EconPapers    
Economics at your fingertips  
 

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

Downloads: (external link)
http://www.nuff.ox.ac.uk/Economics/papers/2001/w27/ArfimaNum.pdf (application/pdf)

Related works:
Journal Article: Computational aspects of maximum likelihood estimation of autoregressive fractionally integrated moving average models (2003) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nuf:econwp:0127

Access Statistics for this paper

More papers in Economics Papers from Economics Group, Nuffield College, University of Oxford Contact information at EDIRC.
Bibliographic data for series maintained by Maxine Collett ().

 
Page updated 2019-09-16
Handle: RePEc:nuf:econwp:0127