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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(104.5,89.1,82.6,102.7,91.8,94.1,103.1,93.2,91,94.3,99.4,115.7,116.8,99.8,96,115.9,109.1,117.3,109.8,112.8,110.7,100,113.3,122.4,112.5,104.2,92.5,117.2,109.3,106.1,118.8,105.3,106,102,112.9,116.5,114.8,100.5,85.4,114.6,109.9,100.7,115.5,100.7,99,102.3,108.8,105.9,113.2,95.7,80.9,113.9,98.1,102.8,104.7,95.9,94.6,101.6,103.9,110.3) > 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] 103.5 88.1 81.6 101.7 90.8 93.1 102.1 92.2 90.0 93.3 98.4 114.7 [13] 115.8 98.8 95.0 114.9 108.1 116.3 108.8 111.8 109.7 99.0 112.3 121.4 [25] 111.5 103.2 91.5 116.2 108.3 105.1 117.8 104.3 105.0 101.0 111.9 115.5 [37] 113.8 99.5 84.4 113.6 108.9 99.7 114.5 99.7 98.0 101.3 107.8 104.9 [49] 112.2 94.7 79.9 112.9 97.1 101.8 103.7 94.9 93.6 100.6 102.9 109.3 > y [1] 136.7 147.3 151.2 168.4 167.6 160.1 173.1 178.0 189.6 189.0 180.6 173.8 [13] 179.5 195.8 192.8 196.0 215.3 220.4 216.9 228.7 226.4 203.2 195.6 197.8 [25] 206.5 189.7 200.6 209.5 222.5 222.8 230.2 243.0 233.7 249.2 264.7 286.6 [37] 282.3 294.4 311.3 332.8 346.7 382.2 406.1 412.6 361.7 320.9 238.4 190.0 [49] 158.7 162.4 156.6 165.2 175.7 197.3 225.2 215.2 234.9 225.9 241.3 252.1 > (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 0.7064 0.5527 > (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 0.2887 0.8334 > postscript(file="/var/www/html/rcomp/tmp/1r5c61260547548.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 0.188 0.197 0.199 0.206 0.207 0.209 0.235 0.197 0.160 0.155 0.150 -3 -2 -1 0 1 2 3 4 5 6 7 0.180 0.173 0.195 0.191 0.186 0.161 0.132 0.105 0.019 -0.045 -0.095 8 9 10 11 12 13 14 -0.125 -0.188 -0.245 -0.279 -0.279 -0.257 -0.249 > (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 0.188 0.197 0.199 0.206 0.207 0.209 0.235 0.197 0.160 0.155 0.150 -3 -2 -1 0 1 2 3 4 5 6 7 0.180 0.173 0.195 0.191 0.186 0.161 0.132 0.105 0.019 -0.045 -0.095 8 9 10 11 12 13 14 -0.125 -0.188 -0.245 -0.279 -0.279 -0.257 -0.249 > par(op) > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2ge8c1260547548.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/3hwvf1260547548.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/4u86a1260547548.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/5vi031260547548.tab") > system("convert tmp/1r5c61260547548.ps tmp/1r5c61260547548.png") > system("convert tmp/2ge8c1260547548.ps tmp/2ge8c1260547548.png") > system("convert tmp/3hwvf1260547548.ps tmp/3hwvf1260547548.png") > > > proc.time() user system elapsed 0.959 0.500 1.120