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Sharp and Robust Estimation of Partially Identified Discrete Response Models

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If you have a question about this talk, please contact Qingyuan Zhao .

Semiparametric discrete choice models are point identified with continuous covariates and may become partially identified with discrete covariates. Classical estimators, such as maximum score (Manski (1975)), lose their desirable properties without point identification. They may not be sharp, converging to outer regions the identified set (Komarova (2013)), and in many discrete designs, weakly converge to random sets. They lack robustness as their distribution limit changes discontinuously with model parameters. We propose a new class of estimators based on the quantile of a random set, which are both sharp and robust, also applicable to single-index and discrete panel data models.

(joint work with S.Khan and D. Nekipelov)

This talk is part of the Statistics series.

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