BANDWIDTH SELECTION FOR KERNEL REGRESSION WITH LONG-RANGE DEPENDENT ERRORS

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.