A Classical MCMC Approach to the Estimation of Limited Dependent Variable Models of Time Series
Computational Economics, Forthcoming
40 Pages Posted: 14 Jul 2012 Last revised: 6 Aug 2012
Date Written: February 2012
Estimating Limited Dependent Variable Time Series models through standard extremum methods can be a daunting computational task because of the need for integration of high order multiple integrals and/or numerical optimization of difficult objective functions. This paper proposes a classical Markov Chain Monte Carlo (MCMC) estimation technique with data augmentation that overcomes both of these problems. The asymptotic properties of the proposed estimator are discussed. Furthermore, a practical and flexible algorithmic framework for this class of models is proposed and is illustrated using simulated data, thus also offering some insight into the small-sample biases of such estimators. Finally, the proposed framework is used to estimate a dynamic, discrete-choice monetary policy reaction function for the United States during the Greenspan years.
Keywords: Discrete Choice models, Censored models, Data Augmentation, Markov Chain Monte Carlo, Gibbs Sampling, Taylor rules, Alan Greenspan
JEL Classification: C15, C24, C25, E52
Suggested Citation: Suggested Citation