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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. Copyright Springer Science+Business Media New York 2013

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File URL: http://hdl.handle.net/10.1007/s10614-012-9339-6
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Article provided by Society for Computational Economics in its journal Computational Economics.

Volume (Year): 42 (2013)
Issue (Month): 1 (June)
Pages: 71-105

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Handle: RePEc:kap:compec:v:42:y:2013:i:1:p:71-105
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