Estimation of long-memory parameters for seasonal fractional ARIMA with stable innovations

We carry out finite sample size parameter estimation methods for long-memory parameters of the class of seasonal fractional ARIMA with stable innovations. In particular, we consider the semiparametric method studied in Reisen et al. (2006) [27] and two Whittle approaches: the classical Whittle method and a method based on a Markov Chains Monte Carlo (MCMC) procedure. The performance of the methods is discussed using a Monte Carlo simulation.