Abstract
A new method for estimating long-memory parameter in ARFIMA Models is proposed based on ARMA approximation. The Kullback-Leibler discrepancy is used to find a best ARMA approximation for a FI(d) model. The performance of the new estimator is investigated and compared to previous methods in finite sample via simulations. The Nile River data are used for illustration.