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Identification in Ascending Auctions, with an Application to Digital Rights Management

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  • Joachim Freyberger
  • Bradley J. Larsen

Abstract

This study provides new identification and estimation results for ascending (traditional English or online) auctions with unobserved auction-level heterogeneity and an unknown number of bidders. When the seller's reserve price and two order statistics of bids are observed, we derive conditions under which the distributions of buyer valuations, unobserved heterogeneity, and number of participants are point identified. We also derive conditions for point identification in cases where reserve prices are binding (in which case bids may be unobserved in some auctions) and present general conditions for partial identification. We propose a nonparametric maximum likelihood approach for estimation and inference. We apply our approach to the online market for used iPhones and analyze the effects of recent regulatory changes banning consumers from circumventing digital rights management technologies used to lock phones to service providers. We find that buyer valuations for unlocked phones dropped after the unlocking ban took effect.

Suggested Citation

  • Joachim Freyberger & Bradley J. Larsen, 2017. "Identification in Ascending Auctions, with an Application to Digital Rights Management," NBER Working Papers 23569, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:23569
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    Cited by:

    1. Yao Luo & Peijun Sang & Ruli Xiao, 2024. "Order Statistics Approaches to Unobserved Heterogeneity in Auctions," Working Papers tecipa-776, University of Toronto, Department of Economics.
    2. Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," Papers 2402.05030, arXiv.org, revised Nov 2024.
    3. Michele Fioretti, 2022. "Caring or Pretending to Care? Social Impact, Firms' Objectives, and Welfare (former title: Social Responsibility and Firm's Objectives)," SciencePo Working papers Main hal-03393065, HAL.
    4. repec:spo:wpecon:info:hdl:2441/5kht5rc22p99sq5tol4efe4ssb is not listed on IDEAS
    5. repec:hal:spmain:info:hdl:2441/5kht5rc22p99sq5tol4efe4ssb is not listed on IDEAS
    6. repec:hal:wpspec:info:hdl:2441/5kht5rc22p99sq5tol4efe4ssb is not listed on IDEAS
    7. Marleen Marra, 2024. "Estimating and Auction Platform Game with Two-Sided Entry," Working Papers hal-03393068, HAL.
    8. JoonHwan Cho & Yao Luo & Ruli Xiao, 2024. "Deconvolution from two order statistics," Papers 2403.17777, arXiv.org.
    9. Emmanuel Guerre & Yao Luo, 2019. "Nonparametric Identification of First-Price Auction with Unobserved Competition: A Density Discontinuity Framework," Papers 1908.05476, arXiv.org, revised Jan 2022.
    10. Kong, Yunmi, 2022. "Identification of English auctions when losing entrants are not observed," International Journal of Industrial Organization, Elsevier, vol. 85(C).
    11. Barkley, Aaron & Groeger, Joachim R. & Miller, Robert A., 2021. "Bidding frictions in ascending auctions," Journal of Econometrics, Elsevier, vol. 223(2), pages 376-400.
    12. Yao Luo & Ruli Xiao, 2019. "Identification of Auction Models Using Order Statistics," Working Papers tecipa-630, University of Toronto, Department of Economics.
    13. Stefan Seifert & Silke Hüttel, 2023. "Is there a risk of a winner’s curse in farmland auctions?," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 50(3), pages 1140-1177.
    14. JoonHwan Cho & Yao Luo & Ruli Xiao, 2022. "Deconvolution from Two Order Statistics," Working Papers tecipa-739, University of Toronto, Department of Economics.
    15. Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," THEMA Working Papers 2024-01, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    16. 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.
    17. Luo, Yao & Xiao, Ruli, 2023. "Identification of auction models using order statistics," Journal of Econometrics, Elsevier, vol. 236(1).
    18. repec:spo:wpmain:info:hdl:2441/5kht5rc22p99sq5tol4efe4ssb is not listed on IDEAS

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    More about this item

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C57 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Econometrics of Games and Auctions
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • L0 - Industrial Organization - - General
    • L96 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Telecommunications
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights

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