This paper provides welfare metrics for dynamic choice. We give estimation and inference methods for functions of the expected value of dynamic choice. These parameters include average value by group, average derivatives with respect to endowments, and structural decompositions. The example of dynamic discrete choice is considered. We give dual and doubly robust representations of these parameters. A least squares estimator of the dynamic Riesz representer for the parameter of interest is given. Debiased machine learners are provided and asymptotic theory given.

翻译：本文为动态选择提供了福利度量指标。我们给出了动态选择期望值函数的估计与推断方法。这些参数包括按群体划分的平均价值、关于禀赋的平均导数以及结构分解。文中以动态离散选择为例进行探讨，给出了这些参数的对偶表示及双重稳健表示。我们提出了目标参数动态Riesz表示的最小二乘估计量，提供了去偏机器学习方法并给出了渐近理论。