  In a classical model of the first-price sealed-bid auction with independent
private values, we develop nonparametric estimation and inference procedures
for a class of policy-relevant metrics, such as total expected surplus and
expected revenue under counterfactual reserve prices. Motivated by the
linearity of these metrics in the quantile function of bidders' values, we
propose a bid spacings-based estimator of the latter and derive its
Bahadur-Kiefer expansion. This makes it possible to construct exact uniform
confidence bands and assess the optimality of a given auction rule. Using the
data on U.S. Forest Service timber auctions, we test whether setting zero
reserve prices in these auctions was revenue maximizing.
