This paper introduces a Simulated Moments Estimator (SME) for estimating parameters in dynamic asset pricing models where the state vector follows a time-homogeneous Markov process. The authors provide conditions for weak and strong consistency, as well as asymptotic normality, of the SME. They discuss trade-offs among the regularity conditions underlying the large sample properties of the SME in the context of an asset pricing model. The SME extends the generalized method-of-moments (GMM) estimator to a broader class of models where the moment restrictions are not analytically representable in terms of observable variables. The paper includes an illustrative asset pricing model to motivate the use of simulation methods and defines the SME. It also presents conditions for consistency, including a uniform weak law of large numbers, and characterizes the asymptotic distribution of the SME. The authors address the challenges posed by the non-stationarity of simulated processes and the parameter dependency of the data generation process. They propose geometric ergodicity as a condition to ensure asymptotic stationarity and impose a damping condition on the feedback effect of parameter choice on the state process. The paper concludes with a discussion of practical considerations for choosing between weak and strong consistency conditions in dynamic asset pricing models.This paper introduces a Simulated Moments Estimator (SME) for estimating parameters in dynamic asset pricing models where the state vector follows a time-homogeneous Markov process. The authors provide conditions for weak and strong consistency, as well as asymptotic normality, of the SME. They discuss trade-offs among the regularity conditions underlying the large sample properties of the SME in the context of an asset pricing model. The SME extends the generalized method-of-moments (GMM) estimator to a broader class of models where the moment restrictions are not analytically representable in terms of observable variables. The paper includes an illustrative asset pricing model to motivate the use of simulation methods and defines the SME. It also presents conditions for consistency, including a uniform weak law of large numbers, and characterizes the asymptotic distribution of the SME. The authors address the challenges posed by the non-stationarity of simulated processes and the parameter dependency of the data generation process. They propose geometric ergodicity as a condition to ensure asymptotic stationarity and impose a damping condition on the feedback effect of parameter choice on the state process. The paper concludes with a discussion of practical considerations for choosing between weak and strong consistency conditions in dynamic asset pricing models.