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Identification and Estimation of ARMA processes Step 2(2)*

*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: Sun, 07 Dec 2008 07:51:36 -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/07/t1228661544akvheia0taw8037.htm/, Retrieved Sun, 07 Dec 2008 14:52:26 +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/07/t1228661544akvheia0taw8037.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

Post a new message
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
1593 1477.9 1733.7 1569.7 1843.7 1950.3 1657.5 1772.1 1568.3 1809.8 1646.7 1808.5 1763.9 1625.5 1538.8 1342.4 1645.1 1619.9 1338.1 1505.5 1529.1 1511.9 1656.7 1694.4 1662.3 1588.7 1483.3 1585.6 1658.9 1584.4 1470.6 1618.7 1407.6 1473.9 1515.3 1485.4 1496.1 1493.5 1298.4 1375.3 1507.9 1455.3 1363.3 1392.8 1348.8 1880.3 1669.2 1543.6 1701.2 1516.5 1466.8 1484.1 1577.2 1684.5 1414.7 1674.5 1598.7 1739.1 1674.6 1671.8 1802 1526.8 1580.9 1634.8 1610.3 1712 1678.8 1708.1 1680.6 2056 1624 2021.4 1861.1 1750.8 1767.5 1710.3 2151.5 2047.9 1915.4 1984.7 1896.5 2170.8 2139.9 2330.5 2121.8 2226.8 1857.9 2155.9 2341.7 2290.2 2006.5 2111.9 1731.3 1762.2 1863.2 1943.5 1975.2
 
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
10.7158197.050
20.6960976.85580
30.6326786.23120
40.6062185.97060
50.6466566.36880
60.6094686.00260
70.6045235.95390
80.5144115.06641e-06
90.4308354.24322.5e-05
100.3996513.93617.8e-05
110.4325334.262.4e-05
120.4790354.71794e-06
130.3426933.37510.000531
140.3352823.30210.000672
150.2394052.35790.010194
160.2317172.28210.012333
170.2344872.30940.01152
180.2300642.26590.012841
190.1946141.91670.029108
200.1629551.60490.055881
210.0835110.82250.206408
220.0747350.73610.231736
230.0921220.90730.183249
240.1044091.02830.153181
250.0186510.18370.427321
260.0037410.03680.48534
27-0.083644-0.82380.206038
28-0.06173-0.6080.272314
29-0.054747-0.53920.295493
30-0.106534-1.04920.148337
31-0.084093-0.82820.20479
32-0.110264-1.0860.140091
33-0.172496-1.69890.046273
34-0.169805-1.67240.048835
35-0.160952-1.58520.058088
36-0.116267-1.14510.127493
37-0.198425-1.95430.026775
38-0.210257-2.07080.020517
39-0.255045-2.51190.00683
40-0.229021-2.25560.013171
41-0.231275-2.27780.012467
42-0.249739-2.45960.007839
43-0.217442-2.14160.017367
44-0.281108-2.76860.003373
45-0.335824-3.30750.000661
46-0.288844-2.84480.002711
47-0.310706-3.06010.001431
48-0.252901-2.49080.007222
49-0.278527-2.74320.003624
50-0.290523-2.86130.002584
51-0.291627-2.87220.002503
52-0.290089-2.8570.002616
53-0.261077-2.57130.005825
54-0.245824-2.42110.008667
55-0.23134-2.27840.012447
56-0.252557-2.48740.007287
57-0.250235-2.46450.007739
58-0.227465-2.24030.013678
59-0.209771-2.0660.020747
60-0.139819-1.37710.085832


Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.7158197.050
20.376743.71050.000173
30.1253411.23450.110005
40.0990380.97540.165892
50.2423692.38710.009461
60.0657410.64750.259428
70.0515840.5080.306287
8-0.134465-1.32430.094253
9-0.198047-1.95050.026999
10-0.087149-0.85830.196415
110.1404821.38360.084831
120.2088842.05730.021172
13-0.268742-2.64680.004741
14-0.047696-0.46980.319792
15-0.064444-0.63470.263559
160.0362680.35720.360858
170.0092580.09120.463767
180.0166250.16370.43514
19-0.136219-1.34160.091429
200.0946760.93250.176709
21-0.00605-0.05960.476304
22-0.005078-0.050.480109
23-0.032093-0.31610.376309
240.0419840.41350.340079
25-0.168408-1.65860.050211
26-0.023445-0.23090.408935
27-0.082532-0.81280.209148
280.0446630.43990.330503
290.0478540.47130.319238
30-0.145686-1.43480.077274
310.0274650.27050.393676
320.1201081.18290.119865
330.0054060.05320.478825
34-0.094282-0.92860.177708
35-0.010407-0.10250.459286
360.0922980.9090.182795
37-0.098797-0.9730.166477
38-0.089197-0.87850.190924
39-0.02958-0.29130.385709
40-0.017386-0.17120.432197
410.0534060.5260.300049
42-0.027752-0.27330.392593
43-0.047396-0.46680.320846
44-0.094285-0.92860.1777
45-0.053976-0.53160.298108
460.1102871.08620.140041
47-0.091135-0.89760.185816
480.0232160.22870.40981
490.1023671.00820.157933
500.0272130.2680.394628
510.0355940.35060.363338
52-0.047881-0.47160.319144
530.0311340.30660.379888
54-0.03079-0.30320.381176
55-0.078107-0.76930.221803
56-0.023817-0.23460.407519
570.0591930.5830.28063
580.0222260.21890.413592
590.0577690.5690.285348
600.0072730.07160.471522
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228661544akvheia0taw8037/1zy4w1228661494.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228661544akvheia0taw8037/1zy4w1228661494.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228661544akvheia0taw8037/2xrlw1228661494.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228661544akvheia0taw8037/2xrlw1228661494.ps (open in new window)


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