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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> y <- c(255
+ ,280.2
+ ,299.9
+ ,339.2
+ ,374.2
+ ,393.5
+ ,389.2
+ ,381.7
+ ,375.2
+ ,369
+ ,357.4
+ ,352.1
+ ,346.5
+ ,342.9
+ ,340.3
+ ,328.3
+ ,322.9
+ ,314.3
+ ,308.9
+ ,294
+ ,285.6
+ ,281.2
+ ,280.3
+ ,278.8
+ ,274.5
+ ,270.4
+ ,263.4
+ ,259.9
+ ,258
+ ,262.7
+ ,284.7
+ ,311.3
+ ,322.1
+ ,327
+ ,331.3
+ ,333.3
+ ,321.4
+ ,327
+ ,320
+ ,314.7
+ ,316.7
+ ,314.4
+ ,321.3
+ ,318.2
+ ,307.2
+ ,301.3
+ ,287.5
+ ,277.7
+ ,274.4
+ ,258.8
+ ,253.3
+ ,251
+ ,248.4
+ ,249.5
+ ,246.1
+ ,244.5
+ ,243.6
+ ,244
+ ,240.8
+ ,249.8
+ ,248
+ ,259.4
+ ,260.5
+ ,260.8
+ ,261.3
+ ,259.5
+ ,256.6
+ ,257.9
+ ,256.5
+ ,254.2
+ ,253.3
+ ,253.8
+ ,255.5
+ ,257.1
+ ,257.3
+ ,253.2
+ ,252.8
+ ,252
+ ,250.7
+ ,252.2
+ ,250
+ ,251
+ ,253.4
+ ,251.2
+ ,255.6
+ ,261.1
+ ,258.9
+ ,259.9
+ ,261.2
+ ,264.7
+ ,267.1
+ ,266.4
+ ,267.7
+ ,268.6
+ ,267.5
+ ,268.5
+ ,268.5
+ ,270.5
+ ,270.9
+ ,270.1
+ ,269.3
+ ,269.8
+ ,270.1
+ ,264.9
+ ,263.7
+ ,264.8
+ ,263.7
+ ,255.9
+ ,276.2
+ ,360.1
+ ,380.5
+ ,373.7
+ ,369.8
+ ,366.6
+ ,359.3
+ ,345.8
+ ,326.2
+ ,324.5
+ ,328.1
+ ,327.5
+ ,324.4
+ ,316.5
+ ,310.9
+ ,301.5
+ ,291.7
+ ,290.4
+ ,287.4
+ ,277.7
+ ,281.6
+ ,288
+ ,276
+ ,272.9
+ ,283
+ ,283.3
+ ,276.8
+ ,284.5
+ ,282.7
+ ,281.2
+ ,287.4
+ ,283.1
+ ,284
+ ,285.5
+ ,289.2
+ ,292.5
+ ,296.4
+ ,305.2
+ ,303.9
+ ,311.5
+ ,316.3
+ ,316.7
+ ,322.5
+ ,317.1
+ ,309.8
+ ,303.8
+ ,290.3
+ ,293.7
+ ,291.7
+ ,296.5
+ ,289.1
+ ,288.5
+ ,293.8
+ ,297.7
+ ,305.4
+ ,302.7
+ ,302.5
+ ,303
+ ,294.5
+ ,294.1
+ ,294.5
+ ,297.1
+ ,289.4
+ ,292.4
+ ,287.9
+ ,286.6
+ ,280.5
+ ,272.4
+ ,269.2
+ ,270.6
+ ,267.3
+ ,262.5
+ ,266.8
+ ,268.8
+ ,263.1
+ ,261.2
+ ,266
+ ,262.5
+ ,265.2
+ ,261.3
+ ,253.7
+ ,249.2
+ ,239.1
+ ,236.4
+ ,235.2
+ ,245.2
+ ,246.2
+ ,247.7
+ ,251.4
+ ,253.3
+ ,254.8
+ ,250
+ ,249.3
+ ,241.5
+ ,243.3
+ ,248
+ ,253
+ ,252.9
+ ,251.5
+ ,251.6
+ ,253.5
+ ,259.8
+ ,334.1
+ ,448
+ ,445.8
+ ,445
+ ,448.2
+ ,438.2
+ ,439.8
+ ,423.4
+ ,410.8
+ ,408.4
+ ,406.7
+ ,405.9
+ ,402.7
+ ,405.1
+ ,399.6
+ ,386.5
+ ,381.4
+ ,375.2
+ ,357.7
+ ,359
+ ,355
+ ,352.7
+ ,344.4
+ ,343.8
+ ,338
+ ,339
+ ,333.3
+ ,334.4
+ ,328.3
+ ,330.7
+ ,330
+ ,331.6
+ ,351.2
+ ,389.4
+ ,410.9
+ ,442.8
+ ,462.8
+ ,466.9
+ ,461.7
+ ,439.2
+ ,430.3
+ ,416.1
+ ,402.5
+ ,397.3
+ ,403.3
+ ,395.9
+ ,387.8
+ ,378.6
+ ,377.1
+ ,370.4
+ ,362
+ ,350.3
+ ,348.2
+ ,344.6
+ ,343.5
+ ,342.8
+ ,347.6
+ ,346.6
+ ,349.5
+ ,342.1
+ ,342
+ ,342.8
+ ,339.3
+ ,348.2
+ ,333.7
+ ,334.7
+ ,354
+ ,367.7
+ ,363.3
+ ,358.4
+ ,353.1
+ ,343.1
+ ,344.6
+ ,344.4
+ ,333.9
+ ,331.7
+ ,324.3
+ ,321.2
+ ,322.4
+ ,321.7
+ ,320.5
+ ,312.8
+ ,309.7
+ ,315.6
+ ,309.7
+ ,304.6
+ ,302.5
+ ,301.5
+ ,298.8
+ ,291.3
+ ,293.6
+ ,294.6
+ ,285.9
+ ,297.6
+ ,301.1
+ ,293.8
+ ,297.7
+ ,292.9
+ ,292.1
+ ,287.2
+ ,288.2
+ ,283.8
+ ,299.9
+ ,292.4
+ ,293.3
+ ,300.8
+ ,293.7
+ ,293.1
+ ,294.4
+ ,292.1
+ ,291.9
+ ,282.5
+ ,277.9
+ ,287.5
+ ,289.2
+ ,285.6
+ ,293.2
+ ,290.8
+ ,283.1
+ ,275
+ ,287.8
+ ,287.8
+ ,287.4
+ ,284
+ ,277.8
+ ,277.6
+ ,304.9
+ ,294
+ ,300.9
+ ,324
+ ,332.9
+ ,341.6
+ ,333.4
+ ,348.2
+ ,344.7
+ ,344.7
+ ,329.3
+ ,323.5
+ ,323.2
+ ,317.4
+ ,330.1
+ ,329.2
+ ,334.9
+ ,315.8
+ ,315.4
+ ,319.6
+ ,317.3
+ ,313.8
+ ,315.8
+ ,311.3)
> x <- c(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
+ ,102.18)
> par8 = '4'
> par7 = '0'
> par6 = '1'
> par5 = '1'
> par4 = '12'
> par3 = '0'
> par2 = '1'
> 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/
> #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 <- 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
  [1]   0.00  -0.19  -0.17   0.67   3.13  -0.03  -0.39  -0.75  -0.61  -0.53
 [11]  -0.59  -0.75  -0.25  -0.42  -0.38  -0.36  -0.34  -0.32  -0.33  -0.73
 [21]  -1.11  -0.88  -0.76  -0.71  -1.62  -9.39  -0.70   1.29   3.89   1.85
 [31]   1.99   3.78   1.50   1.27   0.76   0.15  -0.99  -0.63   2.81  -1.14
 [41]  -1.20  -0.74  -0.88  -0.90  -1.11  -1.13   2.79  -0.44  -0.99  -0.82
 [51]  -0.72  -0.78  -0.96  -0.95  -1.01  -0.98   3.79   2.43  -0.79  -1.16
 [61]  -0.97   3.36  -0.06  -1.05  -1.12  -0.90  -1.11  -0.93   5.96  -1.66
 [71]  -1.60  -1.25  -1.26  -1.46   1.69  -0.35  -1.39  -1.36  -1.50   2.60
 [81]   0.52  -0.37  -1.46  -0.33  -1.43  -0.94  -0.03  -0.34  -0.65  -1.59
 [91]   1.49   0.54  -1.42  -0.76   0.77  -1.44  -1.74   1.43  -3.19  -2.79
[101]   1.68  -2.02  -1.68   1.82  -1.87   3.28  -0.02   5.59   9.62  14.68
[111]   1.41   1.00  -1.04  -1.60  -1.55  -1.80  -1.18   1.05  -1.79  -1.59
[121]  -1.42  -1.53  -1.45  -1.45  -1.29  -1.31  -1.16  -1.04   1.49   1.65
[131]  -0.99   0.12   1.92  -1.70   0.82  -0.66  -1.66  -1.48  -1.44  -1.48
[141]   1.37   4.33  -1.43   0.07  -3.03  -0.89   6.46  -1.69   0.82  -0.53
[151]  -1.50  -1.55  -0.37   0.51  -1.63  -1.75  -1.80   1.98   2.20  -1.69
[161]  -1.61  -1.49  -1.50   6.29  -0.82  -1.41  -1.49  -1.28  -1.15   1.24
[171]   3.66  -1.16  -1.16  -1.11  -1.17  -1.50   1.77  -0.61  -1.27   0.00
[181]  -3.76  -1.86  -0.66  -0.65  -0.66  -3.80  -3.35  -0.56  -0.56  -0.54
[191]  -0.56  -0.50  -0.48  -0.47   2.61   0.33  -0.70  -0.50   6.31  -0.55
[201]  -0.55  -0.28  -0.79  -0.47   6.85  19.65  -1.17  -0.84   0.96  -1.28
[211]  19.60  14.20   2.61  -0.27  -4.08  -1.97  -0.90  -2.08  -0.78   3.03
[221]  -3.38  -1.96  -3.66  -1.47  -3.25   0.12  -0.09  -6.31   2.60  -0.45
[231]  -2.94   0.75  -1.16   2.70  15.79   1.93   2.53   0.04  -7.76  -4.81
[241]  18.99  43.89  -9.95  24.51   0.06 -39.24  -6.69  -4.84  -5.73  -4.97
[251]   7.37  -0.22   7.02 -15.06   1.68 -11.49  -1.57   0.99  -1.23  -8.42
[261]  -1.64   5.43   1.08  -1.22  -2.91   1.17  -1.48  -1.63   0.06  -5.97
[271]  -2.55  -4.20  -0.03   8.84  12.23  -9.25  -5.09   1.09  -4.17  -1.15
[281]  -5.39  -0.32  -3.99  -0.84   1.14   1.31  -1.52  -3.25  -6.34   0.21
[291]   3.20   3.91 -10.66  -0.39   0.62   0.90  -5.86   2.99  -0.93   2.13
[301]  -1.47   5.17   3.07  -5.94  -2.59  -6.91  -1.84   5.43   0.00   3.36
[311]  -7.13   4.22  -1.38  -3.22   2.60   1.51  -3.43   3.98  -2.93   0.47
[321]  -1.21   0.83   1.49   6.38   0.28   2.40  -2.03   1.58   5.42  -8.43
[331]   0.98   6.80  -1.15   8.21   9.63   0.77  13.68  13.95  -7.45  -2.88
[341]  -6.85  -9.81  -0.06  -4.76   9.77   3.76   0.07  15.31  -9.41  -6.05
[351]   1.17  -3.47  -6.99   1.24   6.13  -3.71  -1.42  12.18   7.78
> y
  [1]  25.2  19.7  39.3  35.0  19.3  -4.3  -7.5  -6.5  -6.2 -11.6  -5.3  -5.6
 [13]  -3.6  -2.6 -12.0  -5.4  -8.6  -5.4 -14.9  -8.4  -4.4  -0.9  -1.5  -4.3
 [25]  -4.1  -7.0  -3.5  -1.9   4.7  22.0  26.6  10.8   4.9   4.3   2.0 -11.9
 [37]   5.6  -7.0  -5.3   2.0  -2.3   6.9  -3.1 -11.0  -5.9 -13.8  -9.8  -3.3
 [49] -15.6  -5.5  -2.3  -2.6   1.1  -3.4  -1.6  -0.9   0.4  -3.2   9.0  -1.8
 [61]  11.4   1.1   0.3   0.5  -1.8  -2.9   1.3  -1.4  -2.3  -0.9   0.5   1.7
 [73]   1.6   0.2  -4.1  -0.4  -0.8  -1.3   1.5  -2.2   1.0   2.4  -2.2   4.4
 [85]   5.5  -2.2   1.0   1.3   3.5   2.4  -0.7   1.3   0.9  -1.1   1.0   0.0
 [97]   2.0   0.4  -0.8  -0.8   0.5   0.3  -5.2  -1.2   1.1  -1.1  -7.8  20.3
[109]  83.9  20.4  -6.8  -3.9  -3.2  -7.3 -13.5 -19.6  -1.7   3.6  -0.6  -3.1
[121]  -7.9  -5.6  -9.4  -9.8  -1.3  -3.0  -9.7   3.9   6.4 -12.0  -3.1  10.1
[133]   0.3  -6.5   7.7  -1.8  -1.5   6.2  -4.3   0.9   1.5   3.7   3.3   3.9
[145]   8.8  -1.3   7.6   4.8   0.4   5.8  -5.4  -7.3  -6.0 -13.5   3.4  -2.0
[157]   4.8  -7.4  -0.6   5.3   3.9   7.7  -2.7  -0.2   0.5  -8.5  -0.4   0.4
[169]   2.6  -7.7   3.0  -4.5  -1.3  -6.1  -8.1  -3.2   1.4  -3.3  -4.8   4.3
[181]   2.0  -5.7  -1.9   4.8  -3.5   2.7  -3.9  -7.6  -4.5 -10.1  -2.7  -1.2
[193]  10.0   1.0   1.5   3.7   1.9   1.5  -4.8  -0.7  -7.8   1.8   4.7   5.0
[205]  -0.1  -1.4   0.1   1.9   6.3  74.3 113.9  -2.2  -0.8   3.2 -10.0   1.6
[217] -16.4 -12.6  -2.4  -1.7  -0.8  -3.2   2.4  -5.5 -13.1  -5.1  -6.2 -17.5
[229]   1.3  -4.0  -2.3  -8.3  -0.6  -5.8   1.0  -5.7   1.1  -6.1   2.4  -0.7
[241]   1.6  19.6  38.2  21.5  31.9  20.0   4.1  -5.2 -22.5  -8.9 -14.2 -13.6
[253]  -5.2   6.0  -7.4  -8.1  -9.2  -1.5  -6.7  -8.4 -11.7  -2.1  -3.6  -1.1
[265]  -0.7   4.8  -1.0   2.9  -7.4  -0.1   0.8  -3.5   8.9 -14.5   1.0  19.3
[277]  13.7  -4.4  -4.9  -5.3 -10.0   1.5  -0.2 -10.5  -2.2  -7.4  -3.1   1.2
[289]  -0.7  -1.2  -7.7  -3.1   5.9  -5.9  -5.1  -2.1  -1.0  -2.7  -7.5   2.3
[301]   1.0  -8.7  11.7   3.5  -7.3   3.9  -4.8  -0.8  -4.9   1.0  -4.4  16.1
[313]  -7.5   0.9   7.5  -7.1  -0.6   1.3  -2.3  -0.2  -9.4  -4.6   9.6   1.7
[325]  -3.6   7.6  -2.4  -7.7  -8.1  12.8   0.0  -0.4  -3.4  -6.2  -0.2  27.3
[337] -10.9   6.9  23.1   8.9   8.7  -8.2  14.8  -3.5   0.0 -15.4  -5.8  -0.3
[349]  -5.8  12.7  -0.9   5.7 -19.1  -0.4   4.2  -2.3  -3.5   2.0  -4.5
> (gyx <- grangertest(y ~ x, order=par8))
Granger causality test

Model 1:  ~ Lags(, 1:4) + Lags(, 1:4)
Model 2:  ~ Lags(, 1:4)
  Res.Df  Df      F    Pr(>F)    
1    346                         
2    350  -4 5.4156 0.0003065 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
> (gxy <- grangertest(x ~ y, order=par8))
Granger causality test

Model 1:  ~ Lags(, 1:4) + Lags(, 1:4)
Model 2:  ~ Lags(, 1:4)
  Res.Df  Df      F   Pr(>F)   
1    346                       
2    350  -4 4.5669 0.001318 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
> postscript(file="/var/www/html/rcomp/tmp/1xv3h1270028323.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 
0.112 0.121 0.133 0.148 0.171 0.195 0.224 0.257 0.292 0.328 0.367 0.404 0.442 
   -9    -8    -7    -6    -5    -4    -3    -2    -1     0     1     2     3 
0.480 0.519 0.561 0.605 0.651 0.687 0.712 0.724 0.723 0.708 0.683 0.646 0.601 
    4     5     6     7     8     9    10    11    12    13    14    15    16 
0.561 0.526 0.494 0.466 0.436 0.405 0.370 0.340 0.315 0.293 0.277 0.265 0.253 
   17    18    19    20    21    22 
0.243 0.244 0.244 0.242 0.240 0.238 
> (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 
-0.059 -0.048 -0.046 -0.098 -0.019 -0.069 -0.055 -0.017 -0.023 -0.027  0.020 
   -11    -10     -9     -8     -7     -6     -5     -4     -3     -2     -1 
-0.012  0.011 -0.018 -0.032 -0.040 -0.017  0.134  0.147  0.164  0.190  0.185 
     0      1      2      3      4      5      6      7      8      9     10 
 0.166  0.178  0.095 -0.061 -0.077 -0.035 -0.048  0.005  0.014  0.052 -0.052 
    11     12     13     14     15     16     17     18     19     20     21 
-0.089 -0.035 -0.097 -0.080  0.003 -0.043 -0.123 -0.006  0.032  0.015  0.011 
    22 
 0.013 
> par(op)
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/28mkj1270028323.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/38mkj1270028323.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$Pr[2])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/44e0a1270028323.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Granger Causality Test: X = f(Y)',5,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Model',header=TRUE)
> a<-table.element(a,'Res.DF',header=TRUE)
> a<-table.element(a,'Diff. DF',header=TRUE)
> a<-table.element(a,'F',header=TRUE)
> a<-table.element(a,'p-value',header=TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Complete model',header=TRUE)
> a<-table.element(a,gxy$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/5pfhg1270028323.tab") 
> 
> try(system("convert tmp/1xv3h1270028323.ps tmp/1xv3h1270028323.png",intern=TRUE))
character(0)
> try(system("convert tmp/28mkj1270028323.ps tmp/28mkj1270028323.png",intern=TRUE))
character(0)
> try(system("convert tmp/38mkj1270028323.ps tmp/38mkj1270028323.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.041   0.494   1.401