Bayesian estimation of dynamic discrete choice models

Susumu Imai, Neelam Jain, Andrew Ching

Research output: Contribution to journalArticle

Abstract

We 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.

Original languageEnglish (US)
Pages (from-to)1865-1899
Number of pages35
JournalEconometrica
Volume77
Issue number6
DOIs
StatePublished - Nov 1 2009

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Keywords

  • Bayesian estimation
  • Discrete choice models
  • Dynamic programming
  • Markov chain Monte Carlo

ASJC Scopus subject areas

  • Economics and Econometrics

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