Semiparametric estimation of the optimal reserve price in first-price auctions

Tong Li, Isabelle Perrigne, Quang Vuong

    Research output: Contribution to journalArticle

    Abstract

    The optimal reserve price in the independent private value paradigm is generally expressed as a functional of the latent distribution of private signals, which is by nature unobserved. This feature has limited the implementation of the optimal reserve price in practice. In this article, we consider first-price auctions within the general affiliated private values paradigm. We show that the seller's expected profit can be written as a functional of the observed bid distribution. We propose a semiparametric extremum estimator for estimating consistently the optimal reserve price from observed bids. As an illustration, we consider the Outer Continental Shelf (OCS) wildcat auctions.

    Original languageEnglish (US)
    Pages (from-to)53-64
    Number of pages12
    JournalJournal of Business and Economic Statistics
    Volume21
    Issue number1
    DOIs
    StatePublished - Jan 2003

    Fingerprint

    Semiparametric Estimation
    auction
    Auctions
    Paradigm
    paradigm
    Extremum
    Profit
    Values
    profit
    Estimator
    Semiparametric estimation
    First-price auction
    Reserve price
    Bid

    Keywords

    • Affiliated private value
    • OCS wildcat auction
    • Semiparametric extremum estimator

    ASJC Scopus subject areas

    • Statistics and Probability
    • Economics and Econometrics
    • Social Sciences (miscellaneous)
    • Statistics, Probability and Uncertainty

    Cite this

    Semiparametric estimation of the optimal reserve price in first-price auctions. / Li, Tong; Perrigne, Isabelle; Vuong, Quang.

    In: Journal of Business and Economic Statistics, Vol. 21, No. 1, 01.2003, p. 53-64.

    Research output: Contribution to journalArticle

    Li, Tong ; Perrigne, Isabelle ; Vuong, Quang. / Semiparametric estimation of the optimal reserve price in first-price auctions. In: Journal of Business and Economic Statistics. 2003 ; Vol. 21, No. 1. pp. 53-64.
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