State space modeling of Gegenbauer processes with long memory

G.S. Dissanayake, M.S. Peiris and Tommaso Proietti

Computational Statistics & Data Analysis, 2016, vol. 100, issue C, 115-130

Abstract: An approximation of a Gegenbauer autoregressive moving average (GARMA) process with long memory using a finite order moving average (MA) representation is considered. The state space form of the MA approximation is developed and the corresponding estimates are obtained by pseudo maximum likelihood using the Kalman filter. For comparative purposes the same exercise is executed with an autoregressive (AR) approximation. Using an extensive Monte Carlo experiment, optimal order of the chosen MA approximation is established, and found it was not very large (around 35) and rather insensitive to the sample size. Further evidence suggests the approximation is reliable for forecasting and signal extraction with periodic long memory components. A rolling forecasting experiment was performed to validate the choice of optimal order of both AR and MA approximations in terms of predictive accuracy. Finally, the proposed methodology was applied to two yearly sunspots time series, and compared with corresponding results proposed in the literature.

Keywords: Long memory; Gegenbauer processes; State space models (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947314002758
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:100:y:2016:i:c:p:115-130

DOI: 10.1016/j.csda.2014.09.014

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 ().