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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_cross.wasp
Title produced by softwareCross Correlation Function
Date of computationSun, 30 Nov 2008 03:38:10 -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/Nov/30/t1228041688d5sddmfxk6w4m4q.htm/, Retrieved Sun, 19 May 2024 09:23:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26420, Retrieved Sun, 19 May 2024 09:23:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact218

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] [Opdracht hfdst 7 Q7] [2008-11-30 10:38:10] [e1dd70d3b1099218056e8ae5041dcc2f] [Current]
Feedback Forum
2008-12-04 13:12:37 [Steven Vercammen] [reply] 
De interpretatie is niet volledig correct.
Met de cross correlatiefunctie kan men nagaan in hoeverre Y te verklaren valt door het verleden van X. X = totale productie van intermediaire goederen en Y= totale productie investeringsgoederen. 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. De grafiek geeft een indicatie van seizonaliteit op k=-12 k=0 en k=12 zien we significante correlaties. De rho is ook het grootst op deze momenten.
2008-12-06 12:15:07 [Maarten Van Gucht] [reply] 
De student kon zijn antwoord nog vervolledigen. Met de cross correlatiefunctie kan je inderdaad nagaan in hoeverre Y te verklaren valt door het verleden van X.
We zien hier dat de correlatie het sterkst is als we de tijdreeks niet verschuiven in de tijd. Er is hier inderdaad ook seizoenaliteit waar te nemen in de grafiek. op de k=-12, K=12 en k= 0 kan je zien dat de correlatie ongeveer gelijk zijn aan elkaar. (allemaal significant verschillend, en dus niet aan het toeval te wijten kunnen zijn)
2008-12-08 15:11:24 [Sam De Cuyper] [reply] 
De interpretatie is niet volledig correct. (Idem zie Steven Vercammen)

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
90,7
94,3
104,6
111,1
110,8
107,2
99
99
91
96,2
96,9
96,2
100,1
99
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
106,4
95,7
96
95,8
103
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
107,5
95,9
106,3
105,2
117,2
106,9
108,2
113
97,2
99,9
108,1
118,1
109,1
93,3
112,1
111,8
112,5
116,3
110,3
117,1
103,4
96,2
Dataseries Y:
Download CSV
Histogram 
78,4
114,6
113,3
117
99,6
99,4
101,9
115,2
108,5
113,8
121
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
106,6
90,1
96,9
125,9
112
100
123,9
79,8
83,4
113,6
112,9
104
109,9
99
106,3
128,9
111,1
102,9
130
87
87,5
117,6
103,4
110,8
112,6
102,5
112,4
135,6
105,1
127,7
137
91
90,5
122,4
123,3
124,3
120
118,1
119
142,7
123,6
129,6
151,6
110,4
99,2
130,5
136,2
129,7
128
121,6
135,8
143,8
147,5
136,2
156,6
123,3
100,4

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=26420&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=26420&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26420&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)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])
-16-0.0107772329233710
-150.095054467425015
-14-0.0502739977990478
-130.0824684290666165
-120.446106430740367
-110.063753450132706
-10-0.236551024031425
-90.0903386444359072
-80.154239059345549
-70.133855239571148
-60.270214100295681
-50.196205272996775
-40.0339605236335903
-30.15805417978222
-20.0553975545261384
-10.208013979281944
00.684629200251957
10.169074450795228
2-0.118223414099991
30.127924486289979
40.188276944277364
50.173467632127080
60.244027361229321
70.138179665785592
80.0347454320779522
90.0547634585257016
10-0.0515797580262622
110.172147727337677
120.515956593332206
130.093203139982522
14-0.215746517387777
150.0420958200666728
160.0853300941386552

\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
-16 & -0.0107772329233710 \tabularnewline
-15 & 0.095054467425015 \tabularnewline
-14 & -0.0502739977990478 \tabularnewline
-13 & 0.0824684290666165 \tabularnewline
-12 & 0.446106430740367 \tabularnewline
-11 & 0.063753450132706 \tabularnewline
-10 & -0.236551024031425 \tabularnewline
-9 & 0.0903386444359072 \tabularnewline
-8 & 0.154239059345549 \tabularnewline
-7 & 0.133855239571148 \tabularnewline
-6 & 0.270214100295681 \tabularnewline
-5 & 0.196205272996775 \tabularnewline
-4 & 0.0339605236335903 \tabularnewline
-3 & 0.15805417978222 \tabularnewline
-2 & 0.0553975545261384 \tabularnewline
-1 & 0.208013979281944 \tabularnewline
0 & 0.684629200251957 \tabularnewline
1 & 0.169074450795228 \tabularnewline
2 & -0.118223414099991 \tabularnewline
3 & 0.127924486289979 \tabularnewline
4 & 0.188276944277364 \tabularnewline
5 & 0.173467632127080 \tabularnewline
6 & 0.244027361229321 \tabularnewline
7 & 0.138179665785592 \tabularnewline
8 & 0.0347454320779522 \tabularnewline
9 & 0.0547634585257016 \tabularnewline
10 & -0.0515797580262622 \tabularnewline
11 & 0.172147727337677 \tabularnewline
12 & 0.515956593332206 \tabularnewline
13 & 0.093203139982522 \tabularnewline
14 & -0.215746517387777 \tabularnewline
15 & 0.0420958200666728 \tabularnewline
16 & 0.0853300941386552 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26420&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]-16[/C][C]-0.0107772329233710[/C][/ROW]
[ROW][C]-15[/C][C]0.095054467425015[/C][/ROW]
[ROW][C]-14[/C][C]-0.0502739977990478[/C][/ROW]
[ROW][C]-13[/C][C]0.0824684290666165[/C][/ROW]
[ROW][C]-12[/C][C]0.446106430740367[/C][/ROW]
[ROW][C]-11[/C][C]0.063753450132706[/C][/ROW]
[ROW][C]-10[/C][C]-0.236551024031425[/C][/ROW]
[ROW][C]-9[/C][C]0.0903386444359072[/C][/ROW]
[ROW][C]-8[/C][C]0.154239059345549[/C][/ROW]
[ROW][C]-7[/C][C]0.133855239571148[/C][/ROW]
[ROW][C]-6[/C][C]0.270214100295681[/C][/ROW]
[ROW][C]-5[/C][C]0.196205272996775[/C][/ROW]
[ROW][C]-4[/C][C]0.0339605236335903[/C][/ROW]
[ROW][C]-3[/C][C]0.15805417978222[/C][/ROW]
[ROW][C]-2[/C][C]0.0553975545261384[/C][/ROW]
[ROW][C]-1[/C][C]0.208013979281944[/C][/ROW]
[ROW][C]0[/C][C]0.684629200251957[/C][/ROW]
[ROW][C]1[/C][C]0.169074450795228[/C][/ROW]
[ROW][C]2[/C][C]-0.118223414099991[/C][/ROW]
[ROW][C]3[/C][C]0.127924486289979[/C][/ROW]
[ROW][C]4[/C][C]0.188276944277364[/C][/ROW]
[ROW][C]5[/C][C]0.173467632127080[/C][/ROW]
[ROW][C]6[/C][C]0.244027361229321[/C][/ROW]
[ROW][C]7[/C][C]0.138179665785592[/C][/ROW]
[ROW][C]8[/C][C]0.0347454320779522[/C][/ROW]
[ROW][C]9[/C][C]0.0547634585257016[/C][/ROW]
[ROW][C]10[/C][C]-0.0515797580262622[/C][/ROW]
[ROW][C]11[/C][C]0.172147727337677[/C][/ROW]
[ROW][C]12[/C][C]0.515956593332206[/C][/ROW]
[ROW][C]13[/C][C]0.093203139982522[/C][/ROW]
[ROW][C]14[/C][C]-0.215746517387777[/C][/ROW]
[ROW][C]15[/C][C]0.0420958200666728[/C][/ROW]
[ROW][C]16[/C][C]0.0853300941386552[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26420&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26420&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])
-16-0.0107772329233710
-150.095054467425015
-14-0.0502739977990478
-130.0824684290666165
-120.446106430740367
-110.063753450132706
-10-0.236551024031425
-90.0903386444359072
-80.154239059345549
-70.133855239571148
-60.270214100295681
-50.196205272996775
-40.0339605236335903
-30.15805417978222
-20.0553975545261384
-10.208013979281944
00.684629200251957
10.169074450795228
2-0.118223414099991
30.127924486289979
40.188276944277364
50.173467632127080
60.244027361229321
70.138179665785592
80.0347454320779522
90.0547634585257016
10-0.0515797580262622
110.172147727337677
120.515956593332206
130.093203139982522
14-0.215746517387777
150.0420958200666728
160.0853300941386552


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')