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A Gini-based unit root test

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  • Shelef, Amit

Abstract

A Gini-based statistical test for a unit root is suggested. This test is based on the well-known Dickey–Fuller test, where the ordinary least squares (OLS) regression is replaced by the semi-parametric Gini regression in modeling the AR process. A residual-based bootstrap is used to find critical values. The Gini methodology is a rank-based methodology that takes into account both the variate values and the ranks. Therefore, it provides robust estimators that are rank-based, while avoiding loss of information. Furthermore, the Gini methodology relies on first-order moment assumptions, which validates its use for a wide range of distributions. Simulation results validate the Gini-based test and indicate its superiority in some design settings in comparison to other available procedures. The Gini-based test opens the door for further developments such as a Gini-based cointegration test.

Suggested Citation

  • Shelef, Amit, 2016. "A Gini-based unit root test," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 763-772.
    • Handle: RePEc:eee:csdana:v:100:y:2016:i:c:p:763-772
    DOI: 10.1016/j.csda.2014.08.012
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    References listed on IDEAS

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    Cited by:

    1. Bailey, Natalia & Giraitis, Liudas, 2016. "Spectral approach to parameter-free unit root testing," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 4-16.
    2. Charpentier, Arthur & Mussard, Stéphane & Ouraga, Téa, 2021. "Principal component analysis: A generalized Gini approach," European Journal of Operational Research, Elsevier, vol. 294(1), pages 236-249.
    3. N. V. Gribkova & J. Su & R. Zitikis, 2022. "Empirical tail conditional allocation and its consistency under minimal assumptions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(4), pages 713-735, August.
    4. Sudheesh K. Kattumannil & N. Sreelakshmi & N. Balakrishnan, 2022. "Non-Parametric Inference for Gini Covariance and its Variants," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(2), pages 790-807, August.
    5. Ndéné Ka, 2021. "Proo-poor growth modeling in developing countries: A Gini regression approach," Economics Bulletin, AccessEcon, vol. 41(2), pages 316-327.

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