  This paper investigates bootstrap-based bias correction of semiparametric
estimators of the long memory parameter, $d$, in fractionally integrated
processes. The re-sampling method involves the application of the sieve
bootstrap to data pre-filtered by a preliminary semiparametric estimate of the
long memory parameter. Theoretical justification for using the bootstrap
technique to bias adjust log periodogram and semiparametric local Whittle
estimators of the memory parameter is provided in the case where the true value
of $d$ lies in the range $0\leq d<0.5$. That the bootstrap method provides
confidence intervals with the correct asymptotic coverage is also proven, with
the intervals shown to adjust explicitly for bias, as estimated via the
bootstrap. Simulation evidence comparing the performance of the bootstrap bias
correction with analytical bias-correction techniques is presented. The
bootstrap method is shown to produce notable bias reductions, in particular
when applied to an estimator for which some degree of bias reduction has
already been accomplished by analytical means.
