Nonparametric Identification of Differentiated Products Demand Using Micro Data

A recent literature considers the identification of heterogeneous demand and supply models via \"quasi-experimental\u0027\u0027 variation, as from instrumental variables. In this paper we establish nonparametric identification of differentiated products demand when one has \"micro data\u0027\u0027 linking characteristics of individual consumers to their choices. Micro data provide a panel structure allowing one to exploit variation across consumers within each market, where latent demand shocks are fixed. This facilitates richer demand specifications while substantially softening the reliance on instrumental variables, reducing both the number and types of instruments required. Our results require neither the structure of a \"special regressor\u0027\u0027 nor a \"full support\u0027\u0027 assumption on consumer-level observables.