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Data X:
2.93 0.31 -1.19 0.31 -0.57 -8.9 -0.62 1.25 2.43 2.73 1.65 -0.48 2.06 5.13 2 2.93 0.81 -0.78 1.62 0.42 3.38 3.39 0.74 3.76 0.03 -1.48 -0.77 2.92 0.15 2.01 0.63 -1.32 -2.32 2.39 4.59 -0.38 -0.25 1.23 1.94 1.11 0.3 1.29 -3.26 2.07 1.59 1.51 0.79 -1.37 1.91 0.28 -0.33 -3.82 -4.35 0.37 -1.49 0.7 2.92 0.26 1.31 -0.1 0.95 3.15 4.15 2.46 1.46 1.95 1.27 2.51 1.63 2.12 2.8 1.2 0.06 -0.56 -0.31 1.76 -0.93 0.49 1.37 1.49 4.03 2.8 -0.15 1.39 -0.2 -1.71 1.2 1.33 2.35 0.89 1 -1.8 1.29 1.57 3.32 0.97 2.48 1.63 -0.54 -0.12 -0.44 1.06 0.23 2.33 1.87 1.65 1.57 1.02 1.36 1.99 2.17 0 -0.88 -1.7 1.55 2.26 -2.52 -0.27 -2.53 0.41 -1.41 1 1.52 -1.6 -1.43 -0.5 -6.01 -8.19 -3.66 -1.5 0.37 -1.41 1.28 3.19 4.74 1.53 2.8 2.3 3.76 0.54 1.26 2.36 0.69 0.81 3.04 1.74 -2.66 -1.09 1.97 -0.38 2.52 1.86 0.26 2.67 1.68 1.36 0.36 1.19 -0.47 -1.02 -0.19 -3.72 -3.94 3.03 -1.18 -0.21 2.53 1.86 0.39 -0.33 -2.33 0.18 0.47 1.14 1.28 0.43 1.02 1.99 2.29 0.36 1 0.99 1.84 -1.03 1.39 -0.24 1.54 1.51 1.04 1.19 0 1.87 0.11 0.06 0.92 1.32 -0.64 0.36 -1.44 -1.67 0.52 -0.27 -1.06 2.53 0.38 1.38
Data Y:
5.4% -5.1% 1.4% 9.3% 42.7% -13.7% 17.8% -6.7% -5.4% 2.9% -9.3% -6.1% -2.2% 0.0% 2.2% 32.6% 0.0% -3.3% 0.0% -11.0% 8.6% -4.4% 0.0% 24.8% -4.4% 36.9% -5.6% -3.0% -6.7% -3.9% 0.7% 4.1% -3.9% -12.9% -3.9% 0.0% -4.9% -3.4% 6.2% -16.7% -3.0% 9.3% -15.1% 8.9% -4.6% -16.6% 2.6% 3.1% -5.5% 0.0% 5.1% 4.9% -22.1% 21.6% -3.1% 0.0% -20.3% -4.8% -11.7% -0.4% -18.6% -19.1% 17.8% 51.2% -8.4% 2.1% 20.7% -10.0% 0.0% 8.7% -7.4% -4.6% 15.7% 12.9% 5.1% -4.8% -8.9% -7.6% 5.3% 8.6% -2.0% -8.4% 6.9% 13.0% -10.9% -3.4% -2.1% 8.6% -4.0% 10.3% 8.1% 0.0% 4.5% -9.3% -1.2% 13.3% -3.4% 2.0% 5.1% -5.8% -5.5% 0.2% -9.9% -11.9% 4.6% 6.5% 5.6% 6.7% 0.4% 0.5% 13.8% 14.8% 33.1% -8.1% -6.1% 40.2% -16.1% -9.0% 27.6% -7.3% 1.2% -1.2% 14.5% -2.9% 4.4% 55.2% -17.7% -9.9% -35.5% -2.3% -0.9% -16.8% -2.2% -28.0% 2.2% -16.1% 1.8% -6.5% 3.9% -4.5% -9.6% -4.1% -16.2% -29.5% 35.1% 104.5% -20.4% -50.0% -1.4% 4.4% -15.0% 90.5% -32.5% 13.6% -17.4% -7.9% -2.9% 14.7% 21.8% -3.2% 27.2% 11.1% -3.1% 31.0% 15.8% -37.7% -12.6% -5.8% 18.4% 22.4% -32.4% -15.6% -12.3% -25.4% -24.5% 25.0% 10.0% 21.8% -9.0% 14.8% -17.1% 16.4% -5.9% 8.6% 2.9% -18.3% -14.6% -25.3% 16.2% 16.3% -2.0% 2.0% -6.0% 8.5% -2.0% 52.0% 11.8% 16.5% -15.2% -8.3% 3.9% 13.8% 7.7% -1.0% 0.0%
Box-Cox transformation parameter (X series)
0.0
1
-2.0
-1.9
-1.8
-1.7
-1.6
-1.5
-1.4
-1.3
-1.2
-1.1
-1.0
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
Degree (d) of non-seasonal differencing (X series)
0
0
1
2
Degree (D) of seasonal differencing (X series)
0
0
1
2
Seasonal Period
1
1
2
3
4
12
Box-Cox transformation parameter (Y series)
1
1
-2.0
-1.9
-1.8
-1.7
-1.6
-1.5
-1.4
-1.3
-1.2
-1.1
-1.0
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
Degree (d) of non-seasonal differencing (Y series)
0
0
1
2
Degree (D) of seasonal differencing (Y series)
0
0
1
2
Number of non-seasonal time lags in test
1
1
2
3
4
5
6
7
8
9
10
11
Chart options
Label y-axis:
Label x-axis:
R Code
library(lmtest) 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 y (gyx <- grangertest(y ~ x, order=par8)) (gxy <- grangertest(x ~ y, order=par8)) bitmap(file='test1.png') op <- par(mfrow=c(2,1)) (r <- ccf(ox,oy,main='Cross Correlation Function (raw data)',ylab='CCF',xlab='Lag (k)')) (r <- ccf(x,y,main='Cross Correlation Function (transformed and differenced)',ylab='CCF',xlab='Lag (k)')) par(op) dev.off() bitmap(file='test2.png') 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() bitmap(file='test3.png') 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() load(file='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$Pr[2]) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Granger Causality Test: X = f(Y)',5,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Model',header=TRUE) a<-table.element(a,'Res.DF',header=TRUE) a<-table.element(a,'Diff. DF',header=TRUE) a<-table.element(a,'F',header=TRUE) a<-table.element(a,'p-value',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Complete model',header=TRUE) a<-table.element(a,gxy$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='mytable2.tab')
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