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Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks

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  • Victor Chernozhukov
  • Iván Fernández-Val

Abstract

Quantile regression (QR) is an increasingly important empirical tool in economics and other sciences for analysing the impact a set of regressors has on the conditional distribution of an outcome. Extremal QR, or QR 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. This paper provides feasible inference tools for extremal conditional quantile models that rely on extreme value approximations to the distribution of self-normalized QR statistics. The methods are simple to implement and can be of independent interest even in the univariate (non-regression) case. We illustrate the results with two empirical examples analysing extreme fluctuations of a stock return and extremely low percentiles of live infant birthweight in the range between 250 and 1500 g. Copyright 2011, Oxford University Press.

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Bibliographic Info

Article provided by Oxford University Press in its journal The Review of Economic Studies.

Volume (Year): 78 (2011)
Issue (Month): 2 ()
Pages: 559-589

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Handle: RePEc:oup:restud:v:78:y:2011:i:2:p:559-589

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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, April.
  3. 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.
  4. Flinn, C. & Heckman, J., 1982. "New methods for analyzing structural models of labor force dynamics," Journal of Econometrics, Elsevier, vol. 18(1), pages 115-168, January.
  5. Kiefer, Nicholas M. & Bunzel, Helle & Vogelsang, Timothy & Vogelsang, Timothy & Bunzel, Helle, 2000. "Simple Robust Testing of Regression Hypotheses," Staff General Research Papers 1832, Iowa State University, Department of Economics.
  6. 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.
  7. Sen, Amartya, 1973. "On Economic Inequality," OUP Catalogue, Oxford University Press, number 9780198281931, September.
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Cited by:
  1. 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).
  2. Becker, Sascha O. & Hvide, Hans K., 2013. "Do Entrepreneurs Matter?," IZA Discussion Papers 7146, Institute for the Study of Labor (IZA).
  3. repec:cge:warwcg:108 is not listed on IDEAS
  4. 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.

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