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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 computationWed, 03 Dec 2008 04:54:56 -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/03/t1228305347shcafaoahy6yukv.htm/, Retrieved Fri, 24 May 2024 14:36:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28638, Retrieved Fri, 24 May 2024 14:36:14 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsCross Correlation - werkloosheid
Estimated Impact235

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]
- RMPD  [Cross Correlation Function] [non stationary ti...] [2008-12-02 19:52:31] [47f64d63202c1921bd27f3073f07a153]
F   PD    [Cross Correlation Function] [non stationary ti...] [2008-12-02 20:08:18] [47f64d63202c1921bd27f3073f07a153]
F   P         [Cross Correlation Function] [non stat time ser...] [2008-12-03 11:54:56] [3bdbbe597ac6c61989658933956ee6ac] [Current]
F   P           [Cross Correlation Function] [non stat time ser...] [2008-12-03 13:42:47] [c96f3dce3a823a83b6ede18389e1cfd4]
Feedback Forum
2008-12-06 14:18:31 [Thomas Plasschaert] [reply] 
Met de cross correlatiefunctie kan men nagaan in hoeverre Y te verklaren valt door het verleden van X. X = totale productie van intermediaire goederen en Y= totale productie investeringsgoederen. rho(Y[t],X[t+k]) geeft de correlatie aan tussen het verleden van X en het heden van Y wanneer k kleiner is dan 0. (is er sprake van een leading indicator?) Wanneer k groter is dan 0 geeft het de correlatie weer tussen de toekomstige x en het heden van Y (is er sprake van een lagging indicator)?
2008-12-08 14:41:20 [Sam De Cuyper] [reply] 
Je hebt alles correct uitgerekend en geïnterpreteerd, maar volgens mij is het niet corerct om ineens een waarde voor lambda te kiezen. Normaal gezien moet je de waarde berekenen dmv de SMP.
2008-12-09 23:49:54 [Peter Van Doninck] [reply] 
De conclusie die de student trekt uit de crosscorrelatiefunctie zijn correct. Er is inderdaad een significant verschil, waardoor toeval uitgesloten is.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
8.4
8.4
8.4
8.6
8.9
8.8
8.3
7.5
7.2
7.5
8.8
9.3
9.3
8.7
8.2
8.3
8.5
8.6
8.6
8.2
8.1
8
8.6
8.7
8.8
8.5
8.4
8.5
8.7
8.7
8.6
8.5
8.3
8.1
8.2
8.1
8.1
7.9
7.9
7.9
8
8
7.9
8
7.7
7.2
7.5
7.3
7
7
7
7.2
7.3
7.1
6.8
6.6
6.2
6.2
6.8
6.9
6.8
Dataseries Y:
Download CSV
Histogram 
7.6
7.9
7.9
8.1
8.2
8
7.5
6.8
6.5
6.6
7.6
8
8
7.7
7.5
7.6
7.7
7.9
7.8
7.5
7.5
7.1
7.5
7.5
7.6
7.7
7.7
7.9
8.1
8.2
8.2
8.1
7.9
7.3
6.9
6.6
6.7
6.9
7
7.1
7.2
7.1
6.9
7
6.8
6.4
6.7
6.7
6.4
6.3
6.2
6.5
6.8
6.8
6.5
6.3
5.9
5.9
6.4
6.4
6.5

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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=28638&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=28638&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28638&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 time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






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])
-140.149990559589973
-130.185861325964253
-120.225297525036757
-110.230031305698896
-100.247280712672970
-90.273097296793963
-80.320058642919908
-70.374685950976791
-60.410742592989366
-50.42140749629168
-40.449612300735022
-30.541245758598121
-20.683668326476845
-10.837063189610136
00.919639434552627
10.811423616715479
20.652148324297912
30.524483155580876
40.459041264932223
50.463415390546591
60.495400497340574
70.498141170375977
80.484834579971819
90.479019341050503
100.476425946526319
110.464566933062830
120.430352149647916
130.341028055775768
140.260096528602442

\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
-14 & 0.149990559589973 \tabularnewline
-13 & 0.185861325964253 \tabularnewline
-12 & 0.225297525036757 \tabularnewline
-11 & 0.230031305698896 \tabularnewline
-10 & 0.247280712672970 \tabularnewline
-9 & 0.273097296793963 \tabularnewline
-8 & 0.320058642919908 \tabularnewline
-7 & 0.374685950976791 \tabularnewline
-6 & 0.410742592989366 \tabularnewline
-5 & 0.42140749629168 \tabularnewline
-4 & 0.449612300735022 \tabularnewline
-3 & 0.541245758598121 \tabularnewline
-2 & 0.683668326476845 \tabularnewline
-1 & 0.837063189610136 \tabularnewline
0 & 0.919639434552627 \tabularnewline
1 & 0.811423616715479 \tabularnewline
2 & 0.652148324297912 \tabularnewline
3 & 0.524483155580876 \tabularnewline
4 & 0.459041264932223 \tabularnewline
5 & 0.463415390546591 \tabularnewline
6 & 0.495400497340574 \tabularnewline
7 & 0.498141170375977 \tabularnewline
8 & 0.484834579971819 \tabularnewline
9 & 0.479019341050503 \tabularnewline
10 & 0.476425946526319 \tabularnewline
11 & 0.464566933062830 \tabularnewline
12 & 0.430352149647916 \tabularnewline
13 & 0.341028055775768 \tabularnewline
14 & 0.260096528602442 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28638&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]-14[/C][C]0.149990559589973[/C][/ROW]
[ROW][C]-13[/C][C]0.185861325964253[/C][/ROW]
[ROW][C]-12[/C][C]0.225297525036757[/C][/ROW]
[ROW][C]-11[/C][C]0.230031305698896[/C][/ROW]
[ROW][C]-10[/C][C]0.247280712672970[/C][/ROW]
[ROW][C]-9[/C][C]0.273097296793963[/C][/ROW]
[ROW][C]-8[/C][C]0.320058642919908[/C][/ROW]
[ROW][C]-7[/C][C]0.374685950976791[/C][/ROW]
[ROW][C]-6[/C][C]0.410742592989366[/C][/ROW]
[ROW][C]-5[/C][C]0.42140749629168[/C][/ROW]
[ROW][C]-4[/C][C]0.449612300735022[/C][/ROW]
[ROW][C]-3[/C][C]0.541245758598121[/C][/ROW]
[ROW][C]-2[/C][C]0.683668326476845[/C][/ROW]
[ROW][C]-1[/C][C]0.837063189610136[/C][/ROW]
[ROW][C]0[/C][C]0.919639434552627[/C][/ROW]
[ROW][C]1[/C][C]0.811423616715479[/C][/ROW]
[ROW][C]2[/C][C]0.652148324297912[/C][/ROW]
[ROW][C]3[/C][C]0.524483155580876[/C][/ROW]
[ROW][C]4[/C][C]0.459041264932223[/C][/ROW]
[ROW][C]5[/C][C]0.463415390546591[/C][/ROW]
[ROW][C]6[/C][C]0.495400497340574[/C][/ROW]
[ROW][C]7[/C][C]0.498141170375977[/C][/ROW]
[ROW][C]8[/C][C]0.484834579971819[/C][/ROW]
[ROW][C]9[/C][C]0.479019341050503[/C][/ROW]
[ROW][C]10[/C][C]0.476425946526319[/C][/ROW]
[ROW][C]11[/C][C]0.464566933062830[/C][/ROW]
[ROW][C]12[/C][C]0.430352149647916[/C][/ROW]
[ROW][C]13[/C][C]0.341028055775768[/C][/ROW]
[ROW][C]14[/C][C]0.260096528602442[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28638&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28638&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])
-140.149990559589973
-130.185861325964253
-120.225297525036757
-110.230031305698896
-100.247280712672970
-90.273097296793963
-80.320058642919908
-70.374685950976791
-60.410742592989366
-50.42140749629168
-40.449612300735022
-30.541245758598121
-20.683668326476845
-10.837063189610136
00.919639434552627
10.811423616715479
20.652148324297912
30.524483155580876
40.459041264932223
50.463415390546591
60.495400497340574
70.498141170375977
80.484834579971819
90.479019341050503
100.476425946526319
110.464566933062830
120.430352149647916
130.341028055775768
140.260096528602442


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')