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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 14:00:44 -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/t1228251872lm6xl4nywp6vmhb.htm/, Retrieved Fri, 17 May 2024 06:17:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28455, Retrieved Fri, 17 May 2024 06:17:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact187

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  [Variance Reduction Matrix] [blok 17 Q6 VRM ] [2008-12-02 20:44:02] [6173c35e31b784a490c8cd5476f785d4]
F RMP     [Spectral Analysis] [blok 17 Q6 spectrum] [2008-12-02 20:49:56] [6173c35e31b784a490c8cd5476f785d4]
F RMPD        [Cross Correlation Function] [blok 17 Q7 crossc...] [2008-12-02 21:00:44] [1237f4df7e9be807e4c0a07b90c45721] [Current]
Feedback Forum
2008-12-04 13:29:09 [c97d2ae59c98cf77a04815c1edffab5a] [reply] 
de tabellen en grafieken zijn juist geproduceerd, maar er is geen conclusie gevormd en geen uitleg gegeven.

uitleg crosscorrelation:
berekent correlatie tussen 2 verschillende tijdsreeksen.
- Rho(y(t), x(t))
- Rho (y(t), x(t-1))
=> Geeft het verband weer tussen x(t) en y(t), maar op een dynamische manier - Hoe kan ik y(t) voorspellen?
=> Adhv huidige waarde van x(t)
=> Adhv verleden/vorige waarde van x(t)
! Crosscorrelation zegt niets over de trend en eventuele differentiatie.

hier wordt de cross correlation berekend op de ruwe gegevens van x en y.

conclusie uit tabel:
- in de tabel zie je de herhaling van de parameters die zijn ingevuld (links bovenaan)
-k: het aantal verschuivingen in de tijd.
-K=0 => correlatie tussen y(t) en x(t) ZONDER verschuiving in de tijd
-Boven 0 in tabel (negatieve waarden van k):
= correlatie tussen het verleden van x(t) en huidige y(t)
=>onderzoek of x(t) een leading indicator is (voorloper)/ een voorspellende kracht heeft op het verloop van y(t)
-Onder 0 (positieve waarden van k):
= toekomstige waarde van x(t) gecorreleerd met de huidige waarde van y(t)
=> verleden van y(t) gecorreleerd met de huidige waarde van x(t)

conclusie uit grafiek:
-Betrouwbaarheidsinterval: correlaties die buiten de stippenlijn vallen zijn significant verschillend van 0, en dus niet te wijten aan toeval.
=>Al deze waarden die erbuiten vallen, gaan een effect hebben op het verloop van y(t)
=>Links van 0:
8 correlaties die niet aan toeval zijn te wijten
Je kan bij deze 8 correlaties het verleden van x gebruiken om y te voorspellen
=>Rechts van 0:
9 correlaties die niet aan toeval zijn te wijten
Je kan bij deze 9 correlaties de toekomst van x gebruiken om y te voorspellen.
(of eventueel het verleden van y gebruiken om x te voorspellen)
-Uit deze grafiek kunnen we ook afleiden hoeveel we de tijdsreeks van x(t) moeten vertragen/ versnellen.
=>Vertragen: met periodes van: -10, -9,-8,-7,-5, -3,-2,-1
=>Versnellen met periodes van: 1,2,3,4,5,7,8,10,14
=>Dit zijn de exogene variabelen nodig om y(t) te voorspellen
-We kunnen ook nagaan of x(t+k) een positief/negatief effect heeft op y(t)
=>Hier zijn alle correlatiecoëfficiënten negatief.
=>Dwz dat x(t+k) altijd een negatief effect zal hebben op het verloop van y(t)

2008-12-09 20:44:22 [Anna Hayan] [reply] 
De grafieken zijn juist maar de conlusie ontbreekt

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
98,6
98
106,8
96,6
100,1
107,7
91,5
97,8
107,4
117,5
105,6
97,4
99,5
98
104,3
100,6
101,1
103,9
96,9
95,5
108,4
117
103,8
100,8
110,6
104
112,6
107,3
98,9
109,8
104,9
102,2
123,9
124,9
112,7
121,9
100,6
104,3
120,4
107,5
102,9
125,6
107,5
108,8
128,4
121,1
119,5
128,7
108,7
105,5
119,8
111,3
110,6
120,1
97,5
107,7
127,3
117,2
119,8
116,2
111
112,4
130,6
109,1
118,8
123,9
101,6
112,8
128
129,6
125,8
119,5
Dataseries Y:
Download CSV
Histogram 
98,1
101,1
111,1
93,3
100
108
70,4
75,4
105,5
112,3
102,5
93,5
86,7
95,2
103,8
97
95,5
101
67,5
64
106,7
100,6
101,2
93,1
84,2
85,8
91,8
92,4
80,3
79,7
62,5
57,1
100,8
100,7
86,2
83,2
71,7
77,5
89,8
80,3
78,7
93,8
57,6
60,6
91
85,3
77,4
77,3
68,3
69,9
81,7
75,1
69,9
84
54,3
60
89,9
77
85,3
77,6
69,2
75,5
85,7
72,2
79,9
85,3
52,2
61,2
82,4
85,4
78,2
70,2

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time0 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 & 0 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28455&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]0 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=28455&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28455&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 time0 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])
-15-0.0617051845151407
-14-0.218009657915035
-13-0.160603747770445
-120.164609141410213
-11-0.151499476253291
-10-0.475364016258988
-9-0.308164273874806
-8-0.334133303948397
-7-0.299803698203838
-6-0.0327286673949929
-5-0.302819000409889
-4-0.445927673594421
-3-0.223699391120500
-2-0.480305702594076
-1-0.33341543471637
00.102158957058790
1-0.359689517979094
2-0.635664452420787
3-0.429719842124593
4-0.464102819261748
5-0.34853562868307
6-0.0805507523225808
7-0.290069214566632
8-0.362204970486818
9-0.177623037202522
10-0.396656991777437
11-0.204115423920471
120.119256840066746
13-0.256373285077429
14-0.437999056716572
15-0.271493798089966

\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
-15 & -0.0617051845151407 \tabularnewline
-14 & -0.218009657915035 \tabularnewline
-13 & -0.160603747770445 \tabularnewline
-12 & 0.164609141410213 \tabularnewline
-11 & -0.151499476253291 \tabularnewline
-10 & -0.475364016258988 \tabularnewline
-9 & -0.308164273874806 \tabularnewline
-8 & -0.334133303948397 \tabularnewline
-7 & -0.299803698203838 \tabularnewline
-6 & -0.0327286673949929 \tabularnewline
-5 & -0.302819000409889 \tabularnewline
-4 & -0.445927673594421 \tabularnewline
-3 & -0.223699391120500 \tabularnewline
-2 & -0.480305702594076 \tabularnewline
-1 & -0.33341543471637 \tabularnewline
0 & 0.102158957058790 \tabularnewline
1 & -0.359689517979094 \tabularnewline
2 & -0.635664452420787 \tabularnewline
3 & -0.429719842124593 \tabularnewline
4 & -0.464102819261748 \tabularnewline
5 & -0.34853562868307 \tabularnewline
6 & -0.0805507523225808 \tabularnewline
7 & -0.290069214566632 \tabularnewline
8 & -0.362204970486818 \tabularnewline
9 & -0.177623037202522 \tabularnewline
10 & -0.396656991777437 \tabularnewline
11 & -0.204115423920471 \tabularnewline
12 & 0.119256840066746 \tabularnewline
13 & -0.256373285077429 \tabularnewline
14 & -0.437999056716572 \tabularnewline
15 & -0.271493798089966 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28455&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]-15[/C][C]-0.0617051845151407[/C][/ROW]
[ROW][C]-14[/C][C]-0.218009657915035[/C][/ROW]
[ROW][C]-13[/C][C]-0.160603747770445[/C][/ROW]
[ROW][C]-12[/C][C]0.164609141410213[/C][/ROW]
[ROW][C]-11[/C][C]-0.151499476253291[/C][/ROW]
[ROW][C]-10[/C][C]-0.475364016258988[/C][/ROW]
[ROW][C]-9[/C][C]-0.308164273874806[/C][/ROW]
[ROW][C]-8[/C][C]-0.334133303948397[/C][/ROW]
[ROW][C]-7[/C][C]-0.299803698203838[/C][/ROW]
[ROW][C]-6[/C][C]-0.0327286673949929[/C][/ROW]
[ROW][C]-5[/C][C]-0.302819000409889[/C][/ROW]
[ROW][C]-4[/C][C]-0.445927673594421[/C][/ROW]
[ROW][C]-3[/C][C]-0.223699391120500[/C][/ROW]
[ROW][C]-2[/C][C]-0.480305702594076[/C][/ROW]
[ROW][C]-1[/C][C]-0.33341543471637[/C][/ROW]
[ROW][C]0[/C][C]0.102158957058790[/C][/ROW]
[ROW][C]1[/C][C]-0.359689517979094[/C][/ROW]
[ROW][C]2[/C][C]-0.635664452420787[/C][/ROW]
[ROW][C]3[/C][C]-0.429719842124593[/C][/ROW]
[ROW][C]4[/C][C]-0.464102819261748[/C][/ROW]
[ROW][C]5[/C][C]-0.34853562868307[/C][/ROW]
[ROW][C]6[/C][C]-0.0805507523225808[/C][/ROW]
[ROW][C]7[/C][C]-0.290069214566632[/C][/ROW]
[ROW][C]8[/C][C]-0.362204970486818[/C][/ROW]
[ROW][C]9[/C][C]-0.177623037202522[/C][/ROW]
[ROW][C]10[/C][C]-0.396656991777437[/C][/ROW]
[ROW][C]11[/C][C]-0.204115423920471[/C][/ROW]
[ROW][C]12[/C][C]0.119256840066746[/C][/ROW]
[ROW][C]13[/C][C]-0.256373285077429[/C][/ROW]
[ROW][C]14[/C][C]-0.437999056716572[/C][/ROW]
[ROW][C]15[/C][C]-0.271493798089966[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28455&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28455&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])
-15-0.0617051845151407
-14-0.218009657915035
-13-0.160603747770445
-120.164609141410213
-11-0.151499476253291
-10-0.475364016258988
-9-0.308164273874806
-8-0.334133303948397
-7-0.299803698203838
-6-0.0327286673949929
-5-0.302819000409889
-4-0.445927673594421
-3-0.223699391120500
-2-0.480305702594076
-1-0.33341543471637
00.102158957058790
1-0.359689517979094
2-0.635664452420787
3-0.429719842124593
4-0.464102819261748
5-0.34853562868307
6-0.0805507523225808
7-0.290069214566632
8-0.362204970486818
9-0.177623037202522
10-0.396656991777437
11-0.204115423920471
120.119256840066746
13-0.256373285077429
14-0.437999056716572
15-0.271493798089966


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')