• Open Access

Minimum distance estimators for dynamic games

Authors

  • Sorawoot Srisuma

    1. School of Economics, University of Surrey; s.srisuma@surrey.ac.uk
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    • This paper was previously circulated under the title “Minimum Distance Estimation for a Class of Markov Decision Processes.” It is a significant revision of the second chapter of my doctoral thesis. I am most grateful to my advisor, Oliver Linton, for his encouragement and guidance. I am thankful for many comments and suggestions from a co-editor and three anonymous referees who greatly helped improve the paper. I also wish to thank Guilherme Carmona, Joachim Groeger, Emmanuel Guerre, James Heckman, Javier Hidalgo, Stefan Hoderlein, Tatiana Komarova, Arthur Lewbel, Martin Pesendorfer, Carlos Santos, Marcia Schafgans, Philipp Schmidt-Dengler, Myung Hwan Seo, and seminar participants at numerous conferences and universities for valuable comments and suggestions. This research is supported by the ERC and ESRC.


Abstract

We develop a minimum distance estimator for dynamic games of incomplete information. We take a two-step approach, following Hotz and Miller (1993), based on the pseudo-model that does not solve the dynamic equilibrium so as to circumvent the potential indeterminacy issues associated with multiple equilibria. The class of games estimable by our methodology includes the familiar discrete unordered action games as well as games where players' actions are monotone (discrete, continuous, or mixed) in the their private values. We also provide conditions for the existence of pure strategy Markov perfect equilibria in monotone action games under increasing differences condition.

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