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Type 'q()' to quit R. > y <- c(114.1,110.3,103.9,101.6,94.6,95.9,104.7,102.8,98.1,113.9,80.9,95.7,113.2,105.9,108.8,102.3,99,100.7,115.5,100.7,109.9,114.6,85.4,100.5,114.8,116.5,112.9,102,106,105.3,118.8,106.1,109.3,117.2,92.5,104.2,112.5,122.4,113.3,100,110.7,112.8,109.8,117.3,109.1,115.9,96,99.8,116.8,115.7,99.4,94.3,91,93.2,103.1,94.1,91.8,102.7,82.6,89.1) > x <- c(20366,22782,19169,13807,29743,25591,29096,26482,22405,27044,17970,18730,19684,19785,18479,10698,31956,29506,34506,27165,26736,23691,18157,17328,18205,20995,17382,9367,31124,26551,30651,25859,25100,25778,20418,18688,20424,24776,19814,12738,31566,30111,30019,31934,25826,26835,20205,17789,20520,22518,15572,11509,25447,24090,27786,26195,20516,22759,19028,16971) > par8 = '11' > par7 = '0' > par6 = '1' > par5 = '1' > par4 = '12' > par3 = '0' > par2 = '1' > 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] 2416 -3613 -5362 15936 -4152 3505 -2614 -4077 4639 -9074 760 954 [13] 101 -1306 -7781 21258 -2450 5000 -7341 -429 -3045 -5534 -829 877 [25] 2790 -3613 -8015 21757 -4573 4100 -4792 -759 678 -5360 -1730 1736 [37] 4352 -4962 -7076 18828 -1455 -92 1915 -6108 1009 -6630 -2416 2731 [49] 1998 -6946 -4063 13938 -1357 3696 -1591 -5679 2243 -3731 -2057 > y [1] -3.8 -6.4 -2.3 -7.0 1.3 8.8 -1.9 -4.7 15.8 -33.0 14.8 17.5 [13] -7.3 2.9 -6.5 -3.3 1.7 14.8 -14.8 9.2 4.7 -29.2 15.1 14.3 [25] 1.7 -3.6 -10.9 4.0 -0.7 13.5 -12.7 3.2 7.9 -24.7 11.7 8.3 [37] 9.9 -9.1 -13.3 10.7 2.1 -3.0 7.5 -8.2 6.8 -19.9 3.8 17.0 [49] -1.1 -16.3 -5.1 -3.3 2.2 9.9 -9.0 -2.3 10.9 -20.1 6.5 > (gyx <- grangertest(y ~ x, order=par8)) Granger causality test Model 1: ~ Lags(, 1:11) + Lags(, 1:11) Model 2: ~ Lags(, 1:11) Res.Df Df F Pr(>F) 1 25 2 36 -11 2.8479 0.01461 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > (gxy <- grangertest(x ~ y, order=par8)) Granger causality test Model 1: ~ Lags(, 1:11) + Lags(, 1:11) Model 2: ~ Lags(, 1:11) Res.Df Df F Pr(>F) 1 25 2 36 -11 3.095 0.009316 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > postscript(file="/var/www/html/rcomp/tmp/1gpj61260546350.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 -0.059 -0.061 0.132 -0.107 -0.209 -0.031 0.352 0.297 -0.179 0.110 -0.092 -3 -2 -1 0 1 2 3 4 5 6 7 -0.049 0.100 0.058 0.313 -0.051 -0.186 -0.020 0.312 0.299 -0.116 0.069 8 9 10 11 12 13 14 -0.084 -0.112 -0.021 0.004 0.125 -0.088 -0.195 > (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 0.115 -0.136 0.311 -0.098 -0.195 -0.126 0.264 0.308 -0.558 0.360 -0.159 -3 -2 -1 0 1 2 3 4 5 6 7 -0.085 0.147 -0.209 0.431 -0.178 -0.184 -0.122 0.217 0.309 -0.453 0.244 8 9 10 11 12 13 14 -0.077 -0.090 0.087 -0.069 0.217 -0.082 -0.121 > par(op) > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2jdb91260546350.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/3ujjh1260546350.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/4rm371260546350.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/5iufe1260546350.tab") > > system("convert tmp/1gpj61260546350.ps tmp/1gpj61260546350.png") > system("convert tmp/2jdb91260546350.ps tmp/2jdb91260546350.png") > system("convert tmp/3ujjh1260546350.ps tmp/3ujjh1260546350.png") > > > proc.time() user system elapsed 0.999 0.510 1.535