A Simulation Estimator for Dynamic Models of Discrete Choice
提出一种新的模拟估计量，用于估计离散选择动态模型的结构参数，通过模拟技术处理无终止状态模型，并基于蒙特卡洛研究比较其与最大似然估计的小样本性质。
This paper analyses a new estimator for the structural parameters of dynamic models of discrete choice. Based on an inversion theorem due to Hotz and Miller (1993), which establishes the existence of a one-to-one mapping between the conditional valuation functions for the dynamic problem and their associated conditional choice probabilities, we exploit simulation techniques to estimate models which do not possess terminal states. In this way our Conditional Choice Simulation (CCS) estimator complements the Conditional Choice Probability (CCP) estimator of Hotz and Miller (1993). Drawing on work in empirical process theory by Pakes and Pollard (1989), we establish its large sample properties, and then conduct a Monte Carlo study of Rust's (1987) model of bus engine replacement to compare its small sample properties with those of Maximum Likelihood (ML).