  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.
