Nonstationary linear processes with infinite variance garch errors

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

3 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)892-925
Journal / PublicationEconometric Theory
Volume37
Issue number5
Online published26 Oct 2021
Publication statusPublished - Oct 2021
Externally publishedYes

Abstract

Recently, Cavaliere, Georgiev, and Taylor (2018, Econometric Theory 34, 302-348) (CGT) considered the augmented Dickey-Fuller (ADF) test for a unit-root model with linear noise driven by i.i.d. infinite variance innovations and showed that ordinary least squares (OLS)-based ADF statistics have the same distribution as in Chan and Tran (1989, Econometric Theory 5, 354-362) for i.i.d. infinite variance noise. They also proposed an interesting question to extend their results to the case with infinite variance GARCH innovations as considered in Zhang, Sin, and Ling (2015, Stochastic Processes and their Applications 125, 482-512). This paper addresses this question. In particular, the limit distributions of the ADF for random walk models with short-memory linear noise driven by infinite variance GARCH innovations are studied. We show that when the tail index α < 2, the limit distributions are completely different from that of CGT and the estimator of the parameters of the lag terms used in the ADF regression is not consistent. This paper provides a broad treatment of unit-root models with linear GARCH noises, which encompasses the commonly entertained unit-root IGARCH model as a special case.

Citation Format(s)

Nonstationary linear processes with infinite variance garch errors. / ZHANG, Rongmao; CHAN, Ngai Hang.
In: Econometric Theory, Vol. 37, No. 5, 10.2021, p. 892-925.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review