Wang, Yafeng and Graham, Brett (2009): Generalized Maximum Entropy estimation of discrete sequential move games of perfect information.
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Abstract
We propose a dataconstrained generalized maximum entropy (GME) estimator for discrete sequential move games of perfect information which can be easily implemented on optimization software with highlevel 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) mixedinteger 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.
Item Type:  MPRA Paper 

Original Title:  Generalized Maximum Entropy estimation of discrete sequential move games of perfect information 
Language:  English 
Keywords:  GameTheoretic Econometric Models, SequentialMove Game, Generalized Maximum Entropy, MixedInteger Nonlinear Programming 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C13  Estimation: General C  Mathematical and Quantitative Methods > C0  General > C01  Econometrics 
Item ID:  21331 
Depositing User:  Yafeng Wang 
Date Deposited:  12 Mar 2010 00:41 
Last Modified:  30 Dec 2015 23:56 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/21331 
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