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Estimating discrete-choice games of incomplete information: Simple static examples

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  • Che-Lin Su

Abstract

We investigate the computational aspect of estimating discrete-choice games under incomplete information. In these games, multiple equilibria can exist. Also, different values of structural parameters can result in different numbers of equilibria. Consequently, under maximum-likelihood estimation, the likelihood function is a discontinuous function of the structural parameters. We reformulate the maximum-likelihood estimation problem as a constrained optimization problem in the joint space of structural parameters and economic endogenous variables. Under this formulation, the objective function and structural equations are smooth functions. The constrained optimization approach does not require repeatedly solving the game or finding all the equilibria. We use two static-game models to demonstrate this approach, conducting Monte Carlo experiments to evaluate the finite-sample performance of the maximum-likelihood estimator, two-step estimators, and the nested pseudo-likelihood estimator. Copyright Springer Science+Business Media New York 2014

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  • Che-Lin Su, 2014. "Estimating discrete-choice games of incomplete information: Simple static examples," Quantitative Marketing and Economics (QME), Springer, vol. 12(2), pages 167-207, June.
    • Handle: RePEc:kap:qmktec:v:12:y:2014:i:2:p:167-207
    DOI: 10.1007/s11129-014-9144-8
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    More about this item

    Keywords

    Structural estimation; Discrete-choice games of incomplete information; Constrained optimization; Multiple equilibria; C13; C57; C61;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C57 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Econometrics of Games and Auctions
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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