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Type 'q()' to quit R. > y <- c(22.782,19.169,13.807,29.743,25.591,29.096,26.482,22.405,27.044,17.97,18.73,19.684,19.785,18.479,10.698,31.956,29.506,34.506,27.165,26.736,23.691,18.157,17.328,18.205,20.995,17.382,9.367,31.124,26.551,30.651,25.859,25.1,25.778,20.418,18.688,20.424,24.776,19.814,12.738,31.566,30.111,30.019,31.934,25.826,26.835,20.205,17.789,20.52,22.518,15.572,11.509,25.447,24.09,27.786,26.195,20.516,22.759,19.028,16.971,20.036,22.485) > x <- c(17,14,15,16,16,15,13,12,13,13,12,10,14,14,15,16,16,15,15,13,15,15,15,13,16,16,14,16,15,14,15,15,14,13,12,13,12,9,10,8,11,8,8,8,4,6,8,10,5,6,5,9,8,6,9,11,11,8,11,11,13) > par8 = '6' > 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] 16 13 14 15 15 14 12 11 12 12 11 9 13 13 14 15 15 14 14 12 14 14 14 12 15 [26] 15 13 15 14 13 14 14 13 12 11 12 11 8 9 7 10 7 7 7 3 5 7 9 4 5 [51] 4 8 7 5 8 10 10 7 10 10 12 > y [1] 21.782 18.169 12.807 28.743 24.591 28.096 25.482 21.405 26.044 16.970 [11] 17.730 18.684 18.785 17.479 9.698 30.956 28.506 33.506 26.165 25.736 [21] 22.691 17.157 16.328 17.205 19.995 16.382 8.367 30.124 25.551 29.651 [31] 24.859 24.100 24.778 19.418 17.688 19.424 23.776 18.814 11.738 30.566 [41] 29.111 29.019 30.934 24.826 25.835 19.205 16.789 19.520 21.518 14.572 [51] 10.509 24.447 23.090 26.786 25.195 19.516 21.759 18.028 15.971 19.036 [61] 21.485 > (gyx <- grangertest(y ~ x, order=par8)) Granger causality test Model 1: ~ Lags(, 1:6) + Lags(, 1:6) Model 2: ~ Lags(, 1:6) Res.Df Df F Pr(>F) 1 42 2 48 -6 0.2954 0.9357 > (gxy <- grangertest(x ~ y, order=par8)) Granger causality test Model 1: ~ Lags(, 1:6) + Lags(, 1:6) Model 2: ~ Lags(, 1:6) Res.Df Df F Pr(>F) 1 42 2 48 -6 1.1225 0.3659 > postscript(file="/var/www/html/rcomp/tmp/1nfhg1260548968.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 0.181 0.199 0.184 0.110 -0.001 -0.003 0.025 0.034 0.040 0.094 0.088 -3 -2 -1 0 1 2 3 4 5 6 7 0.136 0.105 0.117 0.086 -0.011 -0.123 -0.073 -0.083 -0.117 -0.118 -0.034 8 9 10 11 12 13 14 -0.073 -0.075 -0.071 -0.031 -0.013 -0.058 -0.093 > (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 0.181 0.199 0.184 0.110 -0.001 -0.003 0.025 0.034 0.040 0.094 0.088 -3 -2 -1 0 1 2 3 4 5 6 7 0.136 0.105 0.117 0.086 -0.011 -0.123 -0.073 -0.083 -0.117 -0.118 -0.034 8 9 10 11 12 13 14 -0.073 -0.075 -0.071 -0.031 -0.013 -0.058 -0.093 > par(op) > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2usko1260548968.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/3fmle1260548968.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/4pxot1260548968.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/50tu91260548968.tab") > > system("convert tmp/1nfhg1260548968.ps tmp/1nfhg1260548968.png") > system("convert tmp/2usko1260548968.ps tmp/2usko1260548968.png") > system("convert tmp/3fmle1260548968.ps tmp/3fmle1260548968.png") > > > proc.time() user system elapsed 0.930 0.451 1.343