Integrated-quantile-based estimation for first price auction models
Yao Luo and
Working Papers from University of Toronto, Department of Economics
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: D44 D82 C12 C14 (search for similar items in EconPapers)
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