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Type 'q()' to quit R. > y <- c(15.89,16.93,20.28,22.52,23.51,22.59,23.51,24.76,26.08,25.29,23.38,25.29,28.42,31.85,30.1,25.45,24.95,26.84,27.52,27.94,25.23,26.53,27.21,28.53,30.35,31.21,32.86,33.2,35.73,34.53,36.54,40.1,40.56,46.14,42.85,38.22,40.18,42.19,47.56,47.26,44.03,49.83,53.35,58.9,59.64,56.99,53.2,53.24,57.85,55.69,55.64,62.52,64.4,64.65,67.71,67.21,59.37,53.26,52.42,55.03) > x <- c(14.3,14.2,15.9,15.3,15.5,15.1,15,12.1,15.8,16.9,15.1,13.7,14.8,14.7,16,15.4,15,15.5,15.1,11.7,16.3,16.7,15,14.9,14.6,15.3,17.9,16.4,15.4,17.9,15.9,13.9,17.8,17.9,17.4,16.7,16,16.6,19.1,17.8,17.2,18.6,16.3,15.1,19.2,17.7,19.1,18,17.5,17.8,21.1,17.2,19.4,19.8,17.6,16.2,19.5,19.9,20,17.3) > par8 = '1' > 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] -0.1 1.7 -0.6 0.2 -0.4 -0.1 -2.9 3.7 1.1 -1.8 -1.4 1.1 -0.1 1.3 -0.6 [16] -0.4 0.5 -0.4 -3.4 4.6 0.4 -1.7 -0.1 -0.3 0.7 2.6 -1.5 -1.0 2.5 -2.0 [31] -2.0 3.9 0.1 -0.5 -0.7 -0.7 0.6 2.5 -1.3 -0.6 1.4 -2.3 -1.2 4.1 -1.5 [46] 1.4 -1.1 -0.5 0.3 3.3 -3.9 2.2 0.4 -2.2 -1.4 3.3 0.4 0.1 -2.7 > y [1] 1.04 3.35 2.24 0.99 -0.92 0.92 1.25 1.32 -0.79 -1.91 1.91 3.13 [13] 3.43 -1.75 -4.65 -0.50 1.89 0.68 0.42 -2.71 1.30 0.68 1.32 1.82 [25] 0.86 1.65 0.34 2.53 -1.20 2.01 3.56 0.46 5.58 -3.29 -4.63 1.96 [37] 2.01 5.37 -0.30 -3.23 5.80 3.52 5.55 0.74 -2.65 -3.79 0.04 4.61 [49] -2.16 -0.05 6.88 1.88 0.25 3.06 -0.50 -7.84 -6.11 -0.84 2.61 > (gyx <- grangertest(y ~ x, order=par8)) Granger causality test Model 1: ~ Lags(, 1:1) + Lags(, 1:1) Model 2: ~ Lags(, 1:1) Res.Df Df F Pr(>F) 1 55 2 56 -1 0.1946 0.6609 > (gxy <- grangertest(x ~ y, order=par8)) Granger causality test Model 1: ~ Lags(, 1:1) + Lags(, 1:1) Model 2: ~ Lags(, 1:1) Res.Df Df F Pr(>F) 1 55 2 56 -1 0.0733 0.7876 > postscript(file="/var/www/html/rcomp/tmp/1ilff1260482997.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 0.240 0.280 0.299 0.343 0.396 0.444 0.448 0.497 0.548 0.605 0.625 0.635 0.659 -1 0 1 2 3 4 5 6 7 8 9 10 11 0.696 0.704 0.714 0.703 0.674 0.641 0.624 0.588 0.561 0.485 0.434 0.405 0.377 12 13 14 0.336 0.321 0.279 > (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 -0.114 0.071 -0.140 -0.024 0.167 0.019 -0.121 0.004 -0.137 0.107 0.222 -3 -2 -1 0 1 2 3 4 5 6 7 -0.055 -0.047 0.012 -0.212 0.103 0.206 -0.036 -0.100 0.070 -0.061 0.036 8 9 10 11 12 13 14 0.138 -0.022 -0.139 -0.048 -0.051 0.125 0.078 > par(op) > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2oa6x1260482997.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/303hq1260482997.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/4cisv1260482997.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/5p1ll1260482997.tab") > > system("convert tmp/1ilff1260482997.ps tmp/1ilff1260482997.png") > system("convert tmp/2oa6x1260482997.ps tmp/2oa6x1260482997.png") > system("convert tmp/303hq1260482997.ps tmp/303hq1260482997.png") > > > proc.time() user system elapsed 0.897 0.487 1.224