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Semiparametric Quantile Models for Ascending Auctions with Asymmetric Bidders

Bhattacharya, Jayeeta and Gimenes, Nathalie and Guerre, Emmanuel (2019) Semiparametric Quantile Models for Ascending Auctions with Asymmetric Bidders. Working paper. arXiv (Unpublished) (KAR id:79049)

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https://arxiv.org/pdf/1911.13063.pdf

Abstract

The paper proposes a parsimonious and flexible semiparametric quantile regression specification for asymmetric bidders within the independent private value framework. Asymmetry is parameterized using powers of a parent private value distribution, which is generated by a quantile regression specification. As noted in Cantillon (2008), this covers and extends models used for efficient collusion, joint bidding and mergers among homogeneous bidders. The specification can be estimated for ascending auctions using the winning bids and the winner’s identity. The estimation is two stage. The asymmetry parameters are estimated

from the winner’s identity using a simple maximum likelihood procedure. The parent quantile regression specification can be estimated using simple modifications of Gimenes (2017). A timber application reveals that weaker bidders have 30% less chances to win the auction than stronger ones. It is also found that increasing participation in an asymmetric ascending auction may not be as beneficial as using an optimal reserve price as would have been expected from a result of Bulow and Klemperer (1996) valid under symmetry.

Item Type: Monograph (Working paper)
Uncontrolled keywords: Private values; asymmetry; ascending auctions; seller expected revenue; quantile regression; two stage quantile regression estimation.
Subjects: H Social Sciences > HA Statistics
H Social Sciences > HB Economic Theory
Divisions: Faculties > Social Sciences > School of Economics
Depositing User: Emmanuel Guerre
Date Deposited: 02 Dec 2019 14:09 UTC
Last Modified: 06 Dec 2019 12:29 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/79049 (The current URI for this page, for reference purposes)
Guerre, Emmanuel: https://orcid.org/0000-0002-4130-0881
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