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*The author of this computation has been verified*

R Software Module

rwasp_cross.wasp

Title produced by software

Cross Correlation Function

Date of computation

Fri, 05 Dec 2008 12:07:41 -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/05/t12285041317ua62my4cddtzxn.htm/, Retrieved Thu, 16 May 2024 09:10:36 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=29400, Retrieved Thu, 16 May 2024 09:10:36 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

196

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

F     [Cross Correlation Function] [Q7 - non stationa...] [2008-12-01 00:17:04] [57850c80fd59ccfb28f882be994e814e]
F   P     [Cross Correlation Function] [q9 - variance red...] [2008-12-05 19:07:41] [0831954c833179c36e9320daee0825b5] [Current]

Feedback Forum

2008-12-05 19:10:23 [Bob Leysen] [reply] 
Uitleg bij verbetering Q9: 
 
Ik weet niet goed wat ik uit onderstaande grafiek kan afleiden, de seizonaliteit is weg maar enkele cross correlatiecoëfficiënten steken toch nog boven het betrouwbaarheidsinterval uit.  

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Dataseries Y:

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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	'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29400&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29400&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29400&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	'George Udny Yule' @ 72.249.76.132

Cross Correlation Function
Parameter	Value
Box-Cox transformation parameter (lambda) of X series	1
Degree of non-seasonal differencing (d) of X series	2
Degree of seasonal differencing (D) of X series	0
Seasonal Period (s)	12
Box-Cox transformation parameter (lambda) of Y series	1
Degree of non-seasonal differencing (d) of Y series	0
Degree of seasonal differencing (D) of Y series	0
k	rho(Y[t],X[t+k])
-15	0.0548542872588133
-14	0.171374251839766
-13	-0.374330409179643
-12	0.111666040591889
-11	0.129309827215945
-10	-0.0130314881020397
-9	-0.0926505586601394
-8	0.116176686707601
-7	-0.154362397252013
-6	0.121371828147568
-5	-0.0629510138264404
-4	-0.0725245906292337
-3	0.142618743174293
-2	0.229588779903307
-1	-0.538864887232287
0	0.183823631153383
1	0.159372687328676
2	-0.0412346106268782
3	-0.0856869112090173
4	0.099046528966826
5	-0.145503232716741
6	0.131733850160514
7	-0.108681532268999
8	-0.0208991308503382
9	0.155871709962424
10	0.0814679720618008
11	-0.368942312413424
12	0.149852412924629
13	0.0622640618910455
14	0.0160656175192513
15	-0.0402991239066184

\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 & 2 \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
-15 & 0.0548542872588133 \tabularnewline
-14 & 0.171374251839766 \tabularnewline
-13 & -0.374330409179643 \tabularnewline
-12 & 0.111666040591889 \tabularnewline
-11 & 0.129309827215945 \tabularnewline
-10 & -0.0130314881020397 \tabularnewline
-9 & -0.0926505586601394 \tabularnewline
-8 & 0.116176686707601 \tabularnewline
-7 & -0.154362397252013 \tabularnewline
-6 & 0.121371828147568 \tabularnewline
-5 & -0.0629510138264404 \tabularnewline
-4 & -0.0725245906292337 \tabularnewline
-3 & 0.142618743174293 \tabularnewline
-2 & 0.229588779903307 \tabularnewline
-1 & -0.538864887232287 \tabularnewline
0 & 0.183823631153383 \tabularnewline
1 & 0.159372687328676 \tabularnewline
2 & -0.0412346106268782 \tabularnewline
3 & -0.0856869112090173 \tabularnewline
4 & 0.099046528966826 \tabularnewline
5 & -0.145503232716741 \tabularnewline
6 & 0.131733850160514 \tabularnewline
7 & -0.108681532268999 \tabularnewline
8 & -0.0208991308503382 \tabularnewline
9 & 0.155871709962424 \tabularnewline
10 & 0.0814679720618008 \tabularnewline
11 & -0.368942312413424 \tabularnewline
12 & 0.149852412924629 \tabularnewline
13 & 0.0622640618910455 \tabularnewline
14 & 0.0160656175192513 \tabularnewline
15 & -0.0402991239066184 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29400&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]2[/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]-15[/C][C]0.0548542872588133[/C][/ROW]
[ROW][C]-14[/C][C]0.171374251839766[/C][/ROW]
[ROW][C]-13[/C][C]-0.374330409179643[/C][/ROW]
[ROW][C]-12[/C][C]0.111666040591889[/C][/ROW]
[ROW][C]-11[/C][C]0.129309827215945[/C][/ROW]
[ROW][C]-10[/C][C]-0.0130314881020397[/C][/ROW]
[ROW][C]-9[/C][C]-0.0926505586601394[/C][/ROW]
[ROW][C]-8[/C][C]0.116176686707601[/C][/ROW]
[ROW][C]-7[/C][C]-0.154362397252013[/C][/ROW]
[ROW][C]-6[/C][C]0.121371828147568[/C][/ROW]
[ROW][C]-5[/C][C]-0.0629510138264404[/C][/ROW]
[ROW][C]-4[/C][C]-0.0725245906292337[/C][/ROW]
[ROW][C]-3[/C][C]0.142618743174293[/C][/ROW]
[ROW][C]-2[/C][C]0.229588779903307[/C][/ROW]
[ROW][C]-1[/C][C]-0.538864887232287[/C][/ROW]
[ROW][C]0[/C][C]0.183823631153383[/C][/ROW]
[ROW][C]1[/C][C]0.159372687328676[/C][/ROW]
[ROW][C]2[/C][C]-0.0412346106268782[/C][/ROW]
[ROW][C]3[/C][C]-0.0856869112090173[/C][/ROW]
[ROW][C]4[/C][C]0.099046528966826[/C][/ROW]
[ROW][C]5[/C][C]-0.145503232716741[/C][/ROW]
[ROW][C]6[/C][C]0.131733850160514[/C][/ROW]
[ROW][C]7[/C][C]-0.108681532268999[/C][/ROW]
[ROW][C]8[/C][C]-0.0208991308503382[/C][/ROW]
[ROW][C]9[/C][C]0.155871709962424[/C][/ROW]
[ROW][C]10[/C][C]0.0814679720618008[/C][/ROW]
[ROW][C]11[/C][C]-0.368942312413424[/C][/ROW]
[ROW][C]12[/C][C]0.149852412924629[/C][/ROW]
[ROW][C]13[/C][C]0.0622640618910455[/C][/ROW]
[ROW][C]14[/C][C]0.0160656175192513[/C][/ROW]
[ROW][C]15[/C][C]-0.0402991239066184[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29400&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29400&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	1
Degree of non-seasonal differencing (d) of X series	2
Degree of seasonal differencing (D) of X series	0
Seasonal Period (s)	12
Box-Cox transformation parameter (lambda) of Y series	1
Degree of non-seasonal differencing (d) of Y series	0
Degree of seasonal differencing (D) of Y series	0
k	rho(Y[t],X[t+k])
-15	0.0548542872588133
-14	0.171374251839766
-13	-0.374330409179643
-12	0.111666040591889
-11	0.129309827215945
-10	-0.0130314881020397
-9	-0.0926505586601394
-8	0.116176686707601
-7	-0.154362397252013
-6	0.121371828147568
-5	-0.0629510138264404
-4	-0.0725245906292337
-3	0.142618743174293
-2	0.229588779903307
-1	-0.538864887232287
0	0.183823631153383
1	0.159372687328676
2	-0.0412346106268782
3	-0.0856869112090173
4	0.099046528966826
5	-0.145503232716741
6	0.131733850160514
7	-0.108681532268999
8	-0.0208991308503382
9	0.155871709962424
10	0.0814679720618008
11	-0.368942312413424
12	0.149852412924629
13	0.0622640618910455
14	0.0160656175192513
15	-0.0402991239066184

Figure 1

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 1 ; par2 = 2 ; par3 = 0 ; par4 = 12 ; par5 = 1 ; par6 = 0 ; par7 = 0 ;

Parameters (R input):

par1 = 1 ; par2 = 2 ; 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')

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Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code