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Type 'q()' to quit R. > library(vars) Loading required package: MASS Loading required package: strucchange Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric Loading required package: sandwich Loading required package: urca Loading required package: 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 = 538.89, 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 = 26.068, 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 = 3.3923, df1 = 6, df2 = 292, p-value = 0.002994 $Instant H0: No instantaneous causality between: e and prod rw U data: VAR object var.2c Chi-squared = 26.068, 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 = 231.59, 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", SVEC+ 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 -33.188338774 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 -0.570210031 -0.079404090 0.008926991 0.770015126 0.569518122 0.074044380 rw.l2 U.l2 trend 0.001470425 0.124883916 -0.012463808 VAR> VAR(Canada, p = 2, type = "both") 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 + trend e.l1 prod.l1 rw.l1 U.l1 e.l2 1.635735e+00 1.716493e-01 -6.005622e-02 2.739686e-01 -4.842222e-01 prod.l2 rw.l2 U.l2 const trend -9.766366e-02 1.689096e-03 1.433151e-01 -1.509574e+02 -5.706013e-03 Estimated coefficients for equation prod: ========================================= Call: prod = e.l1 + prod.l1 + rw.l1 + U.l1 + e.l2 + prod.l2 + rw.l2 + U.l2 + const + trend e.l1 prod.l1 rw.l1 U.l1 e.l2 prod.l2 -0.14816907 1.09880604 0.01519072 -0.57736715 0.23300094 -0.21942106 rw.l2 U.l2 const trend -0.09343379 0.89061470 -2.16644760 0.06728750 Estimated coefficients for equation rw: ======================================= Call: rw = e.l1 + prod.l1 + rw.l1 + U.l1 + e.l2 + prod.l2 + rw.l2 + U.l2 + const + trend e.l1 prod.l1 rw.l1 U.l1 e.l2 prod.l2 -0.24395401 -0.13327924 0.85895095 -0.08786974 0.21384463 -0.05272931 rw.l2 U.l2 const trend 0.07838534 -0.25444874 133.30872014 0.06805925 Estimated coefficients for equation U: ====================================== Call: U = e.l1 + prod.l1 + rw.l1 + U.l1 + e.l2 + prod.l2 + rw.l2 + U.l2 + const + trend e.l1 prod.l1 rw.l1 U.l1 e.l2 prod.l2 -0.57610104 -0.08790304 0.01181620 0.60018959 0.38095481 0.04320519 rw.l2 U.l2 const trend 0.04661948 -0.09492249 180.98536416 0.01275563 > example(VARselect) VARslc> data(Canada) VARslc> VARselect(Canada, lag.max = 5, type="const") $selection AIC(n) HQ(n) SC(n) FPE(n) 3 2 2 3 $criteria 1 2 3 4 5 AIC(n) -5.817851996 -6.35093701 -6.397756084 -6.145942174 -5.926500201 HQ(n) -5.577529641 -5.91835677 -5.772917961 -5.328846166 -4.917146309 SC(n) -5.217991781 -5.27118862 -4.838119523 -4.106417440 -3.407087295 FPE(n) 0.002976003 0.00175206 0.001685528 0.002201523 0.002811116 > example(vec2var) vec2vr> library(urca) vec2vr> data(finland) vec2vr> sjf <- finland vec2vr> sjf.vecm <- ca.jo(sjf, ecdet = "none", type = "eigen", K = 2, vec2vr+ spec = "longrun", season = 4) vec2vr> vec2var(sjf.vecm, r = 2) Coefficient matrix of lagged endogenous variables: A1: lrm1.l1 lny.l1 lnmr.l1 difp.l1 lrm1 0.855185363 -0.28226832 -0.09298924 -0.1750511 lny 0.036993826 0.33057494 -0.06731145 -0.1946863 lnmr -0.156875074 -0.01067717 0.76861874 0.4247362 difp 0.001331951 0.02850137 0.02361709 0.2063468 A2: lrm1.l2 lny.l2 lnmr.l2 difp.l2 lrm1 0.15787622 0.27655060 -0.10255593 -0.52017728 lny -0.02016649 0.65497929 -0.08102873 -0.09357761 lnmr 0.25725652 -0.10358761 -0.24253117 0.26571672 difp -0.01313100 -0.01096218 -0.02802090 0.36002057 Coefficient matrix of deterministic regressor(s). constant sd1 sd2 sd3 lrm1 0.03454360 0.039660747 0.037177941 0.10095683 lny 0.05021877 0.043686282 0.082751819 0.09559270 lnmr 0.22729778 0.008791390 0.012456612 0.02011396 difp -0.03055891 0.001723883 -0.007525805 -0.00835411 > > > > proc.time() user system elapsed 0.81 0.15 0.96