Bandwidth selection for kernel regression with long-range dependent errors

Document Type

Article

Publication Date

1-1-1997

Abstract

We investigate the effect of long-range dependence on bandwidth selection for kernel regression with the plug-in method of Herrmann, Gasser & Kneip (1992). A new bandwidth estimator is proposed to allow for long-range dependence. Properties of the proposed estimator are investigated theoretically and via simulation. We find that the proposed estimator performs well in terms of integrated squared error of the estimated trend, allowing us to incorporate both deterministic nonlinear features having an unknown structure and long-range dependence into a single model. The method is illustrated using biweekly measurements of the volume of the Great Salt Lake.

Identifier

0000610323 (Scopus)

Publication Title

Biometrika

External Full Text Location

https://doi.org/10.1093/biomet/84.4.791

ISSN

00063444

First Page

791

Last Page

802

Issue

4

Volume

84

Fund Ref

National Science Foundation

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