Nonparametric estimation of regression functions with both categorical and continuous data
In this paper we propose a method for nonparametric regression which admits continuous and categorical data in a natural manner using the method of kernels. A data-driven method of bandwidth selection is proposed, and we establish the asymptotic normality of the estimator. We also establish the rate...
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| Veröffentlicht in: | Journal of econometrics 2004-03, Vol.119 (1), p.99-130 |
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| container_title | Journal of econometrics |
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| creator | Racine, Jeff Li, Qi |
| description | In this paper we propose a method for nonparametric regression which admits continuous and categorical data in a natural manner using the method of kernels. A data-driven method of bandwidth selection is proposed, and we establish the asymptotic normality of the estimator. We also establish the rate of convergence of the cross-validated smoothing parameters to their benchmark optimal smoothing parameters. Simulations suggest that the new estimator performs much better than the conventional nonparametric estimator in the presence of mixed data. An empirical application to a widely used and publicly available dynamic panel of patent data demonstrates that the out-of-sample squared prediction error of our proposed estimator is only 14–20% of that obtained by some popular parametric approaches which have been used to model this data set. |
| doi_str_mv | 10.1016/S0304-4076(03)00157-X |
| format | Article |
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| ispartof | Journal of econometrics, 2004-03, Vol.119 (1), p.99-130 |
| issn | 0304-4076 1872-6895 |
| language | eng |
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| source | RePEc; Elsevier ScienceDirect Journals |
| subjects | Applications Asymptotic normality Cross-validation Discrete variables Dynamic models Econometrics Estimating techniques Estimation Exact sciences and technology Factor analysis Insurance, economics, finance Mathematics Nonparametric smoothing Probability and statistics Regression analysis Sciences and techniques of general use Simulation Statistics Studies |
| title | Nonparametric estimation of regression functions with both categorical and continuous data |
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