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Type 'q()' to quit R. > y <- c(108.5,112.3,116.6,115.5,120.1,132.9,128.1,129.3,132.5,131,124.9,120.8,122,122.1,127.4,135.2,137.3,135,136,138.4,134.7,138.4,133.9,133.6,141.2,151.8,155.4,156.6,161.6,160.7,156,159.5,168.7,169.9,169.9,185.9,190.8,195.8,211.9,227.1,251.3,256.7,251.9,251.2,270.3,267.2,243,229.9,187.2,178.2,175.2,192.4,187,184,194.1,212.7,217.5,200.5,205.9,196.5,206.3) > x <- c(98.71,98.54,98.2,96.92,99.06,99.65,99.82,99.99,100.33,99.31,101.1,101.1,100.93,100.85,100.93,99.6,101.88,101.81,102.38,102.74,102.82,101.72,103.47,102.98,102.68,102.9,103.03,101.29,103.69,103.68,104.2,104.08,104.16,103.05,104.66,104.46,104.95,105.85,106.23,104.86,107.44,108.23,108.45,109.39,110.15,109.13,110.28,110.17,109.99,109.26,109.11,107.06,109.53,108.92,109.24,109.12,109,107.23,109.49,109.04,109.02) > 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/ > #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] -0.17 -0.34 -1.28 2.14 0.59 0.17 0.17 0.34 -1.02 1.79 0.00 -0.17 [13] -0.08 0.08 -1.33 2.28 -0.07 0.57 0.36 0.08 -1.10 1.75 -0.49 -0.30 [25] 0.22 0.13 -1.74 2.40 -0.01 0.52 -0.12 0.08 -1.11 1.61 -0.20 0.49 [37] 0.90 0.38 -1.37 2.58 0.79 0.22 0.94 0.76 -1.02 1.15 -0.11 -0.18 [49] -0.73 -0.15 -2.05 2.47 -0.61 0.32 -0.12 -0.12 -1.77 2.26 -0.45 -0.02 > y [1] 3.8 4.3 -1.1 4.6 12.8 -4.8 1.2 3.2 -1.5 -6.1 -4.1 1.2 [13] 0.1 5.3 7.8 2.1 -2.3 1.0 2.4 -3.7 3.7 -4.5 -0.3 7.6 [25] 10.6 3.6 1.2 5.0 -0.9 -4.7 3.5 9.2 1.2 0.0 16.0 4.9 [37] 5.0 16.1 15.2 24.2 5.4 -4.8 -0.7 19.1 -3.1 -24.2 -13.1 -42.7 [49] -9.0 -3.0 17.2 -5.4 -3.0 10.1 18.6 4.8 -17.0 5.4 -9.4 9.8 > (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 26 2 37 -11 1.3361 0.2608 > (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 26 2 37 -11 2.6896 0.01863 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > postscript(file="/var/www/html/rcomp/tmp/1uniy1260441128.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 0.251 0.293 0.335 0.380 0.424 0.451 0.482 0.515 0.552 0.591 0.651 0.705 0.758 -1 0 1 2 3 4 5 6 7 8 9 10 11 0.813 0.869 0.868 0.865 0.844 0.797 0.756 0.710 0.680 0.654 0.621 0.569 0.548 12 13 14 0.523 0.493 0.447 > (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 -0.023 -0.050 -0.101 -0.049 0.079 0.033 -0.053 -0.061 -0.111 -0.237 0.014 -3 -2 -1 0 1 2 3 4 5 6 7 0.107 -0.054 0.011 0.063 0.073 0.144 0.308 -0.050 0.134 -0.120 0.012 8 9 10 11 12 13 14 0.100 0.120 -0.193 0.024 -0.041 -0.072 0.091 > par(op) > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2r1h91260441128.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/3uzgu1260441128.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/4mjhx1260441128.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/5muzz1260441128.tab") > > system("convert tmp/1uniy1260441128.ps tmp/1uniy1260441128.png") > system("convert tmp/2r1h91260441128.ps tmp/2r1h91260441128.png") > system("convert tmp/3uzgu1260441128.ps tmp/3uzgu1260441128.png") > > > proc.time() user system elapsed 0.955 0.471 1.139