Kalman filtering with truncated normal state variables for Bayesian estimation of macroeconomic models
A 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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- Chib, Siddhartha & Greenberg, Edward, 1996.
"Markov Chain Monte Carlo Simulation Methods in Econometrics,"
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- repec:cup:etheor:v:12:y:1996:i:3:p:409-31 is not listed on IDEAS
- Horowitz, Joel L, 1992. "A Smoothed Maximum Score Estimator for the Binary Response Model," Econometrica, Econometric Society, vol. 60(3), pages 505-531, May. Full references (including those not matched with items on IDEAS)
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