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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.
Berry, Steven T. and Haile, Philip A., "Identification in Differentiated Products Markets Using Market Level Data" (2010). Cowles Foundation Discussion Papers. 2070.