A Classical MCMC Approach to the Estimation of Limited Dependent Variable Models of Time Series

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.