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

Abstract: 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)
Pages: 14 pages
Date: 2001-11-29
New Economics Papers: this item is included in nep-cmp, nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Related works:
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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