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Data X:
14.20 18.40 16.60 14.60 17.70 9.90 5.50 13.60 5.90 3.90 6.20 6.30 3.70 6.30 3.70 5.60 8.70 10.10 13.10 10.20 2.40 5.90 4.30 6.40 5.10 7.60 5.00 6.20 2.90 2.90 -5.60 -12.70 -13.00 -15.00 -23.80 -24.00 -23.30 -35.50 -36.80 -57.10 -69.60 -83.50 -78.30 -72.30 -70.70 -56.00 -49.20 -48.40 -51.90 -51.90 -51.40 -36.90 -30.00 -17.90 -28.20 -22.80 -32.90 -50.10 -31.60 -43.90 -40.70 -36.40 -31.10 -28.80 -10.20 2.70 6.40 10.80 19.80 23.00 15.20 23.20 41.70 43.40 38.70 35.60 27.00 15.30 29.70 26.00 33.50 32.60 23.40 27.30 14.90 12.90 23.10 15.10 13.80 16.90 15.00 16.70 13.50 16.30 22.40 16.30 11.40 14.90 0.10 17.90 17.30 12.20 9.80 5.10 6.80 5.20 -2.70 2.00 -2.30 4.10 6.20 -7.10 -7.10 -9.70 -17.60 -19.60 -33.10 -47.90 -51.60 -61.90 -64.50
Data Y:
2.64 2.49 2.79 2.54 2.42 2.39 2.42 2.30 2.46 2.43 2.50 2.34 2.38 2.52 2.38 2.37 2.53 2.53 2.49 2.57 2.34 2.63 2.55 2.56 2.31 2.21 2.06 2.00 1.67 1.62 1.49 1.28 1.24 0.85 0.42 0.22 0.15 -0.06 -0.18 -0.46 -0.70 -1.09 -1.33 -1.41 -1.56 -1.56 -1.41 -1.39 -1.25 -1.23 -1.12 -0.98 -0.64 -0.41 -0.41 -0.23 -0.25 -0.40 -0.37 -0.37 -0.42 -0.33 -0.35 -0.23 -0.17 -0.17 0.05 0.10 0.26 0.68 0.91 1.15 1.21 1.23 1.36 1.39 1.50 1.54 1.55 1.52 1.68 1.52 1.60 1.50 1.62 1.88 1.93 1.86 1.65 1.86 1.88 1.98 2.04 2.14 2.00 1.89 1.76 1.64 1.62 1.69 1.63 1.53 1.54 1.50 1.33 1.26 1.18 1.27 1.27 1.09 0.94 0.88 0.94 0.87 0.81 0.65 0.52 0.33 0.12 -0.12 -0.30
Box-Cox transformation parameter (X 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 (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
2
1
2
3
4
5
6
7
8
9
10
11
Chart options
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Label x-axis:
R Code
par8 <- '6' par7 <- '0' par6 <- '0' par5 <- '1' par4 <- '1' par3 <- '0' par2 <- '0' par1 <- '1' 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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