Abstract
In this paper we introduce a procedure to compute prediction intervals for FARIMA (p d q) processes, taking into account the variability due to model identification and parameter estimation. To this aim, a particular bootstrap technique is developed. The performance of the prediction intervals is then assessed and compared to that of standard bootstrap percentile intervals. The methods are applied to the time series of Nile River annual minima.
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