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Optimal Nonparametric Estimation of First‐price Auctions

Optimal Nonparametric Estimation of First‐price Auctions This paper proposes a general approach and a computationally convenient estimation procedure for the structural analysis of auction data. Considering first‐price sealed‐bid auction models within the independent private value paradigm, we show that the underlying distribution of bidders' private values is identified from observed bids and the number of actual bidders without any parametric assumptions. Using the theory of minimax, we establish the best rate of uniform convergence at which the latent density of private values can be estimated nonparametrically from available data. We then propose a two‐step kernel‐based estimator that converges at the optimal rate. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Econometrica Wiley

Optimal Nonparametric Estimation of First‐price Auctions

Econometrica , Volume 68 (3) – May 1, 2000

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References (51)

Publisher
Wiley
Copyright
Copyright © 2000 Wiley Subscription Services, Inc., A Wiley Company
ISSN
0012-9682
eISSN
1468-0262
DOI
10.1111/1468-0262.00123
Publisher site
See Article on Publisher Site

Abstract

This paper proposes a general approach and a computationally convenient estimation procedure for the structural analysis of auction data. Considering first‐price sealed‐bid auction models within the independent private value paradigm, we show that the underlying distribution of bidders' private values is identified from observed bids and the number of actual bidders without any parametric assumptions. Using the theory of minimax, we establish the best rate of uniform convergence at which the latent density of private values can be estimated nonparametrically from available data. We then propose a two‐step kernel‐based estimator that converges at the optimal rate.

Journal

EconometricaWiley

Published: May 1, 2000

Keywords: ; ; ; ; ;

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