Generalized nonparametric smoothing with mixed discrete and continuous data

Li, Degui, Simar, Leopold and Zelenyuk, Valentin (2014) Generalized nonparametric smoothing with mixed discrete and continuous data. Computational Statistics and Data Analysis, 1-21. doi:10.1016/j.csda.2014.06.003


Author Li, Degui
Simar, Leopold
Zelenyuk, Valentin
Title Generalized nonparametric smoothing with mixed discrete and continuous data
Journal name Computational Statistics and Data Analysis   Check publisher's open access policy
ISSN 0167-9473
1872-7352
Publication date 2014-06-10
Year available 2014
Sub-type Article (original research)
DOI 10.1016/j.csda.2014.06.003
Start page 1
End page 21
Total pages 21
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Collection year 2015
Language eng
Abstract The nonparametric smoothing technique with mixed discrete and continuous regressors is considered. It is generally admitted that it is better to smooth the discrete variables, which is similar to the smoothing technique for continuous regressors but using discrete kernels. However, such an approach might lead to a potential problem which is linked to the bandwidth selection for the continuous regressors due to the presence of the discrete regressors. Through the numerical study, it is found that in many cases, the performance of the resulting nonparametric regression estimates may deteriorate if the discrete variables are smoothed in the way previously addressed, and that a fully separate estimation without any smoothing of the discrete variables may provide significantly better results both for bias and variance. As a solution, it is suggested a simple generalization of the nonparametric smoothing technique with both discrete and continuous data to address this problem and to provide estimates with more robust performance. The asymptotic theory for the new nonparametric smoothing method is developed and the finite sample behavior of the proposed generalized approach is studied through extensive Monte-Carlo experiments as well an empirical illustration.
Keyword Discrete regressors
Nonparametric regression
Kernel smoothing
Cross-validation
Local linear smoothing
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online ahead of print 10 June 2014.

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
School of Economics Publications
 
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