Asymptotic Least Squares Estimators for Dynamic Games
推导了有限行动动态博弈中马尔可夫均衡的方程系统，并基于此定义了一类渐近最小二乘估计量，统一了Hotz-Miller和Aguirregabiria-Mira等经典估计量，通过蒙特卡洛研究比较了不同估计量的小样本表现。
This paper considers the estimation problem in dynamic games with finite actions. we derive the equation system that characterizes the markovian equilibria. the equilibrium equation system enables us to characterize conditions for identification. we consider a class of asymptotic least squares estimators defined by the equilibrium conditions. this class provides a unified framework for a number of well-known estimators including those by Hotz and Miller (1993) and by Aguirregabiria and Mira (2002). We show that these estimators differ in the weight they assign to individual equilibrium conditions. We derive the efficient weight matrix. A Monte Carlo study illustrates the small sample performance and computational feasibility of alternative estimators.