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Minimax Regression Quantiles

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  • Stefan Holst Bache

    ()
    (Aarhus University, School of Economics and Management and CREATES)

Abstract

A new and alternative quantile regression estimator is developed and it is shown that the estimator is root n-consistent and asymptotically normal. The estimator is based on a minimax ‘deviance function’ and has asymptotically equivalent properties to the usual quantile regression estimator. It is, however, a different and therefore new estimator. It allows for both linear- and nonlinear model specifications. A simple algorithm for computing the estimates is proposed. It seems to work quite well in practice but whether it has theoretical justification is still an open question.

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File URL: ftp://ftp.econ.au.dk/creates/rp/10/rp10_54.pdf
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Bibliographic Info

Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2010-54.

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Length: 15
Date of creation: 01 Aug 2010
Date of revision:
Handle: RePEc:aah:create:2010-54

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Web page: http://www.econ.au.dk/afn/

Related research

Keywords: Quantile regression; non-linear quantile regression; estimating functions; minimax estimation; empirical process theory;

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