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autocorr duur cons

*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, 21 Dec 2008 10:16:16 -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/21/t122987984066t6tcs00g69rk4.htm/, Retrieved Sun, 21 Dec 2008 18:17:22 +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/2008/Dec/21/t122987984066t6tcs00g69rk4.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},
}

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

72,5 72,0 98,8 75,2 81,2 88,0 54,6 68,6 101,5 93,4 84,5 91,4 64,5 64,5 117,3 73,5 79,7 102,6 57,9 73,1 102,4 82,3 89,1 84,7 81,4 67,5 113,9 83,8 73,9 103,9 67,9 62,5 125,4 79,1 106,3 96,2 94,3 85,6 117,4 88,5 124,2 119,3 76,8 70,6 122,1 109,5 119,9 102,3 79,6 78,2 103,6 77,8 99,1 105,7 84,1 88,7 108,0 98,1 101,0 82,0

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	1 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Autocorrelation Function
Time lag k	ACF(k)	T-STAT	P-value
1	-0.011986	-0.0928	0.463168
2	-0.076534	-0.5928	0.277761
3	0.295556	2.2894	0.012796
4	-0.096904	-0.7506	0.22791
5	0.035353	0.2738	0.392573
6	0.647909	5.0187	2e-06
7	-0.044741	-0.3466	0.365067
8	-0.047592	-0.3686	0.356844
9	0.208837	1.6176	0.055492
10	-0.108992	-0.8443	0.200941
11	-0.100475	-0.7783	0.219731
12	0.621023	4.8104	5e-06
13	-0.094758	-0.734	0.232907
14	-0.143174	-1.109	0.135924
15	0.19993	1.5487	0.063362
16	-0.175233	-1.3573	0.089877
17	-0.089458	-0.6929	0.245512
18	0.400753	3.1042	0.001455
19	-0.126377	-0.9789	0.165777
20	-0.064227	-0.4975	0.310327
21	0.092541	0.7168	0.238133
22	-0.21417	-1.659	0.051172
23	-0.122279	-0.9472	0.173677
24	0.316329	2.4503	0.008604
25	-0.140876	-1.0912	0.139769
26	-0.125509	-0.9722	0.167429
27	0.04405	0.3412	0.367069
28	-0.241228	-1.8685	0.033285
29	-0.133009	-1.0303	0.153506
30	0.179891	1.3934	0.084316
31	-0.095498	-0.7397	0.231176
32	-0.09389	-0.7273	0.234946
33	-0.049842	-0.3861	0.350404
34	-0.253172	-1.9611	0.027259
35	-0.133644	-1.0352	0.152364
36	0.144001	1.1154	0.134557
37	-0.101802	-0.7886	0.216739
38	-0.109496	-0.8482	0.199862
39	-0.046279	-0.3585	0.360623
40	-0.207834	-1.6099	0.056337
41	-0.087975	-0.6815	0.249104
42	0.068763	0.5326	0.298126
43	-0.052658	-0.4079	0.342403
44	-0.052122	-0.4037	0.343921
45	-0.04379	-0.3392	0.367822
46	-0.089092	-0.6901	0.246394
47	-0.025889	-0.2005	0.420869
48	0.05023	0.3891	0.349297
49	-0.031636	-0.2451	0.403626
50	-0.044739	-0.3465	0.365071
51	-0.020676	-0.1602	0.436647
52	-0.047281	-0.3662	0.357738
53	-0.01472	-0.114	0.454802
54	0.003765	0.0292	0.488415
55	-0.017532	-0.1358	0.446216
56	-0.013236	-0.1025	0.459342
57	-0.022019	-0.1706	0.432572
58	-0.003353	-0.026	0.489682
59	0.006767	0.0524	0.479186
60	NA	NA	NA

Partial Autocorrelation Function
Time lag k	PACF(k)	T-STAT	P-value
1	-0.011986	-0.0928	0.463168
2	-0.076689	-0.594	0.277362
3	0.295429	2.2884	0.012826
4	-0.112235	-0.8694	0.194055
5	0.094683	0.7334	0.233082
6	0.605755	4.6922	8e-06
7	-0.004302	-0.0333	0.486765
8	-0.006841	-0.053	0.478959
9	-0.074902	-0.5802	0.281979
10	-0.027663	-0.2143	0.415529
11	-0.27231	-2.1093	0.01955
12	0.394618	3.0567	0.001669
13	-0.149788	-1.1602	0.125272
14	-0.07946	-0.6155	0.270277
15	-0.039583	-0.3066	0.380101
16	-0.020907	-0.1619	0.435946
17	-0.020154	-0.1561	0.438235
18	-0.198444	-1.5371	0.064758
19	0.13172	1.0203	0.155842
20	0.028782	0.2229	0.412169
21	-0.055702	-0.4315	0.333838
22	-0.136604	-1.0581	0.14712
23	0.109469	0.8479	0.199921
24	-0.092558	-0.717	0.238092
25	-0.068306	-0.5291	0.299346
26	0.027511	0.2131	0.415986
27	-0.103681	-0.8031	0.21254
28	-0.044775	-0.3468	0.364969
29	-0.075256	-0.5829	0.281062
30	0.012743	0.0987	0.460849
31	0.129741	1.005	0.159474
32	-0.120164	-0.9308	0.177848
33	-0.058288	-0.4515	0.326631
34	-0.023166	-0.1794	0.429099
35	-0.016056	-0.1244	0.450718
36	-0.047565	-0.3684	0.356923
37	0.004795	0.0371	0.485248
38	0.021441	0.1661	0.434326
39	-0.049006	-0.3796	0.352792
40	0.068811	0.533	0.297999
41	0.003284	0.0254	0.489896
42	0.025205	0.1952	0.422934
43	-0.092765	-0.7186	0.237603
44	0.027469	0.2128	0.416113
45	0.126889	0.9829	0.164808
46	0.114678	0.8883	0.188965
47	0.043928	0.3403	0.367422
48	-0.081477	-0.6311	0.26518
49	-0.014541	-0.1126	0.455349
50	-0.087507	-0.6778	0.250243
51	-0.063928	-0.4952	0.31114
52	-0.009463	-0.0733	0.470907
53	-0.013773	-0.1067	0.457698
54	-0.049098	-0.3803	0.35253
55	-0.091902	-0.7119	0.239652
56	0.055585	0.4306	0.334167
57	0.008321	0.0645	0.47441
58	-0.057655	-0.4466	0.328388
59	0.000158	0.0012	0.499515
60	NA	NA	NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t122987984066t6tcs00g69rk4/1elco1229879774.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t122987984066t6tcs00g69rk4/1elco1229879774.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t122987984066t6tcs00g69rk4/26d891229879774.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t122987984066t6tcs00g69rk4/26d891229879774.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')

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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