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autocorr nt 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 10:22:22 -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/t1229880228dfcqc10wc9d5k2y.htm/, Retrieved Sun, 21 Dec 2008 18:23:50 +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/2008/Dec/21/t1229880228dfcqc10wc9d5k2y.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 «

95,9 95,3 100,4 97,3 82,3 97,0 93,5 90,9 107,8 110,9 98,1 106,5 93,4 95,7 109,0 97,6 92,7 107,5 91,7 95,7 111,4 106,0 104,8 108,7 97,3 97,1 106,1 98,6 98,5 105,5 86,2 98,3 111,3 105,0 105,7 103,5 96,9 98,1 111,7 94,7 104,2 109,7 91,3 102,6 114,2 115,8 113,5 107,1 104,5 101,9 116,0 102,0 108,1 112,9 104,5 109,1 113,4 123,9 117,7 108,3

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	0.314567	2.4366	0.008905
2	0.143504	1.1116	0.135378
3	0.386667	2.9951	0.001991
4	-0.00844	-0.0654	0.474046
5	0.092325	0.7151	0.238646
6	0.399191	3.0921	0.001507
7	0.02147	0.1663	0.434239
8	0.000608	0.0047	0.498128
9	0.254957	1.9749	0.026444
10	-0.079915	-0.619	0.269123
11	0.16357	1.267	0.105024
12	0.573675	4.4437	1.9e-05
13	0.052538	0.407	0.342743
14	0.030552	0.2367	0.406865
15	0.088084	0.6823	0.248838
16	-0.226762	-1.7565	0.042053
17	-0.035606	-0.2758	0.391822
18	0.151597	1.1743	0.122464
19	-0.129087	-0.9999	0.160686
20	-0.076471	-0.5923	0.277923
21	0.055809	0.4323	0.333539
22	-0.182627	-1.4146	0.081174
23	0.092549	0.7169	0.238115
24	0.277861	2.1523	0.017703
25	-0.03234	-0.2505	0.401528
26	0.007146	0.0554	0.478019
27	-0.024289	-0.1881	0.425699
28	-0.235935	-1.8275	0.036296
29	-0.045998	-0.3563	0.361433
30	0.050379	0.3902	0.348874
31	-0.111684	-0.8651	0.195216
32	-0.041765	-0.3235	0.373717
33	-0.019709	-0.1527	0.439586
34	-0.149204	-1.1557	0.126187
35	0.065488	0.5073	0.306914
36	0.1203	0.9318	0.177577
37	-0.054212	-0.4199	0.338021
38	-0.060828	-0.4712	0.319615
39	-0.103646	-0.8028	0.212619
40	-0.227203	-1.7599	0.04176
41	-0.153789	-1.1912	0.119124
42	-0.10737	-0.8317	0.204442
43	-0.141276	-1.0943	0.139095
44	-0.110023	-0.8522	0.198737
45	-0.126692	-0.9814	0.16518
46	-0.158103	-1.2247	0.112745
47	-0.055921	-0.4332	0.333226
48	-0.03066	-0.2375	0.406542
49	-0.081061	-0.6279	0.266227
50	-0.114629	-0.8879	0.189067
51	-0.15001	-1.162	0.124924
52	-0.172052	-1.3327	0.093833
53	-0.180267	-1.3963	0.083878
54	-0.133699	-1.0356	0.152267
55	-0.092092	-0.7133	0.2392
56	-0.075589	-0.5855	0.2802
57	-0.068604	-0.5314	0.298551
58	-0.036012	-0.279	0.390621
59	-0.009265	-0.0718	0.471515
60	NA	NA	NA

Partial Autocorrelation Function
Time lag k	PACF(k)	T-STAT	P-value
1	0.314567	2.4366	0.008905
2	0.049444	0.383	0.351541
3	0.365139	2.8284	0.003176
4	-0.286732	-2.221	0.015068
5	0.211954	1.6418	0.052933
6	0.240313	1.8615	0.03379
7	-0.139667	-1.0819	0.141823
8	-0.073556	-0.5698	0.285483
9	0.137029	1.0614	0.146375
10	-0.108558	-0.8409	0.201875
11	0.30721	2.3796	0.010263
12	0.36834	2.8531	0.002966
13	-0.261035	-2.022	0.023823
14	-0.156054	-1.2088	0.115743
15	-0.301782	-2.3376	0.011381
16	0.063816	0.4943	0.311445
17	-0.02956	-0.229	0.409835
18	-0.026621	-0.2062	0.418665
19	0.037183	0.288	0.387161
20	-0.061598	-0.4771	0.3175
21	0.059999	0.4648	0.321895
22	0.076821	0.595	0.277024
23	0.047137	0.3651	0.358152
24	-0.086805	-0.6724	0.25196
25	0.012683	0.0982	0.461034
26	-0.033456	-0.2591	0.398205
27	0.083716	0.6485	0.25958
28	-0.058397	-0.4523	0.326328
29	-0.010895	-0.0844	0.466513
30	-0.058136	-0.4503	0.327051
31	0.043252	0.335	0.369385
32	0.001715	0.0133	0.494724
33	-0.017085	-0.1323	0.44758
34	-0.01205	-0.0933	0.462973
35	-0.058051	-0.4497	0.327287
36	-0.018455	-0.143	0.443404
37	-0.038992	-0.302	0.381838
38	-0.111753	-0.8656	0.195069
39	0.033896	0.2626	0.396896
40	-0.038982	-0.302	0.381867
41	-0.135958	-1.0531	0.148253
42	-0.085749	-0.6642	0.254551
43	0.059907	0.464	0.322149
44	0.020436	0.1583	0.437378
45	-0.025062	-0.1941	0.423365
46	-0.046225	-0.3581	0.360779
47	-0.034183	-0.2648	0.396043
48	-0.045989	-0.3562	0.361461
49	0.055546	0.4303	0.334275
50	0.041093	0.3183	0.375678
51	-0.069204	-0.536	0.296953
52	-0.001993	-0.0154	0.493866
53	-0.041925	-0.3247	0.373251
54	0.056308	0.4362	0.332143
55	-0.020653	-0.16	0.436719
56	0.031886	0.247	0.402881
57	-0.025061	-0.1941	0.423368
58	0.021394	0.1657	0.434467
59	0.053893	0.4175	0.338919
60	NA	NA	NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229880228dfcqc10wc9d5k2y/10qfe1229880140.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229880228dfcqc10wc9d5k2y/10qfe1229880140.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229880228dfcqc10wc9d5k2y/2ebm11229880140.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229880228dfcqc10wc9d5k2y/2ebm11229880140.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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