Inference in Dynamic Discrete Choice Models With Serially Correlated Unobserved State Variables
提出一种贝叶斯马尔可夫链蒙特卡洛估计方法，用于处理动态离散选择模型中因序列相关未观测状态变量导致的高维积分问题，并给出一个高效的动态规划求解算法。
This paper develops a method for inference in dynamic discrete choice models with serially correlated unobserved state variables. Estimation of these models involves computing high-dimensional integrals that are present in the solution to the dynamic program and in the likelihood function. First, the paper proposes a Bayesian Markov chain Monte Carlo estimation procedure that can handle the problem of multidimensional integration in the likelihood function. Second, the paper presents an efficient algorithm for solving the dynamic program suitable for use in conjunction with the proposed estimation procedure. Copyright 2009 The Econometric Society.