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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 computationMon, 01 Dec 2008 15:43:06 -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/01/t1228171429ba06izpwvx6hzti.htm/, Retrieved Sun, 05 May 2024 09:30:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27480, Retrieved Sun, 05 May 2024 09:30:40 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact196

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] [] [2008-12-01 22:02:04] [cf9c64468d04c2c4dd548cc66b4e3677]
F    D    [Cross Correlation Function] [Q7] [2008-12-01 22:43:06] [14a75ec03b2c0d8ddd8b141a7b1594fd] [Current]
F           [Cross Correlation Function] [q7] [2008-12-01 23:42:29] [73d6180dc45497329efd1b6934a84aba]
F           [Cross Correlation Function] [] [2008-12-02 07:35:04] [74be16979710d4c4e7c6647856088456]
Feedback Forum
2008-12-07 23:28:59 [Kenny Simons] [reply] 
Deze vraag heb ik goed opgelost, toch is hier nog een woordje meer uitleg.

De cross correlation function kan niet vergeleken worden met de autocorrelation function. Autocorrelatie meet in welke mate een variabele kan voorspeld worden door het verleden van diezelfde variabele. De crosscorrelatie daarentegen meet in welke mate een variabele voorspeld kan worden door het verleden van een andere variabele.

In de tabel zien we:

k=0 => dit is gewoon de correlatie tussen Yt en Xt. Dit resultaat is wat je dus ook zou krijgen als je gewoon de correlatie zou berekenen.

k=-1 => de correlatie tussen Yt en Xt-1 (verleden)

K=+1 => de correlatie tussen yt en Xt+1 (toekomst)

De grafiek (grafiek zonder transformatie)zou willen zeggen dat het verleden van de variabele gecorreleerd is met het heden van de andere variabele (en omgekeerd), Dit is echter een vertekend beeld.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
110.4
112.9
109.4
111.9
108.9
113.8
114.5
113.2
111
114.6
113.1
113.2
115.1
117.6
117.8
115.7
115.7
118.3
117.9
117.3
119.4
117.1
119
120
118.9
116
115.6
119.7
119.7
120.8
120
120.2
121.7
116.9
122.4
122.6
123.7
120.9
124.2
122.6
125.7
123.1
122.2
126.2
124.4
127.8
124.2
126.7
126.1
128.2
130.4
130.2
129.2
129.7
131
129.2
131.1
132.9
135.2
132.3
Dataseries Y:
Download CSV
Histogram 
92.1
88.5
84.6
87
83.6
84.8
84.1
84.1
80.5
82.6
85.6
83.3
86.1
84.7
85.7
84.9
84.2
85.2
86.1
86
84.5
87.2
83.5
81.9
78.5
81.1
79.2
80.9
81.8
79.4
83.4
81.1
79.8
79.7
84
83.7
83.5
83.6
86
86.8
86.9
89
87.8
86.8
88.8
85.9
86.7
87.9
88.5
88.7
88.1
85.7
86.1
85.7
84.3
86.4
85.4
86.9
85.6
86.4

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'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=27480&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=27480&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27480&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'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)1
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])
-140.200956019121005
-130.188982228174416
-120.203558367279007
-110.182474843657705
-100.186177259472829
-90.218278697358665
-80.279825997770716
-70.307157253447433
-60.311805412976251
-50.320664849685356
-40.341569241061380
-30.315198715020914
-20.30911555634819
-10.308773326018296
00.267778247802182
10.263870532612755
20.246881582329239
30.190146548945094
40.186320363386158
50.168023833124102
60.199236132153985
70.165046603960377
80.162506567734251
90.184121353957285
100.138652628445237
110.0724357766822728
120.00830592816534033
130.0244635091596518
14-0.0382894980164581

\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) & 1 \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 & 0.200956019121005 \tabularnewline
-13 & 0.188982228174416 \tabularnewline
-12 & 0.203558367279007 \tabularnewline
-11 & 0.182474843657705 \tabularnewline
-10 & 0.186177259472829 \tabularnewline
-9 & 0.218278697358665 \tabularnewline
-8 & 0.279825997770716 \tabularnewline
-7 & 0.307157253447433 \tabularnewline
-6 & 0.311805412976251 \tabularnewline
-5 & 0.320664849685356 \tabularnewline
-4 & 0.341569241061380 \tabularnewline
-3 & 0.315198715020914 \tabularnewline
-2 & 0.30911555634819 \tabularnewline
-1 & 0.308773326018296 \tabularnewline
0 & 0.267778247802182 \tabularnewline
1 & 0.263870532612755 \tabularnewline
2 & 0.246881582329239 \tabularnewline
3 & 0.190146548945094 \tabularnewline
4 & 0.186320363386158 \tabularnewline
5 & 0.168023833124102 \tabularnewline
6 & 0.199236132153985 \tabularnewline
7 & 0.165046603960377 \tabularnewline
8 & 0.162506567734251 \tabularnewline
9 & 0.184121353957285 \tabularnewline
10 & 0.138652628445237 \tabularnewline
11 & 0.0724357766822728 \tabularnewline
12 & 0.00830592816534033 \tabularnewline
13 & 0.0244635091596518 \tabularnewline
14 & -0.0382894980164581 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27480&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]1[/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]0.200956019121005[/C][/ROW]
[ROW][C]-13[/C][C]0.188982228174416[/C][/ROW]
[ROW][C]-12[/C][C]0.203558367279007[/C][/ROW]
[ROW][C]-11[/C][C]0.182474843657705[/C][/ROW]
[ROW][C]-10[/C][C]0.186177259472829[/C][/ROW]
[ROW][C]-9[/C][C]0.218278697358665[/C][/ROW]
[ROW][C]-8[/C][C]0.279825997770716[/C][/ROW]
[ROW][C]-7[/C][C]0.307157253447433[/C][/ROW]
[ROW][C]-6[/C][C]0.311805412976251[/C][/ROW]
[ROW][C]-5[/C][C]0.320664849685356[/C][/ROW]
[ROW][C]-4[/C][C]0.341569241061380[/C][/ROW]
[ROW][C]-3[/C][C]0.315198715020914[/C][/ROW]
[ROW][C]-2[/C][C]0.30911555634819[/C][/ROW]
[ROW][C]-1[/C][C]0.308773326018296[/C][/ROW]
[ROW][C]0[/C][C]0.267778247802182[/C][/ROW]
[ROW][C]1[/C][C]0.263870532612755[/C][/ROW]
[ROW][C]2[/C][C]0.246881582329239[/C][/ROW]
[ROW][C]3[/C][C]0.190146548945094[/C][/ROW]
[ROW][C]4[/C][C]0.186320363386158[/C][/ROW]
[ROW][C]5[/C][C]0.168023833124102[/C][/ROW]
[ROW][C]6[/C][C]0.199236132153985[/C][/ROW]
[ROW][C]7[/C][C]0.165046603960377[/C][/ROW]
[ROW][C]8[/C][C]0.162506567734251[/C][/ROW]
[ROW][C]9[/C][C]0.184121353957285[/C][/ROW]
[ROW][C]10[/C][C]0.138652628445237[/C][/ROW]
[ROW][C]11[/C][C]0.0724357766822728[/C][/ROW]
[ROW][C]12[/C][C]0.00830592816534033[/C][/ROW]
[ROW][C]13[/C][C]0.0244635091596518[/C][/ROW]
[ROW][C]14[/C][C]-0.0382894980164581[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27480&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27480&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)1
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])
-140.200956019121005
-130.188982228174416
-120.203558367279007
-110.182474843657705
-100.186177259472829
-90.218278697358665
-80.279825997770716
-70.307157253447433
-60.311805412976251
-50.320664849685356
-40.341569241061380
-30.315198715020914
-20.30911555634819
-10.308773326018296
00.267778247802182
10.263870532612755
20.246881582329239
30.190146548945094
40.186320363386158
50.168023833124102
60.199236132153985
70.165046603960377
80.162506567734251
90.184121353957285
100.138652628445237
110.0724357766822728
120.00830592816534033
130.0244635091596518
14-0.0382894980164581


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 1 ; par5 = 1 ; par6 = 0 ; par7 = 0 ;
Parameters (R input):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 1 ; 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')