A point decision for partially identified auction models

This article proposes a decision-theoretic method to choose a single reserve price for partially identified auction models, such as Haile and Tamer (2003), using data on transaction prices from English auctions. The article employs Gilboa and Schmeidler (1989) for inference that is robust with respect to the prior over unidentified parameters. It is optimal to interpret the transaction price as the highest value, and maximize the posterior mean of the seller’s revenue. The Monte Carlo study shows substantial gains relative to the revenues corresponding to a random point and the midpoint in the Haile and Tamer interval.