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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 13:04:13 -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/01/t1228161914avtpomii1i40c64.htm/, Retrieved Sun, 05 May 2024 10:51:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27301, Retrieved Sun, 05 May 2024 10:51:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact250

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] [Q7 - zonder trans...] [2008-12-01 20:04:13] [5e2b1e7aa808f9f0d23fd35605d4968f] [Current]
-           [Cross Correlation Function] [Q7 - 2 ] [2008-12-01 20:11:27] [299afd6311e4c20059ea2f05c8dd029d]
- RM D        [Standard Deviation-Mean Plot] [Verbetering Q7] [2008-12-08 19:50:20] [299afd6311e4c20059ea2f05c8dd029d]
F RM D      [Variance Reduction Matrix] [Q8] [2008-12-01 20:20:44] [299afd6311e4c20059ea2f05c8dd029d]
F    D        [Variance Reduction Matrix] [Q8 - 2] [2008-12-01 20:25:07] [299afd6311e4c20059ea2f05c8dd029d]
- RM            [Standard Deviation-Mean Plot] [Q8 standard devia...] [2008-12-06 19:18:56] [7d3039e6253bb5fb3b26df1537d500b4]
- RM            [Standard Deviation-Mean Plot] [Verbetering Q7 - 2] [2008-12-08 19:52:58] [299afd6311e4c20059ea2f05c8dd029d]
F RM D          [Standard Deviation-Mean Plot] [Deel 2: Step 1] [2008-12-08 20:09:35] [299afd6311e4c20059ea2f05c8dd029d]
- RM D            [Variance Reduction Matrix] [Deel 2: Step 2 - VRM] [2008-12-08 20:13:17] [299afd6311e4c20059ea2f05c8dd029d]
-                   [Variance Reduction Matrix] [Totale Uitvoer - VRM] [2008-12-17 16:00:59] [299afd6311e4c20059ea2f05c8dd029d]
-  MPD                [Variance Reduction Matrix] [] [2010-12-24 11:50:15] [4dfa50539945b119a90a7606969443b9]
- RM D            [(Partial) Autocorrelation Function] [Deel 2: Step 2 -...] [2008-12-08 20:20:51] [299afd6311e4c20059ea2f05c8dd029d]
- RM D            [(Partial) Autocorrelation Function] [Deel 2: Step 2 - ...] [2008-12-08 20:22:18] [299afd6311e4c20059ea2f05c8dd029d]
-   P               [(Partial) Autocorrelation Function] [Uitvoer vanuit Be...] [2008-12-13 16:33:20] [299afd6311e4c20059ea2f05c8dd029d]
-   P                 [(Partial) Autocorrelation Function] [Uitvoer vanuit Be...] [2008-12-13 16:37:32] [299afd6311e4c20059ea2f05c8dd029d]
-   P                   [(Partial) Autocorrelation Function] [d=0 D=1] [2008-12-14 13:55:01] [299afd6311e4c20059ea2f05c8dd029d]
-   P               [(Partial) Autocorrelation Function] [Totale Uitvoer d=...] [2008-12-17 16:03:30] [299afd6311e4c20059ea2f05c8dd029d]
- RM D            [(Partial) Autocorrelation Function] [Deel 2: Step 2 - ...] [2008-12-08 20:24:54] [299afd6311e4c20059ea2f05c8dd029d]
- RM D            [Spectral Analysis] [Deel 2: Step 2 - ...] [2008-12-08 20:27:07] [299afd6311e4c20059ea2f05c8dd029d]
-   P               [Spectral Analysis] [Totale Uitvoer - ...] [2008-12-17 16:07:59] [299afd6311e4c20059ea2f05c8dd029d]
-  M D                [Spectral Analysis] [Spectral Analysis...] [2010-12-16 10:45:51] [616fb52b46273b7e6805de1e68b3a688]
-  MPD                [Spectral Analysis] [Spectral Analysis...] [2010-12-16 11:12:29] [616fb52b46273b7e6805de1e68b3a688]
-   P                   [Spectral Analysis] [Spectral Analysis...] [2010-12-16 11:14:45] [616fb52b46273b7e6805de1e68b3a688]
-  MPD                [Spectral Analysis] [] [2010-12-24 12:24:57] [4dfa50539945b119a90a7606969443b9]
-  MPD                [Spectral Analysis] [] [2010-12-24 12:35:52] [4dfa50539945b119a90a7606969443b9]
F RM D            [Spectral Analysis] [Deel 2: Step 2 - ...] [2008-12-08 20:29:17] [299afd6311e4c20059ea2f05c8dd029d]
-   P               [Spectral Analysis] [Totale Uitvoer - ...] [2008-12-17 16:10:49] [299afd6311e4c20059ea2f05c8dd029d]
F RM D            [ARIMA Backward Selection] [Deel 2: Step 5] [2008-12-08 20:35:27] [299afd6311e4c20059ea2f05c8dd029d]
-   P               [ARIMA Backward Selection] [Uitvoer vanuit Be...] [2008-12-14 15:42:25] [299afd6311e4c20059ea2f05c8dd029d]
- RMPD                [Multiple Regression] [] [2010-12-21 16:18:28] [1c63f3c303537b65dfa698074d619a3e]
F RMP               [ARIMA Forecasting] [Uitvoer vanuit Be...] [2008-12-14 15:56:40] [299afd6311e4c20059ea2f05c8dd029d]
- RMPD                [ARIMA Forecasting] [Arima Forecasting] [2010-12-28 19:01:32] [74be16979710d4c4e7c6647856088456]
-    D            [Standard Deviation-Mean Plot] [Totale Uitvoer - SMP] [2008-12-17 15:57:12] [299afd6311e4c20059ea2f05c8dd029d]
-  M D              [Standard Deviation-Mean Plot] [Standard Deviatio...] [2010-12-24 13:19:31] [9f313cc7203314d73bf17d2b325aee79]
- RM D              [Variance Reduction Matrix] [Variance Reductio...] [2010-12-24 13:29:11] [9f313cc7203314d73bf17d2b325aee79]
- RMPD              [(Partial) Autocorrelation Function] [(Partial) Autocor...] [2010-12-24 13:35:08] [9f313cc7203314d73bf17d2b325aee79]
- RMPD              [(Partial) Autocorrelation Function] [(Partial) Autocor...] [2010-12-24 13:37:51] [9f313cc7203314d73bf17d2b325aee79]
- RMPD              [Spectral Analysis] [Spectral Analysis] [2010-12-24 13:45:16] [9f313cc7203314d73bf17d2b325aee79]
- RMPD              [Spectral Analysis] [Spectral Analysis] [2010-12-24 13:47:24] [9f313cc7203314d73bf17d2b325aee79]
- RMPD              [Spectral Analysis] [Spectral Analysis] [2010-12-24 13:51:23] [9f313cc7203314d73bf17d2b325aee79]
- RMPD              [ARIMA Backward Selection] [ARIMA Backward Se...] [2010-12-24 14:04:47] [9f313cc7203314d73bf17d2b325aee79]
- RMPD              [ARIMA Forecasting] [ARIMA Forecasting] [2010-12-24 14:15:31] [9f313cc7203314d73bf17d2b325aee79]
- RMPD                [Standard Deviation-Mean Plot] [Standard Deviatio...] [2010-12-27 09:50:14] [9f313cc7203314d73bf17d2b325aee79]
- RMPD                [Variance Reduction Matrix] [Variance Reductio...] [2010-12-27 09:53:20] [9f313cc7203314d73bf17d2b325aee79]
- RMPD                [Spectral Analysis] [Spectral Analysis] [2010-12-27 10:01:30] [9f313cc7203314d73bf17d2b325aee79]
- RMPD                [Spectral Analysis] [Spectral Analysis] [2010-12-27 10:03:10] [9f313cc7203314d73bf17d2b325aee79]
- RMPD                [Classical Decomposition] [Classical Decompo...] [2010-12-27 10:06:19] [9f313cc7203314d73bf17d2b325aee79]
- RMPD                [Decomposition by Loess] [Decomposition by ...] [2010-12-27 10:08:53] [9f313cc7203314d73bf17d2b325aee79]
- RMPD                [ARIMA Backward Selection] [ARIMA Backward Se...] [2010-12-27 10:15:12] [9f313cc7203314d73bf17d2b325aee79]

[Truncated]
Feedback Forum
2008-12-06 18:36:57 [Stéphanie Claes] [reply] 
Als we naar de grafiek kijken dan zeggen we dat alles wat binnen het betrouwbaarheidsinterval valt gelijk is aan nul en bijgevolg niet significant. We zien dat er hier heel wat waarden zijn dat buiten het betrouwbaarheidsinterval vallen en deze zijn allemaal positief significant, dit betekent dat het verleden van Xt gecorreleerd is met het heden van Yt en omgekeerd (=simultaan effect).

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12192.5
11268.8
9097.4
12639.8
13040.1
11687.3
11191.7
11391.9
11793.1
13933.2
12778.1
11810.3
13698.4
11956.6
10723.8
13938.9
13979.8
13807.4
12973.9
12509.8
12934.1
14908.3
13772.1
13012.6
14049.9
11816.5
11593.2
14466.2
13615.9
14733.9
13880.7
13527.5
13584
16170.2
13260.6
14741.9
15486.5
13154.5
12621.2
15031.6
15452.4
15428
13105.9
14716.8
14180
16202.2
14392.4
15140.6
15960.1
14351.3
13230.2
15202.1
17157.3
16159.1
13405.7
17224.7
17338.4
17370.6
18817.8
16593.2
17979.5
Dataseries Y:
Download CSV
Histogram 
10772.8
9987.7
8638.7
11063.7
11855.7
10684.5
11337.4
10478
11123.9
12909.3
11339.9
10462.2
12733.5
10519.2
10414.9
12476.8
12384.6
12266.7
12919.9
11497.3
12142
13919.4
12656.8
12034.1
13199.7
10881.3
11301.2
13643.9
12517
13981.1
14275.7
13435
13565.7
16216.3
12970
14079.9
14235
12213.4
12581
14130.4
14210.8
14378.5
13142.8
13714.7
13621.9
15379.8
13306.3
14391.2
14909.9
14025.4
12951.2
14344.3
16213.3
15544.5
14750.6
17292.7
17568.5
17930.8
18644.7
16694.8
17242.8

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=27301&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=27301&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27301&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 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])
-140.154508969466290
-130.211622024327179
-120.397181294940047
-110.289758929852441
-100.196006963941274
-90.301036011009612
-80.322661028186671
-70.316354041901331
-60.416706856507075
-50.433376172731888
-40.442309983734049
-30.532348118003479
-20.557462635537897
-10.648063523110095
00.96090508179136
10.692417139790461
20.558823780056399
30.58633224775227
40.494040689426363
50.421462228667169
60.428080648514087
70.319181717414391
80.273939420812006
90.236235783510122
100.183516197243372
110.23785509743137
120.352521247344583
130.205736493689958
140.110818124535352

\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 & 0.154508969466290 \tabularnewline
-13 & 0.211622024327179 \tabularnewline
-12 & 0.397181294940047 \tabularnewline
-11 & 0.289758929852441 \tabularnewline
-10 & 0.196006963941274 \tabularnewline
-9 & 0.301036011009612 \tabularnewline
-8 & 0.322661028186671 \tabularnewline
-7 & 0.316354041901331 \tabularnewline
-6 & 0.416706856507075 \tabularnewline
-5 & 0.433376172731888 \tabularnewline
-4 & 0.442309983734049 \tabularnewline
-3 & 0.532348118003479 \tabularnewline
-2 & 0.557462635537897 \tabularnewline
-1 & 0.648063523110095 \tabularnewline
0 & 0.96090508179136 \tabularnewline
1 & 0.692417139790461 \tabularnewline
2 & 0.558823780056399 \tabularnewline
3 & 0.58633224775227 \tabularnewline
4 & 0.494040689426363 \tabularnewline
5 & 0.421462228667169 \tabularnewline
6 & 0.428080648514087 \tabularnewline
7 & 0.319181717414391 \tabularnewline
8 & 0.273939420812006 \tabularnewline
9 & 0.236235783510122 \tabularnewline
10 & 0.183516197243372 \tabularnewline
11 & 0.23785509743137 \tabularnewline
12 & 0.352521247344583 \tabularnewline
13 & 0.205736493689958 \tabularnewline
14 & 0.110818124535352 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27301&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]0.154508969466290[/C][/ROW]
[ROW][C]-13[/C][C]0.211622024327179[/C][/ROW]
[ROW][C]-12[/C][C]0.397181294940047[/C][/ROW]
[ROW][C]-11[/C][C]0.289758929852441[/C][/ROW]
[ROW][C]-10[/C][C]0.196006963941274[/C][/ROW]
[ROW][C]-9[/C][C]0.301036011009612[/C][/ROW]
[ROW][C]-8[/C][C]0.322661028186671[/C][/ROW]
[ROW][C]-7[/C][C]0.316354041901331[/C][/ROW]
[ROW][C]-6[/C][C]0.416706856507075[/C][/ROW]
[ROW][C]-5[/C][C]0.433376172731888[/C][/ROW]
[ROW][C]-4[/C][C]0.442309983734049[/C][/ROW]
[ROW][C]-3[/C][C]0.532348118003479[/C][/ROW]
[ROW][C]-2[/C][C]0.557462635537897[/C][/ROW]
[ROW][C]-1[/C][C]0.648063523110095[/C][/ROW]
[ROW][C]0[/C][C]0.96090508179136[/C][/ROW]
[ROW][C]1[/C][C]0.692417139790461[/C][/ROW]
[ROW][C]2[/C][C]0.558823780056399[/C][/ROW]
[ROW][C]3[/C][C]0.58633224775227[/C][/ROW]
[ROW][C]4[/C][C]0.494040689426363[/C][/ROW]
[ROW][C]5[/C][C]0.421462228667169[/C][/ROW]
[ROW][C]6[/C][C]0.428080648514087[/C][/ROW]
[ROW][C]7[/C][C]0.319181717414391[/C][/ROW]
[ROW][C]8[/C][C]0.273939420812006[/C][/ROW]
[ROW][C]9[/C][C]0.236235783510122[/C][/ROW]
[ROW][C]10[/C][C]0.183516197243372[/C][/ROW]
[ROW][C]11[/C][C]0.23785509743137[/C][/ROW]
[ROW][C]12[/C][C]0.352521247344583[/C][/ROW]
[ROW][C]13[/C][C]0.205736493689958[/C][/ROW]
[ROW][C]14[/C][C]0.110818124535352[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27301&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27301&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])
-140.154508969466290
-130.211622024327179
-120.397181294940047
-110.289758929852441
-100.196006963941274
-90.301036011009612
-80.322661028186671
-70.316354041901331
-60.416706856507075
-50.433376172731888
-40.442309983734049
-30.532348118003479
-20.557462635537897
-10.648063523110095
00.96090508179136
10.692417139790461
20.558823780056399
30.58633224775227
40.494040689426363
50.421462228667169
60.428080648514087
70.319181717414391
80.273939420812006
90.236235783510122
100.183516197243372
110.23785509743137
120.352521247344583
130.205736493689958
140.110818124535352


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ;
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')