On the estimation of [beta]-ARCH models

Ouagnina Hili

Statistics & Probability Letters, 1999, vol. 45, issue 4, 285-293

Abstract: The parametric [beta]-ARCH model which is defined byXt=aXt-1+(a0+a1Xt-12[beta])1/2[var epsilon]thas been introduced by Diebolt and Guégan (C. R. Acad. Sci. Paris 312(I) (1991) 33-36). The probabilistic properties of this model are well known (see Guégan and Diebolt, 1994). In this paper, we derive under mild conditions the asymptotic properties (consistency and asymptotic normality) of the minimum Hellinger distance estimates of the parameters a,a0,a1 and [beta]. The generalisation to an homogeneous Markov chain of order p>1 is also considered.

Keywords: Markov; chain; Invertibility; Stationarity; [alpha]-mixing; [beta]-ARCH; model; Existence; of; moments; Hellinger; distance; Estimation; Consistency; Asymptotic; normality (search for similar items in EconPapers)
Date: 1999
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-7152(99)00070-X
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:stapro:v:45:y:1999:i:4:p:285-293

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

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().