Dual regression

Richard Spady, Sami Stouli

Research output: Contribution to journalArticle (Academic Journal)peer-review

2 Citations (Scopus)
179 Downloads (Pure)

Abstract

We propose dual regression as an alternative to quantile regression for the global estimation of conditional distribution functions. Dual regression provides the interpretational power of quantile regression while avoiding the need to repair intersecting conditional quantile surfaces. We introduce 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, and use it to specify and estimate a flexible class of conditional distribution functions. We present asymptotic theory for the corresponding empirical dual regression process.
Original languageEnglish
Article numberasx074
Pages (from-to)1-18
Number of pages18
JournalBiometrika
Volume105
Issue number1
Early online date19 Jan 2018
DOIs
Publication statusPublished - 1 Mar 2018

Structured keywords

  • ECON Econometrics
  • ECON CEPS Data

Keywords

  • Conditional distribution
  • Duality
  • Monotonicity
  • Quantile regression
  • Method of moments
  • Mathematical programming
  • Convex approximation

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