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