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A family of nonparametric unit root tests for processes driven by infinite variance innovations

Author

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  • Gogebakan Kemal Caglar

    (Department of Economics, Bilkent University, Ankara, Turkey)

Abstract

This paper presents extensions to the family of nonparametric fractional variance ratio (FVR) unit root tests of Nielsen (2009. “A Powerful Test of the Autoregressive Unit Root Hypothesis Based on a Tuning Parameter Free Statistic.” Econometric Theory 25: 1515–44) under heavy tailed (infinite variance) innovations. In this regard, we first develop the asymptotic theory for these FVR tests under this setup. We show that the limiting distributions of the tests are free of serial correlation nuisance parameters, but depend on the tail index of the infinite variance process. Then, we compare the finite sample size and power performance of our FVR unit root tests with the well-known parametric ADF test under the impact of the heavy tailed shocks. Simulations demonstrate that under heavy tailed innovations, the nonparametric FVR tests have desirable size and power properties.

Suggested Citation

  • Gogebakan Kemal Caglar, 2022. "A family of nonparametric unit root tests for processes driven by infinite variance innovations," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 26(5), pages 705-721, December.
    • Handle: RePEc:bpj:sndecm:v:26:y:2022:i:5:p:705-721:n:1
    DOI: 10.1515/snde-2021-0058
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    More about this item

    Keywords

    heavy tailed innovation; infinite variance distribution; nonparametric test; unit root test;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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