Discrete-Continuous Dynamic Choice Models: Identification and Conditional Choice Probability Estimation

66 Pages Posted: 1 May 2022

Date Written: April 1, 2022

Abstract

This paper develops a general framework for models and games, static or dynamic, in which individuals simultaneously make both discrete and continuous choices. The framework incorporates a wide range of unobserved heterogeneity. I show that such models are nonparametrically identified. Based on constructive identification arguments, I build a novel two-step estimation method in the lineage of Hotz and Miller (1993) and Arcidiacono and Miller (2011) but extended to simultaneous discrete-continuous choice. In the first step, I recover the (type-dependent) optimal choices with an expectation-maximization algorithm. In the second step, I estimate the primitives of the model taking the estimated optimal choices as given. The method is especially attractive for complex dynamic models because it significantly reduces the computational burden associated with their estimation compared to alternative full solution methods.

Keywords: Discrete and continuous choice, dynamic model, identification, structural estimation, unobserved heterogeneity

Suggested Citation

Bruneel-Zupanc, Christophe, Discrete-Continuous Dynamic Choice Models: Identification and Conditional Choice Probability Estimation (April 1, 2022). Available at SSRN: https://ssrn.com/abstract=4072421 or http://dx.doi.org/10.2139/ssrn.4072421

Christophe Bruneel-Zupanc (Contact Author)

KU Leuven ( email )

Korte Nieuwstraat 33
2000 Antwerpen
Belgium

HOME PAGE: http://https://www.cbruneel.com/

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