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Kalman filtering with truncated normal state variables for Bayesian estimation of macroeconomic models

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Michael Dueker

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Abstract

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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Paper provided by Federal Reserve Bank of St. Louis in its series Working Papers with number 2005-057.

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Date of creation: 2006
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Handle: RePEc:fip:fedlwp:2005-057

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Keywords: Macroeconomics - Econometric models;

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  1. Kuttner, Kenneth N, 1994. "Estimating Potential Output as a Latent Variable," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 361-68, July.
  2. Chib, Siddhartha & Greenberg, Edward, 1996. "Markov Chain Monte Carlo Simulation Methods in Econometrics," Econometric Theory, Cambridge University Press, vol. 12(03), pages 409-431, August. [Downloadable!]
  3. Horowitz, Joel L, 1992. "A Smoothed Maximum Score Estimator for the Binary Response Model," Econometrica, Econometric Society, vol. 60(3), pages 505-31, May. [Downloadable!] (restricted)
  4. Michael Dueker, 2005. "Dynamic Forecasts of Qualitative Variables: A Qual VAR Model of U.S. Recessions," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 96-104, January. [Downloadable!] (restricted)
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  5. repec:cup:etheor:v:12:y:1996:i:3:p:409-31 is not listed on IDEAS
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