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Unobserved Heterogeneity in Auctions

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Abstract

A common concern in the empirical study of auctions is the likely presence of auction-specific factors that are common knowledge among bidders but unobserved to the econometrician. Such unobserved heterogeneity confounds attempts to uncover the underlying structure of demand and information, typically a primary feature of interest in an auction market. Unobserved heterogeneity presents a particular challenge in first-price auctions, where identification arguments rely on the econometrician's ability to reconstruct from observables the conditional probabilities that entered each bidder's equilibrium optimization problem; when bidders condition on unobservables, it is not obvious that this is possible. Here we discuss several approaches to identification developed in recent work on first-price auctions with unobserved heterogeneity. Despite the special challenges of this setting, all of the approaches build on insights developed in other areas of econometrics, including those on control functions, measurement error, and mixture models. Because each strategy relies on different combinations of model restrictions, technical assumptions, and data requirements, their relative attractiveness will vary with the application. However, this varied menu of results suggests both a type of robustness of identifiability and the potential for expanding the frontier with additional work.

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  • Philip A. Haile & Yuichi Kitamura, 2018. "Unobserved Heterogeneity in Auctions," Cowles Foundation Discussion Papers 2141, Cowles Foundation for Research in Economics, Yale University.
  • Handle: RePEc:cwl:cwldpp:2141
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    Cited by:

    1. Giovanni Compiani & Philip Haile & Marcelo Sant’Anna, 2020. "Common Values, Unobserved Heterogeneity, and Endogenous Entry in US Offshore Oil Lease Auctions," Journal of Political Economy, University of Chicago Press, vol. 128(10), pages 3872-3912.
    2. Myśliwski, Mateusz & Rostom, May, 2022. "Value of information, search, and competition in the UK mortgage market," Bank of England working papers 967, Bank of England.
    3. Yuichi Kitamura & Louise Laage, 2018. "Nonparametric Analysis of Finite Mixtures," Papers 1811.02727, arXiv.org.
    4. Gimenes, Nathalie & Guerre, Emmanuel, 2020. "Nonparametric identification of an interdependent value model with buyer covariates from first-price auction bids," Journal of Econometrics, Elsevier, vol. 219(1), pages 1-18.
    5. Nathalie Gimenes & Emmanuel Guerre, 2019. "Nonparametric identification of an interdependent value model with buyer covariates from first-price auction bids," Papers 1910.10646, arXiv.org.
    6. Yao Luo & Ruli Xiao, 2019. "Identification of Auction Models Using Order Statistics," Working Papers tecipa-630, University of Toronto, Department of Economics.
    7. Rodrigo Carril & Andres Gonzalez-Lira & Michael S. Walker, 2022. "Competition under Incomplete Contracts and the Design of Procurement Policies," Working Papers 1327, Barcelona School of Economics.
    8. Stefan Seifert & Silke Hüttel, 2023. "Is there a risk of a winner’s curse in farmland auctions?," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 50(3), pages 1140-1177.
    9. Seifert, Stefan & Hüttel, Silke, 2020. "Common values and unobserved heterogeneity in farmland auctions in Germany," FORLand Working Papers 21 (2020), Humboldt University Berlin, DFG Research Unit 2569 FORLand "Agricultural Land Markets – Efficiency and Regulation".
    10. Pasha Andreyanov & El Hadi Caoui, 2022. "Secret reserve prices by uninformed sellers," Quantitative Economics, Econometric Society, vol. 13(3), pages 1203-1256, July.
    11. Luo, Yao & Xiao, Ruli, 2023. "Identification of auction models using order statistics," Journal of Econometrics, Elsevier, vol. 236(1).

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