Identification of Auction Models Using Order Statistics

Abstract

Auction data often contain information on only the most competitive bids as opposed to all bids. The usual measurement error approaches to unobserved heterogeneity are inapplicable due to dependence among order statistics. We bridge this gap by providing a set of positive results. First, we show that symmetric auctions are identifiable using three consecutive order statistics or two consecutive ones with an instrument. Second, we introduce competition intensity, i.e., the number of bidders, as additional unobserved heterogeneity. Third, we extend our results to asymmetric auctions. Lastly, we apply our methods to U.S. Forest Service timber auctions and find that ignoring unobserved heterogeneity reduces both bidder and auctioneer surplus.