Wang, Yafeng and Graham, Brett (2009): Generalized Maximum Entropy estimation of discrete sequential move games of perfect information.
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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.
|Item Type:||MPRA Paper|
|Original Title:||Generalized Maximum Entropy estimation of discrete sequential move games of perfect information|
|Keywords:||Game-Theoretic Econometric Models, Sequential-Move Game, Generalized Maximum Entropy, Mixed-Integer 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
|Depositing User:||Yafeng Wang|
|Date Deposited:||12 Mar 2010 00:41|
|Last Modified:||30 Dec 2015 23:56|
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