Semiparametric Estimation of First-Price Auction Models

Gaurab Aryal, Maria F. Gabrielli, Quang Vuong

    Research output: Contribution to journalArticlepeer-review


    In this article, we propose a two-step semiparametric procedure to estimate first-price auction models. In the first step, we estimate the bid density and distribution using local polynomial method, and recover a sample of (pseudo) private values. In the second step, we apply the method of moments to the sample of private values to estimate a finite set of parameters that characterize the density of private values. We show that our estimator attains the parametric consistency rate and is asymptotically normal. And we also determine its asymptotic variance. The advantage of our approach is that it can accommodate multiple auction covariates. Monte Carlo exercises show that the estimator performs well both in estimating the value density and in choosing the revenue maximizing reserve price. Supplementary materials for this article are available online.

    Original languageEnglish (US)
    JournalJournal of Business and Economic Statistics
    StateAccepted/In press - 2019


    • Empirical auctions
    • GMM
    • Local polynomial
    • Semiparametric estimator

    ASJC Scopus subject areas

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


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