We describe a two-step algorithm for estimating dynamic games under the assumption
that behavior is consistent with Markov Perfect Equilibrium. In the first step, the
policy functions and the law of motion for the state variables are estimated. In
the second step, the remaining structural parameters are estimated using the optimality
conditions for equilibrium. The second step estimator is a simple simulated minimum
distance estimator. The algorithm applies to a broad class of models, including I.O.
models with both discrete and continuous controls such as the Ericson and Pakes (1995)
model. We test the algorithm on a class of dynamic discrete choice models with normally
distributed errors, and a class of dynamic oligopoly models similar to that of Pakes
and McGuire (1994).