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Data X:
16.345.35 16.793.90 17.339.85 17.354.05 16.983.20 17.671.65 17.618.15 17.132.20 15.763.05 15.721.50 15.582.80 14.631.10 14.690.70 14.529.15 13.634.60 13.981.75 12.968.95 11.642.40 11.247.55 11.387.50 11.073.45 10.302.10 9.580.30 9.859.90 8.597.75 11.201.75 11.962.10 12.168.45 12.056.05 11.877.45 11.474.45 11.023.25 11.118.00 11.788.85 11.922.80 11.748.15 11.623.90 10.792.50 10.830.95 10.862.55 10.876.75 10.386.60 10.930.45 11.680.50 11.356.50 10.714.30 10.736.15 10.739.35 10.113.70 10.492.85 11.027.70 10.530.70 10.226.55 10.335.30 9.788.60 9.917.90 10.077.10 9.520.90 9.621.25 9.304.05 9.173.75 8.879.60 8.561.30 8.185.80 8.224.50 8.625.70 8.611.15 8.786.20 8.638.50 8.287.75 8.160.10 7.849.80 7.738.40 6.987.05 7.563.55 7.946.35 7.935.25 8.065.80 7.948.90 7.971.30 8.532.85 8.368.50 8.433.65 8.181.50 8.491.00 8.901.85 8.808.90 8.282.70 8.588.25 8.322.20 7.964.80 7.954.35 7.721.30 7.611.35 7.229.95 6.696.40 6.704.20 6.276.95 6.089.50 6.304.00 6.176.10 6.299.15 5.735.30 5.471.80 5.742.00 5.842.20 5.985.95 5.930.20 5.682.55 5.693.05 6.034.75 5.905.10 5.879.85 5.619.70 5.703.30 5.258.50 5.229.00 5.278.90 4.924.25 5.248.15 5.295.55 5.385.20 5.199.25 4.624.30 4.832.05 5.326.60 4.943.25 5.001.00 5.482.00 5.647.40 5.560.15 5.749.50 5.833.75 5.333.25 5.505.90 6.134.50 5.862.70 6.017.70 6.029.95 5.402.40 5.367.60 5.312.50 5.086.30 5.278.00 5.249.10 4.922.30 4.882.05 5.201.05 5.032.70 4.711.70 5.083.95 4.662.10 4.636.45 4.291.10 4.448.95 3.473.95 3.020.95 2.763.65 2.874.80 2.959.15 2.755.10 2.885.60 3.921.20 4.360.00 4.332.95 4.040.55 4.870.10 5.165.90 4.734.50 5.223.50 5.137.45 6.138.60 5.762.75 5.900.65 5.021.35 4.464.00 4.528.85 4.318.30 4.295.80 4.087.90 3.821.55 3.745.30 4.082.70 3.966.40 3.954.50 3.744.10 3.588.40 3.413.90 3.143.20 3.128.20 3.071.05 3.557.60 3.402.55 3.074.70 3.001.10 2.836.55 2.652.25 2.370.95 2.601.40 2.384.65 2.312.30 2.220.60 2.087.55 1.902.50 2.035.65 2.103.25 2.057.60 2.080.50 1.958.80 1.786.90 1.745.50 1.631.75 1.632.30 1.505.60 1.483.60 1.796.10 1.771.90 1.800.30 1.809.75 1.879.75 1.615.25 1.555.90 1.417.10 1.356.55 1.185.85 1.134.15 1.006.80 934.05 978.2 1.063.40 1.041.85 1.093.50 1.050.15 951.4 963.15 1.010.60 958.9 1.057.80 1.028.80 1.084.50 1.129.55 1.142.05 1.075.40 1.059.05 1.067.15 971.9 913.85 1.053.75 1.072.85 1.107.90 1.167.90 1.125.25 1.148.20 1.351.40 1.371.70 1.263.55 1.268.15 1.172.75 1.271.65 1.394.10 1.332.85 1.471.45 1.380.45 1.406.55 1.528.45 1.654.80 1.546.20 1.480.45 1.376.15 1.325.45 1.413.10 1.412.00 1.310.15 1.187.70 1.132.30 978.2 1.078.05 981.3 966.2 884.25 817.75 824.55 904.95 852.8 931.4 941.65 1.063.15 1.159.35 1.116.90 1.060.75 963.45
Data Y:
6.07 5.04 5.84 5.56 4.48 4.52 4.4 4.81 5.26 5.58 5.26 5.14 5.67 4.49 3.16 3.69 5.28 5.9 5.64 5.63 5.33 5.06 5.1 5.45 5.5 6.84 7.49 9.63 8.61 7.62 6.98 6.31 5.98 8.59 8.65 8.33 7.67 6.97 6.6 5.24 4.86 5.23 5.61 5.61 5.61 3.93 3.96 3.97 4.36 4.74 5.11 4 3.97 3.24 2.89 2.52 1.79 1.08 1.09 2.21 2.61 2.62 1.86 2.23 2.59 3.35 4.14 5.3 6.46 6.13 6.59 5.86 5.51 5.53 5.91 6.32 6.72 6.32 5.14 4.35 4.37 6.1 5.7 5.79 6.28 6.3 7.17 5.86 4.12 4.98 6.3 6.75 7.23 6.49 7.02 7.08 6.7 6.73 7.24 9.13 11.47 11.06 10.7 10.75 10.85 11.06 10.68 10.24 11.44 12.06 11.62 11.17 9.55 9.6 6.14 10.31 9.84 10.05 10.16 10.22 8.65 7.57 5.32 6.49 9.34 9.39 10.06 8.99 8.43 8.62 8.72 9.41 8.82 8.82 9.3 9.47 8.33 9.7 9.82 9.88 11.25 13.73 13.91 13.33 14.86 14.86 16.22 14.97 13.51 11.49 11.64 11.72 11.89 9.29 8.63 8.7 8.03 9.63 10.45 9.7 10.45 10.45 9.77 9.02 8.33 7.69 7.75 7.81 7.87 5.47 5.51 5.51 5.51 5.51 6.4 7.26 6.45 5.69 6.61 6.67 6.72 7.56 6.72 6.53 5.95 6.92 6.4 5.94 6.33 7.27 5.93 4.65 4.57 4.57 4.37 5.57 5.33 4.18 3.63 3.45 4.06 3.32 3.74 4.96 4.17 4.17 4.37 3.78 4.17 4.57 4.81 4.61 3.19 3.02 2.83 2.23 3.49 4.13 4.35 3.72 3.07 3.29 2.89 3.1 4.16 4.41 4.66 5.12 4.06 3.86 3.43 3.2 3.6 4.06 4.3 3.86 3.89 4.16 4.66 4.69 5.17 5.19 4.94 4.23 4.89 4.23 4.73 5.19 4.04 3.39 2.5 2.28 2.53 3.02 3.25 3.48 2.74 2.75 3.5 3.99 4.95 5.24 5.01 5.54 4.83 3.61 2.62 0.47 0 0.92 2.14 3.15 3.16 5.26 7.71 8.36 8.95 8.64 9.38 15.32 79.67 18.63 16.34 15.04 14.8 12.39 10.51 8.19 8.26 9.14 9.71
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)
1
0
1
2
Degree (D) of seasonal differencing (X series)
2
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)
1
0
1
2
Degree (D) of seasonal differencing (Y series)
2
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
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Label x-axis:
R Code
par8 <- '1' par7 <- '2' par6 <- '1' par5 <- '1' par4 <- '1' par3 <- '2' par2 <- '1' 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) print(x) print(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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