Identification in Differentiated Products Markets Using Market Level Data

Steven Berry () and Philip Haile

Econometrica, 2014, vol. 82, 1749-1797

Abstract: We present new identification results for nonparametric models of differentiated products markets, using only market level observables. We specify a nonparametric random utility discrete choice model of demand allowing rich preference heterogeneity, product/market unobservables, and endogenous prices. Our supply model posits nonparametric cost functions, allows latent cost shocks, and nests a range of standard oligopoly models. We consider identification of demand, identification of changes in aggregate consumer welfare, identification of marginal costs, identification of firms' marginal cost functions, and discrimination between alternative models of firm conduct. We explore two complementary approaches. The first demonstrates identification under the same nonparametric instrumental variables conditions required for identification of regression models. The second treats demand and supply in a system of nonparametric simultaneous equations, leading to constructive proofs exploiting exogenous variation in demand shifters and cost shifters. We also derive testable restrictions that provide the first general formalization of Bresnahan's (1982) intuition for empirically distinguishing between alternative models of oligopoly competition. From a practical perspective, our results clarify the types of instrumental variables needed with market level data, including tradeoffs between functional form and exclusion restrictions.

Date: 2014
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Citations: View citations in EconPapers (174)

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Related works:
Journal Article: Identification in Differentiated Products Markets Using Market Level Data (2014)
Working Paper: Identification in Differentiated Products Markets Using Market Level Data (2012)
Working Paper: Identification in Differentiated Products Markets Using Market Level Data (2010)
Working Paper: Identification in Differentiated Products Markets Using Market Level Data (2010)
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