Working Paper: NBER ID: w24698
Authors: Dominic Coey; Bradley Larsen; Kane Sweeney; Caio Waisman
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.
Keywords: Online Auctions; Reserve Prices; E-commerce; Bidding Data; Profit Maximization
JEL Codes: C10; C55; D44; L10
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| optimal reserve price (D44) | seller profit (D33) |
| two highest bids (D44) | optimal reserve price (D44) |
| optimal reserve price (D44) | expected profit (D33) |
| number of auctions observed (D44) | optimal reserve price (D44) |
| Myerson optimal auction (D44) | higher revenues (H27) |