标题： 不完全离散选择模型的通用推断 显示英文标题

标题： Universal Inference for Incomplete Discrete Choice Models

摘要： 越来越多的经验模型表现出集合值预测。 本文开发了一种适用于此类模型的可处理且具有有限样本有效性的推断方法。 所提出的程序使用了 Wasserman 等人（2020）提出的通用推断框架的稳健版本，并避免使用矩选择调整参数、重抽样或模拟。 该方法旨在构建反事实对象和其他基础参数函数的置信区间。 它可用于涉及模型不完整、离散和连续协变量以及包含讨厌参数的参数的应用场景。 显示英文摘要

摘要： A growing number of empirical models exhibit set-valued predictions. This paper develops a tractable inference method with finite-sample validity for such models. The proposed procedure uses a robust version of the universal inference framework by Wasserman et al. (2020) and avoids using moment selection tuning parameters, resampling, or simulations. The method is designed for constructing confidence intervals for counterfactual objects and other functionals of the underlying parameter. It can be used in applications that involve model incompleteness, discrete and continuous covariates, and parameters containing nuisance components.