Unit Root Tests and Heavy-Tailed Innovations

I Georgiev, Paulo Rodrigues and Robert Taylor

Essex Finance Centre Working Papers from University of Essex, Essex Business School

Abstract: We evaluate the impact of heavy-tailed innovations on some popular unit root tests. In the context of a near-integrated series driven by linear-process shocks, we demonstrate that their limiting distributions are altered under in nite variance vis-�-vis finite variance. Reassuringly, however, simulation results suggest that the impact of heavy-tailed innovations on these tests are relatively small. We use the framework of Amsler and Schmidt (2012) whereby the innovations have local-to- nite variances being generated as a linear combination of draws from a thin- tailed distribution (in the domain of attraction of the Gaussian distribution) and a heavy-tailed distribution (in the normal domain of attraction of a stable law). We also explore the properties of ADF tests which employ Eicker-White standard errors, demonstrating that these can yield significant power improvements over conventional tests.

Keywords: Infinite variance; ?-stable distribution; Eicker-White standard errors; symptotic local power functions; weak dependence (search for similar items in EconPapers)
Date: 2017-01
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (2)

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Journal Article: Unit Root Tests and Heavy-Tailed Innovations (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:esy:uefcwp:18832

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