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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_cross.wasp
Title produced by softwareCross Correlation Function
Date of computationTue, 02 Dec 2008 07:48:03 -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/t1228229346jyuflyxaj00y52p.htm/, Retrieved Sun, 19 May 2024 11:16:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27884, Retrieved Sun, 19 May 2024 11:16:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsjulie govaerts
Estimated Impact201

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] [eigen data] [2008-12-02 14:48:03] [02bc582261bca489735616f51251e20c] [Current]
Feedback Forum
2008-12-06 13:35:07 [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)?
2008-12-07 12:18:01 [Jolien Van Landeghem] [reply] 
Juist, maar hou er wel rekening mee dat er ook sprake kan zijn van schijncorrelatie : het feit dat x in hoge mate gecorreleerd is met y , kan verklaard worden door een derde variabele z.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
519164
517009
509933
509127
500857
506971
569323
579714
577992
565464
547344
554788
562325
560854
555332
543599
536662
542722
593530
610763
612613
611324
594167
595454
590865
589379
584428
573100
567456
569028
620735
628884
628232
612117
595404
597141
593408
590072
579799
574205
572775
572942
619567
625809
619916
587625
565742
557274
560576
548854
531673
525919
511038
498662
555362
564591
541657
527070
509846
514258
Dataseries Y:
Download CSV
Histogram 
345
334
345
333
336
324
320
330
313
301
288
294
302
294
293
290
283
286
293
334
329
411
416
418
408
402
401
400
389
371
364
350
332
323
316
312
315
314
313
314
317
308
312
306
304
297
284
278
273
265
259
252
245
235
232
229
219
218
215
211

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27884&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27884&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27884&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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






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])
-14-0.266716753526285
-13-0.223494984821071
-12-0.190560892480912
-11-0.156329766839237
-10-0.0885671093257054
-9-0.00803456066313102
-80.072846467827294
-70.155595130371720
-60.251896788980797
-50.291839534606383
-40.317512458651537
-30.368256436360806
-20.398585871761856
-10.451938524182591
00.469341646207360
10.432710462454033
20.41445147858775
30.398093729445352
40.395912736087297
50.387902057559947
60.384305371542659
70.3635332138199
80.339670741102560
90.328036707649137
100.292420807262684
110.271770668141470
120.235045838637953
130.194670168125541
140.172678640813045

\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 & -0.266716753526285 \tabularnewline
-13 & -0.223494984821071 \tabularnewline
-12 & -0.190560892480912 \tabularnewline
-11 & -0.156329766839237 \tabularnewline
-10 & -0.0885671093257054 \tabularnewline
-9 & -0.00803456066313102 \tabularnewline
-8 & 0.072846467827294 \tabularnewline
-7 & 0.155595130371720 \tabularnewline
-6 & 0.251896788980797 \tabularnewline
-5 & 0.291839534606383 \tabularnewline
-4 & 0.317512458651537 \tabularnewline
-3 & 0.368256436360806 \tabularnewline
-2 & 0.398585871761856 \tabularnewline
-1 & 0.451938524182591 \tabularnewline
0 & 0.469341646207360 \tabularnewline
1 & 0.432710462454033 \tabularnewline
2 & 0.41445147858775 \tabularnewline
3 & 0.398093729445352 \tabularnewline
4 & 0.395912736087297 \tabularnewline
5 & 0.387902057559947 \tabularnewline
6 & 0.384305371542659 \tabularnewline
7 & 0.3635332138199 \tabularnewline
8 & 0.339670741102560 \tabularnewline
9 & 0.328036707649137 \tabularnewline
10 & 0.292420807262684 \tabularnewline
11 & 0.271770668141470 \tabularnewline
12 & 0.235045838637953 \tabularnewline
13 & 0.194670168125541 \tabularnewline
14 & 0.172678640813045 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27884&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]-0.266716753526285[/C][/ROW]
[ROW][C]-13[/C][C]-0.223494984821071[/C][/ROW]
[ROW][C]-12[/C][C]-0.190560892480912[/C][/ROW]
[ROW][C]-11[/C][C]-0.156329766839237[/C][/ROW]
[ROW][C]-10[/C][C]-0.0885671093257054[/C][/ROW]
[ROW][C]-9[/C][C]-0.00803456066313102[/C][/ROW]
[ROW][C]-8[/C][C]0.072846467827294[/C][/ROW]
[ROW][C]-7[/C][C]0.155595130371720[/C][/ROW]
[ROW][C]-6[/C][C]0.251896788980797[/C][/ROW]
[ROW][C]-5[/C][C]0.291839534606383[/C][/ROW]
[ROW][C]-4[/C][C]0.317512458651537[/C][/ROW]
[ROW][C]-3[/C][C]0.368256436360806[/C][/ROW]
[ROW][C]-2[/C][C]0.398585871761856[/C][/ROW]
[ROW][C]-1[/C][C]0.451938524182591[/C][/ROW]
[ROW][C]0[/C][C]0.469341646207360[/C][/ROW]
[ROW][C]1[/C][C]0.432710462454033[/C][/ROW]
[ROW][C]2[/C][C]0.41445147858775[/C][/ROW]
[ROW][C]3[/C][C]0.398093729445352[/C][/ROW]
[ROW][C]4[/C][C]0.395912736087297[/C][/ROW]
[ROW][C]5[/C][C]0.387902057559947[/C][/ROW]
[ROW][C]6[/C][C]0.384305371542659[/C][/ROW]
[ROW][C]7[/C][C]0.3635332138199[/C][/ROW]
[ROW][C]8[/C][C]0.339670741102560[/C][/ROW]
[ROW][C]9[/C][C]0.328036707649137[/C][/ROW]
[ROW][C]10[/C][C]0.292420807262684[/C][/ROW]
[ROW][C]11[/C][C]0.271770668141470[/C][/ROW]
[ROW][C]12[/C][C]0.235045838637953[/C][/ROW]
[ROW][C]13[/C][C]0.194670168125541[/C][/ROW]
[ROW][C]14[/C][C]0.172678640813045[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27884&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27884&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])
-14-0.266716753526285
-13-0.223494984821071
-12-0.190560892480912
-11-0.156329766839237
-10-0.0885671093257054
-9-0.00803456066313102
-80.072846467827294
-70.155595130371720
-60.251896788980797
-50.291839534606383
-40.317512458651537
-30.368256436360806
-20.398585871761856
-10.451938524182591
00.469341646207360
10.432710462454033
20.41445147858775
30.398093729445352
40.395912736087297
50.387902057559947
60.384305371542659
70.3635332138199
80.339670741102560
90.328036707649137
100.292420807262684
110.271770668141470
120.235045838637953
130.194670168125541
140.172678640813045


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')