Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/241919 
Year of Publication: 
2020
Series/Report no.: 
cemmap working paper No. CWP44/20
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
This paper develops identification and estimation methods for dynamic structural 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 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.
Subjects: 
dynamic discrete choice
unobserved choice
moral hazard
unobserved heterogeneity
dynamic discrete game
nonparametric identification
JEL: 
C10
C14
C18
C51
D72
D82
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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