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

TL;DR

This paper introduces a novel matching-based approach to estimate complex random utility models in demand analysis, including non-invertible cases, demonstrated through an application to European election data.

Contribution

It establishes an equivalence between demand inversion in discrete-choice models and stable outcomes in two-sided matching, enabling new estimation methods for previously intractable models.

Findings

Effective algorithms for demand inversion using matching techniques.

Sharp bounds on utilities for non-invertible models.

Successful empirical application to European election voting data.

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.