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

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.

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

Monokroussos, George, A Classical MCMC Approach to the Estimation of Limited Dependent Variable Models of Time Series (February 2012). Computational Economics, Forthcoming. Available at SSRN: https://ssrn.com/abstract=2105742 or http://dx.doi.org/10.2139/ssrn.2105742

George Monokroussos (Contact Author)

Joint Research Centre, Italy ( email )

Via E. Fermi 1
I-21020 Ispra (VA)
Italy

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