Generalized Maximum Entropy estimation of discrete sequential move games of perfect information

Yafeng Wang () and Brett Graham

MPRA Paper from University Library of Munich, Germany

Abstract: We propose a data-constrained generalized maximum entropy (GME) estimator for discrete sequential move games of perfect information which can be easily implemented on optimization software with high-level interfaces such as GAMS. Unlike most other work on the estimation of complete information games, the method we proposed is data constrained and does not require simulation and normal distribution of random preference shocks. We formulate the GME estimation as a (convex) mixed-integer nonlinear optimization problem (MINLP) which is well developed over the last few years. The model is identified with only weak scale and location normalizations, monte carlo evidence demonstrates that the estimator can perform well in moderately size samples. As an application, we study the social security acceptance decisions in dual career households.

Keywords: Game-Theoretic Econometric Models; Sequential-Move Game; Generalized Maximum Entropy; Mixed-Integer Nonlinear Programming (search for similar items in EconPapers)
JEL-codes: C01 C13 (search for similar items in EconPapers)
Date: 2009-12-20
New Economics Papers: this item is included in nep-ecm and nep-gth
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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