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Type 'q()' to quit R. > y <- c(226.9,235.9,216.2,226.2,198.3,176.7,166.2,157.6,163.4,159.7,191.0,239.4,321.9,362.7,413.6,407.1,383.2,347.7,333.8,312.3,295.4,283.3,287.6,265.7,250.2,234.7,244.0,231.2,223.8,223.5,210.5,201.6,190.7,207.5,198.8,196.6,204.2,227.4,229.7,217.9,221.4,216.3,197.0,193.8,196.8,180.5,174.8,181.6,190.0,190.6,179.0,174.1,161.1,168.6,169.4,152.2,148.3,137.7,145.0,153.4) > x <- c(2.0,0.6,0.9,1.0,1.2,1.5,1.8,2.3,2.7,3.1,3.7,4.5,5.8,7.0,7.9,8.5,8.7,8.7,8.5,8.3,8.3,8.7,8.5,7.6,6.5,5.6,4.5,4.2,4.1,4.0,4.1,4.3,4.0,3.5,3.2,3.2,3.2,3.0,3.0,2.4,2.3,1.7,1.5,1.1,0.8,1.0,1.5,1.9,1.8,1.9,1.7,1.8,1.6,2.2,2.2,2.3,2.3,2.2,2.5,2.1) > 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] 1.0 -0.4 -0.1 0.0 0.2 0.5 0.8 1.3 1.7 2.1 2.7 3.5 4.8 6.0 6.9 [16] 7.5 7.7 7.7 7.5 7.3 7.3 7.7 7.5 6.6 5.5 4.6 3.5 3.2 3.1 3.0 [31] 3.1 3.3 3.0 2.5 2.2 2.2 2.2 2.0 2.0 1.4 1.3 0.7 0.5 0.1 -0.2 [46] 0.0 0.5 0.9 0.8 0.9 0.7 0.8 0.6 1.2 1.2 1.3 1.3 1.2 1.5 1.1 > y [1] 225.9 234.9 215.2 225.2 197.3 175.7 165.2 156.6 162.4 158.7 190.0 238.4 [13] 320.9 361.7 412.6 406.1 382.2 346.7 332.8 311.3 294.4 282.3 286.6 264.7 [25] 249.2 233.7 243.0 230.2 222.8 222.5 209.5 200.6 189.7 206.5 197.8 195.6 [37] 203.2 226.4 228.7 216.9 220.4 215.3 196.0 192.8 195.8 179.5 173.8 180.6 [49] 189.0 189.6 178.0 173.1 160.1 167.6 168.4 151.2 147.3 136.7 144.0 152.4 > (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 5.0958 0.003726 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > (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 0.602 0.6168 > postscript(file="/var/www/html/rcomp/tmp/1g1fh1260571777.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 -0.199 -0.199 -0.179 -0.131 -0.053 0.047 0.156 0.269 0.378 0.481 0.576 -3 -2 -1 0 1 2 3 4 5 6 7 0.661 0.739 0.796 0.829 0.823 0.789 0.728 0.653 0.572 0.490 0.407 8 9 10 11 12 13 14 0.320 0.235 0.146 0.064 -0.001 -0.046 -0.074 > (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 -0.199 -0.199 -0.179 -0.131 -0.053 0.047 0.156 0.269 0.378 0.481 0.576 -3 -2 -1 0 1 2 3 4 5 6 7 0.661 0.739 0.796 0.829 0.823 0.789 0.728 0.653 0.572 0.490 0.407 8 9 10 11 12 13 14 0.320 0.235 0.146 0.064 -0.001 -0.046 -0.074 > par(op) > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2xjcp1260571777.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/3c19j1260571777.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/4rbb61260571777.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/5tmx21260571777.tab") > system("convert tmp/1g1fh1260571777.ps tmp/1g1fh1260571777.png") > system("convert tmp/2xjcp1260571777.ps tmp/2xjcp1260571777.png") > system("convert tmp/3c19j1260571777.ps tmp/3c19j1260571777.png") > > > proc.time() user system elapsed 0.920 0.504 1.455