Binary Response Panel Data Models with Sample Selection and Self Selection

Anastasia Semykina () and Jeffrey Wooldridge
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Anastasia Semykina: Department of Economics, Florida State University

No wp2015_05_01, Working Papers from Department of Economics, Florida State University

Abstract: We consider estimating binary response models on an unbalanced panel, where the outcome of the dependent variable may be missing due to non-random selection, or there is self selection into a treatment. In the present paper, we first consider estimation of sample selection models and treatment effects using a fully parametric approach, where the error distribution is assumed to be normal in both primary and selection equations. Arbitrary time dependence in errors is permitted. Estimation of both coefficients and partial effects, as well as tests for selection bias are discussed. Furthermore, we consider a semiparametric estimator of binary response panel data models with sample selection that is robust to a variety of error distributions. The estimator employs a control function approach to account for endogenous selection and permits consistent estimation of scaled coefficients and relative effects.

Keywords: Binary response models; Sample selection; Panel data; Semiparametric; Treament effect (search for similar items in EconPapers)
JEL-codes: C14 C33 C34 C35 (search for similar items in EconPapers)
Pages: 39
Date: 2015-02
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (1)

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https://coss.fsu.edu/econpapers/wpaper/wp2015_05_01.pdf First version, 2015-02 (application/pdf)

Related works:
Journal Article: Binary response panel data models with sample selection and self‐selection (2018)
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