On the asymptotic variance in nonparametric regression with fractional time-series errors

Yuanhua Feng

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

This paper focuses on deriving explicit formulae of the asymptotic variance in nonparametric regression with fractional time-series errors. Unified formulae are obtained for fixed design models with long-memory, short-memory and antipersistent errors. It is also found that in strongly antipersistent case the Uniform kernel is no longer the minimum variance one and that for a fourth-order kernel the constant in the asymptotic variance for long-memory errors may be clearly smaller than that for i.i.d. errors. The results are applied to improving an existing data-driven algorithm. Practical performance of the proposed algorithm is illustrated with simulated and real data examples.

Original languageEnglish
Pages (from-to)63-76
Number of pages14
JournalJournal of Nonparametric Statistics
Volume19
Issue number2
DOIs
Publication statusPublished - Feb 2007

Keywords

  • Antipersistence
  • Bandwidth selection
  • Long memory
  • Nonparametric regression

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