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This paper investigates the accuracy of bootstrap-based bias correction ofpersistence measures for long memory fractionally integrated processes. Thebootstrap method is based on the semi-parametric sieve approach, with thedynamics in the long memory process captured by an autoregressiveapproximation. With a view to improving accuracy, the sieve method is alsoapplied to data pre-filtered by a semi-parametric estimate of the long memoryparameter. Both versions of the bootstrap technique are used to estimate thefinite sample distributions of the sample autocorrelation coefficients and theimpulse response coefficients and, in turn, to bias-adjust these statistics.The accuracy of the resultant estimators in the case of the autocorrelationcoefficients is also compared with that yielded by analytical bias adjustmentmethods when available. The basic sieve technique is seen to yield a reductionin the bias of both persistence measures. The pre-filtered sieve produces asubstantial further reduction in the bias of the estimated impulse responsefunction, whilst the extra improvement yielded by pre-filtering in the case ofthe sample autocorrelation function is shown to depend heavily on the accuracyof the pre-filter.

Bias Correction of Persistence Measures in Fractionally Integrated Models