Kalman filtering with truncated normal state variables for Bayesian estimation of macroeconomic models
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.
Keywords: Macroeconomics - Econometric models;
File(s): File format is application/pdf http://research.stlouisfed.org/wp/2005/2005-057.pdf
Provider: Federal Reserve Bank of St. Louis
Part of Series: Working Papers
Publication Date: 2006