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选择不可观测时动态结构模型的识别与估计AI 解读

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,…

数据不完整时动态离散选择模型的锐识别AI 解读

In many empirical studies, those states that are relevant for forward-looking economic agents to make decisions may not be included in the data to which researchers have access. This problem often arises in the context of declining/booming industries. In this paper, we develop the sharp identified sets of structural parameters and counterfactuals for dynamic discrete choice models when empirical data do not cover realizations of relevant future states. Applying the proposed…

不等时间间隔动态面板数据的识别与估计AI 解读

We propose conditions under which parameters of fixed-effect dynamic models are identified with unequally spaced panel data. Under predeterminedness, weak stationarity, and empirically testable rank conditions, AR(1) parameters are identified given the availability of “two pairs of two consecutive time gaps”, which generalizes “two pairs of two consecutive time periods”. This result extends to models with multiple covariates, higher-order autoregressions, and partial…