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*Unverified author*

R Software Module

rwasp_cross.wasp

Title produced by software

Cross Correlation Function

Date of computation

Wed, 03 Dec 2008 05:45:14 -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/03/t1228308360hf1lg8msxp1bt4k.htm/, Retrieved Sun, 19 May 2024 06:06:20 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28667, Retrieved Sun, 19 May 2024 06:06:20 +0000

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Estimated Impact

225

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  [Cross Correlation Function] [] [2008-12-02 19:55:38] [b28ef2aea2cd58ceb5ad90223572c703]
F   P       [Cross Correlation Function] [Q9] [2008-12-03 12:45:14] [d41d8cd98f00b204e9800998ecf8427e] [Current]

Feedback Forum

2008-12-03 19:09:11 [407693b66d7f2e0b350979005057872d] [reply] 
Dit antwoord is volledig juist: 
Als we differentiatie toepassen door voor de parameter X en Y de kleine en grote d een 1 nemen zien we nu dat er maar 5 waarden buiten het betrouwbaarheidsinterval liggen . 
 
2008-12-04 16:16:15 [c97d2ae59c98cf77a04815c1edffab5a] [reply] 
deze oplossing is niet juist, omdat de student Q8 niet volledig heeft opgelost. 
(zie Q8) 
 
2008-12-07 18:23:57 [Sandra Hofmans] [reply] 
Het klopt dat we het verband verdwenen is. Dit is te verklaren doordat de correlatie vertekend kan worden door een derde variabele. Deze kan een invloed uitoefenen op zowel x als y. We moeten deze variabele toch elimineren.  
Maar ja had een betere grafiek bekomen indien je lambda ook nog had berekend.  
2008-12-08 13:12:14 [Dave Bellekens] [reply] 
Het gevonden antwoord en de bijhorende uitleg kloppen wel, maar als je in Q8 de Lambda waarde had berekend, had je deze opgave nog beter kunnen oplossen. 
 
We zien hier dus wel dat het aantal significante coëfficiënten sterk is gedaald, omdat de reeksen stationair zijn gemaakt.

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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=28667&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=28667&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28667&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	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])
-18	0.0455789631444636
-17	0.00400323596052212
-16	-0.0755873109858475
-15	-0.00227560442869971
-14	0.194140579281223
-13	-0.0220910322372835
-12	-0.231678361770819
-11	-0.0100392204092405
-10	-0.0079866949022926
-9	0.0624684162268147
-8	0.0635370471582327
-7	0.0320628533820949
-6	0.0672611532898171
-5	0.0112166241785642
-4	0.120453771074033
-3	0.128587616865702
-2	-0.0268846536534497
-1	-0.036304988312156
0	0.807803711523296
1	0.0490167402762864
2	-0.0762356107357594
3	0.060149383588605
4	0.0396014088594873
5	0.0432167268141464
6	0.0769581384606568
7	-0.00611561411843158
8	0.0508795897124187
9	0.0451834432972536
10	-0.0582579279194025
11	0.0307667743247605
12	-0.163559043679027
13	-0.0269307071796227
14	0.202425463126028
15	-0.0209797792852681
16	-0.0480157057088801
17	0.0224979861577774
18	0.0471338752181358

\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
-18 & 0.0455789631444636 \tabularnewline
-17 & 0.00400323596052212 \tabularnewline
-16 & -0.0755873109858475 \tabularnewline
-15 & -0.00227560442869971 \tabularnewline
-14 & 0.194140579281223 \tabularnewline
-13 & -0.0220910322372835 \tabularnewline
-12 & -0.231678361770819 \tabularnewline
-11 & -0.0100392204092405 \tabularnewline
-10 & -0.0079866949022926 \tabularnewline
-9 & 0.0624684162268147 \tabularnewline
-8 & 0.0635370471582327 \tabularnewline
-7 & 0.0320628533820949 \tabularnewline
-6 & 0.0672611532898171 \tabularnewline
-5 & 0.0112166241785642 \tabularnewline
-4 & 0.120453771074033 \tabularnewline
-3 & 0.128587616865702 \tabularnewline
-2 & -0.0268846536534497 \tabularnewline
-1 & -0.036304988312156 \tabularnewline
0 & 0.807803711523296 \tabularnewline
1 & 0.0490167402762864 \tabularnewline
2 & -0.0762356107357594 \tabularnewline
3 & 0.060149383588605 \tabularnewline
4 & 0.0396014088594873 \tabularnewline
5 & 0.0432167268141464 \tabularnewline
6 & 0.0769581384606568 \tabularnewline
7 & -0.00611561411843158 \tabularnewline
8 & 0.0508795897124187 \tabularnewline
9 & 0.0451834432972536 \tabularnewline
10 & -0.0582579279194025 \tabularnewline
11 & 0.0307667743247605 \tabularnewline
12 & -0.163559043679027 \tabularnewline
13 & -0.0269307071796227 \tabularnewline
14 & 0.202425463126028 \tabularnewline
15 & -0.0209797792852681 \tabularnewline
16 & -0.0480157057088801 \tabularnewline
17 & 0.0224979861577774 \tabularnewline
18 & 0.0471338752181358 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28667&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]-18[/C][C]0.0455789631444636[/C][/ROW]
[ROW][C]-17[/C][C]0.00400323596052212[/C][/ROW]
[ROW][C]-16[/C][C]-0.0755873109858475[/C][/ROW]
[ROW][C]-15[/C][C]-0.00227560442869971[/C][/ROW]
[ROW][C]-14[/C][C]0.194140579281223[/C][/ROW]
[ROW][C]-13[/C][C]-0.0220910322372835[/C][/ROW]
[ROW][C]-12[/C][C]-0.231678361770819[/C][/ROW]
[ROW][C]-11[/C][C]-0.0100392204092405[/C][/ROW]
[ROW][C]-10[/C][C]-0.0079866949022926[/C][/ROW]
[ROW][C]-9[/C][C]0.0624684162268147[/C][/ROW]
[ROW][C]-8[/C][C]0.0635370471582327[/C][/ROW]
[ROW][C]-7[/C][C]0.0320628533820949[/C][/ROW]
[ROW][C]-6[/C][C]0.0672611532898171[/C][/ROW]
[ROW][C]-5[/C][C]0.0112166241785642[/C][/ROW]
[ROW][C]-4[/C][C]0.120453771074033[/C][/ROW]
[ROW][C]-3[/C][C]0.128587616865702[/C][/ROW]
[ROW][C]-2[/C][C]-0.0268846536534497[/C][/ROW]
[ROW][C]-1[/C][C]-0.036304988312156[/C][/ROW]
[ROW][C]0[/C][C]0.807803711523296[/C][/ROW]
[ROW][C]1[/C][C]0.0490167402762864[/C][/ROW]
[ROW][C]2[/C][C]-0.0762356107357594[/C][/ROW]
[ROW][C]3[/C][C]0.060149383588605[/C][/ROW]
[ROW][C]4[/C][C]0.0396014088594873[/C][/ROW]
[ROW][C]5[/C][C]0.0432167268141464[/C][/ROW]
[ROW][C]6[/C][C]0.0769581384606568[/C][/ROW]
[ROW][C]7[/C][C]-0.00611561411843158[/C][/ROW]
[ROW][C]8[/C][C]0.0508795897124187[/C][/ROW]
[ROW][C]9[/C][C]0.0451834432972536[/C][/ROW]
[ROW][C]10[/C][C]-0.0582579279194025[/C][/ROW]
[ROW][C]11[/C][C]0.0307667743247605[/C][/ROW]
[ROW][C]12[/C][C]-0.163559043679027[/C][/ROW]
[ROW][C]13[/C][C]-0.0269307071796227[/C][/ROW]
[ROW][C]14[/C][C]0.202425463126028[/C][/ROW]
[ROW][C]15[/C][C]-0.0209797792852681[/C][/ROW]
[ROW][C]16[/C][C]-0.0480157057088801[/C][/ROW]
[ROW][C]17[/C][C]0.0224979861577774[/C][/ROW]
[ROW][C]18[/C][C]0.0471338752181358[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28667&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28667&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])
-18	0.0455789631444636
-17	0.00400323596052212
-16	-0.0755873109858475
-15	-0.00227560442869971
-14	0.194140579281223
-13	-0.0220910322372835
-12	-0.231678361770819
-11	-0.0100392204092405
-10	-0.0079866949022926
-9	0.0624684162268147
-8	0.0635370471582327
-7	0.0320628533820949
-6	0.0672611532898171
-5	0.0112166241785642
-4	0.120453771074033
-3	0.128587616865702
-2	-0.0268846536534497
-1	-0.036304988312156
0	0.807803711523296
1	0.0490167402762864
2	-0.0762356107357594
3	0.060149383588605
4	0.0396014088594873
5	0.0432167268141464
6	0.0769581384606568
7	-0.00611561411843158
8	0.0508795897124187
9	0.0451834432972536
10	-0.0582579279194025
11	0.0307667743247605
12	-0.163559043679027
13	-0.0269307071796227
14	0.202425463126028
15	-0.0209797792852681
16	-0.0480157057088801
17	0.0224979861577774
18	0.0471338752181358

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