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

George Monokroussos

Computational Economics, 2013, vol. 42, issue 1, 105 pages

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. Copyright Springer Science+Business Media New York 2013

Keywords: Discrete choice models; Censored models; Data augmentation; Markov Chain Monte Carlo; Gibbs sampling; Taylor rules; Alan Greenspan; C15; C24; C25; E52 (search for similar items in EconPapers)
Date: 2013
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Working Paper: A Classical MCMC Approach to the Estimation of Limited Dependent Variable Models of Time Series (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:42:y:2013:i:1:p:71-105

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DOI: 10.1007/s10614-012-9339-6

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