Scalable Optimal Online Auctions

Dominic Coey (coey@fb.com), Bradley Larsen, Kane Sweeney (kane@uber.com) and Caio Waisman (caio.waisman@kellogg.northwestern.edu)
Additional contact information
Dominic Coey: Core Data Science, Facebook, Menlo Park, California 94025
Kane Sweeney: Data Science, Mindstrong, Mountain View, California 94041
Caio Waisman: Kellogg School of Management, , Northwestern University, Evanston, Illinois 60208

Marketing Science, 2021, vol. 40, issue 4, 593-618

Abstract: This paper studies reserve prices computed to maximize the expected profit of the seller based on historical observations of the top two bids from online auctions in an asymmetric, correlated private values environment. This direct approach to computing reserve prices circumvents the need to fully recover distributions of bidder valuations. We specify precise conditions under which this approach is valid and derive asymptotic properties of the estimators. We demonstrate in Monte Carlo simulations that directly estimating reserve prices is faster and, outside of independent private values settings, more accurate than fully estimating the distribution of valuations. We apply the approach to e-commerce auction data for used smartphones from eBay, where we examine empirically the benefit of the optimal reserve and the size of the data set required in practice to achieve that benefit. This simple approach to estimating reserves may be particularly useful for auction design in Big Data settings, where traditional empirical auctions methods may be costly to implement, whereas the approach we discuss is immediately scalable.

Keywords: auctions; econometrics; microeconomics (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:40:y:2021:i:4:p:593-618

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