Optimal nonparametric estimation of first-price auctions

Emmanuel Guerre, Isabelle Perrigne, Quang Vuong

    Research output: Contribution to journalArticlepeer-review


    This paper proposes a general approach and a computationally convenient estimation procedure for the structural analysis of auction data. Considering first-price sealed-bid auction models within the independent private value paradigm, we show that the underlying distribution of bidders' private values is identified from observed bids and the number of actual bidders without any parametric assumptions. Using the theory of minimax, we establish the best rate of uniform convergence at which the latent density of private values can be estimated nonparametrically from available data. We then propose a two-step kernel-based estimator that converges at the optimal rate.

    Original languageEnglish (US)
    Pages (from-to)525-574
    Number of pages50
    Issue number3
    StatePublished - 2000


    • First-price auctions
    • Independent private value
    • Kernel estimation
    • Minimax theory
    • Nonparametric identification
    • Two-stage nonparametric estimation

    ASJC Scopus subject areas

    • Economics and Econometrics


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