Nonparametric identification of random coefficients in aggregate demand models for differentiated products

B-Tier
Journal: The Econometrics Journal
Year: 2023
Volume: 26
Issue: 2
Pages: 279-306

Score contribution per author:

0.670 = (α=2.01 / 3 authors) × 1.0x B-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

Abstract

SummaryThis paper studies nonparametric identification in market-level demand models for differentiated products with heterogeneous consumers. We consider a general class of models that allows for the individual-specific coefficients to vary continuously across the population and give conditions under which the density of these coefficients, and hence also functionals such as the fractions of individuals who benefit from a counterfactual intervention, is identified.

Technical Details

RePEc Handle
repec:oup:emjrnl:v:26:y:2023:i:2:p:279-306.
Journal Field
Econometrics
Author Count
3
Added to Database
2026-01-25