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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 11:20:52 -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/t1228155719cs146t77ly96xkr.htm/, Retrieved Sun, 05 May 2024 10:19:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27088, Retrieved Sun, 05 May 2024 10:19:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact239

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...] [2008-12-01 18:20:52] [382e90e66f02be5ed86892bdc1574692] [Current]
- RMPD    [Tukey lambda PPCC Plot] [Non Stationary Ti...] [2008-12-01 20:49:18] [82970caad4b026be9dd352fdec547fe4]
-    D      [Tukey lambda PPCC Plot] [Non Stationary Ti...] [2008-12-01 21:00:44] [82970caad4b026be9dd352fdec547fe4]
-    D        [Tukey lambda PPCC Plot] [NSTS Q8 Brent lambda] [2008-12-01 22:41:57] [d32f94eec6fe2d8c421bd223368a5ced]
F    D      [Tukey lambda PPCC Plot] [NSTS Q8 Referenti...] [2008-12-01 22:39:51] [d32f94eec6fe2d8c421bd223368a5ced]
-    D      [Tukey lambda PPCC Plot] [Paper Lambda waar...] [2008-12-17 15:48:30] [f9b9e85820b2a54b20380c3265aca831]
F   PD    [Cross Correlation Function] [Non Stationary Ti...] [2008-12-01 21:08:35] [82970caad4b026be9dd352fdec547fe4]
-           [Cross Correlation Function] [NSTS Q9] [2008-12-01 22:36:39] [d32f94eec6fe2d8c421bd223368a5ced]
- RM D    [Variance Reduction Matrix] [Paper: variance r...] [2008-12-18 11:39:42] [5faef9c71125a233e2c8b38f64296883]
Feedback Forum
2008-12-08 01:52:30 [Kenny Simons] [reply] 
Je antwoord is hier correct.

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)



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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0,9554
0,9922
0,9778
0,9808
0,9811
1,0014
1,0183
1,0622
1,0773
1,0807
1,0848
1,1582
1,1663
1,1372
1,1139
1,1222
1,1692
1,1702
1,2286
1,2613
1,2646
1,2262
1,1985
1,2007
1,2138
1,2266
1,2176
1,2218
1,249
1,2991
1,3408
1,3119
1,3014
1,3201
1,2938
1,2694
1,2165
1,2037
1,2292
1,2256
1,2015
1,1786
1,1856
1,2103
1,1938
1,202
1,2271
1,277
1,265
1,2684
1,2811
1,2727
1,2611
1,2881
1,3213
1,2999
1,3074
1,3242
1,3516
1,3511
Dataseries Y:
Download CSV
Histogram 
24,67
25,59
26,09
28,37
27,34
24,46
27,46
30,23
32,33
29,87
24,87
25,48
27,28
28,24
29,58
26,95
29,08
28,76
29,59
30,7
30,52
32,67
33,19
37,13
35,54
37,75
41,84
42,94
49,14
44,61
40,22
44,23
45,85
53,38
53,26
51,8
55,3
57,81
63,96
63,77
59,15
56,12
57,42
63,52
61,71
63,01
68,18
72,03
69,75
74,41
74,33
64,24
60,03
59,44
62,5
55,04
58,34
61,92
67,65
67,68

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=27088&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=27088&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27088&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])
-140.503469365744278
-130.506298494633708
-120.522211622016585
-110.543139548849688
-100.567345931651225
-90.579060825722024
-80.578480252395666
-70.574259140189669
-60.576388234718976
-50.598897894803139
-40.609824077273028
-30.616228733294258
-20.623467290582655
-10.646407020977973
00.67584351449053
10.607154654460355
20.546379424562568
30.493764343301414
40.456458675270597
50.411245837835769
60.354568544859934
70.314044960773658
80.279774106247335
90.246467645127337
100.201152501684263
110.151744861862237
120.115517351278241
130.0711251834532888
140.0321098852875895

\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 & 0.503469365744278 \tabularnewline
-13 & 0.506298494633708 \tabularnewline
-12 & 0.522211622016585 \tabularnewline
-11 & 0.543139548849688 \tabularnewline
-10 & 0.567345931651225 \tabularnewline
-9 & 0.579060825722024 \tabularnewline
-8 & 0.578480252395666 \tabularnewline
-7 & 0.574259140189669 \tabularnewline
-6 & 0.576388234718976 \tabularnewline
-5 & 0.598897894803139 \tabularnewline
-4 & 0.609824077273028 \tabularnewline
-3 & 0.616228733294258 \tabularnewline
-2 & 0.623467290582655 \tabularnewline
-1 & 0.646407020977973 \tabularnewline
0 & 0.67584351449053 \tabularnewline
1 & 0.607154654460355 \tabularnewline
2 & 0.546379424562568 \tabularnewline
3 & 0.493764343301414 \tabularnewline
4 & 0.456458675270597 \tabularnewline
5 & 0.411245837835769 \tabularnewline
6 & 0.354568544859934 \tabularnewline
7 & 0.314044960773658 \tabularnewline
8 & 0.279774106247335 \tabularnewline
9 & 0.246467645127337 \tabularnewline
10 & 0.201152501684263 \tabularnewline
11 & 0.151744861862237 \tabularnewline
12 & 0.115517351278241 \tabularnewline
13 & 0.0711251834532888 \tabularnewline
14 & 0.0321098852875895 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27088&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]0.503469365744278[/C][/ROW]
[ROW][C]-13[/C][C]0.506298494633708[/C][/ROW]
[ROW][C]-12[/C][C]0.522211622016585[/C][/ROW]
[ROW][C]-11[/C][C]0.543139548849688[/C][/ROW]
[ROW][C]-10[/C][C]0.567345931651225[/C][/ROW]
[ROW][C]-9[/C][C]0.579060825722024[/C][/ROW]
[ROW][C]-8[/C][C]0.578480252395666[/C][/ROW]
[ROW][C]-7[/C][C]0.574259140189669[/C][/ROW]
[ROW][C]-6[/C][C]0.576388234718976[/C][/ROW]
[ROW][C]-5[/C][C]0.598897894803139[/C][/ROW]
[ROW][C]-4[/C][C]0.609824077273028[/C][/ROW]
[ROW][C]-3[/C][C]0.616228733294258[/C][/ROW]
[ROW][C]-2[/C][C]0.623467290582655[/C][/ROW]
[ROW][C]-1[/C][C]0.646407020977973[/C][/ROW]
[ROW][C]0[/C][C]0.67584351449053[/C][/ROW]
[ROW][C]1[/C][C]0.607154654460355[/C][/ROW]
[ROW][C]2[/C][C]0.546379424562568[/C][/ROW]
[ROW][C]3[/C][C]0.493764343301414[/C][/ROW]
[ROW][C]4[/C][C]0.456458675270597[/C][/ROW]
[ROW][C]5[/C][C]0.411245837835769[/C][/ROW]
[ROW][C]6[/C][C]0.354568544859934[/C][/ROW]
[ROW][C]7[/C][C]0.314044960773658[/C][/ROW]
[ROW][C]8[/C][C]0.279774106247335[/C][/ROW]
[ROW][C]9[/C][C]0.246467645127337[/C][/ROW]
[ROW][C]10[/C][C]0.201152501684263[/C][/ROW]
[ROW][C]11[/C][C]0.151744861862237[/C][/ROW]
[ROW][C]12[/C][C]0.115517351278241[/C][/ROW]
[ROW][C]13[/C][C]0.0711251834532888[/C][/ROW]
[ROW][C]14[/C][C]0.0321098852875895[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27088&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27088&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])
-140.503469365744278
-130.506298494633708
-120.522211622016585
-110.543139548849688
-100.567345931651225
-90.579060825722024
-80.578480252395666
-70.574259140189669
-60.576388234718976
-50.598897894803139
-40.609824077273028
-30.616228733294258
-20.623467290582655
-10.646407020977973
00.67584351449053
10.607154654460355
20.546379424562568
30.493764343301414
40.456458675270597
50.411245837835769
60.354568544859934
70.314044960773658
80.279774106247335
90.246467645127337
100.201152501684263
110.151744861862237
120.115517351278241
130.0711251834532888
140.0321098852875895


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')