Semiparametric Bayesian Estimation of Dynamic Discrete Choice Models

Andriy Norets and Kenichi Shimizu

Working Papers from Business School - Economics, University of Glasgow

Abstract: We propose a tractable semiparametric estimation method for dynamic discrete choice models. The distribution of additive utility shocks is modeled by location-scale mixtures of extreme value distributions with varying numbers of mixture components. Our approach exploits the analytical tractability of extreme value distributions and the flexibility of the location-scale mixtures. We implement the Bayesian approach to inference using Hamiltonian Monte Carlo and an approximately optimal reversible jump algorithm from Norets (2021). For binary dynamic choice model, our approach delivers estimation results that are consistent with the previous literature. We also apply the proposed method to multinomial choice models, for which previous literature does not provide tractable estimation methods in general settings without distributional assumptions on the utility shocks. We develop theoretical results on approximations by location-scale mixtures in an appropriate distance and posterior concentration of the set identified utility parameters and the distribution of shocks in the model.

Keywords: Dynamic Discrete choice; Bayesian nonparametrics; set identification; location-scale mixtures; MCMC; Hamiltonian Monte Carlo; reversible jump (search for similar items in EconPapers)
Date: 2022-02
New Economics Papers: this item is included in nep-dcm, nep-ecm, nep-ore and nep-upt
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Related works:
Journal Article: Semiparametric Bayesian estimation of dynamic discrete choice models (2024)
Working Paper: Semiparametric Bayesian Estimation of Dynamic Discrete Choice Models (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:gla:glaewp:2022_06

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