Yogurts Choose Consumers? Estimation of Random-Utility Models via Two-Sided Matching
发现随机效用离散选择模型的需求反演等价于双边匹配模型中稳定结果的确定，并基于此提出匹配算法来估计模型，应用于1999年欧洲议会选举投票数据。
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.