Events

Dr. Yulong Wang
Assistant Professor
Syracuse University

This paper studies inference in first-price or second-price sealed-bid auctions with a large number of bidders that have symmetric independent private values. In this context, we propose an asymptotic framework where the number of bidders diverges, while the number of auctions remains fixed. This framework allows us to conduct asymptotically exact inference on several important features of the model based exclusively on observations of the transaction prices. In particular, we study inference on the winner’s expected utility, the seller’s expected revenue, and the tail properties of the valuation distribution. Our simulations show that our inference method delivers excellent finite-sample performance. We illustrate our inference method with an application to car license auctions in Hong Kong.