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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.
Authors
Research Fellow Columbia University
Sokbae is an IFS Research Fellow and a Professor at Columbia University, with an interest in Econometrics, Applied Microeconomics and Statistics.
Academia Sinica
Working Paper details
- DOI
- 10.1920/wp.cem.2017.5117
- Publisher
- The IFS
Suggested citation
Chen, L and Lee, S. (2017). Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models. London: The IFS. Available at: https://ifs.org.uk/publications/breaking-curse-dimensionality-conditional-moment-inequalities-discrete-choice-models (accessed: 29 March 2024).
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