论文标题
半参数贝叶斯的动态离散选择模型估计
Semiparametric Bayesian Estimation of Dynamic Discrete Choice Models
论文作者
论文摘要
我们为结构动态离散选择模型提出了一种可拖动的半参数估计方法。在提出的框架中，添加剂冲击的分布是由具有不同混合物组件的极值分布的位置尺度混合物建模的。我们的方法利用了多项式选择设置中极值分布的分析障碍以及位置尺度混合物的灵活性。我们使用汉密尔顿蒙特卡洛（Monte Carlo）和大约最佳的可逆跳跃算法实施了贝叶斯方法的推理方法。在我们的仿真实验中，我们表明标准的动态logit模型可以提供误导性结果，尤其是关于反事实时，当冲击不是极端值分布时。我们的半参数方法在这些设置中提供了可靠的推论。我们根据位置尺度混合物在适当距离的位置尺度混合物和集合已识别的效用参数的后浓度以及模型中冲击分布的情况下开发理论结果。
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.
