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 language | English |
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Pages (from-to) | 63-76 |
Number of pages | 14 |
Journal | Journal of Nonparametric Statistics |
Volume | 19 |
Issue number | 2 |
DOIs | |
Publication status | Published - Feb 2007 |
Keywords
- Antipersistence
- Bandwidth selection
- Long memory
- Nonparametric regression