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Type 'q()' to quit R. > y <- c(151.7,121.3,133.0,119.6,122.2,117.4,106.7,87.5,81.0,110.3,87.0,55.7,146.0,137.5,138.5,135.6,107.3,99.0,91.4,68.4,82.6,98.4,71.3,47.6,130.8,113.6,125.7,113.6,97.1,104.4,91.8,75.1,89.2,110.2,78.4,68.4,122.8,129.7,159.1,139.0,102.2,113.6,81.5,77.4,87.6,101.2,87.2,64.9,133.1,118.0,135.9,125.7,108.0,128.3,84.7,86.4,92.2,95.8,92.3,54.3) > x <- c(105.2,105.2,105.6,105.6,106.2,106.3,106.4,106.9,107.2,107.3,107.3,107.4,107.55,107.87,108.37,108.38,107.92,108.03,108.14,108.3,108.64,108.66,109.04,109.03,109.03,109.54,109.75,109.83,109.65,109.82,109.95,110.12,110.15,110.2,109.99,110.14,110.14,110.81,110.97,110.99,109.73,109.81,110.02,110.18,110.21,110.25,110.36,110.51,110.64,110.95,111.18,111.19,111.69,111.7,111.83,111.77,111.73,112.01,111.86,112.04) > par8 = '6' > 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] 0.00 0.40 0.00 0.60 0.10 0.10 0.50 0.30 0.10 0.00 0.10 0.15 [13] 0.32 0.50 0.01 -0.46 0.11 0.11 0.16 0.34 0.02 0.38 -0.01 0.00 [25] 0.51 0.21 0.08 -0.18 0.17 0.13 0.17 0.03 0.05 -0.21 0.15 0.00 [37] 0.67 0.16 0.02 -1.26 0.08 0.21 0.16 0.03 0.04 0.11 0.15 0.13 [49] 0.31 0.23 0.01 0.50 0.01 0.13 -0.06 -0.04 0.28 -0.15 0.18 > y [1] -30.4 11.7 -13.4 2.6 -4.8 -10.7 -19.2 -6.5 29.3 -23.3 -31.3 90.3 [13] -8.5 1.0 -2.9 -28.3 -8.3 -7.6 -23.0 14.2 15.8 -27.1 -23.7 83.2 [25] -17.2 12.1 -12.1 -16.5 7.3 -12.6 -16.7 14.1 21.0 -31.8 -10.0 54.4 [37] 6.9 29.4 -20.1 -36.8 11.4 -32.1 -4.1 10.2 13.6 -14.0 -22.3 68.2 [49] -15.1 17.9 -10.2 -17.7 20.3 -43.6 1.7 5.8 3.6 -3.5 -38.0 > (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 40 2 46 -6 0.7165 0.6385 > (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 40 2 46 -6 1.9868 0.09038 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > postscript(file="/var/www/html/rcomp/tmp/1jnop1260463651.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 0.025 -0.022 -0.083 -0.016 -0.022 -0.068 -0.045 -0.002 -0.009 -0.046 -0.075 -3 -2 -1 0 1 2 3 4 5 6 7 -0.094 -0.119 -0.118 -0.175 -0.127 -0.146 -0.175 -0.158 -0.111 -0.084 -0.112 8 9 10 11 12 13 14 -0.100 -0.098 -0.116 -0.106 -0.120 -0.062 -0.065 > (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 0.038 0.011 0.021 0.158 0.016 -0.157 -0.246 0.155 0.102 -0.078 -0.015 -3 -2 -1 0 1 2 3 4 5 6 7 -0.107 -0.014 0.081 0.118 0.209 0.018 -0.271 -0.209 0.145 0.075 -0.178 8 9 10 11 12 13 14 -0.078 0.052 -0.062 0.054 -0.038 0.242 -0.044 > par(op) > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2rlan1260463651.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/3jz591260463651.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/4dqyv1260463651.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/57sls1260463651.tab") > > system("convert tmp/1jnop1260463651.ps tmp/1jnop1260463651.png") > system("convert tmp/2rlan1260463651.ps tmp/2rlan1260463651.png") > system("convert tmp/3jz591260463651.ps tmp/3jz591260463651.png") > > > proc.time() user system elapsed 0.895 0.466 1.499