Identification and estimation of dynamic structural models with unobserved choices
研究了当经济主体的行为未被计量经济学家观测到时，如何识别和估计动态离散选择模型，提出了非参数识别条件和筛子最大似然估计方法，蒙特卡洛模拟验证了有效性。
This paper develops identification and estimation methods for dynamic discrete choice models when agents' actions are unobserved by econometricians. We provide conditions under which choice probabilities and latent state transition rules are nonparametrically identified with a continuous state variable in a single-agent dynamic discrete choice model. Our identification strategy from the baseline model can extend to models with serially correlated unobserved heterogeneity, cases in which choices are partially unavailable, and dynamic discrete games. We propose a sieve maximum likelihood estimator for primitives in agents' utility functions and state transition rules. Monte Carlo simulation results support the validity of the proposed approach.