Semiparametric Inference in Dynamic Binary Choice Models
提出一种不假设未观测状态变量分布的方法，用于动态二元选择模型的半参数推断，结合贝叶斯推断与部分识别，并用Rust的公交车引擎更换模型验证，发现分布假设对周期收益估计影响大，但对反事实条件选择概率影响小。
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.