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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F05%3A03108705%21RIV08-GA0-21230___
rdf:type
skos:Concept n14:Vysledek
dcterms:description
Implementing direct Bayesian inference using Monte Carlo methods (Bootstrap filter) we identified Czech macro-economic model based on the work (Clarida et al. 1999). The main concern was to identify model parameters for the prediction of model behavior, which is essential for taking proper economical decisions. Simultaneous estimation of model parameters led to non-linear model. Commonly used Extended Kalman filter failed in this case, therefore we used bootstrap filter, which can handle non-linear and/or non-gaussian systems. The posterior probability density function of states and parameters were obtained from the prior probabilities (represented as a large set of samples), which were updated from measured data according to Bayesian inference. Given only limited data set (quarterly data from 1994) at disposal we incorporated smoothing (backward filtration) into bootstrap filter to maximize the use of information from the data. Použití metody Monte Carlo na odhad parametrů makroekonomického modelu z dat. Vzužita metoda odhadu z celého souboru dat (tzv smoothing). Implementing direct Bayesian inference using Monte Carlo methods (Bootstrap filter) we identified Czech macro-economic model based on the work (Clarida et al. 1999). The main concern was to identify model parameters for the prediction of model behavior, which is essential for taking proper economical decisions. Simultaneous estimation of model parameters led to non-linear model. Commonly used Extended Kalman filter failed in this case, therefore we used bootstrap filter, which can handle non-linear and/or non-gaussian systems. The posterior probability density function of states and parameters were obtained from the prior probabilities (represented as a large set of samples), which were updated from measured data according to Bayesian inference. Given only limited data set (quarterly data from 1994) at disposal we incorporated smoothing (backward filtration) into bootstrap filter to maximize the use of information from the data.
dcterms:title
Bootstrapový filtr požitý pro estimaci parametrů makroekonomického modelu. Bootstrap Filtering for Czech Macro-economic Model Estimation Bootstrap Filtering for Czech Macro-economic Model Estimation
skos:prefLabel
Bootstrap Filtering for Czech Macro-economic Model Estimation Bootstrap Filtering for Czech Macro-economic Model Estimation Bootstrapový filtr požitý pro estimaci parametrů makroekonomického modelu.
skos:notation
RIV/68407700:21230/05:03108705!RIV08-GA0-21230___
n3:strany
Nečíslováno
n3:aktivita
n4:P
n3:aktivity
P(GA402/05/2172)
n3:dodaniDat
n8:2008
n3:domaciTvurceVysledku
n5:1558064 n5:3021580 n5:5325773
n3:druhVysledku
n20:D
n3:duvernostUdaju
n11:S
n3:entitaPredkladatele
n18:predkladatel
n3:idSjednocenehoVysledku
514102
n3:idVysledku
RIV/68407700:21230/05:03108705
n3:jazykVysledku
n15:eng
n3:klicovaSlova
Bayesian state estimation; Bootstrap filter; Economic modeling; Monte Carlo methods; Smoothing
n3:klicoveSlovo
n7:Smoothing n7:Monte%20Carlo%20methods n7:Bayesian%20state%20estimation n7:Economic%20modeling n7:Bootstrap%20filter
n3:kontrolniKodProRIV
[1F1CED483FDF]
n3:mistoKonaniAkce
Štrbské Pleso
n3:mistoVydani
Bratislava
n3:nazevZdroje
15th International Conference on Process Control 05
n3:obor
n6:BC
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n21:GA402%2F05%2F2172
n3:rokUplatneniVysledku
n8:2005
n3:tvurceVysledku
Trnka, Pavel Havlena, Vladimír Štecha, Jan
n3:typAkce
n13:WRD
n3:zahajeniAkce
2005-06-07+02:00
s:numberOfPages
5
n16:hasPublisher
Slovenská technická univerzita v Bratislave
n17:isbn
80-227-2235-9
n12:organizacniJednotka
21230