This paper provides a simulated moments estimator (SME) for the parameters of dynamic models where the state vector follows a time-homogeneous Markov process. The SME extends the generalized method-of-moments (GMM) estimator to a large class of asset pricing models where the moment restrictions of interest do not have analytic representations in terms of observable variables and the unknown parameter vector. The SME is a computationally tractable means of simultaneously solving and estimating an equilibrium asset pricing model. The paper provides conditions for the consistency and asymptotic normality of the SME. It discusses various tradeoffs among the regularity conditions underlying the large sample properties of the SME in the context of an asset pricing model. The paper also addresses the challenges of simulating time series and the non-stationarity of the simulated series. The paper introduces the concept of geometric ergodicity and provides conditions for the consistency and asymptotic normality of the SME. It also discusses the implications of the SME for dynamic asset pricing models and the regularity conditions required for strong consistency. The paper concludes with a discussion of the practical considerations that may influence which, if either, of these results assures consistency for SM estimation of dynamic asset pricing models.This paper provides a simulated moments estimator (SME) for the parameters of dynamic models where the state vector follows a time-homogeneous Markov process. The SME extends the generalized method-of-moments (GMM) estimator to a large class of asset pricing models where the moment restrictions of interest do not have analytic representations in terms of observable variables and the unknown parameter vector. The SME is a computationally tractable means of simultaneously solving and estimating an equilibrium asset pricing model. The paper provides conditions for the consistency and asymptotic normality of the SME. It discusses various tradeoffs among the regularity conditions underlying the large sample properties of the SME in the context of an asset pricing model. The paper also addresses the challenges of simulating time series and the non-stationarity of the simulated series. The paper introduces the concept of geometric ergodicity and provides conditions for the consistency and asymptotic normality of the SME. It also discusses the implications of the SME for dynamic asset pricing models and the regularity conditions required for strong consistency. The paper concludes with a discussion of the practical considerations that may influence which, if either, of these results assures consistency for SM estimation of dynamic asset pricing models.