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Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models

Le-Yu Chen () and Sokbae (Simon) Lee ()

Journal of Econometrics, 2019, vol. 210, issue 2, 482-497

Abstract: This paper studies inference of preference parameters in semiparametric discrete choice models when these parameters are not point-identified and the identified set is characterized by a class of conditional moment inequalities. Exploring the semiparametric modeling restrictions, we show that the identified set can be equivalently formulated by moment inequalities conditional on only two continuous indexing variables. Such formulation holds regardless of the covariate dimension, thereby breaking the curse of dimensionality for nonparametric inference based on the underlying conditional moment inequalities. We further apply this dimension reducing characterization approach to the monotone single index model and to a variety of semiparametric models under which the sign of conditional expectation of a certain transformation of the outcome is the same as that of the indexing variable.

Keywords: Partial identification; Conditional moment inequalities; Discrete choice; Monotone single index model; Curse of dimensionality (search for similar items in EconPapers)
JEL-codes: C14 C25 (search for similar items in EconPapers)
Date: 2019
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Related works:
Working Paper: Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models (2017) Downloads
Working Paper: Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models (2015) Downloads
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