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Type 'q()' to quit R. > y <- c(102.1,102.86,102.99,103.73,105.02,104.43,104.63,104.93,105.87,105.66,106.76,106,107.22,107.33,107.11,108.86,107.72,107.88,108.38,107.72,108.41,109.9,111.45,112.18,113.34,113.46,114.06,115.54,116.39,115.94,116.97,115.94,115.91,116.43,116.26,116.35,117.9,117.7,117.53,117.86,117.65,116.51,115.93,115.31,115) > x <- c(100.35,100.35,100.36,100.39,100.34,100.34,100.35,100.43,100.47,100.67,100.75,100.78,100.79,100.67,100.64,100.64,100.76,100.79,100.79,100.9,100.98,101.11,101.18,101.22,101.23,101.09,101.26,101.28,101.43,101.53,101.54,101.54,101.79,102.18,102.37,102.46,102.46,102.03,102.26,102.33,102.44,102.5,102.52,102.66,102.72) > 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/ > #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] 99.35 99.35 99.36 99.39 99.34 99.34 99.35 99.43 99.47 99.67 [11] 99.75 99.78 99.79 99.67 99.64 99.64 99.76 99.79 99.79 99.90 [21] 99.98 100.11 100.18 100.22 100.23 100.09 100.26 100.28 100.43 100.53 [31] 100.54 100.54 100.79 101.18 101.37 101.46 101.46 101.03 101.26 101.33 [41] 101.44 101.50 101.52 101.66 101.72 > y [1] 101.10 101.86 101.99 102.73 104.02 103.43 103.63 103.93 104.87 104.66 [11] 105.76 105.00 106.22 106.33 106.11 107.86 106.72 106.88 107.38 106.72 [21] 107.41 108.90 110.45 111.18 112.34 112.46 113.06 114.54 115.39 114.94 [31] 115.97 114.94 114.91 115.43 115.26 115.35 116.90 116.70 116.53 116.86 [41] 116.65 115.51 114.93 114.31 114.00 > (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 35 2 38 -3 0.7097 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 35 2 38 -3 3.2858 0.032 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > postscript(file="/var/www/html/rcomp/tmp/1gxlq1260551705.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 0.010 0.058 0.119 0.182 0.253 0.327 0.398 0.475 0.547 0.617 0.687 0.757 0.831 0 1 2 3 4 5 6 7 8 9 10 11 12 0.903 0.894 0.884 0.867 0.845 0.808 0.762 0.705 0.647 0.579 0.531 0.486 0.431 13 0.374 > (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 0.010 0.058 0.119 0.182 0.253 0.327 0.398 0.475 0.547 0.617 0.687 0.757 0.831 0 1 2 3 4 5 6 7 8 9 10 11 12 0.903 0.894 0.884 0.867 0.845 0.808 0.762 0.705 0.647 0.579 0.531 0.486 0.431 13 0.374 > par(op) > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2uc1l1260551705.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/3ocay1260551705.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/4y68v1260551705.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/5go891260551705.tab") > > system("convert tmp/1gxlq1260551705.ps tmp/1gxlq1260551705.png") > system("convert tmp/2uc1l1260551705.ps tmp/2uc1l1260551705.png") > system("convert tmp/3ocay1260551705.ps tmp/3ocay1260551705.png") > > > proc.time() user system elapsed 0.923 0.495 1.570