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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: Mon, 27 Dec 2010 12:43:27 +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/27/t1293454098amkbd672yoskedk.htm/, Retrieved Mon, 27 Dec 2010 13:48:19 +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/27/t1293454098amkbd672yoskedk.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:

Structural Time Series Model - Gemiddelde bouwprijzen

Dataseries X:

» Textbox « » Textfile « » CSV «

26 26 27 28 27 29 27 30 27 30 32 30 32 33 34 32 34 37 37 36 34 38 41 41 44 42 45 45 49 54 52 53 51 55 60 60 63 60 64 65 75 70 72 69 75 74 74 75 79 79 85 78 84 85 85 82 91 90 98 98

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	3 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Structural Time Series Model
t	Observed	Level	Slope	Seasonal	Stand. Residuals
1	26	26	0	0	0
2	26	26	0	0	0
3	27	26.4885072347855	0.122153734869542	0.133847388049581	0.311760983373203
4	28	27.1342140507663	0.23311824673463	0.228731269700097	0.457903128645816
5	27	27.4402761398104	0.247518398811714	-0.618866011025374	0.107913243035474
6	29	28.1643886484897	0.334228847833608	0.269053410840415	0.406258323165324
7	27	27.8313690825885	0.214297167706663	0.123584922206004	-0.655240986100755
8	30	28.6681306494107	0.328271109943408	0.35405035208128	0.654335034038774
9	27	28.576317321562	0.243146163020616	-0.869867345353114	-0.473927411775527
10	30	29.1337628874529	0.300739692884556	0.394864744714539	0.319730896984165
11	32	30.3282472607551	0.466701979632872	0.289088806993994	0.931954385446869
12	30	30.3661383325944	0.385580902031455	0.30439219579371	-0.449556794087097
13	32	31.5406984939938	0.540958999188249	-0.71493877512563	0.816191289314981
14	33	32.3308096372961	0.588054594922128	0.289099785352401	0.257863813925656
15	34	33.2162678006596	0.644462653593476	0.327045445164039	0.309688871900484
16	32	33.1134457193897	0.501792720619522	0.0333057824407519	-0.776046862174301
17	34	34.008654119934	0.577398204151658	-0.58710785485183	0.403193878082839
18	37	35.3867462918656	0.730093190432383	0.403052074205044	0.826299175426268
19	37	36.316722751826	0.768221457622207	0.380058217828084	0.207164676898101
20	36	36.7045391387188	0.695527605608356	-0.126966403018158	-0.393774402828982
21	34	36.4234344849801	0.509339759241467	-0.978061212475755	-1.00499537425496
22	38	37.1447859549601	0.549826232698546	0.537152043775514	0.218276631897497
23	41	38.7261111165581	0.746739704924741	0.722375168594884	1.06586943848126
24	41	40.051835646218	0.857323465316116	0.0750966064658244	0.598406805604166
25	44	42.4186846862801	1.14480755991479	-0.660442329009606	1.55667184981305
26	42	42.9089628410352	1.01986481633982	0.0690264697580345	-0.673474327413291
27	45	44.0900904893035	1.05063598480811	0.668566466483218	0.166393242126038
28	45	45.1442502646329	1.05130858965528	-0.149538449644073	0.00363819199614165
29	49	47.4067457389236	1.28205233790169	-0.206433014708675	1.24926768508418
30	54	50.639090066362	1.654148468336	0.453092220188854	2.00708245691218
31	52	52.0253793239282	1.60306204629076	0.374314631884652	-0.276200561016786
32	53	53.5330229191274	1.58485775079644	-0.390314716962754	-0.098442644017665
33	51	53.8143587681823	1.33646050540347	-0.878126138597903	-1.3440302049865
34	55	54.8722489045787	1.28333136006269	0.542527272141132	-0.286763605978873
35	60	57.3719448247025	1.51524762661149	0.8163826553382	1.25381310449318
36	60	59.3928468945365	1.61168619852704	-0.147279755863603	0.521405424874104
37	63	62.0592725970851	1.81269194816893	-0.625363947786413	1.08713816958434
38	60	62.3855664351422	1.52927152021036	-0.174545350128675	-1.53043687322043
39	64	63.6492332048664	1.47863911662716	0.745870122729308	-0.273719438715818
40	65	65.1654641505836	1.48580698381513	-0.22145531398507	0.0387486134101313
41	75	69.7927887275753	2.08450328632359	0.5439036258708	3.2372342629851
42	70	71.3544359730276	1.98482304912494	-0.577247751785819	-0.538416634812738
43	72	72.6263342257513	1.84893187819805	0.43327509655402	-0.734582307536485
44	69	72.726341383867	1.51550795325048	-1.12452274634797	-1.80228975328815
45	75	74.2201765174614	1.51137767322435	0.811989190289589	-0.0223296173603243
46	74	75.2492004618061	1.41942939833184	-0.532600122988277	-0.496746875207814
47	74	75.4387940458657	1.18502542128246	0.388011891902151	-1.26703978914813
48	75	76.3956125126443	1.14152392319615	-1.05640100116612	-0.235129753300575
49	79	77.7158753559662	1.17558672014267	1.01888124341681	0.184136082383642
50	79	79.0564953373397	1.20704348825421	-0.30157772975425	0.169965468519296
51	85	81.8250755278947	1.50465498799256	0.856346515579268	1.60863265377257
52	78	81.85355671232	1.22328543212972	-1.66062818134754	-1.52077503364625
53	84	83.030073583634	1.21437265239625	1.03932369656857	-0.0481772494322569
54	85	84.6872449161486	1.2987689610205	-0.34463419906309	0.456044564723472
55	85	85.2537374905804	1.15920974745096	0.833250354993239	-0.754314765429988
56	82	85.4024490773193	0.966612620276884	-1.90192010580532	-1.04094519272034
57	91	87.6412294080345	1.20905140629166	1.47118761062908	1.31041896721792
58	90	89.3725787659999	1.30859587625757	-0.147833515032271	0.53793054646883
59	98	92.9981022820614	1.75015240691565	1.56330703668235	2.3865528577459
60	98	96.7594566280031	2.13346405375339	-1.74511373305005	2.07168574310596

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/27/t1293454098amkbd672yoskedk/1xb0h1293453803.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/27/t1293454098amkbd672yoskedk/1xb0h1293453803.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/27/t1293454098amkbd672yoskedk/2xb0h1293453803.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/27/t1293454098amkbd672yoskedk/2xb0h1293453803.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/27/t1293454098amkbd672yoskedk/3xb0h1293453803.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/27/t1293454098amkbd672yoskedk/3xb0h1293453803.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/27/t1293454098amkbd672yoskedk/4qkzk1293453803.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/27/t1293454098amkbd672yoskedk/4qkzk1293453803.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/27/t1293454098amkbd672yoskedk/50tym1293453803.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/27/t1293454098amkbd672yoskedk/50tym1293453803.ps (open in new window)

Parameters (Session):

par1 = 4 ;

Parameters (R input):

par1 = 4 ;

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

bitmap(file='test1.png')

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

grid()

dev.off()

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='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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