Quantile regression methods for first-price auctions

Nathalie Gimenes and Emmanuel Guerre

Journal of Econometrics, 2022, vol. 226, issue 2, 224-247

Abstract: The paper proposes a quantile-regression inference framework for first-price auctions with symmetric risk-neutral bidders under the independent private-value paradigm. It is first shown that a private-value quantile regression generates a quantile regression for the bids. The private-value quantile regression can be easily estimated from the bid quantile regression and its derivative with respect to the quantile level. This also allows to test for various specification or exogeneity null hypothesis using the observed bids in a simple way. A new local polynomial technique is proposed to estimate the latter over the whole quantile level interval. Plug-in estimation of functionals is also considered, as needed for the expected revenue or the case of CRRA risk-averse bidders, which is amenable to our framework. A quantile-regression analysis to USFS timber is found more appropriate than the homogenized-bid methodology and illustrates the contribution of each explanatory variable to the private-value distribution. Linear interactive sieve extensions are proposed and studied in the Appendices.

Keywords: First-price auction; Independent private values; Dimension reduction; Quantile regression; Local polynomial estimation; Specification testing; Boundary correction; Sieve estimation (search for similar items in EconPapers)
JEL-codes: C14 L70 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:226:y:2022:i:2:p:224-247

DOI: 10.1016/j.jeconom.2021.02.009

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