The paper proposes a quantile-regression inference framework for first-price auctions with symmetric risk-neutral bidders under the independent private-value paradigm. It is first shown that a private-value quantile regression generates a quantile regression for the bids. The private-value quantile regression can be easily estimated from the bid quantile regression and its derivative with respect to the quantile level. This also allows to test for various specification or exogeneity null hypothesis using the observed bids in a simple way. A new local polynomial technique is proposed to estimate the latter over the whole quantile level interval. Plug-in estimation of functionals is also considered, as needed for the expected revenue or the case of CRRA risk-averse bidders, which is amenable to our framework. A quantile-regression analysis to USFS timber is found more appropriate than the homogenized-bid methodology and illustrates the contribution of each explanatory variable to the private-value distribution. Linear interactive sieve extensions are proposed and studied in the Appendices.

该论文提出了一个分位数回归推理框架，用于在独立私人价值范式下具有对称风险中性投标人的首价拍卖。首先表明，私人价值分位数回归会为投标生成分位数回归。私人价值分位数回归可以很容易地从投标分位数回归及其相对于分位数水平的导数中估计出来。这也允许以简单的方式使用观察到的投标来测试各种规格或外生性零假设。提出了一种新的局部多项式技术来在整个分位数水平区间上估计后者。根据预期收入或 CRRA 风险规避投标人情况的需要，还考虑了功能的插件估计，这适用于我们的框架。发现对 USFS 木材的分位数回归分析比同质化投标方法更合适，并说明了每个解释变量对私人价值分布的贡献。在附录中提出并研究了线性交互式筛子扩展。