Root-n Uniformly Consistent Density Estimation in Nonparametric Regression Models
Juan Carlos Escanciano
Indiana University Bloomington - Department of Economics
David T. Jacho-Chávez
Emory University - Department of Economics; Indiana University Bloomington - Department of Economics
September 28, 2011
Journal of Econometrics, Vol. 167, Issue 2, pp. 305–316, April 2012
The paper introduces a root-n consistent estimator of the probability density function of the response variable in a nonparametric regression model. The proposed estimator is shown to have a (uniform) asymptotic normal distribution, and it is computationally very simple to calculate. A Monte Carlo experiment confirms our theoretical results, and an empirical application demonstrates its usefulness. The results derived in the paper adapts general U-processes theory to the inclusion of infinite dimensional nuisance parameters.
Number of Pages in PDF File: 26
Keywords: Density Estimation, Kernel Smoothing, U-processes
JEL Classification: C14working papers series
Date posted: July 6, 2008 ; Last revised: March 9, 2012
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