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

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  • Victor Chernozhukov
  • Ivan Fernandez-Val

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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  • Victor Chernozhukov & Ivan Fernandez-Val, 2009. "Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks," Papers 0912.5013, arXiv.org.
  • Handle: RePEc:arx:papers:0912.5013
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    8. 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.
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    Cited by:

    1. 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.
    2. Denis Chetverikov & Bradley Larsen & Christopher Palmer, 2015. "IV Quantile Regression for Group-level Treatments, with an Application to the Distributional Effects of Trade," NBER Working Papers 21033, National Bureau of Economic Research, Inc.
    3. Lee, Ji Hyung, 2016. "Predictive quantile regression with persistent covariates: IVX-QR approach," Journal of Econometrics, Elsevier, vol. 192(1), pages 105-118.
    4. Aida Caldera Sánchez & Oliver Röhn, 2016. "How do policies influence GDP tail risks?," OECD Economics Department Working Papers 1339, OECD Publishing.
    5. White, Halbert & Kim, Tae-Hwan & Manganelli, Simone, 2015. "VAR for VaR: Measuring tail dependence using multivariate regression quantiles," Journal of Econometrics, Elsevier, vol. 187(1), pages 169-188.
    6. David M. Kaplan, 2014. "Nonparametric Inference on Quantile Marginal Effects," Working Papers 1413, Department of Economics, University of Missouri.
    7. Becker, Sascha & Hvide, Hans V, 2013. "Do entrepreneurs matter?," CAGE Online Working Paper Series 109, Competitive Advantage in the Global Economy (CAGE).
    8. D’Haultfœuille, Xavier & Maurel, Arnaud & Zhang, Yichong, 2018. "Extremal quantile regressions for selection models and the black–white wage gap," Journal of Econometrics, Elsevier, vol. 203(1), pages 129-142.
    9. Maria Marino & Alessio Farcomeni, 2015. "Linear quantile regression models for longitudinal experiments: an overview," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 229-247, August.
    10. Calluzzo, Paul & Dong, Gang Nathan, 2015. "Has the financial system become safer after the crisis? The changing nature of financial institution risk," Journal of Banking & Finance, Elsevier, vol. 53(C), pages 233-248.
    11. Xiong, Qizhou, 2015. "Censored Fractional Response Model: Estimating Heterogeneous Relative Risk Aversion of European Households," IWH Discussion Papers 11/2015, Halle Institute for Economic Research (IWH).

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