Distribution-Free Statistical Inferences for Testing Marginal Changes in Inequality Indices

This paper develops asymptotically distribution-free inference for testing inequality indices with dependent samples. It considers the interpolated Gini coefficient and the generalized entropy class, which includes several commonly used inequality indices. We first establish inference tests for changes in inequality indices with completely dependent samples (i.e., matched pairs) and then generalize the inference procedures to cases with partially dependent samples. The effects of sample dependency on standard errors of inequality changes are examined through simulation studies as well as through applications to the CPS and PSID data

Simple dependent pairs of exponential and uniform random variables

A random-coefficient linear function of two independent exponential variables yielding a third exponential variable is used in the construction of simple, dependent pairs of exponential variables. By employing antithetic exponential variables, the constructions are developed to encompass negative dependency. By employing negative exponentiation, the constructions yield simple multiplicative-based models for dependent uniform pairs. The ranges of dependency allowable in the models are assessed by correlation calculations, both of the product moment and Spearman types; broad ranges within the theoretically allowable ranges are found. Because of their simplicity, all models are particularly suitable for simulation and are free of point and line concentrations of valuesPrepared for: Naval Postgraduate Schoolhttp://archive.org/details/simpledependentp00law