Multiscale Adaptive Inference on Conditional Moment Inequalities

Timothy Armstrong and Hock Peng Chan
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Hock Peng Chan: National University of Singapore

No 1885R, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: This paper considers inference for conditional moment inequality models using a multiscale statistic. We derive the asymptotic distribution of this test statistic and use the result to propose feasible critical values that have a simple analytic formula, and to prove the asymptotic validity of a modified bootstrap procedure. The asymptotic distribution is extreme value, and the proof uses new techniques to overcome several technical obstacles. The test detects local alternatives that approach the identified set at the best rate among available tests in a broad class of models, and is adaptive to the smoothness properties of the data generating process. Our results also have implications for the use of moment selection procedures in this setting. We provide a monte carlo study and an empirical illustration to inference in a regression model with endogenously censored and missing data.

Keywords: Moment inequalities; Set inference; Adaptive inference (search for similar items in EconPapers)
JEL-codes: C01 C14 C34 (search for similar items in EconPapers)
Pages: 62 pages
Date: 2013-01, Revised 2015-12
New Economics Papers: this item is included in nep-sea
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Related works:
Journal Article: Multiscale adaptive inference on conditional moment inequalities (2016)
Working Paper: Multiscale Adaptive Inference on Conditional Moment Inequalities (2014)
Working Paper: Multiscale Adaptive Inference on Conditional Moment Inequalities (2013)
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