Abstract
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.
Highlights
Among1 regressions, the Gini regression initiated by Olkin and Yitzhaki (1992) is increasingly used in econometrics
In the context of semi-parametric Gini regressions, we showed that the Aitken transformation (Aitken 1935) for non-spherical disturbances based on the variance provides exactly the same estimator obtained by neutralizing the Gini covariance of the error term
It is shown that the employ of the Aitken-Gini estimator may be preferred to generalized least squares (GLS) when the data are contaminated by outlying observations
Summary
The Gini regression initiated by Olkin and Yitzhaki (1992) is increasingly used in econometrics. Investigated the semi-parametric Gini regression for vector autoregressive models in which non-spherical disturbances occur. They showed that premultiplying the model by a matrix that neutralizes the Gini covariance of the error terms may produce non-biased Gini estimators. In the context of semi-parametric Gini regressions, we showed that the Aitken transformation (Aitken 1935) for non-spherical disturbances based on the variance provides exactly the same estimator obtained by neutralizing the Gini covariance of the error term. It is shown that the usual White test to detect heteroskedasticity should be done in the Gini sense, that is, by testing the Gini covariance of the regressors instead of their variance In this case, more power is obtained for small samples.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
