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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 12:21:29 -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/t12281593558l76cwfgmylpx89.htm/, Retrieved Sun, 05 May 2024 18:33:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27220, Retrieved Sun, 05 May 2024 18:33:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact199

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] [Buitenlandse handel] [2008-10-13 10:26:18] [1ce0d16c8f4225c977b42c8fa93bc163]
F   PD  [Univariate Data Series] [Aantal gebouwde w...] [2008-10-13 20:51:57] [05e9a6d53ace945e674f09e419f751d6]
-   PD    [Univariate Data Series] [Aantal begonnen w...] [2008-10-20 19:29:55] [a7f04e0e73ce3683561193958d653479]
-           [Univariate Data Series] [Aantal begonnen w...] [2008-10-20 19:33:36] [a7f04e0e73ce3683561193958d653479]
-             [Univariate Data Series] [Aantal begonnen w...] [2008-10-20 20:27:32] [a7f04e0e73ce3683561193958d653479]
F RMPD            [Cross Correlation Function] [Q7 Cross Correlation] [2008-12-01 19:21:29] [f1a30f1149cef3ef3ef69d586c6c3c1c] [Current]
F   P               [Cross Correlation Function] [Q8 - Met transfor...] [2008-12-01 19:54:33] [a7f04e0e73ce3683561193958d653479]
Feedback Forum
2008-12-04 16:15:10 [Stijn Van de Velde] [reply] 
Correct.
De cross correlatie functie word gebruikt om het een voorspelling te maken voor tijdreeks Y aan de hand van het verleden van tijdreeks X.

Je ziet dat er hier niet zo veel correlatie is tussen de 2 reeksen, op een 3tal lags na liggen ze allemaal binnen het betrouwbaarheidsinterval.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
10
12
12
13
17
12
15
12
14
19
16
17
16
19
17
17
20
18
16
19
18
23
20
20
15
17
16
15
10
13
10
19
21
17
16
17
14
18
17
14
15
16
11
15
13
17
16
9
17
15
12
12
12
12
4
7
4
3
3
0
5
Dataseries Y:
Download CSV
Histogram 
3431
3874
2617
3580
5267
3832
3441
3228
3397
3971
4625
4486
4131
4686
3174
4282
4209
4159
3936
3153
3620
4227
4441
4808
4850
5040
3546
4669
5410
5134
4864
3999
4459
4622
5360
4658
5173
4845
3325
4720
4895
5071
4895
3805
4187
4435
4475
4774
5161
4529
3284
4303
4610
4691
4200
3471
3132
4226
3723
3576
3397

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=27220&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=27220&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27220&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])
-140.252837328787826
-130.198175772075666
-120.153611100662611
-110.126690236715449
-100.0811665982945745
-90.204732087188302
-80.229516692054786
-70.178742571665979
-60.123992612167403
-50.139459952859815
-40.188570311819479
-30.240522854233223
-20.330073254128364
-10.261714684849599
00.2446809612763
10.130836645839408
20.0283952737262215
30.0571217053120347
40.138653983306779
5-0.0498647754902465
6-0.149215267804809
7-0.159533315542476
8-0.104136677806486
9-0.0214244258378817
10-0.100930016787985
11-0.212963683966317
12-0.211618999348933
13-0.270404382801259
14-0.281177400051775

\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 & 0.252837328787826 \tabularnewline
-13 & 0.198175772075666 \tabularnewline
-12 & 0.153611100662611 \tabularnewline
-11 & 0.126690236715449 \tabularnewline
-10 & 0.0811665982945745 \tabularnewline
-9 & 0.204732087188302 \tabularnewline
-8 & 0.229516692054786 \tabularnewline
-7 & 0.178742571665979 \tabularnewline
-6 & 0.123992612167403 \tabularnewline
-5 & 0.139459952859815 \tabularnewline
-4 & 0.188570311819479 \tabularnewline
-3 & 0.240522854233223 \tabularnewline
-2 & 0.330073254128364 \tabularnewline
-1 & 0.261714684849599 \tabularnewline
0 & 0.2446809612763 \tabularnewline
1 & 0.130836645839408 \tabularnewline
2 & 0.0283952737262215 \tabularnewline
3 & 0.0571217053120347 \tabularnewline
4 & 0.138653983306779 \tabularnewline
5 & -0.0498647754902465 \tabularnewline
6 & -0.149215267804809 \tabularnewline
7 & -0.159533315542476 \tabularnewline
8 & -0.104136677806486 \tabularnewline
9 & -0.0214244258378817 \tabularnewline
10 & -0.100930016787985 \tabularnewline
11 & -0.212963683966317 \tabularnewline
12 & -0.211618999348933 \tabularnewline
13 & -0.270404382801259 \tabularnewline
14 & -0.281177400051775 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27220&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]0.252837328787826[/C][/ROW]
[ROW][C]-13[/C][C]0.198175772075666[/C][/ROW]
[ROW][C]-12[/C][C]0.153611100662611[/C][/ROW]
[ROW][C]-11[/C][C]0.126690236715449[/C][/ROW]
[ROW][C]-10[/C][C]0.0811665982945745[/C][/ROW]
[ROW][C]-9[/C][C]0.204732087188302[/C][/ROW]
[ROW][C]-8[/C][C]0.229516692054786[/C][/ROW]
[ROW][C]-7[/C][C]0.178742571665979[/C][/ROW]
[ROW][C]-6[/C][C]0.123992612167403[/C][/ROW]
[ROW][C]-5[/C][C]0.139459952859815[/C][/ROW]
[ROW][C]-4[/C][C]0.188570311819479[/C][/ROW]
[ROW][C]-3[/C][C]0.240522854233223[/C][/ROW]
[ROW][C]-2[/C][C]0.330073254128364[/C][/ROW]
[ROW][C]-1[/C][C]0.261714684849599[/C][/ROW]
[ROW][C]0[/C][C]0.2446809612763[/C][/ROW]
[ROW][C]1[/C][C]0.130836645839408[/C][/ROW]
[ROW][C]2[/C][C]0.0283952737262215[/C][/ROW]
[ROW][C]3[/C][C]0.0571217053120347[/C][/ROW]
[ROW][C]4[/C][C]0.138653983306779[/C][/ROW]
[ROW][C]5[/C][C]-0.0498647754902465[/C][/ROW]
[ROW][C]6[/C][C]-0.149215267804809[/C][/ROW]
[ROW][C]7[/C][C]-0.159533315542476[/C][/ROW]
[ROW][C]8[/C][C]-0.104136677806486[/C][/ROW]
[ROW][C]9[/C][C]-0.0214244258378817[/C][/ROW]
[ROW][C]10[/C][C]-0.100930016787985[/C][/ROW]
[ROW][C]11[/C][C]-0.212963683966317[/C][/ROW]
[ROW][C]12[/C][C]-0.211618999348933[/C][/ROW]
[ROW][C]13[/C][C]-0.270404382801259[/C][/ROW]
[ROW][C]14[/C][C]-0.281177400051775[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27220&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27220&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])
-140.252837328787826
-130.198175772075666
-120.153611100662611
-110.126690236715449
-100.0811665982945745
-90.204732087188302
-80.229516692054786
-70.178742571665979
-60.123992612167403
-50.139459952859815
-40.188570311819479
-30.240522854233223
-20.330073254128364
-10.261714684849599
00.2446809612763
10.130836645839408
20.0283952737262215
30.0571217053120347
40.138653983306779
5-0.0498647754902465
6-0.149215267804809
7-0.159533315542476
8-0.104136677806486
9-0.0214244258378817
10-0.100930016787985
11-0.212963683966317
12-0.211618999348933
13-0.270404382801259
14-0.281177400051775


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')