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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 computationTue, 02 Dec 2008 11:24:03 -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/t1228242289wxdangw65jxiqb5.htm/, Retrieved Sun, 19 May 2024 12:19:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28217, Retrieved Sun, 19 May 2024 12:19:19 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsjenske_cole@hotmail.com
Estimated Impact162

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] [time series q7] [2008-12-02 18:24:03] [120dfa2440e51a0cfc0f5296bc5d7460] [Current]
Feedback Forum
2008-12-07 14:24:34 [6066575aa30c0611e452e930b1dff53d] [reply] 
Hier werd de grafiek gegeven en besproken. De tabel werd niet gegeven. We kunnen uit de tabel afleiden dat voor k=0 de correlatie tussen Y[t] en X[t] 0.17 bedraagt, dit is de correlatie zonder verschuiving in de tijd. Bovendien kunnen we ook vermelden dat het hier gaat om een ruwe reeks want de tijdreeks werd niet gedifferentieerd en niet getransformeerd (d=0 en D=0).Over de grafiek kunnen we vermelden dat er slechts een beperkt aantal verticale lijntjes zijn die buiten het 95% betrouwbaarheidsinterval liggen en dus significant verschillend zijn van nul.
2008-12-07 14:26:10 [Stephanie Vanderlinden] [reply] 
Ook hier is de verklaring zeer beknopt. De cross correlatie geeft de correlatie weer tussen twee verschillende tijdreeksen (Yt en Xt). Via de cross correlatie kunnen we onderzoeken op Xt een leading indicator is op Yt. Een leading indicator is een indicator die op voorhand de invloed toont op een andere variabele. De blauwe stippellijnen geven het betrouwbaarheidsinterval weer. De correlaties die buiten deze stippellijnen liggen zijn significant verschillend van nul. Deze correlaties zijn niet voorspelbaar door toeval. Het verschil tussen een autocorrelatie en cross-correlatie is foutief uitgelegd. De autocorrelatie meet de mate waarin Xt kan voorspeld worden op basis van zijn eigen verleden. De cross-correlatie geeft weer in welke mate Yt verklaart kan worden door Xt.
2008-12-08 14:09:31 [58d427c57bd46519a715a3a7fea6a80f] [reply] 
Welke transformatie en/of (seizoenaal) differentiatie nodig is om de 2 tijdreeksen (Xt en Yt) stationair te maken.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
98,1
101,1
111,1
93,3
100
108
70,4
75,4
105,5
112,3
102,5
93,5
86,7
95,2
103,8
97
95,5
101
67,5
64
106,7
100,6
101,2
93,1
84,2
85,8
91,8
92,4
80,3
79,7
62,5
57,1
100,8
100,7
86,2
83,2
71,7
77,5
89,8
80,3
78,7
93,8
57,6
60,6
91
85,3
77,4
77,3
68,3
69,9
81,7
75,1
69,9
84
54,3
60
89,9
77
85,3
77,6
69,2
75,5
85,7
72,2
79,9
85,3
52,2
61,2
82,4
85,4
78,2
70,2
Dataseries Y:
Download CSV
Histogram 
13
8
7
3
3
4
4
0
-4
-14
-18
-8
-1
1
2
0
1
0
-1
-3
-3
-3
-4
-8
-9
-13
-18
-11
-9
-10
-13
-11
-5
-15
-6
-6
-3
-1
-3
-4
-6
0
-4
-2
-2
-6
-7
-6
-6
-3
-2
-5
-11
-11
-11
-10
-14
-8
-9
-5
-1
-2
-5
-4
-6
-2
-2
-2
-2
2
1
-8

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=28217&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=28217&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28217&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])
-15-0.0741348436242949
-14-0.0368416976439518
-13-0.0769871411011834
-12-0.00757400510880037
-11-0.013542066565754
-10-0.0481178151990806
-9-0.0752933498958134
-8-0.0774472967662435
-7-0.0497643761275166
-6-0.0503996684120703
-50.0784870609682332
-40.187622370317091
-30.197126444944920
-20.159778643341767
-10.0957480326856056
00.171320321728396
10.186559555810637
20.234393088152289
30.238350822890252
40.198592233606736
50.141808125881076
60.0657245160976217
70.185649931901419
80.35019824934017
90.417064428232424
100.251270988179828
110.110195953369594
120.13594536471368
130.159707068084677
140.202724178382571
150.205314050173996

\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 & -0.0741348436242949 \tabularnewline
-14 & -0.0368416976439518 \tabularnewline
-13 & -0.0769871411011834 \tabularnewline
-12 & -0.00757400510880037 \tabularnewline
-11 & -0.013542066565754 \tabularnewline
-10 & -0.0481178151990806 \tabularnewline
-9 & -0.0752933498958134 \tabularnewline
-8 & -0.0774472967662435 \tabularnewline
-7 & -0.0497643761275166 \tabularnewline
-6 & -0.0503996684120703 \tabularnewline
-5 & 0.0784870609682332 \tabularnewline
-4 & 0.187622370317091 \tabularnewline
-3 & 0.197126444944920 \tabularnewline
-2 & 0.159778643341767 \tabularnewline
-1 & 0.0957480326856056 \tabularnewline
0 & 0.171320321728396 \tabularnewline
1 & 0.186559555810637 \tabularnewline
2 & 0.234393088152289 \tabularnewline
3 & 0.238350822890252 \tabularnewline
4 & 0.198592233606736 \tabularnewline
5 & 0.141808125881076 \tabularnewline
6 & 0.0657245160976217 \tabularnewline
7 & 0.185649931901419 \tabularnewline
8 & 0.35019824934017 \tabularnewline
9 & 0.417064428232424 \tabularnewline
10 & 0.251270988179828 \tabularnewline
11 & 0.110195953369594 \tabularnewline
12 & 0.13594536471368 \tabularnewline
13 & 0.159707068084677 \tabularnewline
14 & 0.202724178382571 \tabularnewline
15 & 0.205314050173996 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28217&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]-0.0741348436242949[/C][/ROW]
[ROW][C]-14[/C][C]-0.0368416976439518[/C][/ROW]
[ROW][C]-13[/C][C]-0.0769871411011834[/C][/ROW]
[ROW][C]-12[/C][C]-0.00757400510880037[/C][/ROW]
[ROW][C]-11[/C][C]-0.013542066565754[/C][/ROW]
[ROW][C]-10[/C][C]-0.0481178151990806[/C][/ROW]
[ROW][C]-9[/C][C]-0.0752933498958134[/C][/ROW]
[ROW][C]-8[/C][C]-0.0774472967662435[/C][/ROW]
[ROW][C]-7[/C][C]-0.0497643761275166[/C][/ROW]
[ROW][C]-6[/C][C]-0.0503996684120703[/C][/ROW]
[ROW][C]-5[/C][C]0.0784870609682332[/C][/ROW]
[ROW][C]-4[/C][C]0.187622370317091[/C][/ROW]
[ROW][C]-3[/C][C]0.197126444944920[/C][/ROW]
[ROW][C]-2[/C][C]0.159778643341767[/C][/ROW]
[ROW][C]-1[/C][C]0.0957480326856056[/C][/ROW]
[ROW][C]0[/C][C]0.171320321728396[/C][/ROW]
[ROW][C]1[/C][C]0.186559555810637[/C][/ROW]
[ROW][C]2[/C][C]0.234393088152289[/C][/ROW]
[ROW][C]3[/C][C]0.238350822890252[/C][/ROW]
[ROW][C]4[/C][C]0.198592233606736[/C][/ROW]
[ROW][C]5[/C][C]0.141808125881076[/C][/ROW]
[ROW][C]6[/C][C]0.0657245160976217[/C][/ROW]
[ROW][C]7[/C][C]0.185649931901419[/C][/ROW]
[ROW][C]8[/C][C]0.35019824934017[/C][/ROW]
[ROW][C]9[/C][C]0.417064428232424[/C][/ROW]
[ROW][C]10[/C][C]0.251270988179828[/C][/ROW]
[ROW][C]11[/C][C]0.110195953369594[/C][/ROW]
[ROW][C]12[/C][C]0.13594536471368[/C][/ROW]
[ROW][C]13[/C][C]0.159707068084677[/C][/ROW]
[ROW][C]14[/C][C]0.202724178382571[/C][/ROW]
[ROW][C]15[/C][C]0.205314050173996[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28217&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28217&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])
-15-0.0741348436242949
-14-0.0368416976439518
-13-0.0769871411011834
-12-0.00757400510880037
-11-0.013542066565754
-10-0.0481178151990806
-9-0.0752933498958134
-8-0.0774472967662435
-7-0.0497643761275166
-6-0.0503996684120703
-50.0784870609682332
-40.187622370317091
-30.197126444944920
-20.159778643341767
-10.0957480326856056
00.171320321728396
10.186559555810637
20.234393088152289
30.238350822890252
40.198592233606736
50.141808125881076
60.0657245160976217
70.185649931901419
80.35019824934017
90.417064428232424
100.251270988179828
110.110195953369594
120.13594536471368
130.159707068084677
140.202724178382571
150.205314050173996


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')