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 InfoPaper provided by Federal Reserve Bank of St. Louis in its series Working Papers with number 2005-057.
Date of creation: 2006
Date of revision:
Other versions of this item:
- Dueker, Michael, 2006. "Kalman filtering with truncated normal state variables for Bayesian estimation of macroeconomic models," Economics Letters, Elsevier, vol. 93(1), pages 58-62, October.
- NEP-ALL-2005-10-04 (All new papers)
- NEP-DCM-2005-10-04 (Discrete Choice Models)
- NEP-DGE-2005-10-04 (Dynamic General Equilibrium)
- NEP-ECM-2005-10-04 (Econometrics)
- NEP-ETS-2005-10-04 (Econometric Time Series)
- NEP-MAC-2005-10-04 (Macroeconomics)
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