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STSM - faillisementen nijverheid

*The author of this computation has been verified*

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

Title produced by software: Structural Time Series Models

Date of computation: Mon, 20 Dec 2010 13:33:30 +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/20/t12928518939uzzi1wlsblmv6s.htm/, Retrieved Mon, 20 Dec 2010 14:31:39 +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/20/t12928518939uzzi1wlsblmv6s.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 «

48 49 59 56 47 56 50 54 79 50 54 56 50 46 47 43 52 48 36 41 34 37 37 34 55 37 27 38 43 26 32 29 41 55 50 30 35 29 22 39 24 38 30 31 39 33 57 49 74 74 115 67 51 114 70 73 77 67 60 73

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	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Structural Time Series Model
t	Observed	Level	Slope	Seasonal	Stand. Residuals
1	48	48	0	0	0
2	49	48.4835553141614	0.0450889725275577	0.0574194441555891	0.066091969717463
3	59	53.2643494624829	0.360307843867307	0.580383001278492	0.715462380994163
4	56	54.5469322069548	0.405758839289783	0.362293766080655	0.147832922569044
5	47	51.3647197378823	0.264550180068174	0.0570801987295856	-0.592327924025418
6	56	53.3467256543456	0.322272568373561	0.490158112550265	0.287976792902266
7	50	51.9923409850031	0.271623751446046	0.144767701651258	-0.283538684676336
8	54	52.8672127992998	0.288591609331212	0.358676134583125	0.102501088069710
9	79	63.9541166313448	0.57792205048404	1.13515216831913	1.83975286392058
10	50	58.2645542614776	0.415674698647807	-0.172007923441882	-1.06947612345479
11	54	56.4738447253675	0.360014282021618	0.379034307642431	-0.376837534559955
12	56	56.3099041325588	0.347066112409889	0.368183901821673	-0.0895373068586133
13	50	55.8700245508463	0.423396028306244	-4.67556943058289	-0.182115530955458
14	46	51.318167087112	0.229352757294846	-0.276639463057748	-0.711172682161763
15	47	49.2805839804731	0.157069976854008	0.315594236406929	-0.356650752245261
16	43	46.5452484430570	0.0845980918405958	-0.00548886692811479	-0.47824852972098
17	52	48.9006252586044	0.132296908902203	0.241495644247069	0.383363870556341
18	48	48.4783546553118	0.121924241583928	0.228431400695907	-0.0944966010247166
19	36	43.341804872008	0.0303823272467762	-0.59956109984278	-0.899954040282278
20	41	42.397216899578	0.0141162088807799	-0.143414480178913	-0.167215267612003
21	34	38.5407702919013	-0.0489054312852072	0.44410939004563	-0.664515984663655
22	37	38.0077627533247	-0.0566821835261305	-0.383849328360152	-0.0831515438298939
23	37	37.5300603118559	-0.06339239211289	0.0127225527401368	-0.0723288157461003
24	34	35.9655273328857	-0.0860159433976338	-0.0279746051358467	-0.257963606941926
25	55	42.2223784964475	-0.316194026372201	3.99385284434166	1.25863970147881
26	37	40.0930989532443	-0.363962033787098	-1.05887172883997	-0.281449568608998
27	27	34.2618258271655	-0.494903159563017	-0.798999419331397	-0.887128446007918
28	38	35.7183980114887	-0.456283143823238	-0.123798575650131	0.326852063380065
29	43	38.639408518804	-0.398128154633761	0.120475154854376	0.573463845087279
30	26	33.2514191353127	-0.476859388042633	-0.935463211082794	-0.85237103158677
31	32	32.7649152503695	-0.477004006568099	-0.752661380197807	-0.0016519921505009
32	29	31.0845224179931	-0.494554749528413	-0.552813916884883	-0.206387555162543
33	41	34.8360934615138	-0.433475829718496	0.754894974281266	0.728638355886549
34	55	43.2995897242132	-0.306226450344172	0.363138359003166	1.52703407745410
35	50	46.1940842554349	-0.260666687655523	-0.273270587371578	0.549373760889409
36	30	39.7485793548057	-0.340508812460426	-1.85367823584773	-1.06194629241181
37	35	36.0225184471337	-0.287856222706459	3.47929100434421	-0.634153899121415
38	29	32.972665987874	-0.345819504485971	-0.747464534478297	-0.444635919758805
39	22	28.4348817274144	-0.433083749095241	-1.43193087036311	-0.689235293669122
40	39	32.5762165334568	-0.35038926497769	0.799095587514981	0.769539450181696
41	24	28.6966790218554	-0.407672498944957	-0.29646923692442	-0.599881827853066
42	38	32.5028141707185	-0.343672540708173	0.211184864255337	0.719460077012531
43	30	31.5980734695633	-0.351881782199347	-0.892308282386911	-0.0959844972251893
44	31	31.4761450543421	-0.348581511199892	-0.7657624608418	0.0393732685344838
45	39	34.3520853799691	-0.302701507804819	0.5845931513874	0.552306577012687
46	33	33.6694266640623	-0.308091597595351	-0.190541108537781	-0.0650870620941148
47	57	42.8881535856428	-0.173950010432760	2.10297935000392	1.63193254999632
48	49	46.1674532643648	-0.130653447497648	-1.52735706863502	0.591751687622329
49	74	55.2484107624321	-0.172674010162554	6.81210555145937	1.67212696377326
50	74	63.4698221324493	-0.0184002463492205	0.538095179017007	1.37828680623751
51	115	85.9338453088701	0.414754718832038	2.13496231858669	3.72386248104591
52	67	78.5360696504965	0.278416861614145	-1.96803720545403	-1.31659649822396
53	51	67.4929457642276	0.0965311869526432	-2.47124245860172	-1.92395211972329
54	114	85.9611730099336	0.376849101965997	5.17381932281773	3.13315429375489
55	70	80.5258621563455	0.290666727290494	-3.2766682016665	-0.992720931796824
56	73	77.8549235568811	0.247378627042241	-1.15761731004758	-0.506167168805854
57	77	77.2359834012412	0.234793498472384	0.845953011599004	-0.148098597441190
58	67	73.6076566416646	0.178817438750667	-1.78240821735651	-0.660446221293281
59	60	67.3231133380331	0.0862338375258085	0.751131174168254	-1.10492777802117
60	73	70.5495565882865	0.126376703068323	-1.47746518484296	0.537001124713946

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/20/t12928518939uzzi1wlsblmv6s/1mf221292852005.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/20/t12928518939uzzi1wlsblmv6s/1mf221292852005.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/20/t12928518939uzzi1wlsblmv6s/2mf221292852005.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/20/t12928518939uzzi1wlsblmv6s/2mf221292852005.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/20/t12928518939uzzi1wlsblmv6s/3f6jm1292852005.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/20/t12928518939uzzi1wlsblmv6s/3f6jm1292852005.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/20/t12928518939uzzi1wlsblmv6s/4f6jm1292852005.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/20/t12928518939uzzi1wlsblmv6s/4f6jm1292852005.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/20/t12928518939uzzi1wlsblmv6s/58yj81292852005.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/20/t12928518939uzzi1wlsblmv6s/58yj81292852005.ps (open in new window)

Parameters (Session):

par1 = 12 ;

Parameters (R input):

par1 = 12 ;

R code (references can be found in the software module):

par1 <- as.numeric(par1)

nx <- length(x)

x <- ts(x,frequency=par1)

m <- StructTS(x,type='BSM')

m$coef

m$fitted

m$resid

mylevel <- as.numeric(m$fitted[,'level'])

myslope <- as.numeric(m$fitted[,'slope'])

myseas <- as.numeric(m$fitted[,'sea'])

myresid <- as.numeric(m$resid)

myfit <- mylevel+myseas

mylagmax <- nx/2

bitmap(file='test2.png')

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

acf(as.numeric(x),lag.max = mylagmax,main='Observed')

acf(mylevel,na.action=na.pass,lag.max = mylagmax,main='Level')

acf(myseas,na.action=na.pass,lag.max = mylagmax,main='Seasonal')

acf(myresid,na.action=na.pass,lag.max = mylagmax,main='Standardized Residals')

par(op)

dev.off()

bitmap(file='test3.png')

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

spectrum(as.numeric(x),main='Observed')

spectrum(mylevel,main='Level')

spectrum(myseas,main='Seasonal')

spectrum(myresid,main='Standardized Residals')

par(op)

dev.off()

bitmap(file='test4.png')

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

cpgram(as.numeric(x),main='Observed')

cpgram(mylevel,main='Level')

cpgram(myseas,main='Seasonal')

cpgram(myresid,main='Standardized Residals')

par(op)

dev.off()

bitmap(file='test1.png')

plot(as.numeric(m$resid),main='Standardized Residuals',ylab='Residuals',xlab='time',type='b')

grid()

dev.off()

bitmap(file='test5.png')

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

hist(m$resid,main='Residual Histogram')

plot(density(m$resid),main='Residual Kernel Density')

qqnorm(m$resid,main='Residual Normal QQ Plot')

qqline(m$resid)

plot(m$resid^2, myfit^2,main='Sq.Resid vs. Sq.Fit',xlab='Squared residuals',ylab='Squared Fit')

par(op)

dev.off()

load(file='createtable')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Structural Time Series Model',6,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'t',header=TRUE)

a<-table.element(a,'Observed',header=TRUE)

a<-table.element(a,'Level',header=TRUE)

a<-table.element(a,'Slope',header=TRUE)

a<-table.element(a,'Seasonal',header=TRUE)

a<-table.element(a,'Stand. Residuals',header=TRUE)

a<-table.row.end(a)

for (i in 1:nx) {

a<-table.row.start(a)

a<-table.element(a,i,header=TRUE)

a<-table.element(a,x[i])

a<-table.element(a,mylevel[i])

a<-table.element(a,myslope[i])

a<-table.element(a,myseas[i])

a<-table.element(a,myresid[i])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable.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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