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Bayesian quantile regression

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Author Info

  • Tony Lancaster

    ()
    (Institute for Fiscal Studies and Brown University)

  • Sung Jae Jun

Abstract

Recent work by Schennach (2005) has opened the way to a Bayesian treatment of quantile regression. Her method, called Bayesian exponentially tilted empirical likelihood (BETEL), provides a likelihood for data y subject only to a set of m moment conditions of the form Eg(y, ?) = 0 where ? is a k dimensional parameter of interest and k may be smaller, equal to or larger than m. The method may be thought of as construction of a likelihood supported on the n data points that is minimally informative, in the sense of maximum entropy, subject to the moment conditions.

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File URL: http://cemmap.ifs.org.uk/wps/cwp0506.pdf
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Bibliographic Info

Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number CWP05/06.

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Length: 16 pp.
Date of creation: Feb 2006
Date of revision:
Handle: RePEc:ifs:cemmap:05/06

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  1. Gary Chamberlain & Guido W. Imbens, 1996. "Nonparametric Applications of Bayesian Inference," Harvard Institute of Economic Research Working Papers 1772, Harvard - Institute of Economic Research.
  2. Susanne M. Schennach, 2005. "Bayesian exponentially tilted empirical likelihood," Biometrika, Biometrika Trust, vol. 92(1), pages 31-46, March.
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Cited by:
  1. Giuseppe Ragusa, 2007. "Bayesian Likelihoods for Moment Condition Models," Working Papers 060714, University of California-Irvine, Department of Economics.

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