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Semiparametric Inference in Dynamic Binary Choice Models

机译:动态二元选择模型中的半参数推理

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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.
机译:我们介绍了一种在动态二元选择模型中进行半参数推理的方法,该方法不会对计量经济学家未观察到的状态变量施加分布假设。所提出的框架结合了贝叶斯推断和部分识别结果。该方法适用于状态空间有限的模型。我们演示了基于Rust的客车发动机更换模型的方法。估计实验表明,关于未观察到的状态的分布的参数假设可能会对每个周期的收益估计产生重大影响。同时,对于大多数观察到的状态,这些假设对反事实条件选择概率的影响可能很小。

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