[Vars-commits] r119 - pkg/tests
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Wed Mar 20 22:18:38 CET 2024
Author: bpfaff
Date: 2024-03-20 22:18:38 +0100 (Wed, 20 Mar 2024)
New Revision: 119
Removed:
pkg/tests/CheckCausality.R
pkg/tests/CheckCausality.Rout.save
pkg/tests/ExamplesTest.R
pkg/tests/ExamplesTest.Rout.save
Log:
Removed test files
Deleted: pkg/tests/CheckCausality.R
===================================================================
--- pkg/tests/CheckCausality.R 2023-11-25 15:39:33 UTC (rev 118)
+++ pkg/tests/CheckCausality.R 2024-03-20 21:18:38 UTC (rev 119)
@@ -1,38 +0,0 @@
-
-
-###test causality
-library(vars)
-data(Canada)
-
-myVar<-VAR(Canada, p=2, season=12)
-
-causality(myVar)
-causality(myVar, cause="e")
-causality(myVar, cause="prod")
-causality(myVar, cause="rw")
-causality(myVar, cause="U")
-
-causality(myVar, cause=c("e", "prod"))
-causality(myVar, cause=c("prod","e"))
-causality(myVar, cause=c("e", "prod","rw"))
-
-myVar2<-VAR(Canada, p=3, type="trend")
-causality(myVar2, cause="e")
-causality(myVar2, cause="prod")
-causality(myVar2, cause="rw")
-causality(myVar2, cause="U")
-
-causality(myVar2, cause=c("e", "prod"))
-causality(myVar, cause=c("prod","e"))
-causality(myVar, cause=c("e", "prod","rw"))
-
- myVar3<-VAR(Canada[,1:3], p=1, exogen=Canada[,4], type="none")
-
-causality(myVar3)
-causality(myVar3, cause="e")
-causality(myVar3, cause="prod")
-causality(myVar3, cause="rw")
-
-causality(myVar3, cause=c("e", "prod"))
-causality(myVar3, cause=c("prod","e"))
-
Deleted: pkg/tests/CheckCausality.Rout.save
===================================================================
--- pkg/tests/CheckCausality.Rout.save 2023-11-25 15:39:33 UTC (rev 118)
+++ pkg/tests/CheckCausality.Rout.save 2024-03-20 21:18:38 UTC (rev 119)
@@ -1,423 +0,0 @@
-
-R version 2.14.0 (2011-10-31)
-Copyright (C) 2011 The R Foundation for Statistical Computing
-ISBN 3-900051-07-0
-Platform: i486-pc-linux-gnu (32-bit)
-
-R ist freie Software und kommt OHNE JEGLICHE GARANTIE.
-Sie sind eingeladen, es unter bestimmten Bedingungen weiter zu verbreiten.
-Tippen Sie 'license()' or 'licence()' für Details dazu.
-
-R ist ein Gemeinschaftsprojekt mit vielen Beitragenden.
-Tippen Sie 'contributors()' für mehr Information und 'citation()',
-um zu erfahren, wie R oder R packages in Publikationen zitiert werden können.
-
-Tippen Sie 'demo()' für einige Demos, 'help()' für on-line Hilfe, oder
-'help.start()' für eine HTML Browserschnittstelle zur Hilfe.
-Tippen Sie 'q()', um R zu verlassen.
-
->
->
-> ###test causality
-> library(vars)
-Lade nötiges Paket: MASS
-Lade nötiges Paket: strucchange
-Lade nötiges Paket: zoo
-
-Attache Paket: ‘zoo’
-
-The following object(s) are masked from ‘package:base’:
-
- as.Date, as.Date.numeric
-
-Lade nötiges Paket: sandwich
-Lade nötiges Paket: urca
-Lade nötiges Paket: lmtest
-> data(Canada)
->
-> myVar<-VAR(Canada, p=2, season=12)
->
-> causality(myVar)
-$Granger
-
- Granger causality H0: e do not Granger-cause prod rw U
-
-data: VAR object myVar
-F-Test = 5.6938, df1 = 6, df2 = 248, p-value = 1.454e-05
-
-
-$Instant
-
- H0: No instantaneous causality between: e and prod rw U
-
-data: VAR object myVar
-Chi-squared = 26.0187, df = 3, p-value = 9.452e-06
-
-
-Warnmeldung:
-In causality(myVar) :
-Argument 'cause' has not been specified;
-using first variable in 'x$y' (e) as cause variable.
-
-> causality(myVar, cause="e")
-$Granger
-
- Granger causality H0: e do not Granger-cause prod rw U
-
-data: VAR object myVar
-F-Test = 5.6938, df1 = 6, df2 = 248, p-value = 1.454e-05
-
-
-$Instant
-
- H0: No instantaneous causality between: e and prod rw U
-
-data: VAR object myVar
-Chi-squared = 26.0187, df = 3, p-value = 9.452e-06
-
-
-> causality(myVar, cause="prod")
-$Granger
-
- Granger causality H0: prod do not Granger-cause e rw U
-
-data: VAR object myVar
-F-Test = 2.4819, df1 = 6, df2 = 248, p-value = 0.02381
-
-
-$Instant
-
- H0: No instantaneous causality between: prod and e rw U
-
-data: VAR object myVar
-Chi-squared = 0.2975, df = 3, p-value = 0.9605
-
-
-> causality(myVar, cause="rw")
-$Granger
-
- Granger causality H0: rw do not Granger-cause e prod U
-
-data: VAR object myVar
-F-Test = 2.1694, df1 = 6, df2 = 248, p-value = 0.04653
-
-
-$Instant
-
- H0: No instantaneous causality between: rw and e prod U
-
-data: VAR object myVar
-Chi-squared = 3.0925, df = 3, p-value = 0.3776
-
-
-> causality(myVar, cause="U")
-$Granger
-
- Granger causality H0: U do not Granger-cause e prod rw
-
-data: VAR object myVar
-F-Test = 2.8427, df1 = 6, df2 = 248, p-value = 0.01076
-
-
-$Instant
-
- H0: No instantaneous causality between: U and e prod rw
-
-data: VAR object myVar
-Chi-squared = 26.2773, df = 3, p-value = 8.344e-06
-
-
->
-> causality(myVar, cause=c("e", "prod"))
-$Granger
-
- Granger causality H0: e prod do not Granger-cause rw U
-
-data: VAR object myVar
-F-Test = 6.1731, df1 = 8, df2 = 248, p-value = 2.868e-07
-
-
-$Instant
-
- H0: No instantaneous causality between: e prod and rw U
-
-data: VAR object myVar
-Chi-squared = 26.2651, df = 4, p-value = 2.798e-05
-
-
-> causality(myVar, cause=c("prod","e"))
-$Granger
-
- Granger causality H0: e prod do not Granger-cause rw U
-
-data: VAR object myVar
-F-Test = 6.1731, df1 = 8, df2 = 248, p-value = 2.868e-07
-
-
-$Instant
-
- H0: No instantaneous causality between: e prod and rw U
-
-data: VAR object myVar
-Chi-squared = 26.2651, df = 4, p-value = 2.798e-05
-
-
-> causality(myVar, cause=c("e", "prod","rw"))
-$Granger
-
- Granger causality H0: e prod rw do not Granger-cause U
-
-data: VAR object myVar
-F-Test = 8.2792, df1 = 6, df2 = 248, p-value = 3.496e-08
-
-
-$Instant
-
- H0: No instantaneous causality between: e prod rw and U
-
-data: VAR object myVar
-Chi-squared = 26.2773, df = 3, p-value = 8.344e-06
-
-
->
-> myVar2<-VAR(Canada, p=3, type="trend")
-> causality(myVar2, cause="e")
-$Granger
-
- Granger causality H0: e do not Granger-cause prod rw U
-
-data: VAR object myVar2
-F-Test = 4.7587, df1 = 9, df2 = 272, p-value = 6.768e-06
-
-
-$Instant
-
- H0: No instantaneous causality between: e and prod rw U
-
-data: VAR object myVar2
-Chi-squared = 29.6646, df = 3, p-value = 1.623e-06
-
-
-> causality(myVar2, cause="prod")
-$Granger
-
- Granger causality H0: prod do not Granger-cause e rw U
-
-data: VAR object myVar2
-F-Test = 2.3964, df1 = 9, df2 = 272, p-value = 0.01256
-
-
-$Instant
-
- H0: No instantaneous causality between: prod and e rw U
-
-data: VAR object myVar2
-Chi-squared = 1.3713, df = 3, p-value = 0.7123
-
-
-> causality(myVar2, cause="rw")
-$Granger
-
- Granger causality H0: rw do not Granger-cause e prod U
-
-data: VAR object myVar2
-F-Test = 2.221, df1 = 9, df2 = 272, p-value = 0.02101
-
-
-$Instant
-
- H0: No instantaneous causality between: rw and e prod U
-
-data: VAR object myVar2
-Chi-squared = 2.3541, df = 3, p-value = 0.5022
-
-
-> causality(myVar2, cause="U")
-$Granger
-
- Granger causality H0: U do not Granger-cause e prod rw
-
-data: VAR object myVar2
-F-Test = 2.129, df1 = 9, df2 = 272, p-value = 0.02738
-
-
-$Instant
-
- H0: No instantaneous causality between: U and e prod rw
-
-data: VAR object myVar2
-Chi-squared = 29.6082, df = 3, p-value = 1.668e-06
-
-
->
-> causality(myVar2, cause=c("e", "prod"))
-$Granger
-
- Granger causality H0: e prod do not Granger-cause rw U
-
-data: VAR object myVar2
-F-Test = 4.3402, df1 = 12, df2 = 272, p-value = 2.544e-06
-
-
-$Instant
-
- H0: No instantaneous causality between: e prod and rw U
-
-data: VAR object myVar2
-Chi-squared = 29.6006, df = 4, p-value = 5.902e-06
-
-
-> causality(myVar, cause=c("prod","e"))
-$Granger
-
- Granger causality H0: e prod do not Granger-cause rw U
-
-data: VAR object myVar
-F-Test = 6.1731, df1 = 8, df2 = 248, p-value = 2.868e-07
-
-
-$Instant
-
- H0: No instantaneous causality between: e prod and rw U
-
-data: VAR object myVar
-Chi-squared = 26.2651, df = 4, p-value = 2.798e-05
-
-
-> causality(myVar, cause=c("e", "prod","rw"))
-$Granger
-
- Granger causality H0: e prod rw do not Granger-cause U
-
-data: VAR object myVar
-F-Test = 8.2792, df1 = 6, df2 = 248, p-value = 3.496e-08
-
-
-$Instant
-
- H0: No instantaneous causality between: e prod rw and U
-
-data: VAR object myVar
-Chi-squared = 26.2773, df = 3, p-value = 8.344e-06
-
-
->
-> myVar3<-VAR(Canada[,1:3], p=1, exogen=Canada[,4], type="none")
-Warnmeldung:
-In VAR(Canada[, 1:3], p = 1, exogen = Canada[, 4], type = "none") :
- No column names supplied in exogen, using: exo1 , instead.
-
->
-> causality(myVar3)
-$Granger
-
- Granger causality H0: e do not Granger-cause prod rw
-
-data: VAR object myVar3
-F-Test = 8.0347, df1 = 2, df2 = 237, p-value = 0.0004205
-
-
-$Instant
-
- H0: No instantaneous causality between: e and prod rw
-
-data: VAR object myVar3
-Chi-squared = 11.2607, df = 2, p-value = 0.003587
-
-
-Warnmeldung:
-In causality(myVar3) :
-Argument 'cause' has not been specified;
-using first variable in 'x$y' (e) as cause variable.
-
-> causality(myVar3, cause="e")
-$Granger
-
- Granger causality H0: e do not Granger-cause prod rw
-
-data: VAR object myVar3
-F-Test = 8.0347, df1 = 2, df2 = 237, p-value = 0.0004205
-
-
-$Instant
-
- H0: No instantaneous causality between: e and prod rw
-
-data: VAR object myVar3
-Chi-squared = 11.2607, df = 2, p-value = 0.003587
-
-
-> causality(myVar3, cause="prod")
-$Granger
-
- Granger causality H0: prod do not Granger-cause e rw
-
-data: VAR object myVar3
-F-Test = 13.585, df1 = 2, df2 = 237, p-value = 2.597e-06
-
-
-$Instant
-
- H0: No instantaneous causality between: prod and e rw
-
-data: VAR object myVar3
-Chi-squared = 4.8762, df = 2, p-value = 0.08732
-
-
-> causality(myVar3, cause="rw")
-$Granger
-
- Granger causality H0: rw do not Granger-cause e prod
-
-data: VAR object myVar3
-F-Test = 3.7897, df1 = 2, df2 = 237, p-value = 0.02399
-
-
-$Instant
-
- H0: No instantaneous causality between: rw and e prod
-
-data: VAR object myVar3
-Chi-squared = 8.1627, df = 2, p-value = 0.01689
-
-
->
-> causality(myVar3, cause=c("e", "prod"))
-$Granger
-
- Granger causality H0: e prod do not Granger-cause rw
-
-data: VAR object myVar3
-F-Test = 33.7688, df1 = 2, df2 = 237, p-value = 1.248e-13
-
-
-$Instant
-
- H0: No instantaneous causality between: e prod and rw
-
-data: VAR object myVar3
-Chi-squared = 8.1627, df = 2, p-value = 0.01689
-
-
-> causality(myVar3, cause=c("prod","e"))
-$Granger
-
- Granger causality H0: e prod do not Granger-cause rw
-
-data: VAR object myVar3
-F-Test = 33.7688, df1 = 2, df2 = 237, p-value = 1.248e-13
-
-
-$Instant
-
- H0: No instantaneous causality between: e prod and rw
-
-data: VAR object myVar3
-Chi-squared = 8.1627, df = 2, p-value = 0.01689
-
-
->
->
-> proc.time()
- User System verstrichen
- 2.336 0.136 4.556
Deleted: pkg/tests/ExamplesTest.R
===================================================================
--- pkg/tests/ExamplesTest.R 2023-11-25 15:39:33 UTC (rev 118)
+++ pkg/tests/ExamplesTest.R 2024-03-20 21:18:38 UTC (rev 119)
@@ -1,26 +0,0 @@
-library(vars)
-
-
-
-example(Acoef)
-example(arch.test)
-example(Bcoef)
-example(BQ)
-example(causality)
-example(fanchart)
-example(fevd)
-example(irf)
-example(normality.test)
-example(Phi)
-example(Psi)
-example(restrict)
-example(roots)
-example(serial.test)
-example(stability)
-example(SVAR)
-example(SVEC)
-example(VAR)
-example(VARselect)
-example(vec2var)
-
-
Deleted: pkg/tests/ExamplesTest.Rout.save
===================================================================
--- pkg/tests/ExamplesTest.Rout.save 2023-11-25 15:39:33 UTC (rev 118)
+++ pkg/tests/ExamplesTest.Rout.save 2024-03-20 21:18:38 UTC (rev 119)
@@ -1,911 +0,0 @@
-
-R version 2.14.0 (2011-10-31)
-Copyright (C) 2011 The R Foundation for Statistical Computing
-ISBN 3-900051-07-0
-Platform: i486-pc-linux-gnu (32-bit)
-
-R ist freie Software und kommt OHNE JEGLICHE GARANTIE.
-Sie sind eingeladen, es unter bestimmten Bedingungen weiter zu verbreiten.
-Tippen Sie 'license()' or 'licence()' für Details dazu.
-
-R ist ein Gemeinschaftsprojekt mit vielen Beitragenden.
-Tippen Sie 'contributors()' für mehr Information und 'citation()',
-um zu erfahren, wie R oder R packages in Publikationen zitiert werden können.
-
-Tippen Sie 'demo()' für einige Demos, 'help()' für on-line Hilfe, oder
-'help.start()' für eine HTML Browserschnittstelle zur Hilfe.
-Tippen Sie 'q()', um R zu verlassen.
-
-[Vorher gesicherter Workspace wiederhergestellt]
-
-> library(vars)
-Lade nötiges Paket: MASS
-Lade nötiges Paket: strucchange
-Lade nötiges Paket: zoo
-
-Attache Paket: ‘zoo’
-
-The following object(s) are masked from ‘package:base’:
-
- as.Date, as.Date.numeric
-
-Lade nötiges Paket: sandwich
-Lade nötiges Paket: urca
-Lade nötiges Paket: lmtest
->
->
->
-> example(Acoef)
-
-Acoef> data(Canada)
-
-Acoef> var.2c <- VAR(Canada, p = 2, type = "const")
-
-Acoef> Acoef(var.2c)
-[[1]]
- e.l1 prod.l1 rw.l1 U.l1
-e 1.6378206 0.16727167 -0.06311863 0.26558478
-prod -0.1727658 1.15042820 0.05130390 -0.47850131
-rw -0.2688329 -0.08106500 0.89547833 0.01213003
-U -0.5807638 -0.07811707 0.01866214 0.61893150
-
-[[2]]
- e.l2 prod.l2 rw.l2 U.l2
-e -0.4971338 -0.101650067 0.003844492 0.13268931
-prod 0.3852589 -0.172411873 -0.118851043 1.01591801
-rw 0.3678489 -0.005180947 0.052676565 -0.12770826
-U 0.4098182 0.052116684 0.041801152 -0.07116885
-
-> example(arch.test)
-
-arch.t> data(Canada)
-
-arch.t> var.2c <- VAR(Canada, p = 2, type = "const")
-
-arch.t> arch.test(var.2c)
-
- ARCH (multivariate)
-
-data: Residuals of VAR object var.2c
-Chi-squared = 538.8897, df = 500, p-value = 0.1112
-
-> example(Bcoef)
-
-Bcoef> data(Canada)
-
-Bcoef> var.2c <- VAR(Canada, p = 2, type = "const")
-
-Bcoef> Bcoef(var.2c)
- e.l1 prod.l1 rw.l1 U.l1 e.l2 prod.l2
-e 1.6378206 0.16727167 -0.06311863 0.26558478 -0.4971338 -0.101650067
-prod -0.1727658 1.15042820 0.05130390 -0.47850131 0.3852589 -0.172411873
-rw -0.2688329 -0.08106500 0.89547833 0.01213003 0.3678489 -0.005180947
-U -0.5807638 -0.07811707 0.01866214 0.61893150 0.4098182 0.052116684
- rw.l2 U.l2 const
-e 0.003844492 0.13268931 -136.99845
-prod -0.118851043 1.01591801 -166.77552
-rw 0.052676565 -0.12770826 -33.18834
-U 0.041801152 -0.07116885 149.78056
-> example(BQ)
-
-BQ> data(Canada)
-
-BQ> var.2c <- VAR(Canada, p = 2, type = "const")
-
-BQ> BQ(var.2c)
-
-SVAR Estimation Results:
-========================
-
-
-Estimated contemporaneous impact matrix:
- e prod rw U
-e -0.007644 -0.28470 0.07374 -0.21234
-prod 0.543663 0.21658 -0.03379 -0.28652
-rw 0.082112 0.28588 0.71874 0.06162
-U 0.129451 0.05668 -0.01039 0.24111
-
-Estimated identified long run impact matrix:
- e prod rw U
-e 104.37 0.0000 0.00 0.0000
-prod 45.35 5.1971 0.00 0.0000
-rw 168.41 -2.1145 10.72 0.0000
-U -19.26 -0.4562 1.41 0.5331
-> example(causality)
-
-caslty> data(Canada)
-
-caslty> var.2c <- VAR(Canada, p = 2, type = "const")
-
-caslty> causality(var.2c, cause = "e")
-$Granger
-
- Granger causality H0: e do not Granger-cause prod rw U
-
-data: VAR object var.2c
-F-Test = 6.2768, df1 = 6, df2 = 292, p-value = 3.206e-06
-
-
-$Instant
-
- H0: No instantaneous causality between: e and prod rw U
-
-data: VAR object var.2c
-Chi-squared = 26.0685, df = 3, p-value = 9.228e-06
-
-
-
-caslty> #use a robust HC variance-covariance matrix for the Granger test:
-caslty> causality(var.2c, cause = "e", vcov.=vcovHC(var.2c))
-$Granger
-
- Granger causality H0: e do not Granger-cause prod rw U
-
-data: VAR object var.2c
-F-Test = 6.4593, df1 = 6, df2 = 292, p-value = 2.072e-06
-
-
-$Instant
-
- H0: No instantaneous causality between: e and prod rw U
-
-data: VAR object var.2c
-Chi-squared = 26.0685, df = 3, p-value = 9.228e-06
-
-
-
-caslty> #use a wild-bootstrap procedure to for the Granger test
-caslty> ## Not run: causality(var.2c, cause = "e", boot=TRUE, boot.runs=1000)
-caslty>
-caslty>
-caslty>
-> example(fanchart)
-
-fnchrt> ## Not run:
-fnchrt> ##D data(Canada)
-fnchrt> ##D var.2c <- VAR(Canada, p = 2, type = "const")
-fnchrt> ##D var.2c.prd <- predict(var.2c, n.ahead = 8, ci = 0.95)
-fnchrt> ##D fanchart(var.2c.prd)
-fnchrt> ## End(Not run)
-fnchrt>
-fnchrt>
-fnchrt>
-> example(fevd)
-
-fevd> data(Canada)
-
-fevd> var.2c <- VAR(Canada, p = 2, type = "const")
-
-fevd> fevd(var.2c, n.ahead = 5)
-$e
- e prod rw U
-[1,] 1.0000000 0.00000000 0.000000000 0.000000000
-[2,] 0.9633815 0.02563062 0.004448081 0.006539797
-[3,] 0.8961692 0.06797131 0.013226872 0.022632567
-[4,] 0.8057174 0.11757589 0.025689192 0.051017495
-[5,] 0.7019003 0.16952744 0.040094324 0.088477959
-
-$prod
- e prod rw U
-[1,] 0.0009954282 0.9990046 0.000000000 0.000000000
-[2,] 0.0004271364 0.9889540 0.001068905 0.009549939
-[3,] 0.0004117451 0.9923920 0.000714736 0.006481511
-[4,] 0.0005161018 0.9808528 0.003294103 0.015337005
-[5,] 0.0030112576 0.9554064 0.008196778 0.033385578
-
-$rw
- e prod rw U
-[1,] 0.02211315 0.014952942 0.9629339 0.000000e+00
-[2,] 0.04843396 0.009077935 0.9424827 5.449178e-06
-[3,] 0.05435699 0.008694159 0.9364211 5.277244e-04
-[4,] 0.04758894 0.015753861 0.9345505 2.106688e-03
-[5,] 0.03957649 0.026911791 0.9287321 4.779622e-03
-
-$U
- e prod rw U
-[1,] 0.4636211 0.003008244 0.002479203 0.5308915
-[2,] 0.7068777 0.008843922 0.003513667 0.2807647
-[3,] 0.7787875 0.037185141 0.020355796 0.1636716
-[4,] 0.7596609 0.079197860 0.046371393 0.1147699
-[5,] 0.6886156 0.128139114 0.076164201 0.1070811
-
-> example(irf)
-
-irf> data(Canada)
-
-irf> ## For VAR
-irf> var.2c <- VAR(Canada, p = 2, type = "const")
-
-irf> irf(var.2c, impulse = "e", response = c("prod", "rw", "U"), boot =
-irf+ FALSE)
-
-Impulse response coefficients
-$e
- prod rw U
- [1,] -0.020585541 -0.116033519 -0.190420048
- [2,] -0.001200947 -0.202083140 -0.329124153
- [3,] 0.014808436 -0.180277335 -0.369053587
- [4,] -0.021571434 -0.100425475 -0.352501745
- [5,] -0.084914238 0.008049928 -0.300681928
- [6,] -0.155700530 0.126762159 -0.229617289
- [7,] -0.221442354 0.241833321 -0.151593876
- [8,] -0.274945401 0.343821736 -0.075179522
- [9,] -0.313059778 0.427131741 -0.005842792
-[10,] -0.335382897 0.489230185 0.053372767
-[11,] -0.343203150 0.529860661 0.101208799
-
-
-irf> ## For SVAR
-irf> amat <- diag(4)
-
-irf> diag(amat) <- NA
-
-irf> svar.a <- SVAR(var.2c, estmethod = "direct", Amat = amat)
-
-irf> irf(svar.a, impulse = "e", response = c("prod", "rw", "U"), boot =
-irf+ FALSE)
-
-Impulse response coefficients
-$e
- prod rw U
- [1,] 0.00000000 0.00000000 0.0000000
- [2,] -0.06268865 -0.09754690 -0.2107321
- [3,] 0.06083197 -0.11111458 -0.3237895
- [4,] 0.12990844 -0.06461442 -0.3815257
- [5,] 0.14414443 0.01290689 -0.3982625
- [6,] 0.12252595 0.11227775 -0.3826520
- [7,] 0.08032392 0.22380618 -0.3444779
- [8,] 0.02978788 0.33828494 -0.2925832
- [9,] -0.02039436 0.44809056 -0.2342600
-[10,] -0.06469598 0.54753637 -0.1750896
-[11,] -0.10009271 0.63288686 -0.1190199
-
-> example(normality.test)
-
-nrmlt.> data(Canada)
-
-nrmlt.> var.2c <- VAR(Canada, p = 2, type = "const")
-
-nrmlt.> normality.test(var.2c)
-$JB
-
- JB-Test (multivariate)
-
-data: Residuals of VAR object var.2c
-Chi-squared = 5.094, df = 8, p-value = 0.7475
-
-
-$Skewness
-
- Skewness only (multivariate)
-
-data: Residuals of VAR object var.2c
-Chi-squared = 1.7761, df = 4, p-value = 0.7769
-
-
-$Kurtosis
-
- Kurtosis only (multivariate)
-
-data: Residuals of VAR object var.2c
-Chi-squared = 3.3179, df = 4, p-value = 0.5061
-
-
-> example(Phi)
-
-Phi> data(Canada)
-
-Phi> var.2c <- VAR(Canada, p = 2, type = "const")
-
-Phi> Phi(var.2c, nstep=4)
-, , 1
-
- [,1] [,2] [,3] [,4]
-[1,] 1 0 0 0
-[2,] 0 1 0 0
-[3,] 0 0 1 0
-[4,] 0 0 0 1
-
-, , 2
-
- [,1] [,2] [,3] [,4]
-[1,] 1.6378206 0.16727167 -0.06311863 0.26558478
-[2,] -0.1727658 1.15042820 0.05130390 -0.47850131
-[3,] -0.2688329 -0.08106500 0.89547833 0.01213003
-[4,] -0.5807638 -0.07811707 0.01866214 0.61893150
-
-, , 3
-
- [,1] [,2] [,3] [,4]
-[1,] 2.0191501 0.3491150 -0.14251580 0.6512430
-[2,] 0.1676489 1.1553945 -0.01191318 0.1240154
-[3,] -0.3062245 -0.2169480 0.86759379 -0.1419466
-[4,] -0.8923428 -0.1847587 0.10271259 0.1952708
-
-, , 4
-
- [,1] [,2] [,3] [,4]
-[1,] 2.2426353 0.5189024 -0.2308082 1.1469646
-[2,] 0.3580192 1.1425299 -0.1143523 0.7415988
-[3,] -0.1780731 -0.3227527 0.8387395 -0.2880998
-[4,] -1.0514599 -0.2807327 0.1763723 -0.2293339
-
-, , 5
-
- [,1] [,2] [,3] [,4]
-[1,] 2.32449796 0.6704429 -0.3142239 1.6488512
-[2,] 0.39725263 1.1159032 -0.1846605 1.1947208
-[3,] 0.03557054 -0.3899379 0.8047533 -0.4227269
-[4,] -1.09758548 -0.3630190 0.2377230 -0.6178418
-
-> example(Psi)
-
-Psi> data(Canada)
-
-Psi> var.2c <- VAR(Canada, p = 2, type = "const")
-
-Psi> Psi(var.2c, nstep=4)
-, , 1
-
- [,1] [,2] [,3] [,4]
-[1,] 0.36281502 0.00000000 0.00000000 0.000000
-[2,] -0.02058554 0.65214032 0.00000000 0.000000
-[3,] -0.11603352 0.09541606 0.76569598 0.000000
-[4,] -0.19042005 0.01533867 0.01392474 0.203767
-
-, , 2
-
- [,1] [,2] [,3] [,4]
-[1,] 0.547533747 0.10713578 -0.04463148 0.054117425
-[2,] -0.001200947 0.74779626 0.03262018 -0.097502799
-[3,] -0.202083140 0.03276331 0.68583307 0.002471701
-[4,] -0.329124153 -0.03966904 0.02290799 0.126117843
-
-, , 3
-
- [,1] [,2] [,3] [,4]
-[1,] 0.61791814 0.22406285 -0.100055386 0.13270186
-[2,] 0.01480844 0.75424487 -0.007394995 0.02527026
-[3,] -0.18027734 -0.06087546 0.662336510 -0.02892404
-[4,] -0.36905359 -0.10769297 0.081365710 0.03978976
-
-, , 4
-
- [,1] [,2] [,3] [,4]
-[1,] 0.61135633 0.3339673 -0.16075773 0.23371359
-[2,] -0.02157143 0.7455539 -0.07723254 0.15111339
-[3,] -0.10042548 -0.1348699 0.63820776 -0.05870525
-[4,] -0.35250174 -0.1697660 0.13185418 -0.04673068
-
-, , 5
-
- [,1] [,2] [,3] [,4]
-[1,] 0.552047524 0.4325320 -0.2176401 0.33598154
-[2,] -0.084914238 0.7284313 -0.1247576 0.24344474
-[3,] 0.008049928 -0.1839919 0.6103100 -0.08613782
-[4,] -0.300681928 -0.2235336 0.1734203 -0.12589580
-
-> example(restrict)
-
-rstrct> data(Canada)
-
-rstrct> var.2c <- VAR(Canada, p = 2, type = "const")
-
-rstrct> ## Restrictions determined by thresh
-rstrct> restrict(var.2c, method = "ser")
-
-VAR Estimation Results:
-=======================
-
-Estimated coefficients for equation e:
-======================================
-Call:
-e = e.l1 + prod.l1 + rw.l1 + U.l1 + e.l2 + const
-
- e.l1 prod.l1 rw.l1 U.l1 e.l2
- 1.72458925 0.07872263 -0.05370603 0.39150612 -0.60070476
- const
--128.89450474
-
-
-Estimated coefficients for equation prod:
-=========================================
-Call:
-prod = prod.l1 + e.l2 + rw.l2 + U.l2 + const
-
- prod.l1 e.l2 rw.l2 U.l2 const
- 1.00809918 0.28943990 -0.09728839 0.75036470 -240.56045005
-
-
-Estimated coefficients for equation rw:
-=======================================
-Call:
-rw = prod.l1 + rw.l1 + e.l2 + U.l2
-
- prod.l1 rw.l1 e.l2 U.l2
--0.11816412 0.96382332 0.07092345 -0.19300125
-
-
-Estimated coefficients for equation U:
-======================================
-Call:
-U = e.l1 + U.l1 + e.l2 + rw.l2 + const
-
- e.l1 U.l1 e.l2 rw.l2 const
- -0.69545493 0.56002276 0.52253799 0.05670873 142.63164064
-
-
-
-rstrct> ## Restrictions set manually
-rstrct> restrict <- matrix(c(1, 1, 1, 1, 1, 1, 0, 0, 0,
-rstrct+ 1, 0, 1, 0, 0, 1, 0, 1, 1,
-rstrct+ 0, 0, 1, 1, 0, 1, 0, 0, 1,
-rstrct+ 1, 1, 1, 0, 1, 1, 0, 1, 0),
-rstrct+ nrow=4, ncol=9, byrow=TRUE)
-
-rstrct> restrict(var.2c, method = "man", resmat = restrict)
-
-VAR Estimation Results:
-=======================
-
-Estimated coefficients for equation e:
-======================================
-Call:
-e = e.l1 + prod.l1 + rw.l1 + U.l1 + e.l2 + prod.l2
-
- e.l1 prod.l1 rw.l1 U.l1 e.l2 prod.l2
- 1.60267870 0.17587118 0.00178938 0.05416930 -0.63243410 -0.10982496
-
-
-Estimated coefficients for equation prod:
-=========================================
-Call:
-prod = e.l1 + rw.l1 + prod.l2 + U.l2 + const
-
- e.l1 rw.l1 prod.l2 U.l2 const
- 0.2963549 -0.0891558 0.9673667 0.8139226 -234.5271114
-
-
-Estimated coefficients for equation rw:
-=======================================
-Call:
-rw = rw.l1 + U.l1 + prod.l2 + const
-
- rw.l1 U.l1 prod.l2 const
- 0.9886096 -0.3319561 -0.1263612 60.6317163
-
-
-Estimated coefficients for equation U:
-======================================
-Call:
-U = e.l1 + prod.l1 + rw.l1 + e.l2 + prod.l2 + U.l2
-
- e.l1 prod.l1 rw.l1 e.l2 prod.l2 U.l2
--1.074192674 -0.085839420 -0.007743894 1.086784714 0.068131754 0.909941549
-
-
-> example(roots)
-
-roots> data(Canada)
-
-roots> var.2c <- VAR(Canada, p = 2, type = "const")
-
-roots> roots(var.2c)
-[1] 0.9950338 0.9081062 0.9081062 0.7380565 0.7380565 0.1856381 0.1428889
-[8] 0.1428889
-> example(serial.test)
-
-srl.ts> data(Canada)
-
-srl.ts> var.2c <- VAR(Canada, p = 2, type = "const")
-
-srl.ts> serial.test(var.2c, lags.pt = 16, type = "PT.adjusted")
-
- Portmanteau Test (adjusted)
-
-data: Residuals of VAR object var.2c
-Chi-squared = 231.5907, df = 224, p-value = 0.3497
-
-> example(stability)
-
-stblty> data(Canada)
-
-stblty> var.2c <- VAR(Canada, p = 2, type = "const")
-
-stblty> var.2c.stabil <- stability(var.2c, type = "OLS-CUSUM")
-
-stblty> var.2c.stabil
-$e
-
-Empirical Fluctuation Process: OLS-based CUSUM test
-
-Call: efp(formula = formula, data = data, type = type, h = h, dynamic = dynamic,
- rescale = rescale)
-
-
-$prod
-
-Empirical Fluctuation Process: OLS-based CUSUM test
-
-Call: efp(formula = formula, data = data, type = type, h = h, dynamic = dynamic,
- rescale = rescale)
-
-
-$rw
-
-Empirical Fluctuation Process: OLS-based CUSUM test
-
-Call: efp(formula = formula, data = data, type = type, h = h, dynamic = dynamic,
- rescale = rescale)
-
-
-$U
-
-Empirical Fluctuation Process: OLS-based CUSUM test
-
-Call: efp(formula = formula, data = data, type = type, h = h, dynamic = dynamic,
- rescale = rescale)
-
-
-
-stblty> ## Not run:
-stblty> ##D plot(var.2c.stabil)
-stblty> ## End(Not run)
-stblty>
-stblty>
-stblty>
-> example(SVAR)
-
-SVAR> data(Canada)
-
-SVAR> var.2c <- VAR(Canada, p = 2, type = "const")
-
-SVAR> amat <- diag(4)
-
-SVAR> diag(amat) <- NA
-
-SVAR> amat[2, 1] <- NA
-
-SVAR> amat[4, 1] <- NA
-
-SVAR> ## Estimation method scoring
-SVAR> SVAR(x = var.2c, estmethod = "scoring", Amat = amat, Bmat = NULL,
-SVAR+ max.iter = 100, maxls = 1000, conv.crit = 1.0e-8)
-
-SVAR Estimation Results:
-========================
-
-
-Estimated A matrix:
- e prod rw U
-e 2.756 0.000 0.000 0.000
-prod 0.087 1.533 0.000 0.000
-rw 0.000 0.000 1.282 0.000
-U 2.562 0.000 0.000 4.882
-
-SVAR> ## Estimation method direct
-SVAR> SVAR(x = var.2c, estmethod = "direct", Amat = amat, Bmat = NULL,
-SVAR+ hessian = TRUE, method="BFGS")
-
-SVAR Estimation Results:
-========================
-
-
-Estimated A matrix:
- e prod rw U
-e 2.756 0.000 0.000 0.000
-prod 0.087 1.533 0.000 0.000
-rw 0.000 0.000 1.282 0.000
-U 2.562 0.000 0.000 4.882
-> example(SVEC)
-
-SVEC> data(Canada)
-
-SVEC> vecm <- ca.jo(Canada[, c("prod", "e", "U", "rw")], type = "trace", ecdet = "trend", K = 3, spec = "transitory")
-
-SVEC> SR <- matrix(NA, nrow = 4, ncol = 4)
-
-SVEC> SR[4, 2] <- 0
-
-SVEC> SR
- [,1] [,2] [,3] [,4]
-[1,] NA NA NA NA
-[2,] NA NA NA NA
-[3,] NA NA NA NA
-[4,] NA 0 NA NA
-
-SVEC> LR <- matrix(NA, nrow = 4, ncol = 4)
-
-SVEC> LR[1, 2:4] <- 0
-
-SVEC> LR[2:4, 4] <- 0
-
-SVEC> LR
- [,1] [,2] [,3] [,4]
-[1,] NA 0 0 0
-[2,] NA NA NA 0
-[3,] NA NA NA 0
-[4,] NA NA NA 0
-
-SVEC> SVEC(vecm, LR = LR, SR = SR, r = 1, lrtest = FALSE, boot = FALSE)
-
-SVEC Estimation Results:
-========================
-
-
-Estimated contemporaneous impact matrix:
- prod e U rw
-prod 0.58402 0.07434 -0.152578 0.06900
-e -0.12029 0.26144 -0.155096 0.08978
-U 0.02526 -0.26720 0.005488 0.04982
-rw 0.11170 0.00000 0.483771 0.48791
-
-Estimated long run impact matrix:
- prod e U rw
-prod 0.7910 0.0000 0.0000 0
-e 0.2024 0.5769 -0.4923 0
-U -0.1592 -0.3409 0.1408 0
-rw -0.1535 0.5961 -0.2495 0
-> example(VAR)
-
-VAR> data(Canada)
-
-VAR> VAR(Canada, p = 2, type = "none")
-
-VAR Estimation Results:
-=======================
-
-Estimated coefficients for equation e:
-======================================
-Call:
-e = e.l1 + prod.l1 + rw.l1 + U.l1 + e.l2 + prod.l2 + rw.l2 + U.l2
-
- e.l1 prod.l1 rw.l1 U.l1 e.l2 prod.l2
- 1.62046761 0.17973134 -0.04425592 0.11310425 -0.64815156 -0.11683270
- rw.l2 U.l2
- 0.04475537 -0.06581206
-
-
-Estimated coefficients for equation prod:
-=========================================
-Call:
-prod = e.l1 + prod.l1 + rw.l1 + U.l1 + e.l2 + prod.l2 + rw.l2 + U.l2
-
- e.l1 prod.l1 rw.l1 U.l1 e.l2 prod.l2
--0.19389053 1.16559603 0.07426648 -0.66412399 0.20141693 -0.19089450
- rw.l2 U.l2
--0.06904805 0.77427171
-
-
-Estimated coefficients for equation rw:
-=======================================
-Call:
-rw = e.l1 + prod.l1 + rw.l1 + U.l1 + e.l2 + prod.l2 + rw.l2 + U.l2
-
- e.l1 prod.l1 rw.l1 U.l1 e.l2 prod.l2
--0.273036691 -0.078046604 0.900047886 -0.024808893 0.331264372 -0.008858991
- rw.l2 U.l2
- 0.062587364 -0.175795886
-
-
-Estimated coefficients for equation U:
-======================================
-Call:
-U = e.l1 + prod.l1 + rw.l1 + U.l1 + e.l2 + prod.l2 + rw.l2 + U.l2
-
- e.l1 prod.l1 rw.l1 U.l1 e.l2 prod.l2
--0.561791776 -0.091739246 -0.001960487 0.785638638 0.574926136 0.068715871
- rw.l2 U.l2
--0.002926763 0.145852929
-
-
-
-VAR> VAR(Canada, p = 2, type = "const")
-
-VAR Estimation Results:
-=======================
-
-Estimated coefficients for equation e:
-======================================
-Call:
-e = e.l1 + prod.l1 + rw.l1 + U.l1 + e.l2 + prod.l2 + rw.l2 + U.l2 + const
-
- e.l1 prod.l1 rw.l1 U.l1 e.l2
- 1.637821e+00 1.672717e-01 -6.311863e-02 2.655848e-01 -4.971338e-01
- prod.l2 rw.l2 U.l2 const
--1.016501e-01 3.844492e-03 1.326893e-01 -1.369984e+02
-
-
-Estimated coefficients for equation prod:
-=========================================
-Call:
-prod = e.l1 + prod.l1 + rw.l1 + U.l1 + e.l2 + prod.l2 + rw.l2 + U.l2 + const
-
- e.l1 prod.l1 rw.l1 U.l1 e.l2 prod.l2
- -0.1727658 1.1504282 0.0513039 -0.4785013 0.3852589 -0.1724119
- rw.l2 U.l2 const
- -0.1188510 1.0159180 -166.7755177
-
-
-Estimated coefficients for equation rw:
-=======================================
-Call:
-rw = e.l1 + prod.l1 + rw.l1 + U.l1 + e.l2 + prod.l2 + rw.l2 + U.l2 + const
-
- e.l1 prod.l1 rw.l1 U.l1 e.l2
- -0.268832871 -0.081065001 0.895478330 0.012130033 0.367848941
- prod.l2 rw.l2 U.l2 const
- -0.005180947 0.052676565 -0.127708256 -33.188338773
-
-
-Estimated coefficients for equation U:
-======================================
-Call:
-U = e.l1 + prod.l1 + rw.l1 + U.l1 + e.l2 + prod.l2 + rw.l2 + U.l2 + const
-
- e.l1 prod.l1 rw.l1 U.l1 e.l2 prod.l2
- -0.58076382 -0.07811707 0.01866214 0.61893150 0.40981822 0.05211668
- rw.l2 U.l2 const
- 0.04180115 -0.07116885 149.78056487
-
-
-
-VAR> VAR(Canada, p = 2, type = "trend")
-
-VAR Estimation Results:
-=======================
-
-Estimated coefficients for equation e:
-======================================
-Call:
-e = e.l1 + prod.l1 + rw.l1 + U.l1 + e.l2 + prod.l2 + rw.l2 + U.l2 + trend
-
- e.l1 prod.l1 rw.l1 U.l1 e.l2 prod.l2
- 1.63082118 0.16456040 -0.05764637 0.13231952 -0.64150027 -0.12338620
- rw.l2 U.l2 trend
- 0.03934730 -0.04002238 0.01532917
-
-
-Estimated coefficients for equation prod:
-=========================================
-Call:
-prod = e.l1 + prod.l1 + rw.l1 + U.l1 + e.l2 + prod.l2 + rw.l2 + U.l2 + trend
-
- e.l1 prod.l1 rw.l1 U.l1 e.l2 prod.l2
--0.14823958 1.09870431 0.01522531 -0.57940001 0.23074378 -0.21979021
- rw.l2 U.l2 trend
--0.09289334 0.88798355 0.06758938
-
-
-Estimated coefficients for equation rw:
-=======================================
-Call:
-rw = e.l1 + prod.l1 + rw.l1 + U.l1 + e.l2 + prod.l2 + rw.l2 + U.l2 + trend
-
- e.l1 prod.l1 rw.l1 U.l1 e.l2 prod.l2
--0.23961487 -0.12701916 0.85682285 0.03721896 0.35273506 -0.03001403
- rw.l2 U.l2 trend
- 0.04512982 -0.09254553 0.04948332
-
-
-Estimated coefficients for equation U:
-======================================
-Call:
-U = e.l1 + prod.l1 + rw.l1 + U.l1 + e.l2 + prod.l2 + rw.l2 + U.l2 + trend
-
- e.l1 prod.l1 rw.l1 U.l1 e.l2 prod.l2
[TRUNCATED]
To get the complete diff run:
svnlook diff /svnroot/vars -r 119
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