Inference Based on Many Conditional Moment Inequalities

Authors

Donald W.K. Andrews
Xiaoxia Shi

Document Type

Discussion Paper

Publication Date

7-1-2015

CFDP Number

2010R

CFDP Revision Date

2016-04-01

CFDP Pages

39

Abstract

In this paper, we construct confidence sets for models defined by many conditional moment inequalities/equalities. The conditional moment restrictions in the models can be finite, countably in finite, or uncountably in finite. To deal with the complication brought about by the vast number of moment restrictions, we exploit the manageability (Pollard (1990)) of the class of moment functions. We verify the manageability condition in five examples from the recent partial identification literature. The proposed confidence sets are shown to have correct asymptotic size in a uniform sense and to exclude parameter values outside the identified set with probability approaching one. Monte Carlo experiments for a conditional stochastic dominance example and a random-coefficients binary-outcome example support the theoretical results.

Comments

Supplement Materials, 41 pp

Recommended Citation

Andrews, Donald W.K. and Shi, Xiaoxia, "Inference Based on Many Conditional Moment Inequalities" (2015). Cowles Foundation Discussion Papers. 2447.
https://elischolar.library.yale.edu/cowles-discussion-paper-series/2447