Publications séminaires GAINS

30 janvier 2018 Oumar Hamdy NDIAYE

with R. Vejlin, M. Hejlesen and H. Bunzel

présente :

VAR models on ℓ1 metric spaces (a Gini approach) : an application to energy and growth

Abstract :
In this paper, it is proven that the usual VAR approach 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 the data are contaminated by extreme values, we show that semi-parametric VAR-Gini regressions may be used to obtain robust estimators.
The inference on 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.

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