A note on limit theory for mildly stationary autoregression with a heavy-tailed GARCH error process

Eunju Hwang

Statistics & Probability Letters, 2019, vol. 152, issue C, 59-68

Abstract: A first-order mildly stationary autoregression with a heavy-tailed GARCH error process is considered to study the limit theory for the least squared estimator of the autoregression coefficient ρ=ρn∈[0,1). A Gaussian limit theory is established as ρn converges to the unity as n→∞, with rate condition (1−ρn)n→∞, as in Giraitis and Philips (2006), who have discussed the limit theory in case that errors are martingale difference sequences. This work addresses asymptotic results in a case of heavy-tailed GARCH errors, and extends the existing one by allowing errors to follow heavy-tailed process as well as conditional heteroscedasticity.

Keywords: Autoregression; Heavy-tailed GARCH process; Least squared estimator; Limit theory (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167715219301129
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:152:y:2019:i:c:p:59-68

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

DOI: 10.1016/j.spl.2019.04.009

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 ().