Identification and Estimation of Dynamic Structural Models with Unobserved Choices

46 Pages Posted: 24 Oct 2020 Last revised: 18 Jan 2022

See all articles by Yingyao Hu

Yingyao Hu

Johns Hopkins University - Department of Economics

Yi Xin

California Institute of Technology

Date Written: January 14, 2022

Abstract

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

Suggested Citation

Hu, Yingyao and Xin, Yi, Identification and Estimation of Dynamic Structural Models with Unobserved Choices (January 14, 2022). Available at SSRN: https://ssrn.com/abstract=3634910 or http://dx.doi.org/10.2139/ssrn.3634910

Yingyao Hu

Johns Hopkins University - Department of Economics ( email )

3400 Charles Street
Baltimore, MD 21218-2685
United States

Yi Xin (Contact Author)

California Institute of Technology ( email )

Pasadena, CA 91125
United States

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