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Type 'q()' to quit R. > y <- c(95.1,97,112.7,102.9,97.4,111.4,87.4,96.8,114.1,110.3,103.9,101.6,94.6,95.9,104.7,102.8,98.1,113.9,80.9,95.7,113.2,105.9,108.8,102.3,99,100.7,115.5,100.7,109.9,114.6,85.4,100.5,114.8,116.5,112.9,102,106,105.3,118.8,106.1,109.3,117.2,92.5,104.2,112.5,122.4,113.3,100,110.7,112.8,109.8,117.3,109.1,115.9,96,99.8,116.8,115.7,99.4,94.3) > x <- c(93.8,93.8,107.6,101,95.4,96.5,89.2,87.1,110.5,110.8,104.2,88.9,89.8,90,93.9,91.3,87.8,99.7,73.5,79.2,96.9,95.2,95.6,89.7,92.8,88,101.1,92.7,95.8,103.8,81.8,87.1,105.9,108.1,102.6,93.7,103.5,100.6,113.3,102.4,102.1,106.9,87.3,93.1,109.1,120.3,104.9,92.6,109.8,111.4,117.9,121.6,117.8,124.2,106.8,102.7,116.8,113.6,96.1,85) > par8 = '3' > 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] 92.8 92.8 106.6 100.0 94.4 95.5 88.2 86.1 109.5 109.8 103.2 87.9 [13] 88.8 89.0 92.9 90.3 86.8 98.7 72.5 78.2 95.9 94.2 94.6 88.7 [25] 91.8 87.0 100.1 91.7 94.8 102.8 80.8 86.1 104.9 107.1 101.6 92.7 [37] 102.5 99.6 112.3 101.4 101.1 105.9 86.3 92.1 108.1 119.3 103.9 91.6 [49] 108.8 110.4 116.9 120.6 116.8 123.2 105.8 101.7 115.8 112.6 95.1 84.0 > y [1] 94.1 96.0 111.7 101.9 96.4 110.4 86.4 95.8 113.1 109.3 102.9 100.6 [13] 93.6 94.9 103.7 101.8 97.1 112.9 79.9 94.7 112.2 104.9 107.8 101.3 [25] 98.0 99.7 114.5 99.7 108.9 113.6 84.4 99.5 113.8 115.5 111.9 101.0 [37] 105.0 104.3 117.8 105.1 108.3 116.2 91.5 103.2 111.5 121.4 112.3 99.0 [49] 109.7 111.8 108.8 116.3 108.1 114.9 95.0 98.8 115.8 114.7 98.4 93.3 > (gyx <- grangertest(y ~ x, order=par8)) Granger causality test Model 1: ~ Lags(, 1:3) + Lags(, 1:3) Model 2: ~ Lags(, 1:3) Res.Df Df F Pr(>F) 1 50 2 53 -3 3.0883 0.03538 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > (gxy <- grangertest(x ~ y, order=par8)) Granger causality test Model 1: ~ Lags(, 1:3) + Lags(, 1:3) Model 2: ~ Lags(, 1:3) Res.Df Df F Pr(>F) 1 50 2 53 -3 3.9707 0.01293 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > postscript(file="/var/www/html/rcomp/tmp/12eea1260525822.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 -0.221 -0.045 0.423 0.089 -0.274 -0.143 -0.031 0.132 0.155 0.191 -0.009 -3 -2 -1 0 1 2 3 4 5 6 7 0.014 -0.071 0.154 0.814 0.300 -0.094 0.102 0.236 0.349 0.315 0.341 8 9 10 11 12 13 14 0.072 0.040 0.002 0.167 0.610 0.175 -0.109 > (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 -0.221 -0.045 0.423 0.089 -0.274 -0.143 -0.031 0.132 0.155 0.191 -0.009 -3 -2 -1 0 1 2 3 4 5 6 7 0.014 -0.071 0.154 0.814 0.300 -0.094 0.102 0.236 0.349 0.315 0.341 8 9 10 11 12 13 14 0.072 0.040 0.002 0.167 0.610 0.175 -0.109 > par(op) > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/202oc1260525822.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/3teh71260525822.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/4fpol1260525822.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/58gdu1260525822.tab") > > system("convert tmp/12eea1260525822.ps tmp/12eea1260525822.png") > system("convert tmp/202oc1260525822.ps tmp/202oc1260525822.png") > system("convert tmp/3teh71260525822.ps tmp/3teh71260525822.png") > > > proc.time() user system elapsed 0.931 0.469 1.774