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STSM

*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: Wed, 29 Dec 2010 17:18:30 +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/29/t1293642989vrpbw45y3kpkgvc.htm/, Retrieved Wed, 29 Dec 2010 18:16:29 +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/29/t1293642989vrpbw45y3kpkgvc.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 «
347553 353245 347966 364343 341713 361162 354400 345183 341807 348712 349011 416259 360289 367557 364611 378688 351763 377765 373212 365819 364686 363333 362536 428803 375361 377205 381266 390516 366117 393928 387784 385656 385320 389053 390595 462440 402532 409070 421329 428841 404864 432633 416886 414893 417914 417518 423137 506710 436264 443712 452849 453009 437876 460041 450426 447873 444955 452043 480877 546696 483668 489627 490007 496590 480846 497677 492795 488495 486769 494455 498054 558648 506424 512528 512866 529324 508431 523026 521355 514103 513885 522150 527384 654047 543115 543200 571201 568892 537223 553186 550954 543433 557195 565522 571691 633972 575265 572364 586744 600389 578540 610778 596577 590558 597294 602384 614190 690042 639497 649304 678762 691885 667973 682032 672651 671865 671463 675917 680952 754718 694413 699390 710573 714217 702996 712370 708445 707083 etc...
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
1347553347553000
2353245351007.436874439131.6725235426322237.555177009340.55953422886651
3347966350082.10609450060.776678347935-2116.10413493129-0.161748021550647
4364343356784.899274472468.6731532802657558.084311957071.25233743414853
5341713351071.004616159141.090575374521-9357.98771836614-1.23661359174482
6361162354485.995379309.4001815722816675.995595585030.654392715677611
7354400355233.854193002333.528202989424-833.8553897365450.0868365688915405
8345183350520.02764137427.9247317530405-5337.01402549934-0.991039106040417
9341807345064.194555605-333.81495424021-3257.1799195774-1.06882412278209
10348712345369.893593164-288.6812315880573342.104715304080.123882273183855
11349011347063.531847741-141.3326280693451947.462947908650.382033043367552
12416259381271.3347160042516.9763994558334987.57566420426.59094037468453
13360289381723.3640827622389.37392815604-21434.3587511623-0.414413611917631
14367557375644.8766726981750.10396295902-8087.85493859488-1.65341484332896
15364611371726.7858135431259.37416995726-7115.7728070868-1.01434123311235
16378688369724.609450199967.3588457843888963.3979646475-0.587254725606943
17351763366221.37637948569.586435872931-14458.3658100647-0.830737153005935
18377765367340.004763132617.83515622839510424.99391481980.103123027700339
19373212368668.898129683679.9552989635274543.100157066580.133303439859693
20365819368377.865508639595.01610800713-2558.86317609789-0.181449712484553
21364686368680.365550033569.30532726247-3994.36484892523-0.0545548437572989
22363333368005.518891591459.509739113201-4672.51591552167-0.231645102667168
23362536372817.462130002843.603811570005-10281.47251320250.808669251878138
24428803382858.5882766251651.4918218481545944.38981137271.71076270172352
25375361389128.7949649402055.92440105793-13767.80605417850.869207895462496
26377205387254.9901543181706.9098352886-10049.9807160574-0.737549285810525
27381266386744.9728074731507.05497654176-5478.96761414195-0.406977610404488
28390516383618.0971233411086.926055775476897.91358848142-0.844798140770417
29366117382124.859049278853.016434663964-16007.8530311752-0.475032927361531
30393928382768.12738338834.03355667691711159.8731112356-0.0389403585179087
31387784382937.891638651774.0194981129834846.10993240706-0.123441485587687
32385656385547.071004563939.58690322753108.9246638444020.340127687337756
33385320388488.8555294431120.01974235903-3168.860241193960.370013773556048
34389053394129.4321657221526.90739155832-5076.442776549190.833688469354853
35390595401756.2163845482075.15459216825-11161.23068529121.12472956523776
36462440411694.3782227432781.1790584195250745.60330269591.45475697469384
37402532415311.6371961492856.31764494724-12779.63917055620.155474461923205
38409070417594.4246088862804.63055234518-8524.42325454598-0.106507440979362
39421329421835.0064188202934.31776643464-506.0097800255460.264472004870887
40428841422867.2750610182762.488318779255973.72936025514-0.348734131753353
41404864422842.1095917612510.77593719928-17978.1030990989-0.512778438658168
42432633423001.3113567152298.481837783329631.6941520759-0.434785596806747
43416886419333.5537269031759.85266933851-2447.5397101291-1.10498459908139
44414893418212.3773741791499.80384414580-3319.37061373647-0.53262819047511
45417914421879.9207453111695.35635628621-3965.925816352780.399580176606595
46417518425857.2619940531901.03871984206-8339.267318512340.419819265438604
47423137433351.0517300622404.72237171632-10214.06477431121.02935647505121
48506710446490.2597615823371.1950561781760219.71514243121.98130319849148
49436264451165.5765192133488.67118126648-14901.57957752070.241357778440593
50443712453766.3862732863408.62195946843-10054.384191458-0.164176568445523
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58452043460250.3956824922243.11355199202-8207.406608364060.862984215651237
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60546696487908.4740545364189.282397097458787.51591698690.792710319769494
61483668495011.1156317734451.66293364552-11343.12243736270.537783395073045
62489627498488.0038790494363.84054289094-8861.00160219929-0.179842461928951
63490007495242.2867397113678.20435231912-5235.26900503834-1.40089764199887
64496590494696.3642719153297.712416435651893.64555184157-0.776446984994298
65480846494738.7659975223004.58574402646-13892.7584269483-0.598678453708193
66497677492327.640122642516.957917554765349.37249358041-0.997718213064352
67492795493567.6042935972401.95693070914-772.601314581112-0.235504520042625
68488495495512.8282409022360.81806885766-7017.82717565489-0.0841873278019302
69486769499924.8702053362545.56766010784-13155.87498233370.377546468209296
70494455507968.9563860213040.68003946129-13513.96917375281.01102067592377
71498054506620.1323645852645.53669515063-8566.12215725576-0.807306996218308
72558648504498.0526291352216.3896464553254149.9584696246-0.877899518532277
73506424510086.6801964792519.97969754488-3662.688055862510.621538644049421
74512528514862.0452543112723.06324045651-2334.050510561970.415571643642118
75512866516570.0616326732631.66253600812-3704.05927001754-0.186800968734912
76529324521479.7577290122836.752342520407844.236975127140.418843704923221
77508431522210.1557746972647.15872366640-13779.1508783897-0.387366305064880
78523026520815.3345019952283.358529670082210.67490264595-0.744076119071165
79521355522249.3867260912206.90783213922-894.384748232047-0.156454185572624
80514103523705.7850191592139.34187311179-9602.78327149214-0.138219149415433
81513885527140.7268352342255.97989381587-13255.72984951050.238396332232445
82522150531096.776451632409.00851551062-8946.780403468020.312614128904064
83527384534131.5797789832465.32903351658-6747.581233506260.115087940914494
84654047567854.8281905155278.3486727652386192.09910940915.75268358813109
85543115566102.488673994645.57756479518-22987.4723105654-1.29465729464029
86543200557193.3019620883425.49672007205-13993.2704142446-2.49565414098087
87571201565414.6614447763857.183008114315786.327401045730.882377059584996
88568892565190.5877372563489.859549909093701.42174796141-0.750481931023478
89537223558387.5376748172563.59006991194-21164.5137553333-1.89289498304378
90553186554291.0007522231964.26154970950-1104.98526667584-1.22551776446521
91550954553205.4763381011689.80138037205-2251.46924318083-0.561428873866394
92543433554392.7479961251644.57348866365-10959.7468270609-0.0924985783870748
93557195564390.7897090442396.39251752342-7195.809132007491.53678587172319
94565522574981.2793121193133.80718638288-9459.29835423631.50684283369963
95571691586483.6167985453886.84579596006-14792.63624442481.53900883695329
96633972568319.8450860011902.6957989425965652.2061724779-4.05691855975876
97575265576766.1757861582491.52997829859-1501.191003748171.20432707851273
98572364584283.9021380332943.84533967648-11919.91382695920.92498287380284
99586744582643.1898713032531.270358773654100.82078626798-0.843366730704952
100600389586383.947939782640.1082539975014005.04924978080.222420729797276
101578540592312.045465882935.96121880545-13772.05310548700.604673010170403
102610778603162.7758827423648.112845510427615.205721900531.45603233927220
103596577605286.4138010913510.93947097214-8709.41025678324-0.280523301022503
104590558608118.0362876213449.81106522716-17560.0347082253-0.124996533641917
105597294610378.6656383183342.8017923928-13084.6628743310-0.218746892700869
106602384612711.6593808573251.93780481107-10327.6570345519-0.185704341514069
107614190617113.3876523733355.39157281577-2923.390323761970.211451732048875
108690042624564.9815011793723.9443738176665477.0089797410.75349493709471
109639497635828.0132338444402.285768208353668.969241825811.38710258640607
110649304650447.7344366085321.65066095213-1143.758187196021.87982729330047
111678762667156.1755389836346.2406287265311605.79799826652.09448318921045
112691885678834.5603926436826.0133074023813050.42721870830.980595824547383
113667973686267.2271389496880.59596414225-18294.22854836410.11156649980341
114682032683636.4275167176024.83823120868-1604.40541555163-1.74951983074410
115672651683739.1183157095492.00141148216-11088.1045520274-1.08949274578217
116671865688020.3808357035383.06373998364-16155.3780217636-0.222733858567334
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118675917690680.6698564424688.96397914533-14763.6620435481-0.618443227160117
119680952691681.66864614357.15335470859-10729.6600782467-0.678237349552907
120754718695728.8760561054329.267532547458989.1246640482-0.0570088078324176
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122699390705811.4462133584460.37905113447-6421.450456880240.335907175260585
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125702996710293.2814402233772.12746886163-7297.286819273580.425821221902316
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127708445716760.9411765133686.46358369133-8315.942177889990.079271545821299
128707083720291.1837474543672.40821968473-13208.1833844491-0.0287356689409133
129700632719976.5489774283313.68847718782-19344.5397137844-0.733312997130711
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188759270768776.526067796697.15361093218-9506.530505043180.351294230281712
189754929767135.932980096486.839557048498-12206.9275495123-0.429935860592744
190766116771307.301127152818.31583238497-5191.309686238780.677618283937201
191765945773498.541807802941.829973169605-7553.544997067750.252492931134411
192814783771614.663524149687.61748756472243168.3430399135-0.519674921813813
193768494771170.810944834585.82569574346-2676.8083164356-0.208089414605631
194769222770503.458178788473.084405238156-1281.45526765426-0.230473147999869
195768977769128.97274769306.869054457527-151.968455801332-0.33978671919552
196783341770925.366949597440.87308747382912415.62959027110.273937220141106
197764061772311.66648055525.927591214085-8250.668676746540.173872278168941
198767394771768.665477778429.762381668502-4374.66299468296-0.196586084132458
199763910771319.148286637350.658760321509-7409.14624408246-0.161708544716737
200761677771127.3392761301.856086205318-9450.3380159522-0.0997654913262386
201759173772332.301150717383.103295759902-13159.30324861810.166090055043810
202762486770490.511734531182.942634652472-8004.50656616415-0.409178008696945
203762690769217.2324840851.9349242150171-6527.22910132278-0.267812142723966
204809542767220.070286561-132.41018737345842321.9344734348-0.376848122428252
205769041768536.355629253-2.07962520723051504.6410054548240.266429554543579
206770889770236.706037053151.078007970507652.2900082285650.313094115163838
207773527772669.150546422356.319211790342857.844154009320.419565380559709
208789890775592.266124485587.23880332021514297.72791292070.472057199338346
209768325776369.86253271604.364148341443-8044.862974904030.0350084618809219
210772712776748.25626164584.03493764147-4036.25573671675-0.0415580547300015
211772944778576.857246598696.001132803494-5632.860137695510.228887825880919
212769637779298.298313711698.289813088498-9661.29837280730.00467865504724287
213768546779995.091223133698.155145498141-11449.0912196562-0.000275294865688443
214775013781242.709493272747.587090036157-6229.710769658310.101051265097758
215776635782312.678083123776.589831539423-5677.678832004630.0592888434732338
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293642989vrpbw45y3kpkgvc/1wuj31293643103.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293642989vrpbw45y3kpkgvc/1wuj31293643103.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293642989vrpbw45y3kpkgvc/27l0o1293643103.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293642989vrpbw45y3kpkgvc/27l0o1293643103.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293642989vrpbw45y3kpkgvc/3ichr1293643103.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293642989vrpbw45y3kpkgvc/3ichr1293643103.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293642989vrpbw45y3kpkgvc/4ichr1293643103.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293642989vrpbw45y3kpkgvc/4ichr1293643103.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293642989vrpbw45y3kpkgvc/5ichr1293643103.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293642989vrpbw45y3kpkgvc/5ichr1293643103.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')
 




Copyright

Creative Commons License

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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