Please use this identifier to cite or link to this item:
Lancaster, Tony
Jun, Sung Jae
Year of Publication: 
Series/Report no.: 
cemmap working paper, Centre for Microdata Methods and Practice CWP05/06
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.
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

Files in This Item:
509.45 kB

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.