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(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)
> par8 = '11'
> par7 = '1'
> par6 = '1'
> par5 = '1'
> par4 = '12'
> par3 = '1'
> 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#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 <- 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]  -0.25  -0.23  -0.21  -1.03  -3.47  -0.29   0.06   0.02  -0.50  -0.35
 [11]  -0.17   0.04  -1.37  -8.97  -0.32   1.65   4.23   2.17   2.32   4.51
 [21]   2.61   2.15   1.52   0.86   0.63   8.76   3.51  -2.43  -5.09  -2.59
 [31]  -2.87  -4.68  -2.61  -2.40   2.03  -0.59   0.00  -0.19  -3.53   0.36
 [41]   0.24  -0.21  -0.13  -0.08   4.90   3.56  -3.58  -0.72   0.02   4.18
 [51]   0.66  -0.27  -0.16   0.05  -0.10   0.05   2.17  -4.09  -0.81  -0.09
 [61]  -0.29  -4.82   1.75   0.70  -0.27  -0.46  -0.39   3.53  -5.44   1.29
 [71]   0.14   0.92  -0.17   0.52  -1.72   0.01   0.74  -0.23   2.99  -2.06
 [81]  -1.94  -0.39   2.23  -1.11  -0.31   2.37  -3.16  -2.45   2.33  -0.43
 [91]  -3.17   1.28  -0.45   4.04  -0.79   7.03  11.36  13.25   4.60   3.79
[101]  -2.72   0.42   0.13  -3.62   0.69  -2.23  -1.77  -7.18 -11.04 -16.21
[111]  -2.86  -2.45  -0.25   0.29   0.39   0.76   2.67   0.60   0.80   1.71
[121]   3.34  -0.17   2.27   0.79  -0.37  -0.17  -0.28  -0.44  -0.12   2.68
[131]  -0.44  -0.05  -4.95   0.81   5.64  -1.03   2.48   0.95  -0.06  -0.07
[141]  -1.74  -3.82  -0.20  -1.82   1.23   2.87  -4.26   0.00  -2.43  -0.96
[151]   0.00   7.84  -0.45  -1.92   0.14   0.47   0.65  -0.74   1.46   0.53
[161]   0.45   0.38   0.33  -7.79   2.59   0.80   0.22   1.28  -2.61  -3.10
[171]  -4.32   0.51   0.50  -2.69  -2.18   0.94  -2.33   0.07   0.71  -0.50
[181]   3.28   1.39   3.27   0.98  -0.04   3.30   9.66   0.01   0.01   0.26
[191]  -0.23   0.03   7.33  20.12  -3.78  -1.17   1.66  -0.78  13.29  14.75
[201]   3.16   0.01  -3.29  -1.50  -7.75 -21.73   0.39   3.87  -4.34  -0.68
[211] -23.26 -15.67  -5.86   0.39   3.99  -4.34   3.50   1.63  -2.16  -2.28
[221]   2.22   4.66  19.45   3.40   5.78  -0.08  -7.67   1.50  16.39  44.34
[231]  -7.01  23.76   1.22 -41.94 -22.48  -6.77  -8.26  -5.01  15.13   4.59
[241] -11.97 -58.95  11.63 -36.00  -1.63  40.23   5.46  -3.58   4.09  10.40
[251]  -6.29  -1.00  -9.93  16.23  -3.16   9.86   1.63  -6.96  -1.32   4.22
[261]   1.61   3.41  11.15  -8.03  -2.18  -0.08  -2.69   0.48  -5.45   5.65
[271]  -1.44   3.36   1.17  -7.53 -13.75   6.00  -1.25  -0.88   7.37   5.06
[281]  -5.27  -0.07   4.61   1.74  -7.00   1.68   0.59   5.38   4.87   4.96
[291]  -0.13  -9.85   8.07  -6.52  -2.46   4.53   5.86   0.37  -6.20   2.09
[301]   0.09  -8.39  -0.47   7.45  -0.84  10.89  -1.09  -4.96  -1.21  -2.53
[311]   8.62   2.16   1.66   5.62  -4.63   0.07   8.85 -12.41   3.91   6.33
[321]   0.06   7.38   8.14  -5.61  13.40  11.55  -5.42  -4.46 -12.27  -1.38
[331]  -1.04 -11.56  10.92  -4.45  -9.56  14.54 -23.09 -20.00   8.62  -0.59
[341]  -0.14  11.05   6.19   1.05 -11.19   8.42
> y
  [1]  -22.3  -51.3  -40.4  -27.9   -1.1   -7.4   -1.9    1.8   10.7    3.8
 [11]    1.3   -0.5   -4.4    8.5    3.5   13.3   27.4   41.5   19.2    9.3
 [21]    5.2    3.5   -7.6    9.7    0.0   -1.8    3.9   -7.0  -15.1  -29.7
 [31]  -21.8  -10.8  -18.1  -11.8    8.6  -21.2    1.5    3.0   -4.6    3.4
 [41]  -10.3    1.5   10.1    6.3   10.6   18.8    1.5   27.0    6.6    2.6
 [51]    3.1   -2.9    0.5    2.9   -0.5   -2.7    2.3   -8.5    3.5   -9.8
 [61]   -0.9   -4.4   -0.9    1.0    1.6    0.2   -0.8    3.3    3.3   -2.7
 [71]    2.7    3.9   -2.4    5.1    1.7    4.3    3.7   -2.2    3.5   -0.1
 [81]   -3.5    3.2   -4.4   -3.5    2.6   -1.8   -2.1   -3.0   -2.1   -4.5
 [91]   -2.5    0.2    0.0   -8.8   20.3   81.9   20.0   -6.0   -3.1   -3.7
[101]   -7.6   -8.3  -18.4   -2.8    4.7    7.2  -23.4  -91.8  -26.0   -2.6
[111]   -5.9    1.9    4.3    3.8   23.5    8.1  -15.6   -2.5   13.2    8.2
[121]   -0.9   17.1    8.0   -0.2    9.2    5.4   -3.0   -4.9   15.7    6.4
[131]   -6.2    8.5    5.2   -0.1    6.6    1.9   -0.4   -1.1   -8.2   -7.5
[141]  -17.2    0.1   -5.9   -4.0   -6.1   -8.2    0.5    3.5    1.9    2.7
[151]    7.1    6.5    5.0   -3.8    2.4   -2.2   -0.3    3.6   -9.8   -5.2
[161]  -13.8   -5.4   -3.0    0.9    5.2   -4.4    3.9   -0.6    2.0   -4.9
[171]    9.3   -2.2    8.8    4.2   -4.4   -5.9   -6.8    2.1   -5.5    8.0
[181]    6.7    3.4   -1.1    5.4   -1.2   -0.9    6.9   -3.3   11.9    7.4
[191]    6.2  -10.1   -2.4   -1.4   -1.8    4.4   72.8  118.7   -1.5    7.0
[201]    1.4  -14.7   -3.4  -16.3  -11.2   -2.5   -3.6   -7.1  -77.5 -111.5
[211]   -3.3  -12.3   -8.3    3.8  -19.1   17.7    8.6    0.1   -6.6    0.2
[221]   -2.6   -1.4   -0.2   14.2   -1.0    8.6   16.8    0.3   23.6   40.5
[231]   29.8   32.5   25.8    3.1    0.5  -23.6   -2.8  -16.6  -12.9   -6.8
[241]  -13.6  -45.6  -29.6  -41.1  -21.5  -10.8   -3.2   10.8    6.8   10.6
[251]   12.5    4.5   -1.2    6.4   11.0    1.8    1.4    7.5    4.9   20.6
[261]  -12.4    4.6   20.4   14.4   -9.2   -3.9   -8.2   -2.6    1.6   -1.0
[271]   -7.0  -11.1    7.1   -4.1  -18.1  -14.4    3.2   -2.8    2.2   15.9
[281]   -7.4   -4.9    8.4    1.2    4.7   -4.4    1.1    1.7   -7.5   19.4
[291]    6.6  -13.2    9.8    0.3    1.3   -3.9    3.7    3.1   13.8   -8.5
[301]    9.6   -4.2  -10.6    6.7   -2.6    2.5    0.6   -4.5   -5.6   14.0
[311]  -14.4    3.9    6.7   -9.9   -0.6   -7.5   11.5    2.3   -0.2    6.0
[321]   -1.6   -9.8   25.6   -7.3   -0.7   25.5   16.6   16.8  -21.0   14.8
[331]   -3.1    3.4   -9.2   -5.6  -27.6    5.1    5.8  -24.0   -3.2  -27.8
[341]    7.8  -10.6    1.2   -3.5   17.4    1.3
> (gyx <- grangertest(y ~ x, order=par8))
Granger causality test

Model 1:  ~ Lags(, 1:11) + Lags(, 1:11)
Model 2:  ~ Lags(, 1:11)
  Res.Df  Df      F  Pr(>F)  
1    312                     
2    323 -11 2.2643 0.01151 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
> (gxy <- grangertest(x ~ y, order=par8))
Granger causality test

Model 1:  ~ Lags(, 1:11) + Lags(, 1:11)
Model 2:  ~ Lags(, 1:11)
  Res.Df  Df      F    Pr(>F)    
1    312                         
2    323 -11 5.5221 4.501e-08 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
> postscript(file="/var/www/html/rcomp/tmp/1q0e91260461284.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 
0.108 0.113 0.121 0.133 0.149 0.172 0.196 0.225 0.258 0.293 0.329 0.368 0.405 
   -9    -8    -7    -6    -5    -4    -3    -2    -1     0     1     2     3 
0.444 0.482 0.521 0.563 0.608 0.654 0.690 0.715 0.727 0.726 0.713 0.687 0.649 
    4     5     6     7     8     9    10    11    12    13    14    15    16 
0.605 0.564 0.530 0.498 0.468 0.438 0.407 0.373 0.342 0.317 0.294 0.276 0.265 
   17    18    19    20    21    22 
0.252 0.244 0.243 0.244 0.243 0.242 
> (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 
-0.048 -0.112 -0.057 -0.065 -0.095  0.019 -0.136 -0.127 -0.078 -0.118 -0.106 
   -11    -10     -9     -8     -7     -6     -5     -4     -3     -2     -1 
-0.052 -0.087 -0.009  0.038  0.038  0.017  0.012  0.166  0.160  0.143  0.220 
     0      1      2      3      4      5      6      7      8      9     10 
 0.243  0.167  0.224  0.135 -0.061 -0.043  0.046 -0.024 -0.087 -0.060 -0.037 
    11     12     13     14     15     16     17     18     19     20     21 
-0.145 -0.156 -0.132 -0.195 -0.135  0.049 -0.028 -0.086 -0.039 -0.134 -0.069 
    22 
-0.064 
> par(op)
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/2ikev1260461284.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/3odle1260461284.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$Pr[2])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/4czb51260461284.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Granger Causality Test: X = f(Y)',5,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Model',header=TRUE)
> a<-table.element(a,'Res.DF',header=TRUE)
> a<-table.element(a,'Diff. DF',header=TRUE)
> a<-table.element(a,'F',header=TRUE)
> a<-table.element(a,'p-value',header=TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Complete model',header=TRUE)
> a<-table.element(a,gxy$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/5mhvc1260461284.tab") 
> 
> system("convert tmp/1q0e91260461284.ps tmp/1q0e91260461284.png")
> system("convert tmp/2ikev1260461284.ps tmp/2ikev1260461284.png")
> system("convert tmp/3odle1260461284.ps tmp/3odle1260461284.png")
> 
> 
> proc.time()
   user  system elapsed 
  1.105   0.498   1.529