A NONPARAMETRIC TEST FOR COMPARING VALUATION DISTRIBUTIONS IN FIRST-PRICE AUCTIONS

This article proposes a nonparametric test for comparing valuation distributions in first-price auctions. Our test is motivated by the fact that two valuation distributions are the same if and only if their integrated quantile functions are the same. Our method avoids estimating unobserved valuations and does not require smooth estimation of bid density. We show that our test is consistent against all fixed alternatives and has nontrivial power against root-N local alternatives. Monte Carlo experiments show that our test performs well in finite samples. We implement our method on data from U.S. Forest Service timber auctions.