M-estimation for autoregressions with infinite variance

Richard A. Davis, Keith Knight and Jian Liu

Stochastic Processes and their Applications, 1992, vol. 40, issue 1, 145-180

Abstract: We study the problem of estimating autoregressive parameters when the observations are from an AR process with innovations in the domain of attraction of a stable law. We show that non-degenerate limit laws exist for M-estimates if the loss function is sufficiently smooth; these results remain valid if location and scale are also estimated. For least absolute deviation (LAD) estimates, similar results hold under conditions on the innovations distribution near 0. We also discuss, under moment conditions on the innovations, consistency properties for M-estimators corresponding to the class of loss functions, [varrho](x) = x [gamma] for some [gamma] > 0.

Keywords: AR; processes; M-estimation; least; squares; estimation; least; absolute; deviation; stable; distribution; domain; of; attraction; point; process (search for similar items in EconPapers)
Date: 1992
References: Add references at CitEc
Citations: View citations in EconPapers (60)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/0304-4149(92)90142-D
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:spapps:v:40:y:1992:i:1:p:145-180

Ordering information: This journal article can be ordered from
http://http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

Access Statistics for this article

Stochastic Processes and their Applications is currently edited by T. Mikosch

More articles in Stochastic Processes and their Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().