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, Perrigne, and Vuong (2000), and is also asymptotically normal with the appropriate choice of the bandwidth.
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Bibliographic InfoPaper provided by Microeconomics.ca Website in its series Micro Theory Working Papers with number marmer-08-01-17-12-16-12.
Length: 33 pages
Date of creation: 17 Jan 2008
Date of revision: 16 May 2013
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First-price auctions; independent private values; nonparametric estimation; kernel estimation; quantiles; optimal reserve price;
Other versions of this item:
- Marmer, Vadim & Shneyerov, Artyom, 2012. "Quantile-based nonparametric inference for first-price auctions," Journal of Econometrics, Elsevier, vol. 167(2), pages 345-357.
- 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-01-26 (All new papers)
- NEP-ECM-2008-01-26 (Econometrics)
- NEP-ORE-2008-01-26 (Operations Research)
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