A bootstrap-based approach for parameter and polyspectral density estimation of a non-minimum phase ARMA process

Shahnoor Shanta and Visakan Kadirkamanathan

International Journal of Systems Science, 2015, vol. 46, issue 3, 418-428

Abstract: A bootstrap-based methodology is developed for parameter estimation and polyspectral density estimation in the case of the approximating model of the underlying stochastic process being non-minimum phase autoregressive-moving-average (ARMA) type, given a finite realisation of a single time series data. The method is based on a minimum phase/maximum phase decomposition of the system function together with a time reversal step for the parameter and polyspectral confidence interval estimation. Simulation examples are provided to illustrate the proposed method.

Date: 2015
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/00207721.2013.784444 (text/html)
Access to full text is restricted to subscribers.

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:taf:tsysxx:v:46:y:2015:i:3:p:418-428

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TSYS20

DOI: 10.1080/00207721.2013.784444

Access Statistics for this article

International Journal of Systems Science is currently edited by Visakan Kadirkamanathan

More articles in International Journal of Systems Science from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().