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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 12:32:23 -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/t1228246455jehoxkb7nbhrymj.htm/, Retrieved Sun, 19 May 2024 11:29:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28241, Retrieved Sun, 19 May 2024 11:29:03 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsnon stationary time series , Q8
Estimated Impact161

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] [loïqueverhasselt] [2008-12-02 18:09:48] [0e879b146665902680dd148a904a2646]
F   PD    [Cross Correlation Function] [loïqueverhasselt] [2008-12-02 18:24:22] [0e879b146665902680dd148a904a2646]
F   P         [Cross Correlation Function] [loïqueverhasselt] [2008-12-02 19:32:23] [6440ec5a21e5d35520cb2ae6b4b70e45] [Current]
Feedback Forum
2008-12-07 09:41:14 [Gert-Jan Geudens] [reply] 
Deze berekening is overbodig en niet correct. Je moet hier de autocorrelatie berekenen.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
99.4
97.5
94.6
92.6
92.5
89.8
88.8
87.4
85.2
83.1
84.7
84.8
85.8
86.3
89
89
89.3
91.9
94.9
94.4
96.8
96.9
98
97.9
100.9
103.9
103.1
102.5
104.3
102.6
101.7
102.8
105.4
110.9
113.5
116.3
124
128.8
133.5
132.6
128.4
127.3
126.7
123.3
123.2
124.4
128.2
128.7
135.7
139
145.4
142.4
137.7
137
137.1
139.3
139.6
140.4
142.3
148.3
Dataseries Y:
Download CSV
Histogram 
93
98.4
92.6
94.6
99.5
97.6
91.3
93.6
93.1
78.4
70.2
69.3
71.1
73.5
85.9
91.5
91.8
88.3
91.3
94
99.3
96.7
88
96.7
106.8
114.3
105.7
90.1
91.6
97.7
100.8
104.6
95.9
102.7
104
107.9
113.8
113.8
123.1
125.1
137.6
134
140.3
152.1
150.6
167.3
153.2
142
154.4
158.5
180.9
181.3
172.4
192
199.3
215.4
214.3
201.5
190.5
196

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 3 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28241&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28241&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28241&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 time3 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001






Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series1
Degree of non-seasonal differencing (d) of X series1
Degree of seasonal differencing (D) of X series1
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-13-0.190327737694998
-120.071358767029408
-11-0.0561006814306917
-10-0.172422185719298
-9-0.0841822169894472
-8-0.121569076340626
-70.160995207465742
-60.145361303100114
-50.0584444755856403
-40.134892699361726
-30.141449404976179
-2-0.0829693967393286
-10.164588253180000
00.200779458074906
1-0.146782089923922
2-0.0169778887403606
30.0730847181327834
40.0343889306267096
5-0.104308609591241
6-0.074925312318367
7-0.163953435477708
8-0.0920466725183322
9-0.192499682768375
100.0217849386509635
11-0.0365827972490159
12-0.0496339329064017
130.133880887504942

\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 & 1 \tabularnewline
Degree of seasonal differencing (D) of X series & 1 \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 & 1 \tabularnewline
Degree of seasonal differencing (D) of Y series & 1 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-13 & -0.190327737694998 \tabularnewline
-12 & 0.071358767029408 \tabularnewline
-11 & -0.0561006814306917 \tabularnewline
-10 & -0.172422185719298 \tabularnewline
-9 & -0.0841822169894472 \tabularnewline
-8 & -0.121569076340626 \tabularnewline
-7 & 0.160995207465742 \tabularnewline
-6 & 0.145361303100114 \tabularnewline
-5 & 0.0584444755856403 \tabularnewline
-4 & 0.134892699361726 \tabularnewline
-3 & 0.141449404976179 \tabularnewline
-2 & -0.0829693967393286 \tabularnewline
-1 & 0.164588253180000 \tabularnewline
0 & 0.200779458074906 \tabularnewline
1 & -0.146782089923922 \tabularnewline
2 & -0.0169778887403606 \tabularnewline
3 & 0.0730847181327834 \tabularnewline
4 & 0.0343889306267096 \tabularnewline
5 & -0.104308609591241 \tabularnewline
6 & -0.074925312318367 \tabularnewline
7 & -0.163953435477708 \tabularnewline
8 & -0.0920466725183322 \tabularnewline
9 & -0.192499682768375 \tabularnewline
10 & 0.0217849386509635 \tabularnewline
11 & -0.0365827972490159 \tabularnewline
12 & -0.0496339329064017 \tabularnewline
13 & 0.133880887504942 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28241&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]1[/C][/ROW]
[ROW][C]Degree of seasonal differencing (D) of X series[/C][C]1[/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]1[/C][/ROW]
[ROW][C]Degree of seasonal differencing (D) of Y series[/C][C]1[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-13[/C][C]-0.190327737694998[/C][/ROW]
[ROW][C]-12[/C][C]0.071358767029408[/C][/ROW]
[ROW][C]-11[/C][C]-0.0561006814306917[/C][/ROW]
[ROW][C]-10[/C][C]-0.172422185719298[/C][/ROW]
[ROW][C]-9[/C][C]-0.0841822169894472[/C][/ROW]
[ROW][C]-8[/C][C]-0.121569076340626[/C][/ROW]
[ROW][C]-7[/C][C]0.160995207465742[/C][/ROW]
[ROW][C]-6[/C][C]0.145361303100114[/C][/ROW]
[ROW][C]-5[/C][C]0.0584444755856403[/C][/ROW]
[ROW][C]-4[/C][C]0.134892699361726[/C][/ROW]
[ROW][C]-3[/C][C]0.141449404976179[/C][/ROW]
[ROW][C]-2[/C][C]-0.0829693967393286[/C][/ROW]
[ROW][C]-1[/C][C]0.164588253180000[/C][/ROW]
[ROW][C]0[/C][C]0.200779458074906[/C][/ROW]
[ROW][C]1[/C][C]-0.146782089923922[/C][/ROW]
[ROW][C]2[/C][C]-0.0169778887403606[/C][/ROW]
[ROW][C]3[/C][C]0.0730847181327834[/C][/ROW]
[ROW][C]4[/C][C]0.0343889306267096[/C][/ROW]
[ROW][C]5[/C][C]-0.104308609591241[/C][/ROW]
[ROW][C]6[/C][C]-0.074925312318367[/C][/ROW]
[ROW][C]7[/C][C]-0.163953435477708[/C][/ROW]
[ROW][C]8[/C][C]-0.0920466725183322[/C][/ROW]
[ROW][C]9[/C][C]-0.192499682768375[/C][/ROW]
[ROW][C]10[/C][C]0.0217849386509635[/C][/ROW]
[ROW][C]11[/C][C]-0.0365827972490159[/C][/ROW]
[ROW][C]12[/C][C]-0.0496339329064017[/C][/ROW]
[ROW][C]13[/C][C]0.133880887504942[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28241&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28241&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 series1
Degree of seasonal differencing (D) of X series1
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-13-0.190327737694998
-120.071358767029408
-11-0.0561006814306917
-10-0.172422185719298
-9-0.0841822169894472
-8-0.121569076340626
-70.160995207465742
-60.145361303100114
-50.0584444755856403
-40.134892699361726
-30.141449404976179
-2-0.0829693967393286
-10.164588253180000
00.200779458074906
1-0.146782089923922
2-0.0169778887403606
30.0730847181327834
40.0343889306267096
5-0.104308609591241
6-0.074925312318367
7-0.163953435477708
8-0.0920466725183322
9-0.192499682768375
100.0217849386509635
11-0.0365827972490159
12-0.0496339329064017
130.133880887504942


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 1 ; par3 = 1 ; par4 = 12 ; par5 = 1 ; par6 = 1 ; par7 = 1 ;
Parameters (R input):
par1 = 1 ; par2 = 1 ; par3 = 1 ; par4 = 12 ; par5 = 1 ; par6 = 1 ; par7 = 1 ;
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')