Quantile-based nonparametric inference for first-price auctions
AbstractWe 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 et al. (2000), and is also asymptotically normal with an appropriate choice of the bandwidth.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Econometrics.
Volume (Year): 167 (2012)
Issue (Month): 2 ()
Contact details of provider:
Web page: http://www.elsevier.com/locate/jeconom
First-price auctions; Independent private values; Nonparametric estimation; Kernel estimation; Quantiles; Optimal reserve price;
Other versions of this item:
- Marmer, Vadim & Shneyerov, Artyom, 2008. "Quantile-Based Nonparametric Inference for First-Price Auctions," Micro Theory Working Papers marmer-08-01-17-12-16-12, Microeconomics.ca Website, revised 09 Sep 2010.
- Marmer, Vadim & Shneyerov, Artyom, 2006. "Quantile-Based Nonparametric Inference for First-Price Auctions," MPRA Paper 5899, University Library of Munich, Germany, revised 02 Mar 2006.
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- D44 - Microeconomics - - Market Structure and Pricing - - - Auctions
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- repec:cup:cbooks:9780521496032 is not listed on IDEAS
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