Identification of Auction Models Using Order Statistics

Abstract

Auction data often contain information on only the most competitive bids as opposed to all bids. The usual measurement error approaches to unobserved heterogeneity are inapplicable due to dependence among order statistics. We bridge this gap by providing a set of positive identification results. First, we show that symmetric auctions with discrete unobserved heterogeneity are identifiable using two consecutive order statistics and an instrument or three consecutive ones. Second, we extend the results to ascending auctions with unknown competition and unobserved heterogeneity.