Computational Aspects of Maximum Likelihood Estimation of Autoregressive Fractionally Integrated Moving Average Models
Jurgen Doornik and
Marius Ooms ()
No 2001-W27, Economics Papers from Economics Group, Nuffield College, University of Oxford
We discuss computational aspects of likelihood-based estimation of univariate 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.
Keywords: Long memory; Bias; Modified profile likelihood; Restricted maximum likelihood estimator; Time-series regression model likelihood (search for similar items in EconPapers)
JEL-codes: C22 C63 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp, nep-ecm and nep-ets
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Journal Article: Computational aspects of maximum likelihood estimation of autoregressive fractionally integrated moving average models (2003)
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Persistent link: https://EconPapers.repec.org/RePEc:nuf:econwp:0127
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