Victor Chernozhukov, Ivan Fernandez-Val
arXiv 26 Dec 2009 · Statistics — Methodology · 3 citations (OpenAlex)
arXiv:0912.5013 · PDF · DOI · OpenAlex · Extracted main text
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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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 (2005) Extremal quantile regression self | 0.874 | 6 | 5 | 67% |
| 2 | Bertail, Haefke, Politis, and White (2004) A subsampling approach to estimating the distribution of diverging extreme statistics with applications to assessing financial m… | 0.843 | 3 | 3 | 100% |
| 3 | Politis, Romano, and Wolf (1999) Subsampling | 0.737 | 4 | 2 | 75% |
| 4 | Chernozhukov and Umantsev (2001) Conditional Value-at-Risk: Aspects of Modeling and Estimation | 0.737 | 3 | 2 | 100% |
| 5 | Engle and Manganelli (2004) CAViaR: conditional autoregressive value at risk by regression quantiles | 0.644 | 2 | 2 | 100% |
| 6 | Koenker (2005) Quantile regression | 0.644 | 2 | 2 | 100% |
| 7 | Geyer (1996) On the asymptotics of convex stochastic optimization | 0.585 | 4 | 3 | 25% |
| 8 | Knight (1999) Epi-convergence and Stochastic Equisemicontinuity | 0.585 | 4 | 3 | 25% |
| 9 | Bassett and Koenker (1982) An empirical quantile function for linear models with iid errors | 0.511 | 2 | 2 | 50% |
| 10 | Meyer (1973) A poisson-type limit theorem for mixing sequences of dependent 'rare' events | 0.511 | 2 | 2 | 50% |
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