Minimum distance estimation of ARFIMA processes

Mauricio Zevallos and Wilfredo Palma

Computational Statistics & Data Analysis, 2013, vol. 58, issue C, 242-256

Abstract: This 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.

Keywords: Autocorrelation; Fractional noise; Fractional filtering; Long-memory; Missing data; Non-Gaussian processes (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947312003088
Full text for ScienceDirect subscribers only.

Related works:
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:eee:csdana:v:58:y:2013:i:c:p:242-256

DOI: 10.1016/j.csda.2012.08.005

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().