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Identification and estimation of dynamic structural models with unobserved choices

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.
Year of publication:
2020
Authors: Hu, Yingyao ; Xin, Yi
Publisher: London : Centre for Microdata Methods and Practice (cemmap)
Subject: dynamic discrete choice | unobserved choice | moral hazard | unobserved heterogeneity | dynamic discrete game | nonparametric identification
Series:
Type of publication: Book / Working Paper
Type of publication (narrower categories): Working Paper
Language: English
Other identifiers:
10.47004/wp.cem.2020.4420 [DOI]
1730506674 [GVK]
hdl:10419/241919 [Handle]
RePEc:ifs:cemmap:44/20 [RePEc]
Classification: C10 - Econometric and Statistical Methods: General. General ; C14 - Semiparametric and Nonparametric Methods ; c18 ; C51 - Model Construction and Estimation ; D72 - Economic Models of Political Processes: Rent-Seeking, Elections, Legistures, and Voting Behavior ; D82 - Asymmetric and Private Information
Source:
Persistent link: https://ebvufind01.dmz1.zbw.eu/10012621121
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