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ws 9 stru

*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: Sun, 29 Nov 2009 06:44:20 -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/Nov/29/t1259502295286re7ykibky86x.htm/, Retrieved Sun, 29 Nov 2009 14:45:01 +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/Nov/29/t1259502295286re7ykibky86x.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 «

103,63 103,64 103,66 103,77 103,88 103,91 103,91 103,92 104,05 104,23 104,30 104,31 104,31 104,34 104,55 104,65 104,73 104,75 104,75 104,76 104,94 105,29 105,38 105,43 105,43 105,42 105,52 105,69 105,72 105,74 105,74 105,74 105,95 106,17 106,34 106,37 106,37 106,36 106,44 106,29 106,23 106,23 106,23 106,23 106,34 106,44 106,44 106,48 106,50 106,57 106,40 106,37 106,25 106,21 106,21 106,24 106,19 106,08 106,13 106,09

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	103.63	103.63	0	0	0
2	103.64	103.639487071081	0.000648910488208842	0.000512928919251249	0.0696065340614257
3	103.66	103.659452606885	0.00215282818789385	0.000547393115085597	0.228816834469941
4	103.77	103.769281152334	0.0137307162788176	0.000718847665878252	1.25458131126306
5	103.88	103.879148055760	0.0262805008831273	0.00085194424015538	1.10537017977190
6	103.91	103.909143671471	0.0268288266841244	0.000856328528564579	0.0422866014374643
7	103.91	103.90917024696	0.0225458493295439	0.00082975304009546	-0.302820103381684
8	103.92	103.919180584254	0.0204358372168553	0.000819415746001782	-0.140884834159692
9	104.05	104.049106020595	0.0395058700814675	0.000893979405276462	1.22601287192371
10	104.23	104.229027430388	0.064519210832272	0.000972569612124645	1.56831943482443
11	104.3	104.299024918589	0.0655097085829571	0.000975081410528457	0.0610814551868003
12	104.31	104.309045714998	0.0553781356693007	0.00095428500233726	-0.617957149978106
13	104.31	104.333556955022	0.0500146811198818	-0.0235569550220304	-0.402404334177696
14	104.34	104.338870570821	0.0419403926630187	0.00112942917926398	-0.42032620218643
15	104.55	104.548963141537	0.0730644551515239	0.00103685846309704	1.86784281917169
16	104.65	104.648975226234	0.0780609571006235	0.00102477376643753	0.299310728749698
17	104.73	104.72897593464	0.0784210348072705	0.00102406535992498	0.0215442088356734
18	104.75	104.748958557484	0.0675645706706305	0.00104144251627414	-0.649048239773017
19	104.75	104.748942197010	0.0550030056910709	0.00105780299042050	-0.750590883487078
20	104.76	104.758933326375	0.0466334496302088	0.00106667362483166	-0.499931189073008
21	104.94	104.938954724502	0.0714418079252465	0.00104527549776337	1.48151251059090
22	105.29	105.288991103171	0.123265314780059	0.00100889682866174	3.09433629892012
23	105.38	105.378987567145	0.117076006931446	0.0010124328549743	-0.369520630496941
24	105.43	105.428981763805	0.104595165365442	0.00101823619525671	-0.7450943267097
25	105.43	105.456662168138	0.0904927124064977	-0.0266621681380912	-0.92733967289765
26	105.42	105.419428309393	0.0672061618174816	0.000571690606598565	-1.28507598990847
27	105.52	105.51944302157	0.0733139288280936	0.000556978429990895	0.364241115792154
28	105.69	105.689478327500	0.091316391971402	0.000521672500332262	1.07392673202998
29	105.72	105.719460105367	0.0799017147403285	0.00053989463314945	-0.681077595004771
30	105.74	105.739445617214	0.0687517714910469	0.000554382785935683	-0.665373333127532
31	105.74	105.739432083502	0.0559555496612175	0.000567916497887755	-0.763684705238315
32	105.74	105.739423118697	0.0455415515773537	0.00057688130312154	-0.621549981318638
33	105.95	105.949444563502	0.0761481398715597	0.000555436498030725	1.82679941144869
34	106.17	106.169459830441	0.102919096149951	0.0005401695586487	1.59790640483198
35	106.34	106.339465624829	0.115402713987890	0.000534375170899903	0.745135995494691
36	106.37	106.369459620652	0.099509610974413	0.000540379348171504	-0.948656153894987
37	106.37	106.386774852444	0.0843515476891247	-0.0167748524439978	-0.965145018751399
38	106.36	106.359667329378	0.063928410541856	0.000332670621857405	-1.15357188907121
39	106.44	106.439672759715	0.0669212106096375	0.000327240285006276	0.178510453829056
40	106.29	106.289613075859	0.0265359572112102	0.000386924141393101	-2.40945134794615
41	106.23	106.229593698370	0.0104275588418632	0.00040630163009411	-0.961214208414722
42	106.23	106.229591797994	0.00848669384763967	0.000408202005607816	-0.115827359427076
43	106.23	106.229590539192	0.00690718277773937	0.000409460808479994	-0.0942693049984303
44	106.23	106.229589705344	0.00562170024160942	0.000410294656446816	-0.0767246436068602
45	106.34	106.339599961054	0.0250467851818298	0.000400038946161406	1.15943279787802
46	106.44	106.439605955061	0.0389955154595105	0.000394044938584992	0.83258115499392
47	106.44	106.439603416928	0.0317385746477565	0.000396583071970762	-0.433163251940526
48	106.48	106.479603854579	0.0332759866165655	0.000396145420547206	0.0917682266489144
49	106.5	106.505118072273	0.0318411940036946	-0.00511807227287477	-0.0899165989108308
50	106.57	106.569260897074	0.037778951324272	0.000739102925939693	0.339590706536093
51	106.4	106.399205020961	-0.000907727381292717	0.000794979039216034	-2.30788000980989
52	106.37	106.369198654227	-0.00632348611387767	0.000801345772593357	-0.323145614471233
53	106.25	106.249178407179	-0.0274827513435613	0.000821592821104137	-1.26269159907276
54	106.21	106.209176592668	-0.0298124748644497	0.000823407332374508	-0.139039868929669
55	106.21	106.209180110003	-0.0242640361638870	0.000819889996553154	0.331154835263960
56	106.24	106.239185320707	-0.0141652357373642	0.000814679293100075	0.602763417353139
57	106.19	106.189182520052	-0.0208341059855329	0.000817479947739854	-0.39805261908213
58	106.08	106.079176848185	-0.0374276775538321	0.000823151814591872	-0.990456617980347
59	106.13	106.129181374549	-0.0211577482227694	0.000818625450808103	0.971149592792165
60	106.09	106.089180580573	-0.0246641899518216	0.000819419426550609	-0.209300528484211

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/29/t1259502295286re7ykibky86x/11au41259502258.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/29/t1259502295286re7ykibky86x/11au41259502258.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/29/t1259502295286re7ykibky86x/290bd1259502258.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/29/t1259502295286re7ykibky86x/290bd1259502258.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/29/t1259502295286re7ykibky86x/39n3o1259502258.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/29/t1259502295286re7ykibky86x/39n3o1259502258.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/29/t1259502295286re7ykibky86x/4v7so1259502258.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/29/t1259502295286re7ykibky86x/4v7so1259502258.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/29/t1259502295286re7ykibky86x/5vrf61259502258.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/29/t1259502295286re7ykibky86x/5vrf61259502258.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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