Identification and Estimation of Dynamic Structural Models with Unobserved Choices
54 Pages Posted: 24 Oct 2020 Last revised: 19 Aug 2021
Date Written: August 17, 2021
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 non-parametrically identified with a continuous state variable in a single-agent dynamic discrete choice model. Our identification results extend to models with serially correlated unobserved heterogeneity, cases in which state variables are discrete or 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.
Keywords: dynamic discrete choice, unobserved choice, unobserved heterogeneity, dynamic discrete game, nonparametric identification
JEL Classification: C10, C14, C18, C51, D72, D82
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