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A Nonparametric Test For Comparing Valuation Distributions In First‐Price Auctions

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  • Nianqing Liu
  • Yao Luo

Abstract

This article proposes a nonparametric test for comparing valuation distributions in first‐price auctions. Our test is motivated by the fact that two valuation distributions are the same if and only if their integrated quantile functions are the same. Our method avoids estimating unobserved valuations and does not require smooth estimation of bid density. We show that our test is consistent against all fixed alternatives and has nontrivial power against root‐N local alternatives. Monte Carlo experiments show that our test performs well in finite samples. We implement our method on data from U.S. Forest Service timber auctions.

Suggested Citation

  • Nianqing Liu & Yao Luo, 2017. "A Nonparametric Test For Comparing Valuation Distributions In First‐Price Auctions," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 58(3), pages 857-888, August.
    • Handle: RePEc:wly:iecrev:v:58:y:2017:i:3:p:857-888
    DOI: 10.1111/iere.12238
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    References listed on IDEAS

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    Cited by:

    1. Federico Zincenko, 2023. "Nonparametric estimation of conditional densities by generalized random forests," Papers 2309.13251, arXiv.org, revised Jan 2024.
    2. Jun, Sung Jae & Zincenko, Federico, 2022. "Testing for risk aversion in first-price sealed-bid auctions," Journal of Econometrics, Elsevier, vol. 226(2), pages 295-320.

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