  Nonparametric density estimation is of great importance when econometricians
want to model the probabilistic or stochastic structure of a data set. This
comprehensive review summarizes the most important theoretical aspects of
kernel density estimation and provides an extensive description of classical
and modern data analytic methods to compute the smoothing parameter. Throughout
the text, several references can be found to the most up-to-date and cut point
research approaches in this area, while econometric data sets are analyzed as
examples. Lastly, we present SIZer, a new approach introduced by Chaudhuri and
Marron (2000), whose objective is to analyze the visible features representing
important underlying structures for different bandwidths.
