Adam Smith Business School

Semiparametric Bayesian Estimation of Dynamic Discrete Choice Models

Friday 21 October, 3pm-4pm

Kenichi Shimizu is an econometrician and a Lecturer in Economics at the Adam Smith Business School. He completed his PhD at Brown University in 2021. He works in the area of econometric theory, discrete choice models, marketing, structural break (change-point) models, Bayesian estimation of high-dimensional economic models, and semi-parametric Bayesian estimation. His works have been published in the Journal of Econometrics and Foundations & Trends in Econometrics. 

Abstract

We propose a tractable semiparametric estimation method for dynamic discrete choice models. The distribution of additive utility shocks is modelled 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. For the 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. 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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For further information, please contact business-school-research@glasgow.ac.uk 

First published: 11 October 2022