Dual regression

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: cemmap working paper ; No. CWP01/19

Classification and Topics

• Classification: Wirtschaft
• Subject: Conditional distribution ; Duality ; Monotonicity ; Quantile regression ; Method of moments ; Mathematical programming ; Convex approximation

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Spady, Richard Henry ; Stouli, Sami
• Event: Veröffentlichung
• (who): Centre for Microdata Methods and Practice (cemmap)
• (where): London
• (when): 2019

Further information

• DOI: doi:10.1920/wp.cem.2019.0119
• Handle: http://hdl.handle.net/10419/211094
• Last update: 10.03.2025, 11:42 AM CET