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Type 'q()' to quit R. > y <- c(2.9,3.6,2.6,2.5,1.4,1.8,1.3,1.4,1.4,1.7,1.3,2.1,0.8,1,0.9,0.9,0.9,0.9,0.7,0.5) > x <- c(87,85,83,76,65,60,58,57,55,49,46,43,42,40,39,37,36,36,35,11) > par8 = '1' > par7 = '0' > par6 = '0' > par5 = '1' > par4 = '1' > par3 = '0' > par2 = '0' > par1 = '1' > par8 <- '1' > par7 <- '0' > par6 <- '0' > par5 <- '1' > par4 <- '1' > 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/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: > library(lmtest) Loading required package: zoo > 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] 86 84 82 75 64 59 57 56 54 48 45 42 41 39 38 36 35 35 34 10 > y [1] 1.9 2.6 1.6 1.5 0.4 0.8 0.3 0.4 0.4 0.7 0.3 1.1 -0.2 0.0 -0.1 [16] -0.1 -0.1 -0.1 -0.3 -0.5 > (gyx <- grangertest(y ~ x, order=par8)) Granger causality test Model 1: y ~ Lags(y, 1:1) + Lags(x, 1:1) Model 2: y ~ Lags(y, 1:1) Res.Df Df F Pr(>F) 1 16 2 17 -1 9.7925 0.00647 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > (gxy <- grangertest(x ~ y, order=par8)) Granger causality test Model 1: x ~ Lags(x, 1:1) + Lags(y, 1:1) Model 2: x ~ Lags(x, 1:1) Res.Df Df F Pr(>F) 1 16 2 17 -1 0.423 0.5247 > postscript(file="/var/wessaorg/rcomp/tmp/1q0ah1341491865.ps",horizontal=F,onefile=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 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 -0.142 -0.044 0.004 0.025 0.040 0.111 0.143 0.301 0.439 0.680 0.885 1 2 3 4 5 6 7 8 9 10 0.750 0.590 0.441 0.314 0.239 0.158 0.097 -0.111 -0.076 -0.163 > (r <- ccf(x,y,main='Cross Correlation Function (transformed and differenced)',ylab='CCF',xlab='Lag (k)')) Autocorrelations of series 'X', by lag -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 -0.142 -0.044 0.004 0.025 0.040 0.111 0.143 0.301 0.439 0.680 0.885 1 2 3 4 5 6 7 8 9 10 0.750 0.590 0.441 0.314 0.239 0.158 0.097 -0.111 -0.076 -0.163 > par(op) > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2jf9n1341491865.ps",horizontal=F,onefile=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/wessaorg/rcomp/tmp/37kas1341491865.ps",horizontal=F,onefile=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/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/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/wessaorg/rcomp/tmp/4rozg1341491865.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/wessaorg/rcomp/tmp/576171341491865.tab") > > try(system("convert tmp/1q0ah1341491865.ps tmp/1q0ah1341491865.png",intern=TRUE)) character(0) > try(system("convert tmp/2jf9n1341491865.ps tmp/2jf9n1341491865.png",intern=TRUE)) character(0) > try(system("convert tmp/37kas1341491865.ps tmp/37kas1341491865.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 1.595 0.314 1.933