Kalman filtering with truncated normal state variables for Bayesian estimation of macroeconomic models
AbstractA pair of simple modifications-in the forecast error and forecast error variance-to the Kalman filter recursions makes possible the filtering of models in which one or more state variables is truncated normal and latent. Such recursions are broadly applicable to macroeconometric models, such as vector autoregressions and estimated dynamic stochastic general equilibrium models, that have one or more probit-type equation.
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Bibliographic InfoArticle provided by Elsevier in its journal Economics Letters.
Volume (Year): 93 (2006)
Issue (Month): 1 (October)
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Web page: http://www.elsevier.com/locate/ecolet
Other versions of this item:
- Michael Dueker, 2006. "Kalman filtering with truncated normal state variables for Bayesian estimation of macroeconomic models," Working Papers 2005-057, Federal Reserve Bank of St. Louis.
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