Backfitting and smooth backfitting in varying coefficient quantile regression

被引：5

作者：

Lee, Young K. ^{[1
]}

Mammen, Enno ^{[2
]}

Park, Byeong U. ^{[3
]}

机构：

[1] Kangwon Natl Univ, Dept Stat, Chunchon 200701, South Korea

[2] Univ Mannheim, Dept Econ, D-68131 Mannheim, Germany

[3] Seoul Natl Univ, Dept Stat, Seoul 151747, South Korea

来源：

ECONOMETRICS JOURNAL | 2014年 / 17卷 / 02期

基金：

新加坡国家研究基金会;

关键词：

Backfitting; Integral equation; Kernel smoothing; Quantile regression; Smooth backfitting; Varying coefficient models;

D O I：

10.1111/ectj.12017

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

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.

引用

页码：S20 / S38

页数：19

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