Template-Type: ReDIF-Article 1.0
Author-Name:  Adriano Z. Zambom
Author-Email: adriano.zambom@gmail.com
Author-Workplace-Name: Universidade Estadual de Campinas
Author-Name:  Ronaldo Dias
Author-Email: dias@unicamp.br
Author-Workplace-Name: Universidade Estadual de Campinas
Title:  A Review of Kernel Density Estimation with Applications to Econometrics
Abstract:  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.
Classification-JEL: C14
Keywords: Nonparametric Density Estimation, SiZer, Plug-In Bandwidth Selectors, Cross- Validation, Smoothing Parameter.
Journal: International Econometric Review
Pages: 20-42
Volume: 5
Issue: 1
Year: 2013
Month: April
File-URL: http://www.era.org.tr/makaleler/13120083.pdf
File-Format: Application/pdf
Handle: RePEc:erh:journl:v:5:y:2013:i:1:p:20-42