Ordinary Least Squares Estimation of a Dynamic Game Model

Miessi Sanches, F.A., Silva Junior, D. & Srisuma, S. (2016). Ordinary Least Squares Estimation of a Dynamic Game Model. International Economic Review, 57(2), pp. 623-634. doi: 10.1111/iere.12170

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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.

Item Type: Article
Additional Information: This is the peer reviewed version of the following article: Miessi Sanches, F.A., Silva Junior, D. & Srisuma, S. (2016). Ordinary Least Squares Estimation of a Dynamic Game Model. International Economic Review, 57(2), pp. 623-634., which has been published in final form at https://dx.doi.org/10.1111/iere.12170. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
Uncontrolled Keywords: Closed-form Estimation, Dynamic Discrete Choice, Markovian Games
Subjects: H Social Sciences > HB Economic Theory
Divisions: School of Social Sciences > Department of Economics
URI: http://openaccess.city.ac.uk/id/eprint/16624

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