Simulated Moments Estimation of Markov Models of Asset Prices
提出一种模拟矩估计方法，用于估计状态向量服从时间齐次马尔可夫过程的动态模型参数，并给出了弱一致性和强一致性以及渐近正态性的条件，讨论了资产定价模型中大样本性质的正则条件之间的权衡。
This paper provides a simulated moments estimator (SME) 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 tradeoff's among the regularity conditions underlying the large sample properties of the SME are discussed in the context of an asset pricing model.