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Correlatiefunctie Golden na 1 keer niet-seizoenaal differentiëren

*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: Thu, 17 Dec 2009 13:39:59 -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/Dec/17/t126108247377lqxj8kw0w5pjp.htm/, Retrieved Thu, 17 Dec 2009 21:41:15 +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/Dec/17/t126108247377lqxj8kw0w5pjp.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:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

1.77 1.76 1.77 1.95 1.98 1.93 1.94 1.92 1.94 1.92 1.92 1.94 1.91 1.88 1.98 2.4 2.47 2.22 1.98 1.89 1.87 1.88 1.86 1.81 1.79 1.78 1.73 1.88 1.91 1.9 1.84 1.85 1.83 1.82 1.82 1.81 1.75 1.74 1.73 1.96 2.07 1.96 1.87 1.84 1.81 1.78 1.72 1.73 1.64 1.61 1.63 1.92 1.88 1.68 1.58 1.49 1.46 1.44 1.44 1.42 1.4 1.38 1.36 1.48 1.56 1.51 1.51 1.42 1.4 1.38 1.35 1.29

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	0.305804	2.5767	0.006026
2	-0.276232	-2.3276	0.011395
3	-0.333486	-2.81	0.003198
4	-0.107304	-0.9042	0.184484
5	0.015825	0.1333	0.447148
6	0.029814	0.2512	0.401185
7	0.017026	0.1435	0.443165
8	-0.054966	-0.4632	0.322336
9	-0.176799	-1.4897	0.070361
10	-0.183155	-1.5433	0.063602
11	0.117298	0.9884	0.163163
12	0.540494	4.5543	1.1e-05
13	0.148242	1.2491	0.107864
14	-0.176052	-1.4834	0.071191
15	-0.195738	-1.6493	0.05175
16	-0.090181	-0.7599	0.224922
17	-0.002955	-0.0249	0.490104
18	0.038117	0.3212	0.374509
19	0.025015	0.2108	0.416832
20	-0.072515	-0.611	0.271569
21	-0.181743	-1.5314	0.065058
22	-0.162346	-1.368	0.087821
23	0.089064	0.7505	0.227726
24	0.41819	3.5237	0.000374
25	0.168032	1.4159	0.080594
26	-0.089877	-0.7573	0.225684
27	-0.110457	-0.9307	0.177573
28	-0.059799	-0.5039	0.307954
29	0.022962	0.1935	0.423568
30	0.016869	0.1421	0.443684
31	0.002021	0.017	0.493231
32	-0.061024	-0.5142	0.304356
33	-0.176533	-1.4875	0.070656
34	-0.119434	-1.0064	0.158828
35	0.154677	1.3033	0.098337
36	0.412531	3.4761	0.000436

Partial Autocorrelation Function
Time lag k	PACF(k)	T-STAT	P-value
1	0.305804	2.5767	0.006026
2	-0.407892	-3.437	0.000494
3	-0.118865	-1.0016	0.159974
4	-0.056485	-0.476	0.317785
5	-0.106621	-0.8984	0.186002
6	-0.048175	-0.4059	0.343006
7	-0.033681	-0.2838	0.388695
8	-0.116277	-0.9798	0.165263
9	-0.203607	-1.7156	0.045296
10	-0.179793	-1.515	0.06711
11	0.097145	0.8186	0.20789
12	0.409875	3.4537	0.000468
13	-0.278369	-2.3456	0.010898
14	0.183055	1.5424	0.063705
15	0.004157	0.035	0.486078
16	-0.107153	-0.9029	0.184818
17	0.055161	0.4648	0.321751
18	-0.012402	-0.1045	0.458532
19	-0.054318	-0.4577	0.324285
20	-0.077869	-0.6561	0.256929
21	-0.126553	-1.0664	0.144938
22	-0.050795	-0.428	0.33497
23	0.004853	0.0409	0.483749
24	0.04921	0.4147	0.339824
25	0.032136	0.2708	0.393672
26	0.015288	0.1288	0.448931
27	0.072891	0.6142	0.270527
28	0.024543	0.2068	0.418377
29	0.084521	0.7122	0.239341
30	-0.045704	-0.3851	0.350653
31	0.043811	0.3692	0.356556
32	0.002656	0.0224	0.491105
33	-0.126196	-1.0633	0.145614
34	0.090805	0.7651	0.223363
35	0.123779	1.043	0.150249
36	0.070072	0.5904	0.278386

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/17/t126108247377lqxj8kw0w5pjp/1il3d1261082397.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t126108247377lqxj8kw0w5pjp/1il3d1261082397.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t126108247377lqxj8kw0w5pjp/2rycy1261082397.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t126108247377lqxj8kw0w5pjp/2rycy1261082397.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 12 ;

Parameters (R input):

par1 = 36 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; 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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