Simulation Based Estimation of Discrete Sequential Move Games of Perfect Information

We propose simulation based estimation for discrete sequential move games of perfect information which relies on the simulated moments and importance sampling. We use importance sampling techniques not only to reduce computational burden and simulation error, but also to overcome non-smoothness problems. The model is identified with only weak scale and location normalizations, monte Carlo evidence demonstrates that the estimator can perform well in moderately-sized samples.