Ordinary Least Squares Estimation for a Dynamic Game

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.