Abstract
Resampling approaches were the first techniques employed to compute a variance for the Gini coefficient. Few authors showed that Gini's coefficient measure can be obtained from a synthetic ordinary linear regression (OLS) based on the data and their ranks, thereby providing also with an exact analytical standard error. We develop these techniques for assessing the quality of credit models and for measuring the confidence interval of Gini coefficient. A special attention is given to low defaults and/or small length datasets and low quality models. In consequence we develop a new sampling-based method (F-Gini) for measuring the standard error of the Gini coefficient more adapted for these situations.
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