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Cited by 710

The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.

Optimal Nonparametric Estimation of First-price Auctions

Guerre, Emmanuel, Perrigne, Isabelle, Vuong, Quang·2000·Econometrica
DOI 10.1111/1468-0262.00123
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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.

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Metadata

Authors
Guerre, E., Perrigne, I., & Vuong, Q.
Venue
Econometrica
Publisher
The Econometric Society
Published
2000-05
Volume
68
Issue
3
Pages
525-574
ISSN
  • 0012-9682 (ISSN-L)
  • 1468-0262
DOI
10.1111/1468-0262.00123
OpenAlex
W2120416704
Cited by
710
References
30
FWCI
20.49
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Optimal Nonparametric Estimation of First-price Auctions. https://app.cochat.ai/papers/10.1111/1468-0262.00123
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Guerre, E., Perrigne, I., & Vuong, Q. (2000). Optimal Nonparametric Estimation of First-price Auctions. Econometrica. https://doi.org/10.1111/1468-0262.00123

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.