Dual regression

Richard Spady and Sami Stouli
Additional contact information
Richard Spady: Institute for Fiscal Studies and Johns Hopkins

No CWP04/16, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract: We propose an alternative ('dual regression') 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 largely avoiding the need for `rearrangement' to repair the intersecting conditional quantile surfaces that quantile regression often produces in practice. Our approach relies on a mathematical programming characterization of conditional distribution functions which, in its simplest form, provides a simultaneous estimator of location and scale parameters in a linear heteroscedastic model. The statistical properties of this estimator are derived.

Date: 2016-01-13
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.ifs.org.uk/uploads/cemmap/wps/cwp041616.pdf (application/pdf)
Our link check indicates that this URL is bad, the error code is: 404 Not Found (https://www.ifs.org.uk/uploads/cemmap/wps/cwp041616.pdf [302 Found]--> https://ifs.org.uk/uploads/cemmap/wps/cwp041616.pdf)

Related works:
Working Paper: Dual regression (2019)
Journal Article: Dual regression (2018)
Working Paper: Dual Regression (2018)
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:ifs:cemmap:04/16

Ordering information: This working paper can be ordered from
The Institute for Fiscal Studies 7 Ridgmount Street LONDON WC1E 7AE

Access Statistics for this paper

More papers in CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies The Institute for Fiscal Studies 7 Ridgmount Street LONDON WC1E 7AE. Contact information at EDIRC.
Bibliographic data for series maintained by Emma Hyman ().