Integrated-quantile-based estimation for first price auction models

Yao Luo and Yuanyuan Wan

Working Papers from University of Toronto, Department of Economics

Abstract: This paper 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 methods using Monte Carlo simulations and an empirical study of the California highway procurements auctions.

Keywords: First Price Auctions; Monotone Bidding Strategy; Nonparametric Estimation; Tuning-Parameter-Free (search for similar items in EconPapers)
JEL-codes: C12 C14 D44 D82 (search for similar items in EconPapers)
Pages: Unknown pages
Date: 2015-05-06
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Integrated-Quantile-Based Estimation for First-Price Auction Models (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:tor:tecipa:tecipa-539

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