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.
- Sprache
-
Englisch
- Erschienen in
-
Series: cemmap working paper ; No. CWP51/17
Semiparametric and Nonparametric Methods: General
Single Equation Models; Single Variables: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
conditional moment inequalities
discrete choice
monotone single index model
curse of dimensionality
Lee, Sokbae
- DOI
-
doi:10.1920/wp.cem.2017.5117
- Handle
- Letzte Aktualisierung
-
20.09.2024, 08:23 MESZ
Objekttyp
- Arbeitspapier
Beteiligte
- Chen, Le-yu
- Lee, Sokbae
- Centre for Microdata Methods and Practice (cemmap)
Entstanden
- 2017
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