Bayesian quantile regression: An application to the wage distribution in 1990s Britain
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.
|Date of creation:||Aug 2004|
|Date of revision:|
|Publication status:||Published in Sankhya, the Indian Journal of Statistics, 2005, vol. 67, no. 2, pp. 359-377|
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- Richard Disney & Amanda Gosling, 1998. "Does it pay to work in the public sector?," Fiscal Studies, Institute for Fiscal Studies, vol. 19(4), pages 347-374, November.
- Koenker, Roger & Bassett, Gilbert, Jr, 1982. "Robust Tests for Heteroscedasticity Based on Regression Quantiles," Econometrica, Econometric Society, vol. 50(1), pages 43-61, January.
- Bilias, Yannis & Chen, Songnian & Ying, Zhiliang, 2000. "Simple resampling methods for censored regression quantiles," Journal of Econometrics, Elsevier, vol. 99(2), pages 373-386, December.
- Willis, Robert J., 1987. "Wage determinants: A survey and reinterpretation of human capital earnings functions," Handbook of Labor Economics, in: O. Ashenfelter & R. Layard (ed.), Handbook of Labor Economics, edition 1, volume 1, chapter 10, pages 525-602 Elsevier.
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