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.
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