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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 14:34:39 -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/01/t1228167320xxdwz986itn9vip.htm/, Retrieved Sun, 05 May 2024 16:23:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27428, Retrieved Sun, 05 May 2024 16:23:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact181

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]
F RMPD    [Cross Correlation Function] [Q7] [2008-12-01 21:34:39] [b0654df83a8a0e1de3ceb7bf60f0d58f] [Current]
Feedback Forum
2008-12-05 11:44:53 [Olivier Uyttendaele] [reply] 
Je hebt volgens mij het model goed opgebouwd.

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.

Op de grafiek kan je zien dat het verleden van Yt positief gecorreleerd is met de toekomst van Xt.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
217859
208679
213188
216234
213587
209465
204045
200237
203666
241476
260307
243324
244460
233575
237217
235243
230354
227184
221678
217142
219452
256446
265845
248624
241114
229245
231805
219277
219313
212610
214771
211142
211457
240048
240636
230580
208795
197922
194596
194581
185686
178106
172608
167302
168053
202300
202388
182516
173476
166444
171297
169701
164182
161914
159612
151001
158114
186530
187069
174330
Dataseries Y:
Download CSV
Histogram 
258778
252791
256389
258961
258647
256304
250498
247883
249552
262626
264416
273049
272441
267564
265952
263937
264765
263386
258985
257334
257477
271486
274488
281274
272674
269704
268227
276444
272247
268516
263406
263619
265905
281681
287413
289423
281242
273878
269022
272630
270287
260447
262248
252806
238663
258438
266719
263279
258064
248828
248284
253376
251846
239494
239709
228793
229521
249999
254016
251178

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=27428&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=27428&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27428&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])
-140.476781790478242
-130.591803573703924
-120.629645159370004
-110.527077078232284
-100.411947431990722
-90.389419264448319
-80.403268934505981
-70.466764697077327
-60.505695811912516
-50.503482311210285
-40.515584044093945
-30.579877953691879
-20.675416290445703
-10.744534189118732
00.714331672197013
10.509105037354381
20.311980848542282
30.215154881549559
40.126670108548864
50.0895530681675343
60.0503831081649686
7-0.0332286049124206
8-0.092112067874883
9-0.0887170512881217
10-0.0364424240600659
11-0.0171041301878307
12-0.063113304016504
13-0.213853746374075
14-0.335452355366604

\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 & 0.476781790478242 \tabularnewline
-13 & 0.591803573703924 \tabularnewline
-12 & 0.629645159370004 \tabularnewline
-11 & 0.527077078232284 \tabularnewline
-10 & 0.411947431990722 \tabularnewline
-9 & 0.389419264448319 \tabularnewline
-8 & 0.403268934505981 \tabularnewline
-7 & 0.466764697077327 \tabularnewline
-6 & 0.505695811912516 \tabularnewline
-5 & 0.503482311210285 \tabularnewline
-4 & 0.515584044093945 \tabularnewline
-3 & 0.579877953691879 \tabularnewline
-2 & 0.675416290445703 \tabularnewline
-1 & 0.744534189118732 \tabularnewline
0 & 0.714331672197013 \tabularnewline
1 & 0.509105037354381 \tabularnewline
2 & 0.311980848542282 \tabularnewline
3 & 0.215154881549559 \tabularnewline
4 & 0.126670108548864 \tabularnewline
5 & 0.0895530681675343 \tabularnewline
6 & 0.0503831081649686 \tabularnewline
7 & -0.0332286049124206 \tabularnewline
8 & -0.092112067874883 \tabularnewline
9 & -0.0887170512881217 \tabularnewline
10 & -0.0364424240600659 \tabularnewline
11 & -0.0171041301878307 \tabularnewline
12 & -0.063113304016504 \tabularnewline
13 & -0.213853746374075 \tabularnewline
14 & -0.335452355366604 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27428&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]0.476781790478242[/C][/ROW]
[ROW][C]-13[/C][C]0.591803573703924[/C][/ROW]
[ROW][C]-12[/C][C]0.629645159370004[/C][/ROW]
[ROW][C]-11[/C][C]0.527077078232284[/C][/ROW]
[ROW][C]-10[/C][C]0.411947431990722[/C][/ROW]
[ROW][C]-9[/C][C]0.389419264448319[/C][/ROW]
[ROW][C]-8[/C][C]0.403268934505981[/C][/ROW]
[ROW][C]-7[/C][C]0.466764697077327[/C][/ROW]
[ROW][C]-6[/C][C]0.505695811912516[/C][/ROW]
[ROW][C]-5[/C][C]0.503482311210285[/C][/ROW]
[ROW][C]-4[/C][C]0.515584044093945[/C][/ROW]
[ROW][C]-3[/C][C]0.579877953691879[/C][/ROW]
[ROW][C]-2[/C][C]0.675416290445703[/C][/ROW]
[ROW][C]-1[/C][C]0.744534189118732[/C][/ROW]
[ROW][C]0[/C][C]0.714331672197013[/C][/ROW]
[ROW][C]1[/C][C]0.509105037354381[/C][/ROW]
[ROW][C]2[/C][C]0.311980848542282[/C][/ROW]
[ROW][C]3[/C][C]0.215154881549559[/C][/ROW]
[ROW][C]4[/C][C]0.126670108548864[/C][/ROW]
[ROW][C]5[/C][C]0.0895530681675343[/C][/ROW]
[ROW][C]6[/C][C]0.0503831081649686[/C][/ROW]
[ROW][C]7[/C][C]-0.0332286049124206[/C][/ROW]
[ROW][C]8[/C][C]-0.092112067874883[/C][/ROW]
[ROW][C]9[/C][C]-0.0887170512881217[/C][/ROW]
[ROW][C]10[/C][C]-0.0364424240600659[/C][/ROW]
[ROW][C]11[/C][C]-0.0171041301878307[/C][/ROW]
[ROW][C]12[/C][C]-0.063113304016504[/C][/ROW]
[ROW][C]13[/C][C]-0.213853746374075[/C][/ROW]
[ROW][C]14[/C][C]-0.335452355366604[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27428&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27428&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])
-140.476781790478242
-130.591803573703924
-120.629645159370004
-110.527077078232284
-100.411947431990722
-90.389419264448319
-80.403268934505981
-70.466764697077327
-60.505695811912516
-50.503482311210285
-40.515584044093945
-30.579877953691879
-20.675416290445703
-10.744534189118732
00.714331672197013
10.509105037354381
20.311980848542282
30.215154881549559
40.126670108548864
50.0895530681675343
60.0503831081649686
7-0.0332286049124206
8-0.092112067874883
9-0.0887170512881217
10-0.0364424240600659
11-0.0171041301878307
12-0.063113304016504
13-0.213853746374075
14-0.335452355366604


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')