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

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 identified set of utility vectors
is a lattice, and the matching algorithms recover sharp upper and lower bounds on the
utilities. For invertible models, our matching approach facilitates estimation of models that
were previously dicult 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

JEL Classification: C51, C60