0s autopkgtest [02:23:31]: starting date and time: 2024-03-28 02:23:31+0000 0s autopkgtest [02:23:31]: git checkout: 4a1cd702 l/adt_testbed: don't blame the testbed for unsolvable build deps 0s autopkgtest [02:23:31]: host juju-7f2275-prod-proposed-migration-environment-3; command line: /home/ubuntu/autopkgtest/runner/autopkgtest --output-dir /tmp/autopkgtest-work.jbk3x6xc/out --timeout-copy=6000 --setup-commands /home/ubuntu/autopkgtest-cloud/worker-config-production/setup-canonical.sh --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 -- ssh -s /home/ubuntu/autopkgtest/ssh-setup/nova -- --flavor autopkgtest --security-groups autopkgtest-juju-7f2275-prod-proposed-migration-environment-3@bos02-s390x-10.secgroup --name adt-noble-s390x-r-cran-systemfit-20240328-022331-juju-7f2275-prod-proposed-migration-environment-3-17c2615b-f3d3-421b-9773-20cb8f5cad77 --image adt/ubuntu-noble-s390x-server --keyname testbed-juju-7f2275-prod-proposed-migration-environment-3 --net-id=net_prod-proposed-migration -e TERM=linux -e ''"'"'http_proxy=http://squid.internal:3128'"'"'' -e ''"'"'https_proxy=http://squid.internal:3128'"'"'' -e ''"'"'no_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'"'"'' --mirror=http://ftpmaster.internal/ubuntu/ 142s autopkgtest [02:25:53]: testbed dpkg architecture: s390x 142s autopkgtest [02:25:53]: testbed apt version: 2.7.12 142s autopkgtest [02:25:53]: @@@@@@@@@@@@@@@@@@@@ test bed setup 144s Get:1 http://ftpmaster.internal/ubuntu noble-proposed InRelease [117 kB] 144s Get:2 http://ftpmaster.internal/ubuntu noble-proposed/multiverse Sources [55.9 kB] 144s Get:3 http://ftpmaster.internal/ubuntu noble-proposed/main Sources [471 kB] 145s Get:4 http://ftpmaster.internal/ubuntu noble-proposed/universe Sources [3949 kB] 147s Get:5 http://ftpmaster.internal/ubuntu noble-proposed/restricted Sources [8504 B] 147s Get:6 http://ftpmaster.internal/ubuntu noble-proposed/main s390x Packages [648 kB] 147s Get:7 http://ftpmaster.internal/ubuntu noble-proposed/main s390x c-n-f Metadata [3032 B] 147s Get:8 http://ftpmaster.internal/ubuntu noble-proposed/restricted s390x Packages [1372 B] 147s Get:9 http://ftpmaster.internal/ubuntu noble-proposed/restricted s390x c-n-f Metadata [116 B] 147s Get:10 http://ftpmaster.internal/ubuntu noble-proposed/universe s390x Packages [4036 kB] 148s Get:11 http://ftpmaster.internal/ubuntu noble-proposed/universe s390x c-n-f Metadata [7292 B] 148s Get:12 http://ftpmaster.internal/ubuntu noble-proposed/multiverse s390x Packages [46.2 kB] 148s Get:13 http://ftpmaster.internal/ubuntu noble-proposed/multiverse s390x c-n-f Metadata [116 B] 150s Fetched 9343 kB in 6s (1664 kB/s) 150s Reading package lists... 152s Reading package lists... 153s Building dependency tree... 153s Reading state information... 153s Calculating upgrade... 153s The following packages will be upgraded: 153s binutils binutils-common binutils-s390x-linux-gnu dmsetup gcc-13-base 153s gcc-14-base initramfs-tools initramfs-tools-bin initramfs-tools-core jq 153s libbinutils libctf-nobfd0 libctf0 libdevmapper1.02.1 libexpat1 libftdi1-2 153s libgcc-s1 libjq1 libpam-modules libpam-modules-bin libpam-runtime libpam0g 153s libseccomp2 libsframe1 libstdc++6 libusb-1.0-0 154s 26 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 154s Need to get 5444 kB of archives. 154s After this operation, 17.4 kB of additional disk space will be used. 154s Get:1 http://ftpmaster.internal/ubuntu noble/main s390x libpam0g s390x 1.5.3-5ubuntu3 [69.8 kB] 154s Get:2 http://ftpmaster.internal/ubuntu noble/main s390x libpam-modules-bin s390x 1.5.3-5ubuntu3 [57.4 kB] 154s Get:3 http://ftpmaster.internal/ubuntu noble/main s390x libpam-modules s390x 1.5.3-5ubuntu3 [289 kB] 154s Get:4 http://ftpmaster.internal/ubuntu noble/main s390x gcc-14-base s390x 14-20240315-1ubuntu1 [47.0 kB] 154s Get:5 http://ftpmaster.internal/ubuntu noble/main s390x libstdc++6 s390x 14-20240315-1ubuntu1 [908 kB] 154s Get:6 http://ftpmaster.internal/ubuntu noble/main s390x libgcc-s1 s390x 14-20240315-1ubuntu1 [35.9 kB] 154s Get:7 http://ftpmaster.internal/ubuntu noble/main s390x libpam-runtime all 1.5.3-5ubuntu3 [40.8 kB] 154s Get:8 http://ftpmaster.internal/ubuntu noble/main s390x libseccomp2 s390x 2.5.5-1ubuntu2 [53.4 kB] 155s Get:9 http://ftpmaster.internal/ubuntu noble/main s390x libdevmapper1.02.1 s390x 2:1.02.185-3ubuntu2 [142 kB] 155s Get:10 http://ftpmaster.internal/ubuntu noble/main s390x dmsetup s390x 2:1.02.185-3ubuntu2 [80.4 kB] 155s Get:11 http://ftpmaster.internal/ubuntu noble/main s390x libexpat1 s390x 2.6.1-2 [94.8 kB] 155s Get:12 http://ftpmaster.internal/ubuntu noble/main s390x libusb-1.0-0 s390x 2:1.0.27-1 [54.8 kB] 155s Get:13 http://ftpmaster.internal/ubuntu noble/main s390x libctf0 s390x 2.42-4ubuntu1 [98.4 kB] 155s Get:14 http://ftpmaster.internal/ubuntu noble/main s390x libctf-nobfd0 s390x 2.42-4ubuntu1 [100 kB] 155s Get:15 http://ftpmaster.internal/ubuntu noble/main s390x binutils-s390x-linux-gnu s390x 2.42-4ubuntu1 [2270 kB] 155s Get:16 http://ftpmaster.internal/ubuntu noble/main s390x libbinutils s390x 2.42-4ubuntu1 [477 kB] 155s Get:17 http://ftpmaster.internal/ubuntu noble/main s390x binutils s390x 2.42-4ubuntu1 [3056 B] 155s Get:18 http://ftpmaster.internal/ubuntu noble/main s390x binutils-common s390x 2.42-4ubuntu1 [217 kB] 156s Get:19 http://ftpmaster.internal/ubuntu noble/main s390x libsframe1 s390x 2.42-4ubuntu1 [14.2 kB] 156s Get:20 http://ftpmaster.internal/ubuntu noble/main s390x gcc-13-base s390x 13.2.0-21ubuntu1 [48.3 kB] 156s Get:21 http://ftpmaster.internal/ubuntu noble/main s390x initramfs-tools all 0.142ubuntu23 [9058 B] 156s Get:22 http://ftpmaster.internal/ubuntu noble/main s390x initramfs-tools-core all 0.142ubuntu23 [50.1 kB] 156s Get:23 http://ftpmaster.internal/ubuntu noble/main s390x initramfs-tools-bin s390x 0.142ubuntu23 [20.5 kB] 156s Get:24 http://ftpmaster.internal/ubuntu noble/main s390x jq s390x 1.7.1-3 [66.5 kB] 156s Get:25 http://ftpmaster.internal/ubuntu noble/main s390x libjq1 s390x 1.7.1-3 [168 kB] 156s Get:26 http://ftpmaster.internal/ubuntu noble/main s390x libftdi1-2 s390x 1.5-6build4 [29.3 kB] 156s Preconfiguring packages ... 156s Fetched 5444 kB in 2s (2437 kB/s) 156s (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 ... 52167 files and directories currently installed.) 156s Preparing to unpack .../libpam0g_1.5.3-5ubuntu3_s390x.deb ... 156s Unpacking libpam0g:s390x (1.5.3-5ubuntu3) over (1.5.2-9.1ubuntu3) ... 156s Setting up libpam0g:s390x (1.5.3-5ubuntu3) ... 157s (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 ... 52167 files and directories currently installed.) 157s Preparing to unpack .../libpam-modules-bin_1.5.3-5ubuntu3_s390x.deb ... 157s Unpacking libpam-modules-bin (1.5.3-5ubuntu3) over (1.5.2-9.1ubuntu3) ... 157s Setting up libpam-modules-bin (1.5.3-5ubuntu3) ... 157s pam_namespace.service is a disabled or a static unit not running, not starting it. 157s (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 ... 52167 files and directories currently installed.) 157s Preparing to unpack .../libpam-modules_1.5.3-5ubuntu3_s390x.deb ... 157s Unpacking libpam-modules:s390x (1.5.3-5ubuntu3) over (1.5.2-9.1ubuntu3) ... 157s Setting up libpam-modules:s390x (1.5.3-5ubuntu3) ... 157s Installing new version of config file /etc/security/namespace.init ... 157s (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 ... 52165 files and directories currently installed.) 157s Preparing to unpack .../gcc-14-base_14-20240315-1ubuntu1_s390x.deb ... 157s Unpacking gcc-14-base:s390x (14-20240315-1ubuntu1) over (14-20240303-1ubuntu1) ... 157s Setting up gcc-14-base:s390x (14-20240315-1ubuntu1) ... 157s (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 ... 52165 files and directories currently installed.) 157s Preparing to unpack .../libstdc++6_14-20240315-1ubuntu1_s390x.deb ... 157s Unpacking libstdc++6:s390x (14-20240315-1ubuntu1) over (14-20240303-1ubuntu1) ... 158s Setting up libstdc++6:s390x (14-20240315-1ubuntu1) ... 158s (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 ... 52165 files and directories currently installed.) 158s Preparing to unpack .../libgcc-s1_14-20240315-1ubuntu1_s390x.deb ... 158s Unpacking libgcc-s1:s390x (14-20240315-1ubuntu1) over (14-20240303-1ubuntu1) ... 158s Setting up libgcc-s1:s390x (14-20240315-1ubuntu1) ... 158s (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 ... 52165 files and directories currently installed.) 158s Preparing to unpack .../libpam-runtime_1.5.3-5ubuntu3_all.deb ... 158s Unpacking libpam-runtime (1.5.3-5ubuntu3) over (1.5.2-9.1ubuntu3) ... 158s Setting up libpam-runtime (1.5.3-5ubuntu3) ... 158s (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 ... 52165 files and directories currently installed.) 158s Preparing to unpack .../libseccomp2_2.5.5-1ubuntu2_s390x.deb ... 158s Unpacking libseccomp2:s390x (2.5.5-1ubuntu2) over (2.5.5-1ubuntu1) ... 158s Setting up libseccomp2:s390x (2.5.5-1ubuntu2) ... 158s (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 ... 52165 files and directories currently installed.) 158s Preparing to unpack .../00-libdevmapper1.02.1_2%3a1.02.185-3ubuntu2_s390x.deb ... 158s Unpacking libdevmapper1.02.1:s390x (2:1.02.185-3ubuntu2) over (2:1.02.185-3ubuntu1) ... 158s Preparing to unpack .../01-dmsetup_2%3a1.02.185-3ubuntu2_s390x.deb ... 158s Unpacking dmsetup (2:1.02.185-3ubuntu2) over (2:1.02.185-3ubuntu1) ... 158s Preparing to unpack .../02-libexpat1_2.6.1-2_s390x.deb ... 158s Unpacking libexpat1:s390x (2.6.1-2) over (2.6.0-1) ... 158s Preparing to unpack .../03-libusb-1.0-0_2%3a1.0.27-1_s390x.deb ... 158s Unpacking libusb-1.0-0:s390x (2:1.0.27-1) over (2:1.0.26-1) ... 158s Preparing to unpack .../04-libctf0_2.42-4ubuntu1_s390x.deb ... 158s Unpacking libctf0:s390x (2.42-4ubuntu1) over (2.42-3ubuntu1) ... 158s Preparing to unpack .../05-libctf-nobfd0_2.42-4ubuntu1_s390x.deb ... 158s Unpacking libctf-nobfd0:s390x (2.42-4ubuntu1) over (2.42-3ubuntu1) ... 158s Preparing to unpack .../06-binutils-s390x-linux-gnu_2.42-4ubuntu1_s390x.deb ... 158s Unpacking binutils-s390x-linux-gnu (2.42-4ubuntu1) over (2.42-3ubuntu1) ... 158s Preparing to unpack .../07-libbinutils_2.42-4ubuntu1_s390x.deb ... 158s Unpacking libbinutils:s390x (2.42-4ubuntu1) over (2.42-3ubuntu1) ... 158s Preparing to unpack .../08-binutils_2.42-4ubuntu1_s390x.deb ... 158s Unpacking binutils (2.42-4ubuntu1) over (2.42-3ubuntu1) ... 159s Preparing to unpack .../09-binutils-common_2.42-4ubuntu1_s390x.deb ... 159s Unpacking binutils-common:s390x (2.42-4ubuntu1) over (2.42-3ubuntu1) ... 159s Preparing to unpack .../10-libsframe1_2.42-4ubuntu1_s390x.deb ... 159s Unpacking libsframe1:s390x (2.42-4ubuntu1) over (2.42-3ubuntu1) ... 159s Preparing to unpack .../11-gcc-13-base_13.2.0-21ubuntu1_s390x.deb ... 159s Unpacking gcc-13-base:s390x (13.2.0-21ubuntu1) over (13.2.0-17ubuntu2) ... 159s Preparing to unpack .../12-initramfs-tools_0.142ubuntu23_all.deb ... 159s Unpacking initramfs-tools (0.142ubuntu23) over (0.142ubuntu20) ... 159s Preparing to unpack .../13-initramfs-tools-core_0.142ubuntu23_all.deb ... 159s Unpacking initramfs-tools-core (0.142ubuntu23) over (0.142ubuntu20) ... 159s Preparing to unpack .../14-initramfs-tools-bin_0.142ubuntu23_s390x.deb ... 159s Unpacking initramfs-tools-bin (0.142ubuntu23) over (0.142ubuntu20) ... 159s Preparing to unpack .../15-jq_1.7.1-3_s390x.deb ... 159s Unpacking jq (1.7.1-3) over (1.7.1-2) ... 159s Preparing to unpack .../16-libjq1_1.7.1-3_s390x.deb ... 159s Unpacking libjq1:s390x (1.7.1-3) over (1.7.1-2) ... 159s Preparing to unpack .../17-libftdi1-2_1.5-6build4_s390x.deb ... 159s Unpacking libftdi1-2:s390x (1.5-6build4) over (1.5-6build3) ... 159s Setting up libexpat1:s390x (2.6.1-2) ... 159s Setting up libjq1:s390x (1.7.1-3) ... 159s Setting up binutils-common:s390x (2.42-4ubuntu1) ... 159s Setting up libctf-nobfd0:s390x (2.42-4ubuntu1) ... 159s Setting up libsframe1:s390x (2.42-4ubuntu1) ... 159s Setting up gcc-13-base:s390x (13.2.0-21ubuntu1) ... 159s Setting up libdevmapper1.02.1:s390x (2:1.02.185-3ubuntu2) ... 159s Setting up dmsetup (2:1.02.185-3ubuntu2) ... 159s Setting up jq (1.7.1-3) ... 159s Setting up libusb-1.0-0:s390x (2:1.0.27-1) ... 159s Setting up libbinutils:s390x (2.42-4ubuntu1) ... 159s Setting up initramfs-tools-bin (0.142ubuntu23) ... 159s Setting up libctf0:s390x (2.42-4ubuntu1) ... 159s Setting up binutils-s390x-linux-gnu (2.42-4ubuntu1) ... 159s Setting up binutils (2.42-4ubuntu1) ... 159s Setting up libftdi1-2:s390x (1.5-6build4) ... 159s Setting up initramfs-tools-core (0.142ubuntu23) ... 159s Setting up initramfs-tools (0.142ubuntu23) ... 159s update-initramfs: deferring update (trigger activated) 159s Processing triggers for man-db (2.12.0-3) ... 160s Processing triggers for libc-bin (2.39-0ubuntu6) ... 160s Processing triggers for initramfs-tools (0.142ubuntu23) ... 160s update-initramfs: Generating /boot/initrd.img-6.8.0-11-generic 160s W: No lz4 in /usr/bin:/sbin:/bin, using gzip 166s Using config file '/etc/zipl.conf' 166s Building bootmap in '/boot' 166s Adding IPL section 'ubuntu' (default) 166s Preparing boot device for LD-IPL: vda (0000). 166s Done. 166s Reading package lists... 167s Building dependency tree... 167s Reading state information... 167s 0 upgraded, 0 newly installed, 0 to remove and 217 not upgraded. 168s Hit:1 http://ftpmaster.internal/ubuntu noble-proposed InRelease 168s Hit:2 http://ftpmaster.internal/ubuntu noble InRelease 168s Hit:3 http://ftpmaster.internal/ubuntu noble-updates InRelease 168s Hit:4 http://ftpmaster.internal/ubuntu noble-security InRelease 170s Reading package lists... 170s Reading package lists... 170s Building dependency tree... 170s Reading state information... 171s Calculating upgrade... 171s The following packages were automatically installed and are no longer required: 171s libaio1 libnetplan0 python3-distutils python3-lib2to3 171s Use 'sudo apt autoremove' to remove them. 171s The following packages will be REMOVED: 171s libapt-pkg6.0 libarchive13 libatm1 libcurl3-gnutls libcurl4 libdb5.3 libelf1 171s libext2fs2 libgdbm-compat4 libgdbm6 libglib2.0-0 libgnutls30 libgpgme11 171s libhogweed6 libmagic1 libnettle8 libnpth0 libnvme1 libparted2 libperl5.38 171s libpng16-16 libpsl5 libreadline8 libreiserfscore0 libssl3 libtirpc3 liburcu8 171s libuv1 171s The following NEW packages will be installed: 171s bpfcc-tools bpftrace fontconfig-config fonts-dejavu-core fonts-dejavu-mono 171s hwdata ieee-data libaio1t64 libapt-pkg6.0t64 libarchive13t64 libatm1t64 171s libbpfcc libc-dev-bin libc-devtools libc6-dev libclang-cpp18 libclang1-18 171s libcrypt-dev libcurl3t64-gnutls libcurl4t64 libdb5.3t64 libdeflate0 171s libdw1t64 libelf1t64 libext2fs2t64 libfontconfig1 libfreetype6 libgd3 171s libgdbm-compat4t64 libgdbm6t64 libglib2.0-0t64 libgnutls30t64 libgpgme11t64 171s libhogweed6t64 libjbig0 libjpeg-turbo8 libjpeg8 libllvm18 libmagic1t64 171s libnetplan1 libnettle8t64 libnpth0t64 libnvme1t64 libparted2t64 171s libperl5.38t64 libpng16-16t64 libpsl5t64 libreadline8t64 libreiserfscore0t64 171s libsharpyuv0 libssl3t64 libtiff6 libtirpc3t64 liburcu8t64 libuv1t64 libwebp7 171s libxpm4 linux-headers-6.8.0-20 linux-headers-6.8.0-20-generic 171s linux-image-6.8.0-20-generic linux-libc-dev linux-modules-6.8.0-20-generic 171s linux-modules-extra-6.8.0-20-generic linux-tools-6.8.0-20 171s linux-tools-6.8.0-20-generic linux-tools-common manpages manpages-dev 171s python3-bpfcc python3-netaddr rpcsvc-proto ubuntu-kernel-accessories 171s xdg-user-dirs 171s The following packages have been kept back: 171s s390-tools 171s The following packages will be upgraded: 171s apparmor apt apt-utils base-files bash bind9-dnsutils bind9-host bind9-libs 171s bolt bsdextrautils bsdutils btrfs-progs coreutils cryptsetup-bin curl dbus 171s dbus-bin dbus-daemon dbus-session-bus-common dbus-system-bus-common 171s dbus-user-session dhcpcd-base dirmngr dpkg dpkg-dev e2fsprogs e2fsprogs-l10n 171s eject fdisk file ftp fwupd gawk gir1.2-girepository-2.0 gir1.2-glib-2.0 171s gnupg gnupg-l10n gnupg-utils gpg gpg-agent gpg-wks-client gpgconf gpgsm gpgv 171s groff-base ibverbs-providers inetutils-telnet info install-info iproute2 171s keyboxd kmod kpartx krb5-locales libapparmor1 libaudit-common libaudit1 171s libblkid1 libblockdev-crypto3 libblockdev-fs3 libblockdev-loop3 171s libblockdev-mdraid3 libblockdev-nvme3 libblockdev-part3 libblockdev-swap3 171s libblockdev-utils3 libblockdev3 libbpf1 libbrotli1 libcap-ng0 libcom-err2 171s libcryptsetup12 libdbus-1-3 libdebconfclient0 libdpkg-perl 171s libevent-core-2.1-7 libfdisk1 libfido2-1 libfwupd2 libgirepository-1.0-1 171s libglib2.0-data libgssapi-krb5-2 libgudev-1.0-0 libgusb2 libibverbs1 171s libjcat1 libjson-glib-1.0-0 libjson-glib-1.0-common libk5crypto3 libkmod2 171s libkrb5-3 libkrb5support0 libldap-common libldap2 liblocale-gettext-perl 171s liblzma5 libmagic-mgc libmbim-glib4 libmbim-proxy libmm-glib0 libmount1 171s libnghttp2-14 libnsl2 libnss-systemd libpam-systemd libplymouth5 171s libpolkit-agent-1-0 libpolkit-gobject-1-0 libproc2-0 libprotobuf-c1 171s libpython3-stdlib libpython3.11-minimal libpython3.11-stdlib 171s libpython3.12-minimal libpython3.12-stdlib libqmi-glib5 libqmi-proxy 171s libqrtr-glib0 librtmp1 libsasl2-2 libsasl2-modules libsasl2-modules-db 171s libselinux1 libsemanage-common libsemanage2 libslang2 libsmartcols1 171s libsqlite3-0 libss2 libssh-4 libsystemd-shared libsystemd0 171s libtext-charwidth-perl libtext-iconv-perl libtirpc-common libudev1 171s libudisks2-0 libuuid1 libvolume-key1 libxml2 libxmlb2 libxmuu1 linux-generic 171s linux-headers-generic linux-headers-virtual linux-image-generic 171s linux-image-virtual linux-virtual logsave lshw lsof man-db motd-news-config 171s mount mtr-tiny multipath-tools netplan-generator netplan.io openssh-client 171s openssh-server openssh-sftp-server openssl parted perl perl-base 171s perl-modules-5.38 pinentry-curses plymouth plymouth-theme-ubuntu-text procps 171s python-apt-common python3 python3-apt python3-cryptography python3-dbus 171s python3-distutils python3-gdbm python3-gi python3-lib2to3 python3-minimal 171s python3-netplan python3-pkg-resources python3-pyrsistent python3-setuptools 171s python3-typing-extensions python3.11 python3.11-minimal python3.12 171s python3.12-minimal readline-common rsync rsyslog s390-tools-data 171s shared-mime-info sudo systemd systemd-dev systemd-resolved systemd-sysv 171s systemd-timesyncd tcpdump telnet tnftp ubuntu-pro-client 171s ubuntu-pro-client-l10n udev udisks2 usb.ids util-linux uuid-runtime 171s vim-common vim-tiny wget xxd xz-utils zlib1g 171s 216 upgraded, 73 newly installed, 28 to remove and 1 not upgraded. 171s Need to get 223 MB of archives. 171s After this operation, 524 MB of additional disk space will be used. 171s Get:1 http://ftpmaster.internal/ubuntu noble-proposed/main s390x motd-news-config all 13ubuntu8 [5098 B] 172s Get:2 http://ftpmaster.internal/ubuntu noble-proposed/main s390x base-files s390x 13ubuntu8 [74.2 kB] 172s Get:3 http://ftpmaster.internal/ubuntu noble-proposed/main s390x bash s390x 5.2.21-2ubuntu3 [845 kB] 172s Get:4 http://ftpmaster.internal/ubuntu noble-proposed/main s390x bsdutils s390x 1:2.39.3-9ubuntu2 [96.1 kB] 172s Get:5 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libbrotli1 s390x 1.1.0-2build1 [375 kB] 172s Get:6 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libgssapi-krb5-2 s390x 1.20.1-6ubuntu1 [149 kB] 172s Get:7 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libkrb5-3 s390x 1.20.1-6ubuntu1 [360 kB] 173s Get:8 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libkrb5support0 s390x 1.20.1-6ubuntu1 [34.6 kB] 173s Get:9 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libk5crypto3 s390x 1.20.1-6ubuntu1 [90.3 kB] 173s Get:10 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libcom-err2 s390x 1.47.0-2.4~exp1ubuntu2 [22.9 kB] 173s Get:11 http://ftpmaster.internal/ubuntu noble-proposed/main s390x zlib1g s390x 1:1.3.dfsg-3.1ubuntu1 [75.7 kB] 173s Get:12 http://ftpmaster.internal/ubuntu noble-proposed/main s390x librtmp1 s390x 2.4+20151223.gitfa8646d.1-2build6 [58.4 kB] 173s Get:13 http://ftpmaster.internal/ubuntu noble-proposed/main s390x udisks2 s390x 2.10.1-6 [298 kB] 173s Get:14 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libudisks2-0 s390x 2.10.1-6 [179 kB] 173s Get:15 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libblkid1 s390x 2.39.3-9ubuntu2 [128 kB] 173s Get:16 http://ftpmaster.internal/ubuntu noble-proposed/main s390x liblzma5 s390x 5.6.0-0.2 [137 kB] 173s Get:17 http://ftpmaster.internal/ubuntu noble-proposed/main s390x kmod s390x 31+20240202-2ubuntu4 [107 kB] 173s Get:18 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libkmod2 s390x 31+20240202-2ubuntu4 [56.3 kB] 173s Get:19 http://ftpmaster.internal/ubuntu noble-proposed/main s390x systemd-dev all 255.4-1ubuntu5 [103 kB] 173s Get:20 http://ftpmaster.internal/ubuntu noble-proposed/main s390x systemd-timesyncd s390x 255.4-1ubuntu5 [35.3 kB] 173s Get:21 http://ftpmaster.internal/ubuntu noble-proposed/main s390x dbus-session-bus-common all 1.14.10-4ubuntu2 [80.3 kB] 173s Get:22 http://ftpmaster.internal/ubuntu noble-proposed/main s390x systemd-sysv s390x 255.4-1ubuntu5 [11.9 kB] 173s Get:23 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libpam-systemd s390x 255.4-1ubuntu5 [242 kB] 173s Get:24 http://ftpmaster.internal/ubuntu noble-proposed/main s390x dbus-user-session s390x 1.14.10-4ubuntu2 [9960 B] 173s Get:25 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libapparmor1 s390x 4.0.0-beta3-0ubuntu2 [50.8 kB] 173s Get:26 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libaudit-common all 1:3.1.2-2.1 [5674 B] 173s Get:27 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libcap-ng0 s390x 0.8.4-2build1 [15.7 kB] 173s Get:28 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libaudit1 s390x 1:3.1.2-2.1 [48.9 kB] 173s Get:29 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libselinux1 s390x 3.5-2ubuntu1 [84.7 kB] 173s Get:30 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libcurl4t64 s390x 8.5.0-2ubuntu8 [363 kB] 173s Get:31 http://ftpmaster.internal/ubuntu noble-proposed/main s390x curl s390x 8.5.0-2ubuntu8 [227 kB] 174s Get:32 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libpsl5t64 s390x 0.21.2-1.1 [57.6 kB] 174s Get:33 http://ftpmaster.internal/ubuntu noble-proposed/main s390x wget s390x 1.21.4-1ubuntu2 [351 kB] 174s Get:34 http://ftpmaster.internal/ubuntu noble-proposed/main s390x tnftp s390x 20230507-2build1 [107 kB] 174s Get:35 http://ftpmaster.internal/ubuntu noble-proposed/main s390x tcpdump s390x 4.99.4-3ubuntu2 [490 kB] 174s Get:36 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libsystemd-shared s390x 255.4-1ubuntu5 [2131 kB] 175s Get:37 http://ftpmaster.internal/ubuntu noble-proposed/main s390x systemd-resolved s390x 255.4-1ubuntu5 [304 kB] 175s Get:38 http://ftpmaster.internal/ubuntu noble-proposed/main s390x sudo s390x 1.9.15p5-3ubuntu3 [968 kB] 175s Get:39 http://ftpmaster.internal/ubuntu noble-proposed/main s390x rsync s390x 3.2.7-1build1 [446 kB] 175s Get:40 http://ftpmaster.internal/ubuntu noble-proposed/main s390x python3-cryptography s390x 41.0.7-4build2 [918 kB] 176s Get:41 http://ftpmaster.internal/ubuntu noble-proposed/main s390x openssl s390x 3.0.13-0ubuntu2 [1010 kB] 176s Get:42 http://ftpmaster.internal/ubuntu noble-proposed/main s390x openssh-sftp-server s390x 1:9.6p1-3ubuntu11 [39.0 kB] 176s Get:43 http://ftpmaster.internal/ubuntu noble-proposed/main s390x openssh-client s390x 1:9.6p1-3ubuntu11 [935 kB] 177s Get:44 http://ftpmaster.internal/ubuntu noble-proposed/main s390x openssh-server s390x 1:9.6p1-3ubuntu11 [529 kB] 177s Get:45 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libssh-4 s390x 0.10.6-2build1 [189 kB] 177s Get:46 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libsasl2-modules s390x 2.1.28+dfsg1-5ubuntu1 [76.6 kB] 177s Get:47 http://ftpmaster.internal/ubuntu noble-proposed/main s390x python3.12 s390x 3.12.2-4build3 [645 kB] 177s Get:48 http://ftpmaster.internal/ubuntu noble-proposed/main s390x python3.12-minimal s390x 3.12.2-4build3 [2419 kB] 178s Get:49 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libpython3.12-minimal s390x 3.12.2-4build3 [829 kB] 178s Get:50 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libparted2t64 s390x 3.6-3.1build2 [172 kB] 178s Get:51 http://ftpmaster.internal/ubuntu noble-proposed/main s390x parted s390x 3.6-3.1build2 [44.6 kB] 178s Get:52 http://ftpmaster.internal/ubuntu noble-proposed/main s390x python3.11 s390x 3.11.8-1build4 [589 kB] 179s Get:53 http://ftpmaster.internal/ubuntu noble-proposed/main s390x python3.11-minimal s390x 3.11.8-1build4 [2280 kB] 180s Get:54 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libpython3.11-minimal s390x 3.11.8-1build4 [838 kB] 180s Get:55 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libpython3.11-stdlib s390x 3.11.8-1build4 [1944 kB] 181s Get:56 http://ftpmaster.internal/ubuntu noble-proposed/main s390x shared-mime-info s390x 2.4-1build1 [474 kB] 181s Get:57 http://ftpmaster.internal/ubuntu noble-proposed/main s390x gir1.2-girepository-2.0 s390x 1.79.1-1ubuntu6 [24.5 kB] 181s Get:58 http://ftpmaster.internal/ubuntu noble-proposed/main s390x gir1.2-glib-2.0 s390x 2.79.3-3ubuntu5 [180 kB] 181s Get:59 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libgirepository-1.0-1 s390x 1.79.1-1ubuntu6 [84.0 kB] 181s Get:60 http://ftpmaster.internal/ubuntu noble-proposed/main s390x python3-gi s390x 3.47.0-3build1 [236 kB] 181s Get:61 http://ftpmaster.internal/ubuntu noble-proposed/main s390x python3-dbus s390x 1.3.2-5build2 [100 kB] 181s Get:62 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libnetplan1 s390x 1.0-1 [123 kB] 181s Get:63 http://ftpmaster.internal/ubuntu noble-proposed/main s390x python3-netplan s390x 1.0-1 [23.0 kB] 181s Get:64 http://ftpmaster.internal/ubuntu noble-proposed/main s390x netplan-generator s390x 1.0-1 [59.1 kB] 181s Get:65 http://ftpmaster.internal/ubuntu noble-proposed/main s390x netplan.io s390x 1.0-1 [65.4 kB] 181s Get:66 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libxmlb2 s390x 0.3.15-1build1 [70.6 kB] 181s Get:67 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libgpgme11t64 s390x 1.18.0-4.1ubuntu3 [150 kB] 181s Get:68 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libvolume-key1 s390x 0.3.12-7build1 [40.8 kB] 181s Get:69 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libqrtr-glib0 s390x 1.2.2-1ubuntu3 [17.5 kB] 181s Get:70 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libqmi-glib5 s390x 1.35.2-0ubuntu1 [918 kB] 182s Get:71 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libqmi-proxy s390x 1.35.2-0ubuntu1 [6122 B] 182s Get:72 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libpolkit-agent-1-0 s390x 124-1ubuntu1 [17.8 kB] 182s Get:73 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libpolkit-gobject-1-0 s390x 124-1ubuntu1 [48.3 kB] 182s Get:74 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libmm-glib0 s390x 1.23.4-0ubuntu1 [251 kB] 182s Get:75 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libmbim-glib4 s390x 1.31.2-0ubuntu2 [238 kB] 182s Get:76 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libmbim-proxy s390x 1.31.2-0ubuntu2 [6154 B] 182s Get:77 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libjson-glib-1.0-common all 1.8.0-2build1 [4210 B] 182s Get:78 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libjson-glib-1.0-0 s390x 1.8.0-2build1 [68.4 kB] 182s Get:79 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libgusb2 s390x 0.4.8-1build1 [39.0 kB] 182s Get:80 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libgudev-1.0-0 s390x 1:238-3ubuntu2 [15.7 kB] 182s Get:81 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libarchive13t64 s390x 3.7.2-1.1ubuntu2 [419 kB] 182s Get:82 http://ftpmaster.internal/ubuntu noble-proposed/main s390x fwupd s390x 1.9.15-2 [4435 kB] 184s Get:83 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libcurl3t64-gnutls s390x 8.5.0-2ubuntu8 [356 kB] 184s Get:84 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libfwupd2 s390x 1.9.15-2 [136 kB] 184s Get:85 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libblockdev3 s390x 3.1.0-1build1 [52.3 kB] 184s Get:86 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libblockdev-utils3 s390x 3.1.0-1build1 [19.2 kB] 184s Get:87 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libblockdev-swap3 s390x 3.1.0-1build1 [7778 B] 184s Get:88 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libblockdev-part3 s390x 3.1.0-1build1 [15.4 kB] 184s Get:89 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libnvme1t64 s390x 1.8-3 [78.7 kB] 184s Get:90 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libblockdev-nvme3 s390x 3.1.0-1build1 [18.3 kB] 184s Get:91 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libblockdev-mdraid3 s390x 3.1.0-1build1 [13.2 kB] 184s Get:92 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libblockdev-loop3 s390x 3.1.0-1build1 [7138 B] 184s Get:93 http://ftpmaster.internal/ubuntu noble-proposed/main s390x logsave s390x 1.47.0-2.4~exp1ubuntu2 [22.5 kB] 184s Get:94 http://ftpmaster.internal/ubuntu noble-proposed/main s390x e2fsprogs-l10n all 1.47.0-2.4~exp1ubuntu2 [5996 B] 184s Get:95 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libext2fs2t64 s390x 1.47.0-2.4~exp1ubuntu2 [235 kB] 184s Get:96 http://ftpmaster.internal/ubuntu noble-proposed/main s390x e2fsprogs s390x 1.47.0-2.4~exp1ubuntu2 [615 kB] 184s Get:97 http://ftpmaster.internal/ubuntu noble/main s390x libreiserfscore0t64 s390x 1:3.6.27-7.1 [85.5 kB] 184s Get:98 http://ftpmaster.internal/ubuntu noble-proposed/main s390x btrfs-progs s390x 6.6.3-1.1build1 [959 kB] 185s Get:99 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libblockdev-fs3 s390x 3.1.0-1build1 [36.5 kB] 185s Get:100 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libblockdev-crypto3 s390x 3.1.0-1build1 [21.6 kB] 185s Get:101 http://ftpmaster.internal/ubuntu noble-proposed/main s390x bolt s390x 0.9.6-2build1 [142 kB] 185s Get:102 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libglib2.0-0t64 s390x 2.79.3-3ubuntu5 [1566 kB] 186s Get:103 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libjcat1 s390x 0.2.0-2build2 [34.4 kB] 186s Get:104 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libldap2 s390x 2.6.7+dfsg-1~exp1ubuntu6 [202 kB] 186s Get:105 http://ftpmaster.internal/ubuntu noble-proposed/main s390x ubuntu-pro-client-l10n s390x 31.2.2 [19.4 kB] 186s Get:106 http://ftpmaster.internal/ubuntu noble-proposed/main s390x ubuntu-pro-client s390x 31.2.2 [214 kB] 186s Get:107 http://ftpmaster.internal/ubuntu noble-proposed/main s390x gnupg-utils s390x 2.4.4-2ubuntu15 [116 kB] 186s Get:108 http://ftpmaster.internal/ubuntu noble-proposed/main s390x keyboxd s390x 2.4.4-2ubuntu15 [83.1 kB] 186s Get:109 http://ftpmaster.internal/ubuntu noble/main s390x libnpth0t64 s390x 1.6-3.1 [8148 B] 186s Get:110 http://ftpmaster.internal/ubuntu noble-proposed/main s390x gpgv s390x 2.4.4-2ubuntu15 [165 kB] 186s Get:111 http://ftpmaster.internal/ubuntu noble-proposed/main s390x gpg-wks-client s390x 2.4.4-2ubuntu15 [76.8 kB] 186s Get:112 http://ftpmaster.internal/ubuntu noble-proposed/main s390x gpg-agent s390x 2.4.4-2ubuntu15 [240 kB] 186s Get:113 http://ftpmaster.internal/ubuntu noble-proposed/main s390x gpg s390x 2.4.4-2ubuntu15 [589 kB] 186s Get:114 http://ftpmaster.internal/ubuntu noble-proposed/main s390x dirmngr s390x 2.4.4-2ubuntu15 [340 kB] 187s Get:115 http://ftpmaster.internal/ubuntu noble-proposed/main s390x gnupg all 2.4.4-2ubuntu15 [359 kB] 187s Get:116 http://ftpmaster.internal/ubuntu noble-proposed/main s390x python3-apt s390x 2.7.7 [171 kB] 187s Get:117 http://ftpmaster.internal/ubuntu noble-proposed/main s390x apt-utils s390x 2.7.14 [214 kB] 187s Get:118 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libapt-pkg6.0t64 s390x 2.7.14 [1014 kB] 187s Get:119 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libnettle8t64 s390x 3.9.1-2.2 [210 kB] 187s Get:120 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libhogweed6t64 s390x 3.9.1-2.2 [204 kB] 187s Get:121 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libgnutls30t64 s390x 3.8.3-1.1ubuntu2 [1044 kB] 187s Get:122 http://ftpmaster.internal/ubuntu noble-proposed/main s390x apt s390x 2.7.14 [1390 kB] 188s Get:123 http://ftpmaster.internal/ubuntu noble-proposed/main s390x gpgconf s390x 2.4.4-2ubuntu15 [111 kB] 188s Get:124 http://ftpmaster.internal/ubuntu noble-proposed/main s390x gpgsm s390x 2.4.4-2ubuntu15 [244 kB] 188s Get:125 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libreadline8t64 s390x 8.2-4 [170 kB] 188s Get:126 http://ftpmaster.internal/ubuntu noble-proposed/main s390x gawk s390x 1:5.2.1-2build2 [496 kB] 188s Get:127 http://ftpmaster.internal/ubuntu noble-proposed/main s390x fdisk s390x 2.39.3-9ubuntu2 [124 kB] 188s Get:128 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libpython3.12-stdlib s390x 3.12.2-4build3 [2046 kB] 188s Get:129 http://ftpmaster.internal/ubuntu noble-proposed/main s390x perl-base s390x 5.38.2-3.2 [1961 kB] 188s Get:130 http://ftpmaster.internal/ubuntu noble-proposed/main s390x perl-modules-5.38 all 5.38.2-3.2 [3110 kB] 189s Get:131 http://ftpmaster.internal/ubuntu noble-proposed/main s390x python3-gdbm s390x 3.12.2-3ubuntu1.1 [19.0 kB] 189s Get:132 http://ftpmaster.internal/ubuntu noble-proposed/main s390x man-db s390x 2.12.0-3build4 [1246 kB] 189s Get:133 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libgdbm6t64 s390x 1.23-5.1 [36.4 kB] 189s Get:134 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libgdbm-compat4t64 s390x 1.23-5.1 [6880 B] 189s Get:135 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libperl5.38t64 s390x 5.38.2-3.2 [5007 kB] 189s Get:136 http://ftpmaster.internal/ubuntu noble-proposed/main s390x perl s390x 5.38.2-3.2 [231 kB] 189s Get:137 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libdb5.3t64 s390x 5.3.28+dfsg2-6 [763 kB] 189s Get:138 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libsasl2-modules-db s390x 2.1.28+dfsg1-5ubuntu1 [21.1 kB] 189s Get:139 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libsasl2-2 s390x 2.1.28+dfsg1-5ubuntu1 [57.8 kB] 189s Get:140 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libfido2-1 s390x 1.14.0-1build1 [81.0 kB] 189s Get:141 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libcryptsetup12 s390x 2:2.7.0-1ubuntu2 [264 kB] 189s Get:142 http://ftpmaster.internal/ubuntu noble-proposed/main s390x dhcpcd-base s390x 1:10.0.6-1ubuntu2 [217 kB] 189s Get:143 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libuv1t64 s390x 1.48.0-1.1 [101 kB] 189s Get:144 http://ftpmaster.internal/ubuntu noble-proposed/main s390x bind9-host s390x 1:9.18.24-0ubuntu3 [50.5 kB] 189s Get:145 http://ftpmaster.internal/ubuntu noble-proposed/main s390x bind9-dnsutils s390x 1:9.18.24-0ubuntu3 [162 kB] 189s Get:146 http://ftpmaster.internal/ubuntu noble-proposed/main s390x bind9-libs s390x 1:9.18.24-0ubuntu3 [1243 kB] 189s Get:147 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libssl3t64 s390x 3.0.13-0ubuntu2 [1675 kB] 189s Get:148 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libnss-systemd s390x 255.4-1ubuntu5 [166 kB] 189s Get:149 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libudev1 s390x 255.4-1ubuntu5 [178 kB] 189s Get:150 http://ftpmaster.internal/ubuntu noble-proposed/main s390x systemd s390x 255.4-1ubuntu5 [3533 kB] 190s Get:151 http://ftpmaster.internal/ubuntu noble-proposed/main s390x udev s390x 255.4-1ubuntu5 [1887 kB] 190s Get:152 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libsystemd0 s390x 255.4-1ubuntu5 [443 kB] 190s Get:153 http://ftpmaster.internal/ubuntu noble-proposed/main s390x dbus-system-bus-common all 1.14.10-4ubuntu2 [81.5 kB] 190s Get:154 http://ftpmaster.internal/ubuntu noble-proposed/main s390x dbus-bin s390x 1.14.10-4ubuntu2 [41.4 kB] 190s Get:155 http://ftpmaster.internal/ubuntu noble-proposed/main s390x dbus s390x 1.14.10-4ubuntu2 [24.3 kB] 190s Get:156 http://ftpmaster.internal/ubuntu noble-proposed/main s390x dbus-daemon s390x 1.14.10-4ubuntu2 [118 kB] 190s Get:157 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libdbus-1-3 s390x 1.14.10-4ubuntu2 [213 kB] 190s Get:158 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libmount1 s390x 2.39.3-9ubuntu2 [138 kB] 190s Get:159 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libuuid1 s390x 2.39.3-9ubuntu2 [35.6 kB] 190s Get:160 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libfdisk1 s390x 2.39.3-9ubuntu2 [151 kB] 190s Get:161 http://ftpmaster.internal/ubuntu noble-proposed/main s390x mount s390x 2.39.3-9ubuntu2 [119 kB] 190s Get:162 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libsqlite3-0 s390x 3.45.1-1ubuntu1 [747 kB] 190s Get:163 http://ftpmaster.internal/ubuntu noble-proposed/main s390x dpkg s390x 1.22.6ubuntu5 [1278 kB] 190s Get:164 http://ftpmaster.internal/ubuntu noble-proposed/main s390x python3-minimal s390x 3.12.2-0ubuntu1 [27.1 kB] 190s Get:165 http://ftpmaster.internal/ubuntu noble-proposed/main s390x python3 s390x 3.12.2-0ubuntu1 [24.1 kB] 190s Get:166 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libpython3-stdlib s390x 3.12.2-0ubuntu1 [9804 B] 190s Get:167 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libsmartcols1 s390x 2.39.3-9ubuntu2 [67.9 kB] 190s Get:168 http://ftpmaster.internal/ubuntu noble-proposed/main s390x bsdextrautils s390x 2.39.3-9ubuntu2 [76.3 kB] 190s Get:169 http://ftpmaster.internal/ubuntu noble-proposed/main s390x groff-base s390x 1.23.0-3build1 [1049 kB] 190s Get:170 http://ftpmaster.internal/ubuntu noble-proposed/main s390x pinentry-curses s390x 1.2.1-3ubuntu4 [37.6 kB] 190s Get:171 http://ftpmaster.internal/ubuntu noble-proposed/main s390x readline-common all 8.2-4 [56.4 kB] 190s Get:172 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libxml2 s390x 2.9.14+dfsg-1.3ubuntu2 [818 kB] 190s Get:173 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libbpf1 s390x 1:1.3.0-2build1 [176 kB] 190s Get:174 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libelf1t64 s390x 0.190-1.1build2 [69.7 kB] 190s Get:175 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libtirpc-common all 1.3.4+ds-1.1 [8018 B] 190s Get:176 http://ftpmaster.internal/ubuntu noble-proposed/main s390x lsof s390x 4.95.0-1build2 [248 kB] 190s Get:177 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libnsl2 s390x 1.3.0-3build2 [44.1 kB] 190s Get:178 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libtirpc3t64 s390x 1.3.4+ds-1.1 [85.8 kB] 190s Get:179 http://ftpmaster.internal/ubuntu noble-proposed/main s390x iproute2 s390x 6.1.0-1ubuntu5 [1156 kB] 190s Get:180 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libprotobuf-c1 s390x 1.4.1-1ubuntu3 [23.4 kB] 190s Get:181 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libnghttp2-14 s390x 1.59.0-1build1 [77.8 kB] 190s Get:182 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libproc2-0 s390x 2:4.0.4-4ubuntu2 [60.1 kB] 190s Get:183 http://ftpmaster.internal/ubuntu noble-proposed/main s390x procps s390x 2:4.0.4-4ubuntu2 [724 kB] 190s Get:184 http://ftpmaster.internal/ubuntu noble-proposed/main s390x coreutils s390x 9.4-3ubuntu3 [1482 kB] 190s Get:185 http://ftpmaster.internal/ubuntu noble-proposed/main s390x util-linux s390x 2.39.3-9ubuntu2 [1143 kB] 190s Get:186 http://ftpmaster.internal/ubuntu noble-proposed/main s390x file s390x 1:5.45-3 [22.2 kB] 190s Get:187 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libmagic-mgc s390x 1:5.45-3 [305 kB] 190s Get:188 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libmagic1t64 s390x 1:5.45-3 [93.1 kB] 190s Get:189 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libplymouth5 s390x 24.004.60-1ubuntu6 [151 kB] 190s Get:190 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libpng16-16t64 s390x 1.6.43-3 [200 kB] 190s Get:191 http://ftpmaster.internal/ubuntu noble-proposed/main s390x multipath-tools s390x 0.9.4-5ubuntu6 [318 kB] 190s Get:192 http://ftpmaster.internal/ubuntu noble/main s390x liburcu8t64 s390x 0.14.0-3.1 [67.3 kB] 190s Get:193 http://ftpmaster.internal/ubuntu noble-proposed/main s390x liblocale-gettext-perl s390x 1.07-6ubuntu4 [15.8 kB] 191s Get:194 http://ftpmaster.internal/ubuntu noble-proposed/main s390x uuid-runtime s390x 2.39.3-9ubuntu2 [33.4 kB] 191s Get:195 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libdebconfclient0 s390x 0.271ubuntu2 [11.4 kB] 191s Get:196 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libsemanage-common all 3.5-1build4 [10.1 kB] 191s Get:197 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libsemanage2 s390x 3.5-1build4 [96.7 kB] 191s Get:198 http://ftpmaster.internal/ubuntu noble-proposed/main s390x install-info s390x 7.1-3build1 [64.5 kB] 191s Get:199 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libss2 s390x 1.47.0-2.4~exp1ubuntu2 [17.2 kB] 191s Get:200 http://ftpmaster.internal/ubuntu noble-proposed/main s390x eject s390x 2.39.3-9ubuntu2 [26.2 kB] 191s Get:201 http://ftpmaster.internal/ubuntu noble-proposed/main s390x krb5-locales all 1.20.1-6ubuntu1 [13.8 kB] 191s Get:202 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libglib2.0-data all 2.79.3-3ubuntu5 [46.6 kB] 191s Get:203 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libslang2 s390x 2.3.3-3build1 [501 kB] 192s Get:204 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libtext-charwidth-perl s390x 0.04-11build2 [9484 B] 192s Get:205 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libtext-iconv-perl s390x 1.7-8build2 [13.8 kB] 192s Get:206 http://ftpmaster.internal/ubuntu noble-proposed/main s390x python-apt-common all 2.7.7 [19.8 kB] 192s Get:207 http://ftpmaster.internal/ubuntu noble-proposed/main s390x python3-setuptools all 68.1.2-2ubuntu1 [396 kB] 192s Get:208 http://ftpmaster.internal/ubuntu noble-proposed/main s390x python3-pkg-resources all 68.1.2-2ubuntu1 [168 kB] 192s Get:209 http://ftpmaster.internal/ubuntu noble-proposed/main s390x rsyslog s390x 8.2312.0-3ubuntu7 [536 kB] 192s Get:210 http://ftpmaster.internal/ubuntu noble-proposed/main s390x vim-tiny s390x 2:9.1.0016-1ubuntu6 [879 kB] 192s Get:211 http://ftpmaster.internal/ubuntu noble-proposed/main s390x vim-common all 2:9.1.0016-1ubuntu6 [385 kB] 192s Get:212 http://ftpmaster.internal/ubuntu noble/main s390x xdg-user-dirs s390x 0.18-1 [18.5 kB] 192s Get:213 http://ftpmaster.internal/ubuntu noble-proposed/main s390x xxd s390x 2:9.1.0016-1ubuntu6 [63.5 kB] 192s Get:214 http://ftpmaster.internal/ubuntu noble-proposed/main s390x apparmor s390x 4.0.0-beta3-0ubuntu2 [710 kB] 193s Get:215 http://ftpmaster.internal/ubuntu noble-proposed/main s390x ftp all 20230507-2build1 [4724 B] 193s Get:216 http://ftpmaster.internal/ubuntu noble-proposed/main s390x inetutils-telnet s390x 2:2.5-3ubuntu3 [105 kB] 193s Get:217 http://ftpmaster.internal/ubuntu noble-proposed/main s390x info s390x 7.1-3build1 [152 kB] 193s Get:218 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libxmuu1 s390x 2:1.1.3-3build1 [8860 B] 193s Get:219 http://ftpmaster.internal/ubuntu noble-proposed/main s390x lshw s390x 02.19.git.2021.06.19.996aaad9c7-2build2 [346 kB] 193s Get:220 http://ftpmaster.internal/ubuntu noble/main s390x manpages all 6.05.01-1 [1340 kB] 193s Get:221 http://ftpmaster.internal/ubuntu noble-proposed/main s390x mtr-tiny s390x 0.95-1.1build1 [57.0 kB] 193s Get:222 http://ftpmaster.internal/ubuntu noble-proposed/main s390x plymouth-theme-ubuntu-text s390x 24.004.60-1ubuntu6 [10.2 kB] 193s Get:223 http://ftpmaster.internal/ubuntu noble-proposed/main s390x plymouth s390x 24.004.60-1ubuntu6 [147 kB] 193s Get:224 http://ftpmaster.internal/ubuntu noble-proposed/main s390x telnet all 0.17+2.5-3ubuntu3 [3682 B] 193s Get:225 http://ftpmaster.internal/ubuntu noble-proposed/main s390x usb.ids all 2024.03.18-1 [223 kB] 193s Get:226 http://ftpmaster.internal/ubuntu noble-proposed/main s390x xz-utils s390x 5.6.0-0.2 [274 kB] 193s Get:227 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libllvm18 s390x 1:18.1.2-1ubuntu2 [33.4 MB] 203s Get:228 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libclang-cpp18 s390x 1:18.1.2-1ubuntu2 [16.1 MB] 207s Get:229 http://ftpmaster.internal/ubuntu noble-proposed/universe s390x libbpfcc s390x 0.29.1+ds-1ubuntu4 [697 kB] 207s Get:230 http://ftpmaster.internal/ubuntu noble-proposed/universe s390x python3-bpfcc all 0.29.1+ds-1ubuntu4 [40.2 kB] 207s Get:231 http://ftpmaster.internal/ubuntu noble/main s390x ieee-data all 20220827.1 [2113 kB] 208s Get:232 http://ftpmaster.internal/ubuntu noble/main s390x python3-netaddr all 0.8.0-2ubuntu1 [319 kB] 208s Get:233 http://ftpmaster.internal/ubuntu noble-proposed/universe s390x bpfcc-tools all 0.29.1+ds-1ubuntu4 [687 kB] 208s Get:234 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libclang1-18 s390x 1:18.1.2-1ubuntu2 [9349 kB] 211s Get:235 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libdw1t64 s390x 0.190-1.1build2 [286 kB] 211s Get:236 http://ftpmaster.internal/ubuntu noble-proposed/universe s390x bpftrace s390x 0.20.2-1ubuntu1 [1139 kB] 211s Get:237 http://ftpmaster.internal/ubuntu noble-proposed/main s390x cryptsetup-bin s390x 2:2.7.0-1ubuntu2 [211 kB] 211s Get:238 http://ftpmaster.internal/ubuntu noble-proposed/main s390x dpkg-dev all 1.22.6ubuntu5 [1074 kB] 212s Get:239 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libdpkg-perl all 1.22.6ubuntu5 [269 kB] 212s Get:240 http://ftpmaster.internal/ubuntu noble/main s390x fonts-dejavu-mono all 2.37-8 [502 kB] 212s Get:241 http://ftpmaster.internal/ubuntu noble/main s390x fonts-dejavu-core all 2.37-8 [835 kB] 213s Get:242 http://ftpmaster.internal/ubuntu noble/main s390x fontconfig-config s390x 2.15.0-1.1ubuntu1 [37.4 kB] 213s Get:243 http://ftpmaster.internal/ubuntu noble-proposed/main s390x gnupg-l10n all 2.4.4-2ubuntu15 [65.8 kB] 213s Get:244 http://ftpmaster.internal/ubuntu noble/main s390x hwdata all 0.379-1 [29.1 kB] 213s Get:245 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libibverbs1 s390x 50.0-2build1 [70.0 kB] 213s Get:246 http://ftpmaster.internal/ubuntu noble-proposed/main s390x ibverbs-providers s390x 50.0-2build1 [408 kB] 213s Get:247 http://ftpmaster.internal/ubuntu noble/main s390x libaio1t64 s390x 0.3.113-6 [7290 B] 213s Get:248 http://ftpmaster.internal/ubuntu noble/main s390x libatm1t64 s390x 1:2.5.1-5.1 [24.5 kB] 213s Get:249 http://ftpmaster.internal/ubuntu noble/main s390x libc-dev-bin s390x 2.39-0ubuntu6 [20.2 kB] 213s Get:250 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libfreetype6 s390x 2.13.2+dfsg-1build2 [437 kB] 213s Get:251 http://ftpmaster.internal/ubuntu noble/main s390x libfontconfig1 s390x 2.15.0-1.1ubuntu1 [150 kB] 213s Get:252 http://ftpmaster.internal/ubuntu noble/main s390x libjpeg-turbo8 s390x 2.1.5-2ubuntu1 [128 kB] 213s Get:253 http://ftpmaster.internal/ubuntu noble/main s390x libjpeg8 s390x 8c-2ubuntu11 [2146 B] 213s Get:254 http://ftpmaster.internal/ubuntu noble/main s390x libdeflate0 s390x 1.19-1 [46.0 kB] 213s Get:255 http://ftpmaster.internal/ubuntu noble/main s390x libjbig0 s390x 2.1-6.1ubuntu1 [29.8 kB] 213s Get:256 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libsharpyuv0 s390x 1.3.2-0.4build2 [14.9 kB] 213s Get:257 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libwebp7 s390x 1.3.2-0.4build2 [207 kB] 213s Get:258 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libtiff6 s390x 4.5.1+git230720-4ubuntu1 [218 kB] 214s Get:259 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libxpm4 s390x 1:3.5.17-1build1 [41.4 kB] 214s Get:260 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libgd3 s390x 2.3.3-9ubuntu3 [141 kB] 214s Get:261 http://ftpmaster.internal/ubuntu noble/main s390x libc-devtools s390x 2.39-0ubuntu6 [30.6 kB] 214s Get:262 http://ftpmaster.internal/ubuntu noble-proposed/main s390x linux-libc-dev s390x 6.8.0-20.20 [1592 kB] 214s Get:263 http://ftpmaster.internal/ubuntu noble/main s390x libcrypt-dev s390x 1:4.4.36-4 [135 kB] 214s Get:264 http://ftpmaster.internal/ubuntu noble/main s390x rpcsvc-proto s390x 1.4.2-0ubuntu6 [64.7 kB] 214s Get:265 http://ftpmaster.internal/ubuntu noble/main s390x libc6-dev s390x 2.39-0ubuntu6 [1629 kB] 215s Get:266 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libevent-core-2.1-7 s390x 2.1.12-stable-9build1 [94.3 kB] 215s Get:267 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libldap-common all 2.6.7+dfsg-1~exp1ubuntu6 [31.3 kB] 215s Get:268 http://ftpmaster.internal/ubuntu noble-proposed/main s390x linux-modules-6.8.0-20-generic s390x 6.8.0-20.20 [21.0 MB] 225s Get:269 http://ftpmaster.internal/ubuntu noble-proposed/main s390x linux-image-6.8.0-20-generic s390x 6.8.0-20.20 [9872 kB] 229s Get:270 http://ftpmaster.internal/ubuntu noble-proposed/main s390x linux-modules-extra-6.8.0-20-generic s390x 6.8.0-20.20 [11.7 MB] 233s Get:271 http://ftpmaster.internal/ubuntu noble-proposed/main s390x linux-generic s390x 6.8.0-20.20+1 [1734 B] 233s Get:272 http://ftpmaster.internal/ubuntu noble-proposed/main s390x linux-image-generic s390x 6.8.0-20.20+1 [9688 B] 233s Get:273 http://ftpmaster.internal/ubuntu noble-proposed/main s390x linux-virtual s390x 6.8.0-20.20+1 [1682 B] 233s Get:274 http://ftpmaster.internal/ubuntu noble-proposed/main s390x linux-image-virtual s390x 6.8.0-20.20+1 [9700 B] 233s Get:275 http://ftpmaster.internal/ubuntu noble-proposed/main s390x linux-headers-virtual s390x 6.8.0-20.20+1 [1642 B] 233s Get:276 http://ftpmaster.internal/ubuntu noble-proposed/main s390x linux-headers-6.8.0-20 all 6.8.0-20.20 [13.6 MB] 238s Get:277 http://ftpmaster.internal/ubuntu noble-proposed/main s390x linux-headers-6.8.0-20-generic s390x 6.8.0-20.20 [2579 kB] 239s Get:278 http://ftpmaster.internal/ubuntu noble-proposed/main s390x linux-headers-generic s390x 6.8.0-20.20+1 [9608 B] 239s Get:279 http://ftpmaster.internal/ubuntu noble-proposed/main s390x linux-tools-common all 6.8.0-20.20 [437 kB] 239s Get:280 http://ftpmaster.internal/ubuntu noble-proposed/main s390x linux-tools-6.8.0-20 s390x 6.8.0-20.20 [2674 kB] 240s Get:281 http://ftpmaster.internal/ubuntu noble-proposed/main s390x linux-tools-6.8.0-20-generic s390x 6.8.0-20.20 [1724 B] 240s Get:282 http://ftpmaster.internal/ubuntu noble/main s390x manpages-dev all 6.05.01-1 [2018 kB] 241s Get:283 http://ftpmaster.internal/ubuntu noble-proposed/main s390x python3-distutils all 3.12.2-3ubuntu1.1 [133 kB] 242s Get:284 http://ftpmaster.internal/ubuntu noble-proposed/main s390x python3-lib2to3 all 3.12.2-3ubuntu1.1 [79.1 kB] 242s Get:285 http://ftpmaster.internal/ubuntu noble-proposed/main s390x python3-pyrsistent s390x 0.20.0-1build1 [55.8 kB] 242s Get:286 http://ftpmaster.internal/ubuntu noble-proposed/main s390x python3-typing-extensions all 4.10.0-1 [60.7 kB] 242s Get:287 http://ftpmaster.internal/ubuntu noble-proposed/main s390x s390-tools-data all 2.31.0-0ubuntu3 [17.8 kB] 242s Get:288 http://ftpmaster.internal/ubuntu noble/main s390x ubuntu-kernel-accessories s390x 1.536build1 [10.5 kB] 242s Get:289 http://ftpmaster.internal/ubuntu noble-proposed/main s390x kpartx s390x 0.9.4-5ubuntu6 [32.8 kB] 243s Preconfiguring packages ... 243s Fetched 223 MB in 1min 10s (3165 kB/s) 243s (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 ... 52166 files and directories currently installed.) 243s Preparing to unpack .../motd-news-config_13ubuntu8_all.deb ... 243s Unpacking motd-news-config (13ubuntu8) over (13ubuntu7) ... 243s Preparing to unpack .../base-files_13ubuntu8_s390x.deb ... 243s Unpacking base-files (13ubuntu8) over (13ubuntu7) ... 243s Setting up base-files (13ubuntu8) ... 244s motd-news.service is a disabled or a static unit not running, not starting it. 244s (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 ... 52166 files and directories currently installed.) 244s Preparing to unpack .../bash_5.2.21-2ubuntu3_s390x.deb ... 244s Unpacking bash (5.2.21-2ubuntu3) over (5.2.21-2ubuntu2) ... 245s Setting up bash (5.2.21-2ubuntu3) ... 245s update-alternatives: using /usr/share/man/man7/bash-builtins.7.gz to provide /usr/share/man/man7/builtins.7.gz (builtins.7.gz) in auto mode 245s (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 ... 52166 files and directories currently installed.) 245s Preparing to unpack .../bsdutils_1%3a2.39.3-9ubuntu2_s390x.deb ... 245s Unpacking bsdutils (1:2.39.3-9ubuntu2) over (1:2.39.3-6ubuntu2) ... 245s Setting up bsdutils (1:2.39.3-9ubuntu2) ... 245s (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 ... 52166 files and directories currently installed.) 245s Preparing to unpack .../0-libbrotli1_1.1.0-2build1_s390x.deb ... 245s Unpacking libbrotli1:s390x (1.1.0-2build1) over (1.1.0-2) ... 245s Preparing to unpack .../1-libgssapi-krb5-2_1.20.1-6ubuntu1_s390x.deb ... 245s Unpacking libgssapi-krb5-2:s390x (1.20.1-6ubuntu1) over (1.20.1-5build1) ... 245s Preparing to unpack .../2-libkrb5-3_1.20.1-6ubuntu1_s390x.deb ... 245s Unpacking libkrb5-3:s390x (1.20.1-6ubuntu1) over (1.20.1-5build1) ... 245s Preparing to unpack .../3-libkrb5support0_1.20.1-6ubuntu1_s390x.deb ... 245s Unpacking libkrb5support0:s390x (1.20.1-6ubuntu1) over (1.20.1-5build1) ... 245s Preparing to unpack .../4-libk5crypto3_1.20.1-6ubuntu1_s390x.deb ... 245s Unpacking libk5crypto3:s390x (1.20.1-6ubuntu1) over (1.20.1-5build1) ... 245s Preparing to unpack .../5-libcom-err2_1.47.0-2.4~exp1ubuntu2_s390x.deb ... 245s Unpacking libcom-err2:s390x (1.47.0-2.4~exp1ubuntu2) over (1.47.0-2ubuntu1) ... 245s Preparing to unpack .../6-zlib1g_1%3a1.3.dfsg-3.1ubuntu1_s390x.deb ... 245s Unpacking zlib1g:s390x (1:1.3.dfsg-3.1ubuntu1) over (1:1.3.dfsg-3ubuntu1) ... 245s Setting up zlib1g:s390x (1:1.3.dfsg-3.1ubuntu1) ... 245s (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 ... 52166 files and directories currently installed.) 245s Preparing to unpack .../librtmp1_2.4+20151223.gitfa8646d.1-2build6_s390x.deb ... 245s Unpacking librtmp1:s390x (2.4+20151223.gitfa8646d.1-2build6) over (2.4+20151223.gitfa8646d.1-2build4) ... 245s Preparing to unpack .../udisks2_2.10.1-6_s390x.deb ... 245s Unpacking udisks2 (2.10.1-6) over (2.10.1-1ubuntu2) ... 245s Preparing to unpack .../libudisks2-0_2.10.1-6_s390x.deb ... 245s Unpacking libudisks2-0:s390x (2.10.1-6) over (2.10.1-1ubuntu2) ... 245s Preparing to unpack .../libblkid1_2.39.3-9ubuntu2_s390x.deb ... 245s Unpacking libblkid1:s390x (2.39.3-9ubuntu2) over (2.39.3-6ubuntu2) ... 245s Setting up libblkid1:s390x (2.39.3-9ubuntu2) ... 245s (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 ... 52166 files and directories currently installed.) 245s Preparing to unpack .../liblzma5_5.6.0-0.2_s390x.deb ... 245s Unpacking liblzma5:s390x (5.6.0-0.2) over (5.4.5-0.3) ... 245s Setting up liblzma5:s390x (5.6.0-0.2) ... 245s (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 ... 52166 files and directories currently installed.) 245s Preparing to unpack .../0-kmod_31+20240202-2ubuntu4_s390x.deb ... 245s Unpacking kmod (31+20240202-2ubuntu4) over (30+20230601-2ubuntu1) ... 246s Preparing to unpack .../1-libkmod2_31+20240202-2ubuntu4_s390x.deb ... 246s Unpacking libkmod2:s390x (31+20240202-2ubuntu4) over (30+20230601-2ubuntu1) ... 246s Preparing to unpack .../2-systemd-dev_255.4-1ubuntu5_all.deb ... 246s Unpacking systemd-dev (255.4-1ubuntu5) over (255.2-3ubuntu2) ... 246s Preparing to unpack .../3-systemd-timesyncd_255.4-1ubuntu5_s390x.deb ... 246s Unpacking systemd-timesyncd (255.4-1ubuntu5) over (255.2-3ubuntu2) ... 246s Preparing to unpack .../4-dbus-session-bus-common_1.14.10-4ubuntu2_all.deb ... 246s Unpacking dbus-session-bus-common (1.14.10-4ubuntu2) over (1.14.10-4ubuntu1) ... 246s Preparing to unpack .../5-systemd-sysv_255.4-1ubuntu5_s390x.deb ... 246s Unpacking systemd-sysv (255.4-1ubuntu5) over (255.2-3ubuntu2) ... 246s Preparing to unpack .../6-libpam-systemd_255.4-1ubuntu5_s390x.deb ... 246s Unpacking libpam-systemd:s390x (255.4-1ubuntu5) over (255.2-3ubuntu2) ... 246s Preparing to unpack .../7-dbus-user-session_1.14.10-4ubuntu2_s390x.deb ... 246s Unpacking dbus-user-session (1.14.10-4ubuntu2) over (1.14.10-4ubuntu1) ... 246s Preparing to unpack .../8-libapparmor1_4.0.0-beta3-0ubuntu2_s390x.deb ... 246s Unpacking libapparmor1:s390x (4.0.0-beta3-0ubuntu2) over (4.0.0~alpha4-0ubuntu1) ... 246s Preparing to unpack .../9-libaudit-common_1%3a3.1.2-2.1_all.deb ... 246s Unpacking libaudit-common (1:3.1.2-2.1) over (1:3.1.2-2) ... 246s Setting up libaudit-common (1:3.1.2-2.1) ... 246s (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 ... 52166 files and directories currently installed.) 246s Preparing to unpack .../libcap-ng0_0.8.4-2build1_s390x.deb ... 246s Unpacking libcap-ng0:s390x (0.8.4-2build1) over (0.8.4-2) ... 246s Setting up libcap-ng0:s390x (0.8.4-2build1) ... 246s (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 ... 52166 files and directories currently installed.) 246s Preparing to unpack .../libaudit1_1%3a3.1.2-2.1_s390x.deb ... 246s Unpacking libaudit1:s390x (1:3.1.2-2.1) over (1:3.1.2-2) ... 246s Setting up libaudit1:s390x (1:3.1.2-2.1) ... 246s (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 ... 52166 files and directories currently installed.) 246s Preparing to unpack .../libselinux1_3.5-2ubuntu1_s390x.deb ... 246s Unpacking libselinux1:s390x (3.5-2ubuntu1) over (3.5-2build1) ... 246s Setting up libselinux1:s390x (3.5-2ubuntu1) ... 246s dpkg: libcurl4:s390x: dependency problems, but removing anyway as you requested: 246s s390-tools depends on libcurl4 (>= 7.16.2). 246s curl depends on libcurl4 (= 8.5.0-2ubuntu2). 246s 246s (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 ... 52166 files and directories currently installed.) 246s Removing libcurl4:s390x (8.5.0-2ubuntu2) ... 246s Selecting previously unselected package libcurl4t64:s390x. 246s (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 ... 52161 files and directories currently installed.) 246s Preparing to unpack .../libcurl4t64_8.5.0-2ubuntu8_s390x.deb ... 246s Unpacking libcurl4t64:s390x (8.5.0-2ubuntu8) ... 246s Preparing to unpack .../curl_8.5.0-2ubuntu8_s390x.deb ... 246s Unpacking curl (8.5.0-2ubuntu8) over (8.5.0-2ubuntu2) ... 246s dpkg: libpsl5:s390x: dependency problems, but removing anyway as you requested: 246s wget depends on libpsl5 (>= 0.16.0). 246s libcurl3-gnutls:s390x depends on libpsl5 (>= 0.16.0). 246s 246s (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 ... 52167 files and directories currently installed.) 246s Removing libpsl5:s390x (0.21.2-1build1) ... 246s Selecting previously unselected package libpsl5t64:s390x. 246s (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 ... 52162 files and directories currently installed.) 246s Preparing to unpack .../00-libpsl5t64_0.21.2-1.1_s390x.deb ... 246s Unpacking libpsl5t64:s390x (0.21.2-1.1) ... 246s Preparing to unpack .../01-wget_1.21.4-1ubuntu2_s390x.deb ... 246s Unpacking wget (1.21.4-1ubuntu2) over (1.21.4-1ubuntu1) ... 246s Preparing to unpack .../02-tnftp_20230507-2build1_s390x.deb ... 246s Unpacking tnftp (20230507-2build1) over (20230507-2) ... 246s Preparing to unpack .../03-tcpdump_4.99.4-3ubuntu2_s390x.deb ... 246s Unpacking tcpdump (4.99.4-3ubuntu2) over (4.99.4-3ubuntu1) ... 246s Preparing to unpack .../04-libsystemd-shared_255.4-1ubuntu5_s390x.deb ... 246s Unpacking libsystemd-shared:s390x (255.4-1ubuntu5) over (255.2-3ubuntu2) ... 246s Preparing to unpack .../05-systemd-resolved_255.4-1ubuntu5_s390x.deb ... 246s Unpacking systemd-resolved (255.4-1ubuntu5) over (255.2-3ubuntu2) ... 246s Preparing to unpack .../06-sudo_1.9.15p5-3ubuntu3_s390x.deb ... 246s Unpacking sudo (1.9.15p5-3ubuntu3) over (1.9.15p5-3ubuntu1) ... 247s Preparing to unpack .../07-rsync_3.2.7-1build1_s390x.deb ... 247s Unpacking rsync (3.2.7-1build1) over (3.2.7-1) ... 247s Preparing to unpack .../08-python3-cryptography_41.0.7-4build2_s390x.deb ... 247s Unpacking python3-cryptography (41.0.7-4build2) over (41.0.7-3) ... 247s Preparing to unpack .../09-openssl_3.0.13-0ubuntu2_s390x.deb ... 247s Unpacking openssl (3.0.13-0ubuntu2) over (3.0.10-1ubuntu4) ... 247s Preparing to unpack .../10-openssh-sftp-server_1%3a9.6p1-3ubuntu11_s390x.deb ... 247s Unpacking openssh-sftp-server (1:9.6p1-3ubuntu11) over (1:9.6p1-3ubuntu2) ... 247s Preparing to unpack .../11-openssh-client_1%3a9.6p1-3ubuntu11_s390x.deb ... 247s Unpacking openssh-client (1:9.6p1-3ubuntu11) over (1:9.6p1-3ubuntu2) ... 247s Preparing to unpack .../12-openssh-server_1%3a9.6p1-3ubuntu11_s390x.deb ... 247s Unpacking openssh-server (1:9.6p1-3ubuntu11) over (1:9.6p1-3ubuntu2) ... 247s Preparing to unpack .../13-libssh-4_0.10.6-2build1_s390x.deb ... 247s Unpacking libssh-4:s390x (0.10.6-2build1) over (0.10.6-2) ... 247s Preparing to unpack .../14-libsasl2-modules_2.1.28+dfsg1-5ubuntu1_s390x.deb ... 247s Unpacking libsasl2-modules:s390x (2.1.28+dfsg1-5ubuntu1) over (2.1.28+dfsg1-4) ... 247s Preparing to unpack .../15-python3.12_3.12.2-4build3_s390x.deb ... 247s Unpacking python3.12 (3.12.2-4build3) over (3.12.2-1) ... 247s Preparing to unpack .../16-python3.12-minimal_3.12.2-4build3_s390x.deb ... 247s Unpacking python3.12-minimal (3.12.2-4build3) over (3.12.2-1) ... 248s Preparing to unpack .../17-libpython3.12-minimal_3.12.2-4build3_s390x.deb ... 248s Unpacking libpython3.12-minimal:s390x (3.12.2-4build3) over (3.12.2-1) ... 248s dpkg: libparted2:s390x: dependency problems, but removing anyway as you requested: 248s parted depends on libparted2 (= 3.6-3). 248s 248s (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 ... 52168 files and directories currently installed.) 248s Removing libparted2:s390x (3.6-3) ... 248s Selecting previously unselected package libparted2t64:s390x. 248s (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 ... 52162 files and directories currently installed.) 248s Preparing to unpack .../00-libparted2t64_3.6-3.1build2_s390x.deb ... 248s Unpacking libparted2t64:s390x (3.6-3.1build2) ... 248s Preparing to unpack .../01-parted_3.6-3.1build2_s390x.deb ... 248s Unpacking parted (3.6-3.1build2) over (3.6-3) ... 248s Preparing to unpack .../02-python3.11_3.11.8-1build4_s390x.deb ... 248s Unpacking python3.11 (3.11.8-1build4) over (3.11.8-1) ... 248s Preparing to unpack .../03-python3.11-minimal_3.11.8-1build4_s390x.deb ... 248s Unpacking python3.11-minimal (3.11.8-1build4) over (3.11.8-1) ... 248s Preparing to unpack .../04-libpython3.11-minimal_3.11.8-1build4_s390x.deb ... 248s Unpacking libpython3.11-minimal:s390x (3.11.8-1build4) over (3.11.8-1) ... 248s Preparing to unpack .../05-libpython3.11-stdlib_3.11.8-1build4_s390x.deb ... 248s Unpacking libpython3.11-stdlib:s390x (3.11.8-1build4) over (3.11.8-1) ... 249s Preparing to unpack .../06-shared-mime-info_2.4-1build1_s390x.deb ... 249s Unpacking shared-mime-info (2.4-1build1) over (2.4-1) ... 249s Preparing to unpack .../07-gir1.2-girepository-2.0_1.79.1-1ubuntu6_s390x.deb ... 249s Unpacking gir1.2-girepository-2.0:s390x (1.79.1-1ubuntu6) over (1.79.1-1) ... 249s Preparing to unpack .../08-gir1.2-glib-2.0_2.79.3-3ubuntu5_s390x.deb ... 249s Unpacking gir1.2-glib-2.0:s390x (2.79.3-3ubuntu5) over (2.79.2-1~ubuntu1) ... 249s Preparing to unpack .../09-libgirepository-1.0-1_1.79.1-1ubuntu6_s390x.deb ... 249s Unpacking libgirepository-1.0-1:s390x (1.79.1-1ubuntu6) over (1.79.1-1) ... 249s Preparing to unpack .../10-python3-gi_3.47.0-3build1_s390x.deb ... 249s Unpacking python3-gi (3.47.0-3build1) over (3.47.0-3) ... 249s Preparing to unpack .../11-python3-dbus_1.3.2-5build2_s390x.deb ... 249s Unpacking python3-dbus (1.3.2-5build2) over (1.3.2-5build1) ... 249s Selecting previously unselected package libnetplan1:s390x. 249s Preparing to unpack .../12-libnetplan1_1.0-1_s390x.deb ... 249s Unpacking libnetplan1:s390x (1.0-1) ... 249s Preparing to unpack .../13-python3-netplan_1.0-1_s390x.deb ... 249s Unpacking python3-netplan (1.0-1) over (0.107.1-3) ... 249s Preparing to unpack .../14-netplan-generator_1.0-1_s390x.deb ... 249s Adding 'diversion of /lib/systemd/system-generators/netplan to /lib/systemd/system-generators/netplan.usr-is-merged by netplan-generator' 249s Unpacking netplan-generator (1.0-1) over (0.107.1-3) ... 249s Preparing to unpack .../15-netplan.io_1.0-1_s390x.deb ... 249s Unpacking netplan.io (1.0-1) over (0.107.1-3) ... 249s Preparing to unpack .../16-libxmlb2_0.3.15-1build1_s390x.deb ... 249s Unpacking libxmlb2:s390x (0.3.15-1build1) over (0.3.15-1) ... 249s dpkg: libgpgme11:s390x: dependency problems, but removing anyway as you requested: 249s libvolume-key1:s390x depends on libgpgme11 (>= 1.4.1). 249s libjcat1:s390x depends on libgpgme11 (>= 1.2.0). 249s 249s (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 ... 52170 files and directories currently installed.) 249s Removing libgpgme11:s390x (1.18.0-4ubuntu1) ... 249s Selecting previously unselected package libgpgme11t64:s390x. 249s (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 ... 52164 files and directories currently installed.) 249s Preparing to unpack .../00-libgpgme11t64_1.18.0-4.1ubuntu3_s390x.deb ... 249s Unpacking libgpgme11t64:s390x (1.18.0-4.1ubuntu3) ... 249s Preparing to unpack .../01-libvolume-key1_0.3.12-7build1_s390x.deb ... 249s Unpacking libvolume-key1:s390x (0.3.12-7build1) over (0.3.12-5build2) ... 249s Preparing to unpack .../02-libqrtr-glib0_1.2.2-1ubuntu3_s390x.deb ... 249s Unpacking libqrtr-glib0:s390x (1.2.2-1ubuntu3) over (1.2.2-1ubuntu2) ... 250s Preparing to unpack .../03-libqmi-glib5_1.35.2-0ubuntu1_s390x.deb ... 250s Unpacking libqmi-glib5:s390x (1.35.2-0ubuntu1) over (1.34.0-2) ... 250s Preparing to unpack .../04-libqmi-proxy_1.35.2-0ubuntu1_s390x.deb ... 250s Unpacking libqmi-proxy (1.35.2-0ubuntu1) over (1.34.0-2) ... 250s Preparing to unpack .../05-libpolkit-agent-1-0_124-1ubuntu1_s390x.deb ... 250s Unpacking libpolkit-agent-1-0:s390x (124-1ubuntu1) over (124-1) ... 250s Preparing to unpack .../06-libpolkit-gobject-1-0_124-1ubuntu1_s390x.deb ... 250s Unpacking libpolkit-gobject-1-0:s390x (124-1ubuntu1) over (124-1) ... 250s Preparing to unpack .../07-libmm-glib0_1.23.4-0ubuntu1_s390x.deb ... 250s Unpacking libmm-glib0:s390x (1.23.4-0ubuntu1) over (1.22.0-3) ... 250s Preparing to unpack .../08-libmbim-glib4_1.31.2-0ubuntu2_s390x.deb ... 250s Unpacking libmbim-glib4:s390x (1.31.2-0ubuntu2) over (1.30.0-1) ... 250s Preparing to unpack .../09-libmbim-proxy_1.31.2-0ubuntu2_s390x.deb ... 250s Unpacking libmbim-proxy (1.31.2-0ubuntu2) over (1.30.0-1) ... 250s Preparing to unpack .../10-libjson-glib-1.0-common_1.8.0-2build1_all.deb ... 250s Unpacking libjson-glib-1.0-common (1.8.0-2build1) over (1.8.0-2) ... 250s Preparing to unpack .../11-libjson-glib-1.0-0_1.8.0-2build1_s390x.deb ... 250s Unpacking libjson-glib-1.0-0:s390x (1.8.0-2build1) over (1.8.0-2) ... 250s Preparing to unpack .../12-libgusb2_0.4.8-1build1_s390x.deb ... 250s Unpacking libgusb2:s390x (0.4.8-1build1) over (0.4.8-1) ... 250s Preparing to unpack .../13-libgudev-1.0-0_1%3a238-3ubuntu2_s390x.deb ... 250s Unpacking libgudev-1.0-0:s390x (1:238-3ubuntu2) over (1:238-3) ... 250s dpkg: libarchive13:s390x: dependency problems, but removing anyway as you requested: 250s fwupd depends on libarchive13 (>= 3.2.1). 250s 250s (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 ... 52171 files and directories currently installed.) 250s Removing libarchive13:s390x (3.7.2-1ubuntu2) ... 250s Selecting previously unselected package libarchive13t64:s390x. 250s (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 ... 52165 files and directories currently installed.) 250s Preparing to unpack .../libarchive13t64_3.7.2-1.1ubuntu2_s390x.deb ... 250s Unpacking libarchive13t64:s390x (3.7.2-1.1ubuntu2) ... 250s Preparing to unpack .../fwupd_1.9.15-2_s390x.deb ... 250s Unpacking fwupd (1.9.15-2) over (1.9.14-1) ... 250s dpkg: libcurl3-gnutls:s390x: dependency problems, but removing anyway as you requested: 250s libfwupd2:s390x depends on libcurl3-gnutls (>= 7.63.0). 250s 250s (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 ... 52172 files and directories currently installed.) 250s Removing libcurl3-gnutls:s390x (8.5.0-2ubuntu2) ... 250s Selecting previously unselected package libcurl3t64-gnutls:s390x. 250s (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 ... 52165 files and directories currently installed.) 250s Preparing to unpack .../0-libcurl3t64-gnutls_8.5.0-2ubuntu8_s390x.deb ... 250s Unpacking libcurl3t64-gnutls:s390x (8.5.0-2ubuntu8) ... 250s Preparing to unpack .../1-libfwupd2_1.9.15-2_s390x.deb ... 250s Unpacking libfwupd2:s390x (1.9.15-2) over (1.9.14-1) ... 250s Preparing to unpack .../2-libblockdev3_3.1.0-1build1_s390x.deb ... 250s Unpacking libblockdev3:s390x (3.1.0-1build1) over (3.1.0-1) ... 250s Preparing to unpack .../3-libblockdev-utils3_3.1.0-1build1_s390x.deb ... 250s Unpacking libblockdev-utils3:s390x (3.1.0-1build1) over (3.1.0-1) ... 250s Preparing to unpack .../4-libblockdev-swap3_3.1.0-1build1_s390x.deb ... 250s Unpacking libblockdev-swap3:s390x (3.1.0-1build1) over (3.1.0-1) ... 250s Preparing to unpack .../5-libblockdev-part3_3.1.0-1build1_s390x.deb ... 250s Unpacking libblockdev-part3:s390x (3.1.0-1build1) over (3.1.0-1) ... 251s dpkg: libnvme1: dependency problems, but removing anyway as you requested: 251s libblockdev-nvme3:s390x depends on libnvme1 (>= 1.7.1). 251s 251s (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 ... 52172 files and directories currently installed.) 251s Removing libnvme1 (1.8-2) ... 251s Selecting previously unselected package libnvme1t64. 251s (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 ... 52165 files and directories currently installed.) 251s Preparing to unpack .../0-libnvme1t64_1.8-3_s390x.deb ... 251s Unpacking libnvme1t64 (1.8-3) ... 251s Preparing to unpack .../1-libblockdev-nvme3_3.1.0-1build1_s390x.deb ... 251s Unpacking libblockdev-nvme3:s390x (3.1.0-1build1) over (3.1.0-1) ... 251s Preparing to unpack .../2-libblockdev-mdraid3_3.1.0-1build1_s390x.deb ... 251s Unpacking libblockdev-mdraid3:s390x (3.1.0-1build1) over (3.1.0-1) ... 251s Preparing to unpack .../3-libblockdev-loop3_3.1.0-1build1_s390x.deb ... 251s Unpacking libblockdev-loop3:s390x (3.1.0-1build1) over (3.1.0-1) ... 251s Preparing to unpack .../4-logsave_1.47.0-2.4~exp1ubuntu2_s390x.deb ... 251s Unpacking logsave (1.47.0-2.4~exp1ubuntu2) over (1.47.0-2ubuntu1) ... 251s Preparing to unpack .../5-e2fsprogs-l10n_1.47.0-2.4~exp1ubuntu2_all.deb ... 251s Unpacking e2fsprogs-l10n (1.47.0-2.4~exp1ubuntu2) over (1.47.0-2ubuntu1) ... 251s dpkg: libext2fs2:s390x: dependency problems, but removing anyway as you requested: 251s libblockdev-fs3:s390x depends on libext2fs2 (>= 1.42.11). 251s e2fsprogs depends on libext2fs2 (= 1.47.0-2ubuntu1). 251s btrfs-progs depends on libext2fs2 (>= 1.42). 251s 251s (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 ... 52173 files and directories currently installed.) 251s Removing libext2fs2:s390x (1.47.0-2ubuntu1) ... 251s Selecting previously unselected package libext2fs2t64:s390x. 251s (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 ... 52166 files and directories currently installed.) 251s Preparing to unpack .../libext2fs2t64_1.47.0-2.4~exp1ubuntu2_s390x.deb ... 251s Adding 'diversion of /lib/s390x-linux-gnu/libe2p.so.2 to /lib/s390x-linux-gnu/libe2p.so.2.usr-is-merged by libext2fs2t64' 251s Adding 'diversion of /lib/s390x-linux-gnu/libe2p.so.2.3 to /lib/s390x-linux-gnu/libe2p.so.2.3.usr-is-merged by libext2fs2t64' 251s Adding 'diversion of /lib/s390x-linux-gnu/libext2fs.so.2 to /lib/s390x-linux-gnu/libext2fs.so.2.usr-is-merged by libext2fs2t64' 251s Adding 'diversion of /lib/s390x-linux-gnu/libext2fs.so.2.4 to /lib/s390x-linux-gnu/libext2fs.so.2.4.usr-is-merged by libext2fs2t64' 251s Unpacking libext2fs2t64:s390x (1.47.0-2.4~exp1ubuntu2) ... 251s Setting up libcom-err2:s390x (1.47.0-2.4~exp1ubuntu2) ... 251s Setting up libext2fs2t64:s390x (1.47.0-2.4~exp1ubuntu2) ... 251s (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 ... 52182 files and directories currently installed.) 251s Preparing to unpack .../e2fsprogs_1.47.0-2.4~exp1ubuntu2_s390x.deb ... 251s Unpacking e2fsprogs (1.47.0-2.4~exp1ubuntu2) over (1.47.0-2ubuntu1) ... 251s dpkg: libreiserfscore0: dependency problems, but removing anyway as you requested: 251s btrfs-progs depends on libreiserfscore0 (>= 1:3.6.27). 251s 251s (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 ... 52182 files and directories currently installed.) 251s Removing libreiserfscore0 (1:3.6.27-7) ... 251s Selecting previously unselected package libreiserfscore0t64. 251s (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 ... 52177 files and directories currently installed.) 251s Preparing to unpack .../libreiserfscore0t64_1%3a3.6.27-7.1_s390x.deb ... 251s Unpacking libreiserfscore0t64 (1:3.6.27-7.1) ... 251s Preparing to unpack .../btrfs-progs_6.6.3-1.1build1_s390x.deb ... 251s Unpacking btrfs-progs (6.6.3-1.1build1) over (6.6.3-1.1) ... 251s Preparing to unpack .../libblockdev-fs3_3.1.0-1build1_s390x.deb ... 251s Unpacking libblockdev-fs3:s390x (3.1.0-1build1) over (3.1.0-1) ... 251s Preparing to unpack .../libblockdev-crypto3_3.1.0-1build1_s390x.deb ... 251s Unpacking libblockdev-crypto3:s390x (3.1.0-1build1) over (3.1.0-1) ... 251s Preparing to unpack .../bolt_0.9.6-2build1_s390x.deb ... 251s Unpacking bolt (0.9.6-2build1) over (0.9.6-2) ... 251s dpkg: libglib2.0-0:s390x: dependency problems, but removing anyway as you requested: 251s s390-tools depends on libglib2.0-0 (>= 2.77.0). 251s libnetplan0:s390x depends on libglib2.0-0 (>= 2.75.3). 251s libjcat1:s390x depends on libglib2.0-0 (>= 2.75.3). 251s 251s (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 ... 52183 files and directories currently installed.) 251s Removing libglib2.0-0:s390x (2.79.2-1~ubuntu1) ... 252s Selecting previously unselected package libglib2.0-0t64:s390x. 252s (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 ... 52158 files and directories currently installed.) 252s Preparing to unpack .../0-libglib2.0-0t64_2.79.3-3ubuntu5_s390x.deb ... 252s libglib2.0-0t64.preinst: Removing /var/lib/dpkg/info/libglib2.0-0:s390x.postrm to avoid loss of /usr/share/glib-2.0/schemas/gschemas.compiled... 252s removed '/var/lib/dpkg/info/libglib2.0-0:s390x.postrm' 252s Unpacking libglib2.0-0t64:s390x (2.79.3-3ubuntu5) ... 252s Preparing to unpack .../1-libjcat1_0.2.0-2build2_s390x.deb ... 252s Unpacking libjcat1:s390x (0.2.0-2build2) over (0.2.0-2) ... 252s Preparing to unpack .../2-libldap2_2.6.7+dfsg-1~exp1ubuntu6_s390x.deb ... 252s Unpacking libldap2:s390x (2.6.7+dfsg-1~exp1ubuntu6) over (2.6.7+dfsg-1~exp1ubuntu1) ... 252s Preparing to unpack .../3-ubuntu-pro-client-l10n_31.2.2_s390x.deb ... 252s Unpacking ubuntu-pro-client-l10n (31.2.2) over (31.1) ... 252s Preparing to unpack .../4-ubuntu-pro-client_31.2.2_s390x.deb ... 252s Unpacking ubuntu-pro-client (31.2.2) over (31.1) ... 252s Preparing to unpack .../5-gnupg-utils_2.4.4-2ubuntu15_s390x.deb ... 252s Unpacking gnupg-utils (2.4.4-2ubuntu15) over (2.4.4-2ubuntu7) ... 252s Preparing to unpack .../6-keyboxd_2.4.4-2ubuntu15_s390x.deb ... 252s Unpacking keyboxd (2.4.4-2ubuntu15) over (2.4.4-2ubuntu7) ... 252s dpkg: libnpth0:s390x: dependency problems, but removing anyway as you requested: 252s gpgv depends on libnpth0 (>= 0.90). 252s gpgsm depends on libnpth0 (>= 0.90). 252s gpg-agent depends on libnpth0 (>= 0.90). 252s gpg depends on libnpth0 (>= 0.90). 252s dirmngr depends on libnpth0 (>= 0.90). 252s 252s (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 ... 52183 files and directories currently installed.) 252s Removing libnpth0:s390x (1.6-3build2) ... 252s Selecting previously unselected package libnpth0t64:s390x. 252s (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 ... 52178 files and directories currently installed.) 252s Preparing to unpack .../libnpth0t64_1.6-3.1_s390x.deb ... 252s Unpacking libnpth0t64:s390x (1.6-3.1) ... 252s Setting up libnpth0t64:s390x (1.6-3.1) ... 252s (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 ... 52184 files and directories currently installed.) 252s Preparing to unpack .../gpgv_2.4.4-2ubuntu15_s390x.deb ... 252s Unpacking gpgv (2.4.4-2ubuntu15) over (2.4.4-2ubuntu7) ... 252s Setting up gpgv (2.4.4-2ubuntu15) ... 252s (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 ... 52184 files and directories currently installed.) 252s Preparing to unpack .../0-gpg-wks-client_2.4.4-2ubuntu15_s390x.deb ... 252s Unpacking gpg-wks-client (2.4.4-2ubuntu15) over (2.4.4-2ubuntu7) ... 252s Preparing to unpack .../1-gpg-agent_2.4.4-2ubuntu15_s390x.deb ... 252s Unpacking gpg-agent (2.4.4-2ubuntu15) over (2.4.4-2ubuntu7) ... 252s Preparing to unpack .../2-gpg_2.4.4-2ubuntu15_s390x.deb ... 252s Unpacking gpg (2.4.4-2ubuntu15) over (2.4.4-2ubuntu7) ... 252s Preparing to unpack .../3-dirmngr_2.4.4-2ubuntu15_s390x.deb ... 252s Unpacking dirmngr (2.4.4-2ubuntu15) over (2.4.4-2ubuntu7) ... 252s Preparing to unpack .../4-gnupg_2.4.4-2ubuntu15_all.deb ... 252s Unpacking gnupg (2.4.4-2ubuntu15) over (2.4.4-2ubuntu7) ... 252s Preparing to unpack .../5-python3-apt_2.7.7_s390x.deb ... 252s Unpacking python3-apt (2.7.7) over (2.7.6) ... 253s Preparing to unpack .../6-apt-utils_2.7.14_s390x.deb ... 253s Unpacking apt-utils (2.7.14) over (2.7.12) ... 253s dpkg: libapt-pkg6.0:s390x: dependency problems, but removing anyway as you requested: 253s apt depends on libapt-pkg6.0 (>= 2.7.12). 253s 253s (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 ... 52182 files and directories currently installed.) 253s Removing libapt-pkg6.0:s390x (2.7.12) ... 253s dpkg: libnettle8:s390x: dependency problems, but removing anyway as you requested: 253s libhogweed6:s390x depends on libnettle8. 253s libgnutls30:s390x depends on libnettle8 (>= 3.9~). 253s 253s Removing libnettle8:s390x (3.9.1-2) ... 253s Selecting previously unselected package libapt-pkg6.0t64:s390x. 253s (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 ... 52126 files and directories currently installed.) 253s Preparing to unpack .../libapt-pkg6.0t64_2.7.14_s390x.deb ... 253s Unpacking libapt-pkg6.0t64:s390x (2.7.14) ... 253s Setting up libapt-pkg6.0t64:s390x (2.7.14) ... 253s Selecting previously unselected package libnettle8t64:s390x. 253s (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 ... 52176 files and directories currently installed.) 253s Preparing to unpack .../libnettle8t64_3.9.1-2.2_s390x.deb ... 253s Unpacking libnettle8t64:s390x (3.9.1-2.2) ... 253s Setting up libnettle8t64:s390x (3.9.1-2.2) ... 253s dpkg: libhogweed6:s390x: dependency problems, but removing anyway as you requested: 253s libgnutls30:s390x depends on libhogweed6 (>= 3.6). 253s 253s (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 ... 52184 files and directories currently installed.) 253s Removing libhogweed6:s390x (3.9.1-2) ... 253s Selecting previously unselected package libhogweed6t64:s390x. 253s (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 ... 52179 files and directories currently installed.) 253s Preparing to unpack .../libhogweed6t64_3.9.1-2.2_s390x.deb ... 253s Unpacking libhogweed6t64:s390x (3.9.1-2.2) ... 253s Setting up libhogweed6t64:s390x (3.9.1-2.2) ... 253s dpkg: libgnutls30:s390x: dependency problems, but removing anyway as you requested: 253s apt depends on libgnutls30 (>= 3.8.1). 253s 253s (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 ... 52185 files and directories currently installed.) 253s Removing libgnutls30:s390x (3.8.3-1ubuntu1) ... 253s Selecting previously unselected package libgnutls30t64:s390x. 253s (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 ... 52176 files and directories currently installed.) 253s Preparing to unpack .../libgnutls30t64_3.8.3-1.1ubuntu2_s390x.deb ... 253s Unpacking libgnutls30t64:s390x (3.8.3-1.1ubuntu2) ... 253s Setting up libgnutls30t64:s390x (3.8.3-1.1ubuntu2) ... 253s (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 ... 52204 files and directories currently installed.) 253s Preparing to unpack .../archives/apt_2.7.14_s390x.deb ... 253s Unpacking apt (2.7.14) over (2.7.12) ... 254s Setting up apt (2.7.14) ... 254s (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 ... 52204 files and directories currently installed.) 254s Preparing to unpack .../gpgconf_2.4.4-2ubuntu15_s390x.deb ... 254s Unpacking gpgconf (2.4.4-2ubuntu15) over (2.4.4-2ubuntu7) ... 254s Preparing to unpack .../gpgsm_2.4.4-2ubuntu15_s390x.deb ... 254s Unpacking gpgsm (2.4.4-2ubuntu15) over (2.4.4-2ubuntu7) ... 254s dpkg: libreadline8:s390x: dependency problems, but removing anyway as you requested: 254s libpython3.12-stdlib:s390x depends on libreadline8 (>= 7.0~beta). 254s gawk depends on libreadline8 (>= 6.0). 254s fdisk depends on libreadline8 (>= 6.0). 254s 255s (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 ... 52204 files and directories currently installed.) 255s Removing libreadline8:s390x (8.2-3) ... 255s Selecting previously unselected package libreadline8t64:s390x. 255s (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 ... 52192 files and directories currently installed.) 255s Preparing to unpack .../libreadline8t64_8.2-4_s390x.deb ... 255s Adding 'diversion of /lib/s390x-linux-gnu/libhistory.so.8 to /lib/s390x-linux-gnu/libhistory.so.8.usr-is-merged by libreadline8t64' 255s Adding 'diversion of /lib/s390x-linux-gnu/libhistory.so.8.2 to /lib/s390x-linux-gnu/libhistory.so.8.2.usr-is-merged by libreadline8t64' 255s Adding 'diversion of /lib/s390x-linux-gnu/libreadline.so.8 to /lib/s390x-linux-gnu/libreadline.so.8.usr-is-merged by libreadline8t64' 255s Adding 'diversion of /lib/s390x-linux-gnu/libreadline.so.8.2 to /lib/s390x-linux-gnu/libreadline.so.8.2.usr-is-merged by libreadline8t64' 255s Unpacking libreadline8t64:s390x (8.2-4) ... 255s Setting up libreadline8t64:s390x (8.2-4) ... 255s (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 ... 52212 files and directories currently installed.) 255s Preparing to unpack .../gawk_1%3a5.2.1-2build2_s390x.deb ... 255s Unpacking gawk (1:5.2.1-2build2) over (1:5.2.1-2) ... 255s Preparing to unpack .../fdisk_2.39.3-9ubuntu2_s390x.deb ... 255s Unpacking fdisk (2.39.3-9ubuntu2) over (2.39.3-6ubuntu2) ... 255s Preparing to unpack .../libpython3.12-stdlib_3.12.2-4build3_s390x.deb ... 255s Unpacking libpython3.12-stdlib:s390x (3.12.2-4build3) over (3.12.2-1) ... 255s Preparing to unpack .../perl-base_5.38.2-3.2_s390x.deb ... 255s Unpacking perl-base (5.38.2-3.2) over (5.38.2-3) ... 255s Setting up perl-base (5.38.2-3.2) ... 255s (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 ... 52210 files and directories currently installed.) 255s Preparing to unpack .../perl-modules-5.38_5.38.2-3.2_all.deb ... 255s Unpacking perl-modules-5.38 (5.38.2-3.2) over (5.38.2-3) ... 256s Preparing to unpack .../python3-gdbm_3.12.2-3ubuntu1.1_s390x.deb ... 256s Unpacking python3-gdbm:s390x (3.12.2-3ubuntu1.1) over (3.11.5-1) ... 256s Preparing to unpack .../man-db_2.12.0-3build4_s390x.deb ... 256s Unpacking man-db (2.12.0-3build4) over (2.12.0-3) ... 256s dpkg: libgdbm-compat4:s390x: dependency problems, but removing anyway as you requested: 256s libperl5.38:s390x depends on libgdbm-compat4 (>= 1.18-3). 256s 256s (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 ... 52210 files and directories currently installed.) 256s Removing libgdbm-compat4:s390x (1.23-5) ... 256s dpkg: libgdbm6:s390x: dependency problems, but removing anyway as you requested: 256s libperl5.38:s390x depends on libgdbm6 (>= 1.21). 256s 256s Removing libgdbm6:s390x (1.23-5) ... 256s Selecting previously unselected package libgdbm6t64:s390x. 256s (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 ... 52200 files and directories currently installed.) 256s Preparing to unpack .../libgdbm6t64_1.23-5.1_s390x.deb ... 256s Unpacking libgdbm6t64:s390x (1.23-5.1) ... 256s Selecting previously unselected package libgdbm-compat4t64:s390x. 256s Preparing to unpack .../libgdbm-compat4t64_1.23-5.1_s390x.deb ... 256s Unpacking libgdbm-compat4t64:s390x (1.23-5.1) ... 256s dpkg: libperl5.38:s390x: dependency problems, but removing anyway as you requested: 256s perl depends on libperl5.38 (= 5.38.2-3). 256s 256s (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 ... 52212 files and directories currently installed.) 256s Removing libperl5.38:s390x (5.38.2-3) ... 256s Selecting previously unselected package libperl5.38t64:s390x. 256s (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 ... 51690 files and directories currently installed.) 256s Preparing to unpack .../libperl5.38t64_5.38.2-3.2_s390x.deb ... 256s Unpacking libperl5.38t64:s390x (5.38.2-3.2) ... 257s Preparing to unpack .../perl_5.38.2-3.2_s390x.deb ... 257s Unpacking perl (5.38.2-3.2) over (5.38.2-3) ... 257s dpkg: libdb5.3:s390x: dependency problems, but removing anyway as you requested: 257s libsasl2-modules-db:s390x depends on libdb5.3. 257s iproute2 depends on libdb5.3. 257s 258s (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 ... 52212 files and directories currently installed.) 258s Removing libdb5.3:s390x (5.3.28+dfsg2-4) ... 258s Selecting previously unselected package libdb5.3t64:s390x. 258s (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 ... 52206 files and directories currently installed.) 258s Preparing to unpack .../0-libdb5.3t64_5.3.28+dfsg2-6_s390x.deb ... 258s Unpacking libdb5.3t64:s390x (5.3.28+dfsg2-6) ... 258s Preparing to unpack .../1-libsasl2-modules-db_2.1.28+dfsg1-5ubuntu1_s390x.deb ... 258s Unpacking libsasl2-modules-db:s390x (2.1.28+dfsg1-5ubuntu1) over (2.1.28+dfsg1-4) ... 258s Preparing to unpack .../2-libsasl2-2_2.1.28+dfsg1-5ubuntu1_s390x.deb ... 258s Unpacking libsasl2-2:s390x (2.1.28+dfsg1-5ubuntu1) over (2.1.28+dfsg1-4) ... 258s Preparing to unpack .../3-libfido2-1_1.14.0-1build1_s390x.deb ... 258s Unpacking libfido2-1:s390x (1.14.0-1build1) over (1.14.0-1) ... 258s Preparing to unpack .../4-libcryptsetup12_2%3a2.7.0-1ubuntu2_s390x.deb ... 258s Unpacking libcryptsetup12:s390x (2:2.7.0-1ubuntu2) over (2:2.7.0-1ubuntu1) ... 258s Preparing to unpack .../5-dhcpcd-base_1%3a10.0.6-1ubuntu2_s390x.deb ... 258s Unpacking dhcpcd-base (1:10.0.6-1ubuntu2) over (1:10.0.6-1ubuntu1) ... 258s dpkg: libuv1:s390x: dependency problems, but removing anyway as you requested: 258s bind9-libs:s390x depends on libuv1 (>= 1.40.0). 258s 258s (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 ... 52212 files and directories currently installed.) 258s Removing libuv1:s390x (1.48.0-1) ... 258s Selecting previously unselected package libuv1t64:s390x. 258s (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 ... 52207 files and directories currently installed.) 258s Preparing to unpack .../libuv1t64_1.48.0-1.1_s390x.deb ... 258s Unpacking libuv1t64:s390x (1.48.0-1.1) ... 258s Preparing to unpack .../bind9-host_1%3a9.18.24-0ubuntu3_s390x.deb ... 258s Unpacking bind9-host (1:9.18.24-0ubuntu3) over (1:9.18.21-0ubuntu1) ... 258s Preparing to unpack .../bind9-dnsutils_1%3a9.18.24-0ubuntu3_s390x.deb ... 258s Unpacking bind9-dnsutils (1:9.18.24-0ubuntu3) over (1:9.18.21-0ubuntu1) ... 258s Preparing to unpack .../bind9-libs_1%3a9.18.24-0ubuntu3_s390x.deb ... 258s Unpacking bind9-libs:s390x (1:9.18.24-0ubuntu3) over (1:9.18.21-0ubuntu1) ... 258s dpkg: libssl3:s390x: dependency problems, but removing anyway as you requested: 258s systemd depends on libssl3 (>= 3.0.0). 258s s390-tools depends on libssl3 (>= 3.0.0). 258s linux-headers-6.8.0-11-generic depends on libssl3 (>= 3.0.0). 258s 258s (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 ... 52213 files and directories currently installed.) 258s Removing libssl3:s390x (3.0.10-1ubuntu4) ... 258s Selecting previously unselected package libssl3t64:s390x. 258s (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 ... 52202 files and directories currently installed.) 258s Preparing to unpack .../libssl3t64_3.0.13-0ubuntu2_s390x.deb ... 258s Unpacking libssl3t64:s390x (3.0.13-0ubuntu2) ... 258s Setting up libssl3t64:s390x (3.0.13-0ubuntu2) ... 258s (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 ... 52215 files and directories currently installed.) 258s Preparing to unpack .../libnss-systemd_255.4-1ubuntu5_s390x.deb ... 258s Unpacking libnss-systemd:s390x (255.4-1ubuntu5) over (255.2-3ubuntu2) ... 258s Preparing to unpack .../libudev1_255.4-1ubuntu5_s390x.deb ... 258s Unpacking libudev1:s390x (255.4-1ubuntu5) over (255.2-3ubuntu2) ... 258s Setting up libudev1:s390x (255.4-1ubuntu5) ... 258s (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 ... 52215 files and directories currently installed.) 259s Preparing to unpack .../libfdisk1_2.39.3-9ubuntu2_s390x.deb ... 259s Unpacking libfdisk1:s390x (2.39.3-9ubuntu2) over (2.39.3-6ubuntu2) ... 259s Preparing to unpack .../mount_2.39.3-9ubuntu2_s390x.deb ... 259s Unpacking mount (2.39.3-9ubuntu2) over (2.39.3-6ubuntu2) ... 259s Preparing to unpack .../libsqlite3-0_3.45.1-1ubuntu1_s390x.deb ... 259s Unpacking libsqlite3-0:s390x (3.45.1-1ubuntu1) over (3.45.1-1) ... 259s Preparing to unpack .../dpkg_1.22.6ubuntu5_s390x.deb ... 259s Unpacking dpkg (1.22.6ubuntu5) over (1.22.4ubuntu5) ... 259s Setting up dpkg (1.22.6ubuntu5) ... 260s Setting up libpython3.12-minimal:s390x (3.12.2-4build3) ... 260s Setting up python3.12-minimal (3.12.2-4build3) ... 261s (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 ... 52215 files and directories currently installed.) 262s Preparing to unpack .../python3_3.12.2-0ubuntu1_s390x.deb ... 262s Unpacking python3 (3.12.2-0ubuntu1) over (3.12.1-0ubuntu2) ... 262s Preparing to unpack .../libpython3-stdlib_3.12.2-0ubuntu1_s390x.deb ... 262s Unpacking libpython3-stdlib:s390x (3.12.2-0ubuntu1) over (3.12.1-0ubuntu2) ... 262s Preparing to unpack .../libsmartcols1_2.39.3-9ubuntu2_s390x.deb ... 262s Unpacking libsmartcols1:s390x (2.39.3-9ubuntu2) over (2.39.3-6ubuntu2) ... 262s Setting up libsmartcols1:s390x (2.39.3-9ubuntu2) ... 262s (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 ... 52215 files and directories currently installed.) 262s Preparing to unpack .../0-bsdextrautils_2.39.3-9ubuntu2_s390x.deb ... 262s Unpacking bsdextrautils (2.39.3-9ubuntu2) over (2.39.3-6ubuntu2) ... 262s Preparing to unpack .../1-groff-base_1.23.0-3build1_s390x.deb ... 262s Unpacking groff-base (1.23.0-3build1) over (1.23.0-3) ... 262s Preparing to unpack .../2-pinentry-curses_1.2.1-3ubuntu4_s390x.deb ... 262s Unpacking pinentry-curses (1.2.1-3ubuntu4) over (1.2.1-3ubuntu1) ... 262s Preparing to unpack .../3-readline-common_8.2-4_all.deb ... 262s Unpacking readline-common (8.2-4) over (8.2-3) ... 262s Preparing to unpack .../4-libxml2_2.9.14+dfsg-1.3ubuntu2_s390x.deb ... 262s Unpacking libxml2:s390x (2.9.14+dfsg-1.3ubuntu2) over (2.9.14+dfsg-1.3ubuntu1) ... 262s Preparing to unpack .../5-libbpf1_1%3a1.3.0-2build1_s390x.deb ... 262s Unpacking libbpf1:s390x (1:1.3.0-2build1) over (1:1.3.0-2) ... 262s dpkg: libelf1:s390x: dependency problems, but removing anyway as you requested: 262s linux-headers-6.8.0-11-generic depends on libelf1 (>= 0.144). 262s iproute2 depends on libelf1 (>= 0.131). 262s 262s (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 ... 52215 files and directories currently installed.) 262s Removing libelf1:s390x (0.190-1) ... 262s Selecting previously unselected package libelf1t64:s390x. 262s (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 ... 52210 files and directories currently installed.) 262s Preparing to unpack .../libelf1t64_0.190-1.1build2_s390x.deb ... 262s Unpacking libelf1t64:s390x (0.190-1.1build2) ... 263s Preparing to unpack .../libtirpc-common_1.3.4+ds-1.1_all.deb ... 263s Unpacking libtirpc-common (1.3.4+ds-1.1) over (1.3.4+ds-1build1) ... 263s Preparing to unpack .../lsof_4.95.0-1build2_s390x.deb ... 263s Unpacking lsof (4.95.0-1build2) over (4.95.0-1build1) ... 263s Preparing to unpack .../libnsl2_1.3.0-3build2_s390x.deb ... 263s Unpacking libnsl2:s390x (1.3.0-3build2) over (1.3.0-3) ... 263s dpkg: libtirpc3:s390x: dependency problems, but removing anyway as you requested: 263s iproute2 depends on libtirpc3 (>= 1.0.2). 263s 263s (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 ... 52215 files and directories currently installed.) 263s Removing libtirpc3:s390x (1.3.4+ds-1build1) ... 263s Selecting previously unselected package libtirpc3t64:s390x. 263s (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 ... 52209 files and directories currently installed.) 263s Preparing to unpack .../0-libtirpc3t64_1.3.4+ds-1.1_s390x.deb ... 263s Adding 'diversion of /lib/s390x-linux-gnu/libtirpc.so.3 to /lib/s390x-linux-gnu/libtirpc.so.3.usr-is-merged by libtirpc3t64' 263s Adding 'diversion of /lib/s390x-linux-gnu/libtirpc.so.3.0.0 to /lib/s390x-linux-gnu/libtirpc.so.3.0.0.usr-is-merged by libtirpc3t64' 263s Unpacking libtirpc3t64:s390x (1.3.4+ds-1.1) ... 263s Preparing to unpack .../1-iproute2_6.1.0-1ubuntu5_s390x.deb ... 263s Unpacking iproute2 (6.1.0-1ubuntu5) over (6.1.0-1ubuntu2) ... 263s Preparing to unpack .../2-libprotobuf-c1_1.4.1-1ubuntu3_s390x.deb ... 263s Unpacking libprotobuf-c1:s390x (1.4.1-1ubuntu3) over (1.4.1-1ubuntu2) ... 263s Preparing to unpack .../3-libnghttp2-14_1.59.0-1build1_s390x.deb ... 263s Unpacking libnghttp2-14:s390x (1.59.0-1build1) over (1.59.0-1) ... 263s Preparing to unpack .../4-libproc2-0_2%3a4.0.4-4ubuntu2_s390x.deb ... 263s Unpacking libproc2-0:s390x (2:4.0.4-4ubuntu2) over (2:4.0.4-4ubuntu1) ... 263s Preparing to unpack .../5-procps_2%3a4.0.4-4ubuntu2_s390x.deb ... 263s Unpacking procps (2:4.0.4-4ubuntu2) over (2:4.0.4-4ubuntu1) ... 263s Preparing to unpack .../6-coreutils_9.4-3ubuntu3_s390x.deb ... 263s Unpacking coreutils (9.4-3ubuntu3) over (9.4-2ubuntu4) ... 263s Setting up coreutils (9.4-3ubuntu3) ... 263s (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 ... 52221 files and directories currently installed.) 263s Preparing to unpack .../util-linux_2.39.3-9ubuntu2_s390x.deb ... 263s Unpacking util-linux (2.39.3-9ubuntu2) over (2.39.3-6ubuntu2) ... 264s Setting up util-linux (2.39.3-9ubuntu2) ... 265s fstrim.service is a disabled or a static unit not running, not starting it. 265s (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 ... 52221 files and directories currently installed.) 265s Removing libatm1:s390x (1:2.5.1-5) ... 265s (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 ... 52216 files and directories currently installed.) 265s Preparing to unpack .../file_1%3a5.45-3_s390x.deb ... 265s Unpacking file (1:5.45-3) over (1:5.45-2) ... 265s (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 ... 52216 files and directories currently installed.) 265s Removing libmagic1:s390x (1:5.45-2) ... 265s (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 ... 52206 files and directories currently installed.) 265s Preparing to unpack .../libmagic-mgc_1%3a5.45-3_s390x.deb ... 265s Unpacking libmagic-mgc (1:5.45-3) over (1:5.45-2) ... 265s Selecting previously unselected package libmagic1t64:s390x. 265s Preparing to unpack .../libmagic1t64_1%3a5.45-3_s390x.deb ... 265s Unpacking libmagic1t64:s390x (1:5.45-3) ... 265s Preparing to unpack .../libplymouth5_24.004.60-1ubuntu6_s390x.deb ... 265s Unpacking libplymouth5:s390x (24.004.60-1ubuntu6) over (24.004.60-1ubuntu3) ... 265s (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 ... 52217 files and directories currently installed.) 265s Removing libpng16-16:s390x (1.6.43-1) ... 265s Selecting previously unselected package libpng16-16t64:s390x. 265s (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 ... 52207 files and directories currently installed.) 265s Preparing to unpack .../libpng16-16t64_1.6.43-3_s390x.deb ... 265s Unpacking libpng16-16t64:s390x (1.6.43-3) ... 265s Preparing to unpack .../multipath-tools_0.9.4-5ubuntu6_s390x.deb ... 265s Unpacking multipath-tools (0.9.4-5ubuntu6) over (0.9.4-5ubuntu3) ... 265s dpkg: liburcu8:s390x: dependency problems, but removing anyway as you requested: 265s xfsprogs depends on liburcu8 (>= 0.13.0). 265s 266s (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 ... 52217 files and directories currently installed.) 266s Removing liburcu8:s390x (0.14.0-3) ... 266s Selecting previously unselected package liburcu8t64:s390x. 266s (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 ... 52198 files and directories currently installed.) 266s Preparing to unpack .../liburcu8t64_0.14.0-3.1_s390x.deb ... 266s Unpacking liburcu8t64:s390x (0.14.0-3.1) ... 266s Preparing to unpack .../liblocale-gettext-perl_1.07-6ubuntu4_s390x.deb ... 266s Unpacking liblocale-gettext-perl (1.07-6ubuntu4) over (1.07-6build1) ... 266s Preparing to unpack .../uuid-runtime_2.39.3-9ubuntu2_s390x.deb ... 266s Unpacking uuid-runtime (2.39.3-9ubuntu2) over (2.39.3-6ubuntu2) ... 266s Preparing to unpack .../libdebconfclient0_0.271ubuntu2_s390x.deb ... 266s Unpacking libdebconfclient0:s390x (0.271ubuntu2) over (0.271ubuntu1) ... 266s Setting up libdebconfclient0:s390x (0.271ubuntu2) ... 266s (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 ... 52218 files and directories currently installed.) 266s Preparing to unpack .../libsemanage-common_3.5-1build4_all.deb ... 266s Unpacking libsemanage-common (3.5-1build4) over (3.5-1build2) ... 266s Setting up libsemanage-common (3.5-1build4) ... 266s (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 ... 52218 files and directories currently installed.) 266s Preparing to unpack .../libsemanage2_3.5-1build4_s390x.deb ... 266s Unpacking libsemanage2:s390x (3.5-1build4) over (3.5-1build2) ... 266s Setting up libsemanage2:s390x (3.5-1build4) ... 266s (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 ... 52218 files and directories currently installed.) 266s Preparing to unpack .../install-info_7.1-3build1_s390x.deb ... 266s Unpacking install-info (7.1-3build1) over (7.1-3) ... 266s Setting up install-info (7.1-3build1) ... 266s (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 ... 52218 files and directories currently installed.) 266s Preparing to unpack .../00-libss2_1.47.0-2.4~exp1ubuntu2_s390x.deb ... 266s Unpacking libss2:s390x (1.47.0-2.4~exp1ubuntu2) over (1.47.0-2ubuntu1) ... 266s Preparing to unpack .../01-eject_2.39.3-9ubuntu2_s390x.deb ... 266s Unpacking eject (2.39.3-9ubuntu2) over (2.39.3-6ubuntu2) ... 266s Preparing to unpack .../02-krb5-locales_1.20.1-6ubuntu1_all.deb ... 266s Unpacking krb5-locales (1.20.1-6ubuntu1) over (1.20.1-5build1) ... 266s Preparing to unpack .../03-libglib2.0-data_2.79.3-3ubuntu5_all.deb ... 266s Unpacking libglib2.0-data (2.79.3-3ubuntu5) over (2.79.2-1~ubuntu1) ... 266s Preparing to unpack .../04-libslang2_2.3.3-3build1_s390x.deb ... 266s Unpacking libslang2:s390x (2.3.3-3build1) over (2.3.3-3) ... 266s Preparing to unpack .../05-libtext-charwidth-perl_0.04-11build2_s390x.deb ... 266s Unpacking libtext-charwidth-perl:s390x (0.04-11build2) over (0.04-11build1) ... 266s Preparing to unpack .../06-libtext-iconv-perl_1.7-8build2_s390x.deb ... 266s Unpacking libtext-iconv-perl:s390x (1.7-8build2) over (1.7-8build1) ... 266s Preparing to unpack .../07-python-apt-common_2.7.7_all.deb ... 266s Unpacking python-apt-common (2.7.7) over (2.7.6) ... 266s Preparing to unpack .../08-python3-setuptools_68.1.2-2ubuntu1_all.deb ... 267s Unpacking python3-setuptools (68.1.2-2ubuntu1) over (68.1.2-2) ... 267s Preparing to unpack .../09-python3-pkg-resources_68.1.2-2ubuntu1_all.deb ... 267s Unpacking python3-pkg-resources (68.1.2-2ubuntu1) over (68.1.2-2) ... 267s Preparing to unpack .../10-rsyslog_8.2312.0-3ubuntu7_s390x.deb ... 267s Unpacking rsyslog (8.2312.0-3ubuntu7) over (8.2312.0-3ubuntu3) ... 267s Preparing to unpack .../11-vim-tiny_2%3a9.1.0016-1ubuntu6_s390x.deb ... 267s Unpacking vim-tiny (2:9.1.0016-1ubuntu6) over (2:9.1.0016-1ubuntu2) ... 267s Preparing to unpack .../12-vim-common_2%3a9.1.0016-1ubuntu6_all.deb ... 267s Unpacking vim-common (2:9.1.0016-1ubuntu6) over (2:9.1.0016-1ubuntu2) ... 267s Selecting previously unselected package xdg-user-dirs. 267s Preparing to unpack .../13-xdg-user-dirs_0.18-1_s390x.deb ... 267s Unpacking xdg-user-dirs (0.18-1) ... 267s Preparing to unpack .../14-xxd_2%3a9.1.0016-1ubuntu6_s390x.deb ... 267s Unpacking xxd (2:9.1.0016-1ubuntu6) over (2:9.1.0016-1ubuntu2) ... 267s Preparing to unpack .../15-apparmor_4.0.0-beta3-0ubuntu2_s390x.deb ... 268s Unpacking apparmor (4.0.0-beta3-0ubuntu2) over (4.0.0~alpha4-0ubuntu1) ... 268s Preparing to unpack .../16-ftp_20230507-2build1_all.deb ... 268s Unpacking ftp (20230507-2build1) over (20230507-2) ... 268s Preparing to unpack .../17-inetutils-telnet_2%3a2.5-3ubuntu3_s390x.deb ... 268s Unpacking inetutils-telnet (2:2.5-3ubuntu3) over (2:2.5-3ubuntu1) ... 268s Preparing to unpack .../18-info_7.1-3build1_s390x.deb ... 268s Unpacking info (7.1-3build1) over (7.1-3) ... 268s Preparing to unpack .../19-libxmuu1_2%3a1.1.3-3build1_s390x.deb ... 268s Unpacking libxmuu1:s390x (2:1.1.3-3build1) over (2:1.1.3-3) ... 268s Preparing to unpack .../20-lshw_02.19.git.2021.06.19.996aaad9c7-2build2_s390x.deb ... 268s Unpacking lshw (02.19.git.2021.06.19.996aaad9c7-2build2) over (02.19.git.2021.06.19.996aaad9c7-2build1) ... 268s Selecting previously unselected package manpages. 268s Preparing to unpack .../21-manpages_6.05.01-1_all.deb ... 268s Unpacking manpages (6.05.01-1) ... 268s Preparing to unpack .../22-mtr-tiny_0.95-1.1build1_s390x.deb ... 268s Unpacking mtr-tiny (0.95-1.1build1) over (0.95-1.1) ... 268s Preparing to unpack .../23-plymouth-theme-ubuntu-text_24.004.60-1ubuntu6_s390x.deb ... 268s Unpacking plymouth-theme-ubuntu-text (24.004.60-1ubuntu6) over (24.004.60-1ubuntu3) ... 269s Preparing to unpack .../24-plymouth_24.004.60-1ubuntu6_s390x.deb ... 269s Unpacking plymouth (24.004.60-1ubuntu6) over (24.004.60-1ubuntu3) ... 269s Preparing to unpack .../25-telnet_0.17+2.5-3ubuntu3_all.deb ... 269s Unpacking telnet (0.17+2.5-3ubuntu3) over (0.17+2.5-3ubuntu1) ... 269s Preparing to unpack .../26-usb.ids_2024.03.18-1_all.deb ... 269s Unpacking usb.ids (2024.03.18-1) over (2024.01.30-1) ... 269s Preparing to unpack .../27-xz-utils_5.6.0-0.2_s390x.deb ... 269s Unpacking xz-utils (5.6.0-0.2) over (5.4.5-0.3) ... 269s Selecting previously unselected package libllvm18:s390x. 269s Preparing to unpack .../28-libllvm18_1%3a18.1.2-1ubuntu2_s390x.deb ... 269s Unpacking libllvm18:s390x (1:18.1.2-1ubuntu2) ... 271s Selecting previously unselected package libclang-cpp18. 271s Preparing to unpack .../29-libclang-cpp18_1%3a18.1.2-1ubuntu2_s390x.deb ... 271s Unpacking libclang-cpp18 (1:18.1.2-1ubuntu2) ... 272s Selecting previously unselected package libbpfcc:s390x. 272s Preparing to unpack .../30-libbpfcc_0.29.1+ds-1ubuntu4_s390x.deb ... 272s Unpacking libbpfcc:s390x (0.29.1+ds-1ubuntu4) ... 272s Selecting previously unselected package python3-bpfcc. 272s Preparing to unpack .../31-python3-bpfcc_0.29.1+ds-1ubuntu4_all.deb ... 272s Unpacking python3-bpfcc (0.29.1+ds-1ubuntu4) ... 272s Selecting previously unselected package ieee-data. 272s Preparing to unpack .../32-ieee-data_20220827.1_all.deb ... 272s Unpacking ieee-data (20220827.1) ... 272s Selecting previously unselected package python3-netaddr. 272s Preparing to unpack .../33-python3-netaddr_0.8.0-2ubuntu1_all.deb ... 272s Unpacking python3-netaddr (0.8.0-2ubuntu1) ... 272s Selecting previously unselected package bpfcc-tools. 272s Preparing to unpack .../34-bpfcc-tools_0.29.1+ds-1ubuntu4_all.deb ... 272s Unpacking bpfcc-tools (0.29.1+ds-1ubuntu4) ... 272s Selecting previously unselected package libclang1-18. 272s Preparing to unpack .../35-libclang1-18_1%3a18.1.2-1ubuntu2_s390x.deb ... 272s Unpacking libclang1-18 (1:18.1.2-1ubuntu2) ... 272s Selecting previously unselected package libdw1t64:s390x. 272s Preparing to unpack .../36-libdw1t64_0.190-1.1build2_s390x.deb ... 272s Unpacking libdw1t64:s390x (0.190-1.1build2) ... 272s Selecting previously unselected package bpftrace. 272s Preparing to unpack .../37-bpftrace_0.20.2-1ubuntu1_s390x.deb ... 272s Unpacking bpftrace (0.20.2-1ubuntu1) ... 273s Preparing to unpack .../38-cryptsetup-bin_2%3a2.7.0-1ubuntu2_s390x.deb ... 273s Unpacking cryptsetup-bin (2:2.7.0-1ubuntu2) over (2:2.7.0-1ubuntu1) ... 273s Preparing to unpack .../39-dpkg-dev_1.22.6ubuntu5_all.deb ... 273s Unpacking dpkg-dev (1.22.6ubuntu5) over (1.22.4ubuntu5) ... 273s Preparing to unpack .../40-libdpkg-perl_1.22.6ubuntu5_all.deb ... 273s Unpacking libdpkg-perl (1.22.6ubuntu5) over (1.22.4ubuntu5) ... 273s Selecting previously unselected package fonts-dejavu-mono. 273s Preparing to unpack .../41-fonts-dejavu-mono_2.37-8_all.deb ... 273s Unpacking fonts-dejavu-mono (2.37-8) ... 273s Selecting previously unselected package fonts-dejavu-core. 273s Preparing to unpack .../42-fonts-dejavu-core_2.37-8_all.deb ... 273s Unpacking fonts-dejavu-core (2.37-8) ... 273s Selecting previously unselected package fontconfig-config. 273s Preparing to unpack .../43-fontconfig-config_2.15.0-1.1ubuntu1_s390x.deb ... 273s Unpacking fontconfig-config (2.15.0-1.1ubuntu1) ... 273s Preparing to unpack .../44-gnupg-l10n_2.4.4-2ubuntu15_all.deb ... 273s Unpacking gnupg-l10n (2.4.4-2ubuntu15) over (2.4.4-2ubuntu7) ... 273s Selecting previously unselected package hwdata. 273s Preparing to unpack .../45-hwdata_0.379-1_all.deb ... 273s Unpacking hwdata (0.379-1) ... 273s Preparing to unpack .../46-libibverbs1_50.0-2build1_s390x.deb ... 273s Unpacking libibverbs1:s390x (50.0-2build1) over (50.0-2) ... 273s Preparing to unpack .../47-ibverbs-providers_50.0-2build1_s390x.deb ... 273s Unpacking ibverbs-providers:s390x (50.0-2build1) over (50.0-2) ... 273s Selecting previously unselected package libaio1t64:s390x. 273s Preparing to unpack .../48-libaio1t64_0.3.113-6_s390x.deb ... 273s Unpacking libaio1t64:s390x (0.3.113-6) ... 273s Selecting previously unselected package libatm1t64:s390x. 273s Preparing to unpack .../49-libatm1t64_1%3a2.5.1-5.1_s390x.deb ... 273s Unpacking libatm1t64:s390x (1:2.5.1-5.1) ... 273s Selecting previously unselected package libc-dev-bin. 273s Preparing to unpack .../50-libc-dev-bin_2.39-0ubuntu6_s390x.deb ... 273s Unpacking libc-dev-bin (2.39-0ubuntu6) ... 273s Selecting previously unselected package libfreetype6:s390x. 273s Preparing to unpack .../51-libfreetype6_2.13.2+dfsg-1build2_s390x.deb ... 273s Unpacking libfreetype6:s390x (2.13.2+dfsg-1build2) ... 273s Selecting previously unselected package libfontconfig1:s390x. 273s Preparing to unpack .../52-libfontconfig1_2.15.0-1.1ubuntu1_s390x.deb ... 273s Unpacking libfontconfig1:s390x (2.15.0-1.1ubuntu1) ... 273s Selecting previously unselected package libjpeg-turbo8:s390x. 273s Preparing to unpack .../53-libjpeg-turbo8_2.1.5-2ubuntu1_s390x.deb ... 273s Unpacking libjpeg-turbo8:s390x (2.1.5-2ubuntu1) ... 273s Selecting previously unselected package libjpeg8:s390x. 273s Preparing to unpack .../54-libjpeg8_8c-2ubuntu11_s390x.deb ... 274s Unpacking libjpeg8:s390x (8c-2ubuntu11) ... 274s Selecting previously unselected package libdeflate0:s390x. 274s Preparing to unpack .../55-libdeflate0_1.19-1_s390x.deb ... 274s Unpacking libdeflate0:s390x (1.19-1) ... 274s Selecting previously unselected package libjbig0:s390x. 274s Preparing to unpack .../56-libjbig0_2.1-6.1ubuntu1_s390x.deb ... 274s Unpacking libjbig0:s390x (2.1-6.1ubuntu1) ... 274s Selecting previously unselected package libsharpyuv0:s390x. 274s Preparing to unpack .../57-libsharpyuv0_1.3.2-0.4build2_s390x.deb ... 274s Unpacking libsharpyuv0:s390x (1.3.2-0.4build2) ... 274s Selecting previously unselected package libwebp7:s390x. 274s Preparing to unpack .../58-libwebp7_1.3.2-0.4build2_s390x.deb ... 274s Unpacking libwebp7:s390x (1.3.2-0.4build2) ... 274s Selecting previously unselected package libtiff6:s390x. 274s Preparing to unpack .../59-libtiff6_4.5.1+git230720-4ubuntu1_s390x.deb ... 274s Unpacking libtiff6:s390x (4.5.1+git230720-4ubuntu1) ... 274s Selecting previously unselected package libxpm4:s390x. 274s Preparing to unpack .../60-libxpm4_1%3a3.5.17-1build1_s390x.deb ... 274s Unpacking libxpm4:s390x (1:3.5.17-1build1) ... 274s Selecting previously unselected package libgd3:s390x. 274s Preparing to unpack .../61-libgd3_2.3.3-9ubuntu3_s390x.deb ... 274s Unpacking libgd3:s390x (2.3.3-9ubuntu3) ... 274s Selecting previously unselected package libc-devtools. 274s Preparing to unpack .../62-libc-devtools_2.39-0ubuntu6_s390x.deb ... 274s Unpacking libc-devtools (2.39-0ubuntu6) ... 274s Selecting previously unselected package linux-libc-dev:s390x. 274s Preparing to unpack .../63-linux-libc-dev_6.8.0-20.20_s390x.deb ... 274s Unpacking linux-libc-dev:s390x (6.8.0-20.20) ... 274s Selecting previously unselected package libcrypt-dev:s390x. 274s Preparing to unpack .../64-libcrypt-dev_1%3a4.4.36-4_s390x.deb ... 274s Unpacking libcrypt-dev:s390x (1:4.4.36-4) ... 274s Selecting previously unselected package rpcsvc-proto. 274s Preparing to unpack .../65-rpcsvc-proto_1.4.2-0ubuntu6_s390x.deb ... 274s Unpacking rpcsvc-proto (1.4.2-0ubuntu6) ... 274s Selecting previously unselected package libc6-dev:s390x. 274s Preparing to unpack .../66-libc6-dev_2.39-0ubuntu6_s390x.deb ... 274s Unpacking libc6-dev:s390x (2.39-0ubuntu6) ... 274s Preparing to unpack .../67-libevent-core-2.1-7_2.1.12-stable-9build1_s390x.deb ... 274s Unpacking libevent-core-2.1-7:s390x (2.1.12-stable-9build1) over (2.1.12-stable-9) ... 274s Preparing to unpack .../68-libldap-common_2.6.7+dfsg-1~exp1ubuntu6_all.deb ... 274s Unpacking libldap-common (2.6.7+dfsg-1~exp1ubuntu6) over (2.6.7+dfsg-1~exp1ubuntu1) ... 274s Selecting previously unselected package linux-modules-6.8.0-20-generic. 274s Preparing to unpack .../69-linux-modules-6.8.0-20-generic_6.8.0-20.20_s390x.deb ... 274s Unpacking linux-modules-6.8.0-20-generic (6.8.0-20.20) ... 274s Selecting previously unselected package linux-image-6.8.0-20-generic. 274s Preparing to unpack .../70-linux-image-6.8.0-20-generic_6.8.0-20.20_s390x.deb ... 274s Unpacking linux-image-6.8.0-20-generic (6.8.0-20.20) ... 275s Selecting previously unselected package linux-modules-extra-6.8.0-20-generic. 275s Preparing to unpack .../71-linux-modules-extra-6.8.0-20-generic_6.8.0-20.20_s390x.deb ... 275s Unpacking linux-modules-extra-6.8.0-20-generic (6.8.0-20.20) ... 275s Preparing to unpack .../72-linux-generic_6.8.0-20.20+1_s390x.deb 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... 281s Unpacking ubuntu-kernel-accessories (1.536build1) ... 281s Preparing to unpack .../90-kpartx_0.9.4-5ubuntu6_s390x.deb ... 281s Unpacking kpartx (0.9.4-5ubuntu6) over (0.9.4-5ubuntu3) ... 281s Setting up pinentry-curses (1.2.1-3ubuntu4) ... 281s Setting up motd-news-config (13ubuntu8) ... 281s Setting up libtext-iconv-perl:s390x (1.7-8build2) ... 281s Setting up libtext-charwidth-perl:s390x (0.04-11build2) ... 281s Setting up libsharpyuv0:s390x (1.3.2-0.4build2) ... 281s Setting up liburcu8t64:s390x (0.14.0-3.1) ... 281s Setting up tcpdump (4.99.4-3ubuntu2) ... 281s Setting up libibverbs1:s390x (50.0-2build1) ... 281s Setting up ubuntu-kernel-accessories (1.536build1) ... 281s Setting up libapparmor1:s390x (4.0.0-beta3-0ubuntu2) ... 281s Setting up libatm1t64:s390x (1:2.5.1-5.1) ... 281s Setting up libgdbm6t64:s390x (1.23-5.1) ... 281s Setting up bsdextrautils (2.39.3-9ubuntu2) ... 281s Setting up libxpm4:s390x (1:3.5.17-1build1) ... 281s Setting up libgdbm-compat4t64:s390x (1.23-5.1) ... 281s Setting up xdg-user-dirs (0.18-1) ... 281s Setting up ibverbs-providers:s390x (50.0-2build1) ... 281s Setting up linux-headers-6.8.0-20 (6.8.0-20.20) ... 281s Setting up libmagic-mgc (1:5.45-3) ... 281s Setting up gawk (1:5.2.1-2build2) ... 281s Setting up manpages (6.05.01-1) ... 281s Setting up libtirpc-common (1.3.4+ds-1.1) ... 281s Setting up libbrotli1:s390x (1.1.0-2build1) ... 281s Setting up libsqlite3-0:s390x (3.45.1-1ubuntu1) ... 281s Setting up libsasl2-modules:s390x (2.1.28+dfsg1-5ubuntu1) ... 281s Setting up libuv1t64:s390x (1.48.0-1.1) ... 281s Setting up libmagic1t64:s390x (1:5.45-3) ... 281s Setting up rsyslog (8.2312.0-3ubuntu7) ... 281s info: The user `syslog' is already a member of `adm'. 282s Setting up libpsl5t64:s390x (0.21.2-1.1) ... 282s Setting up libnghttp2-14:s390x (1.59.0-1build1) ... 282s Setting up libdeflate0:s390x (1.19-1) ... 282s Setting up linux-libc-dev:s390x (6.8.0-20.20) ... 282s Setting up libreiserfscore0t64 (1:3.6.27-7.1) ... 282s Setting up systemd-dev (255.4-1ubuntu5) ... 282s Setting up libparted2t64:s390x (3.6-3.1build2) ... 282s Setting up krb5-locales (1.20.1-6ubuntu1) ... 282s Setting up file (1:5.45-3) ... 282s Setting up lshw (02.19.git.2021.06.19.996aaad9c7-2build2) ... 282s Setting up libldap-common (2.6.7+dfsg-1~exp1ubuntu6) ... 282s Setting up libprotobuf-c1:s390x (1.4.1-1ubuntu3) ... 282s Setting up libjbig0:s390x (2.1-6.1ubuntu1) ... 282s Setting up xxd (2:9.1.0016-1ubuntu6) ... 282s Setting up libelf1t64:s390x (0.190-1.1build2) ... 282s Setting up libkrb5support0:s390x (1.20.1-6ubuntu1) ... 282s Setting up libdw1t64:s390x (0.190-1.1build2) ... 282s Setting up linux-headers-6.8.0-20-generic (6.8.0-20.20) ... 282s Setting up eject (2.39.3-9ubuntu2) ... 282s Setting up apparmor (4.0.0-beta3-0ubuntu2) ... 282s Installing new version of config file /etc/apparmor.d/abstractions/authentication ... 282s Installing new version of config file /etc/apparmor.d/abstractions/crypto ... 282s Installing new version of config file /etc/apparmor.d/abstractions/kde-open5 ... 282s Installing new version of config file /etc/apparmor.d/abstractions/openssl ... 282s Installing new version of config file /etc/apparmor.d/code ... 282s Installing new version of config file /etc/apparmor.d/firefox ... 284s Reloading AppArmor profiles 284s Setting up libglib2.0-0t64:s390x (2.79.3-3ubuntu5) ... 284s No schema files found: doing nothing. 284s Setting up libglib2.0-data (2.79.3-3ubuntu5) ... 284s Setting up rpcsvc-proto (1.4.2-0ubuntu6) ... 284s Setting up vim-common (2:9.1.0016-1ubuntu6) ... 284s Setting up libqrtr-glib0:s390x (1.2.2-1ubuntu3) ... 284s Setting up libslang2:s390x (2.3.3-3build1) ... 284s Setting up libnvme1t64 (1.8-3) ... 284s Setting up mtr-tiny (0.95-1.1build1) ... 284s Setting up gnupg-l10n (2.4.4-2ubuntu15) ... 284s Setting up librtmp1:s390x (2.4+20151223.gitfa8646d.1-2build6) ... 284s Setting up libdbus-1-3:s390x (1.14.10-4ubuntu2) ... 284s Setting up xz-utils (5.6.0-0.2) ... 284s Setting up perl-modules-5.38 (5.38.2-3.2) ... 284s Setting up libproc2-0:s390x (2:4.0.4-4ubuntu2) ... 284s Setting up fonts-dejavu-mono (2.37-8) ... 284s Setting up libpng16-16t64:s390x (1.6.43-3) ... 284s Setting up libevent-core-2.1-7:s390x (2.1.12-stable-9build1) ... 284s Setting up libss2:s390x (1.47.0-2.4~exp1ubuntu2) ... 284s Setting up usb.ids (2024.03.18-1) ... 284s Setting up sudo (1.9.15p5-3ubuntu3) ... 284s Setting up fonts-dejavu-core (2.37-8) ... 284s Setting up dhcpcd-base (1:10.0.6-1ubuntu2) ... 284s Setting up gir1.2-glib-2.0:s390x (2.79.3-3ubuntu5) ... 284s Setting up libk5crypto3:s390x (1.20.1-6ubuntu1) ... 284s Setting up libjpeg-turbo8:s390x (2.1.5-2ubuntu1) ... 284s Setting up logsave (1.47.0-2.4~exp1ubuntu2) ... 284s Setting up libwebp7:s390x (1.3.2-0.4build2) ... 284s Setting up libfdisk1:s390x (2.39.3-9ubuntu2) ... 284s Setting up libdb5.3t64:s390x (5.3.28+dfsg2-6) ... 284s Setting up libaio1t64:s390x (0.3.113-6) ... 284s Setting up python-apt-common (2.7.7) ... 284s Setting up mount (2.39.3-9ubuntu2) ... 284s Setting up uuid-runtime (2.39.3-9ubuntu2) ... 286s uuidd.service is a disabled or a static unit not running, not starting it. 286s Setting up libmm-glib0:s390x (1.23.4-0ubuntu1) ... 286s Setting up groff-base (1.23.0-3build1) ... 286s Setting up libcrypt-dev:s390x (1:4.4.36-4) ... 286s Setting up libplymouth5:s390x (24.004.60-1ubuntu6) ... 286s Setting up dbus-session-bus-common (1.14.10-4ubuntu2) ... 286s Setting up procps (2:4.0.4-4ubuntu2) ... 286s Setting up gpgconf (2.4.4-2ubuntu15) ... 286s Setting up libcryptsetup12:s390x (2:2.7.0-1ubuntu2) ... 286s Setting up libgirepository-1.0-1:s390x (1.79.1-1ubuntu6) ... 286s Setting up libjson-glib-1.0-common (1.8.0-2build1) ... 286s Setting up libkrb5-3:s390x (1.20.1-6ubuntu1) ... 286s Setting up libpython3.11-minimal:s390x (3.11.8-1build4) ... 286s Setting up libperl5.38t64:s390x (5.38.2-3.2) ... 286s Setting up tnftp (20230507-2build1) ... 286s Setting up dbus-system-bus-common (1.14.10-4ubuntu2) ... 286s Setting up libfido2-1:s390x (1.14.0-1build1) ... 286s Setting up libc-dev-bin (2.39-0ubuntu6) ... 286s Setting up openssl (3.0.13-0ubuntu2) ... 286s Setting up linux-modules-6.8.0-20-generic (6.8.0-20.20) ... 287s Setting up readline-common (8.2-4) ... 287s Setting up libxml2:s390x (2.9.14+dfsg-1.3ubuntu2) ... 287s Setting up libxmuu1:s390x (2:1.1.3-3build1) ... 287s Setting up dbus-bin (1.14.10-4ubuntu2) ... 287s Setting up info (7.1-3build1) ... 287s Setting up liblocale-gettext-perl (1.07-6ubuntu4) ... 287s Setting up gpg (2.4.4-2ubuntu15) ... 287s Setting up libgudev-1.0-0:s390x (1:238-3ubuntu2) ... 287s Setting up libpolkit-gobject-1-0:s390x (124-1ubuntu1) ... 287s Setting up libbpf1:s390x (1:1.3.0-2build1) ... 287s Setting up libmbim-glib4:s390x (1.31.2-0ubuntu2) ... 287s Setting up rsync (3.2.7-1build1) ... 288s rsync.service is a disabled or a static unit not running, not starting it. 288s Setting up libudisks2-0:s390x (2.10.1-6) ... 288s Setting up libkmod2:s390x (31+20240202-2ubuntu4) ... 288s Setting up bolt (0.9.6-2build1) ... 288s bolt.service is a disabled or a static unit not running, not starting it. 288s Setting up s390-tools-data (2.31.0-0ubuntu3) ... 288s Setting up libllvm18:s390x (1:18.1.2-1ubuntu2) ... 288s Setting up gnupg-utils (2.4.4-2ubuntu15) ... 288s Setting up libjpeg8:s390x (8c-2ubuntu11) ... 288s Setting up cryptsetup-bin (2:2.7.0-1ubuntu2) ... 288s Setting up python3.11-minimal (3.11.8-1build4) ... 290s Setting up libclang1-18 (1:18.1.2-1ubuntu2) ... 290s Setting up manpages-dev (6.05.01-1) ... 290s Setting up linux-modules-extra-6.8.0-20-generic (6.8.0-20.20) ... 291s Setting up apt-utils (2.7.14) ... 291s Setting up gpg-agent (2.4.4-2ubuntu15) ... 292s Setting up libpython3.12-stdlib:s390x (3.12.2-4build3) ... 292s Setting up wget (1.21.4-1ubuntu2) ... 292s Setting up fontconfig-config (2.15.0-1.1ubuntu1) ... 292s Setting up libxmlb2:s390x (0.3.15-1build1) ... 292s Setting up btrfs-progs (6.6.3-1.1build1) ... 292s Setting up libpython3.11-stdlib:s390x (3.11.8-1build4) ... 292s Setting up python3.12 (3.12.2-4build3) ... 294s Setting up gpgsm (2.4.4-2ubuntu15) ... 294s Setting up inetutils-telnet (2:2.5-3ubuntu3) ... 294s Setting up parted (3.6-3.1build2) ... 294s Setting up e2fsprogs (1.47.0-2.4~exp1ubuntu2) ... 294s update-initramfs: deferring update (trigger activated) 295s e2scrub_all.service is a disabled or a static unit not running, not starting it. 296s Setting up linux-headers-generic (6.8.0-20.20+1) ... 296s Setting up dbus-daemon (1.14.10-4ubuntu2) ... 296s Setting up libmbim-proxy (1.31.2-0ubuntu2) ... 296s Setting up vim-tiny (2:9.1.0016-1ubuntu6) ... 296s Setting up kmod (31+20240202-2ubuntu4) ... 296s Setting up libnetplan1:s390x (1.0-1) ... 296s Setting up man-db (2.12.0-3build4) ... 296s Updating database of manual pages ... 299s man-db.service is a disabled or a static unit not running, not starting it. 299s Setting up fdisk (2.39.3-9ubuntu2) ... 299s Setting up libjson-glib-1.0-0:s390x (1.8.0-2build1) ... 299s Setting up libsasl2-modules-db:s390x (2.1.28+dfsg1-5ubuntu1) ... 299s Setting up hwdata (0.379-1) ... 299s Setting up perl (5.38.2-3.2) ... 299s Setting up libfreetype6:s390x (2.13.2+dfsg-1build2) ... 299s Setting up gir1.2-girepository-2.0:s390x (1.79.1-1ubuntu6) ... 299s Setting up dbus (1.14.10-4ubuntu2) ... 299s A reboot is required to replace the running dbus-daemon. 299s Please reboot the system when convenient. 300s Setting up shared-mime-info (2.4-1build1) ... 300s Setting up libblockdev-utils3:s390x (3.1.0-1build1) ... 300s Setting up libgssapi-krb5-2:s390x (1.20.1-6ubuntu1) ... 300s Setting up libsystemd-shared:s390x (255.4-1ubuntu5) ... 300s Setting up ftp (20230507-2build1) ... 300s Setting up keyboxd (2.4.4-2ubuntu15) ... 301s Setting up libdpkg-perl (1.22.6ubuntu5) ... 301s Setting up libsasl2-2:s390x (2.1.28+dfsg1-5ubuntu1) ... 301s Setting up libssh-4:s390x (0.10.6-2build1) ... 301s Setting up libblockdev-nvme3:s390x (3.1.0-1build1) ... 301s Setting up libblockdev-fs3:s390x (3.1.0-1build1) ... 301s Setting up ieee-data (20220827.1) ... 301s Setting up libtiff6:s390x (4.5.1+git230720-4ubuntu1) ... 301s Setting up libpolkit-agent-1-0:s390x (124-1ubuntu1) ... 301s Setting up libc6-dev:s390x (2.39-0ubuntu6) ... 301s Setting up libgpgme11t64:s390x (1.18.0-4.1ubuntu3) ... 301s Setting up libfontconfig1:s390x (2.15.0-1.1ubuntu1) ... 301s Setting up libclang-cpp18 (1:18.1.2-1ubuntu2) ... 301s Setting up libbpfcc:s390x (0.29.1+ds-1ubuntu4) ... 301s Setting up linux-tools-common (6.8.0-20.20) ... 301s Setting up libarchive13t64:s390x (3.7.2-1.1ubuntu2) ... 301s Setting up libldap2:s390x (2.6.7+dfsg-1~exp1ubuntu6) ... 301s Setting up libpython3-stdlib:s390x (3.12.2-0ubuntu1) ... 301s Setting up python3.11 (3.11.8-1build4) ... 303s Setting up telnet (0.17+2.5-3ubuntu3) ... 303s Setting up libblockdev-mdraid3:s390x (3.1.0-1build1) ... 303s Setting up linux-headers-virtual (6.8.0-20.20+1) ... 303s Setting up libcurl4t64:s390x (8.5.0-2ubuntu8) ... 303s Setting up bpftrace (0.20.2-1ubuntu1) ... 303s Setting up bind9-libs:s390x (1:9.18.24-0ubuntu3) ... 303s Setting up linux-image-6.8.0-20-generic (6.8.0-20.20) ... 303s I: /boot/vmlinuz is now a symlink to vmlinuz-6.8.0-20-generic 303s I: /boot/initrd.img is now a symlink to initrd.img-6.8.0-20-generic 303s Setting up libtirpc3t64:s390x (1.3.4+ds-1.1) ... 303s Setting up e2fsprogs-l10n (1.47.0-2.4~exp1ubuntu2) ... 303s Setting up libblockdev-swap3:s390x (3.1.0-1build1) ... 303s Setting up iproute2 (6.1.0-1ubuntu5) ... 304s Setting up openssh-client (1:9.6p1-3ubuntu11) ... 304s Setting up libgusb2:s390x (0.4.8-1build1) ... 304s Setting up libblockdev-loop3:s390x (3.1.0-1build1) ... 304s Setting up libcurl3t64-gnutls:s390x (8.5.0-2ubuntu8) ... 304s Setting up libqmi-glib5:s390x (1.35.2-0ubuntu1) ... 304s Setting up linux-tools-6.8.0-20 (6.8.0-20.20) ... 304s Setting up python3 (3.12.2-0ubuntu1) ... 304s Setting up systemd (255.4-1ubuntu5) ... 305s Setting up libblockdev3:s390x (3.1.0-1build1) ... 305s Setting up libjcat1:s390x (0.2.0-2build2) ... 305s Setting up dpkg-dev (1.22.6ubuntu5) ... 305s Setting up libblockdev-part3:s390x (3.1.0-1build1) ... 305s Setting up dirmngr (2.4.4-2ubuntu15) ... 305s Setting up linux-tools-6.8.0-20-generic (6.8.0-20.20) ... 305s Setting up python3-cryptography (41.0.7-4build2) ... 306s Setting up python3-gi (3.47.0-3build1) ... 306s Setting up libgd3:s390x (2.3.3-9ubuntu3) ... 306s Setting up systemd-timesyncd (255.4-1ubuntu5) ... 307s Setting up udev (255.4-1ubuntu5) ... 308s Setting up python3-typing-extensions (4.10.0-1) ... 309s Setting up lsof (4.95.0-1build2) ... 309s Setting up python3-pyrsistent:s390x (0.20.0-1build1) ... 309s Setting up python3-netaddr (0.8.0-2ubuntu1) ... 309s Setting up kpartx (0.9.4-5ubuntu6) ... 309s Setting up libnsl2:s390x (1.3.0-3build2) ... 309s Setting up gnupg (2.4.4-2ubuntu15) ... 309s Setting up python3-netplan (1.0-1) ... 309s Setting up curl (8.5.0-2ubuntu8) ... 309s Setting up libvolume-key1:s390x (0.3.12-7build1) ... 309s Setting up linux-image-virtual (6.8.0-20.20+1) ... 309s Setting up netplan-generator (1.0-1) ... 309s Removing 'diversion of /lib/systemd/system-generators/netplan to /lib/systemd/system-generators/netplan.usr-is-merged by netplan-generator' 309s Setting up bind9-host (1:9.18.24-0ubuntu3) ... 309s Setting up python3-lib2to3 (3.12.2-3ubuntu1.1) ... 310s Setting up python3-bpfcc (0.29.1+ds-1ubuntu4) ... 310s Setting up libc-devtools (2.39-0ubuntu6) ... 310s Setting up systemd-resolved (255.4-1ubuntu5) ... 311s Setting up python3-pkg-resources (68.1.2-2ubuntu1) ... 312s Setting up python3-distutils (3.12.2-3ubuntu1.1) ... 312s python3.12: can't get files for byte-compilation 312s Setting up openssh-sftp-server (1:9.6p1-3ubuntu11) ... 312s Setting up linux-image-generic (6.8.0-20.20+1) ... 312s Setting up systemd-sysv (255.4-1ubuntu5) ... 312s Setting up python3-dbus (1.3.2-5build2) ... 312s Setting up python3-setuptools (68.1.2-2ubuntu1) ... 313s Setting up gpg-wks-client (2.4.4-2ubuntu15) ... 313s Setting up openssh-server (1:9.6p1-3ubuntu11) ... 313s Replacing config file /etc/ssh/sshd_config with new version 316s Created symlink /etc/systemd/system/ssh.service.requires/ssh.socket → /usr/lib/systemd/system/ssh.socket. 317s Setting up linux-generic (6.8.0-20.20+1) ... 317s Setting up libblockdev-crypto3:s390x (3.1.0-1build1) ... 317s Setting up python3-gdbm:s390x (3.12.2-3ubuntu1.1) ... 317s Setting up plymouth (24.004.60-1ubuntu6) ... 317s update-initramfs: Generating /boot/initrd.img-6.8.0-11-generic 317s W: No lz4 in /usr/bin:/sbin:/bin, using gzip 323s Not invoking zipl: initrd doesn't exist yet 323s update-rc.d: warning: start and stop actions are no longer supported; falling back to defaults 323s update-rc.d: warning: start and stop actions are no longer supported; falling back to defaults 324s Setting up python3-apt (2.7.7) ... 324s Setting up libfwupd2:s390x (1.9.15-2) ... 324s Setting up libnss-systemd:s390x (255.4-1ubuntu5) ... 324s Setting up libqmi-proxy (1.35.2-0ubuntu1) ... 324s Setting up multipath-tools (0.9.4-5ubuntu6) ... 325s Setting up netplan.io (1.0-1) ... 325s Setting up linux-virtual (6.8.0-20.20+1) ... 325s Setting up plymouth-theme-ubuntu-text (24.004.60-1ubuntu6) ... 325s update-initramfs: deferring update (trigger activated) 325s Setting up bpfcc-tools (0.29.1+ds-1ubuntu4) ... 325s Setting up libpam-systemd:s390x (255.4-1ubuntu5) ... 325s Setting up bind9-dnsutils (1:9.18.24-0ubuntu3) ... 325s Setting up ubuntu-pro-client (31.2.2) ... 328s Setting up fwupd (1.9.15-2) ... 328s fwupd-offline-update.service is a disabled or a static unit not running, not starting it. 328s fwupd-refresh.service is a disabled or a static unit not running, not starting it. 329s Setting up ubuntu-pro-client-l10n (31.2.2) ... 329s Setting up udisks2 (2.10.1-6) ... 330s Setting up dbus-user-session (1.14.10-4ubuntu2) ... 330s Processing triggers for install-info (7.1-3build1) ... 330s Processing triggers for initramfs-tools (0.142ubuntu23) ... 330s update-initramfs: Generating /boot/initrd.img-6.8.0-11-generic 330s W: No lz4 in /usr/bin:/sbin:/bin, using gzip 336s Not invoking zipl: initrd doesn't exist yet 336s Processing triggers for libc-bin (2.39-0ubuntu6) ... 336s Processing triggers for ufw (0.36.2-5) ... 336s Processing triggers for debianutils (5.17) ... 336s Processing triggers for linux-image-6.8.0-20-generic (6.8.0-20.20) ... 336s /etc/kernel/postinst.d/initramfs-tools: 336s update-initramfs: Generating /boot/initrd.img-6.8.0-20-generic 336s W: No lz4 in /usr/bin:/sbin:/bin, using gzip 342s Using config file '/etc/zipl.conf' 342s Building bootmap in '/boot' 342s Adding IPL section 'ubuntu' (default) 342s Preparing boot device for LD-IPL: vda (0000). 342s Done. 342s /etc/kernel/postinst.d/zz-zipl: 342s Using config file '/etc/zipl.conf' 342s Building bootmap in '/boot' 342s Adding IPL section 'ubuntu' (default) 342s Preparing boot device for LD-IPL: vda (0000). 342s Done. 344s Reading package lists... 344s Building dependency tree... 344s Reading state information... 344s The following packages will be REMOVED: 344s libaio1* libnetplan0* python3-distutils* python3-lib2to3* 345s 0 upgraded, 0 newly installed, 4 to remove and 1 not upgraded. 345s After this operation, 1445 kB disk space will be freed. 345s (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 ... 81787 files and directories currently installed.) 345s Removing libaio1:s390x (0.3.113-5) ... 345s Removing libnetplan0:s390x (0.107.1-3) ... 345s Removing python3-distutils (3.12.2-3ubuntu1.1) ... 345s Removing python3-lib2to3 (3.12.2-3ubuntu1.1) ... 345s Processing triggers for libc-bin (2.39-0ubuntu6) ... 346s autopkgtest [02:29:17]: rebooting testbed after setup commands that affected boot 379s autopkgtest [02:29:50]: testbed running kernel: Linux 6.8.0-20-generic #20-Ubuntu SMP Mon Mar 18 10:49:25 UTC 2024 382s autopkgtest [02:29:53]: @@@@@@@@@@@@@@@@@@@@ apt-source r-cran-systemfit 385s Get:1 http://ftpmaster.internal/ubuntu noble/universe r-cran-systemfit 1.1-30-1 (dsc) [2203 B] 385s Get:2 http://ftpmaster.internal/ubuntu noble/universe r-cran-systemfit 1.1-30-1 (tar) [1040 kB] 385s Get:3 http://ftpmaster.internal/ubuntu noble/universe r-cran-systemfit 1.1-30-1 (diff) [2516 B] 385s gpgv: Signature made Wed Jun 28 12:43:54 2023 UTC 385s gpgv: using RSA key F1F007320A035541F0A663CA578A0494D1C646D1 385s gpgv: issuer "tille@debian.org" 385s gpgv: Can't check signature: No public key 385s dpkg-source: warning: cannot verify inline signature for ./r-cran-systemfit_1.1-30-1.dsc: no acceptable signature found 385s autopkgtest [02:29:56]: testing package r-cran-systemfit version 1.1-30-1 385s autopkgtest [02:29:56]: build not needed 388s autopkgtest [02:29:59]: test run-unit-test: preparing testbed 392s Reading package lists... 392s Building dependency tree... 392s Reading state information... 393s Starting pkgProblemResolver with broken count: 0 393s Starting 2 pkgProblemResolver with broken count: 0 393s Done 393s The following additional packages will be installed: 393s fontconfig fonts-glyphicons-halflings fonts-mathjax libblas3 libcairo2 393s libdatrie1 libgfortran5 libgomp1 libgraphite2-3 libharfbuzz0b libice6 393s libjs-bootstrap libjs-highlight.js libjs-jquery libjs-jquery-datatables 393s libjs-mathjax liblapack3 libnlopt0 libpango-1.0-0 libpangocairo-1.0-0 393s libpangoft2-1.0-0 libpaper-utils libpaper1 libpixman-1-0 libsm6 libtcl8.6 393s libthai-data libthai0 libtk8.6 libxcb-render0 libxcb-shm0 libxft2 393s libxrender1 libxss1 libxt6t64 littler node-normalize.css r-base-core 393s r-cran-abind r-cran-backports r-cran-bdsmatrix r-cran-bit r-cran-bit64 393s r-cran-boot r-cran-brio r-cran-broom r-cran-callr r-cran-car r-cran-cardata 393s r-cran-caret r-cran-cellranger r-cran-class r-cran-cli r-cran-clipr 393s r-cran-clock r-cran-codetools r-cran-collapse r-cran-colorspace 393s r-cran-conquer r-cran-cpp11 r-cran-crayon r-cran-curl r-cran-data.table 393s r-cran-desc r-cran-diagram r-cran-diffobj r-cran-digest r-cran-dplyr 393s r-cran-e1071 r-cran-ellipsis r-cran-evaluate r-cran-fansi r-cran-farver 393s r-cran-forcats r-cran-foreach r-cran-foreign r-cran-formula r-cran-fs 393s r-cran-future r-cran-future.apply r-cran-generics r-cran-ggplot2 393s r-cran-globals r-cran-glue r-cran-gower r-cran-gtable r-cran-hardhat 393s r-cran-haven r-cran-highr r-cran-hms r-cran-ipred r-cran-isoband 393s r-cran-iterators r-cran-jsonlite r-cran-kernsmooth r-cran-knitr 393s r-cran-labeling r-cran-lattice r-cran-lava r-cran-lifecycle r-cran-listenv 393s r-cran-littler r-cran-lme4 r-cran-lmtest r-cran-lubridate r-cran-magrittr 393s r-cran-maptools r-cran-mass r-cran-matrix r-cran-matrixmodels 393s r-cran-matrixstats r-cran-maxlik r-cran-mgcv r-cran-minqa r-cran-misctools 393s r-cran-modelmetrics r-cran-munsell r-cran-nlme r-cran-nloptr r-cran-nnet 393s r-cran-numderiv r-cran-openxlsx r-cran-parallelly r-cran-pbkrtest 393s r-cran-pillar r-cran-pkgbuild r-cran-pkgconfig r-cran-pkgkitten 393s r-cran-pkgload r-cran-plm r-cran-plyr r-cran-praise r-cran-prettyunits 393s r-cran-proc r-cran-processx r-cran-prodlim r-cran-progress r-cran-progressr 393s r-cran-proxy r-cran-ps r-cran-purrr r-cran-quantreg r-cran-r.methodss3 393s r-cran-r.oo r-cran-r.utils r-cran-r6 r-cran-rbibutils r-cran-rcolorbrewer 393s r-cran-rcpp r-cran-rcpparmadillo r-cran-rcppeigen r-cran-rdpack r-cran-readr 393s r-cran-readxl r-cran-recipes r-cran-rematch r-cran-rematch2 r-cran-reshape2 393s r-cran-rio r-cran-rlang r-cran-rpart r-cran-rprojroot r-cran-sandwich 393s r-cran-scales r-cran-shape r-cran-sp r-cran-sparsem r-cran-squarem 393s r-cran-statmod r-cran-stringi r-cran-stringr r-cran-survival 393s r-cran-systemfit r-cran-testthat r-cran-tibble r-cran-tidyr 393s r-cran-tidyselect r-cran-timechange r-cran-timedate r-cran-tzdb r-cran-utf8 393s r-cran-vctrs r-cran-viridislite r-cran-vroom r-cran-waldo r-cran-withr 393s r-cran-writexl r-cran-xfun r-cran-yaml r-cran-zip r-cran-zoo unzip 393s x11-common xdg-utils zip 393s Suggested packages: 393s fonts-mathjax-extras fonts-stix libjs-mathjax-doc tcl8.6 tk8.6 393s libjs-html5shiv elpa-ess r-doc-info | r-doc-pdf r-mathlib r-base-html 393s r-cran-roxygen2 r-cran-rmarkdown r-cran-ff r-cran-aer r-cran-bbmle 393s r-cran-cluster r-cran-cmprsk r-cran-coda r-cran-covr r-cran-emmeans 393s r-cran-epir r-cran-gam r-cran-gee r-cran-geepack r-cran-glmnet r-cran-gmm 393s r-cran-hmisc r-cran-irlba r-cran-interp r-cran-ks r-cran-lavaan r-cran-leaps 393s r-cran-lsmeans r-cran-maps r-cran-mclust r-cran-metafor r-cran-modeldata 393s r-cran-multcomp r-cran-network r-cran-ordinal r-cran-psych r-cran-robust 393s r-cran-robustbase r-cran-rsample r-cran-spdep r-cran-spatialreg 393s r-cran-spelling r-cran-survey r-cran-tseries r-cran-bradleyterry2 393s r-cran-ellipse r-cran-mlbench r-cran-party r-cran-pls r-cran-randomforest 393s r-cran-rann r-cran-rstudioapi r-cran-slider r-cran-kernlab r-cran-mvtnorm 393s r-cran-vcd r-cran-shiny r-cran-shinyjs r-cran-png r-cran-jpeg r-cran-viridis 393s r-cran-tinytest r-cran-markdown r-cran-th.data r-cran-magick r-cran-sf 393s r-cran-getopt r-cran-rgeos r-cran-spatstat.geom r-cran-raster 393s r-cran-polyclip r-cran-plotrix r-cran-spatstat.linnet r-cran-spatstat.utils 393s r-cran-spatstat r-cran-clue r-cran-dbi r-cran-formattable r-cran-nanotime 393s r-cran-palmerpenguins r-cran-units r-cran-vdiffr r-cran-inline r-cran-sem 393s r-cran-bench r-cran-blob r-cran-here r-cran-htmltools r-cran-runit 393s Recommended packages: 393s javascript-common r-recommended r-base-dev r-doc-html r-cran-covr 393s r-cran-mockery r-cran-spelling r-cran-earth r-cran-mda r-cran-mlmetrics 393s r-cran-fastica r-cran-kernlab r-cran-themis r-cran-htmltools 393s r-cran-htmlwidgets r-cran-rmarkdown r-cran-rstudioapi r-cran-whoami 393s r-cran-xts r-cran-bench r-cran-decor r-cran-lobstr r-cran-later 393s r-cran-httpuv r-cran-webutils r-cran-nanotime r-cran-gh r-cran-dbi 393s r-cran-dbplyr r-cran-rmysql r-cran-rpostgresql r-cran-rsqlite 393s r-cran-unitizer r-cran-rhpcblasctl r-cran-r.rsp r-cran-markdown 393s r-cran-hexbin r-cran-hmisc r-cran-mapproj r-cran-maps r-cran-multcomp 393s r-cran-profvis r-cran-ragg r-cran-sf r-cran-svglite r-cran-vdiffr 393s r-cran-xml2 r-cran-devtools r-cran-modeldata r-cran-roxygen2 r-cran-usethis 393s r-cran-testit r-cran-mlbench r-cran-httr r-cran-bslib r-cran-formatr 393s r-cran-gridsvg r-cran-jpeg r-cran-magick r-cran-png r-cran-reticulate 393s r-cran-rgl r-cran-sass r-cran-tikzdevice r-cran-tinytex r-cran-webshot 393s node-highlight.js r-cran-ellipse r-cran-fields r-cran-geepack r-bioc-graph 393s r-cran-bookdown r-cran-igraph r-cran-lavasearch2 r-cran-mets r-cran-optimx 393s r-cran-polycor r-cran-lintr r-cran-tidyverse r-cran-base64enc 393s r-cran-r.devices r-cran-runit r-cran-bitops r-cran-mathjaxr r-cran-mockr 393s r-cran-remotes r-cran-aer r-cran-spdep r-cran-urca r-cran-doparallel 393s r-cran-itertools r-cran-logcondens r-cran-webfakes r-cran-pbmcapply 393s r-cran-furrr r-cran-shiny r-cran-commonmark r-cran-cba r-cran-pingr 393s r-cran-gbrd r-cran-ddalpha r-cran-dials r-cran-rann r-cran-rcpproll 393s r-cran-rsample r-cran-rspectra r-cran-splines2 r-cran-dichromat 393s r-cran-deldir r-cran-terra r-cran-raster r-cran-setrng r-cran-formattable 393s r-cran-pkgdown r-cran-zeallot r-cran-mime r-cran-renv libfile-mimeinfo-perl 393s libnet-dbus-perl libx11-protocol-perl x11-utils x11-xserver-utils 393s The following NEW packages will be installed: 393s autopkgtest-satdep fontconfig fonts-glyphicons-halflings fonts-mathjax 393s libblas3 libcairo2 libdatrie1 libgfortran5 libgomp1 libgraphite2-3 393s libharfbuzz0b libice6 libjs-bootstrap libjs-highlight.js libjs-jquery 393s libjs-jquery-datatables libjs-mathjax liblapack3 libnlopt0 libpango-1.0-0 393s libpangocairo-1.0-0 libpangoft2-1.0-0 libpaper-utils libpaper1 libpixman-1-0 393s libsm6 libtcl8.6 libthai-data libthai0 libtk8.6 libxcb-render0 libxcb-shm0 393s libxft2 libxrender1 libxss1 libxt6t64 littler node-normalize.css r-base-core 393s r-cran-abind r-cran-backports r-cran-bdsmatrix r-cran-bit r-cran-bit64 393s r-cran-boot r-cran-brio r-cran-broom r-cran-callr r-cran-car r-cran-cardata 393s r-cran-caret r-cran-cellranger r-cran-class r-cran-cli r-cran-clipr 393s r-cran-clock r-cran-codetools r-cran-collapse r-cran-colorspace 393s r-cran-conquer r-cran-cpp11 r-cran-crayon r-cran-curl r-cran-data.table 393s r-cran-desc r-cran-diagram r-cran-diffobj r-cran-digest r-cran-dplyr 393s r-cran-e1071 r-cran-ellipsis r-cran-evaluate r-cran-fansi r-cran-farver 393s r-cran-forcats r-cran-foreach r-cran-foreign r-cran-formula r-cran-fs 393s r-cran-future r-cran-future.apply r-cran-generics r-cran-ggplot2 393s r-cran-globals r-cran-glue r-cran-gower r-cran-gtable r-cran-hardhat 393s r-cran-haven r-cran-highr r-cran-hms r-cran-ipred r-cran-isoband 393s r-cran-iterators r-cran-jsonlite r-cran-kernsmooth r-cran-knitr 393s r-cran-labeling r-cran-lattice r-cran-lava r-cran-lifecycle r-cran-listenv 393s r-cran-littler r-cran-lme4 r-cran-lmtest r-cran-lubridate r-cran-magrittr 393s r-cran-maptools r-cran-mass r-cran-matrix r-cran-matrixmodels 393s r-cran-matrixstats r-cran-maxlik r-cran-mgcv r-cran-minqa r-cran-misctools 393s r-cran-modelmetrics r-cran-munsell r-cran-nlme r-cran-nloptr r-cran-nnet 393s r-cran-numderiv r-cran-openxlsx r-cran-parallelly r-cran-pbkrtest 393s r-cran-pillar r-cran-pkgbuild r-cran-pkgconfig r-cran-pkgkitten 393s r-cran-pkgload r-cran-plm r-cran-plyr r-cran-praise r-cran-prettyunits 393s r-cran-proc r-cran-processx r-cran-prodlim r-cran-progress r-cran-progressr 393s r-cran-proxy r-cran-ps r-cran-purrr r-cran-quantreg r-cran-r.methodss3 393s r-cran-r.oo r-cran-r.utils r-cran-r6 r-cran-rbibutils r-cran-rcolorbrewer 393s r-cran-rcpp r-cran-rcpparmadillo r-cran-rcppeigen r-cran-rdpack r-cran-readr 393s r-cran-readxl r-cran-recipes r-cran-rematch r-cran-rematch2 r-cran-reshape2 393s r-cran-rio r-cran-rlang r-cran-rpart r-cran-rprojroot r-cran-sandwich 393s r-cran-scales r-cran-shape r-cran-sp r-cran-sparsem r-cran-squarem 393s r-cran-statmod r-cran-stringi r-cran-stringr r-cran-survival 393s r-cran-systemfit r-cran-testthat r-cran-tibble r-cran-tidyr 393s r-cran-tidyselect r-cran-timechange r-cran-timedate r-cran-tzdb r-cran-utf8 393s r-cran-vctrs r-cran-viridislite r-cran-vroom r-cran-waldo r-cran-withr 393s r-cran-writexl r-cran-xfun r-cran-yaml r-cran-zip r-cran-zoo unzip 393s x11-common xdg-utils zip 393s 0 upgraded, 196 newly installed, 0 to remove and 1 not upgraded. 393s Need to get 162 MB/162 MB of archives. 393s After this operation, 330 MB of additional disk space will be used. 393s Get:1 /tmp/autopkgtest.4VHV7n/1-autopkgtest-satdep.deb autopkgtest-satdep s390x 0 [720 B] 394s Get:2 http://ftpmaster.internal/ubuntu noble/main s390x fontconfig s390x 2.15.0-1.1ubuntu1 [191 kB] 394s Get:3 http://ftpmaster.internal/ubuntu noble/universe s390x fonts-glyphicons-halflings all 1.009~3.4.1+dfsg-3 [118 kB] 394s Get:4 http://ftpmaster.internal/ubuntu noble/main s390x fonts-mathjax all 2.7.9+dfsg-1 [2208 kB] 396s Get:5 http://ftpmaster.internal/ubuntu noble/main s390x libblas3 s390x 3.12.0-3 [245 kB] 396s Get:6 http://ftpmaster.internal/ubuntu noble/main s390x libpixman-1-0 s390x 0.42.2-1 [173 kB] 396s Get:7 http://ftpmaster.internal/ubuntu noble/main s390x libxcb-render0 s390x 1.15-1 [17.0 kB] 396s Get:8 http://ftpmaster.internal/ubuntu noble/main s390x libxcb-shm0 s390x 1.15-1 [5782 B] 396s Get:9 http://ftpmaster.internal/ubuntu noble/main s390x libxrender1 s390x 1:0.9.10-1.1 [19.4 kB] 396s Get:10 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libcairo2 s390x 1.18.0-1ubuntu1 [589 kB] 396s Get:11 http://ftpmaster.internal/ubuntu noble/main s390x libdatrie1 s390x 0.2.13-3 [22.6 kB] 396s Get:12 http://ftpmaster.internal/ubuntu noble/main s390x libgfortran5 s390x 14-20240315-1ubuntu1 [600 kB] 396s Get:13 http://ftpmaster.internal/ubuntu noble/main s390x libgomp1 s390x 14-20240315-1ubuntu1 [151 kB] 396s Get:14 http://ftpmaster.internal/ubuntu noble/main s390x libgraphite2-3 s390x 1.3.14-2 [90.4 kB] 396s Get:15 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libharfbuzz0b s390x 8.3.0-2build1 [515 kB] 396s Get:16 http://ftpmaster.internal/ubuntu noble/main s390x x11-common all 1:7.7+23ubuntu2 [23.4 kB] 396s Get:17 http://ftpmaster.internal/ubuntu noble/main s390x libice6 s390x 2:1.0.10-1build2 [40.8 kB] 396s Get:18 http://ftpmaster.internal/ubuntu noble/universe s390x libjs-bootstrap all 3.4.1+dfsg-3 [129 kB] 396s Get:19 http://ftpmaster.internal/ubuntu noble/universe s390x libjs-highlight.js all 9.18.5+dfsg1-2 [385 kB] 396s Get:20 http://ftpmaster.internal/ubuntu noble/main s390x libjs-jquery all 3.6.1+dfsg+~3.5.14-1 [328 kB] 396s Get:21 http://ftpmaster.internal/ubuntu noble/universe s390x libjs-jquery-datatables all 1.11.5+dfsg-2 [146 kB] 397s Get:22 http://ftpmaster.internal/ubuntu noble/main s390x liblapack3 s390x 3.12.0-3 [2979 kB] 397s Get:23 http://ftpmaster.internal/ubuntu noble/main s390x libthai-data all 0.1.29-2 [158 kB] 397s Get:24 http://ftpmaster.internal/ubuntu noble/main s390x libthai0 s390x 0.1.29-2 [20.6 kB] 397s Get:25 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libpango-1.0-0 s390x 1.52.1+ds-1 [242 kB] 397s Get:26 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libpangoft2-1.0-0 s390x 1.52.1+ds-1 [44.2 kB] 397s Get:27 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libpangocairo-1.0-0 s390x 1.52.1+ds-1 [28.7 kB] 397s Get:28 http://ftpmaster.internal/ubuntu noble/main s390x libpaper1 s390x 1.1.29 [13.3 kB] 397s Get:29 http://ftpmaster.internal/ubuntu noble/main s390x libpaper-utils s390x 1.1.29 [8452 B] 397s Get:30 http://ftpmaster.internal/ubuntu noble/main s390x libsm6 s390x 2:1.2.3-1build2 [16.9 kB] 397s Get:31 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libtcl8.6 s390x 8.6.14+dfsg-1 [1037 kB] 397s Get:32 http://ftpmaster.internal/ubuntu noble/main s390x libxft2 s390x 2.3.6-1 [44.3 kB] 397s Get:33 http://ftpmaster.internal/ubuntu noble/main s390x libxss1 s390x 1:1.2.3-1build2 [8192 B] 397s Get:34 http://ftpmaster.internal/ubuntu noble/main s390x libtk8.6 s390x 8.6.14-1 [833 kB] 397s Get:35 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libxt6t64 s390x 1:1.2.1-1.2 [184 kB] 397s Get:36 http://ftpmaster.internal/ubuntu noble/main s390x zip s390x 3.0-13 [175 kB] 397s Get:37 http://ftpmaster.internal/ubuntu noble/main s390x unzip s390x 6.0-28ubuntu3 [180 kB] 397s Get:38 http://ftpmaster.internal/ubuntu noble/main s390x xdg-utils all 1.1.3-4.1ubuntu3 [62.0 kB] 397s Get:39 http://ftpmaster.internal/ubuntu noble-proposed/universe s390x r-base-core s390x 4.3.3-2build1 [27.1 MB] 399s Get:40 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-littler s390x 0.3.19-1 [93.0 kB] 399s Get:41 http://ftpmaster.internal/ubuntu noble/universe s390x littler all 0.3.19-1 [2472 B] 399s Get:42 http://ftpmaster.internal/ubuntu noble/universe s390x node-normalize.css all 8.0.1-5 [10.8 kB] 399s Get:43 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-abind all 1.4-5-2 [63.6 kB] 399s Get:44 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-backports s390x 1.4.1-1 [101 kB] 399s Get:45 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-bdsmatrix s390x 1.3-6-1 [295 kB] 399s Get:46 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-bit s390x 4.0.5-1 [1067 kB] 399s Get:47 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-bit64 s390x 4.0.5-1 [469 kB] 399s Get:48 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-boot all 1.3-30-1 [619 kB] 399s Get:49 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-brio s390x 1.1.4-1 [37.8 kB] 399s Get:50 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-cli s390x 3.6.2-1 [1382 kB] 399s Get:51 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-generics all 0.1.3-1 [81.3 kB] 399s Get:52 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-glue s390x 1.7.0-1 [154 kB] 399s Get:53 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-rlang s390x 1.1.3-1 [1672 kB] 399s Get:54 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-lifecycle all 1.0.4+dfsg-1 [110 kB] 399s Get:55 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-magrittr s390x 2.0.3-1 [154 kB] 399s Get:56 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-fansi s390x 1.0.5-1 [615 kB] 399s Get:57 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-utf8 s390x 1.2.4-1 [143 kB] 399s Get:58 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-vctrs s390x 0.6.5-1 [1448 kB] 399s Get:59 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-pillar all 1.9.0+dfsg-1 [464 kB] 399s Get:60 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-r6 all 2.5.1-1 [99.0 kB] 399s Get:61 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-pkgconfig all 2.0.3-2build1 [19.7 kB] 399s Get:62 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-tibble s390x 3.2.1+dfsg-2 [415 kB] 399s Get:63 http://ftpmaster.internal/ubuntu noble-proposed/universe s390x r-cran-withr all 3.0.0+dfsg-1 [228 kB] 399s Get:64 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-tidyselect s390x 1.2.0+dfsg-1 [218 kB] 399s Get:65 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-dplyr s390x 1.1.4-1 [1515 kB] 399s Get:66 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-ellipsis s390x 0.3.2-2 [35.4 kB] 399s Get:67 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-purrr s390x 1.0.2-1 [501 kB] 399s Get:68 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-stringi s390x 1.8.3-1 [885 kB] 399s Get:69 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-stringr all 1.5.1-1 [290 kB] 399s Get:70 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-cpp11 all 0.4.7-1 [266 kB] 399s Get:71 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-tidyr s390x 1.3.1-1 [1157 kB] 400s Get:72 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-broom all 1.0.5+dfsg-1 [1729 kB] 400s Get:73 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-ps s390x 1.7.6-1 [313 kB] 400s Get:74 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-processx s390x 3.8.3-1 [364 kB] 400s Get:75 http://ftpmaster.internal/ubuntu noble-proposed/universe s390x r-cran-callr all 3.7.5-1 [429 kB] 400s Get:76 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-cardata all 3.0.5-1 [1819 kB] 400s Get:77 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-mass s390x 7.3-60.0.1-1 [1122 kB] 400s Get:78 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-lattice s390x 0.22-5-1 [1341 kB] 400s Get:79 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-nlme s390x 3.1.164-1 [2268 kB] 400s Get:80 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-matrix s390x 1.6-5-1 [3983 kB] 400s Get:81 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-mgcv s390x 1.9-1-1 [3348 kB] 400s Get:82 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-nnet s390x 7.3-19-2 [112 kB] 400s Get:83 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-pkgkitten all 0.2.3-1 [25.1 kB] 400s Get:84 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-rcpp s390x 1.0.12-1 [1985 kB] 400s Get:85 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-minqa s390x 1.2.6-1 [134 kB] 400s Get:86 http://ftpmaster.internal/ubuntu noble/universe s390x libnlopt0 s390x 2.7.1-5build2 [223 kB] 400s Get:87 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-desc all 1.4.3-1 [359 kB] 400s Get:88 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-digest s390x 0.6.34-1 [187 kB] 400s Get:89 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-evaluate all 0.23-1 [90.2 kB] 400s Get:90 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-jsonlite s390x 1.8.8+dfsg-1 [444 kB] 400s Get:91 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-crayon all 1.5.2-1 [164 kB] 400s Get:92 http://ftpmaster.internal/ubuntu noble-proposed/universe s390x r-cran-fs s390x 1.6.3+dfsg-1build1 [229 kB] 400s Get:93 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-pkgbuild all 1.4.3-1 [209 kB] 400s Get:94 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-rprojroot all 2.0.4-1 [124 kB] 400s Get:95 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-pkgload all 1.3.4-1 [207 kB] 400s Get:96 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-praise all 1.0.0-4build1 [20.3 kB] 400s Get:97 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-diffobj s390x 0.3.5-1 [1115 kB] 400s Get:98 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-rematch2 all 2.1.2-2build1 [46.5 kB] 400s Get:99 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-waldo all 0.5.2-1build1 [120 kB] 400s Get:100 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-testthat s390x 3.2.1-1 [1688 kB] 400s Get:101 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-nloptr s390x 2.0.3-1 [373 kB] 400s Get:102 http://ftpmaster.internal/ubuntu noble-proposed/universe s390x r-cran-rcppeigen s390x 0.3.4.0.0-1 [1428 kB] 401s Get:103 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-statmod s390x 1.5.0-1 [294 kB] 401s Get:104 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-lme4 s390x 1.1-35.1-4 [4143 kB] 401s Get:105 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-numderiv all 2016.8-1.1-3 [115 kB] 401s Get:106 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-xfun s390x 0.41+dfsg-1 [415 kB] 401s Get:107 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-highr all 0.10+dfsg-1 [38.3 kB] 401s Get:108 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-yaml s390x 2.3.8-1 [127 kB] 401s Get:109 http://ftpmaster.internal/ubuntu noble/main s390x libjs-mathjax all 2.7.9+dfsg-1 [5665 kB] 402s Get:110 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-knitr all 1.45+dfsg-1 [917 kB] 402s Get:111 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-pbkrtest all 0.5.2-2 [182 kB] 402s Get:112 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-sparsem s390x 1.81-1 [910 kB] 402s Get:113 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-matrixmodels all 0.5-3-1 [361 kB] 402s Get:114 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-survival s390x 3.5-8-1 [6161 kB] 402s Get:115 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-matrixstats s390x 1.2.0-1 [513 kB] 402s Get:116 http://ftpmaster.internal/ubuntu noble-proposed/universe s390x r-cran-rcpparmadillo s390x 0.12.8.1.0-1 [869 kB] 402s Get:117 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-gtable all 0.3.4+dfsg-1 [191 kB] 402s Get:118 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-isoband s390x 0.2.7-1 [1481 kB] 402s Get:119 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-farver s390x 2.1.1-1 [1347 kB] 402s Get:120 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-labeling all 0.4.3-1 [62.1 kB] 402s Get:121 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-colorspace s390x 2.1-0+dfsg-1 [1540 kB] 402s Get:122 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-munsell all 0.5.0-2build1 [208 kB] 402s Get:123 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-rcolorbrewer all 1.1-3-1build1 [55.4 kB] 402s Get:124 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-viridislite all 0.4.2-2 [1088 kB] 402s Get:125 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-scales all 1.3.0-1 [603 kB] 402s Get:126 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-ggplot2 all 3.4.4+dfsg-1 [3411 kB] 402s Get:127 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-class s390x 7.3-22-2 [88.6 kB] 402s Get:128 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-proxy s390x 0.4-27-1 [182 kB] 402s Get:129 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-e1071 s390x 1.7-14-1 [566 kB] 402s Get:130 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-codetools all 0.2-19-1 [90.5 kB] 402s Get:131 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-iterators all 1.0.14-1 [336 kB] 402s Get:132 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-foreach all 1.5.2-1 [124 kB] 402s Get:133 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-data.table s390x 1.14.10+dfsg-1 [1880 kB] 402s Get:134 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-modelmetrics s390x 1.2.2.2-1build1 [122 kB] 402s Get:135 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-plyr s390x 1.8.9-1 [835 kB] 402s Get:136 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-proc s390x 1.18.5-1 [968 kB] 402s Get:137 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-tzdb s390x 0.4.0-2 [514 kB] 402s Get:138 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-clock s390x 0.7.0-1.1 [1799 kB] 402s Get:139 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-gower s390x 1.0.1-1 [207 kB] 402s Get:140 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-hardhat all 1.3.1+dfsg-1 [554 kB] 402s Get:141 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-rpart s390x 4.1.23-1 [667 kB] 402s Get:142 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-shape all 1.4.6-1 [770 kB] 402s Get:143 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-diagram all 1.6.5-2 [656 kB] 402s Get:144 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-kernsmooth s390x 2.23-22-1 [93.0 kB] 402s Get:145 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-globals all 0.16.2-1 [117 kB] 402s Get:146 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-listenv all 0.9.1+dfsg-1 [112 kB] 402s Get:147 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-parallelly s390x 1.37.1-1 [365 kB] 403s Get:148 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-future all 1.33.1+dfsg-1 [634 kB] 403s Get:149 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-future.apply all 1.11.1+dfsg-1 [171 kB] 403s Get:150 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-progressr all 0.14.0-1 [338 kB] 403s Get:151 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-squarem all 2021.1-1 [179 kB] 403s Get:152 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-lava all 1.7.3+dfsg-1 [2166 kB] 403s Get:153 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-prodlim s390x 2023.08.28-1 [412 kB] 403s Get:154 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-ipred s390x 0.9-14-1 [383 kB] 403s Get:155 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-timechange s390x 0.3.0-1 [185 kB] 403s Get:156 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-lubridate s390x 1.9.3+dfsg-1 [1010 kB] 403s Get:157 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-timedate s390x 4032.109-1 [1229 kB] 403s Get:158 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-recipes all 1.0.9+dfsg-1 [1964 kB] 403s Get:159 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-reshape2 s390x 1.4.4-2build1 [111 kB] 403s Get:160 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-caret s390x 6.0-94+dfsg-1 [3433 kB] 403s Get:161 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-conquer s390x 1.3.3-1 [459 kB] 403s Get:162 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-quantreg s390x 5.97-1 [1550 kB] 403s Get:163 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-sp s390x 1:2.1-2+dfsg-1 [1449 kB] 403s Get:164 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-foreign s390x 0.8.86-1 [247 kB] 403s Get:165 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-maptools s390x 1:1.1-8+dfsg-1 [1367 kB] 403s Get:166 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-forcats all 1.0.0-1 [369 kB] 403s Get:167 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-hms all 1.1.3-1 [96.5 kB] 403s Get:168 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-clipr all 0.8.0-1 [53.5 kB] 403s Get:169 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-prettyunits all 1.2.0-1 [162 kB] 403s Get:170 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-progress all 1.2.3-1 [91.9 kB] 403s Get:171 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-vroom s390x 1.6.5-1 [865 kB] 403s Get:172 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-readr s390x 2.1.5-1 [778 kB] 403s Get:173 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-haven s390x 2.5.4-1 [355 kB] 403s Get:174 http://ftpmaster.internal/ubuntu noble-proposed/universe s390x r-cran-curl s390x 5.2.0+dfsg-1build1 [177 kB] 403s Get:175 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-rematch all 2.0.0-1 [18.3 kB] 403s Get:176 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-cellranger all 1.1.0-3 [102 kB] 403s Get:177 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-readxl s390x 1.4.3-1 [730 kB] 403s Get:178 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-writexl s390x 1.5.0-1 [160 kB] 403s Get:179 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-r.methodss3 all 1.8.2-1 [84.0 kB] 403s Get:180 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-r.oo all 1.26.0-1 [955 kB] 403s Get:181 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-r.utils all 2.12.3-1 [1386 kB] 403s Get:182 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-zip s390x 2.3.1-1 [130 kB] 403s Get:183 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-openxlsx s390x 4.2.5.2-1 [1938 kB] 403s Get:184 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-rio all 1.0.1-1 [529 kB] 403s Get:185 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-car all 3.1-2-2 [1692 kB] 403s Get:186 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-collapse s390x 2.0.10-1 [3232 kB] 404s Get:187 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-formula all 1.2-5-1 [158 kB] 404s Get:188 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-zoo s390x 1.8-12-2 [984 kB] 404s Get:189 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-lmtest s390x 0.9.40-1 [396 kB] 404s Get:190 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-misctools all 0.6-28-1 [99.9 kB] 404s Get:191 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-sandwich all 3.1-0-1 [1484 kB] 404s Get:192 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-maxlik all 1.5-2-1 [1550 kB] 404s Get:193 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-rbibutils s390x 2.2.16-1 [775 kB] 404s Get:194 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-rdpack all 2.6-1 [742 kB] 404s Get:195 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-plm all 2.6-3-1 [2141 kB] 404s Get:196 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-systemfit all 1.1-30-1 [1174 kB] 405s Preconfiguring packages ... 405s Fetched 162 MB in 10s (15.7 MB/s) 405s Selecting previously unselected package fontconfig. 405s (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 ... 81563 files and directories currently installed.) 405s Preparing to unpack .../000-fontconfig_2.15.0-1.1ubuntu1_s390x.deb ... 405s Unpacking fontconfig (2.15.0-1.1ubuntu1) ... 405s Selecting previously unselected package fonts-glyphicons-halflings. 405s Preparing to unpack .../001-fonts-glyphicons-halflings_1.009~3.4.1+dfsg-3_all.deb ... 405s Unpacking fonts-glyphicons-halflings (1.009~3.4.1+dfsg-3) ... 405s Selecting previously unselected package fonts-mathjax. 405s Preparing to unpack .../002-fonts-mathjax_2.7.9+dfsg-1_all.deb ... 405s Unpacking fonts-mathjax (2.7.9+dfsg-1) ... 405s Selecting previously unselected package libblas3:s390x. 405s Preparing to unpack .../003-libblas3_3.12.0-3_s390x.deb ... 405s Unpacking libblas3:s390x (3.12.0-3) ... 405s Selecting previously unselected package libpixman-1-0:s390x. 405s Preparing to unpack .../004-libpixman-1-0_0.42.2-1_s390x.deb ... 405s Unpacking libpixman-1-0:s390x (0.42.2-1) ... 405s Selecting previously unselected package libxcb-render0:s390x. 405s Preparing to unpack .../005-libxcb-render0_1.15-1_s390x.deb ... 405s Unpacking libxcb-render0:s390x (1.15-1) ... 405s Selecting previously unselected package libxcb-shm0:s390x. 405s Preparing to unpack .../006-libxcb-shm0_1.15-1_s390x.deb ... 405s Unpacking libxcb-shm0:s390x (1.15-1) ... 405s Selecting previously unselected package libxrender1:s390x. 405s Preparing to unpack .../007-libxrender1_1%3a0.9.10-1.1_s390x.deb ... 405s Unpacking libxrender1:s390x (1:0.9.10-1.1) ... 405s Selecting previously unselected package libcairo2:s390x. 405s Preparing to unpack .../008-libcairo2_1.18.0-1ubuntu1_s390x.deb ... 405s Unpacking libcairo2:s390x (1.18.0-1ubuntu1) ... 405s Selecting previously unselected package libdatrie1:s390x. 405s Preparing to unpack .../009-libdatrie1_0.2.13-3_s390x.deb ... 405s Unpacking libdatrie1:s390x (0.2.13-3) ... 405s Selecting previously unselected package libgfortran5:s390x. 405s Preparing to unpack .../010-libgfortran5_14-20240315-1ubuntu1_s390x.deb ... 405s Unpacking libgfortran5:s390x (14-20240315-1ubuntu1) ... 405s Selecting previously unselected package libgomp1:s390x. 405s Preparing to unpack .../011-libgomp1_14-20240315-1ubuntu1_s390x.deb ... 405s Unpacking libgomp1:s390x (14-20240315-1ubuntu1) ... 405s Selecting previously unselected package libgraphite2-3:s390x. 405s Preparing to unpack .../012-libgraphite2-3_1.3.14-2_s390x.deb ... 405s Unpacking libgraphite2-3:s390x (1.3.14-2) ... 405s Selecting previously unselected package libharfbuzz0b:s390x. 405s Preparing to unpack .../013-libharfbuzz0b_8.3.0-2build1_s390x.deb ... 405s Unpacking libharfbuzz0b:s390x (8.3.0-2build1) ... 405s Selecting previously unselected package x11-common. 405s Preparing to unpack .../014-x11-common_1%3a7.7+23ubuntu2_all.deb ... 405s Unpacking x11-common (1:7.7+23ubuntu2) ... 405s Selecting previously unselected package libice6:s390x. 405s Preparing to unpack .../015-libice6_2%3a1.0.10-1build2_s390x.deb ... 405s Unpacking libice6:s390x (2:1.0.10-1build2) ... 405s Selecting previously unselected package libjs-bootstrap. 405s Preparing to unpack .../016-libjs-bootstrap_3.4.1+dfsg-3_all.deb ... 405s Unpacking libjs-bootstrap (3.4.1+dfsg-3) ... 405s Selecting previously unselected package libjs-highlight.js. 405s Preparing to unpack .../017-libjs-highlight.js_9.18.5+dfsg1-2_all.deb ... 405s Unpacking libjs-highlight.js (9.18.5+dfsg1-2) ... 406s Selecting previously unselected package libjs-jquery. 406s Preparing to unpack .../018-libjs-jquery_3.6.1+dfsg+~3.5.14-1_all.deb ... 406s Unpacking libjs-jquery (3.6.1+dfsg+~3.5.14-1) ... 406s Selecting previously unselected package libjs-jquery-datatables. 406s Preparing to unpack .../019-libjs-jquery-datatables_1.11.5+dfsg-2_all.deb ... 406s Unpacking libjs-jquery-datatables (1.11.5+dfsg-2) ... 406s Selecting previously unselected package liblapack3:s390x. 406s Preparing to unpack .../020-liblapack3_3.12.0-3_s390x.deb ... 406s Unpacking liblapack3:s390x (3.12.0-3) ... 406s Selecting previously unselected package libthai-data. 406s Preparing to unpack .../021-libthai-data_0.1.29-2_all.deb ... 406s Unpacking libthai-data (0.1.29-2) ... 406s Selecting previously unselected package libthai0:s390x. 406s Preparing to unpack .../022-libthai0_0.1.29-2_s390x.deb ... 406s Unpacking libthai0:s390x (0.1.29-2) ... 406s Selecting previously unselected package libpango-1.0-0:s390x. 406s Preparing to unpack .../023-libpango-1.0-0_1.52.1+ds-1_s390x.deb ... 406s Unpacking libpango-1.0-0:s390x (1.52.1+ds-1) ... 406s Selecting previously unselected package libpangoft2-1.0-0:s390x. 406s Preparing to unpack .../024-libpangoft2-1.0-0_1.52.1+ds-1_s390x.deb ... 406s Unpacking libpangoft2-1.0-0:s390x (1.52.1+ds-1) ... 406s Selecting previously unselected package libpangocairo-1.0-0:s390x. 406s Preparing to unpack .../025-libpangocairo-1.0-0_1.52.1+ds-1_s390x.deb ... 406s Unpacking libpangocairo-1.0-0:s390x (1.52.1+ds-1) ... 406s Selecting previously unselected package libpaper1:s390x. 406s Preparing to unpack .../026-libpaper1_1.1.29_s390x.deb ... 406s Unpacking libpaper1:s390x (1.1.29) ... 406s Selecting previously unselected package libpaper-utils. 406s Preparing to unpack .../027-libpaper-utils_1.1.29_s390x.deb ... 406s Unpacking libpaper-utils (1.1.29) ... 406s Selecting previously unselected package libsm6:s390x. 406s Preparing to unpack .../028-libsm6_2%3a1.2.3-1build2_s390x.deb ... 406s Unpacking libsm6:s390x (2:1.2.3-1build2) ... 406s Selecting previously unselected package libtcl8.6:s390x. 406s Preparing to unpack .../029-libtcl8.6_8.6.14+dfsg-1_s390x.deb ... 406s Unpacking libtcl8.6:s390x (8.6.14+dfsg-1) ... 406s Selecting previously unselected package libxft2:s390x. 406s Preparing to unpack .../030-libxft2_2.3.6-1_s390x.deb ... 406s Unpacking libxft2:s390x (2.3.6-1) ... 406s Selecting previously unselected package libxss1:s390x. 406s Preparing to unpack .../031-libxss1_1%3a1.2.3-1build2_s390x.deb ... 406s Unpacking libxss1:s390x (1:1.2.3-1build2) ... 406s Selecting previously unselected package libtk8.6:s390x. 406s Preparing to unpack .../032-libtk8.6_8.6.14-1_s390x.deb ... 406s Unpacking libtk8.6:s390x (8.6.14-1) ... 407s Selecting previously unselected package libxt6t64:s390x. 407s Preparing to unpack .../033-libxt6t64_1%3a1.2.1-1.2_s390x.deb ... 407s Unpacking libxt6t64:s390x (1:1.2.1-1.2) ... 407s Selecting previously unselected package zip. 407s Preparing to unpack .../034-zip_3.0-13_s390x.deb ... 407s Unpacking zip (3.0-13) ... 407s Selecting previously unselected package unzip. 407s Preparing to unpack .../035-unzip_6.0-28ubuntu3_s390x.deb ... 407s Unpacking unzip (6.0-28ubuntu3) ... 407s Selecting previously unselected package xdg-utils. 407s Preparing to unpack .../036-xdg-utils_1.1.3-4.1ubuntu3_all.deb ... 407s Unpacking xdg-utils (1.1.3-4.1ubuntu3) ... 407s Selecting previously unselected package r-base-core. 407s Preparing to unpack .../037-r-base-core_4.3.3-2build1_s390x.deb ... 407s Unpacking r-base-core (4.3.3-2build1) ... 407s Selecting previously unselected package r-cran-littler. 407s Preparing to unpack .../038-r-cran-littler_0.3.19-1_s390x.deb ... 407s Unpacking r-cran-littler (0.3.19-1) ... 407s Selecting previously unselected package littler. 407s Preparing to unpack .../039-littler_0.3.19-1_all.deb ... 407s Unpacking littler (0.3.19-1) ... 407s Selecting previously unselected package node-normalize.css. 407s Preparing to unpack .../040-node-normalize.css_8.0.1-5_all.deb ... 407s Unpacking node-normalize.css (8.0.1-5) ... 407s Selecting previously unselected package r-cran-abind. 407s Preparing to unpack .../041-r-cran-abind_1.4-5-2_all.deb ... 407s Unpacking r-cran-abind (1.4-5-2) ... 407s Selecting previously unselected package r-cran-backports. 407s Preparing to unpack .../042-r-cran-backports_1.4.1-1_s390x.deb ... 407s Unpacking r-cran-backports (1.4.1-1) ... 407s Selecting previously unselected package r-cran-bdsmatrix. 407s Preparing to unpack .../043-r-cran-bdsmatrix_1.3-6-1_s390x.deb ... 407s Unpacking r-cran-bdsmatrix (1.3-6-1) ... 407s Selecting previously unselected package r-cran-bit. 407s Preparing to unpack .../044-r-cran-bit_4.0.5-1_s390x.deb ... 407s Unpacking r-cran-bit (4.0.5-1) ... 407s Selecting previously unselected package r-cran-bit64. 407s Preparing to unpack .../045-r-cran-bit64_4.0.5-1_s390x.deb ... 407s Unpacking r-cran-bit64 (4.0.5-1) ... 407s Selecting previously unselected package r-cran-boot. 407s Preparing to unpack .../046-r-cran-boot_1.3-30-1_all.deb ... 407s Unpacking r-cran-boot (1.3-30-1) ... 407s Selecting previously unselected package r-cran-brio. 407s Preparing to unpack .../047-r-cran-brio_1.1.4-1_s390x.deb ... 407s Unpacking r-cran-brio (1.1.4-1) ... 407s Selecting previously unselected package r-cran-cli. 407s Preparing to unpack .../048-r-cran-cli_3.6.2-1_s390x.deb ... 407s Unpacking r-cran-cli (3.6.2-1) ... 407s Selecting previously unselected package r-cran-generics. 407s Preparing to unpack .../049-r-cran-generics_0.1.3-1_all.deb ... 407s Unpacking r-cran-generics (0.1.3-1) ... 407s Selecting previously unselected package r-cran-glue. 407s Preparing to unpack .../050-r-cran-glue_1.7.0-1_s390x.deb ... 407s Unpacking r-cran-glue (1.7.0-1) ... 407s Selecting previously unselected package r-cran-rlang. 407s Preparing to unpack .../051-r-cran-rlang_1.1.3-1_s390x.deb ... 407s Unpacking r-cran-rlang (1.1.3-1) ... 407s Selecting previously unselected package r-cran-lifecycle. 407s Preparing to unpack .../052-r-cran-lifecycle_1.0.4+dfsg-1_all.deb ... 407s Unpacking r-cran-lifecycle (1.0.4+dfsg-1) ... 407s Selecting previously unselected package r-cran-magrittr. 407s Preparing to unpack .../053-r-cran-magrittr_2.0.3-1_s390x.deb ... 407s Unpacking r-cran-magrittr (2.0.3-1) ... 407s Selecting previously unselected package r-cran-fansi. 407s Preparing to unpack .../054-r-cran-fansi_1.0.5-1_s390x.deb ... 407s Unpacking r-cran-fansi (1.0.5-1) ... 407s Selecting previously unselected package r-cran-utf8. 407s Preparing to unpack .../055-r-cran-utf8_1.2.4-1_s390x.deb ... 407s Unpacking r-cran-utf8 (1.2.4-1) ... 407s Selecting previously unselected package r-cran-vctrs. 407s Preparing to unpack .../056-r-cran-vctrs_0.6.5-1_s390x.deb ... 407s Unpacking r-cran-vctrs (0.6.5-1) ... 407s Selecting previously unselected package r-cran-pillar. 407s Preparing to unpack .../057-r-cran-pillar_1.9.0+dfsg-1_all.deb ... 407s Unpacking r-cran-pillar (1.9.0+dfsg-1) ... 407s Selecting previously unselected package r-cran-r6. 407s Preparing to unpack .../058-r-cran-r6_2.5.1-1_all.deb ... 407s Unpacking r-cran-r6 (2.5.1-1) ... 407s Selecting previously unselected package r-cran-pkgconfig. 407s Preparing to unpack .../059-r-cran-pkgconfig_2.0.3-2build1_all.deb ... 407s Unpacking r-cran-pkgconfig (2.0.3-2build1) ... 407s Selecting previously unselected package r-cran-tibble. 407s Preparing to unpack .../060-r-cran-tibble_3.2.1+dfsg-2_s390x.deb ... 407s Unpacking r-cran-tibble (3.2.1+dfsg-2) ... 407s Selecting previously unselected package r-cran-withr. 407s Preparing to unpack .../061-r-cran-withr_3.0.0+dfsg-1_all.deb ... 407s Unpacking r-cran-withr (3.0.0+dfsg-1) ... 407s Selecting previously unselected package r-cran-tidyselect. 407s Preparing to unpack .../062-r-cran-tidyselect_1.2.0+dfsg-1_s390x.deb ... 407s Unpacking r-cran-tidyselect (1.2.0+dfsg-1) ... 408s Selecting previously unselected package r-cran-dplyr. 408s Preparing to unpack .../063-r-cran-dplyr_1.1.4-1_s390x.deb ... 408s Unpacking r-cran-dplyr (1.1.4-1) ... 408s Selecting previously unselected package r-cran-ellipsis. 408s Preparing to unpack .../064-r-cran-ellipsis_0.3.2-2_s390x.deb ... 408s Unpacking r-cran-ellipsis (0.3.2-2) ... 408s Selecting previously unselected package r-cran-purrr. 408s Preparing to unpack .../065-r-cran-purrr_1.0.2-1_s390x.deb ... 408s Unpacking r-cran-purrr (1.0.2-1) ... 408s Selecting previously unselected package r-cran-stringi. 408s Preparing to unpack .../066-r-cran-stringi_1.8.3-1_s390x.deb ... 408s Unpacking r-cran-stringi (1.8.3-1) ... 408s Selecting previously unselected package r-cran-stringr. 408s Preparing to unpack .../067-r-cran-stringr_1.5.1-1_all.deb ... 408s Unpacking r-cran-stringr (1.5.1-1) ... 408s Selecting previously unselected package r-cran-cpp11. 408s Preparing to unpack .../068-r-cran-cpp11_0.4.7-1_all.deb ... 408s Unpacking r-cran-cpp11 (0.4.7-1) ... 408s Selecting previously unselected package r-cran-tidyr. 408s Preparing to unpack .../069-r-cran-tidyr_1.3.1-1_s390x.deb ... 408s Unpacking r-cran-tidyr (1.3.1-1) ... 408s Selecting previously unselected package r-cran-broom. 408s Preparing to unpack .../070-r-cran-broom_1.0.5+dfsg-1_all.deb ... 408s Unpacking r-cran-broom (1.0.5+dfsg-1) ... 408s Selecting previously unselected package r-cran-ps. 408s Preparing to unpack .../071-r-cran-ps_1.7.6-1_s390x.deb ... 408s Unpacking r-cran-ps (1.7.6-1) ... 408s Selecting previously unselected package r-cran-processx. 408s Preparing to unpack .../072-r-cran-processx_3.8.3-1_s390x.deb ... 408s Unpacking r-cran-processx (3.8.3-1) ... 408s Selecting previously unselected package r-cran-callr. 408s Preparing to unpack .../073-r-cran-callr_3.7.5-1_all.deb ... 408s Unpacking r-cran-callr (3.7.5-1) ... 408s Selecting previously unselected package r-cran-cardata. 408s Preparing to unpack .../074-r-cran-cardata_3.0.5-1_all.deb ... 408s Unpacking r-cran-cardata (3.0.5-1) ... 408s Selecting previously unselected package r-cran-mass. 408s Preparing to unpack .../075-r-cran-mass_7.3-60.0.1-1_s390x.deb ... 408s Unpacking r-cran-mass (7.3-60.0.1-1) ... 408s Selecting previously unselected package r-cran-lattice. 408s Preparing to unpack .../076-r-cran-lattice_0.22-5-1_s390x.deb ... 408s Unpacking r-cran-lattice (0.22-5-1) ... 408s Selecting previously unselected package r-cran-nlme. 408s Preparing to unpack .../077-r-cran-nlme_3.1.164-1_s390x.deb ... 408s Unpacking r-cran-nlme (3.1.164-1) ... 408s Selecting previously unselected package r-cran-matrix. 408s Preparing to unpack .../078-r-cran-matrix_1.6-5-1_s390x.deb ... 408s Unpacking r-cran-matrix (1.6-5-1) ... 409s Selecting previously unselected package r-cran-mgcv. 409s Preparing to unpack .../079-r-cran-mgcv_1.9-1-1_s390x.deb ... 409s Unpacking r-cran-mgcv (1.9-1-1) ... 409s Selecting previously unselected package r-cran-nnet. 409s Preparing to unpack .../080-r-cran-nnet_7.3-19-2_s390x.deb ... 409s Unpacking r-cran-nnet (7.3-19-2) ... 409s Selecting previously unselected package r-cran-pkgkitten. 409s Preparing to unpack .../081-r-cran-pkgkitten_0.2.3-1_all.deb ... 409s Unpacking r-cran-pkgkitten (0.2.3-1) ... 409s Selecting previously unselected package r-cran-rcpp. 409s Preparing to unpack .../082-r-cran-rcpp_1.0.12-1_s390x.deb ... 409s Unpacking r-cran-rcpp (1.0.12-1) ... 409s Selecting previously unselected package r-cran-minqa. 409s Preparing to unpack .../083-r-cran-minqa_1.2.6-1_s390x.deb ... 409s Unpacking r-cran-minqa (1.2.6-1) ... 409s Selecting previously unselected package libnlopt0:s390x. 409s Preparing to unpack .../084-libnlopt0_2.7.1-5build2_s390x.deb ... 409s Unpacking libnlopt0:s390x (2.7.1-5build2) ... 409s Selecting previously unselected package r-cran-desc. 409s Preparing to unpack .../085-r-cran-desc_1.4.3-1_all.deb ... 409s Unpacking r-cran-desc (1.4.3-1) ... 409s Selecting previously unselected package r-cran-digest. 409s Preparing to unpack .../086-r-cran-digest_0.6.34-1_s390x.deb ... 409s Unpacking r-cran-digest (0.6.34-1) ... 409s Selecting previously unselected package r-cran-evaluate. 409s Preparing to unpack .../087-r-cran-evaluate_0.23-1_all.deb ... 409s Unpacking r-cran-evaluate (0.23-1) ... 409s Selecting previously unselected package r-cran-jsonlite. 409s Preparing to unpack .../088-r-cran-jsonlite_1.8.8+dfsg-1_s390x.deb ... 409s Unpacking r-cran-jsonlite (1.8.8+dfsg-1) ... 409s Selecting previously unselected package r-cran-crayon. 409s Preparing to unpack .../089-r-cran-crayon_1.5.2-1_all.deb ... 409s Unpacking r-cran-crayon (1.5.2-1) ... 409s Selecting previously unselected package r-cran-fs. 409s Preparing to unpack .../090-r-cran-fs_1.6.3+dfsg-1build1_s390x.deb ... 409s Unpacking r-cran-fs (1.6.3+dfsg-1build1) ... 409s Selecting previously unselected package r-cran-pkgbuild. 409s Preparing to unpack .../091-r-cran-pkgbuild_1.4.3-1_all.deb ... 409s Unpacking r-cran-pkgbuild (1.4.3-1) ... 409s Selecting previously unselected package r-cran-rprojroot. 409s Preparing to unpack .../092-r-cran-rprojroot_2.0.4-1_all.deb ... 409s Unpacking r-cran-rprojroot (2.0.4-1) ... 409s Selecting previously unselected package r-cran-pkgload. 409s Preparing to unpack .../093-r-cran-pkgload_1.3.4-1_all.deb ... 409s Unpacking r-cran-pkgload (1.3.4-1) ... 409s Selecting previously unselected package r-cran-praise. 409s Preparing to unpack .../094-r-cran-praise_1.0.0-4build1_all.deb ... 409s Unpacking r-cran-praise (1.0.0-4build1) ... 409s Selecting previously unselected package r-cran-diffobj. 409s Preparing to unpack .../095-r-cran-diffobj_0.3.5-1_s390x.deb ... 409s Unpacking r-cran-diffobj (0.3.5-1) ... 410s Selecting previously unselected package r-cran-rematch2. 410s Preparing to unpack .../096-r-cran-rematch2_2.1.2-2build1_all.deb ... 410s Unpacking r-cran-rematch2 (2.1.2-2build1) ... 410s Selecting previously unselected package r-cran-waldo. 410s Preparing to unpack .../097-r-cran-waldo_0.5.2-1build1_all.deb ... 410s Unpacking r-cran-waldo (0.5.2-1build1) ... 410s Selecting previously unselected package r-cran-testthat. 410s Preparing to unpack .../098-r-cran-testthat_3.2.1-1_s390x.deb ... 410s Unpacking r-cran-testthat (3.2.1-1) ... 410s Selecting previously unselected package r-cran-nloptr. 410s Preparing to unpack .../099-r-cran-nloptr_2.0.3-1_s390x.deb ... 410s Unpacking r-cran-nloptr (2.0.3-1) ... 410s Selecting previously unselected package r-cran-rcppeigen. 410s Preparing to unpack .../100-r-cran-rcppeigen_0.3.4.0.0-1_s390x.deb ... 410s Unpacking r-cran-rcppeigen (0.3.4.0.0-1) ... 410s Selecting previously unselected package r-cran-statmod. 410s Preparing to unpack .../101-r-cran-statmod_1.5.0-1_s390x.deb ... 410s Unpacking r-cran-statmod (1.5.0-1) ... 410s Selecting previously unselected package r-cran-lme4. 410s Preparing to unpack .../102-r-cran-lme4_1.1-35.1-4_s390x.deb ... 410s Unpacking r-cran-lme4 (1.1-35.1-4) ... 410s Selecting previously unselected package r-cran-numderiv. 410s Preparing to unpack .../103-r-cran-numderiv_2016.8-1.1-3_all.deb ... 410s Unpacking r-cran-numderiv (2016.8-1.1-3) ... 410s Selecting previously unselected package r-cran-xfun. 410s Preparing to unpack .../104-r-cran-xfun_0.41+dfsg-1_s390x.deb ... 410s Unpacking r-cran-xfun (0.41+dfsg-1) ... 410s Selecting previously unselected package r-cran-highr. 410s Preparing to unpack .../105-r-cran-highr_0.10+dfsg-1_all.deb ... 410s Unpacking r-cran-highr (0.10+dfsg-1) ... 410s Selecting previously unselected package r-cran-yaml. 410s Preparing to unpack .../106-r-cran-yaml_2.3.8-1_s390x.deb ... 410s Unpacking r-cran-yaml (2.3.8-1) ... 411s Selecting previously unselected package libjs-mathjax. 411s Preparing to unpack .../107-libjs-mathjax_2.7.9+dfsg-1_all.deb ... 411s Unpacking libjs-mathjax (2.7.9+dfsg-1) ... 412s Selecting previously unselected package r-cran-knitr. 412s Preparing to unpack .../108-r-cran-knitr_1.45+dfsg-1_all.deb ... 412s Unpacking r-cran-knitr (1.45+dfsg-1) ... 412s Selecting previously unselected package r-cran-pbkrtest. 412s Preparing to unpack .../109-r-cran-pbkrtest_0.5.2-2_all.deb ... 412s Unpacking r-cran-pbkrtest (0.5.2-2) ... 412s Selecting previously unselected package r-cran-sparsem. 412s Preparing to unpack .../110-r-cran-sparsem_1.81-1_s390x.deb ... 412s Unpacking r-cran-sparsem (1.81-1) ... 412s Selecting previously unselected package r-cran-matrixmodels. 413s Preparing to unpack .../111-r-cran-matrixmodels_0.5-3-1_all.deb ... 413s Unpacking r-cran-matrixmodels (0.5-3-1) ... 413s Selecting previously unselected package r-cran-survival. 413s Preparing to unpack .../112-r-cran-survival_3.5-8-1_s390x.deb ... 413s Unpacking r-cran-survival (3.5-8-1) ... 413s Selecting previously unselected package r-cran-matrixstats. 413s Preparing to unpack .../113-r-cran-matrixstats_1.2.0-1_s390x.deb ... 413s Unpacking r-cran-matrixstats (1.2.0-1) ... 413s Selecting previously unselected package r-cran-rcpparmadillo. 413s Preparing to unpack .../114-r-cran-rcpparmadillo_0.12.8.1.0-1_s390x.deb ... 413s Unpacking r-cran-rcpparmadillo (0.12.8.1.0-1) ... 413s Selecting previously unselected package r-cran-gtable. 413s Preparing to unpack .../115-r-cran-gtable_0.3.4+dfsg-1_all.deb ... 413s Unpacking r-cran-gtable (0.3.4+dfsg-1) ... 413s Selecting previously unselected package r-cran-isoband. 413s Preparing to unpack .../116-r-cran-isoband_0.2.7-1_s390x.deb ... 413s Unpacking r-cran-isoband (0.2.7-1) ... 413s Selecting previously unselected package r-cran-farver. 413s Preparing to unpack .../117-r-cran-farver_2.1.1-1_s390x.deb ... 413s Unpacking r-cran-farver (2.1.1-1) ... 413s Selecting previously unselected package r-cran-labeling. 413s Preparing to unpack .../118-r-cran-labeling_0.4.3-1_all.deb ... 413s Unpacking r-cran-labeling (0.4.3-1) ... 413s Selecting previously unselected package r-cran-colorspace. 413s Preparing to unpack .../119-r-cran-colorspace_2.1-0+dfsg-1_s390x.deb ... 413s Unpacking r-cran-colorspace (2.1-0+dfsg-1) ... 413s Selecting previously unselected package r-cran-munsell. 413s Preparing to unpack .../120-r-cran-munsell_0.5.0-2build1_all.deb ... 413s Unpacking r-cran-munsell (0.5.0-2build1) ... 413s Selecting previously unselected package r-cran-rcolorbrewer. 413s Preparing to unpack .../121-r-cran-rcolorbrewer_1.1-3-1build1_all.deb ... 413s Unpacking r-cran-rcolorbrewer (1.1-3-1build1) ... 413s Selecting previously unselected package r-cran-viridislite. 413s Preparing to unpack .../122-r-cran-viridislite_0.4.2-2_all.deb ... 413s Unpacking r-cran-viridislite (0.4.2-2) ... 413s Selecting previously unselected package r-cran-scales. 413s Preparing to unpack .../123-r-cran-scales_1.3.0-1_all.deb ... 413s Unpacking r-cran-scales (1.3.0-1) ... 413s Selecting previously unselected package r-cran-ggplot2. 413s Preparing to unpack .../124-r-cran-ggplot2_3.4.4+dfsg-1_all.deb ... 413s Unpacking r-cran-ggplot2 (3.4.4+dfsg-1) ... 414s Selecting previously unselected package r-cran-class. 414s Preparing to unpack .../125-r-cran-class_7.3-22-2_s390x.deb ... 414s Unpacking r-cran-class (7.3-22-2) ... 414s Selecting previously unselected package r-cran-proxy. 414s Preparing to unpack .../126-r-cran-proxy_0.4-27-1_s390x.deb ... 414s Unpacking r-cran-proxy (0.4-27-1) ... 414s Selecting previously unselected package r-cran-e1071. 414s Preparing to unpack .../127-r-cran-e1071_1.7-14-1_s390x.deb ... 414s Unpacking r-cran-e1071 (1.7-14-1) ... 414s Selecting previously unselected package r-cran-codetools. 414s Preparing to unpack .../128-r-cran-codetools_0.2-19-1_all.deb ... 414s Unpacking r-cran-codetools (0.2-19-1) ... 414s Selecting previously unselected package r-cran-iterators. 414s Preparing to unpack .../129-r-cran-iterators_1.0.14-1_all.deb ... 414s Unpacking r-cran-iterators (1.0.14-1) ... 414s Selecting previously unselected package r-cran-foreach. 414s Preparing to unpack .../130-r-cran-foreach_1.5.2-1_all.deb ... 414s Unpacking r-cran-foreach (1.5.2-1) ... 414s Selecting previously unselected package r-cran-data.table. 414s Preparing to unpack .../131-r-cran-data.table_1.14.10+dfsg-1_s390x.deb ... 414s Unpacking r-cran-data.table (1.14.10+dfsg-1) ... 414s Selecting previously unselected package r-cran-modelmetrics. 414s Preparing to unpack .../132-r-cran-modelmetrics_1.2.2.2-1build1_s390x.deb ... 414s Unpacking r-cran-modelmetrics (1.2.2.2-1build1) ... 414s Selecting previously unselected package r-cran-plyr. 414s Preparing to unpack .../133-r-cran-plyr_1.8.9-1_s390x.deb ... 414s Unpacking r-cran-plyr (1.8.9-1) ... 414s Selecting previously unselected package r-cran-proc. 414s Preparing to unpack .../134-r-cran-proc_1.18.5-1_s390x.deb ... 414s Unpacking r-cran-proc (1.18.5-1) ... 414s Selecting previously unselected package r-cran-tzdb. 414s Preparing to unpack .../135-r-cran-tzdb_0.4.0-2_s390x.deb ... 414s Unpacking r-cran-tzdb (0.4.0-2) ... 414s Selecting previously unselected package r-cran-clock. 414s Preparing to unpack .../136-r-cran-clock_0.7.0-1.1_s390x.deb ... 414s Unpacking r-cran-clock (0.7.0-1.1) ... 414s Selecting previously unselected package r-cran-gower. 414s Preparing to unpack .../137-r-cran-gower_1.0.1-1_s390x.deb ... 414s Unpacking r-cran-gower (1.0.1-1) ... 414s Selecting previously unselected package r-cran-hardhat. 414s Preparing to unpack .../138-r-cran-hardhat_1.3.1+dfsg-1_all.deb ... 414s Unpacking r-cran-hardhat (1.3.1+dfsg-1) ... 414s Selecting previously unselected package r-cran-rpart. 414s Preparing to unpack .../139-r-cran-rpart_4.1.23-1_s390x.deb ... 414s Unpacking r-cran-rpart (4.1.23-1) ... 414s Selecting previously unselected package r-cran-shape. 414s Preparing to unpack .../140-r-cran-shape_1.4.6-1_all.deb ... 414s Unpacking r-cran-shape (1.4.6-1) ... 414s Selecting previously unselected package r-cran-diagram. 414s Preparing to unpack .../141-r-cran-diagram_1.6.5-2_all.deb ... 414s Unpacking r-cran-diagram (1.6.5-2) ... 414s Selecting previously unselected package r-cran-kernsmooth. 414s Preparing to unpack .../142-r-cran-kernsmooth_2.23-22-1_s390x.deb ... 414s Unpacking r-cran-kernsmooth (2.23-22-1) ... 414s Selecting previously unselected package r-cran-globals. 414s Preparing to unpack .../143-r-cran-globals_0.16.2-1_all.deb ... 414s Unpacking r-cran-globals (0.16.2-1) ... 414s Selecting previously unselected package r-cran-listenv. 414s Preparing to unpack .../144-r-cran-listenv_0.9.1+dfsg-1_all.deb ... 414s Unpacking r-cran-listenv (0.9.1+dfsg-1) ... 414s Selecting previously unselected package r-cran-parallelly. 414s Preparing to unpack .../145-r-cran-parallelly_1.37.1-1_s390x.deb ... 414s Unpacking r-cran-parallelly (1.37.1-1) ... 415s Selecting previously unselected package r-cran-future. 415s Preparing to unpack .../146-r-cran-future_1.33.1+dfsg-1_all.deb ... 415s Unpacking r-cran-future (1.33.1+dfsg-1) ... 415s Selecting previously unselected package r-cran-future.apply. 415s Preparing to unpack .../147-r-cran-future.apply_1.11.1+dfsg-1_all.deb ... 415s Unpacking r-cran-future.apply (1.11.1+dfsg-1) ... 415s Selecting previously unselected package r-cran-progressr. 415s Preparing to unpack .../148-r-cran-progressr_0.14.0-1_all.deb ... 415s Unpacking r-cran-progressr (0.14.0-1) ... 415s Selecting previously unselected package r-cran-squarem. 415s Preparing to unpack .../149-r-cran-squarem_2021.1-1_all.deb ... 415s Unpacking r-cran-squarem (2021.1-1) ... 415s Selecting previously unselected package r-cran-lava. 415s Preparing to unpack .../150-r-cran-lava_1.7.3+dfsg-1_all.deb ... 415s Unpacking r-cran-lava (1.7.3+dfsg-1) ... 415s Selecting previously unselected package r-cran-prodlim. 415s Preparing to unpack .../151-r-cran-prodlim_2023.08.28-1_s390x.deb ... 415s Unpacking r-cran-prodlim (2023.08.28-1) ... 415s Selecting previously unselected package r-cran-ipred. 415s Preparing to unpack .../152-r-cran-ipred_0.9-14-1_s390x.deb ... 415s Unpacking r-cran-ipred (0.9-14-1) ... 415s Selecting previously unselected package r-cran-timechange. 415s Preparing to unpack .../153-r-cran-timechange_0.3.0-1_s390x.deb ... 415s Unpacking r-cran-timechange (0.3.0-1) ... 415s Selecting previously unselected package r-cran-lubridate. 415s Preparing to unpack .../154-r-cran-lubridate_1.9.3+dfsg-1_s390x.deb ... 415s Unpacking r-cran-lubridate (1.9.3+dfsg-1) ... 415s Selecting previously unselected package r-cran-timedate. 415s Preparing to unpack .../155-r-cran-timedate_4032.109-1_s390x.deb ... 415s Unpacking r-cran-timedate (4032.109-1) ... 415s Selecting previously unselected package r-cran-recipes. 415s Preparing to unpack .../156-r-cran-recipes_1.0.9+dfsg-1_all.deb ... 415s Unpacking r-cran-recipes (1.0.9+dfsg-1) ... 415s Selecting previously unselected package r-cran-reshape2. 415s Preparing to unpack .../157-r-cran-reshape2_1.4.4-2build1_s390x.deb ... 415s Unpacking r-cran-reshape2 (1.4.4-2build1) ... 415s Selecting previously unselected package r-cran-caret. 415s Preparing to unpack .../158-r-cran-caret_6.0-94+dfsg-1_s390x.deb ... 415s Unpacking r-cran-caret (6.0-94+dfsg-1) ... 415s Selecting previously unselected package r-cran-conquer. 415s Preparing to unpack .../159-r-cran-conquer_1.3.3-1_s390x.deb ... 415s Unpacking r-cran-conquer (1.3.3-1) ... 415s Selecting previously unselected package r-cran-quantreg. 415s Preparing to unpack .../160-r-cran-quantreg_5.97-1_s390x.deb ... 415s Unpacking r-cran-quantreg (5.97-1) ... 415s Selecting previously unselected package r-cran-sp. 415s Preparing to unpack .../161-r-cran-sp_1%3a2.1-2+dfsg-1_s390x.deb ... 415s Unpacking r-cran-sp (1:2.1-2+dfsg-1) ... 415s Selecting previously unselected package r-cran-foreign. 415s Preparing to unpack .../162-r-cran-foreign_0.8.86-1_s390x.deb ... 415s Unpacking r-cran-foreign (0.8.86-1) ... 415s Selecting previously unselected package r-cran-maptools. 415s Preparing to unpack .../163-r-cran-maptools_1%3a1.1-8+dfsg-1_s390x.deb ... 415s Unpacking r-cran-maptools (1:1.1-8+dfsg-1) ... 415s Selecting previously unselected package r-cran-forcats. 415s Preparing to unpack .../164-r-cran-forcats_1.0.0-1_all.deb ... 415s Unpacking r-cran-forcats (1.0.0-1) ... 416s Selecting previously unselected package r-cran-hms. 416s Preparing to unpack .../165-r-cran-hms_1.1.3-1_all.deb ... 416s Unpacking r-cran-hms (1.1.3-1) ... 416s Selecting previously unselected package r-cran-clipr. 416s Preparing to unpack .../166-r-cran-clipr_0.8.0-1_all.deb ... 416s Unpacking r-cran-clipr (0.8.0-1) ... 416s Selecting previously unselected package r-cran-prettyunits. 416s Preparing to unpack .../167-r-cran-prettyunits_1.2.0-1_all.deb ... 416s Unpacking r-cran-prettyunits (1.2.0-1) ... 416s Selecting previously unselected package r-cran-progress. 416s Preparing to unpack .../168-r-cran-progress_1.2.3-1_all.deb ... 416s Unpacking r-cran-progress (1.2.3-1) ... 416s Selecting previously unselected package r-cran-vroom. 416s Preparing to unpack .../169-r-cran-vroom_1.6.5-1_s390x.deb ... 416s Unpacking r-cran-vroom (1.6.5-1) ... 416s Selecting previously unselected package r-cran-readr. 416s Preparing to unpack .../170-r-cran-readr_2.1.5-1_s390x.deb ... 416s Unpacking r-cran-readr (2.1.5-1) ... 416s Selecting previously unselected package r-cran-haven. 416s Preparing to unpack .../171-r-cran-haven_2.5.4-1_s390x.deb ... 416s Unpacking r-cran-haven (2.5.4-1) ... 416s Selecting previously unselected package r-cran-curl. 416s Preparing to unpack .../172-r-cran-curl_5.2.0+dfsg-1build1_s390x.deb ... 416s Unpacking r-cran-curl (5.2.0+dfsg-1build1) ... 416s Selecting previously unselected package r-cran-rematch. 416s Preparing to unpack .../173-r-cran-rematch_2.0.0-1_all.deb ... 416s Unpacking r-cran-rematch (2.0.0-1) ... 416s Selecting previously unselected package r-cran-cellranger. 416s Preparing to unpack .../174-r-cran-cellranger_1.1.0-3_all.deb ... 416s Unpacking r-cran-cellranger (1.1.0-3) ... 416s Selecting previously unselected package r-cran-readxl. 416s Preparing to unpack .../175-r-cran-readxl_1.4.3-1_s390x.deb ... 416s Unpacking r-cran-readxl (1.4.3-1) ... 416s Selecting previously unselected package r-cran-writexl. 416s Preparing to unpack .../176-r-cran-writexl_1.5.0-1_s390x.deb ... 416s Unpacking r-cran-writexl (1.5.0-1) ... 416s Selecting previously unselected package r-cran-r.methodss3. 416s Preparing to unpack .../177-r-cran-r.methodss3_1.8.2-1_all.deb ... 416s Unpacking r-cran-r.methodss3 (1.8.2-1) ... 416s Selecting previously unselected package r-cran-r.oo. 416s Preparing to unpack .../178-r-cran-r.oo_1.26.0-1_all.deb ... 416s Unpacking r-cran-r.oo (1.26.0-1) ... 416s Selecting previously unselected package r-cran-r.utils. 416s Preparing to unpack .../179-r-cran-r.utils_2.12.3-1_all.deb ... 416s Unpacking r-cran-r.utils (2.12.3-1) ... 416s Selecting previously unselected package r-cran-zip. 416s Preparing to unpack .../180-r-cran-zip_2.3.1-1_s390x.deb ... 416s Unpacking r-cran-zip (2.3.1-1) ... 416s Selecting previously unselected package r-cran-openxlsx. 416s Preparing to unpack .../181-r-cran-openxlsx_4.2.5.2-1_s390x.deb ... 416s Unpacking r-cran-openxlsx (4.2.5.2-1) ... 416s Selecting previously unselected package r-cran-rio. 416s Preparing to unpack .../182-r-cran-rio_1.0.1-1_all.deb ... 416s Unpacking r-cran-rio (1.0.1-1) ... 417s Selecting previously unselected package r-cran-car. 417s Preparing to unpack .../183-r-cran-car_3.1-2-2_all.deb ... 417s Unpacking r-cran-car (3.1-2-2) ... 417s Selecting previously unselected package r-cran-collapse. 417s Preparing to unpack .../184-r-cran-collapse_2.0.10-1_s390x.deb ... 417s Unpacking r-cran-collapse (2.0.10-1) ... 417s Selecting previously unselected package r-cran-formula. 417s Preparing to unpack .../185-r-cran-formula_1.2-5-1_all.deb ... 417s Unpacking r-cran-formula (1.2-5-1) ... 417s Selecting previously unselected package r-cran-zoo. 417s Preparing to unpack .../186-r-cran-zoo_1.8-12-2_s390x.deb ... 417s Unpacking r-cran-zoo (1.8-12-2) ... 417s Selecting previously unselected package r-cran-lmtest. 417s Preparing to unpack .../187-r-cran-lmtest_0.9.40-1_s390x.deb ... 417s Unpacking r-cran-lmtest (0.9.40-1) ... 417s Selecting previously unselected package r-cran-misctools. 417s Preparing to unpack .../188-r-cran-misctools_0.6-28-1_all.deb ... 417s Unpacking r-cran-misctools (0.6-28-1) ... 417s Selecting previously unselected package r-cran-sandwich. 417s Preparing to unpack .../189-r-cran-sandwich_3.1-0-1_all.deb ... 417s Unpacking r-cran-sandwich (3.1-0-1) ... 417s Selecting previously unselected package r-cran-maxlik. 417s Preparing to unpack .../190-r-cran-maxlik_1.5-2-1_all.deb ... 417s Unpacking r-cran-maxlik (1.5-2-1) ... 417s Selecting previously unselected package r-cran-rbibutils. 417s Preparing to unpack .../191-r-cran-rbibutils_2.2.16-1_s390x.deb ... 417s Unpacking r-cran-rbibutils (2.2.16-1) ... 417s Selecting previously unselected package r-cran-rdpack. 417s Preparing to unpack .../192-r-cran-rdpack_2.6-1_all.deb ... 417s Unpacking r-cran-rdpack (2.6-1) ... 417s Selecting previously unselected package r-cran-plm. 417s Preparing to unpack .../193-r-cran-plm_2.6-3-1_all.deb ... 417s Unpacking r-cran-plm (2.6-3-1) ... 417s Selecting previously unselected package r-cran-systemfit. 417s Preparing to unpack .../194-r-cran-systemfit_1.1-30-1_all.deb ... 417s Unpacking r-cran-systemfit (1.1-30-1) ... 417s Selecting previously unselected package autopkgtest-satdep. 417s Preparing to unpack .../195-1-autopkgtest-satdep.deb ... 417s Unpacking autopkgtest-satdep (0) ... 417s Setting up libgraphite2-3:s390x (1.3.14-2) ... 417s Setting up libpixman-1-0:s390x (0.42.2-1) ... 417s Setting up libpaper1:s390x (1.1.29) ... 417s 417s Creating config file /etc/papersize with new version 418s Setting up fontconfig (2.15.0-1.1ubuntu1) ... 420s Regenerating fonts cache... done. 420s Setting up fonts-mathjax (2.7.9+dfsg-1) ... 420s Setting up libjs-mathjax (2.7.9+dfsg-1) ... 420s Setting up libxrender1:s390x (1:0.9.10-1.1) ... 420s Setting up libdatrie1:s390x (0.2.13-3) ... 420s Setting up libxcb-render0:s390x (1.15-1) ... 420s Setting up fonts-glyphicons-halflings (1.009~3.4.1+dfsg-3) ... 420s Setting up unzip (6.0-28ubuntu3) ... 420s Setting up x11-common (1:7.7+23ubuntu2) ... 420s Setting up libnlopt0:s390x (2.7.1-5build2) ... 420s Setting up libxcb-shm0:s390x (1.15-1) ... 420s Setting up libpaper-utils (1.1.29) ... 420s Setting up libgomp1:s390x (14-20240315-1ubuntu1) ... 420s Setting up libcairo2:s390x (1.18.0-1ubuntu1) ... 420s Setting up zip (3.0-13) ... 420s Setting up libblas3:s390x (3.12.0-3) ... 420s update-alternatives: using /usr/lib/s390x-linux-gnu/blas/libblas.so.3 to provide /usr/lib/s390x-linux-gnu/libblas.so.3 (libblas.so.3-s390x-linux-gnu) in auto mode 420s Setting up libtcl8.6:s390x (8.6.14+dfsg-1) ... 420s Setting up libgfortran5:s390x (14-20240315-1ubuntu1) ... 420s Setting up libjs-highlight.js (9.18.5+dfsg1-2) ... 420s Setting up libharfbuzz0b:s390x (8.3.0-2build1) ... 420s Setting up libthai-data (0.1.29-2) ... 420s Setting up libxss1:s390x (1:1.2.3-1build2) ... 420s Setting up libjs-jquery (3.6.1+dfsg+~3.5.14-1) ... 420s Setting up node-normalize.css (8.0.1-5) ... 420s Setting up xdg-utils (1.1.3-4.1ubuntu3) ... 420s update-alternatives: using /usr/bin/xdg-open to provide /usr/bin/open (open) in auto mode 420s Setting up libjs-bootstrap (3.4.1+dfsg-3) ... 420s Setting up libice6:s390x (2:1.0.10-1build2) ... 420s Setting up liblapack3:s390x (3.12.0-3) ... 420s update-alternatives: using /usr/lib/s390x-linux-gnu/lapack/liblapack.so.3 to provide /usr/lib/s390x-linux-gnu/liblapack.so.3 (liblapack.so.3-s390x-linux-gnu) in auto mode 420s Setting up libxft2:s390x (2.3.6-1) ... 420s Setting up libtk8.6:s390x (8.6.14-1) ... 420s Setting up libjs-jquery-datatables (1.11.5+dfsg-2) ... 420s Setting up libthai0:s390x (0.1.29-2) ... 420s Setting up libsm6:s390x (2:1.2.3-1build2) ... 420s Setting up libpango-1.0-0:s390x (1.52.1+ds-1) ... 420s Setting up libxt6t64:s390x 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(64-bit) 434s 434s R is free software and comes with ABSOLUTELY NO WARRANTY. 434s You are welcome to redistribute it under certain conditions. 434s Type 'license()' or 'licence()' for distribution details. 434s 434s R is a collaborative project with many contributors. 434s Type 'contributors()' for more information and 434s 'citation()' on how to cite R or R packages in publications. 434s 434s Type 'demo()' for some demos, 'help()' for on-line help, or 434s 'help.start()' for an HTML browser interface to help. 434s Type 'q()' to quit R. 434s 434s > library( "systemfit" ) 434s Loading required package: Matrix 435s Loading required package: car 435s Loading required package: carData 435s Loading required package: lmtest 435s Loading required package: zoo 435s 435s Attaching package: ‘zoo’ 435s 435s The following objects are masked from ‘package:base’: 435s 435s as.Date, as.Date.numeric 435s 435s 435s Please cite the 'systemfit' package as: 435s 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/. 435s 435s If you have questions, suggestions, or comments regarding the 'systemfit' package, please use a forum or 'tracker' at systemfit's R-Forge site: 435s https://r-forge.r-project.org/projects/systemfit/ 435s > library( "sandwich" ) 435s > options( warn = 1 ) 435s > options( digits = 3 ) 435s > 435s > data( "KleinI" ) 435s > eqConsump <- consump ~ corpProf + corpProfLag + wages 435s > eqInvest <- invest ~ corpProf + corpProfLag + capitalLag 435s > eqPrivWage <- privWage ~ gnp + gnpLag + trend 435s > inst <- ~ govExp + taxes + govWage + trend + capitalLag + corpProfLag + gnpLag 435s > system <- list( Consumption = eqConsump, Investment = eqInvest, 435s + PrivateWages = eqPrivWage ) 435s > restrict <- c( "Consumption_corpProf + Investment_capitalLag = 0" ) 435s > restrict2 <- c( restrict, "Consumption_corpProfLag - PrivateWages_trend = 0" ) 435s > 435s > for( dataNo in 1:5 ) { 435s + # set some values of some variables to NA 435s + if( dataNo == 2 ) { 435s + KleinI$gnpLag[ 7 ] <- NA 435s + } else if( dataNo == 3 ) { 435s + KleinI$wages[ 10 ] <- NA 435s + } else if( dataNo == 4 ) { 435s + KleinI$corpProf[ 13 ] <- NA 435s + } else if( dataNo == 5 ) { 435s + KleinI$invest[ 16 ] <- NA 435s + } 435s + 435s + # single-equation OLS 435s + lmConsump <- lm( eqConsump, data = KleinI ) 435s + lmInvest <- lm( eqInvest, data = KleinI ) 435s + lmPrivWage <- lm( eqPrivWage, data = KleinI ) 435s + 435s + for( methodNo in 1:5 ) { 435s + method <- c( "OLS", "2SLS", "SUR", "3SLS", "3SLS" )[ methodNo ] 435s + maxit <- ifelse( methodNo == 5, 500, 1 ) 435s + 435s + cat( "> \n> # ", ifelse( maxit == 1, "", "I" ), method, "\n", sep = "" ) 435s + if( method %in% c( "OLS", "WLS", "SUR" ) ) { 435s + kleinModel <- systemfit( system, method = method, data = KleinI, 435s + methodResidCov = ifelse( method == "OLS", "geomean", "noDfCor" ), 435s + maxit = maxit ) 435s + } else { 435s + kleinModel <- systemfit( system, method = method, data = KleinI, 435s + inst = inst, methodResidCov = "noDfCor", maxit = maxit ) 435s + } 435s + cat( "> summary\n" ) 435s + print( summary( kleinModel ) ) 435s + if( method == "OLS" ) { 435s + cat( "compare coef with single-equation OLS\n" ) 435s + print( all.equal( coef( kleinModel ), 435s + c( coef( lmConsump ), coef( lmInvest ), coef( lmPrivWage ) ), 435s + check.attributes = FALSE ) ) 435s + } 435s + cat( "> residuals\n" ) 435s + print( residuals( kleinModel ) ) 435s + cat( "> fitted\n" ) 435s + print( fitted( kleinModel ) ) 435s + cat( "> predict\n" ) 435s + print( predict( kleinModel, se.fit = TRUE, 435s + interval = ifelse( methodNo %in% c( 1, 4 ), "prediction", "confidence" ), 435s + useDfSys = methodNo %in% c( 1, 3, 5 ) ) ) 435s + cat( "> model.frame\n" ) 435s + if( methodNo == 1 ) { 435s + mfOls <- model.frame( kleinModel ) 435s + print( mfOls ) 435s + } else if( methodNo == 2 ) { 435s + mf2sls <- model.frame( kleinModel ) 435s + print( mf2sls ) 435s + cat( "> Frames of instrumental variables\n" ) 435s + for( i in 1:3 ){ 435s + print( kleinModel$eq[[ i ]]$modelInst ) 435s + } 435s + } else if( methodNo == 3 ) { 435s + print( all.equal( mfOls, model.frame( kleinModel ) ) ) 435s + } else { 435s + print( all.equal( mf2sls, model.frame( kleinModel ) ) ) 435s + } 435s + cat( "> model.matrix\n" ) 435s + if( methodNo == 1 ) { 435s + mmOls <- model.matrix( kleinModel ) 435s + print( mmOls ) 435s + } else { 435s + print( all.equal( mmOls, model.matrix( kleinModel ) ) ) 435s + } 435s + if( methodNo == 2 ) { 435s + cat( "> matrix of instrumental variables\n" ) 435s + print( model.matrix( kleinModel, which = "z" ) ) 435s + cat( "> matrix of fitted regressors\n" ) 435s + print( round( model.matrix( kleinModel, which = "xHat" ), digits = 7 ) ) 435s + } 435s + cat( "> nobs\n" ) 435s + print( nobs( kleinModel ) ) 435s + cat( "> linearHypothesis\n" ) 435s + print( linearHypothesis( kleinModel, restrict ) ) 435s + print( linearHypothesis( kleinModel, restrict, test = "F" ) ) 435s + print( linearHypothesis( kleinModel, restrict, test = "Chisq" ) ) 435s + print( linearHypothesis( kleinModel, restrict2 ) ) 435s + print( linearHypothesis( kleinModel, restrict2, test = "F" ) ) 435s + print( linearHypothesis( kleinModel, restrict2, test = "Chisq" ) ) 435s + cat( "> logLik\n" ) 435s + print( logLik( kleinModel ) ) 435s + print( logLik( kleinModel, residCovDiag = TRUE ) ) 435s + if( method == "OLS" ) { 435s + cat( "compare log likelihood value with single-equation OLS\n" ) 435s + print( all.equal( logLik( kleinModel, residCovDiag = TRUE ), 435s + logLik( lmConsump ) + logLik( lmInvest ) + logLik( lmPrivWage ), 435s + check.attributes = FALSE ) ) 435s + } 435s + 435s + cat( "Estimating function\n" ) 435s + print( round( estfun( kleinModel ), digits = 7 ) ) 435s + print( all.equal( colSums( estfun( kleinModel ) ), 435s + rep( 0, ncol( estfun( kleinModel ) ) ), check.attributes = FALSE ) ) 435s + 435s + cat( "> Bread\n" ) 435s + print( bread( kleinModel ) ) 435s + } 435s + } 435s > 435s > # OLS 436s > summary 436s 436s systemfit results 436s method: OLS 436s 436s N DF SSR detRCov OLS-R2 McElroy-R2 436s system 63 51 45.2 0.371 0.977 0.991 436s 436s N DF SSR MSE RMSE R2 Adj R2 436s Consumption 21 17 17.9 1.052 1.026 0.981 0.978 436s Investment 21 17 17.3 1.019 1.009 0.931 0.919 436s PrivateWages 21 17 10.0 0.589 0.767 0.987 0.985 436s 436s The covariance matrix of the residuals 436s Consumption Investment PrivateWages 436s Consumption 1.0517 0.0611 -0.470 436s Investment 0.0611 1.0190 0.150 436s PrivateWages -0.4704 0.1497 0.589 436s 436s The correlations of the residuals 436s Consumption Investment PrivateWages 436s Consumption 1.0000 0.0591 -0.598 436s Investment 0.0591 1.0000 0.193 436s PrivateWages -0.5979 0.1933 1.000 436s 436s 436s OLS estimates for 'Consumption' (equation 1) 436s Model Formula: consump ~ corpProf + corpProfLag + wages 436s 436s Estimate Std. Error t value Pr(>|t|) 436s (Intercept) 16.2366 1.3027 12.46 5.6e-10 *** 436s corpProf 0.1929 0.0912 2.12 0.049 * 436s corpProfLag 0.0899 0.0906 0.99 0.335 436s wages 0.7962 0.0399 19.93 3.2e-13 *** 436s --- 436s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 436s 436s Residual standard error: 1.026 on 17 degrees of freedom 436s Number of observations: 21 Degrees of Freedom: 17 436s SSR: 17.879 MSE: 1.052 Root MSE: 1.026 436s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.978 436s 436s 436s OLS estimates for 'Investment' (equation 2) 436s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 436s 436s Estimate Std. Error t value Pr(>|t|) 436s (Intercept) 10.1258 5.4655 1.85 0.08137 . 436s corpProf 0.4796 0.0971 4.94 0.00012 *** 436s corpProfLag 0.3330 0.1009 3.30 0.00421 ** 436s capitalLag -0.1118 0.0267 -4.18 0.00062 *** 436s --- 436s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 436s 436s Residual standard error: 1.009 on 17 degrees of freedom 436s Number of observations: 21 Degrees of Freedom: 17 436s SSR: 17.323 MSE: 1.019 Root MSE: 1.009 436s Multiple R-Squared: 0.931 Adjusted R-Squared: 0.919 436s 436s 436s OLS estimates for 'PrivateWages' (equation 3) 436s Model Formula: privWage ~ gnp + gnpLag + trend 436s 436s Estimate Std. Error t value Pr(>|t|) 436s (Intercept) 1.4970 1.2700 1.18 0.25474 436s gnp 0.4395 0.0324 13.56 1.5e-10 *** 436s gnpLag 0.1461 0.0374 3.90 0.00114 ** 436s trend 0.1302 0.0319 4.08 0.00078 *** 436s --- 436s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 436s 436s Residual standard error: 0.767 on 17 degrees of freedom 436s Number of observations: 21 Degrees of Freedom: 17 436s SSR: 10.005 MSE: 0.589 Root MSE: 0.767 436s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 436s 436s compare coef with single-equation OLS 436s [1] TRUE 436s > residuals 436s Consumption Investment PrivateWages 436s 1 NA NA NA 436s 2 -0.32389 -0.0668 -1.2942 436s 3 -1.25001 -0.0476 0.2957 436s 4 -1.56574 1.2467 1.1877 436s 5 -0.49350 -1.3512 -0.1358 436s 6 0.00761 0.4154 -0.4654 436s 7 0.86910 1.4923 -0.4838 436s 8 1.33848 0.7889 -0.7281 436s 9 1.05498 -0.6317 0.3392 436s 10 -0.58856 1.0830 1.1957 436s 11 0.28231 0.2791 -0.1508 436s 12 -0.22965 0.0369 0.5942 436s 13 -0.32213 0.3659 0.1027 436s 14 0.32228 0.2237 0.4503 436s 15 -0.05801 -0.1728 0.2816 436s 16 -0.03466 0.0101 0.0138 436s 17 1.61650 0.9719 -0.8508 436s 18 -0.43597 0.0516 0.9956 436s 19 0.21005 -2.5656 -0.4688 436s 20 0.98920 -0.6866 -0.3795 436s 21 0.78508 -0.7807 -1.0909 436s 22 -2.17345 -0.6623 0.5917 436s > fitted 436s Consumption Investment PrivateWages 436s 1 NA NA NA 436s 2 42.2 -0.133 26.8 436s 3 46.3 1.948 29.0 436s 4 50.8 3.953 32.9 436s 5 51.1 4.351 34.0 436s 6 52.6 4.685 35.9 436s 7 54.2 4.108 37.9 436s 8 54.9 3.411 38.6 436s 9 56.2 3.632 38.9 436s 10 58.4 4.017 40.1 436s 11 54.7 0.721 38.1 436s 12 51.1 -3.437 33.9 436s 13 45.9 -6.566 28.9 436s 14 46.2 -5.324 28.0 436s 15 48.8 -2.827 30.3 436s 16 51.3 -1.310 33.2 436s 17 56.1 1.128 37.7 436s 18 59.1 1.948 40.0 436s 19 57.3 0.666 38.7 436s 20 60.6 1.987 42.0 436s 21 64.2 4.081 46.1 436s 22 71.9 5.562 52.7 436s > predict 436s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 436s 1 NA NA NA NA 436s 2 42.2 0.462 40.0 44.5 436s 3 46.3 0.518 43.9 48.6 436s 4 50.8 0.341 48.6 52.9 436s 5 51.1 0.396 48.9 53.3 436s 6 52.6 0.397 50.4 54.8 436s 7 54.2 0.359 52.0 56.4 436s 8 54.9 0.327 52.7 57.0 436s 9 56.2 0.350 54.1 58.4 436s 10 58.4 0.370 56.2 60.6 436s 11 54.7 0.606 52.3 57.1 436s 12 51.1 0.484 48.9 53.4 436s 13 45.9 0.629 43.5 48.3 436s 14 46.2 0.602 43.8 48.6 436s 15 48.8 0.374 46.6 50.9 436s 16 51.3 0.333 49.2 53.5 436s 17 56.1 0.366 53.9 58.3 436s 18 59.1 0.321 57.0 61.3 436s 19 57.3 0.371 55.1 59.5 436s 20 60.6 0.434 58.4 62.8 436s 21 64.2 0.425 62.0 66.4 436s 22 71.9 0.666 69.4 74.3 436s Investment.pred Investment.se.fit Investment.lwr Investment.upr 436s 1 NA NA NA NA 436s 2 -0.133 0.607 -2.498 2.231 436s 3 1.948 0.499 -0.313 4.208 436s 4 3.953 0.449 1.735 6.171 436s 5 4.351 0.371 2.192 6.510 436s 6 4.685 0.349 2.540 6.829 436s 7 4.108 0.329 1.976 6.239 436s 8 3.411 0.292 1.301 5.521 436s 9 3.632 0.389 1.460 5.804 436s 10 4.017 0.447 1.801 6.233 436s 11 0.721 0.601 -1.638 3.080 436s 12 -3.437 0.507 -5.704 -1.169 436s 13 -6.566 0.616 -8.940 -4.192 436s 14 -5.324 0.694 -7.783 -2.865 436s 15 -2.827 0.373 -4.988 -0.667 436s 16 -1.310 0.320 -3.436 0.816 436s 17 1.128 0.347 -1.015 3.271 436s 18 1.948 0.243 -0.136 4.033 436s 19 0.666 0.312 -1.456 2.787 436s 20 1.987 0.366 -0.169 4.143 436s 21 4.081 0.332 1.948 6.214 436s 22 5.562 0.461 3.334 7.790 436s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 436s 1 NA NA NA NA 436s 2 26.8 0.354 25.1 28.5 436s 3 29.0 0.355 27.3 30.7 436s 4 32.9 0.354 31.2 34.6 436s 5 34.0 0.269 32.4 35.7 436s 6 35.9 0.266 34.2 37.5 436s 7 37.9 0.266 36.3 39.5 436s 8 38.6 0.273 37.0 40.3 436s 9 38.9 0.261 37.2 40.5 436s 10 40.1 0.247 38.5 41.7 436s 11 38.1 0.354 36.4 39.7 436s 12 33.9 0.363 32.2 35.6 436s 13 28.9 0.429 27.1 30.7 436s 14 28.0 0.376 26.3 29.8 436s 15 30.3 0.371 28.6 32.0 436s 16 33.2 0.310 31.5 34.8 436s 17 37.7 0.305 36.0 39.3 436s 18 40.0 0.238 38.4 41.6 436s 19 38.7 0.357 37.0 40.4 436s 20 42.0 0.321 40.3 43.6 436s 21 46.1 0.335 44.4 47.8 436s 22 52.7 0.502 50.9 54.5 436s > model.frame 436s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 436s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 436s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 436s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 436s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 436s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 436s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 436s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 61.0 436s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 436s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 436s 10 57.8 21.7 21.1 45.3 5.1 211 41.3 67.0 64.5 436s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 436s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 436s 13 45.6 7.0 11.4 34.3 -6.2 213 29.0 44.3 53.4 436s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 436s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 436s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 436s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 436s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 436s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 436s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 436s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 436s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 436s trend 436s 1 -11 436s 2 -10 436s 3 -9 436s 4 -8 436s 5 -7 436s 6 -6 436s 7 -5 436s 8 -4 436s 9 -3 436s 10 -2 436s 11 -1 436s 12 0 436s 13 1 436s 14 2 436s 15 3 436s 16 4 436s 17 5 436s 18 6 436s 19 7 436s 20 8 436s 21 9 436s 22 10 436s > model.matrix 436s Consumption_(Intercept) Consumption_corpProf 436s Consumption_2 1 12.4 436s Consumption_3 1 16.9 436s Consumption_4 1 18.4 436s Consumption_5 1 19.4 436s Consumption_6 1 20.1 436s Consumption_7 1 19.6 436s Consumption_8 1 19.8 436s Consumption_9 1 21.1 436s Consumption_10 1 21.7 436s Consumption_11 1 15.6 436s Consumption_12 1 11.4 436s Consumption_13 1 7.0 436s Consumption_14 1 11.2 436s Consumption_15 1 12.3 436s Consumption_16 1 14.0 436s Consumption_17 1 17.6 436s Consumption_18 1 17.3 436s Consumption_19 1 15.3 436s Consumption_20 1 19.0 436s Consumption_21 1 21.1 436s Consumption_22 1 23.5 436s Investment_2 0 0.0 436s Investment_3 0 0.0 436s Investment_4 0 0.0 436s Investment_5 0 0.0 436s Investment_6 0 0.0 436s Investment_7 0 0.0 436s Investment_8 0 0.0 436s Investment_9 0 0.0 436s Investment_10 0 0.0 436s Investment_11 0 0.0 436s Investment_12 0 0.0 436s Investment_13 0 0.0 436s Investment_14 0 0.0 436s Investment_15 0 0.0 436s Investment_16 0 0.0 436s Investment_17 0 0.0 436s Investment_18 0 0.0 436s Investment_19 0 0.0 436s Investment_20 0 0.0 436s Investment_21 0 0.0 436s Investment_22 0 0.0 436s PrivateWages_2 0 0.0 436s PrivateWages_3 0 0.0 436s PrivateWages_4 0 0.0 436s PrivateWages_5 0 0.0 436s PrivateWages_6 0 0.0 436s PrivateWages_7 0 0.0 436s PrivateWages_8 0 0.0 436s PrivateWages_9 0 0.0 436s PrivateWages_10 0 0.0 436s PrivateWages_11 0 0.0 436s PrivateWages_12 0 0.0 436s PrivateWages_13 0 0.0 436s PrivateWages_14 0 0.0 436s PrivateWages_15 0 0.0 436s PrivateWages_16 0 0.0 436s PrivateWages_17 0 0.0 436s PrivateWages_18 0 0.0 436s PrivateWages_19 0 0.0 436s PrivateWages_20 0 0.0 436s PrivateWages_21 0 0.0 436s PrivateWages_22 0 0.0 436s Consumption_corpProfLag Consumption_wages 436s Consumption_2 12.7 28.2 436s Consumption_3 12.4 32.2 436s Consumption_4 16.9 37.0 436s Consumption_5 18.4 37.0 436s Consumption_6 19.4 38.6 436s Consumption_7 20.1 40.7 436s Consumption_8 19.6 41.5 436s Consumption_9 19.8 42.9 436s Consumption_10 21.1 45.3 436s Consumption_11 21.7 42.1 436s Consumption_12 15.6 39.3 436s Consumption_13 11.4 34.3 436s Consumption_14 7.0 34.1 436s Consumption_15 11.2 36.6 436s Consumption_16 12.3 39.3 436s Consumption_17 14.0 44.2 436s Consumption_18 17.6 47.7 436s Consumption_19 17.3 45.9 436s Consumption_20 15.3 49.4 436s Consumption_21 19.0 53.0 436s Consumption_22 21.1 61.8 436s Investment_2 0.0 0.0 436s Investment_3 0.0 0.0 436s Investment_4 0.0 0.0 436s Investment_5 0.0 0.0 436s Investment_6 0.0 0.0 436s Investment_7 0.0 0.0 436s Investment_8 0.0 0.0 436s Investment_9 0.0 0.0 436s Investment_10 0.0 0.0 436s Investment_11 0.0 0.0 436s Investment_12 0.0 0.0 436s Investment_13 0.0 0.0 436s Investment_14 0.0 0.0 436s Investment_15 0.0 0.0 436s Investment_16 0.0 0.0 436s Investment_17 0.0 0.0 436s Investment_18 0.0 0.0 436s Investment_19 0.0 0.0 436s Investment_20 0.0 0.0 436s Investment_21 0.0 0.0 436s Investment_22 0.0 0.0 436s PrivateWages_2 0.0 0.0 436s PrivateWages_3 0.0 0.0 436s PrivateWages_4 0.0 0.0 436s PrivateWages_5 0.0 0.0 436s PrivateWages_6 0.0 0.0 436s PrivateWages_7 0.0 0.0 436s PrivateWages_8 0.0 0.0 436s PrivateWages_9 0.0 0.0 436s PrivateWages_10 0.0 0.0 436s PrivateWages_11 0.0 0.0 436s PrivateWages_12 0.0 0.0 436s PrivateWages_13 0.0 0.0 436s PrivateWages_14 0.0 0.0 436s PrivateWages_15 0.0 0.0 436s PrivateWages_16 0.0 0.0 436s PrivateWages_17 0.0 0.0 436s PrivateWages_18 0.0 0.0 436s PrivateWages_19 0.0 0.0 436s PrivateWages_20 0.0 0.0 436s PrivateWages_21 0.0 0.0 436s PrivateWages_22 0.0 0.0 436s Investment_(Intercept) Investment_corpProf 436s Consumption_2 0 0.0 436s Consumption_3 0 0.0 436s Consumption_4 0 0.0 436s Consumption_5 0 0.0 436s Consumption_6 0 0.0 436s Consumption_7 0 0.0 436s Consumption_8 0 0.0 436s Consumption_9 0 0.0 436s Consumption_10 0 0.0 436s Consumption_11 0 0.0 436s Consumption_12 0 0.0 436s Consumption_13 0 0.0 436s Consumption_14 0 0.0 436s Consumption_15 0 0.0 436s Consumption_16 0 0.0 436s Consumption_17 0 0.0 436s Consumption_18 0 0.0 436s Consumption_19 0 0.0 436s Consumption_20 0 0.0 436s Consumption_21 0 0.0 436s Consumption_22 0 0.0 436s Investment_2 1 12.4 436s Investment_3 1 16.9 436s Investment_4 1 18.4 436s Investment_5 1 19.4 436s Investment_6 1 20.1 436s Investment_7 1 19.6 436s Investment_8 1 19.8 436s Investment_9 1 21.1 436s Investment_10 1 21.7 436s Investment_11 1 15.6 436s Investment_12 1 11.4 436s Investment_13 1 7.0 436s Investment_14 1 11.2 436s Investment_15 1 12.3 436s Investment_16 1 14.0 436s Investment_17 1 17.6 436s Investment_18 1 17.3 436s Investment_19 1 15.3 436s Investment_20 1 19.0 436s Investment_21 1 21.1 436s Investment_22 1 23.5 436s PrivateWages_2 0 0.0 436s PrivateWages_3 0 0.0 436s PrivateWages_4 0 0.0 436s PrivateWages_5 0 0.0 436s PrivateWages_6 0 0.0 436s PrivateWages_7 0 0.0 436s PrivateWages_8 0 0.0 436s PrivateWages_9 0 0.0 436s PrivateWages_10 0 0.0 436s PrivateWages_11 0 0.0 436s PrivateWages_12 0 0.0 436s PrivateWages_13 0 0.0 436s PrivateWages_14 0 0.0 436s PrivateWages_15 0 0.0 436s PrivateWages_16 0 0.0 436s PrivateWages_17 0 0.0 436s PrivateWages_18 0 0.0 436s PrivateWages_19 0 0.0 436s PrivateWages_20 0 0.0 436s PrivateWages_21 0 0.0 436s PrivateWages_22 0 0.0 436s Investment_corpProfLag Investment_capitalLag 436s Consumption_2 0.0 0 436s Consumption_3 0.0 0 436s Consumption_4 0.0 0 436s Consumption_5 0.0 0 436s Consumption_6 0.0 0 436s Consumption_7 0.0 0 436s Consumption_8 0.0 0 436s Consumption_9 0.0 0 436s Consumption_10 0.0 0 436s Consumption_11 0.0 0 436s Consumption_12 0.0 0 436s Consumption_13 0.0 0 436s Consumption_14 0.0 0 436s Consumption_15 0.0 0 436s Consumption_16 0.0 0 436s Consumption_17 0.0 0 436s Consumption_18 0.0 0 436s Consumption_19 0.0 0 436s Consumption_20 0.0 0 436s Consumption_21 0.0 0 436s Consumption_22 0.0 0 436s Investment_2 12.7 183 436s Investment_3 12.4 183 436s Investment_4 16.9 184 436s Investment_5 18.4 190 436s Investment_6 19.4 193 436s Investment_7 20.1 198 436s Investment_8 19.6 203 436s Investment_9 19.8 208 436s Investment_10 21.1 211 436s Investment_11 21.7 216 436s Investment_12 15.6 217 436s Investment_13 11.4 213 436s Investment_14 7.0 207 436s Investment_15 11.2 202 436s Investment_16 12.3 199 436s Investment_17 14.0 198 436s Investment_18 17.6 200 436s Investment_19 17.3 202 436s Investment_20 15.3 200 436s Investment_21 19.0 201 436s Investment_22 21.1 204 436s PrivateWages_2 0.0 0 436s PrivateWages_3 0.0 0 436s PrivateWages_4 0.0 0 436s PrivateWages_5 0.0 0 436s PrivateWages_6 0.0 0 436s PrivateWages_7 0.0 0 436s PrivateWages_8 0.0 0 436s PrivateWages_9 0.0 0 436s PrivateWages_10 0.0 0 436s PrivateWages_11 0.0 0 436s PrivateWages_12 0.0 0 436s PrivateWages_13 0.0 0 436s PrivateWages_14 0.0 0 436s PrivateWages_15 0.0 0 436s PrivateWages_16 0.0 0 436s PrivateWages_17 0.0 0 436s PrivateWages_18 0.0 0 436s PrivateWages_19 0.0 0 436s PrivateWages_20 0.0 0 436s PrivateWages_21 0.0 0 436s PrivateWages_22 0.0 0 436s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 436s Consumption_2 0 0.0 0.0 436s Consumption_3 0 0.0 0.0 436s Consumption_4 0 0.0 0.0 436s Consumption_5 0 0.0 0.0 436s Consumption_6 0 0.0 0.0 436s Consumption_7 0 0.0 0.0 436s Consumption_8 0 0.0 0.0 436s Consumption_9 0 0.0 0.0 436s Consumption_10 0 0.0 0.0 436s Consumption_11 0 0.0 0.0 436s Consumption_12 0 0.0 0.0 436s Consumption_13 0 0.0 0.0 436s Consumption_14 0 0.0 0.0 436s Consumption_15 0 0.0 0.0 436s Consumption_16 0 0.0 0.0 436s Consumption_17 0 0.0 0.0 436s Consumption_18 0 0.0 0.0 436s Consumption_19 0 0.0 0.0 436s Consumption_20 0 0.0 0.0 436s Consumption_21 0 0.0 0.0 436s Consumption_22 0 0.0 0.0 436s Investment_2 0 0.0 0.0 436s Investment_3 0 0.0 0.0 436s Investment_4 0 0.0 0.0 436s Investment_5 0 0.0 0.0 436s Investment_6 0 0.0 0.0 436s Investment_7 0 0.0 0.0 436s Investment_8 0 0.0 0.0 436s Investment_9 0 0.0 0.0 436s Investment_10 0 0.0 0.0 436s Investment_11 0 0.0 0.0 436s Investment_12 0 0.0 0.0 436s Investment_13 0 0.0 0.0 436s Investment_14 0 0.0 0.0 436s Investment_15 0 0.0 0.0 436s Investment_16 0 0.0 0.0 436s Investment_17 0 0.0 0.0 436s Investment_18 0 0.0 0.0 436s Investment_19 0 0.0 0.0 436s Investment_20 0 0.0 0.0 436s Investment_21 0 0.0 0.0 436s Investment_22 0 0.0 0.0 436s PrivateWages_2 1 45.6 44.9 436s PrivateWages_3 1 50.1 45.6 436s PrivateWages_4 1 57.2 50.1 436s PrivateWages_5 1 57.1 57.2 436s PrivateWages_6 1 61.0 57.1 436s PrivateWages_7 1 64.0 61.0 436s PrivateWages_8 1 64.4 64.0 436s PrivateWages_9 1 64.5 64.4 436s PrivateWages_10 1 67.0 64.5 436s PrivateWages_11 1 61.2 67.0 436s PrivateWages_12 1 53.4 61.2 436s PrivateWages_13 1 44.3 53.4 436s PrivateWages_14 1 45.1 44.3 436s PrivateWages_15 1 49.7 45.1 436s PrivateWages_16 1 54.4 49.7 436s PrivateWages_17 1 62.7 54.4 436s PrivateWages_18 1 65.0 62.7 436s PrivateWages_19 1 60.9 65.0 436s PrivateWages_20 1 69.5 60.9 436s PrivateWages_21 1 75.7 69.5 436s PrivateWages_22 1 88.4 75.7 436s PrivateWages_trend 436s Consumption_2 0 436s Consumption_3 0 436s Consumption_4 0 436s Consumption_5 0 436s Consumption_6 0 436s Consumption_7 0 436s Consumption_8 0 436s Consumption_9 0 436s Consumption_10 0 436s Consumption_11 0 436s Consumption_12 0 436s Consumption_13 0 436s Consumption_14 0 436s Consumption_15 0 436s Consumption_16 0 436s Consumption_17 0 436s Consumption_18 0 436s Consumption_19 0 436s Consumption_20 0 436s Consumption_21 0 436s Consumption_22 0 436s Investment_2 0 436s Investment_3 0 436s Investment_4 0 436s Investment_5 0 436s Investment_6 0 436s Investment_7 0 436s Investment_8 0 436s Investment_9 0 436s Investment_10 0 436s Investment_11 0 436s Investment_12 0 436s Investment_13 0 436s Investment_14 0 436s Investment_15 0 436s Investment_16 0 436s Investment_17 0 436s Investment_18 0 436s Investment_19 0 436s Investment_20 0 436s Investment_21 0 436s Investment_22 0 436s PrivateWages_2 -10 436s PrivateWages_3 -9 436s PrivateWages_4 -8 436s PrivateWages_5 -7 436s PrivateWages_6 -6 436s PrivateWages_7 -5 436s PrivateWages_8 -4 436s PrivateWages_9 -3 436s PrivateWages_10 -2 436s PrivateWages_11 -1 436s PrivateWages_12 0 436s PrivateWages_13 1 436s PrivateWages_14 2 436s PrivateWages_15 3 436s PrivateWages_16 4 436s PrivateWages_17 5 436s PrivateWages_18 6 436s PrivateWages_19 7 436s PrivateWages_20 8 436s PrivateWages_21 9 436s PrivateWages_22 10 436s > nobs 436s [1] 63 436s > linearHypothesis 436s Linear hypothesis test (Theil's F test) 436s 436s Hypothesis: 436s Consumption_corpProf + Investment_capitalLag = 0 436s 436s Model 1: restricted model 436s Model 2: kleinModel 436s 436s Res.Df Df F Pr(>F) 436s 1 52 436s 2 51 1 0.82 0.37 436s Linear hypothesis test (F statistic of a Wald test) 436s 436s Hypothesis: 436s Consumption_corpProf + Investment_capitalLag = 0 436s 436s Model 1: restricted model 436s Model 2: kleinModel 436s 436s Res.Df Df F Pr(>F) 436s 1 52 436s 2 51 1 0.73 0.4 436s Linear hypothesis test (Chi^2 statistic of a Wald test) 436s 436s Hypothesis: 436s Consumption_corpProf + Investment_capitalLag = 0 436s 436s Model 1: restricted model 436s Model 2: kleinModel 436s 436s Res.Df Df Chisq Pr(>Chisq) 436s 1 52 436s 2 51 1 0.73 0.39 436s Linear hypothesis test (Theil's F test) 436s 436s Hypothesis: 436s Consumption_corpProf + Investment_capitalLag = 0 436s Consumption_corpProfLag - PrivateWages_trend = 0 436s 436s Model 1: restricted model 436s Model 2: kleinModel 436s 436s Res.Df Df F Pr(>F) 436s 1 53 436s 2 51 2 0.42 0.66 436s Linear hypothesis test (F statistic of a Wald test) 436s 436s Hypothesis: 436s Consumption_corpProf + Investment_capitalLag = 0 436s Consumption_corpProfLag - PrivateWages_trend = 0 436s 436s Model 1: restricted model 436s Model 2: kleinModel 436s 436s Res.Df Df F Pr(>F) 436s 1 53 436s 2 51 2 0.37 0.69 436s Linear hypothesis test (Chi^2 statistic of a Wald test) 436s 436s Hypothesis: 436s Consumption_corpProf + Investment_capitalLag = 0 436s Consumption_corpProfLag - PrivateWages_trend = 0 436s 436s Model 1: restricted model 436s Model 2: kleinModel 436s 436s Res.Df Df Chisq Pr(>Chisq) 436s 1 53 436s 2 51 2 0.74 0.69 436s > logLik 436s 'log Lik.' -72.3 (df=13) 436s 'log Lik.' -77.9 (df=13) 436s compare log likelihood value with single-equation OLS 436s [1] TRUE 436s Estimating function 436s Consumption_(Intercept) Consumption_corpProf 436s Consumption_2 -0.32389 -4.016 436s Consumption_3 -1.25001 -21.125 436s Consumption_4 -1.56574 -28.810 436s Consumption_5 -0.49350 -9.574 436s Consumption_6 0.00761 0.153 436s Consumption_7 0.86910 17.034 436s Consumption_8 1.33848 26.502 436s Consumption_9 1.05498 22.260 436s Consumption_10 -0.58856 -12.772 436s Consumption_11 0.28231 4.404 436s Consumption_12 -0.22965 -2.618 436s Consumption_13 -0.32213 -2.255 436s Consumption_14 0.32228 3.610 436s Consumption_15 -0.05801 -0.714 436s Consumption_16 -0.03466 -0.485 436s Consumption_17 1.61650 28.450 436s Consumption_18 -0.43597 -7.542 436s Consumption_19 0.21005 3.214 436s Consumption_20 0.98920 18.795 436s Consumption_21 0.78508 16.565 436s Consumption_22 -2.17345 -51.076 436s Investment_2 0.00000 0.000 436s Investment_3 0.00000 0.000 436s Investment_4 0.00000 0.000 436s Investment_5 0.00000 0.000 436s Investment_6 0.00000 0.000 436s Investment_7 0.00000 0.000 436s Investment_8 0.00000 0.000 436s Investment_9 0.00000 0.000 436s Investment_10 0.00000 0.000 436s Investment_11 0.00000 0.000 436s Investment_12 0.00000 0.000 436s Investment_13 0.00000 0.000 436s Investment_14 0.00000 0.000 436s Investment_15 0.00000 0.000 436s Investment_16 0.00000 0.000 436s Investment_17 0.00000 0.000 436s Investment_18 0.00000 0.000 436s Investment_19 0.00000 0.000 436s Investment_20 0.00000 0.000 436s Investment_21 0.00000 0.000 436s Investment_22 0.00000 0.000 436s PrivateWages_2 0.00000 0.000 436s PrivateWages_3 0.00000 0.000 436s PrivateWages_4 0.00000 0.000 436s PrivateWages_5 0.00000 0.000 436s PrivateWages_6 0.00000 0.000 436s PrivateWages_7 0.00000 0.000 436s PrivateWages_8 0.00000 0.000 436s PrivateWages_9 0.00000 0.000 436s PrivateWages_10 0.00000 0.000 436s PrivateWages_11 0.00000 0.000 436s PrivateWages_12 0.00000 0.000 436s PrivateWages_13 0.00000 0.000 436s PrivateWages_14 0.00000 0.000 436s PrivateWages_15 0.00000 0.000 436s PrivateWages_16 0.00000 0.000 436s PrivateWages_17 0.00000 0.000 436s PrivateWages_18 0.00000 0.000 436s PrivateWages_19 0.00000 0.000 436s PrivateWages_20 0.00000 0.000 436s PrivateWages_21 0.00000 0.000 436s PrivateWages_22 0.00000 0.000 436s Consumption_corpProfLag Consumption_wages 436s Consumption_2 -4.113 -9.134 436s Consumption_3 -15.500 -40.250 436s Consumption_4 -26.461 -57.932 436s Consumption_5 -9.080 -18.260 436s Consumption_6 0.148 0.294 436s Consumption_7 17.469 35.372 436s Consumption_8 26.234 55.547 436s Consumption_9 20.889 45.259 436s Consumption_10 -12.419 -26.662 436s Consumption_11 6.126 11.885 436s Consumption_12 -3.583 -9.025 436s Consumption_13 -3.672 -11.049 436s Consumption_14 2.256 10.990 436s Consumption_15 -0.650 -2.123 436s Consumption_16 -0.426 -1.362 436s Consumption_17 22.631 71.449 436s Consumption_18 -7.673 -20.796 436s Consumption_19 3.634 9.641 436s Consumption_20 15.135 48.867 436s Consumption_21 14.916 41.609 436s Consumption_22 -45.860 -134.319 436s Investment_2 0.000 0.000 436s Investment_3 0.000 0.000 436s Investment_4 0.000 0.000 436s Investment_5 0.000 0.000 436s Investment_6 0.000 0.000 436s Investment_7 0.000 0.000 436s Investment_8 0.000 0.000 436s Investment_9 0.000 0.000 436s Investment_10 0.000 0.000 436s Investment_11 0.000 0.000 436s Investment_12 0.000 0.000 436s Investment_13 0.000 0.000 436s Investment_14 0.000 0.000 436s Investment_15 0.000 0.000 436s Investment_16 0.000 0.000 436s Investment_17 0.000 0.000 436s Investment_18 0.000 0.000 436s Investment_19 0.000 0.000 436s Investment_20 0.000 0.000 436s Investment_21 0.000 0.000 436s Investment_22 0.000 0.000 436s PrivateWages_2 0.000 0.000 436s PrivateWages_3 0.000 0.000 436s PrivateWages_4 0.000 0.000 436s PrivateWages_5 0.000 0.000 436s PrivateWages_6 0.000 0.000 436s PrivateWages_7 0.000 0.000 436s PrivateWages_8 0.000 0.000 436s PrivateWages_9 0.000 0.000 436s PrivateWages_10 0.000 0.000 436s PrivateWages_11 0.000 0.000 436s PrivateWages_12 0.000 0.000 436s PrivateWages_13 0.000 0.000 436s PrivateWages_14 0.000 0.000 436s PrivateWages_15 0.000 0.000 436s PrivateWages_16 0.000 0.000 436s PrivateWages_17 0.000 0.000 436s PrivateWages_18 0.000 0.000 436s PrivateWages_19 0.000 0.000 436s PrivateWages_20 0.000 0.000 436s PrivateWages_21 0.000 0.000 436s PrivateWages_22 0.000 0.000 436s Investment_(Intercept) Investment_corpProf 436s Consumption_2 0.0000 0.000 436s Consumption_3 0.0000 0.000 436s Consumption_4 0.0000 0.000 436s Consumption_5 0.0000 0.000 436s Consumption_6 0.0000 0.000 436s Consumption_7 0.0000 0.000 436s Consumption_8 0.0000 0.000 436s Consumption_9 0.0000 0.000 436s Consumption_10 0.0000 0.000 436s Consumption_11 0.0000 0.000 436s Consumption_12 0.0000 0.000 436s Consumption_13 0.0000 0.000 436s Consumption_14 0.0000 0.000 436s Consumption_15 0.0000 0.000 436s Consumption_16 0.0000 0.000 436s Consumption_17 0.0000 0.000 436s Consumption_18 0.0000 0.000 436s Consumption_19 0.0000 0.000 436s Consumption_20 0.0000 0.000 436s Consumption_21 0.0000 0.000 436s Consumption_22 0.0000 0.000 436s Investment_2 -0.0668 -0.828 436s Investment_3 -0.0476 -0.804 436s Investment_4 1.2467 22.939 436s Investment_5 -1.3512 -26.213 436s Investment_6 0.4154 8.350 436s Investment_7 1.4923 29.248 436s Investment_8 0.7889 15.620 436s Investment_9 -0.6317 -13.329 436s Investment_10 1.0830 23.500 436s Investment_11 0.2791 4.353 436s Investment_12 0.0369 0.420 436s Investment_13 0.3659 2.561 436s Investment_14 0.2237 2.505 436s Investment_15 -0.1728 -2.126 436s Investment_16 0.0101 0.141 436s Investment_17 0.9719 17.105 436s Investment_18 0.0516 0.893 436s Investment_19 -2.5656 -39.254 436s Investment_20 -0.6866 -13.045 436s Investment_21 -0.7807 -16.474 436s Investment_22 -0.6623 -15.565 436s PrivateWages_2 0.0000 0.000 436s PrivateWages_3 0.0000 0.000 436s PrivateWages_4 0.0000 0.000 436s PrivateWages_5 0.0000 0.000 436s PrivateWages_6 0.0000 0.000 436s PrivateWages_7 0.0000 0.000 436s PrivateWages_8 0.0000 0.000 436s PrivateWages_9 0.0000 0.000 436s PrivateWages_10 0.0000 0.000 436s PrivateWages_11 0.0000 0.000 436s PrivateWages_12 0.0000 0.000 436s PrivateWages_13 0.0000 0.000 436s PrivateWages_14 0.0000 0.000 436s PrivateWages_15 0.0000 0.000 436s PrivateWages_16 0.0000 0.000 436s PrivateWages_17 0.0000 0.000 436s PrivateWages_18 0.0000 0.000 436s PrivateWages_19 0.0000 0.000 436s PrivateWages_20 0.0000 0.000 436s PrivateWages_21 0.0000 0.000 436s PrivateWages_22 0.0000 0.000 436s Investment_corpProfLag Investment_capitalLag 436s Consumption_2 0.000 0.00 436s Consumption_3 0.000 0.00 436s Consumption_4 0.000 0.00 436s Consumption_5 0.000 0.00 436s Consumption_6 0.000 0.00 436s Consumption_7 0.000 0.00 436s Consumption_8 0.000 0.00 436s Consumption_9 0.000 0.00 436s Consumption_10 0.000 0.00 436s Consumption_11 0.000 0.00 436s Consumption_12 0.000 0.00 436s Consumption_13 0.000 0.00 436s Consumption_14 0.000 0.00 436s Consumption_15 0.000 0.00 436s Consumption_16 0.000 0.00 436s Consumption_17 0.000 0.00 436s Consumption_18 0.000 0.00 436s Consumption_19 0.000 0.00 436s Consumption_20 0.000 0.00 436s Consumption_21 0.000 0.00 436s Consumption_22 0.000 0.00 436s Investment_2 -0.848 -12.21 436s Investment_3 -0.590 -8.69 436s Investment_4 21.069 230.01 436s Investment_5 -24.862 -256.32 436s Investment_6 8.059 80.05 436s Investment_7 29.994 295.17 436s Investment_8 15.463 160.46 436s Investment_9 -12.507 -131.14 436s Investment_10 22.850 228.07 436s Investment_11 6.056 60.20 436s Investment_12 0.575 7.99 436s Investment_13 4.172 78.05 436s Investment_14 1.566 46.33 436s Investment_15 -1.936 -34.91 436s Investment_16 0.124 2.01 436s Investment_17 13.606 192.14 436s Investment_18 0.908 10.31 436s Investment_19 -44.385 -517.74 436s Investment_20 -10.505 -137.25 436s Investment_21 -14.834 -157.09 436s Investment_22 -13.975 -135.45 436s PrivateWages_2 0.000 0.00 436s PrivateWages_3 0.000 0.00 436s PrivateWages_4 0.000 0.00 436s PrivateWages_5 0.000 0.00 436s PrivateWages_6 0.000 0.00 436s PrivateWages_7 0.000 0.00 436s PrivateWages_8 0.000 0.00 436s PrivateWages_9 0.000 0.00 436s PrivateWages_10 0.000 0.00 436s PrivateWages_11 0.000 0.00 436s PrivateWages_12 0.000 0.00 436s PrivateWages_13 0.000 0.00 436s PrivateWages_14 0.000 0.00 436s PrivateWages_15 0.000 0.00 436s PrivateWages_16 0.000 0.00 436s PrivateWages_17 0.000 0.00 436s PrivateWages_18 0.000 0.00 436s PrivateWages_19 0.000 0.00 436s PrivateWages_20 0.000 0.00 436s PrivateWages_21 0.000 0.00 436s PrivateWages_22 0.000 0.00 436s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 436s Consumption_2 0.0000 0.000 0.000 436s Consumption_3 0.0000 0.000 0.000 436s Consumption_4 0.0000 0.000 0.000 436s Consumption_5 0.0000 0.000 0.000 436s Consumption_6 0.0000 0.000 0.000 436s Consumption_7 0.0000 0.000 0.000 436s Consumption_8 0.0000 0.000 0.000 436s Consumption_9 0.0000 0.000 0.000 436s Consumption_10 0.0000 0.000 0.000 436s Consumption_11 0.0000 0.000 0.000 436s Consumption_12 0.0000 0.000 0.000 436s Consumption_13 0.0000 0.000 0.000 436s Consumption_14 0.0000 0.000 0.000 436s Consumption_15 0.0000 0.000 0.000 436s Consumption_16 0.0000 0.000 0.000 436s Consumption_17 0.0000 0.000 0.000 436s Consumption_18 0.0000 0.000 0.000 436s Consumption_19 0.0000 0.000 0.000 436s Consumption_20 0.0000 0.000 0.000 436s Consumption_21 0.0000 0.000 0.000 436s Consumption_22 0.0000 0.000 0.000 436s Investment_2 0.0000 0.000 0.000 436s Investment_3 0.0000 0.000 0.000 436s Investment_4 0.0000 0.000 0.000 436s Investment_5 0.0000 0.000 0.000 436s Investment_6 0.0000 0.000 0.000 436s Investment_7 0.0000 0.000 0.000 436s Investment_8 0.0000 0.000 0.000 436s Investment_9 0.0000 0.000 0.000 436s Investment_10 0.0000 0.000 0.000 436s Investment_11 0.0000 0.000 0.000 436s Investment_12 0.0000 0.000 0.000 436s Investment_13 0.0000 0.000 0.000 436s Investment_14 0.0000 0.000 0.000 436s Investment_15 0.0000 0.000 0.000 436s Investment_16 0.0000 0.000 0.000 436s Investment_17 0.0000 0.000 0.000 436s Investment_18 0.0000 0.000 0.000 436s Investment_19 0.0000 0.000 0.000 436s Investment_20 0.0000 0.000 0.000 436s Investment_21 0.0000 0.000 0.000 436s Investment_22 0.0000 0.000 0.000 436s PrivateWages_2 -1.2942 -59.015 -58.109 436s PrivateWages_3 0.2957 14.813 13.482 436s PrivateWages_4 1.1877 67.938 59.505 436s PrivateWages_5 -0.1358 -7.755 -7.768 436s PrivateWages_6 -0.4654 -28.390 -26.575 436s PrivateWages_7 -0.4838 -30.965 -29.514 436s PrivateWages_8 -0.7281 -46.892 -46.601 436s PrivateWages_9 0.3392 21.881 21.847 436s PrivateWages_10 1.1957 80.111 77.122 436s PrivateWages_11 -0.1508 -9.230 -10.105 436s PrivateWages_12 0.5942 31.729 36.364 436s PrivateWages_13 0.1027 4.549 5.483 436s PrivateWages_14 0.4503 20.307 19.947 436s PrivateWages_15 0.2816 13.993 12.698 436s PrivateWages_16 0.0138 0.748 0.684 436s PrivateWages_17 -0.8508 -53.343 -46.282 436s PrivateWages_18 0.9956 64.717 62.427 436s PrivateWages_19 -0.4688 -28.547 -30.469 436s PrivateWages_20 -0.3795 -26.378 -23.114 436s PrivateWages_21 -1.0909 -82.582 -75.818 436s PrivateWages_22 0.5917 52.309 44.794 436s PrivateWages_trend 436s Consumption_2 0.000 436s Consumption_3 0.000 436s Consumption_4 0.000 436s Consumption_5 0.000 436s Consumption_6 0.000 436s Consumption_7 0.000 436s Consumption_8 0.000 436s Consumption_9 0.000 436s Consumption_10 0.000 436s Consumption_11 0.000 436s Consumption_12 0.000 436s Consumption_13 0.000 436s Consumption_14 0.000 436s Consumption_15 0.000 436s Consumption_16 0.000 436s Consumption_17 0.000 436s Consumption_18 0.000 436s Consumption_19 0.000 436s Consumption_20 0.000 436s Consumption_21 0.000 436s Consumption_22 0.000 436s Investment_2 0.000 436s Investment_3 0.000 436s Investment_4 0.000 436s Investment_5 0.000 436s Investment_6 0.000 436s Investment_7 0.000 436s Investment_8 0.000 436s Investment_9 0.000 436s Investment_10 0.000 436s Investment_11 0.000 436s Investment_12 0.000 436s Investment_13 0.000 436s Investment_14 0.000 436s Investment_15 0.000 436s Investment_16 0.000 436s Investment_17 0.000 436s Investment_18 0.000 436s Investment_19 0.000 436s Investment_20 0.000 436s Investment_21 0.000 436s Investment_22 0.000 436s PrivateWages_2 12.942 436s PrivateWages_3 -2.661 436s PrivateWages_4 -9.502 436s PrivateWages_5 0.951 436s PrivateWages_6 2.792 436s PrivateWages_7 2.419 436s PrivateWages_8 2.913 436s PrivateWages_9 -1.018 436s PrivateWages_10 -2.391 436s PrivateWages_11 0.151 436s PrivateWages_12 0.000 436s PrivateWages_13 0.103 436s PrivateWages_14 0.901 436s PrivateWages_15 0.845 436s PrivateWages_16 0.055 436s PrivateWages_17 -4.254 436s PrivateWages_18 5.974 436s PrivateWages_19 -3.281 436s PrivateWages_20 -3.036 436s PrivateWages_21 -9.818 436s PrivateWages_22 5.917 436s [1] TRUE 436s > Bread 436s Consumption_(Intercept) Consumption_corpProf 436s Consumption_(Intercept) 101.65 0.030 436s Consumption_corpProf 0.03 0.498 436s Consumption_corpProfLag -1.06 -0.316 436s Consumption_wages -1.97 -0.079 436s Investment_(Intercept) 0.00 0.000 436s Investment_corpProf 0.00 0.000 436s Investment_corpProfLag 0.00 0.000 436s Investment_capitalLag 0.00 0.000 436s PrivateWages_(Intercept) 0.00 0.000 436s PrivateWages_gnp 0.00 0.000 436s PrivateWages_gnpLag 0.00 0.000 436s PrivateWages_trend 0.00 0.000 436s Consumption_corpProfLag Consumption_wages 436s Consumption_(Intercept) -1.0607 -1.9718 436s Consumption_corpProf -0.3157 -0.0790 436s Consumption_corpProfLag 0.4922 -0.0402 436s Consumption_wages -0.0402 0.0956 436s Investment_(Intercept) 0.0000 0.0000 436s Investment_corpProf 0.0000 0.0000 436s Investment_corpProfLag 0.0000 0.0000 436s Investment_capitalLag 0.0000 0.0000 436s PrivateWages_(Intercept) 0.0000 0.0000 436s PrivateWages_gnp 0.0000 0.0000 436s PrivateWages_gnpLag 0.0000 0.0000 436s PrivateWages_trend 0.0000 0.0000 436s Investment_(Intercept) Investment_corpProf 436s Consumption_(Intercept) 0.00 0.0000 436s Consumption_corpProf 0.00 0.0000 436s Consumption_corpProfLag 0.00 0.0000 436s Consumption_wages 0.00 0.0000 436s Investment_(Intercept) 1846.89 -17.9709 436s Investment_corpProf -17.97 0.5831 436s Investment_corpProfLag 14.67 -0.5008 436s Investment_capitalLag -8.88 0.0814 436s PrivateWages_(Intercept) 0.00 0.0000 436s PrivateWages_gnp 0.00 0.0000 436s PrivateWages_gnpLag 0.00 0.0000 436s PrivateWages_trend 0.00 0.0000 436s Investment_corpProfLag Investment_capitalLag 436s Consumption_(Intercept) 0.0000 0.0000 436s Consumption_corpProf 0.0000 0.0000 436s Consumption_corpProfLag 0.0000 0.0000 436s Consumption_wages 0.0000 0.0000 436s Investment_(Intercept) 14.6742 -8.8813 436s Investment_corpProf -0.5008 0.0814 436s Investment_corpProfLag 0.6289 -0.0824 436s Investment_capitalLag -0.0824 0.0442 436s PrivateWages_(Intercept) 0.0000 0.0000 436s PrivateWages_gnp 0.0000 0.0000 436s PrivateWages_gnpLag 0.0000 0.0000 436s PrivateWages_trend 0.0000 0.0000 436s PrivateWages_(Intercept) PrivateWages_gnp 436s Consumption_(Intercept) 0.000 0.0000 436s Consumption_corpProf 0.000 0.0000 436s Consumption_corpProfLag 0.000 0.0000 436s Consumption_wages 0.000 0.0000 436s Investment_(Intercept) 0.000 0.0000 436s Investment_corpProf 0.000 0.0000 436s Investment_corpProfLag 0.000 0.0000 436s Investment_capitalLag 0.000 0.0000 436s PrivateWages_(Intercept) 172.668 -0.5919 436s PrivateWages_gnp -0.592 0.1124 436s PrivateWages_gnpLag -2.313 -0.1062 436s PrivateWages_trend 1.993 -0.0274 436s PrivateWages_gnpLag PrivateWages_trend 436s Consumption_(Intercept) 0.00000 0.00000 436s Consumption_corpProf 0.00000 0.00000 436s Consumption_corpProfLag 0.00000 0.00000 436s Consumption_wages 0.00000 0.00000 436s Investment_(Intercept) 0.00000 0.00000 436s Investment_corpProf 0.00000 0.00000 436s Investment_corpProfLag 0.00000 0.00000 436s Investment_capitalLag 0.00000 0.00000 436s PrivateWages_(Intercept) -2.31299 1.99284 436s PrivateWages_gnp -0.10624 -0.02738 436s PrivateWages_gnpLag 0.14992 -0.00601 436s PrivateWages_trend -0.00601 0.10900 436s > 436s > # 2SLS 436s > summary 436s 436s systemfit results 436s method: 2SLS 436s 436s N DF SSR detRCov OLS-R2 McElroy-R2 436s system 63 51 61 0.288 0.969 0.992 436s 436s N DF SSR MSE RMSE R2 Adj R2 436s Consumption 21 17 21.9 1.290 1.136 0.977 0.973 436s Investment 21 17 29.0 1.709 1.307 0.885 0.865 436s PrivateWages 21 17 10.0 0.589 0.767 0.987 0.985 436s 436s The covariance matrix of the residuals 436s Consumption Investment PrivateWages 436s Consumption 1.044 0.438 -0.385 436s Investment 0.438 1.383 0.193 436s PrivateWages -0.385 0.193 0.476 436s 436s The correlations of the residuals 436s Consumption Investment PrivateWages 436s Consumption 1.000 0.364 -0.546 436s Investment 0.364 1.000 0.237 436s PrivateWages -0.546 0.237 1.000 436s 436s 436s 2SLS estimates for 'Consumption' (equation 1) 436s Model Formula: consump ~ corpProf + corpProfLag + wages 436s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 436s gnpLag 436s 436s Estimate Std. Error t value Pr(>|t|) 436s (Intercept) 16.5548 1.3208 12.53 5.2e-10 *** 436s corpProf 0.0173 0.1180 0.15 0.89 436s corpProfLag 0.2162 0.1073 2.02 0.06 . 436s wages 0.8102 0.0402 20.13 2.7e-13 *** 436s --- 436s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 436s 436s Residual standard error: 1.136 on 17 degrees of freedom 436s Number of observations: 21 Degrees of Freedom: 17 436s SSR: 21.925 MSE: 1.29 Root MSE: 1.136 436s Multiple R-Squared: 0.977 Adjusted R-Squared: 0.973 436s 436s 436s 2SLS estimates for 'Investment' (equation 2) 436s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 436s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 436s gnpLag 436s 436s Estimate Std. Error t value Pr(>|t|) 436s (Intercept) 20.2782 7.5427 2.69 0.01555 * 436s corpProf 0.1502 0.1732 0.87 0.39792 436s corpProfLag 0.6159 0.1628 3.78 0.00148 ** 436s capitalLag -0.1578 0.0361 -4.37 0.00042 *** 436s --- 436s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 436s 436s Residual standard error: 1.307 on 17 degrees of freedom 436s Number of observations: 21 Degrees of Freedom: 17 436s SSR: 29.047 MSE: 1.709 Root MSE: 1.307 436s Multiple R-Squared: 0.885 Adjusted R-Squared: 0.865 436s 436s 436s 2SLS estimates for 'PrivateWages' (equation 3) 436s Model Formula: privWage ~ gnp + gnpLag + trend 436s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 436s gnpLag 436s 436s Estimate Std. Error t value Pr(>|t|) 436s (Intercept) 1.5003 1.1478 1.31 0.20857 436s gnp 0.4389 0.0356 12.32 6.8e-10 *** 436s gnpLag 0.1467 0.0388 3.78 0.00150 ** 436s trend 0.1304 0.0291 4.47 0.00033 *** 436s --- 436s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 436s 436s Residual standard error: 0.767 on 17 degrees of freedom 436s Number of observations: 21 Degrees of Freedom: 17 436s SSR: 10.005 MSE: 0.589 Root MSE: 0.767 436s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 436s 436s > residuals 436s Consumption Investment PrivateWages 436s 1 NA NA NA 436s 2 -0.46263 -1.320 -1.2940 436s 3 -0.61635 0.257 0.2981 436s 4 -1.30423 0.860 1.1918 436s 5 -0.24588 -1.594 -0.1361 436s 6 0.22948 0.259 -0.4634 436s 7 0.88538 1.207 -0.4824 436s 8 1.44189 0.969 -0.7284 436s 9 1.34190 0.113 0.3387 436s 10 -0.39403 1.796 1.1965 436s 11 -0.62564 -0.953 -0.1552 436s 12 -1.06543 -0.807 0.5882 436s 13 -1.33021 -0.895 0.0955 436s 14 0.61059 1.306 0.4487 436s 15 -0.14208 -0.151 0.2822 436s 16 0.00315 0.142 0.0145 436s 17 2.00337 1.749 -0.8478 436s 18 -0.60552 -0.192 0.9950 436s 19 -0.24771 -3.291 -0.4734 436s 20 1.38510 0.285 -0.3766 436s 21 1.03204 -0.104 -1.0893 436s 22 -1.89319 0.363 0.5974 436s > fitted 436s Consumption Investment PrivateWages 436s 1 NA NA NA 436s 2 42.4 1.120 26.8 436s 3 45.6 1.643 29.0 436s 4 50.5 4.340 32.9 436s 5 50.8 4.594 34.0 436s 6 52.4 4.841 35.9 436s 7 54.2 4.393 37.9 436s 8 54.8 3.231 38.6 436s 9 56.0 2.887 38.9 436s 10 58.2 3.304 40.1 436s 11 55.6 1.953 38.1 436s 12 52.0 -2.593 33.9 436s 13 46.9 -5.305 28.9 436s 14 45.9 -6.406 28.1 436s 15 48.8 -2.849 30.3 436s 16 51.3 -1.442 33.2 436s 17 55.7 0.351 37.6 436s 18 59.3 2.192 40.0 436s 19 57.7 1.391 38.7 436s 20 60.2 1.015 42.0 436s 21 64.0 3.404 46.1 436s 22 71.6 4.537 52.7 436s > predict 436s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 436s 1 NA NA NA NA 436s 2 42.4 0.471 41.4 43.4 436s 3 45.6 0.577 44.4 46.8 436s 4 50.5 0.354 49.8 51.3 436s 5 50.8 0.405 50.0 51.7 436s 6 52.4 0.404 51.5 53.2 436s 7 54.2 0.359 53.5 55.0 436s 8 54.8 0.328 54.1 55.4 436s 9 56.0 0.368 55.2 56.7 436s 10 58.2 0.377 57.4 59.0 436s 11 55.6 0.728 54.1 57.2 436s 12 52.0 0.604 50.7 53.2 436s 13 46.9 0.765 45.3 48.5 436s 14 45.9 0.615 44.6 47.2 436s 15 48.8 0.374 48.1 49.6 436s 16 51.3 0.333 50.6 52.0 436s 17 55.7 0.409 54.8 56.6 436s 18 59.3 0.326 58.6 60.0 436s 19 57.7 0.414 56.9 58.6 436s 20 60.2 0.478 59.2 61.2 436s 21 64.0 0.446 63.0 64.9 436s 22 71.6 0.689 70.1 73.0 436s Investment.pred Investment.se.fit Investment.lwr Investment.upr 436s 1 NA NA NA NA 436s 2 1.120 0.865 -0.706 2.946 436s 3 1.643 0.594 0.390 2.895 436s 4 4.340 0.545 3.190 5.490 436s 5 4.594 0.443 3.660 5.527 436s 6 4.841 0.411 3.973 5.709 436s 7 4.393 0.399 3.550 5.235 436s 8 3.231 0.348 2.497 3.965 436s 9 2.887 0.542 1.744 4.030 436s 10 3.304 0.593 2.054 4.555 436s 11 1.953 0.855 0.148 3.757 436s 12 -2.593 0.679 -4.026 -1.160 436s 13 -5.305 0.876 -7.152 -3.457 436s 14 -6.406 0.916 -8.338 -4.473 436s 15 -2.849 0.435 -3.765 -1.932 436s 16 -1.442 0.376 -2.236 -0.649 436s 17 0.351 0.510 -0.724 1.426 436s 18 2.192 0.299 1.560 2.823 436s 19 1.391 0.464 0.411 2.371 436s 20 1.015 0.576 -0.201 2.230 436s 21 3.404 0.471 2.410 4.398 436s 22 4.537 0.675 3.114 5.961 436s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 436s 1 NA NA NA NA 436s 2 26.8 0.318 26.1 27.5 436s 3 29.0 0.330 28.3 29.7 436s 4 32.9 0.346 32.2 33.6 436s 5 34.0 0.242 33.5 34.5 436s 6 35.9 0.248 35.3 36.4 436s 7 37.9 0.244 37.4 38.4 436s 8 38.6 0.246 38.1 39.1 436s 9 38.9 0.235 38.4 39.4 436s 10 40.1 0.224 39.6 40.6 436s 11 38.1 0.350 37.3 38.8 436s 12 33.9 0.382 33.1 34.7 436s 13 28.9 0.454 27.9 29.9 436s 14 28.1 0.342 27.3 28.8 436s 15 30.3 0.335 29.6 31.0 436s 16 33.2 0.280 32.6 33.8 436s 17 37.6 0.291 37.0 38.3 436s 18 40.0 0.215 39.6 40.5 436s 19 38.7 0.356 37.9 39.4 436s 20 42.0 0.304 41.3 42.6 436s 21 46.1 0.306 45.4 46.7 436s 22 52.7 0.489 51.7 53.7 436s > model.frame 436s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 436s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 436s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 436s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 436s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 436s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 436s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 436s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 61.0 436s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 436s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 436s 10 57.8 21.7 21.1 45.3 5.1 211 41.3 67.0 64.5 436s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 436s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 436s 13 45.6 7.0 11.4 34.3 -6.2 213 29.0 44.3 53.4 436s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 436s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 436s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 436s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 436s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 436s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 436s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 436s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 436s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 436s trend 436s 1 -11 436s 2 -10 436s 3 -9 436s 4 -8 436s 5 -7 436s 6 -6 436s 7 -5 436s 8 -4 436s 9 -3 436s 10 -2 436s 11 -1 436s 12 0 436s 13 1 436s 14 2 436s 15 3 436s 16 4 436s 17 5 436s 18 6 436s 19 7 436s 20 8 436s 21 9 436s 22 10 436s > Frames of instrumental variables 436s govExp taxes govWage trend capitalLag corpProfLag gnpLag 436s 1 2.4 3.4 2.2 -11 180 NA NA 436s 2 3.9 7.7 2.7 -10 183 12.7 44.9 436s 3 3.2 3.9 2.9 -9 183 12.4 45.6 436s 4 2.8 4.7 2.9 -8 184 16.9 50.1 436s 5 3.5 3.8 3.1 -7 190 18.4 57.2 436s 6 3.3 5.5 3.2 -6 193 19.4 57.1 436s 7 3.3 7.0 3.3 -5 198 20.1 61.0 436s 8 4.0 6.7 3.6 -4 203 19.6 64.0 436s 9 4.2 4.2 3.7 -3 208 19.8 64.4 436s 10 4.1 4.0 4.0 -2 211 21.1 64.5 436s 11 5.2 7.7 4.2 -1 216 21.7 67.0 436s 12 5.9 7.5 4.8 0 217 15.6 61.2 436s 13 4.9 8.3 5.3 1 213 11.4 53.4 436s 14 3.7 5.4 5.6 2 207 7.0 44.3 436s 15 4.0 6.8 6.0 3 202 11.2 45.1 436s 16 4.4 7.2 6.1 4 199 12.3 49.7 436s 17 2.9 8.3 7.4 5 198 14.0 54.4 436s 18 4.3 6.7 6.7 6 200 17.6 62.7 436s 19 5.3 7.4 7.7 7 202 17.3 65.0 436s 20 6.6 8.9 7.8 8 200 15.3 60.9 436s 21 7.4 9.6 8.0 9 201 19.0 69.5 436s 22 13.8 11.6 8.5 10 204 21.1 75.7 436s govExp taxes govWage trend capitalLag corpProfLag gnpLag 436s 1 2.4 3.4 2.2 -11 180 NA NA 436s 2 3.9 7.7 2.7 -10 183 12.7 44.9 436s 3 3.2 3.9 2.9 -9 183 12.4 45.6 436s 4 2.8 4.7 2.9 -8 184 16.9 50.1 436s 5 3.5 3.8 3.1 -7 190 18.4 57.2 436s 6 3.3 5.5 3.2 -6 193 19.4 57.1 436s 7 3.3 7.0 3.3 -5 198 20.1 61.0 436s 8 4.0 6.7 3.6 -4 203 19.6 64.0 436s 9 4.2 4.2 3.7 -3 208 19.8 64.4 436s 10 4.1 4.0 4.0 -2 211 21.1 64.5 436s 11 5.2 7.7 4.2 -1 216 21.7 67.0 436s 12 5.9 7.5 4.8 0 217 15.6 61.2 436s 13 4.9 8.3 5.3 1 213 11.4 53.4 436s 14 3.7 5.4 5.6 2 207 7.0 44.3 436s 15 4.0 6.8 6.0 3 202 11.2 45.1 436s 16 4.4 7.2 6.1 4 199 12.3 49.7 436s 17 2.9 8.3 7.4 5 198 14.0 54.4 436s 18 4.3 6.7 6.7 6 200 17.6 62.7 436s 19 5.3 7.4 7.7 7 202 17.3 65.0 436s 20 6.6 8.9 7.8 8 200 15.3 60.9 436s 21 7.4 9.6 8.0 9 201 19.0 69.5 436s 22 13.8 11.6 8.5 10 204 21.1 75.7 436s govExp taxes govWage trend capitalLag corpProfLag gnpLag 436s 1 2.4 3.4 2.2 -11 180 NA NA 436s 2 3.9 7.7 2.7 -10 183 12.7 44.9 436s 3 3.2 3.9 2.9 -9 183 12.4 45.6 436s 4 2.8 4.7 2.9 -8 184 16.9 50.1 436s 5 3.5 3.8 3.1 -7 190 18.4 57.2 436s 6 3.3 5.5 3.2 -6 193 19.4 57.1 436s 7 3.3 7.0 3.3 -5 198 20.1 61.0 436s 8 4.0 6.7 3.6 -4 203 19.6 64.0 436s 9 4.2 4.2 3.7 -3 208 19.8 64.4 436s 10 4.1 4.0 4.0 -2 211 21.1 64.5 436s 11 5.2 7.7 4.2 -1 216 21.7 67.0 436s 12 5.9 7.5 4.8 0 217 15.6 61.2 436s 13 4.9 8.3 5.3 1 213 11.4 53.4 436s 14 3.7 5.4 5.6 2 207 7.0 44.3 436s 15 4.0 6.8 6.0 3 202 11.2 45.1 436s 16 4.4 7.2 6.1 4 199 12.3 49.7 436s 17 2.9 8.3 7.4 5 198 14.0 54.4 436s 18 4.3 6.7 6.7 6 200 17.6 62.7 436s 19 5.3 7.4 7.7 7 202 17.3 65.0 436s 20 6.6 8.9 7.8 8 200 15.3 60.9 436s 21 7.4 9.6 8.0 9 201 19.0 69.5 436s 22 13.8 11.6 8.5 10 204 21.1 75.7 436s > model.matrix 436s [1] TRUE 436s > matrix of instrumental variables 436s Consumption_(Intercept) Consumption_govExp Consumption_taxes 436s Consumption_2 1 3.9 7.7 436s Consumption_3 1 3.2 3.9 436s Consumption_4 1 2.8 4.7 436s Consumption_5 1 3.5 3.8 436s Consumption_6 1 3.3 5.5 436s Consumption_7 1 3.3 7.0 436s Consumption_8 1 4.0 6.7 436s Consumption_9 1 4.2 4.2 436s Consumption_10 1 4.1 4.0 436s Consumption_11 1 5.2 7.7 436s Consumption_12 1 5.9 7.5 436s Consumption_13 1 4.9 8.3 436s Consumption_14 1 3.7 5.4 436s Consumption_15 1 4.0 6.8 436s Consumption_16 1 4.4 7.2 436s Consumption_17 1 2.9 8.3 436s Consumption_18 1 4.3 6.7 436s Consumption_19 1 5.3 7.4 436s Consumption_20 1 6.6 8.9 436s Consumption_21 1 7.4 9.6 436s Consumption_22 1 13.8 11.6 436s Investment_2 0 0.0 0.0 436s Investment_3 0 0.0 0.0 436s Investment_4 0 0.0 0.0 436s Investment_5 0 0.0 0.0 436s Investment_6 0 0.0 0.0 436s Investment_7 0 0.0 0.0 436s Investment_8 0 0.0 0.0 436s Investment_9 0 0.0 0.0 436s Investment_10 0 0.0 0.0 436s Investment_11 0 0.0 0.0 436s Investment_12 0 0.0 0.0 436s Investment_13 0 0.0 0.0 436s Investment_14 0 0.0 0.0 436s Investment_15 0 0.0 0.0 436s Investment_16 0 0.0 0.0 436s Investment_17 0 0.0 0.0 436s Investment_18 0 0.0 0.0 436s Investment_19 0 0.0 0.0 436s Investment_20 0 0.0 0.0 436s Investment_21 0 0.0 0.0 436s Investment_22 0 0.0 0.0 436s PrivateWages_2 0 0.0 0.0 436s PrivateWages_3 0 0.0 0.0 436s PrivateWages_4 0 0.0 0.0 436s PrivateWages_5 0 0.0 0.0 436s PrivateWages_6 0 0.0 0.0 436s PrivateWages_7 0 0.0 0.0 436s PrivateWages_8 0 0.0 0.0 436s PrivateWages_9 0 0.0 0.0 436s PrivateWages_10 0 0.0 0.0 436s PrivateWages_11 0 0.0 0.0 436s PrivateWages_12 0 0.0 0.0 436s PrivateWages_13 0 0.0 0.0 436s PrivateWages_14 0 0.0 0.0 436s PrivateWages_15 0 0.0 0.0 436s PrivateWages_16 0 0.0 0.0 436s PrivateWages_17 0 0.0 0.0 436s PrivateWages_18 0 0.0 0.0 436s PrivateWages_19 0 0.0 0.0 436s PrivateWages_20 0 0.0 0.0 436s PrivateWages_21 0 0.0 0.0 436s PrivateWages_22 0 0.0 0.0 436s Consumption_govWage Consumption_trend Consumption_capitalLag 436s Consumption_2 2.7 -10 183 436s Consumption_3 2.9 -9 183 436s Consumption_4 2.9 -8 184 436s Consumption_5 3.1 -7 190 436s Consumption_6 3.2 -6 193 436s Consumption_7 3.3 -5 198 436s Consumption_8 3.6 -4 203 436s Consumption_9 3.7 -3 208 436s Consumption_10 4.0 -2 211 436s Consumption_11 4.2 -1 216 436s Consumption_12 4.8 0 217 436s Consumption_13 5.3 1 213 436s Consumption_14 5.6 2 207 436s Consumption_15 6.0 3 202 436s Consumption_16 6.1 4 199 436s Consumption_17 7.4 5 198 436s Consumption_18 6.7 6 200 436s Consumption_19 7.7 7 202 436s Consumption_20 7.8 8 200 436s Consumption_21 8.0 9 201 436s Consumption_22 8.5 10 204 436s Investment_2 0.0 0 0 436s Investment_3 0.0 0 0 436s Investment_4 0.0 0 0 436s Investment_5 0.0 0 0 436s Investment_6 0.0 0 0 436s Investment_7 0.0 0 0 436s Investment_8 0.0 0 0 436s Investment_9 0.0 0 0 436s Investment_10 0.0 0 0 436s Investment_11 0.0 0 0 436s Investment_12 0.0 0 0 436s Investment_13 0.0 0 0 436s Investment_14 0.0 0 0 436s Investment_15 0.0 0 0 436s Investment_16 0.0 0 0 436s Investment_17 0.0 0 0 436s Investment_18 0.0 0 0 436s Investment_19 0.0 0 0 436s Investment_20 0.0 0 0 436s Investment_21 0.0 0 0 436s Investment_22 0.0 0 0 436s PrivateWages_2 0.0 0 0 436s PrivateWages_3 0.0 0 0 436s PrivateWages_4 0.0 0 0 436s PrivateWages_5 0.0 0 0 436s PrivateWages_6 0.0 0 0 436s PrivateWages_7 0.0 0 0 436s PrivateWages_8 0.0 0 0 436s PrivateWages_9 0.0 0 0 436s PrivateWages_10 0.0 0 0 436s PrivateWages_11 0.0 0 0 436s PrivateWages_12 0.0 0 0 436s PrivateWages_13 0.0 0 0 436s PrivateWages_14 0.0 0 0 436s PrivateWages_15 0.0 0 0 436s PrivateWages_16 0.0 0 0 436s PrivateWages_17 0.0 0 0 436s PrivateWages_18 0.0 0 0 436s PrivateWages_19 0.0 0 0 436s PrivateWages_20 0.0 0 0 436s PrivateWages_21 0.0 0 0 436s PrivateWages_22 0.0 0 0 436s Consumption_corpProfLag Consumption_gnpLag 436s Consumption_2 12.7 44.9 436s Consumption_3 12.4 45.6 436s Consumption_4 16.9 50.1 436s Consumption_5 18.4 57.2 436s Consumption_6 19.4 57.1 436s Consumption_7 20.1 61.0 436s Consumption_8 19.6 64.0 436s Consumption_9 19.8 64.4 436s Consumption_10 21.1 64.5 436s Consumption_11 21.7 67.0 436s Consumption_12 15.6 61.2 436s Consumption_13 11.4 53.4 436s Consumption_14 7.0 44.3 436s Consumption_15 11.2 45.1 436s Consumption_16 12.3 49.7 436s Consumption_17 14.0 54.4 436s Consumption_18 17.6 62.7 436s Consumption_19 17.3 65.0 436s Consumption_20 15.3 60.9 436s Consumption_21 19.0 69.5 436s Consumption_22 21.1 75.7 436s Investment_2 0.0 0.0 436s Investment_3 0.0 0.0 436s Investment_4 0.0 0.0 436s Investment_5 0.0 0.0 436s Investment_6 0.0 0.0 436s Investment_7 0.0 0.0 436s Investment_8 0.0 0.0 436s Investment_9 0.0 0.0 436s Investment_10 0.0 0.0 436s Investment_11 0.0 0.0 436s Investment_12 0.0 0.0 436s Investment_13 0.0 0.0 436s Investment_14 0.0 0.0 436s Investment_15 0.0 0.0 436s Investment_16 0.0 0.0 436s Investment_17 0.0 0.0 436s Investment_18 0.0 0.0 436s Investment_19 0.0 0.0 436s Investment_20 0.0 0.0 436s Investment_21 0.0 0.0 436s Investment_22 0.0 0.0 436s PrivateWages_2 0.0 0.0 436s PrivateWages_3 0.0 0.0 436s PrivateWages_4 0.0 0.0 436s PrivateWages_5 0.0 0.0 436s PrivateWages_6 0.0 0.0 436s PrivateWages_7 0.0 0.0 436s PrivateWages_8 0.0 0.0 436s PrivateWages_9 0.0 0.0 436s PrivateWages_10 0.0 0.0 436s PrivateWages_11 0.0 0.0 436s PrivateWages_12 0.0 0.0 436s PrivateWages_13 0.0 0.0 436s PrivateWages_14 0.0 0.0 436s PrivateWages_15 0.0 0.0 436s PrivateWages_16 0.0 0.0 436s PrivateWages_17 0.0 0.0 436s PrivateWages_18 0.0 0.0 436s PrivateWages_19 0.0 0.0 436s PrivateWages_20 0.0 0.0 436s PrivateWages_21 0.0 0.0 436s PrivateWages_22 0.0 0.0 436s Investment_(Intercept) Investment_govExp Investment_taxes 436s Consumption_2 0 0.0 0.0 436s Consumption_3 0 0.0 0.0 436s Consumption_4 0 0.0 0.0 436s Consumption_5 0 0.0 0.0 436s Consumption_6 0 0.0 0.0 436s Consumption_7 0 0.0 0.0 436s Consumption_8 0 0.0 0.0 436s Consumption_9 0 0.0 0.0 436s Consumption_10 0 0.0 0.0 436s Consumption_11 0 0.0 0.0 436s Consumption_12 0 0.0 0.0 436s Consumption_13 0 0.0 0.0 436s Consumption_14 0 0.0 0.0 436s Consumption_15 0 0.0 0.0 436s Consumption_16 0 0.0 0.0 436s Consumption_17 0 0.0 0.0 436s Consumption_18 0 0.0 0.0 436s Consumption_19 0 0.0 0.0 436s Consumption_20 0 0.0 0.0 436s Consumption_21 0 0.0 0.0 436s Consumption_22 0 0.0 0.0 436s Investment_2 1 3.9 7.7 436s Investment_3 1 3.2 3.9 436s Investment_4 1 2.8 4.7 436s Investment_5 1 3.5 3.8 436s Investment_6 1 3.3 5.5 436s Investment_7 1 3.3 7.0 436s Investment_8 1 4.0 6.7 436s Investment_9 1 4.2 4.2 436s Investment_10 1 4.1 4.0 436s Investment_11 1 5.2 7.7 436s Investment_12 1 5.9 7.5 436s Investment_13 1 4.9 8.3 436s Investment_14 1 3.7 5.4 436s Investment_15 1 4.0 6.8 436s Investment_16 1 4.4 7.2 436s Investment_17 1 2.9 8.3 436s Investment_18 1 4.3 6.7 436s Investment_19 1 5.3 7.4 436s Investment_20 1 6.6 8.9 436s Investment_21 1 7.4 9.6 436s Investment_22 1 13.8 11.6 436s PrivateWages_2 0 0.0 0.0 436s PrivateWages_3 0 0.0 0.0 436s PrivateWages_4 0 0.0 0.0 436s PrivateWages_5 0 0.0 0.0 436s PrivateWages_6 0 0.0 0.0 436s PrivateWages_7 0 0.0 0.0 436s PrivateWages_8 0 0.0 0.0 436s PrivateWages_9 0 0.0 0.0 436s PrivateWages_10 0 0.0 0.0 436s PrivateWages_11 0 0.0 0.0 436s PrivateWages_12 0 0.0 0.0 436s PrivateWages_13 0 0.0 0.0 436s PrivateWages_14 0 0.0 0.0 436s PrivateWages_15 0 0.0 0.0 436s PrivateWages_16 0 0.0 0.0 436s PrivateWages_17 0 0.0 0.0 436s PrivateWages_18 0 0.0 0.0 436s PrivateWages_19 0 0.0 0.0 436s PrivateWages_20 0 0.0 0.0 436s PrivateWages_21 0 0.0 0.0 436s PrivateWages_22 0 0.0 0.0 436s Investment_govWage Investment_trend Investment_capitalLag 436s Consumption_2 0.0 0 0 436s Consumption_3 0.0 0 0 436s Consumption_4 0.0 0 0 436s Consumption_5 0.0 0 0 436s Consumption_6 0.0 0 0 436s Consumption_7 0.0 0 0 436s Consumption_8 0.0 0 0 436s Consumption_9 0.0 0 0 436s Consumption_10 0.0 0 0 436s Consumption_11 0.0 0 0 436s Consumption_12 0.0 0 0 436s Consumption_13 0.0 0 0 436s Consumption_14 0.0 0 0 436s Consumption_15 0.0 0 0 436s Consumption_16 0.0 0 0 436s Consumption_17 0.0 0 0 436s Consumption_18 0.0 0 0 436s Consumption_19 0.0 0 0 436s Consumption_20 0.0 0 0 436s Consumption_21 0.0 0 0 436s Consumption_22 0.0 0 0 436s Investment_2 2.7 -10 183 436s Investment_3 2.9 -9 183 436s Investment_4 2.9 -8 184 436s Investment_5 3.1 -7 190 436s Investment_6 3.2 -6 193 436s Investment_7 3.3 -5 198 436s Investment_8 3.6 -4 203 436s Investment_9 3.7 -3 208 436s Investment_10 4.0 -2 211 436s Investment_11 4.2 -1 216 436s Investment_12 4.8 0 217 436s Investment_13 5.3 1 213 436s Investment_14 5.6 2 207 436s Investment_15 6.0 3 202 436s Investment_16 6.1 4 199 436s Investment_17 7.4 5 198 436s Investment_18 6.7 6 200 436s Investment_19 7.7 7 202 436s Investment_20 7.8 8 200 436s Investment_21 8.0 9 201 436s Investment_22 8.5 10 204 436s PrivateWages_2 0.0 0 0 436s PrivateWages_3 0.0 0 0 436s PrivateWages_4 0.0 0 0 436s PrivateWages_5 0.0 0 0 436s PrivateWages_6 0.0 0 0 436s PrivateWages_7 0.0 0 0 436s PrivateWages_8 0.0 0 0 436s PrivateWages_9 0.0 0 0 436s PrivateWages_10 0.0 0 0 436s PrivateWages_11 0.0 0 0 436s PrivateWages_12 0.0 0 0 436s PrivateWages_13 0.0 0 0 436s PrivateWages_14 0.0 0 0 436s PrivateWages_15 0.0 0 0 436s PrivateWages_16 0.0 0 0 436s PrivateWages_17 0.0 0 0 436s PrivateWages_18 0.0 0 0 436s PrivateWages_19 0.0 0 0 436s PrivateWages_20 0.0 0 0 436s PrivateWages_21 0.0 0 0 436s PrivateWages_22 0.0 0 0 436s Investment_corpProfLag Investment_gnpLag 436s Consumption_2 0.0 0.0 436s Consumption_3 0.0 0.0 436s Consumption_4 0.0 0.0 436s Consumption_5 0.0 0.0 436s Consumption_6 0.0 0.0 436s Consumption_7 0.0 0.0 436s Consumption_8 0.0 0.0 436s Consumption_9 0.0 0.0 436s Consumption_10 0.0 0.0 436s Consumption_11 0.0 0.0 436s Consumption_12 0.0 0.0 436s Consumption_13 0.0 0.0 436s Consumption_14 0.0 0.0 436s Consumption_15 0.0 0.0 436s Consumption_16 0.0 0.0 436s Consumption_17 0.0 0.0 436s Consumption_18 0.0 0.0 436s Consumption_19 0.0 0.0 436s Consumption_20 0.0 0.0 436s Consumption_21 0.0 0.0 436s Consumption_22 0.0 0.0 436s Investment_2 12.7 44.9 436s Investment_3 12.4 45.6 436s Investment_4 16.9 50.1 436s Investment_5 18.4 57.2 436s Investment_6 19.4 57.1 436s Investment_7 20.1 61.0 436s Investment_8 19.6 64.0 436s Investment_9 19.8 64.4 436s Investment_10 21.1 64.5 436s Investment_11 21.7 67.0 436s Investment_12 15.6 61.2 436s Investment_13 11.4 53.4 436s Investment_14 7.0 44.3 436s Investment_15 11.2 45.1 436s Investment_16 12.3 49.7 436s Investment_17 14.0 54.4 436s Investment_18 17.6 62.7 436s Investment_19 17.3 65.0 436s Investment_20 15.3 60.9 436s Investment_21 19.0 69.5 436s Investment_22 21.1 75.7 436s PrivateWages_2 0.0 0.0 436s PrivateWages_3 0.0 0.0 436s PrivateWages_4 0.0 0.0 436s PrivateWages_5 0.0 0.0 436s PrivateWages_6 0.0 0.0 436s PrivateWages_7 0.0 0.0 436s PrivateWages_8 0.0 0.0 436s PrivateWages_9 0.0 0.0 436s PrivateWages_10 0.0 0.0 436s PrivateWages_11 0.0 0.0 436s PrivateWages_12 0.0 0.0 436s PrivateWages_13 0.0 0.0 436s PrivateWages_14 0.0 0.0 436s PrivateWages_15 0.0 0.0 436s PrivateWages_16 0.0 0.0 436s PrivateWages_17 0.0 0.0 436s PrivateWages_18 0.0 0.0 436s PrivateWages_19 0.0 0.0 436s PrivateWages_20 0.0 0.0 436s PrivateWages_21 0.0 0.0 436s PrivateWages_22 0.0 0.0 436s PrivateWages_(Intercept) PrivateWages_govExp PrivateWages_taxes 436s Consumption_2 0 0.0 0.0 436s Consumption_3 0 0.0 0.0 436s Consumption_4 0 0.0 0.0 436s Consumption_5 0 0.0 0.0 436s Consumption_6 0 0.0 0.0 436s Consumption_7 0 0.0 0.0 436s Consumption_8 0 0.0 0.0 436s Consumption_9 0 0.0 0.0 436s Consumption_10 0 0.0 0.0 436s Consumption_11 0 0.0 0.0 436s Consumption_12 0 0.0 0.0 436s Consumption_13 0 0.0 0.0 436s Consumption_14 0 0.0 0.0 436s Consumption_15 0 0.0 0.0 436s Consumption_16 0 0.0 0.0 436s Consumption_17 0 0.0 0.0 436s Consumption_18 0 0.0 0.0 436s Consumption_19 0 0.0 0.0 436s Consumption_20 0 0.0 0.0 436s Consumption_21 0 0.0 0.0 436s Consumption_22 0 0.0 0.0 436s Investment_2 0 0.0 0.0 436s Investment_3 0 0.0 0.0 436s Investment_4 0 0.0 0.0 436s Investment_5 0 0.0 0.0 436s Investment_6 0 0.0 0.0 436s Investment_7 0 0.0 0.0 436s Investment_8 0 0.0 0.0 436s Investment_9 0 0.0 0.0 436s Investment_10 0 0.0 0.0 436s Investment_11 0 0.0 0.0 436s Investment_12 0 0.0 0.0 436s Investment_13 0 0.0 0.0 436s Investment_14 0 0.0 0.0 436s Investment_15 0 0.0 0.0 436s Investment_16 0 0.0 0.0 436s Investment_17 0 0.0 0.0 436s Investment_18 0 0.0 0.0 436s Investment_19 0 0.0 0.0 436s Investment_20 0 0.0 0.0 436s Investment_21 0 0.0 0.0 436s Investment_22 0 0.0 0.0 436s PrivateWages_2 1 3.9 7.7 436s PrivateWages_3 1 3.2 3.9 436s PrivateWages_4 1 2.8 4.7 436s PrivateWages_5 1 3.5 3.8 436s PrivateWages_6 1 3.3 5.5 436s PrivateWages_7 1 3.3 7.0 436s PrivateWages_8 1 4.0 6.7 436s PrivateWages_9 1 4.2 4.2 436s PrivateWages_10 1 4.1 4.0 436s PrivateWages_11 1 5.2 7.7 436s PrivateWages_12 1 5.9 7.5 436s PrivateWages_13 1 4.9 8.3 436s PrivateWages_14 1 3.7 5.4 436s PrivateWages_15 1 4.0 6.8 436s PrivateWages_16 1 4.4 7.2 436s PrivateWages_17 1 2.9 8.3 436s PrivateWages_18 1 4.3 6.7 436s PrivateWages_19 1 5.3 7.4 436s PrivateWages_20 1 6.6 8.9 436s PrivateWages_21 1 7.4 9.6 436s PrivateWages_22 1 13.8 11.6 436s PrivateWages_govWage PrivateWages_trend PrivateWages_capitalLag 436s Consumption_2 0.0 0 0 436s Consumption_3 0.0 0 0 436s Consumption_4 0.0 0 0 436s Consumption_5 0.0 0 0 436s Consumption_6 0.0 0 0 436s Consumption_7 0.0 0 0 436s Consumption_8 0.0 0 0 436s Consumption_9 0.0 0 0 436s Consumption_10 0.0 0 0 436s Consumption_11 0.0 0 0 436s Consumption_12 0.0 0 0 436s Consumption_13 0.0 0 0 436s Consumption_14 0.0 0 0 436s Consumption_15 0.0 0 0 436s Consumption_16 0.0 0 0 436s Consumption_17 0.0 0 0 436s Consumption_18 0.0 0 0 436s Consumption_19 0.0 0 0 436s Consumption_20 0.0 0 0 436s Consumption_21 0.0 0 0 436s Consumption_22 0.0 0 0 436s Investment_2 0.0 0 0 436s Investment_3 0.0 0 0 436s Investment_4 0.0 0 0 436s Investment_5 0.0 0 0 436s Investment_6 0.0 0 0 436s Investment_7 0.0 0 0 436s Investment_8 0.0 0 0 436s Investment_9 0.0 0 0 436s Investment_10 0.0 0 0 436s Investment_11 0.0 0 0 436s Investment_12 0.0 0 0 436s Investment_13 0.0 0 0 436s Investment_14 0.0 0 0 436s Investment_15 0.0 0 0 436s Investment_16 0.0 0 0 436s Investment_17 0.0 0 0 436s Investment_18 0.0 0 0 436s Investment_19 0.0 0 0 436s Investment_20 0.0 0 0 436s Investment_21 0.0 0 0 436s Investment_22 0.0 0 0 436s PrivateWages_2 2.7 -10 183 436s PrivateWages_3 2.9 -9 183 436s PrivateWages_4 2.9 -8 184 436s PrivateWages_5 3.1 -7 190 436s PrivateWages_6 3.2 -6 193 436s PrivateWages_7 3.3 -5 198 436s PrivateWages_8 3.6 -4 203 436s PrivateWages_9 3.7 -3 208 436s PrivateWages_10 4.0 -2 211 436s PrivateWages_11 4.2 -1 216 436s PrivateWages_12 4.8 0 217 436s PrivateWages_13 5.3 1 213 436s PrivateWages_14 5.6 2 207 436s PrivateWages_15 6.0 3 202 436s PrivateWages_16 6.1 4 199 436s PrivateWages_17 7.4 5 198 436s PrivateWages_18 6.7 6 200 436s PrivateWages_19 7.7 7 202 436s PrivateWages_20 7.8 8 200 436s PrivateWages_21 8.0 9 201 436s PrivateWages_22 8.5 10 204 436s PrivateWages_corpProfLag PrivateWages_gnpLag 436s Consumption_2 0.0 0.0 436s Consumption_3 0.0 0.0 436s Consumption_4 0.0 0.0 436s Consumption_5 0.0 0.0 436s Consumption_6 0.0 0.0 436s Consumption_7 0.0 0.0 436s Consumption_8 0.0 0.0 436s Consumption_9 0.0 0.0 436s Consumption_10 0.0 0.0 436s Consumption_11 0.0 0.0 436s Consumption_12 0.0 0.0 436s Consumption_13 0.0 0.0 436s Consumption_14 0.0 0.0 436s Consumption_15 0.0 0.0 436s Consumption_16 0.0 0.0 436s Consumption_17 0.0 0.0 436s Consumption_18 0.0 0.0 436s Consumption_19 0.0 0.0 436s Consumption_20 0.0 0.0 436s Consumption_21 0.0 0.0 436s Consumption_22 0.0 0.0 436s Investment_2 0.0 0.0 436s Investment_3 0.0 0.0 436s Investment_4 0.0 0.0 436s Investment_5 0.0 0.0 436s Investment_6 0.0 0.0 436s Investment_7 0.0 0.0 436s Investment_8 0.0 0.0 436s Investment_9 0.0 0.0 436s Investment_10 0.0 0.0 436s Investment_11 0.0 0.0 436s Investment_12 0.0 0.0 436s Investment_13 0.0 0.0 436s Investment_14 0.0 0.0 436s Investment_15 0.0 0.0 436s Investment_16 0.0 0.0 436s Investment_17 0.0 0.0 436s Investment_18 0.0 0.0 436s Investment_19 0.0 0.0 436s Investment_20 0.0 0.0 436s Investment_21 0.0 0.0 436s Investment_22 0.0 0.0 436s PrivateWages_2 12.7 44.9 436s PrivateWages_3 12.4 45.6 436s PrivateWages_4 16.9 50.1 436s PrivateWages_5 18.4 57.2 436s PrivateWages_6 19.4 57.1 436s PrivateWages_7 20.1 61.0 436s PrivateWages_8 19.6 64.0 436s PrivateWages_9 19.8 64.4 436s PrivateWages_10 21.1 64.5 436s PrivateWages_11 21.7 67.0 436s PrivateWages_12 15.6 61.2 436s PrivateWages_13 11.4 53.4 436s PrivateWages_14 7.0 44.3 436s PrivateWages_15 11.2 45.1 436s PrivateWages_16 12.3 49.7 436s PrivateWages_17 14.0 54.4 436s PrivateWages_18 17.6 62.7 436s PrivateWages_19 17.3 65.0 436s PrivateWages_20 15.3 60.9 436s PrivateWages_21 19.0 69.5 436s PrivateWages_22 21.1 75.7 436s > matrix of fitted regressors 436s Consumption_(Intercept) Consumption_corpProf 436s Consumption_2 1 13.26 436s Consumption_3 1 16.58 436s Consumption_4 1 19.28 436s Consumption_5 1 20.96 436s Consumption_6 1 19.77 436s Consumption_7 1 18.24 436s Consumption_8 1 17.57 436s Consumption_9 1 19.54 436s Consumption_10 1 20.38 436s Consumption_11 1 17.18 436s Consumption_12 1 12.71 436s Consumption_13 1 9.00 436s Consumption_14 1 9.05 436s Consumption_15 1 12.67 436s Consumption_16 1 14.42 436s Consumption_17 1 14.71 436s Consumption_18 1 19.80 436s Consumption_19 1 19.21 436s Consumption_20 1 17.42 436s Consumption_21 1 20.31 436s Consumption_22 1 22.66 436s Investment_2 0 0.00 436s Investment_3 0 0.00 436s Investment_4 0 0.00 436s Investment_5 0 0.00 436s Investment_6 0 0.00 436s Investment_7 0 0.00 436s Investment_8 0 0.00 436s Investment_9 0 0.00 436s Investment_10 0 0.00 436s Investment_11 0 0.00 436s Investment_12 0 0.00 436s Investment_13 0 0.00 436s Investment_14 0 0.00 436s Investment_15 0 0.00 436s Investment_16 0 0.00 436s Investment_17 0 0.00 436s Investment_18 0 0.00 436s Investment_19 0 0.00 436s Investment_20 0 0.00 436s Investment_21 0 0.00 436s Investment_22 0 0.00 436s PrivateWages_2 0 0.00 436s PrivateWages_3 0 0.00 436s PrivateWages_4 0 0.00 436s PrivateWages_5 0 0.00 436s PrivateWages_6 0 0.00 436s PrivateWages_7 0 0.00 436s PrivateWages_8 0 0.00 436s PrivateWages_9 0 0.00 436s PrivateWages_10 0 0.00 436s PrivateWages_11 0 0.00 436s PrivateWages_12 0 0.00 436s PrivateWages_13 0 0.00 436s PrivateWages_14 0 0.00 436s PrivateWages_15 0 0.00 436s PrivateWages_16 0 0.00 436s PrivateWages_17 0 0.00 436s PrivateWages_18 0 0.00 436s PrivateWages_19 0 0.00 436s PrivateWages_20 0 0.00 436s PrivateWages_21 0 0.00 436s PrivateWages_22 0 0.00 436s Consumption_corpProfLag Consumption_wages 436s Consumption_2 12.7 29.4 436s Consumption_3 12.4 31.8 436s Consumption_4 16.9 35.8 436s Consumption_5 18.4 39.1 436s Consumption_6 19.4 39.1 436s Consumption_7 20.1 39.4 436s Consumption_8 19.6 40.2 436s Consumption_9 19.8 42.3 436s Consumption_10 21.1 44.0 436s Consumption_11 21.7 43.7 436s Consumption_12 15.6 39.5 436s Consumption_13 11.4 35.1 436s Consumption_14 7.0 32.8 436s Consumption_15 11.2 37.5 436s Consumption_16 12.3 40.1 436s Consumption_17 14.0 41.7 436s Consumption_18 17.6 47.9 436s Consumption_19 17.3 49.3 436s Consumption_20 15.3 48.4 436s Consumption_21 19.0 53.4 436s Consumption_22 21.1 60.7 436s Investment_2 0.0 0.0 436s Investment_3 0.0 0.0 436s Investment_4 0.0 0.0 436s Investment_5 0.0 0.0 436s Investment_6 0.0 0.0 436s Investment_7 0.0 0.0 436s Investment_8 0.0 0.0 436s Investment_9 0.0 0.0 436s Investment_10 0.0 0.0 436s Investment_11 0.0 0.0 436s Investment_12 0.0 0.0 436s Investment_13 0.0 0.0 436s Investment_14 0.0 0.0 436s Investment_15 0.0 0.0 436s Investment_16 0.0 0.0 436s Investment_17 0.0 0.0 436s Investment_18 0.0 0.0 436s Investment_19 0.0 0.0 436s Investment_20 0.0 0.0 436s Investment_21 0.0 0.0 436s Investment_22 0.0 0.0 436s PrivateWages_2 0.0 0.0 436s PrivateWages_3 0.0 0.0 436s PrivateWages_4 0.0 0.0 436s PrivateWages_5 0.0 0.0 436s PrivateWages_6 0.0 0.0 436s PrivateWages_7 0.0 0.0 436s PrivateWages_8 0.0 0.0 436s PrivateWages_9 0.0 0.0 436s PrivateWages_10 0.0 0.0 436s PrivateWages_11 0.0 0.0 436s PrivateWages_12 0.0 0.0 436s PrivateWages_13 0.0 0.0 436s PrivateWages_14 0.0 0.0 436s PrivateWages_15 0.0 0.0 436s PrivateWages_16 0.0 0.0 436s PrivateWages_17 0.0 0.0 436s PrivateWages_18 0.0 0.0 436s PrivateWages_19 0.0 0.0 436s PrivateWages_20 0.0 0.0 436s PrivateWages_21 0.0 0.0 436s PrivateWages_22 0.0 0.0 436s Investment_(Intercept) Investment_corpProf 436s Consumption_2 0 0.00 436s Consumption_3 0 0.00 436s Consumption_4 0 0.00 436s Consumption_5 0 0.00 436s Consumption_6 0 0.00 436s Consumption_7 0 0.00 436s Consumption_8 0 0.00 436s Consumption_9 0 0.00 436s Consumption_10 0 0.00 436s Consumption_11 0 0.00 436s Consumption_12 0 0.00 436s Consumption_13 0 0.00 436s Consumption_14 0 0.00 436s Consumption_15 0 0.00 436s Consumption_16 0 0.00 436s Consumption_17 0 0.00 436s Consumption_18 0 0.00 436s Consumption_19 0 0.00 436s Consumption_20 0 0.00 436s Consumption_21 0 0.00 436s Consumption_22 0 0.00 436s Investment_2 1 13.26 436s Investment_3 1 16.58 436s Investment_4 1 19.28 436s Investment_5 1 20.96 436s Investment_6 1 19.77 436s Investment_7 1 18.24 436s Investment_8 1 17.57 436s Investment_9 1 19.54 436s Investment_10 1 20.38 436s Investment_11 1 17.18 436s Investment_12 1 12.71 436s Investment_13 1 9.00 436s Investment_14 1 9.05 436s Investment_15 1 12.67 436s Investment_16 1 14.42 436s Investment_17 1 14.71 436s Investment_18 1 19.80 436s Investment_19 1 19.21 436s Investment_20 1 17.42 436s Investment_21 1 20.31 436s Investment_22 1 22.66 436s PrivateWages_2 0 0.00 436s PrivateWages_3 0 0.00 436s PrivateWages_4 0 0.00 436s PrivateWages_5 0 0.00 436s PrivateWages_6 0 0.00 436s PrivateWages_7 0 0.00 436s PrivateWages_8 0 0.00 436s PrivateWages_9 0 0.00 436s PrivateWages_10 0 0.00 436s PrivateWages_11 0 0.00 436s PrivateWages_12 0 0.00 436s PrivateWages_13 0 0.00 436s PrivateWages_14 0 0.00 436s PrivateWages_15 0 0.00 436s PrivateWages_16 0 0.00 436s PrivateWages_17 0 0.00 436s PrivateWages_18 0 0.00 436s PrivateWages_19 0 0.00 436s PrivateWages_20 0 0.00 436s PrivateWages_21 0 0.00 436s PrivateWages_22 0 0.00 436s Investment_corpProfLag Investment_capitalLag 436s Consumption_2 0.0 0 436s Consumption_3 0.0 0 436s Consumption_4 0.0 0 436s Consumption_5 0.0 0 436s Consumption_6 0.0 0 436s Consumption_7 0.0 0 436s Consumption_8 0.0 0 436s Consumption_9 0.0 0 436s Consumption_10 0.0 0 436s Consumption_11 0.0 0 436s Consumption_12 0.0 0 436s Consumption_13 0.0 0 436s Consumption_14 0.0 0 436s Consumption_15 0.0 0 436s Consumption_16 0.0 0 436s Consumption_17 0.0 0 436s Consumption_18 0.0 0 436s Consumption_19 0.0 0 436s Consumption_20 0.0 0 436s Consumption_21 0.0 0 436s Consumption_22 0.0 0 436s Investment_2 12.7 183 436s Investment_3 12.4 183 436s Investment_4 16.9 184 436s Investment_5 18.4 190 436s Investment_6 19.4 193 436s Investment_7 20.1 198 436s Investment_8 19.6 203 436s Investment_9 19.8 208 436s Investment_10 21.1 211 436s Investment_11 21.7 216 436s Investment_12 15.6 217 436s Investment_13 11.4 213 436s Investment_14 7.0 207 436s Investment_15 11.2 202 436s Investment_16 12.3 199 436s Investment_17 14.0 198 436s Investment_18 17.6 200 436s Investment_19 17.3 202 436s Investment_20 15.3 200 436s Investment_21 19.0 201 436s Investment_22 21.1 204 436s PrivateWages_2 0.0 0 436s PrivateWages_3 0.0 0 436s PrivateWages_4 0.0 0 436s PrivateWages_5 0.0 0 436s PrivateWages_6 0.0 0 436s PrivateWages_7 0.0 0 436s PrivateWages_8 0.0 0 436s PrivateWages_9 0.0 0 436s PrivateWages_10 0.0 0 436s PrivateWages_11 0.0 0 436s PrivateWages_12 0.0 0 436s PrivateWages_13 0.0 0 436s PrivateWages_14 0.0 0 436s PrivateWages_15 0.0 0 436s PrivateWages_16 0.0 0 436s PrivateWages_17 0.0 0 436s PrivateWages_18 0.0 0 436s PrivateWages_19 0.0 0 436s PrivateWages_20 0.0 0 436s PrivateWages_21 0.0 0 436s PrivateWages_22 0.0 0 436s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 436s Consumption_2 0 0.0 0.0 436s Consumption_3 0 0.0 0.0 436s Consumption_4 0 0.0 0.0 436s Consumption_5 0 0.0 0.0 436s Consumption_6 0 0.0 0.0 436s Consumption_7 0 0.0 0.0 436s Consumption_8 0 0.0 0.0 436s Consumption_9 0 0.0 0.0 436s Consumption_10 0 0.0 0.0 436s Consumption_11 0 0.0 0.0 436s Consumption_12 0 0.0 0.0 436s Consumption_13 0 0.0 0.0 436s Consumption_14 0 0.0 0.0 436s Consumption_15 0 0.0 0.0 436s Consumption_16 0 0.0 0.0 436s Consumption_17 0 0.0 0.0 436s Consumption_18 0 0.0 0.0 436s Consumption_19 0 0.0 0.0 436s Consumption_20 0 0.0 0.0 436s Consumption_21 0 0.0 0.0 436s Consumption_22 0 0.0 0.0 436s Investment_2 0 0.0 0.0 436s Investment_3 0 0.0 0.0 436s Investment_4 0 0.0 0.0 436s Investment_5 0 0.0 0.0 436s Investment_6 0 0.0 0.0 436s Investment_7 0 0.0 0.0 436s Investment_8 0 0.0 0.0 436s Investment_9 0 0.0 0.0 436s Investment_10 0 0.0 0.0 436s Investment_11 0 0.0 0.0 436s Investment_12 0 0.0 0.0 436s Investment_13 0 0.0 0.0 436s Investment_14 0 0.0 0.0 436s Investment_15 0 0.0 0.0 436s Investment_16 0 0.0 0.0 436s Investment_17 0 0.0 0.0 436s Investment_18 0 0.0 0.0 436s Investment_19 0 0.0 0.0 436s Investment_20 0 0.0 0.0 436s Investment_21 0 0.0 0.0 436s Investment_22 0 0.0 0.0 436s PrivateWages_2 1 47.7 44.9 436s PrivateWages_3 1 49.3 45.6 436s PrivateWages_4 1 56.8 50.1 436s PrivateWages_5 1 60.7 57.2 436s PrivateWages_6 1 61.2 57.1 436s PrivateWages_7 1 61.3 61.0 436s PrivateWages_8 1 60.9 64.0 436s PrivateWages_9 1 62.4 64.4 436s PrivateWages_10 1 64.4 64.5 436s PrivateWages_11 1 64.4 67.0 436s PrivateWages_12 1 54.9 61.2 436s PrivateWages_13 1 47.1 53.4 436s PrivateWages_14 1 41.6 44.3 436s PrivateWages_15 1 51.0 45.1 436s PrivateWages_16 1 55.7 49.7 436s PrivateWages_17 1 57.3 54.4 436s PrivateWages_18 1 67.7 62.7 436s PrivateWages_19 1 68.2 65.0 436s PrivateWages_20 1 66.9 60.9 436s PrivateWages_21 1 75.3 69.5 436s PrivateWages_22 1 86.5 75.7 436s PrivateWages_trend 436s Consumption_2 0 436s Consumption_3 0 436s Consumption_4 0 436s Consumption_5 0 436s Consumption_6 0 436s Consumption_7 0 436s Consumption_8 0 436s Consumption_9 0 436s Consumption_10 0 436s Consumption_11 0 436s Consumption_12 0 436s Consumption_13 0 436s Consumption_14 0 436s Consumption_15 0 436s Consumption_16 0 436s Consumption_17 0 436s Consumption_18 0 436s Consumption_19 0 436s Consumption_20 0 436s Consumption_21 0 436s Consumption_22 0 436s Investment_2 0 436s Investment_3 0 436s Investment_4 0 436s Investment_5 0 436s Investment_6 0 436s Investment_7 0 436s Investment_8 0 436s Investment_9 0 436s Investment_10 0 436s Investment_11 0 436s Investment_12 0 436s Investment_13 0 436s Investment_14 0 436s Investment_15 0 436s Investment_16 0 436s Investment_17 0 436s Investment_18 0 436s Investment_19 0 436s Investment_20 0 436s Investment_21 0 436s Investment_22 0 436s PrivateWages_2 -10 436s PrivateWages_3 -9 436s PrivateWages_4 -8 436s PrivateWages_5 -7 436s PrivateWages_6 -6 436s PrivateWages_7 -5 436s PrivateWages_8 -4 436s PrivateWages_9 -3 436s PrivateWages_10 -2 436s PrivateWages_11 -1 436s PrivateWages_12 0 436s PrivateWages_13 1 436s PrivateWages_14 2 436s PrivateWages_15 3 436s PrivateWages_16 4 436s PrivateWages_17 5 436s PrivateWages_18 6 436s PrivateWages_19 7 436s PrivateWages_20 8 436s PrivateWages_21 9 436s PrivateWages_22 10 436s > nobs 436s [1] 63 436s > linearHypothesis 436s Linear hypothesis test (Theil's F test) 436s 436s Hypothesis: 436s Consumption_corpProf + Investment_capitalLag = 0 436s 436s Model 1: restricted model 436s Model 2: kleinModel 436s 436s Res.Df Df F Pr(>F) 436s 1 52 436s 2 51 1 1.08 0.3 436s Linear hypothesis test (F statistic of a Wald test) 436s 436s Hypothesis: 436s Consumption_corpProf + Investment_capitalLag = 0 436s 436s Model 1: restricted model 436s Model 2: kleinModel 436s 436s Res.Df Df F Pr(>F) 436s 1 52 436s 2 51 1 1.29 0.26 436s Linear hypothesis test (Chi^2 statistic of a Wald test) 436s 436s Hypothesis: 436s Consumption_corpProf + Investment_capitalLag = 0 436s 436s Model 1: restricted model 436s Model 2: kleinModel 436s 436s Res.Df Df Chisq Pr(>Chisq) 436s 1 52 436s 2 51 1 1.29 0.26 436s Linear hypothesis test (Theil's F test) 436s 436s Hypothesis: 436s Consumption_corpProf + Investment_capitalLag = 0 436s Consumption_corpProfLag - PrivateWages_trend = 0 436s 436s Model 1: restricted model 436s Model 2: kleinModel 436s 436s Res.Df Df F Pr(>F) 436s 1 53 436s 2 51 2 0.54 0.58 436s Linear hypothesis test (F statistic of a Wald test) 436s 436s Hypothesis: 436s Consumption_corpProf + Investment_capitalLag = 0 436s Consumption_corpProfLag - PrivateWages_trend = 0 436s 436s Model 1: restricted model 436s Model 2: kleinModel 436s 436s Res.Df Df F Pr(>F) 436s 1 53 436s 2 51 2 0.65 0.53 436s Linear hypothesis test (Chi^2 statistic of a Wald test) 436s 436s Hypothesis: 436s Consumption_corpProf + Investment_capitalLag = 0 436s Consumption_corpProfLag - PrivateWages_trend = 0 436s 436s Model 1: restricted model 436s Model 2: kleinModel 436s 436s Res.Df Df Chisq Pr(>Chisq) 436s 1 53 436s 2 51 2 1.3 0.52 436s > logLik 436s 'log Lik.' -76.3 (df=13) 436s 'log Lik.' -85.5 (df=13) 436s Estimating function 436s Consumption_(Intercept) Consumption_corpProf 436s Consumption_2 -1.455 -19.28 436s Consumption_3 -0.246 -4.08 436s Consumption_4 -0.309 -5.96 436s Consumption_5 -1.952 -40.92 436s Consumption_6 -0.199 -3.93 436s Consumption_7 2.000 36.47 436s Consumption_8 2.547 44.76 436s Consumption_9 1.829 35.74 436s Consumption_10 0.665 13.55 436s Consumption_11 -1.947 -33.46 436s Consumption_12 -1.232 -15.65 436s Consumption_13 -2.039 -18.35 436s Consumption_14 1.714 15.52 436s Consumption_15 -0.877 -11.11 436s Consumption_16 -0.684 -9.87 436s Consumption_17 4.077 59.98 436s Consumption_18 -0.793 -15.70 436s Consumption_19 -3.072 -59.01 436s Consumption_20 2.230 38.84 436s Consumption_21 0.744 15.11 436s Consumption_22 -1.000 -22.66 436s Investment_2 0.000 0.00 436s Investment_3 0.000 0.00 436s Investment_4 0.000 0.00 436s Investment_5 0.000 0.00 436s Investment_6 0.000 0.00 436s Investment_7 0.000 0.00 436s Investment_8 0.000 0.00 436s Investment_9 0.000 0.00 436s Investment_10 0.000 0.00 436s Investment_11 0.000 0.00 436s Investment_12 0.000 0.00 436s Investment_13 0.000 0.00 436s Investment_14 0.000 0.00 436s Investment_15 0.000 0.00 436s Investment_16 0.000 0.00 436s Investment_17 0.000 0.00 436s Investment_18 0.000 0.00 436s Investment_19 0.000 0.00 436s Investment_20 0.000 0.00 436s Investment_21 0.000 0.00 436s Investment_22 0.000 0.00 436s PrivateWages_2 0.000 0.00 436s PrivateWages_3 0.000 0.00 436s PrivateWages_4 0.000 0.00 436s PrivateWages_5 0.000 0.00 436s PrivateWages_6 0.000 0.00 436s PrivateWages_7 0.000 0.00 436s PrivateWages_8 0.000 0.00 436s PrivateWages_9 0.000 0.00 436s PrivateWages_10 0.000 0.00 436s PrivateWages_11 0.000 0.00 436s PrivateWages_12 0.000 0.00 436s PrivateWages_13 0.000 0.00 436s PrivateWages_14 0.000 0.00 436s PrivateWages_15 0.000 0.00 436s PrivateWages_16 0.000 0.00 436s PrivateWages_17 0.000 0.00 436s PrivateWages_18 0.000 0.00 436s PrivateWages_19 0.000 0.00 436s PrivateWages_20 0.000 0.00 436s PrivateWages_21 0.000 0.00 436s PrivateWages_22 0.000 0.00 436s Consumption_corpProfLag Consumption_wages 436s Consumption_2 -18.47 -42.77 436s Consumption_3 -3.05 -7.82 436s Consumption_4 -5.22 -11.05 436s Consumption_5 -35.93 -76.29 436s Consumption_6 -3.85 -7.77 436s Consumption_7 40.20 78.70 436s Consumption_8 49.93 102.36 436s Consumption_9 36.21 77.42 436s Consumption_10 14.03 29.28 436s Consumption_11 -42.26 -85.10 436s Consumption_12 -19.22 -48.63 436s Consumption_13 -23.25 -71.64 436s Consumption_14 12.00 56.20 436s Consumption_15 -9.82 -32.89 436s Consumption_16 -8.42 -27.47 436s Consumption_17 57.07 170.01 436s Consumption_18 -13.96 -37.97 436s Consumption_19 -53.15 -151.48 436s Consumption_20 34.12 107.90 436s Consumption_21 14.14 39.73 436s Consumption_22 -21.10 -60.72 436s Investment_2 0.00 0.00 436s Investment_3 0.00 0.00 436s Investment_4 0.00 0.00 436s Investment_5 0.00 0.00 436s Investment_6 0.00 0.00 436s Investment_7 0.00 0.00 436s Investment_8 0.00 0.00 436s Investment_9 0.00 0.00 436s Investment_10 0.00 0.00 436s Investment_11 0.00 0.00 436s Investment_12 0.00 0.00 436s Investment_13 0.00 0.00 436s Investment_14 0.00 0.00 436s Investment_15 0.00 0.00 436s Investment_16 0.00 0.00 436s Investment_17 0.00 0.00 436s Investment_18 0.00 0.00 436s Investment_19 0.00 0.00 436s Investment_20 0.00 0.00 436s Investment_21 0.00 0.00 436s Investment_22 0.00 0.00 436s PrivateWages_2 0.00 0.00 436s PrivateWages_3 0.00 0.00 436s PrivateWages_4 0.00 0.00 436s PrivateWages_5 0.00 0.00 436s PrivateWages_6 0.00 0.00 436s PrivateWages_7 0.00 0.00 436s PrivateWages_8 0.00 0.00 436s PrivateWages_9 0.00 0.00 436s PrivateWages_10 0.00 0.00 436s PrivateWages_11 0.00 0.00 436s PrivateWages_12 0.00 0.00 436s PrivateWages_13 0.00 0.00 436s PrivateWages_14 0.00 0.00 436s PrivateWages_15 0.00 0.00 436s PrivateWages_16 0.00 0.00 436s PrivateWages_17 0.00 0.00 436s PrivateWages_18 0.00 0.00 436s PrivateWages_19 0.00 0.00 436s PrivateWages_20 0.00 0.00 436s PrivateWages_21 0.00 0.00 436s PrivateWages_22 0.00 0.00 436s Investment_(Intercept) Investment_corpProf 436s Consumption_2 0.0000 0.000 436s Consumption_3 0.0000 0.000 436s Consumption_4 0.0000 0.000 436s Consumption_5 0.0000 0.000 436s Consumption_6 0.0000 0.000 436s Consumption_7 0.0000 0.000 436s Consumption_8 0.0000 0.000 436s Consumption_9 0.0000 0.000 436s Consumption_10 0.0000 0.000 436s Consumption_11 0.0000 0.000 436s Consumption_12 0.0000 0.000 436s Consumption_13 0.0000 0.000 436s Consumption_14 0.0000 0.000 436s Consumption_15 0.0000 0.000 436s Consumption_16 0.0000 0.000 436s Consumption_17 0.0000 0.000 436s Consumption_18 0.0000 0.000 436s Consumption_19 0.0000 0.000 436s Consumption_20 0.0000 0.000 436s Consumption_21 0.0000 0.000 436s Consumption_22 0.0000 0.000 436s Investment_2 -1.4484 -19.199 436s Investment_3 0.3058 5.070 436s Investment_4 0.7275 14.029 436s Investment_5 -1.8279 -38.314 436s Investment_6 0.3088 6.104 436s Investment_7 1.4119 25.751 436s Investment_8 1.3034 22.906 436s Investment_9 0.3472 6.785 436s Investment_10 1.9947 40.642 436s Investment_11 -1.1903 -20.449 436s Investment_12 -1.0029 -12.742 436s Investment_13 -1.1958 -10.762 436s Investment_14 1.6279 14.739 436s Investment_15 -0.2072 -2.625 436s Investment_16 0.0790 1.140 436s Investment_17 2.1831 32.118 436s Investment_18 -0.5667 -11.219 436s Investment_19 -3.8778 -74.479 436s Investment_20 0.5228 9.107 436s Investment_21 0.0154 0.312 436s Investment_22 0.4893 11.087 436s PrivateWages_2 0.0000 0.000 436s PrivateWages_3 0.0000 0.000 436s PrivateWages_4 0.0000 0.000 436s PrivateWages_5 0.0000 0.000 436s PrivateWages_6 0.0000 0.000 436s PrivateWages_7 0.0000 0.000 436s PrivateWages_8 0.0000 0.000 436s PrivateWages_9 0.0000 0.000 436s PrivateWages_10 0.0000 0.000 436s PrivateWages_11 0.0000 0.000 436s PrivateWages_12 0.0000 0.000 436s PrivateWages_13 0.0000 0.000 436s PrivateWages_14 0.0000 0.000 436s PrivateWages_15 0.0000 0.000 436s PrivateWages_16 0.0000 0.000 436s PrivateWages_17 0.0000 0.000 436s PrivateWages_18 0.0000 0.000 436s PrivateWages_19 0.0000 0.000 436s PrivateWages_20 0.0000 0.000 436s PrivateWages_21 0.0000 0.000 436s PrivateWages_22 0.0000 0.000 436s Investment_corpProfLag Investment_capitalLag 436s Consumption_2 0.000 0.0 436s Consumption_3 0.000 0.0 436s Consumption_4 0.000 0.0 436s Consumption_5 0.000 0.0 436s Consumption_6 0.000 0.0 436s Consumption_7 0.000 0.0 436s Consumption_8 0.000 0.0 436s Consumption_9 0.000 0.0 436s Consumption_10 0.000 0.0 436s Consumption_11 0.000 0.0 436s Consumption_12 0.000 0.0 436s Consumption_13 0.000 0.0 436s Consumption_14 0.000 0.0 436s Consumption_15 0.000 0.0 436s Consumption_16 0.000 0.0 436s Consumption_17 0.000 0.0 436s Consumption_18 0.000 0.0 436s Consumption_19 0.000 0.0 436s Consumption_20 0.000 0.0 436s Consumption_21 0.000 0.0 436s Consumption_22 0.000 0.0 436s Investment_2 -18.395 -264.8 436s Investment_3 3.792 55.8 436s Investment_4 12.295 134.2 436s Investment_5 -33.634 -346.8 436s Investment_6 5.991 59.5 436s Investment_7 28.378 279.3 436s Investment_8 25.548 265.1 436s Investment_9 6.875 72.1 436s Investment_10 42.088 420.1 436s Investment_11 -25.829 -256.7 436s Investment_12 -15.646 -217.3 436s Investment_13 -13.632 -255.1 436s Investment_14 11.395 337.1 436s Investment_15 -2.320 -41.8 436s Investment_16 0.972 15.7 436s Investment_17 30.564 431.6 436s Investment_18 -9.974 -113.2 436s Investment_19 -67.085 -782.5 436s Investment_20 7.999 104.5 436s Investment_21 0.292 3.1 436s Investment_22 10.325 100.1 436s PrivateWages_2 0.000 0.0 436s PrivateWages_3 0.000 0.0 436s PrivateWages_4 0.000 0.0 436s PrivateWages_5 0.000 0.0 436s PrivateWages_6 0.000 0.0 436s PrivateWages_7 0.000 0.0 436s PrivateWages_8 0.000 0.0 436s PrivateWages_9 0.000 0.0 436s PrivateWages_10 0.000 0.0 436s PrivateWages_11 0.000 0.0 436s PrivateWages_12 0.000 0.0 436s PrivateWages_13 0.000 0.0 436s PrivateWages_14 0.000 0.0 436s PrivateWages_15 0.000 0.0 436s PrivateWages_16 0.000 0.0 436s PrivateWages_17 0.000 0.0 436s PrivateWages_18 0.000 0.0 436s PrivateWages_19 0.000 0.0 436s PrivateWages_20 0.000 0.0 436s PrivateWages_21 0.000 0.0 436s PrivateWages_22 0.000 0.0 436s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 436s Consumption_2 0.0000 0.00 0.00 436s Consumption_3 0.0000 0.00 0.00 436s Consumption_4 0.0000 0.00 0.00 436s Consumption_5 0.0000 0.00 0.00 436s Consumption_6 0.0000 0.00 0.00 436s Consumption_7 0.0000 0.00 0.00 436s Consumption_8 0.0000 0.00 0.00 436s Consumption_9 0.0000 0.00 0.00 436s Consumption_10 0.0000 0.00 0.00 436s Consumption_11 0.0000 0.00 0.00 436s Consumption_12 0.0000 0.00 0.00 436s Consumption_13 0.0000 0.00 0.00 436s Consumption_14 0.0000 0.00 0.00 436s Consumption_15 0.0000 0.00 0.00 436s Consumption_16 0.0000 0.00 0.00 436s Consumption_17 0.0000 0.00 0.00 436s Consumption_18 0.0000 0.00 0.00 436s Consumption_19 0.0000 0.00 0.00 436s Consumption_20 0.0000 0.00 0.00 436s Consumption_21 0.0000 0.00 0.00 436s Consumption_22 0.0000 0.00 0.00 436s Investment_2 0.0000 0.00 0.00 436s Investment_3 0.0000 0.00 0.00 436s Investment_4 0.0000 0.00 0.00 436s Investment_5 0.0000 0.00 0.00 436s Investment_6 0.0000 0.00 0.00 436s Investment_7 0.0000 0.00 0.00 436s Investment_8 0.0000 0.00 0.00 436s Investment_9 0.0000 0.00 0.00 436s Investment_10 0.0000 0.00 0.00 436s Investment_11 0.0000 0.00 0.00 436s Investment_12 0.0000 0.00 0.00 436s Investment_13 0.0000 0.00 0.00 436s Investment_14 0.0000 0.00 0.00 436s Investment_15 0.0000 0.00 0.00 436s Investment_16 0.0000 0.00 0.00 436s Investment_17 0.0000 0.00 0.00 436s Investment_18 0.0000 0.00 0.00 436s Investment_19 0.0000 0.00 0.00 436s Investment_20 0.0000 0.00 0.00 436s Investment_21 0.0000 0.00 0.00 436s Investment_22 0.0000 0.00 0.00 436s PrivateWages_2 -2.1987 -104.79 -98.72 436s PrivateWages_3 0.6372 31.43 29.06 436s PrivateWages_4 1.3519 76.84 67.73 436s PrivateWages_5 -1.7306 -105.10 -98.99 436s PrivateWages_6 -0.5521 -33.79 -31.52 436s PrivateWages_7 0.7059 43.27 43.06 436s PrivateWages_8 0.8269 50.32 52.92 436s PrivateWages_9 1.2718 79.33 81.90 436s PrivateWages_10 2.3392 150.64 150.88 436s PrivateWages_11 -1.5500 -99.78 -103.85 436s PrivateWages_12 -0.0625 -3.43 -3.82 436s PrivateWages_13 -1.1474 -54.08 -61.27 436s PrivateWages_14 1.9682 81.95 87.19 436s PrivateWages_15 -0.2753 -14.03 -12.42 436s PrivateWages_16 -0.5389 -30.00 -26.78 436s PrivateWages_17 1.5156 86.87 82.45 436s PrivateWages_18 -0.1787 -12.09 -11.21 436s PrivateWages_19 -3.6814 -251.10 -239.29 436s PrivateWages_20 0.7597 50.83 46.27 436s PrivateWages_21 -0.9040 -68.05 -62.83 436s PrivateWages_22 1.4431 124.79 109.24 436s PrivateWages_trend 436s Consumption_2 0.000 436s Consumption_3 0.000 436s Consumption_4 0.000 436s Consumption_5 0.000 436s Consumption_6 0.000 436s Consumption_7 0.000 436s Consumption_8 0.000 436s Consumption_9 0.000 436s Consumption_10 0.000 436s Consumption_11 0.000 436s Consumption_12 0.000 436s Consumption_13 0.000 436s Consumption_14 0.000 436s Consumption_15 0.000 436s Consumption_16 0.000 436s Consumption_17 0.000 436s Consumption_18 0.000 436s Consumption_19 0.000 436s Consumption_20 0.000 436s Consumption_21 0.000 436s Consumption_22 0.000 436s Investment_2 0.000 436s Investment_3 0.000 436s Investment_4 0.000 436s Investment_5 0.000 436s Investment_6 0.000 436s Investment_7 0.000 436s Investment_8 0.000 436s Investment_9 0.000 436s Investment_10 0.000 436s Investment_11 0.000 436s Investment_12 0.000 436s Investment_13 0.000 436s Investment_14 0.000 436s Investment_15 0.000 436s Investment_16 0.000 436s Investment_17 0.000 436s Investment_18 0.000 436s Investment_19 0.000 436s Investment_20 0.000 436s Investment_21 0.000 436s Investment_22 0.000 436s PrivateWages_2 21.987 436s PrivateWages_3 -5.735 436s PrivateWages_4 -10.815 436s PrivateWages_5 12.114 436s PrivateWages_6 3.312 436s PrivateWages_7 -3.529 436s PrivateWages_8 -3.307 436s PrivateWages_9 -3.815 436s PrivateWages_10 -4.678 436s PrivateWages_11 1.550 436s PrivateWages_12 0.000 436s PrivateWages_13 -1.147 436s PrivateWages_14 3.936 436s PrivateWages_15 -0.826 436s PrivateWages_16 -2.156 436s PrivateWages_17 7.578 436s PrivateWages_18 -1.072 436s PrivateWages_19 -25.769 436s PrivateWages_20 6.078 436s PrivateWages_21 -8.136 436s PrivateWages_22 14.431 436s [1] TRUE 436s > Bread 436s Consumption_(Intercept) Consumption_corpProf 436s Consumption_(Intercept) 105.265 -0.9259 436s Consumption_corpProf -0.926 0.8409 436s Consumption_corpProfLag -0.287 -0.5775 436s Consumption_wages -1.975 -0.0921 436s Investment_(Intercept) 0.000 0.0000 436s Investment_corpProf 0.000 0.0000 436s Investment_corpProfLag 0.000 0.0000 436s Investment_capitalLag 0.000 0.0000 436s PrivateWages_(Intercept) 0.000 0.0000 436s PrivateWages_gnp 0.000 0.0000 436s PrivateWages_gnpLag 0.000 0.0000 436s PrivateWages_trend 0.000 0.0000 436s Consumption_corpProfLag Consumption_wages 436s Consumption_(Intercept) -0.287 -1.9751 436s Consumption_corpProf -0.578 -0.0921 436s Consumption_corpProfLag 0.694 -0.0320 436s Consumption_wages -0.032 0.0978 436s Investment_(Intercept) 0.000 0.0000 436s Investment_corpProf 0.000 0.0000 436s Investment_corpProfLag 0.000 0.0000 436s Investment_capitalLag 0.000 0.0000 436s PrivateWages_(Intercept) 0.000 0.0000 436s PrivateWages_gnp 0.000 0.0000 436s PrivateWages_gnpLag 0.000 0.0000 436s PrivateWages_trend 0.000 0.0000 436s Investment_(Intercept) Investment_corpProf 436s Consumption_(Intercept) 0.0 0.000 436s Consumption_corpProf 0.0 0.000 436s Consumption_corpProfLag 0.0 0.000 436s Consumption_wages 0.0 0.000 436s Investment_(Intercept) 2591.3 -42.124 436s Investment_corpProf -42.1 1.367 436s Investment_corpProfLag 35.4 -1.174 436s Investment_capitalLag -12.3 0.191 436s PrivateWages_(Intercept) 0.0 0.000 436s PrivateWages_gnp 0.0 0.000 436s PrivateWages_gnpLag 0.0 0.000 436s PrivateWages_trend 0.0 0.000 436s Investment_corpProfLag Investment_capitalLag 436s Consumption_(Intercept) 0.000 0.0000 436s Consumption_corpProf 0.000 0.0000 436s Consumption_corpProfLag 0.000 0.0000 436s Consumption_wages 0.000 0.0000 436s Investment_(Intercept) 35.417 -12.2536 436s Investment_corpProf -1.174 0.1908 436s Investment_corpProfLag 1.207 -0.1763 436s Investment_capitalLag -0.176 0.0594 436s PrivateWages_(Intercept) 0.000 0.0000 436s PrivateWages_gnp 0.000 0.0000 436s PrivateWages_gnpLag 0.000 0.0000 436s PrivateWages_trend 0.000 0.0000 436s PrivateWages_(Intercept) PrivateWages_gnp 436s Consumption_(Intercept) 0.000 0.0000 436s Consumption_corpProf 0.000 0.0000 436s Consumption_corpProfLag 0.000 0.0000 436s Consumption_wages 0.000 0.0000 436s Investment_(Intercept) 0.000 0.0000 436s Investment_corpProf 0.000 0.0000 436s Investment_corpProfLag 0.000 0.0000 436s Investment_capitalLag 0.000 0.0000 436s PrivateWages_(Intercept) 174.205 -0.8839 436s PrivateWages_gnp -0.884 0.1679 436s PrivateWages_gnpLag -2.037 -0.1586 436s PrivateWages_trend 2.064 -0.0409 436s PrivateWages_gnpLag PrivateWages_trend 436s Consumption_(Intercept) 0.00000 0.00000 436s Consumption_corpProf 0.00000 0.00000 436s Consumption_corpProfLag 0.00000 0.00000 436s Consumption_wages 0.00000 0.00000 436s Investment_(Intercept) 0.00000 0.00000 436s Investment_corpProf 0.00000 0.00000 436s Investment_corpProfLag 0.00000 0.00000 436s Investment_capitalLag 0.00000 0.00000 436s PrivateWages_(Intercept) -2.03709 2.06394 436s PrivateWages_gnp -0.15864 -0.04088 436s PrivateWages_gnpLag 0.19944 0.00675 436s PrivateWages_trend 0.00675 0.11229 436s > 436s > # SUR 436s > summary 436s 436s systemfit results 436s method: SUR 436s 436s N DF SSR detRCov OLS-R2 McElroy-R2 436s system 63 51 46.5 0.158 0.977 0.993 436s 436s N DF SSR MSE RMSE R2 Adj R2 436s Consumption 21 17 18.1 1.065 1.032 0.981 0.977 436s Investment 21 17 17.6 1.036 1.018 0.930 0.918 436s PrivateWages 21 17 10.8 0.633 0.796 0.986 0.984 436s 436s The covariance matrix of the residuals used for estimation 436s Consumption Investment PrivateWages 436s Consumption 0.8514 0.0495 -0.381 436s Investment 0.0495 0.8249 0.121 436s PrivateWages -0.3808 0.1212 0.476 436s 436s The covariance matrix of the residuals 436s Consumption Investment PrivateWages 436s Consumption 0.8618 0.0766 -0.437 436s Investment 0.0766 0.8384 0.203 436s PrivateWages -0.4368 0.2027 0.513 436s 436s The correlations of the residuals 436s Consumption Investment PrivateWages 436s Consumption 1.0000 0.0901 -0.657 436s Investment 0.0901 1.0000 0.309 436s PrivateWages -0.6572 0.3092 1.000 436s 436s 436s SUR estimates for 'Consumption' (equation 1) 436s Model Formula: consump ~ corpProf + corpProfLag + wages 436s 436s Estimate Std. Error t value Pr(>|t|) 436s (Intercept) 15.9805 1.1687 13.67 1.3e-10 *** 436s corpProf 0.2302 0.0767 3.00 0.008 ** 436s corpProfLag 0.0673 0.0769 0.87 0.394 436s wages 0.7962 0.0353 22.58 4.1e-14 *** 436s --- 436s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 436s 436s Residual standard error: 1.032 on 17 degrees of freedom 436s Number of observations: 21 Degrees of Freedom: 17 436s SSR: 18.098 MSE: 1.065 Root MSE: 1.032 436s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.977 436s 436s 436s SUR estimates for 'Investment' (equation 2) 436s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 436s 436s Estimate Std. Error t value Pr(>|t|) 436s (Intercept) 12.9293 4.8014 2.69 0.01540 * 436s corpProf 0.4429 0.0861 5.15 8.1e-05 *** 436s corpProfLag 0.3655 0.0894 4.09 0.00077 *** 436s capitalLag -0.1253 0.0235 -5.34 5.4e-05 *** 436s --- 436s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 436s 436s Residual standard error: 1.018 on 17 degrees of freedom 436s Number of observations: 21 Degrees of Freedom: 17 436s SSR: 17.606 MSE: 1.036 Root MSE: 1.018 436s Multiple R-Squared: 0.93 Adjusted R-Squared: 0.918 436s 436s 436s SUR estimates for 'PrivateWages' (equation 3) 436s Model Formula: privWage ~ gnp + gnpLag + trend 436s 436s Estimate Std. Error t value Pr(>|t|) 436s (Intercept) 1.6347 1.1173 1.46 0.16 436s gnp 0.4098 0.0273 15.04 3.0e-11 *** 436s gnpLag 0.1744 0.0312 5.59 3.2e-05 *** 436s trend 0.1558 0.0276 5.65 2.9e-05 *** 436s --- 436s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 436s 436s Residual standard error: 0.796 on 17 degrees of freedom 436s Number of observations: 21 Degrees of Freedom: 17 436s SSR: 10.763 MSE: 0.633 Root MSE: 0.796 436s Multiple R-Squared: 0.986 Adjusted R-Squared: 0.984 436s 436s > residuals 436s Consumption Investment PrivateWages 436s 1 NA NA NA 436s 2 -0.24064 -0.3522 -1.0960 436s 3 -1.34080 -0.1605 0.5818 436s 4 -1.61038 1.0687 1.5313 436s 5 -0.54147 -1.4707 -0.0220 436s 6 -0.04372 0.3299 -0.2587 436s 7 0.85234 1.4346 -0.3243 436s 8 1.30302 0.8306 -0.6674 436s 9 0.97574 -0.4918 0.3660 436s 10 -0.66060 1.2434 1.2682 436s 11 0.45069 0.2647 -0.3467 436s 12 -0.04295 0.0795 0.3057 436s 13 -0.06686 0.3369 -0.2602 436s 14 0.32177 0.4080 0.3434 436s 15 -0.00441 -0.1533 0.2628 436s 16 -0.01931 0.0158 -0.0216 436s 17 1.53656 1.0372 -0.7988 436s 18 -0.42317 0.0176 0.8550 436s 19 0.29041 -2.6364 -0.8217 436s 20 0.88685 -0.5822 -0.3869 436s 21 0.68839 -0.7015 -1.1838 436s 22 -2.31147 -0.5183 0.6742 436s > fitted 436s Consumption Investment PrivateWages 436s 1 NA NA NA 436s 2 42.1 0.152 26.6 436s 3 46.3 2.060 28.7 436s 4 50.8 4.131 32.6 436s 5 51.1 4.471 33.9 436s 6 52.6 4.770 35.7 436s 7 54.2 4.165 37.7 436s 8 54.9 3.369 38.6 436s 9 56.3 3.492 38.8 436s 10 58.5 3.857 40.0 436s 11 54.5 0.735 38.2 436s 12 50.9 -3.479 34.2 436s 13 45.7 -6.537 29.3 436s 14 46.2 -5.508 28.2 436s 15 48.7 -2.847 30.3 436s 16 51.3 -1.316 33.2 436s 17 56.2 1.063 37.6 436s 18 59.1 1.982 40.1 436s 19 57.2 0.736 39.0 436s 20 60.7 1.882 42.0 436s 21 64.3 4.002 46.2 436s 22 72.0 5.418 52.6 436s > predict 436s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 436s 1 NA NA NA NA 436s 2 42.1 0.415 41.3 43.0 436s 3 46.3 0.449 45.4 47.2 436s 4 50.8 0.300 50.2 51.4 436s 5 51.1 0.348 50.4 51.8 436s 6 52.6 0.350 51.9 53.3 436s 7 54.2 0.317 53.6 54.9 436s 8 54.9 0.289 54.3 55.5 436s 9 56.3 0.309 55.7 56.9 436s 10 58.5 0.328 57.8 59.1 436s 11 54.5 0.516 53.5 55.6 436s 12 50.9 0.414 50.1 51.8 436s 13 45.7 0.544 44.6 46.8 436s 14 46.2 0.527 45.1 47.2 436s 15 48.7 0.332 48.0 49.4 436s 16 51.3 0.295 50.7 51.9 436s 17 56.2 0.319 55.5 56.8 436s 18 59.1 0.286 58.5 59.7 436s 19 57.2 0.323 56.6 57.9 436s 20 60.7 0.381 59.9 61.5 436s 21 64.3 0.381 63.5 65.1 436s 22 72.0 0.597 70.8 73.2 436s Investment.pred Investment.se.fit Investment.lwr Investment.upr 436s 1 NA NA NA NA 436s 2 0.152 0.536 -0.924 1.229 436s 3 2.060 0.446 1.166 2.955 436s 4 4.131 0.397 3.334 4.929 436s 5 4.471 0.329 3.809 5.132 436s 6 4.770 0.311 4.145 5.395 436s 7 4.165 0.294 3.575 4.756 436s 8 3.369 0.263 2.842 3.897 436s 9 3.492 0.347 2.796 4.188 436s 10 3.857 0.398 3.058 4.656 436s 11 0.735 0.539 -0.346 1.816 436s 12 -3.479 0.454 -4.390 -2.569 436s 13 -6.537 0.552 -7.646 -5.428 436s 14 -5.508 0.617 -6.747 -4.269 436s 15 -2.847 0.335 -3.519 -2.175 436s 16 -1.316 0.287 -1.892 -0.739 436s 17 1.063 0.311 0.439 1.686 436s 18 1.982 0.218 1.545 2.420 436s 19 0.736 0.279 0.176 1.296 436s 20 1.882 0.327 1.227 2.538 436s 21 4.002 0.297 3.405 4.598 436s 22 5.418 0.412 4.591 6.245 436s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 436s 1 NA NA NA NA 436s 2 26.6 0.313 26.0 27.2 436s 3 28.7 0.310 28.1 29.3 436s 4 32.6 0.305 32.0 33.2 436s 5 33.9 0.236 33.4 34.4 436s 6 35.7 0.233 35.2 36.1 436s 7 37.7 0.234 37.3 38.2 436s 8 38.6 0.239 38.1 39.0 436s 9 38.8 0.229 38.4 39.3 436s 10 40.0 0.219 39.6 40.5 436s 11 38.2 0.301 37.6 38.9 436s 12 34.2 0.308 33.6 34.8 436s 13 29.3 0.370 28.5 30.0 436s 14 28.2 0.332 27.5 28.8 436s 15 30.3 0.324 29.7 31.0 436s 16 33.2 0.271 32.7 33.8 436s 17 37.6 0.263 37.1 38.1 436s 18 40.1 0.211 39.7 40.6 436s 19 39.0 0.306 38.4 39.6 436s 20 42.0 0.280 41.4 42.5 436s 21 46.2 0.298 45.6 46.8 436s 22 52.6 0.445 51.7 53.5 436s > model.frame 436s [1] TRUE 436s > model.matrix 436s [1] TRUE 436s > nobs 436s [1] 63 436s > linearHypothesis 436s Linear hypothesis test (Theil's F test) 436s 436s Hypothesis: 436s Consumption_corpProf + Investment_capitalLag = 0 436s 436s Model 1: restricted model 436s Model 2: kleinModel 436s 436s Res.Df Df F Pr(>F) 436s 1 52 436s 2 51 1 1.44 0.24 436s Linear hypothesis test (F statistic of a Wald test) 436s 436s Hypothesis: 436s Consumption_corpProf + Investment_capitalLag = 0 436s 436s Model 1: restricted model 436s Model 2: kleinModel 436s 436s Res.Df Df F Pr(>F) 436s 1 52 436s 2 51 1 1.69 0.2 436s Linear hypothesis test (Chi^2 statistic of a Wald test) 436s 436s Hypothesis: 436s Consumption_corpProf + Investment_capitalLag = 0 436s 436s Model 1: restricted model 436s Model 2: kleinModel 436s 436s Res.Df Df Chisq Pr(>Chisq) 436s 1 52 436s 2 51 1 1.69 0.19 436s Linear hypothesis test (Theil's F test) 436s 436s Hypothesis: 436s Consumption_corpProf + Investment_capitalLag = 0 436s Consumption_corpProfLag - PrivateWages_trend = 0 436s 436s Model 1: restricted model 436s Model 2: kleinModel 436s 436s Res.Df Df F Pr(>F) 436s 1 53 436s 2 51 2 0.77 0.47 436s Linear hypothesis test (F statistic of a Wald test) 436s 436s Hypothesis: 436s Consumption_corpProf + Investment_capitalLag = 0 436s Consumption_corpProfLag - PrivateWages_trend = 0 436s 436s Model 1: restricted model 436s Model 2: kleinModel 436s 436s Res.Df Df F Pr(>F) 436s 1 53 436s 2 51 2 0.91 0.41 436s Linear hypothesis test (Chi^2 statistic of a Wald test) 436s 436s Hypothesis: 436s Consumption_corpProf + Investment_capitalLag = 0 436s Consumption_corpProfLag - PrivateWages_trend = 0 436s 436s Model 1: restricted model 436s Model 2: kleinModel 436s 436s Res.Df Df Chisq Pr(>Chisq) 436s 1 53 436s 2 51 2 1.83 0.4 436s > logLik 436s 'log Lik.' -70 (df=18) 436s 'log Lik.' -79 (df=18) 436s Estimating function 436s Consumption_(Intercept) Consumption_corpProf 436s Consumption_2 -0.46275 -5.7381 436s Consumption_3 -2.57830 -43.5733 436s Consumption_4 -3.09670 -56.9792 436s Consumption_5 -1.04122 -20.1997 436s Consumption_6 -0.08406 -1.6897 436s Consumption_7 1.63901 32.1246 436s Consumption_8 2.50567 49.6122 436s Consumption_9 1.87631 39.5902 436s Consumption_10 -1.27032 -27.5659 436s Consumption_11 0.86667 13.5200 436s Consumption_12 -0.08259 -0.9415 436s Consumption_13 -0.12857 -0.9000 436s Consumption_14 0.61874 6.9299 436s Consumption_15 -0.00847 -0.1042 436s Consumption_16 -0.03714 -0.5200 436s Consumption_17 2.95475 52.0036 436s Consumption_18 -0.81375 -14.0778 436s Consumption_19 0.55845 8.5443 436s Consumption_20 1.70539 32.4023 436s Consumption_21 1.32376 27.9312 436s Consumption_22 -4.44487 -104.4543 436s Investment_2 0.12481 1.5477 436s Investment_3 0.05687 0.9611 436s Investment_4 -0.37877 -6.9693 436s Investment_5 0.52122 10.1116 436s Investment_6 -0.11690 -2.3498 436s Investment_7 -0.50845 -9.9656 436s Investment_8 -0.29439 -5.8289 436s Investment_9 0.17430 3.6777 436s Investment_10 -0.44066 -9.5623 436s Investment_11 -0.09381 -1.4634 436s Investment_12 -0.02816 -0.3210 436s Investment_13 -0.11941 -0.8359 436s Investment_14 -0.14460 -1.6195 436s Investment_15 0.05435 0.6685 436s Investment_16 -0.00559 -0.0783 436s Investment_17 -0.36761 -6.4700 436s Investment_18 -0.00622 -0.1077 436s Investment_19 0.93438 14.2960 436s Investment_20 0.20633 3.9202 436s Investment_21 0.24863 5.2460 436s Investment_22 0.18369 4.3168 436s PrivateWages_2 -1.78352 -22.1156 436s PrivateWages_3 0.94670 15.9992 436s PrivateWages_4 2.49170 45.8473 436s PrivateWages_5 -0.03583 -0.6950 436s PrivateWages_6 -0.42104 -8.4630 436s PrivateWages_7 -0.52776 -10.3441 436s PrivateWages_8 -1.08598 -21.5024 436s PrivateWages_9 0.59560 12.5672 436s PrivateWages_10 2.06359 44.7800 436s PrivateWages_11 -0.56422 -8.8019 436s PrivateWages_12 0.49749 5.6714 436s PrivateWages_13 -0.42337 -2.9636 436s PrivateWages_14 0.55874 6.2579 436s PrivateWages_15 0.42760 5.2595 436s PrivateWages_16 -0.03516 -0.4922 436s PrivateWages_17 -1.29986 -22.8775 436s PrivateWages_18 1.39131 24.0696 436s PrivateWages_19 -1.33711 -20.4578 436s PrivateWages_20 -0.62964 -11.9631 436s PrivateWages_21 -1.92625 -40.6439 436s PrivateWages_22 1.09700 25.7794 436s Consumption_corpProfLag Consumption_wages 436s Consumption_2 -5.8769 -13.049 436s Consumption_3 -31.9709 -83.021 436s Consumption_4 -52.3342 -114.578 436s Consumption_5 -19.1585 -38.525 436s Consumption_6 -1.6308 -3.245 436s Consumption_7 32.9441 66.708 436s Consumption_8 49.1110 103.985 436s Consumption_9 37.1510 80.494 436s Consumption_10 -26.8037 -57.545 436s Consumption_11 18.8066 36.487 436s Consumption_12 -1.2884 -3.246 436s Consumption_13 -1.4658 -4.410 436s Consumption_14 4.3312 21.099 436s Consumption_15 -0.0949 -0.310 436s Consumption_16 -0.4568 -1.460 436s Consumption_17 41.3665 130.600 436s Consumption_18 -14.3220 -38.816 436s Consumption_19 9.6612 25.633 436s Consumption_20 26.0924 84.246 436s Consumption_21 25.1514 70.159 436s Consumption_22 -93.7867 -274.693 436s Investment_2 1.5851 3.520 436s Investment_3 0.7052 1.831 436s Investment_4 -6.4012 -14.014 436s Investment_5 9.5904 19.285 436s Investment_6 -2.2679 -4.513 436s Investment_7 -10.2199 -20.694 436s Investment_8 -5.7700 -12.217 436s Investment_9 3.4511 7.477 436s Investment_10 -9.2979 -19.962 436s Investment_11 -2.0356 -3.949 436s Investment_12 -0.4393 -1.107 436s Investment_13 -1.3613 -4.096 436s Investment_14 -1.0122 -4.931 436s Investment_15 0.6087 1.989 436s Investment_16 -0.0688 -0.220 436s Investment_17 -5.1466 -16.248 436s Investment_18 -0.1095 -0.297 436s Investment_19 16.1648 42.888 436s Investment_20 3.1568 10.193 436s Investment_21 4.7239 13.177 436s Investment_22 3.8759 11.352 436s PrivateWages_2 -22.6507 -50.295 436s PrivateWages_3 11.7391 30.484 436s PrivateWages_4 42.1098 92.193 436s PrivateWages_5 -0.6592 -1.326 436s PrivateWages_6 -8.1683 -16.252 436s PrivateWages_7 -10.6080 -21.480 436s PrivateWages_8 -21.2852 -45.068 436s PrivateWages_9 11.7929 25.551 436s PrivateWages_10 43.5418 93.481 436s PrivateWages_11 -12.2437 -23.754 436s PrivateWages_12 7.7609 19.551 436s PrivateWages_13 -4.8264 -14.521 436s PrivateWages_14 3.9112 19.053 436s PrivateWages_15 4.7891 15.650 436s PrivateWages_16 -0.4325 -1.382 436s PrivateWages_17 -18.1980 -57.454 436s PrivateWages_18 24.4870 66.365 436s PrivateWages_19 -23.1320 -61.373 436s PrivateWages_20 -9.6335 -31.104 436s PrivateWages_21 -36.5988 -102.091 436s PrivateWages_22 23.1466 67.794 436s Investment_(Intercept) Investment_corpProf 436s Consumption_2 0.08529 1.0576 436s Consumption_3 0.47520 8.0308 436s Consumption_4 0.57074 10.5016 436s Consumption_5 0.19190 3.7229 436s Consumption_6 0.01549 0.3114 436s Consumption_7 -0.30208 -5.9207 436s Consumption_8 -0.46181 -9.1438 436s Consumption_9 -0.34582 -7.2967 436s Consumption_10 0.23413 5.0806 436s Consumption_11 -0.15973 -2.4918 436s Consumption_12 0.01522 0.1735 436s Consumption_13 0.02370 0.1659 436s Consumption_14 -0.11404 -1.2772 436s Consumption_15 0.00156 0.0192 436s Consumption_16 0.00685 0.0958 436s Consumption_17 -0.54458 -9.5846 436s Consumption_18 0.14998 2.5946 436s Consumption_19 -0.10293 -1.5748 436s Consumption_20 -0.31431 -5.9719 436s Consumption_21 -0.24398 -5.1479 436s Consumption_22 0.81921 19.2515 436s Investment_2 -0.46650 -5.7846 436s Investment_3 -0.21255 -3.5922 436s Investment_4 1.41568 26.0484 436s Investment_5 -1.94810 -37.7932 436s Investment_6 0.43694 8.7825 436s Investment_7 1.90038 37.2474 436s Investment_8 1.10030 21.7860 436s Investment_9 -0.65146 -13.7457 436s Investment_10 1.64701 35.7401 436s Investment_11 0.35062 5.4696 436s Investment_12 0.10525 1.1998 436s Investment_13 0.44632 3.1242 436s Investment_14 0.54045 6.0530 436s Investment_15 -0.20313 -2.4985 436s Investment_16 0.02090 0.2926 436s Investment_17 1.37398 24.1820 436s Investment_18 0.02326 0.4024 436s Investment_19 -3.49233 -53.4327 436s Investment_20 -0.77116 -14.6521 436s Investment_21 -0.92927 -19.6075 436s Investment_22 -0.68657 -16.1344 436s PrivateWages_2 0.67977 8.4291 436s PrivateWages_3 -0.36082 -6.0979 436s PrivateWages_4 -0.94969 -17.4742 436s PrivateWages_5 0.01365 0.2649 436s PrivateWages_6 0.16048 3.2256 436s PrivateWages_7 0.20115 3.9426 436s PrivateWages_8 0.41391 8.1954 436s PrivateWages_9 -0.22701 -4.7899 436s PrivateWages_10 -0.78652 -17.0674 436s PrivateWages_11 0.21505 3.3548 436s PrivateWages_12 -0.18961 -2.1616 436s PrivateWages_13 0.16136 1.1295 436s PrivateWages_14 -0.21296 -2.3851 436s PrivateWages_15 -0.16298 -2.0046 436s PrivateWages_16 0.01340 0.1876 436s PrivateWages_17 0.49543 8.7195 436s PrivateWages_18 -0.53028 -9.1739 436s PrivateWages_19 0.50963 7.7973 436s PrivateWages_20 0.23998 4.5596 436s PrivateWages_21 0.73417 15.4910 436s PrivateWages_22 -0.41811 -9.8256 436s Investment_corpProfLag Investment_capitalLag 436s Consumption_2 1.0831 15.590 436s Consumption_3 5.8924 86.771 436s Consumption_4 9.6455 105.301 436s Consumption_5 3.5310 36.404 436s Consumption_6 0.3006 2.986 436s Consumption_7 -6.0718 -59.751 436s Consumption_8 -9.0514 -93.932 436s Consumption_9 -6.8471 -71.791 436s Consumption_10 4.9401 49.307 436s Consumption_11 -3.4662 -34.454 436s Consumption_12 0.2375 3.299 436s Consumption_13 0.2701 5.055 436s Consumption_14 -0.7983 -23.617 436s Consumption_15 0.0175 0.315 436s Consumption_16 0.0842 1.362 436s Consumption_17 -7.6241 -107.663 436s Consumption_18 2.6396 29.966 436s Consumption_19 -1.7806 -20.770 436s Consumption_20 -4.8090 -62.831 436s Consumption_21 -4.6355 -49.088 436s Consumption_22 17.2854 167.529 436s Investment_2 -5.9246 -85.277 436s Investment_3 -2.6357 -38.812 436s Investment_4 23.9249 261.192 436s Investment_5 -35.8451 -369.555 436s Investment_6 8.4767 84.199 436s Investment_7 38.1976 375.895 436s Investment_8 21.5660 223.802 436s Investment_9 -12.8988 -135.242 436s Investment_10 34.7519 346.860 436s Investment_11 7.6084 75.628 436s Investment_12 1.6419 22.807 436s Investment_13 5.0880 95.199 436s Investment_14 3.7831 111.927 436s Investment_15 -2.2751 -41.032 436s Investment_16 0.2571 4.159 436s Investment_17 19.2357 271.636 436s Investment_18 0.4094 4.648 436s Investment_19 -60.4174 -704.753 436s Investment_20 -11.7988 -154.156 436s Investment_21 -17.6560 -186.968 436s Investment_22 -14.4866 -140.403 436s PrivateWages_2 8.6331 124.262 436s PrivateWages_3 -4.4742 -65.887 436s PrivateWages_4 -16.0497 -175.217 436s PrivateWages_5 0.2512 2.590 436s PrivateWages_6 3.1132 30.924 436s PrivateWages_7 4.0431 39.788 436s PrivateWages_8 8.1126 84.189 436s PrivateWages_9 -4.4947 -47.127 436s PrivateWages_10 -16.5955 -165.640 436s PrivateWages_11 4.6666 46.386 436s PrivateWages_12 -2.9580 -41.089 436s PrivateWages_13 1.8395 34.418 436s PrivateWages_14 -1.4907 -44.104 436s PrivateWages_15 -1.8253 -32.921 436s PrivateWages_16 0.1648 2.667 436s PrivateWages_17 6.9360 97.946 436s PrivateWages_18 -9.3330 -105.950 436s PrivateWages_19 8.8165 102.843 436s PrivateWages_20 3.6717 47.972 436s PrivateWages_21 13.9492 147.715 436s PrivateWages_22 -8.8221 -85.503 436s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 436s Consumption_2 -0.39158 -17.856 -17.582 436s Consumption_3 -2.18178 -109.307 -99.489 436s Consumption_4 -2.62045 -149.890 -131.285 436s Consumption_5 -0.88109 -50.310 -50.398 436s Consumption_6 -0.07113 -4.339 -4.062 436s Consumption_7 1.38694 88.764 84.604 436s Consumption_8 2.12032 136.548 135.700 436s Consumption_9 1.58775 102.410 102.251 436s Consumption_10 -1.07495 -72.022 -69.335 436s Consumption_11 0.73338 44.883 49.136 436s Consumption_12 -0.06989 -3.732 -4.277 436s Consumption_13 -0.10880 -4.820 -5.810 436s Consumption_14 0.52359 23.614 23.195 436s Consumption_15 -0.00717 -0.356 -0.323 436s Consumption_16 -0.03143 -1.710 -1.562 436s Consumption_17 2.50033 156.771 136.018 436s Consumption_18 -0.68860 -44.759 -43.175 436s Consumption_19 0.47257 28.779 30.717 436s Consumption_20 1.44311 100.296 87.885 436s Consumption_21 1.12017 84.797 77.852 436s Consumption_22 -3.76128 -332.497 -284.729 436s Investment_2 0.21842 9.960 9.807 436s Investment_3 0.09952 4.986 4.538 436s Investment_4 -0.66282 -37.913 -33.207 436s Investment_5 0.91210 52.081 52.172 436s Investment_6 -0.20458 -12.479 -11.681 436s Investment_7 -0.88976 -56.944 -54.275 436s Investment_8 -0.51516 -33.176 -32.970 436s Investment_9 0.30501 19.673 19.643 436s Investment_10 -0.77113 -51.666 -49.738 436s Investment_11 -0.16416 -10.047 -10.999 436s Investment_12 -0.04928 -2.631 -3.016 436s Investment_13 -0.20897 -9.257 -11.159 436s Investment_14 -0.25304 -11.412 -11.210 436s Investment_15 0.09511 4.727 4.289 436s Investment_16 -0.00978 -0.532 -0.486 436s Investment_17 -0.64330 -40.335 -34.995 436s Investment_18 -0.01089 -0.708 -0.683 436s Investment_19 1.63511 99.578 106.282 436s Investment_20 0.36106 25.094 21.989 436s Investment_21 0.43508 32.936 30.238 436s Investment_22 0.32145 28.416 24.334 436s PrivateWages_2 -3.89912 -177.800 -175.070 436s PrivateWages_3 2.06967 103.690 94.377 436s PrivateWages_4 5.44735 311.588 272.912 436s PrivateWages_5 -0.07832 -4.472 -4.480 436s PrivateWages_6 -0.92048 -56.150 -52.560 436s PrivateWages_7 -1.15379 -73.843 -70.381 436s PrivateWages_8 -2.37416 -152.896 -151.946 436s PrivateWages_9 1.30210 83.986 83.855 436s PrivateWages_10 4.51142 302.265 290.986 436s PrivateWages_11 -1.23351 -75.491 -82.645 436s PrivateWages_12 1.08762 58.079 66.562 436s PrivateWages_13 -0.92556 -41.002 -49.425 436s PrivateWages_14 1.22152 55.091 54.114 436s PrivateWages_15 0.93482 46.461 42.160 436s PrivateWages_16 -0.07687 -4.182 -3.820 436s PrivateWages_17 -2.84174 -178.177 -154.591 436s PrivateWages_18 3.04167 197.708 190.713 436s PrivateWages_19 -2.92319 -178.022 -190.007 436s PrivateWages_20 -1.37651 -95.667 -83.829 436s PrivateWages_21 -4.21116 -318.785 -292.676 436s PrivateWages_22 2.39825 212.005 181.548 436s PrivateWages_trend 436s Consumption_2 3.9158 436s Consumption_3 19.6360 436s Consumption_4 20.9636 436s Consumption_5 6.1676 436s Consumption_6 0.4268 436s Consumption_7 -6.9347 436s Consumption_8 -8.4813 436s Consumption_9 -4.7633 436s Consumption_10 2.1499 436s Consumption_11 -0.7334 436s Consumption_12 0.0000 436s Consumption_13 -0.1088 436s Consumption_14 1.0472 436s Consumption_15 -0.0215 436s Consumption_16 -0.1257 436s Consumption_17 12.5017 436s Consumption_18 -4.1316 436s Consumption_19 3.3080 436s Consumption_20 11.5449 436s Consumption_21 10.0816 436s Consumption_22 -37.6128 436s Investment_2 -2.1842 436s Investment_3 -0.8957 436s Investment_4 5.3026 436s Investment_5 -6.3847 436s Investment_6 1.2275 436s Investment_7 4.4488 436s Investment_8 2.0606 436s Investment_9 -0.9150 436s Investment_10 1.5423 436s Investment_11 0.1642 436s Investment_12 0.0000 436s Investment_13 -0.2090 436s Investment_14 -0.5061 436s Investment_15 0.2853 436s Investment_16 -0.0391 436s Investment_17 -3.2165 436s Investment_18 -0.0653 436s Investment_19 11.4458 436s Investment_20 2.8885 436s Investment_21 3.9157 436s Investment_22 3.2145 436s PrivateWages_2 38.9912 436s PrivateWages_3 -18.6270 436s PrivateWages_4 -43.5788 436s PrivateWages_5 0.5483 436s PrivateWages_6 5.5229 436s PrivateWages_7 5.7689 436s PrivateWages_8 9.4967 436s PrivateWages_9 -3.9063 436s PrivateWages_10 -9.0228 436s PrivateWages_11 1.2335 436s PrivateWages_12 0.0000 436s PrivateWages_13 -0.9256 436s PrivateWages_14 2.4431 436s PrivateWages_15 2.8045 436s PrivateWages_16 -0.3075 436s PrivateWages_17 -14.2087 436s PrivateWages_18 18.2500 436s PrivateWages_19 -20.4623 436s PrivateWages_20 -11.0121 436s PrivateWages_21 -37.9005 436s PrivateWages_22 23.9825 436s [1] TRUE 436s > Bread 436s Consumption_(Intercept) Consumption_corpProf Consumption_corpProfLag 436s [1,] 86.0484 -0.02454 -0.83573 436s [2,] -0.0245 0.37055 -0.22831 436s [3,] -0.8357 -0.22831 0.37290 436s [4,] -1.6729 -0.06016 -0.03411 436s [5,] 10.1786 -0.46129 0.72764 436s [6,] -0.1293 0.03988 -0.03792 436s [7,] -0.0505 -0.03436 0.04602 436s [8,] -0.0350 0.00175 -0.00419 436s [9,] -37.4223 0.06800 1.80971 436s [10,] 0.4074 -0.06333 0.04058 436s [11,] 0.2037 0.06442 -0.07324 436s [12,] 0.2057 0.03217 0.03109 436s Consumption_wages Investment_(Intercept) Investment_corpProf 436s [1,] -1.67e+00 10.179 -0.12933 436s [2,] -6.02e-02 -0.461 0.03988 436s [3,] -3.41e-02 0.728 -0.03792 436s [4,] 7.83e-02 -0.341 0.00185 436s [5,] -3.41e-01 1452.346 -13.96098 436s [6,] 1.85e-03 -13.961 0.46676 436s [7,] -2.96e-03 11.230 -0.39879 436s [8,] 1.79e-03 -6.973 0.06288 436s [9,] 1.32e-01 19.427 -0.13338 436s [10,] -5.46e-05 0.416 0.01516 436s [11,] -2.23e-03 -0.760 -0.01340 436s [12,] -3.03e-02 -0.736 0.00571 436s Investment_corpProfLag Investment_capitalLag PrivateWages_(Intercept) 436s [1,] -0.05046 -0.03501 -37.4223 436s [2,] -0.03436 0.00175 0.0680 436s [3,] 0.04602 -0.00419 1.8097 436s [4,] -0.00296 0.00179 0.1325 436s [5,] 11.22954 -6.97254 19.4266 436s [6,] -0.39879 0.06288 -0.1334 436s [7,] 0.50387 -0.06357 -0.5157 436s [8,] -0.06357 0.03467 -0.0417 436s [9,] -0.51574 -0.04172 78.6495 436s [10,] -0.00784 -0.00271 -0.3339 436s [11,] 0.01702 0.00353 -0.9859 436s [12,] -0.01390 0.00432 0.8712 436s PrivateWages_gnp PrivateWages_gnpLag PrivateWages_trend 436s [1,] 4.07e-01 0.20374 0.20573 436s [2,] -6.33e-02 0.06442 0.03217 436s [3,] 4.06e-02 -0.07324 0.03109 436s [4,] -5.46e-05 -0.00223 -0.03033 436s [5,] 4.16e-01 -0.75990 -0.73581 436s [6,] 1.52e-02 -0.01340 0.00571 436s [7,] -7.84e-03 0.01702 -0.01390 436s [8,] -2.71e-03 0.00353 0.00432 436s [9,] -3.34e-01 -0.98593 0.87119 436s [10,] 4.68e-02 -0.04271 -0.01162 436s [11,] -4.27e-02 0.06124 -0.00299 436s [12,] -1.16e-02 -0.00299 0.04791 436s > 436s > # 3SLS 436s > summary 436s 436s systemfit results 436s method: 3SLS 436s 436s N DF SSR detRCov OLS-R2 McElroy-R2 436s system 63 51 73.6 0.283 0.963 0.995 436s 436s N DF SSR MSE RMSE R2 Adj R2 436s Consumption 21 17 18.7 1.102 1.050 0.980 0.977 436s Investment 21 17 44.0 2.586 1.608 0.826 0.795 436s PrivateWages 21 17 10.9 0.642 0.801 0.986 0.984 436s 436s The covariance matrix of the residuals used for estimation 436s Consumption Investment PrivateWages 436s Consumption 1.044 0.438 -0.385 436s Investment 0.438 1.383 0.193 436s PrivateWages -0.385 0.193 0.476 436s 436s The covariance matrix of the residuals 436s Consumption Investment PrivateWages 436s Consumption 0.892 0.411 -0.394 436s Investment 0.411 2.093 0.403 436s PrivateWages -0.394 0.403 0.520 436s 436s The correlations of the residuals 436s Consumption Investment PrivateWages 436s Consumption 1.000 0.301 -0.578 436s Investment 0.301 1.000 0.386 436s PrivateWages -0.578 0.386 1.000 436s 436s 436s 3SLS estimates for 'Consumption' (equation 1) 436s Model Formula: consump ~ corpProf + corpProfLag + wages 436s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 436s gnpLag 436s 436s Estimate Std. Error t value Pr(>|t|) 436s (Intercept) 16.4408 1.3045 12.60 4.7e-10 *** 436s corpProf 0.1249 0.1081 1.16 0.26 436s corpProfLag 0.1631 0.1004 1.62 0.12 436s wages 0.7901 0.0379 20.83 1.5e-13 *** 436s --- 436s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 436s 436s Residual standard error: 1.05 on 17 degrees of freedom 436s Number of observations: 21 Degrees of Freedom: 17 436s SSR: 18.727 MSE: 1.102 Root MSE: 1.05 436s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.977 436s 436s 436s 3SLS estimates for 'Investment' (equation 2) 436s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 436s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 436s gnpLag 436s 436s Estimate Std. Error t value Pr(>|t|) 436s (Intercept) 28.1778 6.7938 4.15 0.00067 *** 436s corpProf -0.0131 0.1619 -0.08 0.93655 436s corpProfLag 0.7557 0.1529 4.94 0.00012 *** 436s capitalLag -0.1948 0.0325 -5.99 1.5e-05 *** 436s --- 436s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 436s 436s Residual standard error: 1.608 on 17 degrees of freedom 436s Number of observations: 21 Degrees of Freedom: 17 436s SSR: 43.954 MSE: 2.586 Root MSE: 1.608 436s Multiple R-Squared: 0.826 Adjusted R-Squared: 0.795 436s 436s 436s 3SLS estimates for 'PrivateWages' (equation 3) 436s Model Formula: privWage ~ gnp + gnpLag + trend 436s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 436s gnpLag 436s 436s Estimate Std. Error t value Pr(>|t|) 436s (Intercept) 1.7972 1.1159 1.61 0.13 436s gnp 0.4005 0.0318 12.59 4.8e-10 *** 436s gnpLag 0.1813 0.0342 5.31 5.8e-05 *** 436s trend 0.1497 0.0279 5.36 5.2e-05 *** 436s --- 436s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 436s 436s Residual standard error: 0.801 on 17 degrees of freedom 436s Number of observations: 21 Degrees of Freedom: 17 436s SSR: 10.921 MSE: 0.642 Root MSE: 0.801 436s Multiple R-Squared: 0.986 Adjusted R-Squared: 0.984 436s 436s > residuals 436s Consumption Investment PrivateWages 436s 1 NA NA NA 436s 2 -0.4416 -2.1951 -1.20287 436s 3 -1.0150 0.1515 0.51834 436s 4 -1.5289 0.4406 1.50936 436s 5 -0.4985 -1.8667 -0.08743 436s 6 -0.0132 0.0713 -0.28089 436s 7 0.7759 1.0294 -0.33908 436s 8 1.3004 1.1011 -0.69282 436s 9 1.0993 0.5853 0.34494 436s 10 -0.5839 2.2952 1.27590 436s 11 -0.1917 -1.3443 -0.40414 436s 12 -0.5598 -0.9944 0.22151 436s 13 -0.6746 -1.3404 -0.36962 436s 14 0.5767 1.9316 0.31006 436s 15 -0.0211 -0.1217 0.27309 436s 16 0.0539 0.1847 0.00716 436s 17 1.8555 2.0937 -0.71866 436s 18 -0.4596 -0.3216 0.90582 436s 19 0.0613 -3.6314 -0.81881 436s 20 1.2602 0.7582 -0.26942 436s 21 0.9500 0.2428 -1.06125 436s 22 -1.9451 0.9302 0.87883 436s > fitted 436s Consumption Investment PrivateWages 436s 1 NA NA NA 436s 2 42.3 1.99510 26.7 436s 3 46.0 1.74850 28.8 436s 4 50.7 4.75942 32.6 436s 5 51.1 4.86672 34.0 436s 6 52.6 5.02874 35.7 436s 7 54.3 4.57056 37.7 436s 8 54.9 3.09893 38.6 436s 9 56.2 2.41471 38.9 436s 10 58.4 2.80476 40.0 436s 11 55.2 2.34425 38.3 436s 12 51.5 -2.40558 34.3 436s 13 46.3 -4.85959 29.4 436s 14 45.9 -7.03164 28.2 436s 15 48.7 -2.87827 30.3 436s 16 51.2 -1.48466 33.2 436s 17 55.8 0.00629 37.5 436s 18 59.2 2.32164 40.1 436s 19 57.4 1.73138 39.0 436s 20 60.3 0.54175 41.9 436s 21 64.1 3.05716 46.1 436s 22 71.6 3.96979 52.4 436s > predict 436s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 436s 1 NA NA NA NA 436s 2 42.3 0.464 39.9 44.8 436s 3 46.0 0.541 43.5 48.5 436s 4 50.7 0.337 48.4 53.1 436s 5 51.1 0.385 48.7 53.5 436s 6 52.6 0.386 50.3 55.0 436s 7 54.3 0.349 52.0 56.7 436s 8 54.9 0.320 52.6 57.2 436s 9 56.2 0.355 53.9 58.5 436s 10 58.4 0.370 56.0 60.7 436s 11 55.2 0.682 52.6 57.8 436s 12 51.5 0.563 48.9 54.0 436s 13 46.3 0.719 43.6 49.0 436s 14 45.9 0.597 43.4 48.5 436s 15 48.7 0.370 46.4 51.1 436s 16 51.2 0.327 48.9 53.6 436s 17 55.8 0.391 53.5 58.2 436s 18 59.2 0.316 56.8 61.5 436s 19 57.4 0.389 55.1 59.8 436s 20 60.3 0.459 57.9 62.8 436s 21 64.1 0.438 61.7 66.4 436s 22 71.6 0.674 69.0 74.3 436s Investment.pred Investment.se.fit Investment.lwr Investment.upr 436s 1 NA NA NA NA 436s 2 1.99510 0.792 -1.787 5.777 436s 3 1.74850 0.585 -1.861 5.358 436s 4 4.75942 0.510 1.200 8.319 436s 5 4.86672 0.423 1.359 8.375 436s 6 5.02874 0.400 1.533 8.525 436s 7 4.57056 0.391 1.079 8.062 436s 8 3.09893 0.345 -0.371 6.568 436s 9 2.41471 0.511 -1.145 5.974 436s 10 2.80476 0.560 -0.788 6.397 436s 11 2.34425 0.839 -1.482 6.170 436s 12 -2.40558 0.673 -6.083 1.272 436s 13 -4.85959 0.862 -8.708 -1.011 436s 14 -7.03164 0.874 -10.893 -3.171 436s 15 -2.87827 0.433 -6.392 0.635 436s 16 -1.48466 0.375 -4.968 1.999 436s 17 0.00629 0.491 -3.541 3.554 436s 18 2.32164 0.294 -1.127 5.771 436s 19 1.73138 0.446 -1.789 5.252 436s 20 0.54175 0.547 -3.042 4.125 436s 21 3.05716 0.454 -0.468 6.582 436s 22 3.96979 0.642 0.317 7.623 436s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 436s 1 NA NA NA NA 436s 2 26.7 0.314 24.9 28.5 436s 3 28.8 0.318 27.0 30.6 436s 4 32.6 0.325 30.8 34.4 436s 5 34.0 0.235 32.2 35.7 436s 6 35.7 0.241 33.9 37.4 436s 7 37.7 0.238 36.0 39.5 436s 8 38.6 0.237 36.8 40.4 436s 9 38.9 0.227 37.1 40.6 436s 10 40.0 0.219 38.3 41.8 436s 11 38.3 0.317 36.5 40.1 436s 12 34.3 0.344 32.4 36.1 436s 13 29.4 0.419 27.5 31.3 436s 14 28.2 0.334 26.4 30.0 436s 15 30.3 0.320 28.5 32.1 436s 16 33.2 0.268 31.4 35.0 436s 17 37.5 0.269 35.7 39.3 436s 18 40.1 0.212 38.3 41.8 436s 19 39.0 0.331 37.2 40.8 436s 20 41.9 0.287 40.1 43.7 436s 21 46.1 0.301 44.3 47.9 436s 22 52.4 0.471 50.5 54.4 436s > model.frame 436s [1] TRUE 436s > model.matrix 436s [1] TRUE 436s > nobs 436s [1] 63 436s > linearHypothesis 436s Linear hypothesis test (Theil's F test) 436s 436s Hypothesis: 436s Consumption_corpProf + Investment_capitalLag = 0 436s 436s Model 1: restricted model 436s Model 2: kleinModel 436s 436s Res.Df Df F Pr(>F) 436s 1 52 436s 2 51 1 0.29 0.59 436s Linear hypothesis test (F statistic of a Wald test) 436s 436s Hypothesis: 436s Consumption_corpProf + Investment_capitalLag = 0 436s 436s Model 1: restricted model 436s Model 2: kleinModel 436s 436s Res.Df Df F Pr(>F) 436s 1 52 436s 2 51 1 0.39 0.54 436s Linear hypothesis test (Chi^2 statistic of a Wald test) 436s 436s Hypothesis: 436s Consumption_corpProf + Investment_capitalLag = 0 436s 436s Model 1: restricted model 436s Model 2: kleinModel 436s 436s Res.Df Df Chisq Pr(>Chisq) 436s 1 52 436s 2 51 1 0.39 0.53 436s Linear hypothesis test (Theil's F test) 436s 436s Hypothesis: 436s Consumption_corpProf + Investment_capitalLag = 0 436s Consumption_corpProfLag - PrivateWages_trend = 0 436s 436s Model 1: restricted model 436s Model 2: kleinModel 436s 436s Res.Df Df F Pr(>F) 436s 1 53 436s 2 51 2 0.3 0.74 436s Linear hypothesis test (F statistic of a Wald test) 436s 436s Hypothesis: 436s Consumption_corpProf + Investment_capitalLag = 0 436s Consumption_corpProfLag - PrivateWages_trend = 0 436s 436s Model 1: restricted model 436s Model 2: kleinModel 436s 436s Res.Df Df F Pr(>F) 436s 1 53 436s 2 51 2 0.4 0.67 436s Linear hypothesis test (Chi^2 statistic of a Wald test) 436s 436s Hypothesis: 436s Consumption_corpProf + Investment_capitalLag = 0 436s Consumption_corpProfLag - PrivateWages_trend = 0 436s 436s Model 1: restricted model 436s Model 2: kleinModel 436s 436s Res.Df Df Chisq Pr(>Chisq) 436s 1 53 436s 2 51 2 0.8 0.67 436s > logLik 436s 'log Lik.' -76.1 (df=18) 436s 'log Lik.' -89.1 (df=18) 436s Estimating function 436s Consumption_(Intercept) Consumption_corpProf 436s Consumption_2 -3.2451 -43.02 436s Consumption_3 -1.3384 -22.19 436s Consumption_4 -1.4130 -27.25 436s Consumption_5 -5.0390 -105.62 436s Consumption_6 -0.8531 -16.86 436s Consumption_7 4.3438 79.23 436s Consumption_8 5.6608 99.48 436s Consumption_9 3.7666 73.61 436s Consumption_10 1.2798 26.08 436s Consumption_11 -3.5695 -61.32 436s Consumption_12 -1.8656 -23.70 436s Consumption_13 -3.4193 -30.77 436s Consumption_14 4.0738 36.88 436s Consumption_15 -1.6814 -21.31 436s Consumption_16 -1.4312 -20.64 436s Consumption_17 9.0552 133.22 436s Consumption_18 -1.9716 -39.03 436s Consumption_19 -6.7338 -129.33 436s Consumption_20 4.8735 84.89 436s Consumption_21 1.6324 33.15 436s Consumption_22 -2.1249 -48.14 436s Investment_2 2.1466 28.45 436s Investment_3 -0.1448 -2.40 436s Investment_4 -0.4444 -8.57 436s Investment_5 1.8148 38.04 436s Investment_6 -0.0658 -1.30 436s Investment_7 -0.9944 -18.14 436s Investment_8 -1.0536 -18.52 436s Investment_9 -0.5553 -10.85 436s Investment_10 -2.2390 -45.62 436s Investment_11 1.3010 22.35 436s Investment_12 0.9607 12.21 436s Investment_13 1.2918 11.63 436s Investment_14 -1.8711 -16.94 436s Investment_15 0.1149 1.46 436s Investment_16 -0.1869 -2.70 436s Investment_17 -2.0208 -29.73 436s Investment_18 0.2841 5.62 436s Investment_19 3.5191 67.59 436s Investment_20 -0.7250 -12.63 436s Investment_21 -0.2285 -4.64 436s Investment_22 -0.9035 -20.47 436s PrivateWages_2 -4.3513 -57.68 436s PrivateWages_3 1.7756 29.44 436s PrivateWages_4 3.5512 68.47 436s PrivateWages_5 -3.3088 -69.35 436s PrivateWages_6 -0.7761 -15.34 436s PrivateWages_7 1.5988 29.16 436s PrivateWages_8 1.5583 27.38 436s PrivateWages_9 2.5665 50.15 436s PrivateWages_10 4.9740 101.35 436s PrivateWages_11 -3.5972 -61.80 436s PrivateWages_12 -0.7986 -10.15 436s PrivateWages_13 -3.2258 -29.03 436s PrivateWages_14 3.6395 32.95 436s PrivateWages_15 -0.5056 -6.41 436s PrivateWages_16 -1.0680 -15.40 436s PrivateWages_17 3.0850 45.39 436s PrivateWages_18 -0.3546 -7.02 436s PrivateWages_19 -8.0362 -154.35 436s PrivateWages_20 1.6465 28.68 436s PrivateWages_21 -1.9137 -38.86 436s PrivateWages_22 3.5407 80.22 436s Consumption_corpProfLag Consumption_wages 436s Consumption_2 -41.21 -95.43 436s Consumption_3 -16.60 -42.49 436s Consumption_4 -23.88 -50.52 436s Consumption_5 -92.72 -196.89 436s Consumption_6 -16.55 -33.39 436s Consumption_7 87.31 170.95 436s Consumption_8 110.95 227.47 436s Consumption_9 74.58 159.45 436s Consumption_10 27.00 56.34 436s Consumption_11 -77.46 -155.98 436s Consumption_12 -29.10 -73.65 436s Consumption_13 -38.98 -120.13 436s Consumption_14 28.52 133.55 436s Consumption_15 -18.83 -63.05 436s Consumption_16 -17.60 -57.45 436s Consumption_17 126.77 377.63 436s Consumption_18 -34.70 -94.39 436s Consumption_19 -116.49 -332.00 436s Consumption_20 74.56 235.83 436s Consumption_21 31.02 87.12 436s Consumption_22 -44.84 -129.02 436s Investment_2 27.26 63.12 436s Investment_3 -1.80 -4.60 436s Investment_4 -7.51 -15.89 436s Investment_5 33.39 70.91 436s Investment_6 -1.28 -2.57 436s Investment_7 -19.99 -39.13 436s Investment_8 -20.65 -42.34 436s Investment_9 -10.99 -23.51 436s Investment_10 -47.24 -98.56 436s Investment_11 28.23 56.85 436s Investment_12 14.99 37.92 436s Investment_13 14.73 45.38 436s Investment_14 -13.10 -61.34 436s Investment_15 1.29 4.31 436s Investment_16 -2.30 -7.50 436s Investment_17 -28.29 -84.27 436s Investment_18 5.00 13.60 436s Investment_19 60.88 173.50 436s Investment_20 -11.09 -35.08 436s Investment_21 -4.34 -12.19 436s Investment_22 -19.06 -54.86 436s PrivateWages_2 -55.26 -127.96 436s PrivateWages_3 22.02 56.38 436s PrivateWages_4 60.01 126.96 436s PrivateWages_5 -60.88 -129.29 436s PrivateWages_6 -15.06 -30.37 436s PrivateWages_7 32.14 62.92 436s PrivateWages_8 30.54 62.62 436s PrivateWages_9 50.82 108.65 436s PrivateWages_10 104.95 218.96 436s PrivateWages_11 -78.06 -157.19 436s PrivateWages_12 -12.46 -31.53 436s PrivateWages_13 -36.77 -113.33 436s PrivateWages_14 25.48 119.32 436s PrivateWages_15 -5.66 -18.96 436s PrivateWages_16 -13.14 -42.87 436s PrivateWages_17 43.19 128.65 436s PrivateWages_18 -6.24 -16.98 436s PrivateWages_19 -139.03 -396.21 436s PrivateWages_20 25.19 79.68 436s PrivateWages_21 -36.36 -102.14 436s PrivateWages_22 74.71 214.98 436s Investment_(Intercept) Investment_corpProf 436s Consumption_2 1.4757 19.56 436s Consumption_3 0.6086 10.09 436s Consumption_4 0.6425 12.39 436s Consumption_5 2.2915 48.03 436s Consumption_6 0.3879 7.67 436s Consumption_7 -1.9753 -36.03 436s Consumption_8 -2.5742 -45.24 436s Consumption_9 -1.7128 -33.47 436s Consumption_10 -0.5820 -11.86 436s Consumption_11 1.6232 27.89 436s Consumption_12 0.8484 10.78 436s Consumption_13 1.5549 13.99 436s Consumption_14 -1.8525 -16.77 436s Consumption_15 0.7646 9.69 436s Consumption_16 0.6508 9.39 436s Consumption_17 -4.1178 -60.58 436s Consumption_18 0.8965 17.75 436s Consumption_19 3.0621 58.81 436s Consumption_20 -2.2162 -38.60 436s Consumption_21 -0.7423 -15.07 436s Consumption_22 0.9663 21.89 436s Investment_2 -2.6492 -35.12 436s Investment_3 0.1787 2.96 436s Investment_4 0.5485 10.58 436s Investment_5 -2.2397 -46.94 436s Investment_6 0.0811 1.60 436s Investment_7 1.2272 22.38 436s Investment_8 1.3003 22.85 436s Investment_9 0.6853 13.39 436s Investment_10 2.7633 56.30 436s Investment_11 -1.6056 -27.58 436s Investment_12 -1.1856 -15.06 436s Investment_13 -1.5943 -14.35 436s Investment_14 2.3092 20.91 436s Investment_15 -0.1418 -1.80 436s Investment_16 0.2307 3.33 436s Investment_17 2.4940 36.69 436s Investment_18 -0.3506 -6.94 436s Investment_19 -4.3431 -83.42 436s Investment_20 0.8947 15.59 436s Investment_21 0.2820 5.73 436s Investment_22 1.1150 25.26 436s PrivateWages_2 2.6070 34.56 436s PrivateWages_3 -1.0638 -17.64 436s PrivateWages_4 -2.1276 -41.02 436s PrivateWages_5 1.9824 41.55 436s PrivateWages_6 0.4650 9.19 436s PrivateWages_7 -0.9579 -17.47 436s PrivateWages_8 -0.9336 -16.41 436s PrivateWages_9 -1.5377 -30.05 436s PrivateWages_10 -2.9800 -60.72 436s PrivateWages_11 2.1552 37.03 436s PrivateWages_12 0.4785 6.08 436s PrivateWages_13 1.9327 17.39 436s PrivateWages_14 -2.1805 -19.74 436s PrivateWages_15 0.3029 3.84 436s PrivateWages_16 0.6398 9.23 436s PrivateWages_17 -1.8483 -27.19 436s PrivateWages_18 0.2125 4.21 436s PrivateWages_19 4.8147 92.47 436s PrivateWages_20 -0.9865 -17.18 436s PrivateWages_21 1.1466 23.28 436s PrivateWages_22 -2.1213 -48.06 436s Investment_corpProfLag Investment_capitalLag 436s Consumption_2 18.74 269.8 436s Consumption_3 7.55 111.1 436s Consumption_4 10.86 118.5 436s Consumption_5 42.16 434.7 436s Consumption_6 7.53 74.8 436s Consumption_7 -39.70 -390.7 436s Consumption_8 -50.45 -523.6 436s Consumption_9 -33.91 -355.6 436s Consumption_10 -12.28 -122.6 436s Consumption_11 35.22 350.1 436s Consumption_12 13.23 183.8 436s Consumption_13 17.73 331.7 436s Consumption_14 -12.97 -383.7 436s Consumption_15 8.56 154.5 436s Consumption_16 8.01 129.5 436s Consumption_17 -57.65 -814.1 436s Consumption_18 15.78 179.1 436s Consumption_19 52.98 617.9 436s Consumption_20 -33.91 -443.0 436s Consumption_21 -14.10 -149.4 436s Consumption_22 20.39 197.6 436s Investment_2 -33.65 -484.3 436s Investment_3 2.22 32.6 436s Investment_4 9.27 101.2 436s Investment_5 -41.21 -424.9 436s Investment_6 1.57 15.6 436s Investment_7 24.67 242.7 436s Investment_8 25.49 264.5 436s Investment_9 13.57 142.3 436s Investment_10 58.30 581.9 436s Investment_11 -34.84 -346.3 436s Investment_12 -18.50 -256.9 436s Investment_13 -18.17 -340.1 436s Investment_14 16.16 478.2 436s Investment_15 -1.59 -28.6 436s Investment_16 2.84 45.9 436s Investment_17 34.92 493.1 436s Investment_18 -6.17 -70.0 436s Investment_19 -75.14 -876.4 436s Investment_20 13.69 178.9 436s Investment_21 5.36 56.7 436s Investment_22 23.53 228.0 436s PrivateWages_2 33.11 476.6 436s PrivateWages_3 -13.19 -194.3 436s PrivateWages_4 -35.96 -392.5 436s PrivateWages_5 36.48 376.1 436s PrivateWages_6 9.02 89.6 436s PrivateWages_7 -19.25 -189.5 436s PrivateWages_8 -18.30 -189.9 436s PrivateWages_9 -30.45 -319.2 436s PrivateWages_10 -62.88 -627.6 436s PrivateWages_11 46.77 464.9 436s PrivateWages_12 7.46 103.7 436s PrivateWages_13 22.03 412.2 436s PrivateWages_14 -15.26 -451.6 436s PrivateWages_15 3.39 61.2 436s PrivateWages_16 7.87 127.3 436s PrivateWages_17 -25.88 -365.4 436s PrivateWages_18 3.74 42.5 436s PrivateWages_19 83.29 971.6 436s PrivateWages_20 -15.09 -197.2 436s PrivateWages_21 21.78 230.7 436s PrivateWages_22 -44.76 -433.8 436s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 436s Consumption_2 -3.220 -153.49 -144.60 436s Consumption_3 -1.328 -65.52 -60.57 436s Consumption_4 -1.402 -79.70 -70.25 436s Consumption_5 -5.001 -303.71 -286.05 436s Consumption_6 -0.847 -51.81 -48.34 436s Consumption_7 4.311 264.22 262.96 436s Consumption_8 5.618 341.88 359.54 436s Consumption_9 3.738 233.16 240.73 436s Consumption_10 1.270 81.79 81.92 436s Consumption_11 -3.542 -228.05 -237.34 436s Consumption_12 -1.851 -101.61 -113.31 436s Consumption_13 -3.393 -159.94 -181.21 436s Consumption_14 4.043 168.34 179.10 436s Consumption_15 -1.669 -85.05 -75.26 436s Consumption_16 -1.420 -79.06 -70.59 436s Consumption_17 8.987 515.06 488.87 436s Consumption_18 -1.957 -132.41 -122.68 436s Consumption_19 -6.683 -455.83 -434.38 436s Consumption_20 4.837 323.61 294.54 436s Consumption_21 1.620 121.95 112.59 436s Consumption_22 -2.109 -182.35 -159.64 436s Investment_2 2.807 133.77 126.02 436s Investment_3 -0.189 -9.34 -8.63 436s Investment_4 -0.581 -33.02 -29.11 436s Investment_5 2.373 144.11 135.73 436s Investment_6 -0.086 -5.26 -4.91 436s Investment_7 -1.300 -79.69 -79.31 436s Investment_8 -1.378 -83.84 -88.17 436s Investment_9 -0.726 -45.28 -46.75 436s Investment_10 -2.928 -188.52 -188.82 436s Investment_11 1.701 109.51 113.97 436s Investment_12 1.256 68.94 76.87 436s Investment_13 1.689 79.61 90.20 436s Investment_14 -2.446 -101.86 -108.38 436s Investment_15 0.150 7.66 6.77 436s Investment_16 -0.244 -13.60 -12.15 436s Investment_17 -2.642 -151.44 -143.74 436s Investment_18 0.371 25.13 23.29 436s Investment_19 4.601 313.85 299.09 436s Investment_20 -0.948 -63.43 -57.73 436s Investment_21 -0.299 -22.49 -20.76 436s Investment_22 -1.181 -102.15 -89.43 436s PrivateWages_2 -8.830 -420.86 -396.47 436s PrivateWages_3 3.603 177.74 164.31 436s PrivateWages_4 7.206 409.57 361.04 436s PrivateWages_5 -6.715 -407.80 -384.07 436s PrivateWages_6 -1.575 -96.39 -89.93 436s PrivateWages_7 3.244 198.86 197.91 436s PrivateWages_8 3.162 192.44 202.38 436s PrivateWages_9 5.208 324.85 335.40 436s PrivateWages_10 10.094 649.99 651.03 436s PrivateWages_11 -7.300 -469.94 -489.08 436s PrivateWages_12 -1.621 -88.94 -99.18 436s PrivateWages_13 -6.546 -308.53 -349.56 436s PrivateWages_14 7.386 307.52 327.18 436s PrivateWages_15 -1.026 -52.30 -46.27 436s PrivateWages_16 -2.167 -120.63 -107.71 436s PrivateWages_17 6.260 358.81 340.56 436s PrivateWages_18 -0.720 -48.70 -45.12 436s PrivateWages_19 -16.308 -1112.35 -1060.00 436s PrivateWages_20 3.341 223.57 203.48 436s PrivateWages_21 -3.883 -292.34 -269.90 436s PrivateWages_22 7.185 621.32 543.91 436s PrivateWages_trend 436s Consumption_2 32.205 436s Consumption_3 11.954 436s Consumption_4 11.218 436s Consumption_5 35.006 436s Consumption_6 5.080 436s Consumption_7 -21.554 436s Consumption_8 -22.471 436s Consumption_9 -11.214 436s Consumption_10 -2.540 436s Consumption_11 3.542 436s Consumption_12 0.000 436s Consumption_13 -3.393 436s Consumption_14 8.086 436s Consumption_15 -5.006 436s Consumption_16 -5.681 436s Consumption_17 44.933 436s Consumption_18 -11.740 436s Consumption_19 -46.779 436s Consumption_20 38.692 436s Consumption_21 14.580 436s Consumption_22 -21.088 436s Investment_2 -28.067 436s Investment_3 1.704 436s Investment_4 4.648 436s Investment_5 -16.610 436s Investment_6 0.516 436s Investment_7 6.501 436s Investment_8 5.511 436s Investment_9 2.178 436s Investment_10 5.855 436s Investment_11 -1.701 436s Investment_12 0.000 436s Investment_13 1.689 436s Investment_14 -4.893 436s Investment_15 0.451 436s Investment_16 -0.978 436s Investment_17 -13.211 436s Investment_18 2.228 436s Investment_19 32.209 436s Investment_20 -7.583 436s Investment_21 -2.689 436s Investment_22 -11.813 436s PrivateWages_2 88.301 436s PrivateWages_3 -32.429 436s PrivateWages_4 -57.650 436s PrivateWages_5 47.002 436s PrivateWages_6 9.450 436s PrivateWages_7 -16.222 436s PrivateWages_8 -12.649 436s PrivateWages_9 -15.624 436s PrivateWages_10 -20.187 436s PrivateWages_11 7.300 436s PrivateWages_12 0.000 436s PrivateWages_13 -6.546 436s PrivateWages_14 14.771 436s PrivateWages_15 -3.078 436s PrivateWages_16 -8.669 436s PrivateWages_17 31.301 436s PrivateWages_18 -4.318 436s PrivateWages_19 -114.154 436s PrivateWages_20 26.730 436s PrivateWages_21 -34.951 436s PrivateWages_22 71.851 436s [1] TRUE 436s > Bread 436s Consumption_(Intercept) Consumption_corpProf Consumption_corpProfLag 436s [1,] 1.07e+02 -1.06982 -0.3515 436s [2,] -1.07e+00 0.73659 -0.5079 436s [3,] -3.51e-01 -0.50793 0.6355 436s [4,] -1.93e+00 -0.07361 -0.0356 436s [5,] 1.24e+02 -0.98618 3.4455 436s [6,] -2.71e+00 0.38390 -0.3719 436s [7,] 9.65e-01 -0.31139 0.3992 436s [8,] -4.61e-01 -0.00199 -0.0185 436s [9,] -3.88e+01 0.05351 1.8003 436s [10,] 6.27e-01 -0.08533 0.0556 436s [11,] -5.96e-04 0.08746 -0.0887 436s [12,] 2.14e-01 0.04029 0.0279 436s Consumption_wages Investment_(Intercept) Investment_corpProf 436s [1,] -1.934840 123.765 -2.71e+00 436s [2,] -0.073613 -0.986 3.84e-01 436s [3,] -0.035606 3.445 -3.72e-01 436s [4,] 0.090675 -3.911 5.58e-02 436s [5,] -3.910682 2907.785 -4.61e+01 436s [6,] 0.055805 -46.132 1.65e+00 436s [7,] -0.054072 38.083 -1.41e+00 436s [8,] 0.019220 -13.707 2.06e-01 436s [9,] 0.174112 17.422 -1.06e-01 436s [10,] -0.002325 2.389 2.04e-03 436s [11,] -0.000594 -2.765 -2.91e-04 436s [12,] -0.032572 -2.080 3.10e-02 436s Investment_corpProfLag Investment_capitalLag PrivateWages_(Intercept) 436s [1,] 0.96474 -0.46130 -38.76422 436s [2,] -0.31139 -0.00199 0.05351 436s [3,] 0.39923 -0.01847 1.80032 436s [4,] -0.05407 0.01922 0.17411 436s [5,] 38.08346 -13.70662 17.42245 436s [6,] -1.40785 0.20597 -0.10564 436s [7,] 1.47348 -0.19170 -0.93153 436s [8,] -0.19170 0.06667 0.00097 436s [9,] -0.93153 0.00097 78.44334 436s [10,] 0.01112 -0.01300 -0.49810 436s [11,] 0.00455 0.01344 -0.81226 436s [12,] -0.04174 0.01117 0.88592 436s PrivateWages_gnp PrivateWages_gnpLag PrivateWages_trend 436s [1,] 0.62679 -0.000596 0.21374 436s [2,] -0.08533 0.087455 0.04029 436s [3,] 0.05563 -0.088660 0.02790 436s [4,] -0.00233 -0.000594 -0.03257 436s [5,] 2.38888 -2.764716 -2.07974 436s [6,] 0.00204 -0.000291 0.03105 436s [7,] 0.01112 0.004547 -0.04174 436s [8,] -0.01300 0.013443 0.01117 436s [9,] -0.49810 -0.812260 0.88592 436s [10,] 0.06376 -0.057450 -0.01781 436s [11,] -0.05745 0.073510 0.00317 436s [12,] -0.01781 0.003170 0.04916 436s > 436s > # I3SLS 436s > summary 436s 436s systemfit results 436s method: iterated 3SLS 436s 436s convergence achieved after 20 iterations 436s 436s N DF SSR detRCov OLS-R2 McElroy-R2 436s system 63 51 128 0.509 0.936 0.996 436s 436s N DF SSR MSE RMSE R2 Adj R2 436s Consumption 21 17 19.2 1.130 1.063 0.980 0.976 436s Investment 21 17 95.7 5.627 2.372 0.621 0.554 436s PrivateWages 21 17 12.7 0.748 0.865 0.984 0.981 436s 436s The covariance matrix of the residuals used for estimation 436s Consumption Investment PrivateWages 436s Consumption 0.915 0.642 -0.435 436s Investment 0.642 4.555 0.734 436s PrivateWages -0.435 0.734 0.606 436s 436s The covariance matrix of the residuals 436s Consumption Investment PrivateWages 436s Consumption 0.915 0.642 -0.435 436s Investment 0.642 4.555 0.734 436s PrivateWages -0.435 0.734 0.606 436s 436s The correlations of the residuals 436s Consumption Investment PrivateWages 436s Consumption 1.000 0.314 -0.584 436s Investment 0.314 1.000 0.442 436s PrivateWages -0.584 0.442 1.000 436s 436s 436s 3SLS estimates for 'Consumption' (equation 1) 436s Model Formula: consump ~ corpProf + corpProfLag + wages 436s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 436s gnpLag 436s 436s Estimate Std. Error t value Pr(>|t|) 436s (Intercept) 16.5590 1.2244 13.52 1.6e-10 *** 436s corpProf 0.1645 0.0962 1.71 0.105 436s corpProfLag 0.1766 0.0901 1.96 0.067 . 436s wages 0.7658 0.0348 22.03 6.1e-14 *** 436s --- 436s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 436s 436s Residual standard error: 1.063 on 17 degrees of freedom 436s Number of observations: 21 Degrees of Freedom: 17 436s SSR: 19.213 MSE: 1.13 Root MSE: 1.063 436s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.976 436s 436s 436s 3SLS estimates for 'Investment' (equation 2) 436s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 436s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 436s gnpLag 436s 437s Estimate Std. Error t value Pr(>|t|) 437s (Intercept) 42.8959 10.5937 4.05 0.00083 *** 437s corpProf -0.3565 0.2602 -1.37 0.18838 437s corpProfLag 1.0113 0.2488 4.07 0.00081 *** 437s capitalLag -0.2602 0.0509 -5.12 8.6e-05 *** 437s --- 437s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 437s 437s Residual standard error: 2.372 on 17 degrees of freedom 437s Number of observations: 21 Degrees of Freedom: 17 437s SSR: 95.661 MSE: 5.627 Root MSE: 2.372 437s Multiple R-Squared: 0.621 Adjusted R-Squared: 0.554 437s 437s 437s 3SLS estimates for 'PrivateWages' (equation 3) 437s Model Formula: privWage ~ gnp + gnpLag + trend 437s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 437s gnpLag 437s 437s Estimate Std. Error t value Pr(>|t|) 437s (Intercept) 2.6247 1.1956 2.20 0.042 * 437s gnp 0.3748 0.0311 12.05 9.4e-10 *** 437s gnpLag 0.1937 0.0324 5.98 1.5e-05 *** 437s trend 0.1679 0.0289 5.80 2.1e-05 *** 437s --- 437s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 437s 437s Residual standard error: 0.865 on 17 degrees of freedom 437s Number of observations: 21 Degrees of Freedom: 17 437s SSR: 12.719 MSE: 0.748 Root MSE: 0.865 437s Multiple R-Squared: 0.984 Adjusted R-Squared: 0.981 437s 437s > residuals 437s Consumption Investment PrivateWages 437s 1 NA NA NA 437s 2 -0.537 -3.95419 -1.2303 437s 3 -1.187 0.00151 0.5797 437s 4 -1.705 -0.22015 1.6794 437s 5 -0.734 -2.22753 -0.0260 437s 6 -0.251 -0.10866 -0.1362 437s 7 0.600 0.83218 -0.1837 437s 8 1.142 1.46624 -0.5825 437s 9 0.921 1.62030 0.4347 437s 10 -0.745 3.40013 1.4104 437s 11 -0.197 -2.15443 -0.4679 437s 12 -0.385 -1.62274 0.0106 437s 13 -0.390 -2.62869 -0.7363 437s 14 0.749 2.80517 0.0581 437s 15 0.112 -0.27710 0.1113 437s 16 0.170 0.13598 -0.1089 437s 17 1.925 2.76200 -0.6976 437s 18 -0.341 -0.53919 0.8651 437s 19 0.219 -4.32845 -1.0116 437s 20 1.383 1.71889 -0.2087 437s 21 1.028 1.06406 -0.9656 437s 22 -1.777 2.25466 1.2061 437s > fitted 437s Consumption Investment PrivateWages 437s 1 NA NA NA 437s 2 42.4 3.754 26.7 437s 3 46.2 1.898 28.7 437s 4 50.9 5.420 32.4 437s 5 51.3 5.228 33.9 437s 6 52.9 5.209 35.5 437s 7 54.5 4.768 37.6 437s 8 55.1 2.734 38.5 437s 9 56.4 1.380 38.8 437s 10 58.5 1.700 39.9 437s 11 55.2 3.154 38.4 437s 12 51.3 -1.777 34.5 437s 13 46.0 -3.571 29.7 437s 14 45.8 -7.905 28.4 437s 15 48.6 -2.723 30.5 437s 16 51.1 -1.436 33.3 437s 17 55.8 -0.662 37.5 437s 18 59.0 2.539 40.1 437s 19 57.3 2.428 39.2 437s 20 60.2 -0.419 41.8 437s 21 64.0 2.236 46.0 437s 22 71.5 2.645 52.1 437s > predict 437s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 437s 1 NA NA NA NA 437s 2 42.4 0.434 41.6 43.3 437s 3 46.2 0.491 45.2 47.2 437s 4 50.9 0.309 50.3 51.5 437s 5 51.3 0.351 50.6 52.0 437s 6 52.9 0.352 52.1 53.6 437s 7 54.5 0.320 53.9 55.1 437s 8 55.1 0.293 54.5 55.6 437s 9 56.4 0.324 55.7 57.0 437s 10 58.5 0.340 57.9 59.2 437s 11 55.2 0.613 54.0 56.4 437s 12 51.3 0.506 50.3 52.3 437s 13 46.0 0.649 44.7 47.3 437s 14 45.8 0.546 44.7 46.8 437s 15 48.6 0.341 47.9 49.3 437s 16 51.1 0.301 50.5 51.7 437s 17 55.8 0.357 55.1 56.5 437s 18 59.0 0.293 58.5 59.6 437s 19 57.3 0.353 56.6 58.0 437s 20 60.2 0.421 59.4 61.1 437s 21 64.0 0.409 63.2 64.8 437s 22 71.5 0.630 70.2 72.7 437s Investment.pred Investment.se.fit Investment.lwr Investment.upr 437s 1 NA NA NA NA 437s 2 3.754 1.263 1.218 6.2906 437s 3 1.898 1.022 -0.153 3.9503 437s 4 5.420 0.853 3.709 7.1317 437s 5 5.228 0.727 3.767 6.6877 437s 6 5.209 0.703 3.797 6.6200 437s 7 4.768 0.688 3.387 6.1487 437s 8 2.734 0.615 1.499 3.9683 437s 9 1.380 0.852 -0.330 3.0893 437s 10 1.700 0.938 -0.184 3.5836 437s 11 3.154 1.437 0.269 6.0398 437s 12 -1.777 1.173 -4.133 0.5780 437s 13 -3.571 1.494 -6.570 -0.5725 437s 14 -7.905 1.479 -10.875 -4.9350 437s 15 -2.723 0.778 -4.285 -1.1613 437s 16 -1.436 0.672 -2.784 -0.0875 437s 17 -0.662 0.832 -2.333 1.0088 437s 18 2.539 0.522 1.491 3.5875 437s 19 2.428 0.753 0.918 3.9392 437s 20 -0.419 0.907 -2.240 1.4019 437s 21 2.236 0.775 0.679 3.7928 437s 22 2.645 1.076 0.486 4.8047 437s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 437s 1 NA NA NA NA 437s 2 26.7 0.340 26.0 27.4 437s 3 28.7 0.339 28.0 29.4 437s 4 32.4 0.340 31.7 33.1 437s 5 33.9 0.250 33.4 34.4 437s 6 35.5 0.258 35.0 36.1 437s 7 37.6 0.256 37.1 38.1 437s 8 38.5 0.252 38.0 39.0 437s 9 38.8 0.241 38.3 39.2 437s 10 39.9 0.239 39.4 40.4 437s 11 38.4 0.314 37.7 39.0 437s 12 34.5 0.342 33.8 35.2 437s 13 29.7 0.430 28.9 30.6 437s 14 28.4 0.361 27.7 29.2 437s 15 30.5 0.336 29.8 31.2 437s 16 33.3 0.281 32.7 33.9 437s 17 37.5 0.270 37.0 38.0 437s 18 40.1 0.231 39.7 40.6 437s 19 39.2 0.343 38.5 39.9 437s 20 41.8 0.294 41.2 42.4 437s 21 46.0 0.326 45.3 46.6 437s 22 52.1 0.501 51.1 53.1 437s > model.frame 437s [1] TRUE 437s > model.matrix 437s [1] TRUE 437s > nobs 437s [1] 63 437s > linearHypothesis 437s Linear hypothesis test (Theil's F test) 437s 437s Hypothesis: 437s Consumption_corpProf + Investment_capitalLag = 0 437s 437s Model 1: restricted model 437s Model 2: kleinModel 437s 437s Res.Df Df F Pr(>F) 437s 1 52 437s 2 51 1 0.59 0.45 437s Linear hypothesis test (F statistic of a Wald test) 437s 437s Hypothesis: 437s Consumption_corpProf + Investment_capitalLag = 0 437s 437s Model 1: restricted model 437s Model 2: kleinModel 437s 437s Res.Df Df F Pr(>F) 437s 1 52 437s 2 51 1 0.73 0.4 437s Linear hypothesis test (Chi^2 statistic of a Wald test) 437s 437s Hypothesis: 437s Consumption_corpProf + Investment_capitalLag = 0 437s 437s Model 1: restricted model 437s Model 2: kleinModel 437s 437s Res.Df Df Chisq Pr(>Chisq) 437s 1 52 437s 2 51 1 0.73 0.39 437s Linear hypothesis test (Theil's F test) 437s 437s Hypothesis: 437s Consumption_corpProf + Investment_capitalLag = 0 437s Consumption_corpProfLag - PrivateWages_trend = 0 437s 437s Model 1: restricted model 437s Model 2: kleinModel 437s 437s Res.Df Df F Pr(>F) 437s 1 53 437s 2 51 2 0.72 0.49 437s Linear hypothesis test (F statistic of a Wald test) 437s 437s Hypothesis: 437s Consumption_corpProf + Investment_capitalLag = 0 437s Consumption_corpProfLag - PrivateWages_trend = 0 437s 437s Model 1: restricted model 437s Model 2: kleinModel 437s 437s Res.Df Df F Pr(>F) 437s 1 53 437s 2 51 2 0.88 0.42 437s Linear hypothesis test (Chi^2 statistic of a Wald test) 437s 437s Hypothesis: 437s Consumption_corpProf + Investment_capitalLag = 0 437s Consumption_corpProfLag - PrivateWages_trend = 0 437s 437s Model 1: restricted model 437s Model 2: kleinModel 437s 437s Res.Df Df Chisq Pr(>Chisq) 437s 1 53 437s 2 51 2 1.77 0.41 437s > logLik 437s 'log Lik.' -82.3 (df=18) 437s 'log Lik.' -99.1 (df=18) 437s Estimating function 437s Consumption_(Intercept) Consumption_corpProf 437s Consumption_2 -6.979 -92.51 437s Consumption_3 -3.442 -57.06 437s Consumption_4 -3.899 -75.19 437s Consumption_5 -11.237 -235.54 437s Consumption_6 -2.642 -52.22 437s Consumption_7 8.084 147.44 437s Consumption_8 10.972 192.80 437s Consumption_9 7.028 137.33 437s Consumption_10 1.972 40.17 437s Consumption_11 -7.325 -125.85 437s Consumption_12 -3.206 -40.73 437s Consumption_13 -5.913 -53.22 437s Consumption_14 9.196 83.26 437s Consumption_15 -2.781 -35.23 437s Consumption_16 -2.363 -34.08 437s Consumption_17 18.799 276.57 437s Consumption_18 -3.872 -76.65 437s Consumption_19 -13.205 -253.63 437s Consumption_20 10.531 183.44 437s Consumption_21 3.807 77.30 437s Consumption_22 -3.522 -79.79 437s Investment_2 5.075 67.27 437s Investment_3 0.158 2.62 437s Investment_4 -0.131 -2.53 437s Investment_5 2.324 48.72 437s Investment_6 0.316 6.26 437s Investment_7 -0.482 -8.80 437s Investment_8 -0.935 -16.43 437s Investment_9 -1.481 -28.94 437s Investment_10 -4.072 -82.96 437s Investment_11 2.213 38.01 437s Investment_12 1.610 20.45 437s Investment_13 2.664 23.98 437s Investment_14 -2.837 -25.69 437s Investment_15 0.201 2.55 437s Investment_16 -0.398 -5.74 437s Investment_17 -2.409 -35.45 437s Investment_18 -0.488 -9.66 437s Investment_19 4.083 78.42 437s Investment_20 -1.607 -27.99 437s Investment_21 -1.086 -22.05 437s Investment_22 -2.718 -61.58 437s PrivateWages_2 -9.649 -127.90 437s PrivateWages_3 4.187 69.41 437s PrivateWages_4 8.749 168.69 437s PrivateWages_5 -6.685 -140.11 437s PrivateWages_6 -1.021 -20.18 437s PrivateWages_7 4.003 73.02 437s PrivateWages_8 3.592 63.12 437s PrivateWages_9 5.932 115.93 437s PrivateWages_10 11.495 234.22 437s PrivateWages_11 -7.992 -137.30 437s PrivateWages_12 -2.626 -33.36 437s PrivateWages_13 -8.660 -77.94 437s PrivateWages_14 6.531 59.13 437s PrivateWages_15 -1.757 -22.27 437s PrivateWages_16 -2.801 -40.40 437s PrivateWages_17 6.362 93.60 437s PrivateWages_18 -0.661 -13.09 437s PrivateWages_19 -18.070 -347.06 437s PrivateWages_20 3.670 63.92 437s PrivateWages_21 -3.889 -78.97 437s PrivateWages_22 9.289 210.47 437s Consumption_corpProfLag Consumption_wages 437s Consumption_2 -88.63 -205.23 437s Consumption_3 -42.68 -109.29 437s Consumption_4 -65.90 -139.41 437s Consumption_5 -206.77 -439.08 437s Consumption_6 -51.26 -103.40 437s Consumption_7 162.48 318.13 437s Consumption_8 215.04 440.87 437s Consumption_9 139.15 297.49 437s Consumption_10 41.60 86.79 437s Consumption_11 -158.95 -320.08 437s Consumption_12 -50.01 -126.56 437s Consumption_13 -67.41 -207.75 437s Consumption_14 64.37 301.49 437s Consumption_15 -31.14 -104.27 437s Consumption_16 -29.07 -94.86 437s Consumption_17 263.19 783.97 437s Consumption_18 -68.15 -185.39 437s Consumption_19 -228.45 -651.06 437s Consumption_20 161.12 509.58 437s Consumption_21 72.33 203.19 437s Consumption_22 -74.31 -213.82 437s Investment_2 64.45 149.24 437s Investment_3 1.96 5.01 437s Investment_4 -2.22 -4.70 437s Investment_5 42.77 90.82 437s Investment_6 6.14 12.39 437s Investment_7 -9.70 -18.98 437s Investment_8 -18.33 -37.57 437s Investment_9 -29.32 -62.69 437s Investment_10 -85.92 -179.25 437s Investment_11 48.02 96.69 437s Investment_12 25.11 63.55 437s Investment_13 30.37 93.60 437s Investment_14 -19.86 -93.02 437s Investment_15 2.25 7.55 437s Investment_16 -4.90 -15.98 437s Investment_17 -33.73 -100.47 437s Investment_18 -8.59 -23.36 437s Investment_19 70.63 201.29 437s Investment_20 -24.59 -77.76 437s Investment_21 -20.63 -57.96 437s Investment_22 -57.35 -165.02 437s PrivateWages_2 -122.54 -283.73 437s PrivateWages_3 51.92 132.94 437s PrivateWages_4 147.85 312.78 437s PrivateWages_5 -123.00 -261.19 437s PrivateWages_6 -19.80 -39.95 437s PrivateWages_7 80.47 157.55 437s PrivateWages_8 70.40 144.33 437s PrivateWages_9 117.46 251.13 437s PrivateWages_10 242.55 506.03 437s PrivateWages_11 -173.42 -349.22 437s PrivateWages_12 -40.96 -103.66 437s PrivateWages_13 -98.72 -304.24 437s PrivateWages_14 45.71 214.10 437s PrivateWages_15 -19.68 -65.90 437s PrivateWages_16 -34.45 -112.44 437s PrivateWages_17 89.07 265.31 437s PrivateWages_18 -11.64 -31.65 437s PrivateWages_19 -312.61 -890.90 437s PrivateWages_20 56.14 177.57 437s PrivateWages_21 -73.89 -207.57 437s PrivateWages_22 196.00 564.00 437s Investment_(Intercept) Investment_corpProf 437s Consumption_2 2.2268 29.52 437s Consumption_3 1.0983 18.21 437s Consumption_4 1.2442 23.99 437s Consumption_5 3.5856 75.15 437s Consumption_6 0.8430 16.66 437s Consumption_7 -2.5793 -47.04 437s Consumption_8 -3.5007 -61.52 437s Consumption_9 -2.2423 -43.82 437s Consumption_10 -0.6291 -12.82 437s Consumption_11 2.3372 40.15 437s Consumption_12 1.0229 13.00 437s Consumption_13 1.8868 16.98 437s Consumption_14 -2.9343 -26.57 437s Consumption_15 0.8872 11.24 437s Consumption_16 0.7541 10.87 437s Consumption_17 -5.9983 -88.25 437s Consumption_18 1.2355 24.46 437s Consumption_19 4.2135 80.93 437s Consumption_20 -3.3600 -58.53 437s Consumption_21 -1.2147 -24.67 437s Consumption_22 1.1237 25.46 437s Investment_2 -2.6152 -34.67 437s Investment_3 -0.0813 -1.35 437s Investment_4 0.0677 1.30 437s Investment_5 -1.1977 -25.10 437s Investment_6 -0.1631 -3.22 437s Investment_7 0.2486 4.53 437s Investment_8 0.4818 8.47 437s Investment_9 0.7630 14.91 437s Investment_10 2.0982 42.75 437s Investment_11 -1.1402 -19.59 437s Investment_12 -0.8295 -10.54 437s Investment_13 -1.3729 -12.36 437s Investment_14 1.4620 13.24 437s Investment_15 -0.1037 -1.31 437s Investment_16 0.2051 2.96 437s Investment_17 1.2415 18.26 437s Investment_18 0.2514 4.98 437s Investment_19 -2.1038 -40.41 437s Investment_20 0.8280 14.42 437s Investment_21 0.5596 11.36 437s Investment_22 1.4005 31.73 437s PrivateWages_2 3.7415 49.60 437s PrivateWages_3 -1.6237 -26.92 437s PrivateWages_4 -3.3924 -65.41 437s PrivateWages_5 2.5921 54.33 437s PrivateWages_6 0.3959 7.82 437s PrivateWages_7 -1.5524 -28.31 437s PrivateWages_8 -1.3929 -24.48 437s PrivateWages_9 -2.3004 -44.95 437s PrivateWages_10 -4.4576 -90.82 437s PrivateWages_11 3.0990 53.24 437s PrivateWages_12 1.0182 12.94 437s PrivateWages_13 3.3581 30.22 437s PrivateWages_14 -2.5324 -22.93 437s PrivateWages_15 0.6815 8.64 437s PrivateWages_16 1.0862 15.66 437s PrivateWages_17 -2.4670 -36.29 437s PrivateWages_18 0.2564 5.07 437s PrivateWages_19 7.0070 134.58 437s PrivateWages_20 -1.4230 -24.79 437s PrivateWages_21 1.5081 30.62 437s PrivateWages_22 -3.6021 -81.61 437s Investment_corpProfLag Investment_capitalLag 437s Consumption_2 28.28 407.1 437s Consumption_3 13.62 200.5 437s Consumption_4 21.03 229.5 437s Consumption_5 65.97 680.2 437s Consumption_6 16.35 162.4 437s Consumption_7 -51.84 -510.2 437s Consumption_8 -68.61 -712.1 437s Consumption_9 -44.40 -465.5 437s Consumption_10 -13.27 -132.5 437s Consumption_11 50.72 504.1 437s Consumption_12 15.96 221.7 437s Consumption_13 21.51 402.5 437s Consumption_14 -20.54 -607.7 437s Consumption_15 9.94 179.2 437s Consumption_16 9.27 150.1 437s Consumption_17 -83.98 -1185.9 437s Consumption_18 21.74 246.9 437s Consumption_19 72.89 850.3 437s Consumption_20 -51.41 -671.7 437s Consumption_21 -23.08 -244.4 437s Consumption_22 23.71 229.8 437s Investment_2 -33.21 -478.1 437s Investment_3 -1.01 -14.9 437s Investment_4 1.14 12.5 437s Investment_5 -22.04 -227.2 437s Investment_6 -3.16 -31.4 437s Investment_7 5.00 49.2 437s Investment_8 9.44 98.0 437s Investment_9 15.11 158.4 437s Investment_10 44.27 441.9 437s Investment_11 -24.74 -245.9 437s Investment_12 -12.94 -179.8 437s Investment_13 -15.65 -292.8 437s Investment_14 10.23 302.8 437s Investment_15 -1.16 -21.0 437s Investment_16 2.52 40.8 437s Investment_17 17.38 245.4 437s Investment_18 4.43 50.2 437s Investment_19 -36.40 -424.5 437s Investment_20 12.67 165.5 437s Investment_21 10.63 112.6 437s Investment_22 29.55 286.4 437s PrivateWages_2 47.52 683.9 437s PrivateWages_3 -20.13 -296.5 437s PrivateWages_4 -57.33 -625.9 437s PrivateWages_5 47.69 491.7 437s PrivateWages_6 7.68 76.3 437s PrivateWages_7 -31.20 -307.1 437s PrivateWages_8 -27.30 -283.3 437s PrivateWages_9 -45.55 -477.6 437s PrivateWages_10 -94.05 -938.8 437s PrivateWages_11 67.25 668.4 437s PrivateWages_12 15.88 220.6 437s PrivateWages_13 38.28 716.3 437s PrivateWages_14 -17.73 -524.5 437s PrivateWages_15 7.63 137.7 437s PrivateWages_16 13.36 216.2 437s PrivateWages_17 -34.54 -487.7 437s PrivateWages_18 4.51 51.2 437s PrivateWages_19 121.22 1414.0 437s PrivateWages_20 -21.77 -284.4 437s PrivateWages_21 28.65 303.4 437s PrivateWages_22 -76.00 -736.6 437s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 437s Consumption_2 -7.713 -367.6 -346.32 437s Consumption_3 -3.804 -187.6 -173.47 437s Consumption_4 -4.309 -244.9 -215.90 437s Consumption_5 -12.419 -754.3 -710.38 437s Consumption_6 -2.920 -178.7 -166.72 437s Consumption_7 8.934 547.6 544.97 437s Consumption_8 12.125 737.9 776.02 437s Consumption_9 7.767 484.4 500.17 437s Consumption_10 2.179 140.3 140.54 437s Consumption_11 -8.095 -521.2 -542.38 437s Consumption_12 -3.543 -194.5 -216.84 437s Consumption_13 -6.535 -308.0 -348.98 437s Consumption_14 10.163 423.2 450.24 437s Consumption_15 -3.073 -156.6 -138.60 437s Consumption_16 -2.612 -145.4 -129.81 437s Consumption_17 20.776 1190.8 1130.21 437s Consumption_18 -4.279 -289.6 -268.32 437s Consumption_19 -14.594 -995.5 -948.61 437s Consumption_20 11.638 778.7 708.75 437s Consumption_21 4.207 316.7 292.41 437s Consumption_22 -3.892 -336.6 -294.62 437s Investment_2 6.817 324.9 306.06 437s Investment_3 0.212 10.5 9.67 437s Investment_4 -0.176 -10.0 -8.84 437s Investment_5 3.122 189.6 178.58 437s Investment_6 0.425 26.0 24.27 437s Investment_7 -0.648 -39.7 -39.52 437s Investment_8 -1.256 -76.4 -80.37 437s Investment_9 -1.989 -124.1 -128.08 437s Investment_10 -5.469 -352.2 -352.75 437s Investment_11 2.972 191.3 199.12 437s Investment_12 2.162 118.7 132.32 437s Investment_13 3.579 168.7 191.09 437s Investment_14 -3.811 -158.7 -168.82 437s Investment_15 0.270 13.8 12.19 437s Investment_16 -0.535 -29.8 -26.57 437s Investment_17 -3.236 -185.5 -176.04 437s Investment_18 -0.655 -44.4 -41.09 437s Investment_19 5.484 374.0 356.44 437s Investment_20 -2.158 -144.4 -131.44 437s Investment_21 -1.459 -109.8 -101.37 437s Investment_22 -3.650 -315.7 -276.34 437s PrivateWages_2 -14.774 -704.2 -663.37 437s PrivateWages_3 6.412 316.3 292.37 437s PrivateWages_4 13.396 761.4 671.14 437s PrivateWages_5 -10.236 -621.6 -585.48 437s PrivateWages_6 -1.563 -95.7 -89.26 437s PrivateWages_7 6.130 375.7 373.95 437s PrivateWages_8 5.500 334.7 352.01 437s PrivateWages_9 9.084 566.6 585.00 437s PrivateWages_10 17.602 1133.5 1135.33 437s PrivateWages_11 -12.237 -787.8 -819.89 437s PrivateWages_12 -4.021 -220.7 -246.06 437s PrivateWages_13 -13.260 -625.0 -708.11 437s PrivateWages_14 10.000 416.4 443.00 437s PrivateWages_15 -2.691 -137.2 -121.37 437s PrivateWages_16 -4.289 -238.7 -213.18 437s PrivateWages_17 9.742 558.3 529.95 437s PrivateWages_18 -1.012 -68.5 -63.47 437s PrivateWages_19 -27.669 -1887.3 -1798.51 437s PrivateWages_20 5.619 376.0 342.19 437s PrivateWages_21 -5.955 -448.3 -413.89 437s PrivateWages_22 14.224 1230.0 1076.76 437s PrivateWages_trend 437s Consumption_2 77.130 437s Consumption_3 34.237 437s Consumption_4 34.475 437s Consumption_5 86.935 437s Consumption_6 17.519 437s Consumption_7 -44.670 437s Consumption_8 -48.501 437s Consumption_9 -23.300 437s Consumption_10 -4.358 437s Consumption_11 8.095 437s Consumption_12 0.000 437s Consumption_13 -6.535 437s Consumption_14 20.327 437s Consumption_15 -9.219 437s Consumption_16 -10.447 437s Consumption_17 103.880 437s Consumption_18 -25.676 437s Consumption_19 -102.158 437s Consumption_20 93.104 437s Consumption_21 37.866 437s Consumption_22 -38.920 437s Investment_2 -68.165 437s Investment_3 -1.908 437s Investment_4 1.411 437s Investment_5 -21.854 437s Investment_6 -2.550 437s Investment_7 3.240 437s Investment_8 5.023 437s Investment_9 5.967 437s Investment_10 10.938 437s Investment_11 -2.972 437s Investment_12 0.000 437s Investment_13 3.579 437s Investment_14 -7.622 437s Investment_15 0.811 437s Investment_16 -2.138 437s Investment_17 -16.180 437s Investment_18 -3.932 437s Investment_19 38.386 437s Investment_20 -17.267 437s Investment_21 -13.128 437s Investment_22 -36.504 437s PrivateWages_2 147.744 437s PrivateWages_3 -57.704 437s PrivateWages_4 -107.168 437s PrivateWages_5 71.650 437s PrivateWages_6 9.379 437s PrivateWages_7 -30.651 437s PrivateWages_8 -22.000 437s PrivateWages_9 -27.251 437s PrivateWages_10 -35.204 437s PrivateWages_11 12.237 437s PrivateWages_12 0.000 437s PrivateWages_13 -13.260 437s PrivateWages_14 20.000 437s PrivateWages_15 -8.073 437s PrivateWages_16 -17.157 437s PrivateWages_17 48.709 437s PrivateWages_18 -6.074 437s PrivateWages_19 -193.685 437s PrivateWages_20 44.952 437s PrivateWages_21 -53.597 437s PrivateWages_22 142.240 437s [1] TRUE 437s > Bread 437s Consumption_(Intercept) Consumption_corpProf Consumption_corpProfLag 437s [1,] 94.44678 -0.9198 -0.3009 437s [2,] -0.91977 0.5830 -0.4036 437s [3,] -0.30085 -0.4036 0.5114 437s [4,] -1.71741 -0.0559 -0.0303 437s [5,] 169.11432 -7.0463 6.8731 437s [6,] -3.78719 0.8222 -0.7139 437s [7,] 1.24504 -0.6799 0.7545 437s [8,] -0.61653 0.0214 -0.0358 437s [9,] -43.93927 0.0941 1.6110 437s [10,] 0.70520 -0.0665 0.0417 437s [11,] 0.00487 0.0673 -0.0710 437s [12,] 0.27782 0.0450 0.0254 437s Consumption_wages Investment_(Intercept) Investment_corpProf 437s [1,] -1.71741 169.11 -3.79e+00 437s [2,] -0.05588 -7.05 8.22e-01 437s [3,] -0.03031 6.87 -7.14e-01 437s [4,] 0.07612 -3.87 3.83e-02 437s [5,] -3.87475 7070.32 -1.04e+02 437s [6,] 0.03834 -104.41 4.26e+00 437s [7,] -0.05106 83.93 -3.59e+00 437s [8,] 0.02027 -33.26 4.55e-01 437s [9,] 0.35346 48.43 -5.08e-01 437s [10,] -0.00637 6.61 4.29e-03 437s [11,] 0.00050 -7.65 4.31e-03 437s [12,] -0.03505 -5.67 7.94e-02 437s Investment_corpProfLag Investment_capitalLag PrivateWages_(Intercept) 437s [1,] 1.24504 -0.6165 -43.9393 437s [2,] -0.67986 0.0214 0.0941 437s [3,] 0.75452 -0.0358 1.6110 437s [4,] -0.05106 0.0203 0.3535 437s [5,] 83.92612 -33.2552 48.4291 437s [6,] -3.59218 0.4550 -0.5077 437s [7,] 3.89889 -0.4344 -3.1131 437s [8,] -0.43443 0.1630 0.0665 437s [9,] -3.11309 0.0665 90.0495 437s [10,] 0.04234 -0.0368 -0.7131 437s [11,] 0.00984 0.0370 -0.7830 437s [12,] -0.11558 0.0310 0.9385 437s PrivateWages_gnp PrivateWages_gnpLag PrivateWages_trend 437s [1,] 0.70520 0.00487 0.27782 437s [2,] -0.06653 0.06728 0.04499 437s [3,] 0.04169 -0.07096 0.02543 437s [4,] -0.00637 0.00050 -0.03505 437s [5,] 6.61461 -7.64810 -5.66883 437s [6,] 0.00429 0.00431 0.07939 437s [7,] 0.04234 0.00984 -0.11558 437s [8,] -0.03681 0.03698 0.03103 437s [9,] -0.71315 -0.78300 0.93852 437s [10,] 0.06094 -0.05082 -0.02122 437s [11,] -0.05082 0.06614 0.00579 437s [12,] -0.02122 0.00579 0.05272 437s > 437s > # OLS 437s Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 437s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 437s > summary 437s 437s systemfit results 437s method: OLS 437s 437s N DF SSR detRCov OLS-R2 McElroy-R2 437s system 62 50 44.9 0.372 0.977 0.991 437s 437s N DF SSR MSE RMSE R2 Adj R2 437s Consumption 21 17 17.88 1.052 1.03 0.981 0.978 437s Investment 21 17 17.32 1.019 1.01 0.931 0.919 437s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 437s 437s The covariance matrix of the residuals 437s Consumption Investment PrivateWages 437s Consumption 1.0703 -0.0161 -0.463 437s Investment -0.0161 0.9435 0.199 437s PrivateWages -0.4633 0.1993 0.609 437s 437s The correlations of the residuals 437s Consumption Investment PrivateWages 437s Consumption 1.0000 -0.0201 -0.575 437s Investment -0.0201 1.0000 0.264 437s PrivateWages -0.5747 0.2639 1.000 437s 437s 437s OLS estimates for 'Consumption' (equation 1) 437s Model Formula: consump ~ corpProf + corpProfLag + wages 437s 437s Estimate Std. Error t value Pr(>|t|) 437s (Intercept) 16.2366 1.3141 12.36 6.4e-10 *** 437s corpProf 0.1929 0.0920 2.10 0.051 . 437s corpProfLag 0.0899 0.0914 0.98 0.339 437s wages 0.7962 0.0403 19.76 3.6e-13 *** 437s --- 437s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 437s 437s Residual standard error: 1.026 on 17 degrees of freedom 437s Number of observations: 21 Degrees of Freedom: 17 437s SSR: 17.879 MSE: 1.052 Root MSE: 1.026 437s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.978 437s 437s 437s OLS estimates for 'Investment' (equation 2) 437s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 437s 437s Estimate Std. Error t value Pr(>|t|) 437s (Intercept) 10.1258 5.2592 1.93 0.07108 . 437s corpProf 0.4796 0.0934 5.13 8.3e-05 *** 437s corpProfLag 0.3330 0.0971 3.43 0.00318 ** 437s capitalLag -0.1118 0.0257 -4.35 0.00044 *** 437s --- 437s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 437s 437s Residual standard error: 1.009 on 17 degrees of freedom 437s Number of observations: 21 Degrees of Freedom: 17 437s SSR: 17.323 MSE: 1.019 Root MSE: 1.009 437s Multiple R-Squared: 0.931 Adjusted R-Squared: 0.919 437s 437s 437s OLS estimates for 'PrivateWages' (equation 3) 437s Model Formula: privWage ~ gnp + gnpLag + trend 437s 437s Estimate Std. Error t value Pr(>|t|) 437s (Intercept) 1.3550 1.3093 1.03 0.3161 437s gnp 0.4417 0.0331 13.33 4.4e-10 *** 437s gnpLag 0.1466 0.0381 3.85 0.0014 ** 437s trend 0.1244 0.0336 3.70 0.0020 ** 437s --- 437s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 437s 437s Residual standard error: 0.78 on 16 degrees of freedom 437s Number of observations: 20 Degrees of Freedom: 16 437s SSR: 9.739 MSE: 0.609 Root MSE: 0.78 437s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 437s 437s compare coef with single-equation OLS 437s [1] TRUE 437s > residuals 437s Consumption Investment PrivateWages 437s 1 NA NA NA 437s 2 -0.32389 -0.0668 -1.3389 437s 3 -1.25001 -0.0476 0.2462 437s 4 -1.56574 1.2467 1.1255 437s 5 -0.49350 -1.3512 -0.1959 437s 6 0.00761 0.4154 -0.5284 437s 7 0.86910 1.4923 NA 437s 8 1.33848 0.7889 -0.7909 437s 9 1.05498 -0.6317 0.2819 437s 10 -0.58856 1.0830 1.1384 437s 11 0.28231 0.2791 -0.1904 437s 12 -0.22965 0.0369 0.5813 437s 13 -0.32213 0.3659 0.1206 437s 14 0.32228 0.2237 0.4773 437s 15 -0.05801 -0.1728 0.3035 437s 16 -0.03466 0.0101 0.0284 437s 17 1.61650 0.9719 -0.8517 437s 18 -0.43597 0.0516 0.9908 437s 19 0.21005 -2.5656 -0.4597 437s 20 0.98920 -0.6866 -0.3819 437s 21 0.78508 -0.7807 -1.1062 437s 22 -2.17345 -0.6623 0.5501 437s > fitted 437s Consumption Investment PrivateWages 437s 1 NA NA NA 437s 2 42.2 -0.133 26.8 437s 3 46.3 1.948 29.1 437s 4 50.8 3.953 33.0 437s 5 51.1 4.351 34.1 437s 6 52.6 4.685 35.9 437s 7 54.2 4.108 NA 437s 8 54.9 3.411 38.7 437s 9 56.2 3.632 38.9 437s 10 58.4 4.017 40.2 437s 11 54.7 0.721 38.1 437s 12 51.1 -3.437 33.9 437s 13 45.9 -6.566 28.9 437s 14 46.2 -5.324 28.0 437s 15 48.8 -2.827 30.3 437s 16 51.3 -1.310 33.2 437s 17 56.1 1.128 37.7 437s 18 59.1 1.948 40.0 437s 19 57.3 0.666 38.7 437s 20 60.6 1.987 42.0 437s 21 64.2 4.081 46.1 437s 22 71.9 5.562 52.7 437s > predict 437s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 437s 1 NA NA NA NA 437s 2 42.2 0.466 40.0 44.5 437s 3 46.3 0.523 43.9 48.6 437s 4 50.8 0.344 48.6 52.9 437s 5 51.1 0.399 48.9 53.3 437s 6 52.6 0.401 50.4 54.8 437s 7 54.2 0.363 52.0 56.4 437s 8 54.9 0.330 52.7 57.0 437s 9 56.2 0.354 54.1 58.4 437s 10 58.4 0.373 56.2 60.6 437s 11 54.7 0.612 52.3 57.1 437s 12 51.1 0.489 48.8 53.4 437s 13 45.9 0.634 43.5 48.3 437s 14 46.2 0.608 43.8 48.6 437s 15 48.8 0.378 46.6 51.0 437s 16 51.3 0.336 49.2 53.5 437s 17 56.1 0.369 53.9 58.3 437s 18 59.1 0.324 57.0 61.3 437s 19 57.3 0.375 55.1 59.5 437s 20 60.6 0.437 58.4 62.9 437s 21 64.2 0.429 62.0 66.4 437s 22 71.9 0.672 69.4 74.3 437s Investment.pred Investment.se.fit Investment.lwr Investment.upr 437s 1 NA NA NA NA 437s 2 -0.133 0.584 -2.476 2.209 437s 3 1.948 0.480 -0.297 4.193 437s 4 3.953 0.432 1.748 6.159 437s 5 4.351 0.357 2.201 6.502 437s 6 4.685 0.336 2.548 6.821 437s 7 4.108 0.316 1.983 6.232 437s 8 3.411 0.281 1.306 5.516 437s 9 3.632 0.374 1.469 5.794 437s 10 4.017 0.430 1.813 6.221 437s 11 0.721 0.579 -1.616 3.058 437s 12 -3.437 0.488 -5.688 -1.185 437s 13 -6.566 0.592 -8.917 -4.215 437s 14 -5.324 0.667 -7.754 -2.893 437s 15 -2.827 0.359 -4.979 -0.675 437s 16 -1.310 0.308 -3.430 0.810 437s 17 1.128 0.334 -1.008 3.264 437s 18 1.948 0.234 -0.133 4.030 437s 19 0.666 0.300 -1.450 2.781 437s 20 1.987 0.353 -0.161 4.134 437s 21 4.081 0.319 1.954 6.207 437s 22 5.562 0.444 3.348 7.777 437s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 437s 1 NA NA NA NA 437s 2 26.8 0.366 25.1 28.6 437s 3 29.1 0.369 27.3 30.8 437s 4 33.0 0.372 31.2 34.7 437s 5 34.1 0.288 32.4 35.8 437s 6 35.9 0.287 34.3 37.6 437s 7 NA NA NA NA 437s 8 38.7 0.293 37.0 40.4 437s 9 38.9 0.279 37.3 40.6 437s 10 40.2 0.266 38.5 41.8 437s 11 38.1 0.365 36.4 39.8 437s 12 33.9 0.369 32.2 35.7 437s 13 28.9 0.438 27.1 30.7 437s 14 28.0 0.385 26.3 29.8 437s 15 30.3 0.379 28.6 32.0 437s 16 33.2 0.316 31.5 34.9 437s 17 37.7 0.310 36.0 39.3 437s 18 40.0 0.243 38.4 41.7 437s 19 38.7 0.363 36.9 40.4 437s 20 42.0 0.326 40.3 43.7 437s 21 46.1 0.341 44.4 47.8 437s 22 52.7 0.514 50.9 54.6 437s > model.frame 437s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 437s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 437s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 437s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 437s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 437s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 437s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 437s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 437s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 437s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 437s 10 57.8 21.7 21.1 45.3 5.1 211 41.3 67.0 64.5 437s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 437s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 437s 13 45.6 7.0 11.4 34.3 -6.2 213 29.0 44.3 53.4 437s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 437s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 437s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 437s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 437s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 437s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 437s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 437s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 437s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 437s trend 437s 1 -11 437s 2 -10 437s 3 -9 437s 4 -8 437s 5 -7 437s 6 -6 437s 7 -5 437s 8 -4 437s 9 -3 437s 10 -2 437s 11 -1 437s 12 0 437s 13 1 437s 14 2 437s 15 3 437s 16 4 437s 17 5 437s 18 6 437s 19 7 437s 20 8 437s 21 9 437s 22 10 437s > model.matrix 437s Consumption_(Intercept) Consumption_corpProf 437s Consumption_2 1 12.4 437s Consumption_3 1 16.9 437s Consumption_4 1 18.4 437s Consumption_5 1 19.4 437s Consumption_6 1 20.1 437s Consumption_7 1 19.6 437s Consumption_8 1 19.8 437s Consumption_9 1 21.1 437s Consumption_10 1 21.7 437s Consumption_11 1 15.6 437s Consumption_12 1 11.4 437s Consumption_13 1 7.0 437s Consumption_14 1 11.2 437s Consumption_15 1 12.3 437s Consumption_16 1 14.0 437s Consumption_17 1 17.6 437s Consumption_18 1 17.3 437s Consumption_19 1 15.3 437s Consumption_20 1 19.0 437s Consumption_21 1 21.1 437s Consumption_22 1 23.5 437s Investment_2 0 0.0 437s Investment_3 0 0.0 437s Investment_4 0 0.0 437s Investment_5 0 0.0 437s Investment_6 0 0.0 437s Investment_7 0 0.0 437s Investment_8 0 0.0 437s Investment_9 0 0.0 437s Investment_10 0 0.0 437s Investment_11 0 0.0 437s Investment_12 0 0.0 437s Investment_13 0 0.0 437s Investment_14 0 0.0 437s Investment_15 0 0.0 437s Investment_16 0 0.0 437s Investment_17 0 0.0 437s Investment_18 0 0.0 437s Investment_19 0 0.0 437s Investment_20 0 0.0 437s Investment_21 0 0.0 437s Investment_22 0 0.0 437s PrivateWages_2 0 0.0 437s PrivateWages_3 0 0.0 437s PrivateWages_4 0 0.0 437s PrivateWages_5 0 0.0 437s PrivateWages_6 0 0.0 437s PrivateWages_8 0 0.0 437s PrivateWages_9 0 0.0 437s PrivateWages_10 0 0.0 437s PrivateWages_11 0 0.0 437s PrivateWages_12 0 0.0 437s PrivateWages_13 0 0.0 437s PrivateWages_14 0 0.0 437s PrivateWages_15 0 0.0 437s PrivateWages_16 0 0.0 437s PrivateWages_17 0 0.0 437s PrivateWages_18 0 0.0 437s PrivateWages_19 0 0.0 437s PrivateWages_20 0 0.0 437s PrivateWages_21 0 0.0 437s PrivateWages_22 0 0.0 437s Consumption_corpProfLag Consumption_wages 437s Consumption_2 12.7 28.2 437s Consumption_3 12.4 32.2 437s Consumption_4 16.9 37.0 437s Consumption_5 18.4 37.0 437s Consumption_6 19.4 38.6 437s Consumption_7 20.1 40.7 437s Consumption_8 19.6 41.5 437s Consumption_9 19.8 42.9 437s Consumption_10 21.1 45.3 437s Consumption_11 21.7 42.1 437s Consumption_12 15.6 39.3 437s Consumption_13 11.4 34.3 437s Consumption_14 7.0 34.1 437s Consumption_15 11.2 36.6 437s Consumption_16 12.3 39.3 437s Consumption_17 14.0 44.2 437s Consumption_18 17.6 47.7 437s Consumption_19 17.3 45.9 437s Consumption_20 15.3 49.4 437s Consumption_21 19.0 53.0 437s Consumption_22 21.1 61.8 437s Investment_2 0.0 0.0 437s Investment_3 0.0 0.0 437s Investment_4 0.0 0.0 437s Investment_5 0.0 0.0 437s Investment_6 0.0 0.0 437s Investment_7 0.0 0.0 437s Investment_8 0.0 0.0 437s Investment_9 0.0 0.0 437s Investment_10 0.0 0.0 437s Investment_11 0.0 0.0 437s Investment_12 0.0 0.0 437s Investment_13 0.0 0.0 437s Investment_14 0.0 0.0 437s Investment_15 0.0 0.0 437s Investment_16 0.0 0.0 437s Investment_17 0.0 0.0 437s Investment_18 0.0 0.0 437s Investment_19 0.0 0.0 437s Investment_20 0.0 0.0 437s Investment_21 0.0 0.0 437s Investment_22 0.0 0.0 437s PrivateWages_2 0.0 0.0 437s PrivateWages_3 0.0 0.0 437s PrivateWages_4 0.0 0.0 437s PrivateWages_5 0.0 0.0 437s PrivateWages_6 0.0 0.0 437s PrivateWages_8 0.0 0.0 437s PrivateWages_9 0.0 0.0 437s PrivateWages_10 0.0 0.0 437s PrivateWages_11 0.0 0.0 437s PrivateWages_12 0.0 0.0 437s PrivateWages_13 0.0 0.0 437s PrivateWages_14 0.0 0.0 437s PrivateWages_15 0.0 0.0 437s PrivateWages_16 0.0 0.0 437s PrivateWages_17 0.0 0.0 437s PrivateWages_18 0.0 0.0 437s PrivateWages_19 0.0 0.0 437s PrivateWages_20 0.0 0.0 437s PrivateWages_21 0.0 0.0 437s PrivateWages_22 0.0 0.0 437s Investment_(Intercept) Investment_corpProf 437s Consumption_2 0 0.0 437s Consumption_3 0 0.0 437s Consumption_4 0 0.0 437s Consumption_5 0 0.0 437s Consumption_6 0 0.0 437s Consumption_7 0 0.0 437s Consumption_8 0 0.0 437s Consumption_9 0 0.0 437s Consumption_10 0 0.0 437s Consumption_11 0 0.0 437s Consumption_12 0 0.0 437s Consumption_13 0 0.0 437s Consumption_14 0 0.0 437s Consumption_15 0 0.0 437s Consumption_16 0 0.0 437s Consumption_17 0 0.0 437s Consumption_18 0 0.0 437s Consumption_19 0 0.0 437s Consumption_20 0 0.0 437s Consumption_21 0 0.0 437s Consumption_22 0 0.0 437s Investment_2 1 12.4 437s Investment_3 1 16.9 437s Investment_4 1 18.4 437s Investment_5 1 19.4 437s Investment_6 1 20.1 437s Investment_7 1 19.6 437s Investment_8 1 19.8 437s Investment_9 1 21.1 437s Investment_10 1 21.7 437s Investment_11 1 15.6 437s Investment_12 1 11.4 437s Investment_13 1 7.0 437s Investment_14 1 11.2 437s Investment_15 1 12.3 437s Investment_16 1 14.0 437s Investment_17 1 17.6 437s Investment_18 1 17.3 437s Investment_19 1 15.3 437s Investment_20 1 19.0 437s Investment_21 1 21.1 437s Investment_22 1 23.5 437s PrivateWages_2 0 0.0 437s PrivateWages_3 0 0.0 437s PrivateWages_4 0 0.0 437s PrivateWages_5 0 0.0 437s PrivateWages_6 0 0.0 437s PrivateWages_8 0 0.0 437s PrivateWages_9 0 0.0 437s PrivateWages_10 0 0.0 437s PrivateWages_11 0 0.0 437s PrivateWages_12 0 0.0 437s PrivateWages_13 0 0.0 437s PrivateWages_14 0 0.0 437s PrivateWages_15 0 0.0 437s PrivateWages_16 0 0.0 437s PrivateWages_17 0 0.0 437s PrivateWages_18 0 0.0 437s PrivateWages_19 0 0.0 437s PrivateWages_20 0 0.0 437s PrivateWages_21 0 0.0 437s PrivateWages_22 0 0.0 437s Investment_corpProfLag Investment_capitalLag 437s Consumption_2 0.0 0 437s Consumption_3 0.0 0 437s Consumption_4 0.0 0 437s Consumption_5 0.0 0 437s Consumption_6 0.0 0 437s Consumption_7 0.0 0 437s Consumption_8 0.0 0 437s Consumption_9 0.0 0 437s Consumption_10 0.0 0 437s Consumption_11 0.0 0 437s Consumption_12 0.0 0 437s Consumption_13 0.0 0 437s Consumption_14 0.0 0 437s Consumption_15 0.0 0 437s Consumption_16 0.0 0 437s Consumption_17 0.0 0 437s Consumption_18 0.0 0 437s Consumption_19 0.0 0 437s Consumption_20 0.0 0 437s Consumption_21 0.0 0 437s Consumption_22 0.0 0 437s Investment_2 12.7 183 437s Investment_3 12.4 183 437s Investment_4 16.9 184 437s Investment_5 18.4 190 437s Investment_6 19.4 193 437s Investment_7 20.1 198 437s Investment_8 19.6 203 437s Investment_9 19.8 208 437s Investment_10 21.1 211 437s Investment_11 21.7 216 437s Investment_12 15.6 217 437s Investment_13 11.4 213 437s Investment_14 7.0 207 437s Investment_15 11.2 202 437s Investment_16 12.3 199 437s Investment_17 14.0 198 437s Investment_18 17.6 200 437s Investment_19 17.3 202 437s Investment_20 15.3 200 437s Investment_21 19.0 201 437s Investment_22 21.1 204 437s PrivateWages_2 0.0 0 437s PrivateWages_3 0.0 0 437s PrivateWages_4 0.0 0 437s PrivateWages_5 0.0 0 437s PrivateWages_6 0.0 0 437s PrivateWages_8 0.0 0 437s PrivateWages_9 0.0 0 437s PrivateWages_10 0.0 0 437s PrivateWages_11 0.0 0 437s PrivateWages_12 0.0 0 437s PrivateWages_13 0.0 0 437s PrivateWages_14 0.0 0 437s PrivateWages_15 0.0 0 437s PrivateWages_16 0.0 0 437s PrivateWages_17 0.0 0 437s PrivateWages_18 0.0 0 437s PrivateWages_19 0.0 0 437s PrivateWages_20 0.0 0 437s PrivateWages_21 0.0 0 437s PrivateWages_22 0.0 0 437s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 437s Consumption_2 0 0.0 0.0 437s Consumption_3 0 0.0 0.0 437s Consumption_4 0 0.0 0.0 437s Consumption_5 0 0.0 0.0 437s Consumption_6 0 0.0 0.0 437s Consumption_7 0 0.0 0.0 437s Consumption_8 0 0.0 0.0 437s Consumption_9 0 0.0 0.0 437s Consumption_10 0 0.0 0.0 437s Consumption_11 0 0.0 0.0 437s Consumption_12 0 0.0 0.0 437s Consumption_13 0 0.0 0.0 437s Consumption_14 0 0.0 0.0 437s Consumption_15 0 0.0 0.0 437s Consumption_16 0 0.0 0.0 437s Consumption_17 0 0.0 0.0 437s Consumption_18 0 0.0 0.0 437s Consumption_19 0 0.0 0.0 437s Consumption_20 0 0.0 0.0 437s Consumption_21 0 0.0 0.0 437s Consumption_22 0 0.0 0.0 437s Investment_2 0 0.0 0.0 437s Investment_3 0 0.0 0.0 437s Investment_4 0 0.0 0.0 437s Investment_5 0 0.0 0.0 437s Investment_6 0 0.0 0.0 437s Investment_7 0 0.0 0.0 437s Investment_8 0 0.0 0.0 437s Investment_9 0 0.0 0.0 437s Investment_10 0 0.0 0.0 437s Investment_11 0 0.0 0.0 437s Investment_12 0 0.0 0.0 437s Investment_13 0 0.0 0.0 437s Investment_14 0 0.0 0.0 437s Investment_15 0 0.0 0.0 437s Investment_16 0 0.0 0.0 437s Investment_17 0 0.0 0.0 437s Investment_18 0 0.0 0.0 437s Investment_19 0 0.0 0.0 437s Investment_20 0 0.0 0.0 437s Investment_21 0 0.0 0.0 437s Investment_22 0 0.0 0.0 437s PrivateWages_2 1 45.6 44.9 437s PrivateWages_3 1 50.1 45.6 437s PrivateWages_4 1 57.2 50.1 437s PrivateWages_5 1 57.1 57.2 437s PrivateWages_6 1 61.0 57.1 437s PrivateWages_8 1 64.4 64.0 437s PrivateWages_9 1 64.5 64.4 437s PrivateWages_10 1 67.0 64.5 437s PrivateWages_11 1 61.2 67.0 437s PrivateWages_12 1 53.4 61.2 437s PrivateWages_13 1 44.3 53.4 437s PrivateWages_14 1 45.1 44.3 437s PrivateWages_15 1 49.7 45.1 437s PrivateWages_16 1 54.4 49.7 437s PrivateWages_17 1 62.7 54.4 437s PrivateWages_18 1 65.0 62.7 437s PrivateWages_19 1 60.9 65.0 437s PrivateWages_20 1 69.5 60.9 437s PrivateWages_21 1 75.7 69.5 437s PrivateWages_22 1 88.4 75.7 437s PrivateWages_trend 437s Consumption_2 0 437s Consumption_3 0 437s Consumption_4 0 437s Consumption_5 0 437s Consumption_6 0 437s Consumption_7 0 437s Consumption_8 0 437s Consumption_9 0 437s Consumption_10 0 437s Consumption_11 0 437s Consumption_12 0 437s Consumption_13 0 437s Consumption_14 0 437s Consumption_15 0 437s Consumption_16 0 437s Consumption_17 0 437s Consumption_18 0 437s Consumption_19 0 437s Consumption_20 0 437s Consumption_21 0 437s Consumption_22 0 437s Investment_2 0 437s Investment_3 0 437s Investment_4 0 437s Investment_5 0 437s Investment_6 0 437s Investment_7 0 437s Investment_8 0 437s Investment_9 0 437s Investment_10 0 437s Investment_11 0 437s Investment_12 0 437s Investment_13 0 437s Investment_14 0 437s Investment_15 0 437s Investment_16 0 437s Investment_17 0 437s Investment_18 0 437s Investment_19 0 437s Investment_20 0 437s Investment_21 0 437s Investment_22 0 437s PrivateWages_2 -10 437s PrivateWages_3 -9 437s PrivateWages_4 -8 437s PrivateWages_5 -7 437s PrivateWages_6 -6 437s PrivateWages_8 -4 437s PrivateWages_9 -3 437s PrivateWages_10 -2 437s PrivateWages_11 -1 437s PrivateWages_12 0 437s PrivateWages_13 1 437s PrivateWages_14 2 437s PrivateWages_15 3 437s PrivateWages_16 4 437s PrivateWages_17 5 437s PrivateWages_18 6 437s PrivateWages_19 7 437s PrivateWages_20 8 437s PrivateWages_21 9 437s PrivateWages_22 10 437s > nobs 437s [1] 62 437s > linearHypothesis 437s Linear hypothesis test (Theil's F test) 437s 437s Hypothesis: 437s Consumption_corpProf + Investment_capitalLag = 0 437s 437s Model 1: restricted model 437s Model 2: kleinModel 437s 437s Res.Df Df F Pr(>F) 437s 1 51 437s 2 50 1 0.8 0.37 437s Linear hypothesis test (F statistic of a Wald test) 437s 437s Hypothesis: 437s Consumption_corpProf + Investment_capitalLag = 0 437s 437s Model 1: restricted model 437s Model 2: kleinModel 437s 437s Res.Df Df F Pr(>F) 437s 1 51 437s 2 50 1 0.72 0.4 437s Linear hypothesis test (Chi^2 statistic of a Wald test) 437s 437s Hypothesis: 437s Consumption_corpProf + Investment_capitalLag = 0 437s 437s Model 1: restricted model 437s Model 2: kleinModel 437s 437s Res.Df Df Chisq Pr(>Chisq) 437s 1 51 437s 2 50 1 0.72 0.4 437s Linear hypothesis test (Theil's F test) 437s 437s Hypothesis: 437s Consumption_corpProf + Investment_capitalLag = 0 437s Consumption_corpProfLag - PrivateWages_trend = 0 437s 437s Model 1: restricted model 437s Model 2: kleinModel 437s 437s Res.Df Df F Pr(>F) 437s 1 52 437s 2 50 2 0.42 0.66 437s Linear hypothesis test (F statistic of a Wald test) 437s 437s Hypothesis: 437s Consumption_corpProf + Investment_capitalLag = 0 437s Consumption_corpProfLag - PrivateWages_trend = 0 437s 437s Model 1: restricted model 437s Model 2: kleinModel 437s 437s Res.Df Df F Pr(>F) 437s 1 52 437s 2 50 2 0.37 0.69 437s Linear hypothesis test (Chi^2 statistic of a Wald test) 437s 437s Hypothesis: 437s Consumption_corpProf + Investment_capitalLag = 0 437s Consumption_corpProfLag - PrivateWages_trend = 0 437s 437s Model 1: restricted model 437s Model 2: kleinModel 437s 437s Res.Df Df Chisq Pr(>Chisq) 437s 1 52 437s 2 50 2 0.75 0.69 437s > logLik 437s 'log Lik.' -71.9 (df=13) 437s 'log Lik.' -77.1 (df=13) 437s compare log likelihood value with single-equation OLS 437s [1] "Mean relative difference: 0.000555" 437s Estimating function 437s Consumption_(Intercept) Consumption_corpProf 437s Consumption_2 -0.32389 -4.016 437s Consumption_3 -1.25001 -21.125 437s Consumption_4 -1.56574 -28.810 437s Consumption_5 -0.49350 -9.574 437s Consumption_6 0.00761 0.153 437s Consumption_7 0.86910 17.034 437s Consumption_8 1.33848 26.502 437s Consumption_9 1.05498 22.260 437s Consumption_10 -0.58856 -12.772 437s Consumption_11 0.28231 4.404 437s Consumption_12 -0.22965 -2.618 437s Consumption_13 -0.32213 -2.255 437s Consumption_14 0.32228 3.610 437s Consumption_15 -0.05801 -0.714 437s Consumption_16 -0.03466 -0.485 437s Consumption_17 1.61650 28.450 437s Consumption_18 -0.43597 -7.542 437s Consumption_19 0.21005 3.214 437s Consumption_20 0.98920 18.795 437s Consumption_21 0.78508 16.565 437s Consumption_22 -2.17345 -51.076 437s Investment_2 0.00000 0.000 437s Investment_3 0.00000 0.000 437s Investment_4 0.00000 0.000 437s Investment_5 0.00000 0.000 437s Investment_6 0.00000 0.000 437s Investment_7 0.00000 0.000 437s Investment_8 0.00000 0.000 437s Investment_9 0.00000 0.000 437s Investment_10 0.00000 0.000 437s Investment_11 0.00000 0.000 437s Investment_12 0.00000 0.000 437s Investment_13 0.00000 0.000 437s Investment_14 0.00000 0.000 437s Investment_15 0.00000 0.000 437s Investment_16 0.00000 0.000 437s Investment_17 0.00000 0.000 437s Investment_18 0.00000 0.000 437s Investment_19 0.00000 0.000 437s Investment_20 0.00000 0.000 437s Investment_21 0.00000 0.000 437s Investment_22 0.00000 0.000 437s PrivateWages_2 0.00000 0.000 437s PrivateWages_3 0.00000 0.000 437s PrivateWages_4 0.00000 0.000 437s PrivateWages_5 0.00000 0.000 437s PrivateWages_6 0.00000 0.000 437s PrivateWages_8 0.00000 0.000 437s PrivateWages_9 0.00000 0.000 437s PrivateWages_10 0.00000 0.000 437s PrivateWages_11 0.00000 0.000 437s PrivateWages_12 0.00000 0.000 437s PrivateWages_13 0.00000 0.000 437s PrivateWages_14 0.00000 0.000 437s PrivateWages_15 0.00000 0.000 437s PrivateWages_16 0.00000 0.000 437s PrivateWages_17 0.00000 0.000 437s PrivateWages_18 0.00000 0.000 437s PrivateWages_19 0.00000 0.000 437s PrivateWages_20 0.00000 0.000 437s PrivateWages_21 0.00000 0.000 437s PrivateWages_22 0.00000 0.000 437s Consumption_corpProfLag Consumption_wages 437s Consumption_2 -4.113 -9.134 437s Consumption_3 -15.500 -40.250 437s Consumption_4 -26.461 -57.932 437s Consumption_5 -9.080 -18.260 437s Consumption_6 0.148 0.294 437s Consumption_7 17.469 35.372 437s Consumption_8 26.234 55.547 437s Consumption_9 20.889 45.259 437s Consumption_10 -12.419 -26.662 437s Consumption_11 6.126 11.885 437s Consumption_12 -3.583 -9.025 437s Consumption_13 -3.672 -11.049 437s Consumption_14 2.256 10.990 437s Consumption_15 -0.650 -2.123 437s Consumption_16 -0.426 -1.362 437s Consumption_17 22.631 71.449 437s Consumption_18 -7.673 -20.796 437s Consumption_19 3.634 9.641 437s Consumption_20 15.135 48.867 437s Consumption_21 14.916 41.609 437s Consumption_22 -45.860 -134.319 437s Investment_2 0.000 0.000 437s Investment_3 0.000 0.000 437s Investment_4 0.000 0.000 437s Investment_5 0.000 0.000 437s Investment_6 0.000 0.000 437s Investment_7 0.000 0.000 437s Investment_8 0.000 0.000 437s Investment_9 0.000 0.000 437s Investment_10 0.000 0.000 437s Investment_11 0.000 0.000 437s Investment_12 0.000 0.000 437s Investment_13 0.000 0.000 437s Investment_14 0.000 0.000 437s Investment_15 0.000 0.000 437s Investment_16 0.000 0.000 437s Investment_17 0.000 0.000 437s Investment_18 0.000 0.000 437s Investment_19 0.000 0.000 437s Investment_20 0.000 0.000 437s Investment_21 0.000 0.000 437s Investment_22 0.000 0.000 437s PrivateWages_2 0.000 0.000 437s PrivateWages_3 0.000 0.000 437s PrivateWages_4 0.000 0.000 437s PrivateWages_5 0.000 0.000 437s PrivateWages_6 0.000 0.000 437s PrivateWages_8 0.000 0.000 437s PrivateWages_9 0.000 0.000 437s PrivateWages_10 0.000 0.000 437s PrivateWages_11 0.000 0.000 437s PrivateWages_12 0.000 0.000 437s PrivateWages_13 0.000 0.000 437s PrivateWages_14 0.000 0.000 437s PrivateWages_15 0.000 0.000 437s PrivateWages_16 0.000 0.000 437s PrivateWages_17 0.000 0.000 437s PrivateWages_18 0.000 0.000 437s PrivateWages_19 0.000 0.000 437s PrivateWages_20 0.000 0.000 437s PrivateWages_21 0.000 0.000 437s PrivateWages_22 0.000 0.000 437s Investment_(Intercept) Investment_corpProf 437s Consumption_2 0.0000 0.000 437s Consumption_3 0.0000 0.000 437s Consumption_4 0.0000 0.000 437s Consumption_5 0.0000 0.000 437s Consumption_6 0.0000 0.000 437s Consumption_7 0.0000 0.000 437s Consumption_8 0.0000 0.000 437s Consumption_9 0.0000 0.000 437s Consumption_10 0.0000 0.000 437s Consumption_11 0.0000 0.000 437s Consumption_12 0.0000 0.000 437s Consumption_13 0.0000 0.000 437s Consumption_14 0.0000 0.000 437s Consumption_15 0.0000 0.000 437s Consumption_16 0.0000 0.000 437s Consumption_17 0.0000 0.000 437s Consumption_18 0.0000 0.000 437s Consumption_19 0.0000 0.000 437s Consumption_20 0.0000 0.000 437s Consumption_21 0.0000 0.000 437s Consumption_22 0.0000 0.000 437s Investment_2 -0.0668 -0.828 437s Investment_3 -0.0476 -0.804 437s Investment_4 1.2467 22.939 437s Investment_5 -1.3512 -26.213 437s Investment_6 0.4154 8.350 437s Investment_7 1.4923 29.248 437s Investment_8 0.7889 15.620 437s Investment_9 -0.6317 -13.329 437s Investment_10 1.0830 23.500 437s Investment_11 0.2791 4.353 437s Investment_12 0.0369 0.420 437s Investment_13 0.3659 2.561 437s Investment_14 0.2237 2.505 437s Investment_15 -0.1728 -2.126 437s Investment_16 0.0101 0.141 437s Investment_17 0.9719 17.105 437s Investment_18 0.0516 0.893 437s Investment_19 -2.5656 -39.254 437s Investment_20 -0.6866 -13.045 437s Investment_21 -0.7807 -16.474 437s Investment_22 -0.6623 -15.565 437s PrivateWages_2 0.0000 0.000 437s PrivateWages_3 0.0000 0.000 437s PrivateWages_4 0.0000 0.000 437s PrivateWages_5 0.0000 0.000 437s PrivateWages_6 0.0000 0.000 437s PrivateWages_8 0.0000 0.000 437s PrivateWages_9 0.0000 0.000 437s PrivateWages_10 0.0000 0.000 437s PrivateWages_11 0.0000 0.000 437s PrivateWages_12 0.0000 0.000 437s PrivateWages_13 0.0000 0.000 437s PrivateWages_14 0.0000 0.000 437s PrivateWages_15 0.0000 0.000 437s PrivateWages_16 0.0000 0.000 437s PrivateWages_17 0.0000 0.000 437s PrivateWages_18 0.0000 0.000 437s PrivateWages_19 0.0000 0.000 437s PrivateWages_20 0.0000 0.000 437s PrivateWages_21 0.0000 0.000 437s PrivateWages_22 0.0000 0.000 437s Investment_corpProfLag Investment_capitalLag 437s Consumption_2 0.000 0.00 437s Consumption_3 0.000 0.00 437s Consumption_4 0.000 0.00 437s Consumption_5 0.000 0.00 437s Consumption_6 0.000 0.00 437s Consumption_7 0.000 0.00 437s Consumption_8 0.000 0.00 437s Consumption_9 0.000 0.00 437s Consumption_10 0.000 0.00 437s Consumption_11 0.000 0.00 437s Consumption_12 0.000 0.00 437s Consumption_13 0.000 0.00 437s Consumption_14 0.000 0.00 437s Consumption_15 0.000 0.00 437s Consumption_16 0.000 0.00 437s Consumption_17 0.000 0.00 437s Consumption_18 0.000 0.00 437s Consumption_19 0.000 0.00 437s Consumption_20 0.000 0.00 437s Consumption_21 0.000 0.00 437s Consumption_22 0.000 0.00 437s Investment_2 -0.848 -12.21 437s Investment_3 -0.590 -8.69 437s Investment_4 21.069 230.01 437s Investment_5 -24.862 -256.32 437s Investment_6 8.059 80.05 437s Investment_7 29.994 295.17 437s Investment_8 15.463 160.46 437s Investment_9 -12.507 -131.14 437s Investment_10 22.850 228.07 437s Investment_11 6.056 60.20 437s Investment_12 0.575 7.99 437s Investment_13 4.172 78.05 437s Investment_14 1.566 46.33 437s Investment_15 -1.936 -34.91 437s Investment_16 0.124 2.01 437s Investment_17 13.606 192.14 437s Investment_18 0.908 10.31 437s Investment_19 -44.385 -517.74 437s Investment_20 -10.505 -137.25 437s Investment_21 -14.834 -157.09 437s Investment_22 -13.975 -135.45 437s PrivateWages_2 0.000 0.00 437s PrivateWages_3 0.000 0.00 437s PrivateWages_4 0.000 0.00 437s PrivateWages_5 0.000 0.00 437s PrivateWages_6 0.000 0.00 437s PrivateWages_8 0.000 0.00 437s PrivateWages_9 0.000 0.00 437s PrivateWages_10 0.000 0.00 437s PrivateWages_11 0.000 0.00 437s PrivateWages_12 0.000 0.00 437s PrivateWages_13 0.000 0.00 437s PrivateWages_14 0.000 0.00 437s PrivateWages_15 0.000 0.00 437s PrivateWages_16 0.000 0.00 437s PrivateWages_17 0.000 0.00 437s PrivateWages_18 0.000 0.00 437s PrivateWages_19 0.000 0.00 437s PrivateWages_20 0.000 0.00 437s PrivateWages_21 0.000 0.00 437s PrivateWages_22 0.000 0.00 437s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 437s Consumption_2 0.0000 0.00 0.00 437s Consumption_3 0.0000 0.00 0.00 437s Consumption_4 0.0000 0.00 0.00 437s Consumption_5 0.0000 0.00 0.00 437s Consumption_6 0.0000 0.00 0.00 437s Consumption_7 0.0000 0.00 0.00 437s Consumption_8 0.0000 0.00 0.00 437s Consumption_9 0.0000 0.00 0.00 437s Consumption_10 0.0000 0.00 0.00 437s Consumption_11 0.0000 0.00 0.00 437s Consumption_12 0.0000 0.00 0.00 437s Consumption_13 0.0000 0.00 0.00 437s Consumption_14 0.0000 0.00 0.00 437s Consumption_15 0.0000 0.00 0.00 437s Consumption_16 0.0000 0.00 0.00 437s Consumption_17 0.0000 0.00 0.00 437s Consumption_18 0.0000 0.00 0.00 437s Consumption_19 0.0000 0.00 0.00 437s Consumption_20 0.0000 0.00 0.00 437s Consumption_21 0.0000 0.00 0.00 437s Consumption_22 0.0000 0.00 0.00 437s Investment_2 0.0000 0.00 0.00 437s Investment_3 0.0000 0.00 0.00 437s Investment_4 0.0000 0.00 0.00 437s Investment_5 0.0000 0.00 0.00 437s Investment_6 0.0000 0.00 0.00 437s Investment_7 0.0000 0.00 0.00 437s Investment_8 0.0000 0.00 0.00 437s Investment_9 0.0000 0.00 0.00 437s Investment_10 0.0000 0.00 0.00 437s Investment_11 0.0000 0.00 0.00 437s Investment_12 0.0000 0.00 0.00 437s Investment_13 0.0000 0.00 0.00 437s Investment_14 0.0000 0.00 0.00 437s Investment_15 0.0000 0.00 0.00 437s Investment_16 0.0000 0.00 0.00 437s Investment_17 0.0000 0.00 0.00 437s Investment_18 0.0000 0.00 0.00 437s Investment_19 0.0000 0.00 0.00 437s Investment_20 0.0000 0.00 0.00 437s Investment_21 0.0000 0.00 0.00 437s Investment_22 0.0000 0.00 0.00 437s PrivateWages_2 -1.3389 -61.06 -60.12 437s PrivateWages_3 0.2462 12.33 11.23 437s PrivateWages_4 1.1255 64.38 56.39 437s PrivateWages_5 -0.1959 -11.18 -11.20 437s PrivateWages_6 -0.5284 -32.23 -30.17 437s PrivateWages_8 -0.7909 -50.94 -50.62 437s PrivateWages_9 0.2819 18.18 18.15 437s PrivateWages_10 1.1384 76.28 73.43 437s PrivateWages_11 -0.1904 -11.65 -12.76 437s PrivateWages_12 0.5813 31.04 35.58 437s PrivateWages_13 0.1206 5.34 6.44 437s PrivateWages_14 0.4773 21.53 21.14 437s PrivateWages_15 0.3035 15.09 13.69 437s PrivateWages_16 0.0284 1.55 1.41 437s PrivateWages_17 -0.8517 -53.40 -46.33 437s PrivateWages_18 0.9908 64.40 62.12 437s PrivateWages_19 -0.4597 -28.00 -29.88 437s PrivateWages_20 -0.3819 -26.54 -23.26 437s PrivateWages_21 -1.1062 -83.74 -76.88 437s PrivateWages_22 0.5501 48.63 41.64 437s PrivateWages_trend 437s Consumption_2 0.000 437s Consumption_3 0.000 437s Consumption_4 0.000 437s Consumption_5 0.000 437s Consumption_6 0.000 437s Consumption_7 0.000 437s Consumption_8 0.000 437s Consumption_9 0.000 437s Consumption_10 0.000 437s Consumption_11 0.000 437s Consumption_12 0.000 437s Consumption_13 0.000 437s Consumption_14 0.000 437s Consumption_15 0.000 437s Consumption_16 0.000 437s Consumption_17 0.000 437s Consumption_18 0.000 437s Consumption_19 0.000 437s Consumption_20 0.000 437s Consumption_21 0.000 437s Consumption_22 0.000 437s Investment_2 0.000 437s Investment_3 0.000 437s Investment_4 0.000 437s Investment_5 0.000 437s Investment_6 0.000 437s Investment_7 0.000 437s Investment_8 0.000 437s Investment_9 0.000 437s Investment_10 0.000 437s Investment_11 0.000 437s Investment_12 0.000 437s Investment_13 0.000 437s Investment_14 0.000 437s Investment_15 0.000 437s Investment_16 0.000 437s Investment_17 0.000 437s Investment_18 0.000 437s Investment_19 0.000 437s Investment_20 0.000 437s Investment_21 0.000 437s Investment_22 0.000 437s PrivateWages_2 13.389 437s PrivateWages_3 -2.216 437s PrivateWages_4 -9.004 437s PrivateWages_5 1.371 437s PrivateWages_6 3.170 437s PrivateWages_8 3.164 437s PrivateWages_9 -0.846 437s PrivateWages_10 -2.277 437s PrivateWages_11 0.190 437s PrivateWages_12 0.000 437s PrivateWages_13 0.121 437s PrivateWages_14 0.955 437s PrivateWages_15 0.911 437s PrivateWages_16 0.114 437s PrivateWages_17 -4.258 437s PrivateWages_18 5.945 437s PrivateWages_19 -3.218 437s PrivateWages_20 -3.055 437s PrivateWages_21 -9.956 437s PrivateWages_22 5.501 437s [1] TRUE 437s > Bread 437s Consumption_(Intercept) Consumption_corpProf 437s Consumption_(Intercept) 100.0401 0.0296 437s Consumption_corpProf 0.0296 0.4904 437s Consumption_corpProfLag -1.0438 -0.3107 437s Consumption_wages -1.9405 -0.0777 437s Investment_(Intercept) 0.0000 0.0000 437s Investment_corpProf 0.0000 0.0000 437s Investment_corpProfLag 0.0000 0.0000 437s Investment_capitalLag 0.0000 0.0000 437s PrivateWages_(Intercept) 0.0000 0.0000 437s PrivateWages_gnp 0.0000 0.0000 437s PrivateWages_gnpLag 0.0000 0.0000 437s PrivateWages_trend 0.0000 0.0000 437s Consumption_corpProfLag Consumption_wages 437s Consumption_(Intercept) -1.0438 -1.9405 437s Consumption_corpProf -0.3107 -0.0777 437s Consumption_corpProfLag 0.4844 -0.0396 437s Consumption_wages -0.0396 0.0941 437s Investment_(Intercept) 0.0000 0.0000 437s Investment_corpProf 0.0000 0.0000 437s Investment_corpProfLag 0.0000 0.0000 437s Investment_capitalLag 0.0000 0.0000 437s PrivateWages_(Intercept) 0.0000 0.0000 437s PrivateWages_gnp 0.0000 0.0000 437s PrivateWages_gnpLag 0.0000 0.0000 437s PrivateWages_trend 0.0000 0.0000 437s Investment_(Intercept) Investment_corpProf 437s Consumption_(Intercept) 0.00 0.0000 437s Consumption_corpProf 0.00 0.0000 437s Consumption_corpProfLag 0.00 0.0000 437s Consumption_wages 0.00 0.0000 437s Investment_(Intercept) 1817.57 -17.6857 437s Investment_corpProf -17.69 0.5738 437s Investment_corpProfLag 14.44 -0.4928 437s Investment_capitalLag -8.74 0.0801 437s PrivateWages_(Intercept) 0.00 0.0000 437s PrivateWages_gnp 0.00 0.0000 437s PrivateWages_gnpLag 0.00 0.0000 437s PrivateWages_trend 0.00 0.0000 437s Investment_corpProfLag Investment_capitalLag 437s Consumption_(Intercept) 0.0000 0.0000 437s Consumption_corpProf 0.0000 0.0000 437s Consumption_corpProfLag 0.0000 0.0000 437s Consumption_wages 0.0000 0.0000 437s Investment_(Intercept) 14.4412 -8.7403 437s Investment_corpProf -0.4928 0.0801 437s Investment_corpProfLag 0.6190 -0.0811 437s Investment_capitalLag -0.0811 0.0435 437s PrivateWages_(Intercept) 0.0000 0.0000 437s PrivateWages_gnp 0.0000 0.0000 437s PrivateWages_gnpLag 0.0000 0.0000 437s PrivateWages_trend 0.0000 0.0000 437s PrivateWages_(Intercept) PrivateWages_gnp 437s Consumption_(Intercept) 0.000 0.000 437s Consumption_corpProf 0.000 0.000 437s Consumption_corpProfLag 0.000 0.000 437s Consumption_wages 0.000 0.000 437s Investment_(Intercept) 0.000 0.000 437s Investment_corpProf 0.000 0.000 437s Investment_corpProfLag 0.000 0.000 437s Investment_capitalLag 0.000 0.000 437s PrivateWages_(Intercept) 174.627 -0.658 437s PrivateWages_gnp -0.658 0.112 437s PrivateWages_gnpLag -2.295 -0.104 437s PrivateWages_trend 2.155 -0.030 437s PrivateWages_gnpLag PrivateWages_trend 437s Consumption_(Intercept) 0.00000 0.00000 437s Consumption_corpProf 0.00000 0.00000 437s Consumption_corpProfLag 0.00000 0.00000 437s Consumption_wages 0.00000 0.00000 437s Investment_(Intercept) 0.00000 0.00000 437s Investment_corpProf 0.00000 0.00000 437s Investment_corpProfLag 0.00000 0.00000 437s Investment_capitalLag 0.00000 0.00000 437s PrivateWages_(Intercept) -2.29451 2.15506 437s PrivateWages_gnp -0.10426 -0.03004 437s PrivateWages_gnpLag 0.14761 -0.00667 437s PrivateWages_trend -0.00667 0.11527 437s > 437s > # 2SLS 437s > summary 437s 437s systemfit results 437s method: 2SLS 437s 437s N DF SSR detRCov OLS-R2 McElroy-R2 437s system 60 48 53.4 0.274 0.973 0.992 437s 437s N DF SSR MSE RMSE R2 Adj R2 437s Consumption 20 16 20.67 1.292 1.14 0.978 0.974 437s Investment 20 16 23.02 1.438 1.20 0.901 0.883 437s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 437s 437s The covariance matrix of the residuals 437s Consumption Investment PrivateWages 437s Consumption 1.034 0.309 -0.383 437s Investment 0.309 1.151 0.202 437s PrivateWages -0.383 0.202 0.487 437s 437s The correlations of the residuals 437s Consumption Investment PrivateWages 437s Consumption 1.000 0.284 -0.540 437s Investment 0.284 1.000 0.269 437s PrivateWages -0.540 0.269 1.000 437s 437s 437s 2SLS estimates for 'Consumption' (equation 1) 437s Model Formula: consump ~ corpProf + corpProfLag + wages 437s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 437s gnpLag 437s 437s Estimate Std. Error t value Pr(>|t|) 437s (Intercept) 16.5093 1.3121 12.58 1.0e-09 *** 437s corpProf 0.0219 0.1159 0.19 0.85 437s corpProfLag 0.1931 0.1071 1.80 0.09 . 437s wages 0.8174 0.0408 20.05 9.2e-13 *** 437s --- 437s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 437s 437s Residual standard error: 1.137 on 16 degrees of freedom 437s Number of observations: 20 Degrees of Freedom: 16 437s SSR: 20.671 MSE: 1.292 Root MSE: 1.137 437s Multiple R-Squared: 0.978 Adjusted R-Squared: 0.974 437s 437s 437s 2SLS estimates for 'Investment' (equation 2) 437s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 437s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 437s gnpLag 437s 437s Estimate Std. Error t value Pr(>|t|) 437s (Intercept) 17.843 6.850 2.60 0.01915 * 437s corpProf 0.217 0.155 1.40 0.18106 437s corpProfLag 0.542 0.148 3.65 0.00216 ** 437s capitalLag -0.145 0.033 -4.41 0.00044 *** 437s --- 437s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 437s 437s Residual standard error: 1.199 on 16 degrees of freedom 437s Number of observations: 20 Degrees of Freedom: 16 437s SSR: 23.016 MSE: 1.438 Root MSE: 1.199 437s Multiple R-Squared: 0.901 Adjusted R-Squared: 0.883 437s 437s 437s 2SLS estimates for 'PrivateWages' (equation 3) 437s Model Formula: privWage ~ gnp + gnpLag + trend 437s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 437s gnpLag 437s 437s Estimate Std. Error t value Pr(>|t|) 437s (Intercept) 1.3431 1.1772 1.14 0.27070 437s gnp 0.4438 0.0358 12.39 1.3e-09 *** 437s gnpLag 0.1447 0.0389 3.72 0.00185 ** 437s trend 0.1238 0.0306 4.05 0.00093 *** 437s --- 437s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 437s 437s Residual standard error: 0.78 on 16 degrees of freedom 437s Number of observations: 20 Degrees of Freedom: 16 437s SSR: 9.741 MSE: 0.609 Root MSE: 0.78 437s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 437s 437s > residuals 437s Consumption Investment PrivateWages 437s 1 NA NA NA 437s 2 -0.383 -1.0104 -1.3401 437s 3 -0.593 0.2478 0.2378 437s 4 -1.219 1.0621 1.1117 437s 5 -0.130 -1.4104 -0.1954 437s 6 0.354 0.4328 -0.5355 437s 7 NA NA NA 437s 8 1.551 1.0463 -0.7908 437s 9 1.440 0.0674 0.2831 437s 10 -0.286 1.7698 1.1353 437s 11 -0.453 -0.5912 -0.1765 437s 12 -0.994 -0.6318 0.6007 437s 13 -1.300 -0.6983 0.1443 437s 14 0.521 0.9724 0.4826 437s 15 -0.157 -0.1827 0.3016 437s 16 -0.014 0.1167 0.0261 437s 17 1.974 1.6266 -0.8614 437s 18 -0.576 -0.0525 0.9927 437s 19 -0.203 -3.0656 -0.4446 437s 20 1.342 0.1393 -0.3914 437s 21 1.039 -0.1305 -1.1115 437s 22 -1.912 0.2922 0.5312 437s > fitted 437s Consumption Investment PrivateWages 437s 1 NA NA NA 437s 2 42.3 0.810 26.8 437s 3 45.6 1.652 29.1 437s 4 50.4 4.138 33.0 437s 5 50.7 4.410 34.1 437s 6 52.2 4.667 35.9 437s 7 NA NA NA 437s 8 54.6 3.154 38.7 437s 9 55.9 2.933 38.9 437s 10 58.1 3.330 40.2 437s 11 55.5 1.591 38.1 437s 12 51.9 -2.768 33.9 437s 13 46.9 -5.502 28.9 437s 14 46.0 -6.072 28.0 437s 15 48.9 -2.817 30.3 437s 16 51.3 -1.417 33.2 437s 17 55.7 0.473 37.7 437s 18 59.3 2.053 40.0 437s 19 57.7 1.166 38.6 437s 20 60.3 1.161 42.0 437s 21 64.0 3.431 46.1 437s 22 71.6 4.608 52.8 437s > predict 437s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 437s 1 NA NA NA NA 437s 2 42.3 0.473 41.3 43.3 437s 3 45.6 0.573 44.4 46.8 437s 4 50.4 0.366 49.6 51.2 437s 5 50.7 0.423 49.8 51.6 437s 6 52.2 0.426 51.3 53.1 437s 7 NA NA NA NA 437s 8 54.6 0.347 53.9 55.4 437s 9 55.9 0.384 55.0 56.7 437s 10 58.1 0.395 57.2 58.9 437s 11 55.5 0.729 53.9 57.0 437s 12 51.9 0.594 50.6 53.2 437s 13 46.9 0.752 45.3 48.5 437s 14 46.0 0.616 44.7 47.3 437s 15 48.9 0.373 48.1 49.6 437s 16 51.3 0.331 50.6 52.0 437s 17 55.7 0.403 54.9 56.6 437s 18 59.3 0.326 58.6 60.0 437s 19 57.7 0.411 56.8 58.6 437s 20 60.3 0.472 59.3 61.3 437s 21 64.0 0.443 63.0 64.9 437s 22 71.6 0.683 70.2 73.1 437s Investment.pred Investment.se.fit Investment.lwr Investment.upr 437s 1 NA NA NA NA 437s 2 0.810 0.786 -0.8569 2.48 437s 3 1.652 0.541 0.5056 2.80 437s 4 4.138 0.511 3.0552 5.22 437s 5 4.410 0.421 3.5172 5.30 437s 6 4.667 0.395 3.8294 5.51 437s 7 NA NA NA NA 437s 8 3.154 0.327 2.4602 3.85 437s 9 2.933 0.489 1.8967 3.97 437s 10 3.330 0.537 2.1915 4.47 437s 11 1.591 0.786 -0.0748 3.26 437s 12 -2.768 0.615 -4.0716 -1.46 437s 13 -5.502 0.787 -7.1696 -3.83 437s 14 -6.072 0.842 -7.8568 -4.29 437s 15 -2.817 0.397 -3.6591 -1.98 437s 16 -1.417 0.343 -2.1436 -0.69 437s 17 0.473 0.457 -0.4954 1.44 437s 18 2.053 0.286 1.4471 2.66 437s 19 1.166 0.430 0.2549 2.08 437s 20 1.161 0.515 0.0698 2.25 437s 21 3.431 0.426 2.5282 4.33 437s 22 4.608 0.606 3.3223 5.89 437s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 437s 1 NA NA NA NA 437s 2 26.8 0.328 26.1 27.5 437s 3 29.1 0.340 28.3 29.8 437s 4 33.0 0.360 32.2 33.8 437s 5 34.1 0.258 33.5 34.6 437s 6 35.9 0.266 35.4 36.5 437s 7 NA NA NA NA 437s 8 38.7 0.262 38.1 39.2 437s 9 38.9 0.250 38.4 39.4 437s 10 40.2 0.240 39.7 40.7 437s 11 38.1 0.355 37.3 38.8 437s 12 33.9 0.382 33.1 34.7 437s 13 28.9 0.456 27.9 29.8 437s 14 28.0 0.348 27.3 28.8 437s 15 30.3 0.339 29.6 31.0 437s 16 33.2 0.284 32.6 33.8 437s 17 37.7 0.293 37.0 38.3 437s 18 40.0 0.218 39.5 40.5 437s 19 38.6 0.358 37.9 39.4 437s 20 42.0 0.307 41.3 42.6 437s 21 46.1 0.310 45.5 46.8 437s 22 52.8 0.496 51.7 53.8 437s > model.frame 437s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 437s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 437s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 437s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 437s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 437s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 437s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 437s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 437s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 437s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 437s 10 57.8 21.7 21.1 45.3 5.1 211 41.3 67.0 64.5 437s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 437s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 437s 13 45.6 7.0 11.4 34.3 -6.2 213 29.0 44.3 53.4 437s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 437s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 437s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 437s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 437s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 437s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 437s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 437s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 437s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 437s trend 437s 1 -11 437s 2 -10 437s 3 -9 437s 4 -8 437s 5 -7 437s 6 -6 437s 7 -5 437s 8 -4 437s 9 -3 437s 10 -2 437s 11 -1 437s 12 0 437s 13 1 437s 14 2 437s 15 3 437s 16 4 437s 17 5 437s 18 6 437s 19 7 437s 20 8 437s 21 9 437s 22 10 437s > Frames of instrumental variables 437s govExp taxes govWage trend capitalLag corpProfLag gnpLag 437s 1 2.4 3.4 2.2 -11 180 NA NA 437s 2 3.9 7.7 2.7 -10 183 12.7 44.9 437s 3 3.2 3.9 2.9 -9 183 12.4 45.6 437s 4 2.8 4.7 2.9 -8 184 16.9 50.1 437s 5 3.5 3.8 3.1 -7 190 18.4 57.2 437s 6 3.3 5.5 3.2 -6 193 19.4 57.1 437s 7 3.3 7.0 3.3 -5 198 20.1 NA 437s 8 4.0 6.7 3.6 -4 203 19.6 64.0 437s 9 4.2 4.2 3.7 -3 208 19.8 64.4 437s 10 4.1 4.0 4.0 -2 211 21.1 64.5 437s 11 5.2 7.7 4.2 -1 216 21.7 67.0 437s 12 5.9 7.5 4.8 0 217 15.6 61.2 437s 13 4.9 8.3 5.3 1 213 11.4 53.4 437s 14 3.7 5.4 5.6 2 207 7.0 44.3 437s 15 4.0 6.8 6.0 3 202 11.2 45.1 437s 16 4.4 7.2 6.1 4 199 12.3 49.7 437s 17 2.9 8.3 7.4 5 198 14.0 54.4 437s 18 4.3 6.7 6.7 6 200 17.6 62.7 437s 19 5.3 7.4 7.7 7 202 17.3 65.0 437s 20 6.6 8.9 7.8 8 200 15.3 60.9 437s 21 7.4 9.6 8.0 9 201 19.0 69.5 437s 22 13.8 11.6 8.5 10 204 21.1 75.7 437s govExp taxes govWage trend capitalLag corpProfLag gnpLag 437s 1 2.4 3.4 2.2 -11 180 NA NA 437s 2 3.9 7.7 2.7 -10 183 12.7 44.9 437s 3 3.2 3.9 2.9 -9 183 12.4 45.6 437s 4 2.8 4.7 2.9 -8 184 16.9 50.1 437s 5 3.5 3.8 3.1 -7 190 18.4 57.2 437s 6 3.3 5.5 3.2 -6 193 19.4 57.1 437s 7 3.3 7.0 3.3 -5 198 20.1 NA 437s 8 4.0 6.7 3.6 -4 203 19.6 64.0 437s 9 4.2 4.2 3.7 -3 208 19.8 64.4 437s 10 4.1 4.0 4.0 -2 211 21.1 64.5 437s 11 5.2 7.7 4.2 -1 216 21.7 67.0 437s 12 5.9 7.5 4.8 0 217 15.6 61.2 437s 13 4.9 8.3 5.3 1 213 11.4 53.4 437s 14 3.7 5.4 5.6 2 207 7.0 44.3 437s 15 4.0 6.8 6.0 3 202 11.2 45.1 437s 16 4.4 7.2 6.1 4 199 12.3 49.7 437s 17 2.9 8.3 7.4 5 198 14.0 54.4 437s 18 4.3 6.7 6.7 6 200 17.6 62.7 437s 19 5.3 7.4 7.7 7 202 17.3 65.0 437s 20 6.6 8.9 7.8 8 200 15.3 60.9 437s 21 7.4 9.6 8.0 9 201 19.0 69.5 437s 22 13.8 11.6 8.5 10 204 21.1 75.7 437s govExp taxes govWage trend capitalLag corpProfLag gnpLag 437s 1 2.4 3.4 2.2 -11 180 NA NA 437s 2 3.9 7.7 2.7 -10 183 12.7 44.9 437s 3 3.2 3.9 2.9 -9 183 12.4 45.6 437s 4 2.8 4.7 2.9 -8 184 16.9 50.1 437s 5 3.5 3.8 3.1 -7 190 18.4 57.2 437s 6 3.3 5.5 3.2 -6 193 19.4 57.1 437s 7 3.3 7.0 3.3 -5 198 20.1 NA 437s 8 4.0 6.7 3.6 -4 203 19.6 64.0 437s 9 4.2 4.2 3.7 -3 208 19.8 64.4 437s 10 4.1 4.0 4.0 -2 211 21.1 64.5 437s 11 5.2 7.7 4.2 -1 216 21.7 67.0 437s 12 5.9 7.5 4.8 0 217 15.6 61.2 437s 13 4.9 8.3 5.3 1 213 11.4 53.4 437s 14 3.7 5.4 5.6 2 207 7.0 44.3 437s 15 4.0 6.8 6.0 3 202 11.2 45.1 437s 16 4.4 7.2 6.1 4 199 12.3 49.7 437s 17 2.9 8.3 7.4 5 198 14.0 54.4 437s 18 4.3 6.7 6.7 6 200 17.6 62.7 437s 19 5.3 7.4 7.7 7 202 17.3 65.0 437s 20 6.6 8.9 7.8 8 200 15.3 60.9 437s 21 7.4 9.6 8.0 9 201 19.0 69.5 437s 22 13.8 11.6 8.5 10 204 21.1 75.7 437s > model.matrix 437s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0323 >" 437s [2] "Attributes: < Component “dimnames”: Component 1: 55 string mismatches >" 437s [3] "Numeric: lengths (744, 720) differ" 437s > matrix of instrumental variables 437s Consumption_(Intercept) Consumption_govExp Consumption_taxes 437s Consumption_2 1 3.9 7.7 437s Consumption_3 1 3.2 3.9 437s Consumption_4 1 2.8 4.7 437s Consumption_5 1 3.5 3.8 437s Consumption_6 1 3.3 5.5 437s Consumption_8 1 4.0 6.7 437s Consumption_9 1 4.2 4.2 437s Consumption_10 1 4.1 4.0 437s Consumption_11 1 5.2 7.7 437s Consumption_12 1 5.9 7.5 437s Consumption_13 1 4.9 8.3 437s Consumption_14 1 3.7 5.4 437s Consumption_15 1 4.0 6.8 437s Consumption_16 1 4.4 7.2 437s Consumption_17 1 2.9 8.3 437s Consumption_18 1 4.3 6.7 437s Consumption_19 1 5.3 7.4 437s Consumption_20 1 6.6 8.9 437s Consumption_21 1 7.4 9.6 437s Consumption_22 1 13.8 11.6 437s Investment_2 0 0.0 0.0 437s Investment_3 0 0.0 0.0 437s Investment_4 0 0.0 0.0 437s Investment_5 0 0.0 0.0 437s Investment_6 0 0.0 0.0 437s Investment_8 0 0.0 0.0 437s Investment_9 0 0.0 0.0 437s Investment_10 0 0.0 0.0 437s Investment_11 0 0.0 0.0 437s Investment_12 0 0.0 0.0 437s Investment_13 0 0.0 0.0 437s Investment_14 0 0.0 0.0 437s Investment_15 0 0.0 0.0 437s Investment_16 0 0.0 0.0 437s Investment_17 0 0.0 0.0 437s Investment_18 0 0.0 0.0 437s Investment_19 0 0.0 0.0 437s Investment_20 0 0.0 0.0 437s Investment_21 0 0.0 0.0 437s Investment_22 0 0.0 0.0 437s PrivateWages_2 0 0.0 0.0 437s PrivateWages_3 0 0.0 0.0 437s PrivateWages_4 0 0.0 0.0 437s PrivateWages_5 0 0.0 0.0 437s PrivateWages_6 0 0.0 0.0 437s PrivateWages_8 0 0.0 0.0 437s PrivateWages_9 0 0.0 0.0 437s PrivateWages_10 0 0.0 0.0 437s PrivateWages_11 0 0.0 0.0 437s PrivateWages_12 0 0.0 0.0 437s PrivateWages_13 0 0.0 0.0 437s PrivateWages_14 0 0.0 0.0 437s PrivateWages_15 0 0.0 0.0 437s PrivateWages_16 0 0.0 0.0 437s PrivateWages_17 0 0.0 0.0 437s PrivateWages_18 0 0.0 0.0 437s PrivateWages_19 0 0.0 0.0 437s PrivateWages_20 0 0.0 0.0 437s PrivateWages_21 0 0.0 0.0 437s PrivateWages_22 0 0.0 0.0 437s Consumption_govWage Consumption_trend Consumption_capitalLag 437s Consumption_2 2.7 -10 183 437s Consumption_3 2.9 -9 183 437s Consumption_4 2.9 -8 184 437s Consumption_5 3.1 -7 190 437s Consumption_6 3.2 -6 193 437s Consumption_8 3.6 -4 203 437s Consumption_9 3.7 -3 208 437s Consumption_10 4.0 -2 211 437s Consumption_11 4.2 -1 216 437s Consumption_12 4.8 0 217 437s Consumption_13 5.3 1 213 437s Consumption_14 5.6 2 207 437s Consumption_15 6.0 3 202 437s Consumption_16 6.1 4 199 437s Consumption_17 7.4 5 198 437s Consumption_18 6.7 6 200 437s Consumption_19 7.7 7 202 437s Consumption_20 7.8 8 200 437s Consumption_21 8.0 9 201 437s Consumption_22 8.5 10 204 437s Investment_2 0.0 0 0 437s Investment_3 0.0 0 0 437s Investment_4 0.0 0 0 437s Investment_5 0.0 0 0 437s Investment_6 0.0 0 0 437s Investment_8 0.0 0 0 437s Investment_9 0.0 0 0 437s Investment_10 0.0 0 0 437s Investment_11 0.0 0 0 437s Investment_12 0.0 0 0 437s Investment_13 0.0 0 0 437s Investment_14 0.0 0 0 437s Investment_15 0.0 0 0 437s Investment_16 0.0 0 0 437s Investment_17 0.0 0 0 437s Investment_18 0.0 0 0 437s Investment_19 0.0 0 0 437s Investment_20 0.0 0 0 437s Investment_21 0.0 0 0 437s Investment_22 0.0 0 0 437s PrivateWages_2 0.0 0 0 437s PrivateWages_3 0.0 0 0 437s PrivateWages_4 0.0 0 0 437s PrivateWages_5 0.0 0 0 437s PrivateWages_6 0.0 0 0 437s PrivateWages_8 0.0 0 0 437s PrivateWages_9 0.0 0 0 437s PrivateWages_10 0.0 0 0 437s PrivateWages_11 0.0 0 0 437s PrivateWages_12 0.0 0 0 437s PrivateWages_13 0.0 0 0 437s PrivateWages_14 0.0 0 0 437s PrivateWages_15 0.0 0 0 437s PrivateWages_16 0.0 0 0 437s PrivateWages_17 0.0 0 0 437s PrivateWages_18 0.0 0 0 437s PrivateWages_19 0.0 0 0 437s PrivateWages_20 0.0 0 0 437s PrivateWages_21 0.0 0 0 437s PrivateWages_22 0.0 0 0 437s Consumption_corpProfLag Consumption_gnpLag 437s Consumption_2 12.7 44.9 437s Consumption_3 12.4 45.6 437s Consumption_4 16.9 50.1 437s Consumption_5 18.4 57.2 437s Consumption_6 19.4 57.1 437s Consumption_8 19.6 64.0 437s Consumption_9 19.8 64.4 437s Consumption_10 21.1 64.5 437s Consumption_11 21.7 67.0 437s Consumption_12 15.6 61.2 437s Consumption_13 11.4 53.4 437s Consumption_14 7.0 44.3 437s Consumption_15 11.2 45.1 437s Consumption_16 12.3 49.7 437s Consumption_17 14.0 54.4 437s Consumption_18 17.6 62.7 437s Consumption_19 17.3 65.0 437s Consumption_20 15.3 60.9 437s Consumption_21 19.0 69.5 437s Consumption_22 21.1 75.7 437s Investment_2 0.0 0.0 437s Investment_3 0.0 0.0 437s Investment_4 0.0 0.0 437s Investment_5 0.0 0.0 437s Investment_6 0.0 0.0 437s Investment_8 0.0 0.0 437s Investment_9 0.0 0.0 437s Investment_10 0.0 0.0 437s Investment_11 0.0 0.0 437s Investment_12 0.0 0.0 437s Investment_13 0.0 0.0 437s Investment_14 0.0 0.0 437s Investment_15 0.0 0.0 437s Investment_16 0.0 0.0 437s Investment_17 0.0 0.0 437s Investment_18 0.0 0.0 437s Investment_19 0.0 0.0 437s Investment_20 0.0 0.0 437s Investment_21 0.0 0.0 437s Investment_22 0.0 0.0 437s PrivateWages_2 0.0 0.0 437s PrivateWages_3 0.0 0.0 437s PrivateWages_4 0.0 0.0 437s PrivateWages_5 0.0 0.0 437s PrivateWages_6 0.0 0.0 437s PrivateWages_8 0.0 0.0 437s PrivateWages_9 0.0 0.0 437s PrivateWages_10 0.0 0.0 437s PrivateWages_11 0.0 0.0 437s PrivateWages_12 0.0 0.0 437s PrivateWages_13 0.0 0.0 437s PrivateWages_14 0.0 0.0 437s PrivateWages_15 0.0 0.0 437s PrivateWages_16 0.0 0.0 437s PrivateWages_17 0.0 0.0 437s PrivateWages_18 0.0 0.0 437s PrivateWages_19 0.0 0.0 437s PrivateWages_20 0.0 0.0 437s PrivateWages_21 0.0 0.0 437s PrivateWages_22 0.0 0.0 437s Investment_(Intercept) Investment_govExp Investment_taxes 437s Consumption_2 0 0.0 0.0 437s Consumption_3 0 0.0 0.0 437s Consumption_4 0 0.0 0.0 437s Consumption_5 0 0.0 0.0 437s Consumption_6 0 0.0 0.0 437s Consumption_8 0 0.0 0.0 437s Consumption_9 0 0.0 0.0 437s Consumption_10 0 0.0 0.0 437s Consumption_11 0 0.0 0.0 437s Consumption_12 0 0.0 0.0 437s Consumption_13 0 0.0 0.0 437s Consumption_14 0 0.0 0.0 437s Consumption_15 0 0.0 0.0 437s Consumption_16 0 0.0 0.0 437s Consumption_17 0 0.0 0.0 437s Consumption_18 0 0.0 0.0 437s Consumption_19 0 0.0 0.0 437s Consumption_20 0 0.0 0.0 437s Consumption_21 0 0.0 0.0 437s Consumption_22 0 0.0 0.0 437s Investment_2 1 3.9 7.7 437s Investment_3 1 3.2 3.9 437s Investment_4 1 2.8 4.7 437s Investment_5 1 3.5 3.8 437s Investment_6 1 3.3 5.5 437s Investment_8 1 4.0 6.7 437s Investment_9 1 4.2 4.2 437s Investment_10 1 4.1 4.0 437s Investment_11 1 5.2 7.7 437s Investment_12 1 5.9 7.5 437s Investment_13 1 4.9 8.3 437s Investment_14 1 3.7 5.4 437s Investment_15 1 4.0 6.8 437s Investment_16 1 4.4 7.2 437s Investment_17 1 2.9 8.3 437s Investment_18 1 4.3 6.7 437s Investment_19 1 5.3 7.4 437s Investment_20 1 6.6 8.9 437s Investment_21 1 7.4 9.6 437s Investment_22 1 13.8 11.6 437s PrivateWages_2 0 0.0 0.0 437s PrivateWages_3 0 0.0 0.0 437s PrivateWages_4 0 0.0 0.0 437s PrivateWages_5 0 0.0 0.0 437s PrivateWages_6 0 0.0 0.0 437s PrivateWages_8 0 0.0 0.0 437s PrivateWages_9 0 0.0 0.0 437s PrivateWages_10 0 0.0 0.0 437s PrivateWages_11 0 0.0 0.0 437s PrivateWages_12 0 0.0 0.0 437s PrivateWages_13 0 0.0 0.0 437s PrivateWages_14 0 0.0 0.0 437s PrivateWages_15 0 0.0 0.0 437s PrivateWages_16 0 0.0 0.0 437s PrivateWages_17 0 0.0 0.0 437s PrivateWages_18 0 0.0 0.0 437s PrivateWages_19 0 0.0 0.0 437s PrivateWages_20 0 0.0 0.0 437s PrivateWages_21 0 0.0 0.0 437s PrivateWages_22 0 0.0 0.0 437s Investment_govWage Investment_trend Investment_capitalLag 437s Consumption_2 0.0 0 0 437s Consumption_3 0.0 0 0 437s Consumption_4 0.0 0 0 437s Consumption_5 0.0 0 0 437s Consumption_6 0.0 0 0 437s Consumption_8 0.0 0 0 437s Consumption_9 0.0 0 0 437s Consumption_10 0.0 0 0 437s Consumption_11 0.0 0 0 437s Consumption_12 0.0 0 0 437s Consumption_13 0.0 0 0 437s Consumption_14 0.0 0 0 437s Consumption_15 0.0 0 0 437s Consumption_16 0.0 0 0 437s Consumption_17 0.0 0 0 437s Consumption_18 0.0 0 0 437s Consumption_19 0.0 0 0 437s Consumption_20 0.0 0 0 437s Consumption_21 0.0 0 0 437s Consumption_22 0.0 0 0 437s Investment_2 2.7 -10 183 437s Investment_3 2.9 -9 183 437s Investment_4 2.9 -8 184 437s Investment_5 3.1 -7 190 437s Investment_6 3.2 -6 193 437s Investment_8 3.6 -4 203 437s Investment_9 3.7 -3 208 437s Investment_10 4.0 -2 211 437s Investment_11 4.2 -1 216 437s Investment_12 4.8 0 217 437s Investment_13 5.3 1 213 437s Investment_14 5.6 2 207 437s Investment_15 6.0 3 202 437s Investment_16 6.1 4 199 437s Investment_17 7.4 5 198 437s Investment_18 6.7 6 200 437s Investment_19 7.7 7 202 437s Investment_20 7.8 8 200 437s Investment_21 8.0 9 201 437s Investment_22 8.5 10 204 437s PrivateWages_2 0.0 0 0 437s PrivateWages_3 0.0 0 0 437s PrivateWages_4 0.0 0 0 437s PrivateWages_5 0.0 0 0 437s PrivateWages_6 0.0 0 0 437s PrivateWages_8 0.0 0 0 437s PrivateWages_9 0.0 0 0 437s PrivateWages_10 0.0 0 0 437s PrivateWages_11 0.0 0 0 437s PrivateWages_12 0.0 0 0 437s PrivateWages_13 0.0 0 0 437s PrivateWages_14 0.0 0 0 437s PrivateWages_15 0.0 0 0 437s PrivateWages_16 0.0 0 0 437s PrivateWages_17 0.0 0 0 437s PrivateWages_18 0.0 0 0 437s PrivateWages_19 0.0 0 0 437s PrivateWages_20 0.0 0 0 437s PrivateWages_21 0.0 0 0 437s PrivateWages_22 0.0 0 0 437s Investment_corpProfLag Investment_gnpLag 437s Consumption_2 0.0 0.0 437s Consumption_3 0.0 0.0 437s Consumption_4 0.0 0.0 437s Consumption_5 0.0 0.0 437s Consumption_6 0.0 0.0 437s Consumption_8 0.0 0.0 437s Consumption_9 0.0 0.0 437s Consumption_10 0.0 0.0 437s Consumption_11 0.0 0.0 437s Consumption_12 0.0 0.0 437s Consumption_13 0.0 0.0 437s Consumption_14 0.0 0.0 437s Consumption_15 0.0 0.0 437s Consumption_16 0.0 0.0 437s Consumption_17 0.0 0.0 437s Consumption_18 0.0 0.0 437s Consumption_19 0.0 0.0 437s Consumption_20 0.0 0.0 437s Consumption_21 0.0 0.0 437s Consumption_22 0.0 0.0 437s Investment_2 12.7 44.9 437s Investment_3 12.4 45.6 437s Investment_4 16.9 50.1 437s Investment_5 18.4 57.2 437s Investment_6 19.4 57.1 437s Investment_8 19.6 64.0 437s Investment_9 19.8 64.4 437s Investment_10 21.1 64.5 437s Investment_11 21.7 67.0 437s Investment_12 15.6 61.2 437s Investment_13 11.4 53.4 437s Investment_14 7.0 44.3 437s Investment_15 11.2 45.1 437s Investment_16 12.3 49.7 437s Investment_17 14.0 54.4 437s Investment_18 17.6 62.7 437s Investment_19 17.3 65.0 437s Investment_20 15.3 60.9 437s Investment_21 19.0 69.5 437s Investment_22 21.1 75.7 437s PrivateWages_2 0.0 0.0 437s PrivateWages_3 0.0 0.0 437s PrivateWages_4 0.0 0.0 437s PrivateWages_5 0.0 0.0 437s PrivateWages_6 0.0 0.0 437s PrivateWages_8 0.0 0.0 437s PrivateWages_9 0.0 0.0 437s PrivateWages_10 0.0 0.0 437s PrivateWages_11 0.0 0.0 437s PrivateWages_12 0.0 0.0 437s PrivateWages_13 0.0 0.0 437s PrivateWages_14 0.0 0.0 437s PrivateWages_15 0.0 0.0 437s PrivateWages_16 0.0 0.0 437s PrivateWages_17 0.0 0.0 437s PrivateWages_18 0.0 0.0 437s PrivateWages_19 0.0 0.0 437s PrivateWages_20 0.0 0.0 437s PrivateWages_21 0.0 0.0 437s PrivateWages_22 0.0 0.0 437s PrivateWages_(Intercept) PrivateWages_govExp PrivateWages_taxes 437s Consumption_2 0 0.0 0.0 437s Consumption_3 0 0.0 0.0 437s Consumption_4 0 0.0 0.0 437s Consumption_5 0 0.0 0.0 437s Consumption_6 0 0.0 0.0 437s Consumption_8 0 0.0 0.0 437s Consumption_9 0 0.0 0.0 437s Consumption_10 0 0.0 0.0 437s Consumption_11 0 0.0 0.0 437s Consumption_12 0 0.0 0.0 437s Consumption_13 0 0.0 0.0 437s Consumption_14 0 0.0 0.0 437s Consumption_15 0 0.0 0.0 437s Consumption_16 0 0.0 0.0 437s Consumption_17 0 0.0 0.0 437s Consumption_18 0 0.0 0.0 437s Consumption_19 0 0.0 0.0 437s Consumption_20 0 0.0 0.0 437s Consumption_21 0 0.0 0.0 437s Consumption_22 0 0.0 0.0 437s Investment_2 0 0.0 0.0 437s Investment_3 0 0.0 0.0 437s Investment_4 0 0.0 0.0 437s Investment_5 0 0.0 0.0 437s Investment_6 0 0.0 0.0 437s Investment_8 0 0.0 0.0 437s Investment_9 0 0.0 0.0 437s Investment_10 0 0.0 0.0 437s Investment_11 0 0.0 0.0 437s Investment_12 0 0.0 0.0 437s Investment_13 0 0.0 0.0 437s Investment_14 0 0.0 0.0 437s Investment_15 0 0.0 0.0 437s Investment_16 0 0.0 0.0 437s Investment_17 0 0.0 0.0 437s Investment_18 0 0.0 0.0 437s Investment_19 0 0.0 0.0 437s Investment_20 0 0.0 0.0 437s Investment_21 0 0.0 0.0 437s Investment_22 0 0.0 0.0 437s PrivateWages_2 1 3.9 7.7 437s PrivateWages_3 1 3.2 3.9 437s PrivateWages_4 1 2.8 4.7 437s PrivateWages_5 1 3.5 3.8 437s PrivateWages_6 1 3.3 5.5 437s PrivateWages_8 1 4.0 6.7 437s PrivateWages_9 1 4.2 4.2 437s PrivateWages_10 1 4.1 4.0 437s PrivateWages_11 1 5.2 7.7 437s PrivateWages_12 1 5.9 7.5 437s PrivateWages_13 1 4.9 8.3 437s PrivateWages_14 1 3.7 5.4 437s PrivateWages_15 1 4.0 6.8 437s PrivateWages_16 1 4.4 7.2 437s PrivateWages_17 1 2.9 8.3 437s PrivateWages_18 1 4.3 6.7 437s PrivateWages_19 1 5.3 7.4 437s PrivateWages_20 1 6.6 8.9 437s PrivateWages_21 1 7.4 9.6 437s PrivateWages_22 1 13.8 11.6 437s PrivateWages_govWage PrivateWages_trend PrivateWages_capitalLag 437s Consumption_2 0.0 0 0 437s Consumption_3 0.0 0 0 437s Consumption_4 0.0 0 0 437s Consumption_5 0.0 0 0 437s Consumption_6 0.0 0 0 437s Consumption_8 0.0 0 0 437s Consumption_9 0.0 0 0 437s Consumption_10 0.0 0 0 437s Consumption_11 0.0 0 0 437s Consumption_12 0.0 0 0 437s Consumption_13 0.0 0 0 437s Consumption_14 0.0 0 0 437s Consumption_15 0.0 0 0 437s Consumption_16 0.0 0 0 437s Consumption_17 0.0 0 0 437s Consumption_18 0.0 0 0 437s Consumption_19 0.0 0 0 437s Consumption_20 0.0 0 0 437s Consumption_21 0.0 0 0 437s Consumption_22 0.0 0 0 437s Investment_2 0.0 0 0 437s Investment_3 0.0 0 0 437s Investment_4 0.0 0 0 437s Investment_5 0.0 0 0 437s Investment_6 0.0 0 0 437s Investment_8 0.0 0 0 437s Investment_9 0.0 0 0 437s Investment_10 0.0 0 0 437s Investment_11 0.0 0 0 437s Investment_12 0.0 0 0 437s Investment_13 0.0 0 0 437s Investment_14 0.0 0 0 437s Investment_15 0.0 0 0 437s Investment_16 0.0 0 0 437s Investment_17 0.0 0 0 437s Investment_18 0.0 0 0 437s Investment_19 0.0 0 0 437s Investment_20 0.0 0 0 437s Investment_21 0.0 0 0 437s Investment_22 0.0 0 0 437s PrivateWages_2 2.7 -10 183 437s PrivateWages_3 2.9 -9 183 437s PrivateWages_4 2.9 -8 184 437s PrivateWages_5 3.1 -7 190 437s PrivateWages_6 3.2 -6 193 437s PrivateWages_8 3.6 -4 203 437s PrivateWages_9 3.7 -3 208 437s PrivateWages_10 4.0 -2 211 437s PrivateWages_11 4.2 -1 216 437s PrivateWages_12 4.8 0 217 437s PrivateWages_13 5.3 1 213 437s PrivateWages_14 5.6 2 207 437s PrivateWages_15 6.0 3 202 437s PrivateWages_16 6.1 4 199 437s PrivateWages_17 7.4 5 198 437s PrivateWages_18 6.7 6 200 437s PrivateWages_19 7.7 7 202 437s PrivateWages_20 7.8 8 200 437s PrivateWages_21 8.0 9 201 437s PrivateWages_22 8.5 10 204 437s PrivateWages_corpProfLag PrivateWages_gnpLag 437s Consumption_2 0.0 0.0 437s Consumption_3 0.0 0.0 437s Consumption_4 0.0 0.0 437s Consumption_5 0.0 0.0 437s Consumption_6 0.0 0.0 437s Consumption_8 0.0 0.0 437s Consumption_9 0.0 0.0 437s Consumption_10 0.0 0.0 437s Consumption_11 0.0 0.0 437s Consumption_12 0.0 0.0 437s Consumption_13 0.0 0.0 437s Consumption_14 0.0 0.0 437s Consumption_15 0.0 0.0 437s Consumption_16 0.0 0.0 437s Consumption_17 0.0 0.0 437s Consumption_18 0.0 0.0 437s Consumption_19 0.0 0.0 437s Consumption_20 0.0 0.0 437s Consumption_21 0.0 0.0 437s Consumption_22 0.0 0.0 437s Investment_2 0.0 0.0 437s Investment_3 0.0 0.0 437s Investment_4 0.0 0.0 437s Investment_5 0.0 0.0 437s Investment_6 0.0 0.0 437s Investment_8 0.0 0.0 437s Investment_9 0.0 0.0 437s Investment_10 0.0 0.0 437s Investment_11 0.0 0.0 437s Investment_12 0.0 0.0 437s Investment_13 0.0 0.0 437s Investment_14 0.0 0.0 437s Investment_15 0.0 0.0 437s Investment_16 0.0 0.0 437s Investment_17 0.0 0.0 437s Investment_18 0.0 0.0 437s Investment_19 0.0 0.0 437s Investment_20 0.0 0.0 437s Investment_21 0.0 0.0 437s Investment_22 0.0 0.0 437s PrivateWages_2 12.7 44.9 437s PrivateWages_3 12.4 45.6 437s PrivateWages_4 16.9 50.1 437s PrivateWages_5 18.4 57.2 437s PrivateWages_6 19.4 57.1 437s PrivateWages_8 19.6 64.0 437s PrivateWages_9 19.8 64.4 437s PrivateWages_10 21.1 64.5 437s PrivateWages_11 21.7 67.0 437s PrivateWages_12 15.6 61.2 437s PrivateWages_13 11.4 53.4 437s PrivateWages_14 7.0 44.3 437s PrivateWages_15 11.2 45.1 437s PrivateWages_16 12.3 49.7 437s PrivateWages_17 14.0 54.4 437s PrivateWages_18 17.6 62.7 437s PrivateWages_19 17.3 65.0 437s PrivateWages_20 15.3 60.9 437s PrivateWages_21 19.0 69.5 437s PrivateWages_22 21.1 75.7 437s > matrix of fitted regressors 437s Consumption_(Intercept) Consumption_corpProf 437s Consumption_2 1 12.96 437s Consumption_3 1 16.70 437s Consumption_4 1 19.14 437s Consumption_5 1 20.94 437s Consumption_6 1 19.47 437s Consumption_8 1 17.14 437s Consumption_9 1 19.49 437s Consumption_10 1 20.46 437s Consumption_11 1 16.85 437s Consumption_12 1 12.68 437s Consumption_13 1 8.92 437s Consumption_14 1 9.30 437s Consumption_15 1 12.79 437s Consumption_16 1 14.26 437s Consumption_17 1 14.75 437s Consumption_18 1 19.54 437s Consumption_19 1 19.36 437s Consumption_20 1 17.39 437s Consumption_21 1 20.10 437s Consumption_22 1 22.86 437s Investment_2 0 0.00 437s Investment_3 0 0.00 437s Investment_4 0 0.00 437s Investment_5 0 0.00 437s Investment_6 0 0.00 437s Investment_8 0 0.00 437s Investment_9 0 0.00 437s Investment_10 0 0.00 437s Investment_11 0 0.00 437s Investment_12 0 0.00 437s Investment_13 0 0.00 437s Investment_14 0 0.00 437s Investment_15 0 0.00 437s Investment_16 0 0.00 437s Investment_17 0 0.00 437s Investment_18 0 0.00 437s Investment_19 0 0.00 437s Investment_20 0 0.00 437s Investment_21 0 0.00 437s Investment_22 0 0.00 437s PrivateWages_2 0 0.00 437s PrivateWages_3 0 0.00 437s PrivateWages_4 0 0.00 437s PrivateWages_5 0 0.00 437s PrivateWages_6 0 0.00 437s PrivateWages_8 0 0.00 437s PrivateWages_9 0 0.00 437s PrivateWages_10 0 0.00 437s PrivateWages_11 0 0.00 437s PrivateWages_12 0 0.00 437s PrivateWages_13 0 0.00 437s PrivateWages_14 0 0.00 437s PrivateWages_15 0 0.00 437s PrivateWages_16 0 0.00 437s PrivateWages_17 0 0.00 437s PrivateWages_18 0 0.00 437s PrivateWages_19 0 0.00 437s PrivateWages_20 0 0.00 437s PrivateWages_21 0 0.00 437s PrivateWages_22 0 0.00 437s Consumption_corpProfLag Consumption_wages 437s Consumption_2 12.7 29.1 437s Consumption_3 12.4 31.9 437s Consumption_4 16.9 35.6 437s Consumption_5 18.4 39.0 437s Consumption_6 19.4 38.8 437s Consumption_8 19.6 39.8 437s Consumption_9 19.8 42.3 437s Consumption_10 21.1 44.1 437s Consumption_11 21.7 43.4 437s Consumption_12 15.6 39.5 437s Consumption_13 11.4 35.1 437s Consumption_14 7.0 33.0 437s Consumption_15 11.2 37.6 437s Consumption_16 12.3 40.0 437s Consumption_17 14.0 41.7 437s Consumption_18 17.6 47.6 437s Consumption_19 17.3 49.5 437s Consumption_20 15.3 48.4 437s Consumption_21 19.0 53.2 437s Consumption_22 21.1 60.9 437s Investment_2 0.0 0.0 437s Investment_3 0.0 0.0 437s Investment_4 0.0 0.0 437s Investment_5 0.0 0.0 437s Investment_6 0.0 0.0 437s Investment_8 0.0 0.0 437s Investment_9 0.0 0.0 437s Investment_10 0.0 0.0 437s Investment_11 0.0 0.0 437s Investment_12 0.0 0.0 437s Investment_13 0.0 0.0 437s Investment_14 0.0 0.0 437s Investment_15 0.0 0.0 437s Investment_16 0.0 0.0 437s Investment_17 0.0 0.0 437s Investment_18 0.0 0.0 437s Investment_19 0.0 0.0 437s Investment_20 0.0 0.0 437s Investment_21 0.0 0.0 437s Investment_22 0.0 0.0 437s PrivateWages_2 0.0 0.0 437s PrivateWages_3 0.0 0.0 437s PrivateWages_4 0.0 0.0 437s PrivateWages_5 0.0 0.0 437s PrivateWages_6 0.0 0.0 437s PrivateWages_8 0.0 0.0 437s PrivateWages_9 0.0 0.0 437s PrivateWages_10 0.0 0.0 437s PrivateWages_11 0.0 0.0 437s PrivateWages_12 0.0 0.0 437s PrivateWages_13 0.0 0.0 437s PrivateWages_14 0.0 0.0 437s PrivateWages_15 0.0 0.0 437s PrivateWages_16 0.0 0.0 437s PrivateWages_17 0.0 0.0 437s PrivateWages_18 0.0 0.0 437s PrivateWages_19 0.0 0.0 437s PrivateWages_20 0.0 0.0 437s PrivateWages_21 0.0 0.0 437s PrivateWages_22 0.0 0.0 437s Investment_(Intercept) Investment_corpProf 437s Consumption_2 0 0.00 437s Consumption_3 0 0.00 437s Consumption_4 0 0.00 437s Consumption_5 0 0.00 437s Consumption_6 0 0.00 437s Consumption_8 0 0.00 437s Consumption_9 0 0.00 437s Consumption_10 0 0.00 437s Consumption_11 0 0.00 437s Consumption_12 0 0.00 437s Consumption_13 0 0.00 437s Consumption_14 0 0.00 437s Consumption_15 0 0.00 437s Consumption_16 0 0.00 437s Consumption_17 0 0.00 437s Consumption_18 0 0.00 437s Consumption_19 0 0.00 437s Consumption_20 0 0.00 437s Consumption_21 0 0.00 437s Consumption_22 0 0.00 437s Investment_2 1 12.96 437s Investment_3 1 16.70 437s Investment_4 1 19.14 437s Investment_5 1 20.94 437s Investment_6 1 19.47 437s Investment_8 1 17.14 437s Investment_9 1 19.49 437s Investment_10 1 20.46 437s Investment_11 1 16.85 437s Investment_12 1 12.68 437s Investment_13 1 8.92 437s Investment_14 1 9.30 437s Investment_15 1 12.79 437s Investment_16 1 14.26 437s Investment_17 1 14.75 437s Investment_18 1 19.54 437s Investment_19 1 19.36 437s Investment_20 1 17.39 437s Investment_21 1 20.10 437s Investment_22 1 22.86 437s PrivateWages_2 0 0.00 437s PrivateWages_3 0 0.00 437s PrivateWages_4 0 0.00 437s PrivateWages_5 0 0.00 437s PrivateWages_6 0 0.00 437s PrivateWages_8 0 0.00 437s PrivateWages_9 0 0.00 437s PrivateWages_10 0 0.00 437s PrivateWages_11 0 0.00 437s PrivateWages_12 0 0.00 437s PrivateWages_13 0 0.00 437s PrivateWages_14 0 0.00 437s PrivateWages_15 0 0.00 437s PrivateWages_16 0 0.00 437s PrivateWages_17 0 0.00 437s PrivateWages_18 0 0.00 437s PrivateWages_19 0 0.00 437s PrivateWages_20 0 0.00 437s PrivateWages_21 0 0.00 437s PrivateWages_22 0 0.00 437s Investment_corpProfLag Investment_capitalLag 437s Consumption_2 0.0 0 437s Consumption_3 0.0 0 437s Consumption_4 0.0 0 437s Consumption_5 0.0 0 437s Consumption_6 0.0 0 437s Consumption_8 0.0 0 437s Consumption_9 0.0 0 437s Consumption_10 0.0 0 437s Consumption_11 0.0 0 437s Consumption_12 0.0 0 437s Consumption_13 0.0 0 437s Consumption_14 0.0 0 437s Consumption_15 0.0 0 437s Consumption_16 0.0 0 437s Consumption_17 0.0 0 437s Consumption_18 0.0 0 437s Consumption_19 0.0 0 437s Consumption_20 0.0 0 437s Consumption_21 0.0 0 437s Consumption_22 0.0 0 437s Investment_2 12.7 183 437s Investment_3 12.4 183 437s Investment_4 16.9 184 437s Investment_5 18.4 190 437s Investment_6 19.4 193 437s Investment_8 19.6 203 437s Investment_9 19.8 208 437s Investment_10 21.1 211 437s Investment_11 21.7 216 437s Investment_12 15.6 217 437s Investment_13 11.4 213 437s Investment_14 7.0 207 437s Investment_15 11.2 202 437s Investment_16 12.3 199 437s Investment_17 14.0 198 437s Investment_18 17.6 200 437s Investment_19 17.3 202 437s Investment_20 15.3 200 437s Investment_21 19.0 201 437s Investment_22 21.1 204 437s PrivateWages_2 0.0 0 437s PrivateWages_3 0.0 0 437s PrivateWages_4 0.0 0 437s PrivateWages_5 0.0 0 437s PrivateWages_6 0.0 0 437s PrivateWages_8 0.0 0 437s PrivateWages_9 0.0 0 437s PrivateWages_10 0.0 0 437s PrivateWages_11 0.0 0 437s PrivateWages_12 0.0 0 437s PrivateWages_13 0.0 0 437s PrivateWages_14 0.0 0 437s PrivateWages_15 0.0 0 437s PrivateWages_16 0.0 0 437s PrivateWages_17 0.0 0 437s PrivateWages_18 0.0 0 437s PrivateWages_19 0.0 0 437s PrivateWages_20 0.0 0 437s PrivateWages_21 0.0 0 437s PrivateWages_22 0.0 0 437s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 437s Consumption_2 0 0.0 0.0 437s Consumption_3 0 0.0 0.0 437s Consumption_4 0 0.0 0.0 437s Consumption_5 0 0.0 0.0 437s Consumption_6 0 0.0 0.0 437s Consumption_8 0 0.0 0.0 437s Consumption_9 0 0.0 0.0 437s Consumption_10 0 0.0 0.0 437s Consumption_11 0 0.0 0.0 437s Consumption_12 0 0.0 0.0 437s Consumption_13 0 0.0 0.0 437s Consumption_14 0 0.0 0.0 437s Consumption_15 0 0.0 0.0 437s Consumption_16 0 0.0 0.0 437s Consumption_17 0 0.0 0.0 437s Consumption_18 0 0.0 0.0 437s Consumption_19 0 0.0 0.0 437s Consumption_20 0 0.0 0.0 437s Consumption_21 0 0.0 0.0 437s Consumption_22 0 0.0 0.0 437s Investment_2 0 0.0 0.0 437s Investment_3 0 0.0 0.0 437s Investment_4 0 0.0 0.0 437s Investment_5 0 0.0 0.0 437s Investment_6 0 0.0 0.0 437s Investment_8 0 0.0 0.0 437s Investment_9 0 0.0 0.0 437s Investment_10 0 0.0 0.0 437s Investment_11 0 0.0 0.0 437s Investment_12 0 0.0 0.0 437s Investment_13 0 0.0 0.0 437s Investment_14 0 0.0 0.0 437s Investment_15 0 0.0 0.0 437s Investment_16 0 0.0 0.0 437s Investment_17 0 0.0 0.0 437s Investment_18 0 0.0 0.0 437s Investment_19 0 0.0 0.0 437s Investment_20 0 0.0 0.0 437s Investment_21 0 0.0 0.0 437s Investment_22 0 0.0 0.0 437s PrivateWages_2 1 47.1 44.9 437s PrivateWages_3 1 49.6 45.6 437s PrivateWages_4 1 56.5 50.1 437s PrivateWages_5 1 60.7 57.2 437s PrivateWages_6 1 60.6 57.1 437s PrivateWages_8 1 60.0 64.0 437s PrivateWages_9 1 62.3 64.4 437s PrivateWages_10 1 64.6 64.5 437s PrivateWages_11 1 63.7 67.0 437s PrivateWages_12 1 54.8 61.2 437s PrivateWages_13 1 47.0 53.4 437s PrivateWages_14 1 42.1 44.3 437s PrivateWages_15 1 51.2 45.1 437s PrivateWages_16 1 55.3 49.7 437s PrivateWages_17 1 57.4 54.4 437s PrivateWages_18 1 67.2 62.7 437s PrivateWages_19 1 68.5 65.0 437s PrivateWages_20 1 66.8 60.9 437s PrivateWages_21 1 74.9 69.5 437s PrivateWages_22 1 86.9 75.7 437s PrivateWages_trend 437s Consumption_2 0 437s Consumption_3 0 437s Consumption_4 0 437s Consumption_5 0 437s Consumption_6 0 437s Consumption_8 0 437s Consumption_9 0 437s Consumption_10 0 437s Consumption_11 0 437s Consumption_12 0 437s Consumption_13 0 437s Consumption_14 0 437s Consumption_15 0 437s Consumption_16 0 437s Consumption_17 0 437s Consumption_18 0 437s Consumption_19 0 437s Consumption_20 0 437s Consumption_21 0 437s Consumption_22 0 437s Investment_2 0 437s Investment_3 0 437s Investment_4 0 437s Investment_5 0 437s Investment_6 0 437s Investment_8 0 437s Investment_9 0 437s Investment_10 0 437s Investment_11 0 437s Investment_12 0 437s Investment_13 0 437s Investment_14 0 437s Investment_15 0 437s Investment_16 0 437s Investment_17 0 437s Investment_18 0 437s Investment_19 0 437s Investment_20 0 437s Investment_21 0 437s Investment_22 0 437s PrivateWages_2 -10 437s PrivateWages_3 -9 437s PrivateWages_4 -8 437s PrivateWages_5 -7 437s PrivateWages_6 -6 437s PrivateWages_8 -4 437s PrivateWages_9 -3 437s PrivateWages_10 -2 437s PrivateWages_11 -1 437s PrivateWages_12 0 437s PrivateWages_13 1 437s PrivateWages_14 2 437s PrivateWages_15 3 437s PrivateWages_16 4 437s PrivateWages_17 5 437s PrivateWages_18 6 437s PrivateWages_19 7 437s PrivateWages_20 8 437s PrivateWages_21 9 437s PrivateWages_22 10 437s > nobs 437s [1] 60 437s > linearHypothesis 437s Linear hypothesis test (Theil's F test) 437s 437s Hypothesis: 437s Consumption_corpProf + Investment_capitalLag = 0 437s 437s Model 1: restricted model 437s Model 2: kleinModel 437s 437s Res.Df Df F Pr(>F) 437s 1 49 437s 2 48 1 0.95 0.34 437s Linear hypothesis test (F statistic of a Wald test) 437s 437s Hypothesis: 437s Consumption_corpProf + Investment_capitalLag = 0 437s 437s Model 1: restricted model 437s Model 2: kleinModel 437s 437s Res.Df Df F Pr(>F) 437s 1 49 437s 2 48 1 1.05 0.31 437s Linear hypothesis test (Chi^2 statistic of a Wald test) 437s 437s Hypothesis: 437s Consumption_corpProf + Investment_capitalLag = 0 437s 437s Model 1: restricted model 437s Model 2: kleinModel 437s 437s Res.Df Df Chisq Pr(>Chisq) 437s 1 49 437s 2 48 1 1.05 0.3 437s Linear hypothesis test (Theil's F test) 437s 437s Hypothesis: 437s Consumption_corpProf + Investment_capitalLag = 0 437s Consumption_corpProfLag - PrivateWages_trend = 0 437s 437s Model 1: restricted model 437s Model 2: kleinModel 437s 437s Res.Df Df F Pr(>F) 437s 1 50 437s 2 48 2 0.48 0.62 437s Linear hypothesis test (F statistic of a Wald test) 437s 437s Hypothesis: 437s Consumption_corpProf + Investment_capitalLag = 0 437s Consumption_corpProfLag - PrivateWages_trend = 0 437s 437s Model 1: restricted model 437s Model 2: kleinModel 437s 437s Res.Df Df F Pr(>F) 437s 1 50 437s 2 48 2 0.53 0.59 437s Linear hypothesis test (Chi^2 statistic of a Wald test) 437s 437s Hypothesis: 437s Consumption_corpProf + Investment_capitalLag = 0 437s Consumption_corpProfLag - PrivateWages_trend = 0 437s 437s Model 1: restricted model 437s Model 2: kleinModel 437s 437s Res.Df Df Chisq Pr(>Chisq) 437s 1 50 437s 2 48 2 1.06 0.59 437s > logLik 437s 'log Lik.' -72.2 (df=13) 437s 'log Lik.' -79.7 (df=13) 437s Estimating function 437s Consumption_(Intercept) Consumption_corpProf 437s Consumption_2 -1.1407 -14.78 437s Consumption_3 -0.3242 -5.42 437s Consumption_4 -0.0963 -1.84 437s Consumption_5 -1.8392 -38.51 437s Consumption_6 0.1702 3.31 437s Consumption_8 3.0349 52.02 437s Consumption_9 1.9822 38.63 437s Consumption_10 0.7162 14.65 437s Consumption_11 -1.5151 -25.52 437s Consumption_12 -1.1471 -14.54 437s Consumption_13 -1.9595 -17.48 437s Consumption_14 1.4394 13.39 437s Consumption_15 -1.0033 -12.84 437s Consumption_16 -0.5750 -8.20 437s Consumption_17 4.0452 59.67 437s Consumption_18 -0.5669 -11.08 437s Consumption_19 -3.1962 -61.88 437s Consumption_20 2.2286 38.75 437s Consumption_21 0.9237 18.57 437s Consumption_22 -1.1770 -26.91 437s Investment_2 0.0000 0.00 437s Investment_3 0.0000 0.00 437s Investment_4 0.0000 0.00 437s Investment_5 0.0000 0.00 437s Investment_6 0.0000 0.00 437s Investment_8 0.0000 0.00 437s Investment_9 0.0000 0.00 437s Investment_10 0.0000 0.00 437s Investment_11 0.0000 0.00 437s Investment_12 0.0000 0.00 437s Investment_13 0.0000 0.00 437s Investment_14 0.0000 0.00 437s Investment_15 0.0000 0.00 437s Investment_16 0.0000 0.00 437s Investment_17 0.0000 0.00 437s Investment_18 0.0000 0.00 437s Investment_19 0.0000 0.00 437s Investment_20 0.0000 0.00 437s Investment_21 0.0000 0.00 437s Investment_22 0.0000 0.00 437s PrivateWages_2 0.0000 0.00 437s PrivateWages_3 0.0000 0.00 437s PrivateWages_4 0.0000 0.00 437s PrivateWages_5 0.0000 0.00 437s PrivateWages_6 0.0000 0.00 437s PrivateWages_8 0.0000 0.00 437s PrivateWages_9 0.0000 0.00 437s PrivateWages_10 0.0000 0.00 437s PrivateWages_11 0.0000 0.00 437s PrivateWages_12 0.0000 0.00 437s PrivateWages_13 0.0000 0.00 437s PrivateWages_14 0.0000 0.00 437s PrivateWages_15 0.0000 0.00 437s PrivateWages_16 0.0000 0.00 437s PrivateWages_17 0.0000 0.00 437s PrivateWages_18 0.0000 0.00 437s PrivateWages_19 0.0000 0.00 437s PrivateWages_20 0.0000 0.00 437s PrivateWages_21 0.0000 0.00 437s PrivateWages_22 0.0000 0.00 437s Consumption_corpProfLag Consumption_wages 437s Consumption_2 -14.49 -33.21 437s Consumption_3 -4.02 -10.33 437s Consumption_4 -1.63 -3.43 437s Consumption_5 -33.84 -71.82 437s Consumption_6 3.30 6.61 437s Consumption_8 59.48 120.65 437s Consumption_9 39.25 83.81 437s Consumption_10 15.11 31.59 437s Consumption_11 -32.88 -65.70 437s Consumption_12 -17.89 -45.25 437s Consumption_13 -22.34 -68.69 437s Consumption_14 10.08 47.54 437s Consumption_15 -11.24 -37.74 437s Consumption_16 -7.07 -22.99 437s Consumption_17 56.63 168.85 437s Consumption_18 -9.98 -27.00 437s Consumption_19 -55.29 -158.06 437s Consumption_20 34.10 107.77 437s Consumption_21 17.55 49.11 437s Consumption_22 -24.84 -71.70 437s Investment_2 0.00 0.00 437s Investment_3 0.00 0.00 437s Investment_4 0.00 0.00 437s Investment_5 0.00 0.00 437s Investment_6 0.00 0.00 437s Investment_8 0.00 0.00 437s Investment_9 0.00 0.00 437s Investment_10 0.00 0.00 437s Investment_11 0.00 0.00 437s Investment_12 0.00 0.00 437s Investment_13 0.00 0.00 437s Investment_14 0.00 0.00 437s Investment_15 0.00 0.00 437s Investment_16 0.00 0.00 437s Investment_17 0.00 0.00 437s Investment_18 0.00 0.00 437s Investment_19 0.00 0.00 437s Investment_20 0.00 0.00 437s Investment_21 0.00 0.00 437s Investment_22 0.00 0.00 437s PrivateWages_2 0.00 0.00 437s PrivateWages_3 0.00 0.00 437s PrivateWages_4 0.00 0.00 437s PrivateWages_5 0.00 0.00 437s PrivateWages_6 0.00 0.00 437s PrivateWages_8 0.00 0.00 437s PrivateWages_9 0.00 0.00 437s PrivateWages_10 0.00 0.00 437s PrivateWages_11 0.00 0.00 437s PrivateWages_12 0.00 0.00 437s PrivateWages_13 0.00 0.00 437s PrivateWages_14 0.00 0.00 437s PrivateWages_15 0.00 0.00 437s PrivateWages_16 0.00 0.00 437s PrivateWages_17 0.00 0.00 437s PrivateWages_18 0.00 0.00 437s PrivateWages_19 0.00 0.00 437s PrivateWages_20 0.00 0.00 437s PrivateWages_21 0.00 0.00 437s PrivateWages_22 0.00 0.00 437s Investment_(Intercept) Investment_corpProf 437s Consumption_2 0.0000 0.000 437s Consumption_3 0.0000 0.000 437s Consumption_4 0.0000 0.000 437s Consumption_5 0.0000 0.000 437s Consumption_6 0.0000 0.000 437s Consumption_8 0.0000 0.000 437s Consumption_9 0.0000 0.000 437s Consumption_10 0.0000 0.000 437s Consumption_11 0.0000 0.000 437s Consumption_12 0.0000 0.000 437s Consumption_13 0.0000 0.000 437s Consumption_14 0.0000 0.000 437s Consumption_15 0.0000 0.000 437s Consumption_16 0.0000 0.000 437s Consumption_17 0.0000 0.000 437s Consumption_18 0.0000 0.000 437s Consumption_19 0.0000 0.000 437s Consumption_20 0.0000 0.000 437s Consumption_21 0.0000 0.000 437s Consumption_22 0.0000 0.000 437s Investment_2 -1.1313 -14.660 437s Investment_3 0.2902 4.847 437s Investment_4 0.9027 17.274 437s Investment_5 -1.7434 -36.502 437s Investment_6 0.5695 11.088 437s Investment_8 1.6225 27.812 437s Investment_9 0.4166 8.119 437s Investment_10 2.0381 41.703 437s Investment_11 -0.8611 -14.505 437s Investment_12 -0.9091 -11.527 437s Investment_13 -1.1148 -9.946 437s Investment_14 1.3841 12.873 437s Investment_15 -0.2900 -3.710 437s Investment_16 0.0605 0.862 437s Investment_17 2.2439 33.101 437s Investment_18 -0.5390 -10.534 437s Investment_19 -3.9452 -76.375 437s Investment_20 0.4890 8.502 437s Investment_21 0.0864 1.737 437s Investment_22 0.4306 9.843 437s PrivateWages_2 0.0000 0.000 437s PrivateWages_3 0.0000 0.000 437s PrivateWages_4 0.0000 0.000 437s PrivateWages_5 0.0000 0.000 437s PrivateWages_6 0.0000 0.000 437s PrivateWages_8 0.0000 0.000 437s PrivateWages_9 0.0000 0.000 437s PrivateWages_10 0.0000 0.000 437s PrivateWages_11 0.0000 0.000 437s PrivateWages_12 0.0000 0.000 437s PrivateWages_13 0.0000 0.000 437s PrivateWages_14 0.0000 0.000 437s PrivateWages_15 0.0000 0.000 437s PrivateWages_16 0.0000 0.000 437s PrivateWages_17 0.0000 0.000 437s PrivateWages_18 0.0000 0.000 437s PrivateWages_19 0.0000 0.000 437s PrivateWages_20 0.0000 0.000 437s PrivateWages_21 0.0000 0.000 437s PrivateWages_22 0.0000 0.000 437s Investment_corpProfLag Investment_capitalLag 437s Consumption_2 0.000 0.0 437s Consumption_3 0.000 0.0 437s Consumption_4 0.000 0.0 437s Consumption_5 0.000 0.0 437s Consumption_6 0.000 0.0 437s Consumption_8 0.000 0.0 437s Consumption_9 0.000 0.0 437s Consumption_10 0.000 0.0 437s Consumption_11 0.000 0.0 437s Consumption_12 0.000 0.0 437s Consumption_13 0.000 0.0 437s Consumption_14 0.000 0.0 437s Consumption_15 0.000 0.0 437s Consumption_16 0.000 0.0 437s Consumption_17 0.000 0.0 437s Consumption_18 0.000 0.0 437s Consumption_19 0.000 0.0 437s Consumption_20 0.000 0.0 437s Consumption_21 0.000 0.0 437s Consumption_22 0.000 0.0 437s Investment_2 -14.368 -206.8 437s Investment_3 3.598 53.0 437s Investment_4 15.256 166.5 437s Investment_5 -32.079 -330.7 437s Investment_6 11.048 109.7 437s Investment_8 31.801 330.0 437s Investment_9 8.248 86.5 437s Investment_10 43.003 429.2 437s Investment_11 -18.685 -185.7 437s Investment_12 -14.182 -197.0 437s Investment_13 -12.709 -237.8 437s Investment_14 9.689 286.6 437s Investment_15 -3.247 -58.6 437s Investment_16 0.744 12.0 437s Investment_17 31.414 443.6 437s Investment_18 -9.486 -107.7 437s Investment_19 -68.252 -796.1 437s Investment_20 7.482 97.7 437s Investment_21 1.642 17.4 437s Investment_22 9.085 88.0 437s PrivateWages_2 0.000 0.0 437s PrivateWages_3 0.000 0.0 437s PrivateWages_4 0.000 0.0 437s PrivateWages_5 0.000 0.0 437s PrivateWages_6 0.000 0.0 437s PrivateWages_8 0.000 0.0 437s PrivateWages_9 0.000 0.0 437s PrivateWages_10 0.000 0.0 437s PrivateWages_11 0.000 0.0 437s PrivateWages_12 0.000 0.0 437s PrivateWages_13 0.000 0.0 437s PrivateWages_14 0.000 0.0 437s PrivateWages_15 0.000 0.0 437s PrivateWages_16 0.000 0.0 437s PrivateWages_17 0.000 0.0 437s PrivateWages_18 0.000 0.0 437s PrivateWages_19 0.000 0.0 437s PrivateWages_20 0.000 0.0 437s PrivateWages_21 0.000 0.0 437s PrivateWages_22 0.000 0.0 437s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 437s Consumption_2 0.0000 0.00 0.00 437s Consumption_3 0.0000 0.00 0.00 437s Consumption_4 0.0000 0.00 0.00 437s Consumption_5 0.0000 0.00 0.00 437s Consumption_6 0.0000 0.00 0.00 437s Consumption_8 0.0000 0.00 0.00 437s Consumption_9 0.0000 0.00 0.00 437s Consumption_10 0.0000 0.00 0.00 437s Consumption_11 0.0000 0.00 0.00 437s Consumption_12 0.0000 0.00 0.00 437s Consumption_13 0.0000 0.00 0.00 437s Consumption_14 0.0000 0.00 0.00 437s Consumption_15 0.0000 0.00 0.00 437s Consumption_16 0.0000 0.00 0.00 437s Consumption_17 0.0000 0.00 0.00 437s Consumption_18 0.0000 0.00 0.00 437s Consumption_19 0.0000 0.00 0.00 437s Consumption_20 0.0000 0.00 0.00 437s Consumption_21 0.0000 0.00 0.00 437s Consumption_22 0.0000 0.00 0.00 437s Investment_2 0.0000 0.00 0.00 437s Investment_3 0.0000 0.00 0.00 437s Investment_4 0.0000 0.00 0.00 437s Investment_5 0.0000 0.00 0.00 437s Investment_6 0.0000 0.00 0.00 437s Investment_8 0.0000 0.00 0.00 437s Investment_9 0.0000 0.00 0.00 437s Investment_10 0.0000 0.00 0.00 437s Investment_11 0.0000 0.00 0.00 437s Investment_12 0.0000 0.00 0.00 437s Investment_13 0.0000 0.00 0.00 437s Investment_14 0.0000 0.00 0.00 437s Investment_15 0.0000 0.00 0.00 437s Investment_16 0.0000 0.00 0.00 437s Investment_17 0.0000 0.00 0.00 437s Investment_18 0.0000 0.00 0.00 437s Investment_19 0.0000 0.00 0.00 437s Investment_20 0.0000 0.00 0.00 437s Investment_21 0.0000 0.00 0.00 437s Investment_22 0.0000 0.00 0.00 437s PrivateWages_2 -1.9924 -93.78 -89.46 437s PrivateWages_3 0.4683 23.22 21.35 437s PrivateWages_4 1.4034 79.35 70.31 437s PrivateWages_5 -1.7870 -108.45 -102.22 437s PrivateWages_6 -0.3627 -21.98 -20.71 437s PrivateWages_8 1.1629 69.77 74.43 437s PrivateWages_9 1.2735 79.30 82.01 437s PrivateWages_10 2.2141 142.96 142.81 437s PrivateWages_11 -1.2912 -82.26 -86.51 437s PrivateWages_12 -0.0350 -1.92 -2.14 437s PrivateWages_13 -1.0438 -49.04 -55.74 437s PrivateWages_14 1.8016 75.90 79.81 437s PrivateWages_15 -0.3714 -19.02 -16.75 437s PrivateWages_16 -0.3904 -21.61 -19.40 437s PrivateWages_17 1.4934 85.71 81.24 437s PrivateWages_18 0.0279 1.88 1.75 437s PrivateWages_19 -3.8229 -261.91 -248.49 437s PrivateWages_20 0.7870 52.61 47.93 437s PrivateWages_21 -0.7415 -55.52 -51.54 437s PrivateWages_22 1.2062 104.79 91.31 437s PrivateWages_trend 437s Consumption_2 0.000 437s Consumption_3 0.000 437s Consumption_4 0.000 437s Consumption_5 0.000 437s Consumption_6 0.000 437s Consumption_8 0.000 437s Consumption_9 0.000 437s Consumption_10 0.000 437s Consumption_11 0.000 437s Consumption_12 0.000 437s Consumption_13 0.000 437s Consumption_14 0.000 437s Consumption_15 0.000 437s Consumption_16 0.000 437s Consumption_17 0.000 437s Consumption_18 0.000 437s Consumption_19 0.000 437s Consumption_20 0.000 437s Consumption_21 0.000 437s Consumption_22 0.000 437s Investment_2 0.000 437s Investment_3 0.000 437s Investment_4 0.000 437s Investment_5 0.000 437s Investment_6 0.000 437s Investment_8 0.000 437s Investment_9 0.000 437s Investment_10 0.000 437s Investment_11 0.000 437s Investment_12 0.000 437s Investment_13 0.000 437s Investment_14 0.000 437s Investment_15 0.000 437s Investment_16 0.000 437s Investment_17 0.000 437s Investment_18 0.000 437s Investment_19 0.000 437s Investment_20 0.000 437s Investment_21 0.000 437s Investment_22 0.000 437s PrivateWages_2 19.924 437s PrivateWages_3 -4.214 437s PrivateWages_4 -11.227 437s PrivateWages_5 12.509 437s PrivateWages_6 2.176 437s PrivateWages_8 -4.652 437s PrivateWages_9 -3.820 437s PrivateWages_10 -4.428 437s PrivateWages_11 1.291 437s PrivateWages_12 0.000 437s PrivateWages_13 -1.044 437s PrivateWages_14 3.603 437s PrivateWages_15 -1.114 437s PrivateWages_16 -1.562 437s PrivateWages_17 7.467 437s PrivateWages_18 0.168 437s PrivateWages_19 -26.760 437s PrivateWages_20 6.296 437s PrivateWages_21 -6.674 437s PrivateWages_22 12.062 437s [1] TRUE 437s > Bread 437s Consumption_(Intercept) Consumption_corpProf 437s Consumption_(Intercept) 99.945 -0.7943 437s Consumption_corpProf -0.794 0.7797 437s Consumption_corpProfLag -0.325 -0.5285 437s Consumption_wages -1.888 -0.0894 437s Investment_(Intercept) 0.000 0.0000 437s Investment_corpProf 0.000 0.0000 437s Investment_corpProfLag 0.000 0.0000 437s Investment_capitalLag 0.000 0.0000 437s PrivateWages_(Intercept) 0.000 0.0000 437s PrivateWages_gnp 0.000 0.0000 437s PrivateWages_gnpLag 0.000 0.0000 437s PrivateWages_trend 0.000 0.0000 437s Consumption_corpProfLag Consumption_wages 437s Consumption_(Intercept) -0.3246 -1.8878 437s Consumption_corpProf -0.5285 -0.0894 437s Consumption_corpProfLag 0.6654 -0.0384 437s Consumption_wages -0.0384 0.0965 437s Investment_(Intercept) 0.0000 0.0000 437s Investment_corpProf 0.0000 0.0000 437s Investment_corpProfLag 0.0000 0.0000 437s Investment_capitalLag 0.0000 0.0000 437s PrivateWages_(Intercept) 0.0000 0.0000 437s PrivateWages_gnp 0.0000 0.0000 437s PrivateWages_gnpLag 0.0000 0.0000 437s PrivateWages_trend 0.0000 0.0000 437s Investment_(Intercept) Investment_corpProf 437s Consumption_(Intercept) 0.0 0.000 437s Consumption_corpProf 0.0 0.000 437s Consumption_corpProfLag 0.0 0.000 437s Consumption_wages 0.0 0.000 437s Investment_(Intercept) 2446.2 -38.918 437s Investment_corpProf -38.9 1.252 437s Investment_corpProfLag 33.4 -1.090 437s Investment_capitalLag -11.6 0.177 437s PrivateWages_(Intercept) 0.0 0.000 437s PrivateWages_gnp 0.0 0.000 437s PrivateWages_gnpLag 0.0 0.000 437s PrivateWages_trend 0.0 0.000 437s Investment_corpProfLag Investment_capitalLag 437s Consumption_(Intercept) 0.000 0.0000 437s Consumption_corpProf 0.000 0.0000 437s Consumption_corpProfLag 0.000 0.0000 437s Consumption_wages 0.000 0.0000 437s Investment_(Intercept) 33.384 -11.6216 437s Investment_corpProf -1.090 0.1774 437s Investment_corpProfLag 1.148 -0.1680 437s Investment_capitalLag -0.168 0.0567 437s PrivateWages_(Intercept) 0.000 0.0000 437s PrivateWages_gnp 0.000 0.0000 437s PrivateWages_gnpLag 0.000 0.0000 437s PrivateWages_trend 0.000 0.0000 437s PrivateWages_(Intercept) PrivateWages_gnp 437s Consumption_(Intercept) 0.000 0.0000 437s Consumption_corpProf 0.000 0.0000 437s Consumption_corpProfLag 0.000 0.0000 437s Consumption_wages 0.000 0.0000 437s Investment_(Intercept) 0.000 0.0000 437s Investment_corpProf 0.000 0.0000 437s Investment_corpProfLag 0.000 0.0000 437s Investment_capitalLag 0.000 0.0000 437s PrivateWages_(Intercept) 170.714 -0.9289 437s PrivateWages_gnp -0.929 0.1580 437s PrivateWages_gnpLag -1.948 -0.1473 437s PrivateWages_trend 2.164 -0.0424 437s PrivateWages_gnpLag PrivateWages_trend 437s Consumption_(Intercept) 0.000 0.0000 437s Consumption_corpProf 0.000 0.0000 437s Consumption_corpProfLag 0.000 0.0000 437s Consumption_wages 0.000 0.0000 437s Investment_(Intercept) 0.000 0.0000 437s Investment_corpProf 0.000 0.0000 437s Investment_corpProfLag 0.000 0.0000 437s Investment_capitalLag 0.000 0.0000 437s PrivateWages_(Intercept) -1.948 2.1641 437s PrivateWages_gnp -0.147 -0.0424 437s PrivateWages_gnpLag 0.186 0.0060 437s PrivateWages_trend 0.006 0.1151 437s > 437s > # SUR 437s > summary 437s 437s Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 437s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 437s systemfit results 437s method: SUR 437s 437s N DF SSR detRCov OLS-R2 McElroy-R2 437s system 62 50 46.2 0.154 0.977 0.993 437s 437s N DF SSR MSE RMSE R2 Adj R2 437s Consumption 21 17 18.1 1.062 1.031 0.981 0.977 437s Investment 21 17 17.5 1.030 1.015 0.931 0.918 437s PrivateWages 20 16 10.6 0.663 0.814 0.987 0.984 437s 437s The covariance matrix of the residuals used for estimation 437s Consumption Investment PrivateWages 437s Consumption 0.8562 -0.0129 -0.371 437s Investment -0.0129 0.7548 0.159 437s PrivateWages -0.3706 0.1594 0.487 437s 437s The covariance matrix of the residuals 437s Consumption Investment PrivateWages 437s Consumption 0.8684 0.0078 -0.442 437s Investment 0.0078 0.7702 0.237 437s PrivateWages -0.4416 0.2366 0.531 437s 437s The correlations of the residuals 437s Consumption Investment PrivateWages 437s Consumption 1.00000 0.00562 -0.651 437s Investment 0.00562 1.00000 0.372 437s PrivateWages -0.65109 0.37198 1.000 437s 437s 437s SUR estimates for 'Consumption' (equation 1) 437s Model Formula: consump ~ corpProf + corpProfLag + wages 437s 437s Estimate Std. Error t value Pr(>|t|) 437s (Intercept) 16.0647 1.1729 13.70 1.3e-10 *** 437s corpProf 0.2283 0.0775 2.94 0.0091 ** 437s corpProfLag 0.0723 0.0771 0.94 0.3615 437s wages 0.7930 0.0352 22.51 4.3e-14 *** 437s --- 437s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 437s 437s Residual standard error: 1.031 on 17 degrees of freedom 437s Number of observations: 21 Degrees of Freedom: 17 437s SSR: 18.06 MSE: 1.062 Root MSE: 1.031 437s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.977 437s 437s 437s SUR estimates for 'Investment' (equation 2) 437s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 437s 437s Estimate Std. Error t value Pr(>|t|) 437s (Intercept) 12.3516 4.5762 2.70 0.01520 * 437s corpProf 0.4461 0.0818 5.45 4.3e-05 *** 437s corpProfLag 0.3609 0.0849 4.25 0.00054 *** 437s capitalLag -0.1224 0.0223 -5.47 4.1e-05 *** 437s --- 437s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 437s 437s Residual standard error: 1.015 on 17 degrees of freedom 437s Number of observations: 21 Degrees of Freedom: 17 437s SSR: 17.514 MSE: 1.03 Root MSE: 1.015 437s Multiple R-Squared: 0.931 Adjusted R-Squared: 0.918 437s 437s 437s SUR estimates for 'PrivateWages' (equation 3) 437s Model Formula: privWage ~ gnp + gnpLag + trend 437s 437s Estimate Std. Error t value Pr(>|t|) 437s (Intercept) 1.5433 1.1371 1.36 0.19 437s gnp 0.4117 0.0279 14.77 9.6e-11 *** 437s gnpLag 0.1743 0.0317 5.50 4.8e-05 *** 437s trend 0.1550 0.0283 5.49 5.0e-05 *** 437s --- 437s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 437s 437s Residual standard error: 0.814 on 16 degrees of freedom 437s Number of observations: 20 Degrees of Freedom: 16 437s SSR: 10.611 MSE: 0.663 Root MSE: 0.814 437s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.984 437s 437s > residuals 437s Consumption Investment PrivateWages 437s 1 NA NA NA 437s 2 -0.27628 -0.3003 -1.0910 437s 3 -1.35400 -0.1239 0.5795 437s 4 -1.62816 1.1154 1.5172 437s 5 -0.56494 -1.4358 -0.0341 437s 6 -0.06584 0.3581 -0.2772 437s 7 0.83245 1.4526 NA 437s 8 1.28855 0.8290 -0.6896 437s 9 0.96709 -0.5092 0.3445 437s 10 -0.66705 1.2210 1.2429 437s 11 0.41992 0.2497 -0.3602 437s 12 -0.05971 0.0470 0.3068 437s 13 -0.08649 0.3096 -0.2426 437s 14 0.33124 0.3652 0.3591 437s 15 -0.00604 -0.1652 0.2710 437s 16 -0.01478 0.0124 -0.0207 437s 17 1.55472 1.0339 -0.8117 437s 18 -0.41250 0.0255 0.8398 437s 19 0.29322 -2.6293 -0.8283 437s 20 0.91756 -0.5906 -0.4091 437s 21 0.71583 -0.7036 -1.2154 437s 22 -2.26223 -0.5283 0.6207 437s > fitted 437s Consumption Investment PrivateWages 437s 1 NA NA NA 437s 2 42.2 0.100 26.6 437s 3 46.4 2.024 28.7 437s 4 50.8 4.085 32.6 437s 5 51.2 4.436 33.9 437s 6 52.7 4.742 35.7 437s 7 54.3 4.147 NA 437s 8 54.9 3.371 38.6 437s 9 56.3 3.509 38.9 437s 10 58.5 3.879 40.1 437s 11 54.6 0.750 38.3 437s 12 51.0 -3.447 34.2 437s 13 45.7 -6.510 29.2 437s 14 46.2 -5.465 28.1 437s 15 48.7 -2.835 30.3 437s 16 51.3 -1.312 33.2 437s 17 56.1 1.066 37.6 437s 18 59.1 1.974 40.2 437s 19 57.2 0.729 39.0 437s 20 60.7 1.891 42.0 437s 21 64.3 4.004 46.2 437s 22 72.0 5.428 52.7 437s > predict 437s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 437s 1 NA NA NA NA 437s 2 42.2 0.414 41.3 43.0 437s 3 46.4 0.451 45.4 47.3 437s 4 50.8 0.296 50.2 51.4 437s 5 51.2 0.342 50.5 51.9 437s 6 52.7 0.342 52.0 53.4 437s 7 54.3 0.309 53.6 54.9 437s 8 54.9 0.282 54.3 55.5 437s 9 56.3 0.303 55.7 56.9 437s 10 58.5 0.321 57.8 59.1 437s 11 54.6 0.515 53.5 55.6 437s 12 51.0 0.418 50.1 51.8 437s 13 45.7 0.548 44.6 46.8 437s 14 46.2 0.528 45.1 47.2 437s 15 48.7 0.333 48.0 49.4 437s 16 51.3 0.296 50.7 51.9 437s 17 56.1 0.321 55.5 56.8 437s 18 59.1 0.287 58.5 59.7 437s 19 57.2 0.325 56.6 57.9 437s 20 60.7 0.383 59.9 61.5 437s 21 64.3 0.382 63.5 65.1 437s 22 72.0 0.599 70.8 73.2 437s Investment.pred Investment.se.fit Investment.lwr Investment.upr 437s 1 NA NA NA NA 437s 2 0.100 0.511 -0.926 1.127 437s 3 2.024 0.425 1.170 2.878 437s 4 4.085 0.378 3.325 4.845 437s 5 4.436 0.313 3.806 5.065 437s 6 4.742 0.296 4.147 5.336 437s 7 4.147 0.279 3.586 4.709 437s 8 3.371 0.250 2.868 3.874 437s 9 3.509 0.331 2.845 4.174 437s 10 3.879 0.380 3.116 4.642 437s 11 0.750 0.512 -0.279 1.779 437s 12 -3.447 0.433 -4.316 -2.578 437s 13 -6.510 0.527 -7.568 -5.451 437s 14 -5.465 0.587 -6.645 -4.285 437s 15 -2.835 0.320 -3.477 -2.193 437s 16 -1.312 0.274 -1.863 -0.761 437s 17 1.066 0.296 0.472 1.661 437s 18 1.974 0.208 1.558 2.391 437s 19 0.729 0.265 0.197 1.262 437s 20 1.891 0.311 1.266 2.515 437s 21 4.004 0.283 3.435 4.572 437s 22 5.428 0.393 4.640 6.217 437s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 437s 1 NA NA NA NA 437s 2 26.6 0.318 26.0 27.2 437s 3 28.7 0.317 28.1 29.4 437s 4 32.6 0.315 32.0 33.2 437s 5 33.9 0.243 33.4 34.4 437s 6 35.7 0.242 35.2 36.2 437s 7 NA NA NA NA 437s 8 38.6 0.247 38.1 39.1 437s 9 38.9 0.236 38.4 39.3 437s 10 40.1 0.227 39.6 40.5 437s 11 38.3 0.306 37.6 38.9 437s 12 34.2 0.312 33.6 34.8 437s 13 29.2 0.376 28.5 30.0 437s 14 28.1 0.337 27.5 28.8 437s 15 30.3 0.328 29.7 31.0 437s 16 33.2 0.274 32.7 33.8 437s 17 37.6 0.266 37.1 38.1 437s 18 40.2 0.213 39.7 40.6 437s 19 39.0 0.310 38.4 39.7 437s 20 42.0 0.282 41.4 42.6 437s 21 46.2 0.300 45.6 46.8 437s 22 52.7 0.451 51.8 53.6 437s > model.frame 437s [1] TRUE 437s > model.matrix 437s [1] TRUE 437s > nobs 437s [1] 62 437s > linearHypothesis 437s Linear hypothesis test (Theil's F test) 437s 437s Hypothesis: 437s Consumption_corpProf + Investment_capitalLag = 0 437s 437s Model 1: restricted model 437s Model 2: kleinModel 437s 437s Res.Df Df F Pr(>F) 437s 1 51 437s 2 50 1 1.39 0.24 437s Linear hypothesis test (F statistic of a Wald test) 437s 437s Hypothesis: 437s Consumption_corpProf + Investment_capitalLag = 0 437s 437s Model 1: restricted model 437s Model 2: kleinModel 437s 437s Res.Df Df F Pr(>F) 437s 1 51 437s 2 50 1 1.7 0.2 437s Linear hypothesis test (Chi^2 statistic of a Wald test) 437s 437s Hypothesis: 437s Consumption_corpProf + Investment_capitalLag = 0 437s 437s Model 1: restricted model 437s Model 2: kleinModel 437s 437s Res.Df Df Chisq Pr(>Chisq) 437s 1 51 437s 2 50 1 1.7 0.19 437s Linear hypothesis test (Theil's F test) 437s 437s Hypothesis: 437s Consumption_corpProf + Investment_capitalLag = 0 437s Consumption_corpProfLag - PrivateWages_trend = 0 437s 437s Model 1: restricted model 437s Model 2: kleinModel 437s 437s Res.Df Df F Pr(>F) 437s 1 52 437s 2 50 2 0.72 0.49 437s Linear hypothesis test (F statistic of a Wald test) 437s 437s Hypothesis: 437s Consumption_corpProf + Investment_capitalLag = 0 437s Consumption_corpProfLag - PrivateWages_trend = 0 437s 437s Model 1: restricted model 437s Model 2: kleinModel 437s 437s Res.Df Df F Pr(>F) 437s 1 52 437s 2 50 2 0.87 0.42 437s Linear hypothesis test (Chi^2 statistic of a Wald test) 437s 437s Hypothesis: 437s Consumption_corpProf + Investment_capitalLag = 0 437s Consumption_corpProfLag - PrivateWages_trend = 0 437s 437s Model 1: restricted model 437s Model 2: kleinModel 437s 437s Res.Df Df Chisq Pr(>Chisq) 437s 1 52 437s 2 50 2 1.75 0.42 437s > logLik 437s 'log Lik.' -69.4 (df=18) 437s 'log Lik.' -78.2 (df=18) 437s Estimating function 437s Consumption_(Intercept) Consumption_corpProf 437s Consumption_2 -0.49572 -6.1470 437s Consumption_3 -2.42943 -41.0573 437s Consumption_4 -2.92134 -53.7526 437s Consumption_5 -1.01365 -19.6648 437s Consumption_6 -0.11814 -2.3746 437s Consumption_7 1.49363 29.2752 437s Consumption_8 2.31199 45.7775 437s Consumption_9 1.73521 36.6129 437s Consumption_10 -1.19687 -25.9720 437s Consumption_11 0.75344 11.7537 437s Consumption_12 -0.10714 -1.2214 437s Consumption_13 -0.15519 -1.0863 437s Consumption_14 0.59434 6.6566 437s Consumption_15 -0.01083 -0.1332 437s Consumption_16 -0.02651 -0.3712 437s Consumption_17 2.78956 49.0963 437s Consumption_18 -0.74013 -12.8043 437s Consumption_19 0.52610 8.0494 437s Consumption_20 1.64635 31.2806 437s Consumption_21 1.28438 27.1004 437s Consumption_22 -4.05902 -95.3870 437s Investment_2 0.08318 1.0314 437s Investment_3 0.03433 0.5802 437s Investment_4 -0.30897 -5.6851 437s Investment_5 0.39771 7.7155 437s Investment_6 -0.09921 -1.9941 437s Investment_7 -0.40237 -7.8864 437s Investment_8 -0.22963 -4.5466 437s Investment_9 0.14106 2.9764 437s Investment_10 -0.33822 -7.3394 437s Investment_11 -0.06917 -1.0790 437s Investment_12 -0.01303 -0.1485 437s Investment_13 -0.08575 -0.6003 437s Investment_14 -0.10117 -1.1331 437s Investment_15 0.04575 0.5628 437s Investment_16 -0.00344 -0.0482 437s Investment_17 -0.28639 -5.0405 437s Investment_18 -0.00707 -0.1223 437s Investment_19 0.72832 11.1433 437s Investment_20 0.16360 3.1083 437s Investment_21 0.19490 4.1123 437s Investment_22 0.14635 3.4391 437s PrivateWages_2 -1.58896 -19.7031 437s PrivateWages_3 0.84394 14.2626 437s PrivateWages_4 2.20977 40.6598 437s PrivateWages_5 -0.04965 -0.9631 437s PrivateWages_6 -0.40373 -8.1150 437s PrivateWages_8 -1.00430 -19.8851 437s PrivateWages_9 0.50179 10.5878 437s PrivateWages_10 1.81021 39.2815 437s PrivateWages_11 -0.52455 -8.1830 437s PrivateWages_12 0.44676 5.0931 437s PrivateWages_13 -0.35330 -2.4731 437s PrivateWages_14 0.52303 5.8579 437s PrivateWages_15 0.39464 4.8541 437s PrivateWages_16 -0.03009 -0.4213 437s PrivateWages_17 -1.18225 -20.8075 437s PrivateWages_18 1.22307 21.1590 437s PrivateWages_19 -1.20633 -18.4569 437s PrivateWages_20 -0.59580 -11.3203 437s PrivateWages_21 -1.77014 -37.3499 437s PrivateWages_22 0.90407 21.2457 437s Consumption_corpProfLag Consumption_wages 437s Consumption_2 -6.2957 -13.979 437s Consumption_3 -30.1249 -78.228 437s Consumption_4 -49.3706 -108.090 437s Consumption_5 -18.6512 -37.505 437s Consumption_6 -2.2919 -4.560 437s Consumption_7 30.0220 60.791 437s Consumption_8 45.3151 95.948 437s Consumption_9 34.3571 74.440 437s Consumption_10 -25.2539 -54.218 437s Consumption_11 16.3496 31.720 437s Consumption_12 -1.6714 -4.211 437s Consumption_13 -1.7691 -5.323 437s Consumption_14 4.1604 20.267 437s Consumption_15 -0.1213 -0.396 437s Consumption_16 -0.3261 -1.042 437s Consumption_17 39.0539 123.299 437s Consumption_18 -13.0263 -35.304 437s Consumption_19 9.1016 24.148 437s Consumption_20 25.1891 81.330 437s Consumption_21 24.4032 68.072 437s Consumption_22 -85.6453 -250.847 437s Investment_2 1.0563 2.346 437s Investment_3 0.4257 1.105 437s Investment_4 -5.2216 -11.432 437s Investment_5 7.3178 14.715 437s Investment_6 -1.9246 -3.829 437s Investment_7 -8.0876 -16.376 437s Investment_8 -4.5007 -9.530 437s Investment_9 2.7930 6.052 437s Investment_10 -7.1364 -15.321 437s Investment_11 -1.5009 -2.912 437s Investment_12 -0.2033 -0.512 437s Investment_13 -0.9776 -2.941 437s Investment_14 -0.7082 -3.450 437s Investment_15 0.5124 1.675 437s Investment_16 -0.0423 -0.135 437s Investment_17 -4.0095 -12.659 437s Investment_18 -0.1244 -0.337 437s Investment_19 12.5999 33.430 437s Investment_20 2.5030 8.082 437s Investment_21 3.7031 10.330 437s Investment_22 3.0879 9.044 437s PrivateWages_2 -20.1798 -44.809 437s PrivateWages_3 10.4649 27.175 437s PrivateWages_4 37.3452 81.762 437s PrivateWages_5 -0.9135 -1.837 437s PrivateWages_6 -7.8324 -15.584 437s PrivateWages_8 -19.6842 -41.678 437s PrivateWages_9 9.9355 21.527 437s PrivateWages_10 38.1953 82.002 437s PrivateWages_11 -11.3827 -22.084 437s PrivateWages_12 6.9695 17.558 437s PrivateWages_13 -4.0277 -12.118 437s PrivateWages_14 3.6612 17.835 437s PrivateWages_15 4.4200 14.444 437s PrivateWages_16 -0.3701 -1.183 437s PrivateWages_17 -16.5515 -52.255 437s PrivateWages_18 21.5260 58.340 437s PrivateWages_19 -20.8696 -55.371 437s PrivateWages_20 -9.1158 -29.433 437s PrivateWages_21 -33.6326 -93.817 437s PrivateWages_22 19.0759 55.872 437s Investment_(Intercept) Investment_corpProf 437s Consumption_2 0.07653 0.9490 437s Consumption_3 0.37506 6.3385 437s Consumption_4 0.45100 8.2984 437s Consumption_5 0.15649 3.0359 437s Consumption_6 0.01824 0.3666 437s Consumption_7 -0.23059 -4.5195 437s Consumption_8 -0.35693 -7.0672 437s Consumption_9 -0.26788 -5.6523 437s Consumption_10 0.18477 4.0096 437s Consumption_11 -0.11632 -1.8145 437s Consumption_12 0.01654 0.1886 437s Consumption_13 0.02396 0.1677 437s Consumption_14 -0.09175 -1.0277 437s Consumption_15 0.00167 0.0206 437s Consumption_16 0.00409 0.0573 437s Consumption_17 -0.43066 -7.5796 437s Consumption_18 0.11426 1.9767 437s Consumption_19 -0.08122 -1.2427 437s Consumption_20 -0.25417 -4.8291 437s Consumption_21 -0.19828 -4.1838 437s Consumption_22 0.62664 14.7260 437s Investment_2 -0.44022 -5.4587 437s Investment_3 -0.18170 -3.0707 437s Investment_4 1.63526 30.0888 437s Investment_5 -2.10489 -40.8348 437s Investment_6 0.52506 10.5537 437s Investment_7 2.12955 41.7392 437s Investment_8 1.21532 24.0633 437s Investment_9 -0.74658 -15.7528 437s Investment_10 1.79005 38.8441 437s Investment_11 0.36607 5.7107 437s Investment_12 0.06896 0.7861 437s Investment_13 0.45385 3.1769 437s Investment_14 0.53544 5.9969 437s Investment_15 -0.24215 -2.9785 437s Investment_16 0.01822 0.2551 437s Investment_17 1.51576 26.6774 437s Investment_18 0.03741 0.6472 437s Investment_19 -3.85468 -58.9766 437s Investment_20 -0.86584 -16.4509 437s Investment_21 -1.03151 -21.7649 437s Investment_22 -0.77455 -18.2019 437s PrivateWages_2 0.75366 9.3454 437s PrivateWages_3 -0.40029 -6.7649 437s PrivateWages_4 -1.04812 -19.2855 437s PrivateWages_5 0.02355 0.4568 437s PrivateWages_6 0.19149 3.8490 437s PrivateWages_8 0.47635 9.4317 437s PrivateWages_9 -0.23801 -5.0219 437s PrivateWages_10 -0.85860 -18.6317 437s PrivateWages_11 0.24880 3.8813 437s PrivateWages_12 -0.21191 -2.4157 437s PrivateWages_13 0.16758 1.1730 437s PrivateWages_14 -0.24808 -2.7785 437s PrivateWages_15 -0.18718 -2.3024 437s PrivateWages_16 0.01427 0.1998 437s PrivateWages_17 0.56075 9.8693 437s PrivateWages_18 -0.58012 -10.0360 437s PrivateWages_19 0.57218 8.7543 437s PrivateWages_20 0.28260 5.3694 437s PrivateWages_21 0.83960 17.7155 437s PrivateWages_22 -0.42881 -10.0771 437s Investment_corpProfLag Investment_capitalLag 437s Consumption_2 0.9719 13.990 437s Consumption_3 4.6507 68.486 437s Consumption_4 7.6219 83.210 437s Consumption_5 2.8794 29.686 437s Consumption_6 0.3538 3.515 437s Consumption_7 -4.6348 -45.611 437s Consumption_8 -6.9958 -72.599 437s Consumption_9 -5.3041 -55.613 437s Consumption_10 3.8987 38.913 437s Consumption_11 -2.5241 -25.090 437s Consumption_12 0.2580 3.584 437s Consumption_13 0.2731 5.110 437s Consumption_14 -0.6423 -19.002 437s Consumption_15 0.0187 0.338 437s Consumption_16 0.0503 0.815 437s Consumption_17 -6.0292 -85.141 437s Consumption_18 2.0110 22.830 437s Consumption_19 -1.4051 -16.390 437s Consumption_20 -3.8887 -50.808 437s Consumption_21 -3.7674 -39.895 437s Consumption_22 13.2221 128.147 437s Investment_2 -5.5908 -80.472 437s Investment_3 -2.2531 -33.179 437s Investment_4 27.6359 301.706 437s Investment_5 -38.7299 -399.297 437s Investment_6 10.1862 101.179 437s Investment_7 42.8040 421.225 437s Investment_8 23.8203 247.196 437s Investment_9 -14.7822 -154.989 437s Investment_10 37.7701 376.985 437s Investment_11 7.9437 78.961 437s Investment_12 1.0757 14.943 437s Investment_13 5.1739 96.806 437s Investment_14 3.7481 110.889 437s Investment_15 -2.7121 -48.915 437s Investment_16 0.2241 3.626 437s Investment_17 21.2206 299.666 437s Investment_18 0.6585 7.475 437s Investment_19 -66.6860 -777.874 437s Investment_20 -13.2473 -173.081 437s Investment_21 -19.5987 -207.540 437s Investment_22 -16.3429 -158.395 437s PrivateWages_2 9.5715 137.769 437s PrivateWages_3 -4.9636 -73.093 437s PrivateWages_4 -17.7133 -193.379 437s PrivateWages_5 0.4333 4.467 437s PrivateWages_6 3.7150 36.901 437s PrivateWages_8 9.3365 96.890 437s PrivateWages_9 -4.7125 -49.410 437s PrivateWages_10 -18.1165 -180.822 437s PrivateWages_11 5.3990 53.666 437s PrivateWages_12 -3.3057 -45.920 437s PrivateWages_13 1.9104 35.744 437s PrivateWages_14 -1.7366 -51.377 437s PrivateWages_15 -2.0965 -37.811 437s PrivateWages_16 0.1756 2.840 437s PrivateWages_17 7.8506 110.861 437s PrivateWages_18 -10.2100 -115.907 437s PrivateWages_19 9.8987 115.466 437s PrivateWages_20 4.3237 56.491 437s PrivateWages_21 15.9524 168.927 437s PrivateWages_22 -9.0479 -87.692 437s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 437s Consumption_2 -0.40239 -18.349 -18.067 437s Consumption_3 -1.97202 -98.798 -89.924 437s Consumption_4 -2.37131 -135.639 -118.803 437s Consumption_5 -0.82280 -46.982 -47.064 437s Consumption_6 -0.09590 -5.850 -5.476 437s Consumption_7 0.00000 0.000 0.000 437s Consumption_8 1.87670 120.859 120.108 437s Consumption_9 1.40851 90.849 90.708 437s Consumption_10 -0.97152 -65.092 -62.663 437s Consumption_11 0.61158 37.429 40.976 437s Consumption_12 -0.08697 -4.644 -5.322 437s Consumption_13 -0.12597 -5.580 -6.727 437s Consumption_14 0.48244 21.758 21.372 437s Consumption_15 -0.00879 -0.437 -0.396 437s Consumption_16 -0.02152 -1.171 -1.070 437s Consumption_17 2.26435 141.975 123.181 437s Consumption_18 -0.60078 -39.051 -37.669 437s Consumption_19 0.42705 26.007 27.758 437s Consumption_20 1.33638 92.878 81.385 437s Consumption_21 1.04256 78.922 72.458 437s Consumption_22 -3.29479 -291.260 -249.416 437s Investment_2 0.20743 9.459 9.314 437s Investment_3 0.08562 4.289 3.904 437s Investment_4 -0.77054 -44.075 -38.604 437s Investment_5 0.99183 56.634 56.733 437s Investment_6 -0.24741 -15.092 -14.127 437s Investment_7 0.00000 0.000 0.000 437s Investment_8 -0.57266 -36.880 -36.650 437s Investment_9 0.35179 22.690 22.655 437s Investment_10 -0.84348 -56.513 -54.405 437s Investment_11 -0.17249 -10.557 -11.557 437s Investment_12 -0.03249 -1.735 -1.989 437s Investment_13 -0.21385 -9.474 -11.420 437s Investment_14 -0.25230 -11.379 -11.177 437s Investment_15 0.11410 5.671 5.146 437s Investment_16 -0.00859 -0.467 -0.427 437s Investment_17 -0.71423 -44.782 -38.854 437s Investment_18 -0.01763 -1.146 -1.105 437s Investment_19 1.81634 110.615 118.062 437s Investment_20 0.40799 28.355 24.846 437s Investment_21 0.48605 36.794 33.781 437s Investment_22 0.36497 32.263 27.628 437s PrivateWages_2 -3.69675 -168.572 -165.984 437s PrivateWages_3 1.96345 98.369 89.533 437s PrivateWages_4 5.14109 294.070 257.568 437s PrivateWages_5 -0.11550 -6.595 -6.607 437s PrivateWages_6 -0.93929 -57.297 -53.633 437s PrivateWages_8 -2.33652 -150.472 -149.537 437s PrivateWages_9 1.16743 75.299 75.183 437s PrivateWages_10 4.21148 282.169 271.641 437s PrivateWages_11 -1.22037 -74.687 -81.765 437s PrivateWages_12 1.03941 55.504 63.612 437s PrivateWages_13 -0.82197 -36.413 -43.893 437s PrivateWages_14 1.21684 54.880 53.906 437s PrivateWages_15 0.91815 45.632 41.409 437s PrivateWages_16 -0.07001 -3.809 -3.480 437s PrivateWages_17 -2.75052 -172.458 -149.628 437s PrivateWages_18 2.84549 184.957 178.412 437s PrivateWages_19 -2.80656 -170.920 -182.427 437s PrivateWages_20 -1.38615 -96.338 -84.417 437s PrivateWages_21 -4.11826 -311.753 -286.219 437s PrivateWages_22 2.10334 185.935 159.223 437s PrivateWages_trend 437s Consumption_2 4.0239 437s Consumption_3 17.7482 437s Consumption_4 18.9705 437s Consumption_5 5.7596 437s Consumption_6 0.5754 437s Consumption_7 0.0000 437s Consumption_8 -7.5068 437s Consumption_9 -4.2255 437s Consumption_10 1.9430 437s Consumption_11 -0.6116 437s Consumption_12 0.0000 437s Consumption_13 -0.1260 437s Consumption_14 0.9649 437s Consumption_15 -0.0264 437s Consumption_16 -0.0861 437s Consumption_17 11.3217 437s Consumption_18 -3.6047 437s Consumption_19 2.9894 437s Consumption_20 10.6910 437s Consumption_21 9.3830 437s Consumption_22 -32.9479 437s Investment_2 -2.0743 437s Investment_3 -0.7706 437s Investment_4 6.1643 437s Investment_5 -6.9428 437s Investment_6 1.4845 437s Investment_7 0.0000 437s Investment_8 2.2907 437s Investment_9 -1.0554 437s Investment_10 1.6870 437s Investment_11 0.1725 437s Investment_12 0.0000 437s Investment_13 -0.2139 437s Investment_14 -0.5046 437s Investment_15 0.3423 437s Investment_16 -0.0343 437s Investment_17 -3.5712 437s Investment_18 -0.1058 437s Investment_19 12.7144 437s Investment_20 3.2639 437s Investment_21 4.3745 437s Investment_22 3.6497 437s PrivateWages_2 36.9675 437s PrivateWages_3 -17.6711 437s PrivateWages_4 -41.1287 437s PrivateWages_5 0.8085 437s PrivateWages_6 5.6357 437s PrivateWages_8 9.3461 437s PrivateWages_9 -3.5023 437s PrivateWages_10 -8.4230 437s PrivateWages_11 1.2204 437s PrivateWages_12 0.0000 437s PrivateWages_13 -0.8220 437s PrivateWages_14 2.4337 437s PrivateWages_15 2.7544 437s PrivateWages_16 -0.2801 437s PrivateWages_17 -13.7526 437s PrivateWages_18 17.0729 437s PrivateWages_19 -19.6459 437s PrivateWages_20 -11.0892 437s PrivateWages_21 -37.0644 437s PrivateWages_22 21.0334 437s [1] TRUE 437s > Bread 437s Consumption_(Intercept) Consumption_corpProf Consumption_corpProfLag 437s [1,] 85.2889 -0.01362 -0.83841 437s [2,] -0.0136 0.37283 -0.23220 437s [3,] -0.8384 -0.23220 0.36858 437s [4,] -1.6590 -0.05994 -0.03120 437s [5,] -3.1844 -0.68255 0.70355 437s [6,] 0.0595 0.01846 -0.01774 437s [7,] -0.0239 -0.01745 0.02009 437s [8,] 0.0127 0.00329 -0.00362 437s [9,] -36.0142 0.07978 1.66083 437s [10,] 0.3888 -0.06209 0.04032 437s [11,] 0.2001 0.06287 -0.07012 437s [12,] 0.1814 0.03185 0.02619 437s Consumption_wages Investment_(Intercept) Investment_corpProf 437s [1,] -1.66e+00 -3.184 0.05950 437s [2,] -5.99e-02 -0.683 0.01846 437s [3,] -3.12e-02 0.704 -0.01774 437s [4,] 7.69e-02 0.082 -0.00204 437s [5,] 8.20e-02 1298.386 -12.39923 437s [6,] -2.04e-03 -12.399 0.41486 437s [7,] -2.16e-05 9.908 -0.35328 437s [8,] -2.54e-04 -6.230 0.05576 437s [9,] 1.50e-01 24.451 -0.18195 437s [10,] 6.53e-06 0.391 0.02158 437s [11,] -2.68e-03 -0.821 -0.01913 437s [12,] -2.78e-02 -0.890 0.00590 437s Investment_corpProfLag Investment_capitalLag PrivateWages_(Intercept) 437s [1,] -2.39e-02 0.012670 -36.0142 437s [2,] -1.75e-02 0.003286 0.0798 437s [3,] 2.01e-02 -0.003616 1.6608 437s [4,] -2.16e-05 -0.000254 0.1499 437s [5,] 9.91e+00 -6.230058 24.4513 437s [6,] -3.53e-01 0.055757 -0.1819 437s [7,] 4.47e-01 -0.056152 -0.6460 437s [8,] -5.62e-02 0.030966 -0.0512 437s [9,] -6.46e-01 -0.051180 80.1680 437s [10,] -1.22e-02 -0.002778 -0.3588 437s [11,] 2.36e-02 0.003775 -0.9890 437s [12,] -1.61e-02 0.005268 0.9201 437s PrivateWages_gnp PrivateWages_gnpLag PrivateWages_trend 437s [1,] 3.89e-01 0.20005 0.18143 437s [2,] -6.21e-02 0.06287 0.03185 437s [3,] 4.03e-02 -0.07012 0.02619 437s [4,] 6.53e-06 -0.00268 -0.02782 437s [5,] 3.91e-01 -0.82129 -0.89038 437s [6,] 2.16e-02 -0.01913 0.00590 437s [7,] -1.22e-02 0.02360 -0.01606 437s [8,] -2.78e-03 0.00377 0.00527 437s [9,] -3.59e-01 -0.98896 0.92007 437s [10,] 4.82e-02 -0.04360 -0.01308 437s [11,] -4.36e-02 0.06217 -0.00244 437s [12,] -1.31e-02 -0.00244 0.04948 437s > 437s > # 3SLS 437s > summary 437s 437s systemfit results 437s method: 3SLS 437s 437s N DF SSR detRCov OLS-R2 McElroy-R2 437s system 60 48 62.6 0.265 0.968 0.994 437s 437s N DF SSR MSE RMSE R2 Adj R2 437s Consumption 20 16 17.8 1.114 1.06 0.981 0.977 437s Investment 20 16 34.3 2.143 1.46 0.853 0.825 437s PrivateWages 20 16 10.5 0.656 0.81 0.987 0.984 437s 437s The covariance matrix of the residuals used for estimation 437s Consumption Investment PrivateWages 437s Consumption 1.034 0.309 -0.383 437s Investment 0.309 1.151 0.202 437s PrivateWages -0.383 0.202 0.487 437s 437s The covariance matrix of the residuals 437s Consumption Investment PrivateWages 437s Consumption 0.891 0.304 -0.391 437s Investment 0.304 1.715 0.388 437s PrivateWages -0.391 0.388 0.525 437s 437s The correlations of the residuals 437s Consumption Investment PrivateWages 437s Consumption 1.000 0.246 -0.571 437s Investment 0.246 1.000 0.409 437s PrivateWages -0.571 0.409 1.000 437s 437s 437s 3SLS estimates for 'Consumption' (equation 1) 437s Model Formula: consump ~ corpProf + corpProfLag + wages 437s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 437s gnpLag 437s 437s Estimate Std. Error t value Pr(>|t|) 437s (Intercept) 16.3668 1.3024 12.57 1.1e-09 *** 437s corpProf 0.1186 0.1073 1.10 0.29 437s corpProfLag 0.1448 0.1008 1.44 0.17 437s wages 0.8006 0.0391 20.47 6.7e-13 *** 437s --- 437s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 437s 437s Residual standard error: 1.056 on 16 degrees of freedom 437s Number of observations: 20 Degrees of Freedom: 16 437s SSR: 17.825 MSE: 1.114 Root MSE: 1.056 437s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.977 437s 437s 437s 3SLS estimates for 'Investment' (equation 2) 437s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 437s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 437s gnpLag 437s 437s Estimate Std. Error t value Pr(>|t|) 437s (Intercept) 24.8872 6.2956 3.95 0.00114 ** 437s corpProf 0.0702 0.1458 0.48 0.63648 437s corpProfLag 0.6688 0.1402 4.77 0.00021 *** 437s capitalLag -0.1786 0.0303 -5.90 2.3e-05 *** 437s --- 437s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 437s 437s Residual standard error: 1.464 on 16 degrees of freedom 437s Number of observations: 20 Degrees of Freedom: 16 437s SSR: 34.295 MSE: 2.143 Root MSE: 1.464 437s Multiple R-Squared: 0.853 Adjusted R-Squared: 0.825 437s 437s 437s 3SLS estimates for 'PrivateWages' (equation 3) 437s Model Formula: privWage ~ gnp + gnpLag + trend 437s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 437s gnpLag 437s 437s Estimate Std. Error t value Pr(>|t|) 437s (Intercept) 1.6387 1.1457 1.43 0.17188 437s gnp 0.4062 0.0324 12.52 1.1e-09 *** 437s gnpLag 0.1784 0.0347 5.14 1.0e-04 *** 437s trend 0.1435 0.0292 4.91 0.00016 *** 437s --- 437s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 437s 437s Residual standard error: 0.81 on 16 degrees of freedom 437s Number of observations: 20 Degrees of Freedom: 16 437s SSR: 10.497 MSE: 0.656 Root MSE: 0.81 437s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.984 437s 437s > residuals 437s Consumption Investment PrivateWages 437s 1 NA NA NA 437s 2 -0.3538 -1.795 -1.2388 437s 3 -0.9465 0.154 0.4649 437s 4 -1.4189 0.678 1.4344 437s 5 -0.3546 -1.666 -0.1354 437s 6 0.1366 0.251 -0.3452 437s 7 NA NA NA 437s 8 1.4213 1.150 -0.7445 437s 9 1.2173 0.476 0.3001 437s 10 -0.4636 2.200 1.2232 437s 11 -0.0650 -0.962 -0.4104 437s 12 -0.5422 -0.808 0.2495 437s 13 -0.7092 -1.098 -0.3057 437s 14 0.4898 1.542 0.3497 437s 15 -0.0502 -0.155 0.2949 437s 16 0.0272 0.154 0.0214 437s 17 1.8311 1.932 -0.7322 437s 18 -0.4567 -0.180 0.9090 437s 19 0.0650 -3.381 -0.7795 437s 20 1.2135 0.557 -0.2847 437s 21 0.9466 0.167 -1.0812 437s 22 -1.9877 0.784 0.8102 437s > fitted 437s Consumption Investment PrivateWages 437s 1 NA NA NA 437s 2 42.3 1.595 26.7 437s 3 45.9 1.746 28.8 437s 4 50.6 4.522 32.7 437s 5 51.0 4.666 34.0 437s 6 52.5 4.849 35.7 437s 7 NA NA NA 437s 8 54.8 3.050 38.6 437s 9 56.1 2.524 38.9 437s 10 58.3 2.900 40.1 437s 11 55.1 1.962 38.3 437s 12 51.4 -2.592 34.3 437s 13 46.3 -5.102 29.3 437s 14 46.0 -6.642 28.2 437s 15 48.8 -2.845 30.3 437s 16 51.3 -1.454 33.2 437s 17 55.9 0.168 37.5 437s 18 59.2 2.180 40.1 437s 19 57.4 1.481 39.0 437s 20 60.4 0.743 41.9 437s 21 64.1 3.133 46.1 437s 22 71.7 4.116 52.5 437s > predict 437s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 437s 1 NA NA NA NA 437s 2 42.3 0.468 39.8 44.7 437s 3 45.9 0.543 43.4 48.5 437s 4 50.6 0.352 48.3 53.0 437s 5 51.0 0.407 48.6 53.4 437s 6 52.5 0.411 50.1 54.9 437s 7 NA NA NA NA 437s 8 54.8 0.340 52.4 57.1 437s 9 56.1 0.372 53.7 58.5 437s 10 58.3 0.387 55.9 60.6 437s 11 55.1 0.687 52.4 57.7 437s 12 51.4 0.558 48.9 54.0 437s 13 46.3 0.713 43.6 49.0 437s 14 46.0 0.599 43.4 48.6 437s 15 48.8 0.368 46.4 51.1 437s 16 51.3 0.326 48.9 53.6 437s 17 55.9 0.388 53.5 58.3 437s 18 59.2 0.319 56.8 61.5 437s 19 57.4 0.391 55.0 59.8 437s 20 60.4 0.457 57.9 62.8 437s 21 64.1 0.437 61.6 66.5 437s 22 71.7 0.674 69.0 74.3 437s Investment.pred Investment.se.fit Investment.lwr Investment.upr 437s 1 NA NA NA NA 437s 2 1.595 0.731 -1.8742 5.065 437s 3 1.746 0.533 -1.5566 5.050 437s 4 4.522 0.484 1.2530 7.791 437s 5 4.666 0.406 1.4458 7.887 437s 6 4.849 0.386 1.6390 8.058 437s 7 NA NA NA NA 437s 8 3.050 0.325 -0.1296 6.229 437s 9 2.524 0.467 -0.7334 5.782 437s 10 2.900 0.515 -0.3900 6.190 437s 11 1.962 0.769 -1.5438 5.467 437s 12 -2.592 0.608 -5.9519 0.769 437s 13 -5.102 0.774 -8.6129 -1.592 437s 14 -6.642 0.807 -10.1867 -3.098 437s 15 -2.845 0.395 -6.0599 0.370 437s 16 -1.454 0.341 -4.6409 1.733 437s 17 0.168 0.442 -3.0739 3.410 437s 18 2.180 0.281 -0.9807 5.340 437s 19 1.481 0.414 -1.7440 4.706 437s 20 0.743 0.492 -2.5310 4.017 437s 21 3.133 0.414 -0.0924 6.358 437s 22 4.116 0.583 0.7756 7.457 437s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 437s 1 NA NA NA NA 437s 2 26.7 0.322 24.9 28.6 437s 3 28.8 0.328 27.0 30.7 437s 4 32.7 0.340 30.8 34.5 437s 5 34.0 0.250 32.2 35.8 437s 6 35.7 0.257 33.9 37.5 437s 7 NA NA NA NA 437s 8 38.6 0.254 36.8 40.4 437s 9 38.9 0.241 37.1 40.7 437s 10 40.1 0.235 38.3 41.9 437s 11 38.3 0.325 36.5 40.2 437s 12 34.3 0.349 32.4 36.1 437s 13 29.3 0.425 27.4 31.2 437s 14 28.2 0.340 26.3 30.0 437s 15 30.3 0.326 28.5 32.2 437s 16 33.2 0.272 31.4 35.0 437s 17 37.5 0.273 35.7 39.3 437s 18 40.1 0.214 38.3 41.9 437s 19 39.0 0.336 37.1 40.8 437s 20 41.9 0.290 40.1 43.7 437s 21 46.1 0.305 44.2 47.9 437s 22 52.5 0.479 50.5 54.5 437s > model.frame 437s [1] TRUE 437s > model.matrix 437s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0323 >" 437s [2] "Attributes: < Component “dimnames”: Component 1: 55 string mismatches >" 437s [3] "Numeric: lengths (744, 720) differ" 437s > nobs 437s [1] 60 437s > linearHypothesis 437s Linear hypothesis test (Theil's F test) 437s 437s Hypothesis: 437s Consumption_corpProf + Investment_capitalLag = 0 437s 437s Model 1: restricted model 437s Model 2: kleinModel 437s 437s Res.Df Df F Pr(>F) 437s 1 49 437s 2 48 1 0.22 0.64 437s Linear hypothesis test (F statistic of a Wald test) 437s 437s Hypothesis: 437s Consumption_corpProf + Investment_capitalLag = 0 437s 437s Model 1: restricted model 437s Model 2: kleinModel 437s 437s Res.Df Df F Pr(>F) 437s 1 49 437s 2 48 1 0.29 0.59 437s Linear hypothesis test (Chi^2 statistic of a Wald test) 437s 437s Hypothesis: 437s Consumption_corpProf + Investment_capitalLag = 0 437s 437s Model 1: restricted model 437s Model 2: kleinModel 437s 437s Res.Df Df Chisq Pr(>Chisq) 437s 1 49 437s 2 48 1 0.29 0.59 437s Linear hypothesis test (Theil's F test) 437s 437s Hypothesis: 437s Consumption_corpProf + Investment_capitalLag = 0 437s Consumption_corpProfLag - PrivateWages_trend = 0 437s 437s Model 1: restricted model 437s Model 2: kleinModel 437s 437s Res.Df Df F Pr(>F) 437s 1 50 437s 2 48 2 0.29 0.75 437s Linear hypothesis test (F statistic of a Wald test) 437s 437s Hypothesis: 437s Consumption_corpProf + Investment_capitalLag = 0 437s Consumption_corpProfLag - PrivateWages_trend = 0 437s 437s Model 1: restricted model 437s Model 2: kleinModel 437s 437s Res.Df Df F Pr(>F) 437s 1 50 437s 2 48 2 0.38 0.68 437s Linear hypothesis test (Chi^2 statistic of a Wald test) 437s 437s Hypothesis: 437s Consumption_corpProf + Investment_capitalLag = 0 437s Consumption_corpProfLag - PrivateWages_trend = 0 437s 437s Model 1: restricted model 437s Model 2: kleinModel 437s 437s Res.Df Df Chisq Pr(>Chisq) 437s 1 50 437s 2 48 2 0.77 0.68 437s > logLik 437s 'log Lik.' -71.9 (df=18) 437s 'log Lik.' -82.9 (df=18) 437s Estimating function 437s Consumption_(Intercept) Consumption_corpProf 437s Consumption_2 -2.1852 -28.316 437s Consumption_3 -1.2615 -21.074 437s Consumption_4 -0.7432 -14.221 437s Consumption_5 -4.1386 -86.649 437s Consumption_6 0.0344 0.669 437s Consumption_8 5.9528 102.039 437s Consumption_9 3.6199 70.548 437s Consumption_10 1.2130 24.820 437s Consumption_11 -2.3309 -39.266 437s Consumption_12 -1.5509 -19.665 437s Consumption_13 -2.9298 -26.139 437s Consumption_14 2.9907 27.815 437s Consumption_15 -1.7611 -22.533 437s Consumption_16 -1.0403 -14.834 437s Consumption_17 7.8605 115.957 437s Consumption_18 -1.2660 -24.744 437s Consumption_19 -6.1974 -119.976 437s Consumption_20 4.2546 73.971 437s Consumption_21 1.7695 35.564 437s Consumption_22 -2.2905 -52.365 437s Investment_2 1.5294 19.818 437s Investment_3 -0.1395 -2.330 437s Investment_4 -0.5222 -9.992 437s Investment_5 1.4794 30.973 437s Investment_6 -0.2466 -4.801 437s Investment_8 -1.1148 -19.108 437s Investment_9 -0.4909 -9.566 437s Investment_10 -1.9066 -39.013 437s Investment_11 0.8748 14.736 437s Investment_12 0.7489 9.496 437s Investment_13 1.0277 9.169 437s Investment_14 -1.3972 -12.995 437s Investment_15 0.1582 2.024 437s Investment_16 -0.1132 -1.614 437s Investment_17 -1.7775 -26.221 437s Investment_18 0.2812 5.496 437s Investment_19 3.0567 59.173 437s Investment_20 -0.5590 -9.719 437s Investment_21 -0.1981 -3.981 437s Investment_22 -0.6908 -15.792 437s PrivateWages_2 -3.3803 -43.802 437s PrivateWages_3 1.2445 20.789 437s PrivateWages_4 3.1328 59.947 437s PrivateWages_5 -2.9316 -61.378 437s PrivateWages_6 -0.3443 -6.703 437s PrivateWages_8 1.9219 32.944 437s PrivateWages_9 2.2216 43.296 437s PrivateWages_10 4.0703 83.288 437s PrivateWages_11 -2.6344 -44.377 437s PrivateWages_12 -0.6120 -7.760 437s PrivateWages_13 -2.5653 -22.887 437s PrivateWages_14 2.8669 26.663 437s PrivateWages_15 -0.5912 -7.565 437s PrivateWages_16 -0.6625 -9.447 437s PrivateWages_17 2.6204 38.656 437s PrivateWages_18 0.0477 0.933 437s PrivateWages_19 -7.1288 -138.006 437s PrivateWages_20 1.4620 25.419 437s PrivateWages_21 -1.3672 -27.479 437s PrivateWages_22 2.6294 60.113 437s Consumption_corpProfLag Consumption_wages 437s Consumption_2 -27.752 -63.61 437s Consumption_3 -15.643 -40.21 437s Consumption_4 -12.560 -26.46 437s Consumption_5 -76.150 -161.61 437s Consumption_6 0.667 1.34 437s Consumption_8 116.675 236.66 437s Consumption_9 71.675 153.05 437s Consumption_10 25.593 53.50 437s Consumption_11 -50.581 -101.08 437s Consumption_12 -24.194 -61.19 437s Consumption_13 -33.399 -102.70 437s Consumption_14 20.935 98.78 437s Consumption_15 -19.724 -66.25 437s Consumption_16 -12.795 -41.59 437s Consumption_17 110.047 328.11 437s Consumption_18 -22.282 -60.30 437s Consumption_19 -107.216 -306.49 437s Consumption_20 65.095 205.74 437s Consumption_21 33.620 94.08 437s Consumption_22 -48.330 -139.53 437s Investment_2 19.424 44.52 437s Investment_3 -1.729 -4.45 437s Investment_4 -8.825 -18.59 437s Investment_5 27.221 57.77 437s Investment_6 -4.784 -9.58 437s Investment_8 -21.849 -44.32 437s Investment_9 -9.719 -20.75 437s Investment_10 -40.229 -84.09 437s Investment_11 18.983 37.94 437s Investment_12 11.683 29.55 437s Investment_13 11.716 36.03 437s Investment_14 -9.780 -46.15 437s Investment_15 1.772 5.95 437s Investment_16 -1.392 -4.53 437s Investment_17 -24.885 -74.20 437s Investment_18 4.949 13.39 437s Investment_19 52.880 151.16 437s Investment_20 -8.553 -27.03 437s Investment_21 -3.764 -10.53 437s Investment_22 -14.576 -42.08 437s PrivateWages_2 -42.929 -98.41 437s PrivateWages_3 15.432 39.67 437s PrivateWages_4 52.944 111.55 437s PrivateWages_5 -53.942 -114.48 437s PrivateWages_6 -6.679 -13.37 437s PrivateWages_8 37.670 76.41 437s PrivateWages_9 43.987 93.93 437s PrivateWages_10 85.884 179.53 437s PrivateWages_11 -57.165 -114.24 437s PrivateWages_12 -9.547 -24.14 437s PrivateWages_13 -29.244 -89.93 437s PrivateWages_14 20.068 94.68 437s PrivateWages_15 -6.622 -22.24 437s PrivateWages_16 -8.149 -26.49 437s PrivateWages_17 36.686 109.38 437s PrivateWages_18 0.840 2.27 437s PrivateWages_19 -123.329 -352.55 437s PrivateWages_20 22.369 70.70 437s PrivateWages_21 -25.977 -72.69 437s PrivateWages_22 55.481 160.18 437s Investment_(Intercept) Investment_corpProf 437s Consumption_2 0.9588 12.424 437s Consumption_3 0.5535 9.246 437s Consumption_4 0.3261 6.240 437s Consumption_5 1.8159 38.018 437s Consumption_6 -0.0151 -0.294 437s Consumption_8 -2.6118 -44.771 437s Consumption_9 -1.5883 -30.954 437s Consumption_10 -0.5322 -10.890 437s Consumption_11 1.0227 17.228 437s Consumption_12 0.6805 8.628 437s Consumption_13 1.2855 11.469 437s Consumption_14 -1.3122 -12.204 437s Consumption_15 0.7727 9.887 437s Consumption_16 0.4564 6.508 437s Consumption_17 -3.4489 -50.877 437s Consumption_18 0.5555 10.857 437s Consumption_19 2.7192 52.640 437s Consumption_20 -1.8667 -32.456 437s Consumption_21 -0.7764 -15.604 437s Consumption_22 1.0050 22.976 437s Investment_2 -2.3899 -30.969 437s Investment_3 0.2179 3.641 437s Investment_4 0.8160 15.614 437s Investment_5 -2.3118 -48.401 437s Investment_6 0.3854 7.502 437s Investment_8 1.7420 29.860 437s Investment_9 0.7670 14.948 437s Investment_10 2.9794 60.964 437s Investment_11 -1.3670 -23.027 437s Investment_12 -1.1702 -14.838 437s Investment_13 -1.6060 -14.328 437s Investment_14 2.1833 20.306 437s Investment_15 -0.2472 -3.163 437s Investment_16 0.1769 2.522 437s Investment_17 2.7776 40.974 437s Investment_18 -0.4394 -8.588 437s Investment_19 -4.7765 -92.468 437s Investment_20 0.8735 15.187 437s Investment_21 0.3095 6.221 437s Investment_22 1.0795 24.678 437s PrivateWages_2 2.1957 28.452 437s PrivateWages_3 -0.8084 -13.504 437s PrivateWages_4 -2.0349 -38.939 437s PrivateWages_5 1.9043 39.869 437s PrivateWages_6 0.2236 4.354 437s PrivateWages_8 -1.2484 -21.399 437s PrivateWages_9 -1.4431 -28.123 437s PrivateWages_10 -2.6439 -54.100 437s PrivateWages_11 1.7112 28.826 437s PrivateWages_12 0.3975 5.041 437s PrivateWages_13 1.6663 14.867 437s PrivateWages_14 -1.8622 -17.319 437s PrivateWages_15 0.3840 4.914 437s PrivateWages_16 0.4304 6.137 437s PrivateWages_17 -1.7021 -25.110 437s PrivateWages_18 -0.0310 -0.606 437s PrivateWages_19 4.6306 89.644 437s PrivateWages_20 -0.9497 -16.511 437s PrivateWages_21 0.8881 17.849 437s PrivateWages_22 -1.7080 -39.047 437s Investment_corpProfLag Investment_capitalLag 437s Consumption_2 12.176 175.26 437s Consumption_3 6.864 101.07 437s Consumption_4 5.511 60.16 437s Consumption_5 33.412 344.47 437s Consumption_6 -0.293 -2.91 437s Consumption_8 -51.192 -531.25 437s Consumption_9 -31.448 -329.73 437s Consumption_10 -11.229 -112.08 437s Consumption_11 22.193 220.60 437s Consumption_12 10.615 147.46 437s Consumption_13 14.654 274.19 437s Consumption_14 -9.185 -271.76 437s Consumption_15 8.654 156.08 437s Consumption_16 5.614 90.83 437s Consumption_17 -48.284 -681.84 437s Consumption_18 9.776 110.98 437s Consumption_19 47.042 548.73 437s Consumption_20 -28.561 -373.16 437s Consumption_21 -14.751 -156.21 437s Consumption_22 21.205 205.52 437s Investment_2 -30.352 -436.88 437s Investment_3 2.702 39.79 437s Investment_4 13.790 150.55 437s Investment_5 -42.537 -438.54 437s Investment_6 7.476 74.26 437s Investment_8 34.143 354.32 437s Investment_9 15.187 159.24 437s Investment_10 62.865 627.45 437s Investment_11 -29.663 -294.86 437s Investment_12 -18.256 -253.59 437s Investment_13 -18.308 -342.55 437s Investment_14 15.283 452.17 437s Investment_15 -2.768 -49.93 437s Investment_16 2.176 35.20 437s Investment_17 38.886 549.13 437s Investment_18 -7.734 -87.79 437s Investment_19 -82.633 -963.90 437s Investment_20 13.365 174.61 437s Investment_21 5.881 62.28 437s Investment_22 22.777 220.75 437s PrivateWages_2 27.885 401.37 437s PrivateWages_3 -10.024 -147.61 437s PrivateWages_4 -34.390 -375.44 437s PrivateWages_5 35.039 361.24 437s PrivateWages_6 4.339 43.10 437s PrivateWages_8 -24.469 -253.93 437s PrivateWages_9 -28.572 -299.58 437s PrivateWages_10 -55.787 -556.81 437s PrivateWages_11 37.132 369.10 437s PrivateWages_12 6.201 86.14 437s PrivateWages_13 18.996 355.42 437s PrivateWages_14 -13.035 -385.66 437s PrivateWages_15 4.301 77.58 437s PrivateWages_16 5.293 85.64 437s PrivateWages_17 -23.830 -336.51 437s PrivateWages_18 -0.546 -6.19 437s PrivateWages_19 80.110 934.46 437s PrivateWages_20 -14.530 -189.84 437s PrivateWages_21 16.874 178.68 437s PrivateWages_22 -36.038 -349.28 437s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 437s Consumption_2 -2.1174 -99.67 -95.07 437s Consumption_3 -1.2224 -60.61 -55.74 437s Consumption_4 -0.7201 -40.72 -36.08 437s Consumption_5 -4.0103 -243.37 -229.39 437s Consumption_6 0.0333 2.02 1.90 437s Consumption_8 5.7682 346.08 369.17 437s Consumption_9 3.5077 218.42 225.90 437s Consumption_10 1.1754 75.89 75.81 437s Consumption_11 -2.2587 -143.90 -151.33 437s Consumption_12 -1.5028 -82.40 -91.97 437s Consumption_13 -2.8389 -133.36 -151.60 437s Consumption_14 2.8980 122.09 128.38 437s Consumption_15 -1.7065 -87.40 -76.96 437s Consumption_16 -1.0080 -55.78 -50.10 437s Consumption_17 7.6168 437.16 414.35 437s Consumption_18 -1.2268 -82.41 -76.92 437s Consumption_19 -6.0053 -411.44 -390.34 437s Consumption_20 4.1227 275.58 251.07 437s Consumption_21 1.7146 128.37 119.16 437s Consumption_22 -2.2195 -192.83 -168.02 437s Investment_2 2.1940 103.27 98.51 437s Investment_3 -0.2001 -9.92 -9.12 437s Investment_4 -0.7491 -42.36 -37.53 437s Investment_5 2.1223 128.79 121.39 437s Investment_6 -0.3538 -21.44 -20.20 437s Investment_8 -1.5992 -95.95 -102.35 437s Investment_9 -0.7042 -43.85 -45.35 437s Investment_10 -2.7351 -176.60 -176.41 437s Investment_11 1.2549 79.95 84.08 437s Investment_12 1.0743 58.91 65.75 437s Investment_13 1.4743 69.26 78.73 437s Investment_14 -2.0044 -84.44 -88.79 437s Investment_15 0.2269 11.62 10.23 437s Investment_16 -0.1624 -8.99 -8.07 437s Investment_17 -2.5499 -146.35 -138.71 437s Investment_18 0.4034 27.10 25.29 437s Investment_19 4.3849 300.42 285.02 437s Investment_20 -0.8019 -53.60 -48.84 437s Investment_21 -0.2842 -21.27 -19.75 437s Investment_22 -0.9910 -86.09 -75.02 437s PrivateWages_2 -7.3399 -345.49 -329.56 437s PrivateWages_3 2.7024 133.99 123.23 437s PrivateWages_4 6.8025 384.63 340.81 437s PrivateWages_5 -6.3658 -386.31 -364.12 437s PrivateWages_6 -0.7476 -45.31 -42.69 437s PrivateWages_8 4.1733 250.39 267.09 437s PrivateWages_9 4.8240 300.38 310.66 437s PrivateWages_10 8.8383 570.68 570.07 437s PrivateWages_11 -5.7203 -364.45 -383.26 437s PrivateWages_12 -1.3289 -72.87 -81.33 437s PrivateWages_13 -5.5702 -261.67 -297.45 437s PrivateWages_14 6.2251 262.25 275.77 437s PrivateWages_15 -1.2838 -65.75 -57.90 437s PrivateWages_16 -1.4387 -79.61 -71.50 437s PrivateWages_17 5.6900 326.57 309.54 437s PrivateWages_18 0.1036 6.96 6.50 437s PrivateWages_19 -15.4796 -1060.55 -1006.17 437s PrivateWages_20 3.1746 212.21 193.34 437s PrivateWages_21 -2.9688 -222.26 -206.33 437s PrivateWages_22 5.7096 496.04 432.21 437s PrivateWages_trend 437s Consumption_2 21.174 437s Consumption_3 11.002 437s Consumption_4 5.761 437s Consumption_5 28.072 437s Consumption_6 -0.200 437s Consumption_8 -23.073 437s Consumption_9 -10.523 437s Consumption_10 -2.351 437s Consumption_11 2.259 437s Consumption_12 0.000 437s Consumption_13 -2.839 437s Consumption_14 5.796 437s Consumption_15 -5.119 437s Consumption_16 -4.032 437s Consumption_17 38.084 437s Consumption_18 -7.361 437s Consumption_19 -42.037 437s Consumption_20 32.981 437s Consumption_21 15.431 437s Consumption_22 -22.195 437s Investment_2 -21.940 437s Investment_3 1.801 437s Investment_4 5.993 437s Investment_5 -14.856 437s Investment_6 2.123 437s Investment_8 6.397 437s Investment_9 2.112 437s Investment_10 5.470 437s Investment_11 -1.255 437s Investment_12 0.000 437s Investment_13 1.474 437s Investment_14 -4.009 437s Investment_15 0.681 437s Investment_16 -0.650 437s Investment_17 -12.749 437s Investment_18 2.420 437s Investment_19 30.694 437s Investment_20 -6.415 437s Investment_21 -2.557 437s Investment_22 -9.910 437s PrivateWages_2 73.399 437s PrivateWages_3 -24.321 437s PrivateWages_4 -54.420 437s PrivateWages_5 44.560 437s PrivateWages_6 4.486 437s PrivateWages_8 -16.693 437s PrivateWages_9 -14.472 437s PrivateWages_10 -17.677 437s PrivateWages_11 5.720 437s PrivateWages_12 0.000 437s PrivateWages_13 -5.570 437s PrivateWages_14 12.450 437s PrivateWages_15 -3.851 437s PrivateWages_16 -5.755 437s PrivateWages_17 28.450 437s PrivateWages_18 0.622 437s PrivateWages_19 -108.357 437s PrivateWages_20 25.397 437s PrivateWages_21 -26.719 437s PrivateWages_22 57.096 437s [1] TRUE 437s > Bread 437s Consumption_(Intercept) Consumption_corpProf Consumption_corpProfLag 437s [1,] 101.7742 -0.858360 -0.3736 437s [2,] -0.8584 0.690973 -0.4670 437s [3,] -0.3736 -0.466994 0.6099 437s [4,] -1.8845 -0.076066 -0.0404 437s [5,] 84.1239 -0.877202 2.8173 437s [6,] -1.7843 0.267204 -0.2636 437s [7,] 0.6061 -0.218819 0.2875 437s [8,] -0.3146 -0.000285 -0.0152 437s [9,] -36.6570 0.120759 1.7724 437s [10,] 0.5673 -0.083944 0.0542 437s [11,] 0.0259 0.084615 -0.0868 437s [12,] 0.2015 0.041756 0.0283 437s Consumption_wages Investment_(Intercept) Investment_corpProf 437s [1,] -1.884465 84.124 -1.7843 437s [2,] -0.076066 -0.877 0.2672 437s [3,] -0.040367 2.817 -0.2636 437s [4,] 0.091823 -2.748 0.0379 437s [5,] -2.748307 2378.068 -36.8158 437s [6,] 0.037919 -36.816 1.2756 437s [7,] -0.038383 31.099 -1.1022 437s [8,] 0.013629 -11.271 0.1659 437s [9,] 0.115318 17.951 -0.1175 437s [10,] -0.000915 1.841 0.0121 437s [11,] -0.000905 -2.197 -0.0106 437s [12,] -0.032751 -1.985 0.0278 437s Investment_corpProfLag Investment_capitalLag PrivateWages_(Intercept) 437s [1,] 0.60609 -3.15e-01 -3.67e+01 437s [2,] -0.21882 -2.85e-04 1.21e-01 437s [3,] 0.28746 -1.52e-02 1.77e+00 437s [4,] -0.03838 1.36e-02 1.15e-01 437s [5,] 31.09923 -1.13e+01 1.80e+01 437s [6,] -1.10217 1.66e-01 -1.17e-01 437s [7,] 1.17984 -1.58e-01 -9.59e-01 437s [8,] -0.15817 5.51e-02 7.31e-04 437s [9,] -0.95890 7.31e-04 7.88e+01 437s [10,] 0.00248 -1.04e-02 -5.11e-01 437s [11,] 0.01419 1.07e-02 -8.12e-01 437s [12,] -0.04010 1.08e-02 9.53e-01 437s PrivateWages_gnp PrivateWages_gnpLag PrivateWages_trend 437s [1,] 0.567318 0.025878 0.20145 437s [2,] -0.083944 0.084615 0.04176 437s [3,] 0.054179 -0.086845 0.02834 437s [4,] -0.000915 -0.000905 -0.03275 437s [5,] 1.840734 -2.196531 -1.98486 437s [6,] 0.012109 -0.010622 0.02782 437s [7,] 0.002479 0.014187 -0.04010 437s [8,] -0.010386 0.010690 0.01081 437s [9,] -0.511083 -0.811688 0.95314 437s [10,] 0.063161 -0.056453 -0.01901 437s [11,] -0.056453 0.072451 0.00297 437s [12,] -0.019011 0.002975 0.05128 437s > 437s > # I3SLS 438s > summary 438s 438s systemfit results 438s method: iterated 3SLS 438s 438s convergence achieved after 22 iterations 438s 438s N DF SSR detRCov OLS-R2 McElroy-R2 438s system 60 48 107 0.47 0.946 0.996 438s 438s N DF SSR MSE RMSE R2 Adj R2 438s Consumption 20 16 18.1 1.13 1.063 0.981 0.977 438s Investment 20 16 76.4 4.77 2.185 0.672 0.610 438s PrivateWages 20 16 12.3 0.77 0.877 0.984 0.982 438s 438s The covariance matrix of the residuals used for estimation 438s Consumption Investment PrivateWages 438s Consumption 0.905 0.509 -0.437 438s Investment 0.509 3.819 0.709 438s PrivateWages -0.437 0.709 0.616 438s 438s The covariance matrix of the residuals 438s Consumption Investment PrivateWages 438s Consumption 0.905 0.509 -0.437 438s Investment 0.509 3.819 0.709 438s PrivateWages -0.437 0.709 0.616 438s 438s The correlations of the residuals 438s Consumption Investment PrivateWages 438s Consumption 1.000 0.274 -0.585 438s Investment 0.274 1.000 0.462 438s PrivateWages -0.585 0.462 1.000 438s 438s 438s 3SLS estimates for 'Consumption' (equation 1) 438s Model Formula: consump ~ corpProf + corpProfLag + wages 438s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 438s gnpLag 438s 438s Estimate Std. Error t value Pr(>|t|) 438s (Intercept) 16.4728 1.2187 13.52 3.6e-10 *** 438s corpProf 0.1642 0.0952 1.73 0.10 438s corpProfLag 0.1552 0.0903 1.72 0.11 438s wages 0.7756 0.0356 21.82 2.5e-13 *** 438s --- 438s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 438s 438s Residual standard error: 1.063 on 16 degrees of freedom 438s Number of observations: 20 Degrees of Freedom: 16 438s SSR: 18.095 MSE: 1.131 Root MSE: 1.063 438s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.977 438s 438s 438s 3SLS estimates for 'Investment' (equation 2) 438s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 438s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 438s gnpLag 438s 438s Estimate Std. Error t value Pr(>|t|) 438s (Intercept) 38.7938 9.7249 3.99 0.00106 ** 438s corpProf -0.2501 0.2337 -1.07 0.30036 438s corpProfLag 0.9129 0.2271 4.02 0.00099 *** 438s capitalLag -0.2409 0.0469 -5.14 9.9e-05 *** 438s --- 438s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 438s 438s Residual standard error: 2.185 on 16 degrees of freedom 438s Number of observations: 20 Degrees of Freedom: 16 438s SSR: 76.371 MSE: 4.773 Root MSE: 2.185 438s Multiple R-Squared: 0.672 Adjusted R-Squared: 0.61 438s 438s 438s 3SLS estimates for 'PrivateWages' (equation 3) 438s Model Formula: privWage ~ gnp + gnpLag + trend 438s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 438s gnpLag 438s 438s Estimate Std. Error t value Pr(>|t|) 438s (Intercept) 2.4620 1.2228 2.01 0.061 . 438s gnp 0.3776 0.0318 11.88 2.4e-09 *** 438s gnpLag 0.1937 0.0331 5.85 2.5e-05 *** 438s trend 0.1619 0.0300 5.40 5.9e-05 *** 438s --- 438s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 438s 438s Residual standard error: 0.877 on 16 degrees of freedom 438s Number of observations: 20 Degrees of Freedom: 16 438s SSR: 12.318 MSE: 0.77 Root MSE: 0.877 438s Multiple R-Squared: 0.984 Adjusted R-Squared: 0.982 438s 438s > residuals 438s Consumption Investment PrivateWages 438s 1 NA NA NA 438s 2 -0.4522 -3.4485 -1.2596 438s 3 -1.1470 0.0027 0.5437 438s 4 -1.6147 0.0274 1.6290 438s 5 -0.6117 -2.0392 -0.0707 438s 6 -0.1229 0.0457 -0.1859 438s 7 NA NA NA 438s 8 1.2461 1.4658 -0.6304 438s 9 1.0158 1.4202 0.3924 438s 10 -0.6460 3.2062 1.3671 438s 11 -0.0554 -1.7386 -0.4891 438s 12 -0.3472 -1.3793 0.0179 438s 13 -0.3947 -2.2646 -0.6968 438s 14 0.6536 2.4092 0.1021 438s 15 0.0821 -0.2787 0.1482 438s 16 0.1381 0.1196 -0.0796 438s 17 1.8826 2.5548 -0.6862 438s 18 -0.3415 -0.4009 0.8755 438s 19 0.2296 -4.0454 -0.9839 438s 20 1.3178 1.4481 -0.1989 438s 21 1.0065 0.9087 -0.9681 438s 22 -1.8388 1.9868 1.1734 438s > fitted 438s Consumption Investment PrivateWages 438s 1 NA NA NA 438s 2 42.4 3.249 26.8 438s 3 46.1 1.897 28.8 438s 4 50.8 5.173 32.5 438s 5 51.2 5.039 34.0 438s 6 52.7 5.054 35.6 438s 7 NA NA NA 438s 8 55.0 2.734 38.5 438s 9 56.3 1.580 38.8 438s 10 58.4 1.894 39.9 438s 11 55.1 2.739 38.4 438s 12 51.2 -2.021 34.5 438s 13 46.0 -3.935 29.7 438s 14 45.8 -7.509 28.4 438s 15 48.6 -2.721 30.5 438s 16 51.2 -1.420 33.3 438s 17 55.8 -0.455 37.5 438s 18 59.0 2.401 40.1 438s 19 57.3 2.145 39.2 438s 20 60.3 -0.148 41.8 438s 21 64.0 2.391 46.0 438s 22 71.5 2.913 52.1 438s > predict 438s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 438s 1 NA NA NA NA 438s 2 42.4 0.437 41.5 43.2 438s 3 46.1 0.492 45.2 47.1 438s 4 50.8 0.321 50.2 51.5 438s 5 51.2 0.369 50.5 52.0 438s 6 52.7 0.372 52.0 53.5 438s 7 NA NA NA NA 438s 8 55.0 0.310 54.3 55.6 438s 9 56.3 0.338 55.6 57.0 438s 10 58.4 0.355 57.7 59.2 438s 11 55.1 0.618 53.8 56.3 438s 12 51.2 0.501 50.2 52.3 438s 13 46.0 0.642 44.7 47.3 438s 14 45.8 0.547 44.7 46.9 438s 15 48.6 0.340 47.9 49.3 438s 16 51.2 0.300 50.6 51.8 438s 17 55.8 0.354 55.1 56.5 438s 18 59.0 0.294 58.4 59.6 438s 19 57.3 0.354 56.6 58.0 438s 20 60.3 0.418 59.4 61.1 438s 21 64.0 0.407 63.2 64.8 438s 22 71.5 0.628 70.3 72.8 438s Investment.pred Investment.se.fit Investment.lwr Investment.upr 438s 1 NA NA NA NA 438s 2 3.249 1.160 0.91672 5.580 438s 3 1.897 0.934 0.02009 3.775 438s 4 5.173 0.803 3.55865 6.787 438s 5 5.039 0.693 3.64486 6.433 438s 6 5.054 0.674 3.69840 6.410 438s 7 NA NA NA NA 438s 8 2.734 0.584 1.56002 3.908 438s 9 1.580 0.783 0.00466 3.155 438s 10 1.894 0.868 0.14846 3.639 438s 11 2.739 1.321 0.08241 5.395 438s 12 -2.021 1.064 -4.16036 0.119 438s 13 -3.935 1.349 -6.64712 -1.224 438s 14 -7.509 1.360 -10.24349 -4.775 438s 15 -2.721 0.712 -4.15288 -1.290 438s 16 -1.420 0.614 -2.65412 -0.185 438s 17 -0.455 0.751 -1.96433 1.055 438s 18 2.401 0.498 1.39939 3.402 438s 19 2.145 0.698 0.74152 3.549 438s 20 -0.148 0.816 -1.78957 1.493 438s 21 2.391 0.713 0.95855 3.824 438s 22 2.913 0.984 0.93419 4.892 438s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 438s 1 NA NA NA NA 438s 2 26.8 0.347 26.1 27.5 438s 3 28.8 0.348 28.1 29.5 438s 4 32.5 0.354 31.8 33.2 438s 5 34.0 0.263 33.4 34.5 438s 6 35.6 0.274 35.0 36.1 438s 7 NA NA NA NA 438s 8 38.5 0.268 38.0 39.1 438s 9 38.8 0.256 38.3 39.3 438s 10 39.9 0.254 39.4 40.4 438s 11 38.4 0.323 37.7 39.0 438s 12 34.5 0.347 33.8 35.2 438s 13 29.7 0.435 28.8 30.6 438s 14 28.4 0.366 27.7 29.1 438s 15 30.5 0.341 29.8 31.1 438s 16 33.3 0.285 32.7 33.9 438s 17 37.5 0.275 36.9 38.0 438s 18 40.1 0.233 39.7 40.6 438s 19 39.2 0.346 38.5 39.9 438s 20 41.8 0.298 41.2 42.4 438s 21 46.0 0.329 45.3 46.6 438s 22 52.1 0.510 51.1 53.2 438s > model.frame 438s [1] TRUE 438s > model.matrix 438s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0323 >" 438s [2] "Attributes: < Component “dimnames”: Component 1: 55 string mismatches >" 438s [3] "Numeric: lengths (744, 720) differ" 438s > nobs 438s [1] 60 438s > linearHypothesis 438s Linear hypothesis test (Theil's F test) 438s 438s Hypothesis: 438s Consumption_corpProf + Investment_capitalLag = 0 438s 438s Model 1: restricted model 438s Model 2: kleinModel 438s 438s Res.Df Df F Pr(>F) 438s 1 49 438s 2 48 1 0.4 0.53 438s Linear hypothesis test (F statistic of a Wald test) 438s 438s Hypothesis: 438s Consumption_corpProf + Investment_capitalLag = 0 438s 438s Model 1: restricted model 438s Model 2: kleinModel 438s 438s Res.Df Df F Pr(>F) 438s 1 49 438s 2 48 1 0.5 0.49 438s Linear hypothesis test (Chi^2 statistic of a Wald test) 438s 438s Hypothesis: 438s Consumption_corpProf + Investment_capitalLag = 0 438s 438s Model 1: restricted model 438s Model 2: kleinModel 438s 438s Res.Df Df Chisq Pr(>Chisq) 438s 1 49 438s 2 48 1 0.5 0.48 438s Linear hypothesis test (Theil's F test) 438s 438s Hypothesis: 438s Consumption_corpProf + Investment_capitalLag = 0 438s Consumption_corpProfLag - PrivateWages_trend = 0 438s 438s Model 1: restricted model 438s Model 2: kleinModel 438s 438s Res.Df Df F Pr(>F) 438s 1 50 438s 2 48 2 0.66 0.52 438s Linear hypothesis test (F statistic of a Wald test) 438s 438s Hypothesis: 438s Consumption_corpProf + Investment_capitalLag = 0 438s Consumption_corpProfLag - PrivateWages_trend = 0 438s 438s Model 1: restricted model 438s Model 2: kleinModel 438s 438s Res.Df Df F Pr(>F) 438s 1 50 438s 2 48 2 0.83 0.44 438s Linear hypothesis test (Chi^2 statistic of a Wald test) 438s 438s Hypothesis: 438s Consumption_corpProf + Investment_capitalLag = 0 438s Consumption_corpProfLag - PrivateWages_trend = 0 438s 438s Model 1: restricted model 438s Model 2: kleinModel 438s 438s Res.Df Df Chisq Pr(>Chisq) 438s 1 50 438s 2 48 2 1.66 0.44 438s > logLik 438s 'log Lik.' -77.6 (df=18) 438s 'log Lik.' -92.7 (df=18) 438s Estimating function 438s Consumption_(Intercept) Consumption_corpProf 438s Consumption_2 -4.9216 -63.77 438s Consumption_3 -3.3974 -56.75 438s Consumption_4 -2.5781 -49.33 438s Consumption_5 -9.6538 -202.12 438s Consumption_6 -0.8124 -15.82 438s Consumption_8 11.9408 204.68 438s Consumption_9 6.9299 135.05 438s Consumption_10 1.8984 38.85 438s Consumption_11 -4.8868 -82.32 438s Consumption_12 -2.6585 -33.71 438s Consumption_13 -5.0990 -45.49 438s Consumption_14 7.0717 65.77 438s Consumption_15 -3.1138 -39.84 438s Consumption_16 -1.6973 -24.20 438s Consumption_17 16.7458 247.03 438s Consumption_18 -2.5779 -50.39 438s Consumption_19 -12.5621 -243.19 438s Consumption_20 9.4057 163.53 438s Consumption_21 4.0953 82.31 438s Consumption_22 -4.1289 -94.39 438s Investment_2 4.3863 56.84 438s Investment_3 0.0612 1.02 438s Investment_4 -0.2801 -5.36 438s Investment_5 2.1936 45.93 438s Investment_6 0.1486 2.89 438s Investment_8 -1.0616 -18.20 438s Investment_9 -1.3484 -26.28 438s Investment_10 -3.8396 -78.57 438s Investment_11 1.8918 31.87 438s Investment_12 1.4041 17.80 438s Investment_13 2.3647 21.10 438s Investment_14 -2.5638 -23.84 438s Investment_15 0.2053 2.63 438s Investment_16 -0.2445 -3.49 438s Investment_17 -2.4423 -36.03 438s Investment_18 -0.2128 -4.16 438s Investment_19 4.0168 77.76 438s Investment_20 -1.3846 -24.07 438s Investment_21 -0.8726 -17.54 438s Investment_22 -2.4220 -55.37 438s PrivateWages_2 -7.8312 -101.48 438s PrivateWages_3 3.1927 53.33 438s PrivateWages_4 8.1013 155.02 438s PrivateWages_5 -6.1495 -128.75 438s PrivateWages_6 -0.1677 -3.26 438s PrivateWages_8 4.4536 76.34 438s PrivateWages_9 5.3302 103.88 438s PrivateWages_10 9.8611 201.78 438s PrivateWages_11 -6.2042 -104.51 438s PrivateWages_12 -2.2572 -28.62 438s PrivateWages_13 -7.3701 -65.76 438s PrivateWages_14 5.2841 49.14 438s PrivateWages_15 -1.8316 -23.44 438s PrivateWages_16 -1.8732 -26.71 438s PrivateWages_17 5.6855 83.87 438s PrivateWages_18 0.2354 4.60 438s PrivateWages_19 -16.6516 -322.36 438s PrivateWages_20 3.4690 60.31 438s PrivateWages_21 -2.8192 -56.66 438s PrivateWages_22 7.5425 172.43 438s Consumption_corpProfLag Consumption_wages 438s Consumption_2 -62.504 -143.28 438s Consumption_3 -42.128 -108.30 438s Consumption_4 -43.571 -91.80 438s Consumption_5 -177.629 -376.98 438s Consumption_6 -15.760 -31.55 438s Consumption_8 234.039 474.72 438s Consumption_9 137.212 292.99 438s Consumption_10 40.056 83.73 438s Consumption_11 -106.045 -211.93 438s Consumption_12 -41.472 -104.88 438s Consumption_13 -58.128 -178.75 438s Consumption_14 49.502 233.56 438s Consumption_15 -34.874 -117.14 438s Consumption_16 -20.877 -67.86 438s Consumption_17 234.441 699.00 438s Consumption_18 -45.372 -122.79 438s Consumption_19 -217.325 -621.24 438s Consumption_20 143.908 454.84 438s Consumption_21 77.811 217.74 438s Consumption_22 -87.120 -251.52 438s Investment_2 55.705 127.69 438s Investment_3 0.759 1.95 438s Investment_4 -4.734 -9.97 438s Investment_5 40.363 85.66 438s Investment_6 2.882 5.77 438s Investment_8 -20.807 -42.21 438s Investment_9 -26.697 -57.01 438s Investment_10 -81.017 -169.36 438s Investment_11 41.052 82.04 438s Investment_12 21.904 55.40 438s Investment_13 26.957 82.89 438s Investment_14 -17.946 -84.67 438s Investment_15 2.299 7.72 438s Investment_16 -3.007 -9.77 438s Investment_17 -34.192 -101.95 438s Investment_18 -3.746 -10.14 438s Investment_19 69.491 198.65 438s Investment_20 -21.185 -66.96 438s Investment_21 -16.580 -46.40 438s Investment_22 -51.104 -147.54 438s PrivateWages_2 -99.457 -227.98 438s PrivateWages_3 39.589 101.77 438s PrivateWages_4 136.911 288.46 438s PrivateWages_5 -113.151 -240.14 438s PrivateWages_6 -3.252 -6.51 438s PrivateWages_8 87.291 177.06 438s PrivateWages_9 105.538 225.36 438s PrivateWages_10 208.070 434.95 438s PrivateWages_11 -134.631 -269.05 438s PrivateWages_12 -35.213 -89.05 438s PrivateWages_13 -84.019 -258.36 438s PrivateWages_14 36.989 174.52 438s PrivateWages_15 -20.514 -68.91 438s PrivateWages_16 -23.040 -74.89 438s PrivateWages_17 79.598 237.33 438s PrivateWages_18 4.143 11.21 438s PrivateWages_19 -288.073 -823.48 438s PrivateWages_20 53.076 167.75 438s PrivateWages_21 -53.565 -149.89 438s PrivateWages_22 159.147 459.47 438s Investment_(Intercept) Investment_corpProf 438s Consumption_2 1.6584 21.489 438s Consumption_3 1.1448 19.123 438s Consumption_4 0.8687 16.623 438s Consumption_5 3.2529 68.104 438s Consumption_6 0.2737 5.329 438s Consumption_8 -4.0235 -68.968 438s Consumption_9 -2.3351 -45.507 438s Consumption_10 -0.6397 -13.089 438s Consumption_11 1.6466 27.739 438s Consumption_12 0.8958 11.358 438s Consumption_13 1.7181 15.329 438s Consumption_14 -2.3828 -22.161 438s Consumption_15 1.0492 13.424 438s Consumption_16 0.5719 8.155 438s Consumption_17 -5.6426 -83.238 438s Consumption_18 0.8686 16.978 438s Consumption_19 4.2329 81.944 438s Consumption_20 -3.1693 -55.102 438s Consumption_21 -1.3799 -27.735 438s Consumption_22 1.3913 31.806 438s Investment_2 -2.5801 -33.433 438s Investment_3 -0.0360 -0.601 438s Investment_4 0.1648 3.153 438s Investment_5 -1.2904 -27.016 438s Investment_6 -0.0874 -1.701 438s Investment_8 0.6245 10.704 438s Investment_9 0.7931 15.457 438s Investment_10 2.2586 46.215 438s Investment_11 -1.1128 -18.746 438s Investment_12 -0.8259 -10.473 438s Investment_13 -1.3910 -12.410 438s Investment_14 1.5081 14.026 438s Investment_15 -0.1208 -1.545 438s Investment_16 0.1438 2.050 438s Investment_17 1.4366 21.193 438s Investment_18 0.1252 2.447 438s Investment_19 -2.3628 -45.741 438s Investment_20 0.8145 14.161 438s Investment_21 0.5133 10.317 438s Investment_22 1.4247 32.570 438s PrivateWages_2 3.3346 43.210 438s PrivateWages_3 -1.3594 -22.709 438s PrivateWages_4 -3.4495 -66.008 438s PrivateWages_5 2.6185 54.822 438s PrivateWages_6 0.0714 1.390 438s PrivateWages_8 -1.8964 -32.506 438s PrivateWages_9 -2.2696 -44.232 438s PrivateWages_10 -4.1989 -85.919 438s PrivateWages_11 2.6418 44.502 438s PrivateWages_12 0.9611 12.187 438s PrivateWages_13 3.1382 27.999 438s PrivateWages_14 -2.2500 -20.926 438s PrivateWages_15 0.7799 9.979 438s PrivateWages_16 0.7976 11.373 438s PrivateWages_17 -2.4209 -35.713 438s PrivateWages_18 -0.1002 -1.959 438s PrivateWages_19 7.0903 137.261 438s PrivateWages_20 -1.4771 -25.682 438s PrivateWages_21 1.2004 24.127 438s PrivateWages_22 -3.2116 -73.422 438s Investment_corpProfLag Investment_capitalLag 438s Consumption_2 21.061 303.15 438s Consumption_3 14.195 209.04 438s Consumption_4 14.681 160.28 438s Consumption_5 59.853 617.07 438s Consumption_6 5.310 52.75 438s Consumption_8 -78.860 -818.38 438s Consumption_9 -46.234 -484.76 438s Consumption_10 -13.497 -134.72 438s Consumption_11 35.732 355.18 438s Consumption_12 13.974 194.12 438s Consumption_13 19.587 366.47 438s Consumption_14 -16.680 -493.49 438s Consumption_15 11.751 211.94 438s Consumption_16 7.034 113.81 438s Consumption_17 -78.996 -1115.54 438s Consumption_18 15.288 173.56 438s Consumption_19 73.229 854.19 438s Consumption_20 -48.490 -633.54 438s Consumption_21 -26.219 -277.64 438s Consumption_22 29.355 284.51 438s Investment_2 -32.767 -471.64 438s Investment_3 -0.446 -6.57 438s Investment_4 2.785 30.40 438s Investment_5 -23.742 -244.78 438s Investment_6 -1.695 -16.84 438s Investment_8 12.239 127.02 438s Investment_9 15.704 164.66 438s Investment_10 47.656 475.66 438s Investment_11 -24.148 -240.03 438s Investment_12 -12.884 -178.98 438s Investment_13 -15.857 -296.69 438s Investment_14 10.556 312.32 438s Investment_15 -1.352 -24.39 438s Investment_16 1.769 28.62 438s Investment_17 20.113 284.02 438s Investment_18 2.203 25.01 438s Investment_19 -40.876 -476.81 438s Investment_20 12.461 162.81 438s Investment_21 9.753 103.28 438s Investment_22 30.061 291.35 438s PrivateWages_2 42.349 609.56 438s PrivateWages_3 -16.857 -248.23 438s PrivateWages_4 -58.297 -636.44 438s PrivateWages_5 48.180 496.72 438s PrivateWages_6 1.385 13.76 438s PrivateWages_8 -37.169 -385.72 438s PrivateWages_9 -44.939 -471.17 438s PrivateWages_10 -88.597 -884.29 438s PrivateWages_11 57.326 569.83 438s PrivateWages_12 14.994 208.28 438s PrivateWages_13 35.776 669.38 438s PrivateWages_14 -15.750 -465.97 438s PrivateWages_15 8.735 157.54 438s PrivateWages_16 9.810 158.72 438s PrivateWages_17 -33.893 -478.62 438s PrivateWages_18 -1.764 -20.03 438s PrivateWages_19 122.662 1430.82 438s PrivateWages_20 -22.600 -295.28 438s PrivateWages_21 22.808 241.53 438s PrivateWages_22 -67.765 -656.78 438s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 438s Consumption_2 -5.3990 -254.13 -242.42 438s Consumption_3 -3.7270 -184.79 -169.95 438s Consumption_4 -2.8282 -159.92 -141.69 438s Consumption_5 -10.5903 -642.68 -605.76 438s Consumption_6 -0.8912 -54.02 -50.89 438s Consumption_8 13.0991 785.91 838.34 438s Consumption_9 7.6022 473.37 489.58 438s Consumption_10 2.0826 134.47 134.33 438s Consumption_11 -5.3609 -341.55 -359.18 438s Consumption_12 -2.9163 -159.91 -178.48 438s Consumption_13 -5.5936 -262.77 -298.70 438s Consumption_14 7.7577 326.81 343.67 438s Consumption_15 -3.4158 -174.95 -154.05 438s Consumption_16 -1.8619 -103.04 -92.54 438s Consumption_17 18.3702 1054.34 999.34 438s Consumption_18 -2.8280 -189.97 -177.32 438s Consumption_19 -13.7808 -944.16 -895.75 438s Consumption_20 10.3182 689.71 628.38 438s Consumption_21 4.4926 336.34 312.24 438s Consumption_22 -4.5294 -393.51 -342.88 438s Investment_2 6.0805 286.21 273.02 438s Investment_3 0.0848 4.21 3.87 438s Investment_4 -0.3883 -21.96 -19.45 438s Investment_5 3.0410 184.55 173.94 438s Investment_6 0.2060 12.48 11.76 438s Investment_8 -1.4717 -88.30 -94.19 438s Investment_9 -1.8692 -116.39 -120.38 438s Investment_10 -5.3228 -343.69 -343.32 438s Investment_11 2.6225 167.09 175.71 438s Investment_12 1.9465 106.73 119.12 438s Investment_13 3.2781 154.00 175.05 438s Investment_14 -3.5541 -149.72 -157.44 438s Investment_15 0.2846 14.58 12.84 438s Investment_16 -0.3389 -18.75 -16.84 438s Investment_17 -3.3857 -194.32 -184.18 438s Investment_18 -0.2951 -19.82 -18.50 438s Investment_19 5.5684 381.50 361.95 438s Investment_20 -1.9195 -128.31 -116.90 438s Investment_21 -1.2097 -90.57 -84.07 438s Investment_22 -3.3575 -291.70 -254.16 438s PrivateWages_2 -12.3381 -580.75 -553.98 438s PrivateWages_3 5.0300 249.39 229.37 438s PrivateWages_4 12.7635 721.68 639.45 438s PrivateWages_5 -9.6885 -587.96 -554.18 438s PrivateWages_6 -0.2641 -16.01 -15.08 438s PrivateWages_8 7.0167 420.99 449.07 438s PrivateWages_9 8.3978 522.92 540.82 438s PrivateWages_10 15.5362 1003.16 1002.09 438s PrivateWages_11 -9.7747 -622.76 -654.90 438s PrivateWages_12 -3.5562 -195.00 -217.64 438s PrivateWages_13 -11.6116 -545.48 -620.06 438s PrivateWages_14 8.3251 350.72 368.80 438s PrivateWages_15 -2.8858 -147.80 -130.15 438s PrivateWages_16 -2.9512 -163.31 -146.67 438s PrivateWages_17 8.9576 514.11 487.29 438s PrivateWages_18 0.3709 24.92 23.26 438s PrivateWages_19 -26.2346 -1797.40 -1705.25 438s PrivateWages_20 5.4654 365.33 332.84 438s PrivateWages_21 -4.4417 -332.53 -308.70 438s PrivateWages_22 11.8832 1032.40 899.56 438s PrivateWages_trend 438s Consumption_2 53.990 438s Consumption_3 33.543 438s Consumption_4 22.626 438s Consumption_5 74.132 438s Consumption_6 5.347 438s Consumption_8 -52.396 438s Consumption_9 -22.806 438s Consumption_10 -4.165 438s Consumption_11 5.361 438s Consumption_12 0.000 438s Consumption_13 -5.594 438s Consumption_14 15.515 438s Consumption_15 -10.247 438s Consumption_16 -7.448 438s Consumption_17 91.851 438s Consumption_18 -16.968 438s Consumption_19 -96.465 438s Consumption_20 82.545 438s Consumption_21 40.433 438s Consumption_22 -45.294 438s Investment_2 -60.805 438s Investment_3 -0.763 438s Investment_4 3.106 438s Investment_5 -21.287 438s Investment_6 -1.236 438s Investment_8 5.887 438s Investment_9 5.608 438s Investment_10 10.646 438s Investment_11 -2.623 438s Investment_12 0.000 438s Investment_13 3.278 438s Investment_14 -7.108 438s Investment_15 0.854 438s Investment_16 -1.356 438s Investment_17 -16.928 438s Investment_18 -1.770 438s Investment_19 38.979 438s Investment_20 -15.356 438s Investment_21 -10.887 438s Investment_22 -33.575 438s PrivateWages_2 123.381 438s PrivateWages_3 -45.270 438s PrivateWages_4 -102.108 438s PrivateWages_5 67.820 438s PrivateWages_6 1.585 438s PrivateWages_8 -28.067 438s PrivateWages_9 -25.193 438s PrivateWages_10 -31.072 438s PrivateWages_11 9.775 438s PrivateWages_12 0.000 438s PrivateWages_13 -11.612 438s PrivateWages_14 16.650 438s PrivateWages_15 -8.657 438s PrivateWages_16 -11.805 438s PrivateWages_17 44.788 438s PrivateWages_18 2.225 438s PrivateWages_19 -183.642 438s PrivateWages_20 43.723 438s PrivateWages_21 -39.975 438s PrivateWages_22 118.832 438s [1] TRUE 438s > Bread 438s Consumption_(Intercept) Consumption_corpProf Consumption_corpProfLag 438s [1,] 89.117 -0.7628 -0.3161 438s [2,] -0.763 0.5437 -0.3702 438s [3,] -0.316 -0.3702 0.4897 438s [4,] -1.650 -0.0567 -0.0339 438s [5,] 127.149 -5.8142 6.0484 438s [6,] -2.757 0.6390 -0.5640 438s [7,] 0.822 -0.5332 0.6080 438s [8,] -0.462 0.0186 -0.0321 438s [9,] -41.723 0.1554 1.5996 438s [10,] 0.652 -0.0670 0.0422 438s [11,] 0.023 0.0665 -0.0715 438s [12,] 0.266 0.0460 0.0263 438s Consumption_wages Investment_(Intercept) Investment_corpProf 438s [1,] -1.649949 127.15 -2.7567 438s [2,] -0.056675 -5.81 0.6390 438s [3,] -0.033922 6.05 -0.5640 438s [4,] 0.075837 -3.04 0.0284 438s [5,] -3.037786 5674.46 -81.6232 438s [6,] 0.028439 -81.62 3.2764 438s [7,] -0.041721 66.55 -2.7837 438s [8,] 0.016133 -26.78 0.3579 438s [9,] 0.286845 49.74 -0.5482 438s [10,] -0.005120 5.39 0.0206 438s [11,] 0.000492 -6.38 -0.0122 438s [12,] -0.035219 -5.00 0.0650 438s Investment_corpProfLag Investment_capitalLag PrivateWages_(Intercept) 438s [1,] 0.8223 -0.4623 -41.7225 438s [2,] -0.5332 0.0186 0.1554 438s [3,] 0.6080 -0.0321 1.5996 438s [4,] -0.0417 0.0161 0.2868 438s [5,] 66.5535 -26.7802 49.7422 438s [6,] -2.7837 0.3579 -0.5482 438s [7,] 3.0944 -0.3490 -2.9105 438s [8,] -0.3490 0.1318 0.0433 438s [9,] -2.9105 0.0433 89.7087 438s [10,] 0.0256 -0.0306 -0.7102 438s [11,] 0.0243 0.0308 -0.7883 438s [12,] -0.1021 0.0277 0.9946 438s PrivateWages_gnp PrivateWages_gnpLag PrivateWages_trend 438s [1,] 0.65175 0.023034 0.26557 438s [2,] -0.06703 0.066494 0.04602 438s [3,] 0.04225 -0.071498 0.02630 438s [4,] -0.00512 0.000492 -0.03522 438s [5,] 5.38683 -6.377135 -4.99571 438s [6,] 0.02064 -0.012164 0.06501 438s [7,] 0.02556 0.024313 -0.10213 438s [8,] -0.03064 0.030839 0.02771 438s [9,] -0.71025 -0.788347 0.99462 438s [10,] 0.06062 -0.050369 -0.02195 438s [11,] -0.05037 0.065741 0.00529 438s [12,] -0.02195 0.005286 0.05391 438s > 438s > # OLS 438s Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 438s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 438s > summary 438s 438s systemfit results 438s method: OLS 438s 438s N DF SSR detRCov OLS-R2 McElroy-R2 438s system 61 49 44.5 0.382 0.977 0.99 438s 438s N DF SSR MSE RMSE R2 Adj R2 438s Consumption 20 16 17.48 1.093 1.04 0.981 0.978 438s Investment 21 17 17.32 1.019 1.01 0.931 0.919 438s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 438s 438s The covariance matrix of the residuals 438s Consumption Investment PrivateWages 438s Consumption 1.124 0.034 -0.442 438s Investment 0.034 0.928 0.130 438s PrivateWages -0.442 0.130 0.563 438s 438s The correlations of the residuals 438s Consumption Investment PrivateWages 438s Consumption 1.0000 0.0266 -0.563 438s Investment 0.0266 1.0000 0.169 438s PrivateWages -0.5630 0.1689 1.000 438s 438s 438s OLS estimates for 'Consumption' (equation 1) 438s Model Formula: consump ~ corpProf + corpProfLag + wages 438s 438s Estimate Std. Error t value Pr(>|t|) 438s (Intercept) 16.1357 1.3571 11.89 2.4e-09 *** 438s corpProf 0.1994 0.0949 2.10 0.052 . 438s corpProfLag 0.0969 0.0944 1.03 0.320 438s wages 0.7940 0.0415 19.16 1.9e-12 *** 438s --- 438s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 438s 438s Residual standard error: 1.045 on 16 degrees of freedom 438s Number of observations: 20 Degrees of Freedom: 16 438s SSR: 17.481 MSE: 1.093 Root MSE: 1.045 438s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.978 438s 438s 438s OLS estimates for 'Investment' (equation 2) 438s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 438s 438s Estimate Std. Error t value Pr(>|t|) 438s (Intercept) 10.1258 5.2164 1.94 0.06901 . 438s corpProf 0.4796 0.0927 5.17 7.6e-05 *** 438s corpProfLag 0.3330 0.0963 3.46 0.00299 ** 438s capitalLag -0.1118 0.0255 -4.38 0.00041 *** 438s --- 438s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 438s 438s Residual standard error: 1.009 on 17 degrees of freedom 438s Number of observations: 21 Degrees of Freedom: 17 438s SSR: 17.323 MSE: 1.019 Root MSE: 1.009 438s Multiple R-Squared: 0.931 Adjusted R-Squared: 0.919 438s 438s 438s OLS estimates for 'PrivateWages' (equation 3) 438s Model Formula: privWage ~ gnp + gnpLag + trend 438s 438s Estimate Std. Error t value Pr(>|t|) 438s (Intercept) 1.3550 1.2591 1.08 0.2978 438s gnp 0.4417 0.0319 13.86 2.5e-10 *** 438s gnpLag 0.1466 0.0366 4.01 0.0010 ** 438s trend 0.1244 0.0323 3.85 0.0014 ** 438s --- 438s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 438s 438s Residual standard error: 0.78 on 16 degrees of freedom 438s Number of observations: 20 Degrees of Freedom: 16 438s SSR: 9.739 MSE: 0.609 Root MSE: 0.78 438s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 438s 438s compare coef with single-equation OLS 438s [1] TRUE 438s > residuals 438s Consumption Investment PrivateWages 438s 1 NA NA NA 438s 2 -0.3304 -0.0668 -1.3389 438s 3 -1.2748 -0.0476 0.2462 438s 4 -1.6213 1.2467 1.1255 438s 5 -0.5661 -1.3512 -0.1959 438s 6 -0.0730 0.4154 -0.5284 438s 7 0.7915 1.4923 NA 438s 8 1.2648 0.7889 -0.7909 438s 9 0.9746 -0.6317 0.2819 438s 10 NA 1.0830 1.1384 438s 11 0.2225 0.2791 -0.1904 438s 12 -0.2256 0.0369 0.5813 438s 13 -0.2711 0.3659 0.1206 438s 14 0.3765 0.2237 0.4773 438s 15 -0.0349 -0.1728 0.3035 438s 16 -0.0243 0.0101 0.0284 438s 17 1.6023 0.9719 -0.8517 438s 18 -0.4658 0.0516 0.9908 438s 19 0.1914 -2.5656 -0.4597 438s 20 0.9683 -0.6866 -0.3819 438s 21 0.7325 -0.7807 -1.1062 438s 22 -2.2370 -0.6623 0.5501 438s > fitted 438s Consumption Investment PrivateWages 438s 1 NA NA NA 438s 2 42.2 -0.133 26.8 438s 3 46.3 1.948 29.1 438s 4 50.8 3.953 33.0 438s 5 51.2 4.351 34.1 438s 6 52.7 4.685 35.9 438s 7 54.3 4.108 NA 438s 8 54.9 3.411 38.7 438s 9 56.3 3.632 38.9 438s 10 NA 4.017 40.2 438s 11 54.8 0.721 38.1 438s 12 51.1 -3.437 33.9 438s 13 45.9 -6.566 28.9 438s 14 46.1 -5.324 28.0 438s 15 48.7 -2.827 30.3 438s 16 51.3 -1.310 33.2 438s 17 56.1 1.128 37.7 438s 18 59.2 1.948 40.0 438s 19 57.3 0.666 38.7 438s 20 60.6 1.987 42.0 438s 21 64.3 4.081 46.1 438s 22 71.9 5.562 52.7 438s > predict 438s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 438s 1 NA NA NA NA 438s 2 42.2 0.478 39.9 44.5 438s 3 46.3 0.537 43.9 48.6 438s 4 50.8 0.364 48.6 53.0 438s 5 51.2 0.427 48.9 53.4 438s 6 52.7 0.433 50.4 54.9 438s 7 54.3 0.394 52.1 56.6 438s 8 54.9 0.360 52.7 57.2 438s 9 56.3 0.387 54.1 58.6 438s 10 NA NA NA NA 438s 11 54.8 0.635 52.3 57.2 438s 12 51.1 0.501 48.8 53.5 438s 13 45.9 0.656 43.4 48.4 438s 14 46.1 0.629 43.7 48.6 438s 15 48.7 0.389 46.5 51.0 438s 16 51.3 0.345 49.1 53.5 438s 17 56.1 0.379 53.9 58.3 438s 18 59.2 0.336 57.0 61.4 438s 19 57.3 0.385 55.1 59.5 438s 20 60.6 0.450 58.3 62.9 438s 21 64.3 0.448 62.0 66.6 438s 22 71.9 0.697 69.4 74.5 438s Investment.pred Investment.se.fit Investment.lwr Investment.upr 438s 1 NA NA NA NA 438s 2 -0.133 0.579 -2.472 2.206 438s 3 1.948 0.476 -0.295 4.190 438s 4 3.953 0.428 1.750 6.157 438s 5 4.351 0.354 2.202 6.501 438s 6 4.685 0.333 2.548 6.821 438s 7 4.108 0.314 1.983 6.232 438s 8 3.411 0.279 1.306 5.516 438s 9 3.632 0.371 1.470 5.793 438s 10 4.017 0.426 1.815 6.219 438s 11 0.721 0.574 -1.613 3.054 438s 12 -3.437 0.484 -5.686 -1.188 438s 13 -6.566 0.588 -8.913 -4.219 438s 14 -5.324 0.662 -7.750 -2.898 438s 15 -2.827 0.356 -4.978 -0.676 438s 16 -1.310 0.305 -3.429 0.809 438s 17 1.128 0.332 -1.007 3.263 438s 18 1.948 0.232 -0.133 4.030 438s 19 0.666 0.298 -1.449 2.781 438s 20 1.987 0.350 -0.160 4.133 438s 21 4.081 0.317 1.955 6.207 438s 22 5.562 0.440 3.349 7.775 438s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 438s 1 NA NA NA NA 438s 2 26.8 0.352 25.1 28.6 438s 3 29.1 0.355 27.3 30.8 438s 4 33.0 0.358 31.2 34.7 438s 5 34.1 0.277 32.4 35.8 438s 6 35.9 0.276 34.3 37.6 438s 7 NA NA NA NA 438s 8 38.7 0.282 37.0 40.4 438s 9 38.9 0.268 37.3 40.6 438s 10 40.2 0.255 38.5 41.8 438s 11 38.1 0.351 36.4 39.8 438s 12 33.9 0.355 32.2 35.6 438s 13 28.9 0.421 27.1 30.7 438s 14 28.0 0.370 26.3 29.8 438s 15 30.3 0.364 28.6 32.0 438s 16 33.2 0.304 31.5 34.9 438s 17 37.7 0.298 36.0 39.3 438s 18 40.0 0.233 38.4 41.6 438s 19 38.7 0.349 36.9 40.4 438s 20 42.0 0.314 40.3 43.7 438s 21 46.1 0.328 44.4 47.8 438s 22 52.7 0.494 50.9 54.6 438s > model.frame 438s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 438s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 438s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 438s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 438s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 438s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 438s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 438s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 438s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 438s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 438s 10 57.8 21.7 21.1 NA 5.1 211 41.3 67.0 64.5 438s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 438s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 438s 13 45.6 7.0 11.4 34.3 -6.2 213 29.0 44.3 53.4 438s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 438s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 438s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 438s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 438s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 438s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 438s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 438s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 438s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 438s trend 438s 1 -11 438s 2 -10 438s 3 -9 438s 4 -8 438s 5 -7 438s 6 -6 438s 7 -5 438s 8 -4 438s 9 -3 438s 10 -2 438s 11 -1 438s 12 0 438s 13 1 438s 14 2 438s 15 3 438s 16 4 438s 17 5 438s 18 6 438s 19 7 438s 20 8 438s 21 9 438s 22 10 438s > model.matrix 438s Consumption_(Intercept) Consumption_corpProf 438s Consumption_2 1 12.4 438s Consumption_3 1 16.9 438s Consumption_4 1 18.4 438s Consumption_5 1 19.4 438s Consumption_6 1 20.1 438s Consumption_7 1 19.6 438s Consumption_8 1 19.8 438s Consumption_9 1 21.1 438s Consumption_11 1 15.6 438s Consumption_12 1 11.4 438s Consumption_13 1 7.0 438s Consumption_14 1 11.2 438s Consumption_15 1 12.3 438s Consumption_16 1 14.0 438s Consumption_17 1 17.6 438s Consumption_18 1 17.3 438s Consumption_19 1 15.3 438s Consumption_20 1 19.0 438s Consumption_21 1 21.1 438s Consumption_22 1 23.5 438s Investment_2 0 0.0 438s Investment_3 0 0.0 438s Investment_4 0 0.0 438s Investment_5 0 0.0 438s Investment_6 0 0.0 438s Investment_7 0 0.0 438s Investment_8 0 0.0 438s Investment_9 0 0.0 438s Investment_10 0 0.0 438s Investment_11 0 0.0 438s Investment_12 0 0.0 438s Investment_13 0 0.0 438s Investment_14 0 0.0 438s Investment_15 0 0.0 438s Investment_16 0 0.0 438s Investment_17 0 0.0 438s Investment_18 0 0.0 438s Investment_19 0 0.0 438s Investment_20 0 0.0 438s Investment_21 0 0.0 438s Investment_22 0 0.0 438s PrivateWages_2 0 0.0 438s PrivateWages_3 0 0.0 438s PrivateWages_4 0 0.0 438s PrivateWages_5 0 0.0 438s PrivateWages_6 0 0.0 438s PrivateWages_8 0 0.0 438s PrivateWages_9 0 0.0 438s PrivateWages_10 0 0.0 438s PrivateWages_11 0 0.0 438s PrivateWages_12 0 0.0 438s PrivateWages_13 0 0.0 438s PrivateWages_14 0 0.0 438s PrivateWages_15 0 0.0 438s PrivateWages_16 0 0.0 438s PrivateWages_17 0 0.0 438s PrivateWages_18 0 0.0 438s PrivateWages_19 0 0.0 438s PrivateWages_20 0 0.0 438s PrivateWages_21 0 0.0 438s PrivateWages_22 0 0.0 438s Consumption_corpProfLag Consumption_wages 438s Consumption_2 12.7 28.2 438s Consumption_3 12.4 32.2 438s Consumption_4 16.9 37.0 438s Consumption_5 18.4 37.0 438s Consumption_6 19.4 38.6 438s Consumption_7 20.1 40.7 438s Consumption_8 19.6 41.5 438s Consumption_9 19.8 42.9 438s Consumption_11 21.7 42.1 438s Consumption_12 15.6 39.3 438s Consumption_13 11.4 34.3 438s Consumption_14 7.0 34.1 438s Consumption_15 11.2 36.6 438s Consumption_16 12.3 39.3 438s Consumption_17 14.0 44.2 438s Consumption_18 17.6 47.7 438s Consumption_19 17.3 45.9 438s Consumption_20 15.3 49.4 438s Consumption_21 19.0 53.0 438s Consumption_22 21.1 61.8 438s Investment_2 0.0 0.0 438s Investment_3 0.0 0.0 438s Investment_4 0.0 0.0 438s Investment_5 0.0 0.0 438s Investment_6 0.0 0.0 438s Investment_7 0.0 0.0 438s Investment_8 0.0 0.0 438s Investment_9 0.0 0.0 438s Investment_10 0.0 0.0 438s Investment_11 0.0 0.0 438s Investment_12 0.0 0.0 438s Investment_13 0.0 0.0 438s Investment_14 0.0 0.0 438s Investment_15 0.0 0.0 438s Investment_16 0.0 0.0 438s Investment_17 0.0 0.0 438s Investment_18 0.0 0.0 438s Investment_19 0.0 0.0 438s Investment_20 0.0 0.0 438s Investment_21 0.0 0.0 438s Investment_22 0.0 0.0 438s PrivateWages_2 0.0 0.0 438s PrivateWages_3 0.0 0.0 438s PrivateWages_4 0.0 0.0 438s PrivateWages_5 0.0 0.0 438s PrivateWages_6 0.0 0.0 438s PrivateWages_8 0.0 0.0 438s PrivateWages_9 0.0 0.0 438s PrivateWages_10 0.0 0.0 438s PrivateWages_11 0.0 0.0 438s PrivateWages_12 0.0 0.0 438s PrivateWages_13 0.0 0.0 438s PrivateWages_14 0.0 0.0 438s PrivateWages_15 0.0 0.0 438s PrivateWages_16 0.0 0.0 438s PrivateWages_17 0.0 0.0 438s PrivateWages_18 0.0 0.0 438s PrivateWages_19 0.0 0.0 438s PrivateWages_20 0.0 0.0 438s PrivateWages_21 0.0 0.0 438s PrivateWages_22 0.0 0.0 438s Investment_(Intercept) Investment_corpProf 438s Consumption_2 0 0.0 438s Consumption_3 0 0.0 438s Consumption_4 0 0.0 438s Consumption_5 0 0.0 438s Consumption_6 0 0.0 438s Consumption_7 0 0.0 438s Consumption_8 0 0.0 438s Consumption_9 0 0.0 438s Consumption_11 0 0.0 438s Consumption_12 0 0.0 438s Consumption_13 0 0.0 438s Consumption_14 0 0.0 438s Consumption_15 0 0.0 438s Consumption_16 0 0.0 438s Consumption_17 0 0.0 438s Consumption_18 0 0.0 438s Consumption_19 0 0.0 438s Consumption_20 0 0.0 438s Consumption_21 0 0.0 438s Consumption_22 0 0.0 438s Investment_2 1 12.4 438s Investment_3 1 16.9 438s Investment_4 1 18.4 438s Investment_5 1 19.4 438s Investment_6 1 20.1 438s Investment_7 1 19.6 438s Investment_8 1 19.8 438s Investment_9 1 21.1 438s Investment_10 1 21.7 438s Investment_11 1 15.6 438s Investment_12 1 11.4 438s Investment_13 1 7.0 438s Investment_14 1 11.2 438s Investment_15 1 12.3 438s Investment_16 1 14.0 438s Investment_17 1 17.6 438s Investment_18 1 17.3 438s Investment_19 1 15.3 438s Investment_20 1 19.0 438s Investment_21 1 21.1 438s Investment_22 1 23.5 438s PrivateWages_2 0 0.0 438s PrivateWages_3 0 0.0 438s PrivateWages_4 0 0.0 438s PrivateWages_5 0 0.0 438s PrivateWages_6 0 0.0 438s PrivateWages_8 0 0.0 438s PrivateWages_9 0 0.0 438s PrivateWages_10 0 0.0 438s PrivateWages_11 0 0.0 438s PrivateWages_12 0 0.0 438s PrivateWages_13 0 0.0 438s PrivateWages_14 0 0.0 438s PrivateWages_15 0 0.0 438s PrivateWages_16 0 0.0 438s PrivateWages_17 0 0.0 438s PrivateWages_18 0 0.0 438s PrivateWages_19 0 0.0 438s PrivateWages_20 0 0.0 438s PrivateWages_21 0 0.0 438s PrivateWages_22 0 0.0 438s Investment_corpProfLag Investment_capitalLag 438s Consumption_2 0.0 0 438s Consumption_3 0.0 0 438s Consumption_4 0.0 0 438s Consumption_5 0.0 0 438s Consumption_6 0.0 0 438s Consumption_7 0.0 0 438s Consumption_8 0.0 0 438s Consumption_9 0.0 0 438s Consumption_11 0.0 0 438s Consumption_12 0.0 0 438s Consumption_13 0.0 0 438s Consumption_14 0.0 0 438s Consumption_15 0.0 0 438s Consumption_16 0.0 0 438s Consumption_17 0.0 0 438s Consumption_18 0.0 0 438s Consumption_19 0.0 0 438s Consumption_20 0.0 0 438s Consumption_21 0.0 0 438s Consumption_22 0.0 0 438s Investment_2 12.7 183 438s Investment_3 12.4 183 438s Investment_4 16.9 184 438s Investment_5 18.4 190 438s Investment_6 19.4 193 438s Investment_7 20.1 198 438s Investment_8 19.6 203 438s Investment_9 19.8 208 438s Investment_10 21.1 211 438s Investment_11 21.7 216 438s Investment_12 15.6 217 438s Investment_13 11.4 213 438s Investment_14 7.0 207 438s Investment_15 11.2 202 438s Investment_16 12.3 199 438s Investment_17 14.0 198 438s Investment_18 17.6 200 438s Investment_19 17.3 202 438s Investment_20 15.3 200 438s Investment_21 19.0 201 438s Investment_22 21.1 204 438s PrivateWages_2 0.0 0 438s PrivateWages_3 0.0 0 438s PrivateWages_4 0.0 0 438s PrivateWages_5 0.0 0 438s PrivateWages_6 0.0 0 438s PrivateWages_8 0.0 0 438s PrivateWages_9 0.0 0 438s PrivateWages_10 0.0 0 438s PrivateWages_11 0.0 0 438s PrivateWages_12 0.0 0 438s PrivateWages_13 0.0 0 438s PrivateWages_14 0.0 0 438s PrivateWages_15 0.0 0 438s PrivateWages_16 0.0 0 438s PrivateWages_17 0.0 0 438s PrivateWages_18 0.0 0 438s PrivateWages_19 0.0 0 438s PrivateWages_20 0.0 0 438s PrivateWages_21 0.0 0 438s PrivateWages_22 0.0 0 438s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 438s Consumption_2 0 0.0 0.0 438s Consumption_3 0 0.0 0.0 438s Consumption_4 0 0.0 0.0 438s Consumption_5 0 0.0 0.0 438s Consumption_6 0 0.0 0.0 438s Consumption_7 0 0.0 0.0 438s Consumption_8 0 0.0 0.0 438s Consumption_9 0 0.0 0.0 438s Consumption_11 0 0.0 0.0 438s Consumption_12 0 0.0 0.0 438s Consumption_13 0 0.0 0.0 438s Consumption_14 0 0.0 0.0 438s Consumption_15 0 0.0 0.0 438s Consumption_16 0 0.0 0.0 438s Consumption_17 0 0.0 0.0 438s Consumption_18 0 0.0 0.0 438s Consumption_19 0 0.0 0.0 438s Consumption_20 0 0.0 0.0 438s Consumption_21 0 0.0 0.0 438s Consumption_22 0 0.0 0.0 438s Investment_2 0 0.0 0.0 438s Investment_3 0 0.0 0.0 438s Investment_4 0 0.0 0.0 438s Investment_5 0 0.0 0.0 438s Investment_6 0 0.0 0.0 438s Investment_7 0 0.0 0.0 438s Investment_8 0 0.0 0.0 438s Investment_9 0 0.0 0.0 438s Investment_10 0 0.0 0.0 438s Investment_11 0 0.0 0.0 438s Investment_12 0 0.0 0.0 438s Investment_13 0 0.0 0.0 438s Investment_14 0 0.0 0.0 438s Investment_15 0 0.0 0.0 438s Investment_16 0 0.0 0.0 438s Investment_17 0 0.0 0.0 438s Investment_18 0 0.0 0.0 438s Investment_19 0 0.0 0.0 438s Investment_20 0 0.0 0.0 438s Investment_21 0 0.0 0.0 438s Investment_22 0 0.0 0.0 438s PrivateWages_2 1 45.6 44.9 438s PrivateWages_3 1 50.1 45.6 438s PrivateWages_4 1 57.2 50.1 438s PrivateWages_5 1 57.1 57.2 438s PrivateWages_6 1 61.0 57.1 438s PrivateWages_8 1 64.4 64.0 438s PrivateWages_9 1 64.5 64.4 438s PrivateWages_10 1 67.0 64.5 438s PrivateWages_11 1 61.2 67.0 438s PrivateWages_12 1 53.4 61.2 438s PrivateWages_13 1 44.3 53.4 438s PrivateWages_14 1 45.1 44.3 438s PrivateWages_15 1 49.7 45.1 438s PrivateWages_16 1 54.4 49.7 438s PrivateWages_17 1 62.7 54.4 438s PrivateWages_18 1 65.0 62.7 438s PrivateWages_19 1 60.9 65.0 438s PrivateWages_20 1 69.5 60.9 438s PrivateWages_21 1 75.7 69.5 438s PrivateWages_22 1 88.4 75.7 438s PrivateWages_trend 438s Consumption_2 0 438s Consumption_3 0 438s Consumption_4 0 438s Consumption_5 0 438s Consumption_6 0 438s Consumption_7 0 438s Consumption_8 0 438s Consumption_9 0 438s Consumption_11 0 438s Consumption_12 0 438s Consumption_13 0 438s Consumption_14 0 438s Consumption_15 0 438s Consumption_16 0 438s Consumption_17 0 438s Consumption_18 0 438s Consumption_19 0 438s Consumption_20 0 438s Consumption_21 0 438s Consumption_22 0 438s Investment_2 0 438s Investment_3 0 438s Investment_4 0 438s Investment_5 0 438s Investment_6 0 438s Investment_7 0 438s Investment_8 0 438s Investment_9 0 438s Investment_10 0 438s Investment_11 0 438s Investment_12 0 438s Investment_13 0 438s Investment_14 0 438s Investment_15 0 438s Investment_16 0 438s Investment_17 0 438s Investment_18 0 438s Investment_19 0 438s Investment_20 0 438s Investment_21 0 438s Investment_22 0 438s PrivateWages_2 -10 438s PrivateWages_3 -9 438s PrivateWages_4 -8 438s PrivateWages_5 -7 438s PrivateWages_6 -6 438s PrivateWages_8 -4 438s PrivateWages_9 -3 438s PrivateWages_10 -2 438s PrivateWages_11 -1 438s PrivateWages_12 0 438s PrivateWages_13 1 438s PrivateWages_14 2 438s PrivateWages_15 3 438s PrivateWages_16 4 438s PrivateWages_17 5 438s PrivateWages_18 6 438s PrivateWages_19 7 438s PrivateWages_20 8 438s PrivateWages_21 9 438s PrivateWages_22 10 438s > nobs 438s [1] 61 438s > linearHypothesis 438s Linear hypothesis test (Theil's F test) 438s 438s Hypothesis: 438s Consumption_corpProf + Investment_capitalLag = 0 438s 438s Model 1: restricted model 438s Model 2: kleinModel 438s 438s Res.Df Df F Pr(>F) 438s 1 50 438s 2 49 1 0.87 0.35 438s Linear hypothesis test (F statistic of a Wald test) 438s 438s Hypothesis: 438s Consumption_corpProf + Investment_capitalLag = 0 438s 438s Model 1: restricted model 438s Model 2: kleinModel 438s 438s Res.Df Df F Pr(>F) 438s 1 50 438s 2 49 1 0.8 0.38 438s Linear hypothesis test (Chi^2 statistic of a Wald test) 438s 438s Hypothesis: 438s Consumption_corpProf + Investment_capitalLag = 0 438s 438s Model 1: restricted model 438s Model 2: kleinModel 438s 438s Res.Df Df Chisq Pr(>Chisq) 438s 1 50 438s 2 49 1 0.8 0.37 438s Linear hypothesis test (Theil's F test) 438s 438s Hypothesis: 438s Consumption_corpProf + Investment_capitalLag = 0 438s Consumption_corpProfLag - PrivateWages_trend = 0 438s 438s Model 1: restricted model 438s Model 2: kleinModel 438s 438s Res.Df Df F Pr(>F) 438s 1 51 438s 2 49 2 0.48 0.62 438s Linear hypothesis test (F statistic of a Wald test) 438s 438s Hypothesis: 438s Consumption_corpProf + Investment_capitalLag = 0 438s Consumption_corpProfLag - PrivateWages_trend = 0 438s 438s Model 1: restricted model 438s Model 2: kleinModel 438s 438s Res.Df Df F Pr(>F) 438s 1 51 438s 2 49 2 0.43 0.65 438s Linear hypothesis test (Chi^2 statistic of a Wald test) 438s 438s Hypothesis: 438s Consumption_corpProf + Investment_capitalLag = 0 438s Consumption_corpProfLag - PrivateWages_trend = 0 438s 438s Model 1: restricted model 438s Model 2: kleinModel 438s 438s Res.Df Df Chisq Pr(>Chisq) 438s 1 51 438s 2 49 2 0.87 0.65 438s > logLik 438s 'log Lik.' -71.7 (df=13) 438s 'log Lik.' -76.1 (df=13) 438s compare log likelihood value with single-equation OLS 438s [1] "Mean relative difference: 0.00159" 438s Estimating function 438s Consumption_(Intercept) Consumption_corpProf 438s Consumption_2 -0.3304 -4.097 438s Consumption_3 -1.2748 -21.544 438s Consumption_4 -1.6213 -29.832 438s Consumption_5 -0.5661 -10.982 438s Consumption_6 -0.0730 -1.467 438s Consumption_7 0.7915 15.513 438s Consumption_8 1.2648 25.043 438s Consumption_9 0.9746 20.563 438s Consumption_11 0.2225 3.470 438s Consumption_12 -0.2256 -2.572 438s Consumption_13 -0.2711 -1.898 438s Consumption_14 0.3765 4.217 438s Consumption_15 -0.0349 -0.429 438s Consumption_16 -0.0243 -0.341 438s Consumption_17 1.6023 28.201 438s Consumption_18 -0.4658 -8.058 438s Consumption_19 0.1914 2.928 438s Consumption_20 0.9683 18.397 438s Consumption_21 0.7325 15.456 438s Consumption_22 -2.2370 -52.569 438s Investment_2 0.0000 0.000 438s Investment_3 0.0000 0.000 438s Investment_4 0.0000 0.000 438s Investment_5 0.0000 0.000 438s Investment_6 0.0000 0.000 438s Investment_7 0.0000 0.000 438s Investment_8 0.0000 0.000 438s Investment_9 0.0000 0.000 438s Investment_10 0.0000 0.000 438s Investment_11 0.0000 0.000 438s Investment_12 0.0000 0.000 438s Investment_13 0.0000 0.000 438s Investment_14 0.0000Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 438s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 438s 0.000 438s Investment_15 0.0000 0.000 438s Investment_16 0.0000 0.000 438s Investment_17 0.0000 0.000 438s Investment_18 0.0000 0.000 438s Investment_19 0.0000 0.000 438s Investment_20 0.0000 0.000 438s Investment_21 0.0000 0.000 438s Investment_22 0.0000 0.000 438s PrivateWages_2 0.0000 0.000 438s PrivateWages_3 0.0000 0.000 438s PrivateWages_4 0.0000 0.000 438s PrivateWages_5 0.0000 0.000 438s PrivateWages_6 0.0000 0.000 438s PrivateWages_8 0.0000 0.000 438s PrivateWages_9 0.0000 0.000 438s PrivateWages_10 0.0000 0.000 438s PrivateWages_11 0.0000 0.000 438s PrivateWages_12 0.0000 0.000 438s PrivateWages_13 0.0000 0.000 438s PrivateWages_14 0.0000 0.000 438s PrivateWages_15 0.0000 0.000 438s PrivateWages_16 0.0000 0.000 438s PrivateWages_17 0.0000 0.000 438s PrivateWages_18 0.0000 0.000 438s PrivateWages_19 0.0000 0.000 438s PrivateWages_20 0.0000 0.000 438s PrivateWages_21 0.0000 0.000 438s PrivateWages_22 0.0000 0.000 438s Consumption_corpProfLag Consumption_wages 438s Consumption_2 -4.196 -9.318 438s Consumption_3 -15.808 -41.049 438s Consumption_4 -27.400 -59.988 438s Consumption_5 -10.416 -20.944 438s Consumption_6 -1.416 -2.817 438s Consumption_7 15.908 32.212 438s Consumption_8 24.790 52.490 438s Consumption_9 19.296 41.809 438s Consumption_11 4.827 9.366 438s Consumption_12 -3.520 -8.867 438s Consumption_13 -3.091 -9.299 438s Consumption_14 2.636 12.839 438s Consumption_15 -0.391 -1.277 438s Consumption_16 -0.299 -0.957 438s Consumption_17 22.433 70.823 438s Consumption_18 -8.197 -22.217 438s Consumption_19 3.311 8.785 438s Consumption_20 14.815 47.833 438s Consumption_21 13.917 38.822 438s Consumption_22 -47.200 -138.245 438s Investment_2 0.000 0.000 438s Investment_3 0.000 0.000 438s Investment_4 0.000 0.000 438s Investment_5 0.000 0.000 438s Investment_6 0.000 0.000 438s Investment_7 0.000 0.000 438s Investment_8 0.000 0.000 438s Investment_9 0.000 0.000 438s Investment_10 0.000 0.000 438s Investment_11 0.000 0.000 438s Investment_12 0.000 0.000 438s Investment_13 0.000 0.000 438s Investment_14 0.000 0.000 438s Investment_15 0.000 0.000 438s Investment_16 0.000 0.000 438s Investment_17 0.000 0.000 438s Investment_18 0.000 0.000 438s Investment_19 0.000 0.000 438s Investment_20 0.000 0.000 438s Investment_21 0.000 0.000 438s Investment_22 0.000 0.000 438s PrivateWages_2 0.000 0.000 438s PrivateWages_3 0.000 0.000 438s PrivateWages_4 0.000 0.000 438s PrivateWages_5 0.000 0.000 438s PrivateWages_6 0.000 0.000 438s PrivateWages_8 0.000 0.000 438s PrivateWages_9 0.000 0.000 438s PrivateWages_10 0.000 0.000 438s PrivateWages_11 0.000 0.000 438s PrivateWages_12 0.000 0.000 438s PrivateWages_13 0.000 0.000 438s PrivateWages_14 0.000 0.000 438s PrivateWages_15 0.000 0.000 438s PrivateWages_16 0.000 0.000 438s PrivateWages_17 0.000 0.000 438s PrivateWages_18 0.000 0.000 438s PrivateWages_19 0.000 0.000 438s PrivateWages_20 0.000 0.000 438s PrivateWages_21 0.000 0.000 438s PrivateWages_22 0.000 0.000 438s Investment_(Intercept) Investment_corpProf 438s Consumption_2 0.0000 0.000 438s Consumption_3 0.0000 0.000 438s Consumption_4 0.0000 0.000 438s Consumption_5 0.0000 0.000 438s Consumption_6 0.0000 0.000 438s Consumption_7 0.0000 0.000 438s Consumption_8 0.0000 0.000 438s Consumption_9 0.0000 0.000 438s Consumption_11 0.0000 0.000 438s Consumption_12 0.0000 0.000 438s Consumption_13 0.0000 0.000 438s Consumption_14 0.0000 0.000 438s Consumption_15 0.0000 0.000 438s Consumption_16 0.0000 0.000 438s Consumption_17 0.0000 0.000 438s Consumption_18 0.0000 0.000 438s Consumption_19 0.0000 0.000 438s Consumption_20 0.0000 0.000 438s Consumption_21 0.0000 0.000 438s Consumption_22 0.0000 0.000 438s Investment_2 -0.0668 -0.828 438s Investment_3 -0.0476 -0.804 438s Investment_4 1.2467 22.939 438s Investment_5 -1.3512 -26.213 438s Investment_6 0.4154 8.350 438s Investment_7 1.4923 29.248 438s Investment_8 0.7889 15.620 438s Investment_9 -0.6317 -13.329 438s Investment_10 1.0830 23.500 438s Investment_11 0.2791 4.353 438s Investment_12 0.0369 0.420 438s Investment_13 0.3659 2.561 438s Investment_14 0.2237 2.505 438s Investment_15 -0.1728 -2.126 438s Investment_16 0.0101 0.141 438s Investment_17 0.9719 17.105 438s Investment_18 0.0516 0.893 438s Investment_19 -2.5656 -39.254 438s Investment_20 -0.6866 -13.045 438s Investment_21 -0.7807 -16.474 438s Investment_22 -0.6623 -15.565 438s PrivateWages_2 0.0000 0.000 438s PrivateWages_3 0.0000 0.000 438s PrivateWages_4 0.0000 0.000 438s PrivateWages_5 0.0000 0.000 438s PrivateWages_6 0.0000 0.000 438s PrivateWages_8 0.0000 0.000 438s PrivateWages_9 0.0000 0.000 438s PrivateWages_10 0.0000 0.000 438s PrivateWages_11 0.0000 0.000 438s PrivateWages_12 0.0000 0.000 438s PrivateWages_13 0.0000 0.000 438s PrivateWages_14 0.0000 0.000 438s PrivateWages_15 0.0000 0.000 438s PrivateWages_16 0.0000 0.000 438s PrivateWages_17 0.0000 0.000 438s PrivateWages_18 0.0000 0.000 438s PrivateWages_19 0.0000 0.000 438s PrivateWages_20 0.0000 0.000 438s PrivateWages_21 0.0000 0.000 438s PrivateWages_22 0.0000 0.000 438s Investment_corpProfLag Investment_capitalLag 438s Consumption_2 0.000 0.00 438s Consumption_3 0.000 0.00 438s Consumption_4 0.000 0.00 438s Consumption_5 0.000 0.00 438s Consumption_6 0.000 0.00 438s Consumption_7 0.000 0.00 438s Consumption_8 0.000 0.00 438s Consumption_9 0.000 0.00 438s Consumption_11 0.000 0.00 438s Consumption_12 0.000 0.00 438s Consumption_13 0.000 0.00 438s Consumption_14 0.000 0.00 438s Consumption_15 0.000 0.00 438s Consumption_16 0.000 0.00 438s Consumption_17 0.000 0.00 438s Consumption_18 0.000 0.00 438s Consumption_19 0.000 0.00 438s Consumption_20 0.000 0.00 438s Consumption_21 0.000 0.00 438s Consumption_22 0.000 0.00 438s Investment_2 -0.848 -12.21 438s Investment_3 -0.590 -8.69 438s Investment_4 21.069 230.01 438s Investment_5 -24.862 -256.32 438s Investment_6 8.059 80.05 438s Investment_7 29.994 295.17 438s Investment_8 15.463 160.46 438s Investment_9 -12.507 -131.14 438s Investment_10 22.850 228.07 438s Investment_11 6.056 60.20 438s Investment_12 0.575 7.99 438s Investment_13 4.172 78.05 438s Investment_14 1.566 46.33 438s Investment_15 -1.936 -34.91 438s Investment_16 0.124 2.01 438s Investment_17 13.606 192.14 438s Investment_18 0.908 10.31 438s Investment_19 -44.385 -517.74 438s Investment_20 -10.505 -137.25 438s Investment_21 -14.834 -157.09 438s Investment_22 -13.975 -135.45 438s PrivateWages_2 0.000 0.00 438s PrivateWages_3 0.000 0.00 438s PrivateWages_4 0.000 0.00 438s PrivateWages_5 0.000 0.00 438s PrivateWages_6 0.000 0.00 438s PrivateWages_8 0.000 0.00 438s PrivateWages_9 0.000 0.00 438s PrivateWages_10 0.000 0.00 438s PrivateWages_11 0.000 0.00 438s PrivateWages_12 0.000 0.00 438s PrivateWages_13 0.000 0.00 438s PrivateWages_14 0.000 0.00 438s PrivateWages_15 0.000 0.00 438s PrivateWages_16 0.000 0.00 438s PrivateWages_17 0.000 0.00 438s PrivateWages_18 0.000 0.00 438s PrivateWages_19 0.000 0.00 438s PrivateWages_20 0.000 0.00 438s PrivateWages_21 0.000 0.00 438s PrivateWages_22 0.000 0.00 438s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 438s Consumption_2 0.0000 0.00 0.00 438s Consumption_3 0.0000 0.00 0.00 438s Consumption_4 0.0000 0.00 0.00 438s Consumption_5 0.0000 0.00 0.00 438s Consumption_6 0.0000 0.00 0.00 438s Consumption_7 0.0000 0.00 0.00 438s Consumption_8 0.0000 0.00 0.00 438s Consumption_9 0.0000 0.00 0.00 438s Consumption_11 0.0000 0.00 0.00 438s Consumption_12 0.0000 0.00 0.00 438s Consumption_13 0.0000 0.00 0.00 438s Consumption_14 0.0000 0.00 0.00 438s Consumption_15 0.0000 0.00 0.00 438s Consumption_16 0.0000 0.00 0.00 438s Consumption_17 0.0000 0.00 0.00 438s Consumption_18 0.0000 0.00 0.00 438s Consumption_19 0.0000 0.00 0.00 438s Consumption_20 0.0000 0.00 0.00 438s Consumption_21 0.0000 0.00 0.00 438s Consumption_22 0.0000 0.00 0.00 438s Investment_2 0.0000 0.00 0.00 438s Investment_3 0.0000 0.00 0.00 438s Investment_4 0.0000 0.00 0.00 438s Investment_5 0.0000 0.00 0.00 438s Investment_6 0.0000 0.00 0.00 438s Investment_7 0.0000 0.00 0.00 438s Investment_8 0.0000 0.00 0.00 438s Investment_9 0.0000 0.00 0.00 438s Investment_10 0.0000 0.00 0.00 438s Investment_11 0.0000 0.00 0.00 438s Investment_12 0.0000 0.00 0.00 438s Investment_13 0.0000 0.00 0.00 438s Investment_14 0.0000 0.00 0.00 438s Investment_15 0.0000 0.00 0.00 438s Investment_16 0.0000 0.00 0.00 438s Investment_17 0.0000 0.00 0.00 438s Investment_18 0.0000 0.00 0.00 438s Investment_19 0.0000 0.00 0.00 438s Investment_20 0.0000 0.00 0.00 438s Investment_21 0.0000 0.00 0.00 438s Investment_22 0.0000 0.00 0.00 438s PrivateWages_2 -1.3389 -61.06 -60.12 438s PrivateWages_3 0.2462 12.33 11.23 438s PrivateWages_4 1.1255 64.38 56.39 438s PrivateWages_5 -0.1959 -11.18 -11.20 438s PrivateWages_6 -0.5284 -32.23 -30.17 438s PrivateWages_8 -0.7909 -50.94 -50.62 438s PrivateWages_9 0.2819 18.18 18.15 438s PrivateWages_10 1.1384 76.28 73.43 438s PrivateWages_11 -0.1904 -11.65 -12.76 438s PrivateWages_12 0.5813 31.04 35.58 438s PrivateWages_13 0.1206 5.34 6.44 438s PrivateWages_14 0.4773 21.53 21.14 438s PrivateWages_15 0.3035 15.09 13.69 438s PrivateWages_16 0.0284 1.55 1.41 438s PrivateWages_17 -0.8517 -53.40 -46.33 438s PrivateWages_18 0.9908 64.40 62.12 438s PrivateWages_19 -0.4597 -28.00 -29.88 438s PrivateWages_20 -0.3819 -26.54 -23.26 438s PrivateWages_21 -1.1062 -83.74 -76.88 438s PrivateWages_22 0.5501 48.63 41.64 438s PrivateWages_trend 438s Consumption_2 0.000 438s Consumption_3 0.000 438s Consumption_4 0.000 438s Consumption_5 0.000 438s Consumption_6 0.000 438s Consumption_7 0.000 438s Consumption_8 0.000 438s Consumption_9 0.000 438s Consumption_11 0.000 438s Consumption_12 0.000 438s Consumption_13 0.000 438s Consumption_14 0.000 438s Consumption_15 0.000 438s Consumption_16 0.000 438s Consumption_17 0.000 438s Consumption_18 0.000 438s Consumption_19 0.000 438s Consumption_20 0.000 438s Consumption_21 0.000 438s Consumption_22 0.000 438s Investment_2 0.000 438s Investment_3 0.000 438s Investment_4 0.000 438s Investment_5 0.000 438s Investment_6 0.000 438s Investment_7 0.000 438s Investment_8 0.000 438s Investment_9 0.000 438s Investment_10 0.000 438s Investment_11 0.000 438s Investment_12 0.000 438s Investment_13 0.000 438s Investment_14 0.000 438s Investment_15 0.000 438s Investment_16 0.000 438s Investment_17 0.000 438s Investment_18 0.000 438s Investment_19 0.000 438s Investment_20 0.000 438s Investment_21 0.000 438s Investment_22 0.000 438s PrivateWages_2 13.389 438s PrivateWages_3 -2.216 438s PrivateWages_4 -9.004 438s PrivateWages_5 1.371 438s PrivateWages_6 3.170 438s PrivateWages_8 3.164 438s PrivateWages_9 -0.846 438s PrivateWages_10 -2.277 438s PrivateWages_11 0.190 438s PrivateWages_12 0.000 438s PrivateWages_13 0.121 438s PrivateWages_14 0.955 438s PrivateWages_15 0.911 438s PrivateWages_16 0.114 438s PrivateWages_17 -4.258 438s PrivateWages_18 5.945 438s PrivateWages_19 -3.218 438s PrivateWages_20 -3.055 438s PrivateWages_21 -9.956 438s PrivateWages_22 5.501 438s [1] TRUE 438s > Bread 438s Consumption_(Intercept) Consumption_corpProf 438s Consumption_(Intercept) 99.9867 -0.0712 438s Consumption_corpProf -0.0712 0.4890 438s Consumption_corpProfLag -1.1355 -0.2987 438s Consumption_wages -1.8752 -0.0787 438s Investment_(Intercept) 0.0000 0.0000 438s Investment_corpProf 0.0000 0.0000 438s Investment_corpProfLag 0.0000 0.0000 438s Investment_capitalLag 0.0000 0.0000 438s PrivateWages_(Intercept) 0.0000 0.0000 438s PrivateWages_gnp 0.0000 0.0000 438s PrivateWages_gnpLag 0.0000 0.0000 438s PrivateWages_trend 0.0000 0.0000 438s Consumption_corpProfLag Consumption_wages 438s Consumption_(Intercept) -1.1355 -1.8752 438s Consumption_corpProf -0.2987 -0.0787 438s Consumption_corpProfLag 0.4841 -0.0413 438s Consumption_wages -0.0413 0.0933 438s Investment_(Intercept) 0.0000 0.0000 438s Investment_corpProf 0.0000 0.0000 438s Investment_corpProfLag 0.0000 0.0000 438s Investment_capitalLag 0.0000 0.0000 438s PrivateWages_(Intercept) 0.0000 0.0000 438s PrivateWages_gnp 0.0000 0.0000 438s PrivateWages_gnpLag 0.0000 0.0000 438s PrivateWages_trend 0.0000 0.0000 438s Investment_(Intercept) Investment_corpProf 438s Consumption_(Intercept) 0.0 0.0000 438s Consumption_corpProf 0.0 0.0000 438s Consumption_corpProfLag 0.0 0.0000 438s Consumption_wages 0.0 0.0000 438s Investment_(Intercept) 1788.3 -17.4004 438s Investment_corpProf -17.4 0.5646 438s Investment_corpProfLag 14.2 -0.4849 438s Investment_capitalLag -8.6 0.0788 438s PrivateWages_(Intercept) 0.0 0.0000 438s PrivateWages_gnp 0.0 0.0000 438s PrivateWages_gnpLag 0.0 0.0000 438s PrivateWages_trend 0.0 0.0000 438s Investment_corpProfLag Investment_capitalLag 438s Consumption_(Intercept) 0.0000 0.0000 438s Consumption_corpProf 0.0000 0.0000 438s Consumption_corpProfLag 0.0000 0.0000 438s Consumption_wages 0.0000 0.0000 438s Investment_(Intercept) 14.2083 -8.5994 438s Investment_corpProf -0.4849 0.0788 438s Investment_corpProfLag 0.6090 -0.0798 438s Investment_capitalLag -0.0798 0.0428 438s PrivateWages_(Intercept) 0.0000 0.0000 438s PrivateWages_gnp 0.0000 0.0000 438s PrivateWages_gnpLag 0.0000 0.0000 438s PrivateWages_trend 0.0000 0.0000 438s PrivateWages_(Intercept) PrivateWages_gnp 438s Consumption_(Intercept) 0.000 0.0000 438s Consumption_corpProf 0.000 0.0000 438s Consumption_corpProfLag 0.000 0.0000 438s Consumption_wages 0.000 0.0000 438s Investment_(Intercept) 0.000 0.0000 438s Investment_corpProf 0.000 0.0000 438s Investment_corpProfLag 0.000 0.0000 438s Investment_capitalLag 0.000 0.0000 438s PrivateWages_(Intercept) 171.811 -0.6470 438s PrivateWages_gnp -0.647 0.1100 438s PrivateWages_gnpLag -2.257 -0.1026 438s PrivateWages_trend 2.120 -0.0296 438s PrivateWages_gnpLag PrivateWages_trend 438s Consumption_(Intercept) 0.00000 0.00000 438s Consumption_corpProf 0.00000 0.00000 438s Consumption_corpProfLag 0.00000 0.00000 438s Consumption_wages 0.00000 0.00000 438s Investment_(Intercept) 0.00000 0.00000 438s Investment_corpProf 0.00000 0.00000 438s Investment_corpProfLag 0.00000 0.00000 438s Investment_capitalLag 0.00000 0.00000 438s PrivateWages_(Intercept) -2.25750 2.12030 438s PrivateWages_gnp -0.10258 -0.02955 438s PrivateWages_gnpLag 0.14523 -0.00656 438s PrivateWages_trend -0.00656 0.11341 438s > 438s > # 2SLS 438s > summary 438s 438s systemfit results 438s method: 2SLS 438s 438s N DF SSR detRCov OLS-R2 McElroy-R2 438s system 59 47 53.2 0.251 0.973 0.991 438s 438s N DF SSR MSE RMSE R2 Adj R2 438s Consumption 19 15 20.49 1.366 1.17 0.978 0.973 438s Investment 20 16 23.02 1.438 1.20 0.901 0.883 438s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 438s 438s The covariance matrix of the residuals 438s Consumption Investment PrivateWages 438s Consumption 1.079 0.354 -0.383 438s Investment 0.354 1.047 0.107 438s PrivateWages -0.383 0.107 0.445 438s 438s The correlations of the residuals 438s Consumption Investment PrivateWages 438s Consumption 1.000 0.335 -0.556 438s Investment 0.335 1.000 0.149 438s PrivateWages -0.556 0.149 1.000 438s 438s 438s 2SLS estimates for 'Consumption' (equation 1) 438s Model Formula: consump ~ corpProf + corpProfLag + wages 438s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 438s gnpLag 438s 438s Estimate Std. Error t value Pr(>|t|) 438s (Intercept) 16.4657 1.3505 12.19 3.5e-09 *** 438s corpProf 0.0243 0.1180 0.21 0.839 438s corpProfLag 0.1981 0.1087 1.82 0.088 . 438s wages 0.8159 0.0420 19.45 4.7e-12 *** 438s --- 438s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 438s 438s Residual standard error: 1.169 on 15 degrees of freedom 438s Number of observations: 19 Degrees of Freedom: 15 438s SSR: 20.493 MSE: 1.366 Root MSE: 1.169 438s Multiple R-Squared: 0.978 Adjusted R-Squared: 0.973 438s 438s 438s 2SLS estimates for 'Investment' (equation 2) 438s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 438s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 438s gnpLag 438s 438s Estimate Std. Error t value Pr(>|t|) 438s (Intercept) 17.8425 6.5319 2.73 0.01478 * 438s corpProf 0.2167 0.1478 1.47 0.16189 438s corpProfLag 0.5416 0.1415 3.83 0.00149 ** 438s capitalLag -0.1455 0.0314 -4.63 0.00028 *** 438s --- 438s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 438s 438s Residual standard error: 1.199 on 16 degrees of freedom 438s Number of observations: 20 Degrees of Freedom: 16 438s SSR: 23.016 MSE: 1.438 Root MSE: 1.199 438s Multiple R-Squared: 0.901 Adjusted R-Squared: 0.883 438s 438s 438s 2SLS estimates for 'PrivateWages' (equation 3) 438s Model Formula: privWage ~ gnp + gnpLag + trend 438s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 438s gnpLag 438s 438s Estimate Std. Error t value Pr(>|t|) 438s (Intercept) 1.3431 1.1250 1.19 0.24995 438s gnp 0.4438 0.0342 12.97 6.6e-10 *** 438s gnpLag 0.1447 0.0371 3.90 0.00128 ** 438s trend 0.1238 0.0292 4.24 0.00063 *** 438s --- 438s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 438s 438s Residual standard error: 0.78 on 16 degrees of freedom 438s Number of observations: 20 Degrees of Freedom: 16 438s SSR: 9.741 MSE: 0.609 Root MSE: 0.78 438s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 438s 438s > residuals 438s Consumption Investment PrivateWages 438s 1 NA NA NA 438s 2 -0.39161 -1.0104 -1.3401 438s 3 -0.60524 0.2478 0.2378 438s 4 -1.24952 1.0621 1.1117 438s 5 -0.17101 -1.4104 -0.1954 438s 6 0.30841 0.4328 -0.5355 438s 7 NA NA NA 438s 8 1.50999 1.0463 -0.7908 438s 9 1.39649 0.0674 0.2831 438s 10 NA 1.7698 1.1353 438s 11 -0.49339 -0.5912 -0.1765 438s 12 -0.99824 -0.6318 0.6007 438s 13 -1.27965 -0.6983 0.1443 438s 14 0.55302 0.9724 0.4826 438s 15 -0.14553 -0.1827 0.3016 438s 16 -0.00773 0.1167 0.0261 438s 17 1.97001 1.6266 -0.8614 438s 18 -0.59152 -0.0525 0.9927 438s 19 -0.21481 -3.0656 -0.4446 438s 20 1.33575 0.1393 -0.3914 438s 21 1.01443 -0.1305 -1.1115 438s 22 -1.93986 0.2922 0.5312 438s > fitted 438s Consumption Investment PrivateWages 438s 1 NA NA NA 438s 2 42.3 0.810 26.8 438s 3 45.6 1.652 29.1 438s 4 50.4 4.138 33.0 438s 5 50.8 4.410 34.1 438s 6 52.3 4.667 35.9 438s 7 NA NA NA 438s 8 54.7 3.154 38.7 438s 9 55.9 2.933 38.9 438s 10 NA 3.330 40.2 438s 11 55.5 1.591 38.1 438s 12 51.9 -2.768 33.9 438s 13 46.9 -5.502 28.9 438s 14 45.9 -6.072 28.0 438s 15 48.8 -2.817 30.3 438s 16 51.3 -1.417 33.2 438s 17 55.7 0.473 37.7 438s 18 59.3 2.053 40.0 438s 19 57.7 1.166 38.6 438s 20 60.3 1.161 42.0 438s 21 64.0 3.431 46.1 438s 22 71.6 4.608 52.8 438s > predict 438s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 438s 1 NA NA NA NA 438s 2 42.3 0.483 41.3 43.3 438s 3 45.6 0.586 44.4 46.9 438s 4 50.4 0.390 49.6 51.3 438s 5 50.8 0.456 49.8 51.7 438s 6 52.3 0.463 51.3 53.3 438s 7 NA NA NA NA 438s 8 54.7 0.382 53.9 55.5 438s 9 55.9 0.422 55.0 56.8 438s 10 NA NA NA NA 438s 11 55.5 0.742 53.9 57.1 438s 12 51.9 0.600 50.6 53.2 438s 13 46.9 0.770 45.2 48.5 438s 14 45.9 0.635 44.6 47.3 438s 15 48.8 0.383 48.0 49.7 438s 16 51.3 0.339 50.6 52.0 438s 17 55.7 0.410 54.9 56.6 438s 18 59.3 0.336 58.6 60.0 438s 19 57.7 0.418 56.8 58.6 438s 20 60.3 0.481 59.2 61.3 438s 21 64.0 0.462 63.0 65.0 438s 22 71.6 0.706 70.1 73.1 438s Investment.pred Investment.se.fit Investment.lwr Investment.upr 438s 1 NA NA NA NA 438s 2 0.810 0.750 -0.77956 2.400 438s 3 1.652 0.516 0.55883 2.746 438s 4 4.138 0.487 3.10541 5.170 438s 5 4.410 0.402 3.55860 5.262 438s 6 4.667 0.377 3.86830 5.466 438s 7 NA NA NA NA 438s 8 3.154 0.312 2.49238 3.815 438s 9 2.933 0.466 1.94478 3.920 438s 10 3.330 0.512 2.24435 4.416 438s 11 1.591 0.749 0.00249 3.180 438s 12 -2.768 0.586 -4.01111 -1.525 438s 13 -5.502 0.750 -7.09222 -3.911 438s 14 -6.072 0.803 -7.77404 -4.371 438s 15 -2.817 0.379 -3.62002 -2.015 438s 16 -1.417 0.327 -2.10985 -0.723 438s 17 0.473 0.436 -0.45046 1.397 438s 18 2.053 0.272 1.47523 2.630 438s 19 1.166 0.410 0.29710 2.034 438s 20 1.161 0.491 0.12044 2.201 438s 21 3.431 0.406 2.57004 4.291 438s 22 4.608 0.578 3.38197 5.834 438s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 438s 1 NA NA NA NA 438s 2 26.8 0.313 26.2 27.5 438s 3 29.1 0.325 28.4 29.8 438s 4 33.0 0.344 32.3 33.7 438s 5 34.1 0.246 33.6 34.6 438s 6 35.9 0.254 35.4 36.5 438s 7 NA NA NA NA 438s 8 38.7 0.251 38.2 39.2 438s 9 38.9 0.239 38.4 39.4 438s 10 40.2 0.229 39.7 40.7 438s 11 38.1 0.339 37.4 38.8 438s 12 33.9 0.365 33.1 34.7 438s 13 28.9 0.436 27.9 29.8 438s 14 28.0 0.333 27.3 28.7 438s 15 30.3 0.324 29.6 31.0 438s 16 33.2 0.271 32.6 33.7 438s 17 37.7 0.280 37.1 38.3 438s 18 40.0 0.208 39.6 40.4 438s 19 38.6 0.342 37.9 39.4 438s 20 42.0 0.293 41.4 42.6 438s 21 46.1 0.296 45.5 46.7 438s 22 52.8 0.474 51.8 53.8 438s > model.frame 438s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 438s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 438s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 438s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 438s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 438s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 438s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 438s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 438s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 438s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 438s 10 57.8 21.7 21.1 NA 5.1 211 41.3 67.0 64.5 438s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 438s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 438s 13 45.6 7.0 11.4 34.3 -6.2 213 29.0 44.3 53.4 438s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 438s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 438s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 438s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 438s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 438s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 438s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 438s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 438s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 438s trend 438s 1 -11 438s 2 -10 438s 3 -9 438s 4 -8 438s 5 -7 438s 6 -6 438s 7 -5 438s 8 -4 438s 9 -3 438s 10 -2 438s 11 -1 438s 12 0 438s 13 1 438s 14 2 438s 15 3 438s 16 4 438s 17 5 438s 18 6 438s 19 7 438s 20 8 438s 21 9 438s 22 10 438s > Frames of instrumental variables 438s govExp taxes govWage trend capitalLag corpProfLag gnpLag 438s 1 2.4 3.4 2.2 -11 180 NA NA 438s 2 3.9 7.7 2.7 -10 183 12.7 44.9 438s 3 3.2 3.9 2.9 -9 183 12.4 45.6 438s 4 2.8 4.7 2.9 -8 184 16.9 50.1 438s 5 3.5 3.8 3.1 -7 190 18.4 57.2 438s 6 3.3 5.5 3.2 -6 193 19.4 57.1 438s 7 3.3 7.0 3.3 -5 198 20.1 NA 438s 8 4.0 6.7 3.6 -4 203 19.6 64.0 438s 9 4.2 4.2 3.7 -3 208 19.8 64.4 438s 10 4.1 4.0 4.0 -2 211 21.1 64.5 438s 11 5.2 7.7 4.2 -1 216 21.7 67.0 438s 12 5.9 7.5 4.8 0 217 15.6 61.2 438s 13 4.9 8.3 5.3 1 213 11.4 53.4 438s 14 3.7 5.4 5.6 2 207 7.0 44.3 438s 15 4.0 6.8 6.0 3 202 11.2 45.1 438s 16 4.4 7.2 6.1 4 199 12.3 49.7 438s 17 2.9 8.3 7.4 5 198 14.0 54.4 438s 18 4.3 6.7 6.7 6 200 17.6 62.7 438s 19 5.3 7.4 7.7 7 202 17.3 65.0 438s 20 6.6 8.9 7.8 8 200 15.3 60.9 438s 21 7.4 9.6 8.0 9 201 19.0 69.5 438s 22 13.8 11.6 8.5 10 204 21.1 75.7 438s govExp taxes govWage trend capitalLag corpProfLag gnpLag 438s 1 2.4 3.4 2.2 -11 180 NA NA 438s 2 3.9 7.7 2.7 -10 183 12.7 44.9 438s 3 3.2 3.9 2.9 -9 183 12.4 45.6 438s 4 2.8 4.7 2.9 -8 184 16.9 50.1 438s 5 3.5 3.8 3.1 -7 190 18.4 57.2 438s 6 3.3 5.5 3.2 -6 193 19.4 57.1 438s 7 3.3 7.0 3.3 -5 198 20.1 NA 438s 8 4.0 6.7 3.6 -4 203 19.6 64.0 438s 9 4.2 4.2 3.7 -3 208 19.8 64.4 438s 10 4.1 4.0 4.0 -2 211 21.1 64.5 438s 11 5.2 7.7 4.2 -1 216 21.7 67.0 438s 12 5.9 7.5 4.8 0 217 15.6 61.2 438s 13 4.9 8.3 5.3 1 213 11.4 53.4 438s 14 3.7 5.4 5.6 2 207 7.0 44.3 438s 15 4.0 6.8 6.0 3 202 11.2 45.1 438s 16 4.4 7.2 6.1 4 199 12.3 49.7 438s 17 2.9 8.3 7.4 5 198 14.0 54.4 438s 18 4.3 6.7 6.7 6 200 17.6 62.7 438s 19 5.3 7.4 7.7 7 202 17.3 65.0 438s 20 6.6 8.9 7.8 8 200 15.3 60.9 438s 21 7.4 9.6 8.0 9 201 19.0 69.5 438s 22 13.8 11.6 8.5 10 204 21.1 75.7 438s govExp taxes govWage trend capitalLag corpProfLag gnpLag 438s 1 2.4 3.4 2.2 -11 180 NA NA 438s 2 3.9 7.7 2.7 -10 183 12.7 44.9 438s 3 3.2 3.9 2.9 -9 183 12.4 45.6 438s 4 2.8 4.7 2.9 -8 184 16.9 50.1 438s 5 3.5 3.8 3.1 -7 190 18.4 57.2 438s 6 3.3 5.5 3.2 -6 193 19.4 57.1 438s 7 3.3 7.0 3.3 -5 198 20.1 NA 438s 8 4.0 6.7 3.6 -4 203 19.6 64.0 438s 9 4.2 4.2 3.7 -3 208 19.8 64.4 438s 10 4.1 4.0 4.0 -2 211 21.1 64.5 438s 11 5.2 7.7 4.2 -1 216 21.7 67.0 438s 12 5.9 7.5 4.8 0 217 15.6 61.2 438s 13 4.9 8.3 5.3 1 213 11.4 53.4 438s 14 3.7 5.4 5.6 2 207 7.0 44.3 438s 15 4.0 6.8 6.0 3 202 11.2 45.1 438s 16 4.4 7.2 6.1 4 199 12.3 49.7 438s 17 2.9 8.3 7.4 5 198 14.0 54.4 438s 18 4.3 6.7 6.7 6 200 17.6 62.7 438s 19 5.3 7.4 7.7 7 202 17.3 65.0 438s 20 6.6 8.9 7.8 8 200 15.3 60.9 438s 21 7.4 9.6 8.0 9 201 19.0 69.5 438s 22 13.8 11.6 8.5 10 204 21.1 75.7 438s > model.matrix 438s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0328 >" 438s [2] "Attributes: < Component “dimnames”: Component 1: 54 string mismatches >" 438s [3] "Numeric: lengths (732, 708) differ" 438s > matrix of instrumental variables 438s Consumption_(Intercept) Consumption_govExp Consumption_taxes 438s Consumption_2 1 3.9 7.7 438s Consumption_3 1 3.2 3.9 438s Consumption_4 1 2.8 4.7 438s Consumption_5 1 3.5 3.8 438s Consumption_6 1 3.3 5.5 438s Consumption_8 1 4.0 6.7 438s Consumption_9 1 4.2 4.2 438s Consumption_11 1 5.2 7.7 438s Consumption_12 1 5.9 7.5 438s Consumption_13 1 4.9 8.3 438s Consumption_14 1 3.7 5.4 438s Consumption_15 1 4.0 6.8 438s Consumption_16 1 4.4 7.2 438s Consumption_17 1 2.9 8.3 438s Consumption_18 1 4.3 6.7 438s Consumption_19 1 5.3 7.4 438s Consumption_20 1 6.6 8.9 438s Consumption_21 1 7.4 9.6 438s Consumption_22 1 13.8 11.6 438s Investment_2 0 0.0 0.0 438s Investment_3 0 0.0 0.0 438s Investment_4 0 0.0 0.0 438s Investment_5 0 0.0 0.0 438s Investment_6 0 0.0 0.0 438s Investment_8 0 0.0 0.0 438s Investment_9 0 0.0 0.0 438s Investment_10 0 0.0 0.0 438s Investment_11 0 0.0 0.0 438s Investment_12 0 0.0 0.0 438s Investment_13 0 0.0 0.0 438s Investment_14 0 0.0 0.0 438s Investment_15 0 0.0 0.0 438s Investment_16 0 0.0 0.0 438s Investment_17 0 0.0 0.0 438s Investment_18 0 0.0 0.0 438s Investment_19 0 0.0 0.0 438s Investment_20 0 0.0 0.0 438s Investment_21 0 0.0 0.0 438s Investment_22 0 0.0 0.0 438s PrivateWages_2 0 0.0 0.0 438s PrivateWages_3 0 0.0 0.0 438s PrivateWages_4 0 0.0 0.0 438s PrivateWages_5 0 0.0 0.0 438s PrivateWages_6 0 0.0 0.0 438s PrivateWages_8 0 0.0 0.0 438s PrivateWages_9 0 0.0 0.0 438s PrivateWages_10 0 0.0 0.0 438s PrivateWages_11 0 0.0 0.0 438s PrivateWages_12 0 0.0 0.0 438s PrivateWages_13 0 0.0 0.0 438s PrivateWages_14 0 0.0 0.0 438s PrivateWages_15 0 0.0 0.0 438s PrivateWages_16 0 0.0 0.0 438s PrivateWages_17 0 0.0 0.0 438s PrivateWages_18 0 0.0 0.0 438s PrivateWages_19 0 0.0 0.0 438s PrivateWages_20 0 0.0 0.0 438s PrivateWages_21 0 0.0 0.0 438s PrivateWages_22 0 0.0 0.0 438s Consumption_govWage Consumption_trend Consumption_capitalLag 438s Consumption_2 2.7 -10 183 438s Consumption_3 2.9 -9 183 438s Consumption_4 2.9 -8 184 438s Consumption_5 3.1 -7 190 438s Consumption_6 3.2 -6 193 438s Consumption_8 3.6 -4 203 438s Consumption_9 3.7 -3 208 438s Consumption_11 4.2 -1 216 438s Consumption_12 4.8 0 217 438s Consumption_13 5.3 1 213 438s Consumption_14 5.6 2 207 438s Consumption_15 6.0 3 202 438s Consumption_16 6.1 4 199 438s Consumption_17 7.4 5 198 438s Consumption_18 6.7 6 200 438s Consumption_19 7.7 7 202 438s Consumption_20 7.8 8 200 438s Consumption_21 8.0 9 201 438s Consumption_22 8.5 10 204 438s Investment_2 0.0 0 0 438s Investment_3 0.0 0 0 438s Investment_4 0.0 0 0 438s Investment_5 0.0 0 0 438s Investment_6 0.0 0 0 438s Investment_8 0.0 0 0 438s Investment_9 0.0 0 0 438s Investment_10 0.0 0 0 438s Investment_11 0.0 0 0 438s Investment_12 0.0 0 0 438s Investment_13 0.0 0 0 438s Investment_14 0.0 0 0 438s Investment_15 0.0 0 0 438s Investment_16 0.0 0 0 438s Investment_17 0.0 0 0 438s Investment_18 0.0 0 0 438s Investment_19 0.0 0 0 438s Investment_20 0.0 0 0 438s Investment_21 0.0 0 0 438s Investment_22 0.0 0 0 438s PrivateWages_2 0.0 0 0 438s PrivateWages_3 0.0 0 0 438s PrivateWages_4 0.0 0 0 438s PrivateWages_5 0.0 0 0 438s PrivateWages_6 0.0 0 0 438s PrivateWages_8 0.0 0 0 438s PrivateWages_9 0.0 0 0 438s PrivateWages_10 0.0 0 0 438s PrivateWages_11 0.0 0 0 438s PrivateWages_12 0.0 0 0 438s PrivateWages_13 0.0 0 0 438s PrivateWages_14 0.0 0 0 438s PrivateWages_15 0.0 0 0 438s PrivateWages_16 0.0 0 0 438s PrivateWages_17 0.0 0 0 438s PrivateWages_18 0.0 0 0 438s PrivateWages_19 0.0 0 0 438s PrivateWages_20 0.0 0 0 438s PrivateWages_21 0.0 0 0 438s PrivateWages_22 0.0 0 0 438s Consumption_corpProfLag Consumption_gnpLag 438s Consumption_2 12.7 44.9 438s Consumption_3 12.4 45.6 438s Consumption_4 16.9 50.1 438s Consumption_5 18.4 57.2 438s Consumption_6 19.4 57.1 438s Consumption_8 19.6 64.0 438s Consumption_9 19.8 64.4 438s Consumption_11 21.7 67.0 438s Consumption_12 15.6 61.2 438s Consumption_13 11.4 53.4 438s Consumption_14 7.0 44.3 438s Consumption_15 11.2 45.1 438s Consumption_16 12.3 49.7 438s Consumption_17 14.0 54.4 438s Consumption_18 17.6 62.7 438s Consumption_19 17.3 65.0 438s Consumption_20 15.3 60.9 438s Consumption_21 19.0 69.5 438s Consumption_22 21.1 75.7 438s Investment_2 0.0 0.0 438s Investment_3 0.0 0.0 438s Investment_4 0.0 0.0 438s Investment_5 0.0 0.0 438s Investment_6 0.0 0.0 438s Investment_8 0.0 0.0 438s Investment_9 0.0 0.0 438s Investment_10 0.0 0.0 438s Investment_11 0.0 0.0 438s Investment_12 0.0 0.0 438s Investment_13 0.0 0.0 438s Investment_14 0.0 0.0 438s Investment_15 0.0 0.0 438s Investment_16 0.0 0.0 438s Investment_17 0.0 0.0 438s Investment_18 0.0 0.0 438s Investment_19 0.0 0.0 438s Investment_20 0.0 0.0 438s Investment_21 0.0 0.0 438s Investment_22 0.0 0.0 438s PrivateWages_2 0.0 0.0 438s PrivateWages_3 0.0 0.0 438s PrivateWages_4 0.0 0.0 438s PrivateWages_5 0.0 0.0 438s PrivateWages_6 0.0 0.0 438s PrivateWages_8 0.0 0.0 438s PrivateWages_9 0.0 0.0 438s PrivateWages_10 0.0 0.0 438s PrivateWages_11 0.0 0.0 438s PrivateWages_12 0.0 0.0 438s PrivateWages_13 0.0 0.0 438s PrivateWages_14 0.0 0.0 438s PrivateWages_15 0.0 0.0 438s PrivateWages_16 0.0 0.0 438s PrivateWages_17 0.0 0.0 438s PrivateWages_18 0.0 0.0 438s PrivateWages_19 0.0 0.0 438s PrivateWages_20 0.0 0.0 438s PrivateWages_21 0.0 0.0 438s PrivateWages_22 0.0 0.0 438s Investment_(Intercept) Investment_govExp Investment_taxes 438s Consumption_2 0 0.0 0.0 438s Consumption_3 0 0.0 0.0 438s Consumption_4 0 0.0 0.0 438s Consumption_5 0 0.0 0.0 438s Consumption_6 0 0.0 0.0 438s Consumption_8 0 0.0 0.0 438s Consumption_9 0 0.0 0.0 438s Consumption_11 0 0.0 0.0 438s Consumption_12 0 0.0 0.0 438s Consumption_13 0 0.0 0.0 438s Consumption_14 0 0.0 0.0 438s Consumption_15 0 0.0 0.0 438s Consumption_16 0 0.0 0.0 438s Consumption_17 0 0.0 0.0 438s Consumption_18 0 0.0 0.0 438s Consumption_19 0 0.0 0.0 438s Consumption_20 0 0.0 0.0 438s Consumption_21 0 0.0 0.0 438s Consumption_22 0 0.0 0.0 438s Investment_2 1 3.9 7.7 438s Investment_3 1 3.2 3.9 438s Investment_4 1 2.8 4.7 438s Investment_5 1 3.5 3.8 438s Investment_6 1 3.3 5.5 438s Investment_8 1 4.0 6.7 438s Investment_9 1 4.2 4.2 438s Investment_10 1 4.1 4.0 438s Investment_11 1 5.2 7.7 438s Investment_12 1 5.9 7.5 438s Investment_13 1 4.9 8.3 438s Investment_14 1 3.7 5.4 438s Investment_15 1 4.0 6.8 438s Investment_16 1 4.4 7.2 438s Investment_17 1 2.9 8.3 438s Investment_18 1 4.3 6.7 438s Investment_19 1 5.3 7.4 438s Investment_20 1 6.6 8.9 438s Investment_21 1 7.4 9.6 438s Investment_22 1 13.8 11.6 438s PrivateWages_2 0 0.0 0.0 438s PrivateWages_3 0 0.0 0.0 438s PrivateWages_4 0 0.0 0.0 438s PrivateWages_5 0 0.0 0.0 438s PrivateWages_6 0 0.0 0.0 438s PrivateWages_8 0 0.0 0.0 438s PrivateWages_9 0 0.0 0.0 438s PrivateWages_10 0 0.0 0.0 438s PrivateWages_11 0 0.0 0.0 438s PrivateWages_12 0 0.0 0.0 438s PrivateWages_13 0 0.0 0.0 438s PrivateWages_14 0 0.0 0.0 438s PrivateWages_15 0 0.0 0.0 438s PrivateWages_16 0 0.0 0.0 438s PrivateWages_17 0 0.0 0.0 438s PrivateWages_18 0 0.0 0.0 438s PrivateWages_19 0 0.0 0.0 438s PrivateWages_20 0 0.0 0.0 438s PrivateWages_21 0 0.0 0.0 438s PrivateWages_22 0 0.0 0.0 438s Investment_govWage Investment_trend Investment_capitalLag 438s Consumption_2 0.0 0 0 438s Consumption_3 0.0 0 0 438s Consumption_4 0.0 0 0 438s Consumption_5 0.0 0 0 438s Consumption_6 0.0 0 0 438s Consumption_8 0.0 0 0 438s Consumption_9 0.0 0 0 438s Consumption_11 0.0 0 0 438s Consumption_12 0.0 0 0 438s Consumption_13 0.0 0 0 438s Consumption_14 0.0 0 0 438s Consumption_15 0.0 0 0 438s Consumption_16 0.0 0 0 438s Consumption_17 0.0 0 0 438s Consumption_18 0.0 0 0 438s Consumption_19 0.0 0 0 438s Consumption_20 0.0 0 0 438s Consumption_21 0.0 0 0 438s Consumption_22 0.0 0 0 438s Investment_2 2.7 -10 183 438s Investment_3 2.9 -9 183 438s Investment_4 2.9 -8 184 438s Investment_5 3.1 -7 190 438s Investment_6 3.2 -6 193 438s Investment_8 3.6 -4 203 438s Investment_9 3.7 -3 208 438s Investment_10 4.0 -2 211 438s Investment_11 4.2 -1 216 438s Investment_12 4.8 0 217 438s Investment_13 5.3 1 213 438s Investment_14 5.6 2 207 438s Investment_15 6.0 3 202 438s Investment_16 6.1 4 199 438s Investment_17 7.4 5 198 438s Investment_18 6.7 6 200 438s Investment_19 7.7 7 202 438s Investment_20 7.8 8 200 438s Investment_21 8.0 9 201 438s Investment_22 8.5 10 204 438s PrivateWages_2 0.0 0 0 438s PrivateWages_3 0.0 0 0 438s PrivateWages_4 0.0 0 0 438s PrivateWages_5 0.0 0 0 438s PrivateWages_6 0.0 0 0 438s PrivateWages_8 0.0 0 0 438s PrivateWages_9 0.0 0 0 438s PrivateWages_10 0.0 0 0 438s PrivateWages_11 0.0 0 0 438s PrivateWages_12 0.0 0 0 438s PrivateWages_13 0.0 0 0 438s PrivateWages_14 0.0 0 0 438s PrivateWages_15 0.0 0 0 438s PrivateWages_16 0.0 0 0 438s PrivateWages_17 0.0 0 0 438s PrivateWages_18 0.0 0 0 438s PrivateWages_19 0.0 0 0 438s PrivateWages_20 0.0 0 0 438s PrivateWages_21 0.0 0 0 438s PrivateWages_22 0.0 0 0 438s Investment_corpProfLag Investment_gnpLag 438s Consumption_2 0.0 0.0 438s Consumption_3 0.0 0.0 438s Consumption_4 0.0 0.0 438s Consumption_5 0.0 0.0 438s Consumption_6 0.0 0.0 438s Consumption_8 0.0 0.0 438s Consumption_9 0.0 0.0 438s Consumption_11 0.0 0.0 438s Consumption_12 0.0 0.0 438s Consumption_13 0.0 0.0 438s Consumption_14 0.0 0.0 438s Consumption_15 0.0 0.0 438s Consumption_16 0.0 0.0 438s Consumption_17 0.0 0.0 438s Consumption_18 0.0 0.0 438s Consumption_19 0.0 0.0 438s Consumption_20 0.0 0.0 438s Consumption_21 0.0 0.0 438s Consumption_22 0.0 0.0 438s Investment_2 12.7 44.9 438s Investment_3 12.4 45.6 438s Investment_4 16.9 50.1 438s Investment_5 18.4 57.2 438s Investment_6 19.4 57.1 438s Investment_8 19.6 64.0 438s Investment_9 19.8 64.4 438s Investment_10 21.1 64.5 438s Investment_11 21.7 67.0 438s Investment_12 15.6 61.2 438s Investment_13 11.4 53.4 438s Investment_14 7.0 44.3 438s Investment_15 11.2 45.1 438s Investment_16 12.3 49.7 438s Investment_17 14.0 54.4 438s Investment_18 17.6 62.7 438s Investment_19 17.3 65.0 438s Investment_20 15.3 60.9 438s Investment_21 19.0 69.5 438s Investment_22 21.1 75.7 438s PrivateWages_2 0.0 0.0 438s PrivateWages_3 0.0 0.0 438s PrivateWages_4 0.0 0.0 438s PrivateWages_5 0.0 0.0 438s PrivateWages_6 0.0 0.0 438s PrivateWages_8 0.0 0.0 438s PrivateWages_9 0.0 0.0 438s PrivateWages_10 0.0 0.0 438s PrivateWages_11 0.0 0.0 438s PrivateWages_12 0.0 0.0 438s PrivateWages_13 0.0 0.0 438s PrivateWages_14 0.0 0.0 438s PrivateWages_15 0.0 0.0 438s PrivateWages_16 0.0 0.0 438s PrivateWages_17 0.0 0.0 438s PrivateWages_18 0.0 0.0 438s PrivateWages_19 0.0 0.0 438s PrivateWages_20 0.0 0.0 438s PrivateWages_21 0.0 0.0 438s PrivateWages_22 0.0 0.0 438s PrivateWages_(Intercept) PrivateWages_govExp PrivateWages_taxes 438s Consumption_2 0 0.0 0.0 438s Consumption_3 0 0.0 0.0 438s Consumption_4 0 0.0 0.0 438s Consumption_5 0 0.0 0.0 438s Consumption_6 0 0.0 0.0 438s Consumption_8 0 0.0 0.0 438s Consumption_9 0 0.0 0.0 438s Consumption_11 0 0.0 0.0 438s Consumption_12 0 0.0 0.0 438s Consumption_13 0 0.0 0.0 438s Consumption_14 0 0.0 0.0 438s Consumption_15 0 0.0 0.0 438s Consumption_16 0 0.0 0.0 438s Consumption_17 0 0.0 0.0 438s Consumption_18 0 0.0 0.0 438s Consumption_19 0 0.0 0.0 438s Consumption_20 0 0.0 0.0 438s Consumption_21 0 0.0 0.0 438s Consumption_22 0 0.0 0.0 438s Investment_2 0 0.0 0.0 438s Investment_3 0 0.0 0.0 438s Investment_4 0 0.0 0.0 438s Investment_5 0 0.0 0.0 438s Investment_6 0 0.0 0.0 438s Investment_8 0 0.0 0.0 438s Investment_9 0 0.0 0.0 438s Investment_10 0 0.0 0.0 438s Investment_11 0 0.0 0.0 438s Investment_12 0 0.0 0.0 438s Investment_13 0 0.0 0.0 438s Investment_14 0 0.0 0.0 438s Investment_15 0 0.0 0.0 438s Investment_16 0 0.0 0.0 438s Investment_17 0 0.0 0.0 438s Investment_18 0 0.0 0.0 438s Investment_19 0 0.0 0.0 438s Investment_20 0 0.0 0.0 438s Investment_21 0 0.0 0.0 438s Investment_22 0 0.0 0.0 438s PrivateWages_2 1 3.9 7.7 438s PrivateWages_3 1 3.2 3.9 438s PrivateWages_4 1 2.8 4.7 438s PrivateWages_5 1 3.5 3.8 438s PrivateWages_6 1 3.3 5.5 438s PrivateWages_8 1 4.0 6.7 438s PrivateWages_9 1 4.2 4.2 438s PrivateWages_10 1 4.1 4.0 438s PrivateWages_11 1 5.2 7.7 438s PrivateWages_12 1 5.9 7.5 438s PrivateWages_13 1 4.9 8.3 438s PrivateWages_14 1 3.7 5.4 438s PrivateWages_15 1 4.0 6.8 438s PrivateWages_16 1 4.4 7.2 438s PrivateWages_17 1 2.9 8.3 438s PrivateWages_18 1 4.3 6.7 438s PrivateWages_19 1 5.3 7.4 438s PrivateWages_20 1 6.6 8.9 438s PrivateWages_21 1 7.4 9.6 438s PrivateWages_22 1 13.8 11.6 438s PrivateWages_govWage PrivateWages_trend PrivateWages_capitalLag 438s Consumption_2 0.0 0 0 438s Consumption_3 0.0 0 0 438s Consumption_4 0.0 0 0 438s Consumption_5 0.0 0 0 438s Consumption_6 0.0 0 0 438s Consumption_8 0.0 0 0 438s Consumption_9 0.0 0 0 438s Consumption_11 0.0 0 0 438s Consumption_12 0.0 0 0 438s Consumption_13 0.0 0 0 438s Consumption_14 0.0 0 0 438s Consumption_15 0.0 0 0 438s Consumption_16 0.0 0 0 438s Consumption_17 0.0 0 0 438s Consumption_18 0.0 0 0 438s Consumption_19 0.0 0 0 438s Consumption_20 0.0 0 0 438s Consumption_21 0.0 0 0 438s Consumption_22 0.0 0 0 438s Investment_2 0.0 0 0 438s Investment_3 0.0 0 0 438s Investment_4 0.0 0 0 438s Investment_5 0.0 0 0 438s Investment_6 0.0 0 0 438s Investment_8 0.0 0 0 438s Investment_9 0.0 0 0 438s Investment_10 0.0 0 0 438s Investment_11 0.0 0 0 438s Investment_12 0.0 0 0 438s Investment_13 0.0 0 0 438s Investment_14 0.0 0 0 438s Investment_15 0.0 0 0 438s Investment_16 0.0 0 0 438s Investment_17 0.0 0 0 438s Investment_18 0.0 0 0 438s Investment_19 0.0 0 0 438s Investment_20 0.0 0 0 438s Investment_21 0.0 0 0 438s Investment_22 0.0 0 0 438s PrivateWages_2 2.7 -10 183 438s PrivateWages_3 2.9 -9 183 438s PrivateWages_4 2.9 -8 184 438s PrivateWages_5 3.1 -7 190 438s PrivateWages_6 3.2 -6 193 438s PrivateWages_8 3.6 -4 203 438s PrivateWages_9 3.7 -3 208 438s PrivateWages_10 4.0 -2 211 438s PrivateWages_11 4.2 -1 216 438s PrivateWages_12 4.8 0 217 438s PrivateWages_13 5.3 1 213 438s PrivateWages_14 5.6 2 207 438s PrivateWages_15 6.0 3 202 438s PrivateWages_16 6.1 4 199 438s PrivateWages_17 7.4 5 198 438s PrivateWages_18 6.7 6 200 438s PrivateWages_19 7.7 7 202 438s PrivateWages_20 7.8 8 200 438s PrivateWages_21 8.0 9 201 438s PrivateWages_22 8.5 10 204 438s PrivateWages_corpProfLag PrivateWages_gnpLag 438s Consumption_2 0.0 0.0 438s Consumption_3 0.0 0.0 438s Consumption_4 0.0 0.0 438s Consumption_5 0.0 0.0 438s Consumption_6 0.0 0.0 438s Consumption_8 0.0 0.0 438s Consumption_9 0.0 0.0 438s Consumption_11 0.0 0.0 438s Consumption_12 0.0 0.0 438s Consumption_13 0.0 0.0 438s Consumption_14 0.0 0.0 438s Consumption_15 0.0 0.0 438s Consumption_16 0.0 0.0 438s Consumption_17 0.0 0.0 438s Consumption_18 0.0 0.0 438s Consumption_19 0.0 0.0 438s Consumption_20 0.0 0.0 438s Consumption_21 0.0 0.0 438s Consumption_22 0.0 0.0 438s Investment_2 0.0 0.0 438s Investment_3 0.0 0.0 438s Investment_4 0.0 0.0 438s Investment_5 0.0 0.0 438s Investment_6 0.0 0.0 438s Investment_8 0.0 0.0 438s Investment_9 0.0 0.0 438s Investment_10 0.0 0.0 438s Investment_11 0.0 0.0 438s Investment_12 0.0 0.0 438s Investment_13 0.0 0.0 438s Investment_14 0.0 0.0 438s Investment_15 0.0 0.0 438s Investment_16 0.0 0.0 438s Investment_17 0.0 0.0 438s Investment_18 0.0 0.0 438s Investment_19 0.0 0.0 438s Investment_20 0.0 0.0 438s Investment_21 0.0 0.0 438s Investment_22 0.0 0.0 438s PrivateWages_2 12.7 44.9 438s PrivateWages_3 12.4 45.6 438s PrivateWages_4 16.9 50.1 438s PrivateWages_5 18.4 57.2 438s PrivateWages_6 19.4 57.1 438s PrivateWages_8 19.6 64.0 438s PrivateWages_9 19.8 64.4 438s PrivateWages_10 21.1 64.5 438s PrivateWages_11 21.7 67.0 438s PrivateWages_12 15.6 61.2 438s PrivateWages_13 11.4 53.4 438s PrivateWages_14 7.0 44.3 438s PrivateWages_15 11.2 45.1 438s PrivateWages_16 12.3 49.7 438s PrivateWages_17 14.0 54.4 438s PrivateWages_18 17.6 62.7 438s PrivateWages_19 17.3 65.0 438s PrivateWages_20 15.3 60.9 438s PrivateWages_21 19.0 69.5 438s PrivateWages_22 21.1 75.7 438s > matrix of fitted regressors 438s Consumption_(Intercept) Consumption_corpProf 438s Consumption_2 1 13.44 438s Consumption_3 1 16.68 438s Consumption_4 1 18.95 438s Consumption_5 1 20.63 438s Consumption_6 1 19.28 438s Consumption_8 1 17.21 438s Consumption_9 1 18.99 438s Consumption_11 1 16.43 438s Consumption_12 1 12.49 438s Consumption_13 1 9.06 438s Consumption_14 1 9.28 438s Consumption_15 1 12.49 438s Consumption_16 1 14.39 438s Consumption_17 1 14.69 438s Consumption_18 1 19.60 438s Consumption_19 1 19.15 438s Consumption_20 1 17.54 438s Consumption_21 1 20.33 438s Consumption_22 1 22.78 438s Investment_2 0 0.00 438s Investment_3 0 0.00 438s Investment_4 0 0.00 438s Investment_5 0 0.00 438s Investment_6 0 0.00 438s Investment_8 0 0.00 438s Investment_9 0 0.00 438s Investment_10 0 0.00 438s Investment_11 0 0.00 438s Investment_12 0 0.00 438s Investment_13 0 0.00 438s Investment_14 0 0.00 438s Investment_15 0 0.00 438s Investment_16 0 0.00 438s Investment_17 0 0.00 438s Investment_18 0 0.00 438s Investment_19 0 0.00 438s Investment_20 0 0.00 438s Investment_21 0 0.00 438s Investment_22 0 0.00 438s PrivateWages_2 0 0.00 438s PrivateWages_3 0 0.00 438s PrivateWages_4 0 0.00 438s PrivateWages_5 0 0.00 438s PrivateWages_6 0 0.00 438s PrivateWages_8 0 0.00 438s PrivateWages_9 0 0.00 438s PrivateWages_10 0 0.00 438s PrivateWages_11 0 0.00 438s PrivateWages_12 0 0.00 438s PrivateWages_13 0 0.00 438s PrivateWages_14 0 0.00 438s PrivateWages_15 0 0.00 438s PrivateWages_16 0 0.00 438s PrivateWages_17 0 0.00 438s PrivateWages_18 0 0.00 438s PrivateWages_19 0 0.00 438s PrivateWages_20 0 0.00 438s PrivateWages_21 0 0.00 438s PrivateWages_22 0 0.00 438s Consumption_corpProfLag Consumption_wages 438s Consumption_2 12.7 29.6 438s Consumption_3 12.4 31.9 438s Consumption_4 16.9 35.4 438s Consumption_5 18.4 38.8 438s Consumption_6 19.4 38.7 438s Consumption_8 19.6 39.8 438s Consumption_9 19.8 41.8 438s Consumption_11 21.7 43.0 438s Consumption_12 15.6 39.3 438s Consumption_13 11.4 35.2 438s Consumption_14 7.0 33.0 438s Consumption_15 11.2 37.3 438s Consumption_16 12.3 40.1 438s Consumption_17 14.0 41.7 438s Consumption_18 17.6 47.7 438s Consumption_19 17.3 49.2 438s Consumption_20 15.3 48.5 438s Consumption_21 19.0 53.4 438s Consumption_22 21.1 60.8 438s Investment_2 0.0 0.0 438s Investment_3 0.0 0.0 438s Investment_4 0.0 0.0 438s Investment_5 0.0 0.0 438s Investment_6 0.0 0.0 438s Investment_8 0.0 0.0 438s Investment_9 0.0 0.0 438s Investment_10 0.0 0.0 438s Investment_11 0.0 0.0 438s Investment_12 0.0 0.0 438s Investment_13 0.0 0.0 438s Investment_14 0.0 0.0 438s Investment_15 0.0 0.0 438s Investment_16 0.0 0.0 438s Investment_17 0.0 0.0 438s Investment_18 0.0 0.0 438s Investment_19 0.0 0.0 438s Investment_20 0.0 0.0 438s Investment_21 0.0 0.0 438s Investment_22 0.0 0.0 438s PrivateWages_2 0.0 0.0 438s PrivateWages_3 0.0 0.0 438s PrivateWages_4 0.0 0.0 438s PrivateWages_5 0.0 0.0 438s PrivateWages_6 0.0 0.0 438s PrivateWages_8 0.0 0.0 438s PrivateWages_9 0.0 0.0 438s PrivateWages_10 0.0 0.0 438s PrivateWages_11 0.0 0.0 438s PrivateWages_12 0.0 0.0 438s PrivateWages_13 0.0 0.0 438s PrivateWages_14 0.0 0.0 438s PrivateWages_15 0.0 0.0 438s PrivateWages_16 0.0 0.0 438s PrivateWages_17 0.0 0.0 438s PrivateWages_18 0.0 0.0 438s PrivateWages_19 0.0 0.0 438s PrivateWages_20 0.0 0.0 438s PrivateWages_21 0.0 0.0 438s PrivateWages_22 0.0 0.0 438s Investment_(Intercept) Investment_corpProf 438s Consumption_2 0 0.00 438s Consumption_3 0 0.00 438s Consumption_4 0 0.00 438s Consumption_5 0 0.00 438s Consumption_6 0 0.00 438s Consumption_8 0 0.00 438s Consumption_9 0 0.00 438s Consumption_11 0 0.00 438s Consumption_12 0 0.00 438s Consumption_13 0 0.00 438s Consumption_14 0 0.00 438s Consumption_15 0 0.00 438s Consumption_16 0 0.00 438s Consumption_17 0 0.00 438s Consumption_18 0 0.00 438s Consumption_19 0 0.00 438s Consumption_20 0 0.00 438s Consumption_21 0 0.00 438s Consumption_22 0 0.00 438s Investment_2 1 12.96 438s Investment_3 1 16.70 438s Investment_4 1 19.14 438s Investment_5 1 20.94 438s Investment_6 1 19.47 438s Investment_8 1 17.14 438s Investment_9 1 19.49 438s Investment_10 1 20.46 438s Investment_11 1 16.85 438s Investment_12 1 12.68 438s Investment_13 1 8.92 438s Investment_14 1 9.30 438s Investment_15 1 12.79 438s Investment_16 1 14.26 438s Investment_17 1 14.75 438s Investment_18 1 19.54 438s Investment_19 1 19.36 438s Investment_20 1 17.39 438s Investment_21 1 20.10 438s Investment_22 1 22.86 438s PrivateWages_2 0 0.00 438s PrivateWages_3 0 0.00 438s PrivateWages_4 0 0.00 438s PrivateWages_5 0 0.00 438s PrivateWages_6 0 0.00 438s PrivateWages_8 0 0.00 438s PrivateWages_9 0 0.00 438s PrivateWages_10 0 0.00 438s PrivateWages_11 0 0.00 438s PrivateWages_12 0 0.00 438s PrivateWages_13 0 0.00 438s PrivateWages_14 0 0.00 438s PrivateWages_15 0 0.00 438s PrivateWages_16 0 0.00 438s PrivateWages_17 0 0.00 438s PrivateWages_18 0 0.00 438s PrivateWages_19 0 0.00 438s PrivateWages_20 0 0.00 438s PrivateWages_21 0 0.00 438s PrivateWages_22 0 0.00 438s Investment_corpProfLag Investment_capitalLag 438s Consumption_2 0.0 0 438s Consumption_3 0.0 0 438s Consumption_4 0.0 0 438s Consumption_5 0.0 0 438s Consumption_6 0.0 0 438s Consumption_8 0.0 0 438s Consumption_9 0.0 0 438s Consumption_11 0.0 0 438s Consumption_12 0.0 0 438s Consumption_13 0.0 0 438s Consumption_14 0.0 0 438s Consumption_15 0.0 0 438s Consumption_16 0.0 0 438s Consumption_17 0.0 0 438s Consumption_18 0.0 0 438s Consumption_19 0.0 0 438s Consumption_20 0.0 0 438s Consumption_21 0.0 0 438s Consumption_22 0.0 0 438s Investment_2 12.7 183 438s Investment_3 12.4 183 438s Investment_4 16.9 184 438s Investment_5 18.4 190 438s Investment_6 19.4 193 438s Investment_8 19.6 203 438s Investment_9 19.8 208 438s Investment_10 21.1 211 438s Investment_11 21.7 216 438s Investment_12 15.6 217 438s Investment_13 11.4 213 438s Investment_14 7.0 207 438s Investment_15 11.2 202 438s Investment_16 12.3 199 438s Investment_17 14.0 198 438s Investment_18 17.6 200 438s Investment_19 17.3 202 438s Investment_20 15.3 200 438s Investment_21 19.0 201 438s Investment_22 21.1 204 438s PrivateWages_2 0.0 0 438s PrivateWages_3 0.0 0 438s PrivateWages_4 0.0 0 438s PrivateWages_5 0.0 0 438s PrivateWages_6 0.0 0 438s PrivateWages_8 0.0 0 438s PrivateWages_9 0.0 0 438s PrivateWages_10 0.0 0 438s PrivateWages_11 0.0 0 438s PrivateWages_12 0.0 0 438s PrivateWages_13 0.0 0 438s PrivateWages_14 0.0 0 438s PrivateWages_15 0.0 0 438s PrivateWages_16 0.0 0 438s PrivateWages_17 0.0 0 438s PrivateWages_18 0.0 0 438s PrivateWages_19 0.0 0 438s PrivateWages_20 0.0 0 438s PrivateWages_21 0.0 0 438s PrivateWages_22 0.0 0 438s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 438s Consumption_2 0 0.0 0.0 438s Consumption_3 0 0.0 0.0 438s Consumption_4 0 0.0 0.0 438s Consumption_5 0 0.0 0.0 438s Consumption_6 0 0.0 0.0 438s Consumption_8 0 0.0 0.0 438s Consumption_9 0 0.0 0.0 438s Consumption_11 0 0.0 0.0 438s Consumption_12 0 0.0 0.0 438s Consumption_13 0 0.0 0.0 438s Consumption_14 0 0.0 0.0 438s Consumption_15 0 0.0 0.0 438s Consumption_16 0 0.0 0.0 438s Consumption_17 0 0.0 0.0 438s Consumption_18 0 0.0 0.0 438s Consumption_19 0 0.0 0.0 438s Consumption_20 0 0.0 0.0 438s Consumption_21 0 0.0 0.0 438s Consumption_22 0 0.0 0.0 438s Investment_2 0 0.0 0.0 438s Investment_3 0 0.0 0.0 438s Investment_4 0 0.0 0.0 438s Investment_5 0 0.0 0.0 438s Investment_6 0 0.0 0.0 438s Investment_8 0 0.0 0.0 438s Investment_9 0 0.0 0.0 438s Investment_10 0 0.0 0.0 438s Investment_11 0 0.0 0.0 438s Investment_12 0 0.0 0.0 438s Investment_13 0 0.0 0.0 438s Investment_14 0 0.0 0.0 438s Investment_15 0 0.0 0.0 438s Investment_16 0 0.0 0.0 438s Investment_17 0 0.0 0.0 438s Investment_18 0 0.0 0.0 438s Investment_19 0 0.0 0.0 438s Investment_20 0 0.0 0.0 438s Investment_21 0 0.0 0.0 438s Investment_22 0 0.0 0.0 438s PrivateWages_2 1 47.1 44.9 438s PrivateWages_3 1 49.6 45.6 438s PrivateWages_4 1 56.5 50.1 438s PrivateWages_5 1 60.7 57.2 438s PrivateWages_6 1 60.6 57.1 438s PrivateWages_8 1 60.0 64.0 438s PrivateWages_9 1 62.3 64.4 438s PrivateWages_10 1 64.6 64.5 438s PrivateWages_11 1 63.7 67.0 438s PrivateWages_12 1 54.8 61.2 438s PrivateWages_13 1 47.0 53.4 438s PrivateWages_14 1 42.1 44.3 438s PrivateWages_15 1 51.2 45.1 438s PrivateWages_16 1 55.3 49.7 438s PrivateWages_17 1 57.4 54.4 438s PrivateWages_18 1 67.2 62.7 438s PrivateWages_19 1 68.5 65.0 438s PrivateWages_20 1 66.8 60.9 438s PrivateWages_21 1 74.9 69.5 438s PrivateWages_22 1 86.9 75.7 438s PrivateWages_trend 438s Consumption_2 0 438s Consumption_3 0 438s Consumption_4 0 438s Consumption_5 0 438s Consumption_6 0 438s Consumption_8 0 438s Consumption_9 0 438s Consumption_11 0 438s Consumption_12 0 438s Consumption_13 0 438s Consumption_14 0 438s Consumption_15 0 438s Consumption_16 0 438s Consumption_17 0 438s Consumption_18 0 438s Consumption_19 0 438s Consumption_20 0 438s Consumption_21 0 438s Consumption_22 0 438s Investment_2 0 438s Investment_3 0 438s Investment_4 0 438s Investment_5 0 438s Investment_6 0 438s Investment_8 0 438s Investment_9 0 438s Investment_10 0 438s Investment_11 0 438s Investment_12 0 438s Investment_13 0 438s Investment_14 0 438s Investment_15 0 438s Investment_16 0 438s Investment_17 0 438s Investment_18 0 438s Investment_19 0 438s Investment_20 0 438s Investment_21 0 438s Investment_22 0 438s PrivateWages_2 -10 438s PrivateWages_3 -9 438s PrivateWages_4 -8 438s PrivateWages_5 -7 438s PrivateWages_6 -6 438s PrivateWages_8 -4 438s PrivateWages_9 -3 438s PrivateWages_10 -2 438s PrivateWages_11 -1 438s PrivateWages_12 0 438s PrivateWages_13 1 438s PrivateWages_14 2 438s PrivateWages_15 3 438s PrivateWages_16 4 438s PrivateWages_17 5 438s PrivateWages_18 6 438s PrivateWages_19 7 438s PrivateWages_20 8 438s PrivateWages_21 9 438s PrivateWages_22 10 438s > nobs 438s [1] 59 438s > linearHypothesis 438s Linear hypothesis test (Theil's F test) 438s 438s Hypothesis: 438s Consumption_corpProf + Investment_capitalLag = 0 438s 438s Model 1: restricted model 438s Model 2: kleinModel 438s 438s Res.Df Df F Pr(>F) 438s 1 48 438s 2 47 1 0.87 0.36 438s Linear hypothesis test (F statistic of a Wald test) 438s 438s Hypothesis: 438s Consumption_corpProf + Investment_capitalLag = 0 438s 438s Model 1: restricted model 438s Model 2: kleinModel 438s 438s Res.Df Df F Pr(>F) 438s 1 48 438s 2 47 1 0.98 0.33 438s Linear hypothesis test (Chi^2 statistic of a Wald test) 438s 438s Hypothesis: 438s Consumption_corpProf + Investment_capitalLag = 0 438s 438s Model 1: restricted model 438s Model 2: kleinModel 438s 438s Res.Df Df Chisq Pr(>Chisq) 438s 1 48 438s 2 47 1 0.98 0.32 438s Linear hypothesis test (Theil's F test) 438s 438s Hypothesis: 438s Consumption_corpProf + Investment_capitalLag = 0 438s Consumption_corpProfLag - PrivateWages_trend = 0 438s 438s Model 1: restricted model 438s Model 2: kleinModel 438s 438s Res.Df Df F Pr(>F) 438s 1 49 438s 2 47 2 0.43 0.65 438s Linear hypothesis test (F statistic of a Wald test) 438s 438s Hypothesis: 438s Consumption_corpProf + Investment_capitalLag = 0 438s Consumption_corpProfLag - PrivateWages_trend = 0 438s 438s Model 1: restricted model 438s Model 2: kleinModel 438s 438s Res.Df Df F Pr(>F) 438s 1 49 438s 2 47 2 0.49 0.61 438s Linear hypothesis test (Chi^2 statistic of a Wald test) 438s 438s Hypothesis: 438s Consumption_corpProf + Investment_capitalLag = 0 438s Consumption_corpProfLag - PrivateWages_trend = 0 438s 438s Model 1: restricted model 438s Model 2: kleinModel 438s 438s Res.Df Df Chisq Pr(>Chisq) 438s 1 49 438s 2 47 2 0.98 0.61 438s > logLik 438s 'log Lik.' -71.5 (df=13) 438s 'log Lik.' -78.7 (df=13) 438s Estimating function 438s Consumption_(Intercept) Consumption_corpProf 438s Consumption_2 -1.5371 -20.65 438s Consumption_3 -0.3191 -5.32 438s Consumption_4 0.0169 0.32 438s Consumption_5 -1.6346 -33.73 438s Consumption_6 0.2820 5.44 438s Consumption_8 2.9429 50.64 438s Consumption_9 2.3495 44.61 438s Consumption_11 -1.2221 -20.08 438s Consumption_12 -1.0034 -12.54 438s Consumption_13 -2.0551 -18.62 438s Consumption_14 1.4937 13.86 438s Consumption_15 -0.7418 -9.26 438s Consumption_16 -0.6703 -9.64 438s Consumption_17 4.0943 60.15 438s Consumption_18 -0.6347 -12.44 438s Consumption_19 -3.0409 -58.22 438s Consumption_20 2.1019 36.86 438s Consumption_21 0.7142 14.52 438s Consumption_22 -1.1363 -25.88 438s Investment_2 0.0000 0.00 438s Investment_3 0.0000 0.00 438s Investment_4 0.0000 0.00 438s Investment_5 0.0000 0.00 438s Investment_6 0.0000 0.00 438s Investment_8 0.0000 0.00 438s Investment_9 0.0000 0.00 438s Investment_10 0.0000 0.00 438s Investment_11 0.0000 0.00 438s Investment_12 0.0000 0.00 438s Investment_13 0.0000 0.00 438s Investment_14 0.0000 0.00 438s Investment_15 0.0000 0.00 438s Investment_16 0.0000 0.00 438s Investment_17 0.0000 0.00 438s Investment_18 0.0000 0.00 438s Investment_19 0.0000 0.00 438s Investment_20 0.0000 0.00 438s Investment_21 0.0000 0.00 438s Investment_22 0.0000 0.00 438s PrivateWages_2 0.0000 0.00 438s PrivateWages_3 0.0000 0.00 438s PrivateWages_4 0.0000 0.00 438s PrivateWages_5 0.0000 0.00 438s PrivateWages_6 0.0000 0.00 438s PrivateWages_8 0.0000 0.00 438s PrivateWages_9 0.0000 0.00 438s PrivateWages_10 0.0000 0.00 438s PrivateWages_11 0.0000 0.00 438s PrivateWages_12 0.0000 0.00 438s PrivateWages_13 0.0000 0.00 438s PrivateWages_14 0.0000 0.00 438s PrivateWages_15 0.0000 0.00 438s PrivateWages_16 0.0000 0.00 438s PrivateWages_17 0.0000 0.00 438s PrivateWages_18 0.0000 0.00 438s PrivateWages_19 0.0000 0.00 438s PrivateWages_20 0.0000 0.00 438s PrivateWages_21 0.0000 0.00 438s PrivateWages_22 0.0000 0.00 438s Consumption_corpProfLag Consumption_wages 438s Consumption_2 -19.521 -45.456 438s Consumption_3 -3.957 -10.167 438s Consumption_4 0.286 0.599 438s Consumption_5 -30.078 -63.354 438s Consumption_6 5.471 10.901 438s Consumption_8 57.681 117.190 438s Consumption_9 46.520 98.197 438s Consumption_11 -26.520 -52.512 438s Consumption_12 -15.653 -39.407 438s Consumption_13 -23.428 -72.317 438s Consumption_14 10.456 49.297 438s Consumption_15 -8.308 -27.687 438s Consumption_16 -8.244 -26.878 438s Consumption_17 57.321 170.665 438s Consumption_18 -11.170 -30.264 438s Consumption_19 -52.608 -149.761 438s Consumption_20 32.159 101.952 438s Consumption_21 13.570 38.131 438s Consumption_22 -23.976 -69.128 438s Investment_2 0.000 0.000 438s Investment_3 0.000 0.000 438s Investment_4 0.000 0.000 438s Investment_5 0.000 0.000 438s Investment_6 0.000 0.000 438s Investment_8 0.000 0.000 438s Investment_9 0.000 0.000 438s Investment_10 0.000 0.000 438s Investment_11 0.000 0.000 438s Investment_12 0.000 0.000 438s Investment_13 0.000 0.000 438s Investment_14 0.000 0.000 438s Investment_15 0.000 0.000 438s Investment_16 0.000 0.000 438s Investment_17 0.000 0.000 438s Investment_18 0.000 0.000 438s Investment_19 0.000 0.000 438s Investment_20 0.000 0.000 438s Investment_21 0.000 0.000 438s Investment_22 0.000 0.000 438s PrivateWages_2 0.000 0.000 438s PrivateWages_3 0.000 0.000 438s PrivateWages_4 0.000 0.000 438s PrivateWages_5 0.000 0.000 438s PrivateWages_6 0.000 0.000 438s PrivateWages_8 0.000 0.000 438s PrivateWages_9 0.000 0.000 438s PrivateWages_10 0.000 0.000 438s PrivateWages_11 0.000 0.000 438s PrivateWages_12 0.000 0.000 438s PrivateWages_13 0.000 0.000 438s PrivateWages_14 0.000 0.000 438s PrivateWages_15 0.000 0.000 438s PrivateWages_16 0.000 0.000 438s PrivateWages_17 0.000 0.000 438s PrivateWages_18 0.000 0.000 438s PrivateWages_19 0.000 0.000 438s PrivateWages_20 0.000 0.000 438s PrivateWages_21 0.000 0.000 438s PrivateWages_22 0.000 0.000 438s Investment_(Intercept) Investment_corpProf 438s Consumption_2 0.0000 0.000 438s Consumption_3 0.0000 0.000 438s Consumption_4 0.0000 0.000 438s Consumption_5 0.0000 0.000 438s Consumption_6 0.0000 0.000 438s Consumption_8 0.0000 0.000 438s Consumption_9 0.0000 0.000 438s Consumption_11 0.0000 0.000 438s Consumption_12 0.0000 0.000 438s Consumption_13 0.0000 0.000 438s Consumption_14 0.0000 0.000 438s Consumption_15 0.0000 0.000 438s Consumption_16 0.0000 0.000 438s Consumption_17 0.0000 0.000 438s Consumption_18 0.0000 0.000 438s Consumption_19 0.0000 0.000 438s Consumption_20 0.0000 0.000 438s Consumption_21 0.0000 0.000 438s Consumption_22 0.0000 0.000 438s Investment_2 -1.1313 -14.660 438s Investment_3 0.2902 4.847 438s Investment_4 0.9027 17.274 438s Investment_5 -1.7434 -36.502 438s Investment_6 0.5695 11.088 438s Investment_8 1.6225 27.812 438s Investment_9 0.4166 8.119 438s Investment_10 2.0381 41.703 438s Investment_11 -0.8611 -14.505 438s Investment_12 -0.9091 -11.527 438s Investment_13 -1.1148 -9.946 438s Investment_14 1.3841 12.873 438s Investment_15 -0.2900 -3.710 438s Investment_16 0.0605 0.862 438s Investment_17 2.2439 33.101 438s Investment_18 -0.5390 -10.534 438s Investment_19 -3.9452 -76.375 438s Investment_20 0.4890 8.502 438s Investment_21 0.0864 1.737 438s Investment_22 0.4306 9.843 438s PrivateWages_2 0.0000 0.000 438s PrivateWages_3 0.0000 0.000 438s PrivateWages_4 0.0000 0.000 438s PrivateWages_5 0.0000 0.000 438s PrivateWages_6 0.0000 0.000 438s PrivateWages_8 0.0000 0.000 438s PrivateWages_9 0.0000 0.000 438s PrivateWages_10 0.0000 0.000 438s PrivateWages_11 0.0000 0.000 438s PrivateWages_12 0.0000 0.000 438s PrivateWages_13 0.0000 0.000 438s PrivateWages_14 0.0000 0.000 438s PrivateWages_15 0.0000 0.000 438s PrivateWages_16 0.0000 0.000 438s PrivateWages_17 0.0000 0.000 438s PrivateWages_18 0.0000 0.000 438s PrivateWages_19 0.0000 0.000 438s PrivateWages_20 0.0000 0.000 438s PrivateWages_21 0.0000 0.000 438s PrivateWages_22 0.0000 0.000 438s Investment_corpProfLag Investment_capitalLag 438s Consumption_2 0.000 0.0 438s Consumption_3 0.000 0.0 438s Consumption_4 0.000 0.0 438s Consumption_5 0.000 0.0 438s Consumption_6 0.000 0.0 438s Consumption_8 0.000 0.0 438s Consumption_9 0.000 0.0 438s Consumption_11 0.000 0.0 438s Consumption_12 0.000 0.0 438s Consumption_13 0.000 0.0 438s Consumption_14 0.000 0.0 438s Consumption_15 0.000 0.0 438s Consumption_16 0.000 0.0 438s Consumption_17 0.000 0.0 438s Consumption_18 0.000 0.0 438s Consumption_19 0.000 0.0 438s Consumption_20 0.000 0.0 438s Consumption_21 0.000 0.0 438s Consumption_22 0.000 0.0 438s Investment_2 -14.368 -206.8 438s Investment_3 3.598 53.0 438s Investment_4 15.256 166.5 438s Investment_5 -32.079 -330.7 438s Investment_6 11.048 109.7 438s Investment_8 31.801 330.0 438s Investment_9 8.248 86.5 438s Investment_10 43.003 429.2 438s Investment_11 -18.685 -185.7 438s Investment_12 -14.182 -197.0 438s Investment_13 -12.709 -237.8 438s Investment_14 9.689 286.6 438s Investment_15 -3.247 -58.6 438s Investment_16 0.744 12.0 438s Investment_17 31.414 443.6 438s Investment_18 -9.486 -107.7 438s Investment_19 -68.252 -796.1 438s Investment_20 7.482 97.7 438s Investment_21 1.642 17.4 438s Investment_22 9.085 88.0 438s PrivateWages_2 0.000 0.0 438s PrivateWages_3 0.000 0.0 438s PrivateWages_4 0.000 0.0 438s PrivateWages_5 0.000 0.0 438s PrivateWages_6 0.000 0.0 438s PrivateWages_8 0.000 0.0 438s PrivateWages_9 0.000 0.0 438s PrivateWages_10 0.000 0.0 438s PrivateWages_11 0.000 0.0 438s PrivateWages_12 0.000 0.0 438s PrivateWages_13 0.000 0.0 438s PrivateWages_14 0.000 0.0 438s PrivateWages_15 0.000 0.0 438s PrivateWages_16 0.000 0.0 438s PrivateWages_17 0.000 0.0 438s PrivateWages_18 0.000 0.0 438s PrivateWages_19 0.000 0.0 438s PrivateWages_20 0.000 0.0 438s PrivateWages_21 0.000 0.0 438s PrivateWages_22 0.000 0.0 438s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 438s Consumption_2 0.0000 0.00 0.00 438s Consumption_3 0.0000 0.00 0.00 438s Consumption_4 0.0000 0.00 0.00 438s Consumption_5 0.0000 0.00 0.00 438s Consumption_6 0.0000 0.00 0.00 438s Consumption_8 0.0000 0.00 0.00 438s Consumption_9 0.0000 0.00 0.00 438s Consumption_11 0.0000 0.00 0.00 438s Consumption_12 0.0000 0.00 0.00 438s Consumption_13 0.0000 0.00 0.00 438s Consumption_14 0.0000 0.00 0.00 438s Consumption_15 0.0000 0.00 0.00 438s Consumption_16 0.0000 0.00 0.00 438s Consumption_17 0.0000 0.00 0.00 438s Consumption_18 0.0000 0.00 0.00 438s Consumption_19 0.0000 0.00 0.00 438s Consumption_20 0.0000 0.00 0.00 438s Consumption_21 0.0000 0.00 0.00 438s Consumption_22 0.0000 0.00 0.00 438s Investment_2 0.0000 0.00 0.00 438s Investment_3 0.0000 0.00 0.00 438s Investment_4 0.0000 0.00 0.00 438s Investment_5 0.0000 0.00 0.00 438s Investment_6 0.0000 0.00 0.00 438s Investment_8 0.0000 0.00 0.00 438s Investment_9 0.0000 0.00 0.00 438s Investment_10 0.0000 0.00 0.00 438s Investment_11 0.0000 0.00 0.00 438s Investment_12 0.0000 0.00 0.00 438s Investment_13 0.0000 0.00 0.00 438s Investment_14 0.0000 0.00 0.00 438s Investment_15 0.0000 0.00 0.00 438s Investment_16 0.0000 0.00 0.00 438s Investment_17 0.0000 0.00 0.00 438s Investment_18 0.0000 0.00 0.00 438s Investment_19 0.0000 0.00 0.00 438s Investment_20 0.0000 0.00 0.00 438s Investment_21 0.0000 0.00 0.00 438s Investment_22 0.0000 0.00 0.00 438s PrivateWages_2 -1.9924 -93.78 -89.46 438s PrivateWages_3 0.4683 23.22 21.35 438s PrivateWages_4 1.4034 79.35 70.31 438s PrivateWages_5 -1.7870 -108.45 -102.22 438s PrivateWages_6 -0.3627 -21.98 -20.71 438s PrivateWages_8 1.1629 69.77 74.43 438s PrivateWages_9 1.2735 79.30 82.01 438s PrivateWages_10 2.2141 142.96 142.81 438s PrivateWages_11 -1.2912 -82.26 -86.51 438s PrivateWages_12 -0.0350 -1.92 -2.14 438s PrivateWages_13 -1.0438 -49.04 -55.74 438s PrivateWages_14 1.8016 75.90 79.81 438s PrivateWages_15 -0.3714 -19.02 -16.75 438s PrivateWages_16 -0.3904 -21.61 -19.40 438s PrivateWages_17 1.4934 85.71 81.24 438s PrivateWages_18 0.0279 1.88 1.75 438s PrivateWages_19 -3.8229 -261.91 -248.49 438s PrivateWages_20 0.7870 52.61 47.93 438s PrivateWages_21 -0.7415 -55.52 -51.54 438s PrivateWages_22 1.2062 104.79 91.31 438s PrivateWages_trend 438s Consumption_2 0.000 438s Consumption_3 0.000 438s Consumption_4 0.000 438s Consumption_5 0.000 438s Consumption_6 0.000 438s Consumption_8 0.000 438s Consumption_9 0.000 438s Consumption_11 0.000 438s Consumption_12 0.000 438s Consumption_13 0.000 438s Consumption_14 0.000 438s Consumption_15 0.000 438s Consumption_16 0.000 438s Consumption_17 0.000 438s Consumption_18 0.000 438s Consumption_19 0.000 438s Consumption_20 0.000 438s Consumption_21 0.000 438s Consumption_22 0.000 438s Investment_2 0.000 438s Investment_3 0.000 438s Investment_4 0.000 438s Investment_5 0.000 438s Investment_6 0.000 438s Investment_8 0.000 438s Investment_9 0.000 438s Investment_10 0.000 438s Investment_11 0.000 438s Investment_12 0.000 438s Investment_13 0.000 438s Investment_14 0.000 438s Investment_15 0.000 438s Investment_16 0.000 438s Investment_17 0.000 438s Investment_18 0.000 438s Investment_19 0.000 438s Investment_20 0.000 438s Investment_21 0.000 438s Investment_22 0.000 438s PrivateWages_2 19.924 438s PrivateWages_3 -4.214 438s PrivateWages_4 -11.227 438s PrivateWages_5 12.509 438s PrivateWages_6 2.176 438s PrivateWages_8 -4.652 438s PrivateWages_9 -3.820 438s PrivateWages_10 -4.428 438s PrivateWages_11 1.291 438s PrivateWages_12 0.000 438s PrivateWages_13 -1.044 438s PrivateWages_14 3.603 438s PrivateWages_15 -1.114 438s PrivateWages_16 -1.562 438s PrivateWages_17 7.467 438s PrivateWages_18 0.168 438s PrivateWages_19 -26.760 438s PrivateWages_20 6.296 438s PrivateWages_21 -6.674 438s PrivateWages_22 12.062 438s [1] TRUE 438s > Bread 438s Consumption_(Intercept) Consumption_corpProf 438s Consumption_(Intercept) 99.763 -0.8715 438s Consumption_corpProf -0.872 0.7621 438s Consumption_corpProfLag -0.479 -0.4940 438s Consumption_wages -1.807 -0.0927 438s Investment_(Intercept) 0.000 0.0000 438s Investment_corpProf 0.000 0.0000 438s Investment_corpProfLag 0.000 0.0000 438s Investment_capitalLag 0.000 0.0000 438s PrivateWages_(Intercept) 0.000 0.0000 438s PrivateWages_gnp 0.000 0.0000 438s PrivateWages_gnpLag 0.000 0.0000 438s PrivateWages_trend 0.000 0.0000 438s Consumption_corpProfLag Consumption_wages 438s Consumption_(Intercept) -0.4786 -1.8068 438s Consumption_corpProf -0.4940 -0.0927 438s Consumption_corpProfLag 0.6462 -0.0403 438s Consumption_wages -0.0403 0.0963 438s Investment_(Intercept) 0.0000 0.0000 438s Investment_corpProf 0.0000 0.0000 438s Investment_corpProfLag 0.0000 0.0000 438s Investment_capitalLag 0.0000 0.0000 438s PrivateWages_(Intercept) 0.0000 0.0000 438s PrivateWages_gnp 0.0000 0.0000 438s PrivateWages_gnpLag 0.0000 0.0000 438s PrivateWages_trend 0.0000 0.0000 438s Investment_(Intercept) Investment_corpProf 438s Consumption_(Intercept) 0.0 0.000 438s Consumption_corpProf 0.0 0.000 438s Consumption_corpProfLag 0.0 0.000 438s Consumption_wages 0.0 0.000 438s Investment_(Intercept) 2405.5 -38.269 438s Investment_corpProf -38.3 1.231 438s Investment_corpProfLag 32.8 -1.072 438s Investment_capitalLag -11.4 0.174 438s PrivateWages_(Intercept) 0.0 0.000 438s PrivateWages_gnp 0.0 0.000 438s PrivateWages_gnpLag 0.0 0.000 438s PrivateWages_trend 0.0 0.000 438s Investment_corpProfLag Investment_capitalLag 438s Consumption_(Intercept) 0.000 0.0000 438s Consumption_corpProf 0.000 0.0000 438s Consumption_corpProfLag 0.000 0.0000 438s Consumption_wages 0.000 0.0000 438s Investment_(Intercept) 32.828 -11.4279 438s Investment_corpProf -1.072 0.1744 438s Investment_corpProfLag 1.129 -0.1652 438s Investment_capitalLag -0.165 0.0557 438s PrivateWages_(Intercept) 0.000 0.0000 438s PrivateWages_gnp 0.000 0.0000 438s PrivateWages_gnpLag 0.000 0.0000 438s PrivateWages_trend 0.000 0.0000 438s PrivateWages_(Intercept) PrivateWages_gnp 438s Consumption_(Intercept) 0.000 0.0000 438s Consumption_corpProf 0.000 0.0000 438s Consumption_corpProfLag 0.000 0.0000 438s Consumption_wages 0.000 0.0000 438s Investment_(Intercept) 0.000 0.0000 438s Investment_corpProf 0.000 0.0000 438s Investment_corpProfLag 0.000 0.0000 438s Investment_capitalLag 0.000 0.0000 438s PrivateWages_(Intercept) 167.869 -0.9135 438s PrivateWages_gnp -0.913 0.1554 438s PrivateWages_gnpLag -1.915 -0.1448 438s PrivateWages_trend 2.128 -0.0417 438s PrivateWages_gnpLag PrivateWages_trend 438s Consumption_(Intercept) 0.0000 0.0000 438s Consumption_corpProf 0.0000 0.0000 438s Consumption_corpProfLag 0.0000 0.0000 438s Consumption_wages 0.0000 0.0000 438s Investment_(Intercept) 0.0000 0.0000 438s Investment_corpProf 0.0000 0.0000 438s Investment_corpProfLag 0.0000 0.0000 438s Investment_capitalLag 0.0000 0.0000 438s PrivateWages_(Intercept) -1.9153 2.1280 438s PrivateWages_gnp -0.1448 -0.0417 438s PrivateWages_gnpLag 0.1830 0.0059 438s PrivateWages_trend 0.0059 0.1132 438s > 438s > # SUR 438s Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 438s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 438s > summary 438s 438s systemfit results 438s method: SUR 438s 438s N DF SSR detRCov OLS-R2 McElroy-R2 438s system 61 49 45.4 0.151 0.977 0.992 438s 438s N DF SSR MSE RMSE R2 Adj R2 438s Consumption 20 16 17.6 1.102 1.050 0.981 0.977 438s Investment 21 17 17.5 1.029 1.015 0.931 0.918 438s PrivateWages 20 16 10.3 0.643 0.802 0.987 0.985 438s 438s The covariance matrix of the residuals used for estimation 438s Consumption Investment PrivateWages 438s Consumption 0.8871 0.0268 -0.349 438s Investment 0.0268 0.7328 0.103 438s PrivateWages -0.3492 0.1029 0.444 438s 438s The covariance matrix of the residuals 438s Consumption Investment PrivateWages 438s Consumption 0.8852 0.0508 -0.406 438s Investment 0.0508 0.7313 0.161 438s PrivateWages -0.4063 0.1609 0.467 438s 438s The correlations of the residuals 438s Consumption Investment PrivateWages 438s Consumption 1.000 0.065 -0.635 438s Investment 0.065 1.000 0.262 438s PrivateWages -0.635 0.262 1.000 438s 438s 438s SUR estimates for 'Consumption' (equation 1) 438s Model Formula: consump ~ corpProf + corpProfLag + wages 438s 438s Estimate Std. Error t value Pr(>|t|) 438s (Intercept) 16.0876 1.2010 13.39 4.1e-10 *** 438s corpProf 0.2173 0.0799 2.72 0.015 * 438s corpProfLag 0.0694 0.0793 0.88 0.394 438s wages 0.7975 0.0360 22.15 2.0e-13 *** 438s --- 438s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 438s 438s Residual standard error: 1.05 on 16 degrees of freedom 438s Number of observations: 20 Degrees of Freedom: 16 438s SSR: 17.63 MSE: 1.102 Root MSE: 1.05 438s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.977 438s 438s 438s SUR estimates for 'Investment' (equation 2) 438s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 438s 438s Estimate Std. Error t value Pr(>|t|) 438s (Intercept) 12.3518 4.5615 2.71 0.01493 * 438s corpProf 0.4511 0.0814 5.54 3.6e-05 *** 438s corpProfLag 0.3570 0.0846 4.22 0.00058 *** 438s capitalLag -0.1225 0.0223 -5.49 4.0e-05 *** 438s --- 438s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 438s 438s Residual standard error: 1.015 on 17 degrees of freedom 438s Number of observations: 21 Degrees of Freedom: 17 438s SSR: 17.5 MSE: 1.029 Root MSE: 1.015 438s Multiple R-Squared: 0.931 Adjusted R-Squared: 0.918 438s 438s 438s SUR estimates for 'PrivateWages' (equation 3) 438s Model Formula: privWage ~ gnp + gnpLag + trend 438s 438s Estimate Std. Error t value Pr(>|t|) 438s (Intercept) 1.3964 1.0825 1.29 0.22 438s gnp 0.4177 0.0269 15.55 4.4e-11 *** 438s gnpLag 0.1709 0.0306 5.59 4.0e-05 *** 438s trend 0.1467 0.0272 5.40 5.9e-05 *** 438s --- 438s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 438s 438s Residual standard error: 0.802 on 16 degrees of freedom 438s Number of observations: 20 Degrees of Freedom: 16 438s SSR: 10.284 MSE: 0.643 Root MSE: 0.802 438s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 438s 438s > residuals 438s Consumption Investment PrivateWages 438s 1 NA NA NA 438s 2 -0.2529 -0.2920 -1.15193 438s 3 -1.2998 -0.1392 0.50193 438s 4 -1.5662 1.1106 1.42026 438s 5 -0.4876 -1.4391 -0.09801 438s 6 0.0149 0.3556 -0.35678 438s 7 0.9002 1.4558 NA 438s 8 1.3535 0.8299 -0.74964 438s 9 1.0406 -0.5136 0.29355 438s 10 NA 1.2191 1.18544 438s 11 0.4417 0.2810 -0.36558 438s 12 -0.0892 0.0754 0.33733 438s 13 -0.1541 0.3429 -0.17490 438s 14 0.2984 0.3597 0.39941 438s 15 -0.0260 -0.1602 0.29441 438s 16 -0.0250 0.0130 -0.00177 438s 17 1.5671 1.0231 -0.81891 438s 18 -0.4089 0.0306 0.85516 438s 19 0.2819 -2.6153 -0.77184 438s 20 0.9257 -0.6030 -0.41040 438s 21 0.7415 -0.7118 -1.21679 438s 22 -2.2437 -0.5398 0.57166 438s > fitted 438s Consumption Investment PrivateWages 438s 1 NA NA NA 438s 2 42.2 0.092 26.7 438s 3 46.3 2.039 28.8 438s 4 50.8 4.089 32.7 438s 5 51.1 4.439 34.0 438s 6 52.6 4.744 35.8 438s 7 54.2 4.144 NA 438s 8 54.8 3.370 38.6 438s 9 56.3 3.514 38.9 438s 10 NA 3.881 40.1 438s 11 54.6 0.719 38.3 438s 12 51.0 -3.475 34.2 438s 13 45.8 -6.543 29.2 438s 14 46.2 -5.460 28.1 438s 15 48.7 -2.840 30.3 438s 16 51.3 -1.313 33.2 438s 17 56.1 1.077 37.6 438s 18 59.1 1.969 40.1 438s 19 57.2 0.715 39.0 438s 20 60.7 1.903 42.0 438s 21 64.3 4.012 46.2 438s 22 71.9 5.440 52.7 438s > predict 438s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 438s 1 NA NA NA NA 438s 2 42.2 0.422 41.3 43.0 438s 3 46.3 0.462 45.4 47.2 438s 4 50.8 0.309 50.1 51.4 438s 5 51.1 0.359 50.4 51.8 438s 6 52.6 0.362 51.9 53.3 438s 7 54.2 0.328 53.5 54.9 438s 8 54.8 0.300 54.2 55.4 438s 9 56.3 0.323 55.6 56.9 438s 10 NA NA NA NA 438s 11 54.6 0.531 53.5 55.6 438s 12 51.0 0.427 50.1 51.8 438s 13 45.8 0.564 44.6 46.9 438s 14 46.2 0.543 45.1 47.3 438s 15 48.7 0.341 48.0 49.4 438s 16 51.3 0.302 50.7 51.9 438s 17 56.1 0.328 55.5 56.8 438s 18 59.1 0.294 58.5 59.7 438s 19 57.2 0.332 56.6 57.9 438s 20 60.7 0.392 59.9 61.5 438s 21 64.3 0.394 63.5 65.0 438s 22 71.9 0.615 70.7 73.2 438s Investment.pred Investment.se.fit Investment.lwr Investment.upr 438s 1 NA NA NA NA 438s 2 0.092 0.508 -0.929 1.113 438s 3 2.039 0.421 1.193 2.885 438s 4 4.089 0.376 3.333 4.846 438s 5 4.439 0.311 3.813 5.065 438s 6 4.744 0.294 4.154 5.335 438s 7 4.144 0.277 3.587 4.701 438s 8 3.370 0.247 2.873 3.867 438s 9 3.514 0.328 2.855 4.172 438s 10 3.881 0.376 3.126 4.636 438s 11 0.719 0.508 -0.301 1.739 438s 12 -3.475 0.428 -4.336 -2.615 438s 13 -6.543 0.521 -7.590 -5.496 438s 14 -5.460 0.583 -6.632 -4.288 438s 15 -2.840 0.316 -3.474 -2.205 438s 16 -1.313 0.271 -1.857 -0.769 438s 17 1.077 0.293 0.488 1.666 438s 18 1.969 0.205 1.557 2.382 438s 19 0.715 0.263 0.187 1.244 438s 20 1.903 0.309 1.283 2.523 438s 21 4.012 0.280 3.449 4.574 438s 22 5.440 0.389 4.659 6.221 438s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 438s 1 NA NA NA NA 438s 2 26.7 0.306 26.0 27.3 438s 3 28.8 0.305 28.2 29.4 438s 4 32.7 0.302 32.1 33.3 438s 5 34.0 0.231 33.5 34.5 438s 6 35.8 0.230 35.3 36.2 438s 7 NA NA NA NA 438s 8 38.6 0.233 38.2 39.1 438s 9 38.9 0.222 38.5 39.4 438s 10 40.1 0.213 39.7 40.5 438s 11 38.3 0.292 37.7 38.9 438s 12 34.2 0.300 33.6 34.8 438s 13 29.2 0.361 28.4 29.9 438s 14 28.1 0.322 27.5 28.7 438s 15 30.3 0.314 29.7 30.9 438s 16 33.2 0.263 32.7 33.7 438s 17 37.6 0.256 37.1 38.1 438s 18 40.1 0.204 39.7 40.6 438s 19 39.0 0.298 38.4 39.6 438s 20 42.0 0.272 41.5 42.6 438s 21 46.2 0.288 45.6 46.8 438s 22 52.7 0.431 51.9 53.6 438s > model.frame 438s [1] TRUE 438s > model.matrix 438s [1] TRUE 438s > nobs 438s [1] 61 438s > linearHypothesis 438s Linear hypothesis test (Theil's F test) 438s 438s Hypothesis: 438s Consumption_corpProf + Investment_capitalLag = 0 438s 438s Model 1: restricted model 438s Model 2: kleinModel 438s 438s Res.Df Df F Pr(>F) 438s 1 50 438s 2 49 1 1.01 0.32 438s Linear hypothesis test (F statistic of a Wald test) 438s 438s Hypothesis: 438s Consumption_corpProf + Investment_capitalLag = 0 438s 438s Model 1: restricted model 438s Model 2: kleinModel 438s 438s Res.Df Df F Pr(>F) 438s 1 50 438s 2 49 1 1.3 0.26 438s Linear hypothesis test (Chi^2 statistic of a Wald test) 438s 438s Hypothesis: 438s Consumption_corpProf + Investment_capitalLag = 0 438s 438s Model 1: restricted model 438s Model 2: kleinModel 438s 438s Res.Df Df Chisq Pr(>Chisq) 438s 1 50 438s 2 49 1 1.3 0.25 438s Linear hypothesis test (Theil's F test) 438s 438s Hypothesis: 438s Consumption_corpProf + Investment_capitalLag = 0 438s Consumption_corpProfLag - PrivateWages_trend = 0 438s 438s Model 1: restricted model 438s Model 2: kleinModel 438s 438s Res.Df Df F Pr(>F) 438s 1 51 438s 2 49 2 0.53 0.59 438s Linear hypothesis test (F statistic of a Wald test) 438s 438s Hypothesis: 438s Consumption_corpProf + Investment_capitalLag = 0 438s Consumption_corpProfLag - PrivateWages_trend = 0 438s 438s Model 1: restricted model 438s Model 2: kleinModel 438s 438s Res.Df Df F Pr(>F) 438s 1 51 438s 2 49 2 0.69 0.51 438s Linear hypothesis test (Chi^2 statistic of a Wald test) 438s 438s Hypothesis: 438s Consumption_corpProf + Investment_capitalLag = 0 438s Consumption_corpProfLag - PrivateWages_trend = 0 438s 438s Model 1: restricted model 438s Model 2: kleinModel 438s 438s Res.Df Df Chisq Pr(>Chisq) 438s 1 51 438s 2 49 2 1.38 0.5 438s > logLik 438s 'log Lik.' -69.6 (df=18) 438s 'log Lik.' -76.9 (df=18) 438s Estimating function 438s Consumption_(Intercept) Consumption_corpProf 438s Consumption_2 -0.42417 -5.2597 438s Consumption_3 -2.17982 -36.8390 438s Consumption_4 -2.62648 -48.3271 438s Consumption_5 -0.81768 -15.8630 438s Consumption_6 0.02500 0.5025 438s Consumption_7 1.50966 29.5894 438s Consumption_8 2.26980 44.9421 438s Consumption_9 1.74517 36.8231 438s Consumption_11 0.74077 11.5559 438s Consumption_12 -0.14959 -1.7053 438s Consumption_13 -0.25842 -1.8090 438s Consumption_14 0.50036 5.6040 438s Consumption_15 -0.04361 -0.5363 438s Consumption_16 -0.04189 -0.5865 438s Consumption_17 2.62802 46.2532 438s Consumption_18 -0.68580 -11.8643 438s Consumption_19 0.47280 7.2339 438s Consumption_20 1.55235 29.4946 438s Consumption_21 1.24350 26.2379 438s Consumption_22 -3.76279 -88.4255 438s Investment_2 0.07441 0.9227 438s Investment_3 0.03547 0.5995 438s Investment_4 -0.28298 -5.2069 438s Investment_5 0.36669 7.1139 438s Investment_6 -0.09061 -1.8212 438s Investment_7 -0.37095 -7.2706 438s Investment_8 -0.21146 -4.1868 438s Investment_9 0.13086 2.7611 438s Investment_10 0.00000 0.0000 438s Investment_11 -0.07161 -1.1172 438s Investment_12 -0.01921 -0.2190 438s Investment_13 -0.08737 -0.6116 438s Investment_14 -0.09166 -1.0266 438s Investment_15 0.04082 0.5021 438s Investment_16 -0.00330 -0.0462 438s Investment_17 -0.26069 -4.5882 438s Investment_18 -0.00779 -0.1348 438s Investment_19 0.66639 10.1958 438s Investment_20 0.15365 2.9194 438s Investment_21 0.18136 3.8268 438s Investment_22 0.13754 3.2323 438s PrivateWages_2 -1.58616 -19.6684 438s PrivateWages_3 0.69114 11.6803 438s PrivateWages_4 1.95564 35.9837 438s PrivateWages_5 -0.13496 -2.6181 438s PrivateWages_6 -0.49127 -9.8746 438s PrivateWages_8 -1.03222 -20.4380 438s PrivateWages_9 0.40421 8.5288 438s PrivateWages_10 0.00000 0.0000 438s PrivateWages_11 -0.50339 -7.8529 438s PrivateWages_12 0.46449 5.2952 438s PrivateWages_13 -0.24083 -1.6858 439s PrivateWages_14 0.54997 6.1596 439s PrivateWages_15 0.40539 4.9863 439s PrivateWages_16 -0.00244 -0.0342 439s PrivateWages_17 -1.12761 -19.8459 439s PrivateWages_18 1.17751 20.3710 439s PrivateWages_19 -1.06279 -16.2607 439s PrivateWages_20 -0.56511 -10.7371 439s PrivateWages_21 -1.67547 -35.3524 439s PrivateWages_22 0.78715 18.4981 439s Consumption_corpProfLag Consumption_wages 439s Consumption_2 -5.3870 -11.962 439s Consumption_3 -27.0298 -70.190 439s Consumption_4 -44.3874 -97.180 439s Consumption_5 -15.0453 -30.254 439s Consumption_6 0.4850 0.965 439s Consumption_7 30.3442 61.443 439s Consumption_8 44.4881 94.197 439s Consumption_9 34.5544 74.868 439s Consumption_11 16.0746 31.186 439s Consumption_12 -2.3336 -5.879 439s Consumption_13 -2.9460 -8.864 439s Consumption_14 3.5025 17.062 439s Consumption_15 -0.4884 -1.596 439s Consumption_16 -0.5153 -1.646 439s Consumption_17 36.7923 116.159 439s Consumption_18 -12.0701 -32.713 439s Consumption_19 8.1795 21.702 439s Consumption_20 23.7509 76.686 439s Consumption_21 23.6265 65.906 439s Consumption_22 -79.3948 -232.540 439s Investment_2 0.9450 2.098 439s Investment_3 0.4399 1.142 439s Investment_4 -4.7824 -10.470 439s Investment_5 6.7472 13.568 439s Investment_6 -1.7577 -3.497 439s Investment_7 -7.4561 -15.098 439s Investment_8 -4.1445 -8.775 439s Investment_9 2.5910 5.614 439s Investment_10 0.0000 0.000 439s Investment_11 -1.5540 -3.015 439s Investment_12 -0.2997 -0.755 439s Investment_13 -0.9961 -2.997 439s Investment_14 -0.6416 -3.126 439s Investment_15 0.4572 1.494 439s Investment_16 -0.0406 -0.130 439s Investment_17 -3.6497 -11.523 439s Investment_18 -0.1371 -0.372 439s Investment_19 11.5286 30.587 439s Investment_20 2.3509 7.590 439s Investment_21 3.4459 9.612 439s Investment_22 2.9022 8.500 439s PrivateWages_2 -20.1442 -44.730 439s PrivateWages_3 8.5702 22.255 439s PrivateWages_4 33.0503 72.359 439s PrivateWages_5 -2.4832 -4.993 439s PrivateWages_6 -9.5307 -18.963 439s PrivateWages_8 -20.2315 -42.837 439s PrivateWages_9 8.0034 17.341 439s PrivateWages_10 0.0000 0.000 439s PrivateWages_11 -10.9235 -21.193 439s PrivateWages_12 7.2461 18.254 439s PrivateWages_13 -2.7454 -8.260 439s PrivateWages_14 3.8498 18.754 439s PrivateWages_15 4.5404 14.837 439s PrivateWages_16 -0.0300 -0.096 439s PrivateWages_17 -15.7865 -49.840 439s PrivateWages_18 20.7242 56.167 439s PrivateWages_19 -18.3863 -48.782 439s PrivateWages_20 -8.6462 -27.916 439s PrivateWages_21 -31.8339 -88.800 439s PrivateWages_22 16.6089 48.646 439s Investment_(Intercept) Investment_corpProf 439s Consumption_2 0.064449 0.7992 439s Consumption_3 0.331201 5.5973 439s Consumption_4 0.399066 7.3428 439s Consumption_5 0.124238 2.4102 439s Consumption_6 -0.003798 -0.0763 439s Consumption_7 -0.229378 -4.4958 439s Consumption_8 -0.344873 -6.8285 439s Consumption_9 -0.265161 -5.5949 439s Consumption_11 -0.112552 -1.7558 439s Consumption_12 0.022729 0.2591 439s Consumption_13 0.039265 0.2749 439s Consumption_14 -0.076024 -0.8515 439s Consumption_15 0.006625 0.0815 439s Consumption_16 0.006365 0.0891 439s Consumption_17 -0.399301 -7.0277 439s Consumption_18 0.104200 1.8027 439s Consumption_19 -0.071838 -1.0991 439s Consumption_20 -0.235863 -4.4814 439s Consumption_21 -0.188937 -3.9866 439s Consumption_22 0.571717 13.4353 439s Investment_2 -0.423201 -5.2477 439s Investment_3 -0.201766 -3.4098 439s Investment_4 1.609495 29.6147 439s Investment_5 -2.085613 -40.4609 439s Investment_6 0.515327 10.3581 439s Investment_7 2.109824 41.3526 439s Investment_8 1.202679 23.8131 439s Investment_9 -0.744277 -15.7042 439s Investment_10 1.766841 38.3405 439s Investment_11 0.407303 6.3539 439s Investment_12 0.109258 1.2455 439s Investment_13 0.496948 3.4786 439s Investment_14 0.521347 5.8391 439s Investment_15 -0.232156 -2.8555 439s Investment_16 0.018782 0.2630 439s Investment_17 1.482721 26.0959 439s Investment_18 0.044303 0.7664 439s Investment_19 -3.790179 -57.9897 439s Investment_20 -0.873905 -16.6042 439s Investment_21 -1.031520 -21.7651 439s Investment_22 -0.782292 -18.3839 439s PrivateWages_2 0.617327 7.6549 439s PrivateWages_3 -0.268990 -4.5459 439s PrivateWages_4 -0.761128 -14.0048 439s PrivateWages_5 0.052525 1.0190 439s PrivateWages_6 0.191202 3.8432 439s PrivateWages_8 0.401737 7.9544 439s PrivateWages_9 -0.157317 -3.3194 439s PrivateWages_10 -0.635285 -13.7857 439s PrivateWages_11 0.195917 3.0563 439s PrivateWages_12 -0.180778 -2.0609 439s PrivateWages_13 0.093729 0.6561 439s PrivateWages_14 -0.214045 -2.3973 439s PrivateWages_15 -0.157776 -1.9406 439s PrivateWages_16 0.000951 0.0133 439s PrivateWages_17 0.438862 7.7240 439s PrivateWages_18 -0.458284 -7.9283 439s PrivateWages_19 0.413636 6.3286 439s PrivateWages_20 0.219939 4.1788 439s PrivateWages_21 0.652086 13.7590 439s PrivateWages_22 -0.306358 -7.1994 439s Investment_corpProfLag Investment_capitalLag 439s Consumption_2 0.8185 11.781 439s Consumption_3 4.1069 60.477 439s Consumption_4 6.7442 73.628 439s Consumption_5 2.2860 23.568 439s Consumption_6 -0.0737 -0.732 439s Consumption_7 -4.6105 -45.371 439s Consumption_8 -6.7595 -70.147 439s Consumption_9 -5.2502 -55.047 439s Consumption_11 -2.4424 -24.277 439s Consumption_12 0.3546 4.925 439s Consumption_13 0.4476 8.375 439s Consumption_14 -0.5322 -15.745 439s Consumption_15 0.0742 1.338 439s Consumption_16 0.0783 1.267 439s Consumption_17 -5.5902 -78.942 439s Consumption_18 1.8339 20.819 439s Consumption_19 -1.2428 -14.497 439s Consumption_20 -3.6087 -47.149 439s Consumption_21 -3.5898 -38.014 439s Consumption_22 12.0632 116.916 439s Investment_2 -5.3746 -77.361 439s Investment_3 -2.5019 -36.842 439s Investment_4 27.2005 296.952 439s Investment_5 -38.3753 -395.641 439s Investment_6 9.9974 99.304 439s Investment_7 42.4075 417.323 439s Investment_8 23.5725 244.625 439s Investment_9 -14.7367 -154.512 439s Investment_10 37.2803 372.097 439s Investment_11 8.8385 87.855 439s Investment_12 1.7044 23.676 439s Investment_13 5.6652 105.999 439s Investment_14 3.6494 107.971 439s Investment_15 -2.6002 -46.896 439s Investment_16 0.2310 3.738 439s Investment_17 20.7581 293.134 439s Investment_18 0.7797 8.852 439s Investment_19 -65.5701 -764.858 439s Investment_20 -13.3707 -174.694 439s Investment_21 -19.5989 -207.542 439s Investment_22 -16.5064 -159.979 439s PrivateWages_2 7.8401 112.847 439s PrivateWages_3 -3.3355 -49.118 439s PrivateWages_4 -12.8631 -140.428 439s PrivateWages_5 0.9665 9.964 439s PrivateWages_6 3.7093 36.845 439s PrivateWages_8 7.8740 81.713 439s PrivateWages_9 -3.1149 -32.659 439s PrivateWages_10 -13.4045 -133.791 439s PrivateWages_11 4.2514 42.259 439s PrivateWages_12 -2.8201 -39.175 439s PrivateWages_13 1.0685 19.992 439s PrivateWages_14 -1.4983 -44.329 439s PrivateWages_15 -1.7671 -31.871 439s PrivateWages_16 0.0117 0.189 439s PrivateWages_17 6.1441 86.763 439s PrivateWages_18 -8.0658 -91.565 439s PrivateWages_19 7.1559 83.472 439s PrivateWages_20 3.3651 43.966 439s PrivateWages_21 12.3896 131.200 439s PrivateWages_22 -6.4641 -62.650 439s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 439s Consumption_2 -0.34828 -15.881 -15.638 439s Consumption_3 -1.78978 -89.668 -81.614 439s Consumption_4 -2.15652 -123.353 -108.042 439s Consumption_5 -0.67137 -38.335 -38.402 439s Consumption_6 0.02052 1.252 1.172 439s Consumption_7 0.00000 0.000 0.000 439s Consumption_8 1.86367 120.020 119.275 439s Consumption_9 1.43291 92.422 92.279 439s Consumption_11 0.60822 37.223 40.751 439s Consumption_12 -0.12282 -6.559 -7.517 439s Consumption_13 -0.21218 -9.400 -11.331 439s Consumption_14 0.41083 18.528 18.200 439s Consumption_15 -0.03580 -1.779 -1.615 439s Consumption_16 -0.03440 -1.871 -1.710 439s Consumption_17 2.15779 135.293 117.384 439s Consumption_18 -0.56309 -36.601 -35.306 439s Consumption_19 0.38821 23.642 25.233 439s Consumption_20 1.27458 88.584 77.622 439s Consumption_21 1.02100 77.290 70.960 439s Consumption_22 -3.08951 -273.113 -233.876 439s Investment_2 0.15649 7.136 7.027 439s Investment_3 0.07461 3.738 3.402 439s Investment_4 -0.59517 -34.043 -29.818 439s Investment_5 0.77123 44.037 44.114 439s Investment_6 -0.19056 -11.624 -10.881 439s Investment_7 0.00000 0.000 0.000 439s Investment_8 -0.44473 -28.641 -28.463 439s Investment_9 0.27522 17.752 17.724 439s Investment_10 -0.65335 -43.774 -42.141 439s Investment_11 -0.15061 -9.218 -10.091 439s Investment_12 -0.04040 -2.157 -2.473 439s Investment_13 -0.18376 -8.141 -9.813 439s Investment_14 -0.19279 -8.695 -8.540 439s Investment_15 0.08585 4.267 3.872 439s Investment_16 -0.00695 -0.378 -0.345 439s Investment_17 -0.54829 -34.378 -29.827 439s Investment_18 -0.01638 -1.065 -1.027 439s Investment_19 1.40155 85.354 91.101 439s Investment_20 0.32316 22.459 19.680 439s Investment_21 0.38144 28.875 26.510 439s Investment_22 0.28928 25.572 21.898 439s PrivateWages_2 -3.98191 -181.575 -178.788 439s PrivateWages_3 1.73505 86.926 79.118 439s PrivateWages_4 4.90946 280.821 245.964 439s PrivateWages_5 -0.33880 -19.345 -19.379 439s PrivateWages_6 -1.23330 -75.231 -70.421 439s PrivateWages_8 -2.59130 -166.880 -165.843 439s PrivateWages_9 1.01473 65.450 65.349 439s PrivateWages_10 4.09774 274.549 264.304 439s PrivateWages_11 -1.26371 -77.339 -84.669 439s PrivateWages_12 1.16606 62.268 71.363 439s PrivateWages_13 -0.60457 -26.783 -32.284 439s PrivateWages_14 1.38064 62.267 61.163 439s PrivateWages_15 1.01769 50.579 45.898 439s PrivateWages_16 -0.00613 -0.334 -0.305 439s PrivateWages_17 -2.83076 -177.489 -153.993 439s PrivateWages_18 2.95604 192.143 185.344 439s PrivateWages_19 -2.66805 -162.484 -173.423 439s PrivateWages_20 -1.41866 -98.597 -86.396 439s PrivateWages_21 -4.20611 -318.403 -292.325 439s PrivateWages_22 1.97608 174.686 149.589 439s PrivateWages_trend 439s Consumption_2 3.4828 439s Consumption_3 16.1081 439s Consumption_4 17.2522 439s Consumption_5 4.6996 439s Consumption_6 -0.1231 439s Consumption_7 0.0000 439s Consumption_8 -7.4547 439s Consumption_9 -4.2987 439s Consumption_11 -0.6082 439s Consumption_12 0.0000 439s Consumption_13 -0.2122 439s Consumption_14 0.8217 439s Consumption_15 -0.1074 439s Consumption_16 -0.1376 439s Consumption_17 10.7889 439s Consumption_18 -3.3785 439s Consumption_19 2.7174 439s Consumption_20 10.1967 439s Consumption_21 9.1890 439s Consumption_22 -30.8951 439s Investment_2 -1.5649 439s Investment_3 -0.6715 439s Investment_4 4.7613 439s Investment_5 -5.3986 439s Investment_6 1.1434 439s Investment_7 0.0000 439s Investment_8 1.7789 439s Investment_9 -0.8257 439s Investment_10 1.3067 439s Investment_11 0.1506 439s Investment_12 0.0000 439s Investment_13 -0.1838 439s Investment_14 -0.3856 439s Investment_15 0.2575 439s Investment_16 -0.0278 439s Investment_17 -2.7414 439s Investment_18 -0.0983 439s Investment_19 9.8108 439s Investment_20 2.5853 439s Investment_21 3.4330 439s Investment_22 2.8928 439s PrivateWages_2 39.8191 439s PrivateWages_3 -15.6154 439s PrivateWages_4 -39.2757 439s PrivateWages_5 2.3716 439s PrivateWages_6 7.3998 439s PrivateWages_8 10.3652 439s PrivateWages_9 -3.0442 439s PrivateWages_10 -8.1955 439s PrivateWages_11 1.2637 439s PrivateWages_12 0.0000 439s PrivateWages_13 -0.6046 439s PrivateWages_14 2.7613 439s PrivateWages_15 3.0531 439s PrivateWages_16 -0.0245 439s PrivateWages_17 -14.1538 439s PrivateWages_18 17.7363 439s PrivateWages_19 -18.6764 439s PrivateWages_20 -11.3493 439s PrivateWages_21 -37.8550 439s PrivateWages_22 19.7608 439s [1] TRUE 439s > Bread 439s Consumption_(Intercept) Consumption_corpProf Consumption_corpProfLag 439s [1,] 87.9904 -0.088084 -0.91416 439s [2,] -0.0881 0.389639 -0.23612 439s [3,] -0.9142 -0.236125 0.38341 439s [4,] -1.6692 -0.062952 -0.03326 439s [5,] 2.6851 -0.188961 0.72342 439s [6,] -0.0355 0.023370 -0.02643 439s [7,] -0.0563 -0.020038 0.03196 439s [8,] -0.0054 0.000618 -0.00397 439s [9,] -33.1687 0.063156 1.54217 439s [10,] 0.3665 -0.059172 0.03813 439s [11,] 0.1741 0.060188 -0.06574 439s [12,] 0.1831 0.029476 0.02425 439s Consumption_wages Investment_(Intercept) Investment_corpProf 439s [1,] -1.669236 2.685 -0.03549 439s [2,] -0.062952 -0.189 0.02337 439s [3,] -0.033257 0.723 -0.02643 439s [4,] 0.079061 -0.248 0.00151 439s [5,] -0.248317 1269.247 -12.23080 439s [6,] 0.001506 -12.231 0.40462 439s [7,] -0.002778 9.884 -0.34614 439s [8,] 0.001327 -6.097 0.05519 439s [9,] 0.134743 17.903 -0.13872 439s [10,] 0.000196 0.262 0.01397 439s [11,] -0.002616 -0.581 -0.01197 439s [12,] -0.026193 -0.551 0.00355 439s Investment_corpProfLag Investment_capitalLag PrivateWages_(Intercept) 439s [1,] -0.05628 -0.005396 -33.1687 439s [2,] -0.02004 0.000618 0.0632 439s [3,] 0.03196 -0.003967 1.5422 439s [4,] -0.00278 0.001327 0.1347 439s [5,] 9.88435 -6.096982 17.9032 439s [6,] -0.34614 0.055190 -0.1387 439s [7,] 0.43632 -0.055785 -0.4000 439s [8,] -0.05578 0.030317 -0.0433 439s [9,] -0.40000 -0.043343 71.4840 439s [10,] -0.00786 -0.001844 -0.3085 439s [11,] 0.01493 0.002686 -0.8909 439s [12,] -0.01033 0.003295 0.8146 439s PrivateWages_gnp PrivateWages_gnpLag PrivateWages_trend 439s [1,] 0.366465 0.17405 0.18311 439s [2,] -0.059172 0.06019 0.02948 439s [3,] 0.038129 -0.06574 0.02425 439s [4,] 0.000196 -0.00262 -0.02619 439s [5,] 0.262390 -0.58123 -0.55064 439s [6,] 0.013966 -0.01197 0.00355 439s [7,] -0.007857 0.01493 -0.01033 439s [8,] -0.001844 0.00269 0.00330 439s [9,] -0.308484 -0.89087 0.81461 439s [10,] 0.044017 -0.04022 -0.01158 439s [11,] -0.040216 0.05696 -0.00212 439s [12,] -0.011575 -0.00212 0.04506 439s > 439s > # 3SLS 439s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 439s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 439s > summary 439s 439s systemfit results 439s method: 3SLS 439s 439s N DF SSR detRCov OLS-R2 McElroy-R2 439s system 59 47 59.5 0.241 0.97 0.994 439s 439s N DF SSR MSE RMSE R2 Adj R2 439s Consumption 19 15 18.1 1.203 1.097 0.980 0.977 439s Investment 20 16 31.1 1.945 1.395 0.866 0.841 439s PrivateWages 20 16 10.3 0.645 0.803 0.987 0.985 439s 439s The covariance matrix of the residuals used for estimation 439s Consumption Investment PrivateWages 439s Consumption 1.079 0.354 -0.383 439s Investment 0.354 1.047 0.107 439s PrivateWages -0.383 0.107 0.445 439s 439s The covariance matrix of the residuals 439s Consumption Investment PrivateWages 439s Consumption 0.950 0.324 -0.395 439s Investment 0.324 1.385 0.242 439s PrivateWages -0.395 0.242 0.475 439s 439s The correlations of the residuals 439s Consumption Investment PrivateWages 439s Consumption 1.000 0.293 -0.582 439s Investment 0.293 1.000 0.292 439s PrivateWages -0.582 0.292 1.000 439s 439s 439s 3SLS estimates for 'Consumption' (equation 1) 439s Model Formula: consump ~ corpProf + corpProfLag + wages 439s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 439s gnpLag 439s 439s Estimate Std. Error t value Pr(>|t|) 439s (Intercept) 16.5606 1.3295 12.46 2.6e-09 *** 439s corpProf 0.1100 0.1098 1.00 0.33 439s corpProfLag 0.1155 0.1007 1.15 0.27 439s wages 0.8086 0.0401 20.18 2.8e-12 *** 439s --- 439s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 439s 439s Residual standard error: 1.097 on 15 degrees of freedom 439s Number of observations: 19 Degrees of Freedom: 15 439s SSR: 18.051 MSE: 1.203 Root MSE: 1.097 439s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.977 439s 439s 439s 3SLS estimates for 'Investment' (equation 2) 439s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 439s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 439s gnpLag 439s 439s Estimate Std. Error t value Pr(>|t|) 439s (Intercept) 23.6871 6.1159 3.87 0.00135 ** 439s corpProf 0.1072 0.1414 0.76 0.45918 439s corpProfLag 0.6278 0.1361 4.61 0.00029 *** 439s capitalLag -0.1726 0.0295 -5.85 2.5e-05 *** 439s --- 439s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 439s 439s Residual standard error: 1.395 on 16 degrees of freedom 439s Number of observations: 20 Degrees of Freedom: 16 439s SSR: 31.126 MSE: 1.945 Root MSE: 1.395 439s Multiple R-Squared: 0.866 Adjusted R-Squared: 0.841 439s 439s 439s 3SLS estimates for 'PrivateWages' (equation 3) 439s Model Formula: privWage ~ gnp + gnpLag + trend 439s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 439s gnpLag 439s 439s Estimate Std. Error t value Pr(>|t|) 439s (Intercept) 1.3603 1.0927 1.24 0.23109 439s gnp 0.4117 0.0315 13.06 6.0e-10 *** 439s gnpLag 0.1782 0.0336 5.31 7.1e-05 *** 439s trend 0.1370 0.0280 4.89 0.00016 *** 439s --- 439s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 439s 439s Residual standard error: 0.803 on 16 degrees of freedom 439s Number of observations: 20 Degrees of Freedom: 16 439s SSR: 10.318 MSE: 0.645 Root MSE: 0.803 439s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 439s 439s > residuals 439s Consumption Investment PrivateWages 439s 1 NA NA NA 439s 2 -0.29542 -1.636 -1.2658 439s 3 -0.89033 0.135 0.4198 439s 4 -1.25669 0.777 1.3578 439s 5 -0.14000 -1.574 -0.2036 439s 6 0.37365 0.341 -0.4283 439s 7 NA NA NA 439s 8 1.63850 1.194 -0.8319 439s 9 1.44030 0.454 0.2186 439s 10 NA 2.192 1.1346 439s 11 0.17274 -0.750 -0.4603 439s 12 -0.49629 -0.698 0.2476 439s 13 -0.78384 -0.976 -0.2528 439s 14 0.32420 1.365 0.4028 439s 15 -0.10364 -0.170 0.3295 439s 16 -0.00105 0.140 0.0377 439s 17 1.84421 1.862 -0.7540 439s 18 -0.36893 -0.103 0.8827 439s 19 0.14129 -3.255 -0.7764 439s 20 1.23511 0.475 -0.3230 439s 21 1.06553 0.152 -1.1453 439s 22 -1.85709 0.746 0.6843 439s > fitted 439s Consumption Investment PrivateWages 439s 1 NA NA NA 439s 2 42.2 1.436 26.8 439s 3 45.9 1.765 28.9 439s 4 50.5 4.423 32.7 439s 5 50.7 4.574 34.1 439s 6 52.2 4.759 35.8 439s 7 NA NA NA 439s 8 54.6 3.006 38.7 439s 9 55.9 2.546 39.0 439s 10 NA 2.908 40.2 439s 11 54.8 1.750 38.4 439s 12 51.4 -2.702 34.3 439s 13 46.4 -5.224 29.3 439s 14 46.2 -6.465 28.1 439s 15 48.8 -2.830 30.3 439s 16 51.3 -1.440 33.2 439s 17 55.9 0.238 37.6 439s 18 59.1 2.103 40.1 439s 19 57.4 1.355 39.0 439s 20 60.4 0.825 41.9 439s 21 63.9 3.148 46.1 439s 22 71.6 4.154 52.6 439s > predict 439s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 439s 1 NA NA NA NA 439s 2 42.2 0.475 39.6 44.7 439s 3 45.9 0.557 43.3 48.5 439s 4 50.5 0.372 48.0 52.9 439s 5 50.7 0.433 48.2 53.3 439s 6 52.2 0.438 49.7 54.7 439s 7 NA NA NA NA 439s 8 54.6 0.362 52.1 57.0 439s 9 55.9 0.401 53.4 58.3 439s 10 NA NA NA NA 439s 11 54.8 0.684 52.1 57.6 439s 12 51.4 0.563 48.8 54.0 439s 13 46.4 0.733 43.6 49.2 439s 14 46.2 0.612 43.5 48.9 439s 15 48.8 0.379 46.3 51.3 439s 16 51.3 0.334 48.9 53.7 439s 17 55.9 0.394 53.4 58.3 439s 18 59.1 0.322 56.6 61.5 439s 19 57.4 0.392 54.9 59.8 439s 20 60.4 0.462 57.8 62.9 439s 21 63.9 0.448 61.4 66.5 439s 22 71.6 0.686 68.8 74.3 439s Investment.pred Investment.se.fit Investment.lwr Investment.upr 439s 1 NA NA NA NA 439s 2 1.436 0.709 -1.8811 4.754 439s 3 1.765 0.512 -1.3848 4.915 439s 4 4.423 0.470 1.3027 7.543 439s 5 4.574 0.392 1.5029 7.645 439s 6 4.759 0.370 1.7000 7.818 439s 7 NA NA NA NA 439s 8 3.006 0.306 -0.0214 6.033 439s 9 2.546 0.444 -0.5575 5.649 439s 10 2.908 0.488 -0.2245 6.041 439s 11 1.750 0.738 -1.5953 5.096 439s 12 -2.702 0.583 -5.9068 0.503 439s 13 -5.224 0.743 -8.5738 -1.874 439s 14 -6.465 0.780 -9.8530 -3.077 439s 15 -2.830 0.378 -5.8936 0.233 439s 16 -1.440 0.326 -4.4762 1.597 439s 17 0.238 0.426 -2.8533 3.329 439s 18 2.103 0.268 -0.9077 5.114 439s 19 1.355 0.399 -1.7201 4.431 439s 20 0.825 0.474 -2.2981 3.947 439s 21 3.148 0.393 0.0761 6.220 439s 22 4.154 0.555 0.9719 7.336 439s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 439s 1 NA NA NA NA 439s 2 26.8 0.309 24.9 28.6 439s 3 28.9 0.315 27.1 30.7 439s 4 32.7 0.326 30.9 34.6 439s 5 34.1 0.236 32.3 35.9 439s 6 35.8 0.244 34.0 37.6 439s 7 NA NA NA NA 439s 8 38.7 0.237 37.0 40.5 439s 9 39.0 0.225 37.2 40.7 439s 10 40.2 0.219 38.4 41.9 439s 11 38.4 0.309 36.5 40.2 439s 12 34.3 0.336 32.4 36.1 439s 13 29.3 0.411 27.3 31.2 439s 14 28.1 0.326 26.3 29.9 439s 15 30.3 0.313 28.4 32.1 439s 16 33.2 0.262 31.4 35.0 439s 17 37.6 0.265 35.8 39.3 439s 18 40.1 0.205 38.4 41.9 439s 19 39.0 0.323 37.1 40.8 439s 20 41.9 0.282 40.1 43.7 439s 21 46.1 0.293 44.3 48.0 439s 22 52.6 0.463 50.7 54.6 439s > model.frame 439s [1] TRUE 439s > model.matrix 439s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0328 >" 439s [2] "Attributes: < Component “dimnames”: Component 1: 54 string mismatches >" 439s [3] "Numeric: lengths (732, 708) differ" 439s > nobs 439s [1] 59 439s > linearHypothesis 439s Linear hypothesis test (Theil's F test) 439s 439s Hypothesis: 439s Consumption_corpProf + Investment_capitalLag = 0 439s 439s Model 1: restricted model 439s Model 2: kleinModel 439s 439s Res.Df Df F Pr(>F) 439s 1 48 439s 2 47 1 0.23 0.64 439s Linear hypothesis test (F statistic of a Wald test) 439s 439s Hypothesis: 439s Consumption_corpProf + Investment_capitalLag = 0 439s 439s Model 1: restricted model 439s Model 2: kleinModel 439s 439s Res.Df Df F Pr(>F) 439s 1 48 439s 2 47 1 0.31 0.58 439s Linear hypothesis test (Chi^2 statistic of a Wald test) 439s 439s Hypothesis: 439s Consumption_corpProf + Investment_capitalLag = 0 439s 439s Model 1: restricted model 439s Model 2: kleinModel 439s 439s Res.Df Df Chisq Pr(>Chisq) 439s 1 48 439s 2 47 1 0.31 0.58 439s Linear hypothesis test (Theil's F test) 439s 439s Hypothesis: 439s Consumption_corpProf + Investment_capitalLag = 0 439s Consumption_corpProfLag - PrivateWages_trend = 0 439s 439s Model 1: restricted model 439s Model 2: kleinModel 439s 439s Res.Df Df F Pr(>F) 439s 1 49 439s 2 47 2 0.5 0.61 439s Linear hypothesis test (F statistic of a Wald test) 439s 439s Hypothesis: 439s Consumption_corpProf + Investment_capitalLag = 0 439s Consumption_corpProfLag - PrivateWages_trend = 0 439s 439s Model 1: restricted model 439s Model 2: kleinModel 439s 439s Res.Df Df F Pr(>F) 439s 1 49 439s 2 47 2 0.68 0.51 439s Linear hypothesis test (Chi^2 statistic of a Wald test) 439s 439s Hypothesis: 439s Consumption_corpProf + Investment_capitalLag = 0 439s Consumption_corpProfLag - PrivateWages_trend = 0 439s 439s Model 1: restricted model 439s Model 2: kleinModel 439s 439s Res.Df Df Chisq Pr(>Chisq) 439s 1 49 439s 2 47 2 1.37 0.5 439s > logLik 439s 'log Lik.' -71 (df=18) 439s 'log Lik.' -81.1 (df=18) 439s Estimating function 439s Consumption_(Intercept) Consumption_corpProf 439s Consumption_2 -2.7455 -36.891 439s Consumption_3 -1.0626 -17.729 439s Consumption_4 -0.0885 -1.678 439s Consumption_5 -3.0649 -63.238 439s Consumption_6 0.7553 14.561 439s Consumption_8 5.9278 102.010 439s Consumption_9 4.6365 88.027 439s Consumption_11 -1.1219 -18.435 439s Consumption_12 -1.0756 -13.439 439s Consumption_13 -3.1243 -28.309 439s Consumption_14 2.5683 23.826 439s Consumption_15 -1.2839 -16.033 439s Consumption_16 -1.2479 -17.951 439s Consumption_17 7.5868 111.454 439s Consumption_18 -1.1010 -21.581 439s Consumption_19 -5.4018 -103.426 439s Consumption_20 3.8300 67.171 439s Consumption_21 1.5068 30.633 439s Consumption_22 -1.8041 -41.092 439s Investment_2 1.3384 17.984 439s Investment_3 -0.1231 -2.053 439s Investment_4 -0.5511 -10.444 439s Investment_5 1.3722 28.313 439s Investment_6 -0.3224 -6.215 439s Investment_8 -1.1676 -20.092 439s Investment_9 -0.4950 -9.397 439s Investment_10 0.0000 0.000 439s Investment_11 0.6975 11.462 439s Investment_12 0.6591 8.235 439s Investment_13 0.9331 8.455 439s Investment_14 -1.2380 -11.485 439s Investment_15 0.1758 2.195 439s Investment_16 -0.0882 -1.269 439s Investment_17 -1.7103 -25.126 439s Investment_18 0.2715 5.322 439s Investment_19 2.9123 55.761 439s Investment_20 -0.5118 -8.975 439s Investment_21 -0.2046 -4.160 439s Investment_22 -0.6426 -14.637 439s PrivateWages_2 -3.2663 -43.888 439s PrivateWages_3 1.1062 18.456 439s PrivateWages_4 2.8429 53.880 439s PrivateWages_5 -2.9330 -60.515 439s PrivateWages_6 -0.4678 -9.018 439s PrivateWages_8 1.7117 29.456 439s PrivateWages_9 1.9856 37.698 439s PrivateWages_10 0.0000 0.000 439s PrivateWages_11 -2.6089 -42.870 439s PrivateWages_12 -0.5972 -7.462 439s PrivateWages_13 -2.3655 -21.434 439s PrivateWages_14 2.8394 26.341 439s PrivateWages_15 -0.5146 -6.427 439s PrivateWages_16 -0.6088 -8.757 439s PrivateWages_17 2.4972 36.686 439s PrivateWages_18 -0.0214 -0.419 439s PrivateWages_19 -6.8265 -130.705 439s PrivateWages_20 1.3447 23.584 439s PrivateWages_21 -1.4002 -28.468 439s PrivateWages_22 2.2878 52.110 439s Consumption_corpProfLag Consumption_wages 439s Consumption_2 -34.868 -81.19 439s Consumption_3 -13.177 -33.85 439s Consumption_4 -1.496 -3.14 439s Consumption_5 -56.394 -118.79 439s Consumption_6 14.654 29.20 439s Consumption_8 116.186 236.05 439s Consumption_9 91.802 193.78 439s Consumption_11 -24.345 -48.21 439s Consumption_12 -16.779 -42.24 439s Consumption_13 -35.617 -109.94 439s Consumption_14 17.978 84.77 439s Consumption_15 -14.380 -47.92 439s Consumption_16 -15.349 -50.04 439s Consumption_17 106.215 316.24 439s Consumption_18 -19.377 -52.50 439s Consumption_19 -93.451 -266.03 439s Consumption_20 58.598 185.77 439s Consumption_21 28.629 80.45 439s Consumption_22 -38.066 -109.75 439s Investment_2 16.998 39.58 439s Investment_3 -1.526 -3.92 439s Investment_4 -9.313 -19.52 439s Investment_5 25.249 53.18 439s Investment_6 -6.254 -12.46 439s Investment_8 -22.884 -46.49 439s Investment_9 -9.800 -20.69 439s Investment_10 0.000 0.00 439s Investment_11 15.136 29.97 439s Investment_12 10.282 25.88 439s Investment_13 10.638 32.84 439s Investment_14 -8.666 -40.86 439s Investment_15 1.969 6.56 439s Investment_16 -1.085 -3.54 439s Investment_17 -23.945 -71.29 439s Investment_18 4.779 12.95 439s Investment_19 50.383 143.43 439s Investment_20 -7.830 -24.82 439s Investment_21 -3.888 -10.92 439s Investment_22 -13.559 -39.09 439s PrivateWages_2 -41.482 -96.59 439s PrivateWages_3 13.717 35.24 439s PrivateWages_4 48.044 100.73 439s PrivateWages_5 -53.966 -113.67 439s PrivateWages_6 -9.075 -18.08 439s PrivateWages_8 33.550 68.16 439s PrivateWages_9 39.314 82.99 439s PrivateWages_10 0.000 0.00 439s PrivateWages_11 -56.613 -112.10 439s PrivateWages_12 -9.317 -23.46 439s PrivateWages_13 -26.967 -83.24 439s PrivateWages_14 19.876 93.71 439s PrivateWages_15 -5.764 -19.21 439s PrivateWages_16 -7.488 -24.41 439s PrivateWages_17 34.961 104.09 439s PrivateWages_18 -0.376 -1.02 439s PrivateWages_19 -118.099 -336.20 439s PrivateWages_20 20.574 65.22 439s PrivateWages_21 -26.605 -74.76 439s PrivateWages_22 48.272 139.18 439s Investment_(Intercept) Investment_corpProf 439s Consumption_2 1.1993 15.540 439s Consumption_3 0.4642 7.754 439s Consumption_4 0.0387 0.740 439s Consumption_5 1.3388 28.029 439s Consumption_6 -0.3299 -6.424 439s Consumption_8 -2.5893 -44.384 439s Consumption_9 -2.0252 -39.469 439s Consumption_11 0.4900 8.255 439s Consumption_12 0.4698 5.957 439s Consumption_13 1.3647 12.176 439s Consumption_14 -1.1219 -10.434 439s Consumption_15 0.5608 7.176 439s Consumption_16 0.5451 7.773 439s Consumption_17 -3.3140 -48.887 439s Consumption_18 0.4809 9.399 439s Consumption_19 2.3595 45.678 439s Consumption_20 -1.6729 -29.086 439s Consumption_21 -0.6582 -13.228 439s Consumption_22 0.7880 18.015 439s Investment_2 -2.2459 -29.102 439s Investment_3 0.2065 3.450 439s Investment_4 0.9247 17.694 439s Investment_5 -2.3026 -48.209 439s Investment_6 0.5410 10.532 439s Investment_8 1.9592 33.583 439s Investment_9 0.8306 16.187 439s Investment_10 3.0781 62.986 439s Investment_11 -1.1704 -19.716 439s Investment_12 -1.1059 -14.023 439s Investment_13 -1.5658 -13.970 439s Investment_14 2.0775 19.321 439s Investment_15 -0.2950 -3.775 439s Investment_16 0.1480 2.111 439s Investment_17 2.8700 42.338 439s Investment_18 -0.4556 -8.905 439s Investment_19 -4.8870 -94.607 439s Investment_20 0.8587 14.930 439s Investment_21 0.3434 6.901 439s Investment_22 1.0783 24.652 439s PrivateWages_2 1.8660 24.179 439s PrivateWages_3 -0.6320 -10.557 439s PrivateWages_4 -1.6241 -31.077 439s PrivateWages_5 1.6755 35.080 439s PrivateWages_6 0.2672 5.203 439s PrivateWages_8 -0.9779 -16.762 439s PrivateWages_9 -1.1343 -22.106 439s PrivateWages_10 -2.1296 -43.576 439s PrivateWages_11 1.4904 25.106 439s PrivateWages_12 0.3412 4.326 439s PrivateWages_13 1.3514 12.057 439s PrivateWages_14 -1.6221 -15.086 439s PrivateWages_15 0.2940 3.762 439s PrivateWages_16 0.3478 4.959 439s PrivateWages_17 -1.4266 -21.045 439s PrivateWages_18 0.0122 0.239 439s PrivateWages_19 3.8998 75.496 439s PrivateWages_20 -0.7682 -13.356 439s PrivateWages_21 0.7999 16.078 439s PrivateWages_22 -1.3070 -29.879 439s Investment_corpProfLag Investment_capitalLag 439s Consumption_2 15.231 219.22 439s Consumption_3 5.756 84.76 439s Consumption_4 0.654 7.13 439s Consumption_5 24.633 253.96 439s Consumption_6 -6.401 -63.58 439s Consumption_8 -50.751 -526.67 439s Consumption_9 -40.100 -420.44 439s Consumption_11 10.634 105.70 439s Consumption_12 7.329 101.81 439s Consumption_13 15.558 291.09 439s Consumption_14 -7.853 -232.34 439s Consumption_15 6.281 113.29 439s Consumption_16 6.705 108.47 439s Consumption_17 -46.395 -655.17 439s Consumption_18 8.464 96.09 439s Consumption_19 40.820 476.15 439s Consumption_20 -25.596 -334.42 439s Consumption_21 -12.505 -132.42 439s Consumption_22 16.627 161.15 439s Investment_2 -28.522 -410.54 439s Investment_3 2.561 37.71 439s Investment_4 15.627 170.61 439s Investment_5 -42.368 -436.81 439s Investment_6 10.495 104.25 439s Investment_8 38.400 398.50 439s Investment_9 16.445 172.43 439s Investment_10 64.949 648.26 439s Investment_11 -25.398 -252.46 439s Investment_12 -17.253 -239.66 439s Investment_13 -17.850 -333.99 439s Investment_14 14.542 430.24 439s Investment_15 -3.304 -59.59 439s Investment_16 1.821 29.46 439s Investment_17 40.180 567.40 439s Investment_18 -8.019 -91.03 439s Investment_19 -84.545 -986.19 439s Investment_20 13.139 171.66 439s Investment_21 6.524 69.08 439s Investment_22 22.753 220.52 439s PrivateWages_2 23.698 341.10 439s PrivateWages_3 -7.836 -115.39 439s PrivateWages_4 -27.446 -299.64 439s PrivateWages_5 30.830 317.85 439s PrivateWages_6 5.185 51.50 439s PrivateWages_8 -19.166 -198.90 439s PrivateWages_9 -22.459 -235.48 439s PrivateWages_10 -44.934 -448.49 439s PrivateWages_11 32.341 321.48 439s PrivateWages_12 5.323 73.94 439s PrivateWages_13 15.406 288.25 439s PrivateWages_14 -11.355 -335.93 439s PrivateWages_15 3.293 59.39 439s PrivateWages_16 4.278 69.21 439s PrivateWages_17 -19.973 -282.04 439s PrivateWages_18 0.215 2.44 439s PrivateWages_19 67.467 786.98 439s PrivateWages_20 -11.753 -153.56 439s PrivateWages_21 15.199 160.94 439s PrivateWages_22 -27.577 -267.27 439s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 439s Consumption_2 -2.6531 -124.88 -119.13 439s Consumption_3 -1.0269 -50.91 -46.83 439s Consumption_4 -0.0856 -4.84 -4.29 439s Consumption_5 -2.9618 -179.74 -169.41 439s Consumption_6 0.7299 44.24 41.68 439s Consumption_8 5.7284 343.69 366.62 439s Consumption_9 4.4804 278.99 288.54 439s Consumption_11 -1.0841 -69.07 -72.64 439s Consumption_12 -1.0394 -56.99 -63.61 439s Consumption_13 -3.0192 -141.83 -161.22 439s Consumption_14 2.4819 104.56 109.95 439s Consumption_15 -1.2407 -63.55 -55.96 439s Consumption_16 -1.2059 -66.73 -59.93 439s Consumption_17 7.3315 420.78 398.83 439s Consumption_18 -1.0639 -71.47 -66.71 439s Consumption_19 -5.2200 -357.64 -339.30 439s Consumption_20 3.7011 247.40 225.40 439s Consumption_21 1.4561 109.01 101.20 439s Consumption_22 -1.7434 -151.46 -131.97 439s Investment_2 1.6915 79.62 75.95 439s Investment_3 -0.1555 -7.71 -7.09 439s Investment_4 -0.6965 -39.38 -34.89 439s Investment_5 1.7343 105.25 99.20 439s Investment_6 -0.4074 -24.70 -23.26 439s Investment_8 -1.4756 -88.53 -94.44 439s Investment_9 -0.6256 -38.95 -40.29 439s Investment_10 -2.3184 -149.69 -149.53 439s Investment_11 0.8815 56.16 59.06 439s Investment_12 0.8330 45.67 50.98 439s Investment_13 1.1793 55.40 62.98 439s Investment_14 -1.5647 -65.92 -69.32 439s Investment_15 0.2222 11.38 10.02 439s Investment_16 -0.1115 -6.17 -5.54 439s Investment_17 -2.1616 -124.06 -117.59 439s Investment_18 0.3432 23.05 21.52 439s Investment_19 3.6807 252.18 239.25 439s Investment_20 -0.6468 -43.23 -39.39 439s Investment_21 -0.2586 -19.36 -17.97 439s Investment_22 -0.8122 -70.56 -61.48 439s PrivateWages_2 -7.4676 -351.50 -335.29 439s PrivateWages_3 2.5291 125.39 115.33 439s PrivateWages_4 6.4995 367.50 325.62 439s PrivateWages_5 -6.7054 -406.93 -383.55 439s PrivateWages_6 -1.0695 -64.82 -61.07 439s PrivateWages_8 3.9134 234.79 250.46 439s PrivateWages_9 4.5395 282.67 292.34 439s PrivateWages_10 8.5226 550.30 549.71 439s PrivateWages_11 -5.9646 -380.01 -399.63 439s PrivateWages_12 -1.3654 -74.87 -83.57 439s PrivateWages_13 -5.4082 -254.06 -288.80 439s PrivateWages_14 6.4916 273.48 287.58 439s PrivateWages_15 -1.1766 -60.26 -53.06 439s PrivateWages_16 -1.3918 -77.02 -69.17 439s PrivateWages_17 5.7093 327.68 310.59 439s PrivateWages_18 -0.0489 -3.28 -3.07 439s PrivateWages_19 -15.6071 -1069.28 -1014.46 439s PrivateWages_20 3.0743 205.50 187.22 439s PrivateWages_21 -3.2013 -239.67 -222.49 439s PrivateWages_22 5.2304 454.42 395.94 439s PrivateWages_trend 439s Consumption_2 26.531 439s Consumption_3 9.242 439s Consumption_4 0.684 439s Consumption_5 20.732 439s Consumption_6 -4.380 439s Consumption_8 -22.913 439s Consumption_9 -13.441 439s Consumption_11 1.084 439s Consumption_12 0.000 439s Consumption_13 -3.019 439s Consumption_14 4.964 439s Consumption_15 -3.722 439s Consumption_16 -4.824 439s Consumption_17 36.658 439s Consumption_18 -6.384 439s Consumption_19 -36.540 439s Consumption_20 29.609 439s Consumption_21 13.105 439s Consumption_22 -17.434 439s Investment_2 -16.915 439s Investment_3 1.400 439s Investment_4 5.572 439s Investment_5 -12.140 439s Investment_6 2.445 439s Investment_8 5.902 439s Investment_9 1.877 439s Investment_10 4.637 439s Investment_11 -0.882 439s Investment_12 0.000 439s Investment_13 1.179 439s Investment_14 -3.129 439s Investment_15 0.667 439s Investment_16 -0.446 439s Investment_17 -10.808 439s Investment_18 2.059 439s Investment_19 25.765 439s Investment_20 -5.174 439s Investment_21 -2.327 439s Investment_22 -8.122 439s PrivateWages_2 74.676 439s PrivateWages_3 -22.762 439s PrivateWages_4 -51.996 439s PrivateWages_5 46.938 439s PrivateWages_6 6.417 439s PrivateWages_8 -15.654 439s PrivateWages_9 -13.618 439s PrivateWages_10 -17.045 439s PrivateWages_11 5.965 439s PrivateWages_12 0.000 439s PrivateWages_13 -5.408 439s PrivateWages_14 12.983 439s PrivateWages_15 -3.530 439s PrivateWages_16 -5.567 439s PrivateWages_17 28.547 439s PrivateWages_18 -0.293 439s PrivateWages_19 -109.250 439s PrivateWages_20 24.594 439s PrivateWages_21 -28.812 439s PrivateWages_22 52.304 439s [1] TRUE 439s > Bread 439s Consumption_(Intercept) Consumption_corpProf Consumption_corpProfLag 439s [1,] 104.28657 -1.0082 -0.4696 439s [2,] -1.00824 0.7107 -0.4494 439s [3,] -0.46959 -0.4494 0.5979 439s [4,] -1.85053 -0.0857 -0.0409 439s [5,] 80.53000 1.3241 3.0428 439s [6,] -1.81359 0.2334 -0.2583 439s [7,] 0.54047 -0.1847 0.2826 439s [8,] -0.28778 -0.0112 -0.0165 439s [9,] -35.77159 0.2050 1.7044 439s [10,] 0.58031 -0.0870 0.0510 439s [11,] -0.00461 0.0862 -0.0821 439s [12,] 0.19369 0.0416 0.0268 439s Consumption_wages Investment_(Intercept) Investment_corpProf 439s [1,] -1.850529 80.530 -1.81359 439s [2,] -0.085701 1.324 0.23344 439s [3,] -0.040883 3.043 -0.25828 439s [4,] 0.094773 -3.542 0.04931 439s [5,] -3.542001 2206.842 -34.41529 439s [6,] 0.049311 -34.415 1.17951 439s [7,] -0.048133 29.517 -1.02562 439s [8,] 0.017421 -10.487 0.15573 439s [9,] 0.083728 18.025 -0.14810 439s [10,] 0.000958 1.156 0.00386 439s [11,] -0.002304 -1.519 -0.00126 439s [12,] -0.031989 -0.955 0.01443 439s Investment_corpProfLag Investment_capitalLag PrivateWages_(Intercept) 439s [1,] 0.54047 -0.28778 -35.7716 439s [2,] -0.18475 -0.01117 0.2050 439s [3,] 0.28258 -0.01647 1.7044 439s [4,] -0.04813 0.01742 0.0837 439s [5,] 29.51706 -10.48672 18.0248 439s [6,] -1.02562 0.15573 -0.1481 439s [7,] 1.09362 -0.14971 -0.4803 439s [8,] -0.14971 0.05132 -0.0381 439s [9,] -0.48030 -0.03806 70.4425 439s [10,] 0.00353 -0.00637 -0.4681 439s [11,] 0.00471 0.00732 -0.7110 439s [12,] -0.02247 0.00534 0.8424 439s PrivateWages_gnp PrivateWages_gnpLag PrivateWages_trend 439s [1,] 0.580315 -0.00461 0.19369 439s [2,] -0.086985 0.08623 0.04160 439s [3,] 0.051027 -0.08213 0.02678 439s [4,] 0.000958 -0.00230 -0.03199 439s [5,] 1.156385 -1.51874 -0.95497 439s [6,] 0.003856 -0.00126 0.01443 439s [7,] 0.003528 0.00471 -0.02247 439s [8,] -0.006374 0.00732 0.00534 439s [9,] -0.468096 -0.71104 0.84245 439s [10,] 0.058634 -0.05251 -0.01709 439s [11,] -0.052508 0.06655 0.00301 439s [12,] -0.017087 0.00301 0.04635 439s > 439s > # I3SLS 439s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 439s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 439s > summary 439s 439s systemfit results 439s method: iterated 3SLS 439s 439s convergence achieved after 15 iterations 439s 439s N DF SSR detRCov OLS-R2 McElroy-R2 439s system 59 47 81.3 0.349 0.958 0.995 439s 439s N DF SSR MSE RMSE R2 Adj R2 439s Consumption 19 15 18.1 1.209 1.100 0.980 0.976 439s Investment 20 16 52.0 3.250 1.803 0.776 0.735 439s PrivateWages 20 16 11.2 0.699 0.836 0.986 0.983 439s 439s The covariance matrix of the residuals used for estimation 439s Consumption Investment PrivateWages 439s Consumption 0.955 0.456 -0.421 439s Investment 0.456 2.294 0.375 439s PrivateWages -0.421 0.375 0.522 439s 439s The covariance matrix of the residuals 439s Consumption Investment PrivateWages 439s Consumption 0.955 0.456 -0.421 439s Investment 0.456 2.294 0.375 439s PrivateWages -0.421 0.375 0.522 439s 439s The correlations of the residuals 439s Consumption Investment PrivateWages 439s Consumption 1.000 0.322 -0.582 439s Investment 0.322 1.000 0.341 439s PrivateWages -0.582 0.341 1.000 439s 439s 439s 3SLS estimates for 'Consumption' (equation 1) 439s Model Formula: consump ~ corpProf + corpProfLag + wages 439s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 439s gnpLag 439s 439s Estimate Std. Error t value Pr(>|t|) 439s (Intercept) 16.8311 1.2489 13.48 8.7e-10 *** 439s corpProf 0.1468 0.0991 1.48 0.16 439s corpProfLag 0.0924 0.0906 1.02 0.32 439s wages 0.7945 0.0371 21.43 1.2e-12 *** 439s --- 439s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 439s 439s Residual standard error: 1.1 on 15 degrees of freedom 439s Number of observations: 19 Degrees of Freedom: 15 439s SSR: 18.14 MSE: 1.209 Root MSE: 1.1 439s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.976 439s 439s 439s 3SLS estimates for 'Investment' (equation 2) 439s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 439s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 439s gnpLag 439s 439s Estimate Std. Error t value Pr(>|t|) 439s (Intercept) 32.4128 8.2695 3.92 0.00122 ** 439s corpProf -0.0799 0.1934 -0.41 0.68498 439s corpProfLag 0.7607 0.1878 4.05 0.00093 *** 439s capitalLag -0.2114 0.0400 -5.29 7.4e-05 *** 439s --- 439s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 439s 439s Residual standard error: 1.803 on 16 degrees of freedom 439s Number of observations: 20 Degrees of Freedom: 16 439s SSR: 51.999 MSE: 3.25 Root MSE: 1.803 439s Multiple R-Squared: 0.776 Adjusted R-Squared: 0.735 439s 439s 439s 3SLS estimates for 'PrivateWages' (equation 3) 439s Model Formula: privWage ~ gnp + gnpLag + trend 439s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 439s gnpLag 439s 439s Estimate Std. Error t value Pr(>|t|) 439s (Intercept) 1.5421 1.1496 1.34 0.19852 439s gnp 0.3936 0.0313 12.57 1.0e-09 *** 439s gnpLag 0.1945 0.0328 5.93 2.1e-05 *** 439s trend 0.1416 0.0286 4.95 0.00014 *** 439s --- 439s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 439s 439s Residual standard error: 0.836 on 16 degrees of freedom 439s Number of observations: 20 Degrees of Freedom: 16 439s SSR: 11.181 MSE: 0.699 Root MSE: 0.836 439s Multiple R-Squared: 0.986 Adjusted R-Squared: 0.983 439s 439s > residuals 439s Consumption Investment PrivateWages 439s 1 NA NA NA 439s 2 -0.3309 -2.6308 -1.3061 439s 3 -1.0419 0.0146 0.4450 439s 4 -1.2918 0.4128 1.4338 439s 5 -0.1772 -1.7488 -0.2494 439s 6 0.3563 0.2807 -0.4066 439s 7 NA NA NA 439s 8 1.6778 1.4671 -0.8700 439s 9 1.4561 1.1068 0.1712 439s 10 NA 2.9002 1.1262 439s 11 0.4237 -1.0652 -0.6189 439s 12 -0.2711 -0.9488 0.0375 439s 13 -0.5643 -1.6241 -0.5055 439s 14 0.2845 1.8477 0.3080 439s 15 -0.0514 -0.2379 0.3003 439s 16 0.0521 0.1268 0.0141 439s 17 1.8733 2.2462 -0.7083 439s 18 -0.1962 -0.1724 0.8305 439s 19 0.3553 -3.5810 -0.9448 439s 20 1.3161 1.0343 -0.2738 439s 21 1.2055 0.6622 -1.1283 439s 22 -1.6327 1.5541 0.8257 439s > fitted 439s Consumption Investment PrivateWages 439s 1 NA NA NA 439s 2 42.2 2.431 26.8 439s 3 46.0 1.885 28.9 439s 4 50.5 4.787 32.7 439s 5 50.8 4.749 34.1 439s 6 52.2 4.819 35.8 439s 7 NA NA NA 439s 8 54.5 2.733 38.8 439s 9 55.8 1.893 39.0 439s 10 NA 2.200 40.2 439s 11 54.6 2.065 38.5 439s 12 51.2 -2.451 34.5 439s 13 46.2 -4.576 29.5 439s 14 46.2 -6.948 28.2 439s 15 48.8 -2.762 30.3 439s 16 51.2 -1.427 33.2 439s 17 55.8 -0.146 37.5 439s 18 58.9 2.172 40.2 439s 19 57.1 1.681 39.1 439s 20 60.3 0.266 41.9 439s 21 63.8 2.638 46.1 439s 22 71.3 3.346 52.5 439s > predict 439s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 439s 1 NA NA NA NA 439s 2 42.2 0.446 41.3 43.1 439s 3 46.0 0.511 45.0 47.1 439s 4 50.5 0.340 49.8 51.2 439s 5 50.8 0.393 50.0 51.6 439s 6 52.2 0.396 51.4 53.0 439s 7 NA NA NA NA 439s 8 54.5 0.326 53.9 55.2 439s 9 55.8 0.362 55.1 56.6 439s 10 NA NA NA NA 439s 11 54.6 0.612 53.3 55.8 439s 12 51.2 0.511 50.1 52.2 439s 13 46.2 0.671 44.8 47.5 439s 14 46.2 0.563 45.1 47.3 439s 15 48.8 0.354 48.0 49.5 439s 16 51.2 0.311 50.6 51.9 439s 17 55.8 0.362 55.1 56.6 439s 18 58.9 0.297 58.3 59.5 439s 19 57.1 0.357 56.4 57.9 439s 20 60.3 0.427 59.4 61.1 439s 21 63.8 0.416 63.0 64.6 439s 22 71.3 0.640 70.0 72.6 439s Investment.pred Investment.se.fit Investment.lwr Investment.upr 439s 1 NA NA NA NA 439s 2 2.431 0.970 0.4798 4.382 439s 3 1.885 0.745 0.3859 3.385 439s 4 4.787 0.664 3.4506 6.124 439s 5 4.749 0.562 3.6174 5.880 439s 6 4.819 0.537 3.7391 5.900 439s 7 NA NA NA NA 439s 8 2.733 0.446 1.8351 3.631 439s 9 1.893 0.620 0.6455 3.141 439s 10 2.200 0.684 0.8232 3.576 439s 11 2.065 1.055 -0.0569 4.187 439s 12 -2.451 0.845 -4.1517 -0.751 439s 13 -4.576 1.070 -6.7293 -2.423 439s 14 -6.948 1.103 -9.1676 -4.728 439s 15 -2.762 0.556 -3.8806 -1.644 439s 16 -1.427 0.480 -2.3919 -0.462 439s 17 -0.146 0.603 -1.3588 1.066 439s 18 2.172 0.390 1.3869 2.958 439s 19 1.681 0.563 0.5476 2.815 439s 20 0.266 0.661 -1.0634 1.595 439s 21 2.638 0.558 1.5144 3.761 439s 22 3.346 0.778 1.7808 4.911 439s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 439s 1 NA NA NA NA 439s 2 26.8 0.326 26.2 27.5 439s 3 28.9 0.328 28.2 29.5 439s 4 32.7 0.334 32.0 33.3 439s 5 34.1 0.242 33.7 34.6 439s 6 35.8 0.252 35.3 36.3 439s 7 NA NA NA NA 439s 8 38.8 0.244 38.3 39.3 439s 9 39.0 0.232 38.6 39.5 439s 10 40.2 0.230 39.7 40.6 439s 11 38.5 0.308 37.9 39.1 439s 12 34.5 0.336 33.8 35.1 439s 13 29.5 0.420 28.7 30.4 439s 14 28.2 0.345 27.5 28.9 439s 15 30.3 0.325 29.6 31.0 439s 16 33.2 0.271 32.6 33.7 439s 17 37.5 0.267 37.0 38.0 439s 18 40.2 0.218 39.7 40.6 439s 19 39.1 0.331 38.5 39.8 439s 20 41.9 0.289 41.3 42.5 439s 21 46.1 0.311 45.5 46.8 439s 22 52.5 0.485 51.5 53.5 439s > model.frame 439s [1] TRUE 439s > model.matrix 439s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0328 >" 439s [2] "Attributes: < Component “dimnames”: Component 1: 54 string mismatches >" 439s [3] "Numeric: lengths (732, 708) differ" 439s > nobs 439s [1] 59 439s > linearHypothesis 439s Linear hypothesis test (Theil's F test) 439s 439s Hypothesis: 439s Consumption_corpProf + Investment_capitalLag = 0 439s 439s Model 1: restricted model 439s Model 2: kleinModel 439s 439s Res.Df Df F Pr(>F) 439s 1 48 439s 2 47 1 0.28 0.6 439s Linear hypothesis test (F statistic of a Wald test) 439s 439s Hypothesis: 439s Consumption_corpProf + Investment_capitalLag = 0 439s 439s Model 1: restricted model 439s Model 2: kleinModel 439s 439s Res.Df Df F Pr(>F) 439s 1 48 439s 2 47 1 0.37 0.55 439s Linear hypothesis test (Chi^2 statistic of a Wald test) 439s 439s Hypothesis: 439s Consumption_corpProf + Investment_capitalLag = 0 439s 439s Model 1: restricted model 439s Model 2: kleinModel 439s 439s Res.Df Df Chisq Pr(>Chisq) 439s 1 48 439s 2 47 1 0.37 0.54 439s Linear hypothesis test (Theil's F test) 439s 439s Hypothesis: 439s Consumption_corpProf + Investment_capitalLag = 0 439s Consumption_corpProfLag - PrivateWages_trend = 0 439s 439s Model 1: restricted model 439s Model 2: kleinModel 439s 439s Res.Df Df F Pr(>F) 439s 1 49 439s 2 47 2 1.25 0.3 439s Linear hypothesis test (F statistic of a Wald test) 439s 439s Hypothesis: 439s Consumption_corpProf + Investment_capitalLag = 0 439s Consumption_corpProfLag - PrivateWages_trend = 0 439s 439s Model 1: restricted model 439s Model 2: kleinModel 439s 439s Res.Df Df F Pr(>F) 439s 1 49 439s 2 47 2 1.64 0.21 439s Linear hypothesis test (Chi^2 statistic of a Wald test) 439s 439s Hypothesis: 439s Consumption_corpProf + Investment_capitalLag = 0 439s Consumption_corpProfLag - PrivateWages_trend = 0 439s 439s Model 1: restricted model 439s Model 2: kleinModel 439s 439s Res.Df Df Chisq Pr(>Chisq) 439s 1 49 439s 2 47 2 3.28 0.19 439s > logLik 439s 'log Lik.' -74.5 (df=18) 439s 'log Lik.' -87.1 (df=18) 439s Estimating function 439s Consumption_(Intercept) Consumption_corpProf 439s Consumption_2 -4.75944 -63.951 439s Consumption_3 -2.22772 -37.167 439s Consumption_4 -0.38275 -7.254 439s Consumption_5 -5.30482 -109.454 439s Consumption_6 1.30597 25.176 439s Consumption_8 10.25777 176.523 439s Consumption_9 7.99665 151.823 439s Consumption_11 -1.17443 -19.299 439s Consumption_12 -1.24242 -15.523 439s Consumption_13 -4.75716 -43.103 439s Consumption_14 4.34635 40.320 439s Consumption_15 -1.98107 -24.739 439s Consumption_16 -1.93670 -27.859 439s Consumption_17 13.00314 191.023 439s Consumption_18 -1.57749 -30.922 439s Consumption_19 -8.67959 -166.185 439s Consumption_20 6.77999 118.909 439s Consumption_21 3.04771 61.962 439s Consumption_22 -2.30170 -52.427 439s Investment_2 2.92832 39.347 439s Investment_3 0.00114 0.019 439s Investment_4 -0.53396 -10.120 439s Investment_5 1.84118 37.989 439s Investment_6 -0.26074 -5.026 439s Investment_8 -1.42063 -24.447 439s Investment_9 -1.10750 -21.027 439s Investment_10 0.00000 0.000 439s Investment_11 1.09344 17.968 439s Investment_12 0.95848 11.975 439s Investment_13 1.66503 15.086 439s Investment_14 -1.92032 -17.814 439s Investment_15 0.22458 2.804 439s Investment_16 -0.16698 -2.402 439s Investment_17 -2.28568 -33.578 439s Investment_18 -0.00785 -0.154 439s Investment_19 3.68757 70.604 439s Investment_20 -1.02511 -17.979 439s Investment_21 -0.65919 -13.402 439s Investment_22 -1.70192 -38.765 439s PrivateWages_2 -6.13297 -82.407 439s PrivateWages_3 2.11354 35.262 439s PrivateWages_4 5.50774 104.386 439s PrivateWages_5 -5.40526 -111.526 439s PrivateWages_6 -0.82424 -15.889 439s PrivateWages_8 2.80754 48.314 439s PrivateWages_9 3.41557 64.847 439s PrivateWages_10 0.00000 0.000 439s PrivateWages_11 -5.23135 -85.964 439s PrivateWages_12 -1.71264 -21.398 439s PrivateWages_13 -5.07393 -45.974 439s PrivateWages_14 4.80915 44.613 439s PrivateWages_15 -0.96519 -12.053 439s PrivateWages_16 -1.15621 -16.632 439s PrivateWages_17 4.49108 65.976 439s PrivateWages_18 -0.08188 -1.605 439s PrivateWages_19 -12.82495 -245.555 439s PrivateWages_20 2.51036 44.027 439s PrivateWages_21 -2.60385 -52.938 439s PrivateWages_22 4.63537 105.582 439s Consumption_corpProfLag Consumption_wages 439s Consumption_2 -60.4449 -140.7509 439s Consumption_3 -27.6237 -70.9657 439s Consumption_4 -6.4685 -13.5614 439s Consumption_5 -97.6087 -205.5997 439s Consumption_6 25.3358 50.4846 439s Consumption_8 201.0522 408.4748 439s Consumption_9 158.3336 334.2197 439s Consumption_11 -25.4852 -50.4634 439s Consumption_12 -19.3817 -48.7944 439s Consumption_13 -54.2317 -167.3998 439s Consumption_14 30.4244 143.4489 439s Consumption_15 -22.1880 -73.9440 439s Consumption_16 -23.8214 -77.6627 439s Consumption_17 182.0440 542.0110 439s Consumption_18 -27.7639 -75.2217 439s Consumption_19 -150.1568 -427.4616 439s Consumption_20 103.7339 328.8605 439s Consumption_21 57.9064 162.7199 439s Consumption_22 -48.5659 -140.0278 439s Investment_2 37.1896 86.5991 439s Investment_3 0.0141 0.0362 439s Investment_4 -9.0240 -18.9190 439s Investment_5 33.8777 71.3589 439s Investment_6 -5.0583 -10.0793 439s Investment_8 -27.8443 -56.5709 439s Investment_9 -21.9285 -46.2880 439s Investment_10 0.0000 0.0000 439s Investment_11 23.7276 46.9832 439s Investment_12 14.9524 37.6432 439s Investment_13 18.9813 58.5907 439s Investment_14 -13.4423 -63.3793 439s Investment_15 2.5153 8.3824 439s Investment_16 -2.0538 -6.6959 439s Investment_17 -31.9996 -95.2743 439s Investment_18 -0.1382 -0.3745 439s Investment_19 63.7949 181.6093 439s Investment_20 -15.6841 -49.7224 439s Investment_21 -12.5246 -35.1949 439s Investment_22 -35.9105 -103.5390 439s PrivateWages_2 -77.8887 -181.3703 439s PrivateWages_3 26.2079 67.3285 439s PrivateWages_4 93.0807 195.1464 439s PrivateWages_5 -99.4568 -209.4924 439s PrivateWages_6 -15.9902 -31.8624 439s PrivateWages_8 55.0278 111.7991 439s PrivateWages_9 67.6282 142.7536 439s PrivateWages_10 0.0000 0.0000 439s PrivateWages_11 -113.5202 -224.7822 439s PrivateWages_12 -26.7172 -67.2617 439s PrivateWages_13 -57.8428 -178.5466 439s PrivateWages_14 33.6641 158.7235 439s PrivateWages_15 -10.8101 -36.0260 439s PrivateWages_16 -14.2214 -46.3646 439s PrivateWages_17 62.8751 187.2021 439s PrivateWages_18 -1.4410 -3.9043 439s PrivateWages_19 -221.8716 -631.6170 439s PrivateWages_20 38.4085 121.7638 439s PrivateWages_21 -49.4732 -139.0222 439s PrivateWages_22 97.8064 282.0006 439s Investment_(Intercept) Investment_corpProf 439s Consumption_2 1.782157 23.0934 439s Consumption_3 0.834162 13.9344 439s Consumption_4 0.143320 2.7425 439s Consumption_5 1.986375 41.5880 439s Consumption_6 -0.489016 -9.5207 439s Consumption_8 -3.840991 -65.8399 439s Consumption_9 -2.994321 -58.3554 439s Consumption_11 0.439763 7.4080 439s Consumption_12 0.465220 5.8989 439s Consumption_13 1.781306 15.8927 439s Consumption_14 -1.627477 -15.1363 439s Consumption_15 0.741807 9.4914 439s Consumption_16 0.725191 10.3407 439s Consumption_17 -4.868989 -71.8262 439s Consumption_18 0.590688 11.5449 439s Consumption_19 3.250046 62.9174 439s Consumption_20 -2.538748 -44.1394 439s Consumption_21 -1.141204 -22.9368 439s Consumption_22 0.861865 19.7035 439s Investment_2 -2.373514 -30.7562 439s Investment_3 -0.000921 -0.0154 439s Investment_4 0.432798 8.2817 439s Investment_5 -1.492349 -31.2447 439s Investment_6 0.211337 4.1146 439s Investment_8 1.151475 19.7379 439s Investment_9 0.897673 17.4945 439s Investment_10 2.570865 52.6054 439s Investment_11 -0.886274 -14.9297 439s Investment_12 -0.776889 -9.8508 439s Investment_13 -1.349570 -12.0408 439s Investment_14 1.556498 14.4761 439s Investment_15 -0.182029 -2.3291 439s Investment_16 0.135342 1.9299 439s Investment_17 1.852635 27.3297 439s Investment_18 0.006366 0.1244 439s Investment_19 -2.988917 -57.8622 439s Investment_20 0.830890 14.4461 439s Investment_21 0.534301 10.7388 439s Investment_22 1.379471 31.5367 439s PrivateWages_2 2.964495 38.4142 439s PrivateWages_3 -1.021623 -17.0659 439s PrivateWages_4 -2.662277 -50.9436 439s PrivateWages_5 2.612743 54.7020 439s PrivateWages_6 0.398411 7.7567 439s PrivateWages_8 -1.357082 -23.2623 439s PrivateWages_9 -1.650985 -32.1755 439s PrivateWages_10 -3.276467 -67.0436 439s PrivateWages_11 2.528678 42.5968 439s PrivateWages_12 0.827840 10.4968 439s PrivateWages_13 2.452590 21.8819 439s PrivateWages_14 -2.324602 -21.6199 439s PrivateWages_15 0.466545 5.9694 439s PrivateWages_16 0.558877 7.9692 439s PrivateWages_17 -2.170857 -32.0240 439s PrivateWages_18 0.039577 0.7735 439s PrivateWages_19 6.199203 120.0098 439s PrivateWages_20 -1.213433 -21.0971 439s PrivateWages_21 1.258626 25.2969 439s PrivateWages_22 -2.240603 -51.2233 439s Investment_corpProfLag Investment_capitalLag 439s Consumption_2 22.6334 325.778 439s Consumption_3 10.3436 152.318 439s Consumption_4 2.4221 26.443 439s Consumption_5 36.5493 376.815 439s Consumption_6 -9.4869 -94.233 439s Consumption_8 -75.2834 -781.258 439s Consumption_9 -59.2876 -621.621 439s Consumption_11 9.5429 94.857 439s Consumption_12 7.2574 100.813 439s Consumption_13 20.3069 379.952 439s Consumption_14 -11.3923 -337.050 439s Consumption_15 8.3082 149.845 439s Consumption_16 8.9199 144.313 439s Consumption_17 -68.1658 -962.599 439s Consumption_18 10.3961 118.019 439s Consumption_19 56.2258 655.859 439s Consumption_20 -38.8428 -507.496 439s Consumption_21 -21.6829 -229.610 439s Consumption_22 18.1854 176.251 439s Investment_2 -30.1436 -433.878 439s Investment_3 -0.0114 -0.168 439s Investment_4 7.3143 79.851 439s Investment_5 -27.4592 -283.099 439s Investment_6 4.0999 40.725 439s Investment_8 22.5689 234.210 439s Investment_9 17.7739 186.357 439s Investment_10 54.2453 541.424 439s Investment_11 -19.2321 -191.169 439s Investment_12 -12.1195 -168.352 439s Investment_13 -15.3851 -287.863 439s Investment_14 10.8955 322.351 439s Investment_15 -2.0387 -36.770 439s Investment_16 1.6647 26.933 439s Investment_17 25.9369 366.266 439s Investment_18 0.1120 1.272 439s Investment_19 -51.7083 -603.163 439s Investment_20 12.7126 166.095 439s Investment_21 10.1517 107.501 439s Investment_22 29.1068 282.102 439s PrivateWages_2 37.6491 541.910 439s PrivateWages_3 -12.6681 -186.548 439s PrivateWages_4 -44.9925 -491.190 439s PrivateWages_5 48.0745 495.637 439s PrivateWages_6 7.7292 76.774 439s PrivateWages_8 -26.5988 -276.031 439s PrivateWages_9 -32.6895 -342.744 439s PrivateWages_10 -69.1335 -690.024 439s PrivateWages_11 54.8723 545.436 439s PrivateWages_12 12.9143 179.393 439s PrivateWages_13 27.9595 523.137 439s PrivateWages_14 -16.2722 -481.425 439s PrivateWages_15 5.2253 94.242 439s PrivateWages_16 6.8742 111.217 439s PrivateWages_17 -30.3920 -429.178 439s PrivateWages_18 0.6966 7.908 439s PrivateWages_19 107.2462 1250.999 439s PrivateWages_20 -18.5655 -242.565 439s PrivateWages_21 23.9139 253.236 439s PrivateWages_22 -47.2767 -458.203 439s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 439s Consumption_2 -5.12212 -2.41e+02 -229.983 439s Consumption_3 -2.39748 -1.19e+02 -109.325 439s Consumption_4 -0.41192 -2.33e+01 -20.637 439s Consumption_5 -5.70906 -3.46e+02 -326.558 439s Consumption_6 1.40549 8.52e+01 80.253 439s Consumption_8 11.03944 6.62e+02 706.524 439s Consumption_9 8.60601 5.36e+Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 439s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 439s 02 554.227 439s Consumption_11 -1.26393 -8.05e+01 -84.683 439s Consumption_12 -1.33709 -7.33e+01 -81.830 439s Consumption_13 -5.11967 -2.41e+02 -273.390 439s Consumption_14 4.67755 1.97e+02 207.216 439s Consumption_15 -2.13204 -1.09e+02 -96.155 439s Consumption_16 -2.08428 -1.15e+02 -103.589 439s Consumption_17 13.99402 8.03e+02 761.275 439s Consumption_18 -1.69770 -1.14e+02 -106.446 439s Consumption_19 -9.34099 -6.40e+02 -607.165 439s Consumption_20 7.29665 4.88e+02 444.366 439s Consumption_21 3.27995 2.46e+02 227.957 439s Consumption_22 -2.47710 -2.15e+02 -187.516 439s Investment_2 4.06820 1.91e+02 182.662 439s Investment_3 0.00158 7.83e-02 0.072 439s Investment_4 -0.74181 -4.19e+01 -37.165 439s Investment_5 2.55788 1.55e+02 146.311 439s Investment_6 -0.36223 -2.20e+01 -20.683 439s Investment_8 -1.97362 -1.18e+02 -126.312 439s Investment_9 -1.53861 -9.58e+01 -99.086 439s Investment_10 -4.40645 -2.85e+02 -284.216 439s Investment_11 1.51907 9.68e+01 101.778 439s Investment_12 1.33159 7.30e+01 81.493 439s Investment_13 2.31316 1.09e+02 123.523 439s Investment_14 -2.66783 -1.12e+02 -118.185 439s Investment_15 0.31200 1.60e+01 14.071 439s Investment_16 -0.23198 -1.28e+01 -11.529 439s Investment_17 -3.17541 -1.82e+02 -172.742 439s Investment_18 -0.01091 -7.33e-01 -0.684 439s Investment_19 5.12299 3.51e+02 332.995 439s Investment_20 -1.42414 -9.52e+01 -86.730 439s Investment_21 -0.91579 -6.86e+01 -63.647 439s Investment_22 -2.36441 -2.05e+02 -178.986 439s PrivateWages_2 -10.69229 -5.03e+02 -480.084 439s PrivateWages_3 3.68477 1.83e+02 168.026 439s PrivateWages_4 9.60226 5.43e+02 481.073 439s PrivateWages_5 -9.42360 -5.72e+02 -539.030 439s PrivateWages_6 -1.43698 -8.71e+01 -82.052 439s PrivateWages_8 4.89470 2.94e+02 313.261 439s PrivateWages_9 5.95474 3.71e+02 383.486 439s PrivateWages_10 11.81751 7.63e+02 762.229 439s PrivateWages_11 -9.12040 -5.81e+02 -611.067 439s PrivateWages_12 -2.98584 -1.64e+02 -182.733 439s PrivateWages_13 -8.84596 -4.16e+02 -472.374 439s PrivateWages_14 8.38434 3.53e+02 371.426 439s PrivateWages_15 -1.68273 -8.62e+01 -75.891 439s PrivateWages_16 -2.01575 -1.12e+02 -100.183 439s PrivateWages_17 7.82981 4.49e+02 425.942 439s PrivateWages_18 -0.14275 -9.59e+00 -8.950 439s PrivateWages_19 -22.35918 -1.53e+03 -1453.347 439s PrivateWages_20 4.37659 2.93e+02 266.534 439s PrivateWages_21 -4.53959 -3.40e+02 -315.502 439s PrivateWages_22 8.08137 7.02e+02 611.760 439s PrivateWages_trend 439s Consumption_2 51.2212 439s Consumption_3 21.5773 439s Consumption_4 3.2953 439s Consumption_5 39.9635 439s Consumption_6 -8.4329 439s Consumption_8 -44.1578 439s Consumption_9 -25.8180 439s Consumption_11 1.2639 439s Consumption_12 0.0000 439s Consumption_13 -5.1197 439s Consumption_14 9.3551 439s Consumption_15 -6.3961 439s Consumption_16 -8.3371 439s Consumption_17 69.9701 439s Consumption_18 -10.1862 439s Consumption_19 -65.3870 439s Consumption_20 58.3732 439s Consumption_21 29.5195 439s Consumption_22 -24.7710 439s Investment_2 -40.6819 439s Investment_3 -0.0142 439s Investment_4 5.9345 439s Investment_5 -17.9052 439s Investment_6 2.1734 439s Investment_8 7.8945 439s Investment_9 4.6158 439s Investment_10 8.8129 439s Investment_11 -1.5191 439s Investment_12 0.0000 439s Investment_13 2.3132 439s Investment_14 -5.3357 439s Investment_15 0.9360 439s Investment_16 -0.9279 439s Investment_17 -15.8771 439s Investment_18 -0.0655 439s Investment_19 35.8610 439s Investment_20 -11.3931 439s Investment_21 -8.2421 439s Investment_22 -23.6441 439s PrivateWages_2 106.9229 439s PrivateWages_3 -33.1629 439s PrivateWages_4 -76.8181 439s PrivateWages_5 65.9652 439s PrivateWages_6 8.6219 439s PrivateWages_8 -19.5788 439s PrivateWages_9 -17.8642 439s PrivateWages_10 -23.6350 439s PrivateWages_11 9.1204 439s PrivateWages_12 0.0000 439s PrivateWages_13 -8.8460 439s PrivateWages_14 16.7687 439s PrivateWages_15 -5.0482 439s PrivateWages_16 -8.0630 439s PrivateWages_17 39.1491 439s PrivateWages_18 -0.8565 439s PrivateWages_19 -156.5143 439s PrivateWages_20 35.0127 439s PrivateWages_21 -40.8563 439s PrivateWages_22 80.8137 439s [1] TRUE 439s > Bread 439s Consumption_(Intercept) Consumption_corpProf Consumption_corpProfLag 439s [1,] 92.02523 -0.8883 -0.3567 439s [2,] -0.88834 0.5799 -0.3635 439s [3,] -0.35667 -0.3635 0.4840 439s [4,] -1.65059 -0.0695 -0.0345 439s [5,] 87.30345 -0.4940 5.6093 439s [6,] -2.09669 0.4100 -0.4129 439s [7,] 0.52353 -0.3352 0.4397 439s [8,] -0.29441 -0.0047 -0.0291 439s [9,] -39.25694 0.2930 1.5879 439s [10,] 0.63395 -0.0766 0.0444 439s [11,] -0.00377 0.0739 -0.0730 439s [12,] 0.26412 0.0450 0.0239 439s Consumption_wages Investment_(Intercept) Investment_corpProf 439s [1,] -1.650593 87.303 -2.09669 439s [2,] -0.069509 -0.494 0.41001 439s [3,] -0.034488 5.609 -0.41285 439s [4,] 0.081060 -3.868 0.04419 439s [5,] -3.867758 4034.682 -59.45928 439s [6,] 0.044186 -59.459 2.20583 439s [7,] -0.048017 50.679 -1.90719 439s [8,] 0.019469 -19.184 0.26586 439s [9,] 0.172081 52.203 -0.49762 439s [10,] -0.001839 2.943 0.01728 439s [11,] -0.000946 -3.971 -0.00883 439s [12,] -0.034168 -2.641 0.03741 439s Investment_corpProfLag Investment_capitalLag PrivateWages_(Intercept) 439s [1,] 0.52353 -0.2944 -39.2569 439s [2,] -0.33517 -0.0047 0.2930 439s [3,] 0.43972 -0.0291 1.5879 439s [4,] -0.04802 0.0195 0.1721 439s [5,] 50.67914 -19.1839 52.2027 439s [6,] -1.90719 0.2659 -0.4976 439s [7,] 2.08136 -0.2612 -1.5286 439s [8,] -0.26125 0.0944 -0.0914 439s [9,] -1.52864 -0.0914 77.9751 439s [10,] 0.00872 -0.0168 -0.5909 439s [11,] 0.01756 0.0191 -0.7086 439s [12,] -0.06267 0.0150 0.8675 439s PrivateWages_gnp PrivateWages_gnpLag PrivateWages_trend 439s [1,] 0.63395 -0.003771 0.26412 439s [2,] -0.07661 0.073937 0.04500 439s [3,] 0.04435 -0.072979 0.02395 439s [4,] -0.00184 -0.000946 -0.03417 439s [5,] 2.94321 -3.971150 -2.64074 439s [6,] 0.01728 -0.008829 0.03741 439s [7,] 0.00872 0.017559 -0.06267 439s [8,] -0.01682 0.019146 0.01504 439s [9,] -0.59094 -0.708614 0.86750 439s [10,] 0.05781 -0.049542 -0.01891 439s [11,] -0.04954 0.063408 0.00453 439s [12,] -0.01891 0.004534 0.04825 439s > 439s > # OLS 439s > summary 439s 439s systemfit results 439s method: OLS 439s 439s N DF SSR detRCov OLS-R2 McElroy-R2 439s system 59 47 44.2 0.453 0.976 0.99 439s 439s N DF SSR MSE RMSE R2 Adj R2 439s Consumption 19 15 17.36 1.157 1.08 0.980 0.976 439s Investment 20 16 17.11 1.069 1.03 0.912 0.895 439s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 439s 439s The covariance matrix of the residuals 439s Consumption Investment PrivateWages 439s Consumption 1.1939 0.0559 -0.474 439s Investment 0.0559 0.9839 0.140 439s PrivateWages -0.4745 0.1403 0.602 439s 439s The correlations of the residuals 439s Consumption Investment PrivateWages 439s Consumption 1.0000 0.0447 -0.568 439s Investment 0.0447 1.0000 0.169 439s PrivateWages -0.5680 0.1689 1.000 439s 439s 439s OLS estimates for 'Consumption' (equation 1) 439s Model Formula: consump ~ corpProf + corpProfLag + wages 439s 439s Estimate Std. Error t value Pr(>|t|) 439s (Intercept) 16.2957 1.4879 10.95 1.5e-08 *** 439s corpProf 0.1796 0.1162 1.55 0.14 439s corpProfLag 0.1032 0.0994 1.04 0.32 439s wages 0.7962 0.0433 18.39 1.1e-11 *** 439s --- 439s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 439s 439s Residual standard error: 1.076 on 15 degrees of freedom 439s Number of observations: 19 Degrees of Freedom: 15 439s SSR: 17.362 MSE: 1.157 Root MSE: 1.076 439s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.976 439s 439s 439s OLS estimates for 'Investment' (equation 2) 439s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 439s 439s Estimate Std. Error t value Pr(>|t|) 439s (Intercept) 10.1813 5.3720 1.90 0.07627 . 439s corpProf 0.5003 0.1052 4.75 0.00022 *** 439s corpProfLag 0.3259 0.1003 3.25 0.00502 ** 439s capitalLag -0.1134 0.0265 -4.28 0.00057 *** 439s --- 439s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 439s 439s Residual standard error: 1.034 on 16 degrees of freedom 439s Number of observations: 20 Degrees of Freedom: 16 439s SSR: 17.109 MSE: 1.069 Root MSE: 1.034 439s Multiple R-Squared: 0.912 Adjusted R-Squared: 0.895 439s 439s 439s OLS estimates for 'PrivateWages' (equation 3) 439s Model Formula: privWage ~ gnp + gnpLag + trend 439s 439s Estimate Std. Error t value Pr(>|t|) 439s (Intercept) 1.3550 1.3021 1.04 0.3135 439s gnp 0.4417 0.0330 13.40 4.1e-10 *** 439s gnpLag 0.1466 0.0379 3.87 0.0013 ** 439s trend 0.1244 0.0335 3.72 0.0019 ** 439s --- 439s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 439s 439s Residual standard error: 0.78 on 16 degrees of freedom 439s Number of observations: 20 Degrees of Freedom: 16 439s SSR: 9.739 MSE: 0.609 Root MSE: 0.78 439s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 439s 439s compare coef with single-equation OLS 439s [1] TRUE 439s > residuals 439s Consumption Investment PrivateWages 439s 1 NA NA NA 439s 2 -0.3863 -0.000301 -1.3389 439s 3 -1.2484 -0.076489 0.2462 439s 4 -1.6040 1.221792 1.1255 439s 5 -0.5384 -1.377872 -0.1959 439s 6 -0.0413 0.386104 -0.5284 439s 7 0.8043 1.486279 NA 439s 8 1.2830 0.784055 -0.7909 439s 9 1.0142 -0.655354 0.2819 439s 10 NA 1.060871 1.1384 439s 11 0.1429 0.395249 -0.1904 439s 12 -0.3439 0.198005 0.5813 439s 13 NA NA 0.1206 439s 14 0.3199 0.312725 0.4773 439s 15 -0.1016 -0.084685 0.3035 439s 16 -0.0702 0.066194 0.0284 439s 17 1.6064 0.963697 -0.8517 439s 18 -0.4980 0.078506 0.9908 439s 19 0.1253 -2.496401 -0.4597 439s 20 0.9805 -0.711004 -0.3819 439s 21 0.7551 -0.820172 -1.1062 439s 22 -2.1992 -0.731199 0.5501 439s > fitted 439s Consumption Investment PrivateWages 439s 1 NA NA NA 439s 2 42.3 -0.200 26.8 439s 3 46.2 1.976 29.1 439s 4 50.8 3.978 33.0 439s 5 51.1 4.378 34.1 439s 6 52.6 4.714 35.9 439s 7 54.3 4.114 NA 439s 8 54.9 3.416 38.7 439s 9 56.3 3.655 38.9 439s 10 NA 4.039 40.2 439s 11 54.9 0.605 38.1 439s 12 51.2 -3.598 33.9 439s 13 NA NA 28.9 439s 14 46.2 -5.413 28.0 439s 15 48.8 -2.915 30.3 439s 16 51.4 -1.366 33.2 439s 17 56.1 1.136 37.7 439s 18 59.2 1.921 40.0 439s 19 57.4 0.596 38.7 439s 20 60.6 2.011 42.0 439s 21 64.2 4.120 46.1 439s 22 71.9 5.631 52.7 439s > predict 439s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 439s 1 NA NA NA NA 439s 2 42.3 0.523 39.9 44.7 439s 3 46.2 0.560 43.8 48.7 439s 4 50.8 0.379 48.5 53.1 439s 5 51.1 0.448 48.8 53.5 439s 6 52.6 0.457 50.3 55.0 439s 7 54.3 0.408 52.0 56.6 439s 8 54.9 0.375 52.6 57.2 439s 9 56.3 0.418 54.0 58.6 439s 10 NA NA NA NA 439s 11 54.9 0.701 52.3 57.4 439s 12 51.2 0.638 48.7 53.8 439s 13 NA NA NA NA 439s 14 46.2 0.673 43.6 48.7 439s 15 48.8 0.453 46.5 51.2 439s 16 51.4 0.384 49.1 53.7 439s 17 56.1 0.391 53.8 58.4 439s 18 59.2 0.361 56.9 61.5 439s 19 57.4 0.449 55.0 59.7 439s 20 60.6 0.465 58.3 63.0 439s 21 64.2 0.468 61.9 66.6 439s 22 71.9 0.728 69.3 74.5 439s Investment.pred Investment.se.fit Investment.lwr Investment.upr 439s 1 NA NA NA NA 439s 2 -0.200 0.613 -2.618 2.219 439s 3 1.976 0.494 -0.329 4.282 439s 4 3.978 0.444 1.714 6.242 439s 5 4.378 0.369 2.169 6.587 439s 6 4.714 0.349 2.519 6.909 439s 7 4.114 0.323 1.934 6.293 439s 8 3.416 0.287 1.257 5.575 439s 9 3.655 0.386 1.435 5.876 439s 10 4.039 0.441 1.777 6.301 439s 11 0.605 0.641 -1.843 3.053 439s 12 -3.598 0.606 -6.010 -1.186 439s 13 NA NA NA NA 439s 14 -5.413 0.708 -7.934 -2.892 439s 15 -2.915 0.412 -5.155 -0.676 439s 16 -1.366 0.336 -3.554 0.821 439s 17 1.136 0.342 -1.055 3.327 439s 18 1.921 0.246 -0.217 4.060 439s 19 0.596 0.341 -1.594 2.787 439s 20 2.011 0.364 -0.194 4.216 439s 21 4.120 0.337 1.932 6.308 439s 22 5.631 0.477 3.341 7.922 439s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 439s 1 NA NA NA NA 439s 2 26.8 0.364 25.1 28.6 439s 3 29.1 0.367 27.3 30.8 439s 4 33.0 0.370 31.2 34.7 439s 5 34.1 0.286 32.4 35.8 439s 6 35.9 0.285 34.3 37.6 439s 7 NA NA NA NA 439s 8 38.7 0.292 37.0 40.4 439s 9 38.9 0.277 37.3 40.6 439s 10 40.2 0.264 38.5 41.8 439s 11 38.1 0.363 36.4 39.8 439s 12 33.9 0.367 32.2 35.7 439s 13 28.9 0.435 27.1 30.7 439s 14 28.0 0.383 26.3 29.8 439s 15 30.3 0.377 28.6 32.0 439s 16 33.2 0.315 31.5 34.9 439s 17 37.7 0.308 36.0 39.3 439s 18 40.0 0.241 38.4 41.7 439s 19 38.7 0.361 36.9 40.4 439s 20 42.0 0.324 40.3 43.7 439s 21 46.1 0.339 44.4 47.8 439s 22 52.7 0.511 50.9 54.6 439s > model.frame 439s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 439s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 439s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 439s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 439s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 439s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 439s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 439s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 439s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 439s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 439s 10 57.8 21.7 21.1 NA 5.1 211 41.3 67.0 64.5 439s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 439s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 439s 13 45.6 NA 11.4 34.3 -6.2 213 29.0 44.3 53.4 439s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 439s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 439s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 439s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 439s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 439s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 439s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 439s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 439s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 439s trend 439s 1 -11 439s 2 -10 439s 3 -9 439s 4 -8 439s 5 -7 439s 6 -6 439s 7 -5 439s 8 -4 439s 9 -3 439s 10 -2 439s 11 -1 439s 12 0 439s 13 1 439s 14 2 439s 15 3 439s 16 4 439s 17 5 439s 18 6 439s 19 7 439s 20 8 439s 21 9 439s 22 10 439s > model.matrix 439s Consumption_(Intercept) Consumption_corpProf 439s Consumption_2 1 12.4 439s Consumption_3 1 16.9 439s Consumption_4 1 18.4 439s Consumption_5 1 19.4 439s Consumption_6 1 20.1 439s Consumption_7 1 19.6 439s Consumption_8 1 19.8 439s Consumption_9 1 21.1 439s Consumption_11 1 15.6 439s Consumption_12 1 11.4 439s Consumption_14 1 11.2 439s Consumption_15 1 12.3 439s Consumption_16 1 14.0 439s Consumption_17 1 17.6 439s Consumption_18 1 17.3 439s Consumption_19 1 15.3 439s Consumption_20 1 19.0 439s Consumption_21 1 21.1 439s Consumption_22 1 23.5 439s Investment_2 0 0.0 439s Investment_3 0 0.0 439s Investment_4 0 0.0 439s Investment_5 0 0.0 439s Investment_6 0 0.0 439s Investment_7 0 0.0 439s Investment_8 0 0.0 439s Investment_9 0 0.0 439s Investment_10 0 0.0 439s Investment_11 0 0.0 439s Investment_12 0 0.0 439s Investment_14 0 0.0 439s Investment_15 0 0.0 439s Investment_16 0 0.0 439s Investment_17 0 0.0 439s Investment_18 0 0.0 439s Investment_19 0 0.0 439s Investment_20 0 0.0 439s Investment_21 0 0.0 439s Investment_22 0 0.0 439s PrivateWages_2 0 0.0 439s PrivateWages_3 0 0.0 439s PrivateWages_4 0 0.0 439s PrivateWages_5 0 0.0 439s PrivateWages_6 0 0.0 439s PrivateWages_8 0 0.0 439s PrivateWages_9 0 0.0 439s PrivateWages_10 0 0.0 439s PrivateWages_11 0 0.0 439s PrivateWages_12 0 0.0 439s PrivateWages_13 0 0.0 439s PrivateWages_14 0 0.0 439s PrivateWages_15 0 0.0 439s PrivateWages_16 0 0.0 439s PrivateWages_17 0 0.0 439s PrivateWages_18 0 0.0 439s PrivateWages_19 0 0.0 439s PrivateWages_20 0 0.0 439s PrivateWages_21 0 0.0 439s PrivateWages_22 0 0.0 439s Consumption_corpProfLag Consumption_wages 439s Consumption_2 12.7 28.2 439s Consumption_3 12.4 32.2 439s Consumption_4 16.9 37.0 439s Consumption_5 18.4 37.0 439s Consumption_6 19.4 38.6 439s Consumption_7 20.1 40.7 439s Consumption_8 19.6 41.5 439s Consumption_9 19.8 42.9 439s Consumption_11 21.7 42.1 439s Consumption_12 15.6 39.3 439s Consumption_14 7.0 34.1 439s Consumption_15 11.2 36.6 439s Consumption_16 12.3 39.3 439s Consumption_17 14.0 44.2 439s Consumption_18 17.6 47.7 439s Consumption_19 17.3 45.9 439s Consumption_20 15.3 49.4 439s Consumption_21 19.0 53.0 439s Consumption_22 21.1 61.8 439s Investment_2 0.0 0.0 439s Investment_3 0.0 0.0 439s Investment_4 0.0 0.0 439s Investment_5 0.0 0.0 439s Investment_6 0.0 0.0 439s Investment_7 0.0 0.0 439s Investment_8 0.0 0.0 439s Investment_9 0.0 0.0 439s Investment_10 0.0 0.0 439s Investment_11 0.0 0.0 439s Investment_12 0.0 0.0 439s Investment_14 0.0 0.0 439s Investment_15 0.0 0.0 439s Investment_16 0.0 0.0 439s Investment_17 0.0 0.0 439s Investment_18 0.0 0.0 439s Investment_19 0.0 0.0 439s Investment_20 0.0 0.0 439s Investment_21 0.0 0.0 439s Investment_22 0.0 0.0 439s PrivateWages_2 0.0 0.0 439s PrivateWages_3 0.0 0.0 439s PrivateWages_4 0.0 0.0 439s PrivateWages_5 0.0 0.0 439s PrivateWages_6 0.0 0.0 439s PrivateWages_8 0.0 0.0 439s PrivateWages_9 0.0 0.0 439s PrivateWages_10 0.0 0.0 439s PrivateWages_11 0.0 0.0 439s PrivateWages_12 0.0 0.0 439s PrivateWages_13 0.0 0.0 439s PrivateWages_14 0.0 0.0 439s PrivateWages_15 0.0 0.0 439s PrivateWages_16 0.0 0.0 439s PrivateWages_17 0.0 0.0 439s PrivateWages_18 0.0 0.0 439s PrivateWages_19 0.0 0.0 439s PrivateWages_20 0.0 0.0 439s PrivateWages_21 0.0 0.0 439s PrivateWages_22 0.0 0.0 439s Investment_(Intercept) Investment_corpProf 439s Consumption_2 0 0.0 439s Consumption_3 0 0.0 439s Consumption_4 0 0.0 439s Consumption_5 0 0.0 439s Consumption_6 0 0.0 439s Consumption_7 0 0.0 439s Consumption_8 0 0.0 439s Consumption_9 0 0.0 439s Consumption_11 0 0.0 439s Consumption_12 0 0.0 439s Consumption_14 0 0.0 439s Consumption_15 0 0.0 439s Consumption_16 0 0.0 439s Consumption_17 0 0.0 439s Consumption_18 0 0.0 439s Consumption_19 0 0.0 439s Consumption_20 0 0.0 439s Consumption_21 0 0.0 439s Consumption_22 0 0.0 439s Investment_2 1 12.4 439s Investment_3 1 16.9 439s Investment_4 1 18.4 439s Investment_5 1 19.4 439s Investment_6 1 20.1 439s Investment_7 1 19.6 439s Investment_8 1 19.8 439s Investment_9 1 21.1 439s Investment_10 1 21.7 439s Investment_11 1 15.6 439s Investment_12 1 11.4 439s Investment_14 1 11.2 439s Investment_15 1 12.3 439s Investment_16 1 14.0 439s Investment_17 1 17.6 439s Investment_18 1 17.3 439s Investment_19 1 15.3 439s Investment_20 1 19.0 439s Investment_21 1 21.1 439s Investment_22 1 23.5 439s PrivateWages_2 0 0.0 439s PrivateWages_3 0 0.0 439s PrivateWages_4 0 0.0 439s PrivateWages_5 0 0.0 439s PrivateWages_6 0 0.0 439s PrivateWages_8 0 0.0 439s PrivateWages_9 0 0.0 439s PrivateWages_10 0 0.0 439s PrivateWages_11 0 0.0 439s PrivateWages_12 0 0.0 439s PrivateWages_13 0 0.0 439s PrivateWages_14 0 0.0 439s PrivateWages_15 0 0.0 439s PrivateWages_16 0 0.0 439s PrivateWages_17 0 0.0 439s PrivateWages_18 0 0.0 439s PrivateWages_19 0 0.0 439s PrivateWages_20 0 0.0 439s PrivateWages_21 0 0.0 439s PrivateWages_22 0 0.0 439s Investment_corpProfLag Investment_capitalLag 439s Consumption_2 0.0 0 439s Consumption_3 0.0 0 439s Consumption_4 0.0 0 439s Consumption_5 0.0 0 439s Consumption_6 0.0 0 439s Consumption_7 0.0 0 439s Consumption_8 0.0 0 439s Consumption_9 0.0 0 439s Consumption_11 0.0 0 439s Consumption_12 0.0 0 439s Consumption_14 0.0 0 439s Consumption_15 0.0 0 439s Consumption_16 0.0 0 439s Consumption_17 0.0 0 439s Consumption_18 0.0 0 439s Consumption_19 0.0 0 439s Consumption_20 0.0 0 439s Consumption_21 0.0 0 439s Consumption_22 0.0 0 439s Investment_2 12.7 183 439s Investment_3 12.4 183 439s Investment_4 16.9 184 439s Investment_5 18.4 190 439s Investment_6 19.4 193 439s Investment_7 20.1 198 439s Investment_8 19.6 203 439s Investment_9 19.8 208 439s Investment_10 21.1 211 439s Investment_11 21.7 216 439s Investment_12 15.6 217 439s Investment_14 7.0 207 439s Investment_15 11.2 202 439s Investment_16 12.3 199 439s Investment_17 14.0 198 439s Investment_18 17.6 200 439s Investment_19 17.3 202 439s Investment_20 15.3 200 439s Investment_21 19.0 201 439s Investment_22 21.1 204 439s PrivateWages_2 0.0 0 439s PrivateWages_3 0.0 0 439s PrivateWages_4 0.0 0 439s PrivateWages_5 0.0 0 439s PrivateWages_6 0.0 0 439s PrivateWages_8 0.0 0 439s PrivateWages_9 0.0 0 439s PrivateWages_10 0.0 0 439s PrivateWages_11 0.0 0 439s PrivateWages_12 0.0 0 439s PrivateWages_13 0.0 0 439s PrivateWages_14 0.0 0 439s PrivateWages_15 0.0 0 439s PrivateWages_16 0.0 0 439s PrivateWages_17 0.0 0 439s PrivateWages_18 0.0 0 439s PrivateWages_19 0.0 0 439s PrivateWages_20 0.0 0 439s PrivateWages_21 0.0 0 439s PrivateWages_22 0.0 0 439s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 439s Consumption_2 0 0.0 0.0 439s Consumption_3 0 0.0 0.0 439s Consumption_4 0 0.0 0.0 439s Consumption_5 0 0.0 0.0 439s Consumption_6 0 0.0 0.0 439s Consumption_7 0 0.0 0.0 439s Consumption_8 0 0.0 0.0 439s Consumption_9 0 0.0 0.0 439s Consumption_11 0 0.0 0.0 439s Consumption_12 0 0.0 0.0 439s Consumption_14 0 0.0 0.0 439s Consumption_15 0 0.0 0.0 439s Consumption_16 0 0.0 0.0 439s Consumption_17 0 0.0 0.0 439s Consumption_18 0 0.0 0.0 439s Consumption_19 0 0.0 0.0 439s Consumption_20 0 0.0 0.0 439s Consumption_21 0 0.0 0.0 439s Consumption_22 0 0.0 0.0 439s Investment_2 0 0.0 0.0 439s Investment_3 0 0.0 0.0 439s Investment_4 0 0.0 0.0 439s Investment_5 0 0.0 0.0 439s Investment_6 0 0.0 0.0 439s Investment_7 0 0.0 0.0 439s Investment_8 0 0.0 0.0 439s Investment_9 0 0.0 0.0 439s Investment_10 0 0.0 0.0 439s Investment_11 0 0.0 0.0 439s Investment_12 0 0.0 0.0 439s Investment_14 0 0.0 0.0 439s Investment_15 0 0.0 0.0 439s Investment_16 0 0.0 0.0 439s Investment_17 0 0.0 0.0 439s Investment_18 0 0.0 0.0 439s Investment_19 0 0.0 0.0 439s Investment_20 0 0.0 0.0 439s Investment_21 0 0.0 0.0 439s Investment_22 0 0.0 0.0 439s PrivateWages_2 1 45.6 44.9 439s PrivateWages_3 1 50.1 45.6 439s PrivateWages_4 1 57.2 50.1 439s PrivateWages_5 1 57.1 57.2 439s PrivateWages_6 1 61.0 57.1 439s PrivateWages_8 1 64.4 64.0 439s PrivateWages_9 1 64.5 64.4 439s PrivateWages_10 1 67.0 64.5 439s PrivateWages_11 1 61.2 67.0 439s PrivateWages_12 1 53.4 61.2 439s PrivateWages_13 1 44.3 53.4 439s PrivateWages_14 1 45.1 44.3 439s PrivateWages_15 1 49.7 45.1 439s PrivateWages_16 1 54.4 49.7 439s PrivateWages_17 1 62.7 54.4 439s PrivateWages_18 1 65.0 62.7 439s PrivateWages_19 1 60.9 65.0 439s PrivateWages_20 1 69.5 60.9 439s PrivateWages_21 1 75.7 69.5 439s PrivateWages_22 1 88.4 75.7 439s PrivateWages_trend 439s Consumption_2 0 439s Consumption_3 0 439s Consumption_4 0 439s Consumption_5 0 439s Consumption_6 0 439s Consumption_7 0 439s Consumption_8 0 439s Consumption_9 0 439s Consumption_11 0 439s Consumption_12 0 439s Consumption_14 0 439s Consumption_15 0 439s Consumption_16 0 439s Consumption_17 0 439s Consumption_18 0 439s Consumption_19 0 439s Consumption_20 0 439s Consumption_21 0 439s Consumption_22 0 439s Investment_2 0 439s Investment_3 0 439s Investment_4 0 439s Investment_5 0 439s Investment_6 0 439s Investment_7 0 439s Investment_8 0 439s Investment_9 0 439s Investment_10 0 439s Investment_11 0 439s Investment_12 0 439s Investment_14 0 439s Investment_15 0 439s Investment_16 0 439s Investment_17 0 439s Investment_18 0 439s Investment_19 0 439s Investment_20 0 439s Investment_21 0 439s Investment_22 0 439s PrivateWages_2 -10 439s PrivateWages_3 -9 439s PrivateWages_4 -8 439s PrivateWages_5 -7 439s PrivateWages_6 -6 439s PrivateWages_8 -4 439s PrivateWages_9 -3 439s PrivateWages_10 -2 439s PrivateWages_11 -1 439s PrivateWages_12 0 439s PrivateWages_13 1 439s PrivateWages_14 2 439s PrivateWages_15 3 439s PrivateWages_16 4 439s PrivateWages_17 5 439s PrivateWages_18 6 439s PrivateWages_19 7 439s PrivateWages_20 8 439s PrivateWages_21 9 439s PrivateWages_22 10 439s > nobs 439s [1] 59 439s > linearHypothesis 439s Linear hypothesis test (Theil's F test) 439s 439s Hypothesis: 439s Consumption_corpProf + Investment_capitalLag = 0 439s 439s Model 1: restricted model 439s Model 2: kleinModel 439s 439s Res.Df Df F Pr(>F) 439s 1 48 439s 2 47 1 0.33 0.57 439s Linear hypothesis test (F statistic of a Wald test) 439s 439s Hypothesis: 439s Consumption_corpProf + Investment_capitalLag = 0 439s 439s Model 1: restricted model 439s Model 2: kleinModel 439s 439s Res.Df Df F Pr(>F) 439s 1 48 439s 2 47 1 0.31 0.58 439s Linear hypothesis test (Chi^2 statistic of a Wald test) 439s 439s Hypothesis: 439s Consumption_corpProf + Investment_capitalLag = 0 439s 439s Model 1: restricted model 439s Model 2: kleinModel 439s 439s Res.Df Df Chisq Pr(>Chisq) 439s 1 48 439s 2 47 1 0.31 0.58 439s Linear hypothesis test (Theil's F test) 439s 439s Hypothesis: 439s Consumption_corpProf + Investment_capitalLag = 0 439s Consumption_corpProfLag - PrivateWages_trend = 0 439s 439s Model 1: restricted model 439s Model 2: kleinModel 439s 439s Res.Df Df F Pr(>F) 439s 1 49 439s 2 47 2 0.17 0.84 439s Linear hypothesis test (F statistic of a Wald test) 439s 439s Hypothesis: 439s Consumption_corpProf + Investment_capitalLag = 0 439s Consumption_corpProfLag - PrivateWages_trend = 0 439s 439s Model 1: restricted model 439s Model 2: kleinModel 439s 439s Res.Df Df F Pr(>F) 439s 1 49 439s 2 47 2 0.16 0.85 439s Linear hypothesis test (Chi^2 statistic of a Wald test) 439s 439s Hypothesis: 439s Consumption_corpProf + Investment_capitalLag = 0 439s Consumption_corpProfLag - PrivateWages_trend = 0 439s 439s Model 1: restricted model 439s Model 2: kleinModel 439s 439s Res.Df Df Chisq Pr(>Chisq) 439s 1 49 439s 2 47 2 0.33 0.85 439s > logLik 439s 'log Lik.' -69.6 (df=13) 439s 'log Lik.' -74.2 (df=13) 439s compare log likelihood value with single-equation OLS 439s [1] "Mean relative difference: 0.00099" 439s Estimating function 439s Consumption_(Intercept) Consumption_corpProf 439s Consumption_2 -0.3863 -4.791 439s Consumption_3 -1.2484 -21.098 439s Consumption_4 -1.6040 -29.514 439s Consumption_5 -0.5384 -10.446 439s Consumption_6 -0.0413 -0.830 439s Consumption_7 0.8043 15.763 439s Consumption_8 1.2830 25.403 439s Consumption_9 1.0142 21.399 439s Consumption_11 0.1429 2.229 439s Consumption_12 -0.3439 -3.920 439s Consumption_14 0.3199 3.583 439s Consumption_15 -0.1016 -1.250 439s Consumption_16 -0.0702 -0.983 439s Consumption_17 1.6064 28.272 439s Consumption_18 -0.4980 -8.616 439s Consumption_19 0.1253 1.917 439s Consumption_20 0.9805 18.629 439s Consumption_21 0.7551 15.933 439s Consumption_22 -2.1992 -51.681 439s Investment_2 0.0000 0.000 439s Investment_3 0.0000 0.000 439s Investment_4 0.0000 0.000 439s Investment_5 0.0000 0.000 439s Investment_6 0.0000 0.000 439s Investment_7 0.0000 0.000 439s Investment_8 0.0000 0.000 439s Investment_9 0.0000 0.000 439s Investment_10 0.0000 0.000 439s Investment_11 0.0000 0.000 439s Investment_12 0.0000 0.000 439s Investment_14 0.0000 0.000 439s Investment_15 0.0000 0.000 439s Investment_16 0.0000 0.000 439s Investment_17 0.0000 0.000 439s Investment_18 0.0000 0.000 439s Investment_19 0.0000 0.000 439s Investment_20 0.0000 0.000 439s Investment_21 0.0000 0.000 439s Investment_22 0.0000 0.000 439s PrivateWages_2 0.0000 0.000 439s PrivateWages_3 0.0000 0.000 439s PrivateWages_4 0.0000 0.000 439s PrivateWages_5 0.0000 0.000 439s PrivateWages_6 0.0000 0.000 439s PrivateWages_8 0.0000 0.000 439s PrivateWages_9 0.0000 0.000 439s PrivateWages_10 0.0000 0.000 439s PrivateWages_11 0.0000 0.000 439s PrivateWages_12 0.0000 0.000 439s PrivateWages_13 0.0000 0.000 439s PrivateWages_14 0.0000 0.000 439s PrivateWages_15 0.0000 0.000 439s PrivateWages_16 0.0000 0.000 439s PrivateWages_17 0.0000 0.000 439s PrivateWages_18 0.0000 0.000 439s PrivateWages_19 0.0000 0.000 439s PrivateWages_20 0.0000 0.000 439s PrivateWages_21 0.0000 0.000 439s PrivateWages_22 0.0000 0.000 439s Consumption_corpProfLag Consumption_wages 439s Consumption_2 -4.907 -10.90 439s Consumption_3 -15.480 -40.20 439s Consumption_4 -27.108 -59.35 439s Consumption_5 -9.907 -19.92 439s Consumption_6 -0.801 -1.59 439s Consumption_7 16.166 32.73 439s Consumption_8 25.146 53.24 439s Consumption_9 20.081 43.51 439s Consumption_11 3.100 6.01 439s Consumption_12 -5.364 -13.51 439s Consumption_14 2.239 10.91 439s Consumption_15 -1.138 -3.72 439s Consumption_16 -0.864 -2.76 439s Consumption_17 22.489 71.00 439s Consumption_18 -8.765 -23.76 439s Consumption_19 2.168 5.75 439s Consumption_20 15.002 48.44 439s Consumption_21 14.348 40.02 439s Consumption_22 -46.403 -135.91 439s Investment_2 0.000 0.00 439s Investment_3 0.000 0.00 439s Investment_4 0.000 0.00 439s Investment_5 0.000 0.00 439s Investment_6 0.000 0.00 439s Investment_7 0.000 0.00 439s Investment_8 0.000 0.00 439s Investment_9 0.000 0.00 439s Investment_10 0.000 0.00 439s Investment_11 0.000 0.00 439s Investment_12 0.000 0.00 439s Investment_14 0.000 0.00 439s Investment_15 0.000 0.00 439s Investment_16 0.000 0.00 439s Investment_17 0.000 0.00 439s Investment_18 0.000 0.00 439s Investment_19 0.000 0.00 439s Investment_20 0.000 0.00 439s Investment_21 0.000 0.00 439s Investment_22 0.000 0.00 439s PrivateWages_2 0.000 0.00 439s PrivateWages_3 0.000 0.00 439s PrivateWages_4 0.000 0.00 439s PrivateWages_5 0.000 0.00 439s PrivateWages_6 0.000 0.00 439s PrivateWages_8 0.000 0.00 439s PrivateWages_9 0.000 0.00 439s PrivateWages_10 0.000 0.00 439s PrivateWages_11 0.000 0.00 439s PrivateWages_12 0.000 0.00 439s PrivateWages_13 0.000 0.00 439s PrivateWages_14 0.000 0.00 439s PrivateWages_15 0.000 0.00 439s PrivateWages_16 0.000 0.00 439s PrivateWages_17 0.000 0.00 439s PrivateWages_18 0.000 0.00 439s PrivateWages_19 0.000 0.00 439s PrivateWages_20 0.000 0.00 439s PrivateWages_21 0.000 0.00 439s PrivateWages_22 0.000 0.00 439s Investment_(Intercept) Investment_corpProf 439s Consumption_2 0.000000 0.00000 439s Consumption_3 0.000000 0.00000 439s Consumption_4 0.000000 0.00000 439s Consumption_5 0.000000 0.00000 439s Consumption_6 0.000000 0.00000 439s Consumption_7 0.000000 0.00000 439s Consumption_8 0.000000 0.00000 439s Consumption_9 0.000000 0.00000 439s Consumption_11 0.000000 0.00000 439s Consumption_12 0.000000 0.00000 439s Consumption_14 0.000000 0.00000 439s Consumption_15 0.000000 0.00000 439s Consumption_16 0.000000 0.00000 439s Consumption_17 0.000000 0.00000 439s Consumption_18 0.000000 0.00000 439s Consumption_19 0.000000 0.00000 439s Consumption_20 0.000000 0.00000 439s Consumption_21 0.000000 0.00000 439s Consumption_22 0.000000 0.00000 439s Investment_2 -0.000301 -0.00373 439s Investment_3 -0.076489 -1.29266 439s Investment_4 1.221792 22.48097 439s Investment_5 -1.377872 -26.73071 439s Investment_6 0.386104 7.76068 439s Investment_7 1.486279 29.13107 439s Investment_8 0.784055 15.52429 439s Investment_9 -0.655354 -13.82796 439s Investment_10 1.060871 23.02091 439s Investment_11 0.395249 6.16588 439s Investment_12 0.198005 2.25726 439s Investment_14 0.312725 3.50252 439s Investment_15 -0.084685 -1.04163 439s Investment_16 0.066194 0.92672 439s Investment_17 0.963697 16.96106 439s Investment_18 0.078506 1.35816 439s Investment_19 -2.496401 -38.19494 439s Investment_20 -0.711004 -13.50907 439s Investment_21 -0.820172 -17.30564 439s Investment_22 -0.731199 -17.18317 439s PrivateWages_2 0.000000 0.00000 439s PrivateWages_3 0.000000 0.00000 439s PrivateWages_4 0.000000 0.00000 439s PrivateWages_5 0.000000 0.00000 439s PrivateWages_6 0.000000 0.00000 439s PrivateWages_8 0.000000 0.00000 439s PrivateWages_9 0.000000 0.00000 439s PrivateWages_10 0.000000 0.00000 439s PrivateWages_11 0.000000 0.00000 439s PrivateWages_12 0.000000 0.00000 439s PrivateWages_13 0.000000 0.00000 439s PrivateWages_14 0.000000 0.00000 439s PrivateWages_15 0.000000 0.00000 439s PrivateWages_16 0.000000 0.00000 439s PrivateWages_17 0.000000 0.00000 439s PrivateWages_18 0.000000 0.00000 439s PrivateWages_19 0.000000 0.00000 439s PrivateWages_20 0.000000 0.00000 439s PrivateWages_21 0.000000 0.00000 439s PrivateWages_22 0.000000 0.00000 439s Investment_corpProfLag Investment_capitalLag 439s Consumption_2 0.00000 0.000 439s Consumption_3 0.00000 0.000 439s Consumption_4 0.00000 0.000 439s Consumption_5 0.00000 0.000 439s Consumption_6 0.00000 0.000 439s Consumption_7 0.00000 0.000 439s Consumption_8 0.00000 0.000 439s Consumption_9 0.00000 0.000 439s Consumption_11 0.00000 0.000 439s Consumption_12 0.00000 0.000 439s Consumption_14 0.00000 0.000 439s Consumption_15 0.00000 0.000 439s Consumption_16 0.00000 0.000 439s Consumption_17 0.00000 0.000 439s Consumption_18 0.00000 0.000 439s Consumption_19 0.00000 0.000 439s Consumption_20 0.00000 0.000 439s Consumption_21 0.00000 0.000 439s Consumption_22 0.00000 0.000 439s Investment_2 -0.00382 -0.055 439s Investment_3 -0.94846 -13.967 439s Investment_4 20.64828 225.421 439s Investment_5 -25.35284 -261.382 439s Investment_6 7.49041 74.402 439s Investment_7 29.87421 293.986 439s Investment_8 15.36748 159.477 439s Investment_9 -12.97600 -136.051 439s Investment_10 22.38438 223.419 439s Investment_11 8.57690 85.255 439s Investment_12 3.08888 42.908 439s Investment_14 2.18907 64.765 439s Investment_15 -0.94848 -17.106 439s Investment_16 0.81419 13.173 439s Investment_17 13.49175 190.523 439s Investment_18 1.38171 15.686 439s Investment_19 -43.18774 -503.774 439s Investment_20 -10.87836 -142.130 439s Investment_21 -15.58327 -165.019 439s Investment_22 -15.42829 -149.530 439s PrivateWages_2 0.00000 0.000 439s PrivateWages_3 0.00000 0.000 439s PrivateWages_4 0.00000 0.000 439s PrivateWages_5 0.00000 0.000 439s PrivateWages_6 0.00000 0.000 439s PrivateWages_8 0.00000 0.000 439s PrivateWages_9 0.00000 0.000 439s PrivateWages_10 0.00000 0.000 439s PrivateWages_11 0.00000 0.000 439s PrivateWages_12 0.00000 0.000 439s PrivateWages_13 0.00000 0.000 439s PrivateWages_14 0.00000 0.000 439s PrivateWages_15 0.00000 0.000 439s PrivateWages_16 0.00000 0.000 439s PrivateWages_17 0.00000 0.000 439s PrivateWages_18 0.00000 0.000 439s PrivateWages_19 0.00000 0.000 439s PrivateWages_20 0.00000 0.000 439s PrivateWages_21 0.00000 0.000 439s PrivateWages_22 0.00000 0.000 439s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 439s Consumption_2 0.0000 0.00 0.00 439s Consumption_3 0.0000 0.00 0.00 439s Consumption_4 0.0000 0.00 0.00 439s Consumption_5 0.0000 0.00 0.00 439s Consumption_6 0.0000 0.00 0.00 439s Consumption_7 0.0000 0.00 0.00 439s Consumption_8 0.0000 0.00 0.00 439s Consumption_9 0.0000 0.00 0.00 439s Consumption_11 0.0000 0.00 0.00 439s Consumption_12 0.0000 0.00 0.00 439s Consumption_14 0.0000 0.00 0.00 439s Consumption_15 0.0000 0.00 0.00 439s Consumption_16 0.0000 0.00 0.00 439s Consumption_17 0.0000 0.00 0.00 439s Consumption_18 0.0000 0.00 0.00 439s Consumption_19 0.0000 0.00 0.00 439s Consumption_20 0.0000 0.00 0.00 439s Consumption_21 0.0000 0.00 0.00 439s Consumption_22 0.0000 0.00 0.00 439s Investment_2 0.0000 0.00 0.00 439s Investment_3 0.0000 0.00 0.00 439s Investment_4 0.0000 0.00 0.00 439s Investment_5 0.0000 0.00 0.00 439s Investment_6 0.0000 0.00 0.00 439s Investment_7 0.0000 0.00 0.00 439s Investment_8 0.0000 0.00 0.00 439s Investment_9 0.0000 0.00 0.00 439s Investment_10 0.0000 0.00 0.00 439s Investment_11 0.0000 0.00 0.00 439s Investment_12 0.0000 0.00 0.00 439s Investment_14 0.0000 0.00 0.00 439s Investment_15 0.0000 0.00 0.00 439s Investment_16 0.0000 0.00 0.00 439s Investment_17 0.0000 0.00 0.00 439s Investment_18 0.0000 0.00 0.00 439s Investment_19 0.0000 0.00 0.00 439s Investment_20 0.0000 0.00 0.00 439s Investment_21 0.0000 0.00 0.00 439s Investment_22 0.0000 0.00 0.00 439s PrivateWages_2 -1.3389 -61.06 -60.12 439s PrivateWages_3 0.2462 12.33 11.23 439s PrivateWages_4 1.1255 64.38 56.39 439s PrivateWages_5 -0.1959 -11.18 -11.20 439s PrivateWages_6 -0.5284 -32.23 -30.17 439s PrivateWages_8 -0.7909 -50.94 -50.62 439s PrivateWages_9 0.2819 18.18 18.15 439s PrivateWages_10 1.1384 76.28 73.43 439s PrivateWages_11 -0.1904 -11.65 -12.76 439s PrivateWages_12 0.5813 31.04 35.58 439s PrivateWages_13 0.1206 5.34 6.44 439s PrivateWages_14 0.4773 21.53 21.14 439s PrivateWages_15 0.3035 15.09 13.69 439s PrivateWages_16 0.0284 1.55 1.41 439s PrivateWages_17 -0.8517 -53.40 -46.33 439s PrivateWages_18 0.9908 64.40 62.12 439s PrivateWages_19 -0.4597 -28.00 -29.88 439s PrivateWages_20 -0.3819 -26.54 -23.26 439s PrivateWages_21 -1.1062 -83.74 -76.88 439s PrivateWages_22 0.5501 48.63 41.64 439s PrivateWages_trend 439s Consumption_2 0.000 439s Consumption_3 0.000 439s Consumption_4 0.000 439s Consumption_5 0.000 439s Consumption_6 0.000 439s Consumption_7 0.000 439s Consumption_8 0.000 439s Consumption_9 0.000 439s Consumption_11 0.000 439s Consumption_12 0.000 439s Consumption_14 0.000 439s Consumption_15 0.000 439s Consumption_16 0.000 439s Consumption_17 0.000 439s Consumption_18 0.000 439s Consumption_19 0.000 439s Consumption_20 0.000 439s Consumption_21 0.000 439s Consumption_22 0.000 439s Investment_2 0.000 439s Investment_3 0.000 439s Investment_4 0.000 439s Investment_5 0.000 439s Investment_6 0.000 439s Investment_7 0.000 439s Investment_8 0.000 439s Investment_9 0.000 439s Investment_10 0.000 439s Investment_11 0.000 439s Investment_12 0.000 439s Investment_14 0.000 439s Investment_15 0.000 439s Investment_16 0.000 439s Investment_17 0.000 439s Investment_18 0.000 439s Investment_19 0.000 439s Investment_20 0.000 439s Investment_21 0.000 439s Investment_22 0.000 439s PrivateWages_2 13.389 439s PrivateWages_3 -2.216 439s PrivateWages_4 -9.004 439s PrivateWages_5 1.371 439s PrivateWages_6 3.170 439s PrivateWages_8 3.164 439s PrivateWages_9 -0.846 439s PrivateWages_10 -2.277 439s PrivateWages_11 0.190 439s PrivateWages_12 0.000 439s PrivateWages_13 0.121 439s PrivateWages_14 0.955 439s PrivateWages_15 0.911 439s PrivateWages_16 0.114 439s PrivateWages_17 -4.258 439s PrivateWages_18 5.945 439s PrivateWages_19 -3.218 439s PrivateWages_20 -3.055 439s PrivateWages_21 -9.956 439s PrivateWages_22 5.501 439s [1] TRUE 439s > Bread 439s Consumption_(Intercept) Consumption_corpProf 439s Consumption_(Intercept) 109.396 -1.6401 439s Consumption_corpProf -1.640 0.6675 439s Consumption_corpProfLag -0.598 -0.3509 439s Consumption_wages -1.641 -0.0975 439s Investment_(Intercept) 0.000 0.0000 439s Investment_corpProf 0.000 0.0000 439s Investment_corpProfLag 0.000 0.0000 439s Investment_capitalLag 0.000 0.0000 439s PrivateWages_(Intercept) 0.000 0.0000 439s PrivateWages_gnp 0.000 0.0000 439s PrivateWages_gnpLag 0.000 0.0000 439s PrivateWages_trend 0.000 0.0000 439s Consumption_corpProfLag Consumption_wages 439s Consumption_(Intercept) -0.5979 -1.6408 439s Consumption_corpProf -0.3509 -0.0975 439s Consumption_corpProfLag 0.4880 -0.0331 439s Consumption_wages -0.0331 0.0926 439s Investment_(Intercept) 0.0000 0.0000 439s Investment_corpProf 0.0000 0.0000 439s Investment_corpProfLag 0.0000 0.0000 439s Investment_capitalLag 0.0000 0.0000 439s PrivateWages_(Intercept) 0.0000 0.0000 439s PrivateWages_gnp 0.0000 0.0000 439s PrivateWages_gnpLag 0.0000 0.0000 439s PrivateWages_trend 0.0000 0.0000 439s Investment_(Intercept) Investment_corpProf 439s Consumption_(Intercept) 0.00 0.0000 439s Consumption_corpProf 0.00 0.0000 439s Consumption_corpProfLag 0.00 0.0000 439s Consumption_wages 0.00 0.0000 439s Investment_(Intercept) 1730.48 -16.5126 439s Investment_corpProf -16.51 0.6641 439s Investment_corpProfLag 13.63 -0.5096 439s Investment_capitalLag -8.34 0.0672 439s PrivateWages_(Intercept) 0.00 0.0000 439s PrivateWages_gnp 0.00 0.0000 439s PrivateWages_gnpLag 0.00 0.0000 439s PrivateWages_trend 0.00 0.0000 439s Investment_corpProfLag Investment_capitalLag 439s Consumption_(Intercept) 0.000 0.0000 439s Consumption_corpProf 0.000 0.0000 439s Consumption_corpProfLag 0.000 0.0000 439s Consumption_wages 0.000 0.0000 439s Investment_(Intercept) 13.633 -8.3416 439s Investment_corpProf -0.510 0.0672 439s Investment_corpProfLag 0.603 -0.0740 439s Investment_capitalLag -0.074 0.0420 439s PrivateWages_(Intercept) 0.000 0.0000 439s PrivateWages_gnp 0.000 0.0000 439s PrivateWages_gnpLag 0.000 0.0000 439s PrivateWages_trend 0.000 0.0000 439s PrivateWages_(Intercept) PrivateWages_gnp 439s Consumption_(Intercept) 0.000 0.0000 439s Consumption_corpProf 0.000 0.0000 439s Consumption_corpProfLag 0.000 0.0000 439s Consumption_wages 0.000 0.0000 439s Investment_(Intercept) 0.000 0.0000 439s Investment_corpProf 0.000 0.0000 439s Investment_corpProfLag 0.000 0.0000 439s Investment_capitalLag 0.000 0.0000 439s PrivateWages_(Intercept) 166.178 -0.6258 439s PrivateWages_gnp -0.626 0.1064 439s PrivateWages_gnpLag -2.183 -0.0992 439s PrivateWages_trend 2.051 -0.0286 439s PrivateWages_gnpLag PrivateWages_trend 439s Consumption_(Intercept) 0.00000 0.00000 439s Consumption_corpProf 0.00000 0.00000 439s Consumption_corpProfLag 0.00000 0.00000 439s Consumption_wages 0.00000 0.00000 439s Investment_(Intercept) 0.00000 0.00000 439s Investment_corpProf 0.00000 0.00000 439s Investment_corpProfLag 0.00000 0.00000 439s Investment_capitalLag 0.00000 0.00000 439s PrivateWages_(Intercept) -2.18348 2.05079 439s PrivateWages_gnp -0.09921 -0.02859 439s PrivateWages_gnpLag 0.14047 -0.00635 439s PrivateWages_trend -0.00635 0.10969 439s > 439s > # 2SLS 439s > summary 439s 439s systemfit results 439s method: 2SLS 439s 439s N DF SSR detRCovWarning in systemfit(system, method = method, data = KleinI, inst = inst, : 439s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 439s OLS-R2 McElroy-R2 439s system 57 45 58.2 0.333 0.968 0.991 439s 439s N DF SSR MSE RMSE R2 Adj R2 439s Consumption 18 14 22.27 1.591 1.26 0.974 0.968 439s Investment 19 15 26.21 1.748 1.32 0.852 0.823 439s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 439s 439s The covariance matrix of the residuals 439s Consumption Investment PrivateWages 439s Consumption 1.237 0.518 -0.408 439s Investment 0.518 1.263 0.113 439s PrivateWages -0.408 0.113 0.468 439s 439s The correlations of the residuals 439s Consumption Investment PrivateWages 439s Consumption 1.000 0.416 -0.538 439s Investment 0.416 1.000 0.139 439s PrivateWages -0.538 0.139 1.000 439s 439s 439s 2SLS estimates for 'Consumption' (equation 1) 439s Model Formula: consump ~ corpProf + corpProfLag + wages 439s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 439s gnpLag 439s 439s Estimate Std. Error t value Pr(>|t|) 439s (Intercept) 17.2849 1.6018 10.79 3.6e-08 *** 439s corpProf -0.0770 0.1637 -0.47 0.645 439s corpProfLag 0.2327 0.1242 1.87 0.082 . 439s wages 0.8259 0.0459 17.98 4.5e-11 *** 439s --- 439s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 439s 439s Residual standard error: 1.261 on 14 degrees of freedom 439s Number of observations: 18 Degrees of Freedom: 14 439s SSR: 22.269 MSE: 1.591 Root MSE: 1.261 439s Multiple R-Squared: 0.974 Adjusted R-Squared: 0.968 439s 439s 439s 2SLS estimates for 'Investment' (equation 2) 439s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 439s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 439s gnpLag 439s 439s Estimate Std. Error t value Pr(>|t|) 439s (Intercept) 18.4005 7.1627 2.57 0.02138 * 439s corpProf 0.1507 0.1905 0.79 0.44118 439s corpProfLag 0.5757 0.1634 3.52 0.00307 ** 439s capitalLag -0.1452 0.0339 -4.28 0.00065 *** 439s --- 439s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 439s 439s Residual standard error: 1.322 on 15 degrees of freedom 439s Number of observations: 19 Degrees of Freedom: 15 439s SSR: 26.213 MSE: 1.748 Root MSE: 1.322 439s Multiple R-Squared: 0.852 Adjusted R-Squared: 0.823 439s 439s 439s 2SLS estimates for 'PrivateWages' (equation 3) 439s Model Formula: privWage ~ gnp + gnpLag + trend 439s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 439s gnpLag 439s 439s Estimate Std. Error t value Pr(>|t|) 439s (Intercept) 1.3431 1.1544 1.16 0.26172 439s gnp 0.4438 0.0351 12.64 9.7e-10 *** 439s gnpLag 0.1447 0.0381 3.80 0.00158 ** 439s trend 0.1238 0.0300 4.13 0.00078 *** 439s --- 439s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 439s 439s Residual standard error: 0.78 on 16 degrees of freedom 439s Number of observations: 20 Degrees of Freedom: 16 439s SSR: 9.741 MSE: 0.609 Root MSE: 0.78 439s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 439s 439s > residuals 439s Consumption Investment PrivateWages 439s 1 NA NA NA 439s 2 -0.6754 -1.23599 -1.3401 439s 3 -0.4627 0.32957 0.2378 439s 4 -1.1585 1.08894 1.1117 439s 5 -0.0305 -1.37017 -0.1954 439s 6 0.4693 0.48431 -0.5355 439s 7 NA NA NA 439s 8 1.6045 1.06811 -0.7908 439s 9 1.6018 0.16695 0.2831 439s 10 NA 1.86380 1.1353 439s 11 -0.9031 -0.92183 -0.1765 439s 12 -1.5948 -1.03217 0.6007 439s 13 NA NA 0.1443 439s 14 0.2854 0.85468 0.4826 439s 15 -0.4718 -0.36943 0.3016 439s 16 -0.2268 0.00554 0.0261 439s 17 2.0079 1.69566 -0.8614 439s 18 -0.7434 -0.12659 0.9927 439s 19 -0.5410 -3.26209 -0.4446 439s 20 1.4186 0.25579 -0.3914 439s 21 1.1462 -0.00185 -1.1115 439s 22 -1.7256 0.50679 0.5312 439s > fitted 439s Consumption Investment PrivateWages 439s 1 NA NA NA 439s 2 42.6 1.036 26.8 439s 3 45.5 1.570 29.1 439s 4 50.4 4.111 33.0 439s 5 50.6 4.370 34.1 439s 6 52.1 4.616 35.9 439s 7 NA NA NA 439s 8 54.6 3.132 38.7 439s 9 55.7 2.833 38.9 439s 10 NA 3.236 40.2 439s 11 55.9 1.922 38.1 439s 12 52.5 -2.368 33.9 439s 13 NA NA 28.9 439s 14 46.2 -5.955 28.0 439s 15 49.2 -2.631 30.3 439s 16 51.5 -1.306 33.2 439s 17 55.7 0.404 37.7 439s 18 59.4 2.127 40.0 439s 19 58.0 1.362 38.6 439s 20 60.2 1.044 42.0 439s 21 63.9 3.302 46.1 439s 22 71.4 4.393 52.8 439s > predict 439s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 439s 1 NA NA NA NA 439s 2 42.6 0.571 41.4 43.8 439s 3 45.5 0.656 44.1 46.9 439s 4 50.4 0.431 49.4 51.3 439s 5 50.6 0.510 49.5 51.7 439s 6 52.1 0.521 51.0 53.2 439s 7 NA NA NA NA 439s 8 54.6 0.419 53.7 55.5 439s 9 55.7 0.496 54.6 56.8 439s 10 NA NA NA NA 439s 11 55.9 0.910 54.0 57.9 439s 12 52.5 0.869 50.6 54.4 439s 13 NA NA NA NA 439s 14 46.2 0.694 44.7 47.7 439s 15 49.2 0.487 48.1 50.2 439s 16 51.5 0.396 50.7 52.4 439s 17 55.7 0.445 54.7 56.6 439s 18 59.4 0.386 58.6 60.3 439s 19 58.0 0.548 56.9 59.2 439s 20 60.2 0.528 59.0 61.3 439s 21 63.9 0.515 62.8 65.0 439s 22 71.4 0.786 69.7 73.1 439s Investment.pred Investment.se.fit Investment.lwr Investment.upr 439s 1 NA NA NA NA 439s 2 1.036 0.892 -0.865 2.937 439s 3 1.570 0.579 0.335 2.805 439s 4 4.111 0.531 2.979 5.243 439s 5 4.370 0.440 3.432 5.308 439s 6 4.616 0.416 3.729 5.502 439s 7 NA NA NA NA 439s 8 3.132 0.344 2.398 3.866 439s 9 2.833 0.533 1.696 3.970 439s 10 3.236 0.580 2.000 4.473 439s 11 1.922 0.959 -0.122 3.966 439s 12 -2.368 0.860 -4.201 -0.534 439s 13 NA NA NA NA 439s 14 -5.955 0.865 -7.799 -4.110 439s 15 -2.631 0.479 -3.652 -1.610 439s 16 -1.306 0.382 -2.120 -0.491 439s 17 0.404 0.487 -0.635 1.443 439s 18 2.127 0.319 1.447 2.806 439s 19 1.362 0.537 0.218 2.506 439s 20 1.044 0.566 -0.162 2.250 439s 21 3.302 0.486 2.265 4.339 439s 22 4.393 0.713 2.874 5.912 439s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 439s 1 NA NA NA NA 439s 2 26.8 0.321 26.2 27.5 439s 3 29.1 0.334 28.4 29.8 439s 4 33.0 0.353 32.2 33.7 439s 5 34.1 0.253 33.6 34.6 439s 6 35.9 0.261 35.4 36.5 439s 7 NA NA NA NA 439s 8 38.7 0.257 38.1 39.2 439s 9 38.9 0.245 38.4 39.4 439s 10 40.2 0.235 39.7 40.7 439s 11 38.1 0.348 37.3 38.8 439s 12 33.9 0.374 33.1 34.7 439s 13 28.9 0.447 27.9 29.8 439s 14 28.0 0.341 27.3 28.7 439s 15 30.3 0.333 29.6 31.0 439s 16 33.2 0.278 32.6 33.8 439s 17 37.7 0.288 37.1 38.3 439s 18 40.0 0.214 39.6 40.5 439s 19 38.6 0.351 37.9 39.4 439s 20 42.0 0.301 41.4 42.6 439s 21 46.1 0.304 45.5 46.8 439s 22 52.8 0.486 51.7 53.8 439s > model.frame 439s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 439s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 439s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 439s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 439s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 439s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 439s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 439s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 439s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 439s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 439s 10 57.8 21.7 21.1 NA 5.1 211 41.3 67.0 64.5 439s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 439s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 439s 13 45.6 NA 11.4 34.3 -6.2 213 29.0 44.3 53.4 439s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 439s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 439s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 439s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 439s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 439s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 439s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 439s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 439s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 439s trend 439s 1 -11 439s 2 -10 439s 3 -9 439s 4 -8 439s 5 -7 439s 6 -6 439s 7 -5 439s 8 -4 439s 9 -3 439s 10 -2 439s 11 -1 439s 12 0 439s 13 1 439s 14 2 439s 15 3 439s 16 4 439s 17 5 439s 18 6 439s 19 7 439s 20 8 439s 21 9 439s 22 10 439s > Frames of instrumental variables 439s govExp taxes govWage trend capitalLag corpProfLag gnpLag 439s 1 2.4 3.4 2.2 -11 180 NA NA 439s 2 3.9 7.7 2.7 -10 183 12.7 44.9 439s 3 3.2 3.9 2.9 -9 183 12.4 45.6 439s 4 2.8 4.7 2.9 -8 184 16.9 50.1 439s 5 3.5 3.8 3.1 -7 190 18.4 57.2 439s 6 3.3 5.5 3.2 -6 193 19.4 57.1 439s 7 3.3 7.0 3.3 -5 198 20.1 NA 439s 8 4.0 6.7 3.6 -4 203 19.6 64.0 439s 9 4.2 4.2 3.7 -3 208 19.8 64.4 439s 10 4.1 4.0 4.0 -2 211 21.1 64.5 439s 11 5.2 7.7 4.2 -1 216 21.7 67.0 439s 12 5.9 7.5 4.8 0 217 15.6 61.2 439s 13 4.9 8.3 5.3 1 213 11.4 53.4 439s 14 3.7 5.4 5.6 2 207 7.0 44.3 439s 15 4.0 6.8 6.0 3 202 11.2 45.1 439s 16 4.4 7.2 6.1 4 199 12.3 49.7 439s 17 2.9 8.3 7.4 5 198 14.0 54.4 439s 18 4.3 6.7 6.7 6 200 17.6 62.7 439s 19 5.3 7.4 7.7 7 202 17.3 65.0 439s 20 6.6 8.9 7.8 8 200 15.3 60.9 439s 21 7.4 9.6 8.0 9 201 19.0 69.5 439s 22 13.8 11.6 8.5 10 204 21.1 75.7 439s govExp taxes govWage trend capitalLag corpProfLag gnpLag 439s 1 2.4 3.4 2.2 -11 180 NA NA 439s 2 3.9 7.7 2.7 -10 183 12.7 44.9 439s 3 3.2 3.9 2.9 -9 183 12.4 45.6 439s 4 2.8 4.7 2.9 -8 184 16.9 50.1 439s 5 3.5 3.8 3.1 -7 190 18.4 57.2 439s 6 3.3 5.5 3.2 -6 193 19.4 57.1 439s 7 3.3 7.0 3.3 -5 198 20.1 NA 439s 8 4.0 6.7 3.6 -4 203 19.6 64.0 439s 9 4.2 4.2 3.7 -3 208 19.8 64.4 439s 10 4.1 4.0 4.0 -2 211 21.1 64.5 439s 11 5.2 7.7 4.2 -1 216 21.7 67.0 439s 12 5.9 7.5 4.8 0 217 15.6 61.2 439s 13 4.9 8.3 5.3 1 213 11.4 53.4 439s 14 3.7 5.4 5.6 2 207 7.0 44.3 439s 15 4.0 6.8 6.0 3 202 11.2 45.1 439s 16 4.4 7.2 6.1 4 199 12.3 49.7 439s 17 2.9 8.3 7.4 5 198 14.0 54.4 439s 18 4.3 6.7 6.7 6 200 17.6 62.7 439s 19 5.3 7.4 7.7 7 202 17.3 65.0 439s 20 6.6 8.9 7.8 8 200 15.3 60.9 439s 21 7.4 9.6 8.0 9 201 19.0 69.5 439s 22 13.8 11.6 8.5 10 204 21.1 75.7 439s govExp taxes govWage trend capitalLag corpProfLag gnpLag 439s 1 2.4 3.4 2.2 -11 180 NA NA 439s 2 3.9 7.7 2.7 -10 183 12.7 44.9 439s 3 3.2 3.9 2.9 -9 183 12.4 45.6 439s 4 2.8 4.7 2.9 -8 184 16.9 50.1 439s 5 3.5 3.8 3.1 -7 190 18.4 57.2 439s 6 3.3 5.5 3.2 -6 193 19.4 57.1 439s 7 3.3 7.0 3.3 -5 198 20.1 NA 439s 8 4.0 6.7 3.6 -4 203 19.6 64.0 439s 9 4.2 4.2 3.7 -3 208 19.8 64.4 439s 10 4.1 4.0 4.0 -2 211 21.1 64.5 439s 11 5.2 7.7 4.2 -1 216 21.7 67.0 439s 12 5.9 7.5 4.8 0 217 15.6 61.2 439s 13 4.9 8.3 5.3 1 213 11.4 53.4 439s 14 3.7 5.4 5.6 2 207 7.0 44.3 439s 15 4.0 6.8 6.0 3 202 11.2 45.1 439s 16 4.4 7.2 6.1 4 199 12.3 49.7 439s 17 2.9 8.3 7.4 5 198 14.0 54.4 439s 18 4.3 6.7 6.7 6 200 17.6 62.7 439s 19 5.3 7.4 7.7 7 202 17.3 65.0 439s 20 6.6 8.9 7.8 8 200 15.3 60.9 439s 21 7.4 9.6 8.0 9 201 19.0 69.5 439s 22 13.8 11.6 8.5 10 204 21.1 75.7 439s > model.matrix 439s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0339 >" 439s [2] "Attributes: < Component “dimnames”: Component 1: 52 string mismatches >" 439s [3] "Numeric: lengths (708, 684) differ" 439s > matrix of instrumental variables 439s Consumption_(Intercept) Consumption_govExp Consumption_taxes 439s Consumption_2 1 3.9 7.7 439s Consumption_3 1 3.2 3.9 439s Consumption_4 1 2.8 4.7 439s Consumption_5 1 3.5 3.8 439s Consumption_6 1 3.3 5.5 439s Consumption_8 1 4.0 6.7 439s Consumption_9 1 4.2 4.2 439s Consumption_11 1 5.2 7.7 439s Consumption_12 1 5.9 7.5 439s Consumption_14 1 3.7 5.4 439s Consumption_15 1 4.0 6.8 439s Consumption_16 1 4.4 7.2 439s Consumption_17 1 2.9 8.3 439s Consumption_18 1 4.3 6.7 439s Consumption_19 1 5.3 7.4 439s Consumption_20 1 6.6 8.9 439s Consumption_21 1 7.4 9.6 439s Consumption_22 1 13.8 11.6 439s Investment_2 0 0.0 0.0 439s Investment_3 0 0.0 0.0 439s Investment_4 0 0.0 0.0 439s Investment_5 0 0.0 0.0 439s Investment_6 0 0.0 0.0 439s Investment_8 0 0.0 0.0 439s Investment_9 0 0.0 0.0 439s Investment_10 0 0.0 0.0 439s Investment_11 0 0.0 0.0 439s Investment_12 0 0.0 0.0 439s Investment_14 0 0.0 0.0 439s Investment_15 0 0.0 0.0 439s Investment_16 0 0.0 0.0 439s Investment_17 0 0.0 0.0 439s Investment_18 0 0.0 0.0 439s Investment_19 0 0.0 0.0 439s Investment_20 0 0.0 0.0 439s Investment_21 0 0.0 0.0 439s Investment_22 0 0.0 0.0 439s PrivateWages_2 0 0.0 0.0 439s PrivateWages_3 0 0.0 0.0 439s PrivateWages_4 0 0.0 0.0 439s PrivateWages_5 0 0.0 0.0 439s PrivateWages_6 0 0.0 0.0 439s PrivateWages_8 0 0.0 0.0 439s PrivateWages_9 0 0.0 0.0 439s PrivateWages_10 0 0.0 0.0 439s PrivateWages_11 0 0.0 0.0 439s PrivateWages_12 0 0.0 0.0 439s PrivateWages_13 0 0.0 0.0 439s PrivateWages_14 0 0.0 0.0 439s PrivateWages_15 0 0.0 0.0 439s PrivateWages_16 0 0.0 0.0 439s PrivateWages_17 0 0.0 0.0 439s PrivateWages_18 0 0.0 0.0 439s PrivateWages_19 0 0.0 0.0 439s PrivateWages_20 0 0.0 0.0 439s PrivateWages_21 0 0.0 0.0 439s PrivateWages_22 0 0.0 0.0 439s Consumption_govWage Consumption_trend Consumption_capitalLag 439s Consumption_2 2.7 -10 183 439s Consumption_3 2.9 -9 183 439s Consumption_4 2.9 -8 184 439s Consumption_5 3.1 -7 190 439s Consumption_6 3.2 -6 193 439s Consumption_8 3.6 -4 203 439s Consumption_9 3.7 -3 208 439s Consumption_11 4.2 -1 216 439s Consumption_12 4.8 0 217 439s Consumption_14 5.6 2 207 439s Consumption_15 6.0 3 202 439s Consumption_16 6.1 4 199 439s Consumption_17 7.4 5 198 439s Consumption_18 6.7 6 200 439s Consumption_19 7.7 7 202 439s Consumption_20 7.8 8 200 439s Consumption_21 8.0 9 201 439s Consumption_22 8.5 10 204 439s Investment_2 0.0 0 0 439s Investment_3 0.0 0 0 439s Investment_4 0.0 0 0 439s Investment_5 0.0 0 0 439s Investment_6 0.0 0 0 439s Investment_8 0.0 0 0 439s Investment_9 0.0 0 0 439s Investment_10 0.0 0 0 439s Investment_11 0.0 0 0 439s Investment_12 0.0 0 0 439s Investment_14 0.0 0 0 439s Investment_15 0.0 0 0 439s Investment_16 0.0 0 0 439s Investment_17 0.0 0 0 439s Investment_18 0.0 0 0 439s Investment_19 0.0 0 0 439s Investment_20 0.0 0 0 439s Investment_21 0.0 0 0 439s Investment_22 0.0 0 0 439s PrivateWages_2 0.0 0 0 439s PrivateWages_3 0.0 0 0 439s PrivateWages_4 0.0 0 0 439s PrivateWages_5 0.0 0 0 439s PrivateWages_6 0.0 0 0 439s PrivateWages_8 0.0 0 0 439s PrivateWages_9 0.0 0 0 439s PrivateWages_10 0.0 0 0 439s PrivateWages_11 0.0 0 0 439s PrivateWages_12 0.0 0 0 439s PrivateWages_13 0.0 0 0 439s PrivateWages_14 0.0 0 0 439s PrivateWages_15 0.0 0 0 439s PrivateWages_16 0.0 0 0 439s PrivateWages_17 0.0 0 0 439s PrivateWages_18 0.0 0 0 439s PrivateWages_19 0.0 0 0 439s PrivateWages_20 0.0 0 0 439s PrivateWages_21 0.0 0 0 439s PrivateWages_22 0.0 0 0 439s Consumption_corpProfLag Consumption_gnpLag 439s Consumption_2 12.7 44.9 439s Consumption_3 12.4 45.6 439s Consumption_4 16.9 50.1 439s Consumption_5 18.4 57.2 439s Consumption_6 19.4 57.1 439s Consumption_8 19.6 64.0 439s Consumption_9 19.8 64.4 439s Consumption_11 21.7 67.0 439s Consumption_12 15.6 61.2 439s Consumption_14 7.0 44.3 439s Consumption_15 11.2 45.1 439s Consumption_16 12.3 49.7 439s Consumption_17 14.0 54.4 439s Consumption_18 17.6 62.7 439s Consumption_19 17.3 65.0 439s Consumption_20 15.3 60.9 439s Consumption_21 19.0 69.5 439s Consumption_22 21.1 75.7 439s Investment_2 0.0 0.0 439s Investment_3 0.0 0.0 439s Investment_4 0.0 0.0 439s Investment_5 0.0 0.0 439s Investment_6 0.0 0.0 439s Investment_8 0.0 0.0 439s Investment_9 0.0 0.0 439s Investment_10 0.0 0.0 439s Investment_11 0.0 0.0 439s Investment_12 0.0 0.0 439s Investment_14 0.0 0.0 439s Investment_15 0.0 0.0 439s Investment_16 0.0 0.0 439s Investment_17 0.0 0.0 439s Investment_18 0.0 0.0 439s Investment_19 0.0 0.0 439s Investment_20 0.0 0.0 439s Investment_21 0.0 0.0 439s Investment_22 0.0 0.0 439s PrivateWages_2 0.0 0.0 439s PrivateWages_3 0.0 0.0 439s PrivateWages_4 0.0 0.0 439s PrivateWages_5 0.0 0.0 439s PrivateWages_6 0.0 0.0 439s PrivateWages_8 0.0 0.0 439s PrivateWages_9 0.0 0.0 439s PrivateWages_10 0.0 0.0 439s PrivateWages_11 0.0 0.0 439s PrivateWages_12 0.0 0.0 439s PrivateWages_13 0.0 0.0 439s PrivateWages_14 0.0 0.0 439s PrivateWages_15 0.0 0.0 439s PrivateWages_16 0.0 0.0 439s PrivateWages_17 0.0 0.0 439s PrivateWages_18 0.0 0.0 439s PrivateWages_19 0.0 0.0 439s PrivateWages_20 0.0 0.0 439s PrivateWages_21 0.0 0.0 439s PrivateWages_22 0.0 0.0 439s Investment_(Intercept) Investment_govExp Investment_taxes 439s Consumption_2 0 0.0 0.0 439s Consumption_3 0 0.0 0.0 439s Consumption_4 0 0.0 0.0 439s Consumption_5 0 0.0 0.0 439s Consumption_6 0 0.0 0.0 439s Consumption_8 0 0.0 0.0 439s Consumption_9 0 0.0 0.0 439s Consumption_11 0 0.0 0.0 439s Consumption_12 0 0.0 0.0 439s Consumption_14 0 0.0 0.0 439s Consumption_15 0 0.0 0.0 439s Consumption_16 0 0.0 0.0 439s Consumption_17 0 0.0 0.0 439s Consumption_18 0 0.0 0.0 439s Consumption_19 0 0.0 0.0 439s Consumption_20 0 0.0 0.0 439s Consumption_21 0 0.0 0.0 439s Consumption_22 0 0.0 0.0 439s Investment_2 1 3.9 7.7 439s Investment_3 1 3.2 3.9 439s Investment_4 1 2.8 4.7 439s Investment_5 1 3.5 3.8 439s Investment_6 1 3.3 5.5 439s Investment_8 1 4.0 6.7 439s Investment_9 1 4.2 4.2 439s Investment_10 1 4.1 4.0 439s Investment_11 1 5.2 7.7 439s Investment_12 1 5.9 7.5 439s Investment_14 1 3.7 5.4 439s Investment_15 1 4.0 6.8 439s Investment_16 1 4.4 7.2 439s Investment_17 1 2.9 8.3 439s Investment_18 1 4.3 6.7 439s Investment_19 1 5.3 7.4 439s Investment_20 1 6.6 8.9 439s Investment_21 1 7.4 9.6 439s Investment_22 1 13.8 11.6 439s PrivateWages_2 0 0.0 0.0 439s PrivateWages_3 0 0.0 0.0 439s PrivateWages_4 0 0.0 0.0 439s PrivateWages_5 0 0.0 0.0 439s PrivateWages_6 0 0.0 0.0 439s PrivateWages_8 0 0.0 0.0 439s PrivateWages_9 0 0.0 0.0 439s PrivateWages_10 0 0.0 0.0 439s PrivateWages_11 0 0.0 0.0 439s PrivateWages_12 0 0.0 0.0 439s PrivateWages_13 0 0.0 0.0 439s PrivateWages_14 0 0.0 0.0 439s PrivateWages_15 0 0.0 0.0 439s PrivateWages_16 0 0.0 0.0 439s PrivateWages_17 0 0.0 0.0 439s PrivateWages_18 0 0.0 0.0 439s PrivateWages_19 0 0.0 0.0 439s PrivateWages_20 0 0.0 0.0 439s PrivateWages_21 0 0.0 0.0 439s PrivateWages_22 0 0.0 0.0 439s Investment_govWage Investment_trend Investment_capitalLag 439s Consumption_2 0.0 0 0 439s Consumption_3 0.0 0 0 439s Consumption_4 0.0 0 0 439s Consumption_5 0.0 0 0 439s Consumption_6 0.0 0 0 439s Consumption_8 0.0 0 0 439s Consumption_9 0.0 0 0 439s Consumption_11 0.0 0 0 439s Consumption_12 0.0 0 0 439s Consumption_14 0.0 0 0 439s Consumption_15 0.0 0 0 439s Consumption_16 0.0 0 0 439s Consumption_17 0.0 0 0 439s Consumption_18 0.0 0 0 439s Consumption_19 0.0 0 0 439s Consumption_20 0.0 0 0 439s Consumption_21 0.0 0 0 439s Consumption_22 0.0 0 0 439s Investment_2 2.7 -10 183 439s Investment_3 2.9 -9 183 439s Investment_4 2.9 -8 184 439s Investment_5 3.1 -7 190 439s Investment_6 3.2 -6 193 439s Investment_8 3.6 -4 203 439s Investment_9 3.7 -3 208 439s Investment_10 4.0 -2 211 439s Investment_11 4.2 -1 216 439s Investment_12 4.8 0 217 439s Investment_14 5.6 2 207 439s Investment_15 6.0 3 202 439s Investment_16 6.1 4 199 439s Investment_17 7.4 5 198 439s Investment_18 6.7 6 200 439s Investment_19 7.7 7 202 439s Investment_20 7.8 8 200 439s Investment_21 8.0 9 201 439s Investment_22 8.5 10 204 439s PrivateWages_2 0.0 0 0 439s PrivateWages_3 0.0 0 0 439s PrivateWages_4 0.0 0 0 439s PrivateWages_5 0.0 0 0 439s PrivateWages_6 0.0 0 0 439s PrivateWages_8 0.0 0 0 439s PrivateWages_9 0.0 0 0 439s PrivateWages_10 0.0 0 0 439s PrivateWages_11 0.0 0 0 439s PrivateWages_12 0.0 0 0 439s PrivateWages_13 0.0 0 0 439s PrivateWages_14 0.0 0 0 439s PrivateWages_15 0.0 0 0 439s PrivateWages_16 0.0 0 0 439s PrivateWages_17 0.0 0 0 439s PrivateWages_18 0.0 0 0 439s PrivateWages_19 0.0 0 0 439s PrivateWages_20 0.0 0 0 439s PrivateWages_21 0.0 0 0 439s PrivateWages_22 0.0 0 0 439s Investment_corpProfLag Investment_gnpLag 439s Consumption_2 0.0 0.0 439s Consumption_3 0.0 0.0 439s Consumption_4 0.0 0.0 439s Consumption_5 0.0 0.0 439s Consumption_6 0.0 0.0 439s Consumption_8 0.0 0.0 439s Consumption_9 0.0 0.0 439s Consumption_11 0.0 0.0 439s Consumption_12 0.0 0.0 439s Consumption_14 0.0 0.0 439s Consumption_15 0.0 0.0 439s Consumption_16 0.0 0.0 439s Consumption_17 0.0 0.0 439s Consumption_18 0.0 0.0 439s Consumption_19 0.0 0.0 439s Consumption_20 0.0 0.0 439s Consumption_21 0.0 0.0 439s Consumption_22 0.0 0.0 439s Investment_2 12.7 44.9 439s Investment_3 12.4 45.6 439s Investment_4 16.9 50.1 439s Investment_5 18.4 57.2 439s Investment_6 19.4 57.1 439s Investment_8 19.6 64.0 439s Investment_9 19.8 64.4 439s Investment_10 21.1 64.5 439s Investment_11 21.7 67.0 439s Investment_12 15.6 61.2 439s Investment_14 7.0 44.3 439s Investment_15 11.2 45.1 439s Investment_16 12.3 49.7 439s Investment_17 14.0 54.4 439s Investment_18 17.6 62.7 439s Investment_19 17.3 65.0 439s Investment_20 15.3 60.9 439s Investment_21 19.0 69.5 439s Investment_22 21.1 75.7 439s PrivateWages_2 0.0 0.0 439s PrivateWages_3 0.0 0.0 439s PrivateWages_4 0.0 0.0 439s PrivateWages_5 0.0 0.0 439s PrivateWages_6 0.0 0.0 439s PrivateWages_8 0.0 0.0 439s PrivateWages_9 0.0 0.0 439s PrivateWages_10 0.0 0.0 439s PrivateWages_11 0.0 0.0 439s PrivateWages_12 0.0 0.0 439s PrivateWages_13 0.0 0.0 439s PrivateWages_14 0.0 0.0 439s PrivateWages_15 0.0 0.0 439s PrivateWages_16 0.0 0.0 439s PrivateWages_17 0.0 0.0 439s PrivateWages_18 0.0 0.0 439s PrivateWages_19 0.0 0.0 439s PrivateWages_20 0.0 0.0 439s PrivateWages_21 0.0 0.0 439s PrivateWages_22 0.0 0.0 439s PrivateWages_(Intercept) PrivateWages_govExp PrivateWages_taxes 439s Consumption_2 0 0.0 0.0 439s Consumption_3 0 0.0 0.0 439s Consumption_4 0 0.0 0.0 439s Consumption_5 0 0.0 0.0 439s Consumption_6 0 0.0 0.0 439s Consumption_8 0 0.0 0.0 439s Consumption_9 0 0.0 0.0 439s Consumption_11 0 0.0 0.0 439s Consumption_12 0 0.0 0.0 439s Consumption_14 0 0.0 0.0 439s Consumption_15 0 0.0 0.0 439s Consumption_16 0 0.0 0.0 439s Consumption_17 0 0.0 0.0 439s Consumption_18 0 0.0 0.0 439s Consumption_19 0 0.0 0.0 439s Consumption_20 0 0.0 0.0 439s Consumption_21 0 0.0 0.0 439s Consumption_22 0 0.0 0.0 439s Investment_2 0 0.0 0.0 439s Investment_3 0 0.0 0.0 439s Investment_4 0 0.0 0.0 439s Investment_5 0 0.0 0.0 439s Investment_6 0 0.0 0.0 439s Investment_8 0 0.0 0.0 439s Investment_9 0 0.0 0.0 439s Investment_10 0 0.0 0.0 439s Investment_11 0 0.0 0.0 439s Investment_12 0 0.0 0.0 439s Investment_14 0 0.0 0.0 439s Investment_15 0 0.0 0.0 439s Investment_16 0 0.0 0.0 439s Investment_17 0 0.0 0.0 439s Investment_18 0 0.0 0.0 439s Investment_19 0 0.0 0.0 439s Investment_20 0 0.0 0.0 439s Investment_21 0 0.0 0.0 439s Investment_22 0 0.0 0.0 439s PrivateWages_2 1 3.9 7.7 439s PrivateWages_3 1 3.2 3.9 439s PrivateWages_4 1 2.8 4.7 439s PrivateWages_5 1 3.5 3.8 439s PrivateWages_6 1 3.3 5.5 439s PrivateWages_8 1 4.0 6.7 439s PrivateWages_9 1 4.2 4.2 439s PrivateWages_10 1 4.1 4.0 439s PrivateWages_11 1 5.2 7.7 439s PrivateWages_12 1 5.9 7.5 439s PrivateWages_13 1 4.9 8.3 439s PrivateWages_14 1 3.7 5.4 439s PrivateWages_15 1 4.0 6.8 439s PrivateWages_16 1 4.4 7.2 439s PrivateWages_17 1 2.9 8.3 439s PrivateWages_18 1 4.3 6.7 439s PrivateWages_19 1 5.3 7.4 439s PrivateWages_20 1 6.6 8.9 439s PrivateWages_21 1 7.4 9.6 439s PrivateWages_22 1 13.8 11.6 439s PrivateWages_govWage PrivateWages_trend PrivateWages_capitalLag 439s Consumption_2 0.0 0 0 439s Consumption_3 0.0 0 0 439s Consumption_4 0.0 0 0 439s Consumption_5 0.0 0 0 439s Consumption_6 0.0 0 0 439s Consumption_8 0.0 0 0 439s Consumption_9 0.0 0 0 439s Consumption_11 0.0 0 0 439s Consumption_12 0.0 0 0 439s Consumption_14 0.0 0 0 439s Consumption_15 0.0 0 0 439s Consumption_16 0.0 0 0 439s Consumption_17 0.0 0 0 439s Consumption_18 0.0 0 0 439s Consumption_19 0.0 0 0 439s Consumption_20 0.0 0 0 439s Consumption_21 0.0 0 0 439s Consumption_22 0.0 0 0 439s Investment_2 0.0 0 0 439s Investment_3 0.0 0 0 439s Investment_4 0.0 0 0 439s Investment_5 0.0 0 0 439s Investment_6 0.0 0 0 439s Investment_8 0.0 0 0 439s Investment_9 0.0 0 0 439s Investment_10 0.0 0 0 439s Investment_11 0.0 0 0 439s Investment_12 0.0 0 0 439s Investment_14 0.0 0 0 439s Investment_15 0.0 0 0 439s Investment_16 0.0 0 0 439s Investment_17 0.0 0 0 439s Investment_18 0.0 0 0 439s Investment_19 0.0 0 0 439s Investment_20 0.0 0 0 439s Investment_21 0.0 0 0 439s Investment_22 0.0 0 0 439s PrivateWages_2 2.7 -10 183 439s PrivateWages_3 2.9 -9 183 439s PrivateWages_4 2.9 -8 184 439s PrivateWages_5 3.1 -7 190 439s PrivateWages_6 3.2 -6 193 439s PrivateWages_8 3.6 -4 203 439s PrivateWages_9 3.7 -3 208 439s PrivateWages_10 4.0 -2 211 439s PrivateWages_11 4.2 -1 216 439s PrivateWages_12 4.8 0 217 439s PrivateWages_13 5.3 1 213 439s PrivateWages_14 5.6 2 207 439s PrivateWages_15 6.0 3 202 439s PrivateWages_16 6.1 4 199 439s PrivateWages_17 7.4 5 198 439s PrivateWages_18 6.7 6 200 439s PrivateWages_19 7.7 7 202 439s PrivateWages_20 7.8 8 200 439s PrivateWages_21 8.0 9 201 439s PrivateWages_22 8.5 10 204 439s PrivateWages_corpProfLag PrivateWages_gnpLag 439s Consumption_2 0.0 0.0 439s Consumption_3 0.0 0.0 439s Consumption_4 0.0 0.0 439s Consumption_5 0.0 0.0 439s Consumption_6 0.0 0.0 439s Consumption_8 0.0 0.0 439s Consumption_9 0.0 0.0 439s Consumption_11 0.0 0.0 439s Consumption_12 0.0 0.0 439s Consumption_14 0.0 0.0 439s Consumption_15 0.0 0.0 439s Consumption_16 0.0 0.0 439s Consumption_17 0.0 0.0 439s Consumption_18 0.0 0.0 439s Consumption_19 0.0 0.0 439s Consumption_20 0.0 0.0 439s Consumption_21 0.0 0.0 439s Consumption_22 0.0 0.0 439s Investment_2 0.0 0.0 439s Investment_3 0.0 0.0 439s Investment_4 0.0 0.0 439s Investment_5 0.0 0.0 439s Investment_6 0.0 0.0 439s Investment_8 0.0 0.0 439s Investment_9 0.0 0.0 439s Investment_10 0.0 0.0 439s Investment_11 0.0 0.0 439s Investment_12 0.0 0.0 439s Investment_14 0.0 0.0 439s Investment_15 0.0 0.0 439s Investment_16 0.0 0.0 439s Investment_17 0.0 0.0 439s Investment_18 0.0 0.0 439s Investment_19 0.0 0.0 439s Investment_20 0.0 0.0 439s Investment_21 0.0 0.0 439s Investment_22 0.0 0.0 439s PrivateWages_2 12.7 44.9 439s PrivateWages_3 12.4 45.6 439s PrivateWages_4 16.9 50.1 439s PrivateWages_5 18.4 57.2 439s PrivateWages_6 19.4 57.1 439s PrivateWages_8 19.6 64.0 439s PrivateWages_9 19.8 64.4 439s PrivateWages_10 21.1 64.5 439s PrivateWages_11 21.7 67.0 439s PrivateWages_12 15.6 61.2 439s PrivateWages_13 11.4 53.4 439s PrivateWages_14 7.0 44.3 439s PrivateWages_15 11.2 45.1 439s PrivateWages_16 12.3 49.7 439s PrivateWages_17 14.0 54.4 439s PrivateWages_18 17.6 62.7 439s PrivateWages_19 17.3 65.0 439s PrivateWages_20 15.3 60.9 439s PrivateWages_21 19.0 69.5 439s PrivateWages_22 21.1 75.7 439s > matrix of fitted regressors 439s Consumption_(Intercept) Consumption_corpProf 439s Consumption_2 1 14.0 439s Consumption_3 1 16.7 439s Consumption_4 1 18.5 439s Consumption_5 1 20.3 439s Consumption_6 1 19.0 439s Consumption_8 1 17.6 439s Consumption_9 1 18.9 439s Consumption_11 1 16.7 439s Consumption_12 1 13.4 439s Consumption_14 1 10.0 439s Consumption_15 1 12.5 439s Consumption_16 1 14.5 439s Consumption_17 1 14.9 439s Consumption_18 1 19.4 439s Consumption_19 1 19.1 439s Consumption_20 1 17.7 439s Consumption_21 1 20.4 439s Consumption_22 1 22.7 439s Investment_2 0 0.0 439s Investment_3 0 0.0 439s Investment_4 0 0.0 439s Investment_5 0 0.0 439s Investment_6 0 0.0 439s Investment_8 0 0.0 439s Investment_9 0 0.0 439s Investment_10 0 0.0 439s Investment_11 0 0.0 439s Investment_12 0 0.0 439s Investment_14 0 0.0 439s Investment_15 0 0.0 439s Investment_16 0 0.0 439s Investment_17 0 0.0 439s Investment_18 0 0.0 439s Investment_19 0 0.0 439s Investment_20 0 0.0 439s Investment_21 0 0.0 439s Investment_22 0 0.0 439s PrivateWages_2 0 0.0 439s PrivateWages_3 0 0.0 439s PrivateWages_4 0 0.0 439s PrivateWages_5 0 0.0 439s PrivateWages_6 0 0.0 439s PrivateWages_8 0 0.0 439s PrivateWages_9 0 0.0 439s PrivateWages_10 0 0.0 439s PrivateWages_11 0 0.0 439s PrivateWages_12 0 0.0 439s PrivateWages_13 0 0.0 439s PrivateWages_14 0 0.0 439s PrivateWages_15 0 0.0 439s PrivateWages_16 0 0.0 439s PrivateWages_17 0 0.0 439s PrivateWages_18 0 0.0 439s PrivateWages_19 0 0.0 439s PrivateWages_20 0 0.0 439s PrivateWages_21 0 0.0 439s PrivateWages_22 0 0.0 439s Consumption_corpProfLag Consumption_wages 439s Consumption_2 12.7 29.8 439s Consumption_3 12.4 31.8 439s Consumption_4 16.9 35.3 439s Consumption_5 18.4 38.6 439s Consumption_6 19.4 38.5 439s Consumption_8 19.6 40.0 439s Consumption_9 19.8 41.8 439s Consumption_11 21.7 43.1 439s Consumption_12 15.6 39.7 439s Consumption_14 7.0 33.3 439s Consumption_15 11.2 37.3 439s Consumption_16 12.3 40.1 439s Consumption_17 14.0 41.8 439s Consumption_18 17.6 47.6 439s Consumption_19 17.3 49.2 439s Consumption_20 15.3 48.6 439s Consumption_21 19.0 53.4 439s Consumption_22 21.1 60.8 439s Investment_2 0.0 0.0 439s Investment_3 0.0 0.0 439s Investment_4 0.0 0.0 439s Investment_5 0.0 0.0 439s Investment_6 0.0 0.0 439s Investment_8 0.0 0.0 439s Investment_9 0.0 0.0 439s Investment_10 0.0 0.0 439s Investment_11 0.0 0.0 439s Investment_12 0.0 0.0 439s Investment_14 0.0 0.0 439s Investment_15 0.0 0.0 439s Investment_16 0.0 0.0 439s Investment_17 0.0 0.0 439s Investment_18 0.0 0.0 439s Investment_19 0.0 0.0 439s Investment_20 0.0 0.0 439s Investment_21 0.0 0.0 439s Investment_22 0.0 0.0 439s PrivateWages_2 0.0 0.0 439s PrivateWages_3 0.0 0.0 439s PrivateWages_4 0.0 0.0 439s PrivateWages_5 0.0 0.0 439s PrivateWages_6 0.0 0.0 439s PrivateWages_8 0.0 0.0 439s PrivateWages_9 0.0 0.0 439s PrivateWages_10 0.0 0.0 439s PrivateWages_11 0.0 0.0 439s PrivateWages_12 0.0 0.0 439s PrivateWages_13 0.0 0.0 439s PrivateWages_14 0.0 0.0 439s PrivateWages_15 0.0 0.0 439s PrivateWages_16 0.0 0.0 439s PrivateWages_17 0.0 0.0 439s PrivateWages_18 0.0 0.0 439s PrivateWages_19 0.0 0.0 439s PrivateWages_20 0.0 0.0 439s PrivateWages_21 0.0 0.0 439s PrivateWages_22 0.0 0.0 439s Investment_(Intercept) Investment_corpProf 439s Consumption_2 0 0.00 439s Consumption_3 0 0.00 439s Consumption_4 0 0.00 439s Consumption_5 0 0.00 439s Consumption_6 0 0.00 439s Consumption_8 0 0.00 439s Consumption_9 0 0.00 439s Consumption_11 0 0.00 439s Consumption_12 0 0.00 439s Consumption_14 0 0.00 439s Consumption_15 0 0.00 439s Consumption_16 0 0.00 439s Consumption_17 0 0.00 439s Consumption_18 0 0.00 439s Consumption_19 0 0.00 439s Consumption_20 0 0.00 439s Consumption_21 0 0.00 439s Consumption_22 0 0.00 439s Investment_2 1 13.41 439s Investment_3 1 16.69 439s Investment_4 1 18.79 439s Investment_5 1 20.65 439s Investment_6 1 19.26 439s Investment_8 1 17.53 439s Investment_9 1 19.53 439s Investment_10 1 20.27 439s Investment_11 1 17.19 439s Investment_12 1 13.52 439s Investment_14 1 9.99 439s Investment_15 1 12.86 439s Investment_16 1 14.33 439s Investment_17 1 14.97 439s Investment_18 1 19.37 439s Investment_19 1 19.36 439s Investment_20 1 17.47 439s Investment_21 1 20.12 439s Investment_22 1 22.78 439s PrivateWages_2 0 0.00 439s PrivateWages_3 0 0.00 439s PrivateWages_4 0 0.00 439s PrivateWages_5 0 0.00 439s PrivateWages_6 0 0.00 439s PrivateWages_8 0 0.00 439s PrivateWages_9 0 0.00 439s PrivateWages_10 0 0.00 439s PrivateWages_11 0 0.00 439s PrivateWages_12 0 0.00 439s PrivateWages_13 0 0.00 439s PrivateWages_14 0 0.00 439s PrivateWages_15 0 0.00 439s PrivateWages_16 0 0.00 439s PrivateWages_17 0 0.00 439s PrivateWages_18 0 0.00 439s PrivateWages_19 0 0.00 439s PrivateWages_20 0 0.00 439s PrivateWages_21 0 0.00 439s PrivateWages_22 0 0.00 439s Investment_corpProfLag Investment_capitalLag 439s Consumption_2 0.0 0 439s Consumption_3 0.0 0 439s Consumption_4 0.0 0 439s Consumption_5 0.0 0 439s Consumption_6 0.0 0 439s Consumption_8 0.0 0 439s Consumption_9 0.0 0 439s Consumption_11 0.0 0 439s Consumption_12 0.0 0 439s Consumption_14 0.0 0 439s Consumption_15 0.0 0 439s Consumption_16 0.0 0 439s Consumption_17 0.0 0 439s Consumption_18 0.0 0 439s Consumption_19 0.0 0 439s Consumption_20 0.0 0 439s Consumption_21 0.0 0 439s Consumption_22 0.0 0 439s Investment_2 12.7 183 439s Investment_3 12.4 183 439s Investment_4 16.9 184 439s Investment_5 18.4 190 439s Investment_6 19.4 193 439s Investment_8 19.6 203 439s Investment_9 19.8 208 439s Investment_10 21.1 211 439s Investment_11 21.7 216 439s Investment_12 15.6 217 439s Investment_14 7.0 207 439s Investment_15 11.2 202 439s Investment_16 12.3 199 439s Investment_17 14.0 198 439s Investment_18 17.6 200 439s Investment_19 17.3 202 439s Investment_20 15.3 200 439s Investment_21 19.0 201 439s Investment_22 21.1 204 439s PrivateWages_2 0.0 0 439s PrivateWages_3 0.0 0 439s PrivateWages_4 0.0 0 439s PrivateWages_5 0.0 0 439s PrivateWages_6 0.0 0 439s PrivateWages_8 0.0 0 439s PrivateWages_9 0.0 0 439s PrivateWages_10 0.0 0 439s PrivateWages_11 0.0 0 439s PrivateWages_12 0.0 0 439s PrivateWages_13 0.0 0 439s PrivateWages_14 0.0 0 439s PrivateWages_15 0.0 0 439s PrivateWages_16 0.0 0 439s PrivateWages_17 0.0 0 439s PrivateWages_18 0.0 0 439s PrivateWages_19 0.0 0 439s PrivateWages_20 0.0 0 439s PrivateWages_21 0.0 0 439s PrivateWages_22 0.0 0 439s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 439s Consumption_2 0 0.0 0.0 439s Consumption_3 0 0.0 0.0 439s Consumption_4 0 0.0 0.0 439s Consumption_5 0 0.0 0.0 439s Consumption_6 0 0.0 0.0 439s Consumption_8 0 0.0 0.0 439s Consumption_9 0 0.0 0.0 439s Consumption_11 0 0.0 0.0 439s Consumption_12 0 0.0 0.0 439s Consumption_14 0 0.0 0.0 439s Consumption_15 0 0.0 0.0 439s Consumption_16 0 0.0 0.0 439s Consumption_17 0 0.0 0.0 439s Consumption_18 0 0.0 0.0 439s Consumption_19 0 0.0 0.0 439s Consumption_20 0 0.0 0.0 439s Consumption_21 0 0.0 0.0 439s Consumption_22 0 0.0 0.0 439s Investment_2 0 0.0 0.0 439s Investment_3 0 0.0 0.0 439s Investment_4 0 0.0 0.0 439s Investment_5 0 0.0 0.0 439s Investment_6 0 0.0 0.0 439s Investment_8 0 0.0 0.0 439s Investment_9 0 0.0 0.0 439s Investment_10 0 0.0 0.0 439s Investment_11 0 0.0 0.0 439s Investment_12 0 0.0 0.0 439s Investment_14 0 0.0 0.0 439s Investment_15 0 0.0 0.0 439s Investment_16 0 0.0 0.0 439s Investment_17 0 0.0 0.0 439s Investment_18 0 0.0 0.0 439s Investment_19 0 0.0 0.0 439s Investment_20 0 0.0 0.0 439s Investment_21 0 0.0 0.0 439s Investment_22 0 0.0 0.0 439s PrivateWages_2 1 47.1 44.9 439s PrivateWages_3 1 49.6 45.6 439s PrivateWages_4 1 56.5 50.1 439s PrivateWages_5 1 60.7 57.2 439s PrivateWages_6 1 60.6 57.1 439s PrivateWages_8 1 60.0 64.0 439s PrivateWages_9 1 62.3 64.4 439s PrivateWages_10 1 64.6 64.5 439s PrivateWages_11 1 63.7 67.0 439s PrivateWages_12 1 54.8 61.2 439s PrivateWages_13 1 47.0 53.4 439s PrivateWages_14 1 42.1 44.3 439s PrivateWages_15 1 51.2 45.1 439s PrivateWages_16 1 55.3 49.7 439s PrivateWages_17 1 57.4 54.4 439s PrivateWages_18 1 67.2 62.7 439s PrivateWages_19 1 68.5 65.0 439s PrivateWages_20 1 66.8 60.9 439s PrivateWages_21 1 74.9 69.5 439s PrivateWages_22 1 86.9 75.7 439s PrivateWages_trend 439s Consumption_2 0 439s Consumption_3 0 439s Consumption_4 0 439s Consumption_5 0 439s Consumption_6 0 439s Consumption_8 0 439s Consumption_9 0 439s Consumption_11 0 439s Consumption_12 0 439s Consumption_14 0 439s Consumption_15 0 439s Consumption_16 0 439s Consumption_17 0 439s Consumption_18 0 439s Consumption_19 0 439s Consumption_20 0 439s Consumption_21 0 439s Consumption_22 0 439s Investment_2 0 439s Investment_3 0 439s Investment_4 0 439s Investment_5 0 439s Investment_6 0 439s Investment_8 0 439s Investment_9 0 439s Investment_10 0 439s Investment_11 0 439s Investment_12 0 439s Investment_14 0 439s Investment_15 0 439s Investment_16 0 439s Investment_17 0 439s Investment_18 0 439s Investment_19 0 439s Investment_20 0 439s Investment_21 0 439s Investment_22 0 439s PrivateWages_2 -10 439s PrivateWages_3 -9 439s PrivateWages_4 -8 439s PrivateWages_5 -7 439s PrivateWages_6 -6 439s PrivateWages_8 -4 439s PrivateWages_9 -3 439s PrivateWages_10 -2 439s PrivateWages_11 -1 439s PrivateWages_12 0 439s PrivateWages_13 1 439s PrivateWages_14 2 439s PrivateWages_15 3 439s PrivateWages_16 4 439s PrivateWages_17 5 439s PrivateWages_18 6 439s PrivateWages_19 7 439s PrivateWages_20 8 439s PrivateWages_21 9 439s PrivateWages_22 10 439s > nobs 439s [1] 57 439s > linearHypothesis 439s Linear hypothesis test (Theil's F test) 439s 439s Hypothesis: 439s Consumption_corpProf + Investment_capitalLag = 0 439s 439s Model 1: restricted model 439s Model 2: kleinModel 439s 439s Res.Df Df F Pr(>F) 439s 1 46 439s 2 45 1 1.37 0.25 439s Linear hypothesis test (F statistic of a Wald test) 439s 439s Hypothesis: 439s Consumption_corpProf + Investment_capitalLag = 0 439s 439s Model 1: restricted model 439s Model 2: kleinModel 439s 439s Res.Df Df F Pr(>F) 439s 1 46 439s 2 45 1 1.77 0.19 439s Linear hypothesis test (Chi^2 statistic of a Wald test) 439s 439s Hypothesis: 439s Consumption_corpProf + Investment_capitalLag = 0 439s 439s Model 1: restricted model 439s Model 2: kleinModel 439s 439s Res.Df Df Chisq Pr(>Chisq) 439s 1 46 439s 2 45 1 1.77 0.18 439s Linear hypothesis test (Theil's F test) 439s 439s Hypothesis: 439s Consumption_corpProf + Investment_capitalLag = 0 439s Consumption_corpProfLag - PrivateWages_trend = 0 439s 439s Model 1: restricted model 439s Model 2: kleinModel 439s 439s Res.Df Df F Pr(>F) 439s 1 47 439s 2 45 2 0.69 0.51 439s Linear hypothesis test (F statistic of a Wald test) 439s 439s Hypothesis: 439s Consumption_corpProf + Investment_capitalLag = 0 439s Consumption_corpProfLag - PrivateWages_trend = 0 439s 439s Model 1: restricted model 439s Model 2: kleinModel 439s 439s Res.Df Df F Pr(>F) 439s 1 47 439s 2 45 2 0.89 0.42 439s Linear hypothesis test (Chi^2 statistic of a Wald test) 439s 439s Hypothesis: 439s Consumption_corpProf + Investment_capitalLag = 0 439s Consumption_corpProfLag - PrivateWages_trend = 0 439s 439s Model 1: restricted model 439s Model 2: kleinModel 439s 439s Res.Df Df Chisq Pr(>Chisq) 439s 1 47 439s 2 45 2 1.78 0.41 439s > logLik 439s 'log Lik.' -70.6 (df=13) 439s 'log Lik.' -78.7 (df=13) 439s Estimating function 439s Consumption_(Intercept) Consumption_corpProf 439s Consumption_2 -1.891 -26.49 439s Consumption_3 -0.190 -3.16 439s Consumption_4 0.294 5.45 439s Consumption_5 -1.285 -26.05 439s Consumption_6 0.431 8.19 439s Consumption_8 2.670 47.11 439s Consumption_9 2.363 44.77 439s Consumption_11 -1.642 -27.49 439s Consumption_12 -1.735 -23.21 439s Consumption_14 0.834 8.35 439s Consumption_15 -1.061 -13.27 439s Consumption_16 -0.885 -12.82 439s Consumption_17 3.801 56.68 439s Consumption_18 -0.502 -9.76 439s Consumption_19 -3.000 -57.33 439s Consumption_20 2.012 35.52 439s Consumption_21 0.746 15.21 439s Consumption_22 -0.957 -21.70 439s Investment_2 0.000 0.00 439s Investment_3 0.000 0.00 439s Investment_4 0.000 0.00 439s Investment_5 0.000 0.00 439s Investment_6 0.000 0.00 439s Investment_8 0.000 0.00 439s Investment_9 0.000 0.00 439s Investment_10 0.000 0.00 439s Investment_11 0.000 0.00 439s Investment_12 0.000 0.00 439s Investment_14 0.000 0.00 439s Investment_15 0.000 0.00 439s Investment_16 0.000 0.00 439s Investment_17 0.000 0.00 439s Investment_18 0.000 0.00 439s Investment_19 0.000 0.00 439s Investment_20 0.000 0.00 439s Investment_21 0.000 0.00 439s Investment_22 0.000 0.00 439s PrivateWages_2 0.000 0.00 439s PrivateWages_3 0.000 0.00 439s PrivateWages_4 0.000 0.00 439s PrivateWages_5 0.000 0.00 439s PrivateWages_6 0.000 0.00 439s PrivateWages_8 0.000 0.00 439s PrivateWages_9 0.000 0.00 439s PrivateWages_10 0.000 0.00 439s PrivateWages_11 0.000 0.00 439s PrivateWages_12 0.000 0.00 439s PrivateWages_13 0.000 0.00 439s PrivateWages_14 0.000 0.00 439s PrivateWages_15 0.000 0.00 439s PrivateWages_16 0.000 0.00 439s PrivateWages_17 0.000 0.00 439s PrivateWages_18 0.000 0.00 439s PrivateWages_19 0.000 0.00 439s PrivateWages_20 0.000 0.00 439s PrivateWages_21 0.000 0.00 439s PrivateWages_22 0.000 0.00 439s Consumption_corpProfLag Consumption_wages 439s Consumption_2 -24.01 -56.38 439s Consumption_3 -2.35 -6.04 439s Consumption_4 4.96 10.35 439s Consumption_5 -23.65 -49.61 439s Consumption_6 8.35 16.60 439s Consumption_8 52.33 106.81 439s Consumption_9 46.80 98.74 439s Consumption_11 -35.64 -70.78 439s Consumption_12 -27.07 -68.81 439s Consumption_14 5.83 27.78 439s Consumption_15 -11.88 -39.61 439s Consumption_16 -10.89 -35.54 439s Consumption_17 53.21 158.79 439s Consumption_18 -8.84 -23.92 439s Consumption_19 -51.90 -147.70 439s Consumption_20 30.78 97.67 439s Consumption_21 14.17 39.83 439s Consumption_22 -20.20 -58.19 439s Investment_2 0.00 0.00 439s Investment_3 0.00 0.00 439s Investment_4 0.00 0.00 439s Investment_5 0.00 0.00 439s Investment_6 0.00 0.00 439s Investment_8 0.00 0.00 439s Investment_9 0.00 0.00 439s Investment_10 0.00 0.00 440s Investment_11 0.00 0.00 440s Investment_12 0.00 0.00 440s Investment_14 0.00 0.00 440s Investment_15 0.00 0.00 440s Investment_16 0.00 0.00 440s Investment_17 0.00 0.00 440s Investment_18 0.00 0.00 440s Investment_19 0.00 0.00 440s Investment_20 0.00 0.00 440s Investment_21 0.00 0.00 440s Investment_22 0.00 0.00 440s PrivateWages_2 0.00 0.00 440s PrivateWages_3 0.00 0.00 440s PrivateWages_4 0.00 0.00 440s PrivateWages_5 0.00 0.00 440s PrivateWages_6 0.00 0.00 440s PrivateWages_8 0.00 0.00 440s PrivateWages_9 0.00 0.00 440s PrivateWages_10 0.00 0.00 440s PrivateWages_11 0.00 0.00 440s PrivateWages_12 0.00 0.00 440s PrivateWages_13 0.00 0.00 440s PrivateWages_14 0.00 0.00 440s PrivateWages_15 0.00 0.00 440s PrivateWages_16 0.00 0.00 440s PrivateWages_17 0.00 0.00 440s PrivateWages_18 0.00 0.00 440s PrivateWages_19 0.00 0.00 440s PrivateWages_20 0.00 0.00 440s PrivateWages_21 0.00 0.00 440s PrivateWages_22 0.00 0.00 440s Investment_(Intercept) Investment_corpProf 440s Consumption_2 0.000 0.000 440s Consumption_3 0.000 0.000 440s Consumption_4 0.000 0.000 440s Consumption_5 0.000 0.000 440s Consumption_6 0.000 0.000 440s Consumption_8 0.000 0.000 440s Consumption_9 0.000 0.000 440s Consumption_11 0.000 0.000 440s Consumption_12 0.000 0.000 440s Consumption_14 0.000 0.000 440s Consumption_15 0.000 0.000 440s Consumption_16 0.000 0.000 440s Consumption_17 0.000 0.000 440s Consumption_18 0.000 0.000 440s Consumption_19 0.000 0.000 440s Consumption_20 0.000 0.000 440s Consumption_21 0.000 0.000 440s Consumption_22 0.000 0.000 440s Investment_2 -1.389 -18.632 440s Investment_3 0.361 6.028 440s Investment_4 1.031 19.362 440s Investment_5 -1.558 -32.177 440s Investment_6 0.610 11.759 440s Investment_8 1.410 24.716 440s Investment_9 0.404 7.885 440s Investment_10 2.080 42.149 440s Investment_11 -1.162 -19.982 440s Investment_12 -1.352 -18.282 440s Investment_14 1.037 10.359 440s Investment_15 -0.454 -5.832 440s Investment_16 -0.044 -0.631 440s Investment_17 2.093 31.318 440s Investment_18 -0.438 -8.488 440s Investment_19 -3.873 -74.977 440s Investment_20 0.486 8.486 440s Investment_21 0.145 2.925 440s Investment_22 0.615 14.015 440s PrivateWages_2 0.000 0.000 440s PrivateWages_3 0.000 0.000 440s PrivateWages_4 0.000 0.000 440s PrivateWages_5 0.000 0.000 440s PrivateWages_6 0.000 0.000 440s PrivateWages_8 0.000 0.000 440s PrivateWages_9 0.000 0.000 440s PrivateWages_10 0.000 0.000 440s PrivateWages_11 0.000 0.000 440s PrivateWages_12 0.000 0.000 440s PrivateWages_13 0.000 0.000 440s PrivateWages_14 0.000 0.000 440s PrivateWages_15 0.000 0.000 440s PrivateWages_16 0.000 0.000 440s PrivateWages_17 0.000 0.000 440s PrivateWages_18 0.000 0.000 440s PrivateWages_19 0.000 0.000 440s PrivateWages_20 0.000 0.000 440s PrivateWages_21 0.000 0.000 440s PrivateWages_22 0.000 0.000 440s Investment_corpProfLag Investment_capitalLag 440s Consumption_2 0.000 0.00 440s Consumption_3 0.000 0.00 440s Consumption_4 0.000 0.00 440s Consumption_5 0.000 0.00 440s Consumption_6 0.000 0.00 440s Consumption_8 0.000 0.00 440s Consumption_9 0.000 0.00 440s Consumption_11 0.000 0.00 440s Consumption_12 0.000 0.00 440s Consumption_14 0.000 0.00 440s Consumption_15 0.000 0.00 440s Consumption_16 0.000 0.00 440s Consumption_17 0.000 0.00 440s Consumption_18 0.000 0.00 440s Consumption_19 0.000 0.00 440s Consumption_20 0.000 0.00 440s Consumption_21 0.000 0.00 440s Consumption_22 0.000 0.00 440s Investment_2 -17.639 -253.89 440s Investment_3 4.479 65.95 440s Investment_4 17.417 190.14 440s Investment_5 -28.673 -295.61 440s Investment_6 11.843 117.63 440s Investment_8 27.629 286.73 440s Investment_9 7.995 83.82 440s Investment_10 43.878 437.95 440s Investment_11 -25.218 -250.67 440s Investment_12 -21.091 -292.97 440s Investment_14 7.256 214.68 440s Investment_15 -5.080 -91.62 440s Investment_16 -0.541 -8.76 440s Investment_17 29.296 413.70 440s Investment_18 -7.713 -87.56 440s Investment_19 -67.010 -781.66 440s Investment_20 7.430 97.07 440s Investment_21 2.762 29.24 440s Investment_22 12.981 125.81 440s PrivateWages_2 0.000 0.00 440s PrivateWages_3 0.000 0.00 440s PrivateWages_4 0.000 0.00 440s PrivateWages_5 0.000 0.00 440s PrivateWages_6 0.000 0.00 440s PrivateWages_8 0.000 0.00 440s PrivateWages_9 0.000 0.00 440s PrivateWages_10 0.000 0.00 440s PrivateWages_11 0.000 0.00 440s PrivateWages_12 0.000 0.00 440s PrivateWages_13 0.000 0.00 440s PrivateWages_14 0.000 Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 440s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 440s 0.00 440s PrivateWages_15 0.000 0.00 440s PrivateWages_16 0.000 0.00 440s PrivateWages_17 0.000 0.00 440s PrivateWages_18 0.000 0.00 440s PrivateWages_19 0.000 0.00 440s PrivateWages_20 0.000 0.00 440s PrivateWages_21 0.000 0.00 440s PrivateWages_22 0.000 0.00 440s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 440s Consumption_2 0.0000 0.00 0.00 440s Consumption_3 0.0000 0.00 0.00 440s Consumption_4 0.0000 0.00 0.00 440s Consumption_5 0.0000 0.00 0.00 440s Consumption_6 0.0000 0.00 0.00 440s Consumption_8 0.0000 0.00 0.00 440s Consumption_9 0.0000 0.00 0.00 440s Consumption_11 0.0000 0.00 0.00 440s Consumption_12 0.0000 0.00 0.00 440s Consumption_14 0.0000 0.00 0.00 440s Consumption_15 0.0000 0.00 0.00 440s Consumption_16 0.0000 0.00 0.00 440s Consumption_17 0.0000 0.00 0.00 440s Consumption_18 0.0000 0.00 0.00 440s Consumption_19 0.0000 0.00 0.00 440s Consumption_20 0.0000 0.00 0.00 440s Consumption_21 0.0000 0.00 0.00 440s Consumption_22 0.0000 0.00 0.00 440s Investment_2 0.0000 0.00 0.00 440s Investment_3 0.0000 0.00 0.00 440s Investment_4 0.0000 0.00 0.00 440s Investment_5 0.0000 0.00 0.00 440s Investment_6 0.0000 0.00 0.00 440s Investment_8 0.0000 0.00 0.00 440s Investment_9 0.0000 0.00 0.00 440s Investment_10 0.0000 0.00 0.00 440s Investment_11 0.0000 0.00 0.00 440s Investment_12 0.0000 0.00 0.00 440s Investment_14 0.0000 0.00 0.00 440s Investment_15 0.0000 0.00 0.00 440s Investment_16 0.0000 0.00 0.00 440s Investment_17 0.0000 0.00 0.00 440s Investment_18 0.0000 0.00 0.00 440s Investment_19 0.0000 0.00 0.00 440s Investment_20 0.0000 0.00 0.00 440s Investment_21 0.0000 0.00 0.00 440s Investment_22 0.0000 0.00 0.00 440s PrivateWages_2 -1.9924 -93.78 -89.46 440s PrivateWages_3 0.4683 23.22 21.35 440s PrivateWages_4 1.4034 79.35 70.31 440s PrivateWages_5 -1.7870 -108.45 -102.22 440s PrivateWages_6 -0.3627 -21.98 -20.71 440s PrivateWages_8 1.1629 69.77 74.43 440s PrivateWages_9 1.2735 79.30 82.01 440s PrivateWages_10 2.2141 142.96 142.81 440s PrivateWages_11 -1.2912 -82.26 -86.51 440s PrivateWages_12 -0.0350 -1.92 -2.14 440s PrivateWages_13 -1.0438 -49.04 -55.74 440s PrivateWages_14 1.8016 75.90 79.81 440s PrivateWages_15 -0.3714 -19.02 -16.75 440s PrivateWages_16 -0.3904 -21.61 -19.40 440s PrivateWages_17 1.4934 85.71 81.24 440s PrivateWages_18 0.0279 1.88 1.75 440s PrivateWages_19 -3.8229 -261.91 -248.49 440s PrivateWages_20 0.7870 52.61 47.93 440s PrivateWages_21 -0.7415 -55.52 -51.54 440s PrivateWages_22 1.2062 104.79 91.31 440s PrivateWages_trend 440s Consumption_2 0.000 440s Consumption_3 0.000 440s Consumption_4 0.000 440s Consumption_5 0.000 440s Consumption_6 0.000 440s Consumption_8 0.000 440s Consumption_9 0.000 440s Consumption_11 0.000 440s Consumption_12 0.000 440s Consumption_14 0.000 440s Consumption_15 0.000 440s Consumption_16 0.000 440s Consumption_17 0.000 440s Consumption_18 0.000 440s Consumption_19 0.000 440s Consumption_20 0.000 440s Consumption_21 0.000 440s Consumption_22 0.000 440s Investment_2 0.000 440s Investment_3 0.000 440s Investment_4 0.000 440s Investment_5 0.000 440s Investment_6 0.000 440s Investment_8 0.000 440s Investment_9 0.000 440s Investment_10 0.000 440s Investment_11 0.000 440s Investment_12 0.000 440s Investment_14 0.000 440s Investment_15 0.000 440s Investment_16 0.000 440s Investment_17 0.000 440s Investment_18 0.000 440s Investment_19 0.000 440s Investment_20 0.000 440s Investment_21 0.000 440s Investment_22 0.000 440s PrivateWages_2 19.924 440s PrivateWages_3 -4.214 440s PrivateWages_4 -11.227 440s PrivateWages_5 12.509 440s PrivateWages_6 2.176 440s PrivateWages_8 -4.652 440s PrivateWages_9 -3.820 440s PrivateWages_10 -4.428 440s PrivateWages_11 1.291 440s PrivateWages_12 0.000 440s PrivateWages_13 -1.044 440s PrivateWages_14 3.603 440s PrivateWages_15 -1.114 440s PrivateWages_16 -1.562 440s PrivateWages_17 7.467 440s PrivateWages_18 0.168 440s PrivateWages_19 -26.760 440s PrivateWages_20 6.296 440s PrivateWages_21 -6.674 440s PrivateWages_22 12.062 440s [1] TRUE 440s > Bread 440s Consumption_(Intercept) Consumption_corpProf 440s Consumption_(Intercept) 118.21 -4.213 440s Consumption_corpProf -4.21 1.235 440s Consumption_corpProfLag 1.03 -0.689 440s Consumption_wages -1.44 -0.136 440s Investment_(Intercept) 0.00 0.000 440s Investment_corpProf 0.00 0.000 440s Investment_corpProfLag 0.00 0.000 440s Investment_capitalLag 0.00 0.000 440s PrivateWages_(Intercept) 0.00 0.000 440s PrivateWages_gnp 0.00 0.000 440s PrivateWages_gnpLag 0.00 0.000 440s PrivateWages_trend 0.00 0.000 440s Consumption_corpProfLag Consumption_wages 440s Consumption_(Intercept) 1.0298 -1.4384 440s Consumption_corpProf -0.6891 -0.1356 440s Consumption_corpProfLag 0.7104 -0.0191 440s Consumption_wages -0.0191 0.0972 440s Investment_(Intercept) 0.0000 0.0000 440s Investment_corpProf 0.0000 0.0000 440s Investment_corpProfLag 0.0000 0.0000 440s Investment_capitalLag 0.0000 0.0000 440s PrivateWages_(Intercept) 0.0000 0.0000 440s PrivateWages_gnp 0.0000 0.0000 440s PrivateWages_gnpLag 0.0000 0.0000 440s PrivateWages_trend 0.0000 0.0000 440s Investment_(Intercept) Investment_corpProf 440s Consumption_(Intercept) 0.0 0.000 440s Consumption_corpProf 0.0 0.000 440s Consumption_corpProfLag 0.0 0.000 440s Consumption_wages 0.0 0.000 440s Investment_(Intercept) 2314.8 -41.107 440s Investment_corpProf -41.1 1.637 440s Investment_corpProfLag 33.2 -1.272 440s Investment_capitalLag -10.7 0.169 440s PrivateWages_(Intercept) 0.0 0.000 440s PrivateWages_gnp 0.0 0.000 440s PrivateWages_gnpLag 0.0 0.000 440s PrivateWages_trend 0.0 0.000 440s Investment_corpProfLag Investment_capitalLag 440s Consumption_(Intercept) 0.000 0.0000 440s Consumption_corpProf 0.000 0.0000 440s Consumption_corpProfLag 0.000 0.0000 440s Consumption_wages 0.000 0.0000 440s Investment_(Intercept) 33.159 -10.7377 440s Investment_corpProf -1.272 0.1688 440s Investment_corpProfLag 1.204 -0.1550 440s Investment_capitalLag -0.155 0.0519 440s PrivateWages_(Intercept) 0.000 0.0000 440s PrivateWages_gnp 0.000 0.0000 440s PrivateWages_gnpLag 0.000 0.0000 440s PrivateWages_trend 0.000 0.0000 440s PrivateWages_(Intercept) PrivateWages_gnp 440s Consumption_(Intercept) 0.000 0.0000 440s Consumption_corpProf 0.000 0.0000 440s Consumption_corpProfLag 0.000 0.0000 440s Consumption_wages 0.000 0.0000 440s Investment_(Intercept) 0.000 0.0000 440s Investment_corpProf 0.000 0.0000 440s Investment_corpProfLag 0.000 0.0000 440s Investment_capitalLag 0.000 0.0000 440s PrivateWages_(Intercept) 162.179 -0.8825 440s PrivateWages_gnp -0.882 0.1501 440s PrivateWages_gnpLag -1.850 -0.1399 440s PrivateWages_trend 2.056 -0.0403 440s PrivateWages_gnpLag PrivateWages_trend 440s Consumption_(Intercept) 0.0000 0.0000 440s Consumption_corpProf 0.0000 0.0000 440s Consumption_corpProfLag 0.0000 0.0000 440s Consumption_wages 0.0000 0.0000 440s Investment_(Intercept) 0.0000 0.0000 440s Investment_corpProf 0.0000 0.0000 440s Investment_corpProfLag 0.0000 0.0000 440s Investment_capitalLag 0.0000 0.0000 440s PrivateWages_(Intercept) -1.8504 2.0559 440s PrivateWages_gnp -0.1399 -0.0403 440s PrivateWages_gnpLag 0.1768 0.0057 440s PrivateWages_trend 0.0057 0.1094 440s > 440s > # SUR 440s > summary 440s 440s systemfit results 440s method: SUR 440s 440s N DF SSR detRCov OLS-R2 McElroy-R2 440s system 59 47 45.1 0.168 0.976 0.992 440s 440s N DF SSR MSE RMSE R2 Adj R2 440s Consumption 19 15 17.5 1.167 1.080 0.980 0.975 440s Investment 20 16 17.3 1.083 1.041 0.911 0.894 440s PrivateWages 20 16 10.3 0.642 0.801 0.987 0.985 440s 440s The covariance matrix of the residuals used for estimation 440s Consumption Investment PrivateWages 440s Consumption 0.9286 0.0435 -0.369 440s Investment 0.0435 0.7653 0.109 440s PrivateWages -0.3690 0.1091 0.468 440s 440s The covariance matrix of the residuals 440s Consumption Investment PrivateWages 440s Consumption 0.9251 0.0748 -0.427 440s Investment 0.0748 0.7653 0.171 440s PrivateWages -0.4268 0.1706 0.492 440s 440s The correlations of the residuals 440s Consumption Investment PrivateWages 440s Consumption 1.0000 0.0888 -0.636 440s Investment 0.0888 1.0000 0.268 440s PrivateWages -0.6364 0.2678 1.000 440s 440s 440s SUR estimates for 'Consumption' (equation 1) 440s Model Formula: consump ~ corpProf + corpProfLag + wages 440s 440s Estimate Std. Error t value Pr(>|t|) 440s (Intercept) 16.2684 1.2781 12.73 1.9e-09 *** 440s corpProf 0.1942 0.0927 2.10 0.054 . 440s corpProfLag 0.0746 0.0819 0.91 0.377 440s wages 0.8011 0.0372 21.53 1.1e-12 *** 440s --- 440s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 440s 440s Residual standard error: 1.08 on 15 degrees of freedom 440s Number of observations: 19 Degrees of Freedom: 15 440s SSR: 17.501 MSE: 1.167 Root MSE: 1.08 440s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.975 440s 440s 440s SUR estimates for 'Investment' (equation 2) 440s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 440s 440s Estimate Std. Error t value Pr(>|t|) 440s (Intercept) 12.6462 4.6500 2.72 0.01515 * 440s corpProf 0.4707 0.0916 5.14 9.9e-05 *** 440s corpProfLag 0.3519 0.0874 4.03 0.00097 *** 440s capitalLag -0.1253 0.0229 -5.47 5.1e-05 *** 440s --- 440s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 440s 440s Residual standard error: 1.041 on 16 degrees of freedom 440s Number of observations: 20 Degrees of Freedom: 16 440s SSR: 17.325 MSE: 1.083 Root MSE: 1.041 440s Multiple R-Squared: 0.911 Adjusted R-Squared: 0.894 440s 440s 440s SUR estimates for 'PrivateWages' (equation 3) 440s Model Formula: privWage ~ gnp + gnpLag + trend 440s 440s Estimate Std. Error t value Pr(>|t|) 440s (Intercept) 1.3245 1.0946 1.21 0.24 440s gnp 0.4184 0.0260 16.08 2.7e-11 *** 440s gnpLag 0.1714 0.0307 5.59 4.1e-05 *** 440s trend 0.1455 0.0276 5.27 7.6e-05 *** 440s --- 440s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 440s 440s Residual standard error: 0.801 on 16 degrees of freedom 440s Number of observations: 20 Degrees of Freedom: 16 440s SSR: 10.265 MSE: 0.642 Root MSE: 0.801 440s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 440s 440s > residuals 440s Consumption Investment PrivateWages 440s 1 NA NA NA 440s 2 -0.3146 -0.2419 -1.1439 440s 3 -1.2707 -0.1795 0.5080 440s 4 -1.5428 1.0691 1.4208 440s 5 -0.4489 -1.4778 -0.1000 440s 6 0.0588 0.3168 -0.3599 440s 7 0.9215 1.4450 NA 440s 8 1.3791 0.8287 -0.7561 440s 9 1.0901 -0.5272 0.2880 440s 10 NA 1.2089 1.1795 440s 11 0.3577 0.4081 -0.3681 440s 12 -0.2286 0.2569 0.3439 440s 13 NA NA -0.1574 440s 14 0.2172 0.4743 0.4225 440s 15 -0.1124 -0.0607 0.3154 440s 16 -0.0876 0.0761 0.0151 440s 17 1.5611 1.0205 -0.8084 440s 18 -0.4529 0.0580 0.8611 440s 19 0.1999 -2.5444 -0.7635 440s 20 0.9266 -0.6202 -0.4039 440s 21 0.7589 -0.7478 -1.2175 440s 22 -2.2135 -0.6029 0.5611 440s > fitted 440s Consumption Investment PrivateWages 440s 1 NA NA NA 440s 2 42.2 0.0419 26.6 440s 3 46.3 2.0795 28.8 440s 4 50.7 4.1309 32.7 440s 5 51.0 4.4778 34.0 440s 6 52.5 4.7832 35.8 440s 7 54.2 4.1550 NA 440s 8 54.8 3.3713 38.7 440s 9 56.2 3.5272 38.9 440s 10 NA 3.8911 40.1 440s 11 54.6 0.5919 38.3 440s 12 51.1 -3.6569 34.2 440s 13 NA NA 29.2 440s 14 46.3 -5.5743 28.1 440s 15 48.8 -2.9393 30.3 440s 16 51.4 -1.3761 33.2 440s 17 56.1 1.0795 37.6 440s 18 59.2 1.9420 40.1 440s 19 57.3 0.6444 39.0 440s 20 60.7 1.9202 42.0 440s 21 64.2 4.0478 46.2 440s 22 71.9 5.5029 52.7 440s > predict 440s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 440s 1 NA NA NA NA 440s 2 42.2 0.448 41.3 43.1 440s 3 46.3 0.476 45.3 47.2 440s 4 50.7 0.318 50.1 51.4 440s 5 51.0 0.373 50.3 51.8 440s 6 52.5 0.378 51.8 53.3 440s 7 54.2 0.337 53.5 54.9 440s 8 54.8 0.310 54.2 55.4 440s 9 56.2 0.343 55.5 56.9 440s 10 NA NA NA NA 440s 11 54.6 0.567 53.5 55.8 440s 12 51.1 0.509 50.1 52.2 440s 13 NA NA NA NA 440s 14 46.3 0.573 45.1 47.4 440s 15 48.8 0.382 48.0 49.6 440s 16 51.4 0.328 50.7 52.0 440s 17 56.1 0.336 55.5 56.8 440s 18 59.2 0.309 58.5 59.8 440s 19 57.3 0.370 56.6 58.0 440s 20 60.7 0.401 59.9 61.5 440s 21 64.2 0.405 63.4 65.1 440s 22 71.9 0.633 70.6 73.2 440s Investment.pred Investment.se.fit Investment.lwr Investment.upr 440s 1 NA NA NA NA 440s 2 0.0419 0.533 -1.0309 1.115 440s 3 2.0795 0.433 1.2082 2.951 440s 4 4.1309 0.387 3.3532 4.909 440s 5 4.4778 0.322 3.8307 5.125 440s 6 4.7832 0.305 4.1700 5.396 440s 7 4.1550 0.283 3.5852 4.725 440s 8 3.3713 0.253 2.8630 3.880 440s 9 3.5272 0.337 2.8488 4.206 440s 10 3.8911 0.386 3.1149 4.667 440s 11 0.5919 0.561 -0.5376 1.722 440s 12 -3.6569 0.530 -4.7223 -2.591 440s 13 NA NA NA NA 440s 14 -5.5743 0.618 -6.8176 -4.331 440s 15 -2.9393 0.362 -3.6671 -2.212 440s 16 -1.3761 0.296 -1.9710 -0.781 440s 17 1.0795 0.300 0.4763 1.683 440s 18 1.9420 0.216 1.5081 2.376 440s 19 0.6444 0.298 0.0451 1.244 440s 20 1.9202 0.318 1.2798 2.561 440s 21 4.0478 0.295 3.4537 4.642 440s 22 5.5029 0.417 4.6638 6.342 440s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 440s 1 NA NA NA NA 440s 2 26.6 0.312 26.0 27.3 440s 3 28.8 0.312 28.2 29.4 440s 4 32.7 0.307 32.1 33.3 440s 5 34.0 0.237 33.5 34.5 440s 6 35.8 0.235 35.3 36.2 440s 7 NA NA NA NA 440s 8 38.7 0.239 38.2 39.1 440s 9 38.9 0.228 38.5 39.4 440s 10 40.1 0.218 39.7 40.6 440s 11 38.3 0.293 37.7 38.9 440s 12 34.2 0.290 33.6 34.7 440s 13 29.2 0.343 28.5 29.8 440s 14 28.1 0.321 27.4 28.7 440s 15 30.3 0.320 29.6 30.9 440s 16 33.2 0.268 32.6 33.7 440s 17 37.6 0.263 37.1 38.1 440s 18 40.1 0.207 39.7 40.6 440s 19 39.0 0.293 38.4 39.6 440s 20 42.0 0.279 41.4 42.6 440s 21 46.2 0.295 45.6 46.8 440s 22 52.7 0.435 51.9 53.6 440s > model.frame 440s [1] TRUE 440s > model.matrix 440s [1] TRUE 440s > nobs 440s [1] 59 440s > linearHypothesis 440s Linear hypothesis test (Theil's F test) 440s 440s Hypothesis: 440s Consumption_corpProf + Investment_capitalLag = 0 440s 440s Model 1: restricted model 440s Model 2: kleinModel 440s 440s Res.Df Df F Pr(>F) 440s 1 48 440s 2 47 1 0.41 0.52 440s Linear hypothesis test (F statistic of a Wald test) 440s 440s Hypothesis: 440s Consumption_corpProf + Investment_capitalLag = 0 440s 440s Model 1: restricted model 440s Model 2: kleinModel 440s 440s Res.Df Df F Pr(>F) 440s 1 48 440s 2 47 1 0.52 0.47 440s Linear hypothesis test (Chi^2 statistic of a Wald test) 440s 440s Hypothesis: 440s Consumption_corpProf + Investment_capitalLag = 0 440s 440s Model 1: restricted model 440s Model 2: kleinModel 440s 440s Res.Df Df Chisq Pr(>Chisq) 440s 1 48 440s 2 47 1 0.52 0.47 440s Linear hypothesis test (Theil's F test) 440s 440s Hypothesis: 440s Consumption_corpProf + Investment_capitalLag = 0 440s Consumption_corpProfLag - PrivateWages_trend = 0 440s 440s Model 1: restricted model 440s Model 2: kleinModel 440s 440s Res.Df Df F Pr(>F) 440s 1 49 440s 2 47 2 0.31 0.73 440s Linear hypothesis test (F statistic of a Wald test) 440s 440s Hypothesis: 440s Consumption_corpProf + Investment_capitalLag = 0 440s Consumption_corpProfLag - PrivateWages_trend = 0 440s 440s Model 1: restricted model 440s Model 2: kleinModel 440s 440s Res.Df Df F Pr(>F) 440s 1 49 440s 2 47 2 0.4 0.67 440s Linear hypothesis test (Chi^2 statistic of a Wald test) 440s 440s Hypothesis: 440s Consumption_corpProf + Investment_capitalLag = 0 440s Consumption_corpProfLag - PrivateWages_trend = 0 440s 440s Model 1: restricted model 440s Model 2: kleinModel 440s 440s Res.Df Df Chisq Pr(>Chisq) 440s 1 49 440s 2 47 2 0.79 0.67 440s > logLik 440s 'log Lik.' -67.3 (df=18) 440s 'log Lik.' -74.9 (df=18) 440s Estimating function 440s Consumption_(Intercept) Consumption_corpProf 440s Consumption_2 -0.5115 -6.342 440s Consumption_3 -2.0659 -34.913 440s Consumption_4 -2.5083 -46.152 440s Consumption_5 -0.7298 -14.158 440s Consumption_6 0.0957 1.923 440s Consumption_7 1.4982 29.364 440s Consumption_8 2.2421 44.394 440s Consumption_9 1.7723 37.396 440s Consumption_11 0.5815 9.072 440s Consumption_12 -0.3716 -4.237 440s Consumption_14 0.3531 3.954 440s Consumption_15 -0.1827 -2.248 440s Consumption_16 -0.1424 -1.993 440s Consumption_17 2.5380 44.669 440s Consumption_18 -0.7363 -12.738 440s Consumption_19 0.3251 4.973 440s Consumption_20 1.5064 28.622 440s Consumption_21 1.2337 26.032 440s Consumption_22 -3.5987 -84.568 440s Investment_2 0.0688 0.854 440s Investment_3 0.0511 0.863 440s Investment_4 -0.3043 -5.599 440s Investment_5 0.4206 8.160 440s Investment_6 -0.0902 -1.813 440s Investment_7 -0.4113 -8.061 440s Investment_8 -0.2359 -4.670 440s Investment_9 0.1501 3.166 440s Investment_10 0.0000 0.000 440s Investment_11 -0.1161 -1.812 440s Investment_12 -0.0731 -0.834 440s Investment_14 -0.1350 -1.512 440s Investment_15 0.0173 0.212 440s Investment_16 -0.0217 -0.303 440s Investment_17 -0.2904 -5.112 440s Investment_18 -0.0165 -0.286 440s Investment_19 0.7242 11.080 440s Investment_20 0.1765 3.354 440s Investment_21 0.2128 4.491 440s Investment_22 0.1716 4.032 440s PrivateWages_2 -1.5418 -19.118 440s PrivateWages_3 0.6847 11.571 440s PrivateWages_4 1.9149 35.234 440s PrivateWages_5 -0.1348 -2.615 440s PrivateWages_6 -0.4851 -9.750 440s PrivateWages_8 -1.0191 -20.178 440s PrivateWages_9 0.3882 8.190 440s PrivateWages_10 0.0000 0.000 440s PrivateWages_11 -0.4961 -7.739 440s PrivateWages_12 0.4635 5.284 440s PrivateWages_13 0.0000 0.000 440s PrivateWages_14 0.5694 6.377 440s PrivateWages_15 0.4251 5.229 440s PrivateWages_16 0.0204 0.286 440s PrivateWages_17 -1.0895 -19.175 440s PrivateWages_18 1.1605 20.077 440s PrivateWages_19 -1.0290 -15.743 440s PrivateWages_20 -0.5443 -10.343 440s PrivateWages_21 -1.6408 -34.622 440s PrivateWages_22 0.7563 17.772 440s Consumption_corpProfLag Consumption_wages 440s Consumption_2 -6.496 -14.423 440s Consumption_3 -25.617 -66.521 440s Consumption_4 -42.390 -92.806 440s Consumption_5 -13.428 -27.003 440s Consumption_6 1.856 3.693 440s Consumption_7 30.114 60.976 440s Consumption_8 43.945 93.047 440s Consumption_9 35.092 76.033 440s Consumption_11 12.619 24.482 440s Consumption_12 -5.798 -14.606 440s Consumption_14 2.471 12.039 440s Consumption_15 -2.047 -6.688 440s Consumption_16 -1.751 -5.595 440s Consumption_17 35.532 112.180 440s Consumption_18 -12.959 -35.121 440s Consumption_19 5.624 14.920 440s Consumption_20 23.048 74.417 440s Consumption_21 23.441 65.389 440s Consumption_22 -75.932 -222.397 440s Investment_2 0.874 1.941 440s Investment_3 0.633 1.645 440s Investment_4 -5.142 -11.258 440s Investment_5 7.739 15.562 440s Investment_6 -1.749 -3.481 440s Investment_7 -8.267 -16.739 440s Investment_8 -4.623 -9.788 440s Investment_9 2.971 6.437 440s Investment_10 0.000 0.000 440s Investment_11 -2.520 -4.889 440s Investment_12 -1.141 -2.873 440s Investment_14 -0.945 -4.603 440s Investment_15 0.193 0.632 440s Investment_16 -0.266 -0.851 440s Investment_17 -4.066 -12.838 440s Investment_18 -0.291 -0.787 440s Investment_19 12.528 33.240 440s Investment_20 2.701 8.720 440s Investment_21 4.044 11.280 440s Investment_22 3.620 10.604 440s PrivateWages_2 -19.580 -43.478 440s PrivateWages_3 8.490 22.046 440s PrivateWages_4 32.362 70.851 440s PrivateWages_5 -2.480 -4.987 440s PrivateWages_6 -9.410 -18.724 440s PrivateWages_8 -19.974 -42.291 440s PrivateWages_9 7.686 16.652 440s PrivateWages_10 0.000 0.000 440s PrivateWages_11 -10.765 -20.886 440s PrivateWages_12 7.230 18.215 440s PrivateWages_13 0.000 0.000 440s PrivateWages_14 3.986 19.417 440s PrivateWages_15 4.762 15.560 440s PrivateWages_16 0.251 0.802 440s PrivateWages_17 -15.253 -48.156 440s PrivateWages_18 20.425 55.356 440s PrivateWages_19 -17.801 -47.230 440s PrivateWages_20 -8.329 -26.891 440s PrivateWages_21 -31.176 -86.965 440s PrivateWages_22 15.957 46.737 440s Investment_(Intercept) Investment_corpProf 440s Consumption_2 0.08954 1.110 440s Consumption_3 0.36165 6.112 440s Consumption_4 0.43910 8.079 440s Consumption_5 0.12776 2.479 440s Consumption_6 -0.01675 -0.337 440s Consumption_7 -0.26227 -5.141 440s Consumption_8 -0.39250 -7.772 440s Consumption_9 -0.31026 -6.547 440s Consumption_11 -0.10180 -1.588 440s Consumption_12 0.06506 0.742 440s Consumption_14 -0.06181 -0.692 440s Consumption_15 0.03199 0.393 440s Consumption_16 0.02492 0.349 440s Consumption_17 -0.44431 -7.820 440s Consumption_18 0.12890 2.230 440s Consumption_19 -0.05691 -0.871 440s Consumption_20 -0.26372 -5.011 440s Consumption_21 -0.21598 -4.557 440s Consumption_22 0.62998 14.805 440s Investment_2 -0.33900 -4.204 440s Investment_3 -0.25149 -4.250 440s Investment_4 1.49825 27.568 440s Investment_5 -2.07104 -40.178 440s Investment_6 0.44402 8.925 440s Investment_7 2.02512 39.692 440s Investment_8 1.16134 22.995 440s Investment_9 -0.73888 -15.590 440s Investment_10 1.69419 36.764 440s Investment_11 0.57188 8.921 440s Investment_12 0.36002 4.104 440s Investment_14 0.66469 7.445 440s Investment_15 -0.08500 -1.046 440s Investment_16 0.10666 1.493 440s Investment_17 1.43016 25.171 440s Investment_18 0.08129 1.406 440s Investment_19 -3.56588 -54.558 440s Investment_20 -0.86923 -16.515 440s Investment_21 -1.04801 -22.113 440s Investment_22 -0.84488 -19.855 440s PrivateWages_2 0.63026 7.815 440s PrivateWages_3 -0.27988 -4.730 440s PrivateWages_4 -0.78278 -14.403 440s PrivateWages_5 0.05510 1.069 440s PrivateWages_6 0.19829 3.986 440s PrivateWages_8 0.41658 8.248 440s PrivateWages_9 -0.15868 -3.348 440s PrivateWages_10 -0.64985 -14.102 440s PrivateWages_11 0.20280 3.164 440s PrivateWages_12 -0.18947 -2.160 440s PrivateWages_13 0.00000 0.000 440s PrivateWages_14 -0.23276 -2.607 440s PrivateWages_15 -0.17379 -2.138 440s PrivateWages_16 -0.00834 -0.117 440s PrivateWages_17 0.44538 7.839 440s PrivateWages_18 -0.47440 -8.207 440s PrivateWages_19 0.42063 6.436 440s PrivateWages_20 0.22252 4.228 440s PrivateWages_21 0.67076 14.153 440s PrivateWages_22 -0.30915 -7.265 440s Investment_corpProfLag Investment_capitalLag 440s Consumption_2 1.137 16.37 440s Consumption_3 4.484 66.04 440s Consumption_4 7.421 81.01 440s Consumption_5 2.351 24.24 440s Consumption_6 -0.325 -3.23 440s Consumption_7 -5.272 -51.88 440s Consumption_8 -7.693 -79.84 440s Consumption_9 -6.143 -64.41 440s Consumption_11 -2.209 -21.96 440s Consumption_12 1.015 14.10 440s Consumption_14 -0.433 -12.80 440s Consumption_15 0.358 6.46 440s Consumption_16 0.307 4.96 440s Consumption_17 -6.220 -87.84 440s Consumption_18 2.269 25.75 440s Consumption_19 -0.984 -11.48 440s Consumption_20 -4.035 -52.72 440s Consumption_21 -4.104 -43.46 440s Consumption_22 13.293 128.83 440s Investment_2 -4.305 -61.97 440s Investment_3 -3.118 -45.92 440s Investment_4 25.320 276.43 440s Investment_5 -38.107 -392.88 440s Investment_6 8.614 85.56 440s Investment_7 40.705 400.57 440s Investment_8 22.762 236.22 440s Investment_9 -14.630 -153.39 440s Investment_10 35.747 356.80 440s Investment_11 12.410 123.35 440s Investment_12 5.616 78.02 440s Investment_14 4.653 137.66 440s Investment_15 -0.952 -17.17 440s Investment_16 1.312 21.22 440s Investment_17 20.022 282.74 440s Investment_18 1.431 16.24 440s Investment_19 -61.690 -719.59 440s Investment_20 -13.299 -173.76 440s Investment_21 -19.912 -210.86 440s Investment_22 -17.827 -172.78 440s PrivateWages_2 8.004 115.21 440s PrivateWages_3 -3.471 -51.11 440s PrivateWages_4 -13.229 -144.42 440s PrivateWages_5 1.014 10.45 440s PrivateWages_6 3.847 38.21 440s PrivateWages_8 8.165 84.73 440s PrivateWages_9 -3.142 -32.94 440s PrivateWages_10 -13.712 -136.86 440s PrivateWages_11 4.401 43.74 440s PrivateWages_12 -2.956 -41.06 440s PrivateWages_13 0.000 0.00 440s PrivateWages_14 -1.629 -48.21 440s PrivateWages_15 -1.946 -35.11 440s PrivateWages_16 -0.103 -1.66 440s PrivateWages_17 6.235 88.05 440s PrivateWages_18 -8.349 -94.78 440s PrivateWages_19 7.277 84.88 440s PrivateWages_20 3.405 44.48 440s PrivateWages_21 12.744 134.96 440s PrivateWages_22 -6.523 -63.22 440s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 440s Consumption_2 -0.4240 -19.33 -19.04 440s Consumption_3 -1.7126 -85.80 -78.09 440s Consumption_4 -2.0793 -118.94 -104.17 440s Consumption_5 -0.6050 -34.54 -34.61 440s Consumption_6 0.0793 4.84 4.53 440s Consumption_7 0.0000 0.00 0.00 440s Consumption_8 1.8587 119.70 118.95 440s Consumption_9 1.4692 94.76 94.62 440s Consumption_11 0.4821 29.50 32.30 440s Consumption_12 -0.3081 -16.45 -18.85 440s Consumption_14 0.2927 13.20 12.97 440s Consumption_15 -0.1515 -7.53 -6.83 440s Consumption_16 -0.1180 -6.42 -5.87 440s Consumption_17 2.1040 131.92 114.46 440s Consumption_18 -0.6104 -39.67 -38.27 440s Consumption_19 0.2695 16.41 17.52 440s Consumption_20 1.2488 86.79 76.05 440s Consumption_21 1.0228 77.42 71.08 440s Consumption_22 -2.9832 -263.72 -225.83 440s Investment_2 0.1333 6.08 5.98 440s Investment_3 0.0989 4.95 4.51 440s Investment_4 -0.5890 -33.69 -29.51 440s Investment_5 0.8142 46.49 46.57 440s Investment_6 -0.1746 -10.65 -9.97 440s Investment_7 0.0000 0.00 0.00 440s Investment_8 -0.4566 -29.40 -29.22 440s Investment_9 0.2905 18.74 18.71 440s Investment_10 -0.6660 -44.62 -42.96 440s Investment_11 -0.2248 -13.76 -15.06 440s Investment_12 -0.1415 -7.56 -8.66 440s Investment_14 -0.2613 -11.79 -11.58 440s Investment_15 0.0334 1.66 1.51 440s Investment_16 -0.0419 -2.28 -2.08 440s Investment_17 -0.5622 -35.25 -30.59 440s Investment_18 -0.0320 -2.08 -2.00 440s Investment_19 1.4018 85.37 91.12 440s Investment_20 0.3417 23.75 20.81 440s Investment_21 0.4120 31.19 28.63 440s Investment_22 0.3321 29.36 25.14 440s PrivateWages_2 -3.8052 -173.52 -170.85 440s PrivateWages_3 1.6898 84.66 77.06 440s PrivateWages_4 4.7261 270.33 236.78 440s PrivateWages_5 -0.3327 -19.00 -19.03 440s PrivateWages_6 -1.1972 -73.03 -68.36 440s PrivateWages_8 -2.5152 -161.98 -160.97 440s PrivateWages_9 0.9580 61.79 61.70 440s PrivateWages_10 3.9235 262.88 253.07 440s PrivateWages_11 -1.2244 -74.93 -82.04 440s PrivateWages_12 1.1439 61.09 70.01 440s PrivateWages_13 -0.5236 -23.19 -27.96 440s PrivateWages_14 1.4053 63.38 62.26 440s PrivateWages_15 1.0493 52.15 47.32 440s PrivateWages_16 0.0503 2.74 2.50 440s PrivateWages_17 -2.6890 -168.60 -146.28 440s PrivateWages_18 2.8642 186.17 179.59 440s PrivateWages_19 -2.5396 -154.66 -165.07 440s PrivateWages_20 -1.3435 -93.37 -81.82 440s PrivateWages_21 -4.0497 -306.57 -281.46 440s PrivateWages_22 1.8665 165.00 141.30 440s PrivateWages_trend 440s Consumption_2 4.240 440s Consumption_3 15.413 440s Consumption_4 16.634 440s Consumption_5 4.235 440s Consumption_6 -0.476 440s Consumption_7 0.000 440s Consumption_8 -7.435 440s Consumption_9 -4.408 440s Consumption_11 -0.482 440s Consumption_12 0.000 440s Consumption_14 0.585 440s Consumption_15 -0.454 440s Consumption_16 -0.472 440s Consumption_17 10.520 440s Consumption_18 -3.662 440s Consumption_19 1.886 440s Consumption_20 9.990 440s Consumption_21 9.205 440s Consumption_22 -29.832 440s Investment_2 -1.333 440s Investment_3 -0.890 440s Investment_4 4.712 440s Investment_5 -5.699 440s Investment_6 1.047 440s Investment_7 0.000 440s Investment_8 1.826 440s Investment_9 -0.871 440s Investment_10 1.332 440s Investment_11 0.225 440s Investment_12 0.000 440s Investment_14 -0.523 440s Investment_15 0.100 440s Investment_16 -0.168 440s Investment_17 -2.811 440s Investment_18 -0.192 440s Investment_19 9.813 440s Investment_20 2.734 440s Investment_21 3.708 440s Investment_22 3.321 440s PrivateWages_2 38.052 440s PrivateWages_3 -15.208 440s PrivateWages_4 -37.809 440s PrivateWages_5 2.329 440s PrivateWages_6 7.183 440s PrivateWages_8 10.061 440s PrivateWages_9 -2.874 440s PrivateWages_10 -7.847 440s PrivateWages_11 1.224 440s PrivateWages_12 0.000 440s PrivateWages_13 -0.524 440s PrivateWages_14 2.811 440s PrivateWages_15 3.148 440s PrivateWages_16 0.201 440s PrivateWages_17 -13.445 440s PrivateWages_18 17.185 440s PrivateWages_19 -17.777 440s PrivateWages_20 -10.748 440s PrivateWages_21 -36.448 440s PrivateWages_22 18.665 440s [1] TRUE 440s > Bread 440s Consumption_(Intercept) Consumption_corpProf Consumption_corpProfLag 440s [1,] 9.64e+01 -1.01207 -0.67760 440s [2,] -1.01e+00 0.50717 -0.26912 440s [3,] -6.78e-01 -0.26912 0.39547 440s [4,] -1.57e+00 -0.07816 -0.02960 440s [5,] 4.72e+00 -0.06998 0.78589 440s [6,] -2.60e-01 0.05062 -0.04147 440s [7,] 5.84e-03 -0.03341 0.04369 440s [8,] -2.63e-04 -0.00132 -0.00391 440s [9,] -3.35e+01 0.06371 1.58512 440s [10,] 2.97e-01 -0.05279 0.03618 440s [11,] 2.54e-01 0.05334 -0.06435 440s [12,] 1.92e-01 0.03084 0.02478 440s Consumption_wages Investment_(Intercept) Investment_corpProf 440s [1,] -1.566759 4.725 -0.25994 440s [2,] -0.078160 -0.070 0.05062 440s [3,] -0.029602 0.786 -0.04147 440s [4,] 0.081697 -0.368 0.00116 440s [5,] -0.368191 1275.706 -12.07893 440s [6,] 0.001158 -12.079 0.49514 440s [7,] -0.003210 9.845 -0.37888 440s [8,] 0.001998 -6.140 0.04890 440s [9,] 0.126305 19.264 -0.14904 440s [10,] -0.000206 0.266 0.01283 440s [11,] -0.002055 -0.608 -0.01053 440s [12,] -0.027162 -0.549 0.00394 440s Investment_corpProfLag Investment_capitalLag PrivateWages_(Intercept) 440s [1,] 0.00584 -0.000263 -33.5037 440s [2,] -0.03341 -0.001318 0.0637 440s [3,] 0.04369 -0.003914 1.5851 440s [4,] -0.00321 0.001998 0.1263 440s [5,] 9.84516 -6.139910 19.2637 440s [6,] -0.37888 0.048897 -0.1490 440s [7,] 0.45026 -0.053769 -0.4040 440s [8,] -0.05377 0.030940 -0.0490 440s [9,] -0.40395 -0.049007 70.6849 440s [10,] -0.00755 -0.001777 -0.2111 440s [11,] 0.01465 0.002709 -0.9817 440s [12,] -0.01065 0.003278 0.7839 440s PrivateWages_gnp PrivateWages_gnpLag PrivateWages_trend 440s [1,] 0.297134 0.25379 0.19157 440s [2,] -0.052789 0.05334 0.03084 440s [3,] 0.036177 -0.06435 0.02478 440s [4,] -0.000206 -0.00206 -0.02716 440s [5,] 0.265808 -0.60808 -0.54935 440s [6,] 0.012829 -0.01053 0.00394 440s [7,] -0.007548 0.01465 -0.01065 440s [8,] -0.001777 0.00271 0.00328 440s [9,] -0.211061 -0.98166 0.78387 440s [10,] 0.039911 -0.03744 -0.00955 440s [11,] -0.037441 0.05550 -0.00377 440s [12,] -0.009553 -0.00377 0.04488 440s > 440s > # 3SLS 440s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 440s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 440s > summary 440s 440s systemfit results 440s method: 3SLS 440s 440s N DF SSR detRCov OLS-R2 McElroy-R2 440s system 57 45 66.8 0.361 0.963 0.993 440s 440s N DF SSR MSE RMSE R2 Adj R2 440s Consumption 18 14 22.6 1.616 1.271 0.974 0.968 440s Investment 19 15 34.1 2.277 1.509 0.807 0.769 440s PrivateWages 20 16 10.1 0.628 0.793 0.987 0.985 440s 440s The covariance matrix of the residuals used for estimation 440s Consumption Investment PrivateWages 440s Consumption 1.237 0.518 -0.408 440s Investment 0.518 1.263 0.113 440s PrivateWages -0.408 0.113 0.468 440s 440s The covariance matrix of the residuals 440s Consumption Investment PrivateWages 440s Consumption 1.257 0.601 -0.421 440s Investment 0.601 1.601 0.214 440s PrivateWages -0.421 0.214 0.491 440s 440s The correlations of the residuals 440s Consumption Investment PrivateWages 440s Consumption 1.000 0.425 -0.537 440s Investment 0.425 1.000 0.239 440s PrivateWages -0.537 0.239 1.000 440s 440s 440s 3SLS estimates for 'Consumption' (equation 1) 440s Model Formula: consump ~ corpProf + corpProfLag + wages 440s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 440s gnpLag 440s 440s Estimate Std. Error t value Pr(>|t|) 440s (Intercept) 18.2100 1.5273 11.92 1e-08 *** 440s corpProf -0.0639 0.1461 -0.44 0.67 440s corpProfLag 0.1687 0.1125 1.50 0.16 440s wages 0.8230 0.0431 19.07 2e-11 *** 440s --- 440s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 440s 440s Residual standard error: 1.271 on 14 degrees of freedom 440s Number of observations: 18 Degrees of Freedom: 14 440s SSR: 22.626 MSE: 1.616 Root MSE: 1.271 440s Multiple R-Squared: 0.974 Adjusted R-Squared: 0.968 440s 440s 440s 3SLS estimates for 'Investment' (equation 2) 440s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 440s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 440s gnpLag 440s 440s Estimate Std. Error t value Pr(>|t|) 440s (Intercept) 24.7534 6.5548 3.78 0.00183 ** 440s corpProf 0.0524 0.1807 0.29 0.77600 440s corpProfLag 0.6584 0.1551 4.24 0.00071 *** 440s capitalLag -0.1756 0.0311 -5.64 4.7e-05 *** 440s --- 440s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 440s 440s Residual standard error: 1.509 on 15 degrees of freedom 440s Number of observations: 19 Degrees of Freedom: 15 440s SSR: 34.149 MSE: 2.277 Root MSE: 1.509 440s Multiple R-Squared: 0.807 Adjusted R-Squared: 0.769 440s 440s 440s 3SLS estimates for 'PrivateWages' (equation 3) 440s Model Formula: privWage ~ gnp + gnpLag + trend 440s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 440s gnpLag 440s 440s Estimate Std. Error t value Pr(>|t|) 440s (Intercept) 0.8154 1.0961 0.74 0.46772 440s gnp 0.4250 0.0299 14.19 1.7e-10 *** 440s gnpLag 0.1731 0.0331 5.23 8.3e-05 *** 440s trend 0.1255 0.0283 4.43 0.00042 *** 440s --- 440s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 440s 440s Residual standard error: 0.793 on 16 degrees of freedom 440s Number of observations: 20 Degrees of Freedom: 16 440s SSR: 10.054 MSE: 0.628 Root MSE: 0.793 440s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 440s 440s > residuals 440s Consumption Investment PrivateWages 440s 1 NA NA NA 440s 2 -0.8680 -1.857 -1.21010 440s 3 -0.7217 0.170 0.43075 440s 4 -1.1353 0.762 1.30899 440s 5 0.0755 -1.565 -0.20270 440s 6 0.6348 0.367 -0.46842 440s 7 NA NA NA 440s 8 1.7953 1.230 -0.85853 440s 9 1.7924 0.568 0.20422 440s 10 NA 2.308 1.09889 440s 11 -0.5211 -0.972 -0.39427 440s 12 -1.5560 -0.960 0.39889 440s 13 NA NA -0.00934 440s 14 -0.2384 1.327 0.59990 440s 15 -0.7342 -0.292 0.48094 440s 16 -0.4331 0.068 0.16188 440s 17 1.8775 1.932 -0.70448 440s 18 -0.6294 -0.154 0.95616 440s 19 -0.4252 -3.400 -0.62489 440s 20 1.3682 0.589 -0.29589 440s 21 1.3155 0.271 -1.14466 440s 22 -1.4276 0.942 0.55941 440s > fitted 440s Consumption Investment PrivateWages 440s 1 NA NA NA 440s 2 42.8 1.657 26.7 440s 3 45.7 1.730 28.9 440s 4 50.3 4.438 32.8 440s 5 50.5 4.565 34.1 440s 6 52.0 4.733 35.9 440s 7 NA NA NA 440s 8 54.4 2.970 38.8 440s 9 55.5 2.432 39.0 440s 10 NA 2.792 40.2 440s 11 55.5 1.972 38.3 440s 12 52.5 -2.440 34.1 440s 13 NA NA 29.0 440s 14 46.7 -6.427 27.9 440s 15 49.4 -2.708 30.1 440s 16 51.7 -1.368 33.0 440s 17 55.8 0.168 37.5 440s 18 59.3 2.154 40.0 440s 19 57.9 1.500 38.8 440s 20 60.2 0.711 41.9 440s 21 63.7 3.029 46.1 440s 22 71.1 3.958 52.7 440s > predict 440s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 440s 1 NA NA NA NA 440s 2 42.8 0.542 39.8 45.7 440s 3 45.7 0.612 42.7 48.7 440s 4 50.3 0.407 47.5 53.2 440s 5 50.5 0.478 47.6 53.4 440s 6 52.0 0.488 49.0 54.9 440s 7 NA NA NA NA 440s 8 54.4 0.394 51.5 57.3 440s 9 55.5 0.464 52.6 58.4 440s 10 NA NA NA NA 440s 11 55.5 0.811 52.3 58.8 440s 12 52.5 0.773 49.3 55.6 440s 13 NA NA NA NA 440s 14 46.7 0.666 43.7 49.8 440s 15 49.4 0.463 46.5 52.3 440s 16 51.7 0.381 48.9 54.6 440s 17 55.8 0.424 52.9 58.7 440s 18 59.3 0.359 56.5 62.2 440s 19 57.9 0.492 55.0 60.8 440s 20 60.2 0.501 57.3 63.2 440s 21 63.7 0.491 60.8 66.6 440s 22 71.1 0.749 68.0 74.3 440s Investment.pred Investment.se.fit Investment.lwr Investment.upr 440s 1 NA NA NA NA 440s 2 1.657 0.831 -2.015 5.329 440s 3 1.730 0.574 -1.711 5.171 440s 4 4.438 0.507 1.045 7.831 440s 5 4.565 0.426 1.223 7.907 440s 6 4.733 0.406 1.402 8.064 440s 7 NA NA NA NA 440s 8 2.970 0.334 -0.324 6.263 440s 9 2.432 0.501 -0.957 5.820 440s 10 2.792 0.544 -0.627 6.211 440s 11 1.972 0.937 -1.814 5.757 440s 12 -2.440 0.849 -6.131 1.250 440s 13 NA NA NA NA 440s 14 -6.427 0.836 -10.104 -2.750 440s 15 -2.708 0.477 -6.081 0.665 440s 16 -1.368 0.381 -4.685 1.949 440s 17 0.168 0.473 -3.202 3.538 440s 18 2.154 0.311 -1.130 5.438 440s 19 1.500 0.518 -1.900 4.900 440s 20 0.711 0.541 -2.705 4.127 440s 21 3.029 0.467 -0.338 6.395 440s 22 3.958 0.677 0.432 7.483 440s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 440s 1 NA NA NA NA 440s 2 26.7 0.315 24.9 28.5 440s 3 28.9 0.322 27.1 30.7 440s 4 32.8 0.330 31.0 34.6 440s 5 34.1 0.241 32.3 35.9 440s 6 35.9 0.249 34.1 37.6 440s 7 NA NA NA NA 440s 8 38.8 0.243 37.0 40.5 440s 9 39.0 0.231 37.2 40.7 440s 10 40.2 0.225 38.5 41.9 440s 11 38.3 0.305 36.5 40.1 440s 12 34.1 0.317 32.3 35.9 440s 13 29.0 0.382 27.1 30.9 440s 14 27.9 0.321 26.1 29.7 440s 15 30.1 0.316 28.3 31.9 440s 16 33.0 0.265 31.3 34.8 440s 17 37.5 0.270 35.7 39.3 440s 18 40.0 0.207 38.3 41.8 440s 19 38.8 0.311 37.0 40.6 440s 20 41.9 0.287 40.1 43.7 440s 21 46.1 0.300 44.3 47.9 440s 22 52.7 0.463 50.8 54.7 440s > model.frame 440s [1] TRUE 440s > model.matrix 440s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0339 >" 440s [2] "Attributes: < Component “dimnames”: Component 1: 52 string mismatches >" 440s [3] "Numeric: lengths (708, 684) differ" 440s > nobs 440s [1] 57 440s > linearHypothesis 440s Linear hypothesis test (Theil's F test) 440s 440s Hypothesis: 440s Consumption_corpProf + Investment_capitalLag = 0 440s 440s Model 1: restricted model 440s Model 2: kleinModel 440s 440s Res.Df Df F Pr(>F) 440s 1 46 440s 2 45 1 1.95 0.17 440s Linear hypothesis test (F statistic of a Wald test) 440s 440s Hypothesis: 440s Consumption_corpProf + Investment_capitalLag = 0 440s 440s Model 1: restricted model 440s Model 2: kleinModel 440s 440s Res.Df Df F Pr(>F) 440s 1 46 440s 2 45 1 2.71 0.11 440s Linear hypothesis test (Chi^2 statistic of a Wald test) 440s 440s Hypothesis: 440s Consumption_corpProf + Investment_capitalLag = 0 440s 440s Model 1: restricted model 440s Model 2: kleinModel 440s 440s Res.Df Df Chisq Pr(>Chisq) 440s 1 46 440s 2 45 1 2.71 0.1 . 440s --- 440s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 440s Linear hypothesis test (Theil's F test) 440s 440s Hypothesis: 440s Consumption_corpProf + Investment_capitalLag = 0 440s Consumption_corpProfLag - PrivateWages_trend = 0 440s 440s Model 1: restricted model 440s Model 2: kleinModel 440s 440s Res.Df Df F Pr(>F) 440s 1 47 440s 2 45 2 1.78 0.18 440s Linear hypothesis test (F statistic of a Wald test) 440s 440s Hypothesis: 440s Consumption_corpProf + Investment_capitalLag = 0 440s Consumption_corpProfLag - PrivateWages_trend = 0 440s 440s Model 1: restricted model 440s Model 2: kleinModel 440s 440s Res.Df Df F Pr(>F) 440s 1 47 440s 2 45 2 2.48 0.095 . 440s --- 440s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 440s Linear hypothesis test (Chi^2 statistic of a Wald test) 440s 440s Hypothesis: 440s Consumption_corpProf + Investment_capitalLag = 0 440s Consumption_corpProfLag - PrivateWages_trend = 0 440s 440s Model 1: restricted model 440s Model 2: kleinModel 440s 440s Res.Df Df Chisq Pr(>Chisq) 440s 1 47 440s 2 45 2 4.95 0.084 . 440s --- 440s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 440s > logLik 440s 'log Lik.' -71.2 (df=18) 440s 'log Lik.' -81.7 (df=18) 440s Estimating function 440s Consumption_(Intercept) Consumption_corpProf 440s Consumption_2 -3.6474 -51.112 440s Consumption_3 -0.7759 -12.930 440s Consumption_4 0.5383 9.982 440s Consumption_5 -2.0601 -41.756 440s Consumption_6 1.0597 20.157 440s Consumption_8 5.0108 88.416 440s Consumption_9 4.4804 84.874 440s Consumption_11 -2.2103 -37.003 440s Consumption_12 -2.9903 -39.999 440s Consumption_14 0.5609 5.622 440s Consumption_15 -2.2997 -28.756 440s Consumption_16 -1.9032 -27.562 440s Consumption_17 6.4249 95.811 440s Consumption_18 -0.7235 -14.050 440s Consumption_19 -5.0805 -97.079 440s Consumption_20 3.4333 60.632 440s Consumption_21 1.6077 32.791 440s Consumption_22 -1.1313 -25.654 440s Investment_2 1.6537 23.174 440s Investment_3 -0.1564 -2.607 440s Investment_4 -0.6420 -11.906 440s Investment_5 1.4113 28.605 440s Investment_6 -0.3557 -6.767 440s Investment_8 -1.1680 -20.610 440s Investment_9 -0.5634 -10.672 440s Investment_10 0.0000 0.000 440s Investment_11 0.9137 15.295 440s Investment_12 0.9272 12.402 440s Investment_14 -1.2036 -12.064 440s Investment_15 0.2779 3.475 440s Investment_16 -0.0439 -0.636 440s Investment_17 -1.7918 -26.720 440s Investment_18 0.2271 4.411 440s Investment_19 3.1278 59.767 440s Investment_20 -0.5790 -10.225 440s Investment_21 -0.2789 -5.690 440s Investment_22 -0.8484 -19.238 440s PrivateWages_2 -3.1568 -44.237 440s PrivateWages_3 1.1209 18.679 440s PrivateWages_4 2.7328 50.677 440s PrivateWages_5 -2.9712 -60.223 440s PrivateWages_6 -0.5212 -9.913 440s PrivateWages_8 1.7420 30.738 440s PrivateWages_9 1.9832 37.569 440s PrivateWages_10 0.0000 0.000 440s PrivateWages_11 -2.5151 -42.105 440s PrivateWages_12 -0.3611 -4.830 440s PrivateWages_13 0.0000 0.000 440s PrivateWages_14 3.2055 32.130 440s PrivateWages_15 -0.2814 -3.519 440s PrivateWages_16 -0.4078 -5.906 440s PrivateWages_17 2.6678 39.784 440s PrivateWages_18 0.0554 1.076 440s PrivateWages_19 -6.6416 -126.909 440s PrivateWages_20 1.4327 25.301 440s PrivateWages_21 -1.3598 -27.735 440s PrivateWages_22 2.0747 47.044 440s Consumption_corpProfLag Consumption_wages 440s Consumption_2 -46.322 -108.77 440s Consumption_3 -9.621 -24.71 440s Consumption_4 9.097 18.98 440s Consumption_5 -37.905 -79.52 440s Consumption_6 20.558 40.85 440s Consumption_8 98.211 200.48 440s Consumption_9 88.711 187.18 440s Consumption_11 -47.964 -95.27 440s Consumption_12 -46.648 -118.58 440s Consumption_14 3.926 18.69 440s Consumption_15 -25.757 -85.85 440s Consumption_16 -23.410 -76.40 440s Consumption_17 89.949 268.43 440s Consumption_18 -12.733 -34.44 440s Consumption_19 -87.892 -250.13 440s Consumption_20 52.529 166.71 440s Consumption_21 30.546 85.88 440s Consumption_22 -23.871 -68.78 440s Investment_2 21.002 49.32 440s Investment_3 -1.940 -4.98 440s Investment_4 -10.851 -22.64 440s Investment_5 25.967 54.47 440s Investment_6 -6.901 -13.71 440s Investment_8 -22.893 -46.73 440s Investment_9 -11.154 -23.53 440s Investment_10 0.000 0.00 440s Investment_11 19.827 39.38 440s Investment_12 14.464 36.77 440s Investment_14 -8.425 -40.11 440s Investment_15 3.113 10.38 440s Investment_16 -0.540 -1.76 440s Investment_17 -25.085 -74.86 440s Investment_18 3.997 10.81 440s Investment_19 54.111 153.99 440s Investment_20 -8.858 -28.11 440s Investment_21 -5.300 -14.90 440s Investment_22 -17.901 -51.58 440s PrivateWages_2 -40.091 -94.14 440s PrivateWages_3 13.899 35.70 440s PrivateWages_4 46.184 96.34 440s PrivateWages_5 -54.670 -114.69 440s PrivateWages_6 -10.110 -20.09 440s PrivateWages_8 34.144 69.70 440s PrivateWages_9 39.267 82.85 440s PrivateWages_10 0.000 0.00 440s PrivateWages_11 -54.578 -108.40 440s PrivateWages_12 -5.633 -14.32 440s PrivateWages_13 0.000 0.00 440s PrivateWages_14 22.438 106.83 440s PrivateWages_15 -3.152 -10.51 440s PrivateWages_16 -5.016 -16.37 440s PrivateWages_17 37.350 111.46 440s PrivateWages_18 0.975 2.64 440s PrivateWages_19 -114.899 -326.98 440s PrivateWages_20 21.920 69.57 440s PrivateWages_21 -25.836 -72.64 440s PrivateWages_22 43.775 126.12 440s Investment_(Intercept) Investment_corpProf 440s Consumption_2 1.8176 24.384 440s Consumption_3 0.3867 6.453 440s Consumption_4 -0.2682 -5.040 440s Consumption_5 1.0266 21.198 440s Consumption_6 -0.5281 -10.172 440s Consumption_8 -2.4970 -43.782 440s Consumption_9 -2.2327 -43.602 440s Consumption_11 1.1015 18.940 440s Consumption_12 1.4902 20.151 440s Consumption_14 -0.2795 -2.793 440s Consumption_15 1.1460 14.736 440s Consumption_16 0.9485 13.590 440s Consumption_17 -3.2018 -47.918 440s Consumption_18 0.3605 6.983 440s Consumption_19 2.5318 49.008 440s Consumption_20 -1.7109 -29.898 440s Consumption_21 -0.8012 -16.122 440s Consumption_22 0.5638 12.844 440s Investment_2 -2.3696 -31.787 440s Investment_3 0.2241 3.741 440s Investment_4 0.9200 17.284 440s Investment_5 -2.0221 -41.754 440s Investment_6 0.5097 9.819 440s Investment_8 1.6736 29.344 440s Investment_9 0.8072 15.764 440s Investment_10 2.9560 59.913 440s Investment_11 -1.3092 -22.510 440s Investment_12 -1.3285 -17.964 440s Investment_14 1.7246 17.233 440s Investment_15 -0.3982 -5.120 440s Investment_16 0.0630 0.902 440s Investment_17 2.5674 38.424 440s Investment_18 -0.3254 -6.303 440s Investment_19 -4.4817 -86.752 440s Investment_20 0.8296 14.497 440s Investment_21 0.3997 8.043 440s Investment_22 1.2156 27.693 440s PrivateWages_2 1.9315 25.910 440s PrivateWages_3 -0.6858 -11.446 440s PrivateWages_4 -1.6720 -31.413 440s PrivateWages_5 1.8179 37.537 440s PrivateWages_6 0.3189 6.142 440s PrivateWages_8 -1.0659 -18.688 440s PrivateWages_9 -1.2134 -23.696 440s PrivateWages_10 -2.2443 -45.488 440s PrivateWages_11 1.5389 26.460 440s PrivateWages_12 0.2209 2.988 440s PrivateWages_13 0.0000 0.000 440s PrivateWages_14 -1.9613 -19.598 440s PrivateWages_15 0.1722 2.214 440s PrivateWages_16 0.2495 3.576 440s PrivateWages_17 -1.6323 -24.429 440s PrivateWages_18 -0.0339 -0.657 440s PrivateWages_19 4.0636 78.659 440s PrivateWages_20 -0.8766 -15.318 440s PrivateWages_21 0.8320 16.742 440s PrivateWages_22 -1.2694 -28.917 440s Investment_corpProfLag Investment_capitalLag 440s Consumption_2 23.084 332.27 440s Consumption_3 4.795 70.60 440s Consumption_4 -4.533 -49.49 440s Consumption_5 18.890 194.75 440s Consumption_6 -10.245 -101.76 440s Consumption_8 -48.942 -507.90 440s Consumption_9 -44.208 -463.52 440s Consumption_11 23.902 237.59 440s Consumption_12 23.247 322.92 440s Consumption_14 -1.957 -57.89 440s Consumption_15 12.836 231.50 440s Consumption_16 11.666 188.74 440s Consumption_17 -44.825 -632.99 440s Consumption_18 6.345 72.04 440s Consumption_19 43.800 510.92 440s Consumption_20 -26.177 -342.01 440s Consumption_21 -15.222 -161.20 440s Consumption_22 11.896 115.30 440s Investment_2 -30.093 -433.16 440s Investment_3 2.779 40.93 440s Investment_4 15.547 169.73 440s Investment_5 -37.208 -383.60 440s Investment_6 9.888 98.22 440s Investment_8 32.803 340.41 440s Investment_9 15.983 167.58 440s Investment_10 62.371 622.53 440s Investment_11 -28.409 -282.39 440s Investment_12 -20.724 -287.88 440s Investment_14 12.072 357.16 440s Investment_15 -4.460 -80.44 440s Investment_16 0.774 12.53 440s Investment_17 35.944 507.58 440s Investment_18 -5.727 -65.02 440s Investment_19 -77.534 -904.41 440s Investment_20 12.693 165.84 440s Investment_21 7.594 80.42 440s Investment_22 25.650 248.60 440s PrivateWages_2 24.530 353.07 440s PrivateWages_3 -8.504 -125.23 440s PrivateWages_4 -28.257 -308.49 440s PrivateWages_5 33.450 344.86 440s PrivateWages_6 6.186 61.45 440s PrivateWages_8 -20.891 -216.79 440s PrivateWages_9 -24.025 -251.90 440s PrivateWages_10 -47.355 -472.65 440s PrivateWages_11 33.393 331.93 440s PrivateWages_12 3.447 47.88 440s PrivateWages_13 0.000 0.00 440s PrivateWages_14 -13.729 -406.18 440s PrivateWages_15 1.929 34.78 440s PrivateWages_16 3.069 49.66 440s PrivateWages_17 -22.852 -322.71 440s PrivateWages_18 -0.597 -6.77 440s PrivateWages_19 70.300 820.04 440s PrivateWages_20 -13.412 -175.23 440s PrivateWages_21 15.807 167.39 440s PrivateWages_22 -26.784 -259.59 440s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 440s Consumption_2 -3.6123 -170.03 -162.19 440s Consumption_3 -0.7684 -38.10 -35.04 440s Consumption_4 0.5331 30.14 26.71 440s Consumption_5 -2.0403 -123.82 -116.70 440s Consumption_6 1.0495 63.61 59.93 440s Consumption_8 4.9625 297.74 317.60 440s Consumption_9 4.4373 276.30 285.76 440s Consumption_11 -2.1891 -139.47 -146.67 440s Consumption_12 -2.9615 -162.39 -181.24 440s Consumption_14 0.5555 23.40 24.61 440s Consumption_15 -2.2776 -116.65 -102.72 440s Consumption_16 -1.8849 -104.31 -93.68 440s Consumption_17 6.3631 365.20 346.15 440s Consumption_18 -0.7165 -48.13 -44.93 440s Consumption_19 -5.0316 -344.73 -327.05 440s Consumption_20 3.4002 227.29 207.07 440s Consumption_21 1.5922 119.20 110.66 440s Consumption_22 -1.1205 -97.34 -84.82 440s Investment_2 2.0108 94.65 90.29 440s Investment_3 -0.1902 -9.43 -8.67 440s Investment_4 -0.7807 -44.14 -39.11 440s Investment_5 1.7160 104.14 98.16 440s Investment_6 -0.4326 -26.22 -24.70 440s Investment_8 -1.4203 -85.21 -90.90 440s Investment_9 -0.6850 -42.65 -44.11 440s Investment_10 -2.5085 -161.97 -161.80 440s Investment_11 1.1110 70.78 74.44 440s Investment_12 1.1274 61.82 69.00 440s Investment_14 -1.4635 -61.65 -64.83 440s Investment_15 0.3379 17.31 15.24 440s Investment_16 -0.0534 -2.96 -2.66 440s Investment_17 -2.1788 -125.05 -118.52 440s Investment_18 0.2762 18.55 17.32 440s Investment_19 3.8033 260.57 247.21 440s Investment_20 -0.7040 -47.06 -42.87 440s Investment_21 -0.3392 -25.39 -23.57 440s Investment_22 -1.0316 -89.62 -78.09 440s PrivateWages_2 -7.1301 -335.61 -320.14 440s PrivateWages_3 2.5317 125.52 115.44 440s PrivateWages_4 6.1723 349.00 309.23 440s PrivateWages_5 -6.7109 -407.26 -383.86 440s PrivateWages_6 -1.1771 -71.34 -67.21 440s PrivateWages_8 3.9346 236.07 251.82 440s PrivateWages_9 4.4793 278.92 288.47 440s PrivateWages_10 8.2849 534.95 534.38 440s PrivateWages_11 -5.6807 -361.93 -380.61 440s PrivateWages_12 -0.8156 -44.72 -49.92 440s PrivateWages_13 -4.4579 -209.42 -238.05 440s PrivateWages_14 7.2401 305.01 320.74 440s PrivateWages_15 -0.6357 -32.56 -28.67 440s PrivateWages_16 -0.9212 -50.98 -45.78 440s PrivateWages_17 6.0257 345.84 327.80 440s PrivateWages_18 0.1252 8.41 7.85 440s PrivateWages_19 -15.0009 -1027.75 -975.06 440s PrivateWages_20 3.2360 216.31 197.07 440s PrivateWages_21 -3.0713 -229.93 -213.45 440s PrivateWages_22 4.6859 407.11 354.72 440s PrivateWages_trend 440s Consumption_2 36.123 440s Consumption_3 6.916 440s Consumption_4 -4.265 440s Consumption_5 14.282 440s Consumption_6 -6.297 440s Consumption_8 -19.850 440s Consumption_9 -13.312 440s Consumption_11 2.189 440s Consumption_12 0.000 440s Consumption_14 1.111 440s Consumption_15 -6.833 440s Consumption_16 -7.540 440s Consumption_17 31.815 440s Consumption_18 -4.299 440s Consumption_19 -35.221 440s Consumption_20 27.202 440s Consumption_21 14.330 440s Consumption_22 -11.205 440s Investment_2 -20.108 440s Investment_3 1.712 440s Investment_4 6.246 440s Investment_5 -12.012 440s Investment_6 2.595 440s Investment_8 5.681 440s Investment_9 2.055 440s Investment_10 5.017 440s Investment_11 -1.111 440s Investment_12 0.000 440s Investment_14 -2.927 440s Investment_15 1.014 440s Investment_16 -0.214 440s Investment_17 -10.894 440s Investment_18 1.657 440s Investment_19 26.623 440s Investment_20 -5.632 440s Investment_21 -3.053 440s Investment_22 -10.316 440s PrivateWages_2 71.301 440s PrivateWages_3 -22.785 440s PrivateWages_4 -49.379 440s PrivateWages_5 46.976 440s PrivateWages_6 7.063 440s PrivateWages_8 -15.738 440s PrivateWages_9 -13.438 440s PrivateWages_10 -16.570 440s PrivateWages_11 5.681 440s PrivateWages_12 0.000 440s PrivateWages_13 -4.458 440s PrivateWages_14 14.480 440s PrivateWages_15 -1.907 440s PrivateWages_16 -3.685 440s PrivateWages_17 30.129 440s PrivateWages_18 0.751 440s PrivateWages_19 -105.007 440s PrivateWages_20 25.888 440s PrivateWages_21 -27.641 440s PrivateWages_22 46.859 440s [1] TRUE 440s > Bread 440s Consumption_(Intercept) Consumption_corpProf Consumption_corpProfLag 440s [1,] 132.9647 -4.1876 0.7762 440s [2,] -4.1876 1.2160 -0.6687 440s [3,] 0.7762 -0.6687 0.7219 440s [4,] -1.6897 -0.1344 -0.0278 440s [5,] 101.6483 3.2473 3.4997 440s [6,] -4.3150 0.5140 -0.4474 440s [7,] 1.5566 -0.3374Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 440s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 440s 0.4240 440s [8,] -0.2539 -0.0329 -0.0138 440s [9,] -35.7522 0.3296 1.6708 440s [10,] 0.5355 -0.0797 0.0478 440s [11,] 0.0459 0.0759 -0.0780 440s [12,] 0.1973 0.0481 0.0250 440s Consumption_wages Investment_(Intercept) Investment_corpProf 440s [1,] -1.689687 101.65 -4.32e+00 440s [2,] -0.134421 3.25 5.14e-01 440s [3,] -0.027837 3.50 -4.47e-01 440s [4,] 0.106098 -5.00 6.63e-02 440s [5,] -4.996393 2449.02 -4.26e+01 440s [6,] 0.066338 -42.57 1.86e+00 440s [7,] -0.064579 34.21 -1.44e+00 440s [8,] 0.024569 -11.36 1.70e-01 440s [9,] 0.047220 27.91 -2.66e-01 440s [10,] 0.000172 1.31 3.12e-04 440s [11,] -0.000827 -1.84 4.41e-03 440s [12,] -0.034079 -0.80 1.58e-02 440s Investment_corpProfLag Investment_capitalLag PrivateWages_(Intercept) 440s [1,] 1.55659 -0.25392 -35.7522 440s [2,] -0.33742 -0.03292 0.3296 440s [3,] 0.42396 -0.01383 1.6708 440s [4,] -0.06458 0.02457 0.0472 440s [5,] 34.20897 -11.35519 27.9136 440s [6,] -1.43523 0.17002 -0.2656 440s [7,] 1.37137 -0.15991 -0.3976 440s [8,] -0.15991 0.05521 -0.0847 440s [9,] -0.39759 -0.08475 68.4821 440s [10,] 0.00601 -0.00701 -0.3279 440s [11,] 0.00088 0.00875 -0.8283 440s [12,] -0.02279 0.00445 0.7887 440s PrivateWages_gnp PrivateWages_gnpLag PrivateWages_trend 440s [1,] 0.535460 0.045866 0.197271 440s [2,] -0.079666 0.075947 0.048142 440s [3,] 0.047829 -0.078006 0.025001 440s [4,] 0.000172 -0.000827 -0.034079 440s [5,] 1.306914 -1.841775 -0.800037 440s [6,] 0.000312 0.004408 0.015824 440s [7,] 0.006007 0.000880 -0.022790 440s [8,] -0.007006 0.008751 0.004448 440s [9,] -0.327909 -0.828330 0.788744 440s [10,] 0.051096 -0.046839 -0.013933 440s [11,] -0.046839 0.062505 0.000532 440s [12,] -0.013933 0.000532 0.045663 440s > 440s > # I3SLS 440s > summary 440s 440s systemfit results 440s method: iterated 3SLS 440s 440s convergence achieved after 9 iterations 440s 440s N DF SSR detRCov OLS-R2 McElroy-R2 440s system 57 45 75 0.422 0.959 0.993 440s 440s N DF SSR MSE RMSE R2 Adj R2 440s Consumption 18 14 22.7 1.622 1.273 0.973 0.968 440s Investment 19 15 42.1 2.809 1.676 0.762 0.715 440s PrivateWages 20 16 10.2 0.638 0.799 0.987 0.985 440s 440s The covariance matrix of the residuals used for estimation 440s Consumption Investment PrivateWages 440s Consumption 1.261 0.675 -0.439 440s Investment 0.675 1.949 0.237 440s PrivateWages -0.439 0.237 0.503 440s 440s The covariance matrix of the residuals 440s Consumption Investment PrivateWages 440s Consumption 1.261 0.675 -0.439 440s Investment 0.675 1.949 0.237 440s PrivateWages -0.439 0.237 0.503 440s 440s The correlations of the residuals 440s Consumption Investment PrivateWages 440s Consumption 1.000 0.431 -0.550 440s Investment 0.431 1.000 0.239 440s PrivateWages -0.550 0.239 1.000 440s 440s 440s 3SLS estimates for 'Consumption' (equation 1) 440s Model Formula: consump ~ corpProf + corpProfLag + wages 440s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 440s gnpLag 440s 440s Estimate Std. Error t value Pr(>|t|) 440s (Intercept) 18.5887 1.5250 12.19 7.6e-09 *** 440s corpProf -0.0438 0.1441 -0.30 0.77 440s corpProfLag 0.1456 0.1109 1.31 0.21 440s wages 0.8141 0.0428 19.01 2.1e-11 *** 440s --- 440s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 440s 440s Residual standard error: 1.273 on 14 degrees of freedom 440s Number of observations: 18 Degrees of Freedom: 14 440s SSR: 22.704 MSE: 1.622 Root MSE: 1.273 440s Multiple R-Squared: 0.973 Adjusted R-Squared: 0.968 440s 440s 440s 3SLS estimates for 'Investment' (equation 2) 440s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 440s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 440s gnpLag 440s 440s Estimate Std. Error t value Pr(>|t|) 440s (Intercept) 29.4725 7.6857 3.83 0.0016 ** 440s corpProf -0.0183 0.2154 -0.09 0.9333 440s corpProfLag 0.7195 0.1850 3.89 0.0015 ** 440s capitalLag -0.1985 0.0366 -5.43 6.9e-05 *** 440s --- 440s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 440s 440s Residual standard error: 1.676 on 15 degrees of freedom 440s Number of observations: 19 Degrees of Freedom: 15 440s SSR: 42.136 MSE: 2.809 Root MSE: 1.676 440s Multiple R-Squared: 0.762 Adjusted R-Squared: 0.715 440s 440s 440s 3SLS estimates for 'PrivateWages' (equation 3) 440s Model Formula: privWage ~ gnp + gnpLag + trend 440s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 440s gnpLag 440s 440s Estimate Std. Error t value Pr(>|t|) 440s (Intercept) 0.5385 1.1055 0.49 0.63277 440s gnp 0.4251 0.0287 14.80 9.3e-11 *** 440s gnpLag 0.1776 0.0322 5.51 4.7e-05 *** 440s trend 0.1211 0.0283 4.28 0.00057 *** 440s --- 440s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 440s 440s Residual standard error: 0.799 on 16 degrees of freedom 440s Number of observations: 20 Degrees of Freedom: 16 440s SSR: 10.204 MSE: 0.638 Root MSE: 0.799 440s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 440s 440s > residuals 440s Consumption Investment PrivateWages 440s 1 NA NA NA 440s 2 -0.9524 -2.2888 -1.1837 440s 3 -0.8681 0.0698 0.4581 440s 4 -1.1653 0.5368 1.3199 440s 5 0.0601 -1.6917 -0.2194 440s 6 0.6426 0.2972 -0.4805 440s 7 NA NA NA 440s 8 1.8394 1.3723 -0.8931 440s 9 1.8275 0.8861 0.1723 440s 10 NA 2.6574 1.0707 440s 11 -0.3387 -0.9736 -0.4288 440s 12 -1.4550 -0.8630 0.3956 440s 13 NA NA 0.0277 440s 14 -0.3782 1.7151 0.6823 440s 15 -0.7768 -0.1993 0.5638 440s 16 -0.4606 0.1448 0.2281 440s 17 1.8605 2.1295 -0.6557 440s 18 -0.5262 -0.1493 0.9718 440s 19 -0.3047 -3.4730 -0.6148 440s 20 1.3992 0.8566 -0.2636 440s 21 1.4216 0.4910 -1.1472 440s 22 -1.2431 1.2792 0.5323 440s > fitted 440s Consumption Investment PrivateWages 440s 1 NA NA NA 440s 2 42.9 2.0888 26.7 440s 3 45.9 1.8302 28.8 440s 4 50.4 4.6632 32.8 440s 5 50.5 4.6917 34.1 440s 6 52.0 4.8028 35.9 440s 7 NA NA NA 440s 8 54.4 2.8277 38.8 440s 9 55.5 2.1139 39.0 440s 10 NA 2.4426 40.2 440s 11 55.3 1.9736 38.3 440s 12 52.4 -2.5370 34.1 440s 13 NA NA 29.0 440s 14 46.9 -6.8151 27.8 440s 15 49.5 -2.8007 30.0 440s 16 51.8 -1.4448 33.0 440s 17 55.8 -0.0295 37.5 440s 18 59.2 2.1493 40.0 440s 19 57.8 1.5730 38.8 440s 20 60.2 0.4434 41.9 440s 21 63.6 2.8090 46.1 440s 22 70.9 3.6208 52.8 440s > predict 440s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 440s 1 NA NA NA NA 440s 2 42.9 0.541 41.8 43.9 440s 3 45.9 0.608 44.6 47.1 440s 4 50.4 0.403 49.6 51.2 440s 5 50.5 0.472 49.6 51.5 440s 6 52.0 0.481 51.0 52.9 440s 7 NA NA NA NA 440s 8 54.4 0.388 53.6 55.1 440s 9 55.5 0.458 54.6 56.4 440s 10 NA NA NA NA 440s 11 55.3 0.795 53.7 56.9 440s 12 52.4 0.762 50.8 53.9 440s 13 NA NA NA NA 440s 14 46.9 0.663 45.5 48.2 440s 15 49.5 0.462 48.5 50.4 440s 16 51.8 0.381 51.0 52.5 440s 17 55.8 0.423 55.0 56.7 440s 18 59.2 0.355 58.5 59.9 440s 19 57.8 0.484 56.8 58.8 440s 20 60.2 0.500 59.2 61.2 440s 21 63.6 0.490 62.6 64.6 440s 22 70.9 0.747 69.4 72.4 440s Investment.pred Investment.se.fit Investment.lwr Investment.upr 440s 1 NA NA NA NA 440s 2 2.0888 0.985 0.105 4.072 440s 3 1.8302 0.708 0.404 3.257 440s 4 4.6632 0.612 3.430 5.897 440s 5 4.6917 0.519 3.645 5.738 440s 6 4.8028 0.498 3.800 5.806 440s 7 NA NA NA NA 440s 8 2.8277 0.410 2.003 3.653 440s 9 2.1139 0.599 0.908 3.320 440s 10 2.4426 0.651 1.131 3.754 440s 11 1.9736 1.138 -0.320 4.267 440s 12 -2.5370 1.038 -4.627 -0.447 440s 13 NA NA NA NA 440s 14 -6.8151 1.011 -8.851 -4.779 440s 15 -2.8007 0.587 -3.984 -1.617 440s 16 -1.4448 0.470 -2.392 -0.498 440s 17 -0.0295 0.573 -1.183 1.124 440s 18 2.1493 0.380 1.384 2.915 440s 19 1.5730 0.624 0.315 2.831 440s 20 0.4434 0.649 -0.864 1.751 440s 21 2.8090 0.565 1.671 3.947 440s 22 3.6208 0.814 1.982 5.260 440s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 440s 1 NA NA NA NA 440s 2 26.7 0.322 26.0 27.3 440s 3 28.8 0.328 28.2 29.5 440s 4 32.8 0.332 32.1 33.4 440s 5 34.1 0.244 33.6 34.6 440s 6 35.9 0.252 35.4 36.4 440s 7 NA NA NA NA 440s 8 38.8 0.246 38.3 39.3 440s 9 39.0 0.234 38.6 39.5 440s 10 40.2 0.230 39.8 40.7 440s 11 38.3 0.299 37.7 38.9 440s 12 34.1 0.304 33.5 34.7 440s 13 29.0 0.366 28.2 29.7 440s 14 27.8 0.321 27.2 28.5 440s 15 30.0 0.317 29.4 30.7 440s 16 33.0 0.266 32.4 33.5 440s 17 37.5 0.270 36.9 38.0 440s 18 40.0 0.211 39.6 40.5 440s 19 38.8 0.305 38.2 39.4 440s 20 41.9 0.290 41.3 42.4 440s 21 46.1 0.309 45.5 46.8 440s 22 52.8 0.468 51.8 53.7 440s > model.frame 440s [1] TRUE 440s > model.matrix 440s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0339 >" 440s [2] "Attributes: < Component “dimnames”: Component 1: 52 string mismatches >" 440s [3] "Numeric: lengths (708, 684) differ" 440s > nobs 440s [1] 57 440s > linearHypothesis 440s Linear hypothesis test (Theil's F test) 440s 440s Hypothesis: 440s Consumption_corpProf + Investment_capitalLag = 0 440s 440s Model 1: restricted model 440s Model 2: kleinModel 440s 440s Res.Df Df F Pr(>F) 440s 1 46 440s 2 45 1 2.17 0.15 440s Linear hypothesis test (F statistic of a Wald test) 440s 440s Hypothesis: 440s Consumption_corpProf + Investment_capitalLag = 0 440s 440s Model 1: restricted model 440s Model 2: kleinModel 440s 440s Res.Df Df F Pr(>F) 440s 1 46 440s 2 45 1 2.84 0.099 . 440s --- 440s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 440s Linear hypothesis test (Chi^2 statistic of a Wald test) 440s 440s Hypothesis: 440s Consumption_corpProf + Investment_capitalLag = 0 440s 440s Model 1: restricted model 440s Model 2: kleinModel 440s 440s Res.Df Df Chisq Pr(>Chisq) 440s 1 46 440s 2 45 1 2.84 0.092 . 440s --- 440s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 440s Linear hypothesis test (Theil's F test) 440s 440s Hypothesis: 440s Consumption_corpProf + Investment_capitalLag = 0 440s Consumption_corpProfLag - PrivateWages_trend = 0 440s 440s Model 1: restricted model 440s Model 2: kleinModel 440s 440s Res.Df Df F Pr(>F) 440s 1 47 440s 2 45 2 2.45 0.098 . 440s --- 440s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 440s Linear hypothesis test (F statistic of a Wald test) 440s 440s Hypothesis: 440s Consumption_corpProf + Investment_capitalLag = 0 440s Consumption_corpProfLag - PrivateWages_trend = 0 440s 440s Model 1: restricted model 440s Model 2: kleinModel 440s 440s Res.Df Df F Pr(>F) 440s 1 47 440s 2 45 2 3.2 0.05 . 440s --- 440s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 440s Linear hypothesis test (Chi^2 statistic of a Wald test) 440s 440s Hypothesis: 440s Consumption_corpProf + Investment_capitalLag = 0 440s Consumption_corpProfLag - PrivateWages_trend = 0 440s 440s Model 1: restricted model 440s Model 2: kleinModel 440s 440s Res.Df Df Chisq Pr(>Chisq) 440s 1 47 440s 2 45 2 6.4 0.041 * 440s --- 440s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 440s > logLik 440s 'log Lik.' -72.7 (df=18) 440s 'log Lik.' -83.9 (df=18) 440s Estimating function 440s Consumption_(Intercept) Consumption_corpProf 440s Consumption_2 -4.8293 -67.67 440s Consumption_3 -1.2969 -21.61 440s Consumption_4 0.5735 10.64 440s Consumption_5 -2.6416 -53.54 440s Consumption_6 1.4014 26.66 440s Consumption_8 6.4885 114.49 440s Consumption_9 5.8062 109.99 440s Consumption_11 -2.4210 -40.53 440s Consumption_12 -3.6335 -48.60 440s Consumption_14 0.4385 4.39 440s Consumption_15 -2.9914 -37.40 440s Consumption_16 -2.4677 -35.74 440s Consumption_17 8.1448 121.46 440s Consumption_18 -0.7823 -15.19 440s Consumption_19 -6.2524 -119.47 440s Consumption_20 4.4447 78.49 440s Consumption_21 2.3016 46.94 440s Consumption_22 -1.0069 -22.83 440s Investment_2 2.3888 33.48 440s Investment_3 -0.0694 -1.16 440s Investment_4 -0.5723 -10.61 440s Investment_5 1.7561 35.59 440s Investment_6 -0.2966 -5.64 440s Investment_8 -1.4003 -24.71 440s Investment_9 -0.9021 -17.09 440s Investment_10 0.0000 0.00 440s Investment_11 0.9937 16.63 440s Investment_12 0.8671 11.60 440s Investment_14 -1.7814 -17.86 440s Investment_15 0.1989 2.49 440s Investment_16 -0.1587 -2.30 440s Investment_17 -2.1900 -32.66 440s Investment_18 0.1172 2.28 440s Investment_19 3.5762 68.34 440s Investment_20 -0.8719 -15.40 440s Investment_21 -0.4978 -10.15 440s Investment_22 -1.3322 -30.21 440s PrivateWages_2 -4.3522 -60.99 440s PrivateWages_3 1.6337 27.22 440s PrivateWages_4 3.8487 71.37 440s PrivateWages_5 -4.1966 -85.06 440s PrivateWages_6 -0.7579 -14.42 440s PrivateWages_8 2.3542 41.54 440s PrivateWages_9 2.6975 51.10 440s PrivateWages_10 0.0000 0.00 440s PrivateWages_11 -3.6015 -60.29 440s PrivateWages_12 -0.5133 -6.87 440s PrivateWages_13 0.0000 0.00 440s PrivateWages_14 4.6825 46.94 440s PrivateWages_15 -0.1944 -2.43 440s PrivateWages_16 -0.4112 -5.96 440s PrivateWages_17 3.8500 57.41 440s PrivateWages_18 0.1148 2.23 440s PrivateWages_19 -9.2669 -177.08 440s PrivateWages_20 2.0821 36.77 440s PrivateWages_21 -1.9079 -38.91 440s PrivateWages_22 2.8370 64.33 440s Consumption_corpProfLag Consumption_wages 440s Consumption_2 -61.332 -144.02 440s Consumption_3 -16.082 -41.30 440s Consumption_4 9.693 20.22 440s Consumption_5 -48.605 -101.97 440s Consumption_6 27.187 54.02 440s Consumption_8 127.174 259.60 440s Consumption_9 114.963 242.56 440s Consumption_11 -52.537 -104.35 440s Consumption_12 -56.683 -144.08 440s Consumption_14 3.069 14.61 440s Consumption_15 -33.504 -111.68 440s Consumption_16 -30.352 -99.06 440s Consumption_17 114.027 340.28 440s Consumption_18 -13.768 -37.24 440s Consumption_19 -108.167 -307.82 440s Consumption_20 68.004 215.82 440s Consumption_21 43.729 122.95 440s Consumption_22 -21.245 -61.21 440s Investment_2 30.338 71.24 440s Investment_3 -0.861 -2.21 440s Investment_4 -9.672 -20.18 440s Investment_5 32.311 67.78 440s Investment_6 -5.754 -11.43 440s Investment_8 -27.445 -56.02 440s Investment_9 -17.861 -37.69 440s Investment_10 0.000 0.00 440s Investment_11 21.563 42.83 440s Investment_12 13.527 34.39 440s Investment_14 -12.470 -59.37 440s Investment_15 2.228 7.43 440s Investment_16 -1.952 -6.37 440s Investment_17 -30.659 -91.49 440s Investment_18 2.063 5.58 440s Investment_19 61.869 176.07 440s Investment_20 -13.340 -42.34 440s Investment_21 -9.458 -26.59 440s Investment_22 -28.109 -80.99 440s PrivateWages_2 -55.273 -129.79 440s PrivateWages_3 20.257 52.03 440s PrivateWages_4 65.044 135.69 440s PrivateWages_5 -77.218 -161.99 440s PrivateWages_6 -14.704 -29.21 440s PrivateWages_8 46.143 94.19 440s PrivateWages_9 53.410 112.69 440s PrivateWages_10 0.000 0.00 440s PrivateWages_11 -78.152 -155.23 440s PrivateWages_12 -8.008 -20.36 440s PrivateWages_13 0.000 0.00 440s PrivateWages_14 32.778 156.05 440s PrivateWages_15 -2.178 -7.26 440s PrivateWages_16 -5.058 -16.51 440s PrivateWages_17 53.901 160.85 440s PrivateWages_18 2.020 5.46 440s PrivateWages_19 -160.318 -456.23 440s PrivateWages_20 31.857 101.10 440s PrivateWages_21 -36.250 -101.92 440s PrivateWages_22 59.861 172.47 440s Investment_(Intercept) Investment_corpProf 440s Consumption_2 2.3171 31.08 440s Consumption_3 0.6223 10.39 440s Consumption_4 -0.2752 -5.17 440s Consumption_5 1.2675 26.17 440s Consumption_6 -0.6724 -12.95 440s Consumption_8 -3.1132 -54.59 440s Consumption_9 -2.7858 -54.40 440s Consumption_11 1.1616 19.97 440s Consumption_12 1.7434 23.57 440s Consumption_14 -0.2104 -2.10 440s Consumption_15 1.4353 18.46 440s Consumption_16 1.1840 16.97 440s Consumption_17 -3.9079 -58.49 440s Consumption_18 0.3753 7.27 440s Consumption_19 2.9999 58.07 440s Consumption_20 -2.1326 -37.27 440s Consumption_21 -1.1043 -22.22 440s Consumption_22 0.4831 11.01 440s Investment_2 -2.3817 -31.95 440s Investment_3 0.0692 1.16 440s Investment_4 0.5706 10.72 440s Investment_5 -1.7509 -36.15 440s Investment_6 0.2957 5.70 440s Investment_8 1.3961 24.48 440s Investment_9 0.8994 17.56 440s Investment_10 2.7604 55.95 440s Investment_11 -0.9907 -17.04 440s Investment_12 -0.8646 -11.69 440s Investment_14 1.7761 17.75 440s Investment_15 -0.1983 -2.55 440s Investment_16 0.1582 2.27 440s Investment_17 2.1835 32.68 440s Investment_18 -0.1169 -2.26 440s Investment_19 -3.5657 -69.02 440s Investment_20 0.8693 15.19 440s Investment_21 0.4963 9.99 440s Investment_22 1.3282 30.26 440s PrivateWages_2 2.5510 34.22 440s PrivateWages_3 -0.9575 -15.98 440s PrivateWages_4 -2.2559 -42.38 440s PrivateWages_5 2.4598 50.79 440s PrivateWages_6 0.4442 8.56 440s PrivateWages_8 -1.3799 -24.19 440s PrivateWages_9 -1.5811 -30.88 440s PrivateWages_10 -2.9678 -60.15 440s PrivateWages_11 2.1109 36.30 440s PrivateWages_12 0.3009 4.07 440s PrivateWages_13 0.0000 0.00 440s PrivateWages_14 -2.7446 -27.43 440s PrivateWages_15 0.1140 1.47 440s PrivateWages_16 0.2410 3.45 440s PrivateWages_17 -2.2567 -33.77 440s PrivateWages_18 -0.0673 -1.30 440s PrivateWages_19 5.4317 105.14 440s PrivateWages_20 -1.2204 -21.33 440s PrivateWages_21 1.1183 22.50 440s PrivateWages_22 -1.6629 -37.88 440s Investment_corpProfLag Investment_capitalLag 440s Consumption_2 29.428 423.6 440s Consumption_3 7.716 113.6 440s Consumption_4 -4.651 -50.8 440s Consumption_5 23.321 240.4 440s Consumption_6 -13.045 -129.6 440s Consumption_8 -61.019 -633.2 440s Consumption_9 -55.160 -578.3 440s Consumption_11 25.207 250.6 440s Consumption_12 27.197 377.8 440s Consumption_14 -1.473 -43.6 440s Consumption_15 16.075 289.9 440s Consumption_16 14.563 235.6 440s Consumption_17 -54.711 -772.6 440s Consumption_18 6.606 75.0 440s Consumption_19 51.899 605.4 440s Consumption_20 -32.629 -426.3 440s Consumption_21 -20.982 -222.2 440s Consumption_22 10.194 98.8 440s Investment_2 -30.248 -435.4 440s Investment_3 0.858 12.6 440s Investment_4 9.643 105.3 440s Investment_5 -32.216 -332.1 440s Investment_6 5.737 57.0 440s Investment_8 27.364 284.0 440s Investment_9 17.808 186.7 440s Investment_10 58.244 581.3 440s Investment_11 -21.499 -213.7 440s Investment_12 -13.487 -187.4 440s Investment_14 12.433 367.8 440s Investment_15 -2.221 -40.1 440s Investment_16 1.946 31.5 440s Investment_17 30.569 431.7 440s Investment_18 -2.057 -23.4 440s Investment_19 -61.686 -719.5 440s Investment_20 13.301 173.8 440s Investment_21 9.430 99.9 440s Investment_22 28.026 271.6 440s PrivateWages_2 32.397 466.3 440s PrivateWages_3 -11.874 -174.8 440s PrivateWages_4 -38.124 -416.2 440s PrivateWages_5 45.260 466.6 440s PrivateWages_6 8.618 85.6 440s PrivateWages_8 -27.046 -280.7 440s PrivateWages_9 -31.306 -328.2 440s PrivateWages_10 -62.621 -625.0 440s PrivateWages_11 45.808 455.3 440s PrivateWages_12 4.694 65.2 440s PrivateWages_13 0.000 0.0 440s PrivateWages_14 -19.212 -568.4 440s PrivateWages_15 1.276 23.0 440s PrivateWages_16 2.965 48.0 440s PrivateWages_17 -31.593 -446.1 440s PrivateWages_18 -1.184 -13.4 440s PrivateWages_19 93.968 1096.1 440s PrivateWages_20 -18.672 -244.0 440s PrivateWages_21 21.247 225.0 440s PrivateWages_22 -35.087 -340.1 440s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 440s Consumption_2 -5.2993 -249.44 -237.94 440s Consumption_3 -1.4232 -70.56 -64.90 440s Consumption_4 0.6293 35.58 31.53 440s Consumption_5 -2.8987 -175.91 -165.80 440s Consumption_6 1.5378 93.21 87.81 440s Consumption_8 7.1199 427.18 455.67 440s Consumption_9 6.3712 396.73 410.31 440s Consumption_11 -2.6567 -169.26 -178.00 440s Consumption_12 -3.9871 -218.62 -244.01 440s Consumption_14 0.4811 20.27 21.31 440s Consumption_15 -3.2826 -168.12 -148.04 440s Consumption_16 -2.7078 -149.85 -134.58 440s Consumption_17 8.9374 512.95 486.20 440s Consumption_18 -0.8584 -57.66 -53.82 440s Consumption_19 -6.8609 -470.06 -445.96 440s Consumption_20 4.8772 326.02 297.02 440s Consumption_21 2.5255 189.08 175.52 440s Consumption_22 -1.1049 -95.99 -83.64 440s Investment_2 3.2022 150.73 143.78 440s Investment_3 -0.0931 -4.61 -4.24 440s Investment_4 -0.7671 -43.38 -38.43 440s Investment_5 2.3540 142.85 134.65 440s Investment_6 -0.3976 -24.10 -22.70 440s Investment_8 -1.8770 -112.62 -120.13 440s Investment_9 -1.2092 -75.30 -77.87 440s Investment_10 -3.7113 -239.64 -239.38 440s Investment_11 1.3320 84.87 89.25 440s Investment_12 1.1624 63.74 71.14 440s Investment_14 -2.3880 -100.60 -105.79 440s Investment_15 0.2667 13.66 12.03 440s Investment_16 -0.2127 -11.77 -10.57 440s Investment_17 -2.9356 -168.49 -159.70 440s Investment_18 0.1571 10.56 9.85 440s Investment_19 4.7939 328.45 311.61 440s Investment_20 -1.1688 -78.13 -71.18 440s Investment_21 -0.6673 -49.96 -46.38 440s Investment_22 -1.7858 -155.15 -135.18 440s PrivateWages_2 -8.5877 -404.22 -385.59 440s PrivateWages_3 3.2235 159.82 146.99 440s PrivateWages_4 7.5943 429.40 380.48 440s PrivateWages_5 -8.2808 -502.53 -473.66 440s PrivateWages_6 -1.4955 -90.64 -85.39 440s PrivateWages_8 4.6454 278.71 297.31 440s PrivateWages_9 5.3226 331.43 342.78 440s PrivateWages_10 9.9910 645.11 644.42 440s PrivateWages_11 -7.1064 -452.76 -476.13 440s PrivateWages_12 -1.0129 -55.54 -61.99 440s PrivateWages_13 -5.2725 -247.69 -281.55 440s PrivateWages_14 9.2395 389.24 409.31 440s PrivateWages_15 -0.3837 -19.65 -17.30 440s PrivateWages_16 -0.8115 -44.91 -40.33 440s PrivateWages_17 7.5969 436.02 413.27 440s PrivateWages_18 0.2264 15.21 14.20 440s PrivateWages_19 -18.2855 -1252.79 -1188.56 440s PrivateWages_20 4.1085 274.63 250.21 440s PrivateWages_21 -3.7647 -281.85 -261.64 440s PrivateWages_22 5.5980 486.35 423.77 440s PrivateWages_trend 440s Consumption_2 52.993 440s Consumption_3 12.808 440s Consumption_4 -5.035 440s Consumption_5 20.291 440s Consumption_6 -9.227 440s Consumption_8 -28.480 440s Consumption_9 -19.114 440s Consumption_11 2.657 440s Consumption_12 0.000 440s Consumption_14 0.962 440s Consumption_15 -9.848 440s Consumption_16 -10.831 440s Consumption_17 44.687 440s Consumption_18 -5.151 440s Consumption_19 -48.026 440s Consumption_20 39.018 440s Consumption_21 22.730 440s Consumption_22 -11.049 440s Investment_2 -32.022 440s Investment_3 0.838 440s Investment_4 6.137 440s Investment_5 -16.478 440s Investment_6 2.386 440s Investment_8 7.508 440s Investment_9 3.628 440s Investment_10 7.423 440s Investment_11 -1.332 440s Investment_12 0.000 440s Investment_14 -4.776 440s Investment_15 0.800 440s Investment_16 -0.851 440s Investment_17 -14.678 440s Investment_18 0.943 440s Investment_19 33.558 440s Investment_20 -9.351 440s Investment_21 -6.006 440s Investment_22 -17.858 440s PrivateWages_2 85.877 440s PrivateWages_3 -29.012 440s PrivateWages_4 -60.755 440s PrivateWages_5 57.966 440s PrivateWages_6 8.973 440s PrivateWages_8 -18.582 440s PrivateWages_9 -15.968 440s PrivateWages_10 -19.982 440s PrivateWages_11 7.106 440s PrivateWages_12 0.000 440s PrivateWages_13 -5.272 440s PrivateWages_14 18.479 440s PrivateWages_15 -1.151 440s PrivateWages_16 -3.246 440s PrivateWages_17 37.985 440s PrivateWages_18 1.359 440s PrivateWages_19 -127.998 440s PrivateWages_20 32.868 440s PrivateWages_21 -33.882 440s PrivateWages_22 55.980 440s [1] TRUE 440s > Bread 440s Consumption_(Intercept) Consumption_corpProf Consumption_corpProfLag 440s [1,] 132.5589 -4.1405 0.7711 440s [2,] -4.1405 1.1839 -0.6491 440s [3,] 0.7711 -0.6491 0.7009 440s [4,] -1.6944 -0.1297 -0.0283 440s [5,] 114.8656 3.1837 5.1587 440s [6,] -5.5704 0.7491 -0.6223 440s [7,] 1.9218 -0.4973 0.5817 440s [8,] -0.2370 -0.0398 -0.0201 440s [9,] -36.8131 0.3292 1.6643 440s [10,] 0.5110 -0.0698 0.0440 440s [11,] 0.0898 0.0655 -0.0737 440s [12,] 0.2835 0.0505 0.0244 440s Consumption_wages Investment_(Intercept) Investment_corpProf 440s [1,] -1.694379 114.87 -5.57043 440s [2,] -0.129702 3.18 0.74914 440s [3,] -0.028262 5.16 -0.62232 440s [4,] 0.104489 -5.87 0.06772 440s [5,] -5.874854 3366.95 -56.98587 440s [6,] 0.067720 -56.99 2.64551 440s [7,] -0.069795 45.44 -2.02544 440s [8,] 0.029271 -15.60Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 440s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 440s 0.22292 440s [9,] 0.075832 53.51 -0.48750 440s [10,] -0.001892 2.12 0.00442 440s [11,] 0.000817 -3.12 0.00410 440s [12,] -0.036920 -1.40 0.02820 440s Investment_corpProfLag Investment_capitalLag PrivateWages_(Intercept) 440s [1,] 1.92185 -0.23700 -36.8131 440s [2,] -0.49725 -0.03983 0.3292 440s [3,] 0.58170 -0.02007 1.6643 440s [4,] -0.06979 0.02927 0.0758 440s [5,] 45.44092 -15.60143 53.5110 440s [6,] -2.02544 0.22292 -0.4875 440s [7,] 1.95029 -0.21271 -0.7904 440s [8,] -0.21271 0.07616 -0.1618 440s [9,] -0.79038 -0.16180 69.6580 440s [10,] 0.00806 -0.01150 -0.3039 440s [11,] 0.00580 0.01472 -0.8753 440s [12,] -0.04133 0.00782 0.7539 440s PrivateWages_gnp PrivateWages_gnpLag PrivateWages_trend 440s [1,] 0.51104 0.089786 0.283482 440s [2,] -0.06979 0.065456 0.050508 440s [3,] 0.04399 -0.073692 0.024378 440s [4,] -0.00189 0.000817 -0.036920 440s [5,] 2.11576 -3.117775 -1.396100 440s [6,] 0.00442 0.004099 0.028202 440s [7,] 0.00806 0.005798 -0.041335 440s [8,] -0.01150 0.014719 0.007824 440s [9,] -0.30387 -0.875279 0.753905 440s [10,] 0.04699 -0.042862 -0.013049 440s [11,] -0.04286 0.059096 0.000172 440s [12,] -0.01305 0.000172 0.045631 440s > 440s > # OLS 440s > summary 440s 440s systemfit results 440s method: OLS 440s 440s N DF SSR detRCov OLS-R2 McElroy-R2 440s system 58 46 44.2 0.565 0.976 0.991 440s 440s N DF SSR MSE RMSE R2 Adj R2 440s Consumption 19 15 17.36 1.157 1.08 0.980 0.976 440s Investment 19 15 17.11 1.140 1.07 0.907 0.889 440s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 440s 440s The covariance matrix of the residuals 440s Consumption Investment PrivateWages 440s Consumption 1.285 0.061 -0.511 440s Investment 0.061 1.059 0.151 440s PrivateWages -0.511 0.151 0.648 440s 440s The correlations of the residuals 440s Consumption Investment PrivateWages 440s Consumption 1.0000 0.0457 -0.568 440s Investment 0.0457 1.0000 0.168 440s PrivateWages -0.5681 0.1676 1.000 440s 440s 440s OLS estimates for 'Consumption' (equation 1) 440s Model Formula: consump ~ corpProf + corpProfLag + wages 440s 440s Estimate Std. Error t value Pr(>|t|) 440s (Intercept) 16.2957 1.5438 10.56 2.4e-08 *** 440s corpProf 0.1796 0.1206 1.49 0.16 440s corpProfLag 0.1032 0.1031 1.00 0.33 440s wages 0.7962 0.0449 17.73 1.8e-11 *** 440s --- 440s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 440s 440s Residual standard error: 1.076 on 15 degrees of freedom 440s Number of observations: 19 Degrees of Freedom: 15 440s SSR: 17.362 MSE: 1.157 Root MSE: 1.076 440s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.976 440s 440s 440s OLS estimates for 'Investment' (equation 2) 440s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 440s 440s Estimate Std. Error t value Pr(>|t|) 440s (Intercept) 10.1724 5.5758 1.82 0.08808 . 440s corpProf 0.5004 0.1092 4.58 0.00036 *** 440s corpProfLag 0.3270 0.1052 3.11 0.00718 ** 440s capitalLag -0.1134 0.0275 -4.13 0.00090 *** 440s --- 440s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 440s 440s Residual standard error: 1.068 on 15 degrees of freedom 440s Number of observations: 19 Degrees of Freedom: 15 440s SSR: 17.105 MSE: 1.14 Root MSE: 1.068 440s Multiple R-Squared: 0.907 Adjusted R-Squared: 0.889 440s 440s 440s OLS estimates for 'PrivateWages' (equation 3) 440s Model Formula: privWage ~ gnp + gnpLag + trend 440s 440s Estimate Std. Error t value Pr(>|t|) 440s (Intercept) 1.3550 1.3512 1.00 0.3309 440s gnp 0.4417 0.0342 12.92 7e-10 *** 440s gnpLag 0.1466 0.0393 3.73 0.0018 ** 440s trend 0.1244 0.0347 3.58 0.0025 ** 440s --- 440s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 440s 440s Residual standard error: 0.78 on 16 degrees of freedom 440s Number of observations: 20 Degrees of Freedom: 16 440s SSR: 9.739 MSE: 0.609 Root MSE: 0.78 440s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 440s 440s compare coef with single-equation OLS 440s [1] TRUE 440s > residuals 440s Consumption Investment PrivateWages 440s 1 NA NA NA 440s 2 -0.3863 0.00693 -1.3389 440s 3 -1.2484 -0.06954 0.2462 440s 4 -1.6040 1.22401 1.1255 440s 5 -0.5384 -1.37697 -0.1959 440s 6 -0.0413 0.38610 -0.5284 440s 7 0.8043 1.48598 NA 440s 8 1.2830 0.78465 -0.7909 440s 9 1.0142 -0.65483 0.2819 440s 10 NA 1.06018 1.1384 440s 11 0.1429 0.39508 -0.1904 440s 12 -0.3439 0.20479 0.5813 440s 13 NA NA 0.1206 440s 14 0.3199 0.32778 0.4773 440s 15 -0.1016 -0.07450 0.3035 440s 16 -0.0702 NA 0.0284 440s 17 1.6064 0.96998 -0.8517 440s 18 -0.4980 0.08124 0.9908 440s 19 0.1253 -2.49295 -0.4597 440s 20 0.9805 -0.70609 -0.3819 440s 21 0.7551 -0.81928 -1.1062 440s 22 -2.1992 -0.73256 0.5501 440s > fitted 440s Consumption Investment PrivateWages 440s 1 NA NA NA 440s 2 42.3 -0.207 26.8 440s 3 46.2 1.970 29.1 440s 4 50.8 3.976 33.0 440s 5 51.1 4.377 34.1 440s 6 52.6 4.714 35.9 440s 7 54.3 4.114 NA 440s 8 54.9 3.415 38.7 440s 9 56.3 3.655 38.9 440s 10 NA 4.040 40.2 440s 11 54.9 0.605 38.1 440s 12 51.2 -3.605 33.9 440s 13 NA NA 28.9 440s 14 46.2 -5.428 28.0 440s 15 48.8 -2.926 30.3 440s 16 51.4 NA 33.2 440s 17 56.1 1.130 37.7 440s 18 59.2 1.919 40.0 440s 19 57.4 0.593 38.7 440s 20 60.6 2.006 42.0 440s 21 64.2 4.119 46.1 440s 22 71.9 5.633 52.7 440s > predict 440s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 440s 1 NA NA NA NA 440s 2 42.3 0.543 39.9 44.7 440s 3 46.2 0.581 43.8 48.7 440s 4 50.8 0.394 48.5 53.1 440s 5 51.1 0.465 48.8 53.5 440s 6 52.6 0.474 50.3 55.0 440s 7 54.3 0.423 52.0 56.6 440s 8 54.9 0.389 52.6 57.2 440s 9 56.3 0.434 54.0 58.6 440s 10 NA NA NA NA 440s 11 54.9 0.727 52.2 57.5 440s 12 51.2 0.662 48.7 53.8 440s 13 NA NA NA NA 440s 14 46.2 0.698 43.6 48.8 440s 15 48.8 0.470 46.4 51.2 440s 16 51.4 0.398 49.1 53.7 440s 17 56.1 0.405 53.8 58.4 440s 18 59.2 0.375 56.9 61.5 440s 19 57.4 0.466 55.0 59.7 440s 20 60.6 0.482 58.2 63.0 440s 21 64.2 0.485 61.9 66.6 440s 22 71.9 0.755 69.3 74.5 440s Investment.pred Investment.se.fit Investment.lwr Investment.upr 440s 1 NA NA NA NA 440s 2 -0.207 0.645 -2.718 2.30 440s 3 1.970 0.523 -0.423 4.36 440s 4 3.976 0.462 1.634 6.32 440s 5 4.377 0.383 2.094 6.66 440s 6 4.714 0.362 2.444 6.98 440s 7 4.114 0.336 1.861 6.37 440s 8 3.415 0.298 1.184 5.65 440s 9 3.655 0.400 1.359 5.95 440s 10 4.040 0.458 1.701 6.38 440s 11 0.605 0.666 -1.928 3.14 440s 12 -3.605 0.637 -6.108 -1.10 440s 13 NA NA NA NA 440s 14 -5.428 0.767 -8.074 -2.78 440s 15 -2.926 0.453 -5.261 -0.59 440s 16 NA NA NA NA 440s 17 1.130 0.366 -1.142 3.40 440s 18 1.919 0.258 -0.293 4.13 440s 19 0.593 0.357 -1.674 2.86 440s 20 2.006 0.384 -0.278 4.29 440s 21 4.119 0.350 1.858 6.38 440s 22 5.633 0.495 3.263 8.00 440s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 440s 1 NA NA NA NA 440s 2 26.8 0.378 25.1 28.6 440s 3 29.1 0.381 27.3 30.8 440s 4 33.0 0.384 31.2 34.7 440s 5 34.1 0.297 32.4 35.8 440s 6 35.9 0.296 34.2 37.6 440s 7 NA NA NA NA 440s 8 38.7 0.303 37.0 40.4 440s 9 38.9 0.288 37.2 40.6 440s 10 40.2 0.274 38.5 41.8 440s 11 38.1 0.377 36.3 39.8 440s 12 33.9 0.381 32.2 35.7 440s 13 28.9 0.452 27.1 30.7 440s 14 28.0 0.397 26.3 29.8 440s 15 30.3 0.391 28.5 32.1 440s 16 33.2 0.327 31.5 34.9 440s 17 37.7 0.320 36.0 39.3 440s 18 40.0 0.250 38.4 41.7 440s 19 38.7 0.375 36.9 40.4 440s 20 42.0 0.337 40.3 43.7 440s 21 46.1 0.352 44.4 47.8 440s 22 52.7 0.530 50.9 54.6 440s > model.frame 440s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 440s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 440s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 440s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 440s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 440s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 440s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 440s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 440s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 440s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 440s 10 57.8 21.7 21.1 NA 5.1 211 41.3 67.0 64.5 440s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 440s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 440s 13 45.6 NA 11.4 34.3 -6.2 213 29.0 44.3 53.4 440s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 440s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 440s 16 51.3 14.0 12.3 39.3 NA 199 33.2 54.4 49.7 440s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 440s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 440s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 440s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 440s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 440s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 440s trend 440s 1 -11 440s 2 -10 440s 3 -9 440s 4 -8 440s 5 -7 440s 6 -6 440s 7 -5 440s 8 -4 440s 9 -3 440s 10 -2 440s 11 -1 440s 12 0 440s 13 1 440s 14 2 440s 15 3 440s 16 4 440s 17 5 440s 18 6 440s 19 7 440s 20 8 440s 21 9 440s 22 10 440s > model.matrix 440s Consumption_(Intercept) Consumption_corpProf 440s Consumption_2 1 12.4 440s Consumption_3 1 16.9 440s Consumption_4 1 18.4 440s Consumption_5 1 19.4 440s Consumption_6 1 20.1 440s Consumption_7 1 19.6 440s Consumption_8 1 19.8 440s Consumption_9 1 21.1 440s Consumption_11 1 15.6 440s Consumption_12 1 11.4 440s Consumption_14 1 11.2 440s Consumption_15 1 12.3 440s Consumption_16 1 14.0 440s Consumption_17 1 17.6 440s Consumption_18 1 17.3 440s Consumption_19 1 15.3 440s Consumption_20 1 19.0 440s Consumption_21 1 21.1 440s Consumption_22 1 23.5 440s Investment_2 0 0.0 440s Investment_3 0 0.0 440s Investment_4 0 0.0 440s Investment_5 0 0.0 440s Investment_6 0 0.0 440s Investment_7 0 0.0 440s Investment_8 0 0.0 440s Investment_9 0 0.0 440s Investment_10 0 0.0 440s Investment_11 0 0.0 440s Investment_12 0 0.0 440s Investment_14 0 0.0 440s Investment_15 0 0.0 440s Investment_17 0 0.0 440s Investment_18 0 0.0 440s Investment_19 0 0.0 440s Investment_20 0 0.0 440s Investment_21 0 0.0 440s Investment_22 0 0.0 440s PrivateWages_2 0 0.0 440s PrivateWages_3 0 0.0 440s PrivateWages_4 0 0.0 440s PrivateWages_5 0 0.0 440s PrivateWages_6 0 0.0 440s PrivateWages_8 0 0.0 440s PrivateWages_9 0 0.0 440s PrivateWages_10 0 0.0 440s PrivateWages_11 0 0.0 440s PrivateWages_12 0 0.0 440s PrivateWages_13 0 0.0 440s PrivateWages_14 0 0.0 440s PrivateWages_15 0 0.0 440s PrivateWages_16 0 0.0 440s PrivateWages_17 0 0.0 440s PrivateWages_18 0 0.0 440s PrivateWages_19 0 0.0 440s PrivateWages_20 0 0.0 440s PrivateWages_21 0 0.0 440s PrivateWages_22 0 0.0 440s Consumption_corpProfLag Consumption_wages 440s Consumption_2 12.7 28.2 440s Consumption_3 12.4 32.2 440s Consumption_4 16.9 37.0 440s Consumption_5 18.4 37.0 440s Consumption_6 19.4 38.6 440s Consumption_7 20.1 40.7 440s Consumption_8 19.6 41.5 440s Consumption_9 19.8 42.9 440s Consumption_11 21.7 42.1 440s Consumption_12 15.6 39.3 440s Consumption_14 7.0 34.1 440s Consumption_15 11.2 36.6 440s Consumption_16 12.3 39.3 440s Consumption_17 14.0 44.2 440s Consumption_18 17.6 47.7 440s Consumption_19 17.3 45.9 440s Consumption_20 15.3 49.4 440s Consumption_21 19.0 53.0 440s Consumption_22 21.1 61.8 440s Investment_2 0.0 0.0 440s Investment_3 0.0 0.0 440s Investment_4 0.0 0.0 440s Investment_5 0.0 0.0 440s Investment_6 0.0 0.0 440s Investment_7 0.0 0.0 440s Investment_8 0.0 0.0 440s Investment_9 0.0 0.0 440s Investment_10 0.0 0.0 440s Investment_11 0.0 0.0 440s Investment_12 0.0 0.0 440s Investment_14 0.0 0.0 440s Investment_15 0.0 0.0 440s Investment_17 0.0 0.0 440s Investment_18 0.0 0.0 440s Investment_19 0.0 0.0 440s Investment_20 0.0 0.0 440s Investment_21 0.0 0.0 440s Investment_22 0.0 0.0 440s PrivateWages_2 0.0 0.0 440s PrivateWages_3 0.0 0.0 440s PrivateWages_4 0.0 0.0 440s PrivateWages_5 0.0 0.0 440s PrivateWages_6 0.0 0.0 440s PrivateWages_8 0.0 0.0 440s PrivateWages_9 0.0 0.0 440s PrivateWages_10 0.0 0.0 440s PrivateWages_11 0.0 0.0 440s PrivateWages_12 0.0 0.0 440s PrivateWages_13 0.0 0.0 440s PrivateWages_14 0.0 0.0 440s PrivateWages_15 0.0 0.0 440s PrivateWages_16 0.0 0.0 440s PrivateWages_17 0.0 0.0 440s PrivateWages_18 0.0 0.0 440s PrivateWages_19 0.0 0.0 440s PrivateWages_20 0.0 0.0 440s PrivateWages_21 0.0 0.0 440s PrivateWages_22 0.0 0.0 440s Investment_(Intercept) Investment_corpProf 440s Consumption_2 0 0.0 440s Consumption_3 0 0.0 440s Consumption_4 0 0.0 440s Consumption_5 0 0.0 440s Consumption_6 0 0.0 440s Consumption_7 0 0.0 440s Consumption_8 0 0.0 440s Consumption_9 0 0.0 440s Consumption_11 0 0.0 440s Consumption_12 0 0.0 440s Consumption_14 0 0.0 440s Consumption_15 0 0.0 440s Consumption_16 0 0.0 440s Consumption_17 0 0.0 440s Consumption_18 0 0.0 440s Consumption_19 0 0.0 440s Consumption_20 0 0.0 440s Consumption_21 0 0.0 440s Consumption_22 0 0.0 440s Investment_2 1 12.4 440s Investment_3 1 16.9 440s Investment_4 1 18.4 440s Investment_5 1 19.4 440s Investment_6 1 20.1 440s Investment_7 1 19.6 440s Investment_8 1 19.8 440s Investment_9 1 21.1 440s Investment_10 1 21.7 440s Investment_11 1 15.6 440s Investment_12 1 11.4 440s Investment_14 1 11.2 440s Investment_15 1 12.3 440s Investment_17 1 17.6 440s Investment_18 1 17.3 440s Investment_19 1 15.3 440s Investment_20 1 19.0 440s Investment_21 1 21.1 440s Investment_22 1 23.5 440s PrivateWages_2 0 0.0 440s PrivateWages_3 0 0.0 440s PrivateWages_4 0 0.0 440s PrivateWages_5 0 0.0 440s PrivateWages_6 0 0.0 440s PrivateWages_8 0 0.0 440s PrivateWages_9 0 0.0 440s PrivateWages_10 0 0.0 440s PrivateWages_11 0 0.0 440s PrivateWages_12 0 0.0 440s PrivateWages_13 0 0.0 440s PrivateWages_14 0 0.0 440s PrivateWages_15 0 0.0 440s PrivateWages_16 0 0.0 440s PrivateWages_17 0 0.0 440s PrivateWages_18 0 0.0 440s PrivateWages_19 0 0.0 440s PrivateWages_20 0 0.0 440s PrivateWages_21 0 0.0 440s PrivateWages_22 0 0.0 440s Investment_corpProfLag Investment_capitalLag 440s Consumption_2 0.0 0 440s Consumption_3 0.0 0 440s Consumption_4 0.0 0 440s Consumption_5 0.0 0 440s Consumption_6 0.0 0 440s Consumption_7 0.0 0 440s Consumption_8 0.0 0 440s Consumption_9 0.0 0 440s Consumption_11 0.0 0 440s Consumption_12 0.0 0 440s Consumption_14 0.0 0 440s Consumption_15 0.0 0 440s Consumption_16 0.0 0 440s Consumption_17 0.0 0 440s Consumption_18 0.0 0 440s Consumption_19 0.0 0 440s Consumption_20 0.0 0 440s Consumption_21 0.0 0 440s Consumption_22 0.0 0 440s Investment_2 12.7 183 440s Investment_3 12.4 183 440s Investment_4 16.9 184 440s Investment_5 18.4 190 440s Investment_6 19.4 193 440s Investment_7 20.1 198 440s Investment_8 19.6 203 440s Investment_9 19.8 208 440s Investment_10 21.1 211 440s Investment_11 21.7 216 440s Investment_12 15.6 217 440s Investment_14 7.0 207 440s Investment_15 11.2 202 440s Investment_17 14.0 198 440s Investment_18 17.6 200 440s Investment_19 17.3 202 440s Investment_20 15.3 200 440s Investment_21 19.0 201 440s Investment_22 21.1 204 440s PrivateWages_2 0.0 0 440s PrivateWages_3 0.0 0 440s PrivateWages_4 0.0 0 440s PrivateWages_5 0.0 0 440s PrivateWages_6 0.0 0 440s PrivateWages_8 0.0 0 440s PrivateWages_9 0.0 0 440s PrivateWages_10 0.0 0 440s PrivateWages_11 0.0 0 440s PrivateWages_12 0.0 0 440s PrivateWages_13 0.0 0 440s PrivateWages_14 0.0 0 440s PrivateWages_15 0.0 0 440s PrivateWages_16 0.0 0 440s PrivateWages_17 0.0 0 440s PrivateWages_18 0.0 0 440s PrivateWages_19 0.0 0 440s PrivateWages_20 0.0 0 440s PrivateWages_21 0.0 0 440s PrivateWages_22 0.0 0 440s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 440s Consumption_2 0 0.0 0.0 440s Consumption_3 0 0.0 0.0 440s Consumption_4 0 0.0 0.0 440s Consumption_5 0 0.0 0.0 440s Consumption_6 0 0.0 0.0 440s Consumption_7 0 0.0 0.0 440s Consumption_8 0 0.0 0.0 440s Consumption_9 0 0.0 0.0 440s Consumption_11 0 0.0 0.0 440s Consumption_12 0 0.0 0.0 440s Consumption_14 0 0.0 0.0 440s Consumption_15 0 0.0 0.0 440s Consumption_16 0 0.0 0.0 440s Consumption_17 0 0.0 0.0 440s Consumption_18 0 0.0 0.0 440s Consumption_19 0 0.0 0.0 440s Consumption_20 0 0.0 0.0 440s Consumption_21 0 0.0 0.0 440s Consumption_22 0 0.0 0.0 440s Investment_2 0 0.0 0.0 440s Investment_3 0 0.0 0.0 440s Investment_4 0 0.0 0.0 440s Investment_5 0 0.0 0.0 440s Investment_6 0 0.0 0.0 440s Investment_7 0 0.0 0.0 440s Investment_8 0 0.0 0.0 440s Investment_9 0 0.0 0.0 440s Investment_10 0 0.0 0.0 440s Investment_11 0 0.0 0.0 440s Investment_12 0 0.0 0.0 440s Investment_14 0 0.0 0.0 440s Investment_15 0 0.0 0.0 440s Investment_17 0 0.0 0.0 440s Investment_18 0 0.0 0.0 440s Investment_19 0 0.0 0.0 440s Investment_20 0 0.0 0.0 440s Investment_21 0 0.0 0.0 440s Investment_22 0 0.0 0.0 440s PrivateWages_2 1 45.6 44.9 440s PrivateWages_3 1 50.1 45.6 440s PrivateWages_4 1 57.2 50.1 440s PrivateWages_5 1 57.1 57.2 440s PrivateWages_6 1 61.0 57.1 440s PrivateWages_8 1 64.4 64.0 440s PrivateWages_9 1 64.5 64.4 440s PrivateWages_10 1 67.0 64.5 440s PrivateWages_11 1 61.2 67.0 440s PrivateWages_12 1 53.4 61.2 440s PrivateWages_13 1 44.3 53.4 440s PrivateWages_14 1 45.1 44.3 440s PrivateWages_15 1 49.7 45.1 440s PrivateWages_16 1 54.4 49.7 440s PrivateWages_17 1 62.7 54.4 440s PrivateWages_18 1 65.0 62.7 440s PrivateWages_19 1 60.9 65.0 440s PrivateWages_20 1 69.5 60.9 440s PrivateWages_21 1 75.7 69.5 440s PrivateWages_22 1 88.4 75.7 440s PrivateWages_trend 440s Consumption_2 0 440s Consumption_3 0 440s Consumption_4 0 440s Consumption_5 0 440s Consumption_6 0 440s Consumption_7 0 440s Consumption_8 0 440s Consumption_9 0 440s Consumption_11 0 440s Consumption_12 0 440s Consumption_14 0 440s Consumption_15 0 440s Consumption_16 0 440s Consumption_17 0 440s Consumption_18 0 440s Consumption_19 0 440s Consumption_20 0 440s Consumption_21 0 440s Consumption_22 0 440s Investment_2 0 440s Investment_3 0 440s Investment_4 0 440s Investment_5 0 440s Investment_6 0 440s Investment_7 0 440s Investment_8 0 440s Investment_9 0 440s Investment_10 0 440s Investment_11 0 440s Investment_12 0 440s Investment_14 0 440s Investment_15 0 440s Investment_17 0 440s Investment_18 0 440s Investment_19 0 440s Investment_20 0 440s Investment_21 0 440s Investment_22 0 440s PrivateWages_2 -10 440s PrivateWages_3 -9 440s PrivateWages_4 -8 440s PrivateWages_5 -7 440s PrivateWages_6 -6 440s PrivateWages_8 -4 440s PrivateWages_9 -3 440s PrivateWages_10 -2 440s PrivateWages_11 -1 440s PrivateWages_12 0 440s PrivateWages_13 1 440s PrivateWages_14 2 440s PrivateWages_15 3 440s PrivateWages_16 4 440s PrivateWages_17 5 440s PrivateWages_18 6 440s PrivateWages_19 7 440s PrivateWages_20 8 440s PrivateWages_21 9 440s PrivateWages_22 10 440s > nobs 440s [1] 58 440s > linearHypothesis 440s Linear hypothesis test (Theil's F test) 440s 440s Hypothesis: 440s Consumption_corpProf + Investment_capitalLag = 0 440s 440s Model 1: restricted model 440s Model 2: kleinModel 440s 440s Res.Df Df F Pr(>F) 440s 1 47 440s 2 46 1 0.3 0.59 440s Linear hypothesis test (F statistic of a Wald test) 440s 440s Hypothesis: 440s Consumption_corpProf + Investment_capitalLag = 0 440s 440s Model 1: restricted model 440s Model 2: kleinModel 440s 440s Res.Df Df F Pr(>F) 440s 1 47 440s 2 46 1 0.29 0.6 440s Linear hypothesis test (Chi^2 statistic of a Wald test) 440s 440s Hypothesis: 440s Consumption_corpProf + Investment_capitalLag = 0 440s 440s Model 1: restricted model 440s Model 2: kleinModel 440s 440s Res.Df Df Chisq Pr(>Chisq) 440s 1 47 440s 2 46 1 0.29 0.59 440s Linear hypothesis test (Theil's F test) 440s 440s Hypothesis: 440s Consumption_corpProf + Investment_capitalLag = 0 440s Consumption_corpProfLag - PrivateWages_trend = 0 440s 440s Model 1: restricted model 440s Model 2: kleinModel 440s 440s Res.Df Df F Pr(>F) 440s 1 48 440s 2 46 2 0.16 0.85 440s Linear hypothesis test (F statistic of a Wald test) 440s 440s Hypothesis: 440s Consumption_corpProf + Investment_capitalLag = 0 440s Consumption_corpProfLag - PrivateWages_trend = 0 440s 440s Model 1: restricted model 440s Model 2: kleinModel 440s 440s Res.Df Df F Pr(>F) 440s 1 48 440s 2 46 2 0.15 0.86 440s Linear hypothesis test (Chi^2 statistic of a Wald test) 440s 440s Hypothesis: 440s Consumption_corpProf + Investment_capitalLag = 0 440s Consumption_corpProfLag - PrivateWages_trend = 0 440s 440s Model 1: restricted model 440s Model 2: kleinModel 440s 440s Res.Df Df Chisq Pr(>Chisq) 440s 1 48 440s 2 46 2 0.3 0.86 440s > logLik 440s 'log Lik.' -68.8 (df=13) 440s 'log Lik.' -73.3 (df=13) 440s compare log likelihood value with single-equation OLS 440s [1] "Mean relative difference: 0.0011" 440s Estimating function 440s Consumption_(Intercept) Consumption_corpProf 440s Consumption_2 -0.3863 -4.791 440s Consumption_3 -1.2484 -21.098 440s Consumption_4 -1.6040 -29.514 440s Consumption_5 -0.5384 -10.446 440s Consumption_6 -0.0413 -0.830 440s Consumption_7 0.8043 15.763 440s Consumption_8 1.2830 25.403 440s Consumption_9 1.0142 21.399Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 440s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 440s 440s Consumption_11 0.1429 2.229 440s Consumption_12 -0.3439 -3.920 440s Consumption_14 0.3199 3.583 440s Consumption_15 -0.1016 -1.250 440s Consumption_16 -0.0702 -0.983 440s Consumption_17 1.6064 28.272 440s Consumption_18 -0.4980 -8.616 440s Consumption_19 0.1253 1.917 440s Consumption_20 0.9805 18.629 440s Consumption_21 0.7551 15.933 440s Consumption_22 -2.1992 -51.681 440s Investment_2 0.0000 0.000 440s Investment_3 0.0000 0.000 440s Investment_4 0.0000 0.000 440s Investment_5 0.0000 0.000 440s Investment_6 0.0000 0.000 440s Investment_7 0.0000 0.000 440s Investment_8 0.0000 0.000 440s Investment_9 0.0000 0.000 440s Investment_10 0.0000 0.000 440s Investment_11 0.0000 0.000 440s Investment_12 0.0000 0.000 440s Investment_14 0.0000 0.000 440s Investment_15 0.0000 0.000 440s Investment_17 0.0000 0.000 440s Investment_18 0.0000 0.000 440s Investment_19 0.0000 0.000 440s Investment_20 0.0000 0.000 440s Investment_21 0.0000 0.000 440s Investment_22 0.0000 0.000 440s PrivateWages_2 0.0000 0.000 440s PrivateWages_3 0.0000 0.000 440s PrivateWages_4 0.0000 0.000 440s PrivateWages_5 0.0000 0.000 440s PrivateWages_6 0.0000 0.000 440s PrivateWages_8 0.0000 0.000 440s PrivateWages_9 0.0000 0.000 440s PrivateWages_10 0.0000 0.000 440s PrivateWages_11 0.0000 0.000 440s PrivateWages_12 0.0000 0.000 440s PrivateWages_13 0.0000 0.000 440s PrivateWages_14 0.0000 0.000 440s PrivateWages_15 0.0000 0.000 440s PrivateWages_16 0.0000 0.000 440s PrivateWages_17 0.0000 0.000 440s PrivateWages_18 0.0000 0.000 440s PrivateWages_19 0.0000 0.000 440s PrivateWages_20 0.0000 0.000 440s PrivateWages_21 0.0000 0.000 440s PrivateWages_22 0.0000 0.000 440s Consumption_corpProfLag Consumption_wages 440s Consumption_2 -4.907 -10.90 440s Consumption_3 -15.480 -40.20 440s Consumption_4 -27.108 -59.35 440s Consumption_5 -9.907 -19.92 440s Consumption_6 -0.801 -1.59 440s Consumption_7 16.166 32.73 440s Consumption_8 25.146 53.24 440s Consumption_9 20.081 43.51 440s Consumption_11 3.100 6.01 440s Consumption_12 -5.364 -13.51 440s Consumption_14 2.239 10.91 440s Consumption_15 -1.138 -3.72 440s Consumption_16 -0.864 -2.76 440s Consumption_17 22.489 71.00 440s Consumption_18 -8.765 -23.76 440s Consumption_19 2.168 5.75 440s Consumption_20 15.002 48.44 440s Consumption_21 14.348 40.02 440s Consumption_22 -46.403 -135.91 440s Investment_2 0.000 0.00 440s Investment_3 0.000 0.00 440s Investment_4 0.000 0.00 440s Investment_5 0.000 0.00 440s Investment_6 0.000 0.00 440s Investment_7 0.000 0.00 440s Investment_8 0.000 0.00 440s Investment_9 0.000 0.00 440s Investment_10 0.000 0.00 440s Investment_11 0.000 0.00 440s Investment_12 0.000 0.00 440s Investment_14 0.000 0.00 440s Investment_15 0.000 0.00 440s Investment_17 0.000 0.00 440s Investment_18 0.000 0.00 440s Investment_19 0.000 0.00 440s Investment_20 0.000 0.00 440s Investment_21 0.000 0.00 440s Investment_22 0.000 0.00 440s PrivateWages_2 0.000 0.00 440s PrivateWages_3 0.000 0.00 440s PrivateWages_4 0.000 0.00 440s PrivateWages_5 0.000 0.00 440s PrivateWages_6 0.000 0.00 440s PrivateWages_8 0.000 0.00 440s PrivateWages_9 0.000 0.00 440s PrivateWages_10 0.000 0.00 440s PrivateWages_11 0.000 0.00 440s PrivateWages_12 0.000 0.00 440s PrivateWages_13 0.000 0.00 440s PrivateWages_14 0.000 0.00 440s PrivateWages_15 0.000 0.00 440s PrivateWages_16 0.000 0.00 440s PrivateWages_17 0.000 0.00 440s PrivateWages_18 0.000 0.00 440s PrivateWages_19 0.000 0.00 440s PrivateWages_20 0.000 0.00 440s PrivateWages_21 0.000 0.00 440s PrivateWages_22 0.000 0.00 440s Investment_(Intercept) Investment_corpProf 440s Consumption_2 0.00000 0.000 440s Consumption_3 0.00000 0.000 440s Consumption_4 0.00000 0.000 440s Consumption_5 0.00000 0.000 440s Consumption_6 0.00000 0.000 440s Consumption_7 0.00000 0.000 440s Consumption_8 0.00000 0.000 440s Consumption_9 0.00000 0.000 440s Consumption_11 0.00000 0.000 440s Consumption_12 0.00000 0.000 440s Consumption_14 0.00000 0.000 440s Consumption_15 0.00000 0.000 440s Consumption_16 0.00000 0.000 440s Consumption_17 0.00000 0.000 440s Consumption_18 0.00000 0.000 440s Consumption_19 0.00000 0.000 440s Consumption_20 0.00000 0.000 440s Consumption_21 0.00000 0.000 440s Consumption_22 0.00000 0.000 440s Investment_2 0.00693 0.086 440s Investment_3 -0.06954 -1.175 440s Investment_4 1.22401 22.522 440s Investment_5 -1.37696 -26.713 440s Investment_6 0.38610 7.761 440s Investment_7 1.48598 29.125 440s Investment_8 0.78465 15.536 440s Investment_9 -0.65483 -13.817 440s Investment_10 1.06018 23.006 440s Investment_11 0.39508 6.163 440s Investment_12 0.20479 2.335 440s Investment_14 0.32778 3.671 440s Investment_15 -0.07450 -0.916 440s Investment_17 0.96998 17.072 440s Investment_18 0.08124 1.405 440s Investment_19 -2.49295 -38.142 440s Investment_20 -0.70609 -13.416 440s Investment_21 -0.81928 -17.287 440s Investment_22 -0.73256 -17.215 440s PrivateWages_2 0.00000 0.000 440s PrivateWages_3 0.00000 0.000 440s PrivateWages_4 0.00000 0.000 440s PrivateWages_5 0.00000 0.000 440s PrivateWages_6 0.00000 0.000 440s PrivateWages_8 0.00000 0.000 440s PrivateWages_9 0.00000 0.000 440s PrivateWages_10 0.00000 0.000 440s PrivateWages_11 0.00000 0.000 440s PrivateWages_12 0.00000 0.000 440s PrivateWages_13 0.00000 0.000 440s PrivateWages_14 0.00000 0.000 440s PrivateWages_15 0.00000 0.000 440s PrivateWages_16 0.00000 0.000 440s PrivateWages_17 0.00000 0.000 440s PrivateWages_18 0.00000 0.000 440s PrivateWages_19 0.00000 0.000 440s PrivateWages_20 0.00000 0.000 440s PrivateWages_21 0.00000 0.000 440s PrivateWages_22 0.00000 0.000 440s Investment_corpProfLag Investment_capitalLag 440s Consumption_2 0.0000 0.00 440s Consumption_3 0.0000 0.00 440s Consumption_4 0.0000 0.00 440s Consumption_5 0.0000 0.00 440s Consumption_6 0.0000 0.00 440s Consumption_7 0.0000 0.00 440s Consumption_8 0.0000 0.00 440s Consumption_9 0.0000 0.00 440s Consumption_11 0.0000 0.00 440s Consumption_12 0.0000 0.00 440s Consumption_14 0.0000 0.00 440s Consumption_15 0.0000 0.00 440s Consumption_16 0.0000 0.00 440s Consumption_17 0.0000 0.00 440s Consumption_18 0.0000 0.00 440s Consumption_19 0.0000 0.00 440s Consumption_20 0.0000 0.00 440s Consumption_21 0.0000 0.00 440s Consumption_22 0.0000 0.00 440s Investment_2 0.0881 1.27 440s Investment_3 -0.8622 -12.70 440s Investment_4 20.6858 225.83 440s Investment_5 -25.3362 -261.21 440s Investment_6 7.4903 74.40 440s Investment_7 29.8681 293.93 440s Investment_8 15.3791 159.60 440s Investment_9 -12.9657 -135.94 440s Investment_10 22.3698 223.27 440s Investment_11 8.5733 85.22 440s Investment_12 3.1947 44.38 440s Investment_14 2.2945 67.88 440s Investment_15 -0.8344 -15.05 440s Investment_17 13.5797 191.77 440s Investment_18 1.4298 16.23 440s Investment_19 -43.1281 -503.08 440s Investment_20 -10.8032 -141.15 440s Investment_21 -15.5663 -164.84 440s Investment_22 -15.4570 -149.81 440s PrivateWages_2 0.0000 0.00 440s PrivateWages_3 0.0000 0.00 440s PrivateWages_4 0.0000 0.00 440s PrivateWages_5 0.0000 0.00 440s PrivateWages_6 0.0000 0.00 440s PrivateWages_8 0.0000 0.00 440s PrivateWages_9 0.0000 0.00 440s PrivateWages_10 0.0000 0.00 440s PrivateWages_11 0.0000 0.00 440s PrivateWages_12 0.0000 0.00 440s PrivateWages_13 0.0000 0.00 440s PrivateWages_14 0.0000 0.00 440s PrivateWages_15 0.0000 0.00 440s PrivateWages_16 0.0000 0.00 440s PrivateWages_17 0.0000 0.00 440s PrivateWages_18 0.0000 0.00 440s PrivateWages_19 0.0000 0.00 440s PrivateWages_20 0.0000 0.00 440s PrivateWages_21 0.0000 0.00 440s PrivateWages_22 0.0000 0.00 440s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 440s Consumption_2 0.0000 0.00 0.00 440s Consumption_3 0.0000 0.00 0.00 440s Consumption_4 0.0000 0.00 0.00 440s Consumption_5 0.0000 0.00 0.00 440s Consumption_6 0.0000 0.00 0.00 440s Consumption_7 0.0000 0.00 0.00 440s Consumption_8 0.0000 0.00 0.00 440s Consumption_9 0.0000 0.00 0.00 440s Consumption_11 0.0000 0.00 0.00 440s Consumption_12 0.0000 0.00 0.00 440s Consumption_14 0.0000 0.00 0.00 440s Consumption_15 0.0000 0.00 0.00 440s Consumption_16 0.0000 0.00 0.00 440s Consumption_17 0.0000 0.00 0.00 440s Consumption_18 0.0000 0.00 0.00 440s Consumption_19 0.0000 0.00 0.00 440s Consumption_20 0.0000 0.00 0.00 440s Consumption_21 0.0000 0.00 0.00 440s Consumption_22 0.0000 0.00 0.00 440s Investment_2 0.0000 0.00 0.00 440s Investment_3 0.0000 0.00 0.00 440s Investment_4 0.0000 0.00 0.00 440s Investment_5 0.0000 0.00 0.00 440s Investment_6 0.0000 0.00 0.00 440s Investment_7 0.0000 0.00 0.00 440s Investment_8 0.0000 0.00 0.00 440s Investment_9 0.0000 0.00 0.00 440s Investment_10 0.0000 0.00 0.00 440s Investment_11 0.0000 0.00 0.00 440s Investment_12 0.0000 0.00 0.00 440s Investment_14 0.0000 0.00 0.00 440s Investment_15 0.0000 0.00 0.00 440s Investment_17 0.0000 0.00 0.00 440s Investment_18 0.0000 0.00 0.00 440s Investment_19 0.0000 0.00 0.00 440s Investment_20 0.0000 0.00 0.00 440s Investment_21 0.0000 0.00 0.00 440s Investment_22 0.0000 0.00 0.00 440s PrivateWages_2 -1.3389 -61.06 -60.12 440s PrivateWages_3 0.2462 12.33 11.23 440s PrivateWages_4 1.1255 64.38 56.39 440s PrivateWages_5 -0.1959 -11.18 -11.20 440s PrivateWages_6 -0.5284 -32.23 -30.17 440s PrivateWages_8 -0.7909 -50.94 -50.62 440s PrivateWages_9 0.2819 18.18 18.15 440s PrivateWages_10 1.1384 76.28 73.43 440s PrivateWages_11 -0.1904 -11.65 -12.76 440s PrivateWages_12 0.5813 31.04 35.58 440s PrivateWages_13 0.1206 5.34 6.44 440s PrivateWages_14 0.4773 21.53 21.14 440s PrivateWages_15 0.3035 15.09 13.69 440s PrivateWages_16 0.0284 1.55 1.41 440s PrivateWages_17 -0.8517 -53.40 -46.33 440s PrivateWages_18 0.9908 64.40 62.12 440s PrivateWages_19 -0.4597 -28.00 -29.88 440s PrivateWages_20 -0.3819 -26.54 -23.26 440s PrivateWages_21 -1.1062 -83.74 -76.88 440s PrivateWages_22 0.5501 48.63 41.64 440s PrivateWages_trend 440s Consumption_2 0.000 440s Consumption_3 0.000 440s Consumption_4 0.000 440s Consumption_5 0.000 440s Consumption_6 0.000 440s Consumption_7 0.000 440s Consumption_8 0.000 440s Consumption_9 0.000 440s Consumption_11 0.000 440s Consumption_12 0.000 440s Consumption_14 0.000 440s Consumption_15 0.000 440s Consumption_16 0.000 440s Consumption_17 0.000 440s Consumption_18 0.000 440s Consumption_19 0.000 440s Consumption_20 0.000 440s Consumption_21 0.000 440s Consumption_22 0.000 440s Investment_2 0.000 440s Investment_3 0.000 440s Investment_4 0.000 440s Investment_5 0.000 440s Investment_6 0.000 440s Investment_7 0.000 440s Investment_8 0.000 440s Investment_9 0.000 440s Investment_10 0.000 440s Investment_11 0.000 440s Investment_12 0.000 440s Investment_14 0.000 440s Investment_15 0.000 440s Investment_17 0.000 440s Investment_18 0.000 440s Investment_19 0.000 440s Investment_20 0.000 440s Investment_21 0.000 440s Investment_22 0.000 440s PrivateWages_2 13.389 440s PrivateWages_3 -2.216 440s PrivateWages_4 -9.004 440s PrivateWages_5 1.371 440s PrivateWages_6 3.170 440s PrivateWages_8 3.164 440s PrivateWages_9 -0.846 440s PrivateWages_10 -2.277 440s PrivateWages_11 0.190 440s PrivateWages_12 0.000 440s PrivateWages_13 0.121 440s PrivateWages_14 0.955 440s PrivateWages_15 0.911 440s PrivateWages_16 0.114 440s PrivateWages_17 -4.258 440s PrivateWages_18 5.945 440s PrivateWages_19 -3.218 440s PrivateWages_20 -3.055 440s PrivateWages_21 -9.956 440s PrivateWages_22 5.501 440s [1] TRUE 440s > Bread 440s Consumption_(Intercept) Consumption_corpProf 440s Consumption_(Intercept) 107.542 -1.6123 440s Consumption_corpProf -1.612 0.6562 440s Consumption_corpProfLag -0.588 -0.3449 440s Consumption_wages -1.613 -0.0959 440s Investment_(Intercept) 0.000 0.0000 440s Investment_corpProf 0.000 0.0000 440s Investment_corpProfLag 0.000 0.0000 440s Investment_capitalLag 0.000 0.0000 440s PrivateWages_(Intercept) 0.000 0.0000 440s PrivateWages_gnp 0.000 0.0000 440s PrivateWages_gnpLag 0.000 0.0000 440s PrivateWages_trend 0.000 0.0000 440s Consumption_corpProfLag Consumption_wages 440s Consumption_(Intercept) -0.5878 -1.6130 440s Consumption_corpProf -0.3449 -0.0959 440s Consumption_corpProfLag 0.4797 -0.0326 440s Consumption_wages -0.0326 0.0910 440s Investment_(Intercept) 0.0000 0.0000 440s Investment_corpProf 0.0000 0.0000 440s Investment_corpProfLag 0.0000 0.0000 440s Investment_capitalLag 0.0000 0.0000 440s PrivateWages_(Intercept) 0.0000 0.0000 440s PrivateWages_gnp 0.0000 0.0000 440s PrivateWages_gnpLag 0.0000 0.0000 440s PrivateWages_trend 0.0000 0.0000 440s Investment_(Intercept) Investment_corpProf 440s Consumption_(Intercept) 0.00 0.000 440s Consumption_corpProf 0.00 0.000 440s Consumption_corpProfLag 0.00 0.000 440s Consumption_wages 0.00 0.000 440s Investment_(Intercept) 1702.08 -16.246 440s Investment_corpProf -16.25 0.653 440s Investment_corpProfLag 13.29 -0.499 440s Investment_capitalLag -8.19 0.066 440s PrivateWages_(Intercept) 0.00 0.000 440s PrivateWages_gnp 0.00 0.000 440s PrivateWages_gnpLag 0.00 0.000 440s PrivateWages_trend 0.00 0.000 440s Investment_corpProfLag Investment_capitalLag 440s Consumption_(Intercept) 0.0000 0.0000 440s Consumption_corpProf 0.0000 0.0000 440s Consumption_corpProfLag 0.0000 0.0000 440s Consumption_wages 0.0000 0.0000 440s Investment_(Intercept) 13.2940 -8.1927 440s Investment_corpProf -0.4994 0.0660 440s Investment_corpProfLag 0.6054 -0.0737 440s Investment_capitalLag -0.0737 0.0414 440s PrivateWages_(Intercept) 0.0000 0.0000 440s PrivateWages_gnp 0.0000 0.0000 440s PrivateWages_gnpLag 0.0000 0.0000 440s PrivateWages_trend 0.0000 0.0000 440s PrivateWages_(Intercept) PrivateWages_gnp 440s Consumption_(Intercept) 0.000 0.0000 440s Consumption_corpProf 0.000 0.0000 440s Consumption_corpProfLag 0.000 0.0000 440s Consumption_wages 0.000 0.0000 440s Investment_(Intercept) 0.000 0.0000 440s Investment_corpProf 0.000 0.0000 440s Investment_corpProfLag 0.000 0.0000 440s Investment_capitalLag 0.000 0.0000 440s PrivateWages_(Intercept) 163.361 -0.6152 440s PrivateWages_gnp -0.615 0.1046 440s PrivateWages_gnpLag -2.146 -0.0975 440s PrivateWages_trend 2.016 -0.0281 440s PrivateWages_gnpLag PrivateWages_trend 440s Consumption_(Intercept) 0.00000 0.00000 440s Consumption_corpProf 0.00000 0.00000 440s Consumption_corpProfLag 0.00000 0.00000 440s Consumption_wages 0.00000 0.00000 440s Investment_(Intercept) 0.00000 0.00000 440s Investment_corpProf 0.00000 0.00000 440s Investment_corpProfLag 0.00000 0.00000 440s Investment_capitalLag 0.00000 0.00000 440s PrivateWages_(Intercept) -2.14647 2.01603 440s PrivateWages_gnp -0.09753 -0.02810 440s PrivateWages_gnpLag 0.13809 -0.00624 440s PrivateWages_trend -0.00624 0.10783 440s > 440s > # 2SLS 440s > summary 440s 440s systemfit results 440s method: 2SLS 440s 440s N DF SSR detRCov OLS-R2 McElroy-R2 440s system 56 44 57.9 0.391 0.968 0.992 440s 440s N DF SSR MSE RMSE R2 Adj R2 440s Consumption 18 14 22.27 1.591 1.26 0.974 0.968 440s Investment 18 14 25.85 1.847 1.36 0.847 0.815 440s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 440s 440s The covariance matrix of the residuals 440s Consumption Investment PrivateWages 440s Consumption 1.307 0.540 -0.431 440s Investment 0.540 1.319 0.119 440s PrivateWages -0.431 0.119 0.496 440s 440s The correlations of the residuals 440s Consumption Investment PrivateWages 440s Consumption 1.000 0.414 -0.538 440s Investment 0.414 1.000 0.139 440s PrivateWages -0.538 0.139 1.000 440s 440s 440s 2SLS estimates for 'Consumption' (equation 1) 440s Model Formula: consump ~ corpProf + corpProfLag + wages 440s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 440s gnpLag 440s 440s Estimate Std. Error t value Pr(>|t|) 440s (Intercept) 17.2849 1.6463 10.50 5.1e-08 *** 440s corpProf -0.0770 0.1683 -0.46 0.65 440s corpProfLag 0.2327 0.1276 1.82 0.09 . 440s wages 0.8259 0.0472 17.49 6.6e-11 *** 440s --- 440s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 440s 440s Residual standard error: 1.261 on 14 degrees of freedom 440s Number of observations: 18 Degrees of Freedom: 14 440s SSR: 22.269 MSE: 1.591 Root MSE: 1.261 440s Multiple R-Squared: 0.974 Adjusted R-Squared: 0.968 440s 440s 440s 2SLS estimates for 'Investment' (equation 2) 440s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 440s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 440s gnpLag 440s 440s Estimate Std. Error t value Pr(>|t|) 440s (Intercept) 18.2571 7.3132 2.50 0.02564 * 440s corpProf 0.1564 0.1942 0.81 0.43408 440s corpProfLag 0.5714 0.1672 3.42 0.00417 ** 440s capitalLag -0.1446 0.0346 -4.18 0.00093 *** 440s --- 440s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 440s 440s Residual standard error: 1.359 on 14 degrees of freedom 440s Number of observations: 18 Degrees of Freedom: 14 440s SSR: 25.852 MSE: 1.847 Root MSE: 1.359 440s Multiple R-Squared: 0.847 Adjusted R-Squared: 0.815 440s 440s 440s 2SLS estimates for 'PrivateWages' (equation 3) 440s Model Formula: privWage ~ gnp + gnpLag + trend 440s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 440s gnpLag 440s 440s Estimate Std. Error t value Pr(>|t|) 440s (Intercept) 1.3431 1.1879 1.13 0.275 440s gnp 0.4438 0.0361 12.28 1.5e-09 *** 440s gnpLag 0.1447 0.0392 3.69 0.002 ** 440s trend 0.1238 0.0308 4.01 0.001 ** 440s --- 440s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 440s 440s Residual standard error: 0.78 on 16 degrees of freedom 440s Number of observations: 20 Degrees of Freedom: 16 440s SSR: 9.741 MSE: 0.609 Root MSE: 0.78 440s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 440s 440s > residuals 440s Consumption Investment PrivateWages 440s 1 NA NA NA 440s 2 -0.6754 -1.214 -1.3401 440s 3 -0.4627 0.325 0.2378 440s 4 -1.1585 1.094 1.1117 440s 5 -0.0305 -1.368 -0.1954 440s 6 0.4693 0.486 -0.5355 440s 7 NA NA NA 440s 8 1.6045 1.066 -0.7908 440s 9 1.6018 0.156 0.2831 440s 10 NA 1.853 1.1353 440s 11 -0.9031 -0.898 -0.1765 440s 12 -1.5948 -1.012 0.6007 440s 13 NA NA 0.1443 440s 14 0.2854 0.845 0.4826 440s 15 -0.4718 -0.365 0.3016 440s 16 -0.2268 NA 0.0261 440s 17 2.0079 1.685 -0.8614 440s 18 -0.7434 -0.121 0.9927 440s 19 -0.5410 -3.248 -0.4446 440s 20 1.4186 0.241 -0.3914 440s 21 1.1462 -0.013 -1.1115 440s 22 -1.7256 0.489 0.5312 440s > fitted 440s Consumption Investment PrivateWages 440s 1 NA NA NA 440s 2 42.6 1.014 26.8 440s 3 45.5 1.575 29.1 440s 4 50.4 4.106 33.0 440s 5 50.6 4.368 34.1 440s 6 52.1 4.614 35.9 440s 7 NA NA NA 440s 8 54.6 3.134 38.7 440s 9 55.7 2.844 38.9 440s 10 NA 3.247 40.2 440s 11 55.9 1.898 38.1 440s 12 52.5 -2.388 33.9 440s 13 NA NA 28.9 440s 14 46.2 -5.945 28.0 440s 15 49.2 -2.635 30.3 440s 16 51.5 NA 33.2 440s 17 55.7 0.415 37.7 440s 18 59.4 2.121 40.0 440s 19 58.0 1.348 38.6 440s 20 60.2 1.059 42.0 440s 21 63.9 3.313 46.1 440s 22 71.4 4.411 52.8 440s > predict 440s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 440s 1 NA NA NA NA 440s 2 42.6 0.586 41.3 43.8 440s 3 45.5 0.674 44.0 46.9 440s 4 50.4 0.443 49.4 51.3 440s 5 50.6 0.524 49.5 51.8 440s 6 52.1 0.535 51.0 53.3 441s 7 NA NA NA NA 441s 8 54.6 0.431 53.7 55.5 441s 9 55.7 0.510 54.6 56.8 441s 10 NA NA NA NA 441s 11 55.9 0.936 53.9 57.9 441s 12 52.5 0.893 50.6 54.4 441s 13 NA NA NA NA 441s 14 46.2 0.713 44.7 47.7 441s 15 49.2 0.501 48.1 50.2 441s 16 51.5 0.407 50.7 52.4 441s 17 55.7 0.457 54.7 56.7 441s 18 59.4 0.397 58.6 60.3 441s 19 58.0 0.564 56.8 59.2 441s 20 60.2 0.543 59.0 61.3 441s 21 63.9 0.529 62.7 65.0 441s 22 71.4 0.808 69.7 73.2 441s Investment.pred Investment.se.fit Investment.lwr Investment.upr 441s 1 NA NA NA NA 441s 2 1.014 0.919 -0.957 2.985 441s 3 1.575 0.602 0.284 2.867 441s 4 4.106 0.544 2.940 5.272 441s 5 4.368 0.450 3.402 5.333 441s 6 4.614 0.425 3.703 5.526 441s 7 NA NA NA NA 441s 8 3.134 0.352 2.380 3.889 441s 9 2.844 0.544 1.677 4.012 441s 10 3.247 0.592 1.976 4.518 441s 11 1.898 0.978 -0.200 3.996 441s 12 -2.388 0.886 -4.289 -0.488 441s 13 NA NA NA NA 441s 14 -5.945 0.916 -7.909 -3.980 441s 15 -2.635 0.518 -3.745 -1.525 441s 16 NA NA NA NA 441s 17 0.415 0.507 -0.671 1.501 441s 18 2.121 0.329 1.416 2.826 441s 19 1.348 0.551 0.166 2.529 441s 20 1.059 0.582 -0.189 2.306 441s 21 3.313 0.496 2.248 4.377 441s 22 4.411 0.728 2.850 5.971 441s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 441s 1 NA NA NA NA 441s 2 26.8 0.330 26.1 27.5 441s 3 29.1 0.344 28.3 29.8 441s 4 33.0 0.363 32.2 33.8 441s 5 34.1 0.260 33.5 34.6 441s 6 35.9 0.268 35.4 36.5 441s 7 NA NA NA NA 441s 8 38.7 0.265 38.1 39.3 441s 9 38.9 0.252 38.4 39.5 441s 10 40.2 0.242 39.7 40.7 441s 11 38.1 0.358 37.3 38.8 441s 12 33.9 0.385 33.1 34.7 441s 13 28.9 0.460 27.9 29.8 441s 14 28.0 0.351 27.3 28.8 441s 15 30.3 0.343 29.6 31.0 441s 16 33.2 0.287 32.6 33.8 441s 17 37.7 0.296 37.0 38.3 441s 18 40.0 0.220 39.5 40.5 441s 19 38.6 0.361 37.9 39.4 441s 20 42.0 0.309 41.3 42.6 441s 21 46.1 0.312 45.4 46.8 441s 22 52.8 0.501 51.7 53.8 441s > model.frame 441s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 441s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 441s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 441s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 441s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 441s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 441s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 441s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 441s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 441s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 441s 10 57.8 21.7 21.1 NA 5.1 211 41.3 67.0 64.5 441s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 441s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 441s 13 45.6 NA 11.4 34.3 -6.2 213 29.0 44.3 53.4 441s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 441s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 441s 16 51.3 14.0 12.3 39.3 NA 199 33.2 54.4 49.7 441s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 441s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 441s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 441s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 441s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 441s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 441s trend 441s 1 -11 441s 2 -10 441s 3 -9 441s 4 -8 441s 5 -7 441s 6 -6 441s 7 -5 441s 8 -4 441s 9 -3 441s 10 -2 441s 11 -1 441s 12 0 441s 13 1 441s 14 2 441s 15 3 441s 16 4 441s 17 5 441s 18 6 441s 19 7 441s 20 8 441s 21 9 441s 22 10 441s > Frames of instrumental variables 441s govExp taxes govWage trend capitalLag corpProfLag gnpLag 441s 1 2.4 3.4 2.2 -11 180 NA NA 441s 2 3.9 7.7 2.7 -10 183 12.7 44.9 441s 3 3.2 3.9 2.9 -9 183 12.4 45.6 441s 4 2.8 4.7 2.9 -8 184 16.9 50.1 441s 5 3.5 3.8 3.1 -7 190 18.4 57.2 441s 6 3.3 5.5 3.2 -6 193 19.4 57.1 441s 7 3.3 7.0 3.3 -5 198 20.1 NA 441s 8 4.0 6.7 3.6 -4 203 19.6 64.0 441s 9 4.2 4.2 3.7 -3 208 19.8 64.4 441s 10 4.1 4.0 4.0 -2 211 21.1 64.5 441s 11 5.2 7.7 4.2 -1 216 21.7 67.0 441s 12 5.9 7.5 4.8 0 217 15.6 61.2 441s 13 4.9 8.3 5.3 1 213 11.4 53.4 441s 14 3.7 5.4 5.6 2 207 7.0 44.3 441s 15 4.0 6.8 6.0 3 202 11.2 45.1 441s 16 4.4 7.2 6.1 4 199 12.3 49.7 441s 17 2.9 8.3 7.4 5 198 14.0 54.4 441s 18 4.3 6.7 6.7 6 200 17.6 62.7 441s 19 5.3 7.4 7.7 7 202 17.3 65.0 441s 20 6.6 8.9 7.8 8 200 15.3 60.9 441s 21 7.4 9.6 8.0 9 201 19.0 69.5 441s 22 13.8 11.6 8.5 10 204 21.1 75.7 441s govExp taxes govWage trend capitalLag corpProfLag gnpLag 441s 1 2.4 3.4 2.2 -11 180 NA NA 441s 2 3.9 7.7 2.7 -10 183 12.7 44.9 441s 3 3.2 3.9 2.9 -9 183 12.4 45.6 441s 4 2.8 4.7 2.9 -8 184 16.9 50.1 441s 5 3.5 3.8 3.1 -7 190 18.4 57.2 441s 6 3.3 5.5 3.2 -6 193 19.4 57.1 441s 7 3.3 7.0 3.3 -5 198 20.1 NA 441s 8 4.0 6.7 3.6 -4 203 19.6 64.0 441s 9 4.2 4.2 3.7 -3 208 19.8 64.4 441s 10 4.1 4.0 4.0 -2 211 21.1 64.5 441s 11 5.2 7.7 4.2 -1 216 21.7 67.0 441s 12 5.9 7.5 4.8 0 217 15.6 61.2 441s 13 4.9 8.3 5.3 1 213 11.4 53.4 441s 14 3.7 5.4 5.6 2 207 7.0 44.3 441s 15 4.0 6.8 6.0 3 202 11.2 45.1 441s 16 4.4 7.2 6.1 4 199 12.3 49.7 441s 17 2.9 8.3 7.4 5 198 14.0 54.4 441s 18 4.3 6.7 6.7 6 200 17.6 62.7 441s 19 5.3 7.4 7.7 7 202 17.3 65.0 441s 20 6.6 8.9 7.8 8 200 15.3 60.9 441s 21 7.4 9.6 8.0 9 201 19.0 69.5 441s 22 13.8 11.6 8.5 10 204 21.1 75.7 441s govExp taxes govWage trend capitalLag corpProfLag gnpLag 441s 1 2.4 3.4 2.2 -11 180 NA NA 441s 2 3.9 7.7 2.7 -10 183 12.7 44.9 441s 3 3.2 3.9 2.9 -9 183 12.4 45.6 441s 4 2.8 4.7 2.9 -8 184 16.9 50.1 441s 5 3.5 3.8 3.1 -7 190 18.4 57.2 441s 6 3.3 5.5 3.2 -6 193 19.4 57.1 441s 7 3.3 7.0 3.3 -5 198 20.1 NA 441s 8 4.0 6.7 3.6 -4 203 19.6 64.0 441s 9 4.2 4.2 3.7 -3 208 19.8 64.4 441s 10 4.1 4.0 4.0 -2 211 21.1 64.5 441s 11 5.2 7.7 4.2 -1 216 21.7 67.0 441s 12 5.9 7.5 4.8 0 217 15.6 61.2 441s 13 4.9 8.3 5.3 1 213 11.4 53.4 441s 14 3.7 5.4 5.6 2 207 7.0 44.3 441s 15 4.0 6.8 6.0 3 202 11.2 45.1 441s 16 4.4 7.2 6.1 4 199 12.3 49.7 441s 17 2.9 8.3 7.4 5 198 14.0 54.4 441s 18 4.3 6.7 6.7 6 200 17.6 62.7 441s 19 5.3 7.4 7.7 7 202 17.3 65.0 441s 20 6.6 8.9 7.8 8 200 15.3 60.9 441s 21 7.4 9.6 8.0 9 201 19.0 69.5 441s 22 13.8 11.6 8.5 10 204 21.1 75.7 441s > model.matrix 441s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0345 >" 441s [2] "Attributes: < Component “dimnames”: Component 1: 51 string mismatches >" 441s [3] "Numeric: lengths (696, 672) differ" 441s > matrix of instrumental variables 441s Consumption_(Intercept) Consumption_govExp Consumption_taxes 441s Consumption_2 1 3.9 7.7 441s Consumption_3 1 3.2 3.9 441s Consumption_4 1 2.8 4.7 441s Consumption_5 1 3.5 3.8 441s Consumption_6 1 3.3 5.5 441s Consumption_8 1 4.0 6.7 441s Consumption_9 1 4.2 4.2 441s Consumption_11 1 5.2 7.7 441s Consumption_12 1 5.9 7.5 441s Consumption_14 1 3.7 5.4 441s Consumption_15 1 4.0 6.8 441s Consumption_16 1 4.4 7.2 441s Consumption_17 1 2.9 8.3 441s Consumption_18 1 4.3 6.7 441s Consumption_19 1 5.3 7.4 441s Consumption_20 1 6.6 8.9 441s Consumption_21 1 7.4 9.6 441s Consumption_22 1 13.8 11.6 441s Investment_2 0 0.0 0.0 441s Investment_3 0 0.0 0.0 441s Investment_4 0 0.0 0.0 441s Investment_5 0 0.0 0.0 441s Investment_6 0 0.0 0.0 441s Investment_8 0 0.0 0.0 441s Investment_9 0 0.0 0.0 441s Investment_10 0 0.0 0.0 441s Investment_11 0 0.0 0.0 441s Investment_12 0 0.0 0.0 441s Investment_14 0 0.0 0.0 441s Investment_15 0 0.0 0.0 441s Investment_17 0 0.0 0.0 441s Investment_18 0 0.0 0.0 441s Investment_19 0 0.0 0.0 441s Investment_20 0 0.0 0.0 441s Investment_21 0 0.0 0.0 441s Investment_22 0 0.0 0.0 441s PrivateWages_2 0 0.0 0.0 441s PrivateWages_3 0 0.0 0.0 441s PrivateWages_4 0 0.0 0.0 441s PrivateWages_5 0 0.0 0.0 441s PrivateWages_6 0 0.0 0.0 441s PrivateWages_8 0 0.0 0.0 441s PrivateWages_9 0 0.0 0.0 441s PrivateWages_10 0 0.0 0.0 441s PrivateWages_11 0 0.0 0.0 441s PrivateWages_12 0 0.0 0.0 441s PrivateWages_13 0 0.0 0.0 441s PrivateWages_14 0 0.0 0.0 441s PrivateWages_15 0 0.0 0.0 441s PrivateWages_16 0 0.0 0.0 441s PrivateWages_17 0 0.0 0.0 441s PrivateWages_18 0 0.0 0.0 441s PrivateWages_19 0 0.0 0.0 441s PrivateWages_20 0 0.0 0.0 441s PrivateWages_21 0 0.0 0.0 441s PrivateWages_22 0 0.0 0.0 441s Consumption_govWage Consumption_trend Consumption_capitalLag 441s Consumption_2 2.7 -10 183 441s Consumption_3 2.9 -9 183 441s Consumption_4 2.9 -8 184 441s Consumption_5 3.1 -7 190 441s Consumption_6 3.2 -6 193 441s Consumption_8 3.6 -4 203 441s Consumption_9 3.7 -3 208 441s Consumption_11 4.2 -1 216 441s Consumption_12 4.8 0 217 441s Consumption_14 5.6 2 207 441s Consumption_15 6.0 3 202 441s Consumption_16 6.1 4 199 441s Consumption_17 7.4 5 198 441s Consumption_18 6.7 6 200 441s Consumption_19 7.7 7 202 441s Consumption_20 7.8 8 200 441s Consumption_21 8.0 9 201 441s Consumption_22 8.5 10 204 441s Investment_2 0.0 0 0 441s Investment_3 0.0 0 0 441s Investment_4 0.0 0 0 441s Investment_5 0.0 0 0 441s Investment_6 0.0 0 0 441s Investment_8 0.0 0 0 441s Investment_9 0.0 0 0 441s Investment_10 0.0 0 0 441s Investment_11 0.0 0 0 441s Investment_12 0.0 0 0 441s Investment_14 0.0 0 0 441s Investment_15 0.0 0 0 441s Investment_17 0.0 0 0 441s Investment_18 0.0 0 0 441s Investment_19 0.0 0 0 441s Investment_20 0.0 0 0 441s Investment_21 0.0 0 0 441s Investment_22 0.0 0 0 441s PrivateWages_2 0.0 0 0 441s PrivateWages_3 0.0 0 0 441s PrivateWages_4 0.0 0 0 441s PrivateWages_5 0.0 0 0 441s PrivateWages_6 0.0 0 0 441s PrivateWages_8 0.0 0 0 441s PrivateWages_9 0.0 0 0 441s PrivateWages_10 0.0 0 0 441s PrivateWages_11 0.0 0 0 441s PrivateWages_12 0.0 0 0 441s PrivateWages_13 0.0 0 0 441s PrivateWages_14 0.0 0 0 441s PrivateWages_15 0.0 0 0 441s PrivateWages_16 0.0 0 0 441s PrivateWages_17 0.0 0 0 441s PrivateWages_18 0.0 0 0 441s PrivateWages_19 0.0 0 0 441s PrivateWages_20 0.0 0 0 441s PrivateWages_21 0.0 0 0 441s PrivateWages_22 0.0 0 0 441s Consumption_corpProfLag Consumption_gnpLag 441s Consumption_2 12.7 44.9 441s Consumption_3 12.4 45.6 441s Consumption_4 16.9 50.1 441s Consumption_5 18.4 57.2 441s Consumption_6 19.4 57.1 441s Consumption_8 19.6 64.0 441s Consumption_9 19.8 64.4 441s Consumption_11 21.7 67.0 441s Consumption_12 15.6 61.2 441s Consumption_14 7.0 44.3 441s Consumption_15 11.2 45.1 441s Consumption_16 12.3 49.7 441s Consumption_17 14.0 54.4 441s Consumption_18 17.6 62.7 441s Consumption_19 17.3 65.0 441s Consumption_20 15.3 60.9 441s Consumption_21 19.0 69.5 441s Consumption_22 21.1 75.7 441s Investment_2 0.0 0.0 441s Investment_3 0.0 0.0 441s Investment_4 0.0 0.0 441s Investment_5 0.0 0.0 441s Investment_6 0.0 0.0 441s Investment_8 0.0 0.0 441s Investment_9 0.0 0.0 441s Investment_10 0.0 0.0 441s Investment_11 0.0 0.0 441s Investment_12 0.0 0.0 441s Investment_14 0.0 0.0 441s Investment_15 0.0 0.0 441s Investment_17 0.0 0.0 441s Investment_18 0.0 0.0 441s Investment_19 0.0 0.0 441s Investment_20 0.0 0.0 441s Investment_21 0.0 0.0 441s Investment_22 0.0 0.0 441s PrivateWages_2 0.0 0.0 441s PrivateWages_3 0.0 0.0 441s PrivateWages_4 0.0 0.0 441s PrivateWages_5 0.0 0.0 441s PrivateWages_6 0.0 0.0 441s PrivateWages_8 0.0 0.0 441s PrivateWages_9 0.0 0.0 441s PrivateWages_10 0.0 0.0 441s PrivateWages_11 0.0 0.0 441s PrivateWages_12 0.0 0.0 441s PrivateWages_13 0.0 0.0 441s PrivateWages_14 0.0 0.0 441s PrivateWages_15 0.0 0.0 441s PrivateWages_16 0.0 0.0 441s PrivateWages_17 0.0 0.0 441s PrivateWages_18 0.0 0.0 441s PrivateWages_19 0.0 0.0 441s PrivateWages_20 0.0 0.0 441s PrivateWages_21 0.0 0.0 441s PrivateWages_22 0.0 0.0 441s Investment_(Intercept) Investment_govExp Investment_taxes 441s Consumption_2 0 0.0 0.0 441s Consumption_3 0 0.0 0.0 441s Consumption_4 0 0.0 0.0 441s Consumption_5 0 0.0 0.0 441s Consumption_6 0 0.0 0.0 441s Consumption_8 0 0.0 0.0 441s Consumption_9 0 0.0 0.0 441s Consumption_11 0 0.0 0.0 441s Consumption_12 0 0.0 0.0 441s Consumption_14 0 0.0 0.0 441s Consumption_15 0 0.0 0.0 441s Consumption_16 0 0.0 0.0 441s Consumption_17 0 0.0 0.0 441s Consumption_18 0 0.0 0.0 441s Consumption_19 0 0.0 0.0 441s Consumption_20 0 0.0 0.0 441s Consumption_21 0 0.0 0.0 441s Consumption_22 0 0.0 0.0 441s Investment_2 1 3.9 7.7 441s Investment_3 1 3.2 3.9 441s Investment_4 1 2.8 4.7 441s Investment_5 1 3.5 3.8 441s Investment_6 1 3.3 5.5 441s Investment_8 1 4.0 6.7 441s Investment_9 1 4.2 4.2 441s Investment_10 1 4.1 4.0 441s Investment_11 1 5.2 7.7 441s Investment_12 1 5.9 7.5 441s Investment_14 1 3.7 5.4 441s Investment_15 1 4.0 6.8 441s Investment_17 1 2.9 8.3 441s Investment_18 1 4.3 6.7 441s Investment_19 1 5.3 7.4 441s Investment_20 1 6.6 8.9 441s Investment_21 1 7.4 9.6 441s Investment_22 1 13.8 11.6 441s PrivateWages_2 0 0.0 0.0 441s PrivateWages_3 0 0.0 0.0 441s PrivateWages_4 0 0.0 0.0 441s PrivateWages_5 0 0.0 0.0 441s PrivateWages_6 0 0.0 0.0 441s PrivateWages_8 0 0.0 0.0 441s PrivateWages_9 0 0.0 0.0 441s PrivateWages_10 0 0.0 0.0 441s PrivateWages_11 0 0.0 0.0 441s PrivateWages_12 0 0.0 0.0 441s PrivateWages_13 0 0.0 0.0 441s PrivateWages_14 0 0.0 0.0 441s PrivateWages_15 0 0.0 0.0 441s PrivateWages_16 0 0.0 0.0 441s PrivateWages_17 0 0.0 0.0 441s PrivateWages_18 0 0.0 0.0 441s PrivateWages_19 0 0.0 0.0 441s PrivateWages_20 0 0.0 0.0 441s PrivateWages_21 0 0.0 0.0 441s PrivateWages_22 0 0.0 0.0 441s Investment_govWage Investment_trend Investment_capitalLag 441s Consumption_2 0.0 0 0 441s Consumption_3 0.0 0 0 441s Consumption_4 0.0 0 0 441s Consumption_5 0.0 0 0 441s Consumption_6 0.0 0 0 441s Consumption_8 0.0 0 0 441s Consumption_9 0.0 0 0 441s Consumption_11 0.0 0 0 441s Consumption_12 0.0 0 0 441s Consumption_14 0.0 0 0 441s Consumption_15 0.0 0 0 441s Consumption_16 0.0 0 0 441s Consumption_17 0.0 0 0 441s Consumption_18 0.0 0 0 441s Consumption_19 0.0 0 0 441s Consumption_20 0.0 0 0 441s Consumption_21 0.0 0 0 441s Consumption_22 0.0 0 0 441s Investment_2 2.7 -10 183 441s Investment_3 2.9 -9 183 441s Investment_4 2.9 -8 184 441s Investment_5 3.1 -7 190 441s Investment_6 3.2 -6 193 441s Investment_8 3.6 -4 203 441s Investment_9 3.7 -3 208 441s Investment_10 4.0 -2 211 441s Investment_11 4.2 -1 216 441s Investment_12 4.8 0 217 441s Investment_14 5.6 2 207 441s Investment_15 6.0 3 202 441s Investment_17 7.4 5 198 441s Investment_18 6.7 6 200 441s Investment_19 7.7 7 202 441s Investment_20 7.8 8 200 441s Investment_21 8.0 9 201 441s Investment_22 8.5 10 204 441s PrivateWages_2 0.0 0 0 441s PrivateWages_3 0.0 0 0 441s PrivateWages_4 0.0 0 0 441s PrivateWages_5 0.0 0 0 441s PrivateWages_6 0.0 0 0 441s PrivateWages_8 0.0 0 0 441s PrivateWages_9 0.0 0 0 441s PrivateWages_10 0.0 0 0 441s PrivateWages_11 0.0 0 0 441s PrivateWages_12 0.0 0 0 441s PrivateWages_13 0.0 0 0 441s PrivateWages_14 0.0 0 0 441s PrivateWages_15 0.0 0 0 441s PrivateWages_16 0.0 0 0 441s PrivateWages_17 0.0 0 0 441s PrivateWages_18 0.0 0 0 441s PrivateWages_19 0.0 0 0 441s PrivateWages_20 0.0 0 0 441s PrivateWages_21 0.0 0 0 441s PrivateWages_22 0.0 0 0 441s Investment_corpProfLag Investment_gnpLag 441s Consumption_2 0.0 0.0 441s Consumption_3 0.0 0.0 441s Consumption_4 0.0 0.0 441s Consumption_5 0.0 0.0 441s Consumption_6 0.0 0.0 441s Consumption_8 0.0 0.0 441s Consumption_9 0.0 0.0 441s Consumption_11 0.0 0.0 441s Consumption_12 0.0 0.0 441s Consumption_14 0.0 0.0 441s Consumption_15 0.0 0.0 441s Consumption_16 0.0 0.0 441s Consumption_17 0.0 0.0 441s Consumption_18 0.0 0.0 441s Consumption_19 0.0 0.0 441s Consumption_20 0.0 0.0 441s Consumption_21 0.0 0.0 441s Consumption_22 0.0 0.0 441s Investment_2 12.7 44.9 441s Investment_3 12.4 45.6 441s Investment_4 16.9 50.1 441s Investment_5 18.4 57.2 441s Investment_6 19.4 57.1 441s Investment_8 19.6 64.0 441s Investment_9 19.8 64.4 441s Investment_10 21.1 64.5 441s Investment_11 21.7 67.0 441s Investment_12 15.6 61.2 441s Investment_14 7.0 44.3 441s Investment_15 11.2 45.1 441s Investment_17 14.0 54.4 441s Investment_18 17.6 62.7 441s Investment_19 17.3 65.0 441s Investment_20 15.3 60.9 441s Investment_21 19.0 69.5 441s Investment_22 21.1 75.7 441s PrivateWages_2 0.0 0.0 441s PrivateWages_3 0.0 0.0 441s PrivateWages_4 0.0 0.0 441s PrivateWages_5 0.0 0.0 441s PrivateWages_6 0.0 0.0 441s PrivateWages_8 0.0 0.0 441s PrivateWages_9 0.0 0.0 441s PrivateWages_10 0.0 0.0 441s PrivateWages_11 0.0 0.0 441s PrivateWages_12 0.0 0.0 441s PrivateWages_13 0.0 0.0 441s PrivateWages_14 0.0 0.0 441s PrivateWages_15 0.0 0.0 441s PrivateWages_16 0.0 0.0 441s PrivateWages_17 0.0 0.0 441s PrivateWages_18 0.0 0.0 441s PrivateWages_19 0.0 0.0 441s PrivateWages_20 0.0 0.0 441s PrivateWages_21 0.0 0.0 441s PrivateWages_22 0.0 0.0 441s PrivateWages_(Intercept) PrivateWages_govExp PrivateWages_taxes 441s Consumption_2 0 0.0 0.0 441s Consumption_3 0 0.0 0.0 441s Consumption_4 0 0.0 0.0 441s Consumption_5 0 0.0 0.0 441s Consumption_6 0 0.0 0.0 441s Consumption_8 0 0.0 0.0 441s Consumption_9 0 0.0 0.0 441s Consumption_11 0 0.0 0.0 441s Consumption_12 0 0.0 0.0 441s Consumption_14 0 0.0 0.0 441s Consumption_15 0 0.0 0.0 441s Consumption_16 0 0.0 0.0 441s Consumption_17 0 0.0 0.0 441s Consumption_18 0 0.0 0.0 441s Consumption_19 0 0.0 0.0 441s Consumption_20 0 0.0 0.0 441s Consumption_21 0 0.0 0.0 441s Consumption_22 0 0.0 0.0 441s Investment_2 0 0.0 0.0 441s Investment_3 0 0.0 0.0 441s Investment_4 0 0.0 0.0 441s Investment_5 0 0.0 0.0 441s Investment_6 0 0.0 0.0 441s Investment_8 0 0.0 0.0 441s Investment_9 0 0.0 0.0 441s Investment_10 0 0.0 0.0 441s Investment_11 0 0.0 0.0 441s Investment_12 0 0.0 0.0 441s Investment_14 0 0.0 0.0 441s Investment_15 0 0.0 0.0 441s Investment_17 0 0.0 0.0 441s Investment_18 0 0.0 0.0 441s Investment_19 0 0.0 0.0 441s Investment_20 0 0.0 0.0 441s Investment_21 0 0.0 0.0 441s Investment_22 0 0.0 0.0 441s PrivateWages_2 1 3.9 7.7 441s PrivateWages_3 1 3.2 3.9 441s PrivateWages_4 1 2.8 4.7 441s PrivateWages_5 1 3.5 3.8 441s PrivateWages_6 1 3.3 5.5 441s PrivateWages_8 1 4.0 6.7 441s PrivateWages_9 1 4.2 4.2 441s PrivateWages_10 1 4.1 4.0 441s PrivateWages_11 1 5.2 7.7 441s PrivateWages_12 1 5.9 7.5 441s PrivateWages_13 1 4.9 8.3 441s PrivateWages_14 1 3.7 5.4 441s PrivateWages_15 1 4.0 6.8 441s PrivateWages_16 1 4.4 7.2 441s PrivateWages_17 1 2.9 8.3 441s PrivateWages_18 1 4.3 6.7 441s PrivateWages_19 1 5.3 7.4 441s PrivateWages_20 1 6.6 8.9 441s PrivateWages_21 1 7.4 9.6 441s PrivateWages_22 1 13.8 11.6 441s PrivateWages_govWage PrivateWages_trend PrivateWages_capitalLag 441s Consumption_2 0.0 0 0 441s Consumption_3 0.0 0 0 441s Consumption_4 0.0 0 0 441s Consumption_5 0.0 0 0 441s Consumption_6 0.0 0 0 441s Consumption_8 0.0 0 0 441s Consumption_9 0.0 0 0 441s Consumption_11 0.0 0 0 441s Consumption_12 0.0 0 0 441s Consumption_14 0.0 0 0 441s Consumption_15 0.0 0 0 441s Consumption_16 0.0 0 0 441s Consumption_17 0.0 0 0 441s Consumption_18 0.0 0 0 441s Consumption_19 0.0 0 0 441s Consumption_20 0.0 0 0 441s Consumption_21 0.0 0 0 441s Consumption_22 0.0 0 0 441s Investment_2 0.0 0 0 441s Investment_3 0.0 0 0 441s Investment_4 0.0 0 0 441s Investment_5 0.0 0 0 441s Investment_6 0.0 0 0 441s Investment_8 0.0 0 0 441s Investment_9 0.0 0 0 441s Investment_10 0.0 0 0 441s Investment_11 0.0 0 0 441s Investment_12 0.0 0 0 441s Investment_14 0.0 0 0 441s Investment_15 0.0 0 0 441s Investment_17 0.0 0 0 441s Investment_18 0.0 0 0 441s Investment_19 0.0 0 0 441s Investment_20 0.0 0 0 441s Investment_21 0.0 0 0 441s Investment_22 0.0 0 0 441s PrivateWages_2 2.7 -10 183 441s PrivateWages_3 2.9 -9 183 441s PrivateWages_4 2.9 -8 184 441s PrivateWages_5 3.1 -7 190 441s PrivateWages_6 3.2 -6 193 441s PrivateWages_8 3.6 -4 203 441s PrivateWages_9 3.7 -3 208 441s PrivateWages_10 4.0 -2 211 441s PrivateWages_11 4.2 -1 216 441s PrivateWages_12 4.8 0 217 441s PrivateWages_13 5.3 1 213 441s PrivateWages_14 5.6 2 207 441s PrivateWages_15 6.0 3 202 441s PrivateWages_16 6.1 4 199 441s PrivateWages_17 7.4 5 198 441s PrivateWages_18 6.7 6 200 441s PrivateWages_19 7.7 7 202 441s PrivateWages_20 7.8 8 200 441s PrivateWages_21 8.0 9 201 441s PrivateWages_22 8.5 10 204 441s PrivateWages_corpProfLag PrivateWages_gnpLag 441s Consumption_2 0.0 0.0 441s Consumption_3 0.0 0.0 441s Consumption_4 0.0 0.0 441s Consumption_5 0.0 0.0 441s Consumption_6 0.0 0.0 441s Consumption_8 0.0 0.0 441s Consumption_9 0.0 0.0 441s Consumption_11 0.0 0.0 441s Consumption_12 0.0 0.0 441s Consumption_14 0.0 0.0 441s Consumption_15 0.0 0.0 441s Consumption_16 0.0 0.0 441s Consumption_17 0.0 0.0 441s Consumption_18 0.0 0.0 441s Consumption_19 0.0 0.0 441s Consumption_20 0.0 0.0 441s Consumption_21 0.0 0.0 441s Consumption_22 0.0 0.0 441s Investment_2 0.0 0.0 441s Investment_3 0.0 0.0 441s Investment_4 0.0 0.0 441s Investment_5 0.0 0.0 441s Investment_6 0.0 0.0 441s Investment_8 0.0 0.0 441s Investment_9 0.0 0.0 441s Investment_10 0.0 0.0 441s Investment_11 0.0 0.0 441s Investment_12 0.0 0.0 441s Investment_14 0.0 0.0 441s Investment_15 0.0 0.0 441s Investment_17 0.0 0.0 441s Investment_18 0.0 0.0 441s Investment_19 0.0 0.0 441s Investment_20 0.0 0.0 441s Investment_21 0.0 0.0 441s Investment_22 0.0 0.0 441s PrivateWages_2 12.7 44.9 441s PrivateWages_3 12.4 45.6 441s PrivateWages_4 16.9 50.1 441s PrivateWages_5 18.4 57.2 441s PrivateWages_6 19.4 57.1 441s PrivateWages_8 19.6 64.0 441s PrivateWages_9 19.8 64.4 441s PrivateWages_10 21.1 64.5 441s PrivateWages_11 21.7 67.0 441s PrivateWages_12 15.6 61.2 441s PrivateWages_13 11.4 53.4 441s PrivateWages_14 7.0 44.3 441s PrivateWages_15 11.2 45.1 441s PrivateWages_16 12.3 49.7 441s PrivateWages_17 14.0 54.4 441s PrivateWages_18 17.6 62.7 441s PrivateWages_19 17.3 65.0 441s PrivateWages_20 15.3 60.9 441s PrivateWages_21 19.0 69.5 441s PrivateWages_22 21.1 75.7 441s > matrix of fitted regressors 441s Consumption_(Intercept) Consumption_corpProf 441s Consumption_2 1 14.0 441s Consumption_3 1 16.7 441s Consumption_4 1 18.5 441s Consumption_5 1 20.3 441s Consumption_6 1 19.0 441s Consumption_8 1 17.6 441s Consumption_9 1 18.9 441s Consumption_11 1 16.7 441s Consumption_12 1 13.4 441s Consumption_14 1 10.0 441s Consumption_15 1 12.5 441s Consumption_16 1 14.5 441s Consumption_17 1 14.9 441s Consumption_18 1 19.4 441s Consumption_19 1 19.1 441s Consumption_20 1 17.7 441s Consumption_21 1 20.4 441s Consumption_22 1 22.7 441s Investment_2 0 0.0 441s Investment_3 0 0.0 441s Investment_4 0 0.0 441s Investment_5 0 0.0 441s Investment_6 0 0.0 441s Investment_8 0 0.0 441s Investment_9 0 0.0 441s Investment_10 0 0.0 441s Investment_11 0 0.0 441s Investment_12 0 0.0 441s Investment_14 0 0.0 441s Investment_15 0 0.0 441s Investment_17 0 0.0 441s Investment_18 0 0.0 441s Investment_19 0 0.0 441s Investment_20 0 0.0 441s Investment_21 0 0.0 441s Investment_22 0 0.0 441s PrivateWages_2 0 0.0 441s PrivateWages_3 0 0.0 441s PrivateWages_4 0 0.0 441s PrivateWages_5 0 0.0 441s PrivateWages_6 0 0.0 441s PrivateWages_8 0 0.0 441s PrivateWages_9 0 0.0 441s PrivateWages_10 0 0.0 441s PrivateWages_11 0 0.0 441s PrivateWages_12 0 0.0 441s PrivateWages_13 0 0.0 441s PrivateWages_14 0 0.0 441s PrivateWages_15 0 0.0 441s PrivateWages_16 0 0.0 441s PrivateWages_17 0 0.0 441s PrivateWages_18 0 0.0 441s PrivateWages_19 0 0.0 441s PrivateWages_20 0 0.0 441s PrivateWages_21 0 0.0 441s PrivateWages_22 0 0.0 441s Consumption_corpProfLag Consumption_wages 441s Consumption_2 12.7 29.8 441s Consumption_3 12.4 31.8 441s Consumption_4 16.9 35.3 441s Consumption_5 18.4 38.6 441s Consumption_6 19.4 38.5 441s Consumption_8 19.6 40.0 441s Consumption_9 19.8 41.8 441s Consumption_11 21.7 43.1 441s Consumption_12 15.6 39.7 441s Consumption_14 7.0 33.3 441s Consumption_15 11.2 37.3 441s Consumption_16 12.3 40.1 441s Consumption_17 14.0 41.8 441s Consumption_18 17.6 47.6 441s Consumption_19 17.3 49.2 441s Consumption_20 15.3 48.6 441s Consumption_21 19.0 53.4 441s Consumption_22 21.1 60.8 441s Investment_2 0.0 0.0 441s Investment_3 0.0 0.0 441s Investment_4 0.0 0.0 441s Investment_5 0.0 0.0 441s Investment_6 0.0 0.0 441s Investment_8 0.0 0.0 441s Investment_9 0.0 0.0 441s Investment_10 0.0 0.0 441s Investment_11 0.0 0.0 441s Investment_12 0.0 0.0 441s Investment_14 0.0 0.0 441s Investment_15 0.0 0.0 441s Investment_17 0.0 0.0 441s Investment_18 0.0 0.0 441s Investment_19 0.0 0.0 441s Investment_20 0.0 0.0 441s Investment_21 0.0 0.0 441s Investment_22 0.0 0.0 441s PrivateWages_2 0.0 0.0 441s PrivateWages_3 0.0 0.0 441s PrivateWages_4 0.0 0.0 441s PrivateWages_5 0.0 0.0 441s PrivateWages_6 0.0 0.0 441s PrivateWages_8 0.0 0.0 441s PrivateWages_9 0.0 0.0 441s PrivateWages_10 0.0 0.0 441s PrivateWages_11 0.0 0.0 441s PrivateWages_12 0.0 0.0 441s PrivateWages_13 0.0 0.0 441s PrivateWages_14 0.0 0.0 441s PrivateWages_15 0.0 0.0 441s PrivateWages_16 0.0 0.0 441s PrivateWages_17 0.0 0.0 441s PrivateWages_18 0.0 0.0 441s PrivateWages_19 0.0 0.0 441s PrivateWages_20 0.0 0.0 441s PrivateWages_21 0.0 0.0 441s PrivateWages_22 0.0 0.0 441s Investment_(Intercept) Investment_corpProf 441s Consumption_2 0 0.0 441s Consumption_3 0 0.0 441s Consumption_4 0 0.0 441s Consumption_5 0 0.0 441s Consumption_6 0 0.0 441s Consumption_8 0 0.0 441s Consumption_9 0 0.0 441s Consumption_11 0 0.0 441s Consumption_12 0 0.0 441s Consumption_14 0 0.0 441s Consumption_15 0 0.0 441s Consumption_16 0 0.0 441s Consumption_17 0 0.0 441s Consumption_18 0 0.0 441s Consumption_19 0 0.0 441s Consumption_20 0 0.0 441s Consumption_21 0 0.0 441s Consumption_22 0 0.0 441s Investment_2 1 13.4 441s Investment_3 1 16.7 441s Investment_4 1 18.8 441s Investment_5 1 20.6 441s Investment_6 1 19.3 441s Investment_8 1 17.5 441s Investment_9 1 19.5 441s Investment_10 1 20.2 441s Investment_11 1 17.2 441s Investment_12 1 13.5 441s Investment_14 1 10.1 441s Investment_15 1 13.0 441s Investment_17 1 14.9 441s Investment_18 1 19.5 441s Investment_19 1 19.3 441s Investment_20 1 17.5 441s Investment_21 1 20.2 441s Investment_22 1 22.8 441s PrivateWages_2 0 0.0 441s PrivateWages_3 0 0.0 441s PrivateWages_4 0 0.0 441s PrivateWages_5 0 0.0 441s PrivateWages_6 0 0.0 441s PrivateWages_8 0 0.0 441s PrivateWages_9 0 0.0 441s PrivateWages_10 0 0.0 441s PrivateWages_11 0 0.0 441s PrivateWages_12 0 0.0 441s PrivateWages_13 0 0.0 441s PrivateWages_14 0 0.0 441s PrivateWages_15 0 0.0 441s PrivateWages_16 0 0.0 441s PrivateWages_17 0 0.0 441s PrivateWages_18 0 0.0 441s PrivateWages_19 0 0.0 441s PrivateWages_20 0 0.0 441s PrivateWages_21 0 0.0 441s PrivateWages_22 0 0.0 441s Investment_corpProfLag Investment_capitalLag 441s Consumption_2 0.0 0 441s Consumption_3 0.0 0 441s Consumption_4 0.0 0 441s Consumption_5 0.0 0 441s Consumption_6 0.0 0 441s Consumption_8 0.0 0 441s Consumption_9 0.0 0 441s Consumption_11 0.0 0 441s Consumption_12 0.0 0 441s Consumption_14 0.0 0 441s Consumption_15 0.0 0 441s Consumption_16 0.0 0 441s Consumption_17 0.0 0 441s Consumption_18 0.0 0 441s Consumption_19 0.0 0 441s Consumption_20 0.0 0 441s Consumption_21 0.0 0 441s Consumption_22 0.0 0 441s Investment_2 12.7 183 441s Investment_3 12.4 183 441s Investment_4 16.9 184 441s Investment_5 18.4 190 441s Investment_6 19.4 193 441s Investment_8 19.6 203 441s Investment_9 19.8 208 441s Investment_10 21.1 211 441s Investment_11 21.7 216 441s Investment_12 15.6 217 441s Investment_14 7.0 207 441s Investment_15 11.2 202 441s Investment_17 14.0 198 441s Investment_18 17.6 200 441s Investment_19 17.3 202 441s Investment_20 15.3 200 441s Investment_21 19.0 201 441s Investment_22 21.1 204 441s PrivateWages_2 0.0 0 441s PrivateWages_3 0.0 0 441s PrivateWages_4 0.0 0 441s PrivateWages_5 0.0 0 441s PrivateWages_6 0.0 0 441s PrivateWages_8 0.0 0 441s PrivateWages_9 0.0 0 441s PrivateWages_10 0.0 0 441s PrivateWages_11 0.0 0 441s PrivateWages_12 0.0 0 441s PrivateWages_13 0.0 0 441s PrivateWages_14 0.0 0 441s PrivateWages_15 0.0 0 441s PrivateWages_16 0.0 0 441s PrivateWages_17 0.0 0 441s PrivateWages_18 0.0 0 441s PrivateWages_19 0.0 0 441s PrivateWages_20 0.0 0 441s PrivateWages_21 0.0 0 441s PrivateWages_22 0.0 0 441s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 441s Consumption_2 0 0.0 0.0 441s Consumption_3 0 0.0 0.0 441s Consumption_4 0 0.0 0.0 441s Consumption_5 0 0.0 0.0 441s Consumption_6 0 0.0 0.0 441s Consumption_8 0 0.0 0.0 441s Consumption_9 0 0.0 0.0 441s Consumption_11 0 0.0 0.0 441s Consumption_12 0 0.0 0.0 441s Consumption_14 0 0.0 0.0 441s Consumption_15 0 0.0 0.0 441s Consumption_16 0 0.0 0.0 441s Consumption_17 0 0.0 0.0 441s Consumption_18 0 0.0 0.0 441s Consumption_19 0 0.0 0.0 441s Consumption_20 0 0.0 0.0 441s Consumption_21 0 0.0 0.0 441s Consumption_22 0 0.0 0.0 441s Investment_2 0 0.0 0.0 441s Investment_3 0 0.0 0.0 441s Investment_4 0 0.0 0.0 441s Investment_5 0 0.0 0.0 441s Investment_6 0 0.0 0.0 441s Investment_8 0 0.0 0.0 441s Investment_9 0 0.0 0.0 441s Investment_10 0 0.0 0.0 441s Investment_11 0 0.0 0.0 441s Investment_12 0 0.0 0.0 441s Investment_14 0 0.0 0.0 441s Investment_15 0 0.0 0.0 441s Investment_17 0 0.0 0.0 441s Investment_18 0 0.0 0.0 441s Investment_19 0 0.0 0.0 441s Investment_20 0 0.0 0.0 441s Investment_21 0 0.0 0.0 441s Investment_22 0 0.0 0.0 441s PrivateWages_2 1 47.1 44.9 441s PrivateWages_3 1 49.6 45.6 441s PrivateWages_4 1 56.5 50.1 441s PrivateWages_5 1 60.7 57.2 441s PrivateWages_6 1 60.6 57.1 441s PrivateWages_8 1 60.0 64.0 441s PrivateWages_9 1 62.3 64.4 441s PrivateWages_10 1 64.6 64.5 441s PrivateWages_11 1 63.7 67.0 441s PrivateWages_12 1 54.8 61.2 441s PrivateWages_13 1 47.0 53.4 441s PrivateWages_14 1 42.1 44.3 441s PrivateWages_15 1 51.2 45.1 441s PrivateWages_16 1 55.3 49.7 441s PrivateWages_17 1 57.4 54.4 441s PrivateWages_18 1 67.2 62.7 441s PrivateWages_19 1 68.5 65.0 441s PrivateWages_20 1 66.8 60.9 441s PrivateWages_21 1 74.9 69.5 441s PrivateWages_22 1 86.9 75.7 441s PrivateWages_trend 441s Consumption_2 0 441s Consumption_3 0 441s Consumption_4 0 441s Consumption_5 0 441s Consumption_6 0 441s Consumption_8 0 441s Consumption_9 0 441s Consumption_11 0 441s Consumption_12 0 441s Consumption_14 0 441s Consumption_15 0 441s Consumption_16 0 441s Consumption_17 0 441s Consumption_18 0 441s Consumption_19 0 441s Consumption_20 0 441s Consumption_21 0 441s Consumption_22 0 441s Investment_2 0 441s Investment_3 0 441s Investment_4 0 441s Investment_5 0 441s Investment_6 0 441s Investment_8 0 441s Investment_9 0 441s Investment_10 0 441s Investment_11 0 441s Investment_12 0 441s Investment_14 0 441s Investment_15 0 441s Investment_17 0 441s Investment_18 0 441s Investment_19 0 441s Investment_20 0 441s Investment_21 0 441s Investment_22 0 441s PrivateWages_2 -10 441s PrivateWages_3 -9 441s PrivateWages_4 -8 441s PrivateWages_5 -7 441s PrivateWages_6 -6 441s PrivateWages_8 -4 441s PrivateWages_9 -3 441s PrivateWages_10 -2 441s PrivateWages_11 -1 441s PrivateWages_12 0 441s PrivateWages_13 1 441s PrivateWages_14 2 441s PrivateWages_15 3 441s PrivateWages_16 4 441s PrivateWages_17 5 441s PrivateWages_18 6 441s PrivateWages_19 7 441s PrivateWages_20 8 441s PrivateWages_21 9 441s PrivateWages_22 10 441s > nobs 441s [1] 56 441s > linearHypothesis 441s Linear hypothesis test (Theil's F test) 441s 441s Hypothesis: 441s Consumption_corpProf + Investment_capitalLag = 0 441s 441s Model 1: restricted model 441s Model 2: kleinModel 441s 441s Res.Df Df F Pr(>F) 441s 1 45 441s 2 44 1 1.27 0.27 441s Linear hypothesis test (F statistic of a Wald test) 441s 441s Hypothesis: 441s Consumption_corpProf + Investment_capitalLag = 0 441s 441s Model 1: restricted model 441s Model 2: kleinModel 441s 441s Res.Df Df F Pr(>F) 441s 1 45 441s 2 44 1 1.66 0.2 441s Linear hypothesis test (Chi^2 statistic of a Wald test) 441s 441s Hypothesis: 441s Consumption_corpProf + Investment_capitalLag = 0 441s 441s Model 1: restricted model 441s Model 2: kleinModel 441s 441s Res.Df Df Chisq Pr(>Chisq) 441s 1 45 441s 2 44 1 1.66 0.2 441s Linear hypothesis test (Theil's F test) 441s 441s Hypothesis: 441s Consumption_corpProf + Investment_capitalLag = 0 441s Consumption_corpProfLag - PrivateWages_trend = 0 441s 441s Model 1: restricted model 441s Model 2: kleinModel 441s 441s Res.Df Df F Pr(>F) 441s 1 46 441s 2 44 2 0.64 0.53 441s Linear hypothesis test (F statistic of a Wald test) 441s 441s Hypothesis: 441s Consumption_corpProf + Investment_capitalLag = 0 441s Consumption_corpProfLag - PrivateWages_trend = 0 441s 441s Model 1: restricted model 441s Model 2: kleinModel 441s 441s Res.Df Df F Pr(>F) 441s 1 46 441s 2 44 2 0.84 0.44 441s Linear hypothesis test (Chi^2 statistic of a Wald test) 441s 441s Hypothesis: 441s Consumption_corpProf + Investment_capitalLag = 0 441s Consumption_corpProfLag - PrivateWages_trend = 0 441s 441s Model 1: restricted model 441s Model 2: kleinModel 441s 441s Res.Df Df Chisq Pr(>Chisq) 441s 1 46 441s 2 44 2 1.68 0.43 441s > logLik 441s 'log Lik.' -69.5 (df=13) 441s 'log Lik.' -77.5 (df=13) 441s Estimating function 441s Consumption_(Intercept) Consumption_corpProf 441s Consumption_2 -1.891 -26.49 441s Consumption_3 -0.190 -3.16 441s Consumption_4 0.294 5.45 441s Consumption_5 -1.285 -26.05 441s Consumption_6 0.431 8.19 441s Consumption_8 2.670 47.11 441s Consumption_9 2.363 44.77 441s Consumption_11 -1.642 -27.49 441s Consumption_12 -1.735 -23.21 441s Consumption_14 0.834 8.35 441s Consumption_15 -1.061 -13.27 441s Consumption_16 -0.885 -12.82 441s Consumption_17 3.801 56.68 441s Consumption_18 -0.502 -9.76 441s Consumption_19 -3.000 -57.33 441s Consumption_20 2.012 35.52 441s Consumption_21 0.746 15.21 441s Consumption_22 -0.957 -21.70 441s Investment_2 0.000 0.00 441s Investment_3 0.000 0.00 441s Investment_4 0.000 0.00 441s Investment_5 0.000 0.00 441s Investment_6 0.000 0.00 441s Investment_8 0.000 0.00 441s Investment_9 0.000 0.00 441s Investment_10 0.000 0.00 441s Investment_11 0.000 0.00 441s Investment_12 0.000 0.00 441s Investment_14 0.000 0.00 441s Investment_15 0.000 0.00 441s Investment_17 0.000 0.00 441s Investment_18 0.000 0.00 441s Investment_19 0.000 0.00 441s Investment_20 0.000 0.00 441s Investment_21 0.000 0.00 441s Investment_22 0.000 0.00 441s PrivateWages_2 0.000 0.00 441s PrivateWages_3 0.000 0.00 441s PrivateWages_4 0.000 0.00 441s PrivateWages_5 0.000 0.00 441s PrivateWages_6 0.000 0.00 441s PrivateWages_8 0.000 0.00 441s PrivateWages_9 0.000 0.00 441s PrivateWages_10 0.000 0.00 441s PrivateWages_11 0.000 0.00 441s PrivateWages_12 0.000 0.00 441s PrivateWages_13 0.000 0.00 441s PrivateWages_14 0.000 0.00 441s PrivateWages_15 0.000 0.00 441s PrivateWages_16 0.000 0.00 441s PrivateWages_17 0.000 0.00 441s PrivateWages_18 0.000 0.00 441s PrivateWages_19 0.000 0.00 441s PrivateWages_20 0.000 0.00 441s PrivateWages_21 0.000 0.00 441s PrivateWages_22 0.000 0.00 441s Consumption_corpProfLag Consumption_wages 441s Consumption_2 -24.01 -56.38 441s Consumption_3 -2.35 -6.04 441s Consumption_4 4.96 10.35 441s Consumption_5 -23.65 -49.61 441s Consumption_6 8.35 16.60 441s Consumption_8 52.33 106.81 441s Consumption_9 46.80 98.74 441s Consumption_11 -35.64 -70.78 441s Consumption_12 -27.07 -68.81 441s Consumption_14 5.83 27.78 441s Consumption_15 -11.88 -39.61 441s Consumption_16 -10.89 -35.54 441s Consumption_17 53.21 158.79 441s Consumption_18 -8.84 -23.92 441s Consumption_19 -51.90 -147.70 441s Consumption_20 30.78 97.67 441s Consumption_21 14.17 39.83 441s Consumption_22 -20.20 -58.19 441s Investment_2 0.00 0.00 441s Investment_3 0.00 0.00 441s Investment_4 0.00 0.00 441s Investment_5 0.00 0.00 441s Investment_6 0.00 0.00 441s Investment_8 0.00 0.00 441s Investment_9 0.00 0.00 441s Investment_10 0.00 0.00 441s Investment_11 0.00 0.00 441s Investment_12 0.00 0.00 441s Investment_14 0.00 0.00 441s Investment_15 0.00 0.00 441s Investment_17 0.00 0.00 441s Investment_18 0.00 0.00 441s Investment_19 0.00 0.00 441s Investment_20 0.00 0.00 441s Investment_21 0.00 0.00 441s Investment_22 0.00 0.00 441s PrivateWages_2 0.00 0.00 441s PrivateWages_3 0.00 0.00 441s PrivateWages_4 0.00 0.00 441s PrivateWages_5 0.00 0.00 441s PrivateWages_6 0.00 0.00 441s PrivateWages_8 0.00 0.00 441s PrivateWages_9 0.00 0.00 441s PrivateWages_10 0.00 0.00 441s PrivateWages_11 0.00 0.00 441s PrivateWages_12 0.00 0.00 441s PrivateWages_13 0.00 0.00 441s PrivateWages_14 0.00 0.00 441s PrivateWages_15 0.00 0.00 441s PrivateWages_16 0.00 0.00 441s PrivateWages_17 0.00 0.00 441s PrivateWages_18 0.00 0.00 441s PrivateWages_19 0.00 0.00 441s PrivateWages_20 0.00 0.00 441s PrivateWages_21 0.00 0.00 441s PrivateWages_22 0.00 0.00 441s Investment_(Intercept) Investment_corpProf 441s Consumption_2 0.000 0.00 441s Consumption_3 0.000 0.00 441s Consumption_4 0.000 0.00 441s Consumption_5 0.000 0.00 441s Consumption_6 0.000 0.00 441s Consumption_8 0.000 0.00 441s Consumption_9 0.000 0.00 441s Consumption_11 0.000 0.00 441s Consumption_12 0.000 0.00 441s Consumption_14 0.000 0.00 441s Consumption_15 0.000 0.00 441s Consumption_16 0.000 0.00 441s Consumption_17 0.000 0.00 441s Consumption_18 0.000 0.00 441s Consumption_19 0.000 0.00 441s Consumption_20 0.000 0.00 441s Consumption_21 0.000 0.00 441s Consumption_22 0.000 0.00 441s Investment_2 -1.375 -18.47 441s Investment_3 0.361 6.02 441s Investment_4 1.027 19.33 441s Investment_5 -1.558 -32.12 441s Investment_6 0.610 11.77 441s Investment_8 1.420 24.90 441s Investment_9 0.404 7.88 441s Investment_10 2.082 42.13 441s Investment_11 -1.150 -19.79 441s Investment_12 -1.339 -18.06 441s Investment_14 1.019 10.28 441s Investment_15 -0.475 -6.17 441s Investment_17 2.105 31.39 441s Investment_18 -0.465 -9.06 441s Investment_19 -3.871 -74.65 441s Investment_20 0.469 8.23 441s Investment_21 0.132 2.65 441s Investment_22 0.603 13.74 441s PrivateWages_2 0.000 0.00 441s PrivateWages_3 0.000 0.00 441s PrivateWages_4 0.000 0.00 441s PrivateWages_5 0.000 0.00 441s PrivateWages_6 0.000 0.00 441s PrivateWages_8 0.000 0.00 441s PrivateWages_9 0.000 0.00 441s PrivateWages_10 0.000 0.00 441s PrivateWages_11 0.000 0.00 441s PrivateWages_12 0.000 0.00 441s PrivateWages_13 0.000 0.00 441s PrivateWages_14 0.000 0.00 441s PrivateWages_15 0.000 0.00 441s PrivateWages_16 0.000 0.00 441s PrivateWages_17 0.000 0.00 441s PrivateWages_18 0.000 0.00 441s PrivateWages_19 0.000 0.00 441s PrivateWages_20 0.000 0.00 441s PrivateWages_21 0.000 0.00 441s PrivateWages_22 0.000 0.00 441s Investment_corpProfLag Investment_capitalLag 441s Consumption_2 0.00 0.0 441s Consumption_3 0.00 0.0 441s Consumption_4 0.00 0.0 441s Consumption_5 0.00 0.0 441s Consumption_6 0.00 0.0 441s Consumption_8 0.00 0.0 441s Consumption_9 0.00 0.0 441s Consumption_11 0.00 0.0 441s Consumption_12 0.00 0.0 441s Consumption_14 0.00 0.0 441s Consumption_15 0.00 0.0 441s Consumption_16 0.00 0.0 441s Consumption_17 0.00 0.0 441s Consumption_18 0.00 0.0 441s Consumption_19 0.00 0.0 441s Consumption_20 0.00 0.0 441s Consumption_21 0.00 0.0 441s Consumption_22 0.00 0.0 441s Investment_2 -17.46 -251.4 441s Investment_3 4.48 65.9 441s Investment_4 17.35 189.4 441s Investment_5 -28.67 -295.5 441s Investment_6 11.83 117.5 441s Investment_8 27.83 288.8 441s Investment_9 8.00 83.9 441s Investment_10 43.93 438.5 441s Investment_11 -24.96 -248.1 441s Investment_12 -20.88 -290.1 441s Investment_14 7.14 211.1 441s Investment_15 -5.32 -95.9 441s Investment_17 29.48 416.3 441s Investment_18 -8.18 -92.9 441s Investment_19 -66.97 -781.2 441s Investment_20 7.18 93.8 441s Investment_21 2.50 26.5 441s Investment_22 12.73 123.4 441s PrivateWages_2 0.00 0.0 441s PrivateWages_3 0.00 0.0 441s PrivateWages_4 0.00 0.0 441s PrivateWages_5 0.00 0.0 441s PrivateWages_6 0.00 0.0 441s PrivateWages_8 0.00 0.0 441s PrivateWages_9 0.00 0.0 441s PrivateWages_10 0.00 0.0 441s PrivateWages_11 0.00 0.0 441s PrivateWages_12 0.00 0.0 441s PrivateWages_13 0.00 0.0 441s PrivateWages_14 0.00 0.0 441s PrivateWages_15 0.00 0.0 441s PrivateWages_16 0.00 0.0 441s PrivateWages_17 0.00 0.0 441s PrivateWages_18 0.00 0.0 441s PrivateWages_19 0.00 0.0 441s PrivateWages_20 0.00 0.0 441s PrivateWages_21 0.00 0.0 441s PrivateWages_22 0.00 0.0 441s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 441s Consumption_2 0.0000 0.00 0.00 441s Consumption_3 0.0000 0.00 0.00 441s Consumption_4 0.0000 0.00 0.00 441s Consumption_5 0.0000 0.00 0.00 441s Consumption_6 0.0000 0.00 0.00 441s Consumption_8 0.0000 0.00 0.00 441s Consumption_9 0.0000 0.00 0.00 441s Consumption_11 0.0000 0.00 0.00 441s Consumption_12 0.0000 0.00 0.00 441s Consumption_14 0.0000 0.00 0.00 441s Consumption_15 0.0000 0.00 0.00 441s Consumption_16 0.0000 0.00 0.00 441s Consumption_17 0.0000 0.00 0.00 441s Consumption_18 0.0000 0.00 0.00 441s Consumption_19 0.0000 0.00 0.00 441s Consumption_20 0.0000 0.00 0.00 441s Consumption_21 0.0000 0.00 0.00 441s Consumption_22 0.0000 0.00 0.00 441s Investment_2 0.0000 0.00 0.00 441s Investment_3 0.0000 0.00 0.00 441s Investment_4 0.0000 0.00 0.00 441s Investment_5 0.0000 0.00 0.00 441s Investment_6 0.0000 0.00 0.00 441s Investment_8 0.0000 0.00 0.00 441s Investment_9 0.0000 0.00 0.00 441s Investment_10 0.0000 0.00 0.00 441s Investment_11 0.0000 0.00 0.00 441s Investment_12 0.0000 0.00 0.00 441s Investment_14 0.0000 0.00 0.00 441s Investment_15 0.0000 0.00 0.00 441s Investment_17 0.0000 0.00 0.00 441s Investment_18 0.0000 0.00 0.00 441s Investment_19 0.0000 0.00 0.00 441s Investment_20 0.0000 0.00 0.00 441s Investment_21 0.0000 0.00 0.00 441s Investment_22 0.0000 0.00 0.00 441s PrivateWages_2 -1.9924 -93.78 -89.46 441s PrivateWages_3 0.4683 23.22 21.35 441s PrivateWages_4 1.4034 79.35 70.31 441s PrivateWages_5 -1.7870 -108.45 -102.22 441s PrivateWages_6 -0.3627 -21.98 -20.71 441s PrivateWages_8 1.1629 69.77 74.43 441s PrivateWages_9 1.2735 79.30 82.01 441s PrivateWages_10 2.2141 142.96 142.81 441s PrivateWages_11 -1.2912 -82.26 -86.51 441s PrivateWages_12 -0.0350 -1.92 -2.14 441s PrivateWages_13 -1.0438 -49.04 -55.74 441s PrivateWages_14 1.8016 75.90 79.81 441s PrivateWages_15 -0.3714 -19.02 -16.75 441s PrivateWages_16 -0.3904 -21.61 -19.40 441s PrivateWages_17 1.4934 85.71 81.24 441s PrivateWages_18 0.0279 1.88 1.75 441s PrivateWages_19 -3.8229 -261.91 -248.49 441s PrivateWages_20 0.7870 52.61 47.93 441s PrivateWages_21 -0.7415 -55.52 -51.54 441s PrivateWages_22 1.2062 104.79 91.31 441s PrivateWages_trend 441s Consumption_2 0.000 441s Consumption_3 0.000 441s Consumption_4 0.000 441s Consumption_5 0.000 441s Consumption_6 0.000 441s Consumption_8 0.000 441s Consumption_9 0.000 441s Consumption_11 0.000 441s Consumption_12 0.000 441s Consumption_14 0.000 441s Consumption_15 0.000 441s Consumption_16 0.000 441s Consumption_17 0.000 441s Consumption_18 0.000 441s Consumption_19 0.000 441s Consumption_20 0.000 441s Consumption_21 0.000 441s Consumption_22 0.000 441s Investment_2 0.000 441s Investment_3 0.000 441s Investment_4 0.000 441s Investment_5 0.000 441s Investment_6 0.000 441s Investment_8 0.000 441s Investment_9 0.000 441s Investment_10 0.000 441s Investment_11 0.000 441s Investment_12 0.000 441s Investment_14 0.000 441s Investment_15 0.000 441s Investment_17 0.000 441s Investment_18 0.000 441s Investment_19 0.000 441s Investment_20 0.000 441s Investment_21 0.000 441s Investment_22 0.000 441s PrivateWages_2 19.924 441s PrivateWages_3 -4.214 441s PrivateWages_4 -11.227 441s PrivateWages_5 12.509 441s PrivateWages_6 2.176 441s PrivateWages_8 -4.652 441s PrivateWages_9 -3.820 441s PrivateWages_10 -4.428 441s PrivateWages_11 1.291 441s PrivateWages_12 0.000 441s PrivateWages_13 -1.044 441s PrivateWages_14 3.603 441s PrivateWages_15 -1.114 441s PrivateWages_16 -1.562 441s PrivateWages_17 7.467 441s PrivateWages_18 0.168 441s PrivateWages_19 -26.760 441s PrivateWages_20 6.296 441s PrivateWages_21 -6.674 441s PrivateWages_22 12.062 441s [1] TRUE 441s > Bread 441s Consumption_(Intercept) Consumption_corpProf 441s Consumption_(Intercept) 116.13 -4.139 441s Consumption_corpProf -4.14 1.213 441s Consumption_corpProfLag 1.01 -0.677 441s Consumption_wages -1.41 -0.133 441s Investment_(Intercept) 0.00 0.000 441s Investment_corpProf 0.00 0.000 441s Investment_corpProfLag 0.00 0.000 441s Investment_capitalLag 0.00 0.000 441s PrivateWages_(Intercept) 0.00 0.000 441s PrivateWages_gnp 0.00 0.000 441s PrivateWages_gnpLag 0.00 0.000 441s PrivateWages_trend 0.00 0.000 441s Consumption_corpProfLag Consumption_wages 441s Consumption_(Intercept) 1.0117 -1.4132 441s Consumption_corpProf -0.6770 -0.1333 441s Consumption_corpProfLag 0.6979 -0.0188 441s Consumption_wages -0.0188 0.0955 441s Investment_(Intercept) 0.0000 0.0000 441s Investment_corpProf 0.0000 0.0000 441s Investment_corpProfLag 0.0000 0.0000 441s Investment_capitalLag 0.0000 0.0000 441s PrivateWages_(Intercept) 0.0000 0.0000 441s PrivateWages_gnp 0.0000 0.0000 441s PrivateWages_gnpLag 0.0000 0.0000 441s PrivateWages_trend Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 441s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 441s 0.0000 0.0000 441s Investment_(Intercept) Investment_corpProf 441s Consumption_(Intercept) 0.0 0.000 441s Consumption_corpProf 0.0 0.000 441s Consumption_corpProfLag 0.0 0.000 441s Consumption_wages 0.0 0.000 441s Investment_(Intercept) 2271.1 -40.229 441s Investment_corpProf -40.2 1.601 441s Investment_corpProfLag 32.3 -1.240 441s Investment_capitalLag -10.5 0.165 441s PrivateWages_(Intercept) 0.0 0.000 441s PrivateWages_gnp 0.0 0.000 441s PrivateWages_gnpLag 0.0 0.000 441s PrivateWages_trend 0.0 0.000 441s Investment_corpProfLag Investment_capitalLag 441s Consumption_(Intercept) 0.000 0.0000 441s Consumption_corpProf 0.000 0.0000 441s Consumption_corpProfLag 0.000 0.0000 441s Consumption_wages 0.000 0.0000 441s Investment_(Intercept) 32.280 -10.5200 441s Investment_corpProf -1.240 0.1648 441s Investment_corpProfLag 1.187 -0.1522 441s Investment_capitalLag -0.152 0.0509 441s PrivateWages_(Intercept) 0.000 0.0000 441s PrivateWages_gnp 0.000 0.0000 441s PrivateWages_gnpLag 0.000 0.0000 441s PrivateWages_trend 0.000 0.0000 441s PrivateWages_(Intercept) PrivateWages_gnp 441s Consumption_(Intercept) 0.000 0.0000 441s Consumption_corpProf 0.000 0.0000 441s Consumption_corpProfLag 0.000 0.0000 441s Consumption_wages 0.000 0.0000 441s Investment_(Intercept) 0.000 0.0000 441s Investment_corpProf 0.000 0.0000 441s Investment_corpProfLag 0.000 0.0000 441s Investment_capitalLag 0.000 0.0000 441s PrivateWages_(Intercept) 159.333 -0.8670 441s PrivateWages_gnp -0.867 0.1475 441s PrivateWages_gnpLag -1.818 -0.1375 441s PrivateWages_trend 2.020 -0.0396 441s PrivateWages_gnpLag PrivateWages_trend 441s Consumption_(Intercept) 0.0000 0.0000 441s Consumption_corpProf 0.0000 0.0000 441s Consumption_corpProfLag 0.0000 0.0000 441s Consumption_wages 0.0000 0.0000 441s Investment_(Intercept) 0.0000 0.0000 441s Investment_corpProf 0.0000 0.0000 441s Investment_corpProfLag 0.0000 0.0000 441s Investment_capitalLag 0.0000 0.0000 441s PrivateWages_(Intercept) -1.8179 2.0198 441s PrivateWages_gnp -0.1375 -0.0396 441s PrivateWages_gnpLag 0.1737 0.0056 441s PrivateWages_trend 0.0056 0.1075 441s > 441s > # SUR 441s > summary 441s 441s systemfit results 441s method: SUR 441s 441s N DF SSR detRCov OLS-R2 McElroy-R2 441s system 58 46 45.1 0.199 0.975 0.993 441s 441s N DF SSR MSE RMSE R2 Adj R2 441s Consumption 19 15 17.5 1.167 1.080 0.980 0.975 441s Investment 19 15 17.3 1.155 1.075 0.906 0.887 441s PrivateWages 20 16 10.3 0.642 0.801 0.987 0.985 441s 441s The covariance matrix of the residuals used for estimation 441s Consumption Investment PrivateWages 441s Consumption 0.9830 0.0466 -0.391 441s Investment 0.0466 0.8101 0.115 441s PrivateWages -0.3906 0.1155 0.496 441s 441s The covariance matrix of the residuals 441s Consumption Investment PrivateWages 441s Consumption 0.979 0.080 -0.452 441s Investment 0.080 0.810 0.181 441s PrivateWages -0.452 0.181 0.521 441s 441s The correlations of the residuals 441s Consumption Investment PrivateWages 441s Consumption 1.0000 0.0907 -0.636 441s Investment 0.0907 1.0000 0.267 441s PrivateWages -0.6362 0.2671 1.000 441s 441s 441s SUR estimates for 'Consumption' (equation 1) 441s Model Formula: consump ~ corpProf + corpProfLag + wages 441s 441s Estimate Std. Error t value Pr(>|t|) 441s (Intercept) 16.2670 1.3148 12.37 2.8e-09 *** 441s corpProf 0.1942 0.0954 2.04 0.06 . 441s corpProfLag 0.0747 0.0842 0.89 0.39 441s wages 0.8011 0.0383 20.93 1.6e-12 *** 441s --- 441s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 441s 441s Residual standard error: 1.08 on 15 degrees of freedom 441s Number of observations: 19 Degrees of Freedom: 15 441s SSR: 17.501 MSE: 1.167 Root MSE: 1.08 441s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.975 441s 441s 441s SUR estimates for 'Investment' (equation 2) 441s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 441s 441s Estimate Std. Error t value Pr(>|t|) 441s (Intercept) 12.6390 4.7856 2.64 0.01852 * 441s corpProf 0.4708 0.0943 4.99 0.00016 *** 441s corpProfLag 0.3533 0.0907 3.89 0.00144 ** 441s capitalLag -0.1254 0.0236 -5.32 8.6e-05 *** 441s --- 441s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 441s 441s Residual standard error: 1.075 on 15 degrees of freedom 441s Number of observations: 19 Degrees of Freedom: 15 441s SSR: 17.321 MSE: 1.155 Root MSE: 1.075 441s Multiple R-Squared: 0.906 Adjusted R-Squared: 0.887 441s 441s 441s SUR estimates for 'PrivateWages' (equation 3) 441s Model Formula: privWage ~ gnp + gnpLag + trend 441s 441s Estimate Std. Error t value Pr(>|t|) 441s (Intercept) 1.3264 1.1240 1.18 0.2552 441s gnp 0.4184 0.0268 15.63 4.1e-11 *** 441s gnpLag 0.1714 0.0315 5.43 5.5e-05 *** 441s trend 0.1456 0.0284 5.13 0.0001 *** 441s --- 441s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 441s 441s Residual standard error: 0.801 on 16 degrees of freedom 441s Number of observations: 20 Degrees of Freedom: 16 441s SSR: 10.266 MSE: 0.642 Root MSE: 0.801 441s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 441s 441s > residuals 441s Consumption Investment PrivateWages 441s 1 NA NA NA 441s 2 -0.3143 -0.2326 -1.1434 441s 3 -1.2700 -0.1705 0.5084 441s 4 -1.5426 1.0718 1.4211 441s 5 -0.4489 -1.4767 -0.0992 441s 6 0.0588 0.3167 -0.3594 441s 7 0.9213 1.4446 NA 441s 8 1.3789 0.8296 -0.7554 441s 9 1.0900 -0.5263 0.2887 441s 10 NA 1.2083 1.1800 441s 11 0.3569 0.4082 -0.3673 441s 12 -0.2288 0.2663 0.3445 441s 13 NA NA -0.1571 441s 14 0.2181 0.4946 0.4220 441s 15 -0.1120 -0.0470 0.3147 441s 16 -0.0872 NA 0.0145 441s 17 1.5615 1.0289 -0.8091 441s 18 -0.4530 0.0617 0.8608 441s 19 0.1997 -2.5397 -0.7635 441s 20 0.9268 -0.6136 -0.4046 441s 21 0.7588 -0.7465 -1.2179 441s 22 -2.2137 -0.6044 0.5606 441s > fitted 441s Consumption Investment PrivateWages 441s 1 NA NA NA 441s 2 42.2 0.0326 26.6 441s 3 46.3 2.0705 28.8 441s 4 50.7 4.1282 32.7 441s 5 51.0 4.4767 34.0 441s 6 52.5 4.7833 35.8 441s 7 54.2 4.1554 NA 441s 8 54.8 3.3704 38.7 441s 9 56.2 3.5263 38.9 441s 10 NA 3.8917 40.1 441s 11 54.6 0.5918 38.3 441s 12 51.1 -3.6663 34.2 441s 13 NA NA 29.2 441s 14 46.3 -5.5946 28.1 441s 15 48.8 -2.9530 30.3 441s 16 51.4 NA 33.2 441s 17 56.1 1.0711 37.6 441s 18 59.2 1.9383 40.1 441s 19 57.3 0.6397 39.0 441s 20 60.7 1.9136 42.0 441s 21 64.2 4.0465 46.2 441s 22 71.9 5.5044 52.7 441s > predict 441s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 441s 1 NA NA NA NA 441s 2 42.2 0.460 41.3 43.1 441s 3 46.3 0.489 45.3 47.3 441s 4 50.7 0.328 50.1 51.4 441s 5 51.0 0.384 50.3 51.8 441s 6 52.5 0.389 51.8 53.3 441s 7 54.2 0.347 53.5 54.9 441s 8 54.8 0.319 54.2 55.5 441s 9 56.2 0.353 55.5 56.9 441s 10 NA NA NA NA 441s 11 54.6 0.583 53.5 55.8 441s 12 51.1 0.524 50.1 52.2 441s 13 NA NA NA NA 441s 14 46.3 0.589 45.1 47.5 441s 15 48.8 0.393 48.0 49.6 441s 16 51.4 0.337 50.7 52.1 441s 17 56.1 0.345 55.4 56.8 441s 18 59.2 0.318 58.5 59.8 441s 19 57.3 0.381 56.5 58.1 441s 20 60.7 0.413 59.8 61.5 441s 21 64.2 0.417 63.4 65.1 441s 22 71.9 0.651 70.6 73.2 441s Investment.pred Investment.se.fit Investment.lwr Investment.upr 441s 1 NA NA NA NA 441s 2 0.0326 0.556 -1.0866 1.15 441s 3 2.0705 0.454 1.1575 2.98 441s 4 4.1282 0.399 3.3256 4.93 441s 5 4.4767 0.331 3.8101 5.14 441s 6 4.7833 0.314 4.1520 5.41 441s 7 4.1554 0.291 3.5687 4.74 441s 8 3.3704 0.260 2.8469 3.89 441s 9 3.5263 0.347 2.8278 4.22 441s 10 3.8917 0.397 3.0924 4.69 441s 11 0.5918 0.578 -0.5711 1.75 441s 12 -3.6663 0.551 -4.7762 -2.56 441s 13 NA NA NA NA 441s 14 -5.5946 0.661 -6.9261 -4.26 441s 15 -2.9530 0.392 -3.7430 -2.16 441s 16 NA NA NA NA 441s 17 1.0711 0.318 0.4315 1.71 441s 18 1.9383 0.225 1.4863 2.39 441s 19 0.6397 0.310 0.0165 1.26 441s 20 1.9136 0.333 1.2436 2.58 441s 21 4.0465 0.304 3.4345 4.66 441s 22 5.5044 0.429 4.6400 6.37 441s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 441s 1 NA NA NA NA 441s 2 26.6 0.321 26.0 27.3 441s 3 28.8 0.321 28.1 29.4 441s 4 32.7 0.316 32.0 33.3 441s 5 34.0 0.244 33.5 34.5 441s 6 35.8 0.242 35.3 36.2 441s 7 NA NA NA NA 441s 8 38.7 0.246 38.2 39.2 441s 9 38.9 0.234 38.4 39.4 441s 10 40.1 0.225 39.7 40.6 441s 11 38.3 0.301 37.7 38.9 441s 12 34.2 0.298 33.6 34.8 441s 13 29.2 0.353 28.4 29.9 441s 14 28.1 0.330 27.4 28.7 441s 15 30.3 0.328 29.6 30.9 441s 16 33.2 0.275 32.6 33.7 441s 17 37.6 0.270 37.1 38.2 441s 18 40.1 0.213 39.7 40.6 441s 19 39.0 0.301 38.4 39.6 441s 20 42.0 0.287 41.4 42.6 441s 21 46.2 0.304 45.6 46.8 441s 22 52.7 0.448 51.8 53.6 441s > model.frame 441s [1] TRUE 441s > model.matrix 441s [1] TRUE 441s > nobs 441s [1] 58 441s > linearHypothesis 441s Linear hypothesis test (Theil's F test) 441s 441s Hypothesis: 441s Consumption_corpProf + Investment_capitalLag = 0 441s 441s Model 1: restricted model 441s Model 2: kleinModel 441s 441s Res.Df Df F Pr(>F) 441s 1 47 441s 2 46 1 0.4 0.53 441s Linear hypothesis test (F statistic of a Wald test) 441s 441s Hypothesis: 441s Consumption_corpProf + Investment_capitalLag = 0 441s 441s Model 1: restricted model 441s Model 2: kleinModel 441s 441s Res.Df Df F Pr(>F) 441s 1 47 441s 2 46 1 0.49 0.49 441s Linear hypothesis test (Chi^2 statistic of a Wald test) 441s 441s Hypothesis: 441s Consumption_corpProf + Investment_capitalLag = 0 441s 441s Model 1: restricted model 441s Model 2: kleinModel 441s 441s Res.Df Df Chisq Pr(>Chisq) 441s 1 47 441s 2 46 1 0.49 0.48 441s Linear hypothesis test (Theil's F test) 441s 441s Hypothesis: 441s Consumption_corpProf + Investment_capitalLag = 0 441s Consumption_corpProfLag - PrivateWages_trend = 0 441s 441s Model 1: restricted model 441s Model 2: kleinModel 441s 441s Res.Df Df F Pr(>F) 441s 1 48 441s 2 46 2 0.31 0.74 441s Linear hypothesis test (F statistic of a Wald test) 441s 441s Hypothesis: 441s Consumption_corpProf + Investment_capitalLag = 0 441s Consumption_corpProfLag - PrivateWages_trend = 0 441s 441s Model 1: restricted model 441s Model 2: kleinModel 441s 441s Res.Df Df F Pr(>F) 441s 1 48 441s 2 46 2 0.37 0.69 441s Linear hypothesis test (Chi^2 statistic of a Wald test) 441s 441s Hypothesis: 441s Consumption_corpProf + Investment_capitalLag = 0 441s Consumption_corpProfLag - PrivateWages_trend = 0 441s 441s Model 1: restricted model 441s Model 2: kleinModel 441s 441s Res.Df Df Chisq Pr(>Chisq) 441s 1 48 441s 2 46 2 0.75 0.69 441s > logLik 441s 'log Lik.' -66.4 (df=18) 441s 'log Lik.' -74.1 (df=18) 441s Estimating function 441s Consumption_(Intercept) Consumption_corpProf 441s Consumption_2 -0.4828 -5.986 441s Consumption_3 -1.9510 -32.972 441s Consumption_4 -2.3698 -43.605 441s Consumption_5 -0.6896 -13.377 441s Consumption_6 0.0903 1.814 441s Consumption_7 1.4152 27.739 441s Consumption_8 2.1183 41.942 441s Consumption_9 1.6745 35.332 441s Consumption_11 0.5483 8.553 441s Consumption_12 -0.3515 -4.008 441s Consumption_14 0.3350 3.752 441s Consumption_15 -0.1720 -2.116 441s Consumption_16 -0.1339 -1.875 441s Consumption_17 2.3987 42.218 441s Consumption_18 -0.6959 -12.040 441s Consumption_19 0.3068 4.694 441s Consumption_20 1.4238 27.052 441s Consumption_21 1.1656 24.594 441s Consumption_22 -3.4008 -79.918 441s Investment_2 0.0628 0.779 441s Investment_3 0.0460 0.778 441s Investment_4 -0.2893 -5.322 441s Investment_5 0.3986 7.732 441s Investment_6 -0.0855 -1.718 441s Investment_7 -0.3899 -7.642 441s Investment_8 -0.2239 -4.433 441s Investment_9 0.1420 2.997 441s Investment_10 0.0000 0.000 441s Investment_11 -0.1102 -1.719 441s Investment_12 -0.0719 -0.819 441s Investment_14 -0.1335 -1.495 441s Investment_15 0.0127 0.156 441s Investment_17 -0.2777 -4.887 441s Investment_18 -0.0167 -0.288 441s Investment_19 0.6855 10.488 441s Investment_20 0.1656 3.146 441s Investment_21 0.2015 4.251 441s Investment_22 0.1631 3.834 441s PrivateWages_2 -1.4560 -18.055 441s PrivateWages_3 0.6473 10.940 441s PrivateWages_4 1.8097 33.298 441s PrivateWages_5 -0.1264 -2.452 441s PrivateWages_6 -0.4576 -9.199 441s PrivateWages_8 -0.9619 -19.046 441s PrivateWages_9 0.3676 7.757 441s PrivateWages_10 0.0000 0.000 441s PrivateWages_11 -0.4677 -7.296 441s PrivateWages_12 0.4387 5.001 441s PrivateWages_13 0.0000 0.000 441s PrivateWages_14 0.5373 6.018 441s PrivateWages_15 0.4008 4.929 441s PrivateWages_16 0.0184 0.258 441s PrivateWages_17 -1.0303 -18.134 441s PrivateWages_18 1.0961 18.963 441s PrivateWages_19 -0.9722 -14.875 441s PrivateWages_20 -0.5153 -9.790 441s PrivateWages_21 -1.5509 -32.724 441s PrivateWages_22 0.7139 16.776 441s Consumption_corpProfLag Consumption_wages 441s Consumption_2 -6.131 -13.614 441s Consumption_3 -24.192 -62.822 441s Consumption_4 -40.050 -87.684 441s Consumption_5 -12.688 -25.514 441s Consumption_6 1.751 3.484 441s Consumption_7 28.447 57.601 441s Consumption_8 41.518 87.909 441s Consumption_9 33.155 71.835 441s Consumption_11 11.898 23.083 441s Consumption_12 -5.484 -13.816 441s Consumption_14 2.345 11.425 441s Consumption_15 -1.926 -6.295 441s Consumption_16 -1.647 -5.263 441s Consumption_17 33.582 106.024 441s Consumption_18 -12.249 -33.196 441s Consumption_19 5.307 14.081 441s Consumption_20 21.784 70.336 441s Consumption_21 22.146 61.777 441s Consumption_22 -71.756 -210.167 441s Investment_2 0.797 1.770 441s Investment_3 0.571 1.482 441s Investment_4 -4.889 -10.703 441s Investment_5 7.333 14.747 441s Investment_6 -1.658 -3.300 441s Investment_7 -7.837 -15.869 441s Investment_8 -4.389 -9.292 441s Investment_9 2.812 6.093 441s Investment_10 0.000 0.000 441s Investment_11 -2.391 -4.638 441s Investment_12 -1.121 -2.825 441s Investment_14 -0.934 -4.552 441s Investment_15 0.142 0.464 441s Investment_17 -3.888 -12.274 441s Investment_18 -0.293 -0.794 441s Investment_19 11.859 31.463 441s Investment_20 2.534 8.181 441s Investment_21 3.828 10.678 441s Investment_22 3.442 10.082 441s PrivateWages_2 -18.491 -41.059 441s PrivateWages_3 8.027 20.845 441s PrivateWages_4 30.584 66.958 441s PrivateWages_5 -2.325 -4.676 441s PrivateWages_6 -8.878 -17.665 441s PrivateWages_8 -18.854 -39.920 441s PrivateWages_9 7.279 15.770 441s PrivateWages_10 0.000 0.000 441s PrivateWages_11 -10.149 -19.690 441s PrivateWages_12 6.843 17.240 441s PrivateWages_13 0.000 0.000 441s PrivateWages_14 3.761 18.323 441s PrivateWages_15 4.489 14.668 441s PrivateWages_16 0.227 0.725 441s PrivateWages_17 -14.424 -45.540 441s PrivateWages_18 19.292 52.286 441s PrivateWages_19 -16.820 -44.626 441s PrivateWages_20 -7.884 -25.455 441s PrivateWages_21 -29.467 -82.197 441s PrivateWages_22 15.062 44.116 441s Investment_(Intercept) Investment_corpProf 441s Consumption_2 0.0848 1.052 441s Consumption_3 0.3428 5.793 441s Consumption_4 0.4164 7.661 441s Consumption_5 0.1211 2.350 441s Consumption_6 -0.0159 -0.319 441s Consumption_7 -0.2486 -4.873 441s Consumption_8 -0.3722 -7.369 441s Consumption_9 -0.2942 -6.207 441s Consumption_11 -0.0963 -1.503 441s Consumption_12 0.0618 0.704 441s Consumption_14 -0.0589 -0.659 441s Consumption_15 0.0302 0.372 441s Consumption_16 0.0000 0.000 441s Consumption_17 -0.4214 -7.417 441s Consumption_18 0.1223 2.115 441s Consumption_19 -0.0539 -0.825 441s Consumption_20 -0.2501 -4.753 441s Consumption_21 -0.2048 -4.321 441s Consumption_22 0.5975 14.041 441s Investment_2 -0.3080 -3.820 441s Investment_3 -0.2258 -3.815 441s Investment_4 1.4192 26.112 441s Investment_5 -1.9554 -37.935 441s Investment_6 0.4194 8.430 441s Investment_7 1.9129 37.493 441s Investment_8 1.0985 21.751 441s Investment_9 -0.6968 -14.703 441s Investment_10 1.6000 34.719 441s Investment_11 0.5405 8.432 441s Investment_12 0.3526 4.020 441s Investment_14 0.6549 7.335 441s Investment_15 -0.0622 -0.766 441s Investment_17 1.3624 23.978 441s Investment_18 0.0817 1.413 441s Investment_19 -3.3630 -51.454 441s Investment_20 -0.8125 -15.437 441s Investment_21 -0.9884 -20.856 441s Investment_22 -0.8004 -18.809 441s PrivateWages_2 0.5958 7.388 441s PrivateWages_3 -0.2649 -4.477 441s PrivateWages_4 -0.7405 -13.626 441s PrivateWages_5 0.0517 1.003 441s PrivateWages_6 0.1873 3.764 441s PrivateWages_8 0.3936 7.794 441s PrivateWages_9 -0.1504 -3.174 441s PrivateWages_10 -0.6149 -13.343 441s PrivateWages_11 0.1914 2.986 441s PrivateWages_12 -0.1795 -2.046 441s PrivateWages_13 0.0000 0.000 441s PrivateWages_14 -0.2199 -2.463 441s PrivateWages_15 -0.1640 -2.017 441s PrivateWages_16 0.0000 0.000 441s PrivateWages_17 0.4216 7.420 441s PrivateWages_18 -0.4485 -7.760 441s PrivateWages_19 0.3978 6.087 441s PrivateWages_20 0.2109 4.006 441s PrivateWages_21 0.6346 13.391 441s PrivateWages_22 -0.2921 -6.865 441s Investment_corpProfLag Investment_capitalLag 441s Consumption_2 1.077 15.50 441s Consumption_3 4.250 62.59 441s Consumption_4 7.036 76.82 441s Consumption_5 2.229 22.98 441s Consumption_6 -0.308 -3.06 441s Consumption_7 -4.998 -49.18 441s Consumption_8 -7.294 -75.70 441s Consumption_9 -5.825 -61.07 441s Consumption_11 -2.090 -20.78 441s Consumption_12 0.963 13.38 441s Consumption_14 -0.412 -12.19 441s Consumption_15 0.338 6.10 441s Consumption_16 0.000 0.00 441s Consumption_17 -5.900 -83.32 441s Consumption_18 2.152 24.43 441s Consumption_19 -0.932 -10.88 441s Consumption_20 -3.827 -50.00 441s Consumption_21 -3.891 -41.20 441s Consumption_22 12.607 122.18 441s Investment_2 -3.912 -56.31 441s Investment_3 -2.799 -41.22 441s Investment_4 23.984 261.83 441s Investment_5 -35.979 -370.94 441s Investment_6 8.137 80.82 441s Investment_7 38.449 378.37 441s Investment_8 21.531 223.44 441s Investment_9 -13.797 -144.66 441s Investment_10 33.759 336.95 441s Investment_11 11.729 116.59 441s Investment_12 5.501 76.41 441s Investment_14 4.584 135.62 441s Investment_15 -0.697 -12.57 441s Investment_17 19.074 269.35 441s Investment_18 1.438 16.32 441s Investment_19 -58.180 -678.65 441s Investment_20 -12.431 -162.42 441s Investment_21 -18.780 -198.88 441s Investment_22 -16.888 -163.68 441s PrivateWages_2 7.567 108.91 441s PrivateWages_3 -3.285 -48.37 441s PrivateWages_4 -12.515 -136.63 441s PrivateWages_5 0.951 9.81 441s PrivateWages_6 3.633 36.09 441s PrivateWages_8 7.715 80.06 441s PrivateWages_9 -2.978 -31.23 441s PrivateWages_10 -12.974 -129.50 441s PrivateWages_11 4.153 41.28 441s PrivateWages_12 -2.800 -38.90 441s PrivateWages_13 0.000 0.00 441s PrivateWages_14 -1.539 -45.54 441s PrivateWages_15 -1.837 -33.13 441s PrivateWages_16 0.000 0.00 441s PrivateWages_17 5.903 83.35 441s PrivateWages_18 -7.894 -89.62 441s PrivateWages_19 6.883 80.29 441s PrivateWages_20 3.226 42.15 441s PrivateWages_21 12.058 127.69 441s PrivateWages_22 -6.164 -59.74 441s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 441s Consumption_2 -0.4002 -18.25 -17.97 441s Consumption_3 -1.6172 -81.02 -73.75 441s Consumption_4 -1.9644 -112.37 Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 441s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 441s -98.42 441s Consumption_5 -0.5716 -32.64 -32.70 441s Consumption_6 0.0748 4.56 4.27 441s Consumption_7 0.0000 0.00 0.00 441s Consumption_8 1.7559 113.08 112.38 441s Consumption_9 1.3880 89.53 89.39 441s Consumption_11 0.4545 27.81 30.45 441s Consumption_12 -0.2914 -15.56 -17.83 441s Consumption_14 0.2777 12.53 12.30 441s Consumption_15 -0.1426 -7.09 -6.43 441s Consumption_16 -0.1110 -6.04 -5.52 441s Consumption_17 1.9884 124.67 108.17 441s Consumption_18 -0.5769 -37.50 -36.17 441s Consumption_19 0.2543 15.49 16.53 441s Consumption_20 1.1803 82.03 71.88 441s Consumption_21 0.9662 73.14 67.15 441s Consumption_22 -2.8190 -249.20 -213.40 441s Investment_2 0.1212 5.53 5.44 441s Investment_3 0.0888 4.45 4.05 441s Investment_4 -0.5585 -31.95 -27.98 441s Investment_5 0.7695 43.94 44.02 441s Investment_6 -0.1651 -10.07 -9.42 441s Investment_7 0.0000 0.00 0.00 441s Investment_8 -0.4323 -27.84 -27.67 441s Investment_9 0.2742 17.69 17.66 441s Investment_10 -0.6296 -42.19 -40.61 441s Investment_11 -0.2127 -13.02 -14.25 441s Investment_12 -0.1388 -7.41 -8.49 441s Investment_14 -0.2577 -11.62 -11.42 441s Investment_15 0.0245 1.22 1.10 441s Investment_17 -0.5361 -33.62 -29.17 441s Investment_18 -0.0322 -2.09 -2.02 441s Investment_19 1.3234 80.60 86.02 441s Investment_20 0.3197 22.22 19.47 441s Investment_21 0.3890 29.45 27.03 441s Investment_22 0.3150 27.84 23.84 441s PrivateWages_2 -3.5926 -163.82 -161.31 441s PrivateWages_3 1.5973 80.02 72.84 441s PrivateWages_4 4.4653 255.42 223.71 441s PrivateWages_5 -0.3118 -17.80 -17.84 441s PrivateWages_6 -1.1292 -68.88 -64.48 441s PrivateWages_8 -2.3735 -152.85 -151.90 441s PrivateWages_9 0.9071 58.50 58.41 441s PrivateWages_10 3.7077 248.42 239.15 441s PrivateWages_11 -1.1540 -70.63 -77.32 441s PrivateWages_12 1.0824 57.80 66.24 441s PrivateWages_13 -0.4937 -21.87 -26.36 441s PrivateWages_14 1.3258 59.79 58.73 441s PrivateWages_15 0.9889 49.15 44.60 441s PrivateWages_16 0.0455 2.48 2.26 441s PrivateWages_17 -2.5423 -159.40 -138.30 441s PrivateWages_18 2.7047 175.80 169.58 441s PrivateWages_19 -2.3990 -146.10 -155.93 441s PrivateWages_20 -1.2714 -88.36 -77.43 441s PrivateWages_21 -3.8267 -289.68 -265.96 441s PrivateWages_22 1.7614 155.71 133.34 441s PrivateWages_trend 441s Consumption_2 4.0019 441s Consumption_3 14.5552 441s Consumption_4 15.7155 441s Consumption_5 4.0012 441s Consumption_6 -0.4490 441s Consumption_7 0.0000 441s Consumption_8 -7.0237 441s Consumption_9 -4.1641 441s Consumption_11 -0.4545 441s Consumption_12 0.0000 441s Consumption_14 0.5555 441s Consumption_15 -0.4277 441s Consumption_16 -0.4440 441s Consumption_17 9.9420 441s Consumption_18 -3.4614 441s Consumption_19 1.7801 441s Consumption_20 9.4420 441s Consumption_21 8.6959 441s Consumption_22 -28.1902 441s Investment_2 -1.2122 441s Investment_3 -0.7996 441s Investment_4 4.4678 441s Investment_5 -5.3865 441s Investment_6 0.9903 441s Investment_7 0.0000 441s Investment_8 1.7292 441s Investment_9 -0.8227 441s Investment_10 1.2593 441s Investment_11 0.2127 441s Investment_12 0.0000 441s Investment_14 -0.5154 441s Investment_15 0.0735 441s Investment_17 -2.6807 441s Investment_18 -0.1929 441s Investment_19 9.2640 441s Investment_20 2.5579 441s Investment_21 3.5008 441s Investment_22 3.1497 441s PrivateWages_2 35.9264 441s PrivateWages_3 -14.3757 441s PrivateWages_4 -35.7225 441s PrivateWages_5 2.1827 441s PrivateWages_6 6.7753 441s PrivateWages_8 9.4940 441s PrivateWages_9 -2.7212 441s PrivateWages_10 -7.4154 441s PrivateWages_11 1.1540 441s PrivateWages_12 0.0000 441s PrivateWages_13 -0.4937 441s PrivateWages_14 2.6517 441s PrivateWages_15 2.9666 441s PrivateWages_16 0.1820 441s PrivateWages_17 -12.7113 441s PrivateWages_18 16.2281 441s PrivateWages_19 -16.7928 441s PrivateWages_20 -10.1714 441s PrivateWages_21 -34.4407 441s PrivateWages_22 17.6141 441s [1] TRUE 441s > Bread 441s Consumption_(Intercept) Consumption_corpProf Consumption_corpProfLag 441s [1,] 1.00e+02 -1.05144 -0.70595 441s [2,] -1.05e+00 0.52767 -0.28007 441s [3,] -7.06e-01 -0.28007 0.41162 441s [4,] -1.63e+00 -0.08132 -0.03081 441s [5,] 5.03e+00 -0.06375 0.80965 441s [6,] -2.73e-01 0.05286 -0.04323 441s [7,] 4.77e-03 -0.03564 0.04677 441s [8,] -4.66e-04 -0.00135 -0.00415 441s [9,] -3.50e+01 0.07154 1.64913 441s [10,] 3.09e-01 -0.05491 0.03767 441s [11,] 2.66e-01 0.05541 -0.06699 441s [12,] 1.98e-01 0.03217 0.02582 441s Consumption_wages Investment_(Intercept) Investment_corpProf 441s [1,] -1.63020 5.0343 -0.27333 441s [2,] -0.08132 -0.0638 0.05286 441s [3,] -0.03081 0.8097 -0.04323 441s [4,] 0.08501 -0.3863 0.00122 441s [5,] -0.38629 1328.3034 -12.58281 441s [6,] 0.00122 -12.5828 0.51550 441s [7,] -0.00347 10.1576 -0.39286 441s [8,] 0.00211 -6.3831 0.05078 441s [9,] 0.13121 19.8408 -0.15336 441s [10,] -0.00022 0.2731 0.01339 441s [11,] -0.00213 -0.6257 -0.01103 441s [12,] -0.02827 -0.5788 0.00418 441s Investment_corpProfLag Investment_capitalLag PrivateWages_(Intercept) 441s [1,] 0.00477 -0.000466 -34.9530 441s [2,] -0.03564 -0.001347 0.0715 441s [3,] 0.04677 -0.004153 1.6491 441s [4,] -0.00347 0.002105 0.1312 441s [5,] 10.15755 -6.383136 19.8408 441s [6,] -0.39286 0.050784 -0.1534 441s [7,] 0.47726 -0.056526 -0.3957 441s [8,] -0.05653 0.032233 -0.0526 441s [9,] -0.39566 -0.052599 73.2779 441s [10,] -0.00743 -0.001878 -0.2209 441s [11,] 0.01439 0.002876 -1.0159 441s [12,] -0.01026 0.003357 0.8108 441s PrivateWages_gnp PrivateWages_gnpLag PrivateWages_trend 441s [1,] 0.30855 0.26619 0.19754 441s [2,] -0.05491 0.05541 0.03217 441s [3,] 0.03767 -0.06699 0.02582 441s [4,] -0.00022 -0.00213 -0.02827 441s [5,] 0.27312 -0.62569 -0.57877 441s [6,] 0.01339 -0.01103 0.00418 441s [7,] -0.00743 0.01439 -0.01026 441s [8,] -0.00188 0.00288 0.00336 441s [9,] -0.22091 -1.01587 0.81082 441s [10,] 0.04154 -0.03895 -0.00995 441s [11,] -0.03895 0.05766 -0.00383 441s [12,] -0.00995 -0.00383 0.04664 441s > 441s > # 3SLS 441s > summary 441s 441s systemfit results 441s method: 3SLS 441s 441s N DF SSR detRCov OLS-R2 McElroy-R2 441s system 56 44 67.5 0.436 0.963 0.993 441s 441s N DF SSR MSE RMSE R2 Adj R2 441s Consumption 18 14 22.4 1.598 1.264 0.974 0.968 441s Investment 18 14 35.0 2.503 1.582 0.793 0.749 441s PrivateWages 20 16 10.1 0.629 0.793 0.987 0.985 441s 441s The covariance matrix of the residuals used for estimation 441s Consumption Investment PrivateWages 441s Consumption 1.307 0.540 -0.431 441s Investment 0.540 1.319 0.119 441s PrivateWages -0.431 0.119 0.496 441s 441s The covariance matrix of the residuals 441s Consumption Investment PrivateWages 441s Consumption 1.309 0.638 -0.440 441s Investment 0.638 1.749 0.233 441s PrivateWages -0.440 0.233 0.519 441s 441s The correlations of the residuals 441s Consumption Investment PrivateWages 441s Consumption 1.000 0.422 -0.532 441s Investment 0.422 1.000 0.247 441s PrivateWages -0.532 0.247 1.000 441s 441s 441s 3SLS estimates for 'Consumption' (equation 1) 441s Model Formula: consump ~ corpProf + corpProfLag + wages 441s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 441s gnpLag 441s 441s Estimate Std. Error t value Pr(>|t|) 441s (Intercept) 18.0338 1.5648 11.52 1.6e-08 *** 441s corpProf -0.0632 0.1500 -0.42 0.68 441s corpProfLag 0.1784 0.1154 1.55 0.14 441s wages 0.8224 0.0444 18.54 3.0e-11 *** 441s --- 441s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 441s 441s Residual standard error: 1.264 on 14 degrees of freedom 441s Number of observations: 18 Degrees of Freedom: 14 441s SSR: 22.377 MSE: 1.598 Root MSE: 1.264 441s Multiple R-Squared: 0.974 Adjusted R-Squared: 0.968 441s 441s 441s 3SLS estimates for 'Investment' (equation 2) 441s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 441s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 441s gnpLag 441s 441s Estimate Std. Error t value Pr(>|t|) 441s (Intercept) 24.6766 6.7008 3.68 0.00246 ** 441s corpProf 0.0472 0.1843 0.26 0.80149 441s corpProfLag 0.6874 0.1577 4.36 0.00065 *** 441s capitalLag -0.1776 0.0318 -5.59 6.7e-05 *** 441s --- 441s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 441s 441s Residual standard error: 1.582 on 14 degrees of freedom 441s Number of observations: 18 Degrees of Freedom: 14 441s SSR: 35.037 MSE: 2.503 Root MSE: 1.582 441s Multiple R-Squared: 0.793 Adjusted R-Squared: 0.749 441s 441s 441s 3SLS estimates for 'PrivateWages' (equation 3) 441s Model Formula: privWage ~ gnp + gnpLag + trend 441s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 441s gnpLag 441s 441s Estimate Std. Error t value Pr(>|t|) 441s (Intercept) 0.7823 1.1254 0.70 0.49695 441s gnp 0.4257 0.0308 13.80 2.6e-10 *** 441s gnpLag 0.1728 0.0341 5.07 0.00011 *** 441s trend 0.1252 0.0291 4.30 0.00055 *** 441s --- 441s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 441s 441s Residual standard error: 0.793 on 16 degrees of freedom 441s Number of observations: 20 Degrees of Freedom: 16 441s SSR: 10.057 MSE: 0.629 Root MSE: 0.793 441s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 441s 441s > residuals 441s Consumption Investment PrivateWages 441s 1 NA NA NA 441s 2 -0.8058 -1.721 -1.20135 441s 3 -0.6573 0.337 0.43696 441s 4 -1.1124 0.810 1.31177 441s 5 0.0833 -1.544 -0.19794 441s 6 0.6334 0.368 -0.46596 441s 7 NA NA NA 441s 8 1.7939 1.245 -0.85614 441s 9 1.7891 0.593 0.20698 441s 10 NA 2.303 1.10034 441s 11 -0.5397 -1.015 -0.38801 441s 12 -1.5147 -0.846 0.40949 441s 13 NA NA 0.00602 441s 14 -0.1171 1.670 0.61306 441s 15 -0.6526 -0.075 0.49152 441s 16 -0.3617 NA 0.17066 441s 17 1.9331 2.086 -0.69991 441s 18 -0.6063 -0.101 0.96136 441s 19 -0.3990 -3.345 -0.61606 441s 20 1.4134 0.717 -0.29343 441s 21 1.3257 0.306 -1.14412 441s 22 -1.4340 0.935 0.55310 441s > fitted 441s Consumption Investment PrivateWages 441s 1 NA NA NA 441s 2 42.7 1.5213 26.7 441s 3 45.7 1.5632 28.9 441s 4 50.3 4.3898 32.8 441s 5 50.5 4.5444 34.1 441s 6 52.0 4.7320 35.9 441s 7 NA NA NA 441s 8 54.4 2.9547 38.8 441s 9 55.5 2.4075 39.0 441s 10 NA 2.7965 40.2 441s 11 55.5 2.0150 38.3 441s 12 52.4 -2.5541 34.1 441s 13 NA NA 29.0 441s 14 46.6 -6.7699 27.9 441s 15 49.4 -2.9250 30.1 441s 16 51.7 NA 33.0 441s 17 55.8 0.0139 37.5 441s 18 59.3 2.1013 40.0 441s 19 57.9 1.4453 38.8 441s 20 60.2 0.5828 41.9 441s 21 63.7 2.9944 46.1 441s 22 71.1 3.9651 52.7 441s > predict 441s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 441s 1 NA NA NA NA 441s 2 42.7 0.555 39.7 45.7 441s 3 45.7 0.628 42.6 48.7 441s 4 50.3 0.418 47.5 53.2 441s 5 50.5 0.492 47.6 53.4 441s 6 52.0 0.501 49.0 54.9 441s 7 NA NA NA NA 441s 8 54.4 0.405 51.6 57.3 441s 9 55.5 0.477 52.6 58.4 441s 10 NA NA NA NA 441s 11 55.5 0.832 52.3 58.8 441s 12 52.4 0.792 49.2 55.6 441s 13 NA NA NA NA 441s 14 46.6 0.676 43.5 49.7 441s 15 49.4 0.470 46.5 52.2 441s 16 51.7 0.386 48.8 54.5 441s 17 55.8 0.433 52.9 58.6 441s 18 59.3 0.368 56.5 62.1 441s 19 57.9 0.504 55.0 60.8 441s 20 60.2 0.513 57.3 63.1 441s 21 63.7 0.505 60.8 66.6 441s 22 71.1 0.771 68.0 74.3 441s Investment.pred Investment.se.fit Investment.lwr Investment.upr 441s 1 NA NA NA NA 441s 2 1.5213 0.857 -2.337 5.380 441s 3 1.5632 0.589 -2.058 5.184 441s 4 4.3898 0.519 0.819 7.961 441s 5 4.5444 0.436 1.025 8.064 441s 6 4.7320 0.415 1.224 8.240 441s 7 NA NA NA NA 441s 8 2.9547 0.342 -0.517 6.426 441s 9 2.4075 0.511 -1.158 5.973 441s 10 2.7965 0.556 -0.800 6.393 441s 11 2.0150 0.955 -1.948 5.978 441s 12 -2.5541 0.874 -6.431 1.323 441s 13 NA NA NA NA 441s 14 -6.7699 0.865 -10.637 -2.903 441s 15 -2.9250 0.503 -6.485 0.635 441s 16 NA NA NA NA 441s 17 0.0139 0.483 -3.534 3.561 441s 18 2.1013 0.320 -1.361 5.563 441s 19 1.4453 0.532 -2.134 5.025 441s 20 0.5828 0.550 -3.010 4.175 441s 21 2.9944 0.476 -0.549 6.538 441s 22 3.9651 0.692 0.261 7.669 441s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 441s 1 NA NA NA NA 441s 2 26.7 0.324 24.9 28.5 441s 3 28.9 0.331 27.0 30.7 441s 4 32.8 0.339 31.0 34.6 441s 5 34.1 0.248 32.3 35.9 441s 6 35.9 0.256 34.1 37.6 441s 7 NA NA NA NA 441s 8 38.8 0.251 37.0 40.5 441s 9 39.0 0.238 37.2 40.7 441s 10 40.2 0.232 38.4 42.0 441s 11 38.3 0.314 36.5 40.1 441s 12 34.1 0.327 32.3 35.9 441s 13 29.0 0.393 27.1 30.9 441s 14 27.9 0.329 26.1 29.7 441s 15 30.1 0.324 28.3 31.9 441s 16 33.0 0.271 31.3 34.8 441s 17 37.5 0.277 35.7 39.3 441s 18 40.0 0.213 38.3 41.8 441s 19 38.8 0.320 37.0 40.6 441s 20 41.9 0.295 40.1 43.7 441s 21 46.1 0.309 44.3 47.9 441s 22 52.7 0.476 50.8 54.7 441s > model.frame 441s [1] TRUE 441s > model.matrix 441s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0345 >" 441s [2] "Attributes: < Component “dimnames”: Component 1: 51 string mismatches >" 441s [3] "Numeric: lengths (696, 672) differ" 441s > nobs 441s [1] 56 441s > linearHypothesis 441s Linear hypothesis test (Theil's F test) 441s 441s Hypothesis: 441s Consumption_corpProf + Investment_capitalLag = 0 441s 441s Model 1: restricted model 441s Model 2: kleinModel 441s 441s Res.Df Df F Pr(>F) 441s 1 45 441s 2 44 1 1.91 0.17 441s Linear hypothesis test (F statistic of a Wald test) 441s 441s Hypothesis: 441s Consumption_corpProf + Investment_capitalLag = 0 441s 441s Model 1: restricted model 441s Model 2: kleinModel 441s 441s Res.Df Df F Pr(>F) 441s 1 45 441s 2 44 1 2.6 0.11 441s Linear hypothesis test (Chi^2 statistic of a Wald test) 441s 441s Hypothesis: 441s Consumption_corpProf + Investment_capitalLag = 0 441s 441s Model 1: restricted model 441s Model 2: kleinModel 441s 441s Res.Df Df Chisq Pr(>Chisq) 441s 1 45 441s 2 44 1 2.6 0.11 441s Linear hypothesis test (Theil's F test) 441s 441s Hypothesis: 441s Consumption_corpProf + Investment_capitalLag = 0 441s Consumption_corpProfLag - PrivateWages_trend = 0 441s 441s Model 1: restricted model 441s Model 2: kleinModel 441s 441s Res.Df Df F Pr(>F) 441s 1 46 441s 2 44 2 1.62 0.21 441s Linear hypothesis test (F statistic of a Wald test) 441s 441s Hypothesis: 441s Consumption_corpProf + Investment_capitalLag = 0 441s Consumption_corpProfLag - PrivateWages_trend = 0 441s 441s Model 1: restricted model 441s Model 2: kleinModel 441s 441s Res.Df Df F Pr(>F) 441s 1 46 441s 2 44 2 2.2 0.12 441s Linear hypothesis test (Chi^2 statistic of a Wald test) 441s 441s Hypothesis: 441s Consumption_corpProf + Investment_capitalLag = 0 441s Consumption_corpProfLag - PrivateWages_trend = 0 441s 441s Model 1: restricted model 441s Model 2: kleinModel 441s 441s Res.Df Df Chisq Pr(>Chisq) 441s 1 46 441s 2 44 2 4.41 0.11 441s > logLik 441s 'log Lik.' -70.1 (df=18) 441s 'log Lik.' -80.6 (df=18) 441s Estimating function 441s Consumption_(Intercept) Consumption_corpProf 441s Consumption_2 -3.3369 -46.76 441s Consumption_3 -0.6260 -10.43 441s Consumption_4 0.5431 10.07 441s Consumption_5 -1.9287 -39.09 441s Consumption_6 0.9979 18.98 441s Consumption_8 4.7224 83.33 441s Consumption_9 4.2195 79.93 441s Consumption_11 -2.1144 -35.40 441s Consumption_12 -2.7531 -36.83 441s Consumption_14 0.7280 7.30 441s Consumption_15 -2.0340 -25.43 441s Consumption_16 -1.6770 -24.29 441s Consumption_17 6.1486 91.69 441s Consumption_18 -0.6466 -12.56 441s Consumption_19 -4.7474 -90.72 441s Consumption_20 3.3112 58.48 441s Consumption_21 1.5335 31.28 441s Consumption_22 -1.0772 -24.43 441s Investment_2 1.4470 20.28 441s Investment_3 -0.2844 -4.74 441s Investment_4 -0.6458 -11.98 441s Investment_5 1.3096 26.54 441s Investment_6 -0.3315 -6.31 441s Investment_8 -1.1056 -19.51 441s Investment_9 -0.5457 -10.34 441s Investment_10 0.0000 0.00 441s Investment_11 0.8919 14.93 441s Investment_12 0.7723 10.33 441s Investment_14 -1.4083 -14.12 441s Investment_15 0.0885 1.11 441s Investment_17 -1.8093 -26.98 441s Investment_18 0.1676 3.25 441s Investment_19 2.8888 55.20 441s Investment_20 -0.6425 -11.35 441s Investment_21 -0.2855 -5.82 441s Investment_22 -0.7925 -17.97 441s PrivateWages_2 -2.9611 -41.49 441s PrivateWages_3 1.0665 17.77 441s PrivateWages_4 2.5794 47.83 441s PrivateWages_5 -2.7951 -56.65 441s PrivateWages_6 -0.4865 -9.25 441s PrivateWages_8 1.6497 29.11 441s PrivateWages_9 1.8751 35.52 441s PrivateWages_10 0.0000 0.00 441s PrivateWages_11 -2.3618 -39.54 441s PrivateWages_12 -0.3246 -4.34 441s PrivateWages_13 0.0000 0.00 441s PrivateWages_14 3.0441 30.51 441s PrivateWages_15 -0.2496 -3.12 441s PrivateWages_16 -0.3710 -5.37 441s PrivateWages_17 2.5263 37.67 441s PrivateWages_18 0.0583 1.13 441s PrivateWages_19 -6.2503 -119.43 441s PrivateWages_20 1.3565 23.96 441s PrivateWages_21 -1.2791 -26.09 441s PrivateWages_22 1.9457 44.12 441s Consumption_corpProfLag Consumption_wages 441s Consumption_2 -42.379 -99.51 441s Consumption_3 -7.762 -19.94 441s Consumption_4 9.179 19.15 441s Consumption_5 -35.489 -74.45 441s Consumption_6 19.359 38.46 441s Consumption_8 92.559 188.94 441s Consumption_9 83.547 176.28 441s Consumption_11 -45.883 -91.13 441s Consumption_12 -42.949 -109.17 441s Consumption_14 5.096 24.26 441s Consumption_15 -22.780 -75.93 441s Consumption_16 -20.627 -67.32 441s Consumption_17 86.080 256.88 441s Consumption_18 -11.379 -30.78 441s Consumption_19 -82.131 -233.73 441s Consumption_20 50.662 160.78 441s Consumption_21 29.137 81.92 441s Consumption_22 -22.729 -65.49 441s Investment_2 18.377 43.15 441s Investment_3 -3.526 -9.06 441s Investment_4 -10.914 -22.77 441s Investment_5 24.097 50.55 441s Investment_6 -6.431 -12.78 441s Investment_8 -21.669 -44.23 441s Investment_9 -10.805 -22.80 441s Investment_10 0.000 0.00 441s Investment_11 19.355 38.44 441s Investment_12 12.047 30.62 441s Investment_14 -9.858 -46.93 441s Investment_15 0.992 3.31 441s Investment_17 -25.331 -75.59 441s Investment_18 2.950 7.98 441s Investment_19 49.976 142.22 441s Investment_20 -9.831 -31.20 441s Investment_21 -5.425 -15.25 441s Investment_22 -16.723 -48.18 441s PrivateWages_2 -37.606 -88.31 441s PrivateWages_3 13.225 33.97 441s PrivateWages_4 43.593 90.94 441s PrivateWages_5 -51.429 -107.89 441s PrivateWages_6 -9.438 -18.75 441s PrivateWages_8 32.333 66.00 441s PrivateWages_9 37.126 78.33 441s PrivateWages_10 0.000 0.00 441s PrivateWages_11 -51.251 -101.80 441s PrivateWages_12 -5.063 -12.87 441s PrivateWages_13 0.000 0.00 441s PrivateWages_14 21.309 101.45 441s PrivateWages_15 -2.796 -9.32 441s PrivateWages_16 -4.563 -14.89 441s PrivateWages_17 35.368 105.55 441s PrivateWages_18 1.025 2.77 441s PrivateWages_19 -108.130 -307.72 441s PrivateWages_20 20.754 65.87 441s PrivateWages_21 -24.303 -68.33 441s PrivateWages_22 41.055 118.29 441s Investment_(Intercept) Investment_corpProf 441s Consumption_2 1.6657 22.369 441s Consumption_3 0.3125 5.208 441s Consumption_4 -0.2711 -5.105 441s Consumption_5 0.9628 19.850 441s Consumption_6 -0.4981 -9.617 441s Consumption_8 -2.3573 -41.335 441s Consumption_9 -2.1063 -41.098 441s Consumption_11 1.0555 18.165 441s Consumption_12 1.3743 18.540 441s Consumption_14 -0.3634 -3.664 441s Consumption_15 1.0153 13.204 441s Consumption_16 0.0000 0.000 441s Consumption_17 -3.0693 -45.765 441s Consumption_18 0.3228 6.293 441s Consumption_19 2.3698 45.702 441s Consumption_20 -1.6529 -29.000 441s Consumption_21 -0.7655 -15.445 441s Consumption_22 0.5377 12.243 441s Investment_2 -2.0943 -28.124 441s Investment_3 0.4116 6.860 441s Investment_4 0.9347 17.600 441s Investment_5 -1.8955 -39.080 441s Investment_6 0.4798 9.263 441s Investment_8 1.6002 28.058 441s Investment_9 0.7899 15.412 441s Investment_10 2.8075 56.810 441s Investment_11 -1.2910 -22.218 441s Investment_12 -1.1178 -15.079 441s Investment_14 2.0383 20.552 441s Investment_15 -0.1282 -1.667 441s Investment_17 2.6188 39.047 441s Investment_18 -0.2426 -4.730 441s Investment_19 -4.1811 -80.631 441s Investment_20 0.9300 16.316 441s Investment_21 0.4133 8.338 441s Investment_22 1.1471 26.118 441s PrivateWages_2 1.8190 24.427 441s PrivateWages_3 -0.6551 -10.919 441s PrivateWages_4 -1.5845 -29.835 441s PrivateWages_5 1.7170 35.400 441s PrivateWages_6 0.2989 5.770 441s PrivateWages_8 -1.0134 -17.769 441s PrivateWages_9 -1.1518 -22.474 441s PrivateWages_10 -2.1257 -43.013 441s PrivateWages_11 1.4508 24.969 441s PrivateWages_12 0.1994 2.690 441s PrivateWages_13 0.0000 0.000 441s PrivateWages_14 -1.8700 -18.855 441s PrivateWages_15 0.1533 1.994 441s PrivateWages_16 0.0000 0.000 441s PrivateWages_17 -1.5519 -23.140 441s PrivateWages_18 -0.0358 -0.698 441s PrivateWages_19 3.8395 74.045 441s PrivateWages_20 -0.8333 -14.620 441s PrivateWages_21 0.7858 15.853 441s PrivateWages_22 -1.1953 -27.215 441s Investment_corpProfLag Investment_capitalLag 441s Consumption_2 21.15 304.50 441s Consumption_3 3.87 57.06 441s Consumption_4 -4.58 -50.02 441s Consumption_5 17.72 182.64 441s Consumption_6 -9.66 -95.99 441s Consumption_8 -46.20 -479.48 441s Consumption_9 -41.70 -437.27 441s Consumption_11 22.90 227.67 441s Consumption_12 21.44 297.81 441s Consumption_14 -2.54 -75.26 441s Consumption_15 11.37 205.09 441s Consumption_16 0.00 0.00 441s Consumption_17 -42.97 -606.79 441s Consumption_18 5.68 64.49 441s Consumption_19 41.00 478.23 441s Consumption_20 -25.29 -330.42 441s Consumption_21 -14.54 -154.02 441s Consumption_22 11.35 109.96 441s Investment_2 -26.60 -382.84 441s Investment_3 5.10 75.16 441s Investment_4 15.80 172.46 441s Investment_5 -34.88 -359.58 441s Investment_6 9.31 92.46 441s Investment_8 31.36 325.47 441s Investment_9 15.64 163.98 441s Investment_10 59.24 591.25 441s Investment_11 -28.01 -278.46 441s Investment_12 -17.44 -242.22 441s Investment_14 14.27 422.14 441s Investment_15 -1.44 -25.89 441s Investment_17 36.66 517.73 441s Investment_18 -4.27 -48.47 441s Investment_19 -72.33 -843.75 441s Investment_20 14.23 185.90 441s Investment_21 7.85 83.15 441s Investment_22 24.20 234.58 441s PrivateWages_2 23.10 332.51 441s PrivateWages_3 -8.12 -119.63 441s PrivateWages_4 -26.78 -292.35 441s PrivateWages_5 31.59 325.71 441s PrivateWages_6 5.80 57.59 441s PrivateWages_8 -19.86 -206.12 441s PrivateWages_9 -22.81 -239.12 441s PrivateWages_10 -44.85 -447.66 441s PrivateWages_11 31.48 312.95 441s PrivateWages_12 3.11 43.21 441s PrivateWages_13 0.00 0.00 441s PrivateWages_14 -13.09 -387.28 441s PrivateWages_15 1.72 30.97 441s PrivateWages_16 0.00 0.00 441s PrivateWages_17 -21.73 -306.81 441s PrivateWages_18 -0.63 -7.15 441s PrivateWages_19 66.42 774.82 441s PrivateWages_20 -12.75 -166.57 441s PrivateWages_21 14.93 158.09 441s PrivateWages_22 -25.22 -244.43 441s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 441s Consumption_2 -3.302 -155.43 -148.27 441s Consumption_3 -0.619 -30.71 -28.25 441s Consumption_4 0.537 30.39 26.93 441s Consumption_5 -1.909 -115.83 -109.18 441s Consumption_6 0.987 59.85 56.39 441s Consumption_8 4.673 280.38 299.09 441s Consumption_9 4.176 260.01 268.91 441s Consumption_11 -2.092 -133.31 -140.19 441s Consumption_12 -2.724 -149.39 -166.74 441s Consumption_14 0.720 30.35 31.91 441s Consumption_15 -2.013 -103.09 -90.78 441s Consumption_16 -1.660 -91.84 -82.48 441s Consumption_17 6.085 349.22 331.00 441s Consumption_18 -0.640 -42.98 -40.12 441s Consumption_19 -4.698 -321.88 -305.37 441s Consumption_20 3.277 219.03 199.56 441s Consumption_21 1.518 113.62 105.47 441s Consumption_22 -1.066 -92.61 -80.70 441s Investment_2 1.762 82.94 79.12 441s Investment_3 -0.346 -17.17 -15.79 441s Investment_4 -0.786 -44.47 -39.40 441s Investment_5 1.595 96.79 91.23 441s Investment_6 -0.404 -24.47 -23.05 441s Investment_8 -1.346 -80.78 -86.17 441s Investment_9 -0.665 -41.38 -42.80 441s Investment_10 -2.362 -152.52 -152.36 441s Investment_11 1.086 69.20 72.78 441s Investment_12 0.940 51.57 57.56 441s Investment_14 -1.715 -72.25 -75.98 441s Investment_15 0.108 5.52 4.86 441s Investment_17 -2.203 -126.46 -119.87 441s Investment_18 0.204 13.71 12.80 441s Investment_19 3.518 241.02 228.67 441s Investment_20 -0.782 -52.30 -47.65 441s Investment_21 -0.348 -26.03 -24.17 441s Investment_22 -0.965 -83.85 -73.06 441s PrivateWages_2 -6.697 -315.21 -300.67 441s PrivateWages_3 2.412 119.58 109.98 441s PrivateWages_4 5.833 329.84 292.25 441s PrivateWages_5 -6.321 -383.60 -361.56 441s PrivateWages_6 -1.100 -66.69 -62.82 441s PrivateWages_8 3.731 223.83 238.77 441s PrivateWages_9 4.240 264.05 273.09 441s PrivateWages_10 7.826 505.29 504.75 441s PrivateWages_11 -5.341 -340.30 -357.86 441s PrivateWages_12 -0.734 -40.25 -44.92 441s PrivateWages_13 -4.155 -195.19 -221.87 441s PrivateWages_14 6.884 290.02 304.97 441s PrivateWages_15 -0.565 -28.91 -25.46 441s PrivateWages_16 -0.839 -46.43 -41.70 441s PrivateWages_17 5.713 327.90 310.80 441s PrivateWages_18 0.132 8.85 8.26 441s PrivateWages_19 -14.135 -968.43 -918.78 441s PrivateWages_20 3.068 205.06 186.82 441s PrivateWages_21 -2.893 -216.57 -201.04 441s PrivateWages_22 4.400 382.29 333.10 441s PrivateWages_trend 441s Consumption_2 33.022 441s Consumption_3 5.575 441s Consumption_4 -4.300 441s Consumption_5 13.361 441s Consumption_6 -5.925 441s Consumption_8 -18.693 441s Consumption_9 -12.527 441s Consumption_11 2.092 441s Consumption_12 0.000 441s Consumption_14 1.441 441s Consumption_15 -6.038 441s Consumption_16 -6.638 441s Consumption_17 30.423 441s Consumption_18 -3.839 441s Consumption_19 -32.886 441s Consumption_20 26.214 441s Consumption_21 13.658 441s Consumption_22 -10.660 441s Investment_2 -17.621 441s Investment_3 3.117 441s Investment_4 6.292 441s Investment_5 -11.164 441s Investment_6 2.422 441s Investment_8 5.385 441s Investment_9 1.994 441s Investment_10 4.724 441s Investment_11 -1.086 441s Investment_12 0.000 441s Investment_14 -3.430 441s Investment_15 0.323 441s Investment_17 -11.017 441s Investment_18 1.225 441s Investment_19 24.626 441s Investment_20 -6.260 441s Investment_21 -3.129 441s Investment_22 -9.652 441s PrivateWages_2 66.965 441s PrivateWages_3 -21.707 441s PrivateWages_4 -46.667 441s PrivateWages_5 44.247 441s PrivateWages_6 6.602 441s PrivateWages_8 -14.923 441s PrivateWages_9 -12.721 441s PrivateWages_10 -15.651 441s PrivateWages_11 5.341 441s PrivateWages_12 0.000 441s PrivateWages_13 -4.155 441s PrivateWages_14 13.769 441s PrivateWages_15 -1.694 441s PrivateWages_16 -3.356 441s PrivateWages_17 28.566 441s PrivateWages_18 0.791 441s PrivateWages_19 -98.946 441s PrivateWages_20 24.542 441s PrivateWages_21 -26.035 441s PrivateWages_22 44.003 441s [1] TRUE 441s > Bread 441s Consumption_(Intercept) Consumption_corpProf Consumption_corpProfLag 441s [1,] 137.1267 -4.2997 0.8463 441s [2,] -4.2997 1.2597 -0.6942 441s [3,] 0.8463 -0.6942 0.7454 441s [4,] -1.7733 -0.1394 -0.0281 441s [5,] 105.0265 3.4241 3.4807 441s [6,] -4.4721 0.5244 -0.4530 441s [7,] 1.6442 -0.3454 0.4268 441s [8,] -0.2644 -0.0340 -0.0134 441s [9,] -38.0151 0.3680 1.7655 441s [10,] 0.5379 -0.0825 0.0502 441s [11,] 0.0809 0.0782 -0.0821 441s [12,] 0.1895 0.0505 0.0265 441s Consumption_wages Investment_(Intercept) Investment_corpProf 441s [1,] -1.773256 105.03 -4.47211 441s [2,] -0.139424 3.42 0.52437 441s [3,] -0.028067 3.48 -0.45300 441s [4,] 0.110155 -5.14 0.06784 441s [5,] -5.138461 2514.46 -43.59967 441s [6,] 0.067843 -43.60 1.90216 441s [7,] -0.064178 34.75 -1.45456 441s [8,] 0.025084 -11.63 0.17310 441s [9,] 0.044238 27.92 -0.25822 441s [10,] 0.000203 1.31 0.00136 441s [11,] -0.000811 -1.85 0.00316 441s [12,] -0.035488 -0.85 0.01679 441s Investment_corpProfLag Investment_capitalLag PrivateWages_(Intercept) 441s [1,] 1.64420 -0.26436 -38.0151 441s [2,] -0.34536 -0.03402 0.3680 441s [3,] 0.42680 -0.01343 1.7655 441s [4,] -0.06418 0.02508 0.0442 441s [5,] 34.75055 -11.63252 27.9186 441s [6,] -1.45456 0.17310 -0.2582 441s [7,] 1.39257 -0.16270 -0.3518 441s [8,] -0.16270 0.05655 -0.0905 441s [9,] -0.35175 -0.09046 70.9283 441s [10,] 0.00769 -0.00730 -0.3444 441s [11,] -0.00156 0.00915 -0.8533 441s [12,] -0.02239 0.00456 0.8163 441s PrivateWages_gnp PrivateWages_gnpLag PrivateWages_trend 441s [1,] 0.537909 0.080946 0.189459 441s [2,] -0.082456 0.078164 0.050460 441s [3,] 0.050248 -0.082092 0.026511 441s [4,] 0.000203 -0.000811 -0.035488 441s [5,] 1.312267 -1.847095 -0.850461 441s [6,] 0.001362 0.003160 0.016792 441s [7,] 0.007689 -0.001565 -0.022388 441s [8,] -0.007301 0.009148 0.004555 441s [9,] -0.344428 -0.853347 0.816265 441s [10,] 0.053258 -0.048785 -0.014522 441s [11,] -0.048785 0.064956 0.000648 441s [12,] -0.014522 0.000648 0.047452 441s > 441s > # I3SLS 441s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 441s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 441s > summary 441s 441s systemfit results 441s method: iterated 3SLS 441s 441s convergence achieved after 10 iterations 441s 441s N DF SSR detRCov OLS-R2 McElroy-R2 441s system 56 44 79.4 0.55 0.956 0.994 441s 441s N DF SSR MSE RMSE R2 Adj R2 441s Consumption 18 14 22.3 1.595 1.263 0.974 0.968 441s Investment 18 14 46.8 3.346 1.829 0.724 0.664 441s PrivateWages 20 16 10.2 0.639 0.799 0.987 0.985 441s 441s The covariance matrix of the residuals used for estimation 441s Consumption Investment PrivateWages 441s Consumption 1.307 0.750 -0.452 441s Investment 0.750 2.318 0.272 441s PrivateWages -0.452 0.272 0.530 441s 441s The covariance matrix of the residuals 441s Consumption Investment PrivateWages 441s Consumption 1.307 0.750 -0.452 441s Investment 0.750 2.318 0.272 441s PrivateWages -0.452 0.272 0.530 441s 441s The correlations of the residuals 441s Consumption Investment PrivateWages 441s Consumption 1.000 0.424 -0.542 441s Investment 0.424 1.000 0.254 441s PrivateWages -0.542 0.254 1.000 441s 441s 441s 3SLS estimates for 'Consumption' (equation 1) 441s Model Formula: consump ~ corpProf + corpProfLag + wages 441s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 441s gnpLag 441s 441s Estimate Std. Error t value Pr(>|t|) 441s (Intercept) 18.3252 1.5452 11.86 1.1e-08 *** 441s corpProf -0.0436 0.1470 -0.30 0.77 441s corpProfLag 0.1614 0.1127 1.43 0.17 441s wages 0.8127 0.0436 18.65 2.8e-11 *** 441s --- 441s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 441s 441s Residual standard error: 1.263 on 14 degrees of freedom 441s Number of observations: 18 Degrees of Freedom: 14 441s SSR: 22.337 MSE: 1.595 Root MSE: 1.263 441s Multiple R-Squared: 0.974 Adjusted R-Squared: 0.968 441s 441s 441s 3SLS estimates for 'Investment' (equation 2) 441s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 441s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 441s gnpLag 441s 441s Estimate Std. Error t value Pr(>|t|) 441s (Intercept) 30.2418 8.3674 3.61 0.00282 ** 441s corpProf -0.0437 0.2341 -0.19 0.85457 441s corpProfLag 0.7856 0.1993 3.94 0.00147 ** 441s capitalLag -0.2065 0.0397 -5.20 0.00014 *** 441s --- 441s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 441s 441s Residual standard error: 1.829 on 14 degrees of freedom 441s Number of observations: 18 Degrees of Freedom: 14 441s SSR: 46.838 MSE: 3.346 Root MSE: 1.829 441s Multiple R-Squared: 0.724 Adjusted R-Squared: 0.664 441s 441s 441s 3SLS estimates for 'PrivateWages' (equation 3) 441s Model Formula: privWage ~ gnp + gnpLag + trend 441s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 441s gnpLag 441s 441s Estimate Std. Error t value Pr(>|t|) 441s (Intercept) 0.4741 1.1280 0.42 0.67983 441s gnp 0.4268 0.0296 14.44 1.4e-10 *** 441s gnpLag 0.1767 0.0330 5.35 6.5e-05 *** 441s trend 0.1201 0.0290 4.14 0.00076 *** 441s --- 441s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 441s 441s Residual standard error: 0.799 on 16 degrees of freedom 441s Number of observations: 20 Degrees of Freedom: 16 441s SSR: 10.218 MSE: 0.639 Root MSE: 0.799 441s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 441s 441s > residuals 441s Consumption Investment PrivateWages 441s 1 NA NA NA 441s 2 -0.8546 -2.1226 -1.1687 441s 3 -0.7611 0.3684 0.4670 441s 4 -1.1233 0.5912 1.3216 441s 5 0.0781 -1.6694 -0.2108 441s 6 0.6467 0.2952 -0.4776 441s 7 NA NA NA 441s 8 1.8444 1.4348 -0.8884 441s 9 1.8309 1.0020 0.1781 441s 10 NA 2.7265 1.0734 441s 11 -0.3652 -1.0581 -0.4134 441s 12 -1.3877 -0.6431 0.4203 441s 13 NA NA 0.0623 441s 14 -0.1818 2.4214 0.7091 441s 15 -0.6438 0.2168 0.5845 441s 16 -0.3417 NA 0.2455 441s 17 1.9583 2.4607 -0.6474 441s 18 -0.4806 -0.0468 0.9840 441s 19 -0.2563 -3.3855 -0.5930 441s 20 1.4832 1.1550 -0.2586 441s 21 1.4514 0.6086 -1.1446 441s 22 -1.2351 1.3453 0.5196 441s > fitted 441s Consumption Investment PrivateWages 441s 1 NA NA NA 441s 2 42.8 1.923 26.7 441s 3 45.8 1.532 28.8 441s 4 50.3 4.609 32.8 441s 5 50.5 4.669 34.1 441s 6 52.0 4.805 35.9 441s 7 NA NA NA 441s 8 54.4 2.765 38.8 441s 9 55.5 1.998 39.0 441s 10 NA 2.373 40.2 441s 11 55.4 2.058 38.3 441s 12 52.3 -2.757 34.1 441s 13 NA NA 28.9 441s 14 46.7 -7.521 27.8 441s 15 49.3 -3.217 30.0 441s 16 51.6 NA 33.0 441s 17 55.7 -0.361 37.4 441s 18 59.2 2.047 40.0 441s 19 57.8 1.485 38.8 441s 20 60.1 0.145 41.9 441s 21 63.5 2.691 46.1 441s 22 70.9 3.555 52.8 441s > predict 441s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 441s 1 NA NA NA NA 441s 2 42.8 0.548 41.7 43.9 441s 3 45.8 0.618 44.5 47.0 441s 4 50.3 0.411 49.5 51.2 441s 5 50.5 0.481 49.6 51.5 441s 6 52.0 0.490 51.0 52.9 441s 7 NA NA NA NA 441s 8 54.4 0.396 53.6 55.2 441s 9 55.5 0.467 54.5 56.4 441s 10 NA NA NA NA 441s 11 55.4 0.811 53.7 57.0 441s 12 52.3 0.775 50.7 53.8 441s 13 NA NA NA NA 441s 14 46.7 0.665 45.3 48.0 441s 15 49.3 0.463 48.4 50.3 441s 16 51.6 0.381 50.9 52.4 441s 17 55.7 0.428 54.9 56.6 441s 18 59.2 0.360 58.5 59.9 441s 19 57.8 0.492 56.8 58.7 441s 20 60.1 0.508 59.1 61.1 441s 21 63.5 0.499 62.5 64.6 441s 22 70.9 0.761 69.4 72.5 441s Investment.pred Investment.se.fit Investment.lwr Investment.upr 441s 1 NA NA NA NA 441s 2 1.923 1.079 -0.2526 4.098 441s 3 1.532 0.766 -0.0119 3.075 441s 4 4.609 0.668 3.2632 5.954 441s 5 4.669 0.566 3.5280 5.811 441s 6 4.805 0.543 3.7104 5.899 441s 7 NA NA NA NA 441s 8 2.765 0.447 1.8648 3.665 441s 9 1.998 0.651 0.6860 3.310 441s 10 2.373 0.710 0.9434 3.804 441s 11 2.058 1.237 -0.4350 4.551 441s 12 -2.757 1.139 -5.0532 -0.461 441s 13 NA NA NA NA 441s 14 -7.521 1.094 -9.7261 -5.317 441s 15 -3.217 0.648 -4.5217 -1.912 441s 16 NA NA NA NA 441s 17 -0.361 0.615 -1.6007 0.879 441s 18 2.047 0.417 1.2060 2.888 441s 19 1.485 0.684 0.1062 2.865 441s 20 0.145 0.699 -1.2632 1.553 441s 21 2.691 0.614 1.4548 3.928 441s 22 3.555 0.887 1.7674 5.342 441s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 441s 1 NA NA NA NA 441s 2 26.7 0.330 26.0 27.3 441s 3 28.8 0.336 28.2 29.5 441s 4 32.8 0.340 32.1 33.5 441s 5 34.1 0.251 33.6 34.6 441s 6 35.9 0.259 35.4 36.4 441s 7 NA NA NA NA 441s 8 38.8 0.253 38.3 39.3 441s 9 39.0 0.240 38.5 39.5 441s 10 40.2 0.236 39.8 40.7 441s 11 38.3 0.307 37.7 38.9 441s 12 34.1 0.313 33.4 34.7 441s 13 28.9 0.376 28.2 29.7 441s 14 27.8 0.327 27.1 28.4 441s 15 30.0 0.322 29.4 30.7 441s 16 33.0 0.270 32.4 33.5 441s 17 37.4 0.275 36.9 38.0 441s 18 40.0 0.216 39.6 40.5 441s 19 38.8 0.314 38.2 39.4 441s 20 41.9 0.296 41.3 42.5 441s 21 46.1 0.317 45.5 46.8 441s 22 52.8 0.480 51.8 53.7 441s > model.frame 441s [1] TRUE 441s > model.matrix 441s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0345 >" 441s [2] "Attributes: < Component “dimnames”: Component 1: 51 string mismatches >" 441s [3] "Numeric: lengths (696, 672) differ" 441s > nobs 441s [1] 56 441s > linearHypothesis 441s Linear hypothesis test (Theil's F test) 441s 441s Hypothesis: 441s Consumption_corpProf + Investment_capitalLag = 0 441s 441s Model 1: restricted model 441s Model 2: kleinModel 441s 441s Res.Df Df F Pr(>F) 441s 1 45 441s 2 44 1 2.29 0.14 441s Linear hypothesis test (F statistic of a Wald test) 441s 441s Hypothesis: 441s Consumption_corpProf + Investment_capitalLag = 0 441s 441s Model 1: restricted model 441s Model 2: kleinModel 441s 441s Res.Df Df F Pr(>F) 441s 1 45 441s 2 44 1 2.89 0.096 . 441s --- 441s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 441s Linear hypothesis test (Chi^2 statistic of a Wald test) 441s 441s Hypothesis: 441s Consumption_corpProf + Investment_capitalLag = 0 441s 441s Model 1: restricted model 441s Model 2: kleinModel 441s 441s Res.Df Df Chisq Pr(>Chisq) 441s 1 45 441s 2 44 1 2.89 0.089 . 441s --- 441s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 441s Linear hypothesis test (Theil's F test) 441s 441s Hypothesis: 441s Consumption_corpProf + Investment_capitalLag = 0 441s Consumption_corpProfLag - PrivateWages_trend = 0 441s 441s Model 1: restricted model 441s Model 2: kleinModel 441s 441s Res.Df Df F Pr(>F) 441s 1 46 441s 2 44 2 2.3 0.11 441s Linear hypothesis test (F statistic of a Wald test) 441s 441s Hypothesis: 441s Consumption_corpProf + Investment_capitalLag = 0 441s Consumption_corpProfLag - PrivateWages_trend = 0 441s 441s Model 1: restricted model 441s Model 2: kleinModel 441s 441s Res.Df Df F Pr(>F) 441s 1 46 441s 2 44 2 2.9 0.066 . 441s --- 441s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 441s Linear hypothesis test (Chi^2 statistic of a Wald test) 441s 441s Hypothesis: 441s Consumption_corpProf + Investment_capitalLag = 0 441s Consumption_corpProfLag - PrivateWages_trend = 0 441s 441s Model 1: restricted model 441s Model 2: kleinModel 441s 441s Res.Df Df Chisq Pr(>Chisq) 441s 1 46 441s 2 44 2 5.79 0.055 . 441s --- 441s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 441s > logLik 441s 'log Lik.' -72.2 (df=18) 441s 'log Lik.' -83.4 (df=18) 441s Estimating function 441s Consumption_(Intercept) Consumption_corpProf 441s Consumption_2 -4.4102 -61.801 441s Consumption_3 -1.0169 -16.947 441s Consumption_4 0.6316 11.712 441s Consumption_5 -2.4849 -50.366 441s Consumption_6 1.3496 25.671 441s Consumption_8 6.2136 109.641 441s Consumption_9 5.5588 105.303 441s Consumption_11 -2.3690 -39.659 441s Consumption_12 -3.3344 -44.601 441s Consumption_14 0.8298 8.317 441s Consumption_15 -2.5803 -32.264 441s Consumption_16 -2.1088 -30.539 441s Consumption_17 7.9903 119.154 441s Consumption_18 -0.6538 -12.697 441s Consumption_19 -5.8714 -112.192 441s Consumption_20 4.4259 78.161 441s Consumption_21 2.2655 46.209 441s Consumption_22 -0.9489 -21.517 441s Investment_2 1.9674 27.570 441s Investment_3 -0.3392 -5.652 441s Investment_4 -0.5776 -10.712 441s Investment_5 1.5305 31.021 441s Investment_6 -0.2467 -4.692 441s Investment_8 -1.2650 -22.320 441s Investment_9 -0.8831 -16.728 441s Investment_10 0.0000 0.000 441s Investment_11 0.9353 15.658 441s Investment_12 0.5224 6.988 441s Investment_14 -2.2467 -22.520 441s Investment_15 -0.2344 -2.931 441s Investment_17 -2.2188 -33.088 441s Investment_18 -0.0466 -0.905 441s Investment_19 3.0409 58.107 441s Investment_20 -1.0335 -18.251 441s Investment_21 -0.5381 -10.975 441s Investment_22 -1.2437 -28.202 441s PrivateWages_2 -4.0943 -57.374 441s PrivateWages_3 1.5700 26.162 441s PrivateWages_4 3.6522 67.727 441s PrivateWages_5 -3.9696 -80.460 441s PrivateWages_6 -0.7099 -13.503 441s PrivateWages_8 2.2578 39.840 441s PrivateWages_9 2.5772 48.821 441s PrivateWages_10 0.0000 0.000 441s PrivateWages_11 -3.3861 -56.686 441s PrivateWages_12 -0.4354 -5.824 441s PrivateWages_13 0.0000 0.000 441s PrivateWages_14 4.5081 45.187 441s PrivateWages_15 -0.1430 -1.788 441s PrivateWages_16 -0.3534 -5.118 441s PrivateWages_17 3.6864 54.972 441s PrivateWages_18 0.1281 2.488 441s PrivateWages_19 -8.7578 -167.347 441s PrivateWages_20 1.9940 35.215 441s PrivateWages_21 -1.7982 -36.678 441s PrivateWages_22 2.6643 60.414 441s Consumption_corpProfLag Consumption_wages 441s Consumption_2 -56.01 -131.52 441s Consumption_3 -12.61 -32.39 441s Consumption_4 10.67 22.27 441s Consumption_5 -45.72 -95.92 441s Consumption_6 26.18 52.02 441s Consumption_8 121.79 248.60 441s Consumption_9 110.06 232.23 441s Consumption_11 -51.41 -102.11 441s Consumption_12 -52.02 -132.22 441s Consumption_14 5.81 27.65 441s Consumption_15 -28.90 -96.33 441s Consumption_16 -25.94 -84.65 441s Consumption_17 111.86 333.82 441s Consumption_18 -11.51 -31.13 441s Consumption_19 -101.57 -289.06 441s Consumption_20 67.72 214.91 441s Consumption_21 43.05 121.02 441s Consumption_22 -20.02 -57.69 441s Investment_2 24.99 58.67 441s Investment_3 -4.21 -10.80 441s Investment_4 -9.76 -20.36 441s Investment_5 28.16 59.08 441s Investment_6 -4.79 -9.51 441s Investment_8 -24.79 -50.61 441s Investment_9 -17.48 -36.89 441s Investment_10 0.00 0.00 441s Investment_11 20.30 40.31 441s Investment_12 8.15 20.72 441s Investment_14 -15.73 -74.88 441s Investment_15 -2.63 -8.75 441s Investment_17 -31.06 -92.70 441s Investment_18 -0.82 -2.22 441s Investment_19 52.61 149.71 441s Investment_20 -15.81 -50.18 441s Investment_21 -10.22 -28.74 441s Investment_22 -26.24 -75.61 441s PrivateWages_2 -52.00 -122.10 441s PrivateWages_3 19.47 50.00 441s PrivateWages_4 61.72 128.76 441s PrivateWages_5 -73.04 -153.23 441s PrivateWages_6 -13.77 -27.36 441s PrivateWages_8 44.25 90.33 441s PrivateWages_9 51.03 107.67 441s PrivateWages_10 0.00 0.00 441s PrivateWages_11 -73.48 -145.95 441s PrivateWages_12 -6.79 -17.27 441s PrivateWages_13 0.00 0.00 441s PrivateWages_14 31.56 150.24 441s PrivateWages_15 -1.60 -5.34 441s PrivateWages_16 -4.35 -14.19 441s PrivateWages_17 51.61 154.01 441s PrivateWages_18 2.25 6.10 441s PrivateWages_19 -151.51 -431.17 441s PrivateWages_20 30.51 96.82 441s PrivateWages_21 -34.17 -96.06 441s PrivateWages_22 56.22 161.97 441s Investment_(Intercept) Investment_corpProf 441s Consumption_2 1.9908 26.734 441s Consumption_3 0.4591 7.651 441s Consumption_4 -0.2851 -5.368 441s Consumption_5 1.1217 23.127 441s Consumption_6 -0.6092 -11.762 441s Consumption_8 -2.8049 -49.183 441s Consumption_9 -2.5093 -48.961 441s Consumption_11 1.0694 18.405 441s Consumption_12 1.5052 20.306 441s Consumption_14 -0.3746 -3.777 441s Consumption_15 1.1648 15.147 441s Consumption_16 0.0000 0.000 441s Consumption_17 -3.6069 -53.782 441s Consumption_18 0.2951 5.754 441s Consumption_19 2.6504 51.112 441s Consumption_20 -1.9979 -35.052 441s Consumption_21 -1.0227 -20.634 441s Consumption_22 0.4283 9.753 441s Investment_2 -1.8422 -24.739 441s Investment_3 0.3176 5.293 441s Investment_4 0.5409 10.184 441s Investment_5 -1.4331 -29.546 441s Investment_6 0.2310 4.459 441s Investment_8 1.1844 20.769 441s Investment_9 0.8269 16.134 441s Investment_10 2.3608 47.771 441s Investment_11 -0.8758 -15.072 441s Investment_12 -0.4892 -6.600 441s Investment_14 2.1037 21.212 441s Investment_15 0.2195 2.854 441s Investment_17 2.0776 30.979 441s Investment_18 0.0436 0.851 441s Investment_19 -2.8474 -54.911 441s Investment_20 0.9677 16.978 441s Investment_21 0.5038 10.165 441s Investment_22 1.1646 26.516 441s PrivateWages_2 2.2726 30.518 441s PrivateWages_3 -0.8714 -14.524 441s PrivateWages_4 -2.0272 -38.170 441s PrivateWages_5 2.2034 45.428 441s PrivateWages_6 0.3940 7.607 441s PrivateWages_8 -1.2532 -21.975 441s PrivateWages_9 -1.4305 -27.911 441s PrivateWages_10 -2.6709 -54.046 441s PrivateWages_11 1.8795 32.347 441s PrivateWages_12 0.2417 3.260 441s PrivateWages_13 0.0000 0.000 441s PrivateWages_14 -2.5023 -25.230 441s PrivateWages_15 0.0794 1.032 441s PrivateWages_16 0.0000 0.000 441s PrivateWages_17 -2.0461 -30.509 441s PrivateWages_18 -0.0711 -1.386 441s PrivateWages_19 4.8611 93.745 441s PrivateWages_20 -1.1068 -19.419 441s PrivateWages_21 0.9981 20.138 441s PrivateWages_22 -1.4788 -33.672 441s Investment_corpProfLag Investment_capitalLag 441s Consumption_2 25.283 363.92 441s Consumption_3 5.692 83.82 441s Consumption_4 -4.818 -52.60 441s Consumption_5 20.639 212.79 441s Consumption_6 -11.819 -117.39 441s Consumption_8 -54.976 -570.52 441s Consumption_9 -49.684 -520.93 441s Consumption_11 23.206 230.67 441s Consumption_12 23.481 326.17 441s Consumption_14 -2.622 -77.57 441s Consumption_15 13.045 235.28 441s Consumption_16 0.000 0.00 441s Consumption_17 -50.497 -713.09 441s Consumption_18 5.194 58.97 441s Consumption_19 45.852 534.85 441s Consumption_20 -30.568 -399.38 441s Consumption_21 -19.431 -205.77 441s Consumption_22 9.038 87.60 441s Investment_2 -23.396 -336.76 441s Investment_3 3.938 57.99 441s Investment_4 9.141 99.79 441s Investment_5 -26.369 -271.86 441s Investment_6 4.481 44.51 441s Investment_8 23.215 240.92 441s Investment_9 16.372 171.66 441s Investment_10 49.812 497.18 441s Investment_11 -19.004 -188.91 441s Investment_12 -7.631 -106.01 441s Investment_14 14.726 435.68 441s Investment_15 2.458 44.34 441s Investment_17 29.086 410.74 441s Investment_18 0.768 8.72 441s Investment_19 -49.260 -574.60 441s Investment_20 14.806 193.44 441s Investment_21 9.573 101.37 441s Investment_22 24.572 238.15 441s PrivateWages_2 28.862 415.43 441s PrivateWages_3 -10.806 -159.12 441s PrivateWages_4 -34.259 -374.01 441s PrivateWages_5 40.542 417.98 441s PrivateWages_6 7.644 75.93 441s PrivateWages_8 -24.563 -254.91 441s PrivateWages_9 -28.324 -296.97 441s PrivateWages_10 -56.356 -562.49 441s PrivateWages_11 40.785 405.41 441s PrivateWages_12 3.770 52.37 441s PrivateWages_13 0.000 0.00 441s PrivateWages_14 -17.516 -518.22 441s PrivateWages_15 0.889 16.03 441s PrivateWages_16 0.000 0.00 441s PrivateWages_17 -28.646 -404.52 441s PrivateWages_18 -1.251 -14.21 441s PrivateWages_19 84.097 980.97 441s PrivateWages_20 -16.934 -221.25 441s PrivateWages_21 18.964 200.82 441s PrivateWages_22 -31.204 -302.42 441s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 441s Consumption_2 -4.7927 -225.59 -215.2 441s Consumption_3 -1.1051 -54.79 -50.4 441s Consumption_4 0.6863 38.81 34.4 441s Consumption_5 -2.7004 -163.88 -154.5 441s Consumption_6 1.4666 88.89 83.7 441s Consumption_8 6.7526 405.14 432.2 441s Consumption_9 6.0409 376.16 389.0 441s Consumption_11 -2.5745 -164.03 -172.5 441s Consumption_12 -3.6236 -198.69 -221.8 441s Consumption_14 0.9017 37.99 39.9 441s Consumption_15 -2.8041 -143.62 -126.5 441s Consumption_16 -2.2917 -126.82 -113.9 441s Consumption_17 8.6833 498.37 472.4 441s Consumption_18 -0.7105 -47.73 -44.5 441s Consumption_19 -6.3806 -437.15 -414.7 441s Consumption_20 4.8097 321.50 292.9 441s Consumption_21 2.4620 184.32 171.1 441s Consumption_22 -1.0312 -89.59 -78.1 441s Investment_2 2.6290 123.75 118.0 441s Investment_3 -0.4532 -22.47 -20.7 441s Investment_4 -0.7719 -43.64 -38.7 441s Investment_5 2.0451 124.11 117.0 441s Investment_6 -0.3296 -19.98 -18.8 441s Investment_8 -1.6903 -101.41 -108.2 441s Investment_9 -1.1800 -73.48 -76.0 441s Investment_10 -3.3690 -217.54 -217.3 441s Investment_11 1.2498 79.63 83.7 441s Investment_12 0.6981 38.28 42.7 441s Investment_14 -3.0022 -126.47 -133.0 441s Investment_15 -0.3132 -16.04 -14.1 441s Investment_17 -2.9649 -170.17 -161.3 441s Investment_18 -0.0623 -4.18 -3.9 441s Investment_19 4.0635 278.40 264.1 441s Investment_20 -1.3810 -92.31 -84.1 441s Investment_21 -0.7190 -53.83 -50.0 441s Investment_22 -1.6619 -144.39 -125.8 441s PrivateWages_2 -8.0595 -379.36 -361.9 441s PrivateWages_3 3.0904 153.23 140.9 441s PrivateWages_4 7.1892 406.50 360.2 441s PrivateWages_5 -7.8142 -474.21 -447.0 441s PrivateWages_6 -1.3974 -84.70 -79.8 441s PrivateWages_8 4.4445 266.66 284.4 441s PrivateWages_9 5.0731 315.90 326.7 441s PrivateWages_10 9.4721 611.61 611.0 441s PrivateWages_11 -6.6655 -424.67 -446.6 441s PrivateWages_12 -0.8571 -46.99 -52.5 441s PrivateWages_13 -4.8476 -227.73 -258.9 441s PrivateWages_14 8.8741 373.85 393.1 441s PrivateWages_15 -0.2815 -14.42 -12.7 441s PrivateWages_16 -0.6957 -38.50 -34.6 441s PrivateWages_17 7.2565 416.48 394.8 441s PrivateWages_18 0.2522 16.94 15.8 441s PrivateWages_19 -17.2396 -1181.13 -1120.6 441s PrivateWages_20 3.9252 262.38 239.0 441s PrivateWages_21 -3.5398 -265.01 -246.0 441s PrivateWages_22 5.2446 455.65 397.0 441s PrivateWages_trend 441s Consumption_2 47.927 441s Consumption_3 9.946 441s Consumption_4 -5.491 441s Consumption_5 18.903 441s Consumption_6 -8.800 441s Consumption_8 -27.010 441s Consumption_9 -18.123 441s Consumption_11 2.574 441s Consumption_12 0.000 441s Consumption_14 1.803 441s Consumption_15 -8.412 441s Consumption_16 -9.167 441s Consumption_17 43.417 441s Consumption_18 -4.263 441s Consumption_19 -44.664 441s Consumption_20 38.478 441s Consumption_21 22.158 441s Consumption_22 -10.312 441s Investment_2 -26.290 441s Investment_3 4.079 441s Investment_4 6.175 441s Investment_5 -14.316 441s Investment_6 1.978 441s Investment_8 6.761 441s Investment_9 3.540 441s Investment_10 6.738 441s Investment_11 -1.250 441s Investment_12 0.000 441s Investment_14 -6.004 441s Investment_15 -0.940 441s Investment_17 -14.825 441s Investment_18 -0.374 441s Investment_19 28.444 441s Investment_20 -11.048 441s Investment_21 -6.471 441s Investment_22 -16.619 441s PrivateWages_2 80.595 441s PrivateWages_3 -27.814 441s PrivateWages_4 -57.514 441s PrivateWages_5 54.699 441s PrivateWages_6 8.384 441s PrivateWages_8 -17.778 441s PrivateWages_9 -15.219 441s PrivateWages_10 -18.944 441s PrivateWages_11 6.666 441s PrivateWages_12 0.000 441s PrivateWages_13 -4.848 441s PrivateWages_14 17.748 441s PrivateWages_15 -0.844 441s PrivateWages_16 -2.783 441s PrivateWages_17 36.283 441s PrivateWages_18 1.513 441s PrivateWages_19 -120.677 441s PrivateWages_20 31.402 441s PrivateWages_21 -31.858 441s PrivateWages_22 52.446 441s [1] TRUE 441s > Bread 441s Consumption_(Intercept) Consumption_corpProf Consumption_corpProfLag 441s [1,] 133.708 -4.1980 0.8576 441s [2,] -4.198 1.2100 -0.6653 441s [3,] 0.858 -0.6653 0.7119 441s [4,] -1.738 -0.1324 -0.0277 441s [5,] 125.235 3.6584 5.4171 441s [6,] -6.184 0.8150 -0.6677 441s [7,] 2.270 -0.5431 0.6187 441s [8,] -0.265 -0.0441 -0.0204 441s [9,] -39.027 0.3871 1.7425 441s [10,] 0.490 -0.0701 0.0456 441s [11,] 0.147 0.0648 -0.0766 441s [12,] 0.260 0.0523 0.0256 441s Consumption_wages Investment_(Intercept) Investment_corpProf 441s [1,] -1.73822 125.23 -6.18369 441s [2,] -0.13241 3.66 0.81500 441s [3,] -0.02768 5.42 -0.66769 441s [4,] 0.10634 -6.40 0.07260 441s [5,] -6.40260 3920.72 -66.16832 441s [6,] 0.07260 -66.17 3.06783 441s [7,] -0.07286 52.35 -2.32206 441s [8,] 0.03170 -18.13 0.25629 441s [9,] 0.06731 57.07 -0.51824 441s [10,] -0.00202 2.27 0.00785 441s [11,] 0.00109 -3.34 0.00101 441s [12,] -0.03773 -1.63 0.03241 441s Investment_corpProfLag Investment_capitalLag PrivateWages_(Intercept) 441s [1,] 2.27003 -0.26469 -39.0267 441s [2,] -0.54312 -0.04408 0.3871 441s [3,] 0.61867 -0.02038 1.7425 441s [4,] -0.07286 0.03170 0.0673 441s [5,] 52.35486 -18.13066 57.0659 441s [6,] -2.32206 0.25629 -0.5182 441s [7,] 2.22379 -0.24386 -0.7311 441s [8,] -0.24386 0.08845 -0.1851 441s [9,] -0.73109 -0.18506 71.2482 441s [10,] 0.01103 -0.01288 -0.3220 441s [11,] 0.00202 0.01653 -0.8851 441s [12,] -0.04341 0.00871 0.7698 441s PrivateWages_gnp PrivateWages_gnpLag PrivateWages_trend 441s [1,] 0.49031 0.147339 0.260437 441s [2,] -0.07008 0.064790 0.052347 441s [3,] 0.04558 -0.076595 0.025629 441s [4,] -0.00202 0.001086 -0.037728 441s [5,] 2.27149 -3.339873 -1.627913 441s [6,] 0.00785 0.001013 0.032414 441s [7,] 0.01103 0.002018 -0.043407 441s [8,] -0.01288 0.016530 0.008714 441s [9,] -0.32199 -0.885080 0.769761 441s [10,] 0.04892 -0.044549 -0.013616 441s [11,] -0.04455 0.061046 0.000449 441s [12,] -0.01362 0.000449 0.047057 441s > 441s BEGIN TEST KleinI_noMat.R 442s 442s R version 4.3.3 (2024-02-29) -- "Angel Food Cake" 442s Copyright (C) 2024 The R Foundation for Statistical Computing 442s Platform: s390x-ibm-linux-gnu (64-bit) 442s 442s R is free software and comes with ABSOLUTELY NO WARRANTY. 442s You are welcome to redistribute it under certain conditions. 442s Type 'license()' or 'licence()' for distribution details. 442s 442s R is a collaborative project with many contributors. 442s Type 'contributors()' for more information and 442s 'citation()' on how to cite R or R packages in publications. 442s 442s Type 'demo()' for some demos, 'help()' for on-line help, or 442s 'help.start()' for an HTML browser interface to help. 442s Type 'q()' to quit R. 442s 442s > library( "systemfit" ) 442s Loading required package: Matrix 443s Loading required package: car 443s Loading required package: carData 443s Loading required package: lmtest 443s Loading required package: zoo 443s 443s Attaching package: ‘zoo’ 443s 443s The following objects are masked from ‘package:base’: 443s 443s as.Date, as.Date.numeric 443s 443s 443s Please cite the 'systemfit' package as: 443s 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/. 443s 443s If you have questions, suggestions, or comments regarding the 'systemfit' package, please use a forum or 'tracker' at systemfit's R-Forge site: 443s https://r-forge.r-project.org/projects/systemfit/ 443s > options( warn = 1 ) 443s > options( digits = 3 ) 443s > 443s > data( "KleinI" ) 443s > eqConsump <- consump ~ corpProf + corpProfLag + wages 443s > eqInvest <- invest ~ corpProf + corpProfLag + capitalLag 443s > eqPrivWage <- privWage ~ gnp + gnpLag + trend 443s > inst <- ~ govExp + taxes + govWage + trend + capitalLag + corpProfLag + gnpLag 443s > system <- list( Consumption = eqConsump, Investment = eqInvest, 443s + PrivateWages = eqPrivWage ) 443s > restrict <- c( "Consumption_corpProf + Investment_capitalLag = 0" ) 443s > restrict2 <- c( restrict, "Consumption_corpProfLag - PrivateWages_trend = 0" ) 443s > 443s > for( dataNo in 1:5 ) { 443s + # set some values of some variables to NA 443s + if( dataNo == 2 ) { 443s + KleinI$gnpLag[ 7 ] <- NA 443s + } else if( dataNo == 3 ) { 443s + KleinI$wages[ 10 ] <- NA 443s + } else if( dataNo == 4 ) { 443s + KleinI$corpProf[ 13 ] <- NA 443s + } else if( dataNo == 5 ) { 443s + KleinI$invest[ 16 ] <- NA 443s + } 443s + 443s + # single-equation OLS 443s + lmConsump <- lm( eqConsump, data = KleinI ) 443s + lmInvest <- lm( eqInvest, data = KleinI ) 443s + lmPrivWage <- lm( eqPrivWage, data = KleinI ) 443s + 443s + for( methodNo in 1:5 ) { 443s + method <- c( "OLS", "2SLS", "SUR", "3SLS", "3SLS" )[ methodNo ] 443s + maxit <- ifelse( methodNo == 5, 500, 1 ) 443s + 443s + cat( "> \n> # ", ifelse( maxit == 1, "", "I" ), method, "\n", sep = "" ) 443s + if( method %in% c( "OLS", "WLS", "SUR" ) ) { 443s + kleinModel <- systemfit( system, method = method, data = KleinI, 443s + methodResidCov = ifelse( method == "OLS", "geomean", "noDfCor" ), 443s + maxit = maxit, useMatrix = FALSE ) 443s + } else { 443s + kleinModel <- systemfit( system, method = method, data = KleinI, 443s + inst = inst, methodResidCov = "noDfCor", maxit = maxit, 443s + useMatrix = FALSE ) 443s + } 443s + cat( "> summary\n" ) 443s + print( summary( kleinModel ) ) 443s + if( method == "OLS" ) { 443s + cat( "compare coef with single-equation OLS\n" ) 443s + print( all.equal( coef( kleinModel ), 443s + c( coef( lmConsump ), coef( lmInvest ), coef( lmPrivWage ) ), 443s + check.attributes = FALSE ) ) 443s + } 443s + cat( "> residuals\n" ) 443s + print( residuals( kleinModel ) ) 443s + cat( "> fitted\n" ) 443s + print( fitted( kleinModel ) ) 443s + cat( "> predict\n" ) 443s + print( predict( kleinModel, se.fit = TRUE, 443s + interval = ifelse( methodNo %in% c( 1, 4 ), "prediction", "confidence" ), 443s + useDfSys = methodNo %in% c( 1, 3, 5 ) ) ) 443s + cat( "> model.frame\n" ) 443s + if( methodNo == 1 ) { 443s + mfOls <- model.frame( kleinModel ) 443s + print( mfOls ) 443s + } else if( methodNo == 2 ) { 443s + mf2sls <- model.frame( kleinModel ) 443s + print( mf2sls ) 443s + } else if( methodNo == 3 ) { 443s + print( all.equal( mfOls, model.frame( kleinModel ) ) ) 443s + } else { 443s + print( all.equal( mf2sls, model.frame( kleinModel ) ) ) 443s + } 443s + cat( "> model.matrix\n" ) 443s + if( methodNo == 1 ) { 443s + mmOls <- model.matrix( kleinModel ) 443s + print( mmOls ) 443s + } else { 443s + print( all.equal( mmOls, model.matrix( kleinModel ) ) ) 443s + } 443s + cat( "> nobs\n" ) 443s + print( nobs( kleinModel ) ) 443s + cat( "> linearHypothesis\n" ) 443s + print( linearHypothesis( kleinModel, restrict ) ) 443s + print( linearHypothesis( kleinModel, restrict, test = "F" ) ) 443s + print( linearHypothesis( kleinModel, restrict, test = "Chisq" ) ) 443s + print( linearHypothesis( kleinModel, restrict2 ) ) 443s + print( linearHypothesis( kleinModel, restrict2, test = "F" ) ) 443s + print( linearHypothesis( kleinModel, restrict2, test = "Chisq" ) ) 443s + cat( "> logLik\n" ) 443s + print( logLik( kleinModel ) ) 443s + print( logLik( kleinModel, residCovDiag = TRUE ) ) 443s + if( method == "OLS" ) { 443s + cat( "compare log likelihood value with single-equation OLS\n" ) 443s + print( all.equal( logLik( kleinModel, residCovDiag = TRUE ), 443s + logLik( lmConsump ) + logLik( lmInvest ) + logLik( lmPrivWage ), 443s + check.attributes = FALSE ) ) 443s + } 443s + } 443s + } 443s > 443s > # OLS 443s > summary 443s 443s systemfit results 443s method: OLS 443s 443s N DF SSR detRCov OLS-R2 McElroy-R2 443s system 63 51 45.2 0.371 0.977 0.991 443s 443s N DF SSR MSE RMSE R2 Adj R2 443s Consumption 21 17 17.9 1.052 1.026 0.981 0.978 443s Investment 21 17 17.3 1.019 1.009 0.931 0.919 443s PrivateWages 21 17 10.0 0.589 0.767 0.987 0.985 443s 443s The covariance matrix of the residuals 443s Consumption Investment PrivateWages 443s Consumption 1.0517 0.0611 -0.470 443s Investment 0.0611 1.0190 0.150 443s PrivateWages -0.4704 0.1497 0.589 443s 443s The correlations of the residuals 443s Consumption Investment PrivateWages 443s Consumption 1.0000 0.0591 -0.598 443s Investment 0.0591 1.0000 0.193 443s PrivateWages -0.5979 0.1933 1.000 443s 443s 443s OLS estimates for 'Consumption' (equation 1) 443s Model Formula: consump ~ corpProf + corpProfLag + wages 443s 443s Estimate Std. Error t value Pr(>|t|) 443s (Intercept) 16.2366 1.3027 12.46 5.6e-10 *** 443s corpProf 0.1929 0.0912 2.12 0.049 * 443s corpProfLag 0.0899 0.0906 0.99 0.335 443s wages 0.7962 0.0399 19.93 3.2e-13 *** 443s --- 443s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 443s 443s Residual standard error: 1.026 on 17 degrees of freedom 443s Number of observations: 21 Degrees of Freedom: 17 443s SSR: 17.879 MSE: 1.052 Root MSE: 1.026 443s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.978 443s 443s 443s OLS estimates for 'Investment' (equation 2) 443s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 443s 443s Estimate Std. Error t value Pr(>|t|) 443s (Intercept) 10.1258 5.4655 1.85 0.08137 . 443s corpProf 0.4796 0.0971 4.94 0.00012 *** 443s corpProfLag 0.3330 0.1009 3.30 0.00421 ** 443s capitalLag -0.1118 0.0267 -4.18 0.00062 *** 443s --- 443s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 443s 443s Residual standard error: 1.009 on 17 degrees of freedom 443s Number of observations: 21 Degrees of Freedom: 17 443s SSR: 17.323 MSE: 1.019 Root MSE: 1.009 443s Multiple R-Squared: 0.931 Adjusted R-Squared: 0.919 443s 443s 443s OLS estimates for 'PrivateWages' (equation 3) 443s Model Formula: privWage ~ gnp + gnpLag + trend 443s 443s Estimate Std. Error t value Pr(>|t|) 443s (Intercept) 1.4970 1.2700 1.18 0.25474 443s gnp 0.4395 0.0324 13.56 1.5e-10 *** 443s gnpLag 0.1461 0.0374 3.90 0.00114 ** 443s trend 0.1302 0.0319 4.08 0.00078 *** 443s --- 443s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 443s 443s Residual standard error: 0.767 on 17 degrees of freedom 443s Number of observations: 21 Degrees of Freedom: 17 443s SSR: 10.005 MSE: 0.589 Root MSE: 0.767 443s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 443s 443s compare coef with single-equation OLS 443s [1] TRUE 443s > residuals 443s Consumption Investment PrivateWages 443s 1 NA NA NA 443s 2 -0.32389 -0.0668 -1.2942 443s 3 -1.25001 -0.0476 0.2957 443s 4 -1.56574 1.2467 1.1877 443s 5 -0.49350 -1.3512 -0.1358 443s 6 0.00761 0.4154 -0.4654 443s 7 0.86910 1.4923 -0.4838 443s 8 1.33848 0.7889 -0.7281 443s 9 1.05498 -0.6317 0.3392 443s 10 -0.58856 1.0830 1.1957 443s 11 0.28231 0.2791 -0.1508 443s 12 -0.22965 0.0369 0.5942 443s 13 -0.32213 0.3659 0.1027 443s 14 0.32228 0.2237 0.4503 443s 15 -0.05801 -0.1728 0.2816 443s 16 -0.03466 0.0101 0.0138 443s 17 1.61650 0.9719 -0.8508 443s 18 -0.43597 0.0516 0.9956 443s 19 0.21005 -2.5656 -0.4688 443s 20 0.98920 -0.6866 -0.3795 443s 21 0.78508 -0.7807 -1.0909 443s 22 -2.17345 -0.6623 0.5917 443s > fitted 443s Consumption Investment PrivateWages 443s 1 NA NA NA 443s 2 42.2 -0.133 26.8 443s 3 46.3 1.948 29.0 443s 4 50.8 3.953 32.9 443s 5 51.1 4.351 34.0 443s 6 52.6 4.685 35.9 443s 7 54.2 4.108 37.9 443s 8 54.9 3.411 38.6 443s 9 56.2 3.632 38.9 443s 10 58.4 4.017 40.1 443s 11 54.7 0.721 38.1 443s 12 51.1 -3.437 33.9 443s 13 45.9 -6.566 28.9 443s 14 46.2 -5.324 28.0 443s 15 48.8 -2.827 30.3 443s 16 51.3 -1.310 33.2 443s 17 56.1 1.128 37.7 443s 18 59.1 1.948 40.0 443s 19 57.3 0.666 38.7 443s 20 60.6 1.987 42.0 443s 21 64.2 4.081 46.1 443s 22 71.9 5.562 52.7 443s > predict 443s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 443s 1 NA NA NA NA 443s 2 42.2 0.462 40.0 44.5 443s 3 46.3 0.518 43.9 48.6 443s 4 50.8 0.341 48.6 52.9 443s 5 51.1 0.396 48.9 53.3 443s 6 52.6 0.397 50.4 54.8 443s 7 54.2 0.359 52.0 56.4 443s 8 54.9 0.327 52.7 57.0 443s 9 56.2 0.350 54.1 58.4 443s 10 58.4 0.370 56.2 60.6 443s 11 54.7 0.606 52.3 57.1 443s 12 51.1 0.484 48.9 53.4 443s 13 45.9 0.629 43.5 48.3 443s 14 46.2 0.602 43.8 48.6 443s 15 48.8 0.374 46.6 50.9 443s 16 51.3 0.333 49.2 53.5 443s 17 56.1 0.366 53.9 58.3 443s 18 59.1 0.321 57.0 61.3 443s 19 57.3 0.371 55.1 59.5 443s 20 60.6 0.434 58.4 62.8 443s 21 64.2 0.425 62.0 66.4 443s 22 71.9 0.666 69.4 74.3 443s Investment.pred Investment.se.fit Investment.lwr Investment.upr 443s 1 NA NA NA NA 443s 2 -0.133 0.607 -2.498 2.231 443s 3 1.948 0.499 -0.313 4.208 443s 4 3.953 0.449 1.735 6.171 443s 5 4.351 0.371 2.192 6.510 443s 6 4.685 0.349 2.540 6.829 443s 7 4.108 0.329 1.976 6.239 443s 8 3.411 0.292 1.301 5.521 443s 9 3.632 0.389 1.460 5.804 443s 10 4.017 0.447 1.801 6.233 443s 11 0.721 0.601 -1.638 3.080 443s 12 -3.437 0.507 -5.704 -1.169 443s 13 -6.566 0.616 -8.940 -4.192 443s 14 -5.324 0.694 -7.783 -2.865 443s 15 -2.827 0.373 -4.988 -0.667 443s 16 -1.310 0.320 -3.436 0.816 443s 17 1.128 0.347 -1.015 3.271 443s 18 1.948 0.243 -0.136 4.033 443s 19 0.666 0.312 -1.456 2.787 443s 20 1.987 0.366 -0.169 4.143 443s 21 4.081 0.332 1.948 6.214 443s 22 5.562 0.461 3.334 7.790 443s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 443s 1 NA NA NA NA 443s 2 26.8 0.354 25.1 28.5 443s 3 29.0 0.355 27.3 30.7 443s 4 32.9 0.354 31.2 34.6 443s 5 34.0 0.269 32.4 35.7 443s 6 35.9 0.266 34.2 37.5 443s 7 37.9 0.266 36.3 39.5 443s 8 38.6 0.273 37.0 40.3 443s 9 38.9 0.261 37.2 40.5 443s 10 40.1 0.247 38.5 41.7 443s 11 38.1 0.354 36.4 39.7 443s 12 33.9 0.363 32.2 35.6 443s 13 28.9 0.429 27.1 30.7 443s 14 28.0 0.376 26.3 29.8 443s 15 30.3 0.371 28.6 32.0 443s 16 33.2 0.310 31.5 34.8 443s 17 37.7 0.305 36.0 39.3 443s 18 40.0 0.238 38.4 41.6 443s 19 38.7 0.357 37.0 40.4 443s 20 42.0 0.321 40.3 43.6 443s 21 46.1 0.335 44.4 47.8 443s 22 52.7 0.502 50.9 54.5 443s > model.frame 443s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 443s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 443s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 443s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 443s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 443s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 443s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 443s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 61.0 443s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 443s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 443s 10 57.8 21.7 21.1 45.3 5.1 211 41.3 67.0 64.5 443s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 443s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 443s 13 45.6 7.0 11.4 34.3 -6.2 213 29.0 44.3 53.4 443s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 443s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 443s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 443s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 443s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 443s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 443s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 443s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 443s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 443s trend 443s 1 -11 443s 2 -10 443s 3 -9 443s 4 -8 443s 5 -7 443s 6 -6 443s 7 -5 443s 8 -4 443s 9 -3 443s 10 -2 443s 11 -1 443s 12 0 443s 13 1 443s 14 2 443s 15 3 443s 16 4 443s 17 5 443s 18 6 443s 19 7 443s 20 8 443s 21 9 443s 22 10 443s > model.matrix 443s Consumption_(Intercept) Consumption_corpProf 443s Consumption_2 1 12.4 443s Consumption_3 1 16.9 443s Consumption_4 1 18.4 443s Consumption_5 1 19.4 443s Consumption_6 1 20.1 443s Consumption_7 1 19.6 443s Consumption_8 1 19.8 443s Consumption_9 1 21.1 443s Consumption_10 1 21.7 443s Consumption_11 1 15.6 443s Consumption_12 1 11.4 443s Consumption_13 1 7.0 443s Consumption_14 1 11.2 443s Consumption_15 1 12.3 443s Consumption_16 1 14.0 443s Consumption_17 1 17.6 443s Consumption_18 1 17.3 443s Consumption_19 1 15.3 443s Consumption_20 1 19.0 443s Consumption_21 1 21.1 443s Consumption_22 1 23.5 443s Investment_2 0 0.0 443s Investment_3 0 0.0 443s Investment_4 0 0.0 443s Investment_5 0 0.0 443s Investment_6 0 0.0 443s Investment_7 0 0.0 443s Investment_8 0 0.0 443s Investment_9 0 0.0 443s Investment_10 0 0.0 443s Investment_11 0 0.0 443s Investment_12 0 0.0 443s Investment_13 0 0.0 443s Investment_14 0 0.0 443s Investment_15 0 0.0 443s Investment_16 0 0.0 443s Investment_17 0 0.0 443s Investment_18 0 0.0 443s Investment_19 0 0.0 443s Investment_20 0 0.0 443s Investment_21 0 0.0 443s Investment_22 0 0.0 443s PrivateWages_2 0 0.0 443s PrivateWages_3 0 0.0 443s PrivateWages_4 0 0.0 443s PrivateWages_5 0 0.0 443s PrivateWages_6 0 0.0 443s PrivateWages_7 0 0.0 443s PrivateWages_8 0 0.0 443s PrivateWages_9 0 0.0 443s PrivateWages_10 0 0.0 443s PrivateWages_11 0 0.0 443s PrivateWages_12 0 0.0 443s PrivateWages_13 0 0.0 443s PrivateWages_14 0 0.0 443s PrivateWages_15 0 0.0 443s PrivateWages_16 0 0.0 443s PrivateWages_17 0 0.0 443s PrivateWages_18 0 0.0 443s PrivateWages_19 0 0.0 443s PrivateWages_20 0 0.0 443s PrivateWages_21 0 0.0 443s PrivateWages_22 0 0.0 443s Consumption_corpProfLag Consumption_wages 443s Consumption_2 12.7 28.2 443s Consumption_3 12.4 32.2 443s Consumption_4 16.9 37.0 443s Consumption_5 18.4 37.0 443s Consumption_6 19.4 38.6 443s Consumption_7 20.1 40.7 443s Consumption_8 19.6 41.5 443s Consumption_9 19.8 42.9 443s Consumption_10 21.1 45.3 443s Consumption_11 21.7 42.1 443s Consumption_12 15.6 39.3 443s Consumption_13 11.4 34.3 443s Consumption_14 7.0 34.1 443s Consumption_15 11.2 36.6 443s Consumption_16 12.3 39.3 443s Consumption_17 14.0 44.2 443s Consumption_18 17.6 47.7 443s Consumption_19 17.3 45.9 443s Consumption_20 15.3 49.4 443s Consumption_21 19.0 53.0 443s Consumption_22 21.1 61.8 443s Investment_2 0.0 0.0 443s Investment_3 0.0 0.0 443s Investment_4 0.0 0.0 443s Investment_5 0.0 0.0 443s Investment_6 0.0 0.0 443s Investment_7 0.0 0.0 443s Investment_8 0.0 0.0 443s Investment_9 0.0 0.0 443s Investment_10 0.0 0.0 443s Investment_11 0.0 0.0 443s Investment_12 0.0 0.0 443s Investment_13 0.0 0.0 443s Investment_14 0.0 0.0 443s Investment_15 0.0 0.0 443s Investment_16 0.0 0.0 443s Investment_17 0.0 0.0 443s Investment_18 0.0 0.0 443s Investment_19 0.0 0.0 443s Investment_20 0.0 0.0 443s Investment_21 0.0 0.0 443s Investment_22 0.0 0.0 443s PrivateWages_2 0.0 0.0 443s PrivateWages_3 0.0 0.0 443s PrivateWages_4 0.0 0.0 443s PrivateWages_5 0.0 0.0 443s PrivateWages_6 0.0 0.0 443s PrivateWages_7 0.0 0.0 443s PrivateWages_8 0.0 0.0 443s PrivateWages_9 0.0 0.0 443s PrivateWages_10 0.0 0.0 443s PrivateWages_11 0.0 0.0 443s PrivateWages_12 0.0 0.0 443s PrivateWages_13 0.0 0.0 443s PrivateWages_14 0.0 0.0 443s PrivateWages_15 0.0 0.0 443s PrivateWages_16 0.0 0.0 443s PrivateWages_17 0.0 0.0 443s PrivateWages_18 0.0 0.0 443s PrivateWages_19 0.0 0.0 443s PrivateWages_20 0.0 0.0 443s PrivateWages_21 0.0 0.0 443s PrivateWages_22 0.0 0.0 443s Investment_(Intercept) Investment_corpProf 443s Consumption_2 0 0.0 443s Consumption_3 0 0.0 443s Consumption_4 0 0.0 443s Consumption_5 0 0.0 443s Consumption_6 0 0.0 443s Consumption_7 0 0.0 443s Consumption_8 0 0.0 443s Consumption_9 0 0.0 443s Consumption_10 0 0.0 443s Consumption_11 0 0.0 443s Consumption_12 0 0.0 443s Consumption_13 0 0.0 443s Consumption_14 0 0.0 443s Consumption_15 0 0.0 443s Consumption_16 0 0.0 443s Consumption_17 0 0.0 443s Consumption_18 0 0.0 443s Consumption_19 0 0.0 443s Consumption_20 0 0.0 443s Consumption_21 0 0.0 443s Consumption_22 0 0.0 443s Investment_2 1 12.4 443s Investment_3 1 16.9 443s Investment_4 1 18.4 443s Investment_5 1 19.4 443s Investment_6 1 20.1 443s Investment_7 1 19.6 443s Investment_8 1 19.8 443s Investment_9 1 21.1 443s Investment_10 1 21.7 443s Investment_11 1 15.6 443s Investment_12 1 11.4 443s Investment_13 1 7.0 443s Investment_14 1 11.2 443s Investment_15 1 12.3 443s Investment_16 1 14.0 443s Investment_17 1 17.6 443s Investment_18 1 17.3 443s Investment_19 1 15.3 443s Investment_20 1 19.0 443s Investment_21 1 21.1 443s Investment_22 1 23.5 443s PrivateWages_2 0 0.0 443s PrivateWages_3 0 0.0 443s PrivateWages_4 0 0.0 443s PrivateWages_5 0 0.0 443s PrivateWages_6 0 0.0 443s PrivateWages_7 0 0.0 443s PrivateWages_8 0 0.0 443s PrivateWages_9 0 0.0 443s PrivateWages_10 0 0.0 443s PrivateWages_11 0 0.0 443s PrivateWages_12 0 0.0 443s PrivateWages_13 0 0.0 443s PrivateWages_14 0 0.0 443s PrivateWages_15 0 0.0 443s PrivateWages_16 0 0.0 443s PrivateWages_17 0 0.0 443s PrivateWages_18 0 0.0 443s PrivateWages_19 0 0.0 443s PrivateWages_20 0 0.0 443s PrivateWages_21 0 0.0 443s PrivateWages_22 0 0.0 443s Investment_corpProfLag Investment_capitalLag 443s Consumption_2 0.0 0 443s Consumption_3 0.0 0 443s Consumption_4 0.0 0 443s Consumption_5 0.0 0 443s Consumption_6 0.0 0 443s Consumption_7 0.0 0 443s Consumption_8 0.0 0 443s Consumption_9 0.0 0 443s Consumption_10 0.0 0 443s Consumption_11 0.0 0 443s Consumption_12 0.0 0 443s Consumption_13 0.0 0 443s Consumption_14 0.0 0 443s Consumption_15 0.0 0 443s Consumption_16 0.0 0 443s Consumption_17 0.0 0 443s Consumption_18 0.0 0 443s Consumption_19 0.0 0 443s Consumption_20 0.0 0 443s Consumption_21 0.0 0 443s Consumption_22 0.0 0 443s Investment_2 12.7 183 443s Investment_3 12.4 183 443s Investment_4 16.9 184 443s Investment_5 18.4 190 443s Investment_6 19.4 193 443s Investment_7 20.1 198 443s Investment_8 19.6 203 443s Investment_9 19.8 208 443s Investment_10 21.1 211 443s Investment_11 21.7 216 443s Investment_12 15.6 217 443s Investment_13 11.4 213 443s Investment_14 7.0 207 443s Investment_15 11.2 202 443s Investment_16 12.3 199 443s Investment_17 14.0 198 443s Investment_18 17.6 200 443s Investment_19 17.3 202 443s Investment_20 15.3 200 443s Investment_21 19.0 201 443s Investment_22 21.1 204 443s PrivateWages_2 0.0 0 443s PrivateWages_3 0.0 0 443s PrivateWages_4 0.0 0 443s PrivateWages_5 0.0 0 443s PrivateWages_6 0.0 0 443s PrivateWages_7 0.0 0 443s PrivateWages_8 0.0 0 443s PrivateWages_9 0.0 0 443s PrivateWages_10 0.0 0 443s PrivateWages_11 0.0 0 443s PrivateWages_12 0.0 0 443s PrivateWages_13 0.0 0 443s PrivateWages_14 0.0 0 443s PrivateWages_15 0.0 0 443s PrivateWages_16 0.0 0 443s PrivateWages_17 0.0 0 443s PrivateWages_18 0.0 0 443s PrivateWages_19 0.0 0 443s PrivateWages_20 0.0 0 443s PrivateWages_21 0.0 0 443s PrivateWages_22 0.0 0 443s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 443s Consumption_2 0 0.0 0.0 443s Consumption_3 0 0.0 0.0 443s Consumption_4 0 0.0 0.0 443s Consumption_5 0 0.0 0.0 443s Consumption_6 0 0.0 0.0 443s Consumption_7 0 0.0 0.0 443s Consumption_8 0 0.0 0.0 443s Consumption_9 0 0.0 0.0 443s Consumption_10 0 0.0 0.0 443s Consumption_11 0 0.0 0.0 443s Consumption_12 0 0.0 0.0 443s Consumption_13 0 0.0 0.0 443s Consumption_14 0 0.0 0.0 443s Consumption_15 0 0.0 0.0 443s Consumption_16 0 0.0 0.0 443s Consumption_17 0 0.0 0.0 443s Consumption_18 0 0.0 0.0 443s Consumption_19 0 0.0 0.0 443s Consumption_20 0 0.0 0.0 443s Consumption_21 0 0.0 0.0 443s Consumption_22 0 0.0 0.0 443s Investment_2 0 0.0 0.0 443s Investment_3 0 0.0 0.0 443s Investment_4 0 0.0 0.0 443s Investment_5 0 0.0 0.0 443s Investment_6 0 0.0 0.0 443s Investment_7 0 0.0 0.0 443s Investment_8 0 0.0 0.0 443s Investment_9 0 0.0 0.0 443s Investment_10 0 0.0 0.0 443s Investment_11 0 0.0 0.0 443s Investment_12 0 0.0 0.0 443s Investment_13 0 0.0 0.0 443s Investment_14 0 0.0 0.0 443s Investment_15 0 0.0 0.0 443s Investment_16 0 0.0 0.0 443s Investment_17 0 0.0 0.0 443s Investment_18 0 0.0 0.0 443s Investment_19 0 0.0 0.0 443s Investment_20 0 0.0 0.0 443s Investment_21 0 0.0 0.0 443s Investment_22 0 0.0 0.0 443s PrivateWages_2 1 45.6 44.9 443s PrivateWages_3 1 50.1 45.6 443s PrivateWages_4 1 57.2 50.1 443s PrivateWages_5 1 57.1 57.2 443s PrivateWages_6 1 61.0 57.1 443s PrivateWages_7 1 64.0 61.0 443s PrivateWages_8 1 64.4 64.0 443s PrivateWages_9 1 64.5 64.4 443s PrivateWages_10 1 67.0 64.5 443s PrivateWages_11 1 61.2 67.0 443s PrivateWages_12 1 53.4 61.2 443s PrivateWages_13 1 44.3 53.4 443s PrivateWages_14 1 45.1 44.3 443s PrivateWages_15 1 49.7 45.1 443s PrivateWages_16 1 54.4 49.7 443s PrivateWages_17 1 62.7 54.4 443s PrivateWages_18 1 65.0 62.7 443s PrivateWages_19 1 60.9 65.0 443s PrivateWages_20 1 69.5 60.9 443s PrivateWages_21 1 75.7 69.5 443s PrivateWages_22 1 88.4 75.7 443s PrivateWages_trend 443s Consumption_2 0 443s Consumption_3 0 443s Consumption_4 0 443s Consumption_5 0 443s Consumption_6 0 443s Consumption_7 0 443s Consumption_8 0 443s Consumption_9 0 443s Consumption_10 0 443s Consumption_11 0 443s Consumption_12 0 443s Consumption_13 0 443s Consumption_14 0 443s Consumption_15 0 443s Consumption_16 0 443s Consumption_17 0 443s Consumption_18 0 443s Consumption_19 0 443s Consumption_20 0 443s Consumption_21 0 443s Consumption_22 0 443s Investment_2 0 443s Investment_3 0 443s Investment_4 0 443s Investment_5 0 443s Investment_6 0 443s Investment_7 0 443s Investment_8 0 443s Investment_9 0 443s Investment_10 0 443s Investment_11 0 443s Investment_12 0 443s Investment_13 0 443s Investment_14 0 443s Investment_15 0 443s Investment_16 0 443s Investment_17 0 443s Investment_18 0 443s Investment_19 0 443s Investment_20 0 443s Investment_21 0 443s Investment_22 0 443s PrivateWages_2 -10 443s PrivateWages_3 -9 443s PrivateWages_4 -8 443s PrivateWages_5 -7 443s PrivateWages_6 -6 443s PrivateWages_7 -5 443s PrivateWages_8 -4 443s PrivateWages_9 -3 443s PrivateWages_10 -2 443s PrivateWages_11 -1 443s PrivateWages_12 0 443s PrivateWages_13 1 443s PrivateWages_14 2 443s PrivateWages_15 3 443s PrivateWages_16 4 443s PrivateWages_17 5 443s PrivateWages_18 6 443s PrivateWages_19 7 443s PrivateWages_20 8 443s PrivateWages_21 9 443s PrivateWages_22 10 443s > nobs 443s [1] 63 443s > linearHypothesis 443s Linear hypothesis test (Theil's F test) 443s 443s Hypothesis: 443s Consumption_corpProf + Investment_capitalLag = 0 443s 443s Model 1: restricted model 443s Model 2: kleinModel 443s 443s Res.Df Df F Pr(>F) 443s 1 52 443s 2 51 1 0.82 0.37 443s Linear hypothesis test (F statistic of a Wald test) 443s 443s Hypothesis: 443s Consumption_corpProf + Investment_capitalLag = 0 443s 443s Model 1: restricted model 443s Model 2: kleinModel 443s 443s Res.Df Df F Pr(>F) 443s 1 52 443s 2 51 1 0.73 0.4 443s Linear hypothesis test (Chi^2 statistic of a Wald test) 443s 443s Hypothesis: 443s Consumption_corpProf + Investment_capitalLag = 0 443s 443s Model 1: restricted model 443s Model 2: kleinModel 443s 443s Res.Df Df Chisq Pr(>Chisq) 443s 1 52 443s 2 51 1 0.73 0.39 443s Linear hypothesis test (Theil's F test) 443s 443s Hypothesis: 443s Consumption_corpProf + Investment_capitalLag = 0 443s Consumption_corpProfLag - PrivateWages_trend = 0 443s 443s Model 1: restricted model 443s Model 2: kleinModel 443s 443s Res.Df Df F Pr(>F) 443s 1 53 443s 2 51 2 0.42 0.66 443s Linear hypothesis test (F statistic of a Wald test) 443s 443s Hypothesis: 443s Consumption_corpProf + Investment_capitalLag = 0 443s Consumption_corpProfLag - PrivateWages_trend = 0 443s 443s Model 1: restricted model 443s Model 2: kleinModel 443s 443s Res.Df Df F Pr(>F) 443s 1 53 443s 2 51 2 0.37 0.69 443s Linear hypothesis test (Chi^2 statistic of a Wald test) 443s 443s Hypothesis: 443s Consumption_corpProf + Investment_capitalLag = 0 443s Consumption_corpProfLag - PrivateWages_trend = 0 443s 443s Model 1: restricted model 443s Model 2: kleinModel 443s 443s Res.Df Df Chisq Pr(>Chisq) 443s 1 53 443s 2 51 2 0.74 0.69 443s > logLik 443s 'log Lik.' -72.3 (df=13) 443s 'log Lik.' -77.9 (df=13) 443s compare log likelihood value with single-equation OLS 443s [1] TRUE 443s > 443s > # 2SLS 443s > summary 444s 444s systemfit results 444s method: 2SLS 444s 444s N DF SSR detRCov OLS-R2 McElroy-R2 444s system 63 51 61 0.288 0.969 0.992 444s 444s N DF SSR MSE RMSE R2 Adj R2 444s Consumption 21 17 21.9 1.290 1.136 0.977 0.973 444s Investment 21 17 29.0 1.709 1.307 0.885 0.865 444s PrivateWages 21 17 10.0 0.589 0.767 0.987 0.985 444s 444s The covariance matrix of the residuals 444s Consumption Investment PrivateWages 444s Consumption 1.044 0.438 -0.385 444s Investment 0.438 1.383 0.193 444s PrivateWages -0.385 0.193 0.476 444s 444s The correlations of the residuals 444s Consumption Investment PrivateWages 444s Consumption 1.000 0.364 -0.546 444s Investment 0.364 1.000 0.237 444s PrivateWages -0.546 0.237 1.000 444s 444s 444s 2SLS estimates for 'Consumption' (equation 1) 444s Model Formula: consump ~ corpProf + corpProfLag + wages 444s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 444s gnpLag 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 16.5548 1.3208 12.53 5.2e-10 *** 444s corpProf 0.0173 0.1180 0.15 0.89 444s corpProfLag 0.2162 0.1073 2.02 0.06 . 444s wages 0.8102 0.0402 20.13 2.7e-13 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 1.136 on 17 degrees of freedom 444s Number of observations: 21 Degrees of Freedom: 17 444s SSR: 21.925 MSE: 1.29 Root MSE: 1.136 444s Multiple R-Squared: 0.977 Adjusted R-Squared: 0.973 444s 444s 444s 2SLS estimates for 'Investment' (equation 2) 444s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 444s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 444s gnpLag 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 20.2782 7.5427 2.69 0.01555 * 444s corpProf 0.1502 0.1732 0.87 0.39792 444s corpProfLag 0.6159 0.1628 3.78 0.00148 ** 444s capitalLag -0.1578 0.0361 -4.37 0.00042 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 1.307 on 17 degrees of freedom 444s Number of observations: 21 Degrees of Freedom: 17 444s SSR: 29.047 MSE: 1.709 Root MSE: 1.307 444s Multiple R-Squared: 0.885 Adjusted R-Squared: 0.865 444s 444s 444s 2SLS estimates for 'PrivateWages' (equation 3) 444s Model Formula: privWage ~ gnp + gnpLag + trend 444s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 444s gnpLag 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 1.5003 1.1478 1.31 0.20857 444s gnp 0.4389 0.0356 12.32 6.8e-10 *** 444s gnpLag 0.1467 0.0388 3.78 0.00150 ** 444s trend 0.1304 0.0291 4.47 0.00033 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 0.767 on 17 degrees of freedom 444s Number of observations: 21 Degrees of Freedom: 17 444s SSR: 10.005 MSE: 0.589 Root MSE: 0.767 444s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 444s 444s > residuals 444s Consumption Investment PrivateWages 444s 1 NA NA NA 444s 2 -0.46263 -1.320 -1.2940 444s 3 -0.61635 0.257 0.2981 444s 4 -1.30423 0.860 1.1918 444s 5 -0.24588 -1.594 -0.1361 444s 6 0.22948 0.259 -0.4634 444s 7 0.88538 1.207 -0.4824 444s 8 1.44189 0.969 -0.7284 444s 9 1.34190 0.113 0.3387 444s 10 -0.39403 1.796 1.1965 444s 11 -0.62564 -0.953 -0.1552 444s 12 -1.06543 -0.807 0.5882 444s 13 -1.33021 -0.895 0.0955 444s 14 0.61059 1.306 0.4487 444s 15 -0.14208 -0.151 0.2822 444s 16 0.00315 0.142 0.0145 444s 17 2.00337 1.749 -0.8478 444s 18 -0.60552 -0.192 0.9950 444s 19 -0.24771 -3.291 -0.4734 444s 20 1.38510 0.285 -0.3766 444s 21 1.03204 -0.104 -1.0893 444s 22 -1.89319 0.363 0.5974 444s > fitted 444s Consumption Investment PrivateWages 444s 1 NA NA NA 444s 2 42.4 1.120 26.8 444s 3 45.6 1.643 29.0 444s 4 50.5 4.340 32.9 444s 5 50.8 4.594 34.0 444s 6 52.4 4.841 35.9 444s 7 54.2 4.393 37.9 444s 8 54.8 3.231 38.6 444s 9 56.0 2.887 38.9 444s 10 58.2 3.304 40.1 444s 11 55.6 1.953 38.1 444s 12 52.0 -2.593 33.9 444s 13 46.9 -5.305 28.9 444s 14 45.9 -6.406 28.1 444s 15 48.8 -2.849 30.3 444s 16 51.3 -1.442 33.2 444s 17 55.7 0.351 37.6 444s 18 59.3 2.192 40.0 444s 19 57.7 1.391 38.7 444s 20 60.2 1.015 42.0 444s 21 64.0 3.404 46.1 444s 22 71.6 4.537 52.7 444s > predict 444s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 444s 1 NA NA NA NA 444s 2 42.4 0.471 41.4 43.4 444s 3 45.6 0.577 44.4 46.8 444s 4 50.5 0.354 49.8 51.3 444s 5 50.8 0.405 50.0 51.7 444s 6 52.4 0.404 51.5 53.2 444s 7 54.2 0.359 53.5 55.0 444s 8 54.8 0.328 54.1 55.4 444s 9 56.0 0.368 55.2 56.7 444s 10 58.2 0.377 57.4 59.0 444s 11 55.6 0.728 54.1 57.2 444s 12 52.0 0.604 50.7 53.2 444s 13 46.9 0.765 45.3 48.5 444s 14 45.9 0.615 44.6 47.2 444s 15 48.8 0.374 48.1 49.6 444s 16 51.3 0.333 50.6 52.0 444s 17 55.7 0.409 54.8 56.6 444s 18 59.3 0.326 58.6 60.0 444s 19 57.7 0.414 56.9 58.6 444s 20 60.2 0.478 59.2 61.2 444s 21 64.0 0.446 63.0 64.9 444s 22 71.6 0.689 70.1 73.0 444s Investment.pred Investment.se.fit Investment.lwr Investment.upr 444s 1 NA NA NA NA 444s 2 1.120 0.865 -0.706 2.946 444s 3 1.643 0.594 0.390 2.895 444s 4 4.340 0.545 3.190 5.490 444s 5 4.594 0.443 3.660 5.527 444s 6 4.841 0.411 3.973 5.709 444s 7 4.393 0.399 3.550 5.235 444s 8 3.231 0.348 2.497 3.965 444s 9 2.887 0.542 1.744 4.030 444s 10 3.304 0.593 2.054 4.555 444s 11 1.953 0.855 0.148 3.757 444s 12 -2.593 0.679 -4.026 -1.160 444s 13 -5.305 0.876 -7.152 -3.457 444s 14 -6.406 0.916 -8.338 -4.473 444s 15 -2.849 0.435 -3.765 -1.932 444s 16 -1.442 0.376 -2.236 -0.649 444s 17 0.351 0.510 -0.724 1.426 444s 18 2.192 0.299 1.560 2.823 444s 19 1.391 0.464 0.411 2.371 444s 20 1.015 0.576 -0.201 2.230 444s 21 3.404 0.471 2.410 4.398 444s 22 4.537 0.675 3.114 5.961 444s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 444s 1 NA NA NA NA 444s 2 26.8 0.318 26.1 27.5 444s 3 29.0 0.330 28.3 29.7 444s 4 32.9 0.346 32.2 33.6 444s 5 34.0 0.242 33.5 34.5 444s 6 35.9 0.248 35.3 36.4 444s 7 37.9 0.244 37.4 38.4 444s 8 38.6 0.246 38.1 39.1 444s 9 38.9 0.235 38.4 39.4 444s 10 40.1 0.224 39.6 40.6 444s 11 38.1 0.350 37.3 38.8 444s 12 33.9 0.382 33.1 34.7 444s 13 28.9 0.454 27.9 29.9 444s 14 28.1 0.342 27.3 28.8 444s 15 30.3 0.335 29.6 31.0 444s 16 33.2 0.280 32.6 33.8 444s 17 37.6 0.291 37.0 38.3 444s 18 40.0 0.215 39.6 40.5 444s 19 38.7 0.356 37.9 39.4 444s 20 42.0 0.304 41.3 42.6 444s 21 46.1 0.306 45.4 46.7 444s 22 52.7 0.489 51.7 53.7 444s > model.frame 444s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 444s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 444s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 444s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 444s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 444s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 444s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 444s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 61.0 444s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 444s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 444s 10 57.8 21.7 21.1 45.3 5.1 211 41.3 67.0 64.5 444s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 444s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 444s 13 45.6 7.0 11.4 34.3 -6.2 213 29.0 44.3 53.4 444s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 444s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 444s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 444s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 444s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 444s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 444s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 444s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 444s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 444s trend 444s 1 -11 444s 2 -10 444s 3 -9 444s 4 -8 444s 5 -7 444s 6 -6 444s 7 -5 444s 8 -4 444s 9 -3 444s 10 -2 444s 11 -1 444s 12 0 444s 13 1 444s 14 2 444s 15 3 444s 16 4 444s 17 5 444s 18 6 444s 19 7 444s 20 8 444s 21 9 444s 22 10 444s > model.matrix 444s [1] TRUE 444s > nobs 444s [1] 63 444s > linearHypothesis 444s Linear hypothesis test (Theil's F test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 52 444s 2 51 1 1.08 0.3 444s Linear hypothesis test (F statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 52 444s 2 51 1 1.29 0.26 444s Linear hypothesis test (Chi^2 statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df Chisq Pr(>Chisq) 444s 1 52 444s 2 51 1 1.29 0.26 444s Linear hypothesis test (Theil's F test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 53 444s 2 51 2 0.54 0.58 444s Linear hypothesis test (F statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 53 444s 2 51 2 0.65 0.53 444s Linear hypothesis test (Chi^2 statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df Chisq Pr(>Chisq) 444s 1 53 444s 2 51 2 1.3 0.52 444s > logLik 444s 'log Lik.' -76.3 (df=13) 444s 'log Lik.' -85.5 (df=13) 444s > 444s > # SUR 444s > summary 444s 444s systemfit results 444s method: SUR 444s 444s N DF SSR detRCov OLS-R2 McElroy-R2 444s system 63 51 46.5 0.158 0.977 0.993 444s 444s N DF SSR MSE RMSE R2 Adj R2 444s Consumption 21 17 18.1 1.065 1.032 0.981 0.977 444s Investment 21 17 17.6 1.036 1.018 0.930 0.918 444s PrivateWages 21 17 10.8 0.633 0.796 0.986 0.984 444s 444s The covariance matrix of the residuals used for estimation 444s Consumption Investment PrivateWages 444s Consumption 0.8514 0.0495 -0.381 444s Investment 0.0495 0.8249 0.121 444s PrivateWages -0.3808 0.1212 0.476 444s 444s The covariance matrix of the residuals 444s Consumption Investment PrivateWages 444s Consumption 0.8618 0.0766 -0.437 444s Investment 0.0766 0.8384 0.203 444s PrivateWages -0.4368 0.2027 0.513 444s 444s The correlations of the residuals 444s Consumption Investment PrivateWages 444s Consumption 1.0000 0.0901 -0.657 444s Investment 0.0901 1.0000 0.309 444s PrivateWages -0.6572 0.3092 1.000 444s 444s 444s SUR estimates for 'Consumption' (equation 1) 444s Model Formula: consump ~ corpProf + corpProfLag + wages 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 15.9805 1.1687 13.67 1.3e-10 *** 444s corpProf 0.2302 0.0767 3.00 0.008 ** 444s corpProfLag 0.0673 0.0769 0.87 0.394 444s wages 0.7962 0.0353 22.58 4.1e-14 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 1.032 on 17 degrees of freedom 444s Number of observations: 21 Degrees of Freedom: 17 444s SSR: 18.098 MSE: 1.065 Root MSE: 1.032 444s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.977 444s 444s 444s SUR estimates for 'Investment' (equation 2) 444s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 12.9293 4.8014 2.69 0.01540 * 444s corpProf 0.4429 0.0861 5.15 8.1e-05 *** 444s corpProfLag 0.3655 0.0894 4.09 0.00077 *** 444s capitalLag -0.1253 0.0235 -5.34 5.4e-05 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 1.018 on 17 degrees of freedom 444s Number of observations: 21 Degrees of Freedom: 17 444s SSR: 17.606 MSE: 1.036 Root MSE: 1.018 444s Multiple R-Squared: 0.93 Adjusted R-Squared: 0.918 444s 444s 444s SUR estimates for 'PrivateWages' (equation 3) 444s Model Formula: privWage ~ gnp + gnpLag + trend 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 1.6347 1.1173 1.46 0.16 444s gnp 0.4098 0.0273 15.04 3.0e-11 *** 444s gnpLag 0.1744 0.0312 5.59 3.2e-05 *** 444s trend 0.1558 0.0276 5.65 2.9e-05 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 0.796 on 17 degrees of freedom 444s Number of observations: 21 Degrees of Freedom: 17 444s SSR: 10.763 MSE: 0.633 Root MSE: 0.796 444s Multiple R-Squared: 0.986 Adjusted R-Squared: 0.984 444s 444s > residuals 444s Consumption Investment PrivateWages 444s 1 NA NA NA 444s 2 -0.24064 -0.3522 -1.0960 444s 3 -1.34080 -0.1605 0.5818 444s 4 -1.61038 1.0687 1.5313 444s 5 -0.54147 -1.4707 -0.0220 444s 6 -0.04372 0.3299 -0.2587 444s 7 0.85234 1.4346 -0.3243 444s 8 1.30302 0.8306 -0.6674 444s 9 0.97574 -0.4918 0.3660 444s 10 -0.66060 1.2434 1.2682 444s 11 0.45069 0.2647 -0.3467 444s 12 -0.04295 0.0795 0.3057 444s 13 -0.06686 0.3369 -0.2602 444s 14 0.32177 0.4080 0.3434 444s 15 -0.00441 -0.1533 0.2628 444s 16 -0.01931 0.0158 -0.0216 444s 17 1.53656 1.0372 -0.7988 444s 18 -0.42317 0.0176 0.8550 444s 19 0.29041 -2.6364 -0.8217 444s 20 0.88685 -0.5822 -0.3869 444s 21 0.68839 -0.7015 -1.1838 444s 22 -2.31147 -0.5183 0.6742 444s > fitted 444s Consumption Investment PrivateWages 444s 1 NA NA NA 444s 2 42.1 0.152 26.6 444s 3 46.3 2.060 28.7 444s 4 50.8 4.131 32.6 444s 5 51.1 4.471 33.9 444s 6 52.6 4.770 35.7 444s 7 54.2 4.165 37.7 444s 8 54.9 3.369 38.6 444s 9 56.3 3.492 38.8 444s 10 58.5 3.857 40.0 444s 11 54.5 0.735 38.2 444s 12 50.9 -3.479 34.2 444s 13 45.7 -6.537 29.3 444s 14 46.2 -5.508 28.2 444s 15 48.7 -2.847 30.3 444s 16 51.3 -1.316 33.2 444s 17 56.2 1.063 37.6 444s 18 59.1 1.982 40.1 444s 19 57.2 0.736 39.0 444s 20 60.7 1.882 42.0 444s 21 64.3 4.002 46.2 444s 22 72.0 5.418 52.6 444s > predict 444s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 444s 1 NA NA NA NA 444s 2 42.1 0.415 41.3 43.0 444s 3 46.3 0.449 45.4 47.2 444s 4 50.8 0.300 50.2 51.4 444s 5 51.1 0.348 50.4 51.8 444s 6 52.6 0.350 51.9 53.3 444s 7 54.2 0.317 53.6 54.9 444s 8 54.9 0.289 54.3 55.5 444s 9 56.3 0.309 55.7 56.9 444s 10 58.5 0.328 57.8 59.1 444s 11 54.5 0.516 53.5 55.6 444s 12 50.9 0.414 50.1 51.8 444s 13 45.7 0.544 44.6 46.8 444s 14 46.2 0.527 45.1 47.2 444s 15 48.7 0.332 48.0 49.4 444s 16 51.3 0.295 50.7 51.9 444s 17 56.2 0.319 55.5 56.8 444s 18 59.1 0.286 58.5 59.7 444s 19 57.2 0.323 56.6 57.9 444s 20 60.7 0.381 59.9 61.5 444s 21 64.3 0.381 63.5 65.1 444s 22 72.0 0.597 70.8 73.2 444s Investment.pred Investment.se.fit Investment.lwr Investment.upr 444s 1 NA NA NA NA 444s 2 0.152 0.536 -0.924 1.229 444s 3 2.060 0.446 1.166 2.955 444s 4 4.131 0.397 3.334 4.929 444s 5 4.471 0.329 3.809 5.132 444s 6 4.770 0.311 4.145 5.395 444s 7 4.165 0.294 3.575 4.756 444s 8 3.369 0.263 2.842 3.897 444s 9 3.492 0.347 2.796 4.188 444s 10 3.857 0.398 3.058 4.656 444s 11 0.735 0.539 -0.346 1.816 444s 12 -3.479 0.454 -4.390 -2.569 444s 13 -6.537 0.552 -7.646 -5.428 444s 14 -5.508 0.617 -6.747 -4.269 444s 15 -2.847 0.335 -3.519 -2.175 444s 16 -1.316 0.287 -1.892 -0.739 444s 17 1.063 0.311 0.439 1.686 444s 18 1.982 0.218 1.545 2.420 444s 19 0.736 0.279 0.176 1.296 444s 20 1.882 0.327 1.227 2.538 444s 21 4.002 0.297 3.405 4.598 444s 22 5.418 0.412 4.591 6.245 444s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 444s 1 NA NA NA NA 444s 2 26.6 0.313 26.0 27.2 444s 3 28.7 0.310 28.1 29.3 444s 4 32.6 0.305 32.0 33.2 444s 5 33.9 0.236 33.4 34.4 444s 6 35.7 0.233 35.2 36.1 444s 7 37.7 0.234 37.3 38.2 444s 8 38.6 0.239 38.1 39.0 444s 9 38.8 0.229 38.4 39.3 444s 10 40.0 0.219 39.6 40.5 444s 11 38.2 0.301 37.6 38.9 444s 12 34.2 0.308 33.6 34.8 444s 13 29.3 0.370 28.5 30.0 444s 14 28.2 0.332 27.5 28.8 444s 15 30.3 0.324 29.7 31.0 444s 16 33.2 0.271 32.7 33.8 444s 17 37.6 0.263 37.1 38.1 444s 18 40.1 0.211 39.7 40.6 444s 19 39.0 0.306 38.4 39.6 444s 20 42.0 0.280 41.4 42.5 444s 21 46.2 0.298 45.6 46.8 444s 22 52.6 0.445 51.7 53.5 444s > model.frame 444s [1] TRUE 444s > model.matrix 444s [1] TRUE 444s > nobs 444s [1] 63 444s > linearHypothesis 444s Linear hypothesis test (Theil's F test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 52 444s 2 51 1 1.44 0.24 444s Linear hypothesis test (F statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 52 444s 2 51 1 1.69 0.2 444s Linear hypothesis test (Chi^2 statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df Chisq Pr(>Chisq) 444s 1 52 444s 2 51 1 1.69 0.19 444s Linear hypothesis test (Theil's F test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 53 444s 2 51 2 0.77 0.47 444s Linear hypothesis test (F statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 53 444s 2 51 2 0.91 0.41 444s Linear hypothesis test (Chi^2 statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df Chisq Pr(>Chisq) 444s 1 53 444s 2 51 2 1.83 0.4 444s > logLik 444s 'log Lik.' -70 (df=18) 444s 'log Lik.' -79 (df=18) 444s > 444s > # 3SLS 444s > summary 444s 444s systemfit results 444s method: 3SLS 444s 444s N DF SSR detRCov OLS-R2 McElroy-R2 444s system 63 51 73.6 0.283 0.963 0.995 444s 444s N DF SSR MSE RMSE R2 Adj R2 444s Consumption 21 17 18.7 1.102 1.050 0.980 0.977 444s Investment 21 17 44.0 2.586 1.608 0.826 0.795 444s PrivateWages 21 17 10.9 0.642 0.801 0.986 0.984 444s 444s The covariance matrix of the residuals used for estimation 444s Consumption Investment PrivateWages 444s Consumption 1.044 0.438 -0.385 444s Investment 0.438 1.383 0.193 444s PrivateWages -0.385 0.193 0.476 444s 444s The covariance matrix of the residuals 444s Consumption Investment PrivateWages 444s Consumption 0.892 0.411 -0.394 444s Investment 0.411 2.093 0.403 444s PrivateWages -0.394 0.403 0.520 444s 444s The correlations of the residuals 444s Consumption Investment PrivateWages 444s Consumption 1.000 0.301 -0.578 444s Investment 0.301 1.000 0.386 444s PrivateWages -0.578 0.386 1.000 444s 444s 444s 3SLS estimates for 'Consumption' (equation 1) 444s Model Formula: consump ~ corpProf + corpProfLag + wages 444s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 444s gnpLag 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 16.4408 1.3045 12.60 4.7e-10 *** 444s corpProf 0.1249 0.1081 1.16 0.26 444s corpProfLag 0.1631 0.1004 1.62 0.12 444s wages 0.7901 0.0379 20.83 1.5e-13 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 1.05 on 17 degrees of freedom 444s Number of observations: 21 Degrees of Freedom: 17 444s SSR: 18.727 MSE: 1.102 Root MSE: 1.05 444s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.977 444s 444s 444s 3SLS estimates for 'Investment' (equation 2) 444s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 444s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 444s gnpLag 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 28.1778 6.7938 4.15 0.00067 *** 444s corpProf -0.0131 0.1619 -0.08 0.93655 444s corpProfLag 0.7557 0.1529 4.94 0.00012 *** 444s capitalLag -0.1948 0.0325 -5.99 1.5e-05 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 1.608 on 17 degrees of freedom 444s Number of observations: 21 Degrees of Freedom: 17 444s SSR: 43.954 MSE: 2.586 Root MSE: 1.608 444s Multiple R-Squared: 0.826 Adjusted R-Squared: 0.795 444s 444s 444s 3SLS estimates for 'PrivateWages' (equation 3) 444s Model Formula: privWage ~ gnp + gnpLag + trend 444s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 444s gnpLag 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 1.7972 1.1159 1.61 0.13 444s gnp 0.4005 0.0318 12.59 4.8e-10 *** 444s gnpLag 0.1813 0.0342 5.31 5.8e-05 *** 444s trend 0.1497 0.0279 5.36 5.2e-05 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 0.801 on 17 degrees of freedom 444s Number of observations: 21 Degrees of Freedom: 17 444s SSR: 10.921 MSE: 0.642 Root MSE: 0.801 444s Multiple R-Squared: 0.986 Adjusted R-Squared: 0.984 444s 444s > residuals 444s Consumption Investment PrivateWages 444s 1 NA NA NA 444s 2 -0.4416 -2.1951 -1.20287 444s 3 -1.0150 0.1515 0.51834 444s 4 -1.5289 0.4406 1.50936 444s 5 -0.4985 -1.8667 -0.08743 444s 6 -0.0132 0.0713 -0.28089 444s 7 0.7759 1.0294 -0.33908 444s 8 1.3004 1.1011 -0.69282 444s 9 1.0993 0.5853 0.34494 444s 10 -0.5839 2.2952 1.27590 444s 11 -0.1917 -1.3443 -0.40414 444s 12 -0.5598 -0.9944 0.22151 444s 13 -0.6746 -1.3404 -0.36962 444s 14 0.5767 1.9316 0.31006 444s 15 -0.0211 -0.1217 0.27309 444s 16 0.0539 0.1847 0.00716 444s 17 1.8555 2.0937 -0.71866 444s 18 -0.4596 -0.3216 0.90582 444s 19 0.0613 -3.6314 -0.81881 444s 20 1.2602 0.7582 -0.26942 444s 21 0.9500 0.2428 -1.06125 444s 22 -1.9451 0.9302 0.87883 444s > fitted 444s Consumption Investment PrivateWages 444s 1 NA NA NA 444s 2 42.3 1.99510 26.7 444s 3 46.0 1.74850 28.8 444s 4 50.7 4.75942 32.6 444s 5 51.1 4.86672 34.0 444s 6 52.6 5.02874 35.7 444s 7 54.3 4.57056 37.7 444s 8 54.9 3.09893 38.6 444s 9 56.2 2.41471 38.9 444s 10 58.4 2.80476 40.0 444s 11 55.2 2.34425 38.3 444s 12 51.5 -2.40558 34.3 444s 13 46.3 -4.85959 29.4 444s 14 45.9 -7.03164 28.2 444s 15 48.7 -2.87827 30.3 444s 16 51.2 -1.48466 33.2 444s 17 55.8 0.00629 37.5 444s 18 59.2 2.32164 40.1 444s 19 57.4 1.73138 39.0 444s 20 60.3 0.54175 41.9 444s 21 64.1 3.05716 46.1 444s 22 71.6 3.96979 52.4 444s > predict 444s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 444s 1 NA NA NA NA 444s 2 42.3 0.464 39.9 44.8 444s 3 46.0 0.541 43.5 48.5 444s 4 50.7 0.337 48.4 53.1 444s 5 51.1 0.385 48.7 53.5 444s 6 52.6 0.386 50.3 55.0 444s 7 54.3 0.349 52.0 56.7 444s 8 54.9 0.320 52.6 57.2 444s 9 56.2 0.355 53.9 58.5 444s 10 58.4 0.370 56.0 60.7 444s 11 55.2 0.682 52.6 57.8 444s 12 51.5 0.563 48.9 54.0 444s 13 46.3 0.719 43.6 49.0 444s 14 45.9 0.597 43.4 48.5 444s 15 48.7 0.370 46.4 51.1 444s 16 51.2 0.327 48.9 53.6 444s 17 55.8 0.391 53.5 58.2 444s 18 59.2 0.316 56.8 61.5 444s 19 57.4 0.389 55.1 59.8 444s 20 60.3 0.459 57.9 62.8 444s 21 64.1 0.438 61.7 66.4 444s 22 71.6 0.674 69.0 74.3 444s Investment.pred Investment.se.fit Investment.lwr Investment.upr 444s 1 NA NA NA NA 444s 2 1.99510 0.792 -1.787 5.777 444s 3 1.74850 0.585 -1.861 5.358 444s 4 4.75942 0.510 1.200 8.319 444s 5 4.86672 0.423 1.359 8.375 444s 6 5.02874 0.400 1.533 8.525 444s 7 4.57056 0.391 1.079 8.062 444s 8 3.09893 0.345 -0.371 6.568 444s 9 2.41471 0.511 -1.145 5.974 444s 10 2.80476 0.560 -0.788 6.397 444s 11 2.34425 0.839 -1.482 6.170 444s 12 -2.40558 0.673 -6.083 1.272 444s 13 -4.85959 0.862 -8.708 -1.011 444s 14 -7.03164 0.874 -10.893 -3.171 444s 15 -2.87827 0.433 -6.392 0.635 444s 16 -1.48466 0.375 -4.968 1.999 444s 17 0.00629 0.491 -3.541 3.554 444s 18 2.32164 0.294 -1.127 5.771 444s 19 1.73138 0.446 -1.789 5.252 444s 20 0.54175 0.547 -3.042 4.125 444s 21 3.05716 0.454 -0.468 6.582 444s 22 3.96979 0.642 0.317 7.623 444s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 444s 1 NA NA NA NA 444s 2 26.7 0.314 24.9 28.5 444s 3 28.8 0.318 27.0 30.6 444s 4 32.6 0.325 30.8 34.4 444s 5 34.0 0.235 32.2 35.7 444s 6 35.7 0.241 33.9 37.4 444s 7 37.7 0.238 36.0 39.5 444s 8 38.6 0.237 36.8 40.4 444s 9 38.9 0.227 37.1 40.6 444s 10 40.0 0.219 38.3 41.8 444s 11 38.3 0.317 36.5 40.1 444s 12 34.3 0.344 32.4 36.1 444s 13 29.4 0.419 27.5 31.3 444s 14 28.2 0.334 26.4 30.0 444s 15 30.3 0.320 28.5 32.1 444s 16 33.2 0.268 31.4 35.0 444s 17 37.5 0.269 35.7 39.3 444s 18 40.1 0.212 38.3 41.8 444s 19 39.0 0.331 37.2 40.8 444s 20 41.9 0.287 40.1 43.7 444s 21 46.1 0.301 44.3 47.9 444s 22 52.4 0.471 50.5 54.4 444s > model.frame 444s [1] TRUE 444s > model.matrix 444s [1] TRUE 444s > nobs 444s [1] 63 444s > linearHypothesis 444s Linear hypothesis test (Theil's F test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 52 444s 2 51 1 0.29 0.59 444s Linear hypothesis test (F statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 52 444s 2 51 1 0.39 0.54 444s Linear hypothesis test (Chi^2 statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df Chisq Pr(>Chisq) 444s 1 52 444s 2 51 1 0.39 0.53 444s Linear hypothesis test (Theil's F test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 53 444s 2 51 2 0.3 0.74 444s Linear hypothesis test (F statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 53 444s 2 51 2 0.4 0.67 444s Linear hypothesis test (Chi^2 statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df Chisq Pr(>Chisq) 444s 1 53 444s 2 51 2 0.8 0.67 444s > logLik 444s 'log Lik.' -76.1 (df=18) 444s 'log Lik.' -89.1 (df=18) 444s > 444s > # I3SLS 444s > summary 444s 444s systemfit results 444s method: iterated 3SLS 444s 444s convergence achieved after 20 iterations 444s 444s N DF SSR detRCov OLS-R2 McElroy-R2 444s system 63 51 128 0.509 0.936 0.996 444s 444s N DF SSR MSE RMSE R2 Adj R2 444s Consumption 21 17 19.2 1.130 1.063 0.980 0.976 444s Investment 21 17 95.7 5.627 2.372 0.621 0.554 444s PrivateWages 21 17 12.7 0.748 0.865 0.984 0.981 444s 444s The covariance matrix of the residuals used for estimation 444s Consumption Investment PrivateWages 444s Consumption 0.915 0.642 -0.435 444s Investment 0.642 4.555 0.734 444s PrivateWages -0.435 0.734 0.606 444s 444s The covariance matrix of the residuals 444s Consumption Investment PrivateWages 444s Consumption 0.915 0.642 -0.435 444s Investment 0.642 4.555 0.734 444s PrivateWages -0.435 0.734 0.606 444s 444s The correlations of the residuals 444s Consumption Investment PrivateWages 444s Consumption 1.000 0.314 -0.584 444s Investment 0.314 1.000 0.442 444s PrivateWages -0.584 0.442 1.000 444s 444s 444s 3SLS estimates for 'Consumption' (equation 1) 444s Model Formula: consump ~ corpProf + corpProfLag + wages 444s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 444s gnpLag 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 16.5590 1.2244 13.52 1.6e-10 *** 444s corpProf 0.1645 0.0962 1.71 0.105 444s corpProfLag 0.1766 0.0901 1.96 0.067 . 444s wages 0.7658 0.0348 22.03 6.1e-14 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 1.063 on 17 degrees of freedom 444s Number of observations: 21 Degrees of Freedom: 17 444s SSR: 19.213 MSE: 1.13 Root MSE: 1.063 444s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.976 444s 444s 444s 3SLS estimates for 'Investment' (equation 2) 444s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 444s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 444s gnpLag 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 42.8959 10.5937 4.05 0.00083 *** 444s corpProf -0.3565 0.2602 -1.37 0.18838 444s corpProfLag 1.0113 0.2488 4.07 0.00081 *** 444s capitalLag -0.2602 0.0509 -5.12 8.6e-05 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 2.372 on 17 degrees of freedom 444s Number of observations: 21 Degrees of Freedom: 17 444s SSR: 95.661 MSE: 5.627 Root MSE: 2.372 444s Multiple R-Squared: 0.621 Adjusted R-Squared: 0.554 444s 444s 444s 3SLS estimates for 'PrivateWages' (equation 3) 444s Model Formula: privWage ~ gnp + gnpLag + trend 444s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 444s gnpLag 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 2.6247 1.1956 2.20 0.042 * 444s gnp 0.3748 0.0311 12.05 9.4e-10 *** 444s gnpLag 0.1937 0.0324 5.98 1.5e-05 *** 444s trend 0.1679 0.0289 5.80 2.1e-05 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 0.865 on 17 degrees of freedom 444s Number of observations: 21 Degrees of Freedom: 17 444s SSR: 12.719 MSE: 0.748 Root MSE: 0.865 444s Multiple R-Squared: 0.984 Adjusted R-Squared: 0.981 444s 444s > residuals 444s Consumption Investment PrivateWages 444s 1 NA NA NA 444s 2 -0.537 -3.95419 -1.2303 444s 3 -1.187 0.00151 0.5797 444s 4 -1.705 -0.22015 1.6794 444s 5 -0.734 -2.22753 -0.0260 444s 6 -0.251 -0.10866 -0.1362 444s 7 0.600 0.83218 -0.1837 444s 8 1.142 1.46624 -0.5825 444s 9 0.921 1.62030 0.4347 444s 10 -0.745 3.40013 1.4104 444s 11 -0.197 -2.15443 -0.4679 444s 12 -0.385 -1.62274 0.0106 444s 13 -0.390 -2.62869 -0.7363 444s 14 0.749 2.80517 0.0581 444s 15 0.112 -0.27710 0.1113 444s 16 0.170 0.13598 -0.1089 444s 17 1.925 2.76200 -0.6976 444s 18 -0.341 -0.53919 0.8651 444s 19 0.219 -4.32845 -1.0116 444s 20 1.383 1.71889 -0.2087 444s 21 1.028 1.06406 -0.9656 444s 22 -1.777 2.25466 1.2061 444s > fitted 444s Consumption Investment PrivateWages 444s 1 NA NA NA 444s 2 42.4 3.754 26.7 444s 3 46.2 1.898 28.7 444s 4 50.9 5.420 32.4 444s 5 51.3 5.228 33.9 444s 6 52.9 5.209 35.5 444s 7 54.5 4.768 37.6 444s 8 55.1 2.734 38.5 444s 9 56.4 1.380 38.8 444s 10 58.5 1.700 39.9 444s 11 55.2 3.154 38.4 444s 12 51.3 -1.777 34.5 444s 13 46.0 -3.571 29.7 444s 14 45.8 -7.905 28.4 444s 15 48.6 -2.723 30.5 444s 16 51.1 -1.436 33.3 444s 17 55.8 -0.662 37.5 444s 18 59.0 2.539 40.1 444s 19 57.3 2.428 39.2 444s 20 60.2 -0.419 41.8 444s 21 64.0 2.236 46.0 444s 22 71.5 2.645 52.1 444s > predict 444s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 444s 1 NA NA NA NA 444s 2 42.4 0.434 41.6 43.3 444s 3 46.2 0.491 45.2 47.2 444s 4 50.9 0.309 50.3 51.5 444s 5 51.3 0.351 50.6 52.0 444s 6 52.9 0.352 52.1 53.6 444s 7 54.5 0.320 53.9 55.1 444s 8 55.1 0.293 54.5 55.6 444s 9 56.4 0.324 55.7 57.0 444s 10 58.5 0.340 57.9 59.2 444s 11 55.2 0.613 54.0 56.4 444s 12 51.3 0.506 50.3 52.3 444s 13 46.0 0.649 44.7 47.3 444s 14 45.8 0.546 44.7 46.8 444s 15 48.6 0.341 47.9 49.3 444s 16 51.1 0.301 50.5 51.7 444s 17 55.8 0.357 55.1 56.5 444s 18 59.0 0.293 58.5 59.6 444s 19 57.3 0.353 56.6 58.0 444s 20 60.2 0.421 59.4 61.1 444s 21 64.0 0.409 63.2 64.8 444s 22 71.5 0.630 70.2 72.7 444s Investment.pred Investment.se.fit Investment.lwr Investment.upr 444s 1 NA NA NA NA 444s 2 3.754 1.263 1.218 6.2906 444s 3 1.898 1.022 -0.153 3.9503 444s 4 5.420 0.853 3.709 7.1317 444s 5 5.228 0.727 3.767 6.6877 444s 6 5.209 0.703 3.797 6.6200 444s 7 4.768 0.688 3.387 6.1487 444s 8 2.734 0.615 1.499 3.9683 444s 9 1.380 0.852 -0.330 3.0893 444s 10 1.700 0.938 -0.184 3.5836 444s 11 3.154 1.437 0.269 6.0398 444s 12 -1.777 1.173 -4.133 0.5780 444s 13 -3.571 1.494 -6.570 -0.5725 444s 14 -7.905 1.479 -10.875 -4.9350 444s 15 -2.723 0.778 -4.285 -1.1613 444s 16 -1.436 0.672 -2.784 -0.0875 444s 17 -0.662 0.832 -2.333 1.0088 444s 18 2.539 0.522 1.491 3.5875 444s 19 2.428 0.753 0.918 3.9392 444s 20 -0.419 0.907 -2.240 1.4019 444s 21 2.236 0.775 0.679 3.7928 444s 22 2.645 1.076 0.486 4.8047 444s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 444s 1 NA NA NA NA 444s 2 26.7 0.340 26.0 27.4 444s 3 28.7 0.339 28.0 29.4 444s 4 32.4 0.340 31.7 33.1 444s 5 33.9 0.250 33.4 34.4 444s 6 35.5 0.258 35.0 36.1 444s 7 37.6 0.256 37.1 38.1 444s 8 38.5 0.252 38.0 39.0 444s 9 38.8 0.241 38.3 39.2 444s 10 39.9 0.239 39.4 40.4 444s 11 38.4 0.314 37.7 39.0 444s 12 34.5 0.342 33.8 35.2 444s 13 29.7 0.430 28.9 30.6 444s 14 28.4 0.361 27.7 29.2 444s 15 30.5 0.336 29.8 31.2 444s 16 33.3 0.281 32.7 33.9 444s 17 37.5 0.270 37.0 38.0 444s 18 40.1 0.231 39.7 40.6 444s 19 39.2 0.343 38.5 39.9 444s 20 41.8 0.294 41.2 42.4 444s 21 46.0 0.326 45.3 46.6 444s 22 52.1 0.501 51.1 53.1 444s > model.frame 444s [1] TRUE 444s > model.matrix 444s [1] TRUE 444s > nobs 444s [1] 63 444s > linearHypothesis 444s Linear hypothesis test (Theil's F test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 52 444s 2 51 1 0.59 0.45 444s Linear hypothesis test (F statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 52 444s 2 51 1 0.73 0.4 444s Linear hypothesis test (Chi^2 statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df Chisq Pr(>Chisq) 444s 1 52 444s 2 51 1 0.73 0.39 444s Linear hypothesis test (Theil's F test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 53 444s 2 51 2 0.72 0.49 444s Linear hypothesis test (F statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 53 444s 2 51 2 0.88 0.42 444s Linear hypothesis test (Chi^2 statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df Chisq Pr(>Chisq) 444s 1 53 444s 2 51 2 1.77 0.41 444s > logLik 444s 'log Lik.' -82.3 (df=18) 444s 'log Lik.' -99.1 (df=18) 444s > 444s > # OLS 444s > summary 444s 444s systemfit results 444s method: OLS 444s 444s N DF SSR detRCov OLS-R2 McElroy-R2 444s system 62 50 44.9 0.372 0.977 0.991 444s 444s N DF SSR MSE RMSE R2 Adj R2 444s Consumption 21 17 17.88 1.052 1.03 0.981 0.978 444s Investment 21 17 17.32 1.019 1.01 0.931 0.919 444s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 444s 444s The covariance matrix of the residuals 444s Consumption Investment PrivateWages 444s Consumption 1.0703 -0.0161 -0.463 444s Investment -0.0161 0.9435Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 444s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 444s 0.199 444s PrivateWages -0.4633 0.1993 0.609 444s 444s The correlations of the residuals 444s Consumption Investment PrivateWages 444s Consumption 1.0000 -0.0201 -0.575 444s Investment -0.0201 1.0000 0.264 444s PrivateWages -0.5747 0.2639 1.000 444s 444s 444s OLS estimates for 'Consumption' (equation 1) 444s Model Formula: consump ~ corpProf + corpProfLag + wages 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 16.2366 1.3141 12.36 6.4e-10 *** 444s corpProf 0.1929 0.0920 2.10 0.051 . 444s corpProfLag 0.0899 0.0914 0.98 0.339 444s wages 0.7962 0.0403 19.76 3.6e-13 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 1.026 on 17 degrees of freedom 444s Number of observations: 21 Degrees of Freedom: 17 444s SSR: 17.879 MSE: 1.052 Root MSE: 1.026 444s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.978 444s 444s 444s OLS estimates for 'Investment' (equation 2) 444s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 10.1258 5.2592 1.93 0.07108 . 444s corpProf 0.4796 0.0934 5.13 8.3e-05 *** 444s corpProfLag 0.3330 0.0971 3.43 0.00318 ** 444s capitalLag -0.1118 0.0257 -4.35 0.00044 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 1.009 on 17 degrees of freedom 444s Number of observations: 21 Degrees of Freedom: 17 444s SSR: 17.323 MSE: 1.019 Root MSE: 1.009 444s Multiple R-Squared: 0.931 Adjusted R-Squared: 0.919 444s 444s 444s OLS estimates for 'PrivateWages' (equation 3) 444s Model Formula: privWage ~ gnp + gnpLag + trend 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 1.3550 1.3093 1.03 0.3161 444s gnp 0.4417 0.0331 13.33 4.4e-10 *** 444s gnpLag 0.1466 0.0381 3.85 0.0014 ** 444s trend 0.1244 0.0336 3.70 0.0020 ** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 0.78 on 16 degrees of freedom 444s Number of observations: 20 Degrees of Freedom: 16 444s SSR: 9.739 MSE: 0.609 Root MSE: 0.78 444s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 444s 444s compare coef with single-equation OLS 444s [1] TRUE 444s > residuals 444s Consumption Investment PrivateWages 444s 1 NA NA NA 444s 2 -0.32389 -0.0668 -1.3389 444s 3 -1.25001 -0.0476 0.2462 444s 4 -1.56574 1.2467 1.1255 444s 5 -0.49350 -1.3512 -0.1959 444s 6 0.00761 0.4154 -0.5284 444s 7 0.86910 1.4923 NA 444s 8 1.33848 0.7889 -0.7909 444s 9 1.05498 -0.6317 0.2819 444s 10 -0.58856 1.0830 1.1384 444s 11 0.28231 0.2791 -0.1904 444s 12 -0.22965 0.0369 0.5813 444s 13 -0.32213 0.3659 0.1206 444s 14 0.32228 0.2237 0.4773 444s 15 -0.05801 -0.1728 0.3035 444s 16 -0.03466 0.0101 0.0284 444s 17 1.61650 0.9719 -0.8517 444s 18 -0.43597 0.0516 0.9908 444s 19 0.21005 -2.5656 -0.4597 444s 20 0.98920 -0.6866 -0.3819 444s 21 0.78508 -0.7807 -1.1062 444s 22 -2.17345 -0.6623 0.5501 444s > fitted 444s Consumption Investment PrivateWages 444s 1 NA NA NA 444s 2 42.2 -0.133 26.8 444s 3 46.3 1.948 29.1 444s 4 50.8 3.953 33.0 444s 5 51.1 4.351 34.1 444s 6 52.6 4.685 35.9 444s 7 54.2 4.108 NA 444s 8 54.9 3.411 38.7 444s 9 56.2 3.632 38.9 444s 10 58.4 4.017 40.2 444s 11 54.7 0.721 38.1 444s 12 51.1 -3.437 33.9 444s 13 45.9 -6.566 28.9 444s 14 46.2 -5.324 28.0 444s 15 48.8 -2.827 30.3 444s 16 51.3 -1.310 33.2 444s 17 56.1 1.128 37.7 444s 18 59.1 1.948 40.0 444s 19 57.3 0.666 38.7 444s 20 60.6 1.987 42.0 444s 21 64.2 4.081 46.1 444s 22 71.9 5.562 52.7 444s > predict 444s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 444s 1 NA NA NA NA 444s 2 42.2 0.466 40.0 44.5 444s 3 46.3 0.523 43.9 48.6 444s 4 50.8 0.344 48.6 52.9 444s 5 51.1 0.399 48.9 53.3 444s 6 52.6 0.401 50.4 54.8 444s 7 54.2 0.363 52.0 56.4 444s 8 54.9 0.330 52.7 57.0 444s 9 56.2 0.354 54.1 58.4 444s 10 58.4 0.373 56.2 60.6 444s 11 54.7 0.612 52.3 57.1 444s 12 51.1 0.489 48.8 53.4 444s 13 45.9 0.634 43.5 48.3 444s 14 46.2 0.608 43.8 48.6 444s 15 48.8 0.378 46.6 51.0 444s 16 51.3 0.336 49.2 53.5 444s 17 56.1 0.369 53.9 58.3 444s 18 59.1 0.324 57.0 61.3 444s 19 57.3 0.375 55.1 59.5 444s 20 60.6 0.437 58.4 62.9 444s 21 64.2 0.429 62.0 66.4 444s 22 71.9 0.672 69.4 74.3 444s Investment.pred Investment.se.fit Investment.lwr Investment.upr 444s 1 NA NA NA NA 444s 2 -0.133 0.584 -2.476 2.209 444s 3 1.948 0.480 -0.297 4.193 444s 4 3.953 0.432 1.748 6.159 444s 5 4.351 0.357 2.201 6.502 444s 6 4.685 0.336 2.548 6.821 444s 7 4.108 0.316 1.983 6.232 444s 8 3.411 0.281 1.306 5.516 444s 9 3.632 0.374 1.469 5.794 444s 10 4.017 0.430 1.813 6.221 444s 11 0.721 0.579 -1.616 3.058 444s 12 -3.437 0.488 -5.688 -1.185 444s 13 -6.566 0.592 -8.917 -4.215 444s 14 -5.324 0.667 -7.754 -2.893 444s 15 -2.827 0.359 -4.979 -0.675 444s 16 -1.310 0.308 -3.430 0.810 444s 17 1.128 0.334 -1.008 3.264 444s 18 1.948 0.234 -0.133 4.030 444s 19 0.666 0.300 -1.450 2.781 444s 20 1.987 0.353 -0.161 4.134 444s 21 4.081 0.319 1.954 6.207 444s 22 5.562 0.444 3.348 7.777 444s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 444s 1 NA NA NA NA 444s 2 26.8 0.366 25.1 28.6 444s 3 29.1 0.369 27.3 30.8 444s 4 33.0 0.372 31.2 34.7 444s 5 34.1 0.288 32.4 35.8 444s 6 35.9 0.287 34.3 37.6 444s 7 NA NA NA NA 444s 8 38.7 0.293 37.0 40.4 444s 9 38.9 0.279 37.3 40.6 444s 10 40.2 0.266 38.5 41.8 444s 11 38.1 0.365 36.4 39.8 444s 12 33.9 0.369 32.2 35.7 444s 13 28.9 0.438 27.1 30.7 444s 14 28.0 0.385 26.3 29.8 444s 15 30.3 0.379 28.6 32.0 444s 16 33.2 0.316 31.5 34.9 444s 17 37.7 0.310 36.0 39.3 444s 18 40.0 0.243 38.4 41.7 444s 19 38.7 0.363 36.9 40.4 444s 20 42.0 0.326 40.3 43.7 444s 21 46.1 0.341 44.4 47.8 444s 22 52.7 0.514 50.9 54.6 444s > model.frame 444s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 444s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 444s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 444s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 444s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 444s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 444s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 444s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 444s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 444s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 444s 10 57.8 21.7 21.1 45.3 5.1 211 41.3 67.0 64.5 444s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 444s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 444s 13 45.6 7.0 11.4 34.3 -6.2 213 29.0 44.3 53.4 444s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 444s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 444s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 444s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 444s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 444s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 444s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 444s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 444s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 444s trend 444s 1 -11 444s 2 -10 444s 3 -9 444s 4 -8 444s 5 -7 444s 6 -6 444s 7 -5 444s 8 -4 444s 9 -3 444s 10 -2 444s 11 -1 444s 12 0 444s 13 1 444s 14 2 444s 15 3 444s 16 4 444s 17 5 444s 18 6 444s 19 7 444s 20 8 444s 21 9 444s 22 10 444s > model.matrix 444s Consumption_(Intercept) Consumption_corpProf 444s Consumption_2 1 12.4 444s Consumption_3 1 16.9 444s Consumption_4 1 18.4 444s Consumption_5 1 19.4 444s Consumption_6 1 20.1 444s Consumption_7 1 19.6 444s Consumption_8 1 19.8 444s Consumption_9 1 21.1 444s Consumption_10 1 21.7 444s Consumption_11 1 15.6 444s Consumption_12 1 11.4 444s Consumption_13 1 7.0 444s Consumption_14 1 11.2 444s Consumption_15 1 12.3 444s Consumption_16 1 14.0 444s Consumption_17 1 17.6 444s Consumption_18 1 17.3 444s Consumption_19 1 15.3 444s Consumption_20 1 19.0 444s Consumption_21 1 21.1 444s Consumption_22 1 23.5 444s Investment_2 0 0.0 444s Investment_3 0 0.0 444s Investment_4 0 0.0 444s Investment_5 0 0.0 444s Investment_6 0 0.0 444s Investment_7 0 0.0 444s Investment_8 0 0.0 444s Investment_9 0 0.0 444s Investment_10 0 0.0 444s Investment_11 0 0.0 444s Investment_12 0 0.0 444s Investment_13 0 0.0 444s Investment_14 0 0.0 444s Investment_15 0 0.0 444s Investment_16 0 0.0 444s Investment_17 0 0.0 444s Investment_18 0 0.0 444s Investment_19 0 0.0 444s Investment_20 0 0.0 444s Investment_21 0 0.0 444s Investment_22 0 0.0 444s PrivateWages_2 0 0.0 444s PrivateWages_3 0 0.0 444s PrivateWages_4 0 0.0 444s PrivateWages_5 0 0.0 444s PrivateWages_6 0 0.0 444s PrivateWages_8 0 0.0 444s PrivateWages_9 0 0.0 444s PrivateWages_10 0 0.0 444s PrivateWages_11 0 0.0 444s PrivateWages_12 0 0.0 444s PrivateWages_13 0 0.0 444s PrivateWages_14 0 0.0 444s PrivateWages_15 0 0.0 444s PrivateWages_16 0 0.0 444s PrivateWages_17 0 0.0 444s PrivateWages_18 0 0.0 444s PrivateWages_19 0 0.0 444s PrivateWages_20 0 0.0 444s PrivateWages_21 0 0.0 444s PrivateWages_22 0 0.0 444s Consumption_corpProfLag Consumption_wages 444s Consumption_2 12.7 28.2 444s Consumption_3 12.4 32.2 444s Consumption_4 16.9 37.0 444s Consumption_5 18.4 37.0 444s Consumption_6 19.4 38.6 444s Consumption_7 20.1 40.7 444s Consumption_8 19.6 41.5 444s Consumption_9 19.8 42.9 444s Consumption_10 21.1 45.3 444s Consumption_11 21.7 42.1 444s Consumption_12 15.6 39.3 444s Consumption_13 11.4 34.3 444s Consumption_14 7.0 34.1 444s Consumption_15 11.2 36.6 444s Consumption_16 12.3 39.3 444s Consumption_17 14.0 44.2 444s Consumption_18 17.6 47.7 444s Consumption_19 17.3 45.9 444s Consumption_20 15.3 49.4 444s Consumption_21 19.0 53.0 444s Consumption_22 21.1 61.8 444s Investment_2 0.0 0.0 444s Investment_3 0.0 0.0 444s Investment_4 0.0 0.0 444s Investment_5 0.0 0.0 444s Investment_6 0.0 0.0 444s Investment_7 0.0 0.0 444s Investment_8 0.0 0.0 444s Investment_9 0.0 0.0 444s Investment_10 0.0 0.0 444s Investment_11 0.0 0.0 444s Investment_12 0.0 0.0 444s Investment_13 0.0 0.0 444s Investment_14 0.0 0.0 444s Investment_15 0.0 0.0 444s Investment_16 0.0 0.0 444s Investment_17 0.0 0.0 444s Investment_18 0.0 0.0 444s Investment_19 0.0 0.0 444s Investment_20 0.0 0.0 444s Investment_21 0.0 0.0 444s Investment_22 0.0 0.0 444s PrivateWages_2 0.0 0.0 444s PrivateWages_3 0.0 0.0 444s PrivateWages_4 0.0 0.0 444s PrivateWages_5 0.0 0.0 444s PrivateWages_6 0.0 0.0 444s PrivateWages_8 0.0 0.0 444s PrivateWages_9 0.0 0.0 444s PrivateWages_10 0.0 0.0 444s PrivateWages_11 0.0 0.0 444s PrivateWages_12 0.0 0.0 444s PrivateWages_13 0.0 0.0 444s PrivateWages_14 0.0 0.0 444s PrivateWages_15 0.0 0.0 444s PrivateWages_16 0.0 0.0 444s PrivateWages_17 0.0 0.0 444s PrivateWages_18 0.0 0.0 444s PrivateWages_19 0.0 0.0 444s PrivateWages_20 0.0 0.0 444s PrivateWages_21 0.0 0.0 444s PrivateWages_22 0.0 0.0 444s Investment_(Intercept) Investment_corpProf 444s Consumption_2 0 0.0 444s Consumption_3 0 0.0 444s Consumption_4 0 0.0 444s Consumption_5 0 0.0 444s Consumption_6 0 0.0 444s Consumption_7 0 0.0 444s Consumption_8 0 0.0 444s Consumption_9 0 0.0 444s Consumption_10 0 0.0 444s Consumption_11 0 0.0 444s Consumption_12 0 0.0 444s Consumption_13 0 0.0 444s Consumption_14 0 0.0 444s Consumption_15 0 0.0 444s Consumption_16 0 0.0 444s Consumption_17 0 0.0 444s Consumption_18 0 0.0 444s Consumption_19 0 0.0 444s Consumption_20 0 0.0 444s Consumption_21 0 0.0 444s Consumption_22 0 0.0 444s Investment_2 1 12.4 444s Investment_3 1 16.9 444s Investment_4 1 18.4 444s Investment_5 1 19.4 444s Investment_6 1 20.1 444s Investment_7 1 19.6 444s Investment_8 1 19.8 444s Investment_9 1 21.1 444s Investment_10 1 21.7 444s Investment_11 1 15.6 444s Investment_12 1 11.4 444s Investment_13 1 7.0 444s Investment_14 1 11.2 444s Investment_15 1 12.3 444s Investment_16 1 14.0 444s Investment_17 1 17.6 444s Investment_18 1 17.3 444s Investment_19 1 15.3 444s Investment_20 1 19.0 444s Investment_21 1 21.1 444s Investment_22 1 23.5 444s PrivateWages_2 0 0.0 444s PrivateWages_3 0 0.0 444s PrivateWages_4 0 0.0 444s PrivateWages_5 0 0.0 444s PrivateWages_6 0 0.0 444s PrivateWages_8 0 0.0 444s PrivateWages_9 0 0.0 444s PrivateWages_10 0 0.0 444s PrivateWages_11 0 0.0 444s PrivateWages_12 0 0.0 444s PrivateWages_13 0 0.0 444s PrivateWages_14 0 0.0 444s PrivateWages_15 0 0.0 444s PrivateWages_16 0 0.0 444s PrivateWages_17 0 0.0 444s PrivateWages_18 0 0.0 444s PrivateWages_19 0 0.0 444s PrivateWages_20 0 0.0 444s PrivateWages_21 0 0.0 444s PrivateWages_22 0 0.0 444s Investment_corpProfLag Investment_capitalLag 444s Consumption_2 0.0 0 444s Consumption_3 0.0 0 444s Consumption_4 0.0 0 444s Consumption_5 0.0 0 444s Consumption_6 0.0 0 444s Consumption_7 0.0 0 444s Consumption_8 0.0 0 444s Consumption_9 0.0 0 444s Consumption_10 0.0 0 444s Consumption_11 0.0 0 444s Consumption_12 0.0 0 444s Consumption_13 0.0 0 444s Consumption_14 0.0 0 444s Consumption_15 0.0 0 444s Consumption_16 0.0 0 444s Consumption_17 0.0 0 444s Consumption_18 0.0 0 444s Consumption_19 0.0 0 444s Consumption_20 0.0 0 444s Consumption_21 0.0 0 444s Consumption_22 0.0 0 444s Investment_2 12.7 183 444s Investment_3 12.4 183 444s Investment_4 16.9 184 444s Investment_5 18.4 190 444s Investment_6 19.4 193 444s Investment_7 20.1 198 444s Investment_8 19.6 203 444s Investment_9 19.8 208 444s Investment_10 21.1 211 444s Investment_11 21.7 216 444s Investment_12 15.6 217 444s Investment_13 11.4 213 444s Investment_14 7.0 207 444s Investment_15 11.2 202 444s Investment_16 12.3 199 444s Investment_17 14.0 198 444s Investment_18 17.6 200 444s Investment_19 17.3 202 444s Investment_20 15.3 200 444s Investment_21 19.0 201 444s Investment_22 21.1 204 444s PrivateWages_2 0.0 0 444s PrivateWages_3 0.0 0 444s PrivateWages_4 0.0 0 444s PrivateWages_5 0.0 0 444s PrivateWages_6 0.0 0 444s PrivateWages_8 0.0 0 444s PrivateWages_9 0.0 0 444s PrivateWages_10 0.0 0 444s PrivateWages_11 0.0 0 444s PrivateWages_12 0.0 0 444s PrivateWages_13 0.0 0 444s PrivateWages_14 0.0 0 444s PrivateWages_15 0.0 0 444s PrivateWages_16 0.0 0 444s PrivateWages_17 0.0 0 444s PrivateWages_18 0.0 0 444s PrivateWages_19 0.0 0 444s PrivateWages_20 0.0 0 444s PrivateWages_21 0.0 0 444s PrivateWages_22 0.0 0 444s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 444s Consumption_2 0 0.0 0.0 444s Consumption_3 0 0.0 0.0 444s Consumption_4 0 0.0 0.0 444s Consumption_5 0 0.0 0.0 444s Consumption_6 0 0.0 0.0 444s Consumption_7 0 0.0 0.0 444s Consumption_8 0 0.0 0.0 444s Consumption_9 0 0.0 0.0 444s Consumption_10 0 0.0 0.0 444s Consumption_11 0 0.0 0.0 444s Consumption_12 0 0.0 0.0 444s Consumption_13 0 0.0 0.0 444s Consumption_14 0 0.0 0.0 444s Consumption_15 0 0.0 0.0 444s Consumption_16 0 0.0 0.0 444s Consumption_17 0 0.0 0.0 444s Consumption_18 0 0.0 0.0 444s Consumption_19 0 0.0 0.0 444s Consumption_20 0 0.0 0.0 444s Consumption_21 0 0.0 0.0 444s Consumption_22 0 0.0 0.0 444s Investment_2 0 0.0 0.0 444s Investment_3 0 0.0 0.0 444s Investment_4 0 0.0 0.0 444s Investment_5 0 0.0 0.0 444s Investment_6 0 0.0 0.0 444s Investment_7 0 0.0 0.0 444s Investment_8 0 0.0 0.0 444s Investment_9 0 0.0 0.0 444s Investment_10 0 0.0 0.0 444s Investment_11 0 0.0 0.0 444s Investment_12 0 0.0 0.0 444s Investment_13 0 0.0 0.0 444s Investment_14 0 0.0 0.0 444s Investment_15 0 0.0 0.0 444s Investment_16 0 0.0 0.0 444s Investment_17 0 0.0 0.0 444s Investment_18 0 0.0 0.0 444s Investment_19 0 0.0 0.0 444s Investment_20 0 0.0 0.0 444s Investment_21 0 0.0 0.0 444s Investment_22 0 0.0 0.0 444s PrivateWages_2 1 45.6 44.9 444s PrivateWages_3 1 50.1 45.6 444s PrivateWages_4 1 57.2 50.1 444s PrivateWages_5 1 57.1 57.2 444s PrivateWages_6 1 61.0 57.1 444s PrivateWages_8 1 64.4 64.0 444s PrivateWages_9 1 64.5 64.4 444s PrivateWages_10 1 67.0 64.5 444s PrivateWages_11 1 61.2 67.0 444s PrivateWages_12 1 53.4 61.2 444s PrivateWages_13 1 44.3 53.4 444s PrivateWages_14 1 45.1 44.3 444s PrivateWages_15 1 49.7 45.1 444s PrivateWages_16 1 54.4 49.7 444s PrivateWages_17 1 62.7 54.4 444s PrivateWages_18 1 65.0 62.7 444s PrivateWages_19 1 60.9 65.0 444s PrivateWages_20 1 69.5 60.9 444s PrivateWages_21 1 75.7 69.5 444s PrivateWages_22 1 88.4 75.7 444s PrivateWages_trend 444s Consumption_2 0 444s Consumption_3 0 444s Consumption_4 0 444s Consumption_5 0 444s Consumption_6 0 444s Consumption_7 0 444s Consumption_8 0 444s Consumption_9 0 444s Consumption_10 0 444s Consumption_11 0 444s Consumption_12 0 444s Consumption_13 0 444s Consumption_14 0 444s Consumption_15 0 444s Consumption_16 0 444s Consumption_17 0 444s Consumption_18 0 444s Consumption_19 0 444s Consumption_20 0 444s Consumption_21 0 444s Consumption_22 0 444s Investment_2 0 444s Investment_3 0 444s Investment_4 0 444s Investment_5 0 444s Investment_6 0 444s Investment_7 0 444s Investment_8 0 444s Investment_9 0 444s Investment_10 0 444s Investment_11 0 444s Investment_12 0 444s Investment_13 0 444s Investment_14 0 444s Investment_15 0 444s Investment_16 0 444s Investment_17 0 444s Investment_18 0 444s Investment_19 0 444s Investment_20 0 444s Investment_21 0 444s Investment_22 0 444s PrivateWages_2 -10 444s PrivateWages_3 -9 444s PrivateWages_4 -8 444s PrivateWages_5 -7 444s PrivateWages_6 -6 444s PrivateWages_8 -4 444s PrivateWages_9 -3 444s PrivateWages_10 -2 444s PrivateWages_11 -1 444s PrivateWages_12 0 444s PrivateWages_13 1 444s PrivateWages_14 2 444s PrivateWages_15 3 444s PrivateWages_16 4 444s PrivateWages_17 5 444s PrivateWages_18 6 444s PrivateWages_19 7 444s PrivateWages_20 8 444s PrivateWages_21 9 444s PrivateWages_22 10 444s > nobs 444s [1] 62 444s > linearHypothesis 444s Linear hypothesis test (Theil's F test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 51 444s 2 50 1 0.8 0.37 444s Linear hypothesis test (F statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 51 444s 2 50 1 0.72 0.4 444s Linear hypothesis test (Chi^2 statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df Chisq Pr(>Chisq) 444s 1 51 444s 2 50 1 0.72 0.4 444s Linear hypothesis test (Theil's F test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 52 444s 2 50 2 0.42 0.66 444s Linear hypothesis test (F statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 52 444s 2 50 2 0.37 0.69 444s Linear hypothesis test (Chi^2 statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df Chisq Pr(>Chisq) 444s 1 52 444s 2 50 2 0.75 0.69 444s > logLik 444s 'log Lik.' -71.9 (df=13) 444s 'log Lik.' -77.1 (df=13) 444s compare log likelihood value with single-equation OLS 444s [1] "Mean relative difference: 0.000555" 444s > 444s > # 2SLS 444s > summary 444s 444s systemfit results 444s method: 2SLS 444s 444s N DF SSR detRCov OLS-R2 McElroy-R2 444s system 60 48 53.4 0.274 0.973 0.992 444s 444s N DF SSR MSE RMSE R2 Adj R2 444s Consumption 20 16 20.67 1.292 1.14 0.978 0.974 444s Investment 20 16 23.02 1.438 1.20 0.901 0.883 444s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 444s 444s The covariance matrix of the residuals 444s Consumption Investment PrivateWages 444s Consumption 1.034 0.309 -0.383 444s Investment 0.309 1.151 0.202 444s PrivateWages -0.383 0.202 0.487 444s 444s The correlations of the residuals 444s Consumption Investment PrivateWages 444s Consumption 1.000 0.284 -0.540 444s Investment 0.284 1.000 0.269 444s PrivateWages -0.540 0.269 1.000 444s 444s 444s 2SLS estimates for 'Consumption' (equation 1) 444s Model Formula: consump ~ corpProf + corpProfLag + wages 444s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 444s gnpLag 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 16.5093 1.3121 12.58 1.0e-09 *** 444s corpProf 0.0219 0.1159 0.19 0.85 444s corpProfLag 0.1931 0.1071 1.80 0.09 . 444s wages 0.8174 0.0408 20.05 9.2e-13 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 1.137 on 16 degrees of freedom 444s Number of observations: 20 Degrees of Freedom: 16 444s SSR: 20.671 MSE: 1.292 Root MSE: 1.137 444s Multiple R-Squared: 0.978 Adjusted R-Squared: 0.974 444s 444s 444s 2SLS estimates for 'Investment' (equation 2) 444s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 444s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 444s gnpLag 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 17.843 6.850 2.60 0.01915 * 444s corpProf 0.217 0.155 1.40 0.18106 444s corpProfLag 0.542 0.148 3.65 0.00216 ** 444s capitalLag -0.145 0.033 -4.41 0.00044 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 1.199 on 16 degrees of freedom 444s Number of observations: 20 Degrees of Freedom: 16 444s SSR: 23.016 MSE: 1.438 Root MSE: 1.199 444s Multiple R-Squared: 0.901 Adjusted R-Squared: 0.883 444s 444s 444s 2SLS estimates for 'PrivateWages' (equation 3) 444s Model Formula: privWage ~ gnp + gnpLag + trend 444s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 444s gnpLag 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 1.3431 1.1772 1.14 0.27070 444s gnp 0.4438 0.0358 12.39 1.3e-09 *** 444s gnpLag 0.1447 0.0389 3.72 0.00185 ** 444s trend 0.1238 0.0306 4.05 0.00093 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 0.78 on 16 degrees of freedom 444s Number of observations: 20 Degrees of Freedom: 16 444s SSR: 9.741 MSE: 0.609 Root MSE: 0.78 444s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 444s 444s > residuals 444s Consumption Investment PrivateWages 444s 1 NA NA NA 444s 2 -0.383 -1.0104 -1.3401 444s 3 -0.593 0.2478 0.2378 444s 4 -1.219 1.0621 1.1117 444s 5 -0.130 -1.4104 -0.1954 444s 6 0.354 0.4328 -0.5355 444s 7 NA NA NA 444s 8 1.551 1.0463 -0.7908 444s 9 1.440 0.0674 0.2831 444s 10 -0.286 1.7698 1.1353 444s 11 -0.453 -0.5912 -0.1765 444s 12 -0.994 -0.6318 0.6007 444s 13 -1.300 -0.6983 0.1443 444s 14 0.521 0.9724 0.4826 444s 15 -0.157 -0.1827 0.3016 444s 16 -0.014 0.1167 0.0261 444s 17 1.974 1.6266 -0.8614 444s 18 -0.576 -0.0525 0.9927 444s 19 -0.203 -3.0656 -0.4446 444s 20 1.342 0.1393 -0.3914 444s 21 1.039 -0.1305 -1.1115 444s 22 -1.912 0.2922 0.5312 444s > fitted 444s Consumption Investment PrivateWages 444s 1 NA NA NA 444s 2 42.3 0.810 26.8 444s 3 45.6 1.652 29.1 444s 4 50.4 4.138 33.0 444s 5 50.7 4.410 34.1 444s 6 52.2 4.667 35.9 444s 7 NA NA NA 444s 8 54.6 3.154 38.7 444s 9 55.9 2.933 38.9 444s 10 58.1 3.330 40.2 444s 11 55.5 1.591 38.1 444s 12 51.9 -2.768 33.9 444s 13 46.9 -5.502 28.9 444s 14 46.0 -6.072 28.0 444s 15 48.9 -2.817 30.3 444s 16 51.3 -1.417 33.2 444s 17 55.7 0.473 37.7 444s 18 59.3 2.053 40.0 444s 19 57.7 1.166 38.6 444s 20 60.3 1.161 42.0 444s 21 64.0 3.431 46.1 444s 22 71.6 4.608 52.8 444s > predict 444s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 444s 1 NA NA NA NA 444s 2 42.3 0.473 41.3 43.3 444s 3 45.6 0.573 44.4 46.8 444s 4 50.4 0.366 49.6 51.2 444s 5 50.7 0.423 49.8 51.6 444s 6 52.2 0.426 51.3 53.1 444s 7 NA NA NA NA 444s 8 54.6 0.347 53.9 55.4 444s 9 55.9 0.384 55.0 56.7 444s 10 58.1 0.395 57.2 58.9 444s 11 55.5 0.729 53.9 57.0 444s 12 51.9 0.594 50.6 53.2 444s 13 46.9 0.752 45.3 48.5 444s 14 46.0 0.616 44.7 47.3 444s 15 48.9 0.373 48.1 49.6 444s 16 51.3 0.331 50.6 52.0 444s 17 55.7 0.403 54.9 56.6 444s 18 59.3 0.326 58.6 60.0 444s 19 57.7 0.411 56.8 58.6 444s 20 60.3 0.472 59.3 61.3 444s 21 64.0 0.443 63.0 64.9 444s 22 71.6 0.683 70.2 73.1 444s Investment.pred Investment.se.fit Investment.lwr Investment.upr 444s 1 NA NA NA NA 444s 2 0.810 0.786 -0.8569 2.48 444s 3 1.652 0.541 0.5056 2.80 444s 4 4.138 0.511 3.0552 5.22 444s 5 4.410 0.421 3.5172 5.30 444s 6 4.667 0.395 3.8294 5.51 444s 7 NA NA NA NA 444s 8 3.154 0.327 2.4602 3.85 444s 9 2.933 0.489 1.8967 3.97 444s 10 3.330 0.537 2.1915 4.47 444s 11 1.591 0.786 -0.0748 3.26 444s 12 -2.768 0.615 -4.0716 -1.46 444s 13 -5.502 0.787 -7.1696 -3.83 444s 14 -6.072 0.842 -7.8568 -4.29 444s 15 -2.817 0.397 -3.6591 -1.98 444s 16 -1.417 0.343 -2.1436 -0.69 444s 17 0.473 0.457 -0.4954 1.44 444s 18 2.053 0.286 1.4471 2.66 444s 19 1.166 0.430 0.2549 2.08 444s 20 1.161 0.515 0.0698 2.25 444s 21 3.431 0.426 2.5282 4.33 444s 22 4.608 0.606 3.3223 5.89 444s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 444s 1 NA NA NA NA 444s 2 26.8 0.328 26.1 27.5 444s 3 29.1 0.340 28.3 29.8 444s 4 33.0 0.360 32.2 33.8 444s 5 34.1 0.258 33.5 34.6 444s 6 35.9 0.266 35.4 36.5 444s 7 NA NA NA NA 444s 8 38.7 0.262 38.1 39.2 444s 9 38.9 0.250 38.4 39.4 444s 10 40.2 0.240 39.7 40.7 444s 11 38.1 0.355 37.3 38.8 444s 12 33.9 0.382 33.1 34.7 444s 13 28.9 0.456 27.9 29.8 444s 14 28.0 0.348 27.3 28.8 444s 15 30.3 0.339 29.6 31.0 444s 16 33.2 0.284 32.6 33.8 444s 17 37.7 0.293 37.0 38.3 444s 18 40.0 0.218 39.5 40.5 444s 19 38.6 0.358 37.9 39.4 444s 20 42.0 0.307 41.3 42.6 444s 21 46.1 0.310 45.5 46.8 444s 22 52.8 0.496 51.7 53.8 444s > model.frame 444s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 444s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 444s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 444s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 444s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 444s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 444s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 444s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 444s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 444s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 444s 10 57.8 21.7 21.1 45.3 5.1 211 41.3 67.0 64.5 444s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 444s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 444s 13 45.6 7.0 11.4 34.3 -6.2 213 29.0 44.3 53.4 444s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 444s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 444s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 444s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 444s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 444s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 444s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 444s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 444s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 444s trend 444s 1 -11 444s 2 -10 444s 3 -9 444s 4 -8 444s 5 -7 444s 6 -6 444s 7 -5 444s 8 -4 444s 9 -3 444s 10 -2 444s 11 -1 444s 12 0 444s 13 1 444s 14 2 444s 15 3 444s 16 4 444s 17 5 444s 18 6 444s 19 7 444s 20 8 444s 21 9 444s 22 10 444s > model.matrix 444s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0323 >" 444s [2] "Attributes: < Component “dimnames”: Component 1: 55 string mismatches >" 444s [3] "Numeric: lengths (744, 720) differ" 444s > nobs 444s [1] 60 444s > linearHypothesis 444s Linear hypothesis test (Theil's F test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 49 444s 2 48 1 0.95 0.34 444s Linear hypothesis test (F statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 49 444s 2 48 1 1.05 0.31 444s Linear hypothesis test (Chi^2 statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df Chisq Pr(>Chisq) 444s 1 49 444s 2 48 1 1.05 0.3 444s Linear hypothesis test (Theil's F test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 50 444s 2 48 2 0.48 0.62 444s Linear hypothesis test (F statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 50 444s 2 48 2 0.53 0.59 444s Linear hypothesis test (Chi^2 statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df Chisq Pr(>Chisq) 444s 1 50 444s 2 48 2 1.06 0.59 444s > logLik 444s 'log Lik.' -72.2 (df=13) 444s 'log Lik.' -79.7 (df=13) 444s > 444s > # SUR 444s Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 444s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 444s > summary 444s 444s systemfit results 444s method: SUR 444s 444s N DF SSR detRCov OLS-R2 McElroy-R2 444s system 62 50 46.2 0.154 0.977 0.993 444s 444s N DF SSR MSE RMSE R2 Adj R2 444s Consumption 21 17 18.1 1.062 1.031 0.981 0.977 444s Investment 21 17 17.5 1.030 1.015 0.931 0.918 444s PrivateWages 20 16 10.6 0.663 0.814 0.987 0.984 444s 444s The covariance matrix of the residuals used for estimation 444s Consumption Investment PrivateWages 444s Consumption 0.8562 -0.0129 -0.371 444s Investment -0.0129 0.7548 0.159 444s PrivateWages -0.3706 0.1594 0.487 444s 444s The covariance matrix of the residuals 444s Consumption Investment PrivateWages 444s Consumption 0.8684 0.0078 -0.442 444s Investment 0.0078 0.7702 0.237 444s PrivateWages -0.4416 0.2366 0.531 444s 444s The correlations of the residuals 444s Consumption Investment PrivateWages 444s Consumption 1.00000 0.00562 -0.651 444s Investment 0.00562 1.00000 0.372 444s PrivateWages -0.65109 0.37198 1.000 444s 444s 444s SUR estimates for 'Consumption' (equation 1) 444s Model Formula: consump ~ corpProf + corpProfLag + wages 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 16.0647 1.1729 13.70 1.3e-10 *** 444s corpProf 0.2283 0.0775 2.94 0.0091 ** 444s corpProfLag 0.0723 0.0771 0.94 0.3615 444s wages 0.7930 0.0352 22.51 4.3e-14 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 1.031 on 17 degrees of freedom 444s Number of observations: 21 Degrees of Freedom: 17 444s SSR: 18.06 MSE: 1.062 Root MSE: 1.031 444s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.977 444s 444s 444s SUR estimates for 'Investment' (equation 2) 444s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 12.3516 4.5762 2.70 0.01520 * 444s corpProf 0.4461 0.0818 5.45 4.3e-05 *** 444s corpProfLag 0.3609 0.0849 4.25 0.00054 *** 444s capitalLag -0.1224 0.0223 -5.47 4.1e-05 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 1.015 on 17 degrees of freedom 444s Number of observations: 21 Degrees of Freedom: 17 444s SSR: 17.514 MSE: 1.03 Root MSE: 1.015 444s Multiple R-Squared: 0.931 Adjusted R-Squared: 0.918 444s 444s 444s SUR estimates for 'PrivateWages' (equation 3) 444s Model Formula: privWage ~ gnp + gnpLag + trend 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 1.5433 1.1371 1.36 0.19 444s gnp 0.4117 0.0279 14.77 9.6e-11 *** 444s gnpLag 0.1743 0.0317 5.50 4.8e-05 *** 444s trend 0.1550 0.0283 5.49 5.0e-05 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 0.814 on 16 degrees of freedom 444s Number of observations: 20 Degrees of Freedom: 16 444s SSR: 10.611 MSE: 0.663 Root MSE: 0.814 444s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.984 444s 444s > residuals 444s Consumption Investment PrivateWages 444s 1 NA NA NA 444s 2 -0.27628 -0.3003 -1.0910 444s 3 -1.35400 -0.1239 0.5795 444s 4 -1.62816 1.1154 1.5172 444s 5 -0.56494 -1.4358 -0.0341 444s 6 -0.06584 0.3581 -0.2772 444s 7 0.83245 1.4526 NA 444s 8 1.28855 0.8290 -0.6896 444s 9 0.96709 -0.5092 0.3445 444s 10 -0.66705 1.2210 1.2429 444s 11 0.41992 0.2497 -0.3602 444s 12 -0.05971 0.0470 0.3068 444s 13 -0.08649 0.3096 -0.2426 444s 14 0.33124 0.3652 0.3591 444s 15 -0.00604 -0.1652 0.2710 444s 16 -0.01478 0.0124 -0.0207 444s 17 1.55472 1.0339 -0.8117 444s 18 -0.41250 0.0255 0.8398 444s 19 0.29322 -2.6293 -0.8283 444s 20 0.91756 -0.5906 -0.4091 444s 21 0.71583 -0.7036 -1.2154 444s 22 -2.26223 -0.5283 0.6207 444s > fitted 444s Consumption Investment PrivateWages 444s 1 NA NA NA 444s 2 42.2 0.100 26.6 444s 3 46.4 2.024 28.7 444s 4 50.8 4.085 32.6 444s 5 51.2 4.436 33.9 444s 6 52.7 4.742 35.7 444s 7 54.3 4.147 NA 444s 8 54.9 3.371 38.6 444s 9 56.3 3.509 38.9 444s 10 58.5 3.879 40.1 444s 11 54.6 0.750 38.3 444s 12 51.0 -3.447 34.2 444s 13 45.7 -6.510 29.2 444s 14 46.2 -5.465 28.1 444s 15 48.7 -2.835 30.3 444s 16 51.3 -1.312 33.2 444s 17 56.1 1.066 37.6 444s 18 59.1 1.974 40.2 444s 19 57.2 0.729 39.0 444s 20 60.7 1.891 42.0 444s 21 64.3 4.004 46.2 444s 22 72.0 5.428 52.7 444s > predict 444s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 444s 1 NA NA NA NA 444s 2 42.2 0.414 41.3 43.0 444s 3 46.4 0.451 45.4 47.3 444s 4 50.8 0.296 50.2 51.4 444s 5 51.2 0.342 50.5 51.9 444s 6 52.7 0.342 52.0 53.4 444s 7 54.3 0.309 53.6 54.9 444s 8 54.9 0.282 54.3 55.5 444s 9 56.3 0.303 55.7 56.9 444s 10 58.5 0.321 57.8 59.1 444s 11 54.6 0.515 53.5 55.6 444s 12 51.0 0.418 50.1 51.8 444s 13 45.7 0.548 44.6 46.8 444s 14 46.2 0.528 45.1 47.2 444s 15 48.7 0.333 48.0 49.4 444s 16 51.3 0.296 50.7 51.9 444s 17 56.1 0.321 55.5 56.8 444s 18 59.1 0.287 58.5 59.7 444s 19 57.2 0.325 56.6 57.9 444s 20 60.7 0.383 59.9 61.5 444s 21 64.3 0.382 63.5 65.1 444s 22 72.0 0.599 70.8 73.2 444s Investment.pred Investment.se.fit Investment.lwr Investment.upr 444s 1 NA NA NA NA 444s 2 0.100 0.511 -0.926 1.127 444s 3 2.024 0.425 1.170 2.878 444s 4 4.085 0.378 3.325 4.845 444s 5 4.436 0.313 3.806 5.065 444s 6 4.742 0.296 4.147 5.336 444s 7 4.147 0.279 3.586 4.709 444s 8 3.371 0.250 2.868 3.874 444s 9 3.509 0.331 2.845 4.174 444s 10 3.879 0.380 3.116 4.642 444s 11 0.750 0.512 -0.279 1.779 444s 12 -3.447 0.433 -4.316 -2.578 444s 13 -6.510 0.527 -7.568 -5.451 444s 14 -5.465 0.587 -6.645 -4.285 444s 15 -2.835 0.320 -3.477 -2.193 444s 16 -1.312 0.274 -1.863 -0.761 444s 17 1.066 0.296 0.472 1.661 444s 18 1.974 0.208 1.558 2.391 444s 19 0.729 0.265 0.197 1.262 444s 20 1.891 0.311 1.266 2.515 444s 21 4.004 0.283 3.435 4.572 444s 22 5.428 0.393 4.640 6.217 444s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 444s 1 NA NA NA NA 444s 2 26.6 0.318 26.0 27.2 444s 3 28.7 0.317 28.1 29.4 444s 4 32.6 0.315 32.0 33.2 444s 5 33.9 0.243 33.4 34.4 444s 6 35.7 0.242 35.2 36.2 444s 7 NA NA NA NA 444s 8 38.6 0.247 38.1 39.1 444s 9 38.9 0.236 38.4 39.3 444s 10 40.1 0.227 39.6 40.5 444s 11 38.3 0.306 37.6 38.9 444s 12 34.2 0.312 33.6 34.8 444s 13 29.2 0.376 28.5 30.0 444s 14 28.1 0.337 27.5 28.8 444s 15 30.3 0.328 29.7 31.0 444s 16 33.2 0.274 32.7 33.8 444s 17 37.6 0.266 37.1 38.1 444s 18 40.2 0.213 39.7 40.6 444s 19 39.0 0.310 38.4 39.7 444s 20 42.0 0.282 41.4 42.6 444s 21 46.2 0.300 45.6 46.8 444s 22 52.7 0.451 51.8 53.6 444s > model.frame 444s [1] TRUE 444s > model.matrix 444s [1] TRUE 444s > nobs 444s [1] 62 444s > linearHypothesis 444s Linear hypothesis test (Theil's F test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 51 444s 2 50 1 1.39 0.24 444s Linear hypothesis test (F statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 51 444s 2 50 1 1.7 0.2 444s Linear hypothesis test (Chi^2 statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df Chisq Pr(>Chisq) 444s 1 51 444s 2 50 1 1.7 0.19 444s Linear hypothesis test (Theil's F test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 52 444s 2 50 2 0.72 0.49 444s Linear hypothesis test (F statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 52 444s 2 50 2 0.87 0.42 444s Linear hypothesis test (Chi^2 statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df Chisq Pr(>Chisq) 444s 1 52 444s 2 50 2 1.75 0.42 444s > logLik 444s 'log Lik.' -69.4 (df=18) 444s 'log Lik.' -78.2 (df=18) 444s > 444s > # 3SLS 444s > summary 444s 444s systemfit results 444s method: 3SLS 444s 444s N DF SSR detRCov OLS-R2 McElroy-R2 444s system 60 48 62.6 0.265 0.968 0.994 444s 444s N DF SSR MSE RMSE R2 Adj R2 444s Consumption 20 16 17.8 1.114 1.06 0.981 0.977 444s Investment 20 16 34.3 2.143 1.46 0.853 0.825 444s PrivateWages 20 16 10.5 0.656 0.81 0.987 0.984 444s 444s The covariance matrix of the residuals used for estimation 444s Consumption Investment PrivateWages 444s Consumption 1.034 0.309 -0.383 444s Investment 0.309 1.151 0.202 444s PrivateWages -0.383 0.202 0.487 444s 444s The covariance matrix of the residuals 444s Consumption Investment PrivateWages 444s Consumption 0.891 0.304 -0.391 444s Investment 0.304 1.715 0.388 444s PrivateWages -0.391 0.388 0.525 444s 444s The correlations of the residuals 444s Consumption Investment PrivateWages 444s Consumption 1.000 0.246 -0.571 444s Investment 0.246 1.000 0.409 444s PrivateWages -0.571 0.409 1.000 444s 444s 444s 3SLS estimates for 'Consumption' (equation 1) 444s Model Formula: consump ~ corpProf + corpProfLag + wages 444s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 444s gnpLag 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 16.3668 1.3024 12.57 1.1e-09 *** 444s corpProf 0.1186 0.1073 1.10 0.29 444s corpProfLag 0.1448 0.1008 1.44 0.17 444s wages 0.8006 0.0391 20.47 6.7e-13 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 1.056 on 16 degrees of freedom 444s Number of observations: 20 Degrees of Freedom: 16 444s SSR: 17.825 MSE: 1.114 Root MSE: 1.056 444s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.977 444s 444s 444s 3SLS estimates for 'Investment' (equation 2) 444s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 444s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 444s gnpLag 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 24.8872 6.2956 3.95 0.00114 ** 444s corpProf 0.0702 0.1458 0.48 0.63648 444s corpProfLag 0.6688 0.1402 4.77 0.00021 *** 444s capitalLag -0.1786 0.0303 -5.90 2.3e-05 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 1.464 on 16 degrees of freedom 444s Number of observations: 20 Degrees of Freedom: 16 444s SSR: 34.295 MSE: 2.143 Root MSE: 1.464 444s Multiple R-Squared: 0.853 Adjusted R-Squared: 0.825 444s 444s 444s 3SLS estimates for 'PrivateWages' (equation 3) 444s Model Formula: privWage ~ gnp + gnpLag + trend 444s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 444s gnpLag 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 1.6387 1.1457 1.43 0.17188 444s gnp 0.4062 0.0324 12.52 1.1e-09 *** 444s gnpLag 0.1784 0.0347 5.14 1.0e-04 *** 444s trend 0.1435 0.0292 4.91 0.00016 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 0.81 on 16 degrees of freedom 444s Number of observations: 20 Degrees of Freedom: 16 444s SSR: 10.497 MSE: 0.656 Root MSE: 0.81 444s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.984 444s 444s > residuals 444s Consumption Investment PrivateWages 444s 1 NA NA NA 444s 2 -0.3538 -1.795 -1.2388 444s 3 -0.9465 0.154 0.4649 444s 4 -1.4189 0.678 1.4344 444s 5 -0.3546 -1.666 -0.1354 444s 6 0.1366 0.251 -0.3452 444s 7 NA NA NA 444s 8 1.4213 1.150 -0.7445 444s 9 1.2173 0.476 0.3001 444s 10 -0.4636 2.200 1.2232 444s 11 -0.0650 -0.962 -0.4104 444s 12 -0.5422 -0.808 0.2495 444s 13 -0.7092 -1.098 -0.3057 444s 14 0.4898 1.542 0.3497 444s 15 -0.0502 -0.155 0.2949 444s 16 0.0272 0.154 0.0214 444s 17 1.8311 1.932 -0.7322 444s 18 -0.4567 -0.180 0.9090 444s 19 0.0650 -3.381 -0.7795 444s 20 1.2135 0.557 -0.2847 444s 21 0.9466 0.167 -1.0812 444s 22 -1.9877 0.784 0.8102 444s > fitted 444s Consumption Investment PrivateWages 444s 1 NA NA NA 444s 2 42.3 1.595 26.7 444s 3 45.9 1.746 28.8 444s 4 50.6 4.522 32.7 444s 5 51.0 4.666 34.0 444s 6 52.5 4.849 35.7 444s 7 NA NA NA 444s 8 54.8 3.050 38.6 444s 9 56.1 2.524 38.9 444s 10 58.3 2.900 40.1 444s 11 55.1 1.962 38.3 444s 12 51.4 -2.592 34.3 444s 13 46.3 -5.102 29.3 444s 14 46.0 -6.642 28.2 444s 15 48.8 -2.845 30.3 444s 16 51.3 -1.454 33.2 444s 17 55.9 0.168 37.5 444s 18 59.2 2.180 40.1 444s 19 57.4 1.481 39.0 444s 20 60.4 0.743 41.9 444s 21 64.1 3.133 46.1 444s 22 71.7 4.116 52.5 444s > predict 444s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 444s 1 NA NA NA NA 444s 2 42.3 0.468 39.8 44.7 444s 3 45.9 0.543 43.4 48.5 444s 4 50.6 0.352 48.3 53.0 444s 5 51.0 0.407 48.6 53.4 444s 6 52.5 0.411 50.1 54.9 444s 7 NA NA NA NA 444s 8 54.8 0.340 52.4 57.1 444s 9 56.1 0.372 53.7 58.5 444s 10 58.3 0.387 55.9 60.6 444s 11 55.1 0.687 52.4 57.7 444s 12 51.4 0.558 48.9 54.0 444s 13 46.3 0.713 43.6 49.0 444s 14 46.0 0.599 43.4 48.6 444s 15 48.8 0.368 46.4 51.1 444s 16 51.3 0.326 48.9 53.6 444s 17 55.9 0.388 53.5 58.3 444s 18 59.2 0.319 56.8 61.5 444s 19 57.4 0.391 55.0 59.8 444s 20 60.4 0.457 57.9 62.8 444s 21 64.1 0.437 61.6 66.5 444s 22 71.7 0.674 69.0 74.3 444s Investment.pred Investment.se.fit Investment.lwr Investment.upr 444s 1 NA NA NA NA 444s 2 1.595 0.731 -1.8742 5.065 444s 3 1.746 0.533 -1.5566 5.050 444s 4 4.522 0.484 1.2530 7.791 444s 5 4.666 0.406 1.4458 7.887 444s 6 4.849 0.386 1.6390 8.058 444s 7 NA NA NA NA 444s 8 3.050 0.325 -0.1296 6.229 444s 9 2.524 0.467 -0.7334 5.782 444s 10 2.900 0.515 -0.3900 6.190 444s 11 1.962 0.769 -1.5438 5.467 444s 12 -2.592 0.608 -5.9519 0.769 444s 13 -5.102 0.774 -8.6129 -1.592 444s 14 -6.642 0.807 -10.1867 -3.098 444s 15 -2.845 0.395 -6.0599 0.370 444s 16 -1.454 0.341 -4.6409 1.733 444s 17 0.168 0.442 -3.0739 3.410 444s 18 2.180 0.281 -0.9807 5.340 444s 19 1.481 0.414 -1.7440 4.706 444s 20 0.743 0.492 -2.5310 4.017 444s 21 3.133 0.414 -0.0924 6.358 444s 22 4.116 0.583 0.7756 7.457 444s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 444s 1 NA NA NA NA 444s 2 26.7 0.322 24.9 28.6 444s 3 28.8 0.328 27.0 30.7 444s 4 32.7 0.340 30.8 34.5 444s 5 34.0 0.250 32.2 35.8 444s 6 35.7 0.257 33.9 37.5 444s 7 NA NA NA NA 444s 8 38.6 0.254 36.8 40.4 444s 9 38.9 0.241 37.1 40.7 444s 10 40.1 0.235 38.3 41.9 444s 11 38.3 0.325 36.5 40.2 444s 12 34.3 0.349 32.4 36.1 444s 13 29.3 0.425 27.4 31.2 444s 14 28.2 0.340 26.3 30.0 444s 15 30.3 0.326 28.5 32.2 444s 16 33.2 0.272 31.4 35.0 444s 17 37.5 0.273 35.7 39.3 444s 18 40.1 0.214 38.3 41.9 444s 19 39.0 0.336 37.1 40.8 444s 20 41.9 0.290 40.1 43.7 444s 21 46.1 0.305 44.2 47.9 444s 22 52.5 0.479 50.5 54.5 444s > model.frame 444s [1] TRUE 444s > model.matrix 444s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0323 >" 444s [2] "Attributes: < Component “dimnames”: Component 1: 55 string mismatches >" 444s [3] "Numeric: lengths (744, 720) differ" 444s > nobs 444s [1] 60 444s > linearHypothesis 444s Linear hypothesis test (Theil's F test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 49 444s 2 48 1 0.22 0.64 444s Linear hypothesis test (F statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 49 444s 2 48 1 0.29 0.59 444s Linear hypothesis test (Chi^2 statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df Chisq Pr(>Chisq) 444s 1 49 444s 2 48 1 0.29 0.59 444s Linear hypothesis test (Theil's F test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 50 444s 2 48 2 0.29 0.75 444s Linear hypothesis test (F statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 50 444s 2 48 2 0.38 0.68 444s Linear hypothesis test (Chi^2 statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df Chisq Pr(>Chisq) 444s 1 50 444s 2 48 2 0.77 0.68 444s > logLik 444s 'log Lik.' -71.9 (df=18) 444s 'log Lik.' -82.9 (df=18) 444s > 444s > # I3SLS 444s > summary 444s 444s systemfit results 444s method: iterated 3SLS 444s 444s convergence achieved after 22 iterations 444s 444s N DF SSR detRCov OLS-R2 McElroy-R2 444s system 60 48 107 0.47 0.946 0.996 444s 444s N DF SSR MSE RMSE R2 Adj R2 444s Consumption 20 16 18.1 1.13 1.063 0.981 0.977 444s Investment 20 16 76.4 4.77 2.185 0.672 0.610 444s PrivateWages 20 16 12.3 0.77 0.877 0.984 0.982 444s 444s The covariance matrix of the residuals used for estimation 444s Consumption Investment PrivateWages 444s Consumption 0.905 0.509 -0.437 444s Investment 0.509 3.819 0.709 444s PrivateWages -0.437 0.709 0.616 444s 444s The covariance matrix of the residuals 444s Consumption Investment PrivateWages 444s Consumption 0.905 0.509 -0.437 444s Investment 0.509 3.819 0.709 444s PrivateWages -0.437 0.709 0.616 444s 444s The correlations of the residuals 444s Consumption Investment PrivateWages 444s Consumption 1.000 0.274 -0.585 444s Investment 0.274 1.000 0.462 444s PrivateWages -0.585 0.462 1.000 444s 444s 444s 3SLS estimates for 'Consumption' (equation 1) 444s Model Formula: consump ~ corpProf + corpProfLag + wages 444s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 444s gnpLag 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 16.4728 1.2187 13.52 3.6e-10 *** 444s corpProf 0.1642 0.0952 1.73 0.10 444s corpProfLag 0.1552 0.0903 1.72 0.11 444s wages 0.7756 0.0356 21.82 2.5e-13 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 1.063 on 16 degrees of freedom 444s Number of observations: 20 Degrees of Freedom: 16 444s SSR: 18.095 MSE: 1.131 Root MSE: 1.063 444s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.977 444s 444s 444s 3SLS estimates for 'Investment' (equation 2) 444s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 444s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 444s gnpLag 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 38.7938 9.7249 3.99 0.00106 ** 444s corpProf -0.2501 0.2337 -1.07 0.30036 444s corpProfLag 0.9129 0.2271 4.02 0.00099 *** 444s capitalLag -0.2409 0.0469 -5.14 9.9e-05 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 2.185 on 16 degrees of freedom 444s Number of observations: 20 Degrees of Freedom: 16 444s SSR: 76.371 MSE: 4.773 Root MSE: 2.185 444s Multiple R-Squared: 0.672 Adjusted R-Squared: 0.61 444s 444s 444s 3SLS estimates for 'PrivateWages' (equation 3) 444s Model Formula: privWage ~ gnp + gnpLag + trend 444s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 444s gnpLag 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 2.4620 1.2228 2.01 0.061 . 444s gnp 0.3776 0.0318 11.88 2.4e-09 *** 444s gnpLag 0.1937 0.0331 5.85 2.5e-05 *** 444s trend 0.1619 0.0300 5.40 5.9e-05 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 0.877 on 16 degrees of freedom 444s Number of observations: 20 Degrees of Freedom: 16 444s SSR: 12.318 MSE: 0.77 Root MSE: 0.877 444s Multiple R-Squared: 0.984 Adjusted R-Squared: 0.982 444s 444s > residuals 444s Consumption Investment PrivateWages 444s 1 NA NA NA 444s 2 -0.4522 -3.4485 -1.2596 444s 3 -1.1470 0.0027 0.5437 444s 4 -1.6147 0.0274 1.6290 444s 5 -0.6117 -2.0392 -0.0707 444s 6 -0.1229 0.0457 -0.1859 444s 7 NA NA NA 444s 8 1.2461 1.4658 -0.6304 444s 9 1.0158 1.4202 0.3924 444s 10 -0.6460 3.2062 1.3671 444s 11 -0.0554 -1.7386 -0.4891 444s 12 -0.3472 -1.3793 0.0179 444s 13 -0.3947 -2.2646 -0.6968 444s 14 0.6536 2.4092 0.1021 444s 15 0.0821 -0.2787 0.1482 444s 16 0.1381 0.1196 -0.0796 444s 17 1.8826 2.5548 -0.6862 444s 18 -0.3415 -0.4009 0.8755 444s 19 0.2296 -4.0454 -0.9839 444s 20 1.3178 1.4481 -0.1989 444s 21 1.0065 0.9087 -0.9681 444s 22 -1.8388 1.9868 1.1734 444s > fitted 444s Consumption Investment PrivateWages 444s 1 NA NA NA 444s 2 42.4 3.249 26.8 444s 3 46.1 1.897 28.8 444s 4 50.8 5.173 32.5 444s 5 51.2 5.039 34.0 444s 6 52.7 5.054 35.6 444s 7 NA NA NA 444s 8 55.0 2.734 38.5 444s 9 56.3 1.580 38.8 444s 10 58.4 1.894 39.9 444s 11 55.1 2.739 38.4 444s 12 51.2 -2.021 34.5 444s 13 46.0 -3.935 29.7 444s 14 45.8 -7.509 28.4 444s 15 48.6 -2.721 30.5 444s 16 51.2 -1.420 33.3 444s 17 55.8 -0.455 37.5 444s 18 59.0 2.401 40.1 444s 19 57.3 2.145 39.2 444s 20 60.3 -0.148 41.8 444s 21 64.0 2.391 46.0 444s 22 71.5 2.913 52.1 444s > predict 444s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 444s 1 NA NA NA NA 444s 2 42.4 0.437 41.5 43.2 444s 3 46.1 0.492 45.2 47.1 444s 4 50.8 0.321 50.2 51.5 444s 5 51.2 0.369 50.5 52.0 444s 6 52.7 0.372 52.0 53.5 444s 7 NA NA NA NA 444s 8 55.0 0.310 54.3 55.6 444s 9 56.3 0.338 55.6 57.0 444s 10 58.4 0.355 57.7 59.2 444s 11 55.1 0.618 53.8 56.3 444s 12 51.2 0.501 50.2 52.3 444s 13 46.0 0.642 44.7 47.3 444s 14 45.8 0.547 44.7 46.9 444s 15 48.6 0.340 47.9 49.3 444s 16 51.2 0.300 50.6 51.8 444s 17 55.8 0.354 55.1 56.5 444s 18 59.0 0.294 58.4 59.6 444s 19 57.3 0.354 56.6 58.0 444s 20 60.3 0.418 59.4 61.1 444s 21 64.0 0.407 63.2 64.8 444s 22 71.5 0.628 70.3 72.8 444s Investment.pred Investment.se.fit Investment.lwr Investment.upr 444s 1 NA NA NA NA 444s 2 3.249 1.160 0.91672 5.580 444s 3 1.897 0.934 0.02009 3.775 444s 4 5.173 0.803 3.55865 6.787 444s 5 5.039 0.693 3.64486 6.433 444s 6 5.054 0.674 3.69840 6.410 444s 7 NA NA NA NA 444s 8 2.734 0.584 1.56002 3.908 444s 9 1.580 0.783 0.00466 3.155 444s 10 1.894 0.868 0.14846 3.639 444s 11 2.739 1.321 0.08241 5.395 444s 12 -2.021 1.064 -4.16036 0.119 444s 13 -3.935 1.349 -6.64712 -1.224 444s 14 -7.509 1.360 -10.24349 -4.775 444s 15 -2.721 0.712 -4.15288 -1.290 444s 16 -1.420 0.614 -2.65412 -0.185 444s 17 -0.455 0.751 -1.96433 1.055 444s 18 2.401 0.498 1.39939 3.402 444s 19 2.145 0.698 0.74152 3.549 444s 20 -0.148 0.816 -1.78957 1.493 444s 21 2.391 0.713 0.95855 3.824 444s 22 2.913 0.984 0.93419 4.892 444s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 444s 1 NA NA NA NA 444s 2 26.8 0.347 26.1 27.5 444s 3 28.8 0.348 28.1 29.5 444s 4 32.5 0.354 31.8 33.2 444s 5 34.0 0.263 33.4 34.5 444s 6 35.6 0.274 35.0 36.1 444s 7 NA NA NA NA 444s 8 38.5 0.268 38.0 39.1 444s 9 38.8 0.256 38.3 39.3 444s 10 39.9 0.254 39.4 40.4 444s 11 38.4 0.323 37.7 39.0 444s 12 34.5 0.347 33.8 35.2 444s 13 29.7 0.435 28.8 30.6 444s 14 28.4 0.366 27.7 29.1 444s 15 30.5 0.341 29.8 31.1 444s 16 33.3 0.285 32.7 33.9 444s 17 37.5 0.275 36.9 38.0 444s 18 40.1 0.233 39.7 40.6 444s 19 39.2 0.346 38.5 39.9 444s 20 41.8 0.298 41.2 42.4 444s 21 46.0 0.329 45.3 46.6 444s 22 52.1 0.510 51.1 53.2 444s > model.frame 444s [1] TRUE 444s > model.matrix 444s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0323 >" 444s [2] "Attributes: < Component “dimnames”: Component 1: 55 string mismatches >" 444s [3] "Numeric: lengths (744, 720) differ" 444s > nobs 444s [1] 60 444s > linearHypothesis 444s Linear hypothesis test (Theil's F test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 49 444s 2 48 1 0.4 0.53 444s Linear hypothesis test (F statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 49 444s 2 48 1 0.5 0.49 444s Linear hypothesis test (Chi^2 statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df Chisq Pr(>Chisq) 444s 1 49 444s 2 48 1 0.5 0.48 444s Linear hypothesis test (Theil's F test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 50 444s 2 48 2 0.66 0.52 444s Linear hypothesis test (F statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 50 444s 2 48 2 0.83 0.44 444s Linear hypothesis test (Chi^2 statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df Chisq Pr(>Chisq) 444s 1 50 444s 2 48 2 1.66 0.44 444s > logLik 444s 'log Lik.' -77.6 (df=18) 444s 'log Lik.' -92.7 (df=18) 444s > 444s > # OLS 444s Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 444s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 444s > summary 444s 444s systemfit results 444s method: OLS 444s 444s N DF SSR detRCov OLS-R2 McElroy-R2 444s system 61 49 44.5 0.382 0.977 0.99 444s 444s N DF SSR MSE RMSE R2 Adj R2 444s Consumption 20 16 17.48 1.093 1.04 0.981 0.978 444s Investment 21 17 17.32 1.019 1.01 0.931 0.919 444s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 444s 444s The covariance matrix of the residuals 444s Consumption Investment PrivateWages 444s Consumption 1.124 0.034 -0.442 444s Investment 0.034 0.928 0.130 444s PrivateWages -0.442 0.130 0.563 444s 444s The correlations of the residuals 444s Consumption Investment PrivateWages 444s Consumption 1.0000 0.0266 -0.563 444s Investment 0.0266 1.0000 0.169 444s PrivateWages -0.5630 0.1689 1.000 444s 444s 444s OLS estimates for 'Consumption' (equation 1) 444s Model Formula: consump ~ corpProf + corpProfLag + wages 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 16.1357 1.3571 11.89 2.4e-09 *** 444s corpProf 0.1994 0.0949 2.10 0.052 . 444s corpProfLag 0.0969 0.0944 1.03 0.320 444s wages 0.7940 0.0415 19.16 1.9e-12 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 1.045 on 16 degrees of freedom 444s Number of observations: 20 Degrees of Freedom: 16 444s SSR: 17.481 MSE: 1.093 Root MSE: 1.045 444s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.978 444s 444s 444s OLS estimates for 'Investment' (equation 2) 444s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 10.1258 5.2164 1.94 0.06901 . 444s corpProf 0.4796 0.0927 5.17 7.6e-05 *** 444s corpProfLag 0.3330 0.0963 3.46 0.00299 ** 444s capitalLag -0.1118 0.0255 -4.38 0.00041 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 1.009 on 17 degrees of freedom 444s Number of observations: 21 Degrees of Freedom: 17 444s SSR: 17.323 MSE: 1.019 Root MSE: 1.009 444s Multiple R-Squared: 0.931 Adjusted R-Squared: 0.919 444s 444s 444s OLS estimates for 'PrivateWages' (equation 3) 444s Model Formula: privWage ~ gnp + gnpLag + trend 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 1.3550 1.2591 1.08 0.2978 444s gnp 0.4417 0.0319 13.86 2.5e-10 *** 444s gnpLag 0.1466 0.0366 4.01 0.0010 ** 444s trend 0.1244 0.0323 3.85 0.0014 ** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 0.78 on 16 degrees of freedom 444s Number of observations: 20 Degrees of Freedom: 16 444s SSR: 9.739 MSE: 0.609 Root MSE: 0.78 444s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 444s 444s compare coef with single-equation OLS 444s [1] TRUE 444s > residuals 444s Consumption Investment PrivateWages 444s 1 NA NA NA 444s 2 -0.3304 -0.0668 -1.3389 444s 3 -1.2748 -0.0476 0.2462 444s 4 -1.6213 1.2467 1.1255 444s 5 -0.5661 -1.3512 -0.1959 444s 6 -0.0730 0.4154 -0.5284 444s 7 0.7915 1.4923 NA 444s 8 1.2648 0.7889 -0.7909 444s 9 0.9746 -0.6317 0.2819 444s 10 NA 1.0830 1.1384 444s 11 0.2225 0.2791 -0.1904 444s 12 -0.2256 0.0369 0.5813 444s 13 -0.2711 0.3659 0.1206 444s 14 0.3765 0.2237 0.4773 444s 15 -0.0349 -0.1728 0.3035 444s 16 -0.0243 0.0101 0.0284 444s 17 1.6023 0.9719 -0.8517 444s 18 -0.4658 0.0516 0.9908 444s 19 0.1914 -2.5656 -0.4597 444s 20 0.9683 -0.6866 -0.3819 444s 21 0.7325 -0.7807 -1.1062 444s 22 -2.2370 -0.6623 0.5501 444s > fitted 444s Consumption Investment PrivateWages 444s 1 NA NA NA 444s 2 42.2 -0.133 26.8 444s 3 46.3 1.948 29.1 444s 4 50.8 3.953 33.0 444s 5 51.2 4.351 34.1 444s 6 52.7 4.685 35.9 444s 7 54.3 4.108 NA 444s 8 54.9 3.411 38.7 444s 9 56.3 3.632 38.9 444s 10 NA 4.017 40.2 444s 11 54.8 0.721 38.1 444s 12 51.1 -3.437 33.9 444s 13 45.9 -6.566 28.9 444s 14 46.1 -5.324 28.0 444s 15 48.7 -2.827 30.3 444s 16 51.3 -1.310 33.2 444s 17 56.1 1.128 37.7 444s 18 59.2 1.948 40.0 444s 19 57.3 0.666 38.7 444s 20 60.6 1.987 42.0 444s 21 64.3 4.081 46.1 444s 22 71.9 5.562 52.7 444s > predict 444s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 444s 1 NA NA NA NA 444s 2 42.2 0.478 39.9 44.5 444s 3 46.3 0.537 43.9 48.6 444s 4 50.8 0.364 48.6 53.0 444s 5 51.2 0.427 48.9 53.4 444s 6 52.7 0.433 50.4 54.9 444s 7 54.3 0.394 52.1 56.6 444s 8 54.9 0.360 52.7 57.2 444s 9 56.3 0.387 54.1 58.6 444s 10 NA NA NA NA 444s 11 54.8 0.635 52.3 57.2 444s 12 51.1 0.501 48.8 53.5 444s 13 45.9 0.656 43.4 48.4 444s 14 46.1 0.629 43.7 48.6 444s 15 48.7 0.389 46.5 51.0 444s 16 51.3 0.345 49.1 53.5 444s 17 56.1 0.379 53.9 58.3 444s 18 59.2 0.336 57.0 61.4 444s 19 57.3 0.385 55.1 59.5 444s 20 60.6 0.450 58.3 62.9 444s 21 64.3 0.448 62.0 66.6 444s 22 71.9 0.697 69.4 74.5 444s Investment.pred Investment.se.fit Investment.lwr Investment.upr 444s 1 NA NA NA NA 444s 2 -0.133 0.579 -2.472 2.206 444s 3 1.948 0.476 -0.295 4.190 444s 4 3.953 0.428 1.750 6.157 444s 5 4.351 0.354 2.202 6.501 444s 6 4.685 0.333 2.548 6.821 444s 7 4.108 0.314 1.983 6.232 444s 8 3.411 0.279 1.306 5.516 444s 9 3.632 0.371 1.470 5.793 444s 10 4.017 0.426 1.815 6.219 444s 11 0.721 0.574 -1.613 3.054 444s 12 -3.437 0.484 -5.686 -1.188 444s 13 -6.566 0.588 -8.913 -4.219 444s 14 -5.324 0.662 -7.750 -2.898 444s 15 -2.827 0.356 -4.978 -0.676 444s 16 -1.310 0.305 -3.429 0.809 444s 17 1.128 0.332 -1.007 3.263 444s 18 1.948 0.232 -0.133 4.030 444s 19 0.666 0.298 -1.449 2.781 444s 20 1.987 0.350 -0.160 4.133 444s 21 4.081 0.317 1.955 6.207 444s 22 5.562 0.440 3.349 7.775 444s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 444s 1 NA NA NA NA 444s 2 26.8 0.352 25.1 28.6 444s 3 29.1 0.355 27.3 30.8 444s 4 33.0 0.358 31.2 34.7 444s 5 34.1 0.277 32.4 35.8 444s 6 35.9 0.276 34.3 37.6 444s 7 NA NA NA NA 444s 8 38.7 0.282 37.0 40.4 444s 9 38.9 0.268 37.3 40.6 444s 10 40.2 0.255 38.5 41.8 444s 11 38.1 0.351 36.4 39.8 444s 12 33.9 0.355 32.2 35.6 444s 13 28.9 0.421 27.1 30.7 444s 14 28.0 0.370 26.3 29.8 444s 15 30.3 0.364 28.6 32.0 444s 16 33.2 0.304 31.5 34.9 444s 17 37.7 0.298 36.0 39.3 444s 18 40.0 0.233 38.4 41.6 444s 19 38.7 0.349 36.9 40.4 444s 20 42.0 0.314 40.3 43.7 444s 21 46.1 0.328 44.4 47.8 444s 22 52.7 0.494 50.9 54.6 444s > model.frame 444s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 444s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 444s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 444s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 444s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 444s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 444s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 444s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 444s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 444s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 444s 10 57.8 21.7 21.1 NA 5.1 211 41.3 67.0 64.5 444s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 444s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 444s 13 45.6 7.0 11.4 34.3 -6.2 213 29.0 44.3 53.4 444s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 444s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 444s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 444s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 444s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 444s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 444s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 444s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 444s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 444s trend 444s 1 -11 444s 2 -10 444s 3 -9 444s 4 -8 444s 5 -7 444s 6 -6 444s 7 -5 444s 8 -4 444s 9 -3 444s 10 -2 444s 11 -1 444s 12 0 444s 13 1 444s 14 2 444s 15 3 444s 16 4 444s 17 5 444s 18 6 444s 19 7 444s 20 8 444s 21 9 444s 22 10 444s > model.matrix 444s Consumption_(Intercept) Consumption_corpProf 444s Consumption_2 1 12.4 444s Consumption_3 1 16.9 444s Consumption_4 1 18.4 444s Consumption_5 1 19.4 444s Consumption_6 1 20.1 444s Consumption_7 1 19.6 444s Consumption_8 1 19.8 444s Consumption_9 1 21.1 444s Consumption_11 1 15.6 444s Consumption_12 1 11.4 444s Consumption_13 1 7.0 444s Consumption_14 1 11.2 444s Consumption_15 1 12.3 444s Consumption_16 1 14.0 444s Consumption_17 1 17.6 444s Consumption_18 1 17.3 444s Consumption_19 1 15.3 444s Consumption_20 1 19.0 444s Consumption_21 1 21.1 444s Consumption_22 1 23.5 444s Investment_2 0 0.0 444s Investment_3 0 0.0 444s Investment_4 0 0.0 444s Investment_5 0 0.0 444s Investment_6 0 0.0 444s Investment_7 0 0.0 444s Investment_8 0 0.0 444s Investment_9 0 0.0 444s Investment_10 0 0.0 444s Investment_11 0 0.0 444s Investment_12 0 0.0 444s Investment_13 0 0.0 444s Investment_14 0 0.0 444s Investment_15 0 0.0 444s Investment_16 0 0.0 444s Investment_17 0 0.0 444s Investment_18 0 0.0 444s Investment_19 0 0.0 444s Investment_20 0 0.0 444s Investment_21 0 0.0 444s Investment_22 0 0.0 444s PrivateWages_2 0 0.0 444s PrivateWages_3 0 0.0 444s PrivateWages_4 0 0.0 444s PrivateWages_5 0 0.0 444s PrivateWages_6 0 0.0 444s PrivateWages_8 0 0.0 444s PrivateWages_9 0 0.0 444s PrivateWages_10 0 0.0 444s PrivateWages_11 0 0.0 444s PrivateWages_12 0 0.0 444s PrivateWages_13 0 0.0 444s PrivateWages_14 0 0.0 444s PrivateWages_15 0 0.0 444s PrivateWages_16 0 0.0 444s PrivateWages_17 0 0.0 444s PrivateWages_18 0 0.0 444s PrivateWages_19 0 0.0 444s PrivateWages_20 0 0.0 444s PrivateWages_21 0 0.0 444s PrivateWages_22 0 0.0 444s Consumption_corpProfLag Consumption_wages 444s Consumption_2 12.7 28.2 444s Consumption_3 12.4 32.2 444s Consumption_4 16.9 37.0 444s Consumption_5 18.4 37.0 444s Consumption_6 19.4 38.6 444s Consumption_7 20.1 40.7 444s Consumption_8 19.6 41.5 444s Consumption_9 19.8 42.9 444s Consumption_11 21.7 42.1 444s Consumption_12 15.6 39.3 444s Consumption_13 11.4 34.3 444s Consumption_14 7.0 34.1 444s Consumption_15 11.2 36.6 444s Consumption_16 12.3 39.3 444s Consumption_17 14.0 44.2 444s Consumption_18 17.6 47.7 444s Consumption_19 17.3 45.9 444s Consumption_20 15.3 49.4 444s Consumption_21 19.0 53.0 444s Consumption_22 21.1 61.8 444s Investment_2 0.0 0.0 444s Investment_3 0.0 0.0 444s Investment_4 0.0 0.0 444s Investment_5 0.0 0.0 444s Investment_6 0.0 0.0 444s Investment_7 0.0 0.0 444s Investment_8 0.0 0.0 444s Investment_9 0.0 0.0 444s Investment_10 0.0 0.0 444s Investment_11 0.0 0.0 444s Investment_12 0.0 0.0 444s Investment_13 0.0 0.0 444s Investment_14 0.0 0.0 444s Investment_15 0.0 0.0 444s Investment_16 0.0 0.0 444s Investment_17 0.0 0.0 444s Investment_18 0.0 0.0 444s Investment_19 0.0 0.0 444s Investment_20 0.0 0.0 444s Investment_21 0.0 0.0 444s Investment_22 0.0 0.0 444s PrivateWages_2 0.0 0.0 444s PrivateWages_3 0.0 0.0 444s PrivateWages_4 0.0 0.0 444s PrivateWages_5 0.0 0.0 444s PrivateWages_6 0.0 0.0 444s PrivateWages_8 0.0 0.0 444s PrivateWages_9 0.0 0.0 444s PrivateWages_10 0.0 0.0 444s PrivateWages_11 0.0 0.0 444s PrivateWages_12 0.0 0.0 444s PrivateWages_13 0.0 0.0 444s PrivateWages_14 0.0 0.0 444s PrivateWages_15 0.0 0.0 444s PrivateWages_16 0.0 0.0 444s PrivateWages_17 0.0 0.0 444s PrivateWages_18 0.0 0.0 444s PrivateWages_19 0.0 0.0 444s PrivateWages_20 0.0 0.0 444s PrivateWages_21 0.0 0.0 444s PrivateWages_22 0.0 0.0 444s Investment_(Intercept) Investment_corpProf 444s Consumption_2 0 0.0 444s Consumption_3 0 0.0 444s Consumption_4 0 0.0 444s Consumption_5 0 0.0 444s Consumption_6 0 0.0 444s Consumption_7 0 0.0 444s Consumption_8 0 0.0 444s Consumption_9 0 0.0 444s Consumption_11 0 0.0 444s Consumption_12 0 0.0 444s Consumption_13 0 0.0 444s Consumption_14 0 0.0 444s Consumption_15 0 0.0 444s Consumption_16 0 0.0 444s Consumption_17 0 0.0 444s Consumption_18 0 0.0 444s Consumption_19 0 0.0 444s Consumption_20 0 0.0 444s Consumption_21 0 0.0 444s Consumption_22 0 0.0 444s Investment_2 1 12.4 444s Investment_3 1 16.9 444s Investment_4 1 18.4 444s Investment_5 1 19.4 444s Investment_6 1 20.1 444s Investment_7 1 19.6 444s Investment_8 1 19.8 444s Investment_9 1 21.1 444s Investment_10 1 21.7 444s Investment_11 1 15.6 444s Investment_12 1 11.4 444s Investment_13 1 7.0 444s Investment_14 1 11.2 444s Investment_15 1 12.3 444s Investment_16 1 14.0 444s Investment_17 1 17.6 444s Investment_18 1 17.3 444s Investment_19 1 15.3 444s Investment_20 1 19.0 444s Investment_21 1 21.1 444s Investment_22 1 23.5 444s PrivateWages_2 0 0.0 444s PrivateWages_3 0 0.0 444s PrivateWages_4 0 0.0 444s PrivateWages_5 0 0.0 444s PrivateWages_6 0 0.0 444s PrivateWages_8 0 0.0 444s PrivateWages_9 0 0.0 444s PrivateWages_10 0 0.0 444s PrivateWages_11 0 0.0 444s PrivateWages_12 0 0.0 444s PrivateWages_13 0 0.0 444s PrivateWages_14 0 0.0 444s PrivateWages_15 0 0.0 444s PrivateWages_16 0 0.0 444s PrivateWages_17 0 0.0 444s PrivateWages_18 0 0.0 444s PrivateWages_19 0 0.0 444s PrivateWages_20 0 0.0 444s PrivateWages_21 0 0.0 444s PrivateWages_22 0 0.0 444s Investment_corpProfLag Investment_capitalLag 444s Consumption_2 0.0 0 444s Consumption_3 0.0 0 444s Consumption_4 0.0 0 444s Consumption_5 0.0 0 444s Consumption_6 0.0 0 444s Consumption_7 0.0 0 444s Consumption_8 0.0 0 444s Consumption_9 0.0 0 444s Consumption_11 0.0 0 444s Consumption_12 0.0 0 444s Consumption_13 0.0 0 444s Consumption_14 0.0 0 444s Consumption_15 0.0 0 444s Consumption_16 0.0 0 444s Consumption_17 0.0 0 444s Consumption_18 0.0 0 444s Consumption_19 0.0 0 444s Consumption_20 0.0 0 444s Consumption_21 0.0 0 444s Consumption_22 0.0 0 444s Investment_2 12.7 183 444s Investment_3 12.4 183 444s Investment_4 16.9 184 444s Investment_5 18.4 190 444s Investment_6 19.4 193 444s Investment_7 20.1 198 444s Investment_8 19.6 203 444s Investment_9 19.8 208 444s Investment_10 21.1 211 444s Investment_11 21.7 216 444s Investment_12 15.6 217 444s Investment_13 11.4 213 444s Investment_14 7.0 207 444s Investment_15 11.2 202 444s Investment_16 12.3 199 444s Investment_17 14.0 198 444s Investment_18 17.6 200 444s Investment_19 17.3 202 444s Investment_20 15.3 200 444s Investment_21 19.0 201 444s Investment_22 21.1 204 444s PrivateWages_2 0.0 0 444s PrivateWages_3 0.0 0 444s PrivateWages_4 0.0 0 444s PrivateWages_5 0.0 0 444s PrivateWages_6 0.0 0 444s PrivateWages_8 0.0 0 444s PrivateWages_9 0.0 0 444s PrivateWages_10 0.0 0 444s PrivateWages_11 0.0 0 444s PrivateWages_12 0.0 0 444s PrivateWages_13 0.0 0 444s PrivateWages_14 0.0 0 444s PrivateWages_15 0.0 0 444s PrivateWages_16 0.0 0 444s PrivateWages_17 0.0 0 444s PrivateWages_18 0.0 0 444s PrivateWages_19 0.0 0 444s PrivateWages_20 0.0 0 444s PrivateWages_21 0.0 0 444s PrivateWages_22 0.0 0 444s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 444s Consumption_2 0 0.0 0.0 444s Consumption_3 0 0.0 0.0 444s Consumption_4 0 0.0 0.0 444s Consumption_5 0 0.0 0.0 444s Consumption_6 0 0.0 0.0 444s Consumption_7 0 0.0 0.0 444s Consumption_8 0 0.0 0.0 444s Consumption_9 0 0.0 0.0 444s Consumption_11 0 0.0 0.0 444s Consumption_12 0 0.0 0.0 444s Consumption_13 0 0.0 0.0 444s Consumption_14 0 0.0 0.0 444s Consumption_15 0 0.0 0.0 444s Consumption_16 0 0.0 0.0 444s Consumption_17 0 0.0 0.0 444s Consumption_18 0 0.0 0.0 444s Consumption_19 0 0.0 0.0 444s Consumption_20 0 0.0 0.0 444s Consumption_21 0 0.0 0.0 444s Consumption_22 0 0.0 0.0 444s Investment_2 0 0.0 0.0 444s Investment_3 0 0.0 0.0 444s Investment_4 0 0.0 0.0 444s Investment_5 0 0.0 0.0 444s Investment_6 0 0.0 0.0 444s Investment_7 0 0.0 0.0 444s Investment_8 0 0.0 0.0 444s Investment_9 0 0.0 0.0 444s Investment_10 0 0.0 0.0 444s Investment_11 0 0.0 0.0 444s Investment_12 0 0.0 0.0 444s Investment_13 0 0.0 0.0 444s Investment_14 0 0.0 0.0 444s Investment_15 0 0.0 0.0 444s Investment_16 0 0.0 0.0 444s Investment_17 0 0.0 0.0 444s Investment_18 0 0.0 0.0 444s Investment_19 0 0.0 0.0 444s Investment_20 0 0.0 0.0 444s Investment_21 0 0.0 0.0 444s Investment_22 0 0.0 0.0 444s PrivateWages_2 1 45.6 44.9 444s PrivateWages_3 1 50.1 45.6 444s PrivateWages_4 1 57.2 50.1 444s PrivateWages_5 1 57.1 57.2 444s PrivateWages_6 1 61.0 57.1 444s PrivateWages_8 1 64.4 64.0 444s PrivateWages_9 1 64.5 64.4 444s PrivateWages_10 1 67.0 64.5 444s PrivateWages_11 1 61.2 67.0 444s PrivateWages_12 1 53.4 61.2 444s PrivateWages_13 1 44.3 53.4 444s PrivateWages_14 1 45.1 44.3 444s PrivateWages_15 1 49.7 45.1 444s PrivateWages_16 1 54.4 49.7 444s PrivateWages_17 1 62.7 54.4 444s PrivateWages_18 1 65.0 62.7 444s PrivateWages_19 1 60.9 65.0 444s PrivateWages_20 1 69.5 60.9 444s PrivateWages_21 1 75.7 69.5 444s PrivateWages_22 1 88.4 75.7 444s PrivateWages_trend 444s Consumption_2 0 444s Consumption_3 0 444s Consumption_4 0 444s Consumption_5 0 444s Consumption_6 0 444s Consumption_7 0 444s Consumption_8 0 444s Consumption_9 0 444s Consumption_11 0 444s Consumption_12 0 444s Consumption_13 0 444s Consumption_14 0 444s Consumption_15 0 444s Consumption_16 0 444s Consumption_17 0 444s Consumption_18 0 444s Consumption_19 0 444s Consumption_20 0 444s Consumption_21 0 444s Consumption_22 0 444s Investment_2 0 444s Investment_3 0 444s Investment_4 0 444s Investment_5 0 444s Investment_6 0 444s Investment_7 0 444s Investment_8 0 444s Investment_9 0 444s Investment_10 0 444s Investment_11 0 444s Investment_12 0 444s Investment_13 0 444s Investment_14 0 444s Investment_15 0 444s Investment_16 0 444s Investment_17 0 444s Investment_18 0 444s Investment_19 0 444s Investment_20 0 444s Investment_21 0 444s Investment_22 0 444s PrivateWages_2 -10 444s PrivateWages_3 -9 444s PrivateWages_4 -8 444s PrivateWages_5 -7 444s PrivateWages_6 -6 444s PrivateWages_8 -4 444s PrivateWages_9 -3 444s PrivateWages_10 -2 444s PrivateWages_11 -1 444s PrivateWages_12 0 444s PrivateWages_13 1 444s PrivateWages_14 2 444s PrivateWages_15 3 444s PrivateWages_16 4 444s PrivateWages_17 5 444s PrivateWages_18 6 444s PrivateWages_19 7 444s PrivateWages_20 8 444s PrivateWages_21 9 444s PrivateWages_22 10 444s > nobs 444s [1] 61 444s > linearHypothesis 444s Linear hypothesis test (Theil's F test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 50 444s 2 49 1 0.87 0.35 444s Linear hypothesis test (F statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 50 444s 2 49 1 0.8 0.38 444s Linear hypothesis test (Chi^2 statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df Chisq Pr(>Chisq) 444s 1 50 444s 2 49 1 0.8 0.37 444s Linear hypothesis test (Theil's F test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 51 444s 2 49 2 0.48 0.62 444s Linear hypothesis test (F statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 51 444s 2 49 2 0.43 0.65 444s Linear hypothesis test (Chi^2 statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df Chisq Pr(>Chisq) 444s 1 51 444s 2 49 2 0.87 0.65 444s > logLik 444s 'log Lik.' -71.7 (df=13) 444s 'log Lik.' -76.1 (df=13) 444s compare log likelihood value with single-equation OLS 444s [1] "Mean relative difference: 0.00159" 444s > 444s > # 2SLS 444s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 444s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 444s > summary 444s 444s systemfit results 444s method: 2SLS 444s 444s N DF SSR detRCov OLS-R2 McElroy-R2 444s system 59 47 53.2 0.251 0.973 0.991 444s 444s N DF SSR MSE RMSE R2 Adj R2 444s Consumption 19 15 20.49 1.366 1.17 0.978 0.973 444s Investment 20 16 23.02 1.438 1.20 0.901 0.883 444s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 444s 444s The covariance matrix of the residuals 444s Consumption Investment PrivateWages 444s Consumption 1.079 0.354 -0.383 444s Investment 0.354 1.047 0.107 444s PrivateWages -0.383 0.107 0.445 444s 444s The correlations of the residuals 444s Consumption Investment PrivateWages 444s Consumption 1.000 0.335 -0.556 444s Investment 0.335 1.000 0.149 444s PrivateWages -0.556 0.149 1.000 444s 444s 444s 2SLS estimates for 'Consumption' (equation 1) 444s Model Formula: consump ~ corpProf + corpProfLag + wages 444s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 444s gnpLag 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 16.4657 1.3505 12.19 3.5e-09 *** 444s corpProf 0.0243 0.1180 0.21 0.839 444s corpProfLag 0.1981 0.1087 1.82 0.088 . 444s wages 0.8159 0.0420 19.45 4.7e-12 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 1.169 on 15 degrees of freedom 444s Number of observations: 19 Degrees of Freedom: 15 444s SSR: 20.493 MSE: 1.366 Root MSE: 1.169 444s Multiple R-Squared: 0.978 Adjusted R-Squared: 0.973 444s 444s 444s 2SLS estimates for 'Investment' (equation 2) 444s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 444s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 444s gnpLag 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 17.8425 6.5319 2.73 0.01478 * 444s corpProf 0.2167 0.1478 1.47 0.16189 444s corpProfLag 0.5416 0.1415 3.83 0.00149 ** 444s capitalLag -0.1455 0.0314 -4.63 0.00028 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 1.199 on 16 degrees of freedom 444s Number of observations: 20 Degrees of Freedom: 16 444s SSR: 23.016 MSE: 1.438 Root MSE: 1.199 444s Multiple R-Squared: 0.901 Adjusted R-Squared: 0.883 444s 444s 444s 2SLS estimates for 'PrivateWages' (equation 3) 444s Model Formula: privWage ~ gnp + gnpLag + trend 444s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 444s gnpLag 444s 444s Estimate Std. Error t value Pr(>|t|) 444s (Intercept) 1.3431 1.1250 1.19 0.24995 444s gnp 0.4438 0.0342 12.97 6.6e-10 *** 444s gnpLag 0.1447 0.0371 3.90 0.00128 ** 444s trend 0.1238 0.0292 4.24 0.00063 *** 444s --- 444s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 444s 444s Residual standard error: 0.78 on 16 degrees of freedom 444s Number of observations: 20 Degrees of Freedom: 16 444s SSR: 9.741 MSE: 0.609 Root MSE: 0.78 444s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 444s 444s > residuals 444s Consumption Investment PrivateWages 444s 1 NA NA NA 444s 2 -0.39161 -1.0104 -1.3401 444s 3 -0.60524 0.2478 0.2378 444s 4 -1.24952 1.0621 1.1117 444s 5 -0.17101 -1.4104 -0.1954 444s 6 0.30841 0.4328 -0.5355 444s 7 NA NA NA 444s 8 1.50999 1.0463 -0.7908 444s 9 1.39649 0.0674 0.2831 444s 10 NA 1.7698 1.1353 444s 11 -0.49339 -0.5912 -0.1765 444s 12 -0.99824 -0.6318 0.6007 444s 13 -1.27965 -0.6983 0.1443 444s 14 0.55302 0.9724 0.4826 444s 15 -0.14553 -0.1827 0.3016 444s 16 -0.00773 0.1167 0.0261 444s 17 1.97001 1.6266 -0.8614 444s 18 -0.59152 -0.0525 0.9927 444s 19 -0.21481 -3.0656 -0.4446 444s 20 1.33575 0.1393 -0.3914 444s 21 1.01443 -0.1305 -1.1115 444s 22 -1.93986 0.2922 0.5312 444s > fitted 444s Consumption Investment PrivateWages 444s 1 NA NA NA 444s 2 42.3 0.810 26.8 444s 3 45.6 1.652 29.1 444s 4 50.4 4.138 33.0 444s 5 50.8 4.410 34.1 444s 6 52.3 4.667 35.9 444s 7 NA NA NA 444s 8 54.7 3.154 38.7 444s 9 55.9 2.933 38.9 444s 10 NA 3.330 40.2 444s 11 55.5 1.591 38.1 444s 12 51.9 -2.768 33.9 444s 13 46.9 -5.502 28.9 444s 14 45.9 -6.072 28.0 444s 15 48.8 -2.817 30.3 444s 16 51.3 -1.417 33.2 444s 17 55.7 0.473 37.7 444s 18 59.3 2.053 40.0 444s 19 57.7 1.166 38.6 444s 20 60.3 1.161 42.0 444s 21 64.0 3.431 46.1 444s 22 71.6 4.608 52.8 444s > predict 444s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 444s 1 NA NA NA NA 444s 2 42.3 0.483 41.3 43.3 444s 3 45.6 0.586 44.4 46.9 444s 4 50.4 0.390 49.6 51.3 444s 5 50.8 0.456 49.8 51.7 444s 6 52.3 0.463 51.3 53.3 444s 7 NA NA NA NA 444s 8 54.7 0.382 53.9 55.5 444s 9 55.9 0.422 55.0 56.8 444s 10 NA NA NA NA 444s 11 55.5 0.742 53.9 57.1 444s 12 51.9 0.600 50.6 53.2 444s 13 46.9 0.770 45.2 48.5 444s 14 45.9 0.635 44.6 47.3 444s 15 48.8 0.383 48.0 49.7 444s 16 51.3 0.339 50.6 52.0 444s 17 55.7 0.410 54.9 56.6 444s 18 59.3 0.336 58.6 60.0 444s 19 57.7 0.418 56.8 58.6 444s 20 60.3 0.481 59.2 61.3 444s 21 64.0 0.462 63.0 65.0 444s 22 71.6 0.706 70.1 73.1 444s Investment.pred Investment.se.fit Investment.lwr Investment.upr 444s 1 NA NA NA NA 444s 2 0.810 0.750 -0.77956 2.400 444s 3 1.652 0.516 0.55883 2.746 444s 4 4.138 0.487 3.10541 5.170 444s 5 4.410 0.402 3.55860 5.262 444s 6 4.667 0.377 3.86830 5.466 444s 7 NA NA NA NA 444s 8 3.154 0.312 2.49238 3.815 444s 9 2.933 0.466 1.94478 3.920 444s 10 3.330 0.512 2.24435 4.416 444s 11 1.591 0.749 0.00249 3.180 444s 12 -2.768 0.586 -4.01111 -1.525 444s 13 -5.502 0.750 -7.09222 -3.911 444s 14 -6.072 0.803 -7.77404 -4.371 444s 15 -2.817 0.379 -3.62002 -2.015 444s 16 -1.417 0.327 -2.10985 -0.723 444s 17 0.473 0.436 -0.45046 1.397 444s 18 2.053 0.272 1.47523 2.630 444s 19 1.166 0.410 0.29710 2.034 444s 20 1.161 0.491 0.12044 2.201 444s 21 3.431 0.406 2.57004 4.291 444s 22 4.608 0.578 3.38197 5.834 444s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 444s 1 NA NA NA NA 444s 2 26.8 0.313 26.2 27.5 444s 3 29.1 0.325 28.4 29.8 444s 4 33.0 0.344 32.3 33.7 444s 5 34.1 0.246 33.6 34.6 444s 6 35.9 0.254 35.4 36.5 444s 7 NA NA NA NA 444s 8 38.7 0.251 38.2 39.2 444s 9 38.9 0.239 38.4 39.4 444s 10 40.2 0.229 39.7 40.7 444s 11 38.1 0.339 37.4 38.8 444s 12 33.9 0.365 33.1 34.7 444s 13 28.9 0.436 27.9 29.8 444s 14 28.0 0.333 27.3 28.7 444s 15 30.3 0.324 29.6 31.0 444s 16 33.2 0.271 32.6 33.7 444s 17 37.7 0.280 37.1 38.3 444s 18 40.0 0.208 39.6 40.4 444s 19 38.6 0.342 37.9 39.4 444s 20 42.0 0.293 41.4 42.6 444s 21 46.1 0.296 45.5 46.7 444s 22 52.8 0.474 51.8 53.8 444s > model.frame 444s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 444s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 444s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 444s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 444s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 444s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 444s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 444s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 444s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 444s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 444s 10 57.8 21.7 21.1 NA 5.1 211 41.3 67.0 64.5 444s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 444s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 444s 13 45.6 7.0 11.4 34.3 -6.2 213 29.0 44.3 53.4 444s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 444s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 444s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 444s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 444s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 444s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 444s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 444s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 444s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 444s trend 444s 1 -11 444s 2 -10 444s 3 -9 444s 4 -8 444s 5 -7 444s 6 -6 444s 7 -5 444s 8 -4 444s 9 -3 444s 10 -2 444s 11 -1 444s 12 0 444s 13 1 444s 14 2 444s 15 3 444s 16 4 444s 17 5 444s 18 6 444s 19 7 444s 20 8 444s 21 9 444s 22 10 444s > model.matrix 444s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0328 >" 444s [2] "Attributes: < Component “dimnames”: Component 1: 54 string mismatches >" 444s [3] "Numeric: lengths (732, 708) differ" 444s > nobs 444s [1] 59 444s > linearHypothesis 444s Linear hypothesis test (Theil's F test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 48 444s 2 47 1 0.87 0.36 444s Linear hypothesis test (F statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 48 444s 2 47 1 0.98 0.33 444s Linear hypothesis test (Chi^2 statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df Chisq Pr(>Chisq) 444s 1 48 444s 2 47 1 0.98 0.32 444s Linear hypothesis test (Theil's F test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 49 444s 2 47 2 0.43 0.65 444s Linear hypothesis test (F statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 444s Res.Df Df F Pr(>F) 444s 1 49 444s 2 47 2 0.49 0.61 444s Linear hypothesis test (Chi^2 statistic of a Wald test) 444s 444s Hypothesis: 444s Consumption_corpProf + Investment_capitalLag = 0 444s Consumption_corpProfLag - PrivateWages_trend = 0 444s 444s Model 1: restricted model 444s Model 2: kleinModel 444s 445s Res.Df Df Chisq Pr(>Chisq) 445s 1 49 445s 2 47 2 0.98 0.61 445s > logLik 445s 'log Lik.' -71.5 (df=13) 445s 'log Lik.' -78.7 (df=13) 445s > 445s > # SUR 445s Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 445s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 445s > summary 445s 445s systemfit results 445s method: SUR 445s 445s N DF SSR detRCov OLS-R2 McElroy-R2 445s system 61 49 45.4 0.151 0.977 0.992 445s 445s N DF SSR MSE RMSE R2 Adj R2 445s Consumption 20 16 17.6 1.102 1.050 0.981 0.977 445s Investment 21 17 17.5 1.029 1.015 0.931 0.918 445s PrivateWages 20 16 10.3 0.643 0.802 0.987 0.985 445s 445s The covariance matrix of the residuals used for estimation 445s Consumption Investment PrivateWages 445s Consumption 0.8871 0.0268 -0.349 445s Investment 0.0268 0.7328 0.103 445s PrivateWages -0.3492 0.1029 0.444 445s 445s The covariance matrix of the residuals 445s Consumption Investment PrivateWages 445s Consumption 0.8852 0.0508 -0.406 445s Investment 0.0508 0.7313 0.161 445s PrivateWages -0.4063 0.1609 0.467 445s 445s The correlations of the residuals 445s Consumption Investment PrivateWages 445s Consumption 1.000 0.065 -0.635 445s Investment 0.065 1.000 0.262 445s PrivateWages -0.635 0.262 1.000 445s 445s 445s SUR estimates for 'Consumption' (equation 1) 445s Model Formula: consump ~ corpProf + corpProfLag + wages 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 16.0876 1.2010 13.39 4.1e-10 *** 445s corpProf 0.2173 0.0799 2.72 0.015 * 445s corpProfLag 0.0694 0.0793 0.88 0.394 445s wages 0.7975 0.0360 22.15 2.0e-13 *** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 1.05 on 16 degrees of freedom 445s Number of observations: 20 Degrees of Freedom: 16 445s SSR: 17.63 MSE: 1.102 Root MSE: 1.05 445s Multiple R-Squared: 0.981 Adjusted R-Squared: 0.977 445s 445s 445s SUR estimates for 'Investment' (equation 2) 445s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 12.3518 4.5615 2.71 0.01493 * 445s corpProf 0.4511 0.0814 5.54 3.6e-05 *** 445s corpProfLag 0.3570 0.0846 4.22 0.00058 *** 445s capitalLag -0.1225 0.0223 -5.49 4.0e-05 *** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 1.015 on 17 degrees of freedom 445s Number of observations: 21 Degrees of Freedom: 17 445s SSR: 17.5 MSE: 1.029 Root MSE: 1.015 445s Multiple R-Squared: 0.931 Adjusted R-Squared: 0.918 445s 445s 445s SUR estimates for 'PrivateWages' (equation 3) 445s Model Formula: privWage ~ gnp + gnpLag + trend 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 1.3964 1.0825 1.29 0.22 445s gnp 0.4177 0.0269 15.55 4.4e-11 *** 445s gnpLag 0.1709 0.0306 5.59 4.0e-05 *** 445s trend 0.1467 0.0272 5.40 5.9e-05 *** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 0.802 on 16 degrees of freedom 445s Number of observations: 20 Degrees of Freedom: 16 445s SSR: 10.284 MSE: 0.643 Root MSE: 0.802 445s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 445s 445s > residuals 445s Consumption Investment PrivateWages 445s 1 NA NA NA 445s 2 -0.2529 -0.2920 -1.15193 445s 3 -1.2998 -0.1392 0.50193 445s 4 -1.5662 1.1106 1.42026 445s 5 -0.4876 -1.4391 -0.09801 445s 6 0.0149 0.3556 -0.35678 445s 7 0.9002 1.4558 NA 445s 8 1.3535 0.8299 -0.74964 445s 9 1.0406 -0.5136 0.29355 445s 10 NA 1.2191 1.18544 445s 11 0.4417 0.2810 -0.36558 445s 12 -0.0892 0.0754 0.33733 445s 13 -0.1541 0.3429 -0.17490 445s 14 0.2984 0.3597 0.39941 445s 15 -0.0260 -0.1602 0.29441 445s 16 -0.0250 0.0130 -0.00177 445s 17 1.5671 1.0231 -0.81891 445s 18 -0.4089 0.0306 0.85516 445s 19 0.2819 -2.6153 -0.77184 445s 20 0.9257 -0.6030 -0.41040 445s 21 0.7415 -0.7118 -1.21679 445s 22 -2.2437 -0.5398 0.57166 445s > fitted 445s Consumption Investment PrivateWages 445s 1 NA NA NA 445s 2 42.2 0.092 26.7 445s 3 46.3 2.039 28.8 445s 4 50.8 4.089 32.7 445s 5 51.1 4.439 34.0 445s 6 52.6 4.744 35.8 445s 7 54.2 4.144 NA 445s 8 54.8 3.370 38.6 445s 9 56.3 3.514 38.9 445s 10 NA 3.881 40.1 445s 11 54.6 0.719 38.3 445s 12 51.0 -3.475 34.2 445s 13 45.8 -6.543 29.2 445s 14 46.2 -5.460 28.1 445s 15 48.7 -2.840 30.3 445s 16 51.3 -1.313 33.2 445s 17 56.1 1.077 37.6 445s 18 59.1 1.969 40.1 445s 19 57.2 0.715 39.0 445s 20 60.7 1.903 42.0 445s 21 64.3 4.012 46.2 445s 22 71.9 5.440 52.7 445s > predict 445s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 445s 1 NA NA NA NA 445s 2 42.2 0.422 41.3 43.0 445s 3 46.3 0.462 45.4 47.2 445s 4 50.8 0.309 50.1 51.4 445s 5 51.1 0.359 50.4 51.8 445s 6 52.6 0.362 51.9 53.3 445s 7 54.2 0.328 53.5 54.9 445s 8 54.8 0.300 54.2 55.4 445s 9 56.3 0.323 55.6 56.9 445s 10 NA NA NA NA 445s 11 54.6 0.531 53.5 55.6 445s 12 51.0 0.427 50.1 51.8 445s 13 45.8 0.564 44.6 46.9 445s 14 46.2 0.543 45.1 47.3 445s 15 48.7 0.341 48.0 49.4 445s 16 51.3 0.302 50.7 51.9 445s 17 56.1 0.328 55.5 56.8 445s 18 59.1 0.294 58.5 59.7 445s 19 57.2 0.332 56.6 57.9 445s 20 60.7 0.392 59.9 61.5 445s 21 64.3 0.394 63.5 65.0 445s 22 71.9 0.615 70.7 73.2 445s Investment.pred Investment.se.fit Investment.lwr Investment.upr 445s 1 NA NA NA NA 445s 2 0.092 0.508 -0.929 1.113 445s 3 2.039 0.421 1.193 2.885 445s 4 4.089 0.376 3.333 4.846 445s 5 4.439 0.311 3.813 5.065 445s 6 4.744 0.294 4.154 5.335 445s 7 4.144 0.277 3.587 4.701 445s 8 3.370 0.247 2.873 3.867 445s 9 3.514 0.328 2.855 4.172 445s 10 3.881 0.376 3.126 4.636 445s 11 0.719 0.508 -0.301 1.739 445s 12 -3.475 0.428 -4.336 -2.615 445s 13 -6.543 0.521 -7.590 -5.496 445s 14 -5.460 0.583 -6.632 -4.288 445s 15 -2.840 0.316 -3.474 -2.205 445s 16 -1.313 0.271 -1.857 -0.769 445s 17 1.077 0.293 0.488 1.666 445s 18 1.969 0.205 1.557 2.382 445s 19 0.715 0.263 0.187 1.244 445s 20 1.903 0.309 1.283 2.523 445s 21 4.012 0.280 3.449 4.574 445s 22 5.440 0.389 4.659 6.221 445s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 445s 1 NA NA NA NA 445s 2 26.7 0.306 26.0 27.3 445s 3 28.8 0.305 28.2 29.4 445s 4 32.7 0.302 32.1 33.3 445s 5 34.0 0.231 33.5 34.5 445s 6 35.8 0.230 35.3 36.2 445s 7 NA NA NA NA 445s 8 38.6 0.233 38.2 39.1 445s 9 38.9 0.222 38.5 39.4 445s 10 40.1 0.213 39.7 40.5 445s 11 38.3 0.292 37.7 38.9 445s 12 34.2 0.300 33.6 34.8 445s 13 29.2 0.361 28.4 29.9 445s 14 28.1 0.322 27.5 28.7 445s 15 30.3 0.314 29.7 30.9 445s 16 33.2 0.263 32.7 33.7 445s 17 37.6 0.256 37.1 38.1 445s 18 40.1 0.204 39.7 40.6 445s 19 39.0 0.298 38.4 39.6 445s 20 42.0 0.272 41.5 42.6 445s 21 46.2 0.288 45.6 46.8 445s 22 52.7 0.431 51.9 53.6 445s > model.frame 445s [1] TRUE 445s > model.matrix 445s [1] TRUE 445s > nobs 445s [1] 61 445s > linearHypothesis 445s Linear hypothesis test (Theil's F test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 50 445s 2 49 1 1.01 0.32 445s Linear hypothesis test (F statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 50 445s 2 49 1 1.3 0.26 445s Linear hypothesis test (Chi^2 statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df Chisq Pr(>Chisq) 445s 1 50 445s 2 49 1 1.3 0.25 445s Linear hypothesis test (Theil's F test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 51 445s 2 49 2 0.53 0.59 445s Linear hypothesis test (F statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 51 445s 2 49 2 0.69 0.51 445s Linear hypothesis test (Chi^2 statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df Chisq Pr(>Chisq) 445s 1 51 445s 2 49 2 1.38 0.5 445s > logLik 445s 'log Lik.' -69.6 (df=18) 445s 'log Lik.' -76.9 (df=18) 445s > 445s > # 3SLS 445s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 445s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 445s > summary 445s 445s systemfit results 445s method: 3SLS 445s 445s N DF SSR detRCov OLS-R2 McElroy-R2 445s system 59 47 59.5 0.241 0.97 0.994 445s 445s N DF SSR MSE RMSE R2 Adj R2 445s Consumption 19 15 18.1 1.203 1.097 0.980 0.977 445s Investment 20 16 31.1 1.945 1.395 0.866 0.841 445s PrivateWages 20 16 10.3 0.645 0.803 0.987 0.985 445s 445s The covariance matrix of the residuals used for estimation 445s Consumption Investment PrivateWages 445s Consumption 1.079 0.354 -0.383 445s Investment 0.354 1.047 0.107 445s PrivateWages -0.383 0.107 0.445 445s 445s The covariance matrix of the residuals 445s Consumption Investment PrivateWages 445s Consumption 0.950 0.324 -0.395 445s Investment 0.324 1.385 0.242 445s PrivateWages -0.395 0.242 0.475 445s 445s The correlations of the residuals 445s Consumption Investment PrivateWages 445s Consumption 1.000 0.293 -0.582 445s Investment 0.293 1.000 0.292 445s PrivateWages -0.582 0.292 1.000 445s 445s 445s 3SLS estimates for 'Consumption' (equation 1) 445s Model Formula: consump ~ corpProf + corpProfLag + wages 445s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 445s gnpLag 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 16.5606 1.3295 12.46 2.6e-09 *** 445s corpProf 0.1100 0.1098 1.00 0.33 445s corpProfLag 0.1155 0.1007 1.15 0.27 445s wages 0.8086 0.0401 20.18 2.8e-12 *** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 1.097 on 15 degrees of freedom 445s Number of observations: 19 Degrees of Freedom: 15 445s SSR: 18.051 MSE: 1.203 Root MSE: 1.097 445s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.977 445s 445s 445s 3SLS estimates for 'Investment' (equation 2) 445s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 445s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 445s gnpLag 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 23.6871 6.1159 3.87 0.00135 ** 445s corpProf 0.1072 0.1414 0.76 0.45918 445s corpProfLag 0.6278 0.1361 4.61 0.00029 *** 445s capitalLag -0.1726 0.0295 -5.85 2.5e-05 *** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 1.395 on 16 degrees of freedom 445s Number of observations: 20 Degrees of Freedom: 16 445s SSR: 31.126 MSE: 1.945 Root MSE: 1.395 445s Multiple R-Squared: 0.866 Adjusted R-Squared: 0.841 445s 445s 445s 3SLS estimates for 'PrivateWages' (equation 3) 445s Model Formula: privWage ~ gnp + gnpLag + trend 445s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 445s gnpLag 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 1.3603 1.0927 1.24 0.23109 445s gnp 0.4117 0.0315 13.06 6.0e-10 *** 445s gnpLag 0.1782 0.0336 5.31 7.1e-05 *** 445s trend 0.1370 0.0280 4.89 0.00016 *** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 0.803 on 16 degrees of freedom 445s Number of observations: 20 Degrees of Freedom: 16 445s SSR: 10.318 MSE: 0.645 Root MSE: 0.803 445s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 445s 445s > residuals 445s Consumption Investment PrivateWages 445s 1 NA NA NA 445s 2 -0.29542 -1.636 -1.2658 445s 3 -0.89033 0.135 0.4198 445s 4 -1.25669 0.777 1.3578 445s 5 -0.14000 -1.574 -0.2036 445s 6 0.37365 0.341 -0.4283 445s 7 NA NA NA 445s 8 1.63850 1.194 -0.8319 445s 9 1.44030 0.454 0.2186 445s 10 NA 2.192 1.1346 445s 11 0.17274 -0.750 -0.4603 445s 12 -0.49629 -0.698 0.2476 445s 13 -0.78384 -0.976 -0.2528 445s 14 0.32420 1.365 0.4028 445s 15 -0.10364 -0.170 0.3295 445s 16 -0.00105 0.140 0.0377 445s 17 1.84421 1.862 -0.7540 445s 18 -0.36893 -0.103 0.8827 445s 19 0.14129 -3.255 -0.7764 445s 20 1.23511 0.475 -0.3230 445s 21 1.06553 0.152 -1.1453 445s 22 -1.85709 0.746 0.6843 445s > fitted 445s Consumption Investment PrivateWages 445s 1 NA NA NA 445s 2 42.2 1.436 26.8 445s 3 45.9 1.765 28.9 445s 4 50.5 4.423 32.7 445s 5 50.7 4.574 34.1 445s 6 52.2 4.759 35.8 445s 7 NA NA NA 445s 8 54.6 3.006 38.7 445s 9 55.9 2.546 39.0 445s 10 NA 2.908 40.2 445s 11 54.8 1.750 38.4 445s 12 51.4 -2.702 34.3 445s 13 46.4 -5.224 29.3 445s 14 46.2 -6.465 28.1 445s 15 48.8 -2.830 30.3 445s 16 51.3 -1.440 33.2 445s 17 55.9 0.238 37.6 445s 18 59.1 2.103 40.1 445s 19 57.4 1.355 39.0 445s 20 60.4 0.825 41.9 445s 21 63.9 3.148 46.1 445s 22 71.6 4.154 52.6 445s > predict 445s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 445s 1 NA NA NA NA 445s 2 42.2 0.475 39.6 44.7 445s 3 45.9 0.557 43.3 48.5 445s 4 50.5 0.372 48.0 52.9 445s 5 50.7 0.433 48.2 53.3 445s 6 52.2 0.438 49.7 54.7 445s 7 NA NA NA NA 445s 8 54.6 0.362 52.1 57.0 445s 9 55.9 0.401 53.4 58.3 445s 10 NA NA NA NA 445s 11 54.8 0.684 52.1 57.6 445s 12 51.4 0.563 48.8 54.0 445s 13 46.4 0.733 43.6 49.2 445s 14 46.2 0.612 43.5 48.9 445s 15 48.8 0.379 46.3 51.3 445s 16 51.3 0.334 48.9 53.7 445s 17 55.9 0.394 53.4 58.3 445s 18 59.1 0.322 56.6 61.5 445s 19 57.4 0.392 54.9 59.8 445s 20 60.4 0.462 57.8 62.9 445s 21 63.9 0.448 61.4 66.5 445s 22 71.6 0.686 68.8 74.3 445s Investment.pred Investment.se.fit Investment.lwr Investment.upr 445s 1 NA NA NA NA 445s 2 1.436 0.709 -1.8811 4.754 445s 3 1.765 0.512 -1.3848 4.915 445s 4 4.423 0.470 1.3027 7.543 445s 5 4.574 0.392 1.5029 7.645 445s 6 4.759 0.370 1.7000 7.818 445s 7 NA NA NA NA 445s 8 3.006 0.306 -0.0214 6.033 445s 9 2.546 0.444 -0.5575 5.649 445s 10 2.908 0.488 -0.2245 6.041 445s 11 1.750 0.738 -1.5953 5.096 445s 12 -2.702 0.583 -5.9068 0.503 445s 13 -5.224 0.743 -8.5738 -1.874 445s 14 -6.465 0.780 -9.8530 -3.077 445s 15 -2.830 0.378 -5.8936 0.233 445s 16 -1.440 0.326 -4.4762 1.597 445s 17 0.238 0.426 -2.8533 3.329 445s 18 2.103 0.268 -0.9077 5.114 445s 19 1.355 0.399 -1.7201 4.431 445s 20 0.825 0.474 -2.2981 3.947 445s 21 3.148 0.393 0.0761 6.220 445s 22 4.154 0.555 0.9719 7.336 445s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 445s 1 NA NA NA NA 445s 2 26.8 0.309 24.9 28.6 445s 3 28.9 0.315 27.1 30.7 445s 4 32.7 0.326 30.9 34.6 445s 5 34.1 0.236 32.3 35.9 445s 6 35.8 0.244 34.0 37.6 445s 7 NA NA NA NA 445s 8 38.7 0.237 37.0 40.5 445s 9 39.0 0.225 37.2 40.7 445s 10 40.2 0.219 38.4 41.9 445s 11 38.4 0.309 36.5 40.2 445s 12 34.3 0.336 32.4 36.1 445s 13 29.3 0.411 27.3 31.2 445s 14 28.1 0.326 26.3 29.9 445s 15 30.3 0.313 28.4 32.1 445s 16 33.2 0.262 31.4 35.0 445s 17 37.6 0.265 35.8 39.3 445s 18 40.1 0.205 38.4 41.9 445s 19 39.0 0.323 37.1 40.8 445s 20 41.9 0.282 40.1 43.7 445s 21 46.1 0.293 44.3 48.0 445s 22 52.6 0.463 50.7 54.6 445s > model.frame 445s [1] TRUE 445s > model.matrix 445s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0328 >" 445s [2] "Attributes: < Component “dimnames”: Component 1: 54 string mismatches >" 445s [3] "Numeric: lengths (732, 708) differ" 445s > nobs 445s [1] 59 445s > linearHypothesis 445s Linear hypothesis test (Theil's F test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 48 445s 2 47 1 0.23 0.64 445s Linear hypothesis test (F statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 48 445s 2 47 1 0.31 0.58 445s Linear hypothesis test (Chi^2 statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df Chisq Pr(>Chisq) 445s 1 48 445s 2 47 1 0.31 0.58 445s Linear hypothesis test (Theil's F test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 49 445s 2 47 2 0.5 0.61 445s Linear hypothesis test (F statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 49 445s 2 47 2 0.68 0.51 445s Linear hypothesis test (Chi^2 statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df Chisq Pr(>Chisq) 445s 1 49 445s 2 47 2 1.37 0.5 445s > logLik 445s 'log Lik.' -71 (df=18) 445s 'log Lik.' -81.1 (df=18) 445s > 445s > # I3SLS 445s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 445s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 445s > summary 445s 445s systemfit results 445s method: iterated 3SLS 445s 445s convergence achieved after 15 iterations 445s 445s N DF SSR detRCov OLS-R2 McElroy-R2 445s system 59 47 81.3 0.349 0.958 0.995 445s 445s N DF SSR MSE RMSE R2 Adj R2 445s Consumption 19 15 18.1 1.209 1.100 0.980 0.976 445s Investment 20 16 52.0 3.250 1.803 0.776 0.735 445s PrivateWages 20 16 11.2 0.699 0.836 0.986 0.983 445s 445s The covariance matrix of the residuals used for estimation 445s Consumption Investment PrivateWages 445s Consumption 0.955 0.456 -0.421 445s Investment 0.456 2.294 0.375 445s PrivateWages -0.421 0.375 0.522 445s 445s The covariance matrix of the residuals 445s Consumption Investment PrivateWages 445s Consumption 0.955 0.456 -0.421 445s Investment 0.456 2.294 0.375 445s PrivateWages -0.421 0.375 0.522 445s 445s The correlations of the residuals 445s Consumption Investment PrivateWages 445s Consumption 1.000 0.322 -0.582 445s Investment 0.322 1.000 0.341 445s PrivateWages -0.582 0.341 1.000 445s 445s 445s 3SLS estimates for 'Consumption' (equation 1) 445s Model Formula: consump ~ corpProf + corpProfLag + wages 445s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 445s gnpLag 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 16.8311 1.2489 13.48 8.7e-10 *** 445s corpProf 0.1468 0.0991 1.48 0.16 445s corpProfLag 0.0924 0.0906 1.02 0.32 445s wages 0.7945 0.0371 21.43 1.2e-12 *** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 1.1 on 15 degrees of freedom 445s Number of observations: 19 Degrees of Freedom: 15 445s SSR: 18.14 MSE: 1.209 Root MSE: 1.1 445s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.976 445s 445s 445s 3SLS estimates for 'Investment' (equation 2) 445s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 445s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 445s gnpLag 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 32.4128 8.2695 3.92 0.00122 ** 445s corpProf -0.0799 0.1934 -0.41 0.68498 445s corpProfLag 0.7607 0.1878 4.05 0.00093 *** 445s capitalLag -0.2114 0.0400 -5.29 7.4e-05 *** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 1.803 on 16 degrees of freedom 445s Number of observations: 20 Degrees of Freedom: 16 445s SSR: 51.999 MSE: 3.25 Root MSE: 1.803 445s Multiple R-Squared: 0.776 Adjusted R-Squared: 0.735 445s 445s 445s 3SLS estimates for 'PrivateWages' (equation 3) 445s Model Formula: privWage ~ gnp + gnpLag + trend 445s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 445s gnpLag 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 1.5421 1.1496 1.34 0.19852 445s gnp 0.3936 0.0313 12.57 1.0e-09 *** 445s gnpLag 0.1945 0.0328 5.93 2.1e-05 *** 445s trend 0.1416 0.0286 4.95 0.00014 *** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 0.836 on 16 degrees of freedom 445s Number of observations: 20 Degrees of Freedom: 16 445s SSR: 11.181 MSE: 0.699 Root MSE: 0.836 445s Multiple R-Squared: 0.986 Adjusted R-Squared: 0.983 445s 445s > residuals 445s Consumption Investment PrivateWages 445s 1 NA NA NA 445s 2 -0.3309 -2.6308 -1.3061 445s 3 -1.0419 0.0146 0.4450 445s 4 -1.2918 0.4128 1.4338 445s 5 -0.1772 -1.7488 -0.2494 445s 6 0.3563 0.2807 -0.4066 445s 7 NA NA NA 445s 8 1.6778 1.4671 -0.8700 445s 9 1.4561 1.1068 0.1712 445s 10 NA 2.9002 1.1262 445s 11 0.4237 -1.0652 -0.6189 445s 12 -0.2711 -0.9488 0.0375 445s 13 -0.5643 -1.6241 -0.5055 445s 14 0.2845 1.8477 0.3080 445s 15 -0.0514 -0.2379 0.3003 445s 16 0.0521 0.1268 0.0141 445s 17 1.8733 2.2462 -0.7083 445s 18 -0.1962 -0.1724 0.8305 445s 19 0.3553 -3.5810 -0.9448 445s 20 1.3161 1.0343 -0.2738 445s 21 1.2055 0.6622 -1.1283 445s 22 -1.6327 1.5541 0.8257 445s > fitted 445s Consumption Investment PrivateWages 445s 1 NA NA NA 445s 2 42.2 2.431 26.8 445s 3 46.0 1.885 28.9 445s 4 50.5 4.787 32.7 445s 5 50.8 4.749 34.1 445s 6 52.2 4.819 35.8 445s 7 NA NA NA 445s 8 54.5 2.733 38.8 445s 9 55.8 1.893 39.0 445s 10 NA 2.200 40.2 445s 11 54.6 2.065 38.5 445s 12 51.2 -2.451 34.5 445s 13 46.2 -4.576 29.5 445s 14 46.2 -6.948 28.2 445s 15 48.8 -2.762 30.3 445s 16 51.2 -1.427 33.2 445s 17 55.8 -0.146 37.5 445s 18 58.9 2.172 40.2 445s 19 57.1 1.681 39.1 445s 20 60.3 0.266 41.9 445s 21 63.8 2.638 46.1 445s 22 71.3 3.346 52.5 445s > predict 445s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 445s 1 NA NA NA NA 445s 2 42.2 0.446 41.3 43.1 445s 3 46.0 0.511 45.0 47.1 445s 4 50.5 0.340 49.8 51.2 445s 5 50.8 0.393 50.0 51.6 445s 6 52.2 0.396 51.4 53.0 445s 7 NA NA NA NA 445s 8 54.5 0.326 53.9 55.2 445s 9 55.8 0.362 55.1 56.6 445s 10 NA NA NA NA 445s 11 54.6 0.612 53.3 55.8 445s 12 51.2 0.511 50.1 52.2 445s 13 46.2 0.671 44.8 47.5 445s 14 46.2 0.563 45.1 47.3 445s 15 48.8 0.354 48.0 49.5 445s 16 51.2 0.311 50.6 51.9 445s 17 55.8 0.362 55.1 56.6 445s 18 58.9 0.297 58.3 59.5 445s 19 57.1 0.357 56.4 57.9 445s 20 60.3 0.427 59.4 61.1 445s 21 63.8 0.416 63.0 64.6 445s 22 71.3 0.640 70.0 72.6 445s Investment.pred Investment.se.fit Investment.lwr Investment.upr 445s 1 NA NA NA NA 445s 2 2.431 0.970 0.4798 4.382 445s 3 1.885 0.745 0.3859 3.385 445s 4 4.787 0.664 3.4506 6.124 445s 5 4.749 0.562 3.6174 5.880 445s 6 4.819 0.537 3.7391 5.900 445s 7 NA NA NA NA 445s 8 2.733 0.446 1.8351 3.631 445s 9 1.893 0.620 0.6455 3.141 445s 10 2.200 0.684 0.8232 3.576 445s 11 2.065 1.055 -0.0569 4.187 445s 12 -2.451 0.845 -4.1517 -0.751 445s 13 -4.576 1.070 -6.7293 -2.423 445s 14 -6.948 1.103 -9.1676 -4.728 445s 15 -2.762 0.556 -3.8806 -1.644 445s 16 -1.427 0.480 -2.3919 -0.462 445s 17 -0.146 0.603 -1.3588 1.066 445s 18 2.172 0.390 1.3869 2.958 445s 19 1.681 0.563 0.5476 2.815 445s 20 0.266 0.661 -1.0634 1.595 445s 21 2.638 0.558 1.5144 3.761 445s 22 3.346 0.778 1.7808 4.911 445s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 445s 1 NA NA NA NA 445s 2 26.8 0.326 26.2 27.5 445s 3 28.9 0.328 28.2 29.5 445s 4 32.7 0.334 32.0 33.3 445s 5 34.1 0.242 33.7 34.6 445s 6 35.8 0.252 35.3 36.3 445s 7 NA NA NA NA 445s 8 38.8 0.244 38.3 39.3 445s 9 39.0 0.232 38.6 39.5 445s 10 40.2 0.230 39.7 40.6 445s 11 38.5 0.308 37.9 39.1 445s 12 34.5 0.336 33.8 35.1 445s 13 29.5 0.420 28.7 30.4 445s 14 28.2 0.345 27.5 28.9 445s 15 30.3 0.325 29.6 31.0 445s 16 33.2 0.271 32.6 33.7 445s 17 37.5 0.267 37.0 38.0 445s 18 40.2 0.218 39.7 40.6 445s 19 39.1 0.331 38.5 39.8 445s 20 41.9 0.289 41.3 42.5 445s 21 46.1 0.311 45.5 46.8 445s 22 52.5 0.485 51.5 53.5 445s > model.frame 445s [1] TRUE 445s > model.matrix 445s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0328 >" 445s [2] "Attributes: < Component “dimnames”: Component 1: 54 string mismatches >" 445s [3] "Numeric: lengths (732, 708) differ" 445s > nobs 445s [1] 59 445s > linearHypothesis 445s Linear hypothesis test (Theil's F test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 48 445s 2 47 1 0.28 0.6 445s Linear hypothesis test (F statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 48 445s 2 47 1 0.37 0.55 445s Linear hypothesis test (Chi^2 statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df Chisq Pr(>Chisq) 445s 1 48 445s 2 47 1 0.37 0.54 445s Linear hypothesis test (Theil's F test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 49 445s 2 47 2 1.25 0.3 445s Linear hypothesis test (F statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 49 445s 2 47 2 1.64 0.21 445s Linear hypothesis test (Chi^2 statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df Chisq Pr(>Chisq) 445s 1 49 445s 2 47 2 3.28 0.19 445s > logLik 445s 'log Lik.' -74.5 (df=18) 445s 'log Lik.' -87.1 (df=18) 445s > 445s > # OLS 445s Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 445s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 445s > summary 445s 445s systemfit results 445s method: OLS 445s 445s N DF SSR detRCov OLS-R2 McElroy-R2 445s system 59 47 44.2 0.453 0.976 0.99 445s 445s N DF SSR MSE RMSE R2 Adj R2 445s Consumption 19 15 17.36 1.157 1.08 0.980 0.976 445s Investment 20 16 17.11 1.069 1.03 0.912 0.895 445s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 445s 445s The covariance matrix of the residuals 445s Consumption Investment PrivateWages 445s Consumption 1.1939 0.0559 -0.474 445s Investment 0.0559 0.9839 0.140 445s PrivateWages -0.4745 0.1403 0.602 445s 445s The correlations of the residuals 445s Consumption Investment PrivateWages 445s Consumption 1.0000 0.0447 -0.568 445s Investment 0.0447 1.0000 0.169 445s PrivateWages -0.5680 0.1689 1.000 445s 445s 445s OLS estimates for 'Consumption' (equation 1) 445s Model Formula: consump ~ corpProf + corpProfLag + wages 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 16.2957 1.4879 10.95 1.5e-08 *** 445s corpProf 0.1796 0.1162 1.55 0.14 445s corpProfLag 0.1032 0.0994 1.04 0.32 445s wages 0.7962 0.0433 18.39 1.1e-11 *** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 1.076 on 15 degrees of freedom 445s Number of observations: 19 Degrees of Freedom: 15 445s SSR: 17.362 MSE: 1.157 Root MSE: 1.076 445s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.976 445s 445s 445s OLS estimates for 'Investment' (equation 2) 445s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 10.1813 5.3720 1.90 0.07627 . 445s corpProf 0.5003 0.1052 4.75 0.00022 *** 445s corpProfLag 0.3259 0.1003 3.25 0.00502 ** 445s capitalLag -0.1134 0.0265 -4.28 0.00057 *** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 1.034 on 16 degrees of freedom 445s Number of observations: 20 Degrees of Freedom: 16 445s SSR: 17.109 MSE: 1.069 Root MSE: 1.034 445s Multiple R-Squared: 0.912 Adjusted R-Squared: 0.895 445s 445s 445s OLS estimates for 'PrivateWages' (equation 3) 445s Model Formula: privWage ~ gnp + gnpLag + trend 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 1.3550 1.3021 1.04 0.3135 445s gnp 0.4417 0.0330 13.40 4.1e-10 *** 445s gnpLag 0.1466 0.0379 3.87 0.0013 ** 445s trend 0.1244 0.0335 3.72 0.0019 ** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 0.78 on 16 degrees of freedom 445s Number of observations: 20 Degrees of Freedom: 16 445s SSR: 9.739 MSE: 0.609 Root MSE: 0.78 445s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 445s 445s compare coef with single-equation OLS 445s [1] TRUE 445s > residuals 445s Consumption Investment PrivateWages 445s 1 NA NA NA 445s 2 -0.3863 -0.000301 -1.3389 445s 3 -1.2484 -0.076489 0.2462 445s 4 -1.6040 1.221792 1.1255 445s 5 -0.5384 -1.377872 -0.1959 445s 6 -0.0413 0.386104 -0.5284 445s 7 0.8043 1.486279 NA 445s 8 1.2830 0.784055 -0.7909 445s 9 1.0142 -0.655354 0.2819 445s 10 NA 1.060871 1.1384 445s 11 0.1429 0.395249 -0.1904 445s 12 -0.3439 0.198005 0.5813 445s 13 NA NA 0.1206 445s 14 0.3199 0.312725 0.4773 445s 15 -0.1016 -0.084685 0.3035 445s 16 -0.0702 0.066194 0.0284 445s 17 1.6064 0.963697 -0.8517 445s 18 -0.4980 0.078506 0.9908 445s 19 0.1253 -2.496401 -0.4597 445s 20 0.9805 -0.711004 -0.3819 445s 21 0.7551 -0.820172 -1.1062 445s 22 -2.1992 -0.731199 0.5501 445s > fitted 445s Consumption Investment PrivateWages 445s 1 NA NA NA 445s 2 42.3 -0.200 26.8 445s 3 46.2 1.976 29.1 445s 4 50.8 3.978 33.0 445s 5 51.1 4.378 34.1 445s 6 52.6 4.714 35.9 445s 7 54.3 4.114 NA 445s 8 54.9 3.416 38.7 445s 9 56.3 3.655 38.9 445s 10 NA 4.039 40.2 445s 11 54.9 0.605 38.1 445s 12 51.2 -3.598 33.9 445s 13 NA NA 28.9 445s 14 46.2 -5.413 28.0 445s 15 48.8 -2.915 30.3 445s 16 51.4 -1.366 33.2 445s 17 56.1 1.136 37.7 445s 18 59.2 1.921 40.0 445s 19 57.4 0.596 38.7 445s 20 60.6 2.011 42.0 445s 21 64.2 4.120 46.1 445s 22 71.9 5.631 52.7 445s > predict 445s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 445s 1 NA NA NA NA 445s 2 42.3 0.523 39.9 44.7 445s 3 46.2 0.560 43.8 48.7 445s 4 50.8 0.379 48.5 53.1 445s 5 51.1 0.448 48.8 53.5 445s 6 52.6 0.457 50.3 55.0 445s 7 54.3 0.408 52.0 56.6 445s 8 54.9 0.375 52.6 57.2 445s 9 56.3 0.418 54.0 58.6 445s 10 NA NA NA NA 445s 11 54.9 0.701 52.3 57.4 445s 12 51.2 0.638 48.7 53.8 445s 13 NA NA NA NA 445s 14 46.2 0.673 43.6 48.7 445s 15 48.8 0.453 46.5 51.2 445s 16 51.4 0.384 49.1 53.7 445s 17 56.1 0.391 53.8 58.4 445s 18 59.2 0.361 56.9 61.5 445s 19 57.4 0.449 55.0 59.7 445s 20 60.6 0.465 58.3 63.0 445s 21 64.2 0.468 61.9 66.6 445s 22 71.9 0.728 69.3 74.5 445s Investment.pred Investment.se.fit Investment.lwr Investment.upr 445s 1 NA NA NA NA 445s 2 -0.200 0.613 -2.618 2.219 445s 3 1.976 0.494 -0.329 4.282 445s 4 3.978 0.444 1.714 6.242 445s 5 4.378 0.369 2.169 6.587 445s 6 4.714 0.349 2.519 6.909 445s 7 4.114 0.323 1.934 6.293 445s 8 3.416 0.287 1.257 5.575 445s 9 3.655 0.386 1.435 5.876 445s 10 4.039 0.441 1.777 6.301 445s 11 0.605 0.641 -1.843 3.053 445s 12 -3.598 0.606 -6.010 -1.186 445s 13 NA NA NA NA 445s 14 -5.413 0.708 -7.934 -2.892 445s 15 -2.915 0.412 -5.155 -0.676 445s 16 -1.366 0.336 -3.554 0.821 445s 17 1.136 0.342 -1.055 3.327 445s 18 1.921 0.246 -0.217 4.060 445s 19 0.596 0.341 -1.594 2.787 445s 20 2.011 0.364 -0.194 4.216 445s 21 4.120 0.337 1.932 6.308 445s 22 5.631 0.477 3.341 7.922 445s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 445s 1 NA NA NA NA 445s 2 26.8 0.364 25.1 28.6 445s 3 29.1 0.367 27.3 30.8 445s 4 33.0 0.370 31.2 34.7 445s 5 34.1 0.286 32.4 35.8 445s 6 35.9 0.285 34.3 37.6 445s 7 NA NA NA NA 445s 8 38.7 0.292 37.0 40.4 445s 9 38.9 0.277 37.3 40.6 445s 10 40.2 0.264 38.5 41.8 445s 11 38.1 0.363 36.4 39.8 445s 12 33.9 0.367 32.2 35.7 445s 13 28.9 0.435 27.1 30.7 445s 14 28.0 0.383 26.3 29.8 445s 15 30.3 0.377 28.6 32.0 445s 16 33.2 0.315 31.5 34.9 445s 17 37.7 0.308 36.0 39.3 445s 18 40.0 0.241 38.4 41.7 445s 19 38.7 0.361 36.9 40.4 445s 20 42.0 0.324 40.3 43.7 445s 21 46.1 0.339 44.4 47.8 445s 22 52.7 0.511 50.9 54.6 445s > model.frame 445s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 445s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 445s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 445s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 445s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 445s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 445s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 445s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 445s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 445s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 445s 10 57.8 21.7 21.1 NA 5.1 211 41.3 67.0 64.5 445s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 445s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 445s 13 45.6 NA 11.4 34.3 -6.2 213 29.0 44.3 53.4 445s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 445s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 445s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 445s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 445s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 445s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 445s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 445s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 445s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 445s trend 445s 1 -11 445s 2 -10 445s 3 -9 445s 4 -8 445s 5 -7 445s 6 -6 445s 7 -5 445s 8 -4 445s 9 -3 445s 10 -2 445s 11 -1 445s 12 0 445s 13 1 445s 14 2 445s 15 3 445s 16 4 445s 17 5 445s 18 6 445s 19 7 445s 20 8 445s 21 9 445s 22 10 445s > model.matrix 445s Consumption_(Intercept) Consumption_corpProf 445s Consumption_2 1 12.4 445s Consumption_3 1 16.9 445s Consumption_4 1 18.4 445s Consumption_5 1 19.4 445s Consumption_6 1 20.1 445s Consumption_7 1 19.6 445s Consumption_8 1 19.8 445s Consumption_9 1 21.1 445s Consumption_11 1 15.6 445s Consumption_12 1 11.4 445s Consumption_14 1 11.2 445s Consumption_15 1 12.3 445s Consumption_16 1 14.0 445s Consumption_17 1 17.6 445s Consumption_18 1 17.3 445s Consumption_19 1 15.3 445s Consumption_20 1 19.0 445s Consumption_21 1 21.1 445s Consumption_22 1 23.5 445s Investment_2 0 0.0 445s Investment_3 0 0.0 445s Investment_4 0 0.0 445s Investment_5 0 0.0 445s Investment_6 0 0.0 445s Investment_7 0 0.0 445s Investment_8 0 0.0 445s Investment_9 0 0.0 445s Investment_10 0 0.0 445s Investment_11 0 0.0 445s Investment_12 0 0.0 445s Investment_14 0 0.0 445s Investment_15 0 0.0 445s Investment_16 0 0.0 445s Investment_17 0 0.0 445s Investment_18 0 0.0 445s Investment_19 0 0.0 445s Investment_20 0 0.0 445s Investment_21 0 0.0 445s Investment_22 0 0.0 445s PrivateWages_2 0 0.0 445s PrivateWages_3 0 0.0 445s PrivateWages_4 0 0.0 445s PrivateWages_5 0 0.0 445s PrivateWages_6 0 0.0 445s PrivateWages_8 0 0.0 445s PrivateWages_9 0 0.0 445s PrivateWages_10 0 0.0 445s PrivateWages_11 0 0.0 445s PrivateWages_12 0 0.0 445s PrivateWages_13 0 0.0 445s PrivateWages_14 0 0.0 445s PrivateWages_15 0 0.0 445s PrivateWages_16 0 0.0 445s PrivateWages_17 0 0.0 445s PrivateWages_18 0 0.0 445s PrivateWages_19 0 0.0 445s PrivateWages_20 0 0.0 445s PrivateWages_21 0 0.0 445s PrivateWages_22 0 0.0 445s Consumption_corpProfLag Consumption_wages 445s Consumption_2 12.7 28.2 445s Consumption_3 12.4 32.2 445s Consumption_4 16.9 37.0 445s Consumption_5 18.4 37.0 445s Consumption_6 19.4 38.6 445s Consumption_7 20.1 40.7 445s Consumption_8 19.6 41.5 445s Consumption_9 19.8 42.9 445s Consumption_11 21.7 42.1 445s Consumption_12 15.6 39.3 445s Consumption_14 7.0 34.1 445s Consumption_15 11.2 36.6 445s Consumption_16 12.3 39.3 445s Consumption_17 14.0 44.2 445s Consumption_18 17.6 47.7 445s Consumption_19 17.3 45.9 445s Consumption_20 15.3 49.4 445s Consumption_21 19.0 53.0 445s Consumption_22 21.1 61.8 445s Investment_2 0.0 0.0 445s Investment_3 0.0 0.0 445s Investment_4 0.0 0.0 445s Investment_5 0.0 0.0 445s Investment_6 0.0 0.0 445s Investment_7 0.0 0.0 445s Investment_8 0.0 0.0 445s Investment_9 0.0 0.0 445s Investment_10 0.0 0.0 445s Investment_11 0.0 0.0 445s Investment_12 0.0 0.0 445s Investment_14 0.0 0.0 445s Investment_15 0.0 0.0 445s Investment_16 0.0 0.0 445s Investment_17 0.0 0.0 445s Investment_18 0.0 0.0 445s Investment_19 0.0 0.0 445s Investment_20 0.0 0.0 445s Investment_21 0.0 0.0 445s Investment_22 0.0 0.0 445s PrivateWages_2 0.0 0.0 445s PrivateWages_3 0.0 0.0 445s PrivateWages_4 0.0 0.0 445s PrivateWages_5 0.0 0.0 445s PrivateWages_6 0.0 0.0 445s PrivateWages_8 0.0 0.0 445s PrivateWages_9 0.0 0.0 445s PrivateWages_10 0.0 0.0 445s PrivateWages_11 0.0 0.0 445s PrivateWages_12 0.0 0.0 445s PrivateWages_13 0.0 0.0 445s PrivateWages_14 0.0 0.0 445s PrivateWages_15 0.0 0.0 445s PrivateWages_16 0.0 0.0 445s PrivateWages_17 0.0 0.0 445s PrivateWages_18 0.0 0.0 445s PrivateWages_19 0.0 0.0 445s PrivateWages_20 0.0 0.0 445s PrivateWages_21 0.0 0.0 445s PrivateWages_22 0.0 0.0 445s Investment_(Intercept) Investment_corpProf 445s Consumption_2 0 0.0 445s Consumption_3 0 0.0 445s Consumption_4 0 0.0 445s Consumption_5 0 0.0 445s Consumption_6 0 0.0 445s Consumption_7 0 0.0 445s Consumption_8 0 0.0 445s Consumption_9 0 0.0 445s Consumption_11 0 0.0 445s Consumption_12 0 0.0 445s Consumption_14 0 0.0 445s Consumption_15 0 0.0 445s Consumption_16 0 0.0 445s Consumption_17 0 0.0 445s Consumption_18 0 0.0 445s Consumption_19 0 0.0 445s Consumption_20 0 0.0 445s Consumption_21 0 0.0 445s Consumption_22 0 0.0 445s Investment_2 1 12.4 445s Investment_3 1 16.9 445s Investment_4 1 18.4 445s Investment_5 1 19.4 445s Investment_6 1 20.1 445s Investment_7 1 19.6 445s Investment_8 1 19.8 445s Investment_9 1 21.1 445s Investment_10 1 21.7 445s Investment_11 1 15.6 445s Investment_12 1 11.4 445s Investment_14 1 11.2 445s Investment_15 1 12.3 445s Investment_16 1 14.0 445s Investment_17 1 17.6 445s Investment_18 1 17.3 445s Investment_19 1 15.3 445s Investment_20 1 19.0 445s Investment_21 1 21.1 445s Investment_22 1 23.5 445s PrivateWages_2 0 0.0 445s PrivateWages_3 0 0.0 445s PrivateWages_4 0 0.0 445s PrivateWages_5 0 0.0 445s PrivateWages_6 0 0.0 445s PrivateWages_8 0 0.0 445s PrivateWages_9 0 0.0 445s PrivateWages_10 0 0.0 445s PrivateWages_11 0 0.0 445s PrivateWages_12 0 0.0 445s PrivateWages_13 0 0.0 445s PrivateWages_14 0 0.0 445s PrivateWages_15 0 0.0 445s PrivateWages_16 0 0.0 445s PrivateWages_17 0 0.0 445s PrivateWages_18 0 0.0 445s PrivateWages_19 0 0.0 445s PrivateWages_20 0 0.0 445s PrivateWages_21 0 0.0 445s PrivateWages_22 0 0.0 445s Investment_corpProfLag Investment_capitalLag 445s Consumption_2 0.0 0 445s Consumption_3 0.0 0 445s Consumption_4 0.0 0 445s Consumption_5 0.0 0 445s Consumption_6 0.0 0 445s Consumption_7 0.0 0 445s Consumption_8 0.0 0 445s Consumption_9 0.0 0 445s Consumption_11 0.0 0 445s Consumption_12 0.0 0 445s Consumption_14 0.0 0 445s Consumption_15 0.0 0 445s Consumption_16 0.0 0 445s Consumption_17 0.0 0 445s Consumption_18 0.0 0 445s Consumption_19 0.0 0 445s Consumption_20 0.0 0 445s Consumption_21 0.0 0 445s Consumption_22 0.0 0 445s Investment_2 12.7 183 445s Investment_3 12.4 183 445s Investment_4 16.9 184 445s Investment_5 18.4 190 445s Investment_6 19.4 193 445s Investment_7 20.1 198 445s Investment_8 19.6 203 445s Investment_9 19.8 208 445s Investment_10 21.1 211 445s Investment_11 21.7 216 445s Investment_12 15.6 217 445s Investment_14 7.0 207 445s Investment_15 11.2 202 445s Investment_16 12.3 199 445s Investment_17 14.0 198 445s Investment_18 17.6 200 445s Investment_19 17.3 202 445s Investment_20 15.3 200 445s Investment_21 19.0 201 445s Investment_22 21.1 204 445s PrivateWages_2 0.0 0 445s PrivateWages_3 0.0 0 445s PrivateWages_4 0.0 0 445s PrivateWages_5 0.0 0 445s PrivateWages_6 0.0 0 445s PrivateWages_8 0.0 0 445s PrivateWages_9 0.0 0 445s PrivateWages_10 0.0 0 445s PrivateWages_11 0.0 0 445s PrivateWages_12 0.0 0 445s PrivateWages_13 0.0 0 445s PrivateWages_14 0.0 0 445s PrivateWages_15 0.0 0 445s PrivateWages_16 0.0 0 445s PrivateWages_17 0.0 0 445s PrivateWages_18 0.0 0 445s PrivateWages_19 0.0 0 445s PrivateWages_20 0.0 0 445s PrivateWages_21 0.0 0 445s PrivateWages_22 0.0 0 445s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 445s Consumption_2 0 0.0 0.0 445s Consumption_3 0 0.0 0.0 445s Consumption_4 0 0.0 0.0 445s Consumption_5 0 0.0 0.0 445s Consumption_6 0 0.0 0.0 445s Consumption_7 0 0.0 0.0 445s Consumption_8 0 0.0 0.0 445s Consumption_9 0 0.0 0.0 445s Consumption_11 0 0.0 0.0 445s Consumption_12 0 0.0 0.0 445s Consumption_14 0 0.0 0.0 445s Consumption_15 0 0.0 0.0 445s Consumption_16 0 0.0 0.0 445s Consumption_17 0 0.0 0.0 445s Consumption_18 0 0.0 0.0 445s Consumption_19 0 0.0 0.0 445s Consumption_20 0 0.0 0.0 445s Consumption_21 0 0.0 0.0 445s Consumption_22 0 0.0 0.0 445s Investment_2 0 0.0 0.0 445s Investment_3 0 0.0 0.0 445s Investment_4 0 0.0 0.0 445s Investment_5 0 0.0 0.0 445s Investment_6 0 0.0 0.0 445s Investment_7 0 0.0 0.0 445s Investment_8 0 0.0 0.0 445s Investment_9 0 0.0 0.0 445s Investment_10 0 0.0 0.0 445s Investment_11 0 0.0 0.0 445s Investment_12 0 0.0 0.0 445s Investment_14 0 0.0 0.0 445s Investment_15 0 0.0 0.0 445s Investment_16 0 0.0 0.0 445s Investment_17 0 0.0 0.0 445s Investment_18 0 0.0 0.0 445s Investment_19 0 0.0 0.0 445s Investment_20 0 0.0 0.0 445s Investment_21 0 0.0 0.0 445s Investment_22 0 0.0 0.0 445s PrivateWages_2 1 45.6 44.9 445s PrivateWages_3 1 50.1 45.6 445s PrivateWages_4 1 57.2 50.1 445s PrivateWages_5 1 57.1 57.2 445s PrivateWages_6 1 61.0 57.1 445s PrivateWages_8 1 64.4 64.0 445s PrivateWages_9 1 64.5 64.4 445s PrivateWages_10 1 67.0 64.5 445s PrivateWages_11 1 61.2 67.0 445s PrivateWages_12 1 53.4 61.2 445s PrivateWages_13 1 44.3 53.4 445s PrivateWages_14 1 45.1 44.3 445s PrivateWages_15 1 49.7 45.1 445s PrivateWages_16 1 54.4 49.7 445s PrivateWages_17 1 62.7 54.4 445s PrivateWages_18 1 65.0 62.7 445s PrivateWages_19 1 60.9 65.0 445s PrivateWages_20 1 69.5 60.9 445s PrivateWages_21 1 75.7 69.5 445s PrivateWages_22 1 88.4 75.7 445s PrivateWages_trend 445s Consumption_2 0 445s Consumption_3 0 445s Consumption_4 0 445s Consumption_5 0 445s Consumption_6 0 445s Consumption_7 0 445s Consumption_8 0 445s Consumption_9 0 445s Consumption_11 0 445s Consumption_12 0 445s Consumption_14 0 445s Consumption_15 0 445s Consumption_16 0 445s Consumption_17 0 445s Consumption_18 0 445s Consumption_19 0 445s Consumption_20 0 445s Consumption_21 0 445s Consumption_22 0 445s Investment_2 0 445s Investment_3 0 445s Investment_4 0 445s Investment_5 0 445s Investment_6 0 445s Investment_7 0 445s Investment_8 0 445s Investment_9 0 445s Investment_10 0 445s Investment_11 0 445s Investment_12 0 445s Investment_14 0 445s Investment_15 0 445s Investment_16 0 445s Investment_17 0 445s Investment_18 0 445s Investment_19 0 445s Investment_20 0 445s Investment_21 0 445s Investment_22 0 445s PrivateWages_2 -10 445s PrivateWages_3 -9 445s PrivateWages_4 -8 445s PrivateWages_5 -7 445s PrivateWages_6 -6 445s PrivateWages_8 -4 445s PrivateWages_9 -3 445s PrivateWages_10 -2 445s PrivateWages_11 -1 445s PrivateWages_12 0 445s PrivateWages_13 1 445s PrivateWages_14 2 445s PrivateWages_15 3 445s PrivateWages_16 4 445s PrivateWages_17 5 445s PrivateWages_18 6 445s PrivateWages_19 7 445s PrivateWages_20 8 445s PrivateWages_21 9 445s PrivateWages_22 10 445s > nobs 445s [1] 59 445s > linearHypothesis 445s Linear hypothesis test (Theil's F test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 48 445s 2 47 1 0.33 0.57 445s Linear hypothesis test (F statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 48 445s 2 47 1 0.31 0.58 445s Linear hypothesis test (Chi^2 statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df Chisq Pr(>Chisq) 445s 1 48 445s 2 47 1 0.31 0.58 445s Linear hypothesis test (Theil's F test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 49 445s 2 47 2 0.17 0.84 445s Linear hypothesis test (F statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 49 445s 2 47 2 0.16 0.85 445s Linear hypothesis test (Chi^2 statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df Chisq Pr(>Chisq) 445s 1 49 445s 2 47 2 0.33 0.85 445s > logLik 445s 'log Lik.' -69.6 (df=13) 445s 'log Lik.' -74.2 (df=13) 445s compare log likelihood value with single-equation OLS 445s [1] "Mean relative difference: 0.00099" 445s > 445s > # 2SLS 445s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 445s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 445s > summary 445s 445s systemfit results 445s method: 2SLS 445s 445s N DF SSR detRCov OLS-R2 McElroy-R2 445s system 57 45 58.2 0.333 0.968 0.991 445s 445s N DF SSR MSE RMSE R2 Adj R2 445s Consumption 18 14 22.27 1.591 1.26 0.974 0.968 445s Investment 19 15 26.21 1.748 1.32 0.852 0.823 445s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 445s 445s The covariance matrix of the residuals 445s Consumption Investment PrivateWages 445s Consumption 1.237 0.518 -0.408 445s Investment 0.518 1.263 0.113 445s PrivateWages -0.408 0.113 0.468 445s 445s The correlations of the residuals 445s Consumption Investment PrivateWages 445s Consumption 1.000 0.416 -0.538 445s Investment 0.416 1.000 0.139 445s PrivateWages -0.538 0.139 1.000 445s 445s 445s 2SLS estimates for 'Consumption' (equation 1) 445s Model Formula: consump ~ corpProf + corpProfLag + wages 445s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 445s gnpLag 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 17.2849 1.6018 10.79 3.6e-08 *** 445s corpProf -0.0770 0.1637 -0.47 0.645 445s corpProfLag 0.2327 0.1242 1.87 0.082 . 445s wages 0.8259 0.0459 17.98 4.5e-11 *** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 1.261 on 14 degrees of freedom 445s Number of observations: 18 Degrees of Freedom: 14 445s SSR: 22.269 MSE: 1.591 Root MSE: 1.261 445s Multiple R-Squared: 0.974 Adjusted R-Squared: 0.968 445s 445s 445s 2SLS estimates for 'Investment' (equation 2) 445s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 445s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 445s gnpLag 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 18.4005 7.1627 2.57 0.02138 * 445s corpProf 0.1507 0.1905 0.79 0.44118 445s corpProfLag 0.5757 0.1634 3.52 0.00307 ** 445s capitalLag -0.1452 0.0339 -4.28 0.00065 *** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 1.322 on 15 degrees of freedom 445s Number of observations: 19 Degrees of Freedom: 15 445s SSR: 26.213 MSE: 1.748 Root MSE: 1.322 445s Multiple R-Squared: 0.852 Adjusted R-Squared: 0.823 445s 445s 445s 2SLS estimates for 'PrivateWages' (equation 3) 445s Model Formula: privWage ~ gnp + gnpLag + trend 445s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 445s gnpLag 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 1.3431 1.1544 1.16 0.26172 445s gnp 0.4438 0.0351 12.64 9.7e-10 *** 445s gnpLag 0.1447 0.0381 3.80 0.00158 ** 445s trend 0.1238 0.0300 4.13 0.00078 *** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 0.78 on 16 degrees of freedom 445s Number of observations: 20 Degrees of Freedom: 16 445s SSR: 9.741 MSE: 0.609 Root MSE: 0.78 445s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 445s 445s > residuals 445s Consumption Investment PrivateWages 445s 1 NA NA NA 445s 2 -0.6754 -1.23599 -1.3401 445s 3 -0.4627 0.32957 0.2378 445s 4 -1.1585 1.08894 1.1117 445s 5 -0.0305 -1.37017 -0.1954 445s 6 0.4693 0.48431 -0.5355 445s 7 NA NA NA 445s 8 1.6045 1.06811 -0.7908 445s 9 1.6018 0.16695 0.2831 445s 10 NA 1.86380 1.1353 445s 11 -0.9031 -0.92183 -0.1765 445s 12 -1.5948 -1.03217 0.6007 445s 13 NA NA 0.1443 445s 14 0.2854 0.85468 0.4826 445s 15 -0.4718 -0.36943 0.3016 445s 16 -0.2268 0.00554 0.0261 445s 17 2.0079 1.69566 -0.8614 445s 18 -0.7434 -0.12659 0.9927 445s 19 -0.5410 -3.26209 -0.4446 445s 20 1.4186 0.25579 -0.3914 445s 21 1.1462 -0.00185 -1.1115 445s 22 -1.7256 0.50679 0.5312 445s > fitted 445s Consumption Investment PrivateWages 445s 1 NA NA NA 445s 2 42.6 1.036 26.8 445s 3 45.5 1.570 29.1 445s 4 50.4 4.111 33.0 445s 5 50.6 4.370 34.1 445s 6 52.1 4.616 35.9 445s 7 NA NA NA 445s 8 54.6 3.132 38.7 445s 9 55.7 2.833 38.9 445s 10 NA 3.236 40.2 445s 11 55.9 1.922 38.1 445s 12 52.5 -2.368 33.9 445s 13 NA NA 28.9 445s 14 46.2 -5.955 28.0 445s 15 49.2 -2.631 30.3 445s 16 51.5 -1.306 33.2 445s 17 55.7 0.404 37.7 445s 18 59.4 2.127 40.0 445s 19 58.0 1.362 38.6 445s 20 60.2 1.044 42.0 445s 21 63.9 3.302 46.1 445s 22 71.4 4.393 52.8 445s > predict 445s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 445s 1 NA NA NA NA 445s 2 42.6 0.571 41.4 43.8 445s 3 45.5 0.656 44.1 46.9 445s 4 50.4 0.431 49.4 51.3 445s 5 50.6 0.510 49.5 51.7 445s 6 52.1 0.521 51.0 53.2 445s 7 NA NA NA NA 445s 8 54.6 0.419 53.7 55.5 445s 9 55.7 0.496 54.6 56.8 445s 10 NA NA NA NA 445s 11 55.9 0.910 54.0 57.9 445s 12 52.5 0.869 50.6 54.4 445s 13 NA NA NA NA 445s 14 46.2 0.694 44.7 47.7 445s 15 49.2 0.487 48.1 50.2 445s 16 51.5 0.396 50.7 52.4 445s 17 55.7 0.445 54.7 56.6 445s 18 59.4 0.386 58.6 60.3 445s 19 58.0 0.548 56.9 59.2 445s 20 60.2 0.528 59.0 61.3 445s 21 63.9 0.515 62.8 65.0 445s 22 71.4 0.786 69.7 73.1 445s Investment.pred Investment.se.fit Investment.lwr Investment.upr 445s 1 NA NA NA NA 445s 2 1.036 0.892 -0.865 2.937 445s 3 1.570 0.579 0.335 2.805 445s 4 4.111 0.531 2.979 5.243 445s 5 4.370 0.440 3.432 5.308 445s 6 4.616 0.416 3.729 5.502 445s 7 NA NA NA NA 445s 8 3.132 0.344 2.398 3.866 445s 9 2.833 0.533 1.696 3.970 445s 10 3.236 0.580 2.000 4.473 445s 11 1.922 0.959 -0.122 3.966 445s 12 -2.368 0.860 -4.201 -0.534 445s 13 NA NA NA NA 445s 14 -5.955 0.865 -7.799 -4.110 445s 15 -2.631 0.479 -3.652 -1.610 445s 16 -1.306 0.382 -2.120 -0.491 445s 17 0.404 0.487 -0.635 1.443 445s 18 2.127 0.319 1.447 2.806 445s 19 1.362 0.537 0.218 2.506 445s 20 1.044 0.566 -0.162 2.250 445s 21 3.302 0.486 2.265 4.339 445s 22 4.393 0.713 2.874 5.912 445s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 445s 1 NA NA NA NA 445s 2 26.8 0.321 26.2 27.5 445s 3 29.1 0.334 28.4 29.8 445s 4 33.0 0.353 32.2 33.7 445s 5 34.1 0.253 33.6 34.6 445s 6 35.9 0.261 35.4 36.5 445s 7 NA NA NA NA 445s 8 38.7 0.257 38.1 39.2 445s 9 38.9 0.245 38.4 39.4 445s 10 40.2 0.235 39.7 40.7 445s 11 38.1 0.348 37.3 38.8 445s 12 33.9 0.374 33.1 34.7 445s 13 28.9 0.447 27.9 29.8 445s 14 28.0 0.341 27.3 28.7 445s 15 30.3 0.333 29.6 31.0 445s 16 33.2 0.278 32.6 33.8 445s 17 37.7 0.288 37.1 38.3 445s 18 40.0 0.214 39.6 40.5 445s 19 38.6 0.351 37.9 39.4 445s 20 42.0 0.301 41.4 42.6 445s 21 46.1 0.304 45.5 46.8 445s 22 52.8 0.486 51.7 53.8 445s > model.frame 445s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 445s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 445s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 445s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 445s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 445s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 445s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 445s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 445s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 445s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 445s 10 57.8 21.7 21.1 NA 5.1 211 41.3 67.0 64.5 445s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 445s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 445s 13 45.6 NA 11.4 34.3 -6.2 213 29.0 44.3 53.4 445s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 445s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 445s 16 51.3 14.0 12.3 39.3 -1.3 199 33.2 54.4 49.7 445s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 445s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 445s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 445s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 445s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 445s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 445s trend 445s 1 -11 445s 2 -10 445s 3 -9 445s 4 -8 445s 5 -7 445s 6 -6 445s 7 -5 445s 8 -4 445s 9 -3 445s 10 -2 445s 11 -1 445s 12 0 445s 13 1 445s 14 2 445s 15 3 445s 16 4 445s 17 5 445s 18 6 445s 19 7 445s 20 8 445s 21 9 445s 22 10 445s > model.matrix 445s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0339 >" 445s [2] "Attributes: < Component “dimnames”: Component 1: 52 string mismatches >" 445s [3] "Numeric: lengths (708, 684) differ" 445s > nobs 445s [1] 57 445s > linearHypothesis 445s Linear hypothesis test (Theil's F test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 46 445s 2 45 1 1.37 0.25 445s Linear hypothesis test (F statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 46 445s 2 45 1 1.77 0.19 445s Linear hypothesis test (Chi^2 statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df Chisq Pr(>Chisq) 445s 1 46 445s 2 45 1 1.77 0.18 445s Linear hypothesis test (Theil's F test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 47 445s 2 45 2 0.69 0.51 445s Linear hypothesis test (F statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 47 445s 2 45 2 0.89 0.42 445s Linear hypothesis test (Chi^2 statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df Chisq Pr(>Chisq) 445s 1 47 445s 2 45 2 1.78 0.41 445s > logLik 445s 'log Lik.' -70.6 (df=13) 445s 'log Lik.' -78.7 (df=13) 445s > 445s > # SUR 445s Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == :> summary 445s 445s systemfit results 445s method: SUR 445s 445s N DF SSR detRCov OLS-R2 McElroy-R2 445s system 59 47 45.1 0.168 0.976 0.992 445s 445s N DF SSR MSE RMSE R2 Adj R2 445s Consumption 19 15 17.5 1.167 1.080 0.980 0.975 445s Investment 20 16 17.3 1.083 1.041 0.911 0.894 445s PrivateWages 20 16 10.3 0.642 0.801 0.987 0.985 445s 445s The covariance matrix of the residuals used for estimation 445s Consumption Investment PrivateWages 445s Consumption 0.9286 0.0435 -0.369 445s Investment 0.0435 0.7653 0.109 445s PrivateWages -0.3690 0.1091 0.468 445s 445s The covariance matrix of the residuals 445s Consumption Investment PrivateWages 445s Consumption 0.9251 0.0748 -0.427 445s Investment 0.0748 0.7653 0.171 445s PrivateWages -0.4268 0.1706 0.492 445s 445s The correlations of the residuals 445s Consumption Investment PrivateWages 445s Consumption 1.0000 0.0888 -0.636 445s Investment 0.0888 1.0000 0.268 445s PrivateWages -0.6364 0.2678 445s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 445s 1.000 445s 445s 445s SUR estimates for 'Consumption' (equation 1) 445s Model Formula: consump ~ corpProf + corpProfLag + wages 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 16.2684 1.2781 12.73 1.9e-09 *** 445s corpProf 0.1942 0.0927 2.10 0.054 . 445s corpProfLag 0.0746 0.0819 0.91 0.377 445s wages 0.8011 0.0372 21.53 1.1e-12 *** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 1.08 on 15 degrees of freedom 445s Number of observations: 19 Degrees of Freedom: 15 445s SSR: 17.501 MSE: 1.167 Root MSE: 1.08 445s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.975 445s 445s 445s SUR estimates for 'Investment' (equation 2) 445s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 12.6462 4.6500 2.72 0.01515 * 445s corpProf 0.4707 0.0916 5.14 9.9e-05 *** 445s corpProfLag 0.3519 0.0874 4.03 0.00097 *** 445s capitalLag -0.1253 0.0229 -5.47 5.1e-05 *** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 1.041 on 16 degrees of freedom 445s Number of observations: 20 Degrees of Freedom: 16 445s SSR: 17.325 MSE: 1.083 Root MSE: 1.041 445s Multiple R-Squared: 0.911 Adjusted R-Squared: 0.894 445s 445s 445s SUR estimates for 'PrivateWages' (equation 3) 445s Model Formula: privWage ~ gnp + gnpLag + trend 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 1.3245 1.0946 1.21 0.24 445s gnp 0.4184 0.0260 16.08 2.7e-11 *** 445s gnpLag 0.1714 0.0307 5.59 4.1e-05 *** 445s trend 0.1455 0.0276 5.27 7.6e-05 *** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 0.801 on 16 degrees of freedom 445s Number of observations: 20 Degrees of Freedom: 16 445s SSR: 10.265 MSE: 0.642 Root MSE: 0.801 445s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 445s 445s > residuals 445s Consumption Investment PrivateWages 445s 1 NA NA NA 445s 2 -0.3146 -0.2419 -1.1439 445s 3 -1.2707 -0.1795 0.5080 445s 4 -1.5428 1.0691 1.4208 445s 5 -0.4489 -1.4778 -0.1000 445s 6 0.0588 0.3168 -0.3599 445s 7 0.9215 1.4450 NA 445s 8 1.3791 0.8287 -0.7561 445s 9 1.0901 -0.5272 0.2880 445s 10 NA 1.2089 1.1795 445s 11 0.3577 0.4081 -0.3681 445s 12 -0.2286 0.2569 0.3439 445s 13 NA NA -0.1574 445s 14 0.2172 0.4743 0.4225 445s 15 -0.1124 -0.0607 0.3154 445s 16 -0.0876 0.0761 0.0151 445s 17 1.5611 1.0205 -0.8084 445s 18 -0.4529 0.0580 0.8611 445s 19 0.1999 -2.5444 -0.7635 445s 20 0.9266 -0.6202 -0.4039 445s 21 0.7589 -0.7478 -1.2175 445s 22 -2.2135 -0.6029 0.5611 445s > fitted 445s Consumption Investment PrivateWages 445s 1 NA NA NA 445s 2 42.2 0.0419 26.6 445s 3 46.3 2.0795 28.8 445s 4 50.7 4.1309 32.7 445s 5 51.0 4.4778 34.0 445s 6 52.5 4.7832 35.8 445s 7 54.2 4.1550 NA 445s 8 54.8 3.3713 38.7 445s 9 56.2 3.5272 38.9 445s 10 NA 3.8911 40.1 445s 11 54.6 0.5919 38.3 445s 12 51.1 -3.6569 34.2 445s 13 NA NA 29.2 445s 14 46.3 -5.5743 28.1 445s 15 48.8 -2.9393 30.3 445s 16 51.4 -1.3761 33.2 445s 17 56.1 1.0795 37.6 445s 18 59.2 1.9420 40.1 445s 19 57.3 0.6444 39.0 445s 20 60.7 1.9202 42.0 445s 21 64.2 4.0478 46.2 445s 22 71.9 5.5029 52.7 445s > predict 445s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 445s 1 NA NA NA NA 445s 2 42.2 0.448 41.3 43.1 445s 3 46.3 0.476 45.3 47.2 445s 4 50.7 0.318 50.1 51.4 445s 5 51.0 0.373 50.3 51.8 445s 6 52.5 0.378 51.8 53.3 445s 7 54.2 0.337 53.5 54.9 445s 8 54.8 0.310 54.2 55.4 445s 9 56.2 0.343 55.5 56.9 445s 10 NA NA NA NA 445s 11 54.6 0.567 53.5 55.8 445s 12 51.1 0.509 50.1 52.2 445s 13 NA NA NA NA 445s 14 46.3 0.573 45.1 47.4 445s 15 48.8 0.382 48.0 49.6 445s 16 51.4 0.328 50.7 52.0 445s 17 56.1 0.336 55.5 56.8 445s 18 59.2 0.309 58.5 59.8 445s 19 57.3 0.370 56.6 58.0 445s 20 60.7 0.401 59.9 61.5 445s 21 64.2 0.405 63.4 65.1 445s 22 71.9 0.633 70.6 73.2 445s Investment.pred Investment.se.fit Investment.lwr Investment.upr 445s 1 NA NA NA NA 445s 2 0.0419 0.533 -1.0309 1.115 445s 3 2.0795 0.433 1.2082 2.951 445s 4 4.1309 0.387 3.3532 4.909 445s 5 4.4778 0.322 3.8307 5.125 445s 6 4.7832 0.305 4.1700 5.396 445s 7 4.1550 0.283 3.5852 4.725 445s 8 3.3713 0.253 2.8630 3.880 445s 9 3.5272 0.337 2.8488 4.206 445s 10 3.8911 0.386 3.1149 4.667 445s 11 0.5919 0.561 -0.5376 1.722 445s 12 -3.6569 0.530 -4.7223 -2.591 445s 13 NA NA NA NA 445s 14 -5.5743 0.618 -6.8176 -4.331 445s 15 -2.9393 0.362 -3.6671 -2.212 445s 16 -1.3761 0.296 -1.9710 -0.781 445s 17 1.0795 0.300 0.4763 1.683 445s 18 1.9420 0.216 1.5081 2.376 445s 19 0.6444 0.298 0.0451 1.244 445s 20 1.9202 0.318 1.2798 2.561 445s 21 4.0478 0.295 3.4537 4.642 445s 22 5.5029 0.417 4.6638 6.342 445s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 445s 1 NA NA NA NA 445s 2 26.6 0.312 26.0 27.3 445s 3 28.8 0.312 28.2 29.4 445s 4 32.7 0.307 32.1 33.3 445s 5 34.0 0.237 33.5 34.5 445s 6 35.8 0.235 35.3 36.2 445s 7 NA NA NA NA 445s 8 38.7 0.239 38.2 39.1 445s 9 38.9 0.228 38.5 39.4 445s 10 40.1 0.218 39.7 40.6 445s 11 38.3 0.293 37.7 38.9 445s 12 34.2 0.290 33.6 34.7 445s 13 29.2 0.343 28.5 29.8 445s 14 28.1 0.321 27.4 28.7 445s 15 30.3 0.320 29.6 30.9 445s 16 33.2 0.268 32.6 33.7 445s 17 37.6 0.263 37.1 38.1 445s 18 40.1 0.207 39.7 40.6 445s 19 39.0 0.293 38.4 39.6 445s 20 42.0 0.279 41.4 42.6 445s 21 46.2 0.295 45.6 46.8 445s 22 52.7 0.435 51.9 53.6 445s > model.frame 445s [1] TRUE 445s > model.matrix 445s [1] TRUE 445s > nobs 445s [1] 59 445s > linearHypothesis 445s Linear hypothesis test (Theil's F test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 48 445s 2 47 1 0.41 0.52 445s Linear hypothesis test (F statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 48 445s 2 47 1 0.52 0.47 445s Linear hypothesis test (Chi^2 statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df Chisq Pr(>Chisq) 445s 1 48 445s 2 47 1 0.52 0.47 445s Linear hypothesis test (Theil's F test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 49 445s 2 47 2 0.31 0.73 445s Linear hypothesis test (F statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 49 445s 2 47 2 0.4 0.67 445s Linear hypothesis test (Chi^2 statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df Chisq Pr(>Chisq) 445s 1 49 445s 2 47 2 0.79 0.67 445s > logLik 445s 'log Lik.' -67.3 (df=18) 445s 'log Lik.' -74.9 (df=18) 445s > 445s > # 3SLS 445s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 445s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 445s > summary 445s 445s systemfit results 445s method: 3SLS 445s 445s N DF SSR detRCov OLS-R2 McElroy-R2 445s system 57 45 66.8 0.361 0.963 0.993 445s 445s N DF SSR MSE RMSE R2 Adj R2 445s Consumption 18 14 22.6 1.616 1.271 0.974 0.968 445s Investment 19 15 34.1 2.277 1.509 0.807 0.769 445s PrivateWages 20 16 10.1 0.628 0.793 0.987 0.985 445s 445s The covariance matrix of the residuals used for estimation 445s Consumption Investment PrivateWages 445s Consumption 1.237 0.518 -0.408 445s Investment 0.518 1.263 0.113 445s PrivateWages -0.408 0.113 0.468 445s 445s The covariance matrix of the residuals 445s Consumption Investment PrivateWages 445s Consumption 1.257 0.601 -0.421 445s Investment 0.601 1.601 0.214 445s PrivateWages -0.421 0.214 0.491 445s 445s The correlations of the residuals 445s Consumption Investment PrivateWages 445s Consumption 1.000 0.425 -0.537 445s Investment 0.425 1.000 0.239 445s PrivateWages -0.537 0.239 1.000 445s 445s 445s 3SLS estimates for 'Consumption' (equation 1) 445s Model Formula: consump ~ corpProf + corpProfLag + wages 445s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 445s gnpLag 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 18.2100 1.5273 11.92 1e-08 *** 445s corpProf -0.0639 0.1461 -0.44 0.67 445s corpProfLag 0.1687 0.1125 1.50 0.16 445s wages 0.8230 0.0431 19.07 2e-11 *** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 1.271 on 14 degrees of freedom 445s Number of observations: 18 Degrees of Freedom: 14 445s SSR: 22.626 MSE: 1.616 Root MSE: 1.271 445s Multiple R-Squared: 0.974 Adjusted R-Squared: 0.968 445s 445s 445s 3SLS estimates for 'Investment' (equation 2) 445s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 445s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 445s gnpLag 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 24.7534 6.5548 3.78 0.00183 ** 445s corpProf 0.0524 0.1807 0.29 0.77600 445s corpProfLag 0.6584 0.1551 4.24 0.00071 *** 445s capitalLag -0.1756 0.0311 -5.64 4.7e-05 *** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 1.509 on 15 degrees of freedom 445s Number of observations: 19 Degrees of Freedom: 15 445s SSR: 34.149 MSE: 2.277 Root MSE: 1.509 445s Multiple R-Squared: 0.807 Adjusted R-Squared: 0.769 445s 445s 445s 3SLS estimates for 'PrivateWages' (equation 3) 445s Model Formula: privWage ~ gnp + gnpLag + trend 445s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 445s gnpLag 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 0.8154 1.0961 0.74 0.46772 445s gnp 0.4250 0.0299 14.19 1.7e-10 *** 445s gnpLag 0.1731 0.0331 5.23 8.3e-05 *** 445s trend 0.1255 0.0283 4.43 0.00042 *** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 0.793 on 16 degrees of freedom 445s Number of observations: 20 Degrees of Freedom: 16 445s SSR: 10.054 MSE: 0.628 Root MSE: 0.793 445s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 445s 445s > residuals 445s Consumption Investment PrivateWages 445s 1 NA NA NA 445s 2 -0.8680 -1.857 -1.21010 445s 3 -0.7217 0.170 0.43075 445s 4 -1.1353 0.762 1.30899 445s 5 0.0755 -1.565 -0.20270 445s 6 0.6348 0.367 -0.46842 445s 7 NA NA NA 445s 8 1.7953 1.230 -0.85853 445s 9 1.7924 0.568 0.20422 445s 10 NA 2.308 1.09889 445s 11 -0.5211 -0.972 -0.39427 445s 12 -1.5560 -0.960 0.39889 445s 13 NA NA -0.00934 445s 14 -0.2384 1.327 0.59990 445s 15 -0.7342 -0.292 0.48094 445s 16 -0.4331 0.068 0.16188 445s 17 1.8775 1.932 -0.70448 445s 18 -0.6294 -0.154 0.95616 445s 19 -0.4252 -3.400 -0.62489 445s 20 1.3682 0.589 -0.29589 445s 21 1.3155 0.271 -1.14466 445s 22 -1.4276 0.942 0.55941 445s > fitted 445s Consumption Investment PrivateWages 445s 1 NA NA NA 445s 2 42.8 1.657 26.7 445s 3 45.7 1.730 28.9 445s 4 50.3 4.438 32.8 445s 5 50.5 4.565 34.1 445s 6 52.0 4.733 35.9 445s 7 NA NA NA 445s 8 54.4 2.970 38.8 445s 9 55.5 2.432 39.0 445s 10 NA 2.792 40.2 445s 11 55.5 1.972 38.3 445s 12 52.5 -2.440 34.1 445s 13 NA NA 29.0 445s 14 46.7 -6.427 27.9 445s 15 49.4 -2.708 30.1 445s 16 51.7 -1.368 33.0 445s 17 55.8 0.168 37.5 445s 18 59.3 2.154 40.0 445s 19 57.9 1.500 38.8 445s 20 60.2 0.711 41.9 445s 21 63.7 3.029 46.1 445s 22 71.1 3.958 52.7 445s > predict 445s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 445s 1 NA NA NA NA 445s 2 42.8 0.542 39.8 45.7 445s 3 45.7 0.612 42.7 48.7 445s 4 50.3 0.407 47.5 53.2 445s 5 50.5 0.478 47.6 53.4 445s 6 52.0 0.488 49.0 54.9 445s 7 NA NA NA NA 445s 8 54.4 0.394 51.5 57.3 445s 9 55.5 0.464 52.6 58.4 445s 10 NA NA NA NA 445s 11 55.5 0.811 52.3 58.8 445s 12 52.5 0.773 49.3 55.6 445s 13 NA NA NA NA 445s 14 46.7 0.666 43.7 49.8 445s 15 49.4 0.463 46.5 52.3 445s 16 51.7 0.381 48.9 54.6 445s 17 55.8 0.424 52.9 58.7 445s 18 59.3 0.359 56.5 62.2 445s 19 57.9 0.492 55.0 60.8 445s 20 60.2 0.501 57.3 63.2 445s 21 63.7 0.491 60.8 66.6 445s 22 71.1 0.749 68.0 74.3 445s Investment.pred Investment.se.fit Investment.lwr Investment.upr 445s 1 NA NA NA NA 445s 2 1.657 0.831 -2.015 5.329 445s 3 1.730 0.574 -1.711 5.171 445s 4 4.438 0.507 1.045 7.831 445s 5 4.565 0.426 1.223 7.907 445s 6 4.733 0.406 1.402 8.064 445s 7 NA NA NA NA 445s 8 2.970 0.334 -0.324 6.263 445s 9 2.432 0.501 -0.957 5.820 445s 10 2.792 0.544 -0.627 6.211 445s 11 1.972 0.937 -1.814 5.757 445s 12 -2.440 0.849 -6.131 1.250 445s 13 NA NA NA NA 445s 14 -6.427 0.836 -10.104 -2.750 445s 15 -2.708 0.477 -6.081 0.665 445s 16 -1.368 0.381 -4.685 1.949 445s 17 0.168 0.473 -3.202 3.538 445s 18 2.154 0.311 -1.130 5.438 445s 19 1.500 0.518 -1.900 4.900 445s 20 0.711 0.541 -2.705 4.127 445s 21 3.029 0.467 -0.338 6.395 445s 22 3.958 0.677 0.432 7.483 445s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 445s 1 NA NA NA NA 445s 2 26.7 0.315 24.9 28.5 445s 3 28.9 0.322 27.1 30.7 445s 4 32.8 0.330 31.0 34.6 445s 5 34.1 0.241 32.3 35.9 445s 6 35.9 0.249 34.1 37.6 445s 7 NA NA NA NA 445s 8 38.8 0.243 37.0 40.5 445s 9 39.0 0.231 37.2 40.7 445s 10 40.2 0.225 38.5 41.9 445s 11 38.3 0.305 36.5 40.1 445s 12 34.1 0.317 32.3 35.9 445s 13 29.0 0.382 27.1 30.9 445s 14 27.9 0.321 26.1 29.7 445s 15 30.1 0.316 28.3 31.9 445s 16 33.0 0.265 31.3 34.8 445s 17 37.5 0.270 35.7 39.3 445s 18 40.0 0.207 38.3 41.8 445s 19 38.8 0.311 37.0 40.6 445s 20 41.9 0.287 40.1 43.7 445s 21 46.1 0.300 44.3 47.9 445s 22 52.7 0.463 50.8 54.7 445s > model.frame 445s [1] TRUE 445s > model.matrix 445s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0339 >" 445s [2] "Attributes: < Component “dimnames”: Component 1: 52 string mismatches >" 445s [3] "Numeric: lengths (708, 684) differ" 445s > nobs 445s [1] 57 445s > linearHypothesis 445s Linear hypothesis test (Theil's F test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 46 445s 2 45 1 1.95 0.17 445s Linear hypothesis test (F statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 46 445s 2 45 1 2.71 0.11 445s Linear hypothesis test (Chi^2 statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df Chisq Pr(>Chisq) 445s 1 46 445s 2 45 1 2.71 0.1 . 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s Linear hypothesis test (Theil's F test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 47 445s 2 45 2 1.78 0.18 445s Linear hypothesis test (F statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 47 445s 2 45 2 2.48 0.095 . 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s Linear hypothesis test (Chi^2 statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df Chisq Pr(>Chisq) 445s 1 47 445s 2 45 2 4.95 0.084 . 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s > logLik 445s 'log Lik.' -71.2 (df=18) 445s 'log Lik.' -81.7 (df=18) 445s > 445s > # I3SLS 445s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 445s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 445s > summary 445s 445s systemfit results 445s method: iterated 3SLS 445s 445s convergence achieved after 9 iterations 445s 445s N DF SSR detRCov OLS-R2 McElroy-R2 445s system 57 45 75 0.422 0.959 0.993 445s 445s N DF SSR MSE RMSE R2 Adj R2 445s Consumption 18 14 22.7 1.622 1.273 0.973 0.968 445s Investment 19 15 42.1 2.809 1.676 0.762 0.715 445s PrivateWages 20 16 10.2 0.638 0.799 0.987 0.985 445s 445s The covariance matrix of the residuals used for estimation 445s Consumption Investment PrivateWages 445s Consumption 1.261 0.675 -0.439 445s Investment 0.675 1.949 0.237 445s PrivateWages -0.439 0.237 0.503 445s 445s The covariance matrix of the residuals 445s Consumption Investment PrivateWages 445s Consumption 1.261 0.675 -0.439 445s Investment 0.675 1.949 0.237 445s PrivateWages -0.439 0.237 0.503 445s 445s The correlations of the residuals 445s Consumption Investment PrivateWages 445s Consumption 1.000 0.431 -0.550 445s Investment 0.431 1.000 0.239 445s PrivateWages -0.550 0.239 1.000 445s 445s 445s 3SLS estimates for 'Consumption' (equation 1) 445s Model Formula: consump ~ corpProf + corpProfLag + wages 445s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 445s gnpLag 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 18.5887 1.5250 12.19 7.6e-09 *** 445s corpProf -0.0438 0.1441 -0.30 0.77 445s corpProfLag 0.1456 0.1109 1.31 0.21 445s wages 0.8141 0.0428 19.01 2.1e-11 *** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 1.273 on 14 degrees of freedom 445s Number of observations: 18 Degrees of Freedom: 14 445s SSR: 22.704 MSE: 1.622 Root MSE: 1.273 445s Multiple R-Squared: 0.973 Adjusted R-Squared: 0.968 445s 445s 445s 3SLS estimates for 'Investment' (equation 2) 445s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 445s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 445s gnpLag 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 29.4725 7.6857 3.83 0.0016 ** 445s corpProf -0.0183 0.2154 -0.09 0.9333 445s corpProfLag 0.7195 0.1850 3.89 0.0015 ** 445s capitalLag -0.1985 0.0366 -5.43 6.9e-05 *** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 1.676 on 15 degrees of freedom 445s Number of observations: 19 Degrees of Freedom: 15 445s SSR: 42.136 MSE: 2.809 Root MSE: 1.676 445s Multiple R-Squared: 0.762 Adjusted R-Squared: 0.715 445s 445s 445s 3SLS estimates for 'PrivateWages' (equation 3) 445s Model Formula: privWage ~ gnp + gnpLag + trend 445s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 445s gnpLag 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 0.5385 1.1055 0.49 0.63277 445s gnp 0.4251 0.0287 14.80 9.3e-11 *** 445s gnpLag 0.1776 0.0322 5.51 4.7e-05 *** 445s trend 0.1211 0.0283 4.28 0.00057 *** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 0.799 on 16 degrees of freedom 445s Number of observations: 20 Degrees of Freedom: 16 445s SSR: 10.204 MSE: 0.638 Root MSE: 0.799 445s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 445s 445s > residuals 445s Consumption Investment PrivateWages 445s 1 NA NA NA 445s 2 -0.9524 -2.2888 -1.1837 445s 3 -0.8681 0.0698 0.4581 445s 4 -1.1653 0.5368 1.3199 445s 5 0.0601 -1.6917 -0.2194 445s 6 0.6426 0.2972 -0.4805 445s 7 NA NA NA 445s 8 1.8394 1.3723 -0.8931 445s 9 1.8275 0.8861 0.1723 445s 10 NA 2.6574 1.0707 445s 11 -0.3387 -0.9736 -0.4288 445s 12 -1.4550 -0.8630 0.3956 445s 13 NA NA 0.0277 445s 14 -0.3782 1.7151 0.6823 445s 15 -0.7768 -0.1993 0.5638 445s 16 -0.4606 0.1448 0.2281 445s 17 1.8605 2.1295 -0.6557 445s 18 -0.5262 -0.1493 0.9718 445s 19 -0.3047 -3.4730 -0.6148 445s 20 1.3992 0.8566 -0.2636 445s 21 1.4216 0.4910 -1.1472 445s 22 -1.2431 1.2792 0.5323 445s > fitted 445s Consumption Investment PrivateWages 445s 1 NA NA NA 445s 2 42.9 2.0888 26.7 445s 3 45.9 1.8302 28.8 445s 4 50.4 4.6632 32.8 445s 5 50.5 4.6917 34.1 445s 6 52.0 4.8028 35.9 445s 7 NA NA NA 445s 8 54.4 2.8277 38.8 445s 9 55.5 2.1139 39.0 445s 10 NA 2.4426 40.2 445s 11 55.3 1.9736 38.3 445s 12 52.4 -2.5370 34.1 445s 13 NA NA 29.0 445s 14 46.9 -6.8151 27.8 445s 15 49.5 -2.8007 30.0 445s 16 51.8 -1.4448 33.0 445s 17 55.8 -0.0295 37.5 445s 18 59.2 2.1493 40.0 445s 19 57.8 1.5730 38.8 445s 20 60.2 0.4434 41.9 445s 21 63.6 2.8090 46.1 445s 22 70.9 3.6208 52.8 445s > predict 445s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 445s 1 NA NA NA NA 445s 2 42.9 0.541 41.8 43.9 445s 3 45.9 0.608 44.6 47.1 445s 4 50.4 0.403 49.6 51.2 445s 5 50.5 0.472 49.6 51.5 445s 6 52.0 0.481 51.0 52.9 445s 7 NA NA NA NA 445s 8 54.4 0.388 53.6 55.1 445s 9 55.5 0.458 54.6 56.4 445s 10 NA NA NA NA 445s 11 55.3 0.795 53.7 56.9 445s 12 52.4 0.762 50.8 53.9 445s 13 NA NA NA NA 445s 14 46.9 0.663 45.5 48.2 445s 15 49.5 0.462 48.5 50.4 445s 16 51.8 0.381 51.0 52.5 445s 17 55.8 0.423 55.0 56.7 445s 18 59.2 0.355 58.5 59.9 445s 19 57.8 0.484 56.8 58.8 445s 20 60.2 0.500 59.2 61.2 445s 21 63.6 0.490 62.6 64.6 445s 22 70.9 0.747 69.4 72.4 445s Investment.pred Investment.se.fit Investment.lwr Investment.upr 445s 1 NA NA NA NA 445s 2 2.0888 0.985 0.105 4.072 445s 3 1.8302 0.708 0.404 3.257 445s 4 4.6632 0.612 3.430 5.897 445s 5 4.6917 0.519 3.645 5.738 445s 6 4.8028 0.498 3.800 5.806 445s 7 NA NA NA NA 445s 8 2.8277 0.410 2.003 3.653 445s 9 2.1139 0.599 0.908 3.320 445s 10 2.4426 0.651 1.131 3.754 445s 11 1.9736 1.138 -0.320 4.267 445s 12 -2.5370 1.038 -4.627 -0.447 445s 13 NA NA NA NA 445s 14 -6.8151 1.011 -8.851 -4.779 445s 15 -2.8007 0.587 -3.984 -1.617 445s 16 -1.4448 0.470 -2.392 -0.498 445s 17 -0.0295 0.573 -1.183 1.124 445s 18 2.1493 0.380 1.384 2.915 445s 19 1.5730 0.624 0.315 2.831 445s 20 0.4434 0.649 -0.864 1.751 445s 21 2.8090 0.565 1.671 3.947 445s 22 3.6208 0.814 1.982 5.260 445s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 445s 1 NA NA NA NA 445s 2 26.7 0.322 26.0 27.3 445s 3 28.8 0.328 28.2 29.5 445s 4 32.8 0.332 32.1 33.4 445s 5 34.1 0.244 33.6 34.6 445s 6 35.9 0.252 35.4 36.4 445s 7 NA NA NA NA 445s 8 38.8 0.246 38.3 39.3 445s 9 39.0 0.234 38.6 39.5 445s 10 40.2 0.230 39.8 40.7 445s 11 38.3 0.299 37.7 38.9 445s 12 34.1 0.304 33.5 34.7 445s 13 29.0 0.366 28.2 29.7 445s 14 27.8 0.321 27.2 28.5 445s 15 30.0 0.317 29.4 30.7 445s 16 33.0 0.266 32.4 33.5 445s 17 37.5 0.270 36.9 38.0 445s 18 40.0 0.211 39.6 40.5 445s 19 38.8 0.305 38.2 39.4 445s 20 41.9 0.290 41.3 42.4 445s 21 46.1 0.309 45.5 46.8 445s 22 52.8 0.468 51.8 53.7 445s > model.frame 445s [1] TRUE 445s > model.matrix 445s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0339 >" 445s [2] "Attributes: < Component “dimnames”: Component 1: 52 string mismatches >" 445s [3] "Numeric: lengths (708, 684) differ" 445s > nobs 445s [1] 57 445s > linearHypothesis 445s Linear hypothesis test (Theil's F test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 46 445s 2 45 1 2.17 0.15 445s Linear hypothesis test (F statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 46 445s 2 45 1 2.84 0.099 . 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s Linear hypothesis test (Chi^2 statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df Chisq Pr(>Chisq) 445s 1 46 445s 2 45 1 2.84 0.092 . 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s Linear hypothesis test (Theil's F test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 47 445s 2 45 2 2.45 0.098 . 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s Linear hypothesis test (F statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 47 445s 2 45 2 3.2 0.05 . 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s Linear hypothesis test (Chi^2 statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df Chisq Pr(>Chisq) 445s 1 47 445s 2 45 2 6.4 0.041 * 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s > logLik 445s 'log Lik.' -72.7 (df=18) 445s 'log Lik.' -83.9 (df=18) 445s > 445s > # OLS 445s Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 445s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 445s > summary 445s 445s systemfit results 445s method: OLS 445s 445s N DF SSR detRCov OLS-R2 McElroy-R2 445s system 58 46 44.2 0.565 0.976 0.991 445s 445s N DF SSR MSE RMSE R2 Adj R2 445s Consumption 19 15 17.36 1.157 1.08 0.980 0.976 445s Investment 19 15 17.11 1.140 1.07 0.907 0.889 445s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 445s 445s The covariance matrix of the residuals 445s Consumption Investment PrivateWages 445s Consumption 1.285 0.061 -0.511 445s Investment 0.061 1.059 0.151 445s PrivateWages -0.511 0.151 0.648 445s 445s The correlations of the residuals 445s Consumption Investment PrivateWages 445s Consumption 1.0000 0.0457 -0.568 445s Investment 0.0457 1.0000 0.168 445s PrivateWages -0.5681 0.1676 1.000 445s 445s 445s OLS estimates for 'Consumption' (equation 1) 445s Model Formula: consump ~ corpProf + corpProfLag + wages 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 16.2957 1.5438 10.56 2.4e-08 *** 445s corpProf 0.1796 0.1206 1.49 0.16 445s corpProfLag 0.1032 0.1031 1.00 0.33 445s wages 0.7962 0.0449 17.73 1.8e-11 *** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 1.076 on 15 degrees of freedom 445s Number of observations: 19 Degrees of Freedom: 15 445s SSR: 17.362 MSE: 1.157 Root MSE: 1.076 445s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.976 445s 445s 445s OLS estimates for 'Investment' (equation 2) 445s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 10.1724 5.5758 1.82 0.08808 . 445s corpProf 0.5004 0.1092 4.58 0.00036 *** 445s corpProfLag 0.3270 0.1052 3.11 0.00718 ** 445s capitalLag -0.1134 0.0275 -4.13 0.00090 *** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 1.068 on 15 degrees of freedom 445s Number of observations: 19 Degrees of Freedom: 15 445s SSR: 17.105 MSE: 1.14 Root MSE: 1.068 445s Multiple R-Squared: 0.907 Adjusted R-Squared: 0.889 445s 445s 445s OLS estimates for 'PrivateWages' (equation 3) 445s Model Formula: privWage ~ gnp + gnpLag + trend 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 1.3550 1.3512 1.00 0.3309 445s gnp 0.4417 0.0342 12.92 7e-10 *** 445s gnpLag 0.1466 0.0393 3.73 0.0018 ** 445s trend 0.1244 0.0347 3.58 0.0025 ** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 0.78 on 16 degrees of freedom 445s Number of observations: 20 Degrees of Freedom: 16 445s SSR: 9.739 MSE: 0.609 Root MSE: 0.78 445s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 445s 445s compare coef with single-equation OLS 445s [1] TRUE 445s > residuals 445s Consumption Investment PrivateWages 445s 1 NA NA NA 445s 2 -0.3863 0.00693 -1.3389 445s 3 -1.2484 -0.06954 0.2462 445s 4 -1.6040 1.22401 1.1255 445s 5 -0.5384 -1.37697 -0.1959 445s 6 -0.0413 0.38610 -0.5284 445s 7 0.8043 1.48598 NA 445s 8 1.2830 0.78465 -0.7909 445s 9 1.0142 -0.65483 0.2819 445s 10 NA 1.06018 1.1384 445s 11 0.1429 0.39508 -0.1904 445s 12 -0.3439 0.20479 0.5813 445s 13 NA NA 0.1206 445s 14 0.3199 0.32778 0.4773 445s 15 -0.1016 -0.07450 0.3035 445s 16 -0.0702 NA 0.0284 445s 17 1.6064 0.96998 -0.8517 445s 18 -0.4980 0.08124 0.9908 445s 19 0.1253 -2.49295 -0.4597 445s 20 0.9805 -0.70609 -0.3819 445s 21 0.7551 -0.81928 -1.1062 445s 22 -2.1992 -0.73256 0.5501 445s > fitted 445s Consumption Investment PrivateWages 445s 1 NA NA NA 445s 2 42.3 -0.207 26.8 445s 3 46.2 1.970 29.1 445s 4 50.8 3.976 33.0 445s 5 51.1 4.377 34.1 445s 6 52.6 4.714 35.9 445s 7 54.3 4.114 NA 445s 8 54.9 3.415 38.7 445s 9 56.3 3.655 38.9 445s 10 NA 4.040 40.2 445s 11 54.9 0.605 38.1 445s 12 51.2 -3.605 33.9 445s 13 NA NA 28.9 445s 14 46.2 -5.428 28.0 445s 15 48.8 -2.926 30.3 445s 16 51.4 NA 33.2 445s 17 56.1 1.130 37.7 445s 18 59.2 1.919 40.0 445s 19 57.4 0.593 38.7 445s 20 60.6 2.006 42.0 445s 21 64.2 4.119 46.1 445s 22 71.9 5.633 52.7 445s > predict 445s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 445s 1 NA NA NA NA 445s 2 42.3 0.543 39.9 44.7 445s 3 46.2 0.581 43.8 48.7 445s 4 50.8 0.394 48.5 53.1 445s 5 51.1 0.465 48.8 53.5 445s 6 52.6 0.474 50.3 55.0 445s 7 54.3 0.423 52.0 56.6 445s 8 54.9 0.389 52.6 57.2 445s 9 56.3 0.434 54.0 58.6 445s 10 NA NA NA NA 445s 11 54.9 0.727 52.2 57.5 445s 12 51.2 0.662 48.7 53.8 445s 13 NA NA NA NA 445s 14 46.2 0.698 43.6 48.8 445s 15 48.8 0.470 46.4 51.2 445s 16 51.4 0.398 49.1 53.7 445s 17 56.1 0.405 53.8 58.4 445s 18 59.2 0.375 56.9 61.5 445s 19 57.4 0.466 55.0 59.7 445s 20 60.6 0.482 58.2 63.0 445s 21 64.2 0.485 61.9 66.6 445s 22 71.9 0.755 69.3 74.5 445s Investment.pred Investment.se.fit Investment.lwr Investment.upr 445s 1 NA NA NA NA 445s 2 -0.207 0.645 -2.718 2.30 445s 3 1.970 0.523 -0.423 4.36 445s 4 3.976 0.462 1.634 6.32 445s 5 4.377 0.383 2.094 6.66 445s 6 4.714 0.362 2.444 6.98 445s 7 4.114 0.336 1.861 6.37 445s 8 3.415 0.298 1.184 5.65 445s 9 3.655 0.400 1.359 5.95 445s 10 4.040 0.458 1.701 6.38 445s 11 0.605 0.666 -1.928 3.14 445s 12 -3.605 0.637 -6.108 -1.10 445s 13 NA NA NA NA 445s 14 -5.428 0.767 -8.074 -2.78 445s 15 -2.926 0.453 -5.261 -0.59 445s 16 NA NA NA NA 445s 17 1.130 0.366 -1.142 3.40 445s 18 1.919 0.258 -0.293 4.13 445s 19 0.593 0.357 -1.674 2.86 445s 20 2.006 0.384 -0.278 4.29 445s 21 4.119 0.350 1.858 6.38 445s 22 5.633 0.495 3.263 8.00 445s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 445s 1 NA NA NA NA 445s 2 26.8 0.378 25.1 28.6 445s 3 29.1 0.381 27.3 30.8 445s 4 33.0 0.384 31.2 34.7 445s 5 34.1 0.297 32.4 35.8 445s 6 35.9 0.296 34.2 37.6 445s 7 NA NA NA NA 445s 8 38.7 0.303 37.0 40.4 445s 9 38.9 0.288 37.2 40.6 445s 10 40.2 0.274 38.5 41.8 445s 11 38.1 0.377 36.3 39.8 445s 12 33.9 0.381 32.2 35.7 445s 13 28.9 0.452 27.1 30.7 445s 14 28.0 0.397 26.3 29.8 445s 15 30.3 0.391 28.5 32.1 445s 16 33.2 0.327 31.5 34.9 445s 17 37.7 0.320 36.0 39.3 445s 18 40.0 0.250 38.4 41.7 445s 19 38.7 0.375 36.9 40.4 445s 20 42.0 0.337 40.3 43.7 445s 21 46.1 0.352 44.4 47.8 445s 22 52.7 0.530 50.9 54.6 445s > model.frame 445s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 445s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 445s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 445s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 445s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 445s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 445s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 445s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 445s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 445s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 445s 10 57.8 21.7 21.1 NA 5.1 211 41.3 67.0 64.5 445s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 445s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 445s 13 45.6 NA 11.4 34.3 -6.2 213 29.0 44.3 53.4 445s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 445s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 445s 16 51.3 14.0 12.3 39.3 NA 199 33.2 54.4 49.7 445s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 445s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 445s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 445s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 445s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 445s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 445s trend 445s 1 -11 445s 2 -10 445s 3 -9 445s 4 -8 445s 5 -7 445s 6 -6 445s 7 -5 445s 8 -4 445s 9 -3 445s 10 -2 445s 11 -1 445s 12 0 445s 13 1 445s 14 2 445s 15 3 445s 16 4 445s 17 5 445s 18 6 445s 19 7 445s 20 8 445s 21 9 445s 22 10 445s > model.matrix 445s Consumption_(Intercept) Consumption_corpProf 445s Consumption_2 1 12.4 445s Consumption_3 1 16.9 445s Consumption_4 1 18.4 445s Consumption_5 1 19.4 445s Consumption_6 1 20.1 445s Consumption_7 1 19.6 445s Consumption_8 1 19.8 445s Consumption_9 1 21.1 445s Consumption_11 1 15.6 445s Consumption_12 1 11.4 445s Consumption_14 1 11.2 445s Consumption_15 1 12.3 445s Consumption_16 1 14.0 445s Consumption_17 1 17.6 445s Consumption_18 1 17.3 445s Consumption_19 1 15.3 445s Consumption_20 1 19.0 445s Consumption_21 1 21.1 445s Consumption_22 1 23.5 445s Investment_2 0 0.0 445s Investment_3 0 0.0 445s Investment_4 0 0.0 445s Investment_5 0 0.0 445s Investment_6 0 0.0 445s Investment_7 0 0.0 445s Investment_8 0 0.0 445s Investment_9 0 0.0 445s Investment_10 0 0.0 445s Investment_11 0 0.0 445s Investment_12 0 0.0 445s Investment_14 0 0.0 445s Investment_15 0 0.0 445s Investment_17 0 0.0 445s Investment_18 0 0.0 445s Investment_19 0 0.0 445s Investment_20 0 0.0 445s Investment_21 0 0.0 445s Investment_22 0 0.0 445s PrivateWages_2 0 0.0 445s PrivateWages_3 0 0.0 445s PrivateWages_4 0 0.0 445s PrivateWages_5 0 0.0 445s PrivateWages_6 0 0.0 445s PrivateWages_8 0 0.0 445s PrivateWages_9 0 0.0 445s PrivateWages_10 0 0.0 445s PrivateWages_11 0 0.0 445s PrivateWages_12 0 0.0 445s PrivateWages_13 0 0.0 445s PrivateWages_14 0 0.0 445s PrivateWages_15 0 0.0 445s PrivateWages_16 0 0.0 445s PrivateWages_17 0 0.0 445s PrivateWages_18 0 0.0 445s PrivateWages_19 0 0.0 445s PrivateWages_20 0 0.0 445s PrivateWages_21 0 0.0 445s PrivateWages_22 0 0.0 445s Consumption_corpProfLag Consumption_wages 445s Consumption_2 12.7 28.2 445s Consumption_3 12.4 32.2 445s Consumption_4 16.9 37.0 445s Consumption_5 18.4 37.0 445s Consumption_6 19.4 38.6 445s Consumption_7 20.1 40.7 445s Consumption_8 19.6 41.5 445s Consumption_9 19.8 42.9 445s Consumption_11 21.7 42.1 445s Consumption_12 15.6 39.3 445s Consumption_14 7.0 34.1 445s Consumption_15 11.2 36.6 445s Consumption_16 12.3 39.3 445s Consumption_17 14.0 44.2 445s Consumption_18 17.6 47.7 445s Consumption_19 17.3 45.9 445s Consumption_20 15.3 49.4 445s Consumption_21 19.0 53.0 445s Consumption_22 21.1 61.8 445s Investment_2 0.0 0.0 445s Investment_3 0.0 0.0 445s Investment_4 0.0 0.0 445s Investment_5 0.0 0.0 445s Investment_6 0.0 0.0 445s Investment_7 0.0 0.0 445s Investment_8 0.0 0.0 445s Investment_9 0.0 0.0 445s Investment_10 0.0 0.0 445s Investment_11 0.0 0.0 445s Investment_12 0.0 0.0 445s Investment_14 0.0 0.0 445s Investment_15 0.0 0.0 445s Investment_17 0.0 0.0 445s Investment_18 0.0 0.0 445s Investment_19 0.0 0.0 445s Investment_20 0.0 0.0 445s Investment_21 0.0 0.0 445s Investment_22 0.0 0.0 445s PrivateWages_2 0.0 0.0 445s PrivateWages_3 0.0 0.0 445s PrivateWages_4 0.0 0.0 445s PrivateWages_5 0.0 0.0 445s PrivateWages_6 0.0 0.0 445s PrivateWages_8 0.0 0.0 445s PrivateWages_9 0.0 0.0 445s PrivateWages_10 0.0 0.0 445s PrivateWages_11 0.0 0.0 445s PrivateWages_12 0.0 0.0 445s PrivateWages_13 0.0 0.0 445s PrivateWages_14 0.0 0.0 445s PrivateWages_15 0.0 0.0 445s PrivateWages_16 0.0 0.0 445s PrivateWages_17 0.0 0.0 445s PrivateWages_18 0.0 0.0 445s PrivateWages_19 0.0 0.0 445s PrivateWages_20 0.0 0.0 445s PrivateWages_21 0.0 0.0 445s PrivateWages_22 0.0 0.0 445s Investment_(Intercept) Investment_corpProf 445s Consumption_2 0 0.0 445s Consumption_3 0 0.0 445s Consumption_4 0 0.0 445s Consumption_5 0 0.0 445s Consumption_6 0 0.0 445s Consumption_7 0 0.0 445s Consumption_8 0 0.0 445s Consumption_9 0 0.0 445s Consumption_11 0 0.0 445s Consumption_12 0 0.0 445s Consumption_14 0 0.0 445s Consumption_15 0 0.0 445s Consumption_16 0 0.0 445s Consumption_17 0 0.0 445s Consumption_18 0 0.0 445s Consumption_19 0 0.0 445s Consumption_20 0 0.0 445s Consumption_21 0 0.0 445s Consumption_22 0 0.0 445s Investment_2 1 12.4 445s Investment_3 1 16.9 445s Investment_4 1 18.4 445s Investment_5 1 19.4 445s Investment_6 1 20.1 445s Investment_7 1 19.6 445s Investment_8 1 19.8 445s Investment_9 1 21.1 445s Investment_10 1 21.7 445s Investment_11 1 15.6 445s Investment_12 1 11.4 445s Investment_14 1 11.2 445s Investment_15 1 12.3 445s Investment_17 1 17.6 445s Investment_18 1 17.3 445s Investment_19 1 15.3 445s Investment_20 1 19.0 445s Investment_21 1 21.1 445s Investment_22 1 23.5 445s PrivateWages_2 0 0.0 445s PrivateWages_3 0 0.0 445s PrivateWages_4 0 0.0 445s PrivateWages_5 0 0.0 445s PrivateWages_6 0 0.0 445s PrivateWages_8 0 0.0 445s PrivateWages_9 0 0.0 445s PrivateWages_10 0 0.0 445s PrivateWages_11 0 0.0 445s PrivateWages_12 0 0.0 445s PrivateWages_13 0 0.0 445s PrivateWages_14 0 0.0 445s PrivateWages_15 0 0.0 445s PrivateWages_16 0 0.0 445s PrivateWages_17 0 0.0 445s PrivateWages_18 0 0.0 445s PrivateWages_19 0 0.0 445s PrivateWages_20 0 0.0 445s PrivateWages_21 0 0.0 445s PrivateWages_22 0 0.0 445s Investment_corpProfLag Investment_capitalLag 445s Consumption_2 0.0 0 445s Consumption_3 0.0 0 445s Consumption_4 0.0 0 445s Consumption_5 0.0 0 445s Consumption_6 0.0 0 445s Consumption_7 0.0 0 445s Consumption_8 0.0 0 445s Consumption_9 0.0 0 445s Consumption_11 0.0 0 445s Consumption_12 0.0 0 445s Consumption_14 0.0 0 445s Consumption_15 0.0 0 445s Consumption_16 0.0 0 445s Consumption_17 0.0 0 445s Consumption_18 0.0 0 445s Consumption_19 0.0 0 445s Consumption_20 0.0 0 445s Consumption_21 0.0 0 445s Consumption_22 0.0 0 445s Investment_2 12.7 183 445s Investment_3 12.4 183 445s Investment_4 16.9 184 445s Investment_5 18.4 190 445s Investment_6 19.4 193 445s Investment_7 20.1 198 445s Investment_8 19.6 203 445s Investment_9 19.8 208 445s Investment_10 21.1 211 445s Investment_11 21.7 216 445s Investment_12 15.6 217 445s Investment_14 7.0 207 445s Investment_15 11.2 202 445s Investment_17 14.0 198 445s Investment_18 17.6 200 445s Investment_19 17.3 202 445s Investment_20 15.3 200 445s Investment_21 19.0 201 445s Investment_22 21.1 204 445s PrivateWages_2 0.0 0 445s PrivateWages_3 0.0 0 445s PrivateWages_4 0.0 0 445s PrivateWages_5 0.0 0 445s PrivateWages_6 0.0 0 445s PrivateWages_8 0.0 0 445s PrivateWages_9 0.0 0 445s PrivateWages_10 0.0 0 445s PrivateWages_11 0.0 0 445s PrivateWages_12 0.0 0 445s PrivateWages_13 0.0 0 445s PrivateWages_14 0.0 0 445s PrivateWages_15 0.0 0 445s PrivateWages_16 0.0 0 445s PrivateWages_17 0.0 0 445s PrivateWages_18 0.0 0 445s PrivateWages_19 0.0 0 445s PrivateWages_20 0.0 0 445s PrivateWages_21 0.0 0 445s PrivateWages_22 0.0 0 445s PrivateWages_(Intercept) PrivateWages_gnp PrivateWages_gnpLag 445s Consumption_2 0 0.0 0.0 445s Consumption_3 0 0.0 0.0 445s Consumption_4 0 0.0 0.0 445s Consumption_5 0 0.0 0.0 445s Consumption_6 0 0.0 0.0 445s Consumption_7 0 0.0 0.0 445s Consumption_8 0 0.0 0.0 445s Consumption_9 0 0.0 0.0 445s Consumption_11 0 0.0 0.0 445s Consumption_12 0 0.0 0.0 445s Consumption_14 0 0.0 0.0 445s Consumption_15 0 0.0 0.0 445s Consumption_16 0 0.0 0.0 445s Consumption_17 0 0.0 0.0 445s Consumption_18 0 0.0 0.0 445s Consumption_19 0 0.0 0.0 445s Consumption_20 0 0.0 0.0 445s Consumption_21 0 0.0 0.0 445s Consumption_22 0 0.0 0.0 445s Investment_2 0 0.0 0.0 445s Investment_3 0 0.0 0.0 445s Investment_4 0 0.0 0.0 445s Investment_5 0 0.0 0.0 445s Investment_6 0 0.0 0.0 445s Investment_7 0 0.0 0.0 445s Investment_8 0 0.0 0.0 445s Investment_9 0 0.0 0.0 445s Investment_10 0 0.0 0.0 445s Investment_11 0 0.0 0.0 445s Investment_12 0 0.0 0.0 445s Investment_14 0 0.0 0.0 445s Investment_15 0 0.0 0.0 445s Investment_17 0 0.0 0.0 445s Investment_18 0 0.0 0.0 445s Investment_19 0 0.0 0.0 445s Investment_20 0 0.0 0.0 445s Investment_21 0 0.0 0.0 445s Investment_22 0 0.0 0.0 445s PrivateWages_2 1 45.6 44.9 445s PrivateWages_3 1 50.1 45.6 445s PrivateWages_4 1 57.2 50.1 445s PrivateWages_5 1 57.1 57.2 445s PrivateWages_6 1 61.0 57.1 445s PrivateWages_8 1 64.4 64.0 445s PrivateWages_9 1 64.5 64.4 445s PrivateWages_10 1 67.0 64.5 445s PrivateWages_11 1 61.2 67.0 445s PrivateWages_12 1 53.4 61.2 445s PrivateWages_13 1 44.3 53.4 445s PrivateWages_14 1 45.1 44.3 445s PrivateWages_15 1 49.7 45.1 445s PrivateWages_16 1 54.4 49.7 445s PrivateWages_17 1 62.7 54.4 445s PrivateWages_18 1 65.0 62.7 445s PrivateWages_19 1 60.9 65.0 445s PrivateWages_20 1 69.5 60.9 445s PrivateWages_21 1 75.7 69.5 445s PrivateWages_22 1 88.4 75.7 445s PrivateWages_trend 445s Consumption_2 0 445s Consumption_3 0 445s Consumption_4 0 445s Consumption_5 0 445s Consumption_6 0 445s Consumption_7 0 445s Consumption_8 0 445s Consumption_9 0 445s Consumption_11 0 445s Consumption_12 0 445s Consumption_14 0 445s Consumption_15 0 445s Consumption_16 0 445s Consumption_17 0 445s Consumption_18 0 445s Consumption_19 0 445s Consumption_20 0 445s Consumption_21 0 445s Consumption_22 0 445s Investment_2 0 445s Investment_3 0 445s Investment_4 0 445s Investment_5 0 445s Investment_6 0 445s Investment_7 0 445s Investment_8 0 445s Investment_9 0 445s Investment_10 0 445s Investment_11 0 445s Investment_12 0 445s Investment_14 0 445s Investment_15 0 445s Investment_17 0 445s Investment_18 0 445s Investment_19 0 445s Investment_20 0 445s Investment_21 0 445s Investment_22 0 445s PrivateWages_2 -10 445s PrivateWages_3 -9 445s PrivateWages_4 -8 445s PrivateWages_5 -7 445s PrivateWages_6 -6 445s PrivateWages_8 -4 445s PrivateWages_9 -3 445s PrivateWages_10 -2 445s PrivateWages_11 -1 445s PrivateWages_12 0 445s PrivateWages_13 1 445s PrivateWages_14 2 445s PrivateWages_15 3 445s PrivateWages_16 4 445s PrivateWages_17 5 445s PrivateWages_18 6 445s PrivateWages_19 7 445s PrivateWages_20 8 445s PrivateWages_21 9 445s PrivateWages_22 10 445s > nobs 445s [1] 58 445s > linearHypothesis 445s Linear hypothesis test (Theil's F test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 47 445s 2 46 1 0.3 0.59 445s Linear hypothesis test (F statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 47 445s 2 46 1 0.29 0.6 445s Linear hypothesis test (Chi^2 statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df Chisq Pr(>Chisq) 445s 1 47 445s 2 46 1 0.29 0.59 445s Linear hypothesis test (Theil's F test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 48 445s 2 46 2 0.16 0.85 445s Linear hypothesis test (F statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 48 445s 2 46 2 0.15 0.86 445s Linear hypothesis test (Chi^2 statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df Chisq Pr(>Chisq) 445s 1 48 445s 2 46 2 0.3 0.86 445s > logLik 445s 'log Lik.' -68.8 (df=13) 445s 'log Lik.' -73.3 (df=13) 445s compare log likelihood value with single-equation OLS 445s [1] "Mean relative difference: 0.0011" 445s > 445s > # 2SLS 445s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 445s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 445s > summary 445s 445s systemfit results 445s method: 2SLS 445s 445s N DF SSR detRCov OLS-R2 McElroy-R2 445s system 56 44 57.9 0.391 0.968 0.992 445s 445s N DF SSR MSE RMSE R2 Adj R2 445s Consumption 18 14 22.27 1.591 1.26 0.974 0.968 445s Investment 18 14 25.85 1.847 1.36 0.847 0.815 445s PrivateWages 20 16 9.74 0.609 0.78 0.988 0.985 445s 445s The covariance matrix of the residuals 445s Consumption Investment PrivateWages 445s Consumption 1.307 0.540 -0.431 445s Investment 0.540 1.319 0.119 445s PrivateWages -0.431 0.119 0.496 445s 445s The correlations of the residuals 445s Consumption Investment PrivateWages 445s Consumption 1.000 0.414 -0.538 445s Investment 0.414 1.000 0.139 445s PrivateWages -0.538 0.139 1.000 445s 445s 445s 2SLS estimates for 'Consumption' (equation 1) 445s Model Formula: consump ~ corpProf + corpProfLag + wages 445s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 445s gnpLag 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 17.2849 1.6463 10.50 5.1e-08 *** 445s corpProf -0.0770 0.1683 -0.46 0.65 445s corpProfLag 0.2327 0.1276 1.82 0.09 . 445s wages 0.8259 0.0472 17.49 6.6e-11 *** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 1.261 on 14 degrees of freedom 445s Number of observations: 18 Degrees of Freedom: 14 445s SSR: 22.269 MSE: 1.591 Root MSE: 1.261 445s Multiple R-Squared: 0.974 Adjusted R-Squared: 0.968 445s 445s 445s 2SLS estimates for 'Investment' (equation 2) 445s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 445s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 445s gnpLag 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 18.2571 7.3132 2.50 0.02564 * 445s corpProf 0.1564 0.1942 0.81 0.43408 445s corpProfLag 0.5714 0.1672 3.42 0.00417 ** 445s capitalLag -0.1446 0.0346 -4.18 0.00093 *** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 1.359 on 14 degrees of freedom 445s Number of observations: 18 Degrees of Freedom: 14 445s SSR: 25.852 MSE: 1.847 Root MSE: 1.359 445s Multiple R-Squared: 0.847 Adjusted R-Squared: 0.815 445s 445s 445s 2SLS estimates for 'PrivateWages' (equation 3) 445s Model Formula: privWage ~ gnp + gnpLag + trend 445s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 445s gnpLag 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 1.3431 1.1879 1.13 0.275 445s gnp 0.4438 0.0361 12.28 1.5e-09 *** 445s gnpLag 0.1447 0.0392 3.69 0.002 ** 445s trend 0.1238 0.0308 4.01 0.001 ** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 0.78 on 16 degrees of freedom 445s Number of observations: 20 Degrees of Freedom: 16 445s SSR: 9.741 MSE: 0.609 Root MSE: 0.78 445s Multiple R-Squared: 0.988 Adjusted R-Squared: 0.985 445s 445s > residuals 445s Consumption Investment PrivateWages 445s 1 NA NA NA 445s 2 -0.6754 -1.214 -1.3401 445s 3 -0.4627 0.325 0.2378 445s 4 -1.1585 1.094 1.1117 445s 5 -0.0305 -1.368 -0.1954 445s 6 0.4693 0.486 -0.5355 445s 7 NA NA NA 445s 8 1.6045 1.066 -0.7908 445s 9 1.6018 0.156 0.2831 445s 10 NA 1.853 1.1353 445s 11 -0.9031 -0.898 -0.1765 445s 12 -1.5948 -1.012 0.6007 445s 13 NA NA 0.1443 445s 14 0.2854 0.845 0.4826 445s 15 -0.4718 -0.365 0.3016 445s 16 -0.2268 NA 0.0261 445s 17 2.0079 1.685 -0.8614 445s 18 -0.7434 -0.121 0.9927 445s 19 -0.5410 -3.248 -0.4446 445s 20 1.4186 0.241 -0.3914 445s 21 1.1462 -0.013 -1.1115 445s 22 -1.7256 0.489 0.5312 445s > fitted 445s Consumption Investment PrivateWages 445s 1 NA NA NA 445s 2 42.6 1.014 26.8 445s 3 45.5 1.575 29.1 445s 4 50.4 4.106 33.0 445s 5 50.6 4.368 34.1 445s 6 52.1 4.614 35.9 445s 7 NA NA NA 445s 8 54.6 3.134 38.7 445s 9 55.7 2.844 38.9 445s 10 NA 3.247 40.2 445s 11 55.9 1.898 38.1 445s 12 52.5 -2.388 33.9 445s 13 NA NA 28.9 445s 14 46.2 -5.945 28.0 445s 15 49.2 -2.635 30.3 445s 16 51.5 NA 33.2 445s 17 55.7 0.415 37.7 445s 18 59.4 2.121 40.0 445s 19 58.0 1.348 38.6 445s 20 60.2 1.059 42.0 445s 21 63.9 3.313 46.1 445s 22 71.4 4.411 52.8 445s > predict 445s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 445s 1 NA NA NA NA 445s 2 42.6 0.586 41.3 43.8 445s 3 45.5 0.674 44.0 46.9 445s 4 50.4 0.443 49.4 51.3 445s 5 50.6 0.524 49.5 51.8 445s 6 52.1 0.535 51.0 53.3 445s 7 NA NA NA NA 445s 8 54.6 0.431 53.7 55.5 445s 9 55.7 0.510 54.6 56.8 445s 10 NA NA NA NA 445s 11 55.9 0.936 53.9 57.9 445s 12 52.5 0.893 50.6 54.4 445s 13 NA NA NA NA 445s 14 46.2 0.713 44.7 47.7 445s 15 49.2 0.501 48.1 50.2 445s 16 51.5 0.407 50.7 52.4 445s 17 55.7 0.457 54.7 56.7 445s 18 59.4 0.397 58.6 60.3 445s 19 58.0 0.564 56.8 59.2 445s 20 60.2 0.543 59.0 61.3 445s 21 63.9 0.529 62.7 65.0 445s 22 71.4 0.808 69.7 73.2 445s Investment.pred Investment.se.fit Investment.lwr Investment.upr 445s 1 NA NA NA NA 445s 2 1.014 0.919 -0.957 2.985 445s 3 1.575 0.602 0.284 2.867 445s 4 4.106 0.544 2.940 5.272 445s 5 4.368 0.450 3.402 5.333 445s 6 4.614 0.425 3.703 5.526 445s 7 NA NA NA NA 445s 8 3.134 0.352 2.380 3.889 445s 9 2.844 0.544 1.677 4.012 445s 10 3.247 0.592 1.976 4.518 445s 11 1.898 0.978 -0.200 3.996 445s 12 -2.388 0.886 -4.289 -0.488 445s 13 NA NA NA NA 445s 14 -5.945 0.916 -7.909 -3.980 445s 15 -2.635 0.518 -3.745 -1.525 445s 16 NA NA NA NA 445s 17 0.415 0.507 -0.671 1.501 445s 18 2.121 0.329 1.416 2.826 445s 19 1.348 0.551 0.166 2.529 445s 20 1.059 0.582 -0.189 2.306 445s 21 3.313 0.496 2.248 4.377 445s 22 4.411 0.728 2.850 5.971 445s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 445s 1 NA NA NA NA 445s 2 26.8 0.330 26.1 27.5 445s 3 29.1 0.344 28.3 29.8 445s 4 33.0 0.363 32.2 33.8 445s 5 34.1 0.260 33.5 34.6 445s 6 35.9 0.268 35.4 36.5 445s 7 NA NA NA NA 445s 8 38.7 0.265 38.1 39.3 445s 9 38.9 0.252 38.4 39.5 445s 10 40.2 0.242 39.7 40.7 445s 11 38.1 0.358 37.3 38.8 445s 12 33.9 0.385 33.1 34.7 445s 13 28.9 0.460 27.9 29.8 445s 14 28.0 0.351 27.3 28.8 445s 15 30.3 0.343 29.6 31.0 445s 16 33.2 0.287 32.6 33.8 445s 17 37.7 0.296 37.0 38.3 445s 18 40.0 0.220 39.5 40.5 445s 19 38.6 0.361 37.9 39.4 445s 20 42.0 0.309 41.3 42.6 445s 21 46.1 0.312 45.4 46.8 445s 22 52.8 0.501 51.7 53.8 445s > model.frame 445s consump corpProf corpProfLag wages invest capitalLag privWage gnp gnpLag 445s 1 39.8 12.7 NA 31.0 2.7 180 28.8 44.9 NA 445s 2 41.9 12.4 12.7 28.2 -0.2 183 25.5 45.6 44.9 445s 3 45.0 16.9 12.4 32.2 1.9 183 29.3 50.1 45.6 445s 4 49.2 18.4 16.9 37.0 5.2 184 34.1 57.2 50.1 445s 5 50.6 19.4 18.4 37.0 3.0 190 33.9 57.1 57.2 445s 6 52.6 20.1 19.4 38.6 5.1 193 35.4 61.0 57.1 445s 7 55.1 19.6 20.1 40.7 5.6 198 37.4 64.0 NA 445s 8 56.2 19.8 19.6 41.5 4.2 203 37.9 64.4 64.0 445s 9 57.3 21.1 19.8 42.9 3.0 208 39.2 64.5 64.4 445s 10 57.8 21.7 21.1 NA 5.1 211 41.3 67.0 64.5 445s 11 55.0 15.6 21.7 42.1 1.0 216 37.9 61.2 67.0 445s 12 50.9 11.4 15.6 39.3 -3.4 217 34.5 53.4 61.2 445s 13 45.6 NA 11.4 34.3 -6.2 213 29.0 44.3 53.4 445s 14 46.5 11.2 7.0 34.1 -5.1 207 28.5 45.1 44.3 445s 15 48.7 12.3 11.2 36.6 -3.0 202 30.6 49.7 45.1 445s 16 51.3 14.0 12.3 39.3 NA 199 33.2 54.4 49.7 445s 17 57.7 17.6 14.0 44.2 2.1 198 36.8 62.7 54.4 445s 18 58.7 17.3 17.6 47.7 2.0 200 41.0 65.0 62.7 445s 19 57.5 15.3 17.3 45.9 -1.9 202 38.2 60.9 65.0 445s 20 61.6 19.0 15.3 49.4 1.3 200 41.6 69.5 60.9 445s 21 65.0 21.1 19.0 53.0 3.3 201 45.0 75.7 69.5 445s 22 69.7 23.5 21.1 61.8 4.9 204 53.3 88.4 75.7 445s trend 445s 1 -11 445s 2 -10 445s 3 -9 445s 4 -8 445s 5 -7 445s 6 -6 445s 7 -5 445s 8 -4 445s 9 -3 445s 10 -2 445s 11 -1 445s 12 0 445s 13 1 445s 14 2 445s 15 3 445s 16 4 445s 17 5 445s 18 6 445s 19 7 445s 20 8 445s 21 9 445s 22 10 445s > model.matrix 445s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0345 >" 445s [2] "Attributes: < Component “dimnames”: Component 1: 51 string mismatches >" 445s [3] "Numeric: lengths (696, 672) differ" 445s > nobs 445s [1] 56 445s > linearHypothesis 445s Linear hypothesis test (Theil's F test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 45 445s 2 44 1 1.27 0.27 445s Linear hypothesis test (F statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 45 445s 2 44 1 1.66 0.2 445s Linear hypothesis test (Chi^2 statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df Chisq Pr(>Chisq) 445s 1 45 445s 2 44 1 1.66 0.2 445s Linear hypothesis test (Theil's F test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 46 445s 2 44 2 0.64 0.53 445s Linear hypothesis test (F statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 46 445s 2 44 2 0.84 0.44 445s Linear hypothesis test (Chi^2 statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df Chisq Pr(>Chisq) 445s 1 46 445s 2 44 2 1.68 0.43 445s > logLik 445s 'log Lik.' -69.5 (df=13) 445s 'log Lik.' -77.5 (df=13) 445s > 445s > # SUR 445s Warning in systemfit(system, method = method, data = KleinI, methodResidCov = ifelse(method == : 445s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 445s > summary 445s 445s systemfit results 445s method: SUR 445s 445s N DF SSR detRCov OLS-R2 McElroy-R2 445s system 58 46 45.1 0.199 0.975 0.993 445s 445s N DF SSR MSE RMSE R2 Adj R2 445s Consumption 19 15 17.5 1.167 1.080 0.980 0.975 445s Investment 19 15 17.3 1.155 1.075 0.906 0.887 445s PrivateWages 20 16 10.3 0.642 0.801 0.987 0.985 445s 445s The covariance matrix of the residuals used for estimation 445s Consumption Investment PrivateWages 445s Consumption 0.9830 0.0466 -0.391 445s Investment 0.0466 0.8101 0.115 445s PrivateWages -0.3906 0.1155 0.496 445s 445s The covariance matrix of the residuals 445s Consumption Investment PrivateWages 445s Consumption 0.979 0.080 -0.452 445s Investment 0.080 0.810 0.181 445s PrivateWages -0.452 0.181 0.521 445s 445s The correlations of the residuals 445s Consumption Investment PrivateWages 445s Consumption 1.0000 0.0907 -0.636 445s Investment 0.0907 1.0000 0.267 445s PrivateWages -0.6362 0.2671 1.000 445s 445s 445s SUR estimates for 'Consumption' (equation 1) 445s Model Formula: consump ~ corpProf + corpProfLag + wages 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 16.2670 1.3148 12.37 2.8e-09 *** 445s corpProf 0.1942 0.0954 2.04 0.06 . 445s corpProfLag 0.0747 0.0842 0.89 0.39 445s wages 0.8011 0.0383 20.93 1.6e-12 *** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 1.08 on 15 degrees of freedom 445s Number of observations: 19 Degrees of Freedom: 15 445s SSR: 17.501 MSE: 1.167 Root MSE: 1.08 445s Multiple R-Squared: 0.98 Adjusted R-Squared: 0.975 445s 445s 445s SUR estimates for 'Investment' (equation 2) 445s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 12.6390 4.7856 2.64 0.01852 * 445s corpProf 0.4708 0.0943 4.99 0.00016 *** 445s corpProfLag 0.3533 0.0907 3.89 0.00144 ** 445s capitalLag -0.1254 0.0236 -5.32 8.6e-05 *** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 1.075 on 15 degrees of freedom 445s Number of observations: 19 Degrees of Freedom: 15 445s SSR: 17.321 MSE: 1.155 Root MSE: 1.075 445s Multiple R-Squared: 0.906 Adjusted R-Squared: 0.887 445s 445s 445s SUR estimates for 'PrivateWages' (equation 3) 445s Model Formula: privWage ~ gnp + gnpLag + trend 445s 445s Estimate Std. Error t value Pr(>|t|) 445s (Intercept) 1.3264 1.1240 1.18 0.2552 445s gnp 0.4184 0.0268 15.63 4.1e-11 *** 445s gnpLag 0.1714 0.0315 5.43 5.5e-05 *** 445s trend 0.1456 0.0284 5.13 0.0001 *** 445s --- 445s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 445s 445s Residual standard error: 0.801 on 16 degrees of freedom 445s Number of observations: 20 Degrees of Freedom: 16 445s SSR: 10.266 MSE: 0.642 Root MSE: 0.801 445s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 445s 445s > residuals 445s Consumption Investment PrivateWages 445s 1 NA NA NA 445s 2 -0.3143 -0.2326 -1.1434 445s 3 -1.2700 -0.1705 0.5084 445s 4 -1.5426 1.0718 1.4211 445s 5 -0.4489 -1.4767 -0.0992 445s 6 0.0588 0.3167 -0.3594 445s 7 0.9213 1.4446 NA 445s 8 1.3789 0.8296 -0.7554 445s 9 1.0900 -0.5263 0.2887 445s 10 NA 1.2083 1.1800 445s 11 0.3569 0.4082 -0.3673 445s 12 -0.2288 0.2663 0.3445 445s 13 NA NA -0.1571 445s 14 0.2181 0.4946 0.4220 445s 15 -0.1120 -0.0470 0.3147 445s 16 -0.0872 NA 0.0145 445s 17 1.5615 1.0289 -0.8091 445s 18 -0.4530 0.0617 0.8608 445s 19 0.1997 -2.5397 -0.7635 445s 20 0.9268 -0.6136 -0.4046 445s 21 0.7588 -0.7465 -1.2179 445s 22 -2.2137 -0.6044 0.5606 445s > fitted 445s Consumption Investment PrivateWages 445s 1 NA NA NA 445s 2 42.2 0.0326 26.6 445s 3 46.3 2.0705 28.8 445s 4 50.7 4.1282 32.7 445s 5 51.0 4.4767 34.0 445s 6 52.5 4.7833 35.8 445s 7 54.2 4.1554 NA 445s 8 54.8 3.3704 38.7 445s 9 56.2 3.5263 38.9 445s 10 NA 3.8917 40.1 445s 11 54.6 0.5918 38.3 445s 12 51.1 -3.6663 34.2 445s 13 NA NA 29.2 445s 14 46.3 -5.5946 28.1 445s 15 48.8 -2.9530 30.3 445s 16 51.4 NA 33.2 445s 17 56.1 1.0711 37.6 445s 18 59.2 1.9383 40.1 445s 19 57.3 0.6397 39.0 445s 20 60.7 1.9136 42.0 445s 21 64.2 4.0465 46.2 445s 22 71.9 5.5044 52.7 445s > predict 445s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 445s 1 NA NA NA NA 445s 2 42.2 0.460 41.3 43.1 445s 3 46.3 0.489 45.3 47.3 445s 4 50.7 0.328 50.1 51.4 445s 5 51.0 0.384 50.3 51.8 445s 6 52.5 0.389 51.8 53.3 445s 7 54.2 0.347 53.5 54.9 445s 8 54.8 0.319 54.2 55.5 445s 9 56.2 0.353 55.5 56.9 445s 10 NA NA NA NA 445s 11 54.6 0.583 53.5 55.8 445s 12 51.1 0.524 50.1 52.2 445s 13 NA NA NA NA 445s 14 46.3 0.589 45.1 47.5 445s 15 48.8 0.393 48.0 49.6 445s 16 51.4 0.337 50.7 52.1 445s 17 56.1 0.345 55.4 56.8 445s 18 59.2 0.318 58.5 59.8 445s 19 57.3 0.381 56.5 58.1 445s 20 60.7 0.413 59.8 61.5 445s 21 64.2 0.417 63.4 65.1 445s 22 71.9 0.651 70.6 73.2 445s Investment.pred Investment.se.fit Investment.lwr Investment.upr 445s 1 NA NA NA NA 445s 2 0.0326 0.556 -1.0866 1.15 445s 3 2.0705 0.454 1.1575 2.98 445s 4 4.1282 0.399 3.3256 4.93 445s 5 4.4767 0.331 3.8101 5.14 445s 6 4.7833 0.314 4.1520 5.41 445s 7 4.1554 0.291 3.5687 4.74 445s 8 3.3704 0.260 2.8469 3.89 445s 9 3.5263 0.347 2.8278 4.22 445s 10 3.8917 0.397 3.0924 4.69 445s 11 0.5918 0.578 -0.5711 1.75 445s 12 -3.6663 0.551 -4.7762 -2.56 445s 13 NA NA NA NA 445s 14 -5.5946 0.661 -6.9261 -4.26 445s 15 -2.9530 0.392 -3.7430 -2.16 445s 16 NA NA NA NA 445s 17 1.0711 0.318 0.4315 1.71 445s 18 1.9383 0.225 1.4863 2.39 445s 19 0.6397 0.310 0.0165 1.26 445s 20 1.9136 0.333 1.2436 2.58 445s 21 4.0465 0.304 3.4345 4.66 445s 22 5.5044 0.429 4.6400 6.37 445s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 445s 1 NA NA NA NA 445s 2 26.6 0.321 26.0 27.3 445s 3 28.8 0.321 28.1 29.4 445s 4 32.7 0.316 32.0 33.3 445s 5 34.0 0.244 33.5 34.5 445s 6 35.8 0.242 35.3 36.2 445s 7 NA NA NA NA 445s 8 38.7 0.246 38.2 39.2 445s 9 38.9 0.234 38.4 39.4 445s 10 40.1 0.225 39.7 40.6 445s 11 38.3 0.301 37.7 38.9 445s 12 34.2 0.298 33.6 34.8 445s 13 29.2 0.353 28.4 29.9 445s 14 28.1 0.330 27.4 28.7 445s 15 30.3 0.328 29.6 30.9 445s 16 33.2 0.275 32.6 33.7 445s 17 37.6 0.270 37.1 38.2 445s 18 40.1 0.213 39.7 40.6 445s 19 39.0 0.301 38.4 39.6 445s 20 42.0 0.287 41.4 42.6 445s 21 46.2 0.304 45.6 46.8 445s 22 52.7 0.448 51.8 53.6 445s > model.frame 445s [1] TRUE 445s > model.matrix 445s [1] TRUE 445s > nobs 445s [1] 58 445s > linearHypothesis 445s Linear hypothesis test (Theil's F test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 47 445s 2 46 1 0.4 0.53 445s Linear hypothesis test (F statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 47 445s 2 46 1 0.49 0.49 445s Linear hypothesis test (Chi^2 statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df Chisq Pr(>Chisq) 445s 1 47 445s 2 46 1 0.49 0.48 445s Linear hypothesis test (Theil's F test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 48 445s 2 46 2 0.31 0.74 445s Linear hypothesis test (F statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df F Pr(>F) 445s 1 48 445s 2 46 2 0.37 0.69 445s Linear hypothesis test (Chi^2 statistic of a Wald test) 445s 445s Hypothesis: 445s Consumption_corpProf + Investment_capitalLag = 0 445s Consumption_corpProfLag - PrivateWages_trend = 0 445s 445s Model 1: restricted model 445s Model 2: kleinModel 445s 445s Res.Df Df Chisq Pr(>Chisq) 445s 1 48 445s 2 46 2 0.75 0.69 445s > logLik 445s 'log Lik.' -66.4 (df=18) 446s 'log Lik.' -74.1 (df=18) 446s > 446s > # 3SLS 446s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 446s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 446s > summary 446s 446s systemfit results 446s method: 3SLS 446s 446s N DF SSR detRCov OLS-R2 McElroy-R2 446s system 56 44 67.5 0.436 0.963 0.993 446s 446s N DF SSR MSE RMSE R2 Adj R2 446s Consumption 18 14 22.4 1.598 1.264 0.974 0.968 446s Investment 18 14 35.0 2.503 1.582 0.793 0.749 446s PrivateWages 20 16 10.1 0.629 0.793 0.987 0.985 446s 446s The covariance matrix of the residuals used for estimation 446s Consumption Investment PrivateWages 446s Consumption 1.307 0.540 -0.431 446s Investment 0.540 1.319 0.119 446s PrivateWages -0.431 0.119 0.496 446s 446s The covariance matrix of the residuals 446s Consumption Investment PrivateWages 446s Consumption 1.309 0.638 -0.440 446s Investment 0.638 1.749 0.233 446s PrivateWages -0.440 0.233 0.519 446s 446s The correlations of the residuals 446s Consumption Investment PrivateWages 446s Consumption 1.000 0.422 -0.532 446s Investment 0.422 1.000 0.247 446s PrivateWages -0.532 0.247 1.000 446s 446s 446s 3SLS estimates for 'Consumption' (equation 1) 446s Model Formula: consump ~ corpProf + corpProfLag + wages 446s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 446s gnpLag 446s 446s Estimate Std. Error t value Pr(>|t|) 446s (Intercept) 18.0338 1.5648 11.52 1.6e-08 *** 446s corpProf -0.0632 0.1500 -0.42 0.68 446s corpProfLag 0.1784 0.1154 1.55 0.14 446s wages 0.8224 0.0444 18.54 3.0e-11 *** 446s --- 446s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 446s 446s Residual standard error: 1.264 on 14 degrees of freedom 446s Number of observations: 18 Degrees of Freedom: 14 446s SSR: 22.377 MSE: 1.598 Root MSE: 1.264 446s Multiple R-Squared: 0.974 Adjusted R-Squared: 0.968 446s 446s 446s 3SLS estimates for 'Investment' (equation 2) 446s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 446s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 446s gnpLag 446s 446s Estimate Std. Error t value Pr(>|t|) 446s (Intercept) 24.6766 6.7008 3.68 0.00246 ** 446s corpProf 0.0472 0.1843 0.26 0.80149 446s corpProfLag 0.6874 0.1577 4.36 0.00065 *** 446s capitalLag -0.1776 0.0318 -5.59 6.7e-05 *** 446s --- 446s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 446s 446s Residual standard error: 1.582 on 14 degrees of freedom 446s Number of observations: 18 Degrees of Freedom: 14 446s SSR: 35.037 MSE: 2.503 Root MSE: 1.582 446s Multiple R-Squared: 0.793 Adjusted R-Squared: 0.749 446s 446s 446s 3SLS estimates for 'PrivateWages' (equation 3) 446s Model Formula: privWage ~ gnp + gnpLag + trend 446s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 446s gnpLag 446s 446s Estimate Std. Error t value Pr(>|t|) 446s (Intercept) 0.7823 1.1254 0.70 0.49695 446s gnp 0.4257 0.0308 13.80 2.6e-10 *** 446s gnpLag 0.1728 0.0341 5.07 0.00011 *** 446s trend 0.1252 0.0291 4.30 0.00055 *** 446s --- 446s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 446s 446s Residual standard error: 0.793 on 16 degrees of freedom 446s Number of observations: 20 Degrees of Freedom: 16 446s SSR: 10.057 MSE: 0.629 Root MSE: 0.793 446s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 446s 446s > residuals 446s Consumption Investment PrivateWages 446s 1 NA NA NA 446s 2 -0.8058 -1.721 -1.20135 446s 3 -0.6573 0.337 0.43696 446s 4 -1.1124 0.810 1.31177 446s 5 0.0833 -1.544 -0.19794 446s 6 0.6334 0.368 -0.46596 446s 7 NA NA NA 446s 8 1.7939 1.245 -0.85614 446s 9 1.7891 0.593 0.20698 446s 10 NA 2.303 1.10034 446s 11 -0.5397 -1.015 -0.38801 446s 12 -1.5147 -0.846 0.40949 446s 13 NA NA 0.00602 446s 14 -0.1171 1.670 0.61306 446s 15 -0.6526 -0.075 0.49152 446s 16 -0.3617 NA 0.17066 446s 17 1.9331 2.086 -0.69991 446s 18 -0.6063 -0.101 0.96136 446s 19 -0.3990 -3.345 -0.61606 446s 20 1.4134 0.717 -0.29343 446s 21 1.3257 0.306 -1.14412 446s 22 -1.4340 0.935 0.55310 446s > fitted 446s Consumption Investment PrivateWages 446s 1 NA NA NA 446s 2 42.7 1.5213 26.7 446s 3 45.7 1.5632 28.9 446s 4 50.3 4.3898 32.8 446s 5 50.5 4.5444 34.1 446s 6 52.0 4.7320 35.9 446s 7 NA NA NA 446s 8 54.4 2.9547 38.8 446s 9 55.5 2.4075 39.0 446s 10 NA 2.7965 40.2 446s 11 55.5 2.0150 38.3 446s 12 52.4 -2.5541 34.1 446s 13 NA NA 29.0 446s 14 46.6 -6.7699 27.9 446s 15 49.4 -2.9250 30.1 446s 16 51.7 NA 33.0 446s 17 55.8 0.0139 37.5 446s 18 59.3 2.1013 40.0 446s 19 57.9 1.4453 38.8 446s 20 60.2 0.5828 41.9 446s 21 63.7 2.9944 46.1 446s 22 71.1 3.9651 52.7 446s > predict 446s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 446s 1 NA NA NA NA 446s 2 42.7 0.555 39.7 45.7 446s 3 45.7 0.628 42.6 48.7 446s 4 50.3 0.418 47.5 53.2 446s 5 50.5 0.492 47.6 53.4 446s 6 52.0 0.501 49.0 54.9 446s 7 NA NA NA NA 446s 8 54.4 0.405 51.6 57.3 446s 9 55.5 0.477 52.6 58.4 446s 10 NA NA NA NA 446s 11 55.5 0.832 52.3 58.8 446s 12 52.4 0.792 49.2 55.6 446s 13 NA NA NA NA 446s 14 46.6 0.676 43.5 49.7 446s 15 49.4 0.470 46.5 52.2 446s 16 51.7 0.386 48.8 54.5 446s 17 55.8 0.433 52.9 58.6 446s 18 59.3 0.368 56.5 62.1 446s 19 57.9 0.504 55.0 60.8 446s 20 60.2 0.513 57.3 63.1 446s 21 63.7 0.505 60.8 66.6 446s 22 71.1 0.771 68.0 74.3 446s Investment.pred Investment.se.fit Investment.lwr Investment.upr 446s 1 NA NA NA NA 446s 2 1.5213 0.857 -2.337 5.380 446s 3 1.5632 0.589 -2.058 5.184 446s 4 4.3898 0.519 0.819 7.961 446s 5 4.5444 0.436 1.025 8.064 446s 6 4.7320 0.415 1.224 8.240 446s 7 NA NA NA NA 446s 8 2.9547 0.342 -0.517 6.426 446s 9 2.4075 0.511 -1.158 5.973 446s 10 2.7965 0.556 -0.800 6.393 446s 11 2.0150 0.955 -1.948 5.978 446s 12 -2.5541 0.874 -6.431 1.323 446s 13 NA NA NA NA 446s 14 -6.7699 0.865 -10.637 -2.903 446s 15 -2.9250 0.503 -6.485 0.635 446s 16 NA NA NA NA 446s 17 0.0139 0.483 -3.534 3.561 446s 18 2.1013 0.320 -1.361 5.563 446s 19 1.4453 0.532 -2.134 5.025 446s 20 0.5828 0.550 -3.010 4.175 446s 21 2.9944 0.476 -0.549 6.538 446s 22 3.9651 0.692 0.261 7.669 446s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 446s 1 NA NA NA NA 446s 2 26.7 0.324 24.9 28.5 446s 3 28.9 0.331 27.0 30.7 446s 4 32.8 0.339 31.0 34.6 446s 5 34.1 0.248 32.3 35.9 446s 6 35.9 0.256 34.1 37.6 446s 7 NA NA NA NA 446s 8 38.8 0.251 37.0 40.5 446s 9 39.0 0.238 37.2 40.7 446s 10 40.2 0.232 38.4 42.0 446s 11 38.3 0.314 36.5 40.1 446s 12 34.1 0.327 32.3 35.9 446s 13 29.0 0.393 27.1 30.9 446s 14 27.9 0.329 26.1 29.7 446s 15 30.1 0.324 28.3 31.9 446s 16 33.0 0.271 31.3 34.8 446s 17 37.5 0.277 35.7 39.3 446s 18 40.0 0.213 38.3 41.8 446s 19 38.8 0.320 37.0 40.6 446s 20 41.9 0.295 40.1 43.7 446s 21 46.1 0.309 44.3 47.9 446s 22 52.7 0.476 50.8 54.7 446s > model.frame 446s [1] TRUE 446s > model.matrix 446s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0345 >" 446s [2] "Attributes: < Component “dimnames”: Component 1: 51 string mismatches >" 446s [3] "Numeric: lengths (696, 672) differ" 446s > nobs 446s [1] 56 446s > linearHypothesis 446s Linear hypothesis test (Theil's F test) 446s 446s Hypothesis: 446s Consumption_corpProf + Investment_capitalLag = 0 446s 446s Model 1: restricted model 446s Model 2: kleinModel 446s 446s Res.Df Df F Pr(>F) 446s 1 45 446s 2 44 1 1.91 0.17 446s Linear hypothesis test (F statistic of a Wald test) 446s 446s Hypothesis: 446s Consumption_corpProf + Investment_capitalLag = 0 446s 446s Model 1: restricted model 446s Model 2: kleinModel 446s 446s Res.Df Df F Pr(>F) 446s 1 45 446s 2 44 1 2.6 0.11 446s Linear hypothesis test (Chi^2 statistic of a Wald test) 446s 446s Hypothesis: 446s Consumption_corpProf + Investment_capitalLag = 0 446s 446s Model 1: restricted model 446s Model 2: kleinModel 446s 446s Res.Df Df Chisq Pr(>Chisq) 446s 1 45 446s 2 44 1 2.6 0.11 446s Linear hypothesis test (Theil's F test) 446s 446s Hypothesis: 446s Consumption_corpProf + Investment_capitalLag = 0 446s Consumption_corpProfLag - PrivateWages_trend = 0 446s 446s Model 1: restricted model 446s Model 2: kleinModel 446s 446s Res.Df Df F Pr(>F) 446s 1 46 446s 2 44 2 1.62 0.21 446s Linear hypothesis test (F statistic of a Wald test) 446s 446s Hypothesis: 446s Consumption_corpProf + Investment_capitalLag = 0 446s Consumption_corpProfLag - PrivateWages_trend = 0 446s 446s Model 1: restricted model 446s Model 2: kleinModel 446s 446s Res.Df Df F Pr(>F) 446s 1 46 446s 2 44 2 2.2 0.12 446s Linear hypothesis test (Chi^2 statistic of a Wald test) 446s 446s Hypothesis: 446s Consumption_corpProf + Investment_capitalLag = 0 446s Consumption_corpProfLag - PrivateWages_trend = 0 446s 446s Model 1: restricted model 446s Model 2: kleinModel 446s 446s Res.Df Df Chisq Pr(>Chisq) 446s 1 46 446s 2 44 2 4.41 0.11 446s > logLik 446s 'log Lik.' -70.1 (df=18) 446s 'log Lik.' -80.6 (df=18) 446s > 446s > # I3SLS 446s Warning in systemfit(system, method = method, data = KleinI, inst = inst, : 446s the estimation of systems of equations with unequal numbers of observations has not been thoroughly tested yet 446s > summary 446s 446s systemfit results 446s method: iterated 3SLS 446s 446s convergence achieved after 10 iterations 446s 446s N DF SSR detRCov OLS-R2 McElroy-R2 446s system 56 44 79.4 0.55 0.956 0.994 446s 446s N DF SSR MSE RMSE R2 Adj R2 446s Consumption 18 14 22.3 1.595 1.263 0.974 0.968 446s Investment 18 14 46.8 3.346 1.829 0.724 0.664 446s PrivateWages 20 16 10.2 0.639 0.799 0.987 0.985 446s 446s The covariance matrix of the residuals used for estimation 446s Consumption Investment PrivateWages 446s Consumption 1.307 0.750 -0.452 446s Investment 0.750 2.318 0.272 446s PrivateWages -0.452 0.272 0.530 446s 446s The covariance matrix of the residuals 446s Consumption Investment PrivateWages 446s Consumption 1.307 0.750 -0.452 446s Investment 0.750 2.318 0.272 446s PrivateWages -0.452 0.272 0.530 446s 446s The correlations of the residuals 446s Consumption Investment PrivateWages 446s Consumption 1.000 0.424 -0.542 446s Investment 0.424 1.000 0.254 446s PrivateWages -0.542 0.254 1.000 446s 446s 446s 3SLS estimates for 'Consumption' (equation 1) 446s Model Formula: consump ~ corpProf + corpProfLag + wages 446s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 446s gnpLag 446s 446s Estimate Std. Error t value Pr(>|t|) 446s (Intercept) 18.3252 1.5452 11.86 1.1e-08 *** 446s corpProf -0.0436 0.1470 -0.30 0.77 446s corpProfLag 0.1614 0.1127 1.43 0.17 446s wages 0.8127 0.0436 18.65 2.8e-11 *** 446s --- 446s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 446s 446s Residual standard error: 1.263 on 14 degrees of freedom 446s Number of observations: 18 Degrees of Freedom: 14 446s SSR: 22.337 MSE: 1.595 Root MSE: 1.263 446s Multiple R-Squared: 0.974 Adjusted R-Squared: 0.968 446s 446s 446s 3SLS estimates for 'Investment' (equation 2) 446s Model Formula: invest ~ corpProf + corpProfLag + capitalLag 446s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 446s gnpLag 446s 446s Estimate Std. Error t value Pr(>|t|) 446s (Intercept) 30.2418 8.3674 3.61 0.00282 ** 446s corpProf -0.0437 0.2341 -0.19 0.85457 446s corpProfLag 0.7856 0.1993 3.94 0.00147 ** 446s capitalLag -0.2065 0.0397 -5.20 0.00014 *** 446s --- 446s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 446s 446s Residual standard error: 1.829 on 14 degrees of freedom 446s Number of observations: 18 Degrees of Freedom: 14 446s SSR: 46.838 MSE: 3.346 Root MSE: 1.829 446s Multiple R-Squared: 0.724 Adjusted R-Squared: 0.664 446s 446s 446s 3SLS estimates for 'PrivateWages' (equation 3) 446s Model Formula: privWage ~ gnp + gnpLag + trend 446s Instruments: ~govExp + taxes + govWage + trend + capitalLag + corpProfLag + 446s gnpLag 446s 446s Estimate Std. Error t value Pr(>|t|) 446s (Intercept) 0.4741 1.1280 0.42 0.67983 446s gnp 0.4268 0.0296 14.44 1.4e-10 *** 446s gnpLag 0.1767 0.0330 5.35 6.5e-05 *** 446s trend 0.1201 0.0290 4.14 0.00076 *** 446s --- 446s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 446s 446s Residual standard error: 0.799 on 16 degrees of freedom 446s Number of observations: 20 Degrees of Freedom: 16 446s SSR: 10.218 MSE: 0.639 Root MSE: 0.799 446s Multiple R-Squared: 0.987 Adjusted R-Squared: 0.985 446s 446s > residuals 446s Consumption Investment PrivateWages 446s 1 NA NA NA 446s 2 -0.8546 -2.1226 -1.1687 446s 3 -0.7611 0.3684 0.4670 446s 4 -1.1233 0.5912 1.3216 446s 5 0.0781 -1.6694 -0.2108 446s 6 0.6467 0.2952 -0.4776 446s 7 NA NA NA 446s 8 1.8444 1.4348 -0.8884 446s 9 1.8309 1.0020 0.1781 446s 10 NA 2.7265 1.0734 446s 11 -0.3652 -1.0581 -0.4134 446s 12 -1.3877 -0.6431 0.4203 446s 13 NA NA 0.0623 446s 14 -0.1818 2.4214 0.7091 446s 15 -0.6438 0.2168 0.5845 446s 16 -0.3417 NA 0.2455 446s 17 1.9583 2.4607 -0.6474 446s 18 -0.4806 -0.0468 0.9840 446s 19 -0.2563 -3.3855 -0.5930 446s 20 1.4832 1.1550 -0.2586 446s 21 1.4514 0.6086 -1.1446 446s 22 -1.2351 1.3453 0.5196 446s > fitted 446s Consumption Investment PrivateWages 446s 1 NA NA NA 446s 2 42.8 1.923 26.7 446s 3 45.8 1.532 28.8 446s 4 50.3 4.609 32.8 446s 5 50.5 4.669 34.1 446s 6 52.0 4.805 35.9 446s 7 NA NA NA 446s 8 54.4 2.765 38.8 446s 9 55.5 1.998 39.0 446s 10 NA 2.373 40.2 446s 11 55.4 2.058 38.3 446s 12 52.3 -2.757 34.1 446s 13 NA NA 28.9 446s 14 46.7 -7.521 27.8 446s 15 49.3 -3.217 30.0 446s 16 51.6 NA 33.0 446s 17 55.7 -0.361 37.4 446s 18 59.2 2.047 40.0 446s 19 57.8 1.485 38.8 446s 20 60.1 0.145 41.9 446s 21 63.5 2.691 46.1 446s 22 70.9 3.555 52.8 446s > predict 446s Consumption.pred Consumption.se.fit Consumption.lwr Consumption.upr 446s 1 NA NA NA NA 446s 2 42.8 0.548 41.7 43.9 446s 3 45.8 0.618 44.5 47.0 446s 4 50.3 0.411 49.5 51.2 446s 5 50.5 0.481 49.6 51.5 446s 6 52.0 0.490 51.0 52.9 446s 7 NA NA NA NA 446s 8 54.4 0.396 53.6 55.2 446s 9 55.5 0.467 54.5 56.4 446s 10 NA NA NA NA 446s 11 55.4 0.811 53.7 57.0 446s 12 52.3 0.775 50.7 53.8 446s 13 NA NA NA NA 446s 14 46.7 0.665 45.3 48.0 446s 15 49.3 0.463 48.4 50.3 446s 16 51.6 0.381 50.9 52.4 446s 17 55.7 0.428 54.9 56.6 446s 18 59.2 0.360 58.5 59.9 446s 19 57.8 0.492 56.8 58.7 446s 20 60.1 0.508 59.1 61.1 446s 21 63.5 0.499 62.5 64.6 446s 22 70.9 0.761 69.4 72.5 446s Investment.pred Investment.se.fit Investment.lwr Investment.upr 446s 1 NA NA NA NA 446s 2 1.923 1.079 -0.2526 4.098 446s 3 1.532 0.766 -0.0119 3.075 446s 4 4.609 0.668 3.2632 5.954 446s 5 4.669 0.566 3.5280 5.811 446s 6 4.805 0.543 3.7104 5.899 446s 7 NA NA NA NA 446s 8 2.765 0.447 1.8648 3.665 446s 9 1.998 0.651 0.6860 3.310 446s 10 2.373 0.710 0.9434 3.804 446s 11 2.058 1.237 -0.4350 4.551 446s 12 -2.757 1.139 -5.0532 -0.461 446s 13 NA NA NA NA 446s 14 -7.521 1.094 -9.7261 -5.317 446s 15 -3.217 0.648 -4.5217 -1.912 446s 16 NA NA NA NA 446s 17 -0.361 0.615 -1.6007 0.879 446s 18 2.047 0.417 1.2060 2.888 446s 19 1.485 0.684 0.1062 2.865 446s 20 0.145 0.699 -1.2632 1.553 446s 21 2.691 0.614 1.4548 3.928 446s 22 3.555 0.887 1.7674 5.342 446s PrivateWages.pred PrivateWages.se.fit PrivateWages.lwr PrivateWages.upr 446s 1 NA NA NA NA 446s 2 26.7 0.330 26.0 27.3 446s 3 28.8 0.336 28.2 29.5 446s 4 32.8 0.340 32.1 33.5 446s 5 34.1 0.251 33.6 34.6 446s 6 35.9 0.259 35.4 36.4 446s 7 NA NA NA NA 446s 8 38.8 0.253 38.3 39.3 446s 9 39.0 0.240 38.5 39.5 446s 10 40.2 0.236 39.8 40.7 446s 11 38.3 0.307 37.7 38.9 446s 12 34.1 0.313 33.4 34.7 446s 13 28.9 0.376 28.2 29.7 446s 14 27.8 0.327 27.1 28.4 446s 15 30.0 0.322 29.4 30.7 446s 16 33.0 0.270 32.4 33.5 446s 17 37.4 0.275 36.9 38.0 446s 18 40.0 0.216 39.6 40.5 446s 19 38.8 0.314 38.2 39.4 446s 20 41.9 0.296 41.3 42.5 446s 21 46.1 0.317 45.5 46.8 446s 22 52.8 0.480 51.8 53.7 446s > model.frame 446s [1] TRUE 446s > model.matrix 446s [1] "Attributes: < Component “dim”: Mean relative difference: 0.0345 >" 446s [2] "Attributes: < Component “dimnames”: Component 1: 51 string mismatches >" 446s [3] "Numeric: lengths (696, 672) differ" 446s > nobs 446s [1] 56 446s > linearHypothesis 446s Linear hypothesis test (Theil's F test) 446s 446s Hypothesis: 446s Consumption_corpProf + Investment_capitalLag = 0 446s 446s Model 1: restricted model 446s Model 2: kleinModel 446s 446s Res.Df Df F Pr(>F) 446s 1 45 446s 2 44 1 2.29 0.14 446s Linear hypothesis test (F statistic of a Wald test) 446s 446s Hypothesis: 446s Consumption_corpProf + Investment_capitalLag = 0 446s 446s Model 1: restricted model 446s Model 2: kleinModel 446s 446s Res.Df Df F Pr(>F) 446s 1 45 446s 2 44 1 2.89 0.096 . 446s --- 446s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 446s Linear hypothesis test (Chi^2 statistic of a Wald test) 446s 446s Hypothesis: 446s Consumption_corpProf + Investment_capitalLag = 0 446s 446s Model 1: restricted model 446s Model 2: kleinModel 446s 446s Res.Df Df Chisq Pr(>Chisq) 446s 1 45 446s 2 44 1 2.89 0.089 . 446s --- 446s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 446s Linear hypothesis test (Theil's F test) 446s 446s Hypothesis: 446s Consumption_corpProf + Investment_capitalLag = 0 446s Consumption_corpProfLag - PrivateWages_trend = 0 446s 446s Model 1: restricted model 446s Model 2: kleinModel 446s 446s Res.Df Df F Pr(>F) 446s 1 46 446s 2 44 2 2.3 0.11 446s Linear hypothesis test (F statistic of a Wald test) 446s 446s Hypothesis: 446s Consumption_corpProf + Investment_capitalLag = 0 446s Consumption_corpProfLag - PrivateWages_trend = 0 446s 446s Model 1: restricted model 446s Model 2: kleinModel 446s 446s Res.Df Df F Pr(>F) 446s 1 46 446s 2 44 2 2.9 0.066 . 446s --- 446s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 446s Linear hypothesis test (Chi^2 statistic of a Wald test) 446s 446s Hypothesis: 446s Consumption_corpProf + Investment_capitalLag = 0 446s Consumption_corpProfLag - PrivateWages_trend = 0 446s 446s Model 1: restricted model 446s Model 2: kleinModel 446s 446s Res.Df Df Chisq Pr(>Chisq) 446s 1 46 446s 2 44 2 5.79 0.055 . 446s --- 446s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 446s > logLik 446s 'log Lik.' -72.2 (df=18) 446s 'log Lik.' -83.4 (df=18) 446s > 446s BEGIN TEST test_2sls.R 446s 446s R version 4.3.3 (2024-02-29) -- "Angel Food Cake" 446s Copyright (C) 2024 The R Foundation for Statistical Computing 446s Platform: s390x-ibm-linux-gnu (64-bit) 446s 446s R is free software and comes with ABSOLUTELY NO WARRANTY. 446s You are welcome to redistribute it under certain conditions. 446s Type 'license()' or 'licence()' for distribution details. 446s 446s R is a collaborative project with many contributors. 446s Type 'contributors()' for more information and 446s 'citation()' on how to cite R or R packages in publications. 446s 446s Type 'demo()' for some demos, 'help()' for on-line help, or 446s 'help.start()' for an HTML browser interface to help. 446s Type 'q()' to quit R. 446s 446s > library( systemfit ) 446s Loading required package: Matrix 447s Loading required package: car 447s Loading required package: carData 447s Loading required package: lmtest 447s Loading required package: zoo 447s 447s Attaching package: ‘zoo’ 447s 447s The following objects are masked from ‘package:base’: 447s 447s as.Date, as.Date.numeric 447s 447s 447s Please cite the 'systemfit' package as: 447s 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/. 447s 447s If you have questions, suggestions, or comments regarding the 'systemfit' package, please use a forum or 'tracker' at systemfit's R-Forge site: 447s https://r-forge.r-project.org/projects/systemfit/ 447s > options( digits = 3 ) 447s > 447s > data( "Kmenta" ) 447s > useMatrix <- FALSE 447s > 447s > demand <- consump ~ price + income 447s > supply <- consump ~ price + farmPrice + trend 447s > inst <- ~ income + farmPrice + trend 447s > inst1 <- ~ income + farmPrice 447s > instlist <- list( inst1, inst ) 447s > system <- list( demand = demand, supply = supply ) 447s > restrm <- matrix(0,1,7) # restriction matrix "R" 447s > restrm[1,3] <- 1 447s > restrm[1,7] <- -1 447s > restrict <- "demand_income - supply_trend = 0" 447s > restr2m <- matrix(0,2,7) # restriction matrix "R" 2 447s > restr2m[1,3] <- 1 447s > restr2m[1,7] <- -1 447s > restr2m[2,2] <- -1 447s > restr2m[2,5] <- 1 447s > restr2q <- c( 0, 0.5 ) # restriction vector "q" 2 447s > restrict2 <- c( "demand_income - supply_trend = 0", 447s + "- demand_price + supply_price = 0.5" ) 447s > tc <- matrix(0,7,6) 447s > tc[1,1] <- 1 447s > tc[2,2] <- 1 447s > tc[3,3] <- 1 447s > tc[4,4] <- 1 447s > tc[5,5] <- 1 447s > tc[6,6] <- 1 447s > tc[7,3] <- 1 447s > restr3m <- matrix(0,1,6) # restriction matrix "R" 2 447s > restr3m[1,2] <- -1 447s > restr3m[1,5] <- 1 447s > restr3q <- c( 0.5 ) # restriction vector "q" 2 447s > restrict3 <- "- C2 + C5 = 0.5" 447s > 447s > # It is not possible to estimate 2SLS with systemfit exactly 447s > # as EViews does, because EViews uses 447s > # methodResidCov == "geomean" for the coefficient covariance matrix and 447s > # methodResidCov == "noDfCor" for the residual covariance matrix. 447s > # systemfit uses always the same formulas for both calculations. 447s > 447s > ## *************** 2SLS estimation ************************ 447s > ## ************ 2SLS estimation (default)********************* 447s > fit2sls1 <- systemfit( system, "2SLS", data = Kmenta, inst = inst, 447s + x = TRUE, useMatrix = useMatrix ) 447s > print( summary( fit2sls1 ) ) 447s 447s systemfit results 447s method: 2SLS 447s 447s N DF SSR detRCov OLS-R2 McElroy-R2 447s system 40 33 162 4.36 0.697 0.548 447s 447s N DF SSR MSE RMSE R2 Adj R2 447s demand 20 17 65.7 3.87 1.97 0.755 0.726 447s supply 20 16 96.6 6.04 2.46 0.640 0.572 447s 447s The covariance matrix of the residuals 447s demand supply 447s demand 3.87 4.36 447s supply 4.36 6.04 447s 447s The correlations of the residuals 447s demand supply 447s demand 1.000 0.902 447s supply 0.902 1.000 447s 447s 447s 2SLS estimates for 'demand' (equation 1) 447s Model Formula: consump ~ price + income 447s Instruments: ~income + farmPrice + trend 447s 447s Estimate Std. Error t value Pr(>|t|) 447s (Intercept) 94.6333 7.9208 11.95 1.1e-09 *** 447s price -0.2436 0.0965 -2.52 0.022 * 447s income 0.3140 0.0469 6.69 3.8e-06 *** 447s --- 447s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 447s 447s Residual standard error: 1.966 on 17 degrees of freedom 447s Number of observations: 20 Degrees of Freedom: 17 447s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 447s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 447s 447s 447s 2SLS estimates for 'supply' (equation 2) 447s Model Formula: consump ~ price + farmPrice + trend 447s Instruments: ~income + farmPrice + trend 447s 447s Estimate Std. Error t value Pr(>|t|) 447s (Intercept) 49.5324 12.0105 4.12 0.0008 *** 447s price 0.2401 0.0999 2.40 0.0288 * 447s farmPrice 0.2556 0.0473 5.41 5.8e-05 *** 447s trend 0.2529 0.0997 2.54 0.0219 * 447s --- 447s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 447s 447s Residual standard error: 2.458 on 16 degrees of freedom 447s Number of observations: 20 Degrees of Freedom: 16 447s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 447s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 447s 447s > nobs( fit2sls1 ) 447s [1] 40 447s > 447s > ## *************** 2SLS estimation (singleEqSigma=F)******************* 447s > fit2sls1s <- systemfit( system, "2SLS", data = Kmenta, inst = inst, 447s + singleEqSigma = FALSE, useMatrix = useMatrix ) 447s > print( summary( fit2sls1s ) ) 447s 447s systemfit results 447s method: 2SLS 447s 447s N DF SSR detRCov OLS-R2 McElroy-R2 447s system 40 33 162 4.36 0.697 0.548 447s 447s N DF SSR MSE RMSE R2 Adj R2 447s demand 20 17 65.7 3.87 1.97 0.755 0.726 447s supply 20 16 96.6 6.04 2.46 0.640 0.572 447s 447s The covariance matrix of the residuals 447s demand supply 447s demand 3.87 4.36 447s supply 4.36 6.04 447s 447s The correlations of the residuals 447s demand supply 447s demand 1.000 0.902 447s supply 0.902 1.000 447s 447s 447s 2SLS estimates for 'demand' (equation 1) 447s Model Formula: consump ~ price + income 447s Instruments: ~income + farmPrice + trend 447s 447s Estimate Std. Error t value Pr(>|t|) 447s (Intercept) 94.633 8.935 10.59 6.6e-09 *** 447s price -0.244 0.109 -2.24 0.039 * 447s income 0.314 0.053 5.93 1.6e-05 *** 447s --- 447s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 447s 447s Residual standard error: 1.966 on 17 degrees of freedom 447s Number of observations: 20 Degrees of Freedom: 17 447s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 447s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 447s 447s 447s 2SLS estimates for 'supply' (equation 2) 447s Model Formula: consump ~ price + farmPrice + trend 447s Instruments: ~income + farmPrice + trend 447s 447s Estimate Std. Error t value Pr(>|t|) 447s (Intercept) 49.5324 10.8404 4.57 0.00032 *** 447s price 0.2401 0.0902 2.66 0.01706 * 447s farmPrice 0.2556 0.0426 5.99 1.9e-05 *** 447s trend 0.2529 0.0899 2.81 0.01253 * 447s --- 447s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 447s 447s Residual standard error: 2.458 on 16 degrees of freedom 447s Number of observations: 20 Degrees of Freedom: 16 447s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 447s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 447s 447s > nobs( fit2sls1s ) 447s [1] 40 447s > 447s > ## ********************* 2SLS (useDfSys = TRUE) ***************** 447s > print( summary( fit2sls1, useDfSys = TRUE ) ) 447s 447s systemfit results 447s method: 2SLS 447s 447s N DF SSR detRCov OLS-R2 McElroy-R2 447s system 40 33 162 4.36 0.697 0.548 447s 447s N DF SSR MSE RMSE R2 Adj R2 447s demand 20 17 65.7 3.87 1.97 0.755 0.726 447s supply 20 16 96.6 6.04 2.46 0.640 0.572 447s 447s The covariance matrix of the residuals 447s demand supply 447s demand 3.87 4.36 447s supply 4.36 6.04 447s 447s The correlations of the residuals 447s demand supply 447s demand 1.000 0.902 447s supply 0.902 1.000 447s 447s 447s 2SLS estimates for 'demand' (equation 1) 447s Model Formula: consump ~ price + income 447s Instruments: ~income + farmPrice + trend 447s 447s Estimate Std. Error t value Pr(>|t|) 447s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 447s price -0.2436 0.0965 -2.52 0.017 * 447s income 0.3140 0.0469 6.69 1.3e-07 *** 447s --- 447s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 447s 447s Residual standard error: 1.966 on 17 degrees of freedom 447s Number of observations: 20 Degrees of Freedom: 17 447s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 447s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 447s 447s 447s 2SLS estimates for 'supply' (equation 2) 447s Model Formula: consump ~ price + farmPrice + trend 447s Instruments: ~income + farmPrice + trend 447s 447s Estimate Std. Error t value Pr(>|t|) 447s (Intercept) 49.5324 12.0105 4.12 0.00024 *** 447s price 0.2401 0.0999 2.40 0.02208 * 447s farmPrice 0.2556 0.0473 5.41 5.5e-06 *** 447s trend 0.2529 0.0997 2.54 0.01605 * 447s --- 447s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 447s 447s Residual standard error: 2.458 on 16 degrees of freedom 447s Number of observations: 20 Degrees of Freedom: 16 447s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 447s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 447s 447s > nobs( fit2sls1 ) 447s [1] 40 447s > 447s > ## ********************* 2SLS (methodResidCov = "noDfCor" ) ***************** 447s > fit2sls1r <- systemfit( system, "2SLS", data = Kmenta, inst = inst, 447s + methodResidCov = "noDfCor", useMatrix = useMatrix ) 448s > print( summary( fit2sls1r ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 33 162 2.97 0.697 0.525 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 65.7 3.87 1.97 0.755 0.726 448s supply 20 16 96.6 6.04 2.46 0.640 0.572 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.29 3.59 448s supply 3.59 4.83 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.902 448s supply 0.902 1.000 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 94.6333 7.3027 12.96 3.1e-10 *** 448s price -0.2436 0.0890 -2.74 0.014 * 448s income 0.3140 0.0433 7.25 1.3e-06 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.966 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 448s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 49.5324 10.7425 4.61 0.00029 *** 448s price 0.2401 0.0894 2.69 0.01623 * 448s farmPrice 0.2556 0.0423 6.05 1.7e-05 *** 448s trend 0.2529 0.0891 2.84 0.01188 * 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.458 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 448s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 448s 448s > nobs( fit2sls1r ) 448s [1] 40 448s > 448s > ## *************** 2SLS (methodResidCov="noDfCor", singleEqSigma=F) ************* 448s > fit2sls1rs <- systemfit( system, "2SLS", data = Kmenta, inst = inst, 448s + methodResidCov = "noDfCor", singleEqSigma = FALSE, useMatrix = useMatrix ) 448s > print( summary( fit2sls1rs ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 33 162 2.97 0.697 0.525 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 65.7 3.87 1.97 0.755 0.726 448s supply 20 16 96.6 6.04 2.46 0.640 0.572 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.29 3.59 448s supply 3.59 4.83 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.902 448s supply 0.902 1.000 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 94.6333 8.1158 11.66 1.6e-09 *** 448s price -0.2436 0.0989 -2.46 0.025 * 448s income 0.3140 0.0481 6.53 5.2e-06 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.966 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 448s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 49.5324 9.8463 5.03 0.00012 *** 448s price 0.2401 0.0819 2.93 0.00980 ** 448s farmPrice 0.2556 0.0387 6.60 6.1e-06 *** 448s trend 0.2529 0.0817 3.10 0.00694 ** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.458 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 448s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 448s 448s > nobs( fit2sls1rs ) 448s [1] 40 448s > 448s > ## ********************* 2SLS with restriction ******************** 448s > ## **************** 2SLS with restriction (default)******************** 448s > fit2sls2 <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restrm, 448s + inst = inst, useMatrix = useMatrix ) 448s > print( summary( fit2sls2 ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 34 166 3.6 0.691 0.553 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 67.4 3.97 1.99 0.749 0.719 448s supply 20 16 98.2 6.13 2.48 0.634 0.565 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.97 4.55 448s supply 4.55 6.13 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.923 448s supply 0.923 1.000 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 94.2816 8.8693 10.63 2.4e-12 *** 448s price -0.2247 0.1034 -2.17 0.037 * 448s income 0.2983 0.0454 6.57 1.6e-07 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.991 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 67.418 MSE: 3.966 Root MSE: 1.991 448s Multiple R-Squared: 0.749 Adjusted R-Squared: 0.719 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 48.1843 10.5384 4.57 6.1e-05 *** 448s price 0.2427 0.0896 2.71 0.011 * 448s farmPrice 0.2619 0.0411 6.38 2.8e-07 *** 448s trend 0.2983 0.0454 6.57 1.6e-07 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.477 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 98.155 MSE: 6.135 Root MSE: 2.477 448s Multiple R-Squared: 0.634 Adjusted R-Squared: 0.565 448s 448s > nobs( fit2sls2 ) 448s [1] 40 448s > # the same with symbolically specified restrictions 448s > fit2sls2Sym <- systemfit( system, "2SLS", data = Kmenta, 448s + restrict.matrix = restrict, inst = inst, useMatrix = useMatrix ) 448s > all.equal( fit2sls2, fit2sls2Sym ) 448s [1] "Component “call”: target, current do not match when deparsed" 448s > nobs( fit2sls2Sym ) 448s [1] 40 448s > 448s > ## ************* 2SLS with restriction (singleEqSigma=T) ***************** 448s > fit2sls2s <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restrm, 448s + inst = inst, singleEqSigma = TRUE, x = TRUE, 448s + useMatrix = useMatrix ) 448s > print( summary( fit2sls2s ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 34 166 3.6 0.691 0.553 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 67.4 3.97 1.99 0.749 0.719 448s supply 20 16 98.2 6.13 2.48 0.634 0.565 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.97 4.55 448s supply 4.55 6.13 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.923 448s supply 0.923 1.000 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 94.2816 8.0090 11.77 1.5e-13 *** 448s price -0.2247 0.0946 -2.37 0.023 * 448s income 0.2983 0.0430 6.94 5.3e-08 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.991 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 67.418 MSE: 3.966 Root MSE: 1.991 448s Multiple R-Squared: 0.749 Adjusted R-Squared: 0.719 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 48.1843 11.8001 4.08 0.00025 *** 448s price 0.2427 0.1006 2.41 0.02135 * 448s farmPrice 0.2619 0.0459 5.70 2.1e-06 *** 448s trend 0.2983 0.0430 6.94 5.3e-08 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.477 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 98.155 MSE: 6.135 Root MSE: 2.477 448s Multiple R-Squared: 0.634 Adjusted R-Squared: 0.565 448s 448s > nobs( fit2sls2s ) 448s [1] 40 448s > 448s > ## ********************* 2SLS with restriction (useDfSys=T) ************** 448s > print( summary( fit2sls2, useDfSys = TRUE ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 34 166 3.6 0.691 0.553 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 67.4 3.97 1.99 0.749 0.719 448s supply 20 16 98.2 6.13 2.48 0.634 0.565 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.97 4.55 448s supply 4.55 6.13 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.923 448s supply 0.923 1.000 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 94.2816 8.8693 10.63 2.4e-12 *** 448s price -0.2247 0.1034 -2.17 0.037 * 448s income 0.2983 0.0454 6.57 1.6e-07 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.991 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 67.418 MSE: 3.966 Root MSE: 1.991 448s Multiple R-Squared: 0.749 Adjusted R-Squared: 0.719 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 48.1843 10.5384 4.57 6.1e-05 *** 448s price 0.2427 0.0896 2.71 0.011 * 448s farmPrice 0.2619 0.0411 6.38 2.8e-07 *** 448s trend 0.2983 0.0454 6.57 1.6e-07 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.477 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 98.155 MSE: 6.135 Root MSE: 2.477 448s Multiple R-Squared: 0.634 Adjusted R-Squared: 0.565 448s 448s > nobs( fit2sls2 ) 448s [1] 40 448s > 448s > ## ********************* 2SLS with restriction (methodResidCov = "noDfCor") ************** 448s > fit2sls2r <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restrm, 448s + inst = inst, methodResidCov = "noDfCor", useMatrix = useMatrix ) 448s > print( summary( fit2sls2r ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 34 166 2.45 0.691 0.526 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 67.4 3.97 1.99 0.749 0.719 448s supply 20 16 98.2 6.13 2.48 0.634 0.565 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.37 3.75 448s supply 3.75 4.91 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.923 448s supply 0.923 1.000 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 94.2816 8.1771 11.53 2.7e-13 *** 448s price -0.2247 0.0954 -2.36 0.024 * 448s income 0.2983 0.0419 7.13 3.1e-08 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.991 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 67.418 MSE: 3.966 Root MSE: 1.991 448s Multiple R-Squared: 0.749 Adjusted R-Squared: 0.719 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 48.1843 9.7159 4.96 1.9e-05 *** 448s price 0.2427 0.0826 2.94 0.0059 ** 448s farmPrice 0.2619 0.0379 6.92 5.7e-08 *** 448s trend 0.2983 0.0419 7.13 3.1e-08 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.477 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 98.155 MSE: 6.135 Root MSE: 2.477 448s Multiple R-Squared: 0.634 Adjusted R-Squared: 0.565 448s 448s > nobs( fit2sls2r ) 448s [1] 40 448s > 448s > ## ******** 2SLS with restriction (methodResidCov="noDfCor", singleEqSigma=TRUE) ********* 448s > fit2sls2rs <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restrm, 448s + inst = inst, methodResidCov = "noDfCor", singleEqSigma = TRUE, 448s + useMatrix = useMatrix ) 448s > print( summary( fit2sls2rs ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 34 166 2.45 0.691 0.526 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 67.4 3.97 1.99 0.749 0.719 448s supply 20 16 98.2 6.13 2.48 0.634 0.565 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.37 3.75 448s supply 3.75 4.91 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.923 448s supply 0.923 1.000 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 94.2816 7.3834 12.77 1.6e-14 *** 448s price -0.2247 0.0871 -2.58 0.014 * 448s income 0.2983 0.0394 7.57 8.5e-09 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.991 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 67.418 MSE: 3.966 Root MSE: 1.991 448s Multiple R-Squared: 0.749 Adjusted R-Squared: 0.719 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 48.1843 10.5574 4.56 6.3e-05 *** 448s price 0.2427 0.0900 2.70 0.011 * 448s farmPrice 0.2619 0.0411 6.37 2.8e-07 *** 448s trend 0.2983 0.0394 7.57 8.5e-09 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.477 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 98.155 MSE: 6.135 Root MSE: 2.477 448s Multiple R-Squared: 0.634 Adjusted R-Squared: 0.565 448s 448s > nobs( fit2sls2rs ) 448s [1] 40 448s > 448s > ## ********************* 2SLS with restriction via restrict.regMat ****************** 448s > ## *************** 2SLS with restriction via restrict.regMat (default )*************** 448s > fit2sls3 <- systemfit( system, "2SLS", data = Kmenta, restrict.regMat = tc, 448s + inst = inst, methodResidCov = "noDfCor", useMatrix = useMatrix ) 448s > print( summary( fit2sls3, useDfSys = TRUE ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 34 166 2.45 0.691 0.526 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 67.4 3.97 1.99 0.749 0.719 448s supply 20 16 98.2 6.13 2.48 0.634 0.565 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.37 3.75 448s supply 3.75 4.91 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.923 448s supply 0.923 1.000 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 94.2816 8.1771 11.53 2.7e-13 *** 448s price -0.2247 0.0954 -2.36 0.024 * 448s income 0.2983 0.0419 7.13 3.1e-08 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.991 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 67.418 MSE: 3.966 Root MSE: 1.991 448s Multiple R-Squared: 0.749 Adjusted R-Squared: 0.719 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 48.1843 9.7159 4.96 1.9e-05 *** 448s price 0.2427 0.0826 2.94 0.0059 ** 448s farmPrice 0.2619 0.0379 6.92 5.7e-08 *** 448s trend 0.2983 0.0419 7.13 3.1e-08 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.477 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 98.155 MSE: 6.135 Root MSE: 2.477 448s Multiple R-Squared: 0.634 Adjusted R-Squared: 0.565 448s 448s > nobs( fit2sls3 ) 448s [1] 40 448s > 448s > 448s > ## ***************** 2SLS with 2 restrictions ******************* 448s > ## ************** 2SLS with 2 restrictions (default) ************** 448s > fit2sls4 <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restr2m, 448s + restrict.rhs = restr2q, inst = inst, useMatrix = useMatrix ) 448s > print( summary( fit2sls4 ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 35 166 3.78 0.69 0.568 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 66.1 3.89 1.97 0.754 0.725 448s supply 20 16 100.0 6.25 2.50 0.627 0.557 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.89 4.53 448s supply 4.53 6.25 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.919 448s supply 0.919 1.000 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 95.7059 6.4189 14.91 < 2e-16 *** 448s price -0.2433 0.0663 -3.67 0.00081 *** 448s income 0.3027 0.0408 7.42 1.1e-08 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.972 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 66.081 MSE: 3.887 Root MSE: 1.972 448s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 46.5637 7.8941 5.90 1.1e-06 *** 448s price 0.2567 0.0663 3.87 0.00045 *** 448s farmPrice 0.2637 0.0398 6.62 1.2e-07 *** 448s trend 0.3027 0.0408 7.42 1.1e-08 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.5 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 99.971 MSE: 6.248 Root MSE: 2.5 448s Multiple R-Squared: 0.627 Adjusted R-Squared: 0.557 448s 448s > nobs( fit2sls4 ) 448s [1] 40 448s > # the same with symbolically specified restrictions 448s > fit2sls4Sym <- systemfit( system, "2SLS", data = Kmenta, 448s + restrict.matrix = restrict2, inst = inst, useMatrix = useMatrix ) 448s > all.equal( fit2sls4, fit2sls4Sym ) 448s [1] "Component “call”: target, current do not match when deparsed" 448s > nobs( fit2sls4Sym ) 448s [1] 40 448s > 448s > ## ************ 2SLS with 2 restrictions (singleEqSigma=T) ************** 448s > fit2sls4s <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restr2m, 448s + restrict.rhs = restr2q, inst = inst, singleEqSigma = TRUE, 448s + useMatrix = useMatrix ) 448s > print( summary( fit2sls4s ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 35 166 3.78 0.69 0.568 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 66.1 3.89 1.97 0.754 0.725 448s supply 20 16 100.0 6.25 2.50 0.627 0.557 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.89 4.53 448s supply 4.53 6.25 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.919 448s supply 0.919 1.000 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 95.7059 6.3056 15.18 < 2e-16 *** 448s price -0.2433 0.0684 -3.56 0.0011 ** 448s income 0.3027 0.0394 7.69 5.1e-09 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.972 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 66.081 MSE: 3.887 Root MSE: 1.972 448s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 46.5637 8.3296 5.59 2.7e-06 *** 448s price 0.2567 0.0684 3.75 0.00064 *** 448s farmPrice 0.2637 0.0455 5.79 1.5e-06 *** 448s trend 0.3027 0.0394 7.69 5.1e-09 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.5 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 99.971 MSE: 6.248 Root MSE: 2.5 448s Multiple R-Squared: 0.627 Adjusted R-Squared: 0.557 448s 448s > nobs( fit2sls4s ) 448s [1] 40 448s > 448s > ## ***************** 2SLS with 2 restrictions (useDfSys=T) ************** 448s > print( summary( fit2sls4, useDfSys = TRUE ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 35 166 3.78 0.69 0.568 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 66.1 3.89 1.97 0.754 0.725 448s supply 20 16 100.0 6.25 2.50 0.627 0.557 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.89 4.53 448s supply 4.53 6.25 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.919 448s supply 0.919 1.000 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 95.7059 6.4189 14.91 < 2e-16 *** 448s price -0.2433 0.0663 -3.67 0.00081 *** 448s income 0.3027 0.0408 7.42 1.1e-08 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.972 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 66.081 MSE: 3.887 Root MSE: 1.972 448s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 46.5637 7.8941 5.90 1.1e-06 *** 448s price 0.2567 0.0663 3.87 0.00045 *** 448s farmPrice 0.2637 0.0398 6.62 1.2e-07 *** 448s trend 0.3027 0.0408 7.42 1.1e-08 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.5 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 99.971 MSE: 6.248 Root MSE: 2.5 448s Multiple R-Squared: 0.627 Adjusted R-Squared: 0.557 448s 448s > nobs( fit2sls4 ) 448s [1] 40 448s > 448s > ## ***************** 2SLS with 2 restrictions (methodResidCov="noDfCor") ************** 448s > fit2sls4r <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restr2m, 448s + restrict.rhs = restr2q, inst = inst, methodResidCov = "noDfCor", 448s + x = TRUE, useMatrix = useMatrix ) 448s > print( summary( fit2sls4r ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 35 166 2.57 0.69 0.54 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 66.1 3.89 1.97 0.754 0.725 448s supply 20 16 100.0 6.25 2.50 0.627 0.557 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.30 3.73 448s supply 3.73 5.00 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.919 448s supply 0.919 1.000 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 95.7059 6.0044 15.94 < 2e-16 *** 448s price -0.2433 0.0621 -3.92 0.00039 *** 448s income 0.3027 0.0382 7.93 2.5e-09 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.972 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 66.081 MSE: 3.887 Root MSE: 1.972 448s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 46.5637 7.3842 6.31 3.1e-07 *** 448s price 0.2567 0.0621 4.14 0.00021 *** 448s farmPrice 0.2637 0.0373 7.08 3.0e-08 *** 448s trend 0.3027 0.0382 7.93 2.5e-09 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.5 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 99.971 MSE: 6.248 Root MSE: 2.5 448s Multiple R-Squared: 0.627 Adjusted R-Squared: 0.557 448s 448s > nobs( fit2sls4r ) 448s [1] 40 448s > 448s > ## ***** 2SLS with 2 restrictions (methodResidCov="noDfCor", singleEqSigma=T) ******* 448s > fit2sls4rs <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restr2m, 448s + restrict.rhs = restr2q, inst = inst, methodResidCov = "noDfCor", 448s + singleEqSigma = TRUE, useMatrix = useMatrix ) 448s > print( summary( fit2sls4rs ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 35 166 2.57 0.69 0.54 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 66.1 3.89 1.97 0.754 0.725 448s supply 20 16 100.0 6.25 2.50 0.627 0.557 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.30 3.73 448s supply 3.73 5.00 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.919 448s supply 0.919 1.000 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 95.7059 5.7579 16.62 < 2e-16 *** 448s price -0.2433 0.0621 -3.92 4e-04 *** 448s income 0.3027 0.0360 8.40 6.6e-10 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.972 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 66.081 MSE: 3.887 Root MSE: 1.972 448s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 46.5637 7.5360 6.18 4.5e-07 *** 448s price 0.2567 0.0621 4.13 0.00021 *** 448s farmPrice 0.2637 0.0407 6.47 1.8e-07 *** 448s trend 0.3027 0.0360 8.40 6.6e-10 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.5 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 99.971 MSE: 6.248 Root MSE: 2.5 448s Multiple R-Squared: 0.627 Adjusted R-Squared: 0.557 448s 448s > nobs( fit2sls4rs ) 448s [1] 40 448s > 448s > ## ************* 2SLS with 2 restrictions via R and restrict.regMat ****************** 448s > ## ******** 2SLS with 2 restrictions via R and restrict.regMat (default) ************* 448s > fit2sls5 <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restr3m, 448s + restrict.rhs = restr3q, restrict.regMat = tc, inst = inst, 448s + useMatrix = useMatrix ) 448s > print( summary( fit2sls5 ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 35 166 3.78 0.69 0.568 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 66.1 3.89 1.97 0.754 0.725 448s supply 20 16 100.0 6.25 2.50 0.627 0.557 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.89 4.53 448s supply 4.53 6.25 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.919 448s supply 0.919 1.000 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 95.7059 6.4189 14.91 < 2e-16 *** 448s price -0.2433 0.0663 -3.67 0.00081 *** 448s income 0.3027 0.0408 7.42 1.1e-08 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.972 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 66.081 MSE: 3.887 Root MSE: 1.972 448s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 46.5637 7.8941 5.90 1.1e-06 *** 448s price 0.2567 0.0663 3.87 0.00045 *** 448s farmPrice 0.2637 0.0398 6.62 1.2e-07 *** 448s trend 0.3027 0.0408 7.42 1.1e-08 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.5 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 99.971 MSE: 6.248 Root MSE: 2.5 448s Multiple R-Squared: 0.627 Adjusted R-Squared: 0.557 448s 448s > nobs( fit2sls5 ) 448s [1] 40 448s > # the same with symbolically specified restrictions 448s > fit2sls5Sym <- systemfit( system, "2SLS", data = Kmenta, 448s + restrict.matrix = restrict3, restrict.regMat = tc, inst = inst, 448s + useMatrix = useMatrix ) 448s > all.equal( fit2sls5, fit2sls5Sym ) 448s [1] "Component “call”: target, current do not match when deparsed" 448s > nobs( fit2sls5Sym ) 448s [1] 40 448s > 448s > ## ******* 2SLS with 2 restrictions via R and restrict.regMat (singleEqSigma=T) ****** 448s > fit2sls5s <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restr3m, 448s + restrict.rhs = restr3q, restrict.regMat = tc, inst = inst, 448s + singleEqSigma = TRUE, useMatrix = useMatrix ) 448s > print( summary( fit2sls5s ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 35 166 3.78 0.69 0.568 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 66.1 3.89 1.97 0.754 0.725 448s supply 20 16 100.0 6.25 2.50 0.627 0.557 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.89 4.53 448s supply 4.53 6.25 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.919 448s supply 0.919 1.000 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 95.7059 6.3056 15.18 < 2e-16 *** 448s price -0.2433 0.0684 -3.56 0.0011 ** 448s income 0.3027 0.0394 7.69 5.1e-09 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.972 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 66.081 MSE: 3.887 Root MSE: 1.972 448s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 46.5637 8.3296 5.59 2.7e-06 *** 448s price 0.2567 0.0684 3.75 0.00064 *** 448s farmPrice 0.2637 0.0455 5.79 1.5e-06 *** 448s trend 0.3027 0.0394 7.69 5.1e-09 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.5 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 99.971 MSE: 6.248 Root MSE: 2.5 448s Multiple R-Squared: 0.627 Adjusted R-Squared: 0.557 448s 448s > nobs( fit2sls5s ) 448s [1] 40 448s > 448s > ## ********** 2SLS with 2 restrictions via R and restrict.regMat (useDfSys=T) ******* 448s > print( summary( fit2sls5, useDfSys = TRUE ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 35 166 3.78 0.69 0.568 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 66.1 3.89 1.97 0.754 0.725 448s supply 20 16 100.0 6.25 2.50 0.627 0.557 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.89 4.53 448s supply 4.53 6.25 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.919 448s supply 0.919 1.000 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 95.7059 6.4189 14.91 < 2e-16 *** 448s price -0.2433 0.0663 -3.67 0.00081 *** 448s income 0.3027 0.0408 7.42 1.1e-08 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.972 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 66.081 MSE: 3.887 Root MSE: 1.972 448s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 46.5637 7.8941 5.90 1.1e-06 *** 448s price 0.2567 0.0663 3.87 0.00045 *** 448s farmPrice 0.2637 0.0398 6.62 1.2e-07 *** 448s trend 0.3027 0.0408 7.42 1.1e-08 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.5 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 99.971 MSE: 6.248 Root MSE: 2.5 448s Multiple R-Squared: 0.627 Adjusted R-Squared: 0.557 448s 448s > nobs( fit2sls5 ) 448s [1] 40 448s > 448s > ## ************* 2SLS with 2 restrictions via R and restrict.regMat (methodResidCov="noDfCor") ********* 448s > fit2sls5r <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restr3m, 448s + restrict.rhs = restr3q, restrict.regMat = tc, inst = inst, 448s + methodResidCov = "noDfCor", useMatrix = useMatrix ) 448s > print( summary( fit2sls5r ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 35 166 2.57 0.69 0.54 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 66.1 3.89 1.97 0.754 0.725 448s supply 20 16 100.0 6.25 2.50 0.627 0.557 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.30 3.73 448s supply 3.73 5.00 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.919 448s supply 0.919 1.000 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 95.7059 6.0044 15.94 < 2e-16 *** 448s price -0.2433 0.0621 -3.92 0.00039 *** 448s income 0.3027 0.0382 7.93 2.5e-09 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.972 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 66.081 MSE: 3.887 Root MSE: 1.972 448s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 46.5637 7.3842 6.31 3.1e-07 *** 448s price 0.2567 0.0621 4.14 0.00021 *** 448s farmPrice 0.2637 0.0373 7.08 3.0e-08 *** 448s trend 0.3027 0.0382 7.93 2.5e-09 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.5 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 99.971 MSE: 6.248 Root MSE: 2.5 448s Multiple R-Squared: 0.627 Adjusted R-Squared: 0.557 448s 448s > nobs( fit2sls5r ) 448s [1] 40 448s > 448s > ## ** 2SLS with 2 restrictions via R and restrict.regMat (methodResidCov="noDfCor", singleEqSigma=T) ** 448s > fit2sls5rs <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restr3m, 448s + restrict.rhs = restr3q, restrict.regMat = tc, inst = inst, 448s + methodResidCov = "noDfCor", singleEqSigma = TRUE, 448s + x = TRUE, useMatrix = useMatrix ) 448s > print( summary( fit2sls5rs ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 35 166 2.57 0.69 0.54 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 66.1 3.89 1.97 0.754 0.725 448s supply 20 16 100.0 6.25 2.50 0.627 0.557 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.30 3.73 448s supply 3.73 5.00 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.919 448s supply 0.919 1.000 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 95.7059 5.7579 16.62 < 2e-16 *** 448s price -0.2433 0.0621 -3.92 4e-04 *** 448s income 0.3027 0.0360 8.40 6.6e-10 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.972 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 66.081 MSE: 3.887 Root MSE: 1.972 448s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 46.5637 7.5360 6.18 4.5e-07 *** 448s price 0.2567 0.0621 4.13 0.00021 *** 448s farmPrice 0.2637 0.0407 6.47 1.8e-07 *** 448s trend 0.3027 0.0360 8.40 6.6e-10 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.5 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 99.971 MSE: 6.248 Root MSE: 2.5 448s Multiple R-Squared: 0.627 Adjusted R-Squared: 0.557 448s 448s > nobs( fit2sls5rs ) 448s [1] 40 448s > 448s > ## *********** 2SLS estimation with different instruments ************** 448s > ## ******* 2SLS estimation with different instruments (default) ********* 448s > fit2slsd1 <- systemfit( system, "2SLS", data = Kmenta, inst = instlist, 448s + useMatrix = useMatrix ) 448s > print( summary( fit2slsd1 ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 33 164 9.25 0.694 0.512 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 67.4 3.97 1.99 0.748 0.719 448s supply 20 16 96.6 6.04 2.46 0.640 0.572 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.97 3.84 448s supply 3.84 6.04 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.784 448s supply 0.784 1.000 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 106.7894 11.1435 9.58 2.9e-08 *** 448s price -0.4116 0.1448 -2.84 0.011 * 448s income 0.3617 0.0564 6.41 6.4e-06 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.992 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 448s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 49.5324 12.0105 4.12 0.0008 *** 448s price 0.2401 0.0999 2.40 0.0288 * 448s farmPrice 0.2556 0.0473 5.41 5.8e-05 *** 448s trend 0.2529 0.0997 2.54 0.0219 * 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.458 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 448s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 448s 448s > nobs( fit2slsd1 ) 448s [1] 40 448s > 448s > ## *********** 2SLS estimation with different instruments (singleEqSigma=F)***** 448s > fit2slsd1s <- systemfit( system, "2SLS", data = Kmenta, inst = instlist, 448s + singleEqSigma = FALSE, useMatrix = useMatrix ) 448s > print( summary( fit2slsd1s ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 33 164 9.25 0.694 0.512 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 67.4 3.97 1.99 0.748 0.719 448s supply 20 16 96.6 6.04 2.46 0.640 0.572 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.97 3.84 448s supply 3.84 6.04 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.784 448s supply 0.784 1.000 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 106.7894 12.4749 8.56 1.4e-07 *** 448s price -0.4116 0.1622 -2.54 0.021 * 448s income 0.3617 0.0631 5.73 2.5e-05 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.992 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 448s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 49.5324 10.8976 4.55 0.00033 *** 448s price 0.2401 0.0907 2.65 0.01755 * 448s farmPrice 0.2556 0.0429 5.96 2e-05 *** 448s trend 0.2529 0.0904 2.80 0.01292 * 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.458 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 448s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 448s 448s > nobs( fit2slsd1s ) 448s [1] 40 448s > 448s > ## ********* 2SLS estimation with different instruments (useDfSys=T) ******* 448s > print( summary( fit2slsd1, useDfSys = TRUE ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 33 164 9.25 0.694 0.512 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 67.4 3.97 1.99 0.748 0.719 448s supply 20 16 96.6 6.04 2.46 0.640 0.572 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.97 3.84 448s supply 3.84 6.04 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.784 448s supply 0.784 1.000 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 106.7894 11.1435 9.58 4.7e-11 *** 448s price -0.4116 0.1448 -2.84 0.0076 ** 448s income 0.3617 0.0564 6.41 2.9e-07 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.992 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 448s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 49.5324 12.0105 4.12 0.00024 *** 448s price 0.2401 0.0999 2.40 0.02208 * 448s farmPrice 0.2556 0.0473 5.41 5.5e-06 *** 448s trend 0.2529 0.0997 2.54 0.01605 * 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.458 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 448s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 448s 448s > nobs( fit2slsd1 ) 448s [1] 40 448s > 448s > ## ********* 2SLS estimation with different instruments (methodResidCov="noDfCor") ****** 448s > fit2slsd1r <- systemfit( system, "2SLS", data = Kmenta, inst = instlist, 448s + methodResidCov = "noDfCor", useMatrix = useMatrix ) 448s > print( summary( fit2slsd1r ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 33 164 6.29 0.694 0.5 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 67.4 3.97 1.99 0.748 0.719 448s supply 20 16 96.6 6.04 2.46 0.640 0.572 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.37 3.16 448s supply 3.16 4.83 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.784 448s supply 0.784 1.000 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 106.789 10.274 10.39 8.8e-09 *** 448s price -0.412 0.134 -3.08 0.0068 ** 448s income 0.362 0.052 6.95 2.3e-06 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.992 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 448s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 49.5324 10.7425 4.61 0.00029 *** 448s price 0.2401 0.0894 2.69 0.01623 * 448s farmPrice 0.2556 0.0423 6.05 1.7e-05 *** 448s trend 0.2529 0.0891 2.84 0.01188 * 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.458 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 448s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 448s 448s > nobs( fit2slsd1r ) 448s [1] 40 448s > 448s > ## 2SLS estimation with different instruments (methodResidCov="noDfCor",singleEqSigma=F) 448s > fit2slsd1r <- systemfit( system, "2SLS", data = Kmenta, inst = instlist, 448s + methodResidCov = "noDfCor", singleEqSigma = FALSE, 448s + useMatrix = useMatrix ) 448s > print( summary( fit2slsd1r ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 33 164 6.29 0.694 0.5 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 67.4 3.97 1.99 0.748 0.719 448s supply 20 16 96.6 6.04 2.46 0.640 0.572 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.37 3.16 448s supply 3.16 4.83 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.784 448s supply 0.784 1.000 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 106.7894 11.3309 9.42 3.7e-08 *** 448s price -0.4116 0.1473 -2.79 0.012 * 448s income 0.3617 0.0574 6.31 7.9e-06 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.992 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 448s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 49.5324 9.8982 5.00 0.00013 *** 448s price 0.2401 0.0824 2.92 0.01012 * 448s farmPrice 0.2556 0.0389 6.56 6.5e-06 *** 448s trend 0.2529 0.0821 3.08 0.00718 ** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.458 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 448s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 448s 448s > nobs( fit2slsd1r ) 448s [1] 40 448s > 448s > ## **** 2SLS estimation with different instruments and restriction ******* 448s > ## ** 2SLS estimation with different instruments and restriction (default) **** 448s > fit2slsd2 <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restrm, 448s + inst = instlist, useMatrix = useMatrix ) 448s > print( summary( fit2slsd2 ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 34 165 4.89 0.693 0.56 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 64.4 3.79 1.95 0.760 0.731 448s supply 20 16 100.3 6.27 2.50 0.626 0.556 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.79 4.35 448s supply 4.35 6.27 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.891 448s supply 0.891 1.000 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 103.5936 11.8930 8.71 3.5e-10 *** 448s price -0.3449 0.1455 -2.37 0.024 * 448s income 0.3260 0.0511 6.38 2.8e-07 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.947 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 64.445 MSE: 3.791 Root MSE: 1.947 448s Multiple R-Squared: 0.76 Adjusted R-Squared: 0.731 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 47.3592 10.5362 4.49 7.7e-05 *** 448s price 0.2443 0.0894 2.73 0.0099 ** 448s farmPrice 0.2657 0.0411 6.47 2.1e-07 *** 448s trend 0.3260 0.0511 6.38 2.8e-07 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.504 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 100.321 MSE: 6.27 Root MSE: 2.504 448s Multiple R-Squared: 0.626 Adjusted R-Squared: 0.556 448s 448s > nobs( fit2slsd2 ) 448s [1] 40 448s > 448s > ## 2SLS estimation with different instruments and restriction (singleEqSigma=T) 448s > fit2slsd2s <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restrm, 448s + inst = instlist, singleEqSigma = TRUE, useMatrix = useMatrix ) 448s > print( summary( fit2slsd2s ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 34 165 4.89 0.693 0.56 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 64.4 3.79 1.95 0.760 0.731 448s supply 20 16 100.3 6.27 2.50 0.626 0.556 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.79 4.35 448s supply 4.35 6.27 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.891 448s supply 0.891 1.000 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 103.5936 10.6344 9.74 2.3e-11 *** 448s price -0.3449 0.1327 -2.60 0.014 * 448s income 0.3260 0.0485 6.73 9.9e-08 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.947 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 64.445 MSE: 3.791 Root MSE: 1.947 448s Multiple R-Squared: 0.76 Adjusted R-Squared: 0.731 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 47.3592 11.9466 3.96 0.00036 *** 448s price 0.2443 0.1017 2.40 0.02188 * 448s farmPrice 0.2657 0.0465 5.71 2.0e-06 *** 448s trend 0.3260 0.0485 6.73 9.9e-08 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.504 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 100.321 MSE: 6.27 Root MSE: 2.504 448s Multiple R-Squared: 0.626 Adjusted R-Squared: 0.556 448s 448s > nobs( fit2slsd2s ) 448s [1] 40 448s > 448s > ## **** 2SLS estimation with different instruments and restriction (useDfSys=F) 448s > print( summary( fit2slsd2, useDfSys = FALSE ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 34 165 4.89 0.693 0.56 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 64.4 3.79 1.95 0.760 0.731 448s supply 20 16 100.3 6.27 2.50 0.626 0.556 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.79 4.35 448s supply 4.35 6.27 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.891 448s supply 0.891 1.000 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 103.5936 11.8930 8.71 1.1e-07 *** 448s price -0.3449 0.1455 -2.37 0.03 * 448s income 0.3260 0.0511 6.38 6.9e-06 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.947 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 64.445 MSE: 3.791 Root MSE: 1.947 448s Multiple R-Squared: 0.76 Adjusted R-Squared: 0.731 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 47.3592 10.5362 4.49 0.00037 *** 448s price 0.2443 0.0894 2.73 0.01475 * 448s farmPrice 0.2657 0.0411 6.47 7.8e-06 *** 448s trend 0.3260 0.0511 6.38 9.1e-06 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.504 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 100.321 MSE: 6.27 Root MSE: 2.504 448s Multiple R-Squared: 0.626 Adjusted R-Squared: 0.556 448s 448s > nobs( fit2slsd2 ) 448s [1] 40 448s > 448s > ## **** 2SLS estimation with different instruments and restriction (methodResidCov="noDfCor") 448s > fit2slsd2r <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restrm, 448s + inst = instlist, methodResidCov = "noDfCor", useMatrix = useMatrix ) 448s > print( summary( fit2slsd2r ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 34 165 3.32 0.693 0.537 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 64.4 3.79 1.95 0.760 0.731 448s supply 20 16 100.3 6.27 2.50 0.626 0.556 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.22 3.58 448s supply 3.58 5.02 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.891 448s supply 0.891 1.000 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 103.5936 10.9648 9.45 4.9e-11 *** 448s price -0.3449 0.1341 -2.57 0.015 * 448s income 0.3260 0.0471 6.92 5.7e-08 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.947 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 64.445 MSE: 3.791 Root MSE: 1.947 448s Multiple R-Squared: 0.76 Adjusted R-Squared: 0.731 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 47.3592 9.7139 4.88 2.5e-05 *** 448s price 0.2443 0.0824 2.96 0.0055 ** 448s farmPrice 0.2657 0.0379 7.01 4.3e-08 *** 448s trend 0.3260 0.0471 6.92 5.7e-08 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.504 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 100.321 MSE: 6.27 Root MSE: 2.504 448s Multiple R-Squared: 0.626 Adjusted R-Squared: 0.556 448s 448s > nobs( fit2slsd2r ) 448s [1] 40 448s > 448s > ## 2SLS estimation with different instr. and restr. (methodResidCov="noDfCor", singleEqSigma=T) 448s > fit2slsd2rs <- systemfit( system, "2SLS", data = Kmenta, restrict.matrix = restrm, 448s + inst = instlist, methodResidCov = "noDfCor", singleEqSigma = TRUE, 448s + useMatrix = useMatrix ) 448s > print( summary( fit2slsd2rs ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 34 165 3.32 0.693 0.537 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 64.4 3.79 1.95 0.760 0.731 448s supply 20 16 100.3 6.27 2.50 0.626 0.556 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.22 3.58 448s supply 3.58 5.02 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.891 448s supply 0.891 1.000 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 103.5936 9.7929 10.58 2.7e-12 *** 448s price -0.3449 0.1220 -2.83 0.0078 ** 448s income 0.3260 0.0444 7.35 1.6e-08 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.947 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 64.445 MSE: 3.791 Root MSE: 1.947 448s Multiple R-Squared: 0.76 Adjusted R-Squared: 0.731 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 47.3592 10.6890 4.43 9.3e-05 *** 448s price 0.2443 0.0910 2.69 0.011 * 448s farmPrice 0.2657 0.0416 6.38 2.8e-07 *** 448s trend 0.3260 0.0444 7.35 1.6e-08 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.504 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 100.321 MSE: 6.27 Root MSE: 2.504 448s Multiple R-Squared: 0.626 Adjusted R-Squared: 0.556 448s 448s > nobs( fit2slsd2rs ) 448s [1] 40 448s > 448s > ## **** 2SLS estimation with different instruments and restriction via restrict.regMat * 448s > ## 2SLS estimation with different instruments and restriction via restrict.regMat (default) 448s > fit2slsd3 <- systemfit( system, "2SLS", data = Kmenta, restrict.regMat = tc, 448s + inst = instlist, useMatrix = useMatrix ) 448s > print( summary( fit2slsd3 ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 34 165 4.89 0.693 0.56 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 64.4 3.79 1.95 0.760 0.731 448s supply 20 16 100.3 6.27 2.50 0.626 0.556 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.79 4.35 448s supply 4.35 6.27 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.891 448s supply 0.891 1.000 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 103.5936 11.8930 8.71 3.5e-10 *** 448s price -0.3449 0.1455 -2.37 0.024 * 448s income 0.3260 0.0511 6.38 2.8e-07 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.947 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 64.445 MSE: 3.791 Root MSE: 1.947 448s Multiple R-Squared: 0.76 Adjusted R-Squared: 0.731 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 47.3592 10.5362 4.49 7.7e-05 *** 448s price 0.2443 0.0894 2.73 0.0099 ** 448s farmPrice 0.2657 0.0411 6.47 2.1e-07 *** 448s trend 0.3260 0.0511 6.38 2.8e-07 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.504 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 100.321 MSE: 6.27 Root MSE: 2.504 448s Multiple R-Squared: 0.626 Adjusted R-Squared: 0.556 448s 448s > nobs( fit2slsd3 ) 448s [1] 40 448s > 448s > ## **** 2SLS estimation with different instr. and restr. via restrict.regMat (methodResidCov="noDfCor") 448s > fit2slsd3r <- systemfit( system, "2SLS", data = Kmenta, restrict.regMat = tc, 448s + inst = instlist, methodResidCov = "noDfCor", useMatrix = useMatrix ) 448s > print( summary( fit2slsd3r ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 34 165 3.32 0.693 0.537 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 64.4 3.79 1.95 0.760 0.731 448s supply 20 16 100.3 6.27 2.50 0.626 0.556 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.22 3.58 448s supply 3.58 5.02 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.891 448s supply 0.891 1.000 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 103.5936 10.9648 9.45 4.9e-11 *** 448s price -0.3449 0.1341 -2.57 0.015 * 448s income 0.3260 0.0471 6.92 5.7e-08 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.947 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 64.445 MSE: 3.791 Root MSE: 1.947 448s Multiple R-Squared: 0.76 Adjusted R-Squared: 0.731 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 47.3592 9.7139 4.88 2.5e-05 *** 448s price 0.2443 0.0824 2.96 0.0055 ** 448s farmPrice 0.2657 0.0379 7.01 4.3e-08 *** 448s trend 0.3260 0.0471 6.92 5.7e-08 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.504 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 100.321 MSE: 6.27 Root MSE: 2.504 448s Multiple R-Squared: 0.626 Adjusted R-Squared: 0.556 448s 448s > nobs( fit2slsd3r ) 448s [1] 40 448s > 448s > 448s > ## *********** estimations with a single regressor ************ 448s > fit2slsS1 <- systemfit( 448s + list( consump ~ price - 1, price ~ consump + trend ), "2SLS", 448s + data = Kmenta, inst = ~ farmPrice + trend + income, useMatrix = useMatrix ) 448s > print( summary( fit2slsS1 ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 36 1544 179 -0.65 0.852 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s eq1 20 19 861 45.3 6.73 -2.213 -2.213 448s eq2 20 17 682 40.1 6.33 -0.022 -0.143 448s 448s The covariance matrix of the residuals 448s eq1 eq2 448s eq1 45.3 -40.5 448s eq2 -40.5 40.1 448s 448s The correlations of the residuals 448s eq1 eq2 448s eq1 1.00 -0.95 448s eq2 -0.95 1.00 448s 448s 448s 2SLS estimates for 'eq1' (equation 1) 448s Model Formula: consump ~ price - 1 448s Instruments: ~farmPrice + trend + income 448s 448s Estimate Std. Error t value Pr(>|t|) 448s price 1.006 0.015 66.9 <2e-16 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 6.734 on 19 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 19 448s SSR: 861.48 MSE: 45.341 Root MSE: 6.734 448s Multiple R-Squared: -2.213 Adjusted R-Squared: -2.213 448s 448s 448s 2SLS estimates for 'eq2' (equation 2) 448s Model Formula: price ~ consump + trend 448s Instruments: ~farmPrice + trend + income 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 55.5365 46.2668 1.20 0.25 448s consump 0.4453 0.4622 0.96 0.35 448s trend -0.0426 0.2496 -0.17 0.87 448s 448s Residual standard error: 6.335 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 682.257 MSE: 40.133 Root MSE: 6.335 448s Multiple R-Squared: -0.022 Adjusted R-Squared: -0.143 448s 448s > nobs( fit2slsS1 ) 448s [1] 40 448s > fit2slsS2 <- systemfit( 448s + list( consump ~ price - 1, consump ~ trend - 1 ), "2SLS", 448s + data = Kmenta, inst = ~ farmPrice + price + income, useMatrix = useMatrix ) 448s > print( summary( fit2slsS2 ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 38 47456 111148 -87.5 -5.28 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s eq1 20 19 861 45.3 6.73 -2.21 -2.21 448s eq2 20 19 46595 2452.3 49.52 -172.79 -172.79 448s 448s The covariance matrix of the residuals 448s eq1 eq2 448s eq1 45.34 -6.33 448s eq2 -6.33 2452.34 448s 448s The correlations of the residuals 448s eq1 eq2 448s eq1 1.0000 -0.0448 448s eq2 -0.0448 1.0000 448s 448s 448s 2SLS estimates for 'eq1' (equation 1) 448s Model Formula: consump ~ price - 1 448s Instruments: ~farmPrice + price + income 448s 448s Estimate Std. Error t value Pr(>|t|) 448s price 1.006 0.015 66.9 <2e-16 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 6.733 on 19 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 19 448s SSR: 861.449 MSE: 45.339 Root MSE: 6.733 448s Multiple R-Squared: -2.213 Adjusted R-Squared: -2.213 448s 448s 448s 2SLS estimates for 'eq2' (equation 2) 448s Model Formula: consump ~ trend - 1 448s Instruments: ~farmPrice + price + income 448s 448s Estimate Std. Error t value Pr(>|t|) 448s trend 7.578 0.934 8.11 1.4e-07 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 49.521 on 19 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 19 448s SSR: 46594.549 MSE: 2452.345 Root MSE: 49.521 448s Multiple R-Squared: -172.786 Adjusted R-Squared: -172.786 448s 448s > nobs( fit2slsS2 ) 448s [1] 40 448s > fit2slsS3 <- systemfit( 448s + list( consump ~ trend - 1, price ~ trend - 1 ), "2SLS", 448s + data = Kmenta, inst = instlist, useMatrix = useMatrix ) 448s > print( summary( fit2slsS3 ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 38 97978 687515 -104 -10.6 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s eq1 20 19 50950 2682 51.8 -189.0 -189.0 448s eq2 20 19 47028 2475 49.8 -69.5 -69.5 448s 448s The covariance matrix of the residuals 448s eq1 eq2 448s eq1 2682 2439 448s eq2 2439 2475 448s 448s The correlations of the residuals 448s eq1 eq2 448s eq1 1.000 0.989 448s eq2 0.989 1.000 448s 448s 448s 2SLS estimates for 'eq1' (equation 1) 448s Model Formula: consump ~ trend - 1 448s Instruments: ~income + farmPrice 448s 448s Estimate Std. Error t value Pr(>|t|) 448s trend 8.65 1.05 8.27 1e-07 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 51.784 on 19 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 19 448s SSR: 50949.985 MSE: 2681.578 Root MSE: 51.784 448s Multiple R-Squared: -189.031 Adjusted R-Squared: -189.031 448s 448s 448s 2SLS estimates for 'eq2' (equation 2) 448s Model Formula: price ~ trend - 1 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s trend 7.318 0.929 7.88 2.1e-07 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 49.751 on 19 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 19 448s SSR: 47028.107 MSE: 2475.164 Root MSE: 49.751 448s Multiple R-Squared: -69.48 Adjusted R-Squared: -69.48 448s 448s > nobs( fit2slsS3 ) 448s [1] 40 448s > fit2slsS4 <- systemfit( 448s + list( consump ~ trend - 1, price ~ trend - 1 ), "2SLS", 448s + data = Kmenta, inst = ~ farmPrice + trend + income, 448s + restrict.matrix = matrix( c( 1, -1 ), nrow = 1 ), useMatrix = useMatrix ) 448s > print( summary( fit2slsS4 ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 39 93548 111736 -99 -1.03 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s eq1 20 19 46514 2448 49.5 -172.5 -172.5 448s eq2 20 19 47033 2475 49.8 -69.5 -69.5 448s 448s The covariance matrix of the residuals 448s eq1 eq2 448s eq1 2448 2439 448s eq2 2439 2475 448s 448s The correlations of the residuals 448s eq1 eq2 448s eq1 1.000 0.988 448s eq2 0.988 1.000 448s 448s 448s 2SLS estimates for 'eq1' (equation 1) 448s Model Formula: consump ~ trend - 1 448s Instruments: ~farmPrice + trend + income 448s 448s Estimate Std. Error t value Pr(>|t|) 448s trend 7.362 0.646 11.4 5.7e-14 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 49.478 on 19 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 19 448s SSR: 46514.283 MSE: 2448.12 Root MSE: 49.478 448s Multiple R-Squared: -172.487 Adjusted R-Squared: -172.487 448s 448s 448s 2SLS estimates for 'eq2' (equation 2) 448s Model Formula: price ~ trend - 1 448s Instruments: ~farmPrice + trend + income 448s 448s Estimate Std. Error t value Pr(>|t|) 448s trend 7.362 0.646 11.4 5.7e-14 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 49.754 on 19 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 19 448s SSR: 47033.469 MSE: 2475.446 Root MSE: 49.754 448s Multiple R-Squared: -69.488 Adjusted R-Squared: -69.488 448s 448s > nobs( fit2slsS4 ) 448s [1] 40 448s > fit2slsS5 <- systemfit( 448s + list( consump ~ 1, price ~ 1 ), "2SLS", 448s + data = Kmenta, inst = ~ farmPrice, useMatrix = useMatrix ) 448s > print( summary( fit2slsS1 ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 36 1544 179 -0.65 0.852 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s eq1 20 19 861 45.3 6.73 -2.213 -2.213 448s eq2 20 17 682 40.1 6.33 -0.022 -0.143 448s 448s The covariance matrix of the residuals 448s eq1 eq2 448s eq1 45.3 -40.5 448s eq2 -40.5 40.1 448s 448s The correlations of the residuals 448s eq1 eq2 448s eq1 1.00 -0.95 448s eq2 -0.95 1.00 448s 448s 448s 2SLS estimates for 'eq1' (equation 1) 448s Model Formula: consump ~ price - 1 448s Instruments: ~farmPrice + trend + income 448s 448s Estimate Std. Error t value Pr(>|t|) 448s price 1.006 0.015 66.9 <2e-16 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 6.734 on 19 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 19 448s SSR: 861.48 MSE: 45.341 Root MSE: 6.734 448s Multiple R-Squared: -2.213 Adjusted R-Squared: -2.213 448s 448s 448s 2SLS estimates for 'eq2' (equation 2) 448s Model Formula: price ~ consump + trend 448s Instruments: ~farmPrice + trend + income 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 55.5365 46.2668 1.20 0.25 448s consump 0.4453 0.4622 0.96 0.35 448s trend -0.0426 0.2496 -0.17 0.87 448s 448s Residual standard error: 6.335 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 682.257 MSE: 40.133 Root MSE: 6.335 448s Multiple R-Squared: -0.022 Adjusted R-Squared: -0.143 448s 448s > 448s > 448s > ## **************** shorter summaries ********************** 448s > print( summary( fit2sls1, useDfSys = TRUE, residCov = FALSE ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 33 162 4.36 0.697 0.548 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 65.7 3.87 1.97 0.755 0.726 448s supply 20 16 96.6 6.04 2.46 0.640 0.572 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 448s price -0.2436 0.0965 -2.52 0.017 * 448s income 0.3140 0.0469 6.69 1.3e-07 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.966 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 448s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 49.5324 12.0105 4.12 0.00024 *** 448s price 0.2401 0.0999 2.40 0.02208 * 448s farmPrice 0.2556 0.0473 5.41 5.5e-06 *** 448s trend 0.2529 0.0997 2.54 0.01605 * 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.458 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 448s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 448s 448s > 448s > print( summary( fit2sls1, equations = FALSE ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 33 162 4.36 0.697 0.548 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 65.7 3.87 1.97 0.755 0.726 448s supply 20 16 96.6 6.04 2.46 0.640 0.572 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.87 4.36 448s supply 4.36 6.04 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.902 448s supply 0.902 1.000 448s 448s 448s Coefficients: 448s Estimate Std. Error t value Pr(>|t|) 448s demand_(Intercept) 94.6333 7.9208 11.95 1.1e-09 *** 448s demand_price -0.2436 0.0965 -2.52 0.0218 * 448s demand_income 0.3140 0.0469 6.69 3.8e-06 *** 448s supply_(Intercept) 49.5324 12.0105 4.12 0.0008 *** 448s supply_price 0.2401 0.0999 2.40 0.0288 * 448s supply_farmPrice 0.2556 0.0473 5.41 5.8e-05 *** 448s supply_trend 0.2529 0.0997 2.54 0.0219 * 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s > 448s > print( summary( fit2sls1rs, residCov = FALSE, equations = FALSE ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 33 162 2.97 0.697 0.525 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 65.7 3.87 1.97 0.755 0.726 448s supply 20 16 96.6 6.04 2.46 0.640 0.572 448s 448s 448s Coefficients: 448s Estimate Std. Error t value Pr(>|t|) 448s demand_(Intercept) 94.6333 8.1158 11.66 1.6e-09 *** 448s demand_price -0.2436 0.0989 -2.46 0.02471 * 448s demand_income 0.3140 0.0481 6.53 5.2e-06 *** 448s supply_(Intercept) 49.5324 9.8463 5.03 0.00012 *** 448s supply_price 0.2401 0.0819 2.93 0.00980 ** 448s supply_farmPrice 0.2556 0.0387 6.60 6.1e-06 *** 448s supply_trend 0.2529 0.0817 3.10 0.00694 ** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s > 448s > print( summary( fit2sls2Sym, useDfSys = FALSE ), equations = FALSE ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 34 166 3.6 0.691 0.553 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 67.4 3.97 1.99 0.749 0.719 448s supply 20 16 98.2 6.13 2.48 0.634 0.565 448s 448s The covariance matrix of the residuals 448s demand supply 448s demand 3.97 4.55 448s supply 4.55 6.13 448s 448s The correlations of the residuals 448s demand supply 448s demand 1.000 0.923 448s supply 0.923 1.000 448s 448s 448s Coefficients: 448s Estimate Std. Error t value Pr(>|t|) 448s demand_(Intercept) 94.2816 8.8693 10.63 6.3e-09 *** 448s demand_price -0.2247 0.1034 -2.17 0.04425 * 448s demand_income 0.2983 0.0454 6.57 4.8e-06 *** 448s supply_(Intercept) 48.1843 10.5384 4.57 0.00031 *** 448s supply_price 0.2427 0.0896 2.71 0.01551 * 448s supply_farmPrice 0.2619 0.0411 6.38 9.1e-06 *** 448s supply_trend 0.2983 0.0454 6.57 6.4e-06 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s > 448s > print( summary( fit2sls2 ), residCov = FALSE ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 34 166 3.6 0.691 0.553 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 67.4 3.97 1.99 0.749 0.719 448s supply 20 16 98.2 6.13 2.48 0.634 0.565 448s 448s 448s 2SLS estimates for 'demand' (equation 1) 448s Model Formula: consump ~ price + income 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 94.2816 8.8693 10.63 2.4e-12 *** 448s price -0.2247 0.1034 -2.17 0.037 * 448s income 0.2983 0.0454 6.57 1.6e-07 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 1.991 on 17 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 17 448s SSR: 67.418 MSE: 3.966 Root MSE: 1.991 448s Multiple R-Squared: 0.749 Adjusted R-Squared: 0.719 448s 448s 448s 2SLS estimates for 'supply' (equation 2) 448s Model Formula: consump ~ price + farmPrice + trend 448s Instruments: ~income + farmPrice + trend 448s 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 48.1843 10.5384 4.57 6.1e-05 *** 448s price 0.2427 0.0896 2.71 0.011 * 448s farmPrice 0.2619 0.0411 6.38 2.8e-07 *** 448s trend 0.2983 0.0454 6.57 1.6e-07 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s 448s Residual standard error: 2.477 on 16 degrees of freedom 448s Number of observations: 20 Degrees of Freedom: 16 448s SSR: 98.155 MSE: 6.135 Root MSE: 2.477 448s Multiple R-Squared: 0.634 Adjusted R-Squared: 0.565 448s 448s > 448s > print( summary( fit2sls3, useDfSys = FALSE, residCov = FALSE, 448s + equations = FALSE ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 34 166 2.45 0.691 0.526 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 67.4 3.97 1.99 0.749 0.719 448s supply 20 16 98.2 6.13 2.48 0.634 0.565 448s 448s 448s Coefficients: 448s Estimate Std. Error t value Pr(>|t|) 448s demand_(Intercept) 94.2816 8.1771 11.53 1.8e-09 *** 448s demand_price -0.2247 0.0954 -2.36 0.03071 * 448s demand_income 0.2983 0.0419 7.13 1.7e-06 *** 448s supply_(Intercept) 48.1843 9.7159 4.96 0.00014 *** 448s supply_price 0.2427 0.0826 2.94 0.00966 ** 448s supply_farmPrice 0.2619 0.0379 6.92 3.5e-06 *** 448s supply_trend 0.2983 0.0419 7.13 2.4e-06 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s > 448s > print( summary( fit2sls4s ), equations = FALSE, residCov = FALSE ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 35 166 3.78 0.69 0.568 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 66.1 3.89 1.97 0.754 0.725 448s supply 20 16 100.0 6.25 2.50 0.627 0.557 448s 448s 448s Coefficients: 448s Estimate Std. Error t value Pr(>|t|) 448s demand_(Intercept) 95.7059 6.3056 15.18 < 2e-16 *** 448s demand_price -0.2433 0.0684 -3.56 0.00110 ** 448s demand_income 0.3027 0.0394 7.69 5.1e-09 *** 448s supply_(Intercept) 46.5637 8.3296 5.59 2.7e-06 *** 448s supply_price 0.2567 0.0684 3.75 0.00064 *** 448s supply_farmPrice 0.2637 0.0455 5.79 1.5e-06 *** 448s supply_trend 0.3027 0.0394 7.69 5.1e-09 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s > 448s > print( summary( fit2sls5r, equations = FALSE, residCov = FALSE ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 35 166 2.57 0.69 0.54 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 66.1 3.89 1.97 0.754 0.725 448s supply 20 16 100.0 6.25 2.50 0.627 0.557 448s 448s 448s Coefficients: 448s Estimate Std. Error t value Pr(>|t|) 448s demand_(Intercept) 95.7059 6.0044 15.94 < 2e-16 *** 448s demand_price -0.2433 0.0621 -3.92 0.00039 *** 448s demand_income 0.3027 0.0382 7.93 2.5e-09 *** 448s supply_(Intercept) 46.5637 7.3842 6.31 3.1e-07 *** 448s supply_price 0.2567 0.0621 4.14 0.00021 *** 448s supply_farmPrice 0.2637 0.0373 7.08 3.0e-08 *** 448s supply_trend 0.3027 0.0382 7.93 2.5e-09 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s > 448s > print( summary( fit2slsd1s ), residCov = FALSE, equations = FALSE ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 33 164 9.25 0.694 0.512 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 67.4 3.97 1.99 0.748 0.719 448s supply 20 16 96.6 6.04 2.46 0.640 0.572 448s 448s 448s Coefficients: 448s Estimate Std. Error t value Pr(>|t|) 448s demand_(Intercept) 106.7894 12.4749 8.56 1.4e-07 *** 448s demand_price -0.4116 0.1622 -2.54 0.02121 * 448s demand_income 0.3617 0.0631 5.73 2.5e-05 *** 448s supply_(Intercept) 49.5324 10.8976 4.55 0.00033 *** 448s supply_price 0.2401 0.0907 2.65 0.01755 * 448s supply_farmPrice 0.2556 0.0429 5.96 2.0e-05 *** 448s supply_trend 0.2529 0.0904 2.80 0.01292 * 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s > 448s > print( summary( fit2slsd2, residCov = FALSE, equations = FALSE ) ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 34 165 4.89 0.693 0.56 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 64.4 3.79 1.95 0.760 0.731 448s supply 20 16 100.3 6.27 2.50 0.626 0.556 448s 448s 448s Coefficients: 448s Estimate Std. Error t value Pr(>|t|) 448s demand_(Intercept) 103.5936 11.8930 8.71 3.5e-10 *** 448s demand_price -0.3449 0.1455 -2.37 0.0236 * 448s demand_income 0.3260 0.0511 6.38 2.8e-07 *** 448s supply_(Intercept) 47.3592 10.5362 4.49 7.7e-05 *** 448s supply_price 0.2443 0.0894 2.73 0.0099 ** 448s supply_farmPrice 0.2657 0.0411 6.47 2.1e-07 *** 448s supply_trend 0.3260 0.0511 6.38 2.8e-07 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s > 448s > print( summary( fit2slsd3r ), residCov = FALSE, equations = FALSE ) 448s 448s systemfit results 448s method: 2SLS 448s 448s N DF SSR detRCov OLS-R2 McElroy-R2 448s system 40 34 165 3.32 0.693 0.537 448s 448s N DF SSR MSE RMSE R2 Adj R2 448s demand 20 17 64.4 3.79 1.95 0.760 0.731 448s supply 20 16 100.3 6.27 2.50 0.626 0.556 448s 448s 448s Coefficients: 448s Estimate Std. Error t value Pr(>|t|) 448s demand_(Intercept) 103.5936 10.9648 9.45 4.9e-11 *** 448s demand_price -0.3449 0.1341 -2.57 0.0147 * 448s demand_income 0.3260 0.0471 6.92 5.7e-08 *** 448s supply_(Intercept) 47.3592 9.7139 4.88 2.5e-05 *** 448s supply_price 0.2443 0.0824 2.96 0.0055 ** 448s supply_farmPrice 0.2657 0.0379 7.01 4.3e-08 *** 448s supply_trend 0.3260 0.0471 6.92 5.7e-08 *** 448s --- 448s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 448s > 448s > 448s > ## ****************** residuals ************************** 448s > print( residuals( fit2sls1 ) ) 448s demand supply 448s 1 0.843 -0.4348 448s 2 -0.698 -1.2131 448s 3 2.359 1.7090 448s 4 1.490 0.7956 448s 5 2.139 1.5942 448s 6 1.277 0.6595 448s 7 1.571 1.4346 448s 8 -3.066 -4.8724 448s 9 -1.125 -2.3975 448s 10 2.492 3.1427 448s 11 -0.108 0.0689 448s 12 -2.292 -1.3978 448s 13 -1.598 -1.1136 448s 14 -0.271 1.1684 448s 15 1.958 3.4865 448s 16 -3.430 -3.8285 448s 17 -0.313 0.6793 448s 18 -2.151 -2.7713 448s 19 1.592 2.6668 448s 20 -0.668 0.6235 448s > print( residuals( fit2sls1$eq[[ 1 ]] ) ) 448s 1 2 3 4 5 6 7 8 9 10 11 448s 0.843 -0.698 2.359 1.490 2.139 1.277 1.571 -3.066 -1.125 2.492 -0.108 448s 12 13 14 15 16 17 18 19 20 448s -2.292 -1.598 -0.271 1.958 -3.430 -0.313 -2.151 1.592 -0.668 448s > 448s > print( residuals( fit2sls2s ) ) 448s demand supply 448s 1 0.678 -0.0135 448s 2 -0.777 -0.8544 448s 3 2.281 2.0245 448s 4 1.416 1.0692 448s 5 2.213 1.7598 448s 6 1.334 0.7923 448s 7 1.640 1.5342 448s 8 -2.994 -4.8544 448s 9 -1.072 -2.3959 448s 10 2.522 3.1637 448s 11 -0.330 0.1628 448s 12 -2.593 -1.2864 448s 13 -1.856 -1.0729 448s 14 -0.356 1.1087 448s 15 2.138 3.2597 448s 16 -3.282 -4.1265 448s 17 -0.076 0.3331 448s 18 -2.119 -3.0961 448s 19 1.690 2.3122 448s 20 -0.458 0.1799 448s > print( residuals( fit2sls2s$eq[[ 2 ]] ) ) 448s 1 2 3 4 5 6 7 8 9 10 448s -0.0135 -0.8544 2.0245 1.0692 1.7598 0.7923 1.5342 -4.8544 -2.3959 3.1637 448s 11 12 13 14 15 16 17 18 19 20 448s 0.1628 -1.2864 -1.0729 1.1087 3.2597 -4.1265 0.3331 -3.0961 2.3122 0.1799 448s > 448s > print( residuals( fit2sls3 ) ) 448s demand supply 448s 1 0.678 -0.0135 448s 2 -0.777 -0.8544 448s 3 2.281 2.0245 448s 4 1.416 1.0692 448s 5 2.213 1.7598 448s 6 1.334 0.7923 448s 7 1.640 1.5342 448s 8 -2.994 -4.8544 448s 9 -1.072 -2.3959 448s 10 2.522 3.1637 448s 11 -0.330 0.1628 448s 12 -2.593 -1.2864 448s 13 -1.856 -1.0729 448s 14 -0.356 1.1087 448s 15 2.138 3.2597 448s 16 -3.282 -4.1265 448s 17 -0.076 0.3331 448s 18 -2.119 -3.0961 448s 19 1.690 2.3122 448s 20 -0.458 0.1799 448s > print( residuals( fit2sls3$eq[[ 1 ]] ) ) 448s 1 2 3 4 5 6 7 8 9 10 11 448s 0.678 -0.777 2.281 1.416 2.213 1.334 1.640 -2.994 -1.072 2.522 -0.330 448s 12 13 14 15 16 17 18 19 20 448s -2.593 -1.856 -0.356 2.138 -3.282 -0.076 -2.119 1.690 -0.458 448s > 448s > print( residuals( fit2sls4r ) ) 448s demand supply 448s 1 0.729 0.0219 448s 2 -0.698 -0.8806 448s 3 2.349 2.0055 448s 4 1.496 1.0326 448s 5 2.165 1.7870 448s 6 1.310 0.7993 448s 7 1.635 1.5189 448s 8 -2.951 -4.9334 448s 9 -1.134 -2.3609 448s 10 2.397 3.2818 448s 11 -0.359 0.2857 448s 12 -2.524 -1.2257 448s 13 -1.745 -1.0782 448s 14 -0.349 1.1382 448s 15 2.022 3.2981 448s 16 -3.345 -4.1440 448s 17 -0.322 0.4686 448s 18 -2.075 -3.1779 448s 19 1.738 2.2072 448s 20 -0.339 -0.0444 448s > print( residuals( fit2sls4r$eq[[ 2 ]] ) ) 448s 1 2 3 4 5 6 7 8 9 10 448s 0.0219 -0.8806 2.0055 1.0326 1.7870 0.7993 1.5189 -4.9334 -2.3609 3.2818 448s 11 12 13 14 15 16 17 18 19 20 448s 0.2857 -1.2257 -1.0782 1.1382 3.2981 -4.1440 0.4686 -3.1779 2.2072 -0.0444 448s > 448s > print( residuals( fit2sls5rs ) ) 448s demand supply 448s 1 0.729 0.0219 448s 2 -0.698 -0.8806 448s 3 2.349 2.0055 448s 4 1.496 1.0326 448s 5 2.165 1.7870 448s 6 1.310 0.7993 448s 7 1.635 1.5189 448s 8 -2.951 -4.9334 448s 9 -1.134 -2.3609 448s 10 2.397 3.2818 448s 11 -0.359 0.2857 448s 12 -2.524 -1.2257 448s 13 -1.745 -1.0782 448s 14 -0.349 1.1382 448s 15 2.022 3.2981 448s 16 -3.345 -4.1440 448s 17 -0.322 0.4686 448s 18 -2.075 -3.1779 448s 19 1.738 2.2072 448s 20 -0.339 -0.0444 448s > print( residuals( fit2sls5rs$eq[[ 1 ]] ) ) 448s 1 2 3 4 5 6 7 8 9 10 11 448s 0.729 -0.698 2.349 1.496 2.165 1.310 1.635 -2.951 -1.134 2.397 -0.359 448s 12 13 14 15 16 17 18 19 20 448s -2.524 -1.745 -0.349 2.022 -3.345 -0.322 -2.075 1.738 -0.339 448s > 448s > print( residuals( fit2slsd1 ) ) 448s demand supply 448s 1 1.3775 -0.4348 448s 2 0.0125 -1.2131 448s 3 2.9728 1.7090 448s 4 2.2121 0.7956 448s 5 1.6920 1.5942 448s 6 1.0407 0.6595 448s 7 1.4768 1.4346 448s 8 -2.7583 -4.8724 448s 9 -1.6807 -2.3975 448s 10 1.4265 3.1427 448s 11 -0.2029 0.0689 448s 12 -1.5123 -1.3978 448s 13 -0.4958 -1.1136 448s 14 -0.1528 1.1684 448s 15 0.8692 3.4865 448s 16 -4.0547 -3.8285 448s 17 -2.5309 0.6793 448s 18 -1.8070 -2.7713 448s 19 1.9299 2.6668 448s 20 0.1853 0.6235 448s > print( residuals( fit2slsd1$eq[[ 2 ]] ) ) 448s 1 2 3 4 5 6 7 8 9 10 448s -0.4348 -1.2131 1.7090 0.7956 1.5942 0.6595 1.4346 -4.8724 -2.3975 3.1427 448s 11 12 13 14 15 16 17 18 19 20 448s 0.0689 -1.3978 -1.1136 1.1684 3.4865 -3.8285 0.6793 -2.7713 2.6668 0.6235 448s > 448s > print( residuals( fit2slsd2r ) ) 448s demand supply 448s 1 0.996 0.2444 448s 2 -0.268 -0.6349 448s 3 2.715 2.2177 448s 4 1.936 1.2367 448s 5 1.907 1.8612 448s 6 1.184 0.8736 448s 7 1.609 1.5951 448s 8 -2.709 -4.8434 448s 9 -1.476 -2.3949 448s 10 1.705 3.1765 448s 11 -0.540 0.2202 448s 12 -2.167 -1.2182 448s 13 -1.150 -1.0480 448s 14 -0.316 1.0722 448s 15 1.395 3.1209 448s 16 -3.680 -4.3088 448s 17 -1.669 0.1212 448s 18 -1.829 -3.2948 448s 19 2.016 2.0952 448s 20 0.341 -0.0916 448s > print( residuals( fit2slsd2r$eq[[ 1 ]] ) ) 448s 1 2 3 4 5 6 7 8 9 10 11 448s 0.996 -0.268 2.715 1.936 1.907 1.184 1.609 -2.709 -1.476 1.705 -0.540 448s 12 13 14 15 16 17 18 19 20 448s -2.167 -1.150 -0.316 1.395 -3.680 -1.669 -1.829 2.016 0.341 448s > 448s > 448s > ## *************** coefficients ********************* 448s > print( round( coef( fit2sls1s ), digits = 6 ) ) 448s demand_(Intercept) demand_price demand_income supply_(Intercept) 448s 94.633 -0.244 0.314 49.532 448s supply_price supply_farmPrice supply_trend 448s 0.240 0.256 0.253 448s > print( round( coef( fit2sls1s$eq[[ 1 ]] ), digits = 6 ) ) 448s (Intercept) price income 448s 94.633 -0.244 0.314 448s > 448s > print( round( coef( fit2sls2 ), digits = 6 ) ) 448s demand_(Intercept) demand_price demand_income supply_(Intercept) 448s 94.282 -0.225 0.298 48.184 448s supply_price supply_farmPrice supply_trend 448s 0.243 0.262 0.298 448s > print( round( coef( fit2sls2$eq[[ 2 ]] ), digits = 6 ) ) 448s (Intercept) price farmPrice trend 448s 48.184 0.243 0.262 0.298 448s > 448s > print( round( coef( fit2sls3 ), digits = 6 ) ) 448s demand_(Intercept) demand_price demand_income supply_(Intercept) 448s 94.282 -0.225 0.298 48.184 448s supply_price supply_farmPrice supply_trend 448s 0.243 0.262 0.298 448s > print( round( coef( fit2sls3, modified.regMat = TRUE ), digits = 6 ) ) 448s C1 C2 C3 C4 C5 C6 448s 94.282 -0.225 0.298 48.184 0.243 0.262 448s > print( round( coef( fit2sls3$eq[[ 1 ]] ), digits = 6 ) ) 448s (Intercept) price income 448s 94.282 -0.225 0.298 448s > 448s > print( round( coef( fit2sls4s ), digits = 6 ) ) 448s demand_(Intercept) demand_price demand_income supply_(Intercept) 448s 95.706 -0.243 0.303 46.564 448s supply_price supply_farmPrice supply_trend 448s 0.257 0.264 0.303 448s > print( round( coef( fit2sls4s$eq[[ 2 ]] ), digits = 6 ) ) 448s (Intercept) price farmPrice trend 448s 46.564 0.257 0.264 0.303 448s > 448s > print( round( coef( fit2sls5r ), digits = 6 ) ) 448s demand_(Intercept) demand_price demand_income supply_(Intercept) 448s 95.706 -0.243 0.303 46.564 448s supply_price supply_farmPrice supply_trend 448s 0.257 0.264 0.303 448s > print( round( coef( fit2sls5r, modified.regMat = TRUE ), digits = 6 ) ) 448s C1 C2 C3 C4 C5 C6 448s 95.706 -0.243 0.303 46.564 0.257 0.264 448s > print( round( coef( fit2sls5r$eq[[ 2 ]] ), digits = 6 ) ) 448s (Intercept) price farmPrice trend 448s 46.564 0.257 0.264 0.303 448s > 448s > 448s > ## *************** coefficients with stats ********************* 448s > print( round( coef( summary( fit2sls1s ) ), digits = 6 ) ) 448s Estimate Std. Error t value Pr(>|t|) 448s demand_(Intercept) 94.633 8.9352 10.59 0.000000 448s demand_price -0.244 0.1088 -2.24 0.038916 448s demand_income 0.314 0.0530 5.93 0.000016 448s supply_(Intercept) 49.532 10.8404 4.57 0.000315 448s supply_price 0.240 0.0902 2.66 0.017058 448s supply_farmPrice 0.256 0.0426 5.99 0.000019 448s supply_trend 0.253 0.0899 2.81 0.012528 448s > print( round( coef( summary( fit2sls1s$eq[[ 1 ]] ) ), digits = 6 ) ) 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 94.633 8.935 10.59 0.000000 448s price -0.244 0.109 -2.24 0.038916 448s income 0.314 0.053 5.93 0.000016 448s > 448s > print( round( coef( summary( fit2sls2, useDfSys = FALSE ) ), digits = 6 ) ) 448s Estimate Std. Error t value Pr(>|t|) 448s demand_(Intercept) 94.282 8.8693 10.63 0.000000 448s demand_price -0.225 0.1034 -2.17 0.044246 448s demand_income 0.298 0.0454 6.57 0.000005 448s supply_(Intercept) 48.184 10.5384 4.57 0.000313 448s supply_price 0.243 0.0896 2.71 0.015508 448s supply_farmPrice 0.262 0.0411 6.38 0.000009 448s supply_trend 0.298 0.0454 6.57 0.000006 448s > print( round( coef( summary( fit2sls2$eq[[ 2 ]], useDfSys = FALSE ) ), 448s + digits = 6 ) ) 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 48.184 10.5384 4.57 0.000313 448s price 0.243 0.0896 2.71 0.015508 448s farmPrice 0.262 0.0411 6.38 0.000009 448s trend 0.298 0.0454 6.57 0.000006 448s > 448s > print( round( coef( summary( fit2sls3 ) ), digits = 6 ) ) 448s Estimate Std. Error t value Pr(>|t|) 448s demand_(Intercept) 94.282 8.1771 11.53 0.000000 448s demand_price -0.225 0.0954 -2.36 0.024352 448s demand_income 0.298 0.0419 7.13 0.000000 448s supply_(Intercept) 48.184 9.7159 4.96 0.000019 448s supply_price 0.243 0.0826 2.94 0.005903 448s supply_farmPrice 0.262 0.0379 6.92 0.000000 448s supply_trend 0.298 0.0419 7.13 0.000000 448s > print( round( coef( summary( fit2sls3 ), modified.regMat = TRUE ), digits = 6 ) ) 448s Estimate Std. Error t value Pr(>|t|) 448s C1 94.282 8.1771 11.53 0.000000 448s C2 -0.225 0.0954 -2.36 0.024352 448s C3 0.298 0.0419 7.13 0.000000 448s C4 48.184 9.7159 4.96 0.000019 448s C5 0.243 0.0826 2.94 0.005903 448s C6 0.262 0.0379 6.92 0.000000 448s > print( round( coef( summary( fit2sls3$eq[[ 1 ]] ) ), digits = 6 ) ) 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 94.282 8.1771 11.53 0.0000 448s price -0.225 0.0954 -2.36 0.0244 448s income 0.298 0.0419 7.13 0.0000 448s > 448s > print( round( coef( summary( fit2sls4s ) ), digits = 6 ) ) 448s Estimate Std. Error t value Pr(>|t|) 448s demand_(Intercept) 95.706 6.3056 15.18 0.000000 448s demand_price -0.243 0.0684 -3.56 0.001104 448s demand_income 0.303 0.0394 7.69 0.000000 448s supply_(Intercept) 46.564 8.3296 5.59 0.000003 448s supply_price 0.257 0.0684 3.75 0.000635 448s supply_farmPrice 0.264 0.0455 5.79 0.000001 448s supply_trend 0.303 0.0394 7.69 0.000000 448s > print( round( coef( summary( fit2sls4s$eq[[ 2 ]] ) ), digits = 6 ) ) 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 46.564 8.3296 5.59 0.000003 448s price 0.257 0.0684 3.75 0.000635 448s farmPrice 0.264 0.0455 5.79 0.000001 448s trend 0.303 0.0394 7.69 0.000000 448s > 448s > print( round( coef( summary( fit2sls5r, useDfSys = FALSE ) ), digits = 6 ) ) 448s Estimate Std. Error t value Pr(>|t|) 448s demand_(Intercept) 95.706 6.0044 15.94 0.000000 448s demand_price -0.243 0.0621 -3.92 0.001102 448s demand_income 0.303 0.0382 7.93 0.000000 448s supply_(Intercept) 46.564 7.3842 6.31 0.000010 448s supply_price 0.257 0.0621 4.14 0.000774 448s supply_farmPrice 0.264 0.0373 7.08 0.000003 448s supply_trend 0.303 0.0382 7.93 0.000001 448s > print( round( coef( summary( fit2sls5r, useDfSys = FALSE ), 448s + modified.regMat = TRUE ), digits = 6 ) ) 448s Estimate Std. Error t value Pr(>|t|) 448s C1 95.706 6.0044 15.94 NA 448s C2 -0.243 0.0621 -3.92 NA 448s C3 0.303 0.0382 7.93 NA 448s C4 46.564 7.3842 6.31 NA 448s C5 0.257 0.0621 4.14 NA 448s C6 0.264 0.0373 7.08 NA 448s > print( round( coef( summary( fit2sls5r$eq[[ 2 ]], useDfSys = FALSE ) ), 448s + digits = 6 ) ) 448s Estimate Std. Error t value Pr(>|t|) 448s (Intercept) 46.564 7.3842 6.31 0.000010 448s price 0.257 0.0621 4.14 0.000774 448s farmPrice 0.264 0.0373 7.08 0.000003 448s trend 0.303 0.0382 7.93 0.000001 448s > 448s > 448s > ## *********** variance covariance matrix of the coefficients ******* 448s > print( round( vcov( fit2sls1s ), digits = 6 ) ) 448s demand_(Intercept) demand_price demand_income 448s demand_(Intercept) 79.8371 -0.85694 0.06274 448s demand_price -0.8569 0.01185 -0.00336 448s demand_income 0.0627 -0.00336 0.00280 448s supply_(Intercept) 0.0000 0.00000 0.00000 448s supply_price 0.0000 0.00000 0.00000 448s supply_farmPrice 0.0000 0.00000 0.00000 448s supply_trend 0.0000 0.00000 0.00000 448s supply_(Intercept) supply_price supply_farmPrice 448s demand_(Intercept) 0.000 0.000000 0.000000 448s demand_price 0.000 0.000000 0.000000 448s demand_income 0.000 0.000000 0.000000 448s supply_(Intercept) 117.514 -0.892363 -0.263795 448s supply_price -0.892 0.008136 0.000763 448s supply_farmPrice -0.264 0.000763 0.001819 448s supply_trend -0.241 0.000472 0.001122 448s supply_trend 448s demand_(Intercept) 0.000000 448s demand_price 0.000000 448s demand_income 0.000000 448s supply_(Intercept) -0.240505 448s supply_price 0.000472 448s supply_farmPrice 0.001122 448s supply_trend 0.008090 448s > print( round( vcov( fit2sls1s$eq[[ 1 ]] ), digits = 6 ) ) 448s (Intercept) price income 448s (Intercept) 79.8371 -0.85694 0.06274 448s price -0.8569 0.01185 -0.00336 448s income 0.0627 -0.00336 0.00280 448s > 448s > print( round( vcov( fit2sls1r ), digits = 6 ) ) 448s demand_(Intercept) demand_price demand_income 448s demand_(Intercept) 53.3287 -0.57241 0.04191 448s demand_price -0.5724 0.00791 -0.00225 448s demand_income 0.0419 -0.00225 0.00187 448s supply_(Intercept) 0.0000 0.00000 0.00000 448s supply_price 0.0000 0.00000 0.00000 448s supply_farmPrice 0.0000 0.00000 0.00000 448s supply_trend 0.0000 0.00000 0.00000 448s supply_(Intercept) supply_price supply_farmPrice 448s demand_(Intercept) 0.000 0.000000 0.000000 448s demand_price 0.000 0.000000 0.000000 448s demand_income 0.000 0.000000 0.000000 448s supply_(Intercept) 115.402 -0.876328 -0.259055 448s supply_price -0.876 0.007989 0.000749 448s supply_farmPrice -0.259 0.000749 0.001786 448s supply_trend -0.236 0.000463 0.001101 448s supply_trend 448s demand_(Intercept) 0.000000 448s demand_price 0.000000 448s demand_income 0.000000 448s supply_(Intercept) -0.236183 448s supply_price 0.000463 448s supply_farmPrice 0.001101 448s supply_trend 0.007945 448s > print( round( vcov( fit2sls1r$eq[[ 2 ]] ), digits = 6 ) ) 448s (Intercept) price farmPrice trend 448s (Intercept) 115.402 -0.876328 -0.259055 -0.236183 448s price -0.876 0.007989 0.000749 0.000463 448s farmPrice -0.259 0.000749 0.001786 0.001101 448s trend -0.236 0.000463 0.001101 0.007945 448s > 448s > print( round( vcov( fit2sls2 ), digits = 6 ) ) 448s demand_(Intercept) demand_price demand_income 448s demand_(Intercept) 78.66379 -0.829021 0.046112 448s demand_price -0.82902 0.010698 -0.002471 448s demand_income 0.04611 -0.002471 0.002061 448s supply_(Intercept) -1.37081 0.073457 -0.061273 448s supply_price 0.00269 -0.000144 0.000120 448s supply_farmPrice 0.00639 -0.000343 0.000286 448s supply_trend 0.04611 -0.002471 0.002061 448s supply_(Intercept) supply_price supply_farmPrice 448s demand_(Intercept) -1.3708 0.002689 0.006393 448s demand_price 0.0735 -0.000144 -0.000343 448s demand_income -0.0613 0.000120 0.000286 448s supply_(Intercept) 111.0580 -0.872938 -0.236592 448s supply_price -0.8729 0.008032 0.000707 448s supply_farmPrice -0.2366 0.000707 0.001686 448s supply_trend -0.0613 0.000120 0.000286 448s supply_trend 448s demand_(Intercept) 0.046112 448s demand_price -0.002471 448s demand_income 0.002061 448s supply_(Intercept) -0.061273 448s supply_price 0.000120 448s supply_farmPrice 0.000286 448s supply_trend 0.002061 448s > print( round( vcov( fit2sls2$eq[[ 1 ]] ), digits = 6 ) ) 448s (Intercept) price income 448s (Intercept) 78.6638 -0.82902 0.04611 448s price -0.8290 0.01070 -0.00247 448s income 0.0461 -0.00247 0.00206 448s > 448s > print( round( vcov( fit2sls3 ), digits = 6 ) ) 448s demand_(Intercept) demand_price demand_income 448s demand_(Intercept) 66.86423 -0.704668 0.039196 448s demand_price -0.70467 0.009094 -0.002100 448s demand_income 0.03920 -0.002100 0.001752 448s supply_(Intercept) -1.16519 0.062438 -0.052082 448s supply_price 0.00229 -0.000122 0.000102 448s supply_farmPrice 0.00543 -0.000291 0.000243 448s supply_trend 0.03920 -0.002100 0.001752 448s supply_(Intercept) supply_price supply_farmPrice 448s demand_(Intercept) -1.1652 0.002285 0.005434 448s demand_price 0.0624 -0.000122 -0.000291 448s demand_income -0.0521 0.000102 0.000243 448s supply_(Intercept) 94.3993 -0.741997 -0.201104 448s supply_price -0.7420 0.006827 0.000601 448s supply_farmPrice -0.2011 0.000601 0.001433 448s supply_trend -0.0521 0.000102 0.000243 448s supply_trend 448s demand_(Intercept) 0.039196 448s demand_price -0.002100 448s demand_income 0.001752 448s supply_(Intercept) -0.052082 448s supply_price 0.000102 448s supply_farmPrice 0.000243 448s supply_trend 0.001752 448s > print( round( vcov( fit2sls3, modified.regMat = TRUE ), digits = 6 ) ) 448s C1 C2 C3 C4 C5 C6 448s C1 66.86423 -0.704668 0.039196 -1.1652 0.002285 0.005434 448s C2 -0.70467 0.009094 -0.002100 0.0624 -0.000122 -0.000291 448s C3 0.03920 -0.002100 0.001752 -0.0521 0.000102 0.000243 448s C4 -1.16519 0.062438 -0.052082 94.3993 -0.741997 -0.201104 448s C5 0.00229 -0.000122 0.000102 -0.7420 0.006827 0.000601 448s C6 0.00543 -0.000291 0.000243 -0.2011 0.000601 0.001433 448s > print( round( vcov( fit2sls3$eq[[ 2 ]] ), digits = 6 ) ) 448s (Intercept) price farmPrice trend 448s (Intercept) 94.3993 -0.741997 -0.201104 -0.052082 448s price -0.7420 0.006827 0.000601 0.000102 448s farmPrice -0.2011 0.000601 0.001433 0.000243 448s trend -0.0521 0.000102 0.000243 0.001752 448s > 448s > print( round( vcov( fit2sls4s ), digits = 6 ) ) 448s demand_(Intercept) demand_price demand_income 448s demand_(Intercept) 39.7610 -0.358128 -0.03842 448s demand_price -0.3581 0.004681 -0.00113 448s demand_income -0.0384 -0.001129 0.00155 448s supply_(Intercept) 39.6949 -0.480685 0.08595 448s supply_price -0.3581 0.004681 -0.00113 448s supply_farmPrice -0.0359 0.000252 0.00011 448s supply_trend -0.0384 -0.001129 0.00155 448s supply_(Intercept) supply_price supply_farmPrice 448s demand_(Intercept) 39.6949 -0.358128 -0.035932 448s demand_price -0.4807 0.004681 0.000252 448s demand_income 0.0859 -0.001129 0.000110 448s supply_(Intercept) 69.3817 -0.480685 -0.226588 448s supply_price -0.4807 0.004681 0.000252 448s supply_farmPrice -0.2266 0.000252 0.002072 448s supply_trend 0.0859 -0.001129 0.000110 448s supply_trend 448s demand_(Intercept) -0.03842 448s demand_price -0.00113 448s demand_income 0.00155 448s supply_(Intercept) 0.08595 448s supply_price -0.00113 448s supply_farmPrice 0.00011 448s supply_trend 0.00155 448s > print( round( vcov( fit2sls4s$eq[[ 1 ]] ), digits = 6 ) ) 448s (Intercept) price income 448s (Intercept) 39.7610 -0.35813 -0.03842 448s price -0.3581 0.00468 -0.00113 448s income -0.0384 -0.00113 0.00155 448s > 448s > print( round( vcov( fit2sls5r ), digits = 6 ) ) 448s demand_(Intercept) demand_price demand_income 448s demand_(Intercept) 36.0523 -0.302514 -0.057288 448s demand_price -0.3025 0.003851 -0.000847 448s demand_income -0.0573 -0.000847 0.001456 448s supply_(Intercept) 34.1121 -0.397307 0.057684 448s supply_price -0.3025 0.003851 -0.000847 448s supply_farmPrice -0.0337 0.000218 0.000122 448s supply_trend -0.0573 -0.000847 0.001456 448s supply_(Intercept) supply_price supply_farmPrice 448s demand_(Intercept) 34.1121 -0.302514 -0.033671 448s demand_price -0.3973 0.003851 0.000218 448s demand_income 0.0577 -0.000847 0.000122 448s supply_(Intercept) 54.5267 -0.397307 -0.157170 448s supply_price -0.3973 0.003851 0.000218 448s supply_farmPrice -0.1572 0.000218 0.001388 448s supply_trend 0.0577 -0.000847 0.000122 448s supply_trend 448s demand_(Intercept) -0.057288 448s demand_price -0.000847 448s demand_income 0.001456 448s supply_(Intercept) 0.057684 448s supply_price -0.000847 448s supply_farmPrice 0.000122 448s supply_trend 0.001456 448s > print( round( vcov( fit2sls5r, modified.regMat = TRUE ), digits = 6 ) ) 448s C1 C2 C3 C4 C5 C6 448s C1 36.0523 -0.302514 -0.057288 34.1121 -0.302514 -0.033671 448s C2 -0.3025 0.003851 -0.000847 -0.3973 0.003851 0.000218 448s C3 -0.0573 -0.000847 0.001456 0.0577 -0.000847 0.000122 448s C4 34.1121 -0.397307 0.057684 54.5267 -0.397307 -0.157170 448s C5 -0.3025 0.003851 -0.000847 -0.3973 0.003851 0.000218 448s C6 -0.0337 0.000218 0.000122 -0.1572 0.000218 0.001388 448s > print( round( vcov( fit2sls5r$eq[[ 2 ]] ), digits = 6 ) ) 448s (Intercept) price farmPrice trend 448s (Intercept) 54.5267 -0.397307 -0.157170 0.057684 448s price -0.3973 0.003851 0.000218 -0.000847 448s farmPrice -0.1572 0.000218 0.001388 0.000122 448s trend 0.0577 -0.000847 0.000122 0.001456 448s > 448s > print( round( vcov( fit2slsd1 ), digits = 6 ) ) 448s demand_(Intercept) demand_price demand_income 448s demand_(Intercept) 124.179 -1.51767 0.28519 448s demand_price -1.518 0.02098 -0.00595 448s demand_income 0.285 -0.00595 0.00318 448s supply_(Intercept) 0.000 0.00000 0.00000 448s supply_price 0.000 0.00000 0.00000 448s supply_farmPrice 0.000 0.00000 0.00000 448s supply_trend 0.000 0.00000 0.00000 448s supply_(Intercept) supply_price supply_farmPrice 448s demand_(Intercept) 0.000 0.000000 0.000000 448s demand_price 0.000 0.000000 0.000000 448s demand_income 0.000 0.000000 0.000000 448s supply_(Intercept) 144.253 -1.095410 -0.323818 448s supply_price -1.095 0.009987 0.000936 448s supply_farmPrice -0.324 0.000936 0.002233 448s supply_trend -0.295 0.000579 0.001377 448s supply_trend 448s demand_(Intercept) 0.000000 448s demand_price 0.000000 448s demand_income 0.000000 448s supply_(Intercept) -0.295229 448s supply_price 0.000579 448s supply_farmPrice 0.001377 448s supply_trend 0.009931 448s > print( round( vcov( fit2slsd1$eq[[ 1 ]] ), digits = 6 ) ) 448s (Intercept) price income 448s (Intercept) 124.179 -1.51767 0.28519 448s price -1.518 0.02098 -0.00595 448s income 0.285 -0.00595 0.00318 448s > 448s > print( round( vcov( fit2slsd2rs ), digits = 6 ) ) 448s demand_(Intercept) demand_price demand_income 448s demand_(Intercept) 95.9017 -1.129212 0.176368 448s demand_price -1.1292 0.014881 -0.003682 448s demand_income 0.1764 -0.003682 0.001968 448s supply_(Intercept) -5.2430 0.109460 -0.058492 448s supply_price 0.0103 -0.000215 0.000115 448s supply_farmPrice 0.0245 -0.000510 0.000273 448s supply_trend 0.1764 -0.003682 0.001968 448s supply_(Intercept) supply_price supply_farmPrice 448s demand_(Intercept) -5.2430 0.010284 0.024451 448s demand_price 0.1095 -0.000215 -0.000510 448s demand_income -0.0585 0.000115 0.000273 448s supply_(Intercept) 114.2555 -0.898881 -0.243056 448s supply_price -0.8989 0.008273 0.000727 448s supply_farmPrice -0.2431 0.000727 0.001733 448s supply_trend -0.0585 0.000115 0.000273 448s supply_trend 448s demand_(Intercept) 0.176368 448s demand_price -0.003682 448s demand_income 0.001968 448s supply_(Intercept) -0.058492 448s supply_price 0.000115 448s supply_farmPrice 0.000273 448s supply_trend 0.001968 448s > print( round( vcov( fit2slsd2rs$eq[[ 2 ]] ), digits = 6 ) ) 448s (Intercept) price farmPrice trend 448s (Intercept) 114.2555 -0.898881 -0.243056 -0.058492 448s price -0.8989 0.008273 0.000727 0.000115 448s farmPrice -0.2431 0.000727 0.001733 0.000273 448s trend -0.0585 0.000115 0.000273 0.001968 448s > 448s > print( round( vcov( fit2slsd3 ), digits = 6 ) ) 448s demand_(Intercept) demand_price demand_income 448s demand_(Intercept) 141.4425 -1.640068 0.234151 448s demand_price -1.6401 0.021165 -0.004888 448s demand_income 0.2342 -0.004888 0.002612 448s supply_(Intercept) -6.9607 0.145321 -0.077656 448s supply_price 0.0137 -0.000285 0.000152 448s supply_farmPrice 0.0325 -0.000678 0.000362 448s supply_trend 0.2342 -0.004888 0.002612 448s supply_(Intercept) supply_price supply_farmPrice 448s demand_(Intercept) -6.9607 0.013653 0.032462 448s demand_price 0.1453 -0.000285 -0.000678 448s demand_income -0.0777 0.000152 0.000362 448s supply_(Intercept) 111.0123 -0.869653 -0.237751 448s supply_price -0.8697 0.007995 0.000708 448s supply_farmPrice -0.2378 0.000708 0.001688 448s supply_trend -0.0777 0.000152 0.000362 448s supply_trend 448s demand_(Intercept) 0.234151 448s demand_price -0.004888 448s demand_income 0.002612 448s supply_(Intercept) -0.077656 448s supply_price 0.000152 448s supply_farmPrice 0.000362 448s supply_trend 0.002612 448s > print( round( vcov( fit2slsd3, modified.regMat = TRUE ), digits = 6 ) ) 448s C1 C2 C3 C4 C5 C6 448s C1 141.4425 -1.640068 0.234151 -6.9607 0.013653 0.032462 448s C2 -1.6401 0.021165 -0.004888 0.1453 -0.000285 -0.000678 448s C3 0.2342 -0.004888 0.002612 -0.0777 0.000152 0.000362 448s C4 -6.9607 0.145321 -0.077656 111.0123 -0.869653 -0.237751 448s C5 0.0137 -0.000285 0.000152 -0.8697 0.007995 0.000708 448s C6 0.0325 -0.000678 0.000362 -0.2378 0.000708 0.001688 448s > print( round( vcov( fit2slsd3$eq[[ 1 ]] ), digits = 6 ) ) 448s (Intercept) price income 448s (Intercept) 141.442 -1.64007 0.23415 448s price -1.640 0.02116 -0.00489 448s income 0.234 -0.00489 0.00261 448s > 448s > 448s > ## *********** confidence intervals of coefficients ************* 448s > print( confint( fit2sls1 ) ) 448s 2.5 % 97.5 % 448s demand_(Intercept) 77.922 111.345 448s demand_price -0.447 -0.040 448s demand_income 0.215 0.413 448s supply_(Intercept) 24.071 74.994 448s supply_price 0.028 0.452 448s supply_farmPrice 0.155 0.356 448s supply_trend 0.042 0.464 448s > print( confint( fit2sls1$eq[[ 1 ]], level = 0.9 ) ) 448s 5 % 95 % 448s (Intercept) 80.854 108.412 448s price -0.411 -0.076 448s income 0.232 0.396 448s > 448s > print( confint( fit2sls2s, level = 0.9 ) ) 448s 5 % 95 % 448s demand_(Intercept) 78.005 110.558 448s demand_price -0.417 -0.032 448s demand_income 0.211 0.386 448s supply_(Intercept) 24.204 72.165 448s supply_price 0.038 0.447 448s supply_farmPrice 0.169 0.355 448s supply_trend 0.211 0.386 448s > print( confint( fit2sls2s$eq[[ 2 ]], level = 0.99 ) ) 448s 0.5 % 99.5 % 448s (Intercept) 15.989 80.380 448s price -0.032 0.517 448s farmPrice 0.137 0.387 448s trend 0.181 0.416 448s > 448s > print( confint( fit2sls3, level = 0.99, useDfSys = TRUE ) ) 448s 0.5 % 99.5 % 448s demand_(Intercept) 77.664 110.899 448s demand_price -0.419 -0.031 448s demand_income 0.213 0.383 448s supply_(Intercept) 28.439 67.929 448s supply_price 0.075 0.411 448s supply_farmPrice 0.185 0.339 448s supply_trend 0.213 0.383 448s > print( confint( fit2sls3$eq[[ 1 ]], level = 0.5, useDfSys = TRUE ) ) 448s 25 % 75 % 448s (Intercept) 88.71 99.857 448s price -0.29 -0.160 448s income 0.27 0.327 448s > 448s > print( confint( fit2sls4r, level = 0.5 ) ) 448s 25 % 75 % 448s demand_(Intercept) 83.516 107.895 448s demand_price -0.369 -0.117 448s demand_income 0.225 0.380 448s supply_(Intercept) 31.573 61.554 448s supply_price 0.131 0.383 448s supply_farmPrice 0.188 0.339 448s supply_trend 0.225 0.380 448s > print( confint( fit2sls4r$eq[[ 2 ]], level = 0.25 ) ) 448s 37.5 % 62.5 % 448s (Intercept) 44.192 48.935 448s price 0.237 0.277 448s farmPrice 0.252 0.276 448s trend 0.290 0.315 448s > 448s > print( confint( fit2sls5rs, level = 0.25 ) ) 448s 37.5 % 62.5 % 448s demand_(Intercept) 84.017 107.395 448s demand_price -0.369 -0.117 448s demand_income 0.230 0.376 448s supply_(Intercept) 31.265 61.863 448s supply_price 0.131 0.383 448s supply_farmPrice 0.181 0.346 448s supply_trend 0.230 0.376 448s > print( confint( fit2sls5rs$eq[[ 1 ]], level = 0.975 ) ) 448s 1.3 % 98.8 % 448s (Intercept) 82.221 109.191 448s price -0.389 -0.098 448s income 0.218 0.387 448s > 448s > print( confint( fit2slsd1, level = 0.975, useDfSys = TRUE ) ) 448s 1.3 % 98.8 % 448s demand_(Intercept) 84.118 129.461 448s demand_price -0.706 -0.117 448s demand_income 0.247 0.476 448s supply_(Intercept) 25.097 73.968 448s supply_price 0.037 0.443 448s supply_farmPrice 0.159 0.352 448s supply_trend 0.050 0.456 448s > print( confint( fit2slsd1$eq[[ 2 ]], level = 0.999, useDfSys = TRUE ) ) 448s 0.1 % 100 % 448s (Intercept) 6.163 92.901 448s price -0.121 0.601 448s farmPrice 0.085 0.426 448s trend -0.107 0.613 448s > 448s > print( confint( fit2slsd2r, level = 0.999 ) ) 448s 0.1 % 100 % 448s demand_(Intercept) 81.311 125.877 448s demand_price -0.617 -0.072 448s demand_income 0.230 0.422 448s supply_(Intercept) 27.618 67.100 448s supply_price 0.077 0.412 448s supply_farmPrice 0.189 0.343 448s supply_trend 0.230 0.422 448s > print( confint( fit2slsd2r$eq[[ 1 ]] ) ) 448s 2.5 % 97.5 % 448s (Intercept) 81.311 125.877 448s price -0.617 -0.072 448s income 0.230 0.422 448s > 448s > 448s > ## *********** fitted values ************* 448s > print( fitted( fit2sls1, se.fit = TRUE, interval = "prediction" ) ) 448s demand supply 448s 1 97.6 98.9 448s 2 99.9 100.4 448s 3 99.8 100.5 448s 4 100.0 100.7 448s 5 102.1 102.6 448s 6 102.0 102.6 448s 7 102.4 102.6 448s 8 103.0 104.8 448s 9 101.5 102.7 448s 10 100.3 99.7 448s 11 95.5 95.4 448s 12 94.7 93.8 448s 13 96.1 95.6 448s 14 99.0 97.6 448s 15 103.8 102.3 448s 16 103.7 104.1 448s 17 103.8 102.8 448s 18 102.1 102.7 448s 19 103.6 102.6 448s 20 106.9 105.6 448s > print( fitted( fit2sls1$eq[[ 1 ]] ) ) 448s 1 2 3 4 5 6 7 8 9 10 11 12 13 448s 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 448s 14 15 16 17 18 19 20 448s 99.0 103.8 103.7 103.8 102.1 103.6 106.9 448s > 448s > print( fitted( fit2sls2s ) ) 448s demand supply 448s 1 97.8 98.5 448s 2 100.0 100.0 448s 3 99.9 100.1 448s 4 100.1 100.4 448s 5 102.0 102.5 448s 6 101.9 102.5 448s 7 102.4 102.5 448s 8 102.9 104.8 448s 9 101.4 102.7 448s 10 100.3 99.7 448s 11 95.8 95.3 448s 12 95.0 93.7 448s 13 96.4 95.6 448s 14 99.1 97.6 448s 15 103.7 102.5 448s 16 103.5 104.4 448s 17 103.6 103.2 448s 18 102.0 103.0 448s 19 103.5 102.9 448s 20 106.7 106.1 448s > print( fitted( fit2sls2s$eq[[ 2 ]] ) ) 448s 1 2 3 4 5 6 7 8 9 10 11 12 13 448s 98.5 100.0 100.1 100.4 102.5 102.5 102.5 104.8 102.7 99.7 95.3 93.7 95.6 448s 14 15 16 17 18 19 20 448s 97.6 102.5 104.4 103.2 103.0 102.9 106.1 448s > 448s > print( fitted( fit2sls3 ) ) 448s demand supply 448s 1 97.8 98.5 448s 2 100.0 100.0 448s 3 99.9 100.1 448s 4 100.1 100.4 448s 5 102.0 102.5 448s 6 101.9 102.5 448s 7 102.4 102.5 448s 8 102.9 104.8 448s 9 101.4 102.7 448s 10 100.3 99.7 448s 11 95.8 95.3 448s 12 95.0 93.7 448s 13 96.4 95.6 448s 14 99.1 97.6 448s 15 103.7 102.5 448s 16 103.5 104.4 448s 17 103.6 103.2 448s 18 102.0 103.0 448s 19 103.5 102.9 448s 20 106.7 106.1 448s > print( fitted( fit2sls3$eq[[ 1 ]] ) ) 448s 1 2 3 4 5 6 7 8 9 10 11 12 13 448s 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 448s 14 15 16 17 18 19 20 448s 99.1 103.7 103.5 103.6 102.0 103.5 106.7 448s > 448s > print( fitted( fit2sls4r ) ) 448s demand supply 448s 1 97.8 98.5 448s 2 99.9 100.1 448s 3 99.8 100.2 448s 4 100.0 100.5 448s 5 102.1 102.5 448s 6 101.9 102.4 448s 7 102.4 102.5 448s 8 102.9 104.8 448s 9 101.5 102.7 448s 10 100.4 99.5 448s 11 95.8 95.1 448s 12 94.9 93.6 448s 13 96.3 95.6 448s 14 99.1 97.6 448s 15 103.8 102.5 448s 16 103.6 104.4 448s 17 103.8 103.1 448s 18 102.0 103.1 448s 19 103.5 103.0 448s 20 106.6 106.3 448s > print( fitted( fit2sls4r$eq[[ 2 ]] ) ) 448s 1 2 3 4 5 6 7 8 9 10 11 12 13 448s 98.5 100.1 100.2 100.5 102.5 102.4 102.5 104.8 102.7 99.5 95.1 93.6 95.6 448s 14 15 16 17 18 19 20 448s 97.6 102.5 104.4 103.1 103.1 103.0 106.3 448s > 448s > print( fitted( fit2sls5rs ) ) 448s demand supply 448s 1 97.8 98.5 448s 2 99.9 100.1 448s 3 99.8 100.2 448s 4 100.0 100.5 448s 5 102.1 102.5 448s 6 101.9 102.4 448s 7 102.4 102.5 448s 8 102.9 104.8 448s 9 101.5 102.7 448s 10 100.4 99.5 448s 11 95.8 95.1 448s 12 94.9 93.6 448s 13 96.3 95.6 448s 14 99.1 97.6 448s 15 103.8 102.5 448s 16 103.6 104.4 448s 17 103.8 103.1 448s 18 102.0 103.1 448s 19 103.5 103.0 448s 20 106.6 106.3 448s > print( fitted( fit2sls5rs$eq[[ 1 ]] ) ) 448s 1 2 3 4 5 6 7 8 9 10 11 12 13 448s 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 448s 14 15 16 17 18 19 20 448s 99.1 103.8 103.6 103.8 102.0 103.5 106.6 448s > 448s > print( fitted( fit2slsd1 ) ) 448s demand supply 448s 1 97.1 98.9 448s 2 99.2 100.4 448s 3 99.2 100.5 448s 4 99.3 100.7 448s 5 102.5 102.6 448s 6 102.2 102.6 448s 7 102.5 102.6 448s 8 102.7 104.8 448s 9 102.0 102.7 448s 10 101.4 99.7 448s 11 95.6 95.4 448s 12 93.9 93.8 448s 13 95.0 95.6 448s 14 98.9 97.6 448s 15 104.9 102.3 448s 16 104.3 104.1 448s 17 106.1 102.8 448s 18 101.7 102.7 448s 19 103.3 102.6 448s 20 106.0 105.6 448s > print( fitted( fit2slsd1$eq[[ 2 ]] ) ) 448s 1 2 3 4 5 6 7 8 9 10 11 12 13 448s 98.9 100.4 100.5 100.7 102.6 102.6 102.6 104.8 102.7 99.7 95.4 93.8 95.6 448s 14 15 16 17 18 19 20 448s 97.6 102.3 104.1 102.8 102.7 102.6 105.6 448s > 448s > print( fitted( fit2slsd2r ) ) 448s demand supply 448s 1 97.5 98.2 448s 2 99.5 99.8 448s 3 99.4 99.9 448s 4 99.6 100.3 448s 5 102.3 102.4 448s 6 102.1 102.4 448s 7 102.4 102.4 448s 8 102.6 104.7 448s 9 101.8 102.7 448s 10 101.1 99.6 448s 11 96.0 95.2 448s 12 94.6 93.6 448s 13 95.7 95.6 448s 14 99.1 97.7 448s 15 104.4 102.7 448s 16 103.9 104.5 448s 17 105.2 103.4 448s 18 101.8 103.2 448s 19 103.2 103.1 448s 20 105.9 106.3 448s > print( fitted( fit2slsd2r$eq[[ 1 ]] ) ) 448s 1 2 3 4 5 6 7 8 9 10 11 12 13 448s 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 448s 14 15 16 17 18 19 20 448s 99.1 104.4 103.9 105.2 101.8 103.2 105.9 448s > 448s > 448s > ## *********** predicted values ************* 448s > predictData <- Kmenta 448s > predictData$consump <- NULL 448s > predictData$price <- Kmenta$price * 0.9 448s > predictData$income <- Kmenta$income * 1.1 448s > 448s > print( predict( fit2sls1, se.fit = TRUE, interval = "prediction" ) ) 448s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 448s 1 97.6 0.661 93.3 102.0 98.9 1.079 448s 2 99.9 0.600 95.5 104.2 100.4 1.064 448s 3 99.8 0.564 95.5 104.1 100.5 0.962 448s 4 100.0 0.605 95.7 104.4 100.7 0.938 448s 5 102.1 0.516 97.8 106.4 102.6 0.914 448s 6 102.0 0.474 97.7 106.2 102.6 0.808 448s 7 102.4 0.493 98.1 106.7 102.6 0.736 448s 8 103.0 0.615 98.6 107.3 104.8 0.994 448s 9 101.5 0.544 97.2 105.8 102.7 0.808 448s 10 100.3 0.822 95.8 104.8 99.7 1.023 448s 11 95.5 0.963 90.9 100.2 95.4 1.228 448s 12 94.7 1.006 90.1 99.4 93.8 1.428 448s 13 96.1 0.915 91.6 100.7 95.6 1.272 448s 14 99.0 0.518 94.7 103.3 97.6 0.917 448s 15 103.8 0.793 99.4 108.3 102.3 0.899 448s 16 103.7 0.636 99.3 108.0 104.1 0.936 448s 17 103.8 1.348 98.8 108.9 102.8 1.665 448s 18 102.1 0.549 97.8 106.4 102.7 0.988 448s 19 103.6 0.695 99.2 108.0 102.6 1.129 448s 20 106.9 1.306 101.9 111.9 105.6 1.733 448s supply.lwr supply.upr 448s 1 93.2 104.6 448s 2 94.7 106.1 448s 3 94.9 106.0 448s 4 95.1 106.3 448s 5 97.1 108.2 448s 6 97.1 108.1 448s 7 97.1 108.0 448s 8 99.2 110.4 448s 9 97.3 108.2 448s 10 94.0 105.3 448s 11 89.5 101.2 448s 12 87.8 99.8 448s 13 89.8 101.5 448s 14 92.0 103.1 448s 15 96.8 107.9 448s 16 98.5 109.6 448s 17 96.5 109.1 448s 18 97.1 108.3 448s 19 96.8 108.3 448s 20 99.2 112.0 448s > print( predict( fit2sls1$eq[[ 1 ]], se.fit = TRUE, interval = "prediction" ) ) 448s fit se.fit lwr upr 448s 1 97.6 0.661 93.3 102.0 448s 2 99.9 0.600 95.5 104.2 448s 3 99.8 0.564 95.5 104.1 448s 4 100.0 0.605 95.7 104.4 448s 5 102.1 0.516 97.8 106.4 448s 6 102.0 0.474 97.7 106.2 448s 7 102.4 0.493 98.1 106.7 448s 8 103.0 0.615 98.6 107.3 448s 9 101.5 0.544 97.2 105.8 448s 10 100.3 0.822 95.8 104.8 448s 11 95.5 0.963 90.9 100.2 448s 12 94.7 1.006 90.1 99.4 448s 13 96.1 0.915 91.6 100.7 448s 14 99.0 0.518 94.7 103.3 448s 15 103.8 0.793 99.4 108.3 448s 16 103.7 0.636 99.3 108.0 448s 17 103.8 1.348 98.8 108.9 448s 18 102.1 0.549 97.8 106.4 448s 19 103.6 0.695 99.2 108.0 448s 20 106.9 1.306 101.9 111.9 448s > 448s > print( predict( fit2sls2s, se.pred = TRUE, interval = "confidence", 448s + level = 0.999, newdata = predictData ) ) 448s demand.pred demand.se.pred demand.lwr demand.upr supply.pred supply.se.pred 448s 1 102.7 2.23 99.1 106 96.1 2.75 448s 2 105.2 2.23 101.6 109 97.5 2.64 448s 3 105.1 2.24 101.4 109 97.6 2.65 448s 4 105.4 2.23 101.8 109 97.9 2.62 448s 5 107.2 2.52 101.7 113 100.1 2.83 448s 6 107.1 2.46 101.9 112 100.0 2.77 448s 7 107.7 2.45 102.6 113 100.0 2.70 448s 8 108.5 2.41 103.6 113 102.2 2.65 448s 9 106.5 2.53 100.9 112 100.4 2.87 448s 10 105.0 2.66 98.7 111 97.4 3.10 448s 11 100.1 2.42 95.1 105 93.0 3.17 448s 12 99.5 2.22 96.0 103 91.3 3.14 448s 13 101.2 2.13 98.5 104 93.1 2.95 448s 14 104.0 2.32 99.7 108 95.3 2.91 448s 15 108.9 2.74 102.1 116 100.2 2.92 448s 16 108.9 2.62 102.7 115 102.0 2.79 448s 17 108.4 3.09 99.9 117 101.1 3.37 448s 18 107.5 2.36 102.9 112 100.5 2.65 448s 19 109.2 2.44 104.1 114 100.3 2.64 448s 20 113.0 2.67 106.6 119 103.3 2.58 448s supply.lwr supply.upr 448s 1 91.8 100.4 448s 2 94.3 100.8 448s 3 94.2 101.0 448s 4 94.8 101.0 448s 5 95.2 105.0 448s 6 95.6 104.5 448s 7 96.1 103.9 448s 8 98.9 105.6 448s 9 95.2 105.6 448s 10 90.7 104.1 448s 11 85.9 100.2 448s 12 84.4 98.3 448s 13 87.3 98.9 448s 14 89.7 100.8 448s 15 94.7 105.8 448s 16 97.3 106.6 448s 17 92.9 109.3 448s 18 97.1 103.9 448s 19 97.1 103.6 448s 20 100.7 105.9 448s > print( predict( fit2sls2s$eq[[ 2 ]], se.pred = TRUE, interval = "confidence", 448s + level = 0.999, newdata = predictData ) ) 448s fit se.pred lwr upr 448s 1 96.1 2.75 91.8 100.4 448s 2 97.5 2.64 94.3 100.8 448s 3 97.6 2.65 94.2 101.0 448s 4 97.9 2.62 94.8 101.0 448s 5 100.1 2.83 95.2 105.0 448s 6 100.0 2.77 95.6 104.5 448s 7 100.0 2.70 96.1 103.9 448s 8 102.2 2.65 98.9 105.6 448s 9 100.4 2.87 95.2 105.6 448s 10 97.4 3.10 90.7 104.1 448s 11 93.0 3.17 85.9 100.2 448s 12 91.3 3.14 84.4 98.3 448s 13 93.1 2.95 87.3 98.9 448s 14 95.3 2.91 89.7 100.8 448s 15 100.2 2.92 94.7 105.8 448s 16 102.0 2.79 97.3 106.6 448s 17 101.1 3.37 92.9 109.3 448s 18 100.5 2.65 97.1 103.9 448s 19 100.3 2.64 97.1 103.6 448s 20 103.3 2.58 100.7 105.9 448s > 448s > print( predict( fit2sls3, se.pred = TRUE, interval = "prediction", 448s + level = 0.975, useDfSys = TRUE ) ) 448s demand.pred demand.se.pred demand.lwr demand.upr supply.pred supply.se.pred 448s 1 97.8 2.09 92.9 103 98.5 2.55 448s 2 100.0 2.08 95.1 105 100.0 2.57 448s 3 99.9 2.07 95.0 105 100.1 2.55 448s 4 100.1 2.08 95.2 105 100.4 2.56 448s 5 102.0 2.06 97.2 107 102.5 2.58 448s 6 101.9 2.05 97.1 107 102.5 2.56 448s 7 102.4 2.05 97.5 107 102.5 2.55 448s 8 102.9 2.09 98.0 108 104.8 2.61 448s 9 101.4 2.07 96.6 106 102.7 2.57 448s 10 100.3 2.17 95.2 105 99.7 2.62 448s 11 95.8 2.20 90.6 101 95.3 2.67 448s 12 95.0 2.20 89.9 100 93.7 2.74 448s 13 96.4 2.17 91.3 101 95.6 2.69 448s 14 99.1 2.06 94.3 104 97.6 2.59 448s 15 103.7 2.14 98.7 109 102.5 2.56 448s 16 103.5 2.08 98.6 108 104.4 2.55 448s 17 103.6 2.40 98.0 109 103.2 2.78 448s 18 102.0 2.07 97.2 107 103.0 2.56 448s 19 103.5 2.11 98.6 108 102.9 2.59 448s 20 106.7 2.38 101.1 112 106.1 2.78 448s supply.lwr supply.upr 448s 1 92.5 104 448s 2 94.0 106 448s 3 94.1 106 448s 4 94.4 106 448s 5 96.4 109 448s 6 96.5 108 448s 7 96.5 108 448s 8 98.6 111 448s 9 96.7 109 448s 10 93.5 106 448s 11 89.0 102 448s 12 87.3 100 448s 13 89.3 102 448s 14 91.6 104 448s 15 96.5 109 448s 16 98.4 110 448s 17 96.7 110 448s 18 97.0 109 448s 19 96.8 109 448s 20 99.5 113 448s > print( predict( fit2sls3$eq[[ 1 ]], se.pred = TRUE, interval = "prediction", 448s + level = 0.975, useDfSys = TRUE ) ) 448s fit se.pred lwr upr 448s 1 97.8 2.09 92.9 103 448s 2 100.0 2.08 95.1 105 448s 3 99.9 2.07 95.0 105 448s 4 100.1 2.08 95.2 105 448s 5 102.0 2.06 97.2 107 448s 6 101.9 2.05 97.1 107 448s 7 102.4 2.05 97.5 107 448s 8 102.9 2.09 98.0 108 448s 9 101.4 2.07 96.6 106 448s 10 100.3 2.17 95.2 105 448s 11 95.8 2.20 90.6 101 448s 12 95.0 2.20 89.9 100 448s 13 96.4 2.17 91.3 101 448s 14 99.1 2.06 94.3 104 448s 15 103.7 2.14 98.7 109 448s 16 103.5 2.08 98.6 108 448s 17 103.6 2.40 98.0 109 448s 18 102.0 2.07 97.2 107 448s 19 103.5 2.11 98.6 108 448s 20 106.7 2.38 101.1 112 448s > 448s > print( predict( fit2sls4r, se.fit = TRUE, interval = "confidence", 448s + level = 0.25 ) ) 448s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 448s 1 97.8 0.602 97.6 97.9 98.5 0.586 448s 2 99.9 0.526 99.7 100.1 100.1 0.672 448s 3 99.8 0.508 99.7 100.0 100.2 0.621 448s 4 100.0 0.530 99.8 100.2 100.5 0.632 448s 5 102.1 0.488 101.9 102.2 102.5 0.704 448s 6 101.9 0.474 101.8 102.1 102.4 0.636 448s 7 102.4 0.498 102.2 102.5 102.5 0.587 448s 8 102.9 0.604 102.7 103.0 104.8 0.764 448s 9 101.5 0.502 101.3 101.6 102.7 0.656 448s 10 100.4 0.696 100.2 100.6 99.5 0.710 448s 11 95.8 0.928 95.5 96.1 95.1 0.885 448s 12 94.9 0.889 94.7 95.2 93.6 1.146 448s 13 96.3 0.739 96.0 96.5 95.6 1.052 448s 14 99.1 0.519 98.9 99.3 97.6 0.746 448s 15 103.8 0.626 103.6 104.0 102.5 0.637 448s 16 103.6 0.566 103.4 103.8 104.4 0.615 448s 17 103.8 0.942 103.5 104.1 103.1 1.153 448s 18 102.0 0.540 101.8 102.2 103.1 0.556 448s 19 103.5 0.677 103.3 103.7 103.0 0.631 448s 20 106.6 1.226 106.2 107.0 106.3 0.900 448s supply.lwr supply.upr 448s 1 98.3 98.7 448s 2 99.9 100.3 448s 3 100.0 100.4 448s 4 100.3 100.7 448s 5 102.2 102.7 448s 6 102.2 102.6 448s 7 102.3 102.7 448s 8 104.6 105.1 448s 9 102.5 102.9 448s 10 99.3 99.8 448s 11 94.9 95.4 448s 12 93.3 94.0 448s 13 95.3 96.0 448s 14 97.4 97.9 448s 15 102.3 102.7 448s 16 104.2 104.6 448s 17 102.7 103.4 448s 18 102.9 103.3 448s 19 102.8 103.2 448s 20 106.0 106.6 448s > print( predict( fit2sls4r$eq[[ 2 ]], se.fit = TRUE, interval = "confidence", 448s + level = 0.25 ) ) 448s fit se.fit lwr upr 448s 1 98.5 0.586 98.3 98.7 448s 2 100.1 0.672 99.9 100.3 448s 3 100.2 0.621 100.0 100.4 448s 4 100.5 0.632 100.3 100.7 448s 5 102.5 0.704 102.2 102.7 448s 6 102.4 0.636 102.2 102.6 448s 7 102.5 0.587 102.3 102.7 448s 8 104.8 0.764 104.6 105.1 448s 9 102.7 0.656 102.5 102.9 448s 10 99.5 0.710 99.3 99.8 448s 11 95.1 0.885 94.9 95.4 448s 12 93.6 1.146 93.3 94.0 448s 13 95.6 1.052 95.3 96.0 448s 14 97.6 0.746 97.4 97.9 448s 15 102.5 0.637 102.3 102.7 448s 16 104.4 0.615 104.2 104.6 448s 17 103.1 1.153 102.7 103.4 448s 18 103.1 0.556 102.9 103.3 448s 19 103.0 0.631 102.8 103.2 448s 20 106.3 0.900 106.0 106.6 448s > 448s > print( predict( fit2sls5rs, se.fit = TRUE, se.pred = TRUE, 448s + interval = "prediction", level = 0.5, newdata = predictData ) ) 448s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 448s 1 102.8 0.713 2.10 101.4 104 95.9 448s 2 105.4 0.742 2.11 103.9 107 97.4 448s 3 105.3 0.751 2.11 103.8 107 97.5 448s 4 105.5 0.749 2.11 104.1 107 97.8 448s 5 107.5 1.080 2.25 105.9 109 99.9 448s 6 107.4 1.031 2.23 105.9 109 99.9 448s 7 107.9 1.040 2.23 106.4 109 99.9 448s 8 108.7 1.044 2.23 107.1 110 102.1 448s 9 106.8 1.073 2.24 105.2 108 100.2 448s 10 105.3 1.188 2.30 103.8 107 97.2 448s 11 100.3 1.013 2.22 98.8 102 92.8 448s 12 99.7 0.770 2.12 98.2 101 91.1 448s 13 101.3 0.584 2.06 99.9 103 93.0 448s 14 104.3 0.833 2.14 102.8 106 95.1 448s 15 109.2 1.310 2.37 107.6 111 100.1 448s 16 109.1 1.214 2.32 107.6 111 101.8 448s 17 108.9 1.582 2.53 107.1 111 100.8 448s 18 107.7 0.958 2.19 106.2 109 100.4 448s 19 109.4 1.111 2.26 107.9 111 100.3 448s 20 113.2 1.529 2.50 111.5 115 103.4 448s supply.se.fit supply.se.pred supply.lwr supply.upr 448s 1 0.746 2.61 94.1 97.7 448s 2 0.628 2.58 95.6 99.1 448s 3 0.642 2.58 95.7 99.3 448s 4 0.607 2.57 96.0 99.5 448s 5 0.978 2.68 98.1 101.8 448s 6 0.881 2.65 98.1 101.7 448s 7 0.786 2.62 98.1 101.7 448s 8 0.780 2.62 100.4 103.9 448s 9 1.031 2.70 98.4 102.1 448s 10 1.212 2.78 95.3 99.1 448s 11 1.339 2.84 90.8 94.7 448s 12 1.478 2.90 89.1 93.1 448s 13 1.292 2.81 91.1 94.9 448s 14 1.123 2.74 93.2 97.0 448s 15 1.105 2.73 98.2 101.9 448s 16 0.996 2.69 100.0 103.7 448s 17 1.636 2.99 98.8 102.9 448s 18 0.777 2.62 98.7 102.2 448s 19 0.775 2.62 98.5 102.1 448s 20 0.600 2.57 101.6 105.1 448s > print( predict( fit2sls5rs$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 448s + interval = "prediction", level = 0.5, newdata = predictData ) ) 448s fit se.fit se.pred lwr upr 448s 1 102.8 0.713 2.10 101.4 104 448s 2 105.4 0.742 2.11 103.9 107 448s 3 105.3 0.751 2.11 103.8 107 448s 4 105.5 0.749 2.11 104.1 107 448s 5 107.5 1.080 2.25 105.9 109 448s 6 107.4 1.031 2.23 105.9 109 448s 7 107.9 1.040 2.23 106.4 109 448s 8 108.7 1.044 2.23 107.1 110 448s 9 106.8 1.073 2.24 105.2 108 448s 10 105.3 1.188 2.30 103.8 107 448s 11 100.3 1.013 2.22 98.8 102 448s 12 99.7 0.770 2.12 98.2 101 448s 13 101.3 0.584 2.06 99.9 103 448s 14 104.3 0.833 2.14 102.8 106 448s 15 109.2 1.310 2.37 107.6 111 448s 16 109.1 1.214 2.32 107.6 111 448s 17 108.9 1.582 2.53 107.1 111 448s 18 107.7 0.958 2.19 106.2 109 448s 19 109.4 1.111 2.26 107.9 111 448s 20 113.2 1.529 2.50 111.5 115 448s > 448s > print( predict( fit2slsd1, se.fit = TRUE, se.pred = TRUE, 448s + interval = "confidence", level = 0.99, useDfSys = TRUE ) ) 448s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 448s 1 97.1 0.751 2.13 95.1 99.2 98.9 448s 2 99.2 0.757 2.13 97.1 101.2 100.4 448s 3 99.2 0.692 2.11 97.3 101.1 100.5 448s 4 99.3 0.766 2.13 97.2 101.4 100.7 448s 5 102.5 0.595 2.08 100.9 104.2 102.6 448s 6 102.2 0.503 2.05 100.8 103.6 102.6 448s 7 102.5 0.503 2.05 101.1 103.9 102.6 448s 8 102.7 0.653 2.10 100.9 104.4 104.8 448s 9 102.0 0.655 2.10 100.2 103.8 102.7 448s 10 101.4 1.074 2.26 98.5 104.3 99.7 448s 11 95.6 0.978 2.22 93.0 98.3 95.4 448s 12 93.9 1.134 2.29 90.8 97.0 93.8 448s 13 95.0 1.162 2.31 91.9 98.2 95.6 448s 14 98.9 0.530 2.06 97.5 100.4 97.6 448s 15 104.9 1.061 2.26 102.0 107.8 102.3 448s 16 104.3 0.757 2.13 102.2 106.3 104.1 448s 17 106.1 1.963 2.80 100.7 111.4 102.8 448s 18 101.7 0.597 2.08 100.1 103.4 102.7 448s 19 103.3 0.736 2.12 101.3 105.3 102.6 448s 20 106.0 1.430 2.45 102.1 110.0 105.6 448s supply.se.fit supply.se.pred supply.lwr supply.upr 448s 1 1.079 2.68 96.0 101.9 448s 2 1.064 2.68 97.5 103.3 448s 3 0.962 2.64 97.8 103.1 448s 4 0.938 2.63 98.1 103.3 448s 5 0.914 2.62 100.1 105.1 448s 6 0.808 2.59 100.4 104.8 448s 7 0.736 2.57 100.5 104.6 448s 8 0.994 2.65 102.1 107.5 448s 9 0.808 2.59 100.5 105.0 448s 10 1.023 2.66 96.9 102.5 448s 11 1.228 2.75 92.0 98.7 448s 12 1.428 2.84 89.9 97.7 448s 13 1.272 2.77 92.2 99.1 448s 14 0.917 2.62 95.1 100.1 448s 15 0.899 2.62 99.9 104.8 448s 16 0.936 2.63 101.5 106.6 448s 17 1.665 2.97 98.3 107.4 448s 18 0.988 2.65 100.0 105.4 448s 19 1.129 2.70 99.5 105.6 448s 20 1.733 3.01 100.9 110.3 448s > print( predict( fit2slsd1$eq[[ 2 ]], se.fit = TRUE, se.pred = TRUE, 448s + interval = "confidence", level = 0.99, useDfSys = TRUE ) ) 448s fit se.fit se.pred lwr upr 448s 1 98.9 1.079 2.68 96.0 101.9 448s 2 100.4 1.064 2.68 97.5 103.3 448s 3 100.5 0.962 2.64 97.8 103.1 448s 4 100.7 0.938 2.63 98.1 103.3 448s 5 102.6 0.914 2.62 100.1 105.1 448s 6 102.6 0.808 2.59 100.4 104.8 448s 7 102.6 0.736 2.57 100.5 104.6 448s 8 104.8 0.994 2.65 102.1 107.5 448s 9 102.7 0.808 2.59 100.5 105.0 448s 10 99.7 1.023 2.66 96.9 102.5 448s 11 95.4 1.228 2.75 92.0 98.7 448s 12 93.8 1.428 2.84 89.9 97.7 448s 13 95.6 1.272 2.77 92.2 99.1 448s 14 97.6 0.917 2.62 95.1 100.1 448s 15 102.3 0.899 2.62 99.9 104.8 448s 16 104.1 0.936 2.63 101.5 106.6 448s 17 102.8 1.665 2.97 98.3 107.4 448s 18 102.7 0.988 2.65 100.0 105.4 448s 19 102.6 1.129 2.70 99.5 105.6 448s 20 105.6 1.733 3.01 100.9 110.3 448s > 448s > print( predict( fit2slsd2r, se.fit = TRUE, interval = "prediction", 448s + level = 0.9, newdata = predictData ) ) 448s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 448s 1 104 1.34 99.8 108 95.8 1.026 448s 2 106 1.27 102.3 110 97.3 0.786 448s 3 106 1.32 102.2 110 97.4 0.804 448s 4 106 1.27 102.4 110 97.7 0.734 448s 5 109 2.06 104.2 114 100.0 1.130 448s 6 109 1.92 104.1 113 99.9 1.014 448s 7 109 1.86 104.7 114 99.9 0.893 448s 8 110 1.67 105.4 114 102.2 0.765 448s 9 108 2.12 103.4 113 100.4 1.187 448s 10 107 2.45 101.9 112 97.4 1.525 448s 11 102 1.85 97.1 106 92.9 1.627 448s 12 101 1.26 96.6 104 91.2 1.587 448s 13 102 0.98 98.3 106 93.1 1.314 448s 14 105 1.63 101.1 110 95.3 1.253 448s 15 111 2.53 105.6 116 100.4 1.269 448s 16 111 2.23 105.7 116 102.1 1.075 448s 17 111 3.28 104.9 118 101.3 1.888 448s 18 109 1.59 104.5 113 100.7 0.796 448s 19 110 1.70 106.1 115 100.5 0.772 448s 20 114 1.87 109.4 119 103.6 0.656 448s supply.lwr supply.upr 448s 1 91.2 100.4 448s 2 92.8 101.7 448s 3 93.0 101.9 448s 4 93.3 102.1 448s 5 95.3 104.6 448s 6 95.4 104.5 448s 7 95.4 104.4 448s 8 97.8 106.6 448s 9 95.7 105.1 448s 10 92.5 102.4 448s 11 87.9 98.0 448s 12 86.2 96.2 448s 13 88.3 97.9 448s 14 90.5 100.0 448s 15 95.6 105.1 448s 16 97.5 106.7 448s 17 96.0 106.6 448s 18 96.2 105.1 448s 19 96.1 105.0 448s 20 99.2 107.9 448s > print( predict( fit2slsd2r$eq[[ 1 ]], se.fit = TRUE, interval = "prediction", 448s + level = 0.9, newdata = predictData ) ) 448s fit se.fit lwr upr 448s 1 104 1.34 99.8 108 448s 2 106 1.27 102.3 110 448s 3 106 1.32 102.2 110 448s 4 106 1.27 102.4 110 448s 5 109 2.06 104.2 114 448s 6 109 1.92 104.1 113 448s 7 109 1.86 104.7 114 448s 8 110 1.67 105.4 114 448s 9 108 2.12 103.4 113 448s 10 107 2.45 101.9 112 448s 11 102 1.85 97.1 106 448s 12 101 1.26 96.6 104 448s 13 102 0.98 98.3 106 448s 14 105 1.63 101.1 110 448s 15 111 2.53 105.6 116 448s 16 111 2.23 105.7 116 448s 17 111 3.28 104.9 118 448s 18 109 1.59 104.5 113 448s 19 110 1.70 106.1 115 448s 20 114 1.87 109.4 119 448s > 448s > # predict just one observation 448s > smallData <- data.frame( price = 130, income = 150, farmPrice = 120, 448s + trend = 25 ) 448s > 448s > print( predict( fit2sls1rs, newdata = smallData ) ) 448s demand.pred supply.pred 448s 1 110 118 448s > print( predict( fit2sls1rs$eq[[ 1 ]], newdata = smallData ) ) 448s fit 448s 1 110 448s > 448s > print( predict( fit2sls2, se.fit = TRUE, level = 0.9, 448s + newdata = smallData ) ) 448s demand.pred demand.se.fit supply.pred supply.se.fit 448s 1 110 2.79 119 3.18 448s > print( predict( fit2sls2$eq[[ 1 ]], se.pred = TRUE, level = 0.99, 448s + newdata = smallData ) ) 448s fit se.pred 448s 1 110 3.42 448s > 448s > print( predict( fit2sls3, interval = "prediction", level = 0.975, 448s + newdata = smallData ) ) 448s demand.pred demand.lwr demand.upr supply.pred supply.lwr supply.upr 448s 1 110 102 117 119 110 128 448s > print( predict( fit2sls3$eq[[ 1 ]], interval = "confidence", level = 0.8, 448s + newdata = smallData ) ) 448s fit lwr upr 448s 1 110 106 113 448s > 448s > print( predict( fit2sls4r, se.fit = TRUE, interval = "confidence", 448s + level = 0.999, newdata = smallData ) ) 448s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 448s 1 109 2.24 101 118 119 2.09 448s supply.lwr supply.upr 448s 1 112 127 448s > print( predict( fit2sls4r$eq[[ 2 ]], se.pred = TRUE, interval = "prediction", 448s + level = 0.75, newdata = smallData ) ) 448s fit se.pred lwr upr 448s 1 119 3.26 115 123 448s > 448s > print( predict( fit2sls5s, se.fit = TRUE, interval = "prediction", 448s + newdata = smallData ) ) 448s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 448s 1 109 2.26 103 116 119 2.33 448s supply.lwr supply.upr 448s 1 112 126 448s > print( predict( fit2sls5s$eq[[ 1 ]], se.pred = TRUE, interval = "confidence", 448s + newdata = smallData ) ) 448s fit se.pred lwr upr 448s 1 109 3 105 114 448s > 448s > print( predict( fit2slsd3, se.fit = TRUE, se.pred = TRUE, 448s + interval = "prediction", level = 0.5, newdata = smallData ) ) 448s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 448s 1 108 3.33 3.86 105 110 119 448s supply.se.fit supply.se.pred supply.lwr supply.upr 448s 1 3.2 4.07 116 122 448s > print( predict( fit2slsd3$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 448s + interval = "confidence", level = 0.25, newdata = smallData ) ) 448s fit se.fit se.pred lwr upr 448s 1 108 3.33 3.86 107 109 448s > 448s > 448s > ## ************ correlation of predicted values *************** 448s > print( correlation.systemfit( fit2sls1, 1, 2 ) ) 448s [,1] 448s [1,] 0 448s [2,] 0 448s [3,] 0 448s [4,] 0 448s [5,] 0 448s [6,] 0 448s [7,] 0 448s [8,] 0 448s [9,] 0 448s [10,] 0 448s [11,] 0 448s [12,] 0 448s [13,] 0 448s [14,] 0 448s [15,] 0 448s [16,] 0 448s [17,] 0 448s [18,] 0 448s [19,] 0 448s [20,] 0 448s > 448s > print( correlation.systemfit( fit2sls2s, 2, 1 ) ) 448s [,1] 448s [1,] 0.413453 448s [2,] 0.153759 448s [3,] 0.152962 448s [4,] 0.112671 448s [5,] -0.071442 448s [6,] -0.053943 448s [7,] -0.050961 448s [8,] -0.005442 448s [9,] -0.000476 448s [10,] -0.001894 448s [11,] 0.047351 448s [12,] 0.064973 448s [13,] 0.024591 448s [14,] -0.028036 448s [15,] 0.175326 448s [16,] 0.254878 448s [17,] 0.104540 448s [18,] 0.065579 448s [19,] 0.147008 448s [20,] 0.124593 448s > 448s > print( correlation.systemfit( fit2sls3, 1, 2 ) ) 448s [,1] 448s [1,] 0.44877 448s [2,] 0.16875 448s [3,] 0.16850 448s [4,] 0.12519 448s [5,] -0.08079 448s [6,] -0.06096 448s [7,] -0.05780 448s [8,] -0.00618 448s [9,] -0.00054 448s [10,] -0.00214 448s [11,] 0.05454 448s [12,] 0.07607 448s [13,] 0.02868 448s [14,] -0.03197 448s [15,] 0.19899 448s [16,] 0.28551 448s [17,] 0.11838 448s [18,] 0.07184 448s [19,] 0.16271 448s [20,] 0.13995 448s > 448s > print( correlation.systemfit( fit2sls4r, 2, 1 ) ) 448s [,1] 448s [1,] 0.4078 448s [2,] 0.2866 448s [3,] 0.2528 448s [4,] 0.2836 448s [5,] -0.0300 448s [6,] -0.0537 448s [7,] -0.0627 448s [8,] 0.1044 448s [9,] 0.1003 448s [10,] 0.4530 448s [11,] 0.1293 448s [12,] 0.0184 448s [13,] 0.0449 448s [14,] -0.0409 448s [15,] 0.4229 448s [16,] 0.2649 448s [17,] 0.6554 448s [18,] 0.2693 448s [19,] 0.3831 448s [20,] 0.5784 448s > 448s > print( correlation.systemfit( fit2sls5rs, 1, 2 ) ) 448s [,1] 448s [1,] 0.38438 448s [2,] 0.30697 448s [3,] 0.26690 448s [4,] 0.30163 448s [5,] -0.02768 448s [6,] -0.05086 448s [7,] -0.05895 448s [8,] 0.10102 448s [9,] 0.10072 448s [10,] 0.45547 448s [11,] 0.10817 448s [12,] 0.00552 448s [13,] 0.04219 448s [14,] -0.04054 448s [15,] 0.42100 448s [16,] 0.24974 448s [17,] 0.65722 448s [18,] 0.24286 448s [19,] 0.34336 448s [20,] 0.54717 448s > 448s > print( correlation.systemfit( fit2slsd1, 2, 1 ) ) 448s [,1] 448s [1,] 0 448s [2,] 0 448s [3,] 0 448s [4,] 0 448s [5,] 0 448s [6,] 0 448s [7,] 0 448s [8,] 0 448s [9,] 0 448s [10,] 0 448s [11,] 0 448s [12,] 0 448s [13,] 0 448s [14,] 0 448s [15,] 0 448s [16,] 0 448s [17,] 0 448s [18,] 0 448s [19,] 0 448s [20,] 0 448s > 448s > print( correlation.systemfit( fit2slsd2r, 1, 2 ) ) 448s [,1] 448s [1,] 0.51320 448s [2,] 0.27263 448s [3,] 0.26221 448s [4,] 0.21307 448s [5,] -0.11973 448s [6,] -0.08282 448s [7,] -0.06158 448s [8,] -0.00225 448s [9,] -0.00103 448s [10,] -0.00892 448s [11,] 0.04576 448s [12,] 0.08710 448s [13,] 0.03423 448s [14,] -0.03425 448s [15,] 0.25625 448s [16,] 0.35070 448s [17,] 0.17505 448s [18,] -0.02443 448s [19,] 0.07277 448s [20,] 0.05142 448s > 448s > 448s > ## ************ Log-Likelihood values *************** 448s > print( logLik( fit2sls1 ) ) 448s 'log Lik.' -67.6 (df=8) 448s > print( logLik( fit2sls1, residCovDiag = TRUE ) ) 448s 'log Lik.' -84.4 (df=8) 448s > 448s > print( logLik( fit2sls2s ) ) 448s 'log Lik.' -65.7 (df=7) 448s > print( logLik( fit2sls2s, residCovDiag = TRUE ) ) 448s 'log Lik.' -84.8 (df=7) 448s > 448s > print( logLik( fit2sls3 ) ) 448s 'log Lik.' -65.7 (df=7) 448s > print( logLik( fit2sls3, residCovDiag = TRUE ) ) 448s 'log Lik.' -84.8 (df=7) 448s > 448s > print( logLik( fit2sls4r ) ) 448s 'log Lik.' -66.2 (df=6) 448s > print( logLik( fit2sls4r, residCovDiag = TRUE ) ) 448s 'log Lik.' -84.8 (df=6) 448s > 448s > print( logLik( fit2sls5rs ) ) 448s 'log Lik.' -66.2 (df=6) 448s > print( logLik( fit2sls5rs, residCovDiag = TRUE ) ) 448s 'log Lik.' -84.8 (df=6) 448s > 448s > print( logLik( fit2slsd1 ) ) 448s 'log Lik.' -75.1 (df=8) 448s > print( logLik( fit2slsd1, residCovDiag = TRUE ) ) 448s 'log Lik.' -84.7 (df=8) 448s > 448s > print( logLik( fit2slsd2r ) ) 448s 'log Lik.' -68.8 (df=7) 448s > print( logLik( fit2slsd2r, residCovDiag = TRUE ) ) 448s 'log Lik.' -84.6 (df=7) 448s > 448s > 448s > ## ************** F tests **************** 448s > # testing first restriction 448s > print( linearHypothesis( fit2sls1, restrm ) ) 448s Linear hypothesis test (Theil's F test) 448s 448s Hypothesis: 448s demand_income - supply_trend = 0 448s 448s Model 1: restricted model 448s Model 2: fit2sls1 448s 448s Res.Df Df F Pr(>F) 448s 1 34 448s 2 33 1 0.06 0.8 448s > linearHypothesis( fit2sls1, restrict ) 448s Linear hypothesis test (Theil's F test) 448s 448s Hypothesis: 448s demand_income - supply_trend = 0 448s 448s Model 1: restricted model 448s Model 2: fit2sls1 448s 448s Res.Df Df F Pr(>F) 448s 1 34 448s 2 33 1 0.06 0.8 448s > 448s > print( linearHypothesis( fit2sls1s, restrm ) ) 448s Linear hypothesis test (Theil's F test) 448s 448s Hypothesis: 448s demand_income - supply_trend = 0 448s 448s Model 1: restricted model 448s Model 2: fit2sls1s 448s 448s Res.Df Df F Pr(>F) 448s 1 34 448s 2 33 1 0.07 0.79 448s > linearHypothesis( fit2sls1s, restrict ) 448s Linear hypothesis test (Theil's F test) 448s 448s Hypothesis: 448s demand_income - supply_trend = 0 448s 448s Model 1: restricted model 448s Model 2: fit2sls1s 448s 448s Res.Df Df F Pr(>F) 448s 1 34 448s 2 33 1 0.07 0.79 448s > 448s > print( linearHypothesis( fit2sls1, restrm ) ) 448s Linear hypothesis test (Theil's F test) 448s 448s Hypothesis: 448s demand_income - supply_trend = 0 448s 448s Model 1: restricted model 448s Model 2: fit2sls1 448s 448s Res.Df Df F Pr(>F) 448s 1 34 448s 2 33 1 0.06 0.8 448s > linearHypothesis( fit2sls1, restrict ) 448s Linear hypothesis test (Theil's F test) 448s 448s Hypothesis: 448s demand_income - supply_trend = 0 448s 448s Model 1: restricted model 448s Model 2: fit2sls1 448s 448s Res.Df Df F Pr(>F) 448s 1 34 448s 2 33 1 0.06 0.8 448s > 448s > print( linearHypothesis( fit2sls1r, restrm ) ) 448s Linear hypothesis test (Theil's F test) 448s 448s Hypothesis: 448s demand_income - supply_trend = 0 448s 448s Model 1: restricted model 448s Model 2: fit2sls1r 448s 448s Res.Df Df F Pr(>F) 448s 1 34 448s 2 33 1 0.08 0.78 448s > linearHypothesis( fit2sls1r, restrict ) 448s Linear hypothesis test (Theil's F test) 448s 448s Hypothesis: 448s demand_income - supply_trend = 0 448s 448s Model 1: restricted model 448s Model 2: fit2sls1r 448s 448s Res.Df Df F Pr(>F) 448s 1 34 448s 2 33 1 0.08 0.78 448s > 448s > # testing second restriction 448s > restrOnly2m <- matrix(0,1,7) 448s > restrOnly2q <- 0.5 448s > restrOnly2m[1,2] <- -1 448s > restrOnly2m[1,5] <- 1 448s > restrictOnly2 <- "- demand_price + supply_price = 0.5" 448s > # first restriction not imposed 448s > print( linearHypothesis( fit2sls1, restrOnly2m, restrOnly2q ) ) 448s Linear hypothesis test (Theil's F test) 448s 448s Hypothesis: 448s - demand_price + supply_price = 0.5 448s 448s Model 1: restricted model 448s Model 2: fit2sls1 448s 448s Res.Df Df F Pr(>F) 448s 1 34 448s 2 33 1 0 0.96 448s > linearHypothesis( fit2sls1, restrictOnly2 ) 448s Linear hypothesis test (Theil's F test) 448s 448s Hypothesis: 448s - demand_price + supply_price = 0.5 448s 448s Model 1: restricted model 448s Model 2: fit2sls1 448s 448s Res.Df Df F Pr(>F) 448s 1 34 448s 2 33 1 0 0.96 448s > 448s > # first restriction imposed 448s > print( linearHypothesis( fit2sls2, restrOnly2m, restrOnly2q ) ) 448s Linear hypothesis test (Theil's F test) 448s 448s Hypothesis: 448s - demand_price + supply_price = 0.5 448s 448s Model 1: restricted model 448s Model 2: fit2sls2 448s 448s Res.Df Df F Pr(>F) 448s 1 35 448s 2 34 1 0.01 0.92 448s > linearHypothesis( fit2sls2, restrictOnly2 ) 448s Linear hypothesis test (Theil's F test) 448s 448s Hypothesis: 448s - demand_price + supply_price = 0.5 448s 448s Model 1: restricted model 448s Model 2: fit2sls2 448s 448s Res.Df Df F Pr(>F) 448s 1 35 448s 2 34 1 0.01 0.92 448s > 448s > print( linearHypothesis( fit2sls2r, restrOnly2m, restrOnly2q ) ) 448s Linear hypothesis test (Theil's F test) 448s 448s Hypothesis: 448s - demand_price + supply_price = 0.5 448s 448s Model 1: restricted model 448s Model 2: fit2sls2r 448s 448s Res.Df Df F Pr(>F) 448s 1 35 448s 2 34 1 0.01 0.91 448s > linearHypothesis( fit2sls2r, restrictOnly2 ) 448s Linear hypothesis test (Theil's F test) 448s 448s Hypothesis: 448s - demand_price + supply_price = 0.5 448s 448s Model 1: restricted model 448s Model 2: fit2sls2r 448s 448s Res.Df Df F Pr(>F) 448s 1 35 448s 2 34 1 0.01 0.91 448s > 448s > print( linearHypothesis( fit2sls3, restrOnly2m, restrOnly2q ) ) 448s Linear hypothesis test (Theil's F test) 448s 448s Hypothesis: 448s - demand_price + supply_price = 0.5 448s 448s Model 1: restricted model 448s Model 2: fit2sls3 448s 448s Res.Df Df F Pr(>F) 448s 1 35 448s 2 34 1 0.01 0.91 448s > linearHypothesis( fit2sls3, restrictOnly2 ) 448s Linear hypothesis test (Theil's F test) 448s 448s Hypothesis: 448s - demand_price + supply_price = 0.5 448s 448s Model 1: restricted model 448s Model 2: fit2sls3 448s 448s Res.Df Df F Pr(>F) 448s 1 35 448s 2 34 1 0.01 0.91 448s > 448s > # testing both of the restrictions 448s > print( linearHypothesis( fit2sls1, restr2m, restr2q ) ) 448s Linear hypothesis test (Theil's F test) 448s 448s Hypothesis: 448s demand_income - supply_trend = 0 448s - demand_price + supply_price = 0.5 448s 448s Model 1: restricted model 448s Model 2: fit2sls1 448s 448s Res.Df Df F Pr(>F) 448s 1 35 448s 2 33 2 0.04 0.97 448s > linearHypothesis( fit2sls1, restrict2 ) 448s Linear hypothesis test (Theil's F test) 448s 448s Hypothesis: 448s demand_income - supply_trend = 0 448s - demand_price + supply_price = 0.5 448s 448s Model 1: restricted model 448s Model 2: fit2sls1 448s 448s Res.Df Df F Pr(>F) 448s 1 35 448s 2 33 2 0.04 0.97 448s > 448s > 448s > ## ************** Wald tests **************** 448s > # testing first restriction 448s > print( linearHypothesis( fit2sls1, restrm, test = "Chisq" ) ) 448s Linear hypothesis test (Chi^2 statistic of a Wald test) 448s 448s Hypothesis: 448s demand_income - supply_trend = 0 448s 448s Model 1: restricted model 448s Model 2: fit2sls1 448s 448s Res.Df Df Chisq Pr(>Chisq) 448s 1 34 448s 2 33 1 0.31 0.58 448s > linearHypothesis( fit2sls1, restrict, test = "Chisq" ) 448s Linear hypothesis test (Chi^2 statistic of a Wald test) 448s 448s Hypothesis: 448s demand_income - supply_trend = 0 448s 448s Model 1: restricted model 448s Model 2: fit2sls1 448s 448s Res.Df Df Chisq Pr(>Chisq) 448s 1 34 448s 2 33 1 0.31 0.58 448s > 448s > print( linearHypothesis( fit2sls1s, restrm, test = "Chisq" ) ) 448s Linear hypothesis test (Chi^2 statistic of a Wald test) 448s 448s Hypothesis: 448s demand_income - supply_trend = 0 448s 448s Model 1: restricted model 448s Model 2: fit2sls1s 448s 448s Res.Df Df Chisq Pr(>Chisq) 448s 1 34 448s 2 33 1 0.34 0.56 448s > linearHypothesis( fit2sls1s, restrict, test = "Chisq" ) 448s Linear hypothesis test (Chi^2 statistic of a Wald test) 448s 448s Hypothesis: 448s demand_income - supply_trend = 0 448s 448s Model 1: restricted model 448s Model 2: fit2sls1s 448s 448s Res.Df Df Chisq Pr(>Chisq) 448s 1 34 448s 2 33 1 0.34 0.56 448s > 448s > print( linearHypothesis( fit2sls1, restrm, test = "Chisq" ) ) 448s Linear hypothesis test (Chi^2 statistic of a Wald test) 448s 448s Hypothesis: 448s demand_income - supply_trend = 0 448s 448s Model 1: restricted model 448s Model 2: fit2sls1 448s 448s Res.Df Df Chisq Pr(>Chisq) 448s 1 34 448s 2 33 1 0.31 0.58 448s > linearHypothesis( fit2sls1, restrict, test = "Chisq" ) 448s Linear hypothesis test (Chi^2 statistic of a Wald test) 448s 448s Hypothesis: 448s demand_income - supply_trend = 0 448s 448s Model 1: restricted model 448s Model 2: fit2sls1 448s 448s Res.Df Df Chisq Pr(>Chisq) 448s 1 34 448s 2 33 1 0.31 0.58 448s > 448s > print( linearHypothesis( fit2sls1r, restrm, test = "Chisq" ) ) 448s Linear hypothesis test (Chi^2 statistic of a Wald test) 448s 448s Hypothesis: 448s demand_income - supply_trend = 0 448s 448s Model 1: restricted model 448s Model 2: fit2sls1r 448s 448s Res.Df Df Chisq Pr(>Chisq) 448s 1 34 448s 2 33 1 0.38 0.54 448s > linearHypothesis( fit2sls1r, restrict, test = "Chisq" ) 448s Linear hypothesis test (Chi^2 statistic of a Wald test) 448s 448s Hypothesis: 448s demand_income - supply_trend = 0 448s 448s Model 1: restricted model 448s Model 2: fit2sls1r 448s 448s Res.Df Df Chisq Pr(>Chisq) 448s 1 34 448s 2 33 1 0.38 0.54 448s > 448s > # testing second restriction 448s > # first restriction not imposed 448s > print( linearHypothesis( fit2sls1, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 448s Linear hypothesis test (Chi^2 statistic of a Wald test) 448s 448s Hypothesis: 448s - demand_price + supply_price = 0.5 448s 448s Model 1: restricted model 448s Model 2: fit2sls1 448s 448s Res.Df Df Chisq Pr(>Chisq) 448s 1 34 448s 2 33 1 0.01 0.91 448s > linearHypothesis( fit2sls1, restrictOnly2, test = "Chisq" ) 448s Linear hypothesis test (Chi^2 statistic of a Wald test) 448s 448s Hypothesis: 448s - demand_price + supply_price = 0.5 448s 448s Model 1: restricted model 448s Model 2: fit2sls1 448s 448s Res.Df Df Chisq Pr(>Chisq) 448s 1 34 448s 2 33 1 0.01 0.91 448s > # first restriction imposed 448s > print( linearHypothesis( fit2sls2, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 448s Linear hypothesis test (Chi^2 statistic of a Wald test) 448s 448s Hypothesis: 448s - demand_price + supply_price = 0.5 448s 448s Model 1: restricted model 448s Model 2: fit2sls2 448s 448s Res.Df Df Chisq Pr(>Chisq) 448s 1 35 448s 2 34 1 0.06 0.81 448s > linearHypothesis( fit2sls2, restrictOnly2, test = "Chisq" ) 448s Linear hypothesis test (Chi^2 statistic of a Wald test) 448s 448s Hypothesis: 448s - demand_price + supply_price = 0.5 448s 448s Model 1: restricted model 448s Model 2: fit2sls2 448s 448s Res.Df Df Chisq Pr(>Chisq) 448s 1 35 448s 2 34 1 0.06 0.81 448s > 448s > print( linearHypothesis( fit2sls2r, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 448s Linear hypothesis test (Chi^2 statistic of a Wald test) 448s 448s Hypothesis: 448s - demand_price + supply_price = 0.5 448s 448s Model 1: restricted model 448s Model 2: fit2sls2r 448s 448s Res.Df Df Chisq Pr(>Chisq) 448s 1 35 448s 2 34 1 0.07 0.8 448s > linearHypothesis( fit2sls2r, restrictOnly2, test = "Chisq" ) 448s Linear hypothesis test (Chi^2 statistic of a Wald test) 448s 448s Hypothesis: 448s - demand_price + supply_price = 0.5 448s 448s Model 1: restricted model 448s Model 2: fit2sls2r 448s 448s Res.Df Df Chisq Pr(>Chisq) 448s 1 35 448s 2 34 1 0.07 0.8 448s > 448s > print( linearHypothesis( fit2sls3, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 448s Linear hypothesis test (Chi^2 statistic of a Wald test) 448s 448s Hypothesis: 448s - demand_price + supply_price = 0.5 448s 448s Model 1: restricted model 448s Model 2: fit2sls3 448s 448s Res.Df Df Chisq Pr(>Chisq) 448s 1 35 448s 2 34 1 0.07 0.8 448s > linearHypothesis( fit2sls3, restrictOnly2, test = "Chisq" ) 448s Linear hypothesis test (Chi^2 statistic of a Wald test) 448s 448s Hypothesis: 448s - demand_price + supply_price = 0.5 448s 448s Model 1: restricted model 448s Model 2: fit2sls3 448s 448s Res.Df Df Chisq Pr(>Chisq) 448s 1 35 448s 2 34 1 0.07 0.8 448s > 448s > # testing both of the restrictions 448s > print( linearHypothesis( fit2sls1, restr2m, restr2q, test = "Chisq" ) ) 448s Linear hypothesis test (Chi^2 statistic of a Wald test) 448s 448s Hypothesis: 448s demand_income - supply_trend = 0 448s - demand_price + supply_price = 0.5 448s 448s Model 1: restricted model 448s Model 2: fit2sls1 448s 448s Res.Df Df Chisq Pr(>Chisq) 448s 1 35 448s 2 33 2 0.35 0.84 448s > linearHypothesis( fit2sls1, restrict2, test = "Chisq" ) 448s Linear hypothesis test (Chi^2 statistic of a Wald test) 448s 448s Hypothesis: 448s demand_income - supply_trend = 0 448s - demand_price + supply_price = 0.5 448s 448s Model 1: restricted model 448s Model 2: fit2sls1 448s 448s Res.Df Df Chisq Pr(>Chisq) 448s 1 35 448s 2 33 2 0.35 0.84 448s > 448s > 448s > ## **************** model frame ************************ 448s > print( mf <- model.frame( fit2sls1 ) ) 448s consump price income farmPrice trend 448s 1 98.5 100.3 87.4 98.0 1 448s 2 99.2 104.3 97.6 99.1 2 448s 3 102.2 103.4 96.7 99.1 3 448s 4 101.5 104.5 98.2 98.1 4 448s 5 104.2 98.0 99.8 110.8 5 448s 6 103.2 99.5 100.5 108.2 6 448s 7 104.0 101.1 103.2 105.6 7 448s 8 99.9 104.8 107.8 109.8 8 448s 9 100.3 96.4 96.6 108.7 9 448s 10 102.8 91.2 88.9 100.6 10 448s 11 95.4 93.1 75.1 81.0 11 448s 12 92.4 98.8 76.9 68.6 12 448s 13 94.5 102.9 84.6 70.9 13 448s 14 98.8 98.8 90.6 81.4 14 448s 15 105.8 95.1 103.1 102.3 15 448s 16 100.2 98.5 105.1 105.0 16 448s 17 103.5 86.5 96.4 110.5 17 448s 18 99.9 104.0 104.4 92.5 18 448s 19 105.2 105.8 110.7 89.3 19 448s 20 106.2 113.5 127.1 93.0 20 448s > print( mf1 <- model.frame( fit2sls1$eq[[ 1 ]] ) ) 448s consump price income 448s 1 98.5 100.3 87.4 448s 2 99.2 104.3 97.6 448s 3 102.2 103.4 96.7 448s 4 101.5 104.5 98.2 448s 5 104.2 98.0 99.8 448s 6 103.2 99.5 100.5 448s 7 104.0 101.1 103.2 448s 8 99.9 104.8 107.8 448s 9 100.3 96.4 96.6 448s 10 102.8 91.2 88.9 448s 11 95.4 93.1 75.1 448s 12 92.4 98.8 76.9 448s 13 94.5 102.9 84.6 448s 14 98.8 98.8 90.6 448s 15 105.8 95.1 103.1 448s 16 100.2 98.5 105.1 448s 17 103.5 86.5 96.4 448s 18 99.9 104.0 104.4 448s 19 105.2 105.8 110.7 448s 20 106.2 113.5 127.1 448s > print( attributes( mf1 )$terms ) 448s consump ~ price + income 448s attr(,"variables") 448s list(consump, price, income) 448s attr(,"factors") 448s price income 448s consump 0 0 448s price 1 0 448s income 0 1 448s attr(,"term.labels") 448s [1] "price" "income" 448s attr(,"order") 448s [1] 1 1 448s attr(,"intercept") 448s [1] 1 448s attr(,"response") 448s [1] 1 448s attr(,".Environment") 448s 448s attr(,"predvars") 448s list(consump, price, income) 448s attr(,"dataClasses") 448s consump price income 448s "numeric" "numeric" "numeric" 448s > print( mf2 <- model.frame( fit2sls1$eq[[ 2 ]] ) ) 448s consump price farmPrice trend 448s 1 98.5 100.3 98.0 1 448s 2 99.2 104.3 99.1 2 448s 3 102.2 103.4 99.1 3 448s 4 101.5 104.5 98.1 4 448s 5 104.2 98.0 110.8 5 448s 6 103.2 99.5 108.2 6 448s 7 104.0 101.1 105.6 7 448s 8 99.9 104.8 109.8 8 448s 9 100.3 96.4 108.7 9 448s 10 102.8 91.2 100.6 10 448s 11 95.4 93.1 81.0 11 448s 12 92.4 98.8 68.6 12 448s 13 94.5 102.9 70.9 13 448s 14 98.8 98.8 81.4 14 448s 15 105.8 95.1 102.3 15 448s 16 100.2 98.5 105.0 16 448s 17 103.5 86.5 110.5 17 448s 18 99.9 104.0 92.5 18 448s 19 105.2 105.8 89.3 19 448s 20 106.2 113.5 93.0 20 448s > print( attributes( mf2 )$terms ) 448s consump ~ price + farmPrice + trend 448s attr(,"variables") 448s list(consump, price, farmPrice, trend) 448s attr(,"factors") 448s price farmPrice trend 448s consump 0 0 0 448s price 1 0 0 448s farmPrice 0 1 0 448s trend 0 0 1 448s attr(,"term.labels") 448s [1] "price" "farmPrice" "trend" 448s attr(,"order") 448s [1] 1 1 1 448s attr(,"intercept") 448s [1] 1 448s attr(,"response") 448s [1] 1 448s attr(,".Environment") 448s 448s attr(,"predvars") 448s list(consump, price, farmPrice, trend) 448s attr(,"dataClasses") 448s consump price farmPrice trend 448s "numeric" "numeric" "numeric" "numeric" 448s > 448s > print( all.equal( mf, model.frame( fit2sls2s ) ) ) 448s [1] TRUE 448s > print( all.equal( mf2, model.frame( fit2sls2s$eq[[ 2 ]] ) ) ) 448s [1] TRUE 448s > 448s > print( all.equal( mf, model.frame( fit2sls3 ) ) ) 448s [1] TRUE 448s > print( all.equal( mf1, model.frame( fit2sls3$eq[[ 1 ]] ) ) ) 448s [1] TRUE 448s > 448s > print( all.equal( mf, model.frame( fit2sls4r ) ) ) 448s [1] TRUE 448s > print( all.equal( mf2, model.frame( fit2sls4r$eq[[ 2 ]] ) ) ) 448s [1] TRUE 448s > 448s > print( all.equal( mf, model.frame( fit2sls5rs ) ) ) 448s [1] TRUE 448s > print( all.equal( mf1, model.frame( fit2sls5rs$eq[[ 1 ]] ) ) ) 448s [1] TRUE 448s > 448s > fit2sls1$eq[[ 1 ]]$modelInst 448s income farmPrice trend 448s 1 87.4 98.0 1 448s 2 97.6 99.1 2 448s 3 96.7 99.1 3 448s 4 98.2 98.1 4 448s 5 99.8 110.8 5 448s 6 100.5 108.2 6 448s 7 103.2 105.6 7 448s 8 107.8 109.8 8 448s 9 96.6 108.7 9 448s 10 88.9 100.6 10 448s 11 75.1 81.0 11 448s 12 76.9 68.6 12 448s 13 84.6 70.9 13 448s 14 90.6 81.4 14 448s 15 103.1 102.3 15 448s 16 105.1 105.0 16 448s 17 96.4 110.5 17 448s 18 104.4 92.5 18 448s 19 110.7 89.3 19 448s 20 127.1 93.0 20 448s > fit2sls1$eq[[ 2 ]]$modelInst 448s income farmPrice trend 448s 1 87.4 98.0 1 448s 2 97.6 99.1 2 448s 3 96.7 99.1 3 448s 4 98.2 98.1 4 448s 5 99.8 110.8 5 448s 6 100.5 108.2 6 448s 7 103.2 105.6 7 448s 8 107.8 109.8 8 448s 9 96.6 108.7 9 448s 10 88.9 100.6 10 448s 11 75.1 81.0 11 448s 12 76.9 68.6 12 448s 13 84.6 70.9 13 448s 14 90.6 81.4 14 448s 15 103.1 102.3 15 448s 16 105.1 105.0 16 448s 17 96.4 110.5 17 448s 18 104.4 92.5 18 448s 19 110.7 89.3 19 448s 20 127.1 93.0 20 448s > 448s > fit2sls2s$eq[[ 1 ]]$modelInst 448s income farmPrice trend 448s 1 87.4 98.0 1 448s 2 97.6 99.1 2 448s 3 96.7 99.1 3 448s 4 98.2 98.1 4 448s 5 99.8 110.8 5 448s 6 100.5 108.2 6 448s 7 103.2 105.6 7 448s 8 107.8 109.8 8 448s 9 96.6 108.7 9 448s 10 88.9 100.6 10 448s 11 75.1 81.0 11 448s 12 76.9 68.6 12 448s 13 84.6 70.9 13 448s 14 90.6 81.4 14 448s 15 103.1 102.3 15 448s 16 105.1 105.0 16 448s 17 96.4 110.5 17 448s 18 104.4 92.5 18 448s 19 110.7 89.3 19 448s 20 127.1 93.0 20 448s > fit2sls2s$eq[[ 2 ]]$modelInst 448s income farmPrice trend 448s 1 87.4 98.0 1 448s 2 97.6 99.1 2 448s 3 96.7 99.1 3 448s 4 98.2 98.1 4 448s 5 99.8 110.8 5 448s 6 100.5 108.2 6 448s 7 103.2 105.6 7 448s 8 107.8 109.8 8 448s 9 96.6 108.7 9 448s 10 88.9 100.6 10 448s 11 75.1 81.0 11 448s 12 76.9 68.6 12 448s 13 84.6 70.9 13 448s 14 90.6 81.4 14 448s 15 103.1 102.3 15 448s 16 105.1 105.0 16 448s 17 96.4 110.5 17 448s 18 104.4 92.5 18 448s 19 110.7 89.3 19 448s 20 127.1 93.0 20 448s > 448s > fit2sls5rs$eq[[ 1 ]]$modelInst 448s income farmPrice trend 448s 1 87.4 98.0 1 448s 2 97.6 99.1 2 448s 3 96.7 99.1 3 448s 4 98.2 98.1 4 448s 5 99.8 110.8 5 448s 6 100.5 108.2 6 448s 7 103.2 105.6 7 448s 8 107.8 109.8 8 448s 9 96.6 108.7 9 448s 10 88.9 100.6 10 448s 11 75.1 81.0 11 448s 12 76.9 68.6 12 448s 13 84.6 70.9 13 448s 14 90.6 81.4 14 448s 15 103.1 102.3 15 448s 16 105.1 105.0 16 448s 17 96.4 110.5 17 448s 18 104.4 92.5 18 448s 19 110.7 89.3 19 448s 20 127.1 93.0 20 448s > fit2sls5rs$eq[[ 2 ]]$modelInst 448s income farmPrice trend 448s 1 87.4 98.0 1 448s 2 97.6 99.1 2 448s 3 96.7 99.1 3 448s 4 98.2 98.1 4 448s 5 99.8 110.8 5 448s 6 100.5 108.2 6 448s 7 103.2 105.6 7 448s 8 107.8 109.8 8 448s 9 96.6 108.7 9 448s 10 88.9 100.6 10 448s 11 75.1 81.0 11 448s 12 76.9 68.6 12 448s 13 84.6 70.9 13 448s 14 90.6 81.4 14 448s 15 103.1 102.3 15 448s 16 105.1 105.0 16 448s 17 96.4 110.5 17 448s 18 104.4 92.5 18 448s 19 110.7 89.3 19 448s 20 127.1 93.0 20 448s > 448s > 448s > ## **************** model matrix ************************ 448s > # with x (returnModelMatrix) = TRUE 448s > print( !is.null( fit2sls1$eq[[ 1 ]]$x ) ) 448s [1] TRUE 448s > print( mm <- model.matrix( fit2sls1 ) ) 448s demand_(Intercept) demand_price demand_income supply_(Intercept) 448s demand_1 1 100.3 87.4 0 448s demand_2 1 104.3 97.6 0 448s demand_3 1 103.4 96.7 0 448s demand_4 1 104.5 98.2 0 448s demand_5 1 98.0 99.8 0 448s demand_6 1 99.5 100.5 0 448s demand_7 1 101.1 103.2 0 448s demand_8 1 104.8 107.8 0 448s demand_9 1 96.4 96.6 0 448s demand_10 1 91.2 88.9 0 448s demand_11 1 93.1 75.1 0 448s demand_12 1 98.8 76.9 0 448s demand_13 1 102.9 84.6 0 448s demand_14 1 98.8 90.6 0 448s demand_15 1 95.1 103.1 0 448s demand_16 1 98.5 105.1 0 448s demand_17 1 86.5 96.4 0 448s demand_18 1 104.0 104.4 0 448s demand_19 1 105.8 110.7 0 448s demand_20 1 113.5 127.1 0 448s supply_1 0 0.0 0.0 1 448s supply_2 0 0.0 0.0 1 448s supply_3 0 0.0 0.0 1 448s supply_4 0 0.0 0.0 1 448s supply_5 0 0.0 0.0 1 448s supply_6 0 0.0 0.0 1 448s supply_7 0 0.0 0.0 1 448s supply_8 0 0.0 0.0 1 448s supply_9 0 0.0 0.0 1 448s supply_10 0 0.0 0.0 1 448s supply_11 0 0.0 0.0 1 448s supply_12 0 0.0 0.0 1 448s supply_13 0 0.0 0.0 1 448s supply_14 0 0.0 0.0 1 448s supply_15 0 0.0 0.0 1 448s supply_16 0 0.0 0.0 1 448s supply_17 0 0.0 0.0 1 448s supply_18 0 0.0 0.0 1 448s supply_19 0 0.0 0.0 1 448s supply_20 0 0.0 0.0 1 448s supply_price supply_farmPrice supply_trend 448s demand_1 0.0 0.0 0 448s demand_2 0.0 0.0 0 448s demand_3 0.0 0.0 0 448s demand_4 0.0 0.0 0 448s demand_5 0.0 0.0 0 448s demand_6 0.0 0.0 0 448s demand_7 0.0 0.0 0 448s demand_8 0.0 0.0 0 448s demand_9 0.0 0.0 0 448s demand_10 0.0 0.0 0 448s demand_11 0.0 0.0 0 448s demand_12 0.0 0.0 0 448s demand_13 0.0 0.0 0 448s demand_14 0.0 0.0 0 448s demand_15 0.0 0.0 0 448s demand_16 0.0 0.0 0 448s demand_17 0.0 0.0 0 448s demand_18 0.0 0.0 0 448s demand_19 0.0 0.0 0 448s demand_20 0.0 0.0 0 448s supply_1 100.3 98.0 1 448s supply_2 104.3 99.1 2 448s supply_3 103.4 99.1 3 448s supply_4 104.5 98.1 4 448s supply_5 98.0 110.8 5 448s supply_6 99.5 108.2 6 448s supply_7 101.1 105.6 7 448s supply_8 104.8 109.8 8 448s supply_9 96.4 108.7 9 448s supply_10 91.2 100.6 10 448s supply_11 93.1 81.0 11 448s supply_12 98.8 68.6 12 448s supply_13 102.9 70.9 13 448s supply_14 98.8 81.4 14 448s supply_15 95.1 102.3 15 448s supply_16 98.5 105.0 16 448s supply_17 86.5 110.5 17 448s supply_18 104.0 92.5 18 448s supply_19 105.8 89.3 19 448s supply_20 113.5 93.0 20 448s > print( mm1 <- model.matrix( fit2sls1$eq[[ 1 ]] ) ) 448s (Intercept) price income 448s 1 1 100.3 87.4 448s 2 1 104.3 97.6 448s 3 1 103.4 96.7 448s 4 1 104.5 98.2 448s 5 1 98.0 99.8 448s 6 1 99.5 100.5 448s 7 1 101.1 103.2 448s 8 1 104.8 107.8 448s 9 1 96.4 96.6 448s 10 1 91.2 88.9 448s 11 1 93.1 75.1 448s 12 1 98.8 76.9 448s 13 1 102.9 84.6 448s 14 1 98.8 90.6 448s 15 1 95.1 103.1 448s 16 1 98.5 105.1 448s 17 1 86.5 96.4 448s 18 1 104.0 104.4 448s 19 1 105.8 110.7 448s 20 1 113.5 127.1 448s attr(,"assign") 448s [1] 0 1 2 448s > print( mm2 <- model.matrix( fit2sls1$eq[[ 2 ]] ) ) 448s (Intercept) price farmPrice trend 448s 1 1 100.3 98.0 1 448s 2 1 104.3 99.1 2 448s 3 1 103.4 99.1 3 448s 4 1 104.5 98.1 4 448s 5 1 98.0 110.8 5 448s 6 1 99.5 108.2 6 448s 7 1 101.1 105.6 7 448s 8 1 104.8 109.8 8 448s 9 1 96.4 108.7 9 448s 10 1 91.2 100.6 10 448s 11 1 93.1 81.0 11 448s 12 1 98.8 68.6 12 448s 13 1 102.9 70.9 13 448s 14 1 98.8 81.4 14 448s 15 1 95.1 102.3 15 448s 16 1 98.5 105.0 16 448s 17 1 86.5 110.5 17 448s 18 1 104.0 92.5 18 448s 19 1 105.8 89.3 19 448s 20 1 113.5 93.0 20 448s attr(,"assign") 448s [1] 0 1 2 3 448s > 448s > # with x (returnModelMatrix) = FALSE 448s > print( all.equal( mm, model.matrix( fit2sls1s ) ) ) 448s [1] TRUE 448s > print( all.equal( mm1, model.matrix( fit2sls1s$eq[[ 1 ]] ) ) ) 448s [1] TRUE 448s > print( all.equal( mm2, model.matrix( fit2sls1s$eq[[ 2 ]] ) ) ) 448s [1] TRUE 448s > print( !is.null( fit2sls1s$eq[[ 1 ]]$x ) ) 448s [1] FALSE 448s > 448s > # with x (returnModelMatrix) = TRUE 448s > print( !is.null( fit2sls2s$eq[[ 1 ]]$x ) ) 448s [1] TRUE 448s > print( all.equal( mm, model.matrix( fit2sls2s ) ) ) 448s [1] TRUE 448s > print( all.equal( mm1, model.matrix( fit2sls2s$eq[[ 1 ]] ) ) ) 448s [1] TRUE 448s > print( all.equal( mm2, model.matrix( fit2sls2s$eq[[ 2 ]] ) ) ) 448s [1] TRUE 448s > 448s > # with x (returnModelMatrix) = FALSE 448s > print( all.equal( mm, model.matrix( fit2sls2Sym ) ) ) 448s [1] TRUE 448s > print( all.equal( mm1, model.matrix( fit2sls2Sym$eq[[ 1 ]] ) ) ) 448s [1] TRUE 448s > print( all.equal( mm2, model.matrix( fit2sls2Sym$eq[[ 2 ]] ) ) ) 448s [1] TRUE 448s > print( !is.null( fit2sls2Sym$eq[[ 1 ]]$x ) ) 448s [1] FALSE 448s > 448s > # with x (returnModelMatrix) = FALSE 448s > print( all.equal( mm, model.matrix( fit2sls3 ) ) ) 448s [1] TRUE 448s > print( all.equal( mm1, model.matrix( fit2sls3$eq[[ 1 ]] ) ) ) 448s [1] TRUE 448s > print( all.equal( mm2, model.matrix( fit2sls3$eq[[ 2 ]] ) ) ) 448s [1] TRUE 448s > print( !is.null( fit2sls3$eq[[ 1 ]]$x ) ) 448s [1] FALSE 448s > 448s > # with x (returnModelMatrix) = TRUE 448s > print( !is.null( fit2sls4r$eq[[ 1 ]]$x ) ) 448s [1] TRUE 448s > print( all.equal( mm, model.matrix( fit2sls4r ) ) ) 448s [1] TRUE 448s > print( all.equal( mm1, model.matrix( fit2sls4r$eq[[ 1 ]] ) ) ) 448s [1] TRUE 448s > print( all.equal( mm2, model.matrix( fit2sls4r$eq[[ 2 ]] ) ) ) 448s [1] TRUE 448s > 448s > # with x (returnModelMatrix) = FALSE 448s > print( all.equal( mm, model.matrix( fit2sls4s ) ) ) 448s [1] TRUE 448s > print( all.equal( mm1, model.matrix( fit2sls4s$eq[[ 1 ]] ) ) ) 448s [1] TRUE 448s > print( all.equal( mm2, model.matrix( fit2sls4s$eq[[ 2 ]] ) ) ) 448s [1] TRUE 448s > print( !is.null( fit2sls4s$eq[[ 1 ]]$x ) ) 448s [1] FALSE 448s > 448s > # with x (returnModelMatrix) = TRUE 448s > print( !is.null( fit2sls5rs$eq[[ 1 ]]$x ) ) 448s [1] TRUE 448s > print( all.equal( mm, model.matrix( fit2sls5rs ) ) ) 448s [1] TRUE 448s > print( all.equal( mm1, model.matrix( fit2sls5rs$eq[[ 1 ]] ) ) ) 448s [1] TRUE 448s > print( all.equal( mm2, model.matrix( fit2sls5rs$eq[[ 2 ]] ) ) ) 448s [1] TRUE 448s > 448s > # with x (returnModelMatrix) = FALSE 448s > print( all.equal( mm, model.matrix( fit2sls5r ) ) ) 448s [1] TRUE 448s > print( all.equal( mm1, model.matrix( fit2sls5r$eq[[ 1 ]] ) ) ) 448s [1] TRUE 448s > print( all.equal( mm2, model.matrix( fit2sls5r$eq[[ 2 ]] ) ) ) 448s [1] TRUE 448s > print( !is.null( fit2sls5r$eq[[ 1 ]]$x ) ) 448s [1] FALSE 448s > 448s > # matrices of instrumental variables 448s > model.matrix( fit2sls1, which = "z" ) 448s demand_(Intercept) demand_income demand_farmPrice demand_trend 448s demand_1 1 87.4 98.0 1 448s demand_2 1 97.6 99.1 2 448s demand_3 1 96.7 99.1 3 448s demand_4 1 98.2 98.1 4 448s demand_5 1 99.8 110.8 5 448s demand_6 1 100.5 108.2 6 448s demand_7 1 103.2 105.6 7 448s demand_8 1 107.8 109.8 8 448s demand_9 1 96.6 108.7 9 448s demand_10 1 88.9 100.6 10 448s demand_11 1 75.1 81.0 11 448s demand_12 1 76.9 68.6 12 448s demand_13 1 84.6 70.9 13 448s demand_14 1 90.6 81.4 14 448s demand_15 1 103.1 102.3 15 448s demand_16 1 105.1 105.0 16 448s demand_17 1 96.4 110.5 17 448s demand_18 1 104.4 92.5 18 448s demand_19 1 110.7 89.3 19 448s demand_20 1 127.1 93.0 20 448s supply_1 0 0.0 0.0 0 448s supply_2 0 0.0 0.0 0 448s supply_3 0 0.0 0.0 0 448s supply_4 0 0.0 0.0 0 448s supply_5 0 0.0 0.0 0 448s supply_6 0 0.0 0.0 0 448s supply_7 0 0.0 0.0 0 448s supply_8 0 0.0 0.0 0 448s supply_9 0 0.0 0.0 0 448s supply_10 0 0.0 0.0 0 448s supply_11 0 0.0 0.0 0 448s supply_12 0 0.0 0.0 0 448s supply_13 0 0.0 0.0 0 448s supply_14 0 0.0 0.0 0 448s supply_15 0 0.0 0.0 0 448s supply_16 0 0.0 0.0 0 448s supply_17 0 0.0 0.0 0 448s supply_18 0 0.0 0.0 0 448s supply_19 0 0.0 0.0 0 448s supply_20 0 0.0 0.0 0 448s supply_(Intercept) supply_income supply_farmPrice supply_trend 448s demand_1 0 0.0 0.0 0 448s demand_2 0 0.0 0.0 0 448s demand_3 0 0.0 0.0 0 448s demand_4 0 0.0 0.0 0 448s demand_5 0 0.0 0.0 0 448s demand_6 0 0.0 0.0 0 448s demand_7 0 0.0 0.0 0 448s demand_8 0 0.0 0.0 0 448s demand_9 0 0.0 0.0 0 448s demand_10 0 0.0 0.0 0 448s demand_11 0 0.0 0.0 0 448s demand_12 0 0.0 0.0 0 448s demand_13 0 0.0 0.0 0 448s demand_14 0 0.0 0.0 0 448s demand_15 0 0.0 0.0 0 448s demand_16 0 0.0 0.0 0 448s demand_17 0 0.0 0.0 0 448s demand_18 0 0.0 0.0 0 448s demand_19 0 0.0 0.0 0 448s demand_20 0 0.0 0.0 0 448s supply_1 1 87.4 98.0 1 448s supply_2 1 97.6 99.1 2 448s supply_3 1 96.7 99.1 3 448s supply_4 1 98.2 98.1 4 448s supply_5 1 99.8 110.8 5 448s supply_6 1 100.5 108.2 6 448s supply_7 1 103.2 105.6 7 448s supply_8 1 107.8 109.8 8 448s supply_9 1 96.6 108.7 9 448s supply_10 1 88.9 100.6 10 448s supply_11 1 75.1 81.0 11 448s supply_12 1 76.9 68.6 12 448s supply_13 1 84.6 70.9 13 448s supply_14 1 90.6 81.4 14 448s supply_15 1 103.1 102.3 15 448s supply_16 1 105.1 105.0 16 448s supply_17 1 96.4 110.5 17 448s supply_18 1 104.4 92.5 18 448s supply_19 1 110.7 89.3 19 448s supply_20 1 127.1 93.0 20 448s > model.matrix( fit2sls1$eq[[ 1 ]], which = "z" ) 448s (Intercept) income farmPrice trend 448s 1 1 87.4 98.0 1 448s 2 1 97.6 99.1 2 448s 3 1 96.7 99.1 3 448s 4 1 98.2 98.1 4 448s 5 1 99.8 110.8 5 448s 6 1 100.5 108.2 6 448s 7 1 103.2 105.6 7 448s 8 1 107.8 109.8 8 448s 9 1 96.6 108.7 9 448s 10 1 88.9 100.6 10 448s 11 1 75.1 81.0 11 448s 12 1 76.9 68.6 12 448s 13 1 84.6 70.9 13 448s 14 1 90.6 81.4 14 448s 15 1 103.1 102.3 15 448s 16 1 105.1 105.0 16 448s 17 1 96.4 110.5 17 448s 18 1 104.4 92.5 18 448s 19 1 110.7 89.3 19 448s 20 1 127.1 93.0 20 448s attr(,"assign") 448s [1] 0 1 2 3 448s > model.matrix( fit2sls1$eq[[ 2 ]], which = "z" ) 448s (Intercept) income farmPrice trend 448s 1 1 87.4 98.0 1 448s 2 1 97.6 99.1 2 448s 3 1 96.7 99.1 3 448s 4 1 98.2 98.1 4 448s 5 1 99.8 110.8 5 448s 6 1 100.5 108.2 6 448s 7 1 103.2 105.6 7 448s 8 1 107.8 109.8 8 448s 9 1 96.6 108.7 9 448s 10 1 88.9 100.6 10 448s 11 1 75.1 81.0 11 448s 12 1 76.9 68.6 12 448s 13 1 84.6 70.9 13 448s 14 1 90.6 81.4 14 448s 15 1 103.1 102.3 15 448s 16 1 105.1 105.0 16 448s 17 1 96.4 110.5 17 448s 18 1 104.4 92.5 18 448s 19 1 110.7 89.3 19 448s 20 1 127.1 93.0 20 448s attr(,"assign") 448s [1] 0 1 2 3 448s > 448s > # matrices of fitted regressors 448s > model.matrix( fit2sls5r, which = "xHat" ) 448s demand_(Intercept) demand_price demand_income supply_(Intercept) 448s demand_1 1 99.6 87.4 0 448s demand_2 1 105.1 97.6 0 448s demand_3 1 103.8 96.7 0 448s demand_4 1 104.5 98.2 0 448s demand_5 1 98.7 99.8 0 448s demand_6 1 99.6 100.5 0 448s demand_7 1 102.0 103.2 0 448s demand_8 1 102.2 107.8 0 448s demand_9 1 94.6 96.6 0 448s demand_10 1 92.7 88.9 0 448s demand_11 1 92.4 75.1 0 448s demand_12 1 98.9 76.9 0 448s demand_13 1 102.2 84.6 0 448s demand_14 1 100.3 90.6 0 448s demand_15 1 97.6 103.1 0 448s demand_16 1 96.9 105.1 0 448s demand_17 1 87.7 96.4 0 448s demand_18 1 101.1 104.4 0 448s demand_19 1 106.1 110.7 0 448s demand_20 1 114.4 127.1 0 448s supply_1 0 0.0 0.0 1 448s supply_2 0 0.0 0.0 1 448s supply_3 0 0.0 0.0 1 448s supply_4 0 0.0 0.0 1 448s supply_5 0 0.0 0.0 1 448s supply_6 0 0.0 0.0 1 448s supply_7 0 0.0 0.0 1 448s supply_8 0 0.0 0.0 1 448s supply_9 0 0.0 0.0 1 448s supply_10 0 0.0 0.0 1 448s supply_11 0 0.0 0.0 1 448s supply_12 0 0.0 0.0 1 448s supply_13 0 0.0 0.0 1 448s supply_14 0 0.0 0.0 1 448s supply_15 0 0.0 0.0 1 448s supply_16 0 0.0 0.0 1 448s supply_17 0 0.0 0.0 1 448s supply_18 0 0.0 0.0 1 448s supply_19 0 0.0 0.0 1 448s supply_20 0 0.0 0.0 1 448s supply_price supply_farmPrice supply_trend 448s demand_1 0.0 0.0 0 448s demand_2 0.0 0.0 0 448s demand_3 0.0 0.0 0 448s demand_4 0.0 0.0 0 448s demand_5 0.0 0.0 0 448s demand_6 0.0 0.0 0 448s demand_7 0.0 0.0 0 448s demand_8 0.0 0.0 0 448s demand_9 0.0 0.0 0 448s demand_10 0.0 0.0 0 448s demand_11 0.0 0.0 0 448s demand_12 0.0 0.0 0 448s demand_13 0.0 0.0 0 448s demand_14 0.0 0.0 0 448s demand_15 0.0 0.0 0 448s demand_16 0.0 0.0 0 448s demand_17 0.0 0.0 0 448s demand_18 0.0 0.0 0 448s demand_19 0.0 0.0 0 448s demand_20 0.0 0.0 0 448s supply_1 99.6 98.0 1 448s supply_2 105.1 99.1 2 448s supply_3 103.8 99.1 3 448s supply_4 104.5 98.1 4 448s supply_5 98.7 110.8 5 448s supply_6 99.6 108.2 6 448s supply_7 102.0 105.6 7 448s supply_8 102.2 109.8 8 448s supply_9 94.6 108.7 9 448s supply_10 92.7 100.6 10 448s supply_11 92.4 81.0 11 448s supply_12 98.9 68.6 12 448s supply_13 102.2 70.9 13 448s supply_14 100.3 81.4 14 448s supply_15 97.6 102.3 15 448s supply_16 96.9 105.0 16 448s supply_17 87.7 110.5 17 448s supply_18 101.1 92.5 18 448s supply_19 106.1 89.3 19 448s supply_20 114.4 93.0 20 448s > model.matrix( fit2sls5r$eq[[ 1 ]], which = "xHat" ) 448s (Intercept) price income 448s 1 1 99.6 87.4 448s 2 1 105.1 97.6 448s 3 1 103.8 96.7 448s 4 1 104.5 98.2 448s 5 1 98.7 99.8 448s 6 1 99.6 100.5 448s 7 1 102.0 103.2 448s 8 1 102.2 107.8 448s 9 1 94.6 96.6 448s 10 1 92.7 88.9 448s 11 1 92.4 75.1 448s 12 1 98.9 76.9 448s 13 1 102.2 84.6 448s 14 1 100.3 90.6 448s 15 1 97.6 103.1 448s 16 1 96.9 105.1 448s 17 1 87.7 96.4 448s 18 1 101.1 104.4 448s 19 1 106.1 110.7 448s 20 1 114.4 127.1 448s > model.matrix( fit2sls5r$eq[[ 2 ]], which = "xHat" ) 448s (Intercept) price farmPrice trend 448s 1 1 99.6 98.0 1 448s 2 1 105.1 99.1 2 448s 3 1 103.8 99.1 3 448s 4 1 104.5 98.1 4 448s 5 1 98.7 110.8 5 448s 6 1 99.6 108.2 6 448s 7 1 102.0 105.6 7 448s 8 1 102.2 109.8 8 448s 9 1 94.6 108.7 9 448s 10 1 92.7 100.6 10 448s 11 1 92.4 81.0 11 448s 12 1 98.9 68.6 12 448s 13 1 102.2 70.9 13 448s 14 1 100.3 81.4 14 448s 15 1 97.6 102.3 15 448s 16 1 96.9 105.0 16 448s 17 1 87.7 110.5 17 448s 18 1 101.1 92.5 18 448s 19 1 106.1 89.3 19 448s 20 1 114.4 93.0 20 448s > 448s > 448s > ## **************** formulas ************************ 448s > formula( fit2sls1 ) 448s $demand 448s consump ~ price + income 448s 448s $supply 448s consump ~ price + farmPrice + trend 448s 448s > formula( fit2sls1$eq[[ 1 ]] ) 448s consump ~ price + income 448s > 448s > formula( fit2sls2s ) 448s $demand 448s consump ~ price + income 448s 448s $supply 448s consump ~ price + farmPrice + trend 448s 448s > formula( fit2sls2s$eq[[ 2 ]] ) 448s consump ~ price + farmPrice + trend 448s > 448s > formula( fit2sls3 ) 448s $demand 448s consump ~ price + income 448s 448s $supply 448s consump ~ price + farmPrice + trend 448s 448s > formula( fit2sls3$eq[[ 1 ]] ) 448s consump ~ price + income 448s > 448s > formula( fit2sls4r ) 448s $demand 448s consump ~ price + income 448s 448s $supply 448s consump ~ price + farmPrice + trend 448s 448s > formula( fit2sls4r$eq[[ 2 ]] ) 448s consump ~ price + farmPrice + trend 448s > 448s > formula( fit2sls5rs ) 448s $demand 448s consump ~ price + income 448s 448s $supply 448s consump ~ price + farmPrice + trend 448s 448s > formula( fit2sls5rs$eq[[ 1 ]] ) 448s consump ~ price + income 448s > 448s > formula( fit2slsd1 ) 448s $demand 448s consump ~ price + income 448s 448s $supply 448s consump ~ price + farmPrice + trend 448s 448s > formula( fit2slsd1$eq[[ 2 ]] ) 448s consump ~ price + farmPrice + trend 448s > 448s > formula( fit2slsd2r ) 448s $demand 448s consump ~ price + income 448s 448s $supply 448s consump ~ price + farmPrice + trend 448s 448s > formula( fit2slsd2r$eq[[ 1 ]] ) 448s consump ~ price + income 448s > 448s > 448s > ## **************** model terms ******************* 448s > terms( fit2sls1 ) 448s $demand 448s consump ~ price + income 448s attr(,"variables") 448s list(consump, price, income) 448s attr(,"factors") 448s price income 448s consump 0 0 448s price 1 0 448s income 0 1 448s attr(,"term.labels") 448s [1] "price" "income" 448s attr(,"order") 448s [1] 1 1 448s attr(,"intercept") 448s [1] 1 448s attr(,"response") 448s [1] 1 448s attr(,".Environment") 448s 448s attr(,"predvars") 448s list(consump, price, income) 448s attr(,"dataClasses") 448s consump price income 448s "numeric" "numeric" "numeric" 448s 448s $supply 448s consump ~ price + farmPrice + trend 448s attr(,"variables") 448s list(consump, price, farmPrice, trend) 448s attr(,"factors") 448s price farmPrice trend 448s consump 0 0 0 448s price 1 0 0 448s farmPrice 0 1 0 448s trend 0 0 1 448s attr(,"term.labels") 448s [1] "price" "farmPrice" "trend" 448s attr(,"order") 448s [1] 1 1 1 448s attr(,"intercept") 448s [1] 1 448s attr(,"response") 448s [1] 1 448s attr(,".Environment") 448s 448s attr(,"predvars") 448s list(consump, price, farmPrice, trend) 448s attr(,"dataClasses") 448s consump price farmPrice trend 448s "numeric" "numeric" "numeric" "numeric" 448s 448s > terms( fit2sls1$eq[[ 1 ]] ) 448s consump ~ price + income 448s attr(,"variables") 448s list(consump, price, income) 448s attr(,"factors") 448s price income 448s consump 0 0 448s price 1 0 448s income 0 1 448s attr(,"term.labels") 448s [1] "price" "income" 448s attr(,"order") 448s [1] 1 1 448s attr(,"intercept") 448s [1] 1 448s attr(,"response") 448s [1] 1 448s attr(,".Environment") 448s 448s attr(,"predvars") 448s list(consump, price, income) 448s attr(,"dataClasses") 448s consump price income 448s "numeric" "numeric" "numeric" 448s > 448s > terms( fit2sls2s ) 448s $demand 448s consump ~ price + income 448s attr(,"variables") 448s list(consump, price, income) 448s attr(,"factors") 448s price income 448s consump 0 0 448s price 1 0 448s income 0 1 448s attr(,"term.labels") 448s [1] "price" "income" 448s attr(,"order") 448s [1] 1 1 448s attr(,"intercept") 448s [1] 1 448s attr(,"response") 448s [1] 1 448s attr(,".Environment") 448s 448s attr(,"predvars") 448s list(consump, price, income) 448s attr(,"dataClasses") 448s consump price income 448s "numeric" "numeric" "numeric" 448s 448s $supply 448s consump ~ price + farmPrice + trend 448s attr(,"variables") 448s list(consump, price, farmPrice, trend) 448s attr(,"factors") 448s price farmPrice trend 448s consump 0 0 0 448s price 1 0 0 448s farmPrice 0 1 0 448s trend 0 0 1 448s attr(,"term.labels") 448s [1] "price" "farmPrice" "trend" 448s attr(,"order") 448s [1] 1 1 1 448s attr(,"intercept") 448s [1] 1 448s attr(,"response") 448s [1] 1 448s attr(,".Environment") 448s 448s attr(,"predvars") 448s list(consump, price, farmPrice, trend) 448s attr(,"dataClasses") 448s consump price farmPrice trend 448s "numeric" "numeric" "numeric" "numeric" 448s 448s > terms( fit2sls2s$eq[[ 2 ]] ) 448s consump ~ price + farmPrice + trend 448s attr(,"variables") 448s list(consump, price, farmPrice, trend) 448s attr(,"factors") 448s price farmPrice trend 448s consump 0 0 0 448s price 1 0 0 448s farmPrice 0 1 0 448s trend 0 0 1 448s attr(,"term.labels") 448s [1] "price" "farmPrice" "trend" 448s attr(,"order") 448s [1] 1 1 1 448s attr(,"intercept") 448s [1] 1 448s attr(,"response") 448s [1] 1 448s attr(,".Environment") 448s 448s attr(,"predvars") 448s list(consump, price, farmPrice, trend) 448s attr(,"dataClasses") 448s consump price farmPrice trend 448s "numeric" "numeric" "numeric" "numeric" 448s > 448s > terms( fit2sls3 ) 448s $demand 448s consump ~ price + income 448s attr(,"variables") 448s list(consump, price, income) 448s attr(,"factors") 448s price income 448s consump 0 0 448s price 1 0 448s income 0 1 448s attr(,"term.labels") 448s [1] "price" "income" 448s attr(,"order") 448s [1] 1 1 448s attr(,"intercept") 448s [1] 1 448s attr(,"response") 448s [1] 1 448s attr(,".Environment") 448s 448s attr(,"predvars") 448s list(consump, price, income) 448s attr(,"dataClasses") 448s consump price income 448s "numeric" "numeric" "numeric" 448s 448s $supply 448s consump ~ price + farmPrice + trend 448s attr(,"variables") 448s list(consump, price, farmPrice, trend) 448s attr(,"factors") 448s price farmPrice trend 448s consump 0 0 0 448s price 1 0 0 448s farmPrice 0 1 0 448s trend 0 0 1 448s attr(,"term.labels") 448s [1] "price" "farmPrice" "trend" 448s attr(,"order") 448s [1] 1 1 1 448s attr(,"intercept") 448s [1] 1 448s attr(,"response") 448s [1] 1 448s attr(,".Environment") 448s 448s attr(,"predvars") 448s list(consump, price, farmPrice, trend) 448s attr(,"dataClasses") 448s consump price farmPrice trend 448s "numeric" "numeric" "numeric" "numeric" 448s 448s > terms( fit2sls3$eq[[ 1 ]] ) 448s consump ~ price + income 448s attr(,"variables") 448s list(consump, price, income) 448s attr(,"factors") 448s price income 448s consump 0 0 448s price 1 0 448s income 0 1 448s attr(,"term.labels") 448s [1] "price" "income" 448s attr(,"order") 448s [1] 1 1 448s attr(,"intercept") 448s [1] 1 448s attr(,"response") 448s [1] 1 448s attr(,".Environment") 448s 448s attr(,"predvars") 448s list(consump, price, income) 448s attr(,"dataClasses") 448s consump price income 448s "numeric" "numeric" "numeric" 448s > 448s > terms( fit2sls4r ) 448s $demand 448s consump ~ price + income 448s attr(,"variables") 448s list(consump, price, income) 448s attr(,"factors") 448s price income 448s consump 0 0 448s price 1 0 448s income 0 1 448s attr(,"term.labels") 448s [1] "price" "income" 448s attr(,"order") 448s [1] 1 1 448s attr(,"intercept") 448s [1] 1 448s attr(,"response") 448s [1] 1 448s attr(,".Environment") 448s 448s attr(,"predvars") 448s list(consump, price, income) 448s attr(,"dataClasses") 448s consump price income 448s "numeric" "numeric" "numeric" 448s 448s $supply 448s consump ~ price + farmPrice + trend 448s attr(,"variables") 448s list(consump, price, farmPrice, trend) 448s attr(,"factors") 448s price farmPrice trend 448s consump 0 0 0 448s price 1 0 0 448s farmPrice 0 1 0 448s trend 0 0 1 448s attr(,"term.labels") 448s [1] "price" "farmPrice" "trend" 448s attr(,"order") 448s [1] 1 1 1 448s attr(,"intercept") 448s [1] 1 448s attr(,"response") 448s [1] 1 448s attr(,".Environment") 448s 448s attr(,"predvars") 448s list(consump, price, farmPrice, trend) 448s attr(,"dataClasses") 448s consump price farmPrice trend 448s "numeric" "numeric" "numeric" "numeric" 448s 448s > terms( fit2sls4r$eq[[ 2 ]] ) 448s consump ~ price + farmPrice + trend 448s attr(,"variables") 448s list(consump, price, farmPrice, trend) 448s attr(,"factors") 448s price farmPrice trend 448s consump 0 0 0 448s price 1 0 0 448s farmPrice 0 1 0 448s trend 0 0 1 448s attr(,"term.labels") 448s [1] "price" "farmPrice" "trend" 448s attr(,"order") 448s [1] 1 1 1 448s attr(,"intercept") 448s [1] 1 448s attr(,"response") 448s [1] 1 448s attr(,".Environment") 448s 448s attr(,"predvars") 448s list(consump, price, farmPrice, trend) 448s attr(,"dataClasses") 448s consump price farmPrice trend 448s "numeric" "numeric" "numeric" "numeric" 448s > 448s > terms( fit2sls5rs ) 448s $demand 448s consump ~ price + income 448s attr(,"variables") 448s list(consump, price, income) 448s attr(,"factors") 448s price income 448s consump 0 0 448s price 1 0 448s income 0 1 448s attr(,"term.labels") 448s [1] "price" "income" 448s attr(,"order") 448s [1] 1 1 448s attr(,"intercept") 448s [1] 1 448s attr(,"response") 448s [1] 1 448s attr(,".Environment") 448s 448s attr(,"predvars") 448s list(consump, price, income) 448s attr(,"dataClasses") 448s consump price income 448s "numeric" "numeric" "numeric" 448s 448s $supply 448s consump ~ price + farmPrice + trend 448s attr(,"variables") 448s list(consump, price, farmPrice, trend) 448s attr(,"factors") 448s price farmPrice trend 448s consump 0 0 0 448s price 1 0 0 448s farmPrice 0 1 0 448s trend 0 0 1 448s attr(,"term.labels") 448s [1] "price" "farmPrice" "trend" 448s attr(,"order") 448s [1] 1 1 1 448s attr(,"intercept") 448s [1] 1 448s attr(,"response") 448s [1] 1 448s attr(,".Environment") 448s 448s attr(,"predvars") 448s list(consump, price, farmPrice, trend) 448s attr(,"dataClasses") 448s consump price farmPrice trend 448s "numeric" "numeric" "numeric" "numeric" 448s 448s > terms( fit2sls5rs$eq[[ 1 ]] ) 448s consump ~ price + income 448s attr(,"variables") 448s list(consump, price, income) 448s attr(,"factors") 448s price income 448s consump 0 0 448s price 1 0 448s income 0 1 448s attr(,"term.labels") 448s [1] "price" "income" 448s attr(,"order") 448s [1] 1 1 448s attr(,"intercept") 448s [1] 1 448s attr(,"response") 448s [1] 1 448s attr(,".Environment") 448s 448s attr(,"predvars") 448s list(consump, price, income) 448s attr(,"dataClasses") 448s consump price income 448s "numeric" "numeric" "numeric" 448s > 448s > terms( fit2slsd1 ) 448s $demand 448s consump ~ price + income 448s attr(,"variables") 448s list(consump, price, income) 448s attr(,"factors") 448s price income 448s consump 0 0 448s price 1 0 448s income 0 1 448s attr(,"term.labels") 448s [1] "price" "income" 448s attr(,"order") 448s [1] 1 1 448s attr(,"intercept") 448s [1] 1 448s attr(,"response") 448s [1] 1 448s attr(,".Environment") 448s 448s attr(,"predvars") 448s list(consump, price, income) 448s attr(,"dataClasses") 448s consump price income 448s "numeric" "numeric" "numeric" 448s 448s $supply 448s consump ~ price + farmPrice + trend 448s attr(,"variables") 448s list(consump, price, farmPrice, trend) 448s attr(,"factors") 448s price farmPrice trend 448s consump 0 0 0 448s price 1 0 0 448s farmPrice 0 1 0 448s trend 0 0 1 448s attr(,"term.labels") 448s [1] "price" "farmPrice" "trend" 448s attr(,"order") 448s [1] 1 1 1 448s attr(,"intercept") 448s [1] 1 448s attr(,"response") 448s [1] 1 448s attr(,".Environment") 448s 448s attr(,"predvars") 448s list(consump, price, farmPrice, trend) 448s attr(,"dataClasses") 448s consump price farmPrice trend 448s "numeric" "numeric" "numeric" "numeric" 448s 448s > terms( fit2slsd1$eq[[ 2 ]] ) 448s consump ~ price + farmPrice + trend 448s attr(,"variables") 448s list(consump, price, farmPrice, trend) 448s attr(,"factors") 448s price farmPrice trend 448s consump 0 0 0 448s price 1 0 0 448s farmPrice 0 1 0 448s trend 0 0 1 448s attr(,"term.labels") 448s [1] "price" "farmPrice" "trend" 448s attr(,"order") 448s [1] 1 1 1 448s attr(,"intercept") 448s [1] 1 448s attr(,"response") 448s [1] 1 448s attr(,".Environment") 448s 448s attr(,"predvars") 448s list(consump, price, farmPrice, trend) 448s attr(,"dataClasses") 448s consump price farmPrice trend 448s "numeric" "numeric" "numeric" "numeric" 448s > 448s > terms( fit2slsd2r ) 448s $demand 448s consump ~ price + income 448s attr(,"variables") 448s list(consump, price, income) 448s attr(,"factors") 448s price income 448s consump 0 0 448s price 1 0 448s income 0 1 448s attr(,"term.labels") 448s [1] "price" "income" 448s attr(,"order") 448s [1] 1 1 448s attr(,"intercept") 448s [1] 1 448s attr(,"response") 448s [1] 1 448s attr(,".Environment") 448s 448s attr(,"predvars") 448s list(consump, price, income) 448s attr(,"dataClasses") 448s consump price income 448s "numeric" "numeric" "numeric" 448s 448s $supply 448s consump ~ price + farmPrice + trend 448s attr(,"variables") 448s list(consump, price, farmPrice, trend) 448s attr(,"factors") 448s price farmPrice trend 448s consump 0 0 0 448s price 1 0 0 448s farmPrice 0 1 0 448s trend 0 0 1 448s attr(,"term.labels") 448s [1] "price" "farmPrice" "trend" 448s attr(,"order") 448s [1] 1 1 1 448s attr(,"intercept") 448s [1] 1 448s attr(,"response") 448s [1] 1 448s attr(,".Environment") 448s 448s attr(,"predvars") 448s list(consump, price, farmPrice, trend) 448s attr(,"dataClasses") 448s consump price farmPrice trend 448s "numeric" "numeric" "numeric" "numeric" 448s 448s > terms( fit2slsd2r$eq[[ 1 ]] ) 448s consump ~ price + income 448s attr(,"variables") 448s list(consump, price, income) 448s attr(,"factors") 448s price income 448s consump 0 0 448s price 1 0 448s income 0 1 448s attr(,"term.labels") 448s [1] "price" "income" 448s attr(,"order") 448s [1] 1 1 448s attr(,"intercept") 448s [1] 1 448s attr(,"response") 448s [1] 1 448s attr(,".Environment") 448s 448s attr(,"predvars") 448s list(consump, price, income) 448s attr(,"dataClasses") 448s consump price income 448s "numeric" "numeric" "numeric" 448s > 448s > 448s > ## **************** terms of instruments ******************* 448s > fit2sls1$eq[[ 1 ]]$termsInst 448s ~income + farmPrice + trend 448s attr(,"variables") 448s list(income, farmPrice, trend) 448s attr(,"factors") 448s income farmPrice trend 448s income 1 0 0 448s farmPrice 0 1 0 448s trend 0 0 1 448s attr(,"term.labels") 448s [1] "income" "farmPrice" "trend" 448s attr(,"order") 448s [1] 1 1 1 448s attr(,"intercept") 448s [1] 1 448s attr(,"response") 448s [1] 0 448s attr(,".Environment") 448s 448s attr(,"predvars") 448s list(income, farmPrice, trend) 448s attr(,"dataClasses") 448s income farmPrice trend 448s "numeric" "numeric" "numeric" 448s > 448s > fit2sls2s$eq[[ 2 ]]$termsInst 448s ~income + farmPrice + trend 448s attr(,"variables") 448s list(income, farmPrice, trend) 448s attr(,"factors") 448s income farmPrice trend 448s income 1 0 0 448s farmPrice 0 1 0 448s trend 0 0 1 448s attr(,"term.labels") 448s [1] "income" "farmPrice" "trend" 448s attr(,"order") 448s [1] 1 1 1 448s attr(,"intercept") 448s [1] 1 448s attr(,"response") 448s [1] 0 448s attr(,".Environment") 448s 448s attr(,"predvars") 448s list(income, farmPrice, trend) 448s attr(,"dataClasses") 448s income farmPrice trend 448s "numeric" "numeric" "numeric" 448s > 448s > fit2sls3$eq[[ 1 ]]$termsInst 448s ~income + farmPrice + trend 448s attr(,"variables") 448s list(income, farmPrice, trend) 448s attr(,"factors") 448s income farmPrice trend 448s income 1 0 0 448s farmPrice 0 1 0 448s trend 0 0 1 448s attr(,"term.labels") 448s [1] "income" "farmPrice" "trend" 448s attr(,"order") 448s [1] 1 1 1 448s attr(,"intercept") 448s [1] 1 448s attr(,"response") 448s [1] 0 448s attr(,".Environment") 448s 448s attr(,"predvars") 448s list(income, farmPrice, trend) 448s attr(,"dataClasses") 448s income farmPrice trend 448s "numeric" "numeric" "numeric" 448s > 448s > fit2sls4r$eq[[ 2 ]]$termsInst 448s ~income + farmPrice + trend 448s attr(,"variables") 448s list(income, farmPrice, trend) 448s attr(,"factors") 448s income farmPrice trend 448s income 1 0 0 448s farmPrice 0 1 0 448s trend 0 0 1 448s attr(,"term.labels") 448s [1] "income" "farmPrice" "trend" 448s attr(,"order") 448s [1] 1 1 1 448s attr(,"intercept") 448s [1] 1 448s attr(,"response") 448s [1] 0 448s attr(,".Environment") 448s 448s attr(,"predvars") 448s list(income, farmPrice, trend) 448s attr(,"dataClasses") 448s income farmPrice trend 448s "numeric" "numeric" "numeric" 448s > 448s > fit2sls5rs$eq[[ 1 ]]$termsInst 448s ~income + farmPrice + trend 448s attr(,"variables") 448s list(income, farmPrice, trend) 448s attr(,"factors") 448s income farmPrice trend 448s income 1 0 0 448s farmPrice 0 1 0 448s trend 0 0 1 448s attr(,"term.labels") 448s [1] "income" "farmPrice" "trend" 448s attr(,"order") 448s [1] 1 1 1 448s attr(,"intercept") 448s [1] 1 448s attr(,"response") 448s [1] 0 448s attr(,".Environment") 448s 448s attr(,"predvars") 448s list(income, farmPrice, trend) 448s attr(,"dataClasses") 448s income farmPrice trend 448s "numeric" "numeric" "numeric" 448s > 448s > fit2slsd1$eq[[ 2 ]]$termsInst 448s ~income + farmPrice + trend 448s attr(,"variables") 448s list(income, farmPrice, trend) 448s attr(,"factors") 448s income farmPrice trend 448s income 1 0 0 448s farmPrice 0 1 0 448s trend 0 0 1 448s attr(,"term.labels") 448s [1] "income" "farmPrice" "trend" 448s attr(,"order") 448s [1] 1 1 1 448s attr(,"intercept") 448s [1] 1 448s attr(,"response") 448s [1] 0 448s attr(,".Environment") 448s 448s attr(,"predvars") 448s list(income, farmPrice, trend) 448s attr(,"dataClasses") 448s income farmPrice trend 448s "numeric" "numeric" "numeric" 448s > 448s > fit2slsd2r$eq[[ 1 ]]$termsInst 448s ~income + farmPrice 448s attr(,"variables") 448s list(income, farmPrice) 448s attr(,"factors") 448s income farmPrice 448s income 1 0 448s farmPrice 0 1 448s attr(,"term.labels") 448s [1] "income" "farmPrice" 448s attr(,"order") 448s [1] 1 1 448s attr(,"intercept") 448s [1] 1 448s attr(,"response") 448s [1] 0 448s attr(,".Environment") 448s 448s attr(,"predvars") 448s list(income, farmPrice) 448s attr(,"dataClasses") 448s income farmPrice 448s "numeric" "numeric" 448s > 448s > 448s > ## **************** estfun ************************ 448s > library( "sandwich" ) 448s > 448s > estfun( fit2sls1 ) 448s demand_(Intercept) demand_price demand_income supply_(Intercept) 448s demand_1 0.6738 67.13 58.89 0.000 448s demand_2 -0.4897 -51.48 -47.80 0.000 448s demand_3 2.4440 253.65 236.33 0.000 448s demand_4 1.4958 156.35 146.88 0.000 448s demand_5 2.2975 226.65 229.29 0.000 448s demand_6 1.3235 131.89 133.02 0.000 448s demand_7 1.7917 182.70 184.90 0.000 448s demand_8 -3.6818 -376.41 -396.90 0.000 448s demand_9 -1.5729 -148.80 -151.94 0.000 448s demand_10 2.8552 264.73 253.83 0.000 448s demand_11 -0.2736 -25.29 -20.55 0.000 448s demand_12 -2.2634 -223.89 -174.06 0.000 448s demand_13 -1.7795 -181.80 -150.55 0.000 448s demand_14 0.0991 9.93 8.98 0.000 448s demand_15 2.5674 250.64 264.70 0.000 448s demand_16 -3.8102 -369.18 -400.45 0.000 448s demand_17 -0.0206 -1.81 -1.99 0.000 448s demand_18 -2.8715 -290.19 -299.78 0.000 448s demand_19 1.6632 176.41 184.12 0.000 448s demand_20 -0.4478 -51.23 -56.92 0.000 448s supply_1 0.0000 0.00 0.00 -0.268 448s supply_2 0.0000 0.00 0.00 -1.418 448s supply_3 0.0000 0.00 0.00 1.625 448s supply_4 0.0000 0.00 0.00 0.790 448s supply_5 0.0000 0.00 0.00 1.438 448s supply_6 0.0000 0.00 0.00 0.613 448s supply_7 0.0000 0.00 0.00 1.217 448s supply_8 0.0000 0.00 0.00 -4.265 448s supply_9 0.0000 0.00 0.00 -1.956 448s supply_10 0.0000 0.00 0.00 2.785 448s supply_11 0.0000 0.00 0.00 0.233 448s supply_12 0.0000 0.00 0.00 -1.426 448s supply_13 0.0000 0.00 0.00 -0.935 448s supply_14 0.0000 0.00 0.00 0.803 448s supply_15 0.0000 0.00 0.00 2.886 448s supply_16 0.0000 0.00 0.00 -3.454 448s supply_17 0.0000 0.00 0.00 0.391 448s supply_18 0.0000 0.00 0.00 -2.061 448s supply_19 0.0000 0.00 0.00 2.596 448s supply_20 0.0000 0.00 0.00 0.406 448s supply_price supply_farmPrice supply_trend 448s demand_1 0.0 0.0 0.000 448s demand_2 0.0 0.0 0.000 448s demand_3 0.0 0.0 0.000 448s demand_4 0.0 0.0 0.000 448s demand_5 0.0 0.0 0.000 448s demand_6 0.0 0.0 0.000 448s demand_7 0.0 0.0 0.000 448s demand_8 0.0 0.0 0.000 448s demand_9 0.0 0.0 0.000 448s demand_10 0.0 0.0 0.000 448s demand_11 0.0 0.0 0.000 448s demand_12 0.0 0.0 0.000 448s demand_13 0.0 0.0 0.000 448s demand_14 0.0 0.0 0.000 448s demand_15 0.0 0.0 0.000 448s demand_16 0.0 0.0 0.000 448s demand_17 0.0 0.0 0.000 448s demand_18 0.0 0.0 0.000 448s demand_19 0.0 0.0 0.000 448s demand_20 0.0 0.0 0.000 448s supply_1 -26.7 -26.3 -0.268 448s supply_2 -149.1 -140.5 -2.836 448s supply_3 168.7 161.1 4.876 448s supply_4 82.6 77.5 3.159 448s supply_5 141.9 159.3 7.190 448s supply_6 61.1 66.4 3.680 448s supply_7 124.1 128.5 8.520 448s supply_8 -436.1 -468.3 -34.122 448s supply_9 -185.0 -212.6 -17.602 448s supply_10 258.2 280.1 27.848 448s supply_11 21.5 18.8 2.558 448s supply_12 -141.0 -97.8 -17.107 448s supply_13 -95.5 -66.3 -12.152 448s supply_14 80.6 65.4 11.246 448s supply_15 281.7 295.2 43.286 448s supply_16 -334.7 -362.7 -55.267 448s supply_17 34.3 43.2 6.650 448s supply_18 -208.3 -190.7 -37.106 448s supply_19 275.4 231.8 49.327 448s supply_20 46.5 37.8 8.122 448s > round( colSums( estfun( fit2sls1 ) ), digits = 7 ) 448s demand_(Intercept) demand_price demand_income supply_(Intercept) 448s 0 0 0 0 448s supply_price supply_farmPrice supply_trend 448s 0 0 0 448s > 448s > estfun( fit2sls1s ) 449s demand_(Intercept) demand_price demand_income supply_(Intercept) 449s demand_1 0.6738 67.13 58.89 0.000 449s demand_2 -0.4897 -51.48 -47.80 0.000 449s demand_3 2.4440 253.65 236.33 0.000 449s demand_4 1.4958 156.35 146.88 0.000 449s demand_5 2.2975 226.65 229.29 0.000 449s demand_6 1.3235 131.89 133.02 0.000 449s demand_7 1.7917 182.70 184.90 0.000 449s demand_8 -3.6818 -376.41 -396.90 0.000 449s demand_9 -1.5729 -148.80 -151.94 0.000 449s demand_10 2.8552 264.73 253.83 0.000 449s demand_11 -0.2736 -25.29 -20.55 0.000 449s demand_12 -2.2634 -223.89 -174.06 0.000 449s demand_13 -1.7795 -181.80 -150.55 0.000 449s demand_14 0.0991 9.93 8.98 0.000 449s demand_15 2.5674 250.64 264.70 0.000 449s demand_16 -3.8102 -369.18 -400.45 0.000 449s demand_17 -0.0206 -1.81 -1.99 0.000 449s demand_18 -2.8715 -290.19 -299.78 0.000 449s demand_19 1.6632 176.41 184.12 0.000 449s demand_20 -0.4478 -51.23 -56.92 0.000 449s supply_1 0.0000 0.00 0.00 -0.268 449s supply_2 0.0000 0.00 0.00 -1.418 449s supply_3 0.0000 0.00 0.00 1.625 449s supply_4 0.0000 0.00 0.00 0.790 449s supply_5 0.0000 0.00 0.00 1.438 449s supply_6 0.0000 0.00 0.00 0.613 449s supply_7 0.0000 0.00 0.00 1.217 449s supply_8 0.0000 0.00 0.00 -4.265 449s supply_9 0.0000 0.00 0.00 -1.956 449s supply_10 0.0000 0.00 0.00 2.785 449s supply_11 0.0000 0.00 0.00 0.233 449s supply_12 0.0000 0.00 0.00 -1.426 449s supply_13 0.0000 0.00 0.00 -0.935 449s supply_14 0.0000 0.00 0.00 0.803 449s supply_15 0.0000 0.00 0.00 2.886 449s supply_16 0.0000 0.00 0.00 -3.454 449s supply_17 0.0000 0.00 0.00 0.391 449s supply_18 0.0000 0.00 0.00 -2.061 449s supply_19 0.0000 0.00 0.00 2.596 449s supply_20 0.0000 0.00 0.00 0.406 449s supply_price supply_farmPrice supply_trend 449s demand_1 0.0 0.0 0.000 449s demand_2 0.0 0.0 0.000 449s demand_3 0.0 0.0 0.000 449s demand_4 0.0 0.0 0.000 449s demand_5 0.0 0.0 0.000 449s demand_6 0.0 0.0 0.000 449s demand_7 0.0 0.0 0.000 449s demand_8 0.0 0.0 0.000 449s demand_9 0.0 0.0 0.000 449s demand_10 0.0 0.0 0.000 449s demand_11 0.0 0.0 0.000 449s demand_12 0.0 0.0 0.000 449s demand_13 0.0 0.0 0.000 449s demand_14 0.0 0.0 0.000 449s demand_15 0.0 0.0 0.000 449s demand_16 0.0 0.0 0.000 449s demand_17 0.0 0.0 0.000 449s demand_18 0.0 0.0 0.000 449s demand_19 0.0 0.0 0.000 449s demand_20 0.0 0.0 0.000 449s supply_1 -26.7 -26.3 -0.268 449s supply_2 -149.1 -140.5 -2.836 449s supply_3 168.7 161.1 4.876 449s supply_4 82.6 77.5 3.159 449s supply_5 141.9 159.3 7.190 449s supply_6 61.1 66.4 3.680 449s supply_7 124.1 128.5 8.520 449s supply_8 -436.1 -468.3 -34.122 449s supply_9 -185.0 -212.6 -17.602 449s supply_10 258.2 280.1 27.848 449s supply_11 21.5 18.8 2.558 449s supply_12 -141.0 -97.8 -17.107 449s supply_13 -95.5 -66.3 -12.152 449s supply_14 80.6 65.4 11.246 449s supply_15 281.7 295.2 43.286 449s supply_16 -334.7 -362.7 -55.267 449s supply_17 34.3 43.2 6.650 449s supply_18 -208.3 -190.7 -37.106 449s supply_19 275.4 231.8 49.327 449s supply_20 46.5 37.8 8.122 449s > round( colSums( estfun( fit2sls1s ) ), digits = 7 ) 449s demand_(Intercept) demand_price demand_income supply_(Intercept) 449s 0 0 0 0 449s supply_price supply_farmPrice supply_trend 449s 0 0 0 449s > 449s > estfun( fit2sls1r ) 449s demand_(Intercept) demand_price demand_income supply_(Intercept) 449s demand_1 0.6738 67.13 58.89 0.000 449s demand_2 -0.4897 -51.48 -47.80 0.000 449s demand_3 2.4440 253.65 236.33 0.000 449s demand_4 1.4958 156.35 146.88 0.000 449s demand_5 2.2975 226.65 229.29 0.000 449s demand_6 1.3235 131.89 133.02 0.000 449s demand_7 1.7917 182.70 184.90 0.000 449s demand_8 -3.6818 -376.41 -396.90 0.000 449s demand_9 -1.5729 -148.80 -151.94 0.000 449s demand_10 2.8552 264.73 253.83 0.000 449s demand_11 -0.2736 -25.29 -20.55 0.000 449s demand_12 -2.2634 -223.89 -174.06 0.000 449s demand_13 -1.7795 -181.80 -150.55 0.000 449s demand_14 0.0991 9.93 8.98 0.000 449s demand_15 2.5674 250.64 264.70 0.000 449s demand_16 -3.8102 -369.18 -400.45 0.000 449s demand_17 -0.0206 -1.81 -1.99 0.000 449s demand_18 -2.8715 -290.19 -299.78 0.000 449s demand_19 1.6632 176.41 184.12 0.000 449s demand_20 -0.4478 -51.23 -56.92 0.000 449s supply_1 0.0000 0.00 0.00 -0.268 449s supply_2 0.0000 0.00 0.00 -1.418 449s supply_3 0.0000 0.00 0.00 1.625 449s supply_4 0.0000 0.00 0.00 0.790 449s supply_5 0.0000 0.00 0.00 1.438 449s supply_6 0.0000 0.00 0.00 0.613 449s supply_7 0.0000 0.00 0.00 1.217 449s supply_8 0.0000 0.00 0.00 -4.265 449s supply_9 0.0000 0.00 0.00 -1.956 449s supply_10 0.0000 0.00 0.00 2.785 449s supply_11 0.0000 0.00 0.00 0.233 449s supply_12 0.0000 0.00 0.00 -1.426 449s supply_13 0.0000 0.00 0.00 -0.935 449s supply_14 0.0000 0.00 0.00 0.803 449s supply_15 0.0000 0.00 0.00 2.886 449s supply_16 0.0000 0.00 0.00 -3.454 449s supply_17 0.0000 0.00 0.00 0.391 449s supply_18 0.0000 0.00 0.00 -2.061 449s supply_19 0.0000 0.00 0.00 2.596 449s supply_20 0.0000 0.00 0.00 0.406 449s supply_price supply_farmPrice supply_trend 449s demand_1 0.0 0.0 0.000 449s demand_2 0.0 0.0 0.000 449s demand_3 0.0 0.0 0.000 449s demand_4 0.0 0.0 0.000 449s demand_5 0.0 0.0 0.000 449s demand_6 0.0 0.0 0.000 449s demand_7 0.0 0.0 0.000 449s demand_8 0.0 0.0 0.000 449s demand_9 0.0 0.0 0.000 449s demand_10 0.0 0.0 0.000 449s demand_11 0.0 0.0 0.000 449s demand_12 0.0 0.0 0.000 449s demand_13 0.0 0.0 0.000 449s demand_14 0.0 0.0 0.000 449s demand_15 0.0 0.0 0.000 449s demand_16 0.0 0.0 0.000 449s demand_17 0.0 0.0 0.000 449s demand_18 0.0 0.0 0.000 449s demand_19 0.0 0.0 0.000 449s demand_20 0.0 0.0 0.000 449s supply_1 -26.7 -26.3 -0.268 449s supply_2 -149.1 -140.5 -2.836 449s supply_3 168.7 161.1 4.876 449s supply_4 82.6 77.5 3.159 449s supply_5 141.9 159.3 7.190 449s supply_6 61.1 66.4 3.680 449s supply_7 124.1 128.5 8.520 449s supply_8 -436.1 -468.3 -34.122 449s supply_9 -185.0 -212.6 -17.602 449s supply_10 258.2 280.1 27.848 449s supply_11 21.5 18.8 2.558 449s supply_12 -141.0 -97.8 -17.107 449s supply_13 -95.5 -66.3 -12.152 449s supply_14 80.6 65.4 11.246 449s supply_15 281.7 295.2 43.286 449s supply_16 -334.7 -362.7 -55.267 449s supply_17 34.3 43.2 6.650 449s supply_18 -208.3 -190.7 -37.106 449s supply_19 275.4 231.8 49.327 449s supply_20 46.5 37.8 8.122 449s > round( colSums( estfun( fit2sls1r ) ), digits = 7 ) 449s demand_(Intercept) demand_price demand_income supply_(Intercept) 449s 0 0 0 0 449s supply_price supply_farmPrice supply_trend 449s 0 0 0 449s > 449s > 449s > ## **************** bread ************************ 449s > bread( fit2sls1 ) 449s demand_(Intercept) demand_price demand_income 449s demand_(Intercept) 649.07 -6.9669 0.5100 449s demand_price -6.97 0.0963 -0.0273 449s demand_income 0.51 -0.0273 0.0228 449s supply_(Intercept) 0.00 0.0000 0.0000 449s supply_price 0.00 0.0000 0.0000 449s supply_farmPrice 0.00 0.0000 0.0000 449s supply_trend 0.00 0.0000 0.0000 449s supply_(Intercept) supply_price supply_farmPrice 449s demand_(Intercept) 0.00 0.00000 0.00000 449s demand_price 0.00 0.00000 0.00000 449s demand_income 0.00 0.00000 0.00000 449s supply_(Intercept) 955.38 -7.25488 -2.14464 449s supply_price -7.25 0.06614 0.00620 449s supply_farmPrice -2.14 0.00620 0.01479 449s supply_trend -1.96 0.00384 0.00912 449s supply_trend 449s demand_(Intercept) 0.00000 449s demand_price 0.00000 449s demand_income 0.00000 449s supply_(Intercept) -1.95529 449s supply_price 0.00384 449s supply_farmPrice 0.00912 449s supply_trend 0.06577 449s > 449s > bread( fit2sls1s ) 449s demand_(Intercept) demand_price demand_income 449s demand_(Intercept) 649.07 -6.9669 0.5100 449s demand_price -6.97 0.0963 -0.0273 449s demand_income 0.51 -0.0273 0.0228 449s supply_(Intercept) 0.00 0.0000 0.0000 449s supply_price 0.00 0.0000 0.0000 449s supply_farmPrice 0.00 0.0000 0.0000 449s supply_trend 0.00 0.0000 0.0000 449s supply_(Intercept) supply_price supply_farmPrice 449s demand_(Intercept) 0.00 0.00000 0.00000 449s demand_price 0.00 0.00000 0.00000 449s demand_income 0.00 0.00000 0.00000 449s supply_(Intercept) 955.38 -7.25488 -2.14464 449s supply_price -7.25 0.06614 0.00620 449s supply_farmPrice -2.14 0.00620 0.01479 449s supply_trend -1.96 0.00384 0.00912 449s supply_trend 449s demand_(Intercept) 0.00000 449s demand_price 0.00000 449s demand_income 0.00000 449s supply_(Intercept) -1.95529 449s supply_price 0.00384 449s supply_farmPrice 0.00912 449s supply_trend 0.06577 449s > 449s > bread( fit2sls1r ) 449s demand_(Intercept) demand_price demand_income 449s demand_(Intercept) 649.07 -6.9669 0.5100 449s demand_price -6.97 0.0963 -0.0273 449s demand_income 0.51 -0.0273 0.0228 449s supply_(Intercept) 0.00 0.0000 0.0000 449s supply_price 0.00 0.0000 0.0000 449s supply_farmPrice 0.00 0.0000 0.0000 449s supply_trend 0.00 0.0000 0.0000 449s supply_(Intercept) supply_price supply_farmPrice 449s demand_(Intercept) 0.00 0.00000 0.00000 449s demand_price 0.00 0.00000 0.00000 449s demand_income 0.00 0.00000 0.00000 449s supply_(Intercept) 955.38 -7.25488 -2.14464 449s supply_price -7.25 0.06614 0.00620 449s supply_farmPrice -2.14 0.00620 0.01479 449s supply_trend -1.96 0.00384 0.00912 449s supply_trend 449s demand_(Intercept) 0.00000 449s demand_price 0.00000 449s demand_income 0.00000 449s supply_(Intercept) -1.95529 449s supply_price 0.00384 449s supply_farmPrice 0.00912 449s supply_trend 0.06577 449s > 449s BEGIN TEST test_3sls.R 449s 449s R version 4.3.3 (2024-02-29) -- "Angel Food Cake" 449s Copyright (C) 2024 The R Foundation for Statistical Computing 449s Platform: s390x-ibm-linux-gnu (64-bit) 449s 449s R is free software and comes with ABSOLUTELY NO WARRANTY. 449s You are welcome to redistribute it under certain conditions. 449s Type 'license()' or 'licence()' for distribution details. 449s 449s R is a collaborative project with many contributors. 449s Type 'contributors()' for more information and 449s 'citation()' on how to cite R or R packages in publications. 449s 449s Type 'demo()' for some demos, 'help()' for on-line help, or 449s 'help.start()' for an HTML browser interface to help. 449s Type 'q()' to quit R. 449s 449s > library( systemfit ) 449s Loading required package: Matrix 450s Loading required package: car 450s Loading required package: carData 450s Loading required package: lmtest 450s Loading required package: zoo 450s 450s Attaching package: ‘zoo’ 450s 450s The following objects are masked from ‘package:base’: 450s 450s as.Date, as.Date.numeric 450s 450s 450s Please cite the 'systemfit' package as: 450s 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/. 450s 450s If you have questions, suggestions, or comments regarding the 'systemfit' package, please use a forum or 'tracker' at systemfit's R-Forge site: 450s https://r-forge.r-project.org/projects/systemfit/ 450s > options( digits = 3 ) 450s > 450s > data( "Kmenta" ) 450s > useMatrix <- FALSE 450s > 450s > demand <- consump ~ price + income 450s > supply <- consump ~ price + farmPrice + trend 450s > inst <- ~ income + farmPrice + trend 450s > inst1 <- ~ income + farmPrice 450s > instlist <- list( inst1, inst ) 450s > system <- list( demand = demand, supply = supply ) 450s > restrm <- matrix(0,1,7) # restriction matrix "R" 450s > restrm[1,3] <- 1 450s > restrm[1,7] <- -1 450s > restrict <- "demand_income - supply_trend = 0" 450s > restr2m <- matrix(0,2,7) # restriction matrix "R" 2 450s > restr2m[1,3] <- 1 450s > restr2m[1,7] <- -1 450s > restr2m[2,2] <- -1 450s > restr2m[2,5] <- 1 450s > restr2q <- c( 0, 0.5 ) # restriction vector "q" 2 450s > restrict2 <- c( "demand_income - supply_trend = 0", 450s + "- demand_price + supply_price = 0.5" ) 450s > tc <- matrix(0,7,6) 450s > tc[1,1] <- 1 450s > tc[2,2] <- 1 450s > tc[3,3] <- 1 450s > tc[4,4] <- 1 450s > tc[5,5] <- 1 450s > tc[6,6] <- 1 450s > tc[7,3] <- 1 450s > restr3m <- matrix(0,1,6) # restriction matrix "R" 2 450s > restr3m[1,2] <- -1 450s > restr3m[1,5] <- 1 450s > restr3q <- c( 0.5 ) # restriction vector "q" 2 450s > restrict3 <- "- C2 + C5 = 0.5" 450s > 450s > 450s > ## *************** 3SLS estimation ************************ 450s > fit3sls <- list() 450s > formulas <- c( "GLS", "IV", "Schmidt", "GMM", "EViews" ) 450s > for( i in seq( along = formulas ) ) { 450s + fit3sls[[ i ]] <- list() 450s + 450s + print( "***************************************************" ) 450s + print( paste( "3SLS formula:", formulas[ i ] ) ) 450s + print( "************* 3SLS *********************************" ) 450s + fit3sls[[ i ]]$e1 <- systemfit( system, "3SLS", data = Kmenta, 450s + inst = inst, method3sls = formulas[ i ], useMatrix = useMatrix ) 450s + print( summary( fit3sls[[ i ]]$e1 ) ) 450s + 450s + print( "********************* 3SLS EViews-like *****************" ) 450s + fit3sls[[ i ]]$e1e <- systemfit( system, "3SLS", data = Kmenta, 450s + inst = inst, methodResidCov = "noDfCor", method3sls = formulas[ i ], 450s + useMatrix = useMatrix ) 450s + print( summary( fit3sls[[ i ]]$e1e, useDfSys = TRUE ) ) 450s + 450s + print( "********************* 3SLS with methodResidCov = Theil *****************" ) 450s + fit3sls[[ i ]]$e1c <- systemfit( system, "3SLS", data = Kmenta, 450s + inst = inst, methodResidCov = "Theil", method3sls = formulas[ i ], 450s + x = TRUE, useMatrix = useMatrix ) 450s + print( summary( fit3sls[[ i ]]$e1c, useDfSys = TRUE ) ) 450s + 450s + print( "*************** W3SLS with methodResidCov = Theil *****************" ) 450s + fit3sls[[ i ]]$e1wc <- systemfit( system, "3SLS", data = Kmenta, 450s + inst = inst, methodResidCov = "Theil", method3sls = formulas[ i ], 450s + residCovWeighted = TRUE, useMatrix = useMatrix ) 450s + print( summary( fit3sls[[ i ]]$e1wc, useDfSys = TRUE ) ) 450s + 450s + 450s + print( "*************** 3SLS with restriction *****************" ) 450s + fit3sls[[ i ]]$e2 <- systemfit( system, "3SLS", data = Kmenta, 450s + inst = inst, restrict.matrix = restrm, method3sls = formulas[ i ], 450s + x = TRUE, useMatrix = useMatrix ) 450s + print( summary( fit3sls[[ i ]]$e2 ) ) 450s + # the same with symbolically specified restrictions 450s + fit3sls[[ i ]]$e2Sym <- systemfit( system, "3SLS", data = Kmenta, 450s + inst = inst, restrict.matrix = restrict, method3sls = formulas[ i ], 450s + x = TRUE, useMatrix = useMatrix ) 450s + print( all.equal( fit3sls[[ i ]]$e2, fit3sls[[ i ]]$e2Sym ) ) 450s + 450s + print( "************** 3SLS with restriction (EViews-like) *****************" ) 450s + fit3sls[[ i ]]$e2e <- systemfit( system, "3SLS", data = Kmenta, 450s + inst = inst, methodResidCov = "noDfCor", restrict.matrix = restrm, 450s + method3sls = formulas[ i ], useMatrix = useMatrix ) 450s + print( summary( fit3sls[[ i ]]$e2e, useDfSys = TRUE ) ) 450s + print( nobs( fit3sls[[i]]$e2e )) 450s + 450s + print( "*************** W3SLS with restriction *****************" ) 450s + fit3sls[[ i ]]$e2w <- systemfit( system, "3SLS", data = Kmenta, 450s + inst = inst, restrict.matrix = restrm, method3sls = formulas[ i ], 450s + residCovWeighted = TRUE, useMatrix = useMatrix ) 450s + print( summary( fit3sls[[ i ]]$e2w ) ) 450s + 450s + 450s + print( "*************** 3SLS with restriction via restrict.regMat ********************" ) 450s + fit3sls[[ i ]]$e3 <- systemfit( system, "3SLS", data = Kmenta, 450s + inst = inst, restrict.regMat = tc, method3sls = formulas[ i ], 450s + useMatrix = useMatrix ) 450s + print( summary( fit3sls[[ i ]]$e3 ) ) 450s + 450s + print( "*************** 3SLS with restriction via restrict.regMat (EViews-like) *******" ) 450s + fit3sls[[ i ]]$e3e <- systemfit( system, "3SLS", data = Kmenta, 450s + inst = inst, methodResidCov = "noDfCor", restrict.regMat = tc, 450s + method3sls = formulas[ i ], x = TRUE, 450s + useMatrix = useMatrix ) 450s + print( summary( fit3sls[[ i ]]$e3e, useDfSys = TRUE ) ) 450s + 450s + print( "**** W3SLS with restriction via restrict.regMat (EViews-like) ****" ) 450s + fit3sls[[ i ]]$e3we <- systemfit( system, "3SLS", data = Kmenta, 450s + inst = inst, methodResidCov = "noDfCor", restrict.regMat = tc, 450s + method3sls = formulas[ i ], residCovWeighted = TRUE, 450s + useMatrix = useMatrix ) 450s + print( summary( fit3sls[[ i ]]$e3we, useDfSys = TRUE ) ) 450s + 450s + 450s + print( "*************** 3SLS with 2 restrictions **********************" ) 450s + fit3sls[[ i ]]$e4 <- systemfit( system, "3SLS", data = Kmenta, 450s + inst = inst, restrict.matrix = restr2m, restrict.rhs = restr2q, 450s + method3sls = formulas[ i ], x = TRUE, 450s + useMatrix = useMatrix ) 450s + print( summary( fit3sls[[ i ]]$e4 ) ) 450s + # the same with symbolically specified restrictions 450s + fit3sls[[ i ]]$e4Sym <- systemfit( system, "3SLS", data = Kmenta, 450s + inst = inst, restrict.matrix = restrict2, method3sls = formulas[ i ], 450s + x = TRUE, useMatrix = useMatrix ) 450s + print( all.equal( fit3sls[[ i ]]$e4, fit3sls[[ i ]]$e4Sym ) ) 450s + 450s + print( "*************** 3SLS with 2 restrictions (EViews-like) ************" ) 450s + fit3sls[[ i ]]$e4e <- systemfit( system, "3SLS", data = Kmenta, 450s + inst = inst, methodResidCov = "noDfCor", restrict.matrix = restr2m, 450s + restrict.rhs = restr2q, method3sls = formulas[ i ], 450s + useMatrix = useMatrix ) 450s + print( summary( fit3sls[[ i ]]$e4e, useDfSys = TRUE ) ) 450s + 450s + print( "********** W3SLS with 2 (symbolic) restrictions ***************" ) 450s + fit3sls[[ i ]]$e4wSym <- systemfit( system, "3SLS", data = Kmenta, 450s + inst = inst, restrict.matrix = restrict2, method3sls = formulas[ i ], 450s + residCovWeighted = TRUE, useMatrix = useMatrix ) 450s + print( summary( fit3sls[[ i ]]$e4wSym ) ) 450s + 450s + 450s + print( "*************** 3SLS with 2 restrictions via R and restrict.regMat **********" ) 450s + fit3sls[[ i ]]$e5 <- systemfit( system, "3SLS", data = Kmenta, 450s + inst = inst, restrict.regMat = tc, restrict.matrix = restr3m, 450s + restrict.rhs = restr3q, method3sls = formulas[ i ], 450s + useMatrix = useMatrix ) 450s + print( summary( fit3sls[[ i ]]$e5 ) ) 450s + # the same with symbolically specified restrictions 450s + fit3sls[[ i ]]$e5Sym <- systemfit( system, "3SLS", data = Kmenta, 450s + inst = inst, restrict.regMat = tc, restrict.matrix = restrict3, 450s + method3sls = formulas[ i ], useMatrix = useMatrix ) 450s + print( all.equal( fit3sls[[ i ]]$e5, fit3sls[[ i ]]$e5Sym ) ) 450s + 450s + print( "******** 3SLS with 2 restrictions via R and restrict.regMat (EViews-like)*****" ) 450s + fit3sls[[ i ]]$e5e <- systemfit( system, "3SLS", data = Kmenta, 450s + inst = inst, restrict.regMat = tc, methodResidCov = "noDfCor", 450s + restrict.matrix = restr3m, restrict.rhs = restr3q, 450s + method3sls = formulas[ i ], x = TRUE, 450s + useMatrix = useMatrix ) 450s + print( summary( fit3sls[[ i ]]$e5e, useDfSys = TRUE ) ) 450s + 450s + print( "*** W3SLS with 2 restrictions via R and restrict.regMat (EViews-like) ***" ) 450s + fit3sls[[ i ]]$e5we <- systemfit( system, "3SLS", data = Kmenta, 450s + inst = inst, restrict.regMat = tc, methodResidCov = "noDfCor", 450s + restrict.matrix = restr3m, restrict.rhs = restr3q, method3sls = formulas[ i ], 450s + residCovWeighted = TRUE, useMatrix = useMatrix ) 450s + print( summary( fit3sls[[ i ]]$e5we, useDfSys = TRUE ) ) 450s + 450s + ## *********** estimations with a single regressor ************ 450s + fit3sls[[ i ]]$S1 <- systemfit( 450s + list( farmPrice ~ consump - 1, price ~ consump + trend ), "3SLS", 450s + data = Kmenta, inst = ~ trend + income, useMatrix = useMatrix ) 450s + print( summary( fit3sls[[ i ]]$S1 ) ) 450s + fit3sls[[ i ]]$S2 <- systemfit( 450s + list( consump ~ farmPrice - 1, consump ~ trend - 1 ), "3SLS", 450s + data = Kmenta, inst = ~ price + income, useMatrix = useMatrix ) 450s + print( summary( fit3sls[[ i ]]$S2 ) ) 450s + fit3sls[[ i ]]$S3 <- systemfit( 450s + list( consump ~ trend - 1, farmPrice ~ trend - 1 ), "3SLS", 450s + data = Kmenta, inst = instlist, useMatrix = useMatrix ) 450s + print( summary( fit3sls[[ i ]]$S3 ) ) 450s + fit3sls[[ i ]]$S4 <- systemfit( 450s + list( consump ~ farmPrice - 1, price ~ trend - 1 ), "3SLS", 450s + data = Kmenta, inst = ~ farmPrice + trend + income, 450s + restrict.matrix = matrix( c( 1, -1 ), nrow = 1 ), useMatrix = useMatrix ) 450s + print( summary( fit3sls[[ i ]]$S4 ) ) 450s + fit3sls[[ i ]]$S5 <- systemfit( 450s + list( consump ~ 1, price ~ 1 ), "3SLS", 450s + data = Kmenta, inst = ~ income, useMatrix = useMatrix ) 450s + print( summary( fit3sls[[ i ]]$S5 ) ) 450s + } 451s [1] "***************************************************" 451s [1] "3SLS formula: GLS" 451s [1] "************* 3SLS *********************************" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 33 174 1.03 0.676 0.786 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 65.7 3.87 1.97 0.755 0.726 451s supply 20 16 107.9 6.75 2.60 0.598 0.522 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.87 4.36 451s supply 4.36 6.04 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.87 5.00 451s supply 5.00 6.74 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.00 0.98 451s supply 0.98 1.00 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 94.6333 7.9208 11.95 1.1e-09 *** 451s price -0.2436 0.0965 -2.52 0.022 * 451s income 0.3140 0.0469 6.69 3.8e-06 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.966 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 451s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 52.1972 11.8934 4.39 0.00046 *** 451s price 0.2286 0.0997 2.29 0.03571 * 451s farmPrice 0.2282 0.0440 5.19 9e-05 *** 451s trend 0.3611 0.0729 4.95 0.00014 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.597 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 107.914 MSE: 6.745 Root MSE: 2.597 451s Multiple R-Squared: 0.598 Adjusted R-Squared: 0.522 451s 451s [1] "********************* 3SLS EViews-like *****************" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 33 173 0.719 0.677 0.748 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 65.7 3.87 1.97 0.755 0.726 451s supply 20 16 107.2 6.70 2.59 0.600 0.525 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.29 3.59 451s supply 3.59 4.83 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.29 4.11 451s supply 4.11 5.36 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.000 0.979 451s supply 0.979 1.000 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 451s price -0.2436 0.0890 -2.74 0.0099 ** 451s income 0.3140 0.0433 7.25 2.5e-08 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.966 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 451s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 52.1176 10.6378 4.90 2.5e-05 *** 451s price 0.2289 0.0892 2.57 0.015 * 451s farmPrice 0.2290 0.0393 5.82 1.6e-06 *** 451s trend 0.3579 0.0652 5.49 4.3e-06 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.589 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 107.216 MSE: 6.701 Root MSE: 2.589 451s Multiple R-Squared: 0.6 Adjusted R-Squared: 0.525 451s 451s [1] "********************* 3SLS with methodResidCov = Theil *****************" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 33 174 -0.718 0.675 0.922 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 65.7 3.87 1.97 0.755 0.726 451s supply 20 16 108.7 6.79 2.61 0.594 0.518 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.87 4.50 451s supply 4.50 6.04 451s 451s warning: this covariance matrix is NOT positive semidefinit! 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.87 5.2 451s supply 5.20 6.8 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.000 0.981 451s supply 0.981 1.000 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 451s price -0.2436 0.0965 -2.52 0.017 * 451s income 0.3140 0.0469 6.69 1.3e-07 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.966 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 451s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 52.2869 11.8853 4.40 0.00011 *** 451s price 0.2282 0.0997 2.29 0.02855 * 451s farmPrice 0.2272 0.0438 5.19 1.0e-05 *** 451s trend 0.3648 0.0707 5.16 1.1e-05 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.607 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 108.727 MSE: 6.795 Root MSE: 2.607 451s Multiple R-Squared: 0.594 Adjusted R-Squared: 0.518 451s 451s [1] "*************** W3SLS with methodResidCov = Theil *****************" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 33 174 -0.718 0.675 0.922 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 65.7 3.87 1.97 0.755 0.726 451s supply 20 16 108.7 6.79 2.61 0.594 0.518 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.87 4.50 451s supply 4.50 6.04 451s 451s warning: this covariance matrix is NOT positive semidefinit! 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.87 5.2 451s supply 5.20 6.8 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.000 0.981 451s supply 0.981 1.000 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 451s price -0.2436 0.0965 -2.52 0.017 * 451s income 0.3140 0.0469 6.69 1.3e-07 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.966 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 451s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 52.2869 11.8853 4.40 0.00011 *** 451s price 0.2282 0.0997 2.29 0.02855 * 451s farmPrice 0.2272 0.0438 5.19 1.0e-05 *** 451s trend 0.3648 0.0707 5.16 1.1e-05 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.607 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 108.727 MSE: 6.795 Root MSE: 2.607 451s Multiple R-Squared: 0.594 Adjusted R-Squared: 0.518 451s 451s [1] "*************** 3SLS with restriction *****************" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 34 173 1.27 0.678 0.722 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 67.8 3.99 2.00 0.747 0.717 451s supply 20 16 104.8 6.55 2.56 0.609 0.536 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.97 4.55 451s supply 4.55 6.13 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.99 4.98 451s supply 4.98 6.55 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.000 0.975 451s supply 0.975 1.000 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 94.222 8.015 11.76 1.6e-13 *** 451s price -0.222 0.096 -2.31 0.027 * 451s income 0.296 0.045 6.57 1.6e-07 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.997 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 67.796 MSE: 3.988 Root MSE: 1.997 451s Multiple R-Squared: 0.747 Adjusted R-Squared: 0.717 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 55.9604 11.5777 4.83 2.8e-05 *** 451s price 0.2193 0.1002 2.19 0.036 * 451s farmPrice 0.2060 0.0403 5.11 1.3e-05 *** 451s trend 0.2956 0.0450 6.57 1.6e-07 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.559 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 104.753 MSE: 6.547 Root MSE: 2.559 451s Multiple R-Squared: 0.609 Adjusted R-Squared: 0.536 451s 451s [1] "Component “call”: target, current do not match when deparsed" 451s [1] "************** 3SLS with restriction (EViews-like) *****************" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 34 171 0.887 0.68 0.678 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 67.5 3.97 1.99 0.748 0.719 451s supply 20 16 104.0 6.50 2.55 0.612 0.539 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.37 3.75 451s supply 3.75 4.91 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.37 4.08 451s supply 4.08 5.20 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.000 0.974 451s supply 0.974 1.000 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 94.2737 7.3905 12.76 1.6e-14 *** 451s price -0.2243 0.0888 -2.53 0.016 * 451s income 0.2979 0.0420 7.10 3.4e-08 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.992 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 67.467 MSE: 3.969 Root MSE: 1.992 451s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 55.4521 10.3994 5.33 6.4e-06 *** 451s price 0.2207 0.0896 2.46 0.019 * 451s farmPrice 0.2095 0.0366 5.73 1.9e-06 *** 451s trend 0.2979 0.0420 7.10 3.4e-08 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.55 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 104.013 MSE: 6.501 Root MSE: 2.55 451s Multiple R-Squared: 0.612 Adjusted R-Squared: 0.539 451s 451s [1] 40 451s [1] "*************** W3SLS with restriction *****************" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 34 173 1.24 0.677 0.725 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 68.1 4.00 2.00 0.746 0.716 451s supply 20 16 105.2 6.57 2.56 0.608 0.534 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.93 4.56 451s supply 4.56 6.15 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 4.00 5.01 451s supply 5.01 6.57 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.000 0.976 451s supply 0.976 1.000 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 94.1823 7.9793 11.8 1.4e-13 *** 451s price -0.2194 0.0954 -2.3 0.028 * 451s income 0.2938 0.0445 6.6 1.4e-07 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.001 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 68.057 MSE: 4.003 Root MSE: 2.001 451s Multiple R-Squared: 0.746 Adjusted R-Squared: 0.716 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 56.2541 11.5687 4.86 2.6e-05 *** 451s price 0.2184 0.1003 2.18 0.036 * 451s farmPrice 0.2040 0.0401 5.09 1.3e-05 *** 451s trend 0.2938 0.0445 6.60 1.4e-07 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.564 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 105.161 MSE: 6.573 Root MSE: 2.564 451s Multiple R-Squared: 0.608 Adjusted R-Squared: 0.534 451s 451s [1] "*************** 3SLS with restriction via restrict.regMat ********************" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 34 173 1.27 0.678 0.722 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 67.8 3.99 2.00 0.747 0.717 451s supply 20 16 104.8 6.55 2.56 0.609 0.536 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.97 4.55 451s supply 4.55 6.13 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.99 4.98 451s supply 4.98 6.55 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.000 0.975 451s supply 0.975 1.000 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 94.222 8.015 11.76 1.6e-13 *** 451s price -0.222 0.096 -2.31 0.027 * 451s income 0.296 0.045 6.57 1.6e-07 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.997 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 67.796 MSE: 3.988 Root MSE: 1.997 451s Multiple R-Squared: 0.747 Adjusted R-Squared: 0.717 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 55.9604 11.5777 4.83 2.8e-05 *** 451s price 0.2193 0.1002 2.19 0.036 * 451s farmPrice 0.2060 0.0403 5.11 1.3e-05 *** 451s trend 0.2956 0.0450 6.57 1.6e-07 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.559 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 104.753 MSE: 6.547 Root MSE: 2.559 451s Multiple R-Squared: 0.609 Adjusted R-Squared: 0.536 451s 451s [1] "*************** 3SLS with restriction via restrict.regMat (EViews-like) *******" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 34 171 0.887 0.68 0.678 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 67.5 3.97 1.99 0.748 0.719 451s supply 20 16 104.0 6.50 2.55 0.612 0.539 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.37 3.75 451s supply 3.75 4.91 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.37 4.08 451s supply 4.08 5.20 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.000 0.974 451s supply 0.974 1.000 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 94.2737 7.3905 12.76 1.6e-14 *** 451s price -0.2243 0.0888 -2.53 0.016 * 451s income 0.2979 0.0420 7.10 3.4e-08 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.992 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 67.467 MSE: 3.969 Root MSE: 1.992 451s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 55.4521 10.3994 5.33 6.4e-06 *** 451s price 0.2207 0.0896 2.46 0.019 * 451s farmPrice 0.2095 0.0366 5.73 1.9e-06 *** 451s trend 0.2979 0.0420 7.10 3.4e-08 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.55 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 104.013 MSE: 6.501 Root MSE: 2.55 451s Multiple R-Squared: 0.612 Adjusted R-Squared: 0.539 451s 451s [1] "**** W3SLS with restriction via restrict.regMat (EViews-like) ****" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 34 172 0.873 0.679 0.681 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 67.7 3.98 2.00 0.748 0.718 451s supply 20 16 104.3 6.52 2.55 0.611 0.538 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.35 3.76 451s supply 3.76 4.92 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.38 4.10 451s supply 4.10 5.22 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.000 0.975 451s supply 0.975 1.000 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 94.2409 7.3617 12.80 1.5e-14 *** 451s price -0.2225 0.0883 -2.52 0.017 * 451s income 0.2964 0.0416 7.13 3.1e-08 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.995 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 67.672 MSE: 3.981 Root MSE: 1.995 451s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.718 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 55.6925 10.3937 5.36 5.9e-06 *** 451s price 0.2201 0.0897 2.45 0.019 * 451s farmPrice 0.2078 0.0364 5.71 2.0e-06 *** 451s trend 0.2964 0.0416 7.13 3.1e-08 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.553 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 104.312 MSE: 6.519 Root MSE: 2.553 451s Multiple R-Squared: 0.611 Adjusted R-Squared: 0.538 451s 451s [1] "*************** 3SLS with 2 restrictions **********************" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 35 171 1.74 0.681 0.696 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 65.8 3.87 1.97 0.755 0.726 451s supply 20 16 105.4 6.59 2.57 0.607 0.533 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.89 4.53 451s supply 4.53 6.25 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.87 4.87 451s supply 4.87 6.59 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.000 0.965 451s supply 0.965 1.000 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 93.9070 7.9234 11.85 8.3e-14 *** 451s price -0.2457 0.0891 -2.76 0.0092 ** 451s income 0.3236 0.0233 13.91 8.9e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.967 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 65.807 MSE: 3.871 Root MSE: 1.967 451s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 49.9049 8.1797 6.10 5.7e-07 *** 451s price 0.2543 0.0891 2.85 0.0072 ** 451s farmPrice 0.2293 0.0241 9.52 3.1e-11 *** 451s trend 0.3236 0.0233 13.91 8.9e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.566 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 105.389 MSE: 6.587 Root MSE: 2.566 451s Multiple R-Squared: 0.607 Adjusted R-Squared: 0.533 451s 451s [1] "Component “call”: target, current do not match when deparsed" 451s [1] "*************** 3SLS with 2 restrictions (EViews-like) ************" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 35 170 1.19 0.683 0.658 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 65.6 3.86 1.96 0.755 0.727 451s supply 20 16 104.6 6.54 2.56 0.610 0.537 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.30 3.73 451s supply 3.73 5.00 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.28 4.00 451s supply 4.00 5.23 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.000 0.965 451s supply 0.965 1.000 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 94.1703 7.3154 12.87 7.8e-15 *** 451s price -0.2494 0.0812 -3.07 0.0041 ** 451s income 0.3248 0.0209 15.57 < 2e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.964 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 65.577 MSE: 3.857 Root MSE: 1.964 451s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.727 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 50.0853 7.5503 6.63 1.1e-07 *** 451s price 0.2506 0.0812 3.09 0.0039 ** 451s farmPrice 0.2312 0.0212 10.88 8.9e-13 *** 451s trend 0.3248 0.0209 15.57 < 2e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.557 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 104.607 MSE: 6.538 Root MSE: 2.557 451s Multiple R-Squared: 0.61 Adjusted R-Squared: 0.537 451s 451s [1] "********** W3SLS with 2 (symbolic) restrictions ***************" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 35 172 1.74 0.68 0.697 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 65.9 3.88 1.97 0.754 0.725 451s supply 20 16 105.7 6.60 2.57 0.606 0.532 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.88 4.55 451s supply 4.55 6.27 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.88 4.88 451s supply 4.88 6.60 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.000 0.965 451s supply 0.965 1.000 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 93.7870 7.9088 11.86 8.2e-14 *** 451s price -0.2443 0.0892 -2.74 0.0096 ** 451s income 0.3234 0.0229 14.14 4.4e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.969 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 65.907 MSE: 3.877 Root MSE: 1.969 451s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 49.8093 8.1522 6.11 5.5e-07 *** 451s price 0.2557 0.0892 2.87 0.0069 ** 451s farmPrice 0.2289 0.0237 9.67 2.0e-11 *** 451s trend 0.3234 0.0229 14.14 4.4e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.57 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 105.659 MSE: 6.604 Root MSE: 2.57 451s Multiple R-Squared: 0.606 Adjusted R-Squared: 0.532 451s 451s [1] "*************** 3SLS with 2 restrictions via R and restrict.regMat **********" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 35 171 1.74 0.681 0.696 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 65.8 3.87 1.97 0.755 0.726 451s supply 20 16 105.4 6.59 2.57 0.607 0.533 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.89 4.53 451s supply 4.53 6.25 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.87 4.87 451s supply 4.87 6.59 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.000 0.965 451s supply 0.965 1.000 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 93.9070 7.9234 11.85 8.3e-14 *** 451s price -0.2457 0.0891 -2.76 0.0092 ** 451s income 0.3236 0.0233 13.91 8.9e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.967 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 65.807 MSE: 3.871 Root MSE: 1.967 451s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 49.9049 8.1797 6.10 5.7e-07 *** 451s price 0.2543 0.0891 2.85 0.0072 ** 451s farmPrice 0.2293 0.0241 9.52 3.1e-11 *** 451s trend 0.3236 0.0233 13.91 8.9e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.566 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 105.389 MSE: 6.587 Root MSE: 2.566 451s Multiple R-Squared: 0.607 Adjusted R-Squared: 0.533 451s 451s [1] "Component “call”: target, current do not match when deparsed" 451s [1] "******** 3SLS with 2 restrictions via R and restrict.regMat (EViews-like)*****" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 35 170 1.19 0.683 0.658 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 65.6 3.86 1.96 0.755 0.727 451s supply 20 16 104.6 6.54 2.56 0.610 0.537 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.30 3.73 451s supply 3.73 5.00 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.28 4.00 451s supply 4.00 5.23 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.000 0.965 451s supply 0.965 1.000 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 94.1703 7.3154 12.87 7.8e-15 *** 451s price -0.2494 0.0812 -3.07 0.0041 ** 451s income 0.3248 0.0209 15.57 < 2e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.964 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 65.577 MSE: 3.857 Root MSE: 1.964 451s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.727 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 50.0853 7.5503 6.63 1.1e-07 *** 451s price 0.2506 0.0812 3.09 0.0039 ** 451s farmPrice 0.2312 0.0212 10.88 8.9e-13 *** 451s trend 0.3248 0.0209 15.57 < 2e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.557 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 104.607 MSE: 6.538 Root MSE: 2.557 451s Multiple R-Squared: 0.61 Adjusted R-Squared: 0.537 451s 451s [1] "*** W3SLS with 2 restrictions via R and restrict.regMat (EViews-like) ***" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 35 170 1.19 0.682 0.659 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 65.6 3.86 1.97 0.755 0.726 451s supply 20 16 104.8 6.55 2.56 0.609 0.536 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.30 3.75 451s supply 3.75 5.01 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.28 4.00 451s supply 4.00 5.24 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.000 0.965 451s supply 0.965 1.000 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 94.0830 7.3058 12.88 7.5e-15 *** 451s price -0.2484 0.0812 -3.06 0.0042 ** 451s income 0.3246 0.0205 15.81 < 2e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.965 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 65.646 MSE: 3.862 Root MSE: 1.965 451s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 50.0190 7.5314 6.64 1.1e-07 *** 451s price 0.2516 0.0812 3.10 0.0038 ** 451s farmPrice 0.2309 0.0209 11.05 5.9e-13 *** 451s trend 0.3246 0.0205 15.81 < 2e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.559 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 104.795 MSE: 6.55 Root MSE: 2.559 451s Multiple R-Squared: 0.609 Adjusted R-Squared: 0.536 451s 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 36 3690 5613 0.012 0.368 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s eq1 20 19 2132 112.2 10.59 0.305 0.305 451s eq2 20 17 1558 91.7 9.57 -1.335 -1.610 451s 451s The covariance matrix of the residuals used for estimation 451s eq1 eq2 451s eq1 112.2 -44.8 451s eq2 -44.8 56.8 451s 451s The covariance matrix of the residuals 451s eq1 eq2 451s eq1 112.2 -68.3 451s eq2 -68.3 91.7 451s 451s The correlations of the residuals 451s eq1 eq2 451s eq1 1.000 -0.674 451s eq2 -0.674 1.000 451s 451s 451s 3SLS estimates for 'eq1' (equation 1) 451s Model Formula: farmPrice ~ consump - 1 451s Instruments: ~trend + income 451s 451s Estimate Std. Error t value Pr(>|t|) 451s consump 0.9588 0.0235 40.9 <2e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 10.592 on 19 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 19 451s SSR: 2131.725 MSE: 112.196 Root MSE: 10.592 451s Multiple R-Squared: 0.305 Adjusted R-Squared: 0.305 451s 451s 451s 3SLS estimates for 'eq2' (equation 2) 451s Model Formula: price ~ consump + trend 451s Instruments: ~trend + income 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) -92.192 49.896 -1.85 0.0821 . 451s consump 1.953 0.499 3.92 0.0011 ** 451s trend -0.469 0.247 -1.90 0.0743 . 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 9.574 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 1558.311 MSE: 91.665 Root MSE: 9.574 451s Multiple R-Squared: -1.335 Adjusted R-Squared: -1.61 451s 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 38 56326 283068 -104 -10.6 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s eq1 20 19 2313 122 11.0 -7.63 -7.63 451s eq2 20 19 54013 2843 53.3 -200.46 -200.46 451s 451s The covariance matrix of the residuals used for estimation 451s eq1 eq2 451s eq1 121 -255 451s eq2 -255 2953 451s 451s The covariance matrix of the residuals 451s eq1 eq2 451s eq1 122 -251 451s eq2 -251 2843 451s 451s The correlations of the residuals 451s eq1 eq2 451s eq1 1.000 -0.433 451s eq2 -0.433 1.000 451s 451s 451s 3SLS estimates for 'eq1' (equation 1) 451s Model Formula: consump ~ farmPrice - 1 451s Instruments: ~price + income 451s 451s Estimate Std. Error t value Pr(>|t|) 451s farmPrice 1.0417 0.0253 41.2 <2e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 11.034 on 19 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 19 451s SSR: 2313.073 MSE: 121.741 Root MSE: 11.034 451s Multiple R-Squared: -7.627 Adjusted R-Squared: -7.627 451s 451s 451s 3SLS estimates for 'eq2' (equation 2) 451s Model Formula: consump ~ trend - 1 451s Instruments: ~price + income 451s 451s Estimate Std. Error t value Pr(>|t|) 451s trend 9.02 1.13 8 1.7e-07 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 53.318 on 19 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 19 451s SSR: 54013.367 MSE: 2842.809 Root MSE: 53.318 451s Multiple R-Squared: -200.456 Adjusted R-Squared: -200.456 451s 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 38 167069 397886 -49.1 -0.82 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s eq1 20 19 76692 4036 63.5 -285.0 -285.0 451s eq2 20 19 90377 4757 69.0 -28.5 -28.5 451s 451s The covariance matrix of the residuals used for estimation 451s eq1 eq2 451s eq1 2682 2547 451s eq2 2547 2741 451s 451s The covariance matrix of the residuals 451s eq1 eq2 451s eq1 4036 4336 451s eq2 4336 4757 451s 451s The correlations of the residuals 451s eq1 eq2 451s eq1 1.000 0.928 451s eq2 0.928 1.000 451s 451s 451s 3SLS estimates for 'eq1' (equation 1) 451s Model Formula: consump ~ trend - 1 451s Instruments: ~income + farmPrice 451s 451s Estimate Std. Error t value Pr(>|t|) 451s trend 4.162 0.723 5.75 1.5e-05 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 63.533 on 19 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 19 451s SSR: 76691.636 MSE: 4036.402 Root MSE: 63.533 451s Multiple R-Squared: -285.041 Adjusted R-Squared: -285.041 451s 451s 451s 3SLS estimates for 'eq2' (equation 2) 451s Model Formula: farmPrice ~ trend - 1 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s trend 3.274 0.676 4.84 0.00011 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 68.969 on 19 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 19 451s SSR: 90377.499 MSE: 4756.71 Root MSE: 68.969 451s Multiple R-Squared: -28.451 Adjusted R-Squared: -28.451 451s 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 39 161126 1162329 -171 -17.4 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s eq1 20 19 3553 187 13.7 -12.3 -12.3 451s eq2 20 19 157573 8293 91.1 -235.2 -235.2 451s 451s The covariance matrix of the residuals used for estimation 451s eq1 eq2 451s eq1 208 -731 451s eq2 -731 8271 451s 451s The covariance matrix of the residuals 451s eq1 eq2 451s eq1 187 -623 451s eq2 -623 8293 451s 451s The correlations of the residuals 451s eq1 eq2 451s eq1 1.000 -0.121 451s eq2 -0.121 1.000 451s 451s 451s 3SLS estimates for 'eq1' (equation 1) 451s Model Formula: consump ~ farmPrice - 1 451s Instruments: ~farmPrice + trend + income 451s 451s Estimate Std. Error t value Pr(>|t|) 451s farmPrice 1.1122 0.0272 40.8 <2e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 13.675 on 19 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 19 451s SSR: 3553.118 MSE: 187.006 Root MSE: 13.675 451s Multiple R-Squared: -12.252 Adjusted R-Squared: -12.252 451s 451s 451s 3SLS estimates for 'eq2' (equation 2) 451s Model Formula: price ~ trend - 1 451s Instruments: ~farmPrice + trend + income 451s 451s Estimate Std. Error t value Pr(>|t|) 451s trend 1.1122 0.0272 40.8 <2e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 91.068 on 19 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 19 451s SSR: 157573.328 MSE: 8293.333 Root MSE: 91.068 451s Multiple R-Squared: -235.153 Adjusted R-Squared: -235.153 451s 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 38 935 491 0 0 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s eq1 20 19 268 14.1 3.76 0 0 451s eq2 20 19 667 35.1 5.93 0 0 451s 451s The covariance matrix of the residuals used for estimation 451s eq1 eq2 451s eq1 14.11 2.18 451s eq2 2.18 35.12 451s 451s The covariance matrix of the residuals 451s eq1 eq2 451s eq1 14.11 2.18 451s eq2 2.18 35.12 451s 451s The correlations of the residuals 451s eq1 eq2 451s eq1 1.0000 0.0981 451s eq2 0.0981 1.0000 451s 451s 451s 3SLS estimates for 'eq1' (equation 1) 451s Model Formula: consump ~ 1 451s Instruments: ~income 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 100.90 0.84 120 <2e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 3.756 on 19 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 19 451s SSR: 268.114 MSE: 14.111 Root MSE: 3.756 451s Multiple R-Squared: 0 Adjusted R-Squared: 0 451s 451s 451s 3SLS estimates for 'eq2' (equation 2) 451s Model Formula: price ~ 1 451s Instruments: ~income 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 100.02 1.33 75.5 <2e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 5.926 on 19 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 19 451s SSR: 667.251 MSE: 35.118 Root MSE: 5.926 451s Multiple R-Squared: 0 Adjusted R-Squared: 0 451s 451s [1] "***************************************************" 451s [1] "3SLS formula: IV" 451s [1] "************* 3SLS *********************************" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 33 174 1.03 0.676 0.786 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 65.7 3.87 1.97 0.755 0.726 451s supply 20 16 107.9 6.75 2.60 0.598 0.522 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.87 4.36 451s supply 4.36 6.04 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.87 5.00 451s supply 5.00 6.74 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.00 0.98 451s supply 0.98 1.00 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 94.6333 7.9208 11.95 1.1e-09 *** 451s price -0.2436 0.0965 -2.52 0.022 * 451s income 0.3140 0.0469 6.69 3.8e-06 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.966 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 451s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 52.1972 11.8934 4.39 0.00046 *** 451s price 0.2286 0.0997 2.29 0.03571 * 451s farmPrice 0.2282 0.0440 5.19 9e-05 *** 451s trend 0.3611 0.0729 4.95 0.00014 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.597 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 107.914 MSE: 6.745 Root MSE: 2.597 451s Multiple R-Squared: 0.598 Adjusted R-Squared: 0.522 451s 451s [1] "********************* 3SLS EViews-like *****************" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 33 173 0.719 0.677 0.748 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 65.7 3.87 1.97 0.755 0.726 451s supply 20 16 107.2 6.70 2.59 0.600 0.525 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.29 3.59 451s supply 3.59 4.83 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.29 4.11 451s supply 4.11 5.36 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.000 0.979 451s supply 0.979 1.000 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 451s price -0.2436 0.0890 -2.74 0.0099 ** 451s income 0.3140 0.0433 7.25 2.5e-08 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.966 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 451s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 52.1176 10.6378 4.90 2.5e-05 *** 451s price 0.2289 0.0892 2.57 0.015 * 451s farmPrice 0.2290 0.0393 5.82 1.6e-06 *** 451s trend 0.3579 0.0652 5.49 4.3e-06 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.589 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 107.216 MSE: 6.701 Root MSE: 2.589 451s Multiple R-Squared: 0.6 Adjusted R-Squared: 0.525 451s 451s [1] "********************* 3SLS with methodResidCov = Theil *****************" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 33 174 -0.718 0.675 0.922 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 65.7 3.87 1.97 0.755 0.726 451s supply 20 16 108.7 6.79 2.61 0.594 0.518 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.87 4.50 451s supply 4.50 6.04 451s 451s warning: this covariance matrix is NOT positive semidefinit! 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.87 5.2 451s supply 5.20 6.8 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.000 0.981 451s supply 0.981 1.000 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 451s price -0.2436 0.0965 -2.52 0.017 * 451s income 0.3140 0.0469 6.69 1.3e-07 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.966 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 451s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 52.2869 11.8853 4.40 0.00011 *** 451s price 0.2282 0.0997 2.29 0.02855 * 451s farmPrice 0.2272 0.0438 5.19 1.0e-05 *** 451s trend 0.3648 0.0707 5.16 1.1e-05 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.607 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 108.727 MSE: 6.795 Root MSE: 2.607 451s Multiple R-Squared: 0.594 Adjusted R-Squared: 0.518 451s 451s [1] "*************** W3SLS with methodResidCov = Theil *****************" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 33 174 -0.718 0.675 0.922 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 65.7 3.87 1.97 0.755 0.726 451s supply 20 16 108.7 6.79 2.61 0.594 0.518 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.87 4.50 451s supply 4.50 6.04 451s 451s warning: this covariance matrix is NOT positive semidefinit! 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.87 5.2 451s supply 5.20 6.8 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.000 0.981 451s supply 0.981 1.000 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 451s price -0.2436 0.0965 -2.52 0.017 * 451s income 0.3140 0.0469 6.69 1.3e-07 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.966 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 451s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 52.2869 11.8853 4.40 0.00011 *** 451s price 0.2282 0.0997 2.29 0.02855 * 451s farmPrice 0.2272 0.0438 5.19 1.0e-05 *** 451s trend 0.3648 0.0707 5.16 1.1e-05 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.607 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 108.727 MSE: 6.795 Root MSE: 2.607 451s Multiple R-Squared: 0.594 Adjusted R-Squared: 0.518 451s 451s [1] "*************** 3SLS with restriction *****************" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 34 173 1.27 0.678 0.722 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 67.8 3.99 2.00 0.747 0.717 451s supply 20 16 104.8 6.55 2.56 0.609 0.536 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.97 4.55 451s supply 4.55 6.13 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.99 4.98 451s supply 4.98 6.55 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.000 0.975 451s supply 0.975 1.000 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 94.222 8.015 11.76 1.6e-13 *** 451s price -0.222 0.096 -2.31 0.027 * 451s income 0.296 0.045 6.57 1.6e-07 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.997 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 67.796 MSE: 3.988 Root MSE: 1.997 451s Multiple R-Squared: 0.747 Adjusted R-Squared: 0.717 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 55.9604 11.5777 4.83 2.8e-05 *** 451s price 0.2193 0.1002 2.19 0.036 * 451s farmPrice 0.2060 0.0403 5.11 1.3e-05 *** 451s trend 0.2956 0.0450 6.57 1.6e-07 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.559 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 104.753 MSE: 6.547 Root MSE: 2.559 451s Multiple R-Squared: 0.609 Adjusted R-Squared: 0.536 451s 451s [1] "Component “call”: target, current do not match when deparsed" 451s [1] "************** 3SLS with restriction (EViews-like) *****************" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 34 171 0.887 0.68 0.678 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 67.5 3.97 1.99 0.748 0.719 451s supply 20 16 104.0 6.50 2.55 0.612 0.539 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.37 3.75 451s supply 3.75 4.91 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.37 4.08 451s supply 4.08 5.20 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.000 0.974 451s supply 0.974 1.000 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 94.2737 7.3905 12.76 1.6e-14 *** 451s price -0.2243 0.0888 -2.53 0.016 * 451s income 0.2979 0.0420 7.10 3.4e-08 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.992 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 67.467 MSE: 3.969 Root MSE: 1.992 451s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 55.4521 10.3994 5.33 6.4e-06 *** 451s price 0.2207 0.0896 2.46 0.019 * 451s farmPrice 0.2095 0.0366 5.73 1.9e-06 *** 451s trend 0.2979 0.0420 7.10 3.4e-08 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.55 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 104.013 MSE: 6.501 Root MSE: 2.55 451s Multiple R-Squared: 0.612 Adjusted R-Squared: 0.539 451s 451s [1] 40 451s [1] "*************** W3SLS with restriction *****************" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 34 173 1.24 0.677 0.725 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 68.1 4.00 2.00 0.746 0.716 451s supply 20 16 105.2 6.57 2.56 0.608 0.534 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.93 4.56 451s supply 4.56 6.15 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 4.00 5.01 451s supply 5.01 6.57 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.000 0.976 451s supply 0.976 1.000 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 94.1823 7.9793 11.8 1.4e-13 *** 451s price -0.2194 0.0954 -2.3 0.028 * 451s income 0.2938 0.0445 6.6 1.4e-07 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.001 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 68.057 MSE: 4.003 Root MSE: 2.001 451s Multiple R-Squared: 0.746 Adjusted R-Squared: 0.716 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 56.2541 11.5687 4.86 2.6e-05 *** 451s price 0.2184 0.1003 2.18 0.036 * 451s farmPrice 0.2040 0.0401 5.09 1.3e-05 *** 451s trend 0.2938 0.0445 6.60 1.4e-07 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.564 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 105.161 MSE: 6.573 Root MSE: 2.564 451s Multiple R-Squared: 0.608 Adjusted R-Squared: 0.534 451s 451s [1] "*************** 3SLS with restriction via restrict.regMat ********************" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 34 173 1.27 0.678 0.722 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 67.8 3.99 2.00 0.747 0.717 451s supply 20 16 104.8 6.55 2.56 0.609 0.536 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.97 4.55 451s supply 4.55 6.13 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.99 4.98 451s supply 4.98 6.55 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.000 0.975 451s supply 0.975 1.000 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 94.222 8.015 11.76 1.6e-13 *** 451s price -0.222 0.096 -2.31 0.027 * 451s income 0.296 0.045 6.57 1.6e-07 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.997 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 67.796 MSE: 3.988 Root MSE: 1.997 451s Multiple R-Squared: 0.747 Adjusted R-Squared: 0.717 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 55.9604 11.5777 4.83 2.8e-05 *** 451s price 0.2193 0.1002 2.19 0.036 * 451s farmPrice 0.2060 0.0403 5.11 1.3e-05 *** 451s trend 0.2956 0.0450 6.57 1.6e-07 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.559 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 104.753 MSE: 6.547 Root MSE: 2.559 451s Multiple R-Squared: 0.609 Adjusted R-Squared: 0.536 451s 451s [1] "*************** 3SLS with restriction via restrict.regMat (EViews-like) *******" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 34 171 0.887 0.68 0.678 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 67.5 3.97 1.99 0.748 0.719 451s supply 20 16 104.0 6.50 2.55 0.612 0.539 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.37 3.75 451s supply 3.75 4.91 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.37 4.08 451s supply 4.08 5.20 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.000 0.974 451s supply 0.974 1.000 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 94.2737 7.3905 12.76 1.6e-14 *** 451s price -0.2243 0.0888 -2.53 0.016 * 451s income 0.2979 0.0420 7.10 3.4e-08 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.992 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 67.467 MSE: 3.969 Root MSE: 1.992 451s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 55.4521 10.3994 5.33 6.4e-06 *** 451s price 0.2207 0.0896 2.46 0.019 * 451s farmPrice 0.2095 0.0366 5.73 1.9e-06 *** 451s trend 0.2979 0.0420 7.10 3.4e-08 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.55 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 104.013 MSE: 6.501 Root MSE: 2.55 451s Multiple R-Squared: 0.612 Adjusted R-Squared: 0.539 451s 451s [1] "**** W3SLS with restriction via restrict.regMat (EViews-like) ****" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 34 172 0.873 0.679 0.681 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 67.7 3.98 2.00 0.748 0.718 451s supply 20 16 104.3 6.52 2.55 0.611 0.538 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.35 3.76 451s supply 3.76 4.92 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.38 4.10 451s supply 4.10 5.22 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.000 0.975 451s supply 0.975 1.000 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 94.2409 7.3617 12.80 1.5e-14 *** 451s price -0.2225 0.0883 -2.52 0.017 * 451s income 0.2964 0.0416 7.13 3.1e-08 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.995 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 67.672 MSE: 3.981 Root MSE: 1.995 451s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.718 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 55.6925 10.3937 5.36 5.9e-06 *** 451s price 0.2201 0.0897 2.45 0.019 * 451s farmPrice 0.2078 0.0364 5.71 2.0e-06 *** 451s trend 0.2964 0.0416 7.13 3.1e-08 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.553 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 104.312 MSE: 6.519 Root MSE: 2.553 451s Multiple R-Squared: 0.611 Adjusted R-Squared: 0.538 451s 451s [1] "*************** 3SLS with 2 restrictions **********************" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 35 171 1.74 0.681 0.696 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 65.8 3.87 1.97 0.755 0.726 451s supply 20 16 105.4 6.59 2.57 0.607 0.533 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.89 4.53 451s supply 4.53 6.25 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.87 4.87 451s supply 4.87 6.59 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.000 0.965 451s supply 0.965 1.000 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 93.9070 7.9234 11.85 8.3e-14 *** 451s price -0.2457 0.0891 -2.76 0.0092 ** 451s income 0.3236 0.0233 13.91 8.9e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.967 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 65.807 MSE: 3.871 Root MSE: 1.967 451s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 49.9049 8.1797 6.10 5.7e-07 *** 451s price 0.2543 0.0891 2.85 0.0072 ** 451s farmPrice 0.2293 0.0241 9.52 3.1e-11 *** 451s trend 0.3236 0.0233 13.91 8.9e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.566 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 105.389 MSE: 6.587 Root MSE: 2.566 451s Multiple R-Squared: 0.607 Adjusted R-Squared: 0.533 451s 451s [1] "Component “call”: target, current do not match when deparsed" 451s [1] "*************** 3SLS with 2 restrictions (EViews-like) ************" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 35 170 1.19 0.683 0.658 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 65.6 3.86 1.96 0.755 0.727 451s supply 20 16 104.6 6.54 2.56 0.610 0.537 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.30 3.73 451s supply 3.73 5.00 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.28 4.00 451s supply 4.00 5.23 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.000 0.965 451s supply 0.965 1.000 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 94.1703 7.3154 12.87 7.8e-15 *** 451s price -0.2494 0.0812 -3.07 0.0041 ** 451s income 0.3248 0.0209 15.57 < 2e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.964 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 65.577 MSE: 3.857 Root MSE: 1.964 451s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.727 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 50.0853 7.5503 6.63 1.1e-07 *** 451s price 0.2506 0.0812 3.09 0.0039 ** 451s farmPrice 0.2312 0.0212 10.88 8.9e-13 *** 451s trend 0.3248 0.0209 15.57 < 2e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.557 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 104.607 MSE: 6.538 Root MSE: 2.557 451s Multiple R-Squared: 0.61 Adjusted R-Squared: 0.537 451s 451s [1] "********** W3SLS with 2 (symbolic) restrictions ***************" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 35 172 1.74 0.68 0.697 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 65.9 3.88 1.97 0.754 0.725 451s supply 20 16 105.7 6.60 2.57 0.606 0.532 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.88 4.55 451s supply 4.55 6.27 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.88 4.88 451s supply 4.88 6.60 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.000 0.965 451s supply 0.965 1.000 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 93.7870 7.9088 11.86 8.2e-14 *** 451s price -0.2443 0.0892 -2.74 0.0096 ** 451s income 0.3234 0.0229 14.14 4.4e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.969 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 65.907 MSE: 3.877 Root MSE: 1.969 451s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 49.8093 8.1522 6.11 5.5e-07 *** 451s price 0.2557 0.0892 2.87 0.0069 ** 451s farmPrice 0.2289 0.0237 9.67 2.0e-11 *** 451s trend 0.3234 0.0229 14.14 4.4e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.57 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 105.659 MSE: 6.604 Root MSE: 2.57 451s Multiple R-Squared: 0.606 Adjusted R-Squared: 0.532 451s 451s [1] "*************** 3SLS with 2 restrictions via R and restrict.regMat **********" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 35 171 1.74 0.681 0.696 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 65.8 3.87 1.97 0.755 0.726 451s supply 20 16 105.4 6.59 2.57 0.607 0.533 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.89 4.53 451s supply 4.53 6.25 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.87 4.87 451s supply 4.87 6.59 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.000 0.965 451s supply 0.965 1.000 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 93.9070 7.9234 11.85 8.3e-14 *** 451s price -0.2457 0.0891 -2.76 0.0092 ** 451s income 0.3236 0.0233 13.91 8.9e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.967 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 65.807 MSE: 3.871 Root MSE: 1.967 451s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 49.9049 8.1797 6.10 5.7e-07 *** 451s price 0.2543 0.0891 2.85 0.0072 ** 451s farmPrice 0.2293 0.0241 9.52 3.1e-11 *** 451s trend 0.3236 0.0233 13.91 8.9e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.566 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 105.389 MSE: 6.587 Root MSE: 2.566 451s Multiple R-Squared: 0.607 Adjusted R-Squared: 0.533 451s 451s [1] "Component “call”: target, current do not match when deparsed" 451s [1] "******** 3SLS with 2 restrictions via R and restrict.regMat (EViews-like)*****" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 35 170 1.19 0.683 0.658 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 65.6 3.86 1.96 0.755 0.727 451s supply 20 16 104.6 6.54 2.56 0.610 0.537 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.30 3.73 451s supply 3.73 5.00 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.28 4.00 451s supply 4.00 5.23 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.000 0.965 451s supply 0.965 1.000 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 94.1703 7.3154 12.87 7.8e-15 *** 451s price -0.2494 0.0812 -3.07 0.0041 ** 451s income 0.3248 0.0209 15.57 < 2e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.964 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 65.577 MSE: 3.857 Root MSE: 1.964 451s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.727 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 50.0853 7.5503 6.63 1.1e-07 *** 451s price 0.2506 0.0812 3.09 0.0039 ** 451s farmPrice 0.2312 0.0212 10.88 8.9e-13 *** 451s trend 0.3248 0.0209 15.57 < 2e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.557 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 104.607 MSE: 6.538 Root MSE: 2.557 451s Multiple R-Squared: 0.61 Adjusted R-Squared: 0.537 451s 451s [1] "*** W3SLS with 2 restrictions via R and restrict.regMat (EViews-like) ***" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 35 170 1.19 0.682 0.659 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 65.6 3.86 1.97 0.755 0.726 451s supply 20 16 104.8 6.55 2.56 0.609 0.536 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.30 3.75 451s supply 3.75 5.01 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.28 4.00 451s supply 4.00 5.24 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.000 0.965 451s supply 0.965 1.000 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 94.0830 7.3058 12.88 7.5e-15 *** 451s price -0.2484 0.0812 -3.06 0.0042 ** 451s income 0.3246 0.0205 15.81 < 2e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.965 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 65.646 MSE: 3.862 Root MSE: 1.965 451s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 50.0190 7.5314 6.64 1.1e-07 *** 451s price 0.2516 0.0812 3.10 0.0038 ** 451s farmPrice 0.2309 0.0209 11.05 5.9e-13 *** 451s trend 0.3246 0.0205 15.81 < 2e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 2.559 on 16 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 16 451s SSR: 104.795 MSE: 6.55 Root MSE: 2.559 451s Multiple R-Squared: 0.609 Adjusted R-Squared: 0.536 451s 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 36 3690 5613 0.012 0.368 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s eq1 20 19 2132 112.2 10.59 0.305 0.305 451s eq2 20 17 1558 91.7 9.57 -1.335 -1.610 451s 451s The covariance matrix of the residuals used for estimation 451s eq1 eq2 451s eq1 112.2 -44.8 451s eq2 -44.8 56.8 451s 451s The covariance matrix of the residuals 451s eq1 eq2 451s eq1 112.2 -68.3 451s eq2 -68.3 91.7 451s 451s The correlations of the residuals 451s eq1 eq2 451s eq1 1.000 -0.674 451s eq2 -0.674 1.000 451s 451s 451s 3SLS estimates for 'eq1' (equation 1) 451s Model Formula: farmPrice ~ consump - 1 451s Instruments: ~trend + income 451s 451s Estimate Std. Error t value Pr(>|t|) 451s consump 0.9588 0.0235 40.9 <2e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 10.592 on 19 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 19 451s SSR: 2131.725 MSE: 112.196 Root MSE: 10.592 451s Multiple R-Squared: 0.305 Adjusted R-Squared: 0.305 451s 451s 451s 3SLS estimates for 'eq2' (equation 2) 451s Model Formula: price ~ consump + trend 451s Instruments: ~trend + income 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) -92.192 49.896 -1.85 0.0821 . 451s consump 1.953 0.499 3.92 0.0011 ** 451s trend -0.469 0.247 -1.90 0.0743 . 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 9.574 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 1558.311 MSE: 91.665 Root MSE: 9.574 451s Multiple R-Squared: -1.335 Adjusted R-Squared: -1.61 451s 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 38 56326 283068 -104 -10.6 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s eq1 20 19 2313 122 11.0 -7.63 -7.63 451s eq2 20 19 54013 2843 53.3 -200.46 -200.46 451s 451s The covariance matrix of the residuals used for estimation 451s eq1 eq2 451s eq1 121 -255 451s eq2 -255 2953 451s 451s The covariance matrix of the residuals 451s eq1 eq2 451s eq1 122 -251 451s eq2 -251 2843 451s 451s The correlations of the residuals 451s eq1 eq2 451s eq1 1.000 -0.433 451s eq2 -0.433 1.000 451s 451s 451s 3SLS estimates for 'eq1' (equation 1) 451s Model Formula: consump ~ farmPrice - 1 451s Instruments: ~price + income 451s 451s Estimate Std. Error t value Pr(>|t|) 451s farmPrice 1.0417 0.0253 41.2 <2e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 11.034 on 19 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 19 451s SSR: 2313.073 MSE: 121.741 Root MSE: 11.034 451s Multiple R-Squared: -7.627 Adjusted R-Squared: -7.627 451s 451s 451s 3SLS estimates for 'eq2' (equation 2) 451s Model Formula: consump ~ trend - 1 451s Instruments: ~price + income 451s 451s Estimate Std. Error t value Pr(>|t|) 451s trend 9.02 1.13 8 1.7e-07 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 53.318 on 19 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 19 451s SSR: 54013.367 MSE: 2842.809 Root MSE: 53.318 451s Multiple R-Squared: -200.456 Adjusted R-Squared: -200.456 451s 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 38 167069 397886 -49.1 -0.82 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s eq1 20 19 76692 4036 63.5 -285.0 -285.0 451s eq2 20 19 90377 4757 69.0 -28.5 -28.5 451s 451s The covariance matrix of the residuals used for estimation 451s eq1 eq2 451s eq1 2682 2547 451s eq2 2547 2741 451s 451s The covariance matrix of the residuals 451s eq1 eq2 451s eq1 4036 4336 451s eq2 4336 4757 451s 451s The correlations of the residuals 451s eq1 eq2 451s eq1 1.000 0.928 451s eq2 0.928 1.000 451s 451s 451s 3SLS estimates for 'eq1' (equation 1) 451s Model Formula: consump ~ trend - 1 451s Instruments: ~income + farmPrice 451s 451s Estimate Std. Error t value Pr(>|t|) 451s trend 4.162 0.723 5.75 1.5e-05 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 63.533 on 19 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 19 451s SSR: 76691.636 MSE: 4036.402 Root MSE: 63.533 451s Multiple R-Squared: -285.041 Adjusted R-Squared: -285.041 451s 451s 451s 3SLS estimates for 'eq2' (equation 2) 451s Model Formula: farmPrice ~ trend - 1 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s trend 3.274 0.676 4.84 0.00011 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 68.969 on 19 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 19 451s SSR: 90377.499 MSE: 4756.71 Root MSE: 68.969 451s Multiple R-Squared: -28.451 Adjusted R-Squared: -28.451 451s 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 39 161126 1162329 -171 -17.4 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s eq1 20 19 3553 187 13.7 -12.3 -12.3 451s eq2 20 19 157573 8293 91.1 -235.2 -235.2 451s 451s The covariance matrix of the residuals used for estimation 451s eq1 eq2 451s eq1 208 -731 451s eq2 -731 8271 451s 451s The covariance matrix of the residuals 451s eq1 eq2 451s eq1 187 -623 451s eq2 -623 8293 451s 451s The correlations of the residuals 451s eq1 eq2 451s eq1 1.000 -0.121 451s eq2 -0.121 1.000 451s 451s 451s 3SLS estimates for 'eq1' (equation 1) 451s Model Formula: consump ~ farmPrice - 1 451s Instruments: ~farmPrice + trend + income 451s 451s Estimate Std. Error t value Pr(>|t|) 451s farmPrice 1.1122 0.0272 40.8 <2e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 13.675 on 19 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 19 451s SSR: 3553.118 MSE: 187.006 Root MSE: 13.675 451s Multiple R-Squared: -12.252 Adjusted R-Squared: -12.252 451s 451s 451s 3SLS estimates for 'eq2' (equation 2) 451s Model Formula: price ~ trend - 1 451s Instruments: ~farmPrice + trend + income 451s 451s Estimate Std. Error t value Pr(>|t|) 451s trend 1.1122 0.0272 40.8 <2e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 91.068 on 19 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 19 451s SSR: 157573.328 MSE: 8293.333 Root MSE: 91.068 451s Multiple R-Squared: -235.153 Adjusted R-Squared: -235.153 451s 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 38 935 491 0 0 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s eq1 20 19 268 14.1 3.76 0 0 451s eq2 20 19 667 35.1 5.93 0 0 451s 451s The covariance matrix of the residuals used for estimation 451s eq1 eq2 451s eq1 14.11 2.18 451s eq2 2.18 35.12 451s 451s The covariance matrix of the residuals 451s eq1 eq2 451s eq1 14.11 2.18 451s eq2 2.18 35.12 451s 451s The correlations of the residuals 451s eq1 eq2 451s eq1 1.0000 0.0981 451s eq2 0.0981 1.0000 451s 451s 451s 3SLS estimates for 'eq1' (equation 1) 451s Model Formula: consump ~ 1 451s Instruments: ~income 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 100.90 0.84 120 <2e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 3.756 on 19 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 19 451s SSR: 268.114 MSE: 14.111 Root MSE: 3.756 451s Multiple R-Squared: 0 Adjusted R-Squared: 0 451s 451s 451s 3SLS estimates for 'eq2' (equation 2) 451s Model Formula: price ~ 1 451s Instruments: ~income 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 100.02 1.33 75.5 <2e-16 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 5.926 on 19 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 19 451s SSR: 667.251 MSE: 35.118 Root MSE: 5.926 451s Multiple R-Squared: 0 Adjusted R-Squared: 0 451s 451s [1] "***************************************************" 451s [1] "3SLS formula: Schmidt" 451s [1] "************* 3SLS *********************************" 451s 451s systemfit results 451s method: 3SLS 451s 451s N DF SSR detRCov OLS-R2 McElroy-R2 451s system 40 33 174 1.03 0.676 0.786 451s 451s N DF SSR MSE RMSE R2 Adj R2 451s demand 20 17 65.7 3.87 1.97 0.755 0.726 451s supply 20 16 107.9 6.75 2.60 0.598 0.522 451s 451s The covariance matrix of the residuals used for estimation 451s demand supply 451s demand 3.87 4.36 451s supply 4.36 6.04 451s 451s The covariance matrix of the residuals 451s demand supply 451s demand 3.87 5.00 451s supply 5.00 6.74 451s 451s The correlations of the residuals 451s demand supply 451s demand 1.00 0.98 451s supply 0.98 1.00 451s 451s 451s 3SLS estimates for 'demand' (equation 1) 451s Model Formula: consump ~ price + income 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 94.6333 7.9208 11.95 1.1e-09 *** 451s price -0.2436 0.0965 -2.52 0.022 * 451s income 0.3140 0.0469 6.69 3.8e-06 *** 451s --- 451s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 451s 451s Residual standard error: 1.966 on 17 degrees of freedom 451s Number of observations: 20 Degrees of Freedom: 17 451s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 451s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 451s 451s 451s 3SLS estimates for 'supply' (equation 2) 451s Model Formula: consump ~ price + farmPrice + trend 451s Instruments: ~income + farmPrice + trend 451s 451s Estimate Std. Error t value Pr(>|t|) 451s (Intercept) 52.1972 11.8934 4.39 0.00046 *** 451s price 0.2286 0.0997 2.29 0.03571 * 451s farmPrice 0.2282 0.0440 5.19 9e-05 *** 452s trend 0.3611 0.0729 4.95 0.00014 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.597 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 107.914 MSE: 6.745 Root MSE: 2.597 452s Multiple R-Squared: 0.598 Adjusted R-Squared: 0.522 452s 452s [1] "********************* 3SLS EViews-like *****************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 33 173 0.719 0.677 0.748 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 65.7 3.87 1.97 0.755 0.726 452s supply 20 16 107.2 6.70 2.59 0.600 0.525 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.29 3.59 452s supply 3.59 4.83 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.29 4.11 452s supply 4.11 5.36 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.979 452s supply 0.979 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 452s price -0.2436 0.0890 -2.74 0.0099 ** 452s income 0.3140 0.0433 7.25 2.5e-08 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.966 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 452s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 52.1176 10.6378 4.90 2.5e-05 *** 452s price 0.2289 0.0892 2.57 0.015 * 452s farmPrice 0.2290 0.0393 5.82 1.6e-06 *** 452s trend 0.3579 0.0652 5.49 4.3e-06 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.589 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 107.216 MSE: 6.701 Root MSE: 2.589 452s Multiple R-Squared: 0.6 Adjusted R-Squared: 0.525 452s 452s [1] "********************* 3SLS with methodResidCov = Theil *****************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 33 174 -0.718 0.675 0.922 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 65.7 3.87 1.97 0.755 0.726 452s supply 20 16 108.7 6.79 2.61 0.594 0.518 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.87 4.50 452s supply 4.50 6.04 452s 452s warning: this covariance matrix is NOT positive semidefinit! 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.87 5.2 452s supply 5.20 6.8 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.981 452s supply 0.981 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 452s price -0.2436 0.0965 -2.52 0.017 * 452s income 0.3140 0.0469 6.69 1.3e-07 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.966 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 452s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 52.2869 11.8853 4.40 0.00011 *** 452s price 0.2282 0.0997 2.29 0.02855 * 452s farmPrice 0.2272 0.0438 5.19 1.0e-05 *** 452s trend 0.3648 0.0707 5.16 1.1e-05 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.607 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 108.727 MSE: 6.795 Root MSE: 2.607 452s Multiple R-Squared: 0.594 Adjusted R-Squared: 0.518 452s 452s [1] "*************** W3SLS with methodResidCov = Theil *****************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 33 174 -0.718 0.675 0.922 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 65.7 3.87 1.97 0.755 0.726 452s supply 20 16 108.7 6.79 2.61 0.594 0.518 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.87 4.50 452s supply 4.50 6.04 452s 452s warning: this covariance matrix is NOT positive semidefinit! 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.87 5.2 452s supply 5.20 6.8 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.981 452s supply 0.981 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 452s price -0.2436 0.0965 -2.52 0.017 * 452s income 0.3140 0.0469 6.69 1.3e-07 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.966 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 452s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 52.2869 11.8853 4.40 0.00011 *** 452s price 0.2282 0.0997 2.29 0.02855 * 452s farmPrice 0.2272 0.0438 5.19 1.0e-05 *** 452s trend 0.3648 0.0707 5.16 1.1e-05 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.607 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 108.727 MSE: 6.795 Root MSE: 2.607 452s Multiple R-Squared: 0.594 Adjusted R-Squared: 0.518 452s 452s [1] "*************** 3SLS with restriction *****************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 34 173 1.27 0.678 0.722 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 67.8 3.99 2.00 0.747 0.717 452s supply 20 16 104.8 6.55 2.56 0.609 0.536 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.97 4.55 452s supply 4.55 6.13 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.99 4.98 452s supply 4.98 6.55 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.975 452s supply 0.975 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.222 8.015 11.76 1.6e-13 *** 452s price -0.222 0.096 -2.31 0.027 * 452s income 0.296 0.045 6.57 1.6e-07 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.997 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 67.796 MSE: 3.988 Root MSE: 1.997 452s Multiple R-Squared: 0.747 Adjusted R-Squared: 0.717 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 55.9604 11.5777 4.83 2.8e-05 *** 452s price 0.2193 0.1002 2.19 0.036 * 452s farmPrice 0.2060 0.0403 5.11 1.3e-05 *** 452s trend 0.2956 0.0450 6.57 1.6e-07 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.559 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 104.753 MSE: 6.547 Root MSE: 2.559 452s Multiple R-Squared: 0.609 Adjusted R-Squared: 0.536 452s 452s [1] "Component “call”: target, current do not match when deparsed" 452s [1] "************** 3SLS with restriction (EViews-like) *****************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 34 171 0.887 0.68 0.678 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 67.5 3.97 1.99 0.748 0.719 452s supply 20 16 104.0 6.50 2.55 0.612 0.539 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.37 3.75 452s supply 3.75 4.91 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.37 4.08 452s supply 4.08 5.20 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.974 452s supply 0.974 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.2737 7.3905 12.76 1.6e-14 *** 452s price -0.2243 0.0888 -2.53 0.016 * 452s income 0.2979 0.0420 7.10 3.4e-08 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.992 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 67.467 MSE: 3.969 Root MSE: 1.992 452s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 55.4521 10.3994 5.33 6.4e-06 *** 452s price 0.2207 0.0896 2.46 0.019 * 452s farmPrice 0.2095 0.0366 5.73 1.9e-06 *** 452s trend 0.2979 0.0420 7.10 3.4e-08 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.55 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 104.013 MSE: 6.501 Root MSE: 2.55 452s Multiple R-Squared: 0.612 Adjusted R-Squared: 0.539 452s 452s [1] 40 452s [1] "*************** W3SLS with restriction *****************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 34 173 1.24 0.677 0.725 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 68.1 4.00 2.00 0.746 0.716 452s supply 20 16 105.2 6.57 2.56 0.608 0.534 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.93 4.56 452s supply 4.56 6.15 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 4.00 5.01 452s supply 5.01 6.57 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.976 452s supply 0.976 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.1823 7.9793 11.8 1.4e-13 *** 452s price -0.2194 0.0954 -2.3 0.028 * 452s income 0.2938 0.0445 6.6 1.4e-07 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.001 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 68.057 MSE: 4.003 Root MSE: 2.001 452s Multiple R-Squared: 0.746 Adjusted R-Squared: 0.716 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 56.2541 11.5687 4.86 2.6e-05 *** 452s price 0.2184 0.1003 2.18 0.036 * 452s farmPrice 0.2040 0.0401 5.09 1.3e-05 *** 452s trend 0.2938 0.0445 6.60 1.4e-07 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.564 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 105.161 MSE: 6.573 Root MSE: 2.564 452s Multiple R-Squared: 0.608 Adjusted R-Squared: 0.534 452s 452s [1] "*************** 3SLS with restriction via restrict.regMat ********************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 34 173 1.27 0.678 0.722 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 67.8 3.99 2.00 0.747 0.717 452s supply 20 16 104.8 6.55 2.56 0.609 0.536 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.97 4.55 452s supply 4.55 6.13 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.99 4.98 452s supply 4.98 6.55 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.975 452s supply 0.975 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.222 8.015 11.76 1.6e-13 *** 452s price -0.222 0.096 -2.31 0.027 * 452s income 0.296 0.045 6.57 1.6e-07 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.997 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 67.796 MSE: 3.988 Root MSE: 1.997 452s Multiple R-Squared: 0.747 Adjusted R-Squared: 0.717 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 55.9604 11.5777 4.83 2.8e-05 *** 452s price 0.2193 0.1002 2.19 0.036 * 452s farmPrice 0.2060 0.0403 5.11 1.3e-05 *** 452s trend 0.2956 0.0450 6.57 1.6e-07 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.559 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 104.753 MSE: 6.547 Root MSE: 2.559 452s Multiple R-Squared: 0.609 Adjusted R-Squared: 0.536 452s 452s [1] "*************** 3SLS with restriction via restrict.regMat (EViews-like) *******" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 34 171 0.887 0.68 0.678 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 67.5 3.97 1.99 0.748 0.719 452s supply 20 16 104.0 6.50 2.55 0.612 0.539 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.37 3.75 452s supply 3.75 4.91 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.37 4.08 452s supply 4.08 5.20 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.974 452s supply 0.974 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.2737 7.3905 12.76 1.6e-14 *** 452s price -0.2243 0.0888 -2.53 0.016 * 452s income 0.2979 0.0420 7.10 3.4e-08 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.992 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 67.467 MSE: 3.969 Root MSE: 1.992 452s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 55.4521 10.3994 5.33 6.4e-06 *** 452s price 0.2207 0.0896 2.46 0.019 * 452s farmPrice 0.2095 0.0366 5.73 1.9e-06 *** 452s trend 0.2979 0.0420 7.10 3.4e-08 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.55 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 104.013 MSE: 6.501 Root MSE: 2.55 452s Multiple R-Squared: 0.612 Adjusted R-Squared: 0.539 452s 452s [1] "**** W3SLS with restriction via restrict.regMat (EViews-like) ****" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 34 172 0.873 0.679 0.681 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 67.7 3.98 2.00 0.748 0.718 452s supply 20 16 104.3 6.52 2.55 0.611 0.538 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.35 3.76 452s supply 3.76 4.92 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.38 4.10 452s supply 4.10 5.22 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.975 452s supply 0.975 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.2409 7.3617 12.80 1.5e-14 *** 452s price -0.2225 0.0883 -2.52 0.017 * 452s income 0.2964 0.0416 7.13 3.1e-08 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.995 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 67.672 MSE: 3.981 Root MSE: 1.995 452s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.718 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 55.6925 10.3937 5.36 5.9e-06 *** 452s price 0.2201 0.0897 2.45 0.019 * 452s farmPrice 0.2078 0.0364 5.71 2.0e-06 *** 452s trend 0.2964 0.0416 7.13 3.1e-08 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.553 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 104.312 MSE: 6.519 Root MSE: 2.553 452s Multiple R-Squared: 0.611 Adjusted R-Squared: 0.538 452s 452s [1] "*************** 3SLS with 2 restrictions **********************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 35 171 1.74 0.681 0.696 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 65.8 3.87 1.97 0.755 0.726 452s supply 20 16 105.4 6.59 2.57 0.607 0.533 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.89 4.53 452s supply 4.53 6.25 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.87 4.87 452s supply 4.87 6.59 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.965 452s supply 0.965 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 93.9070 7.9234 11.85 8.3e-14 *** 452s price -0.2457 0.0891 -2.76 0.0092 ** 452s income 0.3236 0.0233 13.91 8.9e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.967 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 65.807 MSE: 3.871 Root MSE: 1.967 452s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 49.9049 8.1797 6.10 5.7e-07 *** 452s price 0.2543 0.0891 2.85 0.0072 ** 452s farmPrice 0.2293 0.0241 9.52 3.1e-11 *** 452s trend 0.3236 0.0233 13.91 8.9e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.566 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 105.389 MSE: 6.587 Root MSE: 2.566 452s Multiple R-Squared: 0.607 Adjusted R-Squared: 0.533 452s 452s [1] "Component “call”: target, current do not match when deparsed" 452s [1] "*************** 3SLS with 2 restrictions (EViews-like) ************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 35 170 1.19 0.683 0.658 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 65.6 3.86 1.96 0.755 0.727 452s supply 20 16 104.6 6.54 2.56 0.610 0.537 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.30 3.73 452s supply 3.73 5.00 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.28 4.00 452s supply 4.00 5.23 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.965 452s supply 0.965 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.1703 7.3154 12.87 7.8e-15 *** 452s price -0.2494 0.0812 -3.07 0.0041 ** 452s income 0.3248 0.0209 15.57 < 2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.964 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 65.577 MSE: 3.857 Root MSE: 1.964 452s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.727 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 50.0853 7.5503 6.63 1.1e-07 *** 452s price 0.2506 0.0812 3.09 0.0039 ** 452s farmPrice 0.2312 0.0212 10.88 8.9e-13 *** 452s trend 0.3248 0.0209 15.57 < 2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.557 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 104.607 MSE: 6.538 Root MSE: 2.557 452s Multiple R-Squared: 0.61 Adjusted R-Squared: 0.537 452s 452s [1] "********** W3SLS with 2 (symbolic) restrictions ***************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 35 172 1.74 0.68 0.697 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 65.9 3.88 1.97 0.754 0.725 452s supply 20 16 105.7 6.60 2.57 0.606 0.532 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.88 4.55 452s supply 4.55 6.27 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.88 4.88 452s supply 4.88 6.60 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.965 452s supply 0.965 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 93.7870 7.9088 11.86 8.2e-14 *** 452s price -0.2443 0.0892 -2.74 0.0096 ** 452s income 0.3234 0.0229 14.14 4.4e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.969 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 65.907 MSE: 3.877 Root MSE: 1.969 452s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 49.8093 8.1522 6.11 5.5e-07 *** 452s price 0.2557 0.0892 2.87 0.0069 ** 452s farmPrice 0.2289 0.0237 9.67 2.0e-11 *** 452s trend 0.3234 0.0229 14.14 4.4e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.57 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 105.659 MSE: 6.604 Root MSE: 2.57 452s Multiple R-Squared: 0.606 Adjusted R-Squared: 0.532 452s 452s [1] "*************** 3SLS with 2 restrictions via R and restrict.regMat **********" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 35 171 1.74 0.681 0.696 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 65.8 3.87 1.97 0.755 0.726 452s supply 20 16 105.4 6.59 2.57 0.607 0.533 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.89 4.53 452s supply 4.53 6.25 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.87 4.87 452s supply 4.87 6.59 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.965 452s supply 0.965 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 93.9070 7.9234 11.85 8.3e-14 *** 452s price -0.2457 0.0891 -2.76 0.0092 ** 452s income 0.3236 0.0233 13.91 8.9e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.967 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 65.807 MSE: 3.871 Root MSE: 1.967 452s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 49.9049 8.1797 6.10 5.7e-07 *** 452s price 0.2543 0.0891 2.85 0.0072 ** 452s farmPrice 0.2293 0.0241 9.52 3.1e-11 *** 452s trend 0.3236 0.0233 13.91 8.9e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.566 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 105.389 MSE: 6.587 Root MSE: 2.566 452s Multiple R-Squared: 0.607 Adjusted R-Squared: 0.533 452s 452s [1] "Component “call”: target, current do not match when deparsed" 452s [1] "******** 3SLS with 2 restrictions via R and restrict.regMat (EViews-like)*****" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 35 170 1.19 0.683 0.658 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 65.6 3.86 1.96 0.755 0.727 452s supply 20 16 104.6 6.54 2.56 0.610 0.537 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.30 3.73 452s supply 3.73 5.00 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.28 4.00 452s supply 4.00 5.23 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.965 452s supply 0.965 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.1703 7.3154 12.87 7.8e-15 *** 452s price -0.2494 0.0812 -3.07 0.0041 ** 452s income 0.3248 0.0209 15.57 < 2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.964 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 65.577 MSE: 3.857 Root MSE: 1.964 452s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.727 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 50.0853 7.5503 6.63 1.1e-07 *** 452s price 0.2506 0.0812 3.09 0.0039 ** 452s farmPrice 0.2312 0.0212 10.88 8.9e-13 *** 452s trend 0.3248 0.0209 15.57 < 2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.557 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 104.607 MSE: 6.538 Root MSE: 2.557 452s Multiple R-Squared: 0.61 Adjusted R-Squared: 0.537 452s 452s [1] "*** W3SLS with 2 restrictions via R and restrict.regMat (EViews-like) ***" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 35 170 1.19 0.682 0.659 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 65.6 3.86 1.97 0.755 0.726 452s supply 20 16 104.8 6.55 2.56 0.609 0.536 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.30 3.75 452s supply 3.75 5.01 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.28 4.00 452s supply 4.00 5.24 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.965 452s supply 0.965 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.0830 7.3058 12.88 7.5e-15 *** 452s price -0.2484 0.0812 -3.06 0.0042 ** 452s income 0.3246 0.0205 15.81 < 2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.965 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 65.646 MSE: 3.862 Root MSE: 1.965 452s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 50.0190 7.5314 6.64 1.1e-07 *** 452s price 0.2516 0.0812 3.10 0.0038 ** 452s farmPrice 0.2309 0.0209 11.05 5.9e-13 *** 452s trend 0.3246 0.0205 15.81 < 2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.559 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 104.795 MSE: 6.55 Root MSE: 2.559 452s Multiple R-Squared: 0.609 Adjusted R-Squared: 0.536 452s 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 36 3690 5613 0.012 0.368 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s eq1 20 19 2132 112.2 10.59 0.305 0.305 452s eq2 20 17 1558 91.7 9.57 -1.335 -1.610 452s 452s The covariance matrix of the residuals used for estimation 452s eq1 eq2 452s eq1 112.2 -44.8 452s eq2 -44.8 56.8 452s 452s The covariance matrix of the residuals 452s eq1 eq2 452s eq1 112.2 -68.3 452s eq2 -68.3 91.7 452s 452s The correlations of the residuals 452s eq1 eq2 452s eq1 1.000 -0.674 452s eq2 -0.674 1.000 452s 452s 452s 3SLS estimates for 'eq1' (equation 1) 452s Model Formula: farmPrice ~ consump - 1 452s Instruments: ~trend + income 452s 452s Estimate Std. Error t value Pr(>|t|) 452s consump 0.9588 0.0235 40.9 <2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 10.592 on 19 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 19 452s SSR: 2131.725 MSE: 112.196 Root MSE: 10.592 452s Multiple R-Squared: 0.305 Adjusted R-Squared: 0.305 452s 452s 452s 3SLS estimates for 'eq2' (equation 2) 452s Model Formula: price ~ consump + trend 452s Instruments: ~trend + income 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) -92.192 49.896 -1.85 0.0821 . 452s consump 1.953 0.499 3.92 0.0011 ** 452s trend -0.469 0.247 -1.90 0.0743 . 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 9.574 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 1558.311 MSE: 91.665 Root MSE: 9.574 452s Multiple R-Squared: -1.335 Adjusted R-Squared: -1.61 452s 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 38 56326 283068 -104 -10.6 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s eq1 20 19 2313 122 11.0 -7.63 -7.63 452s eq2 20 19 54013 2843 53.3 -200.46 -200.46 452s 452s The covariance matrix of the residuals used for estimation 452s eq1 eq2 452s eq1 121 -255 452s eq2 -255 2953 452s 452s The covariance matrix of the residuals 452s eq1 eq2 452s eq1 122 -251 452s eq2 -251 2843 452s 452s The correlations of the residuals 452s eq1 eq2 452s eq1 1.000 -0.433 452s eq2 -0.433 1.000 452s 452s 452s 3SLS estimates for 'eq1' (equation 1) 452s Model Formula: consump ~ farmPrice - 1 452s Instruments: ~price + income 452s 452s Estimate Std. Error t value Pr(>|t|) 452s farmPrice 1.0417 0.0253 41.2 <2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 11.034 on 19 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 19 452s SSR: 2313.073 MSE: 121.741 Root MSE: 11.034 452s Multiple R-Squared: -7.627 Adjusted R-Squared: -7.627 452s 452s 452s 3SLS estimates for 'eq2' (equation 2) 452s Model Formula: consump ~ trend - 1 452s Instruments: ~price + income 452s 452s Estimate Std. Error t value Pr(>|t|) 452s trend 9.02 1.13 8 1.7e-07 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 53.318 on 19 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 19 452s SSR: 54013.367 MSE: 2842.809 Root MSE: 53.318 452s Multiple R-Squared: -200.456 Adjusted R-Squared: -200.456 452s 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 38 167069 397886 -49.1 -0.82 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s eq1 20 19 76692 4036 63.5 -285.0 -285.0 452s eq2 20 19 90377 4757 69.0 -28.5 -28.5 452s 452s The covariance matrix of the residuals used for estimation 452s eq1 eq2 452s eq1 2682 2547 452s eq2 2547 2741 452s 452s The covariance matrix of the residuals 452s eq1 eq2 452s eq1 4036 4336 452s eq2 4336 4757 452s 452s The correlations of the residuals 452s eq1 eq2 452s eq1 1.000 0.928 452s eq2 0.928 1.000 452s 452s 452s 3SLS estimates for 'eq1' (equation 1) 452s Model Formula: consump ~ trend - 1 452s Instruments: ~income + farmPrice 452s 452s Estimate Std. Error t value Pr(>|t|) 452s trend 4.162 0.723 5.75 1.5e-05 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 63.533 on 19 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 19 452s SSR: 76691.636 MSE: 4036.402 Root MSE: 63.533 452s Multiple R-Squared: -285.041 Adjusted R-Squared: -285.041 452s 452s 452s 3SLS estimates for 'eq2' (equation 2) 452s Model Formula: farmPrice ~ trend - 1 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s trend 3.274 0.676 4.84 0.00011 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 68.969 on 19 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 19 452s SSR: 90377.499 MSE: 4756.71 Root MSE: 68.969 452s Multiple R-Squared: -28.451 Adjusted R-Squared: -28.451 452s 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 39 161126 1162329 -171 -17.4 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s eq1 20 19 3553 187 13.7 -12.3 -12.3 452s eq2 20 19 157573 8293 91.1 -235.2 -235.2 452s 452s The covariance matrix of the residuals used for estimation 452s eq1 eq2 452s eq1 208 -731 452s eq2 -731 8271 452s 452s The covariance matrix of the residuals 452s eq1 eq2 452s eq1 187 -623 452s eq2 -623 8293 452s 452s The correlations of the residuals 452s eq1 eq2 452s eq1 1.000 -0.121 452s eq2 -0.121 1.000 452s 452s 452s 3SLS estimates for 'eq1' (equation 1) 452s Model Formula: consump ~ farmPrice - 1 452s Instruments: ~farmPrice + trend + income 452s 452s Estimate Std. Error t value Pr(>|t|) 452s farmPrice 1.1122 0.0272 40.8 <2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 13.675 on 19 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 19 452s SSR: 3553.118 MSE: 187.006 Root MSE: 13.675 452s Multiple R-Squared: -12.252 Adjusted R-Squared: -12.252 452s 452s 452s 3SLS estimates for 'eq2' (equation 2) 452s Model Formula: price ~ trend - 1 452s Instruments: ~farmPrice + trend + income 452s 452s Estimate Std. Error t value Pr(>|t|) 452s trend 1.1122 0.0272 40.8 <2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 91.068 on 19 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 19 452s SSR: 157573.328 MSE: 8293.333 Root MSE: 91.068 452s Multiple R-Squared: -235.153 Adjusted R-Squared: -235.153 452s 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 38 935 491 0 0 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s eq1 20 19 268 14.1 3.76 0 0 452s eq2 20 19 667 35.1 5.93 0 0 452s 452s The covariance matrix of the residuals used for estimation 452s eq1 eq2 452s eq1 14.11 2.18 452s eq2 2.18 35.12 452s 452s The covariance matrix of the residuals 452s eq1 eq2 452s eq1 14.11 2.18 452s eq2 2.18 35.12 452s 452s The correlations of the residuals 452s eq1 eq2 452s eq1 1.0000 0.0981 452s eq2 0.0981 1.0000 452s 452s 452s 3SLS estimates for 'eq1' (equation 1) 452s Model Formula: consump ~ 1 452s Instruments: ~income 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 100.90 0.84 120 <2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 3.756 on 19 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 19 452s SSR: 268.114 MSE: 14.111 Root MSE: 3.756 452s Multiple R-Squared: 0 Adjusted R-Squared: 0 452s 452s 452s 3SLS estimates for 'eq2' (equation 2) 452s Model Formula: price ~ 1 452s Instruments: ~income 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 100.02 1.33 75.5 <2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 5.926 on 19 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 19 452s SSR: 667.251 MSE: 35.118 Root MSE: 5.926 452s Multiple R-Squared: 0 Adjusted R-Squared: 0 452s 452s [1] "***************************************************" 452s [1] "3SLS formula: GMM" 452s [1] "************* 3SLS *********************************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 33 174 1.03 0.676 0.786 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 65.7 3.87 1.97 0.755 0.726 452s supply 20 16 107.9 6.75 2.60 0.598 0.522 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.87 4.36 452s supply 4.36 6.04 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.87 5.00 452s supply 5.00 6.74 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.00 0.98 452s supply 0.98 1.00 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.6333 7.9208 11.95 1.1e-09 *** 452s price -0.2436 0.0965 -2.52 0.022 * 452s income 0.3140 0.0469 6.69 3.8e-06 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.966 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 452s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 52.1972 11.8934 4.39 0.00046 *** 452s price 0.2286 0.0997 2.29 0.03571 * 452s farmPrice 0.2282 0.0440 5.19 9e-05 *** 452s trend 0.3611 0.0729 4.95 0.00014 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.597 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 107.914 MSE: 6.745 Root MSE: 2.597 452s Multiple R-Squared: 0.598 Adjusted R-Squared: 0.522 452s 452s [1] "********************* 3SLS EViews-like *****************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 33 173 0.719 0.677 0.748 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 65.7 3.87 1.97 0.755 0.726 452s supply 20 16 107.2 6.70 2.59 0.600 0.525 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.29 3.59 452s supply 3.59 4.83 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.29 4.11 452s supply 4.11 5.36 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.979 452s supply 0.979 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 452s price -0.2436 0.0890 -2.74 0.0099 ** 452s income 0.3140 0.0433 7.25 2.5e-08 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.966 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 452s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 52.1176 10.6378 4.90 2.5e-05 *** 452s price 0.2289 0.0892 2.57 0.015 * 452s farmPrice 0.2290 0.0393 5.82 1.6e-06 *** 452s trend 0.3579 0.0652 5.49 4.3e-06 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.589 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 107.216 MSE: 6.701 Root MSE: 2.589 452s Multiple R-Squared: 0.6 Adjusted R-Squared: 0.525 452s 452s [1] "********************* 3SLS with methodResidCov = Theil *****************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 33 174 -0.718 0.675 0.922 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 65.7 3.87 1.97 0.755 0.726 452s supply 20 16 108.7 6.79 2.61 0.594 0.518 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.87 4.50 452s supply 4.50 6.04 452s 452s warning: this covariance matrix is NOT positive semidefinit! 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.87 5.2 452s supply 5.20 6.8 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.981 452s supply 0.981 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 452s price -0.2436 0.0965 -2.52 0.017 * 452s income 0.3140 0.0469 6.69 1.3e-07 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.966 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 452s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 52.2869 11.8853 4.40 0.00011 *** 452s price 0.2282 0.0997 2.29 0.02855 * 452s farmPrice 0.2272 0.0438 5.19 1.0e-05 *** 452s trend 0.3648 0.0707 5.16 1.1e-05 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.607 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 108.727 MSE: 6.795 Root MSE: 2.607 452s Multiple R-Squared: 0.594 Adjusted R-Squared: 0.518 452s 452s [1] "*************** W3SLS with methodResidCov = Theil *****************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 33 174 -0.718 0.675 0.922 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 65.7 3.87 1.97 0.755 0.726 452s supply 20 16 108.7 6.79 2.61 0.594 0.518 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.87 4.50 452s supply 4.50 6.04 452s 452s warning: this covariance matrix is NOT positive semidefinit! 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.87 5.2 452s supply 5.20 6.8 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.981 452s supply 0.981 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 452s price -0.2436 0.0965 -2.52 0.017 * 452s income 0.3140 0.0469 6.69 1.3e-07 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.966 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 452s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 52.2869 11.8853 4.40 0.00011 *** 452s price 0.2282 0.0997 2.29 0.02855 * 452s farmPrice 0.2272 0.0438 5.19 1.0e-05 *** 452s trend 0.3648 0.0707 5.16 1.1e-05 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.607 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 108.727 MSE: 6.795 Root MSE: 2.607 452s Multiple R-Squared: 0.594 Adjusted R-Squared: 0.518 452s 452s [1] "*************** 3SLS with restriction *****************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 34 173 1.27 0.678 0.722 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 67.8 3.99 2.00 0.747 0.717 452s supply 20 16 104.8 6.55 2.56 0.609 0.536 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.97 4.55 452s supply 4.55 6.13 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.99 4.98 452s supply 4.98 6.55 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.975 452s supply 0.975 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.222 8.015 11.76 1.6e-13 *** 452s price -0.222 0.096 -2.31 0.027 * 452s income 0.296 0.045 6.57 1.6e-07 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.997 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 67.796 MSE: 3.988 Root MSE: 1.997 452s Multiple R-Squared: 0.747 Adjusted R-Squared: 0.717 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 55.9604 11.5777 4.83 2.8e-05 *** 452s price 0.2193 0.1002 2.19 0.036 * 452s farmPrice 0.2060 0.0403 5.11 1.3e-05 *** 452s trend 0.2956 0.0450 6.57 1.6e-07 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.559 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 104.753 MSE: 6.547 Root MSE: 2.559 452s Multiple R-Squared: 0.609 Adjusted R-Squared: 0.536 452s 452s [1] "Component “call”: target, current do not match when deparsed" 452s [1] "************** 3SLS with restriction (EViews-like) *****************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 34 171 0.887 0.68 0.678 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 67.5 3.97 1.99 0.748 0.719 452s supply 20 16 104.0 6.50 2.55 0.612 0.539 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.37 3.75 452s supply 3.75 4.91 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.37 4.08 452s supply 4.08 5.20 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.974 452s supply 0.974 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.2737 7.3905 12.76 1.6e-14 *** 452s price -0.2243 0.0888 -2.53 0.016 * 452s income 0.2979 0.0420 7.10 3.4e-08 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.992 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 67.467 MSE: 3.969 Root MSE: 1.992 452s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 55.4521 10.3994 5.33 6.4e-06 *** 452s price 0.2207 0.0896 2.46 0.019 * 452s farmPrice 0.2095 0.0366 5.73 1.9e-06 *** 452s trend 0.2979 0.0420 7.10 3.4e-08 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.55 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 104.013 MSE: 6.501 Root MSE: 2.55 452s Multiple R-Squared: 0.612 Adjusted R-Squared: 0.539 452s 452s [1] 40 452s [1] "*************** W3SLS with restriction *****************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 34 173 1.24 0.677 0.725 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 68.1 4.00 2.00 0.746 0.716 452s supply 20 16 105.2 6.57 2.56 0.608 0.534 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.93 4.56 452s supply 4.56 6.15 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 4.00 5.01 452s supply 5.01 6.57 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.976 452s supply 0.976 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.1823 7.9793 11.8 1.4e-13 *** 452s price -0.2194 0.0954 -2.3 0.028 * 452s income 0.2938 0.0445 6.6 1.4e-07 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.001 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 68.057 MSE: 4.003 Root MSE: 2.001 452s Multiple R-Squared: 0.746 Adjusted R-Squared: 0.716 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 56.2541 11.5687 4.86 2.6e-05 *** 452s price 0.2184 0.1003 2.18 0.036 * 452s farmPrice 0.2040 0.0401 5.09 1.3e-05 *** 452s trend 0.2938 0.0445 6.60 1.4e-07 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.564 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 105.161 MSE: 6.573 Root MSE: 2.564 452s Multiple R-Squared: 0.608 Adjusted R-Squared: 0.534 452s 452s [1] "*************** 3SLS with restriction via restrict.regMat ********************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 34 173 1.27 0.678 0.722 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 67.8 3.99 2.00 0.747 0.717 452s supply 20 16 104.8 6.55 2.56 0.609 0.536 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.97 4.55 452s supply 4.55 6.13 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.99 4.98 452s supply 4.98 6.55 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.975 452s supply 0.975 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.222 8.015 11.76 1.6e-13 *** 452s price -0.222 0.096 -2.31 0.027 * 452s income 0.296 0.045 6.57 1.6e-07 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.997 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 67.796 MSE: 3.988 Root MSE: 1.997 452s Multiple R-Squared: 0.747 Adjusted R-Squared: 0.717 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 55.9604 11.5777 4.83 2.8e-05 *** 452s price 0.2193 0.1002 2.19 0.036 * 452s farmPrice 0.2060 0.0403 5.11 1.3e-05 *** 452s trend 0.2956 0.0450 6.57 1.6e-07 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.559 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 104.753 MSE: 6.547 Root MSE: 2.559 452s Multiple R-Squared: 0.609 Adjusted R-Squared: 0.536 452s 452s [1] "*************** 3SLS with restriction via restrict.regMat (EViews-like) *******" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 34 171 0.887 0.68 0.678 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 67.5 3.97 1.99 0.748 0.719 452s supply 20 16 104.0 6.50 2.55 0.612 0.539 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.37 3.75 452s supply 3.75 4.91 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.37 4.08 452s supply 4.08 5.20 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.974 452s supply 0.974 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.2737 7.3905 12.76 1.6e-14 *** 452s price -0.2243 0.0888 -2.53 0.016 * 452s income 0.2979 0.0420 7.10 3.4e-08 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.992 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 67.467 MSE: 3.969 Root MSE: 1.992 452s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 55.4521 10.3994 5.33 6.4e-06 *** 452s price 0.2207 0.0896 2.46 0.019 * 452s farmPrice 0.2095 0.0366 5.73 1.9e-06 *** 452s trend 0.2979 0.0420 7.10 3.4e-08 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.55 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 104.013 MSE: 6.501 Root MSE: 2.55 452s Multiple R-Squared: 0.612 Adjusted R-Squared: 0.539 452s 452s [1] "**** W3SLS with restriction via restrict.regMat (EViews-like) ****" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 34 172 0.873 0.679 0.681 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 67.7 3.98 2.00 0.748 0.718 452s supply 20 16 104.3 6.52 2.55 0.611 0.538 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.35 3.76 452s supply 3.76 4.92 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.38 4.10 452s supply 4.10 5.22 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.975 452s supply 0.975 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.2409 7.3617 12.80 1.5e-14 *** 452s price -0.2225 0.0883 -2.52 0.017 * 452s income 0.2964 0.0416 7.13 3.1e-08 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.995 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 67.672 MSE: 3.981 Root MSE: 1.995 452s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.718 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 55.6925 10.3937 5.36 5.9e-06 *** 452s price 0.2201 0.0897 2.45 0.019 * 452s farmPrice 0.2078 0.0364 5.71 2.0e-06 *** 452s trend 0.2964 0.0416 7.13 3.1e-08 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.553 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 104.312 MSE: 6.519 Root MSE: 2.553 452s Multiple R-Squared: 0.611 Adjusted R-Squared: 0.538 452s 452s [1] "*************** 3SLS with 2 restrictions **********************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 35 171 1.74 0.681 0.696 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 65.8 3.87 1.97 0.755 0.726 452s supply 20 16 105.4 6.59 2.57 0.607 0.533 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.89 4.53 452s supply 4.53 6.25 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.87 4.87 452s supply 4.87 6.59 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.965 452s supply 0.965 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 93.9070 7.9234 11.85 8.3e-14 *** 452s price -0.2457 0.0891 -2.76 0.0092 ** 452s income 0.3236 0.0233 13.91 8.9e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.967 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 65.807 MSE: 3.871 Root MSE: 1.967 452s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 49.9049 8.1797 6.10 5.7e-07 *** 452s price 0.2543 0.0891 2.85 0.0072 ** 452s farmPrice 0.2293 0.0241 9.52 3.1e-11 *** 452s trend 0.3236 0.0233 13.91 8.9e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.566 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 105.389 MSE: 6.587 Root MSE: 2.566 452s Multiple R-Squared: 0.607 Adjusted R-Squared: 0.533 452s 452s [1] "Component “call”: target, current do not match when deparsed" 452s [1] "*************** 3SLS with 2 restrictions (EViews-like) ************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 35 170 1.19 0.683 0.658 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 65.6 3.86 1.96 0.755 0.727 452s supply 20 16 104.6 6.54 2.56 0.610 0.537 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.30 3.73 452s supply 3.73 5.00 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.28 4.00 452s supply 4.00 5.23 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.965 452s supply 0.965 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.1703 7.3154 12.87 7.8e-15 *** 452s price -0.2494 0.0812 -3.07 0.0041 ** 452s income 0.3248 0.0209 15.57 < 2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.964 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 65.577 MSE: 3.857 Root MSE: 1.964 452s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.727 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 50.0853 7.5503 6.63 1.1e-07 *** 452s price 0.2506 0.0812 3.09 0.0039 ** 452s farmPrice 0.2312 0.0212 10.88 8.9e-13 *** 452s trend 0.3248 0.0209 15.57 < 2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.557 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 104.607 MSE: 6.538 Root MSE: 2.557 452s Multiple R-Squared: 0.61 Adjusted R-Squared: 0.537 452s 452s [1] "********** W3SLS with 2 (symbolic) restrictions ***************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 35 172 1.74 0.68 0.697 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 65.9 3.88 1.97 0.754 0.725 452s supply 20 16 105.7 6.60 2.57 0.606 0.532 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.88 4.55 452s supply 4.55 6.27 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.88 4.88 452s supply 4.88 6.60 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.965 452s supply 0.965 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 93.7870 7.9088 11.86 8.2e-14 *** 452s price -0.2443 0.0892 -2.74 0.0096 ** 452s income 0.3234 0.0229 14.14 4.4e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.969 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 65.907 MSE: 3.877 Root MSE: 1.969 452s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 49.8093 8.1522 6.11 5.5e-07 *** 452s price 0.2557 0.0892 2.87 0.0069 ** 452s farmPrice 0.2289 0.0237 9.67 2.0e-11 *** 452s trend 0.3234 0.0229 14.14 4.4e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.57 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 105.659 MSE: 6.604 Root MSE: 2.57 452s Multiple R-Squared: 0.606 Adjusted R-Squared: 0.532 452s 452s [1] "*************** 3SLS with 2 restrictions via R and restrict.regMat **********" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 35 171 1.74 0.681 0.696 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 65.8 3.87 1.97 0.755 0.726 452s supply 20 16 105.4 6.59 2.57 0.607 0.533 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.89 4.53 452s supply 4.53 6.25 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.87 4.87 452s supply 4.87 6.59 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.965 452s supply 0.965 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 93.9070 7.9234 11.85 8.3e-14 *** 452s price -0.2457 0.0891 -2.76 0.0092 ** 452s income 0.3236 0.0233 13.91 8.9e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.967 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 65.807 MSE: 3.871 Root MSE: 1.967 452s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 49.9049 8.1797 6.10 5.7e-07 *** 452s price 0.2543 0.0891 2.85 0.0072 ** 452s farmPrice 0.2293 0.0241 9.52 3.1e-11 *** 452s trend 0.3236 0.0233 13.91 8.9e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.566 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 105.389 MSE: 6.587 Root MSE: 2.566 452s Multiple R-Squared: 0.607 Adjusted R-Squared: 0.533 452s 452s [1] "Component “call”: target, current do not match when deparsed" 452s [1] "******** 3SLS with 2 restrictions via R and restrict.regMat (EViews-like)*****" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 35 170 1.19 0.683 0.658 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 65.6 3.86 1.96 0.755 0.727 452s supply 20 16 104.6 6.54 2.56 0.610 0.537 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.30 3.73 452s supply 3.73 5.00 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.28 4.00 452s supply 4.00 5.23 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.965 452s supply 0.965 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.1703 7.3154 12.87 7.8e-15 *** 452s price -0.2494 0.0812 -3.07 0.0041 ** 452s income 0.3248 0.0209 15.57 < 2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.964 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 65.577 MSE: 3.857 Root MSE: 1.964 452s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.727 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 50.0853 7.5503 6.63 1.1e-07 *** 452s price 0.2506 0.0812 3.09 0.0039 ** 452s farmPrice 0.2312 0.0212 10.88 8.9e-13 *** 452s trend 0.3248 0.0209 15.57 < 2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.557 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 104.607 MSE: 6.538 Root MSE: 2.557 452s Multiple R-Squared: 0.61 Adjusted R-Squared: 0.537 452s 452s [1] "*** W3SLS with 2 restrictions via R and restrict.regMat (EViews-like) ***" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 35 170 1.19 0.682 0.659 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 65.6 3.86 1.97 0.755 0.726 452s supply 20 16 104.8 6.55 2.56 0.609 0.536 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.30 3.75 452s supply 3.75 5.01 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.28 4.00 452s supply 4.00 5.24 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.965 452s supply 0.965 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.0830 7.3058 12.88 7.5e-15 *** 452s price -0.2484 0.0812 -3.06 0.0042 ** 452s income 0.3246 0.0205 15.81 < 2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.965 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 65.646 MSE: 3.862 Root MSE: 1.965 452s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 50.0190 7.5314 6.64 1.1e-07 *** 452s price 0.2516 0.0812 3.10 0.0038 ** 452s farmPrice 0.2309 0.0209 11.05 5.9e-13 *** 452s trend 0.3246 0.0205 15.81 < 2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.559 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 104.795 MSE: 6.55 Root MSE: 2.559 452s Multiple R-Squared: 0.609 Adjusted R-Squared: 0.536 452s 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 36 3690 5613 0.012 0.368 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s eq1 20 19 2132 112.2 10.59 0.305 0.305 452s eq2 20 17 1558 91.7 9.57 -1.335 -1.610 452s 452s The covariance matrix of the residuals used for estimation 452s eq1 eq2 452s eq1 112.2 -44.8 452s eq2 -44.8 56.8 452s 452s The covariance matrix of the residuals 452s eq1 eq2 452s eq1 112.2 -68.3 452s eq2 -68.3 91.7 452s 452s The correlations of the residuals 452s eq1 eq2 452s eq1 1.000 -0.674 452s eq2 -0.674 1.000 452s 452s 452s 3SLS estimates for 'eq1' (equation 1) 452s Model Formula: farmPrice ~ consump - 1 452s Instruments: ~trend + income 452s 452s Estimate Std. Error t value Pr(>|t|) 452s consump 0.9588 0.0235 40.9 <2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 10.592 on 19 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 19 452s SSR: 2131.725 MSE: 112.196 Root MSE: 10.592 452s Multiple R-Squared: 0.305 Adjusted R-Squared: 0.305 452s 452s 452s 3SLS estimates for 'eq2' (equation 2) 452s Model Formula: price ~ consump + trend 452s Instruments: ~trend + income 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) -92.192 49.896 -1.85 0.0821 . 452s consump 1.953 0.499 3.92 0.0011 ** 452s trend -0.469 0.247 -1.90 0.0743 . 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 9.574 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 1558.311 MSE: 91.665 Root MSE: 9.574 452s Multiple R-Squared: -1.335 Adjusted R-Squared: -1.61 452s 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 38 56326 283068 -104 -10.6 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s eq1 20 19 2313 122 11.0 -7.63 -7.63 452s eq2 20 19 54013 2843 53.3 -200.46 -200.46 452s 452s The covariance matrix of the residuals used for estimation 452s eq1 eq2 452s eq1 121 -255 452s eq2 -255 2953 452s 452s The covariance matrix of the residuals 452s eq1 eq2 452s eq1 122 -251 452s eq2 -251 2843 452s 452s The correlations of the residuals 452s eq1 eq2 452s eq1 1.000 -0.433 452s eq2 -0.433 1.000 452s 452s 452s 3SLS estimates for 'eq1' (equation 1) 452s Model Formula: consump ~ farmPrice - 1 452s Instruments: ~price + income 452s 452s Estimate Std. Error t value Pr(>|t|) 452s farmPrice 1.0417 0.0253 41.2 <2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 11.034 on 19 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 19 452s SSR: 2313.073 MSE: 121.741 Root MSE: 11.034 452s Multiple R-Squared: -7.627 Adjusted R-Squared: -7.627 452s 452s 452s 3SLS estimates for 'eq2' (equation 2) 452s Model Formula: consump ~ trend - 1 452s Instruments: ~price + income 452s 452s Estimate Std. Error t value Pr(>|t|) 452s trend 9.02 1.13 8 1.7e-07 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 53.318 on 19 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 19 452s SSR: 54013.367 MSE: 2842.809 Root MSE: 53.318 452s Multiple R-Squared: -200.456 Adjusted R-Squared: -200.456 452s 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 38 167069 397886 -49.1 -0.82 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s eq1 20 19 76692 4036 63.5 -285.0 -285.0 452s eq2 20 19 90377 4757 69.0 -28.5 -28.5 452s 452s The covariance matrix of the residuals used for estimation 452s eq1 eq2 452s eq1 2682 2547 452s eq2 2547 2741 452s 452s The covariance matrix of the residuals 452s eq1 eq2 452s eq1 4036 4336 452s eq2 4336 4757 452s 452s The correlations of the residuals 452s eq1 eq2 452s eq1 1.000 0.928 452s eq2 0.928 1.000 452s 452s 452s 3SLS estimates for 'eq1' (equation 1) 452s Model Formula: consump ~ trend - 1 452s Instruments: ~income + farmPrice 452s 452s Estimate Std. Error t value Pr(>|t|) 452s trend 4.162 0.723 5.75 1.5e-05 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 63.533 on 19 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 19 452s SSR: 76691.636 MSE: 4036.402 Root MSE: 63.533 452s Multiple R-Squared: -285.041 Adjusted R-Squared: -285.041 452s 452s 452s 3SLS estimates for 'eq2' (equation 2) 452s Model Formula: farmPrice ~ trend - 1 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s trend 3.274 0.676 4.84 0.00011 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 68.969 on 19 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 19 452s SSR: 90377.499 MSE: 4756.71 Root MSE: 68.969 452s Multiple R-Squared: -28.451 Adjusted R-Squared: -28.451 452s 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 39 161126 1162329 -171 -17.4 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s eq1 20 19 3553 187 13.7 -12.3 -12.3 452s eq2 20 19 157573 8293 91.1 -235.2 -235.2 452s 452s The covariance matrix of the residuals used for estimation 452s eq1 eq2 452s eq1 208 -731 452s eq2 -731 8271 452s 452s The covariance matrix of the residuals 452s eq1 eq2 452s eq1 187 -623 452s eq2 -623 8293 452s 452s The correlations of the residuals 452s eq1 eq2 452s eq1 1.000 -0.121 452s eq2 -0.121 1.000 452s 452s 452s 3SLS estimates for 'eq1' (equation 1) 452s Model Formula: consump ~ farmPrice - 1 452s Instruments: ~farmPrice + trend + income 452s 452s Estimate Std. Error t value Pr(>|t|) 452s farmPrice 1.1122 0.0272 40.8 <2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 13.675 on 19 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 19 452s SSR: 3553.118 MSE: 187.006 Root MSE: 13.675 452s Multiple R-Squared: -12.252 Adjusted R-Squared: -12.252 452s 452s 452s 3SLS estimates for 'eq2' (equation 2) 452s Model Formula: price ~ trend - 1 452s Instruments: ~farmPrice + trend + income 452s 452s Estimate Std. Error t value Pr(>|t|) 452s trend 1.1122 0.0272 40.8 <2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 91.068 on 19 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 19 452s SSR: 157573.328 MSE: 8293.333 Root MSE: 91.068 452s Multiple R-Squared: -235.153 Adjusted R-Squared: -235.153 452s 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 38 935 491 0 0 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s eq1 20 19 268 14.1 3.76 0 0 452s eq2 20 19 667 35.1 5.93 0 0 452s 452s The covariance matrix of the residuals used for estimation 452s eq1 eq2 452s eq1 14.11 2.18 452s eq2 2.18 35.12 452s 452s The covariance matrix of the residuals 452s eq1 eq2 452s eq1 14.11 2.18 452s eq2 2.18 35.12 452s 452s The correlations of the residuals 452s eq1 eq2 452s eq1 1.0000 0.0981 452s eq2 0.0981 1.0000 452s 452s 452s 3SLS estimates for 'eq1' (equation 1) 452s Model Formula: consump ~ 1 452s Instruments: ~income 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 100.90 0.84 120 <2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 3.756 on 19 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 19 452s SSR: 268.114 MSE: 14.111 Root MSE: 3.756 452s Multiple R-Squared: 0 Adjusted R-Squared: 0 452s 452s 452s 3SLS estimates for 'eq2' (equation 2) 452s Model Formula: price ~ 1 452s Instruments: ~income 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 100.02 1.33 75.5 <2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 5.926 on 19 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 19 452s SSR: 667.251 MSE: 35.118 Root MSE: 5.926 452s Multiple R-Squared: 0 Adjusted R-Squared: 0 452s 452s [1] "***************************************************" 452s [1] "3SLS formula: EViews" 452s [1] "************* 3SLS *********************************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 33 174 1.03 0.676 0.786 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 65.7 3.87 1.97 0.755 0.726 452s supply 20 16 107.9 6.75 2.60 0.598 0.522 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.87 4.36 452s supply 4.36 6.04 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.87 5.00 452s supply 5.00 6.74 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.00 0.98 452s supply 0.98 1.00 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.6333 7.9208 11.95 1.1e-09 *** 452s price -0.2436 0.0965 -2.52 0.022 * 452s income 0.3140 0.0469 6.69 3.8e-06 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.966 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 452s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 52.1972 11.8934 4.39 0.00046 *** 452s price 0.2286 0.0997 2.29 0.03571 * 452s farmPrice 0.2282 0.0440 5.19 9e-05 *** 452s trend 0.3611 0.0729 4.95 0.00014 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.597 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 107.914 MSE: 6.745 Root MSE: 2.597 452s Multiple R-Squared: 0.598 Adjusted R-Squared: 0.522 452s 452s [1] "********************* 3SLS EViews-like *****************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 33 173 0.719 0.677 0.748 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 65.7 3.87 1.97 0.755 0.726 452s supply 20 16 107.2 6.70 2.59 0.600 0.525 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.29 3.59 452s supply 3.59 4.83 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.29 4.11 452s supply 4.11 5.36 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.979 452s supply 0.979 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 452s price -0.2436 0.0890 -2.74 0.0099 ** 452s income 0.3140 0.0433 7.25 2.5e-08 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.966 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 452s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 52.1176 10.6378 4.90 2.5e-05 *** 452s price 0.2289 0.0892 2.57 0.015 * 452s farmPrice 0.2290 0.0393 5.82 1.6e-06 *** 452s trend 0.3579 0.0652 5.49 4.3e-06 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.589 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 107.216 MSE: 6.701 Root MSE: 2.589 452s Multiple R-Squared: 0.6 Adjusted R-Squared: 0.525 452s 452s [1] "********************* 3SLS with methodResidCov = Theil *****************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 33 174 -0.718 0.675 0.922 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 65.7 3.87 1.97 0.755 0.726 452s supply 20 16 108.7 6.79 2.61 0.594 0.518 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.87 4.50 452s supply 4.50 6.04 452s 452s warning: this covariance matrix is NOT positive semidefinit! 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.87 5.2 452s supply 5.20 6.8 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.981 452s supply 0.981 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 452s price -0.2436 0.0965 -2.52 0.017 * 452s income 0.3140 0.0469 6.69 1.3e-07 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.966 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 452s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 52.2869 11.8853 4.40 0.00011 *** 452s price 0.2282 0.0997 2.29 0.02855 * 452s farmPrice 0.2272 0.0438 5.19 1.0e-05 *** 452s trend 0.3648 0.0707 5.16 1.1e-05 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.607 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 108.727 MSE: 6.795 Root MSE: 2.607 452s Multiple R-Squared: 0.594 Adjusted R-Squared: 0.518 452s 452s [1] "*************** W3SLS with methodResidCov = Theil *****************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 33 174 -0.718 0.675 0.922 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 65.7 3.87 1.97 0.755 0.726 452s supply 20 16 108.7 6.79 2.61 0.594 0.518 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.87 4.50 452s supply 4.50 6.04 452s 452s warning: this covariance matrix is NOT positive semidefinit! 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.87 5.2 452s supply 5.20 6.8 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.981 452s supply 0.981 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 452s price -0.2436 0.0965 -2.52 0.017 * 452s income 0.3140 0.0469 6.69 1.3e-07 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.966 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 452s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 52.2869 11.8853 4.40 0.00011 *** 452s price 0.2282 0.0997 2.29 0.02855 * 452s farmPrice 0.2272 0.0438 5.19 1.0e-05 *** 452s trend 0.3648 0.0707 5.16 1.1e-05 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.607 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 108.727 MSE: 6.795 Root MSE: 2.607 452s Multiple R-Squared: 0.594 Adjusted R-Squared: 0.518 452s 452s [1] "*************** 3SLS with restriction *****************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 34 173 1.27 0.678 0.722 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 67.8 3.99 2.00 0.747 0.717 452s supply 20 16 104.8 6.55 2.56 0.609 0.536 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.97 4.55 452s supply 4.55 6.13 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.99 4.98 452s supply 4.98 6.55 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.975 452s supply 0.975 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.222 8.015 11.76 1.6e-13 *** 452s price -0.222 0.096 -2.31 0.027 * 452s income 0.296 0.045 6.57 1.6e-07 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.997 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 67.796 MSE: 3.988 Root MSE: 1.997 452s Multiple R-Squared: 0.747 Adjusted R-Squared: 0.717 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 55.9604 11.5777 4.83 2.8e-05 *** 452s price 0.2193 0.1002 2.19 0.036 * 452s farmPrice 0.2060 0.0403 5.11 1.3e-05 *** 452s trend 0.2956 0.0450 6.57 1.6e-07 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.559 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 104.753 MSE: 6.547 Root MSE: 2.559 452s Multiple R-Squared: 0.609 Adjusted R-Squared: 0.536 452s 452s [1] "Component “call”: target, current do not match when deparsed" 452s [1] "************** 3SLS with restriction (EViews-like) *****************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 34 171 0.887 0.68 0.678 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 67.5 3.97 1.99 0.748 0.719 452s supply 20 16 104.0 6.50 2.55 0.612 0.539 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.37 3.75 452s supply 3.75 4.91 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.37 4.08 452s supply 4.08 5.20 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.974 452s supply 0.974 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.2737 7.3905 12.76 1.6e-14 *** 452s price -0.2243 0.0888 -2.53 0.016 * 452s income 0.2979 0.0420 7.10 3.4e-08 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.992 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 67.467 MSE: 3.969 Root MSE: 1.992 452s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 55.4521 10.3994 5.33 6.4e-06 *** 452s price 0.2207 0.0896 2.46 0.019 * 452s farmPrice 0.2095 0.0366 5.73 1.9e-06 *** 452s trend 0.2979 0.0420 7.10 3.4e-08 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.55 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 104.013 MSE: 6.501 Root MSE: 2.55 452s Multiple R-Squared: 0.612 Adjusted R-Squared: 0.539 452s 452s [1] 40 452s [1] "*************** W3SLS with restriction *****************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 34 173 1.24 0.677 0.725 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 68.1 4.00 2.00 0.746 0.716 452s supply 20 16 105.2 6.57 2.56 0.608 0.534 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.93 4.56 452s supply 4.56 6.15 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 4.00 5.01 452s supply 5.01 6.57 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.976 452s supply 0.976 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.1823 7.9793 11.8 1.4e-13 *** 452s price -0.2194 0.0954 -2.3 0.028 * 452s income 0.2938 0.0445 6.6 1.4e-07 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.001 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 68.057 MSE: 4.003 Root MSE: 2.001 452s Multiple R-Squared: 0.746 Adjusted R-Squared: 0.716 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 56.2541 11.5687 4.86 2.6e-05 *** 452s price 0.2184 0.1003 2.18 0.036 * 452s farmPrice 0.2040 0.0401 5.09 1.3e-05 *** 452s trend 0.2938 0.0445 6.60 1.4e-07 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.564 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 105.161 MSE: 6.573 Root MSE: 2.564 452s Multiple R-Squared: 0.608 Adjusted R-Squared: 0.534 452s 452s [1] "*************** 3SLS with restriction via restrict.regMat ********************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 34 173 1.27 0.678 0.722 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 67.8 3.99 2.00 0.747 0.717 452s supply 20 16 104.8 6.55 2.56 0.609 0.536 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.97 4.55 452s supply 4.55 6.13 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.99 4.98 452s supply 4.98 6.55 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.975 452s supply 0.975 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.222 8.015 11.76 1.6e-13 *** 452s price -0.222 0.096 -2.31 0.027 * 452s income 0.296 0.045 6.57 1.6e-07 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.997 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 67.796 MSE: 3.988 Root MSE: 1.997 452s Multiple R-Squared: 0.747 Adjusted R-Squared: 0.717 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 55.9604 11.5777 4.83 2.8e-05 *** 452s price 0.2193 0.1002 2.19 0.036 * 452s farmPrice 0.2060 0.0403 5.11 1.3e-05 *** 452s trend 0.2956 0.0450 6.57 1.6e-07 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.559 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 104.753 MSE: 6.547 Root MSE: 2.559 452s Multiple R-Squared: 0.609 Adjusted R-Squared: 0.536 452s 452s [1] "*************** 3SLS with restriction via restrict.regMat (EViews-like) *******" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 34 171 0.887 0.68 0.678 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 67.5 3.97 1.99 0.748 0.719 452s supply 20 16 104.0 6.50 2.55 0.612 0.539 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.37 3.75 452s supply 3.75 4.91 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.37 4.08 452s supply 4.08 5.20 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.974 452s supply 0.974 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.2737 7.3905 12.76 1.6e-14 *** 452s price -0.2243 0.0888 -2.53 0.016 * 452s income 0.2979 0.0420 7.10 3.4e-08 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.992 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 67.467 MSE: 3.969 Root MSE: 1.992 452s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 55.4521 10.3994 5.33 6.4e-06 *** 452s price 0.2207 0.0896 2.46 0.019 * 452s farmPrice 0.2095 0.0366 5.73 1.9e-06 *** 452s trend 0.2979 0.0420 7.10 3.4e-08 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.55 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 104.013 MSE: 6.501 Root MSE: 2.55 452s Multiple R-Squared: 0.612 Adjusted R-Squared: 0.539 452s 452s [1] "**** W3SLS with restriction via restrict.regMat (EViews-like) ****" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 34 172 0.873 0.679 0.681 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 67.7 3.98 2.00 0.748 0.718 452s supply 20 16 104.3 6.52 2.55 0.611 0.538 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.35 3.76 452s supply 3.76 4.92 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 3.38 4.10 452s supply 4.10 5.22 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.975 452s supply 0.975 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 94.2409 7.3617 12.80 1.5e-14 *** 452s price -0.2225 0.0883 -2.52 0.017 * 452s income 0.2964 0.0416 7.13 3.1e-08 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 1.995 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 67.672 MSE: 3.981 Root MSE: 1.995 452s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.718 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 55.6925 10.3937 5.36 5.9e-06 *** 452s price 0.2201 0.0897 2.45 0.019 * 452s farmPrice 0.2078 0.0364 5.71 2.0e-06 *** 452s trend 0.2964 0.0416 7.13 3.1e-08 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 2.553 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 104.312 MSE: 6.519 Root MSE: 2.553 452s Multiple R-Squared: 0.611 Adjusted R-Squared: 0.538 452s 452s [1] "*************** 3SLS with 2 restrictions **********************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 35 442 31.1 0.176 -0.052 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 164 9.66 3.11 0.388 0.316 452s supply 20 16 278 17.36 4.17 -0.036 -0.230 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.89 4.53 452s supply 4.53 6.25 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 9.66 11.7 452s supply 11.69 17.4 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.903 452s supply 0.903 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 91.2986 7.9234 11.52 1.8e-13 *** 452s price -0.4494 0.0891 -5.04 1.4e-05 *** 452s income 0.5592 0.0233 24.04 < 2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 3.108 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 164.177 MSE: 9.657 Root MSE: 3.108 452s Multiple R-Squared: 0.388 Adjusted R-Squared: 0.316 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) -1.8394 8.1797 -0.22 0.82 452s price 0.5506 0.0891 6.18 4.5e-07 *** 452s farmPrice 0.4325 0.0241 17.95 < 2e-16 *** 452s trend 0.5592 0.0233 24.04 < 2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 4.167 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 277.77 MSE: 17.361 Root MSE: 4.167 452s Multiple R-Squared: -0.036 Adjusted R-Squared: -0.23 452s 452s [1] "Component “call”: target, current do not match when deparsed" 452s [1] "*************** 3SLS with 2 restrictions (EViews-like) ************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 35 439 21.3 0.18 -0.18 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 169 9.93 3.15 0.370 0.296 452s supply 20 16 271 16.91 4.11 -0.009 -0.198 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.30 3.73 452s supply 3.73 5.00 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 8.44 9.64 452s supply 9.64 13.53 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.902 452s supply 0.902 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 93.2926 7.3154 12.75 1.0e-14 *** 452s price -0.4781 0.0812 -5.89 1.1e-06 *** 452s income 0.5683 0.0209 27.24 < 2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 3.152 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 168.882 MSE: 9.934 Root MSE: 3.152 452s Multiple R-Squared: 0.37 Adjusted R-Squared: 0.296 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 0.6559 7.5503 0.09 0.93 452s price 0.5219 0.0812 6.43 2.1e-07 *** 452s farmPrice 0.4355 0.0212 20.49 < 2e-16 *** 452s trend 0.5683 0.0209 27.24 < 2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 4.112 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 270.595 MSE: 16.912 Root MSE: 4.112 452s Multiple R-Squared: -0.009 Adjusted R-Squared: -0.198 452s 452s [1] "********** W3SLS with 2 (symbolic) restrictions ***************" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 35 448 31.2 0.165 -0.057 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 166 9.77 3.13 0.38 0.307 452s supply 20 16 281 17.59 4.19 -0.05 -0.246 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.88 4.55 452s supply 4.55 6.27 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 9.77 11.9 452s supply 11.86 17.6 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.905 452s supply 0.905 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 90.6391 7.9088 11.46 2.1e-13 *** 452s price -0.4438 0.0892 -4.98 1.7e-05 *** 452s income 0.5603 0.0229 24.50 < 2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 3.126 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 166.148 MSE: 9.773 Root MSE: 3.126 452s Multiple R-Squared: 0.38 Adjusted R-Squared: 0.307 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) -2.5480 8.1522 -0.31 0.76 452s price 0.5562 0.0892 6.24 3.7e-07 *** 452s farmPrice 0.4340 0.0237 18.33 < 2e-16 *** 452s trend 0.5603 0.0229 24.50 < 2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 4.194 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 281.4 MSE: 17.587 Root MSE: 4.194 452s Multiple R-Squared: -0.05 Adjusted R-Squared: -0.246 452s 452s [1] "*************** 3SLS with 2 restrictions via R and restrict.regMat **********" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 35 442 31.1 0.176 -0.052 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 164 9.66 3.11 0.388 0.316 452s supply 20 16 278 17.36 4.17 -0.036 -0.230 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.89 4.53 452s supply 4.53 6.25 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 9.66 11.7 452s supply 11.69 17.4 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.903 452s supply 0.903 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 91.2986 7.9234 11.52 1.8e-13 *** 452s price -0.4494 0.0891 -5.04 1.4e-05 *** 452s income 0.5592 0.0233 24.04 < 2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 3.108 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 164.177 MSE: 9.657 Root MSE: 3.108 452s Multiple R-Squared: 0.388 Adjusted R-Squared: 0.316 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) -1.8394 8.1797 -0.22 0.82 452s price 0.5506 0.0891 6.18 4.5e-07 *** 452s farmPrice 0.4325 0.0241 17.95 < 2e-16 *** 452s trend 0.5592 0.0233 24.04 < 2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 4.167 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 277.77 MSE: 17.361 Root MSE: 4.167 452s Multiple R-Squared: -0.036 Adjusted R-Squared: -0.23 452s 452s [1] "Component “call”: target, current do not match when deparsed" 452s [1] "******** 3SLS with 2 restrictions via R and restrict.regMat (EViews-like)*****" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 35 439 21.3 0.18 -0.18 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 169 9.93 3.15 0.370 0.296 452s supply 20 16 271 16.91 4.11 -0.009 -0.198 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.30 3.73 452s supply 3.73 5.00 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 8.44 9.64 452s supply 9.64 13.53 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.902 452s supply 0.902 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 93.2926 7.3154 12.75 1.0e-14 *** 452s price -0.4781 0.0812 -5.89 1.1e-06 *** 452s income 0.5683 0.0209 27.24 < 2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 3.152 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 168.882 MSE: 9.934 Root MSE: 3.152 452s Multiple R-Squared: 0.37 Adjusted R-Squared: 0.296 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 0.6559 7.5503 0.09 0.93 452s price 0.5219 0.0812 6.43 2.1e-07 *** 452s farmPrice 0.4355 0.0212 20.49 < 2e-16 *** 452s trend 0.5683 0.0209 27.24 < 2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 4.112 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 270.595 MSE: 16.912 Root MSE: 4.112 452s Multiple R-Squared: -0.009 Adjusted R-Squared: -0.198 452s 452s [1] "*** W3SLS with 2 restrictions via R and restrict.regMat (EViews-like) ***" 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 35 444 21.3 0.172 -0.188 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s demand 20 17 171 10.0 3.17 0.363 0.289 452s supply 20 16 274 17.1 4.13 -0.020 -0.212 452s 452s The covariance matrix of the residuals used for estimation 452s demand supply 452s demand 3.30 3.75 452s supply 3.75 5.01 452s 452s The covariance matrix of the residuals 452s demand supply 452s demand 8.53 9.77 452s supply 9.77 13.68 452s 452s The correlations of the residuals 452s demand supply 452s demand 1.000 0.904 452s supply 0.904 1.000 452s 452s 452s 3SLS estimates for 'demand' (equation 1) 452s Model Formula: consump ~ price + income 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 92.7628 7.3058 12.70 1.2e-14 *** 452s price -0.4740 0.0812 -5.84 1.3e-06 *** 452s income 0.5694 0.0205 27.74 < 2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 3.168 on 17 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 17 452s SSR: 170.659 MSE: 10.039 Root MSE: 3.168 452s Multiple R-Squared: 0.363 Adjusted R-Squared: 0.289 452s 452s 452s 3SLS estimates for 'supply' (equation 2) 452s Model Formula: consump ~ price + farmPrice + trend 452s Instruments: ~income + farmPrice + trend 452s 452s Estimate Std. Error t value Pr(>|t|) 452s (Intercept) 0.0845 7.5314 0.01 0.99 452s price 0.5260 0.0812 6.48 1.8e-07 *** 452s farmPrice 0.4370 0.0209 20.91 < 2e-16 *** 452s trend 0.5694 0.0205 27.74 < 2e-16 *** 452s --- 452s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 452s 452s Residual standard error: 4.135 on 16 degrees of freedom 452s Number of observations: 20 Degrees of Freedom: 16 452s SSR: 273.568 MSE: 17.098 Root MSE: 4.135 452s Multiple R-Squared: -0.02 Adjusted R-Squared: -0.212 452s 452s 452s systemfit results 452s method: 3SLS 452s 452s N DF SSR detRCov OLS-R2 McElroy-R2 452s system 40 36 3690 5613 0.012 0.368 452s 452s N DF SSR MSE RMSE R2 Adj R2 452s eq1 20 19 2132 112.2 10.59 0.305 0.305 453s eq2 20 17 1558 91.7 9.57 -1.335 -1.610 453s 453s The covariance matrix of the residuals used for estimation 453s eq1 eq2 453s eq1 112.2 -44.8 453s eq2 -44.8 56.8 453s 453s The covariance matrix of the residuals 453s eq1 eq2 453s eq1 112.2 -68.3 453s eq2 -68.3 91.7 453s 453s The correlations of the residuals 453s eq1 eq2 453s eq1 1.000 -0.674 453s eq2 -0.674 1.000 453s 453s 453s 3SLS estimates for 'eq1' (equation 1) 453s Model Formula: farmPrice ~ consump - 1 453s Instruments: ~trend + income 453s 453s Estimate Std. Error t value Pr(>|t|) 453s consump 0.9588 0.0235 40.9 <2e-16 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 10.592 on 19 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 19 453s SSR: 2131.725 MSE: 112.196 Root MSE: 10.592 453s Multiple R-Squared: 0.305 Adjusted R-Squared: 0.305 453s 453s 453s 3SLS estimates for 'eq2' (equation 2) 453s Model Formula: price ~ consump + trend 453s Instruments: ~trend + income 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) -92.192 49.896 -1.85 0.0821 . 453s consump 1.953 0.499 3.92 0.0011 ** 453s trend -0.469 0.247 -1.90 0.0743 . 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 9.574 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 1558.311 MSE: 91.665 Root MSE: 9.574 453s Multiple R-Squared: -1.335 Adjusted R-Squared: -1.61 453s 453s 453s systemfit results 453s method: 3SLS 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 38 56326 283068 -104 -10.6 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s eq1 20 19 2313 122 11.0 -7.63 -7.63 453s eq2 20 19 54013 2843 53.3 -200.46 -200.46 453s 453s The covariance matrix of the residuals used for estimation 453s eq1 eq2 453s eq1 121 -255 453s eq2 -255 2953 453s 453s The covariance matrix of the residuals 453s eq1 eq2 453s eq1 122 -251 453s eq2 -251 2843 453s 453s The correlations of the residuals 453s eq1 eq2 453s eq1 1.000 -0.433 453s eq2 -0.433 1.000 453s 453s 453s 3SLS estimates for 'eq1' (equation 1) 453s Model Formula: consump ~ farmPrice - 1 453s Instruments: ~price + income 453s 453s Estimate Std. Error t value Pr(>|t|) 453s farmPrice 1.0417 0.0253 41.2 <2e-16 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 11.034 on 19 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 19 453s SSR: 2313.073 MSE: 121.741 Root MSE: 11.034 453s Multiple R-Squared: -7.627 Adjusted R-Squared: -7.627 453s 453s 453s 3SLS estimates for 'eq2' (equation 2) 453s Model Formula: consump ~ trend - 1 453s Instruments: ~price + income 453s 453s Estimate Std. Error t value Pr(>|t|) 453s trend 9.02 1.13 8 1.7e-07 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 53.318 on 19 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 19 453s SSR: 54013.367 MSE: 2842.809 Root MSE: 53.318 453s Multiple R-Squared: -200.456 Adjusted R-Squared: -200.456 453s 453s 453s systemfit results 453s method: 3SLS 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 38 167069 397886 -49.1 -0.82 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s eq1 20 19 76692 4036 63.5 -285.0 -285.0 453s eq2 20 19 90377 4757 69.0 -28.5 -28.5 453s 453s The covariance matrix of the residuals used for estimation 453s eq1 eq2 453s eq1 2682 2547 453s eq2 2547 2741 453s 453s The covariance matrix of the residuals 453s eq1 eq2 453s eq1 4036 4336 453s eq2 4336 4757 453s 453s The correlations of the residuals 453s eq1 eq2 453s eq1 1.000 0.928 453s eq2 0.928 1.000 453s 453s 453s 3SLS estimates for 'eq1' (equation 1) 453s Model Formula: consump ~ trend - 1 453s Instruments: ~income + farmPrice 453s 453s Estimate Std. Error t value Pr(>|t|) 453s trend 4.162 0.723 5.75 1.5e-05 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 63.533 on 19 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 19 453s SSR: 76691.636 MSE: 4036.402 Root MSE: 63.533 453s Multiple R-Squared: -285.041 Adjusted R-Squared: -285.041 453s 453s 453s 3SLS estimates for 'eq2' (equation 2) 453s Model Formula: farmPrice ~ trend - 1 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s trend 3.274 0.676 4.84 0.00011 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 68.969 on 19 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 19 453s SSR: 90377.499 MSE: 4756.71 Root MSE: 68.969 453s Multiple R-Squared: -28.451 Adjusted R-Squared: -28.451 453s 453s 453s systemfit results 453s method: 3SLS 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 39 161126 1162329 -171 -17.4 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s eq1 20 19 3553 187 13.7 -12.3 -12.3 453s eq2 20 19 157573 8293 91.1 -235.2 -235.2 453s 453s The covariance matrix of the residuals used for estimation 453s eq1 eq2 453s eq1 208 -731 453s eq2 -731 8271 453s 453s The covariance matrix of the residuals 453s eq1 eq2 453s eq1 187 -623 453s eq2 -623 8293 453s 453s The correlations of the residuals 453s eq1 eq2 453s eq1 1.000 -0.121 453s eq2 -0.121 1.000 453s 453s 453s 3SLS estimates for 'eq1' (equation 1) 453s Model Formula: consump ~ farmPrice - 1 453s Instruments: ~farmPrice + trend + income 453s 453s Estimate Std. Error t value Pr(>|t|) 453s farmPrice 1.1122 0.0272 40.8 <2e-16 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 13.675 on 19 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 19 453s SSR: 3553.118 MSE: 187.006 Root MSE: 13.675 453s Multiple R-Squared: -12.252 Adjusted R-Squared: -12.252 453s 453s 453s 3SLS estimates for 'eq2' (equation 2) 453s Model Formula: price ~ trend - 1 453s Instruments: ~farmPrice + trend + income 453s 453s Estimate Std. Error t value Pr(>|t|) 453s trend 1.1122 0.0272 40.8 <2e-16 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 91.068 on 19 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 19 453s SSR: 157573.328 MSE: 8293.333 Root MSE: 91.068 453s Multiple R-Squared: -235.153 Adjusted R-Squared: -235.153 453s 453s 453s systemfit results 453s method: 3SLS 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 38 935 491 0 0 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s eq1 20 19 268 14.1 3.76 0 0 453s eq2 20 19 667 35.1 5.93 0 0 453s 453s The covariance matrix of the residuals used for estimation 453s eq1 eq2 453s eq1 14.11 2.18 453s eq2 2.18 35.12 453s 453s The covariance matrix of the residuals 453s eq1 eq2 453s eq1 14.11 2.18 453s eq2 2.18 35.12 453s 453s The correlations of the residuals 453s eq1 eq2 453s eq1 1.0000 0.0981 453s eq2 0.0981 1.0000 453s 453s 453s 3SLS estimates for 'eq1' (equation 1) 453s Model Formula: consump ~ 1 453s Instruments: ~income 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 100.90 0.84 120 <2e-16 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 3.756 on 19 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 19 453s SSR: 268.114 MSE: 14.111 Root MSE: 3.756 453s Multiple R-Squared: 0 Adjusted R-Squared: 0 453s 453s 453s 3SLS estimates for 'eq2' (equation 2) 453s Model Formula: price ~ 1 453s Instruments: ~income 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 100.02 1.33 75.5 <2e-16 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 5.926 on 19 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 19 453s SSR: 667.251 MSE: 35.118 Root MSE: 5.926 453s Multiple R-Squared: 0 Adjusted R-Squared: 0 453s 453s > 453s > ## ******************** iterated 3SLS ********************** 453s > fit3slsi <- list() 453s > formulas <- c( "GLS", "IV", "Schmidt", "GMM", "EViews" ) 453s > for( i in seq( along = formulas ) ) { 453s + fit3slsi[[ i ]] <- list() 453s + 453s + print( "***************************************************" ) 453s + print( paste( "3SLS formula:", formulas[ i ] ) ) 453s + print( "************* 3SLS *********************************" ) 453s + fit3slsi[[ i ]]$e1 <- systemfit( system, "3SLS", data = Kmenta, 453s + inst = inst, method3sls = formulas[ i ], maxiter = 100, 453s + useMatrix = useMatrix ) 453s + print( summary( fit3slsi[[ i ]]$e1 ) ) 453s + 453s + print( "********************* iterated 3SLS EViews-like ****************" ) 453s + fit3slsi[[ i ]]$e1e <- systemfit( system, "3SLS", data = Kmenta, 453s + inst = inst, methodResidCov = "noDfCor", method3sls = formulas[ i ], 453s + maxiter = 100, useMatrix = useMatrix ) 453s + print( summary( fit3slsi[[ i ]]$e1e, useDfSys = TRUE ) ) 453s + 453s + print( "************** iterated 3SLS with methodResidCov = Theil **************" ) 453s + fit3slsi[[ i ]]$e1c <- systemfit( system, "3SLS", data = Kmenta, 453s + inst = inst, methodResidCov = "Theil", method3sls = formulas[ i ], 453s + maxiter = 100, x = TRUE, useMatrix = useMatrix ) 453s + print( summary( fit3slsi[[ i ]]$e1c, useDfSys = TRUE ) ) 453s + 453s + print( "**************** iterated W3SLS EViews-like ****************" ) 453s + fit3slsi[[ i ]]$e1we <- systemfit( system, "3SLS", data = Kmenta, 453s + inst = inst, methodResidCov = "noDfCor", method3sls = formulas[ i ], 453s + maxiter = 100, residCovWeighted = TRUE, useMatrix = useMatrix ) 453s + print( summary( fit3slsi[[ i ]]$e1we, useDfSys = TRUE ) ) 453s + 453s + 453s + print( "******* iterated 3SLS with restriction *****************" ) 453s + fit3slsi[[ i ]]$e2 <- systemfit( system, "3SLS", data = Kmenta, 453s + inst = inst, restrict.matrix = restrm, method3sls = formulas[ i ], 453s + maxiter = 100, x = TRUE, useMatrix = useMatrix ) 453s + print( summary( fit3slsi[[ i ]]$e2 ) ) 453s + 453s + print( "********* iterated 3SLS with restriction (EViews-like) *********" ) 453s + fit3slsi[[ i ]]$e2e <- systemfit( system, "3SLS", data = Kmenta, 453s + inst = inst, methodResidCov = "noDfCor", restrict.matrix = restrm, 453s + method3sls = formulas[ i ], maxiter = 100, useMatrix = useMatrix ) 453s + print( summary( fit3slsi[[ i ]]$e2e, useDfSys = TRUE ) ) 453s + 453s + print( "******** iterated W3SLS with restriction (EViews-like) *********" ) 453s + fit3slsi[[ i ]]$e2we <- systemfit( system, "3SLS", data = Kmenta, 453s + inst = inst, methodResidCov = "noDfCor", restrict.matrix = restrm, 453s + method3sls = formulas[ i ], maxiter = 100, residCovWeighted = TRUE, 453s + useMatrix = useMatrix ) 453s + print( summary( fit3slsi[[ i ]]$e2we, useDfSys = TRUE ) ) 453s + 453s + 453s + print( "********* iterated 3SLS with restriction via restrict.regMat *****************" ) 453s + fit3slsi[[ i ]]$e3 <- systemfit( system, "3SLS", data = Kmenta, 453s + inst = inst, restrict.regMat = tc, method3sls = formulas[ i ], 453s + maxiter = 100, useMatrix = useMatrix ) 453s + print( summary( fit3slsi[[ i ]]$e3 ) ) 453s + 453s + print( "********* iterated 3SLS with restriction via restrict.regMat (EViews-like) ***" ) 453s + fit3slsi[[ i ]]$e3e <- systemfit( system, "3SLS", data = Kmenta, 453s + inst = inst, methodResidCov = "noDfCor", restrict.regMat = tc, 453s + method3sls = formulas[ i ], maxiter = 100, x = TRUE, 453s + useMatrix = useMatrix ) 453s + print( summary( fit3slsi[[ i ]]$e3e, useDfSys = TRUE ) ) 453s + 453s + print( "***** iterated W3SLS with restriction via restrict.regMat ********" ) 453s + fit3slsi[[ i ]]$e3w <- systemfit( system, "3SLS", data = Kmenta, 453s + inst = inst, restrict.regMat = tc, method3sls = formulas[ i ], maxiter = 100, 453s + residCovWeighted = TRUE, x = TRUE, useMatrix = useMatrix ) 453s + print( summary( fit3slsi[[ i ]]$e3w ) ) 453s + 453s + 453s + print( "******** iterated 3SLS with 2 restrictions *********************" ) 453s + fit3slsi[[ i ]]$e4 <- systemfit( system, "3SLS", data = Kmenta, 453s + inst = inst, restrict.matrix = restr2m, restrict.rhs = restr2q, 453s + method3sls = formulas[ i ], maxiter = 100, x = TRUE, 453s + useMatrix = useMatrix ) 453s + print( summary( fit3slsi[[ i ]]$e4 ) ) 453s + 453s + print( "********* iterated 3SLS with 2 restrictions (EViews-like) *******" ) 453s + fit3slsi[[ i ]]$e4e <- systemfit( system, "3SLS", data = Kmenta, 453s + inst = inst, methodResidCov = "noDfCor", restrict.matrix = restr2m, 453s + restrict.rhs = restr2q, method3sls = formulas[ i ], maxiter = 100, 453s + useMatrix = useMatrix ) 453s + print( summary( fit3slsi[[ i ]]$e4e, useDfSys = TRUE ) ) 453s + 453s + print( "******** iterated W3SLS with 2 restrictions (EViews-like) *******" ) 453s + fit3slsi[[ i ]]$e4we <- systemfit( system, "3SLS", data = Kmenta, 453s + inst = inst, methodResidCov = "noDfCor", restrict.matrix = restr2m, 453s + restrict.rhs = restr2q, method3sls = formulas[ i ], maxiter = 100, 453s + residCovWeighted = TRUE, useMatrix = useMatrix ) 453s + print( summary( fit3slsi[[ i ]]$e4we, useDfSys = TRUE ) ) 453s + 453s + 453s + print( "******** iterated 3SLS with 2 restrictions via R and restrict.regMat *********" ) 453s + fit3slsi[[ i ]]$e5 <- systemfit( system, "3SLS", data = Kmenta, 453s + inst = inst, restrict.regMat = tc, restrict.matrix = restr3m, 453s + restrict.rhs = restr3q, method3sls = formulas[ i ], maxiter = 100, 453s + useMatrix = useMatrix ) 453s + print( summary( fit3slsi[[ i ]]$e5 ) ) 453s + 453s + print( "*** iterated 3SLS with 2 restrictions via R and restrict.regMat (EViews-like)**" ) 453s + fit3slsi[[ i ]]$e5e <- systemfit( system, "3SLS", data = Kmenta, 453s + inst = inst, restrict.regMat = tc, methodResidCov = "noDfCor", 453s + restrict.matrix = restr3m, restrict.rhs = restr3q, 453s + method3sls = formulas[ i ], maxiter = 100, useMatrix = useMatrix ) 453s + print( summary( fit3slsi[[ i ]]$e5e, useDfSys = TRUE ) ) 453s + 453s + print( "** iterated W3SLS with 2 restrictions via R and restrict.regMat ***" ) 453s + fit3slsi[[ i ]]$e5w <- systemfit( system, "3SLS", data = Kmenta, 453s + inst = inst, restrict.regMat = tc, restrict.matrix = restr3m, 453s + restrict.rhs = restr3q, method3sls = formulas[ i ], maxiter = 100, 453s + residCovWeighted = TRUE, x = TRUE, 453s + useMatrix = useMatrix ) 453s + print( summary( fit3slsi[[ i ]]$e5w ) ) 453s + } 453s [1] "***************************************************" 453s [1] "3SLS formula: GLS" 453s [1] "************* 3SLS *********************************" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 6 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 33 178 0.983 0.668 0.814 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 65.7 3.87 1.97 0.755 0.726 453s supply 20 16 112.4 7.03 2.65 0.581 0.502 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 3.87 5.12 453s supply 5.12 7.03 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 3.87 5.12 453s supply 5.12 7.03 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.982 453s supply 0.982 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 94.6333 7.9208 11.95 1.1e-09 *** 453s price -0.2436 0.0965 -2.52 0.022 * 453s income 0.3140 0.0469 6.69 3.8e-06 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 1.966 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 453s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 52.6618 12.8051 4.11 0.00081 *** 453s price 0.2266 0.1075 2.11 0.05110 . 453s farmPrice 0.2234 0.0468 4.78 0.00021 *** 453s trend 0.3800 0.0720 5.28 7.5e-05 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.651 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 112.431 MSE: 7.027 Root MSE: 2.651 453s Multiple R-Squared: 0.581 Adjusted R-Squared: 0.502 453s 453s [1] "********************* iterated 3SLS EViews-like ****************" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 6 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 33 177 0.667 0.67 0.782 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 65.7 3.87 1.97 0.755 0.726 453s supply 20 16 111.3 6.96 2.64 0.585 0.507 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 3.29 4.20 453s supply 4.20 5.57 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 3.29 4.20 453s supply 4.20 5.57 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.982 453s supply 0.982 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 453s price -0.2436 0.0890 -2.74 0.0099 ** 453s income 0.3140 0.0433 7.25 2.5e-08 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 1.966 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 453s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 52.5527 11.3956 4.61 5.8e-05 *** 453s price 0.2271 0.0956 2.37 0.024 * 453s farmPrice 0.2245 0.0416 5.39 5.8e-06 *** 453s trend 0.3756 0.0641 5.86 1.5e-06 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.637 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 111.302 MSE: 6.956 Root MSE: 2.637 453s Multiple R-Squared: 0.585 Adjusted R-Squared: 0.507 453s 453s [1] "************** iterated 3SLS with methodResidCov = Theil **************" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 6 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 33 179 -0.818 0.665 0.957 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 65.7 3.87 1.97 0.755 0.726 453s supply 20 16 113.8 7.11 2.67 0.576 0.496 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 3.87 5.32 453s supply 5.32 7.11 453s 453s warning: this covariance matrix is NOT positive semidefinit! 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 3.87 5.32 453s supply 5.32 7.11 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.982 453s supply 0.982 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 453s price -0.2436 0.0965 -2.52 0.017 * 453s income 0.3140 0.0469 6.69 1.3e-07 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 1.966 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 453s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 52.7863 12.8707 4.10 0.00025 *** 453s price 0.2261 0.1081 2.09 0.04425 * 453s farmPrice 0.2221 0.0467 4.75 3.8e-05 *** 453s trend 0.3851 0.0693 5.55 3.6e-06 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.667 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 113.77 MSE: 7.111 Root MSE: 2.667 453s Multiple R-Squared: 0.576 Adjusted R-Squared: 0.496 453s 453s [1] "**************** iterated W3SLS EViews-like ****************" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 6 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 33 177 0.667 0.67 0.782 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 65.7 3.87 1.97 0.755 0.726 453s supply 20 16 111.3 6.96 2.64 0.585 0.507 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 3.29 4.20 453s supply 4.20 5.57 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 3.29 4.20 453s supply 4.20 5.57 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.982 453s supply 0.982 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 453s price -0.2436 0.0890 -2.74 0.0099 ** 453s income 0.3140 0.0433 7.25 2.5e-08 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 1.966 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 453s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 52.5527 11.3956 4.61 5.8e-05 *** 453s price 0.2271 0.0956 2.37 0.024 * 453s farmPrice 0.2245 0.0416 5.39 5.8e-06 *** 453s trend 0.3756 0.0641 5.86 1.5e-06 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.637 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 111.302 MSE: 6.956 Root MSE: 2.637 453s Multiple R-Squared: 0.585 Adjusted R-Squared: 0.507 453s 453s [1] "******* iterated 3SLS with restriction *****************" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 17 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 34 240 0.56 0.553 0.819 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 98.4 5.79 2.41 0.633 0.590 453s supply 20 16 141.1 8.82 2.97 0.474 0.375 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 5.79 7.11 453s supply 7.11 8.82 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 5.79 7.11 453s supply 7.11 8.82 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.995 453s supply 0.995 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 92.0742 9.6303 9.56 3.6e-11 *** 453s price -0.1064 0.1023 -1.04 0.31 453s income 0.1996 0.0297 6.73 9.9e-08 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.406 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 98.435 MSE: 5.79 Root MSE: 2.406 453s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.59 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 68.8551 12.4839 5.52 3.7e-06 *** 453s price 0.1833 0.1189 1.54 0.13 453s farmPrice 0.1202 0.0260 4.63 5.1e-05 *** 453s trend 0.1996 0.0297 6.73 9.9e-08 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.97 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 141.147 MSE: 8.822 Root MSE: 2.97 453s Multiple R-Squared: 0.474 Adjusted R-Squared: 0.375 453s 453s [1] "********* iterated 3SLS with restriction (EViews-like) *********" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 20 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 34 237 0.364 0.557 0.755 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 99.3 5.84 2.42 0.630 0.586 453s supply 20 16 138.1 8.63 2.94 0.485 0.388 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 4.96 5.82 453s supply 5.82 6.90 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 4.96 5.82 453s supply 5.82 6.90 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.995 453s supply 0.995 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 453s price -0.1043 0.0958 -1.09 0.28 453s income 0.1979 0.0299 6.61 1.4e-07 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.417 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 99.297 MSE: 5.841 Root MSE: 2.417 453s Multiple R-Squared: 0.63 Adjusted R-Squared: 0.586 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 453s price 0.1851 0.1053 1.76 0.088 . 453s farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 453s trend 0.1979 0.0299 6.61 1.4e-07 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.938 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 138.088 MSE: 8.63 Root MSE: 2.938 453s Multiple R-Squared: 0.485 Adjusted R-Squared: 0.388 453s 453s [1] "******** iterated W3SLS with restriction (EViews-like) *********" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 20 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 34 237 0.364 0.557 0.755 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 99.3 5.84 2.42 0.630 0.586 453s supply 20 16 138.1 8.63 2.94 0.485 0.388 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 4.96 5.82 453s supply 5.82 6.90 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 4.96 5.82 453s supply 5.82 6.90 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.995 453s supply 0.995 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 453s price -0.1043 0.0958 -1.09 0.28 453s income 0.1979 0.0299 6.61 1.4e-07 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.417 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 99.297 MSE: 5.841 Root MSE: 2.417 453s Multiple R-Squared: 0.63 Adjusted R-Squared: 0.586 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 453s price 0.1851 0.1053 1.76 0.088 . 453s farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 453s trend 0.1979 0.0299 6.61 1.4e-07 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.938 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 138.088 MSE: 8.63 Root MSE: 2.938 453s Multiple R-Squared: 0.485 Adjusted R-Squared: 0.388 453s 453s [1] "********* iterated 3SLS with restriction via restrict.regMat *****************" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 17 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 34 240 0.56 0.553 0.819 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 98.4 5.79 2.41 0.633 0.590 453s supply 20 16 141.1 8.82 2.97 0.474 0.375 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 5.79 7.11 453s supply 7.11 8.82 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 5.79 7.11 453s supply 7.11 8.82 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.995 453s supply 0.995 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 92.0742 9.6303 9.56 3.6e-11 *** 453s price -0.1064 0.1023 -1.04 0.31 453s income 0.1996 0.0297 6.73 9.9e-08 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.406 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 98.435 MSE: 5.79 Root MSE: 2.406 453s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.59 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 68.8551 12.4839 5.52 3.7e-06 *** 453s price 0.1833 0.1189 1.54 0.13 453s farmPrice 0.1202 0.0260 4.63 5.1e-05 *** 453s trend 0.1996 0.0297 6.73 9.9e-08 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.97 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 141.147 MSE: 8.822 Root MSE: 2.97 453s Multiple R-Squared: 0.474 Adjusted R-Squared: 0.375 453s 453s [1] "********* iterated 3SLS with restriction via restrict.regMat (EViews-like) ***" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 20 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 34 237 0.364 0.557 0.755 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 99.3 5.84 2.42 0.630 0.586 453s supply 20 16 138.1 8.63 2.94 0.485 0.388 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 4.96 5.82 453s supply 5.82 6.90 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 4.96 5.82 453s supply 5.82 6.90 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.995 453s supply 0.995 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 453s price -0.1043 0.0958 -1.09 0.28 453s income 0.1979 0.0299 6.61 1.4e-07 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.417 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 99.297 MSE: 5.841 Root MSE: 2.417 453s Multiple R-Squared: 0.63 Adjusted R-Squared: 0.586 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 453s price 0.1851 0.1053 1.76 0.088 . 453s farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 453s trend 0.1979 0.0299 6.61 1.4e-07 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.938 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 138.088 MSE: 8.63 Root MSE: 2.938 453s Multiple R-Squared: 0.485 Adjusted R-Squared: 0.388 453s 453s [1] "***** iterated W3SLS with restriction via restrict.regMat ********" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 17 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 34 240 0.56 0.553 0.819 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 98.4 5.79 2.41 0.633 0.590 453s supply 20 16 141.1 8.82 2.97 0.474 0.375 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 5.79 7.11 453s supply 7.11 8.82 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 5.79 7.11 453s supply 7.11 8.82 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.995 453s supply 0.995 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 92.0742 9.6303 9.56 3.6e-11 *** 453s price -0.1064 0.1023 -1.04 0.31 453s income 0.1996 0.0297 6.73 9.9e-08 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.406 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 98.435 MSE: 5.79 Root MSE: 2.406 453s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.59 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 68.8551 12.4839 5.52 3.7e-06 *** 453s price 0.1833 0.1189 1.54 0.13 453s farmPrice 0.1202 0.0260 4.63 5.1e-05 *** 453s trend 0.1996 0.0297 6.73 9.9e-08 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.97 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 141.147 MSE: 8.822 Root MSE: 2.97 453s Multiple R-Squared: 0.474 Adjusted R-Squared: 0.375 453s 453s [1] "******** iterated 3SLS with 2 restrictions *********************" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 9 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 35 185 1.76 0.655 0.71 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 69.9 4.11 2.03 0.739 0.709 453s supply 20 16 114.8 7.18 2.68 0.572 0.491 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 4.11 5.27 453s supply 5.27 7.18 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 4.11 5.27 453s supply 5.27 7.18 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.00 0.97 453s supply 0.97 1.00 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 90.1569 7.9174 11.39 2.5e-13 *** 453s price -0.2007 0.0920 -2.18 0.036 * 453s income 0.3159 0.0192 16.42 < 2e-16 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.028 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 69.917 MSE: 4.113 Root MSE: 2.028 453s Multiple R-Squared: 0.739 Adjusted R-Squared: 0.709 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 46.4810 8.0104 5.80 1.4e-06 *** 453s price 0.2993 0.0920 3.25 0.0025 ** 453s farmPrice 0.2190 0.0196 11.20 4.0e-13 *** 453s trend 0.3159 0.0192 16.42 < 2e-16 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.679 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 114.83 MSE: 7.177 Root MSE: 2.679 453s Multiple R-Squared: 0.572 Adjusted R-Squared: 0.491 453s 453s [1] "********* iterated 3SLS with 2 restrictions (EViews-like) *******" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 8 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 35 179 1.19 0.666 0.668 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 68.3 4.02 2.00 0.745 0.715 453s supply 20 16 110.8 6.92 2.63 0.587 0.509 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 3.41 4.21 453s supply 4.21 5.54 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 3.41 4.21 453s supply 4.21 5.54 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.968 453s supply 0.968 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 91.3901 7.3161 12.5 1.9e-14 *** 453s price -0.2168 0.0835 -2.6 0.014 * 453s income 0.3199 0.0168 19.1 < 2e-16 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.004 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 68.293 MSE: 4.017 Root MSE: 2.004 453s Multiple R-Squared: 0.745 Adjusted R-Squared: 0.715 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 47.5787 7.4268 6.41 2.3e-07 *** 453s price 0.2832 0.0835 3.39 0.0017 ** 453s farmPrice 0.2240 0.0168 13.36 2.7e-15 *** 453s trend 0.3199 0.0168 19.07 < 2e-16 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.631 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 110.791 MSE: 6.924 Root MSE: 2.631 453s Multiple R-Squared: 0.587 Adjusted R-Squared: 0.509 453s 453s [1] "******** iterated W3SLS with 2 restrictions (EViews-like) *******" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 8 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 35 179 1.19 0.666 0.668 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 68.3 4.02 2.00 0.745 0.715 453s supply 20 16 110.8 6.92 2.63 0.587 0.509 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 3.41 4.21 453s supply 4.21 5.54 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 3.41 4.21 453s supply 4.21 5.54 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.968 453s supply 0.968 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 91.3901 7.3161 12.5 1.9e-14 *** 453s price -0.2168 0.0835 -2.6 0.014 * 453s income 0.3199 0.0168 19.1 < 2e-16 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.004 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 68.293 MSE: 4.017 Root MSE: 2.004 453s Multiple R-Squared: 0.745 Adjusted R-Squared: 0.715 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 47.5787 7.4268 6.41 2.3e-07 *** 453s price 0.2832 0.0835 3.39 0.0017 ** 453s farmPrice 0.2240 0.0168 13.36 2.7e-15 *** 453s trend 0.3199 0.0168 19.07 < 2e-16 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.631 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 110.791 MSE: 6.924 Root MSE: 2.631 453s Multiple R-Squared: 0.587 Adjusted R-Squared: 0.509 453s 453s [1] "******** iterated 3SLS with 2 restrictions via R and restrict.regMat *********" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 9 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 35 185 1.76 0.655 0.71 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 69.9 4.11 2.03 0.739 0.709 453s supply 20 16 114.8 7.18 2.68 0.572 0.491 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 4.11 5.27 453s supply 5.27 7.18 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 4.11 5.27 453s supply 5.27 7.18 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.00 0.97 453s supply 0.97 1.00 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 90.1569 7.9174 11.39 2.5e-13 *** 453s price -0.2007 0.0920 -2.18 0.036 * 453s income 0.3159 0.0192 16.42 < 2e-16 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.028 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 69.917 MSE: 4.113 Root MSE: 2.028 453s Multiple R-Squared: 0.739 Adjusted R-Squared: 0.709 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 46.4810 8.0104 5.80 1.4e-06 *** 453s price 0.2993 0.0920 3.25 0.0025 ** 453s farmPrice 0.2190 0.0196 11.20 4.0e-13 *** 453s trend 0.3159 0.0192 16.42 < 2e-16 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.679 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 114.83 MSE: 7.177 Root MSE: 2.679 453s Multiple R-Squared: 0.572 Adjusted R-Squared: 0.491 453s 453s [1] "*** iterated 3SLS with 2 restrictions via R and restrict.regMat (EViews-like)**" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 8 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 35 179 1.19 0.666 0.668 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 68.3 4.02 2.00 0.745 0.715 453s supply 20 16 110.8 6.92 2.63 0.587 0.509 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 3.41 4.21 453s supply 4.21 5.54 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 3.41 4.21 453s supply 4.21 5.54 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.968 453s supply 0.968 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 91.3901 7.3161 12.5 1.9e-14 *** 453s price -0.2168 0.0835 -2.6 0.014 * 453s income 0.3199 0.0168 19.1 < 2e-16 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.004 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 68.293 MSE: 4.017 Root MSE: 2.004 453s Multiple R-Squared: 0.745 Adjusted R-Squared: 0.715 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 47.5787 7.4268 6.41 2.3e-07 *** 453s price 0.2832 0.0835 3.39 0.0017 ** 453s farmPrice 0.2240 0.0168 13.36 2.7e-15 *** 453s trend 0.3199 0.0168 19.07 < 2e-16 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.631 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 110.791 MSE: 6.924 Root MSE: 2.631 453s Multiple R-Squared: 0.587 Adjusted R-Squared: 0.509 453s 453s [1] "** iterated W3SLS with 2 restrictions via R and restrict.regMat ***" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 9 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 35 185 1.76 0.655 0.71 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 69.9 4.11 2.03 0.739 0.709 453s supply 20 16 114.8 7.18 2.68 0.572 0.491 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 4.11 5.27 453s supply 5.27 7.18 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 4.11 5.27 453s supply 5.27 7.18 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.00 0.97 453s supply 0.97 1.00 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 90.1569 7.9174 11.39 2.5e-13 *** 453s price -0.2007 0.0920 -2.18 0.036 * 453s income 0.3159 0.0192 16.42 < 2e-16 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.028 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 69.917 MSE: 4.113 Root MSE: 2.028 453s Multiple R-Squared: 0.739 Adjusted R-Squared: 0.709 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 46.4810 8.0104 5.80 1.4e-06 *** 453s price 0.2993 0.0920 3.25 0.0025 ** 453s farmPrice 0.2190 0.0196 11.20 4.0e-13 *** 453s trend 0.3159 0.0192 16.42 < 2e-16 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.679 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 114.83 MSE: 7.177 Root MSE: 2.679 453s Multiple R-Squared: 0.572 Adjusted R-Squared: 0.491 453s 453s [1] "***************************************************" 453s [1] "3SLS formula: IV" 453s [1] "************* 3SLS *********************************" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 6 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 33 178 0.983 0.668 0.814 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 65.7 3.87 1.97 0.755 0.726 453s supply 20 16 112.4 7.03 2.65 0.581 0.502 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 3.87 5.12 453s supply 5.12 7.03 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 3.87 5.12 453s supply 5.12 7.03 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.982 453s supply 0.982 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 94.6333 7.9208 11.95 1.1e-09 *** 453s price -0.2436 0.0965 -2.52 0.022 * 453s income 0.3140 0.0469 6.69 3.8e-06 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 1.966 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 453s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 52.6618 12.8051 4.11 0.00081 *** 453s price 0.2266 0.1075 2.11 0.05110 . 453s farmPrice 0.2234 0.0468 4.78 0.00021 *** 453s trend 0.3800 0.0720 5.28 7.5e-05 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.651 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 112.431 MSE: 7.027 Root MSE: 2.651 453s Multiple R-Squared: 0.581 Adjusted R-Squared: 0.502 453s 453s [1] "********************* iterated 3SLS EViews-like ****************" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 6 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 33 177 0.667 0.67 0.782 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 65.7 3.87 1.97 0.755 0.726 453s supply 20 16 111.3 6.96 2.64 0.585 0.507 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 3.29 4.20 453s supply 4.20 5.57 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 3.29 4.20 453s supply 4.20 5.57 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.982 453s supply 0.982 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 453s price -0.2436 0.0890 -2.74 0.0099 ** 453s income 0.3140 0.0433 7.25 2.5e-08 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 1.966 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 453s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 52.5527 11.3956 4.61 5.8e-05 *** 453s price 0.2271 0.0956 2.37 0.024 * 453s farmPrice 0.2245 0.0416 5.39 5.8e-06 *** 453s trend 0.3756 0.0641 5.86 1.5e-06 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.637 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 111.302 MSE: 6.956 Root MSE: 2.637 453s Multiple R-Squared: 0.585 Adjusted R-Squared: 0.507 453s 453s [1] "************** iterated 3SLS with methodResidCov = Theil **************" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 6 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 33 179 -0.818 0.665 0.957 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 65.7 3.87 1.97 0.755 0.726 453s supply 20 16 113.8 7.11 2.67 0.576 0.496 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 3.87 5.32 453s supply 5.32 7.11 453s 453s warning: this covariance matrix is NOT positive semidefinit! 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 3.87 5.32 453s supply 5.32 7.11 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.982 453s supply 0.982 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 453s price -0.2436 0.0965 -2.52 0.017 * 453s income 0.3140 0.0469 6.69 1.3e-07 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 1.966 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 453s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 52.7863 12.8707 4.10 0.00025 *** 453s price 0.2261 0.1081 2.09 0.04425 * 453s farmPrice 0.2221 0.0467 4.75 3.8e-05 *** 453s trend 0.3851 0.0693 5.55 3.6e-06 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.667 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 113.77 MSE: 7.111 Root MSE: 2.667 453s Multiple R-Squared: 0.576 Adjusted R-Squared: 0.496 453s 453s [1] "**************** iterated W3SLS EViews-like ****************" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 6 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 33 177 0.667 0.67 0.782 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 65.7 3.87 1.97 0.755 0.726 453s supply 20 16 111.3 6.96 2.64 0.585 0.507 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 3.29 4.20 453s supply 4.20 5.57 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 3.29 4.20 453s supply 4.20 5.57 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.982 453s supply 0.982 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 453s price -0.2436 0.0890 -2.74 0.0099 ** 453s income 0.3140 0.0433 7.25 2.5e-08 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 1.966 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 453s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 52.5527 11.3956 4.61 5.8e-05 *** 453s price 0.2271 0.0956 2.37 0.024 * 453s farmPrice 0.2245 0.0416 5.39 5.8e-06 *** 453s trend 0.3756 0.0641 5.86 1.5e-06 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.637 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 111.302 MSE: 6.956 Root MSE: 2.637 453s Multiple R-Squared: 0.585 Adjusted R-Squared: 0.507 453s 453s [1] "******* iterated 3SLS with restriction *****************" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 17 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 34 240 0.56 0.553 0.819 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 98.4 5.79 2.41 0.633 0.590 453s supply 20 16 141.1 8.82 2.97 0.474 0.375 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 5.79 7.11 453s supply 7.11 8.82 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 5.79 7.11 453s supply 7.11 8.82 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.995 453s supply 0.995 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 92.0742 9.6303 9.56 3.6e-11 *** 453s price -0.1064 0.1023 -1.04 0.31 453s income 0.1996 0.0297 6.73 9.9e-08 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.406 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 98.435 MSE: 5.79 Root MSE: 2.406 453s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.59 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 68.8551 12.4839 5.52 3.7e-06 *** 453s price 0.1833 0.1189 1.54 0.13 453s farmPrice 0.1202 0.0260 4.63 5.1e-05 *** 453s trend 0.1996 0.0297 6.73 9.9e-08 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.97 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 141.147 MSE: 8.822 Root MSE: 2.97 453s Multiple R-Squared: 0.474 Adjusted R-Squared: 0.375 453s 453s [1] "********* iterated 3SLS with restriction (EViews-like) *********" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 20 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 34 237 0.364 0.557 0.755 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 99.3 5.84 2.42 0.630 0.586 453s supply 20 16 138.1 8.63 2.94 0.485 0.388 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 4.96 5.82 453s supply 5.82 6.90 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 4.96 5.82 453s supply 5.82 6.90 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.995 453s supply 0.995 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 453s price -0.1043 0.0958 -1.09 0.28 453s income 0.1979 0.0299 6.61 1.4e-07 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.417 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 99.297 MSE: 5.841 Root MSE: 2.417 453s Multiple R-Squared: 0.63 Adjusted R-Squared: 0.586 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 453s price 0.1851 0.1053 1.76 0.088 . 453s farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 453s trend 0.1979 0.0299 6.61 1.4e-07 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.938 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 138.088 MSE: 8.63 Root MSE: 2.938 453s Multiple R-Squared: 0.485 Adjusted R-Squared: 0.388 453s 453s [1] "******** iterated W3SLS with restriction (EViews-like) *********" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 20 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 34 237 0.364 0.557 0.755 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 99.3 5.84 2.42 0.630 0.586 453s supply 20 16 138.1 8.63 2.94 0.485 0.388 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 4.96 5.82 453s supply 5.82 6.90 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 4.96 5.82 453s supply 5.82 6.90 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.995 453s supply 0.995 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 453s price -0.1043 0.0958 -1.09 0.28 453s income 0.1979 0.0299 6.61 1.4e-07 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.417 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 99.297 MSE: 5.841 Root MSE: 2.417 453s Multiple R-Squared: 0.63 Adjusted R-Squared: 0.586 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 453s price 0.1851 0.1053 1.76 0.088 . 453s farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 453s trend 0.1979 0.0299 6.61 1.4e-07 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.938 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 138.088 MSE: 8.63 Root MSE: 2.938 453s Multiple R-Squared: 0.485 Adjusted R-Squared: 0.388 453s 453s [1] "********* iterated 3SLS with restriction via restrict.regMat *****************" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 17 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 34 240 0.56 0.553 0.819 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 98.4 5.79 2.41 0.633 0.590 453s supply 20 16 141.1 8.82 2.97 0.474 0.375 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 5.79 7.11 453s supply 7.11 8.82 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 5.79 7.11 453s supply 7.11 8.82 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.995 453s supply 0.995 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 92.0742 9.6303 9.56 3.6e-11 *** 453s price -0.1064 0.1023 -1.04 0.31 453s income 0.1996 0.0297 6.73 9.9e-08 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.406 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 98.435 MSE: 5.79 Root MSE: 2.406 453s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.59 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 68.8551 12.4839 5.52 3.7e-06 *** 453s price 0.1833 0.1189 1.54 0.13 453s farmPrice 0.1202 0.0260 4.63 5.1e-05 *** 453s trend 0.1996 0.0297 6.73 9.9e-08 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.97 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 141.147 MSE: 8.822 Root MSE: 2.97 453s Multiple R-Squared: 0.474 Adjusted R-Squared: 0.375 453s 453s [1] "********* iterated 3SLS with restriction via restrict.regMat (EViews-like) ***" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 20 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 34 237 0.364 0.557 0.755 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 99.3 5.84 2.42 0.630 0.586 453s supply 20 16 138.1 8.63 2.94 0.485 0.388 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 4.96 5.82 453s supply 5.82 6.90 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 4.96 5.82 453s supply 5.82 6.90 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.995 453s supply 0.995 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 453s price -0.1043 0.0958 -1.09 0.28 453s income 0.1979 0.0299 6.61 1.4e-07 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.417 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 99.297 MSE: 5.841 Root MSE: 2.417 453s Multiple R-Squared: 0.63 Adjusted R-Squared: 0.586 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 453s price 0.1851 0.1053 1.76 0.088 . 453s farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 453s trend 0.1979 0.0299 6.61 1.4e-07 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.938 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 138.088 MSE: 8.63 Root MSE: 2.938 453s Multiple R-Squared: 0.485 Adjusted R-Squared: 0.388 453s 453s [1] "***** iterated W3SLS with restriction via restrict.regMat ********" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 17 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 34 240 0.56 0.553 0.819 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 98.4 5.79 2.41 0.633 0.590 453s supply 20 16 141.1 8.82 2.97 0.474 0.375 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 5.79 7.11 453s supply 7.11 8.82 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 5.79 7.11 453s supply 7.11 8.82 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.995 453s supply 0.995 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 92.0742 9.6303 9.56 3.6e-11 *** 453s price -0.1064 0.1023 -1.04 0.31 453s income 0.1996 0.0297 6.73 9.9e-08 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.406 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 98.435 MSE: 5.79 Root MSE: 2.406 453s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.59 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 68.8551 12.4839 5.52 3.7e-06 *** 453s price 0.1833 0.1189 1.54 0.13 453s farmPrice 0.1202 0.0260 4.63 5.1e-05 *** 453s trend 0.1996 0.0297 6.73 9.9e-08 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.97 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 141.147 MSE: 8.822 Root MSE: 2.97 453s Multiple R-Squared: 0.474 Adjusted R-Squared: 0.375 453s 453s [1] "******** iterated 3SLS with 2 restrictions *********************" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 9 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 35 185 1.76 0.655 0.71 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 69.9 4.11 2.03 0.739 0.709 453s supply 20 16 114.8 7.18 2.68 0.572 0.491 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 4.11 5.27 453s supply 5.27 7.18 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 4.11 5.27 453s supply 5.27 7.18 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.00 0.97 453s supply 0.97 1.00 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 90.1569 7.9174 11.39 2.5e-13 *** 453s price -0.2007 0.0920 -2.18 0.036 * 453s income 0.3159 0.0192 16.42 < 2e-16 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.028 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 69.917 MSE: 4.113 Root MSE: 2.028 453s Multiple R-Squared: 0.739 Adjusted R-Squared: 0.709 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 46.4810 8.0104 5.80 1.4e-06 *** 453s price 0.2993 0.0920 3.25 0.0025 ** 453s farmPrice 0.2190 0.0196 11.20 4.0e-13 *** 453s trend 0.3159 0.0192 16.42 < 2e-16 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.679 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 114.83 MSE: 7.177 Root MSE: 2.679 453s Multiple R-Squared: 0.572 Adjusted R-Squared: 0.491 453s 453s [1] "********* iterated 3SLS with 2 restrictions (EViews-like) *******" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 8 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 35 179 1.19 0.666 0.668 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 68.3 4.02 2.00 0.745 0.715 453s supply 20 16 110.8 6.92 2.63 0.587 0.509 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 3.41 4.21 453s supply 4.21 5.54 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 3.41 4.21 453s supply 4.21 5.54 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.968 453s supply 0.968 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 91.3901 7.3161 12.5 1.9e-14 *** 453s price -0.2168 0.0835 -2.6 0.014 * 453s income 0.3199 0.0168 19.1 < 2e-16 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.004 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 68.293 MSE: 4.017 Root MSE: 2.004 453s Multiple R-Squared: 0.745 Adjusted R-Squared: 0.715 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 47.5787 7.4268 6.41 2.3e-07 *** 453s price 0.2832 0.0835 3.39 0.0017 ** 453s farmPrice 0.2240 0.0168 13.36 2.7e-15 *** 453s trend 0.3199 0.0168 19.07 < 2e-16 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.631 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 110.791 MSE: 6.924 Root MSE: 2.631 453s Multiple R-Squared: 0.587 Adjusted R-Squared: 0.509 453s 453s [1] "******** iterated W3SLS with 2 restrictions (EViews-like) *******" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 8 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 35 179 1.19 0.666 0.668 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 68.3 4.02 2.00 0.745 0.715 453s supply 20 16 110.8 6.92 2.63 0.587 0.509 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 3.41 4.21 453s supply 4.21 5.54 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 3.41 4.21 453s supply 4.21 5.54 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.968 453s supply 0.968 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 91.3901 7.3161 12.5 1.9e-14 *** 453s price -0.2168 0.0835 -2.6 0.014 * 453s income 0.3199 0.0168 19.1 < 2e-16 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.004 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 68.293 MSE: 4.017 Root MSE: 2.004 453s Multiple R-Squared: 0.745 Adjusted R-Squared: 0.715 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 47.5787 7.4268 6.41 2.3e-07 *** 453s price 0.2832 0.0835 3.39 0.0017 ** 453s farmPrice 0.2240 0.0168 13.36 2.7e-15 *** 453s trend 0.3199 0.0168 19.07 < 2e-16 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.631 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 110.791 MSE: 6.924 Root MSE: 2.631 453s Multiple R-Squared: 0.587 Adjusted R-Squared: 0.509 453s 453s [1] "******** iterated 3SLS with 2 restrictions via R and restrict.regMat *********" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 9 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 35 185 1.76 0.655 0.71 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 69.9 4.11 2.03 0.739 0.709 453s supply 20 16 114.8 7.18 2.68 0.572 0.491 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 4.11 5.27 453s supply 5.27 7.18 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 4.11 5.27 453s supply 5.27 7.18 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.00 0.97 453s supply 0.97 1.00 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 90.1569 7.9174 11.39 2.5e-13 *** 453s price -0.2007 0.0920 -2.18 0.036 * 453s income 0.3159 0.0192 16.42 < 2e-16 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.028 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 69.917 MSE: 4.113 Root MSE: 2.028 453s Multiple R-Squared: 0.739 Adjusted R-Squared: 0.709 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 46.4810 8.0104 5.80 1.4e-06 *** 453s price 0.2993 0.0920 3.25 0.0025 ** 453s farmPrice 0.2190 0.0196 11.20 4.0e-13 *** 453s trend 0.3159 0.0192 16.42 < 2e-16 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.679 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 114.83 MSE: 7.177 Root MSE: 2.679 453s Multiple R-Squared: 0.572 Adjusted R-Squared: 0.491 453s 453s [1] "*** iterated 3SLS with 2 restrictions via R and restrict.regMat (EViews-like)**" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 8 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 35 179 1.19 0.666 0.668 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 68.3 4.02 2.00 0.745 0.715 453s supply 20 16 110.8 6.92 2.63 0.587 0.509 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 3.41 4.21 453s supply 4.21 5.54 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 3.41 4.21 453s supply 4.21 5.54 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.968 453s supply 0.968 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 91.3901 7.3161 12.5 1.9e-14 *** 453s price -0.2168 0.0835 -2.6 0.014 * 453s income 0.3199 0.0168 19.1 < 2e-16 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.004 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 68.293 MSE: 4.017 Root MSE: 2.004 453s Multiple R-Squared: 0.745 Adjusted R-Squared: 0.715 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 47.5787 7.4268 6.41 2.3e-07 *** 453s price 0.2832 0.0835 3.39 0.0017 ** 453s farmPrice 0.2240 0.0168 13.36 2.7e-15 *** 453s trend 0.3199 0.0168 19.07 < 2e-16 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.631 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 110.791 MSE: 6.924 Root MSE: 2.631 453s Multiple R-Squared: 0.587 Adjusted R-Squared: 0.509 453s 453s [1] "** iterated W3SLS with 2 restrictions via R and restrict.regMat ***" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 9 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 35 185 1.76 0.655 0.71 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 69.9 4.11 2.03 0.739 0.709 453s supply 20 16 114.8 7.18 2.68 0.572 0.491 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 4.11 5.27 453s supply 5.27 7.18 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 4.11 5.27 453s supply 5.27 7.18 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.00 0.97 453s supply 0.97 1.00 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 90.1569 7.9174 11.39 2.5e-13 *** 453s price -0.2007 0.0920 -2.18 0.036 * 453s income 0.3159 0.0192 16.42 < 2e-16 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.028 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 69.917 MSE: 4.113 Root MSE: 2.028 453s Multiple R-Squared: 0.739 Adjusted R-Squared: 0.709 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 46.4810 8.0104 5.80 1.4e-06 *** 453s price 0.2993 0.0920 3.25 0.0025 ** 453s farmPrice 0.2190 0.0196 11.20 4.0e-13 *** 453s trend 0.3159 0.0192 16.42 < 2e-16 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.679 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 114.83 MSE: 7.177 Root MSE: 2.679 453s Multiple R-Squared: 0.572 Adjusted R-Squared: 0.491 453s 453s [1] "***************************************************" 453s [1] "3SLS formula: Schmidt" 453s [1] "************* 3SLS *********************************" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 6 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 33 178 0.983 0.668 0.814 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 65.7 3.87 1.97 0.755 0.726 453s supply 20 16 112.4 7.03 2.65 0.581 0.502 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 3.87 5.12 453s supply 5.12 7.03 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 3.87 5.12 453s supply 5.12 7.03 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.982 453s supply 0.982 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 94.6333 7.9208 11.95 1.1e-09 *** 453s price -0.2436 0.0965 -2.52 0.022 * 453s income 0.3140 0.0469 6.69 3.8e-06 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 1.966 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 453s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 52.6618 12.8051 4.11 0.00081 *** 453s price 0.2266 0.1075 2.11 0.05110 . 453s farmPrice 0.2234 0.0468 4.78 0.00021 *** 453s trend 0.3800 0.0720 5.28 7.5e-05 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.651 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 112.431 MSE: 7.027 Root MSE: 2.651 453s Multiple R-Squared: 0.581 Adjusted R-Squared: 0.502 453s 453s [1] "********************* iterated 3SLS EViews-like ****************" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 6 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 33 177 0.667 0.67 0.782 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 65.7 3.87 1.97 0.755 0.726 453s supply 20 16 111.3 6.96 2.64 0.585 0.507 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 3.29 4.20 453s supply 4.20 5.57 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 3.29 4.20 453s supply 4.20 5.57 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.982 453s supply 0.982 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 453s price -0.2436 0.0890 -2.74 0.0099 ** 453s income 0.3140 0.0433 7.25 2.5e-08 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 1.966 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 453s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 52.5527 11.3956 4.61 5.8e-05 *** 453s price 0.2271 0.0956 2.37 0.024 * 453s farmPrice 0.2245 0.0416 5.39 5.8e-06 *** 453s trend 0.3756 0.0641 5.86 1.5e-06 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.637 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 111.302 MSE: 6.956 Root MSE: 2.637 453s Multiple R-Squared: 0.585 Adjusted R-Squared: 0.507 453s 453s [1] "************** iterated 3SLS with methodResidCov = Theil **************" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 6 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 33 179 -0.818 0.665 0.957 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 65.7 3.87 1.97 0.755 0.726 453s supply 20 16 113.8 7.11 2.67 0.576 0.496 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 3.87 5.32 453s supply 5.32 7.11 453s 453s warning: this covariance matrix is NOT positive semidefinit! 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 3.87 5.32 453s supply 5.32 7.11 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.982 453s supply 0.982 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 453s price -0.2436 0.0965 -2.52 0.017 * 453s income 0.3140 0.0469 6.69 1.3e-07 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 1.966 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 453s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 52.7863 12.8707 4.10 0.00025 *** 453s price 0.2261 0.1081 2.09 0.04425 * 453s farmPrice 0.2221 0.0467 4.75 3.8e-05 *** 453s trend 0.3851 0.0693 5.55 3.6e-06 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.667 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 113.77 MSE: 7.111 Root MSE: 2.667 453s Multiple R-Squared: 0.576 Adjusted R-Squared: 0.496 453s 453s [1] "**************** iterated W3SLS EViews-like ****************" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 6 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 33 177 0.667 0.67 0.782 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 65.7 3.87 1.97 0.755 0.726 453s supply 20 16 111.3 6.96 2.64 0.585 0.507 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 3.29 4.20 453s supply 4.20 5.57 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 3.29 4.20 453s supply 4.20 5.57 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.982 453s supply 0.982 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 453s price -0.2436 0.0890 -2.74 0.0099 ** 453s income 0.3140 0.0433 7.25 2.5e-08 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 1.966 on 17 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 17 453s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 453s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 453s 453s 453s 3SLS estimates for 'supply' (equation 2) 453s Model Formula: consump ~ price + farmPrice + trend 453s Instruments: ~income + farmPrice + trend 453s 453s Estimate Std. Error t value Pr(>|t|) 453s (Intercept) 52.5527 11.3956 4.61 5.8e-05 *** 453s price 0.2271 0.0956 2.37 0.024 * 453s farmPrice 0.2245 0.0416 5.39 5.8e-06 *** 453s trend 0.3756 0.0641 5.86 1.5e-06 *** 453s --- 453s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 453s 453s Residual standard error: 2.637 on 16 degrees of freedom 453s Number of observations: 20 Degrees of Freedom: 16 453s SSR: 111.302 MSE: 6.956 Root MSE: 2.637 453s Multiple R-Squared: 0.585 Adjusted R-Squared: 0.507 453s 453s [1] "******* iterated 3SLS with restriction *****************" 453s 453s systemfit results 453s method: iterated 3SLS 453s 453s convergence achieved after 17 iterations 453s 453s N DF SSR detRCov OLS-R2 McElroy-R2 453s system 40 34 240 0.56 0.553 0.819 453s 453s N DF SSR MSE RMSE R2 Adj R2 453s demand 20 17 98.4 5.79 2.41 0.633 0.590 453s supply 20 16 141.1 8.82 2.97 0.474 0.375 453s 453s The covariance matrix of the residuals used for estimation 453s demand supply 453s demand 5.79 7.11 453s supply 7.11 8.82 453s 453s The covariance matrix of the residuals 453s demand supply 453s demand 5.79 7.11 453s supply 7.11 8.82 453s 453s The correlations of the residuals 453s demand supply 453s demand 1.000 0.995 453s supply 0.995 1.000 453s 453s 453s 3SLS estimates for 'demand' (equation 1) 453s Model Formula: consump ~ price + income 453s Instruments: ~income + farmPrice + trend 453s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 92.0742 9.6303 9.56 3.6e-11 *** 454s price -0.1064 0.1023 -1.04 0.31 454s income 0.1996 0.0297 6.73 9.9e-08 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.406 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 98.435 MSE: 5.79 Root MSE: 2.406 454s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.59 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 68.8551 12.4839 5.52 3.7e-06 *** 454s price 0.1833 0.1189 1.54 0.13 454s farmPrice 0.1202 0.0260 4.63 5.1e-05 *** 454s trend 0.1996 0.0297 6.73 9.9e-08 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.97 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 141.147 MSE: 8.822 Root MSE: 2.97 454s Multiple R-Squared: 0.474 Adjusted R-Squared: 0.375 454s 454s [1] "********* iterated 3SLS with restriction (EViews-like) *********" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 20 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 34 237 0.364 0.557 0.755 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 99.3 5.84 2.42 0.630 0.586 454s supply 20 16 138.1 8.63 2.94 0.485 0.388 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 4.96 5.82 454s supply 5.82 6.90 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 4.96 5.82 454s supply 5.82 6.90 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.995 454s supply 0.995 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 454s price -0.1043 0.0958 -1.09 0.28 454s income 0.1979 0.0299 6.61 1.4e-07 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.417 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 99.297 MSE: 5.841 Root MSE: 2.417 454s Multiple R-Squared: 0.63 Adjusted R-Squared: 0.586 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 454s price 0.1851 0.1053 1.76 0.088 . 454s farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 454s trend 0.1979 0.0299 6.61 1.4e-07 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.938 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 138.088 MSE: 8.63 Root MSE: 2.938 454s Multiple R-Squared: 0.485 Adjusted R-Squared: 0.388 454s 454s [1] "******** iterated W3SLS with restriction (EViews-like) *********" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 20 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 34 237 0.364 0.557 0.755 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 99.3 5.84 2.42 0.630 0.586 454s supply 20 16 138.1 8.63 2.94 0.485 0.388 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 4.96 5.82 454s supply 5.82 6.90 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 4.96 5.82 454s supply 5.82 6.90 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.995 454s supply 0.995 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 454s price -0.1043 0.0958 -1.09 0.28 454s income 0.1979 0.0299 6.61 1.4e-07 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.417 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 99.297 MSE: 5.841 Root MSE: 2.417 454s Multiple R-Squared: 0.63 Adjusted R-Squared: 0.586 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 454s price 0.1851 0.1053 1.76 0.088 . 454s farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 454s trend 0.1979 0.0299 6.61 1.4e-07 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.938 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 138.088 MSE: 8.63 Root MSE: 2.938 454s Multiple R-Squared: 0.485 Adjusted R-Squared: 0.388 454s 454s [1] "********* iterated 3SLS with restriction via restrict.regMat *****************" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 17 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 34 240 0.56 0.553 0.819 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 98.4 5.79 2.41 0.633 0.590 454s supply 20 16 141.1 8.82 2.97 0.474 0.375 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 5.79 7.11 454s supply 7.11 8.82 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 5.79 7.11 454s supply 7.11 8.82 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.995 454s supply 0.995 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 92.0742 9.6303 9.56 3.6e-11 *** 454s price -0.1064 0.1023 -1.04 0.31 454s income 0.1996 0.0297 6.73 9.9e-08 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.406 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 98.435 MSE: 5.79 Root MSE: 2.406 454s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.59 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 68.8551 12.4839 5.52 3.7e-06 *** 454s price 0.1833 0.1189 1.54 0.13 454s farmPrice 0.1202 0.0260 4.63 5.1e-05 *** 454s trend 0.1996 0.0297 6.73 9.9e-08 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.97 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 141.147 MSE: 8.822 Root MSE: 2.97 454s Multiple R-Squared: 0.474 Adjusted R-Squared: 0.375 454s 454s [1] "********* iterated 3SLS with restriction via restrict.regMat (EViews-like) ***" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 20 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 34 237 0.364 0.557 0.755 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 99.3 5.84 2.42 0.630 0.586 454s supply 20 16 138.1 8.63 2.94 0.485 0.388 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 4.96 5.82 454s supply 5.82 6.90 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 4.96 5.82 454s supply 5.82 6.90 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.995 454s supply 0.995 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 454s price -0.1043 0.0958 -1.09 0.28 454s income 0.1979 0.0299 6.61 1.4e-07 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.417 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 99.297 MSE: 5.841 Root MSE: 2.417 454s Multiple R-Squared: 0.63 Adjusted R-Squared: 0.586 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 454s price 0.1851 0.1053 1.76 0.088 . 454s farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 454s trend 0.1979 0.0299 6.61 1.4e-07 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.938 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 138.088 MSE: 8.63 Root MSE: 2.938 454s Multiple R-Squared: 0.485 Adjusted R-Squared: 0.388 454s 454s [1] "***** iterated W3SLS with restriction via restrict.regMat ********" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 17 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 34 240 0.56 0.553 0.819 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 98.4 5.79 2.41 0.633 0.590 454s supply 20 16 141.1 8.82 2.97 0.474 0.375 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 5.79 7.11 454s supply 7.11 8.82 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 5.79 7.11 454s supply 7.11 8.82 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.995 454s supply 0.995 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 92.0742 9.6303 9.56 3.6e-11 *** 454s price -0.1064 0.1023 -1.04 0.31 454s income 0.1996 0.0297 6.73 9.9e-08 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.406 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 98.435 MSE: 5.79 Root MSE: 2.406 454s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.59 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 68.8551 12.4839 5.52 3.7e-06 *** 454s price 0.1833 0.1189 1.54 0.13 454s farmPrice 0.1202 0.0260 4.63 5.1e-05 *** 454s trend 0.1996 0.0297 6.73 9.9e-08 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.97 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 141.147 MSE: 8.822 Root MSE: 2.97 454s Multiple R-Squared: 0.474 Adjusted R-Squared: 0.375 454s 454s [1] "******** iterated 3SLS with 2 restrictions *********************" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 9 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 35 185 1.76 0.655 0.71 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 69.9 4.11 2.03 0.739 0.709 454s supply 20 16 114.8 7.18 2.68 0.572 0.491 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 4.11 5.27 454s supply 5.27 7.18 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 4.11 5.27 454s supply 5.27 7.18 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.00 0.97 454s supply 0.97 1.00 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 90.1569 7.9174 11.39 2.5e-13 *** 454s price -0.2007 0.0920 -2.18 0.036 * 454s income 0.3159 0.0192 16.42 < 2e-16 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.028 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 69.917 MSE: 4.113 Root MSE: 2.028 454s Multiple R-Squared: 0.739 Adjusted R-Squared: 0.709 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 46.4810 8.0104 5.80 1.4e-06 *** 454s price 0.2993 0.0920 3.25 0.0025 ** 454s farmPrice 0.2190 0.0196 11.20 4.0e-13 *** 454s trend 0.3159 0.0192 16.42 < 2e-16 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.679 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 114.83 MSE: 7.177 Root MSE: 2.679 454s Multiple R-Squared: 0.572 Adjusted R-Squared: 0.491 454s 454s [1] "********* iterated 3SLS with 2 restrictions (EViews-like) *******" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 8 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 35 179 1.19 0.666 0.668 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 68.3 4.02 2.00 0.745 0.715 454s supply 20 16 110.8 6.92 2.63 0.587 0.509 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 3.41 4.21 454s supply 4.21 5.54 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 3.41 4.21 454s supply 4.21 5.54 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.968 454s supply 0.968 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 91.3901 7.3161 12.5 1.9e-14 *** 454s price -0.2168 0.0835 -2.6 0.014 * 454s income 0.3199 0.0168 19.1 < 2e-16 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.004 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 68.293 MSE: 4.017 Root MSE: 2.004 454s Multiple R-Squared: 0.745 Adjusted R-Squared: 0.715 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 47.5787 7.4268 6.41 2.3e-07 *** 454s price 0.2832 0.0835 3.39 0.0017 ** 454s farmPrice 0.2240 0.0168 13.36 2.7e-15 *** 454s trend 0.3199 0.0168 19.07 < 2e-16 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.631 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 110.791 MSE: 6.924 Root MSE: 2.631 454s Multiple R-Squared: 0.587 Adjusted R-Squared: 0.509 454s 454s [1] "******** iterated W3SLS with 2 restrictions (EViews-like) *******" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 8 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 35 179 1.19 0.666 0.668 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 68.3 4.02 2.00 0.745 0.715 454s supply 20 16 110.8 6.92 2.63 0.587 0.509 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 3.41 4.21 454s supply 4.21 5.54 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 3.41 4.21 454s supply 4.21 5.54 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.968 454s supply 0.968 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 91.3901 7.3161 12.5 1.9e-14 *** 454s price -0.2168 0.0835 -2.6 0.014 * 454s income 0.3199 0.0168 19.1 < 2e-16 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.004 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 68.293 MSE: 4.017 Root MSE: 2.004 454s Multiple R-Squared: 0.745 Adjusted R-Squared: 0.715 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 47.5787 7.4268 6.41 2.3e-07 *** 454s price 0.2832 0.0835 3.39 0.0017 ** 454s farmPrice 0.2240 0.0168 13.36 2.7e-15 *** 454s trend 0.3199 0.0168 19.07 < 2e-16 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.631 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 110.791 MSE: 6.924 Root MSE: 2.631 454s Multiple R-Squared: 0.587 Adjusted R-Squared: 0.509 454s 454s [1] "******** iterated 3SLS with 2 restrictions via R and restrict.regMat *********" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 9 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 35 185 1.76 0.655 0.71 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 69.9 4.11 2.03 0.739 0.709 454s supply 20 16 114.8 7.18 2.68 0.572 0.491 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 4.11 5.27 454s supply 5.27 7.18 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 4.11 5.27 454s supply 5.27 7.18 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.00 0.97 454s supply 0.97 1.00 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 90.1569 7.9174 11.39 2.5e-13 *** 454s price -0.2007 0.0920 -2.18 0.036 * 454s income 0.3159 0.0192 16.42 < 2e-16 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.028 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 69.917 MSE: 4.113 Root MSE: 2.028 454s Multiple R-Squared: 0.739 Adjusted R-Squared: 0.709 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 46.4810 8.0104 5.80 1.4e-06 *** 454s price 0.2993 0.0920 3.25 0.0025 ** 454s farmPrice 0.2190 0.0196 11.20 4.0e-13 *** 454s trend 0.3159 0.0192 16.42 < 2e-16 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.679 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 114.83 MSE: 7.177 Root MSE: 2.679 454s Multiple R-Squared: 0.572 Adjusted R-Squared: 0.491 454s 454s [1] "*** iterated 3SLS with 2 restrictions via R and restrict.regMat (EViews-like)**" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 8 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 35 179 1.19 0.666 0.668 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 68.3 4.02 2.00 0.745 0.715 454s supply 20 16 110.8 6.92 2.63 0.587 0.509 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 3.41 4.21 454s supply 4.21 5.54 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 3.41 4.21 454s supply 4.21 5.54 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.968 454s supply 0.968 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 91.3901 7.3161 12.5 1.9e-14 *** 454s price -0.2168 0.0835 -2.6 0.014 * 454s income 0.3199 0.0168 19.1 < 2e-16 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.004 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 68.293 MSE: 4.017 Root MSE: 2.004 454s Multiple R-Squared: 0.745 Adjusted R-Squared: 0.715 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 47.5787 7.4268 6.41 2.3e-07 *** 454s price 0.2832 0.0835 3.39 0.0017 ** 454s farmPrice 0.2240 0.0168 13.36 2.7e-15 *** 454s trend 0.3199 0.0168 19.07 < 2e-16 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.631 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 110.791 MSE: 6.924 Root MSE: 2.631 454s Multiple R-Squared: 0.587 Adjusted R-Squared: 0.509 454s 454s [1] "** iterated W3SLS with 2 restrictions via R and restrict.regMat ***" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 9 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 35 185 1.76 0.655 0.71 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 69.9 4.11 2.03 0.739 0.709 454s supply 20 16 114.8 7.18 2.68 0.572 0.491 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 4.11 5.27 454s supply 5.27 7.18 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 4.11 5.27 454s supply 5.27 7.18 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.00 0.97 454s supply 0.97 1.00 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 90.1569 7.9174 11.39 2.5e-13 *** 454s price -0.2007 0.0920 -2.18 0.036 * 454s income 0.3159 0.0192 16.42 < 2e-16 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.028 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 69.917 MSE: 4.113 Root MSE: 2.028 454s Multiple R-Squared: 0.739 Adjusted R-Squared: 0.709 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 46.4810 8.0104 5.80 1.4e-06 *** 454s price 0.2993 0.0920 3.25 0.0025 ** 454s farmPrice 0.2190 0.0196 11.20 4.0e-13 *** 454s trend 0.3159 0.0192 16.42 < 2e-16 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.679 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 114.83 MSE: 7.177 Root MSE: 2.679 454s Multiple R-Squared: 0.572 Adjusted R-Squared: 0.491 454s 454s [1] "***************************************************" 454s [1] "3SLS formula: GMM" 454s [1] "************* 3SLS *********************************" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 6 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 33 178 0.983 0.668 0.814 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 65.7 3.87 1.97 0.755 0.726 454s supply 20 16 112.4 7.03 2.65 0.581 0.502 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 3.87 5.12 454s supply 5.12 7.03 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 3.87 5.12 454s supply 5.12 7.03 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.982 454s supply 0.982 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 94.6333 7.9208 11.95 1.1e-09 *** 454s price -0.2436 0.0965 -2.52 0.022 * 454s income 0.3140 0.0469 6.69 3.8e-06 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 1.966 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 454s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 52.6618 12.8051 4.11 0.00081 *** 454s price 0.2266 0.1075 2.11 0.05110 . 454s farmPrice 0.2234 0.0468 4.78 0.00021 *** 454s trend 0.3800 0.0720 5.28 7.5e-05 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.651 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 112.431 MSE: 7.027 Root MSE: 2.651 454s Multiple R-Squared: 0.581 Adjusted R-Squared: 0.502 454s 454s [1] "********************* iterated 3SLS EViews-like ****************" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 6 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 33 177 0.667 0.67 0.782 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 65.7 3.87 1.97 0.755 0.726 454s supply 20 16 111.3 6.96 2.64 0.585 0.507 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 3.29 4.20 454s supply 4.20 5.57 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 3.29 4.20 454s supply 4.20 5.57 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.982 454s supply 0.982 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 454s price -0.2436 0.0890 -2.74 0.0099 ** 454s income 0.3140 0.0433 7.25 2.5e-08 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 1.966 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 454s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 52.5527 11.3956 4.61 5.8e-05 *** 454s price 0.2271 0.0956 2.37 0.024 * 454s farmPrice 0.2245 0.0416 5.39 5.8e-06 *** 454s trend 0.3756 0.0641 5.86 1.5e-06 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.637 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 111.302 MSE: 6.956 Root MSE: 2.637 454s Multiple R-Squared: 0.585 Adjusted R-Squared: 0.507 454s 454s [1] "************** iterated 3SLS with methodResidCov = Theil **************" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 6 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 33 179 -0.818 0.665 0.957 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 65.7 3.87 1.97 0.755 0.726 454s supply 20 16 113.8 7.11 2.67 0.576 0.496 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 3.87 5.32 454s supply 5.32 7.11 454s 454s warning: this covariance matrix is NOT positive semidefinit! 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 3.87 5.32 454s supply 5.32 7.11 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.982 454s supply 0.982 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 454s price -0.2436 0.0965 -2.52 0.017 * 454s income 0.3140 0.0469 6.69 1.3e-07 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 1.966 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 454s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 52.7863 12.8707 4.10 0.00025 *** 454s price 0.2261 0.1081 2.09 0.04425 * 454s farmPrice 0.2221 0.0467 4.75 3.8e-05 *** 454s trend 0.3851 0.0693 5.55 3.6e-06 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.667 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 113.77 MSE: 7.111 Root MSE: 2.667 454s Multiple R-Squared: 0.576 Adjusted R-Squared: 0.496 454s 454s [1] "**************** iterated W3SLS EViews-like ****************" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 6 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 33 177 0.667 0.67 0.782 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 65.7 3.87 1.97 0.755 0.726 454s supply 20 16 111.3 6.96 2.64 0.585 0.507 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 3.29 4.20 454s supply 4.20 5.57 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 3.29 4.20 454s supply 4.20 5.57 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.982 454s supply 0.982 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 454s price -0.2436 0.0890 -2.74 0.0099 ** 454s income 0.3140 0.0433 7.25 2.5e-08 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 1.966 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 454s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 52.5527 11.3956 4.61 5.8e-05 *** 454s price 0.2271 0.0956 2.37 0.024 * 454s farmPrice 0.2245 0.0416 5.39 5.8e-06 *** 454s trend 0.3756 0.0641 5.86 1.5e-06 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.637 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 111.302 MSE: 6.956 Root MSE: 2.637 454s Multiple R-Squared: 0.585 Adjusted R-Squared: 0.507 454s 454s [1] "******* iterated 3SLS with restriction *****************" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 17 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 34 240 0.56 0.553 0.819 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 98.4 5.79 2.41 0.633 0.590 454s supply 20 16 141.1 8.82 2.97 0.474 0.375 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 5.79 7.11 454s supply 7.11 8.82 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 5.79 7.11 454s supply 7.11 8.82 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.995 454s supply 0.995 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 92.0742 9.6303 9.56 3.6e-11 *** 454s price -0.1064 0.1023 -1.04 0.31 454s income 0.1996 0.0297 6.73 9.9e-08 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.406 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 98.435 MSE: 5.79 Root MSE: 2.406 454s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.59 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 68.8551 12.4839 5.52 3.7e-06 *** 454s price 0.1833 0.1189 1.54 0.13 454s farmPrice 0.1202 0.0260 4.63 5.1e-05 *** 454s trend 0.1996 0.0297 6.73 9.9e-08 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.97 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 141.147 MSE: 8.822 Root MSE: 2.97 454s Multiple R-Squared: 0.474 Adjusted R-Squared: 0.375 454s 454s [1] "********* iterated 3SLS with restriction (EViews-like) *********" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 20 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 34 237 0.364 0.557 0.755 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 99.3 5.84 2.42 0.630 0.586 454s supply 20 16 138.1 8.63 2.94 0.485 0.388 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 4.96 5.82 454s supply 5.82 6.90 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 4.96 5.82 454s supply 5.82 6.90 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.995 454s supply 0.995 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 454s price -0.1043 0.0958 -1.09 0.28 454s income 0.1979 0.0299 6.61 1.4e-07 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.417 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 99.297 MSE: 5.841 Root MSE: 2.417 454s Multiple R-Squared: 0.63 Adjusted R-Squared: 0.586 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 454s price 0.1851 0.1053 1.76 0.088 . 454s farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 454s trend 0.1979 0.0299 6.61 1.4e-07 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.938 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 138.088 MSE: 8.63 Root MSE: 2.938 454s Multiple R-Squared: 0.485 Adjusted R-Squared: 0.388 454s 454s [1] "******** iterated W3SLS with restriction (EViews-like) *********" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 20 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 34 237 0.364 0.557 0.755 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 99.3 5.84 2.42 0.630 0.586 454s supply 20 16 138.1 8.63 2.94 0.485 0.388 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 4.96 5.82 454s supply 5.82 6.90 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 4.96 5.82 454s supply 5.82 6.90 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.995 454s supply 0.995 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 454s price -0.1043 0.0958 -1.09 0.28 454s income 0.1979 0.0299 6.61 1.4e-07 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.417 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 99.297 MSE: 5.841 Root MSE: 2.417 454s Multiple R-Squared: 0.63 Adjusted R-Squared: 0.586 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 454s price 0.1851 0.1053 1.76 0.088 . 454s farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 454s trend 0.1979 0.0299 6.61 1.4e-07 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.938 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 138.088 MSE: 8.63 Root MSE: 2.938 454s Multiple R-Squared: 0.485 Adjusted R-Squared: 0.388 454s 454s [1] "********* iterated 3SLS with restriction via restrict.regMat *****************" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 17 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 34 240 0.56 0.553 0.819 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 98.4 5.79 2.41 0.633 0.590 454s supply 20 16 141.1 8.82 2.97 0.474 0.375 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 5.79 7.11 454s supply 7.11 8.82 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 5.79 7.11 454s supply 7.11 8.82 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.995 454s supply 0.995 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 92.0742 9.6303 9.56 3.6e-11 *** 454s price -0.1064 0.1023 -1.04 0.31 454s income 0.1996 0.0297 6.73 9.9e-08 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.406 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 98.435 MSE: 5.79 Root MSE: 2.406 454s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.59 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 68.8551 12.4839 5.52 3.7e-06 *** 454s price 0.1833 0.1189 1.54 0.13 454s farmPrice 0.1202 0.0260 4.63 5.1e-05 *** 454s trend 0.1996 0.0297 6.73 9.9e-08 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.97 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 141.147 MSE: 8.822 Root MSE: 2.97 454s Multiple R-Squared: 0.474 Adjusted R-Squared: 0.375 454s 454s [1] "********* iterated 3SLS with restriction via restrict.regMat (EViews-like) ***" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 20 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 34 237 0.364 0.557 0.755 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 99.3 5.84 2.42 0.630 0.586 454s supply 20 16 138.1 8.63 2.94 0.485 0.388 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 4.96 5.82 454s supply 5.82 6.90 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 4.96 5.82 454s supply 5.82 6.90 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.995 454s supply 0.995 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 454s price -0.1043 0.0958 -1.09 0.28 454s income 0.1979 0.0299 6.61 1.4e-07 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.417 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 99.297 MSE: 5.841 Root MSE: 2.417 454s Multiple R-Squared: 0.63 Adjusted R-Squared: 0.586 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 454s price 0.1851 0.1053 1.76 0.088 . 454s farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 454s trend 0.1979 0.0299 6.61 1.4e-07 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.938 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 138.088 MSE: 8.63 Root MSE: 2.938 454s Multiple R-Squared: 0.485 Adjusted R-Squared: 0.388 454s 454s [1] "***** iterated W3SLS with restriction via restrict.regMat ********" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 17 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 34 240 0.56 0.553 0.819 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 98.4 5.79 2.41 0.633 0.590 454s supply 20 16 141.1 8.82 2.97 0.474 0.375 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 5.79 7.11 454s supply 7.11 8.82 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 5.79 7.11 454s supply 7.11 8.82 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.995 454s supply 0.995 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 92.0742 9.6303 9.56 3.6e-11 *** 454s price -0.1064 0.1023 -1.04 0.31 454s income 0.1996 0.0297 6.73 9.9e-08 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.406 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 98.435 MSE: 5.79 Root MSE: 2.406 454s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.59 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 68.8551 12.4839 5.52 3.7e-06 *** 454s price 0.1833 0.1189 1.54 0.13 454s farmPrice 0.1202 0.0260 4.63 5.1e-05 *** 454s trend 0.1996 0.0297 6.73 9.9e-08 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.97 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 141.147 MSE: 8.822 Root MSE: 2.97 454s Multiple R-Squared: 0.474 Adjusted R-Squared: 0.375 454s 454s [1] "******** iterated 3SLS with 2 restrictions *********************" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 9 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 35 185 1.76 0.655 0.71 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 69.9 4.11 2.03 0.739 0.709 454s supply 20 16 114.8 7.18 2.68 0.572 0.491 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 4.11 5.27 454s supply 5.27 7.18 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 4.11 5.27 454s supply 5.27 7.18 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.00 0.97 454s supply 0.97 1.00 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 90.1569 7.9174 11.39 2.5e-13 *** 454s price -0.2007 0.0920 -2.18 0.036 * 454s income 0.3159 0.0192 16.42 < 2e-16 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.028 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 69.917 MSE: 4.113 Root MSE: 2.028 454s Multiple R-Squared: 0.739 Adjusted R-Squared: 0.709 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 46.4810 8.0104 5.80 1.4e-06 *** 454s price 0.2993 0.0920 3.25 0.0025 ** 454s farmPrice 0.2190 0.0196 11.20 4.0e-13 *** 454s trend 0.3159 0.0192 16.42 < 2e-16 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.679 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 114.83 MSE: 7.177 Root MSE: 2.679 454s Multiple R-Squared: 0.572 Adjusted R-Squared: 0.491 454s 454s [1] "********* iterated 3SLS with 2 restrictions (EViews-like) *******" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 8 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 35 179 1.19 0.666 0.668 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 68.3 4.02 2.00 0.745 0.715 454s supply 20 16 110.8 6.92 2.63 0.587 0.509 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 3.41 4.21 454s supply 4.21 5.54 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 3.41 4.21 454s supply 4.21 5.54 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.968 454s supply 0.968 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 91.3901 7.3161 12.5 1.9e-14 *** 454s price -0.2168 0.0835 -2.6 0.014 * 454s income 0.3199 0.0168 19.1 < 2e-16 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.004 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 68.293 MSE: 4.017 Root MSE: 2.004 454s Multiple R-Squared: 0.745 Adjusted R-Squared: 0.715 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 47.5787 7.4268 6.41 2.3e-07 *** 454s price 0.2832 0.0835 3.39 0.0017 ** 454s farmPrice 0.2240 0.0168 13.36 2.7e-15 *** 454s trend 0.3199 0.0168 19.07 < 2e-16 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.631 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 110.791 MSE: 6.924 Root MSE: 2.631 454s Multiple R-Squared: 0.587 Adjusted R-Squared: 0.509 454s 454s [1] "******** iterated W3SLS with 2 restrictions (EViews-like) *******" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 8 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 35 179 1.19 0.666 0.668 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 68.3 4.02 2.00 0.745 0.715 454s supply 20 16 110.8 6.92 2.63 0.587 0.509 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 3.41 4.21 454s supply 4.21 5.54 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 3.41 4.21 454s supply 4.21 5.54 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.968 454s supply 0.968 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 91.3901 7.3161 12.5 1.9e-14 *** 454s price -0.2168 0.0835 -2.6 0.014 * 454s income 0.3199 0.0168 19.1 < 2e-16 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.004 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 68.293 MSE: 4.017 Root MSE: 2.004 454s Multiple R-Squared: 0.745 Adjusted R-Squared: 0.715 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 47.5787 7.4268 6.41 2.3e-07 *** 454s price 0.2832 0.0835 3.39 0.0017 ** 454s farmPrice 0.2240 0.0168 13.36 2.7e-15 *** 454s trend 0.3199 0.0168 19.07 < 2e-16 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.631 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 110.791 MSE: 6.924 Root MSE: 2.631 454s Multiple R-Squared: 0.587 Adjusted R-Squared: 0.509 454s 454s [1] "******** iterated 3SLS with 2 restrictions via R and restrict.regMat *********" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 9 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 35 185 1.76 0.655 0.71 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 69.9 4.11 2.03 0.739 0.709 454s supply 20 16 114.8 7.18 2.68 0.572 0.491 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 4.11 5.27 454s supply 5.27 7.18 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 4.11 5.27 454s supply 5.27 7.18 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.00 0.97 454s supply 0.97 1.00 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 90.1569 7.9174 11.39 2.5e-13 *** 454s price -0.2007 0.0920 -2.18 0.036 * 454s income 0.3159 0.0192 16.42 < 2e-16 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.028 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 69.917 MSE: 4.113 Root MSE: 2.028 454s Multiple R-Squared: 0.739 Adjusted R-Squared: 0.709 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 46.4810 8.0104 5.80 1.4e-06 *** 454s price 0.2993 0.0920 3.25 0.0025 ** 454s farmPrice 0.2190 0.0196 11.20 4.0e-13 *** 454s trend 0.3159 0.0192 16.42 < 2e-16 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.679 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 114.83 MSE: 7.177 Root MSE: 2.679 454s Multiple R-Squared: 0.572 Adjusted R-Squared: 0.491 454s 454s [1] "*** iterated 3SLS with 2 restrictions via R and restrict.regMat (EViews-like)**" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 8 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 35 179 1.19 0.666 0.668 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 68.3 4.02 2.00 0.745 0.715 454s supply 20 16 110.8 6.92 2.63 0.587 0.509 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 3.41 4.21 454s supply 4.21 5.54 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 3.41 4.21 454s supply 4.21 5.54 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.968 454s supply 0.968 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 91.3901 7.3161 12.5 1.9e-14 *** 454s price -0.2168 0.0835 -2.6 0.014 * 454s income 0.3199 0.0168 19.1 < 2e-16 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.004 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 68.293 MSE: 4.017 Root MSE: 2.004 454s Multiple R-Squared: 0.745 Adjusted R-Squared: 0.715 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 47.5787 7.4268 6.41 2.3e-07 *** 454s price 0.2832 0.0835 3.39 0.0017 ** 454s farmPrice 0.2240 0.0168 13.36 2.7e-15 *** 454s trend 0.3199 0.0168 19.07 < 2e-16 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.631 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 110.791 MSE: 6.924 Root MSE: 2.631 454s Multiple R-Squared: 0.587 Adjusted R-Squared: 0.509 454s 454s [1] "** iterated W3SLS with 2 restrictions via R and restrict.regMat ***" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 9 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 35 185 1.76 0.655 0.71 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 69.9 4.11 2.03 0.739 0.709 454s supply 20 16 114.8 7.18 2.68 0.572 0.491 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 4.11 5.27 454s supply 5.27 7.18 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 4.11 5.27 454s supply 5.27 7.18 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.00 0.97 454s supply 0.97 1.00 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 90.1569 7.9174 11.39 2.5e-13 *** 454s price -0.2007 0.0920 -2.18 0.036 * 454s income 0.3159 0.0192 16.42 < 2e-16 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.028 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 69.917 MSE: 4.113 Root MSE: 2.028 454s Multiple R-Squared: 0.739 Adjusted R-Squared: 0.709 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 46.4810 8.0104 5.80 1.4e-06 *** 454s price 0.2993 0.0920 3.25 0.0025 ** 454s farmPrice 0.2190 0.0196 11.20 4.0e-13 *** 454s trend 0.3159 0.0192 16.42 < 2e-16 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.679 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 114.83 MSE: 7.177 Root MSE: 2.679 454s Multiple R-Squared: 0.572 Adjusted R-Squared: 0.491 454s 454s [1] "***************************************************" 454s [1] "3SLS formula: EViews" 454s [1] "************* 3SLS *********************************" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 6 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 33 178 0.983 0.668 0.814 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 65.7 3.87 1.97 0.755 0.726 454s supply 20 16 112.4 7.03 2.65 0.581 0.502 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 3.87 5.12 454s supply 5.12 7.03 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 3.87 5.12 454s supply 5.12 7.03 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.982 454s supply 0.982 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 94.6333 7.9208 11.95 1.1e-09 *** 454s price -0.2436 0.0965 -2.52 0.022 * 454s income 0.3140 0.0469 6.69 3.8e-06 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 1.966 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 454s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 52.6618 12.8051 4.11 0.00081 *** 454s price 0.2266 0.1075 2.11 0.05110 . 454s farmPrice 0.2234 0.0468 4.78 0.00021 *** 454s trend 0.3800 0.0720 5.28 7.5e-05 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.651 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 112.431 MSE: 7.027 Root MSE: 2.651 454s Multiple R-Squared: 0.581 Adjusted R-Squared: 0.502 454s 454s [1] "********************* iterated 3SLS EViews-like ****************" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 6 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 33 177 0.667 0.67 0.782 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 65.7 3.87 1.97 0.755 0.726 454s supply 20 16 111.3 6.96 2.64 0.585 0.507 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 3.29 4.20 454s supply 4.20 5.57 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 3.29 4.20 454s supply 4.20 5.57 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.982 454s supply 0.982 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 454s price -0.2436 0.0890 -2.74 0.0099 ** 454s income 0.3140 0.0433 7.25 2.5e-08 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 1.966 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 454s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 52.5527 11.3956 4.61 5.8e-05 *** 454s price 0.2271 0.0956 2.37 0.024 * 454s farmPrice 0.2245 0.0416 5.39 5.8e-06 *** 454s trend 0.3756 0.0641 5.86 1.5e-06 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.637 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 111.302 MSE: 6.956 Root MSE: 2.637 454s Multiple R-Squared: 0.585 Adjusted R-Squared: 0.507 454s 454s [1] "************** iterated 3SLS with methodResidCov = Theil **************" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 6 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 33 179 -0.818 0.665 0.957 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 65.7 3.87 1.97 0.755 0.726 454s supply 20 16 113.8 7.11 2.67 0.576 0.496 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 3.87 5.32 454s supply 5.32 7.11 454s 454s warning: this covariance matrix is NOT positive semidefinit! 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 3.87 5.32 454s supply 5.32 7.11 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.982 454s supply 0.982 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 94.6333 7.9208 11.95 1.6e-13 *** 454s price -0.2436 0.0965 -2.52 0.017 * 454s income 0.3140 0.0469 6.69 1.3e-07 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 1.966 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 454s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 52.7863 12.8707 4.10 0.00025 *** 454s price 0.2261 0.1081 2.09 0.04425 * 454s farmPrice 0.2221 0.0467 4.75 3.8e-05 *** 454s trend 0.3851 0.0693 5.55 3.6e-06 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.667 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 113.77 MSE: 7.111 Root MSE: 2.667 454s Multiple R-Squared: 0.576 Adjusted R-Squared: 0.496 454s 454s [1] "**************** iterated W3SLS EViews-like ****************" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 6 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 33 177 0.667 0.67 0.782 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 65.7 3.87 1.97 0.755 0.726 454s supply 20 16 111.3 6.96 2.64 0.585 0.507 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 3.29 4.20 454s supply 4.20 5.57 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 3.29 4.20 454s supply 4.20 5.57 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.982 454s supply 0.982 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 454s price -0.2436 0.0890 -2.74 0.0099 ** 454s income 0.3140 0.0433 7.25 2.5e-08 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 1.966 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 454s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 52.5527 11.3956 4.61 5.8e-05 *** 454s price 0.2271 0.0956 2.37 0.024 * 454s farmPrice 0.2245 0.0416 5.39 5.8e-06 *** 454s trend 0.3756 0.0641 5.86 1.5e-06 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.637 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 111.302 MSE: 6.956 Root MSE: 2.637 454s Multiple R-Squared: 0.585 Adjusted R-Squared: 0.507 454s 454s [1] "******* iterated 3SLS with restriction *****************" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 17 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 34 240 0.56 0.553 0.819 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 98.4 5.79 2.41 0.633 0.590 454s supply 20 16 141.1 8.82 2.97 0.474 0.375 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 5.79 7.11 454s supply 7.11 8.82 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 5.79 7.11 454s supply 7.11 8.82 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.995 454s supply 0.995 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 92.0742 9.6303 9.56 3.6e-11 *** 454s price -0.1064 0.1023 -1.04 0.31 454s income 0.1996 0.0297 6.73 9.9e-08 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.406 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 98.435 MSE: 5.79 Root MSE: 2.406 454s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.59 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 68.8551 12.4839 5.52 3.7e-06 *** 454s price 0.1833 0.1189 1.54 0.13 454s farmPrice 0.1202 0.0260 4.63 5.1e-05 *** 454s trend 0.1996 0.0297 6.73 9.9e-08 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.97 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 141.147 MSE: 8.822 Root MSE: 2.97 454s Multiple R-Squared: 0.474 Adjusted R-Squared: 0.375 454s 454s [1] "********* iterated 3SLS with restriction (EViews-like) *********" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 20 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 34 237 0.364 0.557 0.755 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 99.3 5.84 2.42 0.630 0.586 454s supply 20 16 138.1 8.63 2.94 0.485 0.388 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 4.96 5.82 454s supply 5.82 6.90 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 4.96 5.82 454s supply 5.82 6.90 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.995 454s supply 0.995 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 454s price -0.1043 0.0958 -1.09 0.28 454s income 0.1979 0.0299 6.61 1.4e-07 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.417 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 99.297 MSE: 5.841 Root MSE: 2.417 454s Multiple R-Squared: 0.63 Adjusted R-Squared: 0.586 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 454s price 0.1851 0.1053 1.76 0.088 . 454s farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 454s trend 0.1979 0.0299 6.61 1.4e-07 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.938 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 138.088 MSE: 8.63 Root MSE: 2.938 454s Multiple R-Squared: 0.485 Adjusted R-Squared: 0.388 454s 454s [1] "******** iterated W3SLS with restriction (EViews-like) *********" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 20 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 34 237 0.364 0.557 0.755 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 99.3 5.84 2.42 0.630 0.586 454s supply 20 16 138.1 8.63 2.94 0.485 0.388 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 4.96 5.82 454s supply 5.82 6.90 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 4.96 5.82 454s supply 5.82 6.90 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.995 454s supply 0.995 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 454s price -0.1043 0.0958 -1.09 0.28 454s income 0.1979 0.0299 6.61 1.4e-07 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.417 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 99.297 MSE: 5.841 Root MSE: 2.417 454s Multiple R-Squared: 0.63 Adjusted R-Squared: 0.586 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 454s price 0.1851 0.1053 1.76 0.088 . 454s farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 454s trend 0.1979 0.0299 6.61 1.4e-07 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.938 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 138.088 MSE: 8.63 Root MSE: 2.938 454s Multiple R-Squared: 0.485 Adjusted R-Squared: 0.388 454s 454s [1] "********* iterated 3SLS with restriction via restrict.regMat *****************" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 17 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 34 240 0.56 0.553 0.819 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 98.4 5.79 2.41 0.633 0.590 454s supply 20 16 141.1 8.82 2.97 0.474 0.375 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 5.79 7.11 454s supply 7.11 8.82 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 5.79 7.11 454s supply 7.11 8.82 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.995 454s supply 0.995 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 92.0742 9.6303 9.56 3.6e-11 *** 454s price -0.1064 0.1023 -1.04 0.31 454s income 0.1996 0.0297 6.73 9.9e-08 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.406 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 98.435 MSE: 5.79 Root MSE: 2.406 454s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.59 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 68.8551 12.4839 5.52 3.7e-06 *** 454s price 0.1833 0.1189 1.54 0.13 454s farmPrice 0.1202 0.0260 4.63 5.1e-05 *** 454s trend 0.1996 0.0297 6.73 9.9e-08 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.97 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 141.147 MSE: 8.822 Root MSE: 2.97 454s Multiple R-Squared: 0.474 Adjusted R-Squared: 0.375 454s 454s [1] "********* iterated 3SLS with restriction via restrict.regMat (EViews-like) ***" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 20 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 34 237 0.364 0.557 0.755 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 99.3 5.84 2.42 0.630 0.586 454s supply 20 16 138.1 8.63 2.94 0.485 0.388 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 4.96 5.82 454s supply 5.82 6.90 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 4.96 5.82 454s supply 5.82 6.90 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.995 454s supply 0.995 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 454s price -0.1043 0.0958 -1.09 0.28 454s income 0.1979 0.0299 6.61 1.4e-07 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.417 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 99.297 MSE: 5.841 Root MSE: 2.417 454s Multiple R-Squared: 0.63 Adjusted R-Squared: 0.586 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 454s price 0.1851 0.1053 1.76 0.088 . 454s farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 454s trend 0.1979 0.0299 6.61 1.4e-07 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.938 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 138.088 MSE: 8.63 Root MSE: 2.938 454s Multiple R-Squared: 0.485 Adjusted R-Squared: 0.388 454s 454s [1] "***** iterated W3SLS with restriction via restrict.regMat ********" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 17 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 34 240 0.56 0.553 0.819 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 98.4 5.79 2.41 0.633 0.590 454s supply 20 16 141.1 8.82 2.97 0.474 0.375 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 5.79 7.11 454s supply 7.11 8.82 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 5.79 7.11 454s supply 7.11 8.82 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.995 454s supply 0.995 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 92.0742 9.6303 9.56 3.6e-11 *** 454s price -0.1064 0.1023 -1.04 0.31 454s income 0.1996 0.0297 6.73 9.9e-08 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.406 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 98.435 MSE: 5.79 Root MSE: 2.406 454s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.59 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 68.8551 12.4839 5.52 3.7e-06 *** 454s price 0.1833 0.1189 1.54 0.13 454s farmPrice 0.1202 0.0260 4.63 5.1e-05 *** 454s trend 0.1996 0.0297 6.73 9.9e-08 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.97 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 141.147 MSE: 8.822 Root MSE: 2.97 454s Multiple R-Squared: 0.474 Adjusted R-Squared: 0.375 454s 454s [1] "******** iterated 3SLS with 2 restrictions *********************" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s warning: convergence not achieved after 100 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 35 1194 34.7 -1.23 0.688 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 274 16.1 4.02 -0.024 -0.144 454s supply 20 16 920 57.5 7.58 -2.431 -3.074 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 16.1 29.9 454s supply 29.9 57.5 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 16.1 29.9 454s supply 29.9 57.5 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.981 454s supply 0.981 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 43.5261 10.3602 4.20 0.00017 *** 454s price 0.2553 0.1380 1.85 0.07275 . 454s income 0.3264 0.0424 7.71 4.8e-09 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 4.018 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 274.43 MSE: 16.143 Root MSE: 4.018 454s Multiple R-Squared: -0.024 Adjusted R-Squared: -0.144 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) -49.0143 9.6115 -5.10 1.2e-05 *** 454s price 1.2553 0.1380 9.10 9.5e-11 *** 454s farmPrice 0.2166 0.0573 3.78 0.00058 *** 454s trend 0.3264 0.0424 7.71 4.8e-09 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 7.582 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 919.812 MSE: 57.488 Root MSE: 7.582 454s Multiple R-Squared: -2.431 Adjusted R-Squared: -3.074 454s 454s [1] "********* iterated 3SLS with 2 restrictions (EViews-like) *******" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 66 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 35 615 20.5 -0.147 0.48 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 151 8.87 2.98 0.437 0.371 454s supply 20 16 464 29.00 5.38 -0.731 -1.055 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 7.54 12.4 454s supply 12.43 23.2 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 7.54 12.4 454s supply 12.43 23.2 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.939 454s supply 0.939 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 68.3925 9.6792 7.07 3.1e-08 *** 454s price -0.0907 0.1236 -0.73 0.47 454s income 0.4263 0.0385 11.08 5.4e-13 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.979 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 150.821 MSE: 8.872 Root MSE: 2.979 454s Multiple R-Squared: 0.437 Adjusted R-Squared: 0.371 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) -27.3424 9.5498 -2.86 0.007 ** 454s price 0.9093 0.1236 7.36 1.3e-08 *** 454s farmPrice 0.3396 0.0498 6.82 6.5e-08 *** 454s trend 0.4263 0.0385 11.08 5.4e-13 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 5.385 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 464.036 MSE: 29.002 Root MSE: 5.385 454s Multiple R-Squared: -0.731 Adjusted R-Squared: -1.055 454s 454s [1] "******** iterated W3SLS with 2 restrictions (EViews-like) *******" 454s 454s systemfit results 454s method: iterated 3SLS 454s 454s convergence achieved after 66 iterations 454s 454s N DF SSR detRCov OLS-R2 McElroy-R2 454s system 40 35 615 20.5 -0.147 0.48 454s 454s N DF SSR MSE RMSE R2 Adj R2 454s demand 20 17 151 8.87 2.98 0.437 0.371 454s supply 20 16 464 29.00 5.38 -0.731 -1.055 454s 454s The covariance matrix of the residuals used for estimation 454s demand supply 454s demand 7.54 12.4 454s supply 12.43 23.2 454s 454s The covariance matrix of the residuals 454s demand supply 454s demand 7.54 12.4 454s supply 12.43 23.2 454s 454s The correlations of the residuals 454s demand supply 454s demand 1.000 0.939 454s supply 0.939 1.000 454s 454s 454s 3SLS estimates for 'demand' (equation 1) 454s Model Formula: consump ~ price + income 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) 68.3925 9.6792 7.07 3.1e-08 *** 454s price -0.0907 0.1236 -0.73 0.47 454s income 0.4263 0.0385 11.08 5.4e-13 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 2.979 on 17 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 17 454s SSR: 150.821 MSE: 8.872 Root MSE: 2.979 454s Multiple R-Squared: 0.437 Adjusted R-Squared: 0.371 454s 454s 454s 3SLS estimates for 'supply' (equation 2) 454s Model Formula: consump ~ price + farmPrice + trend 454s Instruments: ~income + farmPrice + trend 454s 454s Estimate Std. Error t value Pr(>|t|) 454s (Intercept) -27.3423 9.5498 -2.86 0.007 ** 454s price 0.9093 0.1236 7.36 1.3e-08 *** 454s farmPrice 0.3396 0.0498 6.82 6.5e-08 *** 454s trend 0.4263 0.0385 11.08 5.4e-13 *** 454s --- 454s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 454s 454s Residual standard error: 5.385 on 16 degrees of freedom 454s Number of observations: 20 Degrees of Freedom: 16 454s SSR: 464.036 MSE: 29.002 Root MSE: 5.385 454s Multiple R-Squared: -0.731 Adjusted R-Squared: -1.055 454s 454s [1] "******** iterated 3SLS with 2 restrictions via R and restrict.regMat *********" 455s 455s systemfit results 455s method: iterated 3SLS 455s 455s warning: convergence not achieved after 100 iterations 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 35 1194 34.7 -1.23 0.688 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 274 16.1 4.02 -0.024 -0.144 455s supply 20 16 920 57.5 7.58 -2.431 -3.074 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 16.1 29.9 455s supply 29.9 57.5 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 16.1 29.9 455s supply 29.9 57.5 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.981 455s supply 0.981 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 43.5261 10.3602 4.20 0.00017 *** 455s price 0.2553 0.1380 1.85 0.07275 . 455s income 0.3264 0.0424 7.71 4.8e-09 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 4.018 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 274.43 MSE: 16.143 Root MSE: 4.018 455s Multiple R-Squared: -0.024 Adjusted R-Squared: -0.144 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) -49.0143 9.6115 -5.10 1.2e-05 *** 455s price 1.2553 0.1380 9.10 9.5e-11 *** 455s farmPrice 0.2166 0.0573 3.78 0.00058 *** 455s trend 0.3264 0.0424 7.71 4.8e-09 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 7.582 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 919.812 MSE: 57.488 Root MSE: 7.582 455s Multiple R-Squared: -2.431 Adjusted R-Squared: -3.074 455s 455s [1] "*** iterated 3SLS with 2 restrictions via R and restrict.regMat (EViews-like)**" 455s 455s systemfit results 455s method: iterated 3SLS 455s 455s convergence achieved after 66 iterations 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 35 615 20.5 -0.147 0.48 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 151 8.87 2.98 0.437 0.371 455s supply 20 16 464 29.00 5.38 -0.731 -1.055 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 7.54 12.4 455s supply 12.43 23.2 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 7.54 12.4 455s supply 12.43 23.2 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.939 455s supply 0.939 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 68.3925 9.6792 7.07 3.1e-08 *** 455s price -0.0907 0.1236 -0.73 0.47 455s income 0.4263 0.0385 11.08 5.4e-13 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.979 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 150.821 MSE: 8.872 Root MSE: 2.979 455s Multiple R-Squared: 0.437 Adjusted R-Squared: 0.371 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) -27.3424 9.5498 -2.86 0.007 ** 455s price 0.9093 0.1236 7.36 1.3e-08 *** 455s farmPrice 0.3396 0.0498 6.82 6.5e-08 *** 455s trend 0.4263 0.0385 11.08 5.4e-13 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 5.385 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 464.036 MSE: 29.002 Root MSE: 5.385 455s Multiple R-Squared: -0.731 Adjusted R-Squared: -1.055 455s 455s [1] "** iterated W3SLS with 2 restrictions via R and restrict.regMat ***" 455s 455s systemfit results 455s method: iterated 3SLS 455s 455s warning: convergence not achieved after 100 iterations 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 35 1194 34.7 -1.23 0.688 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 274 16.1 4.02 -0.024 -0.144 455s supply 20 16 920 57.5 7.58 -2.431 -3.074 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 16.1 29.9 455s supply 29.9 57.5 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 16.1 29.9 455s supply 29.9 57.5 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.981 455s supply 0.981 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 43.5261 10.3602 4.20 0.00017 *** 455s price 0.2553 0.1380 1.85 0.07275 . 455s income 0.3264 0.0424 7.71 4.8e-09 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 4.018 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 274.43 MSE: 16.143 Root MSE: 4.018 455s Multiple R-Squared: -0.024 Adjusted R-Squared: -0.144 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) -49.0142 9.6115 -5.10 1.2e-05 *** 455s price 1.2553 0.1380 9.10 9.5e-11 *** 455s farmPrice 0.2166 0.0573 3.78 0.00058 *** 455s trend 0.3264 0.0424 7.71 4.8e-09 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 7.582 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 919.811 MSE: 57.488 Root MSE: 7.582 455s Multiple R-Squared: -2.431 Adjusted R-Squared: -3.074 455s 455s > 455s > ## **************** 3SLS with different instruments ************* 455s > fit3slsd <- list() 455s > formulas <- c( "GLS", "IV", "Schmidt", "GMM", "EViews" ) 455s > for( i in seq( along = formulas ) ) { 455s + fit3slsd[[ i ]] <- list() 455s + 455s + print( "***************************************************" ) 455s + print( paste( "3SLS formula:", formulas[ i ] ) ) 455s + print( "************* 3SLS with different instruments **************" ) 455s + fit3slsd[[ i ]]$e1 <- systemfit( system, "3SLS", data = Kmenta, 455s + inst = instlist, method3sls = formulas[ i ], useMatrix = useMatrix ) 455s + print( summary( fit3slsd[[ i ]]$e1 ) ) 455s + 455s + print( "******* 3SLS with different instruments (EViews-like) **********" ) 455s + fit3slsd[[ i ]]$e1e <- systemfit( system, "3SLS", data = Kmenta, 455s + inst = instlist, methodResidCov = "noDfCor", method3sls = formulas[ i ], 455s + useMatrix = useMatrix ) 455s + print( summary( fit3slsd[[ i ]]$e1e, useDfSys = TRUE ) ) 455s + 455s + print( "**** 3SLS with different instruments and methodResidCov = Theil ***" ) 455s + fit3slsd[[ i ]]$e1c <- systemfit( system, "3SLS", data = Kmenta, 455s + inst = instlist, methodResidCov = "Theil", method3sls = formulas[ i ], 455s + x = TRUE, useMatrix = useMatrix ) 455s + print( summary( fit3slsd[[ i ]]$e1c, useDfSys = TRUE ) ) 455s + 455s + print( "************* W3SLS with different instruments **************" ) 455s + fit3slsd[[ i ]]$e1w <- systemfit( system, "3SLS", data = Kmenta, 455s + inst = instlist, method3sls = formulas[ i ], residCovWeighted = TRUE, 455s + useMatrix = useMatrix ) 455s + print( summary( fit3slsd[[ i ]]$e1w ) ) 455s + 455s + 455s + print( "******* 3SLS with different instruments and restriction ********" ) 455s + fit3slsd[[ i ]]$e2 <- systemfit( system, "3SLS", data = Kmenta, 455s + inst = instlist, restrict.matrix = restrm, method3sls = formulas[ i ], 455s + x = TRUE, useMatrix = useMatrix ) 455s + print( summary( fit3slsd[[ i ]]$e2 ) ) 455s + 455s + print( "** 3SLS with different instruments and restriction (EViews-like) *" ) 455s + fit3slsd[[ i ]]$e2e <- systemfit( system, "3SLS", data = Kmenta, 455s + inst = instlist, methodResidCov = "noDfCor", restrict.matrix = restrm, 455s + method3sls = formulas[ i ], useMatrix = useMatrix ) 455s + print( summary( fit3slsd[[ i ]]$e2e, useDfSys = TRUE ) ) 455s + 455s + print( "** W3SLS with different instruments and restriction (EViews-like) *" ) 455s + fit3slsd[[ i ]]$e2we <- systemfit( system, "3SLS", data = Kmenta, 455s + inst = instlist, methodResidCov = "noDfCor", restrict.matrix = restrm, 455s + method3sls = formulas[ i ], residCovWeighted = TRUE, 455s + useMatrix = useMatrix ) 455s + print( summary( fit3slsd[[ i ]]$e2we, useDfSys = TRUE ) ) 455s + 455s + 455s + print( "** 3SLS with different instruments and restriction via restrict.regMat *******" ) 455s + fit3slsd[[ i ]]$e3 <- systemfit( system, "3SLS", data = Kmenta, 455s + inst = instlist, restrict.regMat = tc, method3sls = formulas[ i ], 455s + useMatrix = useMatrix ) 455s + print( summary( fit3slsd[[ i ]]$e3 ) ) 455s + 455s + print( "3SLS with different instruments with restriction via restrict.regMat (EViews-like)" ) 455s + fit3slsd[[ i ]]$e3e <- systemfit( system, "3SLS", data = Kmenta, 455s + inst = instlist, methodResidCov = "noDfCor", restrict.regMat = tc, 455s + method3sls = formulas[ i ], x = TRUE, 455s + useMatrix = useMatrix ) 455s + print( summary( fit3slsd[[ i ]]$e3e, useDfSys = TRUE ) ) 455s + 455s + print( "** W3SLS with different instr. and restr. via restrict.regMat ****" ) 455s + fit3slsd[[ i ]]$e3w <- systemfit( system, "3SLS", data = Kmenta, 455s + inst = instlist, restrict.regMat = tc, method3sls = formulas[ i ], 455s + residCovWeighted = TRUE, x = TRUE, 455s + useMatrix = useMatrix ) 455s + print( summary( fit3slsd[[ i ]]$e3w ) ) 455s + 455s + 455s + print( "****** 3SLS with different instruments and 2 restrictions *********" ) 455s + fit3slsd[[ i ]]$e4 <- systemfit( system, "3SLS", data = Kmenta, 455s + inst = instlist, restrict.matrix = restr2m, restrict.rhs = restr2q, 455s + method3sls = formulas[ i ], x = TRUE, 455s + useMatrix = useMatrix ) 455s + print( summary( fit3slsd[[ i ]]$e4 ) ) 455s + 455s + print( "** 3SLS with different instruments and 2 restrictions (EViews-like) *" ) 455s + fit3slsd[[ i ]]$e4e <- systemfit( system, "3SLS", data = Kmenta, 455s + inst = instlist, methodResidCov = "noDfCor", restrict.matrix = restr2m, 455s + restrict.rhs = restr2q, method3sls = formulas[ i ], 455s + useMatrix = useMatrix ) 455s + print( summary( fit3slsd[[ i ]]$e4e, useDfSys = TRUE ) ) 455s + 455s + print( "**** W3SLS with different instruments and 2 restrictions *********" ) 455s + fit3slsd[[ i ]]$e4w <- systemfit( system, "3SLS", data = Kmenta, 455s + inst = instlist, restrict.matrix = restr2m, restrict.rhs = restr2q, 455s + method3sls = formulas[ i ], residCovWeighted = TRUE, 455s + useMatrix = useMatrix ) 455s + print( summary( fit3slsd[[ i ]]$e4w ) ) 455s + 455s + 455s + print( " 3SLS with different instruments with 2 restrictions via R and restrict.regMat" ) 455s + fit3slsd[[ i ]]$e5 <- systemfit( system, "3SLS", data = Kmenta, 455s + inst = instlist, restrict.regMat = tc, restrict.matrix = restr3m, 455s + restrict.rhs = restr3q, method3sls = formulas[ i ], 455s + useMatrix = useMatrix ) 455s + print( summary( fit3slsd[[ i ]]$e5 ) ) 455s + 455s + print( "3SLS with diff. instruments and 2 restr. via R and restrict.regMat (EViews-like)" ) 455s + fit3slsd[[ i ]]$e5e <- systemfit( system, "3SLS", data = Kmenta, 455s + inst = instlist, restrict.regMat = tc, methodResidCov = "noDfCor", 455s + restrict.matrix = restr3m, restrict.rhs = restr3q, 455s + method3sls = formulas[ i ], x = TRUE, 455s + useMatrix = useMatrix ) 455s + print( summary( fit3slsd[[ i ]]$e5e, useDfSys = TRUE ) ) 455s + 455s + print( "W3SLS with diff. instr. and 2 restr. via R and restrict.regMat (EViews-like)" ) 455s + fit3slsd[[ i ]]$e5we <- systemfit( system, "3SLS", data = Kmenta, 455s + inst = instlist, restrict.regMat = tc, methodResidCov = "noDfCor", 455s + restrict.matrix = restr3m, restrict.rhs = restr3q, method3sls = formulas[ i ], 455s + residCovWeighted = TRUE, useMatrix = useMatrix ) 455s + print( summary( fit3slsd[[ i ]]$e5we, useDfSys = TRUE ) ) 455s + } 455s [1] "***************************************************" 455s [1] "3SLS formula: GLS" 455s [1] "************* 3SLS with different instruments **************" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 33 170 13.4 0.683 0.52 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 67.4 3.97 1.99 0.748 0.719 455s supply 20 16 102.4 6.40 2.53 0.618 0.546 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.97 3.84 455s supply 3.84 6.04 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 3.97 3.47 455s supply 3.47 6.40 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.688 455s supply 0.688 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 106.7894 11.1435 9.58 2.9e-08 *** 455s price -0.4116 0.1448 -2.84 0.011 * 455s income 0.3617 0.0564 6.41 6.4e-06 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 1.992 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 455s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 46.9385 11.5390 4.07 0.0009 *** 455s price 0.2744 0.0897 3.06 0.0075 ** 455s farmPrice 0.2521 0.0470 5.36 6.4e-05 *** 455s trend 0.2048 0.0781 2.62 0.0185 * 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.53 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 102.443 MSE: 6.403 Root MSE: 2.53 455s Multiple R-Squared: 0.618 Adjusted R-Squared: 0.546 455s 455s [1] "******* 3SLS with different instruments (EViews-like) **********" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 33 170 9 0.684 0.511 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 67.4 3.97 1.99 0.748 0.719 455s supply 20 16 102.2 6.39 2.53 0.619 0.547 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.37 3.16 455s supply 3.16 4.83 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 3.37 2.87 455s supply 2.87 5.11 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.691 455s supply 0.691 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 106.789 10.274 10.39 6.1e-12 *** 455s price -0.412 0.134 -3.08 0.0041 ** 455s income 0.362 0.052 6.95 6.0e-08 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 1.992 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 455s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 47.0160 10.3208 4.56 6.8e-05 *** 455s price 0.2734 0.0802 3.41 0.0017 ** 455s farmPrice 0.2522 0.0421 6.00 9.8e-07 *** 455s trend 0.2062 0.0699 2.95 0.0058 ** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.527 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 102.203 MSE: 6.388 Root MSE: 2.527 455s Multiple R-Squared: 0.619 Adjusted R-Squared: 0.547 455s 455s [1] "**** 3SLS with different instruments and methodResidCov = Theil ***" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 33 170 12.7 0.683 0.502 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 67.4 3.97 1.99 0.748 0.719 455s supply 20 16 102.7 6.42 2.53 0.617 0.545 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.97 3.96 455s supply 3.96 6.04 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 3.97 3.57 455s supply 3.57 6.42 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.685 455s supply 0.685 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 106.7894 11.1435 9.58 4.7e-11 *** 455s price -0.4116 0.1448 -2.84 0.0076 ** 455s income 0.3617 0.0564 6.41 2.9e-07 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 1.992 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 455s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 46.8512 11.5060 4.07 0.00027 *** 455s price 0.2756 0.0889 3.10 0.00395 ** 455s farmPrice 0.2520 0.0470 5.36 6.4e-06 *** 455s trend 0.2032 0.0765 2.66 0.01204 * 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.534 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 102.718 MSE: 6.42 Root MSE: 2.534 455s Multiple R-Squared: 0.617 Adjusted R-Squared: 0.545 455s 455s [1] "************* W3SLS with different instruments **************" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 33 170 13.4 0.683 0.52 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 67.4 3.97 1.99 0.748 0.719 455s supply 20 16 102.4 6.40 2.53 0.618 0.546 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.97 3.84 455s supply 3.84 6.04 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 3.97 3.47 455s supply 3.47 6.40 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.688 455s supply 0.688 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 106.7894 11.1435 9.58 2.9e-08 *** 455s price -0.4116 0.1448 -2.84 0.011 * 455s income 0.3617 0.0564 6.41 6.4e-06 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 1.992 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 455s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 46.9385 11.5390 4.07 0.0009 *** 455s price 0.2744 0.0897 3.06 0.0075 ** 455s farmPrice 0.2521 0.0470 5.36 6.4e-05 *** 455s trend 0.2048 0.0781 2.62 0.0185 * 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.53 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 102.443 MSE: 6.403 Root MSE: 2.53 455s Multiple R-Squared: 0.618 Adjusted R-Squared: 0.546 455s 455s [1] "******* 3SLS with different instruments and restriction ********" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 34 201 2.72 0.626 0.685 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 72.3 4.25 2.06 0.730 0.699 455s supply 20 16 128.3 8.02 2.83 0.521 0.432 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.79 4.35 455s supply 4.35 6.27 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 4.25 5.60 455s supply 5.60 8.02 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.959 455s supply 0.959 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 88.9456 6.3475 14.01 1.1e-15 *** 455s price -0.1778 0.0812 -2.19 0.036 * 455s income 0.3049 0.0474 6.43 2.4e-07 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.062 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 72.262 MSE: 4.251 Root MSE: 2.062 455s Multiple R-Squared: 0.73 Adjusted R-Squared: 0.699 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 40.2918 11.2022 3.60 0.001 ** 455s price 0.3613 0.0785 4.60 5.6e-05 *** 455s farmPrice 0.2201 0.0453 4.86 2.6e-05 *** 455s trend 0.3049 0.0474 6.43 2.4e-07 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.832 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 128.304 MSE: 8.019 Root MSE: 2.832 455s Multiple R-Squared: 0.521 Adjusted R-Squared: 0.432 455s 455s [1] "** 3SLS with different instruments and restriction (EViews-like) *" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 34 200 1.75 0.627 0.651 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 72.7 4.28 2.07 0.729 0.697 455s supply 20 16 127.0 7.94 2.82 0.526 0.437 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.22 3.58 455s supply 3.58 5.02 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 3.64 4.62 455s supply 4.62 6.35 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.961 455s supply 0.961 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 88.7634 5.8428 15.19 < 2e-16 *** 455s price -0.1738 0.0737 -2.36 0.024 * 455s income 0.3027 0.0432 7.00 4.5e-08 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.068 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 72.717 MSE: 4.277 Root MSE: 2.068 455s Multiple R-Squared: 0.729 Adjusted R-Squared: 0.697 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 40.8177 10.0564 4.06 0.00027 *** 455s price 0.3569 0.0705 5.06 1.4e-05 *** 455s farmPrice 0.2195 0.0403 5.45 4.4e-06 *** 455s trend 0.3027 0.0432 7.00 4.5e-08 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.818 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 127.044 MSE: 7.94 Root MSE: 2.818 455s Multiple R-Squared: 0.526 Adjusted R-Squared: 0.437 455s 455s [1] "** W3SLS with different instruments and restriction (EViews-like) *" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 34 199 1.77 0.629 0.65 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 72.4 4.26 2.06 0.730 0.698 455s supply 20 16 126.7 7.92 2.81 0.527 0.439 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.24 3.60 455s supply 3.60 5.06 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 3.62 4.60 455s supply 4.60 6.34 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.961 455s supply 0.961 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 88.9298 5.9083 15.05 < 2e-16 *** 455s price -0.1760 0.0746 -2.36 0.024 * 455s income 0.3032 0.0434 6.99 4.6e-08 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.064 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 72.435 MSE: 4.261 Root MSE: 2.064 455s Multiple R-Squared: 0.73 Adjusted R-Squared: 0.698 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 40.8325 10.1094 4.04 0.00029 *** 455s price 0.3562 0.0711 5.01 1.7e-05 *** 455s farmPrice 0.2200 0.0405 5.43 4.8e-06 *** 455s trend 0.3032 0.0434 6.99 4.6e-08 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.814 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 126.74 MSE: 7.921 Root MSE: 2.814 455s Multiple R-Squared: 0.527 Adjusted R-Squared: 0.439 455s 455s [1] "** 3SLS with different instruments and restriction via restrict.regMat *******" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 34 201 2.72 0.626 0.685 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 72.3 4.25 2.06 0.730 0.699 455s supply 20 16 128.3 8.02 2.83 0.521 0.432 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.79 4.35 455s supply 4.35 6.27 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 4.25 5.60 455s supply 5.60 8.02 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.959 455s supply 0.959 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 88.9456 6.3475 14.01 1.1e-15 *** 455s price -0.1778 0.0812 -2.19 0.036 * 455s income 0.3049 0.0474 6.43 2.4e-07 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.062 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 72.262 MSE: 4.251 Root MSE: 2.062 455s Multiple R-Squared: 0.73 Adjusted R-Squared: 0.699 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 40.2918 11.2022 3.60 0.001 ** 455s price 0.3613 0.0785 4.60 5.6e-05 *** 455s farmPrice 0.2201 0.0453 4.86 2.6e-05 *** 455s trend 0.3049 0.0474 6.43 2.4e-07 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.832 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 128.304 MSE: 8.019 Root MSE: 2.832 455s Multiple R-Squared: 0.521 Adjusted R-Squared: 0.432 455s 455s [1] "3SLS with different instruments with restriction via restrict.regMat (EViews-like)" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 34 200 1.75 0.627 0.651 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 72.7 4.28 2.07 0.729 0.697 455s supply 20 16 127.0 7.94 2.82 0.526 0.437 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.22 3.58 455s supply 3.58 5.02 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 3.64 4.62 455s supply 4.62 6.35 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.961 455s supply 0.961 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 88.7634 5.8428 15.19 < 2e-16 *** 455s price -0.1738 0.0737 -2.36 0.024 * 455s income 0.3027 0.0432 7.00 4.5e-08 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.068 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 72.717 MSE: 4.277 Root MSE: 2.068 455s Multiple R-Squared: 0.729 Adjusted R-Squared: 0.697 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 40.8177 10.0564 4.06 0.00027 *** 455s price 0.3569 0.0705 5.06 1.4e-05 *** 455s farmPrice 0.2195 0.0403 5.45 4.4e-06 *** 455s trend 0.3027 0.0432 7.00 4.5e-08 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.818 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 127.044 MSE: 7.94 Root MSE: 2.818 455s Multiple R-Squared: 0.526 Adjusted R-Squared: 0.437 455s 455s [1] "** W3SLS with different instr. and restr. via restrict.regMat ****" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 34 200 2.75 0.627 0.684 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 71.9 4.23 2.06 0.732 0.700 455s supply 20 16 127.9 8.00 2.83 0.523 0.433 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.81 4.36 455s supply 4.36 6.34 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 4.23 5.58 455s supply 5.58 7.99 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.958 455s supply 0.958 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 89.1391 6.4318 13.86 1.6e-15 *** 455s price -0.1803 0.0823 -2.19 0.035 * 455s income 0.3055 0.0476 6.42 2.5e-07 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.057 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 71.945 MSE: 4.232 Root MSE: 2.057 455s Multiple R-Squared: 0.732 Adjusted R-Squared: 0.7 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 40.3187 11.2699 3.58 0.0011 ** 455s price 0.3604 0.0792 4.55 6.5e-05 *** 455s farmPrice 0.2207 0.0456 4.84 2.8e-05 *** 455s trend 0.3055 0.0476 6.42 2.5e-07 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.828 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 127.918 MSE: 7.995 Root MSE: 2.828 455s Multiple R-Squared: 0.523 Adjusted R-Squared: 0.433 455s 455s [1] "****** 3SLS with different instruments and 2 restrictions *********" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 35 211 2.1 0.606 0.71 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 77.9 4.58 2.14 0.709 0.675 455s supply 20 16 133.2 8.32 2.88 0.503 0.410 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.79 4.45 455s supply 4.45 6.06 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 4.58 6.01 455s supply 6.01 8.32 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.972 455s supply 0.972 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 86.4443 5.3770 16.08 <2e-16 *** 455s price -0.1371 0.0504 -2.72 0.01 * 455s income 0.2888 0.0182 15.89 <2e-16 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.141 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 77.945 MSE: 4.585 Root MSE: 2.141 455s Multiple R-Squared: 0.709 Adjusted R-Squared: 0.675 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 41.8618 5.4316 7.71 4.8e-09 *** 455s price 0.3629 0.0504 7.20 2.1e-08 *** 455s farmPrice 0.2040 0.0205 9.96 9.4e-12 *** 455s trend 0.2888 0.0182 15.89 < 2e-16 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.885 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 133.177 MSE: 8.324 Root MSE: 2.885 455s Multiple R-Squared: 0.503 Adjusted R-Squared: 0.41 455s 455s [1] "** 3SLS with different instruments and 2 restrictions (EViews-like) *" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 35 210 1.42 0.609 0.668 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 77.9 4.58 2.14 0.709 0.675 455s supply 20 16 132.0 8.25 2.87 0.508 0.415 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.22 3.67 455s supply 3.67 4.85 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 3.90 4.93 455s supply 4.93 6.60 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.972 455s supply 0.972 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 86.3521 4.9704 17.4 <2e-16 *** 455s price -0.1376 0.0458 -3.0 0.0049 ** 455s income 0.2902 0.0168 17.3 <2e-16 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.141 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 77.912 MSE: 4.583 Root MSE: 2.141 455s Multiple R-Squared: 0.709 Adjusted R-Squared: 0.675 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 41.6089 4.9950 8.33 8.0e-10 *** 455s price 0.3624 0.0458 7.91 2.6e-09 *** 455s farmPrice 0.2069 0.0184 11.27 3.4e-13 *** 455s trend 0.2902 0.0168 17.27 < 2e-16 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.872 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 131.997 MSE: 8.25 Root MSE: 2.872 455s Multiple R-Squared: 0.508 Adjusted R-Squared: 0.415 455s 455s [1] "**** W3SLS with different instruments and 2 restrictions *********" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 35 214 2.1 0.601 0.713 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 78.9 4.64 2.15 0.706 0.671 455s supply 20 16 135.2 8.45 2.91 0.496 0.401 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.75 4.46 455s supply 4.46 6.04 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 4.64 6.09 455s supply 6.09 8.45 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.973 455s supply 0.973 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 85.9516 5.1136 16.81 <2e-16 *** 455s price -0.1318 0.0479 -2.75 0.0093 ** 455s income 0.2884 0.0171 16.86 <2e-16 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.154 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 78.853 MSE: 4.638 Root MSE: 2.154 455s Multiple R-Squared: 0.706 Adjusted R-Squared: 0.671 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 41.4498 5.1591 8.03 1.9e-09 *** 455s price 0.3682 0.0479 7.69 5.0e-09 *** 455s farmPrice 0.2028 0.0193 10.50 2.3e-12 *** 455s trend 0.2884 0.0171 16.86 < 2e-16 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.907 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 135.215 MSE: 8.451 Root MSE: 2.907 455s Multiple R-Squared: 0.496 Adjusted R-Squared: 0.401 455s 455s [1] " 3SLS with different instruments with 2 restrictions via R and restrict.regMat" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 35 211 2.1 0.606 0.71 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 77.9 4.58 2.14 0.709 0.675 455s supply 20 16 133.2 8.32 2.88 0.503 0.410 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.79 4.45 455s supply 4.45 6.06 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 4.58 6.01 455s supply 6.01 8.32 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.972 455s supply 0.972 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 86.4443 5.3770 16.08 <2e-16 *** 455s price -0.1371 0.0504 -2.72 0.01 * 455s income 0.2888 0.0182 15.89 <2e-16 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.141 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 77.945 MSE: 4.585 Root MSE: 2.141 455s Multiple R-Squared: 0.709 Adjusted R-Squared: 0.675 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 41.8618 5.4316 7.71 4.8e-09 *** 455s price 0.3629 0.0504 7.20 2.1e-08 *** 455s farmPrice 0.2040 0.0205 9.96 9.4e-12 *** 455s trend 0.2888 0.0182 15.89 < 2e-16 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.885 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 133.177 MSE: 8.324 Root MSE: 2.885 455s Multiple R-Squared: 0.503 Adjusted R-Squared: 0.41 455s 455s [1] "3SLS with diff. instruments and 2 restr. via R and restrict.regMat (EViews-like)" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 35 210 1.42 0.609 0.668 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 77.9 4.58 2.14 0.709 0.675 455s supply 20 16 132.0 8.25 2.87 0.508 0.415 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.22 3.67 455s supply 3.67 4.85 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 3.90 4.93 455s supply 4.93 6.60 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.972 455s supply 0.972 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 86.3521 4.9704 17.4 <2e-16 *** 455s price -0.1376 0.0458 -3.0 0.0049 ** 455s income 0.2902 0.0168 17.3 <2e-16 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.141 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 77.912 MSE: 4.583 Root MSE: 2.141 455s Multiple R-Squared: 0.709 Adjusted R-Squared: 0.675 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 41.6089 4.9950 8.33 8.0e-10 *** 455s price 0.3624 0.0458 7.91 2.6e-09 *** 455s farmPrice 0.2069 0.0184 11.27 3.4e-13 *** 455s trend 0.2902 0.0168 17.27 < 2e-16 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.872 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 131.997 MSE: 8.25 Root MSE: 2.872 455s Multiple R-Squared: 0.508 Adjusted R-Squared: 0.415 455s 455s [1] "W3SLS with diff. instr. and 2 restr. via R and restrict.regMat (EViews-like)" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 35 212 1.42 0.604 0.671 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 78.7 4.63 2.15 0.706 0.672 455s supply 20 16 133.7 8.36 2.89 0.501 0.408 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.19 3.68 455s supply 3.68 4.83 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 3.94 4.99 455s supply 4.99 6.69 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.973 455s supply 0.973 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 85.9108 4.7598 18.05 <2e-16 *** 455s price -0.1329 0.0438 -3.03 0.0045 ** 455s income 0.2900 0.0159 18.18 <2e-16 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.152 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 78.713 MSE: 4.63 Root MSE: 2.152 455s Multiple R-Squared: 0.706 Adjusted R-Squared: 0.672 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 41.2362 4.7784 8.63 3.5e-10 *** 455s price 0.3671 0.0438 8.38 7.0e-10 *** 455s farmPrice 0.2060 0.0174 11.81 9.1e-14 *** 455s trend 0.2900 0.0159 18.18 < 2e-16 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.891 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 133.715 MSE: 8.357 Root MSE: 2.891 455s Multiple R-Squared: 0.501 Adjusted R-Squared: 0.408 455s 455s [1] "***************************************************" 455s [1] "3SLS formula: IV" 455s [1] "************* 3SLS with different instruments **************" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 33 174 2.12 0.675 0.659 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 67.4 3.97 1.99 0.748 0.719 455s supply 20 16 106.6 6.66 2.58 0.602 0.528 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.97 3.84 455s supply 3.84 6.04 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 3.97 4.93 455s supply 4.93 6.66 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.959 455s supply 0.959 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 106.7894 11.1435 9.58 2.9e-08 *** 455s price -0.4116 0.1448 -2.84 0.011 * 455s income 0.3617 0.0564 6.41 6.4e-06 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 1.992 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 455s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 57.2953 11.7078 4.89 0.00016 *** 455s price 0.1373 0.0979 1.40 0.17978 455s farmPrice 0.2660 0.0483 5.51 4.8e-05 *** 455s trend 0.3970 0.0672 5.91 2.2e-05 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.582 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 106.628 MSE: 6.664 Root MSE: 2.582 455s Multiple R-Squared: 0.602 Adjusted R-Squared: 0.528 455s 455s [1] "******* 3SLS with different instruments (EViews-like) **********" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 33 173 1.51 0.677 0.612 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 67.4 3.97 1.99 0.748 0.719 455s supply 20 16 105.7 6.61 2.57 0.606 0.532 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.37 3.16 455s supply 3.16 4.83 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 3.37 4.04 455s supply 4.04 5.29 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.957 455s supply 0.957 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 106.789 10.274 10.39 6.1e-12 *** 455s price -0.412 0.134 -3.08 0.0041 ** 455s income 0.362 0.052 6.95 6.0e-08 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 1.992 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 455s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 57.0636 10.4717 5.45 4.9e-06 *** 455s price 0.1403 0.0875 1.60 0.12 455s farmPrice 0.2657 0.0432 6.15 6.2e-07 *** 455s trend 0.3927 0.0601 6.53 2.0e-07 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.571 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 105.735 MSE: 6.608 Root MSE: 2.571 455s Multiple R-Squared: 0.606 Adjusted R-Squared: 0.532 455s 455s [1] "**** 3SLS with different instruments and methodResidCov = Theil ***" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 33 175 0.321 0.673 0.655 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 67.4 3.97 1.99 0.748 0.719 455s supply 20 16 107.7 6.73 2.59 0.598 0.523 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.97 3.96 455s supply 3.96 6.04 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 3.97 5.14 455s supply 5.14 6.73 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.962 455s supply 0.962 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 106.7894 11.1435 9.58 4.7e-11 *** 455s price -0.4116 0.1448 -2.84 0.0076 ** 455s income 0.3617 0.0564 6.41 2.9e-07 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 1.992 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 455s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 57.5567 11.6867 4.92 2.3e-05 *** 455s price 0.1338 0.0977 1.37 0.18 455s farmPrice 0.2664 0.0484 5.51 4.1e-06 *** 455s trend 0.4018 0.0644 6.24 4.8e-07 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.594 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 107.679 MSE: 6.73 Root MSE: 2.594 455s Multiple R-Squared: 0.598 Adjusted R-Squared: 0.523 455s 455s [1] "************* W3SLS with different instruments **************" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 33 174 2.12 0.675 0.659 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 67.4 3.97 1.99 0.748 0.719 455s supply 20 16 106.6 6.66 2.58 0.602 0.528 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.97 3.84 455s supply 3.84 6.04 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 3.97 4.93 455s supply 4.93 6.66 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.959 455s supply 0.959 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 106.7894 11.1435 9.58 2.9e-08 *** 455s price -0.4116 0.1448 -2.84 0.011 * 455s income 0.3617 0.0564 6.41 6.4e-06 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 1.992 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 455s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 57.2953 11.7078 4.89 0.00016 *** 455s price 0.1373 0.0979 1.40 0.17978 455s farmPrice 0.2660 0.0483 5.51 4.8e-05 *** 455s trend 0.3970 0.0672 5.91 2.2e-05 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.582 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 106.628 MSE: 6.664 Root MSE: 2.582 455s Multiple R-Squared: 0.602 Adjusted R-Squared: 0.528 455s 455s [1] "******* 3SLS with different instruments and restriction ********" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 34 397 11.4 0.26 -0.128 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 175 10.3 3.20 0.349 0.273 455s supply 20 16 223 13.9 3.73 0.170 0.014 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.79 4.35 455s supply 4.35 6.27 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 10.3 11.5 455s supply 11.5 13.9 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.959 455s supply 0.959 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 137.2061 12.4591 11.01 9.3e-13 *** 455s price -0.8101 0.1734 -4.67 4.5e-05 *** 455s income 0.4585 0.0659 6.96 5.0e-08 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 3.204 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 174.513 MSE: 10.265 Root MSE: 3.204 455s Multiple R-Squared: 0.349 Adjusted R-Squared: 0.273 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 81.1339 9.1968 8.82 2.6e-10 *** 455s price -0.1765 0.0892 -1.98 0.056 . 455s farmPrice 0.3374 0.0591 5.71 2.1e-06 *** 455s trend 0.4585 0.0659 6.96 5.0e-08 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 3.73 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 222.562 MSE: 13.91 Root MSE: 3.73 455s Multiple R-Squared: 0.17 Adjusted R-Squared: 0.014 455s 455s [1] "** 3SLS with different instruments and restriction (EViews-like) *" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 34 365 7.14 0.319 -0.166 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 163 9.57 3.09 0.393 0.322 455s supply 20 16 202 12.65 3.56 0.245 0.104 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.22 3.58 455s supply 3.58 5.02 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 8.13 8.67 455s supply 8.67 10.12 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.956 455s supply 0.956 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 134.9751 11.3086 11.94 1.0e-13 *** 455s price -0.7834 0.1565 -5.01 1.7e-05 *** 455s income 0.4539 0.0598 7.60 8.0e-09 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 3.093 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 162.635 MSE: 9.567 Root MSE: 3.093 455s Multiple R-Squared: 0.393 Adjusted R-Squared: 0.322 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 78.1824 8.5029 9.19 9.6e-11 *** 455s price -0.1415 0.0807 -1.75 0.089 . 455s farmPrice 0.3322 0.0524 6.34 3.1e-07 *** 455s trend 0.4539 0.0598 7.60 8.0e-09 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 3.557 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 202.39 MSE: 12.649 Root MSE: 3.557 455s Multiple R-Squared: 0.245 Adjusted R-Squared: 0.104 455s 455s [1] "** W3SLS with different instruments and restriction (EViews-like) *" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 34 351 6.72 0.345 -0.118 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 156 9.18 3.03 0.418 0.349 455s supply 20 16 195 12.20 3.49 0.272 0.135 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.24 3.60 455s supply 3.60 5.06 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 7.81 8.34 455s supply 8.34 9.76 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.955 455s supply 0.955 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 133.7954 11.2810 11.86 1.2e-13 *** 455s price -0.7678 0.1558 -4.93 2.1e-05 *** 455s income 0.4501 0.0595 7.56 8.8e-09 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 3.031 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 156.133 MSE: 9.184 Root MSE: 3.031 455s Multiple R-Squared: 0.418 Adjusted R-Squared: 0.349 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 77.4097 8.6219 8.98 1.7e-10 *** 455s price -0.1304 0.0814 -1.60 0.12 455s farmPrice 0.3292 0.0523 6.29 3.6e-07 *** 455s trend 0.4501 0.0595 7.56 8.8e-09 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 3.493 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 195.256 MSE: 12.204 Root MSE: 3.493 455s Multiple R-Squared: 0.272 Adjusted R-Squared: 0.135 455s 455s [1] "** 3SLS with different instruments and restriction via restrict.regMat *******" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 34 397 11.4 0.26 -0.128 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 175 10.3 3.20 0.349 0.273 455s supply 20 16 223 13.9 3.73 0.170 0.014 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.79 4.35 455s supply 4.35 6.27 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 10.3 11.5 455s supply 11.5 13.9 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.959 455s supply 0.959 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 137.2061 12.4591 11.01 9.3e-13 *** 455s price -0.8101 0.1734 -4.67 4.5e-05 *** 455s income 0.4585 0.0659 6.96 5.0e-08 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 3.204 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 174.513 MSE: 10.265 Root MSE: 3.204 455s Multiple R-Squared: 0.349 Adjusted R-Squared: 0.273 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 81.1339 9.1968 8.82 2.6e-10 *** 455s price -0.1765 0.0892 -1.98 0.056 . 455s farmPrice 0.3374 0.0591 5.71 2.1e-06 *** 455s trend 0.4585 0.0659 6.96 5.0e-08 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 3.73 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 222.562 MSE: 13.91 Root MSE: 3.73 455s Multiple R-Squared: 0.17 Adjusted R-Squared: 0.014 455s 455s [1] "3SLS with different instruments with restriction via restrict.regMat (EViews-like)" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 34 365 7.14 0.319 -0.166 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 163 9.57 3.09 0.393 0.322 455s supply 20 16 202 12.65 3.56 0.245 0.104 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.22 3.58 455s supply 3.58 5.02 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 8.13 8.67 455s supply 8.67 10.12 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.956 455s supply 0.956 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 134.9751 11.3086 11.94 1.0e-13 *** 455s price -0.7834 0.1565 -5.01 1.7e-05 *** 455s income 0.4539 0.0598 7.60 8.0e-09 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 3.093 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 162.635 MSE: 9.567 Root MSE: 3.093 455s Multiple R-Squared: 0.393 Adjusted R-Squared: 0.322 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 78.1824 8.5029 9.19 9.6e-11 *** 455s price -0.1415 0.0807 -1.75 0.089 . 455s farmPrice 0.3322 0.0524 6.34 3.1e-07 *** 455s trend 0.4539 0.0598 7.60 8.0e-09 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 3.557 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 202.39 MSE: 12.649 Root MSE: 3.557 455s Multiple R-Squared: 0.245 Adjusted R-Squared: 0.104 455s 455s [1] "** W3SLS with different instr. and restr. via restrict.regMat ****" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 34 378 10.5 0.295 -0.071 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 166 9.74 3.12 0.382 0.309 455s supply 20 16 212 13.26 3.64 0.209 0.060 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.81 4.36 455s supply 4.36 6.34 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 9.75 10.9 455s supply 10.89 13.3 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.958 455s supply 0.958 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 135.6740 12.4146 10.93 1.1e-12 *** 455s price -0.7901 0.1723 -4.59 5.9e-05 *** 455s income 0.4537 0.0655 6.92 5.6e-08 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 3.122 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 165.668 MSE: 9.745 Root MSE: 3.122 455s Multiple R-Squared: 0.382 Adjusted R-Squared: 0.309 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 80.0613 9.3724 8.54 5.6e-10 *** 455s price -0.1614 0.0902 -1.79 0.082 . 455s farmPrice 0.3335 0.0590 5.65 2.4e-06 *** 455s trend 0.4537 0.0655 6.92 5.6e-08 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 3.642 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 212.177 MSE: 13.261 Root MSE: 3.642 455s Multiple R-Squared: 0.209 Adjusted R-Squared: 0.06 455s 455s [1] "****** 3SLS with different instruments and 2 restrictions *********" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 35 362 6.33 0.325 0.259 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 149 8.79 2.96 0.443 0.377 455s supply 20 16 213 13.30 3.65 0.206 0.058 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.79 4.45 455s supply 4.45 6.06 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 8.79 10.5 455s supply 10.51 13.3 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.973 455s supply 0.973 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 135.467 10.955 12.37 2.5e-14 *** 455s price -0.727 0.116 -6.27 3.4e-07 *** 455s income 0.391 0.018 21.77 < 2e-16 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.964 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 149.387 MSE: 8.787 Root MSE: 2.964 455s Multiple R-Squared: 0.443 Adjusted R-Squared: 0.377 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 92.2897 11.0352 8.36 7.3e-10 *** 455s price -0.2272 0.1160 -1.96 0.058 . 455s farmPrice 0.2817 0.0209 13.47 2.0e-15 *** 455s trend 0.3913 0.0180 21.77 < 2e-16 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 3.647 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 212.786 MSE: 13.299 Root MSE: 3.647 455s Multiple R-Squared: 0.206 Adjusted R-Squared: 0.058 455s 455s [1] "** 3SLS with different instruments and 2 restrictions (EViews-like) *" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 35 306 3.37 0.43 0.248 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 127 7.5 2.74 0.525 0.469 455s supply 20 16 178 11.2 3.34 0.334 0.210 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.22 3.67 455s supply 3.67 4.85 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 6.37 7.31 455s supply 7.31 8.92 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.00 0.97 455s supply 0.97 1.00 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 130.7296 9.6847 13.50 2.0e-15 *** 455s price -0.6671 0.1009 -6.61 1.2e-07 *** 455s income 0.3782 0.0159 23.74 < 2e-16 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.738 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 127.413 MSE: 7.495 Root MSE: 2.738 455s Multiple R-Squared: 0.525 Adjusted R-Squared: 0.469 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 87.4510 9.7547 8.96 1.4e-10 *** 455s price -0.1671 0.1009 -1.66 0.11 455s farmPrice 0.2710 0.0183 14.81 < 2e-16 *** 455s trend 0.3782 0.0159 23.74 < 2e-16 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 3.34 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 178.456 MSE: 11.154 Root MSE: 3.34 455s Multiple R-Squared: 0.334 Adjusted R-Squared: 0.21 455s 455s [1] "**** W3SLS with different instruments and 2 restrictions *********" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 35 467 8.98 0.128 0.113 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 193 11.3 3.37 0.282 0.197 455s supply 20 16 275 17.2 4.14 -0.025 -0.217 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.75 4.46 455s supply 4.46 6.04 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 11.3 13.6 455s supply 13.6 17.2 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.977 455s supply 0.977 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 143.4678 11.2566 12.75 1.0e-14 *** 455s price -0.8203 0.1194 -6.87 5.6e-08 *** 455s income 0.4047 0.0168 24.13 < 2e-16 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 3.366 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 192.561 MSE: 11.327 Root MSE: 3.366 455s Multiple R-Squared: 0.282 Adjusted R-Squared: 0.197 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 100.3734 11.3093 8.88 1.7e-10 *** 455s price -0.3203 0.1194 -2.68 0.011 * 455s farmPrice 0.2930 0.0198 14.79 < 2e-16 *** 455s trend 0.4047 0.0168 24.13 < 2e-16 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 4.144 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 274.775 MSE: 17.173 Root MSE: 4.144 455s Multiple R-Squared: -0.025 Adjusted R-Squared: -0.217 455s 455s [1] " 3SLS with different instruments with 2 restrictions via R and restrict.regMat" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 35 362 6.33 0.325 0.259 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 149 8.79 2.96 0.443 0.377 455s supply 20 16 213 13.30 3.65 0.206 0.058 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.79 4.45 455s supply 4.45 6.06 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 8.79 10.5 455s supply 10.51 13.3 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.973 455s supply 0.973 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 135.467 10.955 12.37 2.5e-14 *** 455s price -0.727 0.116 -6.27 3.4e-07 *** 455s income 0.391 0.018 21.77 < 2e-16 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.964 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 149.387 MSE: 8.787 Root MSE: 2.964 455s Multiple R-Squared: 0.443 Adjusted R-Squared: 0.377 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 92.2897 11.0352 8.36 7.3e-10 *** 455s price -0.2272 0.1160 -1.96 0.058 . 455s farmPrice 0.2817 0.0209 13.47 2.0e-15 *** 455s trend 0.3913 0.0180 21.77 < 2e-16 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 3.647 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 212.786 MSE: 13.299 Root MSE: 3.647 455s Multiple R-Squared: 0.206 Adjusted R-Squared: 0.058 455s 455s [1] "3SLS with diff. instruments and 2 restr. via R and restrict.regMat (EViews-like)" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 35 306 3.37 0.43 0.248 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 127 7.5 2.74 0.525 0.469 455s supply 20 16 178 11.2 3.34 0.334 0.210 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.22 3.67 455s supply 3.67 4.85 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 6.37 7.31 455s supply 7.31 8.92 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.00 0.97 455s supply 0.97 1.00 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 130.7296 9.6847 13.50 2.0e-15 *** 455s price -0.6671 0.1009 -6.61 1.2e-07 *** 455s income 0.3782 0.0159 23.74 < 2e-16 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.738 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 127.413 MSE: 7.495 Root MSE: 2.738 455s Multiple R-Squared: 0.525 Adjusted R-Squared: 0.469 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 87.4510 9.7547 8.96 1.4e-10 *** 455s price -0.1671 0.1009 -1.66 0.11 455s farmPrice 0.2710 0.0183 14.81 < 2e-16 *** 455s trend 0.3782 0.0159 23.74 < 2e-16 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 3.34 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 178.456 MSE: 11.154 Root MSE: 3.34 455s Multiple R-Squared: 0.334 Adjusted R-Squared: 0.21 455s 455s [1] "W3SLS with diff. instr. and 2 restr. via R and restrict.regMat (EViews-like)" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 35 365 4.27 0.319 0.127 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 153 8.97 3.00 0.431 0.364 455s supply 20 16 213 13.29 3.65 0.207 0.058 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.19 3.68 455s supply 3.68 4.83 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 7.63 8.77 455s supply 8.77 10.64 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.973 455s supply 0.973 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 136.2729 9.8523 13.83 8.9e-16 *** 455s price -0.7306 0.1027 -7.11 2.7e-08 *** 455s income 0.3865 0.0149 25.95 < 2e-16 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.996 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 152.579 MSE: 8.975 Root MSE: 2.996 455s Multiple R-Squared: 0.431 Adjusted R-Squared: 0.364 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 93.0701 9.9030 9.40 4.2e-11 *** 455s price -0.2306 0.1027 -2.24 0.031 * 455s farmPrice 0.2777 0.0174 15.99 < 2e-16 *** 455s trend 0.3865 0.0149 25.95 < 2e-16 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 3.646 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 212.723 MSE: 13.295 Root MSE: 3.646 455s Multiple R-Squared: 0.207 Adjusted R-Squared: 0.058 455s 455s [1] "***************************************************" 455s [1] "3SLS formula: Schmidt" 455s [1] "************* 3SLS with different instruments **************" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 33 164 9.25 0.694 0.512 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 67.4 3.97 1.99 0.748 0.719 455s supply 20 16 96.6 6.04 2.46 0.640 0.572 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.97 3.84 455s supply 3.84 6.04 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 3.97 3.84 455s supply 3.84 6.04 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.784 455s supply 0.784 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 106.7894 11.1435 9.58 2.9e-08 *** 455s price -0.4116 0.1448 -2.84 0.011 * 455s income 0.3617 0.0564 6.41 6.4e-06 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 1.992 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 455s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 49.5324 12.0105 4.12 0.0008 *** 455s price 0.2401 0.0999 2.40 0.0288 * 455s farmPrice 0.2556 0.0473 5.41 5.8e-05 *** 455s trend 0.2529 0.0997 2.54 0.0219 * 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.458 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 455s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 455s 455s [1] "******* 3SLS with different instruments (EViews-like) **********" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 33 164 6.29 0.694 0.5 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 67.4 3.97 1.99 0.748 0.719 455s supply 20 16 96.6 6.04 2.46 0.640 0.572 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.37 3.16 455s supply 3.16 4.83 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 3.37 3.16 455s supply 3.16 4.83 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.784 455s supply 0.784 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 106.789 10.274 10.39 6.1e-12 *** 455s price -0.412 0.134 -3.08 0.0041 ** 455s income 0.362 0.052 6.95 6.0e-08 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 1.992 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 455s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 49.5324 10.7425 4.61 5.8e-05 *** 455s price 0.2401 0.0894 2.69 0.0112 * 455s farmPrice 0.2556 0.0423 6.05 8.4e-07 *** 455s trend 0.2529 0.0891 2.84 0.0077 ** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.458 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 455s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 455s 455s [1] "**** 3SLS with different instruments and methodResidCov = Theil ***" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 33 164 8.24 0.694 0.481 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 67.4 3.97 1.99 0.748 0.719 455s supply 20 16 96.6 6.04 2.46 0.640 0.572 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.97 3.96 455s supply 3.96 6.04 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 3.97 3.96 455s supply 3.96 6.04 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.784 455s supply 0.784 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 106.7894 11.1435 9.58 4.7e-11 *** 455s price -0.4116 0.1448 -2.84 0.0076 ** 455s income 0.3617 0.0564 6.41 2.9e-07 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 1.992 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 455s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 49.5324 12.0105 4.12 0.00024 *** 455s price 0.2401 0.0999 2.40 0.02208 * 455s farmPrice 0.2556 0.0473 5.41 5.5e-06 *** 455s trend 0.2529 0.0997 2.54 0.01605 * 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.458 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 455s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 455s 455s [1] "************* W3SLS with different instruments **************" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 33 164 9.25 0.694 0.512 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 67.4 3.97 1.99 0.748 0.719 455s supply 20 16 96.6 6.04 2.46 0.640 0.572 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.97 3.84 455s supply 3.84 6.04 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 3.97 3.84 455s supply 3.84 6.04 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.784 455s supply 0.784 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 106.7894 11.1435 9.58 2.9e-08 *** 455s price -0.4116 0.1448 -2.84 0.011 * 455s income 0.3617 0.0564 6.41 6.4e-06 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 1.992 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 455s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 49.5324 12.0105 4.12 0.0008 *** 455s price 0.2401 0.0999 2.40 0.0288 * 455s farmPrice 0.2556 0.0473 5.41 5.8e-05 *** 455s trend 0.2529 0.0997 2.54 0.0219 * 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.458 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 455s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 455s 455s [1] "******* 3SLS with different instruments and restriction ********" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 34 175 2.68 0.673 0.665 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 65 3.82 1.96 0.758 0.729 455s supply 20 16 110 6.90 2.63 0.588 0.511 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.79 4.35 455s supply 4.35 6.27 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 3.82 4.87 455s supply 4.87 6.90 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.948 455s supply 0.948 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 95.0869 9.9882 9.52 4.0e-11 *** 455s price -0.2583 0.1296 -1.99 0.054 . 455s income 0.3244 0.0534 6.08 6.8e-07 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 1.955 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 64.961 MSE: 3.821 Root MSE: 1.955 455s Multiple R-Squared: 0.758 Adjusted R-Squared: 0.729 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 45.4891 12.9647 3.51 0.0013 ** 455s price 0.2929 0.1164 2.52 0.0167 * 455s farmPrice 0.2350 0.0490 4.80 3.1e-05 *** 455s trend 0.3244 0.0534 6.08 6.8e-07 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.627 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 110.382 MSE: 6.899 Root MSE: 2.627 455s Multiple R-Squared: 0.588 Adjusted R-Squared: 0.511 455s 455s [1] "** 3SLS with different instruments and restriction (EViews-like) *" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 34 175 1.75 0.673 0.636 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 65.2 3.83 1.96 0.757 0.728 455s supply 20 16 110.0 6.88 2.62 0.590 0.513 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.22 3.58 455s supply 3.58 5.02 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 3.26 4.02 455s supply 4.02 5.50 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.00 0.95 455s supply 0.95 1.00 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 94.845 9.149 10.37 4.6e-12 *** 455s price -0.254 0.119 -2.14 0.039 * 455s income 0.323 0.049 6.58 1.5e-07 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 1.958 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 65.171 MSE: 3.834 Root MSE: 1.958 455s Multiple R-Squared: 0.757 Adjusted R-Squared: 0.728 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 45.7348 11.5558 3.96 0.00037 *** 455s price 0.2913 0.1036 2.81 0.00814 ** 455s farmPrice 0.2343 0.0438 5.35 6.0e-06 *** 455s trend 0.3226 0.0490 6.58 1.5e-07 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.622 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 110.035 MSE: 6.877 Root MSE: 2.622 455s Multiple R-Squared: 0.59 Adjusted R-Squared: 0.513 455s 455s [1] "** W3SLS with different instruments and restriction (EViews-like) *" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 34 175 1.76 0.674 0.635 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 65.1 3.83 1.96 0.757 0.729 455s supply 20 16 109.9 6.87 2.62 0.590 0.513 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.24 3.60 455s supply 3.60 5.06 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 3.25 4.02 455s supply 4.02 5.50 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.949 455s supply 0.949 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 94.9533 9.1511 10.38 4.5e-12 *** 455s price -0.2555 0.1186 -2.15 0.038 * 455s income 0.3229 0.0491 6.58 1.5e-07 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 1.957 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 65.09 MSE: 3.829 Root MSE: 1.957 455s Multiple R-Squared: 0.757 Adjusted R-Squared: 0.729 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 45.7433 11.6043 3.94 0.00038 *** 455s price 0.2908 0.1039 2.80 0.00839 ** 455s farmPrice 0.2347 0.0440 5.34 6.2e-06 *** 455s trend 0.3229 0.0491 6.58 1.5e-07 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.621 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 109.922 MSE: 6.87 Root MSE: 2.621 455s Multiple R-Squared: 0.59 Adjusted R-Squared: 0.513 455s 455s [1] "** 3SLS with different instruments and restriction via restrict.regMat *******" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 34 175 2.68 0.673 0.665 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 65 3.82 1.96 0.758 0.729 455s supply 20 16 110 6.90 2.63 0.588 0.511 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.79 4.35 455s supply 4.35 6.27 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 3.82 4.87 455s supply 4.87 6.90 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.948 455s supply 0.948 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 95.0869 9.9882 9.52 4.0e-11 *** 455s price -0.2583 0.1296 -1.99 0.054 . 455s income 0.3244 0.0534 6.08 6.8e-07 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 1.955 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 64.961 MSE: 3.821 Root MSE: 1.955 455s Multiple R-Squared: 0.758 Adjusted R-Squared: 0.729 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 45.4891 12.9647 3.51 0.0013 ** 455s price 0.2929 0.1164 2.52 0.0167 * 455s farmPrice 0.2350 0.0490 4.80 3.1e-05 *** 455s trend 0.3244 0.0534 6.08 6.8e-07 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.627 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 110.382 MSE: 6.899 Root MSE: 2.627 455s Multiple R-Squared: 0.588 Adjusted R-Squared: 0.511 455s 455s [1] "3SLS with different instruments with restriction via restrict.regMat (EViews-like)" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 34 175 1.75 0.673 0.636 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 65.2 3.83 1.96 0.757 0.728 455s supply 20 16 110.0 6.88 2.62 0.590 0.513 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.22 3.58 455s supply 3.58 5.02 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 3.26 4.02 455s supply 4.02 5.50 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.00 0.95 455s supply 0.95 1.00 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 94.845 9.149 10.37 4.6e-12 *** 455s price -0.254 0.119 -2.14 0.039 * 455s income 0.323 0.049 6.58 1.5e-07 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 1.958 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 65.171 MSE: 3.834 Root MSE: 1.958 455s Multiple R-Squared: 0.757 Adjusted R-Squared: 0.728 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 45.7348 11.5558 3.96 0.00037 *** 455s price 0.2913 0.1036 2.81 0.00814 ** 455s farmPrice 0.2343 0.0438 5.35 6.0e-06 *** 455s trend 0.3226 0.0490 6.58 1.5e-07 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.622 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 110.035 MSE: 6.877 Root MSE: 2.622 455s Multiple R-Squared: 0.59 Adjusted R-Squared: 0.513 455s 455s [1] "** W3SLS with different instr. and restr. via restrict.regMat ****" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 34 175 2.7 0.673 0.664 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 64.9 3.82 1.95 0.758 0.730 455s supply 20 16 110.2 6.89 2.62 0.589 0.512 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.81 4.36 455s supply 4.36 6.34 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 3.82 4.86 455s supply 4.86 6.89 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.947 455s supply 0.947 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 95.2108 9.9899 9.53 3.9e-11 *** 455s price -0.2599 0.1296 -2.00 0.053 . 455s income 0.3248 0.0535 6.08 6.9e-07 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 1.954 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 64.876 MSE: 3.816 Root MSE: 1.954 455s Multiple R-Squared: 0.758 Adjusted R-Squared: 0.73 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 45.5042 13.0242 3.49 0.0013 ** 455s price 0.2923 0.1167 2.50 0.0172 * 455s farmPrice 0.2354 0.0492 4.78 3.3e-05 *** 455s trend 0.3248 0.0535 6.08 6.9e-07 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.625 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 110.241 MSE: 6.89 Root MSE: 2.625 455s Multiple R-Squared: 0.589 Adjusted R-Squared: 0.512 455s 455s [1] "****** 3SLS with different instruments and 2 restrictions *********" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 35 178 1.92 0.667 0.696 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 67.5 3.97 1.99 0.748 0.719 455s supply 20 16 110.9 6.93 2.63 0.586 0.509 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.79 4.45 455s supply 4.45 6.06 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 3.97 5.06 455s supply 5.06 6.93 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.964 455s supply 0.964 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 93.3937 10.2477 9.11 9.1e-11 *** 455s price -0.2208 0.1165 -1.90 0.066 . 455s income 0.3033 0.0257 11.78 9.9e-14 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 1.993 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 67.513 MSE: 3.971 Root MSE: 1.993 455s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 49.0104 10.4895 4.67 4.3e-05 *** 455s price 0.2792 0.1165 2.40 0.022 * 455s farmPrice 0.2150 0.0247 8.70 2.8e-10 *** 455s trend 0.3033 0.0257 11.78 9.9e-14 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.633 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 110.934 MSE: 6.933 Root MSE: 2.633 455s Multiple R-Squared: 0.586 Adjusted R-Squared: 0.509 455s 455s [1] "** 3SLS with different instruments and 2 restrictions (EViews-like) *" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 35 178 1.3 0.668 0.659 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 67.6 3.98 1.99 0.748 0.718 455s supply 20 16 110.7 6.92 2.63 0.587 0.510 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.22 3.67 455s supply 3.67 4.85 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 3.38 4.17 455s supply 4.17 5.53 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.965 455s supply 0.965 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 93.210 9.365 9.95 9.6e-12 *** 455s price -0.219 0.105 -2.09 0.044 * 455s income 0.304 0.023 13.19 3.8e-15 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 1.994 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 67.616 MSE: 3.977 Root MSE: 1.994 455s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.718 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 48.6930 9.6005 5.07 1.3e-05 *** 455s price 0.2806 0.1052 2.67 0.011 * 455s farmPrice 0.2168 0.0216 10.02 8.1e-12 *** 455s trend 0.3038 0.0230 13.19 3.8e-15 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.63 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 110.672 MSE: 6.917 Root MSE: 2.63 455s Multiple R-Squared: 0.587 Adjusted R-Squared: 0.51 455s 455s [1] "**** W3SLS with different instruments and 2 restrictions *********" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 35 179 1.92 0.666 0.698 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 67.7 3.98 2.00 0.747 0.718 455s supply 20 16 111.6 6.98 2.64 0.584 0.506 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.75 4.46 455s supply 4.46 6.04 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 3.98 5.09 455s supply 5.09 6.98 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.965 455s supply 0.965 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 93.180 10.378 8.98 1.3e-10 *** 455s price -0.218 0.118 -1.85 0.073 . 455s income 0.303 0.025 12.11 4.5e-14 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 1.996 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 67.719 MSE: 3.983 Root MSE: 1.996 455s Multiple R-Squared: 0.747 Adjusted R-Squared: 0.718 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 48.8549 10.5929 4.61 5.1e-05 *** 455s price 0.2817 0.1182 2.38 0.023 * 455s farmPrice 0.2141 0.0239 8.94 1.5e-10 *** 455s trend 0.3030 0.0250 12.11 4.5e-14 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.641 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 111.614 MSE: 6.976 Root MSE: 2.641 455s Multiple R-Squared: 0.584 Adjusted R-Squared: 0.506 455s 455s [1] " 3SLS with different instruments with 2 restrictions via R and restrict.regMat" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 35 178 1.92 0.667 0.696 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 67.5 3.97 1.99 0.748 0.719 455s supply 20 16 110.9 6.93 2.63 0.586 0.509 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.79 4.45 455s supply 4.45 6.06 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 3.97 5.06 455s supply 5.06 6.93 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.964 455s supply 0.964 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 93.3937 10.2477 9.11 9.1e-11 *** 455s price -0.2208 0.1165 -1.90 0.066 . 455s income 0.3033 0.0257 11.78 9.9e-14 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 1.993 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 67.513 MSE: 3.971 Root MSE: 1.993 455s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 49.0104 10.4895 4.67 4.3e-05 *** 455s price 0.2792 0.1165 2.40 0.022 * 455s farmPrice 0.2150 0.0247 8.70 2.8e-10 *** 455s trend 0.3033 0.0257 11.78 9.9e-14 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.633 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 110.934 MSE: 6.933 Root MSE: 2.633 455s Multiple R-Squared: 0.586 Adjusted R-Squared: 0.509 455s 455s [1] "3SLS with diff. instruments and 2 restr. via R and restrict.regMat (EViews-like)" 455s 455s systemfit results 455s method: 3SLS 455s 455s N DF SSR detRCov OLS-R2 McElroy-R2 455s system 40 35 178 1.3 0.668 0.659 455s 455s N DF SSR MSE RMSE R2 Adj R2 455s demand 20 17 67.6 3.98 1.99 0.748 0.718 455s supply 20 16 110.7 6.92 2.63 0.587 0.510 455s 455s The covariance matrix of the residuals used for estimation 455s demand supply 455s demand 3.22 3.67 455s supply 3.67 4.85 455s 455s The covariance matrix of the residuals 455s demand supply 455s demand 3.38 4.17 455s supply 4.17 5.53 455s 455s The correlations of the residuals 455s demand supply 455s demand 1.000 0.965 455s supply 0.965 1.000 455s 455s 455s 3SLS estimates for 'demand' (equation 1) 455s Model Formula: consump ~ price + income 455s Instruments: ~income + farmPrice 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 93.210 9.365 9.95 9.6e-12 *** 455s price -0.219 0.105 -2.09 0.044 * 455s income 0.304 0.023 13.19 3.8e-15 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 1.994 on 17 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 17 455s SSR: 67.616 MSE: 3.977 Root MSE: 1.994 455s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.718 455s 455s 455s 3SLS estimates for 'supply' (equation 2) 455s Model Formula: consump ~ price + farmPrice + trend 455s Instruments: ~income + farmPrice + trend 455s 455s Estimate Std. Error t value Pr(>|t|) 455s (Intercept) 48.6930 9.6005 5.07 1.3e-05 *** 455s price 0.2806 0.1052 2.67 0.011 * 455s farmPrice 0.2168 0.0216 10.02 8.1e-12 *** 455s trend 0.3038 0.0230 13.19 3.8e-15 *** 455s --- 455s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 455s 455s Residual standard error: 2.63 on 16 degrees of freedom 455s Number of observations: 20 Degrees of Freedom: 16 455s SSR: 110.672 MSE: 6.917 Root MSE: 2.63 455s Multiple R-Squared: 0.587 Adjusted R-Squared: 0.51 455s 455s [1] "W3SLS with diff. instr. and 2 restr. via R and restrict.regMat (EViews-like)" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 35 179 1.3 0.666 0.661 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 67.8 3.99 2.00 0.747 0.717 456s supply 20 16 111.2 6.95 2.64 0.585 0.507 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.19 3.68 456s supply 3.68 4.83 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 3.39 4.19 456s supply 4.19 5.56 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.000 0.965 456s supply 0.965 1.000 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 93.0165 9.4718 9.82 1.4e-11 *** 456s price -0.2172 0.1066 -2.04 0.049 * 456s income 0.3036 0.0224 13.56 1.8e-15 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 1.997 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 67.8 MSE: 3.988 Root MSE: 1.997 456s Multiple R-Squared: 0.747 Adjusted R-Squared: 0.717 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 48.5496 9.6886 5.01 1.6e-05 *** 456s price 0.2828 0.1066 2.65 0.012 * 456s farmPrice 0.2161 0.0210 10.30 3.9e-12 *** 456s trend 0.3036 0.0224 13.56 1.8e-15 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.637 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 111.249 MSE: 6.953 Root MSE: 2.637 456s Multiple R-Squared: 0.585 Adjusted R-Squared: 0.507 456s 456s [1] "***************************************************" 456s [1] "3SLS formula: GMM" 456s [1] "************* 3SLS with different instruments **************" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 33 164 9.25 0.694 0.512 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 67.4 3.97 1.99 0.748 0.719 456s supply 20 16 96.6 6.04 2.46 0.640 0.572 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.97 3.84 456s supply 3.84 6.04 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 3.97 3.84 456s supply 3.84 6.04 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.000 0.784 456s supply 0.784 1.000 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 106.7894 11.1435 9.58 2.9e-08 *** 456s price -0.4116 0.1448 -2.84 0.011 * 456s income 0.3617 0.0564 6.41 6.4e-06 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 1.992 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 456s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 49.5324 12.0105 4.12 0.0008 *** 456s price 0.2401 0.0999 2.40 0.0288 * 456s farmPrice 0.2556 0.0473 5.41 5.8e-05 *** 456s trend 0.2529 0.0997 2.54 0.0219 * 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.458 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 456s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 456s 456s [1] "******* 3SLS with different instruments (EViews-like) **********" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 33 164 6.29 0.694 0.5 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 67.4 3.97 1.99 0.748 0.719 456s supply 20 16 96.6 6.04 2.46 0.640 0.572 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.37 3.16 456s supply 3.16 4.83 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 3.37 3.16 456s supply 3.16 4.83 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.000 0.784 456s supply 0.784 1.000 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 106.789 10.274 10.39 6.1e-12 *** 456s price -0.412 0.134 -3.08 0.0041 ** 456s income 0.362 0.052 6.95 6.0e-08 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 1.992 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 456s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 49.5324 10.7425 4.61 5.8e-05 *** 456s price 0.2401 0.0894 2.69 0.0112 * 456s farmPrice 0.2556 0.0423 6.05 8.4e-07 *** 456s trend 0.2529 0.0891 2.84 0.0077 ** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.458 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 456s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 456s 456s [1] "**** 3SLS with different instruments and methodResidCov = Theil ***" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 33 164 8.24 0.694 0.481 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 67.4 3.97 1.99 0.748 0.719 456s supply 20 16 96.6 6.04 2.46 0.640 0.572 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.97 3.96 456s supply 3.96 6.04 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 3.97 3.96 456s supply 3.96 6.04 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.000 0.784 456s supply 0.784 1.000 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 106.7894 11.1435 9.58 4.7e-11 *** 456s price -0.4116 0.1448 -2.84 0.0076 ** 456s income 0.3617 0.0564 6.41 2.9e-07 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 1.992 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 456s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 49.5324 12.0105 4.12 0.00024 *** 456s price 0.2401 0.0999 2.40 0.02208 * 456s farmPrice 0.2556 0.0473 5.41 5.5e-06 *** 456s trend 0.2529 0.0997 2.54 0.01605 * 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.458 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 456s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 456s 456s [1] "************* W3SLS with different instruments **************" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 33 164 9.25 0.694 0.512 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 67.4 3.97 1.99 0.748 0.719 456s supply 20 16 96.6 6.04 2.46 0.640 0.572 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.97 3.84 456s supply 3.84 6.04 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 3.97 3.84 456s supply 3.84 6.04 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.000 0.784 456s supply 0.784 1.000 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 106.7894 11.1435 9.58 2.9e-08 *** 456s price -0.4116 0.1448 -2.84 0.011 * 456s income 0.3617 0.0564 6.41 6.4e-06 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 1.992 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 456s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 49.5324 12.0105 4.12 0.0008 *** 456s price 0.2401 0.0999 2.40 0.0288 * 456s farmPrice 0.2556 0.0473 5.41 5.8e-05 *** 456s trend 0.2529 0.0997 2.54 0.0219 * 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.458 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 456s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 456s 456s [1] "******* 3SLS with different instruments and restriction ********" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 34 166 2.78 0.691 0.636 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 63.4 3.73 1.93 0.764 0.736 456s supply 20 16 102.2 6.39 2.53 0.619 0.547 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.79 4.35 456s supply 4.35 6.27 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 3.73 4.59 456s supply 4.59 6.39 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.00 0.94 456s supply 0.94 1.00 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 100.1363 8.6083 11.63 2.1e-13 *** 456s price -0.3244 0.1114 -2.91 0.0063 ** 456s income 0.3405 0.0509 6.69 1.1e-07 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 1.931 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 63.395 MSE: 3.729 Root MSE: 1.931 456s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 49.7623 12.2354 4.07 0.00027 *** 456s price 0.2366 0.1018 2.33 0.02617 * 456s farmPrice 0.2473 0.0474 5.22 9.0e-06 *** 456s trend 0.3405 0.0509 6.69 1.1e-07 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.527 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 102.181 MSE: 6.386 Root MSE: 2.527 456s Multiple R-Squared: 0.619 Adjusted R-Squared: 0.547 456s 456s [1] "** 3SLS with different instruments and restriction (EViews-like) *" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 34 165 1.84 0.691 0.608 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 63.4 3.73 1.93 0.764 0.736 456s supply 20 16 102.1 6.38 2.53 0.619 0.548 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.22 3.58 456s supply 3.58 5.02 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 3.17 3.79 456s supply 3.79 5.10 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.000 0.941 456s supply 0.941 1.000 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 99.9363 7.9106 12.63 2.1e-14 *** 456s price -0.3212 0.1019 -3.15 0.0034 ** 456s income 0.3393 0.0466 7.28 2.0e-08 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 1.931 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 63.37 MSE: 3.728 Root MSE: 1.931 456s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 49.8516 10.9418 4.56 6.4e-05 *** 456s price 0.2364 0.0910 2.60 0.014 * 456s farmPrice 0.2467 0.0423 5.83 1.4e-06 *** 456s trend 0.3393 0.0466 7.28 2.0e-08 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.526 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 102.07 MSE: 6.379 Root MSE: 2.526 456s Multiple R-Squared: 0.619 Adjusted R-Squared: 0.548 456s 456s [1] "** W3SLS with different instruments and restriction (EViews-like) *" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 34 165 1.85 0.691 0.608 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 63.4 3.73 1.93 0.764 0.736 456s supply 20 16 102.1 6.38 2.53 0.619 0.548 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.24 3.60 456s supply 3.60 5.06 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 3.17 3.78 456s supply 3.78 5.10 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.000 0.941 456s supply 0.941 1.000 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 99.9706 7.9399 12.59 2.4e-14 *** 456s price -0.3217 0.1023 -3.15 0.0034 ** 456s income 0.3394 0.0467 7.26 2.1e-08 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 1.931 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 63.372 MSE: 3.728 Root MSE: 1.931 456s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 49.8336 10.9955 4.53 6.9e-05 *** 456s price 0.2364 0.0915 2.59 0.014 * 456s farmPrice 0.2469 0.0425 5.80 1.6e-06 *** 456s trend 0.3394 0.0467 7.26 2.1e-08 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.526 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 102.073 MSE: 6.38 Root MSE: 2.526 456s Multiple R-Squared: 0.619 Adjusted R-Squared: 0.548 456s 456s [1] "** 3SLS with different instruments and restriction via restrict.regMat *******" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 34 166 2.78 0.691 0.636 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 63.4 3.73 1.93 0.764 0.736 456s supply 20 16 102.2 6.39 2.53 0.619 0.547 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.79 4.35 456s supply 4.35 6.27 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 3.73 4.59 456s supply 4.59 6.39 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.00 0.94 456s supply 0.94 1.00 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 100.1363 8.6083 11.63 2.1e-13 *** 456s price -0.3244 0.1114 -2.91 0.0063 ** 456s income 0.3405 0.0509 6.69 1.1e-07 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 1.931 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 63.395 MSE: 3.729 Root MSE: 1.931 456s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 49.7623 12.2354 4.07 0.00027 *** 456s price 0.2366 0.1018 2.33 0.02617 * 456s farmPrice 0.2473 0.0474 5.22 9.0e-06 *** 456s trend 0.3405 0.0509 6.69 1.1e-07 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.527 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 102.181 MSE: 6.386 Root MSE: 2.527 456s Multiple R-Squared: 0.619 Adjusted R-Squared: 0.547 456s 456s [1] "3SLS with different instruments with restriction via restrict.regMat (EViews-like)" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 34 165 1.84 0.691 0.608 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 63.4 3.73 1.93 0.764 0.736 456s supply 20 16 102.1 6.38 2.53 0.619 0.548 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.22 3.58 456s supply 3.58 5.02 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 3.17 3.79 456s supply 3.79 5.10 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.000 0.941 456s supply 0.941 1.000 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 99.9363 7.9106 12.63 2.1e-14 *** 456s price -0.3212 0.1019 -3.15 0.0034 ** 456s income 0.3393 0.0466 7.28 2.0e-08 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 1.931 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 63.37 MSE: 3.728 Root MSE: 1.931 456s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 49.8516 10.9418 4.56 6.4e-05 *** 456s price 0.2364 0.0910 2.60 0.014 * 456s farmPrice 0.2467 0.0423 5.83 1.4e-06 *** 456s trend 0.3393 0.0466 7.28 2.0e-08 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.526 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 102.07 MSE: 6.379 Root MSE: 2.526 456s Multiple R-Squared: 0.619 Adjusted R-Squared: 0.548 456s 456s [1] "** W3SLS with different instr. and restr. via restrict.regMat ****" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 34 166 2.79 0.691 0.635 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 63.4 3.73 1.93 0.764 0.736 456s supply 20 16 102.2 6.39 2.53 0.619 0.547 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.81 4.36 456s supply 4.36 6.34 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 3.73 4.59 456s supply 4.59 6.39 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.00 0.94 456s supply 0.94 1.00 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 100.174 8.646 11.59 2.4e-13 *** 456s price -0.325 0.112 -2.91 0.0064 ** 456s income 0.341 0.051 6.67 1.2e-07 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 1.931 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 63.398 MSE: 3.729 Root MSE: 1.931 456s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 49.7425 12.3029 4.04 0.00029 *** 456s price 0.2367 0.1023 2.31 0.02691 * 456s farmPrice 0.2474 0.0477 5.19 9.8e-06 *** 456s trend 0.3406 0.0510 6.67 1.2e-07 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.527 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 102.183 MSE: 6.386 Root MSE: 2.527 456s Multiple R-Squared: 0.619 Adjusted R-Squared: 0.547 456s 456s [1] "****** 3SLS with different instruments and 2 restrictions *********" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 35 165 1.89 0.692 0.677 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 64.1 3.77 1.94 0.761 0.733 456s supply 20 16 101.2 6.32 2.52 0.623 0.552 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.79 4.45 456s supply 4.45 6.06 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 3.77 4.68 456s supply 4.68 6.32 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.00 0.96 456s supply 0.96 1.00 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 98.8949 8.2696 11.96 6.4e-14 *** 456s price -0.2870 0.0909 -3.16 0.0033 ** 456s income 0.3148 0.0224 14.04 4.4e-16 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 1.941 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 64.072 MSE: 3.769 Root MSE: 1.941 456s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 54.6693 8.4422 6.48 1.8e-07 *** 456s price 0.2130 0.0909 2.34 0.025 * 456s farmPrice 0.2237 0.0228 9.82 1.3e-11 *** 456s trend 0.3148 0.0224 14.04 4.4e-16 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.515 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 101.181 MSE: 6.324 Root MSE: 2.515 456s Multiple R-Squared: 0.623 Adjusted R-Squared: 0.552 456s 456s [1] "** 3SLS with different instruments and 2 restrictions (EViews-like) *" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 35 165 1.28 0.692 0.642 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 64.1 3.77 1.94 0.761 0.733 456s supply 20 16 101.1 6.32 2.51 0.623 0.552 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.22 3.67 456s supply 3.67 4.85 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 3.21 3.86 456s supply 3.86 5.06 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.00 0.96 456s supply 0.96 1.00 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 98.6650 7.5755 13.02 5.6e-15 *** 456s price -0.2845 0.0822 -3.46 0.0014 ** 456s income 0.3146 0.0203 15.52 < 2e-16 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 1.942 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 64.111 MSE: 3.771 Root MSE: 1.942 456s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 54.3281 7.7347 7.02 3.6e-08 *** 456s price 0.2155 0.0822 2.62 0.013 * 456s farmPrice 0.2247 0.0201 11.16 4.4e-13 *** 456s trend 0.3146 0.0203 15.52 < 2e-16 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.514 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 101.149 MSE: 6.322 Root MSE: 2.514 456s Multiple R-Squared: 0.623 Adjusted R-Squared: 0.552 456s 456s [1] "**** W3SLS with different instruments and 2 restrictions *********" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 35 165 1.89 0.692 0.677 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 64.1 3.77 1.94 0.761 0.733 456s supply 20 16 101.3 6.33 2.52 0.622 0.551 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.75 4.46 456s supply 4.46 6.04 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 3.77 4.69 456s supply 4.69 6.33 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.00 0.96 456s supply 0.96 1.00 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 98.9360 8.2215 12.03 5.4e-14 *** 456s price -0.2872 0.0907 -3.17 0.0032 ** 456s income 0.3147 0.0215 14.64 2.2e-16 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 1.941 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 64.08 MSE: 3.769 Root MSE: 1.941 456s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 54.7520 8.3733 6.54 1.5e-07 *** 456s price 0.2128 0.0907 2.35 0.025 * 456s farmPrice 0.2231 0.0218 10.24 4.5e-12 *** 456s trend 0.3147 0.0215 14.64 2.2e-16 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.516 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 101.278 MSE: 6.33 Root MSE: 2.516 456s Multiple R-Squared: 0.622 Adjusted R-Squared: 0.551 456s 456s [1] " 3SLS with different instruments with 2 restrictions via R and restrict.regMat" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 35 165 1.89 0.692 0.677 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 64.1 3.77 1.94 0.761 0.733 456s supply 20 16 101.2 6.32 2.52 0.623 0.552 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.79 4.45 456s supply 4.45 6.06 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 3.77 4.68 456s supply 4.68 6.32 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.00 0.96 456s supply 0.96 1.00 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 98.8949 8.2696 11.96 6.4e-14 *** 456s price -0.2870 0.0909 -3.16 0.0033 ** 456s income 0.3148 0.0224 14.04 4.4e-16 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 1.941 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 64.072 MSE: 3.769 Root MSE: 1.941 456s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 54.6693 8.4422 6.48 1.8e-07 *** 456s price 0.2130 0.0909 2.34 0.025 * 456s farmPrice 0.2237 0.0228 9.82 1.3e-11 *** 456s trend 0.3148 0.0224 14.04 4.4e-16 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.515 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 101.181 MSE: 6.324 Root MSE: 2.515 456s Multiple R-Squared: 0.623 Adjusted R-Squared: 0.552 456s 456s [1] "3SLS with diff. instruments and 2 restr. via R and restrict.regMat (EViews-like)" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 35 165 1.28 0.692 0.642 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 64.1 3.77 1.94 0.761 0.733 456s supply 20 16 101.1 6.32 2.51 0.623 0.552 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.22 3.67 456s supply 3.67 4.85 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 3.21 3.86 456s supply 3.86 5.06 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.00 0.96 456s supply 0.96 1.00 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 98.6650 7.5755 13.02 5.6e-15 *** 456s price -0.2845 0.0822 -3.46 0.0014 ** 456s income 0.3146 0.0203 15.52 < 2e-16 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 1.942 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 64.111 MSE: 3.771 Root MSE: 1.942 456s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 54.3281 7.7347 7.02 3.6e-08 *** 456s price 0.2155 0.0822 2.62 0.013 * 456s farmPrice 0.2247 0.0201 11.16 4.4e-13 *** 456s trend 0.3146 0.0203 15.52 < 2e-16 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.514 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 101.149 MSE: 6.322 Root MSE: 2.514 456s Multiple R-Squared: 0.623 Adjusted R-Squared: 0.552 456s 456s [1] "W3SLS with diff. instr. and 2 restr. via R and restrict.regMat (EViews-like)" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 35 165 1.28 0.692 0.643 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 64.1 3.77 1.94 0.761 0.733 456s supply 20 16 101.2 6.33 2.52 0.622 0.552 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.19 3.68 456s supply 3.68 4.83 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 3.21 3.87 456s supply 3.87 5.06 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.00 0.96 456s supply 0.96 1.00 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 98.6980 7.5376 13.09 4.9e-15 *** 456s price -0.2847 0.0820 -3.47 0.0014 ** 456s income 0.3145 0.0195 16.13 < 2e-16 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 1.942 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 64.117 MSE: 3.772 Root MSE: 1.942 456s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 54.3972 7.6824 7.08 3.0e-08 *** 456s price 0.2153 0.0820 2.62 0.013 * 456s farmPrice 0.2242 0.0193 11.60 1.5e-13 *** 456s trend 0.3145 0.0195 16.13 < 2e-16 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.515 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 101.231 MSE: 6.327 Root MSE: 2.515 456s Multiple R-Squared: 0.622 Adjusted R-Squared: 0.552 456s 456s [1] "***************************************************" 456s [1] "3SLS formula: EViews" 456s [1] "************* 3SLS with different instruments **************" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 33 174 2.12 0.675 0.659 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 67.4 3.97 1.99 0.748 0.719 456s supply 20 16 106.6 6.66 2.58 0.602 0.528 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.97 3.84 456s supply 3.84 6.04 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 3.97 4.93 456s supply 4.93 6.66 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.000 0.959 456s supply 0.959 1.000 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 106.7894 11.1435 9.58 2.9e-08 *** 456s price -0.4116 0.1448 -2.84 0.011 * 456s income 0.3617 0.0564 6.41 6.4e-06 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 1.992 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 456s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 57.2953 11.5390 4.97 0.00014 *** 456s price 0.1373 0.0897 1.53 0.14529 456s farmPrice 0.2660 0.0470 5.66 3.6e-05 *** 456s trend 0.3970 0.0781 5.08 0.00011 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.582 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 106.628 MSE: 6.664 Root MSE: 2.582 456s Multiple R-Squared: 0.602 Adjusted R-Squared: 0.528 456s 456s [1] "******* 3SLS with different instruments (EViews-like) **********" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 33 173 1.51 0.677 0.612 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 67.4 3.97 1.99 0.748 0.719 456s supply 20 16 105.7 6.61 2.57 0.606 0.532 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.37 3.16 456s supply 3.16 4.83 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 3.37 4.04 456s supply 4.04 5.29 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.000 0.957 456s supply 0.957 1.000 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 106.789 10.274 10.39 6.1e-12 *** 456s price -0.412 0.134 -3.08 0.0041 ** 456s income 0.362 0.052 6.95 6.0e-08 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 1.992 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 456s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 57.0636 10.3208 5.53 3.9e-06 *** 456s price 0.1403 0.0802 1.75 0.089 . 456s farmPrice 0.2657 0.0421 6.32 3.8e-07 *** 456s trend 0.3927 0.0699 5.62 3.0e-06 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.571 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 105.735 MSE: 6.608 Root MSE: 2.571 456s Multiple R-Squared: 0.606 Adjusted R-Squared: 0.532 456s 456s [1] "**** 3SLS with different instruments and methodResidCov = Theil ***" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 33 175 0.321 0.673 0.655 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 67.4 3.97 1.99 0.748 0.719 456s supply 20 16 107.7 6.73 2.59 0.598 0.523 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.97 3.96 456s supply 3.96 6.04 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 3.97 5.14 456s supply 5.14 6.73 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.000 0.962 456s supply 0.962 1.000 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 106.7894 11.1435 9.58 4.7e-11 *** 456s price -0.4116 0.1448 -2.84 0.0076 ** 456s income 0.3617 0.0564 6.41 2.9e-07 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 1.992 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 456s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 57.5567 11.5060 5.00 1.8e-05 *** 456s price 0.1338 0.0889 1.50 0.14 456s farmPrice 0.2664 0.0470 5.66 2.6e-06 *** 456s trend 0.4018 0.0765 5.26 8.7e-06 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.594 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 107.679 MSE: 6.73 Root MSE: 2.594 456s Multiple R-Squared: 0.598 Adjusted R-Squared: 0.523 456s 456s [1] "************* W3SLS with different instruments **************" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 33 174 2.12 0.675 0.659 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 67.4 3.97 1.99 0.748 0.719 456s supply 20 16 106.6 6.66 2.58 0.602 0.528 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.97 3.84 456s supply 3.84 6.04 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 3.97 4.93 456s supply 4.93 6.66 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.000 0.959 456s supply 0.959 1.000 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 106.7894 11.1435 9.58 2.9e-08 *** 456s price -0.4116 0.1448 -2.84 0.011 * 456s income 0.3617 0.0564 6.41 6.4e-06 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 1.992 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 456s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 57.2953 11.5390 4.97 0.00014 *** 456s price 0.1373 0.0897 1.53 0.14529 456s farmPrice 0.2660 0.0470 5.66 3.6e-05 *** 456s trend 0.3970 0.0781 5.08 0.00011 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.582 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 106.628 MSE: 6.664 Root MSE: 2.582 456s Multiple R-Squared: 0.602 Adjusted R-Squared: 0.528 456s 456s [1] "******* 3SLS with different instruments and restriction ********" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 34 174 3.39 0.676 0.542 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 71.1 4.18 2.04 0.735 0.704 456s supply 20 16 102.6 6.41 2.53 0.617 0.546 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.79 4.35 456s supply 4.35 6.27 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 4.18 4.84 456s supply 4.84 6.41 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.000 0.935 456s supply 0.935 1.000 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 109.4916 6.3475 17.25 < 2e-16 *** 456s price -0.4470 0.0812 -5.50 3.8e-06 *** 456s income 0.3703 0.0474 7.81 4.3e-09 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.045 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 71.077 MSE: 4.181 Root MSE: 2.045 456s Multiple R-Squared: 0.735 Adjusted R-Squared: 0.704 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 57.6795 11.2022 5.15 1.1e-05 *** 456s price 0.1324 0.0785 1.69 0.1 456s farmPrice 0.2700 0.0453 5.97 9.5e-07 *** 456s trend 0.3703 0.0474 7.81 4.3e-09 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.532 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 102.574 MSE: 6.411 Root MSE: 2.532 456s Multiple R-Squared: 0.617 Adjusted R-Squared: 0.546 456s 456s [1] "** 3SLS with different instruments and restriction (EViews-like) *" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 34 173 2.29 0.678 0.515 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 70.5 4.15 2.04 0.737 0.706 456s supply 20 16 102.2 6.38 2.53 0.619 0.548 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.22 3.58 456s supply 3.58 5.02 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 3.53 3.96 456s supply 3.96 5.11 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.000 0.934 456s supply 0.934 1.000 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 109.1085 5.8428 18.67 < 2e-16 *** 456s price -0.4422 0.0737 -6.00 8.6e-07 *** 456s income 0.3693 0.0432 8.54 5.6e-10 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.037 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 70.515 MSE: 4.148 Root MSE: 2.037 456s Multiple R-Squared: 0.737 Adjusted R-Squared: 0.706 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 57.2679 10.0564 5.69 2.1e-06 *** 456s price 0.1375 0.0705 1.95 0.06 . 456s farmPrice 0.2691 0.0403 6.68 1.1e-07 *** 456s trend 0.3693 0.0432 8.54 5.6e-10 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.527 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 102.156 MSE: 6.385 Root MSE: 2.527 456s Multiple R-Squared: 0.619 Adjusted R-Squared: 0.548 456s 456s [1] "** W3SLS with different instruments and restriction (EViews-like) *" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 34 173 2.29 0.678 0.515 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 70.5 4.15 2.04 0.737 0.706 456s supply 20 16 102.1 6.38 2.53 0.619 0.548 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.24 3.60 456s supply 3.60 5.06 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 3.52 3.96 456s supply 3.96 5.11 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.000 0.934 456s supply 0.934 1.000 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 109.0818 5.9083 18.46 < 2e-16 *** 456s price -0.4418 0.0746 -5.92 1.1e-06 *** 456s income 0.3692 0.0434 8.51 6.2e-10 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.036 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 70.475 MSE: 4.146 Root MSE: 2.036 456s Multiple R-Squared: 0.737 Adjusted R-Squared: 0.706 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 57.2616 10.1094 5.66 2.4e-06 *** 456s price 0.1376 0.0711 1.94 0.061 . 456s farmPrice 0.2690 0.0405 6.64 1.3e-07 *** 456s trend 0.3692 0.0434 8.51 6.2e-10 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.527 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 102.135 MSE: 6.383 Root MSE: 2.527 456s Multiple R-Squared: 0.619 Adjusted R-Squared: 0.548 456s 456s [1] "** 3SLS with different instruments and restriction via restrict.regMat *******" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 34 174 3.39 0.676 0.542 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 71.1 4.18 2.04 0.735 0.704 456s supply 20 16 102.6 6.41 2.53 0.617 0.546 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.79 4.35 456s supply 4.35 6.27 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 4.18 4.84 456s supply 4.84 6.41 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.000 0.935 456s supply 0.935 1.000 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 109.4916 6.3475 17.25 < 2e-16 *** 456s price -0.4470 0.0812 -5.50 3.8e-06 *** 456s income 0.3703 0.0474 7.81 4.3e-09 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.045 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 71.077 MSE: 4.181 Root MSE: 2.045 456s Multiple R-Squared: 0.735 Adjusted R-Squared: 0.704 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 57.6795 11.2022 5.15 1.1e-05 *** 456s price 0.1324 0.0785 1.69 0.1 456s farmPrice 0.2700 0.0453 5.97 9.5e-07 *** 456s trend 0.3703 0.0474 7.81 4.3e-09 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.532 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 102.574 MSE: 6.411 Root MSE: 2.532 456s Multiple R-Squared: 0.617 Adjusted R-Squared: 0.546 456s 456s [1] "3SLS with different instruments with restriction via restrict.regMat (EViews-like)" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 34 173 2.29 0.678 0.515 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 70.5 4.15 2.04 0.737 0.706 456s supply 20 16 102.2 6.38 2.53 0.619 0.548 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.22 3.58 456s supply 3.58 5.02 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 3.53 3.96 456s supply 3.96 5.11 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.000 0.934 456s supply 0.934 1.000 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 109.1085 5.8428 18.67 < 2e-16 *** 456s price -0.4422 0.0737 -6.00 8.6e-07 *** 456s income 0.3693 0.0432 8.54 5.6e-10 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.037 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 70.515 MSE: 4.148 Root MSE: 2.037 456s Multiple R-Squared: 0.737 Adjusted R-Squared: 0.706 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 57.2679 10.0564 5.69 2.1e-06 *** 456s price 0.1375 0.0705 1.95 0.06 . 456s farmPrice 0.2691 0.0403 6.68 1.1e-07 *** 456s trend 0.3693 0.0432 8.54 5.6e-10 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.527 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 102.156 MSE: 6.385 Root MSE: 2.527 456s Multiple R-Squared: 0.619 Adjusted R-Squared: 0.548 456s 456s [1] "** W3SLS with different instr. and restr. via restrict.regMat ****" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 34 174 3.38 0.676 0.543 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 71 4.18 2.04 0.735 0.704 456s supply 20 16 103 6.41 2.53 0.618 0.546 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.81 4.36 456s supply 4.36 6.34 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 4.18 4.84 456s supply 4.84 6.41 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.000 0.935 456s supply 0.935 1.000 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 109.4522 6.4318 17.02 < 2e-16 *** 456s price -0.4465 0.0823 -5.42 4.8e-06 *** 456s income 0.3702 0.0476 7.78 4.8e-09 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.044 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 71.017 MSE: 4.177 Root MSE: 2.044 456s Multiple R-Squared: 0.735 Adjusted R-Squared: 0.704 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 57.6669 11.2699 5.12 1.2e-05 *** 456s price 0.1326 0.0792 1.67 0.1 456s farmPrice 0.2699 0.0456 5.92 1.1e-06 *** 456s trend 0.3702 0.0476 7.78 4.8e-09 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.532 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 102.539 MSE: 6.409 Root MSE: 2.532 456s Multiple R-Squared: 0.618 Adjusted R-Squared: 0.546 456s 456s [1] "****** 3SLS with different instruments and 2 restrictions *********" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 35 358 32.4 0.333 -0.013 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 141 8.32 2.88 0.472 0.410 456s supply 20 16 216 13.53 3.68 0.193 0.042 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.79 4.45 456s supply 4.45 6.06 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 8.32 8.95 456s supply 8.95 13.53 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.000 0.844 456s supply 0.844 1.000 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 108.5837 5.3770 20.2 < 2e-16 *** 456s price -0.6034 0.0504 -12.0 6.2e-14 *** 456s income 0.5399 0.0182 29.7 < 2e-16 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.884 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 141.436 MSE: 8.32 Root MSE: 2.884 456s Multiple R-Squared: 0.472 Adjusted R-Squared: 0.41 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 14.7043 5.4316 2.71 0.01 * 456s price 0.3966 0.0504 7.87 3e-09 *** 456s farmPrice 0.4228 0.0205 20.65 <2e-16 *** 456s trend 0.5399 0.0182 29.71 <2e-16 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 3.678 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 216.4 MSE: 13.525 Root MSE: 3.678 456s Multiple R-Squared: 0.193 Adjusted R-Squared: 0.042 456s 456s [1] "** 3SLS with different instruments and 2 restrictions (EViews-like) *" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 35 359 21.9 0.331 -0.059 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 143 8.38 2.90 0.468 0.406 456s supply 20 16 216 13.52 3.68 0.193 0.042 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.22 3.67 456s supply 3.67 4.85 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 7.13 7.43 456s supply 7.43 10.82 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.000 0.846 456s supply 0.846 1.000 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 107.9852 4.9704 21.7 < 2e-16 *** 456s price -0.5994 0.0458 -13.1 4.9e-15 *** 456s income 0.5420 0.0168 32.2 < 2e-16 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.896 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 142.542 MSE: 8.385 Root MSE: 2.896 456s Multiple R-Squared: 0.468 Adjusted R-Squared: 0.406 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 14.4922 4.9950 2.90 0.0064 ** 456s price 0.4006 0.0458 8.75 2.5e-10 *** 456s farmPrice 0.4207 0.0184 22.92 < 2e-16 *** 456s trend 0.5420 0.0168 32.25 < 2e-16 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 3.677 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 216.315 MSE: 13.52 Root MSE: 3.677 456s Multiple R-Squared: 0.193 Adjusted R-Squared: 0.042 456s 456s [1] "**** W3SLS with different instruments and 2 restrictions *********" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 35 364 32.3 0.322 -0.022 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 143 8.43 2.90 0.466 0.403 456s supply 20 16 220 13.78 3.71 0.178 0.024 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.75 4.46 456s supply 4.46 6.04 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 8.43 9.15 456s supply 9.15 13.78 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.00 0.85 456s supply 0.85 1.00 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 107.9125 5.1136 21.1 < 2e-16 *** 456s price -0.5996 0.0479 -12.5 1.7e-14 *** 456s income 0.5430 0.0171 31.7 < 2e-16 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.903 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 143.236 MSE: 8.426 Root MSE: 2.903 456s Multiple R-Squared: 0.466 Adjusted R-Squared: 0.403 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 13.9658 5.1591 2.71 0.01 * 456s price 0.4004 0.0479 8.36 7.3e-10 *** 456s farmPrice 0.4263 0.0193 22.08 < 2e-16 *** 456s trend 0.5430 0.0171 31.74 < 2e-16 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 3.712 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 220.468 MSE: 13.779 Root MSE: 3.712 456s Multiple R-Squared: 0.178 Adjusted R-Squared: 0.024 456s 456s [1] " 3SLS with different instruments with 2 restrictions via R and restrict.regMat" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 35 358 32.4 0.333 -0.013 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 141 8.32 2.88 0.472 0.410 456s supply 20 16 216 13.53 3.68 0.193 0.042 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.79 4.45 456s supply 4.45 6.06 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 8.32 8.95 456s supply 8.95 13.53 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.000 0.844 456s supply 0.844 1.000 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 108.5837 5.3770 20.2 < 2e-16 *** 456s price -0.6034 0.0504 -12.0 6.2e-14 *** 456s income 0.5399 0.0182 29.7 < 2e-16 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.884 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 141.436 MSE: 8.32 Root MSE: 2.884 456s Multiple R-Squared: 0.472 Adjusted R-Squared: 0.41 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 14.7043 5.4316 2.71 0.01 * 456s price 0.3966 0.0504 7.87 3e-09 *** 456s farmPrice 0.4228 0.0205 20.65 <2e-16 *** 456s trend 0.5399 0.0182 29.71 <2e-16 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 3.678 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 216.4 MSE: 13.525 Root MSE: 3.678 456s Multiple R-Squared: 0.193 Adjusted R-Squared: 0.042 456s 456s [1] "3SLS with diff. instruments and 2 restr. via R and restrict.regMat (EViews-like)" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 35 359 21.9 0.331 -0.059 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 143 8.38 2.90 0.468 0.406 456s supply 20 16 216 13.52 3.68 0.193 0.042 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.22 3.67 456s supply 3.67 4.85 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 7.13 7.43 456s supply 7.43 10.82 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.000 0.846 456s supply 0.846 1.000 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 107.9852 4.9704 21.7 < 2e-16 *** 456s price -0.5994 0.0458 -13.1 4.9e-15 *** 456s income 0.5420 0.0168 32.2 < 2e-16 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.896 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 142.542 MSE: 8.385 Root MSE: 2.896 456s Multiple R-Squared: 0.468 Adjusted R-Squared: 0.406 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 14.4922 4.9950 2.90 0.0064 ** 456s price 0.4006 0.0458 8.75 2.5e-10 *** 456s farmPrice 0.4207 0.0184 22.92 < 2e-16 *** 456s trend 0.5420 0.0168 32.25 < 2e-16 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 3.677 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 216.315 MSE: 13.52 Root MSE: 3.677 456s Multiple R-Squared: 0.193 Adjusted R-Squared: 0.042 456s 456s [1] "W3SLS with diff. instr. and 2 restr. via R and restrict.regMat (EViews-like)" 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 35 364 21.8 0.321 -0.069 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 144 8.49 2.91 0.462 0.399 456s supply 20 16 220 13.76 3.71 0.179 0.025 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.19 3.68 456s supply 3.68 4.83 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 7.21 7.59 456s supply 7.59 11.00 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.000 0.852 456s supply 0.852 1.000 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 107.3179 4.7598 22.6 < 2e-16 *** 456s price -0.5955 0.0438 -13.6 1.6e-15 *** 456s income 0.5449 0.0159 34.2 < 2e-16 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.913 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 144.274 MSE: 8.487 Root MSE: 2.913 456s Multiple R-Squared: 0.462 Adjusted R-Squared: 0.399 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 13.7761 4.7784 2.88 0.0067 ** 456s price 0.4045 0.0438 9.23 6.6e-11 *** 456s farmPrice 0.4237 0.0174 24.30 < 2e-16 *** 456s trend 0.5449 0.0159 34.17 < 2e-16 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 3.709 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 220.081 MSE: 13.755 Root MSE: 3.709 456s Multiple R-Squared: 0.179 Adjusted R-Squared: 0.025 456s 456s > 456s > 456s > ## **************** shorter summaries ********************** 456s > print( summary( fit3sls[[ 2 ]]$e1c, equations = FALSE ) ) 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 33 174 -0.718 0.675 0.922 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 65.7 3.87 1.97 0.755 0.726 456s supply 20 16 108.7 6.79 2.61 0.594 0.518 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.87 4.50 456s supply 4.50 6.04 456s 456s warning: this covariance matrix is NOT positive semidefinit! 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 3.87 5.2 456s supply 5.20 6.8 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.000 0.981 456s supply 0.981 1.000 456s 456s 456s Coefficients: 456s Estimate Std. Error t value Pr(>|t|) 456s demand_(Intercept) 94.6333 7.9208 11.95 1.1e-09 *** 456s demand_price -0.2436 0.0965 -2.52 0.02183 * 456s demand_income 0.3140 0.0469 6.69 3.8e-06 *** 456s supply_(Intercept) 52.2869 11.8853 4.40 0.00045 *** 456s supply_price 0.2282 0.0997 2.29 0.03595 * 456s supply_farmPrice 0.2272 0.0438 5.19 8.9e-05 *** 456s supply_trend 0.3648 0.0707 5.16 9.5e-05 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s > 456s > print( summary( fit3sls[[ 3 ]]$e2e ), residCov = FALSE ) 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 34 171 0.887 0.68 0.678 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 67.5 3.97 1.99 0.748 0.719 456s supply 20 16 104.0 6.50 2.55 0.612 0.539 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 94.2737 7.3905 12.76 1.6e-14 *** 456s price -0.2243 0.0888 -2.53 0.016 * 456s income 0.2979 0.0420 7.10 3.4e-08 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 1.992 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 67.467 MSE: 3.969 Root MSE: 1.992 456s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 55.4521 10.3994 5.33 6.4e-06 *** 456s price 0.2207 0.0896 2.46 0.019 * 456s farmPrice 0.2095 0.0366 5.73 1.9e-06 *** 456s trend 0.2979 0.0420 7.10 3.4e-08 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.55 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 104.013 MSE: 6.501 Root MSE: 2.55 456s Multiple R-Squared: 0.612 Adjusted R-Squared: 0.539 456s 456s > 456s > print( summary( fit3sls[[ 4 ]]$e3, useDfSys = FALSE ), residCov = FALSE ) 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 34 173 1.27 0.678 0.722 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 67.8 3.99 2.00 0.747 0.717 456s supply 20 16 104.8 6.55 2.56 0.609 0.536 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 94.222 8.015 11.76 1.4e-09 *** 456s price -0.222 0.096 -2.31 0.034 * 456s income 0.296 0.045 6.57 4.8e-06 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 1.997 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 67.796 MSE: 3.988 Root MSE: 1.997 456s Multiple R-Squared: 0.747 Adjusted R-Squared: 0.717 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 55.9604 11.5777 4.83 0.00018 *** 456s price 0.2193 0.1002 2.19 0.04374 * 456s farmPrice 0.2060 0.0403 5.11 0.00011 *** 456s trend 0.2956 0.0450 6.57 6.5e-06 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.559 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 104.753 MSE: 6.547 Root MSE: 2.559 456s Multiple R-Squared: 0.609 Adjusted R-Squared: 0.536 456s 456s > 456s > print( summary( fit3sls[[ 5 ]]$e4e, equations = FALSE ), 456s + equations = FALSE ) 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 35 439 21.3 0.18 -0.18 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 169 9.93 3.15 0.370 0.296 456s supply 20 16 271 16.91 4.11 -0.009 -0.198 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.30 3.73 456s supply 3.73 5.00 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 8.44 9.64 456s supply 9.64 13.53 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.000 0.902 456s supply 0.902 1.000 456s 456s 456s Coefficients: 456s Estimate Std. Error t value Pr(>|t|) 456s demand_(Intercept) 93.2926 7.3154 12.75 1.0e-14 *** 456s demand_price -0.4781 0.0812 -5.89 1.1e-06 *** 456s demand_income 0.5683 0.0209 27.24 < 2e-16 *** 456s supply_(Intercept) 0.6559 7.5503 0.09 0.93 456s supply_price 0.5219 0.0812 6.43 2.1e-07 *** 456s supply_farmPrice 0.4355 0.0212 20.49 < 2e-16 *** 456s supply_trend 0.5683 0.0209 27.24 < 2e-16 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s > 456s > print( summary( fit3sls[[ 1 ]]$e4wSym, residCov = FALSE ), 456s + equations = FALSE ) 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 35 172 1.74 0.68 0.697 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 65.9 3.88 1.97 0.754 0.725 456s supply 20 16 105.7 6.60 2.57 0.606 0.532 456s 456s 456s Coefficients: 456s Estimate Std. Error t value Pr(>|t|) 456s demand_(Intercept) 93.7870 7.9088 11.86 8.2e-14 *** 456s demand_price -0.2443 0.0892 -2.74 0.0096 ** 456s demand_income 0.3234 0.0229 14.14 4.4e-16 *** 456s supply_(Intercept) 49.8093 8.1522 6.11 5.5e-07 *** 456s supply_price 0.2557 0.0892 2.87 0.0069 ** 456s supply_farmPrice 0.2289 0.0237 9.67 2.0e-11 *** 456s supply_trend 0.3234 0.0229 14.14 4.4e-16 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s > 456s > print( summary( fit3sls[[ 2 ]]$e5, residCov = FALSE ), residCov = TRUE ) 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 35 171 1.74 0.681 0.696 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 65.8 3.87 1.97 0.755 0.726 456s supply 20 16 105.4 6.59 2.57 0.607 0.533 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.89 4.53 456s supply 4.53 6.25 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 3.87 4.87 456s supply 4.87 6.59 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.000 0.965 456s supply 0.965 1.000 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 93.9070 7.9234 11.85 8.3e-14 *** 456s price -0.2457 0.0891 -2.76 0.0092 ** 456s income 0.3236 0.0233 13.91 8.9e-16 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 1.967 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 65.807 MSE: 3.871 Root MSE: 1.967 456s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 49.9049 8.1797 6.10 5.7e-07 *** 456s price 0.2543 0.0891 2.85 0.0072 ** 456s farmPrice 0.2293 0.0241 9.52 3.1e-11 *** 456s trend 0.3236 0.0233 13.91 8.9e-16 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.566 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 105.389 MSE: 6.587 Root MSE: 2.566 456s Multiple R-Squared: 0.607 Adjusted R-Squared: 0.533 456s 456s > 456s > print( summary( fit3slsi[[ 3 ]]$e3e, residCov = FALSE, 456s + equations = FALSE ) ) 456s 456s systemfit results 456s method: iterated 3SLS 456s 456s convergence achieved after 20 iterations 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 34 237 0.364 0.557 0.755 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 99.3 5.84 2.42 0.630 0.586 456s supply 20 16 138.1 8.63 2.94 0.485 0.388 456s 456s 456s Coefficients: 456s Estimate Std. Error t value Pr(>|t|) 456s demand_(Intercept) 92.0353 8.9214 10.32 5.2e-12 *** 456s demand_price -0.1043 0.0958 -1.09 0.284 456s demand_income 0.1979 0.0299 6.61 1.4e-07 *** 456s supply_(Intercept) 68.2830 11.1530 6.12 6.0e-07 *** 456s supply_price 0.1851 0.1053 1.76 0.088 . 456s supply_farmPrice 0.1245 0.0251 4.96 1.9e-05 *** 456s supply_trend 0.1979 0.0299 6.61 1.4e-07 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s > 456s > print( summary( fit3slsi[[ 4 ]]$e1we ), equations = FALSE, residCov = FALSE ) 456s 456s systemfit results 456s method: iterated 3SLS 456s 456s convergence achieved after 6 iterations 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 33 177 0.667 0.67 0.782 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 65.7 3.87 1.97 0.755 0.726 456s supply 20 16 111.3 6.96 2.64 0.585 0.507 456s 456s 456s Coefficients: 456s Estimate Std. Error t value Pr(>|t|) 456s demand_(Intercept) 94.6333 7.3027 12.96 3.1e-10 *** 456s demand_price -0.2436 0.0890 -2.74 0.01402 * 456s demand_income 0.3140 0.0433 7.25 1.3e-06 *** 456s supply_(Intercept) 52.5527 11.3956 4.61 0.00029 *** 456s supply_price 0.2271 0.0956 2.37 0.03043 * 456s supply_farmPrice 0.2245 0.0416 5.39 6.0e-05 *** 456s supply_trend 0.3756 0.0641 5.86 2.4e-05 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s > 456s > print( summary( fit3slsd[[ 5 ]]$e4, residCov = FALSE ) ) 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 35 358 32.4 0.333 -0.013 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 141 8.32 2.88 0.472 0.410 456s supply 20 16 216 13.53 3.68 0.193 0.042 456s 456s 456s 3SLS estimates for 'demand' (equation 1) 456s Model Formula: consump ~ price + income 456s Instruments: ~income + farmPrice 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 108.5837 5.3770 20.2 < 2e-16 *** 456s price -0.6034 0.0504 -12.0 6.2e-14 *** 456s income 0.5399 0.0182 29.7 < 2e-16 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 2.884 on 17 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 17 456s SSR: 141.436 MSE: 8.32 Root MSE: 2.884 456s Multiple R-Squared: 0.472 Adjusted R-Squared: 0.41 456s 456s 456s 3SLS estimates for 'supply' (equation 2) 456s Model Formula: consump ~ price + farmPrice + trend 456s Instruments: ~income + farmPrice + trend 456s 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 14.7043 5.4316 2.71 0.01 * 456s price 0.3966 0.0504 7.87 3e-09 *** 456s farmPrice 0.4228 0.0205 20.65 <2e-16 *** 456s trend 0.5399 0.0182 29.71 <2e-16 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s 456s Residual standard error: 3.678 on 16 degrees of freedom 456s Number of observations: 20 Degrees of Freedom: 16 456s SSR: 216.4 MSE: 13.525 Root MSE: 3.678 456s Multiple R-Squared: 0.193 Adjusted R-Squared: 0.042 456s 456s > 456s > print( summary( fit3slsd[[ 1 ]]$e2we, equations = FALSE ) ) 456s 456s systemfit results 456s method: 3SLS 456s 456s N DF SSR detRCov OLS-R2 McElroy-R2 456s system 40 34 199 1.77 0.629 0.65 456s 456s N DF SSR MSE RMSE R2 Adj R2 456s demand 20 17 72.4 4.26 2.06 0.730 0.698 456s supply 20 16 126.7 7.92 2.81 0.527 0.439 456s 456s The covariance matrix of the residuals used for estimation 456s demand supply 456s demand 3.24 3.60 456s supply 3.60 5.06 456s 456s The covariance matrix of the residuals 456s demand supply 456s demand 3.62 4.60 456s supply 4.60 6.34 456s 456s The correlations of the residuals 456s demand supply 456s demand 1.000 0.961 456s supply 0.961 1.000 456s 456s 456s Coefficients: 456s Estimate Std. Error t value Pr(>|t|) 456s demand_(Intercept) 88.9298 5.9083 15.05 < 2e-16 *** 456s demand_price -0.1760 0.0746 -2.36 0.02415 * 456s demand_income 0.3032 0.0434 6.99 4.6e-08 *** 456s supply_(Intercept) 40.8325 10.1094 4.04 0.00029 *** 456s supply_price 0.3562 0.0711 5.01 1.7e-05 *** 456s supply_farmPrice 0.2200 0.0405 5.43 4.8e-06 *** 456s supply_trend 0.3032 0.0434 6.99 4.6e-08 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s > 456s > 456s > ## ****************** residuals ************************** 456s > print( residuals( fit3sls[[ 1 ]]$e1c ) ) 456s demand supply 456s 1 0.843 0.670 456s 2 -0.698 -0.142 456s 3 2.359 2.659 456s 4 1.490 1.618 456s 5 2.139 2.588 456s 6 1.277 1.485 456s 7 1.571 2.093 456s 8 -3.066 -4.163 456s 9 -1.125 -1.929 456s 10 2.492 3.207 456s 11 -0.108 -0.513 456s 12 -2.292 -2.375 456s 13 -1.598 -2.089 456s 14 -0.271 0.330 456s 15 1.958 3.086 456s 16 -3.430 -4.225 456s 17 -0.313 0.185 456s 18 -2.151 -3.680 456s 19 1.592 1.576 456s 20 -0.668 -0.382 456s > print( residuals( fit3sls[[ 1 ]]$e1c$eq[[ 1 ]] ) ) 456s 1 2 3 4 5 6 7 8 9 10 11 456s 0.843 -0.698 2.359 1.490 2.139 1.277 1.571 -3.066 -1.125 2.492 -0.108 456s 12 13 14 15 16 17 18 19 20 456s -2.292 -1.598 -0.271 1.958 -3.430 -0.313 -2.151 1.592 -0.668 456s > 456s > print( residuals( fit3sls[[ 4 ]]$e1wc ) ) 456s demand supply 456s 1 0.843 0.670 456s 2 -0.698 -0.142 456s 3 2.359 2.659 456s 4 1.490 1.618 456s 5 2.139 2.588 456s 6 1.277 1.485 456s 7 1.571 2.093 456s 8 -3.066 -4.163 456s 9 -1.125 -1.929 456s 10 2.492 3.207 456s 11 -0.108 -0.513 456s 12 -2.292 -2.375 456s 13 -1.598 -2.089 456s 14 -0.271 0.330 456s 15 1.958 3.086 456s 16 -3.430 -4.225 456s 17 -0.313 0.185 456s 18 -2.151 -3.680 456s 19 1.592 1.576 456s 20 -0.668 -0.382 456s > print( residuals( fit3sls[[ 4 ]]$e1wc$eq[[ 1 ]] ) ) 456s 1 2 3 4 5 6 7 8 9 10 11 456s 0.843 -0.698 2.359 1.490 2.139 1.277 1.571 -3.066 -1.125 2.492 -0.108 456s 12 13 14 15 16 17 18 19 20 456s -2.292 -1.598 -0.271 1.958 -3.430 -0.313 -2.151 1.592 -0.668 456s > 456s > print( residuals( fit3sls[[ 2 ]]$e2e ) ) 456s demand supply 456s 1 0.6744 0.0619 456s 2 -0.7785 -0.6344 456s 3 2.2797 2.2267 456s 4 1.4140 1.2428 456s 5 2.2144 2.4566 456s 6 1.3352 1.3851 456s 7 1.6419 2.0264 456s 8 -2.9923 -4.0603 456s 9 -1.0710 -1.8419 456s 10 2.5226 3.1787 456s 11 -0.3346 -0.8086 456s 12 -2.5999 -2.7819 456s 13 -1.8617 -2.3572 456s 14 -0.3584 0.2840 456s 15 2.1419 3.4511 456s 16 -3.2786 -3.7199 456s 17 -0.0706 0.7656 456s 18 -2.1179 -3.2218 456s 19 1.6924 2.0576 456s 20 -0.4528 0.2893 456s > print( residuals( fit3sls[[ 2 ]]$e2e$eq[[ 2 ]] ) ) 456s 1 2 3 4 5 6 7 8 9 10 456s 0.0619 -0.6344 2.2267 1.2428 2.4566 1.3851 2.0264 -4.0603 -1.8419 3.1787 456s 11 12 13 14 15 16 17 18 19 20 456s -0.8086 -2.7819 -2.3572 0.2840 3.4511 -3.7199 0.7656 -3.2218 2.0576 0.2893 456s > 456s > print( residuals( fit3sls[[ 3 ]]$e3 ) ) 456s demand supply 456s 1 0.6499 0.045 456s 2 -0.7902 -0.639 456s 3 2.2682 2.223 456s 4 1.4031 1.239 456s 5 2.2253 2.490 456s 6 1.3437 1.414 456s 7 1.6522 2.051 456s 8 -2.9817 -4.013 456s 9 -1.0632 -1.808 456s 10 2.5270 3.179 456s 11 -0.3675 -0.872 456s 12 -2.6445 -2.878 456s 13 -1.8999 -2.437 456s 14 -0.3711 0.237 456s 15 2.1685 3.474 456s 16 -3.2566 -3.680 456s 17 -0.0355 0.809 456s 18 -2.1131 -3.213 456s 19 1.7070 2.060 456s 20 -0.4215 0.319 456s > print( residuals( fit3sls[[ 3 ]]$e3$eq[[ 1 ]] ) ) 456s 1 2 3 4 5 6 7 8 9 10 456s 0.6499 -0.7902 2.2682 1.4031 2.2253 1.3437 1.6522 -2.9817 -1.0632 2.5270 456s 11 12 13 14 15 16 17 18 19 20 456s -0.3675 -2.6445 -1.8999 -0.3711 2.1685 -3.2566 -0.0355 -2.1131 1.7070 -0.4215 456s > 456s > print( residuals( fit3sls[[ 4 ]]$e4e ) ) 456s demand supply 456s 1 0.9543 0.278 456s 2 -0.6734 -0.586 456s 3 2.3881 2.272 456s 4 1.5091 1.252 456s 5 2.1028 2.356 456s 6 1.2414 1.271 456s 7 1.5161 1.894 456s 8 -3.1487 -4.421 456s 9 -1.1358 -1.958 456s 10 2.5334 3.368 456s 11 0.0936 -0.275 456s 12 -2.0762 -2.176 456s 13 -1.4415 -1.951 456s 14 -0.2039 0.559 456s 15 1.8691 3.353 456s 16 -3.5213 -4.003 456s 17 -0.3804 0.692 456s 18 -2.2018 -3.453 456s 19 1.4834 1.817 456s 20 -0.9080 -0.289 456s > print( residuals( fit3sls[[ 4 ]]$e4e$eq[[ 2 ]] ) ) 456s 1 2 3 4 5 6 7 8 9 10 11 456s 0.278 -0.586 2.272 1.252 2.356 1.271 1.894 -4.421 -1.958 3.368 -0.275 456s 12 13 14 15 16 17 18 19 20 456s -2.176 -1.951 0.559 3.353 -4.003 0.692 -3.453 1.817 -0.289 456s > 456s > print( residuals( fit3sls[[ 5 ]]$e5 ) ) 456s demand supply 456s 1 3.391 2.137 456s 2 0.160 -0.366 456s 3 3.267 2.508 456s 4 2.250 1.132 456s 5 1.168 1.398 456s 6 0.434 0.165 456s 7 0.397 0.594 456s 8 -4.607 -7.911 456s 9 -1.631 -2.964 456s 10 2.800 5.323 456s 11 3.967 4.833 456s 12 2.518 3.479 456s 13 2.169 1.774 456s 14 1.169 3.182 456s 15 -0.415 2.626 456s 16 -5.608 -6.508 456s 17 -2.817 0.433 456s 18 -3.012 -5.580 456s 19 -0.454 -0.427 456s 20 -5.146 -5.829 456s > print( residuals( fit3sls[[ 5 ]]$e5$eq[[ 1 ]] ) ) 456s 1 2 3 4 5 6 7 8 9 10 11 456s 3.391 0.160 3.267 2.250 1.168 0.434 0.397 -4.607 -1.631 2.800 3.967 456s 12 13 14 15 16 17 18 19 20 456s 2.518 2.169 1.169 -0.415 -5.608 -2.817 -3.012 -0.454 -5.146 456s > 456s > print( residuals( fit3slsi[[ 2 ]]$e3e ) ) 456s demand supply 456s 1 -0.376 -0.761 456s 2 -1.281 -1.123 456s 3 1.786 1.809 456s 4 0.942 0.878 456s 5 2.683 3.039 456s 6 1.699 1.899 456s 7 2.083 2.477 456s 8 -2.534 -3.021 456s 9 -0.736 -1.093 456s 10 2.713 3.153 456s 11 -1.748 -2.334 456s 12 -4.518 -5.058 456s 13 -3.502 -4.191 456s 14 -0.901 -0.705 456s 15 3.286 4.209 456s 16 -2.334 -2.514 456s 17 1.438 2.113 456s 18 -1.911 -2.680 456s 19 2.320 2.490 456s 20 0.889 1.412 456s > print( residuals( fit3slsi[[ 2 ]]$e3e$eq[[ 1 ]] ) ) 456s 1 2 3 4 5 6 7 8 9 10 11 456s -0.376 -1.281 1.786 0.942 2.683 1.699 2.083 -2.534 -0.736 2.713 -1.748 456s 12 13 14 15 16 17 18 19 20 456s -4.518 -3.502 -0.901 3.286 -2.334 1.438 -1.911 2.320 0.889 456s > 456s > print( residuals( fit3slsi[[ 1 ]]$e2we ) ) 456s demand supply 456s 1 -0.376 -0.761 456s 2 -1.281 -1.123 456s 3 1.786 1.809 456s 4 0.942 0.878 456s 5 2.683 3.039 456s 6 1.699 1.899 456s 7 2.083 2.477 456s 8 -2.534 -3.021 456s 9 -0.736 -1.093 456s 10 2.713 3.153 456s 11 -1.748 -2.334 456s 12 -4.518 -5.058 456s 13 -3.502 -4.191 456s 14 -0.901 -0.705 456s 15 3.286 4.209 456s 16 -2.334 -2.514 456s 17 1.438 2.113 456s 18 -1.911 -2.680 456s 19 2.320 2.490 456s 20 0.889 1.412 456s > print( residuals( fit3slsi[[ 1 ]]$e2we$eq[[ 1 ]] ) ) 456s 1 2 3 4 5 6 7 8 9 10 11 456s -0.376 -1.281 1.786 0.942 2.683 1.699 2.083 -2.534 -0.736 2.713 -1.748 456s 12 13 14 15 16 17 18 19 20 456s -4.518 -3.502 -0.901 3.286 -2.334 1.438 -1.911 2.320 0.889 456s > 456s > print( residuals( fit3slsd[[ 3 ]]$e4 ) ) 456s demand supply 456s 1 0.7282 0.088 456s 2 -0.7938 -0.850 456s 3 2.2722 2.054 456s 4 1.3947 1.007 456s 5 2.2092 2.526 456s 6 1.3211 1.378 456s 7 1.6076 1.935 456s 8 -3.0646 -4.397 456s 9 -1.0534 -1.692 456s 10 2.6003 3.674 456s 11 -0.1888 -0.319 456s 12 -2.4839 -2.564 456s 13 -1.8018 -2.397 456s 14 -0.3164 0.423 456s 15 2.1290 3.682 456s 16 -3.3141 -3.704 456s 17 -0.0169 1.445 456s 18 -2.1692 -3.473 456s 19 1.6008 1.716 456s 20 -0.6603 -0.530 456s > print( residuals( fit3slsd[[ 3 ]]$e4$eq[[ 2 ]] ) ) 456s 1 2 3 4 5 6 7 8 9 10 11 456s 0.088 -0.850 2.054 1.007 2.526 1.378 1.935 -4.397 -1.692 3.674 -0.319 456s 12 13 14 15 16 17 18 19 20 456s -2.564 -2.397 0.423 3.682 -3.704 1.445 -3.473 1.716 -0.530 456s > 456s > print( residuals( fit3slsd[[ 5 ]]$e5we ) ) 456s demand supply 456s 1 3.290 2.057 456s 2 0.781 0.154 456s 3 3.754 2.921 456s 4 2.915 1.707 456s 5 0.906 1.148 456s 6 0.394 0.120 456s 7 0.632 0.775 456s 8 -3.766 -7.138 456s 9 -2.167 -3.402 456s 10 1.391 4.066 456s 11 2.631 3.690 456s 12 2.043 3.077 456s 13 2.405 2.007 456s 14 0.885 2.914 456s 15 -1.051 2.024 456s 16 -5.729 -6.584 456s 17 -4.810 -1.328 456s 18 -2.329 -4.924 456s 19 0.576 0.472 456s 20 -2.753 -3.755 456s > print( residuals( fit3slsd[[ 5 ]]$e5we$eq[[ 2 ]] ) ) 456s 1 2 3 4 5 6 7 8 9 10 11 456s 2.057 0.154 2.921 1.707 1.148 0.120 0.775 -7.138 -3.402 4.066 3.690 456s 12 13 14 15 16 17 18 19 20 456s 3.077 2.007 2.914 2.024 -6.584 -1.328 -4.924 0.472 -3.755 456s > 456s > 456s > ## *************** coefficients ********************* 456s > print( round( coef( fit3sls[[ 3 ]]$e1c ), digits = 6 ) ) 456s demand_(Intercept) demand_price demand_income supply_(Intercept) 456s 94.633 -0.244 0.314 52.287 456s supply_price supply_farmPrice supply_trend 456s 0.228 0.227 0.365 456s > print( round( coef( fit3sls[[ 4 ]]$e1c$eq[[ 2 ]] ), digits = 6 ) ) 456s (Intercept) price farmPrice trend 456s 52.287 0.228 0.227 0.365 456s > 456s > print( round( coef( fit3slsi[[ 4 ]]$e2 ), digits = 6 ) ) 456s demand_(Intercept) demand_price demand_income supply_(Intercept) 456s 92.074 -0.106 0.200 68.855 456s supply_price supply_farmPrice supply_trend 456s 0.183 0.120 0.200 456s > print( round( coef( fit3slsi[[ 5 ]]$e2$eq[[ 1 ]] ), digits = 6 ) ) 456s (Intercept) price income 456s 92.074 -0.106 0.200 456s > 456s > print( round( coef( fit3sls[[ 2 ]]$e2w ), digits = 6 ) ) 456s demand_(Intercept) demand_price demand_income supply_(Intercept) 456s 94.182 -0.219 0.294 56.254 456s supply_price supply_farmPrice supply_trend 456s 0.218 0.204 0.294 456s > print( round( coef( fit3sls[[ 3 ]]$e2w$eq[[ 1 ]] ), digits = 6 ) ) 456s (Intercept) price income 456s 94.182 -0.219 0.294 456s > 456s > print( round( coef( fit3slsd[[ 5 ]]$e3e ), digits = 6 ) ) 456s demand_(Intercept) demand_price demand_income supply_(Intercept) 456s 109.109 -0.442 0.369 57.268 456s supply_price supply_farmPrice supply_trend 456s 0.137 0.269 0.369 456s > print( round( coef( fit3slsd[[ 5 ]]$e3e, modified.regMat = TRUE ), digits = 6 ) ) 456s C1 C2 C3 C4 C5 C6 456s 109.109 -0.442 0.369 57.268 0.137 0.269 456s > print( round( coef( fit3slsd[[ 1 ]]$e3e$eq[[ 2 ]] ), digits = 6 ) ) 456s (Intercept) price farmPrice trend 456s 40.818 0.357 0.219 0.303 456s > 456s > print( round( coef( fit3slsd[[ 4 ]]$e3w ), digits = 6 ) ) 456s demand_(Intercept) demand_price demand_income supply_(Intercept) 456s 100.174 -0.325 0.341 49.743 456s supply_price supply_farmPrice supply_trend 456s 0.237 0.247 0.341 456s > print( round( coef( fit3slsd[[ 4 ]]$e3w, modified.regMat = TRUE ), digits = 6 ) ) 456s C1 C2 C3 C4 C5 C6 456s 100.174 -0.325 0.341 49.743 0.237 0.247 456s > print( round( coef( fit3slsd[[ 5 ]]$e3w$eq[[ 2 ]] ), digits = 6 ) ) 456s (Intercept) price farmPrice trend 456s 57.667 0.133 0.270 0.370 456s > 456s > print( round( coef( fit3sls[[ 1 ]]$e4 ), digits = 6 ) ) 456s demand_(Intercept) demand_price demand_income supply_(Intercept) 456s 93.907 -0.246 0.324 49.905 456s supply_price supply_farmPrice supply_trend 456s 0.254 0.229 0.324 456s > print( round( coef( fit3sls[[ 2 ]]$e4$eq[[ 1 ]] ), digits = 6 ) ) 456s (Intercept) price income 456s 93.907 -0.246 0.324 456s > 456s > print( round( coef( fit3slsi[[ 2 ]]$e4we ), digits = 6 ) ) 456s demand_(Intercept) demand_price demand_income supply_(Intercept) 456s 91.390 -0.217 0.320 47.579 456s supply_price supply_farmPrice supply_trend 456s 0.283 0.224 0.320 456s > print( round( coef( fit3slsi[[ 1 ]]$e4we$eq[[ 1 ]] ), digits = 6 ) ) 456s (Intercept) price income 456s 91.390 -0.217 0.320 456s > 456s > print( round( coef( fit3slsi[[ 2 ]]$e5e ), digits = 6 ) ) 456s demand_(Intercept) demand_price demand_income supply_(Intercept) 456s 91.390 -0.217 0.320 47.579 456s supply_price supply_farmPrice supply_trend 456s 0.283 0.224 0.320 456s > print( round( coef( fit3slsi[[ 2 ]]$e5e, modified.regMat = TRUE ), digits = 6 ) ) 456s C1 C2 C3 C4 C5 C6 456s 91.390 -0.217 0.320 47.579 0.283 0.224 456s > print( round( coef( fit3slsi[[ 3 ]]$e5e$eq[[ 2 ]] ), digits = 6 ) ) 456s (Intercept) price farmPrice trend 456s 47.579 0.283 0.224 0.320 456s > 456s > 456s > ## *************** coefficients with stats ********************* 456s > print( round( coef( summary( fit3sls[[ 3 ]]$e1c, useDfSys = FALSE ) ), 456s + digits = 6 ) ) 456s Estimate Std. Error t value Pr(>|t|) 456s demand_(Intercept) 94.633 7.9208 11.95 0.000000 456s demand_price -0.244 0.0965 -2.52 0.021832 456s demand_income 0.314 0.0469 6.69 0.000004 456s supply_(Intercept) 52.287 11.8853 4.40 0.000448 456s supply_price 0.228 0.0997 2.29 0.035951 456s supply_farmPrice 0.227 0.0438 5.19 0.000089 456s supply_trend 0.365 0.0707 5.16 0.000095 456s > print( round( coef( summary( fit3sls[[ 4 ]]$e1c$eq[[ 2 ]], useDfSys = FALSE ) ), 456s + digits = 6 ) ) 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 52.287 11.8853 4.40 0.000448 456s price 0.228 0.0997 2.29 0.035951 456s farmPrice 0.227 0.0438 5.19 0.000089 456s trend 0.365 0.0707 5.16 0.000095 456s > 456s > print( round( coef( summary( fit3slsd[[ 2 ]]$e1w, useDfSys = FALSE ) ), 456s + digits = 6 ) ) 456s Estimate Std. Error t value Pr(>|t|) 456s demand_(Intercept) 106.789 11.1435 9.58 0.000000 456s demand_price -0.412 0.1448 -2.84 0.011271 456s demand_income 0.362 0.0564 6.41 0.000006 456s supply_(Intercept) 57.295 11.7078 4.89 0.000162 456s supply_price 0.137 0.0979 1.40 0.179781 456s supply_farmPrice 0.266 0.0483 5.51 0.000048 456s supply_trend 0.397 0.0672 5.91 0.000022 456s > print( round( coef( summary( fit3slsd[[ 3 ]]$e1w$eq[[ 2 ]], useDfSys = FALSE ) ), 456s + digits = 3 ) ) 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 49.532 12.011 4.12 0.001 456s price 0.240 0.100 2.40 0.029 456s farmPrice 0.256 0.047 5.41 0.000 456s trend 0.253 0.100 2.54 0.022 456s > 456s > print( round( coef( summary( fit3slsi[[ 4 ]]$e2 ) ), digits = 6 ) ) 456s Estimate Std. Error t value Pr(>|t|) 456s demand_(Intercept) 92.074 9.6303 9.56 0.000000 456s demand_price -0.106 0.1023 -1.04 0.305469 456s demand_income 0.200 0.0297 6.73 0.000000 456s supply_(Intercept) 68.855 12.4839 5.52 0.000004 456s supply_price 0.183 0.1189 1.54 0.132354 456s supply_farmPrice 0.120 0.0260 4.63 0.000051 456s supply_trend 0.200 0.0297 6.73 0.000000 456s > print( round( coef( summary( fit3slsi[[ 5 ]]$e2$eq[[ 1 ]] ) ), digits = 6 ) ) 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 92.074 9.6303 9.56 0.000 456s price -0.106 0.1023 -1.04 0.305 456s income 0.200 0.0297 6.73 0.000 456s > 456s > print( round( coef( summary( fit3slsd[[ 5 ]]$e3e, useDfSys = FALSE ) ), 456s + digits = 6 ) ) 456s Estimate Std. Error t value Pr(>|t|) 456s demand_(Intercept) 109.109 5.8428 18.67 0.000000 456s demand_price -0.442 0.0737 -6.00 0.000014 456s demand_income 0.369 0.0432 8.54 0.000000 456s supply_(Intercept) 57.268 10.0564 5.69 0.000033 456s supply_price 0.137 0.0705 1.95 0.069081 456s supply_farmPrice 0.269 0.0403 6.68 0.000005 456s supply_trend 0.369 0.0432 8.54 0.000000 456s > print( round( coef( summary( fit3slsd[[ 5 ]]$e3e, useDfSys = FALSE ), 456s + modified.regMat = TRUE ), digits = 6 ) ) 456s Estimate Std. Error t value Pr(>|t|) 456s C1 109.109 5.8428 18.67 NA 456s C2 -0.442 0.0737 -6.00 NA 456s C3 0.369 0.0432 8.54 NA 456s C4 57.268 10.0564 5.69 NA 456s C5 0.137 0.0705 1.95 NA 456s C6 0.269 0.0403 6.68 NA 456s > print( round( coef( summary( fit3slsd[[ 1 ]]$e3e$eq[[ 2 ]], useDfSys = FALSE ) ), 456s + digits = 6 ) ) 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 40.818 10.0564 4.06 0.000912 456s price 0.357 0.0705 5.06 0.000116 456s farmPrice 0.219 0.0403 5.45 0.000053 456s trend 0.303 0.0432 7.00 0.000003 456s > 456s > print( round( coef( summary( fit3slsi[[ 4 ]]$e3w, useDfSys = FALSE ) ), 456s + digits = 6 ) ) 456s Estimate Std. Error t value Pr(>|t|) 456s demand_(Intercept) 92.074 9.6303 9.56 0.000000 456s demand_price -0.106 0.1023 -1.04 0.312700 456s demand_income 0.200 0.0297 6.73 0.000004 456s supply_(Intercept) 68.855 12.4839 5.52 0.000047 456s supply_price 0.183 0.1189 1.54 0.142642 456s supply_farmPrice 0.120 0.0260 4.63 0.000278 456s supply_trend 0.200 0.0297 6.73 0.000005 456s > print( round( coef( summary( fit3slsi[[ 4 ]]$e3w, useDfSys = FALSE ), 456s + modified.regMat = TRUE ), digits = 6 ) ) 456s Estimate Std. Error t value Pr(>|t|) 456s C1 92.074 9.6303 9.56 NA 456s C2 -0.106 0.1023 -1.04 NA 456s C3 0.200 0.0297 6.73 NA 456s C4 68.855 12.4839 5.52 NA 456s C5 0.183 0.1189 1.54 NA 456s C6 0.120 0.0260 4.63 NA 456s > print( round( coef( summary( fit3slsi[[ 5 ]]$e3w$eq[[ 2 ]], useDfSys = FALSE ) ), 456s + digits = 6 ) ) 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 68.855 12.4839 5.52 0.000047 456s price 0.183 0.1189 1.54 0.142642 456s farmPrice 0.120 0.0260 4.63 0.000278 456s trend 0.200 0.0297 6.73 0.000005 456s > 456s > print( round( coef( summary( fit3sls[[ 1 ]]$e4 ) ), digits = 6 ) ) 456s Estimate Std. Error t value Pr(>|t|) 456s demand_(Intercept) 93.907 7.9234 11.85 0.000000 456s demand_price -0.246 0.0891 -2.76 0.009212 456s demand_income 0.324 0.0233 13.91 0.000000 456s supply_(Intercept) 49.905 8.1797 6.10 0.000001 456s supply_price 0.254 0.0891 2.85 0.007217 456s supply_farmPrice 0.229 0.0241 9.52 0.000000 456s supply_trend 0.324 0.0233 13.91 0.000000 456s > print( round( coef( summary( fit3sls[[ 2 ]]$e4$eq[[ 1 ]] ) ), digits = 6 ) ) 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 93.907 7.9234 11.85 0.00000 456s price -0.246 0.0891 -2.76 0.00921 456s income 0.324 0.0233 13.91 0.00000 456s > 456s > print( round( coef( summary( fit3slsi[[ 2 ]]$e5e ) ), digits = 6 ) ) 456s Estimate Std. Error t value Pr(>|t|) 456s demand_(Intercept) 91.390 7.3161 12.49 0.00000 456s demand_price -0.217 0.0835 -2.60 0.01365 456s demand_income 0.320 0.0168 19.07 0.00000 456s supply_(Intercept) 47.579 7.4268 6.41 0.00000 456s supply_price 0.283 0.0835 3.39 0.00174 456s supply_farmPrice 0.224 0.0168 13.36 0.00000 456s supply_trend 0.320 0.0168 19.07 0.00000 456s > print( round( coef( summary( fit3slsi[[ 2 ]]$e5e ), modified.regMat = TRUE ), 456s + digits = 6 ) ) 456s Estimate Std. Error t value Pr(>|t|) 456s C1 91.390 7.3161 12.49 0.00000 456s C2 -0.217 0.0835 -2.60 0.01365 456s C3 0.320 0.0168 19.07 0.00000 456s C4 47.579 7.4268 6.41 0.00000 456s C5 0.283 0.0835 3.39 0.00174 456s C6 0.224 0.0168 13.36 0.00000 456s > print( round( coef( summary( fit3slsi[[ 3 ]]$e5e$eq[[ 2 ]] ) ), digits = 6 ) ) 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 47.579 7.4268 6.41 0.00000 456s price 0.283 0.0835 3.39 0.00174 456s farmPrice 0.224 0.0168 13.36 0.00000 456s trend 0.320 0.0168 19.07 0.00000 456s > 456s > print( round( coef( summary( fit3sls[[ 2 ]]$e5we ) ), digits = 6 ) ) 456s Estimate Std. Error t value Pr(>|t|) 456s demand_(Intercept) 94.083 7.3058 12.88 0.00000 456s demand_price -0.248 0.0812 -3.06 0.00424 456s demand_income 0.325 0.0205 15.81 0.00000 456s supply_(Intercept) 50.019 7.5314 6.64 0.00000 456s supply_price 0.252 0.0812 3.10 0.00383 456s supply_farmPrice 0.231 0.0209 11.05 0.00000 456s supply_trend 0.325 0.0205 15.81 0.00000 456s > print( round( coef( summary( fit3sls[[ 2 ]]$e5we ), modified.regMat = TRUE ), 456s + digits = 6 ) ) 456s Estimate Std. Error t value Pr(>|t|) 456s C1 94.083 7.3058 12.88 0.00000 456s C2 -0.248 0.0812 -3.06 0.00424 456s C3 0.325 0.0205 15.81 0.00000 456s C4 50.019 7.5314 6.64 0.00000 456s C5 0.252 0.0812 3.10 0.00383 456s C6 0.231 0.0209 11.05 0.00000 456s > print( round( coef( summary( fit3sls[[ 3 ]]$e5we$eq[[ 2 ]] ) ), digits = 6 ) ) 456s Estimate Std. Error t value Pr(>|t|) 456s (Intercept) 50.019 7.5314 6.64 0.00000 456s price 0.252 0.0812 3.10 0.00383 456s farmPrice 0.231 0.0209 11.05 0.00000 456s trend 0.325 0.0205 15.81 0.00000 456s > 456s > 456s > ## *********** variance covariance matrix of the coefficients ******* 456s > print( round( vcov( fit3sls[[ 3 ]]$e1c ), digits = 5 ) ) 456s demand_(Intercept) demand_price demand_income 456s demand_(Intercept) 62.7397 -0.67342 0.04930 456s demand_price -0.6734 0.00931 -0.00264 456s demand_income 0.0493 -0.00264 0.00220 456s supply_(Intercept) 65.2708 -0.36561 -0.29198 456s supply_price -0.6979 0.00620 0.00079 456s supply_farmPrice 0.0423 -0.00227 0.00189 456s supply_trend 0.0638 -0.00342 0.00285 456s supply_(Intercept) supply_price supply_farmPrice 456s demand_(Intercept) 65.271 -0.69790 0.04230 456s demand_price -0.366 0.00620 -0.00227 456s demand_income -0.292 0.00079 0.00189 456s supply_(Intercept) 141.261 -1.08251 -0.29300 456s supply_price -1.083 0.00993 0.00080 456s supply_farmPrice -0.293 0.00080 0.00192 456s supply_trend -0.417 0.00110 0.00263 456s supply_trend 456s demand_(Intercept) 0.06383 456s demand_price -0.00342 456s demand_income 0.00285 456s supply_(Intercept) -0.41674 456s supply_price 0.00110 456s supply_farmPrice 0.00263 456s supply_trend 0.00500 456s > print( round( vcov( fit3sls[[ 4 ]]$e1c$eq[[ 2 ]] ), digits = 5 ) ) 456s (Intercept) price farmPrice trend 456s (Intercept) 141.261 -1.08251 -0.29300 -0.41674 456s price -1.083 0.00993 0.00080 0.00110 456s farmPrice -0.293 0.00080 0.00192 0.00263 456s trend -0.417 0.00110 0.00263 0.00500 456s > 456s > print( round( vcov( fit3sls[[ 4 ]]$e2 ), digits = 5 ) ) 456s demand_(Intercept) demand_price demand_income 456s demand_(Intercept) 64.2351 -0.68447 0.04535 456s demand_price -0.6845 0.00921 -0.00243 456s demand_income 0.0454 -0.00243 0.00203 456s supply_(Intercept) 67.0281 -0.42600 -0.24804 456s supply_price -0.7080 0.00641 0.00069 456s supply_farmPrice 0.0366 -0.00196 0.00164 456s supply_trend 0.0454 -0.00243 0.00203 456s supply_(Intercept) supply_price supply_farmPrice 456s demand_(Intercept) 67.028 -0.70800 0.03661 456s demand_price -0.426 0.00641 -0.00196 456s demand_income -0.248 0.00069 0.00164 456s supply_(Intercept) 134.043 -1.07653 -0.24277 456s supply_price -1.077 0.01003 0.00068 456s supply_farmPrice -0.243 0.00068 0.00163 456s supply_trend -0.248 0.00069 0.00164 456s supply_trend 456s demand_(Intercept) 0.04535 456s demand_price -0.00243 456s demand_income 0.00203 456s supply_(Intercept) -0.24804 456s supply_price 0.00069 456s supply_farmPrice 0.00164 456s supply_trend 0.00203 456s > print( round( vcov( fit3sls[[ 5 ]]$e2$eq[[ 1 ]] ), digits = 5 ) ) 456s (Intercept) price income 456s (Intercept) 64.2351 -0.68447 0.04535 456s price -0.6845 0.00921 -0.00243 456s income 0.0454 -0.00243 0.00203 456s > 456s > print( round( vcov( fit3sls[[ 5 ]]$e3e ), digits = 5 ) ) 456s demand_(Intercept) demand_price demand_income 456s demand_(Intercept) 54.6190 -0.58283 0.03940 456s demand_price -0.5828 0.00789 -0.00211 456s demand_income 0.0394 -0.00211 0.00176 456s supply_(Intercept) 55.1360 -0.34396 -0.21065 456s supply_price -0.5835 0.00527 0.00058 456s supply_farmPrice 0.0310 -0.00166 0.00139 456s supply_trend 0.0394 -0.00211 0.00176 456s supply_(Intercept) supply_price supply_farmPrice 456s demand_(Intercept) 55.136 -0.58348 0.03102 456s demand_price -0.344 0.00527 -0.00166 456s demand_income -0.211 0.00058 0.00139 456s supply_(Intercept) 108.147 -0.86360 -0.19987 456s supply_price -0.864 0.00803 0.00056 456s supply_farmPrice -0.200 0.00056 0.00134 456s supply_trend -0.211 0.00058 0.00139 456s supply_trend 456s demand_(Intercept) 0.03940 456s demand_price -0.00211 456s demand_income 0.00176 456s supply_(Intercept) -0.21065 456s supply_price 0.00058 456s supply_farmPrice 0.00139 456s supply_trend 0.00176 456s > print( round( vcov( fit3sls[[ 5 ]]$e3e, modified.regMat = TRUE ), digits = 5 ) ) 456s C1 C2 C3 C4 C5 C6 456s C1 54.6190 -0.58283 0.03940 55.136 -0.58348 0.03102 456s C2 -0.5828 0.00789 -0.00211 -0.344 0.00527 -0.00166 456s C3 0.0394 -0.00211 0.00176 -0.211 0.00058 0.00139 456s C4 55.1360 -0.34396 -0.21065 108.147 -0.86360 -0.19987 456s C5 -0.5835 0.00527 0.00058 -0.864 0.00803 0.00056 456s C6 0.0310 -0.00166 0.00139 -0.200 0.00056 0.00134 456s > print( round( vcov( fit3sls[[ 1 ]]$e3e$eq[[ 2 ]] ), digits = 5 ) ) 456s (Intercept) price farmPrice trend 456s (Intercept) 108.147 -0.86360 -0.19987 -0.21065 456s price -0.864 0.00803 0.00056 0.00058 456s farmPrice -0.200 0.00056 0.00134 0.00139 456s trend -0.211 0.00058 0.00139 0.00176 456s > 456s > print( round( vcov( fit3sls[[ 1 ]]$e4 ), digits = 5 ) ) 456s demand_(Intercept) demand_price demand_income 456s demand_(Intercept) 62.7805 -0.68439 0.06014 456s demand_price -0.6844 0.00794 -0.00113 456s demand_income 0.0601 -0.00113 0.00054 456s supply_(Intercept) 63.2287 -0.69892 0.07078 456s supply_price -0.6844 0.00794 -0.00113 456s supply_farmPrice 0.0499 -0.00087 0.00038 456s supply_trend 0.0601 -0.00113 0.00054 456s supply_(Intercept) supply_price supply_farmPrice 456s demand_(Intercept) 63.2287 -0.68439 0.04986 456s demand_price -0.6989 0.00794 -0.00087 456s demand_income 0.0708 -0.00113 0.00038 456s supply_(Intercept) 66.9073 -0.69892 0.02657 456s supply_price -0.6989 0.00794 -0.00087 456s supply_farmPrice 0.0266 -0.00087 0.00058 456s supply_trend 0.0708 -0.00113 0.00038 456s supply_trend 456s demand_(Intercept) 0.06014 456s demand_price -0.00113 456s demand_income 0.00054 456s supply_(Intercept) 0.07078 456s supply_price -0.00113 456s supply_farmPrice 0.00038 456s supply_trend 0.00054 456s > print( round( vcov( fit3sls[[ 2 ]]$e4$eq[[ 1 ]] ), digits = 5 ) ) 456s (Intercept) price income 456s (Intercept) 62.7805 -0.68439 0.06014 456s price -0.6844 0.00794 -0.00113 456s income 0.0601 -0.00113 0.00054 456s > 456s > print( round( vcov( fit3sls[[ 3 ]]$e4wSym ), digits = 5 ) ) 456s demand_(Intercept) demand_price demand_income 456s demand_(Intercept) 62.5490 -0.68436 0.06248 456s demand_price -0.6844 0.00795 -0.00113 456s demand_income 0.0625 -0.00113 0.00052 456s supply_(Intercept) 62.9766 -0.69799 0.07241 456s supply_price -0.6844 0.00795 -0.00113 456s supply_farmPrice 0.0522 -0.00088 0.00037 456s supply_trend 0.0625 -0.00113 0.00052 456s supply_(Intercept) supply_price supply_farmPrice 456s demand_(Intercept) 62.9766 -0.68436 0.05220 456s demand_price -0.6980 0.00795 -0.00088 456s demand_income 0.0724 -0.00113 0.00037 456s supply_(Intercept) 66.4588 -0.69799 0.03007 456s supply_price -0.6980 0.00795 -0.00088 456s supply_farmPrice 0.0301 -0.00088 0.00056 456s supply_trend 0.0724 -0.00113 0.00037 456s supply_trend 456s demand_(Intercept) 0.06248 456s demand_price -0.00113 456s demand_income 0.00052 456s supply_(Intercept) 0.07241 456s supply_price -0.00113 456s supply_farmPrice 0.00037 456s supply_trend 0.00052 456s > print( round( vcov( fit3sls[[ 4 ]]$e4wSym$eq[[ 1 ]] ), digits = 5 ) ) 456s (Intercept) price income 456s (Intercept) 62.5490 -0.68436 0.06248 456s price -0.6844 0.00795 -0.00113 456s income 0.0625 -0.00113 0.00052 456s > 456s > print( round( vcov( fit3sls[[ 2 ]]$e5e ), digits = 5 ) ) 456s demand_(Intercept) demand_price demand_income 456s demand_(Intercept) 53.5147 -0.57537 0.04304 456s demand_price -0.5754 0.00659 -0.00085 456s demand_income 0.0430 -0.00085 0.00044 456s supply_(Intercept) 53.9493 -0.58881 0.05259 456s supply_price -0.5754 0.00659 -0.00085 456s supply_farmPrice 0.0345 -0.00063 0.00029 456s supply_trend 0.0430 -0.00085 0.00044 456s supply_(Intercept) supply_price supply_farmPrice 456s demand_(Intercept) 53.9493 -0.57537 0.03449 456s demand_price -0.5888 0.00659 -0.00063 456s demand_income 0.0526 -0.00085 0.00029 456s supply_(Intercept) 57.0063 -0.58881 0.01639 456s supply_price -0.5888 0.00659 -0.00063 456s supply_farmPrice 0.0164 -0.00063 0.00045 456s supply_trend 0.0526 -0.00085 0.00029 456s supply_trend 456s demand_(Intercept) 0.04304 456s demand_price -0.00085 456s demand_income 0.00044 456s supply_(Intercept) 0.05259 456s supply_price -0.00085 456s supply_farmPrice 0.00029 456s supply_trend 0.00044 456s > print( round( vcov( fit3sls[[ 2 ]]$e5e, modified.regMat = TRUE ), digits = 5 ) ) 456s C1 C2 C3 C4 C5 C6 456s C1 53.5147 -0.57537 0.04304 53.9493 -0.57537 0.03449 456s C2 -0.5754 0.00659 -0.00085 -0.5888 0.00659 -0.00063 456s C3 0.0430 -0.00085 0.00044 0.0526 -0.00085 0.00029 456s C4 53.9493 -0.58881 0.05259 57.0063 -0.58881 0.01639 456s C5 -0.5754 0.00659 -0.00085 -0.5888 0.00659 -0.00063 456s C6 0.0345 -0.00063 0.00029 0.0164 -0.00063 0.00045 456s > print( round( vcov( fit3sls[[ 3 ]]$e5e$eq[[ 2 ]] ), digits = 5 ) ) 456s (Intercept) price farmPrice trend 456s (Intercept) 57.0063 -0.58881 0.01639 0.05259 456s price -0.5888 0.00659 -0.00063 -0.00085 456s farmPrice 0.0164 -0.00063 0.00045 0.00029 456s trend 0.0526 -0.00085 0.00029 0.00044 456s > 456s > print( round( vcov( fit3slsi[[ 4 ]]$e1e ), digits = 5 ) ) 456s demand_(Intercept) demand_price demand_income 456s demand_(Intercept) 53.3287 -0.57241 0.04191 456s demand_price -0.5724 0.00791 -0.00225 456s demand_income 0.0419 -0.00225 0.00187 456s supply_(Intercept) 60.8329 -0.34075 -0.27213 456s supply_price -0.6504 0.00578 0.00074 456s supply_farmPrice 0.0394 -0.00211 0.00176 456s supply_trend 0.0595 -0.00319 0.00266 456s supply_(Intercept) supply_price supply_farmPrice 456s demand_(Intercept) 60.833 -0.65044 0.03942 456s demand_price -0.341 0.00578 -0.00211 456s demand_income -0.272 0.00074 0.00176 456s supply_(Intercept) 129.860 -0.99616 -0.26688 456s supply_price -0.996 0.00915 0.00073 456s supply_farmPrice -0.267 0.00073 0.00173 456s supply_trend -0.396 0.00107 0.00255 456s supply_trend 456s demand_(Intercept) 0.05949 456s demand_price -0.00319 456s demand_income 0.00266 456s supply_(Intercept) -0.39621 456s supply_price 0.00107 456s supply_farmPrice 0.00255 456s supply_trend 0.00411 456s > print( round( vcov( fit3slsi[[ 3 ]]$e1e$eq[[ 1 ]] ), digits = 5 ) ) 456s (Intercept) price income 456s (Intercept) 53.3287 -0.57241 0.04191 456s price -0.5724 0.00791 -0.00225 456s income 0.0419 -0.00225 0.00187 456s > 456s > print( round( vcov( fit3slsi[[ 5 ]]$e1we ), digits = 5 ) ) 456s demand_(Intercept) demand_price demand_income 456s demand_(Intercept) 53.3287 -0.57241 0.04191 456s demand_price -0.5724 0.00791 -0.00225 456s demand_income 0.0419 -0.00225 0.00187 456s supply_(Intercept) 60.8329 -0.34075 -0.27213 456s supply_price -0.6504 0.00578 0.00074 456s supply_farmPrice 0.0394 -0.00211 0.00176 456s supply_trend 0.0595 -0.00319 0.00266 456s supply_(Intercept) supply_price supply_farmPrice 456s demand_(Intercept) 60.833 -0.65044 0.03942 456s demand_price -0.341 0.00578 -0.00211 456s demand_income -0.272 0.00074 0.00176 456s supply_(Intercept) 129.860 -0.99616 -0.26688 456s supply_price -0.996 0.00915 0.00073 456s supply_farmPrice -0.267 0.00073 0.00173 456s supply_trend -0.396 0.00107 0.00255 456s supply_trend 456s demand_(Intercept) 0.05949 456s demand_price -0.00319 456s demand_income 0.00266 456s supply_(Intercept) -0.39621 456s supply_price 0.00107 456s supply_farmPrice 0.00255 456s supply_trend 0.00411 456s > print( round( vcov( fit3slsi[[ 1 ]]$e1we$eq[[ 2 ]] ), digits = 5 ) ) 456s (Intercept) price farmPrice trend 456s (Intercept) 129.860 -0.99616 -0.26688 -0.39621 456s price -0.996 0.00915 0.00073 0.00107 456s farmPrice -0.267 0.00073 0.00173 0.00255 456s trend -0.396 0.00107 0.00255 0.00411 456s > 456s > print( round( vcov( fit3slsi[[ 5 ]]$e2e ), digits = 5 ) ) 456s demand_(Intercept) demand_price demand_income 456s demand_(Intercept) 79.5917 -0.81281 0.02003 456s demand_price -0.8128 0.00917 -0.00107 456s demand_income 0.0200 -0.00107 0.00090 456s supply_(Intercept) 90.3437 -0.79178 -0.11134 456s supply_price -0.9184 0.00888 0.00031 456s supply_farmPrice 0.0165 -0.00088 0.00074 456s supply_trend 0.0200 -0.00107 0.00090 456s supply_(Intercept) supply_price supply_farmPrice 456s demand_(Intercept) 90.3437 -0.91836 0.01646 456s demand_price -0.7918 0.00888 -0.00088 456s demand_income -0.1113 0.00031 0.00074 456s supply_(Intercept) 124.3894 -1.13680 -0.09494 456s supply_price -1.1368 0.01108 0.00026 456s supply_farmPrice -0.0949 0.00026 0.00063 456s supply_trend -0.1113 0.00031 0.00074 456s supply_trend 456s demand_(Intercept) 0.02003 456s demand_price -0.00107 456s demand_income 0.00090 456s supply_(Intercept) -0.11134 456s supply_price 0.00031 456s supply_farmPrice 0.00074 456s supply_trend 0.00090 456s > print( round( vcov( fit3slsi[[ 4 ]]$e2e$eq[[ 2 ]] ), digits = 5 ) ) 456s (Intercept) price farmPrice trend 456s (Intercept) 124.3894 -1.13680 -0.09494 -0.11134 456s price -1.1368 0.01108 0.00026 0.00031 456s farmPrice -0.0949 0.00026 0.00063 0.00074 456s trend -0.1113 0.00031 0.00074 0.00090 456s > 456s > print( round( vcov( fit3slsi[[ 1 ]]$e3 ), digits = 5 ) ) 456s demand_(Intercept) demand_price demand_income 456s demand_(Intercept) 92.7431 -0.94355 0.01968 456s demand_price -0.9435 0.01046 -0.00105 456s demand_income 0.0197 -0.00105 0.00088 456s supply_(Intercept) 110.7701 -0.99345 -0.11331 456s supply_price -1.1222 0.01091 0.00031 456s supply_farmPrice 0.0168 -0.00090 0.00075 456s supply_trend 0.0197 -0.00105 0.00088 456s supply_(Intercept) supply_price supply_farmPrice 456s demand_(Intercept) 110.770 -1.12223 0.01680 456s demand_price -0.993 0.01091 -0.00090 456s demand_income -0.113 0.00031 0.00075 456s supply_(Intercept) 155.849 -1.44407 -0.10125 456s supply_price -1.444 0.01413 0.00028 456s supply_farmPrice -0.101 0.00028 0.00067 456s supply_trend -0.113 0.00031 0.00075 456s supply_trend 456s demand_(Intercept) 0.01968 456s demand_price -0.00105 456s demand_income 0.00088 456s supply_(Intercept) -0.11331 456s supply_price 0.00031 456s supply_farmPrice 0.00075 456s supply_trend 0.00088 456s > print( round( vcov( fit3slsi[[ 1 ]]$e3, modified.regMat = TRUE ), digits = 5 ) ) 456s C1 C2 C3 C4 C5 C6 456s C1 92.7431 -0.94355 0.01968 110.770 -1.12223 0.01680 456s C2 -0.9435 0.01046 -0.00105 -0.993 0.01091 -0.00090 456s C3 0.0197 -0.00105 0.00088 -0.113 0.00031 0.00075 456s C4 110.7701 -0.99345 -0.11331 155.849 -1.44407 -0.10125 456s C5 -1.1222 0.01091 0.00031 -1.444 0.01413 0.00028 456s C6 0.0168 -0.00090 0.00075 -0.101 0.00028 0.00067 456s > print( round( vcov( fit3slsi[[ 5 ]]$e3$eq[[ 1 ]] ), digits = 5 ) ) 456s (Intercept) price income 456s (Intercept) 92.7431 -0.94355 0.01968 456s price -0.9435 0.01046 -0.00105 456s income 0.0197 -0.00105 0.00088 456s > 456s > print( round( vcov( fit3slsi[[ 2 ]]$e4e ), digits = 5 ) ) 456s demand_(Intercept) demand_price demand_income 456s demand_(Intercept) 53.5249 -0.60193 0.07023 456s demand_price -0.6019 0.00697 -0.00098 456s demand_income 0.0702 -0.00098 0.00028 456s supply_(Intercept) 53.7695 -0.60749 0.07383 456s supply_price -0.6019 0.00697 -0.00098 456s supply_farmPrice 0.0611 -0.00082 0.00022 456s supply_trend 0.0702 -0.00098 0.00028 456s supply_(Intercept) supply_price supply_farmPrice 456s demand_(Intercept) 53.7695 -0.60193 0.06114 456s demand_price -0.6075 0.00697 -0.00082 456s demand_income 0.0738 -0.00098 0.00022 456s supply_(Intercept) 55.1575 -0.60749 0.05283 456s supply_price -0.6075 0.00697 -0.00082 456s supply_farmPrice 0.0528 -0.00082 0.00028 456s supply_trend 0.0738 -0.00098 0.00022 456s supply_trend 456s demand_(Intercept) 0.07023 456s demand_price -0.00098 456s demand_income 0.00028 456s supply_(Intercept) 0.07383 456s supply_price -0.00098 456s supply_farmPrice 0.00022 456s supply_trend 0.00028 456s > print( round( vcov( fit3slsi[[ 1 ]]$e4e$eq[[ 2 ]] ), digits = 5 ) ) 456s (Intercept) price farmPrice trend 456s (Intercept) 55.1575 -0.60749 0.05283 0.07383 456s price -0.6075 0.00697 -0.00082 -0.00098 456s farmPrice 0.0528 -0.00082 0.00028 0.00022 456s trend 0.0738 -0.00098 0.00022 0.00028 456s > 456s > print( round( vcov( fit3slsi[[ 3 ]]$e5 ), digits = 5 ) ) 456s demand_(Intercept) demand_price demand_income 456s demand_(Intercept) 62.6857 -0.71803 0.09573 456s demand_price -0.7180 0.00846 -0.00132 456s demand_income 0.0957 -0.00132 0.00037 456s supply_(Intercept) 62.7317 -0.72119 0.09909 456s supply_price -0.7180 0.00846 -0.00132 456s supply_farmPrice 0.0863 -0.00115 0.00030 456s supply_trend 0.0957 -0.00132 0.00037 456s supply_(Intercept) supply_price supply_farmPrice 456s demand_(Intercept) 62.7317 -0.71803 0.08635 456s demand_price -0.7212 0.00846 -0.00115 456s demand_income 0.0991 -0.00132 0.00030 456s supply_(Intercept) 64.1668 -0.72119 0.07539 456s supply_price -0.7212 0.00846 -0.00115 456s supply_farmPrice 0.0754 -0.00115 0.00038 456s supply_trend 0.0991 -0.00132 0.00030 456s supply_trend 456s demand_(Intercept) 0.09573 456s demand_price -0.00132 456s demand_income 0.00037 456s supply_(Intercept) 0.09909 456s supply_price -0.00132 456s supply_farmPrice 0.00030 456s supply_trend 0.00037 456s > print( round( vcov( fit3slsi[[ 3 ]]$e5, modified.regMat = TRUE ), digits = 5 ) ) 456s C1 C2 C3 C4 C5 C6 456s C1 62.6857 -0.71803 0.09573 62.7317 -0.71803 0.08635 456s C2 -0.7180 0.00846 -0.00132 -0.7212 0.00846 -0.00115 456s C3 0.0957 -0.00132 0.00037 0.0991 -0.00132 0.00030 456s C4 62.7317 -0.72119 0.09909 64.1668 -0.72119 0.07539 456s C5 -0.7180 0.00846 -0.00132 -0.7212 0.00846 -0.00115 456s C6 0.0863 -0.00115 0.00030 0.0754 -0.00115 0.00038 456s > print( round( vcov( fit3slsi[[ 2 ]]$e5$eq[[ 1 ]] ), digits = 5 ) ) 456s (Intercept) price income 456s (Intercept) 62.6857 -0.71803 0.09573 456s price -0.7180 0.00846 -0.00132 456s income 0.0957 -0.00132 0.00037 456s > 456s > print( round( vcov( fit3slsi[[ 5 ]]$e5w ), digits = 5 ) ) 456s demand_(Intercept) demand_price demand_income 456s demand_(Intercept) 107.334 -1.39936 0.34281 456s demand_price -1.399 0.01904 -0.00518 456s demand_income 0.343 -0.00518 0.00179 456s supply_(Intercept) 95.422 -1.22389 0.29205 456s supply_price -1.399 0.01904 -0.00518 456s supply_farmPrice 0.439 -0.00648 0.00214 456s supply_trend 0.343 -0.00518 0.00179 456s supply_(Intercept) supply_price supply_farmPrice 456s demand_(Intercept) 95.422 -1.39936 0.43918 456s demand_price -1.224 0.01904 -0.00648 456s demand_income 0.292 -0.00518 0.00214 456s supply_(Intercept) 92.381 -1.22389 0.30881 456s supply_price -1.224 0.01904 -0.00648 456s supply_farmPrice 0.309 -0.00648 0.00328 456s supply_trend 0.292 -0.00518 0.00214 456s supply_trend 456s demand_(Intercept) 0.34281 456s demand_price -0.00518 456s demand_income 0.00179 456s supply_(Intercept) 0.29205 456s supply_price -0.00518 456s supply_farmPrice 0.00214 456s supply_trend 0.00179 456s > print( round( vcov( fit3slsi[[ 5 ]]$e5w, modified.regMat = TRUE ), digits = 5 ) ) 456s C1 C2 C3 C4 C5 C6 456s C1 107.334 -1.39936 0.34281 95.422 -1.39936 0.43918 456s C2 -1.399 0.01904 -0.00518 -1.224 0.01904 -0.00648 456s C3 0.343 -0.00518 0.00179 0.292 -0.00518 0.00214 456s C4 95.422 -1.22389 0.29205 92.381 -1.22389 0.30881 456s C5 -1.399 0.01904 -0.00518 -1.224 0.01904 -0.00648 456s C6 0.439 -0.00648 0.00214 0.309 -0.00648 0.00328 456s > print( round( vcov( fit3slsi[[ 4 ]]$e5w$eq[[ 1 ]] ), digits = 5 ) ) 456s (Intercept) price income 456s (Intercept) 62.6858 -0.71803 0.09573 456s price -0.7180 0.00846 -0.00132 456s income 0.0957 -0.00132 0.00037 456s > 456s > print( round( vcov( fit3slsd[[ 5 ]]$e1c ), digits = 5 ) ) 456s demand_(Intercept) demand_price demand_income 456s demand_(Intercept) 124.179 -1.51767 0.28519 456s demand_price -1.518 0.02098 -0.00595 456s demand_income 0.285 -0.00595 0.00318 456s supply_(Intercept) 45.831 -0.16114 -0.30261 456s supply_price -0.564 0.00477 0.00089 456s supply_farmPrice 0.157 -0.00365 0.00213 456s supply_trend -0.416 0.00351 0.00066 456s supply_(Intercept) supply_price supply_farmPrice 456s demand_(Intercept) 45.831 -0.56422 0.15696 456s demand_price -0.161 0.00477 -0.00365 456s demand_income -0.303 0.00089 0.00213 456s supply_(Intercept) 132.389 -0.93831 -0.33973 456s supply_price -0.938 0.00791 0.00115 456s supply_farmPrice -0.340 0.00115 0.00221 456s supply_trend -0.515 0.00349 0.00108 456s supply_trend 456s demand_(Intercept) -0.41585 456s demand_price 0.00351 456s demand_income 0.00066 456s supply_(Intercept) -0.51541 456s supply_price 0.00349 456s supply_farmPrice 0.00108 456s supply_trend 0.00585 456s > print( round( vcov( fit3slsd[[ 2 ]]$e1c$eq[[ 2 ]] ), digits = 5 ) ) 456s (Intercept) price farmPrice trend 456s (Intercept) 136.580 -1.06234 -0.24479 -0.60682 456s price -0.994 0.00955 -0.00011 0.00471 456s farmPrice -0.334 0.00098 0.00234 0.00096 456s trend -0.438 0.00119 0.00284 0.00415 456s > 456s > print( round( vcov( fit3slsd[[ 1 ]]$e2 ), digits = 5 ) ) 456s demand_(Intercept) demand_price demand_income 456s demand_(Intercept) 40.2908 -0.42351 0.02315 456s demand_price -0.4235 0.00660 -0.00242 456s demand_income 0.0232 -0.00242 0.00225 456s supply_(Intercept) 23.1539 0.17811 -0.41781 456s supply_price -0.2648 0.00059 0.00211 456s supply_farmPrice 0.0342 -0.00220 0.00190 456s supply_trend 0.0232 -0.00242 0.00225 456s supply_(Intercept) supply_price supply_farmPrice 456s demand_(Intercept) 23.154 -0.26482 0.03423 456s demand_price 0.178 0.00059 -0.00220 456s demand_income -0.418 0.00211 0.00190 456s supply_(Intercept) 125.488 -0.81757 -0.40378 456s supply_price -0.818 0.00616 0.00186 456s supply_farmPrice -0.404 0.00186 0.00205 456s supply_trend -0.418 0.00211 0.00190 456s supply_trend 456s demand_(Intercept) 0.02315 456s demand_price -0.00242 456s demand_income 0.00225 456s supply_(Intercept) -0.41781 456s supply_price 0.00211 456s supply_farmPrice 0.00190 456s supply_trend 0.00225 456s > print( round( vcov( fit3slsd[[ 3 ]]$e2$eq[[ 1 ]] ), digits = 5 ) ) 456s (Intercept) price income 456s (Intercept) 99.763 -1.2027 0.21239 456s price -1.203 0.0168 -0.00490 456s income 0.212 -0.0049 0.00285 456s > 456s > print( round( vcov( fit3slsd[[ 5 ]]$e2we ), digits = 5 ) ) 456s demand_(Intercept) demand_price demand_income 456s demand_(Intercept) 34.9080 -0.36232 0.01530 456s demand_price -0.3623 0.00556 -0.00199 456s demand_income 0.0153 -0.00199 0.00188 456s supply_(Intercept) 20.3293 0.13409 -0.34409 456s supply_price -0.2272 0.00057 0.00174 456s supply_farmPrice 0.0249 -0.00176 0.00155 456s supply_trend 0.0153 -0.00199 0.00188 456s supply_(Intercept) supply_price supply_farmPrice 456s demand_(Intercept) 20.329 -0.22716 0.02494 456s demand_price 0.134 0.00057 -0.00176 456s demand_income -0.344 0.00174 0.00155 456s supply_(Intercept) 102.201 -0.66897 -0.32522 456s supply_price -0.669 0.00505 0.00150 456s supply_farmPrice -0.325 0.00150 0.00164 456s supply_trend -0.344 0.00174 0.00155 456s supply_trend 456s demand_(Intercept) 0.01530 456s demand_price -0.00199 456s demand_income 0.00188 456s supply_(Intercept) -0.34409 456s supply_price 0.00174 456s supply_farmPrice 0.00155 456s supply_trend 0.00188 456s > print( round( vcov( fit3slsd[[ 3 ]]$e2we$eq[[ 1 ]] ), digits = 5 ) ) 456s (Intercept) price income 456s (Intercept) 83.743 -1.0065 0.17519 456s price -1.006 0.0141 -0.00410 456s income 0.175 -0.0041 0.00241 456s > 456s > print( round( vcov( fit3slsd[[ 2 ]]$e3 ), digits = 5 ) ) 456s demand_(Intercept) demand_price demand_income 456s demand_(Intercept) 155.228 -2.21373 0.68055 456s demand_price -1.929 0.03005 -0.01103 456s demand_income 0.389 -0.00812 0.00434 456s supply_(Intercept) 120.424 -1.33693 0.13854 456s supply_price -1.546 0.02054 -0.00522 456s supply_farmPrice 0.314 -0.00655 0.00350 456s supply_trend 0.389 -0.00812 0.00434 456s supply_(Intercept) supply_price supply_farmPrice 456s demand_(Intercept) -25.183 -0.42614 0.63002 456s demand_price 0.811 0.00271 -0.01000 456s demand_income -0.572 0.00159 0.00380 456s supply_(Intercept) 84.582 -0.95409 0.10043 456s supply_price -0.279 0.00796 -0.00478 456s supply_farmPrice -0.521 0.00147 0.00350 456s supply_trend -0.572 0.00159 0.00380 456s supply_trend 456s demand_(Intercept) 0.68055 456s demand_price -0.01103 456s demand_income 0.00434 456s supply_(Intercept) 0.13854 456s supply_price -0.00522 456s supply_farmPrice 0.00350 456s supply_trend 0.00434 456s > print( round( vcov( fit3slsd[[ 2 ]]$e3, modified.regMat = TRUE ), digits = 5 ) ) 456s C1 C2 C3 C4 C5 C6 456s C1 155.228 -2.21373 0.68055 -25.183 -0.42614 0.63002 456s C2 -1.929 0.03005 -0.01103 0.811 0.00271 -0.01000 456s C3 0.389 -0.00812 0.00434 -0.572 0.00159 0.00380 456s C4 120.424 -1.33693 0.13854 84.582 -0.95409 0.10043 456s C5 -1.546 0.02054 -0.00522 -0.279 0.00796 -0.00478 456s C6 0.314 -0.00655 0.00350 -0.521 0.00147 0.00350 456s > print( round( vcov( fit3slsd[[ 4 ]]$e3$eq[[ 2 ]] ), digits = 5 ) ) 456s (Intercept) price farmPrice trend 456s (Intercept) 149.704 -1.13641 -0.33425 -0.32676 456s price -1.136 0.01036 0.00094 0.00091 456s farmPrice -0.334 0.00094 0.00225 0.00216 456s trend -0.327 0.00091 0.00216 0.00259 456s > 456s > print( round( vcov( fit3slsd[[ 3 ]]$e4 ), digits = 5 ) ) 456s demand_(Intercept) demand_price demand_income 456s demand_(Intercept) 105.016 -1.17085 0.12591 456s demand_price -1.171 0.01356 -0.00191 456s demand_income 0.126 -0.00191 0.00066 456s supply_(Intercept) 106.127 -1.19320 0.13778 456s supply_price -1.171 0.01356 -0.00191 456s supply_farmPrice 0.102 -0.00148 0.00047 456s supply_trend 0.126 -0.00191 0.00066 456s supply_(Intercept) supply_price supply_farmPrice 456s demand_(Intercept) 106.1266 -1.17085 0.10227 456s demand_price -1.1932 0.01356 -0.00148 456s demand_income 0.1378 -0.00191 0.00047 456s supply_(Intercept) 110.0305 -1.19320 0.08453 456s supply_price -1.1932 0.01356 -0.00148 456s supply_farmPrice 0.0845 -0.00148 0.00061 456s supply_trend 0.1378 -0.00191 0.00047 456s supply_trend 456s demand_(Intercept) 0.12591 456s demand_price -0.00191 456s demand_income 0.00066 456s supply_(Intercept) 0.13778 456s supply_price -0.00191 456s supply_farmPrice 0.00047 456s supply_trend 0.00066 456s > print( round( vcov( fit3slsd[[ 5 ]]$e4$eq[[ 1 ]] ), digits = 5 ) ) 456s (Intercept) price income 456s (Intercept) 28.9118 -0.25481 -0.03319 456s price -0.2548 0.00254 0.00001 456s income -0.0332 0.00001 0.00033 456s > 456s > print( round( vcov( fit3slsd[[ 4 ]]$e5e ), digits = 5 ) ) 456s demand_(Intercept) demand_price demand_income 456s demand_(Intercept) 57.3878 -0.60414 0.03280 456s demand_price -0.6041 0.00675 -0.00073 456s demand_income 0.0328 -0.00073 0.00041 456s supply_(Intercept) 57.4828 -0.61352 0.04167 456s supply_price -0.6041 0.00675 -0.00073 456s supply_farmPrice 0.0288 -0.00056 0.00028 456s supply_trend 0.0328 -0.00073 0.00041 456s supply_(Intercept) supply_price supply_farmPrice 456s demand_(Intercept) 57.4828 -0.60414 0.02879 456s demand_price -0.6135 0.00675 -0.00056 456s demand_income 0.0417 -0.00073 0.00028 456s supply_(Intercept) 59.8263 -0.61352 0.01389 456s supply_price -0.6135 0.00675 -0.00056 456s supply_farmPrice 0.0139 -0.00056 0.00041 456s supply_trend 0.0417 -0.00073 0.00028 456s supply_trend 456s demand_(Intercept) 0.03280 456s demand_price -0.00073 456s demand_income 0.00041 456s supply_(Intercept) 0.04167 456s supply_price -0.00073 456s supply_farmPrice 0.00028 456s supply_trend 0.00041 456s > print( round( vcov( fit3slsd[[ 4 ]]$e5e, modified.regMat = TRUE ), digits = 5 ) ) 456s C1 C2 C3 C4 C5 C6 456s C1 57.3878 -0.60414 0.03280 57.4828 -0.60414 0.02879 456s C2 -0.6041 0.00675 -0.00073 -0.6135 0.00675 -0.00056 456s C3 0.0328 -0.00073 0.00041 0.0417 -0.00073 0.00028 456s C4 57.4828 -0.61352 0.04167 59.8263 -0.61352 0.01389 456s C5 -0.6041 0.00675 -0.00073 -0.6135 0.00675 -0.00056 456s C6 0.0288 -0.00056 0.00028 0.0139 -0.00056 0.00041 456s > print( round( vcov( fit3slsd[[ 1 ]]$e5e$eq[[ 2 ]] ), digits = 5 ) ) 456s (Intercept) price farmPrice trend 456s (Intercept) 24.9502 -0.21066 -0.03490 -0.02530 456s price -0.2107 0.00210 0.00000 0.00004 456s farmPrice -0.0349 0.00000 0.00034 0.00018 456s trend -0.0253 0.00004 0.00018 0.00028 456s > 456s > 456s > ## *********** confidence intervals of coefficients ************* 456s > print( confint( fit3sls[[ 1 ]]$e1c, useDfSys = TRUE ) ) 456s 2.5 % 97.5 % 456s demand_(Intercept) 78.518 110.748 456s demand_price -0.440 -0.047 456s demand_income 0.218 0.409 456s supply_(Intercept) 28.106 76.468 456s supply_price 0.025 0.431 456s supply_farmPrice 0.138 0.316 456s supply_trend 0.221 0.509 456s > print( confint( fit3sls[[ 1 ]]$e1c$eq[[ 1 ]], level = 0.9, useDfSys = TRUE ) ) 456s 5 % 95 % 456s (Intercept) 81.228 108.038 456s price -0.407 -0.080 456s income 0.235 0.393 456s > 456s > print( confint( fit3sls[[ 2 ]]$e2e, level = 0.9, useDfSys = TRUE ) ) 456s 5 % 95 % 456s demand_(Intercept) 79.254 109.293 456s demand_price -0.405 -0.044 456s demand_income 0.213 0.383 456s supply_(Intercept) 34.318 76.586 456s supply_price 0.039 0.403 456s supply_farmPrice 0.135 0.284 456s supply_trend 0.213 0.383 456s > print( confint( fit3sls[[ 2 ]]$e2e$eq[[ 2 ]], level = 0.99, useDfSys = TRUE ) ) 456s 0.5 % 99.5 % 456s (Intercept) 27.079 83.826 456s price -0.024 0.465 456s farmPrice 0.110 0.309 456s trend 0.183 0.412 456s > 456s > print( confint( fit3sls[[ 3 ]]$e3, level = 0.99 ) ) 456s 0.5 % 99.5 % 456s demand_(Intercept) 77.934 110.509 456s demand_price -0.417 -0.026 456s demand_income 0.204 0.387 456s supply_(Intercept) 32.432 79.489 456s supply_price 0.016 0.423 456s supply_farmPrice 0.124 0.288 456s supply_trend 0.204 0.387 456s > print( confint( fit3sls[[ 3 ]]$e3$eq[[ 1 ]], level = 0.5 ) ) 456s 25 % 75 % 456s (Intercept) 88.757 99.686 456s price -0.287 -0.156 456s income 0.265 0.326 456s > 456s > print( confint( fit3sls[[ 5 ]]$e3we, level = 0.99 ) ) 456s 0.5 % 99.5 % 456s demand_(Intercept) 79.280 109.202 456s demand_price -0.402 -0.043 456s demand_income 0.212 0.381 456s supply_(Intercept) 34.570 76.815 456s supply_price 0.038 0.402 456s supply_farmPrice 0.134 0.282 456s supply_trend 0.212 0.381 456s > print( confint( fit3sls[[ 5 ]]$e3we$eq[[ 1 ]], level = 0.5 ) ) 456s 25 % 75 % 456s (Intercept) 89.222 99.260 456s price -0.283 -0.162 456s income 0.268 0.325 456s > 456s > print( confint( fit3sls[[ 4 ]]$e4e, level = 0.5, useDfSys = TRUE ) ) 456s 25 % 75 % 456s demand_(Intercept) 79.319 109.021 456s demand_price -0.414 -0.085 456s demand_income 0.282 0.367 456s supply_(Intercept) 34.758 65.413 456s supply_price 0.086 0.415 456s supply_farmPrice 0.188 0.274 456s supply_trend 0.282 0.367 456s > print( confint( fit3sls[[ 4 ]]$e4e$eq[[ 2 ]], level = 0.25, useDfSys = TRUE ) ) 456s 37.5 % 62.5 % 456s (Intercept) 47.661 52.510 456s price 0.224 0.277 456s farmPrice 0.224 0.238 456s trend 0.318 0.331 456s > 456s > print( confint( fit3sls[[ 5 ]]$e5, level = 0.25 ) ) 456s 37.5 % 62.5 % 456s demand_(Intercept) 75.213 107.384 456s demand_price -0.630 -0.268 456s demand_income 0.512 0.606 456s supply_(Intercept) -18.445 14.766 456s supply_price 0.370 0.732 456s supply_farmPrice 0.384 0.481 456s supply_trend 0.512 0.606 456s > print( confint( fit3sls[[ 5 ]]$e5$eq[[ 1 ]], level = 0.975 ) ) 456s 1.3 % 98.8 % 456s (Intercept) 72.742 109.855 456s price -0.658 -0.241 456s income 0.505 0.614 456s > 456s > print( confint( fit3slsi[[ 2 ]]$e3e, level = 0.975, useDfSys = TRUE ) ) 456s 1.3 % 98.8 % 456s demand_(Intercept) 73.905 110.166 456s demand_price -0.299 0.090 456s demand_income 0.137 0.259 456s supply_(Intercept) 45.617 90.949 456s supply_price -0.029 0.399 456s supply_farmPrice 0.073 0.175 456s supply_trend 0.137 0.259 456s > print( confint( fit3slsi[[ 2 ]]$e3e$eq[[ 1 ]], level = 0.999, useDfSys = TRUE ) ) 456s 0.1 % 100 % 456s (Intercept) 59.912 124.159 456s price -0.449 0.241 456s income 0.090 0.306 456s > 456s > print( confint( fit3slsi[[ 1 ]]$e5w, level = 0.975, useDfSys = TRUE ) ) 456s 1.3 % 98.8 % 456s demand_(Intercept) 74.084 106.230 456s demand_price -0.387 -0.014 456s demand_income 0.277 0.355 456s supply_(Intercept) 30.219 62.743 456s supply_price 0.113 0.486 456s supply_farmPrice 0.179 0.259 456s supply_trend 0.277 0.355 456s > print( confint( fit3slsi[[ 1 ]]$e5w$eq[[ 1 ]], level = 0.999, useDfSys = TRUE ) ) 456s 0.1 % 100 % 456s (Intercept) 61.724 118.590 456s price -0.531 0.130 456s income 0.247 0.385 456s > 456s > print( confint( fit3slsd[[ 3 ]]$e4, level = 0.999 ) ) 456s 0.1 % 100 % 456s demand_(Intercept) 72.590 114.198 456s demand_price -0.457 0.016 456s demand_income 0.251 0.356 456s supply_(Intercept) 27.716 70.305 456s supply_price 0.043 0.516 456s supply_farmPrice 0.165 0.265 456s supply_trend 0.251 0.356 456s > print( confint( fit3slsd[[ 3 ]]$e4$eq[[ 2 ]] ) ) 456s 2.5 % 97.5 % 456s (Intercept) 27.716 70.305 456s price 0.043 0.516 456s farmPrice 0.165 0.265 456s trend 0.251 0.356 456s > 456s > print( confint( fit3slsd[[ 2 ]]$e4w, level = 0.999 ) ) 456s 0.1 % 100 % 456s demand_(Intercept) 120.616 166.320 456s demand_price -1.063 -0.578 456s demand_income 0.371 0.439 456s supply_(Intercept) 77.414 123.333 456s supply_price -0.563 -0.078 456s supply_farmPrice 0.253 0.333 456s supply_trend 0.371 0.439 456s > print( confint( fit3slsd[[ 2 ]]$e4w$eq[[ 2 ]] ) ) 456s 2.5 % 97.5 % 456s (Intercept) 77.414 123.333 456s price -0.563 -0.078 456s farmPrice 0.253 0.333 456s trend 0.371 0.439 456s > 456s > 456s > ## *********** fitted values ************* 456s > print( fitted( fit3sls[[ 2 ]]$e1c ) ) 456s demand supply 456s 1 97.6 97.8 456s 2 99.9 99.3 456s 3 99.8 99.5 456s 4 100.0 99.9 456s 5 102.1 101.7 456s 6 102.0 101.8 456s 7 102.4 101.9 456s 8 103.0 104.1 456s 9 101.5 102.3 456s 10 100.3 99.6 456s 11 95.5 95.9 456s 12 94.7 94.8 456s 13 96.1 96.6 456s 14 99.0 98.4 456s 15 103.8 102.7 456s 16 103.7 104.4 456s 17 103.8 103.3 456s 18 102.1 103.6 456s 19 103.6 103.6 456s 20 106.9 106.6 456s > print( fitted( fit3sls[[ 2 ]]$e1c$eq[[ 1 ]] ) ) 456s 1 2 3 4 5 6 7 8 9 10 11 12 13 456s 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 456s 14 15 16 17 18 19 20 456s 99.0 103.8 103.7 103.8 102.1 103.6 106.9 456s > 456s > print( fitted( fit3sls[[ 1 ]]$e1wc ) ) 456s demand supply 456s 1 97.6 97.8 456s 2 99.9 99.3 456s 3 99.8 99.5 456s 4 100.0 99.9 456s 5 102.1 101.7 456s 6 102.0 101.8 456s 7 102.4 101.9 456s 8 103.0 104.1 456s 9 101.5 102.3 456s 10 100.3 99.6 456s 11 95.5 95.9 456s 12 94.7 94.8 456s 13 96.1 96.6 456s 14 99.0 98.4 456s 15 103.8 102.7 456s 16 103.7 104.4 456s 17 103.8 103.3 456s 18 102.1 103.6 456s 19 103.6 103.6 456s 20 106.9 106.6 456s > print( fitted( fit3sls[[ 1 ]]$e1wc$eq[[ 1 ]] ) ) 456s 1 2 3 4 5 6 7 8 9 10 11 12 13 456s 97.6 99.9 99.8 100.0 102.1 102.0 102.4 103.0 101.5 100.3 95.5 94.7 96.1 456s 14 15 16 17 18 19 20 456s 99.0 103.8 103.7 103.8 102.1 103.6 106.9 456s > 456s > print( fitted( fit3sls[[ 3 ]]$e2e ) ) 456s demand supply 456s 1 97.8 98.4 456s 2 100.0 99.8 456s 3 99.9 99.9 456s 4 100.1 100.3 456s 5 102.0 101.8 456s 6 101.9 101.9 456s 7 102.4 102.0 456s 8 102.9 104.0 456s 9 101.4 102.2 456s 10 100.3 99.6 456s 11 95.8 96.2 456s 12 95.0 95.2 456s 13 96.4 96.9 456s 14 99.1 98.5 456s 15 103.7 102.3 456s 16 103.5 103.9 456s 17 103.6 102.8 456s 18 102.0 103.2 456s 19 103.5 103.2 456s 20 106.7 105.9 456s > print( fitted( fit3sls[[ 3 ]]$e2e$eq[[ 2 ]] ) ) 456s 1 2 3 4 5 6 7 8 9 10 11 12 13 456s 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 456s 14 15 16 17 18 19 20 456s 98.5 102.3 103.9 102.8 103.2 103.2 105.9 456s > 456s > print( fitted( fit3sls[[ 4 ]]$e3 ) ) 456s demand supply 456s 1 97.8 98.4 456s 2 100.0 99.8 456s 3 99.9 99.9 456s 4 100.1 100.3 456s 5 102.0 101.7 456s 6 101.9 101.8 456s 7 102.3 101.9 456s 8 102.9 103.9 456s 9 101.4 102.2 456s 10 100.3 99.6 456s 11 95.8 96.3 456s 12 95.1 95.3 456s 13 96.4 97.0 456s 14 99.1 98.5 456s 15 103.6 102.3 456s 16 103.5 103.9 456s 17 103.6 102.7 456s 18 102.0 103.1 456s 19 103.5 103.2 456s 20 106.7 105.9 456s > print( fitted( fit3sls[[ 4 ]]$e3$eq[[ 1 ]] ) ) 456s 1 2 3 4 5 6 7 8 9 10 11 12 13 456s 97.8 100.0 99.9 100.1 102.0 101.9 102.3 102.9 101.4 100.3 95.8 95.1 96.4 456s 14 15 16 17 18 19 20 456s 99.1 103.6 103.5 103.6 102.0 103.5 106.7 456s > 456s > print( fitted( fit3sls[[ 5 ]]$e4e ) ) 456s demand supply 456s 1 95.0 96.3 456s 2 98.9 99.4 456s 3 98.8 99.5 456s 4 99.1 100.2 456s 5 103.2 102.9 456s 6 102.9 103.1 456s 7 103.6 103.4 456s 8 104.5 107.7 456s 9 102.1 103.4 456s 10 100.2 97.8 456s 11 91.5 90.8 456s 12 89.8 88.9 456s 13 92.2 92.6 456s 14 97.6 95.6 456s 15 106.4 103.4 456s 16 105.9 106.9 456s 17 106.7 103.6 456s 18 102.9 105.4 456s 19 105.6 105.5 456s 20 111.3 111.7 456s > print( fitted( fit3sls[[ 5 ]]$e4e$eq[[ 2 ]] ) ) 456s 1 2 3 4 5 6 7 8 9 10 11 12 13 456s 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 456s 14 15 16 17 18 19 20 456s 95.6 103.4 106.9 103.6 105.4 105.5 111.7 456s > 456s > print( fitted( fit3sls[[ 1 ]]$e5 ) ) 456s demand supply 456s 1 97.5 98.2 456s 2 99.9 99.8 456s 3 99.8 99.9 456s 4 100.0 100.3 456s 5 102.1 101.9 456s 6 102.0 102.0 456s 7 102.5 102.1 456s 8 103.1 104.3 456s 9 101.5 102.3 456s 10 100.3 99.4 456s 11 95.3 95.7 456s 12 94.5 94.6 456s 13 96.0 96.5 456s 14 99.0 98.2 456s 15 103.9 102.4 456s 16 103.7 104.2 456s 17 103.9 102.7 456s 18 102.1 103.4 456s 19 103.7 103.4 456s 20 107.2 106.6 456s > print( fitted( fit3sls[[ 1 ]]$e5$eq[[ 1 ]] ) ) 456s 1 2 3 4 5 6 7 8 9 10 11 12 13 456s 97.5 99.9 99.8 100.0 102.1 102.0 102.5 103.1 101.5 100.3 95.3 94.5 96.0 456s 14 15 16 17 18 19 20 456s 99.0 103.9 103.7 103.9 102.1 103.7 107.2 456s > 456s > print( fitted( fit3slsi[[ 3 ]]$e3e ) ) 456s demand supply 456s 1 98.9 99.2 456s 2 100.5 100.3 456s 3 100.4 100.4 456s 4 100.6 100.6 456s 5 101.6 101.2 456s 6 101.5 101.3 456s 7 101.9 101.5 456s 8 102.4 102.9 456s 9 101.1 101.4 456s 10 100.1 99.7 456s 11 97.2 97.8 456s 12 96.9 97.5 456s 13 98.0 98.7 456s 14 99.7 99.5 456s 15 102.5 101.6 456s 16 102.6 102.7 456s 17 102.1 101.4 456s 18 101.8 102.6 456s 19 102.9 102.7 456s 20 105.3 104.8 456s > print( fitted( fit3slsi[[ 3 ]]$e3e$eq[[ 1 ]] ) ) 456s 1 2 3 4 5 6 7 8 9 10 11 12 13 456s 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 456s 14 15 16 17 18 19 20 456s 99.7 102.5 102.6 102.1 101.8 102.9 105.3 456s > 456s > print( fitted( fit3slsd[[ 4 ]]$e4 ) ) 456s demand supply 456s 1 97.6 98.3 456s 2 99.7 99.7 456s 3 99.7 99.8 456s 4 99.8 100.1 456s 5 102.2 101.9 456s 6 102.0 102.0 456s 7 102.4 102.0 456s 8 102.8 104.1 456s 9 101.6 102.4 456s 10 100.7 99.8 456s 11 95.8 96.1 456s 12 94.8 94.8 456s 13 96.0 96.5 456s 14 99.1 98.3 456s 15 104.1 102.5 456s 16 103.7 104.2 456s 17 104.4 103.2 456s 18 101.9 103.2 456s 19 103.4 103.2 456s 20 106.3 105.9 456s > print( fitted( fit3slsd[[ 4 ]]$e4$eq[[ 2 ]] ) ) 456s 1 2 3 4 5 6 7 8 9 10 11 12 13 456s 98.3 99.7 99.8 100.1 101.9 102.0 102.0 104.1 102.4 99.8 96.1 94.8 96.5 456s 14 15 16 17 18 19 20 456s 98.3 102.5 104.2 103.2 103.2 103.2 105.9 456s > 456s > print( fitted( fit3slsd[[ 2 ]]$e3w ) ) 456s demand supply 456s 1 96.1 97.0 456s 2 97.6 97.2 456s 3 97.8 97.8 456s 4 97.7 97.7 456s 5 103.5 103.5 456s 6 102.7 102.8 456s 7 102.6 102.1 456s 8 101.8 103.4 456s 9 103.3 104.8 456s 10 103.9 103.4 456s 11 96.2 97.0 456s 12 92.5 92.4 456s 13 92.7 93.0 456s 14 98.8 97.6 456s 15 107.3 105.6 456s 16 105.6 106.4 456s 17 111.1 110.7 456s 18 100.9 102.3 456s 19 102.3 101.4 456s 20 103.7 101.8 456s > print( fitted( fit3slsd[[ 2 ]]$e3w$eq[[ 2 ]] ) ) 456s 1 2 3 4 5 6 7 8 9 10 11 12 13 456s 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 456s 14 15 16 17 18 19 20 456s 97.6 105.6 106.4 110.7 102.3 101.4 101.8 456s > 456s > 456s > ## *********** predicted values ************* 456s > predictData <- Kmenta 456s > predictData$consump <- NULL 456s > predictData$price <- Kmenta$price * 0.9 456s > predictData$income <- Kmenta$income * 1.1 456s > 456s > print( predict( fit3sls[[ 2 ]]$e1c, se.fit = TRUE, interval = "prediction", 456s + useDfSys = TRUE ) ) 456s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 456s 1 97.6 0.661 93.4 101.9 97.8 0.826 456s 2 99.9 0.600 95.7 104.1 99.3 0.825 456s 3 99.8 0.564 95.6 104.0 99.5 0.755 456s 4 100.0 0.605 95.8 104.2 99.9 0.783 456s 5 102.1 0.516 98.0 106.2 101.7 0.669 456s 6 102.0 0.474 97.9 106.1 101.8 0.620 456s 7 102.4 0.493 98.3 106.5 101.9 0.608 456s 8 103.0 0.615 98.8 107.2 104.1 0.889 456s 9 101.5 0.544 97.3 105.6 102.3 0.753 456s 10 100.3 0.822 96.0 104.7 99.6 1.022 456s 11 95.5 0.963 91.1 100.0 95.9 1.172 456s 12 94.7 1.006 90.2 99.2 94.8 1.289 456s 13 96.1 0.915 91.7 100.5 96.6 1.114 456s 14 99.0 0.518 94.9 103.2 98.4 0.751 456s 15 103.8 0.793 99.5 108.2 102.7 0.863 456s 16 103.7 0.636 99.5 107.9 104.4 0.902 456s 17 103.8 1.348 99.0 108.7 103.3 1.636 456s 18 102.1 0.549 97.9 106.2 103.6 0.807 456s 19 103.6 0.695 99.4 107.9 103.6 0.898 456s 20 106.9 1.306 102.1 111.7 106.6 1.613 456s supply.lwr supply.upr 456s 1 92.3 103 456s 2 93.8 105 456s 3 94.0 105 456s 4 94.3 105 456s 5 96.2 107 456s 6 96.3 107 456s 7 96.5 107 456s 8 98.5 110 456s 9 96.8 108 456s 10 93.9 105 456s 11 90.1 102 456s 12 88.9 101 456s 13 90.9 102 456s 14 92.9 104 456s 15 97.1 108 456s 16 98.8 110 456s 17 97.1 110 456s 18 98.1 109 456s 19 98.0 109 456s 20 100.4 113 456s > print( predict( fit3sls[[ 2 ]]$e1c$eq[[ 1 ]], se.fit = TRUE, interval = "prediction", 456s + useDfSys = TRUE ) ) 456s fit se.fit lwr upr 456s 1 97.6 0.661 93.4 101.9 456s 2 99.9 0.600 95.7 104.1 456s 3 99.8 0.564 95.6 104.0 456s 4 100.0 0.605 95.8 104.2 456s 5 102.1 0.516 98.0 106.2 456s 6 102.0 0.474 97.9 106.1 456s 7 102.4 0.493 98.3 106.5 456s 8 103.0 0.615 98.8 107.2 456s 9 101.5 0.544 97.3 105.6 456s 10 100.3 0.822 96.0 104.7 456s 11 95.5 0.963 91.1 100.0 456s 12 94.7 1.006 90.2 99.2 456s 13 96.1 0.915 91.7 100.5 456s 14 99.0 0.518 94.9 103.2 456s 15 103.8 0.793 99.5 108.2 456s 16 103.7 0.636 99.5 107.9 456s 17 103.8 1.348 99.0 108.7 456s 18 102.1 0.549 97.9 106.2 456s 19 103.6 0.695 99.4 107.9 456s 20 106.9 1.306 102.1 111.7 456s > 456s > print( predict( fit3sls[[ 3 ]]$e2e, se.pred = TRUE, interval = "confidence", 456s + level = 0.999, newdata = predictData, useDfSys = TRUE ) ) 456s demand.pred demand.se.pred demand.lwr demand.upr supply.pred supply.se.pred 456s 1 102.7 2.20 99.3 106 96.2 2.78 456s 2 105.2 2.21 101.8 109 97.5 2.68 456s 3 105.1 2.22 101.6 109 97.7 2.69 456s 4 105.4 2.21 101.9 109 98.0 2.67 456s 5 107.2 2.47 101.9 112 99.6 2.80 456s 6 107.1 2.43 102.1 112 99.7 2.76 456s 7 107.7 2.42 102.8 113 99.7 2.72 456s 8 108.5 2.38 103.7 113 101.6 2.66 456s 9 106.5 2.48 101.2 112 100.1 2.85 456s 10 105.0 2.59 99.1 111 97.6 3.04 456s 11 100.1 2.36 95.5 105 94.2 3.07 456s 12 99.5 2.19 96.3 103 93.0 3.00 456s 13 101.2 2.11 98.7 104 94.6 2.85 456s 14 104.0 2.29 100.0 108 96.3 2.84 456s 15 108.9 2.68 102.4 115 100.2 2.90 456s 16 108.8 2.57 103.0 115 101.8 2.81 456s 17 108.4 2.99 100.4 116 100.8 3.28 456s 18 107.5 2.34 103.1 112 100.9 2.66 456s 19 109.2 2.42 104.3 114 100.8 2.64 456s 20 113.0 2.63 106.8 119 103.4 2.62 456s supply.lwr supply.upr 456s 1 92.2 100.2 456s 2 94.6 100.5 456s 3 94.6 100.7 456s 4 95.1 100.8 456s 5 95.4 103.8 456s 6 95.8 103.5 456s 7 96.3 103.1 456s 8 98.9 104.4 456s 9 95.4 104.7 456s 10 91.6 103.6 456s 11 88.0 100.4 456s 12 87.3 98.7 456s 13 90.1 99.2 456s 14 91.8 100.8 456s 15 95.3 105.2 456s 16 97.5 106.0 456s 17 93.4 108.3 456s 18 98.1 103.6 456s 19 98.4 103.2 456s 20 101.2 105.6 456s > print( predict( fit3sls[[ 3 ]]$e2e$eq[[ 2 ]], se.pred = TRUE, interval = "confidence", 456s + level = 0.999, newdata = predictData, useDfSys = TRUE ) ) 456s fit se.pred lwr upr 456s 1 96.2 2.78 92.2 100.2 456s 2 97.5 2.68 94.6 100.5 456s 3 97.7 2.69 94.6 100.7 456s 4 98.0 2.67 95.1 100.8 456s 5 99.6 2.80 95.4 103.8 456s 6 99.7 2.76 95.8 103.5 456s 7 99.7 2.72 96.3 103.1 456s 8 101.6 2.66 98.9 104.4 456s 9 100.1 2.85 95.4 104.7 456s 10 97.6 3.04 91.6 103.6 456s 11 94.2 3.07 88.0 100.4 456s 12 93.0 3.00 87.3 98.7 456s 13 94.6 2.85 90.1 99.2 456s 14 96.3 2.84 91.8 100.8 456s 15 100.2 2.90 95.3 105.2 456s 16 101.8 2.81 97.5 106.0 456s 17 100.8 3.28 93.4 108.3 456s 18 100.9 2.66 98.1 103.6 456s 19 100.8 2.64 98.4 103.2 456s 20 103.4 2.62 101.2 105.6 456s > 456s > print( predict( fit3sls[[ 5 ]]$e2w, se.pred = TRUE, interval = "confidence", 456s + level = 0.999, newdata = predictData, useDfSys = TRUE ) ) 456s demand.pred demand.se.pred demand.lwr demand.upr supply.pred supply.se.pred 456s 1 102.6 2.24 99.0 106 96.3 2.84 456s 2 105.1 2.24 101.5 109 97.6 2.72 456s 3 105.0 2.25 101.3 109 97.7 2.73 456s 4 105.3 2.24 101.6 109 98.0 2.71 456s 5 107.1 2.54 101.5 113 99.6 2.88 456s 6 107.0 2.49 101.7 112 99.6 2.82 456s 7 107.6 2.48 102.3 113 99.7 2.77 456s 8 108.3 2.44 103.3 113 101.6 2.70 456s 9 106.4 2.55 100.7 112 100.0 2.94 456s 10 104.9 2.67 98.5 111 97.6 3.17 456s 11 100.1 2.43 95.1 105 94.3 3.20 456s 12 99.5 2.23 96.0 103 93.2 3.11 456s 13 101.2 2.14 98.5 104 94.8 2.92 456s 14 104.0 2.33 99.6 108 96.4 2.92 456s 15 108.7 2.77 101.8 116 100.2 2.99 456s 16 108.7 2.65 102.5 115 101.7 2.88 456s 17 108.3 3.12 99.7 117 100.8 3.45 456s 18 107.4 2.39 102.7 112 100.9 2.70 456s 19 109.1 2.48 103.8 114 100.8 2.67 456s 20 112.9 2.71 106.3 119 103.4 2.65 456s supply.lwr supply.upr 456s 1 91.8 100.7 456s 2 94.3 100.8 456s 3 94.3 101.1 456s 4 94.8 101.1 456s 5 94.9 104.3 456s 6 95.4 103.9 456s 7 95.9 103.5 456s 8 98.5 104.7 456s 9 94.9 105.2 456s 10 90.9 104.4 456s 11 87.4 101.2 456s 12 86.9 99.5 456s 13 89.7 99.8 456s 14 91.4 101.4 456s 15 94.7 105.8 456s 16 97.0 106.5 456s 17 92.5 109.1 456s 18 97.8 103.9 456s 19 98.1 103.5 456s 20 101.0 105.9 456s > print( predict( fit3sls[[ 5 ]]$e2w$eq[[ 2 ]], se.pred = TRUE, interval = "confidence", 456s + level = 0.999, newdata = predictData, useDfSys = TRUE ) ) 456s fit se.pred lwr upr 456s 1 96.3 2.84 91.8 100.7 456s 2 97.6 2.72 94.3 100.8 456s 3 97.7 2.73 94.3 101.1 456s 4 98.0 2.71 94.8 101.1 456s 5 99.6 2.88 94.9 104.3 456s 6 99.6 2.82 95.4 103.9 456s 7 99.7 2.77 95.9 103.5 456s 8 101.6 2.70 98.5 104.7 456s 9 100.0 2.94 94.9 105.2 456s 10 97.6 3.17 90.9 104.4 456s 11 94.3 3.20 87.4 101.2 456s 12 93.2 3.11 86.9 99.5 456s 13 94.8 2.92 89.7 99.8 456s 14 96.4 2.92 91.4 101.4 456s 15 100.2 2.99 94.7 105.8 456s 16 101.7 2.88 97.0 106.5 456s 17 100.8 3.45 92.5 109.1 456s 18 100.9 2.70 97.8 103.9 456s 19 100.8 2.67 98.1 103.5 456s 20 103.4 2.65 101.0 105.9 456s > 456s > print( predict( fit3sls[[ 4 ]]$e3, se.pred = TRUE, interval = "prediction", 456s + level = 0.975 ) ) 456s demand.pred demand.se.pred demand.lwr demand.upr supply.pred supply.se.pred 456s 1 97.8 2.10 92.9 103 98.4 2.64 456s 2 100.0 2.09 95.1 105 99.8 2.66 456s 3 99.9 2.08 95.0 105 99.9 2.65 456s 4 100.1 2.09 95.2 105 100.3 2.66 456s 5 102.0 2.06 97.2 107 101.7 2.65 456s 6 101.9 2.05 97.1 107 101.8 2.63 456s 7 102.3 2.06 97.5 107 101.9 2.63 456s 8 102.9 2.09 98.0 108 103.9 2.71 456s 9 101.4 2.07 96.6 106 102.2 2.67 456s 10 100.3 2.16 95.2 105 99.6 2.76 456s 11 95.8 2.21 90.6 101 96.3 2.80 456s 12 95.1 2.22 89.9 100 95.3 2.84 456s 13 96.4 2.19 91.3 102 97.0 2.78 456s 14 99.1 2.06 94.3 104 98.5 2.67 456s 15 103.6 2.15 98.6 109 102.3 2.68 456s 16 103.5 2.09 98.6 108 103.9 2.68 456s 17 103.6 2.41 97.9 109 102.7 3.00 456s 18 102.0 2.07 97.2 107 103.1 2.66 456s 19 103.5 2.12 98.6 108 103.2 2.69 456s 20 106.7 2.39 101.1 112 105.9 2.98 456s supply.lwr supply.upr 456s 1 92.2 105 456s 2 93.6 106 456s 3 93.7 106 456s 4 94.0 107 456s 5 95.5 108 456s 6 95.7 108 456s 7 95.8 108 456s 8 97.6 110 456s 9 95.9 108 456s 10 93.2 106 456s 11 89.7 103 456s 12 88.6 102 456s 13 90.5 103 456s 14 92.3 105 456s 15 96.0 109 456s 16 97.6 110 456s 17 95.7 110 456s 18 96.9 109 456s 19 96.9 109 456s 20 98.9 113 456s > print( predict( fit3sls[[ 4 ]]$e3$eq[[ 1 ]], se.pred = TRUE, interval = "prediction", 456s + level = 0.975 ) ) 456s fit se.pred lwr upr 456s 1 97.8 2.10 92.9 103 456s 2 100.0 2.09 95.1 105 456s 3 99.9 2.08 95.0 105 456s 4 100.1 2.09 95.2 105 456s 5 102.0 2.06 97.2 107 456s 6 101.9 2.05 97.1 107 456s 7 102.3 2.06 97.5 107 456s 8 102.9 2.09 98.0 108 456s 9 101.4 2.07 96.6 106 456s 10 100.3 2.16 95.2 105 456s 11 95.8 2.21 90.6 101 456s 12 95.1 2.22 89.9 100 456s 13 96.4 2.19 91.3 102 456s 14 99.1 2.06 94.3 104 456s 15 103.6 2.15 98.6 109 456s 16 103.5 2.09 98.6 108 456s 17 103.6 2.41 97.9 109 456s 18 102.0 2.07 97.2 107 456s 19 103.5 2.12 98.6 108 456s 20 106.7 2.39 101.1 112 456s > 456s > print( predict( fit3sls[[ 5 ]]$e4e, se.fit = TRUE, interval = "confidence", 456s + level = 0.25, useDfSys = TRUE ) ) 456s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 456s 1 95.0 0.465 94.8 95.1 96.3 0.536 456s 2 98.9 0.532 98.7 99.1 99.4 0.663 456s 3 98.8 0.497 98.6 99.0 99.5 0.613 456s 4 99.1 0.541 99.0 99.3 100.2 0.662 456s 5 103.2 0.450 103.0 103.3 102.9 0.593 456s 6 102.9 0.417 102.7 103.0 103.1 0.543 456s 7 103.6 0.420 103.5 103.8 103.4 0.524 456s 8 104.5 0.525 104.3 104.6 107.7 0.634 456s 9 102.1 0.494 101.9 102.2 103.4 0.660 456s 10 100.2 0.760 100.0 100.4 97.8 0.895 456s 11 91.5 0.660 91.3 91.7 90.8 0.736 456s 12 89.8 0.563 89.6 89.9 88.9 0.742 456s 13 92.2 0.597 92.0 92.4 92.6 0.806 456s 14 97.6 0.426 97.4 97.7 95.6 0.568 456s 15 106.4 0.619 106.2 106.6 103.4 0.721 456s 16 105.9 0.476 105.8 106.1 106.9 0.608 456s 17 106.7 1.159 106.3 107.1 103.6 1.414 456s 18 102.9 0.494 102.7 103.0 105.4 0.582 456s 19 105.6 0.574 105.4 105.8 105.5 0.676 456s 20 111.3 1.030 110.9 111.6 111.7 1.146 456s supply.lwr supply.upr 456s 1 96.1 96.4 456s 2 99.1 99.6 456s 3 99.3 99.7 456s 4 100.0 100.4 456s 5 102.7 103.1 456s 6 102.9 103.3 456s 7 103.2 103.5 456s 8 107.5 107.9 456s 9 103.2 103.7 456s 10 97.5 98.0 456s 11 90.5 91.0 456s 12 88.7 89.1 456s 13 92.4 92.9 456s 14 95.4 95.8 456s 15 103.1 103.6 456s 16 106.7 107.0 456s 17 103.1 104.0 456s 18 105.3 105.6 456s 19 105.3 105.8 456s 20 111.4 112.1 456s > print( predict( fit3sls[[ 5 ]]$e4e$eq[[ 2 ]], se.fit = TRUE, interval = "confidence", 456s + level = 0.25, useDfSys = TRUE ) ) 456s fit se.fit lwr upr 456s 1 96.3 0.536 96.1 96.4 456s 2 99.4 0.663 99.1 99.6 456s 3 99.5 0.613 99.3 99.7 456s 4 100.2 0.662 100.0 100.4 456s 5 102.9 0.593 102.7 103.1 456s 6 103.1 0.543 102.9 103.3 456s 7 103.4 0.524 103.2 103.5 456s 8 107.7 0.634 107.5 107.9 456s 9 103.4 0.660 103.2 103.7 456s 10 97.8 0.895 97.5 98.0 456s 11 90.8 0.736 90.5 91.0 456s 12 88.9 0.742 88.7 89.1 456s 13 92.6 0.806 92.4 92.9 456s 14 95.6 0.568 95.4 95.8 456s 15 103.4 0.721 103.1 103.6 456s 16 106.9 0.608 106.7 107.0 456s 17 103.6 1.414 103.1 104.0 456s 18 105.4 0.582 105.3 105.6 456s 19 105.5 0.676 105.3 105.8 456s 20 111.7 1.146 111.4 112.1 456s > 456s > print( predict( fit3sls[[ 1 ]]$e5, se.fit = TRUE, se.pred = TRUE, 456s + interval = "prediction", level = 0.5, newdata = predictData ) ) 456s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 456s 1 102.8 0.957 2.19 101.3 104 95.7 456s 2 105.6 0.829 2.13 104.1 107 97.1 456s 3 105.5 0.869 2.15 104.0 107 97.3 456s 4 105.8 0.823 2.13 104.3 107 97.6 456s 5 107.8 1.308 2.36 106.2 109 99.4 456s 6 107.7 1.213 2.31 106.1 109 99.4 456s 7 108.3 1.145 2.28 106.7 110 99.5 456s 8 109.1 0.984 2.20 107.6 111 101.7 456s 9 107.0 1.372 2.40 105.3 109 99.8 456s 10 105.4 1.659 2.57 103.6 107 97.1 456s 11 100.1 1.365 2.39 98.4 102 93.3 456s 12 99.4 0.969 2.19 97.9 101 92.1 456s 13 101.3 0.752 2.11 99.8 103 93.9 456s 14 104.3 1.112 2.26 102.8 106 95.7 456s 15 109.6 1.580 2.52 107.9 111 100.0 456s 16 109.6 1.368 2.40 107.9 111 101.7 456s 17 109.1 2.136 2.90 107.1 111 100.5 456s 18 108.1 0.966 2.19 106.6 110 100.8 456s 19 109.9 0.980 2.20 108.4 111 100.7 456s 20 114.1 0.997 2.21 112.6 116 103.7 456s supply.se.fit supply.se.pred supply.lwr supply.upr 456s 1 0.959 2.74 93.8 97.5 456s 2 0.742 2.67 95.3 99.0 456s 3 0.791 2.69 95.4 99.1 456s 4 0.735 2.67 95.8 99.4 456s 5 1.280 2.87 97.4 101.3 456s 6 1.159 2.82 97.5 101.3 456s 7 1.031 2.77 97.6 101.4 456s 8 0.867 2.71 99.8 103.5 456s 9 1.416 2.93 97.8 101.8 456s 10 1.724 3.09 95.0 99.2 456s 11 1.457 2.95 91.3 95.4 456s 12 1.102 2.79 90.2 94.0 456s 13 0.894 2.72 92.1 95.8 456s 14 1.092 2.79 93.8 97.6 456s 15 1.516 2.98 98.0 102.0 456s 16 1.321 2.89 99.7 103.7 456s 17 2.297 3.44 98.2 102.9 456s 18 0.847 2.70 98.9 102.6 456s 19 0.743 2.67 98.9 102.6 456s 20 0.589 2.63 101.9 105.5 456s > print( predict( fit3sls[[ 1 ]]$e5$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 456s + interval = "prediction", level = 0.5, newdata = predictData ) ) 456s fit se.fit se.pred lwr upr 456s 1 102.8 0.957 2.19 101.3 104 456s 2 105.6 0.829 2.13 104.1 107 456s 3 105.5 0.869 2.15 104.0 107 456s 4 105.8 0.823 2.13 104.3 107 456s 5 107.8 1.308 2.36 106.2 109 456s 6 107.7 1.213 2.31 106.1 109 456s 7 108.3 1.145 2.28 106.7 110 456s 8 109.1 0.984 2.20 107.6 111 456s 9 107.0 1.372 2.40 105.3 109 456s 10 105.4 1.659 2.57 103.6 107 456s 11 100.1 1.365 2.39 98.4 102 456s 12 99.4 0.969 2.19 97.9 101 456s 13 101.3 0.752 2.11 99.8 103 456s 14 104.3 1.112 2.26 102.8 106 456s 15 109.6 1.580 2.52 107.9 111 456s 16 109.6 1.368 2.40 107.9 111 456s 17 109.1 2.136 2.90 107.1 111 456s 18 108.1 0.966 2.19 106.6 110 456s 19 109.9 0.980 2.20 108.4 111 456s 20 114.1 0.997 2.21 112.6 116 456s > 456s > print( predict( fit3slsi[[ 3 ]]$e3e, se.fit = TRUE, se.pred = TRUE, 456s + interval = "confidence", level = 0.99, useDfSys = TRUE ) ) 456s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 456s 1 98.9 0.590 2.49 97.3 100.5 99.2 456s 2 100.5 0.643 2.50 98.7 102.2 100.3 456s 3 100.4 0.602 2.49 98.7 102.0 100.4 456s 4 100.6 0.653 2.50 98.8 102.3 100.6 456s 5 101.6 0.548 2.48 100.1 103.1 101.2 456s 6 101.5 0.512 2.47 100.1 102.9 101.3 456s 7 101.9 0.524 2.47 100.5 103.3 101.5 456s 8 102.4 0.667 2.51 100.6 104.3 102.9 456s 9 101.1 0.599 2.49 99.5 102.7 101.4 456s 10 100.1 0.928 2.59 97.6 102.6 99.7 456s 11 97.2 0.898 2.58 94.7 99.6 97.8 456s 12 96.9 0.767 2.54 94.8 99.0 97.5 456s 13 98.0 0.745 2.53 96.0 100.1 98.7 456s 14 99.7 0.536 2.48 98.2 101.1 99.5 456s 15 102.5 0.745 2.53 100.5 104.5 101.6 456s 16 102.6 0.589 2.49 101.0 104.2 102.7 456s 17 102.1 1.376 2.78 98.3 105.8 101.4 456s 18 101.8 0.615 2.49 100.2 103.5 102.6 456s 19 102.9 0.738 2.53 100.9 104.9 102.7 456s 20 105.3 1.357 2.77 101.6 109.0 104.8 456s supply.se.fit supply.se.pred supply.lwr supply.upr 456s 1 0.638 3.01 97.5 101.0 456s 2 0.752 3.03 98.3 102.4 456s 3 0.700 3.02 98.4 102.3 456s 4 0.761 3.03 98.6 102.7 456s 5 0.649 3.01 99.4 103.0 456s 6 0.610 3.00 99.7 103.0 456s 7 0.613 3.00 99.8 103.2 456s 8 0.829 3.05 100.7 105.2 456s 9 0.731 3.03 99.4 103.4 456s 10 1.092 3.13 96.7 102.6 456s 11 1.037 3.12 94.9 100.6 456s 12 0.902 3.07 95.0 99.9 456s 13 0.855 3.06 96.4 101.1 456s 14 0.670 3.01 97.6 101.3 456s 15 0.812 3.05 99.4 103.8 456s 16 0.707 3.02 100.8 104.7 456s 17 1.584 3.34 97.1 105.7 456s 18 0.740 3.03 100.6 104.6 456s 19 0.852 3.06 100.4 105.1 456s 20 1.564 3.33 100.6 109.1 456s > print( predict( fit3slsi[[ 3 ]]$e3e$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 456s + interval = "confidence", level = 0.99, useDfSys = TRUE ) ) 456s fit se.fit se.pred lwr upr 456s 1 98.9 0.590 2.49 97.3 100.5 456s 2 100.5 0.643 2.50 98.7 102.2 456s 3 100.4 0.602 2.49 98.7 102.0 456s 4 100.6 0.653 2.50 98.8 102.3 456s 5 101.6 0.548 2.48 100.1 103.1 456s 6 101.5 0.512 2.47 100.1 102.9 456s 7 101.9 0.524 2.47 100.5 103.3 456s 8 102.4 0.667 2.51 100.6 104.3 456s 9 101.1 0.599 2.49 99.5 102.7 456s 10 100.1 0.928 2.59 97.6 102.6 456s 11 97.2 0.898 2.58 94.7 99.6 456s 12 96.9 0.767 2.54 94.8 99.0 456s 13 98.0 0.745 2.53 96.0 100.1 456s 14 99.7 0.536 2.48 98.2 101.1 456s 15 102.5 0.745 2.53 100.5 104.5 456s 16 102.6 0.589 2.49 101.0 104.2 456s 17 102.1 1.376 2.78 98.3 105.8 456s 18 101.8 0.615 2.49 100.2 103.5 456s 19 102.9 0.738 2.53 100.9 104.9 456s 20 105.3 1.357 2.77 101.6 109.0 456s > 456s > print( predict( fit3slsi[[ 1 ]]$e5w, se.fit = TRUE, se.pred = TRUE, 456s + interval = "prediction", level = 0.5, newdata = predictData ) ) 456s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 456s 1 102.4 0.986 2.25 100.9 104 95.3 456s 2 105.2 0.851 2.20 103.7 107 96.9 456s 3 105.1 0.896 2.22 103.6 107 97.0 456s 4 105.4 0.844 2.20 103.9 107 97.4 456s 5 107.1 1.351 2.44 105.5 109 98.7 456s 6 107.1 1.250 2.38 105.5 109 98.9 456s 7 107.8 1.173 2.34 106.2 109 99.0 456s 8 108.7 0.983 2.25 107.2 110 101.3 456s 9 106.3 1.420 2.48 104.6 108 99.1 456s 10 104.6 1.713 2.65 102.8 106 96.2 456s 11 99.4 1.372 2.45 97.8 101 92.8 456s 12 99.0 0.965 2.25 97.5 101 91.9 456s 13 101.0 0.768 2.17 99.5 102 93.8 456s 14 103.8 1.149 2.33 102.2 105 95.3 456s 15 108.8 1.631 2.60 107.0 111 99.2 456s 16 108.9 1.405 2.47 107.2 111 101.1 456s 17 108.0 2.211 3.00 106.0 110 99.4 456s 18 107.7 0.978 2.25 106.1 109 100.4 456s 19 109.5 0.964 2.25 108.0 111 100.5 456s 20 113.8 0.818 2.19 112.3 115 103.7 456s supply.se.fit supply.se.pred supply.lwr supply.upr 456s 1 0.987 2.85 93.3 97.2 456s 2 0.772 2.79 95.0 98.8 456s 3 0.824 2.80 95.1 98.9 456s 4 0.767 2.79 95.5 99.3 456s 5 1.341 3.00 96.7 100.8 456s 6 1.215 2.94 96.9 100.9 456s 7 1.084 2.89 97.1 101.0 456s 8 0.907 2.83 99.4 103.2 456s 9 1.483 3.06 97.0 101.2 456s 10 1.795 3.22 94.1 98.4 456s 11 1.455 3.05 90.7 94.8 456s 12 1.002 2.86 90.0 93.9 456s 13 0.805 2.80 91.9 95.7 456s 14 1.087 2.89 93.4 97.3 456s 15 1.585 3.11 97.1 101.4 456s 16 1.383 3.01 99.0 103.1 456s 17 2.399 3.60 96.9 101.8 456s 18 0.883 2.82 98.5 102.4 456s 19 0.770 2.79 98.6 102.4 456s 20 0.616 2.75 101.9 105.6 456s > print( predict( fit3slsi[[ 1 ]]$e5w$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 456s + interval = "prediction", level = 0.5, newdata = predictData ) ) 456s fit se.fit se.pred lwr upr 456s 1 102.4 0.986 2.25 100.9 104 456s 2 105.2 0.851 2.20 103.7 107 456s 3 105.1 0.896 2.22 103.6 107 456s 4 105.4 0.844 2.20 103.9 107 456s 5 107.1 1.351 2.44 105.5 109 456s 6 107.1 1.250 2.38 105.5 109 456s 7 107.8 1.173 2.34 106.2 109 456s 8 108.7 0.983 2.25 107.2 110 456s 9 106.3 1.420 2.48 104.6 108 456s 10 104.6 1.713 2.65 102.8 106 456s 11 99.4 1.372 2.45 97.8 101 456s 12 99.0 0.965 2.25 97.5 101 456s 13 101.0 0.768 2.17 99.5 102 456s 14 103.8 1.149 2.33 102.2 105 456s 15 108.8 1.631 2.60 107.0 111 456s 16 108.9 1.405 2.47 107.2 111 456s 17 108.0 2.211 3.00 106.0 110 456s 18 107.7 0.978 2.25 106.1 109 456s 19 109.5 0.964 2.25 108.0 111 456s 20 113.8 0.818 2.19 112.3 115 456s > 456s > print( predict( fit3slsd[[ 4 ]]$e4, se.fit = TRUE, interval = "prediction", 456s + level = 0.9, newdata = predictData ) ) 456s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 456s 1 103 0.972 99.6 107 96.1 0.980 456s 2 106 0.820 102.2 109 97.5 0.751 456s 3 106 0.863 102.1 109 97.6 0.801 456s 4 106 0.813 102.4 109 97.9 0.741 456s 5 108 1.305 104.2 112 99.8 1.287 456s 6 108 1.206 104.1 112 99.8 1.164 456s 7 109 1.132 104.7 112 99.9 1.035 456s 8 109 0.960 105.5 113 101.8 0.857 456s 9 107 1.377 103.4 111 100.3 1.422 456s 10 106 1.688 101.8 110 97.8 1.748 456s 11 101 1.415 96.8 105 94.1 1.490 456s 12 100 1.004 96.3 104 92.7 1.115 456s 13 102 0.766 98.1 105 94.4 0.891 456s 14 105 1.124 101.0 109 96.2 1.107 456s 15 110 1.575 105.8 114 100.5 1.523 456s 16 110 1.355 105.9 114 102.1 1.318 456s 17 110 2.158 105.0 115 101.3 2.305 456s 18 108 0.947 104.5 112 101.0 0.843 456s 19 110 0.953 106.3 114 100.9 0.735 456s 20 114 0.974 109.9 117 103.5 0.583 456s supply.lwr supply.upr 456s 1 91.6 100.7 456s 2 93.0 101.9 456s 3 93.2 102.1 456s 4 93.5 102.3 456s 5 95.0 104.6 456s 6 95.2 104.5 456s 7 95.3 104.5 456s 8 97.3 106.3 456s 9 95.4 105.2 456s 10 92.6 103.0 456s 11 89.2 99.0 456s 12 88.1 97.4 456s 13 89.8 98.9 456s 14 91.6 100.9 456s 15 95.5 105.5 456s 16 97.3 106.9 456s 17 95.6 107.1 456s 18 96.5 105.5 456s 19 96.5 105.3 456s 20 99.2 107.9 456s > print( predict( fit3slsd[[ 4 ]]$e4$eq[[ 2 ]], se.fit = TRUE, interval = "prediction", 456s + level = 0.9, newdata = predictData ) ) 456s fit se.fit lwr upr 456s 1 96.1 0.980 91.6 100.7 456s 2 97.5 0.751 93.0 101.9 456s 3 97.6 0.801 93.2 102.1 456s 4 97.9 0.741 93.5 102.3 456s 5 99.8 1.287 95.0 104.6 456s 6 99.8 1.164 95.2 104.5 456s 7 99.9 1.035 95.3 104.5 456s 8 101.8 0.857 97.3 106.3 456s 9 100.3 1.422 95.4 105.2 456s 10 97.8 1.748 92.6 103.0 456s 11 94.1 1.490 89.2 99.0 456s 12 92.7 1.115 88.1 97.4 456s 13 94.4 0.891 89.8 98.9 456s 14 96.2 1.107 91.6 100.9 456s 15 100.5 1.523 95.5 105.5 456s 16 102.1 1.318 97.3 106.9 456s 17 101.3 2.305 95.6 107.1 456s 18 101.0 0.843 96.5 105.5 456s 19 100.9 0.735 96.5 105.3 456s 20 103.5 0.583 99.2 107.9 456s > 456s > print( predict( fit3slsd[[ 2 ]]$e3w, se.fit = TRUE, se.pred = TRUE, 456s + interval = "confidence", level = 0.99, useDfSys = TRUE ) ) 456s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 456s 1 96.1 0.832 3.23 93.8 98.3 97.0 456s 2 97.6 0.849 3.24 95.3 99.9 97.2 456s 3 97.8 0.771 3.22 95.7 99.9 97.8 456s 4 97.7 0.857 3.24 95.3 100.0 97.7 456s 5 103.5 0.648 3.19 101.8 105.3 103.5 456s 6 102.7 0.519 3.16 101.3 104.1 102.8 456s 7 102.6 0.499 3.16 101.3 104.0 102.1 456s 8 101.8 0.627 3.18 100.1 103.5 103.4 456s 9 103.3 0.714 3.20 101.3 105.2 104.8 456s 10 103.9 1.172 3.33 100.7 107.1 103.4 456s 11 96.2 0.920 3.25 93.7 98.7 97.0 456s 12 92.5 1.261 3.37 89.1 95.9 92.4 456s 13 92.7 1.364 3.41 89.0 96.5 93.0 456s 14 98.8 0.528 3.17 97.3 100.2 97.6 456s 15 107.3 1.245 3.36 103.9 110.7 105.6 456s 16 105.6 0.856 3.24 103.2 107.9 106.4 456s 17 111.1 2.310 3.88 104.8 117.4 110.7 456s 18 100.9 0.592 3.18 99.2 102.5 102.3 456s 19 102.3 0.700 3.20 100.4 104.2 101.4 456s 20 103.7 1.350 3.40 100.0 107.4 101.8 456s supply.se.fit supply.se.pred supply.lwr supply.upr 456s 1 0.791 3.73 94.8 99.2 456s 2 0.857 3.74 94.8 99.5 456s 3 0.776 3.72 95.7 99.9 456s 4 0.825 3.73 95.5 100.0 456s 5 0.817 3.73 101.2 105.7 456s 6 0.713 3.71 100.9 104.8 456s 7 0.644 3.70 100.4 103.9 456s 8 0.858 3.74 101.0 105.7 456s 9 0.962 3.77 102.2 107.4 456s 10 1.040 3.79 100.6 106.3 456s 11 1.083 3.80 94.1 100.0 456s 12 1.633 3.99 88.0 96.9 456s 13 1.568 3.96 88.7 97.3 456s 14 0.871 3.74 95.2 100.0 456s 15 1.029 3.78 102.8 108.4 456s 16 1.056 3.79 103.6 109.3 456s 17 2.050 4.18 105.1 116.2 456s 18 0.687 3.71 100.4 104.2 456s 19 0.773 3.72 99.3 103.5 456s 20 1.300 3.87 98.3 105.4 456s > print( predict( fit3slsd[[ 2 ]]$e3w$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 456s + interval = "confidence", level = 0.99, useDfSys = TRUE ) ) 456s fit se.fit se.pred lwr upr 456s 1 96.1 0.832 3.23 93.8 98.3 456s 2 97.6 0.849 3.24 95.3 99.9 456s 3 97.8 0.771 3.22 95.7 99.9 456s 4 97.7 0.857 3.24 95.3 100.0 456s 5 103.5 0.648 3.19 101.8 105.3 456s 6 102.7 0.519 3.16 101.3 104.1 456s 7 102.6 0.499 3.16 101.3 104.0 456s 8 101.8 0.627 3.18 100.1 103.5 456s 9 103.3 0.714 3.20 101.3 105.2 456s 10 103.9 1.172 3.33 100.7 107.1 456s 11 96.2 0.920 3.25 93.7 98.7 456s 12 92.5 1.261 3.37 89.1 95.9 456s 13 92.7 1.364 3.41 89.0 96.5 456s 14 98.8 0.528 3.17 97.3 100.2 456s 15 107.3 1.245 3.36 103.9 110.7 456s 16 105.6 0.856 3.24 103.2 107.9 456s 17 111.1 2.310 3.88 104.8 117.4 456s 18 100.9 0.592 3.18 99.2 102.5 456s 19 102.3 0.700 3.20 100.4 104.2 456s 20 103.7 1.350 3.40 100.0 107.4 456s > 456s > 456s > # predict just one observation 456s > smallData <- data.frame( price = 130, income = 150, farmPrice = 120, 456s + trend = 25 ) 456s > 456s > print( predict( fit3sls[[ 3 ]]$e1c, newdata = smallData ) ) 456s demand.pred supply.pred 456s 1 110 118 456s > print( predict( fit3sls[[ 3 ]]$e1c$eq[[ 1 ]], newdata = smallData ) ) 456s fit 456s 1 110 456s > 456s > print( predict( fit3sls[[ 4 ]]$e2e, se.fit = TRUE, level = 0.9, 456s + newdata = smallData ) ) 456s demand.pred demand.se.fit supply.pred supply.se.fit 456s 1 110 2.34 117 3.29 456s > print( predict( fit3sls[[ 5 ]]$e2e$eq[[ 1 ]], se.pred = TRUE, level = 0.99, 456s + newdata = smallData ) ) 456s fit se.pred 456s 1 110 3.07 456s > 456s > print( predict( fit3sls[[ 1]]$e3, interval = "prediction", level = 0.975, 456s + newdata = smallData ) ) 456s demand.pred demand.lwr demand.upr supply.pred supply.lwr supply.upr 456s 1 110 102 117 117 106 127 456s > print( predict( fit3sls[[ 1 ]]$e3$eq[[ 1 ]], interval = "confidence", level = 0.8, 456s + newdata = smallData ) ) 456s fit lwr upr 456s 1 110 106 113 456s > 456s > print( predict( fit3sls[[ 4]]$e3we, interval = "prediction", level = 0.975, 456s + newdata = smallData ) ) 456s demand.pred demand.lwr demand.upr supply.pred supply.lwr supply.upr 456s 1 110 103 117 117 107 126 456s > print( predict( fit3sls[[ 4 ]]$e3we$eq[[ 1 ]], interval = "confidence", level = 0.8, 456s + newdata = smallData ) ) 456s fit lwr upr 456s 1 110 107 113 456s > 456s > print( predict( fit3sls[[ 2 ]]$e4e, se.fit = TRUE, interval = "confidence", 456s + level = 0.999, newdata = smallData ) ) 456s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 456s 1 110 2.14 103 118 119 2.25 456s supply.lwr supply.upr 456s 1 110 127 456s > print( predict( fit3sls[[ 2 ]]$e4e$eq[[ 2 ]], se.pred = TRUE, interval = "prediction", 456s + level = 0.75, newdata = smallData ) ) 456s fit se.pred lwr upr 456s 1 119 3.41 115 123 456s > 456s > print( predict( fit3sls[[ 3 ]]$e5, se.fit = TRUE, interval = "prediction", 456s + newdata = smallData ) ) 456s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 456s 1 111 2.3 104 117 119 2.44 456s supply.lwr supply.upr 456s 1 111 126 456s > print( predict( fit3sls[[ 3 ]]$e5$eq[[ 1 ]], se.pred = TRUE, interval = "confidence", 456s + newdata = smallData ) ) 456s fit se.pred lwr upr 456s 1 111 3.02 106 115 456s > 456s > print( predict( fit3slsi[[ 4 ]]$e3e, se.fit = TRUE, se.pred = TRUE, 456s + interval = "prediction", level = 0.5, newdata = smallData ) ) 456s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 456s 1 108 2.75 3.66 106 111 112 456s supply.se.fit supply.se.pred supply.lwr supply.upr 456s 1 3.46 4.54 109 115 456s > print( predict( fit3slsd[[ 5 ]]$e4$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 456s + interval = "confidence", level = 0.25, newdata = smallData ) ) 456s fit se.fit se.pred lwr upr 456s 1 111 1.85 3.42 111 112 456s > 456s > print( predict( fit3slsd[[ 2 ]]$e2we, se.fit = TRUE, se.pred = TRUE, 456s + interval = "prediction", level = 0.5, newdata = smallData ) ) 456s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 456s 1 101 2.76 4.1 98.7 104 111 456s supply.se.fit supply.se.pred supply.lwr supply.upr 456s 1 2.79 4.47 108 114 456s > print( predict( fit3slsi[[ 3 ]]$e4we$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 456s + interval = "confidence", level = 0.25, newdata = smallData ) ) 456s fit se.fit se.pred lwr upr 456s 1 111 2.03 2.86 111 112 456s > 456s > 456s > ## ************ correlation of predicted values *************** 456s > print( correlation.systemfit( fit3sls[[ 1 ]]$e1c, 2, 1 ) ) 456s [,1] 456s [1,] 0.880 456s [2,] 0.881 456s [3,] 0.886 456s [4,] 0.901 456s [5,] 0.866 456s [6,] 0.881 456s [7,] 0.892 456s [8,] 0.887 456s [9,] 0.901 456s [10,] 0.924 456s [11,] 0.925 456s [12,] 0.916 456s [13,] 0.910 456s [14,] 0.885 456s [15,] 0.909 456s [16,] 0.921 456s [17,] 0.928 456s [18,] 0.845 456s [19,] 0.890 456s [20,] 0.920 456s > 456s > print( correlation.systemfit( fit3sls[[ 2 ]]$e2e, 1, 2 ) ) 456s [,1] 456s [1,] 0.935 456s [2,] 0.927 456s [3,] 0.923 456s [4,] 0.921 456s [5,] 0.876 456s [6,] 0.884 456s [7,] 0.894 456s [8,] 0.875 456s [9,] 0.890 456s [10,] 0.917 456s [11,] 0.911 456s [12,] 0.898 456s [13,] 0.892 456s [14,] 0.871 456s [15,] 0.905 456s [16,] 0.945 456s [17,] 0.926 456s [18,] 0.908 456s [19,] 0.915 456s [20,] 0.926 456s > 456s > print( correlation.systemfit( fit3sls[[ 5 ]]$e2w, 2, 1 ) ) 456s [,1] 456s [1,] 0.932 456s [2,] 0.928 456s [3,] 0.925 456s [4,] 0.923 456s [5,] 0.882 456s [6,] 0.890 456s [7,] 0.899 456s [8,] 0.880 456s [9,] 0.895 456s [10,] 0.921 456s [11,] 0.914 456s [12,] 0.900 456s [13,] 0.895 456s [14,] 0.876 456s [15,] 0.905 456s [16,] 0.947 456s [17,] 0.928 456s [18,] 0.915 456s [19,] 0.916 456s [20,] 0.928 456s > 456s > print( correlation.systemfit( fit3sls[[ 3 ]]$e3, 2, 1 ) ) 456s [,1] 456s [1,] 0.931 456s [2,] 0.925 456s [3,] 0.922 456s [4,] 0.920 456s [5,] 0.877 456s [6,] 0.884 456s [7,] 0.894 456s [8,] 0.875 456s [9,] 0.890 456s [10,] 0.917 456s [11,] 0.910 456s [12,] 0.896 456s [13,] 0.891 456s [14,] 0.871 456s [15,] 0.903 456s [16,] 0.944 456s [17,] 0.925 456s [18,] 0.911 456s [19,] 0.913 456s [20,] 0.925 456s > 456s > print( correlation.systemfit( fit3sls[[ 4 ]]$e4e, 1, 2 ) ) 456s [,1] 456s [1,] 0.924 456s [2,] 0.933 456s [3,] 0.933 456s [4,] 0.938 456s [5,] 0.862 456s [6,] 0.868 456s [7,] 0.874 456s [8,] 0.879 456s [9,] 0.883 456s [10,] 0.943 456s [11,] 0.830 456s [12,] 0.744 456s [13,] 0.826 456s [14,] 0.834 456s [15,] 0.952 456s [16,] 0.918 456s [17,] 0.954 456s [18,] 0.930 456s [19,] 0.890 456s [20,] 0.893 456s > 456s > print( correlation.systemfit( fit3sls[[ 5 ]]$e5, 2, 1 ) ) 456s [,1] 456s [1,] 0.922 456s [2,] 0.935 456s [3,] 0.934 456s [4,] 0.939 456s [5,] 0.863 456s [6,] 0.868 456s [7,] 0.874 456s [8,] 0.876 456s [9,] 0.884 456s [10,] 0.942 456s [11,] 0.824 456s [12,] 0.747 456s [13,] 0.830 456s [14,] 0.833 456s [15,] 0.952 456s [16,] 0.919 456s [17,] 0.955 456s [18,] 0.928 456s [19,] 0.886 456s [20,] 0.888 456s > 456s > print( correlation.systemfit( fit3slsi[[ 2 ]]$e3e, 1, 2 ) ) 456s [,1] 456s [1,] 0.982 456s [2,] 0.994 456s [3,] 0.993 456s [4,] 0.992 456s [5,] 0.990 456s [6,] 0.990 456s [7,] 0.991 456s [8,] 0.978 456s [9,] 0.984 456s [10,] 0.992 456s [11,] 0.991 456s [12,] 0.985 456s [13,] 0.986 456s [14,] 0.980 456s [15,] 0.976 456s [16,] 0.994 456s [17,] 0.992 456s [18,] 0.987 456s [19,] 0.990 456s [20,] 0.991 456s > 456s > print( correlation.systemfit( fit3slsi[[ 4 ]]$e5w, 1, 2 ) ) 456s [,1] 456s [1,] 0.962 456s [2,] 0.975 456s [3,] 0.974 456s [4,] 0.976 456s [5,] 0.946 456s [6,] 0.948 456s [7,] 0.951 456s [8,] 0.944 456s [9,] 0.952 456s [10,] 0.976 456s [11,] 0.912 456s [12,] 0.871 456s [13,] 0.926 456s [14,] 0.927 456s [15,] 0.979 456s [16,] 0.968 456s [17,] 0.981 456s [18,] 0.970 456s [19,] 0.947 456s [20,] 0.943 456s > 456s > print( correlation.systemfit( fit3slsd[[ 3 ]]$e4, 2, 1 ) ) 456s [,1] 456s [1,] 0.932 456s [2,] 0.954 456s [3,] 0.952 456s [4,] 0.957 456s [5,] 0.892 456s [6,] 0.887 456s [7,] 0.887 456s [8,] 0.905 456s [9,] 0.914 456s [10,] 0.963 456s [11,] 0.860 456s [12,] 0.779 456s [13,] 0.878 456s [14,] 0.852 456s [15,] 0.968 456s [16,] 0.938 456s [17,] 0.973 456s [18,] 0.946 456s [19,] 0.913 456s [20,] 0.921 456s > 456s > 456s > ## ************ Log-Likelihood values *************** 456s > print( logLik( fit3sls[[ 1 ]]$e1c ) ) 456s 'log Lik.' -53 (df=10) 456s > print( logLik( fit3sls[[ 1 ]]$e1c, residCovDiag = TRUE ) ) 456s 'log Lik.' -85.6 (df=10) 456s > 456s > print( logLik( fit3sls[[ 2 ]]$e2e ) ) 456s 'log Lik.' -55.6 (df=9) 456s > print( logLik( fit3sls[[ 2 ]]$e2e, residCovDiag = TRUE ) ) 456s 'log Lik.' -85.4 (df=9) 456s > 456s > print( logLik( fit3sls[[ 3 ]]$e3 ) ) 456s 'log Lik.' -55.3 (df=9) 456s > print( logLik( fit3sls[[ 3 ]]$e3, residCovDiag = TRUE ) ) 456s 'log Lik.' -85.5 (df=9) 456s > 456s > print( logLik( fit3sls[[ 4 ]]$e4e ) ) 456s 'log Lik.' -58.5 (df=8) 456s > print( logLik( fit3sls[[ 4 ]]$e4e, residCovDiag = TRUE ) ) 456s 'log Lik.' -85.2 (df=8) 456s > 456s > print( logLik( fit3sls[[ 2 ]]$e4wSym ) ) 456s 'log Lik.' -58.5 (df=8) 456s > print( logLik( fit3sls[[ 2 ]]$e4wSym, residCovDiag = TRUE ) ) 456s 'log Lik.' -85.3 (df=8) 456s > 456s > print( logLik( fit3sls[[ 5 ]]$e5 ) ) 456s 'log Lik.' -87.3 (df=8) 456s > print( logLik( fit3sls[[ 5 ]]$e5, residCovDiag = TRUE ) ) 456s 'log Lik.' -104 (df=8) 456s > 456s > print( logLik( fit3slsi[[ 2 ]]$e3e ) ) 456s 'log Lik.' -46.7 (df=9) 456s > print( logLik( fit3slsi[[ 2 ]]$e3e, residCovDiag = TRUE ) ) 456s 'log Lik.' -92.1 (df=9) 456s > 456s > print( logLik( fit3slsi[[ 1 ]]$e1we ) ) 456s 'log Lik.' -52.7 (df=10) 456s > print( logLik( fit3slsi[[ 1 ]]$e1we, residCovDiag = TRUE ) ) 456s 'log Lik.' -85.8 (df=10) 456s > 456s > print( logLik( fit3slsd[[ 3 ]]$e4 ) ) 456s 'log Lik.' -59.4 (df=8) 456s > print( logLik( fit3slsd[[ 3 ]]$e4, residCovDiag = TRUE ) ) 456s 'log Lik.' -86.1 (df=8) 456s > 456s > print( logLik( fit3slsd[[ 5 ]]$e2we ) ) 456s 'log Lik.' -65 (df=9) 456s > print( logLik( fit3slsd[[ 5 ]]$e2we, residCovDiag = TRUE ) ) 456s 'log Lik.' -85.7 (df=9) 456s > 456s > 456s > ## ************** F tests **************** 456s > # testing first restriction 456s > print( linearHypothesis( fit3sls[[ 1 ]]$e1, restrm ) ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s demand_income - supply_trend = 0 456s 456s Model 1: restricted model 456s Model 2: fit3sls[[1]]$e1 456s 456s Res.Df Df F Pr(>F) 456s 1 34 456s 2 33 1 1.69 0.2 456s > linearHypothesis( fit3sls[[ 1 ]]$e1, restrict ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s demand_income - supply_trend = 0 456s 456s Model 1: restricted model 456s Model 2: fit3sls[[1]]$e1 456s 456s Res.Df Df F Pr(>F) 456s 1 34 456s 2 33 1 1.69 0.2 456s > 456s > print( linearHypothesis( fit3sls[[ 2 ]]$e1e, restrm ) ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s demand_income - supply_trend = 0 456s 456s Model 1: restricted model 456s Model 2: fit3sls[[2]]$e1e 456s 456s Res.Df Df F Pr(>F) 456s 1 34 456s 2 33 1 1.52 0.23 456s > linearHypothesis( fit3sls[[ 2 ]]$e1e, restrict ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s demand_income - supply_trend = 0 456s 456s Model 1: restricted model 456s Model 2: fit3sls[[2]]$e1e 456s 456s Res.Df Df F Pr(>F) 456s 1 34 456s 2 33 1 1.52 0.23 456s > 456s > print( linearHypothesis( fit3sls[[ 3 ]]$e1c, restrm ) ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s demand_income - supply_trend = 0 456s 456s Model 1: restricted model 456s Model 2: fit3sls[[3]]$e1c 456s 456s Res.Df Df F Pr(>F) 456s 1 34 456s 2 33 1 2.47 0.13 456s > linearHypothesis( fit3sls[[ 3 ]]$e1c, restrict ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s demand_income - supply_trend = 0 456s 456s Model 1: restricted model 456s Model 2: fit3sls[[3]]$e1c 456s 456s Res.Df Df F Pr(>F) 456s 1 34 456s 2 33 1 2.47 0.13 456s > 456s > print( linearHypothesis( fit3slsi[[ 4 ]]$e1, restrm ) ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s demand_income - supply_trend = 0 456s 456s Model 1: restricted model 456s Model 2: fit3slsi[[4]]$e1 456s 456s Res.Df Df F Pr(>F) 456s 1 34 456s 2 33 1 4.75 0.037 * 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s > linearHypothesis( fit3slsi[[ 4 ]]$e1, restrict ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s demand_income - supply_trend = 0 456s 456s Model 1: restricted model 456s Model 2: fit3slsi[[4]]$e1 456s 456s Res.Df Df F Pr(>F) 456s 1 34 456s 2 33 1 4.75 0.037 * 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s > 456s > print( linearHypothesis( fit3slsd[[ 5 ]]$e1e, restrm ) ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s demand_income - supply_trend = 0 456s 456s Model 1: restricted model 456s Model 2: fit3slsd[[5]]$e1e 456s 456s Res.Df Df F Pr(>F) 456s 1 34 456s 2 33 1 0.18 0.68 456s > linearHypothesis( fit3slsd[[ 5 ]]$e1e, restrict ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s demand_income - supply_trend = 0 456s 456s Model 1: restricted model 456s Model 2: fit3slsd[[5]]$e1e 456s 456s Res.Df Df F Pr(>F) 456s 1 34 456s 2 33 1 0.18 0.68 456s > 456s > print( linearHypothesis( fit3slsd[[ 2 ]]$e1w, restrm ) ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s demand_income - supply_trend = 0 456s 456s Model 1: restricted model 456s Model 2: fit3slsd[[2]]$e1w 456s 456s Res.Df Df F Pr(>F) 456s 1 34 456s 2 33 1 0.51 0.48 456s > linearHypothesis( fit3slsd[[ 2 ]]$e1w, restrict ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s demand_income - supply_trend = 0 456s 456s Model 1: restricted model 456s Model 2: fit3slsd[[2]]$e1w 456s 456s Res.Df Df F Pr(>F) 456s 1 34 456s 2 33 1 0.51 0.48 456s > 456s > # testing second restriction 456s > restrOnly2m <- matrix(0,1,7) 456s > restrOnly2q <- 0.5 456s > restrOnly2m[1,2] <- -1 456s > restrOnly2m[1,5] <- 1 456s > restrictOnly2 <- "- demand_price + supply_price = 0.5" 456s > # first restriction not imposed 456s > print( linearHypothesis( fit3sls[[ 5 ]]$e1c, restrOnly2m, restrOnly2q ) ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3sls[[5]]$e1c 456s 456s Res.Df Df F Pr(>F) 456s 1 34 456s 2 33 1 0.17 0.69 456s > linearHypothesis( fit3sls[[ 5 ]]$e1c, restrictOnly2 ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3sls[[5]]$e1c 456s 456s Res.Df Df F Pr(>F) 456s 1 34 456s 2 33 1 0.17 0.69 456s > 456s > print( linearHypothesis( fit3slsi[[ 1 ]]$e1e, restrOnly2m, restrOnly2q ) ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3slsi[[1]]$e1e 456s 456s Res.Df Df F Pr(>F) 456s 1 34 456s 2 33 1 0.13 0.72 456s > linearHypothesis( fit3slsi[[ 1 ]]$e1e, restrictOnly2 ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3slsi[[1]]$e1e 456s 456s Res.Df Df F Pr(>F) 456s 1 34 456s 2 33 1 0.13 0.72 456s > 456s > print( linearHypothesis( fit3slsi[[ 3 ]]$e1we, restrOnly2m, restrOnly2q ) ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3slsi[[3]]$e1we 456s 456s Res.Df Df F Pr(>F) 456s 1 34 456s 2 33 1 0.13 0.72 456s > linearHypothesis( fit3slsi[[ 3 ]]$e1we, restrictOnly2 ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3slsi[[3]]$e1we 456s 456s Res.Df Df F Pr(>F) 456s 1 34 456s 2 33 1 0.13 0.72 456s > 456s > print( linearHypothesis( fit3slsd[[ 2 ]]$e1, restrOnly2m, restrOnly2q ) ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3slsd[[2]]$e1 456s 456s Res.Df Df F Pr(>F) 456s 1 34 456s 2 33 1 0.25 0.62 456s > linearHypothesis( fit3slsd[[ 2 ]]$e1, restrictOnly2 ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3slsd[[2]]$e1 456s 456s Res.Df Df F Pr(>F) 456s 1 34 456s 2 33 1 0.25 0.62 456s > 456s > # first restriction imposed 456s > print( linearHypothesis( fit3sls[[ 4 ]]$e2, restrOnly2m, restrOnly2q ) ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3sls[[4]]$e2 456s 456s Res.Df Df F Pr(>F) 456s 1 35 456s 2 34 1 0.81 0.38 456s > linearHypothesis( fit3sls[[ 4 ]]$e2, restrictOnly2 ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3sls[[4]]$e2 456s 456s Res.Df Df F Pr(>F) 456s 1 35 456s 2 34 1 0.81 0.38 456s > 456s > print( linearHypothesis( fit3sls[[ 4 ]]$e3, restrOnly2m, restrOnly2q ) ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3sls[[4]]$e3 456s 456s Res.Df Df F Pr(>F) 456s 1 35 456s 2 34 1 0.81 0.38 456s > linearHypothesis( fit3sls[[ 4 ]]$e3, restrictOnly2 ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3sls[[4]]$e3 456s 456s Res.Df Df F Pr(>F) 456s 1 35 456s 2 34 1 0.81 0.38 456s > 456s > print( linearHypothesis( fit3sls[[ 1 ]]$e2w, restrOnly2m, restrOnly2q ) ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3sls[[1]]$e2w 456s 456s Res.Df Df F Pr(>F) 456s 1 35 456s 2 34 1 0.9 0.35 456s > linearHypothesis( fit3sls[[ 1 ]]$e2w, restrictOnly2 ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3sls[[1]]$e2w 456s 456s Res.Df Df F Pr(>F) 456s 1 35 456s 2 34 1 0.9 0.35 456s > 456s > print( linearHypothesis( fit3sls[[ 1 ]]$e3we, restrOnly2m, restrOnly2q ) ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3sls[[1]]$e3we 456s 456s Res.Df Df F Pr(>F) 456s 1 35 456s 2 34 1 0.75 0.39 456s > linearHypothesis( fit3sls[[ 1 ]]$e3we, restrictOnly2 ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3sls[[1]]$e3we 456s 456s Res.Df Df F Pr(>F) 456s 1 35 456s 2 34 1 0.75 0.39 456s > 456s > print( linearHypothesis( fit3slsi[[ 5 ]]$e2e, restrOnly2m, restrOnly2q ) ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3slsi[[5]]$e2e 456s 456s Res.Df Df F Pr(>F) 456s 1 35 456s 2 34 1 15.1 0.00044 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s > linearHypothesis( fit3slsi[[ 5 ]]$e2e, restrictOnly2 ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3slsi[[5]]$e2e 456s 456s Res.Df Df F Pr(>F) 456s 1 35 456s 2 34 1 15.1 0.00044 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s > 456s > print( linearHypothesis( fit3slsi[[ 5 ]]$e3e, restrOnly2m, restrOnly2q ) ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3slsi[[5]]$e3e 456s 456s Res.Df Df F Pr(>F) 456s 1 35 456s 2 34 1 15.1 0.00044 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s > linearHypothesis( fit3slsi[[ 5 ]]$e3e, restrictOnly2 ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3slsi[[5]]$e3e 456s 456s Res.Df Df F Pr(>F) 456s 1 35 456s 2 34 1 15.1 0.00044 *** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s > 456s > print( linearHypothesis( fit3slsd[[ 1 ]]$e2, restrOnly2m, restrOnly2q ) ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3slsd[[1]]$e2 456s 456s Res.Df Df F Pr(>F) 456s 1 35 456s 2 34 1 0.16 0.69 456s > linearHypothesis( fit3slsd[[ 1 ]]$e2, restrictOnly2 ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3slsd[[1]]$e2 456s 456s Res.Df Df F Pr(>F) 456s 1 35 456s 2 34 1 0.16 0.69 456s > 456s > print( linearHypothesis( fit3slsd[[ 1 ]]$e3, restrOnly2m, restrOnly2q ) ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3slsd[[1]]$e3 456s 456s Res.Df Df F Pr(>F) 456s 1 35 456s 2 34 1 0.16 0.69 456s > linearHypothesis( fit3slsd[[ 1 ]]$e3, restrictOnly2 ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3slsd[[1]]$e3 456s 456s Res.Df Df F Pr(>F) 456s 1 35 456s 2 34 1 0.16 0.69 456s > 456s > # testing both of the restrictions 456s > print( linearHypothesis( fit3sls[[ 2 ]]$e1e, restr2m, restr2q ) ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s demand_income - supply_trend = 0 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3sls[[2]]$e1e 456s 456s Res.Df Df F Pr(>F) 456s 1 35 456s 2 33 2 1 0.38 456s > linearHypothesis( fit3sls[[ 2 ]]$e1e, restrict2 ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s demand_income - supply_trend = 0 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3sls[[2]]$e1e 456s 456s Res.Df Df F Pr(>F) 456s 1 35 456s 2 33 2 1 0.38 456s > 456s > print( linearHypothesis( fit3slsi[[ 3 ]]$e1, restr2m, restr2q ) ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s demand_income - supply_trend = 0 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3slsi[[3]]$e1 456s 456s Res.Df Df F Pr(>F) 456s 1 35 456s 2 33 2 5.59 0.0081 ** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s > linearHypothesis( fit3slsi[[ 3 ]]$e1, restrict2 ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s demand_income - supply_trend = 0 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3slsi[[3]]$e1 456s 456s Res.Df Df F Pr(>F) 456s 1 35 456s 2 33 2 5.59 0.0081 ** 456s --- 456s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 456s > 456s > print( linearHypothesis( fit3slsd[[ 4 ]]$e1e, restr2m, restr2q ) ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s demand_income - supply_trend = 0 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3slsd[[4]]$e1e 456s 456s Res.Df Df F Pr(>F) 456s 1 35 456s 2 33 2 0.64 0.53 456s > linearHypothesis( fit3slsd[[ 4 ]]$e1e, restrict2 ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s demand_income - supply_trend = 0 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3slsd[[4]]$e1e 456s 456s Res.Df Df F Pr(>F) 456s 1 35 456s 2 33 2 0.64 0.53 456s > 456s > print( linearHypothesis( fit3slsd[[ 5 ]]$e1w, restr2m, restr2q ) ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s demand_income - supply_trend = 0 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3slsd[[5]]$e1w 456s 456s Res.Df Df F Pr(>F) 456s 1 35 456s 2 33 2 0.45 0.64 456s > linearHypothesis( fit3slsd[[ 5 ]]$e1w, restrict2 ) 456s Linear hypothesis test (Theil's F test) 456s 456s Hypothesis: 456s demand_income - supply_trend = 0 456s - demand_price + supply_price = 0.5 456s 456s Model 1: restricted model 456s Model 2: fit3slsd[[5]]$e1w 456s 456s Res.Df Df F Pr(>F) 456s 1 35 456s 2 33 2 0.45 0.64 456s > 456s > 456s > ## ************** Wald tests **************** 456s > # testing first restriction 456s > print( linearHypothesis( fit3sls[[ 1 ]]$e1, restrm, test = "Chisq" ) ) 456s Linear hypothesis test (Chi^2 statistic of a Wald test) 456s 456s Hypothesis: 456s demand_income - supply_trend = 0 456s 456s Model 1: restricted model 456s Model 2: fit3sls[[1]]$e1 456s 456s Res.Df Df Chisq Pr(>Chisq) 456s 1 34 456s 2 33 1 1.11 0.29 456s > linearHypothesis( fit3sls[[ 1 ]]$e1, restrict, test = "Chisq" ) 456s Linear hypothesis test (Chi^2 statistic of a Wald test) 456s 456s Hypothesis: 456s demand_income - supply_trend = 0 456s 456s Model 1: restricted model 456s Model 2: fit3sls[[1]]$e1 456s 456s Res.Df Df Chisq Pr(>Chisq) 456s 1 34 456s 2 33 1 1.11 0.29 456s > 456s > print( linearHypothesis( fit3sls[[ 2 ]]$e1e, restrm, test = "Chisq" ) ) 456s Linear hypothesis test (Chi^2 statistic of a Wald test) 456s 456s Hypothesis: 456s demand_income - supply_trend = 0 456s 456s Model 1: restricted model 456s Model 2: fit3sls[[2]]$e1e 456s 456s Res.Df Df Chisq Pr(>Chisq) 456s 1 34 456s 2 33 1 1.23 0.27 456s > linearHypothesis( fit3sls[[ 2 ]]$e1e, restrict, test = "Chisq" ) 456s Linear hypothesis test (Chi^2 statistic of a Wald test) 456s 456s Hypothesis: 456s demand_income - supply_trend = 0 456s 456s Model 1: restricted model 456s Model 2: fit3sls[[2]]$e1e 456s 456s Res.Df Df Chisq Pr(>Chisq) 456s 1 34 456s 2 33 1 1.23 0.27 456s > 456s > print( linearHypothesis( fit3sls[[ 3 ]]$e1c, restrm, test = "Chisq" ) ) 456s Linear hypothesis test (Chi^2 statistic of a Wald test) 456s 456s Hypothesis: 456s demand_income - supply_trend = 0 456s 456s Model 1: restricted model 456s Model 2: fit3sls[[3]]$e1c 456s 456s Res.Df Df Chisq Pr(>Chisq) 456s 1 34 456s 2 33 1 1.73 0.19 456s > linearHypothesis( fit3sls[[ 3 ]]$e1c, restrict, test = "Chisq" ) 456s Linear hypothesis test (Chi^2 statistic of a Wald test) 456s 456s Hypothesis: 456s demand_income - supply_trend = 0 456s 456s Model 1: restricted model 456s Model 2: fit3sls[[3]]$e1c 456s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 34 457s 2 33 1 1.73 0.19 457s > 457s > print( linearHypothesis( fit3slsi[[ 4 ]]$e1, restrm, test = "Chisq" ) ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s demand_income - supply_trend = 0 457s 457s Model 1: restricted model 457s Model 2: fit3slsi[[4]]$e1 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 34 457s 2 33 1 4.81 0.028 * 457s --- 457s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 457s > linearHypothesis( fit3slsi[[ 4 ]]$e1, restrict, test = "Chisq" ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s demand_income - supply_trend = 0 457s 457s Model 1: restricted model 457s Model 2: fit3slsi[[4]]$e1 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 34 457s 2 33 1 4.81 0.028 * 457s --- 457s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 457s > 457s > print( linearHypothesis( fit3slsi[[ 2 ]]$e1we, restrm, test = "Chisq" ) ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s demand_income - supply_trend = 0 457s 457s Model 1: restricted model 457s Model 2: fit3slsi[[2]]$e1we 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 34 457s 2 33 1 5.72 0.017 * 457s --- 457s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 457s > linearHypothesis( fit3slsi[[ 2 ]]$e1we, restrict, test = "Chisq" ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s demand_income - supply_trend = 0 457s 457s Model 1: restricted model 457s Model 2: fit3slsi[[2]]$e1we 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 34 457s 2 33 1 5.72 0.017 * 457s --- 457s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 457s > 457s > print( linearHypothesis( fit3slsd[[ 5 ]]$e1e, restrm, test = "Chisq" ) ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s demand_income - supply_trend = 0 457s 457s Model 1: restricted model 457s Model 2: fit3slsd[[5]]$e1e 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 34 457s 2 33 1 0.15 0.7 457s > linearHypothesis( fit3slsd[[ 5 ]]$e1e, restrict, test = "Chisq" ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s demand_income - supply_trend = 0 457s 457s Model 1: restricted model 457s Model 2: fit3slsd[[5]]$e1e 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 34 457s 2 33 1 0.15 0.7 457s > 457s > # testing second restriction 457s > # first restriction not imposed 457s > print( linearHypothesis( fit3sls[[ 5 ]]$e1c, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3sls[[5]]$e1c 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 34 457s 2 33 1 0.12 0.73 457s > linearHypothesis( fit3sls[[ 5 ]]$e1c, restrictOnly2, test = "Chisq" ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3sls[[5]]$e1c 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 34 457s 2 33 1 0.12 0.73 457s > 457s > print( linearHypothesis( fit3sls[[ 3 ]]$e1wc, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3sls[[3]]$e1wc 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 34 457s 2 33 1 0.12 0.73 457s > linearHypothesis( fit3sls[[ 3 ]]$e1wc, restrictOnly2, test = "Chisq" ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3sls[[3]]$e1wc 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 34 457s 2 33 1 0.12 0.73 457s > 457s > print( linearHypothesis( fit3slsi[[ 1 ]]$e1e, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3slsi[[1]]$e1e 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 34 457s 2 33 1 0.16 0.69 457s > linearHypothesis( fit3slsi[[ 1 ]]$e1e, restrictOnly2, test = "Chisq" ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3slsi[[1]]$e1e 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 34 457s 2 33 1 0.16 0.69 457s > 457s > print( linearHypothesis( fit3slsd[[ 2 ]]$e1, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3slsd[[2]]$e1 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 34 457s 2 33 1 0.17 0.68 457s > linearHypothesis( fit3slsd[[ 2 ]]$e1, restrictOnly2, test = "Chisq" ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3slsd[[2]]$e1 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 34 457s 2 33 1 0.17 0.68 457s > 457s > # first restriction imposed 457s > print( linearHypothesis( fit3sls[[ 4 ]]$e2, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3sls[[4]]$e2 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 35 457s 2 34 1 0.55 0.46 457s > linearHypothesis( fit3sls[[ 4 ]]$e2, restrictOnly2, test = "Chisq" ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3sls[[4]]$e2 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 35 457s 2 34 1 0.55 0.46 457s > 457s > print( linearHypothesis( fit3sls[[ 4 ]]$e3, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3sls[[4]]$e3 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 35 457s 2 34 1 0.55 0.46 457s > linearHypothesis( fit3sls[[ 4 ]]$e3, restrictOnly2, test = "Chisq" ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3sls[[4]]$e3 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 35 457s 2 34 1 0.55 0.46 457s > 457s > print( linearHypothesis( fit3slsi[[ 5 ]]$e2e, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3slsi[[5]]$e2e 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 35 457s 2 34 1 17.8 2.4e-05 *** 457s --- 457s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 457s > linearHypothesis( fit3slsi[[ 5 ]]$e2e, restrictOnly2, test = "Chisq" ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3slsi[[5]]$e2e 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 35 457s 2 34 1 17.8 2.4e-05 *** 457s --- 457s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 457s > 457s > print( linearHypothesis( fit3slsi[[ 5 ]]$e3e, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3slsi[[5]]$e3e 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 35 457s 2 34 1 17.8 2.4e-05 *** 457s --- 457s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 457s > linearHypothesis( fit3slsi[[ 5 ]]$e3e, restrictOnly2, test = "Chisq" ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3slsi[[5]]$e3e 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 35 457s 2 34 1 17.8 2.4e-05 *** 457s --- 457s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 457s > 457s > print( linearHypothesis( fit3slsd[[ 1 ]]$e2, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3slsd[[1]]$e2 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 35 457s 2 34 1 0.13 0.72 457s > linearHypothesis( fit3slsd[[ 1 ]]$e2, restrictOnly2, test = "Chisq" ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3slsd[[1]]$e2 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 35 457s 2 34 1 0.13 0.72 457s > 457s > print( linearHypothesis( fit3slsd[[ 1 ]]$e3, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3slsd[[1]]$e3 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 35 457s 2 34 1 0.13 0.72 457s > linearHypothesis( fit3slsd[[ 1 ]]$e3, restrictOnly2, test = "Chisq" ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3slsd[[1]]$e3 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 35 457s 2 34 1 0.13 0.72 457s > 457s > print( linearHypothesis( fit3slsd[[ 2 ]]$e2we, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3slsd[[2]]$e2we 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 35 457s 2 34 1 1.52 0.22 457s > linearHypothesis( fit3slsd[[ 2 ]]$e2we, restrictOnly2, test = "Chisq" ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3slsd[[2]]$e2we 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 35 457s 2 34 1 1.52 0.22 457s > 457s > print( linearHypothesis( fit3slsd[[ 3 ]]$e3w, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3slsd[[3]]$e3w 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 35 457s 2 34 1 0.23 0.63 457s > linearHypothesis( fit3slsd[[ 3 ]]$e3w, restrictOnly2, test = "Chisq" ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3slsd[[3]]$e3w 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 35 457s 2 34 1 0.23 0.63 457s > 457s > # testing both of the restrictions 457s > print( linearHypothesis( fit3sls[[ 2 ]]$e1e, restr2m, restr2q, test = "Chisq" ) ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s demand_income - supply_trend = 0 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3sls[[2]]$e1e 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 35 457s 2 33 2 1.62 0.44 457s > linearHypothesis( fit3sls[[ 2 ]]$e1e, restrict2, test = "Chisq" ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s demand_income - supply_trend = 0 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3sls[[2]]$e1e 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 35 457s 2 33 2 1.62 0.44 457s > 457s > print( linearHypothesis( fit3sls[[ 5 ]]$e1wc, restr2m, restr2q, test = "Chisq" ) ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s demand_income - supply_trend = 0 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3sls[[5]]$e1wc 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 35 457s 2 33 2 2.43 0.3 457s > linearHypothesis( fit3sls[[ 5 ]]$e1wc, restrict2, test = "Chisq" ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s demand_income - supply_trend = 0 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3sls[[5]]$e1wc 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 35 457s 2 33 2 2.43 0.3 457s > 457s > print( linearHypothesis( fit3slsi[[ 3 ]]$e1, restr2m, restr2q, test = "Chisq" ) ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s demand_income - supply_trend = 0 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3slsi[[3]]$e1 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 35 457s 2 33 2 11.3 0.0035 ** 457s --- 457s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 457s > linearHypothesis( fit3slsi[[ 3 ]]$e1, restrict2, test = "Chisq" ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s demand_income - supply_trend = 0 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3slsi[[3]]$e1 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 35 457s 2 33 2 11.3 0.0035 ** 457s --- 457s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 457s > 457s > print( linearHypothesis( fit3slsd[[ 4 ]]$e1e, restr2m, restr2q, test = "Chisq" ) ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s demand_income - supply_trend = 0 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3slsd[[4]]$e1e 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 35 457s 2 33 2 1.55 0.46 457s > linearHypothesis( fit3slsd[[ 4 ]]$e1e, restrict2, test = "Chisq" ) 457s Linear hypothesis test (Chi^2 statistic of a Wald test) 457s 457s Hypothesis: 457s demand_income - supply_trend = 0 457s - demand_price + supply_price = 0.5 457s 457s Model 1: restricted model 457s Model 2: fit3slsd[[4]]$e1e 457s 457s Res.Df Df Chisq Pr(>Chisq) 457s 1 35 457s 2 33 2 1.55 0.46 457s > 457s > 457s > ## *********** model frame ************* 457s > print( mf <- model.frame( fit3sls[[ 3 ]]$e1c ) ) 457s consump price income farmPrice trend 457s 1 98.5 100.3 87.4 98.0 1 457s 2 99.2 104.3 97.6 99.1 2 457s 3 102.2 103.4 96.7 99.1 3 457s 4 101.5 104.5 98.2 98.1 4 457s 5 104.2 98.0 99.8 110.8 5 457s 6 103.2 99.5 100.5 108.2 6 457s 7 104.0 101.1 103.2 105.6 7 457s 8 99.9 104.8 107.8 109.8 8 457s 9 100.3 96.4 96.6 108.7 9 457s 10 102.8 91.2 88.9 100.6 10 457s 11 95.4 93.1 75.1 81.0 11 457s 12 92.4 98.8 76.9 68.6 12 457s 13 94.5 102.9 84.6 70.9 13 457s 14 98.8 98.8 90.6 81.4 14 457s 15 105.8 95.1 103.1 102.3 15 457s 16 100.2 98.5 105.1 105.0 16 457s 17 103.5 86.5 96.4 110.5 17 457s 18 99.9 104.0 104.4 92.5 18 457s 19 105.2 105.8 110.7 89.3 19 457s 20 106.2 113.5 127.1 93.0 20 457s > print( mf1 <- model.frame( fit3sls[[ 3 ]]$e1c$eq[[ 1 ]] ) ) 457s consump price income 457s 1 98.5 100.3 87.4 457s 2 99.2 104.3 97.6 457s 3 102.2 103.4 96.7 457s 4 101.5 104.5 98.2 457s 5 104.2 98.0 99.8 457s 6 103.2 99.5 100.5 457s 7 104.0 101.1 103.2 457s 8 99.9 104.8 107.8 457s 9 100.3 96.4 96.6 457s 10 102.8 91.2 88.9 457s 11 95.4 93.1 75.1 457s 12 92.4 98.8 76.9 457s 13 94.5 102.9 84.6 457s 14 98.8 98.8 90.6 457s 15 105.8 95.1 103.1 457s 16 100.2 98.5 105.1 457s 17 103.5 86.5 96.4 457s 18 99.9 104.0 104.4 457s 19 105.2 105.8 110.7 457s 20 106.2 113.5 127.1 457s > print( attributes( mf1 )$terms ) 457s consump ~ price + income 457s attr(,"variables") 457s list(consump, price, income) 457s attr(,"factors") 457s price income 457s consump 0 0 457s price 1 0 457s income 0 1 457s attr(,"term.labels") 457s [1] "price" "income" 457s attr(,"order") 457s [1] 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 1 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(consump, price, income) 457s attr(,"dataClasses") 457s consump price income 457s "numeric" "numeric" "numeric" 457s > print( mf2 <- model.frame( fit3sls[[ 3 ]]$e1c$eq[[ 2 ]] ) ) 457s consump price farmPrice trend 457s 1 98.5 100.3 98.0 1 457s 2 99.2 104.3 99.1 2 457s 3 102.2 103.4 99.1 3 457s 4 101.5 104.5 98.1 4 457s 5 104.2 98.0 110.8 5 457s 6 103.2 99.5 108.2 6 457s 7 104.0 101.1 105.6 7 457s 8 99.9 104.8 109.8 8 457s 9 100.3 96.4 108.7 9 457s 10 102.8 91.2 100.6 10 457s 11 95.4 93.1 81.0 11 457s 12 92.4 98.8 68.6 12 457s 13 94.5 102.9 70.9 13 457s 14 98.8 98.8 81.4 14 457s 15 105.8 95.1 102.3 15 457s 16 100.2 98.5 105.0 16 457s 17 103.5 86.5 110.5 17 457s 18 99.9 104.0 92.5 18 457s 19 105.2 105.8 89.3 19 457s 20 106.2 113.5 93.0 20 457s > print( attributes( mf2 )$terms ) 457s consump ~ price + farmPrice + trend 457s attr(,"variables") 457s list(consump, price, farmPrice, trend) 457s attr(,"factors") 457s price farmPrice trend 457s consump 0 0 0 457s price 1 0 0 457s farmPrice 0 1 0 457s trend 0 0 1 457s attr(,"term.labels") 457s [1] "price" "farmPrice" "trend" 457s attr(,"order") 457s [1] 1 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 1 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(consump, price, farmPrice, trend) 457s attr(,"dataClasses") 457s consump price farmPrice trend 457s "numeric" "numeric" "numeric" "numeric" 457s > 457s > print( all.equal( mf, model.frame( fit3sls[[ 3 ]]$e1wc ) ) ) 457s [1] TRUE 457s > print( all.equal( mf2, model.frame( fit3sls[[ 3 ]]$e1wc$eq[[ 2 ]] ) ) ) 457s [1] TRUE 457s > 457s > print( all.equal( mf, model.frame( fit3sls[[ 4 ]]$e2e ) ) ) 457s [1] TRUE 457s > print( all.equal( mf2, model.frame( fit3sls[[ 4 ]]$e2e$eq[[ 2 ]] ) ) ) 457s [1] TRUE 457s > 457s > print( all.equal( mf, model.frame( fit3sls[[ 5 ]]$e3 ) ) ) 457s [1] TRUE 457s > print( all.equal( mf1, model.frame( fit3sls[[ 5 ]]$e3$eq[[ 1 ]] ) ) ) 457s [1] TRUE 457s > 457s > print( all.equal( mf, model.frame( fit3sls[[ 1 ]]$e4e ) ) ) 457s [1] TRUE 457s > print( all.equal( mf2, model.frame( fit3sls[[ 1 ]]$e4e$eq[[ 2 ]] ) ) ) 457s [1] TRUE 457s > 457s > print( all.equal( mf, model.frame( fit3sls[[ 2 ]]$e5 ) ) ) 457s [1] TRUE 457s > print( all.equal( mf1, model.frame( fit3sls[[ 3 ]]$e5$eq[[ 1 ]] ) ) ) 457s [1] TRUE 457s > 457s > print( all.equal( mf, model.frame( fit3slsi[[ 4 ]]$e3e ) ) ) 457s [1] TRUE 457s > print( all.equal( mf1, model.frame( fit3slsi[[ 4 ]]$e3e$eq[[ 1 ]] ) ) ) 457s [1] TRUE 457s > 457s > print( all.equal( mf, model.frame( fit3slsd[[ 5 ]]$e4 ) ) ) 457s [1] TRUE 457s > print( all.equal( mf2, model.frame( fit3slsd[[ 5 ]]$e4$eq[[ 2 ]] ) ) ) 457s [1] TRUE 457s > 457s > fit3sls[[ 3 ]]$e1c$eq[[ 1 ]]$modelInst 457s income farmPrice trend 457s 1 87.4 98.0 1 457s 2 97.6 99.1 2 457s 3 96.7 99.1 3 457s 4 98.2 98.1 4 457s 5 99.8 110.8 5 457s 6 100.5 108.2 6 457s 7 103.2 105.6 7 457s 8 107.8 109.8 8 457s 9 96.6 108.7 9 457s 10 88.9 100.6 10 457s 11 75.1 81.0 11 457s 12 76.9 68.6 12 457s 13 84.6 70.9 13 457s 14 90.6 81.4 14 457s 15 103.1 102.3 15 457s 16 105.1 105.0 16 457s 17 96.4 110.5 17 457s 18 104.4 92.5 18 457s 19 110.7 89.3 19 457s 20 127.1 93.0 20 457s > fit3sls[[ 3 ]]$e1c$eq[[ 2 ]]$modelInst 457s income farmPrice trend 457s 1 87.4 98.0 1 457s 2 97.6 99.1 2 457s 3 96.7 99.1 3 457s 4 98.2 98.1 4 457s 5 99.8 110.8 5 457s 6 100.5 108.2 6 457s 7 103.2 105.6 7 457s 8 107.8 109.8 8 457s 9 96.6 108.7 9 457s 10 88.9 100.6 10 457s 11 75.1 81.0 11 457s 12 76.9 68.6 12 457s 13 84.6 70.9 13 457s 14 90.6 81.4 14 457s 15 103.1 102.3 15 457s 16 105.1 105.0 16 457s 17 96.4 110.5 17 457s 18 104.4 92.5 18 457s 19 110.7 89.3 19 457s 20 127.1 93.0 20 457s > 457s > fit3sls[[ 1 ]]$e3$eq[[ 1 ]]$modelInst 457s income farmPrice trend 457s 1 87.4 98.0 1 457s 2 97.6 99.1 2 457s 3 96.7 99.1 3 457s 4 98.2 98.1 4 457s 5 99.8 110.8 5 457s 6 100.5 108.2 6 457s 7 103.2 105.6 7 457s 8 107.8 109.8 8 457s 9 96.6 108.7 9 457s 10 88.9 100.6 10 457s 11 75.1 81.0 11 457s 12 76.9 68.6 12 457s 13 84.6 70.9 13 457s 14 90.6 81.4 14 457s 15 103.1 102.3 15 457s 16 105.1 105.0 16 457s 17 96.4 110.5 17 457s 18 104.4 92.5 18 457s 19 110.7 89.3 19 457s 20 127.1 93.0 20 457s > fit3sls[[ 1 ]]$e3$eq[[ 2 ]]$modelInst 457s income farmPrice trend 457s 1 87.4 98.0 1 457s 2 97.6 99.1 2 457s 3 96.7 99.1 3 457s 4 98.2 98.1 4 457s 5 99.8 110.8 5 457s 6 100.5 108.2 6 457s 7 103.2 105.6 7 457s 8 107.8 109.8 8 457s 9 96.6 108.7 9 457s 10 88.9 100.6 10 457s 11 75.1 81.0 11 457s 12 76.9 68.6 12 457s 13 84.6 70.9 13 457s 14 90.6 81.4 14 457s 15 103.1 102.3 15 457s 16 105.1 105.0 16 457s 17 96.4 110.5 17 457s 18 104.4 92.5 18 457s 19 110.7 89.3 19 457s 20 127.1 93.0 20 457s > 457s > fit3slsd[[ 5 ]]$e4$eq[[ 1 ]]$modelInst 457s income farmPrice 457s 1 87.4 98.0 457s 2 97.6 99.1 457s 3 96.7 99.1 457s 4 98.2 98.1 457s 5 99.8 110.8 457s 6 100.5 108.2 457s 7 103.2 105.6 457s 8 107.8 109.8 457s 9 96.6 108.7 457s 10 88.9 100.6 457s 11 75.1 81.0 457s 12 76.9 68.6 457s 13 84.6 70.9 457s 14 90.6 81.4 457s 15 103.1 102.3 457s 16 105.1 105.0 457s 17 96.4 110.5 457s 18 104.4 92.5 457s 19 110.7 89.3 457s 20 127.1 93.0 457s > fit3slsd[[ 5 ]]$e4$eq[[ 2 ]]$modelInst 457s income farmPrice trend 457s 1 87.4 98.0 1 457s 2 97.6 99.1 2 457s 3 96.7 99.1 3 457s 4 98.2 98.1 4 457s 5 99.8 110.8 5 457s 6 100.5 108.2 6 457s 7 103.2 105.6 7 457s 8 107.8 109.8 8 457s 9 96.6 108.7 9 457s 10 88.9 100.6 10 457s 11 75.1 81.0 11 457s 12 76.9 68.6 12 457s 13 84.6 70.9 13 457s 14 90.6 81.4 14 457s 15 103.1 102.3 15 457s 16 105.1 105.0 16 457s 17 96.4 110.5 17 457s 18 104.4 92.5 18 457s 19 110.7 89.3 19 457s 20 127.1 93.0 20 457s > 457s > 457s > ## **************** model matrix ************************ 457s > # with x (returnModelMatrix) = TRUE 457s > print( !is.null( fit3sls[[ 4 ]]$e1c$eq[[ 1 ]]$x ) ) 457s [1] TRUE 457s > print( mm <- model.matrix( fit3sls[[ 4 ]]$e1c ) ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s demand_1 1 100.3 87.4 0 457s demand_2 1 104.3 97.6 0 457s demand_3 1 103.4 96.7 0 457s demand_4 1 104.5 98.2 0 457s demand_5 1 98.0 99.8 0 457s demand_6 1 99.5 100.5 0 457s demand_7 1 101.1 103.2 0 457s demand_8 1 104.8 107.8 0 457s demand_9 1 96.4 96.6 0 457s demand_10 1 91.2 88.9 0 457s demand_11 1 93.1 75.1 0 457s demand_12 1 98.8 76.9 0 457s demand_13 1 102.9 84.6 0 457s demand_14 1 98.8 90.6 0 457s demand_15 1 95.1 103.1 0 457s demand_16 1 98.5 105.1 0 457s demand_17 1 86.5 96.4 0 457s demand_18 1 104.0 104.4 0 457s demand_19 1 105.8 110.7 0 457s demand_20 1 113.5 127.1 0 457s supply_1 0 0.0 0.0 1 457s supply_2 0 0.0 0.0 1 457s supply_3 0 0.0 0.0 1 457s supply_4 0 0.0 0.0 1 457s supply_5 0 0.0 0.0 1 457s supply_6 0 0.0 0.0 1 457s supply_7 0 0.0 0.0 1 457s supply_8 0 0.0 0.0 1 457s supply_9 0 0.0 0.0 1 457s supply_10 0 0.0 0.0 1 457s supply_11 0 0.0 0.0 1 457s supply_12 0 0.0 0.0 1 457s supply_13 0 0.0 0.0 1 457s supply_14 0 0.0 0.0 1 457s supply_15 0 0.0 0.0 1 457s supply_16 0 0.0 0.0 1 457s supply_17 0 0.0 0.0 1 457s supply_18 0 0.0 0.0 1 457s supply_19 0 0.0 0.0 1 457s supply_20 0 0.0 0.0 1 457s supply_price supply_farmPrice supply_trend 457s demand_1 0.0 0.0 0 457s demand_2 0.0 0.0 0 457s demand_3 0.0 0.0 0 457s demand_4 0.0 0.0 0 457s demand_5 0.0 0.0 0 457s demand_6 0.0 0.0 0 457s demand_7 0.0 0.0 0 457s demand_8 0.0 0.0 0 457s demand_9 0.0 0.0 0 457s demand_10 0.0 0.0 0 457s demand_11 0.0 0.0 0 457s demand_12 0.0 0.0 0 457s demand_13 0.0 0.0 0 457s demand_14 0.0 0.0 0 457s demand_15 0.0 0.0 0 457s demand_16 0.0 0.0 0 457s demand_17 0.0 0.0 0 457s demand_18 0.0 0.0 0 457s demand_19 0.0 0.0 0 457s demand_20 0.0 0.0 0 457s supply_1 100.3 98.0 1 457s supply_2 104.3 99.1 2 457s supply_3 103.4 99.1 3 457s supply_4 104.5 98.1 4 457s supply_5 98.0 110.8 5 457s supply_6 99.5 108.2 6 457s supply_7 101.1 105.6 7 457s supply_8 104.8 109.8 8 457s supply_9 96.4 108.7 9 457s supply_10 91.2 100.6 10 457s supply_11 93.1 81.0 11 457s supply_12 98.8 68.6 12 457s supply_13 102.9 70.9 13 457s supply_14 98.8 81.4 14 457s supply_15 95.1 102.3 15 457s supply_16 98.5 105.0 16 457s supply_17 86.5 110.5 17 457s supply_18 104.0 92.5 18 457s supply_19 105.8 89.3 19 457s supply_20 113.5 93.0 20 457s > print( mm1 <- model.matrix( fit3sls[[ 4 ]]$e1c$eq[[ 1 ]] ) ) 457s (Intercept) price income 457s 1 1 100.3 87.4 457s 2 1 104.3 97.6 457s 3 1 103.4 96.7 457s 4 1 104.5 98.2 457s 5 1 98.0 99.8 457s 6 1 99.5 100.5 457s 7 1 101.1 103.2 457s 8 1 104.8 107.8 457s 9 1 96.4 96.6 457s 10 1 91.2 88.9 457s 11 1 93.1 75.1 457s 12 1 98.8 76.9 457s 13 1 102.9 84.6 457s 14 1 98.8 90.6 457s 15 1 95.1 103.1 457s 16 1 98.5 105.1 457s 17 1 86.5 96.4 457s 18 1 104.0 104.4 457s 19 1 105.8 110.7 457s 20 1 113.5 127.1 457s attr(,"assign") 457s [1] 0 1 2 457s > print( mm2 <- model.matrix( fit3sls[[ 4 ]]$e1c$eq[[ 2 ]] ) ) 457s (Intercept) price farmPrice trend 457s 1 1 100.3 98.0 1 457s 2 1 104.3 99.1 2 457s 3 1 103.4 99.1 3 457s 4 1 104.5 98.1 4 457s 5 1 98.0 110.8 5 457s 6 1 99.5 108.2 6 457s 7 1 101.1 105.6 7 457s 8 1 104.8 109.8 8 457s 9 1 96.4 108.7 9 457s 10 1 91.2 100.6 10 457s 11 1 93.1 81.0 11 457s 12 1 98.8 68.6 12 457s 13 1 102.9 70.9 13 457s 14 1 98.8 81.4 14 457s 15 1 95.1 102.3 15 457s 16 1 98.5 105.0 16 457s 17 1 86.5 110.5 17 457s 18 1 104.0 92.5 18 457s 19 1 105.8 89.3 19 457s 20 1 113.5 93.0 20 457s attr(,"assign") 457s [1] 0 1 2 3 457s > 457s > # with x (returnModelMatrix) = FALSE 457s > print( all.equal( mm, model.matrix( fit3sls[[ 4 ]]$e1wc ) ) ) 457s [1] TRUE 457s > print( all.equal( mm1, model.matrix( fit3sls[[ 4 ]]$e1wc$eq[[ 1 ]] ) ) ) 457s [1] TRUE 457s > print( all.equal( mm2, model.matrix( fit3sls[[ 4 ]]$e1wc$eq[[ 2 ]] ) ) ) 457s [1] TRUE 457s > print( !is.null( fit3sls[[ 4 ]]$e1wc$eq[[ 1 ]]$x ) ) 457s [1] FALSE 457s > 457s > # with x (returnModelMatrix) = TRUE 457s > print( !is.null( fit3sls[[ 5 ]]$e2$eq[[ 1 ]]$x ) ) 457s [1] TRUE 457s > print( all.equal( mm, model.matrix( fit3sls[[ 5 ]]$e2 ) ) ) 457s [1] TRUE 457s > print( all.equal( mm1, model.matrix( fit3sls[[ 5 ]]$e2$eq[[ 1 ]] ) ) ) 457s [1] TRUE 457s > print( all.equal( mm2, model.matrix( fit3sls[[ 5 ]]$e2$eq[[ 2 ]] ) ) ) 457s [1] TRUE 457s > 457s > # with x (returnModelMatrix) = FALSE 457s > print( all.equal( mm, model.matrix( fit3sls[[ 5 ]]$e2e ) ) ) 457s [1] TRUE 457s > print( all.equal( mm1, model.matrix( fit3sls[[ 5 ]]$e2e$eq[[ 1 ]] ) ) ) 457s [1] TRUE 457s > print( all.equal( mm2, model.matrix( fit3sls[[ 5 ]]$e2e$eq[[ 2 ]] ) ) ) 457s [1] TRUE 457s > print( !is.null( fit3sls[[ 5 ]]$e1wc$e2e[[ 1 ]]$x ) ) 457s [1] FALSE 457s > 457s > # with x (returnModelMatrix) = TRUE 457s > print( !is.null( fit3sls[[ 1 ]]$e3e$eq[[ 1 ]]$x ) ) 457s [1] TRUE 457s > print( all.equal( mm, model.matrix( fit3sls[[ 1 ]]$e3e ) ) ) 457s [1] TRUE 457s > print( all.equal( mm1, model.matrix( fit3sls[[ 1 ]]$e3e$eq[[ 1 ]] ) ) ) 457s [1] TRUE 457s > print( all.equal( mm2, model.matrix( fit3sls[[ 1 ]]$e3e$eq[[ 2 ]] ) ) ) 457s [1] TRUE 457s > 457s > # with x (returnModelMatrix) = FALSE 457s > print( all.equal( mm, model.matrix( fit3sls[[ 1 ]]$e3 ) ) ) 457s [1] TRUE 457s > print( all.equal( mm1, model.matrix( fit3sls[[ 1 ]]$e3$eq[[ 1 ]] ) ) ) 457s [1] TRUE 457s > print( all.equal( mm2, model.matrix( fit3sls[[ 1 ]]$e3$eq[[ 2 ]] ) ) ) 457s [1] TRUE 457s > print( !is.null( fit3sls[[ 1 ]]$e3$eq[[ 1 ]]$x ) ) 457s [1] FALSE 457s > 457s > # with x (returnModelMatrix) = TRUE 457s > print( !is.null( fit3slsi[[ 2 ]]$e4$eq[[ 1 ]]$x ) ) 457s [1] TRUE 457s > print( all.equal( mm, model.matrix( fit3slsi[[ 2 ]]$e4 ) ) ) 457s [1] TRUE 457s > print( all.equal( mm1, model.matrix( fit3slsi[[ 2 ]]$e4$eq[[ 1 ]] ) ) ) 457s [1] TRUE 457s > print( all.equal( mm2, model.matrix( fit3slsi[[ 2 ]]$e4$eq[[ 2 ]] ) ) ) 457s [1] TRUE 457s > 457s > # with x (returnModelMatrix) = FALSE 457s > print( all.equal( mm, model.matrix( fit3slsi[[ 2 ]]$e4we ) ) ) 457s [1] TRUE 457s > print( all.equal( mm1, model.matrix( fit3slsi[[ 2 ]]$e4we$eq[[ 1 ]] ) ) ) 457s [1] TRUE 457s > print( all.equal( mm2, model.matrix( fit3slsi[[ 2 ]]$e4we$eq[[ 2 ]] ) ) ) 457s [1] TRUE 457s > print( !is.null( fit3slsi[[ 2 ]]$e1wc$e4we[[ 1 ]]$x ) ) 457s [1] FALSE 457s > 457s > # with x (returnModelMatrix) = TRUE 457s > print( !is.null( fit3slsi[[ 5 ]]$e5w$eq[[ 1 ]]$x ) ) 457s [1] TRUE 457s > print( all.equal( mm, model.matrix( fit3slsi[[ 5 ]]$e5w ) ) ) 457s [1] TRUE 457s > print( all.equal( mm1, model.matrix( fit3slsi[[ 5 ]]$e5w$eq[[ 1 ]] ) ) ) 457s [1] TRUE 457s > print( all.equal( mm2, model.matrix( fit3slsi[[ 5 ]]$e5w$eq[[ 2 ]] ) ) ) 457s [1] TRUE 457s > 457s > # with x (returnModelMatrix) = FALSE 457s > print( all.equal( mm, model.matrix( fit3slsi[[ 5 ]]$e5 ) ) ) 457s [1] TRUE 457s > print( all.equal( mm1, model.matrix( fit3slsi[[ 5 ]]$e5$eq[[ 1 ]] ) ) ) 457s [1] TRUE 457s > print( all.equal( mm2, model.matrix( fit3slsi[[ 5 ]]$e5$eq[[ 2 ]] ) ) ) 457s [1] TRUE 457s > print( !is.null( fit3slsi[[ 5 ]]$e5$eq[[ 1 ]]$x ) ) 457s [1] FALSE 457s > 457s > # with x (returnModelMatrix) = TRUE 457s > print( !is.null( fit3slsd[[ 3 ]]$e5e$eq[[ 1 ]]$x ) ) 457s [1] TRUE 457s > print( all.equal( mm, model.matrix( fit3slsd[[ 3 ]]$e5e ) ) ) 457s [1] TRUE 457s > print( all.equal( mm1, model.matrix( fit3slsd[[ 3 ]]$e5e$eq[[ 1 ]] ) ) ) 457s [1] TRUE 457s > print( all.equal( mm2, model.matrix( fit3slsd[[ 3 ]]$e5e$eq[[ 2 ]] ) ) ) 457s [1] TRUE 457s > 457s > # with x (returnModelMatrix) = FALSE 457s > print( all.equal( mm, model.matrix( fit3slsd[[ 3 ]]$e5we ) ) ) 457s [1] TRUE 457s > print( all.equal( mm1, model.matrix( fit3slsd[[ 3 ]]$e5we$eq[[ 1 ]] ) ) ) 457s [1] TRUE 457s > print( all.equal( mm2, model.matrix( fit3slsd[[ 3 ]]$e5we$eq[[ 2 ]] ) ) ) 457s [1] TRUE 457s > print( !is.null( fit3sls[[ 3 ]]$e5we$eq[[ 1 ]]$x ) ) 457s [1] FALSE 457s > 457s > # with x (returnModelMatrix) = TRUE 457s > print( !is.null( fit3slsd[[ 2 ]]$e3w$eq[[ 1 ]]$x ) ) 457s [1] TRUE 457s > print( all.equal( mm, model.matrix( fit3slsd[[ 2 ]]$e3w ) ) ) 457s [1] TRUE 457s > print( all.equal( mm1, model.matrix( fit3slsd[[ 2 ]]$e3w$eq[[ 1 ]] ) ) ) 457s [1] TRUE 457s > print( all.equal( mm2, model.matrix( fit3slsd[[ 2 ]]$e3w$eq[[ 2 ]] ) ) ) 457s [1] TRUE 457s > 457s > # with x (returnModelMatrix) = FALSE 457s > print( all.equal( mm, model.matrix( fit3slsd[[ 2 ]]$e3 ) ) ) 457s [1] TRUE 457s > print( all.equal( mm1, model.matrix( fit3slsd[[ 2 ]]$e3$eq[[ 1 ]] ) ) ) 457s [1] TRUE 457s > print( all.equal( mm2, model.matrix( fit3slsd[[ 2 ]]$e3$eq[[ 2 ]] ) ) ) 457s [1] TRUE 457s > print( !is.null( fit3slsd[[ 2 ]]$e3$eq[[ 1 ]]$x ) ) 457s [1] FALSE 457s > 457s > # matrices of instrumental variables 457s > model.matrix( fit3sls[[ 1 ]]$e1c, which = "z" ) 457s demand_(Intercept) demand_income demand_farmPrice demand_trend 457s demand_1 1 87.4 98.0 1 457s demand_2 1 97.6 99.1 2 457s demand_3 1 96.7 99.1 3 457s demand_4 1 98.2 98.1 4 457s demand_5 1 99.8 110.8 5 457s demand_6 1 100.5 108.2 6 457s demand_7 1 103.2 105.6 7 457s demand_8 1 107.8 109.8 8 457s demand_9 1 96.6 108.7 9 457s demand_10 1 88.9 100.6 10 457s demand_11 1 75.1 81.0 11 457s demand_12 1 76.9 68.6 12 457s demand_13 1 84.6 70.9 13 457s demand_14 1 90.6 81.4 14 457s demand_15 1 103.1 102.3 15 457s demand_16 1 105.1 105.0 16 457s demand_17 1 96.4 110.5 17 457s demand_18 1 104.4 92.5 18 457s demand_19 1 110.7 89.3 19 457s demand_20 1 127.1 93.0 20 457s supply_1 0 0.0 0.0 0 457s supply_2 0 0.0 0.0 0 457s supply_3 0 0.0 0.0 0 457s supply_4 0 0.0 0.0 0 457s supply_5 0 0.0 0.0 0 457s supply_6 0 0.0 0.0 0 457s supply_7 0 0.0 0.0 0 457s supply_8 0 0.0 0.0 0 457s supply_9 0 0.0 0.0 0 457s supply_10 0 0.0 0.0 0 457s supply_11 0 0.0 0.0 0 457s supply_12 0 0.0 0.0 0 457s supply_13 0 0.0 0.0 0 457s supply_14 0 0.0 0.0 0 457s supply_15 0 0.0 0.0 0 457s supply_16 0 0.0 0.0 0 457s supply_17 0 0.0 0.0 0 457s supply_18 0 0.0 0.0 0 457s supply_19 0 0.0 0.0 0 457s supply_20 0 0.0 0.0 0 457s supply_(Intercept) supply_income supply_farmPrice supply_trend 457s demand_1 0 0.0 0.0 0 457s demand_2 0 0.0 0.0 0 457s demand_3 0 0.0 0.0 0 457s demand_4 0 0.0 0.0 0 457s demand_5 0 0.0 0.0 0 457s demand_6 0 0.0 0.0 0 457s demand_7 0 0.0 0.0 0 457s demand_8 0 0.0 0.0 0 457s demand_9 0 0.0 0.0 0 457s demand_10 0 0.0 0.0 0 457s demand_11 0 0.0 0.0 0 457s demand_12 0 0.0 0.0 0 457s demand_13 0 0.0 0.0 0 457s demand_14 0 0.0 0.0 0 457s demand_15 0 0.0 0.0 0 457s demand_16 0 0.0 0.0 0 457s demand_17 0 0.0 0.0 0 457s demand_18 0 0.0 0.0 0 457s demand_19 0 0.0 0.0 0 457s demand_20 0 0.0 0.0 0 457s supply_1 1 87.4 98.0 1 457s supply_2 1 97.6 99.1 2 457s supply_3 1 96.7 99.1 3 457s supply_4 1 98.2 98.1 4 457s supply_5 1 99.8 110.8 5 457s supply_6 1 100.5 108.2 6 457s supply_7 1 103.2 105.6 7 457s supply_8 1 107.8 109.8 8 457s supply_9 1 96.6 108.7 9 457s supply_10 1 88.9 100.6 10 457s supply_11 1 75.1 81.0 11 457s supply_12 1 76.9 68.6 12 457s supply_13 1 84.6 70.9 13 457s supply_14 1 90.6 81.4 14 457s supply_15 1 103.1 102.3 15 457s supply_16 1 105.1 105.0 16 457s supply_17 1 96.4 110.5 17 457s supply_18 1 104.4 92.5 18 457s supply_19 1 110.7 89.3 19 457s supply_20 1 127.1 93.0 20 457s > model.matrix( fit3sls[[ 3 ]]$e1c$eq[[ 1 ]], which = "z" ) 457s (Intercept) income farmPrice trend 457s 1 1 87.4 98.0 1 457s 2 1 97.6 99.1 2 457s 3 1 96.7 99.1 3 457s 4 1 98.2 98.1 4 457s 5 1 99.8 110.8 5 457s 6 1 100.5 108.2 6 457s 7 1 103.2 105.6 7 457s 8 1 107.8 109.8 8 457s 9 1 96.6 108.7 9 457s 10 1 88.9 100.6 10 457s 11 1 75.1 81.0 11 457s 12 1 76.9 68.6 12 457s 13 1 84.6 70.9 13 457s 14 1 90.6 81.4 14 457s 15 1 103.1 102.3 15 457s 16 1 105.1 105.0 16 457s 17 1 96.4 110.5 17 457s 18 1 104.4 92.5 18 457s 19 1 110.7 89.3 19 457s 20 1 127.1 93.0 20 457s attr(,"assign") 457s [1] 0 1 2 3 457s > model.matrix( fit3sls[[ 4 ]]$e1c$eq[[ 2 ]], which = "z" ) 457s (Intercept) income farmPrice trend 457s 1 1 87.4 98.0 1 457s 2 1 97.6 99.1 2 457s 3 1 96.7 99.1 3 457s 4 1 98.2 98.1 4 457s 5 1 99.8 110.8 5 457s 6 1 100.5 108.2 6 457s 7 1 103.2 105.6 7 457s 8 1 107.8 109.8 8 457s 9 1 96.6 108.7 9 457s 10 1 88.9 100.6 10 457s 11 1 75.1 81.0 11 457s 12 1 76.9 68.6 12 457s 13 1 84.6 70.9 13 457s 14 1 90.6 81.4 14 457s 15 1 103.1 102.3 15 457s 16 1 105.1 105.0 16 457s 17 1 96.4 110.5 17 457s 18 1 104.4 92.5 18 457s 19 1 110.7 89.3 19 457s 20 1 127.1 93.0 20 457s attr(,"assign") 457s [1] 0 1 2 3 457s > 457s > # matrices of fitted regressors 457s > model.matrix( fit3slsd[[ 1 ]]$e3w, which = "xHat" ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s demand_1 1 95.2 87.4 0 457s demand_2 1 99.3 97.6 0 457s demand_3 1 99.0 96.7 0 457s demand_4 1 99.9 98.2 0 457s demand_5 1 97.0 99.8 0 457s demand_6 1 98.0 100.5 0 457s demand_7 1 99.9 103.2 0 457s demand_8 1 100.7 107.8 0 457s demand_9 1 96.2 96.6 0 457s demand_10 1 95.1 88.9 0 457s demand_11 1 94.7 75.1 0 457s demand_12 1 99.0 76.9 0 457s demand_13 1 101.7 84.6 0 457s demand_14 1 101.3 90.6 0 457s demand_15 1 100.8 103.1 0 457s demand_16 1 100.9 105.1 0 457s demand_17 1 95.6 96.4 0 457s demand_18 1 104.2 104.4 0 457s demand_19 1 107.8 110.7 0 457s demand_20 1 113.9 127.1 0 457s supply_1 0 0.0 0.0 1 457s supply_2 0 0.0 0.0 1 457s supply_3 0 0.0 0.0 1 457s supply_4 0 0.0 0.0 1 457s supply_5 0 0.0 0.0 1 457s supply_6 0 0.0 0.0 1 457s supply_7 0 0.0 0.0 1 457s supply_8 0 0.0 0.0 1 457s supply_9 0 0.0 0.0 1 457s supply_10 0 0.0 0.0 1 457s supply_11 0 0.0 0.0 1 457s supply_12 0 0.0 0.0 1 457s supply_13 0 0.0 0.0 1 457s supply_14 0 0.0 0.0 1 457s supply_15 0 0.0 0.0 1 457s supply_16 0 0.0 0.0 1 457s supply_17 0 0.0 0.0 1 457s supply_18 0 0.0 0.0 1 457s supply_19 0 0.0 0.0 1 457s supply_20 0 0.0 0.0 1 457s supply_price supply_farmPrice supply_trend 457s demand_1 0.0 0.0 0 457s demand_2 0.0 0.0 0 457s demand_3 0.0 0.0 0 457s demand_4 0.0 0.0 0 457s demand_5 0.0 0.0 0 457s demand_6 0.0 0.0 0 457s demand_7 0.0 0.0 0 457s demand_8 0.0 0.0 0 457s demand_9 0.0 0.0 0 457s demand_10 0.0 0.0 0 457s demand_11 0.0 0.0 0 457s demand_12 0.0 0.0 0 457s demand_13 0.0 0.0 0 457s demand_14 0.0 0.0 0 457s demand_15 0.0 0.0 0 457s demand_16 0.0 0.0 0 457s demand_17 0.0 0.0 0 457s demand_18 0.0 0.0 0 457s demand_19 0.0 0.0 0 457s demand_20 0.0 0.0 0 457s supply_1 99.6 98.0 1 457s supply_2 105.1 99.1 2 457s supply_3 103.8 99.1 3 457s supply_4 104.5 98.1 4 457s supply_5 98.7 110.8 5 457s supply_6 99.6 108.2 6 457s supply_7 102.0 105.6 7 457s supply_8 102.2 109.8 8 457s supply_9 94.6 108.7 9 457s supply_10 92.7 100.6 10 457s supply_11 92.4 81.0 11 457s supply_12 98.9 68.6 12 457s supply_13 102.2 70.9 13 457s supply_14 100.3 81.4 14 457s supply_15 97.6 102.3 15 457s supply_16 96.9 105.0 16 457s supply_17 87.7 110.5 17 457s supply_18 101.1 92.5 18 457s supply_19 106.1 89.3 19 457s supply_20 114.4 93.0 20 457s > model.matrix( fit3slsd[[ 3 ]]$e3w$eq[[ 1 ]], which = "xHat" ) 457s (Intercept) price income 457s 1 1 95.2 87.4 457s 2 1 99.3 97.6 457s 3 1 99.0 96.7 457s 4 1 99.9 98.2 457s 5 1 97.0 99.8 457s 6 1 98.0 100.5 457s 7 1 99.9 103.2 457s 8 1 100.7 107.8 457s 9 1 96.2 96.6 457s 10 1 95.1 88.9 457s 11 1 94.7 75.1 457s 12 1 99.0 76.9 457s 13 1 101.7 84.6 457s 14 1 101.3 90.6 457s 15 1 100.8 103.1 457s 16 1 100.9 105.1 457s 17 1 95.6 96.4 457s 18 1 104.2 104.4 457s 19 1 107.8 110.7 457s 20 1 113.9 127.1 457s > model.matrix( fit3slsd[[ 4 ]]$e3w$eq[[ 2 ]], which = "xHat" ) 457s (Intercept) price farmPrice trend 457s 1 1 99.6 98.0 1 457s 2 1 105.1 99.1 2 457s 3 1 103.8 99.1 3 457s 4 1 104.5 98.1 4 457s 5 1 98.7 110.8 5 457s 6 1 99.6 108.2 6 457s 7 1 102.0 105.6 7 457s 8 1 102.2 109.8 8 457s 9 1 94.6 108.7 9 457s 10 1 92.7 100.6 10 457s 11 1 92.4 81.0 11 457s 12 1 98.9 68.6 12 457s 13 1 102.2 70.9 13 457s 14 1 100.3 81.4 14 457s 15 1 97.6 102.3 15 457s 16 1 96.9 105.0 16 457s 17 1 87.7 110.5 17 457s 18 1 101.1 92.5 18 457s 19 1 106.1 89.3 19 457s 20 1 114.4 93.0 20 457s > 457s > 457s > ## **************** formulas ************************ 457s > formula( fit3sls[[ 2 ]]$e1c ) 457s $demand 457s consump ~ price + income 457s 457s $supply 457s consump ~ price + farmPrice + trend 457s 457s > formula( fit3sls[[ 2 ]]$e1c$eq[[ 1 ]] ) 457s consump ~ price + income 457s > 457s > formula( fit3sls[[ 3 ]]$e2e ) 457s $demand 457s consump ~ price + income 457s 457s $supply 457s consump ~ price + farmPrice + trend 457s 457s > formula( fit3sls[[ 3 ]]$e2e$eq[[ 2 ]] ) 457s consump ~ price + farmPrice + trend 457s > 457s > formula( fit3sls[[ 4 ]]$e3 ) 457s $demand 457s consump ~ price + income 457s 457s $supply 457s consump ~ price + farmPrice + trend 457s 457s > formula( fit3sls[[ 4 ]]$e3$eq[[ 1 ]] ) 457s consump ~ price + income 457s > 457s > formula( fit3sls[[ 5 ]]$e4e ) 457s $demand 457s consump ~ price + income 457s 457s $supply 457s consump ~ price + farmPrice + trend 457s 457s > formula( fit3sls[[ 5 ]]$e4e$eq[[ 2 ]] ) 457s consump ~ price + farmPrice + trend 457s > 457s > formula( fit3sls[[ 1 ]]$e5 ) 457s $demand 457s consump ~ price + income 457s 457s $supply 457s consump ~ price + farmPrice + trend 457s 457s > formula( fit3sls[[ 1 ]]$e5$eq[[ 1 ]] ) 457s consump ~ price + income 457s > 457s > formula( fit3slsi[[ 3 ]]$e3e ) 457s $demand 457s consump ~ price + income 457s 457s $supply 457s consump ~ price + farmPrice + trend 457s 457s > formula( fit3slsi[[ 3 ]]$e3e$eq[[ 1 ]] ) 457s consump ~ price + income 457s > 457s > formula( fit3slsd[[ 4 ]]$e4 ) 457s $demand 457s consump ~ price + income 457s 457s $supply 457s consump ~ price + farmPrice + trend 457s 457s > formula( fit3slsd[[ 4 ]]$e4$eq[[ 2 ]] ) 457s consump ~ price + farmPrice + trend 457s > 457s > formula( fit3slsd[[ 2 ]]$e1w ) 457s $demand 457s consump ~ price + income 457s 457s $supply 457s consump ~ price + farmPrice + trend 457s 457s > formula( fit3slsd[[ 2 ]]$e1w$eq[[ 1 ]] ) 457s consump ~ price + income 457s > 457s > 457s > ## **************** model terms ******************* 457s > terms( fit3sls[[ 2 ]]$e1c ) 457s $demand 457s consump ~ price + income 457s attr(,"variables") 457s list(consump, price, income) 457s attr(,"factors") 457s price income 457s consump 0 0 457s price 1 0 457s income 0 1 457s attr(,"term.labels") 457s [1] "price" "income" 457s attr(,"order") 457s [1] 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 1 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(consump, price, income) 457s attr(,"dataClasses") 457s consump price income 457s "numeric" "numeric" "numeric" 457s 457s $supply 457s consump ~ price + farmPrice + trend 457s attr(,"variables") 457s list(consump, price, farmPrice, trend) 457s attr(,"factors") 457s price farmPrice trend 457s consump 0 0 0 457s price 1 0 0 457s farmPrice 0 1 0 457s trend 0 0 1 457s attr(,"term.labels") 457s [1] "price" "farmPrice" "trend" 457s attr(,"order") 457s [1] 1 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 1 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(consump, price, farmPrice, trend) 457s attr(,"dataClasses") 457s consump price farmPrice trend 457s "numeric" "numeric" "numeric" "numeric" 457s 457s > terms( fit3sls[[ 2 ]]$e1c$eq[[ 1 ]] ) 457s consump ~ price + income 457s attr(,"variables") 457s list(consump, price, income) 457s attr(,"factors") 457s price income 457s consump 0 0 457s price 1 0 457s income 0 1 457s attr(,"term.labels") 457s [1] "price" "income" 457s attr(,"order") 457s [1] 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 1 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(consump, price, income) 457s attr(,"dataClasses") 457s consump price income 457s "numeric" "numeric" "numeric" 457s > 457s > terms( fit3sls[[ 3 ]]$e2e ) 457s $demand 457s consump ~ price + income 457s attr(,"variables") 457s list(consump, price, income) 457s attr(,"factors") 457s price income 457s consump 0 0 457s price 1 0 457s income 0 1 457s attr(,"term.labels") 457s [1] "price" "income" 457s attr(,"order") 457s [1] 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 1 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(consump, price, income) 457s attr(,"dataClasses") 457s consump price income 457s "numeric" "numeric" "numeric" 457s 457s $supply 457s consump ~ price + farmPrice + trend 457s attr(,"variables") 457s list(consump, price, farmPrice, trend) 457s attr(,"factors") 457s price farmPrice trend 457s consump 0 0 0 457s price 1 0 0 457s farmPrice 0 1 0 457s trend 0 0 1 457s attr(,"term.labels") 457s [1] "price" "farmPrice" "trend" 457s attr(,"order") 457s [1] 1 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 1 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(consump, price, farmPrice, trend) 457s attr(,"dataClasses") 457s consump price farmPrice trend 457s "numeric" "numeric" "numeric" "numeric" 457s 457s > terms( fit3sls[[ 3 ]]$e2e$eq[[ 2 ]] ) 457s consump ~ price + farmPrice + trend 457s attr(,"variables") 457s list(consump, price, farmPrice, trend) 457s attr(,"factors") 457s price farmPrice trend 457s consump 0 0 0 457s price 1 0 0 457s farmPrice 0 1 0 457s trend 0 0 1 457s attr(,"term.labels") 457s [1] "price" "farmPrice" "trend" 457s attr(,"order") 457s [1] 1 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 1 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(consump, price, farmPrice, trend) 457s attr(,"dataClasses") 457s consump price farmPrice trend 457s "numeric" "numeric" "numeric" "numeric" 457s > 457s > terms( fit3sls[[ 4 ]]$e3 ) 457s $demand 457s consump ~ price + income 457s attr(,"variables") 457s list(consump, price, income) 457s attr(,"factors") 457s price income 457s consump 0 0 457s price 1 0 457s income 0 1 457s attr(,"term.labels") 457s [1] "price" "income" 457s attr(,"order") 457s [1] 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 1 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(consump, price, income) 457s attr(,"dataClasses") 457s consump price income 457s "numeric" "numeric" "numeric" 457s 457s $supply 457s consump ~ price + farmPrice + trend 457s attr(,"variables") 457s list(consump, price, farmPrice, trend) 457s attr(,"factors") 457s price farmPrice trend 457s consump 0 0 0 457s price 1 0 0 457s farmPrice 0 1 0 457s trend 0 0 1 457s attr(,"term.labels") 457s [1] "price" "farmPrice" "trend" 457s attr(,"order") 457s [1] 1 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 1 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(consump, price, farmPrice, trend) 457s attr(,"dataClasses") 457s consump price farmPrice trend 457s "numeric" "numeric" "numeric" "numeric" 457s 457s > terms( fit3sls[[ 4 ]]$e3$eq[[ 1 ]] ) 457s consump ~ price + income 457s attr(,"variables") 457s list(consump, price, income) 457s attr(,"factors") 457s price income 457s consump 0 0 457s price 1 0 457s income 0 1 457s attr(,"term.labels") 457s [1] "price" "income" 457s attr(,"order") 457s [1] 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 1 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(consump, price, income) 457s attr(,"dataClasses") 457s consump price income 457s "numeric" "numeric" "numeric" 457s > 457s > terms( fit3sls[[ 5 ]]$e4e ) 457s $demand 457s consump ~ price + income 457s attr(,"variables") 457s list(consump, price, income) 457s attr(,"factors") 457s price income 457s consump 0 0 457s price 1 0 457s income 0 1 457s attr(,"term.labels") 457s [1] "price" "income" 457s attr(,"order") 457s [1] 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 1 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(consump, price, income) 457s attr(,"dataClasses") 457s consump price income 457s "numeric" "numeric" "numeric" 457s 457s $supply 457s consump ~ price + farmPrice + trend 457s attr(,"variables") 457s list(consump, price, farmPrice, trend) 457s attr(,"factors") 457s price farmPrice trend 457s consump 0 0 0 457s price 1 0 0 457s farmPrice 0 1 0 457s trend 0 0 1 457s attr(,"term.labels") 457s [1] "price" "farmPrice" "trend" 457s attr(,"order") 457s [1] 1 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 1 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(consump, price, farmPrice, trend) 457s attr(,"dataClasses") 457s consump price farmPrice trend 457s "numeric" "numeric" "numeric" "numeric" 457s 457s > terms( fit3sls[[ 5 ]]$e4e$eq[[ 2 ]] ) 457s consump ~ price + farmPrice + trend 457s attr(,"variables") 457s list(consump, price, farmPrice, trend) 457s attr(,"factors") 457s price farmPrice trend 457s consump 0 0 0 457s price 1 0 0 457s farmPrice 0 1 0 457s trend 0 0 1 457s attr(,"term.labels") 457s [1] "price" "farmPrice" "trend" 457s attr(,"order") 457s [1] 1 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 1 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(consump, price, farmPrice, trend) 457s attr(,"dataClasses") 457s consump price farmPrice trend 457s "numeric" "numeric" "numeric" "numeric" 457s > 457s > terms( fit3sls[[ 1 ]]$e5 ) 457s $demand 457s consump ~ price + income 457s attr(,"variables") 457s list(consump, price, income) 457s attr(,"factors") 457s price income 457s consump 0 0 457s price 1 0 457s income 0 1 457s attr(,"term.labels") 457s [1] "price" "income" 457s attr(,"order") 457s [1] 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 1 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(consump, price, income) 457s attr(,"dataClasses") 457s consump price income 457s "numeric" "numeric" "numeric" 457s 457s $supply 457s consump ~ price + farmPrice + trend 457s attr(,"variables") 457s list(consump, price, farmPrice, trend) 457s attr(,"factors") 457s price farmPrice trend 457s consump 0 0 0 457s price 1 0 0 457s farmPrice 0 1 0 457s trend 0 0 1 457s attr(,"term.labels") 457s [1] "price" "farmPrice" "trend" 457s attr(,"order") 457s [1] 1 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 1 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(consump, price, farmPrice, trend) 457s attr(,"dataClasses") 457s consump price farmPrice trend 457s "numeric" "numeric" "numeric" "numeric" 457s 457s > terms( fit3sls[[ 1 ]]$e5$eq[[ 1 ]] ) 457s consump ~ price + income 457s attr(,"variables") 457s list(consump, price, income) 457s attr(,"factors") 457s price income 457s consump 0 0 457s price 1 0 457s income 0 1 457s attr(,"term.labels") 457s [1] "price" "income" 457s attr(,"order") 457s [1] 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 1 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(consump, price, income) 457s attr(,"dataClasses") 457s consump price income 457s "numeric" "numeric" "numeric" 457s > 457s > terms( fit3sls[[ 2 ]]$e4wSym ) 457s $demand 457s consump ~ price + income 457s attr(,"variables") 457s list(consump, price, income) 457s attr(,"factors") 457s price income 457s consump 0 0 457s price 1 0 457s income 0 1 457s attr(,"term.labels") 457s [1] "price" "income" 457s attr(,"order") 457s [1] 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 1 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(consump, price, income) 457s attr(,"dataClasses") 457s consump price income 457s "numeric" "numeric" "numeric" 457s 457s $supply 457s consump ~ price + farmPrice + trend 457s attr(,"variables") 457s list(consump, price, farmPrice, trend) 457s attr(,"factors") 457s price farmPrice trend 457s consump 0 0 0 457s price 1 0 0 457s farmPrice 0 1 0 457s trend 0 0 1 457s attr(,"term.labels") 457s [1] "price" "farmPrice" "trend" 457s attr(,"order") 457s [1] 1 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 1 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(consump, price, farmPrice, trend) 457s attr(,"dataClasses") 457s consump price farmPrice trend 457s "numeric" "numeric" "numeric" "numeric" 457s 457s > terms( fit3sls[[ 2 ]]$e4wSym$eq[[ 1 ]] ) 457s consump ~ price + income 457s attr(,"variables") 457s list(consump, price, income) 457s attr(,"factors") 457s price income 457s consump 0 0 457s price 1 0 457s income 0 1 457s attr(,"term.labels") 457s [1] "price" "income" 457s attr(,"order") 457s [1] 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 1 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(consump, price, income) 457s attr(,"dataClasses") 457s consump price income 457s "numeric" "numeric" "numeric" 457s > 457s > terms( fit3slsi[[ 3 ]]$e3e ) 457s $demand 457s consump ~ price + income 457s attr(,"variables") 457s list(consump, price, income) 457s attr(,"factors") 457s price income 457s consump 0 0 457s price 1 0 457s income 0 1 457s attr(,"term.labels") 457s [1] "price" "income" 457s attr(,"order") 457s [1] 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 1 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(consump, price, income) 457s attr(,"dataClasses") 457s consump price income 457s "numeric" "numeric" "numeric" 457s 457s $supply 457s consump ~ price + farmPrice + trend 457s attr(,"variables") 457s list(consump, price, farmPrice, trend) 457s attr(,"factors") 457s price farmPrice trend 457s consump 0 0 0 457s price 1 0 0 457s farmPrice 0 1 0 457s trend 0 0 1 457s attr(,"term.labels") 457s [1] "price" "farmPrice" "trend" 457s attr(,"order") 457s [1] 1 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 1 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(consump, price, farmPrice, trend) 457s attr(,"dataClasses") 457s consump price farmPrice trend 457s "numeric" "numeric" "numeric" "numeric" 457s 457s > terms( fit3slsi[[ 3 ]]$e3e$eq[[ 1 ]] ) 457s consump ~ price + income 457s attr(,"variables") 457s list(consump, price, income) 457s attr(,"factors") 457s price income 457s consump 0 0 457s price 1 0 457s income 0 1 457s attr(,"term.labels") 457s [1] "price" "income" 457s attr(,"order") 457s [1] 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 1 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(consump, price, income) 457s attr(,"dataClasses") 457s consump price income 457s "numeric" "numeric" "numeric" 457s > 457s > terms( fit3slsd[[ 4 ]]$e4 ) 457s $demand 457s consump ~ price + income 457s attr(,"variables") 457s list(consump, price, income) 457s attr(,"factors") 457s price income 457s consump 0 0 457s price 1 0 457s income 0 1 457s attr(,"term.labels") 457s [1] "price" "income" 457s attr(,"order") 457s [1] 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 1 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(consump, price, income) 457s attr(,"dataClasses") 457s consump price income 457s "numeric" "numeric" "numeric" 457s 457s $supply 457s consump ~ price + farmPrice + trend 457s attr(,"variables") 457s list(consump, price, farmPrice, trend) 457s attr(,"factors") 457s price farmPrice trend 457s consump 0 0 0 457s price 1 0 0 457s farmPrice 0 1 0 457s trend 0 0 1 457s attr(,"term.labels") 457s [1] "price" "farmPrice" "trend" 457s attr(,"order") 457s [1] 1 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 1 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(consump, price, farmPrice, trend) 457s attr(,"dataClasses") 457s consump price farmPrice trend 457s "numeric" "numeric" "numeric" "numeric" 457s 457s > terms( fit3slsd[[ 4 ]]$e4$eq[[ 2 ]] ) 457s consump ~ price + farmPrice + trend 457s attr(,"variables") 457s list(consump, price, farmPrice, trend) 457s attr(,"factors") 457s price farmPrice trend 457s consump 0 0 0 457s price 1 0 0 457s farmPrice 0 1 0 457s trend 0 0 1 457s attr(,"term.labels") 457s [1] "price" "farmPrice" "trend" 457s attr(,"order") 457s [1] 1 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 1 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(consump, price, farmPrice, trend) 457s attr(,"dataClasses") 457s consump price farmPrice trend 457s "numeric" "numeric" "numeric" "numeric" 457s > 457s > terms( fit3slsd[[ 5 ]]$e5we ) 457s $demand 457s consump ~ price + income 457s attr(,"variables") 457s list(consump, price, income) 457s attr(,"factors") 457s price income 457s consump 0 0 457s price 1 0 457s income 0 1 457s attr(,"term.labels") 457s [1] "price" "income" 457s attr(,"order") 457s [1] 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 1 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(consump, price, income) 457s attr(,"dataClasses") 457s consump price income 457s "numeric" "numeric" "numeric" 457s 457s $supply 457s consump ~ price + farmPrice + trend 457s attr(,"variables") 457s list(consump, price, farmPrice, trend) 457s attr(,"factors") 457s price farmPrice trend 457s consump 0 0 0 457s price 1 0 0 457s farmPrice 0 1 0 457s trend 0 0 1 457s attr(,"term.labels") 457s [1] "price" "farmPrice" "trend" 457s attr(,"order") 457s [1] 1 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 1 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(consump, price, farmPrice, trend) 457s attr(,"dataClasses") 457s consump price farmPrice trend 457s "numeric" "numeric" "numeric" "numeric" 457s 457s > terms( fit3slsd[[ 5 ]]$e5we$eq[[ 2 ]] ) 457s consump ~ price + farmPrice + trend 457s attr(,"variables") 457s list(consump, price, farmPrice, trend) 457s attr(,"factors") 457s price farmPrice trend 457s consump 0 0 0 457s price 1 0 0 457s farmPrice 0 1 0 457s trend 0 0 1 457s attr(,"term.labels") 457s [1] "price" "farmPrice" "trend" 457s attr(,"order") 457s [1] 1 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 1 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(consump, price, farmPrice, trend) 457s attr(,"dataClasses") 457s consump price farmPrice trend 457s "numeric" "numeric" "numeric" "numeric" 457s > 457s > 457s > ## **************** terms of instruments ******************* 457s > fit3sls[[ 2 ]]$e1c$eq[[ 1 ]]$termsInst 457s ~income + farmPrice + trend 457s attr(,"variables") 457s list(income, farmPrice, trend) 457s attr(,"factors") 457s income farmPrice trend 457s income 1 0 0 457s farmPrice 0 1 0 457s trend 0 0 1 457s attr(,"term.labels") 457s [1] "income" "farmPrice" "trend" 457s attr(,"order") 457s [1] 1 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 0 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(income, farmPrice, trend) 457s attr(,"dataClasses") 457s income farmPrice trend 457s "numeric" "numeric" "numeric" 457s > 457s > fit3sls[[ 3 ]]$e2e$eq[[ 2 ]]$termsInst 457s ~income + farmPrice + trend 457s attr(,"variables") 457s list(income, farmPrice, trend) 457s attr(,"factors") 457s income farmPrice trend 457s income 1 0 0 457s farmPrice 0 1 0 457s trend 0 0 1 457s attr(,"term.labels") 457s [1] "income" "farmPrice" "trend" 457s attr(,"order") 457s [1] 1 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 0 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(income, farmPrice, trend) 457s attr(,"dataClasses") 457s income farmPrice trend 457s "numeric" "numeric" "numeric" 457s > 457s > fit3sls[[ 4 ]]$e3$eq[[ 1 ]]$termsInst 457s ~income + farmPrice + trend 457s attr(,"variables") 457s list(income, farmPrice, trend) 457s attr(,"factors") 457s income farmPrice trend 457s income 1 0 0 457s farmPrice 0 1 0 457s trend 0 0 1 457s attr(,"term.labels") 457s [1] "income" "farmPrice" "trend" 457s attr(,"order") 457s [1] 1 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 0 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(income, farmPrice, trend) 457s attr(,"dataClasses") 457s income farmPrice trend 457s "numeric" "numeric" "numeric" 457s > 457s > fit3sls[[ 5 ]]$e4e$eq[[ 2 ]]$termsInst 457s ~income + farmPrice + trend 457s attr(,"variables") 457s list(income, farmPrice, trend) 457s attr(,"factors") 457s income farmPrice trend 457s income 1 0 0 457s farmPrice 0 1 0 457s trend 0 0 1 457s attr(,"term.labels") 457s [1] "income" "farmPrice" "trend" 457s attr(,"order") 457s [1] 1 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 0 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(income, farmPrice, trend) 457s attr(,"dataClasses") 457s income farmPrice trend 457s "numeric" "numeric" "numeric" 457s > 457s > fit3sls[[ 1 ]]$e5$eq[[ 1 ]]$termsInst 457s ~income + farmPrice + trend 457s attr(,"variables") 457s list(income, farmPrice, trend) 457s attr(,"factors") 457s income farmPrice trend 457s income 1 0 0 457s farmPrice 0 1 0 457s trend 0 0 1 457s attr(,"term.labels") 457s [1] "income" "farmPrice" "trend" 457s attr(,"order") 457s [1] 1 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 0 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(income, farmPrice, trend) 457s attr(,"dataClasses") 457s income farmPrice trend 457s "numeric" "numeric" "numeric" 457s > 457s > fit3sls[[ 2 ]]$e4wSym$eq[[ 1 ]]$termsInst 457s ~income + farmPrice + trend 457s attr(,"variables") 457s list(income, farmPrice, trend) 457s attr(,"factors") 457s income farmPrice trend 457s income 1 0 0 457s farmPrice 0 1 0 457s trend 0 0 1 457s attr(,"term.labels") 457s [1] "income" "farmPrice" "trend" 457s attr(,"order") 457s [1] 1 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 0 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(income, farmPrice, trend) 457s attr(,"dataClasses") 457s income farmPrice trend 457s "numeric" "numeric" "numeric" 457s > 457s > fit3slsi[[ 3 ]]$e3e$eq[[ 1 ]]$termsInst 457s ~income + farmPrice + trend 457s attr(,"variables") 457s list(income, farmPrice, trend) 457s attr(,"factors") 457s income farmPrice trend 457s income 1 0 0 457s farmPrice 0 1 0 457s trend 0 0 1 457s attr(,"term.labels") 457s [1] "income" "farmPrice" "trend" 457s attr(,"order") 457s [1] 1 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 0 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(income, farmPrice, trend) 457s attr(,"dataClasses") 457s income farmPrice trend 457s "numeric" "numeric" "numeric" 457s > 457s > fit3slsd[[ 4 ]]$e4$eq[[ 2 ]]$termsInst 457s ~income + farmPrice + trend 457s attr(,"variables") 457s list(income, farmPrice, trend) 457s attr(,"factors") 457s income farmPrice trend 457s income 1 0 0 457s farmPrice 0 1 0 457s trend 0 0 1 457s attr(,"term.labels") 457s [1] "income" "farmPrice" "trend" 457s attr(,"order") 457s [1] 1 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 0 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(income, farmPrice, trend) 457s attr(,"dataClasses") 457s income farmPrice trend 457s "numeric" "numeric" "numeric" 457s > 457s > fit3slsd[[ 5 ]]$e5we$eq[[ 2 ]]$termsInst 457s ~income + farmPrice + trend 457s attr(,"variables") 457s list(income, farmPrice, trend) 457s attr(,"factors") 457s income farmPrice trend 457s income 1 0 0 457s farmPrice 0 1 0 457s trend 0 0 1 457s attr(,"term.labels") 457s [1] "income" "farmPrice" "trend" 457s attr(,"order") 457s [1] 1 1 1 457s attr(,"intercept") 457s [1] 1 457s attr(,"response") 457s [1] 0 457s attr(,".Environment") 457s 457s attr(,"predvars") 457s list(income, farmPrice, trend) 457s attr(,"dataClasses") 457s income farmPrice trend 457s "numeric" "numeric" "numeric" 457s > 457s > 457s > ## **************** estfun ************************ 457s > library( "sandwich" ) 457s > 457s > estfun( fit3sls[[ 1 ]]$e1 ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s demand_1 0.93243 92.895 81.494 -0.67273 457s demand_2 -0.67769 -71.238 -66.143 0.48894 457s demand_3 3.38220 351.019 327.058 -2.44019 457s demand_4 2.06995 216.373 203.269 -1.49343 457s demand_5 3.17940 313.652 317.304 -2.29388 457s demand_6 1.83161 182.517 184.077 -1.32147 457s demand_7 2.47947 252.837 255.881 -1.78889 457s demand_8 -5.09517 -520.901 -549.259 3.67607 457s demand_9 -2.17668 -205.928 -210.267 1.57043 457s demand_10 3.95122 366.354 351.263 -2.85073 457s demand_11 -0.37870 -34.993 -28.440 0.27322 457s demand_12 -3.13231 -309.838 -240.875 2.25990 457s demand_13 -2.46263 -251.590 -208.339 1.77674 457s demand_14 0.13711 13.748 12.422 -0.09892 457s demand_15 3.55301 346.849 366.315 -2.56343 457s demand_16 -5.27287 -510.898 -554.179 3.80428 457s demand_17 -0.02852 -2.502 -2.750 0.02058 457s demand_18 -3.97374 -401.582 -414.859 2.86698 457s demand_19 2.30169 244.124 254.797 -1.66062 457s demand_20 -0.61976 -70.898 -78.771 0.44714 457s supply_1 -0.79213 -78.918 -69.232 0.70287 457s supply_2 0.37122 39.022 36.231 -0.32939 457s supply_3 -2.54401 -264.028 -246.006 2.25734 457s supply_4 -1.58295 -165.467 -155.446 1.40458 457s supply_5 -2.40285 -237.044 -239.804 2.13208 457s supply_6 -1.41153 -140.656 -141.858 1.25247 457s supply_7 -1.86174 -189.846 -192.132 1.65195 457s supply_8 3.60208 368.256 388.304 -3.19618 457s supply_9 1.52187 143.979 147.013 -1.35038 457s supply_10 -2.85966 -265.145 -254.224 2.53741 457s supply_11 0.33741 31.177 25.339 -0.29938 457s supply_12 2.36613 234.051 181.956 -2.09950 457s supply_13 1.88385 192.460 159.374 -1.67157 457s supply_14 -0.00962 -0.965 -0.872 0.00854 457s supply_15 -2.52306 -246.304 -260.128 2.23875 457s supply_16 3.84942 372.977 404.574 -3.41564 457s supply_17 0.07279 6.384 7.017 -0.06459 457s supply_18 2.96969 300.114 310.035 -2.63504 457s supply_19 -1.54232 -163.584 -170.735 1.36853 457s supply_20 0.55542 63.538 70.594 -0.49283 457s supply_price supply_farmPrice supply_trend 457s demand_1 -67.022 -65.927 -0.673 457s demand_2 51.397 48.454 0.978 457s demand_3 -253.253 -241.823 -7.321 457s demand_4 -156.109 -146.505 -5.974 457s demand_5 -226.294 -254.162 -11.469 457s demand_6 -131.682 -142.983 -7.929 457s demand_7 -182.417 -188.907 -12.522 457s demand_8 375.820 403.632 29.409 457s demand_9 148.573 170.706 14.134 457s demand_10 -264.317 -286.783 -28.507 457s demand_11 25.247 22.131 3.005 457s demand_12 223.542 155.029 27.119 457s demand_13 181.517 125.971 23.098 457s demand_14 -9.919 -8.052 -1.385 457s demand_15 -250.245 -262.238 -38.451 457s demand_16 368.603 399.449 60.868 457s demand_17 1.805 2.274 0.350 457s demand_18 289.734 265.195 51.606 457s demand_19 -176.131 -148.294 -31.552 457s demand_20 51.151 41.584 8.943 457s supply_1 70.025 68.881 0.703 457s supply_2 -34.625 -32.642 -0.659 457s supply_3 234.276 223.702 6.772 457s supply_4 146.821 137.789 5.618 457s supply_5 210.332 236.235 10.660 457s supply_6 124.806 135.517 7.515 457s supply_7 168.453 174.446 11.564 457s supply_8 -326.759 -350.940 -25.569 457s supply_9 -127.755 -146.786 -12.153 457s supply_10 235.267 255.264 25.374 457s supply_11 -27.664 -24.250 -3.293 457s supply_12 -207.676 -144.026 -25.194 457s supply_13 -170.773 -118.514 -21.730 457s supply_14 0.856 0.695 0.120 457s supply_15 218.549 229.024 33.581 457s supply_16 -330.948 -358.642 -54.650 457s supply_17 -5.665 -7.137 -1.098 457s supply_18 -266.295 -243.742 -47.431 457s supply_19 145.150 122.209 26.002 457s supply_20 -56.378 -45.834 -9.857 457s > round( colSums( estfun( fit3sls[[ 1 ]]$e1 ) ), digits = 7 ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s 0 0 0 0 457s supply_price supply_farmPrice supply_trend 457s 0 0 0 457s > 457s > estfun( fit3sls[[ 2 ]]$e1e ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s demand_1 1.0970 109.29 95.88 -0.8158 457s demand_2 -0.7973 -83.81 -77.82 0.5929 457s demand_3 3.9791 412.96 384.77 -2.9592 457s demand_4 2.4352 254.56 239.14 -1.8110 457s demand_5 3.7405 369.00 373.30 -2.7817 457s demand_6 2.1548 214.73 216.56 -1.6025 457s demand_7 2.9170 297.45 301.04 -2.1693 457s demand_8 -5.9943 -612.82 -646.19 4.4579 457s demand_9 -2.5608 -242.27 -247.37 1.9044 457s demand_10 4.6485 431.00 413.25 -3.4570 457s demand_11 -0.4455 -41.17 -33.46 0.3313 457s demand_12 -3.6851 -364.52 -283.38 2.7405 457s demand_13 -2.8972 -295.99 -245.10 2.1546 457s demand_14 0.1613 16.17 14.61 -0.1200 457s demand_15 4.1800 408.06 430.96 -3.1086 457s demand_16 -6.2034 -601.06 -651.98 4.6134 457s demand_17 -0.0336 -2.94 -3.24 0.0250 457s demand_18 -4.6750 -472.45 -488.07 3.4767 457s demand_19 2.7079 287.21 299.76 -2.0138 457s demand_20 -0.7291 -83.41 -92.67 0.5422 457s supply_1 -0.9222 -91.88 -80.60 0.8435 457s supply_2 0.4880 51.30 47.63 -0.4463 457s supply_3 -3.0517 -316.72 -295.10 2.7912 457s supply_4 -1.8908 -197.65 -185.68 1.7294 457s supply_5 -2.8789 -284.00 -287.31 2.6331 457s supply_6 -1.6828 -167.69 -169.12 1.5391 457s supply_7 -2.2343 -227.83 -230.58 2.0435 457s supply_8 4.3919 449.01 473.45 -4.0170 457s supply_9 1.8611 176.08 179.79 -1.7022 457s supply_10 -3.4650 -321.27 -308.04 3.1691 457s supply_11 0.3885 35.90 29.18 -0.3554 457s supply_12 2.8352 280.45 218.03 -2.5932 457s supply_13 2.2501 229.88 190.36 -2.0580 457s supply_14 -0.0404 -4.05 -3.66 0.0369 457s supply_15 -3.0726 -299.95 -316.79 2.8103 457s supply_16 4.6536 450.90 489.09 -4.2563 457s supply_17 0.0715 6.27 6.89 -0.0654 457s supply_18 3.5683 360.61 372.53 -3.2636 457s supply_19 -1.9084 -202.41 -211.25 1.7454 457s supply_20 0.6388 73.07 81.19 -0.5842 457s supply_price supply_farmPrice supply_trend 457s demand_1 -81.28 -79.95 -0.816 457s demand_2 62.33 58.76 1.186 457s demand_3 -307.11 -293.25 -8.877 457s demand_4 -189.31 -177.66 -7.244 457s demand_5 -274.42 -308.22 -13.909 457s demand_6 -159.69 -173.39 -9.615 457s demand_7 -221.21 -229.08 -15.185 457s demand_8 455.75 489.48 35.663 457s demand_9 180.17 207.01 17.140 457s demand_10 -320.53 -347.78 -34.570 457s demand_11 30.62 26.84 3.645 457s demand_12 271.08 188.00 32.886 457s demand_13 220.12 152.76 28.010 457s demand_14 -12.03 -9.76 -1.679 457s demand_15 -303.47 -318.01 -46.629 457s demand_16 447.00 484.40 73.814 457s demand_17 2.19 2.76 0.424 457s demand_18 351.35 321.60 62.581 457s demand_19 -213.59 -179.83 -38.262 457s demand_20 62.03 50.43 10.845 457s supply_1 84.04 82.66 0.843 457s supply_2 -46.92 -44.23 -0.893 457s supply_3 289.68 276.60 8.373 457s supply_4 180.78 169.66 6.918 457s supply_5 259.76 291.74 13.165 457s supply_6 153.37 166.53 9.235 457s supply_7 208.38 215.80 14.305 457s supply_8 -410.67 -441.06 -32.136 457s supply_9 -161.04 -185.03 -15.320 457s supply_10 293.84 318.82 31.691 457s supply_11 -32.84 -28.78 -3.909 457s supply_12 -256.51 -177.89 -31.118 457s supply_13 -210.25 -145.91 -26.754 457s supply_14 3.70 3.00 0.517 457s supply_15 274.34 287.49 42.154 457s supply_16 -412.40 -446.91 -68.101 457s supply_17 -5.73 -7.23 -1.112 457s supply_18 -329.82 -301.88 -58.745 457s supply_19 185.13 155.87 33.163 457s supply_20 -66.83 -54.33 -11.684 457s > round( colSums( estfun( fit3sls[[ 2 ]]$e1e ) ), digits = 7 ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s 0 0 0 0 457s supply_price supply_farmPrice supply_trend 457s 0 0 0 457s > 457s > estfun( fit3sls[[ 3 ]]$e1c ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s demand_1 1.3280 132.31 116.07 -0.9904 457s demand_2 -0.9652 -101.46 -94.20 0.7198 457s demand_3 4.8171 499.94 465.81 -3.5924 457s demand_4 2.9481 308.17 289.50 -2.1986 457s demand_5 4.5282 446.72 451.92 -3.3770 457s demand_6 2.6087 259.95 262.17 -1.9455 457s demand_7 3.5314 360.10 364.44 -2.6336 457s demand_8 -7.2568 -741.89 -782.28 5.4119 457s demand_9 -3.1001 -293.29 -299.47 2.3120 457s demand_10 5.6275 521.78 500.28 -4.1968 457s demand_11 -0.5394 -49.84 -40.51 0.4022 457s demand_12 -4.4612 -441.28 -343.06 3.3270 457s demand_13 -3.5074 -358.33 -296.72 2.6157 457s demand_14 0.1953 19.58 17.69 -0.1456 457s demand_15 5.0603 494.00 521.72 -3.7739 457s demand_16 -7.5098 -727.64 -789.29 5.6006 457s demand_17 -0.0406 -3.56 -3.92 0.0303 457s demand_18 -5.6596 -571.95 -590.86 4.2207 457s demand_19 3.2782 347.69 362.89 -2.4448 457s demand_20 -0.8827 -100.98 -112.19 0.6583 457s supply_1 -1.2187 -121.42 -106.51 1.0461 457s supply_2 0.4947 52.00 48.29 -0.4247 457s supply_3 -3.7909 -393.44 -366.58 3.2542 457s supply_4 -2.3698 -247.71 -232.71 2.0343 457s supply_5 -3.5854 -353.70 -357.82 3.0777 457s supply_6 -2.1176 -211.02 -212.82 1.8178 457s supply_7 -2.7729 -282.76 -286.16 2.3803 457s supply_8 5.2704 538.82 568.15 -4.5242 457s supply_9 2.2191 209.94 214.37 -1.9049 457s supply_10 -4.2139 -390.71 -374.62 3.6173 457s supply_11 0.5250 48.51 39.42 -0.4506 457s supply_12 3.5301 349.19 271.47 -3.0303 457s supply_13 2.8205 288.15 238.61 -2.4212 457s supply_14 0.0251 2.52 2.28 -0.0216 457s supply_15 -3.6967 -360.87 -381.13 3.1733 457s supply_16 5.6869 551.02 597.70 -4.8817 457s supply_17 0.1301 11.41 12.54 -0.1117 457s supply_18 4.4171 446.39 461.15 -3.7917 457s supply_19 -2.2186 -235.31 -245.60 1.9044 457s supply_20 0.8653 98.99 109.98 -0.7428 457s supply_price supply_farmPrice supply_trend 457s demand_1 -98.67 -97.06 -0.990 457s demand_2 75.67 71.33 1.440 457s demand_3 -372.84 -356.01 -10.777 457s demand_4 -229.82 -215.68 -8.794 457s demand_5 -333.15 -374.17 -16.885 457s demand_6 -193.86 -210.50 -11.673 457s demand_7 -268.55 -278.11 -18.435 457s demand_8 553.28 594.22 43.295 457s demand_9 218.73 251.31 20.808 457s demand_10 -389.13 -422.20 -41.968 457s demand_11 37.17 32.58 4.425 457s demand_12 329.10 228.23 39.924 457s demand_13 267.23 185.45 34.004 457s demand_14 -14.60 -11.85 -2.039 457s demand_15 -368.41 -386.07 -56.608 457s demand_16 542.65 588.07 89.610 457s demand_17 2.66 3.35 0.515 457s demand_18 426.54 390.42 75.973 457s demand_19 -259.30 -218.32 -46.450 457s demand_20 75.30 61.22 13.166 457s supply_1 104.22 102.52 1.046 457s supply_2 -44.64 -42.09 -0.849 457s supply_3 337.73 322.49 9.763 457s supply_4 212.64 199.56 8.137 457s supply_5 303.62 341.01 15.389 457s supply_6 181.14 196.69 10.907 457s supply_7 242.72 251.36 16.662 457s supply_8 -462.53 -496.76 -36.194 457s supply_9 -180.22 -207.07 -17.144 457s supply_10 335.39 363.90 36.173 457s supply_11 -41.64 -36.50 -4.957 457s supply_12 -299.75 -207.88 -36.364 457s supply_13 -247.35 -171.66 -31.475 457s supply_14 -2.16 -1.75 -0.302 457s supply_15 309.78 324.63 47.599 457s supply_16 -473.00 -512.58 -78.108 457s supply_17 -9.80 -12.34 -1.899 457s supply_18 -383.19 -350.73 -68.251 457s supply_19 201.99 170.07 36.184 457s supply_20 -84.97 -69.08 -14.856 457s > round( colSums( estfun( fit3sls[[ 3 ]]$e1c ) ), digits = 7 ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s 0 0 0 0 457s supply_price supply_farmPrice supply_trend 457s 0 0 0 457s > 457s > estfun( fit3sls[[ 4 ]]$e1wc ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s demand_1 1.3280 132.31 116.07 -0.9904 457s demand_2 -0.9652 -101.46 -94.20 0.7198 457s demand_3 4.8171 499.94 465.81 -3.5924 457s demand_4 2.9481 308.17 289.50 -2.1986 457s demand_5 4.5282 446.72 451.92 -3.3770 457s demand_6 2.6087 259.95 262.17 -1.9455 457s demand_7 3.5314 360.10 364.44 -2.6336 457s demand_8 -7.2568 -741.89 -782.28 5.4119 457s demand_9 -3.1001 -293.29 -299.47 2.3120 457s demand_10 5.6275 521.78 500.28 -4.1968 457s demand_11 -0.5394 -49.84 -40.51 0.4022 457s demand_12 -4.4612 -441.28 -343.06 3.3270 457s demand_13 -3.5074 -358.33 -296.72 2.6157 457s demand_14 0.1953 19.58 17.69 -0.1456 457s demand_15 5.0603 494.00 521.72 -3.7739 457s demand_16 -7.5098 -727.64 -789.29 5.6006 457s demand_17 -0.0406 -3.56 -3.92 0.0303 457s demand_18 -5.6596 -571.95 -590.86 4.2207 457s demand_19 3.2782 347.69 362.89 -2.4448 457s demand_20 -0.8827 -100.98 -112.19 0.6583 457s supply_1 -1.2187 -121.42 -106.51 1.0461 457s supply_2 0.4947 52.00 48.29 -0.4247 457s supply_3 -3.7909 -393.44 -366.58 3.2542 457s supply_4 -2.3698 -247.71 -232.71 2.0343 457s supply_5 -3.5854 -353.70 -357.82 3.0777 457s supply_6 -2.1176 -211.02 -212.82 1.8178 457s supply_7 -2.7729 -282.76 -286.16 2.3803 457s supply_8 5.2704 538.82 568.15 -4.5242 457s supply_9 2.2191 209.94 214.37 -1.9049 457s supply_10 -4.2139 -390.71 -374.62 3.6173 457s supply_11 0.5250 48.51 39.42 -0.4506 457s supply_12 3.5301 349.19 271.47 -3.0303 457s supply_13 2.8205 288.15 238.61 -2.4212 457s supply_14 0.0251 2.52 2.28 -0.0216 457s supply_15 -3.6967 -360.87 -381.13 3.1733 457s supply_16 5.6869 551.02 597.70 -4.8817 457s supply_17 0.1301 11.41 12.54 -0.1117 457s supply_18 4.4171 446.39 461.15 -3.7917 457s supply_19 -2.2186 -235.31 -245.60 1.9044 457s supply_20 0.8653 98.99 109.98 -0.7428 457s supply_price supply_farmPrice supply_trend 457s demand_1 -98.67 -97.06 -0.990 457s demand_2 75.67 71.33 1.440 457s demand_3 -372.84 -356.01 -10.777 457s demand_4 -229.82 -215.68 -8.794 457s demand_5 -333.15 -374.17 -16.885 457s demand_6 -193.86 -210.50 -11.673 457s demand_7 -268.55 -278.11 -18.435 457s demand_8 553.28 594.22 43.295 457s demand_9 218.73 251.31 20.808 457s demand_10 -389.13 -422.20 -41.968 457s demand_11 37.17 32.58 4.425 457s demand_12 329.10 228.23 39.924 457s demand_13 267.23 185.45 34.004 457s demand_14 -14.60 -11.85 -2.039 457s demand_15 -368.41 -386.07 -56.608 457s demand_16 542.65 588.07 89.610 457s demand_17 2.66 3.35 0.515 457s demand_18 426.54 390.42 75.973 457s demand_19 -259.30 -218.32 -46.450 457s demand_20 75.30 61.22 13.166 457s supply_1 104.22 102.52 1.046 457s supply_2 -44.64 -42.09 -0.849 457s supply_3 337.73 322.49 9.763 457s supply_4 212.64 199.56 8.137 457s supply_5 303.62 341.01 15.389 457s supply_6 181.14 196.69 10.907 457s supply_7 242.72 251.36 16.662 457s supply_8 -462.53 -496.76 -36.194 457s supply_9 -180.22 -207.07 -17.144 457s supply_10 335.39 363.90 36.173 457s supply_11 -41.64 -36.50 -4.957 457s supply_12 -299.75 -207.88 -36.364 457s supply_13 -247.35 -171.66 -31.475 457s supply_14 -2.16 -1.75 -0.302 457s supply_15 309.78 324.63 47.599 457s supply_16 -473.00 -512.58 -78.108 457s supply_17 -9.80 -12.34 -1.899 457s supply_18 -383.19 -350.73 -68.251 457s supply_19 201.99 170.07 36.184 457s supply_20 -84.97 -69.08 -14.856 457s > 457s > round( colSums( estfun( fit3sls[[ 5 ]]$e1wc ) ), digits = 7 ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s 0 0 0 0 457s supply_price supply_farmPrice supply_trend 457s 0 0 0 457s > round( colSums( estfun( fit3sls[[ 5 ]]$e1wc, residFit = FALSE ) ), digits = 7 ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s 0 0 0 0 457s supply_price supply_farmPrice supply_trend 457s 0 0 0 457s > 457s > round( colSums( estfun( fit3sls[[ 4 ]]$e1wc ) ), digits = 7 ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s 0 0 0 0 457s supply_price supply_farmPrice supply_trend 457s 0 0 0 457s > round( colSums( estfun( fit3sls[[ 4 ]]$e1wc, residFit = FALSE ) ), digits = 7 ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s 0 0 0 0 457s supply_price supply_farmPrice supply_trend 457s 0 0 0 457s > 457s > round( colSums( estfun( fit3sls[[ 3 ]]$e1wc ) ), digits = 7 ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s 0 0 0 0 457s supply_price supply_farmPrice supply_trend 457s 0 0 0 457s > round( colSums( estfun( fit3sls[[ 3 ]]$e1wc, residFit = FALSE ) ), digits = 7 ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s 0 0 0 0 457s supply_price supply_farmPrice supply_trend 457s 0 0 0 457s > 457s > round( colSums( estfun( fit3sls[[ 2 ]]$e1wc ) ), digits = 7 ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s 0 0 0 0 457s supply_price supply_farmPrice supply_trend 457s 0 0 0 457s > round( colSums( estfun( fit3sls[[ 2 ]]$e1wc, residFit = FALSE ) ), digits = 7 ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s 0 0 0 0 457s supply_price supply_farmPrice supply_trend 457s 0 0 0 457s > 457s > round( colSums( estfun( fit3sls[[ 1 ]]$e1wc ) ), digits = 7 ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s 0 0 0 0 457s supply_price supply_farmPrice supply_trend 457s 0 0 0 457s > round( colSums( estfun( fit3sls[[ 1 ]]$e1wc, residFit = FALSE ) ), digits = 7 ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s 0 0 0 0 457s supply_price supply_farmPrice supply_trend 457s 0 0 0 457s > 457s > estfun( fit3slsd[[ 5 ]]$e1w ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s demand_1 -0.471 -44.9 -41.2 0.299 457s demand_2 -1.315 -130.6 -128.3 0.835 457s demand_3 0.736 72.8 71.2 -0.467 457s demand_4 0.203 20.3 19.9 -0.129 457s demand_5 0.825 80.0 82.4 -0.524 457s demand_6 0.290 28.4 29.1 -0.184 457s demand_7 0.657 65.6 67.8 -0.417 457s demand_8 -2.887 -290.8 -311.2 1.833 457s demand_9 -1.172 -112.7 -113.2 0.744 457s demand_10 1.981 188.4 176.1 -1.258 457s demand_11 0.308 29.2 23.1 -0.196 457s demand_12 -0.922 -91.4 -70.9 0.586 457s demand_13 -0.639 -65.0 -54.1 0.406 457s demand_14 0.597 60.5 54.0 -0.379 457s demand_15 2.100 211.7 216.5 -1.333 457s demand_16 -1.984 -200.3 -208.6 1.260 457s demand_17 0.785 75.0 75.7 -0.499 457s demand_18 -1.136 -118.3 -118.6 0.721 457s demand_19 1.814 195.6 200.8 -1.152 457s demand_20 0.232 26.4 29.5 -0.147 457s supply_1 -0.434 -41.3 -37.9 0.449 457s supply_2 -0.126 -12.6 -12.3 0.131 457s supply_3 -1.272 -125.8 -123.0 1.316 457s supply_4 -0.902 -90.1 -88.6 0.933 457s supply_5 -0.805 -78.1 -80.4 0.833 457s supply_6 -0.457 -44.8 -46.0 0.473 457s supply_7 -0.758 -75.8 -78.3 0.784 457s supply_8 1.582 159.3 170.5 -1.636 457s supply_9 1.004 96.6 97.0 -1.039 457s supply_10 -0.856 -81.5 -76.1 0.886 457s supply_11 0.191 18.1 14.3 -0.197 457s supply_12 0.607 60.1 46.7 -0.628 457s supply_13 0.335 34.0 28.3 -0.346 457s supply_14 -0.201 -20.3 -18.2 0.208 457s supply_15 -0.801 -80.8 -82.6 0.829 457s supply_16 1.930 194.8 202.9 -1.997 457s supply_17 0.811 77.5 78.2 -0.839 457s supply_18 1.241 129.3 129.5 -1.283 457s supply_19 -0.858 -92.5 -95.0 0.888 457s supply_20 -0.229 -26.1 -29.1 0.237 457s supply_price supply_farmPrice supply_trend 457s demand_1 29.8 29.3 0.299 457s demand_2 87.8 82.7 1.670 457s demand_3 -48.5 -46.3 -1.402 457s demand_4 -13.5 -12.7 -0.516 457s demand_5 -51.7 -58.1 -2.620 457s demand_6 -18.3 -19.9 -1.105 457s demand_7 -42.5 -44.0 -2.919 457s demand_8 187.4 201.3 14.667 457s demand_9 70.4 80.9 6.698 457s demand_10 -116.6 -126.5 -12.579 457s demand_11 -18.1 -15.8 -2.152 457s demand_12 57.9 40.2 7.029 457s demand_13 41.5 28.8 5.278 457s demand_14 -38.0 -30.8 -5.304 457s demand_15 -130.2 -136.4 -20.000 457s demand_16 122.1 132.3 20.164 457s demand_17 -43.7 -55.1 -8.477 457s demand_18 72.9 66.7 12.986 457s demand_19 -122.2 -102.9 -21.890 457s demand_20 -16.9 -13.7 -2.947 457s supply_1 44.7 44.0 0.449 457s supply_2 13.7 13.0 0.262 457s supply_3 136.5 130.4 3.947 457s supply_4 97.5 91.5 3.731 457s supply_5 82.2 92.3 4.165 457s supply_6 47.1 51.2 2.839 457s supply_7 80.0 82.8 5.491 457s supply_8 -167.3 -179.7 -13.089 457s supply_9 -98.3 -112.9 -9.349 457s supply_10 82.1 89.1 8.857 457s supply_11 -18.2 -16.0 -2.169 457s supply_12 -62.1 -43.1 -7.532 457s supply_13 -35.4 -24.5 -4.499 457s supply_14 20.8 16.9 2.907 457s supply_15 80.9 84.8 12.430 457s supply_16 -193.5 -209.7 -31.948 457s supply_17 -73.6 -92.7 -14.264 457s supply_18 -129.7 -118.7 -23.101 457s supply_19 94.1 79.3 16.863 457s supply_20 27.1 22.1 4.744 457s Warning message: 457s In estfun.systemfit(fit3slsd[[5]]$e1w) : 457s > estfun( fit3slsd[[ 5 ]]$e1w, residFit = FALSE ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s demand_1 0.89947 85.649 78.613 -0.57123 457s demand_2 0.00817 0.811 0.797 -0.00519 457s demand_3 1.94109 192.071 187.703 -1.23275 457s demand_4 1.44439 144.277 141.839 -0.91731 457s demand_5 1.10477 107.119 110.256 -0.70162 457s demand_6 0.67950 66.596 68.290 -0.43154 457s demand_7 0.96428 96.352 99.513 -0.61239 457s demand_8 -1.80100 -181.402 -194.148 1.14378 457s demand_9 -1.09741 the columns of the returned estimating function do not all sum up to zero, which indicates that the wrong estimating function is returned 457s -105.536 -106.009 0.69694 457s demand_10 0.93145 88.611 82.806 -0.59155 457s demand_11 -0.13250 -12.551 -9.951 0.08415 457s demand_12 -0.98743 -97.798 -75.933 0.62710 457s demand_13 -0.32371 -32.932 -27.386 0.20558 457s demand_14 -0.09978 -10.112 -9.040 0.06337 457s demand_15 0.56754 57.219 58.513 -0.36043 457s demand_16 -2.64753 -267.185 -278.255 1.68140 457s demand_17 -1.65258 -157.934 -159.308 1.04952 457s demand_18 -1.17988 -122.919 -123.179 0.74932 457s demand_19 1.26015 135.883 139.499 -0.80030 457s demand_20 0.12101 13.783 15.380 -0.07685 457s supply_1 -0.39424 -37.540 -34.456 0.40779 457s supply_2 -0.17503 -17.388 -17.083 0.18104 457s supply_3 -1.29167 -127.811 -124.905 1.33607 457s supply_4 -0.90312 -90.210 -88.686 0.93416 457s supply_5 -0.84242 -81.682 -84.074 0.87137 457s supply_6 -0.46834 -45.901 -47.069 0.48444 457s supply_7 -0.80988 -80.925 -83.580 0.83772 457s supply_8 1.72577 173.825 186.038 -1.78508 457s supply_9 1.10899 106.650 107.128 -1.14710 457s supply_10 -0.94120 -89.538 -83.673 0.97355 457s supply_11 0.22943 21.733 17.231 -0.23732 457s supply_12 0.60019 59.445 46.155 -0.62082 457s supply_13 0.37695 38.348 31.890 -0.38990 457s supply_14 -0.28729 -29.116 -26.029 0.29717 457s supply_15 -0.94355 -95.128 -97.280 0.97597 457s supply_16 2.01917 203.771 212.215 -2.08856 457s supply_17 0.74286 70.994 71.612 -0.76839 457s supply_18 1.40908 146.797 147.108 -1.45750 457s supply_19 -0.87479 -94.329 -96.840 0.90486 457s supply_20 -0.28090 -31.995 -35.702 0.29055 457s supply_price supply_farmPrice supply_trend 457s demand_1 -56.911 -55.981 -0.5712 457s demand_2 -0.545 -0.514 -0.0104 457s demand_3 -127.940 -122.166 -3.6983 457s demand_4 -95.886 -89.988 -3.6692 457s demand_5 -69.215 -77.739 -3.5081 457s demand_6 -43.002 -46.692 -2.5892 457s demand_7 -62.447 -64.669 -4.2868 457s demand_8 116.934 125.587 9.1502 457s demand_9 65.935 75.758 6.2725 457s demand_10 -54.848 -59.510 -5.9155 457s demand_11 7.776 6.816 0.9257 457s demand_12 62.030 43.019 7.5252 457s demand_13 21.003 14.576 2.6726 457s demand_14 6.354 5.158 0.8871 457s demand_15 -35.186 -36.872 -5.4065 457s demand_16 162.914 176.547 26.9023 457s demand_17 92.041 115.972 17.8418 457s demand_18 75.726 69.312 13.4878 457s demand_19 -84.882 -71.467 -15.2057 457s demand_20 -8.791 -7.147 -1.5370 457s supply_1 40.627 39.963 0.4078 457s supply_2 19.031 17.941 0.3621 457s supply_3 138.662 132.404 4.0082 457s supply_4 97.648 91.641 3.7366 457s supply_5 85.962 96.548 4.3569 457s supply_6 48.274 52.416 2.9066 457s supply_7 85.424 88.463 5.8640 457s supply_8 -182.496 -196.002 -14.2806 457s supply_9 -108.523 -124.690 -10.3239 457s supply_10 90.266 97.939 9.7355 457s supply_11 -21.929 -19.223 -2.6105 457s supply_12 -61.410 -42.588 -7.4498 457s supply_13 -39.834 -27.644 -5.0687 457s supply_14 29.799 24.189 4.1603 457s supply_15 95.276 99.842 14.6396 457s supply_16 -202.365 -219.299 -33.4170 457s supply_17 -67.387 -84.908 -13.0627 457s supply_18 -147.294 -134.819 -26.2351 457s supply_19 95.972 80.804 17.1923 457s supply_20 33.238 27.021 5.8111 457s > 457s > round( colSums( estfun( fit3slsd[[ 5 ]]$e1w ) ), digits = 7 ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s 0.0 0.0 0.0 0.0 457s supply_price supply_farmPrice supply_trend 457s 38.6 0.0 -52.4 457s > round( colSums( estfun( fit3slsd[[ 5 ]]$e1w, residFit = FALSE ) ), digits = 7 ) 457s demand_(Intercept) demand_price demand_income Warning message: 457s In estfun.systemfit(fit3slsd[[5]]$e1w) : 457s the columns of the returned estimating function do not all sum up to zero, which indicates that the wrong estimating function is returned 457s supply_(Intercept) 457s 0 0 0 0 457s supply_price supply_farmPrice supply_trend 457s 0 0 0 457s > 457s > round( colSums( estfun( fit3slsd[[ 4 ]]$e1w ) ), digits = 7 ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s 0.00 0.00 0.00 0.00 457s supply_price supply_farmPrice supply_trend 457s 9.67 0.00 -13.12 457s > round( colSums( estfun( fit3slsd[[ 4 ]]$e1w, residFit = FALSE ) ), digits = 7 ) 457s demand_(Intercept) demand_price demand_income Warning message: 457s In estfun.systemfit(fit3slsd[[4]]$e1w) : 457s the columns of the returned estimating function do not all sum up to zero, which indicates that the wrong estimating function is returned 457s Warning message: 457s In estfun.systemfit(fit3slsd[[4]]$e1w, residFit = FALSE) : 457s the columns of the returned estimating function do not all sum up to zero, which indicates that the wrong estimating function is returned 457s supply_(Intercept) 457s 0.0 0.0 0.0 0.0 457s supply_price supply_farmPrice supply_trend 457s -28.9 0.0 39.3 457s > 457s > round( colSums( estfun( fit3slsd[[ 3 ]]$e1w ) ), digits = 7 ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s 0.00 0.00 0.00 0.00 457s supply_price supply_farmPrice supply_trend 457s 9.67 0.00 -13.12 457s Warning message: 457s In estfun.systemfit(fit3slsd[[3]]$e1w) : 457s the columns of the returned estimating function do not all sum up to zero, which indicates that the wrong estimating function is returned 457s > round( colSums( estfun( fit3slsd[[ 3 ]]$e1w, residFit = FALSE ) ), digits = 7 ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s 0.0 0.0 0.0 0.0 457s supply_price supply_farmPrice supply_trend 457s -28.9 0.0 39.3 457s > 457s > round( colSums( estfun( fit3slsd[[ 2 ]]$e1w ) ), digits = 7 ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s 0.0 0.0 0.0 0.0 457s supply_price supply_farmPrice supply_trend 457s 38.6 0.0 -52.4 457s > round( colSums( estfun( fit3slsd[[ 2 ]]$e1w, residFit = FALSE ) ), digits = 7 ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s 0 0 0 0 457s supply_price supply_farmPrice supply_trend 457s 0 0 0 457s > 457s Warning message: 457s In estfun.systemfit(fit3slsd[[3]]$e1w, residFit = FALSE) : 457s the columns of the returned estimating function do not all sum up to zero, which indicates that the wrong estimating function is returned 457s Warning message: 457s In estfun.systemfit(fit3slsd[[2]]$e1w) : 457s the columns of the returned estimating function do not all sum up to zero, which indicates that the wrong estimating function is returned 457s > round( colSums( estfun( fit3slsd[[ 1 ]]$e1w ) ), digits = 7 ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s 0 0 0 0 457s supply_price supply_farmPrice supply_trend 457s 0 0 0 457s > round( colSums( estfun( fit3slsd[[ 1 ]]$e1w, residFit = FALSE ) ), digits = 7 ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s 0.0 0.0 0.0 0.0 457s supply_price supply_farmPrice supply_trend 457s -38.6 0.0 52.4 457s > Warning message: 457s In estfun.systemfit(fit3slsd[[1]]$e1w, residFit = FALSE) : 457s the columns of the returned estimating function do not all sum up to zero, which indicates that the wrong estimating function is returned 457s 457s > 457s > ## **************** bread ************************ 457s > bread( fit3sls[[ 1 ]]$e1 ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s [1,] 2509.59 -26.9369 1.9721 2525.8 457s [2,] -26.94 0.3724 -0.1057 -14.1 457s [3,] 1.97 -0.1057 0.0881 -11.3 457s [4,] 2525.80 -14.1479 -11.2987 5658.1 457s [5,] -27.01 0.2401 0.0307 -43.3 457s [6,] 1.64 -0.0877 0.0732 -11.8 457s [7,] 2.47 -0.1324 0.1104 -16.4 457s supply_price supply_farmPrice supply_trend 457s [1,] -27.0066 1.6369 2.4699 457s [2,] 0.2401 -0.0877 -0.1324 457s [3,] 0.0307 0.0732 0.1104 457s [4,] -43.3336 -11.7989 -16.3581 457s [5,] 0.3974 0.0325 0.0428 457s [6,] 0.0325 0.0774 0.1019 457s [7,] 0.0428 0.1019 0.2125 457s > 457s > bread( fit3sls[[ 2 ]]$e1e ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s [1,] 2133.15 -22.8963 1.6763 2082.83 457s [2,] -22.90 0.3165 -0.0898 -11.67 457s [3,] 1.68 -0.0898 0.0749 -9.32 457s [4,] 2082.83 -11.6667 -9.3172 4526.47 457s [5,] -22.27 0.1980 0.0253 -34.67 457s [6,] 1.35 -0.0723 0.0603 -9.44 457s [7,] 2.04 -0.1091 0.0910 -13.09 457s supply_price supply_farmPrice supply_trend 457s [1,] -22.2702 1.3498 2.0367 457s [2,] 0.1980 -0.0723 -0.1091 457s [3,] 0.0253 0.0603 0.0910 457s [4,] -34.6668 -9.4391 -13.0865 457s [5,] 0.3179 0.0260 0.0342 457s [6,] 0.0260 0.0619 0.0815 457s [7,] 0.0342 0.0815 0.1700 457s > 457s > bread( fit3sls[[ 3 ]]$e1c ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s [1,] 2509.59 -26.9369 1.9721 2610.8 457s [2,] -26.94 0.3724 -0.1057 -14.6 457s [3,] 1.97 -0.1057 0.0881 -11.7 457s [4,] 2610.83 -14.6243 -11.6791 5650.4 457s [5,] -27.92 0.2482 0.0317 -43.3 457s [6,] 1.69 -0.0907 0.0756 -11.7 457s [7,] 2.55 -0.1368 0.1141 -16.7 457s supply_price supply_farmPrice supply_trend 457s [1,] -27.9159 1.6920 2.5531 457s [2,] 0.2482 -0.0907 -0.1368 457s [3,] 0.0317 0.0756 0.1141 457s [4,] -43.3005 -11.7199 -16.6696 457s [5,] 0.3972 0.0321 0.0441 457s [6,] 0.0321 0.0766 0.1051 457s [7,] 0.0441 0.1051 0.1999 457s > 457s > bread( fit3sls[[ 4 ]]$e1wc ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s [1,] 2509.59 -26.9369 1.9721 2610.8 457s [2,] -26.94 0.3724 -0.1057 -14.6 457s [3,] 1.97 -0.1057 0.0881 -11.7 457s [4,] 2610.83 -14.6243 -11.6791 5650.4 457s [5,] -27.92 0.2482 0.0317 -43.3 457s [6,] 1.69 -0.0907 0.0756 -11.7 457s [7,] 2.55 -0.1368 0.1141 -16.7 457s supply_price supply_farmPrice supply_trend 457s [1,] -27.9159 1.6920 2.5531 457s [2,] 0.2482 -0.0907 -0.1368 457s [3,] 0.0317 0.0756 0.1141 457s [4,] -43.3005 -11.7199 -16.6696 457s [5,] 0.3972 0.0321 0.0441 457s [6,] 0.0321 0.0766 0.1051 457s [7,] 0.0441 0.1051 0.1999 457s > 457s > bread( fit3slsd[[ 5 ]]$e1w ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s [1,] 4967.14 -60.707 11.4076 1773.52 457s [2,] -60.71 0.839 -0.2382 -6.24 457s [3,] 11.41 -0.238 0.1273 -11.71 457s [4,] 1773.52 -6.236 -11.7103 5325.96 457s [5,] -21.83 0.185 0.0346 -37.94 457s [6,] 6.07 -0.141 0.0826 -13.55 457s [7,] -16.09 0.136 0.0255 -20.05 457s supply_price supply_farmPrice supply_trend 457s [1,] -21.8336 6.0740 -16.0922 457s [2,] 0.1845 -0.1413 0.1360 457s [3,] 0.0346 0.0826 0.0255 457s [4,] -37.9350 -13.5483 -20.0519 457s [5,] 0.3216 0.0453 0.1323 457s [6,] 0.0453 0.0885 0.0440 457s [7,] 0.1323 0.0440 0.2443 457s > 457s > bread( fit3slsd[[ 4 ]]$e1w ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s [1,] 4967.14 -60.707 11.4076 1773.52 457s [2,] -60.71 0.839 -0.2382 -6.24 457s [3,] 11.41 -0.238 0.1273 -11.71 457s [4,] 1773.52 -6.236 -11.7103 5325.96 457s [5,] -21.83 0.185 0.0346 -37.94 457s [6,] 6.07 -0.141 0.0826 -13.55 457s [7,] -16.09 0.136 0.0255 -20.05 457s supply_price supply_farmPrice supply_trend 457s [1,] -21.8336 6.0740 -16.0922 457s [2,] 0.1845 -0.1413 0.1360 457s [3,] 0.0346 0.0826 0.0255 457s [4,] -37.9350 -13.5483 -20.0519 457s [5,] 0.3216 0.0453 0.1323 457s [6,] 0.0453 0.0885 0.0440 457s [7,] 0.1323 0.0440 0.2443 457s > 457s > bread( fit3slsd[[ 3 ]]$e1w ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s [1,] 4967.14 -60.707 11.4076 1773.52 457s [2,] -60.71 0.839 -0.2382 -6.24 457s [3,] 11.41 -0.238 0.1273 -11.71 457s [4,] 1773.52 -6.236 -11.7103 5325.96 457s [5,] -21.83 0.185 0.0346 -37.94 457s [6,] 6.07 -0.141 0.0826 -13.55 457s [7,] -16.09 0.136 0.0255 -20.05 457s supply_price supply_farmPrice supply_trend 457s [1,] -21.8336 6.0740 -16.0922 457s [2,] 0.1845 -0.1413 0.1360 457s [3,] 0.0346 0.0826 0.0255 457s [4,] -37.9350 -13.5483 -20.0519 457s [5,] 0.3216 0.0453 0.1323 457s [6,] 0.0453 0.0885 0.0440 457s [7,] 0.1323 0.0440 0.2443 457s > 457s > bread( fit3slsd[[ 2 ]]$e1w ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s [1,] 4967.14 -60.707 11.4076 1773.52 457s [2,] -60.71 0.839 -0.2382 -6.24 457s [3,] 11.41 -0.238 0.1273 -11.71 457s [4,] 1773.52 -6.236 -11.7103 5325.96 457s [5,] -21.83 0.185 0.0346 -37.94 457s [6,] 6.07 -0.141 0.0826 -13.55 457s [7,] -16.09 0.136 0.0255 -20.05 457s supply_price supply_farmPrice supply_trend 457s [1,] -21.8336 6.0740 -16.0922 457s [2,] 0.1845 -0.1413 0.1360 457s [3,] 0.0346 0.0826 0.0255 457s [4,] -37.9350 -13.5483 -20.0519 457s [5,] 0.3216 0.0453 0.1323 457s [6,] 0.0453 0.0885 0.0440 457s [7,] 0.1323 0.0440 0.2443 457s > 457s > bread( fit3slsd[[ 1 ]]$e1w ) 457s demand_(Intercept) demand_price demand_income supply_(Intercept) 457s [1,] 4967.14 -60.707 11.4076 1773.52 457s [2,] -60.71 0.839 -0.2382 -6.24 457s [3,] 11.41 -0.238 0.1273 -11.71 457s [4,] 1773.52 -6.236 -11.7103 5325.96 457s [5,] -21.83 0.185 0.0346 -37.94 457s [6,] 6.07 -0.141 0.0826 -13.55 457s [7,] -16.09 0.136 0.0255 -20.05 457s supply_price supply_farmPrice supply_trend 457s [1,] -21.8336 6.0740 -16.0922 457s [2,] 0.1845 -0.1413 0.1360 457s [3,] 0.0346 0.0826 0.0255 457s [4,] -37.9350 -13.5483 -20.0519 457s [5,] 0.3216 0.0453 0.1323 457s [6,] 0.0453 0.0885 0.0440 457s [7,] 0.1323 0.0440 0.2443 457s > 457s BEGIN TEST test_hausman.R 457s 457s R version 4.3.3 (2024-02-29) -- "Angel Food Cake" 457s Copyright (C) 2024 The R Foundation for Statistical Computing 457s Platform: s390x-ibm-linux-gnu (64-bit) 457s 457s R is free software and comes with ABSOLUTELY NO WARRANTY. 457s You are welcome to redistribute it under certain conditions. 457s Type 'license()' or 'licence()' for distribution details. 457s 457s R is a collaborative project with many contributors. 457s Type 'contributors()' for more information and 457s 'citation()' on how to cite R or R packages in publications. 457s 457s Type 'demo()' for some demos, 'help()' for on-line help, or 457s 'help.start()' for an HTML browser interface to help. 457s Type 'q()' to quit R. 457s 457s > library( "systemfit" ) 457s Loading required package: Matrix 458s Loading required package: car 458s Loading required package: carData 458s Loading required package: lmtest 458s Loading required package: zoo 458s 458s Attaching package: ‘zoo’ 458s 458s The following objects are masked from ‘package:base’: 458s 458s as.Date, as.Date.numeric 458s 458s 458s Please cite the 'systemfit' package as: 458s 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/. 458s 458s If you have questions, suggestions, or comments regarding the 'systemfit' package, please use a forum or 'tracker' at systemfit's R-Forge site: 458s https://r-forge.r-project.org/projects/systemfit/ 458s > options( digits = 5 ) 458s > 458s > data( "Kmenta" ) 458s > useMatrix <- FALSE 458s > 458s > eqDemand <- consump ~ price + income 458s > eqSupply <- consump ~ price + farmPrice + trend 458s > inst <- ~ income + farmPrice + trend 458s > eqSystem <- list( demand = eqDemand, supply = eqSupply ) 458s > restrm <- matrix(0,1,7) # restriction matrix "R" 458s > restrm[1,3] <- 1 458s > restrm[1,7] <- -1 458s > restr2m <- matrix(0,2,7) # restriction matrix "R" 2 458s > restr2m[1,3] <- 1 458s > restr2m[1,7] <- -1 458s > restr2m[2,2] <- -1 458s > restr2m[2,5] <- 1 458s > restr2q <- c( 0, 0.5 ) # restriction vector "q" 2 458s > tc <- matrix(0,7,6) 458s > tc[1,1] <- 1 458s > tc[2,2] <- 1 458s > tc[3,3] <- 1 458s > tc[4,4] <- 1 458s > tc[5,5] <- 1 458s > tc[6,6] <- 1 458s > tc[7,3] <- 1 458s > restr3m <- matrix(0,1,6) # restriction matrix "R" 2 458s > restr3m[1,2] <- -1 458s > restr3m[1,5] <- 1 458s > restr3q <- c( 0.5 ) # restriction vector "q" 2 458s > 458s > 458s > ## ******************* unrestricted estimation ***************** 458s > ## ******************** default estimation ********************* 458s > fit2sls1 <- systemfit( eqSystem, "2SLS", data = Kmenta, inst = inst, 458s + useMatrix = useMatrix ) 458s > fit3sls1 <- systemfit( eqSystem, "3SLS", data = Kmenta, inst = inst, 458s + useMatrix = useMatrix ) 458s > print( hausman.systemfit( fit2sls1, fit3sls1 ) ) 458s 458s Hausman specification test for consistency of the 3SLS estimation 458s 458s data: Kmenta 458s Hausman = 2.54, df = 7, p-value = 0.92 458s 458s > 458s > ## ************** 2SLS estimation with singleEqSigma = FALSE ***************** 458s > fit2sls1s <- systemfit( eqSystem, "2SLS", data = Kmenta, inst = inst, 458s + singleEqSigma = FALSE, useMatrix = useMatrix ) 458s > print( hausman.systemfit( fit2sls1s, fit3sls1 ) ) 458s 458s Hausman specification test for consistency of the 3SLS estimation 458s 458s data: Kmenta 458s Hausman = 3.28, df = 7, p-value = 0.86 458s 458s > 458s > ## ******************* estimations with methodResidCov = 0 ***************** 458s > fit2sls1r <- systemfit( eqSystem, "2SLS", data = Kmenta, inst = inst, 458s + methodResidCov = "noDfCor", useMatrix = useMatrix ) 458s > fit3sls1r <- systemfit( eqSystem, "3SLS", data = Kmenta, inst = inst, 458s + methodResidCov = "noDfCor", useMatrix = useMatrix ) 458s > print( hausman.systemfit( fit2sls1r, fit3sls1r ) ) 458s 458s Hausman specification test for consistency of the 3SLS estimation 458s 458s data: Kmenta 458s Hausman = 2.98, df = 7, p-value = 0.89 458s 458s > 458s > 458s > ## ********************* estimation with restriction ******************** 458s > ## *********************** default estimation *********************** 458s > fit2sls2 <- systemfit( eqSystem, "2SLS", data = Kmenta, restrict.matrix = restrm, 458s + inst = inst, useMatrix = useMatrix ) 458s > fit3sls2 <- systemfit( eqSystem, "3SLS", data = Kmenta, restrict.matrix = restrm, 458s + inst = inst, useMatrix = useMatrix ) 458s > # print( hausman.systemfit( fit2sls2, fit3sls2 ) ) 458s > 458s > ## ************* 2SLS estimation with singleEqSigma = TRUE ***************** 458s > fit2sls2s <- systemfit( eqSystem, "2SLS", data = Kmenta, restrict.matrix = restrm, 458s + inst = inst, singleEqSigma = TRUE, useMatrix = useMatrix ) 458s > # print( hausman.systemfit( fit2sls2s, fit3sls2 ) ) 458s > 458s > ## ********************* estimations with methodResidCov = 0 ************** 458s > fit2sls2r <- systemfit( eqSystem, "2SLS", data = Kmenta, restrict.matrix = restrm, 458s + inst = inst, methodResidCov = "noDfCor", useMatrix = useMatrix ) 458s > fit3sls2r <- systemfit( eqSystem, "3SLS", data = Kmenta, restrict.matrix = restrm, 458s + inst = inst, methodResidCov = "noDfCor", useMatrix = useMatrix ) 458s > # print( hausman.systemfit( fit2sls2r, fit3sls2r ) ) 458s > 458s > 458s > ## ****************** estimation with restriction via restrict.regMat ****************** 458s > ## ********************** default estimation ******************** 458s > fit2sls3 <- systemfit( eqSystem, "2SLS", data = Kmenta, restrict.regMat = tc, 458s + inst = inst, useMatrix = useMatrix ) 458s > fit3sls3 <- systemfit( eqSystem, "3SLS", data = Kmenta, restrict.regMat = tc, 458s + inst = inst, useMatrix = useMatrix ) 458s > print( hausman.systemfit( fit2sls3, fit3sls3 ) ) 458s 458s Hausman specification test for consistency of the 3SLS estimation 458s 458s data: Kmenta 458s Hausman = -0.281, df = 6, p-value = 1 458s 458s > 458s > ## ******************* estimations with methodResidCov = 0 ******* 458s > fit2sls3r <- systemfit( eqSystem, "2SLS", data = Kmenta, restrict.regMat = tc, 458s + inst = inst, methodResidCov = "noDfCor", useMatrix = useMatrix ) 458s > fit3sls3r <- systemfit( eqSystem, "3SLS", data = Kmenta, restrict.regMat = tc, 458s + inst = inst, methodResidCov = "noDfCor", useMatrix = useMatrix ) 458s > print( hausman.systemfit( fit2sls3r, fit3sls3r ) ) 458s 458s Hausman specification test for consistency of the 3SLS estimation 458s 458s data: Kmenta 458s Hausman = -0.0132, df = 6, p-value = 1 458s 458s > 458s > 458s > ## ***************** estimations with 2 restrictions ******************* 458s > ## *********************** default estimations ************** 458s > fit2sls4 <- systemfit( eqSystem, "2SLS", data = Kmenta, restrict.matrix = restr2m, 458s + restrict.rhs = restr2q, inst = inst, useMatrix = useMatrix ) 458s > fit3sls4 <- systemfit( eqSystem, "3SLS", data = Kmenta, restrict.matrix = restr2m, 458s + restrict.rhs = restr2q, inst = inst, useMatrix = useMatrix ) 458s > # print( hausman.systemfit( fit2sls4, fit3sls4 ) ) 458s > 458s > ## ***************** estimations with methodResidCov = 0 ************** 458s > fit2sls4r <- systemfit( eqSystem, "2SLS", data = Kmenta, restrict.matrix = restr2m, 458s + restrict.rhs = restr2q, inst = inst, methodResidCov = "noDfCor", 458s + useMatrix = useMatrix ) 458s > fit3sls4r <- systemfit( eqSystem, "3SLS", data = Kmenta, restrict.matrix = restr2m, 458s + restrict.rhs = restr2q, inst = inst, methodResidCov = "noDfCor", 458s + useMatrix = useMatrix ) 458s > # print( hausman.systemfit( fit2sls4r, fit3sls4r ) ) 458s > 458s > 458s > ## *********** estimations with 2 restrictions via R and restrict.regMat *************** 458s > ## ***************** default estimations ******************* 458s > fit2sls5 <- systemfit( eqSystem, "2SLS", data = Kmenta, restrict.matrix = restr3m, 458s + restrict.rhs = restr3q, restrict.regMat = tc, inst = inst, 458s + useMatrix = useMatrix ) 458s > fit3sls5 <- systemfit( eqSystem, "3SLS", data = Kmenta, restrict.matrix = restr3m, 458s + restrict.rhs = restr3q, restrict.regMat = tc, inst = inst, 458s + useMatrix = useMatrix ) 458s > # print( hausman.systemfit( fit2sls5, fit3sls5 ) ) 458s > 458s > ## ************* estimations with methodResidCov = 0 ********* 458s > fit2sls5r <- systemfit( eqSystem, "2SLS", data = Kmenta, restrict.matrix = restr3m, 458s + restrict.rhs = restr3q, restrict.regMat = tc, inst = inst, 458s + methodResidCov = "noDfCor", useMatrix = useMatrix ) 458s > fit3sls5r <- systemfit( eqSystem, "3SLS", data = Kmenta, restrict.matrix = restr3m, 458s + restrict.rhs = restr3q, restrict.regMat = tc, inst = inst, 458s + methodResidCov = "noDfCor", useMatrix = useMatrix ) 458s > # print( hausman.systemfit( fit2sls5r, fit3sls5r ) ) 458s > 458s BEGIN TEST test_ols.R 459s 459s R version 4.3.3 (2024-02-29) -- "Angel Food Cake" 459s Copyright (C) 2024 The R Foundation for Statistical Computing 459s Platform: s390x-ibm-linux-gnu (64-bit) 459s 459s R is free software and comes with ABSOLUTELY NO WARRANTY. 459s You are welcome to redistribute it under certain conditions. 459s Type 'license()' or 'licence()' for distribution details. 459s 459s R is a collaborative project with many contributors. 459s Type 'contributors()' for more information and 459s 'citation()' on how to cite R or R packages in publications. 459s 459s Type 'demo()' for some demos, 'help()' for on-line help, or 459s 'help.start()' for an HTML browser interface to help. 459s Type 'q()' to quit R. 459s 459s > library( systemfit ) 459s Loading required package: Matrix 460s Loading required package: car 460s Loading required package: carData 460s Loading required package: lmtest 460s Loading required package: zoo 460s 460s Attaching package: ‘zoo’ 460s 460s The following objects are masked from ‘package:base’: 460s 460s as.Date, as.Date.numeric 460s 460s 460s Please cite the 'systemfit' package as: 460s 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/. 460s 460s If you have questions, suggestions, or comments regarding the 'systemfit' package, please use a forum or 'tracker' at systemfit's R-Forge site: 460s https://r-forge.r-project.org/projects/systemfit/ 460s > options( digits = 3 ) 460s > 460s > data( "Kmenta" ) 460s > useMatrix <- FALSE 460s > 460s > demand <- consump ~ price + income 460s > supply <- consump ~ price + farmPrice + trend 460s > system <- list( demand = demand, supply = supply ) 460s > restrm <- matrix(0,1,7) # restriction matrix "R" 460s > restrm[1,3] <- 1 460s > restrm[1,7] <- -1 460s > restrict <- "demand_income - supply_trend = 0" 460s > restr2m <- matrix(0,2,7) # restriction matrix "R" 2 460s > restr2m[1,3] <- 1 460s > restr2m[1,7] <- -1 460s > restr2m[2,2] <- -1 460s > restr2m[2,5] <- 1 460s > restr2q <- c( 0, 0.5 ) # restriction vector "q" 2 460s > restrict2 <- c( "demand_income - supply_trend = 0", 460s + "- demand_price + supply_price = 0.5" ) 460s > tc <- matrix(0,7,6) 460s > tc[1,1] <- 1 460s > tc[2,2] <- 1 460s > tc[3,3] <- 1 460s > tc[4,4] <- 1 460s > tc[5,5] <- 1 460s > tc[6,6] <- 1 460s > tc[7,3] <- 1 460s > restr3m <- matrix(0,1,6) # restriction matrix "R" 2 460s > restr3m[1,2] <- -1 460s > restr3m[1,5] <- 1 460s > restr3q <- c( 0.5 ) # restriction vector "q" 2 460s > restrict3 <- "- C2 + C5 = 0.5" 460s > 460s > # It is not possible to estimate OLS with systemfit 460s > # exactly as EViews does, because EViews uses 460s > # methodResidCov == "geomean" for the coefficient covariance matrix and 460s > # methodResidCov == "noDfCor" for the residual covariance matrix, while 460s > # systemfit uses always the same formulas for both calculations. 460s > 460s > ## ******* single-equation OLS estimations ********************* 460s > lmDemand <- lm( demand, data = Kmenta ) 460s > lmSupply <- lm( supply, data = Kmenta ) 460s > 460s > ## *************** OLS estimation ************************ 460s > ## ********** OLS estimation (default) ******************** 460s > fitols1 <- systemfit( system, "OLS", data = Kmenta, useMatrix = useMatrix ) 460s > print( summary( fitols1 ) ) 460s 460s systemfit results 460s method: OLS 460s 460s N DF SSR detRCov OLS-R2 McElroy-R2 460s system 40 33 156 4.43 0.709 0.558 460s 460s N DF SSR MSE RMSE R2 Adj R2 460s demand 20 17 63.3 3.73 1.93 0.764 0.736 460s supply 20 16 92.6 5.78 2.40 0.655 0.590 460s 460s The covariance matrix of the residuals 460s demand supply 460s demand 3.73 4.14 460s supply 4.14 5.78 460s 460s The correlations of the residuals 460s demand supply 460s demand 1.000 0.891 460s supply 0.891 1.000 460s 460s 460s OLS estimates for 'demand' (equation 1) 460s Model Formula: consump ~ price + income 460s 460s Estimate Std. Error t value Pr(>|t|) 460s (Intercept) 99.8954 7.5194 13.29 2.1e-10 *** 460s price -0.3163 0.0907 -3.49 0.0028 ** 460s income 0.3346 0.0454 7.37 1.1e-06 *** 460s --- 460s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 460s 460s Residual standard error: 1.93 on 17 degrees of freedom 460s Number of observations: 20 Degrees of Freedom: 17 460s SSR: 63.332 MSE: 3.725 Root MSE: 1.93 460s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 460s 460s 460s OLS estimates for 'supply' (equation 2) 460s Model Formula: consump ~ price + farmPrice + trend 460s 460s Estimate Std. Error t value Pr(>|t|) 460s (Intercept) 58.2754 11.4629 5.08 0.00011 *** 460s price 0.1604 0.0949 1.69 0.11039 460s farmPrice 0.2481 0.0462 5.37 6.2e-05 *** 460s trend 0.2483 0.0975 2.55 0.02157 * 460s --- 460s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 460s 460s Residual standard error: 2.405 on 16 degrees of freedom 460s Number of observations: 20 Degrees of Freedom: 16 460s SSR: 92.551 MSE: 5.784 Root MSE: 2.405 460s Multiple R-Squared: 0.655 Adjusted R-Squared: 0.59 460s 460s > nobs( fitols1 ) 460s [1] 40 460s > all.equal( coef( fitols1 ), c( coef( lmDemand ), coef( lmSupply ) ), 460s + check.attributes = FALSE ) 460s [1] TRUE 460s > all.equal( coef( summary( fitols1 ) ), 460s + rbind( coef( summary( lmDemand ) ), coef( summary( lmSupply ) ) ), 460s + check.attributes = FALSE ) 460s [1] TRUE 460s > all.equal( vcov( fitols1 ), 460s + as.matrix( bdiag( vcov( lmDemand ), vcov( lmSupply ) ) ), 460s + check.attributes = FALSE ) 460s [1] TRUE 460s > 460s > ## ********** OLS estimation (no singleEqSigma=F) ****************** 460s > fitols1s <- systemfit( system, "OLS", data = Kmenta, 460s + singleEqSigma = FALSE, useMatrix = useMatrix ) 460s > print( summary( fitols1s ) ) 460s 460s systemfit results 460s method: OLS 460s 460s N DF SSR detRCov OLS-R2 McElroy-R2 460s system 40 33 156 4.43 0.709 0.558 460s 460s N DF SSR MSE RMSE R2 Adj R2 460s demand 20 17 63.3 3.73 1.93 0.764 0.736 460s supply 20 16 92.6 5.78 2.40 0.655 0.590 460s 460s The covariance matrix of the residuals 460s demand supply 460s demand 3.73 4.14 460s supply 4.14 5.78 460s 460s The correlations of the residuals 460s demand supply 460s demand 1.000 0.891 460s supply 0.891 1.000 460s 460s 460s OLS estimates for 'demand' (equation 1) 460s Model Formula: consump ~ price + income 460s 460s Estimate Std. Error t value Pr(>|t|) 460s (Intercept) 99.8954 8.4671 11.80 1.3e-09 *** 460s price -0.3163 0.1021 -3.10 0.0065 ** 460s income 0.3346 0.0511 6.54 5.0e-06 *** 460s --- 460s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 460s 460s Residual standard error: 1.93 on 17 degrees of freedom 460s Number of observations: 20 Degrees of Freedom: 17 460s SSR: 63.332 MSE: 3.725 Root MSE: 1.93 460s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 460s 460s 460s OLS estimates for 'supply' (equation 2) 460s Model Formula: consump ~ price + farmPrice + trend 460s 460s Estimate Std. Error t value Pr(>|t|) 460s (Intercept) 58.2754 10.3587 5.63 3.8e-05 *** 460s price 0.1604 0.0857 1.87 0.080 . 460s farmPrice 0.2481 0.0417 5.94 2.1e-05 *** 460s trend 0.2483 0.0881 2.82 0.012 * 460s --- 460s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 460s 460s Residual standard error: 2.405 on 16 degrees of freedom 460s Number of observations: 20 Degrees of Freedom: 16 460s SSR: 92.551 MSE: 5.784 Root MSE: 2.405 460s Multiple R-Squared: 0.655 Adjusted R-Squared: 0.59 460s 460s > all.equal( coef( fitols1s ), c( coef( lmDemand ), coef( lmSupply ) ), 460s + check.attributes = FALSE ) 460s [1] TRUE 460s > 460s > ## **************** OLS (useDfSys=T) *********************** 460s > print( summary( fitols1, useDfSys = TRUE ) ) 460s 460s systemfit results 460s method: OLS 460s 460s N DF SSR detRCov OLS-R2 McElroy-R2 460s system 40 33 156 4.43 0.709 0.558 460s 460s N DF SSR MSE RMSE R2 Adj R2 460s demand 20 17 63.3 3.73 1.93 0.764 0.736 460s supply 20 16 92.6 5.78 2.40 0.655 0.590 460s 460s The covariance matrix of the residuals 460s demand supply 460s demand 3.73 4.14 460s supply 4.14 5.78 460s 460s The correlations of the residuals 460s demand supply 460s demand 1.000 0.891 460s supply 0.891 1.000 460s 460s 460s OLS estimates for 'demand' (equation 1) 460s Model Formula: consump ~ price + income 460s 460s Estimate Std. Error t value Pr(>|t|) 460s (Intercept) 99.8954 7.5194 13.29 8.4e-15 *** 460s price -0.3163 0.0907 -3.49 0.0014 ** 460s income 0.3346 0.0454 7.37 1.8e-08 *** 460s --- 460s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 460s 460s Residual standard error: 1.93 on 17 degrees of freedom 460s Number of observations: 20 Degrees of Freedom: 17 460s SSR: 63.332 MSE: 3.725 Root MSE: 1.93 460s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 460s 460s 460s OLS estimates for 'supply' (equation 2) 460s Model Formula: consump ~ price + farmPrice + trend 460s 460s Estimate Std. Error t value Pr(>|t|) 460s (Intercept) 58.2754 11.4629 5.08 1.4e-05 *** 460s price 0.1604 0.0949 1.69 0.100 460s farmPrice 0.2481 0.0462 5.37 6.1e-06 *** 460s trend 0.2483 0.0975 2.55 0.016 * 460s --- 460s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 460s 460s Residual standard error: 2.405 on 16 degrees of freedom 460s Number of observations: 20 Degrees of Freedom: 16 460s SSR: 92.551 MSE: 5.784 Root MSE: 2.405 460s Multiple R-Squared: 0.655 Adjusted R-Squared: 0.59 460s 460s > 460s > ## **************** OLS (methodResidCov="noDfCor") *********************** 460s > fitols1r <- systemfit( system, "OLS", data = Kmenta, 460s + methodResidCov = "noDfCor", x = TRUE, 460s + useMatrix = useMatrix ) 460s > print( summary( fitols1r ) ) 460s 460s systemfit results 460s method: OLS 460s 460s N DF SSR detRCov OLS-R2 McElroy-R2 460s system 40 33 156 3.02 0.709 0.537 460s 460s N DF SSR MSE RMSE R2 Adj R2 460s demand 20 17 63.3 3.73 1.93 0.764 0.736 460s supply 20 16 92.6 5.78 2.40 0.655 0.590 460s 460s The covariance matrix of the residuals 460s demand supply 460s demand 3.17 3.41 460s supply 3.41 4.63 460s 460s The correlations of the residuals 460s demand supply 460s demand 1.000 0.891 460s supply 0.891 1.000 460s 460s 460s OLS estimates for 'demand' (equation 1) 460s Model Formula: consump ~ price + income 460s 460s Estimate Std. Error t value Pr(>|t|) 460s (Intercept) 99.8954 6.9325 14.41 5.8e-11 *** 460s price -0.3163 0.0836 -3.78 0.0015 ** 460s income 0.3346 0.0419 7.99 3.7e-07 *** 460s --- 460s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 460s 460s Residual standard error: 1.93 on 17 degrees of freedom 460s Number of observations: 20 Degrees of Freedom: 17 460s SSR: 63.332 MSE: 3.725 Root MSE: 1.93 460s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 460s 460s 460s OLS estimates for 'supply' (equation 2) 460s Model Formula: consump ~ price + farmPrice + trend 460s 460s Estimate Std. Error t value Pr(>|t|) 460s (Intercept) 58.2754 10.2527 5.68 3.4e-05 *** 460s price 0.1604 0.0849 1.89 0.077 . 460s farmPrice 0.2481 0.0413 6.01 1.8e-05 *** 460s trend 0.2483 0.0872 2.85 0.012 * 460s --- 460s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 460s 460s Residual standard error: 2.405 on 16 degrees of freedom 460s Number of observations: 20 Degrees of Freedom: 16 460s SSR: 92.551 MSE: 5.784 Root MSE: 2.405 460s Multiple R-Squared: 0.655 Adjusted R-Squared: 0.59 460s 460s > all.equal( coef( fitols1r ), c( coef( lmDemand ), coef( lmSupply ) ), 460s + check.attributes = FALSE ) 460s [1] TRUE 460s > 460s > ## ******** OLS (methodResidCov="noDfCor", singleEqSigma=F) *********** 460s > fitols1rs <- systemfit( system, "OLS", data = Kmenta, 460s + methodResidCov = "noDfCor", singleEqSigma = FALSE, 460s + useMatrix = useMatrix ) 460s > print( summary( fitols1rs ) ) 460s 460s systemfit results 460s method: OLS 460s 460s N DF SSR detRCov OLS-R2 McElroy-R2 460s system 40 33 156 3.02 0.709 0.537 460s 460s N DF SSR MSE RMSE R2 Adj R2 460s demand 20 17 63.3 3.73 1.93 0.764 0.736 460s supply 20 16 92.6 5.78 2.40 0.655 0.590 460s 460s The covariance matrix of the residuals 460s demand supply 460s demand 3.17 3.41 460s supply 3.41 4.63 460s 460s The correlations of the residuals 460s demand supply 460s demand 1.000 0.891 460s supply 0.891 1.000 460s 460s 460s OLS estimates for 'demand' (equation 1) 460s Model Formula: consump ~ price + income 460s 460s Estimate Std. Error t value Pr(>|t|) 460s (Intercept) 99.8954 7.6907 12.99 3.0e-10 *** 460s price -0.3163 0.0927 -3.41 0.0033 ** 460s income 0.3346 0.0465 7.20 1.5e-06 *** 460s --- 460s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 460s 460s Residual standard error: 1.93 on 17 degrees of freedom 460s Number of observations: 20 Degrees of Freedom: 17 460s SSR: 63.332 MSE: 3.725 Root MSE: 1.93 460s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 460s 460s 460s OLS estimates for 'supply' (equation 2) 460s Model Formula: consump ~ price + farmPrice + trend 460s 460s Estimate Std. Error t value Pr(>|t|) 460s (Intercept) 58.2754 9.4088 6.19 1.3e-05 *** 460s price 0.1604 0.0779 2.06 0.0561 . 460s farmPrice 0.2481 0.0379 6.55 6.7e-06 *** 460s trend 0.2483 0.0800 3.10 0.0068 ** 460s --- 460s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 460s 460s Residual standard error: 2.405 on 16 degrees of freedom 460s Number of observations: 20 Degrees of Freedom: 16 460s SSR: 92.551 MSE: 5.784 Root MSE: 2.405 460s Multiple R-Squared: 0.655 Adjusted R-Squared: 0.59 460s 460s > all.equal( coef( fitols1rs ), c( coef( lmDemand ), coef( lmSupply ) ), 460s + check.attributes = FALSE ) 460s [1] TRUE 460s > 460s > ## **************** OLS (methodResidCov="Theil" ) *********************** 460s > fitols1r <- systemfit( system, "OLS", data = Kmenta, 460s + methodResidCov = "Theil", x = TRUE, 460s + useMatrix = useMatrix ) 460s > print( summary( fitols1r ) ) 460s 460s systemfit results 460s method: OLS 460s 460s N DF SSR detRCov OLS-R2 McElroy-R2 460s system 40 33 156 3.26 0.709 0.503 460s 460s N DF SSR MSE RMSE R2 Adj R2 460s demand 20 17 63.3 3.73 1.93 0.764 0.736 460s supply 20 16 92.6 5.78 2.40 0.655 0.590 460s 460s The covariance matrix of the residuals 460s demand supply 460s demand 3.73 4.28 460s supply 4.28 5.78 460s 460s The correlations of the residuals 460s demand supply 460s demand 1.000 0.891 460s supply 0.891 1.000 460s 460s 460s OLS estimates for 'demand' (equation 1) 460s Model Formula: consump ~ price + income 460s 460s Estimate Std. Error t value Pr(>|t|) 460s (Intercept) 99.8954 7.5194 13.29 2.1e-10 *** 460s price -0.3163 0.0907 -3.49 0.0028 ** 460s income 0.3346 0.0454 7.37 1.1e-06 *** 460s --- 460s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 460s 460s Residual standard error: 1.93 on 17 degrees of freedom 460s Number of observations: 20 Degrees of Freedom: 17 460s SSR: 63.332 MSE: 3.725 Root MSE: 1.93 460s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 460s 460s 460s OLS estimates for 'supply' (equation 2) 460s Model Formula: consump ~ price + farmPrice + trend 460s 460s Estimate Std. Error t value Pr(>|t|) 460s (Intercept) 58.2754 11.4629 5.08 0.00011 *** 460s price 0.1604 0.0949 1.69 0.11039 460s farmPrice 0.2481 0.0462 5.37 6.2e-05 *** 460s trend 0.2483 0.0975 2.55 0.02157 * 460s --- 460s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 460s 460s Residual standard error: 2.405 on 16 degrees of freedom 460s Number of observations: 20 Degrees of Freedom: 16 460s SSR: 92.551 MSE: 5.784 Root MSE: 2.405 460s Multiple R-Squared: 0.655 Adjusted R-Squared: 0.59 460s 460s > all.equal( coef( fitols1r ), c( coef( lmDemand ), coef( lmSupply ) ), 460s + check.attributes = FALSE ) 460s [1] TRUE 460s > 460s > ## **************** OLS (methodResidCov="max") *********************** 460s > fitols1r <- systemfit( system, "OLS", data = Kmenta, 460s + methodResidCov = "max", x = TRUE, 460s + useMatrix = useMatrix ) 460s > print( summary( fitols1r ) ) 460s 460s systemfit results 460s method: OLS 460s 460s N DF SSR detRCov OLS-R2 McElroy-R2 460s system 40 33 156 3.37 0.709 0.509 460s 460s N DF SSR MSE RMSE R2 Adj R2 460s demand 20 17 63.3 3.73 1.93 0.764 0.736 460s supply 20 16 92.6 5.78 2.40 0.655 0.590 460s 460s The covariance matrix of the residuals 460s demand supply 460s demand 3.73 4.26 460s supply 4.26 5.78 460s 460s The correlations of the residuals 460s demand supply 460s demand 1.000 0.891 460s supply 0.891 1.000 460s 460s 460s OLS estimates for 'demand' (equation 1) 460s Model Formula: consump ~ price + income 460s 460s Estimate Std. Error t value Pr(>|t|) 460s (Intercept) 99.8954 7.5194 13.29 2.1e-10 *** 460s price -0.3163 0.0907 -3.49 0.0028 ** 460s income 0.3346 0.0454 7.37 1.1e-06 *** 460s --- 460s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 460s 460s Residual standard error: 1.93 on 17 degrees of freedom 460s Number of observations: 20 Degrees of Freedom: 17 460s SSR: 63.332 MSE: 3.725 Root MSE: 1.93 460s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 460s 460s 460s OLS estimates for 'supply' (equation 2) 460s Model Formula: consump ~ price + farmPrice + trend 460s 460s Estimate Std. Error t value Pr(>|t|) 460s (Intercept) 58.2754 11.4629 5.08 0.00011 *** 460s price 0.1604 0.0949 1.69 0.11039 460s farmPrice 0.2481 0.0462 5.37 6.2e-05 *** 460s trend 0.2483 0.0975 2.55 0.02157 * 460s --- 460s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 460s 460s Residual standard error: 2.405 on 16 degrees of freedom 460s Number of observations: 20 Degrees of Freedom: 16 460s SSR: 92.551 MSE: 5.784 Root MSE: 2.405 460s Multiple R-Squared: 0.655 Adjusted R-Squared: 0.59 460s 460s > 460s > ## ******** OLS (methodResidCov="max", singleEqSigma=F) *********** 460s > fitols1rs <- systemfit( system, "OLS", data = Kmenta, 460s + methodResidCov = "max", singleEqSigma = FALSE, 460s + useMatrix = useMatrix ) 460s > print( summary( fitols1rs ) ) 460s 460s systemfit results 460s method: OLS 460s 460s N DF SSR detRCov OLS-R2 McElroy-R2 460s system 40 33 156 3.37 0.709 0.509 460s 460s N DF SSR MSE RMSE R2 Adj R2 460s demand 20 17 63.3 3.73 1.93 0.764 0.736 460s supply 20 16 92.6 5.78 2.40 0.655 0.590 460s 460s The covariance matrix of the residuals 460s demand supply 460s demand 3.73 4.26 460s supply 4.26 5.78 460s 460s The correlations of the residuals 460s demand supply 460s demand 1.000 0.891 460s supply 0.891 1.000 460s 460s 460s OLS estimates for 'demand' (equation 1) 460s Model Formula: consump ~ price + income 460s 460s Estimate Std. Error t value Pr(>|t|) 460s (Intercept) 99.8954 8.4671 11.80 1.3e-09 *** 460s price -0.3163 0.1021 -3.10 0.0065 ** 460s income 0.3346 0.0511 6.54 5.0e-06 *** 460s --- 460s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 460s 460s Residual standard error: 1.93 on 17 degrees of freedom 460s Number of observations: 20 Degrees of Freedom: 17 460s SSR: 63.332 MSE: 3.725 Root MSE: 1.93 460s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 460s 460s 460s OLS estimates for 'supply' (equation 2) 460s Model Formula: consump ~ price + farmPrice + trend 460s 460s Estimate Std. Error t value Pr(>|t|) 460s (Intercept) 58.2754 10.3587 5.63 3.8e-05 *** 460s price 0.1604 0.0857 1.87 0.080 . 460s farmPrice 0.2481 0.0417 5.94 2.1e-05 *** 460s trend 0.2483 0.0881 2.82 0.012 * 460s --- 460s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 460s 460s Residual standard error: 2.405 on 16 degrees of freedom 460s Number of observations: 20 Degrees of Freedom: 16 460s SSR: 92.551 MSE: 5.784 Root MSE: 2.405 460s Multiple R-Squared: 0.655 Adjusted R-Squared: 0.59 460s 460s > 460s > 460s > ## ********* OLS with cross-equation restriction ************ 460s > ## ****** OLS with cross-equation restriction (default) ********* 460s > fitols2 <- systemfit( system, "OLS", data = Kmenta, 460s + restrict.matrix = restrm, useMatrix = useMatrix ) 460s > print( summary( fitols2 ) ) 460s 460s systemfit results 460s method: OLS 460s 460s N DF SSR detRCov OLS-R2 McElroy-R2 460s system 40 34 159 2.5 0.703 0.608 460s 460s N DF SSR MSE RMSE R2 Adj R2 460s demand 20 17 64.2 3.78 1.94 0.761 0.732 460s supply 20 16 95.1 5.94 2.44 0.645 0.579 460s 460s The covariance matrix of the residuals 460s demand supply 460s demand 3.78 4.47 460s supply 4.47 5.94 460s 460s The correlations of the residuals 460s demand supply 460s demand 1.000 0.943 460s supply 0.943 1.000 460s 460s 460s OLS estimates for 'demand' (equation 1) 460s Model Formula: consump ~ price + income 460s 460s Estimate Std. Error t value Pr(>|t|) 460s (Intercept) 99.5563 8.4225 11.82 1.4e-13 *** 460s price -0.2917 0.0975 -2.99 0.0051 ** 460s income 0.3129 0.0441 7.10 3.3e-08 *** 460s --- 460s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 460s 460s Residual standard error: 1.943 on 17 degrees of freedom 460s Number of observations: 20 Degrees of Freedom: 17 460s SSR: 64.186 MSE: 3.776 Root MSE: 1.943 460s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.732 460s 460s 460s OLS estimates for 'supply' (equation 2) 460s Model Formula: consump ~ price + farmPrice + trend 460s 460s Estimate Std. Error t value Pr(>|t|) 460s (Intercept) 56.3795 10.0721 5.60 2.9e-06 *** 460s price 0.1639 0.0853 1.92 0.063 . 460s farmPrice 0.2571 0.0402 6.39 2.7e-07 *** 460s trend 0.3129 0.0441 7.10 3.3e-08 *** 460s --- 460s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 460s 460s Residual standard error: 2.438 on 16 degrees of freedom 460s Number of observations: 20 Degrees of Freedom: 16 460s SSR: 95.088 MSE: 5.943 Root MSE: 2.438 460s Multiple R-Squared: 0.645 Adjusted R-Squared: 0.579 460s 460s > # the same with symbolically specified restrictions 460s > fitols2Sym <- systemfit( system, "OLS", data = Kmenta, 460s + restrict.matrix = restrict, useMatrix = useMatrix ) 460s > all.equal( fitols2, fitols2Sym ) 460s [1] "Component “call”: target, current do not match when deparsed" 460s > 460s > ## ****** OLS with cross-equation restriction (singleEqSigma=T) ******* 460s > fitols2s <- systemfit( system, "OLS", data = Kmenta, 460s + restrict.matrix = restrm, singleEqSigma = TRUE, 460s + useMatrix = useMatrix ) 460s > print( summary( fitols2s ) ) 460s 460s systemfit results 460s method: OLS 460s 460s N DF SSR detRCov OLS-R2 McElroy-R2 460s system 40 34 159 2.5 0.703 0.608 460s 460s N DF SSR MSE RMSE R2 Adj R2 460s demand 20 17 64.2 3.78 1.94 0.761 0.732 460s supply 20 16 95.1 5.94 2.44 0.645 0.579 460s 460s The covariance matrix of the residuals 460s demand supply 460s demand 3.78 4.47 460s supply 4.47 5.94 460s 460s The correlations of the residuals 460s demand supply 460s demand 1.000 0.943 460s supply 0.943 1.000 460s 460s 460s OLS estimates for 'demand' (equation 1) 460s Model Formula: consump ~ price + income 460s 460s Estimate Std. Error t value Pr(>|t|) 460s (Intercept) 99.5563 7.5640 13.16 6.7e-15 *** 460s price -0.2917 0.0887 -3.29 0.0023 ** 460s income 0.3129 0.0415 7.54 9.4e-09 *** 460s --- 460s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 460s 460s Residual standard error: 1.943 on 17 degrees of freedom 460s Number of observations: 20 Degrees of Freedom: 17 460s SSR: 64.186 MSE: 3.776 Root MSE: 1.943 460s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.732 460s 460s 460s OLS estimates for 'supply' (equation 2) 460s Model Formula: consump ~ price + farmPrice + trend 460s 460s Estimate Std. Error t value Pr(>|t|) 460s (Intercept) 56.3795 11.3165 4.98 1.8e-05 *** 460s price 0.1639 0.0960 1.71 0.097 . 460s farmPrice 0.2571 0.0451 5.69 2.1e-06 *** 460s trend 0.3129 0.0415 7.54 9.4e-09 *** 460s --- 460s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 460s 460s Residual standard error: 2.438 on 16 degrees of freedom 460s Number of observations: 20 Degrees of Freedom: 16 460s SSR: 95.088 MSE: 5.943 Root MSE: 2.438 460s Multiple R-Squared: 0.645 Adjusted R-Squared: 0.579 460s 460s > 460s > ## ****** OLS with cross-equation restriction (useDfSys=F) ******* 460s > print( summary( fitols2, useDfSys = FALSE ) ) 460s 460s systemfit results 460s method: OLS 460s 460s N DF SSR detRCov OLS-R2 McElroy-R2 460s system 40 34 159 2.5 0.703 0.608 460s 460s N DF SSR MSE RMSE R2 Adj R2 460s demand 20 17 64.2 3.78 1.94 0.761 0.732 460s supply 20 16 95.1 5.94 2.44 0.645 0.579 460s 460s The covariance matrix of the residuals 460s demand supply 460s demand 3.78 4.47 460s supply 4.47 5.94 460s 460s The correlations of the residuals 460s demand supply 460s demand 1.000 0.943 460s supply 0.943 1.000 460s 460s 460s OLS estimates for 'demand' (equation 1) 460s Model Formula: consump ~ price + income 460s 460s Estimate Std. Error t value Pr(>|t|) 460s (Intercept) 99.5563 8.4225 11.82 1.3e-09 *** 460s price -0.2917 0.0975 -2.99 0.0082 ** 460s income 0.3129 0.0441 7.10 1.8e-06 *** 460s --- 460s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 460s 460s Residual standard error: 1.943 on 17 degrees of freedom 460s Number of observations: 20 Degrees of Freedom: 17 460s SSR: 64.186 MSE: 3.776 Root MSE: 1.943 460s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.732 460s 460s 460s OLS estimates for 'supply' (equation 2) 460s Model Formula: consump ~ price + farmPrice + trend 460s 460s Estimate Std. Error t value Pr(>|t|) 460s (Intercept) 56.3795 10.0721 5.60 4.0e-05 *** 460s price 0.1639 0.0853 1.92 0.073 . 460s farmPrice 0.2571 0.0402 6.39 8.9e-06 *** 460s trend 0.3129 0.0441 7.10 2.5e-06 *** 460s --- 460s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 460s 460s Residual standard error: 2.438 on 16 degrees of freedom 460s Number of observations: 20 Degrees of Freedom: 16 460s SSR: 95.088 MSE: 5.943 Root MSE: 2.438 460s Multiple R-Squared: 0.645 Adjusted R-Squared: 0.579 460s 460s > 460s > ## ****** OLS with cross-equation restriction (methodResidCov="noDfCor") ******* 460s > fitols2r <- systemfit( system, "OLS", data = Kmenta, 460s + restrict.matrix = restrm, methodResidCov = "noDfCor", 460s + useMatrix = useMatrix ) 460s > print( summary( fitols2r ) ) 460s 460s systemfit results 460s method: OLS 460s 460s N DF SSR detRCov OLS-R2 McElroy-R2 460s system 40 34 159 1.7 0.703 0.577 460s 460s N DF SSR MSE RMSE R2 Adj R2 460s demand 20 17 64.2 3.78 1.94 0.761 0.732 460s supply 20 16 95.1 5.94 2.44 0.645 0.579 460s 460s The covariance matrix of the residuals 460s demand supply 460s demand 3.21 3.68 460s supply 3.68 4.75 460s 460s The correlations of the residuals 460s demand supply 460s demand 1.000 0.943 460s supply 0.943 1.000 460s 460s 460s OLS estimates for 'demand' (equation 1) 460s Model Formula: consump ~ price + income 460s 460s Estimate Std. Error t value Pr(>|t|) 460s (Intercept) 99.5563 7.7652 12.82 1.4e-14 *** 460s price -0.2917 0.0899 -3.25 0.0026 ** 460s income 0.3129 0.0406 7.70 5.9e-09 *** 460s --- 460s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 460s 460s Residual standard error: 1.943 on 17 degrees of freedom 460s Number of observations: 20 Degrees of Freedom: 17 460s SSR: 64.186 MSE: 3.776 Root MSE: 1.943 460s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.732 460s 460s 460s OLS estimates for 'supply' (equation 2) 460s Model Formula: consump ~ price + farmPrice + trend 460s 460s Estimate Std. Error t value Pr(>|t|) 460s (Intercept) 56.3795 9.2860 6.07 7.0e-07 *** 460s price 0.1639 0.0786 2.08 0.045 * 460s farmPrice 0.2571 0.0371 6.93 5.4e-08 *** 460s trend 0.3129 0.0406 7.70 5.9e-09 *** 460s --- 460s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 460s 460s Residual standard error: 2.438 on 16 degrees of freedom 460s Number of observations: 20 Degrees of Freedom: 16 460s SSR: 95.088 MSE: 5.943 Root MSE: 2.438 460s Multiple R-Squared: 0.645 Adjusted R-Squared: 0.579 460s 460s > 460s > ## ** OLS with cross-equation restriction (methodResidCov="noDfCor",singleEqSigma=T) *** 460s > fitols2rs <- systemfit( system, "OLS", data = Kmenta, 460s + restrict.matrix = restrm, methodResidCov = "noDfCor", 460s + x = TRUE, useMatrix = useMatrix ) 460s > print( summary( fitols2rs ) ) 460s 460s systemfit results 460s method: OLS 460s 460s N DF SSR detRCov OLS-R2 McElroy-R2 460s system 40 34 159 1.7 0.703 0.577 460s 460s N DF SSR MSE RMSE R2 Adj R2 460s demand 20 17 64.2 3.78 1.94 0.761 0.732 460s supply 20 16 95.1 5.94 2.44 0.645 0.579 460s 460s The covariance matrix of the residuals 460s demand supply 460s demand 3.21 3.68 460s supply 3.68 4.75 460s 460s The correlations of the residuals 460s demand supply 460s demand 1.000 0.943 460s supply 0.943 1.000 460s 460s 460s OLS estimates for 'demand' (equation 1) 460s Model Formula: consump ~ price + income 460s 460s Estimate Std. Error t value Pr(>|t|) 460s (Intercept) 99.5563 7.7652 12.82 1.4e-14 *** 460s price -0.2917 0.0899 -3.25 0.0026 ** 460s income 0.3129 0.0406 7.70 5.9e-09 *** 460s --- 460s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 460s 460s Residual standard error: 1.943 on 17 degrees of freedom 460s Number of observations: 20 Degrees of Freedom: 17 460s SSR: 64.186 MSE: 3.776 Root MSE: 1.943 460s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.732 460s 460s 460s OLS estimates for 'supply' (equation 2) 460s Model Formula: consump ~ price + farmPrice + trend 460s 460s Estimate Std. Error t value Pr(>|t|) 460s (Intercept) 56.3795 9.2860 6.07 7.0e-07 *** 460s price 0.1639 0.0786 2.08 0.045 * 460s farmPrice 0.2571 0.0371 6.93 5.4e-08 *** 460s trend 0.3129 0.0406 7.70 5.9e-09 *** 460s --- 460s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 460s 460s Residual standard error: 2.438 on 16 degrees of freedom 460s Number of observations: 20 Degrees of Freedom: 16 460s SSR: 95.088 MSE: 5.943 Root MSE: 2.438 460s Multiple R-Squared: 0.645 Adjusted R-Squared: 0.579 460s 460s > 460s > ## *** OLS with cross-equation restriction via restrict.regMat *** 460s > ## *** OLS with cross-equation restriction via restrict.regMat (default) *** 460s > fitols3 <- systemfit( system, "OLS", data = Kmenta, restrict.regMat = tc, 460s + x = TRUE, useMatrix = useMatrix ) 460s > print( summary( fitols3 ) ) 460s 460s systemfit results 460s method: OLS 460s 460s N DF SSR detRCov OLS-R2 McElroy-R2 460s system 40 34 159 2.5 0.703 0.608 460s 460s N DF SSR MSE RMSE R2 Adj R2 460s demand 20 17 64.2 3.78 1.94 0.761 0.732 460s supply 20 16 95.1 5.94 2.44 0.645 0.579 460s 460s The covariance matrix of the residuals 460s demand supply 460s demand 3.78 4.47 460s supply 4.47 5.94 460s 460s The correlations of the residuals 460s demand supply 460s demand 1.000 0.943 461s supply 0.943 1.000 461s 461s 461s OLS estimates for 'demand' (equation 1) 461s Model Formula: consump ~ price + income 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 99.5563 8.4225 11.82 1.4e-13 *** 461s price -0.2917 0.0975 -2.99 0.0051 ** 461s income 0.3129 0.0441 7.10 3.3e-08 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 1.943 on 17 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 17 461s SSR: 64.186 MSE: 3.776 Root MSE: 1.943 461s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.732 461s 461s 461s OLS estimates for 'supply' (equation 2) 461s Model Formula: consump ~ price + farmPrice + trend 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 56.3795 10.0721 5.60 2.9e-06 *** 461s price 0.1639 0.0853 1.92 0.063 . 461s farmPrice 0.2571 0.0402 6.39 2.7e-07 *** 461s trend 0.3129 0.0441 7.10 3.3e-08 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 2.438 on 16 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 16 461s SSR: 95.088 MSE: 5.943 Root MSE: 2.438 461s Multiple R-Squared: 0.645 Adjusted R-Squared: 0.579 461s 461s > 461s > ## *** OLS with cross-equation restriction via restrict.regMat (singleEqSigma=T) *** 461s > fitols3s <- systemfit( system, "OLS", data = Kmenta, 461s + restrict.regMat = tc, singleEqSigma = TRUE, useMatrix = useMatrix ) 461s > print( summary( fitols3s ) ) 461s 461s systemfit results 461s method: OLS 461s 461s N DF SSR detRCov OLS-R2 McElroy-R2 461s system 40 34 159 2.5 0.703 0.608 461s 461s N DF SSR MSE RMSE R2 Adj R2 461s demand 20 17 64.2 3.78 1.94 0.761 0.732 461s supply 20 16 95.1 5.94 2.44 0.645 0.579 461s 461s The covariance matrix of the residuals 461s demand supply 461s demand 3.78 4.47 461s supply 4.47 5.94 461s 461s The correlations of the residuals 461s demand supply 461s demand 1.000 0.943 461s supply 0.943 1.000 461s 461s 461s OLS estimates for 'demand' (equation 1) 461s Model Formula: consump ~ price + income 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 99.5563 7.5640 13.16 6.7e-15 *** 461s price -0.2917 0.0887 -3.29 0.0023 ** 461s income 0.3129 0.0415 7.54 9.4e-09 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 1.943 on 17 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 17 461s SSR: 64.186 MSE: 3.776 Root MSE: 1.943 461s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.732 461s 461s 461s OLS estimates for 'supply' (equation 2) 461s Model Formula: consump ~ price + farmPrice + trend 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 56.3795 11.3165 4.98 1.8e-05 *** 461s price 0.1639 0.0960 1.71 0.097 . 461s farmPrice 0.2571 0.0451 5.69 2.1e-06 *** 461s trend 0.3129 0.0415 7.54 9.4e-09 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 2.438 on 16 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 16 461s SSR: 95.088 MSE: 5.943 Root MSE: 2.438 461s Multiple R-Squared: 0.645 Adjusted R-Squared: 0.579 461s 461s > 461s > ## *** OLS with cross-equation restriction via restrict.regMat (useDfSys=F) *** 461s > print( summary( fitols3, useDfSys = FALSE ) ) 461s 461s systemfit results 461s method: OLS 461s 461s N DF SSR detRCov OLS-R2 McElroy-R2 461s system 40 34 159 2.5 0.703 0.608 461s 461s N DF SSR MSE RMSE R2 Adj R2 461s demand 20 17 64.2 3.78 1.94 0.761 0.732 461s supply 20 16 95.1 5.94 2.44 0.645 0.579 461s 461s The covariance matrix of the residuals 461s demand supply 461s demand 3.78 4.47 461s supply 4.47 5.94 461s 461s The correlations of the residuals 461s demand supply 461s demand 1.000 0.943 461s supply 0.943 1.000 461s 461s 461s OLS estimates for 'demand' (equation 1) 461s Model Formula: consump ~ price + income 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 99.5563 8.4225 11.82 1.3e-09 *** 461s price -0.2917 0.0975 -2.99 0.0082 ** 461s income 0.3129 0.0441 7.10 1.8e-06 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 1.943 on 17 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 17 461s SSR: 64.186 MSE: 3.776 Root MSE: 1.943 461s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.732 461s 461s 461s OLS estimates for 'supply' (equation 2) 461s Model Formula: consump ~ price + farmPrice + trend 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 56.3795 10.0721 5.60 4.0e-05 *** 461s price 0.1639 0.0853 1.92 0.073 . 461s farmPrice 0.2571 0.0402 6.39 8.9e-06 *** 461s trend 0.3129 0.0441 7.10 2.5e-06 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 2.438 on 16 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 16 461s SSR: 95.088 MSE: 5.943 Root MSE: 2.438 461s Multiple R-Squared: 0.645 Adjusted R-Squared: 0.579 461s 461s > 461s > ## *** OLS with cross-equation restriction via restrict.regMat (methodResidCov="noDfCor") *** 461s > fitols3r <- systemfit( system, "OLS", data = Kmenta, 461s + restrict.regMat = tc, methodResidCov = "noDfCor", 461s + useMatrix = useMatrix ) 461s > print( summary( fitols3r ) ) 461s 461s systemfit results 461s method: OLS 461s 461s N DF SSR detRCov OLS-R2 McElroy-R2 461s system 40 34 159 1.7 0.703 0.577 461s 461s N DF SSR MSE RMSE R2 Adj R2 461s demand 20 17 64.2 3.78 1.94 0.761 0.732 461s supply 20 16 95.1 5.94 2.44 0.645 0.579 461s 461s The covariance matrix of the residuals 461s demand supply 461s demand 3.21 3.68 461s supply 3.68 4.75 461s 461s The correlations of the residuals 461s demand supply 461s demand 1.000 0.943 461s supply 0.943 1.000 461s 461s 461s OLS estimates for 'demand' (equation 1) 461s Model Formula: consump ~ price + income 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 99.5563 7.7652 12.82 1.4e-14 *** 461s price -0.2917 0.0899 -3.25 0.0026 ** 461s income 0.3129 0.0406 7.70 5.9e-09 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 1.943 on 17 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 17 461s SSR: 64.186 MSE: 3.776 Root MSE: 1.943 461s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.732 461s 461s 461s OLS estimates for 'supply' (equation 2) 461s Model Formula: consump ~ price + farmPrice + trend 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 56.3795 9.2860 6.07 7.0e-07 *** 461s price 0.1639 0.0786 2.08 0.045 * 461s farmPrice 0.2571 0.0371 6.93 5.4e-08 *** 461s trend 0.3129 0.0406 7.70 5.9e-09 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 2.438 on 16 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 16 461s SSR: 95.088 MSE: 5.943 Root MSE: 2.438 461s Multiple R-Squared: 0.645 Adjusted R-Squared: 0.579 461s 461s > 461s > ## OLS with cross-equation restriction via restrict.regMat (methodResidCov="noDfCor",singleEqSigma=T) 461s > fitols3rs <- systemfit( system, "OLS", data = Kmenta, 461s + restrict.regMat = tc, methodResidCov = "noDfCor", singleEqSigma = TRUE, 461s + useMatrix = useMatrix ) 461s > print( summary( fitols3rs ) ) 461s 461s systemfit results 461s method: OLS 461s 461s N DF SSR detRCov OLS-R2 McElroy-R2 461s system 40 34 159 1.7 0.703 0.577 461s 461s N DF SSR MSE RMSE R2 Adj R2 461s demand 20 17 64.2 3.78 1.94 0.761 0.732 461s supply 20 16 95.1 5.94 2.44 0.645 0.579 461s 461s The covariance matrix of the residuals 461s demand supply 461s demand 3.21 3.68 461s supply 3.68 4.75 461s 461s The correlations of the residuals 461s demand supply 461s demand 1.000 0.943 461s supply 0.943 1.000 461s 461s 461s OLS estimates for 'demand' (equation 1) 461s Model Formula: consump ~ price + income 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 99.5563 6.9734 14.28 6.7e-16 *** 461s price -0.2917 0.0816 -3.57 0.0011 ** 461s income 0.3129 0.0381 8.22 1.4e-09 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 1.943 on 17 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 17 461s SSR: 64.186 MSE: 3.776 Root MSE: 1.943 461s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.732 461s 461s 461s OLS estimates for 'supply' (equation 2) 461s Model Formula: consump ~ price + farmPrice + trend 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 56.3795 10.1248 5.57 3.1e-06 *** 461s price 0.1639 0.0859 1.91 0.065 . 461s farmPrice 0.2571 0.0404 6.36 2.9e-07 *** 461s trend 0.3129 0.0381 8.22 1.4e-09 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 2.438 on 16 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 16 461s SSR: 95.088 MSE: 5.943 Root MSE: 2.438 461s Multiple R-Squared: 0.645 Adjusted R-Squared: 0.579 461s 461s > 461s > ## ********* OLS with 2 cross-equation restrictions *********** 461s > ## ********* OLS with 2 cross-equation restrictions (default) *********** 461s > fitols4 <- systemfit( system, "OLS", data = Kmenta, restrict.matrix = restr2m, 461s + restrict.rhs = restr2q, useMatrix = useMatrix ) 461s > print( summary( fitols4 ) ) 461s 461s systemfit results 461s method: OLS 461s 461s N DF SSR detRCov OLS-R2 McElroy-R2 461s system 40 35 160 2.69 0.702 0.605 461s 461s N DF SSR MSE RMSE R2 Adj R2 461s demand 20 17 64.0 3.77 1.94 0.761 0.733 461s supply 20 16 95.8 5.99 2.45 0.643 0.576 461s 461s The covariance matrix of the residuals 461s demand supply 461s demand 3.76 4.46 461s supply 4.46 5.99 461s 461s The correlations of the residuals 461s demand supply 461s demand 1.000 0.938 461s supply 0.938 1.000 461s 461s 461s OLS estimates for 'demand' (equation 1) 461s Model Formula: consump ~ price + income 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 101.4817 6.1599 16.47 < 2e-16 *** 461s price -0.3168 0.0629 -5.04 1.4e-05 *** 461s income 0.3189 0.0399 8.00 2.0e-09 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 1.94 on 17 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 17 461s SSR: 64.003 MSE: 3.765 Root MSE: 1.94 461s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 461s 461s 461s OLS estimates for 'supply' (equation 2) 461s Model Formula: consump ~ price + farmPrice + trend 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 54.1494 7.5515 7.17 2.3e-08 *** 461s price 0.1832 0.0629 2.91 0.0062 ** 461s farmPrice 0.2595 0.0391 6.64 1.1e-07 *** 461s trend 0.3189 0.0399 8.00 2.0e-09 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 2.447 on 16 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 16 461s SSR: 95.813 MSE: 5.988 Root MSE: 2.447 461s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.576 461s 461s > # the same with symbolically specified restrictions 461s > fitols4Sym <- systemfit( system, "OLS", data = Kmenta, 461s + restrict.matrix = restrict2, useMatrix = useMatrix ) 461s > all.equal( fitols4, fitols4Sym ) 461s [1] "Component “call”: target, current do not match when deparsed" 461s > 461s > ## ****** OLS with 2 cross-equation restrictions (singleEqSigma=T) ******* 461s > fitols4s <- systemfit( system, "OLS", data = Kmenta, restrict.matrix = restr2m, 461s + restrict.rhs = restr2q, singleEqSigma = TRUE, x = TRUE, 461s + useMatrix = useMatrix ) 461s > print( summary( fitols4s ) ) 461s 461s systemfit results 461s method: OLS 461s 461s N DF SSR detRCov OLS-R2 McElroy-R2 461s system 40 35 160 2.69 0.702 0.605 461s 461s N DF SSR MSE RMSE R2 Adj R2 461s demand 20 17 64.0 3.77 1.94 0.761 0.733 461s supply 20 16 95.8 5.99 2.45 0.643 0.576 461s 461s The covariance matrix of the residuals 461s demand supply 461s demand 3.76 4.46 461s supply 4.46 5.99 461s 461s The correlations of the residuals 461s demand supply 461s demand 1.000 0.938 461s supply 0.938 1.000 461s 461s 461s OLS estimates for 'demand' (equation 1) 461s Model Formula: consump ~ price + income 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 101.4817 6.0474 16.78 < 2e-16 *** 461s price -0.3168 0.0648 -4.89 2.3e-05 *** 461s income 0.3189 0.0385 8.29 9.1e-10 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 1.94 on 17 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 17 461s SSR: 64.003 MSE: 3.765 Root MSE: 1.94 461s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 461s 461s 461s OLS estimates for 'supply' (equation 2) 461s Model Formula: consump ~ price + farmPrice + trend 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 54.1494 7.9687 6.80 7.0e-08 *** 461s price 0.1832 0.0648 2.83 0.0077 ** 461s farmPrice 0.2595 0.0446 5.82 1.3e-06 *** 461s trend 0.3189 0.0385 8.29 9.1e-10 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 2.447 on 16 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 16 461s SSR: 95.813 MSE: 5.988 Root MSE: 2.447 461s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.576 461s 461s > 461s > ## ****** OLS with 2 cross-equation restrictions (useDfSys=F) ******* 461s > print( summary( fitols4, useDfSys = FALSE ) ) 461s 461s systemfit results 461s method: OLS 461s 461s N DF SSR detRCov OLS-R2 McElroy-R2 461s system 40 35 160 2.69 0.702 0.605 461s 461s N DF SSR MSE RMSE R2 Adj R2 461s demand 20 17 64.0 3.77 1.94 0.761 0.733 461s supply 20 16 95.8 5.99 2.45 0.643 0.576 461s 461s The covariance matrix of the residuals 461s demand supply 461s demand 3.76 4.46 461s supply 4.46 5.99 461s 461s The correlations of the residuals 461s demand supply 461s demand 1.000 0.938 461s supply 0.938 1.000 461s 461s 461s OLS estimates for 'demand' (equation 1) 461s Model Formula: consump ~ price + income 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 101.4817 6.1599 16.47 6.9e-12 *** 461s price -0.3168 0.0629 -5.04 1e-04 *** 461s income 0.3189 0.0399 8.00 3.6e-07 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 1.94 on 17 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 17 461s SSR: 64.003 MSE: 3.765 Root MSE: 1.94 461s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 461s 461s 461s OLS estimates for 'supply' (equation 2) 461s Model Formula: consump ~ price + farmPrice + trend 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 54.1494 7.5515 7.17 2.2e-06 *** 461s price 0.1832 0.0629 2.91 0.01 * 461s farmPrice 0.2595 0.0391 6.64 5.6e-06 *** 461s trend 0.3189 0.0399 8.00 5.5e-07 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 2.447 on 16 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 16 461s SSR: 95.813 MSE: 5.988 Root MSE: 2.447 461s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.576 461s 461s > 461s > ## ****** OLS with 2 cross-equation restrictions (methodResidCov="noDfCor") ******* 461s > fitols4r <- systemfit( system, "OLS", data = Kmenta, restrict.matrix = restr2m, 461s + restrict.rhs = restr2q, methodResidCov = "noDfCor", 461s + useMatrix = useMatrix ) 461s > print( summary( fitols4r ) ) 461s 461s systemfit results 461s method: OLS 461s 461s N DF SSR detRCov OLS-R2 McElroy-R2 461s system 40 35 160 1.83 0.702 0.575 461s 461s N DF SSR MSE RMSE R2 Adj R2 461s demand 20 17 64.0 3.77 1.94 0.761 0.733 461s supply 20 16 95.8 5.99 2.45 0.643 0.576 461s 461s The covariance matrix of the residuals 461s demand supply 461s demand 3.20 3.67 461s supply 3.67 4.79 461s 461s The correlations of the residuals 461s demand supply 461s demand 1.000 0.938 461s supply 0.938 1.000 461s 461s 461s OLS estimates for 'demand' (equation 1) 461s Model Formula: consump ~ price + income 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 101.4817 5.7621 17.61 < 2e-16 *** 461s price -0.3168 0.0589 -5.38 5.0e-06 *** 461s income 0.3189 0.0373 8.55 4.3e-10 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 1.94 on 17 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 17 461s SSR: 64.003 MSE: 3.765 Root MSE: 1.94 461s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 461s 461s 461s OLS estimates for 'supply' (equation 2) 461s Model Formula: consump ~ price + farmPrice + trend 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 54.1494 7.0638 7.67 5.4e-09 *** 461s price 0.1832 0.0589 3.11 0.0037 ** 461s farmPrice 0.2595 0.0365 7.10 2.8e-08 *** 461s trend 0.3189 0.0373 8.55 4.3e-10 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 2.447 on 16 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 16 461s SSR: 95.813 MSE: 5.988 Root MSE: 2.447 461s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.576 461s 461s > 461s > ## OLS with 2 cross-equation restrictions (methodResidCov="noDfCor", singleEqSigma=T) * 461s > fitols4rs <- systemfit( system, "OLS", data = Kmenta, restrict.matrix = restr2m, 461s + restrict.rhs = restr2q, methodResidCov = "noDfCor", 461s + singleEqSigma = TRUE, useMatrix = useMatrix ) 461s > print( summary( fitols4rs ) ) 461s 461s systemfit results 461s method: OLS 461s 461s N DF SSR detRCov OLS-R2 McElroy-R2 461s system 40 35 160 1.83 0.702 0.575 461s 461s N DF SSR MSE RMSE R2 Adj R2 461s demand 20 17 64.0 3.77 1.94 0.761 0.733 461s supply 20 16 95.8 5.99 2.45 0.643 0.576 461s 461s The covariance matrix of the residuals 461s demand supply 461s demand 3.20 3.67 461s supply 3.67 4.79 461s 461s The correlations of the residuals 461s demand supply 461s demand 1.000 0.938 461s supply 0.938 1.000 461s 461s 461s OLS estimates for 'demand' (equation 1) 461s Model Formula: consump ~ price + income 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 101.4817 5.5234 18.37 < 2e-16 *** 461s price -0.3168 0.0589 -5.38 5.0e-06 *** 461s income 0.3189 0.0352 9.05 1.1e-10 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 1.94 on 17 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 17 461s SSR: 64.003 MSE: 3.765 Root MSE: 1.94 461s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 461s 461s 461s OLS estimates for 'supply' (equation 2) 461s Model Formula: consump ~ price + farmPrice + trend 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 54.1494 7.2089 7.51 8.5e-09 *** 461s price 0.1832 0.0589 3.11 0.0037 ** 461s farmPrice 0.2595 0.0399 6.51 1.7e-07 *** 461s trend 0.3189 0.0352 9.05 1.1e-10 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 2.447 on 16 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 16 461s SSR: 95.813 MSE: 5.988 Root MSE: 2.447 461s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.576 461s 461s > 461s > ## ***** OLS with 2 cross-equation restrictions via R and restrict.regMat **** 461s > ## ***** OLS with 2 cross-equation restrictions via R and restrict.regMat (default) **** 461s > fitols5 <- systemfit( system, "OLS", data = Kmenta, restrict.matrix = restr3m, 461s + restrict.rhs = restr3q, restrict.regMat = tc, methodResidCov = "noDfCor", 461s + useMatrix = useMatrix ) 461s > print( summary( fitols5 ) ) 461s 461s systemfit results 461s method: OLS 461s 461s N DF SSR detRCov OLS-R2 McElroy-R2 461s system 40 35 160 1.83 0.702 0.575 461s 461s N DF SSR MSE RMSE R2 Adj R2 461s demand 20 17 64.0 3.77 1.94 0.761 0.733 461s supply 20 16 95.8 5.99 2.45 0.643 0.576 461s 461s The covariance matrix of the residuals 461s demand supply 461s demand 3.20 3.67 461s supply 3.67 4.79 461s 461s The correlations of the residuals 461s demand supply 461s demand 1.000 0.938 461s supply 0.938 1.000 461s 461s 461s OLS estimates for 'demand' (equation 1) 461s Model Formula: consump ~ price + income 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 101.4817 5.7621 17.61 < 2e-16 *** 461s price -0.3168 0.0589 -5.38 5.0e-06 *** 461s income 0.3189 0.0373 8.55 4.3e-10 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 1.94 on 17 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 17 461s SSR: 64.003 MSE: 3.765 Root MSE: 1.94 461s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 461s 461s 461s OLS estimates for 'supply' (equation 2) 461s Model Formula: consump ~ price + farmPrice + trend 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 54.1494 7.0638 7.67 5.4e-09 *** 461s price 0.1832 0.0589 3.11 0.0037 ** 461s farmPrice 0.2595 0.0365 7.10 2.8e-08 *** 461s trend 0.3189 0.0373 8.55 4.3e-10 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 2.447 on 16 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 16 461s SSR: 95.813 MSE: 5.988 Root MSE: 2.447 461s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.576 461s 461s > # the same with symbolically specified restrictions 461s > fitols5Sym <- systemfit( system, "OLS", data = Kmenta, 461s + restrict.matrix = restrict3, restrict.regMat = tc, 461s + methodResidCov = "noDfCor", useMatrix = useMatrix ) 461s > all.equal( fitols5, fitols5Sym ) 461s [1] "Component “call”: target, current do not match when deparsed" 461s > 461s > ## ***** OLS with 2 cross-equation restrictions via R and restrict.regMat (singleEqSigma=T) **** 461s > fitols5s <- systemfit( system, "OLS", data = Kmenta,restrict.matrix = restr3m, 461s + restrict.rhs = restr3q, restrict.regMat = tc, singleEqSigma = T, 461s + x = TRUE, useMatrix = useMatrix ) 461s > print( summary( fitols5s ) ) 461s 461s systemfit results 461s method: OLS 461s 461s N DF SSR detRCov OLS-R2 McElroy-R2 461s system 40 35 160 2.69 0.702 0.605 461s 461s N DF SSR MSE RMSE R2 Adj R2 461s demand 20 17 64.0 3.77 1.94 0.761 0.733 461s supply 20 16 95.8 5.99 2.45 0.643 0.576 461s 461s The covariance matrix of the residuals 461s demand supply 461s demand 3.76 4.46 461s supply 4.46 5.99 461s 461s The correlations of the residuals 461s demand supply 461s demand 1.000 0.938 461s supply 0.938 1.000 461s 461s 461s OLS estimates for 'demand' (equation 1) 461s Model Formula: consump ~ price + income 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 101.4817 6.0474 16.78 < 2e-16 *** 461s price -0.3168 0.0648 -4.89 2.3e-05 *** 461s income 0.3189 0.0385 8.29 9.1e-10 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 1.94 on 17 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 17 461s SSR: 64.003 MSE: 3.765 Root MSE: 1.94 461s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 461s 461s 461s OLS estimates for 'supply' (equation 2) 461s Model Formula: consump ~ price + farmPrice + trend 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 54.1494 7.9687 6.80 7.0e-08 *** 461s price 0.1832 0.0648 2.83 0.0077 ** 461s farmPrice 0.2595 0.0446 5.82 1.3e-06 *** 461s trend 0.3189 0.0385 8.29 9.1e-10 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 2.447 on 16 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 16 461s SSR: 95.813 MSE: 5.988 Root MSE: 2.447 461s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.576 461s 461s > 461s > ## ***** OLS with 2 cross-equation restrictions via R and restrict.regMat (useDfSys=F) **** 461s > fitols5o <- systemfit( system, "OLS", data = Kmenta,restrict.matrix = restr3m, 461s + restrict.rhs = restr3q, restrict.regMat = tc, useMatrix = useMatrix ) 461s > print( summary( fitols5o, useDfSys = FALSE ) ) 461s 461s systemfit results 461s method: OLS 461s 461s N DF SSR detRCov OLS-R2 McElroy-R2 461s system 40 35 160 2.69 0.702 0.605 461s 461s N DF SSR MSE RMSE R2 Adj R2 461s demand 20 17 64.0 3.77 1.94 0.761 0.733 461s supply 20 16 95.8 5.99 2.45 0.643 0.576 461s 461s The covariance matrix of the residuals 461s demand supply 461s demand 3.76 4.46 461s supply 4.46 5.99 461s 461s The correlations of the residuals 461s demand supply 461s demand 1.000 0.938 461s supply 0.938 1.000 461s 461s 461s OLS estimates for 'demand' (equation 1) 461s Model Formula: consump ~ price + income 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 101.4817 6.1599 16.47 6.9e-12 *** 461s price -0.3168 0.0629 -5.04 1e-04 *** 461s income 0.3189 0.0399 8.00 3.6e-07 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 1.94 on 17 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 17 461s SSR: 64.003 MSE: 3.765 Root MSE: 1.94 461s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 461s 461s 461s OLS estimates for 'supply' (equation 2) 461s Model Formula: consump ~ price + farmPrice + trend 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 54.1494 7.5515 7.17 2.2e-06 *** 461s price 0.1832 0.0629 2.91 0.01 * 461s farmPrice 0.2595 0.0391 6.64 5.6e-06 *** 461s trend 0.3189 0.0399 8.00 5.5e-07 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 2.447 on 16 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 16 461s SSR: 95.813 MSE: 5.988 Root MSE: 2.447 461s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.576 461s 461s > 461s > ## OLS with 2 cross-equation restr. via R and restrict.regMat (methodResidCov="noDfCor",singleEqSigma=T) 461s > fitols5rs <- systemfit( system, "OLS", data = Kmenta,restrict.matrix = restr3m, 461s + restrict.rhs = restr3q, restrict.regMat = tc, methodResidCov = "noDfCor", 461s + singleEqSigma = TRUE, useMatrix = useMatrix ) 461s > print( summary( fitols5rs ) ) 461s 461s systemfit results 461s method: OLS 461s 461s N DF SSR detRCov OLS-R2 McElroy-R2 461s system 40 35 160 1.83 0.702 0.575 461s 461s N DF SSR MSE RMSE R2 Adj R2 461s demand 20 17 64.0 3.77 1.94 0.761 0.733 461s supply 20 16 95.8 5.99 2.45 0.643 0.576 461s 461s The covariance matrix of the residuals 461s demand supply 461s demand 3.20 3.67 461s supply 3.67 4.79 461s 461s The correlations of the residuals 461s demand supply 461s demand 1.000 0.938 461s supply 0.938 1.000 461s 461s 461s OLS estimates for 'demand' (equation 1) 461s Model Formula: consump ~ price + income 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 101.4817 5.5234 18.37 < 2e-16 *** 461s price -0.3168 0.0589 -5.38 5.0e-06 *** 461s income 0.3189 0.0352 9.05 1.1e-10 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 1.94 on 17 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 17 461s SSR: 64.003 MSE: 3.765 Root MSE: 1.94 461s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 461s 461s 461s OLS estimates for 'supply' (equation 2) 461s Model Formula: consump ~ price + farmPrice + trend 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 54.1494 7.2089 7.51 8.5e-09 *** 461s price 0.1832 0.0589 3.11 0.0037 ** 461s farmPrice 0.2595 0.0399 6.51 1.7e-07 *** 461s trend 0.3189 0.0352 9.05 1.1e-10 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 2.447 on 16 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 16 461s SSR: 95.813 MSE: 5.988 Root MSE: 2.447 461s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.576 461s 461s > 461s > 461s > ## *********** estimations with a single regressor ************ 461s > fitolsS1 <- systemfit( 461s + list( consump ~ price - 1, consump ~ price + trend ), "OLS", 461s + data = Kmenta, useMatrix = useMatrix ) 461s > print( summary( fitolsS1 ) ) 461s 461s systemfit results 461s method: OLS 461s 461s N DF SSR detRCov OLS-R2 McElroy-R2 461s system 40 36 1121 484 -1.09 -1.05 461s 461s N DF SSR MSE RMSE R2 Adj R2 461s eq1 20 19 861 45.3 6.73 -2.213 -2.213 461s eq2 20 17 259 15.3 3.91 0.032 -0.082 461s 461s The covariance matrix of the residuals 461s eq1 eq2 461s eq1 45.3 14.4 461s eq2 14.4 15.3 461s 461s The correlations of the residuals 461s eq1 eq2 461s eq1 1.000 0.549 461s eq2 0.549 1.000 461s 461s 461s OLS estimates for 'eq1' (equation 1) 461s Model Formula: consump ~ price - 1 461s 461s Estimate Std. Error t value Pr(>|t|) 461s price 1.006 0.015 66.9 <2e-16 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 6.733 on 19 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 19 461s SSR: 861.449 MSE: 45.339 Root MSE: 6.733 461s Multiple R-Squared: -2.213 Adjusted R-Squared: -2.213 461s 461s 461s OLS estimates for 'eq2' (equation 2) 461s Model Formula: consump ~ price + trend 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 93.6767 15.2367 6.15 1.1e-05 *** 461s price 0.0622 0.1513 0.41 0.69 461s trend 0.0953 0.1515 0.63 0.54 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 3.907 on 17 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 17 461s SSR: 259.497 MSE: 15.265 Root MSE: 3.907 461s Multiple R-Squared: 0.032 Adjusted R-Squared: -0.082 461s 461s > fitolsS2 <- systemfit( 461s + list( consump ~ price - 1, consump ~ trend - 1 ), "OLS", 461s + data = Kmenta, useMatrix = useMatrix ) 461s > print( summary( fitolsS2 ) ) 461s 461s systemfit results 461s method: OLS 461s 461s N DF SSR detRCov OLS-R2 McElroy-R2 461s system 40 38 47370 110957 -87.3 -5.28 461s 461s N DF SSR MSE RMSE R2 Adj R2 461s eq1 20 19 861 45.3 6.73 -2.21 -2.21 461s eq2 20 19 46509 2447.8 49.48 -172.47 -172.47 461s 461s The covariance matrix of the residuals 461s eq1 eq2 461s eq1 45.34 -5.15 461s eq2 -5.15 2447.84 461s 461s The correlations of the residuals 461s eq1 eq2 461s eq1 1.0000 -0.0439 461s eq2 -0.0439 1.0000 461s 461s 461s OLS estimates for 'eq1' (equation 1) 461s Model Formula: consump ~ price - 1 461s 461s Estimate Std. Error t value Pr(>|t|) 461s price 1.006 0.015 66.9 <2e-16 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 6.733 on 19 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 19 461s SSR: 861.449 MSE: 45.339 Root MSE: 6.733 461s Multiple R-Squared: -2.213 Adjusted R-Squared: -2.213 461s 461s 461s OLS estimates for 'eq2' (equation 2) 461s Model Formula: consump ~ trend - 1 461s 461s Estimate Std. Error t value Pr(>|t|) 461s trend 7.405 0.924 8.02 1.6e-07 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 49.476 on 19 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 19 461s SSR: 46508.922 MSE: 2447.838 Root MSE: 49.476 461s Multiple R-Squared: -172.467 Adjusted R-Squared: -172.467 461s 461s > fitolsS3 <- systemfit( 461s + list( consump ~ trend - 1, price ~ trend - 1 ), "OLS", 461s + data = Kmenta, useMatrix = useMatrix ) 461s > print( summary( fitolsS3 ) ) 461s 461s systemfit results 461s method: OLS 461s 461s N DF SSR detRCov OLS-R2 McElroy-R2 461s system 40 38 93537 108970 -99 -0.977 461s 461s N DF SSR MSE RMSE R2 Adj R2 461s eq1 20 19 46509 2448 49.5 -172.5 -172.5 461s eq2 20 19 47028 2475 49.8 -69.5 -69.5 461s 461s The covariance matrix of the residuals 461s eq1 eq2 461s eq1 2448 2439 461s eq2 2439 2475 461s 461s The correlations of the residuals 461s eq1 eq2 461s eq1 1.000 0.988 461s eq2 0.988 1.000 461s 461s 461s OLS estimates for 'eq1' (equation 1) 461s Model Formula: consump ~ trend - 1 461s 461s Estimate Std. Error t value Pr(>|t|) 461s trend 7.405 0.924 8.02 1.6e-07 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 49.476 on 19 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 19 461s SSR: 46508.922 MSE: 2447.838 Root MSE: 49.476 461s Multiple R-Squared: -172.467 Adjusted R-Squared: -172.467 461s 461s 461s OLS estimates for 'eq2' (equation 2) 461s Model Formula: price ~ trend - 1 461s 461s Estimate Std. Error t value Pr(>|t|) 461s trend 7.318 0.929 7.88 2.1e-07 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 49.751 on 19 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 19 461s SSR: 47028.107 MSE: 2475.164 Root MSE: 49.751 461s Multiple R-Squared: -69.48 Adjusted R-Squared: -69.48 461s 461s > fitolsS4 <- systemfit( 461s + list( consump ~ trend - 1, price ~ trend - 1 ), "OLS", 461s + data = Kmenta, restrict.matrix = matrix( c( 1, -1 ), nrow = 1 ), 461s + useMatrix = useMatrix ) 461s > print( summary( fitolsS4 ) ) 461s 461s systemfit results 461s method: OLS 461s 461s N DF SSR detRCov OLS-R2 McElroy-R2 461s system 40 39 93548 111736 -99 -1.03 461s 461s N DF SSR MSE RMSE R2 Adj R2 461s eq1 20 19 46514 2448 49.5 -172.5 -172.5 461s eq2 20 19 47033 2475 49.8 -69.5 -69.5 461s 461s The covariance matrix of the residuals 461s eq1 eq2 461s eq1 2448 2439 461s eq2 2439 2475 461s 461s The correlations of the residuals 461s eq1 eq2 461s eq1 1.000 0.988 461s eq2 0.988 1.000 461s 461s 461s OLS estimates for 'eq1' (equation 1) 461s Model Formula: consump ~ trend - 1 461s 461s Estimate Std. Error t value Pr(>|t|) 461s trend 7.362 0.646 11.4 5.7e-14 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 49.478 on 19 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 19 461s SSR: 46514.283 MSE: 2448.12 Root MSE: 49.478 461s Multiple R-Squared: -172.487 Adjusted R-Squared: -172.487 461s 461s 461s OLS estimates for 'eq2' (equation 2) 461s Model Formula: price ~ trend - 1 461s 461s Estimate Std. Error t value Pr(>|t|) 461s trend 7.362 0.646 11.4 5.7e-14 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 49.754 on 19 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 19 461s SSR: 47033.469 MSE: 2475.446 Root MSE: 49.754 461s Multiple R-Squared: -69.488 Adjusted R-Squared: -69.488 461s 461s > fitolsS5 <- systemfit( 461s + list( consump ~ 1, farmPrice ~ 1 ), "OLS", 461s + data = Kmenta, useMatrix = useMatrix ) 461s > print( summary( fitolsS5 ) ) 461s 461s systemfit results 461s method: OLS 461s 461s N DF SSR detRCov OLS-R2 McElroy-R2 461s system 40 38 3337 1224 0 0 461s 461s N DF SSR MSE RMSE R2 Adj R2 461s eq1 20 19 268 14.1 3.76 0 0 461s eq2 20 19 3069 161.5 12.71 0 0 461s 461s The covariance matrix of the residuals 461s eq1 eq2 461s eq1 14.1 32.5 461s eq2 32.5 161.5 461s 461s The correlations of the residuals 461s eq1 eq2 461s eq1 1.000 0.681 461s eq2 0.681 1.000 461s 461s 461s OLS estimates for 'eq1' (equation 1) 461s Model Formula: consump ~ 1 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 100.90 0.84 120 <2e-16 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 3.756 on 19 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 19 461s SSR: 268.114 MSE: 14.111 Root MSE: 3.756 461s Multiple R-Squared: 0 Adjusted R-Squared: 0 461s 461s 461s OLS estimates for 'eq2' (equation 2) 461s Model Formula: farmPrice ~ 1 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 96.62 2.84 34 <2e-16 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 12.709 on 19 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 19 461s SSR: 3068.757 MSE: 161.514 Root MSE: 12.709 461s Multiple R-Squared: 0 Adjusted R-Squared: 0 461s 461s > 461s > 461s > ## **************** shorter summaries ********************** 461s > print( summary( fitols1, useDfSys = TRUE, equations = FALSE ) ) 461s 461s systemfit results 461s method: OLS 461s 461s N DF SSR detRCov OLS-R2 McElroy-R2 461s system 40 33 156 4.43 0.709 0.558 461s 461s N DF SSR MSE RMSE R2 Adj R2 461s demand 20 17 63.3 3.73 1.93 0.764 0.736 461s supply 20 16 92.6 5.78 2.40 0.655 0.590 461s 461s The covariance matrix of the residuals 461s demand supply 461s demand 3.73 4.14 461s supply 4.14 5.78 461s 461s The correlations of the residuals 461s demand supply 461s demand 1.000 0.891 461s supply 0.891 1.000 461s 461s 461s Coefficients: 461s Estimate Std. Error t value Pr(>|t|) 461s demand_(Intercept) 99.8954 7.5194 13.29 8.4e-15 *** 461s demand_price -0.3163 0.0907 -3.49 0.0014 ** 461s demand_income 0.3346 0.0454 7.37 1.8e-08 *** 461s supply_(Intercept) 58.2754 11.4629 5.08 1.4e-05 *** 461s supply_price 0.1604 0.0949 1.69 0.1004 461s supply_farmPrice 0.2481 0.0462 5.37 6.1e-06 *** 461s supply_trend 0.2483 0.0975 2.55 0.0157 * 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s > 461s > print( summary( fitols2r ), residCov = FALSE, equations = FALSE ) 461s 461s systemfit results 461s method: OLS 461s 461s N DF SSR detRCov OLS-R2 McElroy-R2 461s system 40 34 159 1.7 0.703 0.577 461s 461s N DF SSR MSE RMSE R2 Adj R2 461s demand 20 17 64.2 3.78 1.94 0.761 0.732 461s supply 20 16 95.1 5.94 2.44 0.645 0.579 461s 461s 461s Coefficients: 461s Estimate Std. Error t value Pr(>|t|) 461s demand_(Intercept) 99.5563 7.7652 12.82 1.4e-14 *** 461s demand_price -0.2917 0.0899 -3.25 0.0026 ** 461s demand_income 0.3129 0.0406 7.70 5.9e-09 *** 461s supply_(Intercept) 56.3795 9.2860 6.07 7.0e-07 *** 461s supply_price 0.1639 0.0786 2.08 0.0447 * 461s supply_farmPrice 0.2571 0.0371 6.93 5.4e-08 *** 461s supply_trend 0.3129 0.0406 7.70 5.9e-09 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s > 461s > print( summary( fitols3s, useDfSys = FALSE ), residCov = TRUE ) 461s 461s systemfit results 461s method: OLS 461s 461s N DF SSR detRCov OLS-R2 McElroy-R2 461s system 40 34 159 2.5 0.703 0.608 461s 461s N DF SSR MSE RMSE R2 Adj R2 461s demand 20 17 64.2 3.78 1.94 0.761 0.732 461s supply 20 16 95.1 5.94 2.44 0.645 0.579 461s 461s The covariance matrix of the residuals 461s demand supply 461s demand 3.78 4.47 461s supply 4.47 5.94 461s 461s The correlations of the residuals 461s demand supply 461s demand 1.000 0.943 461s supply 0.943 1.000 461s 461s 461s OLS estimates for 'demand' (equation 1) 461s Model Formula: consump ~ price + income 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 99.5563 7.5640 13.16 2.4e-10 *** 461s price -0.2917 0.0887 -3.29 0.0043 ** 461s income 0.3129 0.0415 7.54 8.1e-07 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 1.943 on 17 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 17 461s SSR: 64.186 MSE: 3.776 Root MSE: 1.943 461s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.732 461s 461s 461s OLS estimates for 'supply' (equation 2) 461s Model Formula: consump ~ price + farmPrice + trend 461s 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 56.3795 11.3165 4.98 0.00014 *** 461s price 0.1639 0.0960 1.71 0.10724 461s farmPrice 0.2571 0.0451 5.69 3.3e-05 *** 461s trend 0.3129 0.0415 7.54 1.2e-06 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s 461s Residual standard error: 2.438 on 16 degrees of freedom 461s Number of observations: 20 Degrees of Freedom: 16 461s SSR: 95.088 MSE: 5.943 Root MSE: 2.438 461s Multiple R-Squared: 0.645 Adjusted R-Squared: 0.579 461s 461s > 461s > print( summary( fitols4rs, residCov = FALSE, equations = FALSE ) ) 461s 461s systemfit results 461s method: OLS 461s 461s N DF SSR detRCov OLS-R2 McElroy-R2 461s system 40 35 160 1.83 0.702 0.575 461s 461s N DF SSR MSE RMSE R2 Adj R2 461s demand 20 17 64.0 3.77 1.94 0.761 0.733 461s supply 20 16 95.8 5.99 2.45 0.643 0.576 461s 461s 461s Coefficients: 461s Estimate Std. Error t value Pr(>|t|) 461s demand_(Intercept) 101.4817 5.5234 18.37 < 2e-16 *** 461s demand_price -0.3168 0.0589 -5.38 5.0e-06 *** 461s demand_income 0.3189 0.0352 9.05 1.1e-10 *** 461s supply_(Intercept) 54.1494 7.2089 7.51 8.5e-09 *** 461s supply_price 0.1832 0.0589 3.11 0.0037 ** 461s supply_farmPrice 0.2595 0.0399 6.51 1.7e-07 *** 461s supply_trend 0.3189 0.0352 9.05 1.1e-10 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s > 461s > print( summary( fitols5, equations = FALSE ), residCov = FALSE ) 461s 461s systemfit results 461s method: OLS 461s 461s N DF SSR detRCov OLS-R2 McElroy-R2 461s system 40 35 160 1.83 0.702 0.575 461s 461s N DF SSR MSE RMSE R2 Adj R2 461s demand 20 17 64.0 3.77 1.94 0.761 0.733 461s supply 20 16 95.8 5.99 2.45 0.643 0.576 461s 461s 461s Coefficients: 461s Estimate Std. Error t value Pr(>|t|) 461s demand_(Intercept) 101.4817 5.7621 17.61 < 2e-16 *** 461s demand_price -0.3168 0.0589 -5.38 5.0e-06 *** 461s demand_income 0.3189 0.0373 8.55 4.3e-10 *** 461s supply_(Intercept) 54.1494 7.0638 7.67 5.4e-09 *** 461s supply_price 0.1832 0.0589 3.11 0.0037 ** 461s supply_farmPrice 0.2595 0.0365 7.10 2.8e-08 *** 461s supply_trend 0.3189 0.0373 8.55 4.3e-10 *** 461s --- 461s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 461s > 461s > 461s > ## ****************** residuals ************************** 461s > print( residuals( fitols1 ) ) 461s demand supply 461s 1 1.074 -0.444 461s 2 -0.390 -0.896 461s 3 2.625 1.965 461s 4 1.802 1.134 461s 5 1.946 1.514 461s 6 1.175 0.680 461s 7 1.530 1.569 461s 8 -2.933 -4.407 461s 9 -1.365 -2.599 461s 10 2.031 2.469 461s 11 -0.149 -0.598 461s 12 -1.954 -1.697 461s 13 -1.121 -1.064 461s 14 -0.220 0.970 461s 15 1.487 3.159 461s 16 -3.701 -3.866 461s 17 -1.273 -0.265 461s 18 -2.002 -2.449 461s 19 1.738 3.110 461s 20 -0.299 1.714 461s > print( residuals( fitols1$eq[[ 2 ]] ) ) 461s 1 2 3 4 5 6 7 8 9 10 11 461s -0.444 -0.896 1.965 1.134 1.514 0.680 1.569 -4.407 -2.599 2.469 -0.598 461s 12 13 14 15 16 17 18 19 20 461s -1.697 -1.064 0.970 3.159 -3.866 -0.265 -2.449 3.110 1.714 461s > 461s > print( residuals( fitols2r ) ) 461s demand supply 461s 1 0.8465 0.156 461s 2 -0.4933 -0.384 461s 3 2.5225 2.415 461s 4 1.7066 1.525 461s 5 2.0445 1.750 461s 6 1.2529 0.870 461s 7 1.6277 1.711 461s 8 -2.8261 -4.380 461s 9 -1.2979 -2.597 461s 10 2.0592 2.497 461s 11 -0.4663 -0.466 461s 12 -2.3732 -1.540 461s 13 -1.4734 -1.006 461s 14 -0.3398 0.885 461s 15 1.7283 2.835 461s 16 -3.4975 -4.290 461s 17 -0.9651 -0.760 461s 18 -1.9512 -2.911 461s 19 1.8829 2.606 461s 20 0.0129 1.085 461s > print( residuals( fitols2r$eq[[ 1 ]] ) ) 461s 1 2 3 4 5 6 7 8 9 10 461s 0.8465 -0.4933 2.5225 1.7066 2.0445 1.2529 1.6277 -2.8261 -1.2979 2.0592 461s 11 12 13 14 15 16 17 18 19 20 461s -0.4663 -2.3732 -1.4734 -0.3398 1.7283 -3.4975 -0.9651 -1.9512 1.8829 0.0129 461s > 461s > print( residuals( fitols3s ) ) 461s demand supply 461s 1 0.8465 0.156 461s 2 -0.4933 -0.384 461s 3 2.5225 2.415 461s 4 1.7066 1.525 461s 5 2.0445 1.750 461s 6 1.2529 0.870 461s 7 1.6277 1.711 461s 8 -2.8261 -4.380 461s 9 -1.2979 -2.597 461s 10 2.0592 2.497 461s 11 -0.4663 -0.466 461s 12 -2.3732 -1.540 461s 13 -1.4734 -1.006 461s 14 -0.3398 0.885 461s 15 1.7283 2.835 461s 16 -3.4975 -4.290 461s 17 -0.9651 -0.760 461s 18 -1.9512 -2.911 461s 19 1.8829 2.606 461s 20 0.0129 1.085 461s > print( residuals( fitols3s$eq[[ 2 ]] ) ) 461s 1 2 3 4 5 6 7 8 9 10 11 461s 0.156 -0.384 2.415 1.525 1.750 0.870 1.711 -4.380 -2.597 2.497 -0.466 461s 12 13 14 15 16 17 18 19 20 461s -1.540 -1.006 0.885 2.835 -4.290 -0.760 -2.911 2.606 1.085 461s > 461s > print( residuals( fitols4rs ) ) 461s demand supply 461s 1 0.915 0.204 461s 2 -0.387 -0.421 461s 3 2.613 2.388 461s 4 1.815 1.474 461s 5 1.980 1.787 461s 6 1.221 0.879 461s 7 1.620 1.690 461s 8 -2.769 -4.489 461s 9 -1.382 -2.549 461s 10 1.890 2.660 461s 11 -0.506 -0.297 461s 12 -2.280 -1.456 461s 13 -1.323 -1.013 461s 14 -0.330 0.925 461s 15 1.572 2.889 461s 16 -3.582 -4.313 461s 17 -1.298 -0.573 461s 18 -1.892 -3.023 461s 19 1.948 2.462 461s 20 0.174 0.777 461s > print( residuals( fitols4rs$eq[[ 1 ]] ) ) 461s 1 2 3 4 5 6 7 8 9 10 11 461s 0.915 -0.387 2.613 1.815 1.980 1.221 1.620 -2.769 -1.382 1.890 -0.506 461s 12 13 14 15 16 17 18 19 20 461s -2.280 -1.323 -0.330 1.572 -3.582 -1.298 -1.892 1.948 0.174 461s > 461s > print( residuals( fitols5 ) ) 461s demand supply 461s 1 0.915 0.204 461s 2 -0.387 -0.421 461s 3 2.613 2.388 461s 4 1.815 1.474 461s 5 1.980 1.787 461s 6 1.221 0.879 461s 7 1.620 1.690 461s 8 -2.769 -4.489 461s 9 -1.382 -2.549 461s 10 1.890 2.660 461s 11 -0.506 -0.297 461s 12 -2.280 -1.456 461s 13 -1.323 -1.013 461s 14 -0.330 0.925 461s 15 1.572 2.889 461s 16 -3.582 -4.313 461s 17 -1.298 -0.573 461s 18 -1.892 -3.023 461s 19 1.948 2.462 461s 20 0.174 0.777 461s > print( residuals( fitols5$eq[[ 2 ]] ) ) 461s 1 2 3 4 5 6 7 8 9 10 11 461s 0.204 -0.421 2.388 1.474 1.787 0.879 1.690 -4.489 -2.549 2.660 -0.297 461s 12 13 14 15 16 17 18 19 20 461s -1.456 -1.013 0.925 2.889 -4.313 -0.573 -3.023 2.462 0.777 461s > 461s > 461s > ## *************** coefficients ********************* 461s > print( round( coef( fitols1rs ), digits = 6 ) ) 461s demand_(Intercept) demand_price demand_income supply_(Intercept) 461s 99.895 -0.316 0.335 58.275 461s supply_price supply_farmPrice supply_trend 461s 0.160 0.248 0.248 461s > print( round( coef( fitols1rs$eq[[ 2 ]] ), digits = 6 ) ) 461s (Intercept) price farmPrice trend 461s 58.275 0.160 0.248 0.248 461s > 461s > print( round( coef( fitols2s ), digits = 6 ) ) 461s demand_(Intercept) demand_price demand_income supply_(Intercept) 461s 99.556 -0.292 0.313 56.380 461s supply_price supply_farmPrice supply_trend 461s 0.164 0.257 0.313 461s > print( round( coef( fitols2s$eq[[ 1 ]] ), digits = 6 ) ) 461s (Intercept) price income 461s 99.556 -0.292 0.313 461s > 461s > print( round( coef( fitols3 ), digits = 6 ) ) 461s demand_(Intercept) demand_price demand_income supply_(Intercept) 461s 99.556 -0.292 0.313 56.380 461s supply_price supply_farmPrice supply_trend 461s 0.164 0.257 0.313 461s > print( round( coef( fitols3, modified.regMat = TRUE ), digits = 6 ) ) 461s C1 C2 C3 C4 C5 C6 461s 99.556 -0.292 0.313 56.380 0.164 0.257 461s > print( round( coef( fitols3$eq[[ 2 ]] ), digits = 6 ) ) 461s (Intercept) price farmPrice trend 461s 56.380 0.164 0.257 0.313 461s > 461s > print( round( coef( fitols4r ), digits = 6 ) ) 461s demand_(Intercept) demand_price demand_income supply_(Intercept) 461s 101.482 -0.317 0.319 54.149 461s supply_price supply_farmPrice supply_trend 461s 0.183 0.260 0.319 461s > print( round( coef( fitols4r$eq[[ 1 ]] ), digits = 6 ) ) 461s (Intercept) price income 461s 101.482 -0.317 0.319 461s > 461s > print( round( coef( fitols5 ), digits = 6 ) ) 461s demand_(Intercept) demand_price demand_income supply_(Intercept) 461s 101.482 -0.317 0.319 54.149 461s supply_price supply_farmPrice supply_trend 461s 0.183 0.260 0.319 461s > print( round( coef( fitols5, modified.regMat = TRUE ), digits = 6 ) ) 461s C1 C2 C3 C4 C5 C6 461s 101.482 -0.317 0.319 54.149 0.183 0.260 461s > print( round( coef( fitols5$eq[[ 2 ]] ), digits = 6 ) ) 461s (Intercept) price farmPrice trend 461s 54.149 0.183 0.260 0.319 461s > 461s > 461s > ## *************** coefficients with stats ********************* 461s > print( round( coef( summary( fitols1rs, useDfSys = FALSE ) ), digits = 6 ) ) 461s Estimate Std. Error t value Pr(>|t|) 461s demand_(Intercept) 99.895 8.4671 11.80 0.000000 461s demand_price -0.316 0.1021 -3.10 0.006536 461s demand_income 0.335 0.0511 6.54 0.000005 461s supply_(Intercept) 58.275 10.3587 5.63 0.000038 461s supply_price 0.160 0.0857 1.87 0.079851 461s supply_farmPrice 0.248 0.0417 5.94 0.000021 461s supply_trend 0.248 0.0881 2.82 0.012382 461s > print( round( coef( summary( fitols1rs$eq[[ 2 ]], useDfSys = FALSE ) ), 461s + digits = 6 ) ) 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 58.275 10.3587 5.63 0.000038 461s price 0.160 0.0857 1.87 0.079851 461s farmPrice 0.248 0.0417 5.94 0.000021 461s trend 0.248 0.0881 2.82 0.012382 461s > 461s > print( round( coef( summary( fitols2s ) ), digits = 6 ) ) 461s Estimate Std. Error t value Pr(>|t|) 461s demand_(Intercept) 99.556 7.5640 13.16 0.000000 461s demand_price -0.292 0.0887 -3.29 0.002340 461s demand_income 0.313 0.0415 7.54 0.000000 461s supply_(Intercept) 56.380 11.3165 4.98 0.000018 461s supply_price 0.164 0.0960 1.71 0.097028 461s supply_farmPrice 0.257 0.0451 5.69 0.000002 461s supply_trend 0.313 0.0415 7.54 0.000000 461s > print( round( coef( summary( fitols2s$eq[[ 1 ]] ) ), digits = 6 ) ) 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 99.556 7.5640 13.16 0.00000 461s price -0.292 0.0887 -3.29 0.00234 461s income 0.313 0.0415 7.54 0.00000 461s > 461s > print( round( coef( summary( fitols3, useDfSys = FALSE ) ), digits = 6 ) ) 461s Estimate Std. Error t value Pr(>|t|) 461s demand_(Intercept) 99.556 8.4225 11.82 0.000000 461s demand_price -0.292 0.0975 -2.99 0.008189 461s demand_income 0.313 0.0441 7.10 0.000002 461s supply_(Intercept) 56.380 10.0721 5.60 0.000040 461s supply_price 0.164 0.0853 1.92 0.072611 461s supply_farmPrice 0.257 0.0402 6.39 0.000009 461s supply_trend 0.313 0.0441 7.10 0.000003 461s > print( round( coef( summary( fitols3, useDfSys = FALSE ), modified.regMat = TRUE ), 461s + digits = 6 ) ) 461s Estimate Std. Error t value Pr(>|t|) 461s C1 99.556 8.4225 11.82 NA 461s C2 -0.292 0.0975 -2.99 NA 461s C3 0.313 0.0441 7.10 NA 461s C4 56.380 10.0721 5.60 NA 461s C5 0.164 0.0853 1.92 NA 461s C6 0.257 0.0402 6.39 NA 461s > print( round( coef( summary( fitols3$eq[[ 2 ]], useDfSys = FALSE ) ), 461s + digits = 6 ) ) 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 56.380 10.0721 5.60 0.000040 461s price 0.164 0.0853 1.92 0.072611 461s farmPrice 0.257 0.0402 6.39 0.000009 461s trend 0.313 0.0441 7.10 0.000003 461s > 461s > print( round( coef( summary( fitols4r, useDfSys = FALSE ) ), digits = 6 ) ) 461s Estimate Std. Error t value Pr(>|t|) 461s demand_(Intercept) 101.482 5.7621 17.61 0.0e+00 461s demand_price -0.317 0.0589 -5.38 5.0e-05 461s demand_income 0.319 0.0373 8.55 0.0e+00 461s supply_(Intercept) 54.149 7.0638 7.67 1.0e-06 461s supply_price 0.183 0.0589 3.11 6.7e-03 461s supply_farmPrice 0.260 0.0365 7.10 3.0e-06 461s supply_trend 0.319 0.0373 8.55 0.0e+00 461s > print( round( coef( summary( fitols4r$eq[[ 1 ]], useDfSys = FALSE ) ), 461s + digits = 6 ) ) 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 101.482 5.7621 17.61 0e+00 461s price -0.317 0.0589 -5.38 5e-05 461s income 0.319 0.0373 8.55 0e+00 461s > 461s > print( round( coef( summary( fitols5 ) ), digits = 6 ) ) 461s Estimate Std. Error t value Pr(>|t|) 461s demand_(Intercept) 101.482 5.7621 17.61 0.000000 461s demand_price -0.317 0.0589 -5.38 0.000005 461s demand_income 0.319 0.0373 8.55 0.000000 461s supply_(Intercept) 54.149 7.0638 7.67 0.000000 461s supply_price 0.183 0.0589 3.11 0.003680 461s supply_farmPrice 0.260 0.0365 7.10 0.000000 461s supply_trend 0.319 0.0373 8.55 0.000000 461s > print( round( coef( summary( fitols5 ), modified.regMat = TRUE ), digits = 6 ) ) 461s Estimate Std. Error t value Pr(>|t|) 461s C1 101.482 5.7621 17.61 0.000000 461s C2 -0.317 0.0589 -5.38 0.000005 461s C3 0.319 0.0373 8.55 0.000000 461s C4 54.149 7.0638 7.67 0.000000 461s C5 0.183 0.0589 3.11 0.003680 461s C6 0.260 0.0365 7.10 0.000000 461s > print( round( coef( summary( fitols5$eq[[ 2 ]] ) ), digits = 6 ) ) 461s Estimate Std. Error t value Pr(>|t|) 461s (Intercept) 54.149 7.0638 7.67 0.00000 461s price 0.183 0.0589 3.11 0.00368 461s farmPrice 0.260 0.0365 7.10 0.00000 461s trend 0.319 0.0373 8.55 0.00000 461s > 461s > 461s > ## *********** variance covariance matrix of the coefficients ******* 461s > print( round( vcov( fitols1rs ), digits = 6 ) ) 461s demand_(Intercept) demand_price demand_income 461s demand_(Intercept) 71.6926 -0.75420 0.04078 461s demand_price -0.7542 0.01043 -0.00296 461s demand_income 0.0408 -0.00296 0.00262 461s supply_(Intercept) 0.0000 0.00000 0.00000 461s supply_price 0.0000 0.00000 0.00000 461s supply_farmPrice 0.0000 0.00000 0.00000 461s supply_trend 0.0000 0.00000 0.00000 461s supply_(Intercept) supply_price supply_farmPrice 461s demand_(Intercept) 0.000 0.000000 0.000000 461s demand_price 0.000 0.000000 0.000000 461s demand_income 0.000 0.000000 0.000000 461s supply_(Intercept) 107.303 -0.806417 -0.248549 461s supply_price -0.806 0.007352 0.000689 461s supply_farmPrice -0.249 0.000689 0.001742 461s supply_trend -0.228 0.000426 0.001074 461s supply_trend 461s demand_(Intercept) 0.000000 461s demand_price 0.000000 461s demand_income 0.000000 461s supply_(Intercept) -0.227988 461s supply_price 0.000426 461s supply_farmPrice 0.001074 461s supply_trend 0.007766 461s > print( round( vcov( fitols1rs$eq[[ 2 ]] ), digits = 6 ) ) 461s (Intercept) price farmPrice trend 461s (Intercept) 107.303 -0.806417 -0.248549 -0.227988 461s price -0.806 0.007352 0.000689 0.000426 461s farmPrice -0.249 0.000689 0.001742 0.001074 461s trend -0.228 0.000426 0.001074 0.007766 461s > 461s > print( round( vcov( fitols2s ), digits = 6 ) ) 461s demand_(Intercept) demand_price demand_income 461s demand_(Intercept) 57.21413 -0.596328 0.026850 461s demand_price -0.59633 0.007862 -0.001948 461s demand_income 0.02685 -0.001948 0.001722 461s supply_(Intercept) -0.78825 0.057190 -0.050565 461s supply_price 0.00147 -0.000107 0.000095 461s supply_farmPrice 0.00371 -0.000269 0.000238 461s supply_trend 0.02685 -0.001948 0.001722 461s supply_(Intercept) supply_price supply_farmPrice 461s demand_(Intercept) -0.7883 0.001474 0.003714 461s demand_price 0.0572 -0.000107 -0.000269 461s demand_income -0.0506 0.000095 0.000238 461s supply_(Intercept) 128.0635 -1.001596 -0.280017 461s supply_price -1.0016 0.009225 0.000806 461s supply_farmPrice -0.2800 0.000806 0.002038 461s supply_trend -0.0506 0.000095 0.000238 461s supply_trend 461s demand_(Intercept) 0.026850 461s demand_price -0.001948 461s demand_income 0.001722 461s supply_(Intercept) -0.050565 461s supply_price 0.000095 461s supply_farmPrice 0.000238 461s supply_trend 0.001722 461s > print( round( vcov( fitols2s$eq[[ 1 ]] ), digits = 6 ) ) 461s (Intercept) price income 461s (Intercept) 57.2141 -0.59633 0.02685 461s price -0.5963 0.00786 -0.00195 461s income 0.0268 -0.00195 0.00172 461s > 461s > print( round( vcov( fitols3 ), digits = 6 ) ) 461s demand_(Intercept) demand_price demand_income 461s demand_(Intercept) 70.93892 -0.736413 0.030252 461s demand_price -0.73641 0.009503 -0.002195 461s demand_income 0.03025 -0.002195 0.001941 461s supply_(Intercept) -0.88813 0.064436 -0.056972 461s supply_price 0.00166 -0.000120 0.000107 461s supply_farmPrice 0.00419 -0.000304 0.000268 461s supply_trend 0.03025 -0.002195 0.001941 461s supply_(Intercept) supply_price supply_farmPrice 461s demand_(Intercept) -0.8881 0.001661 0.004185 461s demand_price 0.0644 -0.000120 -0.000304 461s demand_income -0.0570 0.000107 0.000268 461s supply_(Intercept) 101.4478 -0.790443 -0.223090 461s supply_price -0.7904 0.007274 0.000640 461s supply_farmPrice -0.2231 0.000640 0.001617 461s supply_trend -0.0570 0.000107 0.000268 461s supply_trend 461s demand_(Intercept) 0.030252 461s demand_price -0.002195 461s demand_income 0.001941 461s supply_(Intercept) -0.056972 461s supply_price 0.000107 461s supply_farmPrice 0.000268 461s supply_trend 0.001941 461s > print( round( vcov( fitols3, modified.regMat = TRUE ), digits = 6 ) ) 461s C1 C2 C3 C4 C5 C6 461s C1 70.93892 -0.736413 0.030252 -0.8881 0.001661 0.004185 461s C2 -0.73641 0.009503 -0.002195 0.0644 -0.000120 -0.000304 461s C3 0.03025 -0.002195 0.001941 -0.0570 0.000107 0.000268 461s C4 -0.88813 0.064436 -0.056972 101.4478 -0.790443 -0.223090 461s C5 0.00166 -0.000120 0.000107 -0.7904 0.007274 0.000640 461s C6 0.00419 -0.000304 0.000268 -0.2231 0.000640 0.001617 461s > print( round( vcov( fitols3$eq[[ 2 ]] ), digits = 6 ) ) 461s (Intercept) price farmPrice trend 461s (Intercept) 101.448 -0.790443 -0.223090 -0.056972 461s price -0.790 0.007274 0.000640 0.000107 461s farmPrice -0.223 0.000640 0.001617 0.000268 461s trend -0.057 0.000107 0.000268 0.001941 461s > 461s > print( round( vcov( fitols4r ), digits = 6 ) ) 461s demand_(Intercept) demand_price demand_income 461s demand_(Intercept) 33.2016 -0.272100 -0.059329 461s demand_price -0.2721 0.003464 -0.000762 461s demand_income -0.0593 -0.000762 0.001390 461s supply_(Intercept) 30.8652 -0.357363 0.050012 461s supply_price -0.2721 0.003464 -0.000762 461s supply_farmPrice -0.0313 0.000196 0.000120 461s supply_trend -0.0593 -0.000762 0.001390 461s supply_(Intercept) supply_price supply_farmPrice 461s demand_(Intercept) 30.865 -0.272100 -0.031328 461s demand_price -0.357 0.003464 0.000196 461s demand_income 0.050 -0.000762 0.000120 461s supply_(Intercept) 49.897 -0.357363 -0.149852 461s supply_price -0.357 0.003464 0.000196 461s supply_farmPrice -0.150 0.000196 0.001335 461s supply_trend 0.050 -0.000762 0.000120 461s supply_trend 461s demand_(Intercept) -0.059329 461s demand_price -0.000762 461s demand_income 0.001390 461s supply_(Intercept) 0.050012 461s supply_price -0.000762 461s supply_farmPrice 0.000120 461s supply_trend 0.001390 461s > print( round( vcov( fitols4r$eq[[ 1 ]] ), digits = 6 ) ) 461s (Intercept) price income 461s (Intercept) 33.2016 -0.272100 -0.059329 461s price -0.2721 0.003464 -0.000762 461s income -0.0593 -0.000762 0.001390 461s > 461s > print( round( vcov( fitols5 ), digits = 6 ) ) 461s demand_(Intercept) demand_price demand_income 461s demand_(Intercept) 33.2016 -0.272100 -0.059329 461s demand_price -0.2721 0.003464 -0.000762 461s demand_income -0.0593 -0.000762 0.001390 461s supply_(Intercept) 30.8652 -0.357363 0.050012 461s supply_price -0.2721 0.003464 -0.000762 461s supply_farmPrice -0.0313 0.000196 0.000120 461s supply_trend -0.0593 -0.000762 0.001390 461s supply_(Intercept) supply_price supply_farmPrice 461s demand_(Intercept) 30.865 -0.272100 -0.031328 461s demand_price -0.357 0.003464 0.000196 461s demand_income 0.050 -0.000762 0.000120 461s supply_(Intercept) 49.897 -0.357363 -0.149852 461s supply_price -0.357 0.003464 0.000196 461s supply_farmPrice -0.150 0.000196 0.001335 461s supply_trend 0.050 -0.000762 0.000120 461s supply_trend 461s demand_(Intercept) -0.059329 461s demand_price -0.000762 461s demand_income 0.001390 461s supply_(Intercept) 0.050012 461s supply_price -0.000762 461s supply_farmPrice 0.000120 461s supply_trend 0.001390 461s > print( round( vcov( fitols5, modified.regMat = TRUE ), digits = 6 ) ) 461s C1 C2 C3 C4 C5 C6 461s C1 33.2016 -0.272100 -0.059329 30.865 -0.272100 -0.031328 461s C2 -0.2721 0.003464 -0.000762 -0.357 0.003464 0.000196 461s C3 -0.0593 -0.000762 0.001390 0.050 -0.000762 0.000120 461s C4 30.8652 -0.357363 0.050012 49.897 -0.357363 -0.149852 461s C5 -0.2721 0.003464 -0.000762 -0.357 0.003464 0.000196 461s C6 -0.0313 0.000196 0.000120 -0.150 0.000196 0.001335 461s > print( round( vcov( fitols5$eq[[ 2 ]] ), digits = 6 ) ) 461s (Intercept) price farmPrice trend 461s (Intercept) 49.897 -0.357363 -0.149852 0.050012 461s price -0.357 0.003464 0.000196 -0.000762 461s farmPrice -0.150 0.000196 0.001335 0.000120 461s trend 0.050 -0.000762 0.000120 0.001390 461s > 461s > 461s > ## *********** confidence intervals of coefficients ************* 461s > print( confint( fitols1, useDfSys = TRUE ) ) 461s 2.5 % 97.5 % 461s demand_(Intercept) 84.597 115.194 461s demand_price -0.501 -0.132 461s demand_income 0.242 0.427 461s supply_(Intercept) 34.954 81.597 461s supply_price -0.033 0.353 461s supply_farmPrice 0.154 0.342 461s supply_trend 0.050 0.447 461s > print( confint( fitols1$eq[[ 2 ]], level = 0.9, useDfSys = TRUE ) ) 461s 5 % 95 % 461s (Intercept) 38.876 77.675 461s price 0.000 0.321 461s farmPrice 0.170 0.326 461s trend 0.083 0.413 461s > 461s > print( confint( fitols2r, level = 0.9 ) ) 461s 5 % 95 % 461s demand_(Intercept) 83.776 115.337 461s demand_price -0.474 -0.109 461s demand_income 0.230 0.395 461s supply_(Intercept) 37.508 75.251 461s supply_price 0.004 0.324 461s supply_farmPrice 0.182 0.332 461s supply_trend 0.230 0.395 461s > print( confint( fitols2r$eq[[ 1 ]], level = 0.99 ) ) 461s 0.5 % 99.5 % 461s (Intercept) 78.370 120.743 461s price -0.537 -0.046 461s income 0.202 0.424 461s > 461s > print( confint( fitols3s, level = 0.99 ) ) 461s 0.5 % 99.5 % 461s demand_(Intercept) 84.184 114.928 461s demand_price -0.472 -0.112 461s demand_income 0.229 0.397 461s supply_(Intercept) 33.382 79.377 461s supply_price -0.031 0.359 461s supply_farmPrice 0.165 0.349 461s supply_trend 0.229 0.397 461s > print( confint( fitols3s$eq[[ 2 ]], level = 0.5 ) ) 461s 25 % 75 % 461s (Intercept) 48.664 64.095 461s price 0.098 0.229 461s farmPrice 0.226 0.288 461s trend 0.285 0.341 461s > 461s > print( confint( fitols4rs, level = 0.5 ) ) 461s 25 % 75 % 461s demand_(Intercept) 90.269 112.695 461s demand_price -0.436 -0.197 461s demand_income 0.247 0.390 461s supply_(Intercept) 39.515 68.784 461s supply_price 0.064 0.303 461s supply_farmPrice 0.179 0.340 461s supply_trend 0.247 0.390 461s > print( confint( fitols4rs$eq[[ 1 ]], level = 0.25 ) ) 461s 37.5 % 62.5 % 461s (Intercept) 99.708 103.256 461s price -0.336 -0.298 461s income 0.308 0.330 461s > 461s > print( confint( fitols5, level = 0.25 ) ) 461s 37.5 % 62.5 % 461s demand_(Intercept) 89.784 113.179 461s demand_price -0.436 -0.197 461s demand_income 0.243 0.395 461s supply_(Intercept) 39.809 68.490 461s supply_price 0.064 0.303 461s supply_farmPrice 0.185 0.334 461s supply_trend 0.243 0.395 461s > print( confint( fitols5$eq[[ 2 ]], level = 0.999 ) ) 461s 0.1 % 100 % 461s (Intercept) 28.782 79.517 461s price -0.028 0.395 461s farmPrice 0.128 0.391 461s trend 0.185 0.453 461s > 461s > print( confint( fitols3, level = 0.999, useDfSys = FALSE ) ) 461s 0.1 % 100 % 461s demand_(Intercept) 81.786 117.326 461s demand_price -0.497 -0.086 461s demand_income 0.220 0.406 461s supply_(Intercept) 35.028 77.731 461s supply_price -0.017 0.345 461s supply_farmPrice 0.172 0.342 461s supply_trend 0.219 0.406 461s > print( confint( fitols3$eq[[ 1 ]], useDfSys = FALSE ) ) 461s 2.5 % 97.5 % 461s (Intercept) 81.786 117.326 461s price -0.497 -0.086 461s income 0.220 0.406 461s > 461s > 461s > ## *********** fitted values ************* 461s > print( fitted( fitols1 ) ) 461s demand supply 461s 1 97.4 98.9 461s 2 99.6 100.1 461s 3 99.5 100.2 461s 4 99.7 100.4 461s 5 102.3 102.7 461s 6 102.1 102.6 461s 7 102.5 102.4 461s 8 102.8 104.3 461s 9 101.7 102.9 461s 10 100.8 100.4 461s 11 95.6 96.0 461s 12 94.4 94.1 461s 13 95.7 95.6 461s 14 99.0 97.8 461s 15 104.3 102.6 461s 16 103.9 104.1 461s 17 104.8 103.8 461s 18 101.9 102.4 461s 19 103.5 102.1 461s 20 106.5 104.5 461s > print( fitted( fitols1$eq[[ 2 ]] ) ) 461s 1 2 3 4 5 6 7 8 9 10 11 12 13 461s 98.9 100.1 100.2 100.4 102.7 102.6 102.4 104.3 102.9 100.4 96.0 94.1 95.6 461s 14 15 16 17 18 19 20 461s 97.8 102.6 104.1 103.8 102.4 102.1 104.5 461s > 461s > print( fitted( fitols2r ) ) 461s demand supply 461s 1 97.6 98.3 461s 2 99.7 99.6 461s 3 99.6 99.7 461s 4 99.8 100.0 461s 5 102.2 102.5 461s 6 102.0 102.4 461s 7 102.4 102.3 461s 8 102.7 104.3 461s 9 101.6 102.9 461s 10 100.8 100.3 461s 11 95.9 95.9 461s 12 94.8 94.0 461s 13 96.0 95.5 461s 14 99.1 97.9 461s 15 104.1 103.0 461s 16 103.7 104.5 461s 17 104.5 104.3 461s 18 101.9 102.8 461s 19 103.3 102.6 461s 20 106.2 105.1 461s > print( fitted( fitols2r$eq[[ 1 ]] ) ) 461s 1 2 3 4 5 6 7 8 9 10 11 12 13 461s 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 461s 14 15 16 17 18 19 20 461s 99.1 104.1 103.7 104.5 101.9 103.3 106.2 461s > 461s > print( fitted( fitols3s ) ) 461s demand supply 461s 1 97.6 98.3 461s 2 99.7 99.6 461s 3 99.6 99.7 461s 4 99.8 100.0 461s 5 102.2 102.5 461s 6 102.0 102.4 461s 7 102.4 102.3 461s 8 102.7 104.3 461s 9 101.6 102.9 461s 10 100.8 100.3 461s 11 95.9 95.9 461s 12 94.8 94.0 461s 13 96.0 95.5 461s 14 99.1 97.9 461s 15 104.1 103.0 461s 16 103.7 104.5 461s 17 104.5 104.3 461s 18 101.9 102.8 461s 19 103.3 102.6 461s 20 106.2 105.1 461s > print( fitted( fitols3s$eq[[ 2 ]] ) ) 461s 1 2 3 4 5 6 7 8 9 10 11 12 13 461s 98.3 99.6 99.7 100.0 102.5 102.4 102.3 104.3 102.9 100.3 95.9 94.0 95.5 461s 14 15 16 17 18 19 20 461s 97.9 103.0 104.5 104.3 102.8 102.6 105.1 461s > 461s > print( fitted( fitols4rs ) ) 461s demand supply 461s 1 97.6 98.3 461s 2 99.6 99.6 461s 3 99.5 99.8 461s 4 99.7 100.0 461s 5 102.3 102.5 461s 6 102.0 102.4 461s 7 102.4 102.3 461s 8 102.7 104.4 461s 9 101.7 102.9 461s 10 100.9 100.2 461s 11 95.9 95.7 461s 12 94.7 93.9 461s 13 95.9 95.5 461s 14 99.1 97.8 461s 15 104.2 102.9 461s 16 103.8 104.5 461s 17 104.8 104.1 461s 18 101.8 103.0 461s 19 103.3 102.8 461s 20 106.1 105.5 461s > print( fitted( fitols4rs$eq[[ 1 ]] ) ) 461s 1 2 3 4 5 6 7 8 9 10 11 12 13 461s 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 461s 14 15 16 17 18 19 20 461s 99.1 104.2 103.8 104.8 101.8 103.3 106.1 461s > 461s > print( fitted( fitols5 ) ) 461s demand supply 461s 1 97.6 98.3 461s 2 99.6 99.6 461s 3 99.5 99.8 461s 4 99.7 100.0 461s 5 102.3 102.5 461s 6 102.0 102.4 461s 7 102.4 102.3 461s 8 102.7 104.4 461s 9 101.7 102.9 461s 10 100.9 100.2 461s 11 95.9 95.7 461s 12 94.7 93.9 461s 13 95.9 95.5 461s 14 99.1 97.8 461s 15 104.2 102.9 461s 16 103.8 104.5 461s 17 104.8 104.1 461s 18 101.8 103.0 461s 19 103.3 102.8 461s 20 106.1 105.5 461s > print( fitted( fitols5$eq[[ 2 ]] ) ) 461s 1 2 3 4 5 6 7 8 9 10 11 12 13 461s 98.3 99.6 99.8 100.0 102.5 102.4 102.3 104.4 102.9 100.2 95.7 93.9 95.5 461s 14 15 16 17 18 19 20 461s 97.8 102.9 104.5 104.1 103.0 102.8 105.5 461s > 461s > 461s > ## *********** predicted values ************* 461s > predictData <- Kmenta 461s > predictData$consump <- NULL 461s > predictData$price <- Kmenta$price * 0.9 461s > predictData$income <- Kmenta$income * 1.1 461s > 461s > print( predict( fitols1, se.fit = TRUE, interval = "prediction", 461s + useDfSys = TRUE ) ) 461s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 461s 1 97.4 0.643 93.3 101.5 98.9 1.056 461s 2 99.6 0.577 95.5 103.7 100.1 1.037 461s 3 99.5 0.545 95.5 103.6 100.2 0.939 461s 4 99.7 0.582 95.6 103.8 100.4 0.912 461s 5 102.3 0.502 98.2 106.4 102.7 0.895 461s 6 102.1 0.463 98.0 106.1 102.6 0.791 461s 7 102.5 0.484 98.4 106.5 102.4 0.719 461s 8 102.8 0.601 98.7 106.9 104.3 0.963 461s 9 101.7 0.527 97.6 105.8 102.9 0.788 461s 10 100.8 0.788 96.5 105.0 100.4 0.981 461s 11 95.6 0.946 91.2 100.0 96.0 1.185 461s 12 94.4 0.980 90.0 98.8 94.1 1.394 461s 13 95.7 0.880 91.3 100.0 95.6 1.244 461s 14 99.0 0.508 94.9 103.0 97.8 0.896 461s 15 104.3 0.758 100.1 108.5 102.6 0.874 461s 16 103.9 0.616 99.8 108.0 104.1 0.916 461s 17 104.8 1.273 100.1 109.5 103.8 1.605 461s 18 101.9 0.536 97.9 106.0 102.4 0.962 461s 19 103.5 0.680 99.3 107.6 102.1 1.098 461s 20 106.5 1.274 101.8 111.2 104.5 1.664 461s supply.lwr supply.upr 461s 1 93.6 104.3 461s 2 94.8 105.4 461s 3 94.9 105.5 461s 4 95.1 105.6 461s 5 97.5 107.9 461s 6 97.4 107.7 461s 7 97.3 107.5 461s 8 99.0 109.6 461s 9 97.8 108.1 461s 10 95.1 105.6 461s 11 90.6 101.5 461s 12 88.5 99.8 461s 13 90.1 101.1 461s 14 92.6 103.0 461s 15 97.4 107.8 461s 16 98.9 109.3 461s 17 97.9 109.7 461s 18 97.1 107.6 461s 19 96.7 107.5 461s 20 98.6 110.5 461s > print( predict( fitols1$eq[[ 2 ]], se.fit = TRUE, interval = "prediction", 461s + useDfSys = TRUE ) ) 461s fit se.fit lwr upr 461s 1 98.9 1.056 93.6 104.3 461s 2 100.1 1.037 94.8 105.4 461s 3 100.2 0.939 94.9 105.5 461s 4 100.4 0.912 95.1 105.6 461s 5 102.7 0.895 97.5 107.9 461s 6 102.6 0.791 97.4 107.7 461s 7 102.4 0.719 97.3 107.5 461s 8 104.3 0.963 99.0 109.6 461s 9 102.9 0.788 97.8 108.1 461s 10 100.4 0.981 95.1 105.6 461s 11 96.0 1.185 90.6 101.5 461s 12 94.1 1.394 88.5 99.8 461s 13 95.6 1.244 90.1 101.1 461s 14 97.8 0.896 92.6 103.0 461s 15 102.6 0.874 97.4 107.8 461s 16 104.1 0.916 98.9 109.3 461s 17 103.8 1.605 97.9 109.7 461s 18 102.4 0.962 97.1 107.6 461s 19 102.1 1.098 96.7 107.5 461s 20 104.5 1.664 98.6 110.5 461s > 461s > print( predict( fitols2r, se.pred = TRUE, interval = "confidence", 461s + level = 0.999, newdata = predictData ) ) 461s demand.pred demand.se.pred demand.lwr demand.upr supply.pred supply.se.pred 461s 1 103 2.17 99.9 107 96.7 2.62 461s 2 106 2.16 102.4 109 97.9 2.55 461s 3 106 2.17 102.2 109 98.1 2.55 461s 4 106 2.16 102.5 109 98.3 2.54 461s 5 108 2.43 102.9 113 100.9 2.67 461s 6 108 2.38 103.1 113 100.7 2.63 461s 7 109 2.37 103.7 113 100.6 2.59 461s 8 109 2.33 104.5 114 102.6 2.55 461s 9 107 2.44 102.2 113 101.4 2.69 461s 10 106 2.57 100.2 112 98.8 2.84 461s 11 101 2.36 96.1 106 94.4 2.89 461s 12 100 2.17 96.6 104 92.3 2.88 461s 13 102 2.08 99.0 104 93.9 2.75 461s 14 105 2.25 100.7 109 96.3 2.72 461s 15 110 2.63 103.7 116 101.4 2.72 461s 16 110 2.52 104.1 116 102.9 2.65 461s 17 110 2.96 102.0 118 102.9 3.03 461s 18 108 2.28 103.9 112 101.1 2.55 461s 19 110 2.36 105.1 115 100.9 2.55 461s 20 114 2.57 107.4 120 103.3 2.51 461s supply.lwr supply.upr 461s 1 93.2 100.2 461s 2 95.2 100.5 461s 3 95.3 100.8 461s 4 95.8 100.8 461s 5 97.0 104.8 461s 6 97.2 104.3 461s 7 97.5 103.7 461s 8 99.9 105.2 461s 9 97.3 105.5 461s 10 93.6 104.1 461s 11 88.8 100.0 461s 12 86.8 97.9 461s 13 89.3 98.5 461s 14 91.9 100.6 461s 15 97.0 105.8 461s 16 99.2 106.6 461s 17 96.4 109.4 461s 18 98.4 103.9 461s 19 98.2 103.5 461s 20 101.1 105.5 461s > print( predict( fitols2r$eq[[ 1 ]], se.pred = TRUE, interval = "confidence", 461s + level = 0.999, newdata = predictData ) ) 461s fit se.pred lwr upr 461s 1 103 2.17 99.9 107 461s 2 106 2.16 102.4 109 461s 3 106 2.17 102.2 109 461s 4 106 2.16 102.5 109 461s 5 108 2.43 102.9 113 461s 6 108 2.38 103.1 113 461s 7 109 2.37 103.7 113 461s 8 109 2.33 104.5 114 461s 9 107 2.44 102.2 113 461s 10 106 2.57 100.2 112 461s 11 101 2.36 96.1 106 461s 12 100 2.17 96.6 104 461s 13 102 2.08 99.0 104 461s 14 105 2.25 100.7 109 461s 15 110 2.63 103.7 116 461s 16 110 2.52 104.1 116 461s 17 110 2.96 102.0 118 461s 18 108 2.28 103.9 112 461s 19 110 2.36 105.1 115 461s 20 114 2.57 107.4 120 461s > 461s > print( predict( fitols3s, se.fit = TRUE, se.pred = TRUE, 461s + interval = "prediction", level = 0.5, newdata = predictData ) ) 461s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 461s 1 103 0.940 2.16 101.8 105 96.7 461s 2 106 0.944 2.16 104.3 107 97.9 461s 3 106 0.969 2.17 104.2 107 98.1 461s 4 106 0.949 2.16 104.4 107 98.3 461s 5 108 1.452 2.43 106.5 110 100.9 461s 6 108 1.372 2.38 106.4 110 100.7 461s 7 109 1.356 2.37 106.9 110 100.6 461s 8 109 1.296 2.34 107.6 111 102.6 461s 9 107 1.464 2.43 105.8 109 101.4 461s 10 106 1.652 2.55 104.5 108 98.8 461s 11 101 1.305 2.34 99.4 103 94.4 461s 12 100 0.941 2.16 98.6 102 92.3 461s 13 102 0.725 2.07 100.2 103 93.9 461s 14 105 1.124 2.24 103.3 106 96.3 461s 15 110 1.774 2.63 108.3 112 101.4 461s 16 110 1.606 2.52 108.2 112 102.9 461s 17 110 2.216 2.95 108.0 112 102.9 461s 18 108 1.208 2.29 106.6 110 101.1 461s 19 110 1.356 2.37 108.3 112 100.9 461s 20 114 1.718 2.59 111.7 115 103.3 461s supply.se.fit supply.se.pred supply.lwr supply.upr 461s 1 1.149 2.69 94.8 98.5 461s 2 0.873 2.59 96.1 99.6 461s 3 0.907 2.60 96.3 99.8 461s 4 0.831 2.58 96.5 100.0 461s 5 1.324 2.77 99.0 102.8 461s 6 1.188 2.71 98.9 102.6 461s 7 1.049 2.65 98.8 102.4 461s 8 0.911 2.60 100.8 104.3 461s 9 1.396 2.81 99.5 103.3 461s 10 1.782 3.02 96.8 100.9 461s 11 1.906 3.09 92.3 96.5 461s 12 1.875 3.08 90.2 94.4 461s 13 1.560 2.89 91.9 95.8 461s 14 1.475 2.85 94.3 98.2 461s 15 1.477 2.85 99.5 103.3 461s 16 1.245 2.74 101.0 104.8 461s 17 2.195 3.28 100.6 105.1 461s 18 0.909 2.60 99.4 102.9 461s 19 0.875 2.59 99.1 102.6 461s 20 0.704 2.54 101.6 105.0 461s > print( predict( fitols3s$eq[[ 2 ]], se.fit = TRUE, se.pred = TRUE, 461s + interval = "prediction", level = 0.5, newdata = predictData ) ) 461s fit se.fit se.pred lwr upr 461s 1 96.7 1.149 2.69 94.8 98.5 461s 2 97.9 0.873 2.59 96.1 99.6 461s 3 98.1 0.907 2.60 96.3 99.8 461s 4 98.3 0.831 2.58 96.5 100.0 461s 5 100.9 1.324 2.77 99.0 102.8 461s 6 100.7 1.188 2.71 98.9 102.6 461s 7 100.6 1.049 2.65 98.8 102.4 461s 8 102.6 0.911 2.60 100.8 104.3 461s 9 101.4 1.396 2.81 99.5 103.3 461s 10 98.8 1.782 3.02 96.8 100.9 461s 11 94.4 1.906 3.09 92.3 96.5 461s 12 92.3 1.875 3.08 90.2 94.4 461s 13 93.9 1.560 2.89 91.9 95.8 461s 14 96.3 1.475 2.85 94.3 98.2 461s 15 101.4 1.477 2.85 99.5 103.3 461s 16 102.9 1.245 2.74 101.0 104.8 461s 17 102.9 2.195 3.28 100.6 105.1 461s 18 101.1 0.909 2.60 99.4 102.9 461s 19 100.9 0.875 2.59 99.1 102.6 461s 20 103.3 0.704 2.54 101.6 105.0 461s > 461s > print( predict( fitols4rs, se.fit = TRUE, se.pred = TRUE, 461s + interval = "confidence", level = 0.99 ) ) 461s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 461s 1 97.6 0.541 2.01 96.1 99.0 98.3 461s 2 99.6 0.471 2.00 98.3 100.9 99.6 461s 3 99.5 0.454 1.99 98.3 100.8 99.8 461s 4 99.7 0.475 2.00 98.4 101.0 100.0 461s 5 102.3 0.434 1.99 101.1 103.4 102.5 461s 6 102.0 0.418 1.98 100.9 103.2 102.4 461s 7 102.4 0.440 1.99 101.2 103.6 102.3 461s 8 102.7 0.537 2.01 101.2 104.1 104.4 461s 9 101.7 0.447 1.99 100.5 102.9 102.9 461s 10 100.9 0.628 2.04 99.2 102.6 100.2 461s 11 95.9 0.833 2.11 93.7 98.2 95.7 461s 12 94.7 0.807 2.10 92.5 96.9 93.9 461s 13 95.9 0.677 2.06 94.0 97.7 95.5 461s 14 99.1 0.459 1.99 97.8 100.3 97.8 461s 15 104.2 0.572 2.02 102.7 105.8 102.9 461s 16 103.8 0.509 2.01 102.4 105.2 104.5 461s 17 104.8 0.877 2.13 102.4 107.2 104.1 461s 18 101.8 0.478 2.00 100.5 103.1 103.0 461s 19 103.3 0.604 2.03 101.6 104.9 102.8 461s 20 106.1 1.102 2.23 103.1 109.1 105.5 461s supply.se.fit supply.se.pred supply.lwr supply.upr 461s 1 0.598 2.52 96.7 99.9 461s 2 0.679 2.54 97.8 101.5 461s 3 0.634 2.53 98.0 101.5 461s 4 0.643 2.53 98.3 101.8 461s 5 0.753 2.56 100.4 104.5 461s 6 0.680 2.54 100.5 104.2 461s 7 0.625 2.53 100.6 104.0 461s 8 0.799 2.57 102.2 106.6 461s 9 0.700 2.55 101.0 104.8 461s 10 0.716 2.55 98.2 102.1 461s 11 0.916 2.61 93.2 98.2 461s 12 1.226 2.74 90.5 97.2 461s 13 1.130 2.70 92.5 98.6 461s 14 0.796 2.57 95.7 100.0 461s 15 0.656 2.53 101.1 104.7 461s 16 0.644 2.53 102.8 106.3 461s 17 1.150 2.70 101.0 107.2 461s 18 0.575 2.51 101.4 104.5 461s 19 0.649 2.53 101.0 104.5 461s 20 0.875 2.60 103.1 107.8 461s > print( predict( fitols4rs$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 461s + interval = "confidence", level = 0.99 ) ) 461s fit se.fit se.pred lwr upr 461s 1 97.6 0.541 2.01 96.1 99.0 461s 2 99.6 0.471 2.00 98.3 100.9 461s 3 99.5 0.454 1.99 98.3 100.8 461s 4 99.7 0.475 2.00 98.4 101.0 461s 5 102.3 0.434 1.99 101.1 103.4 461s 6 102.0 0.418 1.98 100.9 103.2 461s 7 102.4 0.440 1.99 101.2 103.6 461s 8 102.7 0.537 2.01 101.2 104.1 461s 9 101.7 0.447 1.99 100.5 102.9 461s 10 100.9 0.628 2.04 99.2 102.6 461s 11 95.9 0.833 2.11 93.7 98.2 461s 12 94.7 0.807 2.10 92.5 96.9 461s 13 95.9 0.677 2.06 94.0 97.7 461s 14 99.1 0.459 1.99 97.8 100.3 461s 15 104.2 0.572 2.02 102.7 105.8 461s 16 103.8 0.509 2.01 102.4 105.2 461s 17 104.8 0.877 2.13 102.4 107.2 461s 18 101.8 0.478 2.00 100.5 103.1 461s 19 103.3 0.604 2.03 101.6 104.9 461s 20 106.1 1.102 2.23 103.1 109.1 461s > 461s > print( predict( fitols5, se.fit = TRUE, interval = "prediction", 461s + level = 0.9, newdata = predictData ) ) 461s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 461s 1 104 0.714 100.0 107 96.4 0.712 461s 2 106 0.748 102.5 110 97.7 0.591 461s 3 106 0.753 102.4 109 97.9 0.602 461s 4 106 0.756 102.6 110 98.1 0.565 461s 5 109 1.055 104.8 112 100.7 0.900 461s 6 108 1.013 104.7 112 100.5 0.811 461s 7 109 1.029 105.2 113 100.5 0.722 461s 8 109 1.055 105.7 113 102.5 0.703 461s 9 108 1.042 104.1 112 101.1 0.952 461s 10 107 1.148 102.8 110 98.5 1.136 461s 11 101 1.026 97.6 105 94.0 1.245 461s 12 100 0.800 96.7 104 92.1 1.347 461s 13 102 0.606 98.4 105 93.7 1.170 461s 14 105 0.820 101.5 109 96.0 1.034 461s 15 111 1.272 106.6 114 101.2 1.031 461s 16 110 1.191 106.4 114 102.7 0.925 461s 17 111 1.513 106.5 115 102.5 1.529 461s 18 108 0.963 104.8 112 101.0 0.720 461s 19 110 1.129 106.4 114 100.8 0.717 461s 20 114 1.601 109.5 118 103.4 0.562 461s supply.lwr supply.upr 461s 1 92.1 100.7 461s 2 93.4 102.0 461s 3 93.6 102.1 461s 4 93.9 102.4 461s 5 96.3 105.1 461s 6 96.2 104.9 461s 7 96.1 104.8 461s 8 98.2 106.8 461s 9 96.7 105.6 461s 10 93.9 103.0 461s 11 89.4 98.7 461s 12 87.4 96.8 461s 13 89.1 98.2 461s 14 91.5 100.5 461s 15 96.7 105.7 461s 16 98.3 107.2 461s 17 97.6 107.4 461s 18 96.7 105.4 461s 19 96.5 105.1 461s 20 99.1 107.6 461s > print( predict( fitols5$eq[[ 2 ]], se.fit = TRUE, interval = "prediction", 461s + level = 0.9, newdata = predictData ) ) 461s fit se.fit lwr upr 461s 1 96.4 0.712 92.1 100.7 461s 2 97.7 0.591 93.4 102.0 461s 3 97.9 0.602 93.6 102.1 461s 4 98.1 0.565 93.9 102.4 461s 5 100.7 0.900 96.3 105.1 461s 6 100.5 0.811 96.2 104.9 461s 7 100.5 0.722 96.1 104.8 461s 8 102.5 0.703 98.2 106.8 461s 9 101.1 0.952 96.7 105.6 461s 10 98.5 1.136 93.9 103.0 461s 11 94.0 1.245 89.4 98.7 461s 12 92.1 1.347 87.4 96.8 461s 13 93.7 1.170 89.1 98.2 461s 14 96.0 1.034 91.5 100.5 461s 15 101.2 1.031 96.7 105.7 461s 16 102.7 0.925 98.3 107.2 461s 17 102.5 1.529 97.6 107.4 461s 18 101.0 0.720 96.7 105.4 461s 19 100.8 0.717 96.5 105.1 461s 20 103.4 0.562 99.1 107.6 461s > 461s > # predict just one observation 461s > smallData <- data.frame( price = 130, income = 150, farmPrice = 120, 461s + trend = 25 ) 461s > 461s > print( predict( fitols1, newdata = smallData ) ) 461s demand.pred supply.pred 461s 1 109 115 461s > print( predict( fitols1$eq[[ 1 ]], newdata = smallData ) ) 461s fit 461s 1 109 461s > 461s > print( predict( fitols2r, se.fit = TRUE, level = 0.9, 461s + newdata = smallData ) ) 461s demand.pred demand.se.fit supply.pred supply.se.fit 461s 1 109 2.48 116 2.8 461s > print( predict( fitols2r$eq[[ 1 ]], se.pred = TRUE, level = 0.99, 461s + newdata = smallData ) ) 461s fit se.pred 461s 1 109 3.15 461s > 461s > print( predict( fitols3s, interval = "prediction", level = 0.975, 461s + newdata = smallData ) ) 461s demand.pred demand.lwr demand.upr supply.pred supply.lwr supply.upr 461s 1 109 101 116 116 107 126 461s > print( predict( fitols3s$eq[[ 1 ]], interval = "confidence", level = 0.8, 461s + newdata = smallData ) ) 461s fit lwr upr 461s 1 109 105 112 461s > 461s > print( predict( fitols4rs, se.fit = TRUE, interval = "confidence", 461s + level = 0.999, newdata = smallData ) ) 461s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 461s 1 108 2.02 101 115 117 2.02 461s supply.lwr supply.upr 461s 1 110 124 461s > print( predict( fitols4rs$eq[[ 2 ]], se.pred = TRUE, interval = "prediction", 461s + level = 0.75, newdata = smallData ) ) 461s fit se.pred lwr upr 461s 1 117 3.18 113 121 461s > 461s > print( predict( fitols5, se.fit = TRUE, interval = "prediction", 461s + newdata = smallData ) ) 461s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 461s 1 108 2.18 102 114 117 2.01 461s supply.lwr supply.upr 461s 1 111 124 461s > print( predict( fitols5$eq[[ 1 ]], se.pred = TRUE, interval = "confidence", 461s + newdata = smallData ) ) 461s fit se.pred lwr upr 461s 1 108 2.92 104 113 461s > 461s > print( predict( fitols5rs, se.fit = TRUE, se.pred = TRUE, 461s + interval = "prediction", level = 0.5, newdata = smallData ) ) 461s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 461s 1 108 2.02 2.8 106 110 117 461s supply.se.fit supply.se.pred supply.lwr supply.upr 461s 1 2.02 3.18 115 119 461s > print( predict( fitols5rs$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 461s + interval = "confidence", level = 0.25, newdata = smallData ) ) 461s fit se.fit se.pred lwr upr 461s 1 108 2.02 2.8 107 109 461s > 461s > 461s > ## ************ correlation of predicted values *************** 461s > print( correlation.systemfit( fitols1, 1, 2 ) ) 461s [,1] 461s [1,] 0 461s [2,] 0 461s [3,] 0 461s [4,] 0 461s [5,] 0 461s [6,] 0 461s [7,] 0 461s [8,] 0 461s [9,] 0 461s [10,] 0 461s [11,] 0 461s [12,] 0 461s [13,] 0 461s [14,] 0 461s [15,] 0 461s [16,] 0 461s [17,] 0 461s [18,] 0 461s [19,] 0 461s [20,] 0 461s > 461s > print( correlation.systemfit( fitols2r, 2, 1 ) ) 461s [,1] 461s [1,] 0.443122 461s [2,] 0.160426 461s [3,] 0.161091 461s [4,] 0.118312 461s [5,] -0.077411 461s [6,] -0.059235 461s [7,] -0.057777 461s [8,] -0.006908 461s [9,] -0.000372 461s [10,] -0.001410 461s [11,] 0.055233 461s [12,] 0.074936 461s [13,] 0.028274 461s [14,] -0.032082 461s [15,] 0.196029 461s [16,] 0.279921 461s [17,] 0.115570 461s [18,] 0.080620 461s [19,] 0.171681 461s [20,] 0.150544 461s > 461s > print( correlation.systemfit( fitols3s, 1, 2 ) ) 461s [,1] 461s [1,] 0.405901 461s [2,] 0.145364 461s [3,] 0.145375 461s [4,] 0.105835 461s [5,] -0.067958 461s [6,] -0.052026 461s [7,] -0.050543 461s [8,] -0.006031 461s [9,] -0.000326 461s [10,] -0.001237 461s [11,] 0.047534 461s [12,] 0.063493 461s [13,] 0.024060 461s [14,] -0.027910 461s [15,] 0.171580 461s [16,] 0.248212 461s [17,] 0.101409 461s [18,] 0.073084 461s [19,] 0.153950 461s [20,] 0.132944 461s > 461s > print( correlation.systemfit( fitols4rs, 2, 1 ) ) 461s [,1] 461s [1,] 0.38162 461s [2,] 0.29173 461s [3,] 0.25421 461s [4,] 0.28598 461s [5,] -0.02775 461s [6,] -0.04974 461s [7,] -0.05850 461s [8,] 0.09388 461s [9,] 0.09469 461s [10,] 0.43814 461s [11,] 0.10559 461s [12,] 0.00876 461s [13,] 0.04090 461s [14,] -0.03984 461s [15,] 0.40767 461s [16,] 0.24571 461s [17,] 0.64160 461s [18,] 0.24037 461s [19,] 0.34075 461s [20,] 0.54270 461s > 461s > print( correlation.systemfit( fitols5, 1, 2 ) ) 461s [,1] 461s [1,] 0.4051 461s [2,] 0.2729 461s [3,] 0.2415 461s [4,] 0.2693 461s [5,] -0.0301 461s [6,] -0.0527 461s [7,] -0.0624 461s [8,] 0.0971 461s [9,] 0.0945 461s [10,] 0.4365 461s [11,] 0.1258 461s [12,] 0.0210 461s [13,] 0.0436 461s [14,] -0.0405 461s [15,] 0.4102 461s [16,] 0.2610 461s [17,] 0.6400 461s [18,] 0.2661 461s [19,] 0.3796 461s [20,] 0.5742 461s > 461s > 461s > ## ************ Log-Likelihood values *************** 461s > print( logLik( fitols1 ) ) 461s 'log Lik.' -67.8 (df=8) 461s > print( logLik( fitols1, residCovDiag = TRUE ) ) 461s 'log Lik.' -83.6 (df=8) 461s > all.equal( logLik( fitols1, residCovDiag = TRUE ), 461s + logLik( lmDemand ) + logLik( lmSupply ), 461s + check.attributes = FALSE ) 461s [1] TRUE 461s > 461s > print( logLik( fitols2r ) ) 461s 'log Lik.' -62 (df=7) 461s > print( logLik( fitols2r, residCovDiag = TRUE ) ) 461s 'log Lik.' -84 (df=7) 461s > 461s > print( logLik( fitols3s ) ) 461s 'log Lik.' -62 (df=7) 461s > print( logLik( fitols3s, residCovDiag = TRUE ) ) 461s 'log Lik.' -84 (df=7) 461s > 461s > print( logLik( fitols4rs ) ) 461s 'log Lik.' -62.8 (df=6) 461s > print( logLik( fitols4rs, residCovDiag = TRUE ) ) 461s 'log Lik.' -84.1 (df=6) 461s > 461s > print( logLik( fitols5 ) ) 461s 'log Lik.' -62.8 (df=6) 461s > print( logLik( fitols5, residCovDiag = TRUE ) ) 461s 'log Lik.' -84.1 (df=6) 461s > 461s > 461s > ## ************** F tests **************** 461s > # testing first restriction 461s > print( linearHypothesis( fitols1, restrm ) ) 461s Linear hypothesis test (Theil's F test) 461s 461s Hypothesis: 461s demand_income - supply_trend = 0 461s 461s Model 1: restricted model 461s Model 2: fitols1 461s 461s Res.Df Df F Pr(>F) 461s 1 34 461s 2 33 1 0.14 0.71 461s > linearHypothesis( fitols1, restrict ) 461s Linear hypothesis test (Theil's F test) 461s 461s Hypothesis: 461s demand_income - supply_trend = 0 461s 461s Model 1: restricted model 461s Model 2: fitols1 461s 461s Res.Df Df F Pr(>F) 461s 1 34 461s 2 33 1 0.14 0.71 461s > 461s > print( linearHypothesis( fitols1s, restrm ) ) 461s Linear hypothesis test (Theil's F test) 461s 461s Hypothesis: 461s demand_income - supply_trend = 0 461s 461s Model 1: restricted model 461s Model 2: fitols1s 461s 461s Res.Df Df F Pr(>F) 461s 1 34 461s 2 33 1 0.15 0.7 461s > linearHypothesis( fitols1s, restrict ) 461s Linear hypothesis test (Theil's F test) 461s 461s Hypothesis: 461s demand_income - supply_trend = 0 461s 461s Model 1: restricted model 461s Model 2: fitols1s 461s 461s Res.Df Df F Pr(>F) 461s 1 34 461s 2 33 1 0.15 0.7 461s > 461s > print( linearHypothesis( fitols1, restrm ) ) 461s Linear hypothesis test (Theil's F test) 461s 461s Hypothesis: 461s demand_income - supply_trend = 0 461s 461s Model 1: restricted model 461s Model 2: fitols1 461s 461s Res.Df Df F Pr(>F) 461s 1 34 461s 2 33 1 0.14 0.71 461s > linearHypothesis( fitols1, restrict ) 461s Linear hypothesis test (Theil's F test) 461s 461s Hypothesis: 461s demand_income - supply_trend = 0 461s 461s Model 1: restricted model 461s Model 2: fitols1 461s 461s Res.Df Df F Pr(>F) 461s 1 34 461s 2 33 1 0.14 0.71 461s > 461s > print( linearHypothesis( fitols1r, restrm ) ) 461s Linear hypothesis test (Theil's F test) 461s 461s Hypothesis: 461s demand_income - supply_trend = 0 461s 461s Model 1: restricted model 461s Model 2: fitols1r 461s 461s Res.Df Df F Pr(>F) 461s 1 34 461s 2 33 1 0.14 0.71 461s > linearHypothesis( fitols1r, restrict ) 461s Linear hypothesis test (Theil's F test) 461s 461s Hypothesis: 461s demand_income - supply_trend = 0 461s 461s Model 1: restricted model 461s Model 2: fitols1r 461s 461s Res.Df Df F Pr(>F) 461s 1 34 461s 2 33 1 0.14 0.71 461s > 461s > # testing second restriction 461s > restrOnly2m <- matrix(0,1,7) 461s > restrOnly2q <- 0.5 461s > restrOnly2m[1,2] <- -1 461s > restrOnly2m[1,5] <- 1 461s > restrictOnly2 <- "- demand_price + supply_price = 0.5" 461s > # first restriction not imposed 461s > print( linearHypothesis( fitols1, restrOnly2m, restrOnly2q ) ) 461s Linear hypothesis test (Theil's F test) 461s 461s Hypothesis: 461s - demand_price + supply_price = 0.5 461s 461s Model 1: restricted model 461s Model 2: fitols1 461s 461s Res.Df Df F Pr(>F) 461s 1 34 461s 2 33 1 0.01 0.94 461s > linearHypothesis( fitols1, restrictOnly2 ) 461s Linear hypothesis test (Theil's F test) 461s 461s Hypothesis: 461s - demand_price + supply_price = 0.5 461s 461s Model 1: restricted model 461s Model 2: fitols1 461s 461s Res.Df Df F Pr(>F) 461s 1 34 461s 2 33 1 0.01 0.94 461s > 461s > # first restriction imposed 461s > print( linearHypothesis( fitols2, restrOnly2m, restrOnly2q ) ) 461s Linear hypothesis test (Theil's F test) 461s 461s Hypothesis: 461s - demand_price + supply_price = 0.5 461s 461s Model 1: restricted model 461s Model 2: fitols2 461s 461s Res.Df Df F Pr(>F) 461s 1 35 461s 2 34 1 0.02 0.88 461s > linearHypothesis( fitols2, restrictOnly2 ) 461s Linear hypothesis test (Theil's F test) 461s 461s Hypothesis: 461s - demand_price + supply_price = 0.5 461s 461s Model 1: restricted model 461s Model 2: fitols2 461s 461s Res.Df Df F Pr(>F) 461s 1 35 461s 2 34 1 0.02 0.88 461s > 461s > print( linearHypothesis( fitols3, restrOnly2m, restrOnly2q ) ) 461s Linear hypothesis test (Theil's F test) 461s 461s Hypothesis: 461s - demand_price + supply_price = 0.5 461s 461s Model 1: restricted model 461s Model 2: fitols3 461s 461s Res.Df Df F Pr(>F) 461s 1 35 461s 2 34 1 0.02 0.88 461s > linearHypothesis( fitols3, restrictOnly2 ) 461s Linear hypothesis test (Theil's F test) 461s 461s Hypothesis: 461s - demand_price + supply_price = 0.5 461s 461s Model 1: restricted model 461s Model 2: fitols3 461s 461s Res.Df Df F Pr(>F) 461s 1 35 461s 2 34 1 0.02 0.88 461s > 461s > # testing both of the restrictions 461s > print( linearHypothesis( fitols1, restr2m, restr2q ) ) 461s Linear hypothesis test (Theil's F test) 461s 461s Hypothesis: 461s demand_income - supply_trend = 0 461s - demand_price + supply_price = 0.5 461s 461s Model 1: restricted model 461s Model 2: fitols1 461s 461s Res.Df Df F Pr(>F) 461s 1 35 461s 2 33 2 0.08 0.93 461s > linearHypothesis( fitols1, restrict2 ) 461s Linear hypothesis test (Theil's F test) 461s 461s Hypothesis: 461s demand_income - supply_trend = 0 461s - demand_price + supply_price = 0.5 461s 461s Model 1: restricted model 461s Model 2: fitols1 461s 461s Res.Df Df F Pr(>F) 461s 1 35 461s 2 33 2 0.08 0.93 461s > 461s > 461s > ## ************** Wald tests **************** 461s > # testing first restriction 461s > print( linearHypothesis( fitols1, restrm, test = "Chisq" ) ) 461s Linear hypothesis test (Chi^2 statistic of a Wald test) 461s 461s Hypothesis: 461s demand_income - supply_trend = 0 461s 461s Model 1: restricted model 461s Model 2: fitols1 461s 461s Res.Df Df Chisq Pr(>Chisq) 461s 1 34 461s 2 33 1 0.64 0.42 461s > linearHypothesis( fitols1, restrict, test = "Chisq" ) 461s Linear hypothesis test (Chi^2 statistic of a Wald test) 461s 461s Hypothesis: 461s demand_income - supply_trend = 0 461s 461s Model 1: restricted model 461s Model 2: fitols1 461s 461s Res.Df Df Chisq Pr(>Chisq) 461s 1 34 461s 2 33 1 0.64 0.42 461s > 461s > print( linearHypothesis( fitols1s, restrm, test = "Chisq" ) ) 461s Linear hypothesis test (Chi^2 statistic of a Wald test) 461s 461s Hypothesis: 461s demand_income - supply_trend = 0 461s 461s Model 1: restricted model 461s Model 2: fitols1s 461s 461s Res.Df Df Chisq Pr(>Chisq) 461s 1 34 461s 2 33 1 0.72 0.4 461s > linearHypothesis( fitols1s, restrict, test = "Chisq" ) 461s Linear hypothesis test (Chi^2 statistic of a Wald test) 461s 461s Hypothesis: 461s demand_income - supply_trend = 0 461s 461s Model 1: restricted model 461s Model 2: fitols1s 461s 461s Res.Df Df Chisq Pr(>Chisq) 461s 1 34 461s 2 33 1 0.72 0.4 461s > 461s > print( linearHypothesis( fitols1, restrm, test = "Chisq" ) ) 461s Linear hypothesis test (Chi^2 statistic of a Wald test) 461s 461s Hypothesis: 461s demand_income - supply_trend = 0 461s 461s Model 1: restricted model 461s Model 2: fitols1 461s 461s Res.Df Df Chisq Pr(>Chisq) 461s 1 34 461s 2 33 1 0.64 0.42 461s > linearHypothesis( fitols1, restrict, test = "Chisq" ) 461s Linear hypothesis test (Chi^2 statistic of a Wald test) 461s 461s Hypothesis: 461s demand_income - supply_trend = 0 461s 461s Model 1: restricted model 461s Model 2: fitols1 461s 461s Res.Df Df Chisq Pr(>Chisq) 461s 1 34 461s 2 33 1 0.64 0.42 461s > 461s > print( linearHypothesis( fitols1r, restrm, test = "Chisq" ) ) 461s Linear hypothesis test (Chi^2 statistic of a Wald test) 461s 461s Hypothesis: 461s demand_income - supply_trend = 0 461s 461s Model 1: restricted model 461s Model 2: fitols1r 461s 461s Res.Df Df Chisq Pr(>Chisq) 461s 1 34 461s 2 33 1 0.64 0.42 461s > linearHypothesis( fitols1r, restrict, test = "Chisq" ) 461s Linear hypothesis test (Chi^2 statistic of a Wald test) 461s 461s Hypothesis: 461s demand_income - supply_trend = 0 461s 461s Model 1: restricted model 461s Model 2: fitols1r 461s 461s Res.Df Df Chisq Pr(>Chisq) 461s 1 34 461s 2 33 1 0.64 0.42 461s > 461s > # testing second restriction 461s > # first restriction not imposed 461s > print( linearHypothesis( fitols1, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 461s Linear hypothesis test (Chi^2 statistic of a Wald test) 461s 461s Hypothesis: 461s - demand_price + supply_price = 0.5 461s 461s Model 1: restricted model 461s Model 2: fitols1 461s 461s Res.Df Df Chisq Pr(>Chisq) 461s 1 34 461s 2 33 1 0.03 0.86 461s > linearHypothesis( fitols1, restrictOnly2, test = "Chisq" ) 461s Linear hypothesis test (Chi^2 statistic of a Wald test) 461s 461s Hypothesis: 461s - demand_price + supply_price = 0.5 461s 461s Model 1: restricted model 461s Model 2: fitols1 461s 461s Res.Df Df Chisq Pr(>Chisq) 461s 1 34 461s 2 33 1 0.03 0.86 461s > # first restriction imposed 461s > print( linearHypothesis( fitols2, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 461s Linear hypothesis test (Chi^2 statistic of a Wald test) 461s 461s Hypothesis: 461s - demand_price + supply_price = 0.5 461s 461s Model 1: restricted model 461s Model 2: fitols2 461s 461s Res.Df Df Chisq Pr(>Chisq) 461s 1 35 461s 2 34 1 0.12 0.73 461s > linearHypothesis( fitols2, restrictOnly2, test = "Chisq" ) 461s Linear hypothesis test (Chi^2 statistic of a Wald test) 461s 461s Hypothesis: 461s - demand_price + supply_price = 0.5 461s 461s Model 1: restricted model 461s Model 2: fitols2 461s 461s Res.Df Df Chisq Pr(>Chisq) 461s 1 35 461s 2 34 1 0.12 0.73 461s > 461s > print( linearHypothesis( fitols3, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 461s Linear hypothesis test (Chi^2 statistic of a Wald test) 461s 461s Hypothesis: 461s - demand_price + supply_price = 0.5 461s 461s Model 1: restricted model 461s Model 2: fitols3 461s 461s Res.Df Df Chisq Pr(>Chisq) 461s 1 35 461s 2 34 1 0.12 0.73 461s > linearHypothesis( fitols3, restrictOnly2, test = "Chisq" ) 461s Linear hypothesis test (Chi^2 statistic of a Wald test) 461s 461s Hypothesis: 461s - demand_price + supply_price = 0.5 461s 461s Model 1: restricted model 461s Model 2: fitols3 461s 461s Res.Df Df Chisq Pr(>Chisq) 461s 1 35 461s 2 34 1 0.12 0.73 461s > 461s > # testing both of the restrictions 461s > print( linearHypothesis( fitols1, restr2m, restr2q, test = "Chisq" ) ) 461s Linear hypothesis test (Chi^2 statistic of a Wald test) 461s 461s Hypothesis: 461s demand_income - supply_trend = 0 461s - demand_price + supply_price = 0.5 461s 461s Model 1: restricted model 461s Model 2: fitols1 461s 461s Res.Df Df Chisq Pr(>Chisq) 461s 1 35 461s 2 33 2 0.72 0.7 461s > linearHypothesis( fitols1, restrict2, test = "Chisq" ) 461s Linear hypothesis test (Chi^2 statistic of a Wald test) 461s 461s Hypothesis: 461s demand_income - supply_trend = 0 461s - demand_price + supply_price = 0.5 461s 461s Model 1: restricted model 461s Model 2: fitols1 461s 461s Res.Df Df Chisq Pr(>Chisq) 461s 1 35 461s 2 33 2 0.72 0.7 461s > 461s > 461s > ## ****************** model frame ************************** 461s > print( mf <- model.frame( fitols1 ) ) 461s consump price income farmPrice trend 461s 1 98.5 100.3 87.4 98.0 1 461s 2 99.2 104.3 97.6 99.1 2 461s 3 102.2 103.4 96.7 99.1 3 461s 4 101.5 104.5 98.2 98.1 4 461s 5 104.2 98.0 99.8 110.8 5 461s 6 103.2 99.5 100.5 108.2 6 461s 7 104.0 101.1 103.2 105.6 7 461s 8 99.9 104.8 107.8 109.8 8 461s 9 100.3 96.4 96.6 108.7 9 461s 10 102.8 91.2 88.9 100.6 10 461s 11 95.4 93.1 75.1 81.0 11 461s 12 92.4 98.8 76.9 68.6 12 461s 13 94.5 102.9 84.6 70.9 13 461s 14 98.8 98.8 90.6 81.4 14 461s 15 105.8 95.1 103.1 102.3 15 461s 16 100.2 98.5 105.1 105.0 16 461s 17 103.5 86.5 96.4 110.5 17 461s 18 99.9 104.0 104.4 92.5 18 461s 19 105.2 105.8 110.7 89.3 19 461s 20 106.2 113.5 127.1 93.0 20 461s > print( mf1 <- model.frame( fitols1$eq[[ 1 ]] ) ) 461s consump price income 461s 1 98.5 100.3 87.4 461s 2 99.2 104.3 97.6 461s 3 102.2 103.4 96.7 461s 4 101.5 104.5 98.2 461s 5 104.2 98.0 99.8 461s 6 103.2 99.5 100.5 461s 7 104.0 101.1 103.2 461s 8 99.9 104.8 107.8 461s 9 100.3 96.4 96.6 461s 10 102.8 91.2 88.9 461s 11 95.4 93.1 75.1 461s 12 92.4 98.8 76.9 461s 13 94.5 102.9 84.6 461s 14 98.8 98.8 90.6 461s 15 105.8 95.1 103.1 461s 16 100.2 98.5 105.1 461s 17 103.5 86.5 96.4 461s 18 99.9 104.0 104.4 461s 19 105.2 105.8 110.7 461s 20 106.2 113.5 127.1 461s > print( attributes( mf1 )$terms ) 461s consump ~ price + income 461s attr(,"variables") 461s list(consump, price, income) 461s attr(,"factors") 461s price income 461s consump 0 0 461s price 1 0 461s income 0 1 461s attr(,"term.labels") 461s [1] "price" "income" 461s attr(,"order") 461s [1] 1 1 461s attr(,"intercept") 461s [1] 1 461s attr(,"response") 461s [1] 1 461s attr(,".Environment") 461s 461s attr(,"predvars") 461s list(consump, price, income) 461s attr(,"dataClasses") 461s consump price income 461s "numeric" "numeric" "numeric" 461s > print( mf2 <- model.frame( fitols1$eq[[ 2 ]] ) ) 461s consump price farmPrice trend 461s 1 98.5 100.3 98.0 1 461s 2 99.2 104.3 99.1 2 461s 3 102.2 103.4 99.1 3 461s 4 101.5 104.5 98.1 4 461s 5 104.2 98.0 110.8 5 461s 6 103.2 99.5 108.2 6 461s 7 104.0 101.1 105.6 7 461s 8 99.9 104.8 109.8 8 461s 9 100.3 96.4 108.7 9 461s 10 102.8 91.2 100.6 10 461s 11 95.4 93.1 81.0 11 461s 12 92.4 98.8 68.6 12 461s 13 94.5 102.9 70.9 13 461s 14 98.8 98.8 81.4 14 461s 15 105.8 95.1 102.3 15 461s 16 100.2 98.5 105.0 16 461s 17 103.5 86.5 110.5 17 461s 18 99.9 104.0 92.5 18 461s 19 105.2 105.8 89.3 19 461s 20 106.2 113.5 93.0 20 461s > print( attributes( mf2 )$terms ) 461s consump ~ price + farmPrice + trend 461s attr(,"variables") 461s list(consump, price, farmPrice, trend) 461s attr(,"factors") 461s price farmPrice trend 461s consump 0 0 0 461s price 1 0 0 461s farmPrice 0 1 0 461s trend 0 0 1 461s attr(,"term.labels") 461s [1] "price" "farmPrice" "trend" 461s attr(,"order") 461s [1] 1 1 1 461s attr(,"intercept") 461s [1] 1 461s attr(,"response") 461s [1] 1 461s attr(,".Environment") 461s 461s attr(,"predvars") 461s list(consump, price, farmPrice, trend) 461s attr(,"dataClasses") 461s consump price farmPrice trend 461s "numeric" "numeric" "numeric" "numeric" 461s > 461s > print( all.equal( mf, model.frame( fitols2r ) ) ) 461s [1] TRUE 461s > print( all.equal( mf1, model.frame( fitols2r$eq[[ 1 ]] ) ) ) 461s [1] TRUE 461s > 461s > print( all.equal( mf, model.frame( fitols3s ) ) ) 461s [1] TRUE 461s > print( all.equal( mf2, model.frame( fitols3s$eq[[ 2 ]] ) ) ) 461s [1] TRUE 461s > 461s > print( all.equal( mf, model.frame( fitols4rs ) ) ) 461s [1] TRUE 461s > print( all.equal( mf1, model.frame( fitols4rs$eq[[ 1 ]] ) ) ) 461s [1] TRUE 461s > 461s > print( all.equal( mf, model.frame( fitols5 ) ) ) 461s [1] TRUE 461s > print( all.equal( mf2, model.frame( fitols5$eq[[ 2 ]] ) ) ) 461s [1] TRUE 461s > 461s > 461s > ## **************** model matrix ************************ 461s > # with x (returnModelMatrix) = TRUE 461s > print( !is.null( fitols1r$eq[[ 1 ]]$x ) ) 461s [1] TRUE 461s > print( mm <- model.matrix( fitols1r ) ) 461s demand_(Intercept) demand_price demand_income supply_(Intercept) 461s demand_1 1 100.3 87.4 0 461s demand_2 1 104.3 97.6 0 461s demand_3 1 103.4 96.7 0 461s demand_4 1 104.5 98.2 0 461s demand_5 1 98.0 99.8 0 461s demand_6 1 99.5 100.5 0 461s demand_7 1 101.1 103.2 0 461s demand_8 1 104.8 107.8 0 461s demand_9 1 96.4 96.6 0 461s demand_10 1 91.2 88.9 0 461s demand_11 1 93.1 75.1 0 461s demand_12 1 98.8 76.9 0 461s demand_13 1 102.9 84.6 0 461s demand_14 1 98.8 90.6 0 461s demand_15 1 95.1 103.1 0 461s demand_16 1 98.5 105.1 0 461s demand_17 1 86.5 96.4 0 461s demand_18 1 104.0 104.4 0 461s demand_19 1 105.8 110.7 0 461s demand_20 1 113.5 127.1 0 461s supply_1 0 0.0 0.0 1 461s supply_2 0 0.0 0.0 1 461s supply_3 0 0.0 0.0 1 461s supply_4 0 0.0 0.0 1 461s supply_5 0 0.0 0.0 1 461s supply_6 0 0.0 0.0 1 461s supply_7 0 0.0 0.0 1 461s supply_8 0 0.0 0.0 1 461s supply_9 0 0.0 0.0 1 461s supply_10 0 0.0 0.0 1 461s supply_11 0 0.0 0.0 1 461s supply_12 0 0.0 0.0 1 461s supply_13 0 0.0 0.0 1 461s supply_14 0 0.0 0.0 1 461s supply_15 0 0.0 0.0 1 461s supply_16 0 0.0 0.0 1 461s supply_17 0 0.0 0.0 1 461s supply_18 0 0.0 0.0 1 461s supply_19 0 0.0 0.0 1 461s supply_20 0 0.0 0.0 1 461s supply_price supply_farmPrice supply_trend 461s demand_1 0.0 0.0 0 461s demand_2 0.0 0.0 0 461s demand_3 0.0 0.0 0 461s demand_4 0.0 0.0 0 461s demand_5 0.0 0.0 0 461s demand_6 0.0 0.0 0 461s demand_7 0.0 0.0 0 461s demand_8 0.0 0.0 0 461s demand_9 0.0 0.0 0 461s demand_10 0.0 0.0 0 461s demand_11 0.0 0.0 0 461s demand_12 0.0 0.0 0 461s demand_13 0.0 0.0 0 461s demand_14 0.0 0.0 0 461s demand_15 0.0 0.0 0 461s demand_16 0.0 0.0 0 461s demand_17 0.0 0.0 0 461s demand_18 0.0 0.0 0 461s demand_19 0.0 0.0 0 461s demand_20 0.0 0.0 0 461s supply_1 100.3 98.0 1 461s supply_2 104.3 99.1 2 461s supply_3 103.4 99.1 3 461s supply_4 104.5 98.1 4 461s supply_5 98.0 110.8 5 461s supply_6 99.5 108.2 6 461s supply_7 101.1 105.6 7 461s supply_8 104.8 109.8 8 461s supply_9 96.4 108.7 9 461s supply_10 91.2 100.6 10 461s supply_11 93.1 81.0 11 461s supply_12 98.8 68.6 12 461s supply_13 102.9 70.9 13 461s supply_14 98.8 81.4 14 461s supply_15 95.1 102.3 15 461s supply_16 98.5 105.0 16 461s supply_17 86.5 110.5 17 461s supply_18 104.0 92.5 18 461s supply_19 105.8 89.3 19 461s supply_20 113.5 93.0 20 461s > print( mm1 <- model.matrix( fitols1r$eq[[ 1 ]] ) ) 461s (Intercept) price income 461s 1 1 100.3 87.4 461s 2 1 104.3 97.6 461s 3 1 103.4 96.7 461s 4 1 104.5 98.2 461s 5 1 98.0 99.8 461s 6 1 99.5 100.5 461s 7 1 101.1 103.2 461s 8 1 104.8 107.8 461s 9 1 96.4 96.6 461s 10 1 91.2 88.9 461s 11 1 93.1 75.1 461s 12 1 98.8 76.9 461s 13 1 102.9 84.6 461s 14 1 98.8 90.6 461s 15 1 95.1 103.1 461s 16 1 98.5 105.1 461s 17 1 86.5 96.4 461s 18 1 104.0 104.4 461s 19 1 105.8 110.7 461s 20 1 113.5 127.1 461s attr(,"assign") 461s [1] 0 1 2 461s > print( mm2 <- model.matrix( fitols1r$eq[[ 2 ]] ) ) 461s (Intercept) price farmPrice trend 461s 1 1 100.3 98.0 1 461s 2 1 104.3 99.1 2 461s 3 1 103.4 99.1 3 461s 4 1 104.5 98.1 4 461s 5 1 98.0 110.8 5 461s 6 1 99.5 108.2 6 461s 7 1 101.1 105.6 7 461s 8 1 104.8 109.8 8 461s 9 1 96.4 108.7 9 461s 10 1 91.2 100.6 10 461s 11 1 93.1 81.0 11 461s 12 1 98.8 68.6 12 461s 13 1 102.9 70.9 13 461s 14 1 98.8 81.4 14 461s 15 1 95.1 102.3 15 461s 16 1 98.5 105.0 16 461s 17 1 86.5 110.5 17 461s 18 1 104.0 92.5 18 461s 19 1 105.8 89.3 19 461s 20 1 113.5 93.0 20 461s attr(,"assign") 461s [1] 0 1 2 3 461s > 461s > # with x (returnModelMatrix) = FALSE 461s > print( all.equal( mm, model.matrix( fitols1rs ) ) ) 461s [1] TRUE 461s > print( all.equal( mm1, model.matrix( fitols1rs$eq[[ 1 ]] ) ) ) 461s [1] TRUE 461s > print( all.equal( mm2, model.matrix( fitols1rs$eq[[ 2 ]] ) ) ) 461s [1] TRUE 461s > print( !is.null( fitols1rs$eq[[ 1 ]]$x ) ) 461s [1] FALSE 461s > 461s > # with x (returnModelMatrix) = TRUE 461s > print( !is.null( fitols2rs$eq[[ 1 ]]$x ) ) 461s [1] TRUE 461s > print( all.equal( mm, model.matrix( fitols2rs ) ) ) 461s [1] TRUE 461s > print( all.equal( mm1, model.matrix( fitols2rs$eq[[ 1 ]] ) ) ) 461s [1] TRUE 461s > print( all.equal( mm2, model.matrix( fitols2rs$eq[[ 2 ]] ) ) ) 461s [1] TRUE 461s > 461s > # with x (returnModelMatrix) = FALSE 461s > print( all.equal( mm, model.matrix( fitols2 ) ) ) 461s [1] TRUE 461s > print( all.equal( mm1, model.matrix( fitols2$eq[[ 1 ]] ) ) ) 461s [1] TRUE 461s > print( all.equal( mm2, model.matrix( fitols2$eq[[ 2 ]] ) ) ) 461s [1] TRUE 461s > print( !is.null( fitols2$eq[[ 1 ]]$x ) ) 461s [1] FALSE 461s > 461s > # with x (returnModelMatrix) = TRUE 461s > print( !is.null( fitols3$eq[[ 1 ]]$x ) ) 461s [1] TRUE 461s > print( all.equal( mm, model.matrix( fitols3 ) ) ) 461s [1] TRUE 461s > print( all.equal( mm1, model.matrix( fitols3$eq[[ 1 ]] ) ) ) 461s [1] TRUE 461s > print( all.equal( mm2, model.matrix( fitols3$eq[[ 2 ]] ) ) ) 461s [1] TRUE 461s > 461s > # with x (returnModelMatrix) = FALSE 461s > print( all.equal( mm, model.matrix( fitols3r ) ) ) 461s [1] TRUE 461s > print( all.equal( mm1, model.matrix( fitols3r$eq[[ 1 ]] ) ) ) 461s [1] TRUE 461s > print( all.equal( mm2, model.matrix( fitols3r$eq[[ 2 ]] ) ) ) 461s [1] TRUE 461s > print( !is.null( fitols3r$eq[[ 1 ]]$x ) ) 461s [1] FALSE 461s > 461s > # with x (returnModelMatrix) = TRUE 461s > print( !is.null( fitols4s$eq[[ 1 ]]$x ) ) 461s [1] TRUE 461s > print( all.equal( mm, model.matrix( fitols4s ) ) ) 461s [1] TRUE 461s > print( all.equal( mm1, model.matrix( fitols4s$eq[[ 1 ]] ) ) ) 461s [1] TRUE 461s > print( all.equal( mm2, model.matrix( fitols4s$eq[[ 2 ]] ) ) ) 461s [1] TRUE 461s > 461s > # with x (returnModelMatrix) = FALSE 461s > print( all.equal( mm, model.matrix( fitols4Sym ) ) ) 461s [1] TRUE 461s > print( all.equal( mm1, model.matrix( fitols4Sym$eq[[ 1 ]] ) ) ) 461s [1] TRUE 461s > print( all.equal( mm2, model.matrix( fitols4Sym$eq[[ 2 ]] ) ) ) 461s [1] TRUE 461s > print( !is.null( fitols4Sym$eq[[ 1 ]]$x ) ) 461s [1] FALSE 461s > 461s > # with x (returnModelMatrix) = TRUE 461s > print( !is.null( fitols5s$eq[[ 1 ]]$x ) ) 461s [1] TRUE 461s > print( all.equal( mm, model.matrix( fitols5s ) ) ) 461s [1] TRUE 461s > print( all.equal( mm1, model.matrix( fitols5s$eq[[ 1 ]] ) ) ) 461s [1] TRUE 461s > print( all.equal( mm2, model.matrix( fitols5s$eq[[ 2 ]] ) ) ) 461s [1] TRUE 461s > 461s > # with x (returnModelMatrix) = FALSE 461s > print( all.equal( mm, model.matrix( fitols5 ) ) ) 461s [1] TRUE 461s > print( all.equal( mm1, model.matrix( fitols5$eq[[ 1 ]] ) ) ) 461s [1] TRUE 461s > print( all.equal( mm2, model.matrix( fitols5$eq[[ 2 ]] ) ) ) 461s [1] TRUE 461s > print( !is.null( fitols5$eq[[ 1 ]]$x ) ) 461s [1]Error in model.matrix.systemfit.equation(object$eq[[i]], which = which) : 461s argument 'which' can only be set to "xHat" or "z" if instruments were used 461s FALSE 461s > 461s > try( model.matrix( fitols1, which = "z" ) ) 461s > 461s > 461s > ## **************** formulas ************************ 461s > formula( fitols1 ) 461s $demand 461s consump ~ price + income 461s 461s $supply 461s consump ~ price + farmPrice + trend 461s 461s > formula( fitols1$eq[[ 2 ]] ) 461s consump ~ price + farmPrice + trend 461s > 461s > formula( fitols2r ) 461s $demand 461s consump ~ price + income 461s 461s $supply 461s consump ~ price + farmPrice + trend 461s 461s > formula( fitols2r$eq[[ 1 ]] ) 461s consump ~ price + income 461s > 461s > formula( fitols3s ) 461s $demand 461s consump ~ price + income 461s 461s $supply 461s consump ~ price + farmPrice + trend 461s 461s > formula( fitols3s$eq[[ 2 ]] ) 461s consump ~ price + farmPrice + trend 461s > 461s > formula( fitols4rs ) 461s $demand 461s consump ~ price + income 461s 461s $supply 461s consump ~ price + farmPrice + trend 461s 461s > formula( fitols4rs$eq[[ 1 ]] ) 461s consump ~ price + income 461s > 461s > formula( fitols5 ) 461s $demand 461s consump ~ price + income 461s 461s $supply 461s consump ~ price + farmPrice + trend 461s 461s > formula( fitols5$eq[[ 2 ]] ) 461s consump ~ price + farmPrice + trend 461s > 461s > 461s > ## **************** model terms ******************* 461s > terms( fitols1 ) 461s $demand 461s consump ~ price + income 461s attr(,"variables") 461s list(consump, price, income) 461s attr(,"factors") 461s price income 461s consump 0 0 461s price 1 0 461s income 0 1 461s attr(,"term.labels") 461s [1] "price" "income" 461s attr(,"order") 461s [1] 1 1 461s attr(,"intercept") 461s [1] 1 461s attr(,"response") 461s [1] 1 461s attr(,".Environment") 461s 461s attr(,"predvars") 461s list(consump, price, income) 461s attr(,"dataClasses") 461s consump price income 461s "numeric" "numeric" "numeric" 461s 461s $supply 461s consump ~ price + farmPrice + trend 461s attr(,"variables") 461s list(consump, price, farmPrice, trend) 461s attr(,"factors") 461s price farmPrice trend 461s consump 0 0 0 461s price 1 0 0 461s farmPrice 0 1 0 461s trend 0 0 1 461s attr(,"term.labels") 461s [1] "price" "farmPrice" "trend" 461s attr(,"order") 461s [1] 1 1 1 461s attr(,"intercept") 461s [1] 1 461s attr(,"response") 461s [1] 1 461s attr(,".Environment") 461s 461s attr(,"predvars") 461s list(consump, price, farmPrice, trend) 461s attr(,"dataClasses") 461s consump price farmPrice trend 461s "numeric" "numeric" "numeric" "numeric" 461s 461s > terms( fitols1$eq[[ 2 ]] ) 461s consump ~ price + farmPrice + trend 461s attr(,"variables") 461s list(consump, price, farmPrice, trend) 461s attr(,"factors") 461s price farmPrice trend 461s consump 0 0 0 461s price 1 0 0 461s farmPrice 0 1 0 461s trend 0 0 1 461s attr(,"term.labels") 461s [1] "price" "farmPrice" "trend" 461s attr(,"order") 461s [1] 1 1 1 461s attr(,"intercept") 461s [1] 1 461s attr(,"response") 461s [1] 1 461s attr(,".Environment") 461s 461s attr(,"predvars") 461s list(consump, price, farmPrice, trend) 461s attr(,"dataClasses") 461s consump price farmPrice trend 461s "numeric" "numeric" "numeric" "numeric" 461s > 461s > terms( fitols2r ) 461s $demand 461s consump ~ price + income 461s attr(,"variables") 461s list(consump, price, income) 461s attr(,"factors") 461s price income 461s consump 0 0 461s price 1 0 461s income 0 1 461s attr(,"term.labels") 461s [1] "price" "income" 461s attr(,"order") 461s [1] 1 1 461s attr(,"intercept") 461s [1] 1 461s attr(,"response") 461s [1] 1 461s attr(,".Environment") 461s 461s attr(,"predvars") 461s list(consump, price, income) 461s attr(,"dataClasses") 461s consump price income 461s "numeric" "numeric" "numeric" 461s 461s $supply 461s consump ~ price + farmPrice + trend 461s attr(,"variables") 461s list(consump, price, farmPrice, trend) 461s attr(,"factors") 461s price farmPrice trend 461s consump 0 0 0 461s price 1 0 0 461s farmPrice 0 1 0 461s trend 0 0 1 461s attr(,"term.labels") 461s [1] "price" "farmPrice" "trend" 461s attr(,"order") 461s [1] 1 1 1 461s attr(,"intercept") 461s [1] 1 461s attr(,"response") 461s [1] 1 461s attr(,".Environment") 461s 461s attr(,"predvars") 461s list(consump, price, farmPrice, trend) 461s attr(,"dataClasses") 461s consump price farmPrice trend 461s "numeric" "numeric" "numeric" "numeric" 461s 461s > terms( fitols2r$eq[[ 1 ]] ) 461s consump ~ price + income 461s attr(,"variables") 461s list(consump, price, income) 461s attr(,"factors") 461s price income 461s consump 0 0 461s price 1 0 461s income 0 1 461s attr(,"term.labels") 461s [1] "price" "income" 461s attr(,"order") 461s [1] 1 1 461s attr(,"intercept") 461s [1] 1 461s attr(,"response") 461s [1] 1 461s attr(,".Environment") 461s 461s attr(,"predvars") 461s list(consump, price, income) 461s attr(,"dataClasses") 461s consump price income 461s "numeric" "numeric" "numeric" 461s > 461s > terms( fitols3s ) 461s $demand 461s consump ~ price + income 461s attr(,"variables") 461s list(consump, price, income) 461s attr(,"factors") 461s price income 461s consump 0 0 461s price 1 0 461s income 0 1 461s attr(,"term.labels") 461s [1] "price" "income" 461s attr(,"order") 461s [1] 1 1 461s attr(,"intercept") 461s [1] 1 461s attr(,"response") 461s [1] 1 461s attr(,".Environment") 461s 461s attr(,"predvars") 461s list(consump, price, income) 461s attr(,"dataClasses") 461s consump price income 461s "numeric" "numeric" "numeric" 461s 461s $supply 461s consump ~ price + farmPrice + trend 461s attr(,"variables") 461s list(consump, price, farmPrice, trend) 461s attr(,"factors") 461s price farmPrice trend 461s consump 0 0 0 461s price 1 0 0 461s farmPrice 0 1 0 461s trend 0 0 1 461s attr(,"term.labels") 461s [1] "price" "farmPrice" "trend" 461s attr(,"order") 461s [1] 1 1 1 461s attr(,"intercept") 461s [1] 1 461s attr(,"response") 461s [1] 1 461s attr(,".Environment") 461s 461s attr(,"predvars") 461s list(consump, price, farmPrice, trend) 461s attr(,"dataClasses") 461s consump price farmPrice trend 461s "numeric" "numeric" "numeric" "numeric" 461s 461s > terms( fitols3s$eq[[ 2 ]] ) 461s consump ~ price + farmPrice + trend 461s attr(,"variables") 461s list(consump, price, farmPrice, trend) 461s attr(,"factors") 461s price farmPrice trend 461s consump 0 0 0 461s price 1 0 0 461s farmPrice 0 1 0 461s trend 0 0 1 461s attr(,"term.labels") 461s [1] "price" "farmPrice" "trend" 461s attr(,"order") 461s [1] 1 1 1 461s attr(,"intercept") 461s [1] 1 461s attr(,"response") 461s [1] 1 461s attr(,".Environment") 461s 461s attr(,"predvars") 461s list(consump, price, farmPrice, trend) 461s attr(,"dataClasses") 461s consump price farmPrice trend 461s "numeric" "numeric" "numeric" "numeric" 461s > 461s > terms( fitols4rs ) 461s $demand 461s consump ~ price + income 461s attr(,"variables") 461s list(consump, price, income) 461s attr(,"factors") 461s price income 461s consump 0 0 461s price 1 0 461s income 0 1 461s attr(,"term.labels") 461s [1] "price" "income" 461s attr(,"order") 461s [1] 1 1 461s attr(,"intercept") 461s [1] 1 461s attr(,"response") 461s [1] 1 461s attr(,".Environment") 461s 461s attr(,"predvars") 461s list(consump, price, income) 461s attr(,"dataClasses") 461s consump price income 461s "numeric" "numeric" "numeric" 461s 461s $supply 461s consump ~ price + farmPrice + trend 461s attr(,"variables") 461s list(consump, price, farmPrice, trend) 461s attr(,"factors") 461s price farmPrice trend 461s consump 0 0 0 461s price 1 0 0 461s farmPrice 0 1 0 461s trend 0 0 1 461s attr(,"term.labels") 461s [1] "price" "farmPrice" "trend" 461s attr(,"order") 461s [1] 1 1 1 461s attr(,"intercept") 461s [1] 1 461s attr(,"response") 461s [1] 1 461s attr(,".Environment") 461s 461s attr(,"predvars") 461s list(consump, price, farmPrice, trend) 461s attr(,"dataClasses") 461s consump price farmPrice trend 461s "numeric" "numeric" "numeric" "numeric" 461s 461s > terms( fitols4rs$eq[[ 1 ]] ) 461s consump ~ price + income 461s attr(,"variables") 461s list(consump, price, income) 461s attr(,"factors") 461s price income 461s consump 0 0 461s price 1 0 461s income 0 1 461s attr(,"term.labels") 461s [1] "price" "income" 461s attr(,"order") 461s [1] 1 1 461s attr(,"intercept") 461s [1] 1 461s attr(,"response") 461s [1] 1 461s attr(,".Environment") 461s 461s attr(,"predvars") 461s list(consump, price, income) 461s attr(,"dataClasses") 461s consump price income 461s "numeric" "numeric" "numeric" 461s > 461s > terms( fitols5 ) 461s $demand 461s consump ~ price + income 461s attr(,"variables") 461s list(consump, price, income) 461s attr(,"factors") 461s price income 461s consump 0 0 461s price 1 0 461s income 0 1 461s attr(,"term.labels") 461s [1] "price" "income" 461s attr(,"order") 461s [1] 1 1 461s attr(,"intercept") 461s [1] 1 461s attr(,"response") 461s [1] 1 461s attr(,".Environment") 461s 461s attr(,"predvars") 461s list(consump, price, income) 461s attr(,"dataClasses") 461s consump price income 461s "numeric" "numeric" "numeric" 461s 461s $supply 461s consump ~ price + farmPrice + trend 461s attr(,"variables") 461s list(consump, price, farmPrice, trend) 461s attr(,"factors") 461s price farmPrice trend 461s consump 0 0 0 461s price 1 0 0 461s farmPrice 0 1 0 461s trend 0 0 1 461s attr(,"term.labels") 461s [1] "price" "farmPrice" "trend" 461s attr(,"order") 461s [1] 1 1 1 461s attr(,"intercept") 461s [1] 1 461s attr(,"response") 461s [1] 1 461s attr(,".Environment") 461s 461s attr(,"predvars") 461s list(consump, price, farmPrice, trend) 461s attr(,"dataClasses") 461s consump price farmPrice trend 461s "numeric" "numeric" "numeric" "numeric" 461s 461s > terms( fitols5$eq[[ 2 ]] ) 461s consump ~ price + farmPrice + trend 461s attr(,"variables") 461s list(consump, price, farmPrice, trend) 461s attr(,"factors") 461s price farmPrice trend 461s consump 0 0 0 461s price 1 0 0 461s farmPrice 0 1 0 461s trend 0 0 1 461s attr(,"term.labels") 461s [1] "price" "farmPrice" "trend" 461s attr(,"order") 461s [1] 1 1 1 461s attr(,"intercept") 461s [1] 1 461s attr(,"response") 461s [1] 1 461s attr(,".Environment") 461s 461s attr(,"predvars") 461s list(consump, price, farmPrice, trend) 461s attr(,"dataClasses") 461s consump price farmPrice trend 461s "numeric" "numeric" "numeric" "numeric" 461s > 461s > 461s > ## **************** estfun ************************ 461s > library( "sandwich" ) 461s > 461s > estfun( fitols1 ) 461s demand_(Intercept) demand_price demand_income supply_(Intercept) 461s demand_1 1.074 107.8 93.9 0.000 461s demand_2 -0.390 -40.7 -38.1 0.000 461s demand_3 2.625 271.5 253.8 0.000 461s demand_4 1.802 188.4 177.0 0.000 461s demand_5 1.946 190.7 194.2 0.000 461s demand_6 1.175 116.8 118.0 0.000 461s demand_7 1.530 154.7 157.9 0.000 461s demand_8 -2.933 -307.2 -316.1 0.000 461s demand_9 -1.365 -131.7 -131.9 0.000 461s demand_10 2.031 185.3 180.5 0.000 461s demand_11 -0.149 -13.9 -11.2 0.000 461s demand_12 -1.954 -193.1 -150.3 0.000 461s demand_13 -1.121 -115.4 -94.8 0.000 461s demand_14 -0.220 -21.7 -19.9 0.000 461s demand_15 1.487 141.4 153.3 0.000 461s demand_16 -3.701 -364.3 -388.9 0.000 461s demand_17 -1.273 -110.1 -122.7 0.000 461s demand_18 -2.002 -208.3 -209.0 0.000 461s demand_19 1.738 183.8 192.4 0.000 461s demand_20 -0.299 -33.9 -38.0 0.000 461s supply_1 0.000 0.0 0.0 -0.444 461s supply_2 0.000 0.0 0.0 -0.896 461s supply_3 0.000 0.0 0.0 1.965 461s supply_4 0.000 0.0 0.0 1.134 461s supply_5 0.000 0.0 0.0 1.514 461s supply_6 0.000 0.0 0.0 0.680 461s supply_7 0.000 0.0 0.0 1.569 461s supply_8 0.000 0.0 0.0 -4.407 461s supply_9 0.000 0.0 0.0 -2.599 461s supply_10 0.000 0.0 0.0 2.469 461s supply_11 0.000 0.0 0.0 -0.598 461s supply_12 0.000 0.0 0.0 -1.697 461s supply_13 0.000 0.0 0.0 -1.064 461s supply_14 0.000 0.0 0.0 0.970 461s supply_15 0.000 0.0 0.0 3.159 461s supply_16 0.000 0.0 0.0 -3.866 461s supply_17 0.000 0.0 0.0 -0.265 461s supply_18 0.000 0.0 0.0 -2.449 461s supply_19 0.000 0.0 0.0 3.110 461s supply_20 0.000 0.0 0.0 1.714 461s supply_price supply_farmPrice supply_trend 461s demand_1 0.0 0.0 0.000 461s demand_2 0.0 0.0 0.000 461s demand_3 0.0 0.0 0.000 461s demand_4 0.0 0.0 0.000 461s demand_5 0.0 0.0 0.000 461s demand_6 0.0 0.0 0.000 461s demand_7 0.0 0.0 0.000 461s demand_8 0.0 0.0 0.000 461s demand_9 0.0 0.0 0.000 461s demand_10 0.0 0.0 0.000 461s demand_11 0.0 0.0 0.000 461s demand_12 0.0 0.0 0.000 461s demand_13 0.0 0.0 0.000 461s demand_14 0.0 0.0 0.000 461s demand_15 0.0 0.0 0.000 461s demand_16 0.0 0.0 0.000 461s demand_17 0.0 0.0 0.000 461s demand_18 0.0 0.0 0.000 461s demand_19 0.0 0.0 0.000 461s demand_20 0.0 0.0 0.000 461s supply_1 -44.6 -43.5 -0.444 461s supply_2 -93.4 -88.7 -1.791 461s supply_3 203.3 194.7 5.895 461s supply_4 118.5 111.3 4.537 461s supply_5 148.4 167.7 7.569 461s supply_6 67.7 73.6 4.082 461s supply_7 158.6 165.7 10.983 461s supply_8 -461.7 -483.9 -35.259 461s supply_9 -250.7 -282.5 -23.391 461s supply_10 225.3 248.4 24.694 461s supply_11 -55.7 -48.5 -6.581 461s supply_12 -167.7 -116.4 -20.369 461s supply_13 -109.5 -75.4 -13.832 461s supply_14 95.8 79.0 13.582 461s supply_15 300.5 323.2 47.386 461s supply_16 -380.6 -405.9 -61.848 461s supply_17 -22.9 -29.2 -4.500 461s supply_18 -254.7 -226.5 -44.080 461s supply_19 328.9 277.7 59.084 461s supply_20 194.5 159.4 34.282 461s > round( colSums( estfun( fitols1 ) ), digits = 7 ) 461s demand_(Intercept) demand_price demand_income supply_(Intercept) 461s 0 0 0 0 461s supply_price supply_farmPrice supply_trend 461s 0 0 0 461s > 461s > estfun( fitols1s ) 461s demand_(Intercept) demand_price demand_income supply_(Intercept) 461s demand_1 1.074 107.8 93.9 0.000 461s demand_2 -0.390 -40.7 -38.1 0.000 461s demand_3 2.625 271.5 253.8 0.000 461s demand_4 1.802 188.4 177.0 0.000 461s demand_5 1.946 190.7 194.2 0.000 461s demand_6 1.175 116.8 118.0 0.000 461s demand_7 1.530 154.7 157.9 0.000 461s demand_8 -2.933 -307.2 -316.1 0.000 461s demand_9 -1.365 -131.7 -131.9 0.000 461s demand_10 2.031 185.3 180.5 0.000 461s demand_11 -0.149 -13.9 -11.2 0.000 461s demand_12 -1.954 -193.1 -150.3 0.000 461s demand_13 -1.121 -115.4 -94.8 0.000 461s demand_14 -0.220 -21.7 -19.9 0.000 461s demand_15 1.487 141.4 153.3 0.000 461s demand_16 -3.701 -364.3 -388.9 0.000 461s demand_17 -1.273 -110.1 -122.7 0.000 461s demand_18 -2.002 -208.3 -209.0 0.000 461s demand_19 1.738 183.8 192.4 0.000 461s demand_20 -0.299 -33.9 -38.0 0.000 461s supply_1 0.000 0.0 0.0 -0.444 461s supply_2 0.000 0.0 0.0 -0.896 461s supply_3 0.000 0.0 0.0 1.965 461s supply_4 0.000 0.0 0.0 1.134 461s supply_5 0.000 0.0 0.0 1.514 461s supply_6 0.000 0.0 0.0 0.680 461s supply_7 0.000 0.0 0.0 1.569 461s supply_8 0.000 0.0 0.0 -4.407 461s supply_9 0.000 0.0 0.0 -2.599 461s supply_10 0.000 0.0 0.0 2.469 461s supply_11 0.000 0.0 0.0 -0.598 461s supply_12 0.000 0.0 0.0 -1.697 461s supply_13 0.000 0.0 0.0 -1.064 461s supply_14 0.000 0.0 0.0 0.970 461s supply_15 0.000 0.0 0.0 3.159 461s supply_16 0.000 0.0 0.0 -3.866 461s supply_17 0.000 0.0 0.0 -0.265 461s supply_18 0.000 0.0 0.0 -2.449 461s supply_19 0.000 0.0 0.0 3.110 461s supply_20 0.000 0.0 0.0 1.714 461s supply_price supply_farmPrice supply_trend 461s demand_1 0.0 0.0 0.000 461s demand_2 0.0 0.0 0.000 461s demand_3 0.0 0.0 0.000 461s demand_4 0.0 0.0 0.000 461s demand_5 0.0 0.0 0.000 461s demand_6 0.0 0.0 0.000 461s demand_7 0.0 0.0 0.000 461s demand_8 0.0 0.0 0.000 461s demand_9 0.0 0.0 0.000 461s demand_10 0.0 0.0 0.000 461s demand_11 0.0 0.0 0.000 461s demand_12 0.0 0.0 0.000 461s demand_13 0.0 0.0 0.000 461s demand_14 0.0 0.0 0.000 461s demand_15 0.0 0.0 0.000 461s demand_16 0.0 0.0 0.000 461s demand_17 0.0 0.0 0.000 461s demand_18 0.0 0.0 0.000 461s demand_19 0.0 0.0 0.000 461s demand_20 0.0 0.0 0.000 461s supply_1 -44.6 -43.5 -0.444 461s supply_2 -93.4 -88.7 -1.791 461s supply_3 203.3 194.7 5.895 461s supply_4 118.5 111.3 4.537 461s supply_5 148.4 167.7 7.569 461s supply_6 67.7 73.6 4.082 461s supply_7 158.6 165.7 10.983 461s supply_8 -461.7 -483.9 -35.259 461s supply_9 -250.7 -282.5 -23.391 461s supply_10 225.3 248.4 24.694 461s supply_11 -55.7 -48.5 -6.581 461s supply_12 -167.7 -116.4 -20.369 461s supply_13 -109.5 -75.4 -13.832 461s supply_14 95.8 79.0 13.582 461s supply_15 300.5 323.2 47.386 461s supply_16 -380.6 -405.9 -61.848 461s supply_17 -22.9 -29.2 -4.500 461s supply_18 -254.7 -226.5 -44.080 461s supply_19 328.9 277.7 59.084 461s supply_20 194.5 159.4 34.282 461s > round( colSums( estfun( fitols1s ) ), digits = 7 ) 461s demand_(Intercept) demand_price demand_income supply_(Intercept) 461s 0 0 0 0 461s supply_price supply_farmPrice supply_trend 461s 0 0 0 461s > 461s > estfun( fitols1r ) 461s demand_(Intercept) demand_price demand_income supply_(Intercept) 461s demand_1 1.074 107.8 93.9 0.000 461s demand_2 -0.390 -40.7 -38.1 0.000 461s demand_3 2.625 271.5 253.8 0.000 461s demand_4 1.802 188.4 177.0 0.000 461s demand_5 1.946 190.7 194.2 0.000 461s demand_6 1.175 116.8 118.0 0.000 461s demand_7 1.530 154.7 157.9 0.000 461s demand_8 -2.933 -307.2 -316.1 0.000 461s demand_9 -1.365 -131.7 -131.9 0.000 461s demand_10 2.031 185.3 180.5 0.000 461s demand_11 -0.149 -13.9 -11.2 0.000 461s demand_12 -1.954 -193.1 -150.3 0.000 461s demand_13 -1.121 -115.4 -94.8 0.000 461s demand_14 -0.220 -21.7 -19.9 0.000 461s demand_15 1.487 141.4 153.3 0.000 461s demand_16 -3.701 -364.3 -388.9 0.000 461s demand_17 -1.273 -110.1 -122.7 0.000 461s demand_18 -2.002 -208.3 -209.0 0.000 461s demand_19 1.738 183.8 192.4 0.000 461s demand_20 -0.299 -33.9 -38.0 0.000 461s supply_1 0.000 0.0 0.0 -0.444 461s supply_2 0.000 0.0 0.0 -0.896 461s supply_3 0.000 0.0 0.0 1.965 461s supply_4 0.000 0.0 0.0 1.134 461s supply_5 0.000 0.0 0.0 1.514 461s supply_6 0.000 0.0 0.0 0.680 461s supply_7 0.000 0.0 0.0 1.569 461s supply_8 0.000 0.0 0.0 -4.407 461s supply_9 0.000 0.0 0.0 -2.599 461s supply_10 0.000 0.0 0.0 2.469 461s supply_11 0.000 0.0 0.0 -0.598 461s supply_12 0.000 0.0 0.0 -1.697 461s supply_13 0.000 0.0 0.0 -1.064 461s supply_14 0.000 0.0 0.0 0.970 461s supply_15 0.000 0.0 0.0 3.159 461s supply_16 0.000 0.0 0.0 -3.866 461s supply_17 0.000 0.0 0.0 -0.265 461s supply_18 0.000 0.0 0.0 -2.449 461s supply_19 0.000 0.0 0.0 3.110 461s supply_20 0.000 0.0 0.0 1.714 461s supply_price supply_farmPrice supply_trend 461s demand_1 0.0 0.0 0.000 461s demand_2 0.0 0.0 0.000 461s demand_3 0.0 0.0 0.000 461s demand_4 0.0 0.0 0.000 461s demand_5 0.0 0.0 0.000 461s demand_6 0.0 0.0 0.000 461s demand_7 0.0 0.0 0.000 461s demand_8 0.0 0.0 0.000 461s demand_9 0.0 0.0 0.000 461s demand_10 0.0 0.0 0.000 461s demand_11 0.0 0.0 0.000 461s demand_12 0.0 0.0 0.000 461s demand_13 0.0 0.0 0.000 461s demand_14 0.0 0.0 0.000 461s demand_15 0.0 0.0 0.000 461s demand_16 0.0 0.0 0.000 461s demand_17 0.0 0.0 0.000 461s demand_18 0.0 0.0 0.000 461s demand_19 0.0 0.0 0.000 461s demand_20 0.0 0.0 0.000 461s supply_1 -44.6 -43.5 -0.444 461s supply_2 -93.4 -88.7 -1.791 461s supply_3 203.3 194.7 5.895 461s supply_4 118.5 111.3 4.537 461s supply_5 148.4 167.7 7.569 461s supply_6 67.7 73.6 4.082 461s supply_7 158.6 165.7 10.983 461s supply_8 -461.7 -483.9 -35.259 461s supply_9 -250.7 -282.5 -23.391 461s supply_10 225.3 248.4 24.694 461s supply_11 -55.7 -48.5 -6.581 461s supply_12 -167.7 -116.4 -20.369 461s supply_13 -109.5 -75.4 -13.832 461s supply_14 95.8 79.0 13.582 461s supply_15 300.5 323.2 47.386 461s supply_16 -380.6 -405.9 -61.848 461s supply_17 -22.9 -29.2 -4.500 461s supply_18 -254.7 -226.5 -44.080 461s supply_19 328.9 277.7 59.084 461s supply_20 194.5 159.4 34.282 461s > round( colSums( estfun( fitols1r ) ), digits = 7 ) 461s demand_(Intercept) demand_price demand_income supply_(Intercept) 461s 0 0 0 0 461s supply_price supply_farmPrice supply_trend 461s 0 Error in estfun.systemfit(fitols2) : 461s returning the estimation function for models with restrictions has not yet been implemented. 461s Error in estfun.systemfit(fitols2Sym) : 461s returning the estimation function for models with restrictions has not yet been implemented. 461s Error in estfun.systemfit(fitols3s) : 461s returning the estimation function for models with restrictions has not yet been implemented. 461s Error in estfun.systemfit(fitols4r) : 461s returning the estimation function for models with restrictions has not yet been implemented. 461s Error in estfun.systemfit(fitols4Sym) : 461s returning the estimation function for models with restrictions has not yet been implemented. 461s Error in estfun.systemfit(fitols5) : 461s returning the estimation function for models with restrictions has not yet been implemented. 461s Error in estfun.systemfit(fitols5Sym) : 461s returning the estimation function for models with restrictions has not yet been implemented. 461s Error in bread.systemfit(fitols2) : 461s returning the 'bread' for models with restrictions has not yet been implemented. 461s 0 0 461s > 461s > try( estfun( fitols2 ) ) 461s > 461s > try( estfun( fitols2Sym ) ) 461s > 461s > try( estfun( fitols3s ) ) 461s > 461s > try( estfun( fitols4r ) ) 461s > 461s > try( estfun( fitols4Sym ) ) 461s > 461s > try( estfun( fitols5 ) ) 461s > 461s > try( estfun( fitols5Sym ) ) 461s > 461s > 461s > ## **************** bread ************************ 461s > bread( fitols1 ) 461s demand_(Intercept) demand_price demand_income 461s demand_(Intercept) 607.086 -6.3865 0.3453 461s demand_price -6.386 0.0883 -0.0251 461s demand_income 0.345 -0.0251 0.0222 461s supply_(Intercept) 0.000 0.0000 0.0000 461s supply_price 0.000 0.0000 0.0000 461s supply_farmPrice 0.000 0.0000 0.0000 461s supply_trend 0.000 0.0000 0.0000 461s supply_(Intercept) supply_price supply_farmPrice 461s demand_(Intercept) 0.00 0.00000 0.00000 461s demand_price 0.00 0.00000 0.00000 461s demand_income 0.00 0.00000 0.00000 461s supply_(Intercept) 908.63 -6.82866 -2.10469 461s supply_price -6.83 0.06226 0.00584 461s supply_farmPrice -2.10 0.00584 0.01475 461s supply_trend -1.93 0.00361 0.00910 461s supply_trend 461s demand_(Intercept) 0.00000 461s demand_price 0.00000 461s demand_income 0.00000 461s supply_(Intercept) -1.93058 461s supply_price 0.00361 461s supply_farmPrice 0.00910 461s supply_trend 0.06576 461s > 461s > bread( fitols1s ) 461s demand_(Intercept) demand_price demand_income 461s demand_(Intercept) 607.086 -6.3865 0.3453 461s demand_price -6.386 0.0883 -0.0251 461s demand_income 0.345 -0.0251 0.0222 461s supply_(Intercept) 0.000 0.0000 0.0000 461s supply_price 0.000 0.0000 0.0000 461s supply_farmPrice 0.000 0.0000 0.0000 461s supply_trend 0.000 0.0000 0.0000 461s supply_(Intercept) supply_price supply_farmPrice 461s demand_(Intercept) 0.00 0.00000 0.00000 461s demand_price 0.00 0.00000 0.00000 461s demand_income 0.00 0.00000 0.00000 461s supply_(Intercept) 908.63 -6.82866 -2.10469 461s supply_price -6.83 0.06226 0.00584 461s supply_farmPrice -2.10 0.00584 0.01475 461s supply_trend -1.93 0.00361 0.00910 461s supply_trend 461s demand_(Intercept) 0.00000 461s demand_price 0.00000 461s demand_income 0.00000 461s supply_(Intercept) -1.93058 461s supply_price 0.00361 461s supply_farmPrice 0.00910 461s supply_trend 0.06576 461s > 461s > bread( fitols1r ) 461s demand_(Intercept) demand_price demand_income 461s demand_(Intercept) 607.086 -6.3865 0.3453 461s demand_price -6.386 0.0883 -0.0251 461s demand_income 0.345 -0.0251 0.0222 461s supply_(Intercept) 0.000 0.0000 0.0000 461s supply_price 0.000 0.0000 0.0000 461s supply_farmPrice 0.000 0.0000 0.0000 461s supply_trend 0.000 0.0000 0.0000 461s supply_(Intercept) supply_price supply_farmPrice 461s demand_(Intercept) 0.00 0.00000 0.00000 461s demand_price 0.00 0.00000 0.00000 461s demand_income 0.00 0.00000 0.00000 461s supply_(Intercept) 908.63 -6.82866 -2.10469 461s supply_price -6.83 0.06226 0.00584 461s supply_farmPrice -2.10 0.00584 0.01475 461s supply_trend -1.93 0.00361 0.00910 461s supply_trend 461s demand_(Intercept) 0.00000 461s demand_price 0.00000 461s demand_income 0.00000 461s supply_(Intercept) -1.93058 461s supply_price 0.00361 461s supply_farmPrice 0.00910 461s supply_trend 0.06576 461s > 461s > try( bread( fitols2 ) ) 461s > 461s BEGIN TEST test_panel.R 461s 461s R version 4.3.3 (2024-02-29) -- "Angel Food Cake" 461s Copyright (C) 2024 The R Foundation for Statistical Computing 461s Platform: s390x-ibm-linux-gnu (64-bit) 461s 461s R is free software and comes with ABSOLUTELY NO WARRANTY. 461s You are welcome to redistribute it under certain conditions. 461s Type 'license()' or 'licence()' for distribution details. 461s 461s R is a collaborative project with many contributors. 461s Type 'contributors()' for more information and 461s 'citation()' on how to cite R or R packages in publications. 461s 461s Type 'demo()' for some demos, 'help()' for on-line help, or 461s 'help.start()' for an HTML browser interface to help. 461s Type 'q()' to quit R. 461s 461s > library( systemfit ) 461s Loading required package: Matrix 463s Loading required package: car 463s Loading required package: carData 463s Loading required package: lmtest 463s Loading required package: zoo 463s 463s Attaching package: ‘zoo’ 463s 463s The following objects are masked from ‘package:base’: 463s 463s as.Date, as.Date.numeric 463s 463s 463s Please cite the 'systemfit' package as: 463s 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/. 463s 463s If you have questions, suggestions, or comments regarding the 'systemfit' package, please use a forum or 'tracker' at systemfit's R-Forge site: 463s https://r-forge.r-project.org/projects/systemfit/ 463s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 463s + library( plm ) 463s + options( digits = 3 ) 463s + useMatrix <- FALSE 463s + } 463s > 463s > ## Repeating the OLS and SUR estimations in Theil (1971, pp. 295, 300) 463s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 463s + data( "GrunfeldGreene" ) 463s + GrunfeldTheil <- subset( GrunfeldGreene, 463s + firm %in% c( "General Electric", "Westinghouse" ) ) 463s + GrunfeldTheil <- pdata.frame( GrunfeldTheil, c( "firm", "year" ) ) 463s + formulaGrunfeld <- invest ~ value + capital 463s + } 463s > 463s > # OLS 463s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 463s + theilOls <- systemfit( formulaGrunfeld, "OLS", 463s + data = GrunfeldTheil, useMatrix = useMatrix ) 463s + print( theilOls ) 463s + print( summary( theilOls ) ) 463s + print( summary( theilOls, useDfSys = TRUE, residCov = FALSE, 463s + equations = FALSE ) ) 463s + print( summary( theilOls, equations = FALSE ) ) 463s + print( coef( theilOls ) ) 463s + print( coef( summary(theilOls ) ) ) 463s + print( vcov( theilOls ) ) 463s + print( residuals( theilOls ) ) 463s + print( confint( theilOls ) ) 463s + print( fitted(theilOls ) ) 463s + print( logLik( theilOls ) ) 463s + print( logLik( theilOls, residCovDiag = TRUE ) ) 463s + print( nobs( theilOls ) ) 463s + print( model.frame( theilOls ) ) 463s + print( model.matrix( theilOls ) ) 463s + print( formula( theilOls ) ) 463s + print( formula( theilOls$eq[[ 1 ]] ) ) 463s + print( terms( theilOls ) ) 463s + print( terms( theilOls$eq[[ 1 ]] ) ) 463s + } 463s 463s systemfit results 463s method: OLS 463s 463s Coefficients: 463s General.Electric_(Intercept) General.Electric_value 463s -9.9563 0.0266 463s General.Electric_capital Westinghouse_(Intercept) 463s 0.1517 -0.5094 463s Westinghouse_value Westinghouse_capital 463s 0.0529 0.0924 463s 463s systemfit results 463s method: OLS 463s 463s N DF SSR detRCov OLS-R2 McElroy-R2 463s system 40 34 14990 38001 0.711 0.618 463s 463s N DF SSR MSE RMSE R2 Adj R2 463s General.Electric 20 17 13217 777 27.9 0.705 0.671 463s Westinghouse 20 17 1773 104 10.2 0.744 0.714 463s 463s The covariance matrix of the residuals 463s General.Electric Westinghouse 463s General.Electric 777 208 463s Westinghouse 208 104 463s 463s The correlations of the residuals 463s General.Electric Westinghouse 463s General.Electric 1.000 0.729 463s Westinghouse 0.729 1.000 463s 463s 463s OLS estimates for 'General.Electric' (equation 1) 463s Model Formula: General.Electric_invest ~ General.Electric_value + General.Electric_capital 463s 463s 463s Estimate Std. Error t value Pr(>|t|) 463s (Intercept) -9.9563 31.3742 -0.32 0.75 463s value 0.0266 0.0156 1.71 0.11 463s capital 0.1517 0.0257 5.90 1.7e-05 *** 463s --- 463s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 463s 463s Residual standard error: 27.883 on 17 degrees of freedom 463s Number of observations: 20 Degrees of Freedom: 17 463s SSR: 13216.588 MSE: 777.446 Root MSE: 27.883 463s Multiple R-Squared: 0.705 Adjusted R-Squared: 0.671 463s 463s 463s OLS estimates for 'Westinghouse' (equation 2) 463s Model Formula: Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 463s 463s 463s Estimate Std. Error t value Pr(>|t|) 463s (Intercept) -0.5094 8.0153 -0.06 0.9501 463s value 0.0529 0.0157 3.37 0.0037 ** 463s capital 0.0924 0.0561 1.65 0.1179 463s --- 463s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 463s 463s Residual standard error: 10.213 on 17 degrees of freedom 463s Number of observations: 20 Degrees of Freedom: 17 463s SSR: 1773.234 MSE: 104.308 Root MSE: 10.213 463s Multiple R-Squared: 0.744 Adjusted R-Squared: 0.714 463s 463s 463s systemfit results 463s method: OLS 463s 463s N DF SSR detRCov OLS-R2 McElroy-R2 463s system 40 34 14990 38001 0.711 0.618 463s 463s N DF SSR MSE RMSE R2 Adj R2 463s General.Electric 20 17 13217 777 27.9 0.705 0.671 463s Westinghouse 20 17 1773 104 10.2 0.744 0.714 463s 463s 463s Coefficients: 463s Estimate Std. Error t value Pr(>|t|) 463s General.Electric_(Intercept) -9.9563 31.3742 -0.32 0.7529 463s General.Electric_value 0.0266 0.0156 1.71 0.0972 . 463s General.Electric_capital 0.1517 0.0257 5.90 1.2e-06 *** 463s Westinghouse_(Intercept) -0.5094 8.0153 -0.06 0.9497 463s Westinghouse_value 0.0529 0.0157 3.37 0.0019 ** 463s Westinghouse_capital 0.0924 0.0561 1.65 0.1087 463s --- 463s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 463s 463s systemfit results 463s method: OLS 463s 463s N DF SSR detRCov OLS-R2 McElroy-R2 463s system 40 34 14990 38001 0.711 0.618 463s 463s N DF SSR MSE RMSE R2 Adj R2 463s General.Electric 20 17 13217 777 27.9 0.705 0.671 463s Westinghouse 20 17 1773 104 10.2 0.744 0.714 463s 463s The covariance matrix of the residuals 463s General.Electric Westinghouse 463s General.Electric 777 208 463s Westinghouse 208 104 463s 463s The correlations of the residuals 463s General.Electric Westinghouse 463s General.Electric 1.000 0.729 463s Westinghouse 0.729 1.000 463s 463s 463s Coefficients: 463s Estimate Std. Error t value Pr(>|t|) 463s General.Electric_(Intercept) -9.9563 31.3742 -0.32 0.7548 463s General.Electric_value 0.0266 0.0156 1.71 0.1063 463s General.Electric_capital 0.1517 0.0257 5.90 1.7e-05 *** 463s Westinghouse_(Intercept) -0.5094 8.0153 -0.06 0.9501 463s Westinghouse_value 0.0529 0.0157 3.37 0.0037 ** 463s Westinghouse_capital 0.0924 0.0561 1.65 0.1179 463s --- 463s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 463s General.Electric_(Intercept) General.Electric_value 463s -9.9563 0.0266 463s General.Electric_capital Westinghouse_(Intercept) 463s 0.1517 -0.5094 463s Westinghouse_value Westinghouse_capital 463s 0.0529 0.0924 463s Estimate Std. Error t value Pr(>|t|) 463s General.Electric_(Intercept) -9.9563 31.3742 -0.3173 7.55e-01 463s General.Electric_value 0.0266 0.0156 1.7057 1.06e-01 463s General.Electric_capital 0.1517 0.0257 5.9015 1.74e-05 463s Westinghouse_(Intercept) -0.5094 8.0153 -0.0636 9.50e-01 463s Westinghouse_value 0.0529 0.0157 3.3677 3.65e-03 463s Westinghouse_capital 0.0924 0.0561 1.6472 1.18e-01 463s General.Electric_(Intercept) 463s General.Electric_(Intercept) 984.344 463s General.Electric_value -0.451 463s General.Electric_capital -0.173 463s Westinghouse_(Intercept) 0.000 463s Westinghouse_value 0.000 463s Westinghouse_capital 0.000 463s General.Electric_value General.Electric_capital 463s General.Electric_(Intercept) -4.51e-01 -1.73e-01 463s General.Electric_value 2.42e-04 -4.73e-05 463s General.Electric_capital -4.73e-05 6.61e-04 463s Westinghouse_(Intercept) 0.00e+00 0.00e+00 463s Westinghouse_value 0.00e+00 0.00e+00 463s Westinghouse_capital 0.00e+00 0.00e+00 463s Westinghouse_(Intercept) Westinghouse_value 463s General.Electric_(Intercept) 0.000 0.000000 463s General.Electric_value 0.000 0.000000 463s General.Electric_capital 0.000 0.000000 463s Westinghouse_(Intercept) 64.245 -0.109545 463s Westinghouse_value -0.110 0.000247 463s Westinghouse_capital 0.169 -0.000653 463s Westinghouse_capital 463s General.Electric_(Intercept) 0.000000 463s General.Electric_value 0.000000 463s General.Electric_capital 0.000000 463s Westinghouse_(Intercept) 0.168911 463s Westinghouse_value -0.000653 463s Westinghouse_capital 0.003147 463s General.Electric Westinghouse 463s X1935 -2.860 3.144 463s X1936 -14.402 -0.958 463s X1937 -5.175 -3.684 463s X1938 -23.295 -7.915 463s X1939 -28.031 -10.322 463s X1940 -0.562 -6.613 463s X1941 40.750 17.265 463s X1942 16.036 8.547 463s X1943 -23.719 -2.916 463s X1944 -26.780 -3.257 463s X1945 1.768 -7.753 463s X1946 58.737 5.796 463s X1947 43.936 15.050 463s X1948 31.227 2.969 463s X1949 -23.552 -11.433 463s X1950 -37.511 -13.481 463s X1951 -4.983 4.619 463s X1952 1.893 13.138 463s X1953 5.087 11.308 463s X1954 -8.563 -13.505 463s 2.5 % 97.5 % 463s General.Electric_(Intercept) -76.150 56.238 463s General.Electric_value -0.006 0.059 463s General.Electric_capital 0.097 0.206 463s Westinghouse_(Intercept) -17.420 16.401 463s Westinghouse_value 0.020 0.086 463s Westinghouse_capital -0.026 0.211 463s General.Electric Westinghouse 463s X1935 36.0 9.79 463s X1936 59.4 26.86 463s X1937 82.4 38.73 463s X1938 67.9 30.81 463s X1939 76.1 29.16 463s X1940 75.0 35.18 463s X1941 72.3 31.25 463s X1942 75.9 34.79 463s X1943 85.0 39.94 463s X1944 83.6 41.07 463s X1945 91.8 47.02 463s X1946 101.2 47.66 463s X1947 103.3 40.51 463s X1948 115.1 46.59 463s X1949 121.9 43.47 463s X1950 131.0 45.72 463s X1951 140.2 49.76 463s X1952 155.4 58.64 463s X1953 174.4 78.77 463s X1954 198.2 82.11 463s 'log Lik.' -159 (df=7) 463s 'log Lik.' -167 (df=7) 463s [1] 40 463s General.Electric_invest General.Electric_value General.Electric_capital 463s X1935 33.1 1171 97.8 463s X1936 45.0 2016 104.4 463s X1937 77.2 2803 118.0 463s X1938 44.6 2040 156.2 463s X1939 48.1 2256 172.6 463s X1940 74.4 2132 186.6 463s X1941 113.0 1834 220.9 463s X1942 91.9 1588 287.8 463s X1943 61.3 1749 319.9 463s X1944 56.8 1687 321.3 463s X1945 93.6 2008 319.6 463s X1946 159.9 2208 346.0 463s X1947 147.2 1657 456.4 463s X1948 146.3 1604 543.4 463s X1949 98.3 1432 618.3 463s X1950 93.5 1610 647.4 463s X1951 135.2 1819 671.3 463s X1952 157.3 2080 726.1 463s X1953 179.5 2372 800.3 463s X1954 189.6 2760 888.9 463s Westinghouse_invest Westinghouse_value Westinghouse_capital 463s X1935 12.9 192 1.8 463s X1936 25.9 516 0.8 463s X1937 35.0 729 7.4 463s X1938 22.9 560 18.1 463s X1939 18.8 520 23.5 463s X1940 28.6 628 26.5 463s X1941 48.5 537 36.2 463s X1942 43.3 561 60.8 463s X1943 37.0 617 84.4 463s X1944 37.8 627 91.2 463s X1945 39.3 737 92.4 463s X1946 53.5 760 86.0 463s X1947 55.6 581 111.1 463s X1948 49.6 662 130.6 463s X1949 32.0 584 141.8 463s X1950 32.2 635 136.7 463s X1951 54.4 724 129.7 463s X1952 71.8 864 145.5 463s X1953 90.1 1194 174.8 463s X1954 68.6 1189 213.5 463s General.Electric_(Intercept) General.Electric_value 463s General.Electric_X1935 1 1171 463s General.Electric_X1936 1 2016 463s General.Electric_X1937 1 2803 463s General.Electric_X1938 1 2040 463s General.Electric_X1939 1 2256 463s General.Electric_X1940 1 2132 463s General.Electric_X1941 1 1834 463s General.Electric_X1942 1 1588 463s General.Electric_X1943 1 1749 463s General.Electric_X1944 1 1687 463s General.Electric_X1945 1 2008 463s General.Electric_X1946 1 2208 463s General.Electric_X1947 1 1657 463s General.Electric_X1948 1 1604 463s General.Electric_X1949 1 1432 463s General.Electric_X1950 1 1610 463s General.Electric_X1951 1 1819 463s General.Electric_X1952 1 2080 463s General.Electric_X1953 1 2372 463s General.Electric_X1954 1 2760 463s Westinghouse_X1935 0 0 463s Westinghouse_X1936 0 0 463s Westinghouse_X1937 0 0 463s Westinghouse_X1938 0 0 463s Westinghouse_X1939 0 0 463s Westinghouse_X1940 0 0 463s Westinghouse_X1941 0 0 463s Westinghouse_X1942 0 0 463s Westinghouse_X1943 0 0 463s Westinghouse_X1944 0 0 463s Westinghouse_X1945 0 0 463s Westinghouse_X1946 0 0 463s Westinghouse_X1947 0 0 463s Westinghouse_X1948 0 0 463s Westinghouse_X1949 0 0 463s Westinghouse_X1950 0 0 463s Westinghouse_X1951 0 0 463s Westinghouse_X1952 0 0 463s Westinghouse_X1953 0 0 463s Westinghouse_X1954 0 0 463s General.Electric_capital Westinghouse_(Intercept) 463s General.Electric_X1935 97.8 0 463s General.Electric_X1936 104.4 0 463s General.Electric_X1937 118.0 0 463s General.Electric_X1938 156.2 0 463s General.Electric_X1939 172.6 0 463s General.Electric_X1940 186.6 0 463s General.Electric_X1941 220.9 0 463s General.Electric_X1942 287.8 0 463s General.Electric_X1943 319.9 0 463s General.Electric_X1944 321.3 0 463s General.Electric_X1945 319.6 0 463s General.Electric_X1946 346.0 0 463s General.Electric_X1947 456.4 0 463s General.Electric_X1948 543.4 0 463s General.Electric_X1949 618.3 0 463s General.Electric_X1950 647.4 0 463s General.Electric_X1951 671.3 0 463s General.Electric_X1952 726.1 0 463s General.Electric_X1953 800.3 0 463s General.Electric_X1954 888.9 0 463s Westinghouse_X1935 0.0 1 463s Westinghouse_X1936 0.0 1 463s Westinghouse_X1937 0.0 1 463s Westinghouse_X1938 0.0 1 463s Westinghouse_X1939 0.0 1 463s Westinghouse_X1940 0.0 1 463s Westinghouse_X1941 0.0 1 463s Westinghouse_X1942 0.0 1 463s Westinghouse_X1943 0.0 1 463s Westinghouse_X1944 0.0 1 463s Westinghouse_X1945 0.0 1 463s Westinghouse_X1946 0.0 1 463s Westinghouse_X1947 0.0 1 463s Westinghouse_X1948 0.0 1 463s Westinghouse_X1949 0.0 1 463s Westinghouse_X1950 0.0 1 463s Westinghouse_X1951 0.0 1 463s Westinghouse_X1952 0.0 1 463s Westinghouse_X1953 0.0 1 463s Westinghouse_X1954 0.0 1 463s Westinghouse_value Westinghouse_capital 463s General.Electric_X1935 0 0.0 463s General.Electric_X1936 0 0.0 463s General.Electric_X1937 0 0.0 463s General.Electric_X1938 0 0.0 463s General.Electric_X1939 0 0.0 463s General.Electric_X1940 0 0.0 463s General.Electric_X1941 0 0.0 463s General.Electric_X1942 0 0.0 463s General.Electric_X1943 0 0.0 463s General.Electric_X1944 0 0.0 463s General.Electric_X1945 0 0.0 463s General.Electric_X1946 0 0.0 463s General.Electric_X1947 0 0.0 463s General.Electric_X1948 0 0.0 463s General.Electric_X1949 0 0.0 463s General.Electric_X1950 0 0.0 463s General.Electric_X1951 0 0.0 463s General.Electric_X1952 0 0.0 463s General.Electric_X1953 0 0.0 463s General.Electric_X1954 0 0.0 463s Westinghouse_X1935 192 1.8 463s Westinghouse_X1936 516 0.8 463s Westinghouse_X1937 729 7.4 463s Westinghouse_X1938 560 18.1 463s Westinghouse_X1939 520 23.5 463s Westinghouse_X1940 628 26.5 463s Westinghouse_X1941 537 36.2 463s Westinghouse_X1942 561 60.8 463s Westinghouse_X1943 617 84.4 463s Westinghouse_X1944 627 91.2 463s Westinghouse_X1945 737 92.4 463s Westinghouse_X1946 760 86.0 463s Westinghouse_X1947 581 111.1 463s Westinghouse_X1948 662 130.6 463s Westinghouse_X1949 584 141.8 463s Westinghouse_X1950 635 136.7 463s Westinghouse_X1951 724 129.7 463s Westinghouse_X1952 864 145.5 463s Westinghouse_X1953 1194 174.8 463s Westinghouse_X1954 1189 213.5 463s $General.Electric 463s General.Electric_invest ~ General.Electric_value + General.Electric_capital 463s 463s 463s $Westinghouse 463s Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 463s 463s 463s General.Electric_invest ~ General.Electric_value + General.Electric_capital 463s 463s $General.Electric 463s General.Electric_invest ~ General.Electric_value + General.Electric_capital 463s attr(,"variables") 463s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 463s attr(,"factors") 463s General.Electric_value General.Electric_capital 463s General.Electric_invest 0 0 463s General.Electric_value 1 0 463s General.Electric_capital 0 1 463s attr(,"term.labels") 463s [1] "General.Electric_value" "General.Electric_capital" 463s attr(,"order") 463s [1] 1 1 463s attr(,"intercept") 463s [1] 1 463s attr(,"response") 463s [1] 1 463s attr(,".Environment") 463s 463s attr(,"predvars") 463s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 463s attr(,"dataClasses") 463s General.Electric_invest General.Electric_value General.Electric_capital 463s "numeric" "numeric" "numeric" 463s 463s $Westinghouse 463s Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 463s attr(,"variables") 463s list(Westinghouse_invest, Westinghouse_value, Westinghouse_capital) 463s attr(,"factors") 463s Westinghouse_value Westinghouse_capital 463s Westinghouse_invest 0 0 463s Westinghouse_value 1 0 463s Westinghouse_capital 0 1 463s attr(,"term.labels") 463s [1] "Westinghouse_value" "Westinghouse_capital" 463s attr(,"order") 463s [1] 1 1 463s attr(,"intercept") 463s [1] 1 463s attr(,"response") 463s [1] 1 463s attr(,".Environment") 463s 463s attr(,"predvars") 463s list(Westinghouse_invest, Westinghouse_value, Westinghouse_capital) 463s attr(,"dataClasses") 463s Westinghouse_invest Westinghouse_value Westinghouse_capital 463s "numeric" "numeric" "numeric" 463s 463s General.Electric_invest ~ General.Electric_value + General.Electric_capital 463s attr(,"variables") 463s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 463s attr(,"factors") 463s General.Electric_value General.Electric_capital 463s General.Electric_invest 0 0 463s General.Electric_value 1 0 463s General.Electric_capital 0 1 463s attr(,"term.labels") 463s [1] "General.Electric_value" "General.Electric_capital" 463s attr(,"order") 463s [1] 1 1 463s attr(,"intercept") 463s [1] 1 463s attr(,"response") 463s [1] 1 463s attr(,".Environment") 463s 463s attr(,"predvars") 463s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 463s attr(,"dataClasses") 463s General.Electric_invest General.Electric_value General.Electric_capital 463s "numeric" "numeric" "numeric" 463s > 463s > # SUR 463s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 463s + theilSur <- systemfit( formulaGrunfeld, "SUR", 463s + data = GrunfeldTheil, methodResidCov = "noDfCor", useMatrix = useMatrix ) 463s + print( theilSur ) 463s + print( summary( theilSur ) ) 463s + print( summary( theilSur, useDfSys = TRUE, equations = FALSE ) ) 463s + print( summary( theilSur, residCov = FALSE, equations = FALSE ) ) 463s + print( coef( theilSur ) ) 463s + print( coef( summary( theilSur ) ) ) 463s + print( vcov( theilSur ) ) 463s + print( residuals( theilSur ) ) 463s + print( confint( theilSur ) ) 463s + print( fitted( theilSur ) ) 463s + print( logLik( theilSur ) ) 463s + print( logLik( theilSur, residCovDiag = TRUE ) ) 463s + print( nobs( theilSur ) ) 463s + print( model.frame( theilSur ) ) 463s + print( model.matrix( theilSur ) ) 463s + print( formula( theilSur ) ) 463s + print( formula( theilSur$eq[[ 2 ]] ) ) 463s + print( terms( theilSur ) ) 463s + print( terms( theilSur$eq[[ 2 ]] ) ) 463s + } 463s 463s systemfit results 463s method: SUR 463s 463s Coefficients: 463s General.Electric_(Intercept) General.Electric_value 463s -27.7193 0.0383 463s General.Electric_capital Westinghouse_(Intercept) 463s 0.1390 -1.2520 463s Westinghouse_value Westinghouse_capital 463s 0.0576 0.0640 463s 463s systemfit results 463s method: SUR 463s 463s N DF SSR detRCov OLS-R2 McElroy-R2 463s system 40 34 15590 25750 0.699 0.615 463s 463s N DF SSR MSE RMSE R2 Adj R2 463s General.Electric 20 17 13788 811 28.5 0.693 0.656 463s Westinghouse 20 17 1801 106 10.3 0.740 0.710 463s 463s The covariance matrix of the residuals used for estimation 463s General.Electric Westinghouse 463s General.Electric 661 176.4 463s Westinghouse 176 88.7 463s 463s The covariance matrix of the residuals 463s General.Electric Westinghouse 463s General.Electric 689 190.6 463s Westinghouse 191 90.1 463s 463s The correlations of the residuals 463s General.Electric Westinghouse 463s General.Electric 1.000 0.765 463s Westinghouse 0.765 1.000 463s 463s 463s SUR estimates for 'General.Electric' (equation 1) 463s Model Formula: General.Electric_invest ~ General.Electric_value + General.Electric_capital 463s 463s 463s Estimate Std. Error t value Pr(>|t|) 463s (Intercept) -27.7193 27.0328 -1.03 0.32 463s value 0.0383 0.0133 2.88 0.01 * 463s capital 0.1390 0.0230 6.04 1.3e-05 *** 463s --- 463s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 463s 463s Residual standard error: 28.479 on 17 degrees of freedom 463s Number of observations: 20 Degrees of Freedom: 17 463s SSR: 13788.376 MSE: 811.081 Root MSE: 28.479 463s Multiple R-Squared: 0.693 Adjusted R-Squared: 0.656 463s 463s 463s SUR estimates for 'Westinghouse' (equation 2) 463s Model Formula: Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 463s 463s 463s Estimate Std. Error t value Pr(>|t|) 463s (Intercept) -1.2520 6.9563 -0.18 0.85930 463s value 0.0576 0.0134 4.30 0.00049 *** 463s capital 0.0640 0.0489 1.31 0.20818 463s --- 463s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 463s 463s Residual standard error: 10.294 on 17 degrees of freedom 463s Number of observations: 20 Degrees of Freedom: 17 463s SSR: 1801.301 MSE: 105.959 Root MSE: 10.294 463s Multiple R-Squared: 0.74 Adjusted R-Squared: 0.71 463s 463s 463s systemfit results 463s method: SUR 463s 463s N DF SSR detRCov OLS-R2 McElroy-R2 463s system 40 34 15590 25750 0.699 0.615 463s 463s N DF SSR MSE RMSE R2 Adj R2 463s General.Electric 20 17 13788 811 28.5 0.693 0.656 463s Westinghouse 20 17 1801 106 10.3 0.740 0.710 463s 463s The covariance matrix of the residuals used for estimation 463s General.Electric Westinghouse 463s General.Electric 661 176.4 463s Westinghouse 176 88.7 463s 463s The covariance matrix of the residuals 463s General.Electric Westinghouse 463s General.Electric 689 190.6 463s Westinghouse 191 90.1 463s 463s The correlations of the residuals 463s General.Electric Westinghouse 463s General.Electric 1.000 0.765 463s Westinghouse 0.765 1.000 463s 463s 463s Coefficients: 463s Estimate Std. Error t value Pr(>|t|) 463s General.Electric_(Intercept) -27.7193 27.0328 -1.03 0.31242 463s General.Electric_value 0.0383 0.0133 2.88 0.00679 ** 463s General.Electric_capital 0.1390 0.0230 6.04 7.7e-07 *** 463s Westinghouse_(Intercept) -1.2520 6.9563 -0.18 0.85824 463s Westinghouse_value 0.0576 0.0134 4.30 0.00014 *** 463s Westinghouse_capital 0.0640 0.0489 1.31 0.19954 463s --- 463s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 463s 463s systemfit results 463s method: SUR 463s 463s N DF SSR detRCov OLS-R2 McElroy-R2 463s system 40 34 15590 25750 0.699 0.615 463s 463s N DF SSR MSE RMSE R2 Adj R2 463s General.Electric 20 17 13788 811 28.5 0.693 0.656 463s Westinghouse 20 17 1801 106 10.3 0.740 0.710 463s 463s 463s Coefficients: 463s Estimate Std. Error t value Pr(>|t|) 463s General.Electric_(Intercept) -27.7193 27.0328 -1.03 0.31955 463s General.Electric_value 0.0383 0.0133 2.88 0.01034 * 463s General.Electric_capital 0.1390 0.0230 6.04 1.3e-05 *** 463s Westinghouse_(Intercept) -1.2520 6.9563 -0.18 0.85930 463s Westinghouse_value 0.0576 0.0134 4.30 0.00049 *** 463s Westinghouse_capital 0.0640 0.0489 1.31 0.20818 463s --- 463s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 463s General.Electric_(Intercept) General.Electric_value 463s -27.7193 0.0383 463s General.Electric_capital Westinghouse_(Intercept) 463s 0.1390 -1.2520 463s Westinghouse_value Westinghouse_capital 463s 0.0576 0.0640 463s Estimate Std. Error t value Pr(>|t|) 463s General.Electric_(Intercept) -27.7193 27.0328 -1.03 3.20e-01 463s General.Electric_value 0.0383 0.0133 2.88 1.03e-02 463s General.Electric_capital 0.1390 0.0230 6.04 1.34e-05 463s Westinghouse_(Intercept) -1.2520 6.9563 -0.18 8.59e-01 463s Westinghouse_value 0.0576 0.0134 4.30 4.88e-04 463s Westinghouse_capital 0.0640 0.0489 1.31 2.08e-01 463s General.Electric_(Intercept) 463s General.Electric_(Intercept) 730.774 463s General.Electric_value -0.329 463s General.Electric_capital -0.146 463s Westinghouse_(Intercept) 126.963 463s Westinghouse_value -0.226 463s Westinghouse_capital 0.393 463s General.Electric_value General.Electric_capital 463s General.Electric_(Intercept) -0.329266 -1.46e-01 463s General.Electric_value 0.000177 -3.40e-05 463s General.Electric_capital -0.000034 5.31e-04 463s Westinghouse_(Intercept) -0.052688 -3.96e-02 463s Westinghouse_value 0.000120 -1.69e-05 463s Westinghouse_capital -0.000325 5.95e-04 463s Westinghouse_(Intercept) Westinghouse_value 463s General.Electric_(Intercept) 126.9626 -2.26e-01 463s General.Electric_value -0.0527 1.20e-04 463s General.Electric_capital -0.0396 -1.69e-05 463s Westinghouse_(Intercept) 48.3908 -8.00e-02 463s Westinghouse_value -0.0800 1.80e-04 463s Westinghouse_capital 0.1136 -4.75e-04 463s Westinghouse_capital 463s General.Electric_(Intercept) 0.392515 463s General.Electric_value -0.000325 463s General.Electric_capital 0.000595 463s Westinghouse_(Intercept) 0.113618 463s Westinghouse_value -0.000475 463s Westinghouse_capital 0.002391 463s General.Electric Westinghouse 463s X1935 2.3756 3.03 463s X1936 -19.0218 -2.64 463s X1937 -18.8820 -6.18 463s X1938 -27.5395 -9.31 463s X1939 -34.6138 -11.37 463s X1940 -5.5099 -8.09 463s X1941 39.7415 16.49 463s X1942 18.7681 8.36 463s X1943 -22.4783 -2.70 463s X1944 -24.7900 -2.89 463s X1945 -0.0321 -7.87 463s X1946 54.9123 5.38 463s X1947 47.9946 16.20 463s X1948 37.0021 4.29 463s X1949 -14.7994 -9.42 463s X1950 -30.4914 -11.86 463s X1951 -0.1173 5.62 463s X1952 4.3913 13.93 463s X1953 5.0921 11.37 463s X1954 -12.0024 -12.32 463s 2.5 % 97.5 % 463s General.Electric_(Intercept) -84.754 29.315 463s General.Electric_value 0.010 0.066 463s General.Electric_capital 0.090 0.188 463s Westinghouse_(Intercept) -15.929 13.425 463s Westinghouse_value 0.029 0.086 463s Westinghouse_capital -0.039 0.167 463s General.Electric Westinghouse 463s X1935 30.7 9.9 463s X1936 64.0 28.5 463s X1937 96.1 41.2 463s X1938 72.1 32.2 463s X1939 82.7 30.2 463s X1940 79.9 36.7 463s X1941 73.3 32.0 463s X1942 73.1 35.0 463s X1943 83.8 39.7 463s X1944 81.6 40.7 463s X1945 93.6 47.1 463s X1946 105.0 48.1 463s X1947 99.2 39.4 463s X1948 109.3 45.3 463s X1949 113.1 41.5 463s X1950 124.0 44.1 463s X1951 135.3 48.8 463s X1952 152.9 57.9 463s X1953 174.4 78.7 463s X1954 201.6 80.9 463s 'log Lik.' -158 (df=9) 463s 'log Lik.' -167 (df=9) 463s [1] 40 463s General.Electric_invest General.Electric_value General.Electric_capital 463s X1935 33.1 1171 97.8 463s X1936 45.0 2016 104.4 463s X1937 77.2 2803 118.0 463s X1938 44.6 2040 156.2 463s X1939 48.1 2256 172.6 463s X1940 74.4 2132 186.6 463s X1941 113.0 1834 220.9 463s X1942 91.9 1588 287.8 463s X1943 61.3 1749 319.9 463s X1944 56.8 1687 321.3 463s X1945 93.6 2008 319.6 463s X1946 159.9 2208 346.0 463s X1947 147.2 1657 456.4 463s X1948 146.3 1604 543.4 463s X1949 98.3 1432 618.3 463s X1950 93.5 1610 647.4 463s X1951 135.2 1819 671.3 463s X1952 157.3 2080 726.1 463s X1953 179.5 2372 800.3 463s X1954 189.6 2760 888.9 463s Westinghouse_invest Westinghouse_value Westinghouse_capital 463s X1935 12.9 192 1.8 463s X1936 25.9 516 0.8 463s X1937 35.0 729 7.4 463s X1938 22.9 560 18.1 463s X1939 18.8 520 23.5 463s X1940 28.6 628 26.5 463s X1941 48.5 537 36.2 463s X1942 43.3 561 60.8 463s X1943 37.0 617 84.4 463s X1944 37.8 627 91.2 463s X1945 39.3 737 92.4 463s X1946 53.5 760 86.0 463s X1947 55.6 581 111.1 463s X1948 49.6 662 130.6 463s X1949 32.0 584 141.8 463s X1950 32.2 635 136.7 463s X1951 54.4 724 129.7 463s X1952 71.8 864 145.5 463s X1953 90.1 1194 174.8 463s X1954 68.6 1189 213.5 463s General.Electric_(Intercept) General.Electric_value 463s General.Electric_X1935 1 1171 463s General.Electric_X1936 1 2016 463s General.Electric_X1937 1 2803 463s General.Electric_X1938 1 2040 463s General.Electric_X1939 1 2256 463s General.Electric_X1940 1 2132 463s General.Electric_X1941 1 1834 463s General.Electric_X1942 1 1588 463s General.Electric_X1943 1 1749 463s General.Electric_X1944 1 1687 463s General.Electric_X1945 1 2008 463s General.Electric_X1946 1 2208 463s General.Electric_X1947 1 1657 463s General.Electric_X1948 1 1604 463s General.Electric_X1949 1 1432 463s General.Electric_X1950 1 1610 463s General.Electric_X1951 1 1819 463s General.Electric_X1952 1 2080 463s General.Electric_X1953 1 2372 463s General.Electric_X1954 1 2760 463s Westinghouse_X1935 0 0 463s Westinghouse_X1936 0 0 463s Westinghouse_X1937 0 0 463s Westinghouse_X1938 0 0 463s Westinghouse_X1939 0 0 463s Westinghouse_X1940 0 0 463s Westinghouse_X1941 0 0 463s Westinghouse_X1942 0 0 463s Westinghouse_X1943 0 0 463s Westinghouse_X1944 0 0 463s Westinghouse_X1945 0 0 463s Westinghouse_X1946 0 0 463s Westinghouse_X1947 0 0 463s Westinghouse_X1948 0 0 463s Westinghouse_X1949 0 0 463s Westinghouse_X1950 0 0 463s Westinghouse_X1951 0 0 463s Westinghouse_X1952 0 0 463s Westinghouse_X1953 0 0 463s Westinghouse_X1954 0 0 463s General.Electric_capital Westinghouse_(Intercept) 463s General.Electric_X1935 97.8 0 463s General.Electric_X1936 104.4 0 463s General.Electric_X1937 118.0 0 463s General.Electric_X1938 156.2 0 463s General.Electric_X1939 172.6 0 463s General.Electric_X1940 186.6 0 463s General.Electric_X1941 220.9 0 463s General.Electric_X1942 287.8 0 463s General.Electric_X1943 319.9 0 463s General.Electric_X1944 321.3 0 463s General.Electric_X1945 319.6 0 463s General.Electric_X1946 346.0 0 463s General.Electric_X1947 456.4 0 463s General.Electric_X1948 543.4 0 463s General.Electric_X1949 618.3 0 463s General.Electric_X1950 647.4 0 463s General.Electric_X1951 671.3 0 463s General.Electric_X1952 726.1 0 463s General.Electric_X1953 800.3 0 463s General.Electric_X1954 888.9 0 463s Westinghouse_X1935 0.0 1 463s Westinghouse_X1936 0.0 1 463s Westinghouse_X1937 0.0 1 463s Westinghouse_X1938 0.0 1 463s Westinghouse_X1939 0.0 1 463s Westinghouse_X1940 0.0 1 463s Westinghouse_X1941 0.0 1 463s Westinghouse_X1942 0.0 1 463s Westinghouse_X1943 0.0 1 463s Westinghouse_X1944 0.0 1 463s Westinghouse_X1945 0.0 1 463s Westinghouse_X1946 0.0 1 463s Westinghouse_X1947 0.0 1 463s Westinghouse_X1948 0.0 1 463s Westinghouse_X1949 0.0 1 463s Westinghouse_X1950 0.0 1 463s Westinghouse_X1951 0.0 1 463s Westinghouse_X1952 0.0 1 463s Westinghouse_X1953 0.0 1 463s Westinghouse_X1954 0.0 1 463s Westinghouse_value Westinghouse_capital 463s General.Electric_X1935 0 0.0 463s General.Electric_X1936 0 0.0 463s General.Electric_X1937 0 0.0 463s General.Electric_X1938 0 0.0 463s General.Electric_X1939 0 0.0 463s General.Electric_X1940 0 0.0 463s General.Electric_X1941 0 0.0 463s General.Electric_X1942 0 0.0 463s General.Electric_X1943 0 0.0 463s General.Electric_X1944 0 0.0 463s General.Electric_X1945 0 0.0 463s General.Electric_X1946 0 0.0 463s General.Electric_X1947 0 0.0 463s General.Electric_X1948 0 0.0 463s General.Electric_X1949 0 0.0 463s General.Electric_X1950 0 0.0 463s General.Electric_X1951 0 0.0 463s General.Electric_X1952 0 0.0 463s General.Electric_X1953 0 0.0 463s General.Electric_X1954 0 0.0 463s Westinghouse_X1935 192 1.8 463s Westinghouse_X1936 516 0.8 463s Westinghouse_X1937 729 7.4 463s Westinghouse_X1938 560 18.1 463s Westinghouse_X1939 520 23.5 463s Westinghouse_X1940 628 26.5 463s Westinghouse_X1941 537 36.2 463s Westinghouse_X1942 561 60.8 463s Westinghouse_X1943 617 84.4 463s Westinghouse_X1944 627 91.2 463s Westinghouse_X1945 737 92.4 463s Westinghouse_X1946 760 86.0 463s Westinghouse_X1947 581 111.1 463s Westinghouse_X1948 662 130.6 463s Westinghouse_X1949 584 141.8 463s Westinghouse_X1950 635 136.7 463s Westinghouse_X1951 724 129.7 463s Westinghouse_X1952 864 145.5 463s Westinghouse_X1953 1194 174.8 463s Westinghouse_X1954 1189 213.5 463s $General.Electric 463s General.Electric_invest ~ General.Electric_value + General.Electric_capital 463s 463s 463s $Westinghouse 463s Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 463s 463s 463s Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 463s 463s $General.Electric 463s General.Electric_invest ~ General.Electric_value + General.Electric_capital 463s attr(,"variables") 463s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 463s attr(,"factors") 463s General.Electric_value General.Electric_capital 463s General.Electric_invest 0 0 463s General.Electric_value 1 0 463s General.Electric_capital 0 1 463s attr(,"term.labels") 463s [1] "General.Electric_value" "General.Electric_capital" 463s attr(,"order") 463s [1] 1 1 463s attr(,"intercept") 463s [1] 1 463s attr(,"response") 463s [1] 1 463s attr(,".Environment") 463s 463s attr(,"predvars") 463s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 463s attr(,"dataClasses") 463s General.Electric_invest General.Electric_value General.Electric_capital 463s "numeric" "numeric" "numeric" 463s 463s $Westinghouse 463s Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 463s attr(,"variables") 463s list(Westinghouse_invest, Westinghouse_value, Westinghouse_capital) 463s attr(,"factors") 463s Westinghouse_value Westinghouse_capital 463s Westinghouse_invest 0 0 463s Westinghouse_value 1 0 463s Westinghouse_capital 0 1 463s attr(,"term.labels") 463s [1] "Westinghouse_value" "Westinghouse_capital" 463s attr(,"order") 463s [1] 1 1 463s attr(,"intercept") 463s [1] 1 463s attr(,"response") 463s [1] 1 463s attr(,".Environment") 463s 463s attr(,"predvars") 463s list(Westinghouse_invest, Westinghouse_value, Westinghouse_capital) 463s attr(,"dataClasses") 463s Westinghouse_invest Westinghouse_value Westinghouse_capital 463s "numeric" "numeric" "numeric" 463s 463s Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 463s attr(,"variables") 463s list(Westinghouse_invest, Westinghouse_value, Westinghouse_capital) 463s attr(,"factors") 463s Westinghouse_value Westinghouse_capital 463s Westinghouse_invest 0 0 463s Westinghouse_value 1 0 463s Westinghouse_capital 0 1 463s attr(,"term.labels") 463s [1] "Westinghouse_value" "Westinghouse_capital" 463s attr(,"order") 463s [1] 1 1 463s attr(,"intercept") 463s [1] 1 463s attr(,"response") 463s [1] 1 463s attr(,".Environment") 463s 463s attr(,"predvars") 463s list(Westinghouse_invest, Westinghouse_value, Westinghouse_capital) 463s attr(,"dataClasses") 463s Westinghouse_invest Westinghouse_value Westinghouse_capital 463s "numeric" "numeric" "numeric" 463s > 463s > ## Repeating the OLS and SUR estimations in Greene (2003, pp. 351) 463s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 463s + GrunfeldGreene <- pdata.frame( GrunfeldGreene, c( "firm", "year" ) ) 463s + formulaGrunfeld <- invest ~ value + capital 463s + } 463s > 463s > # OLS 463s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 463s + greeneOls <- systemfit( formulaGrunfeld, "OLS", 463s + data = GrunfeldGreene, useMatrix = useMatrix ) 463s + print( greeneOls ) 463s + print( summary( greeneOls ) ) 463s + print( summary( greeneOls, useDfSys = TRUE, equations = FALSE ) ) 463s + print( summary( greeneOls, residCov = FALSE ) ) 463s + print( sapply( greeneOls$eq, function(x){return(summary(x)$ssr/20)} ) ) # sigma^2 463s + print( coef( greeneOls ) ) 463s + print( coef( summary( greeneOls ) ) ) 463s + print( vcov( greeneOls ) ) 463s + print( residuals( greeneOls ) ) 463s + print( confint(greeneOls ) ) 463s + print( fitted( greeneOls ) ) 463s + print( logLik( greeneOls ) ) 463s + print( logLik( greeneOls, residCovDiag = TRUE ) ) 463s + print( nobs( greeneOls ) ) 463s + print( model.frame( greeneOls ) ) 463s + print( model.matrix( greeneOls ) ) 463s + print( formula( greeneOls ) ) 463s + print( formula( greeneOls$eq[[ 2 ]] ) ) 463s + print( terms( greeneOls ) ) 463s + print( terms( greeneOls$eq[[ 2 ]] ) ) 463s + } 463s 463s systemfit results 463s method: OLS 463s 463s Coefficients: 463s Chrysler_(Intercept) Chrysler_value 463s -6.1900 0.0779 463s Chrysler_capital General.Electric_(Intercept) 463s 0.3157 -9.9563 463s General.Electric_value General.Electric_capital 463s 0.0266 0.1517 463s General.Motors_(Intercept) General.Motors_value 463s -149.7825 0.1193 463s General.Motors_capital US.Steel_(Intercept) 463s 0.3714 -30.3685 463s US.Steel_value US.Steel_capital 463s 0.1566 0.4239 463s Westinghouse_(Intercept) Westinghouse_value 463s -0.5094 0.0529 463s Westinghouse_capital 463s 0.0924 463s 463s systemfit results 463s method: OLS 463s 463s N DF SSR detRCov OLS-R2 McElroy-R2 463s system 100 85 339121 2.09e+14 0.848 0.862 463s 463s N DF SSR MSE RMSE R2 Adj R2 463s Chrysler 20 17 2997 176 13.3 0.914 0.903 463s General.Electric 20 17 13217 777 27.9 0.705 0.671 463s General.Motors 20 17 143206 8424 91.8 0.921 0.912 463s US.Steel 20 17 177928 10466 102.3 0.440 0.374 463s Westinghouse 20 17 1773 104 10.2 0.744 0.714 463s 463s The covariance matrix of the residuals 463s Chrysler General.Electric General.Motors US.Steel Westinghouse 463s Chrysler 176.3 -25.1 -333 492 15.7 463s General.Electric -25.1 777.4 715 1065 207.6 463s General.Motors -332.7 714.7 8424 -2614 148.4 463s US.Steel 491.9 1064.6 -2614 10466 642.6 463s Westinghouse 15.7 207.6 148 643 104.3 463s 463s The correlations of the residuals 463s Chrysler General.Electric General.Motors US.Steel Westinghouse 463s Chrysler 1.0000 -0.0679 -0.273 0.362 0.115 463s General.Electric -0.0679 1.0000 0.279 0.373 0.729 463s General.Motors -0.2730 0.2793 1.000 -0.278 0.158 463s US.Steel 0.3621 0.3732 -0.278 1.000 0.615 463s Westinghouse 0.1154 0.7290 0.158 0.615 1.000 463s 463s 463s OLS estimates for 'Chrysler' (equation 1) 463s Model Formula: Chrysler_invest ~ Chrysler_value + Chrysler_capital 463s 463s 463s Estimate Std. Error t value Pr(>|t|) 463s (Intercept) -6.1900 13.5065 -0.46 0.6525 463s value 0.0779 0.0200 3.90 0.0011 ** 463s capital 0.3157 0.0288 10.96 4e-09 *** 463s --- 463s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 463s 463s Residual standard error: 13.279 on 17 degrees of freedom 463s Number of observations: 20 Degrees of Freedom: 17 463s SSR: 2997.444 MSE: 176.32 Root MSE: 13.279 463s Multiple R-Squared: 0.914 Adjusted R-Squared: 0.903 463s 463s 463s OLS estimates for 'General.Electric' (equation 2) 463s Model Formula: General.Electric_invest ~ General.Electric_value + General.Electric_capital 463s 463s 463s Estimate Std. Error t value Pr(>|t|) 463s (Intercept) -9.9563 31.3742 -0.32 0.75 463s value 0.0266 0.0156 1.71 0.11 463s capital 0.1517 0.0257 5.90 1.7e-05 *** 463s --- 463s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 463s 463s Residual standard error: 27.883 on 17 degrees of freedom 463s Number of observations: 20 Degrees of Freedom: 17 463s SSR: 13216.588 MSE: 777.446 Root MSE: 27.883 463s Multiple R-Squared: 0.705 Adjusted R-Squared: 0.671 463s 463s 463s OLS estimates for 'General.Motors' (equation 3) 463s Model Formula: General.Motors_invest ~ General.Motors_value + General.Motors_capital 463s 463s 463s Estimate Std. Error t value Pr(>|t|) 463s (Intercept) -149.7825 105.8421 -1.42 0.17508 463s value 0.1193 0.0258 4.62 0.00025 *** 463s capital 0.3714 0.0371 10.02 1.5e-08 *** 463s --- 463s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 463s 463s Residual standard error: 91.782 on 17 degrees of freedom 463s Number of observations: 20 Degrees of Freedom: 17 463s SSR: 143205.877 MSE: 8423.875 Root MSE: 91.782 463s Multiple R-Squared: 0.921 Adjusted R-Squared: 0.912 463s 463s 463s OLS estimates for 'US.Steel' (equation 4) 463s Model Formula: US.Steel_invest ~ US.Steel_value + US.Steel_capital 463s 463s 463s Estimate Std. Error t value Pr(>|t|) 463s (Intercept) -30.3685 157.0477 -0.19 0.849 463s value 0.1566 0.0789 1.98 0.064 . 463s capital 0.4239 0.1552 2.73 0.014 * 463s --- 463s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 463s 463s Residual standard error: 102.305 on 17 degrees of freedom 463s Number of observations: 20 Degrees of Freedom: 17 463s SSR: 177928.314 MSE: 10466.371 Root MSE: 102.305 463s Multiple R-Squared: 0.44 Adjusted R-Squared: 0.374 463s 463s 463s OLS estimates for 'Westinghouse' (equation 5) 463s Model Formula: Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 463s 463s 463s Estimate Std. Error t value Pr(>|t|) 463s (Intercept) -0.5094 8.0153 -0.06 0.9501 463s value 0.0529 0.0157 3.37 0.0037 ** 463s capital 0.0924 0.0561 1.65 0.1179 463s --- 463s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 463s 463s Residual standard error: 10.213 on 17 degrees of freedom 463s Number of observations: 20 Degrees of Freedom: 17 463s SSR: 1773.234 MSE: 104.308 Root MSE: 10.213 463s Multiple R-Squared: 0.744 Adjusted R-Squared: 0.714 463s 463s 463s systemfit results 463s method: OLS 463s 463s N DF SSR detRCov OLS-R2 McElroy-R2 463s system 100 85 339121 2.09e+14 0.848 0.862 463s 463s N DF SSR MSE RMSE R2 Adj R2 463s Chrysler 20 17 2997 176 13.3 0.914 0.903 463s General.Electric 20 17 13217 777 27.9 0.705 0.671 463s General.Motors 20 17 143206 8424 91.8 0.921 0.912 463s US.Steel 20 17 177928 10466 102.3 0.440 0.374 463s Westinghouse 20 17 1773 104 10.2 0.744 0.714 463s 463s The covariance matrix of the residuals 463s Chrysler General.Electric General.Motors US.Steel Westinghouse 463s Chrysler 176.3 -25.1 -333 492 15.7 463s General.Electric -25.1 777.4 715 1065 207.6 463s General.Motors -332.7 714.7 8424 -2614 148.4 463s US.Steel 491.9 1064.6 -2614 10466 642.6 463s Westinghouse 15.7 207.6 148 643 104.3 463s 463s The correlations of the residuals 463s Chrysler General.Electric General.Motors US.Steel Westinghouse 463s Chrysler 1.0000 -0.0679 -0.273 0.362 0.115 463s General.Electric -0.0679 1.0000 0.279 0.373 0.729 463s General.Motors -0.2730 0.2793 1.000 -0.278 0.158 463s US.Steel 0.3621 0.3732 -0.278 1.000 0.615 463s Westinghouse 0.1154 0.7290 0.158 0.615 1.000 463s 463s 463s Coefficients: 463s Estimate Std. Error t value Pr(>|t|) 463s Chrysler_(Intercept) -6.1900 13.5065 -0.46 0.64791 463s Chrysler_value 0.0779 0.0200 3.90 0.00019 *** 463s Chrysler_capital 0.3157 0.0288 10.96 < 2e-16 *** 463s General.Electric_(Intercept) -9.9563 31.3742 -0.32 0.75176 463s General.Electric_value 0.0266 0.0156 1.71 0.09171 . 463s General.Electric_capital 0.1517 0.0257 5.90 7.2e-08 *** 463s General.Motors_(Intercept) -149.7825 105.8421 -1.42 0.16068 463s General.Motors_value 0.1193 0.0258 4.62 1.4e-05 *** 463s General.Motors_capital 0.3714 0.0371 10.02 4.4e-16 *** 463s US.Steel_(Intercept) -30.3685 157.0477 -0.19 0.84713 463s US.Steel_value 0.1566 0.0789 1.98 0.05039 . 463s US.Steel_capital 0.4239 0.1552 2.73 0.00768 ** 463s Westinghouse_(Intercept) -0.5094 8.0153 -0.06 0.94948 463s Westinghouse_value 0.0529 0.0157 3.37 0.00114 ** 463s Westinghouse_capital 0.0924 0.0561 1.65 0.10321 463s --- 463s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 463s 463s systemfit results 463s method: OLS 463s 463s N DF SSR detRCov OLS-R2 McElroy-R2 463s system 100 85 339121 2.09e+14 0.848 0.862 463s 463s N DF SSR MSE RMSE R2 Adj R2 463s Chrysler 20 17 2997 176 13.3 0.914 0.903 463s General.Electric 20 17 13217 777 27.9 0.705 0.671 463s General.Motors 20 17 143206 8424 91.8 0.921 0.912 463s US.Steel 20 17 177928 10466 102.3 0.440 0.374 463s Westinghouse 20 17 1773 104 10.2 0.744 0.714 463s 463s 463s OLS estimates for 'Chrysler' (equation 1) 463s Model Formula: Chrysler_invest ~ Chrysler_value + Chrysler_capital 463s 463s 463s Estimate Std. Error t value Pr(>|t|) 463s (Intercept) -6.1900 13.5065 -0.46 0.6525 463s value 0.0779 0.0200 3.90 0.0011 ** 463s capital 0.3157 0.0288 10.96 4e-09 *** 463s --- 463s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 463s 463s Residual standard error: 13.279 on 17 degrees of freedom 463s Number of observations: 20 Degrees of Freedom: 17 463s SSR: 2997.444 MSE: 176.32 Root MSE: 13.279 463s Multiple R-Squared: 0.914 Adjusted R-Squared: 0.903 463s 463s 463s OLS estimates for 'General.Electric' (equation 2) 463s Model Formula: General.Electric_invest ~ General.Electric_value + General.Electric_capital 463s 463s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -9.9563 31.3742 -0.32 0.75 464s value 0.0266 0.0156 1.71 0.11 464s capital 0.1517 0.0257 5.90 1.7e-05 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 27.883 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 13216.588 MSE: 777.446 Root MSE: 27.883 464s Multiple R-Squared: 0.705 Adjusted R-Squared: 0.671 464s 464s 464s OLS estimates for 'General.Motors' (equation 3) 464s Model Formula: General.Motors_invest ~ General.Motors_value + General.Motors_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -149.7825 105.8421 -1.42 0.17508 464s value 0.1193 0.0258 4.62 0.00025 *** 464s capital 0.3714 0.0371 10.02 1.5e-08 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 91.782 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 143205.877 MSE: 8423.875 Root MSE: 91.782 464s Multiple R-Squared: 0.921 Adjusted R-Squared: 0.912 464s 464s 464s OLS estimates for 'US.Steel' (equation 4) 464s Model Formula: US.Steel_invest ~ US.Steel_value + US.Steel_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -30.3685 157.0477 -0.19 0.849 464s value 0.1566 0.0789 1.98 0.064 . 464s capital 0.4239 0.1552 2.73 0.014 * 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 102.305 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 177928.314 MSE: 10466.371 Root MSE: 102.305 464s Multiple R-Squared: 0.44 Adjusted R-Squared: 0.374 464s 464s 464s OLS estimates for 'Westinghouse' (equation 5) 464s Model Formula: Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -0.5094 8.0153 -0.06 0.9501 464s value 0.0529 0.0157 3.37 0.0037 ** 464s capital 0.0924 0.0561 1.65 0.1179 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 10.213 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 1773.234 MSE: 104.308 Root MSE: 10.213 464s Multiple R-Squared: 0.744 Adjusted R-Squared: 0.714 464s 464s [1] 149.9 660.8 7160.3 8896.4 88.7 464s Chrysler_(Intercept) Chrysler_value 464s -6.1900 0.0779 464s Chrysler_capital General.Electric_(Intercept) 464s 0.3157 -9.9563 464s General.Electric_value General.Electric_capital 464s 0.0266 0.1517 464s General.Motors_(Intercept) General.Motors_value 464s -149.7825 0.1193 464s General.Motors_capital US.Steel_(Intercept) 464s 0.3714 -30.3685 464s US.Steel_value US.Steel_capital 464s 0.1566 0.4239 464s Westinghouse_(Intercept) Westinghouse_value 464s -0.5094 0.0529 464s Westinghouse_capital 464s 0.0924 464s Estimate Std. Error t value Pr(>|t|) 464s Chrysler_(Intercept) -6.1900 13.5065 -0.4583 6.53e-01 464s Chrysler_value 0.0779 0.0200 3.9026 1.15e-03 464s Chrysler_capital 0.3157 0.0288 10.9574 3.99e-09 464s General.Electric_(Intercept) -9.9563 31.3742 -0.3173 7.55e-01 464s General.Electric_value 0.0266 0.0156 1.7057 1.06e-01 464s General.Electric_capital 0.1517 0.0257 5.9015 1.74e-05 464s General.Motors_(Intercept) -149.7825 105.8421 -1.4151 1.75e-01 464s General.Motors_value 0.1193 0.0258 4.6172 2.46e-04 464s General.Motors_capital 0.3714 0.0371 10.0193 1.51e-08 464s US.Steel_(Intercept) -30.3685 157.0477 -0.1934 8.49e-01 464s US.Steel_value 0.1566 0.0789 1.9848 6.35e-02 464s US.Steel_capital 0.4239 0.1552 2.7308 1.42e-02 464s Westinghouse_(Intercept) -0.5094 8.0153 -0.0636 9.50e-01 464s Westinghouse_value 0.0529 0.0157 3.3677 3.65e-03 464s Westinghouse_capital 0.0924 0.0561 1.6472 1.18e-01 464s Chrysler_(Intercept) Chrysler_value 464s Chrysler_(Intercept) 182.4250 -0.254690 464s Chrysler_value -0.2547 0.000399 464s Chrysler_capital 0.0243 -0.000180 464s General.Electric_(Intercept) 0.0000 0.000000 464s General.Electric_value 0.0000 0.000000 464s General.Electric_capital 0.0000 0.000000 464s General.Motors_(Intercept) 0.0000 0.000000 464s General.Motors_value 0.0000 0.000000 464s General.Motors_capital 0.0000 0.000000 464s US.Steel_(Intercept) 0.0000 0.000000 464s US.Steel_value 0.0000 0.000000 464s US.Steel_capital 0.0000 0.000000 464s Westinghouse_(Intercept) 0.0000 0.000000 464s Westinghouse_value 0.0000 0.000000 464s Westinghouse_capital 0.0000 0.000000 464s Chrysler_capital General.Electric_(Intercept) 464s Chrysler_(Intercept) 0.02429 0.000 464s Chrysler_value -0.00018 0.000 464s Chrysler_capital 0.00083 0.000 464s General.Electric_(Intercept) 0.00000 984.344 464s General.Electric_value 0.00000 -0.451 464s General.Electric_capital 0.00000 -0.173 464s General.Motors_(Intercept) 0.00000 0.000 464s General.Motors_value 0.00000 0.000 464s General.Motors_capital 0.00000 0.000 464s US.Steel_(Intercept) 0.00000 0.000 464s US.Steel_value 0.00000 0.000 464s US.Steel_capital 0.00000 0.000 464s Westinghouse_(Intercept) 0.00000 0.000 464s Westinghouse_value 0.00000 0.000 464s Westinghouse_capital 0.00000 0.000 464s General.Electric_value General.Electric_capital 464s Chrysler_(Intercept) 0.00e+00 0.00e+00 464s Chrysler_value 0.00e+00 0.00e+00 464s Chrysler_capital 0.00e+00 0.00e+00 464s General.Electric_(Intercept) -4.51e-01 -1.73e-01 464s General.Electric_value 2.42e-04 -4.73e-05 464s General.Electric_capital -4.73e-05 6.61e-04 464s General.Motors_(Intercept) 0.00e+00 0.00e+00 464s General.Motors_value 0.00e+00 0.00e+00 464s General.Motors_capital 0.00e+00 0.00e+00 464s US.Steel_(Intercept) 0.00e+00 0.00e+00 464s US.Steel_value 0.00e+00 0.00e+00 464s US.Steel_capital 0.00e+00 0.00e+00 464s Westinghouse_(Intercept) 0.00e+00 0.00e+00 464s Westinghouse_value 0.00e+00 0.00e+00 464s Westinghouse_capital 0.00e+00 0.00e+00 464s General.Motors_(Intercept) General.Motors_value 464s Chrysler_(Intercept) 0.000 0.000000 464s Chrysler_value 0.000 0.000000 464s Chrysler_capital 0.000 0.000000 464s General.Electric_(Intercept) 0.000 0.000000 464s General.Electric_value 0.000 0.000000 464s General.Electric_capital 0.000 0.000000 464s General.Motors_(Intercept) 11202.555 -2.623398 464s General.Motors_value -2.623 0.000667 464s General.Motors_capital 0.907 -0.000415 464s US.Steel_(Intercept) 0.000 0.000000 464s US.Steel_value 0.000 0.000000 464s US.Steel_capital 0.000 0.000000 464s Westinghouse_(Intercept) 0.000 0.000000 464s Westinghouse_value 0.000 0.000000 464s Westinghouse_capital 0.000 0.000000 464s General.Motors_capital US.Steel_(Intercept) 464s Chrysler_(Intercept) 0.000000 0.00 464s Chrysler_value 0.000000 0.00 464s Chrysler_capital 0.000000 0.00 464s General.Electric_(Intercept) 0.000000 0.00 464s General.Electric_value 0.000000 0.00 464s General.Electric_capital 0.000000 0.00 464s General.Motors_(Intercept) 0.906860 0.00 464s General.Motors_value -0.000415 0.00 464s General.Motors_capital 0.001374 0.00 464s US.Steel_(Intercept) 0.000000 24663.98 464s US.Steel_value 0.000000 -11.71 464s US.Steel_capital 0.000000 -3.52 464s Westinghouse_(Intercept) 0.000000 0.00 464s Westinghouse_value 0.000000 0.00 464s Westinghouse_capital 0.000000 0.00 464s US.Steel_value US.Steel_capital 464s Chrysler_(Intercept) 0.00000 0.00000 464s Chrysler_value 0.00000 0.00000 464s Chrysler_capital 0.00000 0.00000 464s General.Electric_(Intercept) 0.00000 0.00000 464s General.Electric_value 0.00000 0.00000 464s General.Electric_capital 0.00000 0.00000 464s General.Motors_(Intercept) 0.00000 0.00000 464s General.Motors_value 0.00000 0.00000 464s General.Motors_capital 0.00000 0.00000 464s US.Steel_(Intercept) -11.70740 -3.52078 464s US.Steel_value 0.00622 -0.00188 464s US.Steel_capital -0.00188 0.02409 464s Westinghouse_(Intercept) 0.00000 0.00000 464s Westinghouse_value 0.00000 0.00000 464s Westinghouse_capital 0.00000 0.00000 464s Westinghouse_(Intercept) Westinghouse_value 464s Chrysler_(Intercept) 0.000 0.000000 464s Chrysler_value 0.000 0.000000 464s Chrysler_capital 0.000 0.000000 464s General.Electric_(Intercept) 0.000 0.000000 464s General.Electric_value 0.000 0.000000 464s General.Electric_capital 0.000 0.000000 464s General.Motors_(Intercept) 0.000 0.000000 464s General.Motors_value 0.000 0.000000 464s General.Motors_capital 0.000 0.000000 464s US.Steel_(Intercept) 0.000 0.000000 464s US.Steel_value 0.000 0.000000 464s US.Steel_capital 0.000 0.000000 464s Westinghouse_(Intercept) 64.245 -0.109545 464s Westinghouse_value -0.110 0.000247 464s Westinghouse_capital 0.169 -0.000653 464s Westinghouse_capital 464s Chrysler_(Intercept) 0.000000 464s Chrysler_value 0.000000 464s Chrysler_capital 0.000000 464s General.Electric_(Intercept) 0.000000 464s General.Electric_value 0.000000 464s General.Electric_capital 0.000000 464s General.Motors_(Intercept) 0.000000 464s General.Motors_value 0.000000 464s General.Motors_capital 0.000000 464s US.Steel_(Intercept) 0.000000 464s US.Steel_value 0.000000 464s US.Steel_capital 0.000000 464s Westinghouse_(Intercept) 0.168911 464s Westinghouse_value -0.000653 464s Westinghouse_capital 0.003147 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s X1935 10.622 -2.860 99.14 4.15 3.144 464s X1936 10.425 -14.402 -34.01 81.32 -0.958 464s X1937 -7.404 -5.175 -140.48 31.18 -3.684 464s X1938 7.302 -23.295 -3.28 -99.75 -7.915 464s X1939 -14.682 -28.031 -109.45 -178.23 -10.322 464s X1940 -2.315 -0.562 -19.91 -160.69 -6.613 464s X1941 0.631 40.750 24.12 19.65 17.265 464s X1942 -1.581 16.036 98.02 9.82 8.547 464s X1943 -13.459 -23.719 67.76 -46.76 -2.916 464s X1944 -7.780 -26.780 100.03 -83.74 -3.257 464s X1945 11.757 1.768 35.12 -91.24 -7.753 464s X1946 -16.133 58.737 103.90 28.34 5.796 464s X1947 -6.823 43.936 15.18 57.32 15.050 464s X1948 6.615 31.227 -51.86 140.23 2.969 464s X1949 -7.379 -23.552 -115.39 25.65 -11.433 464s X1950 1.268 -37.511 -63.51 34.88 -13.481 464s X1951 39.502 -4.983 -119.40 115.10 4.619 464s X1952 2.774 1.893 -77.82 149.19 13.138 464s X1953 -6.215 5.087 49.50 89.00 11.308 464s X1954 -7.124 -8.563 142.33 -125.42 -13.505 464s 2.5 % 97.5 % 464s Chrysler_(Intercept) -34.686 22.306 464s Chrysler_value 0.036 0.120 464s Chrysler_capital 0.255 0.377 464s General.Electric_(Intercept) -76.150 56.238 464s General.Electric_value -0.006 0.059 464s General.Electric_capital 0.097 0.206 464s General.Motors_(Intercept) -373.090 73.525 464s General.Motors_value 0.065 0.174 464s General.Motors_capital 0.293 0.450 464s US.Steel_(Intercept) -361.710 300.973 464s US.Steel_value -0.010 0.323 464s US.Steel_capital 0.096 0.751 464s Westinghouse_(Intercept) -17.420 16.401 464s Westinghouse_value 0.020 0.086 464s Westinghouse_capital -0.026 0.211 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s X1935 29.7 36.0 218 206 9.79 464s X1936 62.3 59.4 426 274 26.86 464s X1937 73.7 82.4 551 439 38.73 464s X1938 44.3 67.9 261 362 30.81 464s X1939 67.1 76.1 440 409 29.16 464s X1940 71.7 75.0 481 422 35.18 464s X1941 67.7 72.3 488 453 31.25 464s X1942 48.4 75.9 350 436 34.79 464s X1943 60.9 85.0 432 408 39.94 464s X1944 67.3 83.6 447 372 41.07 464s X1945 77.0 91.8 526 350 47.02 464s X1946 90.3 101.2 584 392 47.66 464s X1947 69.5 103.3 554 363 40.51 464s X1948 82.7 115.1 581 354 46.59 464s X1949 86.4 121.9 670 379 43.47 464s X1950 99.4 131.0 706 384 45.72 464s X1951 121.1 140.2 875 473 49.76 464s X1952 142.2 155.4 969 496 58.64 464s X1953 181.1 174.4 1255 552 78.77 464s X1954 179.6 198.2 1344 585 82.11 464s 'log Lik.' -464 (df=16) 464s 'log Lik.' -481 (df=16) 464s [1] 100 464s Chrysler_invest Chrysler_value Chrysler_capital General.Electric_invest 464s X1935 40.3 418 10.5 33.1 464s X1936 72.8 838 10.2 45.0 464s X1937 66.3 884 34.7 77.2 464s X1938 51.6 438 51.8 44.6 464s X1939 52.4 680 64.3 48.1 464s X1940 69.4 728 67.1 74.4 464s X1941 68.3 644 75.2 113.0 464s X1942 46.8 411 71.4 91.9 464s X1943 47.4 588 67.1 61.3 464s X1944 59.6 698 60.5 56.8 464s X1945 88.8 846 54.6 93.6 464s X1946 74.1 894 84.8 159.9 464s X1947 62.7 579 96.8 147.2 464s X1948 89.4 695 110.2 146.3 464s X1949 79.0 590 147.4 98.3 464s X1950 100.7 694 163.2 93.5 464s X1951 160.6 809 203.5 135.2 464s X1952 145.0 727 290.6 157.3 464s X1953 174.9 1002 346.1 179.5 464s X1954 172.5 703 414.9 189.6 464s General.Electric_value General.Electric_capital General.Motors_invest 464s X1935 1171 97.8 318 464s X1936 2016 104.4 392 464s X1937 2803 118.0 411 464s X1938 2040 156.2 258 464s X1939 2256 172.6 331 464s X1940 2132 186.6 461 464s X1941 1834 220.9 512 464s X1942 1588 287.8 448 464s X1943 1749 319.9 500 464s X1944 1687 321.3 548 464s X1945 2008 319.6 561 464s X1946 2208 346.0 688 464s X1947 1657 456.4 569 464s X1948 1604 543.4 529 464s X1949 1432 618.3 555 464s X1950 1610 647.4 643 464s X1951 1819 671.3 756 464s X1952 2080 726.1 891 464s X1953 2372 800.3 1304 464s X1954 2760 888.9 1487 464s General.Motors_value General.Motors_capital US.Steel_invest 464s X1935 3078 2.8 210 464s X1936 4662 52.6 355 464s X1937 5387 156.9 470 464s X1938 2792 209.2 262 464s X1939 4313 203.4 230 464s X1940 4644 207.2 262 464s X1941 4551 255.2 473 464s X1942 3244 303.7 446 464s X1943 4054 264.1 362 464s X1944 4379 201.6 288 464s X1945 4841 265.0 259 464s X1946 4901 402.2 420 464s X1947 3526 761.5 420 464s X1948 3255 922.4 494 464s X1949 3700 1020.1 405 464s X1950 3756 1099.0 419 464s X1951 4833 1207.7 588 464s X1952 4925 1430.5 645 464s X1953 6242 1777.3 641 464s X1954 5594 2226.3 459 464s US.Steel_value US.Steel_capital Westinghouse_invest Westinghouse_value 464s X1935 1362 53.8 12.9 192 464s X1936 1807 50.5 25.9 516 464s X1937 2676 118.1 35.0 729 464s X1938 1802 260.2 22.9 560 464s X1939 1957 312.7 18.8 520 464s X1940 2203 254.2 28.6 628 464s X1941 2380 261.4 48.5 537 464s X1942 2169 298.7 43.3 561 464s X1943 1985 301.8 37.0 617 464s X1944 1814 279.1 37.8 627 464s X1945 1850 213.8 39.3 737 464s X1946 2068 232.6 53.5 760 464s X1947 1797 264.8 55.6 581 464s X1948 1626 306.9 49.6 662 464s X1949 1667 351.1 32.0 584 464s X1950 1677 357.8 32.2 635 464s X1951 2290 342.1 54.4 724 464s X1952 2159 444.2 71.8 864 464s X1953 2031 623.6 90.1 1194 464s X1954 2116 669.7 68.6 1189 464s Westinghouse_capital 464s X1935 1.8 464s X1936 0.8 464s X1937 7.4 464s X1938 18.1 464s X1939 23.5 464s X1940 26.5 464s X1941 36.2 464s X1942 60.8 464s X1943 84.4 464s X1944 91.2 464s X1945 92.4 464s X1946 86.0 464s X1947 111.1 464s X1948 130.6 464s X1949 141.8 464s X1950 136.7 464s X1951 129.7 464s X1952 145.5 464s X1953 174.8 464s X1954 213.5 464s Chrysler_(Intercept) Chrysler_value Chrysler_capital 464s Chrysler_X1935 1 418 10.5 464s Chrysler_X1936 1 838 10.2 464s Chrysler_X1937 1 884 34.7 464s Chrysler_X1938 1 438 51.8 464s Chrysler_X1939 1 680 64.3 464s Chrysler_X1940 1 728 67.1 464s Chrysler_X1941 1 644 75.2 464s Chrysler_X1942 1 411 71.4 464s Chrysler_X1943 1 588 67.1 464s Chrysler_X1944 1 698 60.5 464s Chrysler_X1945 1 846 54.6 464s Chrysler_X1946 1 894 84.8 464s Chrysler_X1947 1 579 96.8 464s Chrysler_X1948 1 695 110.2 464s Chrysler_X1949 1 590 147.4 464s Chrysler_X1950 1 694 163.2 464s Chrysler_X1951 1 809 203.5 464s Chrysler_X1952 1 727 290.6 464s Chrysler_X1953 1 1002 346.1 464s Chrysler_X1954 1 703 414.9 464s General.Electric_X1935 0 0 0.0 464s General.Electric_X1936 0 0 0.0 464s General.Electric_X1937 0 0 0.0 464s General.Electric_X1938 0 0 0.0 464s General.Electric_X1939 0 0 0.0 464s General.Electric_X1940 0 0 0.0 464s General.Electric_X1941 0 0 0.0 464s General.Electric_X1942 0 0 0.0 464s General.Electric_X1943 0 0 0.0 464s General.Electric_X1944 0 0 0.0 464s General.Electric_X1945 0 0 0.0 464s General.Electric_X1946 0 0 0.0 464s General.Electric_X1947 0 0 0.0 464s General.Electric_X1948 0 0 0.0 464s General.Electric_X1949 0 0 0.0 464s General.Electric_X1950 0 0 0.0 464s General.Electric_X1951 0 0 0.0 464s General.Electric_X1952 0 0 0.0 464s General.Electric_X1953 0 0 0.0 464s 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671.3 0 464s General.Electric_X1952 726.1 0 464s General.Electric_X1953 800.3 0 464s General.Electric_X1954 888.9 0 464s General.Motors_X1935 0.0 1 464s General.Motors_X1936 0.0 1 464s General.Motors_X1937 0.0 1 464s General.Motors_X1938 0.0 1 464s General.Motors_X1939 0.0 1 464s General.Motors_X1940 0.0 1 464s General.Motors_X1941 0.0 1 464s General.Motors_X1942 0.0 1 464s General.Motors_X1943 0.0 1 464s General.Motors_X1944 0.0 1 464s General.Motors_X1945 0.0 1 464s General.Motors_X1946 0.0 1 464s General.Motors_X1947 0.0 1 464s General.Motors_X1948 0.0 1 464s General.Motors_X1949 0.0 1 464s General.Motors_X1950 0.0 1 464s General.Motors_X1951 0.0 1 464s General.Motors_X1952 0.0 1 464s General.Motors_X1953 0.0 1 464s General.Motors_X1954 0.0 1 464s US.Steel_X1935 0.0 0 464s US.Steel_X1936 0.0 0 464s US.Steel_X1937 0.0 0 464s US.Steel_X1938 0.0 0 464s US.Steel_X1939 0.0 0 464s US.Steel_X1940 0.0 0 464s US.Steel_X1941 0.0 0 464s US.Steel_X1942 0.0 0 464s US.Steel_X1943 0.0 0 464s 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Chrysler_X1937 0 0.0 464s Chrysler_X1938 0 0.0 464s Chrysler_X1939 0 0.0 464s Chrysler_X1940 0 0.0 464s Chrysler_X1941 0 0.0 464s Chrysler_X1942 0 0.0 464s Chrysler_X1943 0 0.0 464s Chrysler_X1944 0 0.0 464s Chrysler_X1945 0 0.0 464s Chrysler_X1946 0 0.0 464s Chrysler_X1947 0 0.0 464s Chrysler_X1948 0 0.0 464s Chrysler_X1949 0 0.0 464s Chrysler_X1950 0 0.0 464s Chrysler_X1951 0 0.0 464s Chrysler_X1952 0 0.0 464s Chrysler_X1953 0 0.0 464s Chrysler_X1954 0 0.0 464s General.Electric_X1935 0 0.0 464s General.Electric_X1936 0 0.0 464s General.Electric_X1937 0 0.0 464s General.Electric_X1938 0 0.0 464s General.Electric_X1939 0 0.0 464s General.Electric_X1940 0 0.0 464s General.Electric_X1941 0 0.0 464s General.Electric_X1942 0 0.0 464s General.Electric_X1943 0 0.0 464s General.Electric_X1944 0 0.0 464s General.Electric_X1945 0 0.0 464s General.Electric_X1946 0 0.0 464s General.Electric_X1947 0 0.0 464s General.Electric_X1948 0 0.0 464s General.Electric_X1949 0 0.0 464s General.Electric_X1950 0 0.0 464s General.Electric_X1951 0 0.0 464s General.Electric_X1952 0 0.0 464s General.Electric_X1953 0 0.0 464s General.Electric_X1954 0 0.0 464s General.Motors_X1935 3078 2.8 464s General.Motors_X1936 4662 52.6 464s General.Motors_X1937 5387 156.9 464s General.Motors_X1938 2792 209.2 464s General.Motors_X1939 4313 203.4 464s General.Motors_X1940 4644 207.2 464s General.Motors_X1941 4551 255.2 464s General.Motors_X1942 3244 303.7 464s General.Motors_X1943 4054 264.1 464s General.Motors_X1944 4379 201.6 464s General.Motors_X1945 4841 265.0 464s General.Motors_X1946 4901 402.2 464s General.Motors_X1947 3526 761.5 464s General.Motors_X1948 3255 922.4 464s General.Motors_X1949 3700 1020.1 464s General.Motors_X1950 3756 1099.0 464s General.Motors_X1951 4833 1207.7 464s General.Motors_X1952 4925 1430.5 464s General.Motors_X1953 6242 1777.3 464s General.Motors_X1954 5594 2226.3 464s US.Steel_X1935 0 0.0 464s US.Steel_X1936 0 0.0 464s US.Steel_X1937 0 0.0 464s US.Steel_X1938 0 0.0 464s 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General.Motors_X1950 0 0 464s General.Motors_X1951 0 0 464s General.Motors_X1952 0 0 464s General.Motors_X1953 0 0 464s General.Motors_X1954 0 0 464s US.Steel_X1935 0 0 464s US.Steel_X1936 0 0 464s US.Steel_X1937 0 0 464s US.Steel_X1938 0 0 464s US.Steel_X1939 0 0 464s US.Steel_X1940 0 0 464s US.Steel_X1941 0 0 464s US.Steel_X1942 0 0 464s US.Steel_X1943 0 0 464s US.Steel_X1944 0 0 464s US.Steel_X1945 0 0 464s US.Steel_X1946 0 0 464s US.Steel_X1947 0 0 464s US.Steel_X1948 0 0 464s US.Steel_X1949 0 0 464s US.Steel_X1950 0 0 464s US.Steel_X1951 0 0 464s US.Steel_X1952 0 0 464s US.Steel_X1953 0 0 464s US.Steel_X1954 0 0 464s Westinghouse_X1935 1 192 464s Westinghouse_X1936 1 516 464s Westinghouse_X1937 1 729 464s Westinghouse_X1938 1 560 464s Westinghouse_X1939 1 520 464s Westinghouse_X1940 1 628 464s Westinghouse_X1941 1 537 464s Westinghouse_X1942 1 561 464s Westinghouse_X1943 1 617 464s Westinghouse_X1944 1 627 464s Westinghouse_X1945 1 737 464s Westinghouse_X1946 1 760 464s Westinghouse_X1947 1 581 464s Westinghouse_X1948 1 662 464s Westinghouse_X1949 1 584 464s Westinghouse_X1950 1 635 464s Westinghouse_X1951 1 724 464s Westinghouse_X1952 1 864 464s Westinghouse_X1953 1 1194 464s Westinghouse_X1954 1 1189 464s Westinghouse_capital 464s Chrysler_X1935 0.0 464s Chrysler_X1936 0.0 464s Chrysler_X1937 0.0 464s Chrysler_X1938 0.0 464s Chrysler_X1939 0.0 464s Chrysler_X1940 0.0 464s Chrysler_X1941 0.0 464s Chrysler_X1942 0.0 464s Chrysler_X1943 0.0 464s Chrysler_X1944 0.0 464s Chrysler_X1945 0.0 464s Chrysler_X1946 0.0 464s Chrysler_X1947 0.0 464s Chrysler_X1948 0.0 464s Chrysler_X1949 0.0 464s Chrysler_X1950 0.0 464s Chrysler_X1951 0.0 464s Chrysler_X1952 0.0 464s Chrysler_X1953 0.0 464s Chrysler_X1954 0.0 464s General.Electric_X1935 0.0 464s General.Electric_X1936 0.0 464s General.Electric_X1937 0.0 464s General.Electric_X1938 0.0 464s General.Electric_X1939 0.0 464s General.Electric_X1940 0.0 464s General.Electric_X1941 0.0 464s General.Electric_X1942 0.0 464s General.Electric_X1943 0.0 464s General.Electric_X1944 0.0 464s General.Electric_X1945 0.0 464s General.Electric_X1946 0.0 464s General.Electric_X1947 0.0 464s General.Electric_X1948 0.0 464s General.Electric_X1949 0.0 464s General.Electric_X1950 0.0 464s General.Electric_X1951 0.0 464s General.Electric_X1952 0.0 464s General.Electric_X1953 0.0 464s General.Electric_X1954 0.0 464s General.Motors_X1935 0.0 464s General.Motors_X1936 0.0 464s General.Motors_X1937 0.0 464s General.Motors_X1938 0.0 464s General.Motors_X1939 0.0 464s General.Motors_X1940 0.0 464s General.Motors_X1941 0.0 464s General.Motors_X1942 0.0 464s General.Motors_X1943 0.0 464s General.Motors_X1944 0.0 464s General.Motors_X1945 0.0 464s General.Motors_X1946 0.0 464s General.Motors_X1947 0.0 464s General.Motors_X1948 0.0 464s General.Motors_X1949 0.0 464s General.Motors_X1950 0.0 464s General.Motors_X1951 0.0 464s General.Motors_X1952 0.0 464s General.Motors_X1953 0.0 464s General.Motors_X1954 0.0 464s US.Steel_X1935 0.0 464s US.Steel_X1936 0.0 464s US.Steel_X1937 0.0 464s US.Steel_X1938 0.0 464s US.Steel_X1939 0.0 464s US.Steel_X1940 0.0 464s US.Steel_X1941 0.0 464s US.Steel_X1942 0.0 464s US.Steel_X1943 0.0 464s US.Steel_X1944 0.0 464s US.Steel_X1945 0.0 464s US.Steel_X1946 0.0 464s US.Steel_X1947 0.0 464s US.Steel_X1948 0.0 464s US.Steel_X1949 0.0 464s US.Steel_X1950 0.0 464s US.Steel_X1951 0.0 464s US.Steel_X1952 0.0 464s US.Steel_X1953 0.0 464s US.Steel_X1954 0.0 464s Westinghouse_X1935 1.8 464s Westinghouse_X1936 0.8 464s Westinghouse_X1937 7.4 464s Westinghouse_X1938 18.1 464s Westinghouse_X1939 23.5 464s Westinghouse_X1940 26.5 464s Westinghouse_X1941 36.2 464s Westinghouse_X1942 60.8 464s Westinghouse_X1943 84.4 464s Westinghouse_X1944 91.2 464s Westinghouse_X1945 92.4 464s Westinghouse_X1946 86.0 464s Westinghouse_X1947 111.1 464s Westinghouse_X1948 130.6 464s Westinghouse_X1949 141.8 464s Westinghouse_X1950 136.7 464s Westinghouse_X1951 129.7 464s Westinghouse_X1952 145.5 464s Westinghouse_X1953 174.8 464s Westinghouse_X1954 213.5 464s $Chrysler 464s Chrysler_invest ~ Chrysler_value + Chrysler_capital 464s 464s 464s $General.Electric 464s General.Electric_invest ~ General.Electric_value + General.Electric_capital 464s 464s 464s $General.Motors 464s General.Motors_invest ~ General.Motors_value + General.Motors_capital 464s 464s 464s $US.Steel 464s US.Steel_invest ~ US.Steel_value + US.Steel_capital 464s 464s 464s $Westinghouse 464s Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 464s 464s 464s General.Electric_invest ~ General.Electric_value + General.Electric_capital 464s 464s $Chrysler 464s Chrysler_invest ~ Chrysler_value + Chrysler_capital 464s attr(,"variables") 464s list(Chrysler_invest, Chrysler_value, Chrysler_capital) 464s attr(,"factors") 464s Chrysler_value Chrysler_capital 464s Chrysler_invest 0 0 464s Chrysler_value 1 0 464s Chrysler_capital 0 1 464s attr(,"term.labels") 464s [1] "Chrysler_value" "Chrysler_capital" 464s attr(,"order") 464s [1] 1 1 464s attr(,"intercept") 464s [1] 1 464s attr(,"response") 464s [1] 1 464s attr(,".Environment") 464s 464s attr(,"predvars") 464s list(Chrysler_invest, Chrysler_value, Chrysler_capital) 464s attr(,"dataClasses") 464s Chrysler_invest Chrysler_value Chrysler_capital 464s "numeric" "numeric" "numeric" 464s 464s $General.Electric 464s General.Electric_invest ~ General.Electric_value + General.Electric_capital 464s attr(,"variables") 464s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 464s attr(,"factors") 464s General.Electric_value General.Electric_capital 464s General.Electric_invest 0 0 464s General.Electric_value 1 0 464s General.Electric_capital 0 1 464s attr(,"term.labels") 464s [1] "General.Electric_value" "General.Electric_capital" 464s attr(,"order") 464s [1] 1 1 464s attr(,"intercept") 464s [1] 1 464s attr(,"response") 464s [1] 1 464s attr(,".Environment") 464s 464s attr(,"predvars") 464s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 464s attr(,"dataClasses") 464s General.Electric_invest General.Electric_value General.Electric_capital 464s "numeric" "numeric" "numeric" 464s 464s $General.Motors 464s General.Motors_invest ~ General.Motors_value + General.Motors_capital 464s attr(,"variables") 464s list(General.Motors_invest, General.Motors_value, General.Motors_capital) 464s attr(,"factors") 464s General.Motors_value General.Motors_capital 464s General.Motors_invest 0 0 464s General.Motors_value 1 0 464s General.Motors_capital 0 1 464s attr(,"term.labels") 464s [1] "General.Motors_value" "General.Motors_capital" 464s attr(,"order") 464s [1] 1 1 464s attr(,"intercept") 464s [1] 1 464s attr(,"response") 464s [1] 1 464s attr(,".Environment") 464s 464s attr(,"predvars") 464s list(General.Motors_invest, General.Motors_value, General.Motors_capital) 464s attr(,"dataClasses") 464s General.Motors_invest General.Motors_value General.Motors_capital 464s "numeric" "numeric" "numeric" 464s 464s $US.Steel 464s US.Steel_invest ~ US.Steel_value + US.Steel_capital 464s attr(,"variables") 464s list(US.Steel_invest, US.Steel_value, US.Steel_capital) 464s attr(,"factors") 464s US.Steel_value US.Steel_capital 464s US.Steel_invest 0 0 464s US.Steel_value 1 0 464s US.Steel_capital 0 1 464s attr(,"term.labels") 464s [1] "US.Steel_value" "US.Steel_capital" 464s attr(,"order") 464s [1] 1 1 464s attr(,"intercept") 464s [1] 1 464s attr(,"response") 464s [1] 1 464s attr(,".Environment") 464s 464s attr(,"predvars") 464s list(US.Steel_invest, US.Steel_value, US.Steel_capital) 464s attr(,"dataClasses") 464s US.Steel_invest US.Steel_value US.Steel_capital 464s "numeric" "numeric" "numeric" 464s 464s $Westinghouse 464s Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 464s attr(,"variables") 464s list(Westinghouse_invest, Westinghouse_value, Westinghouse_capital) 464s attr(,"factors") 464s Westinghouse_value Westinghouse_capital 464s Westinghouse_invest 0 0 464s Westinghouse_value 1 0 464s Westinghouse_capital 0 1 464s attr(,"term.labels") 464s [1] "Westinghouse_value" "Westinghouse_capital" 464s attr(,"order") 464s [1] 1 1 464s attr(,"intercept") 464s [1] 1 464s attr(,"response") 464s [1] 1 464s attr(,".Environment") 464s 464s attr(,"predvars") 464s list(Westinghouse_invest, Westinghouse_value, Westinghouse_capital) 464s attr(,"dataClasses") 464s Westinghouse_invest Westinghouse_value Westinghouse_capital 464s "numeric" "numeric" "numeric" 464s 464s General.Electric_invest ~ General.Electric_value + General.Electric_capital 464s attr(,"variables") 464s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 464s attr(,"factors") 464s General.Electric_value General.Electric_capital 464s General.Electric_invest 0 0 464s General.Electric_value 1 0 464s General.Electric_capital 0 1 464s attr(,"term.labels") 464s [1] "General.Electric_value" "General.Electric_capital" 464s attr(,"order") 464s [1] 1 1 464s attr(,"intercept") 464s [1] 1 464s attr(,"response") 464s [1] 1 464s attr(,".Environment") 464s 464s attr(,"predvars") 464s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 464s attr(,"dataClasses") 464s General.Electric_invest General.Electric_value General.Electric_capital 464s "numeric" "numeric" "numeric" 464s > 464s > # OLS Pooled 464s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 464s + greeneOlsPooled <- systemfit( formulaGrunfeld, "OLS", 464s + data = GrunfeldGreene, pooled = TRUE, useMatrix = useMatrix ) 464s + print( greeneOlsPooled ) 464s + print( summary( greeneOlsPooled ) ) 464s + print( summary( greeneOlsPooled, useDfSys = FALSE, residCov = FALSE ) ) 464s + print( summary( greeneOlsPooled, residCov = FALSE, equations = FALSE ) ) 464s + print( sum( sapply( greeneOlsPooled$eq, function(x){return(summary(x)$ssr)}) )/97 ) # sigma^2 464s + print( coef( greeneOlsPooled ) ) 464s + print( coef( greeneOlsPooled, modified.regMat = TRUE ) ) 464s + print( coef( summary( greeneOlsPooled ) ) ) 464s + print( coef( summary( greeneOlsPooled ), modified.regMat = TRUE ) ) 464s + print( vcov( greeneOlsPooled ) ) 464s + print( vcov( greeneOlsPooled, modified.regMat = TRUE ) ) 464s + print( residuals( greeneOlsPooled ) ) 464s + print( confint( greeneOlsPooled ) ) 464s + print( fitted( greeneOlsPooled ) ) 464s + print( logLik( greeneOlsPooled ) ) 464s + print( logLik( greeneOlsPooled, residCovDiag = TRUE ) ) 464s + print( nobs( greeneOlsPooled ) ) 464s + print( model.frame( greeneOlsPooled ) ) 464s + print( model.matrix( greeneOlsPooled ) ) 464s + print( formula( greeneOlsPooled ) ) 464s + print( formula( greeneOlsPooled$eq[[ 1 ]] ) ) 464s + print( terms( greeneOlsPooled ) ) 464s + print( terms( greeneOlsPooled$eq[[ 1 ]] ) ) 464s + } 464s 464s systemfit results 464s method: OLS 464s 464s Coefficients: 464s Chrysler_(Intercept) Chrysler_value 464s -48.030 0.105 464s Chrysler_capital General.Electric_(Intercept) 464s 0.305 -48.030 464s General.Electric_value General.Electric_capital 464s 0.105 0.305 464s General.Motors_(Intercept) General.Motors_value 464s -48.030 0.105 464s General.Motors_capital US.Steel_(Intercept) 464s 0.305 -48.030 464s US.Steel_value US.Steel_capital 464s 0.105 0.305 464s Westinghouse_(Intercept) Westinghouse_value 464s -48.030 0.105 464s Westinghouse_capital 464s 0.305 464s 464s systemfit results 464s method: OLS 464s 464s N DF SSR detRCov OLS-R2 McElroy-R2 464s system 100 97 1570884 4.2e+17 0.294 0.812 464s 464s N DF SSR MSE RMSE R2 Adj R2 464s Chrysler 20 17 15117 889 29.8 0.564 0.513 464s General.Electric 20 17 685770 40339 200.8 -14.291 -16.090 464s General.Motors 20 17 188218 11072 105.2 0.897 0.884 464s US.Steel 20 17 669110 39359 198.4 -1.105 -1.352 464s Westinghouse 20 17 12668 745 27.3 -0.826 -1.041 464s 464s The covariance matrix of the residuals 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 889.2 -4898 -198 4748 -94.6 464s General.Electric -4898.1 40339 -2254 -32821 2658.0 464s General.Motors -197.7 -2254 11072 304 -1328.6 464s US.Steel 4748.1 -32821 304 39359 -1377.3 464s Westinghouse -94.6 2658 -1329 -1377 745.2 464s 464s The correlations of the residuals 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 1.000 0.144 -0.1852 0.2218 0.186 464s General.Electric 0.144 1.000 -0.2592 -0.1216 0.881 464s General.Motors -0.185 -0.259 1.0000 -0.0155 -0.469 464s US.Steel 0.222 -0.122 -0.0155 1.0000 -0.119 464s Westinghouse 0.186 0.881 -0.4689 -0.1186 1.000 464s 464s 464s OLS estimates for 'Chrysler' (equation 1) 464s Model Formula: Chrysler_invest ~ Chrysler_value + Chrysler_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -48.0297 21.4802 -2.24 0.028 * 464s value 0.1051 0.0114 9.24 6.0e-15 *** 464s capital 0.3054 0.0435 7.02 3.1e-10 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 29.82 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 15117.016 MSE: 889.236 Root MSE: 29.82 464s Multiple R-Squared: 0.564 Adjusted R-Squared: 0.513 464s 464s 464s OLS estimates for 'General.Electric' (equation 2) 464s Model Formula: General.Electric_invest ~ General.Electric_value + General.Electric_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -48.0297 21.4802 -2.24 0.028 * 464s value 0.1051 0.0114 9.24 6.0e-15 *** 464s capital 0.3054 0.0435 7.02 3.1e-10 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 200.847 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 685769.815 MSE: 40339.401 Root MSE: 200.847 464s Multiple R-Squared: -14.291 Adjusted R-Squared: -16.09 464s 464s 464s OLS estimates for 'General.Motors' (equation 3) 464s Model Formula: General.Motors_invest ~ General.Motors_value + General.Motors_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -48.0297 21.4802 -2.24 0.028 * 464s value 0.1051 0.0114 9.24 6.0e-15 *** 464s capital 0.3054 0.0435 7.02 3.1e-10 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 105.222 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 188218.158 MSE: 11071.656 Root MSE: 105.222 464s Multiple R-Squared: 0.897 Adjusted R-Squared: 0.884 464s 464s 464s OLS estimates for 'US.Steel' (equation 4) 464s Model Formula: US.Steel_invest ~ US.Steel_value + US.Steel_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -48.0297 21.4802 -2.24 0.028 * 464s value 0.1051 0.0114 9.24 6.0e-15 *** 464s capital 0.3054 0.0435 7.02 3.1e-10 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 198.392 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 669110.225 MSE: 39359.425 Root MSE: 198.392 464s Multiple R-Squared: -1.105 Adjusted R-Squared: -1.352 464s 464s 464s OLS estimates for 'Westinghouse' (equation 5) 464s Model Formula: Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -48.0297 21.4802 -2.24 0.028 * 464s value 0.1051 0.0114 9.24 6.0e-15 *** 464s capital 0.3054 0.0435 7.02 3.1e-10 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 27.298 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 12668.473 MSE: 745.204 Root MSE: 27.298 464s Multiple R-Squared: -0.826 Adjusted R-Squared: -1.041 464s 464s 464s systemfit results 464s method: OLS 464s 464s N DF SSR detRCov OLS-R2 McElroy-R2 464s system 100 97 1570884 4.2e+17 0.294 0.812 464s 464s N DF SSR MSE RMSE R2 Adj R2 464s Chrysler 20 17 15117 889 29.8 0.564 0.513 464s General.Electric 20 17 685770 40339 200.8 -14.291 -16.090 464s General.Motors 20 17 188218 11072 105.2 0.897 0.884 464s US.Steel 20 17 669110 39359 198.4 -1.105 -1.352 464s Westinghouse 20 17 12668 745 27.3 -0.826 -1.041 464s 464s 464s OLS estimates for 'Chrysler' (equation 1) 464s Model Formula: Chrysler_invest ~ Chrysler_value + Chrysler_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -48.0297 21.4802 -2.24 0.039 * 464s value 0.1051 0.0114 9.24 4.9e-08 *** 464s capital 0.3054 0.0435 7.02 2.1e-06 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 29.82 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 15117.016 MSE: 889.236 Root MSE: 29.82 464s Multiple R-Squared: 0.564 Adjusted R-Squared: 0.513 464s 464s 464s OLS estimates for 'General.Electric' (equation 2) 464s Model Formula: General.Electric_invest ~ General.Electric_value + General.Electric_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -48.0297 21.4802 -2.24 0.039 * 464s value 0.1051 0.0114 9.24 4.9e-08 *** 464s capital 0.3054 0.0435 7.02 2.1e-06 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 200.847 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 685769.815 MSE: 40339.401 Root MSE: 200.847 464s Multiple R-Squared: -14.291 Adjusted R-Squared: -16.09 464s 464s 464s OLS estimates for 'General.Motors' (equation 3) 464s Model Formula: General.Motors_invest ~ General.Motors_value + General.Motors_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -48.0297 21.4802 -2.24 0.039 * 464s value 0.1051 0.0114 9.24 4.9e-08 *** 464s capital 0.3054 0.0435 7.02 2.1e-06 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 105.222 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 188218.158 MSE: 11071.656 Root MSE: 105.222 464s Multiple R-Squared: 0.897 Adjusted R-Squared: 0.884 464s 464s 464s OLS estimates for 'US.Steel' (equation 4) 464s Model Formula: US.Steel_invest ~ US.Steel_value + US.Steel_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -48.0297 21.4802 -2.24 0.039 * 464s value 0.1051 0.0114 9.24 4.9e-08 *** 464s capital 0.3054 0.0435 7.02 2.1e-06 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 198.392 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 669110.225 MSE: 39359.425 Root MSE: 198.392 464s Multiple R-Squared: -1.105 Adjusted R-Squared: -1.352 464s 464s 464s OLS estimates for 'Westinghouse' (equation 5) 464s Model Formula: Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -48.0297 21.4802 -2.24 0.039 * 464s value 0.1051 0.0114 9.24 4.9e-08 *** 464s capital 0.3054 0.0435 7.02 2.1e-06 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 27.298 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 12668.473 MSE: 745.204 Root MSE: 27.298 464s Multiple R-Squared: -0.826 Adjusted R-Squared: -1.041 464s 464s 464s systemfit results 464s method: OLS 464s 464s N DF SSR detRCov OLS-R2 McElroy-R2 464s system 100 97 1570884 4.2e+17 0.294 0.812 464s 464s N DF SSR MSE RMSE R2 Adj R2 464s Chrysler 20 17 15117 889 29.8 0.564 0.513 464s General.Electric 20 17 685770 40339 200.8 -14.291 -16.090 464s General.Motors 20 17 188218 11072 105.2 0.897 0.884 464s US.Steel 20 17 669110 39359 198.4 -1.105 -1.352 464s Westinghouse 20 17 12668 745 27.3 -0.826 -1.041 464s 464s 464s Coefficients: 464s Estimate Std. Error t value Pr(>|t|) 464s Chrysler_(Intercept) -48.0297 21.4802 -2.24 0.028 * 464s Chrysler_value 0.1051 0.0114 9.24 6.0e-15 *** 464s Chrysler_capital 0.3054 0.0435 7.02 3.1e-10 *** 464s General.Electric_(Intercept) -48.0297 21.4802 -2.24 0.028 * 464s General.Electric_value 0.1051 0.0114 9.24 6.0e-15 *** 464s General.Electric_capital 0.3054 0.0435 7.02 3.1e-10 *** 464s General.Motors_(Intercept) -48.0297 21.4802 -2.24 0.028 * 464s General.Motors_value 0.1051 0.0114 9.24 6.0e-15 *** 464s General.Motors_capital 0.3054 0.0435 7.02 3.1e-10 *** 464s US.Steel_(Intercept) -48.0297 21.4802 -2.24 0.028 * 464s US.Steel_value 0.1051 0.0114 9.24 6.0e-15 *** 464s US.Steel_capital 0.3054 0.0435 7.02 3.1e-10 *** 464s Westinghouse_(Intercept) -48.0297 21.4802 -2.24 0.028 * 464s Westinghouse_value 0.1051 0.0114 9.24 6.0e-15 *** 464s Westinghouse_capital 0.3054 0.0435 7.02 3.1e-10 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s [1] 16195 464s Chrysler_(Intercept) Chrysler_value 464s -48.030 0.105 464s Chrysler_capital General.Electric_(Intercept) 464s 0.305 -48.030 464s General.Electric_value General.Electric_capital 464s 0.105 0.305 464s General.Motors_(Intercept) General.Motors_value 464s -48.030 0.105 464s General.Motors_capital US.Steel_(Intercept) 464s 0.305 -48.030 464s US.Steel_value US.Steel_capital 464s 0.105 0.305 464s Westinghouse_(Intercept) Westinghouse_value 464s -48.030 0.105 464s Westinghouse_capital 464s 0.305 464s C1 C2 C3 464s -48.030 0.105 0.305 464s Estimate Std. Error t value Pr(>|t|) 464s Chrysler_(Intercept) -48.030 21.4802 -2.24 2.76e-02 464s Chrysler_value 0.105 0.0114 9.24 6.00e-15 464s Chrysler_capital 0.305 0.0435 7.02 3.06e-10 464s General.Electric_(Intercept) -48.030 21.4802 -2.24 2.76e-02 464s General.Electric_value 0.105 0.0114 9.24 6.00e-15 464s General.Electric_capital 0.305 0.0435 7.02 3.06e-10 464s General.Motors_(Intercept) -48.030 21.4802 -2.24 2.76e-02 464s General.Motors_value 0.105 0.0114 9.24 6.00e-15 464s General.Motors_capital 0.305 0.0435 7.02 3.06e-10 464s US.Steel_(Intercept) -48.030 21.4802 -2.24 2.76e-02 464s US.Steel_value 0.105 0.0114 9.24 6.00e-15 464s US.Steel_capital 0.305 0.0435 7.02 3.06e-10 464s Westinghouse_(Intercept) -48.030 21.4802 -2.24 2.76e-02 464s Westinghouse_value 0.105 0.0114 9.24 6.00e-15 464s Westinghouse_capital 0.305 0.0435 7.02 3.06e-10 464s Estimate Std. Error t value Pr(>|t|) 464s C1 -48.030 21.4802 -2.24 2.76e-02 464s C2 0.105 0.0114 9.24 6.00e-15 464s C3 0.305 0.0435 7.02 3.06e-10 464s Chrysler_(Intercept) Chrysler_value 464s Chrysler_(Intercept) 461.39750 -0.154668 464s Chrysler_value -0.15467 0.000129 464s Chrysler_capital -0.00689 -0.000303 464s General.Electric_(Intercept) 461.39750 -0.154668 464s General.Electric_value -0.15467 0.000129 464s General.Electric_capital -0.00689 -0.000303 464s General.Motors_(Intercept) 461.39750 -0.154668 464s General.Motors_value -0.15467 0.000129 464s General.Motors_capital -0.00689 -0.000303 464s US.Steel_(Intercept) 461.39750 -0.154668 464s US.Steel_value -0.15467 0.000129 464s US.Steel_capital -0.00689 -0.000303 464s Westinghouse_(Intercept) 461.39750 -0.154668 464s Westinghouse_value -0.15467 0.000129 464s Westinghouse_capital -0.00689 -0.000303 464s Chrysler_capital General.Electric_(Intercept) 464s Chrysler_(Intercept) -0.006891 461.39750 464s Chrysler_value -0.000303 -0.15467 464s Chrysler_capital 0.001893 -0.00689 464s General.Electric_(Intercept) -0.006891 461.39750 464s General.Electric_value -0.000303 -0.15467 464s General.Electric_capital 0.001893 -0.00689 464s General.Motors_(Intercept) -0.006891 461.39750 464s General.Motors_value -0.000303 -0.15467 464s General.Motors_capital 0.001893 -0.00689 464s US.Steel_(Intercept) -0.006891 461.39750 464s US.Steel_value -0.000303 -0.15467 464s US.Steel_capital 0.001893 -0.00689 464s Westinghouse_(Intercept) -0.006891 461.39750 464s Westinghouse_value -0.000303 -0.15467 464s Westinghouse_capital 0.001893 -0.00689 464s General.Electric_value General.Electric_capital 464s Chrysler_(Intercept) -0.154668 -0.006891 464s Chrysler_value 0.000129 -0.000303 464s Chrysler_capital -0.000303 0.001893 464s General.Electric_(Intercept) -0.154668 -0.006891 464s General.Electric_value 0.000129 -0.000303 464s General.Electric_capital -0.000303 0.001893 464s General.Motors_(Intercept) -0.154668 -0.006891 464s General.Motors_value 0.000129 -0.000303 464s General.Motors_capital -0.000303 0.001893 464s US.Steel_(Intercept) -0.154668 -0.006891 464s US.Steel_value 0.000129 -0.000303 464s US.Steel_capital -0.000303 0.001893 464s Westinghouse_(Intercept) -0.154668 -0.006891 464s Westinghouse_value 0.000129 -0.000303 464s Westinghouse_capital -0.000303 0.001893 464s General.Motors_(Intercept) General.Motors_value 464s Chrysler_(Intercept) 461.39750 -0.154668 464s Chrysler_value -0.15467 0.000129 464s Chrysler_capital -0.00689 -0.000303 464s General.Electric_(Intercept) 461.39750 -0.154668 464s General.Electric_value -0.15467 0.000129 464s General.Electric_capital -0.00689 -0.000303 464s General.Motors_(Intercept) 461.39750 -0.154668 464s General.Motors_value -0.15467 0.000129 464s General.Motors_capital -0.00689 -0.000303 464s US.Steel_(Intercept) 461.39750 -0.154668 464s US.Steel_value -0.15467 0.000129 464s US.Steel_capital -0.00689 -0.000303 464s Westinghouse_(Intercept) 461.39750 -0.154668 464s Westinghouse_value -0.15467 0.000129 464s Westinghouse_capital -0.00689 -0.000303 464s General.Motors_capital US.Steel_(Intercept) 464s Chrysler_(Intercept) -0.006891 461.39750 464s Chrysler_value -0.000303 -0.15467 464s Chrysler_capital 0.001893 -0.00689 464s General.Electric_(Intercept) -0.006891 461.39750 464s General.Electric_value -0.000303 -0.15467 464s General.Electric_capital 0.001893 -0.00689 464s General.Motors_(Intercept) -0.006891 461.39750 464s General.Motors_value -0.000303 -0.15467 464s General.Motors_capital 0.001893 -0.00689 464s US.Steel_(Intercept) -0.006891 461.39750 464s US.Steel_value -0.000303 -0.15467 464s US.Steel_capital 0.001893 -0.00689 464s Westinghouse_(Intercept) -0.006891 461.39750 464s Westinghouse_value -0.000303 -0.15467 464s Westinghouse_capital 0.001893 -0.00689 464s US.Steel_value US.Steel_capital 464s Chrysler_(Intercept) -0.154668 -0.006891 464s Chrysler_value 0.000129 -0.000303 464s Chrysler_capital -0.000303 0.001893 464s General.Electric_(Intercept) -0.154668 -0.006891 464s General.Electric_value 0.000129 -0.000303 464s General.Electric_capital -0.000303 0.001893 464s General.Motors_(Intercept) -0.154668 -0.006891 464s General.Motors_value 0.000129 -0.000303 464s General.Motors_capital -0.000303 0.001893 464s US.Steel_(Intercept) -0.154668 -0.006891 464s US.Steel_value 0.000129 -0.000303 464s US.Steel_capital -0.000303 0.001893 464s Westinghouse_(Intercept) -0.154668 -0.006891 464s Westinghouse_value 0.000129 -0.000303 464s Westinghouse_capital -0.000303 0.001893 464s Westinghouse_(Intercept) Westinghouse_value 464s Chrysler_(Intercept) 461.39750 -0.154668 464s Chrysler_value -0.15467 0.000129 464s Chrysler_capital -0.00689 -0.000303 464s General.Electric_(Intercept) 461.39750 -0.154668 464s General.Electric_value -0.15467 0.000129 464s General.Electric_capital -0.00689 -0.000303 464s General.Motors_(Intercept) 461.39750 -0.154668 464s General.Motors_value -0.15467 0.000129 464s General.Motors_capital -0.00689 -0.000303 464s US.Steel_(Intercept) 461.39750 -0.154668 464s US.Steel_value -0.15467 0.000129 464s US.Steel_capital -0.00689 -0.000303 464s Westinghouse_(Intercept) 461.39750 -0.154668 464s Westinghouse_value -0.15467 0.000129 464s Westinghouse_capital -0.00689 -0.000303 464s Westinghouse_capital 464s Chrysler_(Intercept) -0.006891 464s Chrysler_value -0.000303 464s Chrysler_capital 0.001893 464s General.Electric_(Intercept) -0.006891 464s General.Electric_value -0.000303 464s General.Electric_capital 0.001893 464s General.Motors_(Intercept) -0.006891 464s General.Motors_value -0.000303 464s General.Motors_capital 0.001893 464s US.Steel_(Intercept) -0.006891 464s US.Steel_value -0.000303 464s US.Steel_capital 0.001893 464s Westinghouse_(Intercept) -0.006891 464s Westinghouse_value -0.000303 464s Westinghouse_capital 0.001893 464s C1 C2 C3 464s C1 461.39750 -0.154668 -0.006891 464s C2 -0.15467 0.000129 -0.000303 464s C3 -0.00689 -0.000303 0.001893 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s X1935 41.24 -71.7 41.27 98.333 40.29 464s X1936 29.63 -150.7 -66.11 198.009 19.46 464s X1937 10.81 -205.4 -155.39 200.626 4.21 464s X1938 37.79 -169.4 -51.57 41.520 6.50 464s X1939 9.38 -193.7 -136.54 -22.742 5.06 464s X1940 20.47 -158.6 -42.05 0.513 2.46 464s X1941 25.78 -99.2 3.84 190.851 29.04 464s X1942 29.85 -114.8 62.38 174.529 13.83 464s X1943 13.11 -172.2 41.00 108.865 -5.58 464s X1944 15.73 -170.6 73.77 60.388 -7.87 464s X1945 31.19 -166.9 19.60 47.014 -18.39 464s X1946 2.33 -129.8 98.30 180.017 -4.69 464s X1947 20.31 -118.2 13.81 198.862 8.57 464s X1948 30.75 -140.2 -46.46 277.965 -11.89 464s X1949 19.97 -192.9 -97.21 170.739 -24.58 464s X1950 25.98 -225.4 -39.33 181.300 -28.22 464s X1951 61.49 -213.0 -72.74 291.171 -13.26 464s X1952 27.89 -234.9 -15.13 330.665 -15.43 464s X1953 12.03 -266.1 153.79 285.144 -40.69 464s X1954 19.93 -323.8 267.09 80.518 -73.50 464s 2.5 % 97.5 % 464s Chrysler_(Intercept) -90.662 -5.398 464s Chrysler_value 0.083 0.128 464s Chrysler_capital 0.219 0.392 464s General.Electric_(Intercept) -90.662 -5.398 464s General.Electric_value 0.083 0.128 464s General.Electric_capital 0.219 0.392 464s General.Motors_(Intercept) -90.662 -5.398 464s General.Motors_value 0.083 0.128 464s General.Motors_capital 0.219 0.392 464s US.Steel_(Intercept) -90.662 -5.398 464s US.Steel_value 0.083 0.128 464s US.Steel_capital 0.219 0.392 464s Westinghouse_(Intercept) -90.662 -5.398 464s Westinghouse_value 0.083 0.128 464s Westinghouse_capital 0.219 0.392 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s X1935 -0.95 105 276 112 -27.36 464s X1936 43.13 196 458 157 6.44 464s X1937 55.45 283 566 269 30.84 464s X1938 13.81 214 309 221 16.39 464s X1939 43.03 242 467 253 13.78 464s X1940 48.94 233 503 261 26.11 464s X1941 42.57 212 508 282 19.47 464s X1942 16.95 207 386 271 29.51 464s X1943 34.29 233 459 253 42.60 464s X1944 43.84 227 474 228 45.68 464s X1945 57.59 261 542 212 57.66 464s X1946 71.79 290 590 240 58.15 464s X1947 42.37 265 555 222 46.99 464s X1948 58.61 287 576 217 61.45 464s X1949 59.01 291 652 234 56.62 464s X1950 74.68 319 682 238 60.46 464s X1951 99.13 348 829 297 67.64 464s X1952 117.11 392 906 315 87.21 464s X1953 162.90 446 1151 356 130.77 464s X1954 152.56 513 1220 379 142.10 464s 'log Lik.' -540 (df=4) 464s 'log Lik.' -573 (df=4) 464s [1] 100 464s Chrysler_invest Chrysler_value Chrysler_capital General.Electric_invest 464s X1935 40.3 418 10.5 33.1 464s X1936 72.8 838 10.2 45.0 464s X1937 66.3 884 34.7 77.2 464s X1938 51.6 438 51.8 44.6 464s X1939 52.4 680 64.3 48.1 464s X1940 69.4 728 67.1 74.4 464s X1941 68.3 644 75.2 113.0 464s X1942 46.8 411 71.4 91.9 464s X1943 47.4 588 67.1 61.3 464s X1944 59.6 698 60.5 56.8 464s X1945 88.8 846 54.6 93.6 464s X1946 74.1 894 84.8 159.9 464s X1947 62.7 579 96.8 147.2 464s X1948 89.4 695 110.2 146.3 464s X1949 79.0 590 147.4 98.3 464s X1950 100.7 694 163.2 93.5 464s X1951 160.6 809 203.5 135.2 464s X1952 145.0 727 290.6 157.3 464s X1953 174.9 1002 346.1 179.5 464s X1954 172.5 703 414.9 189.6 464s General.Electric_value General.Electric_capital General.Motors_invest 464s X1935 1171 97.8 318 464s X1936 2016 104.4 392 464s X1937 2803 118.0 411 464s X1938 2040 156.2 258 464s X1939 2256 172.6 331 464s X1940 2132 186.6 461 464s X1941 1834 220.9 512 464s X1942 1588 287.8 448 464s X1943 1749 319.9 500 464s X1944 1687 321.3 548 464s X1945 2008 319.6 561 464s X1946 2208 346.0 688 464s X1947 1657 456.4 569 464s X1948 1604 543.4 529 464s X1949 1432 618.3 555 464s X1950 1610 647.4 643 464s X1951 1819 671.3 756 464s X1952 2080 726.1 891 464s X1953 2372 800.3 1304 464s X1954 2760 888.9 1487 464s General.Motors_value General.Motors_capital US.Steel_invest 464s X1935 3078 2.8 210 464s X1936 4662 52.6 355 464s X1937 5387 156.9 470 464s X1938 2792 209.2 262 464s X1939 4313 203.4 230 464s X1940 4644 207.2 262 464s X1941 4551 255.2 473 464s X1942 3244 303.7 446 464s X1943 4054 264.1 362 464s X1944 4379 201.6 288 464s X1945 4841 265.0 259 464s X1946 4901 402.2 420 464s X1947 3526 761.5 420 464s X1948 3255 922.4 494 464s X1949 3700 1020.1 405 464s X1950 3756 1099.0 419 464s X1951 4833 1207.7 588 464s X1952 4925 1430.5 645 464s X1953 6242 1777.3 641 464s X1954 5594 2226.3 459 464s US.Steel_value US.Steel_capital Westinghouse_invest Westinghouse_value 464s X1935 1362 53.8 12.9 192 464s X1936 1807 50.5 25.9 516 464s X1937 2676 118.1 35.0 729 464s X1938 1802 260.2 22.9 560 464s X1939 1957 312.7 18.8 520 464s X1940 2203 254.2 28.6 628 464s X1941 2380 261.4 48.5 537 464s X1942 2169 298.7 43.3 561 464s X1943 1985 301.8 37.0 617 464s X1944 1814 279.1 37.8 627 464s X1945 1850 213.8 39.3 737 464s X1946 2068 232.6 53.5 760 464s X1947 1797 264.8 55.6 581 464s X1948 1626 306.9 49.6 662 464s X1949 1667 351.1 32.0 584 464s X1950 1677 357.8 32.2 635 464s X1951 2290 342.1 54.4 724 464s X1952 2159 444.2 71.8 864 464s X1953 2031 623.6 90.1 1194 464s X1954 2116 669.7 68.6 1189 464s Westinghouse_capital 464s X1935 1.8 464s X1936 0.8 464s X1937 7.4 464s X1938 18.1 464s X1939 23.5 464s X1940 26.5 464s X1941 36.2 464s X1942 60.8 464s X1943 84.4 464s X1944 91.2 464s X1945 92.4 464s X1946 86.0 464s X1947 111.1 464s X1948 130.6 464s X1949 141.8 464s X1950 136.7 464s X1951 129.7 464s X1952 145.5 464s X1953 174.8 464s X1954 213.5 464s Chrysler_(Intercept) Chrysler_value Chrysler_capital 464s Chrysler_X1935 1 418 10.5 464s Chrysler_X1936 1 838 10.2 464s Chrysler_X1937 1 884 34.7 464s Chrysler_X1938 1 438 51.8 464s Chrysler_X1939 1 680 64.3 464s Chrysler_X1940 1 728 67.1 464s Chrysler_X1941 1 644 75.2 464s Chrysler_X1942 1 411 71.4 464s Chrysler_X1943 1 588 67.1 464s Chrysler_X1944 1 698 60.5 464s Chrysler_X1945 1 846 54.6 464s Chrysler_X1946 1 894 84.8 464s Chrysler_X1947 1 579 96.8 464s Chrysler_X1948 1 695 110.2 464s Chrysler_X1949 1 590 147.4 464s Chrysler_X1950 1 694 163.2 464s Chrysler_X1951 1 809 203.5 464s Chrysler_X1952 1 727 290.6 464s Chrysler_X1953 1 1002 346.1 464s Chrysler_X1954 1 703 414.9 464s General.Electric_X1935 0 0 0.0 464s General.Electric_X1936 0 0 0.0 464s General.Electric_X1937 0 0 0.0 464s General.Electric_X1938 0 0 0.0 464s General.Electric_X1939 0 0 0.0 464s General.Electric_X1940 0 0 0.0 464s General.Electric_X1941 0 0 0.0 464s General.Electric_X1942 0 0 0.0 464s General.Electric_X1943 0 0 0.0 464s General.Electric_X1944 0 0 0.0 464s General.Electric_X1945 0 0 0.0 464s General.Electric_X1946 0 0 0.0 464s General.Electric_X1947 0 0 0.0 464s General.Electric_X1948 0 0 0.0 464s General.Electric_X1949 0 0 0.0 464s General.Electric_X1950 0 0 0.0 464s General.Electric_X1951 0 0 0.0 464s General.Electric_X1952 0 0 0.0 464s General.Electric_X1953 0 0 0.0 464s General.Electric_X1954 0 0 0.0 464s General.Motors_X1935 0 0 0.0 464s General.Motors_X1936 0 0 0.0 464s General.Motors_X1937 0 0 0.0 464s General.Motors_X1938 0 0 0.0 464s General.Motors_X1939 0 0 0.0 464s General.Motors_X1940 0 0 0.0 464s General.Motors_X1941 0 0 0.0 464s General.Motors_X1942 0 0 0.0 464s General.Motors_X1943 0 0 0.0 464s General.Motors_X1944 0 0 0.0 464s General.Motors_X1945 0 0 0.0 464s General.Motors_X1946 0 0 0.0 464s General.Motors_X1947 0 0 0.0 464s General.Motors_X1948 0 0 0.0 464s General.Motors_X1949 0 0 0.0 464s General.Motors_X1950 0 0 0.0 464s General.Motors_X1951 0 0 0.0 464s General.Motors_X1952 0 0 0.0 464s General.Motors_X1953 0 0 0.0 464s General.Motors_X1954 0 0 0.0 464s US.Steel_X1935 0 0 0.0 464s US.Steel_X1936 0 0 0.0 464s US.Steel_X1937 0 0 0.0 464s US.Steel_X1938 0 0 0.0 464s US.Steel_X1939 0 0 0.0 464s US.Steel_X1940 0 0 0.0 464s US.Steel_X1941 0 0 0.0 464s US.Steel_X1942 0 0 0.0 464s US.Steel_X1943 0 0 0.0 464s US.Steel_X1944 0 0 0.0 464s US.Steel_X1945 0 0 0.0 464s US.Steel_X1946 0 0 0.0 464s US.Steel_X1947 0 0 0.0 464s US.Steel_X1948 0 0 0.0 464s US.Steel_X1949 0 0 0.0 464s US.Steel_X1950 0 0 0.0 464s US.Steel_X1951 0 0 0.0 464s US.Steel_X1952 0 0 0.0 464s US.Steel_X1953 0 0 0.0 464s US.Steel_X1954 0 0 0.0 464s Westinghouse_X1935 0 0 0.0 464s Westinghouse_X1936 0 0 0.0 464s Westinghouse_X1937 0 0 0.0 464s Westinghouse_X1938 0 0 0.0 464s Westinghouse_X1939 0 0 0.0 464s Westinghouse_X1940 0 0 0.0 464s Westinghouse_X1941 0 0 0.0 464s Westinghouse_X1942 0 0 0.0 464s Westinghouse_X1943 0 0 0.0 464s Westinghouse_X1944 0 0 0.0 464s Westinghouse_X1945 0 0 0.0 464s Westinghouse_X1946 0 0 0.0 464s Westinghouse_X1947 0 0 0.0 464s Westinghouse_X1948 0 0 0.0 464s Westinghouse_X1949 0 0 0.0 464s Westinghouse_X1950 0 0 0.0 464s Westinghouse_X1951 0 0 0.0 464s Westinghouse_X1952 0 0 0.0 464s Westinghouse_X1953 0 0 0.0 464s Westinghouse_X1954 0 0 0.0 464s General.Electric_(Intercept) General.Electric_value 464s Chrysler_X1935 0 0 464s Chrysler_X1936 0 0 464s Chrysler_X1937 0 0 464s Chrysler_X1938 0 0 464s Chrysler_X1939 0 0 464s Chrysler_X1940 0 0 464s Chrysler_X1941 0 0 464s Chrysler_X1942 0 0 464s Chrysler_X1943 0 0 464s Chrysler_X1944 0 0 464s Chrysler_X1945 0 0 464s Chrysler_X1946 0 0 464s Chrysler_X1947 0 0 464s Chrysler_X1948 0 0 464s Chrysler_X1949 0 0 464s Chrysler_X1950 0 0 464s Chrysler_X1951 0 0 464s Chrysler_X1952 0 0 464s Chrysler_X1953 0 0 464s Chrysler_X1954 0 0 464s General.Electric_X1935 1 1171 464s General.Electric_X1936 1 2016 464s General.Electric_X1937 1 2803 464s General.Electric_X1938 1 2040 464s General.Electric_X1939 1 2256 464s General.Electric_X1940 1 2132 464s General.Electric_X1941 1 1834 464s General.Electric_X1942 1 1588 464s General.Electric_X1943 1 1749 464s General.Electric_X1944 1 1687 464s General.Electric_X1945 1 2008 464s General.Electric_X1946 1 2208 464s General.Electric_X1947 1 1657 464s General.Electric_X1948 1 1604 464s General.Electric_X1949 1 1432 464s General.Electric_X1950 1 1610 464s General.Electric_X1951 1 1819 464s General.Electric_X1952 1 2080 464s General.Electric_X1953 1 2372 464s General.Electric_X1954 1 2760 464s General.Motors_X1935 0 0 464s General.Motors_X1936 0 0 464s General.Motors_X1937 0 0 464s General.Motors_X1938 0 0 464s General.Motors_X1939 0 0 464s General.Motors_X1940 0 0 464s General.Motors_X1941 0 0 464s General.Motors_X1942 0 0 464s General.Motors_X1943 0 0 464s General.Motors_X1944 0 0 464s General.Motors_X1945 0 0 464s General.Motors_X1946 0 0 464s General.Motors_X1947 0 0 464s General.Motors_X1948 0 0 464s General.Motors_X1949 0 0 464s General.Motors_X1950 0 0 464s General.Motors_X1951 0 0 464s General.Motors_X1952 0 0 464s General.Motors_X1953 0 0 464s General.Motors_X1954 0 0 464s US.Steel_X1935 0 0 464s US.Steel_X1936 0 0 464s US.Steel_X1937 0 0 464s US.Steel_X1938 0 0 464s US.Steel_X1939 0 0 464s US.Steel_X1940 0 0 464s US.Steel_X1941 0 0 464s US.Steel_X1942 0 0 464s US.Steel_X1943 0 0 464s US.Steel_X1944 0 0 464s US.Steel_X1945 0 0 464s US.Steel_X1946 0 0 464s US.Steel_X1947 0 0 464s US.Steel_X1948 0 0 464s US.Steel_X1949 0 0 464s US.Steel_X1950 0 0 464s US.Steel_X1951 0 0 464s US.Steel_X1952 0 0 464s US.Steel_X1953 0 0 464s US.Steel_X1954 0 0 464s Westinghouse_X1935 0 0 464s Westinghouse_X1936 0 0 464s Westinghouse_X1937 0 0 464s Westinghouse_X1938 0 0 464s Westinghouse_X1939 0 0 464s Westinghouse_X1940 0 0 464s Westinghouse_X1941 0 0 464s Westinghouse_X1942 0 0 464s Westinghouse_X1943 0 0 464s Westinghouse_X1944 0 0 464s Westinghouse_X1945 0 0 464s Westinghouse_X1946 0 0 464s Westinghouse_X1947 0 0 464s Westinghouse_X1948 0 0 464s Westinghouse_X1949 0 0 464s Westinghouse_X1950 0 0 464s Westinghouse_X1951 0 0 464s Westinghouse_X1952 0 0 464s Westinghouse_X1953 0 0 464s Westinghouse_X1954 0 0 464s General.Electric_capital General.Motors_(Intercept) 464s Chrysler_X1935 0.0 0 464s Chrysler_X1936 0.0 0 464s Chrysler_X1937 0.0 0 464s Chrysler_X1938 0.0 0 464s Chrysler_X1939 0.0 0 464s Chrysler_X1940 0.0 0 464s Chrysler_X1941 0.0 0 464s Chrysler_X1942 0.0 0 464s Chrysler_X1943 0.0 0 464s Chrysler_X1944 0.0 0 464s Chrysler_X1945 0.0 0 464s Chrysler_X1946 0.0 0 464s Chrysler_X1947 0.0 0 464s Chrysler_X1948 0.0 0 464s Chrysler_X1949 0.0 0 464s Chrysler_X1950 0.0 0 464s Chrysler_X1951 0.0 0 464s Chrysler_X1952 0.0 0 464s Chrysler_X1953 0.0 0 464s Chrysler_X1954 0.0 0 464s General.Electric_X1935 97.8 0 464s General.Electric_X1936 104.4 0 464s General.Electric_X1937 118.0 0 464s General.Electric_X1938 156.2 0 464s General.Electric_X1939 172.6 0 464s General.Electric_X1940 186.6 0 464s General.Electric_X1941 220.9 0 464s General.Electric_X1942 287.8 0 464s General.Electric_X1943 319.9 0 464s General.Electric_X1944 321.3 0 464s General.Electric_X1945 319.6 0 464s General.Electric_X1946 346.0 0 464s General.Electric_X1947 456.4 0 464s General.Electric_X1948 543.4 0 464s General.Electric_X1949 618.3 0 464s General.Electric_X1950 647.4 0 464s General.Electric_X1951 671.3 0 464s General.Electric_X1952 726.1 0 464s General.Electric_X1953 800.3 0 464s General.Electric_X1954 888.9 0 464s General.Motors_X1935 0.0 1 464s General.Motors_X1936 0.0 1 464s General.Motors_X1937 0.0 1 464s General.Motors_X1938 0.0 1 464s General.Motors_X1939 0.0 1 464s General.Motors_X1940 0.0 1 464s General.Motors_X1941 0.0 1 464s General.Motors_X1942 0.0 1 464s General.Motors_X1943 0.0 1 464s General.Motors_X1944 0.0 1 464s General.Motors_X1945 0.0 1 464s General.Motors_X1946 0.0 1 464s General.Motors_X1947 0.0 1 464s General.Motors_X1948 0.0 1 464s General.Motors_X1949 0.0 1 464s General.Motors_X1950 0.0 1 464s General.Motors_X1951 0.0 1 464s General.Motors_X1952 0.0 1 464s General.Motors_X1953 0.0 1 464s General.Motors_X1954 0.0 1 464s US.Steel_X1935 0.0 0 464s US.Steel_X1936 0.0 0 464s US.Steel_X1937 0.0 0 464s US.Steel_X1938 0.0 0 464s US.Steel_X1939 0.0 0 464s US.Steel_X1940 0.0 0 464s US.Steel_X1941 0.0 0 464s US.Steel_X1942 0.0 0 464s US.Steel_X1943 0.0 0 464s US.Steel_X1944 0.0 0 464s US.Steel_X1945 0.0 0 464s US.Steel_X1946 0.0 0 464s US.Steel_X1947 0.0 0 464s US.Steel_X1948 0.0 0 464s US.Steel_X1949 0.0 0 464s US.Steel_X1950 0.0 0 464s US.Steel_X1951 0.0 0 464s US.Steel_X1952 0.0 0 464s US.Steel_X1953 0.0 0 464s US.Steel_X1954 0.0 0 464s Westinghouse_X1935 0.0 0 464s Westinghouse_X1936 0.0 0 464s Westinghouse_X1937 0.0 0 464s Westinghouse_X1938 0.0 0 464s Westinghouse_X1939 0.0 0 464s Westinghouse_X1940 0.0 0 464s Westinghouse_X1941 0.0 0 464s Westinghouse_X1942 0.0 0 464s Westinghouse_X1943 0.0 0 464s Westinghouse_X1944 0.0 0 464s Westinghouse_X1945 0.0 0 464s Westinghouse_X1946 0.0 0 464s Westinghouse_X1947 0.0 0 464s Westinghouse_X1948 0.0 0 464s Westinghouse_X1949 0.0 0 464s Westinghouse_X1950 0.0 0 464s Westinghouse_X1951 0.0 0 464s Westinghouse_X1952 0.0 0 464s Westinghouse_X1953 0.0 0 464s Westinghouse_X1954 0.0 0 464s General.Motors_value General.Motors_capital 464s Chrysler_X1935 0 0.0 464s Chrysler_X1936 0 0.0 464s Chrysler_X1937 0 0.0 464s Chrysler_X1938 0 0.0 464s Chrysler_X1939 0 0.0 464s Chrysler_X1940 0 0.0 464s Chrysler_X1941 0 0.0 464s Chrysler_X1942 0 0.0 464s Chrysler_X1943 0 0.0 464s Chrysler_X1944 0 0.0 464s Chrysler_X1945 0 0.0 464s Chrysler_X1946 0 0.0 464s Chrysler_X1947 0 0.0 464s Chrysler_X1948 0 0.0 464s Chrysler_X1949 0 0.0 464s Chrysler_X1950 0 0.0 464s Chrysler_X1951 0 0.0 464s Chrysler_X1952 0 0.0 464s Chrysler_X1953 0 0.0 464s Chrysler_X1954 0 0.0 464s General.Electric_X1935 0 0.0 464s General.Electric_X1936 0 0.0 464s General.Electric_X1937 0 0.0 464s General.Electric_X1938 0 0.0 464s General.Electric_X1939 0 0.0 464s General.Electric_X1940 0 0.0 464s General.Electric_X1941 0 0.0 464s General.Electric_X1942 0 0.0 464s General.Electric_X1943 0 0.0 464s General.Electric_X1944 0 0.0 464s General.Electric_X1945 0 0.0 464s General.Electric_X1946 0 0.0 464s General.Electric_X1947 0 0.0 464s General.Electric_X1948 0 0.0 464s General.Electric_X1949 0 0.0 464s General.Electric_X1950 0 0.0 464s General.Electric_X1951 0 0.0 464s General.Electric_X1952 0 0.0 464s General.Electric_X1953 0 0.0 464s General.Electric_X1954 0 0.0 464s General.Motors_X1935 3078 2.8 464s General.Motors_X1936 4662 52.6 464s General.Motors_X1937 5387 156.9 464s General.Motors_X1938 2792 209.2 464s General.Motors_X1939 4313 203.4 464s General.Motors_X1940 4644 207.2 464s General.Motors_X1941 4551 255.2 464s General.Motors_X1942 3244 303.7 464s General.Motors_X1943 4054 264.1 464s General.Motors_X1944 4379 201.6 464s General.Motors_X1945 4841 265.0 464s General.Motors_X1946 4901 402.2 464s General.Motors_X1947 3526 761.5 464s General.Motors_X1948 3255 922.4 464s General.Motors_X1949 3700 1020.1 464s General.Motors_X1950 3756 1099.0 464s General.Motors_X1951 4833 1207.7 464s General.Motors_X1952 4925 1430.5 464s General.Motors_X1953 6242 1777.3 464s General.Motors_X1954 5594 2226.3 464s US.Steel_X1935 0 0.0 464s US.Steel_X1936 0 0.0 464s US.Steel_X1937 0 0.0 464s US.Steel_X1938 0 0.0 464s US.Steel_X1939 0 0.0 464s US.Steel_X1940 0 0.0 464s US.Steel_X1941 0 0.0 464s US.Steel_X1942 0 0.0 464s US.Steel_X1943 0 0.0 464s US.Steel_X1944 0 0.0 464s US.Steel_X1945 0 0.0 464s US.Steel_X1946 0 0.0 464s US.Steel_X1947 0 0.0 464s US.Steel_X1948 0 0.0 464s US.Steel_X1949 0 0.0 464s US.Steel_X1950 0 0.0 464s US.Steel_X1951 0 0.0 464s US.Steel_X1952 0 0.0 464s US.Steel_X1953 0 0.0 464s US.Steel_X1954 0 0.0 464s Westinghouse_X1935 0 0.0 464s Westinghouse_X1936 0 0.0 464s Westinghouse_X1937 0 0.0 464s Westinghouse_X1938 0 0.0 464s Westinghouse_X1939 0 0.0 464s Westinghouse_X1940 0 0.0 464s Westinghouse_X1941 0 0.0 464s Westinghouse_X1942 0 0.0 464s Westinghouse_X1943 0 0.0 464s Westinghouse_X1944 0 0.0 464s Westinghouse_X1945 0 0.0 464s Westinghouse_X1946 0 0.0 464s Westinghouse_X1947 0 0.0 464s Westinghouse_X1948 0 0.0 464s Westinghouse_X1949 0 0.0 464s Westinghouse_X1950 0 0.0 464s Westinghouse_X1951 0 0.0 464s Westinghouse_X1952 0 0.0 464s Westinghouse_X1953 0 0.0 464s Westinghouse_X1954 0 0.0 464s US.Steel_(Intercept) US.Steel_value US.Steel_capital 464s Chrysler_X1935 0 0 0.0 464s Chrysler_X1936 0 0 0.0 464s Chrysler_X1937 0 0 0.0 464s Chrysler_X1938 0 0 0.0 464s Chrysler_X1939 0 0 0.0 464s Chrysler_X1940 0 0 0.0 464s Chrysler_X1941 0 0 0.0 464s Chrysler_X1942 0 0 0.0 464s Chrysler_X1943 0 0 0.0 464s Chrysler_X1944 0 0 0.0 464s Chrysler_X1945 0 0 0.0 464s Chrysler_X1946 0 0 0.0 464s Chrysler_X1947 0 0 0.0 464s Chrysler_X1948 0 0 0.0 464s Chrysler_X1949 0 0 0.0 464s Chrysler_X1950 0 0 0.0 464s Chrysler_X1951 0 0 0.0 464s Chrysler_X1952 0 0 0.0 464s Chrysler_X1953 0 0 0.0 464s Chrysler_X1954 0 0 0.0 464s General.Electric_X1935 0 0 0.0 464s General.Electric_X1936 0 0 0.0 464s General.Electric_X1937 0 0 0.0 464s General.Electric_X1938 0 0 0.0 464s General.Electric_X1939 0 0 0.0 464s General.Electric_X1940 0 0 0.0 464s General.Electric_X1941 0 0 0.0 464s General.Electric_X1942 0 0 0.0 464s General.Electric_X1943 0 0 0.0 464s General.Electric_X1944 0 0 0.0 464s General.Electric_X1945 0 0 0.0 464s General.Electric_X1946 0 0 0.0 464s General.Electric_X1947 0 0 0.0 464s General.Electric_X1948 0 0 0.0 464s General.Electric_X1949 0 0 0.0 464s General.Electric_X1950 0 0 0.0 464s General.Electric_X1951 0 0 0.0 464s General.Electric_X1952 0 0 0.0 464s General.Electric_X1953 0 0 0.0 464s General.Electric_X1954 0 0 0.0 464s General.Motors_X1935 0 0 0.0 464s General.Motors_X1936 0 0 0.0 464s General.Motors_X1937 0 0 0.0 464s General.Motors_X1938 0 0 0.0 464s General.Motors_X1939 0 0 0.0 464s General.Motors_X1940 0 0 0.0 464s General.Motors_X1941 0 0 0.0 464s General.Motors_X1942 0 0 0.0 464s General.Motors_X1943 0 0 0.0 464s General.Motors_X1944 0 0 0.0 464s General.Motors_X1945 0 0 0.0 464s General.Motors_X1946 0 0 0.0 464s General.Motors_X1947 0 0 0.0 464s General.Motors_X1948 0 0 0.0 464s General.Motors_X1949 0 0 0.0 464s General.Motors_X1950 0 0 0.0 464s General.Motors_X1951 0 0 0.0 464s General.Motors_X1952 0 0 0.0 464s General.Motors_X1953 0 0 0.0 464s General.Motors_X1954 0 0 0.0 464s US.Steel_X1935 1 1362 53.8 464s US.Steel_X1936 1 1807 50.5 464s US.Steel_X1937 1 2676 118.1 464s US.Steel_X1938 1 1802 260.2 464s US.Steel_X1939 1 1957 312.7 464s US.Steel_X1940 1 2203 254.2 464s US.Steel_X1941 1 2380 261.4 464s US.Steel_X1942 1 2169 298.7 464s US.Steel_X1943 1 1985 301.8 464s US.Steel_X1944 1 1814 279.1 464s US.Steel_X1945 1 1850 213.8 464s US.Steel_X1946 1 2068 232.6 464s US.Steel_X1947 1 1797 264.8 464s US.Steel_X1948 1 1626 306.9 464s US.Steel_X1949 1 1667 351.1 464s US.Steel_X1950 1 1677 357.8 464s US.Steel_X1951 1 2290 342.1 464s US.Steel_X1952 1 2159 444.2 464s US.Steel_X1953 1 2031 623.6 464s US.Steel_X1954 1 2116 669.7 464s Westinghouse_X1935 0 0 0.0 464s Westinghouse_X1936 0 0 0.0 464s Westinghouse_X1937 0 0 0.0 464s Westinghouse_X1938 0 0 0.0 464s Westinghouse_X1939 0 0 0.0 464s Westinghouse_X1940 0 0 0.0 464s Westinghouse_X1941 0 0 0.0 464s Westinghouse_X1942 0 0 0.0 464s Westinghouse_X1943 0 0 0.0 464s Westinghouse_X1944 0 0 0.0 464s Westinghouse_X1945 0 0 0.0 464s Westinghouse_X1946 0 0 0.0 464s Westinghouse_X1947 0 0 0.0 464s Westinghouse_X1948 0 0 0.0 464s Westinghouse_X1949 0 0 0.0 464s Westinghouse_X1950 0 0 0.0 464s Westinghouse_X1951 0 0 0.0 464s Westinghouse_X1952 0 0 0.0 464s Westinghouse_X1953 0 0 0.0 464s Westinghouse_X1954 0 0 0.0 464s Westinghouse_(Intercept) Westinghouse_value 464s Chrysler_X1935 0 0 464s Chrysler_X1936 0 0 464s Chrysler_X1937 0 0 464s Chrysler_X1938 0 0 464s Chrysler_X1939 0 0 464s Chrysler_X1940 0 0 464s Chrysler_X1941 0 0 464s Chrysler_X1942 0 0 464s Chrysler_X1943 0 0 464s Chrysler_X1944 0 0 464s Chrysler_X1945 0 0 464s Chrysler_X1946 0 0 464s Chrysler_X1947 0 0 464s Chrysler_X1948 0 0 464s Chrysler_X1949 0 0 464s Chrysler_X1950 0 0 464s Chrysler_X1951 0 0 464s Chrysler_X1952 0 0 464s Chrysler_X1953 0 0 464s Chrysler_X1954 0 0 464s General.Electric_X1935 0 0 464s General.Electric_X1936 0 0 464s General.Electric_X1937 0 0 464s General.Electric_X1938 0 0 464s General.Electric_X1939 0 0 464s General.Electric_X1940 0 0 464s General.Electric_X1941 0 0 464s General.Electric_X1942 0 0 464s General.Electric_X1943 0 0 464s General.Electric_X1944 0 0 464s General.Electric_X1945 0 0 464s General.Electric_X1946 0 0 464s General.Electric_X1947 0 0 464s General.Electric_X1948 0 0 464s General.Electric_X1949 0 0 464s General.Electric_X1950 0 0 464s General.Electric_X1951 0 0 464s General.Electric_X1952 0 0 464s General.Electric_X1953 0 0 464s General.Electric_X1954 0 0 464s General.Motors_X1935 0 0 464s General.Motors_X1936 0 0 464s General.Motors_X1937 0 0 464s General.Motors_X1938 0 0 464s General.Motors_X1939 0 0 464s General.Motors_X1940 0 0 464s General.Motors_X1941 0 0 464s General.Motors_X1942 0 0 464s General.Motors_X1943 0 0 464s General.Motors_X1944 0 0 464s General.Motors_X1945 0 0 464s General.Motors_X1946 0 0 464s General.Motors_X1947 0 0 464s General.Motors_X1948 0 0 464s General.Motors_X1949 0 0 464s General.Motors_X1950 0 0 464s General.Motors_X1951 0 0 464s General.Motors_X1952 0 0 464s General.Motors_X1953 0 0 464s General.Motors_X1954 0 0 464s US.Steel_X1935 0 0 464s US.Steel_X1936 0 0 464s US.Steel_X1937 0 0 464s US.Steel_X1938 0 0 464s US.Steel_X1939 0 0 464s US.Steel_X1940 0 0 464s US.Steel_X1941 0 0 464s US.Steel_X1942 0 0 464s US.Steel_X1943 0 0 464s US.Steel_X1944 0 0 464s US.Steel_X1945 0 0 464s US.Steel_X1946 0 0 464s US.Steel_X1947 0 0 464s US.Steel_X1948 0 0 464s US.Steel_X1949 0 0 464s US.Steel_X1950 0 0 464s US.Steel_X1951 0 0 464s US.Steel_X1952 0 0 464s US.Steel_X1953 0 0 464s US.Steel_X1954 0 0 464s Westinghouse_X1935 1 192 464s Westinghouse_X1936 1 516 464s Westinghouse_X1937 1 729 464s Westinghouse_X1938 1 560 464s Westinghouse_X1939 1 520 464s Westinghouse_X1940 1 628 464s Westinghouse_X1941 1 537 464s Westinghouse_X1942 1 561 464s Westinghouse_X1943 1 617 464s Westinghouse_X1944 1 627 464s Westinghouse_X1945 1 737 464s Westinghouse_X1946 1 760 464s Westinghouse_X1947 1 581 464s Westinghouse_X1948 1 662 464s Westinghouse_X1949 1 584 464s Westinghouse_X1950 1 635 464s Westinghouse_X1951 1 724 464s Westinghouse_X1952 1 864 464s Westinghouse_X1953 1 1194 464s Westinghouse_X1954 1 1189 464s Westinghouse_capital 464s Chrysler_X1935 0.0 464s Chrysler_X1936 0.0 464s Chrysler_X1937 0.0 464s Chrysler_X1938 0.0 464s Chrysler_X1939 0.0 464s Chrysler_X1940 0.0 464s Chrysler_X1941 0.0 464s Chrysler_X1942 0.0 464s Chrysler_X1943 0.0 464s Chrysler_X1944 0.0 464s Chrysler_X1945 0.0 464s Chrysler_X1946 0.0 464s Chrysler_X1947 0.0 464s Chrysler_X1948 0.0 464s Chrysler_X1949 0.0 464s Chrysler_X1950 0.0 464s Chrysler_X1951 0.0 464s Chrysler_X1952 0.0 464s Chrysler_X1953 0.0 464s Chrysler_X1954 0.0 464s General.Electric_X1935 0.0 464s General.Electric_X1936 0.0 464s General.Electric_X1937 0.0 464s General.Electric_X1938 0.0 464s General.Electric_X1939 0.0 464s General.Electric_X1940 0.0 464s General.Electric_X1941 0.0 464s General.Electric_X1942 0.0 464s General.Electric_X1943 0.0 464s General.Electric_X1944 0.0 464s General.Electric_X1945 0.0 464s General.Electric_X1946 0.0 464s General.Electric_X1947 0.0 464s General.Electric_X1948 0.0 464s General.Electric_X1949 0.0 464s General.Electric_X1950 0.0 464s General.Electric_X1951 0.0 464s General.Electric_X1952 0.0 464s General.Electric_X1953 0.0 464s General.Electric_X1954 0.0 464s General.Motors_X1935 0.0 464s General.Motors_X1936 0.0 464s General.Motors_X1937 0.0 464s General.Motors_X1938 0.0 464s General.Motors_X1939 0.0 464s General.Motors_X1940 0.0 464s General.Motors_X1941 0.0 464s General.Motors_X1942 0.0 464s General.Motors_X1943 0.0 464s General.Motors_X1944 0.0 464s General.Motors_X1945 0.0 464s General.Motors_X1946 0.0 464s General.Motors_X1947 0.0 464s General.Motors_X1948 0.0 464s General.Motors_X1949 0.0 464s General.Motors_X1950 0.0 464s General.Motors_X1951 0.0 464s General.Motors_X1952 0.0 464s General.Motors_X1953 0.0 464s General.Motors_X1954 0.0 464s US.Steel_X1935 0.0 464s US.Steel_X1936 0.0 464s US.Steel_X1937 0.0 464s US.Steel_X1938 0.0 464s US.Steel_X1939 0.0 464s US.Steel_X1940 0.0 464s US.Steel_X1941 0.0 464s US.Steel_X1942 0.0 464s US.Steel_X1943 0.0 464s US.Steel_X1944 0.0 464s US.Steel_X1945 0.0 464s US.Steel_X1946 0.0 464s US.Steel_X1947 0.0 464s US.Steel_X1948 0.0 464s US.Steel_X1949 0.0 464s US.Steel_X1950 0.0 464s US.Steel_X1951 0.0 464s US.Steel_X1952 0.0 464s US.Steel_X1953 0.0 464s US.Steel_X1954 0.0 464s Westinghouse_X1935 1.8 464s Westinghouse_X1936 0.8 464s Westinghouse_X1937 7.4 464s Westinghouse_X1938 18.1 464s Westinghouse_X1939 23.5 464s Westinghouse_X1940 26.5 464s Westinghouse_X1941 36.2 464s Westinghouse_X1942 60.8 464s Westinghouse_X1943 84.4 464s Westinghouse_X1944 91.2 464s Westinghouse_X1945 92.4 464s Westinghouse_X1946 86.0 464s Westinghouse_X1947 111.1 464s Westinghouse_X1948 130.6 464s Westinghouse_X1949 141.8 464s Westinghouse_X1950 136.7 464s Westinghouse_X1951 129.7 464s Westinghouse_X1952 145.5 464s Westinghouse_X1953 174.8 464s Westinghouse_X1954 213.5 464s $Chrysler 464s Chrysler_invest ~ Chrysler_value + Chrysler_capital 464s 464s 464s $General.Electric 464s General.Electric_invest ~ General.Electric_value + General.Electric_capital 464s 464s 464s $General.Motors 464s General.Motors_invest ~ General.Motors_value + General.Motors_capital 464s 464s 464s $US.Steel 464s US.Steel_invest ~ US.Steel_value + US.Steel_capital 464s 464s 464s $Westinghouse 464s Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 464s 464s 464s Chrysler_invest ~ Chrysler_value + Chrysler_capital 464s 464s $Chrysler 464s Chrysler_invest ~ Chrysler_value + Chrysler_capital 464s attr(,"variables") 464s list(Chrysler_invest, Chrysler_value, Chrysler_capital) 464s attr(,"factors") 464s Chrysler_value Chrysler_capital 464s Chrysler_invest 0 0 464s Chrysler_value 1 0 464s Chrysler_capital 0 1 464s attr(,"term.labels") 464s [1] "Chrysler_value" "Chrysler_capital" 464s attr(,"order") 464s [1] 1 1 464s attr(,"intercept") 464s [1] 1 464s attr(,"response") 464s [1] 1 464s attr(,".Environment") 464s 464s attr(,"predvars") 464s list(Chrysler_invest, Chrysler_value, Chrysler_capital) 464s attr(,"dataClasses") 464s Chrysler_invest Chrysler_value Chrysler_capital 464s "numeric" "numeric" "numeric" 464s 464s $General.Electric 464s General.Electric_invest ~ General.Electric_value + General.Electric_capital 464s attr(,"variables") 464s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 464s attr(,"factors") 464s General.Electric_value General.Electric_capital 464s General.Electric_invest 0 0 464s General.Electric_value 1 0 464s General.Electric_capital 0 1 464s attr(,"term.labels") 464s [1] "General.Electric_value" "General.Electric_capital" 464s attr(,"order") 464s [1] 1 1 464s attr(,"intercept") 464s [1] 1 464s attr(,"response") 464s [1] 1 464s attr(,".Environment") 464s 464s attr(,"predvars") 464s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 464s attr(,"dataClasses") 464s General.Electric_invest General.Electric_value General.Electric_capital 464s "numeric" "numeric" "numeric" 464s 464s $General.Motors 464s General.Motors_invest ~ General.Motors_value + General.Motors_capital 464s attr(,"variables") 464s list(General.Motors_invest, General.Motors_value, General.Motors_capital) 464s attr(,"factors") 464s General.Motors_value General.Motors_capital 464s General.Motors_invest 0 0 464s General.Motors_value 1 0 464s General.Motors_capital 0 1 464s attr(,"term.labels") 464s [1] "General.Motors_value" "General.Motors_capital" 464s attr(,"order") 464s [1] 1 1 464s attr(,"intercept") 464s [1] 1 464s attr(,"response") 464s [1] 1 464s attr(,".Environment") 464s 464s attr(,"predvars") 464s list(General.Motors_invest, General.Motors_value, General.Motors_capital) 464s attr(,"dataClasses") 464s General.Motors_invest General.Motors_value General.Motors_capital 464s "numeric" "numeric" "numeric" 464s 464s $US.Steel 464s US.Steel_invest ~ US.Steel_value + US.Steel_capital 464s attr(,"variables") 464s list(US.Steel_invest, US.Steel_value, US.Steel_capital) 464s attr(,"factors") 464s US.Steel_value US.Steel_capital 464s US.Steel_invest 0 0 464s US.Steel_value 1 0 464s US.Steel_capital 0 1 464s attr(,"term.labels") 464s [1] "US.Steel_value" "US.Steel_capital" 464s attr(,"order") 464s [1] 1 1 464s attr(,"intercept") 464s [1] 1 464s attr(,"response") 464s [1] 1 464s attr(,".Environment") 464s 464s attr(,"predvars") 464s list(US.Steel_invest, US.Steel_value, US.Steel_capital) 464s attr(,"dataClasses") 464s US.Steel_invest US.Steel_value US.Steel_capital 464s "numeric" "numeric" "numeric" 464s 464s $Westinghouse 464s Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 464s attr(,"variables") 464s list(Westinghouse_invest, Westinghouse_value, Westinghouse_capital) 464s attr(,"factors") 464s Westinghouse_value Westinghouse_capital 464s Westinghouse_invest 0 0 464s Westinghouse_value 1 0 464s Westinghouse_capital 0 1 464s attr(,"term.labels") 464s [1] "Westinghouse_value" "Westinghouse_capital" 464s attr(,"order") 464s [1] 1 1 464s attr(,"intercept") 464s [1] 1 464s attr(,"response") 464s [1] 1 464s attr(,".Environment") 464s 464s attr(,"predvars") 464s list(Westinghouse_invest, Westinghouse_value, Westinghouse_capital) 464s attr(,"dataClasses") 464s Westinghouse_invest Westinghouse_value Westinghouse_capital 464s "numeric" "numeric" "numeric" 464s 464s Chrysler_invest ~ Chrysler_value + Chrysler_capital 464s attr(,"variables") 464s list(Chrysler_invest, Chrysler_value, Chrysler_capital) 464s attr(,"factors") 464s Chrysler_value Chrysler_capital 464s Chrysler_invest 0 0 464s Chrysler_value 1 0 464s Chrysler_capital 0 1 464s attr(,"term.labels") 464s [1] "Chrysler_value" "Chrysler_capital" 464s attr(,"order") 464s [1] 1 1 464s attr(,"intercept") 464s [1] 1 464s attr(,"response") 464s [1] 1 464s attr(,".Environment") 464s 464s attr(,"predvars") 464s list(Chrysler_invest, Chrysler_value, Chrysler_capital) 464s attr(,"dataClasses") 464s Chrysler_invest Chrysler_value Chrysler_capital 464s "numeric" "numeric" "numeric" 464s > 464s > # SUR 464s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 464s + greeneSur <- systemfit( formulaGrunfeld, "SUR", 464s + data = GrunfeldGreene, methodResidCov = "noDfCor", useMatrix = useMatrix ) 464s + print( greeneSur ) 464s + print( summary( greeneSur ) ) 464s + print( summary( greeneSur, useDfSys = TRUE, residCov = FALSE ) ) 464s + print( summary( greeneSur, equations = FALSE ) ) 464s + print( coef( greeneSur ) ) 464s + print( coef( summary( greeneSur ) ) ) 464s + print( vcov( greeneSur ) ) 464s + print( residuals( greeneSur ) ) 464s + print( confint( greeneSur ) ) 464s + print( fitted( greeneSur ) ) 464s + print( logLik( greeneSur ) ) 464s + print( logLik( greeneSur, residCovDiag = TRUE ) ) 464s + print( nobs( greeneSur ) ) 464s + print( model.frame( greeneSur ) ) 464s + print( model.matrix( greeneSur ) ) 464s + print( formula( greeneSur ) ) 464s + print( formula( greeneSur$eq[[ 1 ]] ) ) 464s + print( terms( greeneSur ) ) 464s + print( terms( greeneSur$eq[[ 1 ]] ) ) 464s + } 464s 464s systemfit results 464s method: SUR 464s 464s Coefficients: 464s Chrysler_(Intercept) Chrysler_value 464s 0.5043 0.0695 464s Chrysler_capital General.Electric_(Intercept) 464s 0.3085 -22.4389 464s General.Electric_value General.Electric_capital 464s 0.0373 0.1308 464s General.Motors_(Intercept) General.Motors_value 464s -162.3641 0.1205 464s General.Motors_capital US.Steel_(Intercept) 464s 0.3827 85.4233 464s US.Steel_value US.Steel_capital 464s 0.1015 0.4000 464s Westinghouse_(Intercept) Westinghouse_value 464s 1.0889 0.0570 464s Westinghouse_capital 464s 0.0415 464s 464s systemfit results 464s method: SUR 464s 464s N DF SSR detRCov OLS-R2 McElroy-R2 464s system 100 85 347048 6.18e+13 0.844 0.869 464s 464s N DF SSR MSE RMSE R2 Adj R2 464s Chrysler 20 17 3057 180 13.4 0.912 0.901 464s General.Electric 20 17 14009 824 28.7 0.688 0.651 464s General.Motors 20 17 144321 8489 92.1 0.921 0.911 464s US.Steel 20 17 183763 10810 104.0 0.422 0.354 464s Westinghouse 20 17 1898 112 10.6 0.726 0.694 464s 464s The covariance matrix of the residuals used for estimation 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 149.9 -21.4 -283 418 13.3 464s General.Electric -21.4 660.8 608 905 176.4 464s General.Motors -282.8 607.5 7160 -2222 126.2 464s US.Steel 418.1 905.0 -2222 8896 546.2 464s Westinghouse 13.3 176.4 126 546 88.7 464s 464s The covariance matrix of the residuals 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 152.85 2.05 -314 455 16.7 464s General.Electric 2.05 700.46 605 1224 200.3 464s General.Motors -313.70 605.34 7216 -2687 129.9 464s US.Steel 455.09 1224.41 -2687 9188 652.7 464s Westinghouse 16.66 200.32 130 653 94.9 464s 464s The correlations of the residuals 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 1.00000 0.00626 -0.299 0.384 0.138 464s General.Electric 0.00626 1.00000 0.269 0.483 0.777 464s General.Motors -0.29870 0.26925 1.000 -0.330 0.157 464s US.Steel 0.38402 0.48264 -0.330 1.000 0.699 464s Westinghouse 0.13832 0.77690 0.157 0.699 1.000 464s 464s 464s SUR estimates for 'Chrysler' (equation 1) 464s Model Formula: Chrysler_invest ~ Chrysler_value + Chrysler_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) 0.5043 11.5128 0.04 0.96557 464s value 0.0695 0.0169 4.12 0.00072 *** 464s capital 0.3085 0.0259 11.93 1.1e-09 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 13.41 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 3056.985 MSE: 179.823 Root MSE: 13.41 464s Multiple R-Squared: 0.912 Adjusted R-Squared: 0.901 464s 464s 464s SUR estimates for 'General.Electric' (equation 2) 464s Model Formula: General.Electric_invest ~ General.Electric_value + General.Electric_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -22.4389 25.5186 -0.88 0.3915 464s value 0.0373 0.0123 3.04 0.0074 ** 464s capital 0.1308 0.0220 5.93 1.6e-05 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 28.707 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 14009.115 MSE: 824.066 Root MSE: 28.707 464s Multiple R-Squared: 0.688 Adjusted R-Squared: 0.651 464s 464s 464s SUR estimates for 'General.Motors' (equation 3) 464s Model Formula: General.Motors_invest ~ General.Motors_value + General.Motors_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -162.3641 89.4592 -1.81 0.087 . 464s value 0.1205 0.0216 5.57 3.4e-05 *** 464s capital 0.3827 0.0328 11.68 1.5e-09 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 92.138 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 144320.876 MSE: 8489.463 Root MSE: 92.138 464s Multiple R-Squared: 0.921 Adjusted R-Squared: 0.911 464s 464s 464s SUR estimates for 'US.Steel' (equation 4) 464s Model Formula: US.Steel_invest ~ US.Steel_value + US.Steel_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) 85.4233 111.8774 0.76 0.4556 464s value 0.1015 0.0548 1.85 0.0814 . 464s capital 0.4000 0.1278 3.13 0.0061 ** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 103.969 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 183763.011 MSE: 10809.589 Root MSE: 103.969 464s Multiple R-Squared: 0.422 Adjusted R-Squared: 0.354 464s 464s 464s SUR estimates for 'Westinghouse' (equation 5) 464s Model Formula: Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) 1.0889 6.2588 0.17 0.86394 464s value 0.0570 0.0114 5.02 0.00011 *** 464s capital 0.0415 0.0412 1.01 0.32787 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 10.567 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 1898.249 MSE: 111.662 Root MSE: 10.567 464s Multiple R-Squared: 0.726 Adjusted R-Squared: 0.694 464s 464s 464s systemfit results 464s method: SUR 464s 464s N DF SSR detRCov OLS-R2 McElroy-R2 464s system 100 85 347048 6.18e+13 0.844 0.869 464s 464s N DF SSR MSE RMSE R2 Adj R2 464s Chrysler 20 17 3057 180 13.4 0.912 0.901 464s General.Electric 20 17 14009 824 28.7 0.688 0.651 464s General.Motors 20 17 144321 8489 92.1 0.921 0.911 464s US.Steel 20 17 183763 10810 104.0 0.422 0.354 464s Westinghouse 20 17 1898 112 10.6 0.726 0.694 464s 464s 464s SUR estimates for 'Chrysler' (equation 1) 464s Model Formula: Chrysler_invest ~ Chrysler_value + Chrysler_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) 0.5043 11.5128 0.04 0.97 464s value 0.0695 0.0169 4.12 8.9e-05 *** 464s capital 0.3085 0.0259 11.93 < 2e-16 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 13.41 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 3056.985 MSE: 179.823 Root MSE: 13.41 464s Multiple R-Squared: 0.912 Adjusted R-Squared: 0.901 464s 464s 464s SUR estimates for 'General.Electric' (equation 2) 464s Model Formula: General.Electric_invest ~ General.Electric_value + General.Electric_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -22.4389 25.5186 -0.88 0.3817 464s value 0.0373 0.0123 3.04 0.0031 ** 464s capital 0.1308 0.0220 5.93 6.3e-08 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 28.707 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 14009.115 MSE: 824.066 Root MSE: 28.707 464s Multiple R-Squared: 0.688 Adjusted R-Squared: 0.651 464s 464s 464s SUR estimates for 'General.Motors' (equation 3) 464s Model Formula: General.Motors_invest ~ General.Motors_value + General.Motors_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -162.3641 89.4592 -1.81 0.073 . 464s value 0.1205 0.0216 5.57 2.9e-07 *** 464s capital 0.3827 0.0328 11.68 < 2e-16 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 92.138 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 144320.876 MSE: 8489.463 Root MSE: 92.138 464s Multiple R-Squared: 0.921 Adjusted R-Squared: 0.911 464s 464s 464s SUR estimates for 'US.Steel' (equation 4) 464s Model Formula: US.Steel_invest ~ US.Steel_value + US.Steel_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) 85.4233 111.8774 0.76 0.4473 464s value 0.1015 0.0548 1.85 0.0674 . 464s capital 0.4000 0.1278 3.13 0.0024 ** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 103.969 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 183763.011 MSE: 10809.589 Root MSE: 103.969 464s Multiple R-Squared: 0.422 Adjusted R-Squared: 0.354 464s 464s 464s SUR estimates for 'Westinghouse' (equation 5) 464s Model Formula: Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) 1.0889 6.2588 0.17 0.86 464s value 0.0570 0.0114 5.02 2.8e-06 *** 464s capital 0.0415 0.0412 1.01 0.32 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 10.567 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 1898.249 MSE: 111.662 Root MSE: 10.567 464s Multiple R-Squared: 0.726 Adjusted R-Squared: 0.694 464s 464s 464s systemfit results 464s method: SUR 464s 464s N DF SSR detRCov OLS-R2 McElroy-R2 464s system 100 85 347048 6.18e+13 0.844 0.869 464s 464s N DF SSR MSE RMSE R2 Adj R2 464s Chrysler 20 17 3057 180 13.4 0.912 0.901 464s General.Electric 20 17 14009 824 28.7 0.688 0.651 464s General.Motors 20 17 144321 8489 92.1 0.921 0.911 464s US.Steel 20 17 183763 10810 104.0 0.422 0.354 464s Westinghouse 20 17 1898 112 10.6 0.726 0.694 464s 464s The covariance matrix of the residuals used for estimation 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 149.9 -21.4 -283 418 13.3 464s General.Electric -21.4 660.8 608 905 176.4 464s General.Motors -282.8 607.5 7160 -2222 126.2 464s US.Steel 418.1 905.0 -2222 8896 546.2 464s Westinghouse 13.3 176.4 126 546 88.7 464s 464s The covariance matrix of the residuals 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 152.85 2.05 -314 455 16.7 464s General.Electric 2.05 700.46 605 1224 200.3 464s General.Motors -313.70 605.34 7216 -2687 129.9 464s US.Steel 455.09 1224.41 -2687 9188 652.7 464s Westinghouse 16.66 200.32 130 653 94.9 464s 464s The correlations of the residuals 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 1.00000 0.00626 -0.299 0.384 0.138 464s General.Electric 0.00626 1.00000 0.269 0.483 0.777 464s General.Motors -0.29870 0.26925 1.000 -0.330 0.157 464s US.Steel 0.38402 0.48264 -0.330 1.000 0.699 464s Westinghouse 0.13832 0.77690 0.157 0.699 1.000 464s 464s 464s Coefficients: 464s Estimate Std. Error t value Pr(>|t|) 464s Chrysler_(Intercept) 0.5043 11.5128 0.04 0.96557 464s Chrysler_value 0.0695 0.0169 4.12 0.00072 *** 464s Chrysler_capital 0.3085 0.0259 11.93 1.1e-09 *** 464s General.Electric_(Intercept) -22.4389 25.5186 -0.88 0.39149 464s General.Electric_value 0.0373 0.0123 3.04 0.00738 ** 464s General.Electric_capital 0.1308 0.0220 5.93 1.6e-05 *** 464s General.Motors_(Intercept) -162.3641 89.4592 -1.81 0.08722 . 464s General.Motors_value 0.1205 0.0216 5.57 3.4e-05 *** 464s General.Motors_capital 0.3827 0.0328 11.68 1.5e-09 *** 464s US.Steel_(Intercept) 85.4233 111.8774 0.76 0.45561 464s US.Steel_value 0.1015 0.0548 1.85 0.08142 . 464s US.Steel_capital 0.4000 0.1278 3.13 0.00610 ** 464s Westinghouse_(Intercept) 1.0889 6.2588 0.17 0.86394 464s Westinghouse_value 0.0570 0.0114 5.02 0.00011 *** 464s Westinghouse_capital 0.0415 0.0412 1.01 0.32787 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s Chrysler_(Intercept) Chrysler_value 464s 0.5043 0.0695 464s Chrysler_capital General.Electric_(Intercept) 464s 0.3085 -22.4389 464s General.Electric_value General.Electric_capital 464s 0.0373 0.1308 464s General.Motors_(Intercept) General.Motors_value 464s -162.3641 0.1205 464s General.Motors_capital US.Steel_(Intercept) 464s 0.3827 85.4233 464s US.Steel_value US.Steel_capital 464s 0.1015 0.4000 464s Westinghouse_(Intercept) Westinghouse_value 464s 1.0889 0.0570 464s Westinghouse_capital 464s 0.0415 464s Estimate Std. Error t value Pr(>|t|) 464s Chrysler_(Intercept) 0.5043 11.5128 0.0438 9.66e-01 464s Chrysler_value 0.0695 0.0169 4.1157 7.22e-04 464s Chrysler_capital 0.3085 0.0259 11.9297 1.10e-09 464s General.Electric_(Intercept) -22.4389 25.5186 -0.8793 3.91e-01 464s General.Electric_value 0.0373 0.0123 3.0409 7.38e-03 464s General.Electric_capital 0.1308 0.0220 5.9313 1.64e-05 464s General.Motors_(Intercept) -162.3641 89.4592 -1.8150 8.72e-02 464s General.Motors_value 0.1205 0.0216 5.5709 3.38e-05 464s General.Motors_capital 0.3827 0.0328 11.6805 1.52e-09 464s US.Steel_(Intercept) 85.4233 111.8774 0.7635 4.56e-01 464s US.Steel_value 0.1015 0.0548 1.8523 8.14e-02 464s US.Steel_capital 0.4000 0.1278 3.1300 6.10e-03 464s Westinghouse_(Intercept) 1.0889 6.2588 0.1740 8.64e-01 464s Westinghouse_value 0.0570 0.0114 5.0174 1.06e-04 464s Westinghouse_capital 0.0415 0.0412 1.0074 3.28e-01 464s Chrysler_(Intercept) Chrysler_value 464s Chrysler_(Intercept) 1.33e+02 -1.82e-01 464s Chrysler_value -1.82e-01 2.86e-04 464s Chrysler_capital 9.57e-03 -1.31e-04 464s General.Electric_(Intercept) -2.94e+01 3.74e-02 464s General.Electric_value 1.28e-02 -1.86e-05 464s General.Electric_capital 8.80e-03 -2.96e-06 464s General.Motors_(Intercept) -1.56e+02 1.91e-01 464s General.Motors_value 3.28e-02 -4.91e-05 464s General.Motors_capital -8.18e-04 3.42e-05 464s US.Steel_(Intercept) 1.80e+02 -1.87e-01 464s US.Steel_value -7.46e-02 1.13e-04 464s US.Steel_capital -4.03e-02 -1.22e-04 464s Westinghouse_(Intercept) -3.04e-01 3.03e-03 464s Westinghouse_value 1.14e-03 -3.70e-06 464s Westinghouse_capital 2.42e-03 -6.41e-06 464s Chrysler_capital General.Electric_(Intercept) 464s Chrysler_(Intercept) 9.57e-03 -29.3642 464s Chrysler_value -1.31e-04 0.0374 464s Chrysler_capital 6.69e-04 0.0198 464s General.Electric_(Intercept) 1.98e-02 651.1982 464s General.Electric_value 1.28e-06 -0.2851 464s General.Electric_capital -5.56e-05 -0.1615 464s General.Motors_(Intercept) 7.79e-02 571.3402 464s General.Motors_value 1.03e-05 -0.1196 464s General.Motors_capital -1.89e-04 -0.0352 464s US.Steel_(Intercept) -2.45e-01 644.2920 464s US.Steel_value -3.26e-05 -0.2201 464s US.Steel_capital 1.03e-03 -0.5505 464s Westinghouse_(Intercept) -9.35e-03 102.8679 464s Westinghouse_value 1.18e-05 -0.1700 464s Westinghouse_capital 1.67e-05 0.2338 464s General.Electric_value General.Electric_capital 464s Chrysler_(Intercept) 1.28e-02 8.80e-03 464s Chrysler_value -1.86e-05 -2.96e-06 464s Chrysler_capital 1.28e-06 -5.56e-05 464s General.Electric_(Intercept) -2.85e-01 -1.61e-01 464s General.Electric_value 1.50e-04 -1.70e-05 464s General.Electric_capital -1.70e-05 4.86e-04 464s General.Motors_(Intercept) -2.61e-01 -8.74e-02 464s General.Motors_value 6.35e-05 -9.49e-06 464s General.Motors_capital -2.27e-05 1.98e-04 464s US.Steel_(Intercept) -3.04e-01 -2.30e-02 464s US.Steel_value 1.35e-04 -1.07e-04 464s US.Steel_capital 1.23e-04 7.77e-04 464s Westinghouse_(Intercept) -4.02e-02 -4.02e-02 464s Westinghouse_value 8.74e-05 1.04e-06 464s Westinghouse_capital -2.16e-04 4.61e-04 464s General.Motors_(Intercept) General.Motors_value 464s Chrysler_(Intercept) -1.56e+02 3.28e-02 464s Chrysler_value 1.91e-01 -4.91e-05 464s Chrysler_capital 7.79e-02 1.03e-05 464s General.Electric_(Intercept) 5.71e+02 -1.20e-01 464s General.Electric_value -2.61e-01 6.35e-05 464s General.Electric_capital -8.74e-02 -9.49e-06 464s General.Motors_(Intercept) 8.00e+03 -1.84e+00 464s General.Motors_value -1.84e+00 4.68e-04 464s General.Motors_capital 5.32e-01 -2.83e-04 464s US.Steel_(Intercept) -1.75e+03 3.73e-01 464s US.Steel_value 8.02e-01 -2.06e-04 464s US.Steel_capital 2.01e-01 1.09e-04 464s Westinghouse_(Intercept) 1.10e+02 -2.33e-02 464s Westinghouse_value -2.06e-01 5.10e-05 464s Westinghouse_capital 3.98e-01 -1.28e-04 464s General.Motors_capital US.Steel_(Intercept) 464s Chrysler_(Intercept) -8.18e-04 1.80e+02 464s Chrysler_value 3.42e-05 -1.87e-01 464s Chrysler_capital -1.89e-04 -2.45e-01 464s General.Electric_(Intercept) -3.52e-02 6.44e+02 464s General.Electric_value -2.27e-05 -3.04e-01 464s General.Electric_capital 1.98e-04 -2.30e-02 464s General.Motors_(Intercept) 5.32e-01 -1.75e+03 464s General.Motors_value -2.83e-04 3.73e-01 464s General.Motors_capital 1.07e-03 3.74e-02 464s US.Steel_(Intercept) 3.74e-02 1.25e+04 464s US.Steel_value 1.39e-04 -5.65e+00 464s US.Steel_capital -1.04e-03 -3.12e+00 464s Westinghouse_(Intercept) -4.87e-03 2.74e+02 464s Westinghouse_value -2.38e-05 -5.09e-01 464s Westinghouse_capital 2.43e-04 1.10e+00 464s US.Steel_value US.Steel_capital 464s Chrysler_(Intercept) -7.46e-02 -0.040281 464s Chrysler_value 1.13e-04 -0.000122 464s Chrysler_capital -3.26e-05 0.001031 464s General.Electric_(Intercept) -2.20e-01 -0.550482 464s General.Electric_value 1.35e-04 0.000123 464s General.Electric_capital -1.07e-04 0.000777 464s General.Motors_(Intercept) 8.02e-01 0.200945 464s General.Motors_value -2.06e-04 0.000109 464s General.Motors_capital 1.39e-04 -0.001036 464s US.Steel_(Intercept) -5.65e+00 -3.119830 464s US.Steel_value 3.00e-03 -0.000901 464s US.Steel_capital -9.01e-04 0.016331 464s Westinghouse_(Intercept) -8.35e-02 -0.275101 464s Westinghouse_value 2.23e-04 0.000229 464s Westinghouse_capital -7.74e-04 0.001422 464s Westinghouse_(Intercept) Westinghouse_value 464s Chrysler_(Intercept) -0.30387 1.14e-03 464s Chrysler_value 0.00303 -3.70e-06 464s Chrysler_capital -0.00935 1.18e-05 464s General.Electric_(Intercept) 102.86790 -1.70e-01 464s General.Electric_value -0.04016 8.74e-05 464s General.Electric_capital -0.04021 1.04e-06 464s General.Motors_(Intercept) 110.26166 -2.06e-01 464s General.Motors_value -0.02326 5.10e-05 464s General.Motors_capital -0.00487 -2.38e-05 464s US.Steel_(Intercept) 274.40848 -5.09e-01 464s US.Steel_value -0.08348 2.23e-04 464s US.Steel_capital -0.27510 2.29e-04 464s Westinghouse_(Intercept) 39.17263 -5.99e-02 464s Westinghouse_value -0.05992 1.29e-04 464s Westinghouse_capital 0.06376 -3.12e-04 464s Westinghouse_capital 464s Chrysler_(Intercept) 2.42e-03 464s Chrysler_value -6.41e-06 464s Chrysler_capital 1.67e-05 464s General.Electric_(Intercept) 2.34e-01 464s General.Electric_value -2.16e-04 464s General.Electric_capital 4.61e-04 464s General.Motors_(Intercept) 3.98e-01 464s General.Motors_value -1.28e-04 464s General.Motors_capital 2.43e-04 464s US.Steel_(Intercept) 1.10e+00 464s US.Steel_value -7.74e-04 464s US.Steel_capital 1.42e-03 464s Westinghouse_(Intercept) 6.38e-02 464s Westinghouse_value -3.12e-04 464s Westinghouse_capital 1.70e-03 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s X1935 7.511 -0.905 107.95 -35.3 0.849 464s X1936 10.843 -21.387 -27.67 66.3 -4.639 464s X1937 -6.422 -20.333 -136.20 65.7 -7.906 464s X1938 4.659 -29.453 3.55 -110.1 -10.898 464s X1939 -15.204 -36.171 -104.40 -178.7 -12.863 464s X1940 -2.413 -7.078 -15.30 -149.0 -9.449 464s X1941 -0.116 38.153 28.30 41.3 15.299 464s X1942 -4.311 17.481 103.23 20.6 7.734 464s X1943 -14.728 -23.336 72.44 -46.0 -2.758 464s X1944 -8.172 -25.700 105.03 -92.9 -2.792 464s X1945 12.566 -0.629 38.84 -100.0 -7.681 464s X1946 -14.709 54.737 106.00 32.0 5.446 464s X1947 -7.958 48.169 14.88 46.8 16.715 464s X1948 6.548 37.841 -53.65 121.3 5.293 464s X1949 -8.057 -13.518 -118.82 10.1 -8.216 464s X1950 1.571 -28.788 -67.90 20.0 -10.735 464s X1951 41.064 1.996 -126.32 133.6 6.645 464s X1952 4.273 7.222 -87.37 163.0 15.390 464s X1953 -2.011 8.833 34.43 100.0 13.695 464s X1954 -4.934 -7.135 122.97 -108.7 -9.129 464s 2.5 % 97.5 % 464s Chrysler_(Intercept) -23.786 24.794 464s Chrysler_value 0.034 0.105 464s Chrysler_capital 0.254 0.363 464s General.Electric_(Intercept) -76.278 31.401 464s General.Electric_value 0.011 0.063 464s General.Electric_capital 0.084 0.177 464s General.Motors_(Intercept) -351.107 26.378 464s General.Motors_value 0.075 0.166 464s General.Motors_capital 0.314 0.452 464s US.Steel_(Intercept) -150.617 321.464 464s US.Steel_value -0.014 0.217 464s US.Steel_capital 0.130 0.670 464s Westinghouse_(Intercept) -12.116 14.294 464s Westinghouse_value 0.033 0.081 464s Westinghouse_capital -0.045 0.128 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s X1935 32.8 34.0 210 245 12.1 464s X1936 61.9 66.4 419 289 30.5 464s X1937 72.7 97.5 547 404 43.0 464s X1938 46.9 74.1 254 372 33.8 464s X1939 67.6 84.3 435 409 31.7 464s X1940 71.8 81.5 476 411 38.0 464s X1941 68.5 74.8 484 432 33.2 464s X1942 51.1 74.4 345 425 35.6 464s X1943 62.1 84.6 427 408 39.8 464s X1944 67.7 82.5 442 381 40.6 464s X1945 76.2 94.2 522 359 47.0 464s X1946 88.8 105.2 582 388 48.0 464s X1947 70.6 99.0 554 374 38.8 464s X1948 82.8 108.5 583 373 44.3 464s X1949 87.0 111.8 674 395 40.3 464s X1950 99.1 122.3 711 399 43.0 464s X1951 119.6 133.2 882 455 47.7 464s X1952 140.7 150.1 979 482 56.4 464s X1953 176.9 170.7 1270 541 76.4 464s X1954 177.4 196.7 1364 568 77.7 464s 'log Lik.' -459 (df=30) 464s 'log Lik.' -483 (df=30) 464s [1] 100 464s Chrysler_invest Chrysler_value Chrysler_capital General.Electric_invest 464s X1935 40.3 418 10.5 33.1 464s X1936 72.8 838 10.2 45.0 464s X1937 66.3 884 34.7 77.2 464s X1938 51.6 438 51.8 44.6 464s X1939 52.4 680 64.3 48.1 464s X1940 69.4 728 67.1 74.4 464s X1941 68.3 644 75.2 113.0 464s X1942 46.8 411 71.4 91.9 464s X1943 47.4 588 67.1 61.3 464s X1944 59.6 698 60.5 56.8 464s X1945 88.8 846 54.6 93.6 464s X1946 74.1 894 84.8 159.9 464s X1947 62.7 579 96.8 147.2 464s X1948 89.4 695 110.2 146.3 464s X1949 79.0 590 147.4 98.3 464s X1950 100.7 694 163.2 93.5 464s X1951 160.6 809 203.5 135.2 464s X1952 145.0 727 290.6 157.3 464s X1953 174.9 1002 346.1 179.5 464s X1954 172.5 703 414.9 189.6 464s General.Electric_value General.Electric_capital General.Motors_invest 464s X1935 1171 97.8 318 464s X1936 2016 104.4 392 464s X1937 2803 118.0 411 464s X1938 2040 156.2 258 464s X1939 2256 172.6 331 464s X1940 2132 186.6 461 464s X1941 1834 220.9 512 464s X1942 1588 287.8 448 464s X1943 1749 319.9 500 464s X1944 1687 321.3 548 464s X1945 2008 319.6 561 464s X1946 2208 346.0 688 464s X1947 1657 456.4 569 464s X1948 1604 543.4 529 464s X1949 1432 618.3 555 464s X1950 1610 647.4 643 464s X1951 1819 671.3 756 464s X1952 2080 726.1 891 464s X1953 2372 800.3 1304 464s X1954 2760 888.9 1487 464s General.Motors_value General.Motors_capital US.Steel_invest 464s X1935 3078 2.8 210 464s X1936 4662 52.6 355 464s X1937 5387 156.9 470 464s X1938 2792 209.2 262 464s X1939 4313 203.4 230 464s X1940 4644 207.2 262 464s X1941 4551 255.2 473 464s X1942 3244 303.7 446 464s X1943 4054 264.1 362 464s X1944 4379 201.6 288 464s X1945 4841 265.0 259 464s X1946 4901 402.2 420 464s X1947 3526 761.5 420 464s X1948 3255 922.4 494 464s X1949 3700 1020.1 405 464s X1950 3756 1099.0 419 464s X1951 4833 1207.7 588 464s X1952 4925 1430.5 645 464s X1953 6242 1777.3 641 464s X1954 5594 2226.3 459 464s US.Steel_value US.Steel_capital Westinghouse_invest Westinghouse_value 464s X1935 1362 53.8 12.9 192 464s X1936 1807 50.5 25.9 516 464s X1937 2676 118.1 35.0 729 464s X1938 1802 260.2 22.9 560 464s X1939 1957 312.7 18.8 520 464s X1940 2203 254.2 28.6 628 464s X1941 2380 261.4 48.5 537 464s X1942 2169 298.7 43.3 561 464s X1943 1985 301.8 37.0 617 464s X1944 1814 279.1 37.8 627 464s X1945 1850 213.8 39.3 737 464s X1946 2068 232.6 53.5 760 464s X1947 1797 264.8 55.6 581 464s X1948 1626 306.9 49.6 662 464s X1949 1667 351.1 32.0 584 464s X1950 1677 357.8 32.2 635 464s X1951 2290 342.1 54.4 724 464s X1952 2159 444.2 71.8 864 464s X1953 2031 623.6 90.1 1194 464s X1954 2116 669.7 68.6 1189 464s Westinghouse_capital 464s X1935 1.8 464s X1936 0.8 464s X1937 7.4 464s X1938 18.1 464s X1939 23.5 464s X1940 26.5 464s X1941 36.2 464s X1942 60.8 464s X1943 84.4 464s X1944 91.2 464s X1945 92.4 464s X1946 86.0 464s X1947 111.1 464s X1948 130.6 464s X1949 141.8 464s X1950 136.7 464s X1951 129.7 464s X1952 145.5 464s X1953 174.8 464s X1954 213.5 464s Chrysler_(Intercept) Chrysler_value Chrysler_capital 464s Chrysler_X1935 1 418 10.5 464s Chrysler_X1936 1 838 10.2 464s Chrysler_X1937 1 884 34.7 464s Chrysler_X1938 1 438 51.8 464s Chrysler_X1939 1 680 64.3 464s Chrysler_X1940 1 728 67.1 464s Chrysler_X1941 1 644 75.2 464s Chrysler_X1942 1 411 71.4 464s Chrysler_X1943 1 588 67.1 464s Chrysler_X1944 1 698 60.5 464s Chrysler_X1945 1 846 54.6 464s Chrysler_X1946 1 894 84.8 464s Chrysler_X1947 1 579 96.8 464s Chrysler_X1948 1 695 110.2 464s Chrysler_X1949 1 590 147.4 464s Chrysler_X1950 1 694 163.2 464s Chrysler_X1951 1 809 203.5 464s Chrysler_X1952 1 727 290.6 464s Chrysler_X1953 1 1002 346.1 464s Chrysler_X1954 1 703 414.9 464s General.Electric_X1935 0 0 0.0 464s General.Electric_X1936 0 0 0.0 464s General.Electric_X1937 0 0 0.0 464s General.Electric_X1938 0 0 0.0 464s General.Electric_X1939 0 0 0.0 464s General.Electric_X1940 0 0 0.0 464s General.Electric_X1941 0 0 0.0 464s General.Electric_X1942 0 0 0.0 464s General.Electric_X1943 0 0 0.0 464s General.Electric_X1944 0 0 0.0 464s General.Electric_X1945 0 0 0.0 464s General.Electric_X1946 0 0 0.0 464s General.Electric_X1947 0 0 0.0 464s General.Electric_X1948 0 0 0.0 464s General.Electric_X1949 0 0 0.0 464s General.Electric_X1950 0 0 0.0 464s General.Electric_X1951 0 0 0.0 464s General.Electric_X1952 0 0 0.0 464s General.Electric_X1953 0 0 0.0 464s General.Electric_X1954 0 0 0.0 464s General.Motors_X1935 0 0 0.0 464s General.Motors_X1936 0 0 0.0 464s General.Motors_X1937 0 0 0.0 464s General.Motors_X1938 0 0 0.0 464s General.Motors_X1939 0 0 0.0 464s General.Motors_X1940 0 0 0.0 464s General.Motors_X1941 0 0 0.0 464s General.Motors_X1942 0 0 0.0 464s General.Motors_X1943 0 0 0.0 464s General.Motors_X1944 0 0 0.0 464s General.Motors_X1945 0 0 0.0 464s General.Motors_X1946 0 0 0.0 464s General.Motors_X1947 0 0 0.0 464s General.Motors_X1948 0 0 0.0 464s General.Motors_X1949 0 0 0.0 464s General.Motors_X1950 0 0 0.0 464s General.Motors_X1951 0 0 0.0 464s General.Motors_X1952 0 0 0.0 464s General.Motors_X1953 0 0 0.0 464s General.Motors_X1954 0 0 0.0 464s US.Steel_X1935 0 0 0.0 464s US.Steel_X1936 0 0 0.0 464s US.Steel_X1937 0 0 0.0 464s US.Steel_X1938 0 0 0.0 464s US.Steel_X1939 0 0 0.0 464s US.Steel_X1940 0 0 0.0 464s US.Steel_X1941 0 0 0.0 464s US.Steel_X1942 0 0 0.0 464s US.Steel_X1943 0 0 0.0 464s US.Steel_X1944 0 0 0.0 464s US.Steel_X1945 0 0 0.0 464s US.Steel_X1946 0 0 0.0 464s US.Steel_X1947 0 0 0.0 464s US.Steel_X1948 0 0 0.0 464s US.Steel_X1949 0 0 0.0 464s US.Steel_X1950 0 0 0.0 464s US.Steel_X1951 0 0 0.0 464s US.Steel_X1952 0 0 0.0 464s US.Steel_X1953 0 0 0.0 464s US.Steel_X1954 0 0 0.0 464s Westinghouse_X1935 0 0 0.0 464s Westinghouse_X1936 0 0 0.0 464s Westinghouse_X1937 0 0 0.0 464s Westinghouse_X1938 0 0 0.0 464s Westinghouse_X1939 0 0 0.0 464s Westinghouse_X1940 0 0 0.0 464s Westinghouse_X1941 0 0 0.0 464s Westinghouse_X1942 0 0 0.0 464s Westinghouse_X1943 0 0 0.0 464s Westinghouse_X1944 0 0 0.0 464s Westinghouse_X1945 0 0 0.0 464s Westinghouse_X1946 0 0 0.0 464s Westinghouse_X1947 0 0 0.0 464s Westinghouse_X1948 0 0 0.0 464s Westinghouse_X1949 0 0 0.0 464s Westinghouse_X1950 0 0 0.0 464s Westinghouse_X1951 0 0 0.0 464s Westinghouse_X1952 0 0 0.0 464s Westinghouse_X1953 0 0 0.0 464s Westinghouse_X1954 0 0 0.0 464s General.Electric_(Intercept) General.Electric_value 464s Chrysler_X1935 0 0 464s Chrysler_X1936 0 0 464s Chrysler_X1937 0 0 464s Chrysler_X1938 0 0 464s Chrysler_X1939 0 0 464s Chrysler_X1940 0 0 464s Chrysler_X1941 0 0 464s Chrysler_X1942 0 0 464s Chrysler_X1943 0 0 464s Chrysler_X1944 0 0 464s Chrysler_X1945 0 0 464s Chrysler_X1946 0 0 464s Chrysler_X1947 0 0 464s Chrysler_X1948 0 0 464s Chrysler_X1949 0 0 464s Chrysler_X1950 0 0 464s Chrysler_X1951 0 0 464s Chrysler_X1952 0 0 464s Chrysler_X1953 0 0 464s Chrysler_X1954 0 0 464s General.Electric_X1935 1 1171 464s General.Electric_X1936 1 2016 464s General.Electric_X1937 1 2803 464s General.Electric_X1938 1 2040 464s General.Electric_X1939 1 2256 464s General.Electric_X1940 1 2132 464s General.Electric_X1941 1 1834 464s General.Electric_X1942 1 1588 464s General.Electric_X1943 1 1749 464s General.Electric_X1944 1 1687 464s General.Electric_X1945 1 2008 464s General.Electric_X1946 1 2208 464s General.Electric_X1947 1 1657 464s General.Electric_X1948 1 1604 464s General.Electric_X1949 1 1432 464s General.Electric_X1950 1 1610 464s General.Electric_X1951 1 1819 464s General.Electric_X1952 1 2080 464s General.Electric_X1953 1 2372 464s General.Electric_X1954 1 2760 464s General.Motors_X1935 0 0 464s General.Motors_X1936 0 0 464s General.Motors_X1937 0 0 464s General.Motors_X1938 0 0 464s General.Motors_X1939 0 0 464s General.Motors_X1940 0 0 464s General.Motors_X1941 0 0 464s General.Motors_X1942 0 0 464s General.Motors_X1943 0 0 464s General.Motors_X1944 0 0 464s General.Motors_X1945 0 0 464s General.Motors_X1946 0 0 464s General.Motors_X1947 0 0 464s General.Motors_X1948 0 0 464s General.Motors_X1949 0 0 464s General.Motors_X1950 0 0 464s General.Motors_X1951 0 0 464s General.Motors_X1952 0 0 464s General.Motors_X1953 0 0 464s General.Motors_X1954 0 0 464s US.Steel_X1935 0 0 464s US.Steel_X1936 0 0 464s US.Steel_X1937 0 0 464s US.Steel_X1938 0 0 464s US.Steel_X1939 0 0 464s US.Steel_X1940 0 0 464s US.Steel_X1941 0 0 464s US.Steel_X1942 0 0 464s US.Steel_X1943 0 0 464s US.Steel_X1944 0 0 464s US.Steel_X1945 0 0 464s US.Steel_X1946 0 0 464s US.Steel_X1947 0 0 464s US.Steel_X1948 0 0 464s US.Steel_X1949 0 0 464s US.Steel_X1950 0 0 464s US.Steel_X1951 0 0 464s US.Steel_X1952 0 0 464s US.Steel_X1953 0 0 464s US.Steel_X1954 0 0 464s Westinghouse_X1935 0 0 464s Westinghouse_X1936 0 0 464s Westinghouse_X1937 0 0 464s Westinghouse_X1938 0 0 464s Westinghouse_X1939 0 0 464s Westinghouse_X1940 0 0 464s Westinghouse_X1941 0 0 464s Westinghouse_X1942 0 0 464s Westinghouse_X1943 0 0 464s Westinghouse_X1944 0 0 464s Westinghouse_X1945 0 0 464s Westinghouse_X1946 0 0 464s Westinghouse_X1947 0 0 464s Westinghouse_X1948 0 0 464s Westinghouse_X1949 0 0 464s Westinghouse_X1950 0 0 464s Westinghouse_X1951 0 0 464s Westinghouse_X1952 0 0 464s Westinghouse_X1953 0 0 464s Westinghouse_X1954 0 0 464s General.Electric_capital General.Motors_(Intercept) 464s Chrysler_X1935 0.0 0 464s Chrysler_X1936 0.0 0 464s Chrysler_X1937 0.0 0 464s Chrysler_X1938 0.0 0 464s Chrysler_X1939 0.0 0 464s Chrysler_X1940 0.0 0 464s Chrysler_X1941 0.0 0 464s Chrysler_X1942 0.0 0 464s Chrysler_X1943 0.0 0 464s Chrysler_X1944 0.0 0 464s Chrysler_X1945 0.0 0 464s Chrysler_X1946 0.0 0 464s Chrysler_X1947 0.0 0 464s Chrysler_X1948 0.0 0 464s Chrysler_X1949 0.0 0 464s Chrysler_X1950 0.0 0 464s Chrysler_X1951 0.0 0 464s Chrysler_X1952 0.0 0 464s Chrysler_X1953 0.0 0 464s Chrysler_X1954 0.0 0 464s General.Electric_X1935 97.8 0 464s General.Electric_X1936 104.4 0 464s General.Electric_X1937 118.0 0 464s General.Electric_X1938 156.2 0 464s General.Electric_X1939 172.6 0 464s General.Electric_X1940 186.6 0 464s General.Electric_X1941 220.9 0 464s General.Electric_X1942 287.8 0 464s General.Electric_X1943 319.9 0 464s General.Electric_X1944 321.3 0 464s General.Electric_X1945 319.6 0 464s General.Electric_X1946 346.0 0 464s General.Electric_X1947 456.4 0 464s General.Electric_X1948 543.4 0 464s General.Electric_X1949 618.3 0 464s General.Electric_X1950 647.4 0 464s General.Electric_X1951 671.3 0 464s General.Electric_X1952 726.1 0 464s General.Electric_X1953 800.3 0 464s General.Electric_X1954 888.9 0 464s General.Motors_X1935 0.0 1 464s General.Motors_X1936 0.0 1 464s General.Motors_X1937 0.0 1 464s General.Motors_X1938 0.0 1 464s General.Motors_X1939 0.0 1 464s General.Motors_X1940 0.0 1 464s General.Motors_X1941 0.0 1 464s General.Motors_X1942 0.0 1 464s General.Motors_X1943 0.0 1 464s General.Motors_X1944 0.0 1 464s General.Motors_X1945 0.0 1 464s General.Motors_X1946 0.0 1 464s General.Motors_X1947 0.0 1 464s General.Motors_X1948 0.0 1 464s General.Motors_X1949 0.0 1 464s General.Motors_X1950 0.0 1 464s General.Motors_X1951 0.0 1 464s General.Motors_X1952 0.0 1 464s General.Motors_X1953 0.0 1 464s General.Motors_X1954 0.0 1 464s US.Steel_X1935 0.0 0 464s US.Steel_X1936 0.0 0 464s US.Steel_X1937 0.0 0 464s US.Steel_X1938 0.0 0 464s US.Steel_X1939 0.0 0 464s US.Steel_X1940 0.0 0 464s US.Steel_X1941 0.0 0 464s US.Steel_X1942 0.0 0 464s US.Steel_X1943 0.0 0 464s US.Steel_X1944 0.0 0 464s US.Steel_X1945 0.0 0 464s US.Steel_X1946 0.0 0 464s US.Steel_X1947 0.0 0 464s US.Steel_X1948 0.0 0 464s US.Steel_X1949 0.0 0 464s US.Steel_X1950 0.0 0 464s US.Steel_X1951 0.0 0 464s US.Steel_X1952 0.0 0 464s US.Steel_X1953 0.0 0 464s US.Steel_X1954 0.0 0 464s Westinghouse_X1935 0.0 0 464s Westinghouse_X1936 0.0 0 464s Westinghouse_X1937 0.0 0 464s Westinghouse_X1938 0.0 0 464s Westinghouse_X1939 0.0 0 464s Westinghouse_X1940 0.0 0 464s Westinghouse_X1941 0.0 0 464s Westinghouse_X1942 0.0 0 464s Westinghouse_X1943 0.0 0 464s Westinghouse_X1944 0.0 0 464s Westinghouse_X1945 0.0 0 464s Westinghouse_X1946 0.0 0 464s Westinghouse_X1947 0.0 0 464s Westinghouse_X1948 0.0 0 464s Westinghouse_X1949 0.0 0 464s Westinghouse_X1950 0.0 0 464s Westinghouse_X1951 0.0 0 464s Westinghouse_X1952 0.0 0 464s Westinghouse_X1953 0.0 0 464s Westinghouse_X1954 0.0 0 464s General.Motors_value General.Motors_capital 464s Chrysler_X1935 0 0.0 464s Chrysler_X1936 0 0.0 464s Chrysler_X1937 0 0.0 464s Chrysler_X1938 0 0.0 464s Chrysler_X1939 0 0.0 464s Chrysler_X1940 0 0.0 464s Chrysler_X1941 0 0.0 464s Chrysler_X1942 0 0.0 464s Chrysler_X1943 0 0.0 464s Chrysler_X1944 0 0.0 464s Chrysler_X1945 0 0.0 464s Chrysler_X1946 0 0.0 464s Chrysler_X1947 0 0.0 464s Chrysler_X1948 0 0.0 464s Chrysler_X1949 0 0.0 464s Chrysler_X1950 0 0.0 464s Chrysler_X1951 0 0.0 464s Chrysler_X1952 0 0.0 464s Chrysler_X1953 0 0.0 464s Chrysler_X1954 0 0.0 464s General.Electric_X1935 0 0.0 464s General.Electric_X1936 0 0.0 464s General.Electric_X1937 0 0.0 464s General.Electric_X1938 0 0.0 464s General.Electric_X1939 0 0.0 464s General.Electric_X1940 0 0.0 464s General.Electric_X1941 0 0.0 464s General.Electric_X1942 0 0.0 464s General.Electric_X1943 0 0.0 464s General.Electric_X1944 0 0.0 464s General.Electric_X1945 0 0.0 464s General.Electric_X1946 0 0.0 464s General.Electric_X1947 0 0.0 464s General.Electric_X1948 0 0.0 464s General.Electric_X1949 0 0.0 464s General.Electric_X1950 0 0.0 464s General.Electric_X1951 0 0.0 464s General.Electric_X1952 0 0.0 464s General.Electric_X1953 0 0.0 464s General.Electric_X1954 0 0.0 464s General.Motors_X1935 3078 2.8 464s General.Motors_X1936 4662 52.6 464s General.Motors_X1937 5387 156.9 464s General.Motors_X1938 2792 209.2 464s General.Motors_X1939 4313 203.4 464s General.Motors_X1940 4644 207.2 464s General.Motors_X1941 4551 255.2 464s General.Motors_X1942 3244 303.7 464s General.Motors_X1943 4054 264.1 464s General.Motors_X1944 4379 201.6 464s General.Motors_X1945 4841 265.0 464s General.Motors_X1946 4901 402.2 464s General.Motors_X1947 3526 761.5 464s General.Motors_X1948 3255 922.4 464s General.Motors_X1949 3700 1020.1 464s General.Motors_X1950 3756 1099.0 464s General.Motors_X1951 4833 1207.7 464s General.Motors_X1952 4925 1430.5 464s General.Motors_X1953 6242 1777.3 464s General.Motors_X1954 5594 2226.3 464s US.Steel_X1935 0 0.0 464s US.Steel_X1936 0 0.0 464s US.Steel_X1937 0 0.0 464s US.Steel_X1938 0 0.0 464s US.Steel_X1939 0 0.0 464s US.Steel_X1940 0 0.0 464s US.Steel_X1941 0 0.0 464s US.Steel_X1942 0 0.0 464s US.Steel_X1943 0 0.0 464s US.Steel_X1944 0 0.0 464s US.Steel_X1945 0 0.0 464s US.Steel_X1946 0 0.0 464s US.Steel_X1947 0 0.0 464s US.Steel_X1948 0 0.0 464s US.Steel_X1949 0 0.0 464s US.Steel_X1950 0 0.0 464s US.Steel_X1951 0 0.0 464s US.Steel_X1952 0 0.0 464s US.Steel_X1953 0 0.0 464s US.Steel_X1954 0 0.0 464s Westinghouse_X1935 0 0.0 464s Westinghouse_X1936 0 0.0 464s Westinghouse_X1937 0 0.0 464s Westinghouse_X1938 0 0.0 464s Westinghouse_X1939 0 0.0 464s Westinghouse_X1940 0 0.0 464s Westinghouse_X1941 0 0.0 464s Westinghouse_X1942 0 0.0 464s Westinghouse_X1943 0 0.0 464s Westinghouse_X1944 0 0.0 464s Westinghouse_X1945 0 0.0 464s Westinghouse_X1946 0 0.0 464s Westinghouse_X1947 0 0.0 464s Westinghouse_X1948 0 0.0 464s Westinghouse_X1949 0 0.0 464s Westinghouse_X1950 0 0.0 464s Westinghouse_X1951 0 0.0 464s Westinghouse_X1952 0 0.0 464s Westinghouse_X1953 0 0.0 464s Westinghouse_X1954 0 0.0 464s US.Steel_(Intercept) US.Steel_value US.Steel_capital 464s Chrysler_X1935 0 0 0.0 464s Chrysler_X1936 0 0 0.0 464s Chrysler_X1937 0 0 0.0 464s Chrysler_X1938 0 0 0.0 464s Chrysler_X1939 0 0 0.0 464s Chrysler_X1940 0 0 0.0 464s Chrysler_X1941 0 0 0.0 464s Chrysler_X1942 0 0 0.0 464s Chrysler_X1943 0 0 0.0 464s Chrysler_X1944 0 0 0.0 464s Chrysler_X1945 0 0 0.0 464s Chrysler_X1946 0 0 0.0 464s Chrysler_X1947 0 0 0.0 464s Chrysler_X1948 0 0 0.0 464s Chrysler_X1949 0 0 0.0 464s Chrysler_X1950 0 0 0.0 464s Chrysler_X1951 0 0 0.0 464s Chrysler_X1952 0 0 0.0 464s Chrysler_X1953 0 0 0.0 464s Chrysler_X1954 0 0 0.0 464s General.Electric_X1935 0 0 0.0 464s General.Electric_X1936 0 0 0.0 464s General.Electric_X1937 0 0 0.0 464s General.Electric_X1938 0 0 0.0 464s General.Electric_X1939 0 0 0.0 464s General.Electric_X1940 0 0 0.0 464s General.Electric_X1941 0 0 0.0 464s General.Electric_X1942 0 0 0.0 464s General.Electric_X1943 0 0 0.0 464s General.Electric_X1944 0 0 0.0 464s General.Electric_X1945 0 0 0.0 464s General.Electric_X1946 0 0 0.0 464s General.Electric_X1947 0 0 0.0 464s General.Electric_X1948 0 0 0.0 464s General.Electric_X1949 0 0 0.0 464s General.Electric_X1950 0 0 0.0 464s General.Electric_X1951 0 0 0.0 464s General.Electric_X1952 0 0 0.0 464s General.Electric_X1953 0 0 0.0 464s General.Electric_X1954 0 0 0.0 464s General.Motors_X1935 0 0 0.0 464s General.Motors_X1936 0 0 0.0 464s General.Motors_X1937 0 0 0.0 464s General.Motors_X1938 0 0 0.0 464s General.Motors_X1939 0 0 0.0 464s General.Motors_X1940 0 0 0.0 464s General.Motors_X1941 0 0 0.0 464s General.Motors_X1942 0 0 0.0 464s General.Motors_X1943 0 0 0.0 464s General.Motors_X1944 0 0 0.0 464s General.Motors_X1945 0 0 0.0 464s General.Motors_X1946 0 0 0.0 464s General.Motors_X1947 0 0 0.0 464s General.Motors_X1948 0 0 0.0 464s General.Motors_X1949 0 0 0.0 464s General.Motors_X1950 0 0 0.0 464s General.Motors_X1951 0 0 0.0 464s General.Motors_X1952 0 0 0.0 464s General.Motors_X1953 0 0 0.0 464s General.Motors_X1954 0 0 0.0 464s US.Steel_X1935 1 1362 53.8 464s US.Steel_X1936 1 1807 50.5 464s US.Steel_X1937 1 2676 118.1 464s US.Steel_X1938 1 1802 260.2 464s US.Steel_X1939 1 1957 312.7 464s US.Steel_X1940 1 2203 254.2 464s US.Steel_X1941 1 2380 261.4 464s US.Steel_X1942 1 2169 298.7 464s US.Steel_X1943 1 1985 301.8 464s US.Steel_X1944 1 1814 279.1 464s US.Steel_X1945 1 1850 213.8 464s US.Steel_X1946 1 2068 232.6 464s US.Steel_X1947 1 1797 264.8 464s US.Steel_X1948 1 1626 306.9 464s US.Steel_X1949 1 1667 351.1 464s US.Steel_X1950 1 1677 357.8 464s US.Steel_X1951 1 2290 342.1 464s US.Steel_X1952 1 2159 444.2 464s US.Steel_X1953 1 2031 623.6 464s US.Steel_X1954 1 2116 669.7 464s Westinghouse_X1935 0 0 0.0 464s Westinghouse_X1936 0 0 0.0 464s Westinghouse_X1937 0 0 0.0 464s Westinghouse_X1938 0 0 0.0 464s Westinghouse_X1939 0 0 0.0 464s Westinghouse_X1940 0 0 0.0 464s Westinghouse_X1941 0 0 0.0 464s Westinghouse_X1942 0 0 0.0 464s Westinghouse_X1943 0 0 0.0 464s Westinghouse_X1944 0 0 0.0 464s Westinghouse_X1945 0 0 0.0 464s Westinghouse_X1946 0 0 0.0 464s Westinghouse_X1947 0 0 0.0 464s Westinghouse_X1948 0 0 0.0 464s Westinghouse_X1949 0 0 0.0 464s Westinghouse_X1950 0 0 0.0 464s Westinghouse_X1951 0 0 0.0 464s Westinghouse_X1952 0 0 0.0 464s Westinghouse_X1953 0 0 0.0 464s Westinghouse_X1954 0 0 0.0 464s Westinghouse_(Intercept) Westinghouse_value 464s Chrysler_X1935 0 0 464s Chrysler_X1936 0 0 464s Chrysler_X1937 0 0 464s Chrysler_X1938 0 0 464s Chrysler_X1939 0 0 464s Chrysler_X1940 0 0 464s Chrysler_X1941 0 0 464s Chrysler_X1942 0 0 464s Chrysler_X1943 0 0 464s Chrysler_X1944 0 0 464s Chrysler_X1945 0 0 464s Chrysler_X1946 0 0 464s Chrysler_X1947 0 0 464s Chrysler_X1948 0 0 464s Chrysler_X1949 0 0 464s Chrysler_X1950 0 0 464s Chrysler_X1951 0 0 464s Chrysler_X1952 0 0 464s Chrysler_X1953 0 0 464s Chrysler_X1954 0 0 464s General.Electric_X1935 0 0 464s General.Electric_X1936 0 0 464s General.Electric_X1937 0 0 464s General.Electric_X1938 0 0 464s General.Electric_X1939 0 0 464s General.Electric_X1940 0 0 464s General.Electric_X1941 0 0 464s General.Electric_X1942 0 0 464s General.Electric_X1943 0 0 464s General.Electric_X1944 0 0 464s General.Electric_X1945 0 0 464s General.Electric_X1946 0 0 464s General.Electric_X1947 0 0 464s General.Electric_X1948 0 0 464s General.Electric_X1949 0 0 464s General.Electric_X1950 0 0 464s General.Electric_X1951 0 0 464s General.Electric_X1952 0 0 464s General.Electric_X1953 0 0 464s General.Electric_X1954 0 0 464s General.Motors_X1935 0 0 464s General.Motors_X1936 0 0 464s General.Motors_X1937 0 0 464s General.Motors_X1938 0 0 464s General.Motors_X1939 0 0 464s General.Motors_X1940 0 0 464s General.Motors_X1941 0 0 464s General.Motors_X1942 0 0 464s General.Motors_X1943 0 0 464s General.Motors_X1944 0 0 464s General.Motors_X1945 0 0 464s General.Motors_X1946 0 0 464s General.Motors_X1947 0 0 464s General.Motors_X1948 0 0 464s General.Motors_X1949 0 0 464s General.Motors_X1950 0 0 464s General.Motors_X1951 0 0 464s General.Motors_X1952 0 0 464s General.Motors_X1953 0 0 464s General.Motors_X1954 0 0 464s US.Steel_X1935 0 0 464s US.Steel_X1936 0 0 464s US.Steel_X1937 0 0 464s US.Steel_X1938 0 0 464s US.Steel_X1939 0 0 464s US.Steel_X1940 0 0 464s US.Steel_X1941 0 0 464s US.Steel_X1942 0 0 464s US.Steel_X1943 0 0 464s US.Steel_X1944 0 0 464s US.Steel_X1945 0 0 464s US.Steel_X1946 0 0 464s US.Steel_X1947 0 0 464s US.Steel_X1948 0 0 464s US.Steel_X1949 0 0 464s US.Steel_X1950 0 0 464s US.Steel_X1951 0 0 464s US.Steel_X1952 0 0 464s US.Steel_X1953 0 0 464s US.Steel_X1954 0 0 464s Westinghouse_X1935 1 192 464s Westinghouse_X1936 1 516 464s Westinghouse_X1937 1 729 464s Westinghouse_X1938 1 560 464s Westinghouse_X1939 1 520 464s Westinghouse_X1940 1 628 464s Westinghouse_X1941 1 537 464s Westinghouse_X1942 1 561 464s Westinghouse_X1943 1 617 464s Westinghouse_X1944 1 627 464s Westinghouse_X1945 1 737 464s Westinghouse_X1946 1 760 464s Westinghouse_X1947 1 581 464s Westinghouse_X1948 1 662 464s Westinghouse_X1949 1 584 464s Westinghouse_X1950 1 635 464s Westinghouse_X1951 1 724 464s Westinghouse_X1952 1 864 464s Westinghouse_X1953 1 1194 464s Westinghouse_X1954 1 1189 464s Westinghouse_capital 464s Chrysler_X1935 0.0 464s Chrysler_X1936 0.0 464s Chrysler_X1937 0.0 464s Chrysler_X1938 0.0 464s Chrysler_X1939 0.0 464s Chrysler_X1940 0.0 464s Chrysler_X1941 0.0 464s Chrysler_X1942 0.0 464s Chrysler_X1943 0.0 464s Chrysler_X1944 0.0 464s Chrysler_X1945 0.0 464s Chrysler_X1946 0.0 464s Chrysler_X1947 0.0 464s Chrysler_X1948 0.0 464s Chrysler_X1949 0.0 464s Chrysler_X1950 0.0 464s Chrysler_X1951 0.0 464s Chrysler_X1952 0.0 464s Chrysler_X1953 0.0 464s Chrysler_X1954 0.0 464s General.Electric_X1935 0.0 464s General.Electric_X1936 0.0 464s General.Electric_X1937 0.0 464s General.Electric_X1938 0.0 464s General.Electric_X1939 0.0 464s General.Electric_X1940 0.0 464s General.Electric_X1941 0.0 464s General.Electric_X1942 0.0 464s General.Electric_X1943 0.0 464s General.Electric_X1944 0.0 464s General.Electric_X1945 0.0 464s General.Electric_X1946 0.0 464s General.Electric_X1947 0.0 464s General.Electric_X1948 0.0 464s General.Electric_X1949 0.0 464s General.Electric_X1950 0.0 464s General.Electric_X1951 0.0 464s General.Electric_X1952 0.0 464s General.Electric_X1953 0.0 464s General.Electric_X1954 0.0 464s General.Motors_X1935 0.0 464s General.Motors_X1936 0.0 464s General.Motors_X1937 0.0 464s General.Motors_X1938 0.0 464s General.Motors_X1939 0.0 464s General.Motors_X1940 0.0 464s General.Motors_X1941 0.0 464s General.Motors_X1942 0.0 464s General.Motors_X1943 0.0 464s General.Motors_X1944 0.0 464s General.Motors_X1945 0.0 464s General.Motors_X1946 0.0 464s General.Motors_X1947 0.0 464s General.Motors_X1948 0.0 464s General.Motors_X1949 0.0 464s General.Motors_X1950 0.0 464s General.Motors_X1951 0.0 464s General.Motors_X1952 0.0 464s General.Motors_X1953 0.0 464s General.Motors_X1954 0.0 464s US.Steel_X1935 0.0 464s US.Steel_X1936 0.0 464s US.Steel_X1937 0.0 464s US.Steel_X1938 0.0 464s US.Steel_X1939 0.0 464s US.Steel_X1940 0.0 464s US.Steel_X1941 0.0 464s US.Steel_X1942 0.0 464s US.Steel_X1943 0.0 464s US.Steel_X1944 0.0 464s US.Steel_X1945 0.0 464s US.Steel_X1946 0.0 464s US.Steel_X1947 0.0 464s US.Steel_X1948 0.0 464s US.Steel_X1949 0.0 464s US.Steel_X1950 0.0 464s US.Steel_X1951 0.0 464s US.Steel_X1952 0.0 464s US.Steel_X1953 0.0 464s US.Steel_X1954 0.0 464s Westinghouse_X1935 1.8 464s Westinghouse_X1936 0.8 464s Westinghouse_X1937 7.4 464s Westinghouse_X1938 18.1 464s Westinghouse_X1939 23.5 464s Westinghouse_X1940 26.5 464s Westinghouse_X1941 36.2 464s Westinghouse_X1942 60.8 464s Westinghouse_X1943 84.4 464s Westinghouse_X1944 91.2 464s Westinghouse_X1945 92.4 464s Westinghouse_X1946 86.0 464s Westinghouse_X1947 111.1 464s Westinghouse_X1948 130.6 464s Westinghouse_X1949 141.8 464s Westinghouse_X1950 136.7 464s Westinghouse_X1951 129.7 464s Westinghouse_X1952 145.5 464s Westinghouse_X1953 174.8 464s Westinghouse_X1954 213.5 464s $Chrysler 464s Chrysler_invest ~ Chrysler_value + Chrysler_capital 464s 464s 464s $General.Electric 464s General.Electric_invest ~ General.Electric_value + General.Electric_capital 464s 464s 464s $General.Motors 464s General.Motors_invest ~ General.Motors_value + General.Motors_capital 464s 464s 464s $US.Steel 464s US.Steel_invest ~ US.Steel_value + US.Steel_capital 464s 464s 464s $Westinghouse 464s Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 464s 464s 464s Chrysler_invest ~ Chrysler_value + Chrysler_capital 464s 464s $Chrysler 464s Chrysler_invest ~ Chrysler_value + Chrysler_capital 464s attr(,"variables") 464s list(Chrysler_invest, Chrysler_value, Chrysler_capital) 464s attr(,"factors") 464s Chrysler_value Chrysler_capital 464s Chrysler_invest 0 0 464s Chrysler_value 1 0 464s Chrysler_capital 0 1 464s attr(,"term.labels") 464s [1] "Chrysler_value" "Chrysler_capital" 464s attr(,"order") 464s [1] 1 1 464s attr(,"intercept") 464s [1] 1 464s attr(,"response") 464s [1] 1 464s attr(,".Environment") 464s 464s attr(,"predvars") 464s list(Chrysler_invest, Chrysler_value, Chrysler_capital) 464s attr(,"dataClasses") 464s Chrysler_invest Chrysler_value Chrysler_capital 464s "numeric" "numeric" "numeric" 464s 464s $General.Electric 464s General.Electric_invest ~ General.Electric_value + General.Electric_capital 464s attr(,"variables") 464s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 464s attr(,"factors") 464s General.Electric_value General.Electric_capital 464s General.Electric_invest 0 0 464s General.Electric_value 1 0 464s General.Electric_capital 0 1 464s attr(,"term.labels") 464s [1] "General.Electric_value" "General.Electric_capital" 464s attr(,"order") 464s [1] 1 1 464s attr(,"intercept") 464s [1] 1 464s attr(,"response") 464s [1] 1 464s attr(,".Environment") 464s 464s attr(,"predvars") 464s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 464s attr(,"dataClasses") 464s General.Electric_invest General.Electric_value General.Electric_capital 464s "numeric" "numeric" "numeric" 464s 464s $General.Motors 464s General.Motors_invest ~ General.Motors_value + General.Motors_capital 464s attr(,"variables") 464s list(General.Motors_invest, General.Motors_value, General.Motors_capital) 464s attr(,"factors") 464s General.Motors_value General.Motors_capital 464s General.Motors_invest 0 0 464s General.Motors_value 1 0 464s General.Motors_capital 0 1 464s attr(,"term.labels") 464s [1] "General.Motors_value" "General.Motors_capital" 464s attr(,"order") 464s [1] 1 1 464s attr(,"intercept") 464s [1] 1 464s attr(,"response") 464s [1] 1 464s attr(,".Environment") 464s 464s attr(,"predvars") 464s list(General.Motors_invest, General.Motors_value, General.Motors_capital) 464s attr(,"dataClasses") 464s General.Motors_invest General.Motors_value General.Motors_capital 464s "numeric" "numeric" "numeric" 464s 464s $US.Steel 464s US.Steel_invest ~ US.Steel_value + US.Steel_capital 464s attr(,"variables") 464s list(US.Steel_invest, US.Steel_value, US.Steel_capital) 464s attr(,"factors") 464s US.Steel_value US.Steel_capital 464s US.Steel_invest 0 0 464s US.Steel_value 1 0 464s US.Steel_capital 0 1 464s attr(,"term.labels") 464s [1] "US.Steel_value" "US.Steel_capital" 464s attr(,"order") 464s [1] 1 1 464s attr(,"intercept") 464s [1] 1 464s attr(,"response") 464s [1] 1 464s attr(,".Environment") 464s 464s attr(,"predvars") 464s list(US.Steel_invest, US.Steel_value, US.Steel_capital) 464s attr(,"dataClasses") 464s US.Steel_invest US.Steel_value US.Steel_capital 464s "numeric" "numeric" "numeric" 464s 464s $Westinghouse 464s Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 464s attr(,"variables") 464s list(Westinghouse_invest, Westinghouse_value, Westinghouse_capital) 464s attr(,"factors") 464s Westinghouse_value Westinghouse_capital 464s Westinghouse_invest 0 0 464s Westinghouse_value 1 0 464s Westinghouse_capital 0 1 464s attr(,"term.labels") 464s [1] "Westinghouse_value" "Westinghouse_capital" 464s attr(,"order") 464s [1] 1 1 464s attr(,"intercept") 464s [1] 1 464s attr(,"response") 464s [1] 1 464s attr(,".Environment") 464s 464s attr(,"predvars") 464s list(Westinghouse_invest, Westinghouse_value, Westinghouse_capital) 464s attr(,"dataClasses") 464s Westinghouse_invest Westinghouse_value Westinghouse_capital 464s "numeric" "numeric" "numeric" 464s 464s Chrysler_invest ~ Chrysler_value + Chrysler_capital 464s attr(,"variables") 464s list(Chrysler_invest, Chrysler_value, Chrysler_capital) 464s attr(,"factors") 464s Chrysler_value Chrysler_capital 464s Chrysler_invest 0 0 464s Chrysler_value 1 0 464s Chrysler_capital 0 1 464s attr(,"term.labels") 464s [1] "Chrysler_value" "Chrysler_capital" 464s attr(,"order") 464s [1] 1 1 464s attr(,"intercept") 464s [1] 1 464s attr(,"response") 464s [1] 1 464s attr(,".Environment") 464s 464s attr(,"predvars") 464s list(Chrysler_invest, Chrysler_value, Chrysler_capital) 464s attr(,"dataClasses") 464s Chrysler_invest Chrysler_value Chrysler_capital 464s "numeric" "numeric" "numeric" 464s > 464s > # SUR Pooled 464s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 464s + greeneSurPooled <- systemfit( formulaGrunfeld, "SUR", 464s + data = GrunfeldGreene, pooled = TRUE, methodResidCov = "noDfCor", 464s + residCovWeighted = TRUE, useMatrix = useMatrix ) 464s + print( greeneSurPooled ) 464s + print( summary( greeneSurPooled ) ) 464s + print( summary( greeneSurPooled, useDfSys = FALSE, equations = FALSE ) ) 464s + print( summary( greeneSurPooled, residCov = FALSE, equations = FALSE ) ) 464s + print( coef( greeneSurPooled ) ) 464s + print( coef( greeneSurPooled, modified.regMat = TRUE ) ) 464s + print( coef( summary( greeneSurPooled ) ) ) 464s + print( coef( summary( greeneSurPooled ), modified.regMat = TRUE ) ) 464s + print( vcov( greeneSurPooled ) ) 464s + print( vcov( greeneSurPooled, modified.regMat = TRUE ) ) 464s + print( residuals( greeneSurPooled ) ) 464s + print( confint( greeneSurPooled ) ) 464s + print( fitted( greeneSurPooled ) ) 464s + print( logLik( greeneSurPooled ) ) 464s + print( logLik( greeneSurPooled, residCovDiag = TRUE ) ) 464s + print( nobs( greeneSurPooled ) ) 464s + print( model.frame( greeneSurPooled ) ) 464s + print( model.matrix( greeneSurPooled ) ) 464s + print( formula( greeneSurPooled ) ) 464s + print( formula( greeneSurPooled$eq[[ 1 ]] ) ) 464s + print( terms( greeneSurPooled ) ) 464s + print( terms( greeneSurPooled$eq[[ 1 ]] ) ) 464s + } 464s 464s systemfit results 464s method: SUR 464s 464s Coefficients: 464s Chrysler_(Intercept) Chrysler_value 464s -28.2467 0.0891 464s Chrysler_capital General.Electric_(Intercept) 464s 0.3340 -28.2467 464s General.Electric_value General.Electric_capital 464s 0.0891 0.3340 464s General.Motors_(Intercept) General.Motors_value 464s -28.2467 0.0891 464s General.Motors_capital US.Steel_(Intercept) 464s 0.3340 -28.2467 464s US.Steel_value US.Steel_capital 464s 0.0891 0.3340 464s Westinghouse_(Intercept) Westinghouse_value 464s -28.2467 0.0891 464s Westinghouse_capital 464s 0.3340 464s 464s systemfit results 464s method: SUR 464s 464s N DF SSR detRCov OLS-R2 McElroy-R2 464s system 100 97 1604301 9.95e+16 0.279 0.844 464s 464s N DF SSR MSE RMSE R2 Adj R2 464s Chrysler 20 17 6112 360 19.0 0.824 0.803 464s General.Electric 20 17 691132 40655 201.6 -14.410 -16.223 464s General.Motors 20 17 201010 11824 108.7 0.890 0.877 464s US.Steel 20 17 689380 40552 201.4 -1.168 -1.424 464s Westinghouse 20 17 16667 980 31.3 -1.402 -1.685 464s 464s The covariance matrix of the residuals used for estimation 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 409 -2594 -197 2594 -102 464s General.Electric -2594 36563 -3480 -28623 3797 464s General.Motors -197 -3480 8612 996 -971 464s US.Steel 2594 -28623 996 32903 -2272 464s Westinghouse -102 3797 -971 -2272 778 464s 464s The covariance matrix of the residuals 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 305.61 -1967 -4.81 2159 -124 464s General.Electric -1966.65 34557 -7160.67 -28722 4274 464s General.Motors -4.81 -7161 10050.52 4440 -1401 464s US.Steel 2158.60 -28722 4439.99 34469 -2894 464s Westinghouse -123.92 4274 -1400.75 -2894 833 464s 464s The correlations of the residuals 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 1.000 0.220 -0.3447 0.2008 0.2907 464s General.Electric 0.220 1.000 -0.2233 -0.1587 0.8973 464s General.Motors -0.345 -0.223 1.0000 -0.0924 -0.3760 464s US.Steel 0.201 -0.159 -0.0924 1.0000 -0.0757 464s Westinghouse 0.291 0.897 -0.3760 -0.0757 1.0000 464s 464s 464s SUR estimates for 'Chrysler' (equation 1) 464s Model Formula: Chrysler_invest ~ Chrysler_value + Chrysler_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -28.24669 4.88824 -5.78 9.1e-08 *** 464s value 0.08910 0.00507 17.57 < 2e-16 *** 464s capital 0.33402 0.01671 19.99 < 2e-16 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 18.962 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 6112.2 MSE: 359.541 Root MSE: 18.962 464s Multiple R-Squared: 0.824 Adjusted R-Squared: 0.803 464s 464s 464s SUR estimates for 'General.Electric' (equation 2) 464s Model Formula: General.Electric_invest ~ General.Electric_value + General.Electric_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -28.24669 4.88824 -5.78 9.1e-08 *** 464s value 0.08910 0.00507 17.57 < 2e-16 *** 464s capital 0.33402 0.01671 19.99 < 2e-16 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 201.63 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 691132.056 MSE: 40654.827 Root MSE: 201.63 464s Multiple R-Squared: -14.41 Adjusted R-Squared: -16.223 464s 464s 464s SUR estimates for 'General.Motors' (equation 3) 464s Model Formula: General.Motors_invest ~ General.Motors_value + General.Motors_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -28.24669 4.88824 -5.78 9.1e-08 *** 464s value 0.08910 0.00507 17.57 < 2e-16 *** 464s capital 0.33402 0.01671 19.99 < 2e-16 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 108.739 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 201010.497 MSE: 11824.147 Root MSE: 108.739 464s Multiple R-Squared: 0.89 Adjusted R-Squared: 0.877 464s 464s 464s SUR estimates for 'US.Steel' (equation 4) 464s Model Formula: US.Steel_invest ~ US.Steel_value + US.Steel_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -28.24669 4.88824 -5.78 9.1e-08 *** 464s value 0.08910 0.00507 17.57 < 2e-16 *** 464s capital 0.33402 0.01671 19.99 < 2e-16 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 201.375 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 689379.52 MSE: 40551.736 Root MSE: 201.375 464s Multiple R-Squared: -1.168 Adjusted R-Squared: -1.424 464s 464s 464s SUR estimates for 'Westinghouse' (equation 5) 464s Model Formula: Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -28.24669 4.88824 -5.78 9.1e-08 *** 464s value 0.08910 0.00507 17.57 < 2e-16 *** 464s capital 0.33402 0.01671 19.99 < 2e-16 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 31.312 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 16667.149 MSE: 980.421 Root MSE: 31.312 464s Multiple R-Squared: -1.402 Adjusted R-Squared: -1.685 464s 464s 464s systemfit results 464s method: SUR 464s 464s N DF SSR detRCov OLS-R2 McElroy-R2 464s system 100 97 1604301 9.95e+16 0.279 0.844 464s 464s N DF SSR MSE RMSE R2 Adj R2 464s Chrysler 20 17 6112 360 19.0 0.824 0.803 464s General.Electric 20 17 691132 40655 201.6 -14.410 -16.223 464s General.Motors 20 17 201010 11824 108.7 0.890 0.877 464s US.Steel 20 17 689380 40552 201.4 -1.168 -1.424 464s Westinghouse 20 17 16667 980 31.3 -1.402 -1.685 464s 464s The covariance matrix of the residuals used for estimation 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 409 -2594 -197 2594 -102 464s General.Electric -2594 36563 -3480 -28623 3797 464s General.Motors -197 -3480 8612 996 -971 464s US.Steel 2594 -28623 996 32903 -2272 464s Westinghouse -102 3797 -971 -2272 778 464s 464s The covariance matrix of the residuals 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 305.61 -1967 -4.81 2159 -124 464s General.Electric -1966.65 34557 -7160.67 -28722 4274 464s General.Motors -4.81 -7161 10050.52 4440 -1401 464s US.Steel 2158.60 -28722 4439.99 34469 -2894 464s Westinghouse -123.92 4274 -1400.75 -2894 833 464s 464s The correlations of the residuals 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 1.000 0.220 -0.3447 0.2008 0.2907 464s General.Electric 0.220 1.000 -0.2233 -0.1587 0.8973 464s General.Motors -0.345 -0.223 1.0000 -0.0924 -0.3760 464s US.Steel 0.201 -0.159 -0.0924 1.0000 -0.0757 464s Westinghouse 0.291 0.897 -0.3760 -0.0757 1.0000 464s 464s 464s Coefficients: 464s Estimate Std. Error t value Pr(>|t|) 464s Chrysler_(Intercept) -28.24669 4.88824 -5.78 2.2e-05 *** 464s Chrysler_value 0.08910 0.00507 17.57 2.5e-12 *** 464s Chrysler_capital 0.33402 0.01671 19.99 3.0e-13 *** 464s General.Electric_(Intercept) -28.24669 4.88824 -5.78 2.2e-05 *** 464s General.Electric_value 0.08910 0.00507 17.57 2.5e-12 *** 464s General.Electric_capital 0.33402 0.01671 19.99 3.0e-13 *** 464s General.Motors_(Intercept) -28.24669 4.88824 -5.78 2.2e-05 *** 464s General.Motors_value 0.08910 0.00507 17.57 2.5e-12 *** 464s General.Motors_capital 0.33402 0.01671 19.99 3.0e-13 *** 464s US.Steel_(Intercept) -28.24669 4.88824 -5.78 2.2e-05 *** 464s US.Steel_value 0.08910 0.00507 17.57 2.5e-12 *** 464s US.Steel_capital 0.33402 0.01671 19.99 3.0e-13 *** 464s Westinghouse_(Intercept) -28.24669 4.88824 -5.78 2.2e-05 *** 464s Westinghouse_value 0.08910 0.00507 17.57 2.5e-12 *** 464s Westinghouse_capital 0.33402 0.01671 19.99 3.0e-13 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s systemfit results 464s method: SUR 464s 464s N DF SSR detRCov OLS-R2 McElroy-R2 464s system 100 97 1604301 9.95e+16 0.279 0.844 464s 464s N DF SSR MSE RMSE R2 Adj R2 464s Chrysler 20 17 6112 360 19.0 0.824 0.803 464s General.Electric 20 17 691132 40655 201.6 -14.410 -16.223 464s General.Motors 20 17 201010 11824 108.7 0.890 0.877 464s US.Steel 20 17 689380 40552 201.4 -1.168 -1.424 464s Westinghouse 20 17 16667 980 31.3 -1.402 -1.685 464s 464s 464s Coefficients: 464s Estimate Std. Error t value Pr(>|t|) 464s Chrysler_(Intercept) -28.24669 4.88824 -5.78 9.1e-08 *** 464s Chrysler_value 0.08910 0.00507 17.57 < 2e-16 *** 464s Chrysler_capital 0.33402 0.01671 19.99 < 2e-16 *** 464s General.Electric_(Intercept) -28.24669 4.88824 -5.78 9.1e-08 *** 464s General.Electric_value 0.08910 0.00507 17.57 < 2e-16 *** 464s General.Electric_capital 0.33402 0.01671 19.99 < 2e-16 *** 464s General.Motors_(Intercept) -28.24669 4.88824 -5.78 9.1e-08 *** 464s General.Motors_value 0.08910 0.00507 17.57 < 2e-16 *** 464s General.Motors_capital 0.33402 0.01671 19.99 < 2e-16 *** 464s US.Steel_(Intercept) -28.24669 4.88824 -5.78 9.1e-08 *** 464s US.Steel_value 0.08910 0.00507 17.57 < 2e-16 *** 464s US.Steel_capital 0.33402 0.01671 19.99 < 2e-16 *** 464s Westinghouse_(Intercept) -28.24669 4.88824 -5.78 9.1e-08 *** 464s Westinghouse_value 0.08910 0.00507 17.57 < 2e-16 *** 464s Westinghouse_capital 0.33402 0.01671 19.99 < 2e-16 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s Chrysler_(Intercept) Chrysler_value 464s -28.2467 0.0891 464s Chrysler_capital General.Electric_(Intercept) 464s 0.3340 -28.2467 464s General.Electric_value General.Electric_capital 464s 0.0891 0.3340 464s General.Motors_(Intercept) General.Motors_value 464s -28.2467 0.0891 464s General.Motors_capital US.Steel_(Intercept) 464s 0.3340 -28.2467 464s US.Steel_value US.Steel_capital 464s 0.0891 0.3340 464s Westinghouse_(Intercept) Westinghouse_value 464s -28.2467 0.0891 464s Westinghouse_capital 464s 0.3340 464s C1 C2 C3 464s -28.2467 0.0891 0.3340 464s Estimate Std. Error t value Pr(>|t|) 464s Chrysler_(Intercept) -28.2467 4.88824 -5.78 9.12e-08 464s Chrysler_value 0.0891 0.00507 17.57 0.00e+00 464s Chrysler_capital 0.3340 0.01671 19.99 0.00e+00 464s General.Electric_(Intercept) -28.2467 4.88824 -5.78 9.12e-08 464s General.Electric_value 0.0891 0.00507 17.57 0.00e+00 464s General.Electric_capital 0.3340 0.01671 19.99 0.00e+00 464s General.Motors_(Intercept) -28.2467 4.88824 -5.78 9.12e-08 464s General.Motors_value 0.0891 0.00507 17.57 0.00e+00 464s General.Motors_capital 0.3340 0.01671 19.99 0.00e+00 464s US.Steel_(Intercept) -28.2467 4.88824 -5.78 9.12e-08 464s US.Steel_value 0.0891 0.00507 17.57 0.00e+00 464s US.Steel_capital 0.3340 0.01671 19.99 0.00e+00 464s Westinghouse_(Intercept) -28.2467 4.88824 -5.78 9.12e-08 464s Westinghouse_value 0.0891 0.00507 17.57 0.00e+00 464s Westinghouse_capital 0.3340 0.01671 19.99 0.00e+00 464s Estimate Std. Error t value Pr(>|t|) 464s C1 -28.2467 4.88824 -5.78 9.12e-08 464s C2 0.0891 0.00507 17.57 0.00e+00 464s C3 0.3340 0.01671 19.99 0.00e+00 464s Chrysler_(Intercept) Chrysler_value 464s Chrysler_(Intercept) 23.89487 -1.73e-02 464s Chrysler_value -0.01729 2.57e-05 464s Chrysler_capital 0.00114 -4.74e-05 464s General.Electric_(Intercept) 23.89487 -1.73e-02 464s General.Electric_value -0.01729 2.57e-05 464s General.Electric_capital 0.00114 -4.74e-05 464s General.Motors_(Intercept) 23.89487 -1.73e-02 464s General.Motors_value -0.01729 2.57e-05 464s General.Motors_capital 0.00114 -4.74e-05 464s US.Steel_(Intercept) 23.89487 -1.73e-02 464s US.Steel_value -0.01729 2.57e-05 464s US.Steel_capital 0.00114 -4.74e-05 464s Westinghouse_(Intercept) 23.89487 -1.73e-02 464s Westinghouse_value -0.01729 2.57e-05 464s Westinghouse_capital 0.00114 -4.74e-05 464s Chrysler_capital General.Electric_(Intercept) 464s Chrysler_(Intercept) 1.14e-03 23.89487 464s Chrysler_value -4.74e-05 -0.01729 464s Chrysler_capital 2.79e-04 0.00114 464s General.Electric_(Intercept) 1.14e-03 23.89487 464s General.Electric_value -4.74e-05 -0.01729 464s General.Electric_capital 2.79e-04 0.00114 464s General.Motors_(Intercept) 1.14e-03 23.89487 464s General.Motors_value -4.74e-05 -0.01729 464s General.Motors_capital 2.79e-04 0.00114 464s US.Steel_(Intercept) 1.14e-03 23.89487 464s US.Steel_value -4.74e-05 -0.01729 464s US.Steel_capital 2.79e-04 0.00114 464s Westinghouse_(Intercept) 1.14e-03 23.89487 464s Westinghouse_value -4.74e-05 -0.01729 464s Westinghouse_capital 2.79e-04 0.00114 464s General.Electric_value General.Electric_capital 464s Chrysler_(Intercept) -1.73e-02 1.14e-03 464s Chrysler_value 2.57e-05 -4.74e-05 464s Chrysler_capital -4.74e-05 2.79e-04 464s General.Electric_(Intercept) -1.73e-02 1.14e-03 464s General.Electric_value 2.57e-05 -4.74e-05 464s General.Electric_capital -4.74e-05 2.79e-04 464s General.Motors_(Intercept) -1.73e-02 1.14e-03 464s General.Motors_value 2.57e-05 -4.74e-05 464s General.Motors_capital -4.74e-05 2.79e-04 464s US.Steel_(Intercept) -1.73e-02 1.14e-03 464s US.Steel_value 2.57e-05 -4.74e-05 464s US.Steel_capital -4.74e-05 2.79e-04 464s Westinghouse_(Intercept) -1.73e-02 1.14e-03 464s Westinghouse_value 2.57e-05 -4.74e-05 464s Westinghouse_capital -4.74e-05 2.79e-04 464s General.Motors_(Intercept) General.Motors_value 464s Chrysler_(Intercept) 23.89487 -1.73e-02 464s Chrysler_value -0.01729 2.57e-05 464s Chrysler_capital 0.00114 -4.74e-05 464s General.Electric_(Intercept) 23.89487 -1.73e-02 464s General.Electric_value -0.01729 2.57e-05 464s General.Electric_capital 0.00114 -4.74e-05 464s General.Motors_(Intercept) 23.89487 -1.73e-02 464s General.Motors_value -0.01729 2.57e-05 464s General.Motors_capital 0.00114 -4.74e-05 464s US.Steel_(Intercept) 23.89487 -1.73e-02 464s US.Steel_value -0.01729 2.57e-05 464s US.Steel_capital 0.00114 -4.74e-05 464s Westinghouse_(Intercept) 23.89487 -1.73e-02 464s Westinghouse_value -0.01729 2.57e-05 464s Westinghouse_capital 0.00114 -4.74e-05 464s General.Motors_capital US.Steel_(Intercept) 464s Chrysler_(Intercept) 1.14e-03 23.89487 464s Chrysler_value -4.74e-05 -0.01729 464s Chrysler_capital 2.79e-04 0.00114 464s General.Electric_(Intercept) 1.14e-03 23.89487 464s General.Electric_value -4.74e-05 -0.01729 464s General.Electric_capital 2.79e-04 0.00114 464s General.Motors_(Intercept) 1.14e-03 23.89487 464s General.Motors_value -4.74e-05 -0.01729 464s General.Motors_capital 2.79e-04 0.00114 464s US.Steel_(Intercept) 1.14e-03 23.89487 464s US.Steel_value -4.74e-05 -0.01729 464s US.Steel_capital 2.79e-04 0.00114 464s Westinghouse_(Intercept) 1.14e-03 23.89487 464s Westinghouse_value -4.74e-05 -0.01729 464s Westinghouse_capital 2.79e-04 0.00114 464s US.Steel_value US.Steel_capital 464s Chrysler_(Intercept) -1.73e-02 1.14e-03 464s Chrysler_value 2.57e-05 -4.74e-05 464s Chrysler_capital -4.74e-05 2.79e-04 464s General.Electric_(Intercept) -1.73e-02 1.14e-03 464s General.Electric_value 2.57e-05 -4.74e-05 464s General.Electric_capital -4.74e-05 2.79e-04 464s General.Motors_(Intercept) -1.73e-02 1.14e-03 464s General.Motors_value 2.57e-05 -4.74e-05 464s General.Motors_capital -4.74e-05 2.79e-04 464s US.Steel_(Intercept) -1.73e-02 1.14e-03 464s US.Steel_value 2.57e-05 -4.74e-05 464s US.Steel_capital -4.74e-05 2.79e-04 464s Westinghouse_(Intercept) -1.73e-02 1.14e-03 464s Westinghouse_value 2.57e-05 -4.74e-05 464s Westinghouse_capital -4.74e-05 2.79e-04 464s Westinghouse_(Intercept) Westinghouse_value 464s Chrysler_(Intercept) 23.89487 -1.73e-02 464s Chrysler_value -0.01729 2.57e-05 464s Chrysler_capital 0.00114 -4.74e-05 464s General.Electric_(Intercept) 23.89487 -1.73e-02 464s General.Electric_value -0.01729 2.57e-05 464s General.Electric_capital 0.00114 -4.74e-05 464s General.Motors_(Intercept) 23.89487 -1.73e-02 464s General.Motors_value -0.01729 2.57e-05 464s General.Motors_capital 0.00114 -4.74e-05 464s US.Steel_(Intercept) 23.89487 -1.73e-02 464s US.Steel_value -0.01729 2.57e-05 464s US.Steel_capital 0.00114 -4.74e-05 464s Westinghouse_(Intercept) 23.89487 -1.73e-02 464s Westinghouse_value -0.01729 2.57e-05 464s Westinghouse_capital 0.00114 -4.74e-05 464s Westinghouse_capital 464s Chrysler_(Intercept) 1.14e-03 464s Chrysler_value -4.74e-05 464s Chrysler_capital 2.79e-04 464s General.Electric_(Intercept) 1.14e-03 464s General.Electric_value -4.74e-05 464s General.Electric_capital 2.79e-04 464s General.Motors_(Intercept) 1.14e-03 464s General.Motors_value -4.74e-05 464s General.Motors_capital 2.79e-04 464s US.Steel_(Intercept) 1.14e-03 464s US.Steel_value -4.74e-05 464s US.Steel_capital 2.79e-04 464s Westinghouse_(Intercept) 1.14e-03 464s Westinghouse_value -4.74e-05 464s Westinghouse_capital 2.79e-04 464s C1 C2 C3 464s C1 23.89487 -1.73e-02 1.14e-03 464s C2 -0.01729 2.57e-05 -4.74e-05 464s C3 0.00114 -4.74e-05 2.79e-04 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s X1935 27.830 -75.6 70.61 98.79 23.51 464s X1936 22.951 -141.2 -12.88 205.66 7.90 464s X1937 4.160 -183.7 -93.56 220.24 -4.13 464s X1938 23.527 -161.1 -32.72 43.09 -4.84 464s X1939 -1.382 -182.3 -93.20 -20.20 -7.09 464s X1940 10.397 -149.7 6.46 8.66 -8.03 464s X1941 14.133 -96.0 49.49 201.63 16.81 464s X1942 14.586 -117.5 85.75 180.85 1.28 464s X1943 0.807 -173.2 78.44 112.17 -17.92 464s X1944 5.381 -172.6 118.21 61.60 -20.25 464s X1945 23.374 -163.8 69.60 50.68 -29.03 464s X1946 -5.596 -124.2 145.33 186.62 -14.78 464s X1947 7.005 -124.6 28.58 200.21 -5.11 464s X1948 18.909 -149.9 -40.65 275.38 -24.83 464s X1949 5.397 -207.5 -87.07 167.54 -39.09 464s X1950 12.604 -238.0 -30.56 178.08 -41.77 464s X1951 48.812 -222.9 -49.87 298.18 -25.19 464s X1952 11.406 -242.3 2.83 332.67 -25.56 464s X1953 -1.660 -270.9 182.86 279.96 -46.40 464s X1954 -0.502 -325.0 272.93 75.36 -80.40 464s 2.5 % 97.5 % 464s Chrysler_(Intercept) -37.948 -18.545 464s Chrysler_value 0.079 0.099 464s Chrysler_capital 0.301 0.367 464s General.Electric_(Intercept) -37.948 -18.545 464s General.Electric_value 0.079 0.099 464s General.Electric_capital 0.301 0.367 464s General.Motors_(Intercept) -37.948 -18.545 464s General.Motors_value 0.079 0.099 464s General.Motors_capital 0.301 0.367 464s US.Steel_(Intercept) -37.948 -18.545 464s US.Steel_value 0.079 0.099 464s US.Steel_capital 0.301 0.367 464s Westinghouse_(Intercept) -37.948 -18.545 464s Westinghouse_value 0.079 0.099 464s Westinghouse_capital 0.301 0.367 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s X1935 12.5 109 247 111 -10.6 464s X1936 49.8 186 405 150 18.0 464s X1937 62.1 261 504 250 39.2 464s X1938 28.1 206 290 219 27.7 464s X1939 53.8 230 424 251 25.9 464s X1940 59.0 224 455 253 36.6 464s X1941 54.2 209 463 271 31.7 464s X1942 32.2 209 362 265 42.1 464s X1943 46.6 234 421 249 54.9 464s X1944 54.2 229 429 227 58.1 464s X1945 65.4 257 492 208 68.3 464s X1946 79.7 284 543 234 68.2 464s X1947 55.7 272 540 220 60.7 464s X1948 70.5 296 570 219 74.4 464s X1949 73.6 306 642 238 71.1 464s X1950 88.1 331 673 241 74.0 464s X1951 111.8 358 806 290 79.6 464s X1952 133.6 400 888 313 97.3 464s X1953 176.6 450 1122 361 136.5 464s X1954 173.0 515 1214 384 149.0 464s 'log Lik.' -533 (df=18) 464s 'log Lik.' -568 (df=18) 464s [1] 100 464s Chrysler_invest Chrysler_value Chrysler_capital General.Electric_invest 464s X1935 40.3 418 10.5 33.1 464s X1936 72.8 838 10.2 45.0 464s X1937 66.3 884 34.7 77.2 464s X1938 51.6 438 51.8 44.6 464s X1939 52.4 680 64.3 48.1 464s X1940 69.4 728 67.1 74.4 464s X1941 68.3 644 75.2 113.0 464s X1942 46.8 411 71.4 91.9 464s X1943 47.4 588 67.1 61.3 464s X1944 59.6 698 60.5 56.8 464s X1945 88.8 846 54.6 93.6 464s X1946 74.1 894 84.8 159.9 464s X1947 62.7 579 96.8 147.2 464s X1948 89.4 695 110.2 146.3 464s X1949 79.0 590 147.4 98.3 464s X1950 100.7 694 163.2 93.5 464s X1951 160.6 809 203.5 135.2 464s X1952 145.0 727 290.6 157.3 464s X1953 174.9 1002 346.1 179.5 464s X1954 172.5 703 414.9 189.6 464s General.Electric_value General.Electric_capital General.Motors_invest 464s X1935 1171 97.8 318 464s X1936 2016 104.4 392 464s X1937 2803 118.0 411 464s X1938 2040 156.2 258 464s X1939 2256 172.6 331 464s X1940 2132 186.6 461 464s X1941 1834 220.9 512 464s X1942 1588 287.8 448 464s X1943 1749 319.9 500 464s X1944 1687 321.3 548 464s X1945 2008 319.6 561 464s X1946 2208 346.0 688 464s X1947 1657 456.4 569 464s X1948 1604 543.4 529 464s X1949 1432 618.3 555 464s X1950 1610 647.4 643 464s X1951 1819 671.3 756 464s X1952 2080 726.1 891 464s X1953 2372 800.3 1304 464s X1954 2760 888.9 1487 464s General.Motors_value General.Motors_capital US.Steel_invest 464s X1935 3078 2.8 210 464s X1936 4662 52.6 355 464s X1937 5387 156.9 470 464s X1938 2792 209.2 262 464s X1939 4313 203.4 230 464s X1940 4644 207.2 262 464s X1941 4551 255.2 473 464s X1942 3244 303.7 446 464s X1943 4054 264.1 362 464s X1944 4379 201.6 288 464s X1945 4841 265.0 259 464s X1946 4901 402.2 420 464s X1947 3526 761.5 420 464s X1948 3255 922.4 494 464s X1949 3700 1020.1 405 464s X1950 3756 1099.0 419 464s X1951 4833 1207.7 588 464s X1952 4925 1430.5 645 464s X1953 6242 1777.3 641 464s X1954 5594 2226.3 459 464s US.Steel_value US.Steel_capital Westinghouse_invest Westinghouse_value 464s X1935 1362 53.8 12.9 192 464s X1936 1807 50.5 25.9 516 464s X1937 2676 118.1 35.0 729 464s X1938 1802 260.2 22.9 560 464s X1939 1957 312.7 18.8 520 464s X1940 2203 254.2 28.6 628 464s X1941 2380 261.4 48.5 537 464s X1942 2169 298.7 43.3 561 464s X1943 1985 301.8 37.0 617 464s X1944 1814 279.1 37.8 627 464s X1945 1850 213.8 39.3 737 464s X1946 2068 232.6 53.5 760 464s X1947 1797 264.8 55.6 581 464s X1948 1626 306.9 49.6 662 464s X1949 1667 351.1 32.0 584 464s X1950 1677 357.8 32.2 635 464s X1951 2290 342.1 54.4 724 464s X1952 2159 444.2 71.8 864 464s X1953 2031 623.6 90.1 1194 464s X1954 2116 669.7 68.6 1189 464s Westinghouse_capital 464s X1935 1.8 464s X1936 0.8 464s X1937 7.4 464s X1938 18.1 464s X1939 23.5 464s X1940 26.5 464s X1941 36.2 464s X1942 60.8 464s X1943 84.4 464s X1944 91.2 464s X1945 92.4 464s X1946 86.0 464s X1947 111.1 464s X1948 130.6 464s X1949 141.8 464s X1950 136.7 464s X1951 129.7 464s X1952 145.5 464s X1953 174.8 464s X1954 213.5 464s Chrysler_(Intercept) Chrysler_value Chrysler_capital 464s Chrysler_X1935 1 418 10.5 464s Chrysler_X1936 1 838 10.2 464s Chrysler_X1937 1 884 34.7 464s Chrysler_X1938 1 438 51.8 464s Chrysler_X1939 1 680 64.3 464s Chrysler_X1940 1 728 67.1 464s Chrysler_X1941 1 644 75.2 464s Chrysler_X1942 1 411 71.4 464s Chrysler_X1943 1 588 67.1 464s Chrysler_X1944 1 698 60.5 464s Chrysler_X1945 1 846 54.6 464s Chrysler_X1946 1 894 84.8 464s Chrysler_X1947 1 579 96.8 464s Chrysler_X1948 1 695 110.2 464s Chrysler_X1949 1 590 147.4 464s Chrysler_X1950 1 694 163.2 464s Chrysler_X1951 1 809 203.5 464s Chrysler_X1952 1 727 290.6 464s Chrysler_X1953 1 1002 346.1 464s Chrysler_X1954 1 703 414.9 464s General.Electric_X1935 0 0 0.0 464s General.Electric_X1936 0 0 0.0 464s General.Electric_X1937 0 0 0.0 464s General.Electric_X1938 0 0 0.0 464s General.Electric_X1939 0 0 0.0 464s General.Electric_X1940 0 0 0.0 464s General.Electric_X1941 0 0 0.0 464s General.Electric_X1942 0 0 0.0 464s General.Electric_X1943 0 0 0.0 464s General.Electric_X1944 0 0 0.0 464s General.Electric_X1945 0 0 0.0 464s General.Electric_X1946 0 0 0.0 464s General.Electric_X1947 0 0 0.0 464s General.Electric_X1948 0 0 0.0 464s General.Electric_X1949 0 0 0.0 464s General.Electric_X1950 0 0 0.0 464s General.Electric_X1951 0 0 0.0 464s General.Electric_X1952 0 0 0.0 464s General.Electric_X1953 0 0 0.0 464s General.Electric_X1954 0 0 0.0 464s General.Motors_X1935 0 0 0.0 464s General.Motors_X1936 0 0 0.0 464s General.Motors_X1937 0 0 0.0 464s General.Motors_X1938 0 0 0.0 464s General.Motors_X1939 0 0 0.0 464s General.Motors_X1940 0 0 0.0 464s General.Motors_X1941 0 0 0.0 464s General.Motors_X1942 0 0 0.0 464s General.Motors_X1943 0 0 0.0 464s General.Motors_X1944 0 0 0.0 464s General.Motors_X1945 0 0 0.0 464s General.Motors_X1946 0 0 0.0 464s General.Motors_X1947 0 0 0.0 464s General.Motors_X1948 0 0 0.0 464s General.Motors_X1949 0 0 0.0 464s General.Motors_X1950 0 0 0.0 464s General.Motors_X1951 0 0 0.0 464s General.Motors_X1952 0 0 0.0 464s General.Motors_X1953 0 0 0.0 464s General.Motors_X1954 0 0 0.0 464s US.Steel_X1935 0 0 0.0 464s US.Steel_X1936 0 0 0.0 464s US.Steel_X1937 0 0 0.0 464s US.Steel_X1938 0 0 0.0 464s US.Steel_X1939 0 0 0.0 464s US.Steel_X1940 0 0 0.0 464s US.Steel_X1941 0 0 0.0 464s US.Steel_X1942 0 0 0.0 464s US.Steel_X1943 0 0 0.0 464s US.Steel_X1944 0 0 0.0 464s US.Steel_X1945 0 0 0.0 464s US.Steel_X1946 0 0 0.0 464s US.Steel_X1947 0 0 0.0 464s US.Steel_X1948 0 0 0.0 464s US.Steel_X1949 0 0 0.0 464s US.Steel_X1950 0 0 0.0 464s US.Steel_X1951 0 0 0.0 464s US.Steel_X1952 0 0 0.0 464s US.Steel_X1953 0 0 0.0 464s US.Steel_X1954 0 0 0.0 464s Westinghouse_X1935 0 0 0.0 464s Westinghouse_X1936 0 0 0.0 464s Westinghouse_X1937 0 0 0.0 464s Westinghouse_X1938 0 0 0.0 464s Westinghouse_X1939 0 0 0.0 464s Westinghouse_X1940 0 0 0.0 464s Westinghouse_X1941 0 0 0.0 464s Westinghouse_X1942 0 0 0.0 464s Westinghouse_X1943 0 0 0.0 464s Westinghouse_X1944 0 0 0.0 464s Westinghouse_X1945 0 0 0.0 464s Westinghouse_X1946 0 0 0.0 464s Westinghouse_X1947 0 0 0.0 464s Westinghouse_X1948 0 0 0.0 464s Westinghouse_X1949 0 0 0.0 464s Westinghouse_X1950 0 0 0.0 464s Westinghouse_X1951 0 0 0.0 464s Westinghouse_X1952 0 0 0.0 464s Westinghouse_X1953 0 0 0.0 464s Westinghouse_X1954 0 0 0.0 464s General.Electric_(Intercept) General.Electric_value 464s Chrysler_X1935 0 0 464s Chrysler_X1936 0 0 464s Chrysler_X1937 0 0 464s Chrysler_X1938 0 0 464s Chrysler_X1939 0 0 464s Chrysler_X1940 0 0 464s Chrysler_X1941 0 0 464s Chrysler_X1942 0 0 464s Chrysler_X1943 0 0 464s Chrysler_X1944 0 0 464s Chrysler_X1945 0 0 464s Chrysler_X1946 0 0 464s Chrysler_X1947 0 0 464s Chrysler_X1948 0 0 464s Chrysler_X1949 0 0 464s Chrysler_X1950 0 0 464s Chrysler_X1951 0 0 464s Chrysler_X1952 0 0 464s Chrysler_X1953 0 0 464s Chrysler_X1954 0 0 464s General.Electric_X1935 1 1171 464s General.Electric_X1936 1 2016 464s General.Electric_X1937 1 2803 464s General.Electric_X1938 1 2040 464s General.Electric_X1939 1 2256 464s General.Electric_X1940 1 2132 464s General.Electric_X1941 1 1834 464s General.Electric_X1942 1 1588 464s General.Electric_X1943 1 1749 464s General.Electric_X1944 1 1687 464s General.Electric_X1945 1 2008 464s General.Electric_X1946 1 2208 464s General.Electric_X1947 1 1657 464s General.Electric_X1948 1 1604 464s General.Electric_X1949 1 1432 464s General.Electric_X1950 1 1610 464s General.Electric_X1951 1 1819 464s General.Electric_X1952 1 2080 464s General.Electric_X1953 1 2372 464s General.Electric_X1954 1 2760 464s General.Motors_X1935 0 0 464s General.Motors_X1936 0 0 464s General.Motors_X1937 0 0 464s General.Motors_X1938 0 0 464s General.Motors_X1939 0 0 464s General.Motors_X1940 0 0 464s General.Motors_X1941 0 0 464s General.Motors_X1942 0 0 464s General.Motors_X1943 0 0 464s General.Motors_X1944 0 0 464s General.Motors_X1945 0 0 464s General.Motors_X1946 0 0 464s General.Motors_X1947 0 0 464s General.Motors_X1948 0 0 464s General.Motors_X1949 0 0 464s General.Motors_X1950 0 0 464s General.Motors_X1951 0 0 464s General.Motors_X1952 0 0 464s General.Motors_X1953 0 0 464s General.Motors_X1954 0 0 464s US.Steel_X1935 0 0 464s US.Steel_X1936 0 0 464s US.Steel_X1937 0 0 464s US.Steel_X1938 0 0 464s US.Steel_X1939 0 0 464s US.Steel_X1940 0 0 464s US.Steel_X1941 0 0 464s US.Steel_X1942 0 0 464s US.Steel_X1943 0 0 464s US.Steel_X1944 0 0 464s US.Steel_X1945 0 0 464s US.Steel_X1946 0 0 464s US.Steel_X1947 0 0 464s US.Steel_X1948 0 0 464s US.Steel_X1949 0 0 464s US.Steel_X1950 0 0 464s US.Steel_X1951 0 0 464s US.Steel_X1952 0 0 464s US.Steel_X1953 0 0 464s US.Steel_X1954 0 0 464s Westinghouse_X1935 0 0 464s Westinghouse_X1936 0 0 464s Westinghouse_X1937 0 0 464s Westinghouse_X1938 0 0 464s Westinghouse_X1939 0 0 464s Westinghouse_X1940 0 0 464s Westinghouse_X1941 0 0 464s Westinghouse_X1942 0 0 464s Westinghouse_X1943 0 0 464s Westinghouse_X1944 0 0 464s Westinghouse_X1945 0 0 464s Westinghouse_X1946 0 0 464s Westinghouse_X1947 0 0 464s Westinghouse_X1948 0 0 464s Westinghouse_X1949 0 0 464s Westinghouse_X1950 0 0 464s Westinghouse_X1951 0 0 464s Westinghouse_X1952 0 0 464s Westinghouse_X1953 0 0 464s Westinghouse_X1954 0 0 464s General.Electric_capital General.Motors_(Intercept) 464s Chrysler_X1935 0.0 0 464s Chrysler_X1936 0.0 0 464s Chrysler_X1937 0.0 0 464s Chrysler_X1938 0.0 0 464s Chrysler_X1939 0.0 0 464s Chrysler_X1940 0.0 0 464s Chrysler_X1941 0.0 0 464s Chrysler_X1942 0.0 0 464s Chrysler_X1943 0.0 0 464s Chrysler_X1944 0.0 0 464s Chrysler_X1945 0.0 0 464s Chrysler_X1946 0.0 0 464s Chrysler_X1947 0.0 0 464s Chrysler_X1948 0.0 0 464s Chrysler_X1949 0.0 0 464s Chrysler_X1950 0.0 0 464s Chrysler_X1951 0.0 0 464s Chrysler_X1952 0.0 0 464s Chrysler_X1953 0.0 0 464s Chrysler_X1954 0.0 0 464s General.Electric_X1935 97.8 0 464s General.Electric_X1936 104.4 0 464s General.Electric_X1937 118.0 0 464s General.Electric_X1938 156.2 0 464s General.Electric_X1939 172.6 0 464s General.Electric_X1940 186.6 0 464s General.Electric_X1941 220.9 0 464s General.Electric_X1942 287.8 0 464s General.Electric_X1943 319.9 0 464s General.Electric_X1944 321.3 0 464s General.Electric_X1945 319.6 0 464s General.Electric_X1946 346.0 0 464s General.Electric_X1947 456.4 0 464s General.Electric_X1948 543.4 0 464s General.Electric_X1949 618.3 0 464s General.Electric_X1950 647.4 0 464s General.Electric_X1951 671.3 0 464s General.Electric_X1952 726.1 0 464s General.Electric_X1953 800.3 0 464s General.Electric_X1954 888.9 0 464s General.Motors_X1935 0.0 1 464s General.Motors_X1936 0.0 1 464s General.Motors_X1937 0.0 1 464s General.Motors_X1938 0.0 1 464s General.Motors_X1939 0.0 1 464s General.Motors_X1940 0.0 1 464s General.Motors_X1941 0.0 1 464s General.Motors_X1942 0.0 1 464s General.Motors_X1943 0.0 1 464s General.Motors_X1944 0.0 1 464s General.Motors_X1945 0.0 1 464s General.Motors_X1946 0.0 1 464s General.Motors_X1947 0.0 1 464s General.Motors_X1948 0.0 1 464s General.Motors_X1949 0.0 1 464s General.Motors_X1950 0.0 1 464s General.Motors_X1951 0.0 1 464s General.Motors_X1952 0.0 1 464s General.Motors_X1953 0.0 1 464s General.Motors_X1954 0.0 1 464s US.Steel_X1935 0.0 0 464s US.Steel_X1936 0.0 0 464s US.Steel_X1937 0.0 0 464s US.Steel_X1938 0.0 0 464s US.Steel_X1939 0.0 0 464s US.Steel_X1940 0.0 0 464s US.Steel_X1941 0.0 0 464s US.Steel_X1942 0.0 0 464s US.Steel_X1943 0.0 0 464s US.Steel_X1944 0.0 0 464s US.Steel_X1945 0.0 0 464s US.Steel_X1946 0.0 0 464s US.Steel_X1947 0.0 0 464s US.Steel_X1948 0.0 0 464s US.Steel_X1949 0.0 0 464s US.Steel_X1950 0.0 0 464s US.Steel_X1951 0.0 0 464s US.Steel_X1952 0.0 0 464s US.Steel_X1953 0.0 0 464s US.Steel_X1954 0.0 0 464s Westinghouse_X1935 0.0 0 464s Westinghouse_X1936 0.0 0 464s Westinghouse_X1937 0.0 0 464s Westinghouse_X1938 0.0 0 464s Westinghouse_X1939 0.0 0 464s Westinghouse_X1940 0.0 0 464s Westinghouse_X1941 0.0 0 464s Westinghouse_X1942 0.0 0 464s Westinghouse_X1943 0.0 0 464s Westinghouse_X1944 0.0 0 464s Westinghouse_X1945 0.0 0 464s Westinghouse_X1946 0.0 0 464s Westinghouse_X1947 0.0 0 464s Westinghouse_X1948 0.0 0 464s Westinghouse_X1949 0.0 0 464s Westinghouse_X1950 0.0 0 464s Westinghouse_X1951 0.0 0 464s Westinghouse_X1952 0.0 0 464s Westinghouse_X1953 0.0 0 464s Westinghouse_X1954 0.0 0 464s General.Motors_value General.Motors_capital 464s Chrysler_X1935 0 0.0 464s Chrysler_X1936 0 0.0 464s Chrysler_X1937 0 0.0 464s Chrysler_X1938 0 0.0 464s Chrysler_X1939 0 0.0 464s Chrysler_X1940 0 0.0 464s Chrysler_X1941 0 0.0 464s Chrysler_X1942 0 0.0 464s Chrysler_X1943 0 0.0 464s Chrysler_X1944 0 0.0 464s Chrysler_X1945 0 0.0 464s Chrysler_X1946 0 0.0 464s Chrysler_X1947 0 0.0 464s Chrysler_X1948 0 0.0 464s Chrysler_X1949 0 0.0 464s Chrysler_X1950 0 0.0 464s Chrysler_X1951 0 0.0 464s Chrysler_X1952 0 0.0 464s Chrysler_X1953 0 0.0 464s Chrysler_X1954 0 0.0 464s General.Electric_X1935 0 0.0 464s General.Electric_X1936 0 0.0 464s General.Electric_X1937 0 0.0 464s General.Electric_X1938 0 0.0 464s General.Electric_X1939 0 0.0 464s General.Electric_X1940 0 0.0 464s General.Electric_X1941 0 0.0 464s General.Electric_X1942 0 0.0 464s General.Electric_X1943 0 0.0 464s General.Electric_X1944 0 0.0 464s General.Electric_X1945 0 0.0 464s General.Electric_X1946 0 0.0 464s General.Electric_X1947 0 0.0 464s General.Electric_X1948 0 0.0 464s General.Electric_X1949 0 0.0 464s General.Electric_X1950 0 0.0 464s General.Electric_X1951 0 0.0 464s General.Electric_X1952 0 0.0 464s General.Electric_X1953 0 0.0 464s General.Electric_X1954 0 0.0 464s General.Motors_X1935 3078 2.8 464s General.Motors_X1936 4662 52.6 464s General.Motors_X1937 5387 156.9 464s General.Motors_X1938 2792 209.2 464s General.Motors_X1939 4313 203.4 464s General.Motors_X1940 4644 207.2 464s General.Motors_X1941 4551 255.2 464s General.Motors_X1942 3244 303.7 464s General.Motors_X1943 4054 264.1 464s General.Motors_X1944 4379 201.6 464s General.Motors_X1945 4841 265.0 464s General.Motors_X1946 4901 402.2 464s General.Motors_X1947 3526 761.5 464s General.Motors_X1948 3255 922.4 464s General.Motors_X1949 3700 1020.1 464s General.Motors_X1950 3756 1099.0 464s General.Motors_X1951 4833 1207.7 464s General.Motors_X1952 4925 1430.5 464s General.Motors_X1953 6242 1777.3 464s General.Motors_X1954 5594 2226.3 464s US.Steel_X1935 0 0.0 464s US.Steel_X1936 0 0.0 464s US.Steel_X1937 0 0.0 464s US.Steel_X1938 0 0.0 464s US.Steel_X1939 0 0.0 464s US.Steel_X1940 0 0.0 464s US.Steel_X1941 0 0.0 464s US.Steel_X1942 0 0.0 464s US.Steel_X1943 0 0.0 464s US.Steel_X1944 0 0.0 464s US.Steel_X1945 0 0.0 464s US.Steel_X1946 0 0.0 464s US.Steel_X1947 0 0.0 464s US.Steel_X1948 0 0.0 464s US.Steel_X1949 0 0.0 464s US.Steel_X1950 0 0.0 464s US.Steel_X1951 0 0.0 464s US.Steel_X1952 0 0.0 464s US.Steel_X1953 0 0.0 464s US.Steel_X1954 0 0.0 464s Westinghouse_X1935 0 0.0 464s Westinghouse_X1936 0 0.0 464s Westinghouse_X1937 0 0.0 464s Westinghouse_X1938 0 0.0 464s Westinghouse_X1939 0 0.0 464s Westinghouse_X1940 0 0.0 464s Westinghouse_X1941 0 0.0 464s Westinghouse_X1942 0 0.0 464s Westinghouse_X1943 0 0.0 464s Westinghouse_X1944 0 0.0 464s Westinghouse_X1945 0 0.0 464s Westinghouse_X1946 0 0.0 464s Westinghouse_X1947 0 0.0 464s Westinghouse_X1948 0 0.0 464s Westinghouse_X1949 0 0.0 464s Westinghouse_X1950 0 0.0 464s Westinghouse_X1951 0 0.0 464s Westinghouse_X1952 0 0.0 464s Westinghouse_X1953 0 0.0 464s Westinghouse_X1954 0 0.0 464s US.Steel_(Intercept) US.Steel_value US.Steel_capital 464s Chrysler_X1935 0 0 0.0 464s Chrysler_X1936 0 0 0.0 464s Chrysler_X1937 0 0 0.0 464s Chrysler_X1938 0 0 0.0 464s Chrysler_X1939 0 0 0.0 464s Chrysler_X1940 0 0 0.0 464s Chrysler_X1941 0 0 0.0 464s Chrysler_X1942 0 0 0.0 464s Chrysler_X1943 0 0 0.0 464s Chrysler_X1944 0 0 0.0 464s Chrysler_X1945 0 0 0.0 464s Chrysler_X1946 0 0 0.0 464s Chrysler_X1947 0 0 0.0 464s Chrysler_X1948 0 0 0.0 464s Chrysler_X1949 0 0 0.0 464s Chrysler_X1950 0 0 0.0 464s Chrysler_X1951 0 0 0.0 464s Chrysler_X1952 0 0 0.0 464s Chrysler_X1953 0 0 0.0 464s Chrysler_X1954 0 0 0.0 464s General.Electric_X1935 0 0 0.0 464s General.Electric_X1936 0 0 0.0 464s General.Electric_X1937 0 0 0.0 464s General.Electric_X1938 0 0 0.0 464s General.Electric_X1939 0 0 0.0 464s General.Electric_X1940 0 0 0.0 464s General.Electric_X1941 0 0 0.0 464s General.Electric_X1942 0 0 0.0 464s General.Electric_X1943 0 0 0.0 464s General.Electric_X1944 0 0 0.0 464s General.Electric_X1945 0 0 0.0 464s General.Electric_X1946 0 0 0.0 464s General.Electric_X1947 0 0 0.0 464s General.Electric_X1948 0 0 0.0 464s General.Electric_X1949 0 0 0.0 464s General.Electric_X1950 0 0 0.0 464s General.Electric_X1951 0 0 0.0 464s General.Electric_X1952 0 0 0.0 464s General.Electric_X1953 0 0 0.0 464s General.Electric_X1954 0 0 0.0 464s General.Motors_X1935 0 0 0.0 464s General.Motors_X1936 0 0 0.0 464s General.Motors_X1937 0 0 0.0 464s General.Motors_X1938 0 0 0.0 464s General.Motors_X1939 0 0 0.0 464s General.Motors_X1940 0 0 0.0 464s General.Motors_X1941 0 0 0.0 464s General.Motors_X1942 0 0 0.0 464s General.Motors_X1943 0 0 0.0 464s General.Motors_X1944 0 0 0.0 464s General.Motors_X1945 0 0 0.0 464s General.Motors_X1946 0 0 0.0 464s General.Motors_X1947 0 0 0.0 464s General.Motors_X1948 0 0 0.0 464s General.Motors_X1949 0 0 0.0 464s General.Motors_X1950 0 0 0.0 464s General.Motors_X1951 0 0 0.0 464s General.Motors_X1952 0 0 0.0 464s General.Motors_X1953 0 0 0.0 464s General.Motors_X1954 0 0 0.0 464s US.Steel_X1935 1 1362 53.8 464s US.Steel_X1936 1 1807 50.5 464s US.Steel_X1937 1 2676 118.1 464s US.Steel_X1938 1 1802 260.2 464s US.Steel_X1939 1 1957 312.7 464s US.Steel_X1940 1 2203 254.2 464s US.Steel_X1941 1 2380 261.4 464s US.Steel_X1942 1 2169 298.7 464s US.Steel_X1943 1 1985 301.8 464s US.Steel_X1944 1 1814 279.1 464s US.Steel_X1945 1 1850 213.8 464s US.Steel_X1946 1 2068 232.6 464s US.Steel_X1947 1 1797 264.8 464s US.Steel_X1948 1 1626 306.9 464s US.Steel_X1949 1 1667 351.1 464s US.Steel_X1950 1 1677 357.8 464s US.Steel_X1951 1 2290 342.1 464s US.Steel_X1952 1 2159 444.2 464s US.Steel_X1953 1 2031 623.6 464s US.Steel_X1954 1 2116 669.7 464s Westinghouse_X1935 0 0 0.0 464s Westinghouse_X1936 0 0 0.0 464s Westinghouse_X1937 0 0 0.0 464s Westinghouse_X1938 0 0 0.0 464s Westinghouse_X1939 0 0 0.0 464s Westinghouse_X1940 0 0 0.0 464s Westinghouse_X1941 0 0 0.0 464s Westinghouse_X1942 0 0 0.0 464s Westinghouse_X1943 0 0 0.0 464s Westinghouse_X1944 0 0 0.0 464s Westinghouse_X1945 0 0 0.0 464s Westinghouse_X1946 0 0 0.0 464s Westinghouse_X1947 0 0 0.0 464s Westinghouse_X1948 0 0 0.0 464s Westinghouse_X1949 0 0 0.0 464s Westinghouse_X1950 0 0 0.0 464s Westinghouse_X1951 0 0 0.0 464s Westinghouse_X1952 0 0 0.0 464s Westinghouse_X1953 0 0 0.0 464s Westinghouse_X1954 0 0 0.0 464s Westinghouse_(Intercept) Westinghouse_value 464s Chrysler_X1935 0 0 464s Chrysler_X1936 0 0 464s Chrysler_X1937 0 0 464s Chrysler_X1938 0 0 464s Chrysler_X1939 0 0 464s Chrysler_X1940 0 0 464s Chrysler_X1941 0 0 464s Chrysler_X1942 0 0 464s Chrysler_X1943 0 0 464s Chrysler_X1944 0 0 464s Chrysler_X1945 0 0 464s Chrysler_X1946 0 0 464s Chrysler_X1947 0 0 464s Chrysler_X1948 0 0 464s Chrysler_X1949 0 0 464s Chrysler_X1950 0 0 464s Chrysler_X1951 0 0 464s Chrysler_X1952 0 0 464s Chrysler_X1953 0 0 464s Chrysler_X1954 0 0 464s General.Electric_X1935 0 0 464s General.Electric_X1936 0 0 464s General.Electric_X1937 0 0 464s General.Electric_X1938 0 0 464s General.Electric_X1939 0 0 464s General.Electric_X1940 0 0 464s General.Electric_X1941 0 0 464s General.Electric_X1942 0 0 464s General.Electric_X1943 0 0 464s General.Electric_X1944 0 0 464s General.Electric_X1945 0 0 464s General.Electric_X1946 0 0 464s General.Electric_X1947 0 0 464s General.Electric_X1948 0 0 464s General.Electric_X1949 0 0 464s General.Electric_X1950 0 0 464s General.Electric_X1951 0 0 464s General.Electric_X1952 0 0 464s General.Electric_X1953 0 0 464s General.Electric_X1954 0 0 464s General.Motors_X1935 0 0 464s General.Motors_X1936 0 0 464s General.Motors_X1937 0 0 464s General.Motors_X1938 0 0 464s General.Motors_X1939 0 0 464s General.Motors_X1940 0 0 464s General.Motors_X1941 0 0 464s General.Motors_X1942 0 0 464s General.Motors_X1943 0 0 464s General.Motors_X1944 0 0 464s General.Motors_X1945 0 0 464s General.Motors_X1946 0 0 464s General.Motors_X1947 0 0 464s General.Motors_X1948 0 0 464s General.Motors_X1949 0 0 464s General.Motors_X1950 0 0 464s General.Motors_X1951 0 0 464s General.Motors_X1952 0 0 464s General.Motors_X1953 0 0 464s General.Motors_X1954 0 0 464s US.Steel_X1935 0 0 464s US.Steel_X1936 0 0 464s US.Steel_X1937 0 0 464s US.Steel_X1938 0 0 464s US.Steel_X1939 0 0 464s US.Steel_X1940 0 0 464s US.Steel_X1941 0 0 464s US.Steel_X1942 0 0 464s US.Steel_X1943 0 0 464s US.Steel_X1944 0 0 464s US.Steel_X1945 0 0 464s US.Steel_X1946 0 0 464s US.Steel_X1947 0 0 464s US.Steel_X1948 0 0 464s US.Steel_X1949 0 0 464s US.Steel_X1950 0 0 464s US.Steel_X1951 0 0 464s US.Steel_X1952 0 0 464s US.Steel_X1953 0 0 464s US.Steel_X1954 0 0 464s Westinghouse_X1935 1 192 464s Westinghouse_X1936 1 516 464s Westinghouse_X1937 1 729 464s Westinghouse_X1938 1 560 464s Westinghouse_X1939 1 520 464s Westinghouse_X1940 1 628 464s Westinghouse_X1941 1 537 464s Westinghouse_X1942 1 561 464s Westinghouse_X1943 1 617 464s Westinghouse_X1944 1 627 464s Westinghouse_X1945 1 737 464s Westinghouse_X1946 1 760 464s Westinghouse_X1947 1 581 464s Westinghouse_X1948 1 662 464s Westinghouse_X1949 1 584 464s Westinghouse_X1950 1 635 464s Westinghouse_X1951 1 724 464s Westinghouse_X1952 1 864 464s Westinghouse_X1953 1 1194 464s Westinghouse_X1954 1 1189 464s Westinghouse_capital 464s Chrysler_X1935 0.0 464s Chrysler_X1936 0.0 464s Chrysler_X1937 0.0 464s Chrysler_X1938 0.0 464s Chrysler_X1939 0.0 464s Chrysler_X1940 0.0 464s Chrysler_X1941 0.0 464s Chrysler_X1942 0.0 464s Chrysler_X1943 0.0 464s Chrysler_X1944 0.0 464s Chrysler_X1945 0.0 464s Chrysler_X1946 0.0 464s Chrysler_X1947 0.0 464s Chrysler_X1948 0.0 464s Chrysler_X1949 0.0 464s Chrysler_X1950 0.0 464s Chrysler_X1951 0.0 464s Chrysler_X1952 0.0 464s Chrysler_X1953 0.0 464s Chrysler_X1954 0.0 464s General.Electric_X1935 0.0 464s General.Electric_X1936 0.0 464s General.Electric_X1937 0.0 464s General.Electric_X1938 0.0 464s General.Electric_X1939 0.0 464s General.Electric_X1940 0.0 464s General.Electric_X1941 0.0 464s General.Electric_X1942 0.0 464s General.Electric_X1943 0.0 464s General.Electric_X1944 0.0 464s General.Electric_X1945 0.0 464s General.Electric_X1946 0.0 464s General.Electric_X1947 0.0 464s General.Electric_X1948 0.0 464s General.Electric_X1949 0.0 464s General.Electric_X1950 0.0 464s General.Electric_X1951 0.0 464s General.Electric_X1952 0.0 464s General.Electric_X1953 0.0 464s General.Electric_X1954 0.0 464s General.Motors_X1935 0.0 464s General.Motors_X1936 0.0 464s General.Motors_X1937 0.0 464s General.Motors_X1938 0.0 464s General.Motors_X1939 0.0 464s General.Motors_X1940 0.0 464s General.Motors_X1941 0.0 464s General.Motors_X1942 0.0 464s General.Motors_X1943 0.0 464s General.Motors_X1944 0.0 464s General.Motors_X1945 0.0 464s General.Motors_X1946 0.0 464s General.Motors_X1947 0.0 464s General.Motors_X1948 0.0 464s General.Motors_X1949 0.0 464s General.Motors_X1950 0.0 464s General.Motors_X1951 0.0 464s General.Motors_X1952 0.0 464s General.Motors_X1953 0.0 464s General.Motors_X1954 0.0 464s US.Steel_X1935 0.0 464s US.Steel_X1936 0.0 464s US.Steel_X1937 0.0 464s US.Steel_X1938 0.0 464s US.Steel_X1939 0.0 464s US.Steel_X1940 0.0 464s US.Steel_X1941 0.0 464s US.Steel_X1942 0.0 464s US.Steel_X1943 0.0 464s US.Steel_X1944 0.0 464s US.Steel_X1945 0.0 464s US.Steel_X1946 0.0 464s US.Steel_X1947 0.0 464s US.Steel_X1948 0.0 464s US.Steel_X1949 0.0 464s US.Steel_X1950 0.0 464s US.Steel_X1951 0.0 464s US.Steel_X1952 0.0 464s US.Steel_X1953 0.0 464s US.Steel_X1954 0.0 464s Westinghouse_X1935 1.8 464s Westinghouse_X1936 0.8 464s Westinghouse_X1937 7.4 464s Westinghouse_X1938 18.1 464s Westinghouse_X1939 23.5 464s Westinghouse_X1940 26.5 464s Westinghouse_X1941 36.2 464s Westinghouse_X1942 60.8 464s Westinghouse_X1943 84.4 464s Westinghouse_X1944 91.2 464s Westinghouse_X1945 92.4 464s Westinghouse_X1946 86.0 464s Westinghouse_X1947 111.1 464s Westinghouse_X1948 130.6 464s Westinghouse_X1949 141.8 464s Westinghouse_X1950 136.7 464s Westinghouse_X1951 129.7 464s Westinghouse_X1952 145.5 464s Westinghouse_X1953 174.8 464s Westinghouse_X1954 213.5 464s $Chrysler 464s Chrysler_invest ~ Chrysler_value + Chrysler_capital 464s 464s 464s $General.Electric 464s General.Electric_invest ~ General.Electric_value + General.Electric_capital 464s 464s 464s $General.Motors 464s General.Motors_invest ~ General.Motors_value + General.Motors_capital 464s 464s 464s $US.Steel 464s US.Steel_invest ~ US.Steel_value + US.Steel_capital 464s 464s 464s $Westinghouse 464s Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 464s 464s 464s Chrysler_invest ~ Chrysler_value + Chrysler_capital 464s 464s $Chrysler 464s Chrysler_invest ~ Chrysler_value + Chrysler_capital 464s attr(,"variables") 464s list(Chrysler_invest, Chrysler_value, Chrysler_capital) 464s attr(,"factors") 464s Chrysler_value Chrysler_capital 464s Chrysler_invest 0 0 464s Chrysler_value 1 0 464s Chrysler_capital 0 1 464s attr(,"term.labels") 464s [1] "Chrysler_value" "Chrysler_capital" 464s attr(,"order") 464s [1] 1 1 464s attr(,"intercept") 464s [1] 1 464s attr(,"response") 464s [1] 1 464s attr(,".Environment") 464s 464s attr(,"predvars") 464s list(Chrysler_invest, Chrysler_value, Chrysler_capital) 464s attr(,"dataClasses") 464s Chrysler_invest Chrysler_value Chrysler_capital 464s "numeric" "numeric" "numeric" 464s 464s $General.Electric 464s General.Electric_invest ~ General.Electric_value + General.Electric_capital 464s attr(,"variables") 464s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 464s attr(,"factors") 464s General.Electric_value General.Electric_capital 464s General.Electric_invest 0 0 464s General.Electric_value 1 0 464s General.Electric_capital 0 1 464s attr(,"term.labels") 464s [1] "General.Electric_value" "General.Electric_capital" 464s attr(,"order") 464s [1] 1 1 464s attr(,"intercept") 464s [1] 1 464s attr(,"response") 464s [1] 1 464s attr(,".Environment") 464s 464s attr(,"predvars") 464s list(General.Electric_invest, General.Electric_value, General.Electric_capital) 464s attr(,"dataClasses") 464s General.Electric_invest General.Electric_value General.Electric_capital 464s "numeric" "numeric" "numeric" 464s 464s $General.Motors 464s General.Motors_invest ~ General.Motors_value + General.Motors_capital 464s attr(,"variables") 464s list(General.Motors_invest, General.Motors_value, General.Motors_capital) 464s attr(,"factors") 464s General.Motors_value General.Motors_capital 464s General.Motors_invest 0 0 464s General.Motors_value 1 0 464s General.Motors_capital 0 1 464s attr(,"term.labels") 464s [1] "General.Motors_value" "General.Motors_capital" 464s attr(,"order") 464s [1] 1 1 464s attr(,"intercept") 464s [1] 1 464s attr(,"response") 464s [1] 1 464s attr(,".Environment") 464s 464s attr(,"predvars") 464s list(General.Motors_invest, General.Motors_value, General.Motors_capital) 464s attr(,"dataClasses") 464s General.Motors_invest General.Motors_value General.Motors_capital 464s "numeric" "numeric" "numeric" 464s 464s $US.Steel 464s US.Steel_invest ~ US.Steel_value + US.Steel_capital 464s attr(,"variables") 464s list(US.Steel_invest, US.Steel_value, US.Steel_capital) 464s attr(,"factors") 464s US.Steel_value US.Steel_capital 464s US.Steel_invest 0 0 464s US.Steel_value 1 0 464s US.Steel_capital 0 1 464s attr(,"term.labels") 464s [1] "US.Steel_value" "US.Steel_capital" 464s attr(,"order") 464s [1] 1 1 464s attr(,"intercept") 464s [1] 1 464s attr(,"response") 464s [1] 1 464s attr(,".Environment") 464s 464s attr(,"predvars") 464s list(US.Steel_invest, US.Steel_value, US.Steel_capital) 464s attr(,"dataClasses") 464s US.Steel_invest US.Steel_value US.Steel_capital 464s "numeric" "numeric" "numeric" 464s 464s $Westinghouse 464s Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 464s attr(,"variables") 464s list(Westinghouse_invest, Westinghouse_value, Westinghouse_capital) 464s attr(,"factors") 464s Westinghouse_value Westinghouse_capital 464s Westinghouse_invest 0 0 464s Westinghouse_value 1 0 464s Westinghouse_capital 0 1 464s attr(,"term.labels") 464s [1] "Westinghouse_value" "Westinghouse_capital" 464s attr(,"order") 464s [1] 1 1 464s attr(,"intercept") 464s [1] 1 464s attr(,"response") 464s [1] 1 464s attr(,".Environment") 464s 464s attr(,"predvars") 464s list(Westinghouse_invest, Westinghouse_value, Westinghouse_capital) 464s attr(,"dataClasses") 464s Westinghouse_invest Westinghouse_value Westinghouse_capital 464s "numeric" "numeric" "numeric" 464s 464s Chrysler_invest ~ Chrysler_value + Chrysler_capital 464s attr(,"variables") 464s list(Chrysler_invest, Chrysler_value, Chrysler_capital) 464s attr(,"factors") 464s Chrysler_value Chrysler_capital 464s Chrysler_invest 0 0 464s Chrysler_value 1 0 464s Chrysler_capital 0 1 464s attr(,"term.labels") 464s [1] "Chrysler_value" "Chrysler_capital" 464s attr(,"order") 464s [1] 1 1 464s attr(,"intercept") 464s [1] 1 464s attr(,"response") 464s [1] 1 464s attr(,".Environment") 464s 464s attr(,"predvars") 464s list(Chrysler_invest, Chrysler_value, Chrysler_capital) 464s attr(,"dataClasses") 464s Chrysler_invest Chrysler_value Chrysler_capital 464s "numeric" "numeric" "numeric" 464s > 464s > 464s > ######### IV estimation ####################### 464s > ### 2SLS ### 464s > # instruments = explanatory variables -> 2SLS estimates = OLS estimates 464s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 464s + greene2sls <- systemfit( formulaGrunfeld, inst = ~ value + capital, "2SLS", 464s + data = GrunfeldGreene, useMatrix = useMatrix ) 464s + print( greene2sls ) 464s + print( summary( greene2sls ) ) 464s + print( all.equal( coef( summary( greene2sls ) ), coef( summary( greeneOls ) ) ) ) 464s + print( all.equal( greene2sls[ -c(1,2,6) ], greeneOls[ -c(1,2,6) ] ) ) 464s + for( i in 1:length( greene2sls$eq ) ) { 464s + print( all.equal( greene2sls$eq[[i]][ -c(3,15:17) ], 464s + greeneOls$eq[[i]][-3] ) ) 464s + } 464s + } 464s 464s systemfit results 464s method: 2SLS 464s 464s Coefficients: 464s Chrysler_(Intercept) Chrysler_value 464s -6.1900 0.0779 464s Chrysler_capital General.Electric_(Intercept) 464s 0.3157 -9.9563 464s General.Electric_value General.Electric_capital 464s 0.0266 0.1517 464s General.Motors_(Intercept) General.Motors_value 464s -149.7825 0.1193 464s General.Motors_capital US.Steel_(Intercept) 464s 0.3714 -30.3685 464s US.Steel_value US.Steel_capital 464s 0.1566 0.4239 464s Westinghouse_(Intercept) Westinghouse_value 464s -0.5094 0.0529 464s Westinghouse_capital 464s 0.0924 464s 464s systemfit results 464s method: 2SLS 464s 464s N DF SSR detRCov OLS-R2 McElroy-R2 464s system 100 85 339121 2.09e+14 0.848 0.862 464s 464s N DF SSR MSE RMSE R2 Adj R2 464s Chrysler 20 17 2997 176 13.3 0.914 0.903 464s General.Electric 20 17 13217 777 27.9 0.705 0.671 464s General.Motors 20 17 143206 8424 91.8 0.921 0.912 464s US.Steel 20 17 177928 10466 102.3 0.440 0.374 464s Westinghouse 20 17 1773 104 10.2 0.744 0.714 464s 464s The covariance matrix of the residuals 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 176.3 -25.1 -333 492 15.7 464s General.Electric -25.1 777.4 715 1065 207.6 464s General.Motors -332.7 714.7 8424 -2614 148.4 464s US.Steel 491.9 1064.6 -2614 10466 642.6 464s Westinghouse 15.7 207.6 148 643 104.3 464s 464s The correlations of the residuals 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 1.0000 -0.0679 -0.273 0.362 0.115 464s General.Electric -0.0679 1.0000 0.279 0.373 0.729 464s General.Motors -0.2730 0.2793 1.000 -0.278 0.158 464s US.Steel 0.3621 0.3732 -0.278 1.000 0.615 464s Westinghouse 0.1154 0.7290 0.158 0.615 1.000 464s 464s 464s 2SLS estimates for 'Chrysler' (equation 1) 464s Model Formula: Chrysler_invest ~ Chrysler_value + Chrysler_capital 464s 464s Instruments: ~Chrysler_value + Chrysler_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -6.1900 13.5065 -0.46 0.6525 464s value 0.0779 0.0200 3.90 0.0011 ** 464s capital 0.3157 0.0288 10.96 4e-09 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 13.279 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 2997.444 MSE: 176.32 Root MSE: 13.279 464s Multiple R-Squared: 0.914 Adjusted R-Squared: 0.903 464s 464s 464s 2SLS estimates for 'General.Electric' (equation 2) 464s Model Formula: General.Electric_invest ~ General.Electric_value + General.Electric_capital 464s 464s Instruments: ~General.Electric_value + General.Electric_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -9.9563 31.3742 -0.32 0.75 464s value 0.0266 0.0156 1.71 0.11 464s capital 0.1517 0.0257 5.90 1.7e-05 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 27.883 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 13216.588 MSE: 777.446 Root MSE: 27.883 464s Multiple R-Squared: 0.705 Adjusted R-Squared: 0.671 464s 464s 464s 2SLS estimates for 'General.Motors' (equation 3) 464s Model Formula: General.Motors_invest ~ General.Motors_value + General.Motors_capital 464s 464s Instruments: ~General.Motors_value + General.Motors_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -149.7825 105.8421 -1.42 0.17508 464s value 0.1193 0.0258 4.62 0.00025 *** 464s capital 0.3714 0.0371 10.02 1.5e-08 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 91.782 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 143205.877 MSE: 8423.875 Root MSE: 91.782 464s Multiple R-Squared: 0.921 Adjusted R-Squared: 0.912 464s 464s 464s 2SLS estimates for 'US.Steel' (equation 4) 464s Model Formula: US.Steel_invest ~ US.Steel_value + US.Steel_capital 464s 464s Instruments: ~US.Steel_value + US.Steel_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -30.3685 157.0477 -0.19 0.849 464s value 0.1566 0.0789 1.98 0.064 . 464s capital 0.4239 0.1552 2.73 0.014 * 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 102.305 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 177928.314 MSE: 10466.371 Root MSE: 102.305 464s Multiple R-Squared: 0.44 Adjusted R-Squared: 0.374 464s 464s 464s 2SLS estimates for 'Westinghouse' (equation 5) 464s Model Formula: Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 464s 464s Instruments: ~Westinghouse_value + Westinghouse_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -0.5094 8.0153 -0.06 0.9501 464s value 0.0529 0.0157 3.37 0.0037 ** 464s capital 0.0924 0.0561 1.65 0.1179 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 10.213 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 1773.234 MSE: 104.308 Root MSE: 10.213 464s Multiple R-Squared: 0.744 Adjusted R-Squared: 0.714 464s 464s [1] TRUE 464s [1] TRUE 464s [1] TRUE 464s [1] TRUE 464s [1] TRUE 464s [1] TRUE 464s [1] TRUE 464s > # 'real' IV/2SLS estimation 464s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 464s + greene2slsR <- systemfit( invest ~ capital, inst = ~ value, "2SLS", 464s + data = GrunfeldGreene, useMatrix = useMatrix ) 464s + print( greene2slsR ) 464s + print( summary( greene2slsR ) ) 464s + } 464s 464s systemfit results 464s method: 2SLS 464s 464s Coefficients: 464s Chrysler_(Intercept) Chrysler_capital 464s 4.314 0.675 464s General.Electric_(Intercept) General.Electric_capital 464s -106.788 0.522 464s General.Motors_(Intercept) General.Motors_capital 464s 110.940 0.767 464s US.Steel_(Intercept) US.Steel_capital 464s -323.878 2.432 464s Westinghouse_(Intercept) Westinghouse_capital 464s 13.163 0.347 464s 464s systemfit results 464s method: 2SLS 464s 464s N DF SSR detRCov OLS-R2 McElroy-R2 464s system 100 90 3239824 2.75e+17 -0.456 0.476 464s 464s N DF SSR MSE RMSE R2 Adj R2 464s Chrysler 20 18 30374 1687 41.1 0.124 0.076 464s General.Electric 20 18 174998 9722 98.6 -2.902 -3.119 464s General.Motors 20 18 1100181 61121 247.2 0.396 0.362 464s US.Steel 20 18 1930347 107242 327.5 -5.072 -5.409 464s Westinghouse 20 18 3924 218 14.8 0.434 0.403 464s 464s The covariance matrix of the residuals 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 1687 3089 6820 11741 179 464s General.Electric 3089 9722 20780 23319 886 464s General.Motors 6820 20780 61121 44203 1908 464s US.Steel 11741 23319 44203 107242 1977 464s Westinghouse 179 886 1908 1977 218 464s 464s The correlations of the residuals 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 1.000 0.763 0.672 0.873 0.295 464s General.Electric 0.763 1.000 0.852 0.722 0.608 464s General.Motors 0.672 0.852 1.000 0.546 0.523 464s US.Steel 0.873 0.722 0.546 1.000 0.409 464s Westinghouse 0.295 0.608 0.523 0.409 1.000 464s 464s 464s 2SLS estimates for 'Chrysler' (equation 1) 464s Model Formula: Chrysler_invest ~ Chrysler_capital 464s 464s Instruments: ~Chrysler_value 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) 4.314 34.033 0.13 0.901 464s capital 0.675 0.270 2.50 0.022 * 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 41.078 on 18 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 18 464s SSR: 30373.531 MSE: 1687.418 Root MSE: 41.078 464s Multiple R-Squared: 0.124 Adjusted R-Squared: 0.076 464s 464s 464s 2SLS estimates for 'General.Electric' (equation 2) 464s Model Formula: General.Electric_invest ~ General.Electric_capital 464s 464s Instruments: ~General.Electric_value 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -106.788 306.251 -0.35 0.73 464s capital 0.522 0.763 0.68 0.50 464s 464s Residual standard error: 98.601 on 18 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 18 464s SSR: 174998.166 MSE: 9722.12 Root MSE: 98.601 464s Multiple R-Squared: -2.902 Adjusted R-Squared: -3.119 464s 464s 464s 2SLS estimates for 'General.Motors' (equation 3) 464s Model Formula: General.Motors_invest ~ General.Motors_capital 464s 464s Instruments: ~General.Motors_value 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) 110.940 145.626 0.76 0.4560 464s capital 0.767 0.208 3.69 0.0017 ** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 247.227 on 18 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 18 464s SSR: 1100180.666 MSE: 61121.148 Root MSE: 247.227 464s Multiple R-Squared: 0.396 Adjusted R-Squared: 0.362 464s 464s 464s 2SLS estimates for 'US.Steel' (equation 4) 464s Model Formula: US.Steel_invest ~ US.Steel_capital 464s 464s Instruments: ~US.Steel_value 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -323.88 962.57 -0.34 0.74 464s capital 2.43 3.20 0.76 0.46 464s 464s Residual standard error: 327.478 on 18 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 18 464s SSR: 1930347.395 MSE: 107241.522 Root MSE: 327.478 464s Multiple R-Squared: -5.072 Adjusted R-Squared: -5.409 464s 464s 464s 2SLS estimates for 'Westinghouse' (equation 5) 464s Model Formula: Westinghouse_invest ~ Westinghouse_capital 464s 464s Instruments: ~Westinghouse_value 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) 13.1626 7.0965 1.85 0.08008 . 464s capital 0.3471 0.0734 4.73 0.00017 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 14.765 on 18 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 18 464s SSR: 3923.899 MSE: 217.994 Root MSE: 14.765 464s Multiple R-Squared: 0.434 Adjusted R-Squared: 0.403 464s 464s > 464s > ### 2SLS, pooled ### 464s > # instruments = explanatory variables -> 2SLS estimates = OLS estimates 464s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 464s + greene2slsPooled <- systemfit( formulaGrunfeld, inst = ~ value + capital, "2SLS", 464s + data = GrunfeldGreene, pooled = TRUE, useMatrix = useMatrix ) 464s + print( greene2slsPooled ) 464s + print( summary( greene2slsPooled ) ) 464s + print( all.equal( coef( summary( greene2slsPooled ) ), 464s + coef( summary( greeneOlsPooled ) ) ) ) 464s + print( all.equal( greene2slsPooled[ -c(1,2,6) ], greeneOlsPooled[ -c(1,2,6) ] ) ) 464s + for( i in 1:length( greene2slsPooled$eq ) ) { 464s + print( all.equal( greene2slsPooled$eq[[i]][ -c(3,15:17) ], 464s + greeneOlsPooled$eq[[i]][-3] ) ) 464s + } 464s + } 464s 464s systemfit results 464s method: 2SLS 464s 464s Coefficients: 464s Chrysler_(Intercept) Chrysler_value 464s -48.030 0.105 464s Chrysler_capital General.Electric_(Intercept) 464s 0.305 -48.030 464s General.Electric_value General.Electric_capital 464s 0.105 0.305 464s General.Motors_(Intercept) General.Motors_value 464s -48.030 0.105 464s General.Motors_capital US.Steel_(Intercept) 464s 0.305 -48.030 464s US.Steel_value US.Steel_capital 464s 0.105 0.305 464s Westinghouse_(Intercept) Westinghouse_value 464s -48.030 0.105 464s Westinghouse_capital 464s 0.305 464s 464s systemfit results 464s method: 2SLS 464s 464s N DF SSR detRCov OLS-R2 McElroy-R2 464s system 100 97 1570884 4.2e+17 0.294 0.812 464s 464s N DF SSR MSE RMSE R2 Adj R2 464s Chrysler 20 17 15117 889 29.8 0.564 0.513 464s General.Electric 20 17 685770 40339 200.8 -14.291 -16.090 464s General.Motors 20 17 188218 11072 105.2 0.897 0.884 464s US.Steel 20 17 669110 39359 198.4 -1.105 -1.352 464s Westinghouse 20 17 12668 745 27.3 -0.826 -1.041 464s 464s The covariance matrix of the residuals 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 889.2 -4898 -198 4748 -94.6 464s General.Electric -4898.1 40339 -2254 -32821 2658.0 464s General.Motors -197.7 -2254 11072 304 -1328.6 464s US.Steel 4748.1 -32821 304 39359 -1377.3 464s Westinghouse -94.6 2658 -1329 -1377 745.2 464s 464s The correlations of the residuals 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 1.000 0.144 -0.1852 0.2218 0.186 464s General.Electric 0.144 1.000 -0.2592 -0.1216 0.881 464s General.Motors -0.185 -0.259 1.0000 -0.0155 -0.469 464s US.Steel 0.222 -0.122 -0.0155 1.0000 -0.119 464s Westinghouse 0.186 0.881 -0.4689 -0.1186 1.000 464s 464s 464s 2SLS estimates for 'Chrysler' (equation 1) 464s Model Formula: Chrysler_invest ~ Chrysler_value + Chrysler_capital 464s 464s Instruments: ~Chrysler_value + Chrysler_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -48.0297 21.4802 -2.24 0.028 * 464s value 0.1051 0.0114 9.24 6.0e-15 *** 464s capital 0.3054 0.0435 7.02 3.1e-10 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 29.82 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 15117.016 MSE: 889.236 Root MSE: 29.82 464s Multiple R-Squared: 0.564 Adjusted R-Squared: 0.513 464s 464s 464s 2SLS estimates for 'General.Electric' (equation 2) 464s Model Formula: General.Electric_invest ~ General.Electric_value + General.Electric_capital 464s 464s Instruments: ~General.Electric_value + General.Electric_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -48.0297 21.4802 -2.24 0.028 * 464s value 0.1051 0.0114 9.24 6.0e-15 *** 464s capital 0.3054 0.0435 7.02 3.1e-10 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 200.847 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 685769.815 MSE: 40339.401 Root MSE: 200.847 464s Multiple R-Squared: -14.291 Adjusted R-Squared: -16.09 464s 464s 464s 2SLS estimates for 'General.Motors' (equation 3) 464s Model Formula: General.Motors_invest ~ General.Motors_value + General.Motors_capital 464s 464s Instruments: ~General.Motors_value + General.Motors_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -48.0297 21.4802 -2.24 0.028 * 464s value 0.1051 0.0114 9.24 6.0e-15 *** 464s capital 0.3054 0.0435 7.02 3.1e-10 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 105.222 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 188218.158 MSE: 11071.656 Root MSE: 105.222 464s Multiple R-Squared: 0.897 Adjusted R-Squared: 0.884 464s 464s 464s 2SLS estimates for 'US.Steel' (equation 4) 464s Model Formula: US.Steel_invest ~ US.Steel_value + US.Steel_capital 464s 464s Instruments: ~US.Steel_value + US.Steel_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -48.0297 21.4802 -2.24 0.028 * 464s value 0.1051 0.0114 9.24 6.0e-15 *** 464s capital 0.3054 0.0435 7.02 3.1e-10 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 198.392 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 669110.225 MSE: 39359.425 Root MSE: 198.392 464s Multiple R-Squared: -1.105 Adjusted R-Squared: -1.352 464s 464s 464s 2SLS estimates for 'Westinghouse' (equation 5) 464s Model Formula: Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 464s 464s Instruments: ~Westinghouse_value + Westinghouse_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -48.0297 21.4802 -2.24 0.028 * 464s value 0.1051 0.0114 9.24 6.0e-15 *** 464s capital 0.3054 0.0435 7.02 3.1e-10 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 27.298 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 12668.473 MSE: 745.204 Root MSE: 27.298 464s Multiple R-Squared: -0.826 Adjusted R-Squared: -1.041 464s 464s [1] TRUE 464s [1] TRUE 464s [1] TRUE 464s [1] TRUE 464s [1] TRUE 464s [1] TRUE 464s [1] TRUE 464s > # 'real' IV/2SLS estimation 464s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 464s + greene2slsRPooled <- systemfit( invest ~ capital, inst = ~ value, "2SLS", 464s + data = GrunfeldGreene, pooled = TRUE, useMatrix = useMatrix ) 464s + print( greene2slsRPooled ) 464s + print( summary( greene2slsRPooled ) ) 464s + } 464s 464s systemfit results 464s method: 2SLS 464s 464s Coefficients: 464s Chrysler_(Intercept) Chrysler_capital 464s -15.105 0.849 464s General.Electric_(Intercept) General.Electric_capital 464s -15.105 0.849 464s General.Motors_(Intercept) General.Motors_capital 464s -15.105 0.849 464s US.Steel_(Intercept) US.Steel_capital 464s -15.105 0.849 464s Westinghouse_(Intercept) Westinghouse_capital 464s -15.105 0.849 464s 464s systemfit results 464s method: 2SLS 464s 464s N DF SSR detRCov OLS-R2 McElroy-R2 464s system 100 98 4164182 2.53e+19 -0.871 -0.832 464s 464s N DF SSR MSE RMSE R2 Adj R2 464s Chrysler 20 18 64130 3563 59.7 -0.849 -0.952 464s General.Electric 20 18 1575287 87516 295.8 -34.125 -36.076 464s General.Motors 20 18 1655592 91977 303.3 0.091 0.040 464s US.Steel 20 18 833908 46328 215.2 -1.623 -1.769 464s Westinghouse 20 18 35264 1959 44.3 -4.082 -4.365 464s 464s The covariance matrix of the residuals 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 3563 9506 13222 2659 1862 464s General.Electric 9506 87516 29381 -35898 10615 464s General.Motors 13222 29381 91977 17584 8562 464s US.Steel 2659 -35898 17584 46328 -762 464s Westinghouse 1862 10615 8562 -762 1959 464s 464s The correlations of the residuals 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 1.000 0.843 0.763 0.397 0.742 464s General.Electric 0.843 1.000 0.893 0.226 0.933 464s General.Motors 0.763 0.893 1.000 0.114 0.801 464s US.Steel 0.397 0.226 0.114 1.000 0.375 464s Westinghouse 0.742 0.933 0.801 0.375 1.000 464s 464s 464s 2SLS estimates for 'Chrysler' (equation 1) 464s Model Formula: Chrysler_invest ~ Chrysler_capital 464s 464s Instruments: ~Chrysler_value 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -15.1045 33.8915 -0.45 0.66 464s capital 0.8489 0.0865 9.82 4.4e-16 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 59.689 on 18 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 18 464s SSR: 64130.003 MSE: 3562.778 Root MSE: 59.689 464s Multiple R-Squared: -0.849 Adjusted R-Squared: -0.952 464s 464s 464s 2SLS estimates for 'General.Electric' (equation 2) 464s Model Formula: General.Electric_invest ~ General.Electric_capital 464s 464s Instruments: ~General.Electric_value 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -15.1045 33.8915 -0.45 0.66 464s capital 0.8489 0.0865 9.82 4.4e-16 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 295.831 on 18 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 18 464s SSR: 1575287.29 MSE: 87515.961 Root MSE: 295.831 464s Multiple R-Squared: -34.125 Adjusted R-Squared: -36.076 464s 464s 464s 2SLS estimates for 'General.Motors' (equation 3) 464s Model Formula: General.Motors_invest ~ General.Motors_capital 464s 464s Instruments: ~General.Motors_value 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -15.1045 33.8915 -0.45 0.66 464s capital 0.8489 0.0865 9.82 4.4e-16 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 303.278 on 18 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 18 464s SSR: 1655591.854 MSE: 91977.325 Root MSE: 303.278 464s Multiple R-Squared: 0.091 Adjusted R-Squared: 0.04 464s 464s 464s 2SLS estimates for 'US.Steel' (equation 4) 464s Model Formula: US.Steel_invest ~ US.Steel_capital 464s 464s Instruments: ~US.Steel_value 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -15.1045 33.8915 -0.45 0.66 464s capital 0.8489 0.0865 9.82 4.4e-16 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 215.24 on 18 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 18 464s SSR: 833908.389 MSE: 46328.244 Root MSE: 215.24 464s Multiple R-Squared: -1.623 Adjusted R-Squared: -1.769 464s 464s 464s 2SLS estimates for 'Westinghouse' (equation 5) 464s Model Formula: Westinghouse_invest ~ Westinghouse_capital 464s 464s Instruments: ~Westinghouse_value 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -15.1045 33.8915 -0.45 0.66 464s capital 0.8489 0.0865 9.82 4.4e-16 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 44.262 on 18 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 18 464s SSR: 35264.462 MSE: 1959.137 Root MSE: 44.262 464s Multiple R-Squared: -4.082 Adjusted R-Squared: -4.365 464s 464s > 464s > ### 3SLS ### 464s > # instruments = explanatory variables -> 3SLS estimates = SUR estimates 464s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 464s + greene3sls <- systemfit( formulaGrunfeld, inst = ~ value + capital, "3SLS", 464s + data = GrunfeldGreene, useMatrix = useMatrix, methodResidCov = "noDfCor" ) 464s + print( greene3sls ) 464s + print( summary( greene3sls ) ) 464s + print( all.equal( coef( summary( greene3sls ) ), coef( summary( greeneSur ) ) ) ) 464s + print( all.equal( greene3sls[ -c(1,2,7) ], greeneSur[ -c(1,2,7) ] ) ) 464s + for( i in 1:length( greene3sls$eq ) ) { 464s + print( all.equal( greene3sls$eq[[i]][ -c(3,15:17) ], 464s + greeneSur$eq[[i]][-3] ) ) 464s + } 464s + } 464s 464s systemfit results 464s method: 3SLS 464s 464s Coefficients: 464s Chrysler_(Intercept) Chrysler_value 464s 0.5043 0.0695 464s Chrysler_capital General.Electric_(Intercept) 464s 0.3085 -22.4389 464s General.Electric_value General.Electric_capital 464s 0.0373 0.1308 464s General.Motors_(Intercept) General.Motors_value 464s -162.3641 0.1205 464s General.Motors_capital US.Steel_(Intercept) 464s 0.3827 85.4233 464s US.Steel_value US.Steel_capital 464s 0.1015 0.4000 464s Westinghouse_(Intercept) Westinghouse_value 464s 1.0889 0.0570 464s Westinghouse_capital 464s 0.0415 464s 464s systemfit results 464s method: 3SLS 464s 464s N DF SSR detRCov OLS-R2 McElroy-R2 464s system 100 85 347048 6.18e+13 0.844 0.869 464s 464s N DF SSR MSE RMSE R2 Adj R2 464s Chrysler 20 17 3057 180 13.4 0.912 0.901 464s General.Electric 20 17 14009 824 28.7 0.688 0.651 464s General.Motors 20 17 144321 8489 92.1 0.921 0.911 464s US.Steel 20 17 183763 10810 104.0 0.422 0.354 464s Westinghouse 20 17 1898 112 10.6 0.726 0.694 464s 464s The covariance matrix of the residuals used for estimation 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 149.9 -21.4 -283 418 13.3 464s General.Electric -21.4 660.8 608 905 176.4 464s General.Motors -282.8 607.5 7160 -2222 126.2 464s US.Steel 418.1 905.0 -2222 8896 546.2 464s Westinghouse 13.3 176.4 126 546 88.7 464s 464s The covariance matrix of the residuals 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 152.85 2.05 -314 455 16.7 464s General.Electric 2.05 700.46 605 1224 200.3 464s General.Motors -313.70 605.34 7216 -2687 129.9 464s US.Steel 455.09 1224.41 -2687 9188 652.7 464s Westinghouse 16.66 200.32 130 653 94.9 464s 464s The correlations of the residuals 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 1.00000 0.00626 -0.299 0.384 0.138 464s General.Electric 0.00626 1.00000 0.269 0.483 0.777 464s General.Motors -0.29870 0.26925 1.000 -0.330 0.157 464s US.Steel 0.38402 0.48264 -0.330 1.000 0.699 464s Westinghouse 0.13832 0.77690 0.157 0.699 1.000 464s 464s 464s 3SLS estimates for 'Chrysler' (equation 1) 464s Model Formula: Chrysler_invest ~ Chrysler_value + Chrysler_capital 464s 464s Instruments: ~Chrysler_value + Chrysler_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) 0.5043 11.5128 0.04 0.96557 464s value 0.0695 0.0169 4.12 0.00072 *** 464s capital 0.3085 0.0259 11.93 1.1e-09 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 13.41 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 3056.985 MSE: 179.823 Root MSE: 13.41 464s Multiple R-Squared: 0.912 Adjusted R-Squared: 0.901 464s 464s 464s 3SLS estimates for 'General.Electric' (equation 2) 464s Model Formula: General.Electric_invest ~ General.Electric_value + General.Electric_capital 464s 464s Instruments: ~General.Electric_value + General.Electric_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -22.4389 25.5186 -0.88 0.3915 464s value 0.0373 0.0123 3.04 0.0074 ** 464s capital 0.1308 0.0220 5.93 1.6e-05 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 28.707 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 14009.115 MSE: 824.066 Root MSE: 28.707 464s Multiple R-Squared: 0.688 Adjusted R-Squared: 0.651 464s 464s 464s 3SLS estimates for 'General.Motors' (equation 3) 464s Model Formula: General.Motors_invest ~ General.Motors_value + General.Motors_capital 464s 464s Instruments: ~General.Motors_value + General.Motors_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -162.3641 89.4592 -1.81 0.087 . 464s value 0.1205 0.0216 5.57 3.4e-05 *** 464s capital 0.3827 0.0328 11.68 1.5e-09 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 92.138 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 144320.876 MSE: 8489.463 Root MSE: 92.138 464s Multiple R-Squared: 0.921 Adjusted R-Squared: 0.911 464s 464s 464s 3SLS estimates for 'US.Steel' (equation 4) 464s Model Formula: US.Steel_invest ~ US.Steel_value + US.Steel_capital 464s 464s Instruments: ~US.Steel_value + US.Steel_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) 85.4233 111.8774 0.76 0.4556 464s value 0.1015 0.0548 1.85 0.0814 . 464s capital 0.4000 0.1278 3.13 0.0061 ** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 103.969 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 183763.011 MSE: 10809.589 Root MSE: 103.969 464s Multiple R-Squared: 0.422 Adjusted R-Squared: 0.354 464s 464s 464s 3SLS estimates for 'Westinghouse' (equation 5) 464s Model Formula: Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 464s 464s Instruments: ~Westinghouse_value + Westinghouse_capital 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) 1.0889 6.2588 0.17 0.86394 464s value 0.0570 0.0114 5.02 0.00011 *** 464s capital 0.0415 0.0412 1.01 0.32787 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 10.567 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 1898.249 MSE: 111.662 Root MSE: 10.567 464s Multiple R-Squared: 0.726 Adjusted R-Squared: 0.694 464s 464s [1] TRUE 464s [1] TRUE 464s [1] TRUE 464s [1] TRUE 464s [1] TRUE 464s [1] TRUE 464s [1] TRUE 464s > # 'real' IV/3SLS estimation 464s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 464s + greene3slsR <- systemfit( invest ~ capital, inst = ~ value, "3SLS", 464s + data = GrunfeldGreene, useMatrix = useMatrix ) 464s + print( greene3slsR ) 464s + print( summary( greene3slsR ) ) 464s + } 464s 464s systemfit results 464s method: 3SLS 464s 464s Coefficients: 464s Chrysler_(Intercept) Chrysler_capital 464s 23.499 0.517 464s General.Electric_(Intercept) General.Electric_capital 464s -108.596 0.527 464s General.Motors_(Intercept) General.Motors_capital 464s 199.856 0.629 464s US.Steel_(Intercept) US.Steel_capital 464s 181.691 0.746 464s Westinghouse_(Intercept) Westinghouse_capital 464s 11.668 0.365 464s 464s systemfit results 464s method: 3SLS 464s 464s N DF SSR detRCov OLS-R2 McElroy-R2 464s system 100 90 1026043 4.46e+16 0.539 0.539 464s 464s N DF SSR MSE RMSE R2 Adj R2 464s Chrysler 20 18 12139 674 26.0 0.650 0.631 464s General.Electric 20 18 178965 9942 99.7 -2.990 -3.212 464s General.Motors 20 18 577860 32103 179.2 0.683 0.665 464s US.Steel 20 18 252838 14047 118.5 0.205 0.160 464s Westinghouse 20 18 4241 236 15.3 0.389 0.355 464s 464s The covariance matrix of the residuals used for estimation 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 1687 3089 6820 11741 179 464s General.Electric 3089 9722 20780 23319 886 464s General.Motors 6820 20780 61121 44203 1908 464s US.Steel 11741 23319 44203 107242 1977 464s Westinghouse 179 886 1908 1977 218 464s 464s The covariance matrix of the residuals 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 674 1587 1944 1371 137 464s General.Electric 1587 9942 13003 2009 996 464s General.Motors 1944 13003 32103 -908 1571 464s US.Steel 1371 2009 -908 14047 888 464s Westinghouse 137 996 1571 888 236 464s 464s The correlations of the residuals 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 1.000 0.613 0.4178 0.4454 0.343 464s General.Electric 0.613 1.000 0.7278 0.1700 0.651 464s General.Motors 0.418 0.728 1.0000 -0.0428 0.571 464s US.Steel 0.445 0.170 -0.0428 1.0000 0.488 464s Westinghouse 0.343 0.651 0.5713 0.4880 1.000 464s 464s 464s 3SLS estimates for 'Chrysler' (equation 1) 464s Model Formula: Chrysler_invest ~ Chrysler_capital 464s 464s Instruments: ~Chrysler_value 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) 23.499 17.165 1.37 0.18784 464s capital 0.517 0.120 4.32 0.00041 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 25.969 on 18 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 18 464s SSR: 12138.974 MSE: 674.387 Root MSE: 25.969 464s Multiple R-Squared: 0.65 Adjusted R-Squared: 0.631 464s 464s 464s 3SLS estimates for 'General.Electric' (equation 2) 464s Model Formula: General.Electric_invest ~ General.Electric_capital 464s 464s Instruments: ~General.Electric_value 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -108.596 152.939 -0.71 0.49 464s capital 0.527 0.378 1.39 0.18 464s 464s Residual standard error: 99.712 on 18 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 18 464s SSR: 178964.956 MSE: 9942.498 Root MSE: 99.712 464s Multiple R-Squared: -2.99 Adjusted R-Squared: -3.212 464s 464s 464s 3SLS estimates for 'General.Motors' (equation 3) 464s Model Formula: General.Motors_invest ~ General.Motors_capital 464s 464s Instruments: ~General.Motors_value 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) 199.856 98.953 2.02 0.059 . 464s capital 0.629 0.127 4.97 9.8e-05 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 179.174 on 18 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 18 464s SSR: 577859.714 MSE: 32103.317 Root MSE: 179.174 464s Multiple R-Squared: 0.683 Adjusted R-Squared: 0.665 464s 464s 464s 3SLS estimates for 'US.Steel' (equation 4) 464s Model Formula: US.Steel_invest ~ US.Steel_capital 464s 464s Instruments: ~US.Steel_value 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) 181.691 448.797 0.40 0.69 464s capital 0.746 1.477 0.51 0.62 464s 464s Residual standard error: 118.518 on 18 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 18 464s SSR: 252838.286 MSE: 14046.571 Root MSE: 118.518 464s Multiple R-Squared: 0.205 Adjusted R-Squared: 0.16 464s 464s 464s 3SLS estimates for 'Westinghouse' (equation 5) 464s Model Formula: Westinghouse_invest ~ Westinghouse_capital 464s 464s Instruments: ~Westinghouse_value 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) 11.6685 5.9043 1.98 0.064 . 464s capital 0.3646 0.0572 6.38 5.2e-06 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 15.349 on 18 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 18 464s SSR: 4240.92 MSE: 235.607 Root MSE: 15.349 464s Multiple R-Squared: 0.389 Adjusted R-Squared: 0.355 464s 464s > 464s > ### 3SLS, Pooled ### 464s > # instruments = explanatory variables -> 3SLS estimates = SUR estimates 464s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 464s + greene3slsPooled <- systemfit( formulaGrunfeld, inst = ~ capital + value, "3SLS", 464s + data = GrunfeldGreene, pooled = TRUE, useMatrix = useMatrix, 464s + residCovWeighted = TRUE, methodResidCov = "noDfCor" ) 464s + print( greene3slsPooled ) 464s + print( summary( greene3slsPooled ) ) 464s + print( all.equal( coef( summary( greene3slsPooled ) ), 464s + coef( summary( greeneSurPooled ) ) ) ) 464s + print( all.equal( greene3slsPooled[ -c(1,2,7) ], greeneSurPooled[ -c(1,2,7) ] ) ) 464s + for( i in 1:length( greene3slsPooled$eq ) ) { 464s + print( all.equal( greene3slsPooled$eq[[i]][ -c(3,15:17) ], 464s + greeneSurPooled$eq[[i]][-3] ) ) 464s + } 464s + } 464s 464s systemfit results 464s method: 3SLS 464s 464s Coefficients: 464s Chrysler_(Intercept) Chrysler_value 464s -28.2467 0.0891 464s Chrysler_capital General.Electric_(Intercept) 464s 0.3340 -28.2467 464s General.Electric_value General.Electric_capital 464s 0.0891 0.3340 464s General.Motors_(Intercept) General.Motors_value 464s -28.2467 0.0891 464s General.Motors_capital US.Steel_(Intercept) 464s 0.3340 -28.2467 464s US.Steel_value US.Steel_capital 464s 0.0891 0.3340 464s Westinghouse_(Intercept) Westinghouse_value 464s -28.2467 0.0891 464s Westinghouse_capital 464s 0.3340 464s 464s systemfit results 464s method: 3SLS 464s 464s N DF SSR detRCov OLS-R2 McElroy-R2 464s system 100 97 1604301 9.95e+16 0.279 0.844 464s 464s N DF SSR MSE RMSE R2 Adj R2 464s Chrysler 20 17 6112 360 19.0 0.824 0.803 464s General.Electric 20 17 691132 40655 201.6 -14.410 -16.223 464s General.Motors 20 17 201010 11824 108.7 0.890 0.877 464s US.Steel 20 17 689380 40552 201.4 -1.168 -1.424 464s Westinghouse 20 17 16667 980 31.3 -1.402 -1.685 464s 464s The covariance matrix of the residuals used for estimation 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 409 -2594 -197 2594 -102 464s General.Electric -2594 36563 -3480 -28623 3797 464s General.Motors -197 -3480 8612 996 -971 464s US.Steel 2594 -28623 996 32903 -2272 464s Westinghouse -102 3797 -971 -2272 778 464s 464s The covariance matrix of the residuals 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 305.61 -1967 -4.81 2159 -124 464s General.Electric -1966.65 34557 -7160.67 -28722 4274 464s General.Motors -4.81 -7161 10050.52 4440 -1401 464s US.Steel 2158.60 -28722 4439.99 34469 -2894 464s Westinghouse -123.92 4274 -1400.75 -2894 833 464s 464s The correlations of the residuals 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 1.000 0.220 -0.3447 0.2008 0.2907 464s General.Electric 0.220 1.000 -0.2233 -0.1587 0.8973 464s General.Motors -0.345 -0.223 1.0000 -0.0924 -0.3760 464s US.Steel 0.201 -0.159 -0.0924 1.0000 -0.0757 464s Westinghouse 0.291 0.897 -0.3760 -0.0757 1.0000 464s 464s 464s 3SLS estimates for 'Chrysler' (equation 1) 464s Model Formula: Chrysler_invest ~ Chrysler_value + Chrysler_capital 464s 464s Instruments: ~Chrysler_capital + Chrysler_value 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -28.24669 4.88824 -5.78 9.1e-08 *** 464s value 0.08910 0.00507 17.57 < 2e-16 *** 464s capital 0.33402 0.01671 19.99 < 2e-16 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 18.962 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 6112.2 MSE: 359.541 Root MSE: 18.962 464s Multiple R-Squared: 0.824 Adjusted R-Squared: 0.803 464s 464s 464s 3SLS estimates for 'General.Electric' (equation 2) 464s Model Formula: General.Electric_invest ~ General.Electric_value + General.Electric_capital 464s 464s Instruments: ~General.Electric_capital + General.Electric_value 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -28.24669 4.88824 -5.78 9.1e-08 *** 464s value 0.08910 0.00507 17.57 < 2e-16 *** 464s capital 0.33402 0.01671 19.99 < 2e-16 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 201.63 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 691132.056 MSE: 40654.827 Root MSE: 201.63 464s Multiple R-Squared: -14.41 Adjusted R-Squared: -16.223 464s 464s 464s 3SLS estimates for 'General.Motors' (equation 3) 464s Model Formula: General.Motors_invest ~ General.Motors_value + General.Motors_capital 464s 464s Instruments: ~General.Motors_capital + General.Motors_value 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -28.24669 4.88824 -5.78 9.1e-08 *** 464s value 0.08910 0.00507 17.57 < 2e-16 *** 464s capital 0.33402 0.01671 19.99 < 2e-16 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 108.739 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 201010.497 MSE: 11824.147 Root MSE: 108.739 464s Multiple R-Squared: 0.89 Adjusted R-Squared: 0.877 464s 464s 464s 3SLS estimates for 'US.Steel' (equation 4) 464s Model Formula: US.Steel_invest ~ US.Steel_value + US.Steel_capital 464s 464s Instruments: ~US.Steel_capital + US.Steel_value 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -28.24669 4.88824 -5.78 9.1e-08 *** 464s value 0.08910 0.00507 17.57 < 2e-16 *** 464s capital 0.33402 0.01671 19.99 < 2e-16 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 201.375 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 689379.52 MSE: 40551.736 Root MSE: 201.375 464s Multiple R-Squared: -1.168 Adjusted R-Squared: -1.424 464s 464s 464s 3SLS estimates for 'Westinghouse' (equation 5) 464s Model Formula: Westinghouse_invest ~ Westinghouse_value + Westinghouse_capital 464s 464s Instruments: ~Westinghouse_capital + Westinghouse_value 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -28.24669 4.88824 -5.78 9.1e-08 *** 464s value 0.08910 0.00507 17.57 < 2e-16 *** 464s capital 0.33402 0.01671 19.99 < 2e-16 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 31.312 on 17 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 17 464s SSR: 16667.149 MSE: 980.421 Root MSE: 31.312 464s Multiple R-Squared: -1.402 Adjusted R-Squared: -1.685 464s 464s [1] TRUE 464s [1] TRUE 464s [1] TRUE 464s [1] TRUE 464s [1] TRUE 464s [1] TRUE 464s [1] TRUE 464s > # 'real' IV/3SLS estimation 464s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 464s + greene3slsRPooled <- systemfit( invest ~ capital, inst = ~ value, "3SLS", 464s + data = GrunfeldGreene, useMatrix = useMatrix ) 464s + print( greene3slsRPooled ) 464s + print( summary( greene3slsRPooled ) ) 464s + } 464s 464s systemfit results 464s method: 3SLS 464s 464s Coefficients: 464s Chrysler_(Intercept) Chrysler_capital 464s 23.499 0.517 464s General.Electric_(Intercept) General.Electric_capital 464s -108.596 0.527 464s General.Motors_(Intercept) General.Motors_capital 464s 199.856 0.629 464s US.Steel_(Intercept) US.Steel_capital 464s 181.691 0.746 464s Westinghouse_(Intercept) Westinghouse_capital 464s 11.668 0.365 464s 464s systemfit results 464s method: 3SLS 464s 464s N DF SSR detRCov OLS-R2 McElroy-R2 464s system 100 90 1026043 4.46e+16 0.539 0.539 464s 464s N DF SSR MSE RMSE R2 Adj R2 464s Chrysler 20 18 12139 674 26.0 0.650 0.631 464s General.Electric 20 18 178965 9942 99.7 -2.990 -3.212 464s General.Motors 20 18 577860 32103 179.2 0.683 0.665 464s US.Steel 20 18 252838 14047 118.5 0.205 0.160 464s Westinghouse 20 18 4241 236 15.3 0.389 0.355 464s 464s The covariance matrix of the residuals used for estimation 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 1687 3089 6820 11741 179 464s General.Electric 3089 9722 20780 23319 886 464s General.Motors 6820 20780 61121 44203 1908 464s US.Steel 11741 23319 44203 107242 1977 464s Westinghouse 179 886 1908 1977 218 464s 464s The covariance matrix of the residuals 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 674 1587 1944 1371 137 464s General.Electric 1587 9942 13003 2009 996 464s General.Motors 1944 13003 32103 -908 1571 464s US.Steel 1371 2009 -908 14047 888 464s Westinghouse 137 996 1571 888 236 464s 464s The correlations of the residuals 464s Chrysler General.Electric General.Motors US.Steel Westinghouse 464s Chrysler 1.000 0.613 0.4178 0.4454 0.343 464s General.Electric 0.613 1.000 0.7278 0.1700 0.651 464s General.Motors 0.418 0.728 1.0000 -0.0428 0.571 464s US.Steel 0.445 0.170 -0.0428 1.0000 0.488 464s Westinghouse 0.343 0.651 0.5713 0.4880 1.000 464s 464s 464s 3SLS estimates for 'Chrysler' (equation 1) 464s Model Formula: Chrysler_invest ~ Chrysler_capital 464s 464s Instruments: ~Chrysler_value 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) 23.499 17.165 1.37 0.18784 464s capital 0.517 0.120 4.32 0.00041 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 25.969 on 18 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 18 464s SSR: 12138.974 MSE: 674.387 Root MSE: 25.969 464s Multiple R-Squared: 0.65 Adjusted R-Squared: 0.631 464s 464s 464s 3SLS estimates for 'General.Electric' (equation 2) 464s Model Formula: General.Electric_invest ~ General.Electric_capital 464s 464s Instruments: ~General.Electric_value 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) -108.596 152.939 -0.71 0.49 464s capital 0.527 0.378 1.39 0.18 464s 464s Residual standard error: 99.712 on 18 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 18 464s SSR: 178964.956 MSE: 9942.498 Root MSE: 99.712 464s Multiple R-Squared: -2.99 Adjusted R-Squared: -3.212 464s 464s 464s 3SLS estimates for 'General.Motors' (equation 3) 464s Model Formula: General.Motors_invest ~ General.Motors_capital 464s 464s Instruments: ~General.Motors_value 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) 199.856 98.953 2.02 0.059 . 464s capital 0.629 0.127 4.97 9.8e-05 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 179.174 on 18 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 18 464s SSR: 577859.714 MSE: 32103.317 Root MSE: 179.174 464s Multiple R-Squared: 0.683 Adjusted R-Squared: 0.665 464s 464s 464s 3SLS estimates for 'US.Steel' (equation 4) 464s Model Formula: US.Steel_invest ~ US.Steel_capital 464s 464s Instruments: ~US.Steel_value 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) 181.691 448.797 0.40 0.69 464s capital 0.746 1.477 0.51 0.62 464s 464s Residual standard error: 118.518 on 18 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 18 464s SSR: 252838.286 MSE: 14046.571 Root MSE: 118.518 464s Multiple R-Squared: 0.205 Adjusted R-Squared: 0.16 464s 464s 464s 3SLS estimates for 'Westinghouse' (equation 5) 464s Model Formula: Westinghouse_invest ~ Westinghouse_capital 464s 464s Instruments: ~Westinghouse_value 464s 464s 464s Estimate Std. Error t value Pr(>|t|) 464s (Intercept) 11.6685 5.9043 1.98 0.064 . 464s capital 0.3646 0.0572 6.38 5.2e-06 *** 464s --- 464s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 464s 464s Residual standard error: 15.349 on 18 degrees of freedom 464s Number of observations: 20 Degrees of Freedom: 18 464s SSR: 4240.92 MSE: 235.607 Root MSE: 15.349 464s Multiple R-Squared: 0.389 Adjusted R-Squared: 0.355 464s 464s > 464s > 464s > ## **************** estfun ************************ 464s > library( "sandwich" ) 464s > 464s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 464s + print( estfun( theilOls ) ) 464s + print( round( colSums( estfun( theilOls ) ), digits = 7 ) ) 464s + 464s + print( estfun( theilSur ) ) 464s + print( round( colSums( estfun( theilSur ) ), digits = 7 ) ) 464s + 464s + print( estfun( greeneOls ) ) 464s + print( round( colSums( estfun( greeneOls ) ), digits = 7 ) ) 464s + 464s + print( try( estfun( greeneOlsPooled ) ) ) 464s + 464s + print( estfun( greeneSur ) ) 464s + print( round( colSums( estfun( greeneSur ) ), digits = 7 ) ) 464s + 464s + print( try( estfun( greeneSurPooled ) ) ) 464s + } 464s General.Electric_(Intercept) General.Electric_value 464s General.Electric_X1935 -2.860 -3348 464s General.Electric_X1936 -14.402 -29032 464s General.Electric_X1937 -5.175 -14506 464s General.Electric_X1938 -23.295 -47514 464s General.Electric_X1939 -28.031 -63243 464s General.Electric_X1940 -0.562 -1199 464s General.Electric_X1941 40.750 74739 464s General.Electric_X1942 16.036 25464 464s General.Electric_X1943 -23.719 -41494 464s General.Electric_X1944 -26.780 -45183 464s General.Electric_X1945 1.768 3550 464s General.Electric_X1946 58.737 129709 464s General.Electric_X1947 43.936 72789 464s General.Electric_X1948 31.227 50101 464s General.Electric_X1949 -23.552 -33722 464s General.Electric_X1950 -37.511 -60411 464s General.Electric_X1951 -4.983 -9066 464s General.Electric_X1952 1.893 3937 464s General.Electric_X1953 5.087 12064 464s General.Electric_X1954 -8.563 -23633 464s Westinghouse_X1935 0.000 0 464s Westinghouse_X1936 0.000 0 464s Westinghouse_X1937 0.000 0 464s Westinghouse_X1938 0.000 0 464s Westinghouse_X1939 0.000 0 464s Westinghouse_X1940 0.000 0 464s Westinghouse_X1941 0.000 0 464s Westinghouse_X1942 0.000 0 464s Westinghouse_X1943 0.000 0 464s Westinghouse_X1944 0.000 0 464s Westinghouse_X1945 0.000 0 464s Westinghouse_X1946 0.000 0 464s Westinghouse_X1947 0.000 0 464s Westinghouse_X1948 0.000 0 464s Westinghouse_X1949 0.000 0 464s Westinghouse_X1950 0.000 0 464s Westinghouse_X1951 0.000 0 464s Westinghouse_X1952 0.000 0 464s Westinghouse_X1953 0.000 0 464s Westinghouse_X1954 0.000 0 464s General.Electric_capital Westinghouse_(Intercept) 464s General.Electric_X1935 -280 0.000 464s General.Electric_X1936 -1504 0.000 464s General.Electric_X1937 -611 0.000 464s General.Electric_X1938 -3639 0.000 464s General.Electric_X1939 -4838 0.000 464s General.Electric_X1940 -105 0.000 464s General.Electric_X1941 9002 0.000 464s General.Electric_X1942 4615 0.000 464s General.Electric_X1943 -7588 0.000 464s General.Electric_X1944 -8604 0.000 464s General.Electric_X1945 565 0.000 464s General.Electric_X1946 20323 0.000 464s General.Electric_X1947 20052 0.000 464s General.Electric_X1948 16969 0.000 464s General.Electric_X1949 -14562 0.000 464s General.Electric_X1950 -24285 0.000 464s General.Electric_X1951 -3345 0.000 464s General.Electric_X1952 1374 0.000 464s General.Electric_X1953 4071 0.000 464s General.Electric_X1954 -7612 0.000 464s Westinghouse_X1935 0 3.144 464s Westinghouse_X1936 0 -0.958 464s Westinghouse_X1937 0 -3.684 464s Westinghouse_X1938 0 -7.915 464s Westinghouse_X1939 0 -10.322 464s Westinghouse_X1940 0 -6.613 464s Westinghouse_X1941 0 17.265 464s Westinghouse_X1942 0 8.547 464s Westinghouse_X1943 0 -2.916 464s Westinghouse_X1944 0 -3.257 464s Westinghouse_X1945 0 -7.753 464s Westinghouse_X1946 0 5.796 464s Westinghouse_X1947 0 15.050 464s Westinghouse_X1948 0 2.969 464s Westinghouse_X1949 0 -11.433 464s Westinghouse_X1950 0 -13.481 464s Westinghouse_X1951 0 4.619 464s Westinghouse_X1952 0 13.138 464s Westinghouse_X1953 0 11.308 464s Westinghouse_X1954 0 -13.505 464s Westinghouse_value Westinghouse_capital 464s General.Electric_X1935 0 0.000 464s General.Electric_X1936 0 0.000 464s General.Electric_X1937 0 0.000 464s General.Electric_X1938 0 0.000 464s General.Electric_X1939 0 0.000 464s General.Electric_X1940 0 0.000 464s General.Electric_X1941 0 0.000 464s General.Electric_X1942 0 0.000 464s General.Electric_X1943 0 0.000 464s General.Electric_X1944 0 0.000 464s General.Electric_X1945 0 0.000 464s General.Electric_X1946 0 0.000 464s General.Electric_X1947 0 0.000 464s General.Electric_X1948 0 0.000 464s General.Electric_X1949 0 0.000 464s General.Electric_X1950 0 0.000 464s General.Electric_X1951 0 0.000 464s General.Electric_X1952 0 0.000 464s General.Electric_X1953 0 0.000 464s General.Electric_X1954 0 0.000 464s Westinghouse_X1935 602 5.659 464s Westinghouse_X1936 -494 -0.766 464s Westinghouse_X1937 -2686 -27.263 464s Westinghouse_X1938 -4436 -143.262 464s Westinghouse_X1939 -5366 -242.563 464s Westinghouse_X1940 -4156 -175.254 464s Westinghouse_X1941 9273 624.987 464s Westinghouse_X1942 4797 519.651 464s Westinghouse_X1943 -1800 -246.108 464s Westinghouse_X1944 -2041 -297.023 464s Westinghouse_X1945 -5715 -716.333 464s Westinghouse_X1946 4408 498.495 464s Westinghouse_X1947 8750 1672.098 464s Westinghouse_X1948 1967 387.794 464s Westinghouse_X1949 -6675 -1621.262 464s Westinghouse_X1950 -8563 -1842.843 464s Westinghouse_X1951 3344 599.149 464s Westinghouse_X1952 11353 1911.642 464s Westinghouse_X1953 13496 1976.568 464s Westinghouse_X1954 -16056 -2883.365 464s General.Electric_(Intercept) General.Electric_value 464s 0 0 464s General.Electric_capital Westinghouse_(Intercept) 464s 0 0 464s Westinghouse_value Westinghouse_capital 464s 0 0 464s General.Electric_(Intercept) General.Electric_value 464s General.Electric_X1935 0.007671 8.980 464s General.Electric_X1936 -0.061426 -123.822 464s General.Electric_X1937 -0.060974 -170.929 464s General.Electric_X1938 -0.088931 -181.393 464s General.Electric_X1939 -0.111776 -252.189 464s General.Electric_X1940 -0.017793 -37.937 464s General.Electric_X1941 0.128334 235.378 464s General.Electric_X1942 0.060606 96.243 464s General.Electric_X1943 -0.072587 -126.985 464s General.Electric_X1944 -0.080053 -135.065 464s General.Electric_X1945 -0.000104 -0.208 464s General.Electric_X1946 0.177325 391.586 464s General.Electric_X1947 0.154986 256.765 464s General.Electric_X1948 0.119488 191.707 464s General.Electric_X1949 -0.047791 -68.427 464s General.Electric_X1950 -0.098464 -158.576 464s General.Electric_X1951 -0.000379 -0.689 464s General.Electric_X1952 0.014181 29.492 464s General.Electric_X1953 0.016444 38.998 464s General.Electric_X1954 -0.038758 -106.969 464s Westinghouse_X1935 -0.019477 -22.800 464s Westinghouse_X1936 0.016942 34.151 464s Westinghouse_X1937 0.039739 111.402 464s Westinghouse_X1938 0.059843 122.062 464s Westinghouse_X1939 0.073091 164.909 464s Westinghouse_X1940 0.052015 110.907 464s Westinghouse_X1941 -0.105994 -194.404 464s Westinghouse_X1942 -0.053728 -85.321 464s Westinghouse_X1943 0.017332 30.320 464s Westinghouse_X1944 0.018569 31.330 464s Westinghouse_X1945 0.050605 101.599 464s Westinghouse_X1946 -0.034591 -76.387 464s Westinghouse_X1947 -0.104099 -172.460 464s Westinghouse_X1948 -0.027559 -44.215 464s Westinghouse_X1949 0.060567 86.720 464s Westinghouse_X1950 0.076221 122.754 464s Westinghouse_X1951 -0.036128 -65.731 464s Westinghouse_X1952 -0.089492 -186.117 464s Westinghouse_X1953 -0.073054 -173.256 464s Westinghouse_X1954 0.079198 218.578 464s General.Electric_capital Westinghouse_(Intercept) 464s General.Electric_X1935 0.7503 -0.015267 464s General.Electric_X1936 -6.4128 0.122246 464s General.Electric_X1937 -7.1950 0.121347 464s General.Electric_X1938 -13.8911 0.176986 464s General.Electric_X1939 -19.2925 0.222450 464s General.Electric_X1940 -3.3201 0.035410 464s General.Electric_X1941 28.3490 -0.255403 464s General.Electric_X1942 17.4425 -0.120615 464s General.Electric_X1943 -23.2207 0.144459 464s General.Electric_X1944 -25.7209 0.159316 464s General.Electric_X1945 -0.0331 0.000206 464s General.Electric_X1946 61.3543 -0.352901 464s General.Electric_X1947 70.7355 -0.308443 464s General.Electric_X1948 64.9300 -0.237798 464s General.Electric_X1949 -29.5489 0.095110 464s General.Electric_X1950 -63.7453 0.195956 464s General.Electric_X1951 -0.2543 0.000754 464s General.Electric_X1952 10.2966 -0.028221 464s General.Electric_X1953 13.1598 -0.032725 464s General.Electric_X1954 -34.4523 0.077135 464s Westinghouse_X1935 -1.9049 0.072945 464s Westinghouse_X1936 1.7687 -0.063449 464s Westinghouse_X1937 4.6893 -0.148830 464s Westinghouse_X1938 9.3475 -0.224122 464s Westinghouse_X1939 12.6156 -0.273739 464s Westinghouse_X1940 9.7061 -0.194806 464s Westinghouse_X1941 -23.4141 0.396965 464s Westinghouse_X1942 -15.4630 0.201221 464s Westinghouse_X1943 5.5444 -0.064910 464s Westinghouse_X1944 5.9663 -0.069544 464s Westinghouse_X1945 16.1733 -0.189523 464s Westinghouse_X1946 -11.9684 0.129548 464s Westinghouse_X1947 -47.5107 0.389866 464s Westinghouse_X1948 -14.9755 0.103212 464s Westinghouse_X1949 37.4485 -0.226832 464s Westinghouse_X1950 49.3457 -0.285461 464s Westinghouse_X1951 -24.2526 0.135304 464s Westinghouse_X1952 -64.9804 0.335163 464s Westinghouse_X1953 -58.4654 0.273600 464s Westinghouse_X1954 70.3989 -0.296608 464s Westinghouse_value Westinghouse_capital 464s General.Electric_X1935 -2.924 -0.0275 464s General.Electric_X1936 63.079 0.0978 464s General.Electric_X1937 88.462 0.8980 464s General.Electric_X1938 99.183 3.2034 464s General.Electric_X1939 115.652 5.2276 464s General.Electric_X1940 22.255 0.9384 464s General.Electric_X1941 -137.177 -9.2456 464s General.Electric_X1942 -67.689 -7.3334 464s General.Electric_X1943 89.160 12.1924 464s General.Electric_X1944 99.843 14.5296 464s General.Electric_X1945 0.152 0.0190 464s General.Electric_X1946 -268.381 -30.3494 464s General.Electric_X1947 -179.329 -34.2680 464s General.Electric_X1948 -157.494 -31.0565 464s General.Electric_X1949 55.525 13.4866 464s General.Electric_X1950 124.471 26.7872 464s General.Electric_X1951 0.546 0.0978 464s General.Electric_X1952 -24.386 -4.1062 464s General.Electric_X1953 -39.057 -5.7203 464s General.Electric_X1954 91.705 16.4682 464s Westinghouse_X1935 13.969 0.1313 464s Westinghouse_X1936 -32.740 -0.0508 464s Westinghouse_X1937 -108.497 -1.1013 464s Westinghouse_X1938 -125.598 -4.0566 464s Westinghouse_X1939 -142.317 -6.4329 464s Westinghouse_X1940 -122.436 -5.1624 464s Westinghouse_X1941 213.210 14.3701 464s Westinghouse_X1942 112.925 12.2342 464s Westinghouse_X1943 -40.063 -5.4784 464s Westinghouse_X1944 -43.583 -6.3424 464s Westinghouse_X1945 -139.717 -17.5120 464s Westinghouse_X1946 98.521 11.1411 464s Westinghouse_X1947 226.668 43.3141 464s Westinghouse_X1948 68.357 13.4795 464s Westinghouse_X1949 -132.425 -32.1648 464s Westinghouse_X1950 -181.325 -39.0225 464s Westinghouse_X1951 97.933 17.5490 464s Westinghouse_X1952 289.614 48.7662 464s Westinghouse_X1953 326.541 47.8252 464s Westinghouse_X1954 -352.637 -63.3258 464s General.Electric_(Intercept) General.Electric_value 464s 0 0 464s General.Electric_capital Westinghouse_(Intercept) 464s 0 0 464s Westinghouse_value Westinghouse_capital 464s 0 0 464s Chrysler_(Intercept) Chrysler_value Chrysler_capital 464s Chrysler_X1935 10.622 4435 111.5 464s Chrysler_X1936 10.425 8734 106.3 464s Chrysler_X1937 -7.404 -6544 -256.9 464s Chrysler_X1938 7.302 3198 378.3 464s Chrysler_X1939 -14.682 -9979 -944.0 464s Chrysler_X1940 -2.315 -1685 -155.3 464s Chrysler_X1941 0.631 406 47.4 464s Chrysler_X1942 -1.581 -650 -112.9 464s Chrysler_X1943 -13.459 -7919 -903.1 464s Chrysler_X1944 -7.780 -5433 -470.7 464s Chrysler_X1945 11.757 9951 641.9 464s Chrysler_X1946 -16.133 -14419 -1368.1 464s Chrysler_X1947 -6.823 -3951 -660.5 464s Chrysler_X1948 6.615 4595 729.0 464s Chrysler_X1949 -7.379 -4356 -1087.7 464s Chrysler_X1950 1.268 879 206.9 464s Chrysler_X1951 39.502 31957 8038.6 464s Chrysler_X1952 2.774 2017 806.2 464s Chrysler_X1953 -6.215 -6224 -2151.0 464s Chrysler_X1954 -7.124 -5010 -2955.9 464s General.Electric_X1935 0.000 0 0.0 464s General.Electric_X1936 0.000 0 0.0 464s General.Electric_X1937 0.000 0 0.0 464s General.Electric_X1938 0.000 0 0.0 464s General.Electric_X1939 0.000 0 0.0 464s General.Electric_X1940 0.000 0 0.0 464s General.Electric_X1941 0.000 0 0.0 464s General.Electric_X1942 0.000 0 0.0 464s General.Electric_X1943 0.000 0 0.0 464s General.Electric_X1944 0.000 0 0.0 464s General.Electric_X1945 0.000 0 0.0 464s General.Electric_X1946 0.000 0 0.0 464s General.Electric_X1947 0.000 0 0.0 464s General.Electric_X1948 0.000 0 0.0 464s General.Electric_X1949 0.000 0 0.0 464s General.Electric_X1950 0.000 0 0.0 464s General.Electric_X1951 0.000 0 0.0 464s General.Electric_X1952 0.000 0 0.0 464s General.Electric_X1953 0.000 0 0.0 464s General.Electric_X1954 0.000 0 0.0 464s General.Motors_X1935 0.000 0 0.0 464s General.Motors_X1936 0.000 0 0.0 464s General.Motors_X1937 0.000 0 0.0 464s General.Motors_X1938 0.000 0 0.0 464s General.Motors_X1939 0.000 0 0.0 464s General.Motors_X1940 0.000 0 0.0 464s General.Motors_X1941 0.000 0 0.0 464s General.Motors_X1942 0.000 0 0.0 464s General.Motors_X1943 0.000 0 0.0 464s General.Motors_X1944 0.000 0 0.0 464s General.Motors_X1945 0.000 0 0.0 464s General.Motors_X1946 0.000 0 0.0 464s General.Motors_X1947 0.000 0 0.0 464s General.Motors_X1948 0.000 0 0.0 464s General.Motors_X1949 0.000 0 0.0 464s General.Motors_X1950 0.000 0 0.0 464s General.Motors_X1951 0.000 0 0.0 464s General.Motors_X1952 0.000 0 0.0 464s General.Motors_X1953 0.000 0 0.0 464s General.Motors_X1954 0.000 0 0.0 464s US.Steel_X1935 0.000 0 0.0 464s US.Steel_X1936 0.000 0 0.0 464s US.Steel_X1937 0.000 0 0.0 464s US.Steel_X1938 0.000 0 0.0 464s US.Steel_X1939 0.000 0 0.0 464s US.Steel_X1940 0.000 0 0.0 464s US.Steel_X1941 0.000 0 0.0 464s US.Steel_X1942 0.000 0 0.0 464s US.Steel_X1943 0.000 0 0.0 464s US.Steel_X1944 0.000 0 0.0 464s US.Steel_X1945 0.000 0 0.0 464s US.Steel_X1946 0.000 0 0.0 464s US.Steel_X1947 0.000 0 0.0 464s US.Steel_X1948 0.000 0 0.0 464s US.Steel_X1949 0.000 0 0.0 464s US.Steel_X1950 0.000 0 0.0 464s US.Steel_X1951 0.000 0 0.0 464s US.Steel_X1952 0.000 0 0.0 464s US.Steel_X1953 0.000 0 0.0 464s US.Steel_X1954 0.000 0 0.0 464s Westinghouse_X1935 0.000 0 0.0 464s Westinghouse_X1936 0.000 0 0.0 464s Westinghouse_X1937 0.000 0 0.0 464s Westinghouse_X1938 0.000 0 0.0 464s Westinghouse_X1939 0.000 0 0.0 464s Westinghouse_X1940 0.000 0 0.0 464s Westinghouse_X1941 0.000 0 0.0 464s Westinghouse_X1942 0.000 0 0.0 464s Westinghouse_X1943 0.000 0 0.0 464s Westinghouse_X1944 0.000 0 0.0 464s Westinghouse_X1945 0.000 0 0.0 464s Westinghouse_X1946 0.000 0 0.0 464s Westinghouse_X1947 0.000 0 0.0 464s Westinghouse_X1948 0.000 0 0.0 464s Westinghouse_X1949 0.000 0 0.0 464s Westinghouse_X1950 0.000 0 0.0 464s Westinghouse_X1951 0.000 0 0.0 464s Westinghouse_X1952 0.000 0 0.0 464s Westinghouse_X1953 0.000 0 0.0 464s Westinghouse_X1954 0.000 0 0.0 464s General.Electric_(Intercept) General.Electric_value 464s Chrysler_X1935 0.000 0 464s Chrysler_X1936 0.000 0 464s Chrysler_X1937 0.000 0 464s Chrysler_X1938 0.000 0 464s Chrysler_X1939 0.000 0 464s Chrysler_X1940 0.000 0 464s Chrysler_X1941 0.000 0 464s Chrysler_X1942 0.000 0 464s Chrysler_X1943 0.000 0 464s Chrysler_X1944 0.000 0 464s Chrysler_X1945 0.000 0 464s Chrysler_X1946 0.000 0 464s Chrysler_X1947 0.000 0 464s Chrysler_X1948 0.000 0 464s Chrysler_X1949 0.000 0 464s Chrysler_X1950 0.000 0 464s Chrysler_X1951 0.000 0 464s Chrysler_X1952 0.000 0 464s Chrysler_X1953 0.000 0 464s Chrysler_X1954 0.000 0 464s General.Electric_X1935 -2.860 -3348 464s General.Electric_X1936 -14.402 -29032 464s General.Electric_X1937 -5.175 -14506 464s General.Electric_X1938 -23.295 -47514 464s General.Electric_X1939 -28.031 -63243 464s General.Electric_X1940 -0.562 -1199 464s General.Electric_X1941 40.750 74739 464s General.Electric_X1942 16.036 25464 464s General.Electric_X1943 -23.719 -41494 464s General.Electric_X1944 -26.780 -45183 464s General.Electric_X1945 1.768 3550 464s General.Electric_X1946 58.737 129709 464s General.Electric_X1947 43.936 72789 464s General.Electric_X1948 31.227 50101 464s General.Electric_X1949 -23.552 -33722 464s General.Electric_X1950 -37.511 -60411 464s General.Electric_X1951 -4.983 -9066 464s General.Electric_X1952 1.893 3937 464s General.Electric_X1953 5.087 12064 464s General.Electric_X1954 -8.563 -23633 464s General.Motors_X1935 0.000 0 464s General.Motors_X1936 0.000 0 464s General.Motors_X1937 0.000 0 464s General.Motors_X1938 0.000 0 464s General.Motors_X1939 0.000 0 464s General.Motors_X1940 0.000 0 464s General.Motors_X1941 0.000 0 464s General.Motors_X1942 0.000 0 464s General.Motors_X1943 0.000 0 464s General.Motors_X1944 0.000 0 464s General.Motors_X1945 0.000 0 464s General.Motors_X1946 0.000 0 464s General.Motors_X1947 0.000 0 464s General.Motors_X1948 0.000 0 464s General.Motors_X1949 0.000 0 464s General.Motors_X1950 0.000 0 464s General.Motors_X1951 0.000 0 464s General.Motors_X1952 0.000 0 464s General.Motors_X1953 0.000 0 464s General.Motors_X1954 0.000 0 464s US.Steel_X1935 0.000 0 464s US.Steel_X1936 0.000 0 464s US.Steel_X1937 0.000 0 464s US.Steel_X1938 0.000 0 464s US.Steel_X1939 0.000 0 464s US.Steel_X1940 0.000 0 464s US.Steel_X1941 0.000 0 464s US.Steel_X1942 0.000 0 464s US.Steel_X1943 0.000 0 464s US.Steel_X1944 0.000 0 464s US.Steel_X1945 0.000 0 464s US.Steel_X1946 0.000 0 464s US.Steel_X1947 0.000 0 464s US.Steel_X1948 0.000 0 464s US.Steel_X1949 0.000 0 464s US.Steel_X1950 0.000 0 464s US.Steel_X1951 0.000 0 464s US.Steel_X1952 0.000 0 464s US.Steel_X1953 0.000 0 464s US.Steel_X1954 0.000 0 464s Westinghouse_X1935 0.000 0 464s Westinghouse_X1936 0.000 0 464s Westinghouse_X1937 0.000 0 464s Westinghouse_X1938 0.000 0 464s Westinghouse_X1939 0.000 0 464s Westinghouse_X1940 0.000 0 464s Westinghouse_X1941 0.000 0 464s Westinghouse_X1942 0.000 0 464s Westinghouse_X1943 0.000 0 464s Westinghouse_X1944 0.000 0 464s Westinghouse_X1945 0.000 0 464s Westinghouse_X1946 0.000 0 464s Westinghouse_X1947 0.000 0 464s Westinghouse_X1948 0.000 0 464s Westinghouse_X1949 0.000 0 464s Westinghouse_X1950 0.000 0 464s Westinghouse_X1951 0.000 0 464s Westinghouse_X1952 0.000 0 464s Westinghouse_X1953 0.000 0 464s Westinghouse_X1954 0.000 0 464s General.Electric_capital General.Motors_(Intercept) 464s Chrysler_X1935 0 0.00 464s Chrysler_X1936 0 0.00 464s Chrysler_X1937 0 0.00 464s Chrysler_X1938 0 0.00 464s Chrysler_X1939 0 0.00 464s Chrysler_X1940 0 0.00 464s Chrysler_X1941 0 0.00 464s Chrysler_X1942 0 0.00 464s Chrysler_X1943 0 0.00 464s Chrysler_X1944 0 0.00 464s Chrysler_X1945 0 0.00 464s Chrysler_X1946 0 0.00 464s Chrysler_X1947 0 0.00 464s Chrysler_X1948 0 0.00 464s Chrysler_X1949 0 0.00 464s Chrysler_X1950 0 0.00 464s Chrysler_X1951 0 0.00 464s Chrysler_X1952 0 0.00 464s Chrysler_X1953 0 0.00 464s Chrysler_X1954 0 0.00 464s General.Electric_X1935 -280 0.00 464s General.Electric_X1936 -1504 0.00 464s General.Electric_X1937 -611 0.00 464s General.Electric_X1938 -3639 0.00 464s General.Electric_X1939 -4838 0.00 464s General.Electric_X1940 -105 0.00 464s General.Electric_X1941 9002 0.00 464s General.Electric_X1942 4615 0.00 464s General.Electric_X1943 -7588 0.00 464s General.Electric_X1944 -8604 0.00 464s General.Electric_X1945 565 0.00 464s General.Electric_X1946 20323 0.00 464s General.Electric_X1947 20052 0.00 464s General.Electric_X1948 16969 0.00 464s General.Electric_X1949 -14562 0.00 464s General.Electric_X1950 -24285 0.00 464s General.Electric_X1951 -3345 0.00 464s General.Electric_X1952 1374 0.00 464s General.Electric_X1953 4071 0.00 464s General.Electric_X1954 -7612 0.00 464s General.Motors_X1935 0 99.14 464s General.Motors_X1936 0 -34.01 464s General.Motors_X1937 0 -140.48 464s General.Motors_X1938 0 -3.28 464s General.Motors_X1939 0 -109.45 464s General.Motors_X1940 0 -19.91 464s General.Motors_X1941 0 24.12 464s General.Motors_X1942 0 98.02 464s General.Motors_X1943 0 67.76 464s General.Motors_X1944 0 100.03 464s General.Motors_X1945 0 35.12 464s General.Motors_X1946 0 103.90 464s General.Motors_X1947 0 15.18 464s General.Motors_X1948 0 -51.86 464s General.Motors_X1949 0 -115.39 464s General.Motors_X1950 0 -63.51 464s General.Motors_X1951 0 -119.40 464s General.Motors_X1952 0 -77.82 464s General.Motors_X1953 0 49.50 464s General.Motors_X1954 0 142.33 464s US.Steel_X1935 0 0.00 464s US.Steel_X1936 0 0.00 464s US.Steel_X1937 0 0.00 464s US.Steel_X1938 0 0.00 464s US.Steel_X1939 0 0.00 464s US.Steel_X1940 0 0.00 464s US.Steel_X1941 0 0.00 464s US.Steel_X1942 0 0.00 464s US.Steel_X1943 0 0.00 464s US.Steel_X1944 0 0.00 464s US.Steel_X1945 0 0.00 464s US.Steel_X1946 0 0.00 464s US.Steel_X1947 0 0.00 464s US.Steel_X1948 0 0.00 464s US.Steel_X1949 0 0.00 464s US.Steel_X1950 0 0.00 464s US.Steel_X1951 0 0.00 464s US.Steel_X1952 0 0.00 464s US.Steel_X1953 0 0.00 464s US.Steel_X1954 0 0.00 464s Westinghouse_X1935 0 0.00 464s Westinghouse_X1936 0 0.00 464s Westinghouse_X1937 0 0.00 464s Westinghouse_X1938 0 0.00 464s Westinghouse_X1939 0 0.00 464s Westinghouse_X1940 0 0.00 464s Westinghouse_X1941 0 0.00 464s Westinghouse_X1942 0 0.00 464s Westinghouse_X1943 0 0.00 464s Westinghouse_X1944 0 0.00 464s Westinghouse_X1945 0 0.00 464s Westinghouse_X1946 0 0.00 464s Westinghouse_X1947 0 0.00 464s Westinghouse_X1948 0 0.00 464s Westinghouse_X1949 0 0.00 464s Westinghouse_X1950 0 0.00 464s Westinghouse_X1951 0 0.00 464s Westinghouse_X1952 0 0.00 464s Westinghouse_X1953 0 0.00 464s Westinghouse_X1954 0 0.00 464s General.Motors_value General.Motors_capital 464s Chrysler_X1935 0 0 464s Chrysler_X1936 0 0 464s Chrysler_X1937 0 0 464s Chrysler_X1938 0 0 464s Chrysler_X1939 0 0 464s Chrysler_X1940 0 0 464s Chrysler_X1941 0 0 464s Chrysler_X1942 0 0 464s Chrysler_X1943 0 0 464s Chrysler_X1944 0 0 464s Chrysler_X1945 0 0 464s Chrysler_X1946 0 0 464s Chrysler_X1947 0 0 464s Chrysler_X1948 0 0 464s Chrysler_X1949 0 0 464s Chrysler_X1950 0 0 464s Chrysler_X1951 0 0 464s Chrysler_X1952 0 0 464s Chrysler_X1953 0 0 464s Chrysler_X1954 0 0 464s General.Electric_X1935 0 0 464s General.Electric_X1936 0 0 464s General.Electric_X1937 0 0 464s General.Electric_X1938 0 0 464s General.Electric_X1939 0 0 464s General.Electric_X1940 0 0 464s General.Electric_X1941 0 0 464s General.Electric_X1942 0 0 464s General.Electric_X1943 0 0 464s General.Electric_X1944 0 0 464s General.Electric_X1945 0 0 464s General.Electric_X1946 0 0 464s General.Electric_X1947 0 0 464s General.Electric_X1948 0 0 464s General.Electric_X1949 0 0 464s General.Electric_X1950 0 0 464s General.Electric_X1951 0 0 464s General.Electric_X1952 0 0 464s General.Electric_X1953 0 0 464s General.Electric_X1954 0 0 464s General.Motors_X1935 305191 278 464s General.Motors_X1936 -158530 -1789 464s General.Motors_X1937 -756753 -22041 464s General.Motors_X1938 -9158 -686 464s General.Motors_X1939 -472086 -22262 464s General.Motors_X1940 -92456 -4125 464s General.Motors_X1941 109770 6155 464s General.Motors_X1942 317973 29767 464s General.Motors_X1943 274659 17894 464s General.Motors_X1944 438073 20167 464s General.Motors_X1945 170027 9308 464s General.Motors_X1946 509223 41790 464s General.Motors_X1947 53544 11562 464s General.Motors_X1948 -168794 -47837 464s General.Motors_X1949 -426971 -117711 464s General.Motors_X1950 -238505 -69794 464s General.Motors_X1951 -577039 -144194 464s General.Motors_X1952 -383234 -111315 464s General.Motors_X1953 308954 87974 464s General.Motors_X1954 796113 316860 464s US.Steel_X1935 0 0 464s US.Steel_X1936 0 0 464s US.Steel_X1937 0 0 464s US.Steel_X1938 0 0 464s US.Steel_X1939 0 0 464s US.Steel_X1940 0 0 464s US.Steel_X1941 0 0 464s US.Steel_X1942 0 0 464s US.Steel_X1943 0 0 464s US.Steel_X1944 0 0 464s US.Steel_X1945 0 0 464s US.Steel_X1946 0 0 464s US.Steel_X1947 0 0 464s US.Steel_X1948 0 0 464s US.Steel_X1949 0 0 464s US.Steel_X1950 0 0 464s US.Steel_X1951 0 0 464s US.Steel_X1952 0 0 464s US.Steel_X1953 0 0 464s US.Steel_X1954 0 0 464s Westinghouse_X1935 0 0 464s Westinghouse_X1936 0 0 464s Westinghouse_X1937 0 0 464s Westinghouse_X1938 0 0 464s Westinghouse_X1939 0 0 464s Westinghouse_X1940 0 0 464s Westinghouse_X1941 0 0 464s Westinghouse_X1942 0 0 464s Westinghouse_X1943 0 0 464s Westinghouse_X1944 0 0 464s Westinghouse_X1945 0 0 464s Westinghouse_X1946 0 0 464s Westinghouse_X1947 0 0 464s Westinghouse_X1948 0 0 464s Westinghouse_X1949 0 0 464s Westinghouse_X1950 0 0 464s Westinghouse_X1951 0 0 464s Westinghouse_X1952 0 0 464s Westinghouse_X1953 0 0 464s Westinghouse_X1954 0 0 464s US.Steel_(Intercept) US.Steel_value US.Steel_capital 464s Chrysler_X1935 0.00 0 0 464s Chrysler_X1936 0.00 0 0 464s Chrysler_X1937 0.00 0 0 464s Chrysler_X1938 0.00 0 0 464s Chrysler_X1939 0.00 0 0 464s Chrysler_X1940 0.00 0 0 464s Chrysler_X1941 0.00 0 0 464s Chrysler_X1942 0.00 0 0 464s Chrysler_X1943 0.00 0 0 464s Chrysler_X1944 0.00 0 0 464s Chrysler_X1945 0.00 0 0 464s Chrysler_X1946 0.00 0 0 464s Chrysler_X1947 0.00 0 0 464s Chrysler_X1948 0.00 0 0 464s Chrysler_X1949 0.00 0 0 464s Chrysler_X1950 0.00 0 0 464s Chrysler_X1951 0.00 0 0 464s Chrysler_X1952 0.00 0 0 464s Chrysler_X1953 0.00 0 0 464s Chrysler_X1954 0.00 0 0 464s General.Electric_X1935 0.00 0 0 464s General.Electric_X1936 0.00 0 0 464s General.Electric_X1937 0.00 0 0 464s General.Electric_X1938 0.00 0 0 464s General.Electric_X1939 0.00 0 0 464s General.Electric_X1940 0.00 0 0 464s General.Electric_X1941 0.00 0 0 464s General.Electric_X1942 0.00 0 0 464s General.Electric_X1943 0.00 0 0 464s General.Electric_X1944 0.00 0 0 464s General.Electric_X1945 0.00 0 0 464s General.Electric_X1946 0.00 0 0 464s General.Electric_X1947 0.00 0 0 464s General.Electric_X1948 0.00 0 0 464s General.Electric_X1949 0.00 0 0 464s General.Electric_X1950 0.00 0 0 464s General.Electric_X1951 0.00 0 0 464s General.Electric_X1952 0.00 0 0 464s General.Electric_X1953 0.00 0 0 464s General.Electric_X1954 0.00 0 0 464s General.Motors_X1935 0.00 0 0 464s General.Motors_X1936 0.00 0 0 464s General.Motors_X1937 0.00 0 0 464s General.Motors_X1938 0.00 0 0 464s General.Motors_X1939 0.00 0 0 464s General.Motors_X1940 0.00 0 0 464s General.Motors_X1941 0.00 0 0 464s General.Motors_X1942 0.00 0 0 464s General.Motors_X1943 0.00 0 0 464s General.Motors_X1944 0.00 0 0 464s General.Motors_X1945 0.00 0 0 464s General.Motors_X1946 0.00 0 0 464s General.Motors_X1947 0.00 0 0 464s General.Motors_X1948 0.00 0 0 464s General.Motors_X1949 0.00 0 0 464s General.Motors_X1950 0.00 0 0 464s General.Motors_X1951 0.00 0 0 464s General.Motors_X1952 0.00 0 0 464s General.Motors_X1953 0.00 0 0 464s General.Motors_X1954 0.00 0 0 464s US.Steel_X1935 4.15 5657 223 464s US.Steel_X1936 81.32 146961 4107 464s US.Steel_X1937 31.18 83446 3682 464s US.Steel_X1938 -99.75 -179733 -25954 464s US.Steel_X1939 -178.23 -348850 -55733 464s US.Steel_X1940 -160.69 -353980 -40847 464s US.Steel_X1941 19.65 46784 5137 464s US.Steel_X1942 9.82 21296 2933 464s US.Steel_X1943 -46.76 -92829 -14113 464s US.Steel_X1944 -83.74 -151889 -23371 464s US.Steel_X1945 -91.24 -168815 -19507 464s US.Steel_X1946 28.34 58590 6591 464s US.Steel_X1947 57.32 102983 15178 464s US.Steel_X1948 140.23 227988 43037 464s US.Steel_X1949 25.65 42751 9004 464s US.Steel_X1950 34.88 58503 12479 464s US.Steel_X1951 115.10 263510 39374 464s US.Steel_X1952 149.19 322157 66269 464s US.Steel_X1953 89.00 180793 55503 464s US.Steel_X1954 -125.42 -265326 -83994 464s Westinghouse_X1935 0.00 0 0 464s Westinghouse_X1936 0.00 0 0 464s Westinghouse_X1937 0.00 0 0 464s Westinghouse_X1938 0.00 0 0 464s Westinghouse_X1939 0.00 0 0 464s Westinghouse_X1940 0.00 0 0 464s Westinghouse_X1941 0.00 0 0 464s Westinghouse_X1942 0.00 0 0 464s Westinghouse_X1943 0.00 0 0 464s Westinghouse_X1944 0.00 0 0 464s Westinghouse_X1945 0.00 0 0 464s Westinghouse_X1946 0.00 0 0 464s Westinghouse_X1947 0.00 0 0 464s Westinghouse_X1948 0.00 0 0 464s Westinghouse_X1949 0.00 0 0 464s Westinghouse_X1950 0.00 0 0 464s Westinghouse_X1951 0.00 0 0 464s Westinghouse_X1952 0.00 0 0 464s Westinghouse_X1953 0.00 0 0 464s Westinghouse_X1954 0.00 0 0 464s Westinghouse_(Intercept) Westinghouse_value 464s Chrysler_X1935 0.000 0 464s Chrysler_X1936 0.000 0 464s Chrysler_X1937 0.000 0 464s Chrysler_X1938 0.000 0 464s Chrysler_X1939 0.000 0 464s Chrysler_X1940 0.000 0 464s Chrysler_X1941 0.000 0 464s Chrysler_X1942 0.000 0 464s Chrysler_X1943 0.000 0 464s Chrysler_X1944 0.000 0 464s Chrysler_X1945 0.000 0 464s Chrysler_X1946 0.000 0 464s Chrysler_X1947 0.000 0 464s Chrysler_X1948 0.000 0 464s Chrysler_X1949 0.000 0 464s Chrysler_X1950 0.000 0 464s Chrysler_X1951 0.000 0 464s Chrysler_X1952 0.000 0 464s Chrysler_X1953 0.000 0 464s Chrysler_X1954 0.000 0 464s General.Electric_X1935 0.000 0 464s General.Electric_X1936 0.000 0 464s General.Electric_X1937 0.000 0 464s General.Electric_X1938 0.000 0 464s General.Electric_X1939 0.000 0 464s General.Electric_X1940 0.000 0 464s General.Electric_X1941 0.000 0 464s General.Electric_X1942 0.000 0 464s General.Electric_X1943 0.000 0 464s General.Electric_X1944 0.000 0 464s General.Electric_X1945 0.000 0 464s General.Electric_X1946 0.000 0 464s General.Electric_X1947 0.000 0 464s General.Electric_X1948 0.000 0 464s General.Electric_X1949 0.000 0 464s General.Electric_X1950 0.000 0 464s General.Electric_X1951 0.000 0 464s General.Electric_X1952 0.000 0 464s General.Electric_X1953 0.000 0 464s General.Electric_X1954 0.000 0 464s General.Motors_X1935 0.000 0 464s General.Motors_X1936 0.000 0 464s General.Motors_X1937 0.000 0 464s General.Motors_X1938 0.000 0 464s General.Motors_X1939 0.000 0 464s General.Motors_X1940 0.000 0 464s General.Motors_X1941 0.000 0 464s General.Motors_X1942 0.000 0 464s General.Motors_X1943 0.000 0 464s General.Motors_X1944 0.000 0 464s General.Motors_X1945 0.000 0 464s General.Motors_X1946 0.000 0 464s General.Motors_X1947 0.000 0 464s General.Motors_X1948 0.000 0 464s General.Motors_X1949 0.000 0 464s General.Motors_X1950 0.000 0 464s General.Motors_X1951 0.000 0 464s General.Motors_X1952 0.000 0 464s General.Motors_X1953 0.000 0 464s General.Motors_X1954 0.000 0 464s US.Steel_X1935 0.000 0 464s US.Steel_X1936 0.000 0 464s US.Steel_X1937 0.000 0 464s US.Steel_X1938 0.000 0 464s US.Steel_X1939 0.000 0 464s US.Steel_X1940 0.000 0 464s US.Steel_X1941 0.000 0 464s US.Steel_X1942 0.000 0 464s US.Steel_X1943 0.000 0 464s US.Steel_X1944 0.000 0 464s US.Steel_X1945 0.000 0 464s US.Steel_X1946 0.000 0 464s US.Steel_X1947 0.000 0 464s US.Steel_X1948 0.000 0 464s US.Steel_X1949 0.000 0 464s US.Steel_X1950 0.000 0 464s US.Steel_X1951 0.000 0 464s US.Steel_X1952 0.000 0 464s US.Steel_X1953 0.000 0 464s US.Steel_X1954 0.000 0 464s Westinghouse_X1935 3.144 602 464s Westinghouse_X1936 -0.958 -494 464s Westinghouse_X1937 -3.684 -2686 464s Westinghouse_X1938 -7.915 -4436 464s Westinghouse_X1939 -10.322 -5366 464s Westinghouse_X1940 -6.613 -4156 464s Westinghouse_X1941 17.265 9273 464s Westinghouse_X1942 8.547 4797 464s Westinghouse_X1943 -2.916 -1800 464s Westinghouse_X1944 -3.257 -2041 464s Westinghouse_X1945 -7.753 -5715 464s Westinghouse_X1946 5.796 4408 464s Westinghouse_X1947 15.050 8750 464s Westinghouse_X1948 2.969 1967 464s Westinghouse_X1949 -11.433 -6675 464s Westinghouse_X1950 -13.481 -8563 464s Westinghouse_X1951 4.619 3344 464s Westinghouse_X1952 13.138 11353 464s Westinghouse_X1953 11.308 13496 464s Westinghouse_X1954 -13.505 -16056 464s Westinghouse_capital 464s Chrysler_X1935 0.000 464s Chrysler_X1936 0.000 464s Chrysler_X1937 0.000 464s Chrysler_X1938 0.000 464s Chrysler_X1939 0.000 464s Chrysler_X1940 0.000 464s Chrysler_X1941 0.000 464s Chrysler_X1942 0.000 464s Chrysler_X1943 0.000 464s Chrysler_X1944 0.000 464s Chrysler_X1945 0.000 464s Chrysler_X1946 0.000 464s Chrysler_X1947 0.000 464s Chrysler_X1948 0.000 464s Chrysler_X1949 0.000 464s Chrysler_X1950 0.000 464s Chrysler_X1951 0.000 464s Chrysler_X1952 0.000 464s Chrysler_X1953 0.000 464s Chrysler_X1954 0.000 464s General.Electric_X1935 0.000 464s General.Electric_X1936 0.000 464s General.Electric_X1937 0.000 464s General.Electric_X1938 0.000 464s General.Electric_X1939 0.000 464s General.Electric_X1940 0.000 464s General.Electric_X1941 0.000 464s General.Electric_X1942 0.000 464s General.Electric_X1943 0.000 464s General.Electric_X1944 0.000 464s General.Electric_X1945 0.000 464s General.Electric_X1946 0.000 464s General.Electric_X1947 0.000 464s General.Electric_X1948 0.000 464s General.Electric_X1949 0.000 464s General.Electric_X1950 0.000 464s General.Electric_X1951 0.000 464s General.Electric_X1952 0.000 464s General.Electric_X1953 0.000 464s General.Electric_X1954 0.000 464s General.Motors_X1935 0.000 464s General.Motors_X1936 0.000 464s General.Motors_X1937 0.000 464s General.Motors_X1938 0.000 464s General.Motors_X1939 0.000 464s General.Motors_X1940 0.000 464s General.Motors_X1941 0.000 464s General.Motors_X1942 0.000 464s General.Motors_X1943 0.000 464s General.Motors_X1944 0.000 464s General.Motors_X1945 0.000 464s General.Motors_X1946 0.000 464s General.Motors_X1947 0.000 464s General.Motors_X1948 0.000 464s General.Motors_X1949 0.000 464s General.Motors_X1950 0.000 464s General.Motors_X1951 0.000 464s General.Motors_X1952 0.000 464s General.Motors_X1953 0.000 464s General.Motors_X1954 0.000 464s US.Steel_X1935 0.000 464s US.Steel_X1936 0.000 464s US.Steel_X1937 0.000 464s US.Steel_X1938 0.000 464s US.Steel_X1939 0.000 464s US.Steel_X1940 0.000 464s US.Steel_X1941 0.000 464s US.Steel_X1942 0.000 464s US.Steel_X1943 0.000 464s US.Steel_X1944 0.000 464s US.Steel_X1945 0.000 464s US.Steel_X1946 0.000 464s US.Steel_X1947 0.000 464s US.Steel_X1948 0.000 464s US.Steel_X1949 0.000 464s US.Steel_X1950 0.000 464s US.Steel_X1951 0.000 464s US.Steel_X1952 0.000 464s US.Steel_X1953 0.000 464s US.Steel_X1954 0.000 464s Westinghouse_X1935 5.659 464s Westinghouse_X1936 -0.766 464s Westinghouse_X1937 -27.263 464s Westinghouse_X1938 -143.262 464s Westinghouse_X1939 -242.563 464s Westinghouse_X1940 -175.254 464s Westinghouse_X1941 624.987 464s Westinghouse_X1942 519.651 464s Westinghouse_X1943 -246.108 464s Westinghouse_X1944 -297.023 464s Westinghouse_X1945 -716.333 464s Westinghouse_X1946 498.495 464s Westinghouse_X1947 1672.098 464s Westinghouse_X1948 387.794 464s Westinghouse_X1949 -1621.262 464s Westinghouse_X1950 -1842.843 464s Westinghouse_X1951 599.149 464s Westinghouse_X1952 1911.642 464s Westinghouse_X1953 1976.568 464s Westinghouse_X1954 -2883.365 464s Chrysler_(Intercept) Chrysler_value 464s 0 0 464s Chrysler_capital General.Electric_(Intercept) 464s 0 0 464s General.Electric_value General.Electric_capital 464s 0 0 464s General.Motors_(Intercept) General.Motors_value 464s 0 0 464s General.Motors_capital US.Steel_(Intercept) 464s 0 0 464s US.Steel_value US.Steel_capital 464s 0 0 464s Westinghouse_(Intercept) Westinghouse_value 464s 0 0 464s Westinghouse_capital 464s 0 464s [1] "Error in estfun.systemfit(greeneOlsPooled) : \n returning the estimation function for models with restrictions has not yet been implemented.\n" 464s attr(,"class") 464s [1] "try-error" 464s attr(,"condition") 464s 464s Chrysler_(Intercept) Chrysler_value Chrysler_capital 464s Chrysler_X1935 0.061827 25.813 0.64918 464s Chrysler_X1936 0.089260 74.782 0.91045Error in estfun.systemfit(greeneOlsPooled) : 464s returning the estimation function for models with restrictions has not yet been implemented. 464s 464s Chrysler_X1937 -0.052866 -46.729 -1.83447 464s Chrysler_X1938 0.038353 16.795 1.98668 464s Chrysler_X1939 -0.125156 -85.069 -8.04755 464s Chrysler_X1940 -0.019863 -14.456 -1.33281 464s Chrysler_X1941 -0.000958 -0.617 -0.07206 464s Chrysler_X1942 -0.035485 -14.581 -2.53362 464s Chrysler_X1943 -0.121241 -71.338 -8.13529 464s Chrysler_X1944 -0.067270 -46.981 -4.06984 464s Chrysler_X1945 0.103440 87.551 5.64781 464s Chrysler_X1946 -0.121081 -108.222 -10.26763 464s Chrysler_X1947 -0.065512 -37.931 -6.34155 464s Chrysler_X1948 0.053900 37.439 5.93977 464s Chrysler_X1949 -0.066320 -39.149 -9.77563 464s Chrysler_X1950 0.012935 8.971 2.11101 464s Chrysler_X1951 0.338038 273.472 68.79064 464s Chrysler_X1952 0.035175 25.572 10.22178 464s Chrysler_X1953 -0.016558 -16.583 -5.73086 464s Chrysler_X1954 -0.040615 -28.561 -16.85128 464s General.Electric_X1935 -0.000794 -0.332 -0.00834 464s General.Electric_X1936 -0.018766 -15.722 -0.19142 464s General.Electric_X1937 -0.017841 -15.770 -0.61909 464s General.Electric_X1938 -0.025844 -11.317 -1.33872 464s General.Electric_X1939 -0.031739 -21.573 -2.04083 464s General.Electric_X1940 -0.006211 -4.520 -0.41674 464s General.Electric_X1941 0.033478 21.546 2.51754 464s General.Electric_X1942 0.015339 6.303 1.09520 464s General.Electric_X1943 -0.020477 -12.049 -1.37400 464s General.Electric_X1944 -0.022551 -15.749 -1.36432 464s General.Electric_X1945 -0.000552 -0.467 -0.03015 464s General.Electric_X1946 0.048030 42.930 4.07298 464s General.Electric_X1947 0.042267 24.472 4.09142 464s General.Electric_X1948 0.033204 23.064 3.65913 464s General.Electric_X1949 -0.011862 -7.002 -1.74842 464s General.Electric_X1950 -0.025261 -17.518 -4.12252 464s General.Electric_X1951 0.001752 1.417 0.35646 464s General.Electric_X1952 0.006337 4.607 1.84166 464s General.Electric_X1953 0.007751 7.762 2.68249 464s General.Electric_X1954 -0.006261 -4.402 -2.59748 464s General.Motors_X1935 0.015266 6.374 0.16030 464s General.Motors_X1936 -0.003913 -3.278 -0.03991 464s General.Motors_X1937 -0.019260 -17.024 -0.66833 464s General.Motors_X1938 0.000502 0.220 0.02603 464s General.Motors_X1939 -0.014763 -10.035 -0.94928 464s General.Motors_X1940 -0.002163 -1.575 -0.14517 464s General.Motors_X1941 0.004002 2.576 0.30095 464s General.Motors_X1942 0.014599 5.999 1.04234 464s General.Motors_X1943 0.010244 6.027 0.68736 464s General.Motors_X1944 0.014852 10.373 0.89857 464s General.Motors_X1945 0.005493 4.649 0.29991 464s General.Motors_X1946 0.014990 13.398 1.27114 464s General.Motors_X1947 0.002105 1.219 0.20375 464s General.Motors_X1948 -0.007587 -5.270 -0.83607 464s General.Motors_X1949 -0.016803 -9.919 -2.47682 464s General.Motors_X1950 -0.009602 -6.659 -1.56700 464s General.Motors_X1951 -0.017864 -14.452 -3.63526 464s General.Motors_X1952 -0.012355 -8.982 -3.59050 464s General.Motors_X1953 0.004869 4.876 1.68503 464s General.Motors_X1954 0.017389 12.228 7.21481 464s US.Steel_X1935 0.013928 5.815 0.14625 464s US.Steel_X1936 -0.026161 -21.918 -0.26684 464s US.Steel_X1937 -0.025907 -22.899 -0.89897 464s US.Steel_X1938 0.043429 19.017 2.24961 464s US.Steel_X1939 0.070526 47.937 4.53484 464s US.Steel_X1940 0.058816 42.806 3.94653 464s US.Steel_X1941 -0.016278 -10.476 -1.22408 464s US.Steel_X1942 -0.008142 -3.346 -0.58136 464s US.Steel_X1943 0.018146 10.677 1.21761 464s US.Steel_X1944 0.036672 25.612 2.21866 464s US.Steel_X1945 0.039460 33.399 2.15450 464s US.Steel_X1946 -0.012632 -11.291 -1.07122 464s US.Steel_X1947 -0.018481 -10.700 -1.78894 464s US.Steel_X1948 -0.047880 -33.258 -5.27643 464s US.Steel_X1949 -0.003976 -2.347 -0.58605 464s US.Steel_X1950 -0.007908 -5.484 -1.29060 464s US.Steel_X1951 -0.052722 -42.652 -10.72894 464s US.Steel_X1952 -0.064309 -46.753 -18.68822 464s US.Steel_X1953 -0.039465 -39.524 -13.65875 464s US.Steel_X1954 0.042884 30.156 17.79265 464s Westinghouse_X1935 -0.000639 -0.267 -0.00671 464s Westinghouse_X1936 0.003489 2.923 0.03559 464s Westinghouse_X1937 0.005946 5.256 0.20632 464s Westinghouse_X1938 0.008196 3.589 0.42458 464s Westinghouse_X1939 0.009675 6.576 0.62207 464s Westinghouse_X1940 0.007107 5.172 0.47686 464s Westinghouse_X1941 -0.011506 -7.406 -0.86528 464s Westinghouse_X1942 -0.005817 -2.390 -0.41532 464s Westinghouse_X1943 0.002074 1.221 0.13919 464s Westinghouse_X1944 0.002100 1.466 0.12704 464s Westinghouse_X1945 0.005777 4.890 0.31543 464s Westinghouse_X1946 -0.004096 -3.661 -0.34734 464s Westinghouse_X1947 -0.012571 -7.279 -1.21688 464s Westinghouse_X1948 -0.003981 -2.765 -0.43871 464s Westinghouse_X1949 0.006180 3.648 0.91087 464s Westinghouse_X1950 0.008074 5.599 1.31765 464s Westinghouse_X1951 -0.004997 -4.043 -1.01696 464s Westinghouse_X1952 -0.011575 -8.415 -3.36372 464s Westinghouse_X1953 -0.010300 -10.316 -3.56494 464s Westinghouse_X1954 0.006866 4.828 2.84858 464s General.Electric_(Intercept) General.Electric_value 464s Chrysler_X1935 0.006590 7.715 464s Chrysler_X1936 0.009515 19.180 464s Chrysler_X1937 -0.005635 -15.797 464s Chrysler_X1938 0.004088 8.339 464s Chrysler_X1939 -0.013341 -30.100 464s Chrysler_X1940 -0.002117 -4.514 464s Chrysler_X1941 -0.000102 -0.187 464s Chrysler_X1942 -0.003782 -6.007 464s Chrysler_X1943 -0.012924 -22.609 464s Chrysler_X1944 -0.007171 -12.098 464s Chrysler_X1945 0.011026 22.137 464s Chrysler_X1946 -0.012907 -28.501 464s Chrysler_X1947 -0.006983 -11.569 464s Chrysler_X1948 0.005745 9.218 464s Chrysler_X1949 -0.007069 -10.122 464s Chrysler_X1950 0.001379 2.221 464s Chrysler_X1951 0.036033 65.558 464s Chrysler_X1952 0.003749 7.798 464s Chrysler_X1953 -0.001765 -4.186 464s Chrysler_X1954 -0.004329 -11.949 464s General.Electric_X1935 -0.003192 -3.736 464s General.Electric_X1936 -0.075425 -152.042 464s General.Electric_X1937 -0.071707 -201.016 464s General.Electric_X1938 -0.103871 -211.866 464s General.Electric_X1939 -0.127565 -287.812 464s General.Electric_X1940 -0.024962 -53.224 464s General.Electric_X1941 0.134553 246.784 464s General.Electric_X1942 0.061649 97.899 464s General.Electric_X1943 -0.082300 -143.975 464s General.Electric_X1944 -0.090635 -152.920 464s General.Electric_X1945 -0.002219 -4.456 464s General.Electric_X1946 0.193042 426.295 464s General.Electric_X1947 0.169877 281.435 464s General.Electric_X1948 0.133454 214.114 464s General.Electric_X1949 -0.047674 -68.260 464s General.Electric_X1950 -0.101526 -163.508 464s General.Electric_X1951 0.007040 12.809 464s General.Electric_X1952 0.025471 52.972 464s General.Electric_X1953 0.031151 73.878 464s General.Electric_X1954 -0.025162 -69.445 464s General.Motors_X1935 -0.016212 -18.978 464s General.Motors_X1936 0.004155 8.376 464s General.Motors_X1937 0.020453 57.337 464s General.Motors_X1938 -0.000534 -1.088 464s General.Motors_X1939 0.015678 35.372 464s General.Motors_X1940 0.002297 4.899 464s General.Motors_X1941 -0.004250 -7.795 464s General.Motors_X1942 -0.015503 -24.619 464s General.Motors_X1943 -0.010878 -19.031 464s General.Motors_X1944 -0.015772 -26.611 464s General.Motors_X1945 -0.005833 -11.711 464s General.Motors_X1946 -0.015918 -35.152 464s General.Motors_X1947 -0.002235 -3.703 464s General.Motors_X1948 0.008057 12.926 464s General.Motors_X1949 0.017844 25.549 464s General.Motors_X1950 0.010196 16.421 464s General.Motors_X1951 0.018970 34.514 464s General.Motors_X1952 0.013121 27.287 464s General.Motors_X1953 -0.005170 -12.262 464s General.Motors_X1954 -0.018466 -50.965 464s US.Steel_X1935 0.000660 0.772 464s US.Steel_X1936 -0.001239 -2.497 464s US.Steel_X1937 -0.001227 -3.439 464s US.Steel_X1938 0.002057 4.195 464s US.Steel_X1939 0.003340 7.535 464s US.Steel_X1940 0.002785 5.939 464s US.Steel_X1941 -0.000771 -1.414 464s US.Steel_X1942 -0.000386 -0.612 464s US.Steel_X1943 0.000859 1.503 464s US.Steel_X1944 0.001737 2.930 464s US.Steel_X1945 0.001869 3.752 464s US.Steel_X1946 -0.000598 -1.321 464s US.Steel_X1947 -0.000875 -1.450 464s US.Steel_X1948 -0.002267 -3.638 464s US.Steel_X1949 -0.000188 -0.270 464s US.Steel_X1950 -0.000374 -0.603 464s US.Steel_X1951 -0.002497 -4.542 464s US.Steel_X1952 -0.003045 -6.333 464s US.Steel_X1953 -0.001869 -4.432 464s US.Steel_X1954 0.002031 5.605 464s Westinghouse_X1935 -0.005793 -6.781 464s Westinghouse_X1936 0.031644 63.787 464s Westinghouse_X1937 0.053929 151.178 464s Westinghouse_X1938 0.074341 151.634 464s Westinghouse_X1939 0.087747 197.975 464s Westinghouse_X1940 0.064457 137.434 464s Westinghouse_X1941 -0.104362 -191.410 464s Westinghouse_X1942 -0.052757 -83.779 464s Westinghouse_X1943 0.018814 32.913 464s Westinghouse_X1944 0.019045 32.133 464s Westinghouse_X1945 0.052397 105.198 464s Westinghouse_X1946 -0.037151 -82.040 464s Westinghouse_X1947 -0.114019 -188.895 464s Westinghouse_X1948 -0.036108 -57.931 464s Westinghouse_X1949 0.056048 80.250 464s Westinghouse_X1950 0.073229 117.935 464s Westinghouse_X1951 -0.045325 -82.465 464s Westinghouse_X1952 -0.104985 -218.337 464s Westinghouse_X1953 -0.093423 -221.562 464s Westinghouse_X1954 0.062271 171.863 464s General.Electric_capital General.Motors_(Intercept) 464s Chrysler_X1935 0.6445 1.06e-03 464s Chrysler_X1936 0.9933 1.53e-03 464s Chrysler_X1937 -0.6650 -9.08e-04 464s Chrysler_X1938 0.6386 6.59e-04 464s Chrysler_X1939 -2.3026 -2.15e-03 464s Chrysler_X1940 -0.3951 -3.41e-04 464s Chrysler_X1941 -0.0226 -1.65e-05 464s Chrysler_X1942 -1.0886 -6.10e-04 464s Chrysler_X1943 -4.1343 -2.08e-03 464s Chrysler_X1944 -2.3039 -1.16e-03 464s Chrysler_X1945 3.5239 1.78e-03 464s Chrysler_X1946 -4.4657 -2.08e-03 464s Chrysler_X1947 -3.1871 -1.13e-03 464s Chrysler_X1948 3.1221 9.26e-04 464s Chrysler_X1949 -4.3710 -1.14e-03 464s Chrysler_X1950 0.8926 2.22e-04 464s Chrysler_X1951 24.1889 5.81e-03 464s Chrysler_X1952 2.7225 6.04e-04 464s Chrysler_X1953 -1.4126 -2.84e-04 464s Chrysler_X1954 -3.8484 -6.98e-04 464s General.Electric_X1935 -0.3121 1.36e-04 464s General.Electric_X1936 -7.8744 3.21e-03 464s General.Electric_X1937 -8.4614 3.05e-03 464s General.Electric_X1938 -16.2246 4.42e-03 464s General.Electric_X1939 -22.0177 5.43e-03 464s General.Electric_X1940 -4.6579 1.06e-03 464s General.Electric_X1941 29.7228 -5.73e-03 464s General.Electric_X1942 17.7427 -2.63e-03 464s General.Electric_X1943 -26.3277 3.50e-03 464s General.Electric_X1944 -29.1212 3.86e-03 464s General.Electric_X1945 -0.7094 9.45e-05 464s General.Electric_X1946 66.7926 -8.22e-03 464s General.Electric_X1947 77.5319 -7.23e-03 464s General.Electric_X1948 72.5190 -5.68e-03 464s General.Electric_X1949 -29.4770 2.03e-03 464s General.Electric_X1950 -65.7280 4.32e-03 464s General.Electric_X1951 4.7261 -3.00e-04 464s General.Electric_X1952 18.4946 -1.08e-03 464s General.Electric_X1953 24.9302 -1.33e-03 464s General.Electric_X1954 -22.3665 1.07e-03 464s General.Motors_X1935 -1.5855 2.13e-02 464s General.Motors_X1936 0.4338 -5.46e-03 464s General.Motors_X1937 2.4135 -2.69e-02 464s General.Motors_X1938 -0.0833 7.00e-04 464s General.Motors_X1939 2.7060 -2.06e-02 464s General.Motors_X1940 0.4287 -3.02e-03 464s General.Motors_X1941 -0.9388 5.58e-03 464s General.Motors_X1942 -4.4617 2.04e-02 464s General.Motors_X1943 -3.4800 1.43e-02 464s General.Motors_X1944 -5.0677 2.07e-02 464s General.Motors_X1945 -1.8642 7.66e-03 464s General.Motors_X1946 -5.5077 2.09e-02 464s General.Motors_X1947 -1.0202 2.93e-03 464s General.Motors_X1948 4.3781 -1.06e-02 464s General.Motors_X1949 11.0331 -2.34e-02 464s General.Motors_X1950 6.6012 -1.34e-02 464s General.Motors_X1951 12.7347 -2.49e-02 464s General.Motors_X1952 9.5270 -1.72e-02 464s General.Motors_X1953 -4.1377 6.79e-03 464s General.Motors_X1954 -16.4148 2.42e-02 464s US.Steel_X1935 0.0645 -3.30e-03 464s US.Steel_X1936 -0.1293 6.19e-03 464s US.Steel_X1937 -0.1448 6.13e-03 464s US.Steel_X1938 0.3212 -1.03e-02 464s US.Steel_X1939 0.5764 -1.67e-02 464s US.Steel_X1940 0.5197 -1.39e-02 464s US.Steel_X1941 -0.1703 3.85e-03 464s US.Steel_X1942 -0.1110 1.93e-03 464s US.Steel_X1943 0.2749 -4.29e-03 464s US.Steel_X1944 0.5580 -8.68e-03 464s US.Steel_X1945 0.5972 -9.34e-03 464s US.Steel_X1946 -0.2070 2.99e-03 464s US.Steel_X1947 -0.3994 4.37e-03 464s US.Steel_X1948 -1.2321 1.13e-02 464s US.Steel_X1949 -0.1164 9.41e-04 464s US.Steel_X1950 -0.2424 1.87e-03 464s US.Steel_X1951 -1.6760 1.25e-02 464s US.Steel_X1952 -2.2112 1.52e-02 464s US.Steel_X1953 -1.4956 9.34e-03 464s US.Steel_X1954 1.8051 -1.01e-02 464s Westinghouse_X1935 -0.5665 -4.91e-04 464s Westinghouse_X1936 3.3036 2.68e-03 464s Westinghouse_X1937 6.3636 4.57e-03 464s Westinghouse_X1938 11.6121 6.30e-03 464s Westinghouse_X1939 15.1452 7.44e-03 464s Westinghouse_X1940 12.0276 5.46e-03 464s Westinghouse_X1941 -23.0535 -8.84e-03 464s Westinghouse_X1942 -15.1836 -4.47e-03 464s Westinghouse_X1943 6.0186 1.59e-03 464s Westinghouse_X1944 6.1191 1.61e-03 464s Westinghouse_X1945 16.7462 4.44e-03 464s Westinghouse_X1946 -12.8541 -3.15e-03 464s Westinghouse_X1947 -52.0382 -9.66e-03 464s Westinghouse_X1948 -19.6209 -3.06e-03 464s Westinghouse_X1949 34.6547 4.75e-03 464s Westinghouse_X1950 47.4084 6.21e-03 464s Westinghouse_X1951 -30.4270 -3.84e-03 464s Westinghouse_X1952 -76.2296 -8.90e-03 464s Westinghouse_X1953 -74.7663 -7.92e-03 464s Westinghouse_X1954 55.3529 5.28e-03 464s General.Motors_value General.Motors_capital 464s Chrysler_X1935 3.2697 2.97e-03 464s Chrysler_X1936 7.1482 8.07e-02 464s Chrysler_X1937 -4.8925 -1.42e-01 464s Chrysler_X1938 1.8397 1.38e-01 464s Chrysler_X1939 -9.2736 -4.37e-01 464s Chrysler_X1940 -1.5846 -7.07e-02 464s Chrysler_X1941 -0.0749 -4.20e-03 464s Chrysler_X1942 -1.9776 -1.85e-01 464s Chrysler_X1943 -8.4430 -5.50e-01 464s Chrysler_X1944 -5.0608 -2.33e-01 464s Chrysler_X1945 8.6022 4.71e-01 464s Chrysler_X1946 -10.1940 -8.37e-01 464s Chrysler_X1947 -3.9688 -8.57e-01 464s Chrysler_X1948 3.0137 8.54e-01 464s Chrysler_X1949 -4.2157 -1.16e+00 464s Chrysler_X1950 0.8345 2.44e-01 464s Chrysler_X1951 28.0658 7.01e+00 464s Chrysler_X1952 2.9759 8.64e-01 464s Chrysler_X1953 -1.7755 -5.06e-01 464s Chrysler_X1954 -3.9028 -1.55e+00 464s General.Electric_X1935 0.4184 3.81e-04 464s General.Electric_X1936 14.9723 1.69e-01 464s General.Electric_X1937 16.4491 4.79e-01 464s General.Electric_X1938 12.3500 9.25e-01 464s General.Electric_X1939 23.4292 1.10e+00 464s General.Electric_X1940 4.9361 2.20e-01 464s General.Electric_X1941 -26.0763 -1.46e+00 464s General.Electric_X1942 -8.5163 -7.97e-01 464s General.Electric_X1943 14.2062 9.26e-01 464s General.Electric_X1944 16.9016 7.78e-01 464s General.Electric_X1945 0.4575 2.50e-02 464s General.Electric_X1946 -40.2860 -3.31e+00 464s General.Electric_X1947 -25.5097 -5.51e+00 464s General.Electric_X1948 -18.4956 -5.24e+00 464s General.Electric_X1949 7.5116 2.07e+00 464s General.Electric_X1950 16.2362 4.75e+00 464s General.Electric_X1951 -1.4489 -3.62e-01 464s General.Electric_X1952 -5.3416 -1.55e+00 464s General.Electric_X1953 -8.2795 -2.36e+00 464s General.Electric_X1954 5.9933 2.39e+00 464s General.Motors_X1935 65.5183 5.96e-02 464s General.Motors_X1936 -25.4300 -2.87e-01 464s General.Motors_X1937 -144.6452 -4.21e+00 464s General.Motors_X1938 1.9558 1.47e-01 464s General.Motors_X1939 -88.7707 -4.19e+00 464s General.Motors_X1940 -14.0060 -6.25e-01 464s General.Motors_X1941 25.3914 1.42e+00 464s General.Motors_X1942 66.0227 6.18e+00 464s General.Motors_X1943 57.8898 3.77e+00 464s General.Motors_X1944 90.6754 4.17e+00 464s General.Motors_X1945 37.0686 2.03e+00 464s General.Motors_X1946 102.4144 8.40e+00 464s General.Motors_X1947 10.3479 2.23e+00 464s General.Motors_X1948 -34.4239 -9.76e+00 464s General.Motors_X1949 -86.6782 -2.39e+01 464s General.Motors_X1950 -50.2708 -1.47e+01 464s General.Motors_X1951 -120.3581 -3.01e+01 464s General.Motors_X1952 -84.8289 -2.46e+01 464s General.Motors_X1953 42.3640 1.21e+01 464s General.Motors_X1954 135.6002 5.40e+01 464s US.Steel_X1935 -10.1444 -9.23e-03 464s US.Steel_X1936 28.8526 3.26e-01 464s US.Steel_X1937 33.0183 9.62e-01 464s US.Steel_X1938 -28.6886 -2.15e+00 464s US.Steel_X1939 -71.9676 -3.39e+00 464s US.Steel_X1940 -64.6193 -2.88e+00 464s US.Steel_X1941 17.5269 9.83e-01 464s US.Steel_X1942 6.2492 5.85e-01 464s US.Steel_X1943 -17.4030 -1.13e+00 464s US.Steel_X1944 -37.9949 -1.75e+00 464s US.Steel_X1945 -45.1924 -2.47e+00 464s US.Steel_X1946 14.6469 1.20e+00 464s US.Steel_X1947 15.4188 3.33e+00 464s US.Steel_X1948 36.8685 1.04e+01 464s US.Steel_X1949 3.4806 9.60e-01 464s US.Steel_X1950 7.0265 2.06e+00 464s US.Steel_X1951 60.2830 1.51e+01 464s US.Steel_X1952 74.9299 2.18e+01 464s US.Steel_X1953 58.2771 1.66e+01 464s US.Steel_X1954 -56.7511 -2.26e+01 464s Westinghouse_X1935 -1.5111 -1.37e-03 464s Westinghouse_X1936 12.4999 1.41e-01 464s Westinghouse_X1937 24.6178 7.17e-01 464s Westinghouse_X1938 17.5894 1.32e+00 464s Westinghouse_X1939 32.0707 1.51e+00 464s Westinghouse_X1940 25.3645 1.13e+00 464s Westinghouse_X1941 -40.2479 -2.26e+00 464s Westinghouse_X1942 -14.5028 -1.36e+00 464s Westinghouse_X1943 6.4627 4.21e-01 464s Westinghouse_X1944 7.0674 3.25e-01 464s Westinghouse_X1945 21.4937 1.18e+00 464s Westinghouse_X1946 -15.4283 -1.27e+00 464s Westinghouse_X1947 -34.0718 -7.36e+00 464s Westinghouse_X1948 -9.9583 -2.82e+00 464s Westinghouse_X1949 17.5737 4.84e+00 464s Westinghouse_X1950 23.3044 6.82e+00 464s Westinghouse_X1951 -18.5624 -4.64e+00 464s Westinghouse_X1952 -43.8127 -1.27e+01 464s Westinghouse_X1953 -49.4119 -1.41e+01 464s Westinghouse_X1954 29.5158 1.17e+01 464s US.Steel_(Intercept) US.Steel_value US.Steel_capital 464s Chrysler_X1935 -2.96e-03 -4.0379 -0.15945 464s Chrysler_X1936 -4.28e-03 -7.7323 -0.21608 464s Chrysler_X1937 2.53e-03 6.7824 0.29930 464s Chrysler_X1938 -1.84e-03 -3.3128 -0.47838 464s Chrysler_X1939 6.00e-03 11.7430 1.87608 464s Chrysler_X1940 9.52e-04 2.0975 0.24204 464s Chrysler_X1941 4.59e-05 0.1094 0.01201 464s Chrysler_X1942 1.70e-03 3.6889 0.50810 464s Chrysler_X1943 5.81e-03 11.5373 1.75404 464s Chrysler_X1944 3.22e-03 5.8493 0.90002 464s Chrysler_X1945 -4.96e-03 -9.1744 -1.06014 464s Chrysler_X1946 5.80e-03 12.0014 1.35006 464s Chrysler_X1947 3.14e-03 5.6424 0.83159 464s Chrysler_X1948 -2.58e-03 -4.2007 -0.79297 464s Chrysler_X1949 3.18e-03 5.2997 1.11622 464s Chrysler_X1950 -6.20e-04 -1.0401 -0.22186 464s Chrysler_X1951 -1.62e-02 -37.1002 -5.54355 464s Chrysler_X1952 -1.69e-03 -3.6411 -0.74900 464s Chrysler_X1953 7.94e-04 1.6124 0.49499 464s Chrysler_X1954 1.95e-03 4.1188 1.30389 464s General.Electric_X1935 1.69e-05 0.0230 0.00091 464s General.Electric_X1936 4.00e-04 0.7222 0.02018 464s General.Electric_X1937 3.80e-04 1.0168 0.04487 464s General.Electric_X1938 5.50e-04 0.9917 0.14321 464s General.Electric_X1939 6.76e-04 1.3230 0.21136 464s General.Electric_X1940 1.32e-04 0.2914 0.03362 464s General.Electric_X1941 -7.13e-04 -1.6972 -0.18636 464s General.Electric_X1942 -3.27e-04 -0.7084 -0.09757 464s General.Electric_X1943 4.36e-04 0.8656 0.13161 464s General.Electric_X1944 4.80e-04 0.8711 0.13403 464s General.Electric_X1945 1.18e-05 0.0218 0.00251 464s General.Electric_X1946 -1.02e-03 -2.1149 -0.23791 464s General.Electric_X1947 -9.00e-04 -1.6172 -0.23835 464s General.Electric_X1948 -7.07e-04 -1.1496 -0.21701 464s General.Electric_X1949 2.53e-04 0.4211 0.08869 464s General.Electric_X1950 5.38e-04 0.9023 0.19248 464s General.Electric_X1951 -3.73e-05 -0.0854 -0.01276 464s General.Electric_X1952 -1.35e-04 -0.2914 -0.05995 464s General.Electric_X1953 -1.65e-04 -0.3353 -0.10293 464s General.Electric_X1954 1.33e-04 0.2820 0.08929 464s General.Motors_X1935 1.01e-02 13.7309 0.54222 464s General.Motors_X1936 -2.58e-03 -4.6683 -0.13046 464s General.Motors_X1937 -1.27e-02 -34.0295 -1.50166 464s General.Motors_X1938 3.32e-04 0.5977 0.08631 464s General.Motors_X1939 -9.75e-03 -19.0765 -3.04769 464s General.Motors_X1940 -1.43e-03 -3.1463 -0.36306 464s General.Motors_X1941 2.64e-03 6.2893 0.69062 464s General.Motors_X1942 9.64e-03 20.9002 2.87877 464s General.Motors_X1943 6.76e-03 13.4247 2.04099 464s General.Motors_X1944 9.81e-03 17.7857 2.73663 464s General.Motors_X1945 3.63e-03 6.7092 0.77528 464s General.Motors_X1946 9.90e-03 20.4619 2.30180 464s General.Motors_X1947 1.39e-03 2.4966 0.36796 464s General.Motors_X1948 -5.01e-03 -8.1431 -1.53716 464s General.Motors_X1949 -1.11e-02 -18.4924 -3.89482 464s General.Motors_X1950 -6.34e-03 -10.6327 -2.26803 464s General.Motors_X1951 -1.18e-02 -27.0005 -4.03445 464s General.Motors_X1952 -8.16e-03 -17.6138 -3.62324 464s General.Motors_X1953 3.21e-03 6.5289 2.00435 464s General.Motors_X1954 1.15e-02 24.2859 7.68815 464s US.Steel_X1935 -8.99e-03 -12.2508 -0.48377 464s US.Steel_X1936 1.69e-02 30.5206 0.85291 464s US.Steel_X1937 1.67e-02 44.7615 1.97524 464s US.Steel_X1938 -2.80e-02 -50.5201 -7.29526 464s US.Steel_X1939 -4.55e-02 -89.1179 -14.23756 464s US.Steel_X1940 -3.80e-02 -83.6458 -9.65217 464s US.Steel_X1941 1.05e-02 25.0160 2.74698 464s US.Steel_X1942 5.26e-03 11.3993 1.57013 464s US.Steel_X1943 -1.17e-02 -23.2554 -3.53559 464s US.Steel_X1944 -2.37e-02 -42.9442 -6.60771 464s US.Steel_X1945 -2.55e-02 -47.1333 -5.44650 464s US.Steel_X1946 8.16e-03 16.8627 1.89692 464s US.Steel_X1947 1.19e-02 21.4365 3.15933 464s US.Steel_X1948 3.09e-02 50.2553 9.48663 464s US.Steel_X1949 2.57e-03 4.2789 0.90121 464s US.Steel_X1950 5.11e-03 8.5638 1.82670 464s US.Steel_X1951 3.40e-02 77.9272 11.64398 464s US.Steel_X1952 4.15e-02 89.6523 18.44196 464s US.Steel_X1953 2.55e-02 51.7535 15.88809 464s US.Steel_X1954 -2.77e-02 -58.5688 -18.54102 464s Westinghouse_X1935 -1.36e-03 -1.8578 -0.07336 464s Westinghouse_X1936 7.45e-03 13.4613 0.37618 464s Westinghouse_X1937 1.27e-02 33.9762 1.49930 464s Westinghouse_X1938 1.75e-02 31.5341 4.55362 464s Westinghouse_X1939 2.07e-02 40.4306 6.45923 464s Westinghouse_X1940 1.52e-02 33.4258 3.85712 464s Westinghouse_X1941 -2.46e-02 -58.4830 -6.42196 464s Westinghouse_X1942 -1.24e-02 -26.9329 -3.70970 464s Westinghouse_X1943 4.43e-03 8.7920 1.33667 464s Westinghouse_X1944 4.48e-03 8.1323 1.25129 464s Westinghouse_X1945 1.23e-02 22.8217 2.63717 464s Westinghouse_X1946 -8.75e-03 -18.0831 -2.03421 464s Westinghouse_X1947 -2.68e-02 -48.2250 -7.10746 464s Westinghouse_X1948 -8.50e-03 -13.8193 -2.60865 464s Westinghouse_X1949 1.32e-02 21.9947 4.63248 464s Westinghouse_X1950 1.72e-02 28.9161 6.16798 464s Westinghouse_X1951 -1.07e-02 -24.4289 -3.65019 464s Westinghouse_X1952 -2.47e-02 -53.3679 -10.97807 464s Westinghouse_X1953 -2.20e-02 -44.6732 -13.71448 464s Westinghouse_X1954 1.47e-02 31.0114 9.81721 464s Westinghouse_(Intercept) Westinghouse_value 464s Chrysler_X1935 -5.65e-03 -1.082 464s Chrysler_X1936 -8.16e-03 -4.208 464s Chrysler_X1937 4.83e-03 3.521 464s Chrysler_X1938 -3.50e-03 -1.964 464s Chrysler_X1939 1.14e-02 5.945 464s Chrysler_X1940 1.81e-03 1.141 464s Chrysler_X1941 8.76e-05 0.047 464s Chrysler_X1942 3.24e-03 1.819 464s Chrysler_X1943 1.11e-02 6.837 464s Chrysler_X1944 6.15e-03 3.852 464s Chrysler_X1945 -9.45e-03 -6.967 464s Chrysler_X1946 1.11e-02 8.413 464s Chrysler_X1947 5.99e-03 3.480 464s Chrysler_X1948 -4.92e-03 -3.262 464s Chrysler_X1949 6.06e-03 3.537 464s Chrysler_X1950 -1.18e-03 -0.751 464s Chrysler_X1951 -3.09e-02 -22.354 464s Chrysler_X1952 -3.21e-03 -2.777 464s Chrysler_X1953 1.51e-03 1.806 464s Chrysler_X1954 3.71e-03 4.412 464s General.Electric_X1935 6.17e-03 1.182 464s General.Electric_X1936 1.46e-01 75.280 464s General.Electric_X1937 1.39e-01 101.111 464s General.Electric_X1938 2.01e-01 112.591 464s General.Electric_X1939 2.47e-01 128.281 464s General.Electric_X1940 4.83e-02 30.346 464s General.Electric_X1941 -2.60e-01 -139.785 464s General.Electric_X1942 -1.19e-01 -66.920 464s General.Electric_X1943 1.59e-01 98.251 464s General.Electric_X1944 1.75e-01 109.867 464s General.Electric_X1945 4.29e-03 3.165 464s General.Electric_X1946 -3.73e-01 -283.963 464s General.Electric_X1947 -3.29e-01 -191.038 464s General.Electric_X1948 -2.58e-01 -170.961 464s General.Electric_X1949 9.22e-02 53.834 464s General.Electric_X1950 1.96e-01 124.738 464s General.Electric_X1951 -1.36e-02 -9.856 464s General.Electric_X1952 -4.93e-02 -42.572 464s General.Electric_X1953 -6.03e-02 -71.913 464s General.Electric_X1954 4.87e-02 57.863 464s General.Motors_X1935 -6.24e-02 -11.950 464s General.Motors_X1936 1.60e-02 8.253 464s General.Motors_X1937 7.87e-02 57.392 464s General.Motors_X1938 -2.05e-03 -1.151 464s General.Motors_X1939 6.03e-02 31.373 464s General.Motors_X1940 8.84e-03 5.558 464s General.Motors_X1941 -1.64e-02 -8.786 464s General.Motors_X1942 -5.97e-02 -33.488 464s General.Motors_X1943 -4.19e-02 -25.843 464s General.Motors_X1944 -6.07e-02 -38.047 464s General.Motors_X1945 -2.25e-02 -16.552 464s General.Motors_X1946 -6.13e-02 -46.597 464s General.Motors_X1947 -8.60e-03 -5.002 464s General.Motors_X1948 3.10e-02 20.539 464s General.Motors_X1949 6.87e-02 40.098 464s General.Motors_X1950 3.92e-02 24.930 464s General.Motors_X1951 7.30e-02 52.851 464s General.Motors_X1952 5.05e-02 43.640 464s General.Motors_X1953 -1.99e-02 -23.751 464s General.Motors_X1954 -7.11e-02 -84.506 464s US.Steel_X1935 5.67e-02 10.854 464s US.Steel_X1936 -1.06e-01 -54.933 464s US.Steel_X1937 -1.05e-01 -76.855 464s US.Steel_X1938 1.77e-01 99.039 464s US.Steel_X1939 2.87e-01 149.211 464s US.Steel_X1940 2.39e-01 150.428 464s US.Steel_X1941 -6.62e-02 -35.578 464s US.Steel_X1942 -3.31e-02 -18.595 464s US.Steel_X1943 7.38e-02 45.577 464s US.Steel_X1944 1.49e-01 93.525 464s US.Steel_X1945 1.61e-01 118.378 464s US.Steel_X1946 -5.14e-02 -39.094 464s US.Steel_X1947 -7.52e-02 -43.725 464s US.Steel_X1948 -1.95e-01 -129.046 464s US.Steel_X1949 -1.62e-02 -9.446 464s US.Steel_X1950 -3.22e-02 -20.441 464s US.Steel_X1951 -2.15e-01 -155.289 464s US.Steel_X1952 -2.62e-01 -226.135 464s US.Steel_X1953 -1.61e-01 -191.674 464s US.Steel_X1954 1.75e-01 207.479 464s Westinghouse_X1935 3.03e-02 5.802 464s Westinghouse_X1936 -1.66e-01 -85.410 464s Westinghouse_X1937 -2.82e-01 -205.647 464s Westinghouse_X1938 -3.89e-01 -217.923 464s Westinghouse_X1939 -4.59e-01 -238.632 464s Westinghouse_X1940 -3.37e-01 -211.909 464s Westinghouse_X1941 5.46e-01 293.206 464s Westinghouse_X1942 2.76e-01 154.873 464s Westinghouse_X1943 -9.84e-02 -60.742 464s Westinghouse_X1944 -9.96e-02 -62.433 464s Westinghouse_X1945 -2.74e-01 -202.055 464s Westinghouse_X1946 1.94e-01 147.788 464s Westinghouse_X1947 5.96e-01 346.758 464s Westinghouse_X1948 1.89e-01 125.092 464s Westinghouse_X1949 -2.93e-01 -171.160 464s Westinghouse_X1950 -3.83e-01 -243.315 464s Westinghouse_X1951 2.37e-01 171.608 464s Westinghouse_X1952 5.49e-01 474.533 464s Westinghouse_X1953 4.89e-01 583.245 464s Westinghouse_X1954 -3.26e-01 -387.265 464s Westinghouse_capital 464s Chrysler_X1935 -0.01017 464s Chrysler_X1936 -0.00652 464s Chrysler_X1937 0.03574 464s Chrysler_X1938 -0.06342 464s Chrysler_X1939 0.26872 464s Chrysler_X1940 0.04809 464s Chrysler_X1941 0.00317 464s Chrysler_X1942 0.19712 464s Chrysler_X1943 0.93491 464s Chrysler_X1944 0.56053 464s Chrysler_X1945 -0.87325 464s Chrysler_X1946 0.95137 464s Chrysler_X1947 0.66499 464s Chrysler_X1948 -0.64315 464s Chrysler_X1949 0.85922 464s Chrysler_X1950 -0.16155 464s Chrysler_X1951 -4.00575 464s Chrysler_X1952 -0.46760 464s Chrysler_X1953 0.26445 464s Chrysler_X1954 0.79226 464s General.Electric_X1935 0.01111 464s General.Electric_X1936 0.11671 464s General.Electric_X1937 1.02637 464s General.Electric_X1938 3.63650 464s General.Electric_X1939 5.79842 464s General.Electric_X1940 1.27948 464s General.Electric_X1941 -9.42136 464s General.Electric_X1942 -7.25009 464s General.Electric_X1943 13.43544 464s General.Electric_X1944 15.98834 464s General.Electric_X1945 0.39668 464s General.Electric_X1946 -32.11157 464s General.Electric_X1947 -36.50562 464s General.Electric_X1948 -33.71211 464s General.Electric_X1949 13.07588 464s General.Electric_X1950 26.84462 464s General.Electric_X1951 -1.76618 464s General.Electric_X1952 -7.16842 464s General.Electric_X1953 -10.53235 464s General.Electric_X1954 10.39092 464s General.Motors_X1935 -0.11232 464s General.Motors_X1936 0.01280 464s General.Motors_X1937 0.58258 464s General.Motors_X1938 -0.03717 464s General.Motors_X1939 1.41811 464s General.Motors_X1940 0.23434 464s General.Motors_X1941 -0.59216 464s General.Motors_X1942 -3.62807 464s General.Motors_X1943 -3.53399 464s General.Motors_X1944 -5.53672 464s General.Motors_X1945 -2.07456 464s General.Motors_X1946 -5.26934 464s General.Motors_X1947 -0.95586 464s General.Motors_X1948 4.05009 464s General.Motors_X1949 9.73943 464s General.Motors_X1950 5.36510 464s General.Motors_X1951 9.47046 464s General.Motors_X1952 7.34822 464s General.Motors_X1953 -3.47863 464s General.Motors_X1954 -15.17538 464s US.Steel_X1935 0.10202 464s US.Steel_X1936 -0.08517 464s US.Steel_X1937 -0.78015 464s US.Steel_X1938 3.19880 464s US.Steel_X1939 6.74450 464s US.Steel_X1940 6.34264 464s US.Steel_X1941 -2.39791 464s US.Steel_X1942 -2.01455 464s US.Steel_X1943 6.23245 464s US.Steel_X1944 13.61008 464s US.Steel_X1945 14.83734 464s US.Steel_X1946 -4.42093 464s US.Steel_X1947 -8.35538 464s US.Steel_X1948 -25.44676 464s US.Steel_X1949 -2.29427 464s US.Steel_X1950 -4.39917 464s US.Steel_X1951 -27.82678 464s US.Steel_X1952 -38.07729 464s US.Steel_X1953 -28.07253 464s US.Steel_X1954 37.25854 464s Westinghouse_X1935 0.05454 464s Westinghouse_X1936 -0.13242 464s Westinghouse_X1937 -2.08750 464s Westinghouse_X1938 -7.03855 464s Westinghouse_X1939 -10.78640 464s Westinghouse_X1940 -8.93489 464s Westinghouse_X1941 19.76178 464s Westinghouse_X1942 16.77886 464s Westinghouse_X1943 -8.30621 464s Westinghouse_X1944 -9.08553 464s Westinghouse_X1945 -25.32546 464s Westinghouse_X1946 16.71244 464s Westinghouse_X1947 66.26222 464s Westinghouse_X1948 24.66709 464s Westinghouse_X1949 -41.57334 464s Westinghouse_X1950 -52.36326 464s Westinghouse_X1951 30.75091 464s Westinghouse_X1952 79.90351 464s Westinghouse_X1953 85.42211 464s Westinghouse_X1954 -69.54427 464s Chrysler_(Intercept) Chrysler_value 464s 0 0 464s Chrysler_capital General.Electric_(Intercept) 464s 0 0 464s General.Electric_value General.Electric_capital 464s 0 0 464s General.Motors_(Intercept) General.Motors_value 464s 0 0 464s General.Motors_capital US.Steel_(Intercept) 464s 0 0 464s US.Steel_value US.Steel_capital 464s 0 0 464s Westinghouse_(Intercept) Westinghouse_value 464s 0 0 464s Westinghouse_capital 464s 0 464s Error in estfun.systemfit(greeneSurPooled) : 464s returning the estimation function for models with restrictions has not yet been implemented. 464s [1] "Error in estfun.systemfit(greeneSurPooled) : \n returning the estimation function for models with restrictions has not yet been implemented.\n" 464s attr(,"class") 464s [1] "try-error" 464s attr(,"condition") 464s 464s > 464s > ## **************** bread ************************ 464s > if(requireNamespace( 'plm', quietly = TRUE ) ) { 464s + print( bread( theilOls ) ) 464s + 464s + print( bread( theilSur ) ) 464s + 464s + print( bread( greeneOls ) ) 464s + 464s + print( try( bread( greeneOlsPooled ) ) ) 464s + 464s + print( bread( greeneSur ) ) 464s + 464s + print( try( bread( greeneSurPooled ) ) ) 464s + } 464s General.Electric_(Intercept) 464s General.Electric_(Intercept) 50.64496 464s General.Electric_value -0.02323 464s General.Electric_capital -0.00888 464s Westinghouse_(Intercept) 0.00000 464s Westinghouse_value 0.00000 464s Westinghouse_capital 0.00000 464s General.Electric_value General.Electric_capital 464s General.Electric_(Intercept) -2.32e-02 -8.88e-03 464s General.Electric_value 1.25e-05 -2.43e-06 464s General.Electric_capital -2.43e-06 3.40e-05 464s Westinghouse_(Intercept) 0.00e+00 0.00e+00 464s Westinghouse_value 0.00e+00 0.00e+00 464s Westinghouse_capital 0.00e+00 0.00e+00 464s Westinghouse_(Intercept) Westinghouse_value 464s General.Electric_(Intercept) 0.0000 0.00e+00 464s General.Electric_value 0.0000 0.00e+00 464s General.Electric_capital 0.0000 0.00e+00 464s Westinghouse_(Intercept) 24.6366 -4.20e-02 464s Westinghouse_value -0.0420 9.46e-05 464s Westinghouse_capital 0.0648 -2.51e-04 464s Westinghouse_capital 464s General.Electric_(Intercept) 0.000000 464s General.Electric_value 0.000000 464s General.Electric_capital 0.000000 464s Westinghouse_(Intercept) 0.064774 464s Westinghouse_value -0.000251 464s Westinghouse_capital 0.001207 464s General.Electric_(Intercept) General.Electric_value 464s [1,] 29230.95 -13.17064 464s [2,] -13.17 0.00707 464s [3,] -5.85 -0.00136 464s [4,] 5078.50 -2.10754 464s [5,] -9.05 0.00480 464s [6,] 15.70 -0.01299 464s General.Electric_capital Westinghouse_(Intercept) Westinghouse_value 464s [1,] -5.849668 5078.50 -9.047719 464s [2,] -0.001362 -2.11 0.004800 464s [3,] 0.021226 -1.58 -0.000675 465s [4,] -1.584851 1935.63 -3.200900 465s [5,] -0.000675 -3.20 0.007194 465s [6,] 0.023793 4.54 -0.018984 465s Westinghouse_capital 465s [1,] 15.7006 465s [2,] -0.0130 465s [3,] 0.0238 465s [4,] 4.5447 465s [5,] -0.0190 465s [6,] 0.0957 465s Chrysler_(Intercept) Chrysler_value 465s Chrysler_(Intercept) 103.4623 -0.144448 465s Chrysler_value -0.1444 0.000226 465s Chrysler_capital 0.0138 -0.000102 465s General.Electric_(Intercept) 0.0000 0.000000 465s General.Electric_value 0.0000 0.000000 465s General.Electric_capital 0.0000 0.000000 465s General.Motors_(Intercept) 0.0000 0.000000 465s General.Motors_value 0.0000 0.000000 465s General.Motors_capital 0.0000 0.000000 465s US.Steel_(Intercept) 0.0000 0.000000 465s US.Steel_value 0.0000 0.000000 465s US.Steel_capital 0.0000 0.000000 465s Westinghouse_(Intercept) 0.0000 0.000000 465s Westinghouse_value 0.0000 0.000000 465s Westinghouse_capital 0.0000 0.000000 465s Chrysler_capital General.Electric_(Intercept) 465s Chrysler_(Intercept) 0.013776 0.0000 465s Chrysler_value -0.000102 0.0000 465s Chrysler_capital 0.000471 0.0000 465s General.Electric_(Intercept) 0.000000 126.6124 465s General.Electric_value 0.000000 -0.0581 465s General.Electric_capital 0.000000 -0.0222 465s General.Motors_(Intercept) 0.000000 0.0000 465s General.Motors_value 0.000000 0.0000 465s General.Motors_capital 0.000000 0.0000 465s US.Steel_(Intercept) 0.000000 0.0000 465s US.Steel_value 0.000000 0.0000 465s US.Steel_capital 0.000000 0.0000 465s Westinghouse_(Intercept) 0.000000 0.0000 465s Westinghouse_value 0.000000 0.0000 465s Westinghouse_capital 0.000000 0.0000 465s General.Electric_value General.Electric_capital 465s Chrysler_(Intercept) 0.00e+00 0.00e+00 465s Chrysler_value 0.00e+00 0.00e+00 465s Chrysler_capital 0.00e+00 0.00e+00 465s General.Electric_(Intercept) -5.81e-02 -2.22e-02 465s General.Electric_value 3.12e-05 -6.09e-06 465s General.Electric_capital -6.09e-06 8.50e-05 465s General.Motors_(Intercept) 0.00e+00 0.00e+00 465s General.Motors_value 0.00e+00 0.00e+00 465s General.Motors_capital 0.00e+00 0.00e+00 465s US.Steel_(Intercept) 0.00e+00 0.00e+00 465s US.Steel_value 0.00e+00 0.00e+00 465s US.Steel_capital 0.00e+00 0.00e+00 465s Westinghouse_(Intercept) 0.00e+00 0.00e+00 465s Westinghouse_value 0.00e+00 0.00e+00 465s Westinghouse_capital 0.00e+00 0.00e+00 465s General.Motors_(Intercept) General.Motors_value 465s Chrysler_(Intercept) 0.0000 0.00e+00 465s Chrysler_value 0.0000 0.00e+00 465s Chrysler_capital 0.0000 0.00e+00 465s General.Electric_(Intercept) 0.0000 0.00e+00 465s General.Electric_value 0.0000 0.00e+00 465s General.Electric_capital 0.0000 0.00e+00 465s General.Motors_(Intercept) 132.9858 -3.11e-02 465s General.Motors_value -0.0311 7.92e-06 465s General.Motors_capital 0.0108 -4.93e-06 465s US.Steel_(Intercept) 0.0000 0.00e+00 465s US.Steel_value 0.0000 0.00e+00 465s US.Steel_capital 0.0000 0.00e+00 465s Westinghouse_(Intercept) 0.0000 0.00e+00 465s Westinghouse_value 0.0000 0.00e+00 465s Westinghouse_capital 0.0000 0.00e+00 465s General.Motors_capital US.Steel_(Intercept) 465s Chrysler_(Intercept) 0.00e+00 0.0000 465s Chrysler_value 0.00e+00 0.0000 465s Chrysler_capital 0.00e+00 0.0000 465s General.Electric_(Intercept) 0.00e+00 0.0000 465s General.Electric_value 0.00e+00 0.0000 465s General.Electric_capital 0.00e+00 0.0000 465s General.Motors_(Intercept) 1.08e-02 0.0000 465s General.Motors_value -4.93e-06 0.0000 465s General.Motors_capital 1.63e-05 0.0000 465s US.Steel_(Intercept) 0.00e+00 235.6498 465s US.Steel_value 0.00e+00 -0.1119 465s US.Steel_capital 0.00e+00 -0.0336 465s Westinghouse_(Intercept) 0.00e+00 0.0000 465s Westinghouse_value 0.00e+00 0.0000 465s Westinghouse_capital 0.00e+00 0.0000 465s US.Steel_value US.Steel_capital 465s Chrysler_(Intercept) 0.00e+00 0.00e+00 465s Chrysler_value 0.00e+00 0.00e+00 465s Chrysler_capital 0.00e+00 0.00e+00 465s General.Electric_(Intercept) 0.00e+00 0.00e+00 465s General.Electric_value 0.00e+00 0.00e+00 465s General.Electric_capital 0.00e+00 0.00e+00 465s General.Motors_(Intercept) 0.00e+00 0.00e+00 465s General.Motors_value 0.00e+00 0.00e+00 465s General.Motors_capital 0.00e+00 0.00e+00 465s US.Steel_(Intercept) -1.12e-01 -3.36e-02 465s US.Steel_value 5.95e-05 -1.79e-05 465s US.Steel_capital -1.79e-05 2.30e-04 465s Westinghouse_(Intercept) 0.00e+00 0.00e+00 465s Westinghouse_value 0.00e+00 0.00e+00 465s Westinghouse_capital 0.00e+00 0.00e+00 465s Westinghouse_(Intercept) Westinghouse_value 465s Chrysler_(Intercept) 0.000 0.000000 465s Chrysler_value 0.000 0.000000 465s Chrysler_capital 0.000 0.000000 465s General.Electric_(Intercept) 0.000 0.000000 465s General.Electric_value 0.000 0.000000 465s General.Electric_capital 0.000 0.000000 465s General.Motors_(Intercept) 0.000 0.000000 465s General.Motors_value 0.000 0.000000 465s General.Motors_capital 0.000 0.000000 465s US.Steel_(Intercept) 0.000 0.000000 465s US.Steel_value 0.000 0.000000 465s US.Steel_capital 0.000 0.000000 465s Westinghouse_(Intercept) 61.592 -0.105021 465s Westinghouse_value -0.105 0.000237 465s Westinghouse_capital 0.162 -0.000626 465s Westinghouse_capital 465s Chrysler_(Intercept) 0.000000 465s Chrysler_value 0.000000 465s Chrysler_capital 0.000000 465s General.Electric_(Intercept) 0.000000 465s General.Electric_value 0.000000 465s General.Electric_capital 0.000000 465s General.Motors_(Intercept) 0.000000 465s General.Motors_value 0.000000 465s General.Motors_capital 0.000000 465s US.Steel_(Intercept) 0.000000 465s US.Steel_value 0.000000 465s US.Steel_capital 0.000000 465s Westinghouse_(Intercept) 0.161935 465s Westinghouse_value -0.000626 465s Westinghouse_capital 0.003017 465s [1] "Error in bread.systemfit(greeneOlsPooled) : \n returning the 'bread' for models with restrictions has not yet been implemented.\n" 465s attr(,"class") 465s [1] "try-error" 465s attr(,"condition") 465s 465s Chrysler_(Intercept) Chrysler_value Chrysler_capital 465s [1,] 1.33e+04 -1.82e+01 9.57e-01 465s [2,] -1.82e+01 2.86e-02 -1.31e-02 465s [3,] 9.57e-01 -1.31e-02 6.69e-02 465s [4,] -2.94e+03 3.74e+00 1.98e+00 465s [5,] 1.28e+00 -1.86e-03 1.28e-04 465s [6,] 8.80e-01 -2.96e-04 -5.56e-03 465s [7,] -1.56e+04 1.91e+01 7.79e+00 465s [8,] 3.28e+00Error in bread.systemfit(greeneOlsPooled) : 465s returning the 'bread' for models with restrictions has not yet been implemented. 465s -4.91e-03 1.03e-03 465s [9,] -8.18e-02 3.42e-03 -1.89e-02 465s [10,] 1.80e+04 -1.87e+01 -2.45e+01 465s [11,] -7.46e+00 1.13e-02 -3.26e-03 465s [12,] -4.03e+00 -1.22e-02 1.03e-01 465s [13,] -3.04e+01 3.03e-01 -9.35e-01 465s [14,] 1.14e-01 -3.70e-04 1.18e-03 465s [15,] 2.42e-01 -6.41e-04 1.67e-03 465s General.Electric_(Intercept) General.Electric_value 465s [1,] -2936.42 1.28e+00 465s [2,] 3.74 -1.86e-03 465s [3,] 1.98 1.28e-04 465s [4,] 65119.82 -2.85e+01 465s [5,] -28.51 1.50e-02 465s [6,] -16.15 -1.70e-03 465s [7,] 57134.02 -2.61e+01 465s [8,] -11.96 6.35e-03 465s [9,] -3.52 -2.27e-03 465s [10,] 64429.20 -3.04e+01 465s [11,] -22.01 1.35e-02 465s [12,] -55.05 1.23e-02 465s [13,] 10286.79 -4.02e+00 465s [14,] -17.00 8.74e-03 465s [15,] 23.38 -2.16e-02 465s General.Electric_capital General.Motors_(Intercept) General.Motors_value 465s [1,] 8.80e-01 -1.56e+04 3.28e+00 465s [2,] -2.96e-04 1.91e+01 -4.91e-03 465s [3,] -5.56e-03 7.79e+00 1.03e-03 465s [4,] -1.61e+01 5.71e+04 -1.20e+01 465s [5,] -1.70e-03 -2.61e+01 6.35e-03 465s [6,] 4.86e-02 -8.74e+00 -9.49e-04 465s [7,] -8.74e+00 8.00e+05 -1.84e+02 465s [8,] -9.49e-04 -1.84e+02 4.68e-02 465s [9,] 1.98e-02 5.32e+01 -2.83e-02 465s [10,] -2.30e+00 -1.75e+05 3.73e+01 465s [11,] -1.07e-02 8.02e+01 -2.06e-02 465s [12,] 7.77e-02 2.01e+01 1.09e-02 465s [13,] -4.02e+00 1.10e+04 -2.33e+00 465s [14,] 1.04e-04 -2.06e+01 5.10e-03 465s [15,] 4.61e-02 3.98e+01 -1.28e-02 465s General.Motors_capital US.Steel_(Intercept) US.Steel_value 465s [1,] -0.08183 1.80e+04 -7.46e+00 465s [2,] 0.00342 -1.87e+01 1.13e-02 465s [3,] -0.01889 -2.45e+01 -3.26e-03 465s [4,] -3.51957 6.44e+04 -2.20e+01 465s [5,] -0.00227 -3.04e+01 1.35e-02 465s [6,] 0.01982 -2.30e+00 -1.07e-02 465s [7,] 53.22544 -1.75e+05 8.02e+01 465s [8,] -0.02835 3.73e+01 -2.06e-02 465s [9,] 0.10737 3.74e+00 1.39e-02 465s [10,] 3.74276 1.25e+06 -5.65e+02 465s [11,] 0.01386 -5.65e+02 3.00e-01 465s [12,] -0.10360 -3.12e+02 -9.01e-02 465s [13,] -0.48733 2.74e+04 -8.35e+00 465s [14,] -0.00238 -5.09e+01 2.23e-02 465s [15,] 0.02432 1.10e+02 -7.74e-02 465s US.Steel_capital Westinghouse_(Intercept) Westinghouse_value 465s [1,] -4.0281 -30.387 1.14e-01 465s [2,] -0.0122 0.303 -3.70e-04 465s [3,] 0.1031 -0.935 1.18e-03 465s [4,] -55.0482 10286.790 -1.70e+01 465s [5,] 0.0123 -4.016 8.74e-03 465s [6,] 0.0777 -4.021 1.04e-04 465s [7,] 20.0945 11026.166 -2.06e+01 465s [8,] 0.0109 -2.326 5.10e-03 465s [9,] -0.1036 -0.487 -2.38e-03 465s [10,] -311.9830 27440.848 -5.09e+01 465s [11,] -0.0901 -8.348 2.23e-02 465s [12,] 1.6331 -27.510 2.29e-02 465s [13,] -27.5101 3917.263 -5.99e+00 465s [14,] 0.0229 -5.992 1.29e-02 465s [15,] 0.1422 6.376 -3.12e-02 465s Westinghouse_capital 465s [1,] 2.42e-01 465s [2,] -6.41e-04 465s [3,] 1.67e-03 465s [4,] 2.34e+01 465s [5,] -2.16e-02 465s [6,] 4.61e-02 465s [7,] 3.98e+01 465s [8,] -1.28e-02 465s [9,] 2.43e-02 465s [10,] 1.10e+02 465s [11,] -7.74e-02 465s [12,] 1.42e-01 465s [13,] 6.38e+00 465s [14,] -3.12e-02 465s [15,] 1.70e-01 465s [1] "Error in bread.systemfit(greeneSurPooled) : \n returning the 'bread' for models with restrictions has not yet been implemented.\n"Error in bread.systemfit(greeneSurPooled) : 465s returning the 'bread' for models with restrictions has not yet been implemented. 465s Loading required package: Matrix 465s 465s attr(,"class") 465s [1] "try-error" 465s attr(,"condition") 465s 465s > 465s BEGIN TEST test_sur.R 465s 465s R version 4.3.3 (2024-02-29) -- "Angel Food Cake" 465s Copyright (C) 2024 The R Foundation for Statistical Computing 465s Platform: s390x-ibm-linux-gnu (64-bit) 465s 465s R is free software and comes with ABSOLUTELY NO WARRANTY. 465s You are welcome to redistribute it under certain conditions. 465s Type 'license()' or 'licence()' for distribution details. 465s 465s R is a collaborative project with many contributors. 465s Type 'contributors()' for more information and 465s 'citation()' on how to cite R or R packages in publications. 465s 465s Type 'demo()' for some demos, 'help()' for on-line help, or 465s 'help.start()' for an HTML browser interface to help. 465s Type 'q()' to quit R. 465s 465s > library( systemfit ) 466s Loading required package: car 466s Loading required package: carData 466s Loading required package: lmtest 466s Loading required package: zoo 466s 466s Attaching package: ‘zoo’ 466s 466s The following objects are masked from ‘package:base’: 466s 466s as.Date, as.Date.numeric 466s 466s 466s Please cite the 'systemfit' package as: 466s 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/. 466s 466s If you have questions, suggestions, or comments regarding the 'systemfit' package, please use a forum or 'tracker' at systemfit's R-Forge site: 466s https://r-forge.r-project.org/projects/systemfit/ 466s > options( digits = 3 ) 466s > 466s > data( "Kmenta" ) 466s > useMatrix <- FALSE 466s > 466s > demand <- consump ~ price + income 466s > supply <- consump ~ price + farmPrice + trend 466s > system <- list( demand = demand, supply = supply ) 466s > restrm <- matrix(0,1,7) # restriction matrix "R" 466s > restrm[1,3] <- 1 466s > restrm[1,7] <- -1 466s > restrict <- "demand_income - supply_trend = 0" 466s > restr2m <- matrix(0,2,7) # restriction matrix "R" 2 466s > restr2m[1,3] <- 1 466s > restr2m[1,7] <- -1 466s > restr2m[2,2] <- -1 466s > restr2m[2,5] <- 1 466s > restr2q <- c( 0, 0.5 ) # restriction vector "q" 2 466s > restrict2 <- c( "demand_income - supply_trend = 0", 466s + "- demand_price + supply_price = 0.5" ) 466s > restrict2i <- c( "demand_income - supply_trend = 0", 466s + "- demand_price + supply_income = 0.5" ) 466s > tc <- matrix(0,7,6) 466s > tc[1,1] <- 1 466s > tc[2,2] <- 1 466s > tc[3,3] <- 1 466s > tc[4,4] <- 1 466s > tc[5,5] <- 1 466s > tc[6,6] <- 1 466s > tc[7,3] <- 1 466s > restr3m <- matrix(0,1,6) # restriction matrix "R" 2 466s > restr3m[1,2] <- -1 466s > restr3m[1,5] <- 1 466s > restr3q <- c( 0.5 ) # restriction vector "q" 2 466s > restrict3 <- "- C2 + C5 = 0.5" 466s > 466s > # the standard equations do not converge and lead to a singular weighting matrix 466s > # both in R and in EViews, since both equations have the same endogenous variable 466s > supply2 <- price ~ income + farmPrice + trend 466s > system2 <- list( demand = demand, supply = supply2 ) 466s > 466s > 466s > ## *************** SUR estimation ************************ 466s > fitsur1 <- systemfit( system, "SUR", data = Kmenta, useMatrix = useMatrix ) 466s > print( summary( fitsur1 ) ) 466s 466s systemfit results 466s method: SUR 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 33 170 0.879 0.683 0.789 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 65.7 3.86 1.97 0.755 0.726 466s supply 20 16 104.1 6.50 2.55 0.612 0.539 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.73 4.14 466s supply 4.14 5.78 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.86 4.92 466s supply 4.92 6.50 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.982 466s supply 0.982 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 99.3329 7.5145 13.22 2.3e-10 *** 466s price -0.2755 0.0885 -3.11 0.0063 ** 466s income 0.2986 0.0419 7.12 1.7e-06 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.966 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 65.683 MSE: 3.864 Root MSE: 1.966 466s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: consump ~ price + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 61.9662 11.0808 5.59 4.0e-05 *** 466s price 0.1469 0.0944 1.56 0.13941 466s farmPrice 0.2140 0.0399 5.37 6.3e-05 *** 466s trend 0.3393 0.0679 5.00 0.00013 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.55 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 104.058 MSE: 6.504 Root MSE: 2.55 466s Multiple R-Squared: 0.612 Adjusted R-Squared: 0.539 466s 466s > nobs( fitsur1 ) 466s [1] 40 466s > 466s > ## ********************* SUR (EViews-like) ***************** 466s > fitsur1e <- systemfit( system, "SUR", data = Kmenta, methodResidCov = "noDfCor", 466s + useMatrix = useMatrix ) 466s > print( summary( fitsur1e, useDfSys = TRUE ) ) 466s 466s systemfit results 466s method: SUR 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 33 170 0.598 0.683 0.748 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 66.2 3.89 1.97 0.753 0.724 466s supply 20 16 103.5 6.47 2.54 0.614 0.541 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.17 3.41 466s supply 3.41 4.63 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.31 4.07 466s supply 4.07 5.18 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.982 466s supply 0.982 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 99.2757 6.9280 14.33 8.9e-16 *** 466s price -0.2713 0.0816 -3.33 0.0022 ** 466s income 0.2949 0.0387 7.63 8.9e-09 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.973 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 66.186 MSE: 3.893 Root MSE: 1.973 466s Multiple R-Squared: 0.753 Adjusted R-Squared: 0.724 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: consump ~ price + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 62.2942 9.9110 6.29 4.2e-07 *** 466s price 0.1461 0.0845 1.73 0.093 . 466s farmPrice 0.2121 0.0357 5.95 1.1e-06 *** 466s trend 0.3322 0.0607 5.47 4.6e-06 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.544 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 103.55 MSE: 6.472 Root MSE: 2.544 466s Multiple R-Squared: 0.614 Adjusted R-Squared: 0.541 466s 466s > nobs( fitsur1e ) 466s [1] 40 466s > 466s > ## ********************* SUR (methodResidCov="Theil") ***************** 466s > fitsur1r2 <- systemfit( system, "SUR", data = Kmenta, methodResidCov = "Theil", 466s + useMatrix = useMatrix ) 466s > print( summary( fitsur1r2 ) ) 466s 466s systemfit results 466s method: SUR 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 33 172 -0.896 0.679 1.01 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 66.8 3.93 1.98 0.751 0.722 466s supply 20 16 105.3 6.58 2.57 0.607 0.534 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.73 4.28 466s supply 4.28 5.78 466s 466s warning: this covariance matrix is NOT positive semidefinit! 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.93 5.17 466s supply 5.17 6.58 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.984 466s supply 0.984 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 99.2120 7.5127 13.21 2.3e-10 *** 466s price -0.2667 0.0877 -3.04 0.0074 ** 466s income 0.2908 0.0406 7.16 1.6e-06 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.982 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 66.802 MSE: 3.93 Root MSE: 1.982 466s Multiple R-Squared: 0.751 Adjusted R-Squared: 0.722 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: consump ~ price + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 63.0768 10.9735 5.75 3.0e-05 *** 466s price 0.1439 0.0943 1.52 0.15 466s farmPrice 0.2064 0.0384 5.37 6.2e-05 *** 466s trend 0.3325 0.0640 5.19 8.9e-05 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.566 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 105.322 MSE: 6.583 Root MSE: 2.566 466s Multiple R-Squared: 0.607 Adjusted R-Squared: 0.534 466s 466s > 466s > ## *************** SUR (methodResidCov="Theil", useDfSys = TRUE ) *************** 466s > fitsur1e2 <- systemfit( system, "SUR", data = Kmenta, methodResidCov = "Theil", 466s + x = TRUE, useMatrix = useMatrix ) 466s > print( summary( fitsur1e2, useDfSys = TRUE ) ) 466s 466s systemfit results 466s method: SUR 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 33 172 -0.896 0.679 1.01 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 66.8 3.93 1.98 0.751 0.722 466s supply 20 16 105.3 6.58 2.57 0.607 0.534 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.73 4.28 466s supply 4.28 5.78 466s 466s warning: this covariance matrix is NOT positive semidefinit! 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.93 5.17 466s supply 5.17 6.58 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.984 466s supply 0.984 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 99.2120 7.5127 13.21 1.0e-14 *** 466s price -0.2667 0.0877 -3.04 0.0046 ** 466s income 0.2908 0.0406 7.16 3.3e-08 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.982 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 66.802 MSE: 3.93 Root MSE: 1.982 466s Multiple R-Squared: 0.751 Adjusted R-Squared: 0.722 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: consump ~ price + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 63.0768 10.9735 5.75 2.0e-06 *** 466s price 0.1439 0.0943 1.52 0.14 466s farmPrice 0.2064 0.0384 5.37 6.1e-06 *** 466s trend 0.3325 0.0640 5.19 1.0e-05 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.566 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 105.322 MSE: 6.583 Root MSE: 2.566 466s Multiple R-Squared: 0.607 Adjusted R-Squared: 0.534 466s 466s > 466s > ## ********************* SUR (methodResidCov="max") ***************** 466s > fitsur1r3 <- systemfit( system, "SUR", data = Kmenta, methodResidCov = "max", 466s + useMatrix = useMatrix ) 466s > print( summary( fitsur1r3 ) ) 466s 466s systemfit results 466s method: SUR 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 33 172 -0.735 0.68 0.957 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 66.7 3.92 1.98 0.751 0.722 466s supply 20 16 105.2 6.57 2.56 0.608 0.534 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.73 4.26 466s supply 4.26 5.78 466s 466s warning: this covariance matrix is NOT positive semidefinit! 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.92 5.15 466s supply 5.15 6.57 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.984 466s supply 0.984 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 99.2250 7.5129 13.21 2.3e-10 *** 466s price -0.2677 0.0878 -3.05 0.0073 ** 466s income 0.2916 0.0408 7.15 1.6e-06 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.98 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 66.671 MSE: 3.922 Root MSE: 1.98 466s Multiple R-Squared: 0.751 Adjusted R-Squared: 0.722 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: consump ~ price + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 62.9575 10.9850 5.73 3.1e-05 *** 466s price 0.1442 0.0944 1.53 0.15 466s farmPrice 0.2072 0.0386 5.37 6.2e-05 *** 466s trend 0.3333 0.0644 5.18 9.2e-05 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.564 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 105.187 MSE: 6.574 Root MSE: 2.564 466s Multiple R-Squared: 0.608 Adjusted R-Squared: 0.534 466s 466s > 466s > ## *************** WSUR estimation ************************ 466s > fitsur1w <- systemfit( system, "SUR", data = Kmenta, residCovWeighted = TRUE, 466s + useMatrix = useMatrix ) 466s > summary( fitsur1w ) 466s 466s systemfit results 466s method: SUR 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 33 170 0.879 0.683 0.789 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 65.7 3.86 1.97 0.755 0.726 466s supply 20 16 104.1 6.50 2.55 0.612 0.539 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.73 4.14 466s supply 4.14 5.78 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.86 4.92 466s supply 4.92 6.50 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.982 466s supply 0.982 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 99.3329 7.5145 13.22 2.3e-10 *** 466s price -0.2755 0.0885 -3.11 0.0063 ** 466s income 0.2986 0.0419 7.12 1.7e-06 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.966 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 65.683 MSE: 3.864 Root MSE: 1.966 466s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: consump ~ price + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 61.9662 11.0808 5.59 4.0e-05 *** 466s price 0.1469 0.0944 1.56 0.13941 466s farmPrice 0.2140 0.0399 5.37 6.3e-05 *** 466s trend 0.3393 0.0679 5.00 0.00013 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.55 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 104.058 MSE: 6.504 Root MSE: 2.55 466s Multiple R-Squared: 0.612 Adjusted R-Squared: 0.539 466s 466s > nobs( fitsur1w ) 466s [1] 40 466s > 466s > ## *************** WSUR (methodResidCov="Theil", useDfSys = TRUE ) *************** 466s > fitsur1we2 <- systemfit( system, "SUR", data = Kmenta, methodResidCov = "Theil", 466s + residCovWeighted = TRUE, useMatrix = useMatrix ) 466s > summary( fitsur1we2, useDfSys = TRUE ) 466s 466s systemfit results 466s method: SUR 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 33 172 -0.896 0.679 1.01 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 66.8 3.93 1.98 0.751 0.722 466s supply 20 16 105.3 6.58 2.57 0.607 0.534 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.73 4.28 466s supply 4.28 5.78 466s 466s warning: this covariance matrix is NOT positive semidefinit! 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.93 5.17 466s supply 5.17 6.58 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.984 466s supply 0.984 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 99.2120 7.5127 13.21 1.0e-14 *** 466s price -0.2667 0.0877 -3.04 0.0046 ** 466s income 0.2908 0.0406 7.16 3.3e-08 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.982 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 66.802 MSE: 3.93 Root MSE: 1.982 466s Multiple R-Squared: 0.751 Adjusted R-Squared: 0.722 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: consump ~ price + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 63.0768 10.9735 5.75 2.0e-06 *** 466s price 0.1439 0.0943 1.52 0.14 466s farmPrice 0.2064 0.0384 5.37 6.1e-06 *** 466s trend 0.3325 0.0640 5.19 1.0e-05 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.566 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 105.322 MSE: 6.583 Root MSE: 2.566 466s Multiple R-Squared: 0.607 Adjusted R-Squared: 0.534 466s 466s > 466s > 466s > ## *************** SUR with cross-equation restriction ************** 466s > fitsur2 <- systemfit( system, "SUR", data = Kmenta, restrict.matrix = restrm, 466s + useMatrix = useMatrix ) 466s > print( summary( fitsur2 ) ) 466s 466s systemfit results 466s method: SUR 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 34 179 0.933 0.665 0.753 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 71.6 4.21 2.05 0.733 0.702 466s supply 20 16 107.8 6.74 2.60 0.598 0.523 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.78 4.47 466s supply 4.47 5.94 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 4.21 5.24 466s supply 5.24 6.74 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.983 466s supply 0.983 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 98.8408 7.5581 13.08 8.0e-15 *** 466s price -0.2398 0.0860 -2.79 0.0086 ** 466s income 0.2670 0.0368 7.25 2.2e-08 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.052 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 71.597 MSE: 4.212 Root MSE: 2.052 466s Multiple R-Squared: 0.733 Adjusted R-Squared: 0.702 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: consump ~ price + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 67.4283 10.6647 6.32 3.3e-07 *** 466s price 0.1332 0.0953 1.40 0.17 466s farmPrice 0.1795 0.0337 5.33 6.3e-06 *** 466s trend 0.2670 0.0368 7.25 2.2e-08 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.596 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 107.806 MSE: 6.738 Root MSE: 2.596 466s Multiple R-Squared: 0.598 Adjusted R-Squared: 0.523 466s 466s > nobs( fitsur2 ) 466s [1] 40 466s > # the same with symbolically specified restrictions 466s > fitsur2Sym <- systemfit( system, "SUR", data = Kmenta, 466s + restrict.matrix = restrict, useMatrix = useMatrix ) 466s > all.equal( fitsur2, fitsur2Sym ) 466s [1] "Component “call”: target, current do not match when deparsed" 466s > nobs( fitsur2Sym ) 466s [1] 40 466s > 466s > ## *************** SUR with cross-equation restriction (EViews-like) ** 466s > fitsur2e <- systemfit( system, "SUR", data = Kmenta, restrict.matrix = restrm, 466s + methodResidCov = "noDfCor", x = TRUE, 466s + useMatrix = useMatrix ) 466s > print( summary( fitsur2e ) ) 466s 466s systemfit results 466s method: SUR 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 34 180 0.62 0.663 0.707 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 72.6 4.27 2.07 0.729 0.697 466s supply 20 16 107.9 6.75 2.60 0.597 0.522 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.21 3.68 466s supply 3.68 4.75 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.63 4.35 466s supply 4.35 5.40 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.984 466s supply 0.984 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 98.7799 6.9687 14.17 8.9e-16 *** 466s price -0.2354 0.0795 -2.96 0.0056 ** 466s income 0.2631 0.0344 7.66 6.7e-09 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.066 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 72.577 MSE: 4.269 Root MSE: 2.066 466s Multiple R-Squared: 0.729 Adjusted R-Squared: 0.697 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: consump ~ price + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 67.6039 9.5712 7.06 3.7e-08 *** 466s price 0.1328 0.0853 1.56 0.13 466s farmPrice 0.1785 0.0305 5.85 1.3e-06 *** 466s trend 0.2631 0.0344 7.66 6.7e-09 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.597 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 107.917 MSE: 6.745 Root MSE: 2.597 466s Multiple R-Squared: 0.597 Adjusted R-Squared: 0.522 466s 466s > 466s > ## *************** WSUR with cross-equation restriction (EViews-like) ** 466s > fitsur2we <- systemfit( system, "SUR", data = Kmenta, restrict.matrix = restrm, 466s + methodResidCov = "noDfCor", residCovWeighted = TRUE, 466s + x = TRUE, useMatrix = useMatrix ) 466s > summary( fitsur2we ) 466s 466s systemfit results 466s method: SUR 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 34 182 0.609 0.661 0.711 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 73 4.29 2.07 0.728 0.696 466s supply 20 16 109 6.79 2.61 0.595 0.519 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.19 3.69 466s supply 3.69 4.78 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.65 4.38 466s supply 4.38 5.43 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.985 466s supply 0.985 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 98.7542 6.9468 14.22 6.7e-16 *** 466s price -0.2335 0.0790 -2.96 0.0056 ** 466s income 0.2614 0.0338 7.74 5.3e-09 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.072 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 73.009 MSE: 4.295 Root MSE: 2.072 466s Multiple R-Squared: 0.728 Adjusted R-Squared: 0.696 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: consump ~ price + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 67.8882 9.5640 7.10 3.4e-08 *** 466s price 0.1320 0.0855 1.55 0.13 466s farmPrice 0.1765 0.0301 5.86 1.3e-06 *** 466s trend 0.2614 0.0338 7.74 5.3e-09 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.606 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 108.634 MSE: 6.79 Root MSE: 2.606 466s Multiple R-Squared: 0.595 Adjusted R-Squared: 0.519 466s 466s > 466s > 466s > ## *************** SUR with restriction via restrict.regMat ******************* 466s > fitsur3 <- systemfit( system, "SUR", data = Kmenta, restrict.regMat = tc, 466s + useMatrix = useMatrix ) 466s > print( summary( fitsur3 ) ) 466s 466s systemfit results 466s method: SUR 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 34 179 0.933 0.665 0.753 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 71.6 4.21 2.05 0.733 0.702 466s supply 20 16 107.8 6.74 2.60 0.598 0.523 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.78 4.47 466s supply 4.47 5.94 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 4.21 5.24 466s supply 5.24 6.74 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.983 466s supply 0.983 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 98.8408 7.5581 13.08 8.0e-15 *** 466s price -0.2398 0.0860 -2.79 0.0086 ** 466s income 0.2670 0.0368 7.25 2.2e-08 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.052 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 71.597 MSE: 4.212 Root MSE: 2.052 466s Multiple R-Squared: 0.733 Adjusted R-Squared: 0.702 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: consump ~ price + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 67.4283 10.6647 6.32 3.3e-07 *** 466s price 0.1332 0.0953 1.40 0.17 466s farmPrice 0.1795 0.0337 5.33 6.3e-06 *** 466s trend 0.2670 0.0368 7.25 2.2e-08 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.596 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 107.806 MSE: 6.738 Root MSE: 2.596 466s Multiple R-Squared: 0.598 Adjusted R-Squared: 0.523 466s 466s > nobs( fitsur3 ) 466s [1] 40 466s > 466s > ## *************** SUR with restriction via restrict.regMat (EViews-like) ************** 466s > fitsur3e <- systemfit( system, "SUR", data = Kmenta, restrict.regMat = tc, 466s + methodResidCov = "noDfCor", x = TRUE, 466s + useMatrix = useMatrix ) 466s > print( summary( fitsur3e ) ) 466s 466s systemfit results 466s method: SUR 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 34 180 0.62 0.663 0.707 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 72.6 4.27 2.07 0.729 0.697 466s supply 20 16 107.9 6.75 2.60 0.597 0.522 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.21 3.68 466s supply 3.68 4.75 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.63 4.35 466s supply 4.35 5.40 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.984 466s supply 0.984 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 98.7799 6.9687 14.17 8.9e-16 *** 466s price -0.2354 0.0795 -2.96 0.0056 ** 466s income 0.2631 0.0344 7.66 6.7e-09 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.066 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 72.577 MSE: 4.269 Root MSE: 2.066 466s Multiple R-Squared: 0.729 Adjusted R-Squared: 0.697 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: consump ~ price + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 67.6039 9.5712 7.06 3.7e-08 *** 466s price 0.1328 0.0853 1.56 0.13 466s farmPrice 0.1785 0.0305 5.85 1.3e-06 *** 466s trend 0.2631 0.0344 7.66 6.7e-09 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.597 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 107.917 MSE: 6.745 Root MSE: 2.597 466s Multiple R-Squared: 0.597 Adjusted R-Squared: 0.522 466s 466s > 466s > ## *************** WSUR with restriction via restrict.regMat ******************* 466s > fitsur3w <- systemfit( system, "SUR", data = Kmenta, restrict.regMat = tc, 466s + residCovWeighted = TRUE, x = TRUE, useMatrix = useMatrix ) 466s > summary( fitsur3w ) 466s 466s systemfit results 466s method: SUR 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 34 181 0.919 0.663 0.757 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 72 4.24 2.06 0.731 0.700 466s supply 20 16 109 6.79 2.60 0.595 0.519 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.75 4.48 466s supply 4.48 5.98 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 4.24 5.28 466s supply 5.28 6.79 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.984 466s supply 0.984 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 98.8139 7.5317 13.12 7.3e-15 *** 466s price -0.2378 0.0854 -2.79 0.0087 ** 466s income 0.2653 0.0361 7.34 1.7e-08 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.058 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 72.023 MSE: 4.237 Root MSE: 2.058 466s Multiple R-Squared: 0.731 Adjusted R-Squared: 0.7 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: consump ~ price + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 67.7366 10.6556 6.36 3.0e-07 *** 466s price 0.1324 0.0955 1.39 0.17 466s farmPrice 0.1774 0.0332 5.35 6.1e-06 *** 466s trend 0.2653 0.0361 7.34 1.7e-08 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.605 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 108.579 MSE: 6.786 Root MSE: 2.605 466s Multiple R-Squared: 0.595 Adjusted R-Squared: 0.519 466s 466s > 466s > 466s > ## *************** SUR with 2 restrictions *************************** 466s > fitsur4 <- systemfit( system, "SUR", data = Kmenta, restrict.matrix = restr2m, 466s + restrict.rhs = restr2q, useMatrix = useMatrix ) 466s > print( summary( fitsur4 ) ) 466s 466s systemfit results 466s method: SUR 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 35 165 1.76 0.691 0.69 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 64 3.76 1.94 0.761 0.733 466s supply 20 16 101 6.34 2.52 0.622 0.551 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.76 4.46 466s supply 4.46 5.99 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.76 4.70 466s supply 4.70 6.34 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.962 466s supply 0.962 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 96.8275 7.4665 12.97 6.2e-15 *** 466s price -0.2798 0.0840 -3.33 0.002 ** 466s income 0.3286 0.0206 15.93 < 2e-16 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.94 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 63.987 MSE: 3.764 Root MSE: 1.94 466s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: consump ~ price + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 52.9386 7.6655 6.91 5.1e-08 *** 466s price 0.2202 0.0840 2.62 0.013 * 466s farmPrice 0.2327 0.0212 10.97 7.2e-13 *** 466s trend 0.3286 0.0206 15.93 < 2e-16 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.518 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 101.473 MSE: 6.342 Root MSE: 2.518 466s Multiple R-Squared: 0.622 Adjusted R-Squared: 0.551 466s 466s > nobs( fitsur4 ) 466s [1] 40 466s > # the same with symbolically specified restrictions 466s > fitsur4Sym <- systemfit( system, "SUR", data = Kmenta, 466s + restrict.matrix = restrict2, useMatrix = useMatrix ) 466s > all.equal( fitsur4, fitsur4Sym ) 466s [1] "Component “call”: target, current do not match when deparsed" 466s > 466s > ## *************** SUR with 2 restrictions (EViews-like) ************** 466s > fitsur4e <- systemfit( system, "SUR", data = Kmenta, methodResidCov = "noDfCor", 466s + restrict.matrix = restr2m, restrict.rhs = restr2q, useMatrix = useMatrix ) 466s > print( summary( fitsur4e ) ) 466s 466s systemfit results 466s method: SUR 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 35 165 1.2 0.693 0.653 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 63.8 3.75 1.94 0.762 0.734 466s supply 20 16 100.8 6.30 2.51 0.624 0.553 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.20 3.67 466s supply 3.67 4.79 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.19 3.86 466s supply 3.86 5.04 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.962 466s supply 0.962 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 97.2678 6.9200 14.06 4.4e-16 *** 466s price -0.2851 0.0767 -3.72 7e-04 *** 466s income 0.3296 0.0184 17.86 < 2e-16 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.937 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 63.811 MSE: 3.754 Root MSE: 1.937 466s Multiple R-Squared: 0.762 Adjusted R-Squared: 0.734 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: consump ~ price + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 53.3040 7.1045 7.5 8.7e-09 *** 466s price 0.2149 0.0767 2.8 0.0082 ** 466s farmPrice 0.2343 0.0187 12.6 1.6e-14 *** 466s trend 0.3296 0.0184 17.9 < 2e-16 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.51 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 100.835 MSE: 6.302 Root MSE: 2.51 466s Multiple R-Squared: 0.624 Adjusted R-Squared: 0.553 466s 466s > 466s > ## *************** SUR with 2 restrictions (methodResidCov = "Theil") ************** 466s > fitsur4r2 <- systemfit( system, "SUR", data = Kmenta, methodResidCov = "Theil", 466s + restrict.matrix = restr2m, restrict.rhs = restr2q, useMatrix = useMatrix ) 466s > print( summary( fitsur4r2 ) ) 466s 466s systemfit results 466s method: SUR 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 35 175 0.034 0.673 0.708 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 67 3.94 1.99 0.750 0.721 466s supply 20 16 108 6.76 2.60 0.596 0.521 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.76 4.61 466s supply 4.61 5.99 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.94 5.16 466s supply 5.16 6.76 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.967 466s supply 0.967 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 92.5266 7.2896 12.69 1.2e-14 *** 466s price -0.2304 0.0827 -2.79 0.0086 ** 466s income 0.3221 0.0166 19.37 < 2e-16 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.986 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 67.048 MSE: 3.944 Root MSE: 1.986 466s Multiple R-Squared: 0.75 Adjusted R-Squared: 0.721 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: consump ~ price + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 48.7011 7.4034 6.58 1.3e-07 *** 466s price 0.2696 0.0827 3.26 0.0025 ** 466s farmPrice 0.2261 0.0166 13.62 1.6e-15 *** 466s trend 0.3221 0.0166 19.37 < 2e-16 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.601 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 108.217 MSE: 6.764 Root MSE: 2.601 466s Multiple R-Squared: 0.596 Adjusted R-Squared: 0.521 466s 466s > 466s > ## *************** SUR with 2 restrictions (methodResidCov = "max") ************** 466s > fitsur4r3 <- systemfit( system, "SUR", data = Kmenta, methodResidCov = "max", 466s + restrict.matrix = restr2m, restrict.rhs = restr2q, 466s + x = TRUE, useMatrix = useMatrix ) 466s > print( summary( fitsur4r3 ) ) 466s 466s systemfit results 466s method: SUR 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 35 173 0.217 0.677 0.702 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 66.4 3.91 1.98 0.752 0.723 466s supply 20 16 106.9 6.68 2.58 0.601 0.526 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.76 4.59 466s supply 4.59 5.99 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.91 5.09 466s supply 5.09 6.68 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.966 466s supply 0.966 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 93.1978 7.3168 12.74 1.1e-14 *** 466s price -0.2381 0.0829 -2.87 0.0069 ** 466s income 0.3231 0.0170 18.96 < 2e-16 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.976 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 66.405 MSE: 3.906 Root MSE: 1.976 466s Multiple R-Squared: 0.752 Adjusted R-Squared: 0.723 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: consump ~ price + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 49.3676 7.4381 6.64 1.1e-07 *** 466s price 0.2619 0.0829 3.16 0.0033 ** 466s farmPrice 0.2271 0.0171 13.29 3.1e-15 *** 466s trend 0.3231 0.0170 18.96 < 2e-16 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.585 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 106.924 MSE: 6.683 Root MSE: 2.585 466s Multiple R-Squared: 0.601 Adjusted R-Squared: 0.526 466s 466s > 466s > ## *************** WSUR with 2 restrictions (EViews-like) ************** 466s > fitsur4we <- systemfit( system, "SUR", data = Kmenta, methodResidCov = "noDfCor", 466s + restrict.matrix = restr2m, restrict.rhs = restr2q, residCovWeighted = TRUE, 466s + useMatrix = useMatrix ) 466s > summary( fitsur4we ) 466s 466s systemfit results 466s method: SUR 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 35 165 1.2 0.692 0.654 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 63.9 3.76 1.94 0.762 0.733 466s supply 20 16 101.2 6.33 2.52 0.623 0.552 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.18 3.69 466s supply 3.69 4.81 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.20 3.87 466s supply 3.87 5.06 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.962 466s supply 0.962 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 96.9414 6.8894 14.07 4.4e-16 *** 466s price -0.2814 0.0766 -3.67 8e-04 *** 466s income 0.3291 0.0181 18.18 < 2e-16 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.939 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 63.936 MSE: 3.761 Root MSE: 1.939 466s Multiple R-Squared: 0.762 Adjusted R-Squared: 0.733 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: consump ~ price + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 52.9963 7.0652 7.50 8.7e-09 *** 466s price 0.2186 0.0766 2.85 0.0072 ** 466s farmPrice 0.2337 0.0183 12.76 1.0e-14 *** 466s trend 0.3291 0.0181 18.18 < 2e-16 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.515 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 101.201 MSE: 6.325 Root MSE: 2.515 466s Multiple R-Squared: 0.623 Adjusted R-Squared: 0.552 466s 466s > 466s > 466s > ## *************** SUR with 2 restrictions via R and restrict.regMat **************** 466s > fitsur5 <- systemfit( system, "SUR", data = Kmenta, restrict.matrix = restr3m, 466s + restrict.rhs = restr3q, restrict.regMat = tc, 466s + x = TRUE, useMatrix = useMatrix ) 466s > print( summary( fitsur5 ) ) 466s 466s systemfit results 466s method: SUR 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 35 165 1.76 0.691 0.69 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 64 3.76 1.94 0.761 0.733 466s supply 20 16 101 6.34 2.52 0.622 0.551 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.76 4.46 466s supply 4.46 5.99 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.76 4.70 466s supply 4.70 6.34 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.962 466s supply 0.962 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 96.8275 7.4665 12.97 6.2e-15 *** 466s price -0.2798 0.0840 -3.33 0.002 ** 466s income 0.3286 0.0206 15.93 < 2e-16 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.94 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 63.987 MSE: 3.764 Root MSE: 1.94 466s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: consump ~ price + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 52.9386 7.6655 6.91 5.1e-08 *** 466s price 0.2202 0.0840 2.62 0.013 * 466s farmPrice 0.2327 0.0212 10.97 7.2e-13 *** 466s trend 0.3286 0.0206 15.93 < 2e-16 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.518 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 101.473 MSE: 6.342 Root MSE: 2.518 466s Multiple R-Squared: 0.622 Adjusted R-Squared: 0.551 466s 466s > nobs( fitsur5 ) 466s [1] 40 466s > # the same with symbolically specified restrictions 466s > fitsur5Sym <- systemfit( system, "SUR", data = Kmenta, 466s + restrict.matrix = restrict3, restrict.regMat = tc, 466s + x = TRUE, useMatrix = useMatrix ) 466s > all.equal( fitsur5, fitsur5Sym ) 466s [1] "Component “call”: target, current do not match when deparsed" 466s > 466s > ## *************** SUR with 2 restrictions via R and restrict.regMat (EViews-like) ************** 466s > fitsur5e <- systemfit( system, "SUR", data = Kmenta, methodResidCov = "noDfCor", 466s + restrict.matrix = restr3m, restrict.rhs = restr3q, restrict.regMat = tc, 466s + useMatrix = useMatrix ) 466s > print( summary( fitsur5e ) ) 466s 466s systemfit results 466s method: SUR 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 35 165 1.2 0.693 0.653 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 63.8 3.75 1.94 0.762 0.734 466s supply 20 16 100.8 6.30 2.51 0.624 0.553 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.20 3.67 466s supply 3.67 4.79 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.19 3.86 466s supply 3.86 5.04 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.962 466s supply 0.962 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 97.2678 6.9200 14.06 4.4e-16 *** 466s price -0.2851 0.0767 -3.72 7e-04 *** 466s income 0.3296 0.0184 17.86 < 2e-16 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.937 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 63.811 MSE: 3.754 Root MSE: 1.937 466s Multiple R-Squared: 0.762 Adjusted R-Squared: 0.734 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: consump ~ price + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 53.3040 7.1045 7.5 8.7e-09 *** 466s price 0.2149 0.0767 2.8 0.0082 ** 466s farmPrice 0.2343 0.0187 12.6 1.6e-14 *** 466s trend 0.3296 0.0184 17.9 < 2e-16 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.51 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 100.835 MSE: 6.302 Root MSE: 2.51 466s Multiple R-Squared: 0.624 Adjusted R-Squared: 0.553 466s 466s > 466s > ## ************ WSUR with 2 restrictions via R and restrict.regMat ************ 466s > fitsur5w <- systemfit( system, "SUR", data = Kmenta, restrict.matrix = restr3m, 466s + restrict.rhs = restr3q, restrict.regMat = tc, residCovWeighted = TRUE, 466s + useMatrix = useMatrix ) 466s > summary( fitsur5w ) 466s 466s systemfit results 466s method: SUR 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 35 166 1.75 0.69 0.691 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 64.2 3.77 1.94 0.761 0.733 466s supply 20 16 102.0 6.37 2.52 0.620 0.548 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.74 4.47 466s supply 4.47 6.02 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.77 4.72 466s supply 4.72 6.37 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.963 466s supply 0.963 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 96.4421 7.4234 12.99 6e-15 *** 466s price -0.2753 0.0838 -3.29 0.0023 ** 466s income 0.3280 0.0202 16.21 <2e-16 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.943 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 64.16 MSE: 3.774 Root MSE: 1.943 466s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: consump ~ price + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 52.5761 7.6099 6.91 5.0e-08 *** 466s price 0.2247 0.0838 2.68 0.011 * 466s farmPrice 0.2318 0.0208 11.14 4.7e-13 *** 466s trend 0.3280 0.0202 16.21 < 2e-16 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.524 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 101.967 MSE: 6.373 Root MSE: 2.524 466s Multiple R-Squared: 0.62 Adjusted R-Squared: 0.548 466s 466s > 466s > 466s > ## ************** iterated SUR **************************** 466s > fitsuri1 <- systemfit( system2, "SUR", data = Kmenta, maxit = 100, 466s + useMatrix = useMatrix ) 466s > print( summary( fitsuri1 ) ) 466s 466s systemfit results 466s method: iterated SUR 466s 466s convergence achieved after 6 iterations 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 33 108 4.42 0.885 0.958 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 66.3 3.90 1.98 0.753 0.724 466s supply 20 16 41.4 2.59 1.61 0.938 0.926 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.90 -2.38 466s supply -2.38 2.59 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.90 -2.38 466s supply -2.38 2.59 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 -0.749 466s supply -0.749 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 94.0537 7.4051 12.70 4.2e-10 *** 466s price -0.2355 0.0882 -2.67 0.016 * 466s income 0.3117 0.0457 6.81 3.0e-06 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.975 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 66.286 MSE: 3.899 Root MSE: 1.975 466s Multiple R-Squared: 0.753 Adjusted R-Squared: 0.724 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: price ~ income + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 89.2982 3.3822 26.4 1.3e-14 *** 466s income 0.6655 0.0423 15.7 3.7e-11 *** 466s farmPrice -0.4742 0.0372 -12.7 8.7e-10 *** 466s trend -0.7966 0.0656 -12.2 1.7e-09 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.609 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 41.411 MSE: 2.588 Root MSE: 1.609 466s Multiple R-Squared: 0.938 Adjusted R-Squared: 0.926 466s 466s > nobs( fitsuri1 ) 466s [1] 40 466s > 466s > ## ************** iterated SUR (EViews-like) ***************** 466s > fitsuri1e <- systemfit( system2, "SUR", data = Kmenta, methodResidCov = "noDfCor", 466s + maxit = 100, useMatrix = useMatrix ) 466s > print( summary( fitsuri1e, useDfSys = TRUE ) ) 466s 466s systemfit results 466s method: iterated SUR 466s 466s convergence achieved after 7 iterations 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 33 108 3.01 0.885 0.959 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 66.7 3.93 1.98 0.751 0.722 466s supply 20 16 41.2 2.57 1.60 0.938 0.927 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.34 -1.97 466s supply -1.97 2.06 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.34 -1.97 466s supply -1.97 2.06 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.00 -0.75 466s supply -0.75 1.00 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 93.6193 6.8499 13.67 4.0e-15 *** 466s price -0.2295 0.0816 -2.81 0.0082 ** 466s income 0.3100 0.0423 7.33 2.1e-08 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.981 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 66.742 MSE: 3.926 Root MSE: 1.981 466s Multiple R-Squared: 0.751 Adjusted R-Squared: 0.722 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: price ~ income + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 89.2690 3.0165 29.6 < 2e-16 *** 466s income 0.6641 0.0377 17.6 < 2e-16 *** 466s farmPrice -0.4730 0.0332 -14.2 1.3e-15 *** 466s trend -0.7919 0.0585 -13.6 4.9e-15 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.604 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 41.176 MSE: 2.573 Root MSE: 1.604 466s Multiple R-Squared: 0.938 Adjusted R-Squared: 0.927 466s 466s > 466s > ## ************** iterated SUR (methodResidCov = "Theil") **************************** 466s > fitsuri1r2 <- systemfit( system2, "SUR", data = Kmenta, maxit = 100, 466s + methodResidCov = "Theil", useMatrix = useMatrix ) 466s > print( summary( fitsuri1r2 ) ) 466s 466s systemfit results 466s method: iterated SUR 466s 466s convergence achieved after 7 iterations 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 33 109 4 0.884 0.961 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 66.9 3.94 1.98 0.750 0.721 466s supply 20 16 41.8 2.61 1.62 0.937 0.926 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.94 -2.51 466s supply -2.51 2.61 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.94 -2.51 466s supply -2.51 2.61 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 -0.754 466s supply -0.754 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 93.4405 7.3821 12.66 4.4e-10 *** 466s price -0.2271 0.0877 -2.59 0.019 * 466s income 0.3093 0.0458 6.75 3.4e-06 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.984 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 66.939 MSE: 3.938 Root MSE: 1.984 466s Multiple R-Squared: 0.75 Adjusted R-Squared: 0.721 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: price ~ income + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 89.1602 3.3868 26.3 1.3e-14 *** 466s income 0.6635 0.0423 15.7 3.9e-11 *** 466s farmPrice -0.4710 0.0369 -12.8 8.5e-10 *** 466s trend -0.7952 0.0643 -12.4 1.3e-09 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.616 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 41.764 MSE: 2.61 Root MSE: 1.616 466s Multiple R-Squared: 0.937 Adjusted R-Squared: 0.926 466s 466s > 466s > ## ************** iterated SUR (methodResidCov="Theil", useDfSys=TRUE) ***************** 466s > fitsuri1e2 <- systemfit( system2, "SUR", data = Kmenta, methodResidCov = "Theil", 466s + maxit = 100, x = TRUE, useMatrix = useMatrix ) 466s > print( summary( fitsuri1e2, useDfSys = TRUE ) ) 466s 466s systemfit results 466s method: iterated SUR 466s 466s convergence achieved after 7 iterations 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 33 109 4 0.884 0.961 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 66.9 3.94 1.98 0.750 0.721 466s supply 20 16 41.8 2.61 1.62 0.937 0.926 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.94 -2.51 466s supply -2.51 2.61 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.94 -2.51 466s supply -2.51 2.61 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 -0.754 466s supply -0.754 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 93.4405 7.3821 12.66 3.3e-14 *** 466s price -0.2271 0.0877 -2.59 0.014 * 466s income 0.3093 0.0458 6.75 1.1e-07 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.984 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 66.939 MSE: 3.938 Root MSE: 1.984 466s Multiple R-Squared: 0.75 Adjusted R-Squared: 0.721 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: price ~ income + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 89.1602 3.3868 26.3 < 2e-16 *** 466s income 0.6635 0.0423 15.7 < 2e-16 *** 466s farmPrice -0.4710 0.0369 -12.8 2.7e-14 *** 466s trend -0.7952 0.0643 -12.4 6.0e-14 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.616 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 41.764 MSE: 2.61 Root MSE: 1.616 466s Multiple R-Squared: 0.937 Adjusted R-Squared: 0.926 466s 466s > 466s > ## ************** iterated SUR (methodResidCov = "max") **************************** 466s > fitsuri1r3 <- systemfit( system2, "SUR", data = Kmenta, maxit = 100, 466s + methodResidCov = "max", useMatrix = useMatrix ) 466s > print( summary( fitsuri1r3 ) ) 466s 466s systemfit results 466s method: iterated SUR 466s 466s convergence achieved after 7 iterations 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 33 109 4.06 0.884 0.96 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 66.8 3.93 1.98 0.751 0.721 466s supply 20 16 41.7 2.61 1.61 0.937 0.926 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.93 -2.49 466s supply -2.49 2.61 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.93 -2.49 466s supply -2.49 2.61 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 -0.754 466s supply -0.754 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 93.5427 7.3858 12.67 4.4e-10 *** 466s price -0.2285 0.0877 -2.60 0.019 * 466s income 0.3097 0.0458 6.76 3.3e-06 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.983 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 66.826 MSE: 3.931 Root MSE: 1.983 466s Multiple R-Squared: 0.751 Adjusted R-Squared: 0.721 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: price ~ income + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 89.1830 3.3861 26.3 1.3e-14 *** 466s income 0.6639 0.0423 15.7 3.8e-11 *** 466s farmPrice -0.4715 0.0370 -12.8 8.5e-10 *** 466s trend -0.7955 0.0645 -12.3 1.4e-09 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.615 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 41.708 MSE: 2.607 Root MSE: 1.615 466s Multiple R-Squared: 0.937 Adjusted R-Squared: 0.926 466s 466s > 466s > ## ************** iterated WSUR (methodResidCov = "max") **************************** 466s > fitsuri1wr3 <- systemfit( system2, "SUR", data = Kmenta, maxit = 100, 466s + methodResidCov = "max", residCovWeighted = TRUE, useMatrix = useMatrix ) 466s > summary( fitsuri1wr3 ) 466s 466s systemfit results 466s method: iterated SUR 466s 466s convergence achieved after 7 iterations 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 33 109 4.06 0.884 0.96 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 66.8 3.93 1.98 0.751 0.721 466s supply 20 16 41.7 2.61 1.61 0.937 0.926 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.93 -2.49 466s supply -2.49 2.61 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.93 -2.49 466s supply -2.49 2.61 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 -0.754 466s supply -0.754 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 93.5427 7.3858 12.67 4.4e-10 *** 466s price -0.2285 0.0877 -2.60 0.019 * 466s income 0.3097 0.0458 6.76 3.3e-06 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.983 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 66.826 MSE: 3.931 Root MSE: 1.983 466s Multiple R-Squared: 0.751 Adjusted R-Squared: 0.721 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: price ~ income + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 89.1830 3.3861 26.3 1.3e-14 *** 466s income 0.6639 0.0423 15.7 3.8e-11 *** 466s farmPrice -0.4715 0.0370 -12.8 8.5e-10 *** 466s trend -0.7955 0.0645 -12.3 1.4e-09 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.615 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 41.708 MSE: 2.607 Root MSE: 1.615 466s Multiple R-Squared: 0.937 Adjusted R-Squared: 0.926 466s 466s > 466s > 466s > ## *********** iterated SUR with restriction ******************* 466s > fitsuri2 <- systemfit( system2, "SUR", data = Kmenta, restrict.matrix = restrm, 466s + maxit = 100, useMatrix = useMatrix ) 466s > print( summary( fitsuri2 ) ) 466s 466s systemfit results 466s method: iterated SUR 466s 466s convergence achieved after 21 iterations 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 34 587 110 0.372 0.669 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 67 3.94 1.99 0.75 0.721 466s supply 20 16 520 32.52 5.70 0.22 0.074 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.94 4.24 466s supply 4.24 32.52 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.94 4.24 466s supply 4.24 32.52 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.375 466s supply 0.375 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 107.3678 7.4986 14.32 4.4e-16 *** 466s price -0.3945 0.0912 -4.33 0.00013 *** 466s income 0.3382 0.0466 7.25 2.1e-08 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.986 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 67.024 MSE: 3.943 Root MSE: 1.986 466s Multiple R-Squared: 0.75 Adjusted R-Squared: 0.721 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: price ~ income + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 85.0448 12.1069 7.02 4.2e-08 *** 466s income 0.3125 0.1233 2.53 0.016 * 466s farmPrice -0.1972 0.1157 -1.70 0.097 . 466s trend 0.3382 0.0466 7.25 2.1e-08 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 5.703 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 520.329 MSE: 32.521 Root MSE: 5.703 466s Multiple R-Squared: 0.22 Adjusted R-Squared: 0.074 466s 466s > 466s > ## *********** iterated SUR with restriction (EViews-like) *************** 466s > fitsuri2e <- systemfit( system2, "SUR", data = Kmenta, restrict.matrix = restrm, 466s + methodResidCov = "noDfCor", maxit = 100, x = TRUE, 466s + useMatrix = useMatrix ) 466s > print( summary( fitsuri2e ) ) 466s 466s systemfit results 466s method: iterated SUR 466s 466s convergence achieved after 22 iterations 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 34 588 74.9 0.372 0.664 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 67.5 3.97 1.99 0.748 0.719 466s supply 20 16 520.2 32.51 5.70 0.220 0.074 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.37 3.58 466s supply 3.58 26.01 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.37 3.58 466s supply 3.58 26.01 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.382 466s supply 0.382 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 107.8051 6.9270 15.56 < 2e-16 *** 466s price -0.3986 0.0843 -4.73 3.8e-05 *** 466s income 0.3379 0.0431 7.84 4.0e-09 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.992 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 67.47 MSE: 3.969 Root MSE: 1.992 466s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: price ~ income + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 85.1071 10.8287 7.86 3.8e-09 *** 466s income 0.3106 0.1101 2.82 0.0079 ** 466s farmPrice -0.1960 0.1034 -1.89 0.0667 . 466s trend 0.3379 0.0431 7.84 4.0e-09 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 5.702 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 520.205 MSE: 32.513 Root MSE: 5.702 466s Multiple R-Squared: 0.22 Adjusted R-Squared: 0.074 466s 466s > 466s > ## *********** iterated WSUR with restriction ******************* 466s > fitsuri2w <- systemfit( system2, "SUR", data = Kmenta, restrict.matrix = restrm, 466s + maxit = 100, residCovWeighted = TRUE, useMatrix = useMatrix ) 466s > summary( fitsuri2w ) 466s 466s systemfit results 466s method: iterated SUR 466s 466s convergence achieved after 18 iterations 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 34 587 110 0.372 0.669 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 67 3.94 1.99 0.75 0.721 466s supply 20 16 520 32.52 5.70 0.22 0.074 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.94 4.24 466s supply 4.24 32.52 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.94 4.24 466s supply 4.24 32.52 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.375 466s supply 0.375 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 107.3672 7.4986 14.32 4.4e-16 *** 466s price -0.3945 0.0912 -4.33 0.00013 *** 466s income 0.3382 0.0466 7.25 2.1e-08 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.986 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 67.023 MSE: 3.943 Root MSE: 1.986 466s Multiple R-Squared: 0.75 Adjusted R-Squared: 0.721 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: price ~ income + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 85.0448 12.1069 7.02 4.2e-08 *** 466s income 0.3125 0.1233 2.53 0.016 * 466s farmPrice -0.1972 0.1157 -1.70 0.097 . 466s trend 0.3382 0.0466 7.25 2.1e-08 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 5.703 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 520.327 MSE: 32.52 Root MSE: 5.703 466s Multiple R-Squared: 0.22 Adjusted R-Squared: 0.074 466s 466s > 466s > 466s > ## *********** iterated SUR with restriction via restrict.regMat ******************** 466s > fitsuri3 <- systemfit( system2, "SUR", data = Kmenta, restrict.regMat = tc, 466s + maxit = 100, useMatrix = useMatrix ) 466s > print( summary( fitsuri3 ) ) 466s 466s systemfit results 466s method: iterated SUR 466s 466s convergence achieved after 21 iterations 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 34 587 110 0.372 0.669 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 67 3.94 1.99 0.75 0.721 466s supply 20 16 520 32.52 5.70 0.22 0.074 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.94 4.24 466s supply 4.24 32.52 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.94 4.24 466s supply 4.24 32.52 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.375 466s supply 0.375 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 107.3678 7.4986 14.32 4.4e-16 *** 466s price -0.3945 0.0912 -4.33 0.00013 *** 466s income 0.3382 0.0466 7.25 2.1e-08 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.986 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 67.024 MSE: 3.943 Root MSE: 1.986 466s Multiple R-Squared: 0.75 Adjusted R-Squared: 0.721 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: price ~ income + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 85.0448 12.1069 7.02 4.2e-08 *** 466s income 0.3125 0.1233 2.53 0.016 * 466s farmPrice -0.1972 0.1157 -1.70 0.097 . 466s trend 0.3382 0.0466 7.25 2.1e-08 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 5.703 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 520.329 MSE: 32.521 Root MSE: 5.703 466s Multiple R-Squared: 0.22 Adjusted R-Squared: 0.074 466s 466s > 466s > ## *********** iterated SUR with restriction via restrict.regMat (EViews-like) *************** 466s > fitsuri3e <- systemfit( system2, "SUR", data = Kmenta, restrict.regMat = tc, 466s + methodResidCov = "noDfCor", maxit = 100, x = TRUE, 466s + useMatrix = useMatrix ) 466s > print( summary( fitsuri3e ) ) 466s 466s systemfit results 466s method: iterated SUR 466s 466s convergence achieved after 22 iterations 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 34 588 74.9 0.372 0.664 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 67.5 3.97 1.99 0.748 0.719 466s supply 20 16 520.2 32.51 5.70 0.220 0.074 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.37 3.58 466s supply 3.58 26.01 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.37 3.58 466s supply 3.58 26.01 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.382 466s supply 0.382 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 107.8051 6.9270 15.56 < 2e-16 *** 466s price -0.3986 0.0843 -4.73 3.8e-05 *** 466s income 0.3379 0.0431 7.84 4.0e-09 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.992 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 67.47 MSE: 3.969 Root MSE: 1.992 466s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: price ~ income + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 85.1071 10.8287 7.86 3.8e-09 *** 466s income 0.3106 0.1101 2.82 0.0079 ** 466s farmPrice -0.1960 0.1034 -1.89 0.0667 . 466s trend 0.3379 0.0431 7.84 4.0e-09 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 5.702 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 520.205 MSE: 32.513 Root MSE: 5.702 466s Multiple R-Squared: 0.22 Adjusted R-Squared: 0.074 466s 466s > 466s > ## *********** iterated WSUR with restriction via restrict.regMat (EViews-like) *************** 466s > fitsuri3we <- systemfit( system2, "SUR", data = Kmenta, restrict.regMat = tc, 466s + methodResidCov = "noDfCor", maxit = 100, residCovWeighted = TRUE, 466s + useMatrix = useMatrix ) 466s > summary( fitsuri3we ) 466s 466s systemfit results 466s method: iterated SUR 466s 466s convergence achieved after 20 iterations 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 34 588 74.9 0.372 0.664 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 67.5 3.97 1.99 0.748 0.719 466s supply 20 16 520.2 32.51 5.70 0.220 0.074 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.37 3.58 466s supply 3.58 26.01 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.37 3.58 466s supply 3.58 26.01 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.382 466s supply 0.382 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 107.8055 6.9270 15.56 < 2e-16 *** 466s price -0.3986 0.0843 -4.73 3.8e-05 *** 466s income 0.3379 0.0431 7.84 4.0e-09 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.992 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 67.471 MSE: 3.969 Root MSE: 1.992 466s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: price ~ income + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 85.1071 10.8288 7.86 3.8e-09 *** 466s income 0.3106 0.1101 2.82 0.008 ** 466s farmPrice -0.1960 0.1034 -1.89 0.067 . 466s trend 0.3379 0.0431 7.84 4.0e-09 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 5.702 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 520.206 MSE: 32.513 Root MSE: 5.702 466s Multiple R-Squared: 0.22 Adjusted R-Squared: 0.074 466s 466s > 466s > 466s > ## *************** iterated SUR with 2 restrictions *************************** 466s > fitsurio4 <- systemfit( system, "SUR", data = Kmenta, restrict.matrix = restr2m, 466s + restrict.rhs = restr2q, maxit = 100, useMatrix = useMatrix ) 466s > print( summary( fitsurio4 ) ) 466s 466s systemfit results 466s method: iterated SUR 466s 466s convergence achieved after 10 iterations 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 35 176 1.74 0.671 0.705 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 67.2 3.95 1.99 0.749 0.720 466s supply 20 16 109.2 6.83 2.61 0.593 0.516 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.95 5.02 466s supply 5.02 6.83 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.95 5.02 466s supply 5.02 6.83 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.967 466s supply 0.967 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 92.4262 7.3543 12.57 1.6e-14 *** 466s price -0.2276 0.0850 -2.68 0.011 * 466s income 0.3203 0.0185 17.32 < 2e-16 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.988 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 67.206 MSE: 3.953 Root MSE: 1.988 466s Multiple R-Squared: 0.749 Adjusted R-Squared: 0.72 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: consump ~ price + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 48.7295 7.4587 6.53 1.5e-07 *** 466s price 0.2724 0.0850 3.20 0.0029 ** 466s farmPrice 0.2232 0.0190 11.76 1.0e-13 *** 466s trend 0.3203 0.0185 17.32 < 2e-16 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.613 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 109.234 MSE: 6.827 Root MSE: 2.613 466s Multiple R-Squared: 0.593 Adjusted R-Squared: 0.516 466s 466s > fitsuri4 <- systemfit( system2, "SUR", data = Kmenta, restrict.matrix = restr2m, 466s + restrict.rhs = restr2q, maxit = 100, useMatrix = useMatrix ) 466s > print( summary( fitsuri4 ) ) 466s 466s systemfit results 466s method: iterated SUR 466s 466s convergence achieved after 19 iterations 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 35 575 121 0.385 0.637 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 65.5 3.85 1.96 0.756 0.727 466s supply 20 16 509.3 31.83 5.64 0.237 0.094 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.85 1.23 466s supply 1.23 31.83 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.85 1.23 466s supply 1.23 31.83 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.111 466s supply 0.111 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 98.0356 6.7437 14.54 2.2e-16 *** 466s price -0.2646 0.0777 -3.40 0.0017 ** 466s income 0.3007 0.0436 6.89 5.3e-08 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.963 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 65.532 MSE: 3.855 Root MSE: 1.963 466s Multiple R-Squared: 0.756 Adjusted R-Squared: 0.727 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: price ~ income + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 90.0046 10.4367 8.62 3.5e-10 *** 466s income 0.2354 0.0777 3.03 0.0046 ** 466s farmPrice -0.1667 0.1108 -1.50 0.1416 466s trend 0.3007 0.0436 6.89 5.3e-08 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 5.642 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 509.345 MSE: 31.834 Root MSE: 5.642 466s Multiple R-Squared: 0.237 Adjusted R-Squared: 0.094 466s 466s > 466s > ## *************** iterated SUR with 2 restrictions (EViews-like) ************** 466s > fitsurio4e <- systemfit( system, "SUR", data = Kmenta, methodResidCov = "noDfCor", 466s + restrict.matrix = restr2m, restrict.rhs = restr2q, maxit = 100, 466s + useMatrix = useMatrix ) 466s > print( summary( fitsurio4e ) ) 466s 466s systemfit results 466s method: iterated SUR 466s 466s convergence achieved after 9 iterations 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 35 173 1.18 0.677 0.665 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 66.3 3.90 1.97 0.753 0.724 466s supply 20 16 106.7 6.67 2.58 0.602 0.527 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.31 4.06 466s supply 4.06 5.34 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.31 4.06 466s supply 4.06 5.34 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.966 466s supply 0.966 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 93.3596 6.8576 13.61 1.6e-15 *** 466s price -0.2398 0.0779 -3.08 0.0041 ** 466s income 0.3232 0.0163 19.81 < 2e-16 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.974 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 66.265 MSE: 3.898 Root MSE: 1.974 466s Multiple R-Squared: 0.753 Adjusted R-Squared: 0.724 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: consump ~ price + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 49.5456 6.9727 7.11 2.8e-08 *** 466s price 0.2602 0.0779 3.34 0.002 ** 466s farmPrice 0.2270 0.0164 13.81 8.9e-16 *** 466s trend 0.3232 0.0163 19.81 < 2e-16 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.583 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 106.722 MSE: 6.67 Root MSE: 2.583 466s Multiple R-Squared: 0.602 Adjusted R-Squared: 0.527 466s 466s > fitsuri4e <- systemfit( system2, "SUR", data = Kmenta, methodResidCov = "noDfCor", 466s + restrict.matrix = restr2m, restrict.rhs = restr2q, maxit = 100, 466s + useMatrix = useMatrix ) 466s > print( summary( fitsuri4e ) ) 466s 466s systemfit results 466s method: iterated SUR 466s 466s convergence achieved after 20 iterations 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 35 570 82.4 0.391 0.629 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 66 3.88 1.97 0.754 0.725 466s supply 20 16 504 31.50 5.61 0.245 0.103 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.300 0.876 466s supply 0.876 25.203 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.300 0.876 466s supply 0.876 25.203 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.0000 0.0961 466s supply 0.0961 1.0000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 97.6297 6.1560 15.86 < 2e-16 *** 466s price -0.2576 0.0709 -3.63 0.00089 *** 466s income 0.2976 0.0403 7.38 1.2e-08 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.97 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 65.995 MSE: 3.882 Root MSE: 1.97 466s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: price ~ income + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 89.5437 9.3372 9.59 2.5e-11 *** 466s income 0.2424 0.0709 3.42 0.0016 ** 466s farmPrice -0.1687 0.0988 -1.71 0.0967 . 466s trend 0.2976 0.0403 7.38 1.2e-08 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 5.613 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 504.066 MSE: 31.504 Root MSE: 5.613 466s Multiple R-Squared: 0.245 Adjusted R-Squared: 0.103 466s 466s > 466s > ## *************** iterated WSUR with 2 restrictions *************************** 466s > fitsurio4w <- systemfit( system, "SUR", data = Kmenta, restrict.matrix = restr2m, 466s + restrict.rhs = restr2q, maxit = 100, residCovWeighted = TRUE, 466s + useMatrix = useMatrix ) 466s > summary( fitsurio4w ) 466s 466s systemfit results 466s method: iterated SUR 466s 466s convergence achieved after 10 iterations 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 35 176 1.74 0.671 0.705 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 67.2 3.95 1.99 0.749 0.720 466s supply 20 16 109.2 6.83 2.61 0.593 0.516 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.95 5.02 466s supply 5.02 6.83 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.95 5.02 466s supply 5.02 6.83 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.967 466s supply 0.967 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 92.4262 7.3543 12.57 1.6e-14 *** 466s price -0.2276 0.0850 -2.68 0.011 * 466s income 0.3203 0.0185 17.32 < 2e-16 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.988 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 67.206 MSE: 3.953 Root MSE: 1.988 466s Multiple R-Squared: 0.749 Adjusted R-Squared: 0.72 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: consump ~ price + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 48.7294 7.4587 6.53 1.5e-07 *** 466s price 0.2724 0.0850 3.20 0.0029 ** 466s farmPrice 0.2232 0.0190 11.76 1.0e-13 *** 466s trend 0.3203 0.0185 17.32 < 2e-16 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.613 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 109.234 MSE: 6.827 Root MSE: 2.613 466s Multiple R-Squared: 0.593 Adjusted R-Squared: 0.516 466s 466s > fitsuri4w <- systemfit( system2, "SUR", data = Kmenta, restrict.matrix = restr2m, 466s + restrict.rhs = restr2q, maxit = 100, residCovWeighted = TRUE, 466s + useMatrix = useMatrix ) 466s > summary( fitsuri4w ) 466s 466s systemfit results 466s method: iterated SUR 466s 466s convergence achieved after 18 iterations 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 35 575 121 0.385 0.637 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 65.5 3.85 1.96 0.756 0.727 466s supply 20 16 509.3 31.83 5.64 0.237 0.094 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.85 1.23 466s supply 1.23 31.83 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.85 1.23 466s supply 1.23 31.83 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.111 466s supply 0.111 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 98.0361 6.7437 14.54 2.2e-16 *** 466s price -0.2646 0.0777 -3.40 0.0017 ** 466s income 0.3007 0.0436 6.89 5.3e-08 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.963 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 65.531 MSE: 3.855 Root MSE: 1.963 466s Multiple R-Squared: 0.756 Adjusted R-Squared: 0.727 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: price ~ income + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 90.0052 10.4368 8.62 3.5e-10 *** 466s income 0.2354 0.0777 3.03 0.0046 ** 466s farmPrice -0.1667 0.1108 -1.50 0.1416 466s trend 0.3007 0.0436 6.89 5.3e-08 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 5.642 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 509.349 MSE: 31.834 Root MSE: 5.642 466s Multiple R-Squared: 0.237 Adjusted R-Squared: 0.094 466s 466s > 466s > 466s > ## *************** iterated SUR with 2 restrictions via R and restrict.regMat **************** 466s > fitsurio5 <- systemfit( system, "SUR", data = Kmenta, restrict.matrix = restr3m, 466s + restrict.rhs = restr3q, restrict.regMat = tc, maxit = 100, 466s + useMatrix = useMatrix ) 466s > print( summary( fitsurio5 ) ) 466s 466s systemfit results 466s method: iterated SUR 466s 466s convergence achieved after 10 iterations 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 35 176 1.74 0.671 0.705 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 67.2 3.95 1.99 0.749 0.720 466s supply 20 16 109.2 6.83 2.61 0.593 0.516 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.95 5.02 466s supply 5.02 6.83 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.95 5.02 466s supply 5.02 6.83 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.967 466s supply 0.967 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 92.4262 7.3543 12.57 1.6e-14 *** 466s price -0.2276 0.0850 -2.68 0.011 * 466s income 0.3203 0.0185 17.32 < 2e-16 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.988 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 67.206 MSE: 3.953 Root MSE: 1.988 466s Multiple R-Squared: 0.749 Adjusted R-Squared: 0.72 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: consump ~ price + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 48.7295 7.4587 6.53 1.5e-07 *** 466s price 0.2724 0.0850 3.20 0.0029 ** 466s farmPrice 0.2232 0.0190 11.76 1.0e-13 *** 466s trend 0.3203 0.0185 17.32 < 2e-16 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.613 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 109.234 MSE: 6.827 Root MSE: 2.613 466s Multiple R-Squared: 0.593 Adjusted R-Squared: 0.516 466s 466s > fitsuri5 <- systemfit( system2, "SUR", data = Kmenta, restrict.matrix = restr3m, 466s + restrict.rhs = restr3q, restrict.regMat = tc, maxit = 100, 466s + useMatrix = useMatrix ) 466s > print( summary( fitsuri5 ) ) 466s 466s systemfit results 466s method: iterated SUR 466s 466s convergence achieved after 19 iterations 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 35 575 121 0.385 0.637 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 65.5 3.85 1.96 0.756 0.727 466s supply 20 16 509.3 31.83 5.64 0.237 0.094 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.85 1.23 466s supply 1.23 31.83 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.85 1.23 466s supply 1.23 31.83 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.111 466s supply 0.111 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 98.0356 6.7437 14.54 2.2e-16 *** 466s price -0.2646 0.0777 -3.40 0.0017 ** 466s income 0.3007 0.0436 6.89 5.3e-08 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.963 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 65.532 MSE: 3.855 Root MSE: 1.963 466s Multiple R-Squared: 0.756 Adjusted R-Squared: 0.727 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: price ~ income + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 90.0046 10.4367 8.62 3.5e-10 *** 466s income 0.2354 0.0777 3.03 0.0046 ** 466s farmPrice -0.1667 0.1108 -1.50 0.1416 466s trend 0.3007 0.0436 6.89 5.3e-08 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 5.642 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 509.345 MSE: 31.834 Root MSE: 5.642 466s Multiple R-Squared: 0.237 Adjusted R-Squared: 0.094 466s 466s > 466s > ## ********* iterated SUR with 2 restrictions via R and restrict.regMat (EViews-like) ********** 466s > fitsurio5e <- systemfit( system, "SUR", data = Kmenta, methodResidCov = "noDfCor", 466s + restrict.matrix = restr3m, restrict.rhs = restr3q, restrict.regMat = tc, 466s + maxit = 100, useMatrix = useMatrix ) 466s > print( summary( fitsurio5e ) ) 466s 466s systemfit results 466s method: iterated SUR 466s 466s convergence achieved after 9 iterations 466s 466s N DF SSR detRCov OLS-R2 McElroy-R2 466s system 40 35 173 1.18 0.677 0.665 466s 466s N DF SSR MSE RMSE R2 Adj R2 466s demand 20 17 66.3 3.90 1.97 0.753 0.724 466s supply 20 16 106.7 6.67 2.58 0.602 0.527 466s 466s The covariance matrix of the residuals used for estimation 466s demand supply 466s demand 3.31 4.06 466s supply 4.06 5.34 466s 466s The covariance matrix of the residuals 466s demand supply 466s demand 3.31 4.06 466s supply 4.06 5.34 466s 466s The correlations of the residuals 466s demand supply 466s demand 1.000 0.966 466s supply 0.966 1.000 466s 466s 466s SUR estimates for 'demand' (equation 1) 466s Model Formula: consump ~ price + income 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 93.3596 6.8576 13.61 1.6e-15 *** 466s price -0.2398 0.0779 -3.08 0.0041 ** 466s income 0.3232 0.0163 19.81 < 2e-16 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 1.974 on 17 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 17 466s SSR: 66.265 MSE: 3.898 Root MSE: 1.974 466s Multiple R-Squared: 0.753 Adjusted R-Squared: 0.724 466s 466s 466s SUR estimates for 'supply' (equation 2) 466s Model Formula: consump ~ price + farmPrice + trend 466s 466s Estimate Std. Error t value Pr(>|t|) 466s (Intercept) 49.5456 6.9727 7.11 2.8e-08 *** 466s price 0.2602 0.0779 3.34 0.002 ** 466s farmPrice 0.2270 0.0164 13.81 8.9e-16 *** 466s trend 0.3232 0.0163 19.81 < 2e-16 *** 466s --- 466s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 466s 466s Residual standard error: 2.583 on 16 degrees of freedom 466s Number of observations: 20 Degrees of Freedom: 16 466s SSR: 106.722 MSE: 6.67 Root MSE: 2.583 466s Multiple R-Squared: 0.602 Adjusted R-Squared: 0.527 466s 466s > fitsuri5e <- systemfit( system2, "SUR", data = Kmenta, methodResidCov = "noDfCor", 466s + restrict.matrix = restr3m, restrict.rhs = restr3q, restrict.regMat = tc, 466s + maxit = 100, useMatrix = useMatrix ) 466s > print( summary( fitsuri5e ) ) 466s 466s systemfit results 466s method: iterated SUR 466s 466s convergence achieved after 20 iterations 466s 467s N DF SSR detRCov OLS-R2 McElroy-R2 467s system 40 35 570 82.4 0.391 0.629 467s 467s N DF SSR MSE RMSE R2 Adj R2 467s demand 20 17 66 3.88 1.97 0.754 0.725 467s supply 20 16 504 31.50 5.61 0.245 0.103 467s 467s The covariance matrix of the residuals used for estimation 467s demand supply 467s demand 3.300 0.876 467s supply 0.876 25.203 467s 467s The covariance matrix of the residuals 467s demand supply 467s demand 3.300 0.876 467s supply 0.876 25.203 467s 467s The correlations of the residuals 467s demand supply 467s demand 1.0000 0.0961 467s supply 0.0961 1.0000 467s 467s 467s SUR estimates for 'demand' (equation 1) 467s Model Formula: consump ~ price + income 467s 467s Estimate Std. Error t value Pr(>|t|) 467s (Intercept) 97.6297 6.1560 15.86 < 2e-16 *** 467s price -0.2576 0.0709 -3.63 0.00089 *** 467s income 0.2976 0.0403 7.38 1.2e-08 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s 467s Residual standard error: 1.97 on 17 degrees of freedom 467s Number of observations: 20 Degrees of Freedom: 17 467s SSR: 65.995 MSE: 3.882 Root MSE: 1.97 467s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 467s 467s 467s SUR estimates for 'supply' (equation 2) 467s Model Formula: price ~ income + farmPrice + trend 467s 467s Estimate Std. Error t value Pr(>|t|) 467s (Intercept) 89.5437 9.3372 9.59 2.5e-11 *** 467s income 0.2424 0.0709 3.42 0.0016 ** 467s farmPrice -0.1687 0.0988 -1.71 0.0967 . 467s trend 0.2976 0.0403 7.38 1.2e-08 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s 467s Residual standard error: 5.613 on 16 degrees of freedom 467s Number of observations: 20 Degrees of Freedom: 16 467s SSR: 504.066 MSE: 31.504 Root MSE: 5.613 467s Multiple R-Squared: 0.245 Adjusted R-Squared: 0.103 467s 467s > nobs( fitsuri5e ) 467s [1] 40 467s > 467s > ## ********* iterated SUR with 2 restrictions via R and restrict.regMat (methodResidCov="Theil") ********** 467s > fitsurio5r2 <- systemfit( system, "SUR", data = Kmenta, methodResidCov = "Theil", 467s + restrict.matrix = restr3m, restrict.rhs = restr3q, restrict.regMat = tc, 467s + maxit = 100, x = TRUE, useMatrix = useMatrix ) 467s > print( summary( fitsurio5r2 ) ) 467s 467s systemfit results 467s method: iterated SUR 467s 467s warning: convergence not achieved after 100 iterations 467s 467s N DF SSR detRCov OLS-R2 McElroy-R2 467s system 40 35 253 -1.67 0.527 0.927 467s 467s N DF SSR MSE RMSE R2 Adj R2 467s demand 20 17 95.8 5.63 2.37 0.643 0.601 467s supply 20 16 157.7 9.86 3.14 0.412 0.301 467s 467s The covariance matrix of the residuals used for estimation 467s demand supply 467s demand 4.26 5.29 467s supply 5.29 6.69 467s 467s warning: this covariance matrix is NOT positive semidefinit! 467s 467s The covariance matrix of the residuals 467s demand supply 467s demand 5.63 7.56 467s supply 7.56 9.86 467s 467s The correlations of the residuals 467s demand supply 467s demand 1.000 0.982 467s supply 0.982 1.000 467s 467s 467s SUR estimates for 'demand' (equation 1) 467s Model Formula: consump ~ price + income 467s 467s Estimate Std. Error t value Pr(>|t|) 467s (Intercept) 78.0342 7.1638 10.89 8.6e-13 *** 467s price -0.0647 0.0815 -0.79 0.43 467s income 0.3007 0.0131 23.01 < 2e-16 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s 467s Residual standard error: 2.373 on 17 degrees of freedom 467s Number of observations: 20 Degrees of Freedom: 17 467s SSR: 95.76 MSE: 5.633 Root MSE: 2.373 467s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.601 467s 467s 467s SUR estimates for 'supply' (equation 2) 467s Model Formula: consump ~ price + farmPrice + trend 467s 467s Estimate Std. Error t value Pr(>|t|) 467s (Intercept) 34.1958 7.2257 4.73 3.6e-05 *** 467s price 0.4353 0.0815 5.34 5.7e-06 *** 467s farmPrice 0.2070 0.0124 16.68 < 2e-16 *** 467s trend 0.3007 0.0131 23.01 < 2e-16 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s 467s Residual standard error: 3.14 on 16 degrees of freedom 467s Number of observations: 20 Degrees of Freedom: 16 467s SSR: 157.737 MSE: 9.859 Root MSE: 3.14 467s Multiple R-Squared: 0.412 Adjusted R-Squared: 0.301 467s 467s > fitsuri5r2 <- systemfit( system2, "SUR", data = Kmenta, methodResidCov = "Theil", 467s + restrict.matrix = restr3m, restrict.rhs = restr3q, restrict.regMat = tc, 467s + maxit = 100, x = TRUE, useMatrix = useMatrix ) 467s > print( summary( fitsuri5r2 ) ) 467s 467s systemfit results 467s method: iterated SUR 467s 467s convergence achieved after 21 iterations 467s 467s N DF SSR detRCov OLS-R2 McElroy-R2 467s system 40 35 576 121 0.384 0.637 467s 467s N DF SSR MSE RMSE R2 Adj R2 467s demand 20 17 65.4 3.85 1.96 0.756 0.727 467s supply 20 16 510.8 31.92 5.65 0.235 0.091 467s 467s The covariance matrix of the residuals used for estimation 467s demand supply 467s demand 3.85 1.34 467s supply 1.34 31.92 467s 467s The covariance matrix of the residuals 467s demand supply 467s demand 3.85 1.34 467s supply 1.34 31.92 467s 467s The correlations of the residuals 467s demand supply 467s demand 1.000 0.117 467s supply 0.117 1.000 467s 467s 467s SUR estimates for 'demand' (equation 1) 467s Model Formula: consump ~ price + income 467s 467s Estimate Std. Error t value Pr(>|t|) 467s (Intercept) 98.2200 6.7593 14.53 2.2e-16 *** 467s price -0.2669 0.0778 -3.43 0.0016 ** 467s income 0.3011 0.0435 6.92 4.9e-08 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s 467s Residual standard error: 1.962 on 17 degrees of freedom 467s Number of observations: 20 Degrees of Freedom: 17 467s SSR: 65.447 MSE: 3.85 Root MSE: 1.962 467s Multiple R-Squared: 0.756 Adjusted R-Squared: 0.727 467s 467s 467s SUR estimates for 'supply' (equation 2) 467s Model Formula: price ~ income + farmPrice + trend 467s 467s Estimate Std. Error t value Pr(>|t|) 467s (Intercept) 90.2167 10.4342 8.65 3.3e-10 *** 467s income 0.2331 0.0778 3.00 0.005 ** 467s farmPrice -0.1666 0.1111 -1.50 0.143 467s trend 0.3011 0.0435 6.92 4.9e-08 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s 467s Residual standard error: 5.65 on 16 degrees of freedom 467s Number of observations: 20 Degrees of Freedom: 16 467s SSR: 510.75 MSE: 31.922 Root MSE: 5.65 467s Multiple R-Squared: 0.235 Adjusted R-Squared: 0.091 467s 467s > 467s > ## ********* iterated SUR with 2 restrictions via R and restrict.regMat (methodResidCov="max") ********** 467s > # fitsuri5e <- systemfit( system, "SUR", data = Kmenta, methodResidCov = "max", 467s > # restrict.matrix = restr3m, restrict.rhs = restr3q, restrict.regMat = tc, 467s > # maxit = 100, useMatrix = useMatrix ) 467s > # print( summary( fitsuri5e ) ) 467s > # print( round( vcov( fitsuri5e ), digits = 6 ) ) 467s > # disabled, because the estimation does not converge 467s > 467s > ## ********* iterated WSUR with 2 restrictions via R and restrict.regMat (methodResidCov="Theil") ********** 467s > fitsurio5wr2 <- systemfit( system, "SUR", data = Kmenta, methodResidCov = "Theil", 467s + restrict.matrix = restr3m, restrict.rhs = restr3q, restrict.regMat = tc, 467s + maxit = 100, residCovWeighted = TRUE, useMatrix = useMatrix ) 467s > summary( fitsurio5wr2 ) 467s 467s systemfit results 467s method: iterated SUR 467s 467s warning: convergence not achieved after 100 iterations 467s 467s N DF SSR detRCov OLS-R2 McElroy-R2 467s system 40 35 253 -1.67 0.527 0.927 467s 467s N DF SSR MSE RMSE R2 Adj R2 467s demand 20 17 95.8 5.63 2.37 0.643 0.601 467s supply 20 16 157.7 9.86 3.14 0.412 0.301 467s 467s The covariance matrix of the residuals used for estimation 467s demand supply 467s demand 4.26 5.29 467s supply 5.29 6.69 467s 467s warning: this covariance matrix is NOT positive semidefinit! 467s 467s The covariance matrix of the residuals 467s demand supply 467s demand 5.63 7.56 467s supply 7.56 9.86 467s 467s The correlations of the residuals 467s demand supply 467s demand 1.000 0.982 467s supply 0.982 1.000 467s 467s 467s SUR estimates for 'demand' (equation 1) 467s Model Formula: consump ~ price + income 467s 467s Estimate Std. Error t value Pr(>|t|) 467s (Intercept) 78.0342 7.1638 10.89 8.6e-13 *** 467s price -0.0647 0.0815 -0.79 0.43 467s income 0.3007 0.0131 23.01 < 2e-16 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s 467s Residual standard error: 2.373 on 17 degrees of freedom 467s Number of observations: 20 Degrees of Freedom: 17 467s SSR: 95.76 MSE: 5.633 Root MSE: 2.373 467s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.601 467s 467s 467s SUR estimates for 'supply' (equation 2) 467s Model Formula: consump ~ price + farmPrice + trend 467s 467s Estimate Std. Error t value Pr(>|t|) 467s (Intercept) 34.1958 7.2257 4.73 3.6e-05 *** 467s price 0.4353 0.0815 5.34 5.7e-06 *** 467s farmPrice 0.2070 0.0124 16.68 < 2e-16 *** 467s trend 0.3007 0.0131 23.01 < 2e-16 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s 467s Residual standard error: 3.14 on 16 degrees of freedom 467s Number of observations: 20 Degrees of Freedom: 16 467s SSR: 157.737 MSE: 9.859 Root MSE: 3.14 467s Multiple R-Squared: 0.412 Adjusted R-Squared: 0.301 467s 467s > fitsuri5wr2 <- systemfit( system2, "SUR", data = Kmenta, methodResidCov = "Theil", 467s + restrict.matrix = restr3m, restrict.rhs = restr3q, restrict.regMat = tc, 467s + maxit = 100, residCovWeighted = TRUE, useMatrix = useMatrix ) 467s > summary( fitsuri5wr2 ) 467s 467s systemfit results 467s method: iterated SUR 467s 467s convergence achieved after 19 iterations 467s 467s N DF SSR detRCov OLS-R2 McElroy-R2 467s system 40 35 576 121 0.384 0.637 467s 467s N DF SSR MSE RMSE R2 Adj R2 467s demand 20 17 65.4 3.85 1.96 0.756 0.727 467s supply 20 16 510.8 31.92 5.65 0.235 0.091 467s 467s The covariance matrix of the residuals used for estimation 467s demand supply 467s demand 3.85 1.34 467s supply 1.34 31.92 467s 467s The covariance matrix of the residuals 467s demand supply 467s demand 3.85 1.34 467s supply 1.34 31.92 467s 467s The correlations of the residuals 467s demand supply 467s demand 1.000 0.117 467s supply 0.117 1.000 467s 467s 467s SUR estimates for 'demand' (equation 1) 467s Model Formula: consump ~ price + income 467s 467s Estimate Std. Error t value Pr(>|t|) 467s (Intercept) 98.2200 6.7593 14.53 2.2e-16 *** 467s price -0.2669 0.0778 -3.43 0.0016 ** 467s income 0.3011 0.0435 6.92 4.9e-08 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s 467s Residual standard error: 1.962 on 17 degrees of freedom 467s Number of observations: 20 Degrees of Freedom: 17 467s SSR: 65.447 MSE: 3.85 Root MSE: 1.962 467s Multiple R-Squared: 0.756 Adjusted R-Squared: 0.727 467s 467s 467s SUR estimates for 'supply' (equation 2) 467s Model Formula: price ~ income + farmPrice + trend 467s 467s Estimate Std. Error t value Pr(>|t|) 467s (Intercept) 90.2168 10.4342 8.65 3.3e-10 *** 467s income 0.2331 0.0778 3.00 0.005 ** 467s farmPrice -0.1666 0.1111 -1.50 0.143 467s trend 0.3011 0.0435 6.92 4.9e-08 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s 467s Residual standard error: 5.65 on 16 degrees of freedom 467s Number of observations: 20 Degrees of Freedom: 16 467s SSR: 510.75 MSE: 31.922 Root MSE: 5.65 467s Multiple R-Squared: 0.235 Adjusted R-Squared: 0.091 467s 467s > 467s > 467s > ## *********** estimations with a single regressor ************ 467s > fitsurS1 <- systemfit( 467s + list( price ~ consump - 1, farmPrice ~ consump + trend ), "SUR", 467s + data = Kmenta, useMatrix = useMatrix ) 467s > print( summary( fitsurS1 ) ) 467s 467s systemfit results 467s method: SUR 467s 467s N DF SSR detRCov OLS-R2 McElroy-R2 467s system 40 36 2060 2543 0.449 0.465 467s 467s N DF SSR MSE RMSE R2 Adj R2 467s eq1 20 19 848 44.6 6.68 -0.271 -0.271 467s eq2 20 17 1211 71.3 8.44 0.605 0.559 467s 467s The covariance matrix of the residuals used for estimation 467s eq1 eq2 467s eq1 44.6 -20.5 467s eq2 -20.5 68.9 467s 467s The covariance matrix of the residuals 467s eq1 eq2 467s eq1 44.6 -25.3 467s eq2 -25.3 71.3 467s 467s The correlations of the residuals 467s eq1 eq2 467s eq1 1.000 -0.448 467s eq2 -0.448 1.000 467s 467s 467s SUR estimates for 'eq1' (equation 1) 467s Model Formula: price ~ consump - 1 467s 467s Estimate Std. Error t value Pr(>|t|) 467s consump 0.9902 0.0148 66.9 <2e-16 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s 467s Residual standard error: 6.682 on 19 degrees of freedom 467s Number of observations: 20 Degrees of Freedom: 19 467s SSR: 848.208 MSE: 44.643 Root MSE: 6.682 467s Multiple R-Squared: -0.271 Adjusted R-Squared: -0.271 467s 467s 467s SUR estimates for 'eq2' (equation 2) 467s Model Formula: farmPrice ~ consump + trend 467s 467s Estimate Std. Error t value Pr(>|t|) 467s (Intercept) -108.487 47.754 -2.27 0.03638 * 467s consump 2.123 0.477 4.45 0.00035 *** 467s trend -0.862 0.303 -2.85 0.01111 * 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s 467s Residual standard error: 8.441 on 17 degrees of freedom 467s Number of observations: 20 Degrees of Freedom: 17 467s SSR: 1211.393 MSE: 71.258 Root MSE: 8.441 467s Multiple R-Squared: 0.605 Adjusted R-Squared: 0.559 467s 467s > nobs( fitsurS1 ) 467s [1] 40 467s > fitsurS2 <- systemfit( 467s + list( consump ~ price - 1, consump ~ trend - 1 ), "SUR", 467s + data = Kmenta, useMatrix = useMatrix ) 467s > print( summary( fitsurS2 ) ) 467s 467s systemfit results 467s method: SUR 467s 467s N DF SSR detRCov OLS-R2 McElroy-R2 467s system 40 38 47370 110949 -87.3 -5.28 467s 467s N DF SSR MSE RMSE R2 Adj R2 467s eq1 20 19 861 45.3 6.73 -2.21 -2.21 467s eq2 20 19 46509 2447.8 49.48 -172.47 -172.47 467s 467s The covariance matrix of the residuals used for estimation 467s eq1 eq2 467s eq1 45.34 -5.15 467s eq2 -5.15 2447.84 467s 467s The covariance matrix of the residuals 467s eq1 eq2 467s eq1 45.34 -6.37 467s eq2 -6.37 2447.84 467s 467s The correlations of the residuals 467s eq1 eq2 467s eq1 1.0000 -0.0439 467s eq2 -0.0439 1.0000 467s 467s 467s SUR estimates for 'eq1' (equation 1) 467s Model Formula: consump ~ price - 1 467s 467s Estimate Std. Error t value Pr(>|t|) 467s price 1.006 0.015 67 <2e-16 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s 467s Residual standard error: 6.734 on 19 degrees of freedom 467s Number of observations: 20 Degrees of Freedom: 19 467s SSR: 861.496 MSE: 45.342 Root MSE: 6.734 467s Multiple R-Squared: -2.213 Adjusted R-Squared: -2.213 467s 467s 467s SUR estimates for 'eq2' (equation 2) 467s Model Formula: consump ~ trend - 1 467s 467s Estimate Std. Error t value Pr(>|t|) 467s trend 7.410 0.924 8.02 1.6e-07 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s 467s Residual standard error: 49.476 on 19 degrees of freedom 467s Number of observations: 20 Degrees of Freedom: 19 467s SSR: 46508.986 MSE: 2447.841 Root MSE: 49.476 467s Multiple R-Squared: -172.467 Adjusted R-Squared: -172.467 467s 467s > nobs( fitsurS2 ) 467s [1] 40 467s > fitsurS3 <- systemfit( 467s + list( consump ~ trend - 1, price ~ trend - 1 ), "SUR", 467s + data = Kmenta, useMatrix = useMatrix ) 467s > nobs( fitsurS3 ) 467s [1] 40 467s > print( summary( fitsurS3 ) ) 467s 467s systemfit results 467s method: SUR 467s 467s N DF SSR detRCov OLS-R2 McElroy-R2 467s system 40 38 93537 108970 -99 -0.977 467s 467s N DF SSR MSE RMSE R2 Adj R2 467s eq1 20 19 46509 2448 49.5 -172.5 -172.5 467s eq2 20 19 47028 2475 49.8 -69.5 -69.5 467s 467s The covariance matrix of the residuals used for estimation 467s eq1 eq2 467s eq1 2448 2439 467s eq2 2439 2475 467s 467s The covariance matrix of the residuals 467s eq1 eq2 467s eq1 2448 2439 467s eq2 2439 2475 467s 467s The correlations of the residuals 467s eq1 eq2 467s eq1 1.000 0.988 467s eq2 0.988 1.000 467s 467s 467s SUR estimates for 'eq1' (equation 1) 467s Model Formula: consump ~ trend - 1 467s 467s Estimate Std. Error t value Pr(>|t|) 467s trend 7.405 0.924 8.02 1.6e-07 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s 467s Residual standard error: 49.476 on 19 degrees of freedom 467s Number of observations: 20 Degrees of Freedom: 19 467s SSR: 46508.922 MSE: 2447.838 Root MSE: 49.476 467s Multiple R-Squared: -172.467 Adjusted R-Squared: -172.467 467s 467s 467s SUR estimates for 'eq2' (equation 2) 467s Model Formula: price ~ trend - 1 467s 467s Estimate Std. Error t value Pr(>|t|) 467s trend 7.318 0.929 7.88 2.1e-07 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s 467s Residual standard error: 49.751 on 19 degrees of freedom 467s Number of observations: 20 Degrees of Freedom: 19 467s SSR: 47028.107 MSE: 2475.164 Root MSE: 49.751 467s Multiple R-Squared: -69.48 Adjusted R-Squared: -69.48 467s 467s > fitsurS4 <- systemfit( 467s + list( consump ~ trend - 1, price ~ trend - 1 ), "SUR", 467s + data = Kmenta, restrict.matrix = matrix( c( 1, -1 ), nrow = 1 ), 467s + useMatrix = useMatrix ) 467s > print( summary( fitsurS4 ) ) 467s 467s systemfit results 467s method: SUR 467s 467s N DF SSR detRCov OLS-R2 McElroy-R2 467s system 40 39 93552 111731 -99 -1.03 467s 467s N DF SSR MSE RMSE R2 Adj R2 467s eq1 20 19 46510 2448 49.5 -172.5 -172.5 467s eq2 20 19 47042 2476 49.8 -69.5 -69.5 467s 467s The covariance matrix of the residuals used for estimation 467s eq1 eq2 467s eq1 2448 2439 467s eq2 2439 2475 467s 467s The covariance matrix of the residuals 467s eq1 eq2 467s eq1 2448 2439 467s eq2 2439 2476 467s 467s The correlations of the residuals 467s eq1 eq2 467s eq1 1.000 0.988 467s eq2 0.988 1.000 467s 467s 467s SUR estimates for 'eq1' (equation 1) 467s Model Formula: consump ~ trend - 1 467s 467s Estimate Std. Error t value Pr(>|t|) 467s trend 7.388 0.923 8 9.4e-10 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s 467s Residual standard error: 49.476 on 19 degrees of freedom 467s Number of observations: 20 Degrees of Freedom: 19 467s SSR: 46509.787 MSE: 2447.884 Root MSE: 49.476 467s Multiple R-Squared: -172.47 Adjusted R-Squared: -172.47 467s 467s 467s SUR estimates for 'eq2' (equation 2) 467s Model Formula: price ~ trend - 1 467s 467s Estimate Std. Error t value Pr(>|t|) 467s trend 7.388 0.923 8 9.4e-10 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s 467s Residual standard error: 49.758 on 19 degrees of freedom 467s Number of observations: 20 Degrees of Freedom: 19 467s SSR: 47041.803 MSE: 2475.884 Root MSE: 49.758 467s Multiple R-Squared: -69.501 Adjusted R-Squared: -69.501 467s 467s > nobs( fitsurS4 ) 467s [1] 40 467s > fitsurS5 <- systemfit( 467s + list( consump ~ 1, price ~ 1 ), "SUR", 467s + data = Kmenta, useMatrix = useMatrix ) 467s > print( summary( fitsurS5 ) ) 467s 467s systemfit results 467s method: SUR 467s 467s N DF SSR detRCov OLS-R2 McElroy-R2 467s system 40 38 935 491 0 0 467s 467s N DF SSR MSE RMSE R2 Adj R2 467s eq1 20 19 268 14.1 3.76 0 0 467s eq2 20 19 667 35.1 5.93 0 0 467s 467s The covariance matrix of the residuals used for estimation 467s eq1 eq2 467s eq1 14.11 2.18 467s eq2 2.18 35.12 467s 467s The covariance matrix of the residuals 467s eq1 eq2 467s eq1 14.11 2.18 467s eq2 2.18 35.12 467s 467s The correlations of the residuals 467s eq1 eq2 467s eq1 1.0000 0.0981 467s eq2 0.0981 1.0000 467s 467s 467s SUR estimates for 'eq1' (equation 1) 467s Model Formula: consump ~ 1 467s 467s Estimate Std. Error t value Pr(>|t|) 467s (Intercept) 100.90 0.84 120 <2e-16 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s 467s Residual standard error: 3.756 on 19 degrees of freedom 467s Number of observations: 20 Degrees of Freedom: 19 467s SSR: 268.114 MSE: 14.111 Root MSE: 3.756 467s Multiple R-Squared: 0 Adjusted R-Squared: 0 467s 467s 467s SUR estimates for 'eq2' (equation 2) 467s Model Formula: price ~ 1 467s 467s Estimate Std. Error t value Pr(>|t|) 467s (Intercept) 100.02 1.33 75.5 <2e-16 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s 467s Residual standard error: 5.926 on 19 degrees of freedom 467s Number of observations: 20 Degrees of Freedom: 19 467s SSR: 667.251 MSE: 35.118 Root MSE: 5.926 467s Multiple R-Squared: 0 Adjusted R-Squared: 0 467s 467s > nobs( fitsurS5 ) 467s [1] 40 467s > 467s > 467s > ## **************** shorter summaries ********************** 467s > print( summary( fitsur1e2, useDfSys = TRUE, equations = FALSE ) ) 467s 467s systemfit results 467s method: SUR 467s 467s N DF SSR detRCov OLS-R2 McElroy-R2 467s system 40 33 172 -0.896 0.679 1.01 467s 467s N DF SSR MSE RMSE R2 Adj R2 467s demand 20 17 66.8 3.93 1.98 0.751 0.722 467s supply 20 16 105.3 6.58 2.57 0.607 0.534 467s 467s The covariance matrix of the residuals used for estimation 467s demand supply 467s demand 3.73 4.28 467s supply 4.28 5.78 467s 467s warning: this covariance matrix is NOT positive semidefinit! 467s 467s The covariance matrix of the residuals 467s demand supply 467s demand 3.93 5.17 467s supply 5.17 6.58 467s 467s The correlations of the residuals 467s demand supply 467s demand 1.000 0.984 467s supply 0.984 1.000 467s 467s 467s Coefficients: 467s Estimate Std. Error t value Pr(>|t|) 467s demand_(Intercept) 99.2120 7.5127 13.21 1.0e-14 *** 467s demand_price -0.2667 0.0877 -3.04 0.0046 ** 467s demand_income 0.2908 0.0406 7.16 3.3e-08 *** 467s supply_(Intercept) 63.0768 10.9735 5.75 2.0e-06 *** 467s supply_price 0.1439 0.0943 1.52 0.1368 467s supply_farmPrice 0.2064 0.0384 5.37 6.1e-06 *** 467s supply_trend 0.3325 0.0640 5.19 1.0e-05 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s > 467s > print( summary( fitsur2e, useDfSys = FALSE, residCov = FALSE ) ) 467s 467s systemfit results 467s method: SUR 467s 467s N DF SSR detRCov OLS-R2 McElroy-R2 467s system 40 34 180 0.62 0.663 0.707 467s 467s N DF SSR MSE RMSE R2 Adj R2 467s demand 20 17 72.6 4.27 2.07 0.729 0.697 467s supply 20 16 107.9 6.75 2.60 0.597 0.522 467s 467s 467s SUR estimates for 'demand' (equation 1) 467s Model Formula: consump ~ price + income 467s 467s Estimate Std. Error t value Pr(>|t|) 467s (Intercept) 98.7799 6.9687 14.17 7.6e-11 *** 467s price -0.2354 0.0795 -2.96 0.0088 ** 467s income 0.2631 0.0344 7.66 6.6e-07 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s 467s Residual standard error: 2.066 on 17 degrees of freedom 467s Number of observations: 20 Degrees of Freedom: 17 467s SSR: 72.577 MSE: 4.269 Root MSE: 2.066 467s Multiple R-Squared: 0.729 Adjusted R-Squared: 0.697 467s 467s 467s SUR estimates for 'supply' (equation 2) 467s Model Formula: consump ~ price + farmPrice + trend 467s 467s Estimate Std. Error t value Pr(>|t|) 467s (Intercept) 67.6039 9.5712 7.06 2.7e-06 *** 467s price 0.1328 0.0853 1.56 0.14 467s farmPrice 0.1785 0.0305 5.85 2.5e-05 *** 467s trend 0.2631 0.0344 7.66 9.7e-07 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s 467s Residual standard error: 2.597 on 16 degrees of freedom 467s Number of observations: 20 Degrees of Freedom: 16 467s SSR: 107.917 MSE: 6.745 Root MSE: 2.597 467s Multiple R-Squared: 0.597 Adjusted R-Squared: 0.522 467s 467s > 467s > print( summary( fitsur3 ), equations = FALSE ) 467s 467s systemfit results 467s method: SUR 467s 467s N DF SSR detRCov OLS-R2 McElroy-R2 467s system 40 34 179 0.933 0.665 0.753 467s 467s N DF SSR MSE RMSE R2 Adj R2 467s demand 20 17 71.6 4.21 2.05 0.733 0.702 467s supply 20 16 107.8 6.74 2.60 0.598 0.523 467s 467s The covariance matrix of the residuals used for estimation 467s demand supply 467s demand 3.78 4.47 467s supply 4.47 5.94 467s 467s The covariance matrix of the residuals 467s demand supply 467s demand 4.21 5.24 467s supply 5.24 6.74 467s 467s The correlations of the residuals 467s demand supply 467s demand 1.000 0.983 467s supply 0.983 1.000 467s 467s 467s Coefficients: 467s Estimate Std. Error t value Pr(>|t|) 467s demand_(Intercept) 98.8408 7.5581 13.08 8.0e-15 *** 467s demand_price -0.2398 0.0860 -2.79 0.0086 ** 467s demand_income 0.2670 0.0368 7.25 2.2e-08 *** 467s supply_(Intercept) 67.4283 10.6647 6.32 3.3e-07 *** 467s supply_price 0.1332 0.0953 1.40 0.1713 467s supply_farmPrice 0.1795 0.0337 5.33 6.3e-06 *** 467s supply_trend 0.2670 0.0368 7.25 2.2e-08 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s > 467s > print( summary( fitsur4r3 ), residCov = FALSE, equations = FALSE ) 467s 467s systemfit results 467s method: SUR 467s 467s N DF SSR detRCov OLS-R2 McElroy-R2 467s system 40 35 173 0.217 0.677 0.702 467s 467s N DF SSR MSE RMSE R2 Adj R2 467s demand 20 17 66.4 3.91 1.98 0.752 0.723 467s supply 20 16 106.9 6.68 2.58 0.601 0.526 467s 467s 467s Coefficients: 467s Estimate Std. Error t value Pr(>|t|) 467s demand_(Intercept) 93.1978 7.3168 12.74 1.1e-14 *** 467s demand_price -0.2381 0.0829 -2.87 0.0069 ** 467s demand_income 0.3231 0.0170 18.96 < 2e-16 *** 467s supply_(Intercept) 49.3676 7.4381 6.64 1.1e-07 *** 467s supply_price 0.2619 0.0829 3.16 0.0033 ** 467s supply_farmPrice 0.2271 0.0171 13.29 3.1e-15 *** 467s supply_trend 0.3231 0.0170 18.96 < 2e-16 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s > 467s > print( summary( fitsur5, residCov = FALSE ), equations = FALSE ) 467s 467s systemfit results 467s method: SUR 467s 467s N DF SSR detRCov OLS-R2 McElroy-R2 467s system 40 35 165 1.76 0.691 0.69 467s 467s N DF SSR MSE RMSE R2 Adj R2 467s demand 20 17 64 3.76 1.94 0.761 0.733 467s supply 20 16 101 6.34 2.52 0.622 0.551 467s 467s 467s Coefficients: 467s Estimate Std. Error t value Pr(>|t|) 467s demand_(Intercept) 96.8275 7.4665 12.97 6.2e-15 *** 467s demand_price -0.2798 0.0840 -3.33 0.002 ** 467s demand_income 0.3286 0.0206 15.93 < 2e-16 *** 467s supply_(Intercept) 52.9386 7.6655 6.91 5.1e-08 *** 467s supply_price 0.2202 0.0840 2.62 0.013 * 467s supply_farmPrice 0.2327 0.0212 10.97 7.2e-13 *** 467s supply_trend 0.3286 0.0206 15.93 < 2e-16 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s > 467s > print( summary( fitsur5w, equations = FALSE, residCov = FALSE ), 467s + equations = TRUE ) 467s 467s systemfit results 467s method: SUR 467s 467s N DF SSR detRCov OLS-R2 McElroy-R2 467s system 40 35 166 1.75 0.69 0.691 467s 467s N DF SSR MSE RMSE R2 Adj R2 467s demand 20 17 64.2 3.77 1.94 0.761 0.733 467s supply 20 16 102.0 6.37 2.52 0.620 0.548 467s 467s 467s SUR estimates for 'demand' (equation 1) 467s Model Formula: consump ~ price + income 467s 467s Estimate Std. Error t value Pr(>|t|) 467s (Intercept) 96.4421 7.4234 12.99 6e-15 *** 467s price -0.2753 0.0838 -3.29 0.0023 ** 467s income 0.3280 0.0202 16.21 <2e-16 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s 467s Residual standard error: 1.943 on 17 degrees of freedom 467s Number of observations: 20 Degrees of Freedom: 17 467s SSR: 64.16 MSE: 3.774 Root MSE: 1.943 467s Multiple R-Squared: 0.761 Adjusted R-Squared: 0.733 467s 467s 467s SUR estimates for 'supply' (equation 2) 467s Model Formula: consump ~ price + farmPrice + trend 467s 467s Estimate Std. Error t value Pr(>|t|) 467s (Intercept) 52.5761 7.6099 6.91 5.0e-08 *** 467s price 0.2247 0.0838 2.68 0.011 * 467s farmPrice 0.2318 0.0208 11.14 4.7e-13 *** 467s trend 0.3280 0.0202 16.21 < 2e-16 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s 467s Residual standard error: 2.524 on 16 degrees of freedom 467s Number of observations: 20 Degrees of Freedom: 16 467s SSR: 101.967 MSE: 6.373 Root MSE: 2.524 467s Multiple R-Squared: 0.62 Adjusted R-Squared: 0.548 467s 467s > 467s > print( summary( fitsuri1r3, useDfSys = FALSE ), residCov = FALSE ) 467s 467s systemfit results 467s method: iterated SUR 467s 467s convergence achieved after 7 iterations 467s 467s N DF SSR detRCov OLS-R2 McElroy-R2 467s system 40 33 109 4.06 0.884 0.96 467s 467s N DF SSR MSE RMSE R2 Adj R2 467s demand 20 17 66.8 3.93 1.98 0.751 0.721 467s supply 20 16 41.7 2.61 1.61 0.937 0.926 467s 467s 467s SUR estimates for 'demand' (equation 1) 467s Model Formula: consump ~ price + income 467s 467s Estimate Std. Error t value Pr(>|t|) 467s (Intercept) 93.5427 7.3858 12.67 4.4e-10 *** 467s price -0.2285 0.0877 -2.60 0.019 * 467s income 0.3097 0.0458 6.76 3.3e-06 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s 467s Residual standard error: 1.983 on 17 degrees of freedom 467s Number of observations: 20 Degrees of Freedom: 17 467s SSR: 66.826 MSE: 3.931 Root MSE: 1.983 467s Multiple R-Squared: 0.751 Adjusted R-Squared: 0.721 467s 467s 467s SUR estimates for 'supply' (equation 2) 467s Model Formula: price ~ income + farmPrice + trend 467s 467s Estimate Std. Error t value Pr(>|t|) 467s (Intercept) 89.1830 3.3861 26.3 1.3e-14 *** 467s income 0.6639 0.0423 15.7 3.8e-11 *** 467s farmPrice -0.4715 0.0370 -12.8 8.5e-10 *** 467s trend -0.7955 0.0645 -12.3 1.4e-09 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s 467s Residual standard error: 1.615 on 16 degrees of freedom 467s Number of observations: 20 Degrees of Freedom: 16 467s SSR: 41.708 MSE: 2.607 Root MSE: 1.615 467s Multiple R-Squared: 0.937 Adjusted R-Squared: 0.926 467s 467s > 467s > print( summary( fitsuri2 ), residCov = FALSE ) 467s 467s systemfit results 467s method: iterated SUR 467s 467s convergence achieved after 21 iterations 467s 467s N DF SSR detRCov OLS-R2 McElroy-R2 467s system 40 34 587 110 0.372 0.669 467s 467s N DF SSR MSE RMSE R2 Adj R2 467s demand 20 17 67 3.94 1.99 0.75 0.721 467s supply 20 16 520 32.52 5.70 0.22 0.074 467s 467s 467s SUR estimates for 'demand' (equation 1) 467s Model Formula: consump ~ price + income 467s 467s Estimate Std. Error t value Pr(>|t|) 467s (Intercept) 107.3678 7.4986 14.32 4.4e-16 *** 467s price -0.3945 0.0912 -4.33 0.00013 *** 467s income 0.3382 0.0466 7.25 2.1e-08 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s 467s Residual standard error: 1.986 on 17 degrees of freedom 467s Number of observations: 20 Degrees of Freedom: 17 467s SSR: 67.024 MSE: 3.943 Root MSE: 1.986 467s Multiple R-Squared: 0.75 Adjusted R-Squared: 0.721 467s 467s 467s SUR estimates for 'supply' (equation 2) 467s Model Formula: price ~ income + farmPrice + trend 467s 467s Estimate Std. Error t value Pr(>|t|) 467s (Intercept) 85.0448 12.1069 7.02 4.2e-08 *** 467s income 0.3125 0.1233 2.53 0.016 * 467s farmPrice -0.1972 0.1157 -1.70 0.097 . 467s trend 0.3382 0.0466 7.25 2.1e-08 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s 467s Residual standard error: 5.703 on 16 degrees of freedom 467s Number of observations: 20 Degrees of Freedom: 16 467s SSR: 520.329 MSE: 32.521 Root MSE: 5.703 467s Multiple R-Squared: 0.22 Adjusted R-Squared: 0.074 467s 467s > 467s > print( summary( fitsuri3e, residCov = FALSE, equations = FALSE ) ) 467s 467s systemfit results 467s method: iterated SUR 467s 467s convergence achieved after 22 iterations 467s 467s N DF SSR detRCov OLS-R2 McElroy-R2 467s system 40 34 588 74.9 0.372 0.664 467s 467s N DF SSR MSE RMSE R2 Adj R2 467s demand 20 17 67.5 3.97 1.99 0.748 0.719 467s supply 20 16 520.2 32.51 5.70 0.220 0.074 467s 467s 467s Coefficients: 467s Estimate Std. Error t value Pr(>|t|) 467s demand_(Intercept) 107.8051 6.9270 15.56 < 2e-16 *** 467s demand_price -0.3986 0.0843 -4.73 3.8e-05 *** 467s demand_income 0.3379 0.0431 7.84 4.0e-09 *** 467s supply_(Intercept) 85.1071 10.8287 7.86 3.8e-09 *** 467s supply_income 0.3106 0.1101 2.82 0.0079 ** 467s supply_farmPrice -0.1960 0.1034 -1.89 0.0667 . 467s supply_trend 0.3379 0.0431 7.84 4.0e-09 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s > 467s > print( summary( fitsurio4, residCov = FALSE ), equations = FALSE ) 467s 467s systemfit results 467s method: iterated SUR 467s 467s convergence achieved after 10 iterations 467s 467s N DF SSR detRCov OLS-R2 McElroy-R2 467s system 40 35 176 1.74 0.671 0.705 467s 467s N DF SSR MSE RMSE R2 Adj R2 467s demand 20 17 67.2 3.95 1.99 0.749 0.720 467s supply 20 16 109.2 6.83 2.61 0.593 0.516 467s 467s 467s Coefficients: 467s Estimate Std. Error t value Pr(>|t|) 467s demand_(Intercept) 92.4262 7.3543 12.57 1.6e-14 *** 467s demand_price -0.2276 0.0850 -2.68 0.0112 * 467s demand_income 0.3203 0.0185 17.32 < 2e-16 *** 467s supply_(Intercept) 48.7295 7.4587 6.53 1.5e-07 *** 467s supply_price 0.2724 0.0850 3.20 0.0029 ** 467s supply_farmPrice 0.2232 0.0190 11.76 1.0e-13 *** 467s supply_trend 0.3203 0.0185 17.32 < 2e-16 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s > print( summary( fitsuri4, equations = FALSE ), residCov = FALSE ) 467s 467s systemfit results 467s method: iterated SUR 467s 467s convergence achieved after 19 iterations 467s 467s N DF SSR detRCov OLS-R2 McElroy-R2 467s system 40 35 575 121 0.385 0.637 467s 467s N DF SSR MSE RMSE R2 Adj R2 467s demand 20 17 65.5 3.85 1.96 0.756 0.727 467s supply 20 16 509.3 31.83 5.64 0.237 0.094 467s 467s 467s Coefficients: 467s Estimate Std. Error t value Pr(>|t|) 467s demand_(Intercept) 98.0356 6.7437 14.54 2.2e-16 *** 467s demand_price -0.2646 0.0777 -3.40 0.0017 ** 467s demand_income 0.3007 0.0436 6.89 5.3e-08 *** 467s supply_(Intercept) 90.0046 10.4367 8.62 3.5e-10 *** 467s supply_income 0.2354 0.0777 3.03 0.0046 ** 467s supply_farmPrice -0.1667 0.1108 -1.50 0.1416 467s supply_trend 0.3007 0.0436 6.89 5.3e-08 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s > 467s > print( summary( fitsuri4w, useDfSys = FALSE, equations = FALSE ) ) 467s 467s systemfit results 467s method: iterated SUR 467s 467s convergence achieved after 18 iterations 467s 467s N DF SSR detRCov OLS-R2 McElroy-R2 467s system 40 35 575 121 0.385 0.637 467s 467s N DF SSR MSE RMSE R2 Adj R2 467s demand 20 17 65.5 3.85 1.96 0.756 0.727 467s supply 20 16 509.3 31.83 5.64 0.237 0.094 467s 467s The covariance matrix of the residuals used for estimation 467s demand supply 467s demand 3.85 1.23 467s supply 1.23 31.83 467s 467s The covariance matrix of the residuals 467s demand supply 467s demand 3.85 1.23 467s supply 1.23 31.83 467s 467s The correlations of the residuals 467s demand supply 467s demand 1.000 0.111 467s supply 0.111 1.000 467s 467s 467s Coefficients: 467s Estimate Std. Error t value Pr(>|t|) 467s demand_(Intercept) 98.0361 6.7437 14.54 5.1e-11 *** 467s demand_price -0.2646 0.0777 -3.40 0.0034 ** 467s demand_income 0.3007 0.0436 6.89 2.6e-06 *** 467s supply_(Intercept) 90.0052 10.4368 8.62 2.1e-07 *** 467s supply_income 0.2354 0.0777 3.03 0.0080 ** 467s supply_farmPrice -0.1667 0.1108 -1.50 0.1521 467s supply_trend 0.3007 0.0436 6.89 3.6e-06 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s > 467s > print( summary( fitsurio5r2, equations = FALSE ) ) 467s 467s systemfit results 467s method: iterated SUR 467s 467s warning: convergence not achieved after 100 iterations 467s 467s N DF SSR detRCov OLS-R2 McElroy-R2 467s system 40 35 253 -1.67 0.527 0.927 467s 467s N DF SSR MSE RMSE R2 Adj R2 467s demand 20 17 95.8 5.63 2.37 0.643 0.601 467s supply 20 16 157.7 9.86 3.14 0.412 0.301 467s 467s The covariance matrix of the residuals used for estimation 467s demand supply 467s demand 4.26 5.29 467s supply 5.29 6.69 467s 467s warning: this covariance matrix is NOT positive semidefinit! 467s 467s The covariance matrix of the residuals 467s demand supply 467s demand 5.63 7.56 467s supply 7.56 9.86 467s 467s The correlations of the residuals 467s demand supply 467s demand 1.000 0.982 467s supply 0.982 1.000 467s 467s 467s Coefficients: 467s Estimate Std. Error t value Pr(>|t|) 467s demand_(Intercept) 78.0342 7.1638 10.89 8.6e-13 *** 467s demand_price -0.0647 0.0815 -0.79 0.43 467s demand_income 0.3007 0.0131 23.01 < 2e-16 *** 467s supply_(Intercept) 34.1958 7.2257 4.73 3.6e-05 *** 467s supply_price 0.4353 0.0815 5.34 5.7e-06 *** 467s supply_farmPrice 0.2070 0.0124 16.68 < 2e-16 *** 467s supply_trend 0.3007 0.0131 23.01 < 2e-16 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s > print( summary( fitsuri5r2 ), residCov = FALSE ) 467s 467s systemfit results 467s method: iterated SUR 467s 467s convergence achieved after 21 iterations 467s 467s N DF SSR detRCov OLS-R2 McElroy-R2 467s system 40 35 576 121 0.384 0.637 467s 467s N DF SSR MSE RMSE R2 Adj R2 467s demand 20 17 65.4 3.85 1.96 0.756 0.727 467s supply 20 16 510.8 31.92 5.65 0.235 0.091 467s 467s 467s SUR estimates for 'demand' (equation 1) 467s Model Formula: consump ~ price + income 467s 467s Estimate Std. Error t value Pr(>|t|) 467s (Intercept) 98.2200 6.7593 14.53 2.2e-16 *** 467s price -0.2669 0.0778 -3.43 0.0016 ** 467s income 0.3011 0.0435 6.92 4.9e-08 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s 467s Residual standard error: 1.962 on 17 degrees of freedom 467s Number of observations: 20 Degrees of Freedom: 17 467s SSR: 65.447 MSE: 3.85 Root MSE: 1.962 467s Multiple R-Squared: 0.756 Adjusted R-Squared: 0.727 467s 467s 467s SUR estimates for 'supply' (equation 2) 467s Model Formula: price ~ income + farmPrice + trend 467s 467s Estimate Std. Error t value Pr(>|t|) 467s (Intercept) 90.2167 10.4342 8.65 3.3e-10 *** 467s income 0.2331 0.0778 3.00 0.005 ** 467s farmPrice -0.1666 0.1111 -1.50 0.143 467s trend 0.3011 0.0435 6.92 4.9e-08 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s 467s Residual standard error: 5.65 on 16 degrees of freedom 467s Number of observations: 20 Degrees of Freedom: 16 467s SSR: 510.75 MSE: 31.922 Root MSE: 5.65 467s Multiple R-Squared: 0.235 Adjusted R-Squared: 0.091 467s 467s > 467s > 467s > ## ****************** residuals ************************** 467s > print( residuals( fitsur1e2 ) ) 467s demand supply 467s 1 0.615 0.41825 467s 2 -0.598 -0.00625 467s 3 2.419 2.75649 467s 4 1.609 1.81727 467s 5 2.145 2.53566 467s 6 1.332 1.53338 467s 7 1.727 2.25581 467s 8 -2.718 -3.56834 467s 9 -1.229 -2.02733 467s 10 2.088 2.53245 467s 11 -0.789 -1.40733 467s 12 -2.799 -3.01416 467s 13 -1.831 -2.30119 467s 14 -0.461 0.01871 467s 15 1.974 2.93624 467s 16 -3.291 -4.00484 467s 17 -0.652 -0.45580 467s 18 -1.899 -3.18683 467s 19 2.030 2.18284 467s 20 0.329 0.98497 467s > print( residuals( fitsur1e2$eq[[ 2 ]] ) ) 467s 1 2 3 4 5 6 7 8 467s 0.41825 -0.00625 2.75649 1.81727 2.53566 1.53338 2.25581 -3.56834 467s 9 10 11 12 13 14 15 16 467s -2.02733 2.53245 -1.40733 -3.01416 -2.30119 0.01871 2.93624 -4.00484 467s 17 18 19 20 467s -0.45580 -3.18683 2.18284 0.98497 467s > 467s > print( residuals( fitsur1w ) ) 467s demand supply 467s 1 0.696 0.4713 467s 2 -0.561 0.0197 467s 3 2.455 2.7782 467s 4 1.643 1.8366 467s 5 2.110 2.4709 467s 6 1.304 1.4773 467s 7 1.692 2.2079 467s 8 -2.756 -3.6663 467s 9 -1.253 -2.0985 467s 10 2.078 2.5321 467s 11 -0.675 -1.2705 467s 12 -2.649 -2.8068 467s 13 -1.706 -2.1305 467s 14 -0.419 0.1150 467s 15 1.887 2.8772 467s 16 -3.364 -4.1013 467s 17 -0.762 -0.5650 467s 18 -1.918 -3.2183 467s 19 1.978 2.1637 467s 20 0.218 0.9075 467s > print( residuals( fitsur1w$eq[[ 2 ]] ) ) 467s 1 2 3 4 5 6 7 8 9 10 467s 0.4713 0.0197 2.7782 1.8366 2.4709 1.4773 2.2079 -3.6663 -2.0985 2.5321 467s 11 12 13 14 15 16 17 18 19 20 467s -1.2705 -2.8068 -2.1305 0.1150 2.8772 -4.1013 -0.5650 -3.2183 2.1637 0.9075 467s > 467s > print( residuals( fitsur2e ) ) 467s demand supply 467s 1 0.325 -0.200 467s 2 -0.729 -0.481 467s 3 2.288 2.342 467s 4 1.487 1.457 467s 5 2.271 2.527 467s 6 1.432 1.537 467s 7 1.851 2.275 467s 8 -2.582 -3.322 467s 9 -1.143 -1.834 467s 10 2.124 2.512 467s 11 -1.193 -1.885 467s 12 -3.332 -3.705 467s 13 -2.280 -2.813 467s 14 -0.614 -0.177 467s 15 2.281 3.353 467s 16 -3.032 -3.407 467s 17 -0.260 0.233 467s 18 -1.834 -2.737 467s 19 2.215 2.632 467s 20 0.726 1.692 467s > print( residuals( fitsur2e$eq[[ 1 ]] ) ) 467s 1 2 3 4 5 6 7 8 9 10 11 467s 0.325 -0.729 2.288 1.487 2.271 1.432 1.851 -2.582 -1.143 2.124 -1.193 467s 12 13 14 15 16 17 18 19 20 467s -3.332 -2.280 -0.614 2.281 -3.032 -0.260 -1.834 2.215 0.726 467s > 467s > print( residuals( fitsur3 ) ) 467s demand supply 467s 1 0.366 -0.164 467s 2 -0.711 -0.452 467s 3 2.307 2.368 467s 4 1.504 1.479 467s 5 2.253 2.535 467s 6 1.418 1.544 467s 7 1.833 2.279 467s 8 -2.601 -3.327 467s 9 -1.155 -1.839 467s 10 2.119 2.513 467s 11 -1.136 -1.869 467s 12 -3.257 -3.682 467s 13 -2.217 -2.798 467s 14 -0.593 -0.175 467s 15 2.238 3.332 467s 16 -3.069 -3.436 467s 17 -0.315 0.199 467s 18 -1.844 -2.764 467s 19 2.189 2.604 467s 20 0.671 1.654 467s > print( residuals( fitsur3$eq[[ 2 ]] ) ) 467s 1 2 3 4 5 6 7 8 9 10 11 467s -0.164 -0.452 2.368 1.479 2.535 1.544 2.279 -3.327 -1.839 2.513 -1.869 467s 12 13 14 15 16 17 18 19 20 467s -3.682 -2.798 -0.175 3.332 -3.436 0.199 -2.764 2.604 1.654 467s > 467s > print( residuals( fitsur4r3 ) ) 467s demand supply 467s 1 0.934 0.265 467s 2 -0.721 -0.638 467s 3 2.348 2.232 467s 4 1.459 1.196 467s 5 2.129 2.428 467s 6 1.253 1.318 467s 7 1.514 1.913 467s 8 -3.185 -4.425 467s 9 -1.097 -1.870 467s 10 2.619 3.483 467s 11 0.135 -0.260 467s 12 -2.097 -2.275 467s 13 -1.496 -2.085 467s 14 -0.201 0.516 467s 15 1.934 3.439 467s 16 -3.491 -3.942 467s 17 -0.229 0.913 467s 18 -2.236 -3.503 467s 19 1.440 1.736 467s 20 -1.012 -0.441 467s > print( residuals( fitsur4r3$eq[[ 1 ]] ) ) 467s 1 2 3 4 5 6 7 8 9 10 11 467s 0.934 -0.721 2.348 1.459 2.129 1.253 1.514 -3.185 -1.097 2.619 0.135 467s 12 13 14 15 16 17 18 19 20 467s -2.097 -1.496 -0.201 1.934 -3.491 -0.229 -2.236 1.440 -1.012 467s > 467s > print( residuals( fitsur5 ) ) 467s demand supply 467s 1 1.0025 0.3219 467s 2 -0.5449 -0.4286 467s 3 2.4949 2.4014 467s 4 1.6426 1.4106 467s 5 2.0329 2.2956 467s 6 1.2129 1.2545 467s 7 1.5260 1.9262 467s 8 -3.0444 -4.2868 467s 9 -1.2406 -2.0779 467s 10 2.3001 3.0973 467s 11 -0.0303 -0.4650 467s 12 -2.0337 -2.1783 467s 13 -1.3041 -1.8356 467s 14 -0.2155 0.5292 467s 15 1.6991 3.1787 467s 16 -3.5980 -4.0840 467s 17 -0.7860 0.2371 467s 18 -2.1070 -3.3544 467s 19 1.6070 1.9694 467s 20 -0.6134 0.0885 467s > print( residuals( fitsur5$eq[[ 2 ]] ) ) 467s 1 2 3 4 5 6 7 8 9 10 467s 0.3219 -0.4286 2.4014 1.4106 2.2956 1.2545 1.9262 -4.2868 -2.0779 3.0973 467s 11 12 13 14 15 16 17 18 19 20 467s -0.4650 -2.1783 -1.8356 0.5292 3.1787 -4.0840 0.2371 -3.3544 1.9694 0.0885 467s > 467s > print( residuals( fitsuri1r3 ) ) 467s demand supply 467s 1 0.7952 0.123 467s 2 -0.7614 -1.393 467s 3 2.3039 -0.829 467s 4 1.4250 -0.430 467s 5 2.1792 -1.213 467s 6 1.2979 -0.653 467s 7 1.5795 -1.266 467s 8 -3.0935 2.153 467s 9 -1.0750 1.548 467s 10 2.5876 -1.582 467s 11 -0.0991 0.990 467s 12 -2.3616 0.460 467s 13 -1.6970 1.335 467s 14 -0.2819 -1.054 467s 15 2.0557 -2.339 467s 16 -3.3745 1.734 467s 17 -0.1140 -1.054 467s 18 -2.1822 3.461 467s 19 1.5612 0.318 467s 20 -0.7450 -0.308 467s > print( residuals( fitsuri1r3$eq[[ 1 ]] ) ) 467s 1 2 3 4 5 6 7 8 9 10 467s 0.7952 -0.7614 2.3039 1.4250 2.1792 1.2979 1.5795 -3.0935 -1.0750 2.5876 467s 11 12 13 14 15 16 17 18 19 20 467s -0.0991 -2.3616 -1.6970 -0.2819 2.0557 -3.3745 -0.1140 -2.1822 1.5612 -0.7450 467s > 467s > print( residuals( fitsuri2 ) ) 467s demand supply 467s 1 1.1341 6.955 467s 2 -0.0587 7.587 467s 3 2.8946 6.701 467s 4 2.1508 6.768 467s 5 1.7798 1.930 467s 6 1.1200 2.315 467s 7 1.5920 2.230 467s 8 -2.5983 4.980 467s 9 -1.6414 -0.392 467s 10 1.3742 -5.140 467s 11 -0.6115 -3.174 467s 12 -1.9764 -0.804 467s 13 -0.8493 1.012 467s 14 -0.2942 -3.282 467s 15 1.0840 -7.042 467s 16 -3.8500 -4.140 467s 17 -2.3259 -12.628 467s 18 -1.7141 -1.498 467s 19 2.1409 -2.683 467s 20 0.6494 0.305 467s > print( residuals( fitsuri2$eq[[ 2 ]] ) ) 467s 1 2 3 4 5 6 7 8 9 10 467s 6.955 7.587 6.701 6.768 1.930 2.315 2.230 4.980 -0.392 -5.140 467s 11 12 13 14 15 16 17 18 19 20 467s -3.174 -0.804 1.012 -3.282 -7.042 -4.140 -12.628 -1.498 -2.683 0.305 467s > 467s > print( residuals( fitsuri3e ) ) 467s demand supply 467s 1 1.1327 6.932 467s 2 -0.0412 7.582 467s 3 2.9085 6.695 467s 4 2.1695 6.766 467s 5 1.7721 1.915 467s 6 1.1185 2.305 467s 7 1.5978 2.229 467s 8 -2.5761 4.982 467s 9 -1.6564 -0.410 467s 10 1.3358 -5.161 467s 11 -0.6458 -3.196 467s 12 -1.9868 -0.807 467s 13 -0.8408 1.021 467s 14 -0.3012 -3.275 467s 15 1.0652 -7.037 467s 16 -3.8545 -4.135 467s 17 -2.3819 -12.646 467s 18 -1.6959 -1.478 467s 19 2.1679 -2.647 467s 20 0.7125 0.366 467s > print( residuals( fitsuri3e$eq[[ 1 ]] ) ) 467s 1 2 3 4 5 6 7 8 9 10 467s 1.1327 -0.0412 2.9085 2.1695 1.7721 1.1185 1.5978 -2.5761 -1.6564 1.3358 467s 11 12 13 14 15 16 17 18 19 20 467s -0.6458 -1.9868 -0.8408 -0.3012 1.0652 -3.8545 -2.3819 -1.6959 2.1679 0.7125 467s > 467s > print( residuals( fitsurio4 ) ) 467s demand supply 467s 1 0.9019 0.240 467s 2 -0.7658 -0.697 467s 3 2.3097 2.184 467s 4 1.4141 1.136 467s 5 2.1571 2.490 467s 6 1.2670 1.356 467s 7 1.5188 1.928 467s 8 -3.2060 -4.430 467s 9 -1.0620 -1.789 467s 10 2.6864 3.589 467s 11 0.1438 -0.248 467s 12 -2.1427 -2.369 467s 13 -1.5629 -2.210 467s 14 -0.2076 0.479 467s 15 2.0012 3.526 467s 16 -3.4530 -3.876 467s 17 -0.0902 1.129 467s 18 -2.2581 -3.539 467s 19 1.4172 1.671 467s 20 -1.0688 -0.569 467s > print( residuals( fitsurio4$eq[[ 2 ]] ) ) 467s 1 2 3 4 5 6 7 8 9 10 11 467s 0.240 -0.697 2.184 1.136 2.490 1.356 1.928 -4.430 -1.789 3.589 -0.248 467s 12 13 14 15 16 17 18 19 20 467s -2.369 -2.210 0.479 3.526 -3.876 1.129 -3.539 1.671 -0.569 467s > print( residuals( fitsuri4 ) ) 467s demand supply 467s 1 0.7146 5.775 467s 2 -0.6076 7.198 467s 3 2.4197 6.280 467s 4 1.5931 6.531 467s 5 2.1268 1.465 467s 6 1.3043 2.021 467s 7 1.6685 2.261 467s 8 -2.8295 5.275 467s 9 -1.2125 -0.890 467s 10 2.1921 -5.945 467s 11 -0.5521 -4.407 467s 12 -2.5920 -1.482 467s 13 -1.7095 0.895 467s 14 -0.3902 -3.220 467s 15 1.9290 -6.617 467s 16 -3.3627 -3.607 467s 17 -0.6125 -12.896 467s 18 -1.9758 -0.562 467s 19 1.8877 -1.126 467s 20 0.0085 3.051 467s > print( residuals( fitsuri4$eq[[ 2 ]] ) ) 467s 1 2 3 4 5 6 7 8 9 10 467s 5.775 7.198 6.280 6.531 1.465 2.021 2.261 5.275 -0.890 -5.945 467s 11 12 13 14 15 16 17 18 19 20 467s -4.407 -1.482 0.895 -3.220 -6.617 -3.607 -12.896 -0.562 -1.126 3.051 467s > 467s > print( residuals( fitsuri4w ) ) 467s demand supply 467s 1 0.71463 5.775 467s 2 -0.60754 7.198 467s 3 2.41972 6.280 467s 4 1.59308 6.531 467s 5 2.12679 1.465 467s 6 1.30430 2.021 467s 7 1.66846 2.262 467s 8 -2.82945 5.275 467s 9 -1.21248 -0.890 467s 10 2.19209 -5.946 467s 11 -0.55215 -4.407 467s 12 -2.59194 -1.482 467s 13 -1.70948 0.895 467s 14 -0.39018 -3.220 467s 15 1.92897 -6.617 467s 16 -3.36276 -3.607 467s 17 -0.61256 -12.896 467s 18 -1.97579 -0.562 467s 19 1.88776 -1.126 467s 20 0.00854 3.051 467s > print( residuals( fitsuri4w$eq[[ 2 ]] ) ) 467s 1 2 3 4 5 6 7 8 9 10 467s 5.775 7.198 6.280 6.531 1.465 2.021 2.262 5.275 -0.890 -5.946 467s 11 12 13 14 15 16 17 18 19 20 467s -4.407 -1.482 0.895 -3.220 -6.617 -3.607 -12.896 -0.562 -1.126 3.051 467s > 467s > print( residuals( fitsurio5r2 ) ) 467s demand supply 467s 1 0.655 0.0269 467s 2 -1.456 -1.5152 467s 3 1.737 1.5210 467s 4 0.696 0.3020 467s 5 2.530 2.9397 467s 6 1.417 1.5469 467s 7 1.459 1.8336 467s 8 -3.779 -5.0391 467s 9 -0.498 -1.0416 467s 10 3.950 5.0761 467s 11 0.836 0.6398 467s 12 -2.347 -2.5930 467s 13 -2.286 -3.0468 467s 14 -0.137 0.5081 467s 15 2.908 4.5036 467s 16 -3.050 -3.3786 467s 17 2.091 3.6824 467s 18 -2.775 -4.1107 467s 19 0.737 0.7819 467s 20 -2.686 -2.6370 467s > print( residuals( fitsurio5r2$eq[[ 1 ]] ) ) 467s 1 2 3 4 5 6 7 8 9 10 11 467s 0.655 -1.456 1.737 0.696 2.530 1.417 1.459 -3.779 -0.498 3.950 0.836 467s 12 13 14 15 16 17 18 19 20 467s -2.347 -2.286 -0.137 2.908 -3.050 2.091 -2.775 0.737 -2.686 467s > print( residuals( fitsuri5r2 ) ) 467s demand supply 467s 1 0.7199 5.756 467s 2 -0.5979 7.202 467s 3 2.4279 6.281 467s 4 1.6030 6.535 467s 5 2.1212 1.472 467s 6 1.3017 2.029 467s 7 1.6683 2.275 467s 8 -2.8233 5.299 467s 9 -1.2202 -0.892 467s 10 2.1760 -5.965 467s 11 -0.5578 -4.458 467s 12 -2.5854 -1.528 467s 13 -1.6970 0.866 467s 14 -0.3899 -3.237 467s 15 1.9153 -6.607 467s 16 -3.3698 -3.593 467s 17 -0.6429 -12.902 467s 18 -1.9698 -0.549 467s 19 1.8949 -1.099 467s 20 0.0259 3.114 467s > print( residuals( fitsuri5r2$eq[[ 1 ]] ) ) 467s 1 2 3 4 5 6 7 8 9 10 467s 0.7199 -0.5979 2.4279 1.6030 2.1212 1.3017 1.6683 -2.8233 -1.2202 2.1760 467s 11 12 13 14 15 16 17 18 19 20 467s -0.5578 -2.5854 -1.6970 -0.3899 1.9153 -3.3698 -0.6429 -1.9698 1.8949 0.0259 467s > 467s > 467s > ## *************** coefficients ********************* 467s > print( round( coef( fitsur1r3 ), digits = 6 ) ) 467s demand_(Intercept) demand_price demand_income supply_(Intercept) 467s 99.225 -0.268 0.292 62.958 467s supply_price supply_farmPrice supply_trend 467s 0.144 0.207 0.333 467s > print( round( coef( fitsur1r3$eq[[ 2 ]] ), digits = 6 ) ) 467s (Intercept) price farmPrice trend 467s 62.958 0.144 0.207 0.333 467s > 467s > print( round( coef( fitsuri2 ), digits = 6 ) ) 467s demand_(Intercept) demand_price demand_income supply_(Intercept) 467s 107.368 -0.394 0.338 85.045 467s supply_income supply_farmPrice supply_trend 467s 0.312 -0.197 0.338 467s > print( round( coef( fitsuri2$eq[[ 1 ]] ), digits = 6 ) ) 467s (Intercept) price income 467s 107.368 -0.394 0.338 467s > 467s > print( round( coef( fitsur2we ), digits = 6 ) ) 467s demand_(Intercept) demand_price demand_income supply_(Intercept) 467s 98.754 -0.234 0.261 67.888 467s supply_price supply_farmPrice supply_trend 467s 0.132 0.177 0.261 467s > print( round( coef( fitsur2we$eq[[ 1 ]] ), digits = 6 ) ) 467s (Intercept) price income 467s 98.754 -0.234 0.261 467s > 467s > print( round( coef( fitsur3 ), digits = 6 ) ) 467s demand_(Intercept) demand_price demand_income supply_(Intercept) 467s 98.841 -0.240 0.267 67.428 467s supply_price supply_farmPrice supply_trend 467s 0.133 0.179 0.267 467s > print( round( coef( fitsur3, modified.regMat = TRUE ), digits = 6 ) ) 467s C1 C2 C3 C4 C5 C6 467s 98.841 -0.240 0.267 67.428 0.133 0.179 467s > print( round( coef( fitsur3$eq[[ 2 ]] ), digits = 6 ) ) 467s (Intercept) price farmPrice trend 467s 67.428 0.133 0.179 0.267 467s > 467s > print( round( coef( fitsur4r2 ), digits = 6 ) ) 467s demand_(Intercept) demand_price demand_income supply_(Intercept) 467s 92.527 -0.230 0.322 48.701 467s supply_price supply_farmPrice supply_trend 467s 0.270 0.226 0.322 467s > print( round( coef( fitsur4r2$eq[[ 1 ]] ), digits = 6 ) ) 467s (Intercept) price income 467s 92.527 -0.230 0.322 467s > 467s > print( round( coef( fitsuri5e ), digits = 6 ) ) 467s demand_(Intercept) demand_price demand_income supply_(Intercept) 467s 97.630 -0.258 0.298 89.544 467s supply_income supply_farmPrice supply_trend 467s 0.242 -0.169 0.298 467s > print( round( coef( fitsuri5e, modified.regMat = TRUE ), digits = 6 ) ) 467s C1 C2 C3 C4 C5 C6 467s 97.630 -0.258 0.298 89.544 0.242 -0.169 467s > print( round( coef( fitsuri5e$eq[[ 2 ]] ), digits = 6 ) ) 467s (Intercept) income farmPrice trend 467s 89.544 0.242 -0.169 0.298 467s > 467s > print( round( coef( fitsur5w ), digits = 6 ) ) 467s demand_(Intercept) demand_price demand_income supply_(Intercept) 467s 96.442 -0.275 0.328 52.576 467s supply_price supply_farmPrice supply_trend 467s 0.225 0.232 0.328 467s > print( round( coef( fitsur5w, modified.regMat = TRUE ), digits = 6 ) ) 467s C1 C2 C3 C4 C5 C6 467s 96.442 -0.275 0.328 52.576 0.225 0.232 467s > print( round( coef( fitsur5w$eq[[ 1 ]] ), digits = 6 ) ) 467s (Intercept) price income 467s 96.442 -0.275 0.328 467s > 467s > 467s > ## *************** coefficients with stats ********************* 467s > print( round( coef( summary( fitsur1r3 ) ), digits = 6 ) ) 467s Estimate Std. Error t value Pr(>|t|) 467s demand_(Intercept) 99.225 7.5129 13.21 0.000000 467s demand_price -0.268 0.0878 -3.05 0.007262 467s demand_income 0.292 0.0408 7.15 0.000002 467s supply_(Intercept) 62.958 10.9850 5.73 0.000031 467s supply_price 0.144 0.0944 1.53 0.145991 467s supply_farmPrice 0.207 0.0386 5.37 0.000062 467s supply_trend 0.333 0.0644 5.18 0.000092 467s > print( round( coef( summary( fitsur1r3$eq[[ 2 ]] ) ), digits = 6 ) ) 467s Estimate Std. Error t value Pr(>|t|) 467s (Intercept) 62.958 10.9850 5.73 0.000031 467s price 0.144 0.0944 1.53 0.145991 467s farmPrice 0.207 0.0386 5.37 0.000062 467s trend 0.333 0.0644 5.18 0.000092 467s > 467s > print( round( coef( summary( fitsuri2, useDfSys = FALSE ) ), digits = 6 ) ) 467s Estimate Std. Error t value Pr(>|t|) 467s demand_(Intercept) 107.368 7.4986 14.32 0.000000 467s demand_price -0.394 0.0912 -4.33 0.000459 467s demand_income 0.338 0.0466 7.25 0.000001 467s supply_(Intercept) 85.045 12.1069 7.02 0.000003 467s supply_income 0.312 0.1233 2.53 0.022132 467s supply_farmPrice -0.197 0.1157 -1.70 0.107654 467s supply_trend 0.338 0.0466 7.25 0.000002 467s > print( round( coef( summary( fitsuri2$eq[[ 1 ]], useDfSys = FALSE ) ), 467s + digits = 6 ) ) 467s Estimate Std. Error t value Pr(>|t|) 467s (Intercept) 107.368 7.4986 14.32 0.000000 467s price -0.394 0.0912 -4.33 0.000459 467s income 0.338 0.0466 7.25 0.000001 467s > 467s > print( round( coef( summary( fitsur3 ) ), digits = 6 ) ) 467s Estimate Std. Error t value Pr(>|t|) 467s demand_(Intercept) 98.841 7.5581 13.08 0.000000 467s demand_price -0.240 0.0860 -2.79 0.008613 467s demand_income 0.267 0.0368 7.25 0.000000 467s supply_(Intercept) 67.428 10.6647 6.32 0.000000 467s supply_price 0.133 0.0953 1.40 0.171250 467s supply_farmPrice 0.179 0.0337 5.33 0.000006 467s supply_trend 0.267 0.0368 7.25 0.000000 467s > print( round( coef( summary( fitsur3 ), modified.regMat = TRUE ), digits = 6 ) ) 467s Estimate Std. Error t value Pr(>|t|) 467s C1 98.841 7.5581 13.08 0.000000 467s C2 -0.240 0.0860 -2.79 0.008613 467s C3 0.267 0.0368 7.25 0.000000 467s C4 67.428 10.6647 6.32 0.000000 467s C5 0.133 0.0953 1.40 0.171250 467s C6 0.179 0.0337 5.33 0.000006 467s > print( round( coef( summary( fitsur3$eq[[ 2 ]] ) ), digits = 6 ) ) 467s Estimate Std. Error t value Pr(>|t|) 467s (Intercept) 67.428 10.6647 6.32 0.000000 467s price 0.133 0.0953 1.40 0.171250 467s farmPrice 0.179 0.0337 5.33 0.000006 467s trend 0.267 0.0368 7.25 0.000000 467s > 467s > print( round( coef( summary( fitsuri3we ) ), digits = 6 ) ) 467s Estimate Std. Error t value Pr(>|t|) 467s demand_(Intercept) 107.806 6.9270 15.56 0.000000 467s demand_price -0.399 0.0843 -4.73 0.000038 467s demand_income 0.338 0.0431 7.84 0.000000 467s supply_(Intercept) 85.107 10.8288 7.86 0.000000 467s supply_income 0.311 0.1101 2.82 0.007950 467s supply_farmPrice -0.196 0.1034 -1.89 0.066671 467s supply_trend 0.338 0.0431 7.84 0.000000 467s > print( round( coef( summary( fitsuri3we ), modified.regMat = TRUE ), digits = 6 ) ) 467s Estimate Std. Error t value Pr(>|t|) 467s C1 107.806 6.9270 15.56 0.000000 467s C2 -0.399 0.0843 -4.73 0.000038 467s C3 0.338 0.0431 7.84 0.000000 467s C4 85.107 10.8288 7.86 0.000000 467s C5 0.311 0.1101 2.82 0.007950 467s C6 -0.196 0.1034 -1.89 0.066671 467s > print( round( coef( summary( fitsuri3we$eq[[ 1 ]] ) ), digits = 6 ) ) 467s Estimate Std. Error t value Pr(>|t|) 467s (Intercept) 107.806 6.9270 15.56 0.0e+00 467s price -0.399 0.0843 -4.73 3.8e-05 467s income 0.338 0.0431 7.84 0.0e+00 467s > 467s > print( round( coef( summary( fitsur4r2 ) ), digits = 6 ) ) 467s Estimate Std. Error t value Pr(>|t|) 467s demand_(Intercept) 92.527 7.2896 12.69 0.00000 467s demand_price -0.230 0.0827 -2.79 0.00855 467s demand_income 0.322 0.0166 19.37 0.00000 467s supply_(Intercept) 48.701 7.4034 6.58 0.00000 467s supply_price 0.270 0.0827 3.26 0.00248 467s supply_farmPrice 0.226 0.0166 13.62 0.00000 467s supply_trend 0.322 0.0166 19.37 0.00000 467s > print( round( coef( summary( fitsur4r2$eq[[ 1 ]] ) ), digits = 6 ) ) 467s Estimate Std. Error t value Pr(>|t|) 467s (Intercept) 92.527 7.2896 12.69 0.00000 467s price -0.230 0.0827 -2.79 0.00855 467s income 0.322 0.0166 19.37 0.00000 467s > 467s > print( round( coef( summary( fitsur4we ) ), digits = 6 ) ) 467s Estimate Std. Error t value Pr(>|t|) 467s demand_(Intercept) 96.941 6.8894 14.07 0.000000 467s demand_price -0.281 0.0766 -3.67 0.000796 467s demand_income 0.329 0.0181 18.18 0.000000 467s supply_(Intercept) 52.996 7.0652 7.50 0.000000 467s supply_price 0.219 0.0766 2.85 0.007215 467s supply_farmPrice 0.234 0.0183 12.76 0.000000 467s supply_trend 0.329 0.0181 18.18 0.000000 467s > print( round( coef( summary( fitsur4we$eq[[ 2 ]] ) ), digits = 6 ) ) 467s Estimate Std. Error t value Pr(>|t|) 467s (Intercept) 52.996 7.0652 7.50 0.00000 467s price 0.219 0.0766 2.85 0.00722 467s farmPrice 0.234 0.0183 12.76 0.00000 467s trend 0.329 0.0181 18.18 0.00000 467s > 467s > print( round( coef( summary( fitsuri5e, useDfSys = FALSE ) ), digits = 6 ) ) 467s Estimate Std. Error t value Pr(>|t|) 467s demand_(Intercept) 97.630 6.1560 15.86 0.000000 467s demand_price -0.258 0.0709 -3.63 0.002060 467s demand_income 0.298 0.0403 7.38 0.000001 467s supply_(Intercept) 89.544 9.3372 9.59 0.000000 467s supply_income 0.242 0.0709 3.42 0.003516 467s supply_farmPrice -0.169 0.0988 -1.71 0.107123 467s supply_trend 0.298 0.0403 7.38 0.000002 467s > print( round( coef( summary( fitsuri5e, useDfSys = FALSE ), 467s + modified.regMat = TRUE ), digits = 6 ) ) 467s Estimate Std. Error t value Pr(>|t|) 467s C1 97.630 6.1560 15.86 NA 467s C2 -0.258 0.0709 -3.63 NA 467s C3 0.298 0.0403 7.38 NA 467s C4 89.544 9.3372 9.59 NA 467s C5 0.242 0.0709 3.42 NA 467s C6 -0.169 0.0988 -1.71 NA 467s > print( round( coef( summary( fitsuri5e$eq[[ 2 ]], useDfSys = FALSE ) ), 467s + digits = 6 ) ) 467s Estimate Std. Error t value Pr(>|t|) 467s (Intercept) 89.544 9.3372 9.59 0.000000 467s income 0.242 0.0709 3.42 0.003516 467s farmPrice -0.169 0.0988 -1.71 0.107123 467s trend 0.298 0.0403 7.38 0.000002 467s > 467s > 467s > ## *********** variance covariance matrix of the coefficients ******* 467s > print( round( vcov( fitsur1e2 ), digits = 6 ) ) 467s demand_(Intercept) demand_price demand_income 467s demand_(Intercept) 56.4403 -0.58751 0.025716 467s demand_price -0.5875 0.00769 -0.001866 467s demand_income 0.0257 -0.00187 0.001650 467s supply_(Intercept) 61.0550 -0.40370 -0.209805 467s supply_price -0.6325 0.00579 0.000546 467s supply_farmPrice 0.0215 -0.00156 0.001379 467s supply_trend 0.0327 -0.00237 0.002095 467s supply_(Intercept) supply_price supply_farmPrice 467s demand_(Intercept) 61.055 -0.632489 0.021495 467s demand_price -0.404 0.005792 -0.001559 467s demand_income -0.210 0.000546 0.001379 467s supply_(Intercept) 120.418 -0.954714 -0.221454 467s supply_price -0.955 0.008900 0.000584 467s supply_farmPrice -0.221 0.000584 0.001476 467s supply_trend -0.309 0.000772 0.001950 467s supply_trend 467s demand_(Intercept) 0.032652 467s demand_price -0.002369 467s demand_income 0.002095 467s supply_(Intercept) -0.308674 467s supply_price 0.000772 467s supply_farmPrice 0.001950 467s supply_trend 0.004100 467s > print( round( vcov( fitsur1e2$eq[[ 1 ]] ), digits = 6 ) ) 467s (Intercept) price income 467s (Intercept) 56.4403 -0.58751 0.02572 467s price -0.5875 0.00769 -0.00187 467s income 0.0257 -0.00187 0.00165 467s > 467s > print( round( vcov( fitsur1r3 ), digits = 6 ) ) 467s demand_(Intercept) demand_price demand_income 467s demand_(Intercept) 56.4432 -0.58772 0.025901 467s demand_price -0.5877 0.00771 -0.001879 467s demand_income 0.0259 -0.00188 0.001662 467s supply_(Intercept) 60.8607 -0.40086 -0.210729 467s supply_price -0.6307 0.00577 0.000548 467s supply_farmPrice 0.0216 -0.00157 0.001385 467s supply_trend 0.0328 -0.00238 0.002104 467s supply_(Intercept) supply_price supply_farmPrice 467s demand_(Intercept) 60.861 -0.630659 0.021589 467s demand_price -0.401 0.005771 -0.001566 467s demand_income -0.211 0.000548 0.001385 467s supply_(Intercept) 120.671 -0.955395 -0.223176 467s supply_price -0.955 0.008902 0.000589 467s supply_farmPrice -0.223 0.000589 0.001487 467s supply_trend -0.310 0.000776 0.001959 467s supply_trend 467s demand_(Intercept) 0.032796 467s demand_price -0.002379 467s demand_income 0.002104 467s supply_(Intercept) -0.310422 467s supply_price 0.000776 467s supply_farmPrice 0.001959 467s supply_trend 0.004149 467s > print( round( vcov( fitsur1r3$eq[[ 2 ]] ), digits = 6 ) ) 467s (Intercept) price farmPrice trend 467s (Intercept) 120.671 -0.955395 -0.223176 -0.310422 467s price -0.955 0.008902 0.000589 0.000776 467s farmPrice -0.223 0.000589 0.001487 0.001959 467s trend -0.310 0.000776 0.001959 0.004149 467s > 467s > print( round( vcov( fitsur2e ), digits = 6 ) ) 467s demand_(Intercept) demand_price demand_income 467s demand_(Intercept) 48.5631 -0.50188 0.018400 467s demand_price -0.5019 0.00632 -0.001335 467s demand_income 0.0184 -0.00134 0.001180 467s supply_(Intercept) 53.2014 -0.39283 -0.140738 467s supply_price -0.5462 0.00510 0.000373 467s supply_farmPrice 0.0147 -0.00107 0.000942 467s supply_trend 0.0184 -0.00134 0.001180 467s supply_(Intercept) supply_price supply_farmPrice 467s demand_(Intercept) 53.201 -0.546194 0.014689 467s demand_price -0.393 0.005097 -0.001066 467s demand_income -0.141 0.000373 0.000942 467s supply_(Intercept) 91.607 -0.766739 -0.136644 467s supply_price -0.767 0.007271 0.000368 467s supply_farmPrice -0.137 0.000368 0.000931 467s supply_trend -0.141 0.000373 0.000942 467s supply_trend 467s demand_(Intercept) 0.018400 467s demand_price -0.001335 467s demand_income 0.001180 467s supply_(Intercept) -0.140738 467s supply_price 0.000373 467s supply_farmPrice 0.000942 467s supply_trend 0.001180 467s > print( round( vcov( fitsur2e$eq[[ 1 ]] ), digits = 6 ) ) 467s (Intercept) price income 467s (Intercept) 48.5631 -0.50188 0.01840 467s price -0.5019 0.00632 -0.00134 467s income 0.0184 -0.00134 0.00118 467s > 467s > print( round( vcov( fitsur3 ), digits = 6 ) ) 467s demand_(Intercept) demand_price demand_income 467s demand_(Intercept) 57.1254 -0.58989 0.02116 467s demand_price -0.5899 0.00739 -0.00153 467s demand_income 0.0212 -0.00153 0.00136 467s supply_(Intercept) 64.5952 -0.48211 -0.16560 467s supply_price -0.6626 0.00619 0.00044 467s supply_farmPrice 0.0173 -0.00126 0.00111 467s supply_trend 0.0212 -0.00153 0.00136 467s supply_(Intercept) supply_price supply_farmPrice 467s demand_(Intercept) 64.595 -0.662552 0.017322 467s demand_price -0.482 0.006195 -0.001257 467s demand_income -0.166 0.000440 0.001111 467s supply_(Intercept) 113.736 -0.956493 -0.165927 467s supply_price -0.956 0.009084 0.000448 467s supply_farmPrice -0.166 0.000448 0.001133 467s supply_trend -0.166 0.000440 0.001111 467s supply_trend 467s demand_(Intercept) 0.02116 467s demand_price -0.00153 467s demand_income 0.00136 467s supply_(Intercept) -0.16560 467s supply_price 0.00044 467s supply_farmPrice 0.00111 467s supply_trend 0.00136 467s > print( round( vcov( fitsur3, modified.regMat = TRUE ), digits = 6 ) ) 467s C1 C2 C3 C4 C5 C6 467s C1 57.1254 -0.58989 0.02116 64.595 -0.662552 0.017322 467s C2 -0.5899 0.00739 -0.00153 -0.482 0.006195 -0.001257 467s C3 0.0212 -0.00153 0.00136 -0.166 0.000440 0.001111 467s C4 64.5952 -0.48211 -0.16560 113.736 -0.956493 -0.165927 467s C5 -0.6626 0.00619 0.00044 -0.956 0.009084 0.000448 467s C6 0.0173 -0.00126 0.00111 -0.166 0.000448 0.001133 467s > print( round( vcov( fitsur3$eq[[ 2 ]] ), digits = 6 ) ) 467s (Intercept) price farmPrice trend 467s (Intercept) 113.736 -0.956493 -0.165927 -0.16560 467s price -0.956 0.009084 0.000448 0.00044 467s farmPrice -0.166 0.000448 0.001133 0.00111 467s trend -0.166 0.000440 0.001111 0.00136 467s > 467s > print( round( vcov( fitsur3w ), digits = 6 ) ) 467s demand_(Intercept) demand_price demand_income 467s demand_(Intercept) 56.7267 -0.58513 0.020348 467s demand_price -0.5851 0.00729 -0.001476 467s demand_income 0.0203 -0.00148 0.001305 467s supply_(Intercept) 64.8820 -0.48999 -0.160451 467s supply_price -0.6648 0.00623 0.000426 467s supply_farmPrice 0.0168 -0.00122 0.001077 467s supply_trend 0.0203 -0.00148 0.001305 467s supply_(Intercept) supply_price supply_farmPrice 467s demand_(Intercept) 64.882 -0.664819 0.016795 467s demand_price -0.490 0.006231 -0.001219 467s demand_income -0.160 0.000426 0.001077 467s supply_(Intercept) 113.543 -0.959668 -0.161181 467s supply_price -0.960 0.009129 0.000435 467s supply_farmPrice -0.161 0.000435 0.001100 467s supply_trend -0.160 0.000426 0.001077 467s supply_trend 467s demand_(Intercept) 0.020348 467s demand_price -0.001476 467s demand_income 0.001305 467s supply_(Intercept) -0.160451 467s supply_price 0.000426 467s supply_farmPrice 0.001077 467s supply_trend 0.001305 467s > print( round( vcov( fitsur3w, modified.regMat = TRUE ), digits = 6 ) ) 467s C1 C2 C3 C4 C5 C6 467s C1 56.7267 -0.58513 0.020348 64.882 -0.664819 0.016795 467s C2 -0.5851 0.00729 -0.001476 -0.490 0.006231 -0.001219 467s C3 0.0203 -0.00148 0.001305 -0.160 0.000426 0.001077 467s C4 64.8820 -0.48999 -0.160451 113.543 -0.959668 -0.161181 467s C5 -0.6648 0.00623 0.000426 -0.960 0.009129 0.000435 467s C6 0.0168 -0.00122 0.001077 -0.161 0.000435 0.001100 467s > print( round( vcov( fitsur3w$eq[[ 1 ]] ), digits = 6 ) ) 467s (Intercept) price income 467s (Intercept) 56.7267 -0.58513 0.02035 467s price -0.5851 0.00729 -0.00148 467s income 0.0203 -0.00148 0.00130 467s > 467s > print( round( vcov( fitsur4r2 ), digits = 6 ) ) 467s demand_(Intercept) demand_price demand_income 467s demand_(Intercept) 53.1384 -0.593514 0.065746 467s demand_price -0.5935 0.006838 -0.000927 467s demand_income 0.0657 -0.000927 0.000276 467s supply_(Intercept) 53.3903 -0.599312 0.069540 467s supply_price -0.5935 0.006838 -0.000927 467s supply_farmPrice 0.0570 -0.000775 0.000210 467s supply_trend 0.0657 -0.000927 0.000276 467s supply_(Intercept) supply_price supply_farmPrice 467s demand_(Intercept) 53.3903 -0.593514 0.057048 467s demand_price -0.5993 0.006838 -0.000775 467s demand_income 0.0695 -0.000927 0.000210 467s supply_(Intercept) 54.8108 -0.599312 0.048653 467s supply_price -0.5993 0.006838 -0.000775 467s supply_farmPrice 0.0487 -0.000775 0.000276 467s supply_trend 0.0695 -0.000927 0.000210 467s supply_trend 467s demand_(Intercept) 0.065746 467s demand_price -0.000927 467s demand_income 0.000276 467s supply_(Intercept) 0.069540 467s supply_price -0.000927 467s supply_farmPrice 0.000210 467s supply_trend 0.000276 467s > print( round( vcov( fitsur4r2$eq[[ 1 ]] ), digits = 6 ) ) 467s (Intercept) price income 467s (Intercept) 53.1384 -0.593514 0.065746 467s price -0.5935 0.006838 -0.000927 467s income 0.0657 -0.000927 0.000276 467s > 467s > print( round( vcov( fitsur5e ), digits = 6 ) ) 467s demand_(Intercept) demand_price demand_income 467s demand_(Intercept) 47.8867 -0.516747 0.040579 467s demand_price -0.5167 0.005886 -0.000738 467s demand_income 0.0406 -0.000738 0.000340 467s supply_(Intercept) 48.2187 -0.526670 0.047594 467s supply_price -0.5167 0.005886 -0.000738 467s supply_farmPrice 0.0334 -0.000562 0.000234 467s supply_trend 0.0406 -0.000738 0.000340 467s supply_(Intercept) supply_price supply_farmPrice 467s demand_(Intercept) 48.2187 -0.516747 0.033361 467s demand_price -0.5267 0.005886 -0.000562 467s demand_income 0.0476 -0.000738 0.000234 467s supply_(Intercept) 50.4739 -0.526670 0.020109 467s supply_price -0.5267 0.005886 -0.000562 467s supply_farmPrice 0.0201 -0.000562 0.000348 467s supply_trend 0.0476 -0.000738 0.000234 467s supply_trend 467s demand_(Intercept) 0.040579 467s demand_price -0.000738 467s demand_income 0.000340 467s supply_(Intercept) 0.047594 467s supply_price -0.000738 467s supply_farmPrice 0.000234 467s supply_trend 0.000340 467s > print( round( vcov( fitsur5e, modified.regMat = TRUE ), digits = 6 ) ) 467s C1 C2 C3 C4 C5 C6 467s C1 47.8867 -0.516747 0.040579 48.2187 -0.516747 0.033361 467s C2 -0.5167 0.005886 -0.000738 -0.5267 0.005886 -0.000562 467s C3 0.0406 -0.000738 0.000340 0.0476 -0.000738 0.000234 467s C4 48.2187 -0.526670 0.047594 50.4739 -0.526670 0.020109 467s C5 -0.5167 0.005886 -0.000738 -0.5267 0.005886 -0.000562 467s C6 0.0334 -0.000562 0.000234 0.0201 -0.000562 0.000348 467s > print( round( vcov( fitsur5e$eq[[ 2 ]] ), digits = 6 ) ) 467s (Intercept) price farmPrice trend 467s (Intercept) 50.4739 -0.526670 0.020109 0.047594 467s price -0.5267 0.005886 -0.000562 -0.000738 467s farmPrice 0.0201 -0.000562 0.000348 0.000234 467s trend 0.0476 -0.000738 0.000234 0.000340 467s > 467s > print( round( vcov( fitsuri1r3 ), digits = 6 ) ) 467s demand_(Intercept) demand_price demand_income 467s demand_(Intercept) 54.5505 -0.55698 0.013891 467s demand_price -0.5570 0.00770 -0.002185 467s demand_income 0.0139 -0.00218 0.002098 467s supply_(Intercept) -2.7032 -0.08733 0.115993 467s supply_income 0.2249 -0.00185 -0.000411 467s supply_farmPrice -0.1721 0.00238 -0.000675 467s supply_trend -0.2597 0.00359 -0.001019 467s supply_(Intercept) supply_income supply_farmPrice 467s demand_(Intercept) -2.7032 0.224902 -0.172110 467s demand_price -0.0873 -0.001848 0.002379 467s demand_income 0.1160 -0.000411 -0.000675 467s supply_(Intercept) 11.4659 -0.058750 -0.051728 467s supply_income -0.0587 0.001787 -0.001018 467s supply_farmPrice -0.0517 -0.001018 0.001368 467s supply_trend -0.0578 -0.001631 0.001794 467s supply_trend 467s demand_(Intercept) -0.25970 467s demand_price 0.00359 467s demand_income -0.00102 467s supply_(Intercept) -0.05784 467s supply_income -0.00163 467s supply_farmPrice 0.00179 467s supply_trend 0.00416 467s > print( round( vcov( fitsuri1r3$eq[[ 1 ]] ), digits = 6 ) ) 467s (Intercept) price income 467s (Intercept) 54.5505 -0.55698 0.01389 467s price -0.5570 0.00770 -0.00218 467s income 0.0139 -0.00218 0.00210 467s > 467s > print( round( vcov( fitsuri2 ), digits = 6 ) ) 467s demand_(Intercept) demand_price demand_income 467s demand_(Intercept) 56.2287 -0.59260 0.033216 467s demand_price -0.5926 0.00831 -0.002451 467s demand_income 0.0332 -0.00245 0.002173 467s supply_(Intercept) 5.9548 0.14141 -0.203885 467s supply_income -0.2516 0.00201 0.000518 467s supply_farmPrice 0.1910 -0.00323 0.001351 467s supply_trend 0.0332 -0.00245 0.002173 467s supply_(Intercept) supply_income supply_farmPrice 467s demand_(Intercept) 5.955 -0.251647 0.19097 467s demand_price 0.141 0.002011 -0.00323 467s demand_income -0.204 0.000518 0.00135 467s supply_(Intercept) 146.577 -0.828954 -0.64122 467s supply_income -0.829 0.015214 -0.00683 467s supply_farmPrice -0.641 -0.006835 0.01339 467s supply_trend -0.204 0.000518 0.00135 467s supply_trend 467s demand_(Intercept) 0.033216 467s demand_price -0.002451 467s demand_income 0.002173 467s supply_(Intercept) -0.203885 467s supply_income 0.000518 467s supply_farmPrice 0.001351 467s supply_trend 0.002173 467s > print( round( vcov( fitsuri2$eq[[ 2 ]] ), digits = 6 ) ) 467s (Intercept) income farmPrice trend 467s (Intercept) 146.577 -0.828954 -0.64122 -0.203885 467s income -0.829 0.015214 -0.00683 0.000518 467s farmPrice -0.641 -0.006835 0.01339 0.001351 467s trend -0.204 0.000518 0.00135 0.002173 467s > 467s > print( round( vcov( fitsuri3e ), digits = 6 ) ) 467s demand_(Intercept) demand_price demand_income 467s demand_(Intercept) 47.9834 -0.50592 0.028570 467s demand_price -0.5059 0.00710 -0.002098 467s demand_income 0.0286 -0.00210 0.001859 467s supply_(Intercept) 4.9860 0.11975 -0.172089 467s supply_income -0.2118 0.00170 0.000428 467s supply_farmPrice 0.1609 -0.00273 0.001147 467s supply_trend 0.0286 -0.00210 0.001859 467s supply_(Intercept) supply_income supply_farmPrice 467s demand_(Intercept) 4.986 -0.211763 0.16090 467s demand_price 0.120 0.001700 -0.00273 467s demand_income -0.172 0.000428 0.00115 467s supply_(Intercept) 117.261 -0.661134 -0.51405 467s supply_income -0.661 0.012132 -0.00545 467s supply_farmPrice -0.514 -0.005450 0.01070 467s supply_trend -0.172 0.000428 0.00115 467s supply_trend 467s demand_(Intercept) 0.028570 467s demand_price -0.002098 467s demand_income 0.001859 467s supply_(Intercept) -0.172089 467s supply_income 0.000428 467s supply_farmPrice 0.001147 467s supply_trend 0.001859 467s > print( round( vcov( fitsuri3e, modified.regMat = TRUE ), digits = 6 ) ) 467s C1 C2 C3 C4 C5 C6 467s C1 47.9834 -0.50592 0.028570 4.986 -0.211763 0.16090 467s C2 -0.5059 0.00710 -0.002098 0.120 0.001700 -0.00273 467s C3 0.0286 -0.00210 0.001859 -0.172 0.000428 0.00115 467s C4 4.9860 0.11975 -0.172089 117.261 -0.661134 -0.51405 467s C5 -0.2118 0.00170 0.000428 -0.661 0.012132 -0.00545 467s C6 0.1609 -0.00273 0.001147 -0.514 -0.005450 0.01070 467s > print( round( vcov( fitsuri3e$eq[[ 1 ]] ), digits = 6 ) ) 467s (Intercept) price income 467s (Intercept) 47.9834 -0.5059 0.02857 467s price -0.5059 0.0071 -0.00210 467s income 0.0286 -0.0021 0.00186 467s > 467s > print( round( vcov( fitsurio4e ), digits = 6 ) ) 467s demand_(Intercept) demand_price demand_income 467s demand_(Intercept) 47.0268 -0.525375 0.058300 467s demand_price -0.5254 0.006074 -0.000842 467s demand_income 0.0583 -0.000842 0.000266 467s supply_(Intercept) 47.2346 -0.530682 0.061997 467s supply_price -0.5254 0.006074 -0.000842 467s supply_farmPrice 0.0508 -0.000704 0.000201 467s supply_trend 0.0583 -0.000842 0.000266 467s supply_(Intercept) supply_price supply_farmPrice 467s demand_(Intercept) 47.2346 -0.525375 0.050751 467s demand_price -0.5307 0.006074 -0.000704 467s demand_income 0.0620 -0.000842 0.000201 467s supply_(Intercept) 48.6183 -0.530682 0.042182 467s supply_price -0.5307 0.006074 -0.000704 467s supply_farmPrice 0.0422 -0.000704 0.000270 467s supply_trend 0.0620 -0.000842 0.000201 467s supply_trend 467s demand_(Intercept) 0.058300 467s demand_price -0.000842 467s demand_income 0.000266 467s supply_(Intercept) 0.061997 467s supply_price -0.000842 467s supply_farmPrice 0.000201 467s supply_trend 0.000266 467s > print( round( vcov( fitsurio4e$eq[[ 2 ]] ), digits = 6 ) ) 467s (Intercept) price farmPrice trend 467s (Intercept) 48.6183 -0.530682 0.042182 0.061997 467s price -0.5307 0.006074 -0.000704 -0.000842 467s farmPrice 0.0422 -0.000704 0.000270 0.000201 467s trend 0.0620 -0.000842 0.000201 0.000266 467s > print( round( vcov( fitsuri4e ), digits = 6 ) ) 467s demand_(Intercept) demand_price demand_income 467s demand_(Intercept) 37.8960 -0.36274 -0.01487 467s demand_price -0.3627 0.00503 -0.00144 467s demand_income -0.0149 -0.00144 0.00163 467s supply_(Intercept) 19.0822 -0.20611 0.01617 467s supply_income -0.3627 0.00503 -0.00144 467s supply_farmPrice 0.1707 -0.00279 0.00111 467s supply_trend -0.0149 -0.00144 0.00163 467s supply_(Intercept) supply_income supply_farmPrice 467s demand_(Intercept) 19.0822 -0.36274 0.17073 467s demand_price -0.2061 0.00503 -0.00279 467s demand_income 0.0162 -0.00144 0.00111 467s supply_(Intercept) 87.1827 -0.20611 -0.68294 467s supply_income -0.2061 0.00503 -0.00279 467s supply_farmPrice -0.6829 -0.00279 0.00976 467s supply_trend 0.0162 -0.00144 0.00111 467s supply_trend 467s demand_(Intercept) -0.01487 467s demand_price -0.00144 467s demand_income 0.00163 467s supply_(Intercept) 0.01617 467s supply_income -0.00144 467s supply_farmPrice 0.00111 467s supply_trend 0.00163 467s > print( round( vcov( fitsuri4e$eq[[ 2 ]] ), digits = 6 ) ) 467s (Intercept) income farmPrice trend 467s (Intercept) 87.1827 -0.20611 -0.68294 0.01617 467s income -0.2061 0.00503 -0.00279 -0.00144 467s farmPrice -0.6829 -0.00279 0.00976 0.00111 467s trend 0.0162 -0.00144 0.00111 0.00163 467s > 467s > print( round( vcov( fitsurio5r2 ), digits = 6 ) ) 467s demand_(Intercept) demand_price demand_income 467s demand_(Intercept) 51.3196 -0.579747 0.070528 467s demand_price -0.5797 0.006646 -0.000872 467s demand_income 0.0705 -0.000872 0.000171 467s supply_(Intercept) 51.5518 -0.583025 0.072036 467s supply_price -0.5797 0.006646 -0.000872 467s supply_farmPrice 0.0617 -0.000751 0.000138 467s supply_trend 0.0705 -0.000872 0.000171 467s supply_(Intercept) supply_price supply_farmPrice 467s demand_(Intercept) 51.5518 -0.579747 0.061658 467s demand_price -0.5830 0.006646 -0.000751 467s demand_income 0.0720 -0.000872 0.000138 467s supply_(Intercept) 52.2109 -0.583025 0.058794 467s supply_price -0.5830 0.006646 -0.000751 467s supply_farmPrice 0.0588 -0.000751 0.000154 467s supply_trend 0.0720 -0.000872 0.000138 467s supply_trend 467s demand_(Intercept) 0.070528 467s demand_price -0.000872 467s demand_income 0.000171 467s supply_(Intercept) 0.072036 467s supply_price -0.000872 467s supply_farmPrice 0.000138 467s supply_trend 0.000171 467s > print( round( vcov( fitsurio5r2, modified.regMat = TRUE ), digits = 6 ) ) 467s C1 C2 C3 C4 C5 C6 467s C1 51.3196 -0.579747 0.070528 51.5518 -0.579747 0.061658 467s C2 -0.5797 0.006646 -0.000872 -0.5830 0.006646 -0.000751 467s C3 0.0705 -0.000872 0.000171 0.0720 -0.000872 0.000138 467s C4 51.5518 -0.583025 0.072036 52.2109 -0.583025 0.058794 467s C5 -0.5797 0.006646 -0.000872 -0.5830 0.006646 -0.000751 467s C6 0.0617 -0.000751 0.000138 0.0588 -0.000751 0.000154 467s > print( round( vcov( fitsurio5r2$eq[[ 1 ]] ), digits = 6 ) ) 467s (Intercept) price income 467s (Intercept) 51.3196 -0.579747 0.070528 467s price -0.5797 0.006646 -0.000872 467s income 0.0705 -0.000872 0.000171 467s > print( round( vcov( fitsuri5r2 ), digits = 6 ) ) 467s demand_(Intercept) demand_price demand_income 467s demand_(Intercept) 45.6881 -0.44008 -0.01517 467s demand_price -0.4401 0.00605 -0.00170 467s demand_income -0.0152 -0.00170 0.00190 467s supply_(Intercept) 22.8172 -0.23903 0.01186 467s supply_income -0.4401 0.00605 -0.00170 467s supply_farmPrice 0.2104 -0.00345 0.00138 467s supply_trend -0.0152 -0.00170 0.00190 467s supply_(Intercept) supply_income supply_farmPrice 467s demand_(Intercept) 22.8172 -0.44008 0.21042 467s demand_price -0.2390 0.00605 -0.00345 467s demand_income 0.0119 -0.00170 0.00138 467s supply_(Intercept) 108.8722 -0.23903 -0.87024 467s supply_income -0.2390 0.00605 -0.00345 467s supply_farmPrice -0.8702 -0.00345 0.01234 467s supply_trend 0.0119 -0.00170 0.00138 467s supply_trend 467s demand_(Intercept) -0.01517 467s demand_price -0.00170 467s demand_income 0.00190 467s supply_(Intercept) 0.01186 467s supply_income -0.00170 467s supply_farmPrice 0.00138 467s supply_trend 0.00190 467s > print( round( vcov( fitsuri5r2, modified.regMat = TRUE ), digits = 6 ) ) 467s C1 C2 C3 C4 C5 C6 467s C1 45.6881 -0.44008 -0.01517 22.8172 -0.44008 0.21042 467s C2 -0.4401 0.00605 -0.00170 -0.2390 0.00605 -0.00345 467s C3 -0.0152 -0.00170 0.00190 0.0119 -0.00170 0.00138 467s C4 22.8172 -0.23903 0.01186 108.8722 -0.23903 -0.87024 467s C5 -0.4401 0.00605 -0.00170 -0.2390 0.00605 -0.00345 467s C6 0.2104 -0.00345 0.00138 -0.8702 -0.00345 0.01234 467s > print( round( vcov( fitsuri5r2$eq[[ 1 ]] ), digits = 6 ) ) 467s (Intercept) price income 467s (Intercept) 45.6881 -0.44008 -0.0152 467s price -0.4401 0.00605 -0.0017 467s income -0.0152 -0.00170 0.0019 467s > 467s > print( round( vcov( fitsurio5wr2 ), digits = 6 ) ) 467s demand_(Intercept) demand_price demand_income 467s demand_(Intercept) 51.3196 -0.579747 0.070528 467s demand_price -0.5797 0.006646 -0.000872 467s demand_income 0.0705 -0.000872 0.000171 467s supply_(Intercept) 51.5518 -0.583025 0.072036 467s supply_price -0.5797 0.006646 -0.000872 467s supply_farmPrice 0.0617 -0.000751 0.000138 467s supply_trend 0.0705 -0.000872 0.000171 467s supply_(Intercept) supply_price supply_farmPrice 467s demand_(Intercept) 51.5518 -0.579747 0.061658 467s demand_price -0.5830 0.006646 -0.000751 467s demand_income 0.0720 -0.000872 0.000138 467s supply_(Intercept) 52.2109 -0.583025 0.058794 467s supply_price -0.5830 0.006646 -0.000751 467s supply_farmPrice 0.0588 -0.000751 0.000154 467s supply_trend 0.0720 -0.000872 0.000138 467s supply_trend 467s demand_(Intercept) 0.070528 467s demand_price -0.000872 467s demand_income 0.000171 467s supply_(Intercept) 0.072036 467s supply_price -0.000872 467s supply_farmPrice 0.000138 467s supply_trend 0.000171 467s > print( round( vcov( fitsurio5wr2, modified.regMat = TRUE ), digits = 6 ) ) 467s C1 C2 C3 C4 C5 C6 467s C1 51.3196 -0.579747 0.070528 51.5518 -0.579747 0.061658 467s C2 -0.5797 0.006646 -0.000872 -0.5830 0.006646 -0.000751 467s C3 0.0705 -0.000872 0.000171 0.0720 -0.000872 0.000138 467s C4 51.5518 -0.583025 0.072036 52.2109 -0.583025 0.058794 467s C5 -0.5797 0.006646 -0.000872 -0.5830 0.006646 -0.000751 467s C6 0.0617 -0.000751 0.000138 0.0588 -0.000751 0.000154 467s > print( round( vcov( fitsurio5wr2$eq[[ 2 ]] ), digits = 6 ) ) 467s (Intercept) price farmPrice trend 467s (Intercept) 52.2109 -0.583025 0.058794 0.072036 467s price -0.5830 0.006646 -0.000751 -0.000872 467s farmPrice 0.0588 -0.000751 0.000154 0.000138 467s trend 0.0720 -0.000872 0.000138 0.000171 467s > 467s > 467s > ## *********** confidence intervals of coefficients ************* 467s > print( confint( fitsur1e2, useDfSys = TRUE ) ) 467s 2.5 % 97.5 % 467s demand_(Intercept) 83.927 114.497 467s demand_price -0.445 -0.088 467s demand_income 0.208 0.373 467s supply_(Intercept) 40.751 85.403 467s supply_price -0.048 0.336 467s supply_farmPrice 0.128 0.285 467s supply_trend 0.202 0.463 467s > print( confint( fitsur1e2$eq[[ 2 ]], level = 0.9, useDfSys = TRUE ) ) 467s 5 % 95 % 467s (Intercept) 44.506 81.648 467s price -0.016 0.304 467s farmPrice 0.141 0.271 467s trend 0.224 0.441 467s > 467s > print( confint( fitsur1we2, useDfSys = TRUE ) ) 467s 2.5 % 97.5 % 467s demand_(Intercept) 83.927 114.497 467s demand_price -0.445 -0.088 467s demand_income 0.208 0.373 467s supply_(Intercept) 40.751 85.403 467s supply_price -0.048 0.336 467s supply_farmPrice 0.128 0.285 467s supply_trend 0.202 0.463 467s > print( confint( fitsur1we2$eq[[ 1 ]], level = 0.9, useDfSys = TRUE ) ) 467s 5 % 95 % 467s (Intercept) 86.498 111.926 467s price -0.415 -0.118 467s income 0.222 0.360 467s > 467s > print( confint( fitsur2e, level = 0.9 ) ) 467s 5 % 95 % 467s demand_(Intercept) 84.618 112.942 467s demand_price -0.397 -0.074 467s demand_income 0.193 0.333 467s supply_(Intercept) 48.153 87.055 467s supply_price -0.040 0.306 467s supply_farmPrice 0.116 0.240 467s supply_trend 0.193 0.333 467s > print( confint( fitsur2e$eq[[ 1 ]], level = 0.99 ) ) 467s 0.5 % 99.5 % 467s (Intercept) 79.767 117.793 467s price -0.452 -0.018 467s income 0.169 0.357 467s > 467s > print( confint( fitsur3, level = 0.99 ) ) 467s 0.5 % 99.5 % 467s demand_(Intercept) 83.481 114.201 467s demand_price -0.415 -0.065 467s demand_income 0.192 0.342 467s supply_(Intercept) 45.755 89.102 467s supply_price -0.060 0.327 467s supply_farmPrice 0.111 0.248 467s supply_trend 0.192 0.342 467s > print( confint( fitsur3$eq[[ 2 ]], level = 0.5 ) ) 467s 25 % 75 % 467s (Intercept) 60.157 74.699 467s price 0.068 0.198 467s farmPrice 0.157 0.202 467s trend 0.242 0.292 467s > 467s > print( confint( fitsur4r3, level = 0.5 ) ) 467s 25 % 75 % 467s demand_(Intercept) 78.344 108.052 467s demand_price -0.406 -0.070 467s demand_income 0.289 0.358 467s supply_(Intercept) 34.267 64.468 467s supply_price 0.094 0.430 467s supply_farmPrice 0.192 0.262 467s supply_trend 0.289 0.358 467s > print( confint( fitsur4r3$eq[[ 1 ]], level = 0.25 ) ) 467s 37.5 % 62.5 % 467s (Intercept) 90.848 95.548 467s price -0.265 -0.211 467s income 0.318 0.329 467s > 467s > print( confint( fitsur5, level = 0.25 ) ) 467s 37.5 % 62.5 % 467s demand_(Intercept) 81.670 111.985 467s demand_price -0.450 -0.109 467s demand_income 0.287 0.371 467s supply_(Intercept) 37.377 68.500 467s supply_price 0.050 0.391 467s supply_farmPrice 0.190 0.276 467s supply_trend 0.287 0.371 467s > print( confint( fitsur5$eq[[ 2 ]], level = 0.975 ) ) 467s 1.3 % 98.8 % 467s (Intercept) 34.986 70.891 467s price 0.024 0.417 467s farmPrice 0.183 0.282 467s trend 0.280 0.377 467s > 467s > print( confint( fitsuri1r3, level = 0.975 ) ) 467s 1.3 % 98.8 % 467s demand_(Intercept) 77.960 109.125 467s demand_price -0.414 -0.043 467s demand_income 0.213 0.406 467s supply_(Intercept) 82.005 96.361 467s supply_income 0.574 0.753 467s supply_farmPrice -0.550 -0.393 467s supply_trend -0.932 -0.659 467s > print( confint( fitsuri1r3$eq[[ 1 ]], level = 0.999 ) ) 467s 0.1 % 100 % 467s (Intercept) 64.257 122.828 467s price -0.576 0.119 467s income 0.128 0.491 467s > 467s > print( confint( fitsuri2, level = 0.999 ) ) 467s 0.1 % 100 % 467s demand_(Intercept) 92.129 122.607 467s demand_price -0.580 -0.209 467s demand_income 0.243 0.433 467s supply_(Intercept) 60.441 109.649 467s supply_income 0.062 0.563 467s supply_farmPrice -0.432 0.038 467s supply_trend 0.243 0.433 467s > print( confint( fitsuri2$eq[[ 2 ]], level = 0.1 ) ) 467s 45 % 55 % 467s (Intercept) 83.512 86.578 467s income 0.297 0.328 467s farmPrice -0.212 -0.183 467s trend 0.332 0.344 467s > 467s > print( confint( fitsuri3e, level = 0.1 ) ) 467s 45 % 55 % 467s demand_(Intercept) 93.728 121.882 467s demand_price -0.570 -0.227 467s demand_income 0.250 0.426 467s supply_(Intercept) 63.100 107.114 467s supply_income 0.087 0.534 467s supply_farmPrice -0.406 0.014 467s supply_trend 0.250 0.426 467s > print( confint( fitsuri3e$eq[[ 1 ]], level = 0.01 ) ) 467s 49.5 % 50.5 % 467s (Intercept) 107.718 107.893 467s price -0.400 -0.398 467s income 0.337 0.338 467s > 467s > print( confint( fitsurio4, level = 0.01 ) ) 467s 49.5 % 50.5 % 467s demand_(Intercept) 77.496 107.356 467s demand_price -0.400 -0.055 467s demand_income 0.283 0.358 467s supply_(Intercept) 33.588 63.871 467s supply_price 0.100 0.445 467s supply_farmPrice 0.185 0.262 467s supply_trend 0.283 0.358 467s > print( confint( fitsurio4$eq[[ 2 ]], level = 0.33 ) ) 467s 33.5 % 66.5 % 467s (Intercept) 45.524 51.935 467s price 0.236 0.309 467s farmPrice 0.215 0.231 467s trend 0.312 0.328 467s > print( confint( fitsuri4, level = 0.01 ) ) 467s 49.5 % 50.5 % 467s demand_(Intercept) 84.345 111.726 467s demand_price -0.422 -0.107 467s demand_income 0.212 0.389 467s supply_(Intercept) 68.817 111.192 467s supply_income 0.078 0.393 467s supply_farmPrice -0.392 0.058 467s supply_trend 0.212 0.389 467s > print( confint( fitsuri4$eq[[ 2 ]], level = 0.33 ) ) 467s 33.5 % 66.5 % 467s (Intercept) 85.519 94.490 467s income 0.202 0.269 467s farmPrice -0.214 -0.119 467s trend 0.282 0.319 467s > 467s > print( confint( fitsurio4w, level = 0.01 ) ) 467s 49.5 % 50.5 % 467s demand_(Intercept) 77.496 107.356 467s demand_price -0.400 -0.055 467s demand_income 0.283 0.358 467s supply_(Intercept) 33.587 63.871 467s supply_price 0.100 0.445 467s supply_farmPrice 0.185 0.262 467s supply_trend 0.283 0.358 467s > print( confint( fitsurio4w$eq[[ 1 ]], level = 0.33 ) ) 467s 33.5 % 66.5 % 467s (Intercept) 89.266 95.587 467s price -0.264 -0.191 467s income 0.312 0.328 467s > 467s > print( confint( fitsurio5r2, level = 0.33 ) ) 467s 33.5 % 66.5 % 467s demand_(Intercept) 63.491 92.577 467s demand_price -0.230 0.101 467s demand_income 0.274 0.327 467s supply_(Intercept) 19.527 48.865 467s supply_price 0.270 0.601 467s supply_farmPrice 0.182 0.232 467s supply_trend 0.274 0.327 467s > print( confint( fitsurio5r2$eq[[ 1 ]] ) ) 467s 2.5 % 97.5 % 467s (Intercept) 63.491 92.577 467s price -0.230 0.101 467s income 0.274 0.327 467s > print( confint( fitsuri5r2, level = 0.33 ) ) 467s 33.5 % 66.5 % 467s demand_(Intercept) 84.498 111.942 467s demand_price -0.425 -0.109 467s demand_income 0.213 0.390 467s supply_(Intercept) 69.034 111.399 467s supply_income 0.075 0.391 467s supply_farmPrice -0.392 0.059 467s supply_trend 0.213 0.390 467s > print( confint( fitsuri5r2$eq[[ 1 ]] ) ) 467s 2.5 % 97.5 % 467s (Intercept) 84.498 111.942 467s price -0.425 -0.109 467s income 0.213 0.390 467s > 467s > 467s > ## *********** fitted values ************* 467s > print( fitted( fitsur1e2 ) ) 467s demand supply 467s 1 97.9 98.1 467s 2 99.8 99.2 467s 3 99.7 99.4 467s 4 99.9 99.7 467s 5 102.1 101.7 467s 6 101.9 101.7 467s 7 102.3 101.7 467s 8 102.6 103.5 467s 9 101.6 102.4 467s 10 100.7 100.3 467s 11 96.2 96.8 467s 12 95.2 95.4 467s 13 96.4 96.8 467s 14 99.2 98.7 467s 15 103.8 102.9 467s 16 103.5 104.2 467s 17 104.2 104.0 467s 18 101.8 103.1 467s 19 103.2 103.0 467s 20 105.9 105.2 467s > print( fitted( fitsur1e2$eq[[ 2 ]] ) ) 467s 1 2 3 4 5 6 7 8 9 10 11 12 13 467s 98.1 99.2 99.4 99.7 101.7 101.7 101.7 103.5 102.4 100.3 96.8 95.4 96.8 467s 14 15 16 17 18 19 20 467s 98.7 102.9 104.2 104.0 103.1 103.0 105.2 467s > 467s > print( fitted( fitsur2e ) ) 467s demand supply 467s 1 98.2 98.7 467s 2 99.9 99.7 467s 3 99.9 99.8 467s 4 100.0 100.0 467s 5 102.0 101.7 467s 6 101.8 101.7 467s 7 102.1 101.7 467s 8 102.5 103.2 467s 9 101.5 102.2 467s 10 100.7 100.3 467s 11 96.6 97.3 467s 12 95.8 96.1 467s 13 96.8 97.3 467s 14 99.4 98.9 467s 15 103.5 102.4 467s 16 103.3 103.6 467s 17 103.8 103.3 467s 18 101.8 102.7 467s 19 103.0 102.6 467s 20 105.5 104.5 467s > print( fitted( fitsur2e$eq[[ 1 ]] ) ) 467s 1 2 3 4 5 6 7 8 9 10 11 12 13 467s 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 467s 14 15 16 17 18 19 20 467s 99.4 103.5 103.3 103.8 101.8 103.0 105.5 467s > 467s > print( fitted( fitsur2we ) ) 467s demand supply 467s 1 98.2 98.7 467s 2 99.9 99.7 467s 3 99.9 99.8 467s 4 100.0 100.1 467s 5 102.0 101.7 467s 6 101.8 101.7 467s 7 102.1 101.7 467s 8 102.5 103.2 467s 9 101.5 102.2 467s 10 100.7 100.3 467s 11 96.7 97.4 467s 12 95.8 96.2 467s 13 96.8 97.4 467s 14 99.4 99.0 467s 15 103.5 102.4 467s 16 103.2 103.6 467s 17 103.8 103.3 467s 18 101.8 102.7 467s 19 103.0 102.6 467s 20 105.5 104.5 467s > print( fitted( fitsur2we$eq[[ 2 ]] ) ) 467s 1 2 3 4 5 6 7 8 9 10 11 12 13 467s 98.7 99.7 99.8 100.1 101.7 101.7 101.7 103.2 102.2 100.3 97.4 96.2 97.4 467s 14 15 16 17 18 19 20 467s 99.0 102.4 103.6 103.3 102.7 102.6 104.5 467s > 467s > print( fitted( fitsur3 ) ) 467s demand supply 467s 1 98.1 98.6 467s 2 99.9 99.6 467s 3 99.9 99.8 467s 4 100.0 100.0 467s 5 102.0 101.7 467s 6 101.8 101.7 467s 7 102.2 101.7 467s 8 102.5 103.2 467s 9 101.5 102.2 467s 10 100.7 100.3 467s 11 96.6 97.3 467s 12 95.7 96.1 467s 13 96.8 97.3 467s 14 99.3 98.9 467s 15 103.6 102.5 467s 16 103.3 103.7 467s 17 103.8 103.3 467s 18 101.8 102.7 467s 19 103.0 102.6 467s 20 105.6 104.6 467s > print( fitted( fitsur3$eq[[ 2 ]] ) ) 467s 1 2 3 4 5 6 7 8 9 10 11 12 13 467s 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 467s 14 15 16 17 18 19 20 467s 98.9 102.5 103.7 103.3 102.7 102.6 104.6 467s > 467s > print( fitted( fitsur4r3 ) ) 467s demand supply 467s 1 97.6 98.2 467s 2 99.9 99.8 467s 3 99.8 99.9 467s 4 100.0 100.3 467s 5 102.1 101.8 467s 6 102.0 101.9 467s 7 102.5 102.1 467s 8 103.1 104.3 467s 9 101.4 102.2 467s 10 100.2 99.3 467s 11 95.3 95.7 467s 12 94.5 94.7 467s 13 96.0 96.6 467s 14 99.0 98.2 467s 15 103.9 102.4 467s 16 103.7 104.2 467s 17 103.8 102.6 467s 18 102.2 103.4 467s 19 103.8 103.5 467s 20 107.2 106.7 467s > print( fitted( fitsur4r3$eq[[ 1 ]] ) ) 467s 1 2 3 4 5 6 7 8 9 10 11 12 13 467s 97.6 99.9 99.8 100.0 102.1 102.0 102.5 103.1 101.4 100.2 95.3 94.5 96.0 467s 14 15 16 17 18 19 20 467s 99.0 103.9 103.7 103.8 102.2 103.8 107.2 467s > 467s > print( fitted( fitsur5 ) ) 467s demand supply 467s 1 97.5 98.2 467s 2 99.7 99.6 467s 3 99.7 99.8 467s 4 99.9 100.1 467s 5 102.2 101.9 467s 6 102.0 102.0 467s 7 102.5 102.1 467s 8 102.9 104.2 467s 9 101.6 102.4 467s 10 100.5 99.7 467s 11 95.5 95.9 467s 12 94.5 94.6 467s 13 95.8 96.4 467s 14 99.0 98.2 467s 15 104.1 102.6 467s 16 103.8 104.3 467s 17 104.3 103.3 467s 18 102.0 103.3 467s 19 103.6 103.3 467s 20 106.8 106.1 467s > print( fitted( fitsur5$eq[[ 2 ]] ) ) 467s 1 2 3 4 5 6 7 8 9 10 11 12 13 467s 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 467s 14 15 16 17 18 19 20 467s 98.2 102.6 104.3 103.3 103.3 103.3 106.1 467s > 467s > print( fitted( fitsuri1r3 ) ) 467s demand supply 467s 1 97.7 100.2 467s 2 99.9 105.7 467s 3 99.9 104.3 467s 4 100.1 104.9 467s 5 102.1 99.2 467s 6 101.9 100.1 467s 7 102.4 102.3 467s 8 103.0 102.6 467s 9 101.4 94.9 467s 10 100.2 92.8 467s 11 95.5 92.1 467s 12 94.8 98.3 467s 13 96.2 101.6 467s 14 99.0 99.8 467s 15 103.7 97.5 467s 16 103.6 96.7 467s 17 103.6 87.6 467s 18 102.1 100.6 467s 19 103.7 105.5 467s 20 107.0 113.8 467s > print( fitted( fitsuri1r3$eq[[ 1 ]] ) ) 467s 1 2 3 4 5 6 7 8 9 10 11 12 13 467s 97.7 99.9 99.9 100.1 102.1 101.9 102.4 103.0 101.4 100.2 95.5 94.8 96.2 467s 14 15 16 17 18 19 20 467s 99.0 103.7 103.6 103.6 102.1 103.7 107.0 467s > 467s > print( fitted( fitsuri1wr3 ) ) 467s demand supply 467s 1 97.7 100.2 467s 2 99.9 105.7 467s 3 99.9 104.3 467s 4 100.1 104.9 467s 5 102.1 99.2 467s 6 101.9 100.1 467s 7 102.4 102.3 467s 8 103.0 102.6 467s 9 101.4 94.9 467s 10 100.2 92.8 467s 11 95.5 92.1 467s 12 94.8 98.3 467s 13 96.2 101.6 467s 14 99.0 99.8 467s 15 103.7 97.5 467s 16 103.6 96.7 467s 17 103.6 87.6 467s 18 102.1 100.6 467s 19 103.7 105.5 467s 20 107.0 113.8 467s > print( fitted( fitsuri1wr3$eq[[ 2 ]] ) ) 467s 1 2 3 4 5 6 7 8 9 10 11 12 13 467s 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 467s 14 15 16 17 18 19 20 467s 99.8 97.5 96.7 87.6 100.6 105.5 113.8 467s > 467s > print( fitted( fitsuri2 ) ) 467s demand supply 467s 1 97.4 93.4 467s 2 99.2 96.7 467s 3 99.3 96.7 467s 4 99.4 97.7 467s 5 102.5 96.1 467s 6 102.1 97.1 467s 7 102.4 98.8 467s 8 102.5 99.8 467s 9 102.0 96.8 467s 10 101.4 96.4 467s 11 96.0 96.3 467s 12 94.4 99.6 467s 13 95.4 101.9 467s 14 99.1 102.0 467s 15 104.7 102.2 467s 16 104.1 102.6 467s 17 105.8 99.1 467s 18 101.6 105.5 467s 19 103.1 108.5 467s 20 105.6 113.2 467s > print( fitted( fitsuri2$eq[[ 2 ]] ) ) 467s 1 2 3 4 5 6 7 8 9 10 11 12 13 467s 93.4 96.7 96.7 97.7 96.1 97.1 98.8 99.8 96.8 96.4 96.3 99.6 101.9 467s 14 15 16 17 18 19 20 467s 102.0 102.2 102.6 99.1 105.5 108.5 113.2 467s > 467s > print( fitted( fitsuri3e ) ) 467s demand supply 467s 1 97.4 93.4 467s 2 99.2 96.7 467s 3 99.3 96.7 467s 4 99.3 97.7 467s 5 102.5 96.1 467s 6 102.1 97.2 467s 7 102.4 98.8 467s 8 102.5 99.8 467s 9 102.0 96.9 467s 10 101.5 96.4 467s 11 96.1 96.3 467s 12 94.4 99.6 467s 13 95.4 101.9 467s 14 99.1 102.0 467s 15 104.7 102.2 467s 16 104.1 102.6 467s 17 105.9 99.1 467s 18 101.6 105.5 467s 19 103.1 108.4 467s 20 105.5 113.1 467s > print( fitted( fitsuri3e$eq[[ 1 ]] ) ) 467s 1 2 3 4 5 6 7 8 9 10 11 12 13 467s 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 467s 14 15 16 17 18 19 20 467s 99.1 104.7 104.1 105.9 101.6 103.1 105.5 467s > 467s > print( fitted( fitsurio4 ) ) 467s demand supply 467s 1 97.6 98.2 467s 2 100.0 99.9 467s 3 99.9 100.0 467s 4 100.1 100.4 467s 5 102.1 101.8 467s 6 102.0 101.9 467s 7 102.5 102.1 467s 8 103.1 104.3 467s 9 101.4 102.1 467s 10 100.1 99.2 467s 11 95.3 95.7 467s 12 94.6 94.8 467s 13 96.1 96.7 467s 14 99.0 98.3 467s 15 103.8 102.3 467s 16 103.7 104.1 467s 17 103.6 102.4 467s 18 102.2 103.5 467s 19 103.8 103.6 467s 20 107.3 106.8 467s > print( fitted( fitsurio4$eq[[ 2 ]] ) ) 467s 1 2 3 4 5 6 7 8 9 10 11 12 13 467s 98.2 99.9 100.0 100.4 101.8 101.9 102.1 104.3 102.1 99.2 95.7 94.8 96.7 467s 14 15 16 17 18 19 20 467s 98.3 102.3 104.1 102.4 103.5 103.6 106.8 467s > print( fitted( fitsuri4 ) ) 467s demand supply 467s 1 97.8 94.5 467s 2 99.8 97.1 467s 3 99.7 97.2 467s 4 99.9 98.0 467s 5 102.1 96.5 467s 6 101.9 97.4 467s 7 102.3 98.8 467s 8 102.7 99.5 467s 9 101.6 97.3 467s 10 100.6 97.2 467s 11 96.0 97.5 467s 12 95.0 100.3 467s 13 96.2 102.0 467s 14 99.1 102.0 467s 15 103.9 101.7 467s 16 103.6 102.1 467s 17 104.1 99.4 467s 18 101.9 104.6 467s 19 103.3 106.9 467s 20 106.2 110.4 467s > print( fitted( fitsuri4$eq[[ 2 ]] ) ) 467s 1 2 3 4 5 6 7 8 9 10 11 12 13 467s 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 467s 14 15 16 17 18 19 20 467s 102.0 101.7 102.1 99.4 104.6 106.9 110.4 467s > 467s > print( fitted( fitsurio5r2 ) ) 467s demand supply 467s 1 97.8 98.5 467s 2 100.6 100.7 467s 3 100.4 100.6 467s 4 100.8 101.2 467s 5 101.7 101.3 467s 6 101.8 101.7 467s 7 102.5 102.2 467s 8 103.7 104.9 467s 9 100.8 101.4 467s 10 98.9 97.7 467s 11 94.6 94.8 467s 12 94.8 95.0 467s 13 96.8 97.6 467s 14 98.9 98.2 467s 15 102.9 101.3 467s 16 103.3 103.6 467s 17 101.4 99.8 467s 18 102.7 104.0 467s 19 104.5 104.4 467s 20 108.9 108.9 467s > print( fitted( fitsurio5r2$eq[[ 1 ]] ) ) 467s 1 2 3 4 5 6 7 8 9 10 11 12 13 467s 97.8 100.6 100.4 100.8 101.7 101.8 102.5 103.7 100.8 98.9 94.6 94.8 96.8 467s 14 15 16 17 18 19 20 467s 98.9 102.9 103.3 101.4 102.7 104.5 108.9 467s > print( fitted( fitsuri5r2 ) ) 467s demand supply 467s 1 97.8 94.6 467s 2 99.8 97.1 467s 3 99.7 97.2 467s 4 99.9 98.0 467s 5 102.1 96.5 467s 6 101.9 97.4 467s 7 102.3 98.8 467s 8 102.7 99.5 467s 9 101.6 97.3 467s 10 100.6 97.2 467s 11 96.0 97.5 467s 12 95.0 100.3 467s 13 96.2 102.0 467s 14 99.1 102.0 467s 15 103.9 101.7 467s 16 103.6 102.0 467s 17 104.2 99.4 467s 18 101.9 104.6 467s 19 103.3 106.9 467s 20 106.2 110.4 467s > print( fitted( fitsuri5r2$eq[[ 1 ]] ) ) 467s 1 2 3 4 5 6 7 8 9 10 11 12 13 467s 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 467s 14 15 16 17 18 19 20 467s 99.1 103.9 103.6 104.2 101.9 103.3 106.2 467s > 467s > 467s > ## *********** predicted values ************* 467s > predictData <- Kmenta 467s > predictData$consump <- NULL 467s > predictData$price <- Kmenta$price * 0.9 467s > predictData$income <- Kmenta$income * 1.1 467s > 467s > print( predict( fitsur1e2, se.fit = TRUE, interval = "prediction", 467s + useDfSys = TRUE ) ) 467s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 467s 1 97.9 0.607 93.7 102.1 98.1 0.780 467s 2 99.8 0.569 95.6 104.0 99.2 0.793 467s 3 99.7 0.537 95.6 103.9 99.4 0.728 467s 4 99.9 0.575 95.7 104.1 99.7 0.755 467s 5 102.1 0.493 97.9 106.3 101.7 0.652 467s 6 101.9 0.458 97.8 106.0 101.7 0.605 467s 7 102.3 0.475 98.1 106.4 101.7 0.592 467s 8 102.6 0.593 98.4 106.8 103.5 0.835 467s 9 101.6 0.523 97.4 105.8 102.4 0.717 467s 10 100.7 0.788 96.4 105.1 100.3 0.980 467s 11 96.2 0.898 91.8 100.7 96.8 1.081 467s 12 95.2 0.898 90.8 99.7 95.4 1.159 467s 13 96.4 0.816 92.0 100.7 96.8 1.019 467s 14 99.2 0.495 95.1 103.4 98.7 0.710 467s 15 103.8 0.724 99.5 108.1 102.9 0.816 467s 16 103.5 0.586 99.3 107.7 104.2 0.830 467s 17 104.2 1.240 99.4 108.9 104.0 1.540 467s 18 101.8 0.533 97.7 106.0 103.1 0.770 467s 19 103.2 0.666 98.9 107.4 103.0 0.862 467s 20 105.9 1.240 101.1 110.7 105.2 1.517 467s supply.lwr supply.upr 467s 1 92.6 104 467s 2 93.7 105 467s 3 94.0 105 467s 4 94.2 105 467s 5 96.3 107 467s 6 96.3 107 467s 7 96.4 107 467s 8 98.0 109 467s 9 97.0 108 467s 10 94.7 106 467s 11 91.2 103 467s 12 89.7 101 467s 13 91.2 102 467s 14 93.3 104 467s 15 97.4 108 467s 16 98.7 110 467s 17 97.9 110 467s 18 97.7 109 467s 19 97.5 109 467s 20 99.2 111 467s > print( predict( fitsur1e2$eq[[ 2 ]], se.fit = TRUE, interval = "prediction", 467s + useDfSys = TRUE ) ) 467s fit se.fit lwr upr 467s 1 98.1 0.780 92.6 104 467s 2 99.2 0.793 93.7 105 467s 3 99.4 0.728 94.0 105 467s 4 99.7 0.755 94.2 105 467s 5 101.7 0.652 96.3 107 467s 6 101.7 0.605 96.3 107 467s 7 101.7 0.592 96.4 107 467s 8 103.5 0.835 98.0 109 467s 9 102.4 0.717 97.0 108 467s 10 100.3 0.980 94.7 106 467s 11 96.8 1.081 91.2 103 467s 12 95.4 1.159 89.7 101 467s 13 96.8 1.019 91.2 102 467s 14 98.7 0.710 93.3 104 467s 15 102.9 0.816 97.4 108 467s 16 104.2 0.830 98.7 110 467s 17 104.0 1.540 97.9 110 467s 18 103.1 0.770 97.7 109 467s 19 103.0 0.862 97.5 109 467s 20 105.2 1.517 99.2 111 467s > 467s > print( predict( fitsur2e, se.pred = TRUE, interval = "confidence", 467s + level = 0.999, newdata = predictData ) ) 467s demand.pred demand.se.pred demand.lwr demand.upr supply.pred supply.se.pred 467s 1 103 2.23 99.8 106 97.4 2.80 467s 2 105 2.22 102.0 108 98.3 2.71 467s 3 105 2.23 101.8 108 98.4 2.72 467s 4 105 2.23 102.1 108 98.7 2.70 467s 5 107 2.42 102.3 111 100.4 2.83 467s 6 107 2.39 102.5 111 100.4 2.79 467s 7 107 2.37 103.0 111 100.4 2.75 467s 8 108 2.34 103.8 112 101.8 2.70 467s 9 106 2.44 101.7 111 100.9 2.87 467s 10 105 2.54 99.8 111 99.1 3.05 467s 11 101 2.39 96.5 105 96.1 3.05 467s 12 100 2.24 97.0 103 94.8 2.96 467s 13 101 2.17 99.1 104 96.0 2.83 467s 14 104 2.30 100.5 108 97.6 2.85 467s 15 108 2.58 102.9 114 101.2 2.91 467s 16 108 2.49 103.4 113 102.3 2.83 467s 17 108 2.85 101.3 115 102.1 3.26 467s 18 107 2.31 103.2 111 101.3 2.70 467s 19 108 2.36 104.3 113 101.2 2.68 467s 20 112 2.52 106.4 117 103.0 2.66 467s supply.lwr supply.upr 467s 1 93.6 101.1 467s 2 95.5 101.1 467s 3 95.5 101.3 467s 4 96.0 101.3 467s 5 96.4 104.4 467s 6 96.7 104.1 467s 7 97.1 103.7 467s 8 99.2 104.5 467s 9 96.5 105.3 467s 10 93.4 104.8 467s 11 90.3 101.8 467s 12 89.7 99.9 467s 13 91.9 100.0 467s 14 93.4 101.8 467s 15 96.4 105.9 467s 16 98.3 106.4 467s 17 95.1 109.2 467s 18 98.6 103.9 467s 19 98.9 103.5 467s 20 101.0 105.1 467s > print( predict( fitsur2e$eq[[ 1 ]], se.pred = TRUE, interval = "confidence", 467s + level = 0.999, newdata = predictData ) ) 467s fit se.pred lwr upr 467s 1 103 2.23 99.8 106 467s 2 105 2.22 102.0 108 467s 3 105 2.23 101.8 108 467s 4 105 2.23 102.1 108 467s 5 107 2.42 102.3 111 467s 6 107 2.39 102.5 111 467s 7 107 2.37 103.0 111 467s 8 108 2.34 103.8 112 467s 9 106 2.44 101.7 111 467s 10 105 2.54 99.8 111 467s 11 101 2.39 96.5 105 467s 12 100 2.24 97.0 103 467s 13 101 2.17 99.1 104 467s 14 104 2.30 100.5 108 467s 15 108 2.58 102.9 114 467s 16 108 2.49 103.4 113 467s 17 108 2.85 101.3 115 467s 18 107 2.31 103.2 111 467s 19 108 2.36 104.3 113 467s 20 112 2.52 106.4 117 467s > 467s > print( predict( fitsur3, se.pred = TRUE, interval = "prediction", 467s + level = 0.975 ) ) 467s demand.pred demand.se.pred demand.lwr demand.upr supply.pred supply.se.pred 467s 1 98.1 2.13 93.1 103 98.6 2.67 467s 2 99.9 2.13 94.9 105 99.6 2.69 467s 3 99.9 2.12 94.9 105 99.8 2.68 467s 4 100.0 2.13 95.0 105 100.0 2.69 467s 5 102.0 2.11 97.0 107 101.7 2.67 467s 6 101.8 2.10 96.9 107 101.7 2.66 467s 7 102.2 2.11 97.2 107 101.7 2.66 467s 8 102.5 2.14 97.5 108 103.2 2.72 467s 9 101.5 2.12 96.5 106 102.2 2.69 467s 10 100.7 2.20 95.5 106 100.3 2.78 467s 11 96.6 2.23 91.3 102 97.3 2.80 467s 12 95.7 2.22 90.5 101 96.1 2.81 467s 13 96.8 2.19 91.6 102 97.3 2.77 467s 14 99.3 2.11 94.4 104 98.9 2.69 467s 15 103.6 2.17 98.5 109 102.5 2.71 467s 16 103.3 2.13 98.3 108 103.7 2.69 467s 17 103.8 2.39 98.2 109 103.3 2.99 467s 18 101.8 2.12 96.8 107 102.7 2.69 467s 19 103.0 2.16 98.0 108 102.6 2.71 467s 20 105.6 2.39 100.0 111 104.6 2.97 467s supply.lwr supply.upr 467s 1 92.4 105 467s 2 93.3 106 467s 3 93.5 106 467s 4 93.7 106 467s 5 95.4 108 467s 6 95.5 108 467s 7 95.5 108 467s 8 96.8 110 467s 9 95.9 109 467s 10 93.8 107 467s 11 90.7 104 467s 12 89.5 103 467s 13 90.8 104 467s 14 92.6 105 467s 15 96.1 109 467s 16 97.3 110 467s 17 96.3 110 467s 18 96.4 109 467s 19 96.3 109 467s 20 97.6 112 467s > print( predict( fitsur3$eq[[ 2 ]], se.pred = TRUE, interval = "prediction", 467s + level = 0.975 ) ) 467s fit se.pred lwr upr 467s 1 98.6 2.67 92.4 105 467s 2 99.6 2.69 93.3 106 467s 3 99.8 2.68 93.5 106 467s 4 100.0 2.69 93.7 106 467s 5 101.7 2.67 95.4 108 467s 6 101.7 2.66 95.5 108 467s 7 101.7 2.66 95.5 108 467s 8 103.2 2.72 96.8 110 467s 9 102.2 2.69 95.9 109 467s 10 100.3 2.78 93.8 107 467s 11 97.3 2.80 90.7 104 467s 12 96.1 2.81 89.5 103 467s 13 97.3 2.77 90.8 104 467s 14 98.9 2.69 92.6 105 467s 15 102.5 2.71 96.1 109 467s 16 103.7 2.69 97.3 110 467s 17 103.3 2.99 96.3 110 467s 18 102.7 2.69 96.4 109 467s 19 102.6 2.71 96.3 109 467s 20 104.6 2.97 97.6 112 467s > 467s > print( predict( fitsur4r3, se.fit = TRUE, interval = "confidence", 467s + level = 0.25 ) ) 467s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 467s 1 97.6 0.474 97.4 97.7 98.2 0.571 467s 2 99.9 0.558 99.7 100.1 99.8 0.699 467s 3 99.8 0.523 99.6 100.0 99.9 0.651 467s 4 100.0 0.567 99.9 100.2 100.3 0.701 467s 5 102.1 0.476 102.0 102.3 101.8 0.620 467s 6 102.0 0.443 101.8 102.1 101.9 0.574 467s 7 102.5 0.440 102.3 102.6 102.1 0.559 467s 8 103.1 0.532 102.9 103.3 104.3 0.646 467s 9 101.4 0.520 101.3 101.6 102.2 0.692 467s 10 100.2 0.774 100.0 100.4 99.3 0.939 467s 11 95.3 0.612 95.1 95.5 95.7 0.732 467s 12 94.5 0.525 94.4 94.7 94.7 0.687 467s 13 96.0 0.603 95.8 96.2 96.6 0.791 467s 14 99.0 0.444 98.8 99.1 98.2 0.580 467s 15 103.9 0.643 103.7 104.1 102.4 0.759 467s 16 103.7 0.494 103.6 103.9 104.2 0.634 467s 17 103.8 1.191 103.4 104.1 102.6 1.456 467s 18 102.2 0.510 102.0 102.3 103.4 0.622 467s 19 103.8 0.570 103.6 104.0 103.5 0.714 467s 20 107.2 0.973 106.9 107.6 106.7 1.183 467s supply.lwr supply.upr 467s 1 98.0 98.4 467s 2 99.6 100.0 467s 3 99.7 100.1 467s 4 100.1 100.5 467s 5 101.6 102.0 467s 6 101.7 102.1 467s 7 101.9 102.3 467s 8 104.1 104.5 467s 9 102.0 102.4 467s 10 99.0 99.6 467s 11 95.5 95.9 467s 12 94.5 94.9 467s 13 96.4 96.9 467s 14 98.1 98.4 467s 15 102.1 102.6 467s 16 104.0 104.4 467s 17 102.1 103.1 467s 18 103.2 103.6 467s 19 103.3 103.7 467s 20 106.3 107.1 467s > print( predict( fitsur4r3$eq[[ 1 ]], se.fit = TRUE, interval = "confidence", 467s + level = 0.25 ) ) 467s fit se.fit lwr upr 467s 1 97.6 0.474 97.4 97.7 467s 2 99.9 0.558 99.7 100.1 467s 3 99.8 0.523 99.6 100.0 467s 4 100.0 0.567 99.9 100.2 467s 5 102.1 0.476 102.0 102.3 467s 6 102.0 0.443 101.8 102.1 467s 7 102.5 0.440 102.3 102.6 467s 8 103.1 0.532 102.9 103.3 467s 9 101.4 0.520 101.3 101.6 467s 10 100.2 0.774 100.0 100.4 467s 11 95.3 0.612 95.1 95.5 467s 12 94.5 0.525 94.4 94.7 467s 13 96.0 0.603 95.8 96.2 467s 14 99.0 0.444 98.8 99.1 467s 15 103.9 0.643 103.7 104.1 467s 16 103.7 0.494 103.6 103.9 467s 17 103.8 1.191 103.4 104.1 467s 18 102.2 0.510 102.0 102.3 467s 19 103.8 0.570 103.6 104.0 467s 20 107.2 0.973 106.9 107.6 467s > 467s > print( predict( fitsur4we, se.fit = TRUE, interval = "confidence", 467s + level = 0.25 ) ) 467s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 467s 1 97.5 0.445 97.3 97.6 98.2 0.519 467s 2 99.7 0.514 99.6 99.9 99.6 0.636 467s 3 99.7 0.482 99.5 99.8 99.8 0.591 467s 4 99.9 0.523 99.7 100.0 100.1 0.636 467s 5 102.2 0.438 102.1 102.4 102.0 0.568 467s 6 102.0 0.408 101.9 102.2 102.0 0.523 467s 7 102.5 0.409 102.3 102.6 102.1 0.508 467s 8 102.9 0.503 102.8 103.1 104.2 0.603 467s 9 101.6 0.479 101.4 101.7 102.4 0.631 467s 10 100.5 0.724 100.3 100.8 99.7 0.856 467s 11 95.5 0.612 95.3 95.7 95.9 0.694 467s 12 94.4 0.520 94.3 94.6 94.6 0.677 467s 13 95.8 0.565 95.6 96.0 96.3 0.748 467s 14 99.0 0.414 98.8 99.1 98.2 0.540 467s 15 104.1 0.592 103.9 104.3 102.6 0.690 467s 16 103.8 0.458 103.7 104.0 104.3 0.581 467s 17 104.3 1.100 104.0 104.7 103.3 1.334 467s 18 102.0 0.477 101.9 102.2 103.3 0.564 467s 19 103.6 0.545 103.4 103.8 103.2 0.651 467s 20 106.8 0.958 106.5 107.1 106.1 1.091 467s supply.lwr supply.upr 467s 1 98.0 98.3 467s 2 99.4 99.8 467s 3 99.6 99.9 467s 4 99.9 100.3 467s 5 101.8 102.1 467s 6 101.8 102.2 467s 7 101.9 102.2 467s 8 104.0 104.4 467s 9 102.2 102.6 467s 10 99.5 100.0 467s 11 95.7 96.1 467s 12 94.4 94.8 467s 13 96.1 96.6 467s 14 98.0 98.4 467s 15 102.4 102.9 467s 16 104.1 104.5 467s 17 102.9 103.8 467s 18 103.1 103.5 467s 19 103.0 103.5 467s 20 105.8 106.5 467s > print( predict( fitsur4we$eq[[ 2 ]], se.fit = TRUE, interval = "confidence", 467s + level = 0.25 ) ) 467s fit se.fit lwr upr 467s 1 98.2 0.519 98.0 98.3 467s 2 99.6 0.636 99.4 99.8 467s 3 99.8 0.591 99.6 99.9 467s 4 100.1 0.636 99.9 100.3 467s 5 102.0 0.568 101.8 102.1 467s 6 102.0 0.523 101.8 102.2 467s 7 102.1 0.508 101.9 102.2 467s 8 104.2 0.603 104.0 104.4 467s 9 102.4 0.631 102.2 102.6 467s 10 99.7 0.856 99.5 100.0 467s 11 95.9 0.694 95.7 96.1 467s 12 94.6 0.677 94.4 94.8 467s 13 96.3 0.748 96.1 96.6 467s 14 98.2 0.540 98.0 98.4 467s 15 102.6 0.690 102.4 102.9 467s 16 104.3 0.581 104.1 104.5 467s 17 103.3 1.334 102.9 103.8 467s 18 103.3 0.564 103.1 103.5 467s 19 103.2 0.651 103.0 103.5 467s 20 106.1 1.091 105.8 106.5 467s > 467s > print( predict( fitsur5, se.fit = TRUE, se.pred = TRUE, 467s + interval = "prediction", level = 0.5, newdata = predictData ) ) 467s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 467s 1 103.2 0.911 2.14 101.7 105 96.0 467s 2 105.9 0.786 2.09 104.4 107 97.3 467s 3 105.7 0.824 2.11 104.3 107 97.5 467s 4 106.0 0.780 2.09 104.6 107 97.8 467s 5 108.2 1.233 2.30 106.7 110 99.8 467s 6 108.1 1.143 2.25 106.6 110 99.8 467s 7 108.7 1.076 2.22 107.2 110 99.8 467s 8 109.4 0.919 2.15 108.0 111 101.9 467s 9 107.5 1.295 2.33 105.9 109 100.3 467s 10 106.0 1.568 2.49 104.3 108 97.7 467s 11 100.5 1.292 2.33 98.9 102 93.8 467s 12 99.7 0.921 2.15 98.3 101 92.4 467s 13 101.5 0.720 2.07 100.1 103 94.1 467s 14 104.7 1.054 2.21 103.2 106 96.1 467s 15 110.1 1.485 2.44 108.5 112 100.5 467s 16 110.0 1.284 2.33 108.4 112 102.1 467s 17 109.9 2.013 2.80 108.0 112 101.4 467s 18 108.4 0.906 2.14 106.9 110 101.0 467s 19 110.2 0.911 2.14 108.8 112 100.9 467s 20 114.2 0.898 2.14 112.7 116 103.6 467s supply.se.fit supply.se.pred supply.lwr supply.upr 467s 1 0.916 2.68 94.1 97.8 467s 2 0.715 2.62 95.5 99.1 467s 3 0.760 2.63 95.7 99.3 467s 4 0.708 2.62 96.0 99.6 467s 5 1.213 2.80 97.9 101.7 467s 6 1.100 2.75 97.9 101.7 467s 7 0.982 2.70 98.0 101.7 467s 8 0.825 2.65 100.1 103.7 467s 9 1.339 2.85 98.4 102.2 467s 10 1.631 3.00 95.7 99.8 467s 11 1.375 2.87 91.9 95.8 467s 12 1.025 2.72 90.6 94.3 467s 13 0.831 2.65 92.3 95.9 467s 14 1.033 2.72 94.2 97.9 467s 15 1.434 2.90 98.5 102.5 467s 16 1.249 2.81 100.2 104.1 467s 17 2.163 3.32 99.1 103.6 467s 18 0.809 2.65 99.2 102.8 467s 19 0.712 2.62 99.1 102.7 467s 20 0.572 2.58 101.9 105.4 467s > print( predict( fitsur5$eq[[ 2 ]], se.fit = TRUE, se.pred = TRUE, 467s + interval = "prediction", level = 0.5, newdata = predictData ) ) 467s fit se.fit se.pred lwr upr 467s 1 96.0 0.916 2.68 94.1 97.8 467s 2 97.3 0.715 2.62 95.5 99.1 467s 3 97.5 0.760 2.63 95.7 99.3 467s 4 97.8 0.708 2.62 96.0 99.6 467s 5 99.8 1.213 2.80 97.9 101.7 467s 6 99.8 1.100 2.75 97.9 101.7 467s 7 99.8 0.982 2.70 98.0 101.7 467s 8 101.9 0.825 2.65 100.1 103.7 467s 9 100.3 1.339 2.85 98.4 102.2 467s 10 97.7 1.631 3.00 95.7 99.8 467s 11 93.8 1.375 2.87 91.9 95.8 467s 12 92.4 1.025 2.72 90.6 94.3 467s 13 94.1 0.831 2.65 92.3 95.9 467s 14 96.1 1.033 2.72 94.2 97.9 467s 15 100.5 1.434 2.90 98.5 102.5 467s 16 102.1 1.249 2.81 100.2 104.1 467s 17 101.4 2.163 3.32 99.1 103.6 467s 18 101.0 0.809 2.65 99.2 102.8 467s 19 100.9 0.712 2.62 99.1 102.7 467s 20 103.6 0.572 2.58 101.9 105.4 467s > 467s > print( predict( fitsuri1r3, se.fit = TRUE, se.pred = TRUE, 467s + interval = "confidence", level = 0.99 ) ) 467s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 467s 1 97.7 0.653 2.09 95.8 99.6 100.2 467s 2 99.9 0.578 2.07 98.3 101.6 105.7 467s 3 99.9 0.548 2.06 98.3 101.4 104.3 467s 4 100.1 0.583 2.07 98.4 101.8 104.9 467s 5 102.1 0.509 2.05 100.6 103.5 99.2 467s 6 101.9 0.474 2.04 100.6 103.3 100.1 467s 7 102.4 0.496 2.04 101.0 103.9 102.3 467s 8 103.0 0.615 2.08 101.2 104.8 102.6 467s 9 101.4 0.531 2.05 99.9 103.0 94.9 467s 10 100.2 0.785 2.13 98.0 102.5 92.8 467s 11 95.5 0.971 2.21 92.7 98.3 92.1 467s 12 94.8 0.996 2.22 91.9 97.7 98.3 467s 13 96.2 0.880 2.17 93.7 98.8 101.6 467s 14 99.0 0.521 2.05 97.5 100.5 99.8 467s 15 103.7 0.752 2.12 101.6 105.9 97.5 467s 16 103.6 0.622 2.08 101.8 105.4 96.7 467s 17 103.6 1.241 2.34 100.0 107.2 87.6 467s 18 102.1 0.546 2.06 100.5 103.7 100.6 467s 19 103.7 0.696 2.10 101.6 105.7 105.5 467s 20 107.0 1.299 2.37 103.2 110.7 113.8 467s supply.se.fit supply.se.pred supply.lwr supply.upr 467s 1 0.599 1.72 98.4 101.9 467s 2 0.604 1.72 103.9 107.4 467s 3 0.539 1.70 102.7 105.8 467s 4 0.536 1.70 103.4 106.5 467s 5 0.486 1.69 97.8 100.6 467s 6 0.448 1.68 98.8 101.4 467s 7 0.444 1.67 101.0 103.6 467s 8 0.522 1.70 101.1 104.1 467s 9 0.542 1.70 93.3 96.5 467s 10 0.579 1.72 91.1 94.5 467s 11 0.812 1.81 89.7 94.5 467s 12 0.865 1.83 95.8 100.9 467s 13 0.747 1.78 99.4 103.8 467s 14 0.507 1.69 98.3 101.3 467s 15 0.509 1.69 96.0 98.9 467s 16 0.596 1.72 95.0 98.5 467s 17 0.975 1.89 84.7 90.4 467s 18 0.500 1.69 99.1 102.0 467s 19 0.649 1.74 103.6 107.3 467s 20 1.124 1.97 110.5 117.1 467s > print( predict( fitsuri1r3$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 467s + interval = "confidence", level = 0.99 ) ) 467s fit se.fit se.pred lwr upr 467s 1 97.7 0.653 2.09 95.8 99.6 467s 2 99.9 0.578 2.07 98.3 101.6 467s 3 99.9 0.548 2.06 98.3 101.4 467s 4 100.1 0.583 2.07 98.4 101.8 467s 5 102.1 0.509 2.05 100.6 103.5 467s 6 101.9 0.474 2.04 100.6 103.3 467s 7 102.4 0.496 2.04 101.0 103.9 467s 8 103.0 0.615 2.08 101.2 104.8 467s 9 101.4 0.531 2.05 99.9 103.0 467s 10 100.2 0.785 2.13 98.0 102.5 467s 11 95.5 0.971 2.21 92.7 98.3 467s 12 94.8 0.996 2.22 91.9 97.7 467s 13 96.2 0.880 2.17 93.7 98.8 467s 14 99.0 0.521 2.05 97.5 100.5 467s 15 103.7 0.752 2.12 101.6 105.9 467s 16 103.6 0.622 2.08 101.8 105.4 467s 17 103.6 1.241 2.34 100.0 107.2 467s 18 102.1 0.546 2.06 100.5 103.7 467s 19 103.7 0.696 2.10 101.6 105.7 467s 20 107.0 1.299 2.37 103.2 110.7 467s > 467s > print( predict( fitsuri2, se.fit = TRUE, interval = "prediction", 467s + level = 0.9, newdata = predictData ) ) 467s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 467s 1 104 0.960 100.5 108 96.1 1.37 467s 2 107 1.011 102.9 110 99.7 1.69 467s 3 107 1.032 102.8 110 99.8 1.61 467s 4 107 1.019 103.0 111 100.8 1.76 467s 5 110 1.547 105.4 114 99.2 2.00 467s 6 109 1.468 105.3 114 100.3 1.94 467s 7 110 1.465 105.7 114 102.1 2.12 467s 8 110 1.423 106.1 114 103.2 2.60 467s 9 109 1.543 104.8 113 99.9 1.80 467s 10 108 1.699 103.6 112 99.1 1.35 467s 11 102 1.299 98.2 106 98.6 2.25 467s 12 101 0.939 97.2 105 102.0 3.10 467s 13 102 0.731 98.7 106 104.5 3.01 467s 14 106 1.164 102.1 110 104.9 2.27 467s 15 112 1.896 107.3 117 105.4 2.20 467s 16 112 1.733 107.1 116 105.9 2.40 467s 17 113 2.316 107.4 118 102.1 2.02 467s 18 109 1.316 105.2 113 108.8 2.75 467s 19 111 1.497 106.8 115 111.9 3.73 467s 20 114 1.918 109.7 119 117.2 5.62 467s supply.lwr supply.upr 467s 1 86.2 106 467s 2 89.7 110 467s 3 89.7 110 467s 4 90.7 111 467s 5 89.0 109 467s 6 90.1 110 467s 7 91.8 112 467s 8 92.6 114 467s 9 89.7 110 467s 10 89.2 109 467s 11 88.2 109 467s 12 91.0 113 467s 13 93.6 115 467s 14 94.5 115 467s 15 95.0 116 467s 16 95.4 116 467s 17 91.9 112 467s 18 98.1 119 467s 19 100.4 123 467s 20 103.6 131 467s > print( predict( fitsuri2$eq[[ 2 ]], se.fit = TRUE, interval = "prediction", 467s + level = 0.9, newdata = predictData ) ) 467s fit se.fit lwr upr 467s 1 96.1 1.37 86.2 106 467s 2 99.7 1.69 89.7 110 467s 3 99.8 1.61 89.7 110 467s 4 100.8 1.76 90.7 111 467s 5 99.2 2.00 89.0 109 467s 6 100.3 1.94 90.1 110 467s 7 102.1 2.12 91.8 112 467s 8 103.2 2.60 92.6 114 467s 9 99.9 1.80 89.7 110 467s 10 99.1 1.35 89.2 109 467s 11 98.6 2.25 88.2 109 467s 12 102.0 3.10 91.0 113 467s 13 104.5 3.01 93.6 115 467s 14 104.9 2.27 94.5 115 467s 15 105.4 2.20 95.0 116 467s 16 105.9 2.40 95.4 116 467s 17 102.1 2.02 91.9 112 467s 18 108.8 2.75 98.1 119 467s 19 111.9 3.73 100.4 123 467s 20 117.2 5.62 103.6 131 467s > 467s > print( predict( fitsuri2w, se.fit = TRUE, interval = "prediction", 467s + level = 0.9, newdata = predictData ) ) 467s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 467s 1 104 0.960 100.5 108 96.1 1.37 467s 2 107 1.011 102.9 110 99.7 1.69 467s 3 107 1.032 102.8 110 99.8 1.61 467s 4 107 1.019 103.0 111 100.8 1.76 467s 5 110 1.547 105.4 114 99.2 2.00 467s 6 109 1.468 105.3 114 100.3 1.94 467s 7 110 1.465 105.7 114 102.1 2.12 467s 8 110 1.423 106.1 114 103.2 2.60 467s 9 109 1.543 104.8 113 99.9 1.80 467s 10 108 1.699 103.6 112 99.1 1.35 467s 11 102 1.299 98.2 106 98.6 2.25 467s 12 101 0.939 97.2 105 102.0 3.10 467s 13 102 0.731 98.7 106 104.5 3.01 467s 14 106 1.164 102.1 110 104.9 2.27 467s 15 112 1.896 107.3 117 105.4 2.20 467s 16 112 1.733 107.1 116 105.9 2.40 467s 17 113 2.316 107.4 118 102.1 2.02 467s 18 109 1.316 105.2 113 108.8 2.75 467s 19 111 1.497 106.8 115 111.9 3.73 467s 20 114 1.918 109.7 119 117.2 5.62 467s supply.lwr supply.upr 467s 1 86.2 106 467s 2 89.7 110 467s 3 89.7 110 467s 4 90.7 111 467s 5 89.0 109 467s 6 90.1 110 467s 7 91.8 112 467s 8 92.6 114 467s 9 89.7 110 467s 10 89.2 109 467s 11 88.2 109 467s 12 91.0 113 467s 13 93.6 115 467s 14 94.5 115 467s 15 95.0 116 467s 16 95.4 116 467s 17 91.9 112 467s 18 98.1 119 467s 19 100.4 123 467s 20 103.6 131 467s > print( predict( fitsuri2w$eq[[ 2 ]], se.fit = TRUE, interval = "prediction", 467s + level = 0.9, newdata = predictData ) ) 467s fit se.fit lwr upr 467s 1 96.1 1.37 86.2 106 467s 2 99.7 1.69 89.7 110 467s 3 99.8 1.61 89.7 110 467s 4 100.8 1.76 90.7 111 467s 5 99.2 2.00 89.0 109 467s 6 100.3 1.94 90.1 110 467s 7 102.1 2.12 91.8 112 467s 8 103.2 2.60 92.6 114 467s 9 99.9 1.80 89.7 110 467s 10 99.1 1.35 89.2 109 467s 11 98.6 2.25 88.2 109 467s 12 102.0 3.10 91.0 113 467s 13 104.5 3.01 93.6 115 467s 14 104.9 2.27 94.5 115 467s 15 105.4 2.20 95.0 116 467s 16 105.9 2.40 95.4 116 467s 17 102.1 2.02 91.9 112 467s 18 108.8 2.75 98.1 119 467s 19 111.9 3.73 100.4 123 467s 20 117.2 5.62 103.6 131 467s > 467s > print( predict( fitsuri3e, interval = "prediction", level = 0.925 ) ) 467s demand.pred demand.lwr demand.upr supply.pred supply.lwr supply.upr 467s 1 97.4 93.5 101.2 93.4 82.5 104 467s 2 99.2 95.4 103.0 96.7 86.0 107 467s 3 99.3 95.5 103.0 96.7 86.0 107 467s 4 99.3 95.5 103.1 97.7 87.0 108 467s 5 102.5 98.7 106.2 96.1 85.1 107 467s 6 102.1 98.4 105.9 97.2 86.3 108 467s 7 102.4 98.6 106.2 98.8 88.1 110 467s 8 102.5 98.7 106.3 99.8 88.9 111 467s 9 102.0 98.2 105.8 96.9 85.9 108 467s 10 101.5 97.6 105.4 96.4 85.5 107 467s 11 96.1 92.1 100.1 96.3 84.9 108 467s 12 94.4 90.4 98.4 99.6 87.9 111 467s 13 95.4 91.4 99.3 101.9 90.4 113 467s 14 99.1 95.3 102.8 102.0 91.1 113 467s 15 104.7 100.8 108.6 102.2 91.4 113 467s 16 104.1 100.3 107.9 102.6 91.8 113 467s 17 105.9 101.6 110.2 99.1 88.1 110 467s 18 101.6 97.9 105.4 105.5 94.6 116 467s 19 103.1 99.2 106.9 108.4 97.1 120 467s 20 105.5 101.3 109.8 113.1 100.7 126 467s > print( predict( fitsuri3e$eq[[ 1 ]], interval = "prediction", level = 0.925 ) ) 467s fit lwr upr 467s 1 97.4 93.5 101.2 467s 2 99.2 95.4 103.0 467s 3 99.3 95.5 103.0 467s 4 99.3 95.5 103.1 467s 5 102.5 98.7 106.2 467s 6 102.1 98.4 105.9 467s 7 102.4 98.6 106.2 467s 8 102.5 98.7 106.3 467s 9 102.0 98.2 105.8 467s 10 101.5 97.6 105.4 467s 11 96.1 92.1 100.1 467s 12 94.4 90.4 98.4 467s 13 95.4 91.4 99.3 467s 14 99.1 95.3 102.8 467s 15 104.7 100.8 108.6 467s 16 104.1 100.3 107.9 467s 17 105.9 101.6 110.2 467s 18 101.6 97.9 105.4 467s 19 103.1 99.2 106.9 467s 20 105.5 101.3 109.8 467s > 467s > print( predict( fitsurio4, interval = "confidence", newdata = predictData ) ) 467s demand.pred demand.lwr demand.upr supply.pred supply.lwr supply.upr 467s 1 102.7 100.8 105 95.5 93.6 97.4 467s 2 105.5 103.8 107 97.0 95.5 98.5 467s 3 105.3 103.6 107 97.2 95.6 98.8 467s 4 105.6 104.0 107 97.5 96.0 99.0 467s 5 107.5 105.0 110 99.1 96.5 101.6 467s 6 107.5 105.1 110 99.2 96.9 101.5 467s 7 108.1 105.9 110 99.3 97.2 101.4 467s 8 108.9 107.1 111 101.5 99.7 103.2 467s 9 106.7 104.0 109 99.5 96.7 102.3 467s 10 105.1 101.8 108 96.7 93.4 100.1 467s 11 99.8 97.2 102 93.1 90.4 95.9 467s 12 99.3 97.4 101 92.1 90.1 94.1 467s 13 101.1 99.7 103 93.9 92.3 95.5 467s 14 104.1 101.9 106 95.6 93.5 97.7 467s 15 109.3 106.2 112 99.7 96.7 102.7 467s 16 109.3 106.6 112 101.4 98.8 104.0 467s 17 108.7 104.5 113 100.0 95.5 104.5 467s 18 107.9 106.0 110 100.6 98.9 102.3 467s 19 109.8 107.9 112 100.7 99.2 102.2 467s 20 114.0 112.3 116 103.7 102.5 104.9 467s > print( predict( fitsurio4$eq[[ 2 ]], interval = "confidence", 467s + newdata = predictData ) ) 467s fit lwr upr 467s 1 95.5 93.6 97.4 467s 2 97.0 95.5 98.5 467s 3 97.2 95.6 98.8 467s 4 97.5 96.0 99.0 467s 5 99.1 96.5 101.6 467s 6 99.2 96.9 101.5 467s 7 99.3 97.2 101.4 467s 8 101.5 99.7 103.2 467s 9 99.5 96.7 102.3 467s 10 96.7 93.4 100.1 467s 11 93.1 90.4 95.9 467s 12 92.1 90.1 94.1 467s 13 93.9 92.3 95.5 467s 14 95.6 93.5 97.7 467s 15 99.7 96.7 102.7 467s 16 101.4 98.8 104.0 467s 17 100.0 95.5 104.5 467s 18 100.6 98.9 102.3 467s 19 100.7 99.2 102.2 467s 20 103.7 102.5 104.9 467s > print( predict( fitsuri4, interval = "confidence", newdata = predictData ) ) 467s demand.pred demand.lwr demand.upr supply.pred supply.lwr supply.upr 467s 1 103.1 101.3 105 96.6 93.9 99.3 467s 2 105.5 103.7 107 99.4 96.2 102.5 467s 3 105.4 103.5 107 99.4 96.4 102.5 467s 4 105.6 103.8 107 100.3 97.1 103.5 467s 5 107.7 105.0 110 98.9 94.9 102.9 467s 6 107.6 105.0 110 99.8 96.1 103.5 467s 7 108.1 105.5 111 101.2 97.6 104.9 467s 8 108.7 106.1 111 102.0 97.7 106.4 467s 9 107.0 104.3 110 99.6 96.0 103.2 467s 10 105.7 102.7 109 99.3 96.6 102.0 467s 11 100.7 98.3 103 99.3 95.0 103.5 467s 12 99.9 98.2 102 102.1 95.8 108.4 467s 13 101.5 100.2 103 104.0 97.9 110.1 467s 14 104.5 102.4 107 104.1 99.8 108.4 467s 15 109.5 106.1 113 104.2 100.8 107.5 467s 16 109.4 106.3 112 104.5 100.9 108.2 467s 17 109.3 105.3 113 101.7 97.7 105.6 467s 18 107.8 105.4 110 107.0 103.1 110.9 467s 19 109.5 106.7 112 109.5 104.4 114.6 467s 20 113.0 109.4 117 113.4 106.3 120.6 467s > print( predict( fitsuri4$eq[[ 2 ]], interval = "confidence", 467s + newdata = predictData ) ) 467s fit lwr upr 467s 1 96.6 93.9 99.3 467s 2 99.4 96.2 102.5 467s 3 99.4 96.4 102.5 467s 4 100.3 97.1 103.5 467s 5 98.9 94.9 102.9 467s 6 99.8 96.1 103.5 467s 7 101.2 97.6 104.9 467s 8 102.0 97.7 106.4 467s 9 99.6 96.0 103.2 467s 10 99.3 96.6 102.0 467s 11 99.3 95.0 103.5 467s 12 102.1 95.8 108.4 467s 13 104.0 97.9 110.1 467s 14 104.1 99.8 108.4 467s 15 104.2 100.8 107.5 467s 16 104.5 100.9 108.2 467s 17 101.7 97.7 105.6 467s 18 107.0 103.1 110.9 467s 19 109.5 104.4 114.6 467s 20 113.4 106.3 120.6 467s > 467s > print( predict( fitsurio5r2 ) ) 467s demand.pred supply.pred 467s 1 97.8 98.5 467s 2 100.6 100.7 467s 3 100.4 100.6 467s 4 100.8 101.2 467s 5 101.7 101.3 467s 6 101.8 101.7 467s 7 102.5 102.2 467s 8 103.7 104.9 467s 9 100.8 101.4 467s 10 98.9 97.7 467s 11 94.6 94.8 467s 12 94.8 95.0 467s 13 96.8 97.6 467s 14 98.9 98.2 467s 15 102.9 101.3 467s 16 103.3 103.6 467s 17 101.4 99.8 467s 18 102.7 104.0 467s 19 104.5 104.4 467s 20 108.9 108.9 467s > print( predict( fitsurio5r2$eq[[ 1 ]] ) ) 467s fit 467s 1 97.8 467s 2 100.6 467s 3 100.4 467s 4 100.8 467s 5 101.7 467s 6 101.8 467s 7 102.5 467s 8 103.7 467s 9 100.8 467s 10 98.9 467s 11 94.6 467s 12 94.8 467s 13 96.8 467s 14 98.9 467s 15 102.9 467s 16 103.3 467s 17 101.4 467s 18 102.7 467s 19 104.5 467s 20 108.9 467s > print( predict( fitsuri5r2 ) ) 467s demand.pred supply.pred 467s 1 97.8 94.6 467s 2 99.8 97.1 467s 3 99.7 97.2 467s 4 99.9 98.0 467s 5 102.1 96.5 467s 6 101.9 97.4 467s 7 102.3 98.8 467s 8 102.7 99.5 467s 9 101.6 97.3 467s 10 100.6 97.2 467s 11 96.0 97.5 467s 12 95.0 100.3 467s 13 96.2 102.0 467s 14 99.1 102.0 467s 15 103.9 101.7 467s 16 103.6 102.0 467s 17 104.2 99.4 467s 18 101.9 104.6 467s 19 103.3 106.9 467s 20 106.2 110.4 467s > print( predict( fitsuri5r2$eq[[ 1 ]] ) ) 467s fit 467s 1 97.8 467s 2 99.8 467s 3 99.7 467s 4 99.9 467s 5 102.1 467s 6 101.9 467s 7 102.3 467s 8 102.7 467s 9 101.6 467s 10 100.6 467s 11 96.0 467s 12 95.0 467s 13 96.2 467s 14 99.1 467s 15 103.9 467s 16 103.6 467s 17 104.2 467s 18 101.9 467s 19 103.3 467s 20 106.2 467s > 467s > # predict just one observation 467s > smallData <- data.frame( price = 130, income = 150, farmPrice = 120, 467s + trend = 25 ) 467s > 467s > print( predict( fitsur1e2, newdata = smallData ) ) 467s demand.pred supply.pred 467s 1 108 115 467s > print( predict( fitsur1e2$eq[[ 1 ]], newdata = smallData ) ) 467s fit 467s 1 108 467s > 467s > print( predict( fitsur2e, se.fit = TRUE, level = 0.9, 467s + newdata = smallData ) ) 467s demand.pred demand.se.fit supply.pred supply.se.fit 467s 1 108 2.21 113 3 467s > print( predict( fitsur2e$eq[[ 1 ]], se.pred = TRUE, level = 0.99, 467s + newdata = smallData ) ) 467s fit se.pred 467s 1 108 3.03 467s > 467s > print( predict( fitsur3, interval = "prediction", level = 0.975, 467s + newdata = smallData ) ) 467s demand.pred demand.lwr demand.upr supply.pred supply.lwr supply.upr 467s 1 108 100 115 113 103 123 467s > print( predict( fitsur3$eq[[ 1 ]], interval = "confidence", level = 0.8, 467s + newdata = smallData ) ) 467s fit lwr upr 467s 1 108 105 111 467s > 467s > print( predict( fitsur4r3, se.fit = TRUE, interval = "confidence", 467s + level = 0.999, newdata = smallData ) ) 467s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 467s 1 111 2.06 103 118 119 2.22 467s supply.lwr supply.upr 467s 1 111 127 467s > print( predict( fitsur4r3$eq[[ 2 ]], se.pred = TRUE, interval = "prediction", 467s + level = 0.75, newdata = smallData ) ) 467s fit se.pred lwr upr 467s 1 119 3.41 115 123 467s > 467s > print( predict( fitsur5, se.fit = TRUE, interval = "prediction", 467s + newdata = smallData ) ) 467s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 467s 1 110 2.15 104 116 118 2.29 467s supply.lwr supply.upr 467s 1 111 125 467s > print( predict( fitsur5$eq[[ 1 ]], se.pred = TRUE, interval = "confidence", 467s + newdata = smallData ) ) 467s fit se.pred lwr upr 467s 1 110 2.9 105 114 467s > 467s > print( predict( fitsurio5r2, se.fit = TRUE, se.pred = TRUE, 467s + interval = "prediction", level = 0.5, newdata = smallData ) ) 467s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 467s 1 115 1.98 3.09 113 117 123 467s supply.se.fit supply.se.pred supply.lwr supply.upr 467s 1 2.17 3.82 121 126 467s > print( predict( fitsurio5r2$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 467s + interval = "confidence", level = 0.25, newdata = smallData ) ) 467s fit se.fit se.pred lwr upr 467s 1 115 1.98 3.09 114 115 467s > print( predict( fitsuri5r2, se.fit = TRUE, se.pred = TRUE, 467s + interval = "prediction", level = 0.5, newdata = smallData ) ) 467s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 467s 1 109 2.35 3.06 107 111 113 467s supply.se.fit supply.se.pred supply.lwr supply.upr 467s 1 3.91 6.87 108 117 467s > print( predict( fitsuri5r2$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 467s + interval = "confidence", level = 0.25, newdata = smallData ) ) 467s fit se.fit se.pred lwr upr 467s 1 109 2.35 3.06 108 109 467s > 467s > print( predict( fitsuri5wr2, se.fit = TRUE, se.pred = TRUE, 467s + interval = "prediction", level = 0.5, newdata = smallData ) ) 467s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 467s 1 109 2.35 3.06 107 111 113 467s supply.se.fit supply.se.pred supply.lwr supply.upr 467s 1 3.91 6.87 108 117 467s > print( predict( fitsuri5wr2$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 467s + interval = "confidence", level = 0.25, newdata = smallData ) ) 467s fit se.fit se.pred lwr upr 467s 1 109 2.35 3.06 108 109 467s > 467s > 467s > ## ************ correlation of predicted values *************** 467s > print( correlation.systemfit( fitsur1e2, 2, 1 ) ) 467s [,1] 467s [1,] 0.849 467s [2,] 0.856 467s [3,] 0.864 467s [4,] 0.882 467s [5,] 0.844 467s [6,] 0.861 467s [7,] 0.875 467s [8,] 0.877 467s [9,] 0.884 467s [10,] 0.918 467s [11,] 0.903 467s [12,] 0.884 467s [13,] 0.880 467s [14,] 0.863 467s [15,] 0.896 467s [16,] 0.897 467s [17,] 0.914 467s [18,] 0.839 467s [19,] 0.867 467s [20,] 0.902 467s > 467s > print( correlation.systemfit( fitsur2e, 1, 2 ) ) 467s [,1] 467s [1,] 0.942 467s [2,] 0.944 467s [3,] 0.942 467s [4,] 0.941 467s [5,] 0.902 467s [6,] 0.909 467s [7,] 0.917 467s [8,] 0.903 467s [9,] 0.910 467s [10,] 0.941 467s [11,] 0.923 467s [12,] 0.902 467s [13,] 0.901 467s [14,] 0.893 467s [15,] 0.925 467s [16,] 0.952 467s [17,] 0.944 467s [18,] 0.935 467s [19,] 0.930 467s [20,] 0.938 467s > 467s > print( correlation.systemfit( fitsur3, 2, 1 ) ) 467s [,1] 467s [1,] 0.939 467s [2,] 0.943 467s [3,] 0.941 467s [4,] 0.940 467s [5,] 0.902 467s [6,] 0.909 467s [7,] 0.918 467s [8,] 0.903 467s [9,] 0.910 467s [10,] 0.941 467s [11,] 0.922 467s [12,] 0.900 467s [13,] 0.899 467s [14,] 0.892 467s [15,] 0.923 467s [16,] 0.952 467s [17,] 0.943 467s [18,] 0.936 467s [19,] 0.929 467s [20,] 0.937 467s > 467s > print( correlation.systemfit( fitsur3w, 2, 1 ) ) 467s [,1] 467s [1,] 0.940 467s [2,] 0.946 467s [3,] 0.944 467s [4,] 0.944 467s [5,] 0.908 467s [6,] 0.914 467s [7,] 0.922 467s [8,] 0.907 467s [9,] 0.914 467s [10,] 0.944 467s [11,] 0.926 467s [12,] 0.904 467s [13,] 0.903 467s [14,] 0.897 467s [15,] 0.926 467s [16,] 0.954 467s [17,] 0.946 467s [18,] 0.940 467s [19,] 0.932 467s [20,] 0.940 467s > 467s > print( correlation.systemfit( fitsur4r3, 1, 2 ) ) 467s [,1] 467s [1,] 0.963 467s [2,] 0.971 467s [3,] 0.971 467s [4,] 0.973 467s [5,] 0.940 467s [6,] 0.944 467s [7,] 0.947 467s [8,] 0.942 467s [9,] 0.947 467s [10,] 0.973 467s [11,] 0.910 467s [12,] 0.858 467s [13,] 0.914 467s [14,] 0.923 467s [15,] 0.977 467s [16,] 0.964 467s [17,] 0.978 467s [18,] 0.969 467s [19,] 0.946 467s [20,] 0.941 467s > 467s > print( correlation.systemfit( fitsur5, 2, 1 ) ) 467s [,1] 467s [1,] 0.938 467s [2,] 0.948 467s [3,] 0.948 467s [4,] 0.951 467s [5,] 0.892 467s [6,] 0.897 467s [7,] 0.903 467s [8,] 0.900 467s [9,] 0.907 467s [10,] 0.952 467s [11,] 0.853 467s [12,] 0.784 467s [13,] 0.858 467s [14,] 0.867 467s [15,] 0.961 467s [16,] 0.935 467s [17,] 0.961 467s [18,] 0.944 467s [19,] 0.907 467s [20,] 0.904 467s > 467s > print( correlation.systemfit( fitsuri1r3, 1, 2 ) ) 467s [,1] 467s [1,] -0.662 467s [2,] -0.656 467s [3,] -0.664 467s [4,] -0.689 467s [5,] -0.629 467s [6,] -0.664 467s [7,] -0.696 467s [8,] -0.675 467s [9,] -0.722 467s [10,] -0.757 467s [11,] -0.759 467s [12,] -0.732 467s [13,] -0.710 467s [14,] -0.669 467s [15,] -0.728 467s [16,] -0.737 467s [17,] -0.741 467s [18,] -0.583 467s [19,] -0.684 467s [20,] -0.746 467s > 467s > print( correlation.systemfit( fitsuri2, 2, 1 ) ) 467s [,1] 467s [1,] 0.360 467s [2,] 0.337 467s [3,] 0.337 467s [4,] 0.336 467s [5,] 0.286 467s [6,] 0.299 467s [7,] 0.317 467s [8,] 0.275 467s [9,] 0.322 467s [10,] 0.318 467s [11,] 0.334 467s [12,] 0.334 467s [13,] 0.318 467s [14,] 0.286 467s [15,] 0.358 467s [16,] 0.432 467s [17,] 0.367 467s [18,] 0.362 467s [19,] 0.333 467s [20,] 0.335 467s > 467s > print( correlation.systemfit( fitsuri2w, 1, 2 ) ) 467s [,1] 467s [1,] 0.360 467s [2,] 0.337 467s [3,] 0.337 467s [4,] 0.336 467s [5,] 0.286 467s [6,] 0.299 467s [7,] 0.317 467s [8,] 0.275 467s [9,] 0.322 467s [10,] 0.318 467s [11,] 0.334 467s [12,] 0.334 467s [13,] 0.318 467s [14,] 0.286 467s [15,] 0.358 467s [16,] 0.432 467s [17,] 0.367 467s [18,] 0.362 467s [19,] 0.333 467s [20,] 0.335 467s > 467s > print( correlation.systemfit( fitsuri3e, 1, 2 ) ) 467s [,1] 467s [1,] 0.368 467s [2,] 0.345 467s [3,] 0.344 467s [4,] 0.344 467s [5,] 0.292 467s [6,] 0.305 467s [7,] 0.323 467s [8,] 0.280 467s [9,] 0.329 467s [10,] 0.325 467s [11,] 0.340 467s [12,] 0.340 467s [13,] 0.324 467s [14,] 0.291 467s [15,] 0.366 467s [16,] 0.441 467s [17,] 0.375 467s [18,] 0.369 467s [19,] 0.340 467s [20,] 0.342 467s > 467s > print( correlation.systemfit( fitsurio4, 2, 1 ) ) 467s [,1] 467s [1,] 0.961 467s [2,] 0.971 467s [3,] 0.971 467s [4,] 0.973 467s [5,] 0.940 467s [6,] 0.944 467s [7,] 0.947 467s [8,] 0.939 467s [9,] 0.947 467s [10,] 0.972 467s [11,] 0.904 467s [12,] 0.861 467s [13,] 0.917 467s [14,] 0.922 467s [15,] 0.976 467s [16,] 0.964 467s [17,] 0.978 467s [18,] 0.967 467s [19,] 0.942 467s [20,] 0.934 467s > print( correlation.systemfit( fitsuri4, 2, 1 ) ) 467s [,1] 467s [1,] 0.0384 467s [2,] 0.1213 467s [3,] 0.0975 467s [4,] 0.1381 467s [5,] 0.1295 467s [6,] 0.0937 467s [7,] 0.0630 467s [8,] 0.1056 467s [9,] 0.2180 467s [10,] 0.4042 467s [11,] 0.1074 467s [12,] 0.0337 467s [13,] 0.0760 467s [14,] 0.0701 467s [15,] 0.0680 467s [16,] 0.1263 467s [17,] 0.3859 467s [18,] 0.2715 467s [19,] 0.2850 467s [20,] 0.3967 467s > 467s > print( correlation.systemfit( fitsurio5r2, 1, 2 ) ) 467s [,1] 467s [1,] 0.986 467s [2,] 0.991 467s [3,] 0.991 467s [4,] 0.991 467s [5,] 0.981 467s [6,] 0.983 467s [7,] 0.984 467s [8,] 0.980 467s [9,] 0.982 467s [10,] 0.991 467s [11,] 0.968 467s [12,] 0.947 467s [13,] 0.970 467s [14,] 0.975 467s [15,] 0.991 467s [16,] 0.989 467s [17,] 0.992 467s [18,] 0.990 467s [19,] 0.982 467s [20,] 0.978 467s > print( correlation.systemfit( fitsuri5r2, 1, 2 ) ) 467s [,1] 467s [1,] 0.0440 467s [2,] 0.1279 467s [3,] 0.1045 467s [4,] 0.1451 467s [5,] 0.1375 467s [6,] 0.1021 467s [7,] 0.0719 467s [8,] 0.1124 467s [9,] 0.2252 467s [10,] 0.4097 467s [11,] 0.1145 467s [12,] 0.0410 467s [13,] 0.0834 467s [14,] 0.0778 467s [15,] 0.0750 467s [16,] 0.1344 467s [17,] 0.3900 467s [18,] 0.2789 467s [19,] 0.2897 467s [20,] 0.4005 467s > 467s > 467s > ## ************ Log-Likelihood values *************** 467s > print( logLik( fitsur1e2 ) ) 467s 'log Lik.' -50.9 (df=10) 467s > print( logLik( fitsur1e2, residCovDiag = TRUE ) ) 467s 'log Lik.' -85.4 (df=10) 467s > 467s > print( logLik( fitsur2e ) ) 467s 'log Lik.' -52 (df=9) 467s > print( logLik( fitsur2e, residCovDiag = TRUE ) ) 467s 'log Lik.' -86.5 (df=9) 467s > 467s > print( logLik( fitsur3 ) ) 467s 'log Lik.' -52.2 (df=9) 467s > print( logLik( fitsur3, residCovDiag = TRUE ) ) 467s 'log Lik.' -86.4 (df=9) 467s > 467s > print( logLik( fitsur4r3 ) ) 467s 'log Lik.' -58.4 (df=8) 467s > print( logLik( fitsur4r3, residCovDiag = TRUE ) ) 467s 'log Lik.' -85.5 (df=8) 467s > 467s > print( logLik( fitsur5 ) ) 467s 'log Lik.' -58.5 (df=8) 467s > print( logLik( fitsur5, residCovDiag = TRUE ) ) 467s 'log Lik.' -84.6 (df=8) 467s > 467s > print( logLik( fitsur5w ) ) 467s 'log Lik.' -58.5 (df=8) 467s > print( logLik( fitsur5w, residCovDiag = TRUE ) ) 467s 'log Lik.' -84.7 (df=8) 467s > 467s > print( logLik( fitsuri1r3 ) ) 467s 'log Lik.' -67.8 (df=10) 467s > print( logLik( fitsuri1r3, residCovDiag = TRUE ) ) 467s 'log Lik.' -76.2 (df=10) 467s > 467s > print( logLik( fitsuri2 ) ) 467s 'log Lik.' -99.9 (df=9) 467s > print( logLik( fitsuri2, residCovDiag = TRUE ) ) 467s 'log Lik.' -101 (df=9) 467s > 467s > print( logLik( fitsuri3e ) ) 467s 'log Lik.' -99.9 (df=9) 467s > print( logLik( fitsuri3e, residCovDiag = TRUE ) ) 467s 'log Lik.' -102 (df=9) 467s > 467s > print( logLik( fitsurio4 ) ) 467s 'log Lik.' -58.5 (df=8) 467s > print( logLik( fitsurio4, residCovDiag = TRUE ) ) 467s 'log Lik.' -85.9 (df=8) 467s > 467s > print( logLik( fitsuri4 ) ) 467s 'log Lik.' -101 (df=8) 467s > print( logLik( fitsuri4, residCovDiag = TRUE ) ) 467s 'log Lik.' -101 (df=8) 467s > 467s > print( logLik( fitsuri4w ) ) 467s 'log Lik.' -101 (df=8) 467s > print( logLik( fitsuri4w, residCovDiag = TRUE ) ) 467s 'log Lik.' -101 (df=8) 467s > 467s > print( logLik( fitsurio5r2 ) ) 467s 'log Lik.' -59.8 (df=8) 467s > print( logLik( fitsurio5r2, residCovDiag = TRUE ) ) 467s 'log Lik.' -93.1 (df=8) 467s > 467s > print( logLik( fitsuri5r2 ) ) 467s 'log Lik.' -101 (df=8) 467s > print( logLik( fitsuri5r2, residCovDiag = TRUE ) ) 467s 'log Lik.' -101 (df=8) 467s > 467s > 467s > ## *********** likelihood ratio tests ************* 467s > # testing first restriction 467s > # non-iterating, methodResidCov = 1 467s > print( lrtest( fitsur2, fitsur1 ) ) 467s Likelihood ratio test 467s 467s Model 1: fitsur2 467s Model 2: fitsur1 467s #Df LogLik Df Chisq Pr(>Chisq) 467s 1 9 -52.2 467s 2 10 -51.6 1 1.19 0.28 467s > print( lrtest( fitsur3, fitsur1 ) ) 467s Likelihood ratio test 467s 467s Model 1: fitsur3 467s Model 2: fitsur1 467s #Df LogLik Df Chisq Pr(>Chisq) 467s 1 9 -52.2 467s 2 10 -51.6 1 1.19 0.28 467s > # non-iterating, methodResidCov = 0 467s > print( lrtest( fitsur2e, fitsur1e ) ) 467s Likelihood ratio test 467s 467s Model 1: fitsur2e 467s Model 2: fitsur1e 467s #Df LogLik Df Chisq Pr(>Chisq) 467s 1 9 -52.0 467s 2 10 -51.6 1 0.7 0.4 467s > print( lrtest( fitsur3e, fitsur1e ) ) 467s Likelihood ratio test 467s 467s Model 1: fitsur3e 467s Model 2: fitsur1e 467s #Df LogLik Df Chisq Pr(>Chisq) 467s 1 9 -52.0 467s 2 10 -51.6 1 0.7 0.4 467s > # iterating, methodResidCov = 1 467s > print( lrtest( fitsuri2, fitsuri1 ) ) 467s Likelihood ratio test 467s 467s Model 1: fitsuri2 467s Model 2: fitsuri1 467s #Df LogLik Df Chisq Pr(>Chisq) 467s 1 9 -99.9 467s 2 10 -67.8 1 64.3 1.1e-15 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s > print( lrtest( fitsuri3, fitsuri1 ) ) 467s Likelihood ratio test 467s 467s Model 1: fitsuri3 467s Model 2: fitsuri1 467s #Df LogLik Df Chisq Pr(>Chisq) 467s 1 9 -99.9 467s 2 10 -67.8 1 64.3 1.1e-15 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s > # iterating, methodResidCov = 0 467s > print( lrtest( fitsuri2e, fitsuri1e ) ) 467s Likelihood ratio test 467s 467s Model 1: fitsuri2e 467s Model 2: fitsuri1e 467s #Df LogLik Df Chisq Pr(>Chisq) 467s 1 9 -99.9 467s 2 10 -67.8 1 64.3 1.1e-15 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s > print( lrtest( fitsuri3e, fitsuri1e ) ) 467s Likelihood ratio test 467s 467s Model 1: fitsuri3e 467s Model 2: fitsuri1e 467s #Df LogLik Df Chisq Pr(>Chisq) 467s 1 9 -99.9 467s 2 10 -67.8 1 64.3 1.1e-15 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s > # non-iterating, methodResidCov = 1, WSUR 467s > print( lrtest( fitsur3w, fitsur1w ) ) 467s Likelihood ratio test 467s 467s Model 1: fitsur3w 467s Model 2: fitsur1w 467s #Df LogLik Df Chisq Pr(>Chisq) 467s 1 9 -52.1 467s 2 10 -51.6 1 0.87 0.35 467s > 467s > # testing second restriction 467s > # non-iterating, methodResidCov = 1 467s > print( lrtest( fitsur4, fitsur2 ) ) 467s Likelihood ratio test 467s 467s Model 1: fitsur4 467s Model 2: fitsur2 467s #Df LogLik Df Chisq Pr(>Chisq) 467s 1 8 -58.5 467s 2 9 -52.2 1 12.7 0.00037 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s > print( lrtest( fitsur4, fitsur3 ) ) 467s Likelihood ratio test 467s 467s Model 1: fitsur4 467s Model 2: fitsur3 467s #Df LogLik Df Chisq Pr(>Chisq) 467s 1 8 -58.5 467s 2 9 -52.2 1 12.7 0.00037 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s > print( lrtest( fitsur5, fitsur2 ) ) 467s Likelihood ratio test 467s 467s Model 1: fitsur5 467s Model 2: fitsur2 467s #Df LogLik Df Chisq Pr(>Chisq) 467s 1 8 -58.5 467s 2 9 -52.2 1 12.7 0.00037 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s > print( lrtest( fitsur5, fitsur3 ) ) 467s Likelihood ratio test 467s 467s Model 1: fitsur5 467s Model 2: fitsur3 467s #Df LogLik Df Chisq Pr(>Chisq) 467s 1 8 -58.5 467s 2 9 -52.2 1 12.7 0.00037 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s > # non-iterating, methodResidCov = 0 467s > print( lrtest( fitsur4e, fitsur2e ) ) 467s Likelihood ratio test 467s 467s Model 1: fitsur4e 467s Model 2: fitsur2e 467s #Df LogLik Df Chisq Pr(>Chisq) 467s 1 8 -58.6 467s 2 9 -52.0 1 13.2 0.00028 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s > print( lrtest( fitsur4e, fitsur3e ) ) 467s Likelihood ratio test 467s 467s Model 1: fitsur4e 467s Model 2: fitsur3e 467s #Df LogLik Df Chisq Pr(>Chisq) 467s 1 8 -58.6 467s 2 9 -52.0 1 13.2 0.00028 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s > print( lrtest( fitsur5e, fitsur2e ) ) 467s Likelihood ratio test 467s 467s Model 1: fitsur5e 467s Model 2: fitsur2e 467s #Df LogLik Df Chisq Pr(>Chisq) 467s 1 8 -58.6 467s 2 9 -52.0 1 13.2 0.00028 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s > print( lrtest( fitsur5e, fitsur3e ) ) 467s Likelihood ratio test 467s 467s Model 1: fitsur5e 467s Model 2: fitsur3e 467s #Df LogLik Df Chisq Pr(>Chisq) 467s 1 8 -58.6 467s 2 9 -52.0 1 13.2 0.00028 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s > # iterating, methodResidCov = 1 467s > print( lrtest( fitsurio4, fitsuri2 ) ) 467s Likelihood ratio test 467s 467s Model 1: fitsurio4 467s Model 2: fitsuri2 467s #Df LogLik Df Chisq Pr(>Chisq) 467s 1 8 -58.5 467s 2 9 -99.9 1 82.9 <2e-16 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s > print( lrtest( fitsurio4, fitsuri3 ) ) 467s Warning message: 467s In lrtest.systemfit(fitsurio4, fitsuri2) : 467s model '2' has a smaller log-likelihood value than the more restricted model '1' 467s Likelihood ratio test 467s 467s Model 1: fitsurio4 467s Model 2: fitsuri3 467s #Df LogLik Df Chisq Pr(>Chisq) 467s 1 8 -58.5 467s 2 9 -99.9 1 82.9 <2e-16 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s > print( lrtest( fitsurio5, fitsuri2 ) ) 467s Likelihood ratio test 467s 467s Model 1: fitsurio5 467s Model 2: fitsuri2 467s #Df LogLik Df Chisq Pr(>Chisq) 467s 1 8 -58.5 467s 2 9 -99.9 1 82.9 <2e-16 *** 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s > print( lrtest( fitsurio5, fitsuri3 ) ) 467s Likelihood ratio test 467s 467s Model 1: fitsurio5 467s Model 2: fitsuri3 467s #Df LogLik Df Chisq Pr(>Chisq) 467s 1 8 -58.5 467s 2 9 -99.9 1 82.9 <2e-16 ***Warning message: 467s In lrtest.systemfit(fitsurio4, fitsuri3) : 467s model '2' has a smaller log-likelihood value than the more restricted model '1' 467s Warning message: 467s In lrtest.systemfit(fitsurio5, fitsuri2) : 467s model '2' has a smaller log-likelihood value than the more restricted model '1' 467s Warning message: 467s In lrtest.systemfit(fitsurio5, fitsuri3) : 467s model '2' has a smaller log-likelihood value than the more restricted model '1' 467s 467s --- 467s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 467s > # corrected 467s > print( lrtest( fitsuri2, fitsuri4 ) ) 467s Likelihood ratio test 467s 467s Model 1: fitsuri2 467s Model 2: fitsuri4 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 9 -99.9 468s 2 8 -100.9 -1 1.9 0.17 468s > print( lrtest( fitsuri3, fitsuri4 ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsuri3 468s Model 2: fitsuri4 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 9 -99.9 468s 2 8 -100.9 -1 1.9 0.17 468s > print( lrtest( fitsuri2, fitsuri5 ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsuri2 468s Model 2: fitsuri5 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 9 -99.9 Warning message: 468s In lrtest.systemfit(fitsurio4e, fitsuri2e) : 468s model '2' has a smaller log-likelihood value than the more restricted model '1' 468s Warning message: 468s In lrtest.systemfit(fitsurio4e, fitsuri3e) : 468s model '2' has a smaller log-likelihood value than the more restricted model '1' 468s Warning message: 468s In lrtest.systemfit(fitsurio5e, fitsuri2e) : 468s model '2' has a smaller log-likelihood value than the more restricted model '1' 468s 468s 2 8 -100.9 -1 1.9 0.17 468s > print( lrtest( fitsuri3, fitsuri5 ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsuri3 468s Model 2: fitsuri5 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 9 -99.9 468s 2 8 -100.9 -1 1.9 0.17 468s > 468s > # iterating, methodResidCov = 0 468s > print( lrtest( fitsurio4e, fitsuri2e ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsurio4e 468s Model 2: fitsuri2e 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 8 -58.4 468s 2 9 -99.9 1 83 <2e-16 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > print( lrtest( fitsurio4e, fitsuri3e ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsurio4e 468s Model 2: fitsuri3e 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 8 -58.4 468s 2 9 -99.9 1 83 <2e-16 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > print( lrtest( fitsurio5e, fitsuri2e ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsurio5e 468s Model 2: fitsuri2e 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 8 -58.4 468s 2 9 -99.9 1 83 <2e-16 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > print( lrtest( fitsurio5e, fitsuri3e ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsurio5e 468s Model 2: fitsuri3e 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 8 -58.4 468s 2 9 -99.9 1 83 <2e-16 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > # corrected 468s > print( lrtest( fitsuri2e, fitsuri4e ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsuri2e 468s Model 2: fitsuri4e 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 9 -99.9 468s 2 8Warning message: 468s In lrtest.systemfit(fitsurio5e, fitsuri3e) : 468s model '2' has a smaller log-likelihood value than the more restricted model '1' 468s -100.9 -1 1.9 0.17 468s > print( lrtest( fitsuri3e, fitsuri4e ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsuri3e 468s Model 2: fitsuri4e 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 9 -99.9 468s 2 8 -100.9 -1 1.9 0.17 468s > print( lrtest( fitsuri2e, fitsuri5e ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsuri2e 468s Model 2: fitsuri5e 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 9 -99.9 468s 2 8 -100.9 -1 1.9 0.17 468s > print( lrtest( fitsuri3e, fitsuri5e ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsuri3e 468s Model 2: fitsuri5e 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 9 -99.9 468s 2 8 -100.9 -1 1.9 0.17 468s > 468s > # non-iterating, methodResidCov = 0, WSUR 468s > print( lrtest( fitsur4we, fitsur2we ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsur4we 468s Model 2: fitsur2we 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 8 -58.6 468s 2 9 -51.8 1 13.5 0.00024 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > 468s > # iterating, methodResidCov = 1, WSUR 468s > print( lrtest( fitsuri2w, fitsuri4w ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsuri2w 468s Model 2: fitsuri4w 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 9 -99.9 468s 2 8 -100.9 -1 1.9 0.17 468s > 468s > # testing both of the restrictions 468s > # non-iterating, methodResidCov = 1 468s > print( lrtest( fitsur4, fitsur1 ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsur4 468s Model 2: fitsur1 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 8 -58.5 468s 2 10 -51.6 2 13.8 0.00098 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > print( lrtest( fitsur5, fitsur1 ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsur5 468s Model 2: fitsur1 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 8 -58.5 468s 2 10 -51.6 2 13.8 0.00098 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > # non-iterating, methodResidCov = 0 468s > print( lrtest( fitsur4e, fitsur1e ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsur4e 468s Model 2: fitsur1e 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 8 -58.6 468s 2 10 -51.6 2 13.9 0.00095 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > print( lrtest( fitsur5e, fitsur1e ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsur5e 468s Model 2: fitsur1e 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 8 -58.6 468s 2 10 -51.6 2 13.9 0.00095 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > # iterating, methodResidCov = 1 468s > print( lrtest( fitsurio4, fitsuri1 ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsurio4 468s Model 2: fitsuri1 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 8 -58.5 468s 2 10 -67.8 2 18.6 9e-05 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > print( lrtest( fitsurio5, fitsuri1 ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsurio5 468s Model 2: fitsuri1 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 8 -58.5 468s 2 10 -67.8 2Warning message: 468s In lrtest.systemfit(fitsurio4, fitsuri1) : 468s model '2' has a smaller log-likelihood value than the more restricted model '1' 468s 18.6 9e-05 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > # corrected 468s > print( lrtest( fitsuri1, fitsuri4 ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsuri1 468s Model 2: fitsuri4 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 10Warning message: 468s In lrtest.systemfit(fitsurio5, fitsuri1) : 468s model '2' has a smaller log-likelihood value than the more restricted model '1' 468s -67.8 468s 2 8 -100.9 -2 66.2 4.2e-15 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > print( lrtest( fitsuri1, fitsuri5 ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsuri1 468s Model 2: fitsuri5 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 10 -67.8 468s 2 8 -100.9 -2 66.2 4.2e-15 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > # iterating, methodResidCov = 0 468s > print( lrtest( fitsurio4e, fitsuri1e ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsurio4e 468s Model 2: fitsuri1e 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 8 -58.4 468s 2 10 -67.8 2 18.7 8.9e-05 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s Warning message: 468s In lrtest.systemfit(fitsurio4e, fitsuri1e) : 468s > print( lrtest( fitsurio5e, fitsuri1e ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsurio5e 468s Model 2: fitsuri1e 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 8 -58.4 468s 2 10 -67.8 2 18.7 8.9e-05 *** model '2' has a smaller log-likelihood value than the more restricted model '1' 468s 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > # corrected 468s > print( lrtest( fitsuri1e, fitsuri4e ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsuri1e 468s Model 2: fitsuri4e 468s Warning message: 468s In lrtest.systemfit(fitsurio5e, fitsuri1e) : 468s model '2' has a smaller log-likelihood value than the more restricted model '1' 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 10 -67.8 468s 2 8 -100.9 -2 66.2 4.2e-15 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > print( lrtest( fitsuri1e, fitsuri5e ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsuri1e 468s Model 2: fitsuri5e 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 10 -67.8 468s 2 8 -100.9 -2 66.2 4.2e-15 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > # non-iterating, methodResidCov = 1, WSUR 468s > print( lrtest( fitsur5w, fitsur1w ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsur5w 468s Model 2: fitsur1w 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 8 -58.5 468s 2 10 -51.6 2 13.8 0.001 ** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > 468s > # testing the two restrictions with one call 468s > # non-iterating, methodResidCov = 1 468s > print( lrtest( fitsur4, fitsur2, fitsur1 ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsur4 468s Model 2: fitsur2 468s Model 3: fitsur1 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 8 -58.5 468s 2 9 -52.2 1 12.66 0.00037 *** 468s 3 10 -51.6 1 1.19 0.27520 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > print( lrtest( fitsur5, fitsur3, fitsur1 ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsur5 468s Model 2: fitsur3 468s Model 3: fitsur1 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 8 -58.5 468s 2 9 -52.2 1 12.66 0.00037 *** 468s 3 10 -51.6 1 1.19 0.27520 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > print( lrtest( fitsur1, fitsur3, fitsur5 ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsur1 468s Model 2: fitsur3 468s Model 3: fitsur5 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 10 -51.6 468s 2 9 -52.2 -1 1.19 0.27520 468s 3 8 -58.5 -1 12.66 0.00037 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > print( lrtest( object = fitsur5, fitsur3, fitsur1 ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsur5 468s Model 2: fitsur3 468s Model 3: fitsur1 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 8 -58.5 468s 2 9 -52.2 1 12.66 0.00037 *** 468s 3 10 -51.6 1 1.19 0.27520 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > print( lrtest( fitsur3, object = fitsur5, fitsur1 ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsur5 468s Model 2: fitsur3 468s Model 3: fitsur1 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 8 -58.5 468s 2 9 -52.2 1 12.66 0.00037 *** 468s 3 10 -51.6 1 1.19 0.27520 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > print( lrtest( fitsur3, fitsur1, object = fitsur5 ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsur5 468s Model 2: fitsur3 468s Model 3: fitsur1 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 8 -58.5 468s 2 9 -52.2 1 12.66 0.00037 *** 468s 3 10 -51.6 1 1.19 0.27520 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > # iterating, methodResidCov = 0 468s > print( lrtest( fitsuri4e, fitsuri2e, fitsuri1e ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsuri4e 468s Model 2: fitsuri2e 468s Model 3: fitsuri1e 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 8 -100.9 468s 2 9 -99.9 1 1.9 0.17 468s 3 10 -67.8 1 64.3 1.1e-15 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > print( lrtest( fitsuri5e, fitsuri3e, fitsuri1e ) ) 468s Likelihood ratio test 468s 468s Model 1: fitsuri5e 468s Model 2: fitsuri3e 468s Model 3: fitsuri1e 468s #Df LogLik Df Chisq Pr(>Chisq) 468s 1 8 -100.9 468s 2 9 -99.9 1 1.9 0.17 468s 3 10 -67.8 1 64.3 1.1e-15 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > 468s > ## ************** F tests **************** 468s > # testing first restriction 468s > print( linearHypothesis( fitsur1, restrm ) ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s 468s Model 1: restricted model 468s Model 2: fitsur1 468s 468s Res.Df Df F Pr(>F) 468s 1 34 468s 2 33 1 1.24 0.27 468s > linearHypothesis( fitsur1, restrict ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s 468s Model 1: restricted model 468s Model 2: fitsur1 468s 468s Res.Df Df F Pr(>F) 468s 1 34 468s 2 33 1 1.24 0.27 468s > 468s > print( linearHypothesis( fitsur1r2, restrm ) ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s 468s Model 1: restricted model 468s Model 2: fitsur1r2 468s 468s Res.Df Df F Pr(>F) 468s 1 34 468s 2 33 1 1.65 0.21 468s > linearHypothesis( fitsur1r2, restrict ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s 468s Model 1: restricted model 468s Model 2: fitsur1r2 468s 468s Res.Df Df F Pr(>F) 468s 1 34 468s 2 33 1 1.65 0.21 468s > 468s > print( linearHypothesis( fitsuri1e2, restrm ) ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s 468s Model 1: restricted model 468s Model 2: fitsuri1e2 468s 468s Res.Df Df F Pr(>F) 468s 1 34 468s 2 33 1 140 2.1e-13 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > linearHypothesis( fitsuri1e2, restrict ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s 468s Model 1: restricted model 468s Model 2: fitsuri1e2 468s 468s Res.Df Df F Pr(>F) 468s 1 34 468s 2 33 1 140 2.1e-13 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > 468s > print( linearHypothesis( fitsuri1r3, restrm ) ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s 468s Model 1: restricted model 468s Model 2: fitsuri1r3 468s 468s Res.Df Df F Pr(>F) 468s 1 34 468s 2 33 1 141 1.9e-13 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > linearHypothesis( fitsuri1r3, restrict ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s 468s Model 1: restricted model 468s Model 2: fitsuri1r3 468s 468s Res.Df Df F Pr(>F) 468s 1 34 468s 2 33 1 141 1.9e-13 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > 468s > print( linearHypothesis( fitsur1we2, restrm ) ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s 468s Model 1: restricted model 468s Model 2: fitsur1we2 468s 468s Res.Df Df F Pr(>F) 468s 1 34 468s 2 33 1 1.65 0.21 468s > linearHypothesis( fitsur1we2, restrict ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s 468s Model 1: restricted model 468s Model 2: fitsur1we2 468s 468s Res.Df Df F Pr(>F) 468s 1 34 468s 2 33 1 1.65 0.21 468s > 468s > print( linearHypothesis( fitsuri1wr3, restrm ) ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s 468s Model 1: restricted model 468s Model 2: fitsuri1wr3 468s 468s Res.Df Df F Pr(>F) 468s 1 34 468s 2 33 1 141 1.9e-13 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > linearHypothesis( fitsuri1wr3, restrict ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s 468s Model 1: restricted model 468s Model 2: fitsuri1wr3 468s 468s Res.Df Df F Pr(>F) 468s 1 34 468s 2 33 1 141 1.9e-13 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > 468s > # testing second restriction 468s > restrOnly2m <- matrix(0,1,7) 468s > restrOnly2q <- 0.5 468s > restrOnly2m[1,2] <- -1 468s > restrOnly2m[1,5] <- 1 468s > restrictOnly2 <- "- demand_price + supply_price = 0.5" 468s > restrictOnly2i <- "- demand_price + supply_income = 0.5" 468s > # first restriction not imposed 468s > print( linearHypothesis( fitsur1e2, restrOnly2m, restrOnly2q ) ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s - demand_price + supply_price = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsur1e2 468s 468s Res.Df Df F Pr(>F) 468s 1 34 468s 2 33 1 2.36 0.13 468s > linearHypothesis( fitsur1e2, restrictOnly2 ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s - demand_price + supply_price = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsur1e2 468s 468s Res.Df Df F Pr(>F) 468s 1 34 468s 2 33 1 2.36 0.13 468s > 468s > print( linearHypothesis( fitsuri1, restrOnly2m, restrOnly2q ) ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s - demand_price + supply_income = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsuri1 468s 468s Res.Df Df F Pr(>F) 468s 1 34 468s 2 33 1 12.2 0.0014 ** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > linearHypothesis( fitsuri1, restrictOnly2i ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s - demand_price + supply_income = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsuri1 468s 468s Res.Df Df F Pr(>F) 468s 1 34 468s 2 33 1 12.2 0.0014 ** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > 468s > # first restriction imposed 468s > print( linearHypothesis( fitsur2, restrOnly2m, restrOnly2q ) ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s - demand_price + supply_price = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsur2 468s 468s Res.Df Df F Pr(>F) 468s 1 35 468s 2 34 1 5.5 0.025 * 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > linearHypothesis( fitsur2, restrictOnly2 ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s - demand_price + supply_price = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsur2 468s 468s Res.Df Df F Pr(>F) 468s 1 35 468s 2 34 1 5.5 0.025 * 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > 468s > print( linearHypothesis( fitsur3, restrOnly2m, restrOnly2q ) ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s - demand_price + supply_price = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsur3 468s 468s Res.Df Df F Pr(>F) 468s 1 35 468s 2 34 1 5.5 0.025 * 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > linearHypothesis( fitsur3, restrictOnly2 ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s - demand_price + supply_price = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsur3 468s 468s Res.Df Df F Pr(>F) 468s 1 35 468s 2 34 1 5.5 0.025 * 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > 468s > print( linearHypothesis( fitsuri2e, restrOnly2m, restrOnly2q ) ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s - demand_price + supply_income = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsuri2e 468s 468s Res.Df Df F Pr(>F) 468s 1 35 468s 2 34 1 2.35 0.13 468s > linearHypothesis( fitsuri2e, restrictOnly2i ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s - demand_price + supply_income = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsuri2e 468s 468s Res.Df Df F Pr(>F) 468s 1 35 468s 2 34 1 2.35 0.13 468s > 468s > print( linearHypothesis( fitsuri3e, restrOnly2m, restrOnly2q ) ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s - demand_price + supply_income = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsuri3e 468s 468s Res.Df Df F Pr(>F) 468s 1 35 468s 2 34 1 2.35 0.13 468s > linearHypothesis( fitsuri3e, restrictOnly2i ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s - demand_price + supply_income = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsuri3e 468s 468s Res.Df Df F Pr(>F) 468s 1 35 468s 2 34 1 2.35 0.13 468s > 468s > print( linearHypothesis( fitsur2we, restrOnly2m, restrOnly2q ) ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s - demand_price + supply_price = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsur2we 468s 468s Res.Df Df F Pr(>F) 468s 1 35 468s 2 34 1 6.26 0.017 * 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > linearHypothesis( fitsur2we, restrictOnly2 ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s - demand_price + supply_price = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsur2we 468s 468s Res.Df Df F Pr(>F) 468s 1 35 468s 2 34 1 6.26 0.017 * 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > 468s > print( linearHypothesis( fitsuri3we, restrOnly2m, restrOnly2q ) ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s - demand_price + supply_income = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsuri3we 468s 468s Res.Df Df F Pr(>F) 468s 1 35 468s 2 34 1 2.35 0.13 468s > linearHypothesis( fitsuri3we, restrictOnly2i ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s - demand_price + supply_income = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsuri3we 468s 468s Res.Df Df F Pr(>F) 468s 1 35 468s 2 34 1 2.35 0.13 468s > 468s > # testing both of the restrictions 468s > print( linearHypothesis( fitsur1r3, restr2m, restr2q ) ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s - demand_price + supply_price = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsur1r3 468s 468s Res.Df Df F Pr(>F) 468s 1 35 468s 2 33 2 2.6 0.089 . 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > linearHypothesis( fitsur1r3, restrict2 ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s - demand_price + supply_price = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsur1r3 468s 468s Res.Df Df F Pr(>F) 468s 1 35 468s 2 33 2 2.6 0.089 . 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > 468s > print( linearHypothesis( fitsuri1e2, restr2m, restr2q ) ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s - demand_price + supply_income = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsuri1e2 468s 468s Res.Df Df F Pr(>F) 468s 1 35 468s 2 33 2 89.1 5e-14 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > linearHypothesis( fitsuri1e2, restrict2i ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s - demand_price + supply_income = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsuri1e2 468s 468s Res.Df Df F Pr(>F) 468s 1 35 468s 2 33 2 89.1 5e-14 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > 468s > print( linearHypothesis( fitsur1w, restr2m, restr2q ) ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s - demand_price + supply_price = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsur1w 468s 468s Res.Df Df F Pr(>F) 468s 1 35 468s 2 33 2 1.8 0.18 468s > linearHypothesis( fitsur1w, restrict2 ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s - demand_price + supply_price = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsur1w 468s 468s Res.Df Df F Pr(>F) 468s 1 35 468s 2 33 2 1.8 0.18 468s > 468s > print( linearHypothesis( fitsuri1wr3, restr2m, restr2q ) ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s - demand_price + supply_income = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsuri1wr3 468s 468s Res.Df Df F Pr(>F) 468s 1 35 468s 2 33 2 89.6 4.6e-14 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > linearHypothesis( fitsuri1wr3, restrict2i ) 468s Linear hypothesis test (Theil's F test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s - demand_price + supply_income = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsuri1wr3 468s 468s Res.Df Df F Pr(>F) 468s 1 35 468s 2 33 2 89.6 4.6e-14 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > 468s > 468s > ## ************** Wald tests **************** 468s > # testing first restriction 468s > print( linearHypothesis( fitsur1, restrm, test = "Chisq" ) ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s 468s Model 1: restricted model 468s Model 2: fitsur1 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 34 468s 2 33 1 0.81 0.37 468s > linearHypothesis( fitsur1, restrict, test = "Chisq" ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s 468s Model 1: restricted model 468s Model 2: fitsur1 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 34 468s 2 33 1 0.81 0.37 468s > 468s > print( linearHypothesis( fitsur1r2, restrm, test = "Chisq" ) ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s 468s Model 1: restricted model 468s Model 2: fitsur1r2 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 34 468s 2 33 1 1.12 0.29 468s > linearHypothesis( fitsur1r2, restrict, test = "Chisq" ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s 468s Model 1: restricted model 468s Model 2: fitsur1r2 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 34 468s 2 33 1 1.12 0.29 468s > 468s > print( linearHypothesis( fitsuri1e2, restrm, test = "Chisq" ) ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s 468s Model 1: restricted model 468s Model 2: fitsuri1e2 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 34 468s 2 33 1 147 <2e-16 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > linearHypothesis( fitsuri1e2, restrict, test = "Chisq" ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s 468s Model 1: restricted model 468s Model 2: fitsuri1e2 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 34 468s 2 33 1 147 <2e-16 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > 468s > print( linearHypothesis( fitsuri1r3, restrm, test = "Chisq" ) ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s 468s Model 1: restricted model 468s Model 2: fitsuri1r3 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 34 468s 2 33 1 147 <2e-16 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > linearHypothesis( fitsuri1r3, restrict, test = "Chisq" ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s 468s Model 1: restricted model 468s Model 2: fitsuri1r3 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 34 468s 2 33 1 147 <2e-16 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > 468s > print( linearHypothesis( fitsur1w, restrm, test = "Chisq" ) ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s 468s Model 1: restricted model 468s Model 2: fitsur1w 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 34 468s 2 33 1 0.81 0.37 468s > linearHypothesis( fitsur1w, restrict, test = "Chisq" ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s 468s Model 1: restricted model 468s Model 2: fitsur1w 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 34 468s 2 33 1 0.81 0.37 468s > 468s > # testing second restriction 468s > # first restriction not imposed 468s > print( linearHypothesis( fitsur1e2, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s - demand_price + supply_price = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsur1e2 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 34 468s 2 33 1 1.6 0.21 468s > linearHypothesis( fitsur1e2, restrictOnly2, test = "Chisq" ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s - demand_price + supply_price = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsur1e2 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 34 468s 2 33 1 1.6 0.21 468s > 468s > print( linearHypothesis( fitsuri1, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s - demand_price + supply_income = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsuri1 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 34 468s 2 33 1 12.2 0.00047 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > linearHypothesis( fitsuri1, restrictOnly2i, test = "Chisq" ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s - demand_price + supply_income = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsuri1 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 34 468s 2 33 1 12.2 0.00047 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > 468s > # first restriction imposed 468s > print( linearHypothesis( fitsur2, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s - demand_price + supply_price = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsur2 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 35 468s 2 34 1 3.95 0.047 * 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > linearHypothesis( fitsur2, restrictOnly2, test = "Chisq" ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s - demand_price + supply_price = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsur2 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 35 468s 2 34 1 3.95 0.047 * 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > 468s > print( linearHypothesis( fitsur3, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s - demand_price + supply_price = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsur3 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 35 468s 2 34 1 3.95 0.047 * 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > linearHypothesis( fitsur3, restrictOnly2, test = "Chisq" ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s - demand_price + supply_price = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsur3 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 35 468s 2 34 1 3.95 0.047 * 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > 468s > print( linearHypothesis( fitsuri2e, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s - demand_price + supply_income = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsuri2e 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 35 468s 2 34 1 2.76 0.096 . 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > linearHypothesis( fitsuri2e, restrictOnly2i, test = "Chisq" ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s - demand_price + supply_income = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsuri2e 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 35 468s 2 34 1 2.76 0.096 . 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > 468s > print( linearHypothesis( fitsuri3e, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s - demand_price + supply_income = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsuri3e 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 35 468s 2 34 1 2.76 0.096 . 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > linearHypothesis( fitsuri3e, restrictOnly2i, test = "Chisq" ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s - demand_price + supply_income = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsuri3e 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 35 468s 2 34 1 2.76 0.096 . 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > 468s > print( linearHypothesis( fitsuri2w, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s - demand_price + supply_income = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsuri2w 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 35 468s 2 34 1 2.2 0.14 468s > linearHypothesis( fitsuri2w, restrictOnly2i, test = "Chisq" ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s - demand_price + supply_income = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsuri2w 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 35 468s 2 34 1 2.2 0.14 468s > 468s > print( linearHypothesis( fitsur3w, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s - demand_price + supply_price = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsur3w 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 35 468s 2 34 1 4.26 0.039 * 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > linearHypothesis( fitsur3w, restrictOnly2, test = "Chisq" ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s - demand_price + supply_price = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsur3w 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 35 468s 2 34 1 4.26 0.039 * 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > 468s > 468s > # testing both of the restrictions 468s > print( linearHypothesis( fitsur1r3, restr2m, restr2q, test = "Chisq" ) ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s - demand_price + supply_price = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsur1r3 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 35 468s 2 33 2 3.51 0.17 468s > linearHypothesis( fitsur1r3, restrict2, test = "Chisq" ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s - demand_price + supply_price = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsur1r3 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 35 468s 2 33 2 3.51 0.17 468s > 468s > print( linearHypothesis( fitsuri1e2, restr2m, restr2q, test = "Chisq" ) ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s - demand_price + supply_income = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsuri1e2 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 35 468s 2 33 2 188 <2e-16 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > linearHypothesis( fitsuri1e2, restrict2i, test = "Chisq" ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s - demand_price + supply_income = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsuri1e2 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 35 468s 2 33 2 188 <2e-16 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > 468s > print( linearHypothesis( fitsur1we2, restr2m, restr2q, test = "Chisq" ) ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s - demand_price + supply_price = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsur1we2 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 35 468s 2 33 2 3.66 0.16 468s > linearHypothesis( fitsur1we2, restrict2, test = "Chisq" ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s - demand_price + supply_price = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsur1we2 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 35 468s 2 33 2 3.66 0.16 468s > 468s > print( linearHypothesis( fitsuri1wr3, restr2m, restr2q, test = "Chisq" ) ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s - demand_price + supply_income = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsuri1wr3 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 35 468s 2 33 2 187 <2e-16 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > linearHypothesis( fitsuri1wr3, restrict2i, test = "Chisq" ) 468s Linear hypothesis test (Chi^2 statistic of a Wald test) 468s 468s Hypothesis: 468s demand_income - supply_trend = 0 468s - demand_price + supply_income = 0.5 468s 468s Model 1: restricted model 468s Model 2: fitsuri1wr3 468s 468s Res.Df Df Chisq Pr(>Chisq) 468s 1 35 468s 2 33 2 187 <2e-16 *** 468s --- 468s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 468s > 468s > 468s > ## ****************** model frame ************************** 468s > print( mf <- model.frame( fitsur1e2 ) ) 468s consump price income farmPrice trend 468s 1 98.5 100.3 87.4 98.0 1 468s 2 99.2 104.3 97.6 99.1 2 468s 3 102.2 103.4 96.7 99.1 3 468s 4 101.5 104.5 98.2 98.1 4 468s 5 104.2 98.0 99.8 110.8 5 468s 6 103.2 99.5 100.5 108.2 6 468s 7 104.0 101.1 103.2 105.6 7 468s 8 99.9 104.8 107.8 109.8 8 468s 9 100.3 96.4 96.6 108.7 9 468s 10 102.8 91.2 88.9 100.6 10 468s 11 95.4 93.1 75.1 81.0 11 468s 12 92.4 98.8 76.9 68.6 12 468s 13 94.5 102.9 84.6 70.9 13 468s 14 98.8 98.8 90.6 81.4 14 468s 15 105.8 95.1 103.1 102.3 15 468s 16 100.2 98.5 105.1 105.0 16 468s 17 103.5 86.5 96.4 110.5 17 468s 18 99.9 104.0 104.4 92.5 18 468s 19 105.2 105.8 110.7 89.3 19 468s 20 106.2 113.5 127.1 93.0 20 468s > print( mf1 <- model.frame( fitsur1e2$eq[[ 1 ]] ) ) 468s consump price income 468s 1 98.5 100.3 87.4 468s 2 99.2 104.3 97.6 468s 3 102.2 103.4 96.7 468s 4 101.5 104.5 98.2 468s 5 104.2 98.0 99.8 468s 6 103.2 99.5 100.5 468s 7 104.0 101.1 103.2 468s 8 99.9 104.8 107.8 468s 9 100.3 96.4 96.6 468s 10 102.8 91.2 88.9 468s 11 95.4 93.1 75.1 468s 12 92.4 98.8 76.9 468s 13 94.5 102.9 84.6 468s 14 98.8 98.8 90.6 468s 15 105.8 95.1 103.1 468s 16 100.2 98.5 105.1 468s 17 103.5 86.5 96.4 468s 18 99.9 104.0 104.4 468s 19 105.2 105.8 110.7 468s 20 106.2 113.5 127.1 468s > print( attributes( mf1 )$terms ) 468s consump ~ price + income 468s attr(,"variables") 468s list(consump, price, income) 468s attr(,"factors") 468s price income 468s consump 0 0 468s price 1 0 468s income 0 1 468s attr(,"term.labels") 468s [1] "price" "income" 468s attr(,"order") 468s [1] 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, income) 468s attr(,"dataClasses") 468s consump price income 468s "numeric" "numeric" "numeric" 468s > print( mf2 <- model.frame( fitsur1e2$eq[[ 2 ]] ) ) 468s consump price farmPrice trend 468s 1 98.5 100.3 98.0 1 468s 2 99.2 104.3 99.1 2 468s 3 102.2 103.4 99.1 3 468s 4 101.5 104.5 98.1 4 468s 5 104.2 98.0 110.8 5 468s 6 103.2 99.5 108.2 6 468s 7 104.0 101.1 105.6 7 468s 8 99.9 104.8 109.8 8 468s 9 100.3 96.4 108.7 9 468s 10 102.8 91.2 100.6 10 468s 11 95.4 93.1 81.0 11 468s 12 92.4 98.8 68.6 12 468s 13 94.5 102.9 70.9 13 468s 14 98.8 98.8 81.4 14 468s 15 105.8 95.1 102.3 15 468s 16 100.2 98.5 105.0 16 468s 17 103.5 86.5 110.5 17 468s 18 99.9 104.0 92.5 18 468s 19 105.2 105.8 89.3 19 468s 20 106.2 113.5 93.0 20 468s > print( attributes( mf2 )$terms ) 468s consump ~ price + farmPrice + trend 468s attr(,"variables") 468s list(consump, price, farmPrice, trend) 468s attr(,"factors") 468s price farmPrice trend 468s consump 0 0 0 468s price 1 0 0 468s farmPrice 0 1 0 468s trend 0 0 1 468s attr(,"term.labels") 468s [1] "price" "farmPrice" "trend" 468s attr(,"order") 468s [1] 1 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, farmPrice, trend) 468s attr(,"dataClasses") 468s consump price farmPrice trend 468s "numeric" "numeric" "numeric" "numeric" 468s > 468s > print( all.equal( mf, model.frame( fitsur1w ) ) ) 468s [1] TRUE 468s > print( all.equal( mf1, model.frame( fitsur1w$eq[[ 1 ]] ) ) ) 468s [1] TRUE 468s > 468s > print( all.equal( mf, model.frame( fitsur2e ) ) ) 468s [1] TRUE 468s > print( all.equal( mf1, model.frame( fitsur2e$eq[[ 1 ]] ) ) ) 468s [1] TRUE 468s > 468s > print( all.equal( mf, model.frame( fitsur3 ) ) ) 468s [1] TRUE 468s > print( all.equal( mf2, model.frame( fitsur3$eq[[ 2 ]] ) ) ) 468s [1] TRUE 468s > 468s > print( all.equal( mf, model.frame( fitsur4r3 ) ) ) 468s [1] TRUE 468s > print( all.equal( mf1, model.frame( fitsur4r3$eq[[ 1 ]] ) ) ) 468s [1] TRUE 468s > 468s > print( all.equal( mf, model.frame( fitsur4we ) ) ) 468s [1] TRUE 468s > print( all.equal( mf2, model.frame( fitsur4we$eq[[ 2 ]] ) ) ) 468s [1] TRUE 468s > 468s > print( all.equal( mf, model.frame( fitsur5 ) ) ) 468s [1] TRUE 468s > print( all.equal( mf2, model.frame( fitsur5$eq[[ 2 ]] ) ) ) 468s [1] TRUE 468s > 468s > print( all.equal( mf, model.frame( fitsuri1r3 ) ) ) 468s [1] TRUE 468s > print( all.equal( mf1, model.frame( fitsuri1r3$eq[[ 1 ]] ) ) ) 468s [1] TRUE 468s > 468s > print( all.equal( mf, model.frame( fitsuri2 ) ) ) 468s [1] TRUE 468s > print( all.equal( mf1, model.frame( fitsuri2$eq[[ 1 ]] ) ) ) 468s [1] TRUE 468s > 468s > print( all.equal( mf, model.frame( fitsuri3e ) ) ) 468s [1] TRUE 468s > print( all.equal( mf1, model.frame( fitsuri3e$eq[[ 1 ]] ) ) ) 468s [1] TRUE 468s > 468s > print( all.equal( mf, model.frame( fitsurio4 ) ) ) 468s [1] TRUE 468s > print( all.equal( mf2, model.frame( fitsurio4$eq[[ 2 ]] ) ) ) 468s [1] TRUE 468s > print( all.equal( mf, model.frame( fitsuri4 ) ) ) 468s [1] TRUE 468s > print( all.equal( mf1, model.frame( fitsuri4$eq[[ 1 ]] ) ) ) 468s [1] TRUE 468s > 468s > print( all.equal( mf, model.frame( fitsurio5r2 ) ) ) 468s [1] TRUE 468s > print( all.equal( mf1, model.frame( fitsurio5r2$eq[[ 1 ]] ) ) ) 468s [1] TRUE 468s > print( all.equal( mf, model.frame( fitsuri5r2 ) ) ) 468s [1] TRUE 468s > print( all.equal( mf1, model.frame( fitsuri5r2$eq[[ 1 ]] ) ) ) 468s [1] TRUE 468s > 468s > print( all.equal( mf, model.frame( fitsuri5wr2 ) ) ) 468s [1] TRUE 468s > print( all.equal( mf1, model.frame( fitsuri5wr2$eq[[ 1 ]] ) ) ) 468s [1] TRUE 468s > 468s > 468s > ## **************** model matrix ************************ 468s > # with x (returnModelMatrix) = TRUE 468s > print( !is.null( fitsur1e2$eq[[ 1 ]]$x ) ) 468s [1] TRUE 468s > print( mm <- model.matrix( fitsur1e2 ) ) 468s demand_(Intercept) demand_price demand_income supply_(Intercept) 468s demand_1 1 100.3 87.4 0 468s demand_2 1 104.3 97.6 0 468s demand_3 1 103.4 96.7 0 468s demand_4 1 104.5 98.2 0 468s demand_5 1 98.0 99.8 0 468s demand_6 1 99.5 100.5 0 468s demand_7 1 101.1 103.2 0 468s demand_8 1 104.8 107.8 0 468s demand_9 1 96.4 96.6 0 468s demand_10 1 91.2 88.9 0 468s demand_11 1 93.1 75.1 0 468s demand_12 1 98.8 76.9 0 468s demand_13 1 102.9 84.6 0 468s demand_14 1 98.8 90.6 0 468s demand_15 1 95.1 103.1 0 468s demand_16 1 98.5 105.1 0 468s demand_17 1 86.5 96.4 0 468s demand_18 1 104.0 104.4 0 468s demand_19 1 105.8 110.7 0 468s demand_20 1 113.5 127.1 0 468s supply_1 0 0.0 0.0 1 468s supply_2 0 0.0 0.0 1 468s supply_3 0 0.0 0.0 1 468s supply_4 0 0.0 0.0 1 468s supply_5 0 0.0 0.0 1 468s supply_6 0 0.0 0.0 1 468s supply_7 0 0.0 0.0 1 468s supply_8 0 0.0 0.0 1 468s supply_9 0 0.0 0.0 1 468s supply_10 0 0.0 0.0 1 468s supply_11 0 0.0 0.0 1 468s supply_12 0 0.0 0.0 1 468s supply_13 0 0.0 0.0 1 468s supply_14 0 0.0 0.0 1 468s supply_15 0 0.0 0.0 1 468s supply_16 0 0.0 0.0 1 468s supply_17 0 0.0 0.0 1 468s supply_18 0 0.0 0.0 1 468s supply_19 0 0.0 0.0 1 468s supply_20 0 0.0 0.0 1 468s supply_price supply_farmPrice supply_trend 468s demand_1 0.0 0.0 0 468s demand_2 0.0 0.0 0 468s demand_3 0.0 0.0 0 468s demand_4 0.0 0.0 0 468s demand_5 0.0 0.0 0 468s demand_6 0.0 0.0 0 468s demand_7 0.0 0.0 0 468s demand_8 0.0 0.0 0 468s demand_9 0.0 0.0 0 468s demand_10 0.0 0.0 0 468s demand_11 0.0 0.0 0 468s demand_12 0.0 0.0 0 468s demand_13 0.0 0.0 0 468s demand_14 0.0 0.0 0 468s demand_15 0.0 0.0 0 468s demand_16 0.0 0.0 0 468s demand_17 0.0 0.0 0 468s demand_18 0.0 0.0 0 468s demand_19 0.0 0.0 0 468s demand_20 0.0 0.0 0 468s supply_1 100.3 98.0 1 468s supply_2 104.3 99.1 2 468s supply_3 103.4 99.1 3 468s supply_4 104.5 98.1 4 468s supply_5 98.0 110.8 5 468s supply_6 99.5 108.2 6 468s supply_7 101.1 105.6 7 468s supply_8 104.8 109.8 8 468s supply_9 96.4 108.7 9 468s supply_10 91.2 100.6 10 468s supply_11 93.1 81.0 11 468s supply_12 98.8 68.6 12 468s supply_13 102.9 70.9 13 468s supply_14 98.8 81.4 14 468s supply_15 95.1 102.3 15 468s supply_16 98.5 105.0 16 468s supply_17 86.5 110.5 17 468s supply_18 104.0 92.5 18 468s supply_19 105.8 89.3 19 468s supply_20 113.5 93.0 20 468s > print( mm1 <- model.matrix( fitsur1e2$eq[[ 1 ]] ) ) 468s (Intercept) price income 468s 1 1 100.3 87.4 468s 2 1 104.3 97.6 468s 3 1 103.4 96.7 468s 4 1 104.5 98.2 468s 5 1 98.0 99.8 468s 6 1 99.5 100.5 468s 7 1 101.1 103.2 468s 8 1 104.8 107.8 468s 9 1 96.4 96.6 468s 10 1 91.2 88.9 468s 11 1 93.1 75.1 468s 12 1 98.8 76.9 468s 13 1 102.9 84.6 468s 14 1 98.8 90.6 468s 15 1 95.1 103.1 468s 16 1 98.5 105.1 468s 17 1 86.5 96.4 468s 18 1 104.0 104.4 468s 19 1 105.8 110.7 468s 20 1 113.5 127.1 468s attr(,"assign") 468s [1] 0 1 2 468s > print( mm2 <- model.matrix( fitsur1e2$eq[[ 2 ]] ) ) 468s (Intercept) price farmPrice trend 468s 1 1 100.3 98.0 1 468s 2 1 104.3 99.1 2 468s 3 1 103.4 99.1 3 468s 4 1 104.5 98.1 4 468s 5 1 98.0 110.8 5 468s 6 1 99.5 108.2 6 468s 7 1 101.1 105.6 7 468s 8 1 104.8 109.8 8 468s 9 1 96.4 108.7 9 468s 10 1 91.2 100.6 10 468s 11 1 93.1 81.0 11 468s 12 1 98.8 68.6 12 468s 13 1 102.9 70.9 13 468s 14 1 98.8 81.4 14 468s 15 1 95.1 102.3 15 468s 16 1 98.5 105.0 16 468s 17 1 86.5 110.5 17 468s 18 1 104.0 92.5 18 468s 19 1 105.8 89.3 19 468s 20 1 113.5 93.0 20 468s attr(,"assign") 468s [1] 0 1 2 3 468s > 468s > # with x (returnModelMatrix) = FALSE 468s > print( all.equal( mm, model.matrix( fitsur1r2 ) ) ) 468s [1] TRUE 468s > print( all.equal( mm1, model.matrix( fitsur1r2$eq[[ 1 ]] ) ) ) 468s [1] TRUE 468s > print( all.equal( mm2, model.matrix( fitsur1r2$eq[[ 2 ]] ) ) ) 468s [1] TRUE 468s > print( !is.null( fitsur1r2$eq[[ 1 ]]$x ) ) 468s [1] FALSE 468s > 468s > # with x (returnModelMatrix) = TRUE 468s > print( !is.null( fitsur2e$eq[[ 1 ]]$x ) ) 468s [1] TRUE 468s > print( all.equal( mm, model.matrix( fitsur2e ) ) ) 468s [1] TRUE 468s > print( all.equal( mm1, model.matrix( fitsur2e$eq[[ 1 ]] ) ) ) 468s [1] TRUE 468s > print( all.equal( mm2, model.matrix( fitsur2e$eq[[ 2 ]] ) ) ) 468s [1] TRUE 468s > 468s > # with x (returnModelMatrix) = FALSE 468s > print( all.equal( mm, model.matrix( fitsur2 ) ) ) 468s [1] TRUE 468s > print( all.equal( mm1, model.matrix( fitsur2$eq[[ 1 ]] ) ) ) 468s [1] TRUE 468s > print( all.equal( mm2, model.matrix( fitsur2$eq[[ 2 ]] ) ) ) 468s [1] TRUE 468s > print( !is.null( fitsur2$eq[[ 1 ]]$x ) ) 468s [1] FALSE 468s > 468s > # with x (returnModelMatrix) = TRUE 468s > print( !is.null( fitsur2we$eq[[ 1 ]]$x ) ) 468s [1] TRUE 468s > print( all.equal( mm, model.matrix( fitsur2we ) ) ) 468s [1] TRUE 468s > print( all.equal( mm1, model.matrix( fitsur2we$eq[[ 1 ]] ) ) ) 468s [1] TRUE 468s > print( all.equal( mm2, model.matrix( fitsur2we$eq[[ 2 ]] ) ) ) 468s [1] TRUE 468s > 468s > # with x (returnModelMatrix) = FALSE 468s > print( all.equal( mm, model.matrix( fitsur2 ) ) ) 468s [1] TRUE 468s > print( all.equal( mm1, model.matrix( fitsur2$eq[[ 1 ]] ) ) ) 468s [1] TRUE 468s > print( all.equal( mm2, model.matrix( fitsur2$eq[[ 2 ]] ) ) ) 468s [1] TRUE 468s > print( !is.null( fitsuri2$eq[[ 1 ]]$x ) ) 468s [1] FALSE 468s > 468s > # with x (returnModelMatrix) = TRUE 468s > print( !is.null( fitsur3e$eq[[ 1 ]]$x ) ) 468s [1] TRUE 468s > print( all.equal( mm, model.matrix( fitsur3e ) ) ) 468s [1] TRUE 468s > print( all.equal( mm1, model.matrix( fitsur3e$eq[[ 1 ]] ) ) ) 468s [1] TRUE 468s > print( all.equal( mm2, model.matrix( fitsur3e$eq[[ 2 ]] ) ) ) 468s [1] TRUE 468s > 468s > # with x (returnModelMatrix) = FALSE 468s > print( all.equal( mm, model.matrix( fitsur3 ) ) ) 468s [1] TRUE 468s > print( all.equal( mm1, model.matrix( fitsur3$eq[[ 1 ]] ) ) ) 468s [1] TRUE 468s > print( all.equal( mm2, model.matrix( fitsur3$eq[[ 2 ]] ) ) ) 468s [1] TRUE 468s > print( !is.null( fitsur3$eq[[ 1 ]]$x ) ) 468s [1] FALSE 468s > 468s > # with x (returnModelMatrix) = TRUE 468s > print( !is.null( fitsur3w$eq[[ 1 ]]$x ) ) 468s [1] TRUE 468s > print( all.equal( mm, model.matrix( fitsur3w ) ) ) 468s [1] TRUE 468s > print( all.equal( mm1, model.matrix( fitsur3w$eq[[ 1 ]] ) ) ) 468s [1] TRUE 468s > print( all.equal( mm2, model.matrix( fitsur3w$eq[[ 2 ]] ) ) ) 468s [1] TRUE 468s > 468s > # with x (returnModelMatrix) = FALSE 468s > print( all.equal( mm, model.matrix( fitsur3 ) ) ) 468s [1] TRUE 468s > print( all.equal( mm1, model.matrix( fitsur3$eq[[ 1 ]] ) ) ) 468s [1] TRUE 468s > print( all.equal( mm2, model.matrix( fitsur3$eq[[ 2 ]] ) ) ) 468s [1] TRUE 468s > print( !is.null( fitsuri3$eq[[ 1 ]]$x ) ) 468s [1] FALSE 468s > 468s > # with x (returnModelMatrix) = TRUE 468s > print( !is.null( fitsur4r3$eq[[ 1 ]]$x ) ) 468s [1] TRUE 468s > print( all.equal( mm, model.matrix( fitsur4r3 ) ) ) 468s [1] TRUE 468s > print( all.equal( mm1, model.matrix( fitsur4r3$eq[[ 1 ]] ) ) ) 468s [1] TRUE 468s > print( all.equal( mm2, model.matrix( fitsur4r3$eq[[ 2 ]] ) ) ) 468s [1] TRUE 468s > 468s > # with x (returnModelMatrix) = FALSE 468s > print( all.equal( mm, model.matrix( fitsur4we ) ) ) 468s [1] TRUE 468s > print( all.equal( mm1, model.matrix( fitsur4we$eq[[ 1 ]] ) ) ) 468s [1] TRUE 468s > print( all.equal( mm2, model.matrix( fitsur4we$eq[[ 2 ]] ) ) ) 468s [1] TRUE 468s > print( !is.null( fitsur4we$eq[[ 1 ]]$x ) ) 468s [1] FALSE 468s > 468s > # with x (returnModelMatrix) = TRUE 468s > print( !is.null( fitsurio5r2$eq[[ 1 ]]$x ) ) 468s [1] TRUE 468s > print( !is.null( fitsur5$eq[[ 1 ]]$x ) ) 468s [1] TRUE 468s > print( all.equal( mm, model.matrix( fitsurio5r2 ) ) ) 468s [1] TRUE 468s > print( all.equal( mm1, model.matrix( fitsurio5r2$eq[[ 1 ]] ) ) ) 468s [1] TRUE 468s > print( all.equal( mm, model.matrix( fitsur5 ) ) ) 468s [1] TRUE 468s > print( all.equal( mm1, model.matrix( fitsur5$eq[[ 1 ]] ) ) ) 468s [1] TRUE 468s > #print( all.equal( mm2, model.matrix( fitsuri5r2$eq[[ 2 ]] ) ) ) 468s > 468s > # with x (returnModelMatrix) = FALSE 468s > print( all.equal( mm, model.matrix( fitsurio5 ) ) ) 468s [1] TRUE 468s > print( all.equal( mm1, model.matrix( fitsurio5$eq[[ 1 ]] ) ) ) 468s [1] TRUE 468s > 468s > # with x (returnModelMatrix) = FALSE 468s > print( all.equal( mm, model.matrix( fitsur5w ) ) ) 468s [1] TRUE 468s > print( all.equal( mm1, model.matrix( fitsur5w$eq[[ 1 ]] ) ) ) 468s [1] TRUE 468s > #print( all.equal( mm2, model.matrix( fitsuri5r2$eq[[ 1 ]] ) ) ) 468s > print( !is.null( fitsurio5$eq[[ 1 ]]$x ) ) 468s [1] FALSE 468s > print( !is.null( fitsur5w$eq[[ 1 ]]$x ) ) 468s [1] FALSE 468s > 468s > 468s > ## **************** formulas ************************ 468s > formula( fitsur1e2 ) 468s $demand 468s consump ~ price + income 468s 468s $supply 468s consump ~ price + farmPrice + trend 468s 468s > formula( fitsur1e2$eq[[ 2 ]] ) 468s consump ~ price + farmPrice + trend 468s > 468s > formula( fitsur2e ) 468s $demand 468s consump ~ price + income 468s 468s $supply 468s consump ~ price + farmPrice + trend 468s 468s > formula( fitsur2e$eq[[ 1 ]] ) 468s consump ~ price + income 468s > 468s > formula( fitsur2we ) 468s $demand 468s consump ~ price + income 468s 468s $supply 468s consump ~ price + farmPrice + trend 468s 468s > formula( fitsur2we$eq[[ 1 ]] ) 468s consump ~ price + income 468s > 468s > formula( fitsur3 ) 468s $demand 468s consump ~ price + income 468s 468s $supply 468s consump ~ price + farmPrice + trend 468s 468s > formula( fitsur3$eq[[ 2 ]] ) 468s consump ~ price + farmPrice + trend 468s > 468s > formula( fitsur4r3 ) 468s $demand 468s consump ~ price + income 468s 468s $supply 468s consump ~ price + farmPrice + trend 468s 468s > formula( fitsur4r3$eq[[ 1 ]] ) 468s consump ~ price + income 468s > 468s > formula( fitsur5 ) 468s $demand 468s consump ~ price + income 468s 468s $supply 468s consump ~ price + farmPrice + trend 468s 468s > formula( fitsur5$eq[[ 2 ]] ) 468s consump ~ price + farmPrice + trend 468s > 468s > formula( fitsuri1r3 ) 468s $demand 468s consump ~ price + income 468s 468s $supply 468s price ~ income + farmPrice + trend 468s 468s > formula( fitsuri1r3$eq[[ 1 ]] ) 468s consump ~ price + income 468s > 468s > formula( fitsuri2 ) 468s $demand 468s consump ~ price + income 468s 468s $supply 468s price ~ income + farmPrice + trend 468s 468s > formula( fitsuri2$eq[[ 2 ]] ) 468s price ~ income + farmPrice + trend 468s > 468s > formula( fitsuri3e ) 468s $demand 468s consump ~ price + income 468s 468s $supply 468s price ~ income + farmPrice + trend 468s 468s > formula( fitsuri3e$eq[[ 1 ]] ) 468s consump ~ price + income 468s > 468s > formula( fitsurio4 ) 468s $demand 468s consump ~ price + income 468s 468s $supply 468s consump ~ price + farmPrice + trend 468s 468s > formula( fitsurio4$eq[[ 2 ]] ) 468s consump ~ price + farmPrice + trend 468s > formula( fitsuri4 ) 468s $demand 468s consump ~ price + income 468s 468s $supply 468s price ~ income + farmPrice + trend 468s 468s > formula( fitsuri4$eq[[ 2 ]] ) 468s price ~ income + farmPrice + trend 468s > 468s > formula( fitsurio5r2 ) 468s $demand 468s consump ~ price + income 468s 468s $supply 468s consump ~ price + farmPrice + trend 468s 468s > formula( fitsurio5r2$eq[[ 1 ]] ) 468s consump ~ price + income 468s > formula( fitsuri5r2 ) 468s $demand 468s consump ~ price + income 468s 468s $supply 468s price ~ income + farmPrice + trend 468s 468s > formula( fitsuri5r2$eq[[ 1 ]] ) 468s consump ~ price + income 468s > 468s > formula( fitsuri5wr2 ) 468s $demand 468s consump ~ price + income 468s 468s $supply 468s price ~ income + farmPrice + trend 468s 468s > formula( fitsuri5wr2$eq[[ 1 ]] ) 468s consump ~ price + income 468s > 468s > 468s > ## **************** model terms ******************* 468s > terms( fitsur1e2 ) 468s $demand 468s consump ~ price + income 468s attr(,"variables") 468s list(consump, price, income) 468s attr(,"factors") 468s price income 468s consump 0 0 468s price 1 0 468s income 0 1 468s attr(,"term.labels") 468s [1] "price" "income" 468s attr(,"order") 468s [1] 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, income) 468s attr(,"dataClasses") 468s consump price income 468s "numeric" "numeric" "numeric" 468s 468s $supply 468s consump ~ price + farmPrice + trend 468s attr(,"variables") 468s list(consump, price, farmPrice, trend) 468s attr(,"factors") 468s price farmPrice trend 468s consump 0 0 0 468s price 1 0 0 468s farmPrice 0 1 0 468s trend 0 0 1 468s attr(,"term.labels") 468s [1] "price" "farmPrice" "trend" 468s attr(,"order") 468s [1] 1 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, farmPrice, trend) 468s attr(,"dataClasses") 468s consump price farmPrice trend 468s "numeric" "numeric" "numeric" "numeric" 468s 468s > terms( fitsur1e2$eq[[ 2 ]] ) 468s consump ~ price + farmPrice + trend 468s attr(,"variables") 468s list(consump, price, farmPrice, trend) 468s attr(,"factors") 468s price farmPrice trend 468s consump 0 0 0 468s price 1 0 0 468s farmPrice 0 1 0 468s trend 0 0 1 468s attr(,"term.labels") 468s [1] "price" "farmPrice" "trend" 468s attr(,"order") 468s [1] 1 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, farmPrice, trend) 468s attr(,"dataClasses") 468s consump price farmPrice trend 468s "numeric" "numeric" "numeric" "numeric" 468s > 468s > terms( fitsur2e ) 468s $demand 468s consump ~ price + income 468s attr(,"variables") 468s list(consump, price, income) 468s attr(,"factors") 468s price income 468s consump 0 0 468s price 1 0 468s income 0 1 468s attr(,"term.labels") 468s [1] "price" "income" 468s attr(,"order") 468s [1] 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, income) 468s attr(,"dataClasses") 468s consump price income 468s "numeric" "numeric" "numeric" 468s 468s $supply 468s consump ~ price + farmPrice + trend 468s attr(,"variables") 468s list(consump, price, farmPrice, trend) 468s attr(,"factors") 468s price farmPrice trend 468s consump 0 0 0 468s price 1 0 0 468s farmPrice 0 1 0 468s trend 0 0 1 468s attr(,"term.labels") 468s [1] "price" "farmPrice" "trend" 468s attr(,"order") 468s [1] 1 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, farmPrice, trend) 468s attr(,"dataClasses") 468s consump price farmPrice trend 468s "numeric" "numeric" "numeric" "numeric" 468s 468s > terms( fitsur2e$eq[[ 1 ]] ) 468s consump ~ price + income 468s attr(,"variables") 468s list(consump, price, income) 468s attr(,"factors") 468s price income 468s consump 0 0 468s price 1 0 468s income 0 1 468s attr(,"term.labels") 468s [1] "price" "income" 468s attr(,"order") 468s [1] 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, income) 468s attr(,"dataClasses") 468s consump price income 468s "numeric" "numeric" "numeric" 468s > 468s > terms( fitsur3 ) 468s $demand 468s consump ~ price + income 468s attr(,"variables") 468s list(consump, price, income) 468s attr(,"factors") 468s price income 468s consump 0 0 468s price 1 0 468s income 0 1 468s attr(,"term.labels") 468s [1] "price" "income" 468s attr(,"order") 468s [1] 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, income) 468s attr(,"dataClasses") 468s consump price income 468s "numeric" "numeric" "numeric" 468s 468s $supply 468s consump ~ price + farmPrice + trend 468s attr(,"variables") 468s list(consump, price, farmPrice, trend) 468s attr(,"factors") 468s price farmPrice trend 468s consump 0 0 0 468s price 1 0 0 468s farmPrice 0 1 0 468s trend 0 0 1 468s attr(,"term.labels") 468s [1] "price" "farmPrice" "trend" 468s attr(,"order") 468s [1] 1 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, farmPrice, trend) 468s attr(,"dataClasses") 468s consump price farmPrice trend 468s "numeric" "numeric" "numeric" "numeric" 468s 468s > terms( fitsur3$eq[[ 2 ]] ) 468s consump ~ price + farmPrice + trend 468s attr(,"variables") 468s list(consump, price, farmPrice, trend) 468s attr(,"factors") 468s price farmPrice trend 468s consump 0 0 0 468s price 1 0 0 468s farmPrice 0 1 0 468s trend 0 0 1 468s attr(,"term.labels") 468s [1] "price" "farmPrice" "trend" 468s attr(,"order") 468s [1] 1 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, farmPrice, trend) 468s attr(,"dataClasses") 468s consump price farmPrice trend 468s "numeric" "numeric" "numeric" "numeric" 468s > 468s > terms( fitsur3w ) 468s $demand 468s consump ~ price + income 468s attr(,"variables") 468s list(consump, price, income) 468s attr(,"factors") 468s price income 468s consump 0 0 468s price 1 0 468s income 0 1 468s attr(,"term.labels") 468s [1] "price" "income" 468s attr(,"order") 468s [1] 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, income) 468s attr(,"dataClasses") 468s consump price income 468s "numeric" "numeric" "numeric" 468s 468s $supply 468s consump ~ price + farmPrice + trend 468s attr(,"variables") 468s list(consump, price, farmPrice, trend) 468s attr(,"factors") 468s price farmPrice trend 468s consump 0 0 0 468s price 1 0 0 468s farmPrice 0 1 0 468s trend 0 0 1 468s attr(,"term.labels") 468s [1] "price" "farmPrice" "trend" 468s attr(,"order") 468s [1] 1 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, farmPrice, trend) 468s attr(,"dataClasses") 468s consump price farmPrice trend 468s "numeric" "numeric" "numeric" "numeric" 468s 468s > terms( fitsur3w$eq[[ 2 ]] ) 468s consump ~ price + farmPrice + trend 468s attr(,"variables") 468s list(consump, price, farmPrice, trend) 468s attr(,"factors") 468s price farmPrice trend 468s consump 0 0 0 468s price 1 0 0 468s farmPrice 0 1 0 468s trend 0 0 1 468s attr(,"term.labels") 468s [1] "price" "farmPrice" "trend" 468s attr(,"order") 468s [1] 1 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, farmPrice, trend) 468s attr(,"dataClasses") 468s consump price farmPrice trend 468s "numeric" "numeric" "numeric" "numeric" 468s > 468s > terms( fitsur4r3 ) 468s $demand 468s consump ~ price + income 468s attr(,"variables") 468s list(consump, price, income) 468s attr(,"factors") 468s price income 468s consump 0 0 468s price 1 0 468s income 0 1 468s attr(,"term.labels") 468s [1] "price" "income" 468s attr(,"order") 468s [1] 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, income) 468s attr(,"dataClasses") 468s consump price income 468s "numeric" "numeric" "numeric" 468s 468s $supply 468s consump ~ price + farmPrice + trend 468s attr(,"variables") 468s list(consump, price, farmPrice, trend) 468s attr(,"factors") 468s price farmPrice trend 468s consump 0 0 0 468s price 1 0 0 468s farmPrice 0 1 0 468s trend 0 0 1 468s attr(,"term.labels") 468s [1] "price" "farmPrice" "trend" 468s attr(,"order") 468s [1] 1 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, farmPrice, trend) 468s attr(,"dataClasses") 468s consump price farmPrice trend 468s "numeric" "numeric" "numeric" "numeric" 468s 468s > terms( fitsur4r3$eq[[ 1 ]] ) 468s consump ~ price + income 468s attr(,"variables") 468s list(consump, price, income) 468s attr(,"factors") 468s price income 468s consump 0 0 468s price 1 0 468s income 0 1 468s attr(,"term.labels") 468s [1] "price" "income" 468s attr(,"order") 468s [1] 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, income) 468s attr(,"dataClasses") 468s consump price income 468s "numeric" "numeric" "numeric" 468s > 468s > terms( fitsur4we ) 468s $demand 468s consump ~ price + income 468s attr(,"variables") 468s list(consump, price, income) 468s attr(,"factors") 468s price income 468s consump 0 0 468s price 1 0 468s income 0 1 468s attr(,"term.labels") 468s [1] "price" "income" 468s attr(,"order") 468s [1] 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, income) 468s attr(,"dataClasses") 468s consump price income 468s "numeric" "numeric" "numeric" 468s 468s $supply 468s consump ~ price + farmPrice + trend 468s attr(,"variables") 468s list(consump, price, farmPrice, trend) 468s attr(,"factors") 468s price farmPrice trend 468s consump 0 0 0 468s price 1 0 0 468s farmPrice 0 1 0 468s trend 0 0 1 468s attr(,"term.labels") 468s [1] "price" "farmPrice" "trend" 468s attr(,"order") 468s [1] 1 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, farmPrice, trend) 468s attr(,"dataClasses") 468s consump price farmPrice trend 468s "numeric" "numeric" "numeric" "numeric" 468s 468s > terms( fitsur4we$eq[[ 1 ]] ) 468s consump ~ price + income 468s attr(,"variables") 468s list(consump, price, income) 468s attr(,"factors") 468s price income 468s consump 0 0 468s price 1 0 468s income 0 1 468s attr(,"term.labels") 468s [1] "price" "income" 468s attr(,"order") 468s [1] 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, income) 468s attr(,"dataClasses") 468s consump price income 468s "numeric" "numeric" "numeric" 468s > 468s > terms( fitsur5 ) 468s $demand 468s consump ~ price + income 468s attr(,"variables") 468s list(consump, price, income) 468s attr(,"factors") 468s price income 468s consump 0 0 468s price 1 0 468s income 0 1 468s attr(,"term.labels") 468s [1] "price" "income" 468s attr(,"order") 468s [1] 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, income) 468s attr(,"dataClasses") 468s consump price income 468s "numeric" "numeric" "numeric" 468s 468s $supply 468s consump ~ price + farmPrice + trend 468s attr(,"variables") 468s list(consump, price, farmPrice, trend) 468s attr(,"factors") 468s price farmPrice trend 468s consump 0 0 0 468s price 1 0 0 468s farmPrice 0 1 0 468s trend 0 0 1 468s attr(,"term.labels") 468s [1] "price" "farmPrice" "trend" 468s attr(,"order") 468s [1] 1 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, farmPrice, trend) 468s attr(,"dataClasses") 468s consump price farmPrice trend 468s "numeric" "numeric" "numeric" "numeric" 468s 468s > terms( fitsur5$eq[[ 2 ]] ) 468s consump ~ price + farmPrice + trend 468s attr(,"variables") 468s list(consump, price, farmPrice, trend) 468s attr(,"factors") 468s price farmPrice trend 468s consump 0 0 0 468s price 1 0 0 468s farmPrice 0 1 0 468s trend 0 0 1 468s attr(,"term.labels") 468s [1] "price" "farmPrice" "trend" 468s attr(,"order") 468s [1] 1 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, farmPrice, trend) 468s attr(,"dataClasses") 468s consump price farmPrice trend 468s "numeric" "numeric" "numeric" "numeric" 468s > 468s > terms( fitsuri1r3 ) 468s $demand 468s consump ~ price + income 468s attr(,"variables") 468s list(consump, price, income) 468s attr(,"factors") 468s price income 468s consump 0 0 468s price 1 0 468s income 0 1 468s attr(,"term.labels") 468s [1] "price" "income" 468s attr(,"order") 468s [1] 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, income) 468s attr(,"dataClasses") 468s consump price income 468s "numeric" "numeric" "numeric" 468s 468s $supply 468s price ~ income + farmPrice + trend 468s attr(,"variables") 468s list(price, income, farmPrice, trend) 468s attr(,"factors") 468s income farmPrice trend 468s price 0 0 0 468s income 1 0 0 468s farmPrice 0 1 0 468s trend 0 0 1 468s attr(,"term.labels") 468s [1] "income" "farmPrice" "trend" 468s attr(,"order") 468s [1] 1 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(price, income, farmPrice, trend) 468s attr(,"dataClasses") 468s price income farmPrice trend 468s "numeric" "numeric" "numeric" "numeric" 468s 468s > terms( fitsuri1r3$eq[[ 1 ]] ) 468s consump ~ price + income 468s attr(,"variables") 468s list(consump, price, income) 468s attr(,"factors") 468s price income 468s consump 0 0 468s price 1 0 468s income 0 1 468s attr(,"term.labels") 468s [1] "price" "income" 468s attr(,"order") 468s [1] 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, income) 468s attr(,"dataClasses") 468s consump price income 468s "numeric" "numeric" "numeric" 468s > 468s > terms( fitsuri2 ) 468s $demand 468s consump ~ price + income 468s attr(,"variables") 468s list(consump, price, income) 468s attr(,"factors") 468s price income 468s consump 0 0 468s price 1 0 468s income 0 1 468s attr(,"term.labels") 468s [1] "price" "income" 468s attr(,"order") 468s [1] 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, income) 468s attr(,"dataClasses") 468s consump price income 468s "numeric" "numeric" "numeric" 468s 468s $supply 468s price ~ income + farmPrice + trend 468s attr(,"variables") 468s list(price, income, farmPrice, trend) 468s attr(,"factors") 468s income farmPrice trend 468s price 0 0 0 468s income 1 0 0 468s farmPrice 0 1 0 468s trend 0 0 1 468s attr(,"term.labels") 468s [1] "income" "farmPrice" "trend" 468s attr(,"order") 468s [1] 1 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(price, income, farmPrice, trend) 468s attr(,"dataClasses") 468s price income farmPrice trend 468s "numeric" "numeric" "numeric" "numeric" 468s 468s > terms( fitsuri2$eq[[ 2 ]] ) 468s price ~ income + farmPrice + trend 468s attr(,"variables") 468s list(price, income, farmPrice, trend) 468s attr(,"factors") 468s income farmPrice trend 468s price 0 0 0 468s income 1 0 0 468s farmPrice 0 1 0 468s trend 0 0 1 468s attr(,"term.labels") 468s [1] "income" "farmPrice" "trend" 468s attr(,"order") 468s [1] 1 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(price, income, farmPrice, trend) 468s attr(,"dataClasses") 468s price income farmPrice trend 468s "numeric" "numeric" "numeric" "numeric" 468s > 468s > terms( fitsuri3e ) 468s $demand 468s consump ~ price + income 468s attr(,"variables") 468s list(consump, price, income) 468s attr(,"factors") 468s price income 468s consump 0 0 468s price 1 0 468s income 0 1 468s attr(,"term.labels") 468s [1] "price" "income" 468s attr(,"order") 468s [1] 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, income) 468s attr(,"dataClasses") 468s consump price income 468s "numeric" "numeric" "numeric" 468s 468s $supply 468s price ~ income + farmPrice + trend 468s attr(,"variables") 468s list(price, income, farmPrice, trend) 468s attr(,"factors") 468s income farmPrice trend 468s price 0 0 0 468s income 1 0 0 468s farmPrice 0 1 0 468s trend 0 0 1 468s attr(,"term.labels") 468s [1] "income" "farmPrice" "trend" 468s attr(,"order") 468s [1] 1 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(price, income, farmPrice, trend) 468s attr(,"dataClasses") 468s price income farmPrice trend 468s "numeric" "numeric" "numeric" "numeric" 468s 468s > terms( fitsuri3e$eq[[ 1 ]] ) 468s consump ~ price + income 468s attr(,"variables") 468s list(consump, price, income) 468s attr(,"factors") 468s price income 468s consump 0 0 468s price 1 0 468s income 0 1 468s attr(,"term.labels") 468s [1] "price" "income" 468s attr(,"order") 468s [1] 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, income) 468s attr(,"dataClasses") 468s consump price income 468s "numeric" "numeric" "numeric" 468s > 468s > terms( fitsurio4 ) 468s $demand 468s consump ~ price + income 468s attr(,"variables") 468s list(consump, price, income) 468s attr(,"factors") 468s price income 468s consump 0 0 468s price 1 0 468s income 0 1 468s attr(,"term.labels") 468s [1] "price" "income" 468s attr(,"order") 468s [1] 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, income) 468s attr(,"dataClasses") 468s consump price income 468s "numeric" "numeric" "numeric" 468s 468s $supply 468s consump ~ price + farmPrice + trend 468s attr(,"variables") 468s list(consump, price, farmPrice, trend) 468s attr(,"factors") 468s price farmPrice trend 468s consump 0 0 0 468s price 1 0 0 468s farmPrice 0 1 0 468s trend 0 0 1 468s attr(,"term.labels") 468s [1] "price" "farmPrice" "trend" 468s attr(,"order") 468s [1] 1 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, farmPrice, trend) 468s attr(,"dataClasses") 468s consump price farmPrice trend 468s "numeric" "numeric" "numeric" "numeric" 468s 468s > terms( fitsurio4$eq[[ 2 ]] ) 468s consump ~ price + farmPrice + trend 468s attr(,"variables") 468s list(consump, price, farmPrice, trend) 468s attr(,"factors") 468s price farmPrice trend 468s consump 0 0 0 468s price 1 0 0 468s farmPrice 0 1 0 468s trend 0 0 1 468s attr(,"term.labels") 468s [1] "price" "farmPrice" "trend" 468s attr(,"order") 468s [1] 1 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, farmPrice, trend) 468s attr(,"dataClasses") 468s consump price farmPrice trend 468s "numeric" "numeric" "numeric" "numeric" 468s > terms( fitsuri4 ) 468s $demand 468s consump ~ price + income 468s attr(,"variables") 468s list(consump, price, income) 468s attr(,"factors") 468s price income 468s consump 0 0 468s price 1 0 468s income 0 1 468s attr(,"term.labels") 468s [1] "price" "income" 468s attr(,"order") 468s [1] 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, income) 468s attr(,"dataClasses") 468s consump price income 468s "numeric" "numeric" "numeric" 468s 468s $supply 468s price ~ income + farmPrice + trend 468s attr(,"variables") 468s list(price, income, farmPrice, trend) 468s attr(,"factors") 468s income farmPrice trend 468s price 0 0 0 468s income 1 0 0 468s farmPrice 0 1 0 468s trend 0 0 1 468s attr(,"term.labels") 468s [1] "income" "farmPrice" "trend" 468s attr(,"order") 468s [1] 1 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(price, income, farmPrice, trend) 468s attr(,"dataClasses") 468s price income farmPrice trend 468s "numeric" "numeric" "numeric" "numeric" 468s 468s > terms( fitsuri4$eq[[ 2 ]] ) 468s price ~ income + farmPrice + trend 468s attr(,"variables") 468s list(price, income, farmPrice, trend) 468s attr(,"factors") 468s income farmPrice trend 468s price 0 0 0 468s income 1 0 0 468s farmPrice 0 1 0 468s trend 0 0 1 468s attr(,"term.labels") 468s [1] "income" "farmPrice" "trend" 468s attr(,"order") 468s [1] 1 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(price, income, farmPrice, trend) 468s attr(,"dataClasses") 468s price income farmPrice trend 468s "numeric" "numeric" "numeric" "numeric" 468s > 468s > terms( fitsurio5r2 ) 468s $demand 468s consump ~ price + income 468s attr(,"variables") 468s list(consump, price, income) 468s attr(,"factors") 468s price income 468s consump 0 0 468s price 1 0 468s income 0 1 468s attr(,"term.labels") 468s [1] "price" "income" 468s attr(,"order") 468s [1] 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, income) 468s attr(,"dataClasses") 468s consump price income 468s "numeric" "numeric" "numeric" 468s 468s $supply 468s consump ~ price + farmPrice + trend 468s attr(,"variables") 468s list(consump, price, farmPrice, trend) 468s attr(,"factors") 468s price farmPrice trend 468s consump 0 0 0 468s price 1 0 0 468s farmPrice 0 1 0 468s trend 0 0 1 468s attr(,"term.labels") 468s [1] "price" "farmPrice" "trend" 468s attr(,"order") 468s [1] 1 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, farmPrice, trend) 468s attr(,"dataClasses") 468s consump price farmPrice trend 468s "numeric" "numeric" "numeric" "numeric" 468s 468s > terms( fitsurio5r2$eq[[ 1 ]] ) 468s consump ~ price + income 468s attr(,"variables") 468s list(consump, price, income) 468s attr(,"factors") 468s price income 468s consump 0 0 468s price 1 0 468s income 0 1 468s attr(,"term.labels") 468s [1] "price" "income" 468s attr(,"order") 468s [1] 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, income) 468s attr(,"dataClasses") 468s consump price income 468s "numeric" "numeric" "numeric" 468s > terms( fitsuri5r2 ) 468s $demand 468s consump ~ price + income 468s attr(,"variables") 468s list(consump, price, income) 468s attr(,"factors") 468s price income 468s consump 0 0 468s price 1 0 468s income 0 1 468s attr(,"term.labels") 468s [1] "price" "income" 468s attr(,"order") 468s [1] 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, income) 468s attr(,"dataClasses") 468s consump price income 468s "numeric" "numeric" "numeric" 468s 468s $supply 468s price ~ income + farmPrice + trend 468s attr(,"variables") 468s list(price, income, farmPrice, trend) 468s attr(,"factors") 468s income farmPrice trend 468s price 0 0 0 468s income 1 0 0 468s farmPrice 0 1 0 468s trend 0 0 1 468s attr(,"term.labels") 468s [1] "income" "farmPrice" "trend" 468s attr(,"order") 468s [1] 1 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(price, income, farmPrice, trend) 468s attr(,"dataClasses") 468s price income farmPrice trend 468s "numeric" "numeric" "numeric" "numeric" 468s 468s > terms( fitsuri5r2$eq[[ 1 ]] ) 468s consump ~ price + income 468s attr(,"variables") 468s list(consump, price, income) 468s attr(,"factors") 468s price income 468s consump 0 0 468s price 1 0 468s income 0 1 468s attr(,"term.labels") 468s [1] "price" "income" 468s attr(,"order") 468s [1] 1 1 468s attr(,"intercept") 468s [1] 1 468s attr(,"response") 468s [1] 1 468s attr(,".Environment") 468s 468s attr(,"predvars") 468s list(consump, price, income) 468s attr(,"dataClasses") 468s consump price income 468s "numeric" "numeric" "numeric" 468s > 468s > 468s > ## **************** estfun ************************ 468s > library( "sandwich" ) 468s > 468s > estfun( fitsur1 ) 468s demand_(Intercept) demand_price demand_income supply_(Intercept) 468s demand_1 0.9083 91.12 79.38 -0.6496 468s demand_2 -0.7320 -76.32 -71.44 0.5235 468s demand_3 3.2023 331.23 309.66 -2.2902 468s demand_4 2.1435 224.00 210.49 -1.5330 468s demand_5 2.7516 269.66 274.61 -1.9679 468s demand_6 1.7015 169.22 171.00 -1.2169 468s demand_7 2.2068 223.03 227.74 -1.5783 468s demand_8 -3.5946 -376.58 -387.50 2.5708 468s demand_9 -1.6348 -157.67 -157.92 1.1692 468s demand_10 2.7103 247.26 240.95 -1.9384 468s demand_11 -0.8810 -82.01 -66.16 0.6301 468s demand_12 -3.4554 -341.39 -265.72 2.4712 468s demand_13 -2.2246 -228.93 -188.20 1.5910 468s demand_14 -0.5461 -53.93 -49.48 0.3906 468s demand_15 2.4619 234.17 253.82 -1.7607 468s demand_16 -4.3873 -431.94 -461.11 3.1378 468s demand_17 -0.9942 -85.99 -95.84 0.7110 468s demand_18 -2.5012 -260.17 -261.13 1.7888 468s demand_19 2.5805 272.93 285.66 -1.8455 468s demand_20 0.2846 32.30 36.17 -0.2036 468s supply_1 -0.4396 -44.11 -38.42 0.3959 468s supply_2 -0.0184 -1.92 -1.79 0.0166 468s supply_3 -2.5916 -268.06 -250.60 2.3337 468s supply_4 -1.7132 -179.04 -168.24 1.5428 468s supply_5 -2.3049 -225.88 -230.03 2.0756 468s supply_6 -1.3780 -137.06 -138.49 1.2410 468s supply_7 -2.0596 -208.16 -212.55 1.8547 468s supply_8 3.4200 358.29 368.68 -3.0798 468s supply_9 1.9576 188.80 189.10 -1.7628 468s supply_10 -2.3620 -215.48 -209.98 2.1270 468s supply_11 1.1852 110.32 89.01 -1.0673 468s supply_12 2.6183 258.69 201.34 -2.3578 468s supply_13 1.9874 204.52 168.14 -1.7897 468s supply_14 -0.1072 -10.59 -9.72 0.0966 468s supply_15 -2.6839 -255.29 -276.71 2.4169 468s supply_16 3.8259 376.66 402.10 -3.4452 468s supply_17 0.5270 45.59 50.80 -0.4746 468s supply_18 3.0021 312.27 313.42 -2.7035 468s supply_19 -2.0184 -213.48 -223.44 1.8176 468s supply_20 -0.8466 -96.08 -107.60 0.7623 468s supply_price supply_farmPrice supply_trend 468s demand_1 -65.17 -63.66 -0.6496 468s demand_2 54.58 51.88 1.0470 468s demand_3 -236.89 -226.96 -6.8707 468s demand_4 -160.20 -150.38 -6.1319 468s demand_5 -192.86 -218.05 -9.8397 468s demand_6 -121.02 -131.66 -7.3012 468s demand_7 -159.51 -166.67 -11.0480 468s demand_8 269.33 282.28 20.5665 468s demand_9 112.76 127.09 10.5227 468s demand_10 -176.84 -195.00 -19.3840 468s demand_11 58.65 51.04 6.9309 468s demand_12 244.16 169.53 29.6547 468s demand_13 163.73 112.80 20.6833 468s demand_14 38.57 31.79 5.4681 468s demand_15 -167.48 -180.12 -26.4104 468s demand_16 308.92 329.47 50.2044 468s demand_17 61.50 78.57 12.0871 468s demand_18 186.07 165.47 32.1991 468s demand_19 -195.20 -164.81 -35.0650 468s demand_20 -23.10 -18.93 -4.0710 468s supply_1 39.72 38.80 0.3959 468s supply_2 1.73 1.64 0.0331 468s supply_3 241.39 231.27 7.0012 468s supply_4 161.23 151.34 6.1710 468s supply_5 203.41 229.98 10.3781 468s supply_6 123.42 134.27 7.4457 468s supply_7 187.45 195.86 12.9829 468s supply_8 -322.64 -338.16 -24.6380 468s supply_9 -170.02 -191.62 -15.8653 468s supply_10 194.04 213.98 21.2699 468s supply_11 -99.35 -86.45 -11.7402 468s supply_12 -232.95 -161.74 -28.2933 468s supply_13 -184.18 -126.89 -23.2663 468s supply_14 9.54 7.86 1.3521 468s supply_15 229.90 247.25 36.2539 468s supply_16 -339.19 -361.75 -55.1237 468s supply_17 -41.05 -52.44 -8.0678 468s supply_18 -281.20 -250.07 -48.6623 468s supply_19 192.24 162.31 34.5341 468s supply_20 86.52 70.90 15.2466 468s > round( colSums( estfun( fitsur1 ) ), digits = 7 ) 468s demand_(Intercept) demand_price demand_income supply_(Intercept) 468s 0 0 0 0 468s supply_price supply_farmPrice supply_trend 468s 0 0 0 468s > 468s > estfun( fitsur1e2 ) 468s demand_(Intercept) demand_price demand_income supply_(Intercept) 468s demand_1 1.09034 109.386 95.295 -0.80605 468s demand_2 -1.05992 -110.511 -103.448 0.78356 468s demand_3 4.28760 443.488 414.611 -3.16968 468s demand_4 2.85253 298.107 280.119 -2.10878 468s demand_5 3.80226 372.625 379.466 -2.81088 468s demand_6 2.36197 234.912 237.378 -1.74612 468s demand_7 3.06088 309.351 315.883 -2.26280 468s demand_8 -4.81806 -504.754 -519.386 3.56182 468s demand_9 -2.17915 -210.170 -210.506 1.61097 468s demand_10 3.70159 337.689 329.071 -2.73646 468s demand_11 -1.39799 -130.132 -104.989 1.03349 468s demand_12 -4.96091 -490.143 -381.494 3.66743 468s demand_13 -3.24623 -334.063 -274.631 2.39983 468s demand_14 -0.81794 -80.776 -74.105 0.60467 468s demand_15 3.49861 332.784 360.707 -2.58640 468s demand_16 -5.83443 -574.406 -613.199 4.31320 468s demand_17 -1.15650 -100.035 -111.487 0.85496 468s demand_18 -3.36717 -350.239 -351.532 2.48923 468s demand_19 3.59870 380.631 398.376 -2.66040 468s demand_20 0.58382 66.257 74.203 -0.43160 468s supply_1 -0.54811 -54.988 -47.905 0.47751 468s supply_2 0.00819 0.854 0.799 -0.00713 468s supply_3 -3.61236 -373.644 -349.315 3.14703 468s supply_4 -2.38151 -248.882 -233.865 2.07474 468s supply_5 -3.32295 -325.653 -331.631 2.89490 468s supply_6 -2.00948 -199.855 -201.953 1.75063 468s supply_7 -2.95622 -298.773 -305.081 2.57541 468s supply_8 4.67628 489.901 504.103 -4.07390 468s supply_9 2.65680 256.238 256.647 -2.31456 468s supply_10 -3.31875 -302.763 -295.037 2.89124 468s supply_11 1.84429 171.676 138.506 -1.60672 468s supply_12 3.95003 390.267 303.757 -3.44120 468s supply_13 3.01568 310.338 255.127 -2.62722 468s supply_14 -0.02452 -2.421 -2.221 0.02136 468s supply_15 -3.84791 -366.010 -396.720 3.35224 468s supply_16 5.24831 516.701 551.597 -4.57224 468s supply_17 0.59732 51.667 57.582 -0.52037 468s supply_18 4.17631 434.404 436.007 -3.63834 468s supply_19 -2.86060 -302.562 -316.668 2.49211 468s supply_20 -1.29079 -146.492 -164.060 1.12452 468s supply_price supply_farmPrice supply_trend 468s demand_1 -80.865 -78.993 -0.8060 468s demand_2 81.697 77.651 1.5671 468s demand_3 -327.856 -314.115 -9.5090 468s demand_4 -220.380 -206.871 -8.4351 468s demand_5 -275.469 -311.446 -14.0544 468s demand_6 -173.662 -188.931 -10.4767 468s demand_7 -228.692 -238.952 -15.8396 468s demand_8 373.147 391.088 28.4946 468s demand_9 155.372 175.113 14.4987 468s demand_10 -249.642 -275.288 -27.3646 468s demand_11 96.202 83.712 11.3683 468s demand_12 362.346 251.586 44.0092 468s demand_13 246.962 170.148 31.1978 468s demand_14 59.715 49.220 8.4654 468s demand_15 -246.016 -264.589 -38.7961 468s demand_16 424.638 452.886 69.0111 468s demand_17 73.953 94.473 14.5344 468s demand_18 258.920 230.254 44.8061 468s demand_19 -281.388 -237.573 -50.5475 468s demand_20 -48.982 -40.138 -8.6319 468s supply_1 47.905 46.796 0.4775 468s supply_2 -0.744 -0.707 -0.0143 468s supply_3 325.513 311.871 9.4411 468s supply_4 216.822 203.532 8.2989 468s supply_5 283.704 320.755 14.4745 468s supply_6 174.111 189.418 10.5038 468s supply_7 260.286 271.963 18.0279 468s supply_8 -426.794 -447.314 -32.5912 468s supply_9 -223.230 -251.593 -20.8310 468s supply_10 263.762 290.859 28.9124 468s supply_11 -149.561 -130.144 -17.6739 468s supply_12 -339.994 -236.066 -41.2944 468s supply_13 -270.361 -186.270 -34.1538 468s supply_14 2.109 1.739 0.2990 468s supply_15 318.862 342.934 50.2836 468s supply_16 -450.142 -480.085 -73.1559 468s supply_17 -45.011 -57.501 -8.8464 468s supply_18 -378.445 -336.546 -65.4901 468s supply_19 263.588 222.545 47.3500 468s supply_20 127.621 104.580 22.4903 468s > round( colSums( estfun( fitsur1e2 ) ), digits = 7 ) 468s demand_(Intercept) demand_price demand_income supply_(Intercept) 468s 0 0 0 0 468s supply_price supply_farmPrice supply_trend 468s 0 0 0 468s > 468s > estfun( fitsur1r3 ) 468s demand_(Intercept) demand_price demand_income supply_(Intercept) 468s demand_1 1.07229 107.575 93.718 -0.79049 468s demand_2 -1.02096 -106.450 -99.646 0.75265 468s demand_3 4.16424 430.729 402.682 -3.06988 468s demand_4 2.77231 289.723 272.240 -2.04374 468s demand_5 3.68037 360.680 367.301 -2.71316 468s demand_6 2.28513 227.270 229.656 -1.68460 468s demand_7 2.96157 299.314 305.634 -2.18327 468s demand_8 -4.67889 -490.175 -504.385 3.44927 468s demand_9 -2.11749 -204.223 -204.549 1.56101 468s demand_10 3.58740 327.271 318.920 -2.64463 468s demand_11 -1.33464 -124.235 -100.231 0.98389 468s demand_12 -4.78276 -472.541 -367.794 3.52584 468s demand_13 -3.12449 -321.535 -264.332 2.30337 468s demand_14 -0.78522 -77.545 -71.141 0.57886 468s demand_15 3.37652 321.171 348.119 -2.48917 468s demand_16 -5.67080 -558.296 -596.001 4.18051 468s demand_17 -1.14172 -98.757 -110.062 0.84168 468s demand_18 -3.26836 -339.962 -341.217 2.40943 468s demand_19 3.47995 368.071 385.231 -2.56542 468s demand_20 0.54555 61.914 69.339 -0.40218 468s supply_1 -0.53834 -54.008 -47.051 0.47031 468s supply_2 0.00335 0.349 0.327 -0.00293 468s supply_3 -3.49682 -361.694 -338.143 3.05492 468s supply_4 -2.30621 -241.013 -226.470 2.01477 468s supply_5 -3.20507 -314.100 -319.866 2.80004 468s supply_6 -1.93606 -192.553 -194.574 1.69139 468s supply_7 -2.85248 -288.289 -294.376 2.49200 468s supply_8 4.53460 475.059 488.830 -3.96155 468s supply_9 2.57840 248.676 249.073 -2.25256 468s supply_10 -3.20906 -292.756 -285.286 2.80352 468s supply_11 1.76494 164.289 132.547 -1.54190 468s supply_12 3.79168 374.622 291.580 -3.31251 468s supply_13 2.89330 297.744 244.773 -2.52766 468s supply_14 -0.03625 -3.580 -3.284 0.03167 468s supply_15 -3.71220 -353.101 -382.728 3.24307 468s supply_16 5.08854 500.972 534.805 -4.44548 468s supply_17 0.59312 51.303 57.176 -0.51816 468s supply_18 4.04346 420.584 422.137 -3.53247 468s supply_19 -2.76240 -292.176 -305.797 2.41330 468s supply_20 -1.23648 -140.329 -157.157 1.08023 468s supply_price supply_farmPrice supply_trend 468s demand_1 -79.304 -77.47 -0.79049 468s demand_2 78.475 74.59 1.50531 468s demand_3 -317.533 -304.22 -9.20963 468s demand_4 -213.583 -200.49 -8.17496 468s demand_5 -265.893 -300.62 -13.56581 468s demand_6 -167.543 -182.27 -10.10759 468s demand_7 -220.654 -230.55 -15.28289 468s demand_8 361.356 378.73 27.59420 468s demand_9 150.553 169.68 14.04907 468s demand_10 -241.264 -266.05 -26.44627 468s demand_11 91.586 79.70 10.82281 468s demand_12 348.357 241.87 42.31014 468s demand_13 237.035 163.31 29.94383 468s demand_14 57.166 47.12 8.10410 468s demand_15 -236.767 -254.64 -37.33751 468s demand_16 411.575 438.95 66.88809 468s demand_17 72.803 93.01 14.30850 468s demand_18 250.619 222.87 43.36977 468s demand_19 -271.341 -229.09 -48.74290 468s demand_20 -45.643 -37.40 -8.04353 468s supply_1 47.183 46.09 0.47031 468s supply_2 -0.305 -0.29 -0.00585 468s supply_3 315.985 302.74 9.16476 468s supply_4 210.555 197.65 8.05908 468s supply_5 274.406 310.24 14.00018 468s supply_6 168.219 183.01 10.14835 468s supply_7 251.857 263.16 17.44401 468s supply_8 -415.024 -434.98 -31.69241 468s supply_9 -217.250 -244.85 -20.27300 468s supply_10 255.760 282.03 28.03523 468s supply_11 -143.528 -124.89 -16.96088 468s supply_12 -327.279 -227.24 -39.75013 468s supply_13 -260.117 -179.21 -32.85963 468s supply_14 3.128 2.58 0.44339 468s supply_15 308.478 331.77 48.64611 468s supply_16 -437.662 -466.78 -71.12773 468s supply_17 -44.820 -57.26 -8.80876 468s supply_18 -367.434 -326.75 -63.58452 468s supply_19 255.253 215.51 45.85274 468s supply_20 122.595 100.46 21.60450 468s > round( colSums( estfun( fitsur1r3 ) ), digits = 7 ) 468s demand_(Intercept) demand_price demand_income supply_(Intercept) 468s 0 0 0 0 468s supply_price supply_farmPrice supply_trend 468s 0 0 0 468s > 468s > estfun( fitsur1w ) 468s demand_(Intercept) demand_price demand_income supply_(Intercept) 468s demand_1 0.9083 91.12 79.38 -0.6496 468s demand_2 -0.7320 -76.32 -71.44 0.5235 468s demand_3 3.2023 331.23 309.66 -2.2902 468s demand_4 2.1435 224.00 210.49 -1.5330 468s demand_5 2.7516 269.66 274.61 -1.9679 468s demand_6 1.7015 169.22 171.00 -1.2169 468s demand_7 2.2068 223.03 227.74 -1.5783 468s demand_8 -3.5946 -376.58 -387.50 2.5708 468s demand_9 -1.6348 -157.67 -157.92 1.1692 468s demand_10 2.7103 247.26 240.95 -1.9384 468s demand_11 -0.8810 -82.01 -66.16 0.6301 468s demand_12 -3.4554 -341.39 -265.72 2.4712 468s demand_13 -2.2246 -228.93 -188.20 1.5910 468s demand_14 -0.5461 -53.93 -49.48 0.3906 468s demand_15 2.4619 234.17 253.82 -1.7607 468s demand_16 -4.3873 -431.94 -461.11 3.1378 468s demand_17 -0.9942 -85.99 -95.84 0.7110 468s demand_18 -2.5012 -260.17 -261.13 1.7888 468s demand_19 2.5805 272.93 285.66 -1.8455 468s demand_20 0.2846 32.30 36.17 -0.2036 468s supply_1 -0.4396 -44.11 -38.42 0.3959 468s supply_2 -0.0184 -1.92 -1.79 0.0166 468s supply_3 -2.5916 -268.06 -250.60 2.3337 468s supply_4 -1.7132 -179.04 -168.24 1.5428 468s supply_5 -2.3049 -225.88 -230.03 2.0756 468s supply_6 -1.3780 -137.06 -138.49 1.2410 468s supply_7 -2.0596 -208.16 -212.55 1.8547 468s supply_8 3.4200 358.29 368.68 -3.0798 468s supply_9 1.9576 188.80 189.10 -1.7628 468s supply_10 -2.3620 -215.48 -209.98 2.1270 468s supply_11 1.1852 110.32 89.01 -1.0673 468s supply_12 2.6183 258.69 201.34 -2.3578 468s supply_13 1.9874 204.52 168.14 -1.7897 468s supply_14 -0.1072 -10.59 -9.72 0.0966 468s supply_15 -2.6839 -255.29 -276.71 2.4169 468s supply_16 3.8259 376.66 402.10 -3.4452 468s supply_17 0.5270 45.59 50.80 -0.4746 468s supply_18 3.0021 312.27 313.42 -2.7035 468s supply_19 -2.0184 -213.48 -223.44 1.8176 468s supply_20 -0.8466 -96.08 -107.60 0.7623 468s supply_price supply_farmPrice supply_trend 468s demand_1 -65.17 -63.66 -0.6496 468s demand_2 54.58 51.88 1.0470 468s demand_3 -236.89 -226.96 -6.8707 468s demand_4 -160.20 -150.38 -6.1319 468s demand_5 -192.86 -218.05 -9.8397 468s demand_6 -121.02 -131.66 -7.3012 468s demand_7 -159.51 -166.67 -11.0480 468s demand_8 269.33 282.28 20.5665 468s demand_9 112.76 127.09 10.5227 468s demand_10 -176.84 -195.00 -19.3840 468s demand_11 58.65 51.04 6.9309 468s demand_12 244.16 169.53 29.6547 468s demand_13 163.73 112.80 20.6833 468s demand_14 38.57 31.79 5.4681 468s demand_15 -167.48 -180.12 -26.4104 468s demand_16 308.92 329.47 50.2044 468s demand_17 61.50 78.57 12.0871 468s demand_18 186.07 165.47 32.1991 468s demand_19 -195.20 -164.81 -35.0650 468s demand_20 -23.10 -18.93 -4.0710 468s supply_1 39.72 38.80 0.3959 468s supply_2 1.73 1.64 0.0331 468s supply_3 241.39 231.27 7.0012 468s supply_4 161.23 151.34 6.1710 468s supply_5 203.41 229.98 10.3781 468s supply_6 123.42 134.27 7.4457 468s supply_7 187.45 195.86 12.9829 468s supply_8 -322.64 -338.16 -24.6380 468s supply_9 -170.02 -191.62 -15.8653 468s supply_10 194.04 213.98 21.2699 468s supply_11 -99.35 -86.45 -11.7402 468s supply_12 -232.95 -161.74 -28.2933 468s supply_13 -184.18 -126.89 -23.2663 468s supply_14 9.54 7.86 1.3521 468s supply_15 229.90 247.25 36.2539 468s supply_16 -339.19 -361.75 -55.1237 468s supply_17 -41.05 -52.44 -8.0678 468s supply_18 -281.20 -250.07 -48.6623 468s supply_19 192.24 162.31 34.5341 468s supply_20 86.52 70.90 15.2466 468s > round( colSums( estfun( fitsur1w ) ), digits = 7 ) 468s demand_(Intercept) demand_price demand_income supply_(Intercept) 468s 0 0 0 0 468s supply_price supply_farmPrice supply_trend 468s 0 0 0 468s > 468s > estfun( fitsuri1e ) 468s demand_(Intercept) demand_price demand_income supply_(Intercept) 468s demand_1 0.5467 54.84 47.78 0.5219 468s demand_2 -0.5182 -54.03 -50.58 -0.4947 468s demand_3 1.5799 163.41 152.77 1.5082 468s demand_4 0.9787 102.28 96.11 0.9343 468s demand_5 1.4899 146.02 148.70 1.4224 468s demand_6 0.8875 88.27 89.19 0.8472 468s demand_7 1.0809 109.24 111.55 1.0319 468s demand_8 -2.1165 -221.73 -228.15 -2.0205 468s demand_9 -0.7383 -71.21 -71.32 -0.7049 468s demand_10 1.7668 161.19 157.07 1.6867 468s demand_11 -0.0682 -6.35 -5.12 -0.0651 468s demand_12 -1.6133 -159.40 -124.07 -1.5402 468s demand_13 -1.1570 -119.06 -97.88 -1.1045 468s demand_14 -0.1925 -19.01 -17.44 -0.1838 468s demand_15 1.4026 133.41 144.61 1.3390 468s demand_16 -2.3128 -227.70 -243.08 -2.2080 468s demand_17 -0.0876 -7.58 -8.44 -0.0836 468s demand_18 -1.4924 -155.23 -155.81 -1.4247 468s demand_19 1.0702 113.20 118.47 1.0217 468s demand_20 -0.5064 -57.47 -64.36 -0.4834 468s supply_1 0.1054 10.57 9.21 0.1789 468s supply_2 -0.8882 -92.60 -86.68 -1.5080 468s supply_3 -0.5218 -53.97 -50.46 -0.8859 468s supply_4 -0.2644 -27.63 -25.96 -0.4489 468s supply_5 -0.7666 -75.13 -76.51 -1.3016 468s supply_6 -0.4056 -40.34 -40.77 -0.6887 468s supply_7 -0.8114 -82.00 -83.74 -1.3777 468s supply_8 1.4243 149.22 153.54 2.4183 468s supply_9 1.0270 99.05 99.21 1.7438 468s supply_10 -1.0278 -93.77 -91.37 -1.7451 468s supply_11 0.6336 58.98 47.58 1.0758 468s supply_12 0.2724 26.92 20.95 0.4626 468s supply_13 0.8434 86.79 71.35 1.4319 468s supply_14 -0.7107 -70.19 -64.39 -1.2067 468s supply_15 -1.5343 -145.94 -158.18 -2.6050 468s supply_16 1.1276 111.01 118.51 1.9145 468s supply_17 -0.6907 -59.75 -66.58 -1.1727 468s supply_18 2.2394 232.94 233.79 3.8022 468s supply_19 0.1792 18.96 19.84 0.3043 468s supply_20 -0.2309 -26.21 -29.35 -0.3921 468s supply_income supply_farmPrice supply_trend 468s demand_1 45.61 51.15 0.522 468s demand_2 -48.28 -49.03 -0.989 468s demand_3 145.85 149.47 4.525 468s demand_4 91.75 91.66 3.737 468s demand_5 141.95 157.60 7.112 468s demand_6 85.15 91.67 5.083 468s demand_7 106.49 108.97 7.223 468s demand_8 -217.81 -221.85 -16.164 468s demand_9 -68.09 -76.62 -6.344 468s demand_10 149.95 169.69 16.867 468s demand_11 -4.89 -5.28 -0.717 468s demand_12 -118.44 -105.66 -18.482 468s demand_13 -93.44 -78.31 -14.359 468s demand_14 -16.65 -14.96 -2.573 468s demand_15 138.05 136.98 20.085 468s demand_16 -232.06 -231.84 -35.327 468s demand_17 -8.06 -9.24 -1.421 468s demand_18 -148.74 -131.79 -25.645 468s demand_19 113.10 91.24 19.412 468s demand_20 -61.44 -44.96 -9.668 468s supply_1 15.64 17.53 0.179 468s supply_2 -147.18 -149.44 -3.016 468s supply_3 -85.67 -87.79 -2.658 468s supply_4 -44.08 -44.04 -1.796 468s supply_5 -129.90 -144.21 -6.508 468s supply_6 -69.22 -74.52 -4.132 468s supply_7 -142.17 -145.48 -9.644 468s supply_8 260.69 265.53 19.346 468s supply_9 168.45 189.55 15.694 468s supply_10 -155.14 -175.56 -17.451 468s supply_11 80.79 87.14 11.833 468s supply_12 35.57 31.73 5.551 468s supply_13 121.14 101.52 18.615 468s supply_14 -109.33 -98.23 -16.894 468s supply_15 -268.57 -266.49 -39.075 468s supply_16 201.22 201.03 30.633 468s supply_17 -113.05 -129.59 -19.937 468s supply_18 396.95 351.71 68.440 468s supply_19 33.69 27.18 5.782 468s supply_20 -49.83 -36.46 -7.841 468s > round( colSums( estfun( fitsuri1e ) ), digits = 7 ) 468s demand_(Intercept) demand_price demand_income supply_(Intercept) 468s 0 0 0 0 468s supply_income supply_farmPrice supply_trend 468s 0 0 0 468s > 468s > estfun( fitsuri1wr3 ) 468s demand_(Intercept) demand_price demand_income supply_(Intercept) 468s demand_1 0.5102 51.19 44.59 0.4867 468s demand_2 -0.4886 -50.94 -47.68 -0.4661 468s demand_3 1.4782 152.90 142.94 1.4102 468s demand_4 0.9143 95.55 89.79 0.8722 468s demand_5 1.3982 137.03 139.54 1.3339 468s demand_6 0.8327 82.82 83.69 0.7944 468s demand_7 1.0134 102.42 104.59 0.9668 468s demand_8 -1.9849 -207.94 -213.97 -1.8935 468s demand_9 -0.6897 -66.52 -66.63 -0.6580 468s demand_10 1.6602 151.46 147.60 1.5838 468s demand_11 -0.0636 -5.92 -4.77 -0.0606 468s demand_12 -1.5152 -149.71 -116.52 -1.4455 468s demand_13 -1.0888 -112.05 -92.11 -1.0387 468s demand_14 -0.1809 -17.86 -16.39 -0.1726 468s demand_15 1.3190 125.46 135.99 1.2583 468s demand_16 -2.1651 -213.16 -227.55 -2.0655 468s demand_17 -0.0731 -6.33 -7.05 -0.0698 468s demand_18 -1.4001 -145.63 -146.17 -1.3357 468s demand_19 1.0017 105.95 110.89 0.9556 468s demand_20 -0.4780 -54.25 -60.76 -0.4560 468s supply_1 0.0755 7.57 6.60 0.1193 468s supply_2 -0.8526 -88.90 -83.22 -1.3478 468s supply_3 -0.5074 -52.48 -49.07 -0.8021 468s supply_4 -0.2631 -27.49 -25.83 -0.4159 468s supply_5 -0.7425 -72.77 -74.10 -1.1737 468s supply_6 -0.3998 -39.77 -40.18 -0.6320 468s supply_7 -0.7750 -78.33 -79.98 -1.2251 468s supply_8 1.3178 138.06 142.06 2.0831 468s supply_9 0.9476 91.39 91.54 1.4979 468s supply_10 -0.9683 -88.34 -86.08 -1.5306 468s supply_11 0.6060 56.40 45.51 0.9578 468s supply_12 0.2813 27.79 21.63 0.4446 468s supply_13 0.8170 84.07 69.12 1.2914 468s supply_14 -0.6451 -63.71 -58.44 -1.0197 468s supply_15 -1.4315 -136.17 -147.59 -2.2629 468s supply_16 1.0615 104.50 111.56 1.6779 468s supply_17 -0.6453 -55.82 -62.21 -1.0200 468s supply_18 2.1183 220.33 221.15 3.3484 468s supply_19 0.1946 20.58 21.54 0.3076 468s supply_20 -0.1888 -21.42 -23.99 -0.2984 468s supply_income supply_farmPrice supply_trend 468s demand_1 42.54 47.70 0.487 468s demand_2 -45.49 -46.19 -0.932 468s demand_3 136.37 139.75 4.231 468s demand_4 85.65 85.57 3.489 468s demand_5 133.12 147.79 6.669 468s demand_6 79.84 85.95 4.766 468s demand_7 99.77 102.09 6.768 468s demand_8 -204.12 -207.91 -15.148 468s demand_9 -63.56 -71.52 -5.922 468s demand_10 140.80 159.34 15.838 468s demand_11 -4.55 -4.91 -0.667 468s demand_12 -111.16 -99.16 -17.346 468s demand_13 -87.88 -73.64 -13.503 468s demand_14 -15.63 -14.05 -2.416 468s demand_15 129.73 128.72 18.874 468s demand_16 -217.08 -216.88 -33.048 468s demand_17 -6.73 -7.71 -1.186 468s demand_18 -139.45 -123.55 -24.042 468s demand_19 105.78 85.33 18.156 468s demand_20 -57.96 -42.41 -9.120 468s supply_1 10.43 11.69 0.119 468s supply_2 -131.54 -133.56 -2.696 468s supply_3 -77.56 -79.49 -2.406 468s supply_4 -40.84 -40.80 -1.663 468s supply_5 -117.13 -130.04 -5.868 468s supply_6 -63.52 -68.39 -3.792 468s supply_7 -126.43 -129.37 -8.575 468s supply_8 224.56 228.72 16.665 468s supply_9 144.70 162.82 13.481 468s supply_10 -136.07 -153.98 -15.306 468s supply_11 71.93 77.58 10.536 468s supply_12 34.19 30.50 5.335 468s supply_13 109.25 91.56 16.788 468s supply_14 -92.38 -83.00 -14.276 468s supply_15 -233.30 -231.49 -33.943 468s supply_16 176.34 176.17 26.846 468s supply_17 -98.33 -112.71 -17.341 468s supply_18 349.57 309.73 60.271 468s supply_19 34.05 27.47 5.845 468s supply_20 -37.92 -27.75 -5.967 468s > round( colSums( estfun( fitsuri1wr3 ) ), digits = 7 ) 468s demand_(Intercept) demand_price demand_income supply_(Intercept) 468s 0 0 0 0 468s supply_income supply_farmPrice supply_trend 468s 0 0 0 468s > 468s > estfun( fitsurS1 ) 468s eq1_consump eq2_(Intercept) eq2_consump eq2_trend 468s eq1_1 7.162 0.02160 2.127 0.0216 468s eq1_2 15.562 0.04659 4.621 0.0932 468s eq1_3 6.026 0.01752 1.789 0.0525 468s eq1_4 10.524 0.03079 3.125 0.1232 468s eq1_5 -14.099 -0.04017 -4.187 -0.2008 468s eq1_6 -7.426 -0.02136 -2.205 -0.1282 468s eq1_7 -5.141 -0.01468 -1.527 -0.1028 468s eq1_8 15.138 0.04500 4.495 0.3600 468s eq1_9 -7.596 -0.02248 -2.256 -0.2023 468s eq1_10 -28.217 -0.08150 -8.379 -0.8150 468s eq1_11 -3.498 -0.01088 -1.039 -0.1197 468s eq1_12 17.457 0.05609 5.184 0.6731 468s eq1_13 22.800 0.07162 6.771 0.9311 468s eq1_14 2.479 0.00746 0.736 0.1044 468s eq1_15 -26.446 -0.07423 -7.853 -1.1135 468s eq1_16 -2.054 -0.00609 -0.610 -0.0974 468s eq1_17 -42.973 -0.12327 -12.761 -2.0956 468s eq1_18 13.132 0.03902 3.900 0.7024 468s eq1_19 4.307 0.01216 1.279 0.2310 468s eq1_20 22.866 0.06392 6.790 1.2784 468s eq2_1 -1.322 -0.02928 -2.884 -0.0293 468s eq2_2 -0.971 -0.02136 -2.118 -0.0427 468s eq2_3 -5.293 -0.11298 -11.542 -0.3389 468s eq2_4 -4.273 -0.09180 -9.318 -0.3672 468s eq2_5 1.836 0.03840 4.003 0.1920 468s eq2_6 2.119 0.04477 4.622 0.2686 468s eq2_7 -0.532 -0.01115 -1.160 -0.0781 468s eq2_8 10.068 0.21978 21.956 1.7582 468s eq2_9 9.192 0.19974 20.044 1.7977 468s eq2_10 -0.465 -0.00986 -1.014 -0.0986 468s eq2_11 -2.679 -0.06122 -5.843 -0.6735 468s eq2_12 -6.257 -0.14762 -13.644 -1.7715 468s eq2_13 -7.360 -0.16978 -16.050 -2.2072 468s eq2_14 -5.865 -0.12951 -12.790 -1.8131 468s eq2_15 -0.730 -0.01505 -1.593 -0.2258 468s eq2_16 11.188 0.24342 24.396 3.8947 468s eq2_17 11.047 0.23271 24.091 3.9561 468s eq2_18 3.346 0.07302 7.297 1.3144 468s eq2_19 -7.478 -0.15498 -16.307 -2.9445 468s eq2_20 -5.570 -0.11434 -12.146 -2.2868 468s > round( colSums( estfun( fitsurS1 ) ), digits = 7 ) 468s eq1_consump eq2_(Intercept) eq2_consump eq2_trend 468s 0 0 0 0 468s > 468s > estfun( fitsurS2 ) 468s eq1_price eq2_trend 468s eq1_1 -5.42871 -0.000114 468s eq1_2 -13.14782 -0.000531 468s eq1_3 -4.34907 -0.000266 468s eq1_4 -8.39779 -0.000677 468s eq1_5 12.19030 0.001310 468s eq1_6 6.97176 0.000886 468s eq1_7 5.14513 0.000750 468s eq1_8 -12.72321 -0.002046 468s eq1_9 7.04895 0.001385 468s eq1_10 22.20478 0.005126 468s eq1_11 3.65437 0.000909 468s eq1_12 -15.21951 -0.003893 468s eq1_13 -20.44077 -0.005438 468s eq1_14 -1.31641 -0.000393 468s eq1_15 21.18383 0.007035 468s eq1_16 2.54257 0.000870 468s eq1_17 31.47441 0.013026 468s eq1_18 -10.84129 -0.003951 468s eq1_19 -2.78655 -0.001054 468s eq1_20 -19.91341 -0.007390 468s eq2_1 0.42448 0.037215 468s eq2_2 0.40866 0.068949 468s eq2_3 0.38411 0.097989 468s eq2_4 0.34891 0.117463 468s eq2_5 0.30591 0.137281 468s eq2_6 0.27161 0.144126 468s eq2_7 0.24474 0.149098 468s eq2_8 0.19771 0.132796 468s eq2_9 0.15083 0.123801 468s eq2_10 0.12174 0.117373 468s eq2_11 0.06024 0.062610 468s eq2_12 0.01611 0.017205 468s eq2_13 -0.00856 -0.009507 468s eq2_14 -0.02284 -0.028474 468s eq2_15 -0.02363 -0.032773 468s eq2_16 -0.08383 -0.119831 468s eq2_17 -0.09018 -0.155889 468s eq2_18 -0.16161 -0.245985 468s eq2_19 -0.17473 -0.276076 468s eq2_20 -0.22123 -0.342915 468s > round( colSums( estfun( fitsurS2 ) ), digits = 7 ) 468s eq1_price eq2_trend 468s 0 0 468s > 468s > estfun( fitsurS3 ) 468s eq1_trend eq2_trend 468s eq1_1 2.069 -2.039 468s eq1_2 3.833 -3.777 468s eq1_3 5.448 -5.369 468s eq1_4 6.531 -6.436 468s eq1_5 7.634 -7.523 468s eq1_6 8.015 -7.899 468s eq1_7 8.293 -8.173 468s eq1_8 7.389 -7.281 468s eq1_9 6.890 -6.790 468s eq1_10 6.535 -6.440 468s eq1_11 3.493 -3.443 468s eq1_12 0.972 -0.958 468s eq1_13 -0.510 0.503 468s eq1_14 -1.562 1.539 468s eq1_15 -1.798 1.772 468s eq1_16 -6.634 6.537 468s eq1_17 -8.634 8.509 468s eq1_18Error in estfun.systemfit(fitsurS4) : 468s returning the estimation function for models with restrictions has not yet been impleme -13.639 13.441 468s eq1_19 -15.308 15.085 468s eq1_20 -19.019 18.743 468s eq2_1 -2.082 2.089 468s eq2_2 -4.012 4.027 468s eq2_3 -5.472 5.491 468s eq2_4 -6.736 6.760 468s eq2_5 -6.873 6.897 468s eq2_6 -7.460 7.486 468s eq2_7 -7.809 7.837 468s eq2_8 -8.276 8.305 468s eq2_9 -6.161 6.182 468s eq2_10 -4.039 4.053 468s eq2_11 -3.098 3.109 468s eq2_12 -2.949 2.960 468s eq2_13 -2.261 2.269 468s eq2_14 1.160 -1.164 468s eq2_15 4.921 -4.939 468s eq2_16 6.677 -6.701 468s eq2_17 14.428 -14.479 468s eq2_18 11.167 -11.207 468s eq2_19 14.155 -14.205 468s eq2_20 14.719 -14.771 468s > round( colSums( estfun( fitsurS3 ) ), digits = 7 ) 468s eq1_trend eq2_trend 468s 0 0 468s > 468s > try( estfun( fitsurS4 ) ) 468s nted. 468s > 468s > estfun( fitsurS5 ) 468s eq1_(Intercept) eq2_(Intercept) 468s eq1_1 -0.17267 0.01074 468s eq1_2 -0.12244 0.00761 468s eq1_3 0.09050 -0.00563 468s eq1_4 0.04335 -0.00270 468s eq1_5 0.23912 -0.01487 468s eq1_6 0.16778 -0.01043 468s eq1_7 0.22144 -0.01377 468s eq1_8 -0.07143 0.00444 468s eq1_9 -0.03923 0.00244 468s eq1_10 0.13751 -0.00855 468s eq1_11 -0.39091 0.02431 468s eq1_12 -0.60636 0.03770 468s eq1_13 -0.45531 0.02831 468s eq1_14 -0.15321 0.00953 468s eq1_15 0.35053 -0.02180 468s eq1_16 -0.04817 0.00300 468s eq1_17 0.18774 -0.01167 468s eq1_18 -0.06935 0.00431 468s eq1_19 0.30946 -0.01924 468s eq1_20 0.38165 -0.02373 468s eq2_1 -0.00135 0.00874 468s eq2_2 -0.01889 0.12205 468s eq2_3 -0.01520 0.09821 468s eq2_4 -0.01996 0.12901 468s eq2_5 0.00898 -0.05802 468s eq2_6 0.00251 -0.01619 468s eq2_7 -0.00466 0.03010 468s eq2_8 -0.02111 0.13640 468s eq2_9 0.01590 -0.10273 468s eq2_10 0.03911 -0.25276 468s eq2_11 0.03085 -0.19937 468s eq2_12 0.00542 -0.03502 468s eq2_13 -0.01285 0.08306 468s eq2_14 0.00562 -0.03631 468s eq2_15 0.02180 -0.14088 468s eq2_16 0.00698 -0.04508 468s eq2_17 0.06016 -0.38875 468s eq2_18 -0.01778 0.11492 468s eq2_19 -0.02558 0.16532 468s eq2_20 -0.05994 0.38731 468s > round( colSums( estfun( fitsurS5 ) ), digits = 7 ) 468s eq1_(Intercept) eq2_(Intercept) 468s 0 0 468s > 468s > 468s > ## **************** bread ************************ 468s > round( bread( fitsur1 ), digits = 7 ) 468s demand_(Intercept) demand_price demand_income supply_(Intercept) 468s [1,] 2258.680 -23.5779 1.0971 2354.23 468s [2,] -23.578 0.3134 -0.0796 -15.01 468s [3,] 1.097 -0.0796 0.0704 -8.66 468s [4,] 2354.232 -15.0109 -8.6593 4911.36 468s [5,] -24.454 0.2225 0.0225 -38.45 468s [6,] 0.887 -0.0644 0.0569 -9.51 468s [7,] 1.348 -0.0978 0.0864 -12.94 468s supply_price supply_farmPrice supply_trend 468s [1,] -24.4536 0.8871 1.3477 468s [2,] 0.2225 -0.0644 -0.0978 468s [3,] 0.0225 0.0569 0.0864 468s [4,] -38.4456 -9.5077 -12.9352 468s [5,] 0.3567 0.0252 0.0320 468s [6,] 0.0252 0.0636 0.0807 468s [7,] 0.0320 0.0807 0.1845 468s > 468s > round( bread( fitsur1e2 ), digits = 7 ) 468s demand_(Intercept) demand_price demand_income supply_(Intercept) 468s [1,] 2257.61 -23.5004 1.0286 2442.20 468s [2,] -23.50 0.3077 -0.0746 -16.15 468s [3,] 1.03 -0.0746 0.0660 -8.39 468s [4,] 2442.20 -16.1480 -8.3922 4816.72 468s [5,] -25.30 0.2317 0.0218 -38.19 468s [6,] 0.86 -0.0624 0.0552 -8.86 468s [7,] 1.31 -0.0948 0.0838 -12.35 468s supply_price supply_farmPrice supply_trend 468s [1,] -25.2995 0.8598 1.3061 468s [2,] 0.2317 -0.0624 -0.0948 468s [3,] 0.0218 0.0552 0.0838 468s [4,] -38.1886 -8.8582 -12.3470 468s [5,] 0.3560 0.0234 0.0309 468s [6,] 0.0234 0.0590 0.0780 468s [7,] 0.0309 0.0780 0.1640 468s > 468s > round( bread( fitsur1r3 ), digits = 7 ) 468s demand_(Intercept) demand_price demand_income supply_(Intercept) 468s [1,] 2257.728 -23.5088 1.0361 2434.43 468s [2,] -23.509 0.3083 -0.0752 -16.03 468s [3,] 1.036 -0.0752 0.0665 -8.43 468s [4,] 2434.429 -16.0346 -8.4292 4826.83 468s [5,] -25.226 0.2308 0.0219 -38.22 468s [6,] 0.864 -0.0627 0.0554 -8.93 468s [7,] 1.312 -0.0952 0.0842 -12.42 468s supply_price supply_farmPrice supply_trend 468s [1,] -25.2264 0.8636 1.3118 468s [2,] 0.2308 -0.0627 -0.0952 468s [3,] 0.0219 0.0554 0.0842 468s [4,] -38.2158 -8.9270 -12.4169 468s [5,] 0.3561 0.0235 0.0310 468s [6,] 0.0235 0.0595 0.0784 468s [7,] 0.0310 0.0784 0.1660 468s > 468s > round( bread( fitsur1w ), digits = 7 ) 468s demand_(Intercept) demand_price demand_income supply_(Intercept) 468s [1,] 2258.680 -23.5779 1.0971 2354.23 468s [2,] -23.578 0.3134 -0.0796 -15.01 468s [3,] 1.097 -0.0796 0.0704 -8.66 468s [4,] 2354.232 -15.0109 -8.6593 4911.36 468s [5,] -24.454 0.2225 0.0225 -38.45 468s [6,] 0.887 -0.0644 0.0569 -9.51 468s [7,] 1.348 -0.0978 0.0864 -12.94 468s supply_price supply_farmPrice supply_trend 468s [1,] -24.4536 0.8871 1.3477 468s [2,] 0.2225 -0.0644 -0.0978 468s [3,] 0.0225 0.0569 0.0864 468s [4,] -38.4456 -9.5077 -12.9352 468s [5,] 0.3567 0.0252 0.0320 468s [6,] 0.0252 0.0636 0.0807 468s [7,] 0.0320 0.0807 0.1845 468s > 468s > round( bread( fitsuri1e ), digits = 7 ) 468s demand_(Intercept) demand_price demand_income supply_(Intercept) 468s [1,] 1876.862 -19.2519 0.5677 -81.89 468s [2,] -19.252 0.2661 -0.0755 -2.81 468s [3,] 0.568 -0.0755 0.0716 3.68 468s [4,] -81.887 -2.8102 3.6811 363.96 468s [5,] 7.186 -0.0595 -0.0127 -1.84 468s [6,] -5.538 0.0766 -0.0217 -1.67 468s [7,] -8.357 0.1155 -0.0328 -1.82 468s supply_income supply_farmPrice supply_trend 468s [1,] 7.1857 -5.5385 -8.3572 468s [2,] -0.0595 0.0766 0.1155 468s [3,] -0.0127 -0.0217 -0.0328 468s [4,] -1.8380 -1.6714 -1.8169 468s [5,] 0.0569 -0.0327 -0.0527 468s [6,] -0.0327 0.0441 0.0571 468s [7,] -0.0527 0.0571 0.1367 468s > 468s > round( bread( fitsuri1wr3 ), digits = 7 ) 468s demand_(Intercept) demand_price demand_income supply_(Intercept) 468s [1,] 2182.020 -22.2793 0.5557 -108.13 468s [2,] -22.279 0.3080 -0.0874 -3.49 468s [3,] 0.556 -0.0874 0.0839 4.64 468s [4,] -108.127 -3.4932 4.6397 458.64 468s [5,] 8.996 -0.0739 -0.0164 -2.35 468s [6,] -6.884 0.0952 -0.0270 -2.07 468s [7,] -10.388 0.1436 -0.0408 -2.31 468s supply_income supply_farmPrice supply_trend 468s [1,] 8.9961 -6.8844 -10.3882 468s [2,] -0.0739 0.0952 0.1436 468s [3,] -0.0164 -0.0270 -0.0408 468s [4,] -2.3500 -2.0691 -2.3134 468s [5,] 0.0715 -0.0407 -0.0653 468s [6,] -0.0407 0.0547 0.0717 468s [7,] -0.0653 0.0717 0.1662 468s > 468s > round( bread( fitsurS1 ), digits = 7 ) 468s eq1_consump eq2_(Intercept) eq2_consump eq2_trend 468s [1,] 0.00876 0.0 -4.02e-03 0.000 468s [2,] 0.00000 91218.4 -9.08e+02 48.892 468s [3,] -0.00402 -908.0 9.09e+00 -0.866 468s [4,] 0.00000 48.9 -8.66e-01 3.664 468s > 468s > round( bread( fitsurS2 ), digits = 7 ) 468s eq1_price eq2_trend 468s [1,] 0.00903 -0.00752 468s [2,] -0.00752 34.11430 468s > 468s > round( bread( fitsurS3 ), digits = 7 ) 468s eq1_trend eq2_trend 468s [1,] 34.1 34.0 468s [2,] 34.0 34.5 468s > 468s > try( bread( fitsurS4 ) ) 468s > 468s BEGIN TEST test_w2sls.R 468s Error in bread.systemfit(fitsurS4) : 468s returning the 'bread' for models with restrictions has not yet been implemented. 468s 468s R version 4.3.3 (2024-02-29) -- "Angel Food Cake" 468s Copyright (C) 2024 The R Foundation for Statistical Computing 468s Platform: s390x-ibm-linux-gnu (64-bit) 468s 468s R is free software and comes with ABSOLUTELY NO WARRANTY. 468s You are welcome to redistribute it under certain conditions. 468s Type 'license()' or 'licence()' for distribution details. 468s 468s R is a collaborative project with many contributors. 468s Type 'contributors()' for more information and 468s 'citation()' on how to cite R or R packages in publications. 468s 468s Type 'demo()' for some demos, 'help()' for on-line help, or 468s 'help.start()' for an HTML browser interface to help. 468s Type 'q()' to quit R. 468s 468s > library( systemfit ) 468s Loading required package: Matrix 469s Loading required package: car 469s Loading required package: carData 469s Loading required package: lmtest 469s Loading required package: zoo 469s 469s Attaching package: ‘zoo’ 469s 469s The following objects are masked from ‘package:base’: 469s 469s as.Date, as.Date.numeric 469s 470s 470s Please cite the 'systemfit' package as: 470s 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/. 470s 470s If you have questions, suggestions, or comments regarding the 'systemfit' package, please use a forum or 'tracker' at systemfit's R-Forge site: 470s https://r-forge.r-project.org/projects/systemfit/ 470s > options( digits = 3 ) 470s > 470s > data( "Kmenta" ) 470s > useMatrix <- FALSE 470s > 470s > demand <- consump ~ price + income 470s > supply <- consump ~ price + farmPrice + trend 470s > inst <- ~ income + farmPrice + trend 470s > inst1 <- ~ income + farmPrice 470s > instlist <- list( inst1, inst ) 470s > system <- list( demand = demand, supply = supply ) 470s > restrm <- matrix(0,1,7) # restriction matrix "R" 470s > restrm[1,3] <- 1 470s > restrm[1,7] <- -1 470s > restrict <- "demand_income - supply_trend = 0" 470s > restr2m <- matrix(0,2,7) # restriction matrix "R" 2 470s > restr2m[1,3] <- 1 470s > restr2m[1,7] <- -1 470s > restr2m[2,2] <- -1 470s > restr2m[2,5] <- 1 470s > restr2q <- c( 0, 0.5 ) # restriction vector "q" 2 470s > restrict2 <- c( "demand_income - supply_trend = 0", 470s + "- demand_price + supply_price = 0.5" ) 470s > tc <- matrix(0,7,6) 470s > tc[1,1] <- 1 470s > tc[2,2] <- 1 470s > tc[3,3] <- 1 470s > tc[4,4] <- 1 470s > tc[5,5] <- 1 470s > tc[6,6] <- 1 470s > tc[7,3] <- 1 470s > restr3m <- matrix(0,1,6) # restriction matrix "R" 2 470s > restr3m[1,2] <- -1 470s > restr3m[1,5] <- 1 470s > restr3q <- c( 0.5 ) # restriction vector "q" 2 470s > restrict3 <- "- C2 + C5 = 0.5" 470s > 470s > 470s > ## ********************* W2SLS ***************** 470s > fitw2sls1 <- systemfit( system, "W2SLS", data = Kmenta, inst = inst, 470s + useMatrix = useMatrix ) 470s > print( summary( fitw2sls1 ) ) 470s 470s systemfit results 470s method: W2SLS 470s 470s N DF SSR detRCov OLS-R2 McElroy-R2 470s system 40 33 162 4.36 0.697 0.548 470s 470s N DF SSR MSE RMSE R2 Adj R2 470s demand 20 17 65.7 3.87 1.97 0.755 0.726 470s supply 20 16 96.6 6.04 2.46 0.640 0.572 470s 470s The covariance matrix of the residuals used for estimation 470s demand supply 470s demand 3.87 0.00 470s supply 0.00 6.04 470s 470s The covariance matrix of the residuals 470s demand supply 470s demand 3.87 4.36 470s supply 4.36 6.04 470s 470s The correlations of the residuals 470s demand supply 470s demand 1.000 0.902 470s supply 0.902 1.000 470s 470s 470s W2SLS estimates for 'demand' (equation 1) 470s Model Formula: consump ~ price + income 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 94.6333 7.9208 11.95 1.1e-09 *** 470s price -0.2436 0.0965 -2.52 0.022 * 470s income 0.3140 0.0469 6.69 3.8e-06 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 1.966 on 17 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 17 470s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 470s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 470s 470s 470s W2SLS estimates for 'supply' (equation 2) 470s Model Formula: consump ~ price + farmPrice + trend 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 49.5324 12.0105 4.12 0.0008 *** 470s price 0.2401 0.0999 2.40 0.0288 * 470s farmPrice 0.2556 0.0473 5.41 5.8e-05 *** 470s trend 0.2529 0.0997 2.54 0.0219 * 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 2.458 on 16 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 16 470s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 470s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 470s 470s > 470s > ## ********************* W2SLS (EViews-like) ***************** 470s > fitw2sls1e <- systemfit( system, "W2SLS", data = Kmenta, inst = inst, 470s + methodResidCov = "noDfCor", x = TRUE, 470s + useMatrix = useMatrix ) 470s > print( summary( fitw2sls1e, useDfSys = TRUE ) ) 470s 470s systemfit results 470s method: W2SLS 470s 470s N DF SSR detRCov OLS-R2 McElroy-R2 470s system 40 33 162 2.97 0.697 0.525 470s 470s N DF SSR MSE RMSE R2 Adj R2 470s demand 20 17 65.7 3.87 1.97 0.755 0.726 470s supply 20 16 96.6 6.04 2.46 0.640 0.572 470s 470s The covariance matrix of the residuals used for estimation 470s demand supply 470s demand 3.29 0.00 470s supply 0.00 4.83 470s 470s The covariance matrix of the residuals 470s demand supply 470s demand 3.29 3.59 470s supply 3.59 4.83 470s 470s The correlations of the residuals 470s demand supply 470s demand 1.000 0.902 470s supply 0.902 1.000 470s 470s 470s W2SLS estimates for 'demand' (equation 1) 470s Model Formula: consump ~ price + income 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 94.6333 7.3027 12.96 1.7e-14 *** 470s price -0.2436 0.0890 -2.74 0.0099 ** 470s income 0.3140 0.0433 7.25 2.5e-08 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 1.966 on 17 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 17 470s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 470s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 470s 470s 470s W2SLS estimates for 'supply' (equation 2) 470s Model Formula: consump ~ price + farmPrice + trend 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 49.5324 10.7425 4.61 5.8e-05 *** 470s price 0.2401 0.0894 2.69 0.0112 * 470s farmPrice 0.2556 0.0423 6.05 8.4e-07 *** 470s trend 0.2529 0.0891 2.84 0.0077 ** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 2.458 on 16 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 16 470s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 470s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 470s 470s > 470s > ## ********************* W2SLS with restriction ******************* 470s > fitw2sls2 <- systemfit( system, "W2SLS", data = Kmenta, restrict.matrix = restrm, 470s + inst = inst, useMatrix = useMatrix ) 470s > print( summary( fitw2sls2 ) ) 470s 470s systemfit results 470s method: W2SLS 470s 470s N DF SSR detRCov OLS-R2 McElroy-R2 470s system 40 34 165 3.41 0.692 0.565 470s 470s N DF SSR MSE RMSE R2 Adj R2 470s demand 20 17 66.8 3.93 1.98 0.751 0.721 470s supply 20 16 98.4 6.15 2.48 0.633 0.564 470s 470s The covariance matrix of the residuals used for estimation 470s demand supply 470s demand 3.97 0.00 470s supply 0.00 6.13 470s 470s The covariance matrix of the residuals 470s demand supply 470s demand 3.93 4.56 470s supply 4.56 6.15 470s 470s The correlations of the residuals 470s demand supply 470s demand 1.000 0.927 470s supply 0.927 1.000 470s 470s 470s W2SLS estimates for 'demand' (equation 1) 470s Model Formula: consump ~ price + income 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 94.3832 8.0090 11.78 1.5e-13 *** 470s price -0.2302 0.0946 -2.43 0.02 * 470s income 0.3028 0.0430 7.05 3.9e-08 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 1.983 on 17 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 17 470s SSR: 66.838 MSE: 3.932 Root MSE: 1.983 470s Multiple R-Squared: 0.751 Adjusted R-Squared: 0.721 470s 470s 470s W2SLS estimates for 'supply' (equation 2) 470s Model Formula: consump ~ price + farmPrice + trend 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 48.0494 11.8001 4.07 0.00026 *** 470s price 0.2430 0.1006 2.42 0.02122 * 470s farmPrice 0.2625 0.0459 5.72 2.0e-06 *** 470s trend 0.3028 0.0430 7.05 3.9e-08 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 2.48 on 16 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 16 470s SSR: 98.445 MSE: 6.153 Root MSE: 2.48 470s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.564 470s 470s > # the same with symbolically specified restrictions 470s > fitw2sls2Sym <- systemfit( system, "W2SLS", data = Kmenta, 470s + restrict.matrix = restrict, inst = inst, useMatrix = useMatrix ) 470s > all.equal( fitw2sls2, fitw2sls2Sym ) 470s [1] "Component “call”: target, current do not match when deparsed" 470s > 470s > ## ********************* W2SLS with restriction (EViews-like) ************** 470s > fitw2sls2e <- systemfit( system, "W2SLS", data = Kmenta, restrict.matrix = restrm, 470s + inst = inst, methodResidCov = "noDfCor", x = TRUE, 470s + useMatrix = useMatrix ) 470s > print( summary( fitw2sls2e, useDfSys = TRUE ) ) 470s 470s systemfit results 470s method: W2SLS 470s 470s N DF SSR detRCov OLS-R2 McElroy-R2 470s system 40 34 165 2.33 0.692 0.535 470s 470s N DF SSR MSE RMSE R2 Adj R2 470s demand 20 17 66.9 3.94 1.98 0.750 0.721 470s supply 20 16 98.4 6.15 2.48 0.633 0.564 470s 470s The covariance matrix of the residuals used for estimation 470s demand supply 470s demand 3.37 0.00 470s supply 0.00 4.91 470s 470s The covariance matrix of the residuals 470s demand supply 470s demand 3.35 3.76 470s supply 3.76 4.92 470s 470s The correlations of the residuals 470s demand supply 470s demand 1.000 0.926 470s supply 0.926 1.000 470s 470s 470s W2SLS estimates for 'demand' (equation 1) 470s Model Formula: consump ~ price + income 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 94.3706 7.3834 12.78 1.6e-14 *** 470s price -0.2295 0.0871 -2.63 0.013 * 470s income 0.3022 0.0394 7.67 6.4e-09 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 1.984 on 17 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 17 470s SSR: 66.906 MSE: 3.936 Root MSE: 1.984 470s Multiple R-Squared: 0.75 Adjusted R-Squared: 0.721 470s 470s 470s W2SLS estimates for 'supply' (equation 2) 470s Model Formula: consump ~ price + farmPrice + trend 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 48.0661 10.5574 4.55 6.5e-05 *** 470s price 0.2430 0.0900 2.70 0.011 * 470s farmPrice 0.2624 0.0411 6.39 2.7e-07 *** 470s trend 0.3022 0.0394 7.67 6.4e-09 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 2.48 on 16 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 16 470s SSR: 98.408 MSE: 6.15 Root MSE: 2.48 470s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.564 470s 470s > nobs( fitw2sls2e ) 470s [1] 40 470s > 470s > ## ********************* W2SLS with restriction via restrict.regMat ******************* 470s > fitw2sls3 <- systemfit( system, "W2SLS", data = Kmenta, restrict.regMat = tc, 470s + inst = inst, x = TRUE, useMatrix = useMatrix ) 470s > print( summary( fitw2sls3 ) ) 470s 470s systemfit results 470s method: W2SLS 470s 470s N DF SSR detRCov OLS-R2 McElroy-R2 470s system 40 34 165 3.41 0.692 0.565 470s 470s N DF SSR MSE RMSE R2 Adj R2 470s demand 20 17 66.8 3.93 1.98 0.751 0.721 470s supply 20 16 98.4 6.15 2.48 0.633 0.564 470s 470s The covariance matrix of the residuals used for estimation 470s demand supply 470s demand 3.97 0.00 470s supply 0.00 6.13 470s 470s The covariance matrix of the residuals 470s demand supply 470s demand 3.93 4.56 470s supply 4.56 6.15 470s 470s The correlations of the residuals 470s demand supply 470s demand 1.000 0.927 470s supply 0.927 1.000 470s 470s 470s W2SLS estimates for 'demand' (equation 1) 470s Model Formula: consump ~ price + income 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 94.3832 8.0090 11.78 1.5e-13 *** 470s price -0.2302 0.0946 -2.43 0.02 * 470s income 0.3028 0.0430 7.05 3.9e-08 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 1.983 on 17 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 17 470s SSR: 66.838 MSE: 3.932 Root MSE: 1.983 470s Multiple R-Squared: 0.751 Adjusted R-Squared: 0.721 470s 470s 470s W2SLS estimates for 'supply' (equation 2) 470s Model Formula: consump ~ price + farmPrice + trend 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 48.0494 11.8001 4.07 0.00026 *** 470s price 0.2430 0.1006 2.42 0.02122 * 470s farmPrice 0.2625 0.0459 5.72 2.0e-06 *** 470s trend 0.3028 0.0430 7.05 3.9e-08 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 2.48 on 16 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 16 470s SSR: 98.445 MSE: 6.153 Root MSE: 2.48 470s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.564 470s 470s > 470s > ## ********************* W2SLS with restriction via restrict.regMat (EViews-like) ************** 470s > fitw2sls3e <- systemfit( system, "W2SLS", data = Kmenta, restrict.regMat = tc, 470s + inst = inst, methodResidCov = "noDfCor", useMatrix = useMatrix ) 470s > print( summary( fitw2sls3e, useDfSys = TRUE ) ) 470s 470s systemfit results 470s method: W2SLS 470s 470s N DF SSR detRCov OLS-R2 McElroy-R2 470s system 40 34 165 2.33 0.692 0.535 470s 470s N DF SSR MSE RMSE R2 Adj R2 470s demand 20 17 66.9 3.94 1.98 0.750 0.721 470s supply 20 16 98.4 6.15 2.48 0.633 0.564 470s 470s The covariance matrix of the residuals used for estimation 470s demand supply 470s demand 3.37 0.00 470s supply 0.00 4.91 470s 470s The covariance matrix of the residuals 470s demand supply 470s demand 3.35 3.76 470s supply 3.76 4.92 470s 470s The correlations of the residuals 470s demand supply 470s demand 1.000 0.926 470s supply 0.926 1.000 470s 470s 470s W2SLS estimates for 'demand' (equation 1) 470s Model Formula: consump ~ price + income 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 94.3706 7.3834 12.78 1.6e-14 *** 470s price -0.2295 0.0871 -2.63 0.013 * 470s income 0.3022 0.0394 7.67 6.4e-09 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 1.984 on 17 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 17 470s SSR: 66.906 MSE: 3.936 Root MSE: 1.984 470s Multiple R-Squared: 0.75 Adjusted R-Squared: 0.721 470s 470s 470s W2SLS estimates for 'supply' (equation 2) 470s Model Formula: consump ~ price + farmPrice + trend 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 48.0661 10.5574 4.55 6.5e-05 *** 470s price 0.2430 0.0900 2.70 0.011 * 470s farmPrice 0.2624 0.0411 6.39 2.7e-07 *** 470s trend 0.3022 0.0394 7.67 6.4e-09 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 2.48 on 16 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 16 470s SSR: 98.408 MSE: 6.15 Root MSE: 2.48 470s Multiple R-Squared: 0.633 Adjusted R-Squared: 0.564 470s 470s > 470s > ## ***************** W2SLS with 2 restrictions ******************** 470s > fitw2sls4 <- systemfit( system, "W2SLS", data = Kmenta, restrict.matrix = restr2m, 470s + restrict.rhs = restr2q, inst = inst, x = TRUE, 470s + useMatrix = useMatrix ) 470s > print( summary( fitw2sls4 ) ) 470s 470s systemfit results 470s method: W2SLS 470s 470s N DF SSR detRCov OLS-R2 McElroy-R2 470s system 40 35 166 3.57 0.69 0.575 470s 470s N DF SSR MSE RMSE R2 Adj R2 470s demand 20 17 65.9 3.88 1.97 0.754 0.725 470s supply 20 16 100.3 6.27 2.50 0.626 0.556 470s 470s The covariance matrix of the residuals used for estimation 470s demand supply 470s demand 3.89 0.00 470s supply 0.00 6.25 470s 470s The covariance matrix of the residuals 470s demand supply 470s demand 3.88 4.55 470s supply 4.55 6.27 470s 470s The correlations of the residuals 470s demand supply 470s demand 1.000 0.924 470s supply 0.924 1.000 470s 470s 470s W2SLS estimates for 'demand' (equation 1) 470s Model Formula: consump ~ price + income 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 95.3043 6.3056 15.11 < 2e-16 *** 470s price -0.2428 0.0684 -3.55 0.0011 ** 470s income 0.3063 0.0394 7.78 3.9e-09 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 1.969 on 17 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 17 470s SSR: 65.931 MSE: 3.878 Root MSE: 1.969 470s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 470s 470s 470s W2SLS estimates for 'supply' (equation 2) 470s Model Formula: consump ~ price + farmPrice + trend 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 46.4229 8.3296 5.57 2.8e-06 *** 470s price 0.2572 0.0684 3.76 0.00062 *** 470s farmPrice 0.2642 0.0455 5.80 1.4e-06 *** 470s trend 0.3063 0.0394 7.78 3.9e-09 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 2.503 on 16 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 16 470s SSR: 100.255 MSE: 6.266 Root MSE: 2.503 470s Multiple R-Squared: 0.626 Adjusted R-Squared: 0.556 470s 470s > # the same with symbolically specified restrictions 470s > fitw2sls4Sym <- systemfit( system, "W2SLS", data = Kmenta, 470s + restrict.matrix = restrict2, inst = inst, x = TRUE, 470s + useMatrix = useMatrix ) 470s > all.equal( fitw2sls4, fitw2sls4Sym ) 470s [1] "Component “call”: target, current do not match when deparsed" 470s > 470s > ## ***************** W2SLS with 2 restrictions (EViews-like) ************** 470s > fitw2sls4e <- systemfit( system, "W2SLS", data = Kmenta, restrict.matrix = restr2m, 470s + restrict.rhs = restr2q, inst = inst, methodResidCov = "noDfCor", 470s + useMatrix = useMatrix ) 470s > print( summary( fitw2sls4e, useDfSys = TRUE ) ) 470s 470s systemfit results 470s method: W2SLS 470s 470s N DF SSR detRCov OLS-R2 McElroy-R2 470s system 40 35 166 2.44 0.69 0.546 470s 470s N DF SSR MSE RMSE R2 Adj R2 470s demand 20 17 65.9 3.88 1.97 0.754 0.725 470s supply 20 16 100.2 6.26 2.50 0.626 0.556 470s 470s The covariance matrix of the residuals used for estimation 470s demand supply 470s demand 3.3 0 470s supply 0.0 5 470s 470s The covariance matrix of the residuals 470s demand supply 470s demand 3.30 3.75 470s supply 3.75 5.01 470s 470s The correlations of the residuals 470s demand supply 470s demand 1.000 0.923 470s supply 0.923 1.000 470s 470s 470s W2SLS estimates for 'demand' (equation 1) 470s Model Formula: consump ~ price + income 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 95.3470 5.7579 16.56 < 2e-16 *** 470s price -0.2428 0.0621 -3.91 0.00041 *** 470s income 0.3059 0.0360 8.49 5.1e-10 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 1.97 on 17 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 17 470s SSR: 65.945 MSE: 3.879 Root MSE: 1.97 470s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 470s 470s 470s W2SLS estimates for 'supply' (equation 2) 470s Model Formula: consump ~ price + farmPrice + trend 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 46.4366 7.5360 6.16 4.7e-07 *** 470s price 0.2572 0.0621 4.14 0.00021 *** 470s farmPrice 0.2642 0.0407 6.48 1.8e-07 *** 470s trend 0.3059 0.0360 8.49 5.1e-10 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 2.503 on 16 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 16 470s SSR: 100.225 MSE: 6.264 Root MSE: 2.503 470s Multiple R-Squared: 0.626 Adjusted R-Squared: 0.556 470s 470s > 470s > ## ***************** W2SLS with 2 restrictions via R and restrict.regMat ****************** 470s > fitw2sls5 <- systemfit( system, "W2SLS", data = Kmenta, restrict.matrix = restr3m, 470s + restrict.rhs = restr3q, restrict.regMat = tc, inst = inst, 470s + x = TRUE, useMatrix = useMatrix ) 470s > print( summary( fitw2sls5 ) ) 470s 470s systemfit results 470s method: W2SLS 470s 470s N DF SSR detRCov OLS-R2 McElroy-R2 470s system 40 35 166 3.57 0.69 0.575 470s 470s N DF SSR MSE RMSE R2 Adj R2 470s demand 20 17 65.9 3.88 1.97 0.754 0.725 470s supply 20 16 100.3 6.27 2.50 0.626 0.556 470s 470s The covariance matrix of the residuals used for estimation 470s demand supply 470s demand 3.89 0.00 470s supply 0.00 6.25 470s 470s The covariance matrix of the residuals 470s demand supply 470s demand 3.88 4.55 470s supply 4.55 6.27 470s 470s The correlations of the residuals 470s demand supply 470s demand 1.000 0.924 470s supply 0.924 1.000 470s 470s 470s W2SLS estimates for 'demand' (equation 1) 470s Model Formula: consump ~ price + income 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 95.3043 6.3056 15.11 < 2e-16 *** 470s price -0.2428 0.0684 -3.55 0.0011 ** 470s income 0.3063 0.0394 7.78 3.9e-09 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 1.969 on 17 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 17 470s SSR: 65.931 MSE: 3.878 Root MSE: 1.969 470s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 470s 470s 470s W2SLS estimates for 'supply' (equation 2) 470s Model Formula: consump ~ price + farmPrice + trend 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 46.4229 8.3296 5.57 2.8e-06 *** 470s price 0.2572 0.0684 3.76 0.00062 *** 470s farmPrice 0.2642 0.0455 5.80 1.4e-06 *** 470s trend 0.3063 0.0394 7.78 3.9e-09 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 2.503 on 16 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 16 470s SSR: 100.255 MSE: 6.266 Root MSE: 2.503 470s Multiple R-Squared: 0.626 Adjusted R-Squared: 0.556 470s 470s > # the same with symbolically specified restrictions 470s > fitw2sls5Sym <- systemfit( system, "W2SLS", data = Kmenta, 470s + restrict.matrix = restrict3, restrict.regMat = tc, inst = inst, 470s + x = TRUE, useMatrix = useMatrix ) 470s > all.equal( fitw2sls5, fitw2sls5Sym ) 470s [1] "Component “call”: target, current do not match when deparsed" 470s > 470s > ## ***************** W2SLS with 2 restrictions via R and restrict.regMat (EViews-like) ************** 470s > fitw2sls5e <- systemfit( system, "W2SLS", data = Kmenta, restrict.matrix = restr3m, 470s + restrict.rhs = restr3q, restrict.regMat = tc, inst = inst, 470s + methodResidCov = "noDfCor", useMatrix = useMatrix ) 470s > print( summary( fitw2sls5e, useDfSys = TRUE ) ) 470s 470s systemfit results 470s method: W2SLS 470s 470s N DF SSR detRCov OLS-R2 McElroy-R2 470s system 40 35 166 2.44 0.69 0.546 470s 470s N DF SSR MSE RMSE R2 Adj R2 470s demand 20 17 65.9 3.88 1.97 0.754 0.725 470s supply 20 16 100.2 6.26 2.50 0.626 0.556 470s 470s The covariance matrix of the residuals used for estimation 470s demand supply 470s demand 3.3 0 470s supply 0.0 5 470s 470s The covariance matrix of the residuals 470s demand supply 470s demand 3.30 3.75 470s supply 3.75 5.01 470s 470s The correlations of the residuals 470s demand supply 470s demand 1.000 0.923 470s supply 0.923 1.000 470s 470s 470s W2SLS estimates for 'demand' (equation 1) 470s Model Formula: consump ~ price + income 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 95.3470 5.7579 16.56 < 2e-16 *** 470s price -0.2428 0.0621 -3.91 0.00041 *** 470s income 0.3059 0.0360 8.49 5.1e-10 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 1.97 on 17 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 17 470s SSR: 65.945 MSE: 3.879 Root MSE: 1.97 470s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 470s 470s 470s W2SLS estimates for 'supply' (equation 2) 470s Model Formula: consump ~ price + farmPrice + trend 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 46.4366 7.5360 6.16 4.7e-07 *** 470s price 0.2572 0.0621 4.14 0.00021 *** 470s farmPrice 0.2642 0.0407 6.48 1.8e-07 *** 470s trend 0.3059 0.0360 8.49 5.1e-10 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 2.503 on 16 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 16 470s SSR: 100.225 MSE: 6.264 Root MSE: 2.503 470s Multiple R-Squared: 0.626 Adjusted R-Squared: 0.556 470s 470s > 470s > ## ****** 2SLS estimation with different instruments ********************** 470s > fitw2slsd1 <- systemfit( system, "W2SLS", data = Kmenta, inst = instlist, 470s + useMatrix = useMatrix ) 470s > print( summary( fitw2slsd1 ) ) 470s 470s systemfit results 470s method: W2SLS 470s 470s N DF SSR detRCov OLS-R2 McElroy-R2 470s system 40 33 164 9.25 0.694 0.512 470s 470s N DF SSR MSE RMSE R2 Adj R2 470s demand 20 17 67.4 3.97 1.99 0.748 0.719 470s supply 20 16 96.6 6.04 2.46 0.640 0.572 470s 470s The covariance matrix of the residuals used for estimation 470s demand supply 470s demand 3.97 0.00 470s supply 0.00 6.04 470s 470s The covariance matrix of the residuals 470s demand supply 470s demand 3.97 3.84 470s supply 3.84 6.04 470s 470s The correlations of the residuals 470s demand supply 470s demand 1.000 0.784 470s supply 0.784 1.000 470s 470s 470s W2SLS estimates for 'demand' (equation 1) 470s Model Formula: consump ~ price + income 470s Instruments: ~income + farmPrice 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 106.7894 11.1435 9.58 2.9e-08 *** 470s price -0.4116 0.1448 -2.84 0.011 * 470s income 0.3617 0.0564 6.41 6.4e-06 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 1.992 on 17 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 17 470s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 470s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 470s 470s 470s W2SLS estimates for 'supply' (equation 2) 470s Model Formula: consump ~ price + farmPrice + trend 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 49.5324 12.0105 4.12 0.0008 *** 470s price 0.2401 0.0999 2.40 0.0288 * 470s farmPrice 0.2556 0.0473 5.41 5.8e-05 *** 470s trend 0.2529 0.0997 2.54 0.0219 * 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 2.458 on 16 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 16 470s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 470s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 470s 470s > 470s > ## ****** 2SLS estimation with different instruments (EViews-like)****************** 470s > fitw2slsd1e <- systemfit( system, "W2SLS", data = Kmenta, inst = instlist, 470s + methodResidCov = "noDfCor", x = TRUE, 470s + useMatrix = useMatrix ) 470s > print( summary( fitw2slsd1e, useDfSys = TRUE ) ) 470s 470s systemfit results 470s method: W2SLS 470s 470s N DF SSR detRCov OLS-R2 McElroy-R2 470s system 40 33 164 6.29 0.694 0.5 470s 470s N DF SSR MSE RMSE R2 Adj R2 470s demand 20 17 67.4 3.97 1.99 0.748 0.719 470s supply 20 16 96.6 6.04 2.46 0.640 0.572 470s 470s The covariance matrix of the residuals used for estimation 470s demand supply 470s demand 3.37 0.00 470s supply 0.00 4.83 470s 470s The covariance matrix of the residuals 470s demand supply 470s demand 3.37 3.16 470s supply 3.16 4.83 470s 470s The correlations of the residuals 470s demand supply 470s demand 1.000 0.784 470s supply 0.784 1.000 470s 470s 470s W2SLS estimates for 'demand' (equation 1) 470s Model Formula: consump ~ price + income 470s Instruments: ~income + farmPrice 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 106.789 10.274 10.39 6.1e-12 *** 470s price -0.412 0.134 -3.08 0.0041 ** 470s income 0.362 0.052 6.95 6.0e-08 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 1.992 on 17 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 17 470s SSR: 67.447 MSE: 3.967 Root MSE: 1.992 470s Multiple R-Squared: 0.748 Adjusted R-Squared: 0.719 470s 470s 470s W2SLS estimates for 'supply' (equation 2) 470s Model Formula: consump ~ price + farmPrice + trend 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 49.5324 10.7425 4.61 5.8e-05 *** 470s price 0.2401 0.0894 2.69 0.0112 * 470s farmPrice 0.2556 0.0423 6.05 8.4e-07 *** 470s trend 0.2529 0.0891 2.84 0.0077 ** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 2.458 on 16 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 16 470s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 470s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 470s 470s > 470s > ## **** W2SLS estimation with different instruments and restriction ******** 470s > fitw2slsd2 <- systemfit( system, "W2SLS", data = Kmenta, restrict.matrix = restrm, 470s + inst = instlist, useMatrix = useMatrix ) 470s > print( summary( fitw2slsd2 ) ) 470s 470s systemfit results 470s method: W2SLS 470s 470s N DF SSR detRCov OLS-R2 McElroy-R2 470s system 40 34 166 5.11 0.69 0.557 470s 470s N DF SSR MSE RMSE R2 Adj R2 470s demand 20 17 64.8 3.81 1.95 0.758 0.730 470s supply 20 16 101.4 6.34 2.52 0.622 0.551 470s 470s The covariance matrix of the residuals used for estimation 470s demand supply 470s demand 3.79 0.00 470s supply 0.00 6.27 470s 470s The covariance matrix of the residuals 470s demand supply 470s demand 3.81 4.36 470s supply 4.36 6.34 470s 470s The correlations of the residuals 470s demand supply 470s demand 1.000 0.888 470s supply 0.888 1.000 470s 470s 470s W2SLS estimates for 'demand' (equation 1) 470s Model Formula: consump ~ price + income 470s Instruments: ~income + farmPrice 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 104.5695 10.6344 9.83 1.8e-11 *** 470s price -0.3653 0.1327 -2.75 0.0094 ** 470s income 0.3369 0.0485 6.95 5.1e-08 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 1.952 on 17 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 17 470s SSR: 64.776 MSE: 3.81 Root MSE: 1.952 470s Multiple R-Squared: 0.758 Adjusted R-Squared: 0.73 470s 470s 470s W2SLS estimates for 'supply' (equation 2) 470s Model Formula: consump ~ price + farmPrice + trend 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 47.0356 11.9466 3.94 0.00039 *** 470s price 0.2450 0.1017 2.41 0.02156 * 470s farmPrice 0.2672 0.0465 5.74 1.9e-06 *** 470s trend 0.3369 0.0485 6.95 5.1e-08 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 2.518 on 16 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 16 470s SSR: 101.426 MSE: 6.339 Root MSE: 2.518 470s Multiple R-Squared: 0.622 Adjusted R-Squared: 0.551 470s 470s > 470s > ## **** W2SLS estimation with different instruments and restriction (EViews-like)* 470s > fitw2slsd2e <- systemfit( system, "W2SLS", data = Kmenta, restrict.matrix = restrm, 470s + inst = instlist, methodResidCov = "noDfCor", x = TRUE, 470s + useMatrix = useMatrix ) 470s > print( summary( fitw2slsd2e, useDfSys = TRUE ) ) 470s 470s systemfit results 470s method: W2SLS 470s 470s N DF SSR detRCov OLS-R2 McElroy-R2 470s system 40 34 166 3.45 0.69 0.535 470s 470s N DF SSR MSE RMSE R2 Adj R2 470s demand 20 17 64.7 3.81 1.95 0.759 0.730 470s supply 20 16 101.3 6.33 2.52 0.622 0.551 470s 470s The covariance matrix of the residuals used for estimation 470s demand supply 470s demand 3.22 0.00 470s supply 0.00 5.02 470s 470s The covariance matrix of the residuals 470s demand supply 470s demand 3.24 3.60 470s supply 3.60 5.06 470s 470s The correlations of the residuals 470s demand supply 470s demand 1.000 0.888 470s supply 0.888 1.000 470s 470s 470s W2SLS estimates for 'demand' (equation 1) 470s Model Formula: consump ~ price + income 470s Instruments: ~income + farmPrice 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 104.4638 9.7929 10.67 2.2e-12 *** 470s price -0.3630 0.1220 -2.98 0.0053 ** 470s income 0.3357 0.0444 7.57 8.6e-09 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 1.951 on 17 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 17 470s SSR: 64.715 MSE: 3.807 Root MSE: 1.951 470s Multiple R-Squared: 0.759 Adjusted R-Squared: 0.73 470s 470s 470s W2SLS estimates for 'supply' (equation 2) 470s Model Formula: consump ~ price + farmPrice + trend 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 47.0706 10.6890 4.40 0.0001 *** 470s price 0.2449 0.0910 2.69 0.0109 * 470s farmPrice 0.2671 0.0416 6.41 2.5e-07 *** 470s trend 0.3357 0.0444 7.57 8.6e-09 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 2.516 on 16 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 16 470s SSR: 101.299 MSE: 6.331 Root MSE: 2.516 470s Multiple R-Squared: 0.622 Adjusted R-Squared: 0.551 470s 470s > 470s > ## ** W2SLS estimation with different instruments and restriction via restrict.regMat **** 470s > fitw2slsd3 <- systemfit( system, "W2SLS", data = Kmenta, restrict.regMat = tc, 470s + inst = instlist, x = TRUE, useMatrix = useMatrix ) 470s > print( summary( fitw2slsd3 ) ) 470s 470s systemfit results 470s method: W2SLS 470s 470s N DF SSR detRCov OLS-R2 McElroy-R2 470s system 40 34 166 5.11 0.69 0.557 470s 470s N DF SSR MSE RMSE R2 Adj R2 470s demand 20 17 64.8 3.81 1.95 0.758 0.730 470s supply 20 16 101.4 6.34 2.52 0.622 0.551 470s 470s The covariance matrix of the residuals used for estimation 470s demand supply 470s demand 3.79 0.00 470s supply 0.00 6.27 470s 470s The covariance matrix of the residuals 470s demand supply 470s demand 3.81 4.36 470s supply 4.36 6.34 470s 470s The correlations of the residuals 470s demand supply 470s demand 1.000 0.888 470s supply 0.888 1.000 470s 470s 470s W2SLS estimates for 'demand' (equation 1) 470s Model Formula: consump ~ price + income 470s Instruments: ~income + farmPrice 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 104.5695 10.6344 9.83 1.8e-11 *** 470s price -0.3653 0.1327 -2.75 0.0094 ** 470s income 0.3369 0.0485 6.95 5.1e-08 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 1.952 on 17 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 17 470s SSR: 64.776 MSE: 3.81 Root MSE: 1.952 470s Multiple R-Squared: 0.758 Adjusted R-Squared: 0.73 470s 470s 470s W2SLS estimates for 'supply' (equation 2) 470s Model Formula: consump ~ price + farmPrice + trend 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 47.0356 11.9466 3.94 0.00039 *** 470s price 0.2450 0.1017 2.41 0.02156 * 470s farmPrice 0.2672 0.0465 5.74 1.9e-06 *** 470s trend 0.3369 0.0485 6.95 5.1e-08 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 2.518 on 16 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 16 470s SSR: 101.426 MSE: 6.339 Root MSE: 2.518 470s Multiple R-Squared: 0.622 Adjusted R-Squared: 0.551 470s 470s > 470s > ## W2SLS estimation with different instruments and restriction via restrict.regMat (EViews-like) 470s > fitw2slsd3e <- systemfit( system, "W2SLS", data = Kmenta, restrict.regMat = tc, 470s + inst = instlist, methodResidCov = "noDfCor", useMatrix = useMatrix ) 470s > print( summary( fitw2slsd3e, useDfSys = TRUE ) ) 470s 470s systemfit results 470s method: W2SLS 470s 470s N DF SSR detRCov OLS-R2 McElroy-R2 470s system 40 34 166 3.45 0.69 0.535 470s 470s N DF SSR MSE RMSE R2 Adj R2 470s demand 20 17 64.7 3.81 1.95 0.759 0.730 470s supply 20 16 101.3 6.33 2.52 0.622 0.551 470s 470s The covariance matrix of the residuals used for estimation 470s demand supply 470s demand 3.22 0.00 470s supply 0.00 5.02 470s 470s The covariance matrix of the residuals 470s demand supply 470s demand 3.24 3.60 470s supply 3.60 5.06 470s 470s The correlations of the residuals 470s demand supply 470s demand 1.000 0.888 470s supply 0.888 1.000 470s 470s 470s W2SLS estimates for 'demand' (equation 1) 470s Model Formula: consump ~ price + income 470s Instruments: ~income + farmPrice 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 104.4638 9.7929 10.67 2.2e-12 *** 470s price -0.3630 0.1220 -2.98 0.0053 ** 470s income 0.3357 0.0444 7.57 8.6e-09 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 1.951 on 17 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 17 470s SSR: 64.715 MSE: 3.807 Root MSE: 1.951 470s Multiple R-Squared: 0.759 Adjusted R-Squared: 0.73 470s 470s 470s W2SLS estimates for 'supply' (equation 2) 470s Model Formula: consump ~ price + farmPrice + trend 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 47.0706 10.6890 4.40 0.0001 *** 470s price 0.2449 0.0910 2.69 0.0109 * 470s farmPrice 0.2671 0.0416 6.41 2.5e-07 *** 470s trend 0.3357 0.0444 7.57 8.6e-09 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 2.516 on 16 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 16 470s SSR: 101.299 MSE: 6.331 Root MSE: 2.516 470s Multiple R-Squared: 0.622 Adjusted R-Squared: 0.551 470s 470s > 470s > 470s > ## *********** estimations with a single regressor ************ 470s > fitw2slsS1 <- systemfit( 470s + list( consump ~ price - 1, price ~ consump + trend ), "W2SLS", 470s + data = Kmenta, inst = ~ farmPrice + trend + income, useMatrix = useMatrix ) 470s > print( summary( fitw2slsS1 ) ) 470s 470s systemfit results 470s method: W2SLS 470s 470s N DF SSR detRCov OLS-R2 McElroy-R2 470s system 40 36 1544 179 -0.65 0.852 470s 470s N DF SSR MSE RMSE R2 Adj R2 470s eq1 20 19 861 45.3 6.73 -2.213 -2.213 470s eq2 20 17 682 40.1 6.33 -0.022 -0.143 470s 470s The covariance matrix of the residuals used for estimation 470s eq1 eq2 470s eq1 45.3 0.0 470s eq2 0.0 40.1 470s 470s The covariance matrix of the residuals 470s eq1 eq2 470s eq1 45.3 -40.5 470s eq2 -40.5 40.1 470s 470s The correlations of the residuals 470s eq1 eq2 470s eq1 1.00 -0.95 470s eq2 -0.95 1.00 470s 470s 470s W2SLS estimates for 'eq1' (equation 1) 470s Model Formula: consump ~ price - 1 470s Instruments: ~farmPrice + trend + income 470s 470s Estimate Std. Error t value Pr(>|t|) 470s price 1.006 0.015 66.9 <2e-16 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 6.734 on 19 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 19 470s SSR: 861.48 MSE: 45.341 Root MSE: 6.734 470s Multiple R-Squared: -2.213 Adjusted R-Squared: -2.213 470s 470s 470s W2SLS estimates for 'eq2' (equation 2) 470s Model Formula: price ~ consump + trend 470s Instruments: ~farmPrice + trend + income 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 55.5365 46.2668 1.20 0.25 470s consump 0.4453 0.4622 0.96 0.35 470s trend -0.0426 0.2496 -0.17 0.87 470s 470s Residual standard error: 6.335 on 17 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 17 470s SSR: 682.257 MSE: 40.133 Root MSE: 6.335 470s Multiple R-Squared: -0.022 Adjusted R-Squared: -0.143 470s 470s > fitw2slsS2 <- systemfit( 470s + list( consump ~ price - 1, consump ~ trend - 1 ), "W2SLS", 470s + data = Kmenta, inst = ~ farmPrice + price + income, useMatrix = useMatrix ) 470s > print( summary( fitw2slsS2 ) ) 470s 470s systemfit results 470s method: W2SLS 470s 470s N DF SSR detRCov OLS-R2 McElroy-R2 470s system 40 38 47456 111148 -87.5 -5.28 470s 470s N DF SSR MSE RMSE R2 Adj R2 470s eq1 20 19 861 45.3 6.73 -2.21 -2.21 470s eq2 20 19 46595 2452.3 49.52 -172.79 -172.79 470s 470s The covariance matrix of the residuals used for estimation 470s eq1 eq2 470s eq1 45.3 0 470s eq2 0.0 2452 470s 470s The covariance matrix of the residuals 470s eq1 eq2 470s eq1 45.34 -6.33 470s eq2 -6.33 2452.34 470s 470s The correlations of the residuals 470s eq1 eq2 470s eq1 1.0000 -0.0448 470s eq2 -0.0448 1.0000 470s 470s 470s W2SLS estimates for 'eq1' (equation 1) 470s Model Formula: consump ~ price - 1 470s Instruments: ~farmPrice + price + income 470s 470s Estimate Std. Error t value Pr(>|t|) 470s price 1.006 0.015 66.9 <2e-16 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 6.733 on 19 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 19 470s SSR: 861.449 MSE: 45.339 Root MSE: 6.733 470s Multiple R-Squared: -2.213 Adjusted R-Squared: -2.213 470s 470s 470s W2SLS estimates for 'eq2' (equation 2) 470s Model Formula: consump ~ trend - 1 470s Instruments: ~farmPrice + price + income 470s 470s Estimate Std. Error t value Pr(>|t|) 470s trend 7.578 0.934 8.11 1.4e-07 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 49.521 on 19 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 19 470s SSR: 46594.549 MSE: 2452.345 Root MSE: 49.521 470s Multiple R-Squared: -172.786 Adjusted R-Squared: -172.786 470s 470s > fitw2slsS3 <- systemfit( 470s + list( consump ~ trend - 1, price ~ trend - 1 ), "W2SLS", 470s + data = Kmenta, inst = instlist, useMatrix = useMatrix ) 470s > print( summary( fitw2slsS3 ) ) 470s 470s systemfit results 470s method: W2SLS 470s 470s N DF SSR detRCov OLS-R2 McElroy-R2 470s system 40 38 97978 687515 -104 -10.6 470s 470s N DF SSR MSE RMSE R2 Adj R2 470s eq1 20 19 50950 2682 51.8 -189.0 -189.0 470s eq2 20 19 47028 2475 49.8 -69.5 -69.5 470s 470s The covariance matrix of the residuals used for estimation 470s eq1 eq2 470s eq1 2682 0 470s eq2 0 2475 470s 470s The covariance matrix of the residuals 470s eq1 eq2 470s eq1 2682 2439 470s eq2 2439 2475 470s 470s The correlations of the residuals 470s eq1 eq2 470s eq1 1.000 0.989 470s eq2 0.989 1.000 470s 470s 470s W2SLS estimates for 'eq1' (equation 1) 470s Model Formula: consump ~ trend - 1 470s Instruments: ~income + farmPrice 470s 470s Estimate Std. Error t value Pr(>|t|) 470s trend 8.65 1.05 8.27 1e-07 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 51.784 on 19 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 19 470s SSR: 50949.985 MSE: 2681.578 Root MSE: 51.784 470s Multiple R-Squared: -189.031 Adjusted R-Squared: -189.031 470s 470s 470s W2SLS estimates for 'eq2' (equation 2) 470s Model Formula: price ~ trend - 1 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s trend 7.318 0.929 7.88 2.1e-07 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 49.751 on 19 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 19 470s SSR: 47028.107 MSE: 2475.164 Root MSE: 49.751 470s Multiple R-Squared: -69.48 Adjusted R-Squared: -69.48 470s 470s > fitw2slsS4 <- systemfit( 470s + list( consump ~ trend - 1, price ~ trend - 1 ), "W2SLS", 470s + data = Kmenta, inst = ~ farmPrice + trend + income, 470s + restrict.matrix = matrix( c( 1, -1 ), nrow = 1 ), useMatrix = useMatrix ) 470s > print( summary( fitw2slsS4 ) ) 470s 470s systemfit results 470s method: W2SLS 470s 470s N DF SSR detRCov OLS-R2 McElroy-R2 470s system 40 39 93548 111736 -99 -1.03 470s 470s N DF SSR MSE RMSE R2 Adj R2 470s eq1 20 19 46514 2448 49.5 -172.5 -172.5 470s eq2 20 19 47034 2475 49.8 -69.5 -69.5 470s 470s The covariance matrix of the residuals used for estimation 470s eq1 eq2 470s eq1 2448 0 470s eq2 0 2475 470s 470s The covariance matrix of the residuals 470s eq1 eq2 470s eq1 2448 2439 470s eq2 2439 2475 470s 470s The correlations of the residuals 470s eq1 eq2 470s eq1 1.000 0.988 470s eq2 0.988 1.000 470s 470s 470s W2SLS estimates for 'eq1' (equation 1) 470s Model Formula: consump ~ trend - 1 470s Instruments: ~farmPrice + trend + income 470s 470s Estimate Std. Error t value Pr(>|t|) 470s trend 7.362 0.655 11.2 8.4e-14 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 49.478 on 19 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 19 470s SSR: 46514.224 MSE: 2448.117 Root MSE: 49.478 470s Multiple R-Squared: -172.487 Adjusted R-Squared: -172.487 470s 470s 470s W2SLS estimates for 'eq2' (equation 2) 470s Model Formula: price ~ trend - 1 470s Instruments: ~farmPrice + trend + income 470s 470s Estimate Std. Error t value Pr(>|t|) 470s trend 7.362 0.655 11.2 8.4e-14 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 49.754 on 19 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 19 470s SSR: 47033.528 MSE: 2475.449 Root MSE: 49.754 470s Multiple R-Squared: -69.488 Adjusted R-Squared: -69.488 470s 470s > fitw2slsS5 <- systemfit( 470s + list( consump ~ 1, price ~ 1 ), "W2SLS", 470s + data = Kmenta, inst = instlist, useMatrix = useMatrix ) 470s > print( summary( fitw2slsS5 ) ) 470s 470s systemfit results 470s method: W2SLS 470s 470s N DF SSR detRCov OLS-R2 McElroy-R2 470s system 40 38 935 491 0 0 470s 470s N DF SSR MSE RMSE R2 Adj R2 470s eq1 20 19 268 14.1 3.76 0 0 470s eq2 20 19 667 35.1 5.93 0 0 470s 470s The covariance matrix of the residuals used for estimation 470s eq1 eq2 470s eq1 14.1 0.0 470s eq2 0.0 35.1 470s 470s The covariance matrix of the residuals 470s eq1 eq2 470s eq1 14.11 2.18 470s eq2 2.18 35.12 470s 470s The correlations of the residuals 470s eq1 eq2 470s eq1 1.0000 0.0981 470s eq2 0.0981 1.0000 470s 470s 470s W2SLS estimates for 'eq1' (equation 1) 470s Model Formula: consump ~ 1 470s Instruments: ~income + farmPrice 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 100.90 0.84 120 <2e-16 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 3.756 on 19 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 19 470s SSR: 268.114 MSE: 14.111 Root MSE: 3.756 470s Multiple R-Squared: 0 Adjusted R-Squared: 0 470s 470s 470s W2SLS estimates for 'eq2' (equation 2) 470s Model Formula: price ~ 1 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 100.02 1.33 75.5 <2e-16 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 5.926 on 19 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 19 470s SSR: 667.251 MSE: 35.118 Root MSE: 5.926 470s Multiple R-Squared: 0 Adjusted R-Squared: 0 470s 470s > 470s > 470s > ## **************** shorter summaries ********************** 470s > print( summary( fitw2sls1e, residCov = FALSE ) ) 470s 470s systemfit results 470s method: W2SLS 470s 470s N DF SSR detRCov OLS-R2 McElroy-R2 470s system 40 33 162 2.97 0.697 0.525 470s 470s N DF SSR MSE RMSE R2 Adj R2 470s demand 20 17 65.7 3.87 1.97 0.755 0.726 470s supply 20 16 96.6 6.04 2.46 0.640 0.572 470s 470s 470s W2SLS estimates for 'demand' (equation 1) 470s Model Formula: consump ~ price + income 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 94.6333 7.3027 12.96 3.1e-10 *** 470s price -0.2436 0.0890 -2.74 0.014 * 470s income 0.3140 0.0433 7.25 1.3e-06 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 1.966 on 17 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 17 470s SSR: 65.729 MSE: 3.866 Root MSE: 1.966 470s Multiple R-Squared: 0.755 Adjusted R-Squared: 0.726 470s 470s 470s W2SLS estimates for 'supply' (equation 2) 470s Model Formula: consump ~ price + farmPrice + trend 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 49.5324 10.7425 4.61 0.00029 *** 470s price 0.2401 0.0894 2.69 0.01623 * 470s farmPrice 0.2556 0.0423 6.05 1.7e-05 *** 470s trend 0.2529 0.0891 2.84 0.01188 * 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 2.458 on 16 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 16 470s SSR: 96.633 MSE: 6.04 Root MSE: 2.458 470s Multiple R-Squared: 0.64 Adjusted R-Squared: 0.572 470s 470s > 470s > print( summary( fitw2sls2, residCov = FALSE, equations = FALSE ) ) 470s 470s systemfit results 470s method: W2SLS 470s 470s N DF SSR detRCov OLS-R2 McElroy-R2 470s system 40 34 165 3.41 0.692 0.565 470s 470s N DF SSR MSE RMSE R2 Adj R2 470s demand 20 17 66.8 3.93 1.98 0.751 0.721 470s supply 20 16 98.4 6.15 2.48 0.633 0.564 470s 470s 470s Coefficients: 470s Estimate Std. Error t value Pr(>|t|) 470s demand_(Intercept) 94.3832 8.0090 11.78 1.5e-13 *** 470s demand_price -0.2302 0.0946 -2.43 0.02042 * 470s demand_income 0.3028 0.0430 7.05 3.9e-08 *** 470s supply_(Intercept) 48.0494 11.8001 4.07 0.00026 *** 470s supply_price 0.2430 0.1006 2.42 0.02122 * 470s supply_farmPrice 0.2625 0.0459 5.72 2.0e-06 *** 470s supply_trend 0.3028 0.0430 7.05 3.9e-08 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s > 470s > print( summary( fitw2sls3, useDfSys = FALSE ), equations = FALSE ) 470s 470s systemfit results 470s method: W2SLS 470s 470s N DF SSR detRCov OLS-R2 McElroy-R2 470s system 40 34 165 3.41 0.692 0.565 470s 470s N DF SSR MSE RMSE R2 Adj R2 470s demand 20 17 66.8 3.93 1.98 0.751 0.721 470s supply 20 16 98.4 6.15 2.48 0.633 0.564 470s 470s The covariance matrix of the residuals used for estimation 470s demand supply 470s demand 3.97 0.00 470s supply 0.00 6.13 470s 470s The covariance matrix of the residuals 470s demand supply 470s demand 3.93 4.56 470s supply 4.56 6.15 470s 470s The correlations of the residuals 470s demand supply 470s demand 1.000 0.927 470s supply 0.927 1.000 470s 470s 470s Coefficients: 470s Estimate Std. Error t value Pr(>|t|) 470s demand_(Intercept) 94.3832 8.0090 11.78 1.3e-09 *** 470s demand_price -0.2302 0.0946 -2.43 0.02634 * 470s demand_income 0.3028 0.0430 7.05 2.0e-06 *** 470s supply_(Intercept) 48.0494 11.8001 4.07 0.00089 *** 470s supply_price 0.2430 0.1006 2.42 0.02802 * 470s supply_farmPrice 0.2625 0.0459 5.72 3.2e-05 *** 470s supply_trend 0.3028 0.0430 7.05 2.8e-06 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s > 470s > print( summary( fitw2sls4e ), residCov = FALSE ) 470s 470s systemfit results 470s method: W2SLS 470s 470s N DF SSR detRCov OLS-R2 McElroy-R2 470s system 40 35 166 2.44 0.69 0.546 470s 470s N DF SSR MSE RMSE R2 Adj R2 470s demand 20 17 65.9 3.88 1.97 0.754 0.725 470s supply 20 16 100.2 6.26 2.50 0.626 0.556 470s 470s 470s W2SLS estimates for 'demand' (equation 1) 470s Model Formula: consump ~ price + income 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 95.3470 5.7579 16.56 < 2e-16 *** 470s price -0.2428 0.0621 -3.91 0.00041 *** 470s income 0.3059 0.0360 8.49 5.1e-10 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 1.97 on 17 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 17 470s SSR: 65.945 MSE: 3.879 Root MSE: 1.97 470s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 470s 470s 470s W2SLS estimates for 'supply' (equation 2) 470s Model Formula: consump ~ price + farmPrice + trend 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 46.4366 7.5360 6.16 4.7e-07 *** 470s price 0.2572 0.0621 4.14 0.00021 *** 470s farmPrice 0.2642 0.0407 6.48 1.8e-07 *** 470s trend 0.3059 0.0360 8.49 5.1e-10 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 2.503 on 16 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 16 470s SSR: 100.225 MSE: 6.264 Root MSE: 2.503 470s Multiple R-Squared: 0.626 Adjusted R-Squared: 0.556 470s 470s > 470s > print( summary( fitw2sls5, useDfSys = FALSE, residCov = FALSE ) ) 470s 470s systemfit results 470s method: W2SLS 470s 470s N DF SSR detRCov OLS-R2 McElroy-R2 470s system 40 35 166 3.57 0.69 0.575 470s 470s N DF SSR MSE RMSE R2 Adj R2 470s demand 20 17 65.9 3.88 1.97 0.754 0.725 470s supply 20 16 100.3 6.27 2.50 0.626 0.556 470s 470s 470s W2SLS estimates for 'demand' (equation 1) 470s Model Formula: consump ~ price + income 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 95.3043 6.3056 15.11 2.7e-11 *** 470s price -0.2428 0.0684 -3.55 0.0025 ** 470s income 0.3063 0.0394 7.78 5.4e-07 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 1.969 on 17 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 17 470s SSR: 65.931 MSE: 3.878 Root MSE: 1.969 470s Multiple R-Squared: 0.754 Adjusted R-Squared: 0.725 470s 470s 470s W2SLS estimates for 'supply' (equation 2) 470s Model Formula: consump ~ price + farmPrice + trend 470s Instruments: ~income + farmPrice + trend 470s 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 46.4229 8.3296 5.57 4.2e-05 *** 470s price 0.2572 0.0684 3.76 0.0017 ** 470s farmPrice 0.2642 0.0455 5.80 2.7e-05 *** 470s trend 0.3063 0.0394 7.78 8.0e-07 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s 470s Residual standard error: 2.503 on 16 degrees of freedom 470s Number of observations: 20 Degrees of Freedom: 16 470s SSR: 100.255 MSE: 6.266 Root MSE: 2.503 470s Multiple R-Squared: 0.626 Adjusted R-Squared: 0.556 470s 470s > 470s > print( summary( fitw2slsd1, useDfSys = TRUE ), residCov = FALSE, 470s + equations = FALSE ) 470s 470s systemfit results 470s method: W2SLS 470s 470s N DF SSR detRCov OLS-R2 McElroy-R2 470s system 40 33 164 9.25 0.694 0.512 470s 470s N DF SSR MSE RMSE R2 Adj R2 470s demand 20 17 67.4 3.97 1.99 0.748 0.719 470s supply 20 16 96.6 6.04 2.46 0.640 0.572 470s 470s 470s Coefficients: 470s Estimate Std. Error t value Pr(>|t|) 470s demand_(Intercept) 106.7894 11.1435 9.58 4.7e-11 *** 470s demand_price -0.4116 0.1448 -2.84 0.00764 ** 470s demand_income 0.3617 0.0564 6.41 2.9e-07 *** 470s supply_(Intercept) 49.5324 12.0105 4.12 0.00024 *** 470s supply_price 0.2401 0.0999 2.40 0.02208 * 470s supply_farmPrice 0.2556 0.0473 5.41 5.5e-06 *** 470s supply_trend 0.2529 0.0997 2.54 0.01605 * 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s > 470s > print( summary( fitw2slsd2e, equations = TRUE ), equations = FALSE ) 470s 470s systemfit results 470s method: W2SLS 470s 470s N DF SSR detRCov OLS-R2 McElroy-R2 470s system 40 34 166 3.45 0.69 0.535 470s 470s N DF SSR MSE RMSE R2 Adj R2 470s demand 20 17 64.7 3.81 1.95 0.759 0.730 470s supply 20 16 101.3 6.33 2.52 0.622 0.551 470s 470s The covariance matrix of the residuals used for estimation 470s demand supply 470s demand 3.22 0.00 470s supply 0.00 5.02 470s 470s The covariance matrix of the residuals 470s demand supply 470s demand 3.24 3.60 470s supply 3.60 5.06 470s 470s The correlations of the residuals 470s demand supply 470s demand 1.000 0.888 470s supply 0.888 1.000 470s 470s 470s Coefficients: 470s Estimate Std. Error t value Pr(>|t|) 470s demand_(Intercept) 104.4638 9.7929 10.67 2.2e-12 *** 470s demand_price -0.3630 0.1220 -2.98 0.0053 ** 470s demand_income 0.3357 0.0444 7.57 8.6e-09 *** 470s supply_(Intercept) 47.0706 10.6890 4.40 0.0001 *** 470s supply_price 0.2449 0.0910 2.69 0.0109 * 470s supply_farmPrice 0.2671 0.0416 6.41 2.5e-07 *** 470s supply_trend 0.3357 0.0444 7.57 8.6e-09 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s > 470s > print( summary( fitw2slsd3e, equations = FALSE ), residCov = FALSE ) 470s 470s systemfit results 470s method: W2SLS 470s 470s N DF SSR detRCov OLS-R2 McElroy-R2 470s system 40 34 166 3.45 0.69 0.535 470s 470s N DF SSR MSE RMSE R2 Adj R2 470s demand 20 17 64.7 3.81 1.95 0.759 0.730 470s supply 20 16 101.3 6.33 2.52 0.622 0.551 470s 470s 470s Coefficients: 470s Estimate Std. Error t value Pr(>|t|) 470s demand_(Intercept) 104.4638 9.7929 10.67 2.2e-12 *** 470s demand_price -0.3630 0.1220 -2.98 0.0053 ** 470s demand_income 0.3357 0.0444 7.57 8.6e-09 *** 470s supply_(Intercept) 47.0706 10.6890 4.40 0.0001 *** 470s supply_price 0.2449 0.0910 2.69 0.0109 * 470s supply_farmPrice 0.2671 0.0416 6.41 2.5e-07 *** 470s supply_trend 0.3357 0.0444 7.57 8.6e-09 *** 470s --- 470s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 470s > 470s > 470s > ## ****************** residuals ************************** 470s > print( residuals( fitw2sls1e ) ) 470s demand supply 470s 1 0.843 -0.4348 470s 2 -0.698 -1.2131 470s 3 2.359 1.7090 470s 4 1.490 0.7956 470s 5 2.139 1.5942 470s 6 1.277 0.6595 470s 7 1.571 1.4346 470s 8 -3.066 -4.8724 470s 9 -1.125 -2.3975 470s 10 2.492 3.1427 470s 11 -0.108 0.0689 470s 12 -2.292 -1.3978 470s 13 -1.598 -1.1136 470s 14 -0.271 1.1684 470s 15 1.958 3.4865 470s 16 -3.430 -3.8285 470s 17 -0.313 0.6793 470s 18 -2.151 -2.7713 470s 19 1.592 2.6668 470s 20 -0.668 0.6235 470s > print( residuals( fitw2sls1e$eq[[ 1 ]] ) ) 470s 1 2 3 4 5 6 7 8 9 10 11 470s 0.843 -0.698 2.359 1.490 2.139 1.277 1.571 -3.066 -1.125 2.492 -0.108 470s 12 13 14 15 16 17 18 19 20 470s -2.292 -1.598 -0.271 1.958 -3.430 -0.313 -2.151 1.592 -0.668 470s > 470s > print( residuals( fitw2sls2 ) ) 470s demand supply 470s 1 0.726 0.0287 470s 2 -0.754 -0.8185 470s 3 2.304 2.0561 470s 4 1.437 1.0966 470s 5 2.191 1.7764 470s 6 1.317 0.8056 470s 7 1.620 1.5441 470s 8 -3.015 -4.8526 470s 9 -1.087 -2.3957 470s 10 2.513 3.1658 470s 11 -0.265 0.1722 470s 12 -2.506 -1.2753 470s 13 -1.781 -1.0688 470s 14 -0.332 1.1028 470s 15 2.086 3.2370 470s 16 -3.325 -4.1563 470s 17 -0.144 0.2984 470s 18 -2.128 -3.1286 470s 19 1.662 2.2767 470s 20 -0.518 0.1355 470s > print( residuals( fitw2sls2$eq[[ 2 ]] ) ) 470s 1 2 3 4 5 6 7 8 9 10 470s 0.0287 -0.8185 2.0561 1.0966 1.7764 0.8056 1.5441 -4.8526 -2.3957 3.1658 470s 11 12 13 14 15 16 17 18 19 20 470s 0.1722 -1.2753 -1.0688 1.1028 3.2370 -4.1563 0.2984 -3.1286 2.2767 0.1355 470s > 470s > print( residuals( fitw2sls3 ) ) 470s demand supply 470s 1 0.726 0.0287 470s 2 -0.754 -0.8185 470s 3 2.304 2.0561 470s 4 1.437 1.0966 470s 5 2.191 1.7764 470s 6 1.317 0.8056 470s 7 1.620 1.5441 470s 8 -3.015 -4.8526 470s 9 -1.087 -2.3957 470s 10 2.513 3.1658 470s 11 -0.265 0.1722 470s 12 -2.506 -1.2753 470s 13 -1.781 -1.0688 470s 14 -0.332 1.1028 470s 15 2.086 3.2370 470s 16 -3.325 -4.1563 470s 17 -0.144 0.2984 470s 18 -2.128 -3.1286 470s 19 1.662 2.2767 470s 20 -0.518 0.1355 470s > print( residuals( fitw2sls3$eq[[ 1 ]] ) ) 470s 1 2 3 4 5 6 7 8 9 10 11 470s 0.726 -0.754 2.304 1.437 2.191 1.317 1.620 -3.015 -1.087 2.513 -0.265 470s 12 13 14 15 16 17 18 19 20 470s -2.506 -1.781 -0.332 2.086 -3.325 -0.144 -2.128 1.662 -0.518 470s > 470s > print( residuals( fitw2sls4e ) ) 470s demand supply 470s 1 0.761 0.0514 470s 2 -0.700 -0.8567 470s 3 2.350 2.0266 470s 4 1.492 1.0504 470s 5 2.159 1.7988 470s 6 1.301 0.8085 470s 7 1.616 1.5253 470s 8 -2.986 -4.9339 470s 9 -1.130 -2.3600 470s 10 2.429 3.2858 470s 11 -0.284 0.2948 470s 12 -2.458 -1.2168 470s 13 -1.705 -1.0756 470s 14 -0.327 1.1348 470s 15 2.007 3.2835 470s 16 -3.368 -4.1646 470s 17 -0.312 0.4480 470s 18 -2.099 -3.2018 470s 19 1.694 2.1807 470s 20 -0.439 -0.0794 470s > print( residuals( fitw2sls4e$eq[[ 2 ]] ) ) 470s 1 2 3 4 5 6 7 8 9 10 470s 0.0514 -0.8567 2.0266 1.0504 1.7988 0.8085 1.5253 -4.9339 -2.3600 3.2858 470s 11 12 13 14 15 16 17 18 19 20 470s 0.2948 -1.2168 -1.0756 1.1348 3.2835 -4.1646 0.4480 -3.2018 2.1807 -0.0794 470s > 470s > print( residuals( fitw2sls5 ) ) 470s demand supply 470s 1 0.765 0.0551 470s 2 -0.701 -0.8537 470s 3 2.350 2.0293 470s 4 1.491 1.0527 470s 5 2.158 1.8003 470s 6 1.300 0.8097 470s 7 1.614 1.5262 470s 8 -2.991 -4.9339 470s 9 -1.129 -2.3600 470s 10 2.433 3.2862 470s 11 -0.275 0.2958 470s 12 -2.450 -1.2157 470s 13 -1.700 -1.0752 470s 14 -0.324 1.1344 470s 15 2.005 3.2816 470s 16 -3.371 -4.1672 470s 17 -0.311 0.4452 470s 18 -2.102 -3.2047 470s 19 1.688 2.1776 470s 20 -0.451 -0.0835 470s > print( residuals( fitw2sls5$eq[[ 1 ]] ) ) 470s 1 2 3 4 5 6 7 8 9 10 11 470s 0.765 -0.701 2.350 1.491 2.158 1.300 1.614 -2.991 -1.129 2.433 -0.275 470s 12 13 14 15 16 17 18 19 20 470s -2.450 -1.700 -0.324 2.005 -3.371 -0.311 -2.102 1.688 -0.451 470s > 470s > print( residuals( fitw2slsd1 ) ) 470s demand supply 470s 1 1.3775 -0.4348 470s 2 0.0125 -1.2131 470s 3 2.9728 1.7090 470s 4 2.2121 0.7956 470s 5 1.6920 1.5942 470s 6 1.0407 0.6595 470s 7 1.4768 1.4346 470s 8 -2.7583 -4.8724 470s 9 -1.6807 -2.3975 470s 10 1.4265 3.1427 470s 11 -0.2029 0.0689 470s 12 -1.5123 -1.3978 470s 13 -0.4958 -1.1136 470s 14 -0.1528 1.1684 470s 15 0.8692 3.4865 470s 16 -4.0547 -3.8285 470s 17 -2.5309 0.6793 470s 18 -1.8070 -2.7713 470s 19 1.9299 2.6668 470s 20 0.1853 0.6235 470s > print( residuals( fitw2slsd1$eq[[ 2 ]] ) ) 470s 1 2 3 4 5 6 7 8 9 10 470s -0.4348 -1.2131 1.7090 0.7956 1.5942 0.6595 1.4346 -4.8724 -2.3975 3.1427 470s 11 12 13 14 15 16 17 18 19 20 470s 0.0689 -1.3978 -1.1136 1.1684 3.4865 -3.8285 0.6793 -2.7713 2.6668 0.6235 470s > 470s > print( residuals( fitw2slsd2e ) ) 470s demand supply 470s 1 1.100 0.3346 470s 2 -0.192 -0.5581 470s 3 2.785 2.2852 470s 4 2.012 1.2953 470s 5 1.849 1.8966 470s 6 1.145 0.9020 470s 7 1.573 1.6164 470s 8 -2.722 -4.8395 470s 9 -1.531 -2.3946 470s 10 1.629 3.1810 470s 11 -0.448 0.2403 470s 12 -1.988 -1.1944 470s 13 -0.972 -1.0393 470s 14 -0.271 1.0594 470s 15 1.251 3.0723 470s 16 -3.782 -4.3726 470s 17 -1.904 0.0471 470s 18 -1.823 -3.3644 470s 19 1.992 2.0193 470s 20 0.298 -0.1866 470s > print( residuals( fitw2slsd2e$eq[[ 1 ]] ) ) 470s 1 2 3 4 5 6 7 8 9 10 11 470s 1.100 -0.192 2.785 2.012 1.849 1.145 1.573 -2.722 -1.531 1.629 -0.448 470s 12 13 14 15 16 17 18 19 20 470s -1.988 -0.972 -0.271 1.251 -3.782 -1.904 -1.823 1.992 0.298 470s > 470s > print( residuals( fitw2slsd3e ) ) 470s demand supply 470s 1 1.100 0.3346 470s 2 -0.192 -0.5581 470s 3 2.785 2.2852 470s 4 2.012 1.2953 470s 5 1.849 1.8966 470s 6 1.145 0.9020 470s 7 1.573 1.6164 470s 8 -2.722 -4.8395 470s 9 -1.531 -2.3946 470s 10 1.629 3.1810 470s 11 -0.448 0.2403 470s 12 -1.988 -1.1944 470s 13 -0.972 -1.0393 470s 14 -0.271 1.0594 470s 15 1.251 3.0723 470s 16 -3.782 -4.3726 470s 17 -1.904 0.0471 470s 18 -1.823 -3.3644 470s 19 1.992 2.0193 470s 20 0.298 -0.1866 470s > print( residuals( fitw2slsd3e$eq[[ 2 ]] ) ) 470s 1 2 3 4 5 6 7 8 9 10 470s 0.3346 -0.5581 2.2852 1.2953 1.8966 0.9020 1.6164 -4.8395 -2.3946 3.1810 470s 11 12 13 14 15 16 17 18 19 20 470s 0.2403 -1.1944 -1.0393 1.0594 3.0723 -4.3726 0.0471 -3.3644 2.0193 -0.1866 470s > 470s > 470s > ## *************** coefficients ********************* 470s > print( round( coef( fitw2sls1e ), digits = 6 ) ) 470s demand_(Intercept) demand_price demand_income supply_(Intercept) 470s 94.633 -0.244 0.314 49.532 470s supply_price supply_farmPrice supply_trend 470s 0.240 0.256 0.253 470s > print( round( coef( fitw2sls1e$eq[[ 2 ]] ), digits = 6 ) ) 470s (Intercept) price farmPrice trend 470s 49.532 0.240 0.256 0.253 470s > 470s > print( round( coef( fitw2slsd2e ), digits = 6 ) ) 470s demand_(Intercept) demand_price demand_income supply_(Intercept) 470s 104.464 -0.363 0.336 47.071 470s supply_price supply_farmPrice supply_trend 470s 0.245 0.267 0.336 470s > print( round( coef( fitw2slsd2e$eq[[ 1 ]] ), digits = 6 ) ) 470s (Intercept) price income 470s 104.464 -0.363 0.336 470s > 470s > print( round( coef( fitw2slsd3e ), digits = 6 ) ) 470s demand_(Intercept) demand_price demand_income supply_(Intercept) 470s 104.464 -0.363 0.336 47.071 470s supply_price supply_farmPrice supply_trend 470s 0.245 0.267 0.336 470s > print( round( coef( fitw2slsd3e, modified.regMat = TRUE ), digits = 6 ) ) 470s C1 C2 C3 C4 C5 C6 470s 104.464 -0.363 0.336 47.071 0.245 0.267 470s > print( round( coef( fitw2slsd3e$eq[[ 2 ]] ), digits = 6 ) ) 470s (Intercept) price farmPrice trend 470s 47.071 0.245 0.267 0.336 470s > 470s > print( round( coef( fitw2sls4 ), digits = 6 ) ) 470s demand_(Intercept) demand_price demand_income supply_(Intercept) 470s 95.304 -0.243 0.306 46.423 470s supply_price supply_farmPrice supply_trend 470s 0.257 0.264 0.306 470s > print( round( coef( fitw2sls4$eq[[ 1 ]] ), digits = 6 ) ) 470s (Intercept) price income 470s 95.304 -0.243 0.306 470s > 470s > print( round( coef( fitw2sls5 ), digits = 6 ) ) 470s demand_(Intercept) demand_price demand_income supply_(Intercept) 470s 95.304 -0.243 0.306 46.423 470s supply_price supply_farmPrice supply_trend 470s 0.257 0.264 0.306 470s > print( round( coef( fitw2sls5, modified.regMat = TRUE ), digits = 6 ) ) 470s C1 C2 C3 C4 C5 C6 470s 95.304 -0.243 0.306 46.423 0.257 0.264 470s > print( round( coef( fitw2sls5$eq[[ 2 ]] ), digits = 6 ) ) 470s (Intercept) price farmPrice trend 470s 46.423 0.257 0.264 0.306 470s > 470s > 470s > ## *************** coefficients with stats ********************* 470s > print( round( coef( summary( fitw2sls1e, useDfSys = FALSE ) ), digits = 6 ) ) 470s Estimate Std. Error t value Pr(>|t|) 470s demand_(Intercept) 94.633 7.3027 12.96 0.000000 470s demand_price -0.244 0.0890 -2.74 0.014016 470s demand_income 0.314 0.0433 7.25 0.000001 470s supply_(Intercept) 49.532 10.7425 4.61 0.000289 470s supply_price 0.240 0.0894 2.69 0.016234 470s supply_farmPrice 0.256 0.0423 6.05 0.000017 470s supply_trend 0.253 0.0891 2.84 0.011883 470s > print( round( coef( summary( fitw2sls1e$eq[[ 2 ]], useDfSys = FALSE ) ), 470s + digits = 6 ) ) 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 49.532 10.7425 4.61 0.000289 470s price 0.240 0.0894 2.69 0.016234 470s farmPrice 0.256 0.0423 6.05 0.000017 470s trend 0.253 0.0891 2.84 0.011883 470s > 470s > print( round( coef( summary( fitw2slsd2e ) ), digits = 6 ) ) 470s Estimate Std. Error t value Pr(>|t|) 470s demand_(Intercept) 104.464 9.7929 10.67 0.00000 470s demand_price -0.363 0.1220 -2.98 0.00534 470s demand_income 0.336 0.0444 7.57 0.00000 470s supply_(Intercept) 47.071 10.6890 4.40 0.00010 470s supply_price 0.245 0.0910 2.69 0.01093 470s supply_farmPrice 0.267 0.0416 6.41 0.00000 470s supply_trend 0.336 0.0444 7.57 0.00000 470s > print( round( coef( summary( fitw2slsd2e$eq[[ 1 ]] ) ), digits = 6 ) ) 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 104.464 9.7929 10.67 0.00000 470s price -0.363 0.1220 -2.98 0.00534 470s income 0.336 0.0444 7.57 0.00000 470s > 470s > print( round( coef( summary( fitw2slsd3e, useDfSys = FALSE ) ), digits = 6 ) ) 470s Estimate Std. Error t value Pr(>|t|) 470s demand_(Intercept) 104.464 9.7929 10.67 0.000000 470s demand_price -0.363 0.1220 -2.98 0.008475 470s demand_income 0.336 0.0444 7.57 0.000001 470s supply_(Intercept) 47.071 10.6890 4.40 0.000444 470s supply_price 0.245 0.0910 2.69 0.016014 470s supply_farmPrice 0.267 0.0416 6.41 0.000009 470s supply_trend 0.336 0.0444 7.57 0.000001 470s > print( round( coef( summary( fitw2slsd3e, useDfSys = FALSE ), 470s + modified.regMat = TRUE ), digits = 6 ) ) 470s Estimate Std. Error t value Pr(>|t|) 470s C1 104.464 9.7929 10.67 NA 470s C2 -0.363 0.1220 -2.98 NA 470s C3 0.336 0.0444 7.57 NA 470s C4 47.071 10.6890 4.40 NA 470s C5 0.245 0.0910 2.69 NA 470s C6 0.267 0.0416 6.41 NA 470s > print( round( coef( summary( fitw2slsd3e$eq[[ 2 ]], useDfSys = FALSE ) ), 470s + digits = 6 ) ) 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 47.071 10.6890 4.40 0.000444 470s price 0.245 0.0910 2.69 0.016014 470s farmPrice 0.267 0.0416 6.41 0.000009 470s trend 0.336 0.0444 7.57 0.000001 470s > 470s > print( round( coef( summary( fitw2sls4 ) ), digits = 6 ) ) 470s Estimate Std. Error t value Pr(>|t|) 470s demand_(Intercept) 95.304 6.3056 15.11 0.000000 470s demand_price -0.243 0.0684 -3.55 0.001128 470s demand_income 0.306 0.0394 7.78 0.000000 470s supply_(Intercept) 46.423 8.3296 5.57 0.000003 470s supply_price 0.257 0.0684 3.76 0.000622 470s supply_farmPrice 0.264 0.0455 5.80 0.000001 470s supply_trend 0.306 0.0394 7.78 0.000000 470s > print( round( coef( summary( fitw2sls4$eq[[ 1 ]] ) ), digits = 6 ) ) 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 95.304 6.3056 15.11 0.00000 470s price -0.243 0.0684 -3.55 0.00113 470s income 0.306 0.0394 7.78 0.00000 470s > 470s > print( round( coef( summary( fitw2sls5 ) ), digits = 6 ) ) 470s Estimate Std. Error t value Pr(>|t|) 470s demand_(Intercept) 95.304 6.3056 15.11 0.000000 470s demand_price -0.243 0.0684 -3.55 0.001128 470s demand_income 0.306 0.0394 7.78 0.000000 470s supply_(Intercept) 46.423 8.3296 5.57 0.000003 470s supply_price 0.257 0.0684 3.76 0.000622 470s supply_farmPrice 0.264 0.0455 5.80 0.000001 470s supply_trend 0.306 0.0394 7.78 0.000000 470s > print( round( coef( summary( fitw2sls5 ), modified.regMat = TRUE ), digits = 6 ) ) 470s Estimate Std. Error t value Pr(>|t|) 470s C1 95.304 6.3056 15.11 0.000000 470s C2 -0.243 0.0684 -3.55 0.001128 470s C3 0.306 0.0394 7.78 0.000000 470s C4 46.423 8.3296 5.57 0.000003 470s C5 0.257 0.0684 3.76 0.000622 470s C6 0.264 0.0455 5.80 0.000001 470s > print( round( coef( summary( fitw2sls5$eq[[ 2 ]] ) ), digits = 6 ) ) 470s Estimate Std. Error t value Pr(>|t|) 470s (Intercept) 46.423 8.3296 5.57 0.000003 470s price 0.257 0.0684 3.76 0.000622 470s farmPrice 0.264 0.0455 5.80 0.000001 470s trend 0.306 0.0394 7.78 0.000000 470s > 470s > 470s > ## *********** variance covariance matrix of the coefficients ******* 470s > print( round( vcov( fitw2sls1e ), digits = 6 ) ) 470s demand_(Intercept) demand_price demand_income 470s demand_(Intercept) 53.3287 -0.57241 0.04191 470s demand_price -0.5724 0.00791 -0.00225 470s demand_income 0.0419 -0.00225 0.00187 470s supply_(Intercept) 0.0000 0.00000 0.00000 470s supply_price 0.0000 0.00000 0.00000 470s supply_farmPrice 0.0000 0.00000 0.00000 470s supply_trend 0.0000 0.00000 0.00000 470s supply_(Intercept) supply_price supply_farmPrice 470s demand_(Intercept) 0.000 0.000000 0.000000 470s demand_price 0.000 0.000000 0.000000 470s demand_income 0.000 0.000000 0.000000 470s supply_(Intercept) 115.402 -0.876328 -0.259055 470s supply_price -0.876 0.007989 0.000749 470s supply_farmPrice -0.259 0.000749 0.001786 470s supply_trend -0.236 0.000463 0.001101 470s supply_trend 470s demand_(Intercept) 0.000000 470s demand_price 0.000000 470s demand_income 0.000000 470s supply_(Intercept) -0.236183 470s supply_price 0.000463 470s supply_farmPrice 0.001101 470s supply_trend 0.007945 470s > print( round( vcov( fitw2sls1e$eq[[ 2 ]] ), digits = 6 ) ) 470s (Intercept) price farmPrice trend 470s (Intercept) 115.402 -0.876328 -0.259055 -0.236183 470s price -0.876 0.007989 0.000749 0.000463 470s farmPrice -0.259 0.000749 0.001786 0.001101 470s trend -0.236 0.000463 0.001101 0.007945 470s > 470s > print( round( vcov( fitw2sls2 ), digits = 6 ) ) 470s demand_(Intercept) demand_price demand_income 470s demand_(Intercept) 64.14482 -0.679629 0.041312 470s demand_price -0.67963 0.008954 -0.002214 470s demand_income 0.04131 -0.002214 0.001847 470s supply_(Intercept) -1.22810 0.065809 -0.054894 470s supply_price 0.00241 -0.000129 0.000108 470s supply_farmPrice 0.00573 -0.000307 0.000256 470s supply_trend 0.04131 -0.002214 0.001847 470s supply_(Intercept) supply_price supply_farmPrice 470s demand_(Intercept) -1.2281 0.002409 0.005727 470s demand_price 0.0658 -0.000129 -0.000307 470s demand_income -0.0549 0.000108 0.000256 470s supply_(Intercept) 139.2416 -1.098376 -0.294954 470s supply_price -1.0984 0.010116 0.000884 470s supply_farmPrice -0.2950 0.000884 0.002109 470s supply_trend -0.0549 0.000108 0.000256 470s supply_trend 470s demand_(Intercept) 0.041312 470s demand_price -0.002214 470s demand_income 0.001847 470s supply_(Intercept) -0.054894 470s supply_price 0.000108 470s supply_farmPrice 0.000256 470s supply_trend 0.001847 470s > print( round( vcov( fitw2sls2$eq[[ 1 ]] ), digits = 6 ) ) 470s (Intercept) price income 470s (Intercept) 64.1448 -0.67963 0.04131 470s price -0.6796 0.00895 -0.00221 470s income 0.0413 -0.00221 0.00185 470s > 470s > print( round( vcov( fitw2sls3e ), digits = 6 ) ) 470s demand_(Intercept) demand_price demand_income 470s demand_(Intercept) 54.51421 -0.577209 0.034718 470s demand_price -0.57721 0.007585 -0.001860 470s demand_income 0.03472 -0.001860 0.001552 470s supply_(Intercept) -1.03208 0.055305 -0.046132 470s supply_price 0.00202 -0.000108 0.000090 470s supply_farmPrice 0.00481 -0.000258 0.000215 470s supply_trend 0.03472 -0.001860 0.001552 470s supply_(Intercept) supply_price supply_farmPrice 470s demand_(Intercept) -1.0321 0.002024 0.004813 470s demand_price 0.0553 -0.000108 -0.000258 470s demand_income -0.0461 0.000090 0.000215 470s supply_(Intercept) 111.4592 -0.878830 -0.236271 470s supply_price -0.8788 0.008093 0.000708 470s supply_farmPrice -0.2363 0.000708 0.001689 470s supply_trend -0.0461 0.000090 0.000215 470s supply_trend 470s demand_(Intercept) 0.034718 470s demand_price -0.001860 470s demand_income 0.001552 470s supply_(Intercept) -0.046132 470s supply_price 0.000090 470s supply_farmPrice 0.000215 470s supply_trend 0.001552 470s > print( round( vcov( fitw2sls3e, modified.regMat = TRUE ), digits = 6 ) ) 470s C1 C2 C3 C4 C5 C6 470s C1 54.51421 -0.577209 0.034718 -1.0321 0.002024 0.004813 470s C2 -0.57721 0.007585 -0.001860 0.0553 -0.000108 -0.000258 470s C3 0.03472 -0.001860 0.001552 -0.0461 0.000090 0.000215 470s C4 -1.03208 0.055305 -0.046132 111.4592 -0.878830 -0.236271 470s C5 0.00202 -0.000108 0.000090 -0.8788 0.008093 0.000708 470s C6 0.00481 -0.000258 0.000215 -0.2363 0.000708 0.001689 470s > print( round( vcov( fitw2sls3e$eq[[ 2 ]] ), digits = 6 ) ) 470s (Intercept) price farmPrice trend 470s (Intercept) 111.4592 -0.878830 -0.236271 -0.046132 470s price -0.8788 0.008093 0.000708 0.000090 470s farmPrice -0.2363 0.000708 0.001689 0.000215 470s trend -0.0461 0.000090 0.000215 0.001552 470s > 470s > print( round( vcov( fitw2sls4 ), digits = 6 ) ) 470s demand_(Intercept) demand_price demand_income 470s demand_(Intercept) 39.7610 -0.358128 -0.03842 470s demand_price -0.3581 0.004681 -0.00113 470s demand_income -0.0384 -0.001129 0.00155 470s supply_(Intercept) 39.6949 -0.480685 0.08595 470s supply_price -0.3581 0.004681 -0.00113 470s supply_farmPrice -0.0359 0.000252 0.00011 470s supply_trend -0.0384 -0.001129 0.00155 470s supply_(Intercept) supply_price supply_farmPrice 470s demand_(Intercept) 39.6949 -0.358128 -0.035932 470s demand_price -0.4807 0.004681 0.000252 470s demand_income 0.0859 -0.001129 0.000110 470s supply_(Intercept) 69.3817 -0.480685 -0.226588 470s supply_price -0.4807 0.004681 0.000252 470s supply_farmPrice -0.2266 0.000252 0.002072 470s supply_trend 0.0859 -0.001129 0.000110 470s supply_trend 470s demand_(Intercept) -0.03842 470s demand_price -0.00113 470s demand_income 0.00155 470s supply_(Intercept) 0.08595 470s supply_price -0.00113 470s supply_farmPrice 0.00011 470s supply_trend 0.00155 470s > print( round( vcov( fitw2sls4$eq[[ 1 ]] ), digits = 6 ) ) 470s (Intercept) price income 470s (Intercept) 39.7610 -0.35813 -0.03842 470s price -0.3581 0.00468 -0.00113 470s income -0.0384 -0.00113 0.00155 470s > 470s > print( round( vcov( fitw2sls5 ), digits = 6 ) ) 470s demand_(Intercept) demand_price demand_income 470s demand_(Intercept) 39.7610 -0.358128 -0.03842 470s demand_price -0.3581 0.004681 -0.00113 470s demand_income -0.0384 -0.001129 0.00155 470s supply_(Intercept) 39.6949 -0.480685 0.08595 470s supply_price -0.3581 0.004681 -0.00113 470s supply_farmPrice -0.0359 0.000252 0.00011 470s supply_trend -0.0384 -0.001129 0.00155 470s supply_(Intercept) supply_price supply_farmPrice 470s demand_(Intercept) 39.6949 -0.358128 -0.035932 470s demand_price -0.4807 0.004681 0.000252 470s demand_income 0.0859 -0.001129 0.000110 470s supply_(Intercept) 69.3817 -0.480685 -0.226588 470s supply_price -0.4807 0.004681 0.000252 470s supply_farmPrice -0.2266 0.000252 0.002072 470s supply_trend 0.0859 -0.001129 0.000110 470s supply_trend 470s demand_(Intercept) -0.03842 470s demand_price -0.00113 470s demand_income 0.00155 470s supply_(Intercept) 0.08595 470s supply_price -0.00113 470s supply_farmPrice 0.00011 470s supply_trend 0.00155 470s > print( round( vcov( fitw2sls5, modified.regMat = TRUE ), digits = 6 ) ) 470s C1 C2 C3 C4 C5 C6 470s C1 39.7610 -0.358128 -0.03842 39.6949 -0.358128 -0.035932 470s C2 -0.3581 0.004681 -0.00113 -0.4807 0.004681 0.000252 470s C3 -0.0384 -0.001129 0.00155 0.0859 -0.001129 0.000110 470s C4 39.6949 -0.480685 0.08595 69.3817 -0.480685 -0.226588 470s C5 -0.3581 0.004681 -0.00113 -0.4807 0.004681 0.000252 470s C6 -0.0359 0.000252 0.00011 -0.2266 0.000252 0.002072 470s > print( round( vcov( fitw2sls5$eq[[ 2 ]] ), digits = 6 ) ) 470s (Intercept) price farmPrice trend 470s (Intercept) 69.3817 -0.480685 -0.226588 0.08595 470s price -0.4807 0.004681 0.000252 -0.00113 470s farmPrice -0.2266 0.000252 0.002072 0.00011 470s trend 0.0859 -0.001129 0.000110 0.00155 470s > 470s > print( round( vcov( fitw2slsd1 ), digits = 6 ) ) 470s demand_(Intercept) demand_price demand_income 470s demand_(Intercept) 124.179 -1.51767 0.28519 470s demand_price -1.518 0.02098 -0.00595 470s demand_income 0.285 -0.00595 0.00318 470s supply_(Intercept) 0.000 0.00000 0.00000 470s supply_price 0.000 0.00000 0.00000 470s supply_farmPrice 0.000 0.00000 0.00000 470s supply_trend 0.000 0.00000 0.00000 470s supply_(Intercept) supply_price supply_farmPrice 470s demand_(Intercept) 0.000 0.000000 0.000000 470s demand_price 0.000 0.000000 0.000000 470s demand_income 0.000 0.000000 0.000000 470s supply_(Intercept) 144.253 -1.095410 -0.323818 470s supply_price -1.095 0.009987 0.000936 470s supply_farmPrice -0.324 0.000936 0.002233 470s supply_trend -0.295 0.000579 0.001377 470s supply_trend 470s demand_(Intercept) 0.000000 470s demand_price 0.000000 470s demand_income 0.000000 470s supply_(Intercept) -0.295229 470s supply_price 0.000579 470s supply_farmPrice 0.001377 470s supply_trend 0.009931 470s > print( round( vcov( fitw2slsd1$eq[[ 1 ]] ), digits = 6 ) ) 470s (Intercept) price income 470s (Intercept) 124.179 -1.51767 0.28519 470s price -1.518 0.02098 -0.00595 470s income 0.285 -0.00595 0.00318 470s > 470s > print( round( vcov( fitw2slsd2e ), digits = 6 ) ) 470s demand_(Intercept) demand_price demand_income 470s demand_(Intercept) 95.9017 -1.129212 0.176368 470s demand_price -1.1292 0.014881 -0.003682 470s demand_income 0.1764 -0.003682 0.001968 470s supply_(Intercept) -5.2430 0.109460 -0.058492 470s supply_price 0.0103 -0.000215 0.000115 470s supply_farmPrice 0.0245 -0.000510 0.000273 470s supply_trend 0.1764 -0.003682 0.001968 470s supply_(Intercept) supply_price supply_farmPrice 470s demand_(Intercept) -5.2430 0.010284 0.024451 470s demand_price 0.1095 -0.000215 -0.000510 470s demand_income -0.0585 0.000115 0.000273 470s supply_(Intercept) 114.2555 -0.898881 -0.243056 470s supply_price -0.8989 0.008273 0.000727 470s supply_farmPrice -0.2431 0.000727 0.001733 470s supply_trend -0.0585 0.000115 0.000273 470s supply_trend 470s demand_(Intercept) 0.176368 470s demand_price -0.003682 470s demand_income 0.001968 470s supply_(Intercept) -0.058492 470s supply_price 0.000115 470s supply_farmPrice 0.000273 470s supply_trend 0.001968 470s > print( round( vcov( fitw2slsd2e$eq[[ 2 ]] ), digits = 6 ) ) 470s (Intercept) price farmPrice trend 470s (Intercept) 114.2555 -0.898881 -0.243056 -0.058492 470s price -0.8989 0.008273 0.000727 0.000115 470s farmPrice -0.2431 0.000727 0.001733 0.000273 470s trend -0.0585 0.000115 0.000273 0.001968 470s > 470s > print( round( vcov( fitw2slsd3 ), digits = 6 ) ) 470s demand_(Intercept) demand_price demand_income 470s demand_(Intercept) 113.0903 -1.334011 0.210445 470s demand_price -1.3340 0.017622 -0.004394 470s demand_income 0.2104 -0.004394 0.002348 470s supply_(Intercept) -6.2560 0.130609 -0.069794 470s supply_price 0.0123 -0.000256 0.000137 470s supply_farmPrice 0.0292 -0.000609 0.000325 470s supply_trend 0.2104 -0.004394 0.002348 470s supply_(Intercept) supply_price supply_farmPrice 470s demand_(Intercept) -6.2560 0.012271 0.029175 470s demand_price 0.1306 -0.000256 -0.000609 470s demand_income -0.0698 0.000137 0.000325 470s supply_(Intercept) 142.7207 -1.123408 -0.303360 470s supply_price -1.1234 0.010341 0.000908 470s supply_farmPrice -0.3034 0.000908 0.002165 470s supply_trend -0.0698 0.000137 0.000325 470s supply_trend 470s demand_(Intercept) 0.210445 470s demand_price -0.004394 470s demand_income 0.002348 470s supply_(Intercept) -0.069794 470s supply_price 0.000137 470s supply_farmPrice 0.000325 470s supply_trend 0.002348 470s > print( round( vcov( fitw2slsd3, modified.regMat = TRUE ), digits = 6 ) ) 470s C1 C2 C3 C4 C5 C6 470s C1 113.0903 -1.334011 0.210445 -6.2560 0.012271 0.029175 470s C2 -1.3340 0.017622 -0.004394 0.1306 -0.000256 -0.000609 470s C3 0.2104 -0.004394 0.002348 -0.0698 0.000137 0.000325 470s C4 -6.2560 0.130609 -0.069794 142.7207 -1.123408 -0.303360 470s C5 0.0123 -0.000256 0.000137 -1.1234 0.010341 0.000908 470s C6 0.0292 -0.000609 0.000325 -0.3034 0.000908 0.002165 470s > print( round( vcov( fitw2slsd3$eq[[ 1 ]] ), digits = 6 ) ) 470s (Intercept) price income 470s (Intercept) 113.09 -1.33401 0.21044 470s price -1.33 0.01762 -0.00439 470s income 0.21 -0.00439 0.00235 470s > 470s > 470s > ## *********** confidence intervals of coefficients ************* 470s > print( confint( fitw2sls1e, useDfSys = TRUE ) ) 470s 2.5 % 97.5 % 470s demand_(Intercept) 79.776 109.491 470s demand_price -0.425 -0.063 470s demand_income 0.226 0.402 470s supply_(Intercept) 27.677 71.388 470s supply_price 0.058 0.422 470s supply_farmPrice 0.170 0.342 470s supply_trend 0.072 0.434 470s > print( confint( fitw2sls1e$eq[[ 1 ]], level = 0.9, useDfSys = TRUE ) ) 470s 5 % 95 % 470s (Intercept) 82.275 106.992 470s price -0.394 -0.093 470s income 0.241 0.387 470s > 470s > print( confint( fitw2sls2, level = 0.9 ) ) 470s 5 % 95 % 470s demand_(Intercept) 78.107 110.660 470s demand_price -0.422 -0.038 470s demand_income 0.215 0.390 470s supply_(Intercept) 24.069 72.030 470s supply_price 0.039 0.447 470s supply_farmPrice 0.169 0.356 470s supply_trend 0.215 0.390 470s > print( confint( fitw2sls2$eq[[ 2 ]], level = 0.99 ) ) 470s 0.5 % 99.5 % 470s (Intercept) 15.854 80.245 470s price -0.031 0.517 470s farmPrice 0.137 0.388 470s trend 0.186 0.420 470s > 470s > print( confint( fitw2sls3, level = 0.99 ) ) 470s 0.5 % 99.5 % 470s demand_(Intercept) 78.107 110.660 470s demand_price -0.422 -0.038 470s demand_income 0.215 0.390 470s supply_(Intercept) 24.069 72.030 470s supply_price 0.039 0.447 470s supply_farmPrice 0.169 0.356 470s supply_trend 0.215 0.390 470s > print( confint( fitw2sls3$eq[[ 1 ]], level = 0.5 ) ) 470s 25 % 75 % 470s (Intercept) 88.923 99.844 470s price -0.295 -0.166 470s income 0.274 0.332 470s > 470s > print( confint( fitw2sls4e, level = 0.5, useDfSys = TRUE ) ) 470s 25 % 75 % 470s demand_(Intercept) 83.658 107.036 470s demand_price -0.369 -0.117 470s demand_income 0.233 0.379 470s supply_(Intercept) 31.138 61.736 470s supply_price 0.131 0.383 470s supply_farmPrice 0.181 0.347 470s supply_trend 0.233 0.379 470s > print( confint( fitw2sls4e$eq[[ 2 ]], level = 0.25, useDfSys = TRUE ) ) 470s 37.5 % 62.5 % 470s (Intercept) 44.016 48.857 470s price 0.237 0.277 470s farmPrice 0.251 0.277 470s trend 0.294 0.317 470s > 470s > print( confint( fitw2sls5, level = 0.25 ) ) 470s 37.5 % 62.5 % 470s demand_(Intercept) 82.503 108.105 470s demand_price -0.382 -0.104 470s demand_income 0.226 0.386 470s supply_(Intercept) 29.513 63.333 470s supply_price 0.118 0.396 470s supply_farmPrice 0.172 0.357 470s supply_trend 0.226 0.386 470s > print( confint( fitw2sls5$eq[[ 1 ]], level = 0.975 ) ) 470s 1.3 % 98.8 % 470s (Intercept) 80.537 110.072 470s price -0.403 -0.083 470s income 0.214 0.399 470s > 470s > print( confint( fitw2slsd1, level = 0.975 ) ) 470s 1.3 % 98.8 % 470s demand_(Intercept) 83.279 130.300 470s demand_price -0.717 -0.106 470s demand_income 0.243 0.481 470s supply_(Intercept) 24.071 74.994 470s supply_price 0.028 0.452 470s supply_farmPrice 0.155 0.356 470s supply_trend 0.042 0.464 470s > print( confint( fitw2slsd1$eq[[ 2 ]], level = 0.999 ) ) 470s 0.1 % 100 % 470s (Intercept) 1.310 97.755 470s price -0.161 0.641 470s farmPrice 0.066 0.445 470s trend -0.147 0.653 470s > 470s > print( confint( fitw2slsd2e, level = 0.999, useDfSys = TRUE ) ) 470s 0.1 % 100 % 470s demand_(Intercept) 84.562 124.365 470s demand_price -0.611 -0.115 470s demand_income 0.246 0.426 470s supply_(Intercept) 25.348 68.793 470s supply_price 0.060 0.430 470s supply_farmPrice 0.182 0.352 470s supply_trend 0.246 0.426 470s > print( confint( fitw2slsd2e$eq[[ 1 ]], level = 0.01, useDfSys = TRUE ) ) 470s 49.5 % 50.5 % 470s (Intercept) 104.340 104.587 470s price -0.365 -0.362 470s income 0.335 0.336 470s > 470s > print( confint( fitw2slsd3e, level = 0.01, useDfSys = TRUE ) ) 470s 49.5 % 50.5 % 470s demand_(Intercept) 84.562 124.365 470s demand_price -0.611 -0.115 470s demand_income 0.246 0.426 470s supply_(Intercept) 25.348 68.793 470s supply_price 0.060 0.430 470s supply_farmPrice 0.182 0.352 470s supply_trend 0.246 0.426 470s > print( confint( fitw2slsd3e$eq[[ 2 ]], useDfSys = TRUE ) ) 470s 2.5 % 97.5 % 470s (Intercept) 25.348 68.793 470s price 0.060 0.430 470s farmPrice 0.182 0.352 470s trend 0.246 0.426 470s > 470s > 470s > ## *********** fitted values ************* 470s > print( fitted( fitw2sls1e ) ) 470s demand supply 470s 1 97.6 98.9 470s 2 99.9 100.4 470s 3 99.8 100.5 470s 4 100.0 100.7 470s 5 102.1 102.6 470s 6 102.0 102.6 470s 7 102.4 102.6 470s 8 103.0 104.8 470s 9 101.5 102.7 470s 10 100.3 99.7 470s 11 95.5 95.4 470s 12 94.7 93.8 470s 13 96.1 95.6 470s 14 99.0 97.6 470s 15 103.8 102.3 470s 16 103.7 104.1 470s 17 103.8 102.8 470s 18 102.1 102.7 470s 19 103.6 102.6 470s 20 106.9 105.6 470s > print( fitted( fitw2sls1e$eq[[ 1 ]] ) ) 470s 1 2 3 4 5 6 7 8 9 10 11 12 13 470s 97.6 99.9 99.8 100.0 102.1 102.0 102.4 103.0 101.5 100.3 95.5 94.7 96.1 470s 14 15 16 17 18 19 20 470s 99.0 103.8 103.7 103.8 102.1 103.6 106.9 470s > 470s > print( fitted( fitw2sls2 ) ) 470s demand supply 470s 1 97.8 98.5 470s 2 99.9 100.0 470s 3 99.9 100.1 470s 4 100.1 100.4 470s 5 102.0 102.5 470s 6 101.9 102.4 470s 7 102.4 102.4 470s 8 102.9 104.8 470s 9 101.4 102.7 470s 10 100.3 99.7 470s 11 95.7 95.3 470s 12 94.9 93.7 470s 13 96.3 95.6 470s 14 99.1 97.7 470s 15 103.7 102.6 470s 16 103.5 104.4 470s 17 103.7 103.2 470s 18 102.1 103.1 470s 19 103.6 102.9 470s 20 106.8 106.1 470s > print( fitted( fitw2sls2$eq[[ 2 ]] ) ) 470s 1 2 3 4 5 6 7 8 9 10 11 12 13 470s 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 470s 14 15 16 17 18 19 20 470s 97.7 102.6 104.4 103.2 103.1 102.9 106.1 470s > 470s > print( fitted( fitw2sls3 ) ) 470s demand supply 470s 1 97.8 98.5 470s 2 99.9 100.0 470s 3 99.9 100.1 470s 4 100.1 100.4 470s 5 102.0 102.5 470s 6 101.9 102.4 470s 7 102.4 102.4 470s 8 102.9 104.8 470s 9 101.4 102.7 470s 10 100.3 99.7 470s 11 95.7 95.3 470s 12 94.9 93.7 470s 13 96.3 95.6 470s 14 99.1 97.7 470s 15 103.7 102.6 470s 16 103.5 104.4 470s 17 103.7 103.2 470s 18 102.1 103.1 470s 19 103.6 102.9 470s 20 106.8 106.1 470s > print( fitted( fitw2sls3$eq[[ 1 ]] ) ) 470s 1 2 3 4 5 6 7 8 9 10 11 12 13 470s 97.8 99.9 99.9 100.1 102.0 101.9 102.4 102.9 101.4 100.3 95.7 94.9 96.3 470s 14 15 16 17 18 19 20 470s 99.1 103.7 103.5 103.7 102.1 103.6 106.8 470s > 470s > print( fitted( fitw2sls4e ) ) 470s demand supply 470s 1 97.7 98.4 470s 2 99.9 100.0 470s 3 99.8 100.1 470s 4 100.0 100.5 470s 5 102.1 102.4 470s 6 101.9 102.4 470s 7 102.4 102.5 470s 8 102.9 104.8 470s 9 101.5 102.7 470s 10 100.4 99.5 470s 11 95.7 95.1 470s 12 94.9 93.6 470s 13 96.2 95.6 470s 14 99.1 97.6 470s 15 103.8 102.5 470s 16 103.6 104.4 470s 17 103.8 103.1 470s 18 102.0 103.1 470s 19 103.5 103.0 470s 20 106.7 106.3 470s > print( fitted( fitw2sls4e$eq[[ 2 ]] ) ) 470s 1 2 3 4 5 6 7 8 9 10 11 12 13 470s 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 470s 14 15 16 17 18 19 20 470s 97.6 102.5 104.4 103.1 103.1 103.0 106.3 470s > 470s > print( fitted( fitw2sls5 ) ) 470s demand supply 470s 1 97.7 98.4 470s 2 99.9 100.0 470s 3 99.8 100.1 470s 4 100.0 100.5 470s 5 102.1 102.4 470s 6 101.9 102.4 470s 7 102.4 102.5 470s 8 102.9 104.8 470s 9 101.5 102.7 470s 10 100.4 99.5 470s 11 95.7 95.1 470s 12 94.9 93.6 470s 13 96.2 95.6 470s 14 99.1 97.6 470s 15 103.8 102.5 470s 16 103.6 104.4 470s 17 103.8 103.1 470s 18 102.0 103.1 470s 19 103.5 103.0 470s 20 106.7 106.3 470s > print( fitted( fitw2sls5$eq[[ 1 ]] ) ) 470s 1 2 3 4 5 6 7 8 9 10 11 12 13 470s 97.7 99.9 99.8 100.0 102.1 101.9 102.4 102.9 101.5 100.4 95.7 94.9 96.2 470s 14 15 16 17 18 19 20 470s 99.1 103.8 103.6 103.8 102.0 103.5 106.7 470s > 470s > print( fitted( fitw2slsd1 ) ) 470s demand supply 470s 1 97.1 98.9 470s 2 99.2 100.4 470s 3 99.2 100.5 470s 4 99.3 100.7 470s 5 102.5 102.6 470s 6 102.2 102.6 470s 7 102.5 102.6 470s 8 102.7 104.8 470s 9 102.0 102.7 470s 10 101.4 99.7 470s 11 95.6 95.4 470s 12 93.9 93.8 470s 13 95.0 95.6 470s 14 98.9 97.6 470s 15 104.9 102.3 470s 16 104.3 104.1 470s 17 106.1 102.8 470s 18 101.7 102.7 470s 19 103.3 102.6 470s 20 106.0 105.6 470s > print( fitted( fitw2slsd1$eq[[ 2 ]] ) ) 470s 1 2 3 4 5 6 7 8 9 10 11 12 13 470s 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 470s 14 15 16 17 18 19 20 470s 97.6 102.3 104.1 102.8 102.7 102.6 105.6 470s > 470s > print( fitted( fitw2slsd2e ) ) 470s demand supply 470s 1 97.4 98.2 470s 2 99.4 99.7 470s 3 99.4 99.9 470s 4 99.5 100.2 470s 5 102.4 102.3 470s 6 102.1 102.3 470s 7 102.4 102.4 470s 8 102.6 104.7 470s 9 101.9 102.7 470s 10 101.2 99.6 470s 11 95.9 95.2 470s 12 94.4 93.6 470s 13 95.5 95.6 470s 14 99.0 97.7 470s 15 104.5 102.7 470s 16 104.0 104.6 470s 17 105.4 103.5 470s 18 101.8 103.3 470s 19 103.2 103.2 470s 20 105.9 106.4 470s > print( fitted( fitw2slsd2e$eq[[ 1 ]] ) ) 470s 1 2 3 4 5 6 7 8 9 10 11 12 13 470s 97.4 99.4 99.4 99.5 102.4 102.1 102.4 102.6 101.9 101.2 95.9 94.4 95.5 470s 14 15 16 17 18 19 20 470s 99.0 104.5 104.0 105.4 101.8 103.2 105.9 470s > 470s > print( fitted( fitw2slsd3e ) ) 470s demand supply 470s 1 97.4 98.2 470s 2 99.4 99.7 470s 3 99.4 99.9 470s 4 99.5 100.2 470s 5 102.4 102.3 470s 6 102.1 102.3 470s 7 102.4 102.4 470s 8 102.6 104.7 470s 9 101.9 102.7 470s 10 101.2 99.6 470s 11 95.9 95.2 470s 12 94.4 93.6 470s 13 95.5 95.6 470s 14 99.0 97.7 470s 15 104.5 102.7 470s 16 104.0 104.6 470s 17 105.4 103.5 470s 18 101.8 103.3 470s 19 103.2 103.2 470s 20 105.9 106.4 470s > print( fitted( fitw2slsd3e$eq[[ 2 ]] ) ) 470s 1 2 3 4 5 6 7 8 9 10 11 12 13 470s 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 470s 14 15 16 17 18 19 20 470s 97.7 102.7 104.6 103.5 103.3 103.2 106.4 470s > 470s > 470s > ## *********** predicted values ************* 470s > predictData <- Kmenta 470s > predictData$consump <- NULL 470s > predictData$price <- Kmenta$price * 0.9 470s > predictData$income <- Kmenta$income * 1.1 470s > 470s > print( predict( fitw2sls1e, se.fit = TRUE, interval = "prediction", 470s + useDfSys = TRUE ) ) 470s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 470s 1 97.6 0.609 93.5 101.8 98.9 0.965 470s 2 99.9 0.553 95.7 104.0 100.4 0.952 470s 3 99.8 0.520 95.7 103.9 100.5 0.861 470s 4 100.0 0.558 95.9 104.2 100.7 0.839 470s 5 102.1 0.476 98.0 106.2 102.6 0.818 470s 6 102.0 0.437 97.9 106.1 102.6 0.723 470s 7 102.4 0.454 98.3 106.5 102.6 0.658 470s 8 103.0 0.567 98.8 107.1 104.8 0.889 470s 9 101.5 0.502 97.3 105.6 102.7 0.723 470s 10 100.3 0.758 96.0 104.6 99.7 0.915 470s 11 95.5 0.888 91.2 99.9 95.4 1.098 470s 12 94.7 0.928 90.3 99.1 93.8 1.277 470s 13 96.1 0.844 91.8 100.5 95.6 1.137 470s 14 99.0 0.477 94.9 103.1 97.6 0.820 470s 15 103.8 0.731 99.6 108.1 102.3 0.804 470s 16 103.7 0.587 99.5 107.8 104.1 0.837 470s 17 103.8 1.243 99.1 108.6 102.8 1.489 470s 18 102.1 0.506 97.9 106.2 102.7 0.884 470s 19 103.6 0.641 99.4 107.8 102.6 1.010 470s 20 106.9 1.204 102.2 111.6 105.6 1.550 470s supply.lwr supply.upr 470s 1 93.5 104.3 470s 2 95.0 105.8 470s 3 95.2 105.8 470s 4 95.4 106.0 470s 5 97.4 107.9 470s 6 97.4 107.8 470s 7 97.4 107.7 470s 8 99.5 110.1 470s 9 97.5 108.0 470s 10 94.3 105.0 470s 11 89.9 100.8 470s 12 88.2 99.5 470s 13 90.1 101.2 470s 14 92.3 102.9 470s 15 97.1 107.6 470s 16 98.8 109.3 470s 17 97.0 108.7 470s 18 97.4 108.0 470s 19 97.2 108.0 470s 20 99.7 111.5 470s > print( predict( fitw2sls1e$eq[[ 1 ]], se.fit = TRUE, interval = "prediction", 470s + useDfSys = TRUE ) ) 470s fit se.fit lwr upr 470s 1 97.6 0.609 93.5 101.8 470s 2 99.9 0.553 95.7 104.0 470s 3 99.8 0.520 95.7 103.9 470s 4 100.0 0.558 95.9 104.2 470s 5 102.1 0.476 98.0 106.2 470s 6 102.0 0.437 97.9 106.1 470s 7 102.4 0.454 98.3 106.5 470s 8 103.0 0.567 98.8 107.1 470s 9 101.5 0.502 97.3 105.6 470s 10 100.3 0.758 96.0 104.6 470s 11 95.5 0.888 91.2 99.9 470s 12 94.7 0.928 90.3 99.1 470s 13 96.1 0.844 91.8 100.5 470s 14 99.0 0.477 94.9 103.1 470s 15 103.8 0.731 99.6 108.1 470s 16 103.7 0.587 99.5 107.8 470s 17 103.8 1.243 99.1 108.6 470s 18 102.1 0.506 97.9 106.2 470s 19 103.6 0.641 99.4 107.8 470s 20 106.9 1.204 102.2 111.6 470s > 470s > print( predict( fitw2sls2, se.pred = TRUE, interval = "confidence", 470s + level = 0.999, newdata = predictData ) ) 470s demand.pred demand.se.pred demand.lwr demand.upr supply.pred supply.se.pred 470s 1 102.7 2.22 99.1 106 96.0 2.75 470s 2 105.3 2.22 101.7 109 97.5 2.64 470s 3 105.2 2.23 101.5 109 97.6 2.65 470s 4 105.4 2.22 101.9 109 97.9 2.62 470s 5 107.3 2.51 101.8 113 100.1 2.83 470s 6 107.3 2.46 102.0 112 100.0 2.77 470s 7 107.8 2.44 102.7 113 100.0 2.71 470s 8 108.6 2.40 103.7 113 102.2 2.65 470s 9 106.6 2.52 101.0 112 100.4 2.87 470s 10 105.1 2.65 98.8 111 97.4 3.10 470s 11 100.1 2.41 95.2 105 93.0 3.18 470s 12 99.5 2.21 96.0 103 91.3 3.15 470s 13 101.2 2.12 98.5 104 93.1 2.95 470s 14 104.1 2.31 99.8 108 95.3 2.91 470s 15 109.0 2.73 102.3 116 100.2 2.92 470s 16 109.0 2.61 102.9 115 102.0 2.80 470s 17 108.6 3.08 100.1 117 101.1 3.37 470s 18 107.6 2.35 103.0 112 100.5 2.65 470s 19 109.3 2.44 104.2 114 100.4 2.64 470s 20 113.2 2.66 106.8 120 103.3 2.58 470s supply.lwr supply.upr 470s 1 91.7 100.3 470s 2 94.2 100.7 470s 3 94.2 101.0 470s 4 94.8 101.0 470s 5 95.1 105.0 470s 6 95.6 104.4 470s 7 96.1 103.9 470s 8 98.8 105.6 470s 9 95.2 105.6 470s 10 90.7 104.1 470s 11 85.9 100.1 470s 12 84.3 98.3 470s 13 87.3 98.9 470s 14 89.7 100.8 470s 15 94.7 105.8 470s 16 97.3 106.6 470s 17 92.9 109.4 470s 18 97.1 103.9 470s 19 97.1 103.6 470s 20 100.7 105.9 470s > print( predict( fitw2sls2$eq[[ 2 ]], se.pred = TRUE, interval = "confidence", 470s + level = 0.999, newdata = predictData ) ) 470s fit se.pred lwr upr 470s 1 96.0 2.75 91.7 100.3 470s 2 97.5 2.64 94.2 100.7 470s 3 97.6 2.65 94.2 101.0 470s 4 97.9 2.62 94.8 101.0 470s 5 100.1 2.83 95.1 105.0 470s 6 100.0 2.77 95.6 104.4 470s 7 100.0 2.71 96.1 103.9 470s 8 102.2 2.65 98.8 105.6 470s 9 100.4 2.87 95.2 105.6 470s 10 97.4 3.10 90.7 104.1 470s 11 93.0 3.18 85.9 100.1 470s 12 91.3 3.15 84.3 98.3 470s 13 93.1 2.95 87.3 98.9 470s 14 95.3 2.91 89.7 100.8 470s 15 100.2 2.92 94.7 105.8 470s 16 102.0 2.80 97.3 106.6 470s 17 101.1 3.37 92.9 109.4 470s 18 100.5 2.65 97.1 103.9 470s 19 100.4 2.64 97.1 103.6 470s 20 103.3 2.58 100.7 105.9 470s > 470s > print( predict( fitw2sls3, se.pred = TRUE, interval = "prediction", 470s + level = 0.975 ) ) 470s demand.pred demand.se.pred demand.lwr demand.upr supply.pred supply.se.pred 470s 1 97.8 2.08 92.9 103 98.5 2.57 470s 2 99.9 2.07 95.1 105 100.0 2.61 470s 3 99.9 2.06 95.0 105 100.1 2.59 470s 4 100.1 2.07 95.2 105 100.4 2.60 470s 5 102.0 2.05 97.2 107 102.5 2.63 470s 6 101.9 2.04 97.1 107 102.4 2.60 470s 7 102.4 2.04 97.6 107 102.4 2.58 470s 8 102.9 2.08 98.0 108 104.8 2.68 470s 9 101.4 2.06 96.6 106 102.7 2.61 470s 10 100.3 2.15 95.3 105 99.7 2.69 470s 11 95.7 2.19 90.6 101 95.3 2.77 470s 12 94.9 2.20 89.8 100 93.7 2.86 470s 13 96.3 2.16 91.2 101 95.6 2.79 470s 14 99.1 2.05 94.3 104 97.7 2.64 470s 15 103.7 2.13 98.7 109 102.6 2.60 470s 16 103.5 2.08 98.7 108 104.4 2.59 470s 17 103.7 2.39 98.1 109 103.2 2.91 470s 18 102.1 2.06 97.2 107 103.1 2.59 470s 19 103.6 2.10 98.6 108 102.9 2.64 470s 20 106.8 2.37 101.2 112 106.1 2.90 470s supply.lwr supply.upr 470s 1 92.4 104 470s 2 93.9 106 470s 3 94.0 106 470s 4 94.3 106 470s 5 96.3 109 470s 6 96.3 109 470s 7 96.4 109 470s 8 98.5 111 470s 9 96.6 109 470s 10 93.4 106 470s 11 88.8 102 470s 12 87.0 100 470s 13 89.1 102 470s 14 91.5 104 470s 15 96.5 109 470s 16 98.3 110 470s 17 96.4 110 470s 18 97.0 109 470s 19 96.8 109 470s 20 99.3 113 470s > print( predict( fitw2sls3$eq[[ 1 ]], se.pred = TRUE, interval = "prediction", 470s + level = 0.975 ) ) 470s fit se.pred lwr upr 470s 1 97.8 2.08 92.9 103 470s 2 99.9 2.07 95.1 105 470s 3 99.9 2.06 95.0 105 470s 4 100.1 2.07 95.2 105 470s 5 102.0 2.05 97.2 107 470s 6 101.9 2.04 97.1 107 470s 7 102.4 2.04 97.6 107 470s 8 102.9 2.08 98.0 108 470s 9 101.4 2.06 96.6 106 470s 10 100.3 2.15 95.3 105 470s 11 95.7 2.19 90.6 101 470s 12 94.9 2.20 89.8 100 470s 13 96.3 2.16 91.2 101 470s 14 99.1 2.05 94.3 104 470s 15 103.7 2.13 98.7 109 470s 16 103.5 2.08 98.7 108 470s 17 103.7 2.39 98.1 109 470s 18 102.1 2.06 97.2 107 470s 19 103.6 2.10 98.6 108 470s 20 106.8 2.37 101.2 112 470s > 470s > print( predict( fitw2sls4e, se.fit = TRUE, interval = "confidence", 470s + level = 0.25, useDfSys = TRUE ) ) 470s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 470s 1 97.7 0.552 97.5 97.9 98.4 0.611 470s 2 99.9 0.484 99.7 100.0 100.0 0.700 470s 3 99.8 0.465 99.7 100.0 100.1 0.652 470s 4 100.0 0.488 99.9 100.2 100.5 0.664 470s 5 102.1 0.443 101.9 102.2 102.4 0.769 470s 6 101.9 0.425 101.8 102.1 102.4 0.695 470s 7 102.4 0.447 102.2 102.5 102.5 0.639 470s 8 102.9 0.547 102.7 103.1 104.8 0.821 470s 9 101.5 0.458 101.3 101.6 102.7 0.716 470s 10 100.4 0.648 100.2 100.6 99.5 0.743 470s 11 95.7 0.847 95.4 96.0 95.1 0.944 470s 12 94.9 0.823 94.6 95.1 93.6 1.254 470s 13 96.2 0.695 96.0 96.5 95.6 1.154 470s 14 99.1 0.467 98.9 99.2 97.6 0.814 470s 15 103.8 0.590 103.6 104.0 102.5 0.675 470s 16 103.6 0.520 103.4 103.8 104.4 0.659 470s 17 103.8 0.919 103.5 104.1 103.1 1.196 470s 18 102.0 0.487 101.9 102.2 103.1 0.587 470s 19 103.5 0.615 103.3 103.7 103.0 0.664 470s 20 106.7 1.126 106.3 107.0 106.3 0.909 470s supply.lwr supply.upr 470s 1 98.2 98.6 470s 2 99.8 100.3 470s 3 99.9 100.3 470s 4 100.2 100.7 470s 5 102.2 102.7 470s 6 102.2 102.7 470s 7 102.3 102.7 470s 8 104.6 105.1 470s 9 102.5 102.9 470s 10 99.3 99.8 470s 11 94.8 95.4 470s 12 93.2 94.0 470s 13 95.2 96.0 470s 14 97.4 97.9 470s 15 102.3 102.7 470s 16 104.2 104.6 470s 17 102.7 103.5 470s 18 102.9 103.3 470s 19 102.8 103.3 470s 20 106.0 106.6 470s > print( predict( fitw2sls4e$eq[[ 2 ]], se.fit = TRUE, interval = "confidence", 470s + level = 0.25, useDfSys = TRUE ) ) 470s fit se.fit lwr upr 470s 1 98.4 0.611 98.2 98.6 470s 2 100.0 0.700 99.8 100.3 470s 3 100.1 0.652 99.9 100.3 470s 4 100.5 0.664 100.2 100.7 470s 5 102.4 0.769 102.2 102.7 470s 6 102.4 0.695 102.2 102.7 470s 7 102.5 0.639 102.3 102.7 470s 8 104.8 0.821 104.6 105.1 470s 9 102.7 0.716 102.5 102.9 470s 10 99.5 0.743 99.3 99.8 470s 11 95.1 0.944 94.8 95.4 470s 12 93.6 1.254 93.2 94.0 470s 13 95.6 1.154 95.2 96.0 470s 14 97.6 0.814 97.4 97.9 470s 15 102.5 0.675 102.3 102.7 470s 16 104.4 0.659 104.2 104.6 470s 17 103.1 1.196 102.7 103.5 470s 18 103.1 0.587 102.9 103.3 470s 19 103.0 0.664 102.8 103.3 470s 20 106.3 0.909 106.0 106.6 470s > 470s > print( predict( fitw2sls5, se.fit = TRUE, se.pred = TRUE, 470s + interval = "prediction", level = 0.5, newdata = predictData ) ) 470s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 470s 1 102.8 0.781 2.12 101.4 104 95.8 470s 2 105.4 0.812 2.13 104.0 107 97.4 470s 3 105.3 0.824 2.13 103.8 107 97.5 470s 4 105.6 0.820 2.13 104.1 107 97.8 470s 5 107.5 1.186 2.30 106.0 109 99.9 470s 6 107.4 1.133 2.27 105.9 109 99.9 470s 7 108.0 1.141 2.28 106.4 110 99.9 470s 8 108.7 1.143 2.28 107.2 110 102.1 470s 9 106.8 1.179 2.30 105.2 108 100.2 470s 10 105.3 1.307 2.36 103.7 107 97.2 470s 11 100.3 1.108 2.26 98.7 102 92.7 470s 12 99.6 0.841 2.14 98.2 101 91.1 470s 13 101.3 0.638 2.07 99.9 103 93.0 470s 14 104.3 0.914 2.17 102.8 106 95.1 470s 15 109.3 1.440 2.44 107.6 111 100.1 470s 16 109.2 1.333 2.38 107.6 111 101.9 470s 17 108.9 1.742 2.63 107.1 111 100.9 470s 18 107.8 1.049 2.23 106.2 109 100.5 470s 19 109.5 1.216 2.31 107.9 111 100.3 470s 20 113.3 1.669 2.58 111.6 115 103.4 470s supply.se.fit supply.se.pred supply.lwr supply.upr 470s 1 0.825 2.64 94.1 97.6 470s 2 0.696 2.60 95.6 99.1 470s 3 0.712 2.60 95.7 99.2 470s 4 0.674 2.59 96.0 99.5 470s 5 1.087 2.73 98.1 101.8 470s 6 0.979 2.69 98.0 101.7 470s 7 0.874 2.65 98.1 101.7 470s 8 0.871 2.65 100.3 103.9 470s 9 1.143 2.75 98.4 102.1 470s 10 1.338 2.84 95.3 99.1 470s 11 1.483 2.91 90.8 94.7 470s 12 1.645 3.00 89.1 93.1 470s 13 1.440 2.89 91.0 94.9 470s 14 1.247 2.80 93.2 97.0 470s 15 1.222 2.79 98.2 102.0 470s 16 1.104 2.74 100.0 103.7 470s 17 1.808 3.09 98.7 103.0 470s 18 0.861 2.65 98.7 102.3 470s 19 0.861 2.65 98.5 102.1 470s 20 0.666 2.59 101.6 105.2 470s > print( predict( fitw2sls5$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 470s + interval = "prediction", level = 0.5, newdata = predictData ) ) 470s fit se.fit se.pred lwr upr 470s 1 102.8 0.781 2.12 101.4 104 470s 2 105.4 0.812 2.13 104.0 107 470s 3 105.3 0.824 2.13 103.8 107 470s 4 105.6 0.820 2.13 104.1 107 470s 5 107.5 1.186 2.30 106.0 109 470s 6 107.4 1.133 2.27 105.9 109 470s 7 108.0 1.141 2.28 106.4 110 470s 8 108.7 1.143 2.28 107.2 110 470s 9 106.8 1.179 2.30 105.2 108 470s 10 105.3 1.307 2.36 103.7 107 470s 11 100.3 1.108 2.26 98.7 102 470s 12 99.6 0.841 2.14 98.2 101 470s 13 101.3 0.638 2.07 99.9 103 470s 14 104.3 0.914 2.17 102.8 106 470s 15 109.3 1.440 2.44 107.6 111 470s 16 109.2 1.333 2.38 107.6 111 470s 17 108.9 1.742 2.63 107.1 111 470s 18 107.8 1.049 2.23 106.2 109 470s 19 109.5 1.216 2.31 107.9 111 470s 20 113.3 1.669 2.58 111.6 115 470s > 470s > print( predict( fitw2slsd1, se.fit = TRUE, se.pred = TRUE, 470s + interval = "confidence", level = 0.99 ) ) 470s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 470s 1 97.1 0.751 2.13 94.9 99.3 98.9 470s 2 99.2 0.757 2.13 97.0 101.4 100.4 470s 3 99.2 0.692 2.11 97.2 101.2 100.5 470s 4 99.3 0.766 2.13 97.1 101.5 100.7 470s 5 102.5 0.595 2.08 100.8 104.3 102.6 470s 6 102.2 0.503 2.05 100.7 103.7 102.6 470s 7 102.5 0.503 2.05 101.1 104.0 102.6 470s 8 102.7 0.653 2.10 100.8 104.5 104.8 470s 9 102.0 0.655 2.10 100.1 103.9 102.7 470s 10 101.4 1.074 2.26 98.3 104.5 99.7 470s 11 95.6 0.978 2.22 92.8 98.5 95.4 470s 12 93.9 1.134 2.29 90.7 97.2 93.8 470s 13 95.0 1.162 2.31 91.7 98.4 95.6 470s 14 98.9 0.530 2.06 97.4 100.4 97.6 470s 15 104.9 1.061 2.26 101.9 108.0 102.3 470s 16 104.3 0.757 2.13 102.1 106.5 104.1 470s 17 106.1 1.963 2.80 100.4 111.7 102.8 470s 18 101.7 0.597 2.08 100.0 103.5 102.7 470s 19 103.3 0.736 2.12 101.2 105.4 102.6 470s 20 106.0 1.430 2.45 101.9 110.2 105.6 470s supply.se.fit supply.se.pred supply.lwr supply.upr 470s 1 1.079 2.68 95.8 102.1 470s 2 1.064 2.68 97.3 103.5 470s 3 0.962 2.64 97.6 103.3 470s 4 0.938 2.63 98.0 103.4 470s 5 0.914 2.62 100.0 105.3 470s 6 0.808 2.59 100.2 104.9 470s 7 0.736 2.57 100.4 104.7 470s 8 0.994 2.65 101.9 107.7 470s 9 0.808 2.59 100.4 105.1 470s 10 1.023 2.66 96.7 102.7 470s 11 1.228 2.75 91.8 99.0 470s 12 1.428 2.84 89.7 98.0 470s 13 1.272 2.77 91.9 99.4 470s 14 0.917 2.62 94.9 100.3 470s 15 0.899 2.62 99.7 104.9 470s 16 0.936 2.63 101.3 106.8 470s 17 1.665 2.97 98.0 107.7 470s 18 0.988 2.65 99.8 105.6 470s 19 1.129 2.70 99.3 105.9 470s 20 1.733 3.01 100.5 110.7 470s > print( predict( fitw2slsd1$eq[[ 2 ]], se.fit = TRUE, se.pred = TRUE, 470s + interval = "confidence", level = 0.99 ) ) 470s fit se.fit se.pred lwr upr 470s 1 98.9 1.079 2.68 95.8 102.1 470s 2 100.4 1.064 2.68 97.3 103.5 470s 3 100.5 0.962 2.64 97.6 103.3 470s 4 100.7 0.938 2.63 98.0 103.4 470s 5 102.6 0.914 2.62 100.0 105.3 470s 6 102.6 0.808 2.59 100.2 104.9 470s 7 102.6 0.736 2.57 100.4 104.7 470s 8 104.8 0.994 2.65 101.9 107.7 470s 9 102.7 0.808 2.59 100.4 105.1 470s 10 99.7 1.023 2.66 96.7 102.7 470s 11 95.4 1.228 2.75 91.8 99.0 470s 12 93.8 1.428 2.84 89.7 98.0 470s 13 95.6 1.272 2.77 91.9 99.4 470s 14 97.6 0.917 2.62 94.9 100.3 470s 15 102.3 0.899 2.62 99.7 104.9 470s 16 104.1 0.936 2.63 101.3 106.8 470s 17 102.8 1.665 2.97 98.0 107.7 470s 18 102.7 0.988 2.65 99.8 105.6 470s 19 102.6 1.129 2.70 99.3 105.9 470s 20 105.6 1.733 3.01 100.5 110.7 470s > 470s > print( predict( fitw2slsd2e, se.fit = TRUE, interval = "prediction", 470s + level = 0.9, newdata = predictData, useDfSys = TRUE ) ) 470s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 470s 1 104 1.214 100.1 108 95.7 1.100 470s 2 106 1.169 102.6 110 97.2 0.835 470s 3 106 1.216 102.5 110 97.3 0.864 470s 4 107 1.169 102.7 110 97.6 0.789 470s 5 109 1.897 104.7 114 99.9 1.242 470s 6 109 1.773 104.6 114 99.9 1.115 470s 7 110 1.718 105.2 114 99.9 0.983 470s 8 110 1.552 105.8 114 102.2 0.843 470s 9 109 1.939 104.0 113 100.4 1.310 470s 10 107 2.229 102.5 112 97.4 1.683 470s 11 102 1.655 97.5 106 92.9 1.794 470s 12 101 1.125 96.8 104 91.2 1.750 470s 13 102 0.879 98.5 106 93.1 1.449 470s 14 106 1.480 101.5 110 95.3 1.383 470s 15 111 2.331 106.3 117 100.4 1.395 470s 16 111 2.064 106.3 116 102.2 1.175 470s 17 112 3.001 105.7 118 101.4 2.074 470s 18 109 1.475 104.9 113 100.7 0.861 470s 19 111 1.589 106.5 115 100.6 0.829 470s 20 114 1.756 109.9 119 103.6 0.680 470s supply.lwr supply.upr 470s 1 91.1 100.3 470s 2 92.7 101.7 470s 3 92.8 101.8 470s 4 93.2 102.1 470s 5 95.2 104.7 470s 6 95.3 104.6 470s 7 95.3 104.5 470s 8 97.7 106.7 470s 9 95.6 105.2 470s 10 92.3 102.5 470s 11 87.7 98.1 470s 12 86.0 96.4 470s 13 88.1 98.0 470s 14 90.4 100.1 470s 15 95.5 105.3 470s 16 97.5 106.9 470s 17 95.8 106.9 470s 18 96.2 105.2 470s 19 96.1 105.1 470s 20 99.2 108.0 470s > print( predict( fitw2slsd2e$eq[[ 1 ]], se.fit = TRUE, interval = "prediction", 470s + level = 0.9, newdata = predictData, useDfSys = TRUE ) ) 470s fit se.fit lwr upr 470s 1 104 1.214 100.1 108 470s 2 106 1.169 102.6 110 470s 3 106 1.216 102.5 110 470s 4 107 1.169 102.7 110 470s 5 109 1.897 104.7 114 470s 6 109 1.773 104.6 114 470s 7 110 1.718 105.2 114 470s 8 110 1.552 105.8 114 470s 9 109 1.939 104.0 113 470s 10 107 2.229 102.5 112 470s 11 102 1.655 97.5 106 470s 12 101 1.125 96.8 104 470s 13 102 0.879 98.5 106 470s 14 106 1.480 101.5 110 470s 15 111 2.331 106.3 117 470s 16 111 2.064 106.3 116 470s 17 112 3.001 105.7 118 470s 18 109 1.475 104.9 113 470s 19 111 1.589 106.5 115 470s 20 114 1.756 109.9 119 470s > 470s > print( predict( fitw2slsd3e, se.fit = TRUE, se.pred = TRUE, 470s + interval = "prediction", level = 0.01, useDfSys = TRUE ) ) 470s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 470s 1 97.4 0.622 2.05 97.4 97.4 98.2 470s 2 99.4 0.654 2.06 99.4 99.4 99.7 470s 3 99.4 0.598 2.04 99.4 99.4 99.9 470s 4 99.5 0.663 2.06 99.5 99.5 100.2 470s 5 102.4 0.515 2.02 102.4 102.4 102.3 470s 6 102.1 0.442 2.00 102.1 102.1 102.3 470s 7 102.4 0.444 2.00 102.4 102.4 102.4 470s 8 102.6 0.587 2.04 102.6 102.6 104.7 470s 9 101.9 0.573 2.03 101.9 101.9 102.7 470s 10 101.2 0.948 2.17 101.2 101.2 99.6 470s 11 95.9 0.849 2.13 95.9 95.9 95.2 470s 12 94.4 0.914 2.15 94.4 94.4 93.6 470s 13 95.5 0.943 2.17 95.5 95.5 95.6 470s 14 99.0 0.464 2.01 99.0 99.1 97.7 470s 15 104.5 0.883 2.14 104.5 104.6 102.7 470s 16 104.0 0.631 2.05 104.0 104.0 104.6 470s 17 105.4 1.665 2.56 105.4 105.5 103.5 470s 18 101.8 0.538 2.02 101.7 101.8 103.3 470s 19 103.2 0.661 2.06 103.2 103.3 103.2 470s 20 105.9 1.284 2.34 105.9 106.0 106.4 470s supply.se.fit supply.se.pred supply.lwr supply.upr 470s 1 0.652 2.60 98.1 98.2 470s 2 0.740 2.62 99.7 99.8 470s 3 0.682 2.61 99.8 99.9 470s 4 0.708 2.61 100.2 100.2 470s 5 0.782 2.63 102.3 102.4 470s 6 0.699 2.61 102.3 102.4 470s 7 0.648 2.60 102.3 102.4 470s 8 0.906 2.67 104.7 104.8 470s 9 0.736 2.62 102.7 102.8 470s 10 0.931 2.68 99.6 99.7 470s 11 1.107 2.75 95.2 95.2 470s 12 1.287 2.83 93.6 93.7 470s 13 1.157 2.77 95.5 95.6 470s 14 0.829 2.65 97.7 97.7 470s 15 0.717 2.62 102.7 102.8 470s 16 0.676 2.61 104.6 104.6 470s 17 1.392 2.88 103.4 103.5 470s 18 0.699 2.61 103.3 103.3 470s 19 0.822 2.65 103.2 103.2 470s 20 1.376 2.87 106.4 106.5 470s > print( predict( fitw2slsd3e$eq[[ 2 ]], se.fit = TRUE, se.pred = TRUE, 470s + interval = "prediction", level = 0.01, useDfSys = TRUE ) ) 470s fit se.fit se.pred lwr upr 470s 1 98.2 0.652 2.60 98.1 98.2 470s 2 99.7 0.740 2.62 99.7 99.8 470s 3 99.9 0.682 2.61 99.8 99.9 470s 4 100.2 0.708 2.61 100.2 100.2 470s 5 102.3 0.782 2.63 102.3 102.4 470s 6 102.3 0.699 2.61 102.3 102.4 470s 7 102.4 0.648 2.60 102.3 102.4 470s 8 104.7 0.906 2.67 104.7 104.8 470s 9 102.7 0.736 2.62 102.7 102.8 470s 10 99.6 0.931 2.68 99.6 99.7 470s 11 95.2 1.107 2.75 95.2 95.2 470s 12 93.6 1.287 2.83 93.6 93.7 470s 13 95.6 1.157 2.77 95.5 95.6 470s 14 97.7 0.829 2.65 97.7 97.7 470s 15 102.7 0.717 2.62 102.7 102.8 470s 16 104.6 0.676 2.61 104.6 104.6 470s 17 103.5 1.392 2.88 103.4 103.5 470s 18 103.3 0.699 2.61 103.3 103.3 470s 19 103.2 0.822 2.65 103.2 103.2 470s 20 106.4 1.376 2.87 106.4 106.5 470s > 470s > # predict just one observation 470s > smallData <- data.frame( price = 130, income = 150, farmPrice = 120, 470s + trend = 25 ) 470s > 470s > print( predict( fitw2sls1e, newdata = smallData ) ) 470s demand.pred supply.pred 470s 1 110 118 470s > print( predict( fitw2sls1e$eq[[ 1 ]], newdata = smallData ) ) 470s fit 470s 1 110 470s > 470s > print( predict( fitw2sls2, se.fit = TRUE, level = 0.9, 470s + newdata = smallData ) ) 470s demand.pred demand.se.fit supply.pred supply.se.fit 470s 1 110 2.52 119 3.53 470s > print( predict( fitw2sls2$eq[[ 1 ]], se.pred = TRUE, level = 0.99, 470s + newdata = smallData ) ) 470s fit se.pred 470s 1 110 3.21 470s > 470s > print( predict( fitw2sls3, interval = "prediction", level = 0.975, 470s + newdata = smallData ) ) 470s demand.pred demand.lwr demand.upr supply.pred supply.lwr supply.upr 470s 1 110 102 117 119 109 129 470s > print( predict( fitw2sls3$eq[[ 1 ]], interval = "confidence", level = 0.8, 470s + newdata = smallData ) ) 470s fit lwr upr 470s 1 110 107 113 470s > 470s > print( predict( fitw2sls4e, se.fit = TRUE, interval = "confidence", 470s + level = 0.999, newdata = smallData ) ) 470s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 470s 1 110 2.08 102 117 119 2.11 470s supply.lwr supply.upr 470s 1 112 127 470s > print( predict( fitw2sls4e$eq[[ 2 ]], se.pred = TRUE, interval = "prediction", 470s + level = 0.75, newdata = smallData ) ) 470s fit se.pred lwr upr 470s 1 119 3.27 115 123 470s > 470s > print( predict( fitw2sls5, se.fit = TRUE, interval = "prediction", 470s + newdata = smallData ) ) 470s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 470s 1 110 2.26 104 116 119 2.33 470s supply.lwr supply.upr 470s 1 112 126 470s > print( predict( fitw2sls5$eq[[ 1 ]], se.pred = TRUE, interval = "confidence", 470s + newdata = smallData ) ) 470s fit se.pred lwr upr 470s 1 110 3 105 114 470s > 470s > print( predict( fitw2slsd2e, se.fit = TRUE, se.pred = TRUE, 470s + interval = "prediction", level = 0.5, newdata = smallData ) ) 470s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 470s 1 108 2.71 3.34 105 110 119 470s supply.se.fit supply.se.pred supply.lwr supply.upr 470s 1 3.22 4.08 117 122 470s > print( predict( fitw2slsd2e$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 470s + interval = "confidence", level = 0.25, newdata = smallData ) ) 470s fit se.fit se.pred lwr upr 470s 1 108 2.71 3.34 107 109 470s > 470s > 470s > ## ************ correlation of predicted values *************** 470s > print( correlation.systemfit( fitw2sls1e, 1, 2 ) ) 470s [,1] 470s [1,] 0 470s [2,] 0 470s [3,] 0 470s [4,] 0 470s [5,] 0 470s [6,] 0 470s [7,] 0 470s [8,] 0 470s [9,] 0 470s [10,] 0 470s [11,] 0 470s [12,] 0 470s [13,] 0 470s [14,] 0 470s [15,] 0 470s [16,] 0 470s [17,] 0 470s [18,] 0 470s [19,] 0 470s [20,] 0 470s > 470s > print( correlation.systemfit( fitw2sls2, 2, 1 ) ) 470s [,1] 470s [1,] 0.413453 470s [2,] 0.153759 470s [3,] 0.152962 470s [4,] 0.112671 470s [5,] -0.071442 470s [6,] -0.053943 470s [7,] -0.050961 470s [8,] -0.005442 470s [9,] -0.000476 470s [10,] -0.001894 470s [11,] 0.047351 470s [12,] 0.064973 470s [13,] 0.024591 470s [14,] -0.028036 470s [15,] 0.175326 470s [16,] 0.254878 470s [17,] 0.104540 470s [18,] 0.065579 470s [19,] 0.147008 470s [20,] 0.124593 470s > 470s > print( correlation.systemfit( fitw2sls3, 1, 2 ) ) 470s [,1] 470s [1,] 0.413453 470s [2,] 0.153759 470s [3,] 0.152962 470s [4,] 0.112671 470s [5,] -0.071442 470s [6,] -0.053943 470s [7,] -0.050961 470s [8,] -0.005442 470s [9,] -0.000476 470s [10,] -0.001894 470s [11,] 0.047351 470s [12,] 0.064973 470s [13,] 0.024591 470s [14,] -0.028036 470s [15,] 0.175326 470s [16,] 0.254878 470s [17,] 0.104540 470s [18,] 0.065579 470s [19,] 0.147008 470s [20,] 0.124593 470s > 470s > print( correlation.systemfit( fitw2sls4e, 2, 1 ) ) 470s [,1] 470s [1,] 0.38438 470s [2,] 0.30697 470s [3,] 0.26690 470s [4,] 0.30163 470s [5,] -0.02768 470s [6,] -0.05086 470s [7,] -0.05895 470s [8,] 0.10102 470s [9,] 0.10072 470s [10,] 0.45547 470s [11,] 0.10817 470s [12,] 0.00552 470s [13,] 0.04219 470s [14,] -0.04054 470s [15,] 0.42100 470s [16,] 0.24974 470s [17,] 0.65722 470s [18,] 0.24286 470s [19,] 0.34336 470s [20,] 0.54717 470s > 470s > print( correlation.systemfit( fitw2sls5, 1, 2 ) ) 470s [,1] 470s [1,] 0.38030 470s [2,] 0.30892 470s [3,] 0.26808 470s [4,] 0.30325 470s [5,] -0.02730 470s [6,] -0.05035 470s [7,] -0.05831 470s [8,] 0.10036 470s [9,] 0.10045 470s [10,] 0.45492 470s [11,] 0.10525 470s [12,] 0.00394 470s [13,] 0.04171 470s [14,] -0.04037 470s [15,] 0.41958 470s [16,] 0.24706 470s [17,] 0.65619 470s [18,] 0.23872 470s [19,] 0.33729 470s [20,] 0.54239 470s > 470s > print( correlation.systemfit( fitw2slsd1, 2, 1 ) ) 470s [,1] 470s [1,] 0 470s [2,] 0 470s [3,] 0 470s [4,] 0 470s [5,] 0 470s [6,] 0 470s [7,] 0 470s [8,] 0 470s [9,] 0 470s [10,] 0 470s [11,] 0 470s [12,] 0 470s [13,] 0 470s [14,] 0 470s [15,] 0 470s [16,] 0 470s [17,] 0 470s [18,] 0 470s [19,] 0 470s [20,] 0 470s > 470s > print( correlation.systemfit( fitw2slsd2e, 1, 2 ) ) 470s [,1] 470s [1,] 0.482214 470s [2,] 0.253368 470s [3,] 0.242824 470s [4,] 0.195411 470s [5,] -0.107828 470s [6,] -0.074958 470s [7,] -0.055696 470s [8,] -0.002037 470s [9,] -0.000921 470s [10,] -0.008040 470s [11,] 0.040999 470s [12,] 0.075418 470s [13,] 0.029702 470s [14,] -0.030775 470s [15,] 0.229063 470s [16,] 0.318607 470s [17,] 0.156734 470s [18,] -0.023016 470s [19,] 0.068128 470s [20,] 0.047481 470s > 470s > print( correlation.systemfit( fitw2slsd3e, 2, 1 ) ) 470s [,1] 470s [1,] 0.482214 470s [2,] 0.253368 470s [3,] 0.242824 470s [4,] 0.195411 470s [5,] -0.107828 470s [6,] -0.074958 470s [7,] -0.055696 470s [8,] -0.002037 470s [9,] -0.000921 470s [10,] -0.008040 470s [11,] 0.040999 470s [12,] 0.075418 470s [13,] 0.029702 470s [14,] -0.030775 470s [15,] 0.229063 470s [16,] 0.318607 470s [17,] 0.156734 470s [18,] -0.023016 470s [19,] 0.068128 470s [20,] 0.047481 470s > 470s > 470s > ## ************ LOG-Likelihood values *************** 470s > print( logLik( fitw2sls1e ) ) 470s 'log Lik.' -67.6 (df=9) 470s > print( logLik( fitw2sls1e, residCovDiag = TRUE ) ) 470s 'log Lik.' -84.4 (df=9) 470s > 470s > print( logLik( fitw2sls2 ) ) 470s 'log Lik.' -65.2 (df=8) 470s > print( logLik( fitw2sls2, residCovDiag = TRUE ) ) 470s 'log Lik.' -84.8 (df=8) 470s > 470s > print( logLik( fitw2sls3 ) ) 470s 'log Lik.' -65.2 (df=8) 470s > print( logLik( fitw2sls3, residCovDiag = TRUE ) ) 470s 'log Lik.' -84.8 (df=8) 470s > 470s > print( logLik( fitw2sls4e ) ) 470s 'log Lik.' -65.7 (df=7) 470s > print( logLik( fitw2sls4e, residCovDiag = TRUE ) ) 470s 'log Lik.' -84.8 (df=7) 470s > 470s > print( logLik( fitw2sls5 ) ) 470s 'log Lik.' -65.6 (df=7) 470s > print( logLik( fitw2sls5, residCovDiag = TRUE ) ) 470s 'log Lik.' -84.8 (df=7) 470s > 470s > print( logLik( fitw2slsd1 ) ) 470s 'log Lik.' -75.1 (df=9) 470s > print( logLik( fitw2slsd1, residCovDiag = TRUE ) ) 470s 'log Lik.' -84.7 (df=9) 470s > 470s > print( logLik( fitw2slsd2e ) ) 470s 'log Lik.' -69.1 (df=8) 470s > print( logLik( fitw2slsd2e, residCovDiag = TRUE ) ) 470s 'log Lik.' -84.7 (df=8) 470s > 470s > print( logLik( fitw2slsd3e ) ) 470s 'log Lik.' -69.1 (df=8) 470s > print( logLik( fitw2slsd3e, residCovDiag = TRUE ) ) 470s 'log Lik.' -84.7 (df=8) 470s > 470s > 470s > ## ************** F tests **************** 470s > # testing first restriction 470s > print( linearHypothesis( fitw2sls1, restrm ) ) 470s Linear hypothesis test (Theil's F test) 470s 470s Hypothesis: 470s demand_income - supply_trend = 0 470s 470s Model 1: restricted model 470s Model 2: fitw2sls1 470s 470s Res.Df Df F Pr(>F) 470s 1 34 470s 2 33 1 0.31 0.58 470s > linearHypothesis( fitw2sls1, restrict ) 470s Linear hypothesis test (Theil's F test) 470s 470s Hypothesis: 470s demand_income - supply_trend = 0 470s 470s Model 1: restricted model 470s Model 2: fitw2sls1 470s 470s Res.Df Df F Pr(>F) 470s 1 34 470s 2 33 1 0.31 0.58 470s > 470s > print( linearHypothesis( fitw2slsd1e, restrm ) ) 470s Linear hypothesis test (Theil's F test) 470s 470s Hypothesis: 470s demand_income - supply_trend = 0 470s 470s Model 1: restricted model 470s Model 2: fitw2slsd1e 470s 470s Res.Df Df F Pr(>F) 470s 1 34 470s 2 33 1 0.92 0.35 470s > linearHypothesis( fitw2slsd1e, restrict ) 470s Linear hypothesis test (Theil's F test) 470s 470s Hypothesis: 470s demand_income - supply_trend = 0 470s 470s Model 1: restricted model 470s Model 2: fitw2slsd1e 470s 470s Res.Df Df F Pr(>F) 470s 1 34 470s 2 33 1 0.92 0.35 470s > 470s > # testing second restriction 470s > restrOnly2m <- matrix(0,1,7) 470s > restrOnly2q <- 0.5 470s > restrOnly2m[1,2] <- -1 470s > restrOnly2m[1,5] <- 1 470s > restrictOnly2 <- "- demand_price + supply_price = 0.5" 470s > # first restriction not imposed 470s > print( linearHypothesis( fitw2sls1e, restrOnly2m, restrOnly2q ) ) 470s Linear hypothesis test (Theil's F test) 470s 470s Hypothesis: 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2sls1e 470s 470s Res.Df Df F Pr(>F) 470s 1 34 470s 2 33 1 0.01 0.91 470s > linearHypothesis( fitw2sls1e, restrictOnly2 ) 470s Linear hypothesis test (Theil's F test) 470s 470s Hypothesis: 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2sls1e 470s 470s Res.Df Df F Pr(>F) 470s 1 34 470s 2 33 1 0.01 0.91 470s > 470s > print( linearHypothesis( fitw2slsd1, restrOnly2m, restrOnly2q ) ) 470s Linear hypothesis test (Theil's F test) 470s 470s Hypothesis: 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2slsd1 470s 470s Res.Df Df F Pr(>F) 470s 1 34 470s 2 33 1 0.74 0.39 470s > linearHypothesis( fitw2slsd1, restrictOnly2 ) 470s Linear hypothesis test (Theil's F test) 470s 470s Hypothesis: 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2slsd1 470s 470s Res.Df Df F Pr(>F) 470s 1 34 470s 2 33 1 0.74 0.39 470s > 470s > # first restriction imposed 470s > print( linearHypothesis( fitw2sls2, restrOnly2m, restrOnly2q ) ) 470s Linear hypothesis test (Theil's F test) 470s 470s Hypothesis: 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2sls2 470s 470s Res.Df Df F Pr(>F) 470s 1 35 470s 2 34 1 0.04 0.85 470s > linearHypothesis( fitw2sls2, restrictOnly2 ) 470s Linear hypothesis test (Theil's F test) 470s 470s Hypothesis: 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2sls2 470s 470s Res.Df Df F Pr(>F) 470s 1 35 470s 2 34 1 0.04 0.85 470s > 470s > print( linearHypothesis( fitw2sls3, restrOnly2m, restrOnly2q ) ) 470s Linear hypothesis test (Theil's F test) 470s 470s Hypothesis: 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2sls3 470s 470s Res.Df Df F Pr(>F) 470s 1 35 470s 2 34 1 0.04 0.85 470s > linearHypothesis( fitw2sls3, restrictOnly2 ) 470s Linear hypothesis test (Theil's F test) 470s 470s Hypothesis: 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2sls3 470s 470s Res.Df Df F Pr(>F) 470s 1 35 470s 2 34 1 0.04 0.85 470s > 470s > print( linearHypothesis( fitw2slsd2e, restrOnly2m, restrOnly2q ) ) 470s Linear hypothesis test (Theil's F test) 470s 470s Hypothesis: 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2slsd2e 470s 470s Res.Df Df F Pr(>F) 470s 1 35 470s 2 34 1 0.42 0.52 470s > linearHypothesis( fitw2slsd2e, restrictOnly2 ) 470s Linear hypothesis test (Theil's F test) 470s 470s Hypothesis: 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2slsd2e 470s 470s Res.Df Df F Pr(>F) 470s 1 35 470s 2 34 1 0.42 0.52 470s > 470s > print( linearHypothesis( fitw2slsd3e, restrOnly2m, restrOnly2q ) ) 470s Linear hypothesis test (Theil's F test) 470s 470s Hypothesis: 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2slsd3e 470s 470s Res.Df Df F Pr(>F) 470s 1 35 470s 2 34 1 0.42 0.52 470s > linearHypothesis( fitw2slsd3e, restrictOnly2 ) 470s Linear hypothesis test (Theil's F test) 470s 470s Hypothesis: 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2slsd3e 470s 470s Res.Df Df F Pr(>F) 470s 1 35 470s 2 34 1 0.42 0.52 470s > 470s > # testing both of the restrictions 470s > print( linearHypothesis( fitw2sls1e, restr2m, restr2q ) ) 470s Linear hypothesis test (Theil's F test) 470s 470s Hypothesis: 470s demand_income - supply_trend = 0 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2sls1e 470s 470s Res.Df Df F Pr(>F) 470s 1 35 470s 2 33 2 0.18 0.84 470s > linearHypothesis( fitw2sls1e, restrict2 ) 470s Linear hypothesis test (Theil's F test) 470s 470s Hypothesis: 470s demand_income - supply_trend = 0 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2sls1e 470s 470s Res.Df Df F Pr(>F) 470s 1 35 470s 2 33 2 0.18 0.84 470s > 470s > print( linearHypothesis( fitw2slsd1, restr2m, restr2q ) ) 470s Linear hypothesis test (Theil's F test) 470s 470s Hypothesis: 470s demand_income - supply_trend = 0 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2slsd1 470s 470s Res.Df Df F Pr(>F) 470s 1 35 470s 2 33 2 0.65 0.53 470s > linearHypothesis( fitw2slsd1, restrict2 ) 470s Linear hypothesis test (Theil's F test) 470s 470s Hypothesis: 470s demand_income - supply_trend = 0 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2slsd1 470s 470s Res.Df Df F Pr(>F) 470s 1 35 470s 2 33 2 0.65 0.53 470s > 470s > 470s > ## ************** Wald tests **************** 470s > # testing first restriction 470s > print( linearHypothesis( fitw2sls1, restrm, test = "Chisq" ) ) 470s Linear hypothesis test (Chi^2 statistic of a Wald test) 470s 470s Hypothesis: 470s demand_income - supply_trend = 0 470s 470s Model 1: restricted model 470s Model 2: fitw2sls1 470s 470s Res.Df Df Chisq Pr(>Chisq) 470s 1 34 470s 2 33 1 0.31 0.58 470s > linearHypothesis( fitw2sls1, restrict, test = "Chisq" ) 470s Linear hypothesis test (Chi^2 statistic of a Wald test) 470s 470s Hypothesis: 470s demand_income - supply_trend = 0 470s 470s Model 1: restricted model 470s Model 2: fitw2sls1 470s 470s Res.Df Df Chisq Pr(>Chisq) 470s 1 34 470s 2 33 1 0.31 0.58 470s > 470s > print( linearHypothesis( fitw2slsd1e, restrm, test = "Chisq" ) ) 470s Linear hypothesis test (Chi^2 statistic of a Wald test) 470s 470s Hypothesis: 470s demand_income - supply_trend = 0 470s 470s Model 1: restricted model 470s Model 2: fitw2slsd1e 470s 470s Res.Df Df Chisq Pr(>Chisq) 470s 1 34 470s 2 33 1 1.11 0.29 470s > linearHypothesis( fitw2slsd1e, restrict, test = "Chisq" ) 470s Linear hypothesis test (Chi^2 statistic of a Wald test) 470s 470s Hypothesis: 470s demand_income - supply_trend = 0 470s 470s Model 1: restricted model 470s Model 2: fitw2slsd1e 470s 470s Res.Df Df Chisq Pr(>Chisq) 470s 1 34 470s 2 33 1 1.11 0.29 470s > 470s > # testing second restriction 470s > # first restriction not imposed 470s > print( linearHypothesis( fitw2sls1e, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 470s Linear hypothesis test (Chi^2 statistic of a Wald test) 470s 470s Hypothesis: 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2sls1e 470s 470s Res.Df Df Chisq Pr(>Chisq) 470s 1 34 470s 2 33 1 0.02 0.9 470s > linearHypothesis( fitw2sls1e, restrictOnly2, test = "Chisq" ) 470s Linear hypothesis test (Chi^2 statistic of a Wald test) 470s 470s Hypothesis: 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2sls1e 470s 470s Res.Df Df Chisq Pr(>Chisq) 470s 1 34 470s 2 33 1 0.02 0.9 470s > 470s > print( linearHypothesis( fitw2slsd1, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 470s Linear hypothesis test (Chi^2 statistic of a Wald test) 470s 470s Hypothesis: 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2slsd1 470s 470s Res.Df Df Chisq Pr(>Chisq) 470s 1 34 470s 2 33 1 0.74 0.39 470s > linearHypothesis( fitw2slsd1, restrictOnly2, test = "Chisq" ) 470s Linear hypothesis test (Chi^2 statistic of a Wald test) 470s 470s Hypothesis: 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2slsd1 470s 470s Res.Df Df Chisq Pr(>Chisq) 470s 1 34 470s 2 33 1 0.74 0.39 470s > # first restriction imposed 470s > print( linearHypothesis( fitw2sls2, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 470s Linear hypothesis test (Chi^2 statistic of a Wald test) 470s 470s Hypothesis: 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2sls2 470s 470s Res.Df Df Chisq Pr(>Chisq) 470s 1 35 470s 2 34 1 0.04 0.85 470s > linearHypothesis( fitw2sls2, restrictOnly2, test = "Chisq" ) 470s Linear hypothesis test (Chi^2 statistic of a Wald test) 470s 470s Hypothesis: 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2sls2 470s 470s Res.Df Df Chisq Pr(>Chisq) 470s 1 35 470s 2 34 1 0.04 0.85 470s > 470s > print( linearHypothesis( fitw2sls3, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 470s Linear hypothesis test (Chi^2 statistic of a Wald test) 470s 470s Hypothesis: 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2sls3 470s 470s Res.Df Df Chisq Pr(>Chisq) 470s 1 35 470s 2 34 1 0.04 0.85 470s > linearHypothesis( fitw2sls3, restrictOnly2, test = "Chisq" ) 470s Linear hypothesis test (Chi^2 statistic of a Wald test) 470s 470s Hypothesis: 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2sls3 470s 470s Res.Df Df Chisq Pr(>Chisq) 470s 1 35 470s 2 34 1 0.04 0.85 470s > 470s > print( linearHypothesis( fitw2slsd2e, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 470s Linear hypothesis test (Chi^2 statistic of a Wald test) 470s 470s Hypothesis: 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2slsd2e 470s 470s Res.Df Df Chisq Pr(>Chisq) 470s 1 35 470s 2 34 1 0.49 0.48 470s > linearHypothesis( fitw2slsd2e, restrictOnly2, test = "Chisq" ) 470s Linear hypothesis test (Chi^2 statistic of a Wald test) 470s 470s Hypothesis: 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2slsd2e 470s 470s Res.Df Df Chisq Pr(>Chisq) 470s 1 35 470s 2 34 1 0.49 0.48 470s > 470s > print( linearHypothesis( fitw2slsd3e, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 470s Linear hypothesis test (Chi^2 statistic of a Wald test) 470s 470s Hypothesis: 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2slsd3e 470s 470s Res.Df Df Chisq Pr(>Chisq) 470s 1 35 470s 2 34 1 0.49 0.48 470s > linearHypothesis( fitw2slsd3e, restrictOnly2, test = "Chisq" ) 470s Linear hypothesis test (Chi^2 statistic of a Wald test) 470s 470s Hypothesis: 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2slsd3e 470s 470s Res.Df Df Chisq Pr(>Chisq) 470s 1 35 470s 2 34 1 0.49 0.48 470s > 470s > # testing both of the restrictions 470s > print( linearHypothesis( fitw2sls1e, restr2m, restr2q, test = "Chisq" ) ) 470s Linear hypothesis test (Chi^2 statistic of a Wald test) 470s 470s Hypothesis: 470s demand_income - supply_trend = 0 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2sls1e 470s 470s Res.Df Df Chisq Pr(>Chisq) 470s 1 35 470s 2 33 2 0.43 0.81 470s > linearHypothesis( fitw2sls1e, restrict2, test = "Chisq" ) 470s Linear hypothesis test (Chi^2 statistic of a Wald test) 470s 470s Hypothesis: 470s demand_income - supply_trend = 0 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2sls1e 470s 470s Res.Df Df Chisq Pr(>Chisq) 470s 1 35 470s 2 33 2 0.43 0.81 470s > 470s > print( linearHypothesis( fitw2slsd1, restr2m, restr2q, test = "Chisq" ) ) 470s Linear hypothesis test (Chi^2 statistic of a Wald test) 470s 470s Hypothesis: 470s demand_income - supply_trend = 0 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2slsd1 470s 470s Res.Df Df Chisq Pr(>Chisq) 470s 1 35 470s 2 33 2 1.3 0.52 470s > linearHypothesis( fitw2slsd1, restrict2, test = "Chisq" ) 470s Linear hypothesis test (Chi^2 statistic of a Wald test) 470s 470s Hypothesis: 470s demand_income - supply_trend = 0 470s - demand_price + supply_price = 0.5 470s 470s Model 1: restricted model 470s Model 2: fitw2slsd1 470s 470s Res.Df Df Chisq Pr(>Chisq) 470s 1 35 470s 2 33 2 1.3 0.52 470s > 470s > 470s > ## ****************** model frame ************************** 470s > print( mf <- model.frame( fitw2sls1e ) ) 470s consump price income farmPrice trend 470s 1 98.5 100.3 87.4 98.0 1 470s 2 99.2 104.3 97.6 99.1 2 470s 3 102.2 103.4 96.7 99.1 3 470s 4 101.5 104.5 98.2 98.1 4 470s 5 104.2 98.0 99.8 110.8 5 470s 6 103.2 99.5 100.5 108.2 6 470s 7 104.0 101.1 103.2 105.6 7 470s 8 99.9 104.8 107.8 109.8 8 470s 9 100.3 96.4 96.6 108.7 9 470s 10 102.8 91.2 88.9 100.6 10 470s 11 95.4 93.1 75.1 81.0 11 470s 12 92.4 98.8 76.9 68.6 12 470s 13 94.5 102.9 84.6 70.9 13 470s 14 98.8 98.8 90.6 81.4 14 470s 15 105.8 95.1 103.1 102.3 15 470s 16 100.2 98.5 105.1 105.0 16 470s 17 103.5 86.5 96.4 110.5 17 470s 18 99.9 104.0 104.4 92.5 18 470s 19 105.2 105.8 110.7 89.3 19 470s 20 106.2 113.5 127.1 93.0 20 470s > print( mf1 <- model.frame( fitw2sls1e$eq[[ 1 ]] ) ) 470s consump price income 470s 1 98.5 100.3 87.4 470s 2 99.2 104.3 97.6 470s 3 102.2 103.4 96.7 470s 4 101.5 104.5 98.2 470s 5 104.2 98.0 99.8 470s 6 103.2 99.5 100.5 470s 7 104.0 101.1 103.2 470s 8 99.9 104.8 107.8 470s 9 100.3 96.4 96.6 470s 10 102.8 91.2 88.9 470s 11 95.4 93.1 75.1 470s 12 92.4 98.8 76.9 470s 13 94.5 102.9 84.6 470s 14 98.8 98.8 90.6 470s 15 105.8 95.1 103.1 470s 16 100.2 98.5 105.1 470s 17 103.5 86.5 96.4 470s 18 99.9 104.0 104.4 470s 19 105.2 105.8 110.7 470s 20 106.2 113.5 127.1 470s > print( attributes( mf1 )$terms ) 470s consump ~ price + income 470s attr(,"variables") 470s list(consump, price, income) 470s attr(,"factors") 470s price income 470s consump 0 0 470s price 1 0 470s income 0 1 470s attr(,"term.labels") 470s [1] "price" "income" 470s attr(,"order") 470s [1] 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 1 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(consump, price, income) 470s attr(,"dataClasses") 470s consump price income 470s "numeric" "numeric" "numeric" 470s > print( mf2 <- model.frame( fitw2sls1e$eq[[ 2 ]] ) ) 470s consump price farmPrice trend 470s 1 98.5 100.3 98.0 1 470s 2 99.2 104.3 99.1 2 470s 3 102.2 103.4 99.1 3 470s 4 101.5 104.5 98.1 4 470s 5 104.2 98.0 110.8 5 470s 6 103.2 99.5 108.2 6 470s 7 104.0 101.1 105.6 7 470s 8 99.9 104.8 109.8 8 470s 9 100.3 96.4 108.7 9 470s 10 102.8 91.2 100.6 10 470s 11 95.4 93.1 81.0 11 470s 12 92.4 98.8 68.6 12 470s 13 94.5 102.9 70.9 13 470s 14 98.8 98.8 81.4 14 470s 15 105.8 95.1 102.3 15 470s 16 100.2 98.5 105.0 16 470s 17 103.5 86.5 110.5 17 470s 18 99.9 104.0 92.5 18 470s 19 105.2 105.8 89.3 19 470s 20 106.2 113.5 93.0 20 470s > print( attributes( mf2 )$terms ) 470s consump ~ price + farmPrice + trend 470s attr(,"variables") 470s list(consump, price, farmPrice, trend) 470s attr(,"factors") 470s price farmPrice trend 470s consump 0 0 0 470s price 1 0 0 470s farmPrice 0 1 0 470s trend 0 0 1 470s attr(,"term.labels") 470s [1] "price" "farmPrice" "trend" 470s attr(,"order") 470s [1] 1 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 1 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(consump, price, farmPrice, trend) 470s attr(,"dataClasses") 470s consump price farmPrice trend 470s "numeric" "numeric" "numeric" "numeric" 470s > 470s > print( all.equal( mf, model.frame( fitw2sls2 ) ) ) 470s [1] TRUE 470s > print( all.equal( mf2, model.frame( fitw2sls2$eq[[ 2 ]] ) ) ) 470s [1] TRUE 470s > 470s > print( all.equal( mf, model.frame( fitw2sls3 ) ) ) 470s [1] TRUE 470s > print( all.equal( mf1, model.frame( fitw2sls3$eq[[ 1 ]] ) ) ) 470s [1] TRUE 470s > 470s > print( all.equal( mf, model.frame( fitw2sls4e ) ) ) 470s [1] TRUE 470s > print( all.equal( mf2, model.frame( fitw2sls4e$eq[[ 2 ]] ) ) ) 470s [1] TRUE 470s > 470s > print( all.equal( mf, model.frame( fitw2sls5 ) ) ) 470s [1] TRUE 470s > print( all.equal( mf1, model.frame( fitw2sls5$eq[[ 1 ]] ) ) ) 470s [1] TRUE 470s > 470s > print( all.equal( mf, model.frame( fitw2slsd1 ) ) ) 470s [1] TRUE 470s > print( all.equal( mf2, model.frame( fitw2slsd1$eq[[ 2 ]] ) ) ) 470s [1] TRUE 470s > 470s > print( all.equal( mf, model.frame( fitw2slsd2e ) ) ) 470s [1] TRUE 470s > print( all.equal( mf1, model.frame( fitw2slsd2e$eq[[ 1 ]] ) ) ) 470s [1] TRUE 470s > 470s > print( all.equal( mf, model.frame( fitw2slsd3e ) ) ) 470s [1] TRUE 470s > print( all.equal( mf2, model.frame( fitw2slsd3e$eq[[ 2 ]] ) ) ) 470s [1] TRUE 470s > 470s > fitw2sls1e$eq[[ 1 ]]$modelInst 470s income farmPrice trend 470s 1 87.4 98.0 1 470s 2 97.6 99.1 2 470s 3 96.7 99.1 3 470s 4 98.2 98.1 4 470s 5 99.8 110.8 5 470s 6 100.5 108.2 6 470s 7 103.2 105.6 7 470s 8 107.8 109.8 8 470s 9 96.6 108.7 9 470s 10 88.9 100.6 10 470s 11 75.1 81.0 11 470s 12 76.9 68.6 12 470s 13 84.6 70.9 13 470s 14 90.6 81.4 14 470s 15 103.1 102.3 15 470s 16 105.1 105.0 16 470s 17 96.4 110.5 17 470s 18 104.4 92.5 18 470s 19 110.7 89.3 19 470s 20 127.1 93.0 20 470s > fitw2sls1e$eq[[ 2 ]]$modelInst 470s income farmPrice trend 470s 1 87.4 98.0 1 470s 2 97.6 99.1 2 470s 3 96.7 99.1 3 470s 4 98.2 98.1 4 470s 5 99.8 110.8 5 470s 6 100.5 108.2 6 470s 7 103.2 105.6 7 470s 8 107.8 109.8 8 470s 9 96.6 108.7 9 470s 10 88.9 100.6 10 470s 11 75.1 81.0 11 470s 12 76.9 68.6 12 470s 13 84.6 70.9 13 470s 14 90.6 81.4 14 470s 15 103.1 102.3 15 470s 16 105.1 105.0 16 470s 17 96.4 110.5 17 470s 18 104.4 92.5 18 470s 19 110.7 89.3 19 470s 20 127.1 93.0 20 470s > 470s > fitw2sls4Sym$eq[[ 1 ]]$modelInst 470s income farmPrice trend 470s 1 87.4 98.0 1 470s 2 97.6 99.1 2 470s 3 96.7 99.1 3 470s 4 98.2 98.1 4 470s 5 99.8 110.8 5 470s 6 100.5 108.2 6 470s 7 103.2 105.6 7 470s 8 107.8 109.8 8 470s 9 96.6 108.7 9 470s 10 88.9 100.6 10 470s 11 75.1 81.0 11 470s 12 76.9 68.6 12 470s 13 84.6 70.9 13 470s 14 90.6 81.4 14 470s 15 103.1 102.3 15 470s 16 105.1 105.0 16 470s 17 96.4 110.5 17 470s 18 104.4 92.5 18 470s 19 110.7 89.3 19 470s 20 127.1 93.0 20 470s > fitw2sls4Sym$eq[[ 2 ]]$modelInst 470s income farmPrice trend 470s 1 87.4 98.0 1 470s 2 97.6 99.1 2 470s 3 96.7 99.1 3 470s 4 98.2 98.1 4 470s 5 99.8 110.8 5 470s 6 100.5 108.2 6 470s 7 103.2 105.6 7 470s 8 107.8 109.8 8 470s 9 96.6 108.7 9 470s 10 88.9 100.6 10 470s 11 75.1 81.0 11 470s 12 76.9 68.6 12 470s 13 84.6 70.9 13 470s 14 90.6 81.4 14 470s 15 103.1 102.3 15 470s 16 105.1 105.0 16 470s 17 96.4 110.5 17 470s 18 104.4 92.5 18 470s 19 110.7 89.3 19 470s 20 127.1 93.0 20 470s > 470s > fitw2sls5$eq[[ 1 ]]$modelInst 470s income farmPrice trend 470s 1 87.4 98.0 1 470s 2 97.6 99.1 2 470s 3 96.7 99.1 3 470s 4 98.2 98.1 4 470s 5 99.8 110.8 5 470s 6 100.5 108.2 6 470s 7 103.2 105.6 7 470s 8 107.8 109.8 8 470s 9 96.6 108.7 9 470s 10 88.9 100.6 10 470s 11 75.1 81.0 11 470s 12 76.9 68.6 12 470s 13 84.6 70.9 13 470s 14 90.6 81.4 14 470s 15 103.1 102.3 15 470s 16 105.1 105.0 16 470s 17 96.4 110.5 17 470s 18 104.4 92.5 18 470s 19 110.7 89.3 19 470s 20 127.1 93.0 20 470s > fitw2sls5$eq[[ 2 ]]$modelInst 470s income farmPrice trend 470s 1 87.4 98.0 1 470s 2 97.6 99.1 2 470s 3 96.7 99.1 3 470s 4 98.2 98.1 4 470s 5 99.8 110.8 5 470s 6 100.5 108.2 6 470s 7 103.2 105.6 7 470s 8 107.8 109.8 8 470s 9 96.6 108.7 9 470s 10 88.9 100.6 10 470s 11 75.1 81.0 11 470s 12 76.9 68.6 12 470s 13 84.6 70.9 13 470s 14 90.6 81.4 14 470s 15 103.1 102.3 15 470s 16 105.1 105.0 16 470s 17 96.4 110.5 17 470s 18 104.4 92.5 18 470s 19 110.7 89.3 19 470s 20 127.1 93.0 20 470s > 470s > 470s > ## **************** model matrix ************************ 470s > # with x (returnModelMatrix) = TRUE 470s > print( !is.null( fitw2sls1e$eq[[ 1 ]]$x ) ) 470s [1] TRUE 470s > print( mm <- model.matrix( fitw2sls1e ) ) 470s demand_(Intercept) demand_price demand_income supply_(Intercept) 470s demand_1 1 100.3 87.4 0 470s demand_2 1 104.3 97.6 0 470s demand_3 1 103.4 96.7 0 470s demand_4 1 104.5 98.2 0 470s demand_5 1 98.0 99.8 0 470s demand_6 1 99.5 100.5 0 470s demand_7 1 101.1 103.2 0 470s demand_8 1 104.8 107.8 0 470s demand_9 1 96.4 96.6 0 470s demand_10 1 91.2 88.9 0 470s demand_11 1 93.1 75.1 0 470s demand_12 1 98.8 76.9 0 470s demand_13 1 102.9 84.6 0 470s demand_14 1 98.8 90.6 0 470s demand_15 1 95.1 103.1 0 470s demand_16 1 98.5 105.1 0 470s demand_17 1 86.5 96.4 0 470s demand_18 1 104.0 104.4 0 470s demand_19 1 105.8 110.7 0 470s demand_20 1 113.5 127.1 0 470s supply_1 0 0.0 0.0 1 470s supply_2 0 0.0 0.0 1 470s supply_3 0 0.0 0.0 1 470s supply_4 0 0.0 0.0 1 470s supply_5 0 0.0 0.0 1 470s supply_6 0 0.0 0.0 1 470s supply_7 0 0.0 0.0 1 470s supply_8 0 0.0 0.0 1 470s supply_9 0 0.0 0.0 1 470s supply_10 0 0.0 0.0 1 470s supply_11 0 0.0 0.0 1 470s supply_12 0 0.0 0.0 1 470s supply_13 0 0.0 0.0 1 470s supply_14 0 0.0 0.0 1 470s supply_15 0 0.0 0.0 1 470s supply_16 0 0.0 0.0 1 470s supply_17 0 0.0 0.0 1 470s supply_18 0 0.0 0.0 1 470s supply_19 0 0.0 0.0 1 470s supply_20 0 0.0 0.0 1 470s supply_price supply_farmPrice supply_trend 470s demand_1 0.0 0.0 0 470s demand_2 0.0 0.0 0 470s demand_3 0.0 0.0 0 470s demand_4 0.0 0.0 0 470s demand_5 0.0 0.0 0 470s demand_6 0.0 0.0 0 470s demand_7 0.0 0.0 0 470s demand_8 0.0 0.0 0 470s demand_9 0.0 0.0 0 470s demand_10 0.0 0.0 0 470s demand_11 0.0 0.0 0 470s demand_12 0.0 0.0 0 470s demand_13 0.0 0.0 0 470s demand_14 0.0 0.0 0 470s demand_15 0.0 0.0 0 470s demand_16 0.0 0.0 0 470s demand_17 0.0 0.0 0 470s demand_18 0.0 0.0 0 470s demand_19 0.0 0.0 0 470s demand_20 0.0 0.0 0 470s supply_1 100.3 98.0 1 470s supply_2 104.3 99.1 2 470s supply_3 103.4 99.1 3 470s supply_4 104.5 98.1 4 470s supply_5 98.0 110.8 5 470s supply_6 99.5 108.2 6 470s supply_7 101.1 105.6 7 470s supply_8 104.8 109.8 8 470s supply_9 96.4 108.7 9 470s supply_10 91.2 100.6 10 470s supply_11 93.1 81.0 11 470s supply_12 98.8 68.6 12 470s supply_13 102.9 70.9 13 470s supply_14 98.8 81.4 14 470s supply_15 95.1 102.3 15 470s supply_16 98.5 105.0 16 470s supply_17 86.5 110.5 17 470s supply_18 104.0 92.5 18 470s supply_19 105.8 89.3 19 470s supply_20 113.5 93.0 20 470s > print( mm1 <- model.matrix( fitw2sls1e$eq[[ 1 ]] ) ) 470s (Intercept) price income 470s 1 1 100.3 87.4 470s 2 1 104.3 97.6 470s 3 1 103.4 96.7 470s 4 1 104.5 98.2 470s 5 1 98.0 99.8 470s 6 1 99.5 100.5 470s 7 1 101.1 103.2 470s 8 1 104.8 107.8 470s 9 1 96.4 96.6 470s 10 1 91.2 88.9 470s 11 1 93.1 75.1 470s 12 1 98.8 76.9 470s 13 1 102.9 84.6 470s 14 1 98.8 90.6 470s 15 1 95.1 103.1 470s 16 1 98.5 105.1 470s 17 1 86.5 96.4 470s 18 1 104.0 104.4 470s 19 1 105.8 110.7 470s 20 1 113.5 127.1 470s attr(,"assign") 470s [1] 0 1 2 470s > print( mm2 <- model.matrix( fitw2sls1e$eq[[ 2 ]] ) ) 470s (Intercept) price farmPrice trend 470s 1 1 100.3 98.0 1 470s 2 1 104.3 99.1 2 470s 3 1 103.4 99.1 3 470s 4 1 104.5 98.1 4 470s 5 1 98.0 110.8 5 470s 6 1 99.5 108.2 6 470s 7 1 101.1 105.6 7 470s 8 1 104.8 109.8 8 470s 9 1 96.4 108.7 9 470s 10 1 91.2 100.6 10 470s 11 1 93.1 81.0 11 470s 12 1 98.8 68.6 12 470s 13 1 102.9 70.9 13 470s 14 1 98.8 81.4 14 470s 15 1 95.1 102.3 15 470s 16 1 98.5 105.0 16 470s 17 1 86.5 110.5 17 470s 18 1 104.0 92.5 18 470s 19 1 105.8 89.3 19 470s 20 1 113.5 93.0 20 470s attr(,"assign") 470s [1] 0 1 2 3 470s > 470s > # with x (returnModelMatrix) = FALSE 470s > print( all.equal( mm, model.matrix( fitw2sls1 ) ) ) 470s [1] TRUE 470s > print( all.equal( mm1, model.matrix( fitw2sls1$eq[[ 1 ]] ) ) ) 470s [1] TRUE 470s > print( all.equal( mm2, model.matrix( fitw2sls1$eq[[ 2 ]] ) ) ) 470s [1] TRUE 470s > print( !is.null( fitw2sls1$eq[[ 1 ]]$x ) ) 470s [1] FALSE 470s > 470s > # with x (returnModelMatrix) = TRUE 470s > print( !is.null( fitw2sls2e$eq[[ 1 ]]$x ) ) 470s [1] TRUE 470s > print( all.equal( mm, model.matrix( fitw2sls2e ) ) ) 470s [1] TRUE 470s > print( all.equal( mm1, model.matrix( fitw2sls2e$eq[[ 1 ]] ) ) ) 470s [1] TRUE 470s > print( all.equal( mm2, model.matrix( fitw2sls2e$eq[[ 2 ]] ) ) ) 470s [1] TRUE 470s > 470s > # with x (returnModelMatrix) = FALSE 470s > print( all.equal( mm, model.matrix( fitw2sls2Sym ) ) ) 470s [1] TRUE 470s > print( all.equal( mm1, model.matrix( fitw2sls2Sym$eq[[ 1 ]] ) ) ) 470s [1] TRUE 470s > print( all.equal( mm2, model.matrix( fitw2sls2Sym$eq[[ 2 ]] ) ) ) 470s [1] TRUE 470s > print( !is.null( fitw2sls2Sym$eq[[ 1 ]]$x ) ) 470s [1] FALSE 470s > 470s > # with x (returnModelMatrix) = TRUE 470s > print( !is.null( fitw2slsd3$eq[[ 1 ]]$x ) ) 470s [1] TRUE 470s > print( all.equal( mm, model.matrix( fitw2slsd3 ) ) ) 470s [1] TRUE 470s > print( all.equal( mm1, model.matrix( fitw2slsd3$eq[[ 1 ]] ) ) ) 470s [1] TRUE 470s > print( all.equal( mm2, model.matrix( fitw2slsd3$eq[[ 2 ]] ) ) ) 470s [1] TRUE 470s > 470s > # with x (returnModelMatrix) = FALSE 470s > print( all.equal( mm, model.matrix( fitw2slsd3e ) ) ) 470s [1] TRUE 470s > print( all.equal( mm1, model.matrix( fitw2slsd3e$eq[[ 1 ]] ) ) ) 470s [1] TRUE 470s > print( all.equal( mm2, model.matrix( fitw2slsd3e$eq[[ 2 ]] ) ) ) 470s [1] TRUE 470s > print( !is.null( fitw2slsd3e$eq[[ 1 ]]$x ) ) 470s [1] FALSE 470s > 470s > # with x (returnModelMatrix) = TRUE 470s > print( !is.null( fitw2sls4$eq[[ 1 ]]$x ) ) 470s [1] TRUE 470s > print( all.equal( mm, model.matrix( fitw2sls4 ) ) ) 470s [1] TRUE 470s > print( all.equal( mm1, model.matrix( fitw2sls4$eq[[ 1 ]] ) ) ) 470s [1] TRUE 470s > print( all.equal( mm2, model.matrix( fitw2sls4$eq[[ 2 ]] ) ) ) 470s [1] TRUE 470s > 470s > # with x (returnModelMatrix) = FALSE 470s > print( all.equal( mm, model.matrix( fitw2sls4e ) ) ) 470s [1] TRUE 470s > print( all.equal( mm1, model.matrix( fitw2sls4e$eq[[ 1 ]] ) ) ) 470s [1] TRUE 470s > print( all.equal( mm2, model.matrix( fitw2sls4e$eq[[ 2 ]] ) ) ) 470s [1] TRUE 470s > print( !is.null( fitw2sls4e$eq[[ 1 ]]$x ) ) 470s [1] FALSE 470s > 470s > # with x (returnModelMatrix) = TRUE 470s > print( !is.null( fitw2sls5$eq[[ 1 ]]$x ) ) 470s [1] TRUE 470s > print( all.equal( mm, model.matrix( fitw2sls5 ) ) ) 470s [1] TRUE 470s > print( all.equal( mm1, model.matrix( fitw2sls5$eq[[ 1 ]] ) ) ) 470s [1] TRUE 470s > print( all.equal( mm2, model.matrix( fitw2sls5$eq[[ 2 ]] ) ) ) 470s [1] TRUE 470s > 470s > # with x (returnModelMatrix) = FALSE 470s > print( all.equal( mm, model.matrix( fitw2sls5e ) ) ) 470s [1] TRUE 470s > print( all.equal( mm1, model.matrix( fitw2sls5e$eq[[ 1 ]] ) ) ) 470s [1] TRUE 470s > print( all.equal( mm2, model.matrix( fitw2sls5e$eq[[ 2 ]] ) ) ) 470s [1] TRUE 470s > print( !is.null( fitw2sls5e$eq[[ 1 ]]$x ) ) 470s [1] FALSE 470s > 470s > # matrices of instrumental variables 470s > model.matrix( fitw2sls1, which = "z" ) 470s demand_(Intercept) demand_income demand_farmPrice demand_trend 470s demand_1 1 87.4 98.0 1 470s demand_2 1 97.6 99.1 2 470s demand_3 1 96.7 99.1 3 470s demand_4 1 98.2 98.1 4 470s demand_5 1 99.8 110.8 5 470s demand_6 1 100.5 108.2 6 470s demand_7 1 103.2 105.6 7 470s demand_8 1 107.8 109.8 8 470s demand_9 1 96.6 108.7 9 470s demand_10 1 88.9 100.6 10 470s demand_11 1 75.1 81.0 11 470s demand_12 1 76.9 68.6 12 470s demand_13 1 84.6 70.9 13 470s demand_14 1 90.6 81.4 14 470s demand_15 1 103.1 102.3 15 470s demand_16 1 105.1 105.0 16 470s demand_17 1 96.4 110.5 17 470s demand_18 1 104.4 92.5 18 470s demand_19 1 110.7 89.3 19 470s demand_20 1 127.1 93.0 20 470s supply_1 0 0.0 0.0 0 470s supply_2 0 0.0 0.0 0 470s supply_3 0 0.0 0.0 0 470s supply_4 0 0.0 0.0 0 470s supply_5 0 0.0 0.0 0 470s supply_6 0 0.0 0.0 0 470s supply_7 0 0.0 0.0 0 470s supply_8 0 0.0 0.0 0 470s supply_9 0 0.0 0.0 0 470s supply_10 0 0.0 0.0 0 470s supply_11 0 0.0 0.0 0 470s supply_12 0 0.0 0.0 0 470s supply_13 0 0.0 0.0 0 470s supply_14 0 0.0 0.0 0 470s supply_15 0 0.0 0.0 0 470s supply_16 0 0.0 0.0 0 470s supply_17 0 0.0 0.0 0 470s supply_18 0 0.0 0.0 0 470s supply_19 0 0.0 0.0 0 470s supply_20 0 0.0 0.0 0 470s supply_(Intercept) supply_income supply_farmPrice supply_trend 470s demand_1 0 0.0 0.0 0 470s demand_2 0 0.0 0.0 0 470s demand_3 0 0.0 0.0 0 470s demand_4 0 0.0 0.0 0 470s demand_5 0 0.0 0.0 0 470s demand_6 0 0.0 0.0 0 470s demand_7 0 0.0 0.0 0 470s demand_8 0 0.0 0.0 0 470s demand_9 0 0.0 0.0 0 470s demand_10 0 0.0 0.0 0 470s demand_11 0 0.0 0.0 0 470s demand_12 0 0.0 0.0 0 470s demand_13 0 0.0 0.0 0 470s demand_14 0 0.0 0.0 0 470s demand_15 0 0.0 0.0 0 470s demand_16 0 0.0 0.0 0 470s demand_17 0 0.0 0.0 0 470s demand_18 0 0.0 0.0 0 470s demand_19 0 0.0 0.0 0 470s demand_20 0 0.0 0.0 0 470s supply_1 1 87.4 98.0 1 470s supply_2 1 97.6 99.1 2 470s supply_3 1 96.7 99.1 3 470s supply_4 1 98.2 98.1 4 470s supply_5 1 99.8 110.8 5 470s supply_6 1 100.5 108.2 6 470s supply_7 1 103.2 105.6 7 470s supply_8 1 107.8 109.8 8 470s supply_9 1 96.6 108.7 9 470s supply_10 1 88.9 100.6 10 470s supply_11 1 75.1 81.0 11 470s supply_12 1 76.9 68.6 12 470s supply_13 1 84.6 70.9 13 470s supply_14 1 90.6 81.4 14 470s supply_15 1 103.1 102.3 15 470s supply_16 1 105.1 105.0 16 470s supply_17 1 96.4 110.5 17 470s supply_18 1 104.4 92.5 18 470s supply_19 1 110.7 89.3 19 470s supply_20 1 127.1 93.0 20 470s > model.matrix( fitw2sls1$eq[[ 1 ]], which = "z" ) 470s (Intercept) income farmPrice trend 470s 1 1 87.4 98.0 1 470s 2 1 97.6 99.1 2 470s 3 1 96.7 99.1 3 470s 4 1 98.2 98.1 4 470s 5 1 99.8 110.8 5 470s 6 1 100.5 108.2 6 470s 7 1 103.2 105.6 7 470s 8 1 107.8 109.8 8 470s 9 1 96.6 108.7 9 470s 10 1 88.9 100.6 10 470s 11 1 75.1 81.0 11 470s 12 1 76.9 68.6 12 470s 13 1 84.6 70.9 13 470s 14 1 90.6 81.4 14 470s 15 1 103.1 102.3 15 470s 16 1 105.1 105.0 16 470s 17 1 96.4 110.5 17 470s 18 1 104.4 92.5 18 470s 19 1 110.7 89.3 19 470s 20 1 127.1 93.0 20 470s attr(,"assign") 470s [1] 0 1 2 3 470s > model.matrix( fitw2sls1$eq[[ 2 ]], which = "z" ) 470s (Intercept) income farmPrice trend 470s 1 1 87.4 98.0 1 470s 2 1 97.6 99.1 2 470s 3 1 96.7 99.1 3 470s 4 1 98.2 98.1 4 470s 5 1 99.8 110.8 5 470s 6 1 100.5 108.2 6 470s 7 1 103.2 105.6 7 470s 8 1 107.8 109.8 8 470s 9 1 96.6 108.7 9 470s 10 1 88.9 100.6 10 470s 11 1 75.1 81.0 11 470s 12 1 76.9 68.6 12 470s 13 1 84.6 70.9 13 470s 14 1 90.6 81.4 14 470s 15 1 103.1 102.3 15 470s 16 1 105.1 105.0 16 470s 17 1 96.4 110.5 17 470s 18 1 104.4 92.5 18 470s 19 1 110.7 89.3 19 470s 20 1 127.1 93.0 20 470s attr(,"assign") 470s [1] 0 1 2 3 470s > 470s > # matrices of fitted regressors 470s > model.matrix( fitw2sls5e, which = "xHat" ) 470s demand_(Intercept) demand_price demand_income supply_(Intercept) 470s demand_1 1 99.6 87.4 0 470s demand_2 1 105.1 97.6 0 470s demand_3 1 103.8 96.7 0 470s demand_4 1 104.5 98.2 0 470s demand_5 1 98.7 99.8 0 470s demand_6 1 99.6 100.5 0 470s demand_7 1 102.0 103.2 0 470s demand_8 1 102.2 107.8 0 470s demand_9 1 94.6 96.6 0 470s demand_10 1 92.7 88.9 0 470s demand_11 1 92.4 75.1 0 470s demand_12 1 98.9 76.9 0 470s demand_13 1 102.2 84.6 0 470s demand_14 1 100.3 90.6 0 470s demand_15 1 97.6 103.1 0 470s demand_16 1 96.9 105.1 0 470s demand_17 1 87.7 96.4 0 470s demand_18 1 101.1 104.4 0 470s demand_19 1 106.1 110.7 0 470s demand_20 1 114.4 127.1 0 470s supply_1 0 0.0 0.0 1 470s supply_2 0 0.0 0.0 1 470s supply_3 0 0.0 0.0 1 470s supply_4 0 0.0 0.0 1 470s supply_5 0 0.0 0.0 1 470s supply_6 0 0.0 0.0 1 470s supply_7 0 0.0 0.0 1 470s supply_8 0 0.0 0.0 1 470s supply_9 0 0.0 0.0 1 470s supply_10 0 0.0 0.0 1 470s supply_11 0 0.0 0.0 1 470s supply_12 0 0.0 0.0 1 470s supply_13 0 0.0 0.0 1 470s supply_14 0 0.0 0.0 1 470s supply_15 0 0.0 0.0 1 470s supply_16 0 0.0 0.0 1 470s supply_17 0 0.0 0.0 1 470s supply_18 0 0.0 0.0 1 470s supply_19 0 0.0 0.0 1 470s supply_20 0 0.0 0.0 1 470s supply_price supply_farmPrice supply_trend 470s demand_1 0.0 0.0 0 470s demand_2 0.0 0.0 0 470s demand_3 0.0 0.0 0 470s demand_4 0.0 0.0 0 470s demand_5 0.0 0.0 0 470s demand_6 0.0 0.0 0 470s demand_7 0.0 0.0 0 470s demand_8 0.0 0.0 0 470s demand_9 0.0 0.0 0 470s demand_10 0.0 0.0 0 470s demand_11 0.0 0.0 0 470s demand_12 0.0 0.0 0 470s demand_13 0.0 0.0 0 470s demand_14 0.0 0.0 0 470s demand_15 0.0 0.0 0 470s demand_16 0.0 0.0 0 470s demand_17 0.0 0.0 0 470s demand_18 0.0 0.0 0 470s demand_19 0.0 0.0 0 470s demand_20 0.0 0.0 0 470s supply_1 99.6 98.0 1 470s supply_2 105.1 99.1 2 470s supply_3 103.8 99.1 3 470s supply_4 104.5 98.1 4 470s supply_5 98.7 110.8 5 470s supply_6 99.6 108.2 6 470s supply_7 102.0 105.6 7 470s supply_8 102.2 109.8 8 470s supply_9 94.6 108.7 9 470s supply_10 92.7 100.6 10 470s supply_11 92.4 81.0 11 470s supply_12 98.9 68.6 12 470s supply_13 102.2 70.9 13 470s supply_14 100.3 81.4 14 470s supply_15 97.6 102.3 15 470s supply_16 96.9 105.0 16 470s supply_17 87.7 110.5 17 470s supply_18 101.1 92.5 18 470s supply_19 106.1 89.3 19 470s supply_20 114.4 93.0 20 470s > model.matrix( fitw2sls5e$eq[[ 1 ]], which = "xHat" ) 470s (Intercept) price income 470s 1 1 99.6 87.4 470s 2 1 105.1 97.6 470s 3 1 103.8 96.7 470s 4 1 104.5 98.2 470s 5 1 98.7 99.8 470s 6 1 99.6 100.5 470s 7 1 102.0 103.2 470s 8 1 102.2 107.8 470s 9 1 94.6 96.6 470s 10 1 92.7 88.9 470s 11 1 92.4 75.1 470s 12 1 98.9 76.9 470s 13 1 102.2 84.6 470s 14 1 100.3 90.6 470s 15 1 97.6 103.1 470s 16 1 96.9 105.1 470s 17 1 87.7 96.4 470s 18 1 101.1 104.4 470s 19 1 106.1 110.7 470s 20 1 114.4 127.1 470s > model.matrix( fitw2sls5e$eq[[ 2 ]], which = "xHat" ) 470s (Intercept) price farmPrice trend 470s 1 1 99.6 98.0 1 470s 2 1 105.1 99.1 2 470s 3 1 103.8 99.1 3 470s 4 1 104.5 98.1 4 470s 5 1 98.7 110.8 5 470s 6 1 99.6 108.2 6 470s 7 1 102.0 105.6 7 470s 8 1 102.2 109.8 8 470s 9 1 94.6 108.7 9 470s 10 1 92.7 100.6 10 470s 11 1 92.4 81.0 11 470s 12 1 98.9 68.6 12 470s 13 1 102.2 70.9 13 470s 14 1 100.3 81.4 14 470s 15 1 97.6 102.3 15 470s 16 1 96.9 105.0 16 470s 17 1 87.7 110.5 17 470s 18 1 101.1 92.5 18 470s 19 1 106.1 89.3 19 470s 20 1 114.4 93.0 20 470s > 470s > 470s > ## **************** formulas ************************ 470s > formula( fitw2sls1e ) 470s $demand 470s consump ~ price + income 470s 470s $supply 470s consump ~ price + farmPrice + trend 470s 470s > formula( fitw2sls1e$eq[[ 1 ]] ) 470s consump ~ price + income 470s > 470s > formula( fitw2sls2 ) 470s $demand 470s consump ~ price + income 470s 470s $supply 470s consump ~ price + farmPrice + trend 470s 470s > formula( fitw2sls2$eq[[ 2 ]] ) 470s consump ~ price + farmPrice + trend 470s > 470s > formula( fitw2sls3 ) 470s $demand 470s consump ~ price + income 470s 470s $supply 470s consump ~ price + farmPrice + trend 470s 470s > formula( fitw2sls3$eq[[ 1 ]] ) 470s consump ~ price + income 470s > 470s > formula( fitw2sls4e ) 470s $demand 470s consump ~ price + income 470s 470s $supply 470s consump ~ price + farmPrice + trend 470s 470s > formula( fitw2sls4e$eq[[ 2 ]] ) 470s consump ~ price + farmPrice + trend 470s > 470s > formula( fitw2sls5 ) 470s $demand 470s consump ~ price + income 470s 470s $supply 470s consump ~ price + farmPrice + trend 470s 470s > formula( fitw2sls5$eq[[ 1 ]] ) 470s consump ~ price + income 470s > 470s > formula( fitw2slsd1 ) 470s $demand 470s consump ~ price + income 470s 470s $supply 470s consump ~ price + farmPrice + trend 470s 470s > formula( fitw2slsd1$eq[[ 2 ]] ) 470s consump ~ price + farmPrice + trend 470s > 470s > formula( fitw2slsd2e ) 470s $demand 470s consump ~ price + income 470s 470s $supply 470s consump ~ price + farmPrice + trend 470s 470s > formula( fitw2slsd2e$eq[[ 1 ]] ) 470s consump ~ price + income 470s > 470s > formula( fitw2slsd3e ) 470s $demand 470s consump ~ price + income 470s 470s $supply 470s consump ~ price + farmPrice + trend 470s 470s > formula( fitw2slsd3e$eq[[ 2 ]] ) 470s consump ~ price + farmPrice + trend 470s > 470s > 470s > ## **************** model terms ******************* 470s > terms( fitw2sls1e ) 470s $demand 470s consump ~ price + income 470s attr(,"variables") 470s list(consump, price, income) 470s attr(,"factors") 470s price income 470s consump 0 0 470s price 1 0 470s income 0 1 470s attr(,"term.labels") 470s [1] "price" "income" 470s attr(,"order") 470s [1] 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 1 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(consump, price, income) 470s attr(,"dataClasses") 470s consump price income 470s "numeric" "numeric" "numeric" 470s 470s $supply 470s consump ~ price + farmPrice + trend 470s attr(,"variables") 470s list(consump, price, farmPrice, trend) 470s attr(,"factors") 470s price farmPrice trend 470s consump 0 0 0 470s price 1 0 0 470s farmPrice 0 1 0 470s trend 0 0 1 470s attr(,"term.labels") 470s [1] "price" "farmPrice" "trend" 470s attr(,"order") 470s [1] 1 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 1 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(consump, price, farmPrice, trend) 470s attr(,"dataClasses") 470s consump price farmPrice trend 470s "numeric" "numeric" "numeric" "numeric" 470s 470s > terms( fitw2sls1e$eq[[ 1 ]] ) 470s consump ~ price + income 470s attr(,"variables") 470s list(consump, price, income) 470s attr(,"factors") 470s price income 470s consump 0 0 470s price 1 0 470s income 0 1 470s attr(,"term.labels") 470s [1] "price" "income" 470s attr(,"order") 470s [1] 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 1 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(consump, price, income) 470s attr(,"dataClasses") 470s consump price income 470s "numeric" "numeric" "numeric" 470s > 470s > terms( fitw2sls2 ) 470s $demand 470s consump ~ price + income 470s attr(,"variables") 470s list(consump, price, income) 470s attr(,"factors") 470s price income 470s consump 0 0 470s price 1 0 470s income 0 1 470s attr(,"term.labels") 470s [1] "price" "income" 470s attr(,"order") 470s [1] 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 1 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(consump, price, income) 470s attr(,"dataClasses") 470s consump price income 470s "numeric" "numeric" "numeric" 470s 470s $supply 470s consump ~ price + farmPrice + trend 470s attr(,"variables") 470s list(consump, price, farmPrice, trend) 470s attr(,"factors") 470s price farmPrice trend 470s consump 0 0 0 470s price 1 0 0 470s farmPrice 0 1 0 470s trend 0 0 1 470s attr(,"term.labels") 470s [1] "price" "farmPrice" "trend" 470s attr(,"order") 470s [1] 1 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 1 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(consump, price, farmPrice, trend) 470s attr(,"dataClasses") 470s consump price farmPrice trend 470s "numeric" "numeric" "numeric" "numeric" 470s 470s > terms( fitw2sls2$eq[[ 2 ]] ) 470s consump ~ price + farmPrice + trend 470s attr(,"variables") 470s list(consump, price, farmPrice, trend) 470s attr(,"factors") 470s price farmPrice trend 470s consump 0 0 0 470s price 1 0 0 470s farmPrice 0 1 0 470s trend 0 0 1 470s attr(,"term.labels") 470s [1] "price" "farmPrice" "trend" 470s attr(,"order") 470s [1] 1 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 1 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(consump, price, farmPrice, trend) 470s attr(,"dataClasses") 470s consump price farmPrice trend 470s "numeric" "numeric" "numeric" "numeric" 470s > 470s > terms( fitw2sls3 ) 470s $demand 470s consump ~ price + income 470s attr(,"variables") 470s list(consump, price, income) 470s attr(,"factors") 470s price income 470s consump 0 0 470s price 1 0 470s income 0 1 470s attr(,"term.labels") 470s [1] "price" "income" 470s attr(,"order") 470s [1] 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 1 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(consump, price, income) 470s attr(,"dataClasses") 470s consump price income 470s "numeric" "numeric" "numeric" 470s 470s $supply 470s consump ~ price + farmPrice + trend 470s attr(,"variables") 470s list(consump, price, farmPrice, trend) 470s attr(,"factors") 470s price farmPrice trend 470s consump 0 0 0 470s price 1 0 0 470s farmPrice 0 1 0 470s trend 0 0 1 470s attr(,"term.labels") 470s [1] "price" "farmPrice" "trend" 470s attr(,"order") 470s [1] 1 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 1 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(consump, price, farmPrice, trend) 470s attr(,"dataClasses") 470s consump price farmPrice trend 470s "numeric" "numeric" "numeric" "numeric" 470s 470s > terms( fitw2sls3$eq[[ 1 ]] ) 470s consump ~ price + income 470s attr(,"variables") 470s list(consump, price, income) 470s attr(,"factors") 470s price income 470s consump 0 0 470s price 1 0 470s income 0 1 470s attr(,"term.labels") 470s [1] "price" "income" 470s attr(,"order") 470s [1] 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 1 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(consump, price, income) 470s attr(,"dataClasses") 470s consump price income 470s "numeric" "numeric" "numeric" 470s > 470s > terms( fitw2sls4e ) 470s $demand 470s consump ~ price + income 470s attr(,"variables") 470s list(consump, price, income) 470s attr(,"factors") 470s price income 470s consump 0 0 470s price 1 0 470s income 0 1 470s attr(,"term.labels") 470s [1] "price" "income" 470s attr(,"order") 470s [1] 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 1 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(consump, price, income) 470s attr(,"dataClasses") 470s consump price income 470s "numeric" "numeric" "numeric" 470s 470s $supply 470s consump ~ price + farmPrice + trend 470s attr(,"variables") 470s list(consump, price, farmPrice, trend) 470s attr(,"factors") 470s price farmPrice trend 470s consump 0 0 0 470s price 1 0 0 470s farmPrice 0 1 0 470s trend 0 0 1 470s attr(,"term.labels") 470s [1] "price" "farmPrice" "trend" 470s attr(,"order") 470s [1] 1 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 1 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(consump, price, farmPrice, trend) 470s attr(,"dataClasses") 470s consump price farmPrice trend 470s "numeric" "numeric" "numeric" "numeric" 470s 470s > terms( fitw2sls4e$eq[[ 2 ]] ) 470s consump ~ price + farmPrice + trend 470s attr(,"variables") 470s list(consump, price, farmPrice, trend) 470s attr(,"factors") 470s price farmPrice trend 470s consump 0 0 0 470s price 1 0 0 470s farmPrice 0 1 0 470s trend 0 0 1 470s attr(,"term.labels") 470s [1] "price" "farmPrice" "trend" 470s attr(,"order") 470s [1] 1 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 1 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(consump, price, farmPrice, trend) 470s attr(,"dataClasses") 470s consump price farmPrice trend 470s "numeric" "numeric" "numeric" "numeric" 470s > 470s > terms( fitw2sls5 ) 470s $demand 470s consump ~ price + income 470s attr(,"variables") 470s list(consump, price, income) 470s attr(,"factors") 470s price income 470s consump 0 0 470s price 1 0 470s income 0 1 470s attr(,"term.labels") 470s [1] "price" "income" 470s attr(,"order") 470s [1] 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 1 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(consump, price, income) 470s attr(,"dataClasses") 470s consump price income 470s "numeric" "numeric" "numeric" 470s 470s $supply 470s consump ~ price + farmPrice + trend 470s attr(,"variables") 470s list(consump, price, farmPrice, trend) 470s attr(,"factors") 470s price farmPrice trend 470s consump 0 0 0 470s price 1 0 0 470s farmPrice 0 1 0 470s trend 0 0 1 470s attr(,"term.labels") 470s [1] "price" "farmPrice" "trend" 470s attr(,"order") 470s [1] 1 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 1 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(consump, price, farmPrice, trend) 470s attr(,"dataClasses") 470s consump price farmPrice trend 470s "numeric" "numeric" "numeric" "numeric" 470s 470s > terms( fitw2sls5$eq[[ 1 ]] ) 470s consump ~ price + income 470s attr(,"variables") 470s list(consump, price, income) 470s attr(,"factors") 470s price income 470s consump 0 0 470s price 1 0 470s income 0 1 470s attr(,"term.labels") 470s [1] "price" "income" 470s attr(,"order") 470s [1] 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 1 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(consump, price, income) 470s attr(,"dataClasses") 470s consump price income 470s "numeric" "numeric" "numeric" 470s > 470s > terms( fitw2slsd1 ) 470s $demand 470s consump ~ price + income 470s attr(,"variables") 470s list(consump, price, income) 470s attr(,"factors") 470s price income 470s consump 0 0 470s price 1 0 470s income 0 1 470s attr(,"term.labels") 470s [1] "price" "income" 470s attr(,"order") 470s [1] 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 1 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(consump, price, income) 470s attr(,"dataClasses") 470s consump price income 470s "numeric" "numeric" "numeric" 470s 470s $supply 470s consump ~ price + farmPrice + trend 470s attr(,"variables") 470s list(consump, price, farmPrice, trend) 470s attr(,"factors") 470s price farmPrice trend 470s consump 0 0 0 470s price 1 0 0 470s farmPrice 0 1 0 470s trend 0 0 1 470s attr(,"term.labels") 470s [1] "price" "farmPrice" "trend" 470s attr(,"order") 470s [1] 1 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 1 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(consump, price, farmPrice, trend) 470s attr(,"dataClasses") 470s consump price farmPrice trend 470s "numeric" "numeric" "numeric" "numeric" 470s 470s > terms( fitw2slsd1$eq[[ 2 ]] ) 470s consump ~ price + farmPrice + trend 470s attr(,"variables") 470s list(consump, price, farmPrice, trend) 470s attr(,"factors") 470s price farmPrice trend 470s consump 0 0 0 470s price 1 0 0 470s farmPrice 0 1 0 470s trend 0 0 1 470s attr(,"term.labels") 470s [1] "price" "farmPrice" "trend" 470s attr(,"order") 470s [1] 1 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 1 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(consump, price, farmPrice, trend) 470s attr(,"dataClasses") 470s consump price farmPrice trend 470s "numeric" "numeric" "numeric" "numeric" 470s > 470s > terms( fitw2slsd2e ) 470s $demand 470s consump ~ price + income 470s attr(,"variables") 470s list(consump, price, income) 470s attr(,"factors") 470s price income 470s consump 0 0 470s price 1 0 470s income 0 1 470s attr(,"term.labels") 470s [1] "price" "income" 470s attr(,"order") 470s [1] 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 1 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(consump, price, income) 470s attr(,"dataClasses") 470s consump price income 470s "numeric" "numeric" "numeric" 470s 470s $supply 470s consump ~ price + farmPrice + trend 470s attr(,"variables") 470s list(consump, price, farmPrice, trend) 470s attr(,"factors") 470s price farmPrice trend 470s consump 0 0 0 470s price 1 0 0 470s farmPrice 0 1 0 470s trend 0 0 1 470s attr(,"term.labels") 470s [1] "price" "farmPrice" "trend" 470s attr(,"order") 470s [1] 1 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 1 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(consump, price, farmPrice, trend) 470s attr(,"dataClasses") 470s consump price farmPrice trend 470s "numeric" "numeric" "numeric" "numeric" 470s 470s > terms( fitw2slsd2e$eq[[ 1 ]] ) 470s consump ~ price + income 470s attr(,"variables") 470s list(consump, price, income) 470s attr(,"factors") 470s price income 470s consump 0 0 470s price 1 0 470s income 0 1 470s attr(,"term.labels") 470s [1] "price" "income" 470s attr(,"order") 470s [1] 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 1 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(consump, price, income) 470s attr(,"dataClasses") 470s consump price income 470s "numeric" "numeric" "numeric" 470s > 470s > terms( fitw2slsd3e ) 470s $demand 470s consump ~ price + income 470s attr(,"variables") 470s list(consump, price, income) 470s attr(,"factors") 470s price income 470s consump 0 0 470s price 1 0 470s income 0 1 470s attr(,"term.labels") 470s [1] "price" "income" 470s attr(,"order") 470s [1] 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 1 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(consump, price, income) 470s attr(,"dataClasses") 470s consump price income 470s "numeric" "numeric" "numeric" 470s 470s $supply 470s consump ~ price + farmPrice + trend 470s attr(,"variables") 470s list(consump, price, farmPrice, trend) 470s attr(,"factors") 470s price farmPrice trend 470s consump 0 0 0 470s price 1 0 0 470s farmPrice 0 1 0 470s trend 0 0 1 470s attr(,"term.labels") 470s [1] "price" "farmPrice" "trend" 470s attr(,"order") 470s [1] 1 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 1 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(consump, price, farmPrice, trend) 470s attr(,"dataClasses") 470s consump price farmPrice trend 470s "numeric" "numeric" "numeric" "numeric" 470s 470s > terms( fitw2slsd3e$eq[[ 2 ]] ) 470s consump ~ price + farmPrice + trend 470s attr(,"variables") 470s list(consump, price, farmPrice, trend) 470s attr(,"factors") 470s price farmPrice trend 470s consump 0 0 0 470s price 1 0 0 470s farmPrice 0 1 0 470s trend 0 0 1 470s attr(,"term.labels") 470s [1] "price" "farmPrice" "trend" 470s attr(,"order") 470s [1] 1 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 1 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(consump, price, farmPrice, trend) 470s attr(,"dataClasses") 470s consump price farmPrice trend 470s "numeric" "numeric" "numeric" "numeric" 470s > 470s > 470s > ## **************** terms of instruments ******************* 470s > fitw2sls1e$eq[[ 1 ]]$termsInst 470s ~income + farmPrice + trend 470s attr(,"variables") 470s list(income, farmPrice, trend) 470s attr(,"factors") 470s income farmPrice trend 470s income 1 0 0 470s farmPrice 0 1 0 470s trend 0 0 1 470s attr(,"term.labels") 470s [1] "income" "farmPrice" "trend" 470s attr(,"order") 470s [1] 1 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 0 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(income, farmPrice, trend) 470s attr(,"dataClasses") 470s income farmPrice trend 470s "numeric" "numeric" "numeric" 470s > 470s > fitw2sls2$eq[[ 2 ]]$termsInst 470s ~income + farmPrice + trend 470s attr(,"variables") 470s list(income, farmPrice, trend) 470s attr(,"factors") 470s income farmPrice trend 470s income 1 0 0 470s farmPrice 0 1 0 470s trend 0 0 1 470s attr(,"term.labels") 470s [1] "income" "farmPrice" "trend" 470s attr(,"order") 470s [1] 1 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 0 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(income, farmPrice, trend) 470s attr(,"dataClasses") 470s income farmPrice trend 470s "numeric" "numeric" "numeric" 470s > 470s > fitw2sls3$eq[[ 1 ]]$termsInst 470s ~income + farmPrice + trend 470s attr(,"variables") 470s list(income, farmPrice, trend) 470s attr(,"factors") 470s income farmPrice trend 470s income 1 0 0 470s farmPrice 0 1 0 470s trend 0 0 1 470s attr(,"term.labels") 470s [1] "income" "farmPrice" "trend" 470s attr(,"order") 470s [1] 1 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 0 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(income, farmPrice, trend) 470s attr(,"dataClasses") 470s income farmPrice trend 470s "numeric" "numeric" "numeric" 470s > 470s > fitw2sls4e$eq[[ 2 ]]$termsInst 470s ~income + farmPrice + trend 470s attr(,"variables") 470s list(income, farmPrice, trend) 470s attr(,"factors") 470s income farmPrice trend 470s income 1 0 0 470s farmPrice 0 1 0 470s trend 0 0 1 470s attr(,"term.labels") 470s [1] "income" "farmPrice" "trend" 470s attr(,"order") 470s [1] 1 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 0 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(income, farmPrice, trend) 470s attr(,"dataClasses") 470s income farmPrice trend 470s "numeric" "numeric" "numeric" 470s > 470s > fitw2sls5$eq[[ 1 ]]$termsInst 470s ~income + farmPrice + trend 470s attr(,"variables") 470s list(income, farmPrice, trend) 470s attr(,"factors") 470s income farmPrice trend 470s income 1 0 0 470s farmPrice 0 1 0 470s trend 0 0 1 470s attr(,"term.labels") 470s [1] "income" "farmPrice" "trend" 470s attr(,"order") 470s [1] 1 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 0 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(income, farmPrice, trend) 470s attr(,"dataClasses") 470s income farmPrice trend 470s "numeric" "numeric" "numeric" 470s > 470s > fitw2slsd1$eq[[ 2 ]]$termsInst 470s ~income + farmPrice + trend 470s attr(,"variables") 470s list(income, farmPrice, trend) 470s attr(,"factors") 470s income farmPrice trend 470s income 1 0 0 470s farmPrice 0 1 0 470s trend 0 0 1 470s attr(,"term.labels") 470s [1] "income" "farmPrice" "trend" 470s attr(,"order") 470s [1] 1 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 0 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(income, farmPrice, trend) 470s attr(,"dataClasses") 470s income farmPrice trend 470s "numeric" "numeric" "numeric" 470s > 470s > fitw2slsd2e$eq[[ 1 ]]$termsInst 470s ~income + farmPrice 470s attr(,"variables") 470s list(income, farmPrice) 470s attr(,"factors") 470s income farmPrice 470s income 1 0 470s farmPrice 0 1 470s attr(,"term.labels") 470s [1] "income" "farmPrice" 470s attr(,"order") 470s [1] 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 0 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(income, farmPrice) 470s attr(,"dataClasses") 470s income farmPrice 470s "numeric" "numeric" 470s > 470s > fitw2slsd3e$eq[[ 2 ]]$termsInst 470s ~income + farmPrice + trend 470s attr(,"variables") 470s list(income, farmPrice, trend) 470s attr(,"factors") 470s income farmPrice trend 470s income 1 0 0 470s farmPrice 0 1 0 470s trend 0 0 1 470s attr(,"term.labels") 470s [1] "income" "farmPrice" "trend" 470s attr(,"order") 470s [1] 1 1 1 470s attr(,"intercept") 470s [1] 1 470s attr(,"response") 470s [1] 0 470s attr(,".Environment") 470s 470s attr(,"predvars") 470s list(income, farmPrice, trend) 470s attr(,"dataClasses") 470s income farmPrice trend 470s "numeric" "numeric" "numeric" 470s > 470s > 470s > ## **************** estfun ************************ 470s > library( "sandwich" ) 470s > 470s > estfun( fitw2sls1 ) 470s demand_(Intercept) demand_price demand_income supply_(Intercept) 470s demand_1 0.17426 17.362 15.231 0.0000 470s demand_2 -0.12666 -13.314 -12.362 0.0000 470s demand_3 0.63211 65.603 61.125 0.0000 470s demand_4 0.38686 40.439 37.990 0.0000 470s demand_5 0.59421 58.619 59.302 0.0000 470s demand_6 0.34231 34.111 34.403 0.0000 470s demand_7 0.46340 47.253 47.822 0.0000 470s demand_8 -0.95225 -97.353 -102.653 0.0000 470s demand_9 -0.40681 -38.486 -39.297 0.0000 470s demand_10 0.73846 68.469 65.649 0.0000 470s demand_11 -0.07078 -6.540 -5.315 0.0000 470s demand_12 -0.58541 -57.907 -45.018 0.0000 470s demand_13 -0.46025 -47.020 -38.937 0.0000 470s demand_14 0.02562 2.569 2.322 0.0000 470s demand_15 0.66403 64.824 68.462 0.0000 470s demand_16 -0.98546 -95.483 -103.572 0.0000 470s demand_17 -0.00533 -0.468 -0.514 0.0000 470s demand_18 -0.74266 -75.053 -77.534 0.0000 470s demand_19 0.43017 45.625 47.620 0.0000 470s demand_20 -0.11583 -13.250 -14.722 0.0000 470s supply_1 0.00000 0.000 0.000 -0.0444 470s supply_2 0.00000 0.000 0.000 -0.2348 470s supply_3 0.00000 0.000 0.000 0.2691 470s supply_4 0.00000 0.000 0.000 0.1308 470s supply_5 0.00000 0.000 0.000 0.2381 470s supply_6 0.00000 0.000 0.000 0.1015 470s supply_7 0.00000 0.000 0.000 0.2015 470s supply_8 0.00000 0.000 0.000 -0.7062 470s supply_9 0.00000 0.000 0.000 -0.3238 470s supply_10 0.00000 0.000 0.000 0.4611 470s supply_11 0.00000 0.000 0.000 0.0385 470s supply_12 0.00000 0.000 0.000 -0.2360 470s supply_13 0.00000 0.000 0.000 -0.1548 470s supply_14 0.00000 0.000 0.000 0.1330 470s supply_15 0.00000 0.000 0.000 0.4778 470s supply_16 0.00000 0.000 0.000 -0.5719 470s supply_17 0.00000 0.000 0.000 0.0648 470s supply_18 0.00000 0.000 0.000 -0.3413 470s supply_19 0.00000 0.000 0.000 0.4299 470s supply_20 0.00000 0.000 0.000 0.0672 470s supply_price supply_farmPrice supply_trend 470s demand_1 0.00 0.00 0.0000 470s demand_2 0.00 0.00 0.0000 470s demand_3 0.00 0.00 0.0000 470s demand_4 0.00 0.00 0.0000 470s demand_5 0.00 0.00 0.0000 470s demand_6 0.00 0.00 0.0000 470s demand_7 0.00 0.00 0.0000 470s demand_8 0.00 0.00 0.0000 470s demand_9 0.00 0.00 0.0000 470s demand_10 0.00 0.00 0.0000 470s demand_11 0.00 0.00 0.0000 470s demand_12 0.00 0.00 0.0000 470s demand_13 0.00 0.00 0.0000 470s demand_14 0.00 0.00 0.0000 470s demand_15 0.00 0.00 0.0000 470s demand_16 0.00 0.00 0.0000 470s demand_17 0.00 0.00 0.0000 470s demand_18 0.00 0.00 0.0000 470s demand_19 0.00 0.00 0.0000 470s demand_20 0.00 0.00 0.0000 470s supply_1 -4.42 -4.35 -0.0444 470s supply_2 -24.68 -23.27 -0.4696 470s supply_3 27.93 26.67 0.8073 470s supply_4 13.67 12.83 0.5230 470s supply_5 23.49 26.38 1.1905 470s supply_6 10.12 10.99 0.6093 470s supply_7 20.55 21.28 1.4107 470s supply_8 -72.20 -77.54 -5.6498 470s supply_9 -30.64 -35.20 -2.9145 470s supply_10 42.75 46.39 4.6109 470s supply_11 3.56 3.12 0.4235 470s supply_12 -23.35 -16.19 -2.8326 470s supply_13 -15.81 -10.97 -2.0121 470s supply_14 13.34 10.83 1.8621 470s supply_15 46.64 48.88 7.1671 470s supply_16 -55.42 -60.05 -9.1508 470s supply_17 5.68 7.16 1.1011 470s supply_18 -34.49 -31.57 -6.1438 470s supply_19 45.59 38.39 8.1674 470s supply_20 7.69 6.25 1.3448 470s > round( colSums( estfun( fitw2sls1 ) ), digits = 7 ) 470s demand_(Intercept) demand_price demand_income supply_(Intercept) 470s 0 0 0 0 470s supply_price supply_farmPrice supply_trend 470s 0 0 0 470s > 470s > estfun( fitw2sls1e ) 470s demand_(Intercept) demand_price demand_income supply_(Intercept) 470s demand_1 0.20502 20.43 17.918 0.0000 470s demand_2 -0.14901 -15.66 -14.543 0.0000 470s demand_3 0.74366 77.18 71.912 0.0000 470s demand_4 0.45513 47.57 44.694 0.0000 470s demand_5 0.69907 68.96 69.767 0.0000 470s demand_6 0.40272 40.13 40.474 0.0000 470s demand_7 0.54517 55.59 56.262 0.0000 470s demand_8 -1.12030 -114.53 -120.768 0.0000 470s demand_9 -0.47860 -45.28 -46.232 0.0000 470s demand_10 0.86877 80.55 77.234 0.0000 470s demand_11 -0.08327 -7.69 -6.253 0.0000 470s demand_12 -0.68871 -68.13 -52.962 0.0000 470s demand_13 -0.54147 -55.32 -45.808 0.0000 470s demand_14 0.03015 3.02 2.731 0.0000 470s demand_15 0.78121 76.26 80.543 0.0000 470s demand_16 -1.15937 -112.33 -121.850 0.0000 470s demand_17 -0.00627 -0.55 -0.605 0.0000 470s demand_18 -0.87372 -88.30 -91.217 0.0000 470s demand_19 0.50608 53.68 56.023 0.0000 470s demand_20 -0.13627 -15.59 -17.320 0.0000 470s supply_1 0.00000 0.00 0.000 -0.0554 470s supply_2 0.00000 0.00 0.000 -0.2935 470s supply_3 0.00000 0.00 0.000 0.3364 470s supply_4 0.00000 0.00 0.000 0.1634 470s supply_5 0.00000 0.00 0.000 0.2976 470s supply_6 0.00000 0.00 0.000 0.1269 470s supply_7 0.00000 0.00 0.000 0.2519 470s supply_8 0.00000 0.00 0.000 -0.8828 470s supply_9 0.00000 0.00 0.000 -0.4048 470s supply_10 0.00000 0.00 0.000 0.5764 470s supply_11 0.00000 0.00 0.000 0.0481 470s supply_12 0.00000 0.00 0.000 -0.2951 470s supply_13 0.00000 0.00 0.000 -0.1935 470s supply_14 0.00000 0.00 0.000 0.1663 470s supply_15 0.00000 0.00 0.000 0.5973 470s supply_16 0.00000 0.00 0.000 -0.7149 470s supply_17 0.00000 0.00 0.000 0.0810 470s supply_18 0.00000 0.00 0.000 -0.4267 470s supply_19 0.00000 0.00 0.000 0.5373 470s supply_20 0.00000 0.00 0.000 0.0841 470s supply_price supply_farmPrice supply_trend 470s demand_1 0.00 0.00 0.0000 470s demand_2 0.00 0.00 0.0000 470s demand_3 0.00 0.00 0.0000 470s demand_4 0.00 0.00 0.0000 470s demand_5 0.00 0.00 0.0000 470s demand_6 0.00 0.00 0.0000 470s demand_7 0.00 0.00 0.0000 470s demand_8 0.00 0.00 0.0000 470s demand_9 0.00 0.00 0.0000 470s demand_10 0.00 0.00 0.0000 470s demand_11 0.00 0.00 0.0000 470s demand_12 0.00 0.00 0.0000 470s demand_13 0.00 0.00 0.0000 470s demand_14 0.00 0.00 0.0000 470s demand_15 0.00 0.00 0.0000 470s demand_16 0.00 0.00 0.0000 470s demand_17 0.00 0.00 0.0000 470s demand_18 0.00 0.00 0.0000 470s demand_19 0.00 0.00 0.0000 470s demand_20 0.00 0.00 0.0000 470s supply_1 -5.52 -5.43 -0.0554 470s supply_2 -30.85 -29.09 -0.5870 470s supply_3 34.91 33.33 1.0091 470s supply_4 17.09 16.03 0.6538 470s supply_5 29.36 32.98 1.4882 470s supply_6 12.65 13.73 0.7616 470s supply_7 25.69 26.60 1.7633 470s supply_8 -90.25 -96.93 -7.0623 470s supply_9 -38.30 -44.00 -3.6431 470s supply_10 53.44 57.98 5.7636 470s supply_11 4.45 3.90 0.5294 470s supply_12 -29.19 -20.24 -3.5407 470s supply_13 -19.77 -13.72 -2.5151 470s supply_14 16.67 13.53 2.3277 470s supply_15 58.30 61.10 8.9588 470s supply_16 -69.27 -75.07 -11.4386 470s supply_17 7.10 8.95 1.3763 470s supply_18 -43.12 -39.47 -7.6797 470s supply_19 56.99 47.98 10.2092 470s supply_20 9.62 7.82 1.6810 470s > round( colSums( estfun( fitw2sls1e ) ), digits = 7 ) 470s demand_(Intercept) demand_price demand_income supply_(Intercept) 470s 0 0 0 0 470s supply_price supply_farmPrice supply_trend 470s 0 0 0 470s > 470s > estfun( fitw2slsd1e ) 470s demand_(Intercept) demand_price demand_income supply_(Intercept) 470s demand_1 -0.2141 -20.39 -18.71 0.0000 470s demand_2 -0.5971 -59.32 -58.28 0.0000 470s demand_3 0.3342 33.06 32.31 0.0000 470s demand_4 0.0923 9.21 9.06 0.0000 470s demand_5 0.3748 36.34 37.40 0.0000 470s demand_6 0.1317 12.91 13.23 0.0000 470s demand_7 0.2982 29.80 30.78 0.0000 470s demand_8 -1.3110 -132.05 -141.32 0.0000 470s demand_9 -0.5322 -51.18 -51.41 0.0000 470s demand_10 0.8995 85.57 79.97 0.0000 470s demand_11 0.1399 13.25 10.51 0.0000 470s demand_12 -0.4189 -41.49 -32.21 0.0000 470s demand_13 -0.2903 -29.54 -24.56 0.0000 470s demand_14 0.2709 27.46 24.55 0.0000 470s demand_15 0.9535 96.13 98.30 0.0000 470s demand_16 -0.9012 -90.95 -94.71 0.0000 470s demand_17 0.3566 34.08 34.37 0.0000 470s demand_18 -0.5159 -53.75 -53.86 0.0000 470s demand_19 0.8239 88.84 91.20 0.0000 470s demand_20 0.1054 12.00 13.39 0.0000 470s supply_1 0.0000 0.00 0.00 -0.0554 470s supply_2 0.0000 0.00 0.00 -0.2935 470s supply_3 0.0000 0.00 0.00 0.3364 470s supply_4 0.0000 0.00 0.00 0.1634 470s supply_5 0.0000 0.00 0.00 0.2976 470s supply_6 0.0000 0.00 0.00 0.1269 470s supply_7 0.0000 0.00 0.00 0.2519 470s supply_8 0.0000 0.00 0.00 -0.8828 470s supply_9 0.0000 0.00 0.00 -0.4048 470s supply_10 0.0000 0.00 0.00 0.5764 470s supply_11 0.0000 0.00 0.00 0.0481 470s supply_12 0.0000 0.00 0.00 -0.2951 470s supply_13 0.0000 0.00 0.00 -0.1935 470s supply_14 0.0000 0.00 0.00 0.1663 470s supply_15 0.0000 0.00 0.00 0.5973 470s supply_16 0.0000 0.00 0.00 -0.7149 470s supply_17 0.0000 0.00 0.00 0.0810 470s supply_18 0.0000 0.00 0.00 -0.4267 470s supply_19 0.0000 0.00 0.00 0.5373 470s supply_20 0.0000 0.00 0.00 0.0841 470s supply_price supply_farmPrice supply_trend 470s demand_1 0.00 0.00 0.0000 470s demand_2 0.00 0.00 0.0000 470s demand_3 0.00 0.00 0.0000 470s demand_4 0.00 0.00 0.0000 470s demand_5 0.00 0.00 0.0000 470s demand_6 0.00 0.00 0.0000 470s demand_7 0.00 0.00 0.0000 470s demand_8 0.00 0.00 0.0000 470s demand_9 0.00 0.00 0.0000 470s demand_10 0.00 0.00 0.0000 470s demand_11 0.00 0.00 0.0000 470s demand_12 0.00 0.00 0.0000 470s demand_13 0.00 0.00 0.0000 470s demand_14 0.00 0.00 0.0000 470s demand_15 0.00 0.00 0.0000 470s demand_16 0.00 0.00 0.0000 470s demand_17 0.00 0.00 0.0000 470s demand_18 0.00 0.00 0.0000 470s demand_19 0.00 0.00 0.0000 470s demand_20 0.00 0.00 0.0000 470s supply_1 -5.52 -5.43 -0.0554 470s supply_2 -30.85 -29.09 -0.5870 470s supply_3 34.91 33.33 1.0091 470s supply_4 17.09 16.03 0.6538 470s supply_5 29.36 32.98 1.4882 470s supply_6 12.65 13.73 0.7616 470s supply_7 25.69 26.60 1.7633 470s supply_8 -90.25 -96.93 -7.0623 470s supply_9 -38.30 -44.00 -3.6431 470s supply_10 53.44 57.98 5.7636 470s supply_11 4.45 3.90 0.5294 470s supply_12 -29.19 -20.24 -3.5407 470s supply_13 -19.77 -13.72 -2.5151 470s supply_14 16.67 13.53 2.3277 470s supply_15 58.30 61.10 8.9588 470s supply_16 -69.27 -75.07 -11.4386 470s supply_17 7.10 8.95 1.3763 470s supply_18 -43.12 -39.47 -7.6797 470s supply_19 56.99 47.98 10.2092 470s supply_20 9.62 7.82 1.6810 470s > round( colSums( estfun( fitw2slsd1e ) ), digits = 7 ) 470s demand_(Intercept) demand_price demand_income supply_(Intercept) 470s 0 0 0 0 470s supply_price supply_farmPrice supply_trend 470s 0 0 0 470s > 470s > 470s > ## **************** bread ************************ 470s > bread( fitw2sls1 ) 470s demand_(Intercept) demand_price demand_income supply_(Intercept) 470s [1,] 2509.59 -26.937 1.9721 0.0 470s [2,] -26.94 0.372 -0.1057 0.0 470s [3,] 1.97 -0.106 0.0881 0.0 470s [4,] 0.00 0.000 0.0000 5770.1 470s [5,] 0.00 0.000 0.0000 -43.8 470s [6,] 0.00 0.000 0.0000 -13.0 470s [7,] 0.00 0.000 0.0000 -11.8 470s supply_price supply_farmPrice supply_trend 470s [1,] 0.0000 0.0000 0.0000 470s [2,] 0.0000 0.0000 0.0000 470s [3,] 0.0000 0.0000 0.0000 470s [4,] -43.8164 -12.9527 -11.8092 470s [5,] 0.3995 0.0374 0.0232 470s [6,] 0.0374 0.0893 0.0551 470s [7,] 0.0232 0.0551 0.3972 470s > 470s > bread( fitw2sls1e ) 470s demand_(Intercept) demand_price demand_income supply_(Intercept) 470s [1,] 2133.15 -22.8963 1.6763 0.00 470s [2,] -22.90 0.3165 -0.0898 0.00 470s [3,] 1.68 -0.0898 0.0749 0.00 470s [4,] 0.00 0.0000 0.0000 4616.09 470s [5,] 0.00 0.0000 0.0000 -35.05 470s [6,] 0.00 0.0000 0.0000 -10.36 470s [7,] 0.00 0.0000 0.0000 -9.45 470s supply_price supply_farmPrice supply_trend 470s [1,] 0.0000 0.0000 0.0000 470s [2,] 0.0000 0.0000 0.0000 470s [3,] 0.0000 0.0000 0.0000 470s [4,] -35.0531 -10.3622 -9.4473 470s [5,] 0.3196 0.0300 0.0185 470s [6,] 0.0300 0.0714 0.0441 470s [7,] 0.0185 0.0441 0.3178 470s > 470s > bread( fitw2slsd1e ) 470s demand_(Intercept) demand_price demand_income supply_(Intercept) 470s [1,] 4222.1 -51.601 9.696 0.00 470s [2,] -51.6 0.713 -0.202 0.00 470s [3,] 9.7 -0.202 0.108 0.00 470s [4,] 0.0 0.000 0.000 4616.09 470s [5,] 0.0 0.000 0.000 -35.05 470s [6,] 0.0 0.000 0.000 -10.36 470s [7,] 0.0 0.000 0.000 -9.45 470s supply_price supply_farmPrice supply_trend 470s [1,] 0.0000 0.0000 0.0000 470s [2,] 0.0000 0.0000 0.0000 470s [3,] 0.0000 0.0000 0.0000 470s [4,] -35.0531 -10.3622 -9.4473 470s [5,] 0.3196 0.0300 0.0185 470s [6,] 0.0300 0.0714 0.0441 470s [7,] 0.0185 0.0441 0.3178 470s > 470s BEGIN TEST test_wls.R 470s 470s R version 4.3.3 (2024-02-29) -- "Angel Food Cake" 470s Copyright (C) 2024 The R Foundation for Statistical Computing 470s Platform: s390x-ibm-linux-gnu (64-bit) 470s 470s R is free software and comes with ABSOLUTELY NO WARRANTY. 470s You are welcome to redistribute it under certain conditions. 470s Type 'license()' or 'licence()' for distribution details. 470s 470s R is a collaborative project with many contributors. 470s Type 'contributors()' for more information and 470s 'citation()' on how to cite R or R packages in publications. 470s 471s Type 'demo()' for some demos, 'help()' for on-line help, or 471s 'help.start()' for an HTML browser interface to help. 471s Type 'q()' to quit R. 471s 471s > library( systemfit ) 471s Loading required package: Matrix 472s Loading required package: car 472s Loading required package: carData 472s Loading required package: lmtest 472s Loading required package: zoo 472s 472s Attaching package: ‘zoo’ 472s 472s The following objects are masked from ‘package:base’: 472s 472s as.Date, as.Date.numeric 472s 472s 472s Please cite the 'systemfit' package as: 472s 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/. 472s 472s If you have questions, suggestions, or comments regarding the 'systemfit' package, please use a forum or 'tracker' at systemfit's R-Forge site: 472s https://r-forge.r-project.org/projects/systemfit/ 472s > options( digits = 3 ) 472s > 472s > data( "Kmenta" ) 472s > useMatrix <- FALSE 472s > 472s > demand <- consump ~ price + income 472s > supply <- consump ~ price + farmPrice + trend 472s > system <- list( demand = demand, supply = supply ) 472s > restrm <- matrix(0,1,7) # restriction matrix "R" 472s > restrm[1,3] <- 1 472s > restrm[1,7] <- -1 472s > restrict <- "demand_income - supply_trend = 0" 472s > restr2m <- matrix(0,2,7) # restriction matrix "R" 2 472s > restr2m[1,3] <- 1 472s > restr2m[1,7] <- -1 472s > restr2m[2,2] <- -1 472s > restr2m[2,5] <- 1 472s > restr2q <- c( 0, 0.5 ) # restriction vector "q" 2 472s > restrict2 <- c( "demand_income - supply_trend = 0", 472s + "- demand_price + supply_price = 0.5" ) 472s > tc <- matrix(0,7,6) 472s > tc[1,1] <- 1 472s > tc[2,2] <- 1 472s > tc[3,3] <- 1 472s > tc[4,4] <- 1 472s > tc[5,5] <- 1 472s > tc[6,6] <- 1 472s > tc[7,3] <- 1 472s > restr3m <- matrix(0,1,6) # restriction matrix "R" 2 472s > restr3m[1,2] <- -1 472s > restr3m[1,5] <- 1 472s > restr3q <- c( 0.5 ) # restriction vector "q" 2 472s > restrict3 <- "- C2 + C5 = 0.5" 472s > 472s > 472s > ## ******* single-equation OLS estimations ********************* 472s > lmDemand <- lm( demand, data = Kmenta ) 472s > lmSupply <- lm( supply, data = Kmenta ) 472s > 472s > ## *************** WLS estimation ************************ 472s > fitwls1 <- systemfit( system, "WLS", data = Kmenta, useMatrix = useMatrix ) 472s > print( summary( fitwls1 ) ) 472s 472s systemfit results 472s method: WLS 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 33 156 4.43 0.709 0.558 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s demand 20 17 63.3 3.73 1.93 0.764 0.736 472s supply 20 16 92.6 5.78 2.40 0.655 0.590 472s 472s The covariance matrix of the residuals used for estimation 472s demand supply 472s demand 3.73 0.00 472s supply 0.00 5.78 472s 472s The covariance matrix of the residuals 472s demand supply 472s demand 3.73 4.14 472s supply 4.14 5.78 472s 472s The correlations of the residuals 472s demand supply 472s demand 1.000 0.891 472s supply 0.891 1.000 472s 472s 472s WLS estimates for 'demand' (equation 1) 472s Model Formula: consump ~ price + income 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 99.8954 7.5194 13.29 2.1e-10 *** 472s price -0.3163 0.0907 -3.49 0.0028 ** 472s income 0.3346 0.0454 7.37 1.1e-06 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 1.93 on 17 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 17 472s SSR: 63.332 MSE: 3.725 Root MSE: 1.93 472s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 472s 472s 472s WLS estimates for 'supply' (equation 2) 472s Model Formula: consump ~ price + farmPrice + trend 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 58.2754 11.4629 5.08 0.00011 *** 472s price 0.1604 0.0949 1.69 0.11039 472s farmPrice 0.2481 0.0462 5.37 6.2e-05 *** 472s trend 0.2483 0.0975 2.55 0.02157 * 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 2.405 on 16 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 16 472s SSR: 92.551 MSE: 5.784 Root MSE: 2.405 472s Multiple R-Squared: 0.655 Adjusted R-Squared: 0.59 472s 472s > all.equal( coef( fitwls1 ), c( coef( lmDemand ), coef( lmSupply ) ), 472s + check.attributes = FALSE ) 472s [1] TRUE 472s > all.equal( coef( summary( fitwls1 ) ), 472s + rbind( coef( summary( lmDemand ) ), coef( summary( lmSupply ) ) ), 472s + check.attributes = FALSE ) 472s [1] TRUE 472s > all.equal( vcov( fitwls1 ), 472s + as.matrix( bdiag( vcov( lmDemand ), vcov( lmSupply ) ) ), 472s + check.attributes = FALSE ) 472s [1] TRUE 472s > 472s > ## *************** WLS estimation (EViews-like) ************************ 472s > fitwls1e <- systemfit( system, "WLS", data = Kmenta, methodResidCov = "noDfCor", 472s + x = TRUE, useMatrix = useMatrix ) 472s > print( summary( fitwls1e, useDfSys = TRUE ) ) 472s 472s systemfit results 472s method: WLS 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 33 156 3.02 0.709 0.537 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s demand 20 17 63.3 3.73 1.93 0.764 0.736 472s supply 20 16 92.6 5.78 2.40 0.655 0.590 472s 472s The covariance matrix of the residuals used for estimation 472s demand supply 472s demand 3.17 0.00 472s supply 0.00 4.63 472s 472s The covariance matrix of the residuals 472s demand supply 472s demand 3.17 3.41 472s supply 3.41 4.63 472s 472s The correlations of the residuals 472s demand supply 472s demand 1.000 0.891 472s supply 0.891 1.000 472s 472s 472s WLS estimates for 'demand' (equation 1) 472s Model Formula: consump ~ price + income 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 99.8954 6.9325 14.41 8.9e-16 *** 472s price -0.3163 0.0836 -3.78 0.00062 *** 472s income 0.3346 0.0419 7.99 3.2e-09 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 1.93 on 17 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 17 472s SSR: 63.332 MSE: 3.725 Root MSE: 1.93 472s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 472s 472s 472s WLS estimates for 'supply' (equation 2) 472s Model Formula: consump ~ price + farmPrice + trend 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 58.2754 10.2527 5.68 2.4e-06 *** 472s price 0.1604 0.0849 1.89 0.0676 . 472s farmPrice 0.2481 0.0413 6.01 9.5e-07 *** 472s trend 0.2483 0.0872 2.85 0.0075 ** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 2.405 on 16 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 16 472s SSR: 92.551 MSE: 5.784 Root MSE: 2.405 472s Multiple R-Squared: 0.655 Adjusted R-Squared: 0.59 472s 472s > all.equal( coef( fitwls1e ), c( coef( lmDemand ), coef( lmSupply ) ), 472s + check.attributes = FALSE ) 472s [1] TRUE 472s > 472s > ## ************** WLS with cross-equation restriction *************** 472s > fitwls2 <- systemfit( system, "WLS", data = Kmenta, restrict.matrix = restrm, 472s + x = TRUE, useMatrix = useMatrix ) 472s > print( summary( fitwls2 ) ) 472s 472s systemfit results 472s method: WLS 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 34 159 2.35 0.703 0.622 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s demand 20 17 63.8 3.75 1.94 0.762 0.734 472s supply 20 16 95.6 5.98 2.44 0.643 0.576 472s 472s The covariance matrix of the residuals used for estimation 472s demand supply 472s demand 3.78 0.00 472s supply 0.00 5.94 472s 472s The covariance matrix of the residuals 472s demand supply 472s demand 3.75 4.48 472s supply 4.48 5.98 472s 472s The correlations of the residuals 472s demand supply 472s demand 1.000 0.946 472s supply 0.946 1.000 472s 472s 472s WLS estimates for 'demand' (equation 1) 472s Model Formula: consump ~ price + income 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 99.6582 7.5640 13.18 6.4e-15 *** 472s price -0.2991 0.0887 -3.37 0.0019 ** 472s income 0.3194 0.0415 7.70 6.0e-09 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 1.936 on 17 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 17 472s SSR: 63.75 MSE: 3.75 Root MSE: 1.936 472s Multiple R-Squared: 0.762 Adjusted R-Squared: 0.734 472s 472s 472s WLS estimates for 'supply' (equation 2) 472s Model Formula: consump ~ price + farmPrice + trend 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 56.1877 11.3165 4.97 1.9e-05 *** 472s price 0.1643 0.0960 1.71 0.096 . 472s farmPrice 0.2580 0.0451 5.71 2.0e-06 *** 472s trend 0.3194 0.0415 7.70 6.0e-09 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 2.445 on 16 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 16 472s SSR: 95.627 MSE: 5.977 Root MSE: 2.445 472s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.576 472s 472s > # the same with symbolically specified restrictions 472s > fitwls2Sym <- systemfit( system, "WLS", data = Kmenta, 472s + restrict.matrix = restrict, x = TRUE, 472s + useMatrix = useMatrix ) 472s > all.equal( fitwls2, fitwls2Sym ) 472s [1] "Component “call”: target, current do not match when deparsed" 472s > 472s > ## ************** WLS with cross-equation restriction (EViews-like) ******* 472s > fitwls2e <- systemfit( system, "WLS", data = Kmenta, restrict.matrix = restrm, 472s + methodResidCov = "noDfCor", useMatrix = useMatrix ) 472s > print( summary( fitwls2e ) ) 472s 472s systemfit results 472s method: WLS 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 34 159 1.61 0.703 0.589 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s demand 20 17 63.8 3.75 1.94 0.762 0.734 472s supply 20 16 95.6 5.97 2.44 0.644 0.577 472s 472s The covariance matrix of the residuals used for estimation 472s demand supply 472s demand 3.21 0.00 472s supply 0.00 4.75 472s 472s The covariance matrix of the residuals 472s demand supply 472s demand 3.19 3.69 472s supply 3.69 4.78 472s 472s The correlations of the residuals 472s demand supply 472s demand 1.000 0.946 472s supply 0.946 1.000 472s 472s 472s WLS estimates for 'demand' (equation 1) 472s Model Formula: consump ~ price + income 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 99.6461 6.9734 14.29 6.7e-16 *** 472s price -0.2982 0.0816 -3.65 0.00086 *** 472s income 0.3186 0.0381 8.37 8.9e-10 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 1.937 on 17 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 17 472s SSR: 63.794 MSE: 3.753 Root MSE: 1.937 472s Multiple R-Squared: 0.762 Adjusted R-Squared: 0.734 472s 472s 472s WLS estimates for 'supply' (equation 2) 472s Model Formula: consump ~ price + farmPrice + trend 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 56.2104 10.1248 5.55 3.3e-06 *** 472s price 0.1642 0.0859 1.91 0.064 . 472s farmPrice 0.2579 0.0404 6.38 2.7e-07 *** 472s trend 0.3186 0.0381 8.37 8.9e-10 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 2.444 on 16 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 16 472s SSR: 95.561 MSE: 5.973 Root MSE: 2.444 472s Multiple R-Squared: 0.644 Adjusted R-Squared: 0.577 472s 472s > 472s > ## ******* WLS with cross-equation restriction via restrict.regMat ********** 472s > fitwls3 <- systemfit( system,"WLS", data = Kmenta, restrict.regMat = tc, 472s + x = TRUE, useMatrix = useMatrix ) 472s > print( summary( fitwls3 ) ) 472s 472s systemfit results 472s method: WLS 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 34 159 2.35 0.703 0.622 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s demand 20 17 63.8 3.75 1.94 0.762 0.734 472s supply 20 16 95.6 5.98 2.44 0.643 0.576 472s 472s The covariance matrix of the residuals used for estimation 472s demand supply 472s demand 3.78 0.00 472s supply 0.00 5.94 472s 472s The covariance matrix of the residuals 472s demand supply 472s demand 3.75 4.48 472s supply 4.48 5.98 472s 472s The correlations of the residuals 472s demand supply 472s demand 1.000 0.946 472s supply 0.946 1.000 472s 472s 472s WLS estimates for 'demand' (equation 1) 472s Model Formula: consump ~ price + income 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 99.6582 7.5640 13.18 6.4e-15 *** 472s price -0.2991 0.0887 -3.37 0.0019 ** 472s income 0.3194 0.0415 7.70 6.0e-09 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 1.936 on 17 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 17 472s SSR: 63.75 MSE: 3.75 Root MSE: 1.936 472s Multiple R-Squared: 0.762 Adjusted R-Squared: 0.734 472s 472s 472s WLS estimates for 'supply' (equation 2) 472s Model Formula: consump ~ price + farmPrice + trend 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 56.1877 11.3165 4.97 1.9e-05 *** 472s price 0.1643 0.0960 1.71 0.096 . 472s farmPrice 0.2580 0.0451 5.71 2.0e-06 *** 472s trend 0.3194 0.0415 7.70 6.0e-09 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 2.445 on 16 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 16 472s SSR: 95.627 MSE: 5.977 Root MSE: 2.445 472s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.576 472s 472s > 472s > ## ******* WLS with cross-equation restriction via restrict.regMat (EViews-like) ***** 472s > fitwls3e <- systemfit( system,"WLS", data = Kmenta, restrict.regMat = tc, 472s + methodResidCov = "noDfCor", useMatrix = useMatrix ) 472s > print( summary( fitwls3e ) ) 472s 472s systemfit results 472s method: WLS 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 34 159 1.61 0.703 0.589 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s demand 20 17 63.8 3.75 1.94 0.762 0.734 472s supply 20 16 95.6 5.97 2.44 0.644 0.577 472s 472s The covariance matrix of the residuals used for estimation 472s demand supply 472s demand 3.21 0.00 472s supply 0.00 4.75 472s 472s The covariance matrix of the residuals 472s demand supply 472s demand 3.19 3.69 472s supply 3.69 4.78 472s 472s The correlations of the residuals 472s demand supply 472s demand 1.000 0.946 472s supply 0.946 1.000 472s 472s 472s WLS estimates for 'demand' (equation 1) 472s Model Formula: consump ~ price + income 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 99.6461 6.9734 14.29 6.7e-16 *** 472s price -0.2982 0.0816 -3.65 0.00086 *** 472s income 0.3186 0.0381 8.37 8.9e-10 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 1.937 on 17 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 17 472s SSR: 63.794 MSE: 3.753 Root MSE: 1.937 472s Multiple R-Squared: 0.762 Adjusted R-Squared: 0.734 472s 472s 472s WLS estimates for 'supply' (equation 2) 472s Model Formula: consump ~ price + farmPrice + trend 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 56.2104 10.1248 5.55 3.3e-06 *** 472s price 0.1642 0.0859 1.91 0.064 . 472s farmPrice 0.2579 0.0404 6.38 2.7e-07 *** 472s trend 0.3186 0.0381 8.37 8.9e-10 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 2.444 on 16 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 16 472s SSR: 95.561 MSE: 5.973 Root MSE: 2.444 472s Multiple R-Squared: 0.644 Adjusted R-Squared: 0.577 472s 472s > 472s > ## ***** WLS with 2 cross-equation restrictions *************** 472s > fitwls4 <- systemfit( system,"WLS", data = Kmenta, restrict.matrix = restr2m, 472s + restrict.rhs = restr2q, useMatrix = useMatrix ) 472s > print( summary( fitwls4 ) ) 472s 472s systemfit results 472s method: WLS 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 35 160 2.51 0.702 0.619 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s demand 20 17 63.6 3.74 1.94 0.763 0.735 472s supply 20 16 96.3 6.02 2.45 0.641 0.574 472s 472s The covariance matrix of the residuals used for estimation 472s demand supply 472s demand 3.76 0.00 472s supply 0.00 5.99 472s 472s The covariance matrix of the residuals 472s demand supply 472s demand 3.74 4.47 472s supply 4.47 6.02 472s 472s The correlations of the residuals 472s demand supply 472s demand 1.000 0.943 472s supply 0.943 1.000 472s 472s 472s WLS estimates for 'demand' (equation 1) 472s Model Formula: consump ~ price + income 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 100.9138 6.0474 16.69 < 2e-16 *** 472s price -0.3160 0.0648 -4.87 2.3e-05 *** 472s income 0.3238 0.0385 8.42 6.3e-10 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 1.935 on 17 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 17 472s SSR: 63.636 MSE: 3.743 Root MSE: 1.935 472s Multiple R-Squared: 0.763 Adjusted R-Squared: 0.735 472s 472s 472s WLS estimates for 'supply' (equation 2) 472s Model Formula: consump ~ price + farmPrice + trend 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 53.9416 7.9687 6.77 7.6e-08 *** 472s price 0.1840 0.0648 2.84 0.0075 ** 472s farmPrice 0.2603 0.0446 5.84 1.3e-06 *** 472s trend 0.3238 0.0385 8.42 6.3e-10 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 2.453 on 16 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 16 472s SSR: 96.268 MSE: 6.017 Root MSE: 2.453 472s Multiple R-Squared: 0.641 Adjusted R-Squared: 0.574 472s 472s > # the same with symbolically specified restrictions 472s > fitwls4Sym <- systemfit( system, "WLS", data = Kmenta, 472s + restrict.matrix = restrict2, useMatrix = useMatrix ) 472s > all.equal( fitwls4, fitwls4Sym ) 472s [1] "Component “call”: target, current do not match when deparsed" 472s > 472s > ## ***** WLS with 2 cross-equation restrictions (EViews-like) ********** 472s > fitwls4e <- systemfit( system,"WLS", data = Kmenta, methodResidCov = "noDfCor", 472s + restrict.matrix = restr2m, restrict.rhs = restr2q, 472s + x = TRUE, useMatrix = useMatrix ) 472s > print( summary( fitwls4e ) ) 472s 472s systemfit results 472s method: WLS 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 35 160 1.72 0.702 0.586 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s demand 20 17 63.7 3.75 1.94 0.763 0.735 472s supply 20 16 96.2 6.01 2.45 0.641 0.574 472s 472s The covariance matrix of the residuals used for estimation 472s demand supply 472s demand 3.2 0.00 472s supply 0.0 4.79 472s 472s The covariance matrix of the residuals 472s demand supply 472s demand 3.18 3.69 472s supply 3.69 4.81 472s 472s The correlations of the residuals 472s demand supply 472s demand 1.000 0.942 472s supply 0.942 1.000 472s 472s 472s WLS estimates for 'demand' (equation 1) 472s Model Formula: consump ~ price + income 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 100.9762 5.5234 18.28 < 2e-16 *** 472s price -0.3160 0.0589 -5.37 5.3e-06 *** 472s income 0.3233 0.0352 9.18 7.6e-11 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 1.935 on 17 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 17 472s SSR: 63.67 MSE: 3.745 Root MSE: 1.935 472s Multiple R-Squared: 0.763 Adjusted R-Squared: 0.735 472s 472s 472s WLS estimates for 'supply' (equation 2) 472s Model Formula: consump ~ price + farmPrice + trend 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 53.9630 7.2089 7.49 9.1e-09 *** 472s price 0.1840 0.0589 3.13 0.0036 ** 472s farmPrice 0.2602 0.0399 6.53 1.6e-07 *** 472s trend 0.3233 0.0352 9.18 7.6e-11 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 2.452 on 16 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 16 472s SSR: 96.215 MSE: 6.013 Root MSE: 2.452 472s Multiple R-Squared: 0.641 Adjusted R-Squared: 0.574 472s 472s > 472s > ## *********** WLS with 2 cross-equation restrictions via R and restrict.regMat ****** 472s > fitwls5 <- systemfit( system, "WLS", data = Kmenta, restrict.matrix = restr3m, 472s + restrict.rhs = restr3q, restrict.regMat = tc, 472s + x = TRUE, useMatrix = useMatrix ) 472s > print( summary( fitwls5 ) ) 472s 472s systemfit results 472s method: WLS 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 35 160 2.51 0.702 0.619 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s demand 20 17 63.6 3.74 1.94 0.763 0.735 472s supply 20 16 96.3 6.02 2.45 0.641 0.574 472s 472s The covariance matrix of the residuals used for estimation 472s demand supply 472s demand 3.76 0.00 472s supply 0.00 5.99 472s 472s The covariance matrix of the residuals 472s demand supply 472s demand 3.74 4.47 472s supply 4.47 6.02 472s 472s The correlations of the residuals 472s demand supply 472s demand 1.000 0.943 472s supply 0.943 1.000 472s 472s 472s WLS estimates for 'demand' (equation 1) 472s Model Formula: consump ~ price + income 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 100.9138 6.0474 16.69 < 2e-16 *** 472s price -0.3160 0.0648 -4.87 2.3e-05 *** 472s income 0.3238 0.0385 8.42 6.3e-10 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 1.935 on 17 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 17 472s SSR: 63.636 MSE: 3.743 Root MSE: 1.935 472s Multiple R-Squared: 0.763 Adjusted R-Squared: 0.735 472s 472s 472s WLS estimates for 'supply' (equation 2) 472s Model Formula: consump ~ price + farmPrice + trend 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 53.9416 7.9687 6.77 7.6e-08 *** 472s price 0.1840 0.0648 2.84 0.0075 ** 472s farmPrice 0.2603 0.0446 5.84 1.3e-06 *** 472s trend 0.3238 0.0385 8.42 6.3e-10 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 2.453 on 16 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 16 472s SSR: 96.268 MSE: 6.017 Root MSE: 2.453 472s Multiple R-Squared: 0.641 Adjusted R-Squared: 0.574 472s 472s > # the same with symbolically specified restrictions 472s > fitwls5Sym <- systemfit( system, "WLS", data = Kmenta, 472s + restrict.matrix = restrict3, restrict.regMat = tc, 472s + x = TRUE, useMatrix = useMatrix ) 472s > all.equal( fitwls5, fitwls5Sym ) 472s [1] "Component “call”: target, current do not match when deparsed" 472s > 472s > ## *********** WLS with 2 cross-equation restrictions via R and restrict.regMat (EViews-like) 472s > fitwls5e <- systemfit( system, "WLS", data = Kmenta, methodResidCov = "noDfCor", 472s + restrict.matrix = restr3m, restrict.rhs = restr3q, restrict.regMat = tc, 472s + useMatrix = useMatrix ) 472s > print( summary( fitwls5e ) ) 472s 472s systemfit results 472s method: WLS 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 35 160 1.72 0.702 0.586 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s demand 20 17 63.7 3.75 1.94 0.763 0.735 472s supply 20 16 96.2 6.01 2.45 0.641 0.574 472s 472s The covariance matrix of the residuals used for estimation 472s demand supply 472s demand 3.2 0.00 472s supply 0.0 4.79 472s 472s The covariance matrix of the residuals 472s demand supply 472s demand 3.18 3.69 472s supply 3.69 4.81 472s 472s The correlations of the residuals 472s demand supply 472s demand 1.000 0.942 472s supply 0.942 1.000 472s 472s 472s WLS estimates for 'demand' (equation 1) 472s Model Formula: consump ~ price + income 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 100.9762 5.5234 18.28 < 2e-16 *** 472s price -0.3160 0.0589 -5.37 5.3e-06 *** 472s income 0.3233 0.0352 9.18 7.6e-11 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 1.935 on 17 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 17 472s SSR: 63.67 MSE: 3.745 Root MSE: 1.935 472s Multiple R-Squared: 0.763 Adjusted R-Squared: 0.735 472s 472s 472s WLS estimates for 'supply' (equation 2) 472s Model Formula: consump ~ price + farmPrice + trend 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 53.9630 7.2089 7.49 9.1e-09 *** 472s price 0.1840 0.0589 3.13 0.0036 ** 472s farmPrice 0.2602 0.0399 6.53 1.6e-07 *** 472s trend 0.3233 0.0352 9.18 7.6e-11 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 2.452 on 16 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 16 472s SSR: 96.215 MSE: 6.013 Root MSE: 2.452 472s Multiple R-Squared: 0.641 Adjusted R-Squared: 0.574 472s 472s > 472s > ## *************** iterated WLS estimation ********************* 472s > fitwlsi1 <- systemfit( system, "WLS", data = Kmenta, 472s + maxit = 100, useMatrix = useMatrix ) 472s > print( summary( fitwlsi1, useDfSys = TRUE ) ) 472s 472s systemfit results 472s method: WLS 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 33 156 4.43 0.709 0.558 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s demand 20 17 63.3 3.73 1.93 0.764 0.736 472s supply 20 16 92.6 5.78 2.40 0.655 0.590 472s 472s The covariance matrix of the residuals used for estimation 472s demand supply 472s demand 3.73 0.00 472s supply 0.00 5.78 472s 472s The covariance matrix of the residuals 472s demand supply 472s demand 3.73 4.14 472s supply 4.14 5.78 472s 472s The correlations of the residuals 472s demand supply 472s demand 1.000 0.891 472s supply 0.891 1.000 472s 472s 472s WLS estimates for 'demand' (equation 1) 472s Model Formula: consump ~ price + income 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 99.8954 7.5194 13.29 8.4e-15 *** 472s price -0.3163 0.0907 -3.49 0.0014 ** 472s income 0.3346 0.0454 7.37 1.8e-08 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 1.93 on 17 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 17 472s SSR: 63.332 MSE: 3.725 Root MSE: 1.93 472s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 472s 472s 472s WLS estimates for 'supply' (equation 2) 472s Model Formula: consump ~ price + farmPrice + trend 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 58.2754 11.4629 5.08 1.4e-05 *** 472s price 0.1604 0.0949 1.69 0.100 472s farmPrice 0.2481 0.0462 5.37 6.1e-06 *** 472s trend 0.2483 0.0975 2.55 0.016 * 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 2.405 on 16 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 16 472s SSR: 92.551 MSE: 5.784 Root MSE: 2.405 472s Multiple R-Squared: 0.655 Adjusted R-Squared: 0.59 472s 472s > 472s > ## *************** iterated WLS estimation (EViews-like) ************ 472s > fitwlsi1e <- systemfit( system, "WLS", data = Kmenta, methodResidCov = "noDfCor", 472s + maxit = 100, x = TRUE, useMatrix = useMatrix ) 472s > print( summary( fitwlsi1e, useDfSys = TRUE ) ) 472s 472s systemfit results 472s method: WLS 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 33 156 3.02 0.709 0.537 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s demand 20 17 63.3 3.73 1.93 0.764 0.736 472s supply 20 16 92.6 5.78 2.40 0.655 0.590 472s 472s The covariance matrix of the residuals used for estimation 472s demand supply 472s demand 3.17 0.00 472s supply 0.00 4.63 472s 472s The covariance matrix of the residuals 472s demand supply 472s demand 3.17 3.41 472s supply 3.41 4.63 472s 472s The correlations of the residuals 472s demand supply 472s demand 1.000 0.891 472s supply 0.891 1.000 472s 472s 472s WLS estimates for 'demand' (equation 1) 472s Model Formula: consump ~ price + income 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 99.8954 6.9325 14.41 8.9e-16 *** 472s price -0.3163 0.0836 -3.78 0.00062 *** 472s income 0.3346 0.0419 7.99 3.2e-09 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 1.93 on 17 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 17 472s SSR: 63.332 MSE: 3.725 Root MSE: 1.93 472s Multiple R-Squared: 0.764 Adjusted R-Squared: 0.736 472s 472s 472s WLS estimates for 'supply' (equation 2) 472s Model Formula: consump ~ price + farmPrice + trend 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 58.2754 10.2527 5.68 2.4e-06 *** 472s price 0.1604 0.0849 1.89 0.0676 . 472s farmPrice 0.2481 0.0413 6.01 9.5e-07 *** 472s trend 0.2483 0.0872 2.85 0.0075 ** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 2.405 on 16 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 16 472s SSR: 92.551 MSE: 5.784 Root MSE: 2.405 472s Multiple R-Squared: 0.655 Adjusted R-Squared: 0.59 472s 472s > 472s > ## ****** iterated WLS with cross-equation restriction *************** 472s > fitwlsi2 <- systemfit( system, "WLS", data = Kmenta, restrict.matrix = restrm, 472s + maxit = 100, x = TRUE, useMatrix = useMatrix ) 472s > print( summary( fitwlsi2 ) ) 472s 472s systemfit results 472s method: iterated WLS 472s 472s convergence achieved after 3 iterations 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 34 159 2.34 0.703 0.623 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s demand 20 17 63.7 3.75 1.94 0.762 0.734 472s supply 20 16 95.6 5.98 2.44 0.643 0.576 472s 472s The covariance matrix of the residuals used for estimation 472s demand supply 472s demand 3.75 0.00 472s supply 0.00 5.98 472s 472s The covariance matrix of the residuals 472s demand supply 472s demand 3.75 4.48 472s supply 4.48 5.98 472s 472s The correlations of the residuals 472s demand supply 472s demand 1.000 0.946 472s supply 0.946 1.000 472s 472s 472s WLS estimates for 'demand' (equation 1) 472s Model Formula: consump ~ price + income 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 99.6607 7.5378 13.22 5.8e-15 *** 472s price -0.2993 0.0884 -3.39 0.0018 ** 472s income 0.3196 0.0414 7.72 5.6e-09 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 1.936 on 17 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 17 472s SSR: 63.741 MSE: 3.749 Root MSE: 1.936 472s Multiple R-Squared: 0.762 Adjusted R-Squared: 0.734 472s 472s 472s WLS estimates for 'supply' (equation 2) 472s Model Formula: consump ~ price + farmPrice + trend 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 56.1830 11.3487 4.95 2.0e-05 *** 472s price 0.1643 0.0963 1.71 0.097 . 472s farmPrice 0.2580 0.0453 5.70 2.1e-06 *** 472s trend 0.3196 0.0414 7.72 5.6e-09 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 2.445 on 16 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 16 472s SSR: 95.641 MSE: 5.978 Root MSE: 2.445 472s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.576 472s 472s > 472s > ## ****** iterated WLS with cross-equation restriction (EViews-like) ******** 472s > fitwlsi2e <- systemfit( system, "WLS", data = Kmenta, restrict.matrix = restrm, 472s + methodResidCov = "noDfCor", maxit = 100, useMatrix = useMatrix ) 472s > print( summary( fitwlsi2e ) ) 472s 472s systemfit results 472s method: iterated WLS 472s 472s convergence achieved after 3 iterations 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 34 159 1.6 0.703 0.589 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s demand 20 17 63.8 3.75 1.94 0.762 0.734 472s supply 20 16 95.6 5.97 2.44 0.644 0.577 472s 472s The covariance matrix of the residuals used for estimation 472s demand supply 472s demand 3.19 0.00 472s supply 0.00 4.78 472s 472s The covariance matrix of the residuals 472s demand supply 472s demand 3.19 3.69 472s supply 3.69 4.78 472s 472s The correlations of the residuals 472s demand supply 472s demand 1.000 0.946 472s supply 0.946 1.000 472s 472s 472s WLS estimates for 'demand' (equation 1) 472s Model Formula: consump ~ price + income 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 99.6484 6.9516 14.33 4.4e-16 *** 472s price -0.2984 0.0814 -3.67 0.00083 *** 472s income 0.3188 0.0380 8.39 8.4e-10 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 1.937 on 17 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 17 472s SSR: 63.785 MSE: 3.752 Root MSE: 1.937 472s Multiple R-Squared: 0.762 Adjusted R-Squared: 0.734 472s 472s 472s WLS estimates for 'supply' (equation 2) 472s Model Formula: consump ~ price + farmPrice + trend 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 56.2061 10.1500 5.54 3.4e-06 *** 472s price 0.1642 0.0861 1.91 0.065 . 472s farmPrice 0.2579 0.0405 6.37 2.9e-07 *** 472s trend 0.3188 0.0380 8.39 8.4e-10 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 2.444 on 16 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 16 472s SSR: 95.573 MSE: 5.973 Root MSE: 2.444 472s Multiple R-Squared: 0.644 Adjusted R-Squared: 0.577 472s 472s > 472s > ## ******* iterated WLS with cross-equation restriction via restrict.regMat ********** 472s > fitwlsi3 <- systemfit( system, "WLS", data = Kmenta, restrict.regMat = tc, 472s + maxit = 100, x = TRUE, useMatrix = useMatrix ) 472s > print( summary( fitwlsi3 ) ) 472s 472s systemfit results 472s method: iterated WLS 472s 472s convergence achieved after 3 iterations 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 34 159 2.34 0.703 0.623 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s demand 20 17 63.7 3.75 1.94 0.762 0.734 472s supply 20 16 95.6 5.98 2.44 0.643 0.576 472s 472s The covariance matrix of the residuals used for estimation 472s demand supply 472s demand 3.75 0.00 472s supply 0.00 5.98 472s 472s The covariance matrix of the residuals 472s demand supply 472s demand 3.75 4.48 472s supply 4.48 5.98 472s 472s The correlations of the residuals 472s demand supply 472s demand 1.000 0.946 472s supply 0.946 1.000 472s 472s 472s WLS estimates for 'demand' (equation 1) 472s Model Formula: consump ~ price + income 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 99.6607 7.5378 13.22 5.8e-15 *** 472s price -0.2993 0.0884 -3.39 0.0018 ** 472s income 0.3196 0.0414 7.72 5.6e-09 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 1.936 on 17 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 17 472s SSR: 63.741 MSE: 3.749 Root MSE: 1.936 472s Multiple R-Squared: 0.762 Adjusted R-Squared: 0.734 472s 472s 472s WLS estimates for 'supply' (equation 2) 472s Model Formula: consump ~ price + farmPrice + trend 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 56.1830 11.3487 4.95 2.0e-05 *** 472s price 0.1643 0.0963 1.71 0.097 . 472s farmPrice 0.2580 0.0453 5.70 2.1e-06 *** 472s trend 0.3196 0.0414 7.72 5.6e-09 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 2.445 on 16 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 16 472s SSR: 95.641 MSE: 5.978 Root MSE: 2.445 472s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.576 472s 472s > 472s > ## ******* iterated WLS with cross-equation restriction via restrict.regMat (EViews-like) *** 472s > fitwlsi3e <- systemfit( system, "WLS", data = Kmenta, restrict.regMat = tc, 472s + methodResidCov = "noDfCor", maxit = 100, useMatrix = useMatrix ) 472s > print( summary( fitwlsi3e ) ) 472s 472s systemfit results 472s method: iterated WLS 472s 472s convergence achieved after 3 iterations 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 34 159 1.6 0.703 0.589 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s demand 20 17 63.8 3.75 1.94 0.762 0.734 472s supply 20 16 95.6 5.97 2.44 0.644 0.577 472s 472s The covariance matrix of the residuals used for estimation 472s demand supply 472s demand 3.19 0.00 472s supply 0.00 4.78 472s 472s The covariance matrix of the residuals 472s demand supply 472s demand 3.19 3.69 472s supply 3.69 4.78 472s 472s The correlations of the residuals 472s demand supply 472s demand 1.000 0.946 472s supply 0.946 1.000 472s 472s 472s WLS estimates for 'demand' (equation 1) 472s Model Formula: consump ~ price + income 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 99.6484 6.9516 14.33 4.4e-16 *** 472s price -0.2984 0.0814 -3.67 0.00083 *** 472s income 0.3188 0.0380 8.39 8.4e-10 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 1.937 on 17 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 17 472s SSR: 63.785 MSE: 3.752 Root MSE: 1.937 472s Multiple R-Squared: 0.762 Adjusted R-Squared: 0.734 472s 472s 472s WLS estimates for 'supply' (equation 2) 472s Model Formula: consump ~ price + farmPrice + trend 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 56.2061 10.1500 5.54 3.4e-06 *** 472s price 0.1642 0.0861 1.91 0.065 . 472s farmPrice 0.2579 0.0405 6.37 2.9e-07 *** 472s trend 0.3188 0.0380 8.39 8.4e-10 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 2.444 on 16 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 16 472s SSR: 95.573 MSE: 5.973 Root MSE: 2.444 472s Multiple R-Squared: 0.644 Adjusted R-Squared: 0.577 472s 472s > nobs( fitwlsi3e ) 472s [1] 40 472s > 472s > ## ******* iterated WLS with 2 cross-equation restrictions *********** 472s > fitwlsi4 <- systemfit( system, "WLS", data = Kmenta, restrict.matrix = restr2m, 472s + restrict.rhs = restr2q, maxit = 100, useMatrix = useMatrix ) 472s > print( summary( fitwlsi4 ) ) 472s 472s systemfit results 472s method: iterated WLS 472s 472s convergence achieved after 3 iterations 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 35 160 2.51 0.702 0.619 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s demand 20 17 63.6 3.74 1.94 0.763 0.735 472s supply 20 16 96.3 6.02 2.45 0.641 0.574 472s 472s The covariance matrix of the residuals used for estimation 472s demand supply 472s demand 3.74 0.00 472s supply 0.00 6.02 472s 472s The covariance matrix of the residuals 472s demand supply 472s demand 3.74 4.47 472s supply 4.47 6.02 472s 472s The correlations of the residuals 472s demand supply 472s demand 1.000 0.943 472s supply 0.943 1.000 472s 472s 472s WLS estimates for 'demand' (equation 1) 472s Model Formula: consump ~ price + income 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 100.9031 6.0396 16.71 < 2e-16 *** 472s price -0.3159 0.0648 -4.88 2.3e-05 *** 472s income 0.3239 0.0384 8.43 6.0e-10 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 1.935 on 17 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 17 472s SSR: 63.63 MSE: 3.743 Root MSE: 1.935 472s Multiple R-Squared: 0.763 Adjusted R-Squared: 0.735 472s 472s 472s WLS estimates for 'supply' (equation 2) 472s Model Formula: consump ~ price + farmPrice + trend 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 53.9379 7.9718 6.77 7.7e-08 *** 472s price 0.1841 0.0648 2.84 0.0075 ** 472s farmPrice 0.2603 0.0447 5.83 1.3e-06 *** 472s trend 0.3239 0.0384 8.43 6.0e-10 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 2.453 on 16 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 16 472s SSR: 96.277 MSE: 6.017 Root MSE: 2.453 472s Multiple R-Squared: 0.641 Adjusted R-Squared: 0.574 472s 472s > 472s > ## ******* iterated WLS with 2 cross-equation restrictions (EViews-like) ***** 472s > fitwlsi4e <- systemfit( system, "WLS", data = Kmenta, methodResidCov = "noDfCor", 472s + restrict.matrix = restr2m, restrict.rhs = restr2q, maxit = 100, 472s + x = TRUE, useMatrix = useMatrix ) 472s > print( summary( fitwlsi4e ) ) 472s 472s systemfit results 472s method: iterated WLS 472s 472s convergence achieved after 3 iterations 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 35 160 1.72 0.702 0.586 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s demand 20 17 63.7 3.75 1.94 0.763 0.735 472s supply 20 16 96.2 6.01 2.45 0.641 0.574 472s 472s The covariance matrix of the residuals used for estimation 472s demand supply 472s demand 3.18 0.00 472s supply 0.00 4.81 472s 472s The covariance matrix of the residuals 472s demand supply 472s demand 3.18 3.69 472s supply 3.69 4.81 472s 472s The correlations of the residuals 472s demand supply 472s demand 1.000 0.942 472s supply 0.942 1.000 472s 472s 472s WLS estimates for 'demand' (equation 1) 472s Model Formula: consump ~ price + income 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 100.9662 5.5170 18.30 < 2e-16 *** 472s price -0.3160 0.0589 -5.37 5.2e-06 *** 472s income 0.3234 0.0352 9.20 7.3e-11 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 1.935 on 17 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 17 472s SSR: 63.665 MSE: 3.745 Root MSE: 1.935 472s Multiple R-Squared: 0.763 Adjusted R-Squared: 0.735 472s 472s 472s WLS estimates for 'supply' (equation 2) 472s Model Formula: consump ~ price + farmPrice + trend 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 53.9595 7.2114 7.48 9.2e-09 *** 472s price 0.1840 0.0589 3.13 0.0036 ** 472s farmPrice 0.2602 0.0400 6.51 1.6e-07 *** 472s trend 0.3234 0.0352 9.20 7.3e-11 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 2.452 on 16 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 16 472s SSR: 96.223 MSE: 6.014 Root MSE: 2.452 472s Multiple R-Squared: 0.641 Adjusted R-Squared: 0.574 472s 472s > 472s > ## ***** iterated WLS with 2 cross-equation restrictions via R and restrict.regMat ****** 472s > fitwlsi5 <- systemfit( system, "WLS", data = Kmenta, restrict.matrix = restr3m, 472s + restrict.rhs = restr3q, restrict.regMat = tc, maxit = 100, 472s + x = TRUE, useMatrix = useMatrix ) 472s > print( summary( fitwlsi5 ) ) 472s 472s systemfit results 472s method: iterated WLS 472s 472s convergence achieved after 3 iterations 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 35 160 2.51 0.702 0.619 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s demand 20 17 63.6 3.74 1.94 0.763 0.735 472s supply 20 16 96.3 6.02 2.45 0.641 0.574 472s 472s The covariance matrix of the residuals used for estimation 472s demand supply 472s demand 3.74 0.00 472s supply 0.00 6.02 472s 472s The covariance matrix of the residuals 472s demand supply 472s demand 3.74 4.47 472s supply 4.47 6.02 472s 472s The correlations of the residuals 472s demand supply 472s demand 1.000 0.943 472s supply 0.943 1.000 472s 472s 472s WLS estimates for 'demand' (equation 1) 472s Model Formula: consump ~ price + income 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 100.9031 6.0396 16.71 < 2e-16 *** 472s price -0.3159 0.0648 -4.88 2.3e-05 *** 472s income 0.3239 0.0384 8.43 6.0e-10 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 1.935 on 17 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 17 472s SSR: 63.63 MSE: 3.743 Root MSE: 1.935 472s Multiple R-Squared: 0.763 Adjusted R-Squared: 0.735 472s 472s 472s WLS estimates for 'supply' (equation 2) 472s Model Formula: consump ~ price + farmPrice + trend 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 53.9379 7.9718 6.77 7.7e-08 *** 472s price 0.1841 0.0648 2.84 0.0075 ** 472s farmPrice 0.2603 0.0447 5.83 1.3e-06 *** 472s trend 0.3239 0.0384 8.43 6.0e-10 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 2.453 on 16 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 16 472s SSR: 96.277 MSE: 6.017 Root MSE: 2.453 472s Multiple R-Squared: 0.641 Adjusted R-Squared: 0.574 472s 472s > 472s > ## *** iterated WLS with 2 cross-equation restrictions via R and restrict.regMat (EViews-like) 472s > fitwlsi5e <- systemfit( system, "WLS", data = Kmenta, methodResidCov = "noDfCor", 472s + restrict.matrix = restr3m, restrict.rhs = restr3q, restrict.regMat = tc, 472s + maxit = 100, useMatrix = useMatrix ) 472s > print( summary( fitwlsi5e ) ) 472s 472s systemfit results 472s method: iterated WLS 472s 472s convergence achieved after 3 iterations 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 35 160 1.72 0.702 0.586 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s demand 20 17 63.7 3.75 1.94 0.763 0.735 472s supply 20 16 96.2 6.01 2.45 0.641 0.574 472s 472s The covariance matrix of the residuals used for estimation 472s demand supply 472s demand 3.18 0.00 472s supply 0.00 4.81 472s 472s The covariance matrix of the residuals 472s demand supply 472s demand 3.18 3.69 472s supply 3.69 4.81 472s 472s The correlations of the residuals 472s demand supply 472s demand 1.000 0.942 472s supply 0.942 1.000 472s 472s 472s WLS estimates for 'demand' (equation 1) 472s Model Formula: consump ~ price + income 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 100.9662 5.5170 18.30 < 2e-16 *** 472s price -0.3160 0.0589 -5.37 5.2e-06 *** 472s income 0.3234 0.0352 9.20 7.3e-11 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 1.935 on 17 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 17 472s SSR: 63.665 MSE: 3.745 Root MSE: 1.935 472s Multiple R-Squared: 0.763 Adjusted R-Squared: 0.735 472s 472s 472s WLS estimates for 'supply' (equation 2) 472s Model Formula: consump ~ price + farmPrice + trend 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 53.9595 7.2114 7.48 9.2e-09 *** 472s price 0.1840 0.0589 3.13 0.0036 ** 472s farmPrice 0.2602 0.0400 6.51 1.6e-07 *** 472s trend 0.3234 0.0352 9.20 7.3e-11 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 2.452 on 16 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 16 472s SSR: 96.223 MSE: 6.014 Root MSE: 2.452 472s Multiple R-Squared: 0.641 Adjusted R-Squared: 0.574 472s 472s > 472s > 472s > ## *********** estimations with a single regressor ************ 472s > fitwlsS1 <- systemfit( 472s + list( consump ~ price - 1, consump ~ price + trend ), "WLS", 472s + data = Kmenta, useMatrix = useMatrix ) 472s > print( summary( fitwlsS1 ) ) 472s 472s systemfit results 472s method: WLS 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 36 1121 484 -1.09 -1.05 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s eq1 20 19 861 45.3 6.73 -2.213 -2.213 472s eq2 20 17 259 15.3 3.91 0.032 -0.082 472s 472s The covariance matrix of the residuals used for estimation 472s eq1 eq2 472s eq1 45.3 0.0 472s eq2 0.0 15.3 472s 472s The covariance matrix of the residuals 472s eq1 eq2 472s eq1 45.3 14.4 472s eq2 14.4 15.3 472s 472s The correlations of the residuals 472s eq1 eq2 472s eq1 1.000 0.549 472s eq2 0.549 1.000 472s 472s 472s WLS estimates for 'eq1' (equation 1) 472s Model Formula: consump ~ price - 1 472s 472s Estimate Std. Error t value Pr(>|t|) 472s price 1.006 0.015 66.9 <2e-16 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 6.733 on 19 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 19 472s SSR: 861.449 MSE: 45.339 Root MSE: 6.733 472s Multiple R-Squared: -2.213 Adjusted R-Squared: -2.213 472s 472s 472s WLS estimates for 'eq2' (equation 2) 472s Model Formula: consump ~ price + trend 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 93.6767 15.2367 6.15 1.1e-05 *** 472s price 0.0622 0.1513 0.41 0.69 472s trend 0.0953 0.1515 0.63 0.54 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 3.907 on 17 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 17 472s SSR: 259.497 MSE: 15.265 Root MSE: 3.907 472s Multiple R-Squared: 0.032 Adjusted R-Squared: -0.082 472s 472s > fitwlsS2 <- systemfit( 472s + list( consump ~ price - 1, consump ~ trend - 1 ), "WLS", 472s + data = Kmenta, useMatrix = useMatrix ) 472s > print( summary( fitwlsS2 ) ) 472s 472s systemfit results 472s method: WLS 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 38 47370 110957 -87.3 -5.28 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s eq1 20 19 861 45.3 6.73 -2.21 -2.21 472s eq2 20 19 46509 2447.8 49.48 -172.47 -172.47 472s 472s The covariance matrix of the residuals used for estimation 472s eq1 eq2 472s eq1 45.3 0 472s eq2 0.0 2448 472s 472s The covariance matrix of the residuals 472s eq1 eq2 472s eq1 45.34 -5.15 472s eq2 -5.15 2447.84 472s 472s The correlations of the residuals 472s eq1 eq2 472s eq1 1.0000 -0.0439 472s eq2 -0.0439 1.0000 472s 472s 472s WLS estimates for 'eq1' (equation 1) 472s Model Formula: consump ~ price - 1 472s 472s Estimate Std. Error t value Pr(>|t|) 472s price 1.006 0.015 66.9 <2e-16 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 6.733 on 19 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 19 472s SSR: 861.449 MSE: 45.339 Root MSE: 6.733 472s Multiple R-Squared: -2.213 Adjusted R-Squared: -2.213 472s 472s 472s WLS estimates for 'eq2' (equation 2) 472s Model Formula: consump ~ trend - 1 472s 472s Estimate Std. Error t value Pr(>|t|) 472s trend 7.405 0.924 8.02 1.6e-07 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 49.476 on 19 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 19 472s SSR: 46508.922 MSE: 2447.838 Root MSE: 49.476 472s Multiple R-Squared: -172.467 Adjusted R-Squared: -172.467 472s 472s > fitwlsS3 <- systemfit( 472s + list( consump ~ trend - 1, price ~ trend - 1 ), "WLS", 472s + data = Kmenta, useMatrix = useMatrix ) 472s > print( summary( fitwlsS3 ) ) 472s 472s systemfit results 472s method: WLS 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 38 93537 108970 -99 -0.977 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s eq1 20 19 46509 2448 49.5 -172.5 -172.5 472s eq2 20 19 47028 2475 49.8 -69.5 -69.5 472s 472s The covariance matrix of the residuals used for estimation 472s eq1 eq2 472s eq1 2448 0 472s eq2 0 2475 472s 472s The covariance matrix of the residuals 472s eq1 eq2 472s eq1 2448 2439 472s eq2 2439 2475 472s 472s The correlations of the residuals 472s eq1 eq2 472s eq1 1.000 0.988 472s eq2 0.988 1.000 472s 472s 472s WLS estimates for 'eq1' (equation 1) 472s Model Formula: consump ~ trend - 1 472s 472s Estimate Std. Error t value Pr(>|t|) 472s trend 7.405 0.924 8.02 1.6e-07 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 49.476 on 19 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 19 472s SSR: 46508.922 MSE: 2447.838 Root MSE: 49.476 472s Multiple R-Squared: -172.467 Adjusted R-Squared: -172.467 472s 472s 472s WLS estimates for 'eq2' (equation 2) 472s Model Formula: price ~ trend - 1 472s 472s Estimate Std. Error t value Pr(>|t|) 472s trend 7.318 0.929 7.88 2.1e-07 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 49.751 on 19 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 19 472s SSR: 47028.107 MSE: 2475.164 Root MSE: 49.751 472s Multiple R-Squared: -69.48 Adjusted R-Squared: -69.48 472s 472s > fitwlsS4 <- systemfit( 472s + list( consump ~ trend - 1, price ~ trend - 1 ), "WLS", 472s + data = Kmenta, restrict.matrix = matrix( c( 1, -1 ), nrow = 1 ), 472s + useMatrix = useMatrix ) 472s > print( summary( fitwlsS4 ) ) 472s 472s systemfit results 472s method: WLS 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 39 93548 111736 -99 -1.03 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s eq1 20 19 46514 2448 49.5 -172.5 -172.5 472s eq2 20 19 47034 2475 49.8 -69.5 -69.5 472s 472s The covariance matrix of the residuals used for estimation 472s eq1 eq2 472s eq1 2448 0 472s eq2 0 2475 472s 472s The covariance matrix of the residuals 472s eq1 eq2 472s eq1 2448 2439 472s eq2 2439 2475 472s 472s The correlations of the residuals 472s eq1 eq2 472s eq1 1.000 0.988 472s eq2 0.988 1.000 472s 472s 472s WLS estimates for 'eq1' (equation 1) 472s Model Formula: consump ~ trend - 1 472s 472s Estimate Std. Error t value Pr(>|t|) 472s trend 7.362 0.655 11.2 8.4e-14 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 49.478 on 19 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 19 472s SSR: 46514.224 MSE: 2448.117 Root MSE: 49.478 472s Multiple R-Squared: -172.487 Adjusted R-Squared: -172.487 472s 472s 472s WLS estimates for 'eq2' (equation 2) 472s Model Formula: price ~ trend - 1 472s 472s Estimate Std. Error t value Pr(>|t|) 472s trend 7.362 0.655 11.2 8.4e-14 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 49.754 on 19 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 19 472s SSR: 47033.528 MSE: 2475.449 Root MSE: 49.754 472s Multiple R-Squared: -69.488 Adjusted R-Squared: -69.488 472s 472s > fitwlsS5 <- systemfit( 472s + list( consump ~ 1, price ~ 1 ), "WLS", 472s + data = Kmenta, useMatrix = useMatrix ) 472s > print( summary( fitwlsS5) ) 472s 472s systemfit results 472s method: WLS 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 38 935 491 0 0 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s eq1 20 19 268 14.1 3.76 0 0 472s eq2 20 19 667 35.1 5.93 0 0 472s 472s The covariance matrix of the residuals used for estimation 472s eq1 eq2 472s eq1 14.1 0.0 472s eq2 0.0 35.1 472s 472s The covariance matrix of the residuals 472s eq1 eq2 472s eq1 14.11 2.18 472s eq2 2.18 35.12 472s 472s The correlations of the residuals 472s eq1 eq2 472s eq1 1.0000 0.0981 472s eq2 0.0981 1.0000 472s 472s 472s WLS estimates for 'eq1' (equation 1) 472s Model Formula: consump ~ 1 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 100.90 0.84 120 <2e-16 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 3.756 on 19 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 19 472s SSR: 268.114 MSE: 14.111 Root MSE: 3.756 472s Multiple R-Squared: 0 Adjusted R-Squared: 0 472s 472s 472s WLS estimates for 'eq2' (equation 2) 472s Model Formula: price ~ 1 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 100.02 1.33 75.5 <2e-16 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 5.926 on 19 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 19 472s SSR: 667.251 MSE: 35.118 Root MSE: 5.926 472s Multiple R-Squared: 0 Adjusted R-Squared: 0 472s 472s > 472s > 472s > ## **************** shorter summaries ********************** 472s > print( summary( fitwls1 ), residCov = FALSE, equations = FALSE ) 472s 472s systemfit results 472s method: WLS 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 33 156 4.43 0.709 0.558 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s demand 20 17 63.3 3.73 1.93 0.764 0.736 472s supply 20 16 92.6 5.78 2.40 0.655 0.590 472s 472s 472s Coefficients: 472s Estimate Std. Error t value Pr(>|t|) 472s demand_(Intercept) 99.8954 7.5194 13.29 2.1e-10 *** 472s demand_price -0.3163 0.0907 -3.49 0.00282 ** 472s demand_income 0.3346 0.0454 7.37 1.1e-06 *** 472s supply_(Intercept) 58.2754 11.4629 5.08 0.00011 *** 472s supply_price 0.1604 0.0949 1.69 0.11039 472s supply_farmPrice 0.2481 0.0462 5.37 6.2e-05 *** 472s supply_trend 0.2483 0.0975 2.55 0.02157 * 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s > 472s > print( summary( fitwls2e, useDfSys = FALSE, residCov = FALSE ), 472s + equations = FALSE ) 472s 472s systemfit results 472s method: WLS 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 34 159 1.61 0.703 0.589 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s demand 20 17 63.8 3.75 1.94 0.762 0.734 472s supply 20 16 95.6 5.97 2.44 0.644 0.577 472s 472s 472s Coefficients: 472s Estimate Std. Error t value Pr(>|t|) 472s demand_(Intercept) 99.6461 6.9734 14.29 6.7e-11 *** 472s demand_price -0.2982 0.0816 -3.65 0.002 ** 472s demand_income 0.3186 0.0381 8.37 2.0e-07 *** 472s supply_(Intercept) 56.2104 10.1248 5.55 4.4e-05 *** 472s supply_price 0.1642 0.0859 1.91 0.074 . 472s supply_farmPrice 0.2579 0.0404 6.38 9.1e-06 *** 472s supply_trend 0.3186 0.0381 8.37 3.1e-07 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s > 472s > print( summary( fitwls3 ), residCov = FALSE ) 472s 472s systemfit results 472s method: WLS 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 34 159 2.35 0.703 0.622 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s demand 20 17 63.8 3.75 1.94 0.762 0.734 472s supply 20 16 95.6 5.98 2.44 0.643 0.576 472s 472s 472s WLS estimates for 'demand' (equation 1) 472s Model Formula: consump ~ price + income 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 99.6582 7.5640 13.18 6.4e-15 *** 472s price -0.2991 0.0887 -3.37 0.0019 ** 472s income 0.3194 0.0415 7.70 6.0e-09 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 1.936 on 17 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 17 472s SSR: 63.75 MSE: 3.75 Root MSE: 1.936 472s Multiple R-Squared: 0.762 Adjusted R-Squared: 0.734 472s 472s 472s WLS estimates for 'supply' (equation 2) 472s Model Formula: consump ~ price + farmPrice + trend 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 56.1877 11.3165 4.97 1.9e-05 *** 472s price 0.1643 0.0960 1.71 0.096 . 472s farmPrice 0.2580 0.0451 5.71 2.0e-06 *** 472s trend 0.3194 0.0415 7.70 6.0e-09 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 2.445 on 16 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 16 472s SSR: 95.627 MSE: 5.977 Root MSE: 2.445 472s Multiple R-Squared: 0.643 Adjusted R-Squared: 0.576 472s 472s > 472s > print( summary( fitwls4e, residCov = FALSE, equations = FALSE ) ) 472s 472s systemfit results 472s method: WLS 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 35 160 1.72 0.702 0.586 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s demand 20 17 63.7 3.75 1.94 0.763 0.735 472s supply 20 16 96.2 6.01 2.45 0.641 0.574 472s 472s 472s Coefficients: 472s Estimate Std. Error t value Pr(>|t|) 472s demand_(Intercept) 100.9762 5.5234 18.28 < 2e-16 *** 472s demand_price -0.3160 0.0589 -5.37 5.3e-06 *** 472s demand_income 0.3233 0.0352 9.18 7.6e-11 *** 472s supply_(Intercept) 53.9630 7.2089 7.49 9.1e-09 *** 472s supply_price 0.1840 0.0589 3.13 0.0036 ** 472s supply_farmPrice 0.2602 0.0399 6.53 1.6e-07 *** 472s supply_trend 0.3233 0.0352 9.18 7.6e-11 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s > 472s > print( summary( fitwls5, useDfSys = FALSE ), residCov = FALSE ) 472s 472s systemfit results 472s method: WLS 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 35 160 2.51 0.702 0.619 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s demand 20 17 63.6 3.74 1.94 0.763 0.735 472s supply 20 16 96.3 6.02 2.45 0.641 0.574 472s 472s 472s WLS estimates for 'demand' (equation 1) 472s Model Formula: consump ~ price + income 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 100.9138 6.0474 16.69 5.6e-12 *** 472s price -0.3160 0.0648 -4.87 0.00014 *** 472s income 0.3238 0.0385 8.42 1.8e-07 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 1.935 on 17 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 17 472s SSR: 63.636 MSE: 3.743 Root MSE: 1.935 472s Multiple R-Squared: 0.763 Adjusted R-Squared: 0.735 472s 472s 472s WLS estimates for 'supply' (equation 2) 472s Model Formula: consump ~ price + farmPrice + trend 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 53.9416 7.9687 6.77 4.5e-06 *** 472s price 0.1840 0.0648 2.84 0.012 * 472s farmPrice 0.2603 0.0446 5.84 2.5e-05 *** 472s trend 0.3238 0.0385 8.42 2.9e-07 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 2.453 on 16 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 16 472s SSR: 96.268 MSE: 6.017 Root MSE: 2.453 472s Multiple R-Squared: 0.641 Adjusted R-Squared: 0.574 472s 472s > 472s > print( summary( fitwlsi1e, useDfSys = TRUE, equations = FALSE ) ) 472s 472s systemfit results 472s method: WLS 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 33 156 3.02 0.709 0.537 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s demand 20 17 63.3 3.73 1.93 0.764 0.736 472s supply 20 16 92.6 5.78 2.40 0.655 0.590 472s 472s The covariance matrix of the residuals used for estimation 472s demand supply 472s demand 3.17 0.00 472s supply 0.00 4.63 472s 472s The covariance matrix of the residuals 472s demand supply 472s demand 3.17 3.41 472s supply 3.41 4.63 472s 472s The correlations of the residuals 472s demand supply 472s demand 1.000 0.891 472s supply 0.891 1.000 472s 472s 472s Coefficients: 472s Estimate Std. Error t value Pr(>|t|) 472s demand_(Intercept) 99.8954 6.9325 14.41 8.9e-16 *** 472s demand_price -0.3163 0.0836 -3.78 0.00062 *** 472s demand_income 0.3346 0.0419 7.99 3.2e-09 *** 472s supply_(Intercept) 58.2754 10.2527 5.68 2.4e-06 *** 472s supply_price 0.1604 0.0849 1.89 0.06762 . 472s supply_farmPrice 0.2481 0.0413 6.01 9.5e-07 *** 472s supply_trend 0.2483 0.0872 2.85 0.00754 ** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s > 472s > print( summary( fitwlsi2, equations = FALSE, residCov = FALSE ), 472s + residCov = TRUE ) 472s 472s systemfit results 472s method: iterated WLS 472s 472s convergence achieved after 3 iterations 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 34 159 2.34 0.703 0.623 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s demand 20 17 63.7 3.75 1.94 0.762 0.734 472s supply 20 16 95.6 5.98 2.44 0.643 0.576 472s 472s The covariance matrix of the residuals used for estimation 472s demand supply 472s demand 3.75 0.00 472s supply 0.00 5.98 472s 472s The covariance matrix of the residuals 472s demand supply 472s demand 3.75 4.48 472s supply 4.48 5.98 472s 472s The correlations of the residuals 472s demand supply 472s demand 1.000 0.946 472s supply 0.946 1.000 472s 472s 472s Coefficients: 472s Estimate Std. Error t value Pr(>|t|) 472s demand_(Intercept) 99.6607 7.5378 13.22 5.8e-15 *** 472s demand_price -0.2993 0.0884 -3.39 0.0018 ** 472s demand_income 0.3196 0.0414 7.72 5.6e-09 *** 472s supply_(Intercept) 56.1830 11.3487 4.95 2.0e-05 *** 472s supply_price 0.1643 0.0963 1.71 0.0972 . 472s supply_farmPrice 0.2580 0.0453 5.70 2.1e-06 *** 472s supply_trend 0.3196 0.0414 7.72 5.6e-09 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s > 472s > print( summary( fitwlsi3e ), equations = FALSE, residCov = FALSE ) 472s 472s systemfit results 472s method: iterated WLS 472s 472s convergence achieved after 3 iterations 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 34 159 1.6 0.703 0.589 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s demand 20 17 63.8 3.75 1.94 0.762 0.734 472s supply 20 16 95.6 5.97 2.44 0.644 0.577 472s 472s 472s Coefficients: 472s Estimate Std. Error t value Pr(>|t|) 472s demand_(Intercept) 99.6484 6.9516 14.33 4.4e-16 *** 472s demand_price -0.2984 0.0814 -3.67 0.00083 *** 472s demand_income 0.3188 0.0380 8.39 8.4e-10 *** 472s supply_(Intercept) 56.2061 10.1500 5.54 3.4e-06 *** 472s supply_price 0.1642 0.0861 1.91 0.06502 . 472s supply_farmPrice 0.2579 0.0405 6.37 2.9e-07 *** 472s supply_trend 0.3188 0.0380 8.39 8.4e-10 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s > 472s > print( summary( fitwlsi4, equations = FALSE ), equations = TRUE ) 472s 472s systemfit results 472s method: iterated WLS 472s 472s convergence achieved after 3 iterations 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 35 160 2.51 0.702 0.619 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s demand 20 17 63.6 3.74 1.94 0.763 0.735 472s supply 20 16 96.3 6.02 2.45 0.641 0.574 472s 472s The covariance matrix of the residuals used for estimation 472s demand supply 472s demand 3.74 0.00 472s supply 0.00 6.02 472s 472s The covariance matrix of the residuals 472s demand supply 472s demand 3.74 4.47 472s supply 4.47 6.02 472s 472s The correlations of the residuals 472s demand supply 472s demand 1.000 0.943 472s supply 0.943 1.000 472s 472s 472s WLS estimates for 'demand' (equation 1) 472s Model Formula: consump ~ price + income 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 100.9031 6.0396 16.71 < 2e-16 *** 472s price -0.3159 0.0648 -4.88 2.3e-05 *** 472s income 0.3239 0.0384 8.43 6.0e-10 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 1.935 on 17 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 17 472s SSR: 63.63 MSE: 3.743 Root MSE: 1.935 472s Multiple R-Squared: 0.763 Adjusted R-Squared: 0.735 472s 472s 472s WLS estimates for 'supply' (equation 2) 472s Model Formula: consump ~ price + farmPrice + trend 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 53.9379 7.9718 6.77 7.7e-08 *** 472s price 0.1841 0.0648 2.84 0.0075 ** 472s farmPrice 0.2603 0.0447 5.83 1.3e-06 *** 472s trend 0.3239 0.0384 8.43 6.0e-10 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 2.453 on 16 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 16 472s SSR: 96.277 MSE: 6.017 Root MSE: 2.453 472s Multiple R-Squared: 0.641 Adjusted R-Squared: 0.574 472s 472s > 472s > print( summary( fitwlsi5e, useDfSys = FALSE, residCov = FALSE ) ) 472s 472s systemfit results 472s method: iterated WLS 472s 472s convergence achieved after 3 iterations 472s 472s N DF SSR detRCov OLS-R2 McElroy-R2 472s system 40 35 160 1.72 0.702 0.586 472s 472s N DF SSR MSE RMSE R2 Adj R2 472s demand 20 17 63.7 3.75 1.94 0.763 0.735 472s supply 20 16 96.2 6.01 2.45 0.641 0.574 472s 472s 472s WLS estimates for 'demand' (equation 1) 472s Model Formula: consump ~ price + income 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 100.9662 5.5170 18.30 1.3e-12 *** 472s price -0.3160 0.0589 -5.37 5.1e-05 *** 472s income 0.3234 0.0352 9.20 5.2e-08 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 1.935 on 17 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 17 472s SSR: 63.665 MSE: 3.745 Root MSE: 1.935 472s Multiple R-Squared: 0.763 Adjusted R-Squared: 0.735 472s 472s 472s WLS estimates for 'supply' (equation 2) 472s Model Formula: consump ~ price + farmPrice + trend 472s 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 53.9595 7.2114 7.48 1.3e-06 *** 472s price 0.1840 0.0589 3.13 0.0065 ** 472s farmPrice 0.2602 0.0400 6.51 7.2e-06 *** 472s trend 0.3234 0.0352 9.20 8.7e-08 *** 472s --- 472s Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 472s 472s Residual standard error: 2.452 on 16 degrees of freedom 472s Number of observations: 20 Degrees of Freedom: 16 472s SSR: 96.223 MSE: 6.014 Root MSE: 2.452 472s Multiple R-Squared: 0.641 Adjusted R-Squared: 0.574 472s 472s > 472s > 472s > ## ****************** residuals ************************** 472s > print( residuals( fitwls1 ) ) 472s demand supply 472s 1 1.074 -0.444 472s 2 -0.390 -0.896 472s 3 2.625 1.965 472s 4 1.802 1.134 472s 5 1.946 1.514 472s 6 1.175 0.680 472s 7 1.530 1.569 472s 8 -2.933 -4.407 472s 9 -1.365 -2.599 472s 10 2.031 2.469 472s 11 -0.149 -0.598 472s 12 -1.954 -1.697 472s 13 -1.121 -1.064 472s 14 -0.220 0.970 472s 15 1.487 3.159 472s 16 -3.701 -3.866 472s 17 -1.273 -0.265 472s 18 -2.002 -2.449 472s 19 1.738 3.110 472s 20 -0.299 1.714 472s > print( residuals( fitwls1$eq[[ 2 ]] ) ) 472s 1 2 3 4 5 6 7 8 9 10 11 472s -0.444 -0.896 1.965 1.134 1.514 0.680 1.569 -4.407 -2.599 2.469 -0.598 472s 12 13 14 15 16 17 18 19 20 472s -1.697 -1.064 0.970 3.159 -3.866 -0.265 -2.449 3.110 1.714 472s > 472s > print( residuals( fitwls2e ) ) 472s demand supply 472s 1 0.9069 0.209 472s 2 -0.4660 -0.338 472s 3 2.5495 2.455 472s 4 1.7320 1.560 472s 5 2.0183 1.771 472s 6 1.2321 0.886 472s 7 1.6019 1.724 472s 8 -2.8544 -4.378 472s 9 -1.3158 -2.597 472s 10 2.0517 2.500 472s 11 -0.3823 -0.455 472s 12 -2.2623 -1.525 472s 13 -1.3801 -1.001 472s 14 -0.3081 0.877 472s 15 1.6643 2.806 472s 16 -3.5513 -4.328 472s 17 -1.0466 -0.805 472s 18 -1.9647 -2.952 472s 19 1.8446 2.561 472s 20 -0.0697 1.029 472s > print( residuals( fitwls2e$eq[[ 1 ]] ) ) 472s 1 2 3 4 5 6 7 8 9 10 472s 0.9069 -0.4660 2.5495 1.7320 2.0183 1.2321 1.6019 -2.8544 -1.3158 2.0517 472s 11 12 13 14 15 16 17 18 19 20 472s -0.3823 -2.2623 -1.3801 -0.3081 1.6643 -3.5513 -1.0466 -1.9647 1.8446 -0.0697 472s > 472s > print( residuals( fitwls3 ) ) 472s demand supply 472s 1 0.9150 0.217 472s 2 -0.4624 -0.332 472s 3 2.5532 2.461 472s 4 1.7354 1.564 472s 5 2.0148 1.773 472s 6 1.2293 0.889 472s 7 1.5984 1.725 472s 8 -2.8582 -4.378 472s 9 -1.3182 -2.597 472s 10 2.0507 2.500 472s 11 -0.3710 -0.453 472s 12 -2.2473 -1.524 472s 13 -1.3675 -1.000 472s 14 -0.3038 0.876 472s 15 1.6557 2.802 472s 16 -3.5586 -4.333 472s 17 -1.0576 -0.811 472s 18 -1.9666 -2.957 472s 19 1.8394 2.555 472s 20 -0.0808 1.022 472s > print( residuals( fitwls3$eq[[ 2 ]] ) ) 472s 1 2 3 4 5 6 7 8 9 10 11 472s 0.217 -0.332 2.461 1.564 1.773 0.889 1.725 -4.378 -2.597 2.500 -0.453 472s 12 13 14 15 16 17 18 19 20 472s -1.524 -1.000 0.876 2.802 -4.333 -0.811 -2.957 2.555 1.022 472s > 472s > print( residuals( fitwls4e ) ) 472s demand supply 472s 1 0.9593 0.244 472s 2 -0.3907 -0.388 472s 3 2.6143 2.417 472s 4 1.8088 1.498 472s 5 1.9718 1.803 472s 6 1.2083 0.892 472s 7 1.5943 1.699 472s 8 -2.8174 -4.491 472s 9 -1.3751 -2.548 472s 10 1.9351 2.667 472s 11 -0.4019 -0.284 472s 12 -2.1883 -1.443 472s 13 -1.2686 -1.010 472s 14 -0.2984 0.921 472s 15 1.5512 2.869 472s 16 -3.6143 -4.342 472s 17 -1.2823 -0.600 472s 18 -1.9253 -3.056 472s 19 1.8860 2.425 472s 20 0.0333 0.728 472s > print( residuals( fitwls4e$eq[[ 1 ]] ) ) 472s 1 2 3 4 5 6 7 8 9 10 472s 0.9593 -0.3907 2.6143 1.8088 1.9718 1.2083 1.5943 -2.8174 -1.3751 1.9351 472s 11 12 13 14 15 16 17 18 19 20 472s -0.4019 -2.1883 -1.2686 -0.2984 1.5512 -3.6143 -1.2823 -1.9253 1.8860 0.0333 472s > 472s > print( residuals( fitwls5 ) ) 472s demand supply 472s 1 0.9649 0.249 472s 2 -0.3911 -0.384 472s 3 2.6145 2.421 472s 4 1.8081 1.501 472s 5 1.9707 1.805 472s 6 1.2067 0.893 472s 7 1.5910 1.700 472s 8 -2.8235 -4.491 472s 9 -1.3743 -2.548 472s 10 1.9406 2.667 472s 11 -0.3887 -0.282 472s 12 -2.1767 -1.442 472s 13 -1.2616 -1.009 472s 14 -0.2944 0.920 472s 15 1.5485 2.866 472s 16 -3.6185 -4.345 472s 17 -1.2806 -0.604 472s 18 -1.9295 -3.060 472s 19 1.8782 2.420 472s 20 0.0157 0.721 472s > print( residuals( fitwls5$eq[[ 2 ]] ) ) 472s 1 2 3 4 5 6 7 8 9 10 11 472s 0.249 -0.384 2.421 1.501 1.805 0.893 1.700 -4.491 -2.548 2.667 -0.282 472s 12 13 14 15 16 17 18 19 20 472s -1.442 -1.009 0.920 2.866 -4.345 -0.604 -3.060 2.420 0.721 472s > 472s > print( residuals( fitwlsi1e ) ) 472s demand supply 472s 1 1.074 -0.444 472s 2 -0.390 -0.896 472s 3 2.625 1.965 472s 4 1.802 1.134 472s 5 1.946 1.514 472s 6 1.175 0.680 472s 7 1.530 1.569 472s 8 -2.933 -4.407 472s 9 -1.365 -2.599 472s 10 2.031 2.469 472s 11 -0.149 -0.598 472s 12 -1.954 -1.697 472s 13 -1.121 -1.064 472s 14 -0.220 0.970 472s 15 1.487 3.159 472s 16 -3.701 -3.866 472s 17 -1.273 -0.265 472s 18 -2.002 -2.449 472s 19 1.738 3.110 472s 20 -0.299 1.714 472s > print( residuals( fitwlsi1e$eq[[ 1 ]] ) ) 472s 1 2 3 4 5 6 7 8 9 10 11 472s 1.074 -0.390 2.625 1.802 1.946 1.175 1.530 -2.933 -1.365 2.031 -0.149 472s 12 13 14 15 16 17 18 19 20 472s -1.954 -1.121 -0.220 1.487 -3.701 -1.273 -2.002 1.738 -0.299 472s > 472s > print( residuals( fitwlsi2 ) ) 472s demand supply 472s 1 0.9167 0.218 472s 2 -0.4616 -0.331 472s 3 2.5539 2.462 472s 4 1.7361 1.565 472s 5 2.0140 1.774 472s 6 1.2288 0.889 472s 7 1.5977 1.726 472s 8 -2.8589 -4.378 472s 9 -1.3187 -2.597 472s 10 2.0505 2.500 472s 11 -0.3686 -0.453 472s 12 -2.2443 -1.523 472s 13 -1.3649 -1.000 472s 14 -0.3029 0.876 472s 15 1.6539 2.802 472s 16 -3.5601 -4.334 472s 17 -1.0599 -0.812 472s 18 -1.9669 -2.958 472s 19 1.8383 2.554 472s 20 -0.0831 1.020 472s > print( residuals( fitwlsi2$eq[[ 2 ]] ) ) 472s 1 2 3 4 5 6 7 8 9 10 11 472s 0.218 -0.331 2.462 1.565 1.774 0.889 1.726 -4.378 -2.597 2.500 -0.453 472s 12 13 14 15 16 17 18 19 20 472s -1.523 -1.000 0.876 2.802 -4.334 -0.812 -2.958 2.554 1.020 472s > 472s > print( residuals( fitwlsi3e ) ) 472s demand supply 472s 1 0.9084 0.211 472s 2 -0.4653 -0.337 472s 3 2.5502 2.456 472s 4 1.7326 1.561 472s 5 2.0176 1.771 472s 6 1.2316 0.887 472s 7 1.6012 1.724 472s 8 -2.8551 -4.378 472s 9 -1.3162 -2.597 472s 10 2.0515 2.500 472s 11 -0.3801 -0.454 472s 12 -2.2594 -1.525 472s 13 -1.3777 -1.001 472s 14 -0.3073 0.877 472s 15 1.6627 2.806 472s 16 -3.5527 -4.329 472s 17 -1.0487 -0.806 472s 18 -1.9651 -2.953 472s 19 1.8436 2.560 472s 20 -0.0718 1.028 472s > print( residuals( fitwlsi3e$eq[[ 1 ]] ) ) 472s 1 2 3 4 5 6 7 8 9 10 472s 0.9084 -0.4653 2.5502 1.7326 2.0176 1.2316 1.6012 -2.8551 -1.3162 2.0515 472s 11 12 13 14 15 16 17 18 19 20 472s -0.3801 -2.2594 -1.3777 -0.3073 1.6627 -3.5527 -1.0487 -1.9651 1.8436 -0.0718 472s > 472s > print( residuals( fitwlsi4 ) ) 472s demand supply 472s 1 0.9659 0.250 472s 2 -0.3911 -0.383 472s 3 2.6145 2.421 472s 4 1.8080 1.502 472s 5 1.9705 1.805 472s 6 1.2064 0.893 472s 7 1.5905 1.700 472s 8 -2.8246 -4.491 472s 9 -1.3742 -2.547 472s 10 1.9415 2.667 472s 11 -0.3865 -0.282 472s 12 -2.1747 -1.442 472s 13 -1.2604 -1.009 472s 14 -0.2938 0.920 472s 15 1.5480 2.866 472s 16 -3.6192 -4.346 472s 17 -1.2804 -0.604 472s 18 -1.9302 -3.061 472s 19 1.8768 2.420 472s 20 0.0127 0.720 472s > print( residuals( fitwlsi4$eq[[ 2 ]] ) ) 472s 1 2 3 4 5 6 7 8 9 10 11 472s 0.250 -0.383 2.421 1.502 1.805 0.893 1.700 -4.491 -2.547 2.667 -0.282 472s 12 13 14 15 16 17 18 19 20 472s -1.442 -1.009 0.920 2.866 -4.346 -0.604 -3.061 2.420 0.720 472s > 472s > print( residuals( fitwlsi5e ) ) 472s demand supply 472s 1 0.9602 0.245 472s 2 -0.3908 -0.388 472s 3 2.6143 2.418 472s 4 1.8087 1.498 472s 5 1.9716 1.803 472s 6 1.2081 0.892 472s 7 1.5938 1.699 472s 8 -2.8184 -4.491 472s 9 -1.3750 -2.548 472s 10 1.9360 2.667 472s 11 -0.3997 -0.284 472s 12 -2.1865 -1.443 472s 13 -1.2675 -1.010 472s 14 -0.2978 0.921 472s 15 1.5508 2.869 472s 16 -3.6150 -4.342 472s 17 -1.2820 -0.601 472s 18 -1.9260 -3.057 472s 19 1.8848 2.424 472s 20 0.0305 0.727 472s > print( residuals( fitwlsi5e$eq[[ 1 ]] ) ) 472s 1 2 3 4 5 6 7 8 9 10 472s 0.9602 -0.3908 2.6143 1.8087 1.9716 1.2081 1.5938 -2.8184 -1.3750 1.9360 472s 11 12 13 14 15 16 17 18 19 20 472s -0.3997 -2.1865 -1.2675 -0.2978 1.5508 -3.6150 -1.2820 -1.9260 1.8848 0.0305 472s > 472s > 472s > ## *************** coefficients ********************* 472s > print( round( coef( fitwls1e ), digits = 6 ) ) 472s demand_(Intercept) demand_price demand_income supply_(Intercept) 472s 99.895 -0.316 0.335 58.275 472s supply_price supply_farmPrice supply_trend 472s 0.160 0.248 0.248 472s > print( round( coef( fitwls1e$eq[[ 1 ]] ), digits = 6 ) ) 472s (Intercept) price income 472s 99.895 -0.316 0.335 472s > 472s > print( round( coef( fitwlsi2 ), digits = 6 ) ) 472s demand_(Intercept) demand_price demand_income supply_(Intercept) 472s 99.661 -0.299 0.320 56.183 472s supply_price supply_farmPrice supply_trend 472s 0.164 0.258 0.320 472s > print( round( coef( fitwlsi2$eq[[ 2 ]] ), digits = 6 ) ) 472s (Intercept) price farmPrice trend 472s 56.183 0.164 0.258 0.320 472s > 472s > print( round( coef( fitwls3e ), digits = 6 ) ) 472s demand_(Intercept) demand_price demand_income supply_(Intercept) 472s 99.646 -0.298 0.319 56.210 472s supply_price supply_farmPrice supply_trend 472s 0.164 0.258 0.319 472s > print( round( coef( fitwls3e, modified.regMat = TRUE ), digits = 6 ) ) 472s C1 C2 C3 C4 C5 C6 472s 99.646 -0.298 0.319 56.210 0.164 0.258 472s > print( round( coef( fitwls3e$eq[[ 1 ]] ), digits = 6 ) ) 472s (Intercept) price income 472s 99.646 -0.298 0.319 472s > 472s > print( round( coef( fitwls4 ), digits = 6 ) ) 472s demand_(Intercept) demand_price demand_income supply_(Intercept) 472s 100.914 -0.316 0.324 53.942 472s supply_price supply_farmPrice supply_trend 472s 0.184 0.260 0.324 472s > print( round( coef( fitwls4$eq[[ 2 ]] ), digits = 6 ) ) 472s (Intercept) price farmPrice trend 472s 53.942 0.184 0.260 0.324 472s > 472s > print( round( coef( fitwlsi5 ), digits = 6 ) ) 472s demand_(Intercept) demand_price demand_income supply_(Intercept) 472s 100.903 -0.316 0.324 53.938 472s supply_price supply_farmPrice supply_trend 472s 0.184 0.260 0.324 472s > print( round( coef( fitwlsi5, modified.regMat = TRUE ), digits = 6 ) ) 472s C1 C2 C3 C4 C5 C6 472s 100.903 -0.316 0.324 53.938 0.184 0.260 472s > print( round( coef( fitwlsi5$eq[[ 1 ]] ), digits = 6 ) ) 472s (Intercept) price income 472s 100.903 -0.316 0.324 472s > 472s > 472s > ## *************** coefficients with stats ********************* 472s > print( round( coef( summary( fitwls1e ) ), digits = 6 ) ) 472s Estimate Std. Error t value Pr(>|t|) 472s demand_(Intercept) 99.895 6.9325 14.41 0.000000 472s demand_price -0.316 0.0836 -3.78 0.001483 472s demand_income 0.335 0.0419 7.99 0.000000 472s supply_(Intercept) 58.275 10.2527 5.68 0.000034 472s supply_price 0.160 0.0849 1.89 0.077067 472s supply_farmPrice 0.248 0.0413 6.01 0.000018 472s supply_trend 0.248 0.0872 2.85 0.011659 472s > print( round( coef( summary( fitwls1e$eq[[ 1 ]] ) ), digits = 6 ) ) 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 99.895 6.9325 14.41 0.00000 472s price -0.316 0.0836 -3.78 0.00148 472s income 0.335 0.0419 7.99 0.00000 472s > 472s > print( round( coef( summary( fitwlsi2 ) ), digits = 6 ) ) 472s Estimate Std. Error t value Pr(>|t|) 472s demand_(Intercept) 99.661 7.5378 13.22 0.000000 472s demand_price -0.299 0.0884 -3.39 0.001805 472s demand_income 0.320 0.0414 7.72 0.000000 472s supply_(Intercept) 56.183 11.3487 4.95 0.000020 472s supply_price 0.164 0.0963 1.71 0.097239 472s supply_farmPrice 0.258 0.0453 5.70 0.000002 472s supply_trend 0.320 0.0414 7.72 0.000000 472s > print( round( coef( summary( fitwlsi2$eq[[ 2 ]] ) ), digits = 6 ) ) 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 56.183 11.3487 4.95 0.000020 472s price 0.164 0.0963 1.71 0.097239 472s farmPrice 0.258 0.0453 5.70 0.000002 472s trend 0.320 0.0414 7.72 0.000000 472s > 472s > print( round( coef( summary( fitwls3e ) ), digits = 6 ) ) 472s Estimate Std. Error t value Pr(>|t|) 472s demand_(Intercept) 99.646 6.9734 14.29 0.000000 472s demand_price -0.298 0.0816 -3.65 0.000863 472s demand_income 0.319 0.0381 8.37 0.000000 472s supply_(Intercept) 56.210 10.1248 5.55 0.000003 472s supply_price 0.164 0.0859 1.91 0.064384 472s supply_farmPrice 0.258 0.0404 6.38 0.000000 472s supply_trend 0.319 0.0381 8.37 0.000000 472s > print( round( coef( summary( fitwls3e ), modified.regMat = TRUE ), digits = 6 ) ) 472s Estimate Std. Error t value Pr(>|t|) 472s C1 99.646 6.9734 14.29 0.000000 472s C2 -0.298 0.0816 -3.65 0.000863 472s C3 0.319 0.0381 8.37 0.000000 472s C4 56.210 10.1248 5.55 0.000003 472s C5 0.164 0.0859 1.91 0.064384 472s C6 0.258 0.0404 6.38 0.000000 472s > print( round( coef( summary( fitwls3e$eq[[ 1 ]] ) ), digits = 6 ) ) 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 99.646 6.9734 14.29 0.000000 472s price -0.298 0.0816 -3.65 0.000863 472s income 0.319 0.0381 8.37 0.000000 472s > 472s > print( round( coef( summary( fitwls4, useDfSys = FALSE ) ), digits = 6 ) ) 472s Estimate Std. Error t value Pr(>|t|) 472s demand_(Intercept) 100.914 6.0474 16.69 0.000000 472s demand_price -0.316 0.0648 -4.87 0.000143 472s demand_income 0.324 0.0385 8.42 0.000000 472s supply_(Intercept) 53.942 7.9687 6.77 0.000005 472s supply_price 0.184 0.0648 2.84 0.011833 472s supply_farmPrice 0.260 0.0446 5.84 0.000025 472s supply_trend 0.324 0.0385 8.42 0.000000 472s > print( round( coef( summary( fitwls4$eq[[ 2 ]], useDfSys = FALSE ) ), 472s + digits = 6 ) ) 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 53.942 7.9687 6.77 0.000005 472s price 0.184 0.0648 2.84 0.011833 472s farmPrice 0.260 0.0446 5.84 0.000025 472s trend 0.324 0.0385 8.42 0.000000 472s > 472s > print( round( coef( summary( fitwlsi5, useDfSys = FALSE ) ), digits = 6 ) ) 472s Estimate Std. Error t value Pr(>|t|) 472s demand_(Intercept) 100.903 6.0396 16.71 0.000000 472s demand_price -0.316 0.0648 -4.88 0.000142 472s demand_income 0.324 0.0384 8.43 0.000000 472s supply_(Intercept) 53.938 7.9718 6.77 0.000005 472s supply_price 0.184 0.0648 2.84 0.011806 472s supply_farmPrice 0.260 0.0447 5.83 0.000026 472s supply_trend 0.324 0.0384 8.43 0.000000 472s > print( round( coef( summary( fitwlsi5, useDfSys = FALSE ), 472s + modified.regMat = TRUE ), digits = 6 ) ) 472s Estimate Std. Error t value Pr(>|t|) 472s C1 100.903 6.0396 16.71 NA 472s C2 -0.316 0.0648 -4.88 NA 472s C3 0.324 0.0384 8.43 NA 472s C4 53.938 7.9718 6.77 NA 472s C5 0.184 0.0648 2.84 NA 472s C6 0.260 0.0447 5.83 NA 472s > print( round( coef( summary( fitwlsi5$eq[[ 1 ]], useDfSys = FALSE ) ), 472s + digits = 6 ) ) 472s Estimate Std. Error t value Pr(>|t|) 472s (Intercept) 100.903 6.0396 16.71 0.000000 472s price -0.316 0.0648 -4.88 0.000142 472s income 0.324 0.0384 8.43 0.000000 472s > 472s > 472s > ## *********** variance covariance matrix of the coefficients ******* 472s > print( round( vcov( fitwls1e ), digits = 6 ) ) 472s demand_(Intercept) demand_price demand_income 472s demand_(Intercept) 48.0597 -0.50558 0.02734 472s demand_price -0.5056 0.00699 -0.00198 472s demand_income 0.0273 -0.00198 0.00175 472s supply_(Intercept) 0.0000 0.00000 0.00000 472s supply_price 0.0000 0.00000 0.00000 472s supply_farmPrice 0.0000 0.00000 0.00000 472s supply_trend 0.0000 0.00000 0.00000 472s supply_(Intercept) supply_price supply_farmPrice 472s demand_(Intercept) 0.000 0.000000 0.000000 472s demand_price 0.000 0.000000 0.000000 472s demand_income 0.000 0.000000 0.000000 472s supply_(Intercept) 105.119 -0.790000 -0.243489 472s supply_price -0.790 0.007202 0.000675 472s supply_farmPrice -0.243 0.000675 0.001707 472s supply_trend -0.223 0.000418 0.001052 472s supply_trend 472s demand_(Intercept) 0.000000 472s demand_price 0.000000 472s demand_income 0.000000 472s supply_(Intercept) -0.223347 472s supply_price 0.000418 472s supply_farmPrice 0.001052 472s supply_trend 0.007608 472s > print( round( vcov( fitwls1e$eq[[ 1 ]] ), digits = 6 ) ) 472s (Intercept) price income 472s (Intercept) 48.0597 -0.50558 0.02734 472s price -0.5056 0.00699 -0.00198 472s income 0.0273 -0.00198 0.00175 472s > 472s > print( round( vcov( fitwls2 ), digits = 6 ) ) 472s demand_(Intercept) demand_price demand_income 472s demand_(Intercept) 57.21413 -0.596328 0.026850 472s demand_price -0.59633 0.007862 -0.001948 472s demand_income 0.02685 -0.001948 0.001722 472s supply_(Intercept) -0.78825 0.057190 -0.050565 472s supply_price 0.00147 -0.000107 0.000095 472s supply_farmPrice 0.00371 -0.000269 0.000238 472s supply_trend 0.02685 -0.001948 0.001722 472s supply_(Intercept) supply_price supply_farmPrice 472s demand_(Intercept) -0.7883 0.001474 0.003714 472s demand_price 0.0572 -0.000107 -0.000269 472s demand_income -0.0506 0.000095 0.000238 472s supply_(Intercept) 128.0635 -1.001596 -0.280017 472s supply_price -1.0016 0.009225 0.000806 472s supply_farmPrice -0.2800 0.000806 0.002038 472s supply_trend -0.0506 0.000095 0.000238 472s supply_trend 472s demand_(Intercept) 0.026850 472s demand_price -0.001948 472s demand_income 0.001722 472s supply_(Intercept) -0.050565 472s supply_price 0.000095 472s supply_farmPrice 0.000238 472s supply_trend 0.001722 472s > print( round( vcov( fitwls2$eq[[ 2 ]] ), digits = 6 ) ) 472s (Intercept) price farmPrice trend 472s (Intercept) 128.0635 -1.001596 -0.280017 -0.050565 472s price -1.0016 0.009225 0.000806 0.000095 472s farmPrice -0.2800 0.000806 0.002038 0.000238 472s trend -0.0506 0.000095 0.000238 0.001722 472s > 472s > print( round( vcov( fitwls3e ), digits = 6 ) ) 472s demand_(Intercept) demand_price demand_income 472s demand_(Intercept) 48.62814 -0.506597 0.022574 472s demand_price -0.50660 0.006662 -0.001638 472s demand_income 0.02257 -0.001638 0.001448 472s supply_(Intercept) -0.66271 0.048082 -0.042512 472s supply_price 0.00124 -0.000090 0.000079 472s supply_farmPrice 0.00312 -0.000227 0.000200 472s supply_trend 0.02257 -0.001638 0.001448 472s supply_(Intercept) supply_price supply_farmPrice 472s demand_(Intercept) -0.6627 0.001239 0.003123 472s demand_price 0.0481 -0.000090 -0.000227 472s demand_income -0.0425 0.000079 0.000200 472s supply_(Intercept) 102.5112 -0.801390 -0.224299 472s supply_price -0.8014 0.007381 0.000645 472s supply_farmPrice -0.2243 0.000645 0.001632 472s supply_trend -0.0425 0.000079 0.000200 472s supply_trend 472s demand_(Intercept) 0.022574 472s demand_price -0.001638 472s demand_income 0.001448 472s supply_(Intercept) -0.042512 472s supply_price 0.000079 472s supply_farmPrice 0.000200 472s supply_trend 0.001448 472s > print( round( vcov( fitwls3e, modified.regMat = TRUE ), digits = 6 ) ) 472s C1 C2 C3 C4 C5 C6 472s C1 48.62814 -0.506597 0.022574 -0.6627 0.001239 0.003123 472s C2 -0.50660 0.006662 -0.001638 0.0481 -0.000090 -0.000227 472s C3 0.02257 -0.001638 0.001448 -0.0425 0.000079 0.000200 472s C4 -0.66271 0.048082 -0.042512 102.5112 -0.801390 -0.224299 472s C5 0.00124 -0.000090 0.000079 -0.8014 0.007381 0.000645 472s C6 0.00312 -0.000227 0.000200 -0.2243 0.000645 0.001632 472s > print( round( vcov( fitwls3e$eq[[ 1 ]] ), digits = 6 ) ) 472s (Intercept) price income 472s (Intercept) 48.6281 -0.50660 0.02257 472s price -0.5066 0.00666 -0.00164 472s income 0.0226 -0.00164 0.00145 472s > 472s > print( round( vcov( fitwls4 ), digits = 6 ) ) 472s demand_(Intercept) demand_price demand_income 472s demand_(Intercept) 36.5710 -0.321554 -0.043279 472s demand_price -0.3216 0.004201 -0.001011 472s demand_income -0.0433 -0.001011 0.001481 472s supply_(Intercept) 35.8467 -0.431417 0.074877 472s supply_price -0.3216 0.004201 -0.001011 472s supply_farmPrice -0.0334 0.000226 0.000111 472s supply_trend -0.0433 -0.001011 0.001481 472s supply_(Intercept) supply_price supply_farmPrice 472s demand_(Intercept) 35.8467 -0.321554 -0.033436 472s demand_price -0.4314 0.004201 0.000226 472s demand_income 0.0749 -0.001011 0.000111 472s supply_(Intercept) 63.5001 -0.431417 -0.215648 472s supply_price -0.4314 0.004201 0.000226 472s supply_farmPrice -0.2156 0.000226 0.001986 472s supply_trend 0.0749 -0.001011 0.000111 472s supply_trend 472s demand_(Intercept) -0.043279 472s demand_price -0.001011 472s demand_income 0.001481 472s supply_(Intercept) 0.074877 472s supply_price -0.001011 472s supply_farmPrice 0.000111 472s supply_trend 0.001481 472s > print( round( vcov( fitwls4$eq[[ 2 ]] ), digits = 6 ) ) 472s (Intercept) price farmPrice trend 472s (Intercept) 63.5001 -0.431417 -0.215648 0.074877 472s price -0.4314 0.004201 0.000226 -0.001011 472s farmPrice -0.2156 0.000226 0.001986 0.000111 472s trend 0.0749 -0.001011 0.000111 0.001481 472s > 472s > print( round( vcov( fitwls5 ), digits = 6 ) ) 472s demand_(Intercept) demand_price demand_income 472s demand_(Intercept) 36.5710 -0.321554 -0.043279 472s demand_price -0.3216 0.004201 -0.001011 472s demand_income -0.0433 -0.001011 0.001481 472s supply_(Intercept) 35.8467 -0.431417 0.074877 472s supply_price -0.3216 0.004201 -0.001011 472s supply_farmPrice -0.0334 0.000226 0.000111 472s supply_trend -0.0433 -0.001011 0.001481 472s supply_(Intercept) supply_price supply_farmPrice 472s demand_(Intercept) 35.8467 -0.321554 -0.033436 472s demand_price -0.4314 0.004201 0.000226 472s demand_income 0.0749 -0.001011 0.000111 472s supply_(Intercept) 63.5001 -0.431417 -0.215648 472s supply_price -0.4314 0.004201 0.000226 472s supply_farmPrice -0.2156 0.000226 0.001986 472s supply_trend 0.0749 -0.001011 0.000111 472s supply_trend 472s demand_(Intercept) -0.043279 472s demand_price -0.001011 472s demand_income 0.001481 472s supply_(Intercept) 0.074877 472s supply_price -0.001011 472s supply_farmPrice 0.000111 472s supply_trend 0.001481 472s > print( round( vcov( fitwls5, modified.regMat = TRUE ), digits = 6 ) ) 472s C1 C2 C3 C4 C5 C6 472s C1 36.5710 -0.321554 -0.043279 35.8467 -0.321554 -0.033436 472s C2 -0.3216 0.004201 -0.001011 -0.4314 0.004201 0.000226 472s C3 -0.0433 -0.001011 0.001481 0.0749 -0.001011 0.000111 472s C4 35.8467 -0.431417 0.074877 63.5001 -0.431417 -0.215648 472s C5 -0.3216 0.004201 -0.001011 -0.4314 0.004201 0.000226 472s C6 -0.0334 0.000226 0.000111 -0.2156 0.000226 0.001986 472s > print( round( vcov( fitwls5$eq[[ 1 ]] ), digits = 6 ) ) 472s (Intercept) price income 472s (Intercept) 36.5710 -0.32155 -0.04328 472s price -0.3216 0.00420 -0.00101 472s income -0.0433 -0.00101 0.00148 472s > 472s > print( round( vcov( fitwlsi1 ), digits = 6 ) ) 472s demand_(Intercept) demand_price demand_income 472s demand_(Intercept) 56.5408 -0.59480 0.03216 472s demand_price -0.5948 0.00822 -0.00233 472s demand_income 0.0322 -0.00233 0.00206 472s supply_(Intercept) 0.0000 0.00000 0.00000 472s supply_price 0.0000 0.00000 0.00000 472s supply_farmPrice 0.0000 0.00000 0.00000 472s supply_trend 0.0000 0.00000 0.00000 472s supply_(Intercept) supply_price supply_farmPrice 472s demand_(Intercept) 0.000 0.000000 0.000000 472s demand_price 0.000 0.000000 0.000000 472s demand_income 0.000 0.000000 0.000000 472s supply_(Intercept) 131.398 -0.987500 -0.304361 472s supply_price -0.988 0.009003 0.000844 472s supply_farmPrice -0.304 0.000844 0.002133 472s supply_trend -0.279 0.000522 0.001316 472s supply_trend 472s demand_(Intercept) 0.000000 472s demand_price 0.000000 472s demand_income 0.000000 472s supply_(Intercept) -0.279183 472s supply_price 0.000522 472s supply_farmPrice 0.001316 472s supply_trend 0.009510 472s > print( round( vcov( fitwlsi1$eq[[ 2 ]] ), digits = 6 ) ) 472s (Intercept) price farmPrice trend 472s (Intercept) 131.398 -0.987500 -0.304361 -0.279183 472s price -0.988 0.009003 0.000844 0.000522 472s farmPrice -0.304 0.000844 0.002133 0.001316 472s trend -0.279 0.000522 0.001316 0.009510 472s > 472s > print( round( vcov( fitwlsi2e ), digits = 6 ) ) 472s demand_(Intercept) demand_price demand_income 472s demand_(Intercept) 48.32515 -0.503487 0.022480 472s demand_price -0.50349 0.006624 -0.001631 472s demand_income 0.02248 -0.001631 0.001442 472s supply_(Intercept) -0.65995 0.047882 -0.042335 472s supply_price 0.00123 -0.000090 0.000079 472s supply_farmPrice 0.00311 -0.000226 0.000199 472s supply_trend 0.02248 -0.001631 0.001442 472s supply_(Intercept) supply_price supply_farmPrice 472s demand_(Intercept) -0.6600 0.001234 0.003110 472s demand_price 0.0479 -0.000090 -0.000226 472s demand_income -0.0423 0.000079 0.000199 472s supply_(Intercept) 103.0226 -0.805456 -0.225388 472s supply_price -0.8055 0.007418 0.000649 472s supply_farmPrice -0.2254 0.000649 0.001640 472s supply_trend -0.0423 0.000079 0.000199 472s supply_trend 472s demand_(Intercept) 0.022480 472s demand_price -0.001631 472s demand_income 0.001442 472s supply_(Intercept) -0.042335 472s supply_price 0.000079 472s supply_farmPrice 0.000199 472s supply_trend 0.001442 472s > print( round( vcov( fitwlsi2e$eq[[ 1 ]] ), digits = 6 ) ) 472s (Intercept) price income 472s (Intercept) 48.3251 -0.50349 0.02248 472s price -0.5035 0.00662 -0.00163 472s income 0.0225 -0.00163 0.00144 472s > 472s > print( round( vcov( fitwlsi3 ), digits = 6 ) ) 472s demand_(Intercept) demand_price demand_income 472s demand_(Intercept) 56.81857 -0.592263 0.026724 472s demand_price -0.59226 0.007812 -0.001939 472s demand_income 0.02672 -0.001939 0.001714 472s supply_(Intercept) -0.78454 0.056921 -0.050327 472s supply_price 0.00147 -0.000106 0.000094 472s supply_farmPrice 0.00370 -0.000268 0.000237 472s supply_trend 0.02672 -0.001939 0.001714 472s supply_(Intercept) supply_price supply_farmPrice 472s demand_(Intercept) -0.7845 0.001467 0.003697 472s demand_price 0.0569 -0.000106 -0.000268 472s demand_income -0.0503 0.000094 0.000237 472s supply_(Intercept) 128.7924 -1.007391 -0.281572 472s supply_price -1.0074 0.009279 0.000811 472s supply_farmPrice -0.2816 0.000811 0.002049 472s supply_trend -0.0503 0.000094 0.000237 472s supply_trend 472s demand_(Intercept) 0.026724 472s demand_price -0.001939 472s demand_income 0.001714 472s supply_(Intercept) -0.050327 472s supply_price 0.000094 472s supply_farmPrice 0.000237 472s supply_trend 0.001714 472s > print( round( vcov( fitwlsi3, modified.regMat = TRUE ), digits = 6 ) ) 472s C1 C2 C3 C4 C5 C6 472s C1 56.81857 -0.592263 0.026724 -0.7845 0.001467 0.003697 472s C2 -0.59226 0.007812 -0.001939 0.0569 -0.000106 -0.000268 472s C3 0.02672 -0.001939 0.001714 -0.0503 0.000094 0.000237 472s C4 -0.78454 0.056921 -0.050327 128.7924 -1.007391 -0.281572 472s C5 0.00147 -0.000106 0.000094 -1.0074 0.009279 0.000811 472s C6 0.00370 -0.000268 0.000237 -0.2816 0.000811 0.002049 472s > print( round( vcov( fitwlsi3$eq[[ 2 ]] ), digits = 6 ) ) 472s (Intercept) price farmPrice trend 472s (Intercept) 128.7924 -1.007391 -0.281572 -0.050327 472s price -1.0074 0.009279 0.000811 0.000094 472s farmPrice -0.2816 0.000811 0.002049 0.000237 472s trend -0.0503 0.000094 0.000237 0.001714 472s > 472s > print( round( vcov( fitwlsi4e ), digits = 6 ) ) 472s demand_(Intercept) demand_price demand_income 472s demand_(Intercept) 30.4377 -0.265752 -0.037918 472s demand_price -0.2658 0.003463 -0.000827 472s demand_income -0.0379 -0.000827 0.001237 472s supply_(Intercept) 29.6762 -0.355820 0.060620 472s supply_price -0.2658 0.003463 -0.000827 472s supply_farmPrice -0.0279 0.000187 0.000094 472s supply_trend -0.0379 -0.000827 0.001237 472s supply_(Intercept) supply_price supply_farmPrice 472s demand_(Intercept) 29.6762 -0.265752 -0.027921 472s demand_price -0.3558 0.003463 0.000187 472s demand_income 0.0606 -0.000827 0.000094 472s supply_(Intercept) 52.0044 -0.355820 -0.173988 472s supply_price -0.3558 0.003463 0.000187 472s supply_farmPrice -0.1740 0.000187 0.001596 472s supply_trend 0.0606 -0.000827 0.000094 472s supply_trend 472s demand_(Intercept) -0.037918 472s demand_price -0.000827 472s demand_income 0.001237 472s supply_(Intercept) 0.060620 472s supply_price -0.000827 472s supply_farmPrice 0.000094 472s supply_trend 0.001237 472s > print( round( vcov( fitwlsi4e$eq[[ 1 ]] ), digits = 6 ) ) 472s (Intercept) price income 472s (Intercept) 30.4377 -0.265752 -0.037918 472s price -0.2658 0.003463 -0.000827 472s income -0.0379 -0.000827 0.001237 472s > 472s > print( round( vcov( fitwlsi5e ), digits = 6 ) ) 472s demand_(Intercept) demand_price demand_income 472s demand_(Intercept) 30.4377 -0.265752 -0.037918 472s demand_price -0.2658 0.003463 -0.000827 472s demand_income -0.0379 -0.000827 0.001237 472s supply_(Intercept) 29.6762 -0.355820 0.060620 472s supply_price -0.2658 0.003463 -0.000827 472s supply_farmPrice -0.0279 0.000187 0.000094 472s supply_trend -0.0379 -0.000827 0.001237 472s supply_(Intercept) supply_price supply_farmPrice 472s demand_(Intercept) 29.6762 -0.265752 -0.027921 472s demand_price -0.3558 0.003463 0.000187 472s demand_income 0.0606 -0.000827 0.000094 472s supply_(Intercept) 52.0044 -0.355820 -0.173988 472s supply_price -0.3558 0.003463 0.000187 472s supply_farmPrice -0.1740 0.000187 0.001596 472s supply_trend 0.0606 -0.000827 0.000094 472s supply_trend 472s demand_(Intercept) -0.037918 472s demand_price -0.000827 472s demand_income 0.001237 472s supply_(Intercept) 0.060620 472s supply_price -0.000827 472s supply_farmPrice 0.000094 472s supply_trend 0.001237 472s > print( round( vcov( fitwlsi5e, modified.regMat = TRUE ), digits = 6 ) ) 472s C1 C2 C3 C4 C5 C6 472s C1 30.4377 -0.265752 -0.037918 29.6762 -0.265752 -0.027921 472s C2 -0.2658 0.003463 -0.000827 -0.3558 0.003463 0.000187 472s C3 -0.0379 -0.000827 0.001237 0.0606 -0.000827 0.000094 472s C4 29.6762 -0.355820 0.060620 52.0044 -0.355820 -0.173988 472s C5 -0.2658 0.003463 -0.000827 -0.3558 0.003463 0.000187 472s C6 -0.0279 0.000187 0.000094 -0.1740 0.000187 0.001596 472s > print( round( vcov( fitwlsi5e$eq[[ 2 ]] ), digits = 6 ) ) 472s (Intercept) price farmPrice trend 472s (Intercept) 52.0044 -0.355820 -0.173988 0.060620 472s price -0.3558 0.003463 0.000187 -0.000827 472s farmPrice -0.1740 0.000187 0.001596 0.000094 472s trend 0.0606 -0.000827 0.000094 0.001237 472s > 472s > 472s > ## *********** confidence intervals of coefficients ************* 472s > print( confint( fitwls1 ) ) 472s 2.5 % 97.5 % 472s demand_(Intercept) 84.031 115.760 472s demand_price -0.508 -0.125 472s demand_income 0.239 0.430 472s supply_(Intercept) 33.975 82.576 472s supply_price -0.041 0.362 472s supply_farmPrice 0.150 0.346 472s supply_trend 0.042 0.455 472s > print( confint( fitwls1$eq[[ 2 ]], level = 0.9 ) ) 472s 5 % 95 % 472s (Intercept) 38.263 78.288 472s price -0.005 0.326 472s farmPrice 0.167 0.329 472s trend 0.078 0.419 472s > 472s > print( confint( fitwls2e, level = 0.9 ) ) 472s 5 % 95 % 472s demand_(Intercept) 85.474 113.818 472s demand_price -0.464 -0.132 472s demand_income 0.241 0.396 472s supply_(Intercept) 35.634 76.786 472s supply_price -0.010 0.339 472s supply_farmPrice 0.176 0.340 472s supply_trend 0.241 0.396 472s > print( confint( fitwls2e$eq[[ 1 ]], level = 0.99 ) ) 472s 0.5 % 99.5 % 472s (Intercept) 80.620 118.672 472s price -0.521 -0.076 472s income 0.215 0.422 472s > 472s > print( confint( fitwls3, level = 0.99 ) ) 472s 0.5 % 99.5 % 472s demand_(Intercept) 84.286 115.030 472s demand_price -0.479 -0.119 472s demand_income 0.235 0.404 472s supply_(Intercept) 33.190 79.186 472s supply_price -0.031 0.359 472s supply_farmPrice 0.166 0.350 472s supply_trend 0.235 0.404 472s > print( confint( fitwls3$eq[[ 2 ]], level = 0.5 ) ) 472s 25 % 75 % 472s (Intercept) 48.472 63.903 472s price 0.099 0.230 472s farmPrice 0.227 0.289 472s trend 0.291 0.348 472s > 472s > print( confint( fitwls4e, level = 0.5 ) ) 472s 25 % 75 % 472s demand_(Intercept) 89.763 112.189 472s demand_price -0.436 -0.197 472s demand_income 0.252 0.395 472s supply_(Intercept) 39.328 68.598 472s supply_price 0.064 0.303 472s supply_farmPrice 0.179 0.341 472s supply_trend 0.252 0.395 472s > print( confint( fitwls4e$eq[[ 1 ]], level = 0.25 ) ) 472s 37.5 % 62.5 % 472s (Intercept) 99.202 102.750 472s price -0.335 -0.297 472s income 0.312 0.335 472s > 472s > print( confint( fitwls5, level = 0.25 ) ) 472s 37.5 % 62.5 % 472s demand_(Intercept) 88.637 113.191 472s demand_price -0.448 -0.184 472s demand_income 0.246 0.402 472s supply_(Intercept) 37.764 70.119 472s supply_price 0.052 0.316 472s supply_farmPrice 0.170 0.351 472s supply_trend 0.246 0.402 472s > print( confint( fitwls5$eq[[ 2 ]], level = 0.975 ) ) 472s 1.3 % 98.8 % 472s (Intercept) 35.279 72.604 472s price 0.032 0.336 472s farmPrice 0.156 0.365 472s trend 0.234 0.414 472s > 472s > print( confint( fitwlsi1e, level = 0.975, useDfSys = TRUE ) ) 472s 1.3 % 98.8 % 472s demand_(Intercept) 85.791 114.000 472s demand_price -0.486 -0.146 472s demand_income 0.249 0.420 472s supply_(Intercept) 37.416 79.135 472s supply_price -0.012 0.333 472s supply_farmPrice 0.164 0.332 472s supply_trend 0.071 0.426 472s > print( confint( fitwlsi1e$eq[[ 1 ]], level = 0.999, useDfSys = TRUE ) ) 472s 0.1 % 100 % 472s (Intercept) 74.863 124.928 472s price -0.618 -0.014 472s income 0.183 0.486 472s > 472s > print( confint( fitwlsi2, level = 0.999 ) ) 472s 0.1 % 100 % 472s demand_(Intercept) 84.342 114.979 472s demand_price -0.479 -0.120 472s demand_income 0.235 0.404 472s supply_(Intercept) 33.120 79.246 472s supply_price -0.031 0.360 472s supply_farmPrice 0.166 0.350 472s supply_trend 0.235 0.404 472s > print( confint( fitwlsi2$eq[[ 2 ]], level = 0.1 ) ) 472s 45 % 55 % 472s (Intercept) 54.746 57.620 472s price 0.152 0.176 472s farmPrice 0.252 0.264 472s trend 0.314 0.325 472s > 472s > print( confint( fitwlsi3e, level = 0.1 ) ) 472s 45 % 55 % 472s demand_(Intercept) 85.521 113.776 472s demand_price -0.464 -0.133 472s demand_income 0.242 0.396 472s supply_(Intercept) 35.579 76.833 472s supply_price -0.011 0.339 472s supply_farmPrice 0.176 0.340 472s supply_trend 0.242 0.396 472s > print( confint( fitwlsi3e$eq[[ 1 ]], level = 0.01 ) ) 472s 49.5 % 50.5 % 472s (Intercept) 99.561 99.736 472s price -0.299 -0.297 472s income 0.318 0.319 472s > 472s > print( confint( fitwlsi4, level = 0.01 ) ) 472s 49.5 % 50.5 % 472s demand_(Intercept) 88.642 113.164 472s demand_price -0.447 -0.184 472s demand_income 0.246 0.402 472s supply_(Intercept) 37.754 70.122 472s supply_price 0.053 0.316 472s supply_farmPrice 0.170 0.351 472s supply_trend 0.246 0.402 472s > print( confint( fitwlsi4$eq[[ 2 ]], level = 0.33 ) ) 472s 33.5 % 66.5 % 472s (Intercept) 50.512 57.364 472s price 0.156 0.212 472s farmPrice 0.241 0.279 472s trend 0.307 0.340 472s > 472s > print( confint( fitwlsi5e, level = 0.33 ) ) 472s 33.5 % 66.5 % 472s demand_(Intercept) 89.766 112.166 472s demand_price -0.435 -0.197 472s demand_income 0.252 0.395 472s supply_(Intercept) 39.320 68.599 472s supply_price 0.065 0.303 472s supply_farmPrice 0.179 0.341 472s supply_trend 0.252 0.395 472s > print( confint( fitwlsi5e$eq[[ 1 ]] ) ) 472s 2.5 % 97.5 % 472s (Intercept) 89.766 112.166 472s price -0.435 -0.197 472s income 0.252 0.395 472s > 472s > 472s > ## *********** fitted values ************* 472s > print( fitted( fitwls1 ) ) 472s demand supply 472s 1 97.4 98.9 472s 2 99.6 100.1 472s 3 99.5 100.2 472s 4 99.7 100.4 472s 5 102.3 102.7 472s 6 102.1 102.6 472s 7 102.5 102.4 472s 8 102.8 104.3 472s 9 101.7 102.9 472s 10 100.8 100.4 472s 11 95.6 96.0 472s 12 94.4 94.1 472s 13 95.7 95.6 472s 14 99.0 97.8 472s 15 104.3 102.6 472s 16 103.9 104.1 472s 17 104.8 103.8 472s 18 101.9 102.4 472s 19 103.5 102.1 472s 20 106.5 104.5 472s > print( fitted( fitwls1$eq[[ 2 ]] ) ) 472s 1 2 3 4 5 6 7 8 9 10 11 12 13 472s 98.9 100.1 100.2 100.4 102.7 102.6 102.4 104.3 102.9 100.4 96.0 94.1 95.6 472s 14 15 16 17 18 19 20 472s 97.8 102.6 104.1 103.8 102.4 102.1 104.5 472s > 472s > print( fitted( fitwls2e ) ) 472s demand supply 472s 1 97.6 98.3 472s 2 99.7 99.5 472s 3 99.6 99.7 472s 4 99.8 99.9 472s 5 102.2 102.5 472s 6 102.0 102.4 472s 7 102.4 102.3 472s 8 102.8 104.3 472s 9 101.7 102.9 472s 10 100.8 100.3 472s 11 95.8 95.9 472s 12 94.7 93.9 472s 13 95.9 95.5 472s 14 99.1 97.9 472s 15 104.1 103.0 472s 16 103.8 104.6 472s 17 104.6 104.3 472s 18 101.9 102.9 472s 19 103.4 102.7 472s 20 106.3 105.2 472s > print( fitted( fitwls2e$eq[[ 1 ]] ) ) 472s 1 2 3 4 5 6 7 8 9 10 11 12 13 472s 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 472s 14 15 16 17 18 19 20 472s 99.1 104.1 103.8 104.6 101.9 103.4 106.3 472s > 472s > print( fitted( fitwls3 ) ) 472s demand supply 472s 1 97.6 98.3 472s 2 99.6 99.5 472s 3 99.6 99.7 472s 4 99.8 99.9 472s 5 102.2 102.5 472s 6 102.0 102.4 472s 7 102.4 102.3 472s 8 102.8 104.3 472s 9 101.7 102.9 472s 10 100.8 100.3 472s 11 95.8 95.9 472s 12 94.7 93.9 472s 13 95.9 95.5 472s 14 99.1 97.9 472s 15 104.1 103.0 472s 16 103.8 104.6 472s 17 104.6 104.3 472s 18 101.9 102.9 472s 19 103.4 102.7 472s 20 106.3 105.2 472s > print( fitted( fitwls3$eq[[ 2 ]] ) ) 472s 1 2 3 4 5 6 7 8 9 10 11 12 13 472s 98.3 99.5 99.7 99.9 102.5 102.4 102.3 104.3 102.9 100.3 95.9 93.9 95.5 472s 14 15 16 17 18 19 20 472s 97.9 103.0 104.6 104.3 102.9 102.7 105.2 472s > 472s > print( fitted( fitwls4e ) ) 472s demand supply 472s 1 97.5 98.2 472s 2 99.6 99.6 472s 3 99.5 99.7 472s 4 99.7 100.0 472s 5 102.3 102.4 472s 6 102.0 102.4 472s 7 102.4 102.3 472s 8 102.7 104.4 472s 9 101.7 102.9 472s 10 100.9 100.2 472s 11 95.8 95.7 472s 12 94.6 93.9 472s 13 95.8 95.5 472s 14 99.1 97.8 472s 15 104.2 102.9 472s 16 103.8 104.6 472s 17 104.8 104.1 472s 18 101.9 103.0 472s 19 103.3 102.8 472s 20 106.2 105.5 472s > print( fitted( fitwls4e$eq[[ 1 ]] ) ) 472s 1 2 3 4 5 6 7 8 9 10 11 12 13 472s 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 472s 14 15 16 17 18 19 20 472s 99.1 104.2 103.8 104.8 101.9 103.3 106.2 472s > 472s > print( fitted( fitwls5 ) ) 472s demand supply 472s 1 97.5 98.2 472s 2 99.6 99.6 472s 3 99.5 99.7 472s 4 99.7 100.0 472s 5 102.3 102.4 472s 6 102.0 102.3 472s 7 102.4 102.3 472s 8 102.7 104.4 472s 9 101.7 102.9 472s 10 100.9 100.2 472s 11 95.8 95.7 472s 12 94.6 93.9 472s 13 95.8 95.5 472s 14 99.1 97.8 472s 15 104.2 102.9 472s 16 103.8 104.6 472s 17 104.8 104.1 472s 18 101.9 103.0 472s 19 103.3 102.8 472s 20 106.2 105.5 472s > print( fitted( fitwls5$eq[[ 2 ]] ) ) 472s 1 2 3 4 5 6 7 8 9 10 11 12 13 472s 98.2 99.6 99.7 100.0 102.4 102.3 102.3 104.4 102.9 100.2 95.7 93.9 95.5 472s 14 15 16 17 18 19 20 472s 97.8 102.9 104.6 104.1 103.0 102.8 105.5 472s > 472s > print( fitted( fitwlsi1e ) ) 472s demand supply 472s 1 97.4 98.9 472s 2 99.6 100.1 472s 3 99.5 100.2 472s 4 99.7 100.4 472s 5 102.3 102.7 472s 6 102.1 102.6 472s 7 102.5 102.4 472s 8 102.8 104.3 472s 9 101.7 102.9 472s 10 100.8 100.4 472s 11 95.6 96.0 472s 12 94.4 94.1 472s 13 95.7 95.6 472s 14 99.0 97.8 472s 15 104.3 102.6 472s 16 103.9 104.1 472s 17 104.8 103.8 472s 18 101.9 102.4 472s 19 103.5 102.1 472s 20 106.5 104.5 472s > print( fitted( fitwlsi1e$eq[[ 1 ]] ) ) 472s 1 2 3 4 5 6 7 8 9 10 11 12 13 472s 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 472s 14 15 16 17 18 19 20 472s 99.0 104.3 103.9 104.8 101.9 103.5 106.5 472s > 472s > print( fitted( fitwlsi2 ) ) 472s demand supply 472s 1 97.6 98.3 472s 2 99.6 99.5 472s 3 99.6 99.7 472s 4 99.8 99.9 472s 5 102.2 102.5 472s 6 102.0 102.4 472s 7 102.4 102.3 472s 8 102.8 104.3 472s 9 101.7 102.9 472s 10 100.8 100.3 472s 11 95.8 95.9 472s 12 94.7 93.9 472s 13 95.9 95.5 472s 14 99.1 97.9 472s 15 104.1 103.0 472s 16 103.8 104.6 472s 17 104.6 104.3 472s 18 101.9 102.9 472s 19 103.4 102.7 472s 20 106.3 105.2 472s > print( fitted( fitwlsi2$eq[[ 2 ]] ) ) 472s 1 2 3 4 5 6 7 8 9 10 11 12 13 472s 98.3 99.5 99.7 99.9 102.5 102.4 102.3 104.3 102.9 100.3 95.9 93.9 95.5 472s 14 15 16 17 18 19 20 472s 97.9 103.0 104.6 104.3 102.9 102.7 105.2 472s > 472s > print( fitted( fitwlsi3e ) ) 472s demand supply 472s 1 97.6 98.3 472s 2 99.7 99.5 472s 3 99.6 99.7 472s 4 99.8 99.9 472s 5 102.2 102.5 472s 6 102.0 102.4 472s 7 102.4 102.3 472s 8 102.8 104.3 472s 9 101.7 102.9 472s 10 100.8 100.3 472s 11 95.8 95.9 472s 12 94.7 93.9 472s 13 95.9 95.5 472s 14 99.1 97.9 472s 15 104.1 103.0 472s 16 103.8 104.6 472s 17 104.6 104.3 472s 18 101.9 102.9 472s 19 103.4 102.7 472s 20 106.3 105.2 472s > print( fitted( fitwlsi3e$eq[[ 1 ]] ) ) 472s 1 2 3 4 5 6 7 8 9 10 11 12 13 472s 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 472s 14 15 16 17 18 19 20 472s 99.1 104.1 103.8 104.6 101.9 103.4 106.3 472s > 472s > print( fitted( fitwlsi4 ) ) 472s demand supply 472s 1 97.5 98.2 472s 2 99.6 99.6 472s 3 99.5 99.7 472s 4 99.7 100.0 472s 5 102.3 102.4 472s 6 102.0 102.3 472s 7 102.4 102.3 472s 8 102.7 104.4 472s 9 101.7 102.9 472s 10 100.9 100.2 472s 11 95.8 95.7 472s 12 94.6 93.9 472s 13 95.8 95.5 472s 14 99.1 97.8 472s 15 104.2 102.9 472s 16 103.8 104.6 472s 17 104.8 104.1 472s 18 101.9 103.0 472s 19 103.3 102.8 472s 20 106.2 105.5 472s > print( fitted( fitwlsi4$eq[[ 2 ]] ) ) 472s 1 2 3 4 5 6 7 8 9 10 11 12 13 472s 98.2 99.6 99.7 100.0 102.4 102.3 102.3 104.4 102.9 100.2 95.7 93.9 95.5 472s 14 15 16 17 18 19 20 472s 97.8 102.9 104.6 104.1 103.0 102.8 105.5 472s > 472s > print( fitted( fitwlsi5e ) ) 472s demand supply 472s 1 97.5 98.2 472s 2 99.6 99.6 472s 3 99.5 99.7 472s 4 99.7 100.0 472s 5 102.3 102.4 472s 6 102.0 102.4 472s 7 102.4 102.3 472s 8 102.7 104.4 472s 9 101.7 102.9 472s 10 100.9 100.2 472s 11 95.8 95.7 472s 12 94.6 93.9 472s 13 95.8 95.5 472s 14 99.1 97.8 472s 15 104.2 102.9 472s 16 103.8 104.6 472s 17 104.8 104.1 472s 18 101.9 103.0 472s 19 103.3 102.8 472s 20 106.2 105.5 472s > print( fitted( fitwlsi5e$eq[[ 1 ]] ) ) 472s 1 2 3 4 5 6 7 8 9 10 11 12 13 472s 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 472s 14 15 16 17 18 19 20 472s 99.1 104.2 103.8 104.8 101.9 103.3 106.2 472s > 472s > 472s > ## *********** predicted values ************* 472s > predictData <- Kmenta 472s > predictData$consump <- NULL 472s > predictData$price <- Kmenta$price * 0.9 472s > predictData$income <- Kmenta$income * 1.1 472s > 472s > print( predict( fitwls1, se.fit = TRUE, interval = "prediction" ) ) 472s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 472s 1 97.4 0.643 93.1 101.7 98.9 1.056 472s 2 99.6 0.577 95.3 103.8 100.1 1.037 472s 3 99.5 0.545 95.3 103.8 100.2 0.939 472s 4 99.7 0.582 95.4 104.0 100.4 0.912 472s 5 102.3 0.502 98.1 106.5 102.7 0.895 472s 6 102.1 0.463 97.9 106.3 102.6 0.791 472s 7 102.5 0.484 98.3 106.7 102.4 0.719 472s 8 102.8 0.601 98.6 107.1 104.3 0.963 472s 9 101.7 0.527 97.5 105.9 102.9 0.788 472s 10 100.8 0.788 96.4 105.2 100.4 0.981 472s 11 95.6 0.946 91.0 100.1 96.0 1.185 472s 12 94.4 0.980 89.8 98.9 94.1 1.394 472s 13 95.7 0.880 91.2 100.1 95.6 1.244 472s 14 99.0 0.508 94.8 103.2 97.8 0.896 472s 15 104.3 0.758 99.9 108.7 102.6 0.874 472s 16 103.9 0.616 99.7 108.2 104.1 0.916 472s 17 104.8 1.273 99.9 109.7 103.8 1.605 472s 18 101.9 0.536 97.7 106.2 102.4 0.962 472s 19 103.5 0.680 99.2 107.8 102.1 1.098 472s 20 106.5 1.274 101.7 111.4 104.5 1.664 472s supply.lwr supply.upr 472s 1 93.4 104 472s 2 94.5 106 472s 3 94.7 106 472s 4 94.9 106 472s 5 97.3 108 472s 6 97.2 108 472s 7 97.1 108 472s 8 98.8 110 472s 9 97.6 108 472s 10 94.8 106 472s 11 90.3 102 472s 12 88.2 100 472s 13 89.9 101 472s 14 92.3 103 472s 15 97.2 108 472s 16 98.6 110 472s 17 97.7 110 472s 18 96.9 108 472s 19 96.5 108 472s 20 98.3 111 472s > print( predict( fitwls1$eq[[ 2 ]], se.fit = TRUE, interval = "prediction" ) ) 472s fit se.fit lwr upr 472s 1 98.9 1.056 93.4 104 472s 2 100.1 1.037 94.5 106 472s 3 100.2 0.939 94.7 106 472s 4 100.4 0.912 94.9 106 472s 5 102.7 0.895 97.3 108 472s 6 102.6 0.791 97.2 108 472s 7 102.4 0.719 97.1 108 472s 8 104.3 0.963 98.8 110 472s 9 102.9 0.788 97.6 108 472s 10 100.4 0.981 94.8 106 472s 11 96.0 1.185 90.3 102 472s 12 94.1 1.394 88.2 100 472s 13 95.6 1.244 89.9 101 472s 14 97.8 0.896 92.3 103 472s 15 102.6 0.874 97.2 108 472s 16 104.1 0.916 98.6 110 472s 17 103.8 1.605 97.7 110 472s 18 102.4 0.962 96.9 108 472s 19 102.1 1.098 96.5 108 472s 20 104.5 1.664 98.3 111 472s > 472s > print( predict( fitwls2e, se.pred = TRUE, interval = "confidence", 472s + level = 0.999, newdata = predictData ) ) 472s demand.pred demand.se.pred demand.lwr demand.upr supply.pred supply.se.pred 472s 1 103 2.12 100.2 106 96.6 2.65 472s 2 106 2.12 102.7 109 97.8 2.57 472s 3 106 2.13 102.6 109 98.0 2.58 472s 4 106 2.12 102.9 109 98.2 2.56 472s 5 108 2.35 103.5 113 100.9 2.72 472s 6 108 2.31 103.6 113 100.7 2.67 472s 7 109 2.30 104.2 113 100.6 2.62 472s 8 109 2.27 105.0 114 102.6 2.58 472s 9 108 2.36 102.8 112 101.4 2.74 472s 10 106 2.46 100.8 112 98.8 2.92 472s 11 101 2.28 96.7 105 94.4 2.98 472s 12 100 2.12 97.0 103 92.3 2.96 472s 13 102 2.05 99.3 104 93.8 2.81 472s 14 105 2.20 101.2 109 96.3 2.78 472s 15 110 2.53 104.4 116 101.4 2.78 472s 16 110 2.44 104.7 115 102.9 2.69 472s 17 110 2.81 102.9 118 102.9 3.14 472s 18 108 2.23 104.3 112 101.2 2.58 472s 19 110 2.30 105.6 115 100.9 2.57 472s 20 114 2.50 108.1 119 103.3 2.52 472s supply.lwr supply.upr 472s 1 92.9 100.3 472s 2 95.0 100.6 472s 3 95.1 100.9 472s 4 95.5 100.9 472s 5 96.6 105.1 472s 6 96.9 104.6 472s 7 97.2 104.0 472s 8 99.6 105.5 472s 9 96.9 105.9 472s 10 93.1 104.6 472s 11 88.2 100.5 472s 12 86.3 98.4 472s 13 88.8 98.9 472s 14 91.5 101.0 472s 15 96.7 106.2 472s 16 98.9 106.9 472s 17 95.8 110.0 472s 18 98.2 104.1 472s 19 98.1 103.8 472s 20 101.1 105.6 472s > print( predict( fitwls2e$eq[[ 1 ]], se.pred = TRUE, interval = "confidence", 472s + level = 0.999, newdata = predictData ) ) 472s fit se.pred lwr upr 472s 1 103 2.12 100.2 106 472s 2 106 2.12 102.7 109 472s 3 106 2.13 102.6 109 472s 4 106 2.12 102.9 109 472s 5 108 2.35 103.5 113 472s 6 108 2.31 103.6 113 472s 7 109 2.30 104.2 113 472s 8 109 2.27 105.0 114 472s 9 108 2.36 102.8 112 472s 10 106 2.46 100.8 112 472s 11 101 2.28 96.7 105 472s 12 100 2.12 97.0 103 472s 13 102 2.05 99.3 104 472s 14 105 2.20 101.2 109 472s 15 110 2.53 104.4 116 472s 16 110 2.44 104.7 115 472s 17 110 2.81 102.9 118 472s 18 108 2.23 104.3 112 472s 19 110 2.30 105.6 115 472s 20 114 2.50 108.1 119 472s > 472s > print( predict( fitwls3, se.pred = TRUE, interval = "prediction", 472s + level = 0.975 ) ) 472s demand.pred demand.se.pred demand.lwr demand.upr supply.pred supply.se.pred 472s 1 97.6 2.03 92.8 102.3 98.3 2.54 472s 2 99.6 2.02 94.9 104.4 99.5 2.56 472s 3 99.6 2.01 94.9 104.3 99.7 2.55 472s 4 99.8 2.02 95.0 104.5 99.9 2.56 472s 5 102.2 2.00 97.5 106.9 102.5 2.59 472s 6 102.0 1.99 97.3 106.7 102.4 2.56 472s 7 102.4 1.99 97.7 107.1 102.3 2.54 472s 8 102.8 2.03 98.0 107.5 104.3 2.63 472s 9 101.7 2.01 97.0 106.4 102.9 2.57 472s 10 100.8 2.09 95.9 105.7 100.3 2.64 472s 11 95.8 2.14 90.8 100.8 95.9 2.72 472s 12 94.7 2.14 89.6 99.7 93.9 2.82 472s 13 95.9 2.11 91.0 100.8 95.5 2.75 472s 14 99.1 2.00 94.4 103.8 97.9 2.61 472s 15 104.1 2.07 99.3 109.0 103.0 2.56 472s 16 103.8 2.03 99.0 108.5 104.6 2.55 472s 17 104.6 2.31 99.2 110.0 104.3 2.85 472s 18 101.9 2.01 97.2 106.6 102.9 2.55 472s 19 103.4 2.05 98.6 108.2 102.7 2.59 472s 20 106.3 2.31 100.9 111.7 105.2 2.84 472s supply.lwr supply.upr 472s 1 92.3 104 472s 2 93.5 106 472s 3 93.7 106 472s 4 93.9 106 472s 5 96.4 109 472s 6 96.4 108 472s 7 96.3 108 472s 8 98.1 110 472s 9 96.9 109 472s 10 94.1 107 472s 11 89.5 102 472s 12 87.3 101 472s 13 89.1 102 472s 14 91.8 104 472s 15 97.0 109 472s 16 98.6 111 472s 17 97.6 111 472s 18 96.9 109 472s 19 96.6 109 472s 20 98.6 112 472s > print( predict( fitwls3$eq[[ 2 ]], se.pred = TRUE, interval = "prediction", 472s + level = 0.975 ) ) 472s fit se.pred lwr upr 472s 1 98.3 2.54 92.3 104 472s 2 99.5 2.56 93.5 106 472s 3 99.7 2.55 93.7 106 472s 4 99.9 2.56 93.9 106 472s 5 102.5 2.59 96.4 109 472s 6 102.4 2.56 96.4 108 472s 7 102.3 2.54 96.3 108 472s 8 104.3 2.63 98.1 110 472s 9 102.9 2.57 96.9 109 472s 10 100.3 2.64 94.1 107 472s 11 95.9 2.72 89.5 102 472s 12 93.9 2.82 87.3 101 472s 13 95.5 2.75 89.1 102 472s 14 97.9 2.61 91.8 104 472s 15 103.0 2.56 97.0 109 472s 16 104.6 2.55 98.6 111 472s 17 104.3 2.85 97.6 111 472s 18 102.9 2.55 96.9 109 472s 19 102.7 2.59 96.6 109 472s 20 105.2 2.84 98.6 112 472s > 472s > print( predict( fitwls4e, se.fit = TRUE, interval = "confidence", 472s + level = 0.25 ) ) 472s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 472s 1 97.5 0.541 97.4 97.7 98.2 0.598 472s 2 99.6 0.471 99.4 99.7 99.6 0.679 472s 3 99.5 0.454 99.4 99.7 99.7 0.634 472s 4 99.7 0.475 99.5 99.8 100.0 0.643 472s 5 102.3 0.434 102.1 102.4 102.4 0.753 472s 6 102.0 0.418 101.9 102.2 102.4 0.680 472s 7 102.4 0.440 102.3 102.5 102.3 0.625 472s 8 102.7 0.537 102.5 102.9 104.4 0.799 472s 9 101.7 0.447 101.6 101.9 102.9 0.700 472s 10 100.9 0.628 100.7 101.1 100.2 0.716 472s 11 95.8 0.833 95.6 96.1 95.7 0.916 472s 12 94.6 0.807 94.4 94.9 93.9 1.226 472s 13 95.8 0.677 95.6 96.0 95.5 1.130 472s 14 99.1 0.459 98.9 99.2 97.8 0.796 472s 15 104.2 0.572 104.1 104.4 102.9 0.656 472s 16 103.8 0.509 103.7 104.0 104.6 0.644 472s 17 104.8 0.877 104.5 105.1 104.1 1.150 472s 18 101.9 0.478 101.7 102.0 103.0 0.575 472s 19 103.3 0.604 103.1 103.5 102.8 0.649 472s 20 106.2 1.102 105.8 106.6 105.5 0.875 472s supply.lwr supply.upr 472s 1 98.0 98.4 472s 2 99.4 99.8 472s 3 99.5 99.9 472s 4 99.8 100.2 472s 5 102.2 102.7 472s 6 102.1 102.6 472s 7 102.1 102.5 472s 8 104.1 104.6 472s 9 102.7 103.1 472s 10 99.9 100.4 472s 11 95.4 96.0 472s 12 93.5 94.3 472s 13 95.2 95.9 472s 14 97.6 98.1 472s 15 102.7 103.1 472s 16 104.4 104.8 472s 17 103.8 104.5 472s 18 102.8 103.2 472s 19 102.6 103.0 472s 20 105.2 105.8 472s > print( predict( fitwls4e$eq[[ 1 ]], se.fit = TRUE, interval = "confidence", 472s + level = 0.25 ) ) 472s fit se.fit lwr upr 472s 1 97.5 0.541 97.4 97.7 472s 2 99.6 0.471 99.4 99.7 472s 3 99.5 0.454 99.4 99.7 472s 4 99.7 0.475 99.5 99.8 472s 5 102.3 0.434 102.1 102.4 472s 6 102.0 0.418 101.9 102.2 472s 7 102.4 0.440 102.3 102.5 472s 8 102.7 0.537 102.5 102.9 472s 9 101.7 0.447 101.6 101.9 472s 10 100.9 0.628 100.7 101.1 472s 11 95.8 0.833 95.6 96.1 472s 12 94.6 0.807 94.4 94.9 472s 13 95.8 0.677 95.6 96.0 472s 14 99.1 0.459 98.9 99.2 472s 15 104.2 0.572 104.1 104.4 472s 16 103.8 0.509 103.7 104.0 472s 17 104.8 0.877 104.5 105.1 472s 18 101.9 0.478 101.7 102.0 472s 19 103.3 0.604 103.1 103.5 472s 20 106.2 1.102 105.8 106.6 472s > 472s > print( predict( fitwls5, se.fit = TRUE, se.pred = TRUE, 472s + interval = "prediction", level = 0.5, newdata = predictData ) ) 472s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 472s 1 104 0.749 2.07 102.1 105 96.4 472s 2 106 0.784 2.09 104.6 107 97.7 472s 3 106 0.793 2.09 104.5 107 97.8 472s 4 106 0.792 2.09 104.8 108 98.1 472s 5 109 1.136 2.24 107.1 110 100.6 472s 6 108 1.086 2.22 106.9 110 100.5 472s 7 109 1.097 2.22 107.4 110 100.4 472s 8 110 1.107 2.23 108.0 111 102.5 472s 9 108 1.126 2.24 106.4 109 101.1 472s 10 107 1.243 2.30 105.1 108 98.5 472s 11 101 1.066 2.21 99.7 103 94.0 472s 12 100 0.814 2.10 98.8 102 92.0 472s 13 102 0.617 2.03 100.4 103 93.7 472s 14 105 0.874 2.12 103.7 107 96.0 472s 15 111 1.377 2.37 109.0 112 101.2 472s 16 110 1.279 2.32 108.8 112 102.8 472s 17 111 1.656 2.55 108.9 112 102.5 472s 18 109 1.014 2.18 107.0 110 101.1 472s 19 110 1.180 2.27 108.7 112 100.9 472s 20 114 1.635 2.53 112.2 116 103.4 472s supply.se.fit supply.se.pred supply.lwr supply.upr 472s 1 0.799 2.58 94.6 98.1 472s 2 0.679 2.55 95.9 99.4 472s 3 0.692 2.55 96.1 99.6 472s 4 0.657 2.54 96.3 99.8 472s 5 1.051 2.67 98.8 102.5 472s 6 0.947 2.63 98.7 102.3 472s 7 0.845 2.59 98.7 102.2 472s 8 0.849 2.60 100.7 104.2 472s 9 1.100 2.69 99.3 103.0 472s 10 1.276 2.77 96.6 100.4 472s 11 1.422 2.84 92.1 95.9 472s 12 1.595 2.93 90.1 94.0 472s 13 1.401 2.82 91.7 95.6 472s 14 1.201 2.73 94.2 97.9 472s 15 1.169 2.72 99.3 103.0 472s 16 1.060 2.67 100.9 104.6 472s 17 1.727 3.00 100.5 104.6 472s 18 0.831 2.59 99.3 102.8 472s 19 0.834 2.59 99.1 102.6 472s 20 0.653 2.54 101.7 105.2 472s > print( predict( fitwls5$eq[[ 2 ]], se.fit = TRUE, se.pred = TRUE, 472s + interval = "prediction", level = 0.5, newdata = predictData ) ) 472s fit se.fit se.pred lwr upr 472s 1 96.4 0.799 2.58 94.6 98.1 472s 2 97.7 0.679 2.55 95.9 99.4 472s 3 97.8 0.692 2.55 96.1 99.6 472s 4 98.1 0.657 2.54 96.3 99.8 472s 5 100.6 1.051 2.67 98.8 102.5 472s 6 100.5 0.947 2.63 98.7 102.3 472s 7 100.4 0.845 2.59 98.7 102.2 472s 8 102.5 0.849 2.60 100.7 104.2 472s 9 101.1 1.100 2.69 99.3 103.0 472s 10 98.5 1.276 2.77 96.6 100.4 472s 11 94.0 1.422 2.84 92.1 95.9 472s 12 92.0 1.595 2.93 90.1 94.0 472s 13 93.7 1.401 2.82 91.7 95.6 472s 14 96.0 1.201 2.73 94.2 97.9 472s 15 101.2 1.169 2.72 99.3 103.0 472s 16 102.8 1.060 2.67 100.9 104.6 472s 17 102.5 1.727 3.00 100.5 104.6 472s 18 101.1 0.831 2.59 99.3 102.8 472s 19 100.9 0.834 2.59 99.1 102.6 472s 20 103.4 0.653 2.54 101.7 105.2 472s > 472s > print( predict( fitwlsi1e, se.fit = TRUE, se.pred = TRUE, 472s + interval = "confidence", level = 0.99, useDfSys = TRUE ) ) 472s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 472s 1 97.4 0.593 2.02 95.8 99.0 98.9 472s 2 99.6 0.532 2.00 98.1 101.0 100.1 472s 3 99.5 0.502 1.99 98.2 100.9 100.2 472s 4 99.7 0.537 2.00 98.2 101.2 100.4 472s 5 102.3 0.463 1.98 101.0 103.6 102.7 472s 6 102.1 0.427 1.98 100.9 103.2 102.6 472s 7 102.5 0.446 1.98 101.2 103.7 102.4 472s 8 102.8 0.554 2.01 101.3 104.3 104.3 472s 9 101.7 0.486 1.99 100.4 103.0 102.9 472s 10 100.8 0.727 2.06 98.8 102.8 100.4 472s 11 95.6 0.872 2.12 93.2 98.0 96.0 472s 12 94.4 0.903 2.13 91.9 96.8 94.1 472s 13 95.7 0.811 2.09 93.4 97.9 95.6 472s 14 99.0 0.468 1.99 97.7 100.3 97.8 472s 15 104.3 0.699 2.05 102.4 106.2 102.6 472s 16 103.9 0.568 2.01 102.4 105.5 104.1 472s 17 104.8 1.174 2.26 101.6 108.0 103.8 472s 18 101.9 0.494 1.99 100.6 103.3 102.4 472s 19 103.5 0.627 2.03 101.8 105.2 102.1 472s 20 106.5 1.175 2.26 103.3 109.7 104.5 472s supply.se.fit supply.se.pred supply.lwr supply.upr 472s 1 0.945 2.58 96.3 101.5 472s 2 0.928 2.58 97.5 102.6 472s 3 0.839 2.55 97.9 102.5 472s 4 0.816 2.54 98.1 102.6 472s 5 0.800 2.53 100.5 104.9 472s 6 0.707 2.51 100.6 104.5 472s 7 0.643 2.49 100.7 104.2 472s 8 0.862 2.55 102.0 106.7 472s 9 0.705 2.51 101.0 104.9 472s 10 0.877 2.56 98.0 102.7 472s 11 1.060 2.63 93.1 98.9 472s 12 1.247 2.71 90.7 97.5 472s 13 1.113 2.65 92.6 98.6 472s 14 0.801 2.53 95.6 100.0 472s 15 0.782 2.53 100.5 104.8 472s 16 0.819 2.54 101.9 106.3 472s 17 1.436 2.80 99.9 107.7 472s 18 0.861 2.55 100.0 104.7 472s 19 0.982 2.60 99.4 104.8 472s 20 1.489 2.83 100.4 108.6 472s > print( predict( fitwlsi1e$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 472s + interval = "confidence", level = 0.99, useDfSys = TRUE ) ) 472s fit se.fit se.pred lwr upr 472s 1 97.4 0.593 2.02 95.8 99.0 472s 2 99.6 0.532 2.00 98.1 101.0 472s 3 99.5 0.502 1.99 98.2 100.9 472s 4 99.7 0.537 2.00 98.2 101.2 472s 5 102.3 0.463 1.98 101.0 103.6 472s 6 102.1 0.427 1.98 100.9 103.2 472s 7 102.5 0.446 1.98 101.2 103.7 472s 8 102.8 0.554 2.01 101.3 104.3 472s 9 101.7 0.486 1.99 100.4 103.0 472s 10 100.8 0.727 2.06 98.8 102.8 472s 11 95.6 0.872 2.12 93.2 98.0 472s 12 94.4 0.903 2.13 91.9 96.8 472s 13 95.7 0.811 2.09 93.4 97.9 472s 14 99.0 0.468 1.99 97.7 100.3 472s 15 104.3 0.699 2.05 102.4 106.2 472s 16 103.9 0.568 2.01 102.4 105.5 472s 17 104.8 1.174 2.26 101.6 108.0 472s 18 101.9 0.494 1.99 100.6 103.3 472s 19 103.5 0.627 2.03 101.8 105.2 472s 20 106.5 1.175 2.26 103.3 109.7 472s > 472s > print( predict( fitwlsi2, se.fit = TRUE, interval = "prediction", 472s + level = 0.9, newdata = predictData ) ) 472s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 472s 1 103 0.937 99.7 107 96.6 1.151 472s 2 106 0.942 102.2 110 97.8 0.875 472s 3 106 0.966 102.1 109 98.0 0.909 472s 4 106 0.947 102.4 110 98.2 0.833 472s 5 108 1.448 104.3 112 100.9 1.327 472s 6 108 1.368 104.2 112 100.7 1.192 472s 7 109 1.352 104.7 113 100.6 1.052 472s 8 109 1.293 105.4 113 102.6 0.914 472s 9 108 1.459 103.5 112 101.4 1.400 472s 10 106 1.647 102.0 111 98.8 1.787 472s 11 101 1.300 97.0 105 94.4 1.911 472s 12 100 0.938 96.4 104 92.3 1.880 472s 13 102 0.722 98.2 105 93.8 1.565 472s 14 105 1.121 101.1 109 96.3 1.479 472s 15 110 1.769 105.8 115 101.4 1.481 472s 16 110 1.602 105.8 114 102.9 1.248 472s 17 110 2.210 105.3 115 102.9 2.201 472s 18 108 1.205 104.5 112 101.2 0.911 472s 19 110 1.353 106.1 114 100.9 0.877 472s 20 114 1.714 109.4 118 103.3 0.705 472s supply.lwr supply.upr 472s 1 92.0 101.2 472s 2 93.4 102.2 472s 3 93.6 102.4 472s 4 93.9 102.6 472s 5 96.2 105.6 472s 6 96.1 105.3 472s 7 96.1 105.1 472s 8 98.1 107.0 472s 9 96.6 106.1 472s 10 93.7 103.9 472s 11 89.1 99.6 472s 12 87.1 97.5 472s 13 88.9 98.8 472s 14 91.4 101.1 472s 15 96.6 106.3 472s 16 98.3 107.6 472s 17 97.4 108.5 472s 18 96.8 105.6 472s 19 96.5 105.3 472s 20 99.0 107.7 472s > print( predict( fitwlsi2$eq[[ 2 ]], se.fit = TRUE, interval = "prediction", 472s + level = 0.9, newdata = predictData ) ) 472s fit se.fit lwr upr 472s 1 96.6 1.151 92.0 101.2 472s 2 97.8 0.875 93.4 102.2 472s 3 98.0 0.909 93.6 102.4 472s 4 98.2 0.833 93.9 102.6 472s 5 100.9 1.327 96.2 105.6 472s 6 100.7 1.192 96.1 105.3 472s 7 100.6 1.052 96.1 105.1 472s 8 102.6 0.914 98.1 107.0 472s 9 101.4 1.400 96.6 106.1 472s 10 98.8 1.787 93.7 103.9 472s 11 94.4 1.911 89.1 99.6 472s 12 92.3 1.880 87.1 97.5 472s 13 93.8 1.565 88.9 98.8 472s 14 96.3 1.479 91.4 101.1 472s 15 101.4 1.481 96.6 106.3 472s 16 102.9 1.248 98.3 107.6 472s 17 102.9 2.201 97.4 108.5 472s 18 101.2 0.911 96.8 105.6 472s 19 100.9 0.877 96.5 105.3 472s 20 103.3 0.705 99.0 107.7 472s > 472s > print( predict( fitwlsi3e, interval = "prediction", level = 0.925 ) ) 472s demand.pred demand.lwr demand.upr supply.pred supply.lwr supply.upr 472s 1 97.6 93.9 101.3 98.3 93.6 103 472s 2 99.7 96.0 103.3 99.5 94.9 104 472s 3 99.6 95.9 103.3 99.7 95.1 104 472s 4 99.8 96.1 103.5 99.9 95.3 105 472s 5 102.2 98.6 105.9 102.5 97.8 107 472s 6 102.0 98.4 105.7 102.4 97.7 107 472s 7 102.4 98.7 106.0 102.3 97.6 107 472s 8 102.8 99.1 106.5 104.3 99.5 109 472s 9 101.7 98.0 105.3 102.9 98.3 108 472s 10 100.8 97.0 104.6 100.3 95.5 105 472s 11 95.8 91.9 99.7 95.9 91.0 101 472s 12 94.7 90.8 98.6 93.9 88.9 99 472s 13 95.9 92.1 99.7 95.5 90.6 100 472s 14 99.1 95.4 102.7 97.9 93.2 103 472s 15 104.1 100.4 107.9 103.0 98.3 108 472s 16 103.8 100.1 107.5 104.6 99.9 109 472s 17 104.6 100.4 108.7 104.3 99.2 109 472s 18 101.9 98.2 105.6 102.9 98.2 108 472s 19 103.4 99.6 107.1 102.7 98.0 107 472s 20 106.3 102.2 110.4 105.2 100.1 110 472s > print( predict( fitwlsi3e$eq[[ 1 ]], interval = "prediction", level = 0.925 ) ) 472s fit lwr upr 472s 1 97.6 93.9 101.3 472s 2 99.7 96.0 103.3 472s 3 99.6 95.9 103.3 472s 4 99.8 96.1 103.5 472s 5 102.2 98.6 105.9 472s 6 102.0 98.4 105.7 472s 7 102.4 98.7 106.0 472s 8 102.8 99.1 106.5 472s 9 101.7 98.0 105.3 472s 10 100.8 97.0 104.6 472s 11 95.8 91.9 99.7 472s 12 94.7 90.8 98.6 472s 13 95.9 92.1 99.7 472s 14 99.1 95.4 102.7 472s 15 104.1 100.4 107.9 472s 16 103.8 100.1 107.5 472s 17 104.6 100.4 108.7 472s 18 101.9 98.2 105.6 472s 19 103.4 99.6 107.1 472s 20 106.3 102.2 110.4 472s > 472s > print( predict( fitwlsi4, interval = "confidence", newdata = predictData ) ) 472s demand.pred demand.lwr demand.upr supply.pred supply.lwr supply.upr 472s 1 104 102.0 105 96.4 94.8 98.0 472s 2 106 104.4 108 97.7 96.3 99.0 472s 3 106 104.3 108 97.8 96.4 99.2 472s 4 106 104.6 108 98.1 96.7 99.4 472s 5 109 106.3 111 100.6 98.5 102.8 472s 6 108 106.2 111 100.5 98.6 102.4 472s 7 109 106.7 111 100.4 98.7 102.2 472s 8 110 107.3 112 102.5 100.7 104.2 472s 9 108 105.6 110 101.1 98.9 103.4 472s 10 107 104.1 109 98.5 95.9 101.1 472s 11 101 99.0 103 94.0 91.1 96.9 472s 12 100 98.6 102 92.0 88.8 95.3 472s 13 102 100.5 103 93.7 90.8 96.5 472s 14 105 103.3 107 96.0 93.6 98.5 472s 15 111 107.8 113 101.2 98.8 103.6 472s 16 110 107.8 113 102.8 100.6 104.9 472s 17 111 107.3 114 102.5 99.0 106.0 472s 18 109 106.5 111 101.1 99.4 102.8 472s 19 110 107.9 113 100.9 99.2 102.6 472s 20 114 110.6 117 103.4 102.1 104.7 472s > print( predict( fitwlsi4$eq[[ 2 ]], interval = "confidence", 472s + newdata = predictData ) ) 472s fit lwr upr 472s 1 96.4 94.8 98.0 472s 2 97.7 96.3 99.0 472s 3 97.8 96.4 99.2 472s 4 98.1 96.7 99.4 472s 5 100.6 98.5 102.8 472s 6 100.5 98.6 102.4 472s 7 100.4 98.7 102.2 472s 8 102.5 100.7 104.2 472s 9 101.1 98.9 103.4 472s 10 98.5 95.9 101.1 472s 11 94.0 91.1 96.9 472s 12 92.0 88.8 95.3 472s 13 93.7 90.8 96.5 472s 14 96.0 93.6 98.5 472s 15 101.2 98.8 103.6 472s 16 102.8 100.6 104.9 472s 17 102.5 99.0 106.0 472s 18 101.1 99.4 102.8 472s 19 100.9 99.2 102.6 472s 20 103.4 102.1 104.7 472s > 472s > print( predict( fitwlsi5e, se.fit = TRUE, se.pred = TRUE, 472s + interval = "prediction", level = 0.01 ) ) 472s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 472s 1 97.5 0.540 2.01 97.5 97.6 98.2 472s 2 99.6 0.470 1.99 99.6 99.6 99.6 472s 3 99.5 0.453 1.99 99.5 99.6 99.7 472s 4 99.7 0.474 1.99 99.7 99.7 100.0 472s 5 102.3 0.433 1.98 102.2 102.3 102.4 472s 6 102.0 0.417 1.98 102.0 102.1 102.4 472s 7 102.4 0.439 1.98 102.4 102.4 102.3 472s 8 102.7 0.536 2.01 102.7 102.7 104.4 472s 9 101.7 0.446 1.99 101.7 101.8 102.9 472s 10 100.9 0.627 2.03 100.9 100.9 100.2 472s 11 95.8 0.831 2.11 95.8 95.9 95.7 472s 12 94.6 0.806 2.10 94.6 94.6 93.9 472s 13 95.8 0.676 2.05 95.8 95.8 95.5 472s 14 99.1 0.458 1.99 99.0 99.1 97.8 472s 15 104.2 0.571 2.02 104.2 104.3 102.9 472s 16 103.8 0.508 2.00 103.8 103.9 104.6 472s 17 104.8 0.877 2.12 104.8 104.8 104.1 472s 18 101.9 0.477 1.99 101.8 101.9 103.0 472s 19 103.3 0.602 2.03 103.3 103.4 102.8 472s 20 106.2 1.100 2.23 106.2 106.2 105.5 472s supply.se.fit supply.se.pred supply.lwr supply.upr 472s 1 0.598 2.52 98.2 98.3 472s 2 0.680 2.54 99.5 99.6 472s 3 0.634 2.53 99.7 99.8 472s 4 0.644 2.54 100.0 100.0 472s 5 0.754 2.57 102.4 102.5 472s 6 0.681 2.55 102.3 102.4 472s 7 0.626 2.53 102.3 102.3 472s 8 0.800 2.58 104.4 104.4 472s 9 0.701 2.55 102.9 102.9 472s 10 0.716 2.55 100.1 100.2 472s 11 0.918 2.62 95.7 95.8 472s 12 1.229 2.74 93.8 93.9 472s 13 1.132 2.70 95.5 95.6 472s 14 0.797 2.58 97.8 97.9 472s 15 0.657 2.54 102.9 103.0 472s 16 0.645 2.54 104.5 104.6 472s 17 1.151 2.71 104.1 104.2 472s 18 0.575 2.52 103.0 103.0 472s 19 0.649 2.54 102.8 102.8 472s 20 0.875 2.60 105.5 105.5 472s > print( predict( fitwlsi5e$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 472s + interval = "prediction", level = 0.01 ) ) 472s fit se.fit se.pred lwr upr 472s 1 97.5 0.540 2.01 97.5 97.6 472s 2 99.6 0.470 1.99 99.6 99.6 472s 3 99.5 0.453 1.99 99.5 99.6 472s 4 99.7 0.474 1.99 99.7 99.7 472s 5 102.3 0.433 1.98 102.2 102.3 472s 6 102.0 0.417 1.98 102.0 102.1 472s 7 102.4 0.439 1.98 102.4 102.4 472s 8 102.7 0.536 2.01 102.7 102.7 472s 9 101.7 0.446 1.99 101.7 101.8 472s 10 100.9 0.627 2.03 100.9 100.9 472s 11 95.8 0.831 2.11 95.8 95.9 472s 12 94.6 0.806 2.10 94.6 94.6 472s 13 95.8 0.676 2.05 95.8 95.8 472s 14 99.1 0.458 1.99 99.0 99.1 472s 15 104.2 0.571 2.02 104.2 104.3 472s 16 103.8 0.508 2.00 103.8 103.9 472s 17 104.8 0.877 2.12 104.8 104.8 472s 18 101.9 0.477 1.99 101.8 101.9 472s 19 103.3 0.602 2.03 103.3 103.4 472s 20 106.2 1.100 2.23 106.2 106.2 472s > 472s > # predict just one observation 472s > smallData <- data.frame( price = 130, income = 150, farmPrice = 120, 472s + trend = 25 ) 472s > 472s > print( predict( fitwls1, newdata = smallData ) ) 472s demand.pred supply.pred 472s 1 109 115 472s > print( predict( fitwls1$eq[[ 1 ]], newdata = smallData ) ) 472s fit 472s 1 109 472s > 472s > print( predict( fitwls2e, se.fit = TRUE, level = 0.9, 472s + newdata = smallData ) ) 472s demand.pred demand.se.fit supply.pred supply.se.fit 472s 1 109 2.23 116 3.03 472s > print( predict( fitwls2e$eq[[ 1 ]], se.pred = TRUE, level = 0.99, 472s + newdata = smallData ) ) 472s fit se.pred 472s 1 109 2.96 472s > 472s > print( predict( fitwls3, interval = "prediction", level = 0.975, 472s + newdata = smallData ) ) 472s demand.pred demand.lwr demand.upr supply.pred supply.lwr supply.upr 472s 1 109 101 116 116 107 126 472s > print( predict( fitwls3$eq[[ 1 ]], interval = "confidence", level = 0.8, 472s + newdata = smallData ) ) 472s fit lwr upr 472s 1 109 106 112 472s > 472s > print( predict( fitwls4e, se.fit = TRUE, interval = "confidence", 472s + level = 0.999, newdata = smallData ) ) 472s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 472s 1 108 2.02 101 116 117 2.02 472s supply.lwr supply.upr 472s 1 110 124 472s > print( predict( fitwls4e$eq[[ 2 ]], se.pred = TRUE, interval = "prediction", 472s + level = 0.75, newdata = smallData ) ) 472s fit se.pred lwr upr 472s 1 117 3.18 113 121 472s > 472s > print( predict( fitwls5, se.fit = TRUE, interval = "prediction", 472s + newdata = smallData ) ) 472s demand.pred demand.se.fit demand.lwr demand.upr supply.pred supply.se.fit 472s 1 108 2.2 102 114 117 2.23 472s supply.lwr supply.upr 472s 1 110 124 472s > print( predict( fitwls5$eq[[ 1 ]], se.pred = TRUE, interval = "confidence", 472s + newdata = smallData ) ) 472s fit se.pred lwr upr 472s 1 108 2.93 104 113 472s > 472s > print( predict( fitwlsi3e, se.fit = TRUE, se.pred = TRUE, 472s + interval = "prediction", level = 0.5, newdata = smallData ) ) 472s demand.pred demand.se.fit demand.se.pred demand.lwr demand.upr supply.pred 472s 1 109 2.23 2.95 107 111 116 472s supply.se.fit supply.se.pred supply.lwr supply.upr 472s 1 3.04 3.9 114 119 472s > print( predict( fitwlsi3e$eq[[ 1 ]], se.fit = TRUE, se.pred = TRUE, 472s + interval = "confidence", level = 0.25, newdata = smallData ) ) 472s fit se.fit se.pred lwr upr 472s 1 109 2.23 2.95 108 109 472s > 472s > 472s > ## ************ correlation of predicted values *************** 472s > print( correlation.systemfit( fitwls1, 2, 1 ) ) 472s [,1] 472s [1,] 0 472s [2,] 0 472s [3,] 0 472s [4,] 0 472s [5,] 0 472s [6,] 0 472s [7,] 0 472s [8,] 0 472s [9,] 0 472s [10,] 0 472s [11,] 0 472s [12,] 0 472s [13,] 0 472s [14,] 0 472s [15,] 0 472s [16,] 0 472s [17,] 0 472s [18,] 0 472s [19,] 0 472s [20,] 0 472s > 472s > print( correlation.systemfit( fitwls2e, 1, 2 ) ) 472s [,1] 472s [1,] 0.411525 472s [2,] 0.147624 472s [3,] 0.147711 472s [4,] 0.107654 472s [5,] -0.069284 472s [6,] -0.053039 472s [7,] -0.051551 472s [8,] -0.006153 472s [9,] -0.000333 472s [10,] -0.001262 472s [11,] 0.048574 472s [12,] 0.064996 472s [13,] 0.024618 472s [14,] -0.028485 472s [15,] 0.174980 472s [16,] 0.252722 472s [17,] 0.103392 472s [18,] 0.074219 472s [19,] 0.156545 472s [20,] 0.135438 472s > 472s > print( correlation.systemfit( fitwls3, 2, 1 ) ) 472s [,1] 472s [1,] 0.405901 472s [2,] 0.145364 472s [3,] 0.145375 472s [4,] 0.105835 472s [5,] -0.067958 472s [6,] -0.052026 472s [7,] -0.050543 472s [8,] -0.006031 472s [9,] -0.000326 472s [10,] -0.001237 472s [11,] 0.047534 472s [12,] 0.063493 472s [13,] 0.024060 472s [14,] -0.027910 472s [15,] 0.171580 472s [16,] 0.248212 472s [17,] 0.101409 472s [18,] 0.073084 472s [19,] 0.153950 472s [20,] 0.132944 472s > 472s > print( correlation.systemfit( fitwls4e, 1, 2 ) ) 472s [,1] 472s [1,] 0.38162 472s [2,] 0.29173 472s [3,] 0.25421 472s [4,] 0.28598 472s [5,] -0.02775 472s [6,] -0.04974 472s [7,] -0.05850 472s [8,] 0.09388 472s [9,] 0.09469 472s [10,] 0.43814 472s [11,] 0.10559 472s [12,] 0.00876 472s [13,] 0.04090 472s [14,] -0.03984 472s [15,] 0.40767 472s [16,] 0.24571 472s [17,] 0.64160 472s [18,] 0.24037 472s [19,] 0.34075 472s [20,] 0.54270 472s > 472s > print( correlation.systemfit( fitwls5, 2, 1 ) ) 472s [,1] 472s [1,] 0.3775 472s [2,] 0.2936 472s [3,] 0.2553 472s [4,] 0.2875 472s [5,] -0.0274 472s [6,] -0.0492 472s [7,] -0.0578 472s [8,] 0.0932 472s [9,] 0.0944 472s [10,] 0.4375 472s [11,] 0.1027 472s [12,] 0.0072 472s [13,] 0.0404 472s [14,] -0.0396 472s [15,] 0.4062 472s [16,] 0.2430 472s [17,] 0.6406 472s [18,] 0.2362 472s [19,] 0.3347 472s [20,] 0.5378 472s > 472s > print( correlation.systemfit( fitwlsi1e, 1, 2 ) ) 472s [,1] 472s [1,] 0 472s [2,] 0 472s [3,] 0 472s [4,] 0 472s [5,] 0 472s [6,] 0 472s [7,] 0 472s [8,] 0 472s [9,] 0 472s [10,] 0 472s [11,] 0 472s [12,] 0 472s [13,] 0 472s [14,] 0 472s [15,] 0 472s [16,] 0 472s [17,] 0 472s [18,] 0 472s [19,] 0 472s [20,] 0 472s > 472s > print( correlation.systemfit( fitwlsi2, 2, 1 ) ) 472s [,1] 472s [1,] 0.404696 472s [2,] 0.144881 472s [3,] 0.144877 472s [4,] 0.105448 472s [5,] -0.067678 472s [6,] -0.051812 472s [7,] -0.050330 472s [8,] -0.006005 472s [9,] -0.000325 472s [10,] -0.001232 472s [11,] 0.047315 472s [12,] 0.063179 472s [13,] 0.023943 472s [14,] -0.027789 472s [15,] 0.170862 472s [16,] 0.247256 472s [17,] 0.100990 472s [18,] 0.072842 472s [19,] 0.153398 472s [20,] 0.132415 472s > 472s > print( correlation.systemfit( fitwlsi3e, 1, 2 ) ) 472s [,1] 472s [1,] 0.410485 472s [2,] 0.147206 472s [3,] 0.147278 472s [4,] 0.107316 472s [5,] -0.069036 472s [6,] -0.052850 472s [7,] -0.051363 472s [8,] -0.006130 472s [9,] -0.000331 472s [10,] -0.001257 472s [11,] 0.048379 472s [12,] 0.064714 472s [13,] 0.024513 472s [14,] -0.028377 472s [15,] 0.174345 472s [16,] 0.251882 472s [17,] 0.103022 472s [18,] 0.074009 472s [19,] 0.156063 472s [20,] 0.134974 472s > 472s > print( correlation.systemfit( fitwlsi4, 2, 1 ) ) 472s [,1] 472s [1,] 0.37672 472s [2,] 0.29387 472s [3,] 0.25544 472s [4,] 0.28775 472s [5,] -0.02729 472s [6,] -0.04911 472s [7,] -0.05771 472s [8,] 0.09311 472s [9,] 0.09437 472s [10,] 0.43736 472s [11,] 0.10223 472s [12,] 0.00693 472s [13,] 0.04035 472s [14,] -0.03961 472s [15,] 0.40591 472s [16,] 0.24248 472s [17,] 0.64034 472s [18,] 0.23551 472s [19,] 0.33360 472s [20,] 0.53687 472s > 472s > print( correlation.systemfit( fitwlsi5e, 1, 2 ) ) 472s [,1] 472s [1,] 0.38098 472s [2,] 0.29204 472s [3,] 0.25439 472s [4,] 0.28624 472s [5,] -0.02769 472s [6,] -0.04966 472s [7,] -0.05840 472s [8,] 0.09378 472s [9,] 0.09465 472s [10,] 0.43805 472s [11,] 0.10513 472s [12,] 0.00851 472s [13,] 0.04083 472s [14,] -0.03981 472s [15,] 0.40746 472s [16,] 0.24528 472s [17,] 0.64146 472s [18,] 0.23972 472s [19,] 0.33979 472s [20,] 0.54192 472s > 472s > 472s > ## ************ Log-Likelihood values *************** 472s > print( logLik( fitwls1 ) ) 472s 'log Lik.' -67.8 (df=9) 472s > print( logLik( fitwls1, residCovDiag = TRUE ) ) 472s 'log Lik.' -83.6 (df=9) 472s > all.equal( logLik( fitwls1, residCovDiag = TRUE ), 473s + logLik( lmDemand ) + logLik( lmSupply ), 473s + check.attributes = FALSE ) 473s [1] TRUE 473s > 473s > print( logLik( fitwls2e ) ) 473s 'log Lik.' -61.5 (df=8) 473s > print( logLik( fitwls2e, residCovDiag = TRUE ) ) 473s 'log Lik.' -84 (df=8) 473s > 473s > print( logLik( fitwls3 ) ) 473s 'log Lik.' -61.4 (df=8) 473s > print( logLik( fitwls3, residCovDiag = TRUE ) ) 473s 'log Lik.' -84 (df=8) 473s > 473s > print( logLik( fitwls4e ) ) 473s 'log Lik.' -62.2 (df=7) 473s > print( logLik( fitwls4e, residCovDiag = TRUE ) ) 473s 'log Lik.' -84 (df=7) 473s > 473s > print( logLik( fitwls5 ) ) 473s 'log Lik.' -62.1 (df=7) 473s > print( logLik( fitwls5, residCovDiag = TRUE ) ) 473s 'log Lik.' -84 (df=7) 473s > 473s > print( logLik( fitwlsi1e ) ) 473s 'log Lik.' -67.8 (df=9) 473s > print( logLik( fitwlsi1e, residCovDiag = TRUE ) ) 473s 'log Lik.' -83.6 (df=9) 473s > 473s > print( logLik( fitwlsi2 ) ) 473s 'log Lik.' -61.4 (df=8) 473s > print( logLik( fitwlsi2, residCovDiag = TRUE ) ) 473s 'log Lik.' -84 (df=8) 473s > 473s > print( logLik( fitwlsi3e ) ) 473s 'log Lik.' -61.5 (df=8) 473s > print( logLik( fitwlsi3e, residCovDiag = TRUE ) ) 473s 'log Lik.' -84 (df=8) 473s > 473s > print( logLik( fitwlsi4 ) ) 473s 'log Lik.' -62.1 (df=7) 473s > print( logLik( fitwlsi4, residCovDiag = TRUE ) ) 473s 'log Lik.' -84 (df=7) 473s > 473s > print( logLik( fitwlsi5e ) ) 473s 'log Lik.' -62.2 (df=7) 473s > print( logLik( fitwlsi5e, residCovDiag = TRUE ) ) 473s 'log Lik.' -84 (df=7) 473s > 473s > 473s > ## ************** F tests **************** 473s > # testing first restriction 473s > print( linearHypothesis( fitwls1, restrm ) ) 473s Linear hypothesis test (Theil's F test) 473s 473s Hypothesis: 473s demand_income - supply_trend = 0 473s 473s Model 1: restricted model 473s Model 2: fitwls1 473s 473s Res.Df Df F Pr(>F) 473s 1 34 473s 2 33 1 0.64 0.43 473s > linearHypothesis( fitwls1, restrict ) 473s Linear hypothesis test (Theil's F test) 473s 473s Hypothesis: 473s demand_income - supply_trend = 0 473s 473s Model 1: restricted model 473s Model 2: fitwls1 473s 473s Res.Df Df F Pr(>F) 473s 1 34 473s 2 33 1 0.64 0.43 473s > 473s > print( linearHypothesis( fitwlsi1e, restrm ) ) 473s Linear hypothesis test (Theil's F test) 473s 473s Hypothesis: 473s demand_income - supply_trend = 0 473s 473s Model 1: restricted model 473s Model 2: fitwlsi1e 473s 473s Res.Df Df F Pr(>F) 473s 1 34 473s 2 33 1 0.66 0.42 473s > linearHypothesis( fitwlsi1e, restrict ) 473s Linear hypothesis test (Theil's F test) 473s 473s Hypothesis: 473s demand_income - supply_trend = 0 473s 473s Model 1: restricted model 473s Model 2: fitwlsi1e 473s 473s Res.Df Df F Pr(>F) 473s 1 34 473s 2 33 1 0.66 0.42 473s > 473s > # testing second restriction 473s > restrOnly2m <- matrix(0,1,7) 473s > restrOnly2q <- 0.5 473s > restrOnly2m[1,2] <- -1 473s > restrOnly2m[1,5] <- 1 473s > restrictOnly2 <- "- demand_price + supply_price = 0.5" 473s > # first restriction not imposed 473s > print( linearHypothesis( fitwls1e, restrOnly2m, restrOnly2q ) ) 473s Linear hypothesis test (Theil's F test) 473s 473s Hypothesis: 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwls1e 473s 473s Res.Df Df F Pr(>F) 473s 1 34 473s 2 33 1 0.03 0.86 473s > linearHypothesis( fitwls1e, restrictOnly2 ) 473s Linear hypothesis test (Theil's F test) 473s 473s Hypothesis: 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwls1e 473s 473s Res.Df Df F Pr(>F) 473s 1 34 473s 2 33 1 0.03 0.86 473s > 473s > print( linearHypothesis( fitwlsi1, restrOnly2m, restrOnly2q ) ) 473s Linear hypothesis test (Theil's F test) 473s 473s Hypothesis: 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwlsi1 473s 473s Res.Df Df F Pr(>F) 473s 1 34 473s 2 33 1 0.03 0.86 473s > linearHypothesis( fitwlsi1, restrictOnly2 ) 473s Linear hypothesis test (Theil's F test) 473s 473s Hypothesis: 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwlsi1 473s 473s Res.Df Df F Pr(>F) 473s 1 34 473s 2 33 1 0.03 0.86 473s > 473s > # first restriction imposed 473s > print( linearHypothesis( fitwls2, restrOnly2m, restrOnly2q ) ) 473s Linear hypothesis test (Theil's F test) 473s 473s Hypothesis: 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwls2 473s 473s Res.Df Df F Pr(>F) 473s 1 35 473s 2 34 1 0.08 0.78 473s > linearHypothesis( fitwls2, restrictOnly2 ) 473s Linear hypothesis test (Theil's F test) 473s 473s Hypothesis: 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwls2 473s 473s Res.Df Df F Pr(>F) 473s 1 35 473s 2 34 1 0.08 0.78 473s > 473s > print( linearHypothesis( fitwls3, restrOnly2m, restrOnly2q ) ) 473s Linear hypothesis test (Theil's F test) 473s 473s Hypothesis: 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwls3 473s 473s Res.Df Df F Pr(>F) 473s 1 35 473s 2 34 1 0.08 0.78 473s > linearHypothesis( fitwls3, restrictOnly2 ) 473s Linear hypothesis test (Theil's F test) 473s 473s Hypothesis: 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwls3 473s 473s Res.Df Df F Pr(>F) 473s 1 35 473s 2 34 1 0.08 0.78 473s > 473s > print( linearHypothesis( fitwlsi2e, restrOnly2m, restrOnly2q ) ) 473s Linear hypothesis test (Theil's F test) 473s 473s Hypothesis: 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwlsi2e 473s 473s Res.Df Df F Pr(>F) 473s 1 35 473s 2 34 1 0.08 0.77 473s > linearHypothesis( fitwlsi2e, restrictOnly2 ) 473s Linear hypothesis test (Theil's F test) 473s 473s Hypothesis: 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwlsi2e 473s 473s Res.Df Df F Pr(>F) 473s 1 35 473s 2 34 1 0.08 0.77 473s > 473s > print( linearHypothesis( fitwlsi3e, restrOnly2m, restrOnly2q ) ) 473s Linear hypothesis test (Theil's F test) 473s 473s Hypothesis: 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwlsi3e 473s 473s Res.Df Df F Pr(>F) 473s 1 35 473s 2 34 1 0.08 0.77 473s > linearHypothesis( fitwlsi3e, restrictOnly2 ) 473s Linear hypothesis test (Theil's F test) 473s 473s Hypothesis: 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwlsi3e 473s 473s Res.Df Df F Pr(>F) 473s 1 35 473s 2 34 1 0.08 0.77 473s > 473s > # testing both of the restrictions 473s > print( linearHypothesis( fitwls1e, restr2m, restr2q ) ) 473s Linear hypothesis test (Theil's F test) 473s 473s Hypothesis: 473s demand_income - supply_trend = 0 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwls1e 473s 473s Res.Df Df F Pr(>F) 473s 1 35 473s 2 33 2 0.37 0.69 473s > linearHypothesis( fitwls1e, restrict2 ) 473s Linear hypothesis test (Theil's F test) 473s 473s Hypothesis: 473s demand_income - supply_trend = 0 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwls1e 473s 473s Res.Df Df F Pr(>F) 473s 1 35 473s 2 33 2 0.37 0.69 473s > 473s > print( linearHypothesis( fitwlsi1, restr2m, restr2q ) ) 473s Linear hypothesis test (Theil's F test) 473s 473s Hypothesis: 473s demand_income - supply_trend = 0 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwlsi1 473s 473s Res.Df Df F Pr(>F) 473s 1 35 473s 2 33 2 0.36 0.7 473s > linearHypothesis( fitwlsi1, restrict2 ) 473s Linear hypothesis test (Theil's F test) 473s 473s Hypothesis: 473s demand_income - supply_trend = 0 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwlsi1 473s 473s Res.Df Df F Pr(>F) 473s 1 35 473s 2 33 2 0.36 0.7 473s > 473s > 473s > ## ************** Wald tests **************** 473s > # testing first restriction 473s > print( linearHypothesis( fitwls1, restrm, test = "Chisq" ) ) 473s Linear hypothesis test (Chi^2 statistic of a Wald test) 473s 473s Hypothesis: 473s demand_income - supply_trend = 0 473s 473s Model 1: restricted model 473s Model 2: fitwls1 473s 473s Res.Df Df Chisq Pr(>Chisq) 473s 1 34 473s 2 33 1 0.64 0.42 473s > linearHypothesis( fitwls1, restrict, test = "Chisq" ) 473s Linear hypothesis test (Chi^2 statistic of a Wald test) 473s 473s Hypothesis: 473s demand_income - supply_trend = 0 473s 473s Model 1: restricted model 473s Model 2: fitwls1 473s 473s Res.Df Df Chisq Pr(>Chisq) 473s 1 34 473s 2 33 1 0.64 0.42 473s > 473s > print( linearHypothesis( fitwlsi1e, restrm, test = "Chisq" ) ) 473s Linear hypothesis test (Chi^2 statistic of a Wald test) 473s 473s Hypothesis: 473s demand_income - supply_trend = 0 473s 473s Model 1: restricted model 473s Model 2: fitwlsi1e 473s 473s Res.Df Df Chisq Pr(>Chisq) 473s 1 34 473s 2 33 1 0.8 0.37 473s > linearHypothesis( fitwlsi1e, restrict, test = "Chisq" ) 473s Linear hypothesis test (Chi^2 statistic of a Wald test) 473s 473s Hypothesis: 473s demand_income - supply_trend = 0 473s 473s Model 1: restricted model 473s Model 2: fitwlsi1e 473s 473s Res.Df Df Chisq Pr(>Chisq) 473s 1 34 473s 2 33 1 0.8 0.37 473s > 473s > # testing second restriction 473s > # first restriction not imposed 473s > print( linearHypothesis( fitwls1e, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 473s Linear hypothesis test (Chi^2 statistic of a Wald test) 473s 473s Hypothesis: 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwls1e 473s 473s Res.Df Df Chisq Pr(>Chisq) 473s 1 34 473s 2 33 1 0.04 0.84 473s > linearHypothesis( fitwls1e, restrictOnly2, test = "Chisq" ) 473s Linear hypothesis test (Chi^2 statistic of a Wald test) 473s 473s Hypothesis: 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwls1e 473s 473s Res.Df Df Chisq Pr(>Chisq) 473s 1 34 473s 2 33 1 0.04 0.84 473s > 473s > print( linearHypothesis( fitwlsi1, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 473s Linear hypothesis test (Chi^2 statistic of a Wald test) 473s 473s Hypothesis: 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwlsi1 473s 473s Res.Df Df Chisq Pr(>Chisq) 473s 1 34 473s 2 33 1 0.03 0.86 473s > linearHypothesis( fitwlsi1, restrictOnly2, test = "Chisq" ) 473s Linear hypothesis test (Chi^2 statistic of a Wald test) 473s 473s Hypothesis: 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwlsi1 473s 473s Res.Df Df Chisq Pr(>Chisq) 473s 1 34 473s 2 33 1 0.03 0.86 473s > 473s > # first restriction imposed 473s > print( linearHypothesis( fitwls2, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 473s Linear hypothesis test (Chi^2 statistic of a Wald test) 473s 473s Hypothesis: 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwls2 473s 473s Res.Df Df Chisq Pr(>Chisq) 473s 1 35 473s 2 34 1 0.08 0.78 473s > linearHypothesis( fitwls2, restrictOnly2, test = "Chisq" ) 473s Linear hypothesis test (Chi^2 statistic of a Wald test) 473s 473s Hypothesis: 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwls2 473s 473s Res.Df Df Chisq Pr(>Chisq) 473s 1 35 473s 2 34 1 0.08 0.78 473s > 473s > print( linearHypothesis( fitwls3, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 473s Linear hypothesis test (Chi^2 statistic of a Wald test) 473s 473s Hypothesis: 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwls3 473s 473s Res.Df Df Chisq Pr(>Chisq) 473s 1 35 473s 2 34 1 0.08 0.78 473s > linearHypothesis( fitwls3, restrictOnly2, test = "Chisq" ) 473s Linear hypothesis test (Chi^2 statistic of a Wald test) 473s 473s Hypothesis: 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwls3 473s 473s Res.Df Df Chisq Pr(>Chisq) 473s 1 35 473s 2 34 1 0.08 0.78 473s > 473s > print( linearHypothesis( fitwlsi2e, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 473s Linear hypothesis test (Chi^2 statistic of a Wald test) 473s 473s Hypothesis: 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwlsi2e 473s 473s Res.Df Df Chisq Pr(>Chisq) 473s 1 35 473s 2 34 1 0.1 0.75 473s > linearHypothesis( fitwlsi2e, restrictOnly2, test = "Chisq" ) 473s Linear hypothesis test (Chi^2 statistic of a Wald test) 473s 473s Hypothesis: 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwlsi2e 473s 473s Res.Df Df Chisq Pr(>Chisq) 473s 1 35 473s 2 34 1 0.1 0.75 473s > 473s > print( linearHypothesis( fitwlsi3e, restrOnly2m, restrOnly2q, test = "Chisq" ) ) 473s Linear hypothesis test (Chi^2 statistic of a Wald test) 473s 473s Hypothesis: 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwlsi3e 473s 473s Res.Df Df Chisq Pr(>Chisq) 473s 1 35 473s 2 34 1 0.1 0.75 473s > linearHypothesis( fitwlsi3e, restrictOnly2, test = "Chisq" ) 473s Linear hypothesis test (Chi^2 statistic of a Wald test) 473s 473s Hypothesis: 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwlsi3e 473s 473s Res.Df Df Chisq Pr(>Chisq) 473s 1 35 473s 2 34 1 0.1 0.75 473s > 473s > # testing both of the restrictions 473s > print( linearHypothesis( fitwls1e, restr2m, restr2q, test = "Chisq" ) ) 473s Linear hypothesis test (Chi^2 statistic of a Wald test) 473s 473s Hypothesis: 473s demand_income - supply_trend = 0 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwls1e 473s 473s Res.Df Df Chisq Pr(>Chisq) 473s 1 35 473s 2 33 2 0.9 0.64 473s > linearHypothesis( fitwls1e, restrict2, test = "Chisq" ) 473s Linear hypothesis test (Chi^2 statistic of a Wald test) 473s 473s Hypothesis: 473s demand_income - supply_trend = 0 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwls1e 473s 473s Res.Df Df Chisq Pr(>Chisq) 473s 1 35 473s 2 33 2 0.9 0.64 473s > 473s > print( linearHypothesis( fitwlsi1, restr2m, restr2q, test = "Chisq" ) ) 473s Linear hypothesis test (Chi^2 statistic of a Wald test) 473s 473s Hypothesis: 473s demand_income - supply_trend = 0 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwlsi1 473s 473s Res.Df Df Chisq Pr(>Chisq) 473s 1 35 473s 2 33 2 0.72 0.7 473s > linearHypothesis( fitwlsi1, restrict2, test = "Chisq" ) 473s Linear hypothesis test (Chi^2 statistic of a Wald test) 473s 473s Hypothesis: 473s demand_income - supply_trend = 0 473s - demand_price + supply_price = 0.5 473s 473s Model 1: restricted model 473s Model 2: fitwlsi1 473s 473s Res.Df Df Chisq Pr(>Chisq) 473s 1 35 473s 2 33 2 0.72 0.7 473s > 473s > 473s > ## ****************** model frame ************************** 473s > print( mf <- model.frame( fitwls1 ) ) 473s consump price income farmPrice trend 473s 1 98.5 100.3 87.4 98.0 1 473s 2 99.2 104.3 97.6 99.1 2 473s 3 102.2 103.4 96.7 99.1 3 473s 4 101.5 104.5 98.2 98.1 4 473s 5 104.2 98.0 99.8 110.8 5 473s 6 103.2 99.5 100.5 108.2 6 473s 7 104.0 101.1 103.2 105.6 7 473s 8 99.9 104.8 107.8 109.8 8 473s 9 100.3 96.4 96.6 108.7 9 473s 10 102.8 91.2 88.9 100.6 10 473s 11 95.4 93.1 75.1 81.0 11 473s 12 92.4 98.8 76.9 68.6 12 473s 13 94.5 102.9 84.6 70.9 13 473s 14 98.8 98.8 90.6 81.4 14 473s 15 105.8 95.1 103.1 102.3 15 473s 16 100.2 98.5 105.1 105.0 16 473s 17 103.5 86.5 96.4 110.5 17 473s 18 99.9 104.0 104.4 92.5 18 473s 19 105.2 105.8 110.7 89.3 19 473s 20 106.2 113.5 127.1 93.0 20 473s > print( mf1 <- model.frame( fitwls1$eq[[ 1 ]] ) ) 473s consump price income 473s 1 98.5 100.3 87.4 473s 2 99.2 104.3 97.6 473s 3 102.2 103.4 96.7 473s 4 101.5 104.5 98.2 473s 5 104.2 98.0 99.8 473s 6 103.2 99.5 100.5 473s 7 104.0 101.1 103.2 473s 8 99.9 104.8 107.8 473s 9 100.3 96.4 96.6 473s 10 102.8 91.2 88.9 473s 11 95.4 93.1 75.1 473s 12 92.4 98.8 76.9 473s 13 94.5 102.9 84.6 473s 14 98.8 98.8 90.6 473s 15 105.8 95.1 103.1 473s 16 100.2 98.5 105.1 473s 17 103.5 86.5 96.4 473s 18 99.9 104.0 104.4 473s 19 105.2 105.8 110.7 473s 20 106.2 113.5 127.1 473s > print( attributes( mf1 )$terms ) 473s consump ~ price + income 473s attr(,"variables") 473s list(consump, price, income) 473s attr(,"factors") 473s price income 473s consump 0 0 473s price 1 0 473s income 0 1 473s attr(,"term.labels") 473s [1] "price" "income" 473s attr(,"order") 473s [1] 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, income) 473s attr(,"dataClasses") 473s consump price income 473s "numeric" "numeric" "numeric" 473s > print( mf2 <- model.frame( fitwls1$eq[[ 2 ]] ) ) 473s consump price farmPrice trend 473s 1 98.5 100.3 98.0 1 473s 2 99.2 104.3 99.1 2 473s 3 102.2 103.4 99.1 3 473s 4 101.5 104.5 98.1 4 473s 5 104.2 98.0 110.8 5 473s 6 103.2 99.5 108.2 6 473s 7 104.0 101.1 105.6 7 473s 8 99.9 104.8 109.8 8 473s 9 100.3 96.4 108.7 9 473s 10 102.8 91.2 100.6 10 473s 11 95.4 93.1 81.0 11 473s 12 92.4 98.8 68.6 12 473s 13 94.5 102.9 70.9 13 473s 14 98.8 98.8 81.4 14 473s 15 105.8 95.1 102.3 15 473s 16 100.2 98.5 105.0 16 473s 17 103.5 86.5 110.5 17 473s 18 99.9 104.0 92.5 18 473s 19 105.2 105.8 89.3 19 473s 20 106.2 113.5 93.0 20 473s > print( attributes( mf2 )$terms ) 473s consump ~ price + farmPrice + trend 473s attr(,"variables") 473s list(consump, price, farmPrice, trend) 473s attr(,"factors") 473s price farmPrice trend 473s consump 0 0 0 473s price 1 0 0 473s farmPrice 0 1 0 473s trend 0 0 1 473s attr(,"term.labels") 473s [1] "price" "farmPrice" "trend" 473s attr(,"order") 473s [1] 1 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, farmPrice, trend) 473s attr(,"dataClasses") 473s consump price farmPrice trend 473s "numeric" "numeric" "numeric" "numeric" 473s > 473s > print( all.equal( mf, model.frame( fitwls2e ) ) ) 473s [1] TRUE 473s > print( all.equal( mf1, model.frame( fitwls2e$eq[[ 1 ]] ) ) ) 473s [1] TRUE 473s > 473s > print( all.equal( mf, model.frame( fitwls3 ) ) ) 473s [1] TRUE 473s > print( all.equal( mf2, model.frame( fitwls3$eq[[ 2 ]] ) ) ) 473s [1] TRUE 473s > 473s > print( all.equal( mf, model.frame( fitwls4e ) ) ) 473s [1] TRUE 473s > print( all.equal( mf1, model.frame( fitwls4e$eq[[ 1 ]] ) ) ) 473s [1] TRUE 473s > 473s > print( all.equal( mf, model.frame( fitwls5 ) ) ) 473s [1] TRUE 473s > print( all.equal( mf2, model.frame( fitwls5$eq[[ 2 ]] ) ) ) 473s [1] TRUE 473s > 473s > print( all.equal( mf, model.frame( fitwlsi1e ) ) ) 473s [1] TRUE 473s > print( all.equal( mf1, model.frame( fitwlsi1e$eq[[ 1 ]] ) ) ) 473s [1] TRUE 473s > 473s > print( all.equal( mf, model.frame( fitwlsi2 ) ) ) 473s [1] TRUE 473s > print( all.equal( mf2, model.frame( fitwlsi2$eq[[ 2 ]] ) ) ) 473s [1] TRUE 473s > 473s > print( all.equal( mf, model.frame( fitwlsi3e ) ) ) 473s [1] TRUE 473s > print( all.equal( mf1, model.frame( fitwlsi3e$eq[[ 1 ]] ) ) ) 473s [1] TRUE 473s > 473s > print( all.equal( mf, model.frame( fitwlsi4 ) ) ) 473s [1] TRUE 473s > print( all.equal( mf2, model.frame( fitwlsi4$eq[[ 2 ]] ) ) ) 473s [1] TRUE 473s > 473s > print( all.equal( mf, model.frame( fitwlsi5e ) ) ) 473s [1] TRUE 473s > print( all.equal( mf1, model.frame( fitwlsi5e$eq[[ 1 ]] ) ) ) 473s [1] TRUE 473s > 473s > 473s > ## **************** model matrix ************************ 473s > # with x (returnModelMatrix) = TRUE 473s > print( !is.null( fitwls1e$eq[[ 1 ]]$x ) ) 473s [1] TRUE 473s > print( mm <- model.matrix( fitwlsi1e ) ) 473s demand_(Intercept) demand_price demand_income supply_(Intercept) 473s demand_1 1 100.3 87.4 0 473s demand_2 1 104.3 97.6 0 473s demand_3 1 103.4 96.7 0 473s demand_4 1 104.5 98.2 0 473s demand_5 1 98.0 99.8 0 473s demand_6 1 99.5 100.5 0 473s demand_7 1 101.1 103.2 0 473s demand_8 1 104.8 107.8 0 473s demand_9 1 96.4 96.6 0 473s demand_10 1 91.2 88.9 0 473s demand_11 1 93.1 75.1 0 473s demand_12 1 98.8 76.9 0 473s demand_13 1 102.9 84.6 0 473s demand_14 1 98.8 90.6 0 473s demand_15 1 95.1 103.1 0 473s demand_16 1 98.5 105.1 0 473s demand_17 1 86.5 96.4 0 473s demand_18 1 104.0 104.4 0 473s demand_19 1 105.8 110.7 0 473s demand_20 1 113.5 127.1 0 473s supply_1 0 0.0 0.0 1 473s supply_2 0 0.0 0.0 1 473s supply_3 0 0.0 0.0 1 473s supply_4 0 0.0 0.0 1 473s supply_5 0 0.0 0.0 1 473s supply_6 0 0.0 0.0 1 473s supply_7 0 0.0 0.0 1 473s supply_8 0 0.0 0.0 1 473s supply_9 0 0.0 0.0 1 473s supply_10 0 0.0 0.0 1 473s supply_11 0 0.0 0.0 1 473s supply_12 0 0.0 0.0 1 473s supply_13 0 0.0 0.0 1 473s supply_14 0 0.0 0.0 1 473s supply_15 0 0.0 0.0 1 473s supply_16 0 0.0 0.0 1 473s supply_17 0 0.0 0.0 1 473s supply_18 0 0.0 0.0 1 473s supply_19 0 0.0 0.0 1 473s supply_20 0 0.0 0.0 1 473s supply_price supply_farmPrice supply_trend 473s demand_1 0.0 0.0 0 473s demand_2 0.0 0.0 0 473s demand_3 0.0 0.0 0 473s demand_4 0.0 0.0 0 473s demand_5 0.0 0.0 0 473s demand_6 0.0 0.0 0 473s demand_7 0.0 0.0 0 473s demand_8 0.0 0.0 0 473s demand_9 0.0 0.0 0 473s demand_10 0.0 0.0 0 473s demand_11 0.0 0.0 0 473s demand_12 0.0 0.0 0 473s demand_13 0.0 0.0 0 473s demand_14 0.0 0.0 0 473s demand_15 0.0 0.0 0 473s demand_16 0.0 0.0 0 473s demand_17 0.0 0.0 0 473s demand_18 0.0 0.0 0 473s demand_19 0.0 0.0 0 473s demand_20 0.0 0.0 0 473s supply_1 100.3 98.0 1 473s supply_2 104.3 99.1 2 473s supply_3 103.4 99.1 3 473s supply_4 104.5 98.1 4 473s supply_5 98.0 110.8 5 473s supply_6 99.5 108.2 6 473s supply_7 101.1 105.6 7 473s supply_8 104.8 109.8 8 473s supply_9 96.4 108.7 9 473s supply_10 91.2 100.6 10 473s supply_11 93.1 81.0 11 473s supply_12 98.8 68.6 12 473s supply_13 102.9 70.9 13 473s supply_14 98.8 81.4 14 473s supply_15 95.1 102.3 15 473s supply_16 98.5 105.0 16 473s supply_17 86.5 110.5 17 473s supply_18 104.0 92.5 18 473s supply_19 105.8 89.3 19 473s supply_20 113.5 93.0 20 473s > print( mm1 <- model.matrix( fitwlsi1e$eq[[ 1 ]] ) ) 473s (Intercept) price income 473s 1 1 100.3 87.4 473s 2 1 104.3 97.6 473s 3 1 103.4 96.7 473s 4 1 104.5 98.2 473s 5 1 98.0 99.8 473s 6 1 99.5 100.5 473s 7 1 101.1 103.2 473s 8 1 104.8 107.8 473s 9 1 96.4 96.6 473s 10 1 91.2 88.9 473s 11 1 93.1 75.1 473s 12 1 98.8 76.9 473s 13 1 102.9 84.6 473s 14 1 98.8 90.6 473s 15 1 95.1 103.1 473s 16 1 98.5 105.1 473s 17 1 86.5 96.4 473s 18 1 104.0 104.4 473s 19 1 105.8 110.7 473s 20 1 113.5 127.1 473s attr(,"assign") 473s [1] 0 1 2 473s > print( mm2 <- model.matrix( fitwlsi1e$eq[[ 2 ]] ) ) 473s (Intercept) price farmPrice trend 473s 1 1 100.3 98.0 1 473s 2 1 104.3 99.1 2 473s 3 1 103.4 99.1 3 473s 4 1 104.5 98.1 4 473s 5 1 98.0 110.8 5 473s 6 1 99.5 108.2 6 473s 7 1 101.1 105.6 7 473s 8 1 104.8 109.8 8 473s 9 1 96.4 108.7 9 473s 10 1 91.2 100.6 10 473s 11 1 93.1 81.0 11 473s 12 1 98.8 68.6 12 473s 13 1 102.9 70.9 13 473s 14 1 98.8 81.4 14 473s 15 1 95.1 102.3 15 473s 16 1 98.5 105.0 16 473s 17 1 86.5 110.5 17 473s 18 1 104.0 92.5 18 473s 19 1 105.8 89.3 19 473s 20 1 113.5 93.0 20 473s attr(,"assign") 473s [1] 0 1 2 3 473s > 473s > # with x (returnModelMatrix) = FALSE 473s > print( all.equal( mm, model.matrix( fitwlsi1 ) ) ) 473s [1] TRUE 473s > print( all.equal( mm1, model.matrix( fitwlsi1$eq[[ 1 ]] ) ) ) 473s [1] TRUE 473s > print( all.equal( mm2, model.matrix( fitwlsi1$eq[[ 2 ]] ) ) ) 473s [1] TRUE 473s > print( !is.null( fitwls1$eq[[ 1 ]]$x ) ) 473s [1] FALSE 473s > 473s > # with x (returnModelMatrix) = TRUE 473s > print( !is.null( fitwls2$eq[[ 1 ]]$x ) ) 473s [1] TRUE 473s > print( all.equal( mm, model.matrix( fitwls2 ) ) ) 473s [1] TRUE 473s > print( all.equal( mm1, model.matrix( fitwls2$eq[[ 1 ]] ) ) ) 473s [1] TRUE 473s > print( all.equal( mm2, model.matrix( fitwls2$eq[[ 2 ]] ) ) ) 473s [1] TRUE 473s > 473s > # with x (returnModelMatrix) = FALSE 473s > print( all.equal( mm, model.matrix( fitwls2e ) ) ) 473s [1] TRUE 473s > print( all.equal( mm1, model.matrix( fitwls2e$eq[[ 1 ]] ) ) ) 473s [1] TRUE 473s > print( all.equal( mm2, model.matrix( fitwls2e$eq[[ 2 ]] ) ) ) 473s [1] TRUE 473s > print( !is.null( fitwls2e$eq[[ 1 ]]$x ) ) 473s [1] FALSE 473s > 473s > # with x (returnModelMatrix) = TRUE 473s > print( !is.null( fitwlsi3$eq[[ 1 ]]$x ) ) 473s [1] TRUE 473s > print( all.equal( mm, model.matrix( fitwlsi3 ) ) ) 473s [1] TRUE 473s > print( all.equal( mm1, model.matrix( fitwlsi3$eq[[ 1 ]] ) ) ) 473s [1] TRUE 473s > print( all.equal( mm2, model.matrix( fitwlsi3$eq[[ 2 ]] ) ) ) 473s [1] TRUE 473s > 473s > # with x (returnModelMatrix) = FALSE 473s > print( all.equal( mm, model.matrix( fitwlsi3e ) ) ) 473s [1] TRUE 473s > print( all.equal( mm1, model.matrix( fitwlsi3e$eq[[ 1 ]] ) ) ) 473s [1] TRUE 473s > print( all.equal( mm2, model.matrix( fitwlsi3e$eq[[ 2 ]] ) ) ) 473s [1] TRUE 473s > print( !is.null( fitwlsi3e$eq[[ 1 ]]$x ) ) 473s [1] FALSE 473s > 473s > # with x (returnModelMatrix) = TRUE 473s > print( !is.null( fitwls4e$eq[[ 1 ]]$x ) ) 473s [1] TRUE 473s > print( all.equal( mm, model.matrix( fitwls4e ) ) ) 473s [1] TRUE 473s > print( all.equal( mm1, model.matrix( fitwls4e$eq[[ 1 ]] ) ) ) 473s [1] TRUE 473s > print( all.equal( mm2, model.matrix( fitwls4e$eq[[ 2 ]] ) ) ) 473s [1] TRUE 473s > 473s > # with x (returnModelMatrix) = FALSE 473s > print( all.equal( mm, model.matrix( fitwls4Sym ) ) ) 473s [1] TRUE 473s > print( all.equal( mm1, model.matrix( fitwls4Sym$eq[[ 1 ]] ) ) ) 473s [1] TRUE 473s > print( all.equal( mm2, model.matrix( fitwls4Sym$eq[[ 2 ]] ) ) ) 473s [1] TRUE 473s > print( !is.null( fitwls4Sym$eq[[ 1 ]]$x ) ) 473s [1] FALSE 473s > 473s > # with x (returnModelMatrix) = TRUE 473s > print( !is.null( fitwls5$eq[[ 1 ]]$x ) ) 473s [1] TRUE 473s > print( all.equal( mm, model.matrix( fitwls5 ) ) ) 473s [1] TRUE 473s > print( all.equal( mm1, model.matrix( fitwls5$eq[[ 1 ]] ) ) ) 473s [1] TRUE 473s > print( all.equal( mm2, model.matrix( fitwls5$eq[[ 2 ]] ) ) ) 473s [1] TRUE 473s > 473s > # with x (returnModelMatrix) = FALSE 473s > print( all.equal( mm, model.matrix( fitwls5e ) ) ) 473s [1] TRUE 473s > print( all.equal( mm1, model.matrix( fitwls5e$eq[[ 1 ]] ) ) ) 473s [1] TRUE 473s > print( all.equal( mm2, model.matrix( fitwls5e$eq[[ 2 ]] ) ) ) 473s [1] TRUE 473s > print( !is.null( fitwls5e$eq[[ 1 ]]$x ) ) 473s [1] FALSE 473s > 473s > 473s > ## **************** formulas ************************ 473s > formula( fitwls1 ) 473s $demand 473s consump ~ price + income 473s 473s $supply 473s consump ~ price + farmPrice + trend 473s 473s > formula( fitwls1$eq[[ 2 ]] ) 473s consump ~ price + farmPrice + trend 473s > 473s > formula( fitwls2e ) 473s $demand 473s consump ~ price + income 473s 473s $supply 473s consump ~ price + farmPrice + trend 473s 473s > formula( fitwls2e$eq[[ 1 ]] ) 473s consump ~ price + income 473s > 473s > formula( fitwls3 ) 473s $demand 473s consump ~ price + income 473s 473s $supply 473s consump ~ price + farmPrice + trend 473s 473s > formula( fitwls3$eq[[ 2 ]] ) 473s consump ~ price + farmPrice + trend 473s > 473s > formula( fitwls4e ) 473s $demand 473s consump ~ price + income 473s 473s $supply 473s consump ~ price + farmPrice + trend 473s 473s > formula( fitwls4e$eq[[ 1 ]] ) 473s consump ~ price + income 473s > 473s > formula( fitwls5 ) 473s $demand 473s consump ~ price + income 473s 473s $supply 473s consump ~ price + farmPrice + trend 473s 473s > formula( fitwls5$eq[[ 2 ]] ) 473s consump ~ price + farmPrice + trend 473s > 473s > formula( fitwlsi1e ) 473s $demand 473s consump ~ price + income 473s 473s $supply 473s consump ~ price + farmPrice + trend 473s 473s > formula( fitwlsi1e$eq[[ 1 ]] ) 473s consump ~ price + income 473s > 473s > formula( fitwlsi2 ) 473s $demand 473s consump ~ price + income 473s 473s $supply 473s consump ~ price + farmPrice + trend 473s 473s > formula( fitwlsi2$eq[[ 2 ]] ) 473s consump ~ price + farmPrice + trend 473s > 473s > formula( fitwlsi3e ) 473s $demand 473s consump ~ price + income 473s 473s $supply 473s consump ~ price + farmPrice + trend 473s 473s > formula( fitwlsi3e$eq[[ 1 ]] ) 473s consump ~ price + income 473s > 473s > formula( fitwlsi4 ) 473s $demand 473s consump ~ price + income 473s 473s $supply 473s consump ~ price + farmPrice + trend 473s 473s > formula( fitwlsi4$eq[[ 2 ]] ) 473s consump ~ price + farmPrice + trend 473s > 473s > formula( fitwlsi5e ) 473s $demand 473s consump ~ price + income 473s 473s $supply 473s consump ~ price + farmPrice + trend 473s 473s > formula( fitwlsi5e$eq[[ 1 ]] ) 473s consump ~ price + income 473s > 473s > 473s > ## **************** model terms ******************* 473s > terms( fitwls1 ) 473s $demand 473s consump ~ price + income 473s attr(,"variables") 473s list(consump, price, income) 473s attr(,"factors") 473s price income 473s consump 0 0 473s price 1 0 473s income 0 1 473s attr(,"term.labels") 473s [1] "price" "income" 473s attr(,"order") 473s [1] 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, income) 473s attr(,"dataClasses") 473s consump price income 473s "numeric" "numeric" "numeric" 473s 473s $supply 473s consump ~ price + farmPrice + trend 473s attr(,"variables") 473s list(consump, price, farmPrice, trend) 473s attr(,"factors") 473s price farmPrice trend 473s consump 0 0 0 473s price 1 0 0 473s farmPrice 0 1 0 473s trend 0 0 1 473s attr(,"term.labels") 473s [1] "price" "farmPrice" "trend" 473s attr(,"order") 473s [1] 1 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, farmPrice, trend) 473s attr(,"dataClasses") 473s consump price farmPrice trend 473s "numeric" "numeric" "numeric" "numeric" 473s 473s > terms( fitwls1$eq[[ 2 ]] ) 473s consump ~ price + farmPrice + trend 473s attr(,"variables") 473s list(consump, price, farmPrice, trend) 473s attr(,"factors") 473s price farmPrice trend 473s consump 0 0 0 473s price 1 0 0 473s farmPrice 0 1 0 473s trend 0 0 1 473s attr(,"term.labels") 473s [1] "price" "farmPrice" "trend" 473s attr(,"order") 473s [1] 1 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, farmPrice, trend) 473s attr(,"dataClasses") 473s consump price farmPrice trend 473s "numeric" "numeric" "numeric" "numeric" 473s > 473s > terms( fitwls2e ) 473s $demand 473s consump ~ price + income 473s attr(,"variables") 473s list(consump, price, income) 473s attr(,"factors") 473s price income 473s consump 0 0 473s price 1 0 473s income 0 1 473s attr(,"term.labels") 473s [1] "price" "income" 473s attr(,"order") 473s [1] 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, income) 473s attr(,"dataClasses") 473s consump price income 473s "numeric" "numeric" "numeric" 473s 473s $supply 473s consump ~ price + farmPrice + trend 473s attr(,"variables") 473s list(consump, price, farmPrice, trend) 473s attr(,"factors") 473s price farmPrice trend 473s consump 0 0 0 473s price 1 0 0 473s farmPrice 0 1 0 473s trend 0 0 1 473s attr(,"term.labels") 473s [1] "price" "farmPrice" "trend" 473s attr(,"order") 473s [1] 1 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, farmPrice, trend) 473s attr(,"dataClasses") 473s consump price farmPrice trend 473s "numeric" "numeric" "numeric" "numeric" 473s 473s > terms( fitwls2e$eq[[ 1 ]] ) 473s consump ~ price + income 473s attr(,"variables") 473s list(consump, price, income) 473s attr(,"factors") 473s price income 473s consump 0 0 473s price 1 0 473s income 0 1 473s attr(,"term.labels") 473s [1] "price" "income" 473s attr(,"order") 473s [1] 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, income) 473s attr(,"dataClasses") 473s consump price income 473s "numeric" "numeric" "numeric" 473s > 473s > terms( fitwls3 ) 473s $demand 473s consump ~ price + income 473s attr(,"variables") 473s list(consump, price, income) 473s attr(,"factors") 473s price income 473s consump 0 0 473s price 1 0 473s income 0 1 473s attr(,"term.labels") 473s [1] "price" "income" 473s attr(,"order") 473s [1] 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, income) 473s attr(,"dataClasses") 473s consump price income 473s "numeric" "numeric" "numeric" 473s 473s $supply 473s consump ~ price + farmPrice + trend 473s attr(,"variables") 473s list(consump, price, farmPrice, trend) 473s attr(,"factors") 473s price farmPrice trend 473s consump 0 0 0 473s price 1 0 0 473s farmPrice 0 1 0 473s trend 0 0 1 473s attr(,"term.labels") 473s [1] "price" "farmPrice" "trend" 473s attr(,"order") 473s [1] 1 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, farmPrice, trend) 473s attr(,"dataClasses") 473s consump price farmPrice trend 473s "numeric" "numeric" "numeric" "numeric" 473s 473s > terms( fitwls3$eq[[ 2 ]] ) 473s consump ~ price + farmPrice + trend 473s attr(,"variables") 473s list(consump, price, farmPrice, trend) 473s attr(,"factors") 473s price farmPrice trend 473s consump 0 0 0 473s price 1 0 0 473s farmPrice 0 1 0 473s trend 0 0 1 473s attr(,"term.labels") 473s [1] "price" "farmPrice" "trend" 473s attr(,"order") 473s [1] 1 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, farmPrice, trend) 473s attr(,"dataClasses") 473s consump price farmPrice trend 473s "numeric" "numeric" "numeric" "numeric" 473s > 473s > terms( fitwls4e ) 473s $demand 473s consump ~ price + income 473s attr(,"variables") 473s list(consump, price, income) 473s attr(,"factors") 473s price income 473s consump 0 0 473s price 1 0 473s income 0 1 473s attr(,"term.labels") 473s [1] "price" "income" 473s attr(,"order") 473s [1] 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, income) 473s attr(,"dataClasses") 473s consump price income 473s "numeric" "numeric" "numeric" 473s 473s $supply 473s consump ~ price + farmPrice + trend 473s attr(,"variables") 473s list(consump, price, farmPrice, trend) 473s attr(,"factors") 473s price farmPrice trend 473s consump 0 0 0 473s price 1 0 0 473s farmPrice 0 1 0 473s trend 0 0 1 473s attr(,"term.labels") 473s [1] "price" "farmPrice" "trend" 473s attr(,"order") 473s [1] 1 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, farmPrice, trend) 473s attr(,"dataClasses") 473s consump price farmPrice trend 473s "numeric" "numeric" "numeric" "numeric" 473s 473s > terms( fitwls4e$eq[[ 1 ]] ) 473s consump ~ price + income 473s attr(,"variables") 473s list(consump, price, income) 473s attr(,"factors") 473s price income 473s consump 0 0 473s price 1 0 473s income 0 1 473s attr(,"term.labels") 473s [1] "price" "income" 473s attr(,"order") 473s [1] 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, income) 473s attr(,"dataClasses") 473s consump price income 473s "numeric" "numeric" "numeric" 473s > 473s > terms( fitwls5 ) 473s $demand 473s consump ~ price + income 473s attr(,"variables") 473s list(consump, price, income) 473s attr(,"factors") 473s price income 473s consump 0 0 473s price 1 0 473s income 0 1 473s attr(,"term.labels") 473s [1] "price" "income" 473s attr(,"order") 473s [1] 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, income) 473s attr(,"dataClasses") 473s consump price income 473s "numeric" "numeric" "numeric" 473s 473s $supply 473s consump ~ price + farmPrice + trend 473s attr(,"variables") 473s list(consump, price, farmPrice, trend) 473s attr(,"factors") 473s price farmPrice trend 473s consump 0 0 0 473s price 1 0 0 473s farmPrice 0 1 0 473s trend 0 0 1 473s attr(,"term.labels") 473s [1] "price" "farmPrice" "trend" 473s attr(,"order") 473s [1] 1 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, farmPrice, trend) 473s attr(,"dataClasses") 473s consump price farmPrice trend 473s "numeric" "numeric" "numeric" "numeric" 473s 473s > terms( fitwls5$eq[[ 2 ]] ) 473s consump ~ price + farmPrice + trend 473s attr(,"variables") 473s list(consump, price, farmPrice, trend) 473s attr(,"factors") 473s price farmPrice trend 473s consump 0 0 0 473s price 1 0 0 473s farmPrice 0 1 0 473s trend 0 0 1 473s attr(,"term.labels") 473s [1] "price" "farmPrice" "trend" 473s attr(,"order") 473s [1] 1 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, farmPrice, trend) 473s attr(,"dataClasses") 473s consump price farmPrice trend 473s "numeric" "numeric" "numeric" "numeric" 473s > 473s > terms( fitwlsi1e ) 473s $demand 473s consump ~ price + income 473s attr(,"variables") 473s list(consump, price, income) 473s attr(,"factors") 473s price income 473s consump 0 0 473s price 1 0 473s income 0 1 473s attr(,"term.labels") 473s [1] "price" "income" 473s attr(,"order") 473s [1] 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, income) 473s attr(,"dataClasses") 473s consump price income 473s "numeric" "numeric" "numeric" 473s 473s $supply 473s consump ~ price + farmPrice + trend 473s attr(,"variables") 473s list(consump, price, farmPrice, trend) 473s attr(,"factors") 473s price farmPrice trend 473s consump 0 0 0 473s price 1 0 0 473s farmPrice 0 1 0 473s trend 0 0 1 473s attr(,"term.labels") 473s [1] "price" "farmPrice" "trend" 473s attr(,"order") 473s [1] 1 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, farmPrice, trend) 473s attr(,"dataClasses") 473s consump price farmPrice trend 473s "numeric" "numeric" "numeric" "numeric" 473s 473s > terms( fitwlsi1e$eq[[ 1 ]] ) 473s consump ~ price + income 473s attr(,"variables") 473s list(consump, price, income) 473s attr(,"factors") 473s price income 473s consump 0 0 473s price 1 0 473s income 0 1 473s attr(,"term.labels") 473s [1] "price" "income" 473s attr(,"order") 473s [1] 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, income) 473s attr(,"dataClasses") 473s consump price income 473s "numeric" "numeric" "numeric" 473s > 473s > terms( fitwlsi2 ) 473s $demand 473s consump ~ price + income 473s attr(,"variables") 473s list(consump, price, income) 473s attr(,"factors") 473s price income 473s consump 0 0 473s price 1 0 473s income 0 1 473s attr(,"term.labels") 473s [1] "price" "income" 473s attr(,"order") 473s [1] 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, income) 473s attr(,"dataClasses") 473s consump price income 473s "numeric" "numeric" "numeric" 473s 473s $supply 473s consump ~ price + farmPrice + trend 473s attr(,"variables") 473s list(consump, price, farmPrice, trend) 473s attr(,"factors") 473s price farmPrice trend 473s consump 0 0 0 473s price 1 0 0 473s farmPrice 0 1 0 473s trend 0 0 1 473s attr(,"term.labels") 473s [1] "price" "farmPrice" "trend" 473s attr(,"order") 473s [1] 1 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, farmPrice, trend) 473s attr(,"dataClasses") 473s consump price farmPrice trend 473s "numeric" "numeric" "numeric" "numeric" 473s 473s > terms( fitwlsi2$eq[[ 2 ]] ) 473s consump ~ price + farmPrice + trend 473s attr(,"variables") 473s list(consump, price, farmPrice, trend) 473s attr(,"factors") 473s price farmPrice trend 473s consump 0 0 0 473s price 1 0 0 473s farmPrice 0 1 0 473s trend 0 0 1 473s attr(,"term.labels") 473s [1] "price" "farmPrice" "trend" 473s attr(,"order") 473s [1] 1 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, farmPrice, trend) 473s attr(,"dataClasses") 473s consump price farmPrice trend 473s "numeric" "numeric" "numeric" "numeric" 473s > 473s > terms( fitwlsi3e ) 473s $demand 473s consump ~ price + income 473s attr(,"variables") 473s list(consump, price, income) 473s attr(,"factors") 473s price income 473s consump 0 0 473s price 1 0 473s income 0 1 473s attr(,"term.labels") 473s [1] "price" "income" 473s attr(,"order") 473s [1] 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, income) 473s attr(,"dataClasses") 473s consump price income 473s "numeric" "numeric" "numeric" 473s 473s $supply 473s consump ~ price + farmPrice + trend 473s attr(,"variables") 473s list(consump, price, farmPrice, trend) 473s attr(,"factors") 473s price farmPrice trend 473s consump 0 0 0 473s price 1 0 0 473s farmPrice 0 1 0 473s trend 0 0 1 473s attr(,"term.labels") 473s [1] "price" "farmPrice" "trend" 473s attr(,"order") 473s [1] 1 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, farmPrice, trend) 473s attr(,"dataClasses") 473s consump price farmPrice trend 473s "numeric" "numeric" "numeric" "numeric" 473s 473s > terms( fitwlsi3e$eq[[ 1 ]] ) 473s consump ~ price + income 473s attr(,"variables") 473s list(consump, price, income) 473s attr(,"factors") 473s price income 473s consump 0 0 473s price 1 0 473s income 0 1 473s attr(,"term.labels") 473s [1] "price" "income" 473s attr(,"order") 473s [1] 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, income) 473s attr(,"dataClasses") 473s consump price income 473s "numeric" "numeric" "numeric" 473s > 473s > terms( fitwlsi4 ) 473s $demand 473s consump ~ price + income 473s attr(,"variables") 473s list(consump, price, income) 473s attr(,"factors") 473s price income 473s consump 0 0 473s price 1 0 473s income 0 1 473s attr(,"term.labels") 473s [1] "price" "income" 473s attr(,"order") 473s [1] 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, income) 473s attr(,"dataClasses") 473s consump price income 473s "numeric" "numeric" "numeric" 473s 473s $supply 473s consump ~ price + farmPrice + trend 473s attr(,"variables") 473s list(consump, price, farmPrice, trend) 473s attr(,"factors") 473s price farmPrice trend 473s consump 0 0 0 473s price 1 0 0 473s farmPrice 0 1 0 473s trend 0 0 1 473s attr(,"term.labels") 473s [1] "price" "farmPrice" "trend" 473s attr(,"order") 473s [1] 1 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, farmPrice, trend) 473s attr(,"dataClasses") 473s consump price farmPrice trend 473s "numeric" "numeric" "numeric" "numeric" 473s 473s > terms( fitwlsi4$eq[[ 2 ]] ) 473s consump ~ price + farmPrice + trend 473s attr(,"variables") 473s list(consump, price, farmPrice, trend) 473s attr(,"factors") 473s price farmPrice trend 473s consump 0 0 0 473s price 1 0 0 473s farmPrice 0 1 0 473s trend 0 0 1 473s attr(,"term.labels") 473s [1] "price" "farmPrice" "trend" 473s attr(,"order") 473s [1] 1 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, farmPrice, trend) 473s attr(,"dataClasses") 473s consump price farmPrice trend 473s "numeric" "numeric" "numeric" "numeric" 473s > 473s > terms( fitwlsi5e ) 473s $demand 473s consump ~ price + income 473s attr(,"variables") 473s list(consump, price, income) 473s attr(,"factors") 473s price income 473s consump 0 0 473s price 1 0 473s income 0 1 473s attr(,"term.labels") 473s [1] "price" "income" 473s attr(,"order") 473s [1] 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, income) 473s attr(,"dataClasses") 473s consump price income 473s "numeric" "numeric" "numeric" 473s 473s $supply 473s consump ~ price + farmPrice + trend 473s attr(,"variables") 473s list(consump, price, farmPrice, trend) 473s attr(,"factors") 473s price farmPrice trend 473s consump 0 0 0 473s price 1 0 0 473s farmPrice 0 1 0 473s trend 0 0 1 473s attr(,"term.labels") 473s [1] "price" "farmPrice" "trend" 473s attr(,"order") 473s [1] 1 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, farmPrice, trend) 473s attr(,"dataClasses") 473s consump price farmPrice trend 473s "numeric" "numeric" "numeric" "numeric" 473s 473s > terms( fitwlsi5e$eq[[ 1 ]] ) 473s consump ~ price + income 473s attr(,"variables") 473s list(consump, price, income) 473s attr(,"factors") 473s price income 473s consump 0 0 473s price 1 0 473s income 0 1 473s attr(,"term.labels") 473s [1] "price" "income" 473s attr(,"order") 473s [1] 1 1 473s attr(,"intercept") 473s [1] 1 473s attr(,"response") 473s [1] 1 473s attr(,".Environment") 473s 473s attr(,"predvars") 473s list(consump, price, income) 473s attr(,"dataClasses") 473s consump price income 473s "numeric" "numeric" "numeric" 473s > 473s > 473s > ## **************** estfun ************************ 473s > library( "sandwich" ) 473s > 473s > estfun( fitwls1 ) 473s demand_(Intercept) demand_price demand_income supply_(Intercept) 473s demand_1 0.2884 28.93 25.21 0.0000 473s demand_2 -0.1048 -10.92 -10.22 0.0000 473s demand_3 0.7045 72.87 68.13 0.0000 473s demand_4 0.4838 50.56 47.51 0.0000 473s demand_5 0.5222 51.18 52.12 0.0000 473s demand_6 0.3153 31.36 31.68 0.0000 473s demand_7 0.4108 41.51 42.39 0.0000 473s demand_8 -0.7872 -82.47 -84.86 0.0000 473s demand_9 -0.3665 -35.35 -35.41 0.0000 473s demand_10 0.5451 49.73 48.46 0.0000 473s demand_11 -0.0400 -3.72 -3.00 0.0000 473s demand_12 -0.5246 -51.83 -40.34 0.0000 473s demand_13 -0.3009 -30.96 -25.45 0.0000 473s demand_14 -0.0591 -5.83 -5.35 0.0000 473s demand_15 0.3991 37.96 41.14 0.0000 473s demand_16 -0.9934 -97.80 -104.40 0.0000 473s demand_17 -0.3417 -29.56 -32.94 0.0000 473s demand_18 -0.5375 -55.90 -56.11 0.0000 473s demand_19 0.4665 49.34 51.65 0.0000 473s demand_20 -0.0802 -9.10 -10.20 0.0000 473s supply_1 0.0000 0.00 0.00 -0.0768 473s supply_2 0.0000 0.00 0.00 -0.1548 473s supply_3 0.0000 0.00 0.00 0.3397 473s supply_4 0.0000 0.00 0.00 0.1961 473s supply_5 0.0000 0.00 0.00 0.2617 473s supply_6 0.0000 0.00 0.00 0.1176 473s supply_7 0.0000 0.00 0.00 0.2712 473s supply_8 0.0000 0.00 0.00 -0.7619 473s supply_9 0.0000 0.00 0.00 -0.4493 473s supply_10 0.0000 0.00 0.00 0.4269 473s supply_11 0.0000 0.00 0.00 -0.1034 473s supply_12 0.0000 0.00 0.00 -0.2934 473s supply_13 0.0000 0.00 0.00 -0.1839 473s supply_14 0.0000 0.00 0.00 0.1677 473s supply_15 0.0000 0.00 0.00 0.5461 473s supply_16 0.0000 0.00 0.00 -0.6683 473s supply_17 0.0000 0.00 0.00 -0.0458 473s supply_18 0.0000 0.00 0.00 -0.4234 473s supply_19 0.0000 0.00 0.00 0.5376 473s supply_20 0.0000 0.00 0.00 0.2963 473s supply_price supply_farmPrice supply_trend 473s demand_1 0.00 0.00 0.0000 473s demand_2 0.00 0.00 0.0000 473s demand_3 0.00 0.00 0.0000 473s demand_4 0.00 0.00 0.0000 473s demand_5 0.00 0.00 0.0000 473s demand_6 0.00 0.00 0.0000 473s demand_7 0.00 0.00 0.0000 473s demand_8 0.00 0.00 0.0000 473s demand_9 0.00 0.00 0.0000 473s demand_10 0.00 0.00 0.0000 473s demand_11 0.00 0.00 0.0000 473s demand_12 0.00 0.00 0.0000 473s demand_13 0.00 0.00 0.0000 473s demand_14 0.00 0.00 0.0000 473s demand_15 0.00 0.00 0.0000 473s demand_16 0.00 0.00 0.0000 473s demand_17 0.00 0.00 0.0000 473s demand_18 0.00 0.00 0.0000 473s demand_19 0.00 0.00 0.0000 473s demand_20 0.00 0.00 0.0000 473s supply_1 -7.70 -7.53 -0.0768 473s supply_2 -16.14 -15.34 -0.3096 473s supply_3 35.14 33.67 1.0192 473s supply_4 20.49 19.24 0.7843 473s supply_5 25.65 29.00 1.3085 473s supply_6 11.70 12.73 0.7057 473s supply_7 27.41 28.64 1.8987 473s supply_8 -79.82 -83.66 -6.0955 473s supply_9 -43.33 -48.84 -4.0437 473s supply_10 38.95 42.95 4.2691 473s supply_11 -9.63 -8.38 -1.1377 473s supply_12 -28.99 -20.13 -3.5213 473s supply_13 -18.93 -13.04 -2.3913 473s supply_14 16.56 13.65 2.3480 473s supply_15 51.95 55.87 8.1920 473s supply_16 -65.79 -70.17 -10.6922 473s supply_17 -3.96 -5.06 -0.7779 473s supply_18 -44.04 -39.16 -7.6205 473s supply_19 56.86 48.01 10.2144 473s supply_20 33.63 27.56 5.9267 473s > round( colSums( estfun( fitwls1 ) ), digits = 7 ) 473s demand_(Intercept) demand_price demand_income supply_(Intercept) 473s 0 0 0 0 473s supply_price supply_farmPrice supply_trend 473s 0 0 0 473s > 473s > estfun( fitwlsi1e ) 473s demand_(Intercept) demand_price demand_income supply_(Intercept) 473s demand_1 0.3393 34.04 29.66 0.0000 473s demand_2 -0.1232 -12.85 -12.03 0.0000 473s demand_3 0.8289 85.73 80.15 0.0000 473s demand_4 0.5692 59.49 55.90 0.0000 473s demand_5 0.6144 60.21 61.32 0.0000 473s demand_6 0.3709 36.89 37.28 0.0000 473s demand_7 0.4832 48.84 49.87 0.0000 473s demand_8 -0.9261 -97.03 -99.84 0.0000 473s demand_9 -0.4312 -41.59 -41.66 0.0000 473s demand_10 0.6413 58.51 57.01 0.0000 473s demand_11 -0.0470 -4.38 -3.53 0.0000 473s demand_12 -0.6172 -60.98 -47.46 0.0000 473s demand_13 -0.3540 -36.43 -29.95 0.0000 473s demand_14 -0.0695 -6.86 -6.29 0.0000 473s demand_15 0.4695 44.66 48.40 0.0000 473s demand_16 -1.1687 -115.06 -122.83 0.0000 473s demand_17 -0.4020 -34.78 -38.76 0.0000 473s demand_18 -0.6323 -65.77 -66.01 0.0000 473s demand_19 0.5489 58.05 60.76 0.0000 473s demand_20 -0.0944 -10.71 -12.00 0.0000 473s supply_1 0.0000 0.00 0.00 -0.0960 473s supply_2 0.0000 0.00 0.00 -0.1935 473s supply_3 0.0000 0.00 0.00 0.4247 473s supply_4 0.0000 0.00 0.00 0.2451 473s supply_5 0.0000 0.00 0.00 0.3271 473s supply_6 0.0000 0.00 0.00 0.1470 473s supply_7 0.0000 0.00 0.00 0.3390 473s supply_8 0.0000 0.00 0.00 -0.9524 473s supply_9 0.0000 0.00 0.00 -0.5616 473s supply_10 0.0000 0.00 0.00 0.5336 473s supply_11 0.0000 0.00 0.00 -0.1293 473s supply_12 0.0000 0.00 0.00 -0.3668 473s supply_13 0.0000 0.00 0.00 -0.2299 473s supply_14 0.0000 0.00 0.00 0.2096 473s supply_15 0.0000 0.00 0.00 0.6827 473s supply_16 0.0000 0.00 0.00 -0.8353 473s supply_17 0.0000 0.00 0.00 -0.0572 473s supply_18 0.0000 0.00 0.00 -0.5292 473s supply_19 0.0000 0.00 0.00 0.6720 473s supply_20 0.0000 0.00 0.00 0.3704 473s supply_price supply_farmPrice supply_trend 473s demand_1 0.00 0.00 0.000 473s demand_2 0.00 0.00 0.000 473s demand_3 0.00 0.00 0.000 473s demand_4 0.00 0.00 0.000 473s demand_5 0.00 0.00 0.000 473s demand_6 0.00 0.00 0.000 473s demand_7 0.00 0.00 0.000 473s demand_8 0.00 0.00 0.000 473s demand_9 0.00 0.00 0.000 473s demand_10 0.00 0.00 0.000 473s demand_11 0.00 0.00 0.000 473s demand_12 0.00 0.00 0.000 473s demand_13 0.00 0.00 0.000 473s demand_14 0.00 0.00 0.000 473s demand_15 0.00 0.00 0.000 473s demand_16 0.00 0.00 0.000 473s demand_17 0.00 0.00 0.000 473s demand_18 0.00 0.00 0.000 473s demand_19 0.00 0.00 0.000 473s demand_20 0.00 0.00 0.000 473s supply_1 -9.63 -9.41 -0.096 473s supply_2 -20.18 -19.18 -0.387 473s supply_3 43.92 42.08 1.274 473s supply_4 25.61 24.04 0.980 473s supply_5 32.06 36.25 1.636 473s supply_6 14.62 15.91 0.882 473s supply_7 34.27 35.80 2.373 473s supply_8 -99.78 -104.58 -7.619 473s supply_9 -54.17 -61.05 -5.055 473s supply_10 48.68 53.68 5.336 473s supply_11 -12.03 -10.47 -1.422 473s supply_12 -36.24 -25.16 -4.402 473s supply_13 -23.66 -16.30 -2.989 473s supply_14 20.70 17.06 2.935 473s supply_15 64.93 69.84 10.240 473s supply_16 -82.24 -87.71 -13.365 473s supply_17 -4.95 -6.32 -0.972 473s supply_18 -55.05 -48.95 -9.526 473s supply_19 71.08 60.01 12.768 473s supply_20 42.04 34.45 7.408 473s > round( colSums( estfun( fitwlsi1e ) ), digits = 7 ) 473s demand_(Intercept) demand_price demand_income supply_(Intercept) 473s 0 0 0 0 473s supply_price supply_farmPrice supply_trend 473s 0 0 0 473s > 473s > 473s > ## **************** bread ************************ 473s > bread( fitwls1 ) 473s demand_(Intercept) demand_price demand_income supply_(Intercept) 473s [1,] 2261.63 -23.7921 1.2865 0.0 473s [2,] -23.79 0.3289 -0.0933 0.0 473s [3,] 1.29 -0.0933 0.0825 0.0 473s [4,] 0.00 0.0000 0.0000 5255.9 473s [5,] 0.00 0.0000 0.0000 -39.5 473s [6,] 0.00 0.0000 0.0000 -12.2 473s [7,] 0.00 0.0000 0.0000 -11.2 473s supply_price supply_farmPrice supply_trend 473s [1,] 0.0000 0.0000 0.0000 473s [2,] 0.0000 0.0000 0.0000 473s [3,] 0.0000 0.0000 0.0000 473s [4,] -39.5000 -12.1744 -11.1673 473s [5,] 0.3601 0.0338 0.0209 473s [6,] 0.0338 0.0853 0.0526 473s [7,] 0.0209 0.0526 0.3804 473s > 473s > bread( fitwlsi1e ) 473s demand_(Intercept) demand_price demand_income supply_(Intercept) 473s [1,] 1922.39 -20.2232 1.0935 0.00 473s [2,] -20.22 0.2796 -0.0793 0.00 473s [3,] 1.09 -0.0793 0.0701 0.00 473s [4,] 0.00 0.0000 0.0000 4204.75 473s [5,] 0.00 0.0000 0.0000 -31.60 473s [6,] 0.00 0.0000 0.0000 -9.74 473s [7,] 0.00 0.0000 0.0000 -8.93 473s supply_price supply_farmPrice supply_trend 473s [1,] 0.0000 0.0000 0.0000 473s [2,] 0.0000 0.0000 0.0000 473s [3,] 0.0000 0.0000 0.0000 473s [4,] -31.6000 -9.7395 -8.9339 473s [5,] 0.2881 0.0270 0.0167 473s [6,] 0.0270 0.0683 0.0421 473s [7,] 0.0167 0.0421 0.3043 473s > 473s autopkgtest [02:31:24]: test run-unit-test: -----------------------] 476s autopkgtest [02:31:27]: test run-unit-test: - - - - - - - - - - results - - - - - - - - - - 476s run-unit-test PASS 476s autopkgtest [02:31:27]: test pkg-r-autopkgtest: preparing testbed 479s Reading package lists... 479s Building dependency tree... 479s Reading state information... 479s Starting pkgProblemResolver with broken count: 0 479s Starting 2 pkgProblemResolver with broken count: 0 479s Done 480s The following additional packages will be installed: 480s build-essential cpp cpp-13 cpp-13-s390x-linux-gnu cpp-s390x-linux-gnu 480s dctrl-tools g++ g++-13 g++-13-s390x-linux-gnu g++-s390x-linux-gnu gcc gcc-13 480s gcc-13-s390x-linux-gnu gcc-s390x-linux-gnu gfortran gfortran-13 480s gfortran-13-s390x-linux-gnu gfortran-s390x-linux-gnu icu-devtools libasan8 480s libatomic1 libblas-dev libbz2-dev libcc1-0 libgcc-13-dev libgfortran-13-dev 480s libicu-dev libisl23 libitm1 libjpeg-dev libjpeg-turbo8-dev libjpeg8-dev 480s liblapack-dev liblzma-dev libmpc3 libncurses-dev libpcre2-16-0 libpcre2-32-0 480s libpcre2-dev libpcre2-posix3 libpkgconf3 libpng-dev libreadline-dev 480s libstdc++-13-dev libubsan1 pkg-config pkg-r-autopkgtest pkgconf pkgconf-bin 480s r-base-dev r-cran-arm r-cran-coda r-cran-mi r-cran-sem zlib1g-dev 480s Suggested packages: 480s cpp-doc gcc-13-locales cpp-13-doc debtags g++-multilib g++-13-multilib 480s gcc-13-doc gcc-multilib autoconf automake libtool flex bison gdb gcc-doc 480s gcc-13-multilib gdb-s390x-linux-gnu gfortran-multilib gfortran-doc 480s gfortran-13-multilib gfortran-13-doc libcoarrays-dev liblapack-doc icu-doc 480s liblzma-doc ncurses-doc readline-doc libstdc++-13-doc texlive-base 480s texlive-latex-base texlive-plain-generic texlive-fonts-recommended 480s texlive-fonts-extra texlive-extra-utils texlive-latex-recommended 480s texlive-latex-extra texinfo r-cran-sn r-cran-polycor 480s Recommended packages: 480s bzip2-doc libpng-tools r-cran-truncnorm 480s The following NEW packages will be installed: 480s autopkgtest-satdep build-essential cpp cpp-13 cpp-13-s390x-linux-gnu 480s cpp-s390x-linux-gnu dctrl-tools g++ g++-13 g++-13-s390x-linux-gnu 480s g++-s390x-linux-gnu gcc gcc-13 gcc-13-s390x-linux-gnu gcc-s390x-linux-gnu 480s gfortran gfortran-13 gfortran-13-s390x-linux-gnu gfortran-s390x-linux-gnu 480s icu-devtools libasan8 libatomic1 libblas-dev libbz2-dev libcc1-0 480s libgcc-13-dev libgfortran-13-dev libicu-dev libisl23 libitm1 libjpeg-dev 480s libjpeg-turbo8-dev libjpeg8-dev liblapack-dev liblzma-dev libmpc3 480s libncurses-dev libpcre2-16-0 libpcre2-32-0 libpcre2-dev libpcre2-posix3 480s libpkgconf3 libpng-dev libreadline-dev libstdc++-13-dev libubsan1 pkg-config 480s pkg-r-autopkgtest pkgconf pkgconf-bin r-base-dev r-cran-arm r-cran-coda 480s r-cran-mi r-cran-sem zlib1g-dev 480s 0 upgraded, 56 newly installed, 0 to remove and 1 not upgraded. 480s Need to get 85.7 MB/85.7 MB of archives. 480s After this operation, 286 MB of additional disk space will be used. 480s Get:1 /tmp/autopkgtest.4VHV7n/2-autopkgtest-satdep.deb autopkgtest-satdep s390x 0 [740 B] 480s Get:2 http://ftpmaster.internal/ubuntu noble/main s390x libisl23 s390x 0.26-3 [722 kB] 480s Get:3 http://ftpmaster.internal/ubuntu noble/main s390x libmpc3 s390x 1.3.1-1 [54.9 kB] 480s Get:4 http://ftpmaster.internal/ubuntu noble/main s390x cpp-13-s390x-linux-gnu s390x 13.2.0-21ubuntu1 [9935 kB] 481s Get:5 http://ftpmaster.internal/ubuntu noble/main s390x cpp-13 s390x 13.2.0-21ubuntu1 [1026 B] 481s Get:6 http://ftpmaster.internal/ubuntu noble/main s390x cpp-s390x-linux-gnu s390x 4:13.2.0-7ubuntu1 [5308 B] 481s Get:7 http://ftpmaster.internal/ubuntu noble/main s390x cpp s390x 4:13.2.0-7ubuntu1 [22.4 kB] 481s Get:8 http://ftpmaster.internal/ubuntu noble/main s390x libcc1-0 s390x 14-20240315-1ubuntu1 [50.0 kB] 481s Get:9 http://ftpmaster.internal/ubuntu noble/main s390x libitm1 s390x 14-20240315-1ubuntu1 [31.1 kB] 481s Get:10 http://ftpmaster.internal/ubuntu noble/main s390x libatomic1 s390x 14-20240315-1ubuntu1 [9396 B] 481s Get:11 http://ftpmaster.internal/ubuntu noble/main s390x libasan8 s390x 14-20240315-1ubuntu1 [2997 kB] 481s Get:12 http://ftpmaster.internal/ubuntu noble/main s390x libubsan1 s390x 14-20240315-1ubuntu1 [1186 kB] 481s Get:13 http://ftpmaster.internal/ubuntu noble/main s390x libgcc-13-dev s390x 13.2.0-21ubuntu1 [1003 kB] 481s Get:14 http://ftpmaster.internal/ubuntu noble/main s390x gcc-13-s390x-linux-gnu s390x 13.2.0-21ubuntu1 [19.1 MB] 481s Get:15 http://ftpmaster.internal/ubuntu noble/main s390x gcc-13 s390x 13.2.0-21ubuntu1 [469 kB] 481s Get:16 http://ftpmaster.internal/ubuntu noble/main s390x gcc-s390x-linux-gnu s390x 4:13.2.0-7ubuntu1 [1208 B] 481s Get:17 http://ftpmaster.internal/ubuntu noble/main s390x gcc s390x 4:13.2.0-7ubuntu1 [5014 B] 481s Get:18 http://ftpmaster.internal/ubuntu noble/main s390x libstdc++-13-dev s390x 13.2.0-21ubuntu1 [2494 kB] 481s Get:19 http://ftpmaster.internal/ubuntu noble/main s390x g++-13-s390x-linux-gnu s390x 13.2.0-21ubuntu1 [11.3 MB] 482s Get:20 http://ftpmaster.internal/ubuntu noble/main s390x g++-13 s390x 13.2.0-21ubuntu1 [14.4 kB] 482s Get:21 http://ftpmaster.internal/ubuntu noble/main s390x g++-s390x-linux-gnu s390x 4:13.2.0-7ubuntu1 [956 B] 482s Get:22 http://ftpmaster.internal/ubuntu noble/main s390x g++ s390x 4:13.2.0-7ubuntu1 [1096 B] 482s Get:23 http://ftpmaster.internal/ubuntu noble/main s390x build-essential s390x 12.10ubuntu1 [4930 B] 482s Get:24 http://ftpmaster.internal/ubuntu noble/main s390x dctrl-tools s390x 2.24-3build2 [65.4 kB] 482s Get:25 http://ftpmaster.internal/ubuntu noble/main s390x libgfortran-13-dev s390x 13.2.0-21ubuntu1 [623 kB] 482s Get:26 http://ftpmaster.internal/ubuntu noble/main s390x gfortran-13-s390x-linux-gnu s390x 13.2.0-21ubuntu1 [10.4 MB] 482s Get:27 http://ftpmaster.internal/ubuntu noble/main s390x gfortran-13 s390x 13.2.0-21ubuntu1 [10.9 kB] 482s Get:28 http://ftpmaster.internal/ubuntu noble/main s390x gfortran-s390x-linux-gnu s390x 4:13.2.0-7ubuntu1 [1016 B] 482s Get:29 http://ftpmaster.internal/ubuntu noble/main s390x gfortran s390x 4:13.2.0-7ubuntu1 [1174 B] 482s Get:30 http://ftpmaster.internal/ubuntu noble/main s390x icu-devtools s390x 74.2-1ubuntu1 [224 kB] 482s Get:31 http://ftpmaster.internal/ubuntu noble/main s390x libblas-dev s390x 3.12.0-3 [239 kB] 482s Get:32 http://ftpmaster.internal/ubuntu noble/main s390x libbz2-dev s390x 1.0.8-5ubuntu1 [39.4 kB] 482s Get:33 http://ftpmaster.internal/ubuntu noble/main s390x libicu-dev s390x 74.2-1ubuntu1 [11.9 MB] 483s Get:34 http://ftpmaster.internal/ubuntu noble/main s390x libjpeg-turbo8-dev s390x 2.1.5-2ubuntu1 [264 kB] 483s Get:35 http://ftpmaster.internal/ubuntu noble/main s390x libjpeg8-dev s390x 8c-2ubuntu11 [1484 B] 483s Get:36 http://ftpmaster.internal/ubuntu noble/main s390x libjpeg-dev s390x 8c-2ubuntu11 [1484 B] 483s Get:37 http://ftpmaster.internal/ubuntu noble/main s390x liblapack-dev s390x 3.12.0-3 [5983 kB] 483s Get:38 http://ftpmaster.internal/ubuntu noble/main s390x libncurses-dev s390x 6.4+20240113-1ubuntu1 [412 kB] 483s Get:39 http://ftpmaster.internal/ubuntu noble/main s390x libpcre2-16-0 s390x 10.42-4ubuntu1 [229 kB] 483s Get:40 http://ftpmaster.internal/ubuntu noble/main s390x libpcre2-32-0 s390x 10.42-4ubuntu1 [217 kB] 483s Get:41 http://ftpmaster.internal/ubuntu noble/main s390x libpcre2-posix3 s390x 10.42-4ubuntu1 [6704 B] 483s Get:42 http://ftpmaster.internal/ubuntu noble/main s390x libpcre2-dev s390x 10.42-4ubuntu1 [805 kB] 483s Get:43 http://ftpmaster.internal/ubuntu noble/main s390x libpkgconf3 s390x 1.8.1-2 [30.4 kB] 483s Get:44 http://ftpmaster.internal/ubuntu noble-proposed/main s390x zlib1g-dev s390x 1:1.3.dfsg-3.1ubuntu1 [904 kB] 483s Get:45 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libpng-dev s390x 1.6.43-3 [277 kB] 483s Get:46 http://ftpmaster.internal/ubuntu noble-proposed/main s390x libreadline-dev s390x 8.2-4 [189 kB] 483s Get:47 http://ftpmaster.internal/ubuntu noble/main s390x pkgconf-bin s390x 1.8.1-2 [20.8 kB] 483s Get:48 http://ftpmaster.internal/ubuntu noble/main s390x pkgconf s390x 1.8.1-2 [16.7 kB] 483s Get:49 http://ftpmaster.internal/ubuntu noble/main s390x pkg-config s390x 1.8.1-2 [7170 B] 483s Get:50 http://ftpmaster.internal/ubuntu noble-proposed/main s390x liblzma-dev s390x 5.6.0-0.2 [185 kB] 483s Get:51 http://ftpmaster.internal/ubuntu noble-proposed/universe s390x r-base-dev all 4.3.3-2build1 [4334 B] 483s Get:52 http://ftpmaster.internal/ubuntu noble/universe s390x pkg-r-autopkgtest all 20231212ubuntu1 [6448 B] 483s Get:53 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-coda all 0.19-4.1-1 [321 kB] 483s Get:54 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-arm all 1.13-1-1 [407 kB] 483s Get:55 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-mi all 1.1-1 [1840 kB] 483s Get:56 http://ftpmaster.internal/ubuntu noble/universe s390x r-cran-sem s390x 3.1.15-1 [628 kB] 484s Fetched 85.7 MB in 3s (25.3 MB/s) 484s Selecting previously unselected package libisl23:s390x. 484s (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 ... 102695 files and directories currently installed.) 484s Preparing to unpack .../00-libisl23_0.26-3_s390x.deb ... 484s Unpacking libisl23:s390x (0.26-3) ... 484s Selecting previously unselected package libmpc3:s390x. 484s Preparing to unpack .../01-libmpc3_1.3.1-1_s390x.deb ... 484s Unpacking libmpc3:s390x (1.3.1-1) ... 484s Selecting previously unselected package cpp-13-s390x-linux-gnu. 484s Preparing to unpack .../02-cpp-13-s390x-linux-gnu_13.2.0-21ubuntu1_s390x.deb ... 484s Unpacking cpp-13-s390x-linux-gnu (13.2.0-21ubuntu1) ... 484s Selecting previously unselected package cpp-13. 484s Preparing to unpack .../03-cpp-13_13.2.0-21ubuntu1_s390x.deb ... 484s Unpacking cpp-13 (13.2.0-21ubuntu1) ... 484s Selecting previously unselected package cpp-s390x-linux-gnu. 484s Preparing to unpack .../04-cpp-s390x-linux-gnu_4%3a13.2.0-7ubuntu1_s390x.deb ... 484s Unpacking cpp-s390x-linux-gnu (4:13.2.0-7ubuntu1) ... 484s Selecting previously unselected package cpp. 484s Preparing to unpack .../05-cpp_4%3a13.2.0-7ubuntu1_s390x.deb ... 484s Unpacking cpp (4:13.2.0-7ubuntu1) ... 484s Selecting previously unselected package libcc1-0:s390x. 484s Preparing to unpack .../06-libcc1-0_14-20240315-1ubuntu1_s390x.deb ... 484s Unpacking libcc1-0:s390x (14-20240315-1ubuntu1) ... 484s Selecting previously unselected package libitm1:s390x. 484s Preparing to unpack .../07-libitm1_14-20240315-1ubuntu1_s390x.deb ... 484s Unpacking libitm1:s390x (14-20240315-1ubuntu1) ... 484s Selecting previously unselected package libatomic1:s390x. 484s Preparing to unpack .../08-libatomic1_14-20240315-1ubuntu1_s390x.deb ... 484s Unpacking libatomic1:s390x (14-20240315-1ubuntu1) ... 484s Selecting previously unselected package libasan8:s390x. 484s Preparing to unpack .../09-libasan8_14-20240315-1ubuntu1_s390x.deb ... 484s Unpacking libasan8:s390x (14-20240315-1ubuntu1) ... 485s Selecting previously unselected package libubsan1:s390x. 485s Preparing to unpack .../10-libubsan1_14-20240315-1ubuntu1_s390x.deb ... 485s Unpacking libubsan1:s390x (14-20240315-1ubuntu1) ... 485s Selecting previously unselected package libgcc-13-dev:s390x. 485s Preparing to unpack .../11-libgcc-13-dev_13.2.0-21ubuntu1_s390x.deb ... 485s Unpacking libgcc-13-dev:s390x (13.2.0-21ubuntu1) ... 485s Selecting previously unselected package gcc-13-s390x-linux-gnu. 485s Preparing to unpack .../12-gcc-13-s390x-linux-gnu_13.2.0-21ubuntu1_s390x.deb ... 485s Unpacking gcc-13-s390x-linux-gnu (13.2.0-21ubuntu1) ... 485s Selecting previously unselected package gcc-13. 485s Preparing to unpack .../13-gcc-13_13.2.0-21ubuntu1_s390x.deb ... 485s Unpacking gcc-13 (13.2.0-21ubuntu1) ... 485s Selecting previously unselected package gcc-s390x-linux-gnu. 485s Preparing to unpack .../14-gcc-s390x-linux-gnu_4%3a13.2.0-7ubuntu1_s390x.deb ... 485s Unpacking gcc-s390x-linux-gnu (4:13.2.0-7ubuntu1) ... 485s Selecting previously unselected package gcc. 485s Preparing to unpack .../15-gcc_4%3a13.2.0-7ubuntu1_s390x.deb ... 485s Unpacking gcc (4:13.2.0-7ubuntu1) ... 485s Selecting previously unselected package libstdc++-13-dev:s390x. 485s Preparing to unpack .../16-libstdc++-13-dev_13.2.0-21ubuntu1_s390x.deb ... 485s Unpacking libstdc++-13-dev:s390x (13.2.0-21ubuntu1) ... 486s Selecting previously unselected package g++-13-s390x-linux-gnu. 486s Preparing to unpack .../17-g++-13-s390x-linux-gnu_13.2.0-21ubuntu1_s390x.deb ... 486s Unpacking g++-13-s390x-linux-gnu (13.2.0-21ubuntu1) ... 486s Selecting previously unselected package g++-13. 486s Preparing to unpack .../18-g++-13_13.2.0-21ubuntu1_s390x.deb ... 486s Unpacking g++-13 (13.2.0-21ubuntu1) ... 486s Selecting previously unselected package g++-s390x-linux-gnu. 486s Preparing to unpack .../19-g++-s390x-linux-gnu_4%3a13.2.0-7ubuntu1_s390x.deb ... 486s Unpacking g++-s390x-linux-gnu (4:13.2.0-7ubuntu1) ... 486s Selecting previously unselected package g++. 486s Preparing to unpack .../20-g++_4%3a13.2.0-7ubuntu1_s390x.deb ... 486s Unpacking g++ (4:13.2.0-7ubuntu1) ... 486s Selecting previously unselected package build-essential. 486s Preparing to unpack .../21-build-essential_12.10ubuntu1_s390x.deb ... 486s Unpacking build-essential (12.10ubuntu1) ... 486s Selecting previously unselected package dctrl-tools. 486s Preparing to unpack .../22-dctrl-tools_2.24-3build2_s390x.deb ... 486s Unpacking dctrl-tools (2.24-3build2) ... 486s Selecting previously unselected package libgfortran-13-dev:s390x. 486s Preparing to unpack .../23-libgfortran-13-dev_13.2.0-21ubuntu1_s390x.deb ... 486s Unpacking libgfortran-13-dev:s390x (13.2.0-21ubuntu1) ... 486s Selecting previously unselected package gfortran-13-s390x-linux-gnu. 486s Preparing to unpack .../24-gfortran-13-s390x-linux-gnu_13.2.0-21ubuntu1_s390x.deb ... 486s Unpacking gfortran-13-s390x-linux-gnu (13.2.0-21ubuntu1) ... 486s Selecting previously unselected package gfortran-13. 486s Preparing to unpack .../25-gfortran-13_13.2.0-21ubuntu1_s390x.deb ... 486s Unpacking gfortran-13 (13.2.0-21ubuntu1) ... 486s Selecting previously unselected package gfortran-s390x-linux-gnu. 486s Preparing to unpack .../26-gfortran-s390x-linux-gnu_4%3a13.2.0-7ubuntu1_s390x.deb ... 486s Unpacking gfortran-s390x-linux-gnu (4:13.2.0-7ubuntu1) ... 486s Selecting previously unselected package gfortran. 486s Preparing to unpack .../27-gfortran_4%3a13.2.0-7ubuntu1_s390x.deb ... 486s Unpacking gfortran (4:13.2.0-7ubuntu1) ... 486s Selecting previously unselected package icu-devtools. 486s Preparing to unpack .../28-icu-devtools_74.2-1ubuntu1_s390x.deb ... 486s Unpacking icu-devtools (74.2-1ubuntu1) ... 486s Selecting previously unselected package libblas-dev:s390x. 486s Preparing to unpack .../29-libblas-dev_3.12.0-3_s390x.deb ... 486s Unpacking libblas-dev:s390x (3.12.0-3) ... 486s Selecting previously unselected package libbz2-dev:s390x. 486s Preparing to unpack .../30-libbz2-dev_1.0.8-5ubuntu1_s390x.deb ... 486s Unpacking libbz2-dev:s390x (1.0.8-5ubuntu1) ... 486s Selecting previously unselected package libicu-dev:s390x. 486s Preparing to unpack .../31-libicu-dev_74.2-1ubuntu1_s390x.deb ... 486s Unpacking libicu-dev:s390x (74.2-1ubuntu1) ... 487s Selecting previously unselected package libjpeg-turbo8-dev:s390x. 487s Preparing to unpack .../32-libjpeg-turbo8-dev_2.1.5-2ubuntu1_s390x.deb ... 487s Unpacking libjpeg-turbo8-dev:s390x (2.1.5-2ubuntu1) ... 487s Selecting previously unselected package libjpeg8-dev:s390x. 487s Preparing to unpack .../33-libjpeg8-dev_8c-2ubuntu11_s390x.deb ... 487s Unpacking libjpeg8-dev:s390x (8c-2ubuntu11) ... 487s Selecting previously unselected package libjpeg-dev:s390x. 487s Preparing to unpack .../34-libjpeg-dev_8c-2ubuntu11_s390x.deb ... 487s Unpacking libjpeg-dev:s390x (8c-2ubuntu11) ... 487s Selecting previously unselected package liblapack-dev:s390x. 487s Preparing to unpack .../35-liblapack-dev_3.12.0-3_s390x.deb ... 487s Unpacking liblapack-dev:s390x (3.12.0-3) ... 487s Selecting previously unselected package libncurses-dev:s390x. 487s Preparing to unpack .../36-libncurses-dev_6.4+20240113-1ubuntu1_s390x.deb ... 487s Unpacking libncurses-dev:s390x (6.4+20240113-1ubuntu1) ... 487s Selecting previously unselected package libpcre2-16-0:s390x. 487s Preparing to unpack .../37-libpcre2-16-0_10.42-4ubuntu1_s390x.deb ... 487s Unpacking libpcre2-16-0:s390x (10.42-4ubuntu1) ... 487s Selecting previously unselected package libpcre2-32-0:s390x. 487s 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.../43-libpng-dev_1.6.43-3_s390x.deb ... 487s Unpacking libpng-dev:s390x (1.6.43-3) ... 487s Selecting previously unselected package libreadline-dev:s390x. 487s Preparing to unpack .../44-libreadline-dev_8.2-4_s390x.deb ... 487s Unpacking libreadline-dev:s390x (8.2-4) ... 487s Selecting previously unselected package pkgconf-bin. 487s Preparing to unpack .../45-pkgconf-bin_1.8.1-2_s390x.deb ... 487s Unpacking pkgconf-bin (1.8.1-2) ... 488s Selecting previously unselected package pkgconf:s390x. 488s Preparing to unpack .../46-pkgconf_1.8.1-2_s390x.deb ... 488s Unpacking pkgconf:s390x (1.8.1-2) ... 488s Selecting previously unselected package pkg-config:s390x. 488s Preparing to unpack .../47-pkg-config_1.8.1-2_s390x.deb ... 488s Unpacking pkg-config:s390x (1.8.1-2) ... 488s Selecting previously unselected package liblzma-dev:s390x. 488s Preparing to unpack .../48-liblzma-dev_5.6.0-0.2_s390x.deb ... 488s Unpacking liblzma-dev:s390x (5.6.0-0.2) ... 488s Selecting previously unselected package r-base-dev. 488s Preparing to unpack .../49-r-base-dev_4.3.3-2build1_all.deb ... 488s Unpacking r-base-dev (4.3.3-2build1) ... 488s Selecting previously unselected package pkg-r-autopkgtest. 488s Preparing to unpack .../50-pkg-r-autopkgtest_20231212ubuntu1_all.deb ... 488s Unpacking pkg-r-autopkgtest (20231212ubuntu1) ... 488s Selecting previously unselected package r-cran-coda. 488s Preparing to unpack .../51-r-cran-coda_0.19-4.1-1_all.deb ... 488s Unpacking r-cran-coda (0.19-4.1-1) ... 488s Selecting previously unselected package r-cran-arm. 488s Preparing to unpack .../52-r-cran-arm_1.13-1-1_all.deb ... 488s Unpacking r-cran-arm (1.13-1-1) ... 488s Selecting previously unselected package r-cran-mi. 488s Preparing to unpack .../53-r-cran-mi_1.1-1_all.deb ... 488s Unpacking r-cran-mi (1.1-1) ... 488s Selecting previously unselected package r-cran-sem. 488s Preparing to unpack .../54-r-cran-sem_3.1.15-1_s390x.deb ... 488s Unpacking r-cran-sem (3.1.15-1) ... 488s Selecting previously unselected package autopkgtest-satdep. 488s Preparing to unpack .../55-2-autopkgtest-satdep.deb ... 488s Unpacking autopkgtest-satdep (0) ... 488s Setting up libjpeg-turbo8-dev:s390x (2.1.5-2ubuntu1) ... 488s Setting up libncurses-dev:s390x (6.4+20240113-1ubuntu1) ... 488s Setting up libreadline-dev:s390x (8.2-4) ... 488s Setting up libpcre2-16-0:s390x (10.42-4ubuntu1) ... 488s Setting up libpcre2-32-0:s390x (10.42-4ubuntu1) ... 488s Setting up libpkgconf3:s390x (1.8.1-2) ... 488s Setting up libmpc3:s390x (1.3.1-1) ... 488s Setting up libatomic1:s390x (14-20240315-1ubuntu1) ... 488s Setting up icu-devtools (74.2-1ubuntu1) ... 488s Setting up pkgconf-bin (1.8.1-2) ... 488s Setting up liblzma-dev:s390x (5.6.0-0.2) ... 488s Setting up libubsan1:s390x (14-20240315-1ubuntu1) ... 488s Setting up zlib1g-dev:s390x (1:1.3.dfsg-3.1ubuntu1) ... 488s Setting up libpcre2-posix3:s390x (10.42-4ubuntu1) ... 488s Setting up libasan8:s390x (14-20240315-1ubuntu1) ... 488s Setting up libjpeg8-dev:s390x (8c-2ubuntu11) ... 488s Setting up libisl23:s390x (0.26-3) ... 488s Setting up r-cran-coda (0.19-4.1-1) ... 488s Setting up libicu-dev:s390x (74.2-1ubuntu1) ... 488s Setting up libcc1-0:s390x (14-20240315-1ubuntu1) ... 488s Setting up libblas-dev:s390x (3.12.0-3) ... 488s update-alternatives: using /usr/lib/s390x-linux-gnu/blas/libblas.so to provide /usr/lib/s390x-linux-gnu/libblas.so (libblas.so-s390x-linux-gnu) in auto mode 488s Setting up dctrl-tools (2.24-3build2) ... 488s Setting up libitm1:s390x (14-20240315-1ubuntu1) ... 488s Setting up r-cran-arm (1.13-1-1) ... 488s Setting up libbz2-dev:s390x (1.0.8-5ubuntu1) ... 488s Setting up libpcre2-dev:s390x (10.42-4ubuntu1) ... 488s Setting up libpng-dev:s390x (1.6.43-3) ... 488s Setting up libjpeg-dev:s390x (8c-2ubuntu11) ... 488s Setting up pkgconf:s390x (1.8.1-2) ... 488s Setting up cpp-13-s390x-linux-gnu (13.2.0-21ubuntu1) ... 488s Setting up r-cran-mi (1.1-1) ... 488s Setting up liblapack-dev:s390x (3.12.0-3) ... 488s update-alternatives: using /usr/lib/s390x-linux-gnu/lapack/liblapack.so to provide /usr/lib/s390x-linux-gnu/liblapack.so (liblapack.so-s390x-linux-gnu) in auto mode 488s Setting up pkg-config:s390x (1.8.1-2) ... 488s Setting up libgcc-13-dev:s390x (13.2.0-21ubuntu1) ... 488s Setting up libgfortran-13-dev:s390x (13.2.0-21ubuntu1) ... 488s Setting up r-cran-sem (3.1.15-1) ... 488s Setting up libstdc++-13-dev:s390x (13.2.0-21ubuntu1) ... 488s Setting up cpp-13 (13.2.0-21ubuntu1) ... 488s Setting up cpp-s390x-linux-gnu (4:13.2.0-7ubuntu1) ... 488s Setting up gcc-13-s390x-linux-gnu (13.2.0-21ubuntu1) ... 488s Setting up gcc-s390x-linux-gnu (4:13.2.0-7ubuntu1) ... 488s Setting up g++-13-s390x-linux-gnu (13.2.0-21ubuntu1) ... 488s Setting up gcc-13 (13.2.0-21ubuntu1) ... 488s Setting up cpp (4:13.2.0-7ubuntu1) ... 488s Setting up gfortran-13-s390x-linux-gnu (13.2.0-21ubuntu1) ... 488s Setting up g++-13 (13.2.0-21ubuntu1) ... 488s Setting up gfortran-s390x-linux-gnu (4:13.2.0-7ubuntu1) ... 488s Setting up g++-s390x-linux-gnu (4:13.2.0-7ubuntu1) ... 488s Setting up gcc (4:13.2.0-7ubuntu1) ... 488s Setting up gfortran-13 (13.2.0-21ubuntu1) ... 488s Setting up g++ (4:13.2.0-7ubuntu1) ... 488s update-alternatives: using /usr/bin/g++ to provide /usr/bin/c++ (c++) in auto mode 488s Setting up build-essential (12.10ubuntu1) ... 488s Setting up gfortran (4:13.2.0-7ubuntu1) ... 488s update-alternatives: using /usr/bin/gfortran to provide /usr/bin/f95 (f95) in auto mode 488s 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 488s update-alternatives: using /usr/bin/gfortran to provide /usr/bin/f77 (f77) in auto mode 488s 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 488s Setting up r-base-dev (4.3.3-2build1) ... 488s Setting up pkg-r-autopkgtest (20231212ubuntu1) ... 488s Setting up autopkgtest-satdep (0) ... 488s Processing triggers for man-db (2.12.0-3build4) ... 489s Processing triggers for install-info (7.1-3build1) ... 489s Processing triggers for libc-bin (2.39-0ubuntu6) ... 492s (Reading database ... 104685 files and directories currently installed.) 492s Removing autopkgtest-satdep (0) ... 493s autopkgtest [02:31:44]: test pkg-r-autopkgtest: /usr/share/dh-r/pkg-r-autopkgtest 493s autopkgtest [02:31:44]: test pkg-r-autopkgtest: [----------------------- 493s Test: Try to load the R library systemfit 493s 493s R version 4.3.3 (2024-02-29) -- "Angel Food Cake" 493s Copyright (C) 2024 The R Foundation for Statistical Computing 493s Platform: s390x-ibm-linux-gnu (64-bit) 493s 493s R is free software and comes with ABSOLUTELY NO WARRANTY. 493s You are welcome to redistribute it under certain conditions. 493s Type 'license()' or 'licence()' for distribution details. 493s 493s R is a collaborative project with many contributors. 493s Type 'contributors()' for more information and 493s 'citation()' on how to cite R or R packages in publications. 493s 493s Type 'demo()' for some demos, 'help()' for on-line help, or 493s 'help.start()' for an HTML browser interface to help. 493s Type 'q()' to quit R. 493s 493s > library('systemfit') 493s Loading required package: Matrix 494s Loading required package: car 494s Loading required package: carData 494s Loading required package: lmtest 494s Loading required package: zoo 494s 494s Attaching package: ‘zoo’ 494s 494s The following objects are masked from ‘package:base’: 494s 494s as.Date, as.Date.numeric 494s 494s 494s Please cite the 'systemfit' package as: 494s 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/. 494s 494s If you have questions, suggestions, or comments regarding the 'systemfit' package, please use a forum or 'tracker' at systemfit's R-Forge site: 494s https://r-forge.r-project.org/projects/systemfit/ 494s > 494s > 494s Other tests are currently unsupported! 494s They will be progressively added. 495s autopkgtest [02:31:46]: test pkg-r-autopkgtest: -----------------------] 495s autopkgtest [02:31:46]: test pkg-r-autopkgtest: - - - - - - - - - - results - - - - - - - - - - 495s pkg-r-autopkgtest PASS 496s autopkgtest [02:31:47]: @@@@@@@@@@@@@@@@@@@@ summary 496s run-unit-test PASS 496s pkg-r-autopkgtest PASS 511s Creating nova instance adt-noble-s390x-r-cran-systemfit-20240328-022331-juju-7f2275-prod-proposed-migration-environment-3-17c2615b-f3d3-421b-9773-20cb8f5cad77 from image adt/ubuntu-noble-s390x-server-20240327.img (UUID 4dc0c4c2-a3ae-40cd-8411-e7fc228c10ae)...