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*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 12:54:02 -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/t12599565296fkpvsmdnmxloc7.htm/, Retrieved Fri, 04 Dec 2009 20:55:36 +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/t12599565296fkpvsmdnmxloc7.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 «

6802.96 7132.68 7073.29 7264.5 7105.33 7218.71 7225.72 7354.25 7745.46 8070.26 8366.33 8667.51 8854.34 9218.1 9332.9 9358.31 9248.66 9401.2 9652.04 9957.38 10110.63 10169.26 10343.78 10750.21 11337.5 11786.96 12083.04 12007.74 11745.93 11051.51 11445.9 11924.88 12247.63 12690.91 12910.7 13202.12 13654.67 13862.82 13523.93 14211.17 14510.35 14289.23 14111.82 13086.59 13351.54 13747.69 12855.61 12926.93 12121.95 11731.65 11639.51 12163.78 12029.53 11234.18 9852.13 9709.04 9332.75 7108.6 6691.49 6143.05

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	6802.96	6802.96	0	0	0
2	7132.68	7115.77563241975	21.5403700970263	16.9043675802509	0.44932039843554
3	7073.29	7056.5241675713	15.0618853433054	16.7658324286956	-0.187157310312754
4	7264.5	7247.46592070621	34.5037055181225	17.0340792937899	0.400585664535751
5	7105.33	7088.55141182042	8.60186963447032	16.7785881795752	-0.434618377625661
6	7218.71	7201.81405160398	24.4157005239324	16.8959483960158	0.232785747773486
7	7225.72	7208.84036689836	21.5761459462609	16.879633101641	-0.0383911627018807
8	7354.25	7337.28731741045	39.9300347164757	16.9626825895496	0.234708154437045
9	7745.46	7728.2729314206	102.238363758226	17.1870685793982	0.768187935665514
10	8070.26	8052.95653538964	142.580095167837	17.3034646103613	0.485552554967699
11	8366.33	8348.96102145352	170.800360654781	17.3689785464834	0.334330505643150
12	8667.51	8650.09569932421	194.996648391566	17.4143006757929	0.283691475180607
13	8854.34	8986.2839086275	219.973027741281	-131.943908627501	0.359635051844466
14	9218.1	9206.1084578784	219.945816682235	11.9915421216060	-0.000272798080751261
15	9332.9	9320.84782803717	200.163002948292	12.0521719628253	-0.228425201984916
16	9358.31	9346.17604535292	167.236354640652	12.1339546470779	-0.379580573024516
17	9248.66	9236.42090370243	115.016374376586	12.2390962975663	-0.601357869219266
18	9401.2	9388.97246353382	122.097643836741	12.2275364661838	0.0814900739143552
19	9652.04	9639.84463716831	146.403233070117	12.1953628316859	0.279576471329508
20	9957.38	9945.21685584504	176.417407958718	12.1631441549546	0.345135700576902
21	10110.63	10098.4630465251	172.041616032482	12.1669534749371	-0.0503076431132427
22	10169.26	10157.0779212804	150.618258985056	12.1820787195792	-0.246268095762417
23	10343.78	10331.6005067622	155.133624593544	12.1794932377652	0.0519010639206832
24	10750.21	10738.0525543023	202.609346611612	12.1574456977001	0.54567039270012
25	11337.5	11382.6139947891	284.885344435431	-45.1139947891237	1.04140885574006
26	11786.96	11781.2103252723	305.924230335852	5.74967472768438	0.223580283598754
27	12083.04	12077.2857953009	304.062622388883	5.7542046990833	-0.0213735503522545
28	12007.74	12001.8441075777	232.344077608046	5.89589242226916	-0.823691780188378
29	11745.93	11739.8844309555	138.942546862464	6.04556904451604	-1.07295750145121
30	11051.51	11045.2597145929	-18.5534296086276	6.25028540714393	-1.80950861620265
31	11445.9	11439.7319852051	59.4815146922311	6.16801479493144	0.896647080864921
32	11924.88	11918.7797689900	138.750751010964	6.10023101003502	0.91088637295186
33	12247.63	12241.5538822784	173.518322073997	6.076117721643	0.399532138145045
34	12690.91	12684.8625549436	224.489896123933	6.04744505639516	0.585756541844059
35	12910.7	12904.6521497850	223.601863058376	6.047850215031	-0.0102053042766483
36	13202.12	13196.0768914709	236.415788262714	6.04310852908574	0.147259758283031
37	13654.67	13687.6172392769	284.183214630968	-32.9472392769434	0.585523185737997
38	13862.82	13861.0096038404	263.577959410468	1.81039615962577	-0.224104445005227
39	13523.93	13521.9109588416	149.667940807775	2.01904115836383	-1.30809171207160
40	14211.17	14209.3019416875	251.283889260531	1.86805831250890	1.16722051432893
41	14510.35	14508.4928513925	260.33616598997	1.85714860747044	0.103997847996357
42	14289.23	14287.2839104520	169.351155986596	1.94608954802054	-1.04540852861792
43	14111.82	14109.8219569976	103.825062483787	1.99804300243684	-0.75294479695948
44	13086.59	13084.4547606289	-109.518582329184	2.13523937110385	-2.45160251972505
45	13351.54	13349.4416659797	-38.7619240925203	2.09833402034564	0.81311467280819
46	13747.69	13745.6264294931	43.4140599160744	2.06357050690771	0.944362420580778
47	12855.61	12853.4857819718	-133.3439705084	2.12421802815459	-2.03132331885016
48	12926.93	12924.8165432518	-94.6738157006432	2.11345674821098	0.444405919288936
49	12121.95	12206.1650627209	-211.779732513143	-84.215062720917	-1.41287088696063
50	11731.65	11726.2662482176	-261.808808565566	5.3837517823917	-0.550929871409648
51	11639.51	11634.1729791624	-229.733647282277	5.33702083757955	0.368394716670721
52	12163.78	12158.6113803851	-87.2193582425677	5.16861961493199	1.63717218769508
53	12029.53	12024.3528615403	-96.1075213887502	5.17713845970975	-0.102119361652775
54	11234.18	11228.9001394462	-228.244232118209	5.27986055382809	-1.51830533203426
55	9852.13	9846.71266864719	-446.268627516585	5.41733135281309	-2.50534035565272
56	9709.04	9703.65196554225	-388.981685709453	5.38803445775575	0.65831597897221
57	9332.75	9327.36296023724	-386.583591275167	5.38703976276094	0.0275585458283186
58	7108.6	7103.09615486353	-733.786521822718	5.50384513647265	-3.99007266202074
59	6691.49	6686.00248109009	-673.951997646962	5.4875189099058	0.687628923348309
60	6143.05	6137.56772923764	-650.237258773505	5.48227076236093	0.272535980107564

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599565296fkpvsmdnmxloc7/1bf481259956440.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599565296fkpvsmdnmxloc7/1bf481259956440.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599565296fkpvsmdnmxloc7/22kyn1259956440.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599565296fkpvsmdnmxloc7/22kyn1259956440.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599565296fkpvsmdnmxloc7/3gcj21259956440.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599565296fkpvsmdnmxloc7/3gcj21259956440.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599565296fkpvsmdnmxloc7/4ekd81259956440.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599565296fkpvsmdnmxloc7/4ekd81259956440.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599565296fkpvsmdnmxloc7/5byp71259956440.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599565296fkpvsmdnmxloc7/5byp71259956440.ps (open in new window)

Parameters (Session):

par1 = Aandelenkoers ; par2 = belgostat ; par3 = euronext brussel ;

Parameters (R input):

par1 = 12 ; par2 = belgostat ; par3 = euronext brussel ;

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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