Panel data, frequently employed in empirical investigations, provide estimators being strongly biased in the presence of atypical observations. The aim of this work is to propose a 1 Gini regression for panel data. It is shown that the fixed effects withingroup Gini estimator is more robust than the OLS one when the data are contaminated by outliers. This semi-parametric Gini estimator is proven to be an U -statistics, consequently, it is asymptotically normal.Keywords: Gini, Panel, Regression, U -statistics. * The authors are greatly indebted to Shlomo Yitzhaki for very helpful comments and advices. They acknowledge Benoît Mulkay for stimulating discussions. Two anonymous referees are also acknowledged for very insightful comments and suggestions. The usual disclaimer applies.
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.
The aim of this paper is to analyze the dynamic evolution of six liquidity proxies on time and to find their causality with the French CAC 40 stock market index returns, over the period from January 2007 to December 2018. To this end, we use a vector autoregressive approach and the impulse response function and we perform the Granger causality test between the CAC 40 index returns and six different liquidity proxies. Empirical results suggest a significant short-term relationship between the returns and the liquidity. As for causality test, the results reveal that there is unidirectional causality running from returns to liquidity.
The widely used Prais–Winsten technique for estimating parameters of linear regression model with serial correlation is sensitive to outliers. In this paper, an alternative method based on Gini mean difference (GMD) is proposed. A Monte Carlo simulation is used to show that the Gini estimator is more robust than the general least squares one when the data are contaminated by outliers.
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