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 -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
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Charpentier, A.; Ka, N.; Mussard, S.; Ndiaye, O.H. Gini Regressions and Heteroskedasticity. Econometrics 2019, 7, 4.
Show more citation formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
