Estimating Dynamic Discrete-Choice Games of Incomplete Information

33 Pages Posted: 6 Oct 2012 Last revised: 29 Aug 2014

See all articles by Michael Egesdal

Michael Egesdal

Airbnb

Zhenyu Lai

Harvard University - Department of Economics

Che-Lin Su

University of Chicago - Booth School of Business

Date Written: August 22, 2014

Abstract

We investigate the estimation of models of dynamic discrete-choice games of incomplete information, formulating the maximum-likelihood estimation exercise as a constrained optimization problem which can be solved using state-of-the-art constrained optimization solvers. Under the assumption that only one equilibrium is played in the data, our approach avoids repeatedly solving the dynamic game or finding all equilibria for each candidate vector of the structural parameters. We conduct Monte Carlo experiments to investigate the numerical performance and finite-sample properties of the constrained optimization approach for computing the maximum-likelihood estimator, the two-step pseudo maximum-likelihood estimator and the nested pseudo-likelihood estimator, implemented by both the nested pseudo-likelihood algorithm and a modified nested pseudo-likelihood algorithm.

Keywords: dynamic discrete-choice games of incomplete information, maximum-likelihood estimator, constrained optimization, nested pseudo-likelihood estimator

JEL Classification: C13, C61

Suggested Citation

Egesdal, Michael and Lai, Zhenyu and Su, Che-Lin, Estimating Dynamic Discrete-Choice Games of Incomplete Information (August 22, 2014). Available at SSRN: https://ssrn.com/abstract=2157329 or http://dx.doi.org/10.2139/ssrn.2157329

Michael Egesdal

Airbnb ( email )

888 Brannan St
San Francisco, CA 94103
United States

Zhenyu Lai

Harvard University - Department of Economics ( email )

Littauer Center
Cambridge, MA 02138
United States

Che-Lin Su (Contact Author)

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

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