Inference for Linear Conditional Moment Inequalities
针对经济应用中常见的线性条件矩不等式，提出了一种混合推断方法，在存在冗余参数时仍能构建计算可行的置信集，并通过模拟验证了其优于现有方法的功效和计算效率。
Abstract We show that moment inequalities in a wide variety of economic applications have a particular linear conditional structure. We use this structure to construct uniformly valid confidence sets that remain computationally tractable even in settings with nuisance parameters. We first introduce least-favorable critical values which deliver non-conservative tests if all moments are binding. Next, we introduce a novel conditional inference approach which ensures a strong form of insensitivity to slack moments. Our recommended approach is a hybrid technique which combines desirable aspects of the least favorable and conditional methods. The hybrid approach performs well in simulations calibrated to Wollmann (2018, American Economic Review, 108, 1364–1406), with favorable power and computational time comparisons relative to existing alternatives.