Publication type
Research Paper
Series Number
2004-10
Series
IRISS Working Paper Series
Authors
Publication date
June 1, 2004
Abstract:
This paper illustrates application of Bayesian inference to quantile regression. Bayesian inference regards unknown parameters as random variables, and we describe an MCMC algorithm to estimate the posterior densities of quantile regression parameters. Parameter uncertainty is taken into account without relying on asymptotic approximations. Bayesian inference revealed effective in our application to the wage structure among working males in Britain between 1991 and 2001 using data from the British Household Panel Survey. Looking at different points along the conditional wage distribution uncovered important features of wage returns to education, experience and public sector employment that would be concealed by mean regression.
Subjects
Link
- http://ideas.repec.org/p/irs/iriswp/2004-10.html
Notes
working paper
Paper download#508081