Victor Chernozhukov, Iván Fernández-Val, Tetsuya Kaji
arXiv 20 Dec 2016 · Statistics — Methodology · 16 citations (OpenAlex)
arXiv:1612.06850 · PDF · DOI · OpenAlex · Extracted main text
Extremal quantile regression, i.e. quantile regression applied to the tails of the conditional distribution, counts with an increasing number of economic and financial applications such as value-at-risk, production frontiers, determinants of low infant birth weights, and auction models. This chapter provides an overview of recent developments in the theory and empirics of extremal quantile regression. The advances in the theory have relied on the use of extreme value approximations to the law of the Koenker and Bassett (1978) quantile regression estimator. Extreme value laws not only have been shown to provide more accurate approximations than Gaussian laws at the tails, but also have served as the basis to develop bias corrected estimators and inference methods using simulation and suitable variations of bootstrap and subsampling. The applicability of these methods is illustrated with two empirical examples on conditional value-at-risk and financial contagion.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chernozhukov, V., Fernández-Val, I (2011) Inference for extremal conditional quantile models, with an application to market and birthweight risks self | 1.000 | 15 | 4 | 100% |
| 2 | Embrechts, P., Klüppelberg, C., Mikosch, T (1997) Modelling extremal events 33 | 0.737 | 3 | 2 | 100% |
| 3 | Koenker, R., Bassett, G. S (1978) Regression quantiles | 0.737 | 3 | 2 | 100% |
| 4 | Chernozhukov, V (2005) Extremal quantile regression self | 0.737 | 3 | 2 | 100% |
| 5 | Chernozhukov, V (1998) Nonparametric extreme regression quantiles, working paper, Standord Univ self | 0.644 | 2 | 2 | 100% |
| 6 | Feigin, P. D., Resnick, S. I (1994) Limit distributions for linear programming time series estimators | 0.644 | 2 | 2 | 100% |
| 7 | Gnedenko, B (1943) Sur la distribution limité du terme d' une série alétoire | 0.644 | 2 | 2 | 100% |
| 8 | Knight, K (2001) Limiting distributions of linear programming estimators | 0.644 | 2 | 2 | 100% |
| 9 | Portnoy, S., Jurecková, J (1999) On extreme regression quantiles | 0.644 | 2 | 2 | 100% |
| 10 | Smith, R. L (1994) Nonregular regression | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 52 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Fixed-$k$ Inference for Conditional Extremal Quantiles | 0.405 | 1 | 1 |
| 2 | Fast Inference for Quantile Regression with Tens of Millions of Observations | 0.405 | 1 | 1 |
| 3 | Genuinely Robust Inference for Clustered Data | 0.405 | 1 | 1 |