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Acknowledgement to Reviewers of Econometrics in 2018

Gini Regressions and Heteroskedasticity †

Centre de Recherche en Economie et Management (CREM), Université de Rennes, 35000 Rennes, France
Département D’économie, Université Alioune Diop de Bambey, Bambey BP 30, Senegal
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;
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.

AMA Style

Charpentier A, Ka N, Mussard S, Ndiaye OH. Gini Regressions and Heteroskedasticity. Econometrics. 2019; 7(1):4.

Chicago/Turabian Style

Charpentier, Arthur, Ndéné Ka, Stéphane Mussard, and Oumar H. Ndiaye. 2019. "Gini Regressions and Heteroskedasticity" Econometrics 7, no. 1: 4.

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