Nonparametric Identification of Random Coefficients in Endogenous and Heterogeneous Aggregate Demand Models

TL;DR

This paper establishes conditions for nonparametric identification of consumer heterogeneity in demand models, including BLP and pure characteristics models, enabling analysis of welfare and other functionals.

Contribution

It provides the first nonparametric identification conditions for continuous heterogeneity in market demand models, highlighting differences between BLP and pure characteristics models.

Findings

Identification conditions differ for BLP and pure characteristics models.

Density of consumer coefficients can be nonparametrically identified.

Supports of product characteristics are crucial for identification.

Abstract

This 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 welfare measures, is identified. A key finding is that two leading models, the BLP-model (Berry, Levinsohn, and Pakes, 1995) and the pure characteristics model (Berry and Pakes, 2007), require considerably different conditions on the support of the product characteristics.