Quantile-Based Nonparametric Inference for First-Price Auctions

Vadim Marmer () and Artyom Shneyerov ()

MPRA Paper from University Library of Munich, Germany

Abstract: We propose a quantile-based nonparametric approach to inference on the probability density function (PDF) of the private values in first-price sealed-bid auctions with independent private values. Our method of inference is based on a fully nonparametric kernel-based estimator of the quantiles and PDF of observable bids. Our estimator attains the optimal rate of Guerre, Perrigne, and Vuong (2000), and is also asymptotically normal with the appropriate choice of the bandwidth. As an application, we consider the problem of inference on the optimal reserve price.

Keywords: First-price auctions; independent private values; nonparametric estimation; kernel estimation; quantiles; optimal reserve price (search for similar items in EconPapers)
JEL-codes: C14 D44 (search for similar items in EconPapers)
Date: 2006-10, Revised 2006-03-02
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Citations: View citations in EconPapers (8)

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Related works:
Working Paper: Quantile-Based Nonparametric Inference for First-Price Auctions (2013)
Journal Article: Quantile-based nonparametric inference for first-price auctions (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:5899

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