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Identification With Additively Separable Heterogeneity
Author(s) -
Allen Roy,
Rehbeck John
Publication year - 2019
Publication title -
econometrica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 16.7
H-Index - 199
eISSN - 1468-0262
pISSN - 0012-9682
DOI - 10.3982/ecta15867
Subject(s) - unobservable , nonparametric statistics , identification (biology) , separable space , econometrics , discrete choice , matching (statistics) , independence (probability theory) , class (philosophy) , economics , welfare , revealed preference , mathematics , computer science , statistics , artificial intelligence , mathematical analysis , botany , biology , market economy
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.