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Quantile methods for first-price auction: A signal approach

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  • Nathalie Gimenes
  • Emmanuel Guerre

Abstract

This paper considers a quantile signal framework for first-price auction. Under the independent private value paradigm, a key stability property is that a linear specification for the private value conditional quantile function generates a linear specification for the bids one, from which it can be easily identified. This applies in particular for standard quantile regression models but also to more flexible additive sieve specification which are not affected by the curse of dimensionality. A combination of local polynomial and sieve methods allows to estimate the private value quantile function with a fast optimal rate and for all quantile levels in [0; 1] without boundary effects. The choice of the smoothing parameters is also discussed. Extensions to interdependent values including bidder specific variables are also possible under some functional restrictions, which tie up the signal to the bidder covariate. The identification of this new model is established and some estimation methods are suggested.

Suggested Citation

  • Nathalie Gimenes & Emmanuel Guerre, 2016. "Quantile methods for first-price auction: A signal approach," Working Papers, Department of Economics 2016_23, University of São Paulo (FEA-USP).
    • Handle: RePEc:spa:wpaper:2016wpecon23
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    References listed on IDEAS

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    1. Guerre, Emmanuel & Sabbah, Camille, 2012. "Uniform Bias Study And Bahadur Representation For Local Polynomial Estimators Of The Conditional Quantile Function," Econometric Theory, Cambridge University Press, vol. 28(1), pages 87-129, February.
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    Cited by:

    1. Enache, Andreea & Florens, Jean-Pierre, 2020. "Quantile Analysis of "Hazard-Rate" Game Models," TSE Working Papers 20-1117, Toulouse School of Economics (TSE).
    2. Luo, Yao, 2020. "Unobserved heterogeneity in auctions under restricted stochastic dominance," Journal of Econometrics, Elsevier, vol. 216(2), pages 354-374.
    3. Serafin J. Grundl & Yu Zhu, 2019. "Robust Inference in First-Price Auctions : Experimental Findings as Identifying Restrictions," Finance and Economics Discussion Series 2019-006, Board of Governors of the Federal Reserve System (U.S.).
    4. Ma, Jun & Marmer, Vadim & Shneyerov, Artyom, 2019. "Inference for first-price auctions with Guerre, Perrigne, and Vuong’s estimator," Journal of Econometrics, Elsevier, vol. 211(2), pages 507-538.
    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. Nathalie Gimenes, 2014. "Econometrics of Ascending Auctions by Quantile Regression," Working Papers, Department of Economics 2014_25, University of São Paulo (FEA-USP).
    7. Andreea Enache & Jean-Pierre Florens, 2020. "Identification and Estimation in a Third-Price Auction Model," Post-Print hal-02929530, HAL.
    8. Enache, Andreea & Florens, Jean-Pierre, 2019. "Identification and Estimation in a Third-Price Auction Model," TSE Working Papers 19-989, Toulouse School of Economics (TSE).

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    More about this item

    Keywords

    First-price auction; independent private value; quantile regression; local polynomial estimation; sieve estimation; dimension reduction; boundary correction; interdependent values;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • L70 - Industrial Organization - - Industry Studies: Primary Products and Construction - - - General

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