Dual Regression

Richard Spady and Sami Stouli

Papers from arXiv.org

Abstract: We propose dual regression as an alternative to the quantile regression process for the global estimation of conditional distribution functions under minimal assumptions. Dual regression provides all the interpretational power of the quantile regression process while avoiding the need for repairing the intersecting conditional quantile surfaces that quantile regression often produces in practice. Our approach introduces a mathematical programming characterization of conditional distribution functions which, in its simplest form, is the dual program of a simultaneous estimator for linear location-scale models. We apply our general characterization to the specification and estimation of a flexible class of conditional distribution functions, and present asymptotic theory for the corresponding empirical dual regression process.

Date: 2012-10, Revised 2018-09
References: View references in EconPapers View complete reference list from CitEc
Citations:

Published in Biometrika. Vol. 105(1), pp.1-18 (2018)

Downloads: (external link)
http://arxiv.org/pdf/1210.6958 Latest version (application/pdf)

Related works:
Working Paper: Dual regression (2019)
Journal Article: Dual regression (2018)
Working Paper: Dual regression (2016)
Working Paper: Dual Regression (2016)
Working Paper: Dual regression (2016)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1210.6958

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().