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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_cross.wasp
Title produced by softwareCross Correlation Function
Date of computationTue, 02 Dec 2008 10:59:36 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/02/t12282408205f87l8tyqs1u1g7.htm/, Retrieved Sun, 19 May 2024 10:46:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28159, Retrieved Sun, 19 May 2024 10:46:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact164

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [Airline data] [2007-10-18 09:58:47] [42daae401fd3def69a25014f2252b4c2]
F RMPD  [(Partial) Autocorrelation Function] [nonstationaryques...] [2008-12-01 19:09:36] [922d8ae7bd2fd460a62d9020ccd4931a]
F RMPD      [Cross Correlation Function] [nonstationaryques...] [2008-12-02 17:59:36] [89a49ebb3ece8e9a225c7f9f53a14c57] [Current]
F RMPD        [Variance Reduction Matrix] [nonstationaryques...] [2008-12-02 18:13:31] [922d8ae7bd2fd460a62d9020ccd4931a]
F    D          [Variance Reduction Matrix] [nonstationaryques...] [2008-12-02 18:23:40] [922d8ae7bd2fd460a62d9020ccd4931a]
F   P         [Cross Correlation Function] [nonstationaryques...] [2008-12-02 19:43:19] [922d8ae7bd2fd460a62d9020ccd4931a]
F   PD          [Cross Correlation Function] [nonstationaryques...] [2008-12-02 19:52:09] [922d8ae7bd2fd460a62d9020ccd4931a]
Feedback Forum
2008-12-07 10:17:15 [6066575aa30c0611e452e930b1dff53d] [reply] 
Hier werd de cross correlation function berekent tussen investeringsgoederen en consumptiegoederen. Bovendien werd er ook vermeld dat het hier gaat om een ruwe reeks want de tijdreeks werd niet gedifferentieerd en niet getransformeerd (d=0 en D=0). In de tabel zien we dat voor k=0 de correlatie tussen Y[t] en X[t] 0.686100625132908 bedraagt, dit is de correlatie zonder verschuiving in de tijd. Zo is ook, bijvoorbeeld, 0.0142990905644138 de correlatie tussen Y[t] en X[t-15]. In de grafiek van de cross correlation function worden deze cijfers grafisch voorgesteld. In deze grafiek zien we dat er veel verticale lijnen buiten het 95% betrouwbaarheidsinterval liggen. Al de verticale lijnen die buiten dit betrouwbaarheidsinterval liggen, zijn significant verschillend van nul. We zien ook dat rechts van nul, waar de k waarde positief is, zeer veel verticale lijnen buiten het 95% betrouwbaarheidsinterval liggen.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
78,4
114,6
113,3
117
99,6
99,4
101,9
115,2
108,5
113,8
121
92,2
90,2
101,5
126,6
93,9
89,8
93,4
101,5
110,4
105,9
108,4
113,9
86,1
69,4
101,2
100,5
98
106,6
90,1
96,9
125,9
112
100
123,9
79,8
83,4
113,6
112,9
104
109,9
99
106,3
128,9
111,1
102,9
130
87
87,5
117,6
103,4
110,8
112,6
102,5
112,4
135,6
105,1
127,7
137
91
90,5
122,4
123,3
124,3
120
118,1
119
142,7
123,6
129,6
151,6
110,4
99,2
130,5
136,2
129,7
128
121,6
135,8
143,8
147,5
136,2
156,6
123,3
100,4
Dataseries Y:
Download CSV
Histogram 
97,8
107,4
117,5
105,6
97,4
99,5
98
104,3
100,6
101,1
103,9
96,9
95,5
108,4
117
103,8
100,8
110,6
104
112,6
107,3
98,9
109,8
104,9
102,2
123,9
124,9
112,7
121,9
100,6
104,3
120,4
107,5
102,9
125,6
107,5
108,8
128,4
121,1
119,5
128,7
108,7
105,5
119,8
111,3
110,6
120,1
97,5
107,7
127,3
117,2
119,8
116,2
111
112,4
130,6
109,1
118,8
123,9
101,6
112,8
128
129,6
125,8
119,5
115,7
113,6
129,7
112
116,8
127
112,1
114,2
121,1
131,6
125
120,4
117,7
117,5
120,6
127,5
112,3
124,5
115,2
105,4

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28159&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28159&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28159&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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series1
Degree of non-seasonal differencing (d) of X series0
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-16-0.0700213890544833
-150.0142990905644138
-14-0.193365488956066
-13-0.0450890745987972
-120.377965898280041
-110.0519906803063849
-10-0.0746958549880802
-90.225762577193397
-80.0630934719706794
-70.132622602806340
-60.426786401686938
-50.201701426202626
-40.119284736444523
-30.237122659296598
-2-0.0886280683008975
-10.108125980015205
00.686100625132908
10.230290912212587
20.139624344725165
30.441454371940561
40.232682044125126
50.306827743228845
60.590262231124296
70.308599562256039
80.281539285337359
90.32576371189541
10-0.0360541777512949
110.178304899355525
120.623018971583643
130.262213896511307
140.158442969008772
150.420419585992301
160.187429749797794

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & 1 \tabularnewline
Degree of non-seasonal differencing (d) of X series & 0 \tabularnewline
Degree of seasonal differencing (D) of X series & 0 \tabularnewline
Seasonal Period (s) & 12 \tabularnewline
Box-Cox transformation parameter (lambda) of Y series & 1 \tabularnewline
Degree of non-seasonal differencing (d) of Y series & 0 \tabularnewline
Degree of seasonal differencing (D) of Y series & 0 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-16 & -0.0700213890544833 \tabularnewline
-15 & 0.0142990905644138 \tabularnewline
-14 & -0.193365488956066 \tabularnewline
-13 & -0.0450890745987972 \tabularnewline
-12 & 0.377965898280041 \tabularnewline
-11 & 0.0519906803063849 \tabularnewline
-10 & -0.0746958549880802 \tabularnewline
-9 & 0.225762577193397 \tabularnewline
-8 & 0.0630934719706794 \tabularnewline
-7 & 0.132622602806340 \tabularnewline
-6 & 0.426786401686938 \tabularnewline
-5 & 0.201701426202626 \tabularnewline
-4 & 0.119284736444523 \tabularnewline
-3 & 0.237122659296598 \tabularnewline
-2 & -0.0886280683008975 \tabularnewline
-1 & 0.108125980015205 \tabularnewline
0 & 0.686100625132908 \tabularnewline
1 & 0.230290912212587 \tabularnewline
2 & 0.139624344725165 \tabularnewline
3 & 0.441454371940561 \tabularnewline
4 & 0.232682044125126 \tabularnewline
5 & 0.306827743228845 \tabularnewline
6 & 0.590262231124296 \tabularnewline
7 & 0.308599562256039 \tabularnewline
8 & 0.281539285337359 \tabularnewline
9 & 0.32576371189541 \tabularnewline
10 & -0.0360541777512949 \tabularnewline
11 & 0.178304899355525 \tabularnewline
12 & 0.623018971583643 \tabularnewline
13 & 0.262213896511307 \tabularnewline
14 & 0.158442969008772 \tabularnewline
15 & 0.420419585992301 \tabularnewline
16 & 0.187429749797794 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28159&T=1

[TABLE]
[ROW][C]Cross Correlation Function[/C][/ROW]
[ROW][C]Parameter[/C][C]Value[/C][/ROW]
[ROW][C]Box-Cox transformation parameter (lambda) of X series[/C][C]1[/C][/ROW]
[ROW][C]Degree of non-seasonal differencing (d) of X series[/C][C]0[/C][/ROW]
[ROW][C]Degree of seasonal differencing (D) of X series[/C][C]0[/C][/ROW]
[ROW][C]Seasonal Period (s)[/C][C]12[/C][/ROW]
[ROW][C]Box-Cox transformation parameter (lambda) of Y series[/C][C]1[/C][/ROW]
[ROW][C]Degree of non-seasonal differencing (d) of Y series[/C][C]0[/C][/ROW]
[ROW][C]Degree of seasonal differencing (D) of Y series[/C][C]0[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-16[/C][C]-0.0700213890544833[/C][/ROW]
[ROW][C]-15[/C][C]0.0142990905644138[/C][/ROW]
[ROW][C]-14[/C][C]-0.193365488956066[/C][/ROW]
[ROW][C]-13[/C][C]-0.0450890745987972[/C][/ROW]
[ROW][C]-12[/C][C]0.377965898280041[/C][/ROW]
[ROW][C]-11[/C][C]0.0519906803063849[/C][/ROW]
[ROW][C]-10[/C][C]-0.0746958549880802[/C][/ROW]
[ROW][C]-9[/C][C]0.225762577193397[/C][/ROW]
[ROW][C]-8[/C][C]0.0630934719706794[/C][/ROW]
[ROW][C]-7[/C][C]0.132622602806340[/C][/ROW]
[ROW][C]-6[/C][C]0.426786401686938[/C][/ROW]
[ROW][C]-5[/C][C]0.201701426202626[/C][/ROW]
[ROW][C]-4[/C][C]0.119284736444523[/C][/ROW]
[ROW][C]-3[/C][C]0.237122659296598[/C][/ROW]
[ROW][C]-2[/C][C]-0.0886280683008975[/C][/ROW]
[ROW][C]-1[/C][C]0.108125980015205[/C][/ROW]
[ROW][C]0[/C][C]0.686100625132908[/C][/ROW]
[ROW][C]1[/C][C]0.230290912212587[/C][/ROW]
[ROW][C]2[/C][C]0.139624344725165[/C][/ROW]
[ROW][C]3[/C][C]0.441454371940561[/C][/ROW]
[ROW][C]4[/C][C]0.232682044125126[/C][/ROW]
[ROW][C]5[/C][C]0.306827743228845[/C][/ROW]
[ROW][C]6[/C][C]0.590262231124296[/C][/ROW]
[ROW][C]7[/C][C]0.308599562256039[/C][/ROW]
[ROW][C]8[/C][C]0.281539285337359[/C][/ROW]
[ROW][C]9[/C][C]0.32576371189541[/C][/ROW]
[ROW][C]10[/C][C]-0.0360541777512949[/C][/ROW]
[ROW][C]11[/C][C]0.178304899355525[/C][/ROW]
[ROW][C]12[/C][C]0.623018971583643[/C][/ROW]
[ROW][C]13[/C][C]0.262213896511307[/C][/ROW]
[ROW][C]14[/C][C]0.158442969008772[/C][/ROW]
[ROW][C]15[/C][C]0.420419585992301[/C][/ROW]
[ROW][C]16[/C][C]0.187429749797794[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28159&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28159&T=1

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


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

Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series1
Degree of non-seasonal differencing (d) of X series0
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-16-0.0700213890544833
-150.0142990905644138
-14-0.193365488956066
-13-0.0450890745987972
-120.377965898280041
-110.0519906803063849
-10-0.0746958549880802
-90.225762577193397
-80.0630934719706794
-70.132622602806340
-60.426786401686938
-50.201701426202626
-40.119284736444523
-30.237122659296598
-2-0.0886280683008975
-10.108125980015205
00.686100625132908
10.230290912212587
20.139624344725165
30.441454371940561
40.232682044125126
50.306827743228845
60.590262231124296
70.308599562256039
80.281539285337359
90.32576371189541
10-0.0360541777512949
110.178304899355525
120.623018971583643
130.262213896511307
140.158442969008772
150.420419585992301
160.187429749797794


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 12 ; par5 = 1 ; par6 = 0 ; par7 = 0 ;
Parameters (R input):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 12 ; par5 = 1 ; par6 = 0 ; par7 = 0 ;
R code (references can be found in the software module):
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)
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
bitmap(file='test1.png')
(r <- ccf(x,y,main='Cross Correlation Function',ylab='CCF',xlab='Lag (k)'))
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Cross Correlation Function',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'Value',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Box-Cox transformation parameter (lambda) of X series',header=TRUE)
a<-table.element(a,par1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degree of non-seasonal differencing (d) of X series',header=TRUE)
a<-table.element(a,par2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degree of seasonal differencing (D) of X series',header=TRUE)
a<-table.element(a,par3)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Seasonal Period (s)',header=TRUE)
a<-table.element(a,par4)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Box-Cox transformation parameter (lambda) of Y series',header=TRUE)
a<-table.element(a,par5)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degree of non-seasonal differencing (d) of Y series',header=TRUE)
a<-table.element(a,par6)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degree of seasonal differencing (D) of Y series',header=TRUE)
a<-table.element(a,par7)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'k',header=TRUE)
a<-table.element(a,'rho(Y[t],X[t+k])',header=TRUE)
a<-table.row.end(a)
mylength <- length(r$acf)
myhalf <- floor((mylength-1)/2)
for (i in 1:mylength) {
a<-table.row.start(a)
a<-table.element(a,i-myhalf-1,header=TRUE)
a<-table.element(a,r$acf[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')