R version 2.9.0 (2009-04-17) Copyright (C) 2009 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > y <- c(23,19,18,19,19,22,23,20,14,14,14,15,11,17,16,20,24,23,20,21,19,23,23,23,23,27,26,17,24,26,24,27,27,26,24,23,23,24,17,21,19,22,22,18,16,14,12,14,16,8,3,0,5,1,1,3,6,7,8,14) > x <- c(25.7,24.7,24.2,23.6,24.4,22.5,19.4,18.1,18.1,20.7,19.1,18.3,16.9,17.9,20.2,21.2,23.8,24,26.6,25.3,27.6,24.7,26.6,24.4,24.6,26,24.8,24,22.7,23,24.1,24,22.7,22.6,23.1,24.4,23,22,21.3,21.5,21.3,23.2,21.8,23.3,21,22.4,20.4,19.9,21.3,18.9,15.6,12.5,7.8,5.5,4,3.3,3.7,3.1,5,6.3) > 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/ > #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] 24.7 23.7 23.2 22.6 23.4 21.5 18.4 17.1 17.1 19.7 18.1 17.3 15.9 16.9 19.2 [16] 20.2 22.8 23.0 25.6 24.3 26.6 23.7 25.6 23.4 23.6 25.0 23.8 23.0 21.7 22.0 [31] 23.1 23.0 21.7 21.6 22.1 23.4 22.0 21.0 20.3 20.5 20.3 22.2 20.8 22.3 20.0 [46] 21.4 19.4 18.9 20.3 17.9 14.6 11.5 6.8 4.5 3.0 2.3 2.7 2.1 4.0 5.3 > y [1] 22 18 17 18 18 21 22 19 13 13 13 14 10 16 15 19 23 22 19 20 18 22 22 22 22 [26] 26 25 16 23 25 23 26 26 25 23 22 22 23 16 20 18 21 21 17 15 13 11 13 15 7 [51] 2 -1 4 0 0 2 5 6 7 13 > (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 1.0211 0.4563 > (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 1.6391 0.1457 > postscript(file="/var/www/html/rcomp/tmp/16k1g1260464309.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 -3 -2 0.013 0.028 0.034 0.037 0.034 0.053 0.093 0.169 0.256 0.334 0.426 0.523 0.623 -1 0 1 2 3 4 5 6 7 8 9 10 11 0.727 0.816 0.859 0.850 0.820 0.768 0.673 0.572 0.466 0.381 0.292 0.191 0.126 12 13 14 0.094 0.053 0.005 > (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 -3 -2 0.013 0.028 0.034 0.037 0.034 0.053 0.093 0.169 0.256 0.334 0.426 0.523 0.623 -1 0 1 2 3 4 5 6 7 8 9 10 11 0.727 0.816 0.859 0.850 0.820 0.768 0.673 0.572 0.466 0.381 0.292 0.191 0.126 12 13 14 0.094 0.053 0.005 > par(op) > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2vgew1260464309.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/3t1ed1260464309.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/40n3m1260464309.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/5h0ap1260464309.tab") > > system("convert tmp/16k1g1260464309.ps tmp/16k1g1260464309.png") > system("convert tmp/2vgew1260464309.ps tmp/2vgew1260464309.png") > system("convert tmp/3t1ed1260464309.ps tmp/3t1ed1260464309.png") > > > proc.time() user system elapsed 0.959 0.485 1.144