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Unit Root Tests and Heavy-Tailed Innovations

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  • Georgiev, I
  • Rodrigues, PMM
  • Taylor, AMR

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.

Suggested Citation

  • Georgiev, I & Rodrigues, PMM & Taylor, AMR, 2017. "Unit Root Tests and Heavy-Tailed Innovations," Essex Finance Centre Working Papers 18832, University of Essex, Essex Business School.
    • Handle: RePEc:esy:uefcwp:18832
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    Cited by:

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    2. Paulo M.M. Rodrigues & Matei Demetrescu, 2018. "Testing the fractionally integrated hypothesis using M estimation: With an application to stock market volatility," Working Papers w201817, Banco de Portugal, Economics and Research Department.

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    Keywords

    Infinite variance; ?-stable distribution; Eicker-White standard errors; symptotic local power functions; weak dependence;
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