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Type 'q()' to quit R. > y <- c(-3,-1,-3,-4,-6,0,-4,-2,-2,-6,-7,-6,-6,-3,-2,-5,-11,-11,-11,-10,-14,-8,-9,-5,-1,-2,-5,-4,-6,-2,-2,-2,-2,2,1,-8,-1,1,-1,2,2,1,-1,-2,-2,-1,-8,-4,-6,-3,-3,-7,-9,-11,-13,-11,-9,-17,-22,-25) > x <- c(6802.96,7132.68,7073.29,7264.5,7105.33,7218.71,7225.72,7354.25,7745.46,8070.26,8366.33,8667.51,8854.34,9218.1,9332.9,9358.31,9248.66,9401.2,9652.04,9957.38,10110.63,10169.26,10343.78,10750.21,11337.5,11786.96,12083.04,12007.74,11745.93,11051.51,11445.9,11924.88,12247.63,12690.91,12910.7,13202.12,13654.67,13862.82,13523.93,14211.17,14510.35,14289.23,14111.82,13086.59,13351.54,13747.69,12855.61,12926.93,12121.95,11731.65,11639.51,12163.78,12029.53,11234.18,9852.13,9709.04,9332.75,7108.6,6691.49,6143.05) > par8 = '3' > par7 = '0' > par6 = '0' > par5 = '1' > par4 = '12' > par3 = '0' > par2 = '0' > par1 = '1' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: Wessa P., (2008), Bivariate Granger Causality (v1.0.0) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_grangercausality.wasp#output/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric > par1 <- as.numeric(par1) > par2 <- as.numeric(par2) > par3 <- as.numeric(par3) > par4 <- as.numeric(par4) > par5 <- as.numeric(par5) > par6 <- as.numeric(par6) > par7 <- as.numeric(par7) > par8 <- as.numeric(par8) > ox <- x > oy <- y > if (par1 == 0) { + x <- log(x) + } else { + x <- (x ^ par1 - 1) / par1 + } > if (par5 == 0) { + y <- log(y) + } else { + y <- (y ^ par5 - 1) / par5 + } > if (par2 > 0) x <- diff(x,lag=1,difference=par2) > if (par6 > 0) y <- diff(y,lag=1,difference=par6) > if (par3 > 0) x <- diff(x,lag=par4,difference=par3) > if (par7 > 0) y <- diff(y,lag=par4,difference=par7) > x [1] 6801.96 7131.68 7072.29 7263.50 7104.33 7217.71 7224.72 7353.25 [9] 7744.46 8069.26 8365.33 8666.51 8853.34 9217.10 9331.90 9357.31 [17] 9247.66 9400.20 9651.04 9956.38 10109.63 10168.26 10342.78 10749.21 [25] 11336.50 11785.96 12082.04 12006.74 11744.93 11050.51 11444.90 11923.88 [33] 12246.63 12689.91 12909.70 13201.12 13653.67 13861.82 13522.93 14210.17 [41] 14509.35 14288.23 14110.82 13085.59 13350.54 13746.69 12854.61 12925.93 [49] 12120.95 11730.65 11638.51 12162.78 12028.53 11233.18 9851.13 9708.04 [57] 9331.75 7107.60 6690.49 6142.05 > y [1] -4 -2 -4 -5 -7 -1 -5 -3 -3 -7 -8 -7 -7 -4 -3 -6 -12 -12 -12 [20] -11 -15 -9 -10 -6 -2 -3 -6 -5 -7 -3 -3 -3 -3 1 0 -9 -2 0 [39] -2 1 1 0 -2 -3 -3 -2 -9 -5 -7 -4 -4 -8 -10 -12 -14 -12 -10 [58] -18 -23 -26 > (gyx <- grangertest(y ~ x, order=par8)) Granger causality test Model 1: ~ Lags(, 1:3) + Lags(, 1:3) Model 2: ~ Lags(, 1:3) Res.Df Df F Pr(>F) 1 50 2 53 -3 0.6699 0.5745 > (gxy <- grangertest(x ~ y, order=par8)) Granger causality test Model 1: ~ Lags(, 1:3) + Lags(, 1:3) Model 2: ~ Lags(, 1:3) Res.Df Df F Pr(>F) 1 50 2 53 -3 0.9262 0.435 > postscript(file="/var/www/html/rcomp/tmp/1ln8p1260473455.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > op <- par(mfrow=c(2,1)) > (r <- ccf(ox,oy,main='Cross Correlation Function (raw data)',ylab='CCF',xlab='Lag (k)')) Autocorrelations of series 'X', by lag -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -0.147 -0.130 -0.100 -0.051 -0.001 0.038 0.039 0.038 0.084 0.141 0.204 -3 -2 -1 0 1 2 3 4 5 6 7 0.248 0.321 0.389 0.478 0.414 0.357 0.315 0.290 0.255 0.217 0.174 8 9 10 11 12 13 14 0.145 0.111 0.103 0.117 0.078 0.039 -0.038 > (r <- ccf(x,y,main='Cross Correlation Function (transformed and differenced)',ylab='CCF',xlab='Lag (k)')) Autocorrelations of series 'X', by lag -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -0.147 -0.130 -0.100 -0.051 -0.001 0.038 0.039 0.038 0.084 0.141 0.204 -3 -2 -1 0 1 2 3 4 5 6 7 0.248 0.321 0.389 0.478 0.414 0.357 0.315 0.290 0.255 0.217 0.174 8 9 10 11 12 13 14 0.145 0.111 0.103 0.117 0.078 0.039 -0.038 > par(op) > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2pypw1260473455.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > op <- par(mfrow=c(2,1)) > acf(ox,lag.max=round(length(x)/2),main='ACF of x (raw)') > acf(x,lag.max=round(length(x)/2),main='ACF of x (transformed and differenced)') > par(op) > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/3ve091260473455.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > op <- par(mfrow=c(2,1)) > acf(oy,lag.max=round(length(y)/2),main='ACF of y (raw)') > acf(y,lag.max=round(length(y)/2),main='ACF of y (transformed and differenced)') > par(op) > dev.off() null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Granger Causality Test: Y = f(X)',5,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Model',header=TRUE) > a<-table.element(a,'Res.DF',header=TRUE) > a<-table.element(a,'Diff. DF',header=TRUE) > a<-table.element(a,'F',header=TRUE) > a<-table.element(a,'p-value',header=TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Complete model',header=TRUE) > a<-table.element(a,gyx$Res.Df[1]) > a<-table.element(a,'') > a<-table.element(a,'') > a<-table.element(a,'') > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Reduced model',header=TRUE) > a<-table.element(a,gyx$Res.Df[2]) > a<-table.element(a,gyx$Df[2]) > a<-table.element(a,gyx$F[2]) > a<-table.element(a,gyx$Pr[2]) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/4qxku1260473455.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Granger Causality Test: X = f(Y)',5,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Model',header=TRUE) > a<-table.element(a,'Res.DF',header=TRUE) > a<-table.element(a,'Diff. DF',header=TRUE) > a<-table.element(a,'F',header=TRUE) > a<-table.element(a,'p-value',header=TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Complete model',header=TRUE) > a<-table.element(a,gxy$Res.Df[1]) > a<-table.element(a,'') > a<-table.element(a,'') > a<-table.element(a,'') > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Reduced model',header=TRUE) > a<-table.element(a,gxy$Res.Df[2]) > a<-table.element(a,gxy$Df[2]) > a<-table.element(a,gxy$F[2]) > a<-table.element(a,gxy$Pr[2]) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/5qusi1260473455.tab") > > system("convert tmp/1ln8p1260473455.ps tmp/1ln8p1260473455.png") > system("convert tmp/2pypw1260473455.ps tmp/2pypw1260473455.png") > system("convert tmp/3ve091260473455.ps tmp/3ve091260473455.png") > > > proc.time() user system elapsed 0.916 0.463 1.104