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Identification in Differentiated Products Markets

Author

Listed:
  • Steven Berry

    () (Department of Economics and Cowles Foundation, Yale University, New Haven, Connecticut 06520)

  • Philip Haile

    () (Department of Economics and Cowles Foundation, Yale University, New Haven, Connecticut 06520)

Abstract

Empirical models of differentiated products demand (and often supply) are widely used in industrial organization and other fields of economics. We review some recent work studying identification in a broad class of such models. This work shows that the parametric functional forms and distributional assumptions commonly used for estimation are not essential for identification. Rather, identification relies primarily on the standard requirement that instruments be available for the endogenous variables—here, typically, prices and quantities. We discuss the types of instruments that can suffice, as well as how instrumental variables requirements can be relaxed by the availability of individual-level data or through restrictions on preferences. We also review new results on discrimination between alternative models of oligopoly competition. Together, these results reveal a strong nonparametric foundation for a broad applied literature, provide practical guidance for applied work, and may suggest new approaches to estimation and testing.

Suggested Citation

  • Steven Berry & Philip Haile, 2016. "Identification in Differentiated Products Markets," Annual Review of Economics, Annual Reviews, vol. 8(1), pages 27-52, October.
  • Handle: RePEc:anr:reveco:v:8:y:2016:p:27-52
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    File URL: http://www.annualreviews.org/doi/10.1146/annurev-economics-080315-015050
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    References listed on IDEAS

    as
    1. Reynaert, Mathias & Verboven, Frank, 2014. "Improving the performance of random coefficients demand models: The role of optimal instruments," Journal of Econometrics, Elsevier, vol. 179(1), pages 83-98.
    2. Lau, Lawrence J., 1982. "On identifying the degree of competitiveness from industry price and output data," Economics Letters, Elsevier, vol. 10(1-2), pages 93-99.
    3. Victor Chernozhukov & Christian Hansen, 2005. "An IV Model of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 73(1), pages 245-261, January.
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    Cited by:

    1. Deng, Zhongqi & Chen, Yongjun, 2016. "Markups and Firm-Level Export Status: Comment," MPRA Paper 74494, University Library of Munich, Germany.
    2. Mogens Fosgerau & Abhishek Ranjan, 2017. "A note on identification in discrete choice models with partial observability," Theory and Decision, Springer, vol. 83(2), pages 283-292, August.

    More about this item

    Keywords

    nonparametric identification; instrumental variables; discrete choice; differentiated products oligopoly; demand and supply; firm conduct;

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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