Minimum distance estimation of ARFIMA processes
AbstractThis paper proposes a new minimum distance methodology for the estimation of ARFIMA processes with Gaussian and non-Gaussian errors. The main advantage of this method is that it allows for a computationally efficient estimation when the long-memory parameter is in the interval d∈(−12,12). Previous minimum distance estimation techniques are usually limited to the range d∈(−12,14), leaving outside the very important case of strong long memory with d∈[14,12). It is shown that the new estimator satisfies a central limit theorem and Monte Carlo experiments indicate that the proposed estimator performs very well even for small sample sizes. The methodology is illustrated with three applications. The first two examples involve real-life time series while the third application illustrates that the proposed methodology is a sound alternative for dealing with incomplete time series.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 58 (2013)
Issue (Month): C ()
Contact details of provider:
Web page: http://www.elsevier.com/locate/csda
Autocorrelation; Fractional noise; Fractional filtering; Long-memory; Missing data; Non-Gaussian processes;
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Baillie, Richard T. & Kapetanios, George & Papailias, Fotis, 2014. "Modified information criteria and selection of long memory time series models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 116-131.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.