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Acknowledgement to Reviewers of Econometrics in 2018
Open AccessArticle

Gini Regressions and Heteroskedasticity

1
Centre de Recherche en Economie et Management (CREM), Université de Rennes, 35000 Rennes, France
2
Département D’économie, Université Alioune Diop de Bambey, Bambey BP 30, Senegal
3
Chrome, Université de Nîmes, 30000 Nîmes, France
*
Author to whom correspondence should be addressed.
The authors would like to thank their three reviewers. The usual disclaimer applies.
Econometrics 2019, 7(1), 4; https://doi.org/10.3390/econometrics7010004
Received: 20 July 2018 / Revised: 19 November 2018 / Accepted: 4 January 2019 / Published: 14 January 2019
We propose an Aitken estimator for Gini regression. The suggested A-Gini estimator is proven to be a U-statistics. Monte Carlo simulations are provided to deal with heteroskedasticity and to make some comparisons between the generalized least squares and the Gini regression. A Gini-White test is proposed and shows that a better power is obtained compared with the usual White test when outlying observations contaminate the data. View Full-Text
Keywords: Gini; heteroskedasticity; jackknife; U-statistics Gini; heteroskedasticity; jackknife; U-statistics
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MDPI and ACS Style

Charpentier, A.; Ka, N.; Mussard, S.; Ndiaye, O.H. Gini Regressions and Heteroskedasticity. Econometrics 2019, 7, 4. https://doi.org/10.3390/econometrics7010004

AMA Style

Charpentier A, Ka N, Mussard S, Ndiaye OH. Gini Regressions and Heteroskedasticity. Econometrics. 2019; 7(1):4. https://doi.org/10.3390/econometrics7010004

Chicago/Turabian Style

Charpentier, Arthur; Ka, Ndéné; Mussard, Stéphane; Ndiaye, Oumar H. 2019. "Gini Regressions and Heteroskedasticity" Econometrics 7, no. 1: 4. https://doi.org/10.3390/econometrics7010004

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