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Bayesian Estimation of Dynamic Discrete Choice Models

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Author Info
Susumu Imai () (Queen's University)
Neelam Jain () (Northern Illinois University)
Andrew Ching () (University of Toronto)

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Abstract

We propose a new methodology for structural estimation of 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 per solution-estimation iteration, the number of grid points on the state variable is small, 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.

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File URL: http://www.econ.queensu.ca/working_papers/papers/qed_wp_1118.pdf
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Publisher Info
Paper provided by Queen's University, Department of Economics in its series Working Papers with number 1118.

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Length: 77 pages
Date of creation: Dec 2006
Date of revision:
Handle: RePEc:qed:wpaper:1118

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Related research
Keywords: Bayesian Estimation; Dynamic Discrete Choice Model; Dynamic Programming; Markov Chain Monte Carlo; Bayesian Dynamic Programming Estimation;

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Find related papers by JEL classification:
C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
C61 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Optimization Techniques; Programming Models; Dynamic Analysis
C63 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Computational Techniques
L00 - Industrial Organization - - General - - - General

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Lancaster, Tony, 1997. "Exact Structural Inference in Optimal Job-Search Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(2), pages 165-79, April.
  2. Houser, Daniel, 2003. "Bayesian analysis of a dynamic stochastic model of labor supply and saving," Journal of Econometrics, Elsevier, vol. 113(2), pages 289-335, April. [Downloadable!] (restricted)
  3. McCulloch, Robert & Rossi, Peter E., 1994. "An exact likelihood analysis of the multinomial probit model," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 207-240. [Downloadable!] (restricted)
  4. Chib, Siddhartha & Greenberg, Edward, 1996. "Markov Chain Monte Carlo Simulation Methods in Econometrics," Econometric Theory, Cambridge University Press, vol. 12(03), pages 409-431, August. [Downloadable!]
  5. repec:cup:etheor:v:12:y:1996:i:3:p:409-31 is not listed on IDEAS
  6. Hotz, V Joseph & Robert A. Miller & Seth Sanders & Jeffrey Smith, 1994. "A Simulation Estimator for Dynamic Models of Discrete Choice," Review of Economic Studies, Blackwell Publishing, vol. 61(2), pages 265-89, April. [Downloadable!] (restricted)
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Jean-Pierre Dubé & K. Sudhir & Andrew Ching & Gregory Crawford & Michaela Draganska & Jeremy Fox & Wesley Hartmann & Günter Hitsch & V. Viard & Miguel Villas-Boas & Naufel Vilcassim, 2005. "Recent Advances in Structural Econometric Modeling: Dynamics, Product Positioning and Entry," Marketing Letters, Springer, vol. 16(3), pages 209-224, December. [Downloadable!] (restricted)
  2. Yingyao Hu & Matthew Shum, 2008. "Nonparametric Identification of Dynamic Models with Unobserved State Variables," Economics Working Paper Archive 543, The Johns Hopkins University,Department of Economics. [Downloadable!]
    Other versions:
  3. Victor Aguirregabiria & Pedro mira, 2007. "Dynamic Discrete Choice Structural Models: A Survey," Working Papers tecipa-297, University of Toronto, Department of Economics. [Downloadable!]
    Other versions:
  4. Michael P. Keane & Kenneth I. Wolpin, 2009. "Empirical Applications of Discrete Choice Dynamic Programming Models," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 12(1), pages 1-22, January. [Downloadable!] (restricted)
  5. Bajari, Patrick & Benkard, C. Lanier & Levin, Jonathan, 2007. "Estimating Dynamic Models of Imperfect Competition," Research Papers 1852r1, Stanford University, Graduate School of Business. [Downloadable!]
    Other versions:
  6. Daniel Ackerberg, 2009. "A new use of importance sampling to reduce computational burden in simulation estimation," Quantitative Marketing and Economics, Springer, vol. 7(4), pages 343-376, December. [Downloadable!] (restricted)
  7. Christopher Ferrall, 2005. "Solving Finite Mixture Models: Efficient Computation in Economics Under Serial and Parallel Execution," Computational Economics, Springer, vol. 25(4), pages 343-379, June. [Downloadable!] (restricted)
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