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Bestedingen consument ACF of Y[t] (d=0,D=1,Lambda=1)

*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: Tue, 24 Nov 2009 08:36:35 -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/Nov/24/t1259077068qp94l60l6s0wjwb.htm/, Retrieved Tue, 24 Nov 2009 16:37:50 +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/Nov/24/t1259077068qp94l60l6s0wjwb.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:

Uitleg in Word document

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

96.96 93.11 95.62 98.30 96.38 100.82 99.06 94.03 102.07 99.31 98.64 101.82 99.14 97.63 100.06 101.32 101.49 105.43 105.09 99.48 108.53 104.34 106.10 107.35 103.00 104.50 105.17 104.84 106.18 108.86 107.77 102.74 112.63 106.26 108.86 111.38 106.85 107.86 107.94 111.38 111.29 113.72 111.88 109.87 113.72 111.71 114.81 112.05 111.54 110.87 110.87 115.48 111.63 116.24 113.56 106.01 110.45 107.77 108.61 108.19

Output produced by software:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Autocorrelation Function
Time lag k	ACF(k)	T-STAT	P-value
1	0.656847	4.5508	1.8e-05
2	0.566059	3.9218	0.000139
3	0.551059	3.8178	0.000193
4	0.294666	2.0415	0.023359
5	0.195942	1.3575	0.090482
6	0.155668	1.0785	0.143101
7	0.076742	0.5317	0.298699
8	0.04375	0.3031	0.381559
9	0.002647	0.0183	0.492722
10	-0.048731	-0.3376	0.368561
11	-0.007202	-0.0499	0.480205
12	-0.085983	-0.5957	0.277084
13	-0.14695	-1.0181	0.156867
14	-0.04437	-0.3074	0.379933
15	-0.097753	-0.6773	0.250749
16	-0.160682	-1.1132	0.135575
17	-0.052581	-0.3643	0.35862
18	-0.041522	-0.2877	0.387419
19	-0.050931	-0.3529	0.362868
20	0.0195	0.1351	0.446548
21	0.082792	0.5736	0.284459
22	0.03574	0.2476	0.402744
23	0.086843	0.6017	0.275114
24	0.037654	0.2609	0.397652
25	0.067041	0.4645	0.322204
26	0.047156	0.3267	0.372655
27	-0.036169	-0.2506	0.401601
28	-0.020414	-0.1414	0.444059
29	-0.058067	-0.4023	0.344625
30	-0.144821	-1.0034	0.160362
31	-0.165813	-1.1488	0.128169
32	-0.162386	-1.125	0.133083
33	-0.249909	-1.7314	0.0449
34	-0.244522	-1.6941	0.048364
35	-0.216172	-1.4977	0.070381
36	-0.272132	-1.8854	0.032719

Partial Autocorrelation Function
Time lag k	PACF(k)	T-STAT	P-value
1	0.656847	4.5508	1.8e-05
2	0.236761	1.6403	0.053737
3	0.208243	1.4427	0.077793
4	-0.323596	-2.2419	0.014811
5	-0.090894	-0.6297	0.265929
6	0.023156	0.1604	0.436609
7	0.108048	0.7486	0.228881
8	0.008403	0.0582	0.47691
9	-0.09743	-0.675	0.251453
10	-0.104413	-0.7234	0.236474
11	0.13011	0.9014	0.185932
12	-0.091308	-0.6326	0.264998
13	-0.120334	-0.8337	0.20429
14	0.125936	0.8725	0.193637
15	0.015244	0.1056	0.458166
16	-0.12205	-0.8456	0.200989
17	0.041696	0.2889	0.386959
18	0.092948	0.644	0.261332
19	0.052042	0.3606	0.360006
20	-0.016817	-0.1165	0.453867
21	0.084483	0.5853	0.280539
22	-0.132952	-0.9211	0.180798
23	0.089354	0.6191	0.269402
24	-0.127535	-0.8836	0.190661
25	0.128732	0.8919	0.188454
26	-0.093853	-0.6502	0.259321
27	-0.01849	-0.1281	0.449301
28	-0.13358	-0.9255	0.179677
29	-0.004912	-0.034	0.486496
30	-0.048199	-0.3339	0.369943
31	-0.044333	-0.3071	0.380031
32	-0.05558	-0.3851	0.350944
33	-0.062984	-0.4364	0.332263
34	-0.003594	-0.0249	0.490119
35	0.003677	0.0255	0.489892
36	-0.10026	-0.6946	0.24532

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/24/t1259077068qp94l60l6s0wjwb/1jufn1259076993.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/24/t1259077068qp94l60l6s0wjwb/1jufn1259076993.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/24/t1259077068qp94l60l6s0wjwb/2ve5y1259076993.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/24/t1259077068qp94l60l6s0wjwb/2ve5y1259076993.ps (open in new window)

Parameters (Session):

par1 = 36 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = MA ; par7 = 0.95 ;

Parameters (R input):

par1 = 36 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = MA ; 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')

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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