Arbeitspapier

Dual regression

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.

Sprache
Englisch

Erschienen in
Series: cemmap working paper ; No. CWP01/19

Klassifikation
Wirtschaft
Thema
Conditional distribution
Duality
Monotonicity
Quantile regression
Method of moments
Mathematical programming
Convex approximation

Ereignis
Geistige Schöpfung
(wer)
Spady, Richard Henry
Stouli, Sami
Ereignis
Veröffentlichung
(wer)
Centre for Microdata Methods and Practice (cemmap)
(wo)
London
(wann)
2019

DOI
doi:10.1920/wp.cem.2019.0119
Handle
Letzte Aktualisierung
10.03.2025, 11:42 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Spady, Richard Henry
  • Stouli, Sami
  • Centre for Microdata Methods and Practice (cemmap)

Entstanden

  • 2019

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