ORDINARY LEAST SQUARES ESTIMATION OF A DYNAMIC GAME MODEL
针对线性参数形式的动态博弈模型，提出一种无需数值优化的OLS/GLS闭式估计量，在蒙特卡洛实验中表现良好。
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.