We investigate the effect of long-range dependence on bandwidth select
ion for kernel regression with the plug-in method of Herrmann, Gasser
& Kneip (1992). A new bandwidth estimator is proposed to allow for lon
g-range dependence. Properties of the proposed estimator are investiga
ted theoretically and via simulation. We find that the proposed estima
tor performs well in terms of integrated squared error of the estimate
d trend, allowing us to incorporate both deterministic nonlinear featu
res having an unknown structure and long-range dependence into a singl
e model. The method is illustrated using biweekly measurements of the
volume of the Great Salt Lake.