Semiparametric Inference in Dynamic Binary Choice Models

• 成果类型: Article
• 署名作者: Norets, A.; Tang, X.
• 署名单位: University of Illinois System; University of Illinois Urbana-Champaign; University of Pennsylvania
• 刊物名称: REVIEW OF ECONOMIC STUDIES
• ISSN/ISSBN: 0034-6527
• DOI: 10.1093/restud/rdt050
• 发表日期: 2014
• 页码: 1229-1262
• 关键词: discrete-choice; nonparametric identification; posterior distributions; confidence-intervals; estimators; parameters; time; sets

摘要

We introduce an approach for semiparametric inference in dynamic binary choice models that does not impose distributional assumptions on the state variables unobserved by the econometrician. The proposed framework combines Bayesian inference with partial identification results. The method is applicable to models with finite space of observed states. We demonstrate the method on Rust's model of bus engine replacement. The estimation experiments show that the parametric assumptions about the distribution of the unobserved states can have a considerable effect on the estimates of per-period payoffs. At the same time, the effect of these assumptions on counterfactual conditional choice probabilities can be small for most of the observed states.