ESTIMATION OF DYNAMIC DISCRETE CHOICE MODELS BY MAXIMUM LIKELIHOOD AND THE SIMULATED METHOD OF MOMENTS
比较了最大似然和模拟矩方法在动态离散选择模型中的估计表现，基于美国1980-1990年代教育选择数据构建简化模型，通过模拟数据评估两种方法恢复参数的能力。
We compare the performance of maximum likelihood (ML) and simulated method of moments (SMM) estimators for dynamic discrete choice models. We construct and estimate a simplified dynamic structural model of education that captures some basic features of educational choices in the United States in the 1980s and early 1990s. We use estimates from our model to simulate a synthetic data set and assess the ability of ML and SMM to recover the model parameters on this sample. We investigate the performance of alternative tuning parameters for SMM.