Inference for Linear Conditional Moment Inequalities

Isaiah Andrews, Jonathan Roth and Ariel Pakes

The Review of Economic Studies, 2023, vol. 90, issue 6, 2763-2791

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.

Keywords: Moment inequalities; Subvector inference; Uniform inference (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Related works:
Working Paper: Inference for Linear Conditional Moment Inequalities (2022)
Working Paper: Inference for Linear Conditional Moment Inequalities (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:oup:restud:v:90:y:2023:i:6:p:2763-2791.

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