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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 computationSun, 30 Nov 2008 15:18:34 -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/Nov/30/t1228083745g5tjka4pse87b7d.htm/, Retrieved Sun, 19 May 2024 11:34:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26770, Retrieved Sun, 19 May 2024 11:34:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact127

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] [q7] [2008-11-30 22:18:34] [e11d930c9e2984715c66c796cf63ef19] [Current]
Feedback Forum
2008-12-05 19:14:09 [Olivier Uyttendaele] [reply] 
De crosscorrelatie kan niet vergeleken worden met de autocorrelatie. Autocorrelatie gaat proberen een voorspelling van een tijdreeks (vb.Yt) te doen aan de hand van zijn eigen verleden. De crosscorrelatie gaat proberen een voorspelling te doen van een tijdreeks (vb. Yt) aan de hand van een andere variabele (vb.Xt). Iets theoretischer kan gezegd worden dat dit model probeert te berekenen in welke mate een endogene dataserie een invloed ondergaat van een exogene dataserie, rekening houdend met een vertraging in de tijd.
Op de X-as staan k-waardes (lags) gerangschikt van negatief naar positief. Op de Y-as staat de correlatie tussen 1&0.

Het model dat je krijgt bestaat uit een tabel en een grafiek.
In de tabel vindt je een aantal waarden. De waarde k=0, dit is de correlatie die je zou krijgen als je een gewone autocorrelatie zou berekenen.
k-waarde negatief = het verleden
k-waarde positief = de toekomst.

Bij de negatieve k-waardes ga je kijken hoe het verleden van Yt gecorreleerd is met de toekomst van Xt.
Bij de positieve k-waardes is dit vanzelfsprekend omgekeerd.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
15859,4
15258,9
15498,6
15106,5
15023,6
12083
15761,3
16942,6
15070,3
13659,6
14768,9
14725,1
15998,1
15370,6
14956,9
15469,7
15101,8
11703,7
16283,6
16726,5
14968,9
14861
14583,3
15305,8
17903,9
16379,4
15420,3
17870,5
15912,8
13866,5
17823,2
17872
17422
16704,5
15991,2
16583,6
19123,5
17838,7
17209,4
18586,5
16258,1
15141,6
19202,1
17746,5
19090,1
18040,3
17515,5
17751,8
21072,4
17170
19439,5
19795,4
17574,9
16165,4
19464,6
19932,1
19961,2
17343,4
18924,2
18574,1
21350,6
18594,6
19823,1
20844,4
19640,2
17735,4
19813,6
22238,5
20682,2
17818,6
21872,1
22117
21865,9
Dataseries Y:
Download CSV
Histogram 
12710,3
12120,8
12469,5
12054,6
12112,9
9617,2
12645,8
13581,3
12162,3
10969,7
11880
11887,6
12926,9
12300
12092,8
12380,8
12196,9
9455
13168
13427,9
11980,5
11884,8
11691,7
12233,8
14341,4
13130,7
12421,1
14285,8
12864,6
11160,2
14316,2
14388,7
14013,9
13419
12769,6
13315,5
15332,9
14243
13824,4
14962,9
13202,9
12199
15508,9
14199,8
15169,6
14058
13786,2
14147,9
16541,7
13587,5
15582,4
15802,8
14130,5
12923,2
15612,2
16033,7
16036,6
14037,8
15330,6
15038,3
17401,8
14992,5
16043,7
16929,6
15921,3
14417,2
15961
17851,9
16483,9
14215,5
17429,7
17839,5
17629,2

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=26770&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=26770&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26770&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)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])
-150.256705142875821
-140.227825464572014
-130.294674924455551
-120.58542492237246
-110.385152843012632
-100.305102760671459
-90.437348053783706
-80.52208132342175
-70.44565837660062
-60.557138232675573
-50.544644244576638
-40.633769261434538
-30.573438002001274
-20.4913975923261
-10.628275459602402
00.998199142212563
10.629168244516984
20.489668948821833
30.571727965262977
40.633981751752039
50.543584606394885
60.563703097629806
70.454350166205168
80.530437016787906
90.439860823126138
100.302533681325983
110.380547440331663
120.581509444477227
130.290338451908459
140.219873192355127
150.239211996748903

\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
-15 & 0.256705142875821 \tabularnewline
-14 & 0.227825464572014 \tabularnewline
-13 & 0.294674924455551 \tabularnewline
-12 & 0.58542492237246 \tabularnewline
-11 & 0.385152843012632 \tabularnewline
-10 & 0.305102760671459 \tabularnewline
-9 & 0.437348053783706 \tabularnewline
-8 & 0.52208132342175 \tabularnewline
-7 & 0.44565837660062 \tabularnewline
-6 & 0.557138232675573 \tabularnewline
-5 & 0.544644244576638 \tabularnewline
-4 & 0.633769261434538 \tabularnewline
-3 & 0.573438002001274 \tabularnewline
-2 & 0.4913975923261 \tabularnewline
-1 & 0.628275459602402 \tabularnewline
0 & 0.998199142212563 \tabularnewline
1 & 0.629168244516984 \tabularnewline
2 & 0.489668948821833 \tabularnewline
3 & 0.571727965262977 \tabularnewline
4 & 0.633981751752039 \tabularnewline
5 & 0.543584606394885 \tabularnewline
6 & 0.563703097629806 \tabularnewline
7 & 0.454350166205168 \tabularnewline
8 & 0.530437016787906 \tabularnewline
9 & 0.439860823126138 \tabularnewline
10 & 0.302533681325983 \tabularnewline
11 & 0.380547440331663 \tabularnewline
12 & 0.581509444477227 \tabularnewline
13 & 0.290338451908459 \tabularnewline
14 & 0.219873192355127 \tabularnewline
15 & 0.239211996748903 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26770&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]-15[/C][C]0.256705142875821[/C][/ROW]
[ROW][C]-14[/C][C]0.227825464572014[/C][/ROW]
[ROW][C]-13[/C][C]0.294674924455551[/C][/ROW]
[ROW][C]-12[/C][C]0.58542492237246[/C][/ROW]
[ROW][C]-11[/C][C]0.385152843012632[/C][/ROW]
[ROW][C]-10[/C][C]0.305102760671459[/C][/ROW]
[ROW][C]-9[/C][C]0.437348053783706[/C][/ROW]
[ROW][C]-8[/C][C]0.52208132342175[/C][/ROW]
[ROW][C]-7[/C][C]0.44565837660062[/C][/ROW]
[ROW][C]-6[/C][C]0.557138232675573[/C][/ROW]
[ROW][C]-5[/C][C]0.544644244576638[/C][/ROW]
[ROW][C]-4[/C][C]0.633769261434538[/C][/ROW]
[ROW][C]-3[/C][C]0.573438002001274[/C][/ROW]
[ROW][C]-2[/C][C]0.4913975923261[/C][/ROW]
[ROW][C]-1[/C][C]0.628275459602402[/C][/ROW]
[ROW][C]0[/C][C]0.998199142212563[/C][/ROW]
[ROW][C]1[/C][C]0.629168244516984[/C][/ROW]
[ROW][C]2[/C][C]0.489668948821833[/C][/ROW]
[ROW][C]3[/C][C]0.571727965262977[/C][/ROW]
[ROW][C]4[/C][C]0.633981751752039[/C][/ROW]
[ROW][C]5[/C][C]0.543584606394885[/C][/ROW]
[ROW][C]6[/C][C]0.563703097629806[/C][/ROW]
[ROW][C]7[/C][C]0.454350166205168[/C][/ROW]
[ROW][C]8[/C][C]0.530437016787906[/C][/ROW]
[ROW][C]9[/C][C]0.439860823126138[/C][/ROW]
[ROW][C]10[/C][C]0.302533681325983[/C][/ROW]
[ROW][C]11[/C][C]0.380547440331663[/C][/ROW]
[ROW][C]12[/C][C]0.581509444477227[/C][/ROW]
[ROW][C]13[/C][C]0.290338451908459[/C][/ROW]
[ROW][C]14[/C][C]0.219873192355127[/C][/ROW]
[ROW][C]15[/C][C]0.239211996748903[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26770&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26770&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])
-150.256705142875821
-140.227825464572014
-130.294674924455551
-120.58542492237246
-110.385152843012632
-100.305102760671459
-90.437348053783706
-80.52208132342175
-70.44565837660062
-60.557138232675573
-50.544644244576638
-40.633769261434538
-30.573438002001274
-20.4913975923261
-10.628275459602402
00.998199142212563
10.629168244516984
20.489668948821833
30.571727965262977
40.633981751752039
50.543584606394885
60.563703097629806
70.454350166205168
80.530437016787906
90.439860823126138
100.302533681325983
110.380547440331663
120.581509444477227
130.290338451908459
140.219873192355127
150.239211996748903


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')