Inference Based on Many Conditional Moment Inequalities

Donald Andrews () and Xiaoxia Shi

No 2010, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

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 infinite, or uncountably infinite. 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.

Keywords: Asymptotic size; Conditional moment inequalities; Confidence set; Many moments; Multiple equilibria; Partial identification; Random coefficients; Stochastic dominance; Test (search for similar items in EconPapers)
JEL-codes: C1 C2 C3 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2015-07
New Economics Papers: this item is included in nep-ecm and nep-ore
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Inference based on many conditional moment inequalities (2017)
Working Paper: Inference Based on Many Conditional Moment Inequalities (2016)
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