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tijdreeks werkloosheid

*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, 26 Dec 2010 13:22:00 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/26/t1293369575a9w563go6kj2tu5.htm/, Retrieved Sun, 26 Dec 2010 14:19:36 +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/2010/Dec/26/t1293369575a9w563go6kj2tu5.htm/},
    year = {2010},
}
@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 = {2010},
    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 «

332 369 384 373 378 426 423 397 422 409 430 412 396 470 491 504 484 474 508 492 452 457 457 471 451 476 493 514 522 490 484 506 501 462 465 454 464 427 482 460 473 465 422 415 413 420 363 376 380 384 346 410 389 407 393 346 348 353 364 305 307 312 312 286 344 324 336 327 302 299 311 315 264 278 278 287 279 300 324 354 354 360 363 385 412 370 389 395 417 404 377 456 478 468 437 432 441 449 386 396 394

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	'RServer@AstonUniversity' @ vre.aston.ac.uk

Autocorrelation Function
Time lag k	ACF(k)	T-STAT	P-value
1	-0.208188	-2.0819	0.019953
2	-0.086344	-0.8634	0.194982
3	-0.051841	-0.5184	0.30266
4	0.225522	2.2552	0.013149
5	0.065577	0.6558	0.256738
6	-0.191458	-1.9146	0.029202
7	-0.119321	-1.1932	0.117804
8	0.024271	0.2427	0.404364
9	0.202401	2.024	0.022818
10	-0.058732	-0.5873	0.279155
11	-0.07692	-0.7692	0.221794
12	-0.111683	-1.1168	0.133371
13	0.631099	6.311	0
14	-0.176493	-1.7649	0.040314
15	-0.066519	-0.6652	0.25373
16	-0.149097	-1.491	0.069558
17	0.19646	1.9646	0.026118
18	-0.029919	-0.2992	0.382708
19	-0.203577	-2.0358	0.022209
20	-0.005846	-0.0585	0.476751
21	-0.115216	-1.1522	0.126001
22	0.182072	1.8207	0.03582
23	-0.065388	-0.6539	0.257345
24	-0.095499	-0.955	0.170942
25	-0.049451	-0.4945	0.311015
26	0.372569	3.7257	0.000161
27	-0.069406	-0.6941	0.244628
28	-0.084606	-0.8461	0.199769
29	-0.18583	-1.8583	0.033034
30	0.225205	2.2521	0.013253
31	-0.064643	-0.6464	0.25974
32	-0.190156	-1.9016	0.030054
33	0.027709	0.2771	0.391141
34	-0.149176	-1.4918	0.069456
35	0.201587	2.0159	0.023248
36	-0.121625	-1.2163	0.113375
37	-0.09595	-0.9595	0.169811
38	-0.010815	-0.1082	0.457046
39	0.224618	2.2462	0.013446
40	-0.000496	-0.005	0.498026
41	-0.139881	-1.3988	0.082483
42	-0.161947	-1.6195	0.054248
43	0.203639	2.0364	0.022177
44	-0.037397	-0.374	0.354611
45	-0.182316	-1.8232	0.035633
46	0.00843	0.0843	0.466492
47	-0.115077	-1.1508	0.126285
48	0.169567	1.6957	0.04653

Partial Autocorrelation Function
Time lag k	PACF(k)	T-STAT	P-value
1	-0.208188	-2.0819	0.019953
2	-0.135562	-1.3556	0.089138
3	-0.106994	-1.0699	0.143611
4	0.189967	1.8997	0.030179
5	0.162324	1.6232	0.053843
6	-0.109787	-1.0979	0.137449
7	-0.174671	-1.7467	0.041879
8	-0.132653	-1.3265	0.093842
9	0.127639	1.2764	0.102387
10	0.101948	1.0195	0.155219
11	0.058982	0.5898	0.278321
12	-0.145024	-1.4502	0.075061
13	0.552894	5.5289	0
14	0.021851	0.2185	0.41374
15	0.017573	0.1757	0.430433
16	-0.197602	-1.976	0.025454
17	-0.081974	-0.8197	0.207157
18	-0.203294	-2.0329	0.022354
19	-0.07795	-0.7795	0.218764
20	0.135475	1.3548	0.089275
21	-0.119419	-1.1942	0.117614
22	-0.02244	-0.2244	0.411451
23	-0.020344	-0.2034	0.419601
24	-0.114771	-1.1477	0.126913
25	-0.007381	-0.0738	0.470654
26	0.000471	0.0047	0.498127
27	0.143592	1.4359	0.077073
28	0.015709	0.1571	0.437745
29	-0.05401	-0.5401	0.295165
30	0.069249	0.6925	0.245117
31	-0.033019	-0.3302	0.370972
32	-0.083437	-0.8344	0.203031
33	-0.008909	-0.0891	0.464595
34	-0.061611	-0.6161	0.269611
35	0.012582	0.1258	0.450062
36	-0.082114	-0.8211	0.206759
37	-0.115622	-1.1562	0.125174
38	-0.032233	-0.3223	0.373936
39	-0.079035	-0.7904	0.215596
40	-0.003563	-0.0356	0.485826
41	-0.007923	-0.0792	0.468506
42	-0.072288	-0.7229	0.235722
43	-0.019468	-0.1947	0.423019
44	0.044753	0.4475	0.327732
45	-0.036175	-0.3617	0.359152
46	0.010419	0.1042	0.458615
47	-0.05573	-0.5573	0.289283
48	-0.041193	-0.4119	0.340635

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293369575a9w563go6kj2tu5/1jz321293369717.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293369575a9w563go6kj2tu5/1jz321293369717.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293369575a9w563go6kj2tu5/2cq351293369717.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293369575a9w563go6kj2tu5/2cq351293369717.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293369575a9w563go6kj2tu5/3cq351293369717.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293369575a9w563go6kj2tu5/3cq351293369717.ps (open in new window)

Parameters (Session):

par1 = 48 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;

Parameters (R input):

par1 = 48 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; 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 (par8 != '') par8 <- as.numeric(par8)

ox <- x

if (par8 == '') {

if (par2 == 0) {

x <- log(x)

} else {

x <- (x ^ par2 - 1) / par2

}

} else {

x <- log(x,base=par8)

}

if (par3 > 0) x <- diff(x,lag=1,difference=par3)

if (par4 > 0) x <- diff(x,lag=par5,difference=par4)

bitmap(file='picts.png')

op <- par(mfrow=c(2,1))

plot(ox,type='l',main='Original Time Series',xlab='time',ylab='value')

if (par8=='') {

mytitle <- paste('Working Time Series (lambda=',par2,', d=',par3,', D=',par4,')',sep='')

mysub <- paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='')

} else {

mytitle <- paste('Working Time Series (base=',par8,', d=',par3,', D=',par4,')',sep='')

mysub <- paste('(base=',par8,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='')

}

plot(x,type='l', main=mytitle,xlab='time',ylab='value')

par(op)

dev.off()

bitmap(file='pic1.png')

racf <- acf(x, par1, main='Autocorrelation', xlab='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=mysub)

dev.off()

bitmap(file='pic2.png')

rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF',sub=mysub)

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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