Résumé : In the log-periodogram regression, the Fourier frequencies are used to define the estimator of the long memory parameter . Moreover the number of frequencies considered depends on the sample size through the condition as . However, a rigorous asymptotic semiparametric theory to give a satisfactory choice for m is still lacking. The main objective of this paper is to fill this gap. We define a non-Fourier logperiodogram estimator by performing an OLS regression, in which non-Fourier frequencies independent of the sample size n are used. We show that this new estimator is consistent and asymptotically normal if and without imposing the rate condition . Based on the rate of convergence in the Central Limit Theorem, a moderate , say, is sufficient to obtain a reliable confidence interval for . Vous pouvez entendre l'intervention, tout en visualisant le Power Point, en cliquant sur ce lien : http://epn.univ-paris1.fr/modules/UFR27semSAMOS/SeminaireSAMM_20091016_Boutahar/SeminaireSAMM_20091016_Boutahar.html. Ecouter l'intervention : Bande son disponible au format mp3 Durée : 1H04