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Type 'q()' to quit R. > y <- c(595,591,589,584,573,567,569,621,629,628,612,595,597,593,590,580,574,573,573,620,626,620,588,566,557,561,549,532,526,511,499,555,565,542,527,510,514,517,508,493,490,469,478,528,534,518,506,502,516,528,533,536,537,524,536,587,597,581,564,564) > x <- c(100.00,100.83,101.51,102.16,102.39,102.54,102.85,103.47,103.57,103.69,103.50,103.47,103.45,103.48,103.93,103.89,104.40,104.79,104.77,105.13,105.26,104.96,104.75,105.01,105.15,105.20,105.77,105.78,106.26,106.13,106.12,106.57,106.44,106.54,107.10,108.10,108.40,108.84,109.62,110.42,110.67,111.66,112.28,112.87,112.18,112.36,112.16,111.49,111.25,111.36,111.74,111.10,111.33,111.25,111.04,110.97,111.31,111.02,111.07,111.36) > 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] 0.83 0.68 0.65 0.23 0.15 0.31 0.62 0.10 0.12 -0.19 -0.03 -0.02 [13] 0.03 0.45 -0.04 0.51 0.39 -0.02 0.36 0.13 -0.30 -0.21 0.26 0.14 [25] 0.05 0.57 0.01 0.48 -0.13 -0.01 0.45 -0.13 0.10 0.56 1.00 0.30 [37] 0.44 0.78 0.80 0.25 0.99 0.62 0.59 -0.69 0.18 -0.20 -0.67 -0.24 [49] 0.11 0.38 -0.64 0.23 -0.08 -0.21 -0.07 0.34 -0.29 0.05 0.29 > y [1] -4 -2 -5 -11 -6 2 52 8 -1 -16 -17 2 -4 -3 -10 -6 -1 0 47 [20] 6 -6 -32 -22 -9 4 -12 -17 -6 -15 -12 56 10 -23 -15 -17 4 3 -9 [39] -15 -3 -21 9 50 6 -16 -12 -4 14 12 5 3 1 -13 12 51 10 -16 [58] -17 0 > (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 25 2 36 -11 1.4373 0.2176 > (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 25 2 36 -11 2.6626 0.02062 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > postscript(file="/var/www/html/rcomp/tmp/1dgl51260816749.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 0.008 -0.035 -0.088 -0.148 -0.218 -0.293 -0.366 -0.427 -0.442 -0.460 -0.493 -3 -2 -1 0 1 2 3 4 5 6 7 -0.531 -0.571 -0.613 -0.658 -0.679 -0.693 -0.707 -0.725 -0.733 -0.710 -0.681 8 9 10 11 12 13 14 -0.669 -0.650 -0.631 -0.613 -0.586 -0.551 -0.506 > (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 0.158 0.141 0.203 -0.070 -0.118 -0.048 -0.070 -0.088 0.113 0.226 -0.101 -3 -2 -1 0 1 2 3 4 5 6 7 -0.009 0.010 0.023 0.073 -0.203 -0.230 -0.156 -0.127 -0.115 0.000 0.173 8 9 10 11 12 13 14 -0.114 0.044 0.123 -0.026 -0.006 -0.200 -0.109 > par(op) > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2uoxg1260816749.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/3zlxh1260816749.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/4t1tt1260816749.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/5sr6s1260816749.tab") > try(system("convert tmp/1dgl51260816749.ps tmp/1dgl51260816749.png",intern=TRUE)) character(0) > try(system("convert tmp/2uoxg1260816749.ps tmp/2uoxg1260816749.png",intern=TRUE)) character(0) > try(system("convert tmp/3zlxh1260816749.ps tmp/3zlxh1260816749.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 0.965 0.476 1.120