Home » date » 2010 » Dec » 11 »

STSM: Faillissementen

*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: Sat, 11 Dec 2010 13:09:34 +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/11/t1292073520ty2di7sz9l6t1fd.htm/, Retrieved Sat, 11 Dec 2010 14:20:45 +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/11/t1292073520ty2di7sz9l6t1fd.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 «

46 62 66 59 58 61 41 27 58 70 49 59 44 36 72 45 56 54 53 35 61 52 47 51 52 63 74 45 51 64 36 30 55 64 39 40 63 45 59 55 40 64 27 28 45 57 45 69 60 56 58 50 51 53 37 22 55 70 62 58 39 49 58 47 42 62 39 40 72 70 54 65

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	5 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Structural Time Series Model
t	Observed	Level	Slope	Seasonal	Stand. Residuals
1	46	46	0	0	0
2	62	47.9109909952398	0.457697990806483	3.2732664441853	1.53740801159490
3	66	52.2181091947632	1.23549467053244	3.11059823021179	1.66340889373574
4	59	54.8085379684848	1.45917282107061	0.885365209157957	0.524695635574755
5	58	56.4533173431123	1.48496120504533	1.09744791255815	0.0712082144066377
6	61	58.3025660066566	1.52862181359353	1.78775491036663	0.143265671402083
7	41	55.5098165849483	1.07341424352759	-3.20527967546787	-1.76638134616308
8	27	49.8660729865789	0.442407654810065	-4.36853300101980	-2.8676301503383
9	58	51.1348726332422	0.512449702602629	4.46984428057707	0.368629103685274
10	70	55.1496258961828	0.782855525330754	4.1921840034356	1.62955096914827
11	49	54.8312529351815	0.70477805531319	-2.32506185403912	-0.532976650346298
12	59	55.9269518611341	0.730405529209196	1.77587094147030	0.196188580476513
13	44	56.3322175718715	0.736551767164163	-10.1266938932362	-0.329507017818823
14	36	53.3161772825258	0.551370851135196	-4.85096872699698	-1.97250971184503
15	72	56.1461430448546	0.678454030087005	9.18232891129205	1.08285304248221
16	45	55.0580889491326	0.58285030399649	-4.74389861543802	-0.862737430640465
17	56	55.4247969873688	0.571809017501688	1.26024583279445	-0.110650260025021
18	54	55.121655007196	0.529781916320623	1.7913244432931	-0.46809716299563
19	53	55.2064826223997	0.509645646754152	-0.661514110110605	-0.247188712475582
20	35	53.2929661513387	0.405995059714273	-9.57646047019087	-1.38962527012726
21	61	54.0446390815115	0.420016801398219	5.67413367370527	0.203654897469071
22	52	53.516129640001	0.383447121500262	2.09151193050818	-0.571991077014357
23	47	53.1837978677296	0.35732766183705	-3.39326960331675	-0.441277084928003
24	51	52.7576108386102	0.331818398229640	1.42353098950672	-0.500327135262522
25	52	53.393667285405	0.334464713935509	-2.91243454034330	0.238016698230939
26	63	55.7506299100905	0.38638105192157	-1.08043725288232	1.33115093069008
27	74	57.4971548438824	0.429789242304816	11.3360004076134	0.836132538702332
28	45	57.0091907078008	0.399552381535283	-8.55575179521326	-0.560666586608115
29	51	56.1634701526185	0.359391328280942	-0.410074478700819	-0.77134350606446
30	64	57.0084644010645	0.37447520059378	5.10111512384537	0.306300014569987
31	36	54.4646481401095	0.287519041872966	-6.88373841602748	-1.87336479220132
32	30	52.3779147597683	0.21965600670457	-12.7918471892073	-1.54833518956462
33	55	51.6827015107932	0.194559197170161	7.06832201189843	-0.605031776510308
34	64	52.8949348380383	0.221277167221656	6.87173219683117	0.681880685163791
35	39	51.8538986252562	0.190131975351364	-7.51371554446141	-0.858516679986867
36	40	50.3170461622272	0.153398183928063	-2.78147007264109	-1.20700649875124
37	63	52.1336680183341	0.172850915525717	2.99194230221404	1.25744345028980
38	45	51.8776996365236	0.165163670186253	-4.98992881429014	-0.303709661346437
39	59	51.3081383160931	0.148939140248051	10.7703334599327	-0.498755239588257
40	55	52.7154226513219	0.178504347188585	-2.91213341571516	0.844384311829116
41	40	51.4607842867597	0.144739891109483	-5.52656594183589	-0.964741535525588
42	64	51.9113099812074	0.151794417157173	10.8111285654316	0.207607035354958
43	27	49.569636349891	0.0959863921279493	-12.0411062303291	-1.70961224487393
44	28	48.1399265740086	0.062982215133594	-13.6297315937725	-1.05622452453552
45	45	46.7736261612595	0.0331938580705787	4.38718216971831	-0.99864912109608
46	57	46.7764793982211	0.0325895571864374	10.3556758144455	-0.0213994993193094
47	45	47.2931297999486	0.0415731598280422	-4.42947123934695	0.345405804996543
48	69	50.4129403149992	0.09094566749257	4.70460948531226	2.23928536291905
49	60	51.5905180742013	0.103646048994531	3.32766272724329	0.817930460757922
50	56	52.9226572106711	0.121202400532406	-2.48904466989071	0.898735338757708
51	58	52.6695761920148	0.114880325275220	6.97612087111293	-0.266788689700409
52	50	52.5872799375226	0.111304197834970	-1.73128769627027	-0.139074697674046
53	51	53.0528115129368	0.117841905695975	-3.58859194197530	0.249708537245788
54	53	51.5548081476913	0.08828433395974	8.48023758748402	-1.14383034506231
55	37	50.8845016112866	0.074707801614123	-10.5605720733483	-0.540234450862252
56	22	48.8797431387795	0.0385570759719390	-17.7030046038042	-1.49067364552621
57	55	48.855315335794	0.0375004495205275	6.42471567921443	-0.0454581979877187
58	70	50.2752416536181	0.0596248123260531	13.5280250958367	1.00508440250233
59	62	52.7882342599294	0.096142318248043	-1.89890031614101	1.80000855050602
60	58	53.4735459799282	0.103874688591409	1.81880755891090	0.43799085902189
61	39	51.8304693207109	0.0847434072077441	-4.66011567788266	-1.31980013724254
62	49	51.5694135624134	0.0805485381092885	-0.976452219488245	-0.257688832278507
63	58	51.4574552413979	0.077901625644829	7.41308611130829	-0.141163896816139
64	47	51.1239732030668	0.0718433881375621	-2.28203160692184	-0.299163295924578
65	42	50.2367277875602	0.0573621991680391	-3.9531025424518	-0.696230570898364
66	62	50.3680812137623	0.0584785151567812	11.3008276782143	0.0538204409745376
67	39	50.1104978807176	0.0537837861268707	-9.69046163350035	-0.23079008958385
68	40	51.0556766264015	0.066694662416012	-15.0810395854609	0.653973747021786
69	72	53.1372293709243	0.0948982810964554	9.71202050467582	1.48589765140273
70	70	54.1771896194629	0.107521723003607	11.5027849270950	0.701013670982159
71	54	54.6367172058021	0.111910649706803	-2.258546771144	0.262945414533897
72	65	55.5498728742939	0.120927455412735	5.72160125878757	0.603880419807055

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292073520ty2di7sz9l6t1fd/13n8w1292072969.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292073520ty2di7sz9l6t1fd/13n8w1292072969.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292073520ty2di7sz9l6t1fd/2ew7z1292072969.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292073520ty2di7sz9l6t1fd/2ew7z1292072969.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292073520ty2di7sz9l6t1fd/3ew7z1292072969.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292073520ty2di7sz9l6t1fd/3ew7z1292072969.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292073520ty2di7sz9l6t1fd/4oooj1292072969.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292073520ty2di7sz9l6t1fd/4oooj1292072969.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292073520ty2di7sz9l6t1fd/5oooj1292072969.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292073520ty2di7sz9l6t1fd/5oooj1292072969.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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