Economics at your fingertips  

Mildly Explosive Autoregression Under Stationary Conditional Heteroskedasticity

Stelios Arvanitis and Tassos Magdalinos

Journal of Time Series Analysis, 2018, vol. 39, issue 6, 892-908

Abstract: A limit theory is developed for mildly explosive autoregressions under stationary (weakly or strongly dependent) conditionally heteroskedastic errors. The conditional variance process is allowed to be stationary, integrable and mixingale, thus encompassing general classes of generalized autoregressive conditional heteroskedasticity‐type or stochastic volatility models. No mixing conditions or moments of higher order than 2 are assumed for the innovation process. As in Magdalinos (), we find that the asymptotic behaviour of the sample moments is affected by the memory of the innovation process both in the form of the limiting distribution and, in the case of long range dependence, the rate of convergence, while conditional heteroskedasticity affects only the asymptotic variance. These effects are cancelled out in least squares regression theory, and thereby, the Cauchy limit theory of Phillips and Magdalinos () remains invariant to a wide class of stationary conditionally heteroskedastic innovations processes.

Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)

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:

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 ().

Page updated 2019-02-23
Handle: RePEc:bla:jtsera:v:39:y:2018:i:6:p:892-908