Bayesian Identification of Macroeconomic Model
Macroeconomic model builders try to construct models for the Czech transition economy. They face constrains of data scarcity in terms of short time series of data. Parameters are non-constancy and underspecificated. Since model builder assumptions of identification with respect to data, homogenity and parameter constancy he cannot builds the model. A realist alternative is to choose Bayesian approach to parameters estimation of such a model. The parameter estimation subjects to a learning process and preliminary knowledge about a parameter development is employed. Original extended Kalman filter with backward filter runs is used for simultaneous parameter tracking. The filter is described in the paper. The results of the parameter estimation for the macroeconomic model of the Czech economy are based on real macroeconomic data obtained from the Czech National Bank and the Czech Statistical Office.
Volume (Year): 2 (1995)
Issue (Month): 3 ()
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