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E-Book E-Book AUM Main Library 519.5 (Browse Shelf) Not for loan

Introduction -- More Than a Dozen Alternative Ways of Spelling Gini -- The Gini equivalents of the covariance, the correlation and the regression coefficient -- Decompositions of the GMD -- The Lorenz curve and the concentration curve -- The extended Gini family of measures -- Gini Simple Regressions -- Multiple Regressions -- Inference on Gini-based parameters -estimation -- Inference on Gini-based parameters -testing -- Inference on Lorenz and on Concentration curves -- Introduction to applications -- Social welfare, relative deprivation and the Gini coefficient -- Policy Analysis.-  Policy Analysis Using the Decomposition of the Gini by non-marginal analysis.- Incorporating poverty in Policy Analysis - the Marginal Analysis case -- Introduction to applications of the GMD and the Lorenz curve in finance -- The mean-Gini portfolio and the pricing of capital assets -- Applications of Gini methodology in regression analysis -- Gini's multiple regressions: two approaches and their interaction -- Mixed OLS, Gini and extended Gini regressions.-  An application in statistics - ANOGI -- Suggestions for further research   .

Gini's mean difference (GMD) was first introduced by Corrado Gini in 1912 as an alternative measure of variability. GMD and the parameters which are derived from it (such as the Gini coefficient or the concentration ratio) have been in use in the area of income distribution for almost a century. In practice, the use of GMD as a measure of variability is justified whenever the investigator is not ready to impose, without questioning, the convenient world of normality. This makes the GMD of critical importance in the complex research of statisticians, economists, econometricians, and policy makers. This book focuses on imitating analyses that are based on variance by replacing variance with the GMD and its variants. In this way, the text showcases how almost everything that can be done with the variance as a measure of variability, can be replicated by using Gini. Beyond this, there are marked benefits to utilizing Gini as opposed to other methods. One of the advantages of using Gini methodology is that it provides a unified system that enables the user to learn about various aspects of the underlying distribution. It also provides a systematic method and a unified terminology. Using Gini methodology can reduce the risk of imposing assumptions that are not supported by the data on the model.  With these benefits in mind the text uses the covariance-based approach, though applications to other approaches are mentioned as well.

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