Arbeitspapier

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

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.

Language
Englisch

Bibliographic citation
Series: cemmap working paper ; No. CWP51/17

Classification
Wirtschaft
Semiparametric and Nonparametric Methods: General
Single Equation Models; Single Variables: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
Subject
partial identification
conditional moment inequalities
discrete choice
monotone single index model
curse of dimensionality

Event
Geistige Schöpfung
(who)
Chen, Le-yu
Lee, Sokbae
Event
Veröffentlichung
(who)
Centre for Microdata Methods and Practice (cemmap)
(where)
London
(when)
2017

DOI
doi:10.1920/wp.cem.2017.5117
Handle
Last update
20.09.2024, 8:23 AM CEST

Data provider

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Object type

  • Arbeitspapier

Associated

  • Chen, Le-yu
  • Lee, Sokbae
  • Centre for Microdata Methods and Practice (cemmap)

Time of origin

  • 2017

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