R version 2.9.0 (2009-04-17)
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ISBN 3-900051-07-0

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> y <- c(100.00,99.95,102.07,102.02,102.63,102.88,103.01,104.33,105.21,104.73,104.17,103.69,104.33,105.21,105.31,105.54,106.70,106.60,105.01,104.33,104.17,103.42,102.33,101.82,103.57,103.90,104.58,105.03,105.67,105.69,105.72,105.84,105.79,105.39,105.97,106.50,107.13,109.36,109.36,108.42,107.94,108.10,108.40,110.55,111.81,112.55,111.66,111.38,113.10,115.18,121.07,123.07,123.40,122.92,122.39,123.55,124.97,124.87,123.27,121.96,122.49,125.68,126.76,126.44,125.35,126.01,127.45,130.58,133.09,133.34,132.84,133.80)
> x <- c(100.00,99.97,101.03,101.00,101.30,101.43,101.49,102.14,102.58,102.34,102.07,101.83,102.14,102.58,102.63,102.74,103.32,103.27,102.48,102.14,102.07,101.69,101.15,100.90,101.77,101.93,102.27,102.49,102.80,102.82,102.83,102.89,102.87,102.67,102.96,103.22,103.53,104.63,104.63,104.17,103.93,104.01,104.16,105.22,105.85,106.21,105.77,105.63,106.49,107.51,110.43,111.42,111.58,111.34,111.08,111.66,112.36,112.31,111.52,110.87,111.13,112.71,113.25,113.09,112.55,112.87,113.59,115.14,116.38,116.50,116.25,116.73)
> par8 = '1'
> 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]  99.00  98.97 100.03 100.00 100.30 100.43 100.49 101.14 101.58 101.34
[11] 101.07 100.83 101.14 101.58 101.63 101.74 102.32 102.27 101.48 101.14
[21] 101.07 100.69 100.15  99.90 100.77 100.93 101.27 101.49 101.80 101.82
[31] 101.83 101.89 101.87 101.67 101.96 102.22 102.53 103.63 103.63 103.17
[41] 102.93 103.01 103.16 104.22 104.85 105.21 104.77 104.63 105.49 106.51
[51] 109.43 110.42 110.58 110.34 110.08 110.66 111.36 111.31 110.52 109.87
[61] 110.13 111.71 112.25 112.09 111.55 111.87 112.59 114.14 115.38 115.50
[71] 115.25 115.73
> y
 [1]  99.00  98.95 101.07 101.02 101.63 101.88 102.01 103.33 104.21 103.73
[11] 103.17 102.69 103.33 104.21 104.31 104.54 105.70 105.60 104.01 103.33
[21] 103.17 102.42 101.33 100.82 102.57 102.90 103.58 104.03 104.67 104.69
[31] 104.72 104.84 104.79 104.39 104.97 105.50 106.13 108.36 108.36 107.42
[41] 106.94 107.10 107.40 109.55 110.81 111.55 110.66 110.38 112.10 114.18
[51] 120.07 122.07 122.40 121.92 121.39 122.55 123.97 123.87 122.27 120.96
[61] 121.49 124.68 125.76 125.44 124.35 125.01 126.45 129.58 132.09 132.34
[71] 131.84 132.80
> (gyx <- grangertest(y ~ x, order=par8))
Granger causality test

Model 1:  ~ Lags(, 1:1) + Lags(, 1:1)
Model 2:  ~ Lags(, 1:1)
  Res.Df Df      F Pr(>F)
1     68                 
2     69 -1 0.7959 0.3755
> (gxy <- grangertest(x ~ y, order=par8))
Granger causality test

Model 1:  ~ Lags(, 1:1) + Lags(, 1:1)
Model 2:  ~ Lags(, 1:1)
  Res.Df Df     F Pr(>F)
1     68                
2     69 -1 0.884 0.3504
> postscript(file="/var/www/html/rcomp/tmp/17rv41260532084.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

  -15   -14   -13   -12   -11   -10    -9    -8    -7    -6    -5    -4    -3 
0.362 0.406 0.445 0.478 0.509 0.545 0.584 0.623 0.662 0.701 0.738 0.782 0.835 
   -2    -1     0     1     2     3     4     5     6     7     8     9    10 
0.891 0.948 1.000 0.948 0.891 0.834 0.782 0.738 0.701 0.662 0.623 0.584 0.545 
   11    12    13    14    15 
0.509 0.478 0.445 0.406 0.362 
> (r <- ccf(x,y,main='Cross Correlation Function (transformed and differenced)',ylab='CCF',xlab='Lag (k)'))

Autocorrelations of series 'X', by lag

  -15   -14   -13   -12   -11   -10    -9    -8    -7    -6    -5    -4    -3 
0.362 0.406 0.445 0.478 0.509 0.545 0.584 0.623 0.662 0.701 0.738 0.782 0.835 
   -2    -1     0     1     2     3     4     5     6     7     8     9    10 
0.891 0.948 1.000 0.948 0.891 0.834 0.782 0.738 0.701 0.662 0.623 0.584 0.545 
   11    12    13    14    15 
0.509 0.478 0.445 0.406 0.362 
> par(op)
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/2ta3c1260532084.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/3zviu1260532084.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$Pr[2])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/4tupv1260532084.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Granger Causality Test: X = f(Y)',5,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Model',header=TRUE)
> a<-table.element(a,'Res.DF',header=TRUE)
> a<-table.element(a,'Diff. DF',header=TRUE)
> a<-table.element(a,'F',header=TRUE)
> a<-table.element(a,'p-value',header=TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Complete model',header=TRUE)
> a<-table.element(a,gxy$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/5902q1260532085.tab") 
> 
> system("convert tmp/17rv41260532084.ps tmp/17rv41260532084.png")
> system("convert tmp/2ta3c1260532084.ps tmp/2ta3c1260532084.png")
> system("convert tmp/3zviu1260532084.ps tmp/3zviu1260532084.png")
> 
> 
> proc.time()
   user  system elapsed 
  0.923   0.479   1.096