Sequential Monte Carlo Methods for Estimating Dynamic Microeconomic Models
利用序贯蒙特卡洛方法估计带有序列相关不可观测状态变量的动态微观经济模型，提出全解极大似然和两步法两种估计量，并用资本替换模型验证其有效性。
Summary This paper develops estimators for dynamic microeconomic models with serially correlated unobserved state variables using sequential Monte Carlo methods to estimate the parameters and the distribution of the unobservables. If persistent unobservables are ignored, the estimates can be subject to a dynamic form of sample selection bias. We focus on single‐agent dynamic discrete‐choice models and dynamic games of incomplete information. We propose a full‐solution maximum likelihood procedure and a two‐step method and use them to estimate an extended version of the capital replacement model of Rust with the original data and in a Monte Carlo study. Copyright © 2015 John Wiley & Sons, Ltd.