Generalized Maximum Entropy estimation of discrete sequential move games of perfect information
AbstractWe 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.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 21331.
Date of creation: 20 Dec 2009
Date of revision:
Game-Theoretic Econometric Models; Sequential-Move Game; Generalized Maximum Entropy; Mixed-Integer Nonlinear Programming;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-03-28 (All new papers)
- NEP-ECM-2010-03-28 (Econometrics)
- NEP-GTH-2010-03-28 (Game Theory)
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