Computational Aspects of Maximum Likelihood Estimation of Autoregressive Fractionally Integrated Moving Average Models
We discuss computational aspects of likelihood-based estimation of unvariate ARFIMA (p,d,q) models. We show how efficient computation and simulation is feasible, even for large samples. We also discuss the implementation of analytical bias corrections.
|Date of creation:||01 Nov 2001|
|Date of revision:|
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