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Estimation in English auctions with unobserved heterogeneity

Author

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  • Cristián Hernández
  • Daniel Quint
  • Christopher Turansick

Abstract

We propose a framework for identification and estimation of a private values model with unobserved heterogeneity from bid data in English auctions, using variation in the number of bidders across auctions, and extend the framework to settings where the number of bidders is not cleanly observed in each auction. We illustrate our method on data from eBay Motors auctions. We find that unobserved heterogeneity is important, accounting for two thirds of price variation after controlling for observables, and that welfare measures would be dramatically misestimated if unobserved heterogeneity were ignored.

Suggested Citation

  • Cristián Hernández & Daniel Quint & Christopher Turansick, 2020. "Estimation in English auctions with unobserved heterogeneity," RAND Journal of Economics, RAND Corporation, vol. 51(3), pages 868-904, September.
    • Handle: RePEc:bla:randje:v:51:y:2020:i:3:p:868-904
    DOI: 10.1111/1756-2171.12343
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    References listed on IDEAS

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    Cited by:

    1. Luo, Yao & Xiao, Ruli, 2023. "Identification of auction models using order statistics," Journal of Econometrics, Elsevier, vol. 236(1).
    2. JoonHwan Cho & Yao Luo & Ruli Xiao, 2022. "Deconvolution from Two Order Statistics," Working Papers tecipa-739, University of Toronto, Department of Economics.
    3. Dominic Coey & Bradley J. Larsen & Kane Sweeney & Caio Waisman, 2021. "Scalable Optimal Online Auctions," Marketing Science, INFORMS, vol. 40(4), pages 593-618, July.
    4. Joachim Freyberger & Bradley J. Larsen, 2022. "Identification in ascending auctions, with an application to digital rights management," Quantitative Economics, Econometric Society, vol. 13(2), pages 505-543, May.

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