Identification with Additively Separable Heterogeneity
68 Pages Posted: 21 Nov 2017 Last revised: 19 Feb 2019
Date Written: December 18, 2018
This paper provides nonparametric identification results for a class of latent utility models with additively separable unobservable heterogeneity. These results apply to existing models of discrete choice, bundles, decisions under uncertainty, and matching. Under an independence assumption, such models admit a representative agent. As a result, we can identify how regressors alter the desirability of goods using only average demands. Moreover, average indirect utility (“welfare”) is identified without needing to specify or identify the distribution of unobservable heterogeneity.
Keywords: Nonparametric Identification, Representative Agent, Symmetry, Welfare
JEL Classification: C01, C14, C25, C30, C43
Suggested Citation: Suggested Citation