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Structural Analysis of First-Price Auction Data: Insights from the Laboratory

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  • Paul Pezanis-Christou

    (School of Economics, University of Adelaide)

  • Andrés Romeu

    (Fundamentos del Analisis Economico, Universidad de Murcia)

Abstract

We use laboratory data from first-price auctions and manipulate the quantity and the quality of information available to assess the robustness of structural inferences (i.e., estimates, revenue predictions and optimal reserve price recommendations). We show that the latter are sensitive to the quantity of information when quality is low such as in field settings, and that improving quality in such settings dampens the effect of quantity and unveils out-of-equilibrium bidding patterns. Yet, a counterfactual analysis of the seller's revenues and optimal reserve prices indicates that behavior is best explained by the usual Nash equilibrium model with either risk aversion or a power form of probability misperception. When the information available is of the highest quality, as in laboratory settings, this model is typically rejected because of a nonlinear bidding behavior. We consider two rationales for such behavior and find that they leave revenue predictions and optimal price recommendations hardly affected.

Suggested Citation

  • Paul Pezanis-Christou & Andrés Romeu, 2018. "Structural Analysis of First-Price Auction Data: Insights from the Laboratory," School of Economics Working Papers 2018-08, University of Adelaide, School of Economics.
  • Handle: RePEc:adl:wpaper:2018-08
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    References listed on IDEAS

    as
    1. Paul Pezanis-Christou & Andres Romeu, 2002. "Structural Inferences from First-Price Auction Experiments," UFAE and IAE Working Papers 531.02, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
    2. Li, Huagang & Riley, John G., 2007. "Auction choice," International Journal of Industrial Organization, Elsevier, vol. 25(6), pages 1269-1298, December.
    3. Kenneth Hendricks & Harry J. Paarsch, 1995. "A Survey of Recent Empirical Work Concerning Auctions," Canadian Journal of Economics, Canadian Economics Association, vol. 28(2), pages 403-426, May.
    4. Mark Van Boening & Stephen Rassenti & Vernon Smith, 1998. "Numerical Computation of Equilibrium Bid Functions in a First-Price Auction with Heterogeneous Risk Attitudes," Experimental Economics, Springer;Economic Science Association, vol. 1(2), pages 147-159, September.
    5. Patrick Bajari & Ali Hortacsu, 2005. "Are Structural Estimates of Auction Models Reasonable? Evidence from Experimental Data," Journal of Political Economy, University of Chicago Press, vol. 113(4), pages 703-741, August.
    6. Olivier Armantier, 2002. "Deciding between the Common and Private Values Paradigm: An Application to Experimental Data," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 43(3), pages 783-801, August.
    7. Douglas Dyer & John H. Kagel & Dan Levin, 1989. "Resolving Uncertainty about the Number of Bidders in Independent Private-Value Auctions: An Experimental Analysis," RAND Journal of Economics, The RAND Corporation, vol. 20(2), pages 268-279, Summer.
    8. Harry J. Paarsch & Han Hong, 2006. "An Introduction to the Structural Econometrics of Auction Data," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262162350.
    9. Harrison, Glenn W, 1990. "Risk Attitudes in First-Price Auction Experiments: A Bayesian Analysis," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 541-546, August.
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    Cited by:

    1. Paul Pezanis-Christou & Hang Wu, 2017. "A Naïve Approach to Bidding," School of Economics Working Papers 2017-03, University of Adelaide, School of Economics.
    2. Antonio Merlo & Thomas R. Palfrey, 2018. "External validation of voter turnout models by concealed parameter recovery," Public Choice, Springer, vol. 176(1), pages 297-314, July.
    3. Paul Pezanis-Christou & Hang Wu, 2018. "A non-game-theoretic approach to bidding in first-price and all-pay auctions," School of Economics Working Papers 2018-12, University of Adelaide, School of Economics.

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    More about this item

    Keywords

    first-price sealed bid auctions; structural econometrics of auctions; constant relative risk aversion; probability misperception; expected revenue predictions; optimal reserve prices; experiments;
    All these keywords.

    JEL classification:

    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • D47 - Microeconomics - - Market Structure, Pricing, and Design - - - Market Design
    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance

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