Binary Regressions with Bounded Median Dependence

In this paper we study the identification and estimation of a class of binary regressions where conditional medians of additive disturbances are bounded between known or exogenously identified functions of regressors. This class includes several important microeconometric models, such as simultaneous discrete games with incomplete information, binary regressions with censored regressors, and binary regressions with interval data or measurement errors on regressors. We characterize the identification region of linear coefficients in this class of models and show how point-identification can be achieved in various microeconometric models under fairly general restrictions on structural primitives. We define a novel, two-step smooth extreme estimator, and prove its consistency for the identification region of coefficients. We also provide encouraging Monte Carlo evidence of the estimator’s performance in finite samples.