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Licensed Unlicensed Requires Authentication Published by De Gruyter February 27, 2015

Root-n Consistent Kernel Density Estimation in Practice

Daniel J. Henderson and Christopher F. Parmeter


This paper details implementation of the recently proposed root-n kernel density estimator of (Escanciano, J. C., and D. T. Jacho-Chávez. 2012. “n-uniformly consistent density estimation in nonparametric regression models.” Journal of Econometrics 167: 305–316.) that circumvents the slow rate of convergence of traditional nonparametric kernel density estimators. We discuss implementation issues such as bandwidth selection and controlling for heteroskedasticity. Two empirical examples are provided; we re-examine the classic study of the emerging multimodality of the cross-country distribution of income per capita, finding more local structure with this new method, and we study the distribution of lean body mass across gender, where we demonstrate robustness of the new methods to alternative bandwidth selection mechanisms.

Corresponding author: Daniel J. Henderson, Department of Economics, Finance and Legal Studies, University of Alabama, Tuscaloosa, AL 35487-0224, USA; e-mail:


We wish the thank the editor, Jason Abrevaya as well as David Jacho-Chávez for insightful comments which greatly improved the paper. All errors are ours alone.


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Supplemental Material

The online version of this article (DOI: 10.1515/jem-2014-0010) offers supplementary material, available to authorized users.

Published Online: 2015-2-27
Published in Print: 2017-1-1

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