Conditional Choice Probability Estimation of Dynamic Discrete Choice Models With Unobserved Heterogeneity

• 成果类型: Article
• 署名作者: Arcidiacono, Peter; Miller, Robert A.
• 署名单位: Duke University; Carnegie Mellon University
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA7743
• 发表日期: 2011
• 页码: 1823-1867
• 关键词: sequential estimation; likelihood; inference; algorithm; school; IMPACT; games

摘要

We adapt the expectationmaximization algorithm to incorporate unobserved heterogeneity into conditional choice probability (CCP) estimators of dynamic discrete choice problems. The unobserved heterogeneity can be time-invariant or follow a Markov chain. By developing a class of problems where the difference in future value terms depends on a few conditional choice probabilities, we extend the class of dynamic optimization problems where CCP estimators provide a computationally cheap alternative to full solution methods. Monte Carlo results confirm that our algorithms perform quite well, both in terms of computational time and in the precision of the parameter estimates.