Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/130091 
Year of Publication: 
2015
Series/Report no.: 
cemmap working paper No. CWP04/16
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
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.
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

Files in This Item:
File
Size
631.57 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.