Computational Economics

, Volume 42, Issue 1, pp 71–105 | Cite as

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

  • George Monokroussos


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.


Discrete choice models Censored models Data augmentation Markov Chain Monte Carlo Gibbs sampling Taylor rules Alan Greenspan 

JEL Classification

C15 C24 C25 E52 


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Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.Department of EconomicsUniversity at Albany, SUNY, Business Administration BuildingAlbanyUSA

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