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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 computationTue, 02 Dec 2008 14:19:39 -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/02/t12282543274y4e9rdivmtp2ah.htm/, Retrieved Sun, 19 May 2024 12:37:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28491, Retrieved Sun, 19 May 2024 12:37:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsJonas Scheltjens
Estimated Impact139

Tree of Dependent Computations

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 21:19:39] [f4960a11bac8b7f1cb71c83b5826d5bd] [Current]
Feedback Forum
2008-12-07 09:43:37 [Jolien Van Landeghem] [reply] 
We zien hier inderdaad dat de correlatie het sterkst is als we de tijdreeks niet verschuiven in de tijd. Hier is de correlatie dan 89%. We moeten wel rekening houden met de mogelijkheid van valse correlatie. Op de cross correlation function zie je dat ze allemaal uitsteken boven het betrouwbaarheidsinterval, wat erop wijst dat de verschillen significant zijn. We zien ook dat x en y elkaar beïnvloeden en dat er geen leading indicator is. We gaan bij de Cross Correlation Function een tijdreeks verklaren adhv een andere tijdreeks.
2008-12-09 20:13:29 [Gert-Jan Geudens] [reply] 
We kunnen hier inderdaad zien dat we yt kunnen verklaren aan de hand van het verleden en de toekomst van xt. Zo zien we vooral een grotere (nog niet zo heel groot) correlatie bij een k van -12 , -6 , 0 , 6 en 12. Dit kan misschien iets met seizonaliteit te maken hebben maar aangezien we dit niet besproken hebben , weten we dit niet zeker. Het kan dan ook interessant zijn om dit verder te onderzoeken.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
101,2
100,5
98
106,6
90,1
96,9
125,9
112
100
123,9
79,8
83,4
113,6
112,9
104
109,9
99
106,3
128,9
111,1
102,9
130
87
87,5
117,6
103,4
110,8
112,6
102,5
112,4
135,6
105,1
127,7
137
91
90,5
122,4
123,3
124,3
120
118,1
119
142,7
123,6
129,6
151,6
110,4
99,2
130,5
136,2
129,7
128
121,6
135,8
143,8
147,5
136,2
156,6
123,3
100,4
Dataseries Y:
Download CSV
Histogram 
123,9
124,9
112,7
121,9
100,6
104,3
120,4
107,5
102,9
125,6
107,5
108,8
128,4
121,1
119,5
128,7
108,7
105,5
119,8
111,3
110,6
120,1
97,5
107,7
127,3
117,2
119,8
116,2
111
112,4
130,6
109,1
118,8
123,9
101,6
112,8
128
129,6
125,8
119,5
115,7
113,6
129,7
112
116,8
127
112,1
114,2
121,1
131,6
125
120,4
117,7
117,5
120,6
127,5
112,3
124,5
115,2
105,4

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time0 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=28491&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=28491&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28491&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 time0 seconds
R Server'George Udny Yule' @ 72.249.76.132






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])
-14-0.221829476189632
-13-0.0557093561181141
-120.402077457746795
-11-0.0222912652263954
-10-0.131237008747128
-90.211699255911890
-8-0.0198355447538236
-70.081563222278917
-60.408351970137872
-50.0697685075756811
-4-0.0185335941593820
-30.207580683344334
-2-0.176426698477295
-1-0.0398164282125121
00.677224492204039
10.0602486947680432
2-0.0473800380126331
30.399179915156598
40.039304457455705
50.106584414296492
60.555515057722359
70.139926304070668
80.044967786482402
90.183372449958717
10-0.270866213216714
11-0.133197580404100
120.458192533634466
130.0360243222784831
14-0.120967848062859

\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 & -0.221829476189632 \tabularnewline
-13 & -0.0557093561181141 \tabularnewline
-12 & 0.402077457746795 \tabularnewline
-11 & -0.0222912652263954 \tabularnewline
-10 & -0.131237008747128 \tabularnewline
-9 & 0.211699255911890 \tabularnewline
-8 & -0.0198355447538236 \tabularnewline
-7 & 0.081563222278917 \tabularnewline
-6 & 0.408351970137872 \tabularnewline
-5 & 0.0697685075756811 \tabularnewline
-4 & -0.0185335941593820 \tabularnewline
-3 & 0.207580683344334 \tabularnewline
-2 & -0.176426698477295 \tabularnewline
-1 & -0.0398164282125121 \tabularnewline
0 & 0.677224492204039 \tabularnewline
1 & 0.0602486947680432 \tabularnewline
2 & -0.0473800380126331 \tabularnewline
3 & 0.399179915156598 \tabularnewline
4 & 0.039304457455705 \tabularnewline
5 & 0.106584414296492 \tabularnewline
6 & 0.555515057722359 \tabularnewline
7 & 0.139926304070668 \tabularnewline
8 & 0.044967786482402 \tabularnewline
9 & 0.183372449958717 \tabularnewline
10 & -0.270866213216714 \tabularnewline
11 & -0.133197580404100 \tabularnewline
12 & 0.458192533634466 \tabularnewline
13 & 0.0360243222784831 \tabularnewline
14 & -0.120967848062859 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28491&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]-0.221829476189632[/C][/ROW]
[ROW][C]-13[/C][C]-0.0557093561181141[/C][/ROW]
[ROW][C]-12[/C][C]0.402077457746795[/C][/ROW]
[ROW][C]-11[/C][C]-0.0222912652263954[/C][/ROW]
[ROW][C]-10[/C][C]-0.131237008747128[/C][/ROW]
[ROW][C]-9[/C][C]0.211699255911890[/C][/ROW]
[ROW][C]-8[/C][C]-0.0198355447538236[/C][/ROW]
[ROW][C]-7[/C][C]0.081563222278917[/C][/ROW]
[ROW][C]-6[/C][C]0.408351970137872[/C][/ROW]
[ROW][C]-5[/C][C]0.0697685075756811[/C][/ROW]
[ROW][C]-4[/C][C]-0.0185335941593820[/C][/ROW]
[ROW][C]-3[/C][C]0.207580683344334[/C][/ROW]
[ROW][C]-2[/C][C]-0.176426698477295[/C][/ROW]
[ROW][C]-1[/C][C]-0.0398164282125121[/C][/ROW]
[ROW][C]0[/C][C]0.677224492204039[/C][/ROW]
[ROW][C]1[/C][C]0.0602486947680432[/C][/ROW]
[ROW][C]2[/C][C]-0.0473800380126331[/C][/ROW]
[ROW][C]3[/C][C]0.399179915156598[/C][/ROW]
[ROW][C]4[/C][C]0.039304457455705[/C][/ROW]
[ROW][C]5[/C][C]0.106584414296492[/C][/ROW]
[ROW][C]6[/C][C]0.555515057722359[/C][/ROW]
[ROW][C]7[/C][C]0.139926304070668[/C][/ROW]
[ROW][C]8[/C][C]0.044967786482402[/C][/ROW]
[ROW][C]9[/C][C]0.183372449958717[/C][/ROW]
[ROW][C]10[/C][C]-0.270866213216714[/C][/ROW]
[ROW][C]11[/C][C]-0.133197580404100[/C][/ROW]
[ROW][C]12[/C][C]0.458192533634466[/C][/ROW]
[ROW][C]13[/C][C]0.0360243222784831[/C][/ROW]
[ROW][C]14[/C][C]-0.120967848062859[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28491&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28491&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])
-14-0.221829476189632
-13-0.0557093561181141
-120.402077457746795
-11-0.0222912652263954
-10-0.131237008747128
-90.211699255911890
-8-0.0198355447538236
-70.081563222278917
-60.408351970137872
-50.0697685075756811
-4-0.0185335941593820
-30.207580683344334
-2-0.176426698477295
-1-0.0398164282125121
00.677224492204039
10.0602486947680432
2-0.0473800380126331
30.399179915156598
40.039304457455705
50.106584414296492
60.555515057722359
70.139926304070668
80.044967786482402
90.183372449958717
10-0.270866213216714
11-0.133197580404100
120.458192533634466
130.0360243222784831
14-0.120967848062859


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')