R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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'help.start()' for an HTML browser interface to help.
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> y <- c(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)
> x <- c(87.28
+ ,87.28
+ ,87.09
+ ,86.92
+ ,87.59
+ ,90.72
+ ,90.69
+ ,90.30
+ ,89.55
+ ,88.94
+ ,88.41
+ ,87.82
+ ,87.07
+ ,86.82
+ ,86.40
+ ,86.02
+ ,85.66
+ ,85.32
+ ,85.00
+ ,84.67
+ ,83.94
+ ,82.83
+ ,81.95
+ ,81.19
+ ,80.48
+ ,78.86
+ ,69.47
+ ,68.77
+ ,70.06
+ ,73.95
+ ,75.80
+ ,77.79
+ ,81.57
+ ,83.07
+ ,84.34
+ ,85.10
+ ,85.25
+ ,84.26
+ ,83.63
+ ,86.44
+ ,85.30
+ ,84.10
+ ,83.36
+ ,82.48
+ ,81.58
+ ,80.47
+ ,79.34
+ ,82.13
+ ,81.69
+ ,80.70
+ ,79.88
+ ,79.16
+ ,78.38
+ ,77.42
+ ,76.47
+ ,75.46
+ ,74.48
+ ,78.27
+ ,80.70
+ ,79.91
+ ,78.75
+ ,77.78
+ ,81.14
+ ,81.08
+ ,80.03
+ ,78.91
+ ,78.01
+ ,76.90
+ ,75.97
+ ,81.93
+ ,80.27
+ ,78.67
+ ,77.42
+ ,76.16
+ ,74.70
+ ,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
+ ,69.80
+ ,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
+ ,81.70
+ ,80.25
+ ,78.80
+ ,77.51
+ ,76.20
+ ,75.04
+ ,74.00
+ ,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
+ ,71.90
+ ,71.01
+ ,77.47
+ ,75.78
+ ,76.60
+ ,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
+ ,64.70
+ ,64.70
+ ,60.94
+ ,59.08
+ ,58.42
+ ,57.77
+ ,57.11
+ ,53.31
+ ,49.96
+ ,49.40
+ ,48.84
+ ,48.30
+ ,47.74
+ ,47.24
+ ,46.76
+ ,46.29
+ ,48.90
+ ,49.23
+ ,48.53
+ ,48.03
+ ,54.34
+ ,53.79
+ ,53.24
+ ,52.96
+ ,52.17
+ ,51.70
+ ,58.55
+ ,78.20
+ ,77.03
+ ,76.19
+ ,77.15
+ ,75.87
+ ,95.47
+ ,109.67
+ ,112.28
+ ,112.01
+ ,107.93
+ ,105.96
+ ,105.06
+ ,102.98
+ ,102.20
+ ,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
+ ,99.20
+ ,97.97
+ ,89.55
+ ,87.91
+ ,93.34
+ ,94.42
+ ,93.20
+ ,90.29
+ ,91.46
+ ,89.98
+ ,88.35
+ ,88.41
+ ,82.44
+ ,79.89
+ ,75.69
+ ,75.66
+ ,84.50
+ ,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
+ ,65.30
+ ,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
+ ,50.00
+ ,50.00
+ ,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
+ ,69.60
+ ,79.23
+ ,80.00
+ ,93.68
+ ,107.63
+ ,100.18
+ ,97.30
+ ,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)
> par8 = '3'
> 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 <- 6
> 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]  86.28  86.28  86.09  85.92  86.59  89.72  89.69  89.30  88.55  87.94
 [11]  87.41  86.82  86.07  85.82  85.40  85.02  84.66  84.32  84.00  83.67
 [21]  82.94  81.83  80.95  80.19  79.48  77.86  68.47  67.77  69.06  72.95
 [31]  74.80  76.79  80.57  82.07  83.34  84.10  84.25  83.26  82.63  85.44
 [41]  84.30  83.10  82.36  81.48  80.58  79.47  78.34  81.13  80.69  79.70
 [51]  78.88  78.16  77.38  76.42  75.47  74.46  73.48  77.27  79.70  78.91
 [61]  77.75  76.78  80.14  80.08  79.03  77.91  77.01  75.90  74.97  80.93
 [71]  79.27  77.67  76.42  75.16  73.70  75.39  75.04  73.65  72.29  70.79
 [81]  73.39  73.91  73.54  72.08  71.75  70.32  69.38  69.35  69.01  68.36
 [91]  66.77  68.26  68.80  67.38  66.62  67.39  65.95  64.21  65.64  62.45
[101]  59.66  61.34  59.32  57.64  59.46  57.59  60.87  60.85  66.44  76.06
[111]  90.74  92.15  93.15  92.11  90.51  88.96  87.16  85.98  87.03  85.24
[121]  83.65  82.23  80.70  79.25  77.80  76.51  75.20  74.04  73.00  74.49
[131]  76.14  75.15  75.27  77.19  75.49  76.31  75.65  73.99  72.51  71.07
[141]  69.59  70.96  75.29  73.86  73.93  70.90  70.01  76.47  74.78  75.60
[151]  75.07  73.57  72.02  71.65  72.16  70.53  68.78  66.98  68.96  71.16
[161]  69.47  67.86  66.37  64.87  71.16  70.34  68.93  67.44  66.16  65.01
[171]  66.25  69.91  68.75  67.59  66.48  65.31  63.81  65.58  64.97  63.70
[181]  63.70  59.94  58.08  57.42  56.77  56.11  52.31  48.96  48.40  47.84
[191]  47.30  46.74  46.24  45.76  45.29  47.90  48.23  47.53  47.03  53.34
[201]  52.79  52.24  51.96  51.17  50.70  57.55  77.20  76.03  75.19  76.15
[211]  74.87  94.47 108.67 111.28 111.01 106.93 104.96 104.06 101.98 101.20
[221] 104.23 100.85  98.89  95.23  93.76  90.51  90.63  90.54  84.23  86.83
[231]  86.38  83.44  84.19  83.03  85.73 101.52 103.45 105.98 106.02  98.26
[241]  93.45 112.44 156.33 146.38 170.89 170.95 131.71 125.02 120.18 114.45
[251] 109.48 116.85 116.63 123.65 108.59 110.27  98.78  97.21  98.20  96.97
[261]  88.55  86.91  92.34  93.42  92.20  89.29  90.46  88.98  87.35  87.41
[271]  81.44  78.89  74.69  74.66  83.50  95.73  86.48  81.39  82.48  78.31
[281]  77.16  71.77  71.45  67.46  66.62  67.76  69.07  67.55  64.30  57.96
[291]  58.17  61.37  65.28  54.62  54.23  54.85  55.75  49.89  52.88  51.95
[301]  54.08  52.61  57.78  60.85  54.91  52.32  45.41  43.57  49.00  49.00
[311]  52.36  45.23  49.45  48.07  44.85  47.45  48.96  45.53  49.51  46.58
[321]  47.05  45.84  46.67  48.16  54.54  54.82  57.22  55.19  56.77  62.19
[331]  53.76  54.74  61.54  60.39  68.60  78.23  79.00  92.68 106.63  99.18
[341]  96.30  89.45  79.64  79.58  74.82  84.59  88.35  88.42 103.73  94.32
[351]  88.27  89.44  85.97  78.98  80.22  86.35  82.64  81.22
> y
  [1] 298.9 338.2 373.2 392.5 388.2 380.7 374.2 368.0 356.4 351.1 345.5 341.9
 [13] 339.3 327.3 321.9 313.3 307.9 293.0 284.6 280.2 279.3 277.8 273.5 269.4
 [25] 262.4 258.9 257.0 261.7 283.7 310.3 321.1 326.0 330.3 332.3 320.4 326.0
 [37] 319.0 313.7 315.7 313.4 320.3 317.2 306.2 300.3 286.5 276.7 273.4 257.8
 [49] 252.3 250.0 247.4 248.5 245.1 243.5 242.6 243.0 239.8 248.8 247.0 258.4
 [61] 259.5 259.8 260.3 258.5 255.6 256.9 255.5 253.2 252.3 252.8 254.5 256.1
 [73] 256.3 252.2 251.8 251.0 249.7 251.2 249.0 250.0 252.4 250.2 254.6 260.1
 [85] 257.9 258.9 260.2 263.7 266.1 265.4 266.7 267.6 266.5 267.5 267.5 269.5
 [97] 269.9 269.1 268.3 268.8 269.1 263.9 262.7 263.8 262.7 254.9 275.2 359.1
[109] 379.5 372.7 368.8 365.6 358.3 344.8 325.2 323.5 327.1 326.5 323.4 315.5
[121] 309.9 300.5 290.7 289.4 286.4 276.7 280.6 287.0 275.0 271.9 282.0 282.3
[133] 275.8 283.5 281.7 280.2 286.4 282.1 283.0 284.5 288.2 291.5 295.4 304.2
[145] 302.9 310.5 315.3 315.7 321.5 316.1 308.8 302.8 289.3 292.7 290.7 295.5
[157] 288.1 287.5 292.8 296.7 304.4 301.7 301.5 302.0 293.5 293.1 293.5 296.1
[169] 288.4 291.4 286.9 285.6 279.5 271.4 268.2 269.6 266.3 261.5 265.8 267.8
[181] 262.1 260.2 265.0 261.5 264.2 260.3 252.7 248.2 238.1 235.4 234.2 244.2
[193] 245.2 246.7 250.4 252.3 253.8 249.0 248.3 240.5 242.3 247.0 252.0 251.9
[205] 250.5 250.6 252.5 258.8 333.1 447.0 444.8 444.0 447.2 437.2 438.8 422.4
[217] 409.8 407.4 405.7 404.9 401.7 404.1 398.6 385.5 380.4 374.2 356.7 358.0
[229] 354.0 351.7 343.4 342.8 337.0 338.0 332.3 333.4 327.3 329.7 329.0 330.6
[241] 350.2 388.4 409.9 441.8 461.8 465.9 460.7 438.2 429.3 415.1 401.5 396.3
[253] 402.3 394.9 386.8 377.6 376.1 369.4 361.0 349.3 347.2 343.6 342.5 341.8
[265] 346.6 345.6 348.5 341.1 341.0 341.8 338.3 347.2 332.7 333.7 353.0 366.7
[277] 362.3 357.4 352.1 342.1 343.6 343.4 332.9 330.7 323.3 320.2 321.4 320.7
[289] 319.5 311.8 308.7 314.6 308.7 303.6 301.5 300.5 297.8 290.3 292.6 293.6
[301] 284.9 296.6 300.1 292.8 296.7 291.9 291.1 286.2 287.2 282.8 298.9 291.4
[313] 292.3 299.8 292.7 292.1 293.4 291.1 290.9 281.5 276.9 286.5 288.2 284.6
[325] 292.2 289.8 282.1 274.0 286.8 286.8 286.4 283.0 276.8 276.6 303.9 293.0
[337] 299.9 323.0 331.9 340.6 332.4 347.2 343.7 343.7 328.3 322.5 322.2 316.4
[349] 329.1 328.2 333.9 314.8 314.4 318.6 316.3 312.8 314.8 310.3
> (gyx <- grangertest(y ~ x, order=par8))
Granger causality test

Model 1:  ~ Lags(, 1:6) + Lags(, 1:6)
Model 2:  ~ Lags(, 1:6)
  Res.Df  Df     F  Pr(>F)  
1    339                    
2    345  -6 2.557 0.01955 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
> (gxy <- grangertest(x ~ y, order=par8))
Granger causality test

Model 1:  ~ Lags(, 1:6) + Lags(, 1:6)
Model 2:  ~ Lags(, 1:6)
  Res.Df  Df      F   Pr(>F)    
1    339                        
2    345  -6 7.2619 2.61e-07 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
> postscript(file="/var/www/html/rcomp/tmp/1c8ji1260291435.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

  -22   -21   -20   -19   -18   -17   -16   -15   -14   -13   -12   -11   -10 
0.105 0.109 0.113 0.122 0.134 0.149 0.172 0.196 0.225 0.259 0.293 0.330 0.369 
   -9    -8    -7    -6    -5    -4    -3    -2    -1     0     1     2     3 
0.406 0.445 0.483 0.522 0.564 0.609 0.655 0.691 0.716 0.729 0.728 0.714 0.689 
    4     5     6     7     8     9    10    11    12    13    14    15    16 
0.651 0.606 0.566 0.532 0.499 0.469 0.439 0.408 0.373 0.342 0.317 0.293 0.276 
   17    18    19    20    21    22 
0.264 0.252 0.243 0.243 0.244 0.244 
> (r <- ccf(x,y,main='Cross Correlation Function (transformed and differenced)',ylab='CCF',xlab='Lag (k)'))

Autocorrelations of series 'X', by lag

  -22   -21   -20   -19   -18   -17   -16   -15   -14   -13   -12   -11   -10 
0.105 0.109 0.113 0.122 0.134 0.149 0.172 0.196 0.225 0.259 0.293 0.330 0.369 
   -9    -8    -7    -6    -5    -4    -3    -2    -1     0     1     2     3 
0.406 0.445 0.483 0.522 0.564 0.609 0.655 0.691 0.716 0.729 0.728 0.714 0.689 
    4     5     6     7     8     9    10    11    12    13    14    15    16 
0.651 0.606 0.566 0.532 0.499 0.469 0.439 0.408 0.373 0.342 0.317 0.293 0.276 
   17    18    19    20    21    22 
0.264 0.252 0.243 0.243 0.244 0.244 
> par(op)
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/24zni1260291435.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/3wmhj1260291435.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$Pr[2])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/4jakg1260291435.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Granger Causality Test: X = f(Y)',5,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Model',header=TRUE)
> a<-table.element(a,'Res.DF',header=TRUE)
> a<-table.element(a,'Diff. DF',header=TRUE)
> a<-table.element(a,'F',header=TRUE)
> a<-table.element(a,'p-value',header=TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Complete model',header=TRUE)
> a<-table.element(a,gxy$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/5s0jj1260291435.tab") 
> 
> system("convert tmp/1c8ji1260291435.ps tmp/1c8ji1260291435.png")
> system("convert tmp/24zni1260291435.ps tmp/24zni1260291435.png")
> system("convert tmp/3wmhj1260291435.ps tmp/3wmhj1260291435.png")
> 
> 
> proc.time()
   user  system elapsed 
  1.055   0.480   1.265