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

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  • George Monokroussos

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 established. 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 versatility of the proposed framework is illustrated with an application of a dynamic tobit model for the Open Market Desk's Daily Reaction Function.

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File URL: http://www.albany.edu/economics/research/workingp/2009/mcmcldv.pdf
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Paper provided by University at Albany, SUNY, Department of Economics in its series Discussion Papers with number 09-07.

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Date of creation: 2009
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Handle: RePEc:nya:albaec:09-07

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Postal: Department of Economics, BA 110 University at Albany State University of New York Albany, NY 12222 U.S.A.
Phone: (518) 442-4735
Fax: (518) 442-4736

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Postal: Department of Economics, BA 110 University at Albany State University of New York Albany, NY 12222 U.S.A.
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Web: http://www.albany.edu/economics/research/workingp/index.shtml

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Cited by:
  1. George Monokroussos, 2011. "Dynamic Limited Dependent Variable Modeling and U.S. Monetary Policy," Journal of Money, Credit and Banking, Blackwell Publishing, Blackwell Publishing, vol. 43, pages 519-534, 03.
  2. George Monokroussos, 2006. "A Dynamic Tobit Model for the Open Market Desk's Daily Reaction Function," Computing in Economics and Finance 2006, Society for Computational Economics 390, Society for Computational Economics.

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