Yogurts Choose Consumers? Estimation of Random-Utility Models via Two-Sided Matching

Odran Bonnet, Alfred Galichon, Yu-Wei Hsieh, Keith O’Hara and Matt Shum

The Review of Economic Studies, 2022, vol. 89, issue 6, 3085-3114

Abstract: The problem of demand inversion—a crucial step in the estimation of random utility discrete-choice models—is equivalent to the determination of stable outcomes in two-sided matching models. This equivalence applies to random utility models that are not necessarily additive, smooth, nor even invertible. Based on this equivalence, algorithms for the determination of stable matchings provide effective computational methods for estimating these models. For non-invertible models, the identified set of utility vectors is a lattice, and the matching algorithms recover sharp upper and lower bounds on the utilities. Our matching approach facilitates estimation of models that were previously difficult to estimate, such as the pure characteristics model. An empirical application to voting data from the 1999 European Parliament elections illustrates the good performance of our matching-based demand inversion algorithms in practice.

Keywords: Random utility models; Demand inversion; Two-sided matching; Discrete-choice demand models; Partial identification; Pure characteristics model; C51; C60 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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Related works:
Working Paper: Yogurts Choose Consumers? Estimation of Random-Utility Models via Two-Sided Matching (2022)
Working Paper: Yogurts Choose Consumers? Estimation of Random-Utility Models via Two-Sided Matching (2022)
Working Paper: Yogurts Choose Consumers? Estimation of Random-Utility Models via Two-Sided Matching (2021)
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