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Autocorrelatiefunctie zonder differentiatie (verkoopprijzen)

*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: Wed, 30 Dec 2009 06:05:14 -0700
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/30/t1262178566igy017tus5jqyu3.htm/, Retrieved Wed, 30 Dec 2009 14:09:28 +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/2009/Dec/30/t1262178566igy017tus5jqyu3.htm/},
    year = {2009},
}
@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 = {2009},
    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 «
2072.65 2020.13 2032.76 2050.31 2128.98 2122.14 2122.89 2091.95 2002.97 1923.21 1834.44 1819.15 1792.00 1822.40 1900.70 1903.00 1958.80 1820.50 1719.80 1661.10 1664.40 1703.40 1774.90 1795.00 1816.30 1867.40 1900.00 1961.10 2065.70 2073.50 2080.80 2118.00 2099.00 2085.20 1937.70 1749.50 1750.30 1675.60 1697.50 1699.80 1655.90 1636.00 1614.20 1602.30 1548.70 1556.10 1526.90 1509.20 1566.30 1596.00 1654.50 1664.20 1687.70 1691.00 1664.60 1697.50 1685.10 1643.00 1559.60 1560.20 1590.16 1604.93 1661.80 1670.73 1692.40 1688.17 1658.04 1613.46 1595.11 1558.83 1526.65 1475.19
 
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
10.9254387.85260
20.8268567.01610
30.7088716.0150
40.5928545.03052e-06
50.4876294.13774.7e-05
60.397523.37316e-04
70.3243342.75210.003744
80.2614142.21820.014849
90.2115251.79480.038437
100.1676981.4230.079532
110.127431.08130.14159
120.0894210.75880.225237
130.0517540.43910.330934
140.0303910.25790.398618
150.0217880.18490.426922
160.0315230.26750.39493
170.0390390.33130.370706
180.0488170.41420.33997
190.0769310.65280.257989
200.1234681.04770.14915
210.1759071.49260.069953
220.2343071.98820.025298
230.2715542.30420.012049
240.2875992.44040.008567
250.2752812.33580.011144
260.2428282.06050.021484
270.1982091.68190.048465
280.1354721.14950.127073
290.0653920.55490.290353
30-0.005486-0.04650.481501
31-0.065032-0.55180.291391
32-0.117717-0.99890.160603
33-0.16615-1.40980.081448
34-0.216131-1.83390.035398
35-0.26835-2.2770.012879
36-0.298715-2.53470.006715
37-0.339197-2.87820.002631
38-0.354104-3.00470.001828
39-0.35211-2.98780.001921
40-0.351178-2.97980.001965
41-0.339945-2.88450.002584
42-0.328569-2.7880.003389
43-0.305268-2.59030.0058
44-0.270699-2.2970.012266
45-0.234021-1.98570.025436
46-0.198864-1.68740.047926
47-0.166337-1.41140.081214
48-0.137601-1.16760.123414
49-0.12514-1.06180.145927
50-0.124125-1.05320.147878
51-0.142069-1.20550.11598
52-0.167381-1.42030.079921
53-0.19474-1.65240.051401
54-0.21337-1.81050.037195
55-0.211782-1.7970.038262
56-0.193877-1.64510.052154
57-0.181745-1.54220.063709
58-0.168174-1.4270.078951
59-0.15922-1.3510.090459
60-0.158396-1.3440.091578


Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.9254387.85260
2-0.206036-1.74830.042339
3-0.16963-1.43940.077191
4-0.020603-0.17480.430855
50.0129430.10980.456428
60.0131740.11180.455651
70.0175760.14910.440931
8-0.027451-0.23290.408238
90.0126180.10710.457516
10-0.022664-0.19230.42402
11-0.029999-0.25450.399899
12-0.022723-0.19280.423825
13-0.031315-0.26570.395608
140.0894110.75870.225262
150.0451510.38310.351382
160.0711030.60330.274093
17-0.069188-0.58710.279494
180.0020570.01750.493063
190.1626071.37980.085965
200.1474161.25090.107516
210.025020.21230.416235
220.0667020.5660.286581
23-0.110663-0.9390.175433
24-0.059468-0.50460.30769
25-0.091141-0.77340.220922
26-0.05572-0.47280.318893
27-0.01255-0.10650.457743
28-0.116405-0.98770.163296
29-0.066376-0.56320.287519
30-0.053172-0.45120.326609
31-0.008577-0.07280.471092
32-0.044933-0.38130.352064
33-0.039976-0.33920.36772
34-0.059786-0.50730.306747
35-0.056935-0.48310.315241
360.0908310.77070.221694
37-0.170371-1.44560.076308
380.0965890.81960.207579
390.0698940.59310.277496
40-0.104351-0.88550.189431
410.0140340.11910.452771
42-0.086651-0.73530.232285
43-0.008766-0.07440.470457
440.0561820.47670.317502
45-0.038691-0.32830.371819
46-0.032205-0.27330.392713
47-0.021982-0.18650.42628
48-0.025393-0.21550.415007
49-0.061929-0.52550.300429
50-0.025044-0.21250.416157
51-0.053298-0.45220.326226
52-0.008043-0.06830.472888
530.0254230.21570.414906
540.0680560.57750.282711
550.0885750.75160.227376
560.1107270.93950.175295
57-0.01864-0.15820.437384
580.0197670.16770.433634
590.0473320.40160.344573
60-0.076232-0.64690.259893
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Dec/30/t1262178566igy017tus5jqyu3/11h2m1262178312.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/30/t1262178566igy017tus5jqyu3/11h2m1262178312.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/30/t1262178566igy017tus5jqyu3/2bpzq1262178312.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/30/t1262178566igy017tus5jqyu3/2bpzq1262178312.ps (open in new window)


 
Parameters (Session):
par1 = 60 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
 
Parameters (R input):
par1 = 60 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
 
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 (par6 == 'White Noise') par6 <- 'white' else par6 <- 'ma'
par7 <- as.numeric(par7)
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='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep=''))
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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