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Author

*Unverified author*

R Software Module

rwasp_cross.wasp

Title produced by software

Cross Correlation Function

Date of computation

Tue, 02 Dec 2008 08:13:35 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/02/t1228230874v9ugwba2ttaexc7.htm/, Retrieved Sun, 19 May 2024 10:24:04 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27930, Retrieved Sun, 19 May 2024 10:24:04 +0000

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Original text written by user:

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No (this computation is public)

User-defined keywords

Estimated Impact

177

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

F     [Cross Correlation Function] [Non-stationary ti...] [2008-12-02 14:37:27] [6c955a33a02d5e30e404487434e7a5c9]
F   PD    [Cross Correlation Function] [Non-stationary ti...] [2008-12-02 15:13:35] [a57a97ff9690154d18ed2c72b6ae351a] [Current]

Feedback Forum

2008-12-04 11:05:50 [Steven Vercammen] [reply] 
Deze vraag heb ik correct beantwoord. 
We zien dat er nog steeds sprake is van voorspelbaarheid voor k=-4, k=-1en k=2, maar de voorspelbaarheid is al stuk minder dan in Q7. Dit betekent dus dat er sprake was van een schijncorrelatie. Er was een ‘Z’ die invloed had op beide variabelen en dus zorgde voor een schijnbare correlatie tussen X en Y. In bovenstaande grafiek is de invloed van die Z geëlimineerd. Dit geeft dus een betrouwbaarder beeld en toont aan dat we Y veel minder goed kunnen voorspellen op basis van vroegere en toekomstige waarden van X.  
2008-12-07 10:10:20 [Käthe Vanderheggen] [reply] 
Dit is zeer goed opgelost. Niet de hele tijdreeks kan verklaard worden door deze te differentiëren want er liggen nog steeds waarden buiten het betrouwbaarheidsinterval.
2008-12-08 18:47:13 [Koen Van Baelen] [reply] 
Correct. Er is inderdaad nog maar voorspelbaarheid in enkele gevallen, namelijk waar het betrouwbaarheidsinterval overschreden wordt. Hierdoor kunnen we concluderen dat er sprake was van een nonsencorrelatie hiervoor. Dit kan bijvoorbeeld zoals de student juist uitlegt dat er een variabele Z invloed uitoefent op X en Y en zorgt voor een schijnbaar verband maar dat dus niet waar is.

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Dataseries X:

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Dataseries Y:

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Histogram

2.84
2.78
2.63
2.54
2.56
2.19
2.09
2.06
2.08
2.05
2.03
2.04
2.03
2.01
2.01
2.01
2.01
2.01
2.01
2.02
2.02
2.03
2.05
2.08
2.07
2.06
2.05
2.05
2.05
2.05
2.05
2.06
2.06
2.07
2.07
2.30
2.31
2.31
2.53
2.58
2.59
2.73
2.82
3.00
3.04
3.23
3.32
3.49
3.57
3.56
3.72
3.82
3.82
3.98
4.06
4.08
4.19
4.16
4.17
4.21
4.21
4.17
4.19
4.25
4.25
4.20
4.33
4.41
4.56

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27930&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27930&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27930&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Cross Correlation Function
Parameter	Value
Box-Cox transformation parameter (lambda) of X series	0.5
Degree of non-seasonal differencing (d) of X series	0
Degree of seasonal differencing (D) of X series	1
Seasonal Period (s)	12
Box-Cox transformation parameter (lambda) of Y series	-1.7
Degree of non-seasonal differencing (d) of Y series	1
Degree of seasonal differencing (D) of Y series	1
k	rho(Y[t],X[t+k])
-14	-0.00431381240300947
-13	0.111397822504153
-12	-0.0475687178125955
-11	-0.117181358801256
-10	-0.0258154857728156
-9	-0.200918647992180
-8	-0.195718265186021
-7	-0.0599250514734523
-6	-0.229863660149624
-5	-0.205488708237757
-4	-0.32764257167239
-3	-0.230142225957488
-2	-0.0249063662922654
-1	-0.421482615356873
0	-0.223432118665445
1	-0.0479046609271945
2	-0.392007548062422
3	0.220533792759816
4	0.061980169537483
5	-0.236049022115555
6	0.181761626372412
7	-0.113597353913149
8	0.0380518057770374
9	-0.00686882982049242
10	-0.0726888673777125
11	0.052332641520823
12	-0.0523534017676355
13	0.00513184563827432
14	0.0921411155103819

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & 0.5 \tabularnewline
Degree of non-seasonal differencing (d) of X series & 0 \tabularnewline
Degree of seasonal differencing (D) of X series & 1 \tabularnewline
Seasonal Period (s) & 12 \tabularnewline
Box-Cox transformation parameter (lambda) of Y series & -1.7 \tabularnewline
Degree of non-seasonal differencing (d) of Y series & 1 \tabularnewline
Degree of seasonal differencing (D) of Y series & 1 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-14 & -0.00431381240300947 \tabularnewline
-13 & 0.111397822504153 \tabularnewline
-12 & -0.0475687178125955 \tabularnewline
-11 & -0.117181358801256 \tabularnewline
-10 & -0.0258154857728156 \tabularnewline
-9 & -0.200918647992180 \tabularnewline
-8 & -0.195718265186021 \tabularnewline
-7 & -0.0599250514734523 \tabularnewline
-6 & -0.229863660149624 \tabularnewline
-5 & -0.205488708237757 \tabularnewline
-4 & -0.32764257167239 \tabularnewline
-3 & -0.230142225957488 \tabularnewline
-2 & -0.0249063662922654 \tabularnewline
-1 & -0.421482615356873 \tabularnewline
0 & -0.223432118665445 \tabularnewline
1 & -0.0479046609271945 \tabularnewline
2 & -0.392007548062422 \tabularnewline
3 & 0.220533792759816 \tabularnewline
4 & 0.061980169537483 \tabularnewline
5 & -0.236049022115555 \tabularnewline
6 & 0.181761626372412 \tabularnewline
7 & -0.113597353913149 \tabularnewline
8 & 0.0380518057770374 \tabularnewline
9 & -0.00686882982049242 \tabularnewline
10 & -0.0726888673777125 \tabularnewline
11 & 0.052332641520823 \tabularnewline
12 & -0.0523534017676355 \tabularnewline
13 & 0.00513184563827432 \tabularnewline
14 & 0.0921411155103819 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27930&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]0.5[/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]1[/C][/ROW]
[ROW][C]Seasonal Period (s)[/C][C]12[/C][/ROW]
[ROW][C]Box-Cox transformation parameter (lambda) of Y series[/C][C]-1.7[/C][/ROW]
[ROW][C]Degree of non-seasonal differencing (d) of Y series[/C][C]1[/C][/ROW]
[ROW][C]Degree of seasonal differencing (D) of Y series[/C][C]1[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-14[/C][C]-0.00431381240300947[/C][/ROW]
[ROW][C]-13[/C][C]0.111397822504153[/C][/ROW]
[ROW][C]-12[/C][C]-0.0475687178125955[/C][/ROW]
[ROW][C]-11[/C][C]-0.117181358801256[/C][/ROW]
[ROW][C]-10[/C][C]-0.0258154857728156[/C][/ROW]
[ROW][C]-9[/C][C]-0.200918647992180[/C][/ROW]
[ROW][C]-8[/C][C]-0.195718265186021[/C][/ROW]
[ROW][C]-7[/C][C]-0.0599250514734523[/C][/ROW]
[ROW][C]-6[/C][C]-0.229863660149624[/C][/ROW]
[ROW][C]-5[/C][C]-0.205488708237757[/C][/ROW]
[ROW][C]-4[/C][C]-0.32764257167239[/C][/ROW]
[ROW][C]-3[/C][C]-0.230142225957488[/C][/ROW]
[ROW][C]-2[/C][C]-0.0249063662922654[/C][/ROW]
[ROW][C]-1[/C][C]-0.421482615356873[/C][/ROW]
[ROW][C]0[/C][C]-0.223432118665445[/C][/ROW]
[ROW][C]1[/C][C]-0.0479046609271945[/C][/ROW]
[ROW][C]2[/C][C]-0.392007548062422[/C][/ROW]
[ROW][C]3[/C][C]0.220533792759816[/C][/ROW]
[ROW][C]4[/C][C]0.061980169537483[/C][/ROW]
[ROW][C]5[/C][C]-0.236049022115555[/C][/ROW]
[ROW][C]6[/C][C]0.181761626372412[/C][/ROW]
[ROW][C]7[/C][C]-0.113597353913149[/C][/ROW]
[ROW][C]8[/C][C]0.0380518057770374[/C][/ROW]
[ROW][C]9[/C][C]-0.00686882982049242[/C][/ROW]
[ROW][C]10[/C][C]-0.0726888673777125[/C][/ROW]
[ROW][C]11[/C][C]0.052332641520823[/C][/ROW]
[ROW][C]12[/C][C]-0.0523534017676355[/C][/ROW]
[ROW][C]13[/C][C]0.00513184563827432[/C][/ROW]
[ROW][C]14[/C][C]0.0921411155103819[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27930&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27930&T=1

As an alternative you can also use a QR Code:

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

Cross Correlation Function
Parameter	Value
Box-Cox transformation parameter (lambda) of X series	0.5
Degree of non-seasonal differencing (d) of X series	0
Degree of seasonal differencing (D) of X series	1
Seasonal Period (s)	12
Box-Cox transformation parameter (lambda) of Y series	-1.7
Degree of non-seasonal differencing (d) of Y series	1
Degree of seasonal differencing (D) of Y series	1
k	rho(Y[t],X[t+k])
-14	-0.00431381240300947
-13	0.111397822504153
-12	-0.0475687178125955
-11	-0.117181358801256
-10	-0.0258154857728156
-9	-0.200918647992180
-8	-0.195718265186021
-7	-0.0599250514734523
-6	-0.229863660149624
-5	-0.205488708237757
-4	-0.32764257167239
-3	-0.230142225957488
-2	-0.0249063662922654
-1	-0.421482615356873
0	-0.223432118665445
1	-0.0479046609271945
2	-0.392007548062422
3	0.220533792759816
4	0.061980169537483
5	-0.236049022115555
6	0.181761626372412
7	-0.113597353913149
8	0.0380518057770374
9	-0.00686882982049242
10	-0.0726888673777125
11	0.052332641520823
12	-0.0523534017676355
13	0.00513184563827432
14	0.0921411155103819

Figure 1

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 0.5 ; par2 = 0 ; par3 = 1 ; par4 = 12 ; par5 = -1.7 ; par6 = 1 ; par7 = 1 ;

Parameters (R input):

par1 = 0.5 ; par2 = 0 ; par3 = 1 ; par4 = 12 ; par5 = -1.7 ; par6 = 1 ; par7 = 1 ;

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')

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Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code