Inference for Linear Conditional Moment Inequalities Isaiah Andrews Jonathan Roth Ariel Pakes NBER Working Paper No. 26374

Issued in October 2019

NBER Program(s):Industrial Organization

We consider inference based on linear conditional moment inequalities, which arise in a wide variety of economic applications, including many structural models. We show that linear conditional structure greatly simplifies confidence set construction, allowing for computationally tractable projection inference in settings with nuisance parameters. Next, we derive least favorable critical values that avoid conservativeness due to projection. Finally, we introduce a conditional inference approach which ensures a strong form of insensitivity to slack moments, as well as a hybrid technique which combines the least favorable and conditional methods. Our conditional and hybrid approaches are new even in settings without nuisance parameters. We find good performance in simulations based on Wollmann (2018), especially for the hybrid approach. You may purchase this paper on-line in .pdf format from SSRN.com ($5) for electronic delivery. Access to NBER Papers You are eligible for a free download if you are a subscriber, a corporate associate of the NBER, a journalist, an employee of the U.S. federal government with a ".GOV" domain name, or a resident of nearly any developing country or transition economy. If you usually get free papers at work/university but do not at home, you can either connect to your work VPN or proxy (if any) or elect to have a link to the paper emailed to your work email address below. The email address must be connected to a subscribing college, university, or other subscribing institution. Gmail and other free email addresses will not have access. E-mail:

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data appendix Acknowledgments Machine-readable bibliographic record - MARC, RIS, BibTeX Document Object Identifier (DOI): 10.3386/w26374