Brent R. Hickman
Timothy P. Hubbard

replacing sample trimming with boundary correction in nonparametric estimation of first-price auctions (replication data)

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.

Data and Resources

Suggested Citation

Hickman, Brent R.; Hubbard, Timothy P. (2015): Replacing Sample Trimming with Boundary Correction in Nonparametric Estimation of First-Price Auctions (replication data). Version: 1. Journal of Applied Econometrics. Dataset.