We propose a tractable semiparametric estimation method for structural dynamic discrete choice models. The distribution of additive utility shocks in the proposed framework is modeled by location-scale mixtures of extreme value distributions with varying numbers of mixture components. Our approach exploits the analytical tractability of extreme value distributions in the multinomial choice settings and the flexibility of the location-scale mixtures. We implement the Bayesian approach to inference using Hamiltonian Monte Carlo and an approximately optimal reversible jump algorithm. In our simulation experiments, we show that the standard dynamic logit model can deliver misleading results, especially about counterfactuals, when the shocks are not extreme value distributed. Our semiparametric approach delivers reliable inference in these settings. We develop theoretical results on approximations by location-scale mixtures in an appropriate distance and posterior concentration of the set identified utility parameters and the distribution of shocks in the model.

翻译：本文提出了一种可计算的半参数估计方法用于结构动态离散选择模型。在该框架中，加性效用冲击的分布通过具有可变混合成分数量的极值分布位置-尺度混合模型进行建模。我们的方法利用了极值分布在多项选择设定中的解析可处理性以及位置-尺度混合模型的灵活性。我们采用哈密顿蒙特卡洛方法与近似最优可逆跳跃算法实现贝叶斯推断。仿真实验表明，当冲击不服从极值分布时，标准动态logit模型可能产生误导性结果（尤其是关于反事实推断）。而我们的半参数方法在这些设定中能够提供可靠的推断。我们建立了关于位置-尺度混合模型在适当距离下的近似理论结果，以及模型中被识别效用参数集合与冲击分布的后验集中性理论。