Simulated Moments Estimation of Markov Models of Asset Prices.

This paper provides a simulated moments estimator of the parameters of dynamic models in which the state vector follows a time-homogeneous Markov process. Conditions are provided for both weak and strong consistency as well as asymptotic normality. Various trade-offs among the regularity conditions underlying the large sample properties of the simulated moments estimator are discussed in the context of an asset-pricing model. Geometric ergodicity of the underlying Markov process plays a central role in the analysis, ensuring that the simulated processes are asymptotically stationary with an ergodic distribution that is independent of starting values. Copyright 1993 by The Econometric Society.