Inference for Linear Conditional Moment Inequalities

88 Pages Posted: 21 Oct 2019 Last revised: 26 Jul 2023

See all articles by Isaiah Andrews

Isaiah Andrews

Harvard Society of Fellows

Jonathan Roth

Harvard University

Ariel Pakes

Harvard University; National Bureau of Economic Research (NBER)

Date Written: October 2019

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.

Suggested Citation

Andrews, Isaiah and Roth, Jonathan and Pakes, Ariel, Inference for Linear Conditional Moment Inequalities (October 2019). NBER Working Paper No. w26374, Available at SSRN: https://ssrn.com/abstract=3472809

Isaiah Andrews (Contact Author)

Harvard Society of Fellows ( email )

1875 Cambridge Street
Cambridge, MA 02138
United States

Jonathan Roth

Harvard University ( email )

Ariel Pakes

Harvard University ( email )

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
16
Abstract Views
480
PlumX Metrics