Yulong Wang is an Associate Professor of Economics at Lehigh University. He received his B.A. from Tsinghua University and his Ph.D. in Economics from Princeton University.

His research lies at the intersection of econometrics and applied economics, with a focus on developing robust statistical methods for settings where classical asymptotic frameworks fall short — particularly in the tails of distributions. His work has broad empirical applications, including the study of social segregation, extreme weather events, health expenditure, income inequality, and auction theory.

His research has appeared in leading journals such as the Journal of American Statistical Association, Journal of Econometrics, Journal of Business & Economic Statistics, Journal of International Economics, and Journal of Health Economics.

This paper studies inference in first-price 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. 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. Other relevant applications include online and art auctions.