Replacing Sample Trimming with Boundary Correction in Nonparametric Estimation of First‐Price Auctions
针对拍卖数据非参数估计中因边界效应导致的样本修剪问题，提出边界校正方法，蒙特卡洛实验和石油租赁拍卖数据表明该方法显著改善了有限样本表现，并揭示了修剪掩盖的竞标者不对称与配置低效。
Summary 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.