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Identification and Estimation of ARMA processes B Step 2 ACF D=1

*Unverified author*
R Software Module: rwasp_autocorrelation.wasp (opens new window with default values)
Title produced by software: (Partial) Autocorrelation Function
Date of computation: Tue, 09 Dec 2008 15:08:19 -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/09/t1228860538bs4e8grlxmg1sw3.htm/, Retrieved Tue, 09 Dec 2008 22:09:00 +0000
 
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/09/t1228860538bs4e8grlxmg1sw3.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},
}
 
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
 
Feedback Forum:
2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc] [reply
test

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Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
93.7 105.7 109.5 105.3 102.8 100.6 97.6 110.3 107.2 107.2 108.1 97.1 92.2 112.2 111.6 115.7 111.3 104.2 103.2 112.7 106.4 102.6 110.6 95.2 89 112.5 116.8 107.2 113.6 101.8 102.6 122.7 110.3 110.5 121.6 100.3 100.7 123.4 127.1 124.1 131.2 111.6 114.2 130.1 125.9 119 133.8 107.5 113.5 134.4 126.8 135.6 139.9 129.8 131 153.1 134.1 144.1 155.9 123.3 128.1 144.3 153 149.9 150.9 141 138.9 157.4 142.9 151.7 161 138.5 135.9 151.5 164 159.1 157 142.1 144.8 152.1 154.6 148.7 157.7 146.4 136.5
 
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
10.4792034.09435.4e-05
20.5821624.9742e-06
30.5286184.51651.2e-05
40.4111733.51310.000382
50.422553.61030.000279
60.318632.72240.004051
70.2697362.30460.012017
80.1907911.63010.053692
90.2767792.36480.01035
100.0522030.4460.328451
110.1050530.89760.186181
120.0115870.0990.460704
130.0526210.44960.327167
140.0611560.52250.301445
150.035480.30310.381322
160.0175920.15030.44047
17-0.035278-0.30140.381978
180.0712810.6090.272198
19-0.165435-1.41350.080883
20-0.084434-0.72140.236483
21-0.1009-0.86210.19573
22-0.157836-1.34860.090825
23-0.054656-0.4670.320953
24-0.246051-2.10230.01949
25-0.200041-1.70920.045836
26-0.204963-1.75120.042055
27-0.241701-2.06510.021233
28-0.295188-2.52210.006923
29-0.169675-1.44970.075712
30-0.312024-2.66590.004724
31-0.212905-1.81910.036502
32-0.221993-1.89670.030911
33-0.3173-2.7110.004179
34-0.26674-2.2790.012795
35-0.30235-2.58330.005894
36-0.249356-2.13050.01825
37-0.255034-2.1790.016279
38-0.21749-1.85820.033584
39-0.231101-1.97450.026052
40-0.167559-1.43160.078259
41-0.223761-1.91180.029912
42-0.190987-1.63180.053514
43-0.161987-1.3840.085286
44-0.114422-0.97760.165744
45-0.147436-1.25970.105896
46-0.064476-0.55090.291699
47-0.017441-0.1490.440978
48-0.068237-0.5830.280838
490.0459690.39280.34782
50-0.016812-0.14360.44309
51-0.006385-0.05460.478323
520.1074660.91820.180772
530.047130.40270.344182
540.1085750.92770.17832
550.1279961.09360.138863
560.0953330.81450.208996
570.104530.89310.187369
580.1222831.04480.149784
590.0696620.59520.276777
600.0878340.75050.227697


Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.4792034.09435.4e-05
20.457613.90980.000102
30.2594512.21680.014878
4-0.026293-0.22460.411441
50.0165810.14170.443866
6-0.059493-0.50830.306386
7-0.070399-0.60150.274689
8-0.10823-0.92470.17908
90.1856221.5860.058536
10-0.180452-1.54180.063725
11-0.067149-0.57370.283959
12-0.092846-0.79330.215094
130.1525961.30380.098203
140.0999950.85440.197851
150.0813320.69490.244662
16-0.073572-0.62860.265787
17-0.122094-1.04320.150156
180.050470.43120.333791
19-0.245938-2.10130.019534
20-0.123147-1.05220.148096
210.0951980.81340.209324
22-0.002078-0.01780.492941
230.1151430.98380.164236
24-0.232281-1.98460.025473
25-0.061642-0.52670.300008
260.0452970.3870.349933
27-0.056737-0.48480.314648
28-0.062296-0.53230.298084
290.1807351.54420.063432
30-0.158176-1.35150.090361
31-0.075446-0.64460.260599
32-0.131699-1.12520.132087
330.0226970.19390.423388
34-0.069843-0.59670.276263
35-0.00344-0.02940.488315
36-0.03336-0.2850.388216
370.1603641.37020.08742
38-0.098328-0.84010.201795
39-0.019451-0.16620.434234
400.0249770.21340.415803
41-0.103382-0.88330.189989
420.0435650.37220.355403
43-0.002657-0.02270.490974
44-0.017782-0.15190.439832
45-0.074246-0.63440.263915
460.0011350.00970.496146
470.1199641.0250.15438
480.0715990.61170.271305
490.0428960.36650.357525
50-0.038463-0.32860.37169
51-0.117759-1.00610.158838
520.0152960.13070.448191
530.0212120.18120.428342
540.0208110.17780.429683
55-0.000791-0.00680.497314
560.0313580.26790.394756
57-0.0826-0.70570.241299
58-0.139913-1.19540.117896
590.0587550.5020.308587
60-0.062238-0.53180.298253
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/09/t1228860538bs4e8grlxmg1sw3/1c4m81228860498.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/09/t1228860538bs4e8grlxmg1sw3/1c4m81228860498.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/09/t1228860538bs4e8grlxmg1sw3/232ge1228860498.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/09/t1228860538bs4e8grlxmg1sw3/232ge1228860498.ps (open in new window)


 
Parameters (Session):
par1 = 60 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ;
 
Parameters (R input):
par1 = 60 ; par2 = 1 ; par3 = 0 ; 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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