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Data X:
87.28 87.28 87.09 86.92 87.59 90.72 90.69 90.3 89.55 88.94 88.41 87.82 87.07 86.82 86.4 86.02 85.66 85.32 85 84.67 83.94 82.83 81.95 81.19 80.48 78.86 69.47 68.77 70.06 73.95 75.8 77.79 81.57 83.07 84.34 85.1 85.25 84.26 83.63 86.44 85.3 84.1 83.36 82.48 81.58 80.47 79.34 82.13 81.69 80.7 79.88 79.16 78.38 77.42 76.47 75.46 74.48 78.27 80.7 79.91 78.75 77.78 81.14 81.08 80.03 78.91 78.01 76.9 75.97 81.93 80.27 78.67 77.42 76.16 74.7 76.39 76.04 74.65 73.29 71.79 74.39 74.91 74.54 73.08 72.75 71.32 70.38 70.35 70.01 69.36 67.77 69.26 69.8 68.38 67.62 68.39 66.95 65.21 66.64 63.45 60.66 62.34 60.32 58.64 60.46 58.59 61.87 61.85 67.44 77.06 91.74 93.15 94.15 93.11 91.51 89.96 88.16 86.98 88.03 86.24 84.65 83.23 81.7 80.25 78.8 77.51 76.2 75.04 74 75.49 77.14 76.15 76.27 78.19 76.49 77.31 76.65 74.99 73.51 72.07 70.59 71.96 76.29 74.86 74.93 71.9 71.01 77.47 75.78 76.6 76.07 74.57 73.02 72.65 73.16 71.53 69.78 67.98 69.96 72.16 70.47 68.86 67.37 65.87 72.16 71.34 69.93 68.44 67.16 66.01 67.25 70.91 69.75 68.59 67.48 66.31 64.81 66.58 65.97 64.7 64.7 60.94 59.08 58.42 57.77 57.11 53.31 49.96 49.4 48.84 48.3 47.74 47.24 46.76 46.29 48.9 49.23 48.53 48.03 54.34 53.79 53.24 52.96 52.17 51.7 58.55 78.2 77.03 76.19 77.15 75.87 95.47 109.67 112.28 112.01 107.93 105.96 105.06 102.98 102.2 105.23 101.85 99.89 96.23 94.76 91.51 91.63 91.54 85.23 87.83 87.38 84.44 85.19 84.03 86.73 102.52 104.45 106.98 107.02 99.26 94.45 113.44 157.33 147.38 171.89 171.95 132.71 126.02 121.18 115.45 110.48 117.85 117.63 124.65 109.59 111.27 99.78 98.21 99.2 97.97 89.55 87.91 93.34 94.42 93.2 90.29 91.46 89.98 88.35 88.41 82.44 79.89 75.69 75.66 84.5 96.73 87.48 82.39 83.48 79.31 78.16 72.77 72.45 68.46 67.62 68.76 70.07 68.55 65.3 58.96 59.17 62.37 66.28 55.62 55.23 55.85 56.75 50.89 53.88 52.95 55.08 53.61 58.78 61.85 55.91 53.32 46.41 44.57 50 50 53.36 46.23 50.45 49.07 45.85 48.45 49.96 46.53 50.51 47.58 48.05 46.84 47.67 49.16 55.54 55.82 58.22 56.19 57.77 63.19 54.76 55.74 62.54 61.39 69.6 79.23 80 93.68 107.63 100.18 97.3 90.45 80.64 80.58 75.82 85.59 89.35 89.42 104.73 95.32 89.27 90.44 86.97 79.98 81.22 87.35 83.64 82.22 94.4
Data Y:
280.20 299.90 339.20 374.20 393.50 389.20 381.70 375.20 369.00 357.40 352.10 346.50 342.90 340.30 328.30 322.90 314.30 308.90 294.00 285.60 281.20 280.30 278.80 274.50 270.40 263.40 259.90 258.00 262.70 284.70 311.30 322.10 327.00 331.30 333.30 321.40 327.00 320.00 314.70 316.70 314.40 321.30 318.20 307.20 301.30 287.50 277.70 274.40 258.80 253.30 251.00 248.40 249.50 246.10 244.50 243.60 244.00 240.80 249.80 248.00 259.40 260.50 260.80 261.30 259.50 256.60 257.90 256.50 254.20 253.30 253.80 255.50 257.10 257.30 253.20 252.80 252.00 250.70 252.20 250.00 251.00 253.40 251.20 255.60 261.10 258.90 259.90 261.20 264.70 267.10 266.40 267.70 268.60 267.50 268.50 268.50 270.50 270.90 270.10 269.30 269.80 270.10 264.90 263.70 264.80 263.70 255.90 276.20 360.10 380.50 373.70 369.80 366.60 359.30 345.80 326.20 324.50 328.10 327.50 324.40 316.50 310.90 301.50 291.70 290.40 287.40 277.70 281.60 288.00 276.00 272.90 283.00 283.30 276.80 284.50 282.70 281.20 287.40 283.10 284.00 285.50 289.20 292.50 296.40 305.20 303.90 311.50 316.30 316.70 322.50 317.10 309.80 303.80 290.30 293.70 291.70 296.50 289.10 288.50 293.80 297.70 305.40 302.70 302.50 303.00 294.50 294.10 294.50 297.10 289.40 292.40 287.90 286.60 280.50 272.40 269.20 270.60 267.30 262.50 266.80 268.80 263.10 261.20 266.00 262.50 265.20 261.30 253.70 249.20 239.10 236.40 235.20 245.20 246.20 247.70 251.40 253.30 254.80 250.00 249.30 241.50 243.30 248.00 253.00 252.90 251.50 251.60 253.50 259.80 334.10 448.00 445.80 445.00 448.20 438.20 439.80 423.40 410.80 408.40 406.70 405.90 402.70 405.10 399.60 386.50 381.40 375.20 357.70 359.00 355.00 352.70 344.40 343.80 338.00 339.00 333.30 334.40 328.30 330.70 330.00 331.60 351.20 389.40 410.90 442.80 462.80 466.90 461.70 439.20 430.30 416.10 402.50 397.30 403.30 395.90 387.80 378.60 377.10 370.40 362.00 350.30 348.20 344.60 343.50 342.80 347.60 346.60 349.50 342.10 342.00 342.80 339.30 348.20 333.70 334.70 354.00 367.70 363.30 358.40 353.10 343.10 344.60 344.40 333.90 331.70 324.30 321.20 322.40 321.70 320.50 312.80 309.70 315.60 309.70 304.60 302.50 301.50 298.80 291.30 293.60 294.60 285.90 297.60 301.10 293.80 297.70 292.90 292.10 287.20 288.20 283.80 299.90 292.40 293.30 300.80 293.70 293.10 294.40 292.10 291.90 282.50 277.90 287.50 289.20 285.60 293.20 290.80 283.10 275.00 287.80 287.80 287.40 284.00 277.80 277.60 304.90 294.00 300.90 324.00 332.90 341.60 333.40 348.20 344.70 344.70 329.30 323.50 323.20 317.40 330.10 329.20 334.90 315.80 315.40 319.60 317.30 313.80 315.80 311.30
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
12
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
6
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')
Compute
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Raw Input
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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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