CFDP 2010R

Inference Based on Many Conditional Moment Inequalities


Publication Date: July 2015

Revision Date: April 2016

Pages: 39


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.

Supplemental Material: Supplemental material

Supplement pages: 41


Asymptotic size, Conditional moment inequalities, Confidence set, Many moments, Multiple equilibria, Partial identification, Random coefficients, Stochastic dominance, Test

JEL Classification Codes:  C1, C2, C3