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Type 'q()' to quit R. > y <- c(112,304,794,901,1232,1240,1032,1145,1588,2264,2209,2917,243,558,1238,1502,2000,2146,2066,2046,1952,2771,3278,4000,410,1107,1622,1986,2036,2400,2736,2901,2883,3747,4075,4996,575,999,1411,1493,1846,2899,2372,2856,3468,4193,4440,4186,655,1453,1989,2209,2667,3005,2195,2236,2489,2651,2636,2819) > x <- c(285,574,865,1147,1516,1789,2087,2372,2669,2966,3270,3652,329,658,988,1303,1603,1929,2235,2544,2872,3198,3544,3903,332,665,1001,1329,1639,1975,2304,2640,2992,3330,3690,4063,368,738,1103,1474,1846,2224,2608,2984,3351,3736,4122,4558,378,749,1113,1500,1867,2244,2621,2988,3349,3723,4108,4514) > 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] 284 573 864 1146 1515 1788 2086 2371 2668 2965 3269 3651 328 657 987 [16] 1302 1602 1928 2234 2543 2871 3197 3543 3902 331 664 1000 1328 1638 1974 [31] 2303 2639 2991 3329 3689 4062 367 737 1102 1473 1845 2223 2607 2983 3350 [46] 3735 4121 4557 377 748 1112 1499 1866 2243 2620 2987 3348 3722 4107 4513 > y [1] 111 303 793 900 1231 1239 1031 1144 1587 2263 2208 2916 242 557 1237 [16] 1501 1999 2145 2065 2045 1951 2770 3277 3999 409 1106 1621 1985 2035 2399 [31] 2735 2900 2882 3746 4074 4995 574 998 1410 1492 1845 2898 2371 2855 3467 [46] 4192 4439 4185 654 1452 1988 2208 2666 3004 2194 2235 2488 2650 2635 2818 > (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 2.2347 0.05882 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > (gxy <- grangertest(x ~ y, order=par8)) Granger causality test Model 1: ~ Lags(, 1:6) + Lags(, 1:6) Model 2: ~ Lags(, 1:6) Res.Df Df F Pr(>F) 1 41 2 47 -6 3.508 0.00682 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > postscript(file="/var/www/html/rcomp/tmp/1pvgy1260558439.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 0.092 0.321 0.651 0.316 0.076 -0.115 -0.165 -0.167 -0.135 -0.172 -0.118 -3 -2 -1 0 1 2 3 4 5 6 7 0.008 0.176 0.459 0.862 0.469 0.177 -0.068 -0.155 -0.186 -0.176 -0.221 8 9 10 11 12 13 14 -0.175 -0.057 0.107 0.395 0.803 0.448 0.197 > (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 0.092 0.321 0.651 0.316 0.076 -0.115 -0.165 -0.167 -0.135 -0.172 -0.118 -3 -2 -1 0 1 2 3 4 5 6 7 0.008 0.176 0.459 0.862 0.469 0.177 -0.068 -0.155 -0.186 -0.176 -0.221 8 9 10 11 12 13 14 -0.175 -0.057 0.107 0.395 0.803 0.448 0.197 > par(op) > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/29ei51260558439.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/3sa9x1260558439.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/4xrju1260558439.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/5x3d31260558439.tab") > > system("convert tmp/1pvgy1260558439.ps tmp/1pvgy1260558439.png") > system("convert tmp/29ei51260558439.ps tmp/29ei51260558439.png") > system("convert tmp/3sa9x1260558439.ps tmp/3sa9x1260558439.png") > > > proc.time() user system elapsed 0.939 0.459 1.106