Semiparametric Estimation of Dynamic Binary Choice Panel Data Models

Fu Ouyang () and Thomas Tao Yang

ANU Working Papers in Economics and Econometrics from Australian National University, College of Business and Economics, School of Economics

Abstract: We propose a new approach to the semiparametric analysis of panel data binary choice models with fixed effects and dynamics (lagged dependent variables). The model we consider has the same random utility framework as in Honore and Kyriazidou ´ (2000). We demonstrate that, with additional serial dependence conditions on the process of deterministic utility and tail restrictions on the error distribution, the (point) identification of the model can proceed in two steps, and only requires matching the value of an index function of explanatory variables over time, as opposed to that of each explanatory variable. Our identification approach motivates an easily implementable, two-step maximum score (2SMS) procedure – producing estimators whose rates of convergence, in contrast to Honore and Kyriazidou ´ ’s (2000) methods, are independent of the model dimension. We then derive the asymptotic properties of the 2SMS procedure and propose bootstrap-based distributional approximations for inference. Monte Carlo evidence indicates that our procedure performs adequately in finite samples. We then apply the proposed estimators to study labor market dependence and the effects of health shocks, using data from the Household, Income and Labor Dynamics in Australia (HILDA) survey.

Keywords: Semiparametric estimation; Binary choice model; Panel data; Fixed effects; Dynamics; Maximum score; Bootstrap (search for similar items in EconPapers)
JEL-codes: C14 C23 C35 (search for similar items in EconPapers)
Date: 2020-05
New Economics Papers: this item is included in nep-dcm, nep-ecm, nep-ore and nep-upt
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Citations: View citations in EconPapers (1)

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