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Identification and Estimation of ARMA processes Step 2 (4)*

*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 07:58:01 -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/t1228662009pqbkakftu4iampk.htm/, Retrieved Sun, 07 Dec 2008 15:00:11 +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/t1228662009pqbkakftu4iampk.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
1-0.493897-4.52661e-05
20.0319860.29320.385062
30.2031341.86180.033068
4-0.229968-2.10770.019019
50.1599851.46630.073153
6-0.13555-1.24230.108785
70.0846650.7760.219974
8-0.033556-0.30750.379595
90.0044960.04120.483615
10-0.116912-1.07150.143504
110.2349162.1530.017091
12-0.278561-2.55310.006243
130.0019660.0180.492835
140.1538331.40990.081131
15-0.08422-0.77190.221173
160.0676850.62030.268356
17-0.204485-1.87410.032194
180.291122.66820.004575
19-0.191339-1.75370.041569
200.0168080.1540.438971
210.1387611.27180.103483
22-0.163126-1.49510.069321
230.1767611.620.054486
24-0.119528-1.09550.138216
250.0269080.24660.402902
260.1069710.98040.164851
27-0.1633-1.49670.069114
280.0365290.33480.369308
290.1272911.16660.123329
30-0.189616-1.73790.04295
310.0268830.24640.402991
320.1020330.93520.176195
33-0.078344-0.7180.237364
340.0016450.01510.494002
350.0071790.06580.473848
360.0249030.22820.410009
370.0111120.10180.459562
38-0.031395-0.28770.387126
39-0.011848-0.10860.456894
400.0386620.35430.361986
41-0.056541-0.51820.302838
420.0165340.15150.439956
430.0358660.32870.371593
440.021340.19560.422702
45-0.12569-1.1520.1263
460.1460561.33860.092153
47-0.034818-0.31910.375216
48-0.177727-1.62890.05354
490.1813381.6620.050121
50-0.141094-1.29310.099753
510.0879330.80590.211282
52-0.00411-0.03770.48502
53-0.044216-0.40520.343165
540.0548370.50260.308284
55-0.034778-0.31880.375354
560.0314940.28860.386782
57-0.015334-0.14050.444286
580.0634560.58160.281203
59-0.117433-1.07630.14244
600.1357941.24460.108375


Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.493897-4.52661e-05
2-0.280331-2.56930.005979
30.1218771.1170.133585
4-0.060848-0.55770.289272
50.0513930.4710.319421
6-0.125215-1.14760.127193
70.0258250.23670.406737
8-0.040081-0.36730.357141
90.0462030.42350.336521
10-0.219587-2.01250.023683
110.1853411.69870.04654
12-0.211301-1.93660.028078
13-0.168315-1.54260.063339
14-0.130199-1.19330.118057
150.2004031.83670.034894
160.0185520.170.432699
17-0.219512-2.01190.02372
180.0507060.46470.321665
190.0239920.21990.413246
20-0.00679-0.06220.475263
210.0388930.35650.361196
22-0.081687-0.74870.228073
230.1526821.39940.082695
240.0063650.05830.476811
25-0.139285-1.27660.102637
260.0879540.80610.211228
270.0487360.44670.328131
28-0.00805-0.07380.470679
29-0.106847-0.97930.16513
30-0.040795-0.37390.354715
31-0.027234-0.24960.401753
320.0162710.14910.440907
330.0826790.75780.225357
34-0.117195-1.07410.142925
350.0763130.69940.243111
360.0387990.35560.361515
37-0.011562-0.1060.457929
380.0447710.41030.341303
39-0.064085-0.58740.279271
40-0.017409-0.15960.436808
41-0.092126-0.84430.200438
42-0.011961-0.10960.456486
430.0126040.11550.454154
440.0467680.42860.334641
45-0.036254-0.33230.370255
460.0300150.27510.391961
47-0.059108-0.54170.294719
48-0.146747-1.3450.091129
49-0.062523-0.5730.284075
50-0.025194-0.23090.408974
51-0.012527-0.11480.454432
52-0.010996-0.10080.459983
53-0.008573-0.07860.468781
54-0.012815-0.11740.453393
55-0.004123-0.03780.484973
560.0514560.47160.319217
57-0.01367-0.12530.450297
580.0048720.04460.482247
59-0.062374-0.57170.284537
60-0.012024-0.11020.456255
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228662009pqbkakftu4iampk/10jb61228661880.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228662009pqbkakftu4iampk/10jb61228661880.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228662009pqbkakftu4iampk/2akg71228661880.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228662009pqbkakftu4iampk/2akg71228661880.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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