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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 04:34:07 -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/t1228131374ye17hx64tn0w8jt.htm/, Retrieved Sun, 05 May 2024 12:12:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26880, Retrieved Sun, 05 May 2024 12:12:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact225

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Law of Averages] [Random Walk Simul...] [2008-11-25 18:40:39] [b98453cac15ba1066b407e146608df68]
F RM D  [Variance Reduction Matrix] [Q8 RVM Aantal ins...] [2008-11-27 16:54:08] [6fea0e9a9b3b29a63badf2c274e82506]
- RMPD    [Cross Correlation Function] [Q7 CCF] [2008-11-28 21:49:23] [819b576fab25b35cfda70f80599828ec]
F   P         [Cross Correlation Function] [Cross Correlation Q7] [2008-12-01 11:34:07] [286e96bd53289970f8e5f25a93fb50b3] [Current]
-               [Cross Correlation Function] [] [2008-12-08 18:59:41] [888addc516c3b812dd7be4bd54caa358]
-               [Cross Correlation Function] [] [2008-12-09 08:23:09] [888addc516c3b812dd7be4bd54caa358]
Feedback Forum
2008-12-07 12:06:13 [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.
2008-12-09 01:19:03 [Michael Van Spaandonck] [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 voorgesteld, staan alle parameters op de defaultwaarden. Een groot deel van de cross correlatiewaarden ligt buiten het betrouwbaarheidsinterval en is significant verschillend.

Tot deze conclusie wordt ook gekomen in het bijhorende document.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
58.972
59.249
63.955
53.785
52.760
44.795
37.348
32.370
32.717
40.974
33.591
21.124
58.608
46.865
51.378
46.235
47.206
45.382
41.227
33.795
31.295
42.625
33.625
21.538
56.421
53.152
53.536
52.408
41.454
38.271
35.306
26.414
31.917
38.030
27.534
18.387
50.556
43.901
48.572
43.899
37.532
40.357
35.489
29.027
34.485
42.598
30.306
26.451
47.460
50.104
61.465
53.726
39.477
43.895
31.481
29.896
33.842
39.120
33.702
25.094
Dataseries Y:
Download CSV
Histogram 
54.281
63.654
68.918
58.686
67.074
60.183
54.326
54.085
53.564
60.873
53.398
45.164
59.672
56.298
62.361
56.930
62.954
62.431
52.528
54.060
53.093
52.695
52.333
41.747
58.576
57.851
63.721
63.384
61.141
59.231
63.472
49.214
55.816
61.713
48.664
45.351
57.888
54.091
59.098
58.962
55.433
60.403
60.721
48.440
57.981
60.258
47.312
46.980
54.846
56.824
67.744
62.849
54.691
65.461
53.724
54.560
57.722
55.458
48.490
46.362

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=26880&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=26880&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26880&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)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])
-140.259059649088100
-130.241192044381458
-120.526878358263875
-110.0174453553470836
-10-0.300781308114282
-9-0.242938078656849
-8-0.221550955219530
-7-0.360707606322461
-6-0.235807994650614
-5-0.0254154080161034
-40.126724135215846
-30.304935957430246
-20.352992040815625
-10.409245340814917
00.769273216056776
10.128451536048101
2-0.209240792743404
3-0.173482410029074
4-0.270052929683563
5-0.315735836598653
6-0.303382505109709
7-0.15291266378235
80.0924562057852841
90.0966192898778728
100.18358134158408
110.297897324586841
120.482438597117547
130.0654513056608115
14-0.1572652655354

\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 & 0.259059649088100 \tabularnewline
-13 & 0.241192044381458 \tabularnewline
-12 & 0.526878358263875 \tabularnewline
-11 & 0.0174453553470836 \tabularnewline
-10 & -0.300781308114282 \tabularnewline
-9 & -0.242938078656849 \tabularnewline
-8 & -0.221550955219530 \tabularnewline
-7 & -0.360707606322461 \tabularnewline
-6 & -0.235807994650614 \tabularnewline
-5 & -0.0254154080161034 \tabularnewline
-4 & 0.126724135215846 \tabularnewline
-3 & 0.304935957430246 \tabularnewline
-2 & 0.352992040815625 \tabularnewline
-1 & 0.409245340814917 \tabularnewline
0 & 0.769273216056776 \tabularnewline
1 & 0.128451536048101 \tabularnewline
2 & -0.209240792743404 \tabularnewline
3 & -0.173482410029074 \tabularnewline
4 & -0.270052929683563 \tabularnewline
5 & -0.315735836598653 \tabularnewline
6 & -0.303382505109709 \tabularnewline
7 & -0.15291266378235 \tabularnewline
8 & 0.0924562057852841 \tabularnewline
9 & 0.0966192898778728 \tabularnewline
10 & 0.18358134158408 \tabularnewline
11 & 0.297897324586841 \tabularnewline
12 & 0.482438597117547 \tabularnewline
13 & 0.0654513056608115 \tabularnewline
14 & -0.1572652655354 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26880&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]0.259059649088100[/C][/ROW]
[ROW][C]-13[/C][C]0.241192044381458[/C][/ROW]
[ROW][C]-12[/C][C]0.526878358263875[/C][/ROW]
[ROW][C]-11[/C][C]0.0174453553470836[/C][/ROW]
[ROW][C]-10[/C][C]-0.300781308114282[/C][/ROW]
[ROW][C]-9[/C][C]-0.242938078656849[/C][/ROW]
[ROW][C]-8[/C][C]-0.221550955219530[/C][/ROW]
[ROW][C]-7[/C][C]-0.360707606322461[/C][/ROW]
[ROW][C]-6[/C][C]-0.235807994650614[/C][/ROW]
[ROW][C]-5[/C][C]-0.0254154080161034[/C][/ROW]
[ROW][C]-4[/C][C]0.126724135215846[/C][/ROW]
[ROW][C]-3[/C][C]0.304935957430246[/C][/ROW]
[ROW][C]-2[/C][C]0.352992040815625[/C][/ROW]
[ROW][C]-1[/C][C]0.409245340814917[/C][/ROW]
[ROW][C]0[/C][C]0.769273216056776[/C][/ROW]
[ROW][C]1[/C][C]0.128451536048101[/C][/ROW]
[ROW][C]2[/C][C]-0.209240792743404[/C][/ROW]
[ROW][C]3[/C][C]-0.173482410029074[/C][/ROW]
[ROW][C]4[/C][C]-0.270052929683563[/C][/ROW]
[ROW][C]5[/C][C]-0.315735836598653[/C][/ROW]
[ROW][C]6[/C][C]-0.303382505109709[/C][/ROW]
[ROW][C]7[/C][C]-0.15291266378235[/C][/ROW]
[ROW][C]8[/C][C]0.0924562057852841[/C][/ROW]
[ROW][C]9[/C][C]0.0966192898778728[/C][/ROW]
[ROW][C]10[/C][C]0.18358134158408[/C][/ROW]
[ROW][C]11[/C][C]0.297897324586841[/C][/ROW]
[ROW][C]12[/C][C]0.482438597117547[/C][/ROW]
[ROW][C]13[/C][C]0.0654513056608115[/C][/ROW]
[ROW][C]14[/C][C]-0.1572652655354[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26880&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26880&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])
-140.259059649088100
-130.241192044381458
-120.526878358263875
-110.0174453553470836
-10-0.300781308114282
-9-0.242938078656849
-8-0.221550955219530
-7-0.360707606322461
-6-0.235807994650614
-5-0.0254154080161034
-40.126724135215846
-30.304935957430246
-20.352992040815625
-10.409245340814917
00.769273216056776
10.128451536048101
2-0.209240792743404
3-0.173482410029074
4-0.270052929683563
5-0.315735836598653
6-0.303382505109709
7-0.15291266378235
80.0924562057852841
90.0966192898778728
100.18358134158408
110.297897324586841
120.482438597117547
130.0654513056608115
14-0.1572652655354


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')