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

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Abstract

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.

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Authors and Affiliations

  1. Department of Economics, University at Albany, SUNY, Business Administration Building, Room 110, 1400 Washington Avenue, Albany, NY, 12222, USA

    George Monokroussos

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  1. George Monokroussos
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Correspondence to George Monokroussos.

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Monokroussos, G. A Classical MCMC Approach to the Estimation of Limited Dependent Variable Models of Time Series. Comput Econ 42, 71–105 (2013). https://doi.org/10.1007/s10614-012-9339-6

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  • DOI: https://doi.org/10.1007/s10614-012-9339-6

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