Statistical analysis of incomplete long-range dependent data

This paper addresses both theoretical and methodological issues related to the prediction of long-memory models with incomplete data. Estimates and fo recasts are calculated by means of state space models and the influence of data gaps on the performance of short and long run predictions is investiga ted. These techniques are illustrated with a statistical analysis of the mi nimum water levels of the Nile river, a time series exhibiting strong depen dency.