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, 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.

Biography

Yulong Wang is an associate professor of economics in the Maxwell School and a senior research associate in the Center for Policy Research at Syracuse University. His current research focuses on theory and application of extreme values and tail features. His research has been published in top econometrics and statistics journals such as Journal of the American Statistical Association, Journal of Econometrics, Journal of Business and Economic Statistics, Econometric Theory, and Journal of Applied Econometrics. Before joining Syracuse University, Wang earned a B.A. in finance at Tsinghua University in Beijing and a Ph.D. in economics from Princeton University.