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...

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Bibliographic Details
Published in:Econometrica 2000-05, Vol.68 (3), p.525-574
Main Authors: Guerre, Emmanuel, Perrigne, Isabelle, Vuong, Quang
Format: Article
Language:English
Subjects:
Applied sciences
Auctions
Bids
Consistent estimators
Convergence
Decision theory
Density estimation
Distribution
Econometrics
Economic models
Economic theory
Economic value
Empirical research
Estimating techniques
Estimation
Estimation methods
Estimators
Exact sciences and technology
First-price auctions
independent private value
Industrial organization
kernel estimation
Mathematics
minimax theory
Multivariate analysis
Nash equilibrium
nonparametric identification
Nonparametric inference
Operational research and scientific management
Operational research. Management science
Partial derivatives
Probability and statistics
Reservation prices
Sciences and techniques of general use
Statistics
Studies
two-stage nonparametric estimation
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Summary: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.
ISSN:0012-9682
1468-0262
DOI:10.1111/1468-0262.00123