Vector autoregressive models: A Gini approach

Stéphane Mussard and Oumar Hamady Ndiaye

Physica A: Statistical Mechanics and its Applications, 2018, vol. 492, issue C, 1967-1979

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.

Keywords: Causality; Gini regression; Response function; U-statistics; VAR (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:492:y:2018:i:c:p:1967-1979

DOI: 10.1016/j.physa.2017.11.111

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