This paper is a study of the application of Bayesian exponentially tilted empirical likelihood to inference about quantile regressions. In the case of simple quantiles we show the exact form for the likelihood implied by this method and compare it with the Bayesian bootstrap and with Jeffreys' method. For regression quantiles we derive the asymptotic form of the posterior density. We also examine Markov chain Monte Carlo simulations with a proposal density formed from an overdispersed version of the limiting normal density. We show that the algorithm works well even in models with an endogenous regressor when the instruments are not too weak.
Authors
Tony Lancaster
Pennsylvania State University
Journal article details
- DOI
- 10.1002/jae.1069
- Publisher
- Wiley Online Library
- Issue
- Volume 25, Issue 2, April 2009
Suggested citation
Jun, S and Lancaster, T. (2009). 'Bayesian quantile regression methods' 25(2/2009)
More from IFS
Understand this issue
Raising revenue from closing inheritance tax loopholes
18 April 2024
Sure Start achieved its aims, then we threw it away
15 April 2024
Should we worry about government debt?
11 April 2024
Policy analysis
Recent trends in and the outlook for health-related benefits
19 April 2024
4.2 million working-age people now claiming health-related benefits, could rise by 30% by the end of the decade
19 April 2024
Progression of nurses within the NHS
12 April 2024
Academic research
The employment and distributional impacts of nationwide minimum wage changes
10 April 2024
Willingness to pay for improved public education and public healthcare systems: the role of income mobility prospects
14 March 2024
Unfunded mandates and taxation
14 March 2024