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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

Mon, 01 Dec 2008 15:39:59 -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/01/t122817124580uckwjzbp8oy81.htm/, Retrieved Sun, 05 May 2024 18:36:45 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27475, Retrieved Sun, 05 May 2024 18:36:45 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Non Stationary Time Series

Estimated Impact

177

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

-     [Cross Correlation Function] [Q7 3] [2008-11-26 19:25:05] [8eb83367d7ce233bbf617141d324189b]
F         [Cross Correlation Function] [question 7 cross ...] [2008-12-01 22:39:59] [3efbb18563b4564408d69b3c9a8e9a6e] [Current]

Feedback Forum

2008-12-08 07:46:52 [0762c65deec3d397cd9f26b3749a0847] [reply] 
Je kan de correlatie berekening tussen 101 reeksen en je zal in veel gevallen een positief correlatie bekomen, dit wil niet zeggen dat de reeksen effectief iets met elkaar te maken hebben. Daarom ga je reeksen differentiëren en ga je de 'nonsens' correlatie eruit halen. het is duidelijk dat jou tijdreeksen 'iets' met elkaar te maken dus je kan zeggen dat je na differentiatie alleen zuivere correlatie gegevens bekomt. 

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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	0 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 & 0 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27475&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]0 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=27475&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27475&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	0 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	1
Degree of seasonal differencing (D) of X series	1
Seasonal Period (s)	12
Box-Cox transformation parameter (lambda) of Y series	1
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])
-16	-0.081794596234103
-15	-0.0108757632078465
-14	0.145270271813004
-13	-0.00830672290926244
-12	-0.211315240409863
-11	0.0862806470227862
-10	-0.105651486559672
-9	0.172690998559717
-8	-0.0354215587534432
-7	-0.0207586901030551
-6	0.0118559008682719
-5	-0.110009077753571
-4	0.121170183432084
-3	0.137563182474806
-2	-0.264646202071002
-1	-0.332341184333816
0	0.898712953093486
1	-0.325488753041874
2	-0.0608459594312344
3	-0.0361065066821259
4	0.110124684006107
5	-0.111896182831683
6	0.0877048063119969
7	-0.0110676955496053
8	-0.0904236947653907
9	-0.017884718057844
10	0.139002815869754
11	0.0973452242631403
12	-0.214099959512389
13	-0.121046805533731
14	0.157204873882816
15	0.0301006100349092
16	-0.0570339244323336

\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 & 1 \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 \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
-16 & -0.081794596234103 \tabularnewline
-15 & -0.0108757632078465 \tabularnewline
-14 & 0.145270271813004 \tabularnewline
-13 & -0.00830672290926244 \tabularnewline
-12 & -0.211315240409863 \tabularnewline
-11 & 0.0862806470227862 \tabularnewline
-10 & -0.105651486559672 \tabularnewline
-9 & 0.172690998559717 \tabularnewline
-8 & -0.0354215587534432 \tabularnewline
-7 & -0.0207586901030551 \tabularnewline
-6 & 0.0118559008682719 \tabularnewline
-5 & -0.110009077753571 \tabularnewline
-4 & 0.121170183432084 \tabularnewline
-3 & 0.137563182474806 \tabularnewline
-2 & -0.264646202071002 \tabularnewline
-1 & -0.332341184333816 \tabularnewline
0 & 0.898712953093486 \tabularnewline
1 & -0.325488753041874 \tabularnewline
2 & -0.0608459594312344 \tabularnewline
3 & -0.0361065066821259 \tabularnewline
4 & 0.110124684006107 \tabularnewline
5 & -0.111896182831683 \tabularnewline
6 & 0.0877048063119969 \tabularnewline
7 & -0.0110676955496053 \tabularnewline
8 & -0.0904236947653907 \tabularnewline
9 & -0.017884718057844 \tabularnewline
10 & 0.139002815869754 \tabularnewline
11 & 0.0973452242631403 \tabularnewline
12 & -0.214099959512389 \tabularnewline
13 & -0.121046805533731 \tabularnewline
14 & 0.157204873882816 \tabularnewline
15 & 0.0301006100349092 \tabularnewline
16 & -0.0570339244323336 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27475&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]1[/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[/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]-16[/C][C]-0.081794596234103[/C][/ROW]
[ROW][C]-15[/C][C]-0.0108757632078465[/C][/ROW]
[ROW][C]-14[/C][C]0.145270271813004[/C][/ROW]
[ROW][C]-13[/C][C]-0.00830672290926244[/C][/ROW]
[ROW][C]-12[/C][C]-0.211315240409863[/C][/ROW]
[ROW][C]-11[/C][C]0.0862806470227862[/C][/ROW]
[ROW][C]-10[/C][C]-0.105651486559672[/C][/ROW]
[ROW][C]-9[/C][C]0.172690998559717[/C][/ROW]
[ROW][C]-8[/C][C]-0.0354215587534432[/C][/ROW]
[ROW][C]-7[/C][C]-0.0207586901030551[/C][/ROW]
[ROW][C]-6[/C][C]0.0118559008682719[/C][/ROW]
[ROW][C]-5[/C][C]-0.110009077753571[/C][/ROW]
[ROW][C]-4[/C][C]0.121170183432084[/C][/ROW]
[ROW][C]-3[/C][C]0.137563182474806[/C][/ROW]
[ROW][C]-2[/C][C]-0.264646202071002[/C][/ROW]
[ROW][C]-1[/C][C]-0.332341184333816[/C][/ROW]
[ROW][C]0[/C][C]0.898712953093486[/C][/ROW]
[ROW][C]1[/C][C]-0.325488753041874[/C][/ROW]
[ROW][C]2[/C][C]-0.0608459594312344[/C][/ROW]
[ROW][C]3[/C][C]-0.0361065066821259[/C][/ROW]
[ROW][C]4[/C][C]0.110124684006107[/C][/ROW]
[ROW][C]5[/C][C]-0.111896182831683[/C][/ROW]
[ROW][C]6[/C][C]0.0877048063119969[/C][/ROW]
[ROW][C]7[/C][C]-0.0110676955496053[/C][/ROW]
[ROW][C]8[/C][C]-0.0904236947653907[/C][/ROW]
[ROW][C]9[/C][C]-0.017884718057844[/C][/ROW]
[ROW][C]10[/C][C]0.139002815869754[/C][/ROW]
[ROW][C]11[/C][C]0.0973452242631403[/C][/ROW]
[ROW][C]12[/C][C]-0.214099959512389[/C][/ROW]
[ROW][C]13[/C][C]-0.121046805533731[/C][/ROW]
[ROW][C]14[/C][C]0.157204873882816[/C][/ROW]
[ROW][C]15[/C][C]0.0301006100349092[/C][/ROW]
[ROW][C]16[/C][C]-0.0570339244323336[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27475&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27475&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	1
Degree of seasonal differencing (D) of X series	1
Seasonal Period (s)	12
Box-Cox transformation parameter (lambda) of Y series	1
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])
-16	-0.081794596234103
-15	-0.0108757632078465
-14	0.145270271813004
-13	-0.00830672290926244
-12	-0.211315240409863
-11	0.0862806470227862
-10	-0.105651486559672
-9	0.172690998559717
-8	-0.0354215587534432
-7	-0.0207586901030551
-6	0.0118559008682719
-5	-0.110009077753571
-4	0.121170183432084
-3	0.137563182474806
-2	-0.264646202071002
-1	-0.332341184333816
0	0.898712953093486
1	-0.325488753041874
2	-0.0608459594312344
3	-0.0361065066821259
4	0.110124684006107
5	-0.111896182831683
6	0.0877048063119969
7	-0.0110676955496053
8	-0.0904236947653907
9	-0.017884718057844
10	0.139002815869754
11	0.0973452242631403
12	-0.214099959512389
13	-0.121046805533731
14	0.157204873882816
15	0.0301006100349092
16	-0.0570339244323336

Figure 1

PNG link

Postscript link

PDF link

Parameters (Session):

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

Parameters (R input):

par1 = 1 ; par2 = 1 ; par3 = 1 ; par4 = 12 ; par5 = 1 ; 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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Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code