Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/150394
Authors: 
Egesdal, Michael
Lai, Zhenyu
Su, Che-Lin
Year of Publication: 
2015
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 6 [Year:] 2015 [Issue:] 3 [Pages:] 567-597
Abstract: 
We investigate the estimation of models of dynamic discrete-choice games of incomplete information, formulating the maximum-likelihood estimation exercise as a constrained optimization problem that can be solved using state-of-the-art constrained optimization solvers. Under the assumption that only one equilibrium is played in the data, our approach avoids repeatedly solving the dynamic game or finding all equilibria for each candidate vector of the structural parameters. We conduct Monte Carlo experiments to investigate the numerical performance and finite-sample properties of the constrained optimization approach for computing the maximum-likelihood estimator, the two-step pseudo-maximum-likelihood estimator, and the nested pseudo-likelihood estimator, implemented by both the nested pseudo-likelihood algorithm and a modified nested pseudo-likelihood algorithm.
Subjects: 
Dynamic discrete-choice games of incomplete information
maximum-likelihood estimator
constrained optimization
nested pseudo-likelihood estimator
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by-nc/3.0/
Document Type: 
Article

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