Bayesian Estimation of Dynamic Discrete Choice Models
AbstractWe propose a new methodology for structural estimation of infinite horizon dynamic discrete choice models. We combine the dynamic programming (DP) solution algorithm with the Bayesian Markov chain Monte Carlo algorithm into a single algorithm that solves the DP problem and estimates the parameters simultaneously. As a result, the computational burden of estimating a dynamic model becomes comparable to that of a static model. Another feature of our algorithm is that even though the number of grid points on the state variable is small per solution-estimation iteration, the number of effective grid points increases with the number of estimation iterations. This is how we help ease the "curse of dimensionality." We simulate and estimate several versions of a simple model of entry and exit to illustrate our methodology. We also prove that under standard conditions, the parameters converge in probability to the true posterior distribution, regardless of the starting values. Copyright 2009 The Econometric Society.
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Bibliographic InfoPaper provided by Society for Economic Dynamics in its series 2005 Meeting Papers with number 432.
Date of creation: 2005
Date of revision:
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Postal: Society for Economic Dynamics Christian Zimmermann Economic Research Federal Reserve Bank of St. Louis PO Box 442 St. Louis MO 63166-0442 USA
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Structural estimation; Dynamic programming; MCMC;
Other versions of this item:
- Susumu Imai & Neelam Jain & Andrew Ching, 2009. "Bayesian Estimation of Dynamic Discrete Choice Models," Econometrica, Econometric Society, vol. 77(6), pages 1865-1899, November.
- Susumu Imai & Neelam Jain & Andrew Ching, 2006. "Bayesian Estimation of Dynamic Discrete Choice Models," Working Papers 1118, Queen's University, Department of Economics.
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
- L00 - Industrial Organization - - General - - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2005-12-01 (All new papers)
- NEP-DCM-2005-12-01 (Discrete Choice Models)
- NEP-DGE-2005-12-01 (Dynamic General Equilibrium)
- NEP-ECM-2005-12-01 (Econometrics)
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