Computational aspects of maximum likelihood of autoregressive fractionally integrated moving average models

M. Ooms, J.A. Doornik

Research output: Contribution to JournalArticleAcademic

Abstract

Computational aspects of likelihood-based estimation of univariate ARFIMA (p,d,q) models are addressed. Particular issues are the numerically stable evaluation of the autocovariances and efficient handling of the variance matrix which has dimension equal to the sample size. It is shown how efficient computation and simulation are feasible, even for large samples. Implementation of analytical bias corrections in ARFIMA regression models is also discussed. © 2002 Elsevier Science B.V. All rights reserved.
Original languageEnglish
Pages (from-to)333-348
Number of pages16
JournalComputational Statistics and Data Analysis
Volume42
Issue number3
DOIs
Publication statusPublished - 2003

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