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ACF Handelsbalans Zonder Seizoenaliteit

*The author of this computation has been verified*
R Software Module: rwasp_autocorrelation.wasp (opens new window with default values)
Title produced by software: (Partial) Autocorrelation Function
Date of computation: Mon, 22 Dec 2008 09:52:55 -0700
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/22/t1229964817efkfvxm1yxrhxjz.htm/, Retrieved Mon, 22 Dec 2008 17:53:39 +0100
 
BibTeX entries for LaTeX users:
@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2008/Dec/22/t1229964817efkfvxm1yxrhxjz.htm/},
    year = {2008},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2008},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
1384.2 1368.9 -275.1 -408.9 -37.5 171.5 671.8 -18.5 231.6 747.5 1505.7 -83.6 1173.2 1452.1 777 -52.8 861.2 735.2 1073.6 966.9 1189.8 1093.5 1782.7 -70.4 1471.6 1273.8 900.8 -910.2 299.8 460.2 677.2 937.1 1265.4 1275.6 1582.6 -154.2 1667.6 1083.1 891.7 -26.5 423.4 662.8 711.4 993.3 1133.2 343.9 1415.8 -531.8 1193.6 1201.3 805.6 -164.8 327.3 223.7 675.8 949.7 704.4 265.6 1206 -558.2 1066.8 977.8 207.1 -980.7 -586.4 -24.3 -417.5 104.7 749.5 842.3 1176 -730.3 911.6 662.1 539.1 -236 286.9 497.4 912 519.4 260.1 945.2 412.7 -54.2 592.8 179.9 -548.6 -1685.8 -2041 -1048.7 -1708.4 -1550.7 -1650.2 -911.3
 
Output produced by software:


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


Autocorrelation Function
Time lag kACF(k)T-STATP-value
1-0.409844-3.68860.000203
20.1085060.97650.16585
30.0061990.05580.477823
4-0.072216-0.64990.258782
5-0.029345-0.26410.396186
60.0668070.60130.274672
7-0.067543-0.60790.27248
80.1129141.01620.156274
9-0.096061-0.86450.194921
100.1070510.96350.169093
11-0.012095-0.10890.456793
12-0.253824-2.28440.012482
130.0507490.45670.324539
14-0.059173-0.53260.297898
150.0527490.47470.318126
160.0020780.01870.492561
17-0.050381-0.45340.325725
180.101840.91660.181048
19-0.023339-0.210.417079
200.0333390.30.382454
21-0.070524-0.63470.263702
220.0433150.38980.34884
23-0.048661-0.43790.331294
240.0692760.62350.26736
25-0.059047-0.53140.298289
260.1159811.04380.149836
27-0.070394-0.63350.264081
28-0.028068-0.25260.400604
290.0666780.60010.275056
300.0201010.18090.428446
31-0.171911-1.54720.062857
320.1038350.93450.176407
33-0.070181-0.63160.264705
34-0.034892-0.3140.377154
350.1162891.04660.149198
36-0.044109-0.3970.346212
370.1125651.01310.157018
38-0.131998-1.1880.119156
390.0849840.76490.223289
400.0303090.27280.392857
41-0.094053-0.84650.199891
420.0060250.05420.478445
430.0577550.51980.302312
44-0.045677-0.41110.341045
450.1077020.96930.167637
46-0.0039-0.03510.486044
47-0.075969-0.68370.248051
48-0.022586-0.20330.419716
49-0.062084-0.55880.288935
500.0717060.64540.260261
51-0.103632-0.93270.176877
520.0475190.42770.335012
53-0.012162-0.10950.456557
54-0.002915-0.02620.489566
550.0870350.78330.217863
56-0.085646-0.77080.22153
570.0202170.1820.428036
58-0.026286-0.23660.406792
590.0673280.6060.273119
60-0.026044-0.23440.407636


Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.409844-3.68860.000203
2-0.071472-0.64320.26094
30.0296640.2670.395083
4-0.061675-0.55510.290187
5-0.104331-0.9390.175266
60.0224180.20180.420303
7-0.024695-0.22230.412338
80.0845140.76060.224546
9-0.034213-0.30790.379467
100.0693070.62380.267269
110.0704130.63370.264025
12-0.281759-2.53580.00657
13-0.215512-1.93960.027954
14-0.134924-1.21430.114078
150.0295780.26620.39538
16-0.019161-0.17250.431756
17-0.124542-1.12090.132826
180.0524950.47250.318938
190.0871320.78420.217609
200.1267211.14050.128722
21-0.082075-0.73870.231119
220.043640.39280.347765
230.0127090.11440.454608
24-0.04849-0.43640.331849
25-0.176872-1.59180.057657
26-0.025494-0.22940.40955
270.0663550.59720.276022
28-0.117727-1.05950.14625
29-0.046312-0.41680.338962
300.1451811.30660.097518
31-0.025142-0.22630.410779
32-0.027077-0.24370.404044
33-0.103553-0.9320.177059
34-0.074438-0.66990.252401
350.0977590.87980.190776
360.0024770.02230.491135
370.0743380.6690.252685
38-0.050445-0.4540.325519
390.0900110.81010.210129
400.0732990.65970.255661
41-0.030687-0.27620.391554
42-0.056659-0.50990.305743
43-0.022005-0.1980.421752
44-0.027713-0.24940.401836
45-0.044955-0.40460.343422
460.0526320.47370.318497
47-0.012375-0.11140.455798
48-0.081979-0.73780.231382
49-0.040816-0.36730.357159
500.0333930.30050.382269
518.8e-058e-040.499685
52-0.030248-0.27220.393067
53-0.051894-0.4670.32086
54-0.056144-0.50530.307361
550.0616660.5550.290215
56-0.029591-0.26630.395337
570.048840.43960.330713
580.0222010.19980.421064
590.049340.44410.329093
60-0.003749-0.03370.486582
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229964817efkfvxm1yxrhxjz/1q16d1229964773.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229964817efkfvxm1yxrhxjz/1q16d1229964773.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229964817efkfvxm1yxrhxjz/2w9891229964773.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229964817efkfvxm1yxrhxjz/2w9891229964773.ps (open in new window)


 
Parameters (Session):
par1 = 60 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ;
 
Parameters (R input):
par1 = 60 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ;
 
R code (references can be found in the software module):
if (par1 == 'Default') {
par1 = 10*log10(length(x))
} else {
par1 <- as.numeric(par1)
}
par2 <- as.numeric(par2)
par3 <- as.numeric(par3)
par4 <- as.numeric(par4)
par5 <- as.numeric(par5)
if (par2 == 0) {
x <- log(x)
} else {
x <- (x ^ par2 - 1) / par2
}
if (par3 > 0) x <- diff(x,lag=1,difference=par3)
if (par4 > 0) x <- diff(x,lag=par5,difference=par4)
bitmap(file='pic1.png')
racf <- acf(x,par1,main='Autocorrelation',xlab='lags',ylab='ACF')
dev.off()
bitmap(file='pic2.png')
rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF')
dev.off()
(myacf <- c(racf$acf))
(mypacf <- c(rpacf$acf))
lengthx <- length(x)
sqrtn <- sqrt(lengthx)
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Autocorrelation Function',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Time lag k',header=TRUE)
a<-table.element(a,hyperlink('http://www.xycoon.com/basics.htm','ACF(k)','click here for more information about the Autocorrelation Function'),header=TRUE)
a<-table.element(a,'T-STAT',header=TRUE)
a<-table.element(a,'P-value',header=TRUE)
a<-table.row.end(a)
for (i in 2:(par1+1)) {
a<-table.row.start(a)
a<-table.element(a,i-1,header=TRUE)
a<-table.element(a,round(myacf[i],6))
mytstat <- myacf[i]*sqrtn
a<-table.element(a,round(mytstat,4))
a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Partial Autocorrelation Function',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Time lag k',header=TRUE)
a<-table.element(a,hyperlink('http://www.xycoon.com/basics.htm','PACF(k)','click here for more information about the Partial Autocorrelation Function'),header=TRUE)
a<-table.element(a,'T-STAT',header=TRUE)
a<-table.element(a,'P-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:par1) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,round(mypacf[i],6))
mytstat <- mypacf[i]*sqrtn
a<-table.element(a,round(mytstat,4))
a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
 




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