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Identifier to cite or link to this item: https://hdl.handle.net/10086/18811

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Title
Nonparametric Quantile Regression with Heavy-Tailed and Strongly Dependent Errors
Creator
Creator Name
Honda, Toshio Honda, Toshio
Affiliation
Affiliation Name
Hitotsubashi University
Editor
Contributor Name
社会科学の高度統計・実証分析拠点構築 = Research Unit for Statistical and Empirical Analysis in Social Sciences
Affiliation
Affiliation Name
Hitotsubashi University
Key Word
conditional quantile
random design
check function
local linear regression
stable distribution
linear process
long-range dependence
martingale central limit theorem
Abstract
We consider nonparametric estimation of the conditional qth quantile for stationary time series. We deal with stationary time series with strong time dependence and heavy tails under the setting of random design. We estimate the conditional qth quantile by local linear regression and investigate the asymptotic properties. It is shown that the asymptotic properties are affected by both the time dependence and the tail index of the errors. The results of a small simulation study are also given.
Publisher
Institute of Economic Research, Hitotsubashi University
Issued Date
2010-12
Language
English(eng)
Resource Type
technical report
Version Type
VoR
Is Part OfRelation -
Related Title
Global COE Hi-Stat Discussion Paper Series ; No. 157
Number of Pages
35
Sponsorship
グローバルCOEプログラム = Global COE Program
Appears in Collections
Global COE Hi-Stat Discussion Paper Series
URL
https://hdl.handle.net/10086/18811