Nonparametric Identification of Differentiated Products Demand Using Micro Data

S-Tier
Journal: Econometrica
Year: 2024
Volume: 92
Issue: 4
Pages: 1135-1162

Authors (2)

Steven T. Berry (Yale University) Philip A. Haile (not in RePEc)

Score contribution per author:

4.022 = (α=2.01 / 2 authors) × 4.0x S-tier

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

Abstract

We examine identification of differentiated products demand when one has “micro data” linking the characteristics and choices of individual consumers. Our model nests standard specifications featuring rich observed and unobserved consumer heterogeneity as well as product/market‐level unobservables that introduce the problem of econometric endogeneity. Previous work establishes identification of such models using market‐level data and instruments for all prices and quantities. Micro data provides a panel structure that facilitates richer demand specifications and reduces requirements on both the number and types of instrumental variables. We address identification of demand in the standard case in which nonprice product characteristics are assumed exogenous, but also cover identification of demand elasticities and other key features when these product characteristics are endogenous and not instrumented. We discuss implications of these results for applied work.

Technical Details

RePEc Handle
repec:wly:emetrp:v:92:y:2024:i:4:p:1135-1162
Journal Field
General
Author Count
2
Added to Database
2026-01-24