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Semiparametric Bayesian Estimation of Dynamic Discrete Choice Models

Andriy Norets, Kenichi Shimizu

arXiv 9 Feb 2022 · Econometrics · publishedJournal of Econometrics (2023) · 3 citations (OpenAlex)

arXiv:2202.04339 · PDF · DOI · OpenAlex · Extracted main text

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.

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38
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in-text mentions
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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Rust, J (1987) Optimal replacement of GMC bus engines: an empirical model of Harold Zurcher0.96911591%
2Norets, A. and X. Tang (2013) Semiparametric Inference in Dynamic Binary Choice Models self0.95616688%
3Gilleskie, D (1998) A Dynamic Stochastic Model of Medical Care Use and Work Absence0.9098475%
4Norets, A (2021) Optimal Auxiliary Priors and Reversible Jump Proposals for a Class of Variable Dimension Models self0.81142100%
5Norets, A (2011) Semiparametric Identification of Dynamic Multinomial Choice Models, Unpublished Manuscript, Princeton University self0.7374350%
6Norets, A. and J. Pelenis (2022) Adaptive Bayesian Estimation of Discrete-Continuous Distributions Under Smoothness and Sparsity self0.73732100%
7Manski, C. F (1999) Identification Problems in the Social Sciences0.64422100%
8Moon, H. R. and F. Schorfheide (2012) Bayesian and Frequentist Inference in Partially Identified Models0.64422100%
9Norets, A (2010) Continuity and Differentiability of Expected Value Functions in Dynamic Discrete Choice Models self0.64422100%
10Shen, W., S. T. Tokdar, and S. Ghosal (2013) Adaptive Bayesian multivariate density estimation with Dirichlet mixtures0.64422100%

Showing the top 10 of 38 scored citations.