Semiparametric Estimation of Dynamic Discrete-Choice Models

TL;DR

This paper introduces a novel semiparametric approach for estimating dynamic discrete-choice models, allowing flexible shock distributions and providing simple, computationally efficient estimators with demonstrated effectiveness in simulations and real data.

Contribution

It develops a new Bellman-like recursive representation for shock quantile functions and proposes straightforward estimators that do not require iteration, advancing the estimation methodology for these models.

Findings

Estimators perform well in small samples.

New recursive representation aids identification.

Application to NYC taxi drivers illustrates practical utility.

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 mod- els. 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.