Seminar

Binary Choice Models with Discrete Regressors: Identification and Misspecification

Tatiana Komarova (London School of Economics)

June 15, 2009, 14:00–15:30

Toulouse

Room MF 323

Econometrics Seminar

Abstract

In semiparametric binary response models, support conditions on the regressors are required to guarantee point identification of the parameter of interest. For example, one regressor is usually assumed to have continuous support conditional on the other regressors. In some instances, such conditions have precluded the use of these models; in others, practitioners have failed to consider whether the conditions are satisfied in their data. This paper explores the inferential question in these semi-parametric models when the continuous support condition is not satisfied and all regressors have discrete support. I suggest a recursive procedure that finds sharp bounds on the parameter of interest and outline several applications. After deriving closed-form bounds on the parameter, I show how these formulas can help analyze cases where one regressor's support becomes increasingly dense. Furthermore, I investigate asymptotic properties of estimators of the identification set. I also propose three approaches to address the problem of empty identification sets when a model is misspecified. Finally, I present a Monte Carlo experiment and an empirical illustration to compare several estimation techniques.

Keywords

Binary response models; Discrete regressors; Partial identification; Misspecification; Linear programming;

JEL codes

  • C10: General
  • C14: Semiparametric and Nonparametric Methods: General
  • C2: Single Equation Models • Single Variables
  • C25: Discrete Regression and Qualitative Choice Models • Discrete Regressors • Proportions