发表机构
University of Florida; European Research University（佛罗里达大学; 欧洲研究大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文在无排除限制的三角系统中，利用辅助变量引起的分布偏移和局部密度比限制，推导矩不等式并开发推断程序，从而恢复内生回归元的结构参数识别。
AI 中文摘要
本文研究了一个包含内生回归元的三角系统中，当排除限制不可用且结构扰动之间的依赖通过无限制控制函数建模时的识别与推断问题。在此设定下，标准正交条件无法提供点识别，因为未知控制函数可以合理化广泛的结构系数。我们表明，识别信息可以从由可能直接影响结果的辅助变量引起的第一阶段扰动的分布偏移中提取。通过对对数密度比施加局部限制，并同时给出控制函数筛逼近误差的显式界，我们推导出约束结构参数的不等式。我们开发了一种基于检验反转和乘子自助法的实用推断程序，该程序能够适应生成回归元、交叉拟合筛估计以及局部估计的密度比干扰项。研究结果阐明了在缺乏经典工具变量的情况下，如何从弱的局部分布结构中恢复识别。
英文摘要
This paper studies identification and inference in a triangular system with an endogenous regressor when exclusion restrictions are unavailable and the dependence between structural disturbances is modeled through an unrestricted control function. In this setting, standard orthogonality conditions do not deliver point identification, as the unknown control function can rationalize a wide range of structural coefficients. We show that identifying information can be extracted from distributional shifts in the first-stage disturbance induced by an auxiliary variable that may directly affect the outcome. Imposing a local restriction on the log density ratio, together with an explicit bound on the sieve approximation error of the control function, we derive moment inequalities that restrict the structural parameter. We develop a practical inference procedure based on test inversion and multiplier bootstrap that accommodates generated regressors, cross-fitted sieve estimation, and locally estimated density-ratio nuisances. The results clarify how identification can be recovered from weak local distributional structure in the absence of classical instruments.
