发表机构
McMaster University; University of North Carolina Wilmington（麦克马斯特大学; 北卡罗来纳大学威尔明顿分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对线性测度表示的经济计量模型，提出对抗性差异函数框架，通过有限线性规划计算识别集，并应用于二元选择面板和进入博弈，恢复锐利识别区域。
AI 中文摘要
我们开发了一个框架，用于具有线性测度表示的经济计量模型中的识别、计算与推断。这些模型将维持性约束表示为关于观测和潜在输入联合概率测度的线性矩条件，并将该测度线性映射到输出分布，即使结果方程是非线性的。我们构造了一个对抗性差异函数，其零点刻画了结构参数和反事实参数的识别集。在有限输出支撑下，有限线性规划可以计算差异函数或提供认证界限，即使潜在输入具有无限支撑；惩罚自助法可产生具有均匀逐点覆盖的置信集。我们将该框架应用于二元选择面板中具有固定效应和离散协变量的两个开放情形：具有未指定条件边际误差分布的序列外生性，以及具有已知条件边际误差分布但序列相关性不受限制的情形。在具有多重均衡的进入博弈中，该框架恢复了已知的锐利识别区域。
英文摘要
We develop a framework for identification, computation, and inference in econometric models with a linear-in-measures representation. These models express maintained restrictions as moment conditions linear in the joint probability measure of observed and latent inputs, and map that measure linearly to the distribution of outputs, even with nonlinear outcome equations. We construct an adversarial discrepancy function whose zeros characterize the identified set for structural and counterfactual parameters. With finite output support, finite linear programs compute the discrepancy function or provide certified bounds even when latent inputs have infinite support, and a penalized bootstrap yields confidence sets with uniform per-point coverage. We apply the framework to two open cases in binary choice panels with fixed effects and discrete covariates: sequential exogeneity with unspecified conditional marginal error distributions, and known conditional marginal error distributions with unrestricted serial dependence. In an entry game with multiple equilibria, the framework recovers the known sharp identification region.
Comments84 pages, 8 figures. Includes supplemental and additional appendices
