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Which quantile is the most informative? Maximum likelihood, maximum entropy and quantile regression

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Author Info

  • Bera, A. K.
  • Galvao Jr, A. F.
  • Montes-Rojas, G.
  • Park, S. Y.

Abstract

This paper studies the connections among quantile regression, the asymmetric Laplace distribution, maximum likelihood and maximum entropy. We show that the maximum likelihood problem is equivalent to the solution of a maximum entropy problem where we impose moment constraints given by the joint consideration of the mean and median. Using the resulting score functions we propose an estimator based on the joint estimating equations. This approach delivers estimates for the slope parameters together with the associated “most probable” quantile. Similarly, this method can be seen as a penalized quantile regression estimator, where the penalty is given by deviations from the median regression. We derive the asymptotic properties of this estimator by showing consistency and asymptotic normality under certain regularity conditions. Finally, we illustrate the use of the estimator with a simple application to the U.S. wage data to evaluate the effect of training on wages.

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File URL: http://openaccess.city.ac.uk/1483/1/Which_Quantile_is_the_Most_Informative.pdf
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Bibliographic Info

Paper provided by Department of Economics, City University London in its series Working Papers with number 10/08.

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Date of creation: 2010
Date of revision:
Handle: RePEc:cty:dpaper:10/08

Contact details of provider:
Postal: Department of Economics, Social Sciences Building, City University London, Whiskin Street, London, EC1R 0JD, United Kingdom,
Phone: +44 (0)20 7040 8500
Web page: http://www.city.ac.uk
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Related research

Keywords: quantile regression; treatment effects; asymmetric laplace distribution;

References

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  1. Komunjer, Ivana, 2005. "Quasi-maximum likelihood estimation for conditional quantiles," Journal of Econometrics, Elsevier, vol. 128(1), pages 137-164, September.
  2. repec:cup:cbooks:9780521496032 is not listed on IDEAS
  3. Victor Chernozhukov & Iván Fernández-Val & Blaise Melly, 2013. "Inference on counterfactual distributions," CeMMAP working papers CWP17/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  4. Schennach, Susanne M., 2008. "Quantile Regression With Mismeasured Covariates," Econometric Theory, Cambridge University Press, vol. 24(04), pages 1010-1043, August.
  5. Hinkley, David V. & Revankar, Nagesh S., 1977. "Estimation of the Pareto law from underreported data : A further analysis," Journal of Econometrics, Elsevier, vol. 5(1), pages 1-11, January.
  6. Chernozhukov, Victor & Hansen, Christian, 2006. "Instrumental quantile regression inference for structural and treatment effect models," Journal of Econometrics, Elsevier, vol. 132(2), pages 491-525, June.
  7. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, April.
  8. Joshua Angrist & Victor Chernozhukov & Ivan Fernandez-Val, 2004. "Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure," NBER Working Papers 10428, National Bureau of Economic Research, Inc.
  9. Machado, José A.F., 1993. "Robust Model Selection and M-Estimation," Econometric Theory, Cambridge University Press, vol. 9(03), pages 478-493, June.
  10. Chernozhukov, Victor & Hansen, Christian, 2008. "Instrumental variable quantile regression: A robust inference approach," Journal of Econometrics, Elsevier, vol. 142(1), pages 379-398, January.
  11. Park, Sung Y. & Bera, Anil K., 2009. "Maximum entropy autoregressive conditional heteroskedasticity model," Journal of Econometrics, Elsevier, vol. 150(2), pages 219-230, June.
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Cited by:
  1. Andrew Friedson & Thomas Kniesner, 2012. "Losers and losers: Some demographics of medical malpractice tort reforms," Journal of Risk and Uncertainty, Springer, vol. 45(2), pages 115-133, October.

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