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Type 'q()' to quit R. > y <- c(137.7,148.3,152.2,169.4,168.6,161.1,174.1,179,190.6,190,181.6,174.8,180.5,196.8,193.8,197,216.3,221.4,217.9,229.7,227.4,204.2,196.6,198.8,207.5,190.7,201.6,210.5,223.5,223.8,231.2,244,234.7,250.2,265.7,287.6,283.3,295.4,312.3,333.8,347.7,383.2,407.1,413.6,362.7,321.9,239.4,191,159.7,163.4,157.6,166.2,176.7,198.3,226.2,216.2,235.9,226.9,242.3,253.1) > x <- c(1.71,0.81,0.71,1.01,0.01,0.71,1.51,2.41,2.31,3.61,3.81,4.41,4.91,6.51,7.21,7.11,7.61,7.51,6.81,5.81,6.11,5.31,5.21,4.81,4.61,3.91,3.11,2.91,3.01,3.01,3.01,3.51,3.51,3.51,3.41,3.81,3.71,3.41,3.61,4.01,4.11,4.21,4.51,4.31,3.91,4.51,4.51,4.51,4.01,3.91,4.71,4.61,4.41,4.41,4.01,4.11,4.51,4.01,3.71,3.61) > par8 = '1' > 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] -0.9 -0.1 0.3 -1.0 0.7 0.8 0.9 -0.1 1.3 0.2 0.6 0.5 1.6 0.7 -0.1 [16] 0.5 -0.1 -0.7 -1.0 0.3 -0.8 -0.1 -0.4 -0.2 -0.7 -0.8 -0.2 0.1 0.0 0.0 [31] 0.5 0.0 0.0 -0.1 0.4 -0.1 -0.3 0.2 0.4 0.1 0.1 0.3 -0.2 -0.4 0.6 [46] 0.0 0.0 -0.5 -0.1 0.8 -0.1 -0.2 0.0 -0.4 0.1 0.4 -0.5 -0.3 -0.1 > y [1] 10.6 3.9 17.2 -0.8 -7.5 13.0 4.9 11.6 -0.6 -8.4 -6.8 5.7 [13] 16.3 -3.0 3.2 19.3 5.1 -3.5 11.8 -2.3 -23.2 -7.6 2.2 8.7 [25] -16.8 10.9 8.9 13.0 0.3 7.4 12.8 -9.3 15.5 15.5 21.9 -4.3 [37] 12.1 16.9 21.5 13.9 35.5 23.9 6.5 -50.9 -40.8 -82.5 -48.4 -31.3 [49] 3.7 -5.8 8.6 10.5 21.6 27.9 -10.0 19.7 -9.0 15.4 10.8 > (gyx <- grangertest(y ~ x, order=par8)) Granger causality test Model 1: ~ Lags(, 1:1) + Lags(, 1:1) Model 2: ~ Lags(, 1:1) Res.Df Df F Pr(>F) 1 55 2 56 -1 1.1966 0.2788 > (gxy <- grangertest(x ~ y, order=par8)) Granger causality test Model 1: ~ Lags(, 1:1) + Lags(, 1:1) Model 2: ~ Lags(, 1:1) Res.Df Df F Pr(>F) 1 55 2 56 -1 0.0317 0.8593 > postscript(file="/var/www/html/rcomp/tmp/10mhz1260550890.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 0.010 -0.026 -0.051 -0.055 -0.060 -0.066 -0.061 -0.043 -0.018 0.006 0.051 -3 -2 -1 0 1 2 3 4 5 6 7 0.091 0.120 0.144 0.177 0.167 0.138 0.106 0.079 0.029 -0.013 -0.043 8 9 10 11 12 13 14 -0.070 -0.110 -0.131 -0.150 -0.170 -0.191 -0.186 > (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 -0.081 -0.062 -0.220 -0.026 0.008 -0.112 -0.118 -0.044 0.019 -0.225 0.046 -3 -2 -1 0 1 2 3 4 5 6 7 0.155 0.030 -0.054 0.113 0.047 -0.002 -0.011 0.055 0.033 -0.035 0.083 8 9 10 11 12 13 14 0.115 0.003 0.010 0.094 0.110 0.016 0.022 > par(op) > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2h8ry1260550890.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/3t74n1260550890.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/464371260550891.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/54tlx1260550891.tab") > system("convert tmp/10mhz1260550890.ps tmp/10mhz1260550890.png") > system("convert tmp/2h8ry1260550890.ps tmp/2h8ry1260550890.png") > system("convert tmp/3t74n1260550890.ps tmp/3t74n1260550890.png") > > > proc.time() user system elapsed 0.938 0.483 1.086