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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 12:13:02 -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/t1228158840bg2rztal6wu3847.htm/, Retrieved Sun, 05 May 2024 12:33:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27198, Retrieved Sun, 05 May 2024 12:33:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact300

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]
- RM D  [Standard Deviation-Mean Plot] [Opdracht 1 - Blok...] [2008-11-26 22:10:36] [8094ad203a218aaca2d1cea2c78c2d6e]
-   P     [Standard Deviation-Mean Plot] [Q5- Airline] [2008-11-28 13:14:59] [e5d91604aae608e98a8ea24759233f66]
F RMPD        [Cross Correlation Function] [Q7-random walk (1)] [2008-12-01 19:13:02] [55ca0ca4a201c9689dcf5fae352c92eb] [Current]
F   PD          [Cross Correlation Function] [Q7-random walk (2)] [2008-12-01 19:15:28] [e5d91604aae608e98a8ea24759233f66]
Feedback Forum
2008-12-06 16:57:31 [Kevin Engels] [reply] 
De studente geeft het correcte antwoord.
2008-12-07 13:04:22 [Kevin Neelen] [reply] 
Bij de cross correlatie wordt een verband gelegd tussen een tijdreeks nu, en een andere tjidreeks in het verleden of de toekomst. Zo wordt bijvoorbeeld de cross correlatie tussen Yt en Xt-1, Xt-2, enz. berekend.
Zoals tijdens het hoorcollege staan alle parameters op de defaultwaarden. Een groot deel van de cross correlatiewaarden ligt buiten het betrouwbaarheidsinterval en zijn significant verschillend.
De conclusie van de studente klopt.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1,21
1,74
1,76
1,48
1,04
1,62
1,49
1,79
1,8
1,58
1,86
1,74
1,59
1,26
1,13
1,92
2,61
2,26
2,41
2,26
2,03
2,86
2,55
2,27
2,26
2,57
3,07
2,76
2,51
2,87
3,14
3,11
3,16
2,47
2,57
2,89
2,63
2,38
1,69
1,96
2,19
1,87
1,6
1,63
1,22
1,21
1,49
1,64
1,66
1,77
1,82
1,78
1,28
1,29
1,37
1,12
1,51
2,24
2,94
3,09
Dataseries Y:
Download CSV
Histogram 
99,29
98,69
107,92
101,03
97,55
103,02
94,08
94,12
115,08
116,48
103,42
112,51
95,55
97,53
119,26
100,94
97,73
115,25
92,8
99,2
118,69
110,12
110,26
112,9
102,17
99,38
116,1
103,77
101,81
113,74
89,67
99,5
122,89
108,61
114,37
110,5
104,08
103,64
121,61
101,14
115,97
120,12
95,97
105,01
124,68
123,89
123,61
114,76
108,75
106,09
123,17
106,16
115,18
120,6
109,48
114,44
121,44
129,48
124,32
112,59

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=27198&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=27198&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27198&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)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])
-140.103274658331096
-130.0811130589754812
-120.0455859680402585
-110.0424033222880012
-100.0559596425248009
-9-0.0106070250047541
-8-0.0630961473622912
-7-0.0420949051777839
-6-0.0269488191120835
-5-0.073537682385314
-4-0.0599638847493832
-3-0.109442824766533
-2-0.145152099465970
-1-0.0867816290532318
0-0.0694227592837247
10.0194241620384242
20.0279730657667591
3-0.0494917829918217
4-0.0911350985177075
5-0.098488903473464
6-0.089883715792384
7-0.0473629884005968
8-0.0433171467444423
9-0.0902986445979236
10-0.134634133393480
11-0.117984575979425
12-0.0452972370479236
130.0334849575230235
14-0.03132473819862

\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 & 0.103274658331096 \tabularnewline
-13 & 0.0811130589754812 \tabularnewline
-12 & 0.0455859680402585 \tabularnewline
-11 & 0.0424033222880012 \tabularnewline
-10 & 0.0559596425248009 \tabularnewline
-9 & -0.0106070250047541 \tabularnewline
-8 & -0.0630961473622912 \tabularnewline
-7 & -0.0420949051777839 \tabularnewline
-6 & -0.0269488191120835 \tabularnewline
-5 & -0.073537682385314 \tabularnewline
-4 & -0.0599638847493832 \tabularnewline
-3 & -0.109442824766533 \tabularnewline
-2 & -0.145152099465970 \tabularnewline
-1 & -0.0867816290532318 \tabularnewline
0 & -0.0694227592837247 \tabularnewline
1 & 0.0194241620384242 \tabularnewline
2 & 0.0279730657667591 \tabularnewline
3 & -0.0494917829918217 \tabularnewline
4 & -0.0911350985177075 \tabularnewline
5 & -0.098488903473464 \tabularnewline
6 & -0.089883715792384 \tabularnewline
7 & -0.0473629884005968 \tabularnewline
8 & -0.0433171467444423 \tabularnewline
9 & -0.0902986445979236 \tabularnewline
10 & -0.134634133393480 \tabularnewline
11 & -0.117984575979425 \tabularnewline
12 & -0.0452972370479236 \tabularnewline
13 & 0.0334849575230235 \tabularnewline
14 & -0.03132473819862 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27198&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]0.103274658331096[/C][/ROW]
[ROW][C]-13[/C][C]0.0811130589754812[/C][/ROW]
[ROW][C]-12[/C][C]0.0455859680402585[/C][/ROW]
[ROW][C]-11[/C][C]0.0424033222880012[/C][/ROW]
[ROW][C]-10[/C][C]0.0559596425248009[/C][/ROW]
[ROW][C]-9[/C][C]-0.0106070250047541[/C][/ROW]
[ROW][C]-8[/C][C]-0.0630961473622912[/C][/ROW]
[ROW][C]-7[/C][C]-0.0420949051777839[/C][/ROW]
[ROW][C]-6[/C][C]-0.0269488191120835[/C][/ROW]
[ROW][C]-5[/C][C]-0.073537682385314[/C][/ROW]
[ROW][C]-4[/C][C]-0.0599638847493832[/C][/ROW]
[ROW][C]-3[/C][C]-0.109442824766533[/C][/ROW]
[ROW][C]-2[/C][C]-0.145152099465970[/C][/ROW]
[ROW][C]-1[/C][C]-0.0867816290532318[/C][/ROW]
[ROW][C]0[/C][C]-0.0694227592837247[/C][/ROW]
[ROW][C]1[/C][C]0.0194241620384242[/C][/ROW]
[ROW][C]2[/C][C]0.0279730657667591[/C][/ROW]
[ROW][C]3[/C][C]-0.0494917829918217[/C][/ROW]
[ROW][C]4[/C][C]-0.0911350985177075[/C][/ROW]
[ROW][C]5[/C][C]-0.098488903473464[/C][/ROW]
[ROW][C]6[/C][C]-0.089883715792384[/C][/ROW]
[ROW][C]7[/C][C]-0.0473629884005968[/C][/ROW]
[ROW][C]8[/C][C]-0.0433171467444423[/C][/ROW]
[ROW][C]9[/C][C]-0.0902986445979236[/C][/ROW]
[ROW][C]10[/C][C]-0.134634133393480[/C][/ROW]
[ROW][C]11[/C][C]-0.117984575979425[/C][/ROW]
[ROW][C]12[/C][C]-0.0452972370479236[/C][/ROW]
[ROW][C]13[/C][C]0.0334849575230235[/C][/ROW]
[ROW][C]14[/C][C]-0.03132473819862[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27198&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27198&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])
-140.103274658331096
-130.0811130589754812
-120.0455859680402585
-110.0424033222880012
-100.0559596425248009
-9-0.0106070250047541
-8-0.0630961473622912
-7-0.0420949051777839
-6-0.0269488191120835
-5-0.073537682385314
-4-0.0599638847493832
-3-0.109442824766533
-2-0.145152099465970
-1-0.0867816290532318
0-0.0694227592837247
10.0194241620384242
20.0279730657667591
3-0.0494917829918217
4-0.0911350985177075
5-0.098488903473464
6-0.089883715792384
7-0.0473629884005968
8-0.0433171467444423
9-0.0902986445979236
10-0.134634133393480
11-0.117984575979425
12-0.0452972370479236
130.0334849575230235
14-0.03132473819862


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')