Inference for Linear Conditional Moment Inequalities

TL;DR

This paper develops a new inference method for linear conditional moment inequalities in economics, combining least favorable and conditional approaches to improve power and computational efficiency.

Contribution

It introduces a hybrid inference technique that is both computationally tractable and robust to slack moments, advancing econometric analysis of moment inequalities.

Findings

Hybrid approach outperforms existing methods in simulations

Method remains valid with nuisance parameters

Approach offers a good balance of power and computational speed

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), with favorable power and computational time comparisons relative to existing alternatives.