Backfitting and smooth backfitting in varying coefficient quantile regression | Zendy

Lee Young K. | Zendy; Mammen Enno | Zendy; Park Byeong U. | Zendy

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Backfitting and smooth backfitting in varying coefficient quantile regression

Author(s) -

Lee Young K.,

Mammen Enno,

Park Byeong U.

Publication year - 2014

Publication title -

the econometrics journal

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.861

H-Index - 36

eISSN - 1368-423X

pISSN - 1368-4221

DOI - 10.1111/ectj.12017

Subject(s) - quantile , quantile regression , estimator , mathematics , additive model , econometrics , mathematical optimization , statistics

Summary In this paper, we study ordinary backfitting and smooth backfitting as methods of fitting varying coefficient quantile models. We do this in a unified framework that accommodates various types of varying coefficient models. Our framework also covers the additive quantile model as a special case. Under a set of weak conditions, we derive the asymptotic distributions of the backfitting estimators. We also briefly report on the results of a simulation study.

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