Uniform Bias Study And Bahadur Representation For Local Polynomial Estimators Of The Conditional Quantile Function
This paper investigates the bias and the weak Bahadur representation of a local polynomial estimator of the conditional quantile function and its derivatives. The bias and Bahadur remainder term are studied uniformly with respect to the quantile level, the covariates, and the smoothing parameter. The order of the local polynomial estimator can be higher than the differentiability order of the conditional quantile function. Applications of the results deal with global optimal consistency rates of the local polynomial quantile estimator, performance of random bandwidths, and estimation of the conditional quantile density function. The latter allows us to obtain a simple estimator of the conditional quantile function of the private values in a first-price sealed bids auction under the independent private values paradigm and risk neutrality.
Volume (Year): 28 (2012)
Issue (Month): 01 (February)
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- Emmanuel Guerre & Isabelle Perrigne & Quang Vuong, 2000. "Optimal Nonparametric Estimation of First-Price Auctions," Econometrica, Econometric Society, vol. 68(3), pages 525-574, May.
- Emmanuel Guerre & Isabelle Perrigne & Quang Vuong, 2009. "Nonparametric Identification of Risk Aversion in First-Price Auctions Under Exclusion Restrictions," Econometrica, Econometric Society, vol. 77(4), pages 1193-1227, 07.
- Marmer, Vadim & Shneyerov, Artyom, 2012.
"Quantile-based nonparametric inference for first-price auctions,"
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- 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.
- Marmer, Vadim & Shneyerov, Artyom, 2008. "Quantile-Based Nonparametric Inference for First-Price Auctions," Microeconomics.ca working papers marmer-08-01-17-12-16-12, Vancouver School of Economics, revised 16 May 2013.
- Li, Qi & Racine, Jeffrey S, 2008. "Nonparametric Estimation of Conditional CDF and Quantile Functions With Mixed Categorical and Continuous Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 423-434.
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