A dynamic programming approach to the estimation of markov switching regression models
- 1 February 1993
- journal article
- research article
- Published by Taylor & Francis in Journal of Statistical Computation and Simulation
- Vol. 45 (1-2) , 61-76
- https://doi.org/10.1080/00949659308811472
Abstract
This paper suggests an alternative two-stage estimation method for Markov Switching Regression models. The procedure, based on the Maximum A Posteriori (MAP) decision rule, is essentially related to Bellman's dynamic programming algorithm. Monte Carlo results indicate that this procedure is a good alternative to Hamilton's method. The empirical application shows that the dating of the U.S. business cycles by the MAP procedure is closer to the dating by NBER than that given by Hamilton's method.Keywords
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