. . "Ne\u010D\u00EDslov\u00E1no" . . . . . . "Trnka, Pavel" . "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." . "Bootstrap Filtering for Czech Macro-economic Model Estimation"@en . . "Bootstrap Filtering for Czech Macro-economic Model Estimation" . "RIV/68407700:21230/05:03108705" . "Bootstrapov\u00FD filtr po\u017Eit\u00FD pro estimaci parametr\u016F makroekonomick\u00E9ho modelu."@cs . "21230" . . . . "\u0160trbsk\u00E9 Pleso" . . "Bootstrap Filtering for Czech Macro-economic Model Estimation" . "Bratislava" . "P(GA402/05/2172)" . . . "RIV/68407700:21230/05:03108705!RIV08-GA0-21230___" . . . "Havlena, Vladim\u00EDr" . "Slovensk\u00E1 technick\u00E1 univerzita v Bratislave" . "Bootstrap Filtering for Czech Macro-economic Model Estimation"@en . "[1F1CED483FDF]" . "Bayesian state estimation; Bootstrap filter; Economic modeling; Monte Carlo methods; Smoothing"@en . "Pou\u017Eit\u00ED metody Monte Carlo na odhad parametr\u016F makroekonomick\u00E9ho modelu z dat. Vzu\u017Eita metoda odhadu z cel\u00E9ho souboru dat (tzv smoothing)."@cs . "80-227-2235-9" . . "514102" . . "\u0160techa, Jan" . "3"^^ . "5"^^ . "2005-06-07+02:00"^^ . . "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."@en . "Bootstrapov\u00FD filtr po\u017Eit\u00FD pro estimaci parametr\u016F makroekonomick\u00E9ho modelu."@cs . "15th International Conference on Process Control 05" . . "3"^^ .