Nonparametric Identification of Differentiated Products Demand Using Micro Data

Steven T. Berry, Yale University
Philip A. Haile, Yale University


We examine identification of differentiated products demand when one has “micro data” linking the characteristics and choices of individual con-sumers. 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 non-price 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.