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Type 'q()' to quit R. > y <- c(1901,1395,1639,1643,1751,1797,1373,1558,1555,2061,2010,2119,1985,1963,2017,1975,1589,1679,1392,1511,1449,1767,1899,2179,2217,2049,2343,2175,1607,1702,1764,1766,1615,1953,2091,2411,2550,2351,2786,2525,2474,2332,1978,1789,1904,1997,2207,2453,1948,1384,1989,2140,2100,2045,2083,2022,1950,1422,1859,2147) > x <- c(10436,9314,9717,8997,9062,8885,9058,9095,9149,9857,9848,10269,10341,9690,10125,9349,9224,9224,9454,9347,9430,9933,10148,10677,10735,9760,10567,9333,9409,9502,9348,9319,9594,10160,10182,10810,11105,9874,10958,9311,9610,9398,9784,9425,9557,10166,10337,10770,11265,10183,10941,9628,9709,9637,9579,9741,9754,10508,10749,11079) > par8 = '11' > 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] 10435 9313 9716 8996 9061 8884 9057 9094 9148 9856 9847 10268 [13] 10340 9689 10124 9348 9223 9223 9453 9346 9429 9932 10147 10676 [25] 10734 9759 10566 9332 9408 9501 9347 9318 9593 10159 10181 10809 [37] 11104 9873 10957 9310 9609 9397 9783 9424 9556 10165 10336 10769 [49] 11264 10182 10940 9627 9708 9636 9578 9740 9753 10507 10748 11078 > y [1] 1900 1394 1638 1642 1750 1796 1372 1557 1554 2060 2009 2118 1984 1962 2016 [16] 1974 1588 1678 1391 1510 1448 1766 1898 2178 2216 2048 2342 2174 1606 1701 [31] 1763 1765 1614 1952 2090 2410 2549 2350 2785 2524 2473 2331 1977 1788 1903 [46] 1996 2206 2452 1947 1383 1988 2139 2099 2044 2082 2021 1949 1421 1858 2146 > (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 2.0919 0.05961 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > (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 1.5387 0.1772 > postscript(file="/var/www/html/rcomp/tmp/1h9cz1260291508.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 0.305 0.289 0.318 0.093 -0.038 -0.137 -0.278 -0.296 -0.153 0.072 0.185 -3 -2 -1 0 1 2 3 4 5 6 7 0.391 0.426 0.423 0.503 0.220 0.151 0.026 -0.155 -0.186 -0.162 0.025 8 9 10 11 12 13 14 0.083 0.314 0.349 0.485 0.537 0.263 0.122 > (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 0.305 0.289 0.318 0.093 -0.038 -0.137 -0.278 -0.296 -0.153 0.072 0.185 -3 -2 -1 0 1 2 3 4 5 6 7 0.391 0.426 0.423 0.503 0.220 0.151 0.026 -0.155 -0.186 -0.162 0.025 8 9 10 11 12 13 14 0.083 0.314 0.349 0.485 0.537 0.263 0.122 > par(op) > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2ppfj1260291508.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/3h7pe1260291509.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/4nanl1260291509.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/5dibt1260291509.tab") > > system("convert tmp/1h9cz1260291508.ps tmp/1h9cz1260291508.png") > system("convert tmp/2ppfj1260291508.ps tmp/2ppfj1260291508.png") > system("convert tmp/3h7pe1260291509.ps tmp/3h7pe1260291509.png") > > > proc.time() user system elapsed 0.956 0.517 1.555