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Bayesian quantile regression: An application to the wage distribution in 1990s Britain

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  • VAN KERM Philippe
  • YU Keming
  • ZHANG Jin

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 symptotic 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.

Suggested Citation

  • VAN KERM Philippe & YU Keming & ZHANG Jin, 2004. "Bayesian quantile regression: An application to the wage distribution in 1990s Britain," IRISS Working Paper Series 2004-10, IRISS at CEPS/INSTEAD.
    • Handle: RePEc:irs:iriswp:2004-10
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    References listed on IDEAS

    as
    1. 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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    3. Jacob A. Mincer, 1974. "Schooling, Experience, and Earnings," NBER Books, National Bureau of Economic Research, Inc, number minc74-1, January.
    4. 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.
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    7. Koenker, Roger & Bassett, Gilbert, Jr, 1982. "Robust Tests for Heteroscedasticity Based on Regression Quantiles," Econometrica, Econometric Society, vol. 50(1), pages 43-61, January.
    8. Jacob A. Mincer, 1974. "Schooling and Earnings," NBER Chapters, in: Schooling, Experience, and Earnings, pages 41-63, National Bureau of Economic Research, Inc.
    9. Nigel F. B. Allington & Philip I. Morgan, 2003. "Does it Pay to Work in the Public Sector? Evidence from Three Decades of Econometric Analyses," Public Money & Management, Taylor & Francis Journals, vol. 23(4), pages 253-262, October.
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    Cited by:

    1. Chatterji, Monojit & Mumford, Karen A., 2007. "The Public-Private Sector Wage Differential for Full-Time Male Employees in Britain: A Preliminary Analysis," IZA Discussion Papers 2781, Institute of Labor Economics (IZA).
    2. Monojit Chatterji & Karen Mumford, 2007. "Flying High and Laying Low in the Public and Private Sectors: A Comparison of Pay Differentials for Full-Time Male Employees in Britain," Dundee Discussion Papers in Economics 209, Economic Studies, University of Dundee.
    3. Raquel Rangel de Meireles Guimarães & Luisa Pimenta Terra & Anna Carolina Martins Pinto & Cibele Comini César, 2010. "Diferenciais Regionais No Retorno À Participação No Setor Público No Brasil, 2005," Anais do XIV Semin·rio sobre a Economia Mineira [Proceedings of the 14th Seminar on the Economy of Minas Gerais], in: Anais do XIV Seminário sobre a Economia Mineira [Proceedings of the 14th Seminar on the Economy of Minas Gerais], Cedeplar, Universidade Federal de Minas Gerais.
    4. Van Kerm, Philippe, 2006. "Comparisons of income mobility profiles," ISER Working Paper Series 2006-36, Institute for Social and Economic Research.
    5. Chen, Cathy W.S. & Gerlach, Richard & Wei, D.C.M., 2009. "Bayesian causal effects in quantiles: Accounting for heteroscedasticity," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 1993-2007, April.
    6. Alessio Fusco & Paul Dickes, 2008. "The Rasch Model and Multidimensional Poverty Measurement," Palgrave Macmillan Books, in: Nanak Kakwani & Jacques Silber (ed.), Quantitative Approaches to Multidimensional Poverty Measurement, chapter 3, pages 49-62, Palgrave Macmillan.

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