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structurele tijdreeksanalyse

*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: Fri, 04 Dec 2009 12:23:56 -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/04/t1259954787vmh2fr4stafanuh.htm/, Retrieved Fri, 04 Dec 2009 20:26:34 +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/04/t1259954787vmh2fr4stafanuh.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 «

107.11 107.57 107.81 108.75 109.43 109.62 109.54 109.53 109.84 109.67 109.79 109.56 110.22 110.40 110.69 110.72 110.89 110.58 110.94 110.91 111.22 111.09 111.00 111.06 111.55 112.32 112.64 112.36 112.04 112.37 112.59 112.89 113.22 112.85 113.06 112.99 113.32 113.74 113.91 114.52 114.96 114.91 115.30 115.44 115.52 116.08 115.94 115.56 115.88 116.66 117.41 117.68 117.85 118.21 118.92 119.03 119.17 118.95 118.92 118.90

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	'Gwilym Jenkins' @ 72.249.127.135

Structural Time Series Model
t	Observed	Level	Slope	Seasonal	Stand. Residuals
1	107.11	107.11	0	0	0
2	107.57	107.546132163312	0.0248710415626975	0.0238678366882145	0.80932357825462
3	107.81	107.785356069424	0.02842448512212	0.0246439305758953	0.660158303308612
4	108.75	108.722142242238	0.0491488765966463	0.0278577577617172	2.78846269843622
5	109.43	109.399981820168	0.0672141384954426	0.0300181798316920	1.92390558859739
6	109.62	109.589575695473	0.0714101077081737	0.0304243045274854	0.373391103144814
7	109.54	109.510056810592	0.0654581191638331	0.0299431894082669	-0.459211087677599
8	109.53	109.500286026673	0.0621358155348681	0.0297139733274101	-0.228293953230411
9	109.84	109.809569481716	0.0741128209252116	0.0304305182838847	0.748212577696612
10	109.67	109.640238349600	0.0613719343064332	0.0297616503998263	-0.735388071316504
11	109.79	109.760086649944	0.0646350590294024	0.0299133500556561	0.176293439538470
12	109.56	109.530804235667	0.0473286642117155	0.0291957643325901	-0.884515685621176
13	110.22	110.240559239838	0.0695504960309077	-0.0205592398383125	2.3772269632765
14	110.4	110.396108642431	0.0760214359755692	0.00389135756885429	0.219720051864291
15	110.69	110.685934845870	0.089989104261826	0.00406515412978858	0.640144318683956
16	110.72	110.715980295387	0.0859588438282492	0.00401970461350254	-0.179281923321855
17	110.89	110.885921006497	0.0917442199615598	0.00407899350259487	0.250940159190765
18	110.58	110.576184509721	0.0635120682337714	0.00381549027873475	-1.19865191998523
19	110.94	110.936003950518	0.084714501519575	0.00399604948214656	0.884018034400308
20	110.91	110.906068740568	0.0763887507126039	0.00393125943161672	-0.341840065690101
21	111.22	111.215946495325	0.0935577840398648	0.00405350467457289	0.695799014551623
22	111.09	111.086054790175	0.0769519807033824	0.00394520982473786	-0.665572913361689
23	111	110.996129602582	0.0644385160812172	0.0038703974178266	-0.496866304380876
24	111.06	111.056131441294	0.0641032845879002	0.00386855870591091	-0.0132053727894251
25	111.55	111.508447830662	0.0914111177547482	0.0415521693381841	1.26395208859022
26	112.32	112.313222594141	0.147938276846614	0.00677740585939785	1.93598588472383
27	112.64	112.633200123872	0.161144444006279	0.00679987612787951	0.511574972303186
28	112.36	112.353253302590	0.127148238819144	0.00674669741047049	-1.31139231639904
29	112.04	112.033303036844	0.092570875710737	0.00669696315605846	-1.32904900082609
30	112.37	112.363278676750	0.110984492046492	0.00672132324975234	0.705619178937622
31	112.59	112.583268361405	0.119460013991292	0.00673163859538028	0.32395112996589
32	112.89	112.883252609154	0.133525736145575	0.00674739084569808	0.53644623471777
33	113.22	113.213236804564	0.148860136770374	0.00676319543575702	0.583748530658545
34	112.85	112.843275279748	0.108303035734343	0.0067247202520081	-1.54149694762890
35	113.06	113.053268328800	0.11626244341824	0.00673167119978386	0.302118108131986
36	112.99	112.983280062281	0.101668590466631	0.00671993771869417	-0.553317003168438
37	113.32	113.355048343419	0.122402313356986	-0.0350483434186155	0.852793714384377
38	113.74	113.734037405220	0.142674071186415	0.00596259478007121	0.71485211291891
39	113.91	113.904038088777	0.144820126058072	0.00596191122346658	0.0811671446970093
40	114.52	114.51404873156	0.181373921268729	0.00595126844010006	1.38170618435842
41	114.96	114.954054183443	0.201706497071668	0.00594581655735244	0.768171527454756
42	114.91	114.904049294742	0.181909858498757	0.00595070525752811	-0.747605978788942
43	115.3	115.294053018338	0.198281743219668	0.00594698166244223	0.61804975136406
44	115.44	115.434052057512	0.193694971677420	0.0059479424880309	-0.173100779246586
45	115.52	115.514050330700	0.184744952994161	0.0059496693003318	-0.337678281972651
46	116.08	116.074055581366	0.214291107239876	0.00594441863392370	1.11451134619512
47	115.94	115.934051014424	0.186390573490672	0.00594898557588432	-1.0522401694583
48	115.56	115.554044288484	0.141780357204694	0.00595571151553915	-1.68216200287080
49	115.88	115.921218306884	0.159388179443751	-0.0412183068836902	0.701055604658623
50	116.66	116.651372321110	0.204319201119141	0.00862767888981664	1.61318168627207
51	117.41	117.401403041318	0.247314894565440	0.00859695868229918	1.62062785099945
52	117.68	117.671404217303	0.249102442466250	0.00859578269676911	0.0673727249895382
53	117.85	117.841400439811	0.242868931114299	0.00859956018895737	-0.234926546174502
54	118.21	118.201405592529	0.252099681534979	0.0085944074705657	0.347867105878057
55	118.92	118.911424148525	0.288186994904836	0.0085758514747072	1.35991386955892
56	119.03	119.021417496759	0.274143490997354	0.00858250324107727	-0.52919506096991
57	119.17	119.161412883826	0.263570872844714	0.0085871161735572	-0.398390346991871
58	118.95	118.941397565435	0.225456875072972	0.00860243456487196	-1.43614684198450
59	118.92	118.91139011099	0.205321873750939	0.00860988901002041	-0.758675210934209
60	118.9	118.891384054164	0.187561763821288	0.00861594583578748	-0.669177455035412

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259954787vmh2fr4stafanuh/13k0i1259954634.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259954787vmh2fr4stafanuh/13k0i1259954634.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259954787vmh2fr4stafanuh/2dj2g1259954634.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259954787vmh2fr4stafanuh/2dj2g1259954634.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259954787vmh2fr4stafanuh/37jt31259954634.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259954787vmh2fr4stafanuh/37jt31259954634.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259954787vmh2fr4stafanuh/47x4g1259954634.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259954787vmh2fr4stafanuh/47x4g1259954634.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259954787vmh2fr4stafanuh/5sowl1259954634.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259954787vmh2fr4stafanuh/5sowl1259954634.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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