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cemmap Working Papers
February 2006 CWP05/06
Article
Bayesian quantile regression
Type: cemmap Working Papers
Authors: Tony Lancaster and Sung Jae Jun
ISSN: 1753-9196
Volume, issue, pages: 16 pp.

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, θ) = 0 where θ 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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