Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/211094
Authors: 
Spady, Richard Henry
Stouli, Sami
Year of Publication: 
2019
Series/Report no.: 
cemmap working paper No. CWP01/19
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.
Subjects: 
Conditional distribution
Duality
Monotonicity
Quantile regression
Method of moments
Mathematical programming
Convex approximation
Document Type: 
Working Paper

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