Semiparametric Estimation of Dynamic Discrete Choice Models

30 Pages Posted: 10 May 2019 Last revised: 29 Sep 2019

See all articles by Nicholas Buchholz

Nicholas Buchholz

Princeton University Department of Economics

Matthew Shum

California Institute of Technology

Haiqing Xu

Department of Economics, University of Texas at Austin

Date Written: February 6, 2019

Abstract

We consider the estimation of dynamic binary 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, and resemble classic closed-form estimators from the literature on semiparametric regression and average derivative estimation. Monte Carlo simulations demonstrate that our estimator performs well in small samples.

Keywords: Semiparametric estimation, Dynamic discrete choice model, Average derivative estimation

JEL Classification: C14, D91, C41, L91

Suggested Citation

Buchholz, Nicholas and Shum, Matthew and Xu, Haiqing, Semiparametric Estimation of Dynamic Discrete Choice Models (February 6, 2019). Available at SSRN: https://ssrn.com/abstract=3378724 or http://dx.doi.org/10.2139/ssrn.3378724

Nicholas Buchholz

Princeton University Department of Economics ( email )

Joseph Henry House
Princeton, NJ 08542
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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