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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_grangercausality.wasp
Title produced by softwareBivariate Granger Causality
Date of computationThu, 07 Jul 2016 17:06:19 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Jul/07/t14679076258wrx7as2rpke6q5.htm/, Retrieved Fri, 03 May 2024 06:38:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=295817, Retrieved Fri, 03 May 2024 06:38:34 +0000
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IsPrivate?No (this computation is public)
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Estimated Impact177

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bivariate Granger Causality] [] [2016-07-07 16:06:19] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

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Dataseries Y:
Download CSV
Histogram 
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11.13
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18.110001
20.379999
19.719999
19.450001
19.709999
17.08
16.799999
16.66
17.5
17.1
18.049999
18.719999
17.950001
18.360001
16.74
17.110001
16.48
16.16
15.45
16.27
18.620001
20.469999
19.34
17.530001
16.700001
18.030001
18.93
18.959999
17.57
15.64
15.95
15.99
15.32
15.28
14.74
13.7
14.85
14.63
14.29
13.45
14.02
15.02
15.11
15.96
15.18
16.35
16.49
17.059999
17.83
17.469999
17.98
17.74
15.63
14.38
16.280001
16.41
15.8
14.05
14.51
14.59
14.18
13.88
14.07
13.98
14.15
15.43
16.809999
14.84
15.73
16.32
15.71
15.43
14.55
14.33
15.11
16.370001
16.290001
15.59
16.139999
15.27
15.22
15.07
15.03
16.969999
16.620001
18.83
18.33
18.120001
17.809999
18.6
16.690001
17.59
18.42
17.58
19.08
18.49
18.610001
16.68
16.65
17.92
17.99
16.41
15.24
15.08
15.31
15.14
15.5
15.92
15.51
15.06
15.87
16.639999
17.120001
16.459999
16.58
15.87
16.049999
15.57
15.95
16.559999
16.99
16.34
15.57
17.360001
17.67
17.84
17.84
19.48
19.469999
21.790001
18.02
14.68
14.56
13.83
13.79
13.62
13.81
13.49
13.36
13.52
13.55
13.42
13.57
12.46
12.43
12.46
12.69
12.89
13.57
13.31
14.32
14.38
12.9
14.67
13.72
13.41
13.5
13.02
12.94
12.64
12.98
12.66
12.46
12.31
14.68
15.22
14.17
18.99
16.870001
14.73
15.51
15.36
14.01
13.48
13.53
13.06
12.59
11.56
12.27
11.83
11.3
11.3
13.36
14.39
12.67
13.99
13.57
13.74
12.77
13.15
12.7
13.58
12.76
14.21
13.89
13.92
13.19
12.84
12.36
12.24
12.06
17.27
13.96
16.51
17.559999
14.97
14.39
13.48
13.61
13.62
13.61
13.71
13.52
14.49
13.59
12.85
12.66
12.83
12.66
13.13
12.59
12.55
12.77
12.81
13.07
12.45
13.02
13.37
13.82
14.07
13.99
14.48
14.83
14.53
16.299999
16.280001
16.27
17.5
16.629999
15.14
15.44
17.07
18.59
16.41
17.15
16.799999
16.610001
16.639999
20.49
18.9
20.110001
18.469999
17.209999
16.860001
16.860001
16.370001
16.440001
16.200001
14.89
14.78
14.35
14.21
14.01
13.84
13.79
14.42
13.78
13.77
12.54
12.29
12.66
13.18
12.97
12.72
13.39
13.39
13.45
12.99
11.98
11.84
12.72
12.98
12.73
13.41
12.81
12.31
13.04
14.73
14.37
15.1
14.91
15.94
14.76
14.06
14.99
16.82
16.49
16.809999
17.01
16.610001
15.88
15.77
15.85
15.63
14.53
13.82
14.29
14.16
14.42
14.53
13.59
13.16
13.12
14.31
14.08
14.01
14.06
15.46
16.6
15.54
16.6
17.67
16.74
19.41
20.34
19.6
16.48
15.72
16.07
18.66
14.71
13.48
13.04
13.16
13.33
13.42
13.2
13.09
13.31
13.41
13.65
13.75
13.28
12.93
13.27
12.68
13.91
12.9
12.53
12.82
12.52
12.37
12.19
13.1
13.42
13.4
12.66
12.26
12.79
12.81
12.98
13.7
14.23
14.55
14.7
15.08
13.79
13.49
13.91
15.35
15.54
15.76
16.030001
16.209999
13.8
14.15
13.79
13.04
12.48
12.33
12.46
13.56
13.72
14.23
13.76
13.55
12.92
12.87
12.89
12.14
13.28
12.28
12.28
12.53
12.44
12.87
12.84
13.77
17.879999
17.42
15.8
17.35
17.290001
18.41
21.440001
19.110001
19.950001
17.23
15.31
15.26
14.51
14.3
14.14
13.57
13.87
15.5
14.79
14.68
14.23
13.67
14.35
14.04
14
16
14.1
13.89
14.21
14.11
14.2
14.8
14.47
16.219999
17.82
15.64
14.52
15.12
14.52
15
15.09
14.02
14.93
14.62
14.41
13.88
13.1
13.09
13.37
13.96
15.57
14.89
13.82
15.89
17.030001
16.18
15.61
14.18
13.36
13.25
13.19
13.27
13.32
14.06
13.97
13.71
13.41
13.25
12.91
13.29
13.8
13.4
13.43
12.92
12.23
12.13
12.17
13.17
12.44
12.42
12.96
11.91
12.03
11.36
11.51
11.68
11.57
11.4
11.58
11.87
12.08
11.68
10.73
11.15
10.99
11.6
12.56
12.18
12.65
12.06
10.61
10.62
10.85
10.98
12.13
11.59
11.63
11.26
11.57
11.15
10.82
10.32
11.33
11.98
11.65
12.59
12.08
11.82
11.96
11
14.54
12.06
12.81
12.24
11.52
11.84
12.69
12.56
13.28
13.33
16.950001
17.030001
15.12
16.870001
16.370001
16.66
15.77
14.23
14.13
12.9
12.42
13.15
12.32
12.21
11.78
11.76
11.47
11.7
11.63
11.78
12.05
12.09
12.25
12.36
12.64
12.09
12.66
13.5
12.88
12.8
13.31
14.12
12.73
12.65
12.03
12.11
13.69
14.93
13.27
15.64
14.85
15.98
16.309999
16.709999
16.16
14.55
15.46
17.200001
15.11
18.76
21.24
24.639999
22.790001
25.27
25.200001
21.99
18.57
16.08
17.870001
16.530001
16.110001
16.040001
14.39
15.15
14.52
14.03
14.73
14.89
14.17
13.67
13.12
12.67
12.92
13.02
13.79
13.31
13.99
13.86
13.96
13.58
12.9
12.62
12.25
12.07
13.33
14.16
12.85
12.5
12.38
11.89
14.21
15.35
18.530001
20.08
21.08
20.42
23.57
19.440001
16.809999
16.49
15.25
14.8
14.37
14.5
15.06
15.92
19.200001
17.790001
19.92
21.120001
19.309999
17.01
17.549999
19.6
20.559999
21.48
22.389999
20.950001
19.889999
18.85
16.4
16.66
15.52
17.219999
20.440001
18.76
20.969999
19.43
17.33
18.33
16.85
17.290001
18.549999
17.23
16.959999
15.34
14.69
15.8
15.45
15.29
14.3
14.56
13.69
13.84
13.91
13.34
13.04
13.86
14.23
14.04
15.2
15.06
16.690001
16.870001
15.42
16
15.61
15.66
13.97
14.07
13.02
13.41
13.62
15.44
15.8
15.07
14.51
15.29
15.11
14.67
14.74
14.78
13.98
13.09
12.58
13.94
13.67
12.84
12.6
13.89
13.3
13.25
12.71
12.48
12.29
13.12
12.41
13.39
14.55
12.7
12.85
14.31
15.15
15.13
12.86
13.85
13.86
13.76
12.74
12.38
12.73
12.85
12.88
12.11
12.13
14.06
13.27
13.31
13.84
13.97
14.24
13.66
14.71
14.21
15.29
14.47
13.22
12.85
13.78
15.39
14.81
14.5
13.19
13.96
12.74
12.11
13.26
14.01
14.02
18.85
18.23
16.09
16.790001
17.01
16.09
19.66
19.969999
16.83
13.9
13.37
13.23
12.11
11.95
12.25
12.22
12.12
12.64
13.74
15.6
13.44
12.5
12.13
12.12
12.56
13
12.51
13.77
13.39
12.23
13.71
13.61
13.49
12.83
13.02
13.79
15.25
19.139999
28.030001
40.740002
36.02
30.32
26.1
26.049999
28.43
31.4
26.09
25.610001
27.799999
24.9
26.23
24.370001
23.200001
24.25
22.540001
21.35
21.139999
22.280001
20.139999
22.440001
22.129999
23.469999
23.620001
27.629999
26.83
24.5
22.549999
20.940001
19.540001
19.4
18.4
17.42
17.08
16.17
17.67
18.030001
16.049999
15.05
14.98
15.75
16.700001
14.45
14.46
15.29
15.43
14.33
14.61
15.07
14.15
14.54
15.51
15.05
14.33
16.52
15.29
16.059999
18.370001
20.08
18.16
18.84
16.85
16.99
15.47
15.62
15.93
15.19
15.12
16.129999
14.67
15.91
18.110001
14.81
15.84
17.6
19.610001
19.34
24.389999
22.73
20.950001
17.860001
18.940001
20.700001
18.700001
16.6
15.57
15.74
16.91
16.08
17.290001
18.209999
20.700001
19.34
20.59
24.99
27.01
24.299999
22.469999
25.219999
23.950001
27.02
26.049999
27.59
26.690001
22.34
24.15
22.5
23.110001
22.42
20.200001
19.98
21.98
21.65
21.84
23.379999
26
26.540001
26.290001
28.139999
25.4
24.110001
22.309999
21.639999
20.530001
19.379999
20.98
20.719999
19.110001
19.809999
20.549999
17.700001
17.09
16.700001
16.860001
17.35
18.67
18.34
18.049999
16.5
16.92
16.84
14.99
14.44
14.02
13.79
14.17
14.94
14.74
15.24
13.82
13.56
13.95
13.1
14.12
15.42
14.09
16.16
15.36
16.26
14.85
13.84
13.72
13.62
13.35
13.24
13.28
13.95
13.22
14.08
13.96
13.77
15.22
15.7
14.68
15.6
16.049999
15.91
14.72
14.57
13.63
14.69
14.41
15.04
14.68
15.57
15.95
16.33
15.2
15.82
14.42
13.9
13.43
13.12
14.19
14.2
13.63
13.47
13.65
14.05
14.08
14.64
17.030001
20.969999
20.5
20.139999
19.370001
19.41
18.370001
18.48
21.17
17.25
25.76
23.85
18.75
16.639999
15.63
14.77
15.58

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=295817&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=295817&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=295817&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 1 ; par5 = 1 ; par6 = 0 ; par7 = 0 ; par8 = 1 ;
Parameters (R input):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 1 ; par5 = 1 ; par6 = 0 ; par7 = 0 ; par8 = 1 ;
R code (references can be found in the software module):
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')