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Type 'q()' to quit R. > y <- c(2.05,2.11,2.09,2.05,2.08,2.06,2.06,2.08,2.07,2.06,2.07,2.06,2.09,2.07,2.09,2.28,2.33,2.35,2.52,2.63,2.58,2.70,2.81,2.97,3.04,3.28,3.33,3.50,3.56,3.57,3.69,3.82,3.79,3.96,4.06,4.05,4.03,3.94,4.02,3.88,4.02,4.03,4.09,3.99,4.01,4.01,4.19,4.30,4.27,3.82,3.15,2.49,1.81,1.26,1.06,0.84,0.78,0.70,0.36,0.35) > x <- c(1.00,1.00,1.00,1.00,1.00,1.00,1.00,1.00,1.00,1.00,1.00,1.00,1.00,1.00,1.00,1.25,1.25,1.25,1.50,1.50,1.50,1.75,1.75,2.00,2.00,2.25,2.25,2.50,2.50,2.50,2.75,2.75,2.75,3.00,3.00,3.00,3.00,3.00,3.00,3.00,3.00,3.00,3.00,3.00,3.00,3.00,3.25,3.25,3.25,3.25,2.75,2.00,1.00,1.00,0.50,0.25,0.25,0.25,0.25,0.25) > par8 = '6' > 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#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 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 [13] 0.00 0.00 0.00 0.25 0.25 0.25 0.50 0.50 0.50 0.75 0.75 1.00 [25] 1.00 1.25 1.25 1.50 1.50 1.50 1.75 1.75 1.75 2.00 2.00 2.00 [37] 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.25 2.25 [49] 2.25 2.25 1.75 1.00 0.00 0.00 -0.50 -0.75 -0.75 -0.75 -0.75 -0.75 > y [1] 1.05 1.11 1.09 1.05 1.08 1.06 1.06 1.08 1.07 1.06 1.07 1.06 [13] 1.09 1.07 1.09 1.28 1.33 1.35 1.52 1.63 1.58 1.70 1.81 1.97 [25] 2.04 2.28 2.33 2.50 2.56 2.57 2.69 2.82 2.79 2.96 3.06 3.05 [37] 3.03 2.94 3.02 2.88 3.02 3.03 3.09 2.99 3.01 3.01 3.19 3.30 [49] 3.27 2.82 2.15 1.49 0.81 0.26 0.06 -0.16 -0.22 -0.30 -0.64 -0.65 > (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 41 2 47 -6 1.2981 0.2797 > (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 41 2 47 -6 11.510 1.628e-07 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > postscript(file="/var/www/html/rcomp/tmp/1uknx1260531051.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 -0.137 -0.101 -0.066 -0.032 0.002 0.054 0.133 0.245 0.353 0.473 0.600 -3 -2 -1 0 1 2 3 4 5 6 7 0.720 0.825 0.913 0.976 0.935 0.868 0.782 0.682 0.575 0.469 0.366 8 9 10 11 12 13 14 0.276 0.201 0.141 0.100 0.068 0.036 0.000 > (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 -0.137 -0.101 -0.066 -0.032 0.002 0.054 0.133 0.245 0.353 0.473 0.600 -3 -2 -1 0 1 2 3 4 5 6 7 0.720 0.825 0.913 0.976 0.935 0.868 0.782 0.682 0.575 0.469 0.366 8 9 10 11 12 13 14 0.276 0.201 0.141 0.100 0.068 0.036 0.000 > par(op) > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/21xeu1260531051.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/3jp221260531051.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/40s0q1260531051.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/5cny81260531051.tab") > > system("convert tmp/1uknx1260531051.ps tmp/1uknx1260531051.png") > system("convert tmp/21xeu1260531051.ps tmp/21xeu1260531051.png") > system("convert tmp/3jp221260531051.ps tmp/3jp221260531051.png") > > > proc.time() user system elapsed 0.942 0.457 1.149