  The purpose of this paper is to study the convergence of the quasi-maximum
likelihood (QML) estimator for long memory linear processes. We first establish
a correspondence between the long-memory linear process representation and the
long-memory AR$(\infty)$ process representation. We then establish the almost
sure consistency and asymptotic normality of the QML estimator. Numerical
simulations illustrate the theoretical results and confirm the good performance
of the estimator.
