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Yule–Walker Equations Using a Gini Covariance Matrix for the High-Dimensional Heavy-Tailed PVAR Model

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Listed:
  • Jin Zou

    (School of Mathematics Science, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Dong Han

    (School of Mathematics Science, Shanghai Jiao Tong University, Shanghai 200240, China)

Abstract

Gini covariance plays a vital role in analyzing the relationship between random variables with heavy-tailed distributions. In this papaer, with the existence of a finite second moment, we establish the Gini–Yule–Walker equation to estimate the transition matrix of high-dimensional periodic vector autoregressive (PVAR) processes, the asymptotic results of estimators have been established. We apply this method to study the Granger causality of the heavy-tailed PVAR process, and the results show that the robust transfer matrix estimation induces sign consistency in the value of Granger causality. Effectiveness of the proposed method is verified by both synthetic and real data.

Suggested Citation

  • Jin Zou & Dong Han, 2021. "Yule–Walker Equations Using a Gini Covariance Matrix for the High-Dimensional Heavy-Tailed PVAR Model," Mathematics, MDPI, vol. 9(6), pages 1-15, March.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:6:p:614-:d:516997
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    References listed on IDEAS

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    6. Song, Song & Bickel, Peter J., 2011. "Large vector auto regressions," SFB 649 Discussion Papers 2011-048, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
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