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

Le-Yu Chen and Sokbae (Simon) Lee

No 26/15, CeMMAP working papers from Institute for Fiscal Studies

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 also extend this dimension reducing characterization result to a variety of semi-parametric models under which the sign of conditional expectation of a certain transformation of the outcome is the same as that of the indexing variable.

Date: 2015-06-18
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Related works:
Journal Article: Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models (2019)
Working Paper: Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models (2017)
Working Paper: Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models (2017)
Working Paper: Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:azt:cemmap:26/15

DOI: 10.1920/wp.cem.2015.2615

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