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Type 'q()' to quit R. > y <- c(110.8,119.3,128.1,127.6,137.9,151.4,143.6,143.4,141.9,135.2,133.1,129.6,134.1,136.8,143.5,162.5,163.1,157.2,158.8,155.4,148.5,154.2,153.3,149.4,147.9,156.0,163.0,159.1,159.5,157.3,156.4,156.6,162.4,166.8,162.6,168.1,171.3,171.0,178.7,187.5,211.4,211.6,199.4,198.7,209.3,215.5,212.8,203.2,175.7,171.0,172.5,183.5,185.4,180.9,187.3,202.0,203.3,205.8,221.5,226.6) > x <- c(149,139,135,130,127,122,117,112,113,149,157,157,147,137,132,125,123,117,114,111,112,144,150,149,134,123,116,117,111,105,102,95,93,124,130,124,115,106,105,105,101,95,93,84,87,116,120,117,109,105,107,109,109,108,107,99,103,131,137,135) > par8 = '11' > 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] 148 138 134 129 126 121 116 111 112 148 156 156 146 136 131 124 122 116 113 [20] 110 111 143 149 148 133 122 115 116 110 104 101 94 92 123 129 123 114 105 [39] 104 104 100 94 92 83 86 115 119 116 108 104 106 108 108 107 106 98 102 [58] 130 136 134 > y [1] 109.8 118.3 127.1 126.6 136.9 150.4 142.6 142.4 140.9 134.2 132.1 128.6 [13] 133.1 135.8 142.5 161.5 162.1 156.2 157.8 154.4 147.5 153.2 152.3 148.4 [25] 146.9 155.0 162.0 158.1 158.5 156.3 155.4 155.6 161.4 165.8 161.6 167.1 [37] 170.3 170.0 177.7 186.5 210.4 210.6 198.4 197.7 208.3 214.5 211.8 202.2 [49] 174.7 170.0 171.5 182.5 184.4 179.9 186.3 201.0 202.3 204.8 220.5 225.6 > (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 26 2 37 -11 0.7866 0.651 > (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 26 2 37 -11 0.5145 0.876 > postscript(file="/var/www/html/rcomp/tmp/17pzl1260383256.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 -0.420 -0.393 -0.373 -0.390 -0.420 -0.445 -0.430 -0.382 -0.361 -0.384 -0.488 -3 -2 -1 0 1 2 3 4 5 6 7 -0.592 -0.593 -0.560 -0.528 -0.469 -0.430 -0.388 -0.314 -0.233 -0.192 -0.212 8 9 10 11 12 13 14 -0.294 -0.382 -0.361 -0.281 -0.173 -0.100 -0.054 > (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 -0.420 -0.393 -0.373 -0.390 -0.420 -0.445 -0.430 -0.382 -0.361 -0.384 -0.488 -3 -2 -1 0 1 2 3 4 5 6 7 -0.592 -0.593 -0.560 -0.528 -0.469 -0.430 -0.388 -0.314 -0.233 -0.192 -0.212 8 9 10 11 12 13 14 -0.294 -0.382 -0.361 -0.281 -0.173 -0.100 -0.054 > par(op) > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/29gd21260383256.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/3sy1m1260383256.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/40pav1260383256.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/56cax1260383256.tab") > system("convert tmp/17pzl1260383256.ps tmp/17pzl1260383256.png") > system("convert tmp/29gd21260383256.ps tmp/29gd21260383256.png") > system("convert tmp/3sy1m1260383256.ps tmp/3sy1m1260383256.png") > > > proc.time() user system elapsed 0.980 0.477 1.126