Existing simulation-based estimation methods are either general-purpose but asymptotically inefficient or symptotically efficient but only suitable for restricted classes of odels. This paper studies a simulated maximum-likelihood method that rests on estimating the likelihood nonparametrically on a simulated ample. We prove that this method, which can be used on very general models, is consistent and asymptotically efficient for static models. We then extend it to dynamic models and give some Monte-Carlo simulation results on three dynamic latent variable models.