Integrated-Quantile-Based Estimation for First-Price Auction Models
提出一种无需调参的估计方法，用于一级价格拍卖中投标者估值的分位数函数，在较弱平滑性假设下具有立方根n一致性和渐近正态性，并通过蒙特卡洛模拟和加州公路采购拍卖数据验证。
This article considers nonparametric estimation of first-price auction models under the monotonicity restriction on the bidding strategy. Based on an integrated-quantile representation of the first-order condition, we propose a tuning-parameter-free estimator for the valuation quantile function. We establish its cube-root-<i>n</i> consistency and asymptotic distribution under weaker smoothness assumptions than those typically assumed in the empirical literature. If the latter are true, we also provide a trimming-free smoothed estimator and show that it is asymptotically normal and achieves the optimal rate of Guerre, Perrigne, and Vuong (2000). We illustrate our method using Monte Carlo simulations and an empirical study of the California highway procurement auctions. Supplementary materials for this article are available online.