An M-estimator for the long-memory parameter

- We propose a novel estimator for the long-memory parameter.
- It is an alternative to the classical GPH estimator.
- It has the same asymptotic properties as the GPH estimator.
- It is less sensitive to the presence of additive outliers in the data.

This paper proposes an M-estimator for the fractional parameter of stationary long-range dependent processes as an alternative to the classical GPH (Geweke and Porter-Hudak, 1983) method. Under very general assumptions on the long-range dependent process the consistency and the asymptotic normal distribution are established for the proposed method. One of the main results is that the convergence rate of the M-estimator is Nβ/2, for some positive β, which is the same rate as the standard GPH estimator. The asymptotic properties of the M-estimation method is investigated through Monte-Carlo simulations under the scenarios of ARFIMA models using contaminated with additive outliers and outlier-free data. The GPH approach is also considered in the study for comparison purposes, since this method is widely used in the literature of long-memory time series. The empirical investigation shows that M and GPH-estimator methods display standardized densities fairly close to the standard Gaussian density in the context of non-contaminated data. On the other hand, in the presence of additive outliers, the M-estimator remains unaffected with the presence of additive outliers while the GPH is totally corrupted, which was an expected performance of this estimator. Therefore, the M-estimator here proposed becomes an alternative method to estimate the long-memory parameter when dealing with long-memory time series with and without outliers.