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Semiparametric Estimation of Dynamic Discrete Choice Models

34 Pages Posted: 17 Mar 2016 Last revised: 3 Feb 2017

Nicholas Buchholz

University of Texas at Austin

Matthew Shum

California Institute of Technology

Haiqing Xu

Department of Economics, University of Texas at Austin

Date Written: February 2, 2017

Abstract

We consider the estimation of dynamic discrete choice models in a semiparametric setting, in which the per-period utility functions are known up to a finite number of parameters, but the distribution of utility shocks is left unspecified. This semiparametric setup differs from most of the existing identification and estimation literature for dynamic discrete choice models. To show identification we derive and exploit a new Bellman-like recursive representation for the unknown quantile function of the utility shocks. Our estimators are straightforward to compute; some are simple and require no iteration, and resemble classic estimators from the literature on semiparametric regression and average derivative estimation. Monte Carlo simulations demonstrate that our estimator performs well in small samples. To highlight features of this estimator, we estimate a structural model of dynamic labor supply for New York City taxicab drivers.

Keywords: Semiparametric estimation, Dynamic discrete choice model, Average derivative estimation, Taxicab industry, Labor supply

JEL Classification: C14, D91, C41, L91

Suggested Citation

Buchholz, Nicholas and Shum, Matthew and Xu, Haiqing, Semiparametric Estimation of Dynamic Discrete Choice Models (February 2, 2017). Available at SSRN: https://ssrn.com/abstract=2748697 or http://dx.doi.org/10.2139/ssrn.2748697

Nicholas Buchholz

University of Texas at Austin ( email )

Austin, TX 78712
United States

Matthew Shum (Contact Author)

California Institute of Technology ( email )

Pasadena, CA 91125
United States

Haiqing Xu

Department of Economics, University of Texas at Austin ( email )

Austin, TX 78712
United States

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