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Vector autoregressive models: A Gini approach

Author

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  • Mussard, Stéphane
  • Ndiaye, Oumar Hamady

Abstract

In this paper, it is proven that the usual VAR models may be performed in the Gini sense, that is, on a ℓ1 metric space. The Gini regression is robust to outliers. As a consequence, when data are contaminated by extreme values, we show that semi-parametric VAR-Gini regressions may be used to obtain robust estimators. The inference about the estimators is made with the ℓ1 norm. Also, impulse response functions and Gini decompositions for prevision errors are introduced. Finally, Granger’s causality tests are properly derived based on U-statistics.

Suggested Citation

  • Mussard, Stéphane & Ndiaye, Oumar Hamady, 2018. "Vector autoregressive models: A Gini approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 1967-1979.
    • Handle: RePEc:eee:phsmap:v:492:y:2018:i:c:p:1967-1979
    DOI: 10.1016/j.physa.2017.11.111
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    Cited by:

    1. Arthur Charpentier & Ndéné Ka & Stéphane Mussard & Oumar Hamady Ndiaye, 2019. "Gini Regressions and Heteroskedasticity," Econometrics, MDPI, vol. 7(1), pages 1-16, January.
    2. Anastasia Dimiski, 2020. "Factors that affect Students’ performance in Science: An application using Gini-BMA methodology in PISA 2015 dataset," Working Papers 2004, University of Guelph, Department of Economics and Finance.
    3. Rui Yang & Xin An & Yingwen Chen & Xiuli Yang, 2023. "The Knowledge Analysis of Panel Vector Autoregression: A Systematic Review," SAGE Open, , vol. 13(4), pages 21582440231, December.

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