We consider nonparametric identification in models of differentiated products markets, using only market level observables.On the demand side we consider a nonparametric random utility model nesting random coefficients discrete choice models widely used in applied work.We allow for product/market-specific unobservables, endogenous product characteristics (e.g., prices), and high-dimensional taste shocks with arbitrary correlation and heteroskedasticity.On the supply side we specify marginal costs nonparametrically, allow for unobserved firm heterogeneity, and nest a variety of equilibrium oligopoly models.We pursue two approaches to identification.One relies on instrumental variables conditions used previously to demonstrate identification in a nonparametric regression framework.With this approach we can show identification of the demand side without reference to a particular supply model.Adding the supply side allows identification of firms' marginal costs as well.Our second approach, more closely linked to classical identification arguments for supply and demand models, employs a change of variables approach.This leads to constructive identification results relying on exclusion and support conditions.Our results lead to a testable restriction that provides the first general formalization of Bresnahan's (1982) intuition for empirically discriminating between alternative models of oligopoly competition.
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