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

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  • Wang, Yafeng
  • Graham, Brett

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.

Suggested Citation

  • Wang, Yafeng & Graham, Brett, 2009. "Generalized Maximum Entropy estimation of discrete sequential move games of perfect information," MPRA Paper 21331, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:21331
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    Cited by:

    1. Wang, Yafeng & Graham, Brett, 2010. "Simulation Based Estimation of Discrete Sequential Move Games of Perfect Information," MPRA Paper 23153, University Library of Munich, Germany.

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    More about this item

    Keywords

    Game-Theoretic Econometric Models; Sequential-Move Game; Generalized Maximum Entropy; Mixed-Integer Nonlinear Programming;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics

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