s > 313s > fitw2slsd1$eq[[ 2 ]]$termsInst 313s ~income + farmPrice + trend 313s attr(,"variables") 313s list(income, farmPrice, trend) 313s attr(,"factors") 313s income farmPrice trend 313s income 1 0 0 313s farmPrice 0 1 0 313s trend 0 0 1 313s attr(,"term.labels") 313s [1] "income" "farmPrice" "trend" 313s attr(,"order") 313s [1] 1 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 0 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(income, farmPrice, trend) 313s attr(,"dataClasses") 313s income farmPrice trend 313s "numeric" "numeric" "numeric" 313s > 313s > fitw2slsd2e$eq[[ 1 ]]$termsInst 313s ~income + farmPrice 313s attr(,"variables") 313s list(income, farmPrice) 313s attr(,"factors") 313s income farmPrice 313s income 1 0 313s farmPrice 0 1 313s attr(,"term.labels") 313s [1] "income" "farmPrice" 313s attr(,"order") 313s [1] 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 0 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(income, farmPrice) 313s attr(,"dataClasses") 313s income farmPrice 313s "numeric" "numeric" 313s > 313s > fitw2slsd3e$eq[[ 2 ]]$termsInst 313s ~income + farmPrice + trend 313s attr(,"variables") 313s list(income, farmPrice, trend) 313s attr(,"factors") 313s income farmPrice trend 313s income 1 0 0 313s farmPrice 0 1 0 313s trend 0 0 1 313s attr(,"term.labels") 313s [1] "income" "farmPrice" "trend" 313s attr(,"order") 313s [1] 1 1 1 313s attr(,"intercept") 313s [1] 1 313s attr(,"response") 313s [1] 0 313s attr(,".Environment") 313s 313s attr(,"predvars") 313s list(income, farmPrice, trend) 313s attr(,"dataClasses") 313s income farmPrice trend 313s "numeric" "numeric" "numeric" 313s > 313s > 313s > ## **************** estfun ************************ 313s > library( "sandwich" ) 313s > 313s > estfun( fitw2sls1 ) 313s demand_(Intercept) demand_price demand_income supply_(Intercept) 313s demand_1 0.17426 17.362 15.231 0.0000 313s demand_2 -0.12666 -13.314 -12.362 0.0000 313s demand_3 0.63211 65.603 61.125 0.0000 313s demand_4 0.38686 40.439 37.990 0.0000 313s demand_5 0.59421 58.619 59.302 0.0000 313s demand_6 0.34231 34.111 34.403 0.0000 313s demand_7 0.46340 47.253 47.822 0.0000 313s demand_8 -0.95225 -97.353 -102.653 0.0000 313s demand_9 -0.40681 -38.486 -39.297 0.0000 313s demand_10 0.73846 68.469 65.649 0.0000 313s demand_11 -0.07078 -6.540 -5.315 0.0000 313s demand_12 -0.58541 -57.907 -45.018 0.0000 313s demand_13 -0.46025 -47.020 -38.937 0.0000 313s demand_14 0.02562 2.569 2.322 0.0000 313s demand_15 0.66403 64.824 68.462 0.0000 313s demand_16 -0.98546 -95.483 -103.572 0.0000 313s demand_17 -0.00533 -0.468 -0.514 0.0000 313s demand_18 -0.74266 -75.053 -77.534 0.0000 313s demand_19 0.43017 45.625 47.620 0.0000 313s demand_20 -0.11583 -13.250 -14.722 0.0000 313s supply_1 0.00000 0.000 0.000 -0.0444 313s supply_2 0.00000 0.000 0.000 -0.2348 313s supply_3 0.00000 0.000 0.000 0.2691 313s supply_4 0.00000 0.000 0.000 0.1308 313s supply_5 0.00000 0.000 0.000 0.2381 313s supply_6 0.00000 0.000 0.000 0.1015 313s supply_7 0.00000 0.000 0.000 0.2015 313s supply_8 0.00000 0.000 0.000 -0.7062 313s supply_9 0.00000 0.000 0.000 -0.3238 313s supply_10 0.00000 0.000 0.000 0.4611 313s supply_11 0.00000 0.000 0.000 0.0385 313s supply_12 0.00000 0.000 0.000 -0.2360 313s supply_13 0.00000 0.000 0.000 -0.1548 313s supply_14 0.00000 0.000 0.000 0.1330 313s supply_15 0.00000 0.000 0.000 0.4778 313s supply_16 0.00000 0.000 0.000 -0.5719 313s supply_17 0.00000 0.000 0.000 0.0648 313s supply_18 0.00000 0.000 0.000 -0.3413 313s supply_19 0.00000 0.000 0.000 0.4299 313s supply_20 0.00000 0.000 0.000 0.0672 313s supply_price supply_farmPrice supply_trend 313s demand_1 0.00 0.00 0.0000 313s demand_2 0.00 0.00 0.0000 313s demand_3 0.00 0.00 0.0000 313s demand_4 0.00 0.00 0.0000 313s demand_5 0.00 0.00 0.0000 313s demand_6 0.00 0.00 0.0000 313s demand_7 0.00 0.00 0.0000 313s demand_8 0.00 0.00 0.0000 313s demand_9 0.00 0.00 0.0000 313s demand_10 0.00 0.00 0.0000 313s demand_11 0.00 0.00 0.0000 313s demand_12 0.00 0.00 0.0000 313s demand_13 0.00 0.00 0.0000 313s demand_14 0.00 0.00 0.0000 313s demand_15 0.00 0.00 0.0000 313s demand_16 0.00 0.00 0.0000 313s demand_17 0.00 0.00 0.0000 313s demand_18 0.00 0.00 0.0000 313s demand_19 0.00 0.00 0.0000 313s demand_20 0.00 0.00 0.0000 313s supply_1 -4.42 -4.35 -0.0444 313s supply_2 -24.68 -23.27 -0.4696 313s supply_3 27.93 26.67 0.8073 313s supply_4 13.67 12.83 0.5230 313s supply_5 23.49 26.38 1.1905 313s supply_6 10.12 10.99 0.6093 313s supply_7 20.55 21.28 1.4107 313s supply_8 -72.20 -77.54 -5.6498 313s supply_9 -30.64 -35.20 -2.9145 313s supply_10 42.75 46.39 4.6109 313s supply_11 3.56 3.12 0.4235 313s supply_12 -23.35 -16.19 -2.8326 313s supply_13 -15.81 -10.97 -2.0121 313s supply_14 13.34 10.83 1.8621 313s supply_15 46.64 48.88 7.1671 313s supply_16 -55.42 -60.05 -9.1508 313s supply_17 5.68 7.16 1.1011 313s supply_18 -34.49 -31.57 -6.1438 313s supply_19 45.59 38.39 8.1674 313s supply_20 7.69 6.25 1.3448 313s > round( colSums( estfun( fitw2sls1 ) ), digits = 7 ) 313s demand_(Intercept) demand_price demand_income supply_(Intercept) 313s 0 0 0 0 313s supply_price supply_farmPrice supply_trend 313s 0 0 0 313s > 313s > estfun( fitw2sls1e ) 313s demand_(Intercept) demand_price demand_income supply_(Intercept) 313s demand_1 0.20502 20.43 17.918 0.0000 313s demand_2 -0.14901 -15.66 -14.543 0.0000 313s demand_3 0.74366 77.18 71.912 0.0000 313s demand_4 0.45513 47.57 44.694 0.0000 313s demand_5 0.69907 68.96 69.767 0.0000 313s demand_6 0.40272 40.13 40.474 0.0000 313s demand_7 0.54517 55.59 56.262 0.0000 313s demand_8 -1.12030 -114.53 -120.768 0.0000 313s demand_9 -0.47860 -45.28 -46.232 0.0000 313s demand_10 0.86877 80.55 77.234 0.0000 313s demand_11 -0.08327 -7.69 -6.253 0.0000 313s demand_12 -0.68871 -68.13 -52.962 0.0000 313s demand_13 -0.54147 -55.32 -45.808 0.0000 313s demand_14 0.03015 3.02 2.731 0.0000 313s demand_15 0.78121 76.26 80.543 0.0000 313s demand_16 -1.15937 -112.33 -121.850 0.0000 313s demand_17 -0.00627 -0.55 -0.605 0.0000 313s demand_18 -0.87372 -88.30 -91.217 0.0000 313s demand_19 0.50608 53.68 56.023 0.0000 313s demand_20 -0.13627 -15.59 -17.320 0.0000 313s supply_1 0.00000 0.00 0.000 -0.0554 313s supply_2 0.00000 0.00 0.000 -0.2935 313s supply_3 0.00000 0.00 0.000 0.3364 313s supply_4 0.00000 0.00 0.000 0.1634 313s supply_5 0.00000 0.00 0.000 0.2976 313s supply_6 0.00000 0.00 0.000 0.1269 313s supply_7 0.00000 0.00 0.000 0.2519 313s supply_8 0.00000 0.00 0.000 -0.8828 313s supply_9 0.00000 0.00 0.000 -0.4048 313s supply_10 0.00000 0.00 0.000 0.5764 313s supply_11 0.00000 0.00 0.000 0.0481 313s supply_12 0.00000 0.00 0.000 -0.2951 313s supply_13 0.00000 0.00 0.000 -0.1935 313s supply_14 0.00000 0.00 0.000 0.1663 313s supply_15 0.00000 0.00 0.000 0.5973 313s supply_16 0.00000 0.00 0.000 -0.7149 313s supply_17 0.00000 0.00 0.000 0.0810 313s supply_18 0.00000 0.00 0.000 -0.4267 313s supply_19 0.00000 0.00 0.000 0.5373 313s supply_20 0.00000 0.00 0.000 0.0841 313s supply_price supply_farmPrice supply_trend 313s demand_1 0.00 0.00 0.0000 313s demand_2 0.00 0.00 0.0000 313s demand_3 0.00 0.00 0.0000 313s demand_4 0.00 0.00 0.0000 313s demand_5 0.00 0.00 0.0000 313s demand_6 0.00 0.00 0.0000 313s demand_7 0.00 0.00 0.0000 313s demand_8 0.00 0.00 0.0000 313s demand_9 0.00 0.00 0.0000 313s demand_10 0.00 0.00 0.0000 313s demand_11 0.00 0.00 0.0000 313s demand_12 0.00 0.00 0.0000 313s demand_13 0.00 0.00 0.0000 313s demand_14 0.00 0.00 0.0000 313s demand_15 0.00 0.00 0.0000 313s demand_16 0.00 0.00 0.0000 313s demand_17 0.00 0.00 0.0000 313s demand_18 0.00 0.00 0.0000 313s demand_19 0.00 0.00 0.0000 313s demand_20 0.00 0.00 0.0000 313s supply_1 -5.52 -5.43 -0.0554 313s supply_2 -30.85 -29.09 -0.5870 313s supply_3 34.91 33.33 1.0091 313s supply_4 17.09 16.03 0.6538 313s supply_5 29.36 32.98 1.4882 313s supply_6 12.65 13.73 0.7616 313s supply_7 25.69 26.60 1.7633 313s supply_8 -90.25 -96.93 -7.0623 313s supply_9 -38.30 -44.00 -3.6431 313s supply_10 53.44 57.98 5.7636 313s supply_11 4.45 3.90 0.5294 313s supply_12 -29.19 -20.24 -3.5407 313s supply_13 -19.77 -13.72 -2.5151 313s supply_14 16.67 13.53 2.3277 313s supply_15 58.30 61.10 8.9588 313s supply_16 -69.27 -75.07 -11.4386 313s supply_17 7.10 8.95 1.3763 313s supply_18 -43.12 -39.47 -7.6797 313s supply_19 56.99 47.98 10.2092 313s supply_20 9.62 7.82 1.6810 313s > round( colSums( estfun( fitw2sls1e ) ), digits = 7 ) 313s demand_(Intercept) demand_price demand_income supply_(Intercept) 313s 0 0 0 0 313s supply_price supply_farmPrice supply_trend 313s 0 0 0 313s > 313s > estfun( fitw2slsd1e ) 313s demand_(Intercept) demand_price demand_income supply_(Intercept) 313s demand_1 -0.2141 -20.39 -18.71 0.0000 313s demand_2 -0.5971 -59.32 -58.28 0.0000 313s demand_3 0.3342 33.06 32.31 0.0000 313s demand_4 0.0923 9.21 9.06 0.0000 313s demand_5 0.3748 36.34 37.40 0.0000 313s demand_6 0.1317 12.91 13.23 0.0000 313s demand_7 0.2982 29.80 30.78 0.0000 313s demand_8 -1.3110 -132.05 -141.32 0.0000 313s demand_9 -0.5322 -51.18 -51.41 0.0000 313s demand_10 0.8995 85.57 79.97 0.0000 313s demand_11 0.1399 13.25 10.51 0.0000 313s demand_12 -0.4189 -41.49 -32.21 0.0000 313s demand_13 -0.2903 -29.54 -24.56 0.0000 313s demand_14 0.2709 27.46 24.55 0.0000 313s demand_15 0.9535 96.13 98.30 0.0000 313s demand_16 -0.9012 -90.95 -94.71 0.0000 313s demand_17 0.3566 34.08 34.37 0.0000 313s demand_18 -0.5159 -53.75 -53.86 0.0000 313s demand_19 0.8239 88.84 91.20 0.0000 313s demand_20 0.1054 12.00 13.39 0.0000 313s supply_1 0.0000 0.00 0.00 -0.0554 313s supply_2 0.0000 0.00 0.00 -0.2935 313s supply_3 0.0000 0.00 0.00 0.3364 313s supply_4 0.0000 0.00 0.00 0.1634 313s supply_5 0.0000 0.00 0.00 0.2976 313s supply_6 0.0000 0.00 0.00 0.1269 313s supply_7 0.0000 0.00 0.00 0.2519 313s supply_8 0.0000 0.00 0.00 -0.8828 313s supply_9 0.0000 0.00 0.00 -0.4048 313s supply_10 0.0000 0.00 0.00 0.5764 313s supply_11 0.0000 0.00 0.00 0.0481 313s supply_12 0.0000 0.00 0.00 -0.2951 313s supply_13 0.0000 0.00 0.00 -0.1935 313s supply_14 0.0000 0.00 0.00 0.1663 313s supply_15 0.0000 0.00 0.00 0.5973 313s supply_16 0.0000 0.00 0.00 -0.7149 313s supply_17 0.0000 0.00 0.00 0.0810 313s supply_18 0.0000 0.00 0.00 -0.4267 313s supply_19 0.0000 0.00 0.00 0.5373 313s supply_20 0.0000 0.00 0.00 0.0841 313s supply_price supply_farmPrice supply_trend 313s demand_1 0.00 0.00 0.0000 313s demand_2 0.00 0.00 0.0000 313s demand_3 0.00 0.00 0.0000 313s demand_4 0.00 0.00 0.0000 313s demand_5 0.00 0.00 0.0000 313s demand_6 0.00 0.00 0.0000 313s demand_7 0.00 0.00 0.0000 313s demand_8 0.00 0.00 0.0000 313s demand_9 0.00 0.00 0.0000 313s demand_10 0.00 0.00 0.0000 313s demand_11 0.00 0.00 0.0000 313s demand_12 0.00 0.00 0.0000 313s demand_13 0.00 0.00 0.0000 313s demand_14 0.00 0.00 0.0000 313s demand_15 0.00 0.00 0.0000 313s demand_16 0.00 0.00 0.0000 313s demand_17 0.00 0.00 0.0000 313s demand_18 0.00 0.00 0.0000 313s demand_19 0.00 0.00 0.0000 313s demand_20 0.00 0.00 0.0000 313s supply_1 -5.52 -5.43 -0.0554 313s supply_2 -30.85 -29.09 -0.5870 313s supply_3 34.91 33.33 1.0091 313s supply_4 17.09 16.03 0.6538 313s supply_5 29.36 32.98 1.4882 313s supply_6 12.65 13.73 0.7616 313s supply_7 25.69 26.60 1.7633 313s supply_8 -90.25 -96.93 -7.0623 313s supply_9 -38.30 -44.00 -3.6431 313s supply_10 53.44 57.98 5.7636 313s supply_11 4.45 3.90 0.5294 313s supply_12 -29.19 -20.24 -3.5407 313s supply_13 -19.77 -13.72 -2.5151 313s supply_14 16.67 13.53 2.3277 313s supply_15 58.30 61.10 8.9588 313s supply_16 -69.27 -75.07 -11.4386 313s supply_17 7.10 8.95 1.3763 313s supply_18 -43.12 -39.47 -7.6797 313s supply_19 56.99 47.98 10.2092 313s supply_20 9.62 7.82 1.6810 313s > round( colSums( estfun( fitw2slsd1e ) ), digits = 7 ) 313s demand_(Intercept) demand_price demand_income supply_(Intercept) 313s 0 0 0 0 313s supply_price supply_farmPrice supply_trend 313s 0 0 0 313s > 313s > 313s > ## **************** bread ************************ 313s > bread( fitw2sls1 ) 313s demand_(Intercept) demand_price demand_income supply_(Intercept) 313s [1,] 2509.59 -26.937 1.9721 0.0 313s [2,] -26.94 0.372 -0.1057 0.0 313s [3,] 1.97 -0.106 0.0881 0.0 313s [4,] 0.00 0.000 0.0000 5770.1 313s [5,] 0.00 0.000 0.0000 -43.8 313s [6,] 0.00 0.000 0.0000 -13.0 313s [7,] 0.00 0.000 0.0000 -11.8 313s supply_price supply_farmPrice supply_trend 313s [1,] 0.0000 0.0000 0.0000 313s [2,] 0.0000 0.0000 0.0000 313s [3,] 0.0000 0.0000 0.0000 313s [4,] -43.8164 -12.9527 -11.8092 313s [5,] 0.3995 0.0374 0.0232 313s [6,] 0.0374 0.0893 0.0551 313s [7,] 0.0232 0.0551 0.3972 313s > 313s > bread( fitw2sls1e ) 313s demand_(Intercept) demand_price demand_income supply_(Intercept) 313s [1,] 2133.15 -22.8963 1.6763 0.00 313s [2,] -22.90 0.3165 -0.0898 0.00 313s [3,] 1.68 -0.0898 0.0749 0.00 313s [4,] 0.00 0.0000 0.0000 4616.09 313s [5,] 0.00 0.0000 0.0000 -35.05 313s [6,] 0.00 0.0000 0.0000 -10.36 313s [7,] 0.00 0.0000 0.0000 -9.45 313s supply_price supply_farmPrice supply_trend 313s [1,] 0.0000 0.0000 0.0000 313s [2,] 0.0000 0.0000 0.0000 313s [3,] 0.0000 0.0000 0.0000 313s [4,] -35.0531 -10.3622 -9.4473 313s [5,] 0.3196 0.0300 0.0185 313s [6,] 0.0300 0.0714 0.0441 313s [7,] 0.0185 0.0441 0.3178 313s > 313s > bread( fitw2slsd1e ) 313s demand_(Intercept) demand_price demand_income supply_(Intercept) 313s [1,] 4222.1 -51.601 9.696 0.00 313s [2,] -51.6 0.713 -0.202 0.00 313s [3,] 9.7 -0.202 0.108 0.00 313s [4,] 0.0 0.000 0.000 4616.09 313s [5,] 0.0 0.000 0.000 -35.05 313s [6,] 0.0 0.000 0.000 -10.36 313s [7,] 0.0 0.000 0.000 -9.45 313s supply_price supply_farmPrice supply_trend 313s [1,] 0.0000 0.0000 0.0000 313s [2,] 0.0000 0.0000 0.0000 313s [3,] 0.0000 0.0000 0.0000 313s [4,] -35.0531 -10.3622 -9.4473 313s [5,] 0.3196 0.0300 0.0185 313s [6,] 0.0300 0.0714 0.0441 313s [7,] 0.0185 0.0441 0.3178 313s > 313s BEGIN TEST test_wls.R 313s 313s R version 4.3.3 (2024-02-29) -- "Angel Food Cake" 313s Copyright (C) 2024 The R Foundation for Statistical Computing 313s Platform: arm-unknown-linux-gnueabihf (32-bit) 313s 313s R is free software and comes with ABSOLUTELY NO WARRANTY. 313s You are welcome to redistribute it under certain conditions. 313s Type 'license()' or 'licence()' for distribution details. 313s 313s R is a collaborative project with many contributors. 313s Type 'contributors()' for more information and 313s 'citation()' on how to cite R or R packages in publications. 313s 313s Type 'demo()' for some demos, 'help()' for on-line help, or 313s 'help.start()' for an HTML browser interface to help. 313s Type 'q()' to quit R. 313s 313s Loading required package: Matrix 313s > library( systemfit ) 314s Loading required package: car 314s Loading required package: carData 314s Loading required package: lmtest 314s Loading required package: zoo 314s 314s Attaching package: ‘zoo’ 314s 314s The following objects are masked from ‘package:base’: 314s 314s as.Date, as.Date.numeric 314s 314s 314s Please cite the 'systemfit' package as: 314s Arne Henningsen and Jeff D. Hamann (2007). systemfit: A Package for Estimating Systems of Simultaneous Equations in R. Journal of Statistical Software 23(4), 1-40. http://www.jstatsoft.org/v23/i04/. 314s 314s If you have questions, suggestions, or comments regarding the 'systemfit' package, please use a forum or 'tracker' at systemfit's R-Forge site: 314s https://r-forge.r-project.org/projects/systemfit/ 314s > options( digits = 3 ) 314s > 314s > data( "Kmenta" ) 314s > useMatrix <- FALSE 314s > 314s > demand <- consump ~ price + income 314s > supply <- consump ~ price + farmPrice + trend 314s > system <- list( demand = demand, supply = supply ) 314s > restrm <- matrix(0,1,7) # restriction matrix "R" 314s > restrm[1,3] <- 1 314s > restrm[1,7] <- -1 314s > restrict <- "demand_income - supply_trend = 0" 314s > restr2m <- matrix(0,2,7) # restriction matrix "R" 2 314s > restr2m[1,3] <- 1 314s > restr2m[1,7] <- -1 314s > restr2m[2,2] <- -1 314s > restr2m[2,5] <- 1 314s > restr2q <- c( 0, 0.5 ) # restriction vector "q" 2 314s > restrict2 <- c( "demand_income - supply_trend = 0", 314s + "- demand_price + supply_price = 0.5" ) 314s > tc <- matrix(0,7,6) 314s > tc[1,1] <- 1 314s > tc[2,2] <- 1 314s > tc[3,3] <- 1 314s > tc[4,4] <- 1 314s > tc[5,5] <- 1 314s > tc[6,6] <- 1 314s > tc[7,3] <- 1 314s > restr3m <- matrix(0,1,6) # restriction matrix "R" 2 314s > restr3m[1,2] <- -1 314s > restr3m[1,5] <- 1 314s > restr3q <- c( 0.5 ) # restriction vector "q" 2 314s > restrict3 <- "- C2 + C5 = 0.5" 314s > 314s > 314s > ## ******* single-equation OLS estimations ********************* 314s > lmDemand <- lm( demand, data = Kmenta ) 314s > lmSupply <- lm( supply, data = Kmenta ) 314s > 314s > ## *************** WLS estimation ************************ 314s > fitwls1 <- systemfit( system, "WLS", data = Kmenta, useMatrix = useMatrix ) 314s > print( summary( fitwls1 ) ) 314s 314s systemfit results 314s method: WLS 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 33 156 4.43 0.709 0.558 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s demand 20 17 63.3 3.73 1.93 0.764 0.736 314s supply 20 16 92.6 5.78 2.40 0.655 0.590 314s 314s The covariance matrix of the residuals used for estimation 314s demand supply 314s demand 3.73 0.00 314s supply 0.00 5.78 314s 314s The covariance matrix of the residuals 314s demand supply 314s demand 3.73 4.14 314s supply 4.14 5.78 314s 314s The correlations of the residuals 314s demand supply 314s demand 1.000 0.891 314s supply 0.891 1.000 314s 314s 314s WLS estimates for 'demand' (equation 1) 314s Model Formula: consump ~ price + income 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 99.8954 7.5194 13.29 2.1e-10 *** 314s price -0.3163 0.0907 -3.49 0.0028 ** 314s income 0.3346 0.0454 7.37 1.1e-06 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 1.93 on 17 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 17 314s SSR: 63.332 MSE: 3.725 Root MSE: 1.93 314s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 314s 314s 314s WLS estimates for 'supply' (equation 2) 314s Model Formula: consump ~ price + farmPrice + trend 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 58.2754 11.4629 5.08 0.00011 *** 314s price 0.1604 0.0949 1.69 0.11039 314s farmPrice 0.2481 0.0462 5.37 6.2e-05 *** 314s trend 0.2483 0.0975 2.55 0.02157 * 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 2.405 on 16 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 16 314s SSR: 92.551 MSE: 5.784 Root MSE: 2.405 314s Multiple R-Squared: 0.655 Adjusted R-Squared: 0.59 314s 314s > all.equal( coef( fitwls1 ), c( coef( lmDemand ), coef( lmSupply ) ), 314s + check.attributes = FALSE ) 314s [1] TRUE 314s > all.equal( coef( summary( fitwls1 ) ), 314s + rbind( coef( summary( lmDemand ) ), coef( summary( lmSupply ) ) ), 314s + check.attributes = FALSE ) 314s [1] TRUE 314s > all.equal( vcov( fitwls1 ), 314s + as.matrix( bdiag( vcov( lmDemand ), vcov( lmSupply ) ) ), 314s + check.attributes = FALSE ) 314s [1] TRUE 314s > 314s > ## *************** WLS estimation (EViews-like) ************************ 314s > fitwls1e <- systemfit( system, "WLS", data = Kmenta, methodResidCov = "noDfCor", 314s + x = TRUE, useMatrix = useMatrix ) 314s > print( summary( fitwls1e, useDfSys = TRUE ) ) 314s 314s systemfit results 314s method: WLS 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 33 156 3.02 0.709 0.537 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s demand 20 17 63.3 3.73 1.93 0.764 0.736 314s supply 20 16 92.6 5.78 2.40 0.655 0.590 314s 314s The covariance matrix of the residuals used for estimation 314s demand supply 314s demand 3.17 0.00 314s supply 0.00 4.63 314s 314s The covariance matrix of the residuals 314s demand supply 314s demand 3.17 3.41 314s supply 3.41 4.63 314s 314s The correlations of the residuals 314s demand supply 314s demand 1.000 0.891 314s supply 0.891 1.000 314s 314s 314s WLS estimates for 'demand' (equation 1) 314s Model Formula: consump ~ price + income 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 99.8954 6.9325 14.41 8.9e-16 *** 314s price -0.3163 0.0836 -3.78 0.00062 *** 314s income 0.3346 0.0419 7.99 3.2e-09 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 1.93 on 17 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 17 314s SSR: 63.332 MSE: 3.725 Root MSE: 1.93 314s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 314s 314s 314s WLS estimates for 'supply' (equation 2) 314s Model Formula: consump ~ price + farmPrice + trend 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 58.2754 10.2527 5.68 2.4e-06 *** 314s price 0.1604 0.0849 1.89 0.0676 . 314s farmPrice 0.2481 0.0413 6.01 9.5e-07 *** 314s trend 0.2483 0.0872 2.85 0.0075 ** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 2.405 on 16 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 16 314s SSR: 92.551 MSE: 5.784 Root MSE: 2.405 314s Multiple R-Squared: 0.655 Adjusted R-Squared: 0.59 314s 314s > all.equal( coef( fitwls1e ), c( coef( lmDemand ), coef( lmSupply ) ), 314s + check.attributes = FALSE ) 314s [1] TRUE 314s > 314s > ## ************** WLS with cross-equation restriction *************** 314s > fitwls2 <- systemfit( system, "WLS", data = Kmenta, restrict.matrix = restrm, 314s + x = TRUE, useMatrix = useMatrix ) 314s > print( summary( fitwls2 ) ) 314s 314s systemfit results 314s method: WLS 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 34 159 2.35 0.703 0.622 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s demand 20 17 63.8 3.75 1.94 0.762 0.734 314s supply 20 16 95.6 5.98 2.44 0.643 0.576 314s 314s The covariance matrix of the residuals used for estimation 314s demand supply 314s demand 3.78 0.00 314s supply 0.00 5.94 314s 314s The covariance matrix of the residuals 314s demand supply 314s demand 3.75 4.48 314s supply 4.48 5.98 314s 314s The correlations of the residuals 314s demand supply 314s demand 1.000 0.946 314s supply 0.946 1.000 314s 314s 314s WLS estimates for 'demand' (equation 1) 314s Model Formula: consump ~ price + income 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 99.6582 7.5640 13.18 6.4e-15 *** 314s price -0.2991 0.0887 -3.37 0.0019 ** 314s income 0.3194 0.0415 7.70 6.0e-09 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 1.936 on 17 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 17 314s SSR: 63.75 MSE: 3.75 Root MSE: 1.936 314s Multiple R-Squared: 0.762 Adjusted R-Squared: 0.734 314s 314s 314s WLS estimates for 'supply' (equation 2) 314s Model Formula: consump ~ price + farmPrice + trend 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 56.1877 11.3165 4.97 1.9e-05 *** 314s price 0.1643 0.0960 1.71 0.096 . 314s farmPrice 0.2580 0.0451 5.71 2.0e-06 *** 314s trend 0.3194 0.0415 7.70 6.0e-09 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 2.445 on 16 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 16 314s SSR: 95.627 MSE: 5.977 Root MSE: 2.445 314s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.576 314s 314s > # the same with symbolically specified restrictions 314s > fitwls2Sym <- systemfit( system, "WLS", data = Kmenta, 314s + restrict.matrix = restrict, x = TRUE, 314s + useMatrix = useMatrix ) 314s > all.equal( fitwls2, fitwls2Sym ) 314s [1] "Component “call”: target, current do not match when deparsed" 314s > 314s > ## ************** WLS with cross-equation restriction (EViews-like) ******* 314s > fitwls2e <- systemfit( system, "WLS", data = Kmenta, restrict.matrix = restrm, 314s + methodResidCov = "noDfCor", useMatrix = useMatrix ) 314s > print( summary( fitwls2e ) ) 314s 314s systemfit results 314s method: WLS 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 34 159 1.61 0.703 0.589 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s demand 20 17 63.8 3.75 1.94 0.762 0.734 314s supply 20 16 95.6 5.97 2.44 0.644 0.577 314s 314s The covariance matrix of the residuals used for estimation 314s demand supply 314s demand 3.21 0.00 314s supply 0.00 4.75 314s 314s The covariance matrix of the residuals 314s demand supply 314s demand 3.19 3.69 314s supply 3.69 4.78 314s 314s The correlations of the residuals 314s demand supply 314s demand 1.000 0.946 314s supply 0.946 1.000 314s 314s 314s WLS estimates for 'demand' (equation 1) 314s Model Formula: consump ~ price + income 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 99.6461 6.9734 14.29 6.7e-16 *** 314s price -0.2982 0.0816 -3.65 0.00086 *** 314s income 0.3186 0.0381 8.37 8.9e-10 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 1.937 on 17 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 17 314s SSR: 63.794 MSE: 3.753 Root MSE: 1.937 314s Multiple R-Squared: 0.762 Adjusted R-Squared: 0.734 314s 314s 314s WLS estimates for 'supply' (equation 2) 314s Model Formula: consump ~ price + farmPrice + trend 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 56.2104 10.1248 5.55 3.3e-06 *** 314s price 0.1642 0.0859 1.91 0.064 . 314s farmPrice 0.2579 0.0404 6.38 2.7e-07 *** 314s trend 0.3186 0.0381 8.37 8.9e-10 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 2.444 on 16 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 16 314s SSR: 95.561 MSE: 5.973 Root MSE: 2.444 314s Multiple R-Squared: 0.644 Adjusted R-Squared: 0.577 314s 314s > 314s > ## ******* WLS with cross-equation restriction via restrict.regMat ********** 314s > fitwls3 <- systemfit( system,"WLS", data = Kmenta, restrict.regMat = tc, 314s + x = TRUE, useMatrix = useMatrix ) 314s > print( summary( fitwls3 ) ) 314s 314s systemfit results 314s method: WLS 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 34 159 2.35 0.703 0.622 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s demand 20 17 63.8 3.75 1.94 0.762 0.734 314s supply 20 16 95.6 5.98 2.44 0.643 0.576 314s 314s The covariance matrix of the residuals used for estimation 314s demand supply 314s demand 3.78 0.00 314s supply 0.00 5.94 314s 314s The covariance matrix of the residuals 314s demand supply 314s demand 3.75 4.48 314s supply 4.48 5.98 314s 314s The correlations of the residuals 314s demand supply 314s demand 1.000 0.946 314s supply 0.946 1.000 314s 314s 314s WLS estimates for 'demand' (equation 1) 314s Model Formula: consump ~ price + income 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 99.6582 7.5640 13.18 6.4e-15 *** 314s price -0.2991 0.0887 -3.37 0.0019 ** 314s income 0.3194 0.0415 7.70 6.0e-09 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 1.936 on 17 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 17 314s SSR: 63.75 MSE: 3.75 Root MSE: 1.936 314s Multiple R-Squared: 0.762 Adjusted R-Squared: 0.734 314s 314s 314s WLS estimates for 'supply' (equation 2) 314s Model Formula: consump ~ price + farmPrice + trend 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 56.1877 11.3165 4.97 1.9e-05 *** 314s price 0.1643 0.0960 1.71 0.096 . 314s farmPrice 0.2580 0.0451 5.71 2.0e-06 *** 314s trend 0.3194 0.0415 7.70 6.0e-09 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 2.445 on 16 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 16 314s SSR: 95.627 MSE: 5.977 Root MSE: 2.445 314s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.576 314s 314s > 314s > ## ******* WLS with cross-equation restriction via restrict.regMat (EViews-like) ***** 314s > fitwls3e <- systemfit( system,"WLS", data = Kmenta, restrict.regMat = tc, 314s + methodResidCov = "noDfCor", useMatrix = useMatrix ) 314s > print( summary( fitwls3e ) ) 314s 314s systemfit results 314s method: WLS 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 34 159 1.61 0.703 0.589 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s demand 20 17 63.8 3.75 1.94 0.762 0.734 314s supply 20 16 95.6 5.97 2.44 0.644 0.577 314s 314s The covariance matrix of the residuals used for estimation 314s demand supply 314s demand 3.21 0.00 314s supply 0.00 4.75 314s 314s The covariance matrix of the residuals 314s demand supply 314s demand 3.19 3.69 314s supply 3.69 4.78 314s 314s The correlations of the residuals 314s demand supply 314s demand 1.000 0.946 314s supply 0.946 1.000 314s 314s 314s WLS estimates for 'demand' (equation 1) 314s Model Formula: consump ~ price + income 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 99.6461 6.9734 14.29 6.7e-16 *** 314s price -0.2982 0.0816 -3.65 0.00086 *** 314s income 0.3186 0.0381 8.37 8.9e-10 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 1.937 on 17 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 17 314s SSR: 63.794 MSE: 3.753 Root MSE: 1.937 314s Multiple R-Squared: 0.762 Adjusted R-Squared: 0.734 314s 314s 314s WLS estimates for 'supply' (equation 2) 314s Model Formula: consump ~ price + farmPrice + trend 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 56.2104 10.1248 5.55 3.3e-06 *** 314s price 0.1642 0.0859 1.91 0.064 . 314s farmPrice 0.2579 0.0404 6.38 2.7e-07 *** 314s trend 0.3186 0.0381 8.37 8.9e-10 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 2.444 on 16 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 16 314s SSR: 95.561 MSE: 5.973 Root MSE: 2.444 314s Multiple R-Squared: 0.644 Adjusted R-Squared: 0.577 314s 314s > 314s > ## ***** WLS with 2 cross-equation restrictions *************** 314s > fitwls4 <- systemfit( system,"WLS", data = Kmenta, restrict.matrix = restr2m, 314s + restrict.rhs = restr2q, useMatrix = useMatrix ) 314s > print( summary( fitwls4 ) ) 314s 314s systemfit results 314s method: WLS 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 35 160 2.51 0.702 0.619 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s demand 20 17 63.6 3.74 1.94 0.763 0.735 314s supply 20 16 96.3 6.02 2.45 0.641 0.574 314s 314s The covariance matrix of the residuals used for estimation 314s demand supply 314s demand 3.76 0.00 314s supply 0.00 5.99 314s 314s The covariance matrix of the residuals 314s demand supply 314s demand 3.74 4.47 314s supply 4.47 6.02 314s 314s The correlations of the residuals 314s demand supply 314s demand 1.000 0.943 314s supply 0.943 1.000 314s 314s 314s WLS estimates for 'demand' (equation 1) 314s Model Formula: consump ~ price + income 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 100.9138 6.0474 16.69 < 2e-16 *** 314s price -0.3160 0.0648 -4.87 2.3e-05 *** 314s income 0.3238 0.0385 8.42 6.3e-10 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 1.935 on 17 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 17 314s SSR: 63.636 MSE: 3.743 Root MSE: 1.935 314s Multiple R-Squared: 0.763 Adjusted R-Squared: 0.735 314s 314s 314s WLS estimates for 'supply' (equation 2) 314s Model Formula: consump ~ price + farmPrice + trend 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 53.9416 7.9687 6.77 7.6e-08 *** 314s price 0.1840 0.0648 2.84 0.0075 ** 314s farmPrice 0.2603 0.0446 5.84 1.3e-06 *** 314s trend 0.3238 0.0385 8.42 6.3e-10 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 2.453 on 16 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 16 314s SSR: 96.268 MSE: 6.017 Root MSE: 2.453 314s Multiple R-Squared: 0.641 Adjusted R-Squared: 0.574 314s 314s > # the same with symbolically specified restrictions 314s > fitwls4Sym <- systemfit( system, "WLS", data = Kmenta, 314s + restrict.matrix = restrict2, useMatrix = useMatrix ) 314s > all.equal( fitwls4, fitwls4Sym ) 314s [1] "Component “call”: target, current do not match when deparsed" 314s > 314s > ## ***** WLS with 2 cross-equation restrictions (EViews-like) ********** 314s > fitwls4e <- systemfit( system,"WLS", data = Kmenta, methodResidCov = "noDfCor", 314s + restrict.matrix = restr2m, restrict.rhs = restr2q, 314s + x = TRUE, useMatrix = useMatrix ) 314s > print( summary( fitwls4e ) ) 314s 314s systemfit results 314s method: WLS 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 35 160 1.72 0.702 0.586 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s demand 20 17 63.7 3.75 1.94 0.763 0.735 314s supply 20 16 96.2 6.01 2.45 0.641 0.574 314s 314s The covariance matrix of the residuals used for estimation 314s demand supply 314s demand 3.2 0.00 314s supply 0.0 4.79 314s 314s The covariance matrix of the residuals 314s demand supply 314s demand 3.18 3.69 314s supply 3.69 4.81 314s 314s The correlations of the residuals 314s demand supply 314s demand 1.000 0.942 314s supply 0.942 1.000 314s 314s 314s WLS estimates for 'demand' (equation 1) 314s Model Formula: consump ~ price + income 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 100.9762 5.5234 18.28 < 2e-16 *** 314s price -0.3160 0.0589 -5.37 5.3e-06 *** 314s income 0.3233 0.0352 9.18 7.6e-11 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 1.935 on 17 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 17 314s SSR: 63.67 MSE: 3.745 Root MSE: 1.935 314s Multiple R-Squared: 0.763 Adjusted R-Squared: 0.735 314s 314s 314s WLS estimates for 'supply' (equation 2) 314s Model Formula: consump ~ price + farmPrice + trend 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 53.9630 7.2089 7.49 9.1e-09 *** 314s price 0.1840 0.0589 3.13 0.0036 ** 314s farmPrice 0.2602 0.0399 6.53 1.6e-07 *** 314s trend 0.3233 0.0352 9.18 7.6e-11 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 2.452 on 16 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 16 314s SSR: 96.215 MSE: 6.013 Root MSE: 2.452 314s Multiple R-Squared: 0.641 Adjusted R-Squared: 0.574 314s 314s > 314s > ## *********** WLS with 2 cross-equation restrictions via R and restrict.regMat ****** 314s > fitwls5 <- systemfit( system, "WLS", data = Kmenta, restrict.matrix = restr3m, 314s + restrict.rhs = restr3q, restrict.regMat = tc, 314s + x = TRUE, useMatrix = useMatrix ) 314s > print( summary( fitwls5 ) ) 314s 314s systemfit results 314s method: WLS 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 35 160 2.51 0.702 0.619 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s demand 20 17 63.6 3.74 1.94 0.763 0.735 314s supply 20 16 96.3 6.02 2.45 0.641 0.574 314s 314s The covariance matrix of the residuals used for estimation 314s demand supply 314s demand 3.76 0.00 314s supply 0.00 5.99 314s 314s The covariance matrix of the residuals 314s demand supply 314s demand 3.74 4.47 314s supply 4.47 6.02 314s 314s The correlations of the residuals 314s demand supply 314s demand 1.000 0.943 314s supply 0.943 1.000 314s 314s 314s WLS estimates for 'demand' (equation 1) 314s Model Formula: consump ~ price + income 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 100.9138 6.0474 16.69 < 2e-16 *** 314s price -0.3160 0.0648 -4.87 2.3e-05 *** 314s income 0.3238 0.0385 8.42 6.3e-10 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 1.935 on 17 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 17 314s SSR: 63.636 MSE: 3.743 Root MSE: 1.935 314s Multiple R-Squared: 0.763 Adjusted R-Squared: 0.735 314s 314s 314s WLS estimates for 'supply' (equation 2) 314s Model Formula: consump ~ price + farmPrice + trend 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 53.9416 7.9687 6.77 7.6e-08 *** 314s price 0.1840 0.0648 2.84 0.0075 ** 314s farmPrice 0.2603 0.0446 5.84 1.3e-06 *** 314s trend 0.3238 0.0385 8.42 6.3e-10 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 2.453 on 16 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 16 314s SSR: 96.268 MSE: 6.017 Root MSE: 2.453 314s Multiple R-Squared: 0.641 Adjusted R-Squared: 0.574 314s 314s > # the same with symbolically specified restrictions 314s > fitwls5Sym <- systemfit( system, "WLS", data = Kmenta, 314s + restrict.matrix = restrict3, restrict.regMat = tc, 314s + x = TRUE, useMatrix = useMatrix ) 314s > all.equal( fitwls5, fitwls5Sym ) 314s [1] "Component “call”: target, current do not match when deparsed" 314s > 314s > ## *********** WLS with 2 cross-equation restrictions via R and restrict.regMat (EViews-like) 314s > fitwls5e <- systemfit( system, "WLS", data = Kmenta, methodResidCov = "noDfCor", 314s + restrict.matrix = restr3m, restrict.rhs = restr3q, restrict.regMat = tc, 314s + useMatrix = useMatrix ) 314s > print( summary( fitwls5e ) ) 314s 314s systemfit results 314s method: WLS 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 35 160 1.72 0.702 0.586 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s demand 20 17 63.7 3.75 1.94 0.763 0.735 314s supply 20 16 96.2 6.01 2.45 0.641 0.574 314s 314s The covariance matrix of the residuals used for estimation 314s demand supply 314s demand 3.2 0.00 314s supply 0.0 4.79 314s 314s The covariance matrix of the residuals 314s demand supply 314s demand 3.18 3.69 314s supply 3.69 4.81 314s 314s The correlations of the residuals 314s demand supply 314s demand 1.000 0.942 314s supply 0.942 1.000 314s 314s 314s WLS estimates for 'demand' (equation 1) 314s Model Formula: consump ~ price + income 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 100.9762 5.5234 18.28 < 2e-16 *** 314s price -0.3160 0.0589 -5.37 5.3e-06 *** 314s income 0.3233 0.0352 9.18 7.6e-11 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 1.935 on 17 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 17 314s SSR: 63.67 MSE: 3.745 Root MSE: 1.935 314s Multiple R-Squared: 0.763 Adjusted R-Squared: 0.735 314s 314s 314s WLS estimates for 'supply' (equation 2) 314s Model Formula: consump ~ price + farmPrice + trend 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 53.9630 7.2089 7.49 9.1e-09 *** 314s price 0.1840 0.0589 3.13 0.0036 ** 314s farmPrice 0.2602 0.0399 6.53 1.6e-07 *** 314s trend 0.3233 0.0352 9.18 7.6e-11 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 2.452 on 16 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 16 314s SSR: 96.215 MSE: 6.013 Root MSE: 2.452 314s Multiple R-Squared: 0.641 Adjusted R-Squared: 0.574 314s 314s > 314s > ## *************** iterated WLS estimation ********************* 314s > fitwlsi1 <- systemfit( system, "WLS", data = Kmenta, 314s + maxit = 100, useMatrix = useMatrix ) 314s > print( summary( fitwlsi1, useDfSys = TRUE ) ) 314s 314s systemfit results 314s method: WLS 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 33 156 4.43 0.709 0.558 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s demand 20 17 63.3 3.73 1.93 0.764 0.736 314s supply 20 16 92.6 5.78 2.40 0.655 0.590 314s 314s The covariance matrix of the residuals used for estimation 314s demand supply 314s demand 3.73 0.00 314s supply 0.00 5.78 314s 314s The covariance matrix of the residuals 314s demand supply 314s demand 3.73 4.14 314s supply 4.14 5.78 314s 314s The correlations of the residuals 314s demand supply 314s demand 1.000 0.891 314s supply 0.891 1.000 314s 314s 314s WLS estimates for 'demand' (equation 1) 314s Model Formula: consump ~ price + income 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 99.8954 7.5194 13.29 8.4e-15 *** 314s price -0.3163 0.0907 -3.49 0.0014 ** 314s income 0.3346 0.0454 7.37 1.8e-08 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 1.93 on 17 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 17 314s SSR: 63.332 MSE: 3.725 Root MSE: 1.93 314s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 314s 314s 314s WLS estimates for 'supply' (equation 2) 314s Model Formula: consump ~ price + farmPrice + trend 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 58.2754 11.4629 5.08 1.4e-05 *** 314s price 0.1604 0.0949 1.69 0.100 314s farmPrice 0.2481 0.0462 5.37 6.1e-06 *** 314s trend 0.2483 0.0975 2.55 0.016 * 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 2.405 on 16 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 16 314s SSR: 92.551 MSE: 5.784 Root MSE: 2.405 314s Multiple R-Squared: 0.655 Adjusted R-Squared: 0.59 314s 314s > 314s > ## *************** iterated WLS estimation (EViews-like) ************ 314s > fitwlsi1e <- systemfit( system, "WLS", data = Kmenta, methodResidCov = "noDfCor", 314s + maxit = 100, x = TRUE, useMatrix = useMatrix ) 314s > print( summary( fitwlsi1e, useDfSys = TRUE ) ) 314s 314s systemfit results 314s method: WLS 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 33 156 3.02 0.709 0.537 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s demand 20 17 63.3 3.73 1.93 0.764 0.736 314s supply 20 16 92.6 5.78 2.40 0.655 0.590 314s 314s The covariance matrix of the residuals used for estimation 314s demand supply 314s demand 3.17 0.00 314s supply 0.00 4.63 314s 314s The covariance matrix of the residuals 314s demand supply 314s demand 3.17 3.41 314s supply 3.41 4.63 314s 314s The correlations of the residuals 314s demand supply 314s demand 1.000 0.891 314s supply 0.891 1.000 314s 314s 314s WLS estimates for 'demand' (equation 1) 314s Model Formula: consump ~ price + income 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 99.8954 6.9325 14.41 8.9e-16 *** 314s price -0.3163 0.0836 -3.78 0.00062 *** 314s income 0.3346 0.0419 7.99 3.2e-09 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 1.93 on 17 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 17 314s SSR: 63.332 MSE: 3.725 Root MSE: 1.93 314s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 314s 314s 314s WLS estimates for 'supply' (equation 2) 314s Model Formula: consump ~ price + farmPrice + trend 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 58.2754 10.2527 5.68 2.4e-06 *** 314s price 0.1604 0.0849 1.89 0.0676 . 314s farmPrice 0.2481 0.0413 6.01 9.5e-07 *** 314s trend 0.2483 0.0872 2.85 0.0075 ** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 2.405 on 16 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 16 314s SSR: 92.551 MSE: 5.784 Root MSE: 2.405 314s Multiple R-Squared: 0.655 Adjusted R-Squared: 0.59 314s 314s > 314s > ## ****** iterated WLS with cross-equation restriction *************** 314s > fitwlsi2 <- systemfit( system, "WLS", data = Kmenta, restrict.matrix = restrm, 314s + maxit = 100, x = TRUE, useMatrix = useMatrix ) 314s > print( summary( fitwlsi2 ) ) 314s 314s systemfit results 314s method: iterated WLS 314s 314s convergence achieved after 3 iterations 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 34 159 2.34 0.703 0.623 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s demand 20 17 63.7 3.75 1.94 0.762 0.734 314s supply 20 16 95.6 5.98 2.44 0.643 0.576 314s 314s The covariance matrix of the residuals used for estimation 314s demand supply 314s demand 3.75 0.00 314s supply 0.00 5.98 314s 314s The covariance matrix of the residuals 314s demand supply 314s demand 3.75 4.48 314s supply 4.48 5.98 314s 314s The correlations of the residuals 314s demand supply 314s demand 1.000 0.946 314s supply 0.946 1.000 314s 314s 314s WLS estimates for 'demand' (equation 1) 314s Model Formula: consump ~ price + income 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 99.6607 7.5378 13.22 5.8e-15 *** 314s price -0.2993 0.0884 -3.39 0.0018 ** 314s income 0.3196 0.0414 7.72 5.6e-09 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 1.936 on 17 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 17 314s SSR: 63.741 MSE: 3.749 Root MSE: 1.936 314s Multiple R-Squared: 0.762 Adjusted R-Squared: 0.734 314s 314s 314s WLS estimates for 'supply' (equation 2) 314s Model Formula: consump ~ price + farmPrice + trend 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 56.1830 11.3487 4.95 2.0e-05 *** 314s price 0.1643 0.0963 1.71 0.097 . 314s farmPrice 0.2580 0.0453 5.70 2.1e-06 *** 314s trend 0.3196 0.0414 7.72 5.6e-09 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 2.445 on 16 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 16 314s SSR: 95.641 MSE: 5.978 Root MSE: 2.445 314s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.576 314s 314s > 314s > ## ****** iterated WLS with cross-equation restriction (EViews-like) ******** 314s > fitwlsi2e <- systemfit( system, "WLS", data = Kmenta, restrict.matrix = restrm, 314s + methodResidCov = "noDfCor", maxit = 100, useMatrix = useMatrix ) 314s > print( summary( fitwlsi2e ) ) 314s 314s systemfit results 314s method: iterated WLS 314s 314s convergence achieved after 3 iterations 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 34 159 1.6 0.703 0.589 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s demand 20 17 63.8 3.75 1.94 0.762 0.734 314s supply 20 16 95.6 5.97 2.44 0.644 0.577 314s 314s The covariance matrix of the residuals used for estimation 314s demand supply 314s demand 3.19 0.00 314s supply 0.00 4.78 314s 314s The covariance matrix of the residuals 314s demand supply 314s demand 3.19 3.69 314s supply 3.69 4.78 314s 314s The correlations of the residuals 314s demand supply 314s demand 1.000 0.946 314s supply 0.946 1.000 314s 314s 314s WLS estimates for 'demand' (equation 1) 314s Model Formula: consump ~ price + income 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 99.6484 6.9516 14.33 4.4e-16 *** 314s price -0.2984 0.0814 -3.67 0.00083 *** 314s income 0.3188 0.0380 8.39 8.4e-10 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 1.937 on 17 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 17 314s SSR: 63.785 MSE: 3.752 Root MSE: 1.937 314s Multiple R-Squared: 0.762 Adjusted R-Squared: 0.734 314s 314s 314s WLS estimates for 'supply' (equation 2) 314s Model Formula: consump ~ price + farmPrice + trend 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 56.2061 10.1500 5.54 3.4e-06 *** 314s price 0.1642 0.0861 1.91 0.065 . 314s farmPrice 0.2579 0.0405 6.37 2.9e-07 *** 314s trend 0.3188 0.0380 8.39 8.4e-10 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 2.444 on 16 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 16 314s SSR: 95.573 MSE: 5.973 Root MSE: 2.444 314s Multiple R-Squared: 0.644 Adjusted R-Squared: 0.577 314s 314s > 314s > ## ******* iterated WLS with cross-equation restriction via restrict.regMat ********** 314s > fitwlsi3 <- systemfit( system, "WLS", data = Kmenta, restrict.regMat = tc, 314s + maxit = 100, x = TRUE, useMatrix = useMatrix ) 314s > print( summary( fitwlsi3 ) ) 314s 314s systemfit results 314s method: iterated WLS 314s 314s convergence achieved after 3 iterations 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 34 159 2.34 0.703 0.623 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s demand 20 17 63.7 3.75 1.94 0.762 0.734 314s supply 20 16 95.6 5.98 2.44 0.643 0.576 314s 314s The covariance matrix of the residuals used for estimation 314s demand supply 314s demand 3.75 0.00 314s supply 0.00 5.98 314s 314s The covariance matrix of the residuals 314s demand supply 314s demand 3.75 4.48 314s supply 4.48 5.98 314s 314s The correlations of the residuals 314s demand supply 314s demand 1.000 0.946 314s supply 0.946 1.000 314s 314s 314s WLS estimates for 'demand' (equation 1) 314s Model Formula: consump ~ price + income 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 99.6607 7.5378 13.22 5.8e-15 *** 314s price -0.2993 0.0884 -3.39 0.0018 ** 314s income 0.3196 0.0414 7.72 5.6e-09 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 1.936 on 17 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 17 314s SSR: 63.741 MSE: 3.749 Root MSE: 1.936 314s Multiple R-Squared: 0.762 Adjusted R-Squared: 0.734 314s 314s 314s WLS estimates for 'supply' (equation 2) 314s Model Formula: consump ~ price + farmPrice + trend 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 56.1830 11.3487 4.95 2.0e-05 *** 314s price 0.1643 0.0963 1.71 0.097 . 314s farmPrice 0.2580 0.0453 5.70 2.1e-06 *** 314s trend 0.3196 0.0414 7.72 5.6e-09 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 2.445 on 16 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 16 314s SSR: 95.641 MSE: 5.978 Root MSE: 2.445 314s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.576 314s 314s > 314s > ## ******* iterated WLS with cross-equation restriction via restrict.regMat (EViews-like) *** 314s > fitwlsi3e <- systemfit( system, "WLS", data = Kmenta, restrict.regMat = tc, 314s + methodResidCov = "noDfCor", maxit = 100, useMatrix = useMatrix ) 314s > print( summary( fitwlsi3e ) ) 314s 314s systemfit results 314s method: iterated WLS 314s 314s convergence achieved after 3 iterations 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 34 159 1.6 0.703 0.589 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s demand 20 17 63.8 3.75 1.94 0.762 0.734 314s supply 20 16 95.6 5.97 2.44 0.644 0.577 314s 314s The covariance matrix of the residuals used for estimation 314s demand supply 314s demand 3.19 0.00 314s supply 0.00 4.78 314s 314s The covariance matrix of the residuals 314s demand supply 314s demand 3.19 3.69 314s supply 3.69 4.78 314s 314s The correlations of the residuals 314s demand supply 314s demand 1.000 0.946 314s supply 0.946 1.000 314s 314s 314s WLS estimates for 'demand' (equation 1) 314s Model Formula: consump ~ price + income 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 99.6484 6.9516 14.33 4.4e-16 *** 314s price -0.2984 0.0814 -3.67 0.00083 *** 314s income 0.3188 0.0380 8.39 8.4e-10 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 1.937 on 17 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 17 314s SSR: 63.785 MSE: 3.752 Root MSE: 1.937 314s Multiple R-Squared: 0.762 Adjusted R-Squared: 0.734 314s 314s 314s WLS estimates for 'supply' (equation 2) 314s Model Formula: consump ~ price + farmPrice + trend 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 56.2061 10.1500 5.54 3.4e-06 *** 314s price 0.1642 0.0861 1.91 0.065 . 314s farmPrice 0.2579 0.0405 6.37 2.9e-07 *** 314s trend 0.3188 0.0380 8.39 8.4e-10 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 2.444 on 16 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 16 314s SSR: 95.573 MSE: 5.973 Root MSE: 2.444 314s Multiple R-Squared: 0.644 Adjusted R-Squared: 0.577 314s 314s > nobs( fitwlsi3e ) 314s [1] 40 314s > 314s > ## ******* iterated WLS with 2 cross-equation restrictions *********** 314s > fitwlsi4 <- systemfit( system, "WLS", data = Kmenta, restrict.matrix = restr2m, 314s + restrict.rhs = restr2q, maxit = 100, useMatrix = useMatrix ) 314s > print( summary( fitwlsi4 ) ) 314s 314s systemfit results 314s method: iterated WLS 314s 314s convergence achieved after 3 iterations 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 35 160 2.51 0.702 0.619 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s demand 20 17 63.6 3.74 1.94 0.763 0.735 314s supply 20 16 96.3 6.02 2.45 0.641 0.574 314s 314s The covariance matrix of the residuals used for estimation 314s demand supply 314s demand 3.74 0.00 314s supply 0.00 6.02 314s 314s The covariance matrix of the residuals 314s demand supply 314s demand 3.74 4.47 314s supply 4.47 6.02 314s 314s The correlations of the residuals 314s demand supply 314s demand 1.000 0.943 314s supply 0.943 1.000 314s 314s 314s WLS estimates for 'demand' (equation 1) 314s Model Formula: consump ~ price + income 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 100.9031 6.0396 16.71 < 2e-16 *** 314s price -0.3159 0.0648 -4.88 2.3e-05 *** 314s income 0.3239 0.0384 8.43 6.0e-10 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 1.935 on 17 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 17 314s SSR: 63.63 MSE: 3.743 Root MSE: 1.935 314s Multiple R-Squared: 0.763 Adjusted R-Squared: 0.735 314s 314s 314s WLS estimates for 'supply' (equation 2) 314s Model Formula: consump ~ price + farmPrice + trend 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 53.9379 7.9718 6.77 7.7e-08 *** 314s price 0.1841 0.0648 2.84 0.0075 ** 314s farmPrice 0.2603 0.0447 5.83 1.3e-06 *** 314s trend 0.3239 0.0384 8.43 6.0e-10 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 2.453 on 16 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 16 314s SSR: 96.277 MSE: 6.017 Root MSE: 2.453 314s Multiple R-Squared: 0.641 Adjusted R-Squared: 0.574 314s 314s > 314s > ## ******* iterated WLS with 2 cross-equation restrictions (EViews-like) ***** 314s > fitwlsi4e <- systemfit( system, "WLS", data = Kmenta, methodResidCov = "noDfCor", 314s + restrict.matrix = restr2m, restrict.rhs = restr2q, maxit = 100, 314s + x = TRUE, useMatrix = useMatrix ) 314s > print( summary( fitwlsi4e ) ) 314s 314s systemfit results 314s method: iterated WLS 314s 314s convergence achieved after 3 iterations 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 35 160 1.72 0.702 0.586 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s demand 20 17 63.7 3.75 1.94 0.763 0.735 314s supply 20 16 96.2 6.01 2.45 0.641 0.574 314s 314s The covariance matrix of the residuals used for estimation 314s demand supply 314s demand 3.18 0.00 314s supply 0.00 4.81 314s 314s The covariance matrix of the residuals 314s demand supply 314s demand 3.18 3.69 314s supply 3.69 4.81 314s 314s The correlations of the residuals 314s demand supply 314s demand 1.000 0.942 314s supply 0.942 1.000 314s 314s 314s WLS estimates for 'demand' (equation 1) 314s Model Formula: consump ~ price + income 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 100.9662 5.5170 18.30 < 2e-16 *** 314s price -0.3160 0.0589 -5.37 5.2e-06 *** 314s income 0.3234 0.0352 9.20 7.3e-11 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 1.935 on 17 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 17 314s SSR: 63.665 MSE: 3.745 Root MSE: 1.935 314s Multiple R-Squared: 0.763 Adjusted R-Squared: 0.735 314s 314s 314s WLS estimates for 'supply' (equation 2) 314s Model Formula: consump ~ price + farmPrice + trend 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 53.9595 7.2114 7.48 9.2e-09 *** 314s price 0.1840 0.0589 3.13 0.0036 ** 314s farmPrice 0.2602 0.0400 6.51 1.6e-07 *** 314s trend 0.3234 0.0352 9.20 7.3e-11 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 2.452 on 16 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 16 314s SSR: 96.223 MSE: 6.014 Root MSE: 2.452 314s Multiple R-Squared: 0.641 Adjusted R-Squared: 0.574 314s 314s > 314s > ## ***** iterated WLS with 2 cross-equation restrictions via R and restrict.regMat ****** 314s > fitwlsi5 <- systemfit( system, "WLS", data = Kmenta, restrict.matrix = restr3m, 314s + restrict.rhs = restr3q, restrict.regMat = tc, maxit = 100, 314s + x = TRUE, useMatrix = useMatrix ) 314s > print( summary( fitwlsi5 ) ) 314s 314s systemfit results 314s method: iterated WLS 314s 314s convergence achieved after 3 iterations 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 35 160 2.51 0.702 0.619 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s demand 20 17 63.6 3.74 1.94 0.763 0.735 314s supply 20 16 96.3 6.02 2.45 0.641 0.574 314s 314s The covariance matrix of the residuals used for estimation 314s demand supply 314s demand 3.74 0.00 314s supply 0.00 6.02 314s 314s The covariance matrix of the residuals 314s demand supply 314s demand 3.74 4.47 314s supply 4.47 6.02 314s 314s The correlations of the residuals 314s demand supply 314s demand 1.000 0.943 314s supply 0.943 1.000 314s 314s 314s WLS estimates for 'demand' (equation 1) 314s Model Formula: consump ~ price + income 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 100.9031 6.0396 16.71 < 2e-16 *** 314s price -0.3159 0.0648 -4.88 2.3e-05 *** 314s income 0.3239 0.0384 8.43 6.0e-10 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 1.935 on 17 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 17 314s SSR: 63.63 MSE: 3.743 Root MSE: 1.935 314s Multiple R-Squared: 0.763 Adjusted R-Squared: 0.735 314s 314s 314s WLS estimates for 'supply' (equation 2) 314s Model Formula: consump ~ price + farmPrice + trend 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 53.9379 7.9718 6.77 7.7e-08 *** 314s price 0.1841 0.0648 2.84 0.0075 ** 314s farmPrice 0.2603 0.0447 5.83 1.3e-06 *** 314s trend 0.3239 0.0384 8.43 6.0e-10 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 2.453 on 16 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 16 314s SSR: 96.277 MSE: 6.017 Root MSE: 2.453 314s Multiple R-Squared: 0.641 Adjusted R-Squared: 0.574 314s 314s > 314s > ## *** iterated WLS with 2 cross-equation restrictions via R and restrict.regMat (EViews-like) 314s > fitwlsi5e <- systemfit( system, "WLS", data = Kmenta, methodResidCov = "noDfCor", 314s + restrict.matrix = restr3m, restrict.rhs = restr3q, restrict.regMat = tc, 314s + maxit = 100, useMatrix = useMatrix ) 314s > print( summary( fitwlsi5e ) ) 314s 314s systemfit results 314s method: iterated WLS 314s 314s convergence achieved after 3 iterations 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 35 160 1.72 0.702 0.586 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s demand 20 17 63.7 3.75 1.94 0.763 0.735 314s supply 20 16 96.2 6.01 2.45 0.641 0.574 314s 314s The covariance matrix of the residuals used for estimation 314s demand supply 314s demand 3.18 0.00 314s supply 0.00 4.81 314s 314s The covariance matrix of the residuals 314s demand supply 314s demand 3.18 3.69 314s supply 3.69 4.81 314s 314s The correlations of the residuals 314s demand supply 314s demand 1.000 0.942 314s supply 0.942 1.000 314s 314s 314s WLS estimates for 'demand' (equation 1) 314s Model Formula: consump ~ price + income 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 100.9662 5.5170 18.30 < 2e-16 *** 314s price -0.3160 0.0589 -5.37 5.2e-06 *** 314s income 0.3234 0.0352 9.20 7.3e-11 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 1.935 on 17 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 17 314s SSR: 63.665 MSE: 3.745 Root MSE: 1.935 314s Multiple R-Squared: 0.763 Adjusted R-Squared: 0.735 314s 314s 314s WLS estimates for 'supply' (equation 2) 314s Model Formula: consump ~ price + farmPrice + trend 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 53.9595 7.2114 7.48 9.2e-09 *** 314s price 0.1840 0.0589 3.13 0.0036 ** 314s farmPrice 0.2602 0.0400 6.51 1.6e-07 *** 314s trend 0.3234 0.0352 9.20 7.3e-11 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 2.452 on 16 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 16 314s SSR: 96.223 MSE: 6.014 Root MSE: 2.452 314s Multiple R-Squared: 0.641 Adjusted R-Squared: 0.574 314s 314s > 314s > 314s > ## *********** estimations with a single regressor ************ 314s > fitwlsS1 <- systemfit( 314s + list( consump ~ price - 1, consump ~ price + trend ), "WLS", 314s + data = Kmenta, useMatrix = useMatrix ) 314s > print( summary( fitwlsS1 ) ) 314s 314s systemfit results 314s method: WLS 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 36 1121 484 -1.09 -1.05 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s eq1 20 19 861 45.3 6.73 -2.213 -2.213 314s eq2 20 17 259 15.3 3.91 0.032 -0.082 314s 314s The covariance matrix of the residuals used for estimation 314s eq1 eq2 314s eq1 45.3 0.0 314s eq2 0.0 15.3 314s 314s The covariance matrix of the residuals 314s eq1 eq2 314s eq1 45.3 14.4 314s eq2 14.4 15.3 314s 314s The correlations of the residuals 314s eq1 eq2 314s eq1 1.000 0.549 314s eq2 0.549 1.000 314s 314s 314s WLS estimates for 'eq1' (equation 1) 314s Model Formula: consump ~ price - 1 314s 314s Estimate Std. Error t value Pr(>|t|) 314s price 1.006 0.015 66.9 <2e-16 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 6.733 on 19 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 19 314s SSR: 861.449 MSE: 45.339 Root MSE: 6.733 314s Multiple R-Squared: -2.213 Adjusted R-Squared: -2.213 314s 314s 314s WLS estimates for 'eq2' (equation 2) 314s Model Formula: consump ~ price + trend 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 93.6767 15.2367 6.15 1.1e-05 *** 314s price 0.0622 0.1513 0.41 0.69 314s trend 0.0953 0.1515 0.63 0.54 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 3.907 on 17 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 17 314s SSR: 259.497 MSE: 15.265 Root MSE: 3.907 314s Multiple R-Squared: 0.032 Adjusted R-Squared: -0.082 314s 314s > fitwlsS2 <- systemfit( 314s + list( consump ~ price - 1, consump ~ trend - 1 ), "WLS", 314s + data = Kmenta, useMatrix = useMatrix ) 314s > print( summary( fitwlsS2 ) ) 314s 314s systemfit results 314s method: WLS 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 38 47370 110957 -87.3 -5.28 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s eq1 20 19 861 45.3 6.73 -2.21 -2.21 314s eq2 20 19 46509 2447.8 49.48 -172.47 -172.47 314s 314s The covariance matrix of the residuals used for estimation 314s eq1 eq2 314s eq1 45.3 0 314s eq2 0.0 2448 314s 314s The covariance matrix of the residuals 314s eq1 eq2 314s eq1 45.34 -5.15 314s eq2 -5.15 2447.84 314s 314s The correlations of the residuals 314s eq1 eq2 314s eq1 1.0000 -0.0439 314s eq2 -0.0439 1.0000 314s 314s 314s WLS estimates for 'eq1' (equation 1) 314s Model Formula: consump ~ price - 1 314s 314s Estimate Std. Error t value Pr(>|t|) 314s price 1.006 0.015 66.9 <2e-16 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 6.733 on 19 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 19 314s SSR: 861.449 MSE: 45.339 Root MSE: 6.733 314s Multiple R-Squared: -2.213 Adjusted R-Squared: -2.213 314s 314s 314s WLS estimates for 'eq2' (equation 2) 314s Model Formula: consump ~ trend - 1 314s 314s Estimate Std. Error t value Pr(>|t|) 314s trend 7.405 0.924 8.02 1.6e-07 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 49.476 on 19 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 19 314s SSR: 46508.922 MSE: 2447.838 Root MSE: 49.476 314s Multiple R-Squared: -172.467 Adjusted R-Squared: -172.467 314s 314s > fitwlsS3 <- systemfit( 314s + list( consump ~ trend - 1, price ~ trend - 1 ), "WLS", 314s + data = Kmenta, useMatrix = useMatrix ) 314s > print( summary( fitwlsS3 ) ) 314s 314s systemfit results 314s method: WLS 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 38 93537 108970 -99 -0.977 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s eq1 20 19 46509 2448 49.5 -172.5 -172.5 314s eq2 20 19 47028 2475 49.8 -69.5 -69.5 314s 314s The covariance matrix of the residuals used for estimation 314s eq1 eq2 314s eq1 2448 0 314s eq2 0 2475 314s 314s The covariance matrix of the residuals 314s eq1 eq2 314s eq1 2448 2439 314s eq2 2439 2475 314s 314s The correlations of the residuals 314s eq1 eq2 314s eq1 1.000 0.988 314s eq2 0.988 1.000 314s 314s 314s WLS estimates for 'eq1' (equation 1) 314s Model Formula: consump ~ trend - 1 314s 314s Estimate Std. Error t value Pr(>|t|) 314s trend 7.405 0.924 8.02 1.6e-07 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 49.476 on 19 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 19 314s SSR: 46508.922 MSE: 2447.838 Root MSE: 49.476 314s Multiple R-Squared: -172.467 Adjusted R-Squared: -172.467 314s 314s 314s WLS estimates for 'eq2' (equation 2) 314s Model Formula: price ~ trend - 1 314s 314s Estimate Std. Error t value Pr(>|t|) 314s trend 7.318 0.929 7.88 2.1e-07 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 49.751 on 19 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 19 314s SSR: 47028.107 MSE: 2475.164 Root MSE: 49.751 314s Multiple R-Squared: -69.48 Adjusted R-Squared: -69.48 314s 314s > fitwlsS4 <- systemfit( 314s + list( consump ~ trend - 1, price ~ trend - 1 ), "WLS", 314s + data = Kmenta, restrict.matrix = matrix( c( 1, -1 ), nrow = 1 ), 314s + useMatrix = useMatrix ) 314s > print( summary( fitwlsS4 ) ) 314s 314s systemfit results 314s method: WLS 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 39 93548 111736 -99 -1.03 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s eq1 20 19 46514 2448 49.5 -172.5 -172.5 314s eq2 20 19 47034 2475 49.8 -69.5 -69.5 314s 314s The covariance matrix of the residuals used for estimation 314s eq1 eq2 314s eq1 2448 0 314s eq2 0 2475 314s 314s The covariance matrix of the residuals 314s eq1 eq2 314s eq1 2448 2439 314s eq2 2439 2475 314s 314s The correlations of the residuals 314s eq1 eq2 314s eq1 1.000 0.988 314s eq2 0.988 1.000 314s 314s 314s WLS estimates for 'eq1' (equation 1) 314s Model Formula: consump ~ trend - 1 314s 314s Estimate Std. Error t value Pr(>|t|) 314s trend 7.362 0.655 11.2 8.4e-14 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 49.478 on 19 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 19 314s SSR: 46514.224 MSE: 2448.117 Root MSE: 49.478 314s Multiple R-Squared: -172.487 Adjusted R-Squared: -172.487 314s 314s 314s WLS estimates for 'eq2' (equation 2) 314s Model Formula: price ~ trend - 1 314s 314s Estimate Std. Error t value Pr(>|t|) 314s trend 7.362 0.655 11.2 8.4e-14 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 49.754 on 19 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 19 314s SSR: 47033.528 MSE: 2475.449 Root MSE: 49.754 314s Multiple R-Squared: -69.488 Adjusted R-Squared: -69.488 314s 314s > fitwlsS5 <- systemfit( 314s + list( consump ~ 1, price ~ 1 ), "WLS", 314s + data = Kmenta, useMatrix = useMatrix ) 314s > print( summary( fitwlsS5) ) 314s 314s systemfit results 314s method: WLS 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 38 935 491 0 0 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s eq1 20 19 268 14.1 3.76 0 0 314s eq2 20 19 667 35.1 5.93 0 0 314s 314s The covariance matrix of the residuals used for estimation 314s eq1 eq2 314s eq1 14.1 0.0 314s eq2 0.0 35.1 314s 314s The covariance matrix of the residuals 314s eq1 eq2 314s eq1 14.11 2.18 314s eq2 2.18 35.12 314s 314s The correlations of the residuals 314s eq1 eq2 314s eq1 1.0000 0.0981 314s eq2 0.0981 1.0000 314s 314s 314s WLS estimates for 'eq1' (equation 1) 314s Model Formula: consump ~ 1 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 100.90 0.84 120 <2e-16 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 3.756 on 19 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 19 314s SSR: 268.114 MSE: 14.111 Root MSE: 3.756 314s Multiple R-Squared: 0 Adjusted R-Squared: 0 314s 314s 314s WLS estimates for 'eq2' (equation 2) 314s Model Formula: price ~ 1 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 100.02 1.33 75.5 <2e-16 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 5.926 on 19 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 19 314s SSR: 667.251 MSE: 35.118 Root MSE: 5.926 314s Multiple R-Squared: 0 Adjusted R-Squared: 0 314s 314s > 314s > 314s > ## **************** shorter summaries ********************** 314s > print( summary( fitwls1 ), residCov = FALSE, equations = FALSE ) 314s 314s systemfit results 314s method: WLS 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 33 156 4.43 0.709 0.558 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s demand 20 17 63.3 3.73 1.93 0.764 0.736 314s supply 20 16 92.6 5.78 2.40 0.655 0.590 314s 314s 314s Coefficients: 314s Estimate Std. Error t value Pr(>|t|) 314s demand_(Intercept) 99.8954 7.5194 13.29 2.1e-10 *** 314s demand_price -0.3163 0.0907 -3.49 0.00282 ** 314s demand_income 0.3346 0.0454 7.37 1.1e-06 *** 314s supply_(Intercept) 58.2754 11.4629 5.08 0.00011 *** 314s supply_price 0.1604 0.0949 1.69 0.11039 314s supply_farmPrice 0.2481 0.0462 5.37 6.2e-05 *** 314s supply_trend 0.2483 0.0975 2.55 0.02157 * 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s > 314s > print( summary( fitwls2e, useDfSys = FALSE, residCov = FALSE ), 314s + equations = FALSE ) 314s 314s systemfit results 314s method: WLS 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 34 159 1.61 0.703 0.589 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s demand 20 17 63.8 3.75 1.94 0.762 0.734 314s supply 20 16 95.6 5.97 2.44 0.644 0.577 314s 314s 314s Coefficients: 314s Estimate Std. Error t value Pr(>|t|) 314s demand_(Intercept) 99.6461 6.9734 14.29 6.7e-11 *** 314s demand_price -0.2982 0.0816 -3.65 0.002 ** 314s demand_income 0.3186 0.0381 8.37 2.0e-07 *** 314s supply_(Intercept) 56.2104 10.1248 5.55 4.4e-05 *** 314s supply_price 0.1642 0.0859 1.91 0.074 . 314s supply_farmPrice 0.2579 0.0404 6.38 9.1e-06 *** 314s supply_trend 0.3186 0.0381 8.37 3.1e-07 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s > 314s > print( summary( fitwls3 ), residCov = FALSE ) 314s 314s systemfit results 314s method: WLS 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 34 159 2.35 0.703 0.622 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s demand 20 17 63.8 3.75 1.94 0.762 0.734 314s supply 20 16 95.6 5.98 2.44 0.643 0.576 314s 314s 314s WLS estimates for 'demand' (equation 1) 314s Model Formula: consump ~ price + income 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 99.6582 7.5640 13.18 6.4e-15 *** 314s price -0.2991 0.0887 -3.37 0.0019 ** 314s income 0.3194 0.0415 7.70 6.0e-09 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 1.936 on 17 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 17 314s SSR: 63.75 MSE: 3.75 Root MSE: 1.936 314s Multiple R-Squared: 0.762 Adjusted R-Squared: 0.734 314s 314s 314s WLS estimates for 'supply' (equation 2) 314s Model Formula: consump ~ price + farmPrice + trend 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 56.1877 11.3165 4.97 1.9e-05 *** 314s price 0.1643 0.0960 1.71 0.096 . 314s farmPrice 0.2580 0.0451 5.71 2.0e-06 *** 314s trend 0.3194 0.0415 7.70 6.0e-09 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 2.445 on 16 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 16 314s SSR: 95.627 MSE: 5.977 Root MSE: 2.445 314s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.576 314s 314s > 314s > print( summary( fitwls4e, residCov = FALSE, equations = FALSE ) ) 314s 314s systemfit results 314s method: WLS 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 35 160 1.72 0.702 0.586 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s demand 20 17 63.7 3.75 1.94 0.763 0.735 314s supply 20 16 96.2 6.01 2.45 0.641 0.574 314s 314s 314s Coefficients: 314s Estimate Std. Error t value Pr(>|t|) 314s demand_(Intercept) 100.9762 5.5234 18.28 < 2e-16 *** 314s demand_price -0.3160 0.0589 -5.37 5.3e-06 *** 314s demand_income 0.3233 0.0352 9.18 7.6e-11 *** 314s supply_(Intercept) 53.9630 7.2089 7.49 9.1e-09 *** 314s supply_price 0.1840 0.0589 3.13 0.0036 ** 314s supply_farmPrice 0.2602 0.0399 6.53 1.6e-07 *** 314s supply_trend 0.3233 0.0352 9.18 7.6e-11 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s > 314s > print( summary( fitwls5, useDfSys = FALSE ), residCov = FALSE ) 314s 314s systemfit results 314s method: WLS 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 35 160 2.51 0.702 0.619 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s demand 20 17 63.6 3.74 1.94 0.763 0.735 314s supply 20 16 96.3 6.02 2.45 0.641 0.574 314s 314s 314s WLS estimates for 'demand' (equation 1) 314s Model Formula: consump ~ price + income 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 100.9138 6.0474 16.69 5.6e-12 *** 314s price -0.3160 0.0648 -4.87 0.00014 *** 314s income 0.3238 0.0385 8.42 1.8e-07 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 1.935 on 17 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 17 314s SSR: 63.636 MSE: 3.743 Root MSE: 1.935 314s Multiple R-Squared: 0.763 Adjusted R-Squared: 0.735 314s 314s 314s WLS estimates for 'supply' (equation 2) 314s Model Formula: consump ~ price + farmPrice + trend 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 53.9416 7.9687 6.77 4.5e-06 *** 314s price 0.1840 0.0648 2.84 0.012 * 314s farmPrice 0.2603 0.0446 5.84 2.5e-05 *** 314s trend 0.3238 0.0385 8.42 2.9e-07 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 2.453 on 16 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 16 314s SSR: 96.268 MSE: 6.017 Root MSE: 2.453 314s Multiple R-Squared: 0.641 Adjusted R-Squared: 0.574 314s 314s > 314s > print( summary( fitwlsi1e, useDfSys = TRUE, equations = FALSE ) ) 314s 314s systemfit results 314s method: WLS 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 33 156 3.02 0.709 0.537 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s demand 20 17 63.3 3.73 1.93 0.764 0.736 314s supply 20 16 92.6 5.78 2.40 0.655 0.590 314s 314s The covariance matrix of the residuals used for estimation 314s demand supply 314s demand 3.17 0.00 314s supply 0.00 4.63 314s 314s The covariance matrix of the residuals 314s demand supply 314s demand 3.17 3.41 314s supply 3.41 4.63 314s 314s The correlations of the residuals 314s demand supply 314s demand 1.000 0.891 314s supply 0.891 1.000 314s 314s 314s Coefficients: 314s Estimate Std. Error t value Pr(>|t|) 314s demand_(Intercept) 99.8954 6.9325 14.41 8.9e-16 *** 314s demand_price -0.3163 0.0836 -3.78 0.00062 *** 314s demand_income 0.3346 0.0419 7.99 3.2e-09 *** 314s supply_(Intercept) 58.2754 10.2527 5.68 2.4e-06 *** 314s supply_price 0.1604 0.0849 1.89 0.06762 . 314s supply_farmPrice 0.2481 0.0413 6.01 9.5e-07 *** 314s supply_trend 0.2483 0.0872 2.85 0.00754 ** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s > 314s > print( summary( fitwlsi2, equations = FALSE, residCov = FALSE ), 314s + residCov = TRUE ) 314s 314s systemfit results 314s method: iterated WLS 314s 314s convergence achieved after 3 iterations 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 34 159 2.34 0.703 0.623 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s demand 20 17 63.7 3.75 1.94 0.762 0.734 314s supply 20 16 95.6 5.98 2.44 0.643 0.576 314s 314s The covariance matrix of the residuals used for estimation 314s demand supply 314s demand 3.75 0.00 314s supply 0.00 5.98 314s 314s The covariance matrix of the residuals 314s demand supply 314s demand 3.75 4.48 314s supply 4.48 5.98 314s 314s The correlations of the residuals 314s demand supply 314s demand 1.000 0.946 314s supply 0.946 1.000 314s 314s 314s Coefficients: 314s Estimate Std. Error t value Pr(>|t|) 314s demand_(Intercept) 99.6607 7.5378 13.22 5.8e-15 *** 314s demand_price -0.2993 0.0884 -3.39 0.0018 ** 314s demand_income 0.3196 0.0414 7.72 5.6e-09 *** 314s supply_(Intercept) 56.1830 11.3487 4.95 2.0e-05 *** 314s supply_price 0.1643 0.0963 1.71 0.0972 . 314s supply_farmPrice 0.2580 0.0453 5.70 2.1e-06 *** 314s supply_trend 0.3196 0.0414 7.72 5.6e-09 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s > 314s > print( summary( fitwlsi3e ), equations = FALSE, residCov = FALSE ) 314s 314s systemfit results 314s method: iterated WLS 314s 314s convergence achieved after 3 iterations 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 34 159 1.6 0.703 0.589 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s demand 20 17 63.8 3.75 1.94 0.762 0.734 314s supply 20 16 95.6 5.97 2.44 0.644 0.577 314s 314s 314s Coefficients: 314s Estimate Std. Error t value Pr(>|t|) 314s demand_(Intercept) 99.6484 6.9516 14.33 4.4e-16 *** 314s demand_price -0.2984 0.0814 -3.67 0.00083 *** 314s demand_income 0.3188 0.0380 8.39 8.4e-10 *** 314s supply_(Intercept) 56.2061 10.1500 5.54 3.4e-06 *** 314s supply_price 0.1642 0.0861 1.91 0.06502 . 314s supply_farmPrice 0.2579 0.0405 6.37 2.9e-07 *** 314s supply_trend 0.3188 0.0380 8.39 8.4e-10 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s > 314s > print( summary( fitwlsi4, equations = FALSE ), equations = TRUE ) 314s 314s systemfit results 314s method: iterated WLS 314s 314s convergence achieved after 3 iterations 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 35 160 2.51 0.702 0.619 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s demand 20 17 63.6 3.74 1.94 0.763 0.735 314s supply 20 16 96.3 6.02 2.45 0.641 0.574 314s 314s The covariance matrix of the residuals used for estimation 314s demand supply 314s demand 3.74 0.00 314s supply 0.00 6.02 314s 314s The covariance matrix of the residuals 314s demand supply 314s demand 3.74 4.47 314s supply 4.47 6.02 314s 314s The correlations of the residuals 314s demand supply 314s demand 1.000 0.943 314s supply 0.943 1.000 314s 314s 314s WLS estimates for 'demand' (equation 1) 314s Model Formula: consump ~ price + income 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 100.9031 6.0396 16.71 < 2e-16 *** 314s price -0.3159 0.0648 -4.88 2.3e-05 *** 314s income 0.3239 0.0384 8.43 6.0e-10 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 1.935 on 17 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 17 314s SSR: 63.63 MSE: 3.743 Root MSE: 1.935 314s Multiple R-Squared: 0.763 Adjusted R-Squared: 0.735 314s 314s 314s WLS estimates for 'supply' (equation 2) 314s Model Formula: consump ~ price + farmPrice + trend 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 53.9379 7.9718 6.77 7.7e-08 *** 314s price 0.1841 0.0648 2.84 0.0075 ** 314s farmPrice 0.2603 0.0447 5.83 1.3e-06 *** 314s trend 0.3239 0.0384 8.43 6.0e-10 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 2.453 on 16 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 16 314s SSR: 96.277 MSE: 6.017 Root MSE: 2.453 314s Multiple R-Squared: 0.641 Adjusted R-Squared: 0.574 314s 314s > 314s > print( summary( fitwlsi5e, useDfSys = FALSE, residCov = FALSE ) ) 314s 314s systemfit results 314s method: iterated WLS 314s 314s convergence achieved after 3 iterations 314s 314s N DF SSR detRCov OLS-R2 McElroy-R2 314s system 40 35 160 1.72 0.702 0.586 314s 314s N DF SSR MSE RMSE R2 Adj R2 314s demand 20 17 63.7 3.75 1.94 0.763 0.735 314s supply 20 16 96.2 6.01 2.45 0.641 0.574 314s 314s 314s WLS estimates for 'demand' (equation 1) 314s Model Formula: consump ~ price + income 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 100.9662 5.5170 18.30 1.3e-12 *** 314s price -0.3160 0.0589 -5.37 5.1e-05 *** 314s income 0.3234 0.0352 9.20 5.2e-08 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 1.935 on 17 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 17 314s SSR: 63.665 MSE: 3.745 Root MSE: 1.935 314s Multiple R-Squared: 0.763 Adjusted R-Squared: 0.735 314s 314s 314s WLS estimates for 'supply' (equation 2) 314s Model Formula: consump ~ price + farmPrice + trend 314s 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 53.9595 7.2114 7.48 1.3e-06 *** 314s price 0.1840 0.0589 3.13 0.0065 ** 314s farmPrice 0.2602 0.0400 6.51 7.2e-06 *** 314s trend 0.3234 0.0352 9.20 8.7e-08 *** 314s --- 314s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 314s 314s Residual standard error: 2.452 on 16 degrees of freedom 314s Number of observations: 20 Degrees of Freedom: 16 314s SSR: 96.223 MSE: 6.014 Root MSE: 2.452 314s Multiple R-Squared: 0.641 Adjusted R-Squared: 0.574 314s 314s > 314s > 314s > ## ****************** residuals ************************** 314s > print( residuals( fitwls1 ) ) 314s demand supply 314s 1 1.074 -0.444 314s 2 -0.390 -0.896 314s 3 2.625 1.965 314s 4 1.802 1.134 314s 5 1.946 1.514 314s 6 1.175 0.680 314s 7 1.530 1.569 314s 8 -2.933 -4.407 314s 9 -1.365 -2.599 314s 10 2.031 2.469 314s 11 -0.149 -0.598 314s 12 -1.954 -1.697 314s 13 -1.121 -1.064 314s 14 -0.220 0.970 314s 15 1.487 3.159 314s 16 -3.701 -3.866 314s 17 -1.273 -0.265 314s 18 -2.002 -2.449 314s 19 1.738 3.110 314s 20 -0.299 1.714 314s > print( residuals( fitwls1$eq[[ 2 ]] ) ) 314s 1 2 3 4 5 6 7 8 9 10 11 314s -0.444 -0.896 1.965 1.134 1.514 0.680 1.569 -4.407 -2.599 2.469 -0.598 314s 12 13 14 15 16 17 18 19 20 314s -1.697 -1.064 0.970 3.159 -3.866 -0.265 -2.449 3.110 1.714 314s > 314s > print( residuals( fitwls2e ) ) 314s demand supply 314s 1 0.9069 0.209 314s 2 -0.4660 -0.338 314s 3 2.5495 2.455 314s 4 1.7320 1.560 314s 5 2.0183 1.771 314s 6 1.2321 0.886 314s 7 1.6019 1.724 314s 8 -2.8544 -4.378 314s 9 -1.3158 -2.597 314s 10 2.0517 2.500 314s 11 -0.3823 -0.455 314s 12 -2.2623 -1.525 314s 13 -1.3801 -1.001 314s 14 -0.3081 0.877 314s 15 1.6643 2.806 314s 16 -3.5513 -4.328 314s 17 -1.0466 -0.805 314s 18 -1.9647 -2.952 314s 19 1.8446 2.561 314s 20 -0.0697 1.029 314s > print( residuals( fitwls2e$eq[[ 1 ]] ) ) 314s 1 2 3 4 5 6 7 8 9 10 314s 0.9069 -0.4660 2.5495 1.7320 2.0183 1.2321 1.6019 -2.8544 -1.3158 2.0517 314s 11 12 13 14 15 16 17 18 19 20 314s -0.3823 -2.2623 -1.3801 -0.3081 1.6643 -3.5513 -1.0466 -1.9647 1.8446 -0.0697 314s > 314s > print( residuals( fitwls3 ) ) 314s demand supply 314s 1 0.9150 0.217 314s 2 -0.4624 -0.332 314s 3 2.5532 2.461 314s 4 1.7354 1.564 314s 5 2.0148 1.773 314s 6 1.2293 0.889 314s 7 1.5984 1.725 314s 8 -2.8582 -4.378 314s 9 -1.3182 -2.597 314s 10 2.0507 2.500 314s 11 -0.3710 -0.453 314s 12 -2.2473 -1.524 314s 13 -1.3675 -1.000 314s 14 -0.3038 0.876 314s 15 1.6557 2.802 314s 16 -3.5586 -4.333 314s 17 -1.0576 -0.811 314s 18 -1.9666 -2.957 314s 19 1.8394 2.555 314s 20 -0.0808 1.022 314s > print( residuals( fitwls3$eq[[ 2 ]] ) ) 314s 1 2 3 4 5 6 7 8 9 10 11 314s 0.217 -0.332 2.461 1.564 1.773 0.889 1.725 -4.378 -2.597 2.500 -0.453 314s 12 13 14 15 16 17 18 19 20 314s -1.524 -1.000 0.876 2.802 -4.333 -0.811 -2.957 2.555 1.022 314s > 314s > print( residuals( fitwls4e ) ) 314s demand supply 314s 1 0.9593 0.244 314s 2 -0.3907 -0.388 314s 3 2.6143 2.417 314s 4 1.8088 1.498 314s 5 1.9718 1.803 314s 6 1.2083 0.892 314s 7 1.5943 1.699 314s 8 -2.8174 -4.491 314s 9 -1.3751 -2.548 314s 10 1.9351 2.667 314s 11 -0.4019 -0.284 314s 12 -2.1883 -1.443 314s 13 -1.2686 -1.010 314s 14 -0.2984 0.921 314s 15 1.5512 2.869 314s 16 -3.6143 -4.342 314s 17 -1.2823 -0.600 314s 18 -1.9253 -3.056 314s 19 1.8860 2.425 314s 20 0.0333 0.728 314s > print( residuals( fitwls4e$eq[[ 1 ]] ) ) 314s 1 2 3 4 5 6 7 8 9 10 314s 0.9593 -0.3907 2.6143 1.8088 1.9718 1.2083 1.5943 -2.8174 -1.3751 1.9351 314s 11 12 13 14 15 16 17 18 19 20 314s -0.4019 -2.1883 -1.2686 -0.2984 1.5512 -3.6143 -1.2823 -1.9253 1.8860 0.0333 314s > 314s > print( residuals( fitwls5 ) ) 314s demand supply 314s 1 0.9649 0.249 314s 2 -0.3911 -0.384 314s 3 2.6145 2.421 314s 4 1.8081 1.501 314s 5 1.9707 1.805 314s 6 1.2067 0.893 314s 7 1.5910 1.700 314s 8 -2.8235 -4.491 314s 9 -1.3743 -2.548 314s 10 1.9406 2.667 314s 11 -0.3887 -0.282 314s 12 -2.1767 -1.442 314s 13 -1.2616 -1.009 314s 14 -0.2944 0.920 314s 15 1.5485 2.866 314s 16 -3.6185 -4.345 314s 17 -1.2806 -0.604 314s 18 -1.9295 -3.060 314s 19 1.8782 2.420 314s 20 0.0157 0.721 314s > print( residuals( fitwls5$eq[[ 2 ]] ) ) 314s 1 2 3 4 5 6 7 8 9 10 11 314s 0.249 -0.384 2.421 1.501 1.805 0.893 1.700 -4.491 -2.548 2.667 -0.282 314s 12 13 14 15 16 17 18 19 20 314s -1.442 -1.009 0.920 2.866 -4.345 -0.604 -3.060 2.420 0.721 314s > 314s > print( residuals( fitwlsi1e ) ) 314s demand supply 314s 1 1.074 -0.444 314s 2 -0.390 -0.896 314s 3 2.625 1.965 314s 4 1.802 1.134 314s 5 1.946 1.514 314s 6 1.175 0.680 314s 7 1.530 1.569 314s 8 -2.933 -4.407 314s 9 -1.365 -2.599 314s 10 2.031 2.469 314s 11 -0.149 -0.598 314s 12 -1.954 -1.697 314s 13 -1.121 -1.064 314s 14 -0.220 0.970 314s 15 1.487 3.159 314s 16 -3.701 -3.866 314s 17 -1.273 -0.265 314s 18 -2.002 -2.449 314s 19 1.738 3.110 314s 20 -0.299 1.714 314s > print( residuals( fitwlsi1e$eq[[ 1 ]] ) ) 314s 1 2 3 4 5 6 7 8 9 10 11 314s 1.074 -0.390 2.625 1.802 1.946 1.175 1.530 -2.933 -1.365 2.031 -0.149 314s 12 13 14 15 16 17 18 19 20 314s -1.954 -1.121 -0.220 1.487 -3.701 -1.273 -2.002 1.738 -0.299 314s > 314s > print( residuals( fitwlsi2 ) ) 314s demand supply 314s 1 0.9167 0.218 314s 2 -0.4616 -0.331 314s 3 2.5539 2.462 314s 4 1.7361 1.565 314s 5 2.0140 1.774 314s 6 1.2288 0.889 314s 7 1.5977 1.726 314s 8 -2.8589 -4.378 314s 9 -1.3187 -2.597 314s 10 2.0505 2.500 314s 11 -0.3686 -0.453 314s 12 -2.2443 -1.523 314s 13 -1.3649 -1.000 314s 14 -0.3029 0.876 314s 15 1.6539 2.802 314s 16 -3.5601 -4.334 314s 17 -1.0599 -0.812 314s 18 -1.9669 -2.958 314s 19 1.8383 2.554 314s 20 -0.0831 1.020 314s > print( residuals( fitwlsi2$eq[[ 2 ]] ) ) 314s 1 2 3 4 5 6 7 8 9 10 11 314s 0.218 -0.331 2.462 1.565 1.774 0.889 1.726 -4.378 -2.597 2.500 -0.453 314s 12 13 14 15 16 17 18 19 20 314s -1.523 -1.000 0.876 2.802 -4.334 -0.812 -2.958 2.554 1.020 314s > 314s > print( residuals( fitwlsi3e ) ) 314s demand supply 314s 1 0.9084 0.211 314s 2 -0.4653 -0.337 314s 3 2.5502 2.456 314s 4 1.7326 1.561 314s 5 2.0176 1.771 314s 6 1.2316 0.887 314s 7 1.6012 1.724 314s 8 -2.8551 -4.378 314s 9 -1.3162 -2.597 314s 10 2.0515 2.500 314s 11 -0.3801 -0.454 314s 12 -2.2594 -1.525 314s 13 -1.3777 -1.001 314s 14 -0.3073 0.877 314s 15 1.6627 2.806 314s 16 -3.5527 -4.329 314s 17 -1.0487 -0.806 314s 18 -1.9651 -2.953 314s 19 1.8436 2.560 314s 20 -0.0718 1.028 314s > print( residuals( fitwlsi3e$eq[[ 1 ]] ) ) 314s 1 2 3 4 5 6 7 8 9 10 314s 0.9084 -0.4653 2.5502 1.7326 2.0176 1.2316 1.6012 -2.8551 -1.3162 2.0515 314s 11 12 13 14 15 16 17 18 19 20 314s -0.3801 -2.2594 -1.3777 -0.3073 1.6627 -3.5527 -1.0487 -1.9651 1.8436 -0.0718 314s > 314s > print( residuals( fitwlsi4 ) ) 314s demand supply 314s 1 0.9659 0.250 314s 2 -0.3911 -0.383 314s 3 2.6145 2.421 314s 4 1.8080 1.502 314s 5 1.9705 1.805 314s 6 1.2064 0.893 314s 7 1.5905 1.700 314s 8 -2.8246 -4.491 314s 9 -1.3742 -2.547 314s 10 1.9415 2.667 314s 11 -0.3865 -0.282 314s 12 -2.1747 -1.442 314s 13 -1.2604 -1.009 314s 14 -0.2938 0.920 314s 15 1.5480 2.866 314s 16 -3.6192 -4.346 314s 17 -1.2804 -0.604 314s 18 -1.9302 -3.061 314s 19 1.8768 2.420 314s 20 0.0127 0.720 314s > print( residuals( fitwlsi4$eq[[ 2 ]] ) ) 314s 1 2 3 4 5 6 7 8 9 10 11 314s 0.250 -0.383 2.421 1.502 1.805 0.893 1.700 -4.491 -2.547 2.667 -0.282 314s 12 13 14 15 16 17 18 19 20 314s -1.442 -1.009 0.920 2.866 -4.346 -0.604 -3.061 2.420 0.720 314s > 314s > print( residuals( fitwlsi5e ) ) 314s demand supply 314s 1 0.9602 0.245 314s 2 -0.3908 -0.388 314s 3 2.6143 2.418 314s 4 1.8087 1.498 314s 5 1.9716 1.803 314s 6 1.2081 0.892 314s 7 1.5938 1.699 314s 8 -2.8184 -4.491 314s 9 -1.3750 -2.548 314s 10 1.9360 2.667 314s 11 -0.3997 -0.284 314s 12 -2.1865 -1.443 314s 13 -1.2675 -1.010 314s 14 -0.2978 0.921 314s 15 1.5508 2.869 314s 16 -3.6150 -4.342 314s 17 -1.2820 -0.601 314s 18 -1.9260 -3.057 314s 19 1.8848 2.424 314s 20 0.0305 0.727 314s > print( residuals( fitwlsi5e$eq[[ 1 ]] ) ) 314s 1 2 3 4 5 6 7 8 9 10 314s 0.9602 -0.3908 2.6143 1.8087 1.9716 1.2081 1.5938 -2.8184 -1.3750 1.9360 314s 11 12 13 14 15 16 17 18 19 20 314s -0.3997 -2.1865 -1.2675 -0.2978 1.5508 -3.6150 -1.2820 -1.9260 1.8848 0.0305 314s > 314s > 314s > ## *************** coefficients ********************* 314s > print( round( coef( fitwls1e ), digits = 6 ) ) 314s demand_(Intercept) demand_price demand_income supply_(Intercept) 314s 99.895 -0.316 0.335 58.275 314s supply_price supply_farmPrice supply_trend 314s 0.160 0.248 0.248 314s > print( round( coef( fitwls1e$eq[[ 1 ]] ), digits = 6 ) ) 314s (Intercept) price income 314s 99.895 -0.316 0.335 314s > 314s > print( round( coef( fitwlsi2 ), digits = 6 ) ) 314s demand_(Intercept) demand_price demand_income supply_(Intercept) 314s 99.661 -0.299 0.320 56.183 314s supply_price supply_farmPrice supply_trend 314s 0.164 0.258 0.320 314s > print( round( coef( fitwlsi2$eq[[ 2 ]] ), digits = 6 ) ) 314s (Intercept) price farmPrice trend 314s 56.183 0.164 0.258 0.320 314s > 314s > print( round( coef( fitwls3e ), digits = 6 ) ) 314s demand_(Intercept) demand_price demand_income supply_(Intercept) 314s 99.646 -0.298 0.319 56.210 314s supply_price supply_farmPrice supply_trend 314s 0.164 0.258 0.319 314s > print( round( coef( fitwls3e, modified.regMat = TRUE ), digits = 6 ) ) 314s C1 C2 C3 C4 C5 C6 314s 99.646 -0.298 0.319 56.210 0.164 0.258 314s > print( round( coef( fitwls3e$eq[[ 1 ]] ), digits = 6 ) ) 314s (Intercept) price income 314s 99.646 -0.298 0.319 314s > 314s > print( round( coef( fitwls4 ), digits = 6 ) ) 314s demand_(Intercept) demand_price demand_income supply_(Intercept) 314s 100.914 -0.316 0.324 53.942 314s supply_price supply_farmPrice supply_trend 314s 0.184 0.260 0.324 314s > print( round( coef( fitwls4$eq[[ 2 ]] ), digits = 6 ) ) 314s (Intercept) price farmPrice trend 314s 53.942 0.184 0.260 0.324 314s > 314s > print( round( coef( fitwlsi5 ), digits = 6 ) ) 314s demand_(Intercept) demand_price demand_income supply_(Intercept) 314s 100.903 -0.316 0.324 53.938 314s supply_price supply_farmPrice supply_trend 314s 0.184 0.260 0.324 314s > print( round( coef( fitwlsi5, modified.regMat = TRUE ), digits = 6 ) ) 314s C1 C2 C3 C4 C5 C6 314s 100.903 -0.316 0.324 53.938 0.184 0.260 314s > print( round( coef( fitwlsi5$eq[[ 1 ]] ), digits = 6 ) ) 314s (Intercept) price income 314s 100.903 -0.316 0.324 314s > 314s > 314s > ## *************** coefficients with stats ********************* 314s > print( round( coef( summary( fitwls1e ) ), digits = 6 ) ) 314s Estimate Std. Error t value Pr(>|t|) 314s demand_(Intercept) 99.895 6.9325 14.41 0.000000 314s demand_price -0.316 0.0836 -3.78 0.001483 314s demand_income 0.335 0.0419 7.99 0.000000 314s supply_(Intercept) 58.275 10.2527 5.68 0.000034 314s supply_price 0.160 0.0849 1.89 0.077067 314s supply_farmPrice 0.248 0.0413 6.01 0.000018 314s supply_trend 0.248 0.0872 2.85 0.011659 314s > print( round( coef( summary( fitwls1e$eq[[ 1 ]] ) ), digits = 6 ) ) 314s Estimate Std. Error t value Pr(>|t|) 314s (Intercept) 99.895 6.9325 14.41 0.00000 314s price -0.316 0.0836 -3.78 0.00148 314s income 0.335 0.0419 7.99 0.00000 314s > 314s > print( round( coef( summary( fitwlsi2 ) ), digits = 6 ) ) 315s Estimate Std. Error t value Pr(>|t|) 315s demand_(Intercept) 99.661 7.5378 13.22 0.000000 315s demand_price -0.299 0.0884 -3.39 0.001805 315s demand_income 0.320 0.0414 7.72 0.000000 315s supply_(Intercept) 56.183 11.3487 4.95 0.000020 315s supply_price 0.164 0.0963 1.71 0.097239 315s supply_farmPrice 0.258 0.0453 5.70 0.000002 315s supply_trend 0.320 0.0414 7.72 0.000000 315s > print( round( coef( summary( fitwlsi2$eq[[ 2 ]] ) ), digits = 6 ) ) 315s Estimate Std. Error t value Pr(>|t|) 315s (Intercept) 56.183 11.3487 4.95 0.000020 315s price 0.164 0.0963 1.71 0.097239 315s farmPrice 0.258 0.0453 5.70 0.000002 315s trend 0.320 0.0414 7.72 0.000000 315s > 315s > print( round( coef( summary( fitwls3e ) ), digits = 6 ) ) 315s Estimate Std. Error t value Pr(>|t|) 315s demand_(Intercept) 99.646 6.9734 14.29 0.000000 315s demand_price -0.298 0.0816 -3.65 0.000863 315s demand_income 0.319 0.0381 8.37 0.000000 315s supply_(Intercept) 56.210 10.1248 5.55 0.000003 315s supply_price 0.164 0.0859 1.91 0.064384 315s supply_farmPrice 0.258 0.0404 6.38 0.000000 315s supply_trend 0.319 0.0381 8.37 0.000000 315s > print( round( coef( summary( fitwls3e ), modified.regMat = TRUE ), digits = 6 ) ) 315s Estimate Std. Error t value Pr(>|t|) 315s C1 99.646 6.9734 14.29 0.000000 315s C2 -0.298 0.0816 -3.65 0.000863 315s C3 0.319 0.0381 8.37 0.000000 315s C4 56.210 10.1248 5.55 0.000003 315s C5 0.164 0.0859 1.91 0.064384 315s C6 0.258 0.0404 6.38 0.000000 315s > print( round( coef( summary( fitwls3e$eq[[ 1 ]] ) ), digits = 6 ) ) 315s Estimate Std. Error t value Pr(>|t|) 315s (Intercept) 99.646 6.9734 14.29 0.000000 315s price -0.298 0.0816 -3.65 0.000863 315s income 0.319 0.0381 8.37 0.000000 315s > 315s > print( round( coef( summary( fitwls4, useDfSys = FALSE ) ), digits = 6 ) ) 315s Estimate Std. Error t value Pr(>|t|) 315s demand_(Intercept) 100.914 6.0474 16.69 0.000000 315s demand_price -0.316 0.0648 -4.87 0.000143 315s demand_income 0.324 0.0385 8.42 0.000000 315s supply_(Intercept) 53.942 7.9687 6.77 0.000005 315s supply_price 0.184 0.0648 2.84 0.011833 315s supply_farmPrice 0.260 0.0446 5.84 0.000025 315s supply_trend 0.324 0.0385 8.42 0.000000 315s > print( round( coef( summary( fitwls4$eq[[ 2 ]], useDfSys = FALSE ) ), 315s + digits = 6 ) ) 315s Estimate Std. Error t value Pr(>|t|) 315s (Intercept) 53.942 7.9687 6.77 0.000005 315s price 0.184 0.0648 2.84 0.011833 315s farmPrice 0.260 0.0446 5.84 0.000025 315s trend 0.324 0.0385 8.42 0.000000 315s > 315s > print( round( coef( summary( fitwlsi5, useDfSys = FALSE ) ), digits = 6 ) ) 315s Estimate Std. Error t value Pr(>|t|) 315s demand_(Intercept) 100.903 6.0396 16.71 0.000000 315s demand_price -0.316 0.0648 -4.88 0.000142 315s demand_income 0.324 0.0384 8.43 0.000000 315s supply_(Intercept) 53.938 7.9718 6.77 0.000005 315s supply_price 0.184 0.0648 2.84 0.011806 315s supply_farmPrice 0.260 0.0447 5.83 0.000026 315s supply_trend 0.324 0.0384 8.43 0.000000 315s > print( round( coef( summary( fitwlsi5, useDfSys = FALSE ), 315s + modified.regMat = TRUE ), digits = 6 ) ) 315s Estimate Std. Error t value Pr(>|t|) 315s C1 100.903 6.0396 16.71 NA 315s C2 -0.316 0.0648 -4.88 NA 315s C3 0.324 0.0384 8.43 NA 315s C4 53.938 7.9718 6.77 NA 315s C5 0.184 0.0648 2.84 NA 315s C6 0.260 0.0447 5.83 NA 315s > print( round( coef( summary( fitwlsi5$eq[[ 1 ]], useDfSys = FALSE ) ), 315s + digits = 6 ) ) 315s Estimate Std. Error t value Pr(>|t|) 315s (Intercept) 100.903 6.0396 16.71 0.000000 315s price -0.316 0.0648 -4.88 0.000142 315s income 0.324 0.0384 8.43 0.000000 315s > 315s > 315s > ## *********** variance covariance matrix of the coefficients ******* 315s > print( round( vcov( fitwls1e ), digits = 6 ) ) 315s demand_(Intercept) demand_price demand_income 315s demand_(Intercept) 48.0597 -0.50558 0.02734 315s demand_price -0.5056 0.00699 -0.00198 315s demand_income 0.0273 -0.00198 0.00175 315s supply_(Intercept) 0.0000 0.00000 0.00000 315s supply_price 0.0000 0.00000 0.00000 315s supply_farmPrice 0.0000 0.00000 0.00000 315s supply_trend 0.0000 0.00000 0.00000 315s supply_(Intercept) supply_price supply_farmPrice 315s demand_(Intercept) 0.000 0.000000 0.000000 315s demand_price 0.000 0.000000 0.000000 315s demand_income 0.000 0.000000 0.000000 315s supply_(Intercept) 105.119 -0.790000 -0.243489 315s supply_price -0.790 0.007202 0.000675 315s supply_farmPrice -0.243 0.000675 0.001707 315s supply_trend -0.223 0.000418 0.001052 315s supply_trend 315s demand_(Intercept) 0.000000 315s demand_price 0.000000 315s demand_income 0.000000 315s supply_(Intercept) -0.223347 315s supply_price 0.000418 315s supply_farmPrice 0.001052 315s supply_trend 0.007608 315s > print( round( vcov( fitwls1e$eq[[ 1 ]] ), digits = 6 ) ) 315s (Intercept) price income 315s (Intercept) 48.0597 -0.50558 0.02734 315s price -0.5056 0.00699 -0.00198 315s income 0.0273 -0.00198 0.00175 315s > 315s > print( round( vcov( fitwls2 ), digits = 6 ) ) 315s demand_(Intercept) demand_price demand_income 315s demand_(Intercept) 57.21413 -0.596328 0.026850 315s demand_price -0.59633 0.007862 -0.001948 315s demand_income 0.02685 -0.001948 0.001722 315s supply_(Intercept) -0.78825 0.057190 -0.050565 315s supply_price 0.00147 -0.000107 0.000095 315s supply_farmPrice 0.00371 -0.000269 0.000238 315s supply_trend 0.02685 -0.001948 0.001722 315s supply_(Intercept) supply_price supply_farmPrice 315s demand_(Intercept) -0.7883 0.001474 0.003714 315s demand_price 0.0572 -0.000107 -0.000269 315s demand_income -0.0506 0.000095 0.000238 315s supply_(Intercept) 128.0635 -1.001596 -0.280017 315s supply_price -1.0016 0.009225 0.000806 315s supply_farmPrice -0.2800 0.000806 0.002038 315s supply_trend -0.0506 0.000095 0.000238 315s supply_trend 315s demand_(Intercept) 0.026850 315s demand_price -0.001948 315s demand_income 0.001722 315s supply_(Intercept) -0.050565 315s supply_price 0.000095 315s supply_farmPrice 0.000238 315s supply_trend 0.001722 315s > print( round( vcov( fitwls2$eq[[ 2 ]] ), digits = 6 ) ) 315s (Intercept) price farmPrice trend 315s (Intercept) 128.0635 -1.001596 -0.280017 -0.050565 315s price -1.0016 0.009225 0.000806 0.000095 315s farmPrice -0.2800 0.000806 0.002038 0.000238 315s trend -0.0506 0.000095 0.000238 0.001722 315s > 315s > print( round( vcov( fitwls3e ), digits = 6 ) ) 315s demand_(Intercept) demand_price demand_income 315s demand_(Intercept) 48.62814 -0.506597 0.022574 315s demand_price -0.50660 0.006662 -0.001638 315s demand_income 0.02257 -0.001638 0.001448 315s supply_(Intercept) -0.66271 0.048082 -0.042512 315s supply_price 0.00124 -0.000090 0.000079 315s supply_farmPrice 0.00312 -0.000227 0.000200 315s supply_trend 0.02257 -0.001638 0.001448 315s supply_(Intercept) supply_price supply_farmPrice 315s demand_(Intercept) -0.6627 0.001239 0.003123 315s demand_price 0.0481 -0.000090 -0.000227 315s demand_income -0.0425 0.000079 0.000200 315s supply_(Intercept) 102.5112 -0.801390 -0.224299 315s supply_price -0.8014 0.007381 0.000645 315s supply_farmPrice -0.2243 0.000645 0.001632 315s supply_trend -0.0425 0.000079 0.000200 315s supply_trend 315s demand_(Intercept) 0.022574 315s demand_price -0.001638 315s demand_income 0.001448 315s supply_(Intercept) -0.042512 315s supply_price 0.000079 315s supply_farmPrice 0.000200 315s supply_trend 0.001448 315s > print( round( vcov( fitwls3e, modified.regMat = TRUE ), digits = 6 ) ) 315s C1 C2 C3 C4 C5 C6 315s C1 48.62814 -0.506597 0.022574 -0.6627 0.001239 0.003123 315s C2 -0.50660 0.006662 -0.001638 0.0481 -0.000090 -0.000227 315s C3 0.02257 -0.001638 0.001448 -0.0425 0.000079 0.000200 315s C4 -0.66271 0.048082 -0.042512 102.5112 -0.801390 -0.224299 315s C5 0.00124 -0.000090 0.000079 -0.8014 0.007381 0.000645 315s C6 0.00312 -0.000227 0.000200 -0.2243 0.000645 0.001632 315s > print( round( vcov( fitwls3e$eq[[ 1 ]] ), digits = 6 ) ) 315s (Intercept) price income 315s (Intercept) 48.6281 -0.50660 0.02257 315s price -0.5066 0.00666 -0.00164 315s income 0.0226 -0.00164 0.00145 315s > 315s > print( round( vcov( fitwls4 ), digits = 6 ) ) 315s demand_(Intercept) demand_price demand_income 315s demand_(Intercept) 36.5710 -0.321554 -0.043279 315s demand_price -0.3216 0.004201 -0.001011 315s demand_income -0.0433 -0.001011 0.001481 315s supply_(Intercept) 35.8467 -0.431417 0.074877 315s supply_price -0.3216 0.004201 -0.001011 315s supply_farmPrice -0.0334 0.000226 0.000111 315s supply_trend -0.0433 -0.001011 0.001481 315s supply_(Intercept) supply_price supply_farmPrice 315s demand_(Intercept) 35.8467 -0.321554 -0.033436 315s demand_price -0.4314 0.004201 0.000226 315s demand_income 0.0749 -0.001011 0.000111 315s supply_(Intercept) 63.5001 -0.431417 -0.215648 315s supply_price -0.4314 0.004201 0.000226 315s supply_farmPrice -0.2156 0.000226 0.001986 315s supply_trend 0.0749 -0.001011 0.000111 315s supply_trend 315s demand_(Intercept) -0.043279 315s demand_price -0.001011 315s demand_income 0.001481 315s supply_(Intercept) 0.074877 315s supply_price -0.001011 315s supply_farmPrice 0.000111 315s supply_trend 0.001481 315s > print( round( vcov( fitwls4$eq[[ 2 ]] ), digits = 6 ) ) 315s (Intercept) price farmPrice trend 315s (Intercept) 63.5001 -0.431417 -0.215648 0.074877 315s price -0.4314 0.004201 0.000226 -0.001011 315s farmPrice -0.2156 0.000226 0.001986 0.000111 315s trend 0.0749 -0.001011 0.000111 0.001481 315s > 315s > print( round( vcov( fitwls5 ), digits = 6 ) ) 315s demand_(Intercept) demand_price demand_income 315s demand_(Intercept) 36.5710 -0.321554 -0.043279 315s demand_price -0.3216 0.004201 -0.001011 315s demand_income -0.0433 -0.001011 0.001481 315s supply_(Intercept) 35.8467 -0.431417 0.074877 315s supply_price -0.3216 0.004201 -0.001011 315s supply_farmPrice -0.0334 0.000226 0.000111 315s supply_trend -0.0433 -0.001011 0.001481 315s supply_(Intercept) supply_price supply_farmPrice 315s demand_(Intercept) 35.8467 -0.321554 -0.033436 315s demand_price -0.4314 0.004201 0.000226 315s demand_income 0.0749 -0.001011 0.000111 315s supply_(Intercept) 63.5001 -0.431417 -0.215648 315s supply_price -0.4314 0.004201 0.000226 315s supply_farmPrice -0.2156 0.000226 0.001986 315s supply_trend 0.0749 -0.001011 0.000111 315s supply_trend 315s demand_(Intercept) -0.043279 315s demand_price -0.001011 315s demand_income 0.001481 315s supply_(Intercept) 0.074877 315s supply_price -0.001011 315s supply_farmPrice 0.000111 315s supply_trend 0.001481 315s > print( round( vcov( fitwls5, modified.regMat = TRUE ), digits = 6 ) ) 315s C1 C2 C3 C4 C5 C6 315s C1 36.5710 -0.321554 -0.043279 35.8467 -0.321554 -0.033436 315s C2 -0.3216 0.004201 -0.001011 -0.4314 0.004201 0.000226 315s C3 -0.0433 -0.001011 0.001481 0.0749 -0.001011 0.000111 315s C4 35.8467 -0.431417 0.074877 63.5001 -0.431417 -0.215648 315s C5 -0.3216 0.004201 -0.001011 -0.4314 0.004201 0.000226 315s C6 -0.0334 0.000226 0.000111 -0.2156 0.000226 0.001986 315s > print( round( vcov( fitwls5$eq[[ 1 ]] ), digits = 6 ) ) 315s (Intercept) price income 315s (Intercept) 36.5710 -0.32155 -0.04328 315s price -0.3216 0.00420 -0.00101 315s income -0.0433 -0.00101 0.00148 315s > 315s > print( round( vcov( fitwlsi1 ), digits = 6 ) ) 315s demand_(Intercept) demand_price demand_income 315s demand_(Intercept) 56.5408 -0.59480 0.03216 315s demand_price -0.5948 0.00822 -0.00233 315s demand_income 0.0322 -0.00233 0.00206 315s supply_(Intercept) 0.0000 0.00000 0.00000 315s supply_price 0.0000 0.00000 0.00000 315s supply_farmPrice 0.0000 0.00000 0.00000 315s supply_trend 0.0000 0.00000 0.00000 315s supply_(Intercept) supply_price supply_farmPrice 315s demand_(Intercept) 0.000 0.000000 0.000000 315s demand_price 0.000 0.000000 0.000000 315s demand_income 0.000 0.000000 0.000000 315s supply_(Intercept) 131.398 -0.987500 -0.304361 315s supply_price -0.988 0.009003 0.000844 315s supply_farmPrice -0.304 0.000844 0.002133 315s supply_trend -0.279 0.000522 0.001316 315s supply_trend 315s demand_(Intercept) 0.000000 315s demand_price 0.000000 315s demand_income 0.000000 315s supply_(Intercept) -0.279183 315s supply_price 0.000522 315s supply_farmPrice 0.001316 315s supply_trend 0.009510 315s > print( round( vcov( fitwlsi1$eq[[ 2 ]] ), digits = 6 ) ) 315s (Intercept) price farmPrice trend 315s (Intercept) 131.398 -0.987500 -0.304361 -0.279183 315s price -0.988 0.009003 0.000844 0.000522 315s farmPrice -0.304 0.000844 0.002133 0.001316 315s trend -0.279 0.000522 0.001316 0.009510 315s > 315s > print( round( vcov( fitwlsi2e ), digits = 6 ) ) 315s demand_(Intercept) demand_price demand_income 315s demand_(Intercept) 48.32515 -0.503487 0.022480 315s demand_price -0.50349 0.006624 -0.001631 315s demand_income 0.02248 -0.001631 0.001442 315s supply_(Intercept) -0.65995 0.047882 -0.042335 315s supply_price 0.00123 -0.000090 0.000079 315s supply_farmPrice 0.00311 -0.000226 0.000199 315s supply_trend 0.02248 -0.001631 0.001442 315s supply_(Intercept) supply_price supply_farmPrice 315s demand_(Intercept) -0.6600 0.001234 0.003110 315s demand_price 0.0479 -0.000090 -0.000226 315s demand_income -0.0423 0.000079 0.000199 315s supply_(Intercept) 103.0226 -0.805456 -0.225388 315s supply_price -0.8055 0.007418 0.000649 315s supply_farmPrice -0.2254 0.000649 0.001640 315s supply_trend -0.0423 0.000079 0.000199 315s supply_trend 315s demand_(Intercept) 0.022480 315s demand_price -0.001631 315s demand_income 0.001442 315s supply_(Intercept) -0.042335 315s supply_price 0.000079 315s supply_farmPrice 0.000199 315s supply_trend 0.001442 315s > print( round( vcov( fitwlsi2e$eq[[ 1 ]] ), digits = 6 ) ) 315s (Intercept) price income 315s (Intercept) 48.3251 -0.50349 0.02248 315s price -0.5035 0.00662 -0.00163 315s income 0.0225 -0.00163 0.00144 315s > 315s > print( round( vcov( fitwlsi3 ), digits = 6 ) ) 315s demand_(Intercept) demand_price demand_income 315s demand_(Intercept) 56.81857 -0.592263 0.026724 315s demand_price -0.59226 0.007812 -0.001939 315s demand_income 0.02672 -0.001939 0.001714 315s supply_(Intercept) -0.78454 0.056921 -0.050327 315s supply_price 0.00147 -0.000106 0.000094 315s supply_farmPrice 0.00370 -0.000268 0.000237 315s supply_trend 0.02672 -0.001939 0.001714 315s supply_(Intercept) supply_price supply_farmPrice 315s demand_(Intercept) -0.7845 0.001467 0.003697 315s demand_price 0.0569 -0.000106 -0.000268 315s demand_income -0.0503 0.000094 0.000237 315s supply_(Intercept) 128.7924 -1.007391 -0.281572 315s supply_price -1.0074 0.009279 0.000811 315s supply_farmPrice -0.2816 0.000811 0.002049 315s supply_trend -0.0503 0.000094 0.000237 315s supply_trend 315s demand_(Intercept) 0.026724 315s demand_price -0.001939 315s demand_income 0.001714 315s supply_(Intercept) -0.050327 315s supply_price 0.000094 315s supply_farmPrice 0.000237 315s supply_trend 0.001714 315s > print( round( vcov( fitwlsi3, modified.regMat = TRUE ), digits = 6 ) ) 315s C1 C2 C3 C4 C5 C6 315s C1 56.81857 -0.592263 0.026724 -0.7845 0.001467 0.003697 315s C2 -0.59226 0.007812 -0.001939 0.0569 -0.000106 -0.000268 315s C3 0.02672 -0.001939 0.001714 -0.0503 0.000094 0.000237 315s C4 -0.78454 0.056921 -0.050327 128.7924 -1.007391 -0.281572 315s C5 0.00147 -0.000106 0.000094 -1.0074 0.009279 0.000811 315s C6 0.00370 -0.000268 0.000237 -0.2816 0.000811 0.002049 315s > print( round( vcov( fitwlsi3$eq[[ 2 ]] ), digits = 6 ) ) 315s (Intercept) price farmPrice trend 315s (Intercept) 128.7924 -1.007391 -0.281572 -0.050327 315s price -1.0074 0.009279 0.000811 0.000094 315s farmPrice -0.2816 0.000811 0.002049 0.000237 315s trend -0.0503 0.000094 0.000237 0.001714 315s > 315s > print( round( vcov( fitwlsi4e ), digits = 6 ) ) 315s demand_(Intercept) demand_price demand_income 315s demand_(Intercept) 30.4377 -0.265752 -0.037918 315s demand_price -0.2658 0.003463 -0.000827 315s demand_income -0.0379 -0.000827 0.001237 315s supply_(Intercept) 29.6762 -0.355820 0.060620 315s supply_price -0.2658 0.003463 -0.000827 315s supply_farmPrice -0.0279 0.000187 0.000094 315s supply_trend -0.0379 -0.000827 0.001237 315s supply_(Intercept) supply_price supply_farmPrice 315s demand_(Intercept) 29.6762 -0.265752 -0.027921 315s demand_price -0.3558 0.003463 0.000187 315s demand_income 0.0606 -0.000827 0.000094 315s supply_(Intercept) 52.0044 -0.355820 -0.173988 315s supply_price -0.3558 0.003463 0.000187 315s supply_farmPrice -0.1740 0.000187 0.001596 315s supply_trend 0.0606 -0.000827 0.000094 315s supply_trend 315s demand_(Intercept) -0.037918 315s demand_price -0.000827 315s demand_income 0.001237 315s supply_(Intercept) 0.060620 315s supply_price -0.000827 315s supply_farmPrice 0.000094 315s supply_trend 0.001237 315s > print( round( vcov( fitwlsi4e$eq[[ 1 ]] ), digits = 6 ) ) 315s (Intercept) price income 315s (Intercept) 30.4377 -0.265752 -0.037918 315s price -0.2658 0.003463 -0.000827 315s income -0.0379 -0.000827 0.001237 315s > 315s > print( round( vcov( fitwlsi5e ), digits = 6 ) ) 315s demand_(Intercept) demand_price demand_income 315s demand_(Intercept) 30.4377 -0.265752 -0.037918 315s demand_price -0.2658 0.003463 -0.000827 315s demand_income -0.0379 -0.000827 0.001237 315s supply_(Intercept) 29.6762 -0.355820 0.060620 315s supply_price -0.2658 0.003463 -0.000827 315s supply_farmPrice -0.0279 0.000187 0.000094 315s supply_trend -0.0379 -0.000827 0.001237 315s supply_(Intercept) supply_price supply_farmPrice 315s demand_(Intercept) 29.6762 -0.265752 -0.027921 315s demand_price -0.3558 0.003463 0.000187 315s demand_income 0.0606 -0.000827 0.000094 315s supply_(Intercept) 52.0044 -0.355820 -0.173988 315s supply_price -0.3558 0.003463 0.000187 315s supply_farmPrice -0.1740 0.000187 0.001596 315s supply_trend 0.0606 -0.000827 0.000094 315s supply_trend 315s demand_(Intercept) -0.037918 315s demand_price -0.000827 315s demand_income 0.001237 315s supply_(Intercept) 0.060620 315s supply_price -0.000827 315s supply_farmPrice 0.000094 315s supply_trend 0.001237 315s > print( round( vcov( fitwlsi5e, modified.regMat = TRUE ), digits = 6 ) ) 315s C1 C2 C3 C4 C5 C6 315s C1 30.4377 -0.265752 -0.037918 29.6762 -0.265752 -0.027921 315s C2 -0.2658 0.003463 -0.000827 -0.3558 0.003463 0.000187 315s C3 -0.0379 -0.000827 0.001237 0.0606 -0.000827 0.000094 315s C4 29.6762 -0.355820 0.060620 52.0044 -0.355820 -0.173988 315s C5 -0.2658 0.003463 -0.000827 -0.3558 0.003463 0.000187 315s C6 -0.0279 0.000187 0.000094 -0.1740 0.000187 0.001596 315s > print( round( vcov( fitwlsi5e$eq[[ 2 ]] ), digits = 6 ) ) 315s (Intercept) price farmPrice trend 315s (Intercept) 52.0044 -0.355820 -0.173988 0.060620 315s price -0.3558 0.003463 0.000187 -0.000827 315s farmPrice -0.1740 0.000187 0.001596 0.000094 315s trend 0.0606 -0.000827 0.000094 0.001237 315s > 315s > 315s > ## *********** confidence intervals of coefficients ************* 315s > print( confint( fitwls1 ) ) 315s 2.5 % 97.5 % 315s demand_(Intercept) 84.031 115.760 315s demand_price -0.508 -0.125 315s demand_income 0.239 0.430 315s supply_(Intercept) 33.975 82.576 315s supply_price -0.041 0.362 315s supply_farmPrice 0.150 0.346 315s supply_trend 0.042 0.455 315s > print( confint( fitwls1$eq[[ 2 ]], level = 0.9 ) ) 315s 5 % 95 % 315s (Intercept) 38.263 78.288 315s price -0.005 0.326 315s farmPrice 0.167 0.329 315s trend 0.078 0.419 315s > 315s > print( confint( fitwls2e, level = 0.9 ) ) 315s 5 % 95 % 315s demand_(Intercept) 85.474 113.818 315s demand_price -0.464 -0.132 315s demand_income 0.241 0.396 315s supply_(Intercept) 35.634 76.786 315s supply_price -0.010 0.339 315s supply_farmPrice 0.176 0.340 315s supply_trend 0.241 0.396 315s > print( confint( fitwls2e$eq[[ 1 ]], level = 0.99 ) ) 315s 0.5 % 99.5 % 315s (Intercept) 80.620 118.672 315s price -0.521 -0.076 315s income 0.215 0.422 315s > 315s > print( confint( fitwls3, level = 0.99 ) ) 315s 0.5 % 99.5 % 315s demand_(Intercept) 84.286 115.030 315s demand_price -0.479 -0.119 315s demand_income 0.235 0.404 315s supply_(Intercept) 33.190 79.186 315s supply_price -0.031 0.359 315s supply_farmPrice 0.166 0.350 315s supply_trend 0.235 0.404 315s > print( confint( fitwls3$eq[[ 2 ]], level = 0.5 ) ) 315s 25 % 75 % 315s (Intercept) 48.472 63.903 315s price 0.099 0.230 315s farmPrice 0.227 0.289 315s trend 0.291 0.348 315s > 315s > print( confint( fitwls4e, level = 0.5 ) ) 315s 25 % 75 % 315s demand_(Intercept) 89.763 112.189 315s demand_price -0.436 -0.197 315s demand_income 0.252 0.395 315s supply_(Intercept) 39.328 68.598 315s supply_price 0.064 0.303 315s supply_farmPrice 0.179 0.341 315s supply_trend 0.252 0.395 315s > print( confint( fitwls4e$eq[[ 1 ]], level = 0.25 ) ) 315s 37.5 % 62.5 % 315s (Intercept) 99.202 102.750 315s price -0.335 -0.297 315s income 0.312 0.335 315s > 315s > print( confint( fitwls5, level = 0.25 ) ) 315s 37.5 % 62.5 % 315s demand_(Intercept) 88.637 113.191 315s demand_price -0.448 -0.184 315s demand_income 0.246 0.402 315s supply_(Intercept) 37.764 70.119 315s supply_price 0.052 0.316 315s supply_farmPrice 0.170 0.351 315s supply_trend 0.246 0.402 315s > print( confint( fitwls5$eq[[ 2 ]], level = 0.975 ) ) 315s 1.3 % 98.8 % 315s (Intercept) 35.279 72.604 315s price 0.032 0.336 315s farmPrice 0.156 0.365 315s trend 0.234 0.414 315s > 315s > print( confint( fitwlsi1e, level = 0.975, useDfSys = TRUE ) ) 315s 1.3 % 98.8 % 315s demand_(Intercept) 85.791 114.000 315s demand_price -0.486 -0.146 315s demand_income 0.249 0.420 315s supply_(Intercept) 37.416 79.135 315s supply_price -0.012 0.333 315s supply_farmPrice 0.164 0.332 315s supply_trend 0.071 0.426 315s > print( confint( fitwlsi1e$eq[[ 1 ]], level = 0.999, useDfSys = TRUE ) ) 315s 0.1 % 100 % 315s (Intercept) 74.863 124.928 315s price -0.618 -0.014 315s income 0.183 0.486 315s > 315s > print( confint( fitwlsi2, level = 0.999 ) ) 315s 0.1 % 100 % 315s demand_(Intercept) 84.342 114.979 315s demand_price -0.479 -0.120 315s demand_income 0.235 0.404 315s supply_(Intercept) 33.120 79.246 315s supply_price -0.031 0.360 315s supply_farmPrice 0.166 0.350 315s supply_trend 0.235 0.404 315s > print( confint( fitwlsi2$eq[[ 2 ]], level = 0.1 ) ) 315s 45 % 55 % 315s (Intercept) 54.746 57.620 315s price 0.152 0.176 315s farmPrice 0.252 0.264 315s trend 0.314 0.325 315s > 315s > print( confint( fitwlsi3e, level = 0.1 ) ) 315s 45 % 55 % 315s demand_(Intercept) 85.521 113.776 315s demand_price -0.464 -0.133 315s demand_income 0.242 0.396 315s supply_(Intercept) 35.579 76.833 315s supply_price -0.011 0.339 315s supply_farmPrice 0.176 0.340 315s supply_trend 0.242 0.396 315s > print( confint( fitwlsi3e$eq[[ 1 ]], level = 0.01 ) ) 315s 49.5 % 50.5 % 315s (Intercept) 99.561 99.736 315s price -0.299 -0.297 315s income 0.318 0.319 315s > 315s > print( confint( fitwlsi4, level = 0.01 ) ) 315s 49.5 % 50.5 % 315s demand_(Intercept) 88.642 113.164 315s demand_price -0.447 -0.184 315s demand_income 0.246 0.402 315s supply_(Intercept) 37.754 70.122 315s supply_price 0.053 0.316 315s supply_farmPrice 0.170 0.351 315s supply_trend 0.246 0.402 315s > print( confint( fitwlsi4$eq[[ 2 ]], level = 0.33 ) ) 315s 33.5 % 66.5 % 315s (Intercept) 50.512 57.364 315s price 0.156 0.212 315s farmPrice 0.241 0.279 315s trend 0.307 0.340 315s > 315s > print( confint( fitwlsi5e, level = 0.33 ) ) 315s 33.5 % 66.5 % 315s demand_(Intercept) 89.766 112.166 315s demand_price -0.435 -0.197 315s demand_income 0.252 0.395 315s supply_(Intercept) 39.320 68.599 315s supply_price 0.065 0.303 315s supply_farmPrice 0.179 0.341 315s supply_trend 0.252 0.395 315s > print( confint( fitwlsi5e$eq[[ 1 ]] ) ) 315s 2.5 % 97.5 % 315s (Intercept) 89.766 112.166 315s price -0.435 -0.197 315s income 0.252 0.395 315s > 315s > 315s > ## *********** fitted values ************* 315s > print( fitted( fitwls1 ) ) 315s demand supply 315s 1 97.4 98.9 315s 2 99.6 100.1 315s 3 99.5 100.2 315s 4 99.7 100.4 315s 5 102.3 102.7 315s 6 102.1 102.6 315s 7 102.5 102.4 315s 8 102.8 104.3 315s 9 101.7 102.9 315s 10 100.8 100.4 315s 11 95.6 96.0 315s 12 94.4 94.1 315s 13 95.7 95.6 315s 14 99.0 97.8 315s 15 104.3 102.6 315s 16 103.9 104.1 315s 17 104.8 103.8 315s 18 101.9 102.4 315s 19 103.5 102.1 315s 20 106.5 104.5 315s > print( fitted( fitwls1$eq[[ 2 ]] ) ) 315s 1 2 3 4 5 6 7 8 9 10 11 12 13 315s 98.9 100.1 100.2 100.4 102.7 102.6 102.4 104.3 102.9 100.4 96.0 94.1 95.6 315s 14 15 16 17 18 19 20 315s 97.8 102.6 104.1 103.8 102.4 102.1 104.5 315s > 315s > print( fitted( fitwls2e ) ) 315s demand supply 315s 1 97.6 98.3 315s 2 99.7 99.5 315s 3 99.6 99.7 315s 4 99.8 99.9 315s 5 102.2 102.5 315s 6 102.0 102.4 315s 7 102.4 102.3 315s 8 102.8 104.3 315s 9 101.7 102.9 315s 10 100.8 100.3 315s 11 95.8 95.9 315s 12 94.7 93.9 315s 13 95.9 95.5 315s 14 99.1 97.9 315s 15 104.1 103.0 315s 16 103.8 104.6 315s 17 104.6 104.3 315s 18 101.9 102.9 315s 19 103.4 102.7 315s 20 106.3 105.2 315s > print( fitted( fitwls2e$eq[[ 1 ]] ) ) 315s 1 2 3 4 5 6 7 8 9 10 11 12 13 315s 97.6 99.7 99.6 99.8 102.2 102.0 102.4 102.8 101.7 100.8 95.8 94.7 95.9 315s 14 15 16 17 18 19 20 315s 99.1 104.1 103.8 104.6 101.9 103.4 106.3 315s > 315s > print( fitted( fitwls3 ) ) 315s demand supply 315s 1 97.6 98.3 315s 2 99.6 99.5 315s 3 99.6 99.7 315s 4 99.8 99.9 315s 5 102.2 102.5 315s 6 102.0 102.4 315s 7 102.4 102.3 315s 8 102.8 104.3 315s 9 101.7 102.9 315s 10 100.8 100.3 315s 11 95.8 95.9 315s 12 94.7 93.9 315s 13 95.9 95.5 315s 14 99.1 97.9 315s 15 104.1 103.0 315s 16 103.8 104.6 315s 17 104.6 104.3 315s 18 101.9 102.9 315s 19 103.4 102.7 315s 20 106.3 105.2 315s > print( fitted( fitwls3$eq[[ 2 ]] ) ) 315s 1 2 3 4 5 6 7 8 9 10 11 12 13 315s 98.3 99.5 99.7 99.9 102.5 102.4 102.3 104.3 102.9 100.3 95.9 93.9 95.5 315s 14 15 16 17 18 19 20 315s 97.9 103.0 104.6 104.3 102.9 102.7 105.2 315s > 315s > print( fitted( fitwls4e ) ) 315s demand supply 315s 1 97.5 98.2 315s 2 99.6 99.6 315s 3 99.5 99.7 315s 4 99.7 100.0 315s 5 102.3 102.4 315s 6 102.0 102.4 315s 7 102.4 102.3 315s 8 102.7 104.4 315s 9 101.7 102.9 315s 10 100.9 100.2 315s 11 95.8 95.7 315s 12 94.6 93.9 315s 13 95.8 95.5 315s 14 99.1 97.8 315s 15 104.2 102.9 315s 16 103.8 104.6 315s 17 104.8 104.1 315s 18 101.9 103.0 315s 19 103.3 102.8 315s 20 106.2 105.5 315s > print( fitted( fitwls4e$eq[[ 1 ]] ) ) 315s 1 2 3 4 5 6 7 8 9 10 11 12 13 315s 97.5 99.6 99.5 99.7 102.3 102.0 102.4 102.7 101.7 100.9 95.8 94.6 95.8 315s 14 15 16 17 18 19 20 315s 99.1 104.2 103.8 104.8 101.9 103.3 106.2 315s > 315s > print( fitted( fitwls5 ) ) 315s demand supply 315s 1 97.5 98.2 315s 2 99.6 99.6 315s 3 99.5 99.7 315s 4 99.7 100.0 315s 5 102.3 102.4 315s 6 102.0 102.3 315s 7 102.4 102.3 315s 8 102.7 104.4 315s 9 101.7 102.9 315s 10 100.9 100.2 315s 11 95.8 95.7 315s 12 94.6 93.9 315s 13 95.8 95.5 315s 14 99.1 97.8 315s 15 104.2 102.9 315s 16 103.8 104.6 315s 17 104.8 104.1 315s 18 101.9 103.0 315s 19 103.3 102.8 315s 20 106.2 105.5 315s > print( fitted( fitwls5$eq[[ 2 ]] ) ) 315s 1 2 3 4 5 6 7 8 9 10 11 12 13 315s 98.2 99.6 99.7 100.0 102.4 102.3 102.3 104.4 102.9 100.2 95.7 93.9 95.5 315s 14 15 16 17 18 19 20 315s 97.8 102.9 104.6 104.1 103.0 102.8 105.5 315s > 315s > print( fitted( fitwlsi1e ) ) 315s demand supply 315s 1 97.4 98.9 315s 2 99.6 100.1 315s 3 99.5 100.2 315s 4 99.7 100.4 315s 5 102.3 102.7 315s 6 102.1 102.6 315s 7 102.5 102.4 315s 8 102.8 104.3 315s 9 101.7 102.9 315s 10 100.8 100.4 315s 11 95.6 96.0 315s 12 94.4 94.1 315s 13 95.7 95.6 315s 14 99.0 97.8 315s 15 104.3 102.6 315s 16 103.9 104.1 315s 17 104.8 103.8 315s 18 101.9 102.4 315s 19 103.5 102.1 315s 20 106.5 104.5 315s > print( fitted( fitwlsi1e$eq[[ 1 ]] ) ) 315s 1 2 3 4 5 6 7 8 9 10 11 12 13 315s 97.4 99.6 99.5 99.7 102.3 102.1 102.5 102.8 101.7 100.8 95.6 94.4 95.7 315s 14 15 16 17 18 19 20 315s 99.0 104.3 103.9 104.8 101.9 103.5 106.5 315s > 315s > print( fitted( fitwlsi2 ) ) 315s demand supply 315s 1 97.6 98.3 315s 2 99.6 99.5 315s 3 99.6 99.7 315s 4 99.8 99.9 315s 5 102.2 102.5 315s 6 102.0 102.4 315s 7 102.4 102.3 315s 8 102.8 104.3 315s 9 101.7 102.9 315s 10 100.8 100.3 315s 11 95.8 95.9 315s 12 94.7 93.9 315s 13 95.9 95.5 315s 14 99.1 97.9 315s 15 104.1 103.0 315s 16 103.8 104.6 315s 17 104.6 104.3 315s 18 101.9 102.9 315s 19 103.4 102.7 315s 20 106.3 105.2 315s > print( fitted( fitwlsi2$eq[[ 2 ]] ) ) 315s 1 2 3 4 5 6 7 8 9 10 11 12 13 315s 98.3 99.5 99.7 99.9 102.5 102.4 102.3 104.3 102.9 100.3 95.9 93.9 95.5 315s 14 15 16 17 18 19 20 315s 97.9 103.0 104.6 104.3 102.9 102.7 105.2 315s > 315s > print( fitted( fitwlsi3e ) ) 315s demand supply 315s 1 97.6 98.3 315s 2 99.7 99.5 315s 3 99.6 99.7 315s 4 99.8 99.9 315s 5 102.2 102.5 315s 6 102.0 102.4 315s 7 102.4 102.3 315s 8 102.8 104.3 315s 9 101.7 102.9 315s 10 100.8 100.3 315s 11 95.8 95.9 315s 12 94.7 93.9 315s 13 95.9 95.5 315s 14 99.1 97.9 315s 15 104.1 103.0 315s 16 103.8 104.6 315s 17 104.6 104.3 315s 18 101.9 102.9 315s 19 103.4 102.7 315s 20 106.3 105.2 315s > print( fitted( fitwlsi3e$eq[[ 1 ]] ) ) 315s 1 2 3 4 5 6 7 8 9 10 11 12 13 315s 97.6 99.7 99.6 99.8 102.2 102.0 102.4 102.8 101.7 100.8 95.8 94.7 95.9 315s 14 15 16 17 18 19 20 315s 99.1 104.1 103.8 104.6 101.9 103.4 106.3 315s > 315s > print( fitted( fitwlsi4 ) ) 315s demand supply 315s 1 97.5 98.2 315s 2 99.6 99.6 315s 3 99.5 99.7 315s 4 99.7 100.0 315s 5 102.3 102.4 315s 6 102.0 102.3 315s 7 102.4 102.3 315s 8 102.7 104.4 315s 9 101.7 102.9 315s 10 100.9 100.2 315s 11 95.8 95.7 315s 12 94.6 93.9 315s 13 95.8 95.5 315s 14 99.1 97.8 315s 15 104.2 102.9 315s 16 103.8 104.6 315s 17 104.8 104.1 315s 18 101.9 103.0 315s 19 103.3 102.8 315s 20 106.2 105.5 315s > print( fitted( fitwlsi4$eq[[ 2 ]] ) ) 315s 1 2 3 4 5 6 7 8 9 10 11 12 13 315s 98.2 99.6 99.7 100.0 102.4 102.3 102.3 104.4 102.9 100.2 95.7 93.9 95.5 315s 14 15 16 17 18 19 20 315s 97.8 102.9 104.6 104.1 103.0 102.8 105.5 315s > 315s > print( fitted( fitwlsi5e ) ) 315s demand supply 315s 1 97.5 98.2 315s 2 99.6 99.6 315s 3 99.5 99.7 315s 4 99.7 100.0 315s 5 102.3 102.4 315s 6 102.0 102.4 315s 7 102.4 102.3 315s 8 102.7 104.4 315s 9 101.7 102.9 315s 10 100.9 100.2 315s 11 95.8 95.7 315s 12 94.6 93.9 315s 13 95.8 95.5 315s 14 99.1 97.8 315s 15 104.2 102.9 315s 16 103.8 104.6 315s 17 104.8 104.1 315s 18 101.9 103.0 315s 19 103.3 102.8 315s 20 106.2 105.5 315s > print( fitted( fitwlsi5e$eq[[ 1 ]] ) ) 315s 1 2 3 4 5 6 7 8 9 10 11 12 13 315s 97.5 99.6 99.5 99.7 102.3 102.0 102.4 102.7 101.7 100.9 95.8 94.6 95.8 315s 14 15 16 17 18 19 20 315s 99.1 104.2 103.8 104.8 101.9 103.3 106.2 315s > 315s > 315s > ## *********** predicted values ************* 315s > predictData <- Kmenta 315s > predictData$consump <- NULL 315s > predictData$price <- Kmenta$price * 0.9 315s > predictData$income <- Kmenta$income * 1.1 315s > 315s > print( predict( fitwls1, se.fit = TRUE, interval = "prediction" ) ) 315s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 315s 1 97.4 0.643 93.1 101.7 98.9 1.056 315s 2 99.6 0.577 95.3 103.8 100.1 1.037 315s 3 99.5 0.545 95.3 103.8 100.2 0.939 315s 4 99.7 0.582 95.4 104.0 100.4 0.912 315s 5 102.3 0.502 98.1 106.5 102.7 0.895 315s 6 102.1 0.463 97.9 106.3 102.6 0.791 315s 7 102.5 0.484 98.3 106.7 102.4 0.719 315s 8 102.8 0.601 98.6 107.1 104.3 0.963 315s 9 101.7 0.527 97.5 105.9 102.9 0.788 315s 10 100.8 0.788 96.4 105.2 100.4 0.981 315s 11 95.6 0.946 91.0 100.1 96.0 1.185 315s 12 94.4 0.980 89.8 98.9 94.1 1.394 315s 13 95.7 0.880 91.2 100.1 95.6 1.244 315s 14 99.0 0.508 94.8 103.2 97.8 0.896 315s 15 104.3 0.758 99.9 108.7 102.6 0.874 315s 16 103.9 0.616 99.7 108.2 104.1 0.916 315s 17 104.8 1.273 99.9 109.7 103.8 1.605 315s 18 101.9 0.536 97.7 106.2 102.4 0.962 315s 19 103.5 0.680 99.2 107.8 102.1 1.098 315s 20 106.5 1.274 101.7 111.4 104.5 1.664 315s supply.lwr supply.upr 315s 1 93.4 104 315s 2 94.5 106 315s 3 94.7 106 315s 4 94.9 106 315s 5 97.3 108 315s 6 97.2 108 315s 7 97.1 108 315s 8 98.8 110 315s 9 97.6 108 315s 10 94.8 106 315s 11 90.3 102 315s 12 88.2 100 315s 13 89.9 101 315s 14 92.3 103 315s 15 97.2 108 315s 16 98.6 110 315s 17 97.7 110 315s 18 96.9 108 315s 19 96.5 108 315s 20 98.3 111 315s > print( predict( fitwls1$eq[[ 2 ]], se.fit = TRUE, interval = "prediction" ) ) 315s fit se.fit lwr upr 315s 1 98.9 1.056 93.4 104 315s 2 100.1 1.037 94.5 106 315s 3 100.2 0.939 94.7 106 315s 4 100.4 0.912 94.9 106 315s 5 102.7 0.895 97.3 108 315s 6 102.6 0.791 97.2 108 315s 7 102.4 0.719 97.1 108 315s 8 104.3 0.963 98.8 110 315s 9 102.9 0.788 97.6 108 315s 10 100.4 0.981 94.8 106 315s 11 96.0 1.185 90.3 102 315s 12 94.1 1.394 88.2 100 315s 13 95.6 1.244 89.9 101 315s 14 97.8 0.896 92.3 103 315s 15 102.6 0.874 97.2 108 315s 16 104.1 0.916 98.6 110 315s 17 103.8 1.605 97.7 110 315s 18 102.4 0.962 96.9 108 315s 19 102.1 1.098 96.5 108 315s 20 104.5 1.664 98.3 111 315s > 315s > print( predict( fitwls2e, se.pred = TRUE, interval = "confidence", 315s + level = 0.999, newdata = predictData ) ) 315s demand.pred demand.se.pred demand.lwr demand.upr supply.pred supply.se.pred 315s 1 103 2.12 100.2 106 96.6 2.65 315s 2 106 2.12 102.7 109 97.8 2.57 315s 3 106 2.13 102.6 109 98.0 2.58 315s 4 106 2.12 102.9 109 98.2 2.56 315s 5 108 2.35 103.5 113 100.9 2.72 315s 6 108 2.31 103.6 113 100.7 2.67 315s 7 109 2.30 104.2 113 100.6 2.62 315s 8 109 2.27 105.0 114 102.6 2.58 315s 9 108 2.36 102.8 112 101.4 2.74 315s 10 106 2.46 100.8 112 98.8 2.92 315s 11 101 2.28 96.7 105 94.4 2.98 315s 12 100 2.12 97.0 103 92.3 2.96 315s 13 102 2.05 99.3 104 93.8 2.81 315s 14 105 2.20 101.2 109 96.3 2.78 315s 15 110 2.53 104.4 116 101.4 2.78 315s 16 110 2.44 104.7 115 102.9 2.69 315s 17 110 2.81 102.9 118 102.9 3.14 315s 18 108 2.23 104.3 112 101.2 2.58 315s 19 110 2.30 105.6 115 100.9 2.57 315s 20 114 2.50 108.1 119 103.3 2.52 315s supply.lwr supply.upr 315s 1 92.9 100.3 315s 2 95.0 100.6 315s 3 95.1 100.9 315s 4 95.5 100.9 315s 5 96.6 105.1 315s 6 96.9 104.6 315s 7 97.2 104.0 315s 8 99.6 105.5 315s 9 96.9 105.9 315s 10 93.1 104.6 315s 11 88.2 100.5 315s 12 86.3 98.4 315s 13 88.8 98.9 315s 14 91.5 101.0 315s 15 96.7 106.2 315s 16 98.9 106.9 315s 17 95.8 110.0 315s 18 98.2 104.1 315s 19 98.1 103.8 315s 20 101.1 105.6 315s > print( predict( fitwls2e$eq[[ 1 ]], se.pred = TRUE, interval = "confidence", 315s + level = 0.999, newdata = predictData ) ) 315s fit se.pred lwr upr 315s 1 103 2.12 100.2 106 315s 2 106 2.12 102.7 109 315s 3 106 2.13 102.6 109 315s 4 106 2.12 102.9 109 315s 5 108 2.35 103.5 113 315s 6 108 2.31 103.6 113 315s 7 109 2.30 104.2 113 315s 8 109 2.27 105.0 114 315s 9 108 2.36 102.8 112 315s 10 106 2.46 100.8 112 315s 11 101 2.28 96.7 105 315s 12 100 2.12 97.0 103 315s 13 102 2.05 99.3 104 315s 14 105 2.20 101.2 109 315s 15 110 2.53 104.4 116 315s 16 110 2.44 104.7 115 315s 17 110 2.81 102.9 118 315s 18 108 2.23 104.3 112 315s 19 110 2.30 105.6 115 315s 20 114 2.50 108.1 119 315s > 315s > print( predict( fitwls3, se.pred = TRUE, interval = "prediction", 315s + level = 0.975 ) ) 315s demand.pred demand.se.pred demand.lwr demand.upr supply.pred supply.se.pred 315s 1 97.6 2.03 92.8 102.3 98.3 2.54 315s 2 99.6 2.02 94.9 104.4 99.5 2.56 315s 3 99.6 2.01 94.9 104.3 99.7 2.55 315s 4 99.8 2.02 95.0 104.5 99.9 2.56 315s 5 102.2 2.00 97.5 106.9 102.5 2.59 315s 6 102.0 1.99 97.3 106.7 102.4 2.56 315s 7 102.4 1.99 97.7 107.1 102.3 2.54 315s 8 102.8 2.03 98.0 107.5 104.3 2.63 315s 9 101.7 2.01 97.0 106.4 102.9 2.57 315s 10 100.8 2.09 95.9 105.7 100.3 2.64 315s 11 95.8 2.14 90.8 100.8 95.9 2.72 315s 12 94.7 2.14 89.6 99.7 93.9 2.82 315s 13 95.9 2.11 91.0 100.8 95.5 2.75 315s 14 99.1 2.00 94.4 103.8 97.9 2.61 315s 15 104.1 2.07 99.3 109.0 103.0 2.56 315s 16 103.8 2.03 99.0 108.5 104.6 2.55 315s 17 104.6 2.31 99.2 110.0 104.3 2.85 315s 18 101.9 2.01 97.2 106.6 102.9 2.55 315s 19 103.4 2.05 98.6 108.2 102.7 2.59 315s 20 106.3 2.31 100.9 111.7 105.2 2.84 315s supply.lwr supply.upr 315s 1 92.3 104 315s 2 93.5 106 315s 3 93.7 106 315s 4 93.9 106 315s 5 96.4 109 315s 6 96.4 108 315s 7 96.3 108 315s 8 98.1 110 315s 9 96.9 109 315s 10 94.1 107 315s 11 89.5 102 315s 12 87.3 101 315s 13 89.1 102 315s 14 91.8 104 315s 15 97.0 109 315s 16 98.6 111 315s 17 97.6 111 315s 18 96.9 109 315s 19 96.6 109 315s 20 98.6 112 315s > print( predict( fitwls3$eq[[ 2 ]], se.pred = TRUE, interval = "prediction", 315s + level = 0.975 ) ) 315s fit se.pred lwr upr 315s 1 98.3 2.54 92.3 104 315s 2 99.5 2.56 93.5 106 315s 3 99.7 2.55 93.7 106 315s 4 99.9 2.56 93.9 106 315s 5 102.5 2.59 96.4 109 315s 6 102.4 2.56 96.4 108 315s 7 102.3 2.54 96.3 108 315s 8 104.3 2.63 98.1 110 315s 9 102.9 2.57 96.9 109 315s 10 100.3 2.64 94.1 107 315s 11 95.9 2.72 89.5 102 315s 12 93.9 2.82 87.3 101 315s 13 95.5 2.75 89.1 102 315s 14 97.9 2.61 91.8 104 315s 15 103.0 2.56 97.0 109 315s 16 104.6 2.55 98.6 111 315s 17 104.3 2.85 97.6 111 315s 18 102.9 2.55 96.9 109 315s 19 102.7 2.59 96.6 109 315s 20 105.2 2.84 98.6 112 315s > 315s > print( predict( fitwls4e, se.fit = TRUE, interval = "confidence", 315s + level = 0.25 ) ) 315s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 315s 1 97.5 0.541 97.4 97.7 98.2 0.598 315s 2 99.6 0.471 99.4 99.7 99.6 0.679 315s 3 99.5 0.454 99.4 99.7 99.7 0.634 315s 4 99.7 0.475 99.5 99.8 100.0 0.643 315s 5 102.3 0.434 102.1 102.4 102.4 0.753 315s 6 102.0 0.418 101.9 102.2 102.4 0.680 315s 7 102.4 0.440 102.3 102.5 102.3 0.625 315s 8 102.7 0.537 102.5 102.9 104.4 0.799 315s 9 101.7 0.447 101.6 101.9 102.9 0.700 315s 10 100.9 0.628 100.7 101.1 100.2 0.716 315s 11 95.8 0.833 95.6 96.1 95.7 0.916 315s 12 94.6 0.807 94.4 94.9 93.9 1.226 315s 13 95.8 0.677 95.6 96.0 95.5 1.130 315s 14 99.1 0.459 98.9 99.2 97.8 0.796 315s 15 104.2 0.572 104.1 104.4 102.9 0.656 315s 16 103.8 0.509 103.7 104.0 104.6 0.644 315s 17 104.8 0.877 104.5 105.1 104.1 1.150 315s 18 101.9 0.478 101.7 102.0 103.0 0.575 315s 19 103.3 0.604 103.1 103.5 102.8 0.649 315s 20 106.2 1.102 105.8 106.6 105.5 0.875 315s supply.lwr supply.upr 315s 1 98.0 98.4 315s 2 99.4 99.8 315s 3 99.5 99.9 315s 4 99.8 100.2 315s 5 102.2 102.7 315s 6 102.1 102.6 315s 7 102.1 102.5 315s 8 104.1 104.6 315s 9 102.7 103.1 315s 10 99.9 100.4 315s 11 95.4 96.0 315s 12 93.5 94.3 315s 13 95.2 95.9 315s 14 97.6 98.1 315s 15 102.7 103.1 315s 16 104.4 104.8 315s 17 103.8 104.5 315s 18 102.8 103.2 315s 19 102.6 103.0 315s 20 105.2 105.8 315s > print( predict( fitwls4e$eq[[ 1 ]], se.fit = TRUE, interval = "confidence", 315s + level = 0.25 ) ) 315s fit se.fit lwr upr 315s 1 97.5 0.541 97.4 97.7 315s 2 99.6 0.471 99.4 99.7 315s 3 99.5 0.454 99.4 99.7 315s 4 99.7 0.475 99.5 99.8 315s 5 102.3 0.434 102.1 102.4 315s 6 102.0 0.418 101.9 102.2 315s 7 102.4 0.440 102.3 102.5 315s 8 102.7 0.537 102.5 102.9 315s 9 101.7 0.447 101.6 101.9 315s 10 100.9 0.628 100.7 101.1 315s 11 95.8 0.833 95.6 96.1 315s 12 94.6 0.807 94.4 94.9 315s 13 95.8 0.677 95.6 96.0 315s 14 99.1 0.459 98.9 99.2 315s 15 104.2 0.572 104.1 104.4 315s 16 103.8 0.509 103.7 104.0 315s 17 104.8 0.877 104.5 105.1 315s 18 101.9 0.478 101.7 102.0 315s 19 103.3 0.604 103.1 103.5 315s 20 106.2 1.102 105.8 106.6 315s > 315s > print( predict( fitwls5, se.fit = TRUE, se.pred = TRUE, 315s + interval = "prediction", level = 0.5, newdata = predictData ) ) 315s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 315s 1 104 0.749 2.07 102.1 105 96.4 315s 2 106 0.784 2.09 104.6 107 97.7 315s 3 106 0.793 2.09 104.5 107 97.8 315s 4 106 0.792 2.09 104.8 108 98.1 315s 5 109 1.136 2.24 107.1 110 100.6 315s 6 108 1.086 2.22 106.9 110 100.5 315s 7 109 1.097 2.22 107.4 110 100.4 315s 8 110 1.107 2.23 108.0 111 102.5 315s 9 108 1.126 2.24 106.4 109 101.1 315s 10 107 1.243 2.30 105.1 108 98.5 315s 11 101 1.066 2.21 99.7 103 94.0 315s 12 100 0.814 2.10 98.8 102 92.0 315s 13 102 0.617 2.03 100.4 103 93.7 315s 14 105 0.874 2.12 103.7 107 96.0 315s 15 111 1.377 2.37 109.0 112 101.2 315s 16 110 1.279 2.32 108.8 112 102.8 315s 17 111 1.656 2.55 108.9 112 102.5 315s 18 109 1.014 2.18 107.0 110 101.1 315s 19 110 1.180 2.27 108.7 112 100.9 315s 20 114 1.635 2.53 112.2 116 103.4 315s supply.se.fit supply.se.pred supply.lwr supply.upr 315s 1 0.799 2.58 94.6 98.1 315s 2 0.679 2.55 95.9 99.4 315s 3 0.692 2.55 96.1 99.6 315s 4 0.657 2.54 96.3 99.8 315s 5 1.051 2.67 98.8 102.5 315s 6 0.947 2.63 98.7 102.3 315s 7 0.845 2.59 98.7 102.2 315s 8 0.849 2.60 100.7 104.2 315s 9 1.100 2.69 99.3 103.0 315s 10 1.276 2.77 96.6 100.4 315s 11 1.422 2.84 92.1 95.9 315s 12 1.595 2.93 90.1 94.0 315s 13 1.401 2.82 91.7 95.6 315s 14 1.201 2.73 94.2 97.9 315s 15 1.169 2.72 99.3 103.0 315s 16 1.060 2.67 100.9 104.6 315s 17 1.727 3.00 100.5 104.6 315s 18 0.831 2.59 99.3 102.8 315s 19 0.834 2.59 99.1 102.6 315s 20 0.653 2.54 101.7 105.2 315s > print( predict( fitwls5$eq[[ 2 ]], se.fit = TRUE, se.pred = TRUE, 315s + interval = "prediction", level = 0.5, newdata = predictData ) ) 315s fit se.fit se.pred lwr upr 315s 1 96.4 0.799 2.58 94.6 98.1 315s 2 97.7 0.679 2.55 95.9 99.4 315s 3 97.8 0.692 2.55 96.1 99.6 315s 4 98.1 0.657 2.54 96.3 99.8 315s 5 100.6 1.051 2.67 98.8 102.5 315s 6 100.5 0.947 2.63 98.7 102.3 315s 7 100.4 0.845 2.59 98.7 102.2 315s 8 102.5 0.849 2.60 100.7 104.2 315s 9 101.1 1.100 2.69 99.3 103.0 315s 10 98.5 1.276 2.77 96.6 100.4 315s 11 94.0 1.422 2.84 92.1 95.9 315s 12 92.0 1.595 2.93 90.1 94.0 315s 13 93.7 1.401 2.82 91.7 95.6 315s 14 96.0 1.201 2.73 94.2 97.9 315s 15 101.2 1.169 2.72 99.3 103.0 315s 16 102.8 1.060 2.67 100.9 104.6 315s 17 102.5 1.727 3.00 100.5 104.6 315s 18 101.1 0.831 2.59 99.3 102.8 315s 19 100.9 0.834 2.59 99.1 102.6 315s 20 103.4 0.653 2.54 101.7 105.2 315s > 315s > print( predict( fitwlsi1e, se.fit = TRUE, se.pred = TRUE, 315s + interval = "confidence", level = 0.99, useDfSys = TRUE ) ) 315s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 315s 1 97.4 0.593 2.02 95.8 99.0 98.9 315s 2 99.6 0.532 2.00 98.1 101.0 100.1 315s 3 99.5 0.502 1.99 98.2 100.9 100.2 315s 4 99.7 0.537 2.00 98.2 101.2 100.4 315s 5 102.3 0.463 1.98 101.0 103.6 102.7 315s 6 102.1 0.427 1.98 100.9 103.2 102.6 315s 7 102.5 0.446 1.98 101.2 103.7 102.4 315s 8 102.8 0.554 2.01 101.3 104.3 104.3 315s 9 101.7 0.486 1.99 100.4 103.0 102.9 315s 10 100.8 0.727 2.06 98.8 102.8 100.4 315s 11 95.6 0.872 2.12 93.2 98.0 96.0 315s 12 94.4 0.903 2.13 91.9 96.8 94.1 315s 13 95.7 0.811 2.09 93.4 97.9 95.6 315s 14 99.0 0.468 1.99 97.7 100.3 97.8 315s 15 104.3 0.699 2.05 102.4 106.2 102.6 315s 16 103.9 0.568 2.01 102.4 105.5 104.1 315s 17 104.8 1.174 2.26 101.6 108.0 103.8 315s 18 101.9 0.494 1.99 100.6 103.3 102.4 315s 19 103.5 0.627 2.03 101.8 105.2 102.1 315s 20 106.5 1.175 2.26 103.3 109.7 104.5 315s supply.se.fit supply.se.pred supply.lwr supply.upr 315s 1 0.945 2.58 96.3 101.5 315s 2 0.928 2.58 97.5 102.6 315s 3 0.839 2.55 97.9 102.5 315s 4 0.816 2.54 98.1 102.6 315s 5 0.800 2.53 100.5 104.9 315s 6 0.707 2.51 100.6 104.5 315s 7 0.643 2.49 100.7 104.2 315s 8 0.862 2.55 102.0 106.7 315s 9 0.705 2.51 101.0 104.9 315s 10 0.877 2.56 98.0 102.7 315s 11 1.060 2.63 93.1 98.9 315s 12 1.247 2.71 90.7 97.5 315s 13 1.113 2.65 92.6 98.6 315s 14 0.801 2.53 95.6 100.0 315s 15 0.782 2.53 100.5 104.8 315s 16 0.819 2.54 101.9 106.3 315s 17 1.436 2.80 99.9 107.7 315s 18 0.861 2.55 100.0 104.7 315s 19 0.982 2.60 99.4 104.8 315s 20 1.489 2.83 100.4 108.6 315s > print( predict( fitwlsi1e$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 315s + interval = "confidence", level = 0.99, useDfSys = TRUE ) ) 315s fit se.fit se.pred lwr upr 315s 1 97.4 0.593 2.02 95.8 99.0 315s 2 99.6 0.532 2.00 98.1 101.0 315s 3 99.5 0.502 1.99 98.2 100.9 315s 4 99.7 0.537 2.00 98.2 101.2 315s 5 102.3 0.463 1.98 101.0 103.6 315s 6 102.1 0.427 1.98 100.9 103.2 315s 7 102.5 0.446 1.98 101.2 103.7 315s 8 102.8 0.554 2.01 101.3 104.3 315s 9 101.7 0.486 1.99 100.4 103.0 315s 10 100.8 0.727 2.06 98.8 102.8 315s 11 95.6 0.872 2.12 93.2 98.0 315s 12 94.4 0.903 2.13 91.9 96.8 315s 13 95.7 0.811 2.09 93.4 97.9 315s 14 99.0 0.468 1.99 97.7 100.3 315s 15 104.3 0.699 2.05 102.4 106.2 315s 16 103.9 0.568 2.01 102.4 105.5 315s 17 104.8 1.174 2.26 101.6 108.0 315s 18 101.9 0.494 1.99 100.6 103.3 315s 19 103.5 0.627 2.03 101.8 105.2 315s 20 106.5 1.175 2.26 103.3 109.7 315s > 315s > print( predict( fitwlsi2, se.fit = TRUE, interval = "prediction", 315s + level = 0.9, newdata = predictData ) ) 315s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 315s 1 103 0.937 99.7 107 96.6 1.151 315s 2 106 0.942 102.2 110 97.8 0.875 315s 3 106 0.966 102.1 109 98.0 0.909 315s 4 106 0.947 102.4 110 98.2 0.833 315s 5 108 1.448 104.3 112 100.9 1.327 315s 6 108 1.368 104.2 112 100.7 1.192 315s 7 109 1.352 104.7 113 100.6 1.052 315s 8 109 1.293 105.4 113 102.6 0.914 315s 9 108 1.459 103.5 112 101.4 1.400 315s 10 106 1.647 102.0 111 98.8 1.787 315s 11 101 1.300 97.0 105 94.4 1.911 315s 12 100 0.938 96.4 104 92.3 1.880 315s 13 102 0.722 98.2 105 93.8 1.565 315s 14 105 1.121 101.1 109 96.3 1.479 315s 15 110 1.769 105.8 115 101.4 1.481 315s 16 110 1.602 105.8 114 102.9 1.248 315s 17 110 2.210 105.3 115 102.9 2.201 315s 18 108 1.205 104.5 112 101.2 0.911 315s 19 110 1.353 106.1 114 100.9 0.877 315s 20 114 1.714 109.4 118 103.3 0.705 315s supply.lwr supply.upr 315s 1 92.0 101.2 315s 2 93.4 102.2 315s 3 93.6 102.4 315s 4 93.9 102.6 315s 5 96.2 105.6 315s 6 96.1 105.3 315s 7 96.1 105.1 315s 8 98.1 107.0 315s 9 96.6 106.1 315s 10 93.7 103.9 315s 11 89.1 99.6 315s 12 87.1 97.5 315s 13 88.9 98.8 315s 14 91.4 101.1 315s 15 96.6 106.3 315s 16 98.3 107.6 315s 17 97.4 108.5 315s 18 96.8 105.6 315s 19 96.5 105.3 315s 20 99.0 107.7 315s > print( predict( fitwlsi2$eq[[ 2 ]], se.fit = TRUE, interval = "prediction", 315s + level = 0.9, newdata = predictData ) ) 315s fit se.fit lwr upr 315s 1 96.6 1.151 92.0 101.2 315s 2 97.8 0.875 93.4 102.2 315s 3 98.0 0.909 93.6 102.4 315s 4 98.2 0.833 93.9 102.6 315s 5 100.9 1.327 96.2 105.6 315s 6 100.7 1.192 96.1 105.3 315s 7 100.6 1.052 96.1 105.1 315s 8 102.6 0.914 98.1 107.0 315s 9 101.4 1.400 96.6 106.1 315s 10 98.8 1.787 93.7 103.9 315s 11 94.4 1.911 89.1 99.6 315s 12 92.3 1.880 87.1 97.5 315s 13 93.8 1.565 88.9 98.8 315s 14 96.3 1.479 91.4 101.1 315s 15 101.4 1.481 96.6 106.3 315s 16 102.9 1.248 98.3 107.6 315s 17 102.9 2.201 97.4 108.5 315s 18 101.2 0.911 96.8 105.6 315s 19 100.9 0.877 96.5 105.3 315s 20 103.3 0.705 99.0 107.7 315s > 315s > print( predict( fitwlsi3e, interval = "prediction", level = 0.925 ) ) 315s demand.pred demand.lwr demand.upr supply.pred supply.lwr supply.upr 315s 1 97.6 93.9 101.3 98.3 93.6 103 315s 2 99.7 96.0 103.3 99.5 94.9 104 315s 3 99.6 95.9 103.3 99.7 95.1 104 315s 4 99.8 96.1 103.5 99.9 95.3 105 315s 5 102.2 98.6 105.9 102.5 97.8 107 315s 6 102.0 98.4 105.7 102.4 97.7 107 315s 7 102.4 98.7 106.0 102.3 97.6 107 315s 8 102.8 99.1 106.5 104.3 99.5 109 315s 9 101.7 98.0 105.3 102.9 98.3 108 315s 10 100.8 97.0 104.6 100.3 95.5 105 315s 11 95.8 91.9 99.7 95.9 91.0 101 315s 12 94.7 90.8 98.6 93.9 88.9 99 315s 13 95.9 92.1 99.7 95.5 90.6 100 315s 14 99.1 95.4 102.7 97.9 93.2 103 315s 15 104.1 100.4 107.9 103.0 98.3 108 315s 16 103.8 100.1 107.5 104.6 99.9 109 315s 17 104.6 100.4 108.7 104.3 99.2 109 315s 18 101.9 98.2 105.6 102.9 98.2 108 315s 19 103.4 99.6 107.1 102.7 98.0 107 315s 20 106.3 102.2 110.4 105.2 100.1 110 315s > print( predict( fitwlsi3e$eq[[ 1 ]], interval = "prediction", level = 0.925 ) ) 315s fit lwr upr 315s 1 97.6 93.9 101.3 315s 2 99.7 96.0 103.3 315s 3 99.6 95.9 103.3 315s 4 99.8 96.1 103.5 315s 5 102.2 98.6 105.9 315s 6 102.0 98.4 105.7 315s 7 102.4 98.7 106.0 315s 8 102.8 99.1 106.5 315s 9 101.7 98.0 105.3 315s 10 100.8 97.0 104.6 315s 11 95.8 91.9 99.7 315s 12 94.7 90.8 98.6 315s 13 95.9 92.1 99.7 315s 14 99.1 95.4 102.7 315s 15 104.1 100.4 107.9 315s 16 103.8 100.1 107.5 315s 17 104.6 100.4 108.7 315s 18 101.9 98.2 105.6 315s 19 103.4 99.6 107.1 315s 20 106.3 102.2 110.4 315s > 315s > print( predict( fitwlsi4, interval = "confidence", newdata = predictData ) ) 315s demand.pred demand.lwr demand.upr supply.pred supply.lwr supply.upr 315s 1 104 102.0 105 96.4 94.8 98.0 315s 2 106 104.4 108 97.7 96.3 99.0 315s 3 106 104.3 108 97.8 96.4 99.2 315s 4 106 104.6 108 98.1 96.7 99.4 315s 5 109 106.3 111 100.6 98.5 102.8 315s 6 108 106.2 111 100.5 98.6 102.4 315s 7 109 106.7 111 100.4 98.7 102.2 315s 8 110 107.3 112 102.5 100.7 104.2 315s 9 108 105.6 110 101.1 98.9 103.4 315s 10 107 104.1 109 98.5 95.9 101.1 315s 11 101 99.0 103 94.0 91.1 96.9 315s 12 100 98.6 102 92.0 88.8 95.3 315s 13 102 100.5 103 93.7 90.8 96.5 315s 14 105 103.3 107 96.0 93.6 98.5 315s 15 111 107.8 113 101.2 98.8 103.6 315s 16 110 107.8 113 102.8 100.6 104.9 315s 17 111 107.3 114 102.5 99.0 106.0 315s 18 109 106.5 111 101.1 99.4 102.8 315s 19 110 107.9 113 100.9 99.2 102.6 315s 20 114 110.6 117 103.4 102.1 104.7 315s > print( predict( fitwlsi4$eq[[ 2 ]], interval = "confidence", 315s + newdata = predictData ) ) 315s fit lwr upr 315s 1 96.4 94.8 98.0 315s 2 97.7 96.3 99.0 315s 3 97.8 96.4 99.2 315s 4 98.1 96.7 99.4 315s 5 100.6 98.5 102.8 315s 6 100.5 98.6 102.4 315s 7 100.4 98.7 102.2 315s 8 102.5 100.7 104.2 315s 9 101.1 98.9 103.4 315s 10 98.5 95.9 101.1 315s 11 94.0 91.1 96.9 315s 12 92.0 88.8 95.3 315s 13 93.7 90.8 96.5 315s 14 96.0 93.6 98.5 315s 15 101.2 98.8 103.6 315s 16 102.8 100.6 104.9 315s 17 102.5 99.0 106.0 315s 18 101.1 99.4 102.8 315s 19 100.9 99.2 102.6 315s 20 103.4 102.1 104.7 315s > 315s > print( predict( fitwlsi5e, se.fit = TRUE, se.pred = TRUE, 315s + interval = "prediction", level = 0.01 ) ) 315s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 315s 1 97.5 0.540 2.01 97.5 97.6 98.2 315s 2 99.6 0.470 1.99 99.6 99.6 99.6 315s 3 99.5 0.453 1.99 99.5 99.6 99.7 315s 4 99.7 0.474 1.99 99.7 99.7 100.0 315s 5 102.3 0.433 1.98 102.2 102.3 102.4 315s 6 102.0 0.417 1.98 102.0 102.1 102.4 315s 7 102.4 0.439 1.98 102.4 102.4 102.3 315s 8 102.7 0.536 2.01 102.7 102.7 104.4 315s 9 101.7 0.446 1.99 101.7 101.8 102.9 315s 10 100.9 0.627 2.03 100.9 100.9 100.2 315s 11 95.8 0.831 2.11 95.8 95.9 95.7 315s 12 94.6 0.806 2.10 94.6 94.6 93.9 315s 13 95.8 0.676 2.05 95.8 95.8 95.5 315s 14 99.1 0.458 1.99 99.0 99.1 97.8 315s 15 104.2 0.571 2.02 104.2 104.3 102.9 315s 16 103.8 0.508 2.00 103.8 103.9 104.6 315s 17 104.8 0.877 2.12 104.8 104.8 104.1 315s 18 101.9 0.477 1.99 101.8 101.9 103.0 315s 19 103.3 0.602 2.03 103.3 103.4 102.8 315s 20 106.2 1.100 2.23 106.2 106.2 105.5 315s supply.se.fit supply.se.pred supply.lwr supply.upr 315s 1 0.598 2.52 98.2 98.3 315s 2 0.680 2.54 99.5 99.6 315s 3 0.634 2.53 99.7 99.8 315s 4 0.644 2.54 100.0 100.0 315s 5 0.754 2.57 102.4 102.5 315s 6 0.681 2.55 102.3 102.4 315s 7 0.626 2.53 102.3 102.3 315s 8 0.800 2.58 104.4 104.4 315s 9 0.701 2.55 102.9 102.9 315s 10 0.716 2.55 100.1 100.2 315s 11 0.918 2.62 95.7 95.8 315s 12 1.229 2.74 93.8 93.9 315s 13 1.132 2.70 95.5 95.6 315s 14 0.797 2.58 97.8 97.9 315s 15 0.657 2.54 102.9 103.0 315s 16 0.645 2.54 104.5 104.6 315s 17 1.151 2.71 104.1 104.2 315s 18 0.575 2.52 103.0 103.0 315s 19 0.649 2.54 102.8 102.8 315s 20 0.875 2.60 105.5 105.5 315s > print( predict( fitwlsi5e$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 315s + interval = "prediction", level = 0.01 ) ) 315s fit se.fit se.pred lwr upr 315s 1 97.5 0.540 2.01 97.5 97.6 315s 2 99.6 0.470 1.99 99.6 99.6 315s 3 99.5 0.453 1.99 99.5 99.6 315s 4 99.7 0.474 1.99 99.7 99.7 315s 5 102.3 0.433 1.98 102.2 102.3 315s 6 102.0 0.417 1.98 102.0 102.1 315s 7 102.4 0.439 1.98 102.4 102.4 315s 8 102.7 0.536 2.01 102.7 102.7 315s 9 101.7 0.446 1.99 101.7 101.8 315s 10 100.9 0.627 2.03 100.9 100.9 315s 11 95.8 0.831 2.11 95.8 95.9 315s 12 94.6 0.806 2.10 94.6 94.6 315s 13 95.8 0.676 2.05 95.8 95.8 315s 14 99.1 0.458 1.99 99.0 99.1 315s 15 104.2 0.571 2.02 104.2 104.3 315s 16 103.8 0.508 2.00 103.8 103.9 315s 17 104.8 0.877 2.12 104.8 104.8 315s 18 101.9 0.477 1.99 101.8 101.9 315s 19 103.3 0.602 2.03 103.3 103.4 315s 20 106.2 1.100 2.23 106.2 106.2 315s > 315s > # predict just one observation 315s > smallData <- data.frame( price = 130, income = 150, farmPrice = 120, 315s + trend = 25 ) 315s > 315s > print( predict( fitwls1, newdata = smallData ) ) 315s demand.pred supply.pred 315s 1 109 115 315s > print( predict( fitwls1$eq[[ 1 ]], newdata = smallData ) ) 315s fit 315s 1 109 315s > 315s > print( predict( fitwls2e, se.fit = TRUE, level = 0.9, 315s + newdata = smallData ) ) 315s demand.pred demand.se.fit supply.pred supply.se.fit 315s 1 109 2.23 116 3.03 315s > print( predict( fitwls2e$eq[[ 1 ]], se.pred = TRUE, level = 0.99, 315s + newdata = smallData ) ) 315s fit se.pred 315s 1 109 2.96 315s > 315s > print( predict( fitwls3, interval = "prediction", level = 0.975, 315s + newdata = smallData ) ) 315s demand.pred demand.lwr demand.upr supply.pred supply.lwr supply.upr 315s 1 109 101 116 116 107 126 315s > print( predict( fitwls3$eq[[ 1 ]], interval = "confidence", level = 0.8, 315s + newdata = smallData ) ) 315s fit lwr upr 315s 1 109 106 112 315s > 315s > print( predict( fitwls4e, se.fit = TRUE, interval = "confidence", 315s + level = 0.999, newdata = smallData ) ) 315s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 315s 1 108 2.02 101 116 117 2.02 315s supply.lwr supply.upr 315s 1 110 124 315s > print( predict( fitwls4e$eq[[ 2 ]], se.pred = TRUE, interval = "prediction", 315s + level = 0.75, newdata = smallData ) ) 315s fit se.pred lwr upr 315s 1 117 3.18 113 121 315s > 315s > print( predict( fitwls5, se.fit = TRUE, interval = "prediction", 315s + newdata = smallData ) ) 315s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 315s 1 108 2.2 102 114 117 2.23 315s supply.lwr supply.upr 315s 1 110 124 315s > print( predict( fitwls5$eq[[ 1 ]], se.pred = TRUE, interval = "confidence", 315s + newdata = smallData ) ) 315s fit se.pred lwr upr 315s 1 108 2.93 104 113 315s > 315s > print( predict( fitwlsi3e, se.fit = TRUE, se.pred = TRUE, 315s + interval = "prediction", level = 0.5, newdata = smallData ) ) 315s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 315s 1 109 2.23 2.95 107 111 116 315s supply.se.fit supply.se.pred supply.lwr supply.upr 315s 1 3.04 3.9 114 119 315s > print( predict( fitwlsi3e$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 315s + interval = "confidence", level = 0.25, newdata = smallData ) ) 315s fit se.fit se.pred lwr upr 315s 1 109 2.23 2.95 108 109 315s > 315s > 315s > ## ************ correlation of predicted values *************** 315s > print( correlation.systemfit( fitwls1, 2, 1 ) ) 315s [,1] 315s [1,] 0 315s [2,] 0 315s [3,] 0 315s [4,] 0 315s [5,] 0 315s [6,] 0 315s [7,] 0 315s [8,] 0 315s [9,] 0 315s [10,] 0 315s [11,] 0 315s [12,] 0 315s [13,] 0 315s [14,] 0 315s [15,] 0 315s [16,] 0 315s [17,] 0 315s [18,] 0 315s [19,] 0 315s [20,] 0 315s > 315s > print( correlation.systemfit( fitwls2e, 1, 2 ) ) 315s [,1] 315s [1,] 0.411525 315s [2,] 0.147624 315s [3,] 0.147711 315s [4,] 0.107654 315s [5,] -0.069284 315s [6,] -0.053039 315s [7,] -0.051551 315s [8,] -0.006153 315s [9,] -0.000333 315s [10,] -0.001262 315s [11,] 0.048574 315s [12,] 0.064996 315s [13,] 0.024618 315s [14,] -0.028485 315s [15,] 0.174980 315s [16,] 0.252722 315s [17,] 0.103392 315s [18,] 0.074219 315s [19,] 0.156545 315s [20,] 0.135438 315s > 315s > print( correlation.systemfit( fitwls3, 2, 1 ) ) 315s [,1] 315s [1,] 0.405901 315s [2,] 0.145364 315s [3,] 0.145375 315s [4,] 0.105835 315s [5,] -0.067958 315s [6,] -0.052026 315s [7,] -0.050543 315s [8,] -0.006031 315s [9,] -0.000326 315s [10,] -0.001237 315s [11,] 0.047534 315s [12,] 0.063493 315s [13,] 0.024060 315s [14,] -0.027910 315s [15,] 0.171580 315s [16,] 0.248212 315s [17,] 0.101409 315s [18,] 0.073084 315s [19,] 0.153950 315s [20,] 0.132944 315s > 315s > print( correlation.systemfit( fitwls4e, 1, 2 ) ) 315s [,1] 315s [1,] 0.38162 315s [2,] 0.29173 315s [3,] 0.25421 315s [4,] 0.28598 315s [5,] -0.02775 315s [6,] -0.04974 315s [7,] -0.05850 315s [8,] 0.09388 315s [9,] 0.09469 315s [10,] 0.43814 315s [11,] 0.10559 315s [12,] 0.00876 315s [13,] 0.04090 315s [14,] -0.03984 315s [15,] 0.40767 315s [16,] 0.24571 315s [17,] 0.64160 315s [18,] 0.24037 315s [19,] 0.34075 315s [20,] 0.54270 315s > 315s > print( correlation.systemfit( fitwls5, 2, 1 ) ) 315s [,1] 315s [1,] 0.3775 315s [2,] 0.2936 315s [3,] 0.2553 315s [4,] 0.2875 315s [5,] -0.0274 315s [6,] -0.0492 315s [7,] -0.0578 315s [8,] 0.0932 315s [9,] 0.0944 315s [10,] 0.4375 315s [11,] 0.1027 315s [12,] 0.0072 315s [13,] 0.0404 315s [14,] -0.0396 315s [15,] 0.4062 315s [16,] 0.2430 315s [17,] 0.6406 315s [18,] 0.2362 315s [19,] 0.3347 315s [20,] 0.5378 315s > 315s > print( correlation.systemfit( fitwlsi1e, 1, 2 ) ) 315s [,1] 315s [1,] 0 315s [2,] 0 315s [3,] 0 315s [4,] 0 315s [5,] 0 315s [6,] 0 315s [7,] 0 315s [8,] 0 315s [9,] 0 315s [10,] 0 315s [11,] 0 315s [12,] 0 315s [13,] 0 315s [14,] 0 315s [15,] 0 315s [16,] 0 315s [17,] 0 315s [18,] 0 315s [19,] 0 315s [20,] 0 315s > 315s > print( correlation.systemfit( fitwlsi2, 2, 1 ) ) 315s [,1] 315s [1,] 0.404696 315s [2,] 0.144881 315s [3,] 0.144877 315s [4,] 0.105448 315s [5,] -0.067678 315s [6,] -0.051812 315s [7,] -0.050330 315s [8,] -0.006005 315s [9,] -0.000325 315s [10,] -0.001232 315s [11,] 0.047315 315s [12,] 0.063179 315s [13,] 0.023943 315s [14,] -0.027789 315s [15,] 0.170862 315s [16,] 0.247256 315s [17,] 0.100990 315s [18,] 0.072842 315s [19,] 0.153398 315s [20,] 0.132415 315s > 315s > print( correlation.systemfit( fitwlsi3e, 1, 2 ) ) 315s [,1] 315s [1,] 0.410485 315s [2,] 0.147206 315s [3,] 0.147278 315s [4,] 0.107316 315s [5,] -0.069036 315s [6,] -0.052850 315s [7,] -0.051363 315s [8,] -0.006130 315s [9,] -0.000331 315s [10,] -0.001257 315s [11,] 0.048379 315s [12,] 0.064714 315s [13,] 0.024513 315s [14,] -0.028377 315s [15,] 0.174345 315s [16,] 0.251882 315s [17,] 0.103022 315s [18,] 0.074009 315s [19,] 0.156063 315s [20,] 0.134974 315s > 315s > print( correlation.systemfit( fitwlsi4, 2, 1 ) ) 315s [,1] 315s [1,] 0.37672 315s [2,] 0.29387 315s [3,] 0.25544 315s [4,] 0.28775 315s [5,] -0.02729 315s [6,] -0.04911 315s [7,] -0.05771 315s [8,] 0.09311 315s [9,] 0.09437 315s [10,] 0.43736 315s [11,] 0.10223 315s [12,] 0.00693 315s [13,] 0.04035 315s [14,] -0.03961 315s [15,] 0.40591 315s [16,] 0.24248 315s [17,] 0.64034 315s [18,] 0.23551 315s [19,] 0.33360 315s [20,] 0.53687 315s > 315s > print( correlation.systemfit( fitwlsi5e, 1, 2 ) ) 315s [,1] 315s [1,] 0.38098 315s [2,] 0.29204 315s [3,] 0.25439 315s [4,] 0.28624 315s [5,] -0.02769 315s [6,] -0.04966 315s [7,] -0.05840 315s [8,] 0.09378 315s [9,] 0.09465 315s [10,] 0.43805 315s [11,] 0.10513 315s [12,] 0.00851 315s [13,] 0.04083 315s [14,] -0.03981 315s [15,] 0.40746 315s [16,] 0.24528 315s [17,] 0.64146 315s [18,] 0.23972 315s [19,] 0.33979 315s [20,] 0.54192 315s > 315s > 315s > ## ************ Log-Likelihood values *************** 315s > print( logLik( fitwls1 ) ) 315s 'log Lik.' -67.8 (df=9) 315s > print( logLik( fitwls1, residCovDiag = TRUE ) ) 315s 'log Lik.' -83.6 (df=9) 315s > all.equal( logLik( fitwls1, residCovDiag = TRUE ), 315s + logLik( lmDemand ) + logLik( lmSupply ), 315s + check.attributes = FALSE ) 315s [1] TRUE 315s > 315s > print( logLik( fitwls2e ) ) 315s 'log Lik.' -61.5 (df=8) 315s > print( logLik( fitwls2e, residCovDiag = TRUE ) ) 315s 'log Lik.' -84 (df=8) 315s > 315s > print( logLik( fitwls3 ) ) 315s 'log Lik.' -61.4 (df=8) 315s > print( logLik( fitwls3, residCovDiag = TRUE ) ) 315s 'log Lik.' -84 (df=8) 315s > 315s > print( logLik( fitwls4e ) ) 315s 'log Lik.' -62.2 (df=7) 315s > print( logLik( fitwls4e, residCovDiag = TRUE ) ) 315s 'log Lik.' -84 (df=7) 315s > 315s > print( logLik( fitwls5 ) ) 315s 'log Lik.' -62.1 (df=7) 315s > print( logLik( fitwls5, residCovDiag = TRUE ) ) 315s 'log Lik.' -84 (df=7) 315s > 315s > print( logLik( fitwlsi1e ) ) 315s 'log Lik.' -67.8 (df=9) 315s > print( logLik( fitwlsi1e, residCovDiag = TRUE ) ) 315s 'log Lik.' -83.6 (df=9) 315s > 315s > print( logLik( fitwlsi2 ) ) 315s 'log Lik.' -61.4 (df=8) 315s > print( logLik( fitwlsi2, residCovDiag = TRUE ) ) 315s 'log Lik.' -84 (df=8) 315s > 315s > print( logLik( fitwlsi3e ) ) 315s 'log Lik.' -61.5 (df=8) 315s > print( logLik( fitwlsi3e, residCovDiag = TRUE ) ) 315s 'log Lik.' -84 (df=8) 315s > 315s > print( logLik( fitwlsi4 ) ) 315s 'log Lik.' -62.1 (df=7) 315s > print( logLik( fitwlsi4, residCovDiag = TRUE ) ) 315s 'log Lik.' -84 (df=7) 315s > 315s > print( logLik( fitwlsi5e ) ) 315s 'log Lik.' -62.2 (df=7) 315s > print( logLik( fitwlsi5e, residCovDiag = TRUE ) ) 315s 'log Lik.' -84 (df=7) 315s > 315s > 315s > ## ************** F tests **************** 315s > # testing first restriction 315s > print( linearHypothesis( fitwls1, restrm ) ) 315s Linear hypothesis test (Theil's F test) 315s 315s Hypothesis: 315s demand_income - supply_trend = 0 315s 315s Model 1: restricted model 315s Model 2: fitwls1 315s 315s Res.Df Df F Pr(>F) 315s 1 34 315s 2 33 1 0.64 0.43 315s > linearHypothesis( fitwls1, restrict ) 315s Linear hypothesis test (Theil's F test) 315s 315s Hypothesis: 315s demand_income - supply_trend = 0 315s 315s Model 1: restricted model 315s Model 2: fitwls1 315s 315s Res.Df Df F Pr(>F) 315s 1 34 315s 2 33 1 0.64 0.43 315s > 315s > print( linearHypothesis( fitwlsi1e, restrm ) ) 315s Linear hypothesis test (Theil's F test) 315s 315s Hypothesis: 315s demand_income - supply_trend = 0 315s 315s Model 1: restricted model 315s Model 2: fitwlsi1e 315s 315s Res.Df Df F Pr(>F) 315s 1 34 315s 2 33 1 0.66 0.42 315s > linearHypothesis( fitwlsi1e, restrict ) 315s Linear hypothesis test (Theil's F test) 315s 315s Hypothesis: 315s demand_income - supply_trend = 0 315s 315s Model 1: restricted model 315s Model 2: fitwlsi1e 315s 315s Res.Df Df F Pr(>F) 315s 1 34 315s 2 33 1 0.66 0.42 315s > 315s > # testing second restriction 315s > restrOnly2m <- matrix(0,1,7) 315s > restrOnly2q <- 0.5 315s > restrOnly2m[1,2] <- -1 315s > restrOnly2m[1,5] <- 1 315s > restrictOnly2 <- "- demand_price + supply_price = 0.5" 315s > # first restriction not imposed 315s > print( linearHypothesis( fitwls1e, restrOnly2m, restrOnly2q ) ) 315s Linear hypothesis test (Theil's F test) 315s 315s Hypothesis: 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwls1e 315s 315s Res.Df Df F Pr(>F) 315s 1 34 315s 2 33 1 0.03 0.86 315s > linearHypothesis( fitwls1e, restrictOnly2 ) 315s Linear hypothesis test (Theil's F test) 315s 315s Hypothesis: 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwls1e 315s 315s Res.Df Df F Pr(>F) 315s 1 34 315s 2 33 1 0.03 0.86 315s > 315s > print( linearHypothesis( fitwlsi1, restrOnly2m, restrOnly2q ) ) 315s Linear hypothesis test (Theil's F test) 315s 315s Hypothesis: 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwlsi1 315s 315s Res.Df Df F Pr(>F) 315s 1 34 315s 2 33 1 0.03 0.86 315s > linearHypothesis( fitwlsi1, restrictOnly2 ) 315s Linear hypothesis test (Theil's F test) 315s 315s Hypothesis: 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwlsi1 315s 315s Res.Df Df F Pr(>F) 315s 1 34 315s 2 33 1 0.03 0.86 315s > 315s > # first restriction imposed 315s > print( linearHypothesis( fitwls2, restrOnly2m, restrOnly2q ) ) 315s Linear hypothesis test (Theil's F test) 315s 315s Hypothesis: 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwls2 315s 315s Res.Df Df F Pr(>F) 315s 1 35 315s 2 34 1 0.08 0.78 315s > linearHypothesis( fitwls2, restrictOnly2 ) 315s Linear hypothesis test (Theil's F test) 315s 315s Hypothesis: 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwls2 315s 315s Res.Df Df F Pr(>F) 315s 1 35 315s 2 34 1 0.08 0.78 315s > 315s > print( linearHypothesis( fitwls3, restrOnly2m, restrOnly2q ) ) 315s Linear hypothesis test (Theil's F test) 315s 315s Hypothesis: 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwls3 315s 315s Res.Df Df F Pr(>F) 315s 1 35 315s 2 34 1 0.08 0.78 315s > linearHypothesis( fitwls3, restrictOnly2 ) 315s Linear hypothesis test (Theil's F test) 315s 315s Hypothesis: 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwls3 315s 315s Res.Df Df F Pr(>F) 315s 1 35 315s 2 34 1 0.08 0.78 315s > 315s > print( linearHypothesis( fitwlsi2e, restrOnly2m, restrOnly2q ) ) 315s Linear hypothesis test (Theil's F test) 315s 315s Hypothesis: 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwlsi2e 315s 315s Res.Df Df F Pr(>F) 315s 1 35 315s 2 34 1 0.08 0.77 315s > linearHypothesis( fitwlsi2e, restrictOnly2 ) 315s Linear hypothesis test (Theil's F test) 315s 315s Hypothesis: 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwlsi2e 315s 315s Res.Df Df F Pr(>F) 315s 1 35 315s 2 34 1 0.08 0.77 315s > 315s > print( linearHypothesis( fitwlsi3e, restrOnly2m, restrOnly2q ) ) 315s Linear hypothesis test (Theil's F test) 315s 315s Hypothesis: 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwlsi3e 315s 315s Res.Df Df F Pr(>F) 315s 1 35 315s 2 34 1 0.08 0.77 315s > linearHypothesis( fitwlsi3e, restrictOnly2 ) 315s Linear hypothesis test (Theil's F test) 315s 315s Hypothesis: 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwlsi3e 315s 315s Res.Df Df F Pr(>F) 315s 1 35 315s 2 34 1 0.08 0.77 315s > 315s > # testing both of the restrictions 315s > print( linearHypothesis( fitwls1e, restr2m, restr2q ) ) 315s Linear hypothesis test (Theil's F test) 315s 315s Hypothesis: 315s demand_income - supply_trend = 0 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwls1e 315s 315s Res.Df Df F Pr(>F) 315s 1 35 315s 2 33 2 0.37 0.69 315s > linearHypothesis( fitwls1e, restrict2 ) 315s Linear hypothesis test (Theil's F test) 315s 315s Hypothesis: 315s demand_income - supply_trend = 0 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwls1e 315s 315s Res.Df Df F Pr(>F) 315s 1 35 315s 2 33 2 0.37 0.69 315s > 315s > print( linearHypothesis( fitwlsi1, restr2m, restr2q ) ) 315s Linear hypothesis test (Theil's F test) 315s 315s Hypothesis: 315s demand_income - supply_trend = 0 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwlsi1 315s 315s Res.Df Df F Pr(>F) 315s 1 35 315s 2 33 2 0.36 0.7 315s > linearHypothesis( fitwlsi1, restrict2 ) 315s Linear hypothesis test (Theil's F test) 315s 315s Hypothesis: 315s demand_income - supply_trend = 0 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwlsi1 315s 315s Res.Df Df F Pr(>F) 315s 1 35 315s 2 33 2 0.36 0.7 315s > 315s > 315s > ## ************** Wald tests **************** 315s > # testing first restriction 315s > print( linearHypothesis( fitwls1, restrm, test = "Chisq" ) ) 315s Linear hypothesis test (Chi^2 statistic of a Wald test) 315s 315s Hypothesis: 315s demand_income - supply_trend = 0 315s 315s Model 1: restricted model 315s Model 2: fitwls1 315s 315s Res.Df Df Chisq Pr(>Chisq) 315s 1 34 315s 2 33 1 0.64 0.42 315s > linearHypothesis( fitwls1, restrict, test = "Chisq" ) 315s Linear hypothesis test (Chi^2 statistic of a Wald test) 315s 315s Hypothesis: 315s demand_income - supply_trend = 0 315s 315s Model 1: restricted model 315s Model 2: fitwls1 315s 315s Res.Df Df Chisq Pr(>Chisq) 315s 1 34 315s 2 33 1 0.64 0.42 315s > 315s > print( linearHypothesis( fitwlsi1e, restrm, test = "Chisq" ) ) 315s Linear hypothesis test (Chi^2 statistic of a Wald test) 315s 315s Hypothesis: 315s demand_income - supply_trend = 0 315s 315s Model 1: restricted model 315s Model 2: fitwlsi1e 315s 315s Res.Df Df Chisq Pr(>Chisq) 315s 1 34 315s 2 33 1 0.8 0.37 315s > linearHypothesis( fitwlsi1e, restrict, test = "Chisq" ) 315s Linear hypothesis test (Chi^2 statistic of a Wald test) 315s 315s Hypothesis: 315s demand_income - supply_trend = 0 315s 315s Model 1: restricted model 315s Model 2: fitwlsi1e 315s 315s Res.Df Df Chisq Pr(>Chisq) 315s 1 34 315s 2 33 1 0.8 0.37 315s > 315s > # testing second restriction 315s > # first restriction not imposed 315s > print( linearHypothesis( fitwls1e, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 315s Linear hypothesis test (Chi^2 statistic of a Wald test) 315s 315s Hypothesis: 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwls1e 315s 315s Res.Df Df Chisq Pr(>Chisq) 315s 1 34 315s 2 33 1 0.04 0.84 315s > linearHypothesis( fitwls1e, restrictOnly2, test = "Chisq" ) 315s Linear hypothesis test (Chi^2 statistic of a Wald test) 315s 315s Hypothesis: 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwls1e 315s 315s Res.Df Df Chisq Pr(>Chisq) 315s 1 34 315s 2 33 1 0.04 0.84 315s > 315s > print( linearHypothesis( fitwlsi1, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 315s Linear hypothesis test (Chi^2 statistic of a Wald test) 315s 315s Hypothesis: 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwlsi1 315s 315s Res.Df Df Chisq Pr(>Chisq) 315s 1 34 315s 2 33 1 0.03 0.86 315s > linearHypothesis( fitwlsi1, restrictOnly2, test = "Chisq" ) 315s Linear hypothesis test (Chi^2 statistic of a Wald test) 315s 315s Hypothesis: 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwlsi1 315s 315s Res.Df Df Chisq Pr(>Chisq) 315s 1 34 315s 2 33 1 0.03 0.86 315s > 315s > # first restriction imposed 315s > print( linearHypothesis( fitwls2, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 315s Linear hypothesis test (Chi^2 statistic of a Wald test) 315s 315s Hypothesis: 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwls2 315s 315s Res.Df Df Chisq Pr(>Chisq) 315s 1 35 315s 2 34 1 0.08 0.78 315s > linearHypothesis( fitwls2, restrictOnly2, test = "Chisq" ) 315s Linear hypothesis test (Chi^2 statistic of a Wald test) 315s 315s Hypothesis: 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwls2 315s 315s Res.Df Df Chisq Pr(>Chisq) 315s 1 35 315s 2 34 1 0.08 0.78 315s > 315s > print( linearHypothesis( fitwls3, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 315s Linear hypothesis test (Chi^2 statistic of a Wald test) 315s 315s Hypothesis: 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwls3 315s 315s Res.Df Df Chisq Pr(>Chisq) 315s 1 35 315s 2 34 1 0.08 0.78 315s > linearHypothesis( fitwls3, restrictOnly2, test = "Chisq" ) 315s Linear hypothesis test (Chi^2 statistic of a Wald test) 315s 315s Hypothesis: 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwls3 315s 315s Res.Df Df Chisq Pr(>Chisq) 315s 1 35 315s 2 34 1 0.08 0.78 315s > 315s > print( linearHypothesis( fitwlsi2e, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 315s Linear hypothesis test (Chi^2 statistic of a Wald test) 315s 315s Hypothesis: 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwlsi2e 315s 315s Res.Df Df Chisq Pr(>Chisq) 315s 1 35 315s 2 34 1 0.1 0.75 315s > linearHypothesis( fitwlsi2e, restrictOnly2, test = "Chisq" ) 315s Linear hypothesis test (Chi^2 statistic of a Wald test) 315s 315s Hypothesis: 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwlsi2e 315s 315s Res.Df Df Chisq Pr(>Chisq) 315s 1 35 315s 2 34 1 0.1 0.75 315s > 315s > print( linearHypothesis( fitwlsi3e, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 315s Linear hypothesis test (Chi^2 statistic of a Wald test) 315s 315s Hypothesis: 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwlsi3e 315s 315s Res.Df Df Chisq Pr(>Chisq) 315s 1 35 315s 2 34 1 0.1 0.75 315s > linearHypothesis( fitwlsi3e, restrictOnly2, test = "Chisq" ) 315s Linear hypothesis test (Chi^2 statistic of a Wald test) 315s 315s Hypothesis: 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwlsi3e 315s 315s Res.Df Df Chisq Pr(>Chisq) 315s 1 35 315s 2 34 1 0.1 0.75 315s > 315s > # testing both of the restrictions 315s > print( linearHypothesis( fitwls1e, restr2m, restr2q, test = "Chisq" ) ) 315s Linear hypothesis test (Chi^2 statistic of a Wald test) 315s 315s Hypothesis: 315s demand_income - supply_trend = 0 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwls1e 315s 315s Res.Df Df Chisq Pr(>Chisq) 315s 1 35 315s 2 33 2 0.9 0.64 315s > linearHypothesis( fitwls1e, restrict2, test = "Chisq" ) 315s Linear hypothesis test (Chi^2 statistic of a Wald test) 315s 315s Hypothesis: 315s demand_income - supply_trend = 0 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwls1e 315s 315s Res.Df Df Chisq Pr(>Chisq) 315s 1 35 315s 2 33 2 0.9 0.64 315s > 315s > print( linearHypothesis( fitwlsi1, restr2m, restr2q, test = "Chisq" ) ) 315s Linear hypothesis test (Chi^2 statistic of a Wald test) 315s 315s Hypothesis: 315s demand_income - supply_trend = 0 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwlsi1 315s 315s Res.Df Df Chisq Pr(>Chisq) 315s 1 35 315s 2 33 2 0.72 0.7 315s > linearHypothesis( fitwlsi1, restrict2, test = "Chisq" ) 315s Linear hypothesis test (Chi^2 statistic of a Wald test) 315s 315s Hypothesis: 315s demand_income - supply_trend = 0 315s - demand_price + supply_price = 0.5 315s 315s Model 1: restricted model 315s Model 2: fitwlsi1 315s 315s Res.Df Df Chisq Pr(>Chisq) 315s 1 35 315s 2 33 2 0.72 0.7 315s > 315s > 315s > ## ****************** model frame ************************** 315s > print( mf <- model.frame( fitwls1 ) ) 315s consump price income farmPrice trend 315s 1 98.5 100.3 87.4 98.0 1 315s 2 99.2 104.3 97.6 99.1 2 315s 3 102.2 103.4 96.7 99.1 3 315s 4 101.5 104.5 98.2 98.1 4 315s 5 104.2 98.0 99.8 110.8 5 315s 6 103.2 99.5 100.5 108.2 6 315s 7 104.0 101.1 103.2 105.6 7 315s 8 99.9 104.8 107.8 109.8 8 315s 9 100.3 96.4 96.6 108.7 9 315s 10 102.8 91.2 88.9 100.6 10 315s 11 95.4 93.1 75.1 81.0 11 315s 12 92.4 98.8 76.9 68.6 12 315s 13 94.5 102.9 84.6 70.9 13 315s 14 98.8 98.8 90.6 81.4 14 315s 15 105.8 95.1 103.1 102.3 15 315s 16 100.2 98.5 105.1 105.0 16 315s 17 103.5 86.5 96.4 110.5 17 315s 18 99.9 104.0 104.4 92.5 18 315s 19 105.2 105.8 110.7 89.3 19 315s 20 106.2 113.5 127.1 93.0 20 315s > print( mf1 <- model.frame( fitwls1$eq[[ 1 ]] ) ) 315s consump price income 315s 1 98.5 100.3 87.4 315s 2 99.2 104.3 97.6 315s 3 102.2 103.4 96.7 315s 4 101.5 104.5 98.2 315s 5 104.2 98.0 99.8 315s 6 103.2 99.5 100.5 315s 7 104.0 101.1 103.2 315s 8 99.9 104.8 107.8 315s 9 100.3 96.4 96.6 315s 10 102.8 91.2 88.9 315s 11 95.4 93.1 75.1 315s 12 92.4 98.8 76.9 315s 13 94.5 102.9 84.6 315s 14 98.8 98.8 90.6 315s 15 105.8 95.1 103.1 315s 16 100.2 98.5 105.1 315s 17 103.5 86.5 96.4 315s 18 99.9 104.0 104.4 315s 19 105.2 105.8 110.7 315s 20 106.2 113.5 127.1 315s > print( attributes( mf1 )$terms ) 315s consump ~ price + income 315s attr(,"variables") 315s list(consump, price, income) 315s attr(,"factors") 315s price income 315s consump 0 0 315s price 1 0 315s income 0 1 315s attr(,"term.labels") 315s [1] "price" "income" 315s attr(,"order") 315s [1] 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, income) 315s attr(,"dataClasses") 315s consump price income 315s "numeric" "numeric" "numeric" 315s > print( mf2 <- model.frame( fitwls1$eq[[ 2 ]] ) ) 315s consump price farmPrice trend 315s 1 98.5 100.3 98.0 1 315s 2 99.2 104.3 99.1 2 315s 3 102.2 103.4 99.1 3 315s 4 101.5 104.5 98.1 4 315s 5 104.2 98.0 110.8 5 315s 6 103.2 99.5 108.2 6 315s 7 104.0 101.1 105.6 7 315s 8 99.9 104.8 109.8 8 315s 9 100.3 96.4 108.7 9 315s 10 102.8 91.2 100.6 10 315s 11 95.4 93.1 81.0 11 315s 12 92.4 98.8 68.6 12 315s 13 94.5 102.9 70.9 13 315s 14 98.8 98.8 81.4 14 315s 15 105.8 95.1 102.3 15 315s 16 100.2 98.5 105.0 16 315s 17 103.5 86.5 110.5 17 315s 18 99.9 104.0 92.5 18 315s 19 105.2 105.8 89.3 19 315s 20 106.2 113.5 93.0 20 315s > print( attributes( mf2 )$terms ) 315s consump ~ price + farmPrice + trend 315s attr(,"variables") 315s list(consump, price, farmPrice, trend) 315s attr(,"factors") 315s price farmPrice trend 315s consump 0 0 0 315s price 1 0 0 315s farmPrice 0 1 0 315s trend 0 0 1 315s attr(,"term.labels") 315s [1] "price" "farmPrice" "trend" 315s attr(,"order") 315s [1] 1 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, farmPrice, trend) 315s attr(,"dataClasses") 315s consump price farmPrice trend 315s "numeric" "numeric" "numeric" "numeric" 315s > 315s > print( all.equal( mf, model.frame( fitwls2e ) ) ) 315s [1] TRUE 315s > print( all.equal( mf1, model.frame( fitwls2e$eq[[ 1 ]] ) ) ) 315s [1] TRUE 315s > 315s > print( all.equal( mf, model.frame( fitwls3 ) ) ) 315s [1] TRUE 315s > print( all.equal( mf2, model.frame( fitwls3$eq[[ 2 ]] ) ) ) 315s [1] TRUE 315s > 315s > print( all.equal( mf, model.frame( fitwls4e ) ) ) 315s [1] TRUE 315s > print( all.equal( mf1, model.frame( fitwls4e$eq[[ 1 ]] ) ) ) 315s [1] TRUE 315s > 315s > print( all.equal( mf, model.frame( fitwls5 ) ) ) 315s [1] TRUE 315s > print( all.equal( mf2, model.frame( fitwls5$eq[[ 2 ]] ) ) ) 315s [1] TRUE 315s > 315s > print( all.equal( mf, model.frame( fitwlsi1e ) ) ) 315s [1] TRUE 315s > print( all.equal( mf1, model.frame( fitwlsi1e$eq[[ 1 ]] ) ) ) 315s [1] TRUE 315s > 315s > print( all.equal( mf, model.frame( fitwlsi2 ) ) ) 315s [1] TRUE 315s > print( all.equal( mf2, model.frame( fitwlsi2$eq[[ 2 ]] ) ) ) 315s [1] TRUE 315s > 315s > print( all.equal( mf, model.frame( fitwlsi3e ) ) ) 315s [1] TRUE 315s > print( all.equal( mf1, model.frame( fitwlsi3e$eq[[ 1 ]] ) ) ) 315s [1] TRUE 315s > 315s > print( all.equal( mf, model.frame( fitwlsi4 ) ) ) 315s [1] TRUE 315s > print( all.equal( mf2, model.frame( fitwlsi4$eq[[ 2 ]] ) ) ) 315s [1] TRUE 315s > 315s > print( all.equal( mf, model.frame( fitwlsi5e ) ) ) 315s [1] TRUE 315s > print( all.equal( mf1, model.frame( fitwlsi5e$eq[[ 1 ]] ) ) ) 315s [1] TRUE 315s > 315s > 315s > ## **************** model matrix ************************ 315s > # with x (returnModelMatrix) = TRUE 315s > print( !is.null( fitwls1e$eq[[ 1 ]]$x ) ) 315s [1] TRUE 315s > print( mm <- model.matrix( fitwlsi1e ) ) 315s demand_(Intercept) demand_price demand_income supply_(Intercept) 315s demand_1 1 100.3 87.4 0 315s demand_2 1 104.3 97.6 0 315s demand_3 1 103.4 96.7 0 315s demand_4 1 104.5 98.2 0 315s demand_5 1 98.0 99.8 0 315s demand_6 1 99.5 100.5 0 315s demand_7 1 101.1 103.2 0 315s demand_8 1 104.8 107.8 0 315s demand_9 1 96.4 96.6 0 315s demand_10 1 91.2 88.9 0 315s demand_11 1 93.1 75.1 0 315s demand_12 1 98.8 76.9 0 315s demand_13 1 102.9 84.6 0 315s demand_14 1 98.8 90.6 0 315s demand_15 1 95.1 103.1 0 315s demand_16 1 98.5 105.1 0 315s demand_17 1 86.5 96.4 0 315s demand_18 1 104.0 104.4 0 315s demand_19 1 105.8 110.7 0 315s demand_20 1 113.5 127.1 0 315s supply_1 0 0.0 0.0 1 315s supply_2 0 0.0 0.0 1 315s supply_3 0 0.0 0.0 1 315s supply_4 0 0.0 0.0 1 315s supply_5 0 0.0 0.0 1 315s supply_6 0 0.0 0.0 1 315s supply_7 0 0.0 0.0 1 315s supply_8 0 0.0 0.0 1 315s supply_9 0 0.0 0.0 1 315s supply_10 0 0.0 0.0 1 315s supply_11 0 0.0 0.0 1 315s supply_12 0 0.0 0.0 1 315s supply_13 0 0.0 0.0 1 315s supply_14 0 0.0 0.0 1 315s supply_15 0 0.0 0.0 1 315s supply_16 0 0.0 0.0 1 315s supply_17 0 0.0 0.0 1 315s supply_18 0 0.0 0.0 1 315s supply_19 0 0.0 0.0 1 315s supply_20 0 0.0 0.0 1 315s supply_price supply_farmPrice supply_trend 315s demand_1 0.0 0.0 0 315s demand_2 0.0 0.0 0 315s demand_3 0.0 0.0 0 315s demand_4 0.0 0.0 0 315s demand_5 0.0 0.0 0 315s demand_6 0.0 0.0 0 315s demand_7 0.0 0.0 0 315s demand_8 0.0 0.0 0 315s demand_9 0.0 0.0 0 315s demand_10 0.0 0.0 0 315s demand_11 0.0 0.0 0 315s demand_12 0.0 0.0 0 315s demand_13 0.0 0.0 0 315s demand_14 0.0 0.0 0 315s demand_15 0.0 0.0 0 315s demand_16 0.0 0.0 0 315s demand_17 0.0 0.0 0 315s demand_18 0.0 0.0 0 315s demand_19 0.0 0.0 0 315s demand_20 0.0 0.0 0 315s supply_1 100.3 98.0 1 315s supply_2 104.3 99.1 2 315s supply_3 103.4 99.1 3 315s supply_4 104.5 98.1 4 315s supply_5 98.0 110.8 5 315s supply_6 99.5 108.2 6 315s supply_7 101.1 105.6 7 315s supply_8 104.8 109.8 8 315s supply_9 96.4 108.7 9 315s supply_10 91.2 100.6 10 315s supply_11 93.1 81.0 11 315s supply_12 98.8 68.6 12 315s supply_13 102.9 70.9 13 315s supply_14 98.8 81.4 14 315s supply_15 95.1 102.3 15 315s supply_16 98.5 105.0 16 315s supply_17 86.5 110.5 17 315s supply_18 104.0 92.5 18 315s supply_19 105.8 89.3 19 315s supply_20 113.5 93.0 20 315s > print( mm1 <- model.matrix( fitwlsi1e$eq[[ 1 ]] ) ) 315s (Intercept) price income 315s 1 1 100.3 87.4 315s 2 1 104.3 97.6 315s 3 1 103.4 96.7 315s 4 1 104.5 98.2 315s 5 1 98.0 99.8 315s 6 1 99.5 100.5 315s 7 1 101.1 103.2 315s 8 1 104.8 107.8 315s 9 1 96.4 96.6 315s 10 1 91.2 88.9 315s 11 1 93.1 75.1 315s 12 1 98.8 76.9 315s 13 1 102.9 84.6 315s 14 1 98.8 90.6 315s 15 1 95.1 103.1 315s 16 1 98.5 105.1 315s 17 1 86.5 96.4 315s 18 1 104.0 104.4 315s 19 1 105.8 110.7 315s 20 1 113.5 127.1 315s attr(,"assign") 315s [1] 0 1 2 315s > print( mm2 <- model.matrix( fitwlsi1e$eq[[ 2 ]] ) ) 315s (Intercept) price farmPrice trend 315s 1 1 100.3 98.0 1 315s 2 1 104.3 99.1 2 315s 3 1 103.4 99.1 3 315s 4 1 104.5 98.1 4 315s 5 1 98.0 110.8 5 315s 6 1 99.5 108.2 6 315s 7 1 101.1 105.6 7 315s 8 1 104.8 109.8 8 315s 9 1 96.4 108.7 9 315s 10 1 91.2 100.6 10 315s 11 1 93.1 81.0 11 315s 12 1 98.8 68.6 12 315s 13 1 102.9 70.9 13 315s 14 1 98.8 81.4 14 315s 15 1 95.1 102.3 15 315s 16 1 98.5 105.0 16 315s 17 1 86.5 110.5 17 315s 18 1 104.0 92.5 18 315s 19 1 105.8 89.3 19 315s 20 1 113.5 93.0 20 315s attr(,"assign") 315s [1] 0 1 2 3 315s > 315s > # with x (returnModelMatrix) = FALSE 315s > print( all.equal( mm, model.matrix( fitwlsi1 ) ) ) 315s [1] TRUE 315s > print( all.equal( mm1, model.matrix( fitwlsi1$eq[[ 1 ]] ) ) ) 315s [1] TRUE 315s > print( all.equal( mm2, model.matrix( fitwlsi1$eq[[ 2 ]] ) ) ) 315s [1] TRUE 315s > print( !is.null( fitwls1$eq[[ 1 ]]$x ) ) 315s [1] FALSE 315s > 315s > # with x (returnModelMatrix) = TRUE 315s > print( !is.null( fitwls2$eq[[ 1 ]]$x ) ) 315s [1] TRUE 315s > print( all.equal( mm, model.matrix( fitwls2 ) ) ) 315s [1] TRUE 315s > print( all.equal( mm1, model.matrix( fitwls2$eq[[ 1 ]] ) ) ) 315s [1] TRUE 315s > print( all.equal( mm2, model.matrix( fitwls2$eq[[ 2 ]] ) ) ) 315s [1] TRUE 315s > 315s > # with x (returnModelMatrix) = FALSE 315s > print( all.equal( mm, model.matrix( fitwls2e ) ) ) 315s [1] TRUE 315s > print( all.equal( mm1, model.matrix( fitwls2e$eq[[ 1 ]] ) ) ) 315s [1] TRUE 315s > print( all.equal( mm2, model.matrix( fitwls2e$eq[[ 2 ]] ) ) ) 315s [1] TRUE 315s > print( !is.null( fitwls2e$eq[[ 1 ]]$x ) ) 315s [1] FALSE 315s > 315s > # with x (returnModelMatrix) = TRUE 315s > print( !is.null( fitwlsi3$eq[[ 1 ]]$x ) ) 315s [1] TRUE 315s > print( all.equal( mm, model.matrix( fitwlsi3 ) ) ) 315s [1] TRUE 315s > print( all.equal( mm1, model.matrix( fitwlsi3$eq[[ 1 ]] ) ) ) 315s [1] TRUE 315s > print( all.equal( mm2, model.matrix( fitwlsi3$eq[[ 2 ]] ) ) ) 315s [1] TRUE 315s > 315s > # with x (returnModelMatrix) = FALSE 315s > print( all.equal( mm, model.matrix( fitwlsi3e ) ) ) 315s [1] TRUE 315s > print( all.equal( mm1, model.matrix( fitwlsi3e$eq[[ 1 ]] ) ) ) 315s [1] TRUE 315s > print( all.equal( mm2, model.matrix( fitwlsi3e$eq[[ 2 ]] ) ) ) 315s [1] TRUE 315s > print( !is.null( fitwlsi3e$eq[[ 1 ]]$x ) ) 315s [1] FALSE 315s > 315s > # with x (returnModelMatrix) = TRUE 315s > print( !is.null( fitwls4e$eq[[ 1 ]]$x ) ) 315s [1] TRUE 315s > print( all.equal( mm, model.matrix( fitwls4e ) ) ) 315s [1] TRUE 315s > print( all.equal( mm1, model.matrix( fitwls4e$eq[[ 1 ]] ) ) ) 315s [1] TRUE 315s > print( all.equal( mm2, model.matrix( fitwls4e$eq[[ 2 ]] ) ) ) 315s [1] TRUE 315s > 315s > # with x (returnModelMatrix) = FALSE 315s > print( all.equal( mm, model.matrix( fitwls4Sym ) ) ) 315s [1] TRUE 315s > print( all.equal( mm1, model.matrix( fitwls4Sym$eq[[ 1 ]] ) ) ) 315s [1] TRUE 315s > print( all.equal( mm2, model.matrix( fitwls4Sym$eq[[ 2 ]] ) ) ) 315s [1] TRUE 315s > print( !is.null( fitwls4Sym$eq[[ 1 ]]$x ) ) 315s [1] FALSE 315s > 315s > # with x (returnModelMatrix) = TRUE 315s > print( !is.null( fitwls5$eq[[ 1 ]]$x ) ) 315s [1] TRUE 315s > print( all.equal( mm, model.matrix( fitwls5 ) ) ) 315s [1] TRUE 315s > print( all.equal( mm1, model.matrix( fitwls5$eq[[ 1 ]] ) ) ) 315s [1] TRUE 315s > print( all.equal( mm2, model.matrix( fitwls5$eq[[ 2 ]] ) ) ) 315s [1] TRUE 315s > 315s > # with x (returnModelMatrix) = FALSE 315s > print( all.equal( mm, model.matrix( fitwls5e ) ) ) 315s [1] TRUE 315s > print( all.equal( mm1, model.matrix( fitwls5e$eq[[ 1 ]] ) ) ) 315s [1] TRUE 315s > print( all.equal( mm2, model.matrix( fitwls5e$eq[[ 2 ]] ) ) ) 315s [1] TRUE 315s > print( !is.null( fitwls5e$eq[[ 1 ]]$x ) ) 315s [1] FALSE 315s > 315s > 315s > ## **************** formulas ************************ 315s > formula( fitwls1 ) 315s $demand 315s consump ~ price + income 315s 315s $supply 315s consump ~ price + farmPrice + trend 315s 315s > formula( fitwls1$eq[[ 2 ]] ) 315s consump ~ price + farmPrice + trend 315s > 315s > formula( fitwls2e ) 315s $demand 315s consump ~ price + income 315s 315s $supply 315s consump ~ price + farmPrice + trend 315s 315s > formula( fitwls2e$eq[[ 1 ]] ) 315s consump ~ price + income 315s > 315s > formula( fitwls3 ) 315s $demand 315s consump ~ price + income 315s 315s $supply 315s consump ~ price + farmPrice + trend 315s 315s > formula( fitwls3$eq[[ 2 ]] ) 315s consump ~ price + farmPrice + trend 315s > 315s > formula( fitwls4e ) 315s $demand 315s consump ~ price + income 315s 315s $supply 315s consump ~ price + farmPrice + trend 315s 315s > formula( fitwls4e$eq[[ 1 ]] ) 315s consump ~ price + income 315s > 315s > formula( fitwls5 ) 315s $demand 315s consump ~ price + income 315s 315s $supply 315s consump ~ price + farmPrice + trend 315s 315s > formula( fitwls5$eq[[ 2 ]] ) 315s consump ~ price + farmPrice + trend 315s > 315s > formula( fitwlsi1e ) 315s $demand 315s consump ~ price + income 315s 315s $supply 315s consump ~ price + farmPrice + trend 315s 315s > formula( fitwlsi1e$eq[[ 1 ]] ) 315s consump ~ price + income 315s > 315s > formula( fitwlsi2 ) 315s $demand 315s consump ~ price + income 315s 315s $supply 315s consump ~ price + farmPrice + trend 315s 315s > formula( fitwlsi2$eq[[ 2 ]] ) 315s consump ~ price + farmPrice + trend 315s > 315s > formula( fitwlsi3e ) 315s $demand 315s consump ~ price + income 315s 315s $supply 315s consump ~ price + farmPrice + trend 315s 315s > formula( fitwlsi3e$eq[[ 1 ]] ) 315s consump ~ price + income 315s > 315s > formula( fitwlsi4 ) 315s $demand 315s consump ~ price + income 315s 315s $supply 315s consump ~ price + farmPrice + trend 315s 315s > formula( fitwlsi4$eq[[ 2 ]] ) 315s consump ~ price + farmPrice + trend 315s > 315s > formula( fitwlsi5e ) 315s $demand 315s consump ~ price + income 315s 315s $supply 315s consump ~ price + farmPrice + trend 315s 315s > formula( fitwlsi5e$eq[[ 1 ]] ) 315s consump ~ price + income 315s > 315s > 315s > ## **************** model terms ******************* 315s > terms( fitwls1 ) 315s $demand 315s consump ~ price + income 315s attr(,"variables") 315s list(consump, price, income) 315s attr(,"factors") 315s price income 315s consump 0 0 315s price 1 0 315s income 0 1 315s attr(,"term.labels") 315s [1] "price" "income" 315s attr(,"order") 315s [1] 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, income) 315s attr(,"dataClasses") 315s consump price income 315s "numeric" "numeric" "numeric" 315s 315s $supply 315s consump ~ price + farmPrice + trend 315s attr(,"variables") 315s list(consump, price, farmPrice, trend) 315s attr(,"factors") 315s price farmPrice trend 315s consump 0 0 0 315s price 1 0 0 315s farmPrice 0 1 0 315s trend 0 0 1 315s attr(,"term.labels") 315s [1] "price" "farmPrice" "trend" 315s attr(,"order") 315s [1] 1 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, farmPrice, trend) 315s attr(,"dataClasses") 315s consump price farmPrice trend 315s "numeric" "numeric" "numeric" "numeric" 315s 315s > terms( fitwls1$eq[[ 2 ]] ) 315s consump ~ price + farmPrice + trend 315s attr(,"variables") 315s list(consump, price, farmPrice, trend) 315s attr(,"factors") 315s price farmPrice trend 315s consump 0 0 0 315s price 1 0 0 315s farmPrice 0 1 0 315s trend 0 0 1 315s attr(,"term.labels") 315s [1] "price" "farmPrice" "trend" 315s attr(,"order") 315s [1] 1 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, farmPrice, trend) 315s attr(,"dataClasses") 315s consump price farmPrice trend 315s "numeric" "numeric" "numeric" "numeric" 315s > 315s > terms( fitwls2e ) 315s $demand 315s consump ~ price + income 315s attr(,"variables") 315s list(consump, price, income) 315s attr(,"factors") 315s price income 315s consump 0 0 315s price 1 0 315s income 0 1 315s attr(,"term.labels") 315s [1] "price" "income" 315s attr(,"order") 315s [1] 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, income) 315s attr(,"dataClasses") 315s consump price income 315s "numeric" "numeric" "numeric" 315s 315s $supply 315s consump ~ price + farmPrice + trend 315s attr(,"variables") 315s list(consump, price, farmPrice, trend) 315s attr(,"factors") 315s price farmPrice trend 315s consump 0 0 0 315s price 1 0 0 315s farmPrice 0 1 0 315s trend 0 0 1 315s attr(,"term.labels") 315s [1] "price" "farmPrice" "trend" 315s attr(,"order") 315s [1] 1 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, farmPrice, trend) 315s attr(,"dataClasses") 315s consump price farmPrice trend 315s "numeric" "numeric" "numeric" "numeric" 315s 315s > terms( fitwls2e$eq[[ 1 ]] ) 315s consump ~ price + income 315s attr(,"variables") 315s list(consump, price, income) 315s attr(,"factors") 315s price income 315s consump 0 0 315s price 1 0 315s income 0 1 315s attr(,"term.labels") 315s [1] "price" "income" 315s attr(,"order") 315s [1] 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, income) 315s attr(,"dataClasses") 315s consump price income 315s "numeric" "numeric" "numeric" 315s > 315s > terms( fitwls3 ) 315s $demand 315s consump ~ price + income 315s attr(,"variables") 315s list(consump, price, income) 315s attr(,"factors") 315s price income 315s consump 0 0 315s price 1 0 315s income 0 1 315s attr(,"term.labels") 315s [1] "price" "income" 315s attr(,"order") 315s [1] 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, income) 315s attr(,"dataClasses") 315s consump price income 315s "numeric" "numeric" "numeric" 315s 315s $supply 315s consump ~ price + farmPrice + trend 315s attr(,"variables") 315s list(consump, price, farmPrice, trend) 315s attr(,"factors") 315s price farmPrice trend 315s consump 0 0 0 315s price 1 0 0 315s farmPrice 0 1 0 315s trend 0 0 1 315s attr(,"term.labels") 315s [1] "price" "farmPrice" "trend" 315s attr(,"order") 315s [1] 1 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, farmPrice, trend) 315s attr(,"dataClasses") 315s consump price farmPrice trend 315s "numeric" "numeric" "numeric" "numeric" 315s 315s > terms( fitwls3$eq[[ 2 ]] ) 315s consump ~ price + farmPrice + trend 315s attr(,"variables") 315s list(consump, price, farmPrice, trend) 315s attr(,"factors") 315s price farmPrice trend 315s consump 0 0 0 315s price 1 0 0 315s farmPrice 0 1 0 315s trend 0 0 1 315s attr(,"term.labels") 315s [1] "price" "farmPrice" "trend" 315s attr(,"order") 315s [1] 1 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, farmPrice, trend) 315s attr(,"dataClasses") 315s consump price farmPrice trend 315s "numeric" "numeric" "numeric" "numeric" 315s > 315s > terms( fitwls4e ) 315s $demand 315s consump ~ price + income 315s attr(,"variables") 315s list(consump, price, income) 315s attr(,"factors") 315s price income 315s consump 0 0 315s price 1 0 315s income 0 1 315s attr(,"term.labels") 315s [1] "price" "income" 315s attr(,"order") 315s [1] 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, income) 315s attr(,"dataClasses") 315s consump price income 315s "numeric" "numeric" "numeric" 315s 315s $supply 315s consump ~ price + farmPrice + trend 315s attr(,"variables") 315s list(consump, price, farmPrice, trend) 315s attr(,"factors") 315s price farmPrice trend 315s consump 0 0 0 315s price 1 0 0 315s farmPrice 0 1 0 315s trend 0 0 1 315s attr(,"term.labels") 315s [1] "price" "farmPrice" "trend" 315s attr(,"order") 315s [1] 1 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, farmPrice, trend) 315s attr(,"dataClasses") 315s consump price farmPrice trend 315s "numeric" "numeric" "numeric" "numeric" 315s 315s > terms( fitwls4e$eq[[ 1 ]] ) 315s consump ~ price + income 315s attr(,"variables") 315s list(consump, price, income) 315s attr(,"factors") 315s price income 315s consump 0 0 315s price 1 0 315s income 0 1 315s attr(,"term.labels") 315s [1] "price" "income" 315s attr(,"order") 315s [1] 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, income) 315s attr(,"dataClasses") 315s consump price income 315s "numeric" "numeric" "numeric" 315s > 315s > terms( fitwls5 ) 315s $demand 315s consump ~ price + income 315s attr(,"variables") 315s list(consump, price, income) 315s attr(,"factors") 315s price income 315s consump 0 0 315s price 1 0 315s income 0 1 315s attr(,"term.labels") 315s [1] "price" "income" 315s attr(,"order") 315s [1] 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, income) 315s attr(,"dataClasses") 315s consump price income 315s "numeric" "numeric" "numeric" 315s 315s $supply 315s consump ~ price + farmPrice + trend 315s attr(,"variables") 315s list(consump, price, farmPrice, trend) 315s attr(,"factors") 315s price farmPrice trend 315s consump 0 0 0 315s price 1 0 0 315s farmPrice 0 1 0 315s trend 0 0 1 315s attr(,"term.labels") 315s [1] "price" "farmPrice" "trend" 315s attr(,"order") 315s [1] 1 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, farmPrice, trend) 315s attr(,"dataClasses") 315s consump price farmPrice trend 315s "numeric" "numeric" "numeric" "numeric" 315s 315s > terms( fitwls5$eq[[ 2 ]] ) 315s consump ~ price + farmPrice + trend 315s attr(,"variables") 315s list(consump, price, farmPrice, trend) 315s attr(,"factors") 315s price farmPrice trend 315s consump 0 0 0 315s price 1 0 0 315s farmPrice 0 1 0 315s trend 0 0 1 315s attr(,"term.labels") 315s [1] "price" "farmPrice" "trend" 315s attr(,"order") 315s [1] 1 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, farmPrice, trend) 315s attr(,"dataClasses") 315s consump price farmPrice trend 315s "numeric" "numeric" "numeric" "numeric" 315s > 315s > terms( fitwlsi1e ) 315s $demand 315s consump ~ price + income 315s attr(,"variables") 315s list(consump, price, income) 315s attr(,"factors") 315s price income 315s consump 0 0 315s price 1 0 315s income 0 1 315s attr(,"term.labels") 315s [1] "price" "income" 315s attr(,"order") 315s [1] 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, income) 315s attr(,"dataClasses") 315s consump price income 315s "numeric" "numeric" "numeric" 315s 315s $supply 315s consump ~ price + farmPrice + trend 315s attr(,"variables") 315s list(consump, price, farmPrice, trend) 315s attr(,"factors") 315s price farmPrice trend 315s consump 0 0 0 315s price 1 0 0 315s farmPrice 0 1 0 315s trend 0 0 1 315s attr(,"term.labels") 315s [1] "price" "farmPrice" "trend" 315s attr(,"order") 315s [1] 1 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, farmPrice, trend) 315s attr(,"dataClasses") 315s consump price farmPrice trend 315s "numeric" "numeric" "numeric" "numeric" 315s 315s > terms( fitwlsi1e$eq[[ 1 ]] ) 315s consump ~ price + income 315s attr(,"variables") 315s list(consump, price, income) 315s attr(,"factors") 315s price income 315s consump 0 0 315s price 1 0 315s income 0 1 315s attr(,"term.labels") 315s [1] "price" "income" 315s attr(,"order") 315s [1] 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, income) 315s attr(,"dataClasses") 315s consump price income 315s "numeric" "numeric" "numeric" 315s > 315s > terms( fitwlsi2 ) 315s $demand 315s consump ~ price + income 315s attr(,"variables") 315s list(consump, price, income) 315s attr(,"factors") 315s price income 315s consump 0 0 315s price 1 0 315s income 0 1 315s attr(,"term.labels") 315s [1] "price" "income" 315s attr(,"order") 315s [1] 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, income) 315s attr(,"dataClasses") 315s consump price income 315s "numeric" "numeric" "numeric" 315s 315s $supply 315s consump ~ price + farmPrice + trend 315s attr(,"variables") 315s list(consump, price, farmPrice, trend) 315s attr(,"factors") 315s price farmPrice trend 315s consump 0 0 0 315s price 1 0 0 315s farmPrice 0 1 0 315s trend 0 0 1 315s attr(,"term.labels") 315s [1] "price" "farmPrice" "trend" 315s attr(,"order") 315s [1] 1 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, farmPrice, trend) 315s attr(,"dataClasses") 315s consump price farmPrice trend 315s "numeric" "numeric" "numeric" "numeric" 315s 315s > terms( fitwlsi2$eq[[ 2 ]] ) 315s consump ~ price + farmPrice + trend 315s attr(,"variables") 315s list(consump, price, farmPrice, trend) 315s attr(,"factors") 315s price farmPrice trend 315s consump 0 0 0 315s price 1 0 0 315s farmPrice 0 1 0 315s trend 0 0 1 315s attr(,"term.labels") 315s [1] "price" "farmPrice" "trend" 315s attr(,"order") 315s [1] 1 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, farmPrice, trend) 315s attr(,"dataClasses") 315s consump price farmPrice trend 315s "numeric" "numeric" "numeric" "numeric" 315s > 315s > terms( fitwlsi3e ) 315s $demand 315s consump ~ price + income 315s attr(,"variables") 315s list(consump, price, income) 315s attr(,"factors") 315s price income 315s consump 0 0 315s price 1 0 315s income 0 1 315s attr(,"term.labels") 315s [1] "price" "income" 315s attr(,"order") 315s [1] 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, income) 315s attr(,"dataClasses") 315s consump price income 315s "numeric" "numeric" "numeric" 315s 315s $supply 315s consump ~ price + farmPrice + trend 315s attr(,"variables") 315s list(consump, price, farmPrice, trend) 315s attr(,"factors") 315s price farmPrice trend 315s consump 0 0 0 315s price 1 0 0 315s farmPrice 0 1 0 315s trend 0 0 1 315s attr(,"term.labels") 315s [1] "price" "farmPrice" "trend" 315s attr(,"order") 315s [1] 1 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, farmPrice, trend) 315s attr(,"dataClasses") 315s consump price farmPrice trend 315s "numeric" "numeric" "numeric" "numeric" 315s 315s > terms( fitwlsi3e$eq[[ 1 ]] ) 315s consump ~ price + income 315s attr(,"variables") 315s list(consump, price, income) 315s attr(,"factors") 315s price income 315s consump 0 0 315s price 1 0 315s income 0 1 315s attr(,"term.labels") 315s [1] "price" "income" 315s attr(,"order") 315s [1] 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, income) 315s attr(,"dataClasses") 315s consump price income 315s "numeric" "numeric" "numeric" 315s > 315s > terms( fitwlsi4 ) 315s $demand 315s consump ~ price + income 315s attr(,"variables") 315s list(consump, price, income) 315s attr(,"factors") 315s price income 315s consump 0 0 315s price 1 0 315s income 0 1 315s attr(,"term.labels") 315s [1] "price" "income" 315s attr(,"order") 315s [1] 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, income) 315s attr(,"dataClasses") 315s consump price income 315s "numeric" "numeric" "numeric" 315s 315s $supply 315s consump ~ price + farmPrice + trend 315s attr(,"variables") 315s list(consump, price, farmPrice, trend) 315s attr(,"factors") 315s price farmPrice trend 315s consump 0 0 0 315s price 1 0 0 315s farmPrice 0 1 0 315s trend 0 0 1 315s attr(,"term.labels") 315s [1] "price" "farmPrice" "trend" 315s attr(,"order") 315s [1] 1 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, farmPrice, trend) 315s attr(,"dataClasses") 315s consump price farmPrice trend 315s "numeric" "numeric" "numeric" "numeric" 315s 315s > terms( fitwlsi4$eq[[ 2 ]] ) 315s consump ~ price + farmPrice + trend 315s attr(,"variables") 315s list(consump, price, farmPrice, trend) 315s attr(,"factors") 315s price farmPrice trend 315s consump 0 0 0 315s price 1 0 0 315s farmPrice 0 1 0 315s trend 0 0 1 315s attr(,"term.labels") 315s [1] "price" "farmPrice" "trend" 315s attr(,"order") 315s [1] 1 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, farmPrice, trend) 315s attr(,"dataClasses") 315s consump price farmPrice trend 315s "numeric" "numeric" "numeric" "numeric" 315s > 315s > terms( fitwlsi5e ) 315s $demand 315s consump ~ price + income 315s attr(,"variables") 315s list(consump, price, income) 315s attr(,"factors") 315s price income 315s consump 0 0 315s price 1 0 315s income 0 1 315s attr(,"term.labels") 315s [1] "price" "income" 315s attr(,"order") 315s [1] 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, income) 315s attr(,"dataClasses") 315s consump price income 315s "numeric" "numeric" "numeric" 315s 315s $supply 315s consump ~ price + farmPrice + trend 315s attr(,"variables") 315s list(consump, price, farmPrice, trend) 315s attr(,"factors") 315s price farmPrice trend 315s consump 0 0 0 315s price 1 0 0 315s farmPrice 0 1 0 315s trend 0 0 1 315s attr(,"term.labels") 315s [1] "price" "farmPrice" "trend" 315s attr(,"order") 315s [1] 1 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, farmPrice, trend) 315s attr(,"dataClasses") 315s consump price farmPrice trend 315s "numeric" "numeric" "numeric" "numeric" 315s 315s > terms( fitwlsi5e$eq[[ 1 ]] ) 315s consump ~ price + income 315s attr(,"variables") 315s list(consump, price, income) 315s attr(,"factors") 315s price income 315s consump 0 0 315s price 1 0 315s income 0 1 315s attr(,"term.labels") 315s [1] "price" "income" 315s attr(,"order") 315s [1] 1 1 315s attr(,"intercept") 315s [1] 1 315s attr(,"response") 315s [1] 1 315s attr(,".Environment") 315s 315s attr(,"predvars") 315s list(consump, price, income) 315s attr(,"dataClasses") 315s consump price income 315s "numeric" "numeric" "numeric" 315s > 315s > 315s > ## **************** estfun ************************ 315s > library( "sandwich" ) 315s > 315s > estfun( fitwls1 ) 315s demand_(Intercept) demand_price demand_income supply_(Intercept) 315s demand_1 0.2884 28.93 25.21 0.0000 315s demand_2 -0.1048 -10.92 -10.22 0.0000 315s demand_3 0.7045 72.87 68.13 0.0000 315s demand_4 0.4838 50.56 47.51 0.0000 315s demand_5 0.5222 51.18 52.12 0.0000 315s demand_6 0.3153 31.36 31.68 0.0000 315s demand_7 0.4108 41.51 42.39 0.0000 315s demand_8 -0.7872 -82.47 -84.86 0.0000 315s demand_9 -0.3665 -35.35 -35.41 0.0000 315s demand_10 0.5451 49.73 48.46 0.0000 315s demand_11 -0.0400 -3.72 -3.00 0.0000 315s demand_12 -0.5246 -51.83 -40.34 0.0000 315s demand_13 -0.3009 -30.96 -25.45 0.0000 315s demand_14 -0.0591 -5.83 -5.35 0.0000 315s demand_15 0.3991 37.96 41.14 0.0000 315s demand_16 -0.9934 -97.80 -104.40 0.0000 315s demand_17 -0.3417 -29.56 -32.94 0.0000 315s demand_18 -0.5375 -55.90 -56.11 0.0000 315s demand_19 0.4665 49.34 51.65 0.0000 315s demand_20 -0.0802 -9.10 -10.20 0.0000 315s supply_1 0.0000 0.00 0.00 -0.0768 315s supply_2 0.0000 0.00 0.00 -0.1548 315s supply_3 0.0000 0.00 0.00 0.3397 315s supply_4 0.0000 0.00 0.00 0.1961 315s supply_5 0.0000 0.00 0.00 0.2617 315s supply_6 0.0000 0.00 0.00 0.1176 315s supply_7 0.0000 0.00 0.00 0.2712 315s supply_8 0.0000 0.00 0.00 -0.7619 315s supply_9 0.0000 0.00 0.00 -0.4493 315s supply_10 0.0000 0.00 0.00 0.4269 315s supply_11 0.0000 0.00 0.00 -0.1034 315s supply_12 0.0000 0.00 0.00 -0.2934 315s supply_13 0.0000 0.00 0.00 -0.1839 315s supply_14 0.0000 0.00 0.00 0.1677 315s supply_15 0.0000 0.00 0.00 0.5461 315s supply_16 0.0000 0.00 0.00 -0.6683 315s supply_17 0.0000 0.00 0.00 -0.0458 315s supply_18 0.0000 0.00 0.00 -0.4234 315s supply_19 0.0000 0.00 0.00 0.5376 315s supply_20 0.0000 0.00 0.00 0.2963 315s supply_price supply_farmPrice supply_trend 315s demand_1 0.00 0.00 0.0000 315s demand_2 0.00 0.00 0.0000 315s demand_3 0.00 0.00 0.0000 315s demand_4 0.00 0.00 0.0000 315s demand_5 0.00 0.00 0.0000 315s demand_6 0.00 0.00 0.0000 315s demand_7 0.00 0.00 0.0000 315s demand_8 0.00 0.00 0.0000 315s demand_9 0.00 0.00 0.0000 315s demand_10 0.00 0.00 0.0000 315s demand_11 0.00 0.00 0.0000 315s demand_12 0.00 0.00 0.0000 315s demand_13 0.00 0.00 0.0000 315s demand_14 0.00 0.00 0.0000 315s demand_15 0.00 0.00 0.0000 315s demand_16 0.00 0.00 0.0000 315s demand_17 0.00 0.00 0.0000 315s demand_18 0.00 0.00 0.0000 315s demand_19 0.00 0.00 0.0000 315s demand_20 0.00 0.00 0.0000 315s supply_1 -7.70 -7.53 -0.0768 315s supply_2 -16.14 -15.34 -0.3096 315s supply_3 35.14 33.67 1.0192 315s supply_4 20.49 19.24 0.7843 315s supply_5 25.65 29.00 1.3085 315s supply_6 11.70 12.73 0.7057 315s supply_7 27.41 28.64 1.8987 315s supply_8 -79.82 -83.66 -6.0955 315s supply_9 -43.33 -48.84 -4.0437 315s supply_10 38.95 42.95 4.2691 315s supply_11 -9.63 -8.38 -1.1377 315s supply_12 -28.99 -20.13 -3.5213 315s supply_13 -18.93 -13.04 -2.3913 315s supply_14 16.56 13.65 2.3480 315s supply_15 51.95 55.87 8.1920 315s supply_16 -65.79 -70.17 -10.6922 315s supply_17 -3.96 -5.06 -0.7779 315s supply_18 -44.04 -39.16 -7.6205 315s supply_19 56.86 48.01 10.2144 315s supply_20 33.63 27.56 5.9267 315s > round( colSums( estfun( fitwls1 ) ), digits = 7 ) 315s demand_(Intercept) demand_price demand_income supply_(Intercept) 315s 0 0 0 0 315s supply_price supply_farmPrice supply_trend 315s 0 0 0 315s > 315s > estfun( fitwlsi1e ) 315s demand_(Intercept) demand_price demand_income supply_(Intercept) 315s demand_1 0.3393 34.04 29.66 0.0000 315s demand_2 -0.1232 -12.85 -12.03 0.0000 315s demand_3 0.8289 85.73 80.15 0.0000 315s demand_4 0.5692 59.49 55.90 0.0000 315s demand_5 0.6144 60.21 61.32 0.0000 315s demand_6 0.3709 36.89 37.28 0.0000 315s demand_7 0.4832 48.84 49.87 0.0000 315s demand_8 -0.9261 -97.03 -99.84 0.0000 315s demand_9 -0.4312 -41.59 -41.66 0.0000 315s demand_10 0.6413 58.51 57.01 0.0000 315s demand_11 -0.0470 -4.38 -3.53 0.0000 315s demand_12 -0.6172 -60.98 -47.46 0.0000 315s demand_13 -0.3540 -36.43 -29.95 0.0000 315s demand_14 -0.0695 -6.86 -6.29 0.0000 315s demand_15 0.4695 44.66 48.40 0.0000 315s demand_16 -1.1687 -115.06 -122.83 0.0000 315s demand_17 -0.4020 -34.78 -38.76 0.0000 315s demand_18 -0.6323 -65.77 -66.01 0.0000 315s demand_19 0.5489 58.05 60.76 0.0000 315s demand_20 -0.0944 -10.71 -12.00 0.0000 315s supply_1 0.0000 0.00 0.00 -0.0960 315s supply_2 0.0000 0.00 0.00 -0.1935 315s supply_3 0.0000 0.00 0.00 0.4247 315s supply_4 0.0000 0.00 0.00 0.2451 315s supply_5 0.0000 0.00 0.00 0.3271 315s supply_6 0.0000 0.00 0.00 0.1470 315s supply_7 0.0000 0.00 0.00 0.3390 315s supply_8 0.0000 0.00 0.00 -0.9524 315s supply_9 0.0000 0.00 0.00 -0.5616 315s supply_10 0.0000 0.00 0.00 0.5336 315s supply_11 0.0000 0.00 0.00 -0.1293 315s supply_12 0.0000 0.00 0.00 -0.3668 315s supply_13 0.0000 0.00 0.00 -0.2299 315s supply_14 0.0000 0.00 0.00 0.2096 315s supply_15 0.0000 0.00 0.00 0.6827 315s supply_16 0.0000 0.00 0.00 -0.8353 315s supply_17 0.0000 0.00 0.00 -0.0572 315s supply_18 0.0000 0.00 0.00 -0.5292 315s supply_19 0.0000 0.00 0.00 0.6720 315s supply_20 0.0000 0.00 0.00 0.3704 315s supply_price supply_farmPrice supply_trend 315s demand_1 0.00 0.00 0.000 315s demand_2 0.00 0.00 0.000 315s demand_3 0.00 0.00 0.000 315s demand_4 0.00 0.00 0.000 315s demand_5 0.00 0.00 0.000 315s demand_6 0.00 0.00 0.000 315s demand_7 0.00 0.00 0.000 315s demand_8 0.00 0.00 0.000 315s demand_9 0.00 0.00 0.000 315s demand_10 0.00 0.00 0.000 315s demand_11 0.00 0.00 0.000 315s demand_12 0.00 0.00 0.000 315s demand_13 0.00 0.00 0.000 315s demand_14 0.00 0.00 0.000 315s demand_15 0.00 0.00 0.000 315s demand_16 0.00 0.00 0.000 315s demand_17 0.00 0.00 0.000 315s demand_18 0.00 0.00 0.000 315s demand_19 0.00 0.00 0.000 315s demand_20 0.00 0.00 0.000 315s supply_1 -9.63 -9.41 -0.096 315s supply_2 -20.18 -19.18 -0.387 315s supply_3 43.92 42.08 1.274 315s supply_4 25.61 24.04 0.980 315s supply_5 32.06 36.25 1.636 315s supply_6 14.62 15.91 0.882 315s supply_7 34.27 35.80 2.373 315s supply_8 -99.78 -104.58 -7.619 315s supply_9 -54.17 -61.05 -5.055 315s supply_10 48.68 53.68 5.336 315s supply_11 -12.03 -10.47 -1.422 315s supply_12 -36.24 -25.16 -4.402 315s supply_13 -23.66 -16.30 -2.989 315s supply_14 20.70 17.06 2.935 315s supply_15 64.93 69.84 10.240 315s supply_16 -82.24 -87.71 -13.365 315s supply_17 -4.95 -6.32 -0.972 315s supply_18 -55.05 -48.95 -9.526 315s supply_19 71.08 60.01 12.768 315s supply_20 42.04 34.45 7.408 315s > round( colSums( estfun( fitwlsi1e ) ), digits = 7 ) 315s demand_(Intercept) demand_price demand_income supply_(Intercept) 315s 0 0 0 0 315s supply_price supply_farmPrice supply_trend 315s 0 0 0 315s > 315s > 315s > ## **************** bread ************************ 315s > bread( fitwls1 ) 315s demand_(Intercept) demand_price demand_income supply_(Intercept) 315s [1,] 2261.63 -23.7921 1.2865 0.0 315s [2,] -23.79 0.3289 -0.0933 0.0 315s [3,] 1.29 -0.0933 0.0825 0.0 315s [4,] 0.00 0.0000 0.0000 5255.9 315s [5,] 0.00 0.0000 0.0000 -39.5 315s [6,] 0.00 0.0000 0.0000 -12.2 315s [7,] 0.00 0.0000 0.0000 -11.2 315s supply_price supply_farmPrice supply_trend 315s [1,] 0.0000 0.0000 0.0000 315s [2,] 0.0000 0.0000 0.0000 315s [3,] 0.0000 0.0000 0.0000 315s [4,] -39.5000 -12.1744 -11.1673 315s [5,] 0.3601 0.0338 0.0209 315s [6,] 0.0338 0.0853 0.0526 315s [7,] 0.0209 0.0526 0.3804 315s > 315s > bread( fitwlsi1e ) 315s demand_(Intercept) demand_price demand_income supply_(Intercept) 315s [1,] 1922.39 -20.2232 1.0935 0.00 315s [2,] -20.22 0.2796 -0.0793 0.00 315s [3,] 1.09 -0.0793 0.0701 0.00 315s [4,] 0.00 0.0000 0.0000 4204.75 315s [5,] 0.00 0.0000 0.0000 -31.60 315s [6,] 0.00 0.0000 0.0000 -9.74 315s [7,] 0.00 0.0000 0.0000 -8.93 315s supply_price supply_farmPrice supply_trend 315s [1,] 0.0000 0.0000 0.0000 315s [2,] 0.0000 0.0000 0.0000 315s [3,] 0.0000 0.0000 0.0000 315s [4,] -31.6000 -9.7395 -8.9339 315s [5,] 0.2881 0.0270 0.0167 315s [6,] 0.0270 0.0683 0.0421 315s [7,] 0.0167 0.0421 0.3043 315s > 315s autopkgtest [12:48:14]: test run-unit-test: -----------------------] 319s autopkgtest [12:48:18]: test run-unit-test: - - - - - - - - - - results - - - - - - - - - - 319s run-unit-test PASS 323s autopkgtest [12:48:22]: test pkg-r-autopkgtest: preparing testbed 332s Reading package lists... 333s Building dependency tree... 333s Reading state information... 333s Starting pkgProblemResolver with broken count: 0 333s Starting 2 pkgProblemResolver with broken count: 0 333s Done 334s The following additional packages will be installed: 334s build-essential cpp cpp-13 cpp-13-arm-linux-gnueabihf 334s cpp-arm-linux-gnueabihf dctrl-tools g++ g++-13 g++-13-arm-linux-gnueabihf 334s g++-arm-linux-gnueabihf gcc gcc-13 gcc-13-arm-linux-gnueabihf 334s gcc-arm-linux-gnueabihf gfortran gfortran-13 gfortran-13-arm-linux-gnueabihf 334s gfortran-arm-linux-gnueabihf icu-devtools libasan8 libatomic1 libblas-dev 334s libbz2-dev libc-dev-bin libc6-dev libcc1-0 libcrypt-dev libgcc-13-dev 334s libgfortran-13-dev libicu-dev libisl23 libjpeg-dev libjpeg-turbo8-dev 334s libjpeg8-dev liblapack-dev liblzma-dev libmpc3 libncurses-dev libpcre2-16-0 334s libpcre2-32-0 libpcre2-dev libpcre2-posix3 libpkgconf3 libpng-dev 334s libreadline-dev libstdc++-13-dev libubsan1 linux-libc-dev pkg-config 334s pkg-r-autopkgtest pkgconf pkgconf-bin r-base-dev r-cran-arm r-cran-coda 334s r-cran-mi r-cran-sem rpcsvc-proto zlib1g-dev 334s Suggested packages: 334s cpp-doc gcc-13-locales cpp-13-doc debtags gcc-13-doc gcc-multilib 334s manpages-dev autoconf automake libtool flex bison gdb gcc-doc 334s gdb-arm-linux-gnueabihf gfortran-doc gfortran-13-doc libcoarrays-dev 334s liblapack-doc glibc-doc icu-doc liblzma-doc ncurses-doc readline-doc 334s libstdc++-13-doc texlive-base texlive-latex-base texlive-plain-generic 334s texlive-fonts-recommended texlive-fonts-extra texlive-extra-utils 334s texlive-latex-recommended texlive-latex-extra texinfo r-cran-sn 334s r-cran-polycor 334s Recommended packages: 334s bzip2-doc manpages manpages-dev libc-devtools libpng-tools r-cran-truncnorm 334s The following NEW packages will be installed: 334s autopkgtest-satdep build-essential cpp cpp-13 cpp-13-arm-linux-gnueabihf 334s cpp-arm-linux-gnueabihf dctrl-tools g++ g++-13 g++-13-arm-linux-gnueabihf 334s g++-arm-linux-gnueabihf gcc gcc-13 gcc-13-arm-linux-gnueabihf 334s gcc-arm-linux-gnueabihf gfortran gfortran-13 gfortran-13-arm-linux-gnueabihf 334s gfortran-arm-linux-gnueabihf icu-devtools libasan8 libatomic1 libblas-dev 334s libbz2-dev libc-dev-bin libc6-dev libcc1-0 libcrypt-dev libgcc-13-dev 334s libgfortran-13-dev libicu-dev libisl23 libjpeg-dev libjpeg-turbo8-dev 334s libjpeg8-dev liblapack-dev liblzma-dev libmpc3 libncurses-dev libpcre2-16-0 334s libpcre2-32-0 libpcre2-dev libpcre2-posix3 libpkgconf3 libpng-dev 334s libreadline-dev libstdc++-13-dev libubsan1 linux-libc-dev pkg-config 334s pkg-r-autopkgtest pkgconf pkgconf-bin r-base-dev r-cran-arm r-cran-coda 334s r-cran-mi r-cran-sem rpcsvc-proto zlib1g-dev 334s 0 upgraded, 60 newly installed, 0 to remove and 1 not upgraded. 334s Need to get 77.6 MB/77.6 MB of archives. 334s After this operation, 236 MB of additional disk space will be used. 334s Get:1 /tmp/autopkgtest.coZgcK/2-autopkgtest-satdep.deb autopkgtest-satdep armhf 0 [732 B] 334s Get:2 http://ftpmaster.internal/ubuntu noble/main armhf libc-dev-bin armhf 2.39-0ubuntu6 [19.1 kB] 334s Get:3 http://ftpmaster.internal/ubuntu noble-proposed/main armhf linux-libc-dev armhf 6.8.0-20.20 [1555 kB] 334s Get:4 http://ftpmaster.internal/ubuntu noble/main armhf libcrypt-dev armhf 1:4.4.36-4 [136 kB] 334s Get:5 http://ftpmaster.internal/ubuntu noble/main armhf rpcsvc-proto armhf 1.4.2-0ubuntu6 [63.7 kB] 334s Get:6 http://ftpmaster.internal/ubuntu noble/main armhf libc6-dev armhf 2.39-0ubuntu6 [1351 kB] 334s Get:7 http://ftpmaster.internal/ubuntu noble/main armhf libisl23 armhf 0.26-3 [595 kB] 334s Get:8 http://ftpmaster.internal/ubuntu noble/main armhf libmpc3 armhf 1.3.1-1 [46.4 kB] 334s Get:9 http://ftpmaster.internal/ubuntu noble-proposed/main armhf cpp-13-arm-linux-gnueabihf armhf 13.2.0-21ubuntu1 [8757 kB] 335s Get:10 http://ftpmaster.internal/ubuntu noble-proposed/main armhf cpp-13 armhf 13.2.0-21ubuntu1 [1028 B] 335s Get:11 http://ftpmaster.internal/ubuntu noble/main armhf cpp-arm-linux-gnueabihf armhf 4:13.2.0-7ubuntu1 [5320 B] 335s Get:12 http://ftpmaster.internal/ubuntu noble/main armhf cpp armhf 4:13.2.0-7ubuntu1 [22.4 kB] 335s Get:13 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libcc1-0 armhf 14-20240315-1ubuntu1 [39.0 kB] 335s Get:14 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libatomic1 armhf 14-20240315-1ubuntu1 [7824 B] 335s Get:15 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libasan8 armhf 14-20240315-1ubuntu1 [2941 kB] 335s Get:16 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libubsan1 armhf 14-20240315-1ubuntu1 [1152 kB] 335s Get:17 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libgcc-13-dev armhf 13.2.0-21ubuntu1 [899 kB] 335s Get:18 http://ftpmaster.internal/ubuntu noble-proposed/main armhf gcc-13-arm-linux-gnueabihf armhf 13.2.0-21ubuntu1 [16.8 MB] 335s Get:19 http://ftpmaster.internal/ubuntu noble-proposed/main armhf gcc-13 armhf 13.2.0-21ubuntu1 [450 kB] 335s Get:20 http://ftpmaster.internal/ubuntu noble/main armhf gcc-arm-linux-gnueabihf armhf 4:13.2.0-7ubuntu1 [1220 B] 335s Get:21 http://ftpmaster.internal/ubuntu noble/main armhf gcc armhf 4:13.2.0-7ubuntu1 [5022 B] 335s Get:22 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libstdc++-13-dev armhf 13.2.0-21ubuntu1 [2456 kB] 335s Get:23 http://ftpmaster.internal/ubuntu noble-proposed/main armhf g++-13-arm-linux-gnueabihf armhf 13.2.0-21ubuntu1 [9938 kB] 336s Get:24 http://ftpmaster.internal/ubuntu noble-proposed/main armhf g++-13 armhf 13.2.0-21ubuntu1 [14.5 kB] 336s Get:25 http://ftpmaster.internal/ubuntu noble/main armhf g++-arm-linux-gnueabihf armhf 4:13.2.0-7ubuntu1 [966 B] 336s Get:26 http://ftpmaster.internal/ubuntu noble/main armhf g++ armhf 4:13.2.0-7ubuntu1 [1090 B] 336s Get:27 http://ftpmaster.internal/ubuntu noble/main armhf build-essential armhf 12.10ubuntu1 [4928 B] 336s Get:28 http://ftpmaster.internal/ubuntu noble/main armhf dctrl-tools armhf 2.24-3build2 [57.2 kB] 336s Get:29 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libgfortran-13-dev armhf 13.2.0-21ubuntu1 [359 kB] 336s Get:30 http://ftpmaster.internal/ubuntu noble-proposed/main armhf gfortran-13-arm-linux-gnueabihf armhf 13.2.0-21ubuntu1 [9179 kB] 336s Get:31 http://ftpmaster.internal/ubuntu noble-proposed/main armhf gfortran-13 armhf 13.2.0-21ubuntu1 [10.9 kB] 336s Get:32 http://ftpmaster.internal/ubuntu noble/main armhf gfortran-arm-linux-gnueabihf armhf 4:13.2.0-7ubuntu1 [1024 B] 336s Get:33 http://ftpmaster.internal/ubuntu noble/main armhf gfortran armhf 4:13.2.0-7ubuntu1 [1166 B] 336s Get:34 http://ftpmaster.internal/ubuntu noble/main armhf icu-devtools armhf 74.2-1ubuntu1 [201 kB] 336s Get:35 http://ftpmaster.internal/ubuntu noble/main armhf libblas-dev armhf 3.12.0-3 [132 kB] 336s Get:36 http://ftpmaster.internal/ubuntu noble/main armhf libbz2-dev armhf 1.0.8-5ubuntu1 [30.4 kB] 336s Get:37 http://ftpmaster.internal/ubuntu noble/main armhf libicu-dev armhf 74.2-1ubuntu1 [11.6 MB] 336s Get:38 http://ftpmaster.internal/ubuntu noble/main armhf libjpeg-turbo8-dev armhf 2.1.5-2ubuntu1 [263 kB] 336s Get:39 http://ftpmaster.internal/ubuntu noble/main armhf libjpeg8-dev armhf 8c-2ubuntu11 [1484 B] 336s Get:40 http://ftpmaster.internal/ubuntu noble/main armhf libjpeg-dev armhf 8c-2ubuntu11 [1482 B] 336s Get:41 http://ftpmaster.internal/ubuntu noble/main armhf liblapack-dev armhf 3.12.0-3 [2177 kB] 336s Get:42 http://ftpmaster.internal/ubuntu noble/main armhf libncurses-dev armhf 6.4+20240113-1ubuntu1 [501 kB] 336s Get:43 http://ftpmaster.internal/ubuntu noble/main armhf libpcre2-16-0 armhf 10.42-4ubuntu1 [180 kB] 336s Get:44 http://ftpmaster.internal/ubuntu noble/main armhf libpcre2-32-0 armhf 10.42-4ubuntu1 [171 kB] 336s Get:45 http://ftpmaster.internal/ubuntu noble/main armhf libpcre2-posix3 armhf 10.42-4ubuntu1 [6096 B] 336s Get:46 http://ftpmaster.internal/ubuntu noble/main armhf libpcre2-dev armhf 10.42-4ubuntu1 [664 kB] 336s Get:47 http://ftpmaster.internal/ubuntu noble/main armhf libpkgconf3 armhf 1.8.1-2 [26.3 kB] 336s Get:48 http://ftpmaster.internal/ubuntu noble-proposed/main armhf zlib1g-dev armhf 1:1.3.dfsg-3.1ubuntu1 [880 kB] 336s Get:49 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libpng-dev armhf 1.6.43-3 [248 kB] 336s Get:50 http://ftpmaster.internal/ubuntu noble-proposed/main armhf libreadline-dev armhf 8.2-4 [153 kB] 336s Get:51 http://ftpmaster.internal/ubuntu noble/main armhf pkgconf-bin armhf 1.8.1-2 [20.6 kB] 336s Get:52 http://ftpmaster.internal/ubuntu noble/main armhf pkgconf armhf 1.8.1-2 [16.7 kB] 336s Get:53 http://ftpmaster.internal/ubuntu noble/main armhf pkg-config armhf 1.8.1-2 [7170 B] 336s Get:54 http://ftpmaster.internal/ubuntu noble-proposed/main armhf liblzma-dev armhf 5.6.0-0.2 [166 kB] 336s Get:55 http://ftpmaster.internal/ubuntu noble-proposed/universe armhf r-base-dev all 4.3.3-2build1 [4334 B] 336s Get:56 http://ftpmaster.internal/ubuntu noble/universe armhf pkg-r-autopkgtest all 20231212ubuntu1 [6448 B] 336s Get:57 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-coda all 0.19-4.1-1 [321 kB] 336s Get:58 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-arm all 1.13-1-1 [407 kB] 336s Get:59 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-mi all 1.1-1 [1840 kB] 336s Get:60 http://ftpmaster.internal/ubuntu noble/universe armhf r-cran-sem armhf 3.1.15-1 [623 kB] 337s Fetched 77.6 MB in 3s (29.8 MB/s) 337s Selecting previously unselected package libc-dev-bin. 337s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 79749 files and directories currently installed.) 337s Preparing to unpack .../00-libc-dev-bin_2.39-0ubuntu6_armhf.deb ... 337s Unpacking libc-dev-bin (2.39-0ubuntu6) ... 337s Selecting previously unselected package linux-libc-dev:armhf. 337s Preparing to unpack .../01-linux-libc-dev_6.8.0-20.20_armhf.deb ... 337s Unpacking linux-libc-dev:armhf (6.8.0-20.20) ... 337s Selecting previously unselected package libcrypt-dev:armhf. 337s Preparing to unpack .../02-libcrypt-dev_1%3a4.4.36-4_armhf.deb ... 337s Unpacking libcrypt-dev:armhf (1:4.4.36-4) ... 337s Selecting previously unselected package rpcsvc-proto. 337s Preparing to unpack .../03-rpcsvc-proto_1.4.2-0ubuntu6_armhf.deb ... 337s Unpacking rpcsvc-proto (1.4.2-0ubuntu6) ... 337s Selecting previously unselected package libc6-dev:armhf. 337s Preparing to unpack .../04-libc6-dev_2.39-0ubuntu6_armhf.deb ... 337s Unpacking libc6-dev:armhf (2.39-0ubuntu6) ... 337s Selecting previously unselected package libisl23:armhf. 337s Preparing to unpack .../05-libisl23_0.26-3_armhf.deb ... 337s Unpacking libisl23:armhf (0.26-3) ... 337s Selecting previously unselected package libmpc3:armhf. 337s Preparing to unpack .../06-libmpc3_1.3.1-1_armhf.deb ... 337s Unpacking libmpc3:armhf (1.3.1-1) ... 337s Selecting previously unselected package cpp-13-arm-linux-gnueabihf. 337s Preparing to unpack .../07-cpp-13-arm-linux-gnueabihf_13.2.0-21ubuntu1_armhf.deb ... 337s Unpacking cpp-13-arm-linux-gnueabihf (13.2.0-21ubuntu1) ... 338s Selecting previously unselected package cpp-13. 338s Preparing to unpack .../08-cpp-13_13.2.0-21ubuntu1_armhf.deb ... 338s Unpacking cpp-13 (13.2.0-21ubuntu1) ... 338s Selecting previously unselected package cpp-arm-linux-gnueabihf. 338s Preparing to unpack .../09-cpp-arm-linux-gnueabihf_4%3a13.2.0-7ubuntu1_armhf.deb ... 338s Unpacking cpp-arm-linux-gnueabihf (4:13.2.0-7ubuntu1) ... 338s Selecting previously unselected package cpp. 338s Preparing to unpack .../10-cpp_4%3a13.2.0-7ubuntu1_armhf.deb ... 338s Unpacking cpp (4:13.2.0-7ubuntu1) ... 338s Selecting previously unselected package libcc1-0:armhf. 338s Preparing to unpack .../11-libcc1-0_14-20240315-1ubuntu1_armhf.deb ... 338s Unpacking libcc1-0:armhf (14-20240315-1ubuntu1) ... 338s Selecting previously unselected package libatomic1:armhf. 338s Preparing to unpack .../12-libatomic1_14-20240315-1ubuntu1_armhf.deb ... 338s Unpacking libatomic1:armhf (14-20240315-1ubuntu1) ... 338s Selecting previously unselected package libasan8:armhf. 338s Preparing to unpack .../13-libasan8_14-20240315-1ubuntu1_armhf.deb ... 338s Unpacking libasan8:armhf (14-20240315-1ubuntu1) ... 338s Selecting previously unselected package libubsan1:armhf. 338s Preparing to unpack .../14-libubsan1_14-20240315-1ubuntu1_armhf.deb ... 338s Unpacking libubsan1:armhf (14-20240315-1ubuntu1) ... 338s Selecting previously unselected package libgcc-13-dev:armhf. 338s Preparing to unpack .../15-libgcc-13-dev_13.2.0-21ubuntu1_armhf.deb ... 338s Unpacking libgcc-13-dev:armhf (13.2.0-21ubuntu1) ... 338s Selecting previously unselected package gcc-13-arm-linux-gnueabihf. 338s Preparing to unpack .../16-gcc-13-arm-linux-gnueabihf_13.2.0-21ubuntu1_armhf.deb ... 338s Unpacking gcc-13-arm-linux-gnueabihf (13.2.0-21ubuntu1) ... 338s Selecting previously unselected package gcc-13. 338s Preparing to unpack .../17-gcc-13_13.2.0-21ubuntu1_armhf.deb ... 338s Unpacking gcc-13 (13.2.0-21ubuntu1) ... 338s Selecting previously unselected package gcc-arm-linux-gnueabihf. 338s Preparing to unpack .../18-gcc-arm-linux-gnueabihf_4%3a13.2.0-7ubuntu1_armhf.deb ... 338s Unpacking gcc-arm-linux-gnueabihf (4:13.2.0-7ubuntu1) ... 338s Selecting previously unselected package gcc. 338s Preparing to unpack .../19-gcc_4%3a13.2.0-7ubuntu1_armhf.deb ... 338s Unpacking gcc (4:13.2.0-7ubuntu1) ... 338s Selecting previously unselected package libstdc++-13-dev:armhf. 338s Preparing to unpack .../20-libstdc++-13-dev_13.2.0-21ubuntu1_armhf.deb ... 338s Unpacking libstdc++-13-dev:armhf (13.2.0-21ubuntu1) ... 339s Selecting previously unselected package g++-13-arm-linux-gnueabihf. 339s Preparing to unpack .../21-g++-13-arm-linux-gnueabihf_13.2.0-21ubuntu1_armhf.deb ... 339s Unpacking g++-13-arm-linux-gnueabihf (13.2.0-21ubuntu1) ... 339s Selecting previously unselected package g++-13. 339s Preparing to unpack .../22-g++-13_13.2.0-21ubuntu1_armhf.deb ... 339s Unpacking g++-13 (13.2.0-21ubuntu1) ... 339s Selecting previously unselected package g++-arm-linux-gnueabihf. 339s Preparing to unpack .../23-g++-arm-linux-gnueabihf_4%3a13.2.0-7ubuntu1_armhf.deb ... 339s Unpacking g++-arm-linux-gnueabihf (4:13.2.0-7ubuntu1) ... 339s Selecting previously unselected package g++. 339s Preparing to unpack .../24-g++_4%3a13.2.0-7ubuntu1_armhf.deb ... 339s Unpacking g++ (4:13.2.0-7ubuntu1) ... 339s Selecting previously unselected package build-essential. 339s Preparing to unpack .../25-build-essential_12.10ubuntu1_armhf.deb ... 339s Unpacking build-essential (12.10ubuntu1) ... 339s Selecting previously unselected package dctrl-tools. 339s Preparing to unpack .../26-dctrl-tools_2.24-3build2_armhf.deb ... 339s Unpacking dctrl-tools (2.24-3build2) ... 339s Selecting previously unselected package libgfortran-13-dev:armhf. 339s Preparing to unpack .../27-libgfortran-13-dev_13.2.0-21ubuntu1_armhf.deb ... 339s Unpacking libgfortran-13-dev:armhf (13.2.0-21ubuntu1) ... 339s Selecting previously unselected package gfortran-13-arm-linux-gnueabihf. 339s Preparing to unpack .../28-gfortran-13-arm-linux-gnueabihf_13.2.0-21ubuntu1_armhf.deb ... 339s Unpacking gfortran-13-arm-linux-gnueabihf (13.2.0-21ubuntu1) ... 339s Selecting previously unselected package gfortran-13. 339s Preparing to unpack .../29-gfortran-13_13.2.0-21ubuntu1_armhf.deb ... 339s Unpacking gfortran-13 (13.2.0-21ubuntu1) ... 339s Selecting previously unselected package gfortran-arm-linux-gnueabihf. 339s Preparing to unpack .../30-gfortran-arm-linux-gnueabihf_4%3a13.2.0-7ubuntu1_armhf.deb ... 339s Unpacking gfortran-arm-linux-gnueabihf (4:13.2.0-7ubuntu1) ... 339s Selecting previously unselected package gfortran. 339s Preparing to unpack .../31-gfortran_4%3a13.2.0-7ubuntu1_armhf.deb ... 339s Unpacking gfortran (4:13.2.0-7ubuntu1) ... 339s Selecting previously unselected package icu-devtools. 339s Preparing to unpack .../32-icu-devtools_74.2-1ubuntu1_armhf.deb ... 339s Unpacking icu-devtools (74.2-1ubuntu1) ... 339s Selecting previously unselected package libblas-dev:armhf. 339s Preparing to unpack .../33-libblas-dev_3.12.0-3_armhf.deb ... 339s Unpacking libblas-dev:armhf (3.12.0-3) ... 339s Selecting previously unselected package libbz2-dev:armhf. 339s Preparing to unpack .../34-libbz2-dev_1.0.8-5ubuntu1_armhf.deb ... 339s Unpacking libbz2-dev:armhf (1.0.8-5ubuntu1) ... 339s Selecting previously unselected package libicu-dev:armhf. 340s Preparing to unpack .../35-libicu-dev_74.2-1ubuntu1_armhf.deb ... 340s Unpacking libicu-dev:armhf (74.2-1ubuntu1) ... 340s Selecting previously unselected package libjpeg-turbo8-dev:armhf. 340s Preparing to unpack .../36-libjpeg-turbo8-dev_2.1.5-2ubuntu1_armhf.deb ... 340s Unpacking libjpeg-turbo8-dev:armhf (2.1.5-2ubuntu1) ... 340s Selecting previously unselected package libjpeg8-dev:armhf. 340s Preparing to unpack .../37-libjpeg8-dev_8c-2ubuntu11_armhf.deb ... 340s Unpacking libjpeg8-dev:armhf (8c-2ubuntu11) ... 340s Selecting previously unselected package libjpeg-dev:armhf. 340s Preparing to unpack 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up libjpeg-dev:armhf (8c-2ubuntu11) ... 341s Setting up gcc (4:13.2.0-7ubuntu1) ... 341s Setting up gfortran-arm-linux-gnueabihf (4:13.2.0-7ubuntu1) ... 341s Setting up gfortran-13 (13.2.0-21ubuntu1) ... 341s Setting up g++ (4:13.2.0-7ubuntu1) ... 341s update-alternatives: using /usr/bin/g++ to provide /usr/bin/c++ (c++) in auto mode 341s Setting up build-essential (12.10ubuntu1) ... 341s Setting up gfortran (4:13.2.0-7ubuntu1) ... 341s update-alternatives: using /usr/bin/gfortran to provide /usr/bin/f95 (f95) in auto mode 341s update-alternatives: warning: skip creation of /usr/share/man/man1/f95.1.gz because associated file /usr/share/man/man1/gfortran.1.gz (of link group f95) doesn't exist 341s update-alternatives: using /usr/bin/gfortran to provide /usr/bin/f77 (f77) in auto mode 341s update-alternatives: warning: skip creation of /usr/share/man/man1/f77.1.gz because associated file /usr/share/man/man1/gfortran.1.gz (of link group f77) doesn't exist 341s Setting up r-base-dev (4.3.3-2build1) ... 341s Setting up pkg-r-autopkgtest (20231212ubuntu1) ... 341s Setting up autopkgtest-satdep (0) ... 341s Processing triggers for man-db (2.12.0-3build4) ... 342s Processing triggers for install-info (7.1-3build1) ... 342s Processing triggers for libc-bin (2.39-0ubuntu6) ... 353s (Reading database ... 83330 files and directories currently installed.) 353s Removing autopkgtest-satdep (0) ... 358s autopkgtest [12:48:57]: test pkg-r-autopkgtest: /usr/share/dh-r/pkg-r-autopkgtest 358s autopkgtest [12:48:57]: test pkg-r-autopkgtest: [----------------------- 360s Test: Try to load the R library systemfit 360s 360s R version 4.3.3 (2024-02-29) -- "Angel Food Cake" 360s Copyright (C) 2024 The R Foundation for Statistical Computing 360s Platform: arm-unknown-linux-gnueabihf (32-bit) 360s 360s R is free software and comes with ABSOLUTELY NO WARRANTY. 360s You are welcome to redistribute it under certain conditions. 360s Type 'license()' or 'licence()' for distribution details. 360s 360s R is a collaborative project with many contributors. 360s Type 'contributors()' for more information and 360s 'citation()' on how to cite R or R packages in publications. 360s 360s Type 'demo()' for some demos, 'help()' for on-line help, or 360s 'help.start()' for an HTML browser interface to help. 360s Type 'q()' to quit R. 360s 361s > library('systemfit') 361s Loading required package: Matrix 361s Loading required package: car 361s Loading required package: carData 362s Loading required package: lmtest 362s Loading required package: zoo 362s 362s Attaching package: ‘zoo’ 362s 362s The following objects are masked from ‘package:base’: 362s 362s as.Date, as.Date.numeric 362s 362s 362s Please cite the 'systemfit' package as: 362s Arne Henningsen and Jeff D. Hamann (2007). systemfit: A Package for Estimating Systems of Simultaneous Equations in R. Journal of Statistical Software 23(4), 1-40. http://www.jstatsoft.org/v23/i04/. 362s 362s If you have questions, suggestions, or comments regarding the 'systemfit' package, please use a forum or 'tracker' at systemfit's R-Forge site: 362s https://r-forge.r-project.org/projects/systemfit/ 362s > 362s > 362s Other tests are currently unsupported! 362s They will be progressively added. 362s autopkgtest [12:49:01]: test pkg-r-autopkgtest: -----------------------] 366s autopkgtest [12:49:05]: test pkg-r-autopkgtest: - - - - - - - - - - results - - - - - - - - - - 366s pkg-r-autopkgtest PASS 369s autopkgtest [12:49:08]: @@@@@@@@@@@@@@@@@@@@ summary 369s run-unit-test PASS 369s pkg-r-autopkgtest PASS