Semiparametric Estimation of Dynamic Discrete Choice Models
34 Pages
Posted: 17 Mar 2016
Last revised: 3 Feb 2017
University of Texas at Austin
California Institute of Technology
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:
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
