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.
Authors
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Tony Lancaster
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Pennsylvania State University
Working Paper details
- DOI
- 10.1920/wp.cem.2006.0506
- Publisher
- Institute for Fiscal Studies
Suggested citation
Jun, S and Lancaster, T. (2006). Bayesian quantile regression. London: Institute for Fiscal Studies. Available at: https://ifs.org.uk/publications/bayesian-quantile-regression (accessed: 30 June 2024).
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