Semiparametric Bayesian estimation of dynamic discrete choice models

Andriy Norets and Kenichi Shimizu

Journal of Econometrics, 2024, vol. 238, issue 2

Abstract: We propose a tractable semiparametric estimation method for structural dynamic discrete choice models. The distribution of additive utility shocks in the proposed framework 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 in the multinomial choice settings 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. In our simulation experiments, we show that the standard dynamic logit model can deliver misleading results, especially about counterfactuals, when the shocks are not extreme value distributed. Our semiparametric approach delivers reliable inference in these settings. 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; Hamiltonian Monte Carlo; Reversible jump (search for similar items in EconPapers)
Date: 2024
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Related works:
Working Paper: Semiparametric Bayesian Estimation of Dynamic Discrete Choice Models (2023)
Working Paper: Semiparametric Bayesian Estimation of Dynamic Discrete Choice Models (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:238:y:2024:i:2:s0304407623003585

DOI: 10.1016/j.jeconom.2023.105642

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