标题： 一种对抗性方法用于识别 显示英文标题

标题： An Adversarial Approach to Identification

摘要： 我们引入了一个新的框架，用于表征计量经济模型中结构参数和反事实参数的可识别集。 通过将识别问题重新表述为集合成员问题，我们在观测概率测度的空间中利用分离超平面定理，通过一个具有对抗博弈解释的偏差函数的零点来表征可识别集。 该集合可以是单例，从而得到点识别。 许多计量经济模型的一个特征是，无论是否对误差项做出分布假设，观测变量的概率测度都可以表示为潜在变量概率测度的线性变换。 这种结构提供了一个统一的框架，并通过线性规划促进了计算和推断。 我们通过将其应用于具有固定效应的非线性面板模型，具有参数和非参数误差分布，并在各种外生性限制下（包括严格和顺序的）来展示我们方法的灵活性。 显示英文摘要

摘要： We introduce a new framework for characterizing identified sets of structural and counterfactual parameters in econometric models. By reformulating the identification problem as a set membership question, we leverage the separating hyperplane theorem in the space of observed probability measures to characterize the identified set through the zeros of a discrepancy function with an adversarial game interpretation. The set can be a singleton, resulting in point identification. A feature of many econometric models, with or without distributional assumptions on the error terms, is that the probability measure of observed variables can be expressed as a linear transformation of the probability measure of latent variables. This structure provides a unifying framework and facilitates computation and inference via linear programming. We demonstrate the versatility of our approach by applying it to nonlinear panel models with fixed effects, with parametric and nonparametric error distributions, and across various exogeneity restrictions, including strict and sequential.