Integrated-Quantile-Based Estimation for First-Price Auction Models. Issue 1 (2nd January 2018)

• Record Type: Journal Article
• Authors: Luo, Yao; Wan, Yuanyuan
• Is Part Of: Journal of business & economic statistics. Volume 36:Issue 1(2018)
• Page Start: 173
• Page End: 180
• Publication Date: 2018-01-02
• Subjects: First price auctions -- Monotone bidding strategy -- Nonparametric estimation -- Tuning-Parameter-free; Economics -- Statistical methods -- Periodicals; Commercial statistics -- Periodicals; Économie politique -- Méthodes statistiques -- Périodiques; Statistique commerciale -- Périodiques; 330.015195
• DOI: 10.1080/07350015.2016.1166119
• Languages: English
• ISSNs: 0735-0015

Abstract

Abstract : 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- n 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.