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opgave 6bis.2 stap 2 degree of non-seasonal differencing op 1

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R Software Module: /rwasp_autocorrelation.wasp (opens new window with default values)

Title produced by software: (Partial) Autocorrelation Function

Date of computation: Sat, 18 Dec 2010 13:56:19 +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/18/t1292680788c1c0mwc7bq1zsx3.htm/, Retrieved Sat, 18 Dec 2010 14:59:51 +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/18/t1292680788c1c0mwc7bq1zsx3.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 «

100.4 97.7 97 96.5 98.4 106.3 103.1 102.4 95 98.1 106.1 99.1 101.2 95.5 99.8 97.1 97.5 96.8 97.7 100.9 94.3 99.5 100.8 97 99.2 101 102.3 97 91.2 97.6 95.7 100.5 94.4 102.9 105.1 98.8 100.7 99.6 107.7 102.9 101.6 102.7 110.5 109.8 94.3 102.5 105 102.3 107.7 100.3 99.5 95 97.7 96.3 97.8 106.4 96.1 106.2 114.7 111.9 121 117.7 115.4 114.3 109.5 108.1 108.2 99.1 101.2 98.1 95.5 97.9 98.2 98.7 95.6 95.8 94.4 96.5 103.3 104.3 104.5 102.3 103.8 103.1 102.2 106.3 102.1 94 102.6 102.6 106.7 107.9 109.3 105.9 109.1 108.5 111.7 109.8 109.1 108.5 108.5 106.2 117.1 109.8 115.2 115.9 119.2 121 118.6 117.6 114.6 110.6 102.5 101.6 107.4 105.8 102.8 104 100.4 100.6

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	'George Udny Yule' @ 72.249.76.132

Autocorrelation Function
Time lag k	ACF(k)	T-STAT	P-value
1	-0.323623	-3.5303	0.000296
2	-0.017327	-0.189	0.4252
3	0.059099	0.6447	0.260183
4	-0.101128	-1.1032	0.136089
5	0.190833	2.0817	0.019755
6	-0.194064	-2.117	0.018172
7	0.068342	0.7455	0.228714
8	-0.06741	-0.7354	0.231785
9	-0.029379	-0.3205	0.374581
10	-0.04783	-0.5218	0.301404
11	-0.203783	-2.223	0.014052
12	0.316234	3.4497	0.000389
13	-0.129062	-1.4079	0.080883
14	-0.020321	-0.2217	0.412471
15	-0.028697	-0.313	0.377398
16	-0.03238	-0.3532	0.362274
17	0.174555	1.9042	0.029651
18	-0.175605	-1.9156	0.028906
19	0.095439	1.0411	0.149967
20	0.026413	0.2881	0.386873
21	0.022046	0.2405	0.405181
22	-0.083091	-0.9064	0.183272
23	-0.058244	-0.6354	0.263204
24	0.21403	2.3348	0.010615
25	-0.129801	-1.416	0.079699
26	0.071189	0.7766	0.219475
27	-0.088109	-0.9612	0.169212
28	0.023481	0.2562	0.399138
29	-0.003029	-0.033	0.486849
30	-0.092379	-1.0077	0.157813
31	0.09987	1.0895	0.139077
32	-0.084367	-0.9203	0.179629
33	0.084287	0.9195	0.179857
34	-0.103807	-1.1324	0.129872
35	-0.110796	-1.2086	0.114598
36	0.203143	2.216	0.014296
37	-0.017337	-0.1891	0.425158
38	-0.010513	-0.1147	0.454445
39	-0.049151	-0.5362	0.296418
40	-0.033146	-0.3616	0.359155
41	-0.041896	-0.457	0.324239
42	0.055244	0.6026	0.273948
43	0.066124	0.7213	0.236061
44	-0.009425	-0.1028	0.459142
45	0.019335	0.2109	0.416656
46	-0.068318	-0.7453	0.22879
47	0.080814	0.8816	0.189891
48	0.057828	0.6308	0.264682

Partial Autocorrelation Function
Time lag k	PACF(k)	T-STAT	P-value
1	-0.323623	-3.5303	0.000296
2	-0.136338	-1.4873	0.069794
3	0.009793	0.1068	0.45755
4	-0.08947	-0.976	0.16552
5	0.152678	1.6655	0.04922
6	-0.10844	-1.1829	0.119595
7	0.000683	0.0075	0.497033
8	-0.101997	-1.1127	0.134049
9	-0.052478	-0.5725	0.284043
10	-0.154601	-1.6865	0.04716
11	-0.270151	-2.947	0.001931
12	0.145812	1.5906	0.057174
13	0.014183	0.1547	0.438652
14	-0.023999	-0.2618	0.396965
15	-0.107777	-1.1757	0.12103
16	-0.042546	-0.4641	0.321705
17	0.025285	0.2758	0.39158
18	-0.099364	-1.0839	0.140293
19	-0.023408	-0.2554	0.399446
20	0.033244	0.3627	0.358754
21	0.054293	0.5923	0.277396
22	-0.129782	-1.4158	0.079729
23	-0.027136	-0.296	0.383864
24	0.084505	0.9218	0.179237
25	-0.052774	-0.5757	0.282955
26	0.026042	0.2841	0.38842
27	-0.028219	-0.3078	0.379375
28	0.041179	0.4492	0.32705
29	-0.165713	-1.8077	0.036589
30	-0.044121	-0.4813	0.315592
31	0.028168	0.3073	0.379586
32	-0.060275	-0.6575	0.256058
33	-0.019945	-0.2176	0.414067
34	-0.035924	-0.3919	0.347922
35	-0.114404	-1.248	0.10724
36	-0.030484	-0.3325	0.370032
37	0.08854	0.9659	0.168037
38	-0.042514	-0.4638	0.321827
39	-0.041417	-0.4518	0.326116
40	-0.161912	-1.7662	0.039959
41	-0.209708	-2.2876	0.011962
42	0.023735	0.2589	0.398073
43	0.008527	0.093	0.463022
44	0.054312	0.5925	0.27733
45	-0.052748	-0.5754	0.283048
46	-0.026933	-0.2938	0.384709
47	0.08258	0.9008	0.184747
48	0.018279	0.1994	0.421145

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292680788c1c0mwc7bq1zsx3/16i0r1292680577.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292680788c1c0mwc7bq1zsx3/16i0r1292680577.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292680788c1c0mwc7bq1zsx3/2hrhc1292680577.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292680788c1c0mwc7bq1zsx3/2hrhc1292680577.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292680788c1c0mwc7bq1zsx3/3hrhc1292680577.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292680788c1c0mwc7bq1zsx3/3hrhc1292680577.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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