Inference in Auctions with Many Bidders Using Transaction Prices

This paper considers inference in first-price and second-price sealed-bid auctions in empirical settings where we observe auctions with a large number of bidders. Relevant applications include online auctions, treasury auctions, spectrum auctions, art auctions, and IPO auctions, among others. Given the abundance of bidders in each auction, we propose an asymptotic framework in which the number of bidders diverges while the number of auctions remains fixed. This framework allows us to perform asymptotically exact inference on key model features using only transaction price data. Specifically, we examine inference on the expected utility of the auction winner, the expected revenue of the seller, and the tail properties of the valuation distribution. Simulations confirm the accuracy of our inference methods in finite samples. Finally, we also apply them to Hong Kong car license auction data.