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

Author

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  • 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.

Suggested Citation

  • Bera, A. K. & Galvao Jr, A. F. & Montes-Rojas, G. & Park, S. Y., 2010. "Which quantile is the most informative? Maximum likelihood, maximum entropy and quantile regression," Working Papers 10/08, Department of Economics, City University London.
  • Handle: RePEc:cty:dpaper:10/08
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    File URL: http://openaccess.city.ac.uk/1483/1/Which_Quantile_is_the_Most_Informative.pdf
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    References listed on IDEAS

    as
    1. Park, Sung Y. & Bera, Anil K., 2009. "Maximum entropy autoregressive conditional heteroskedasticity model," Journal of Econometrics, Elsevier, vol. 150(2), pages 219-230, June.
    2. Komunjer, Ivana, 2005. "Quasi-maximum likelihood estimation for conditional quantiles," Journal of Econometrics, Elsevier, vol. 128(1), pages 137-164, September.
    3. Victor Chernozhukov & Iván Fernández‐Val & Blaise Melly, 2013. "Inference on Counterfactual Distributions," Econometrica, Econometric Society, vol. 81(6), pages 2205-2268, November.
    4. Sergio Firpo, 2007. "Efficient Semiparametric Estimation of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 75(1), pages 259-276, January.
    5. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, March.
    6. 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.
    7. Machado, José A.F., 1993. "Robust Model Selection and M-Estimation," Econometric Theory, Cambridge University Press, vol. 9(03), pages 478-493, June.
    8. Joshua Angrist & Victor Chernozhukov & Iván Fernández-Val, 2006. "Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure," Econometrica, Econometric Society, vol. 74(2), pages 539-563, March.
    9. Schennach, Susanne M., 2008. "Quantile Regression With Mismeasured Covariates," Econometric Theory, Cambridge University Press, vol. 24(04), pages 1010-1043, August.
    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. 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.
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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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