Dual regression

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: cemmap working paper ; No. CWP04/16

Classification and Topics

• Classification: Wirtschaft

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Spady, Richard H. ; Stouli, Sami
• Event: Veröffentlichung
• (who): Centre for Microdata Methods and Practice (cemmap)
• (where): London
• (when): 2015

Further information

• DOI: doi:10.1920/wp.cem.2016.0416
• Handle: http://hdl.handle.net/10419/130091
• Last update: 10.03.2025, 11:41 AM CET