Inference in Auctions with Many Bidders Using Transaction Prices

Abstract: This paper studies inference in first- and second-price sealed-bid auctions with many bidders, using an asymptotic framework where the number of bidders increases while the number of auctions remains fixed. Relevant applications include online, treasury, spectrum, and art auctions. Our approach enables asymptotically exact inference on key features such as the winner's expected utility, the seller's expected revenue, and the tail of the valuation distribution using only transaction price data. Our simulations demonstrate the accuracy of the methods in finite samples. We apply our methods to Hong Kong vehicle license auctions, focusing on high-priced, single-letter plates.