Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks
Original manuscript 26 Dec 2009
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Oxford University Press
2013
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| Online Access: | http://hdl.handle.net/1721.1/82629 https://orcid.org/0000-0002-3250-6714 |
| _version_ | 1826197234413207552 |
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| author | Fernandez-Val, Ivan Chernozhukov, Victor V. |
| author2 | Massachusetts Institute of Technology. Department of Economics |
| author_facet | Massachusetts Institute of Technology. Department of Economics Fernandez-Val, Ivan Chernozhukov, Victor V. |
| author_sort | Fernandez-Val, Ivan |
| collection | MIT |
| description | Original manuscript 26 Dec 2009 |
| first_indexed | 2024-09-23T10:44:34Z |
| format | Article |
| id | mit-1721.1/82629 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:44:34Z |
| publishDate | 2013 |
| publisher | Oxford University Press |
| record_format | dspace |
| spelling | mit-1721.1/826292022-09-27T14:38:21Z Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks Fernandez-Val, Ivan Chernozhukov, Victor V. Massachusetts Institute of Technology. Department of Economics Chernozhukov, Victor V. Original manuscript 26 Dec 2009 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. 2013-12-02T20:25:09Z 2013-12-02T20:25:09Z 2011-03 2010-06 Article http://purl.org/eprint/type/JournalArticle 0034-6527 1467-937X http://hdl.handle.net/1721.1/82629 Chernozhukov, V., and I. Fernandez-Val. “Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks.” The Review of Economic Studies 78, no. 2 (March 21, 2011): 559-589. https://orcid.org/0000-0002-3250-6714 en_US http://dx.doi.org/10.1093/restud/rdq020 Review of Economic Studies Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf Oxford University Press arXiv |
| spellingShingle | Fernandez-Val, Ivan Chernozhukov, Victor V. Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks |
| title | Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks |
| title_full | Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks |
| title_fullStr | Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks |
| title_full_unstemmed | Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks |
| title_short | Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks |
| title_sort | inference for extremal conditional quantile models with an application to market and birthweight risks |
| url | http://hdl.handle.net/1721.1/82629 https://orcid.org/0000-0002-3250-6714 |
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