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Inference for extremal conditional quantile models, with an application to market and birthweight risks

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  • Victor Chernozhukov

    ()
    (Institute for Fiscal Studies and MIT)

  • Iván Fernández-Val

    (Institute for Fiscal Studies and Boston University)

Abstract

Quantile regression is an increasingly important empirical tool in economics and other sciences for analyzing the impact of a set of regressors on the conditional distribution of an outcome. Extremal quantile regression, or quantile regression applied to the tails, is of interest in many economic and financial applications, such as conditional value-at-risk, production efficiency, and adjustment bands in (S,s) models. In this paper we provide feasible inference tools for extremal conditional quantile models that rely upon extreme value approximations to the distribution of self-normalized quantile regression statistics. The methods are simple to implement and can be of independent interest even in the non-regression case. We illustrate the results with two empirical examples analyzing extreme fluctuations of a stock return and extremely low percentiles of live infants' birthweights in the range between 250 and 1500 grams.

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Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number CWP40/11.

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Date of creation: Dec 2011
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Handle: RePEc:ifs:cemmap:40/11

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  1. Engle, Robert F & Manganelli, Simone, 1999. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," University of California at San Diego, Economics Working Paper Series qt06m3d6nv, Department of Economics, UC San Diego.
  2. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521608275.
  3. Nicholas M. Kiefer & Timothy J. Vogelsang & Helle Bunzel, 2000. "Simple Robust Testing of Regression Hypotheses," Econometrica, Econometric Society, vol. 68(3), pages 695-714, May.
  4. Peter Christoffersen & Jinyong Hahn & Atsushi Inoue, 1999. "Testing, Comparing, and Combining Value at Risk Measures," Center for Financial Institutions Working Papers 99-44, Wharton School Center for Financial Institutions, University of Pennsylvania.
  5. James J. Heckman & Christopher J. Flinn, 1982. "New Methods for Analyzing Structural Models of Labor Force Dynamics," NBER Working Papers 0856, National Bureau of Economic Research, Inc.
  6. Sen, Amartya, 1973. "On Economic Inequality," OUP Catalogue, Oxford University Press, number 9780198281931, September.
  7. Patrice Bertail & Christian Haefke & Dimitris N. Politis & Halbert White, 2001. "A subsampling approach to estimating the distribution of diversing statistics with application to assessing financial market risks," Economics Working Papers 599, Department of Economics and Business, Universitat Pompeu Fabra.
  8. Donald, Stephen G. & Paarsch, Harry J., 2002. "Superconsistent estimation and inference in structural econometric models using extreme order statistics," Journal of Econometrics, Elsevier, vol. 109(2), pages 305-340, August.
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Cited by:
  1. Becker, Sascha O. & Hvide, Hans K., 2013. "Do Entrepreneurs Matter?," IZA Discussion Papers 7146, Institute for the Study of Labor (IZA).
  2. d'Haultfoeuille, Xavier & Maurel, Arnaud & Zhang, Yichong, 2014. "Extremal Quantile Regressions for Selection Models and the Black-White Wage Gap," IZA Discussion Papers 8256, Institute for the Study of Labor (IZA).
  3. Florens, Jean-Pierre & Simar, Léopold & Van Keilegom, Ingrid, 2014. "Frontier estimation in nonparametric location-scale models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 456-470.
  4. repec:cge:warwcg:108 is not listed on IDEAS

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