n-uniformly consistent density estimation in nonparametric regression models

The paper introduces a 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 experi...

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Bibliographic Details
Published in:Journal of econometrics 2012-04, Vol.167 (2), p.305-316
Main Authors: Escanciano, Juan Carlos, Jacho-Chávez, David T.
Format: Article
Language:English
Subjects:
[formula omitted]-processes
Computational methods
Density estimation
Dimensional nuisance parameters
Econometric models
Estimation
Kernel smoothing
Monte Carlo simulation
Probability
Regression analysis
U-processes theory
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Summary:The paper introduces a 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. The results derived in the paper adapt general U-processes theory to the inclusion of infinite dimensional nuisance parameters.
ISSN:0304-4076
1872-6895
DOI:10.1016/j.jeconom.2011.09.017