Rank‐based estimation for autoregressive moving average time series models

Beth Andrews

Journal of Time Series Analysis, 2008, vol. 29, issue 1, 51-73

Abstract: Abstract. We establish asymptotic normality and consistency for rank‐based estimators of autoregressive‐moving average model parameters. The estimators are obtained by minimizing a rank‐based residual dispersion function similar to the one given by L.A. Jaeckel [Ann. Math. Stat. Vol. 43 (1972) 1449–1458]. These estimators can have the same asymptotic efficiency as maximum likelihood estimators and are robust. The quality of the asymptotic approximations for finite samples is studied via simulation.

Date: 2008
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://doi.org/10.1111/j.1467-9892.2007.00545.x

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:bla:jtsera:v:29:y:2008:i:1:p:51-73

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0143-9782

Access Statistics for this article

Journal of Time Series Analysis is currently edited by M.B. Priestley

More articles in Journal of Time Series Analysis from Wiley Blackwell
Bibliographic data for series maintained by Wiley Content Delivery ().