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Type 'q()' to quit R. > y <- c(117.09,116.77,119.39,122.49,124.08,118.29,112.94,113.79,114.43,118.70,120.36,118.27,118.34,117.82,117.65,118.18,121.02,124.78,131.16,130.14,131.75,134.73,135.35,140.32,136.35,131.60,128.90,133.89,138.25,146.23,144.76,149.30,156.80,159.08,165.12,163.14,153.43,151.01,154.72,154.58,155.63,161.67,163.51,162.91,164.80,164.98,154.54,148.60,149.19,150.61) > x <- c(87.00,96.30,107.1,115.2,106.1,89.50,91.30,97.60,100.7,104.6,94.70,101.8,102.5,105.3,110.3,109.8,117.3,118.8,131.3,125.9,133.1,147.0,145.8,164.4,149.8,137.7,151.7,156.8,180.0,180.4,170.4,191.6,199.5,218.2,217.5,205.0,194.0,199.3,219.3,211.1,215.2,240.2,242.2,240.7,255.4,253.0,218.2,203.7,205.6,215.6) > 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] 86.0 95.3 106.1 114.2 105.1 88.5 90.3 96.6 99.7 103.6 93.7 100.8 [13] 101.5 104.3 109.3 108.8 116.3 117.8 130.3 124.9 132.1 146.0 144.8 163.4 [25] 148.8 136.7 150.7 155.8 179.0 179.4 169.4 190.6 198.5 217.2 216.5 204.0 [37] 193.0 198.3 218.3 210.1 214.2 239.2 241.2 239.7 254.4 252.0 217.2 202.7 [49] 204.6 214.6 > y [1] 116.09 115.77 118.39 121.49 123.08 117.29 111.94 112.79 113.43 117.70 [11] 119.36 117.27 117.34 116.82 116.65 117.18 120.02 123.78 130.16 129.14 [21] 130.75 133.73 134.35 139.32 135.35 130.60 127.90 132.89 137.25 145.23 [31] 143.76 148.30 155.80 158.08 164.12 162.14 152.43 150.01 153.72 153.58 [41] 154.63 160.67 162.51 161.91 163.80 163.98 153.54 147.60 148.19 149.61 > (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 31 2 37 -6 2.1169 0.07958 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > (gxy <- grangertest(x ~ y, order=par8)) Granger causality test Model 1: ~ Lags(, 1:6) + Lags(, 1:6) Model 2: ~ Lags(, 1:6) Res.Df Df F Pr(>F) 1 31 2 37 -6 1.0179 0.432 > postscript(file="/var/www/html/rcomp/tmp/17w071260380911.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 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0.223 0.294 0.364 0.413 0.461 0.525 0.596 0.662 0.717 0.771 0.824 0.880 0.943 0 1 2 3 4 5 6 7 8 9 10 11 12 0.979 0.930 0.886 0.858 0.825 0.771 0.709 0.651 0.601 0.559 0.517 0.449 0.370 13 0.297 > (r <- ccf(x,y,main='Cross Correlation Function (transformed and differenced)',ylab='CCF',xlab='Lag (k)')) Autocorrelations of series 'X', by lag -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0.223 0.294 0.364 0.413 0.461 0.525 0.596 0.662 0.717 0.771 0.824 0.880 0.943 0 1 2 3 4 5 6 7 8 9 10 11 12 0.979 0.930 0.886 0.858 0.825 0.771 0.709 0.651 0.601 0.559 0.517 0.449 0.370 13 0.297 > par(op) > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2v4k11260380911.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/3wxs11260380911.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/46icf1260380912.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/5m74g1260380912.tab") > > system("convert tmp/17w071260380911.ps tmp/17w071260380911.png") > system("convert tmp/2v4k11260380911.ps tmp/2v4k11260380911.png") > system("convert tmp/3wxs11260380911.ps tmp/3wxs11260380911.png") > > > proc.time() user system elapsed 0.957 0.476 1.834