An Alternative Asymptotic Least Squares Estimator for Dynamic Games

The estimation of dynamic games is known to be a numerically challenging task. In this paper we propose an alternative class of asymptotic least squares estimators to Pesendorfer and Schmidt-Dengler's (2008), which includes several well known estimators in the literature as special cases. Our estimator can be substantially easier to compute. In the leading case with linear payoffs specification our estimator has a familiar OLS/GLS closed-form that does not require any optimization. When payoffs have partially linear form, we propose a sequential estimator where the parameters in the nonlinear term can be estimated independently of the linear components, the latter can then be obtained in closed-form. We show the class of estimators we propose and Pesendorfer and Schmidt-Dengler's are in fact asymptotically equivalent. Hence there is no theoretical cost in reducing the computational burden. Our estimator appears to perform well in a simple Monte Carlo experiment