Replacing Sample Trimming with Boundary Correction in Nonparametric Estimation of First‐Price Auctions

Two‐step nonparametric estimators have become standard in empirical auctions. A drawback concerns boundary effects which cause inconsistencies near the endpoints of the support and bias in finite samples. To cope, sample trimming is typically used, which leads to non‐random data loss. Monte Carlo experiments show this leads to poor performance near the support boundaries and on the interior due to bandwidth selection issues. We propose a modification that employs boundary correction techniques, and we demonstrate substantial improvement in finite‐sample performance. We implement the new estimator using oil lease auctions data and find that trimming masks a substantial degree of bidder asymmetry and inefficiency in allocations. Copyright © 2014 John Wiley & Sons, Ltd.