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Ordinary Least Squares Estimation for a Dynamic Game

Fabio A. Miessi Sanches () and Sorawoot Srisuma

No 2014_19, Working Papers, Department of Economics from University of São Paulo (FEA-USP)

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 case. Our proposed estimator has a closed-form. It can be computed without any numerical optimization and always minimizes the least squares objective function. Our estimator is also asymptotically equivalent to the asymptotic least squares estimator of Pesendorfer and Schmidt-Dengler (2008). Our estimator appears to perform well in a simple Monte Carlo experiment.

Keywords: Closed-from Estimation; Dynamic Discrete Choice; Markovian Games (search for similar items in EconPapers)
JEL-codes: C14 C25 C61 (search for similar items in EconPapers)
Date: 2014-10-16, Revised 2015-02-23
New Economics Papers: this item is included in nep-dcm, nep-ecm and nep-ore
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