A General Frequency Domain Estimation Method for Gegenbauer Processes

Richard Hunt (), Peiris Shelton and Weber Neville
Additional contact information
Peiris Shelton: The University of Sydney, School of Mathematics and Statistics, Sydney, New South Wales, Australia
Weber Neville: The University of Sydney, School of Mathematics and Statistics, Sydney, New South Wales, Australia

Journal of Time Series Econometrics, 2021, vol. 13, issue 2, 119-144

Abstract: In this paper a new method for estimation of all the parameters of a k-factor Gegenbauer process is developed using a broadband nonlinear least-squares regression technique in the frequency-domain, with similarities to a Whittle estimator. Simulation studies where the underlying distribution is symmetric suggest that while the new method may have a slightly lower level of accuracy than existing methods (Whittle, conditional sum-of-squares), it can improve the accuracy in determining the values for the short-memory parameters of highly skewed non-Gaussian data (e.g., χ2), while having the added advantage of being evaluated considerably faster. In a supplementary addendum we provide some theoretical results under a Gaussian assumption.

Keywords: GARMA; Gegenbauer (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.1515/jtse-2019-0031 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

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:bpj:jtsmet:v:13:y:2021:i:2:p:119-144:n:4

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/jtse/html

DOI: 10.1515/jtse-2019-0031

Access Statistics for this article

Journal of Time Series Econometrics is currently edited by Javier Hidalgo

More articles in Journal of Time Series Econometrics from De Gruyter
Bibliographic data for series maintained by Peter Golla ().