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(1.6,1.4,0.8,1.1,1.3,1.2,1.3,1.1,1.3,1.2,1.6,1.7,1.5,0.9,1.5,1.4,1.6,1.7,1.4,1.8,1.7,1.4,1.2,1.0,1.7,2.4,2.0,2.1,2.0,1.8,2.7,2.3,1.9,2.0,2.3,2.8,2.4,2.3,2.7,2.7,2.9,3.0,2.2,2.3,2.8,2.8,2.8,2.2,2.6,2.8,2.5,2.4,2.3,1.9,1.7,2.0,2.1,1.7,1.8,1.8,1.8,1.3,1.3,1.3,1.2,1.4,2.2,2.9,3.1,3.5,3.6,4.4,4.1,5.1,5.8,5.9,5.4,5.5,4.8,3.2,2.7,2.1,1.9,0.6,0.7) > x <- c(7.1,6.9,6.8,7.5,7.6,7.8,8.0,8.1,8.2,8.3,8.2,8.0,7.9,7.6,7.6,8.3,8.4,8.4,8.4,8.4,8.6,8.9,8.8,8.3,7.5,7.2,7.4,8.8,9.3,9.3,8.7,8.2,8.3,8.5,8.6,8.5,8.2,8.1,7.9,8.6,8.7,8.7,8.5,8.4,8.5,8.7,8.7,8.6,8.5,8.3,8.0,8.2,8.1,8.1,8.0,7.9,7.9,8.0,8.0,7.9,8.0,7.7,7.2,7.5,7.3,7.0,7.0,7.0,7.2,7.3,7.1,6.8,6.4,6.1,6.5,7.7,7.9,7.5,6.9,6.6,6.9,7.7,8.0,8.0,7.7) > par8 = '11' > par7 = '0' > par6 = '1' > par5 = '1' > par4 = '12' > par3 = '0' > par2 = '1' > 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] -0.2 -0.1 0.7 0.1 0.2 0.2 0.1 0.1 0.1 -0.1 -0.2 -0.1 -0.3 0.0 0.7 [16] 0.1 0.0 0.0 0.0 0.2 0.3 -0.1 -0.5 -0.8 -0.3 0.2 1.4 0.5 0.0 -0.6 [31] -0.5 0.1 0.2 0.1 -0.1 -0.3 -0.1 -0.2 0.7 0.1 0.0 -0.2 -0.1 0.1 0.2 [46] 0.0 -0.1 -0.1 -0.2 -0.3 0.2 -0.1 0.0 -0.1 -0.1 0.0 0.1 0.0 -0.1 0.1 [61] -0.3 -0.5 0.3 -0.2 -0.3 0.0 0.0 0.2 0.1 -0.2 -0.3 -0.4 -0.3 0.4 1.2 [76] 0.2 -0.4 -0.6 -0.3 0.3 0.8 0.3 0.0 -0.3 > y [1] -0.2 -0.6 0.3 0.2 -0.1 0.1 -0.2 0.2 -0.1 0.4 0.1 -0.2 -0.6 0.6 -0.1 [16] 0.2 0.1 -0.3 0.4 -0.1 -0.3 -0.2 -0.2 0.7 0.7 -0.4 0.1 -0.1 -0.2 0.9 [31] -0.4 -0.4 0.1 0.3 0.5 -0.4 -0.1 0.4 0.0 0.2 0.1 -0.8 0.1 0.5 0.0 [46] 0.0 -0.6 0.4 0.2 -0.3 -0.1 -0.1 -0.4 -0.2 0.3 0.1 -0.4 0.1 0.0 0.0 [61] -0.5 0.0 0.0 -0.1 0.2 0.8 0.7 0.2 0.4 0.1 0.8 -0.3 1.0 0.7 0.1 [76] -0.5 0.1 -0.7 -1.6 -0.5 -0.6 -0.2 -1.3 0.1 > (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 50 2 61 -11 0.7312 0.7036 > (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 50 2 61 -11 0.8498 0.5929 > postscript(file="/var/www/html/rcomp/tmp/1ip6z1260540550.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 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 0.023 -0.029 -0.066 -0.083 -0.094 -0.098 -0.125 -0.182 -0.243 -0.279 -0.312 -5 -4 -3 -2 -1 0 1 2 3 4 5 -0.330 -0.348 -0.352 -0.350 -0.341 -0.313 -0.294 -0.288 -0.285 -0.271 -0.254 6 7 8 9 10 11 12 13 14 15 16 -0.223 -0.159 -0.093 -0.059 -0.049 -0.048 -0.056 -0.072 -0.081 -0.084 -0.094 > (r <- ccf(x,y,main='Cross Correlation Function (transformed and differenced)',ylab='CCF',xlab='Lag (k)')) Autocorrelations of series 'X', by lag -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 0.036 -0.060 -0.023 -0.023 -0.008 0.069 0.031 -0.010 -0.062 0.042 -0.030 -5 -4 -3 -2 -1 0 1 2 3 4 5 -0.047 -0.143 -0.050 -0.070 -0.070 -0.018 0.051 0.033 -0.019 0.001 -0.057 6 7 8 9 10 11 12 13 14 15 16 -0.126 0.004 0.153 0.131 0.037 0.016 0.021 -0.050 -0.038 0.063 0.007 > par(op) > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2idwt1260540550.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/3gfdw1260540550.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/47c8q1260540550.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/5mfu21260540550.tab") > > system("convert tmp/1ip6z1260540550.ps tmp/1ip6z1260540550.png") > system("convert tmp/2idwt1260540550.ps tmp/2idwt1260540550.png") > system("convert tmp/3gfdw1260540550.ps tmp/3gfdw1260540550.png") > > > proc.time() user system elapsed 0.986 0.486 2.291