Bootstrap approaches for estimation and condence intervals of long memory processes.

In this work we investigate an alternative bootstrap approach based on a result of Ramsey (1974) and on the Durbin-Levinson algorithm to obtain surrogate series from linear Gaussian processes with long range dependence. We compare this bootstrap method with other existing procedures in a wide Monte Carlo experiment by estimating, parametrically and semiparametrically, the memory parameter d. We consider Gaussian and non-Gaussian processes to prove the robustness of the method to deviations from Normality. The approach is useful also to estimate condence intervals for the memory parameter d by improving the coverage level of the interval.