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"Semiparametric Identification in Panel Data Discrete Response Models"
Abstract
This paper studies partial identification in fixed effects panel data discrete response models. In particular, semiparametric identification in linear index binary and ordered response panel data models with fixed effects is examined. It is shown that under unrestrictive distributional assumptions on the fixed effect and the time-varying unobservables and failure of point identification, informative bounds on the regression coefficients can still be derived under fairly weak conditions. Partial identification is achieved by eliminating the fixed effect and finding features of the distribution that do not depend on the unobserved heterogeneity. Numerical analysis illustrates how these sets shrink as the support of the explanatory variables increases and how higher variation in the unobservables results in wider identification bounds.
