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Ordinary Least Squares Estimation Of A Dynamic Game Model

Author

Listed:
  • Fabio A. Miessi Sanches
  • Daniel Junior Silva
  • Sorawoot Srisuma

Abstract

Estimation of dynamic games is known to be a numerically challenging task. A common form of the payoff functions employed in practice takes the linear‐in‐parameter specification. We show a least squares estimator taking a familiar OLS/GLS expression is available in such a case. Our proposed estimator has a closed form. It can be computed without any numerical optimization and always minimizes the least squares objective function. We specify the optimally weighted GLS estimator that is efficient in the class of estimators under consideration. Our estimator appears to perform well in a simple Monte Carlo experiment.

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  • Fabio A. Miessi Sanches & Daniel Junior Silva & Sorawoot Srisuma, 2016. "Ordinary Least Squares Estimation Of A Dynamic Game Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 57(2), pages 623-634, May.
    • Handle: RePEc:wly:iecrev:v:57:y:2016:i:2:p:623-634
    DOI: 10.1111/iere.12170
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    References listed on IDEAS

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    10. Hiroyuki Kasahara & Katsumi Shimotsu, 2009. "Nonparametric Identification of Finite Mixture Models of Dynamic Discrete Choices," Econometrica, Econometric Society, vol. 77(1), pages 135-175, January.
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    Cited by:

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    3. Taisuke Otsu & Martin Pesendorfer, 2023. "Equilibrium multiplicity in dynamic games: Testing and estimation," The Econometrics Journal, Royal Economic Society, vol. 26(1), pages 26-42.

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