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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 06:17:13 -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/t1228224346wx0qdtb0pysb5td.htm/, Retrieved Sat, 18 May 2024 08:06:39 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27748, Retrieved Sat, 18 May 2024 08:06:39 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact175

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Cross Correlation Function] [CCF] [2008-12-02 13:17:13] [09074fbe368d26382bb94e5bb318a104] [Current]
F   PD    [Cross Correlation Function] [CCF] [2008-12-02 13:45:44] [a4602103a5e123497aa555277d0e627b]
Feedback Forum
2008-12-04 13:56:19 [Steven Vercammen] [reply] 
Dit klopt.
Met de cross correlatiefunctie kan men nagaan in hoeverre Y te verklaren valt door het verleden van X. X = investeringsgoederen en Y= intermediaire goederen. rho(Y[t],X[t+k]) geeft de correlatie aan tussen het verleden van X en het heden van Y wanneer k kleiner is dan 0. (is er sprake van een leading indicator?) Wanneer k groter is dan 0 geeft het de correlatie weer tussen de toekomstige x en het heden van Y (is er sprake van een lagging indicator)? In dit geval is er sprake van beide. Er zijn zowel verticale lijntjes voor als na k=0 overstijgen het betrouwbaarheidsinterval en verschillen dus significant van 0. Volgens deze grafiek zou dus zowel het vroegere als de toekomstige X-waarden informatie bevatten om Y te voorspellen. Het is opvallend dat deze waarden rond k=12 k=0 en k=-12 vallen. Dit zou seizonaliteit kunnen impliceren.
2008-12-08 19:27:00 [5faab2fc6fb120339944528a32d48a04] [reply] 
Aan de hand van deze grafiek kunnen we vaststellen wat het verband is tss 2 variabelen.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
103,1
100,6
103,1
95,5
90,5
90,9
88,8
90,7
94,3
104,6
111,1
110,8
107,2
99,0
99,0
91,0
96,2
96,9
96,2
100,1
99,0
115,4
106,9
107,1
99,3
99,2
108,3
105,6
99,5
107,4
93,1
88,1
110,7
113,1
99,6
93,6
98,6
99,6
114,3
107,8
101,2
112,5
100,5
93,9
116,2
112,0
106,4
95,7
96,0
95,8
103,0
102,2
98,4
111,4
86,6
91,3
107,9
101,8
104,4
93,4
100,1
98,5
112,9
101,4
107,1
110,8
90,3
95,5
111,4
113,0
107,5
95,9
106,3
105,2
117,2
106,9
108,2
113,0
97,2
99,9
108,1
118,1
109,1
93,3
112,1
Dataseries Y:
Download CSV
Histogram 
119,5
125,0
145,0
105,3
116,9
120,1
88,9
78,4
114,6
113,3
117,0
99,6
99,4
101,9
115,2
108,5
113,8
121,0
92,2
90,2
101,5
126,6
93,9
89,8
93,4
101,5
110,4
105,9
108,4
113,9
86,1
69,4
101,2
100,5
98,0
106,6
90,1
96,9
125,9
112,0
100,0
123,9
79,8
83,4
113,6
112,9
104,0
109,9
99,0
106,3
128,9
111,1
102,9
130,0
87,0
87,5
117,6
103,4
110,8
112,6
102,5
112,4
135,6
105,1
127,7
137,0
91,0
90,5
122,4
123,3
124,3
120,0
118,1
119,0
142,7
123,6
129,6
151,6
110,4
99,2
130,5
136,2
129,7
128,0
121,6

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=27748&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=27748&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27748&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])
-16-0.0310946605113673
-150.0604131181495997
-14-0.0535231887826068
-130.00687404157874838
-120.381017816923367
-11-0.00583408742077162
-10-0.284078039319054
-90.0617655605992916
-80.101768840219023
-70.139523392197282
-60.281937485806357
-50.120182543053055
-4-0.0398352384780394
-30.129910527117841
-20.00448710883175743
-10.113869725255704
00.580471399357484
10.047595900957849
2-0.271959218171184
3-0.0261616297225557
40.0360728593729617
50.0402209236664738
60.143522733670509
70.0875819585907979
8-0.00434602815547668
90.0444974714238492
10-0.0819841021821792
110.0979330696712692
120.417860717736214
13-0.051159609837347
14-0.326773606494212
15-0.105350669758714
16-0.00460481347624146

\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
-16 & -0.0310946605113673 \tabularnewline
-15 & 0.0604131181495997 \tabularnewline
-14 & -0.0535231887826068 \tabularnewline
-13 & 0.00687404157874838 \tabularnewline
-12 & 0.381017816923367 \tabularnewline
-11 & -0.00583408742077162 \tabularnewline
-10 & -0.284078039319054 \tabularnewline
-9 & 0.0617655605992916 \tabularnewline
-8 & 0.101768840219023 \tabularnewline
-7 & 0.139523392197282 \tabularnewline
-6 & 0.281937485806357 \tabularnewline
-5 & 0.120182543053055 \tabularnewline
-4 & -0.0398352384780394 \tabularnewline
-3 & 0.129910527117841 \tabularnewline
-2 & 0.00448710883175743 \tabularnewline
-1 & 0.113869725255704 \tabularnewline
0 & 0.580471399357484 \tabularnewline
1 & 0.047595900957849 \tabularnewline
2 & -0.271959218171184 \tabularnewline
3 & -0.0261616297225557 \tabularnewline
4 & 0.0360728593729617 \tabularnewline
5 & 0.0402209236664738 \tabularnewline
6 & 0.143522733670509 \tabularnewline
7 & 0.0875819585907979 \tabularnewline
8 & -0.00434602815547668 \tabularnewline
9 & 0.0444974714238492 \tabularnewline
10 & -0.0819841021821792 \tabularnewline
11 & 0.0979330696712692 \tabularnewline
12 & 0.417860717736214 \tabularnewline
13 & -0.051159609837347 \tabularnewline
14 & -0.326773606494212 \tabularnewline
15 & -0.105350669758714 \tabularnewline
16 & -0.00460481347624146 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27748&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]-16[/C][C]-0.0310946605113673[/C][/ROW]
[ROW][C]-15[/C][C]0.0604131181495997[/C][/ROW]
[ROW][C]-14[/C][C]-0.0535231887826068[/C][/ROW]
[ROW][C]-13[/C][C]0.00687404157874838[/C][/ROW]
[ROW][C]-12[/C][C]0.381017816923367[/C][/ROW]
[ROW][C]-11[/C][C]-0.00583408742077162[/C][/ROW]
[ROW][C]-10[/C][C]-0.284078039319054[/C][/ROW]
[ROW][C]-9[/C][C]0.0617655605992916[/C][/ROW]
[ROW][C]-8[/C][C]0.101768840219023[/C][/ROW]
[ROW][C]-7[/C][C]0.139523392197282[/C][/ROW]
[ROW][C]-6[/C][C]0.281937485806357[/C][/ROW]
[ROW][C]-5[/C][C]0.120182543053055[/C][/ROW]
[ROW][C]-4[/C][C]-0.0398352384780394[/C][/ROW]
[ROW][C]-3[/C][C]0.129910527117841[/C][/ROW]
[ROW][C]-2[/C][C]0.00448710883175743[/C][/ROW]
[ROW][C]-1[/C][C]0.113869725255704[/C][/ROW]
[ROW][C]0[/C][C]0.580471399357484[/C][/ROW]
[ROW][C]1[/C][C]0.047595900957849[/C][/ROW]
[ROW][C]2[/C][C]-0.271959218171184[/C][/ROW]
[ROW][C]3[/C][C]-0.0261616297225557[/C][/ROW]
[ROW][C]4[/C][C]0.0360728593729617[/C][/ROW]
[ROW][C]5[/C][C]0.0402209236664738[/C][/ROW]
[ROW][C]6[/C][C]0.143522733670509[/C][/ROW]
[ROW][C]7[/C][C]0.0875819585907979[/C][/ROW]
[ROW][C]8[/C][C]-0.00434602815547668[/C][/ROW]
[ROW][C]9[/C][C]0.0444974714238492[/C][/ROW]
[ROW][C]10[/C][C]-0.0819841021821792[/C][/ROW]
[ROW][C]11[/C][C]0.0979330696712692[/C][/ROW]
[ROW][C]12[/C][C]0.417860717736214[/C][/ROW]
[ROW][C]13[/C][C]-0.051159609837347[/C][/ROW]
[ROW][C]14[/C][C]-0.326773606494212[/C][/ROW]
[ROW][C]15[/C][C]-0.105350669758714[/C][/ROW]
[ROW][C]16[/C][C]-0.00460481347624146[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27748&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27748&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])
-16-0.0310946605113673
-150.0604131181495997
-14-0.0535231887826068
-130.00687404157874838
-120.381017816923367
-11-0.00583408742077162
-10-0.284078039319054
-90.0617655605992916
-80.101768840219023
-70.139523392197282
-60.281937485806357
-50.120182543053055
-4-0.0398352384780394
-30.129910527117841
-20.00448710883175743
-10.113869725255704
00.580471399357484
10.047595900957849
2-0.271959218171184
3-0.0261616297225557
40.0360728593729617
50.0402209236664738
60.143522733670509
70.0875819585907979
8-0.00434602815547668
90.0444974714238492
10-0.0819841021821792
110.0979330696712692
120.417860717736214
13-0.051159609837347
14-0.326773606494212
15-0.105350669758714
16-0.00460481347624146


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')