Non-Parametric Estimation in DAD

Abstract

It is often useful to visualize the shapes of income distributions. There are essentially two main approaches to doing so, and a mixture of the two. The first approach uses parametric models of income distributions. These models assume that the income distribution follows a known particular functional form, but with unknown parameters. Popular examples of such functional forms include the log-normal, the Pareto, and variants of the beta or gamma distributions. The main statistical challenge is then to estimate the unknown parameters of that functional form, and to test whether a given functional form appears to estimate better the observed distribution of income than another functional form.