The Simple Empirics of Optimal Online Auctions

46 Pages Posted: 13 Jun 2018 Last revised: 26 Jun 2023

See all articles by Dominic Coey

Dominic Coey

eBay Research Labs

Bradley Larsen

Olin Business School - Washington University in St. Louis; National Bureau of Economic Research (NBER); eBay Research Labs

Kane Sweeney

eBay Research Labs

Caio Waisman

Stanford University

Date Written: June 2018

Abstract

We study reserve prices computed to maximize the expected profit of the seller based on historical observations of incomplete bid data typically available to the auction designer in online auctions for advertising or e-commerce. This direct approach to computing reserve prices circumvents the need to fully recover distributions of bidder valuations. We derive asymptotic results and also provide a new bound, based on the empirical Rademacher complexity, for the number of historical auction observations needed in order for revenue under the estimated reserve price to approximate revenue under the optimal reserve arbitrarily closely. 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. We illustrate the approach with e-commerce auction data from eBay. We also demonstrate how this idea can be extended to estimate all objects necessary to implement the Myerson (1981) optimal auction.

Suggested Citation

Coey, Dominic and Larsen, Bradley and Sweeney, Kane and Waisman, Caio, The Simple Empirics of Optimal Online Auctions (June 2018). NBER Working Paper No. w24698, Available at SSRN: https://ssrn.com/abstract=3194742

Dominic Coey (Contact Author)

eBay Research Labs ( email )

Bradley Larsen

Olin Business School - Washington University in St. Louis ( email )

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

eBay Research Labs ( email )

2065 Hamilton Avenue
San Jose, CA
United States

Kane Sweeney

eBay Research Labs ( email )

2065 Hamilton Avenue
San Jose, CA
United States

Caio Waisman

Stanford University

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