FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_cross.wasp
Title produced by softwareCross Correlation Function
Date of computationMon, 26 Nov 2007 03:52:08 -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/2007/Nov/26/t11960738318dfolrtllv9weng.htm/, Retrieved Thu, 02 May 2024 21:47:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=6573, Retrieved Thu, 02 May 2024 21:47:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsQ5. Compute the Cross Correlation Function between (at least) one Yt (endogenous) and one Xt (exogenous) variable. Interpret the results.
Estimated Impact215

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [workshop 7] [2007-11-22 14:50:26] [fd843084b1505c7608c1b4c5365ff9f3]
- RMPD    [Cross Correlation Function] [Q5. Compute the C...] [2007-11-26 10:52:08] [d41d8cd98f00b204e9800998ecf8427e] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
105,5227475
99,96697807
99,29276788
97,61994679
92,62666004
90,32801498
85,52083209
92,71339449
96,51595626
103,9418082
106,9973474
113,2595822
109,7190934
98,37704602
95,34417091
93,02005401
98,46060437
96,29026019
92,64756809
102,3220594
101,3264016
114,6738496
102,9524431
109,4774482
101,6334513
98,57578752
104,3007445
107,9441506
101,8381511
106,7241893
89,66204355
90,05567866
113,30134
112,3883223
95,92201437
95,67776984
100,917002
98,97327054
110,0791791
110,1929871
103,5780994
111,7920977
96,78877956
95,98442935
118,9305845
111,295244
102,4709069
97,82438647
98,25590457
95,1971819
99,19646064
104,468676
100,7123022
110,6990195
83,40207273
93,32671353
110,4355427
101,1594271
100,544762
95,47333016
102,4522505
97,88019225
108,7308777
103,6509173
109,6167435
110,1027949
86,96544074
97,61994679
114,0177893
112,2889515
103,5302866
98,02882615
108,7979443
104,5380327
112,8720892
109,2730085
110,7425924
109,3078289
92,55126085
102,8331586
Dataseries Y:
Download CSV
Histogram 
101,6041349
96,48530635
101,0183779
100,9789536
103,1498368
106,0353826
86,54664403
102,2333505
110,6722524
115,6839132
99,88333999
101,8152182
102,531556
96,48530635
98,65371554
105,1602767
104,1803047
102,2941156
91,65431482
99,8290897
111,7027203
115,1916413
98,18078306
105,3693429
113,9697497
102,39257
106,5043947
112,1639929
101,9132753
108,1029249
99,22123452
106,8328059
127,6749728
122,9695384
106,5989812
127,4258224
103,6650707
102,6879332
113,8821414
112,3730591
106,0351469
123,6587192
101,6804834
113,7319891
132,3120783
119,2282714
113,030863
134,5340717
112,0118607
103,8693859
113,3146225
116,3453161
113,9697497
118,2437275
92,2218338
112,5821252
131,1785637
115,38855
113,3146225
121,4674369
114,3819369
110,6627391
123,529964
114,0455883
122,4195865
121,9849945
96,09988014
117,9133122
131,8998912
127,5968949
118,9898122
124,9170285
119,225136
111,8441918
122,6786856
117,0770476
120,3586507
124,3478999
106,7881542
121,1538377

Tables (Output of Computation)





Summary of compuational 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 compuational 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=6573&T=0

[TABLE]
[ROW][C]Summary of compuational 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=6573&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=6573&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 compuational 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 series1
krho(Y[t],X[t+k])
-15-0.145053772375437
-140.0324789366082915
-130.428852881196256
-12-0.375148136232122
-11-0.195763962709013
-100.293390192673532
-9-0.102540119859827
-8-0.0949541448579465
-70.249995005523389
-6-0.198685663726885
-50.00336964494792187
-40.152855419870542
-3-0.249642694182971
-20.145187756727366
-10.558697781062267
0-0.572631999091446
1-0.146700257144615
20.248546831769132
3-0.157352622027792
4-0.000112802237635372
50.193879770485007
6-0.266801920629453
70.099138341396598
80.109216083916216
9-0.272782261482346
100.205549587293035
110.372864281588813
12-0.452683366124496
13-0.0365854446369496
140.143451802078459
15-0.142122844422988

\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 & 1 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-15 & -0.145053772375437 \tabularnewline
-14 & 0.0324789366082915 \tabularnewline
-13 & 0.428852881196256 \tabularnewline
-12 & -0.375148136232122 \tabularnewline
-11 & -0.195763962709013 \tabularnewline
-10 & 0.293390192673532 \tabularnewline
-9 & -0.102540119859827 \tabularnewline
-8 & -0.0949541448579465 \tabularnewline
-7 & 0.249995005523389 \tabularnewline
-6 & -0.198685663726885 \tabularnewline
-5 & 0.00336964494792187 \tabularnewline
-4 & 0.152855419870542 \tabularnewline
-3 & -0.249642694182971 \tabularnewline
-2 & 0.145187756727366 \tabularnewline
-1 & 0.558697781062267 \tabularnewline
0 & -0.572631999091446 \tabularnewline
1 & -0.146700257144615 \tabularnewline
2 & 0.248546831769132 \tabularnewline
3 & -0.157352622027792 \tabularnewline
4 & -0.000112802237635372 \tabularnewline
5 & 0.193879770485007 \tabularnewline
6 & -0.266801920629453 \tabularnewline
7 & 0.099138341396598 \tabularnewline
8 & 0.109216083916216 \tabularnewline
9 & -0.272782261482346 \tabularnewline
10 & 0.205549587293035 \tabularnewline
11 & 0.372864281588813 \tabularnewline
12 & -0.452683366124496 \tabularnewline
13 & -0.0365854446369496 \tabularnewline
14 & 0.143451802078459 \tabularnewline
15 & -0.142122844422988 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=6573&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]1[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-15[/C][C]-0.145053772375437[/C][/ROW]
[ROW][C]-14[/C][C]0.0324789366082915[/C][/ROW]
[ROW][C]-13[/C][C]0.428852881196256[/C][/ROW]
[ROW][C]-12[/C][C]-0.375148136232122[/C][/ROW]
[ROW][C]-11[/C][C]-0.195763962709013[/C][/ROW]
[ROW][C]-10[/C][C]0.293390192673532[/C][/ROW]
[ROW][C]-9[/C][C]-0.102540119859827[/C][/ROW]
[ROW][C]-8[/C][C]-0.0949541448579465[/C][/ROW]
[ROW][C]-7[/C][C]0.249995005523389[/C][/ROW]
[ROW][C]-6[/C][C]-0.198685663726885[/C][/ROW]
[ROW][C]-5[/C][C]0.00336964494792187[/C][/ROW]
[ROW][C]-4[/C][C]0.152855419870542[/C][/ROW]
[ROW][C]-3[/C][C]-0.249642694182971[/C][/ROW]
[ROW][C]-2[/C][C]0.145187756727366[/C][/ROW]
[ROW][C]-1[/C][C]0.558697781062267[/C][/ROW]
[ROW][C]0[/C][C]-0.572631999091446[/C][/ROW]
[ROW][C]1[/C][C]-0.146700257144615[/C][/ROW]
[ROW][C]2[/C][C]0.248546831769132[/C][/ROW]
[ROW][C]3[/C][C]-0.157352622027792[/C][/ROW]
[ROW][C]4[/C][C]-0.000112802237635372[/C][/ROW]
[ROW][C]5[/C][C]0.193879770485007[/C][/ROW]
[ROW][C]6[/C][C]-0.266801920629453[/C][/ROW]
[ROW][C]7[/C][C]0.099138341396598[/C][/ROW]
[ROW][C]8[/C][C]0.109216083916216[/C][/ROW]
[ROW][C]9[/C][C]-0.272782261482346[/C][/ROW]
[ROW][C]10[/C][C]0.205549587293035[/C][/ROW]
[ROW][C]11[/C][C]0.372864281588813[/C][/ROW]
[ROW][C]12[/C][C]-0.452683366124496[/C][/ROW]
[ROW][C]13[/C][C]-0.0365854446369496[/C][/ROW]
[ROW][C]14[/C][C]0.143451802078459[/C][/ROW]
[ROW][C]15[/C][C]-0.142122844422988[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=6573&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=6573&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 series1
krho(Y[t],X[t+k])
-15-0.145053772375437
-140.0324789366082915
-130.428852881196256
-12-0.375148136232122
-11-0.195763962709013
-100.293390192673532
-9-0.102540119859827
-8-0.0949541448579465
-70.249995005523389
-6-0.198685663726885
-50.00336964494792187
-40.152855419870542
-3-0.249642694182971
-20.145187756727366
-10.558697781062267
0-0.572631999091446
1-0.146700257144615
20.248546831769132
3-0.157352622027792
4-0.000112802237635372
50.193879770485007
6-0.266801920629453
70.099138341396598
80.109216083916216
9-0.272782261482346
100.205549587293035
110.372864281588813
12-0.452683366124496
13-0.0365854446369496
140.143451802078459
15-0.142122844422988


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 1 ; par5 = 1 ; par6 = 0 ; par7 = 1 ;
Parameters (R input):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 1 ; par5 = 1 ; par6 = 0 ; 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) x <- diff(y,lag=par4,difference=par7)
x
y
bitmap(file='test1.png')
(r <- ccf(x,y,main='Cross Correlation Function',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')