Nonparametric estimation of regression functions with both categorical and continuous data
Nonparametric estimation of regression functions with both categorical and continuous data
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DOI：
10.1016/s0304-4076(03)00157-x
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发表时间：
2004-03-01
影响因子：
6.3
通讯作者：
Li, Q
中科院分区：
文献类型：
--
作者：
Racine, J;Li, Q
在本文中，我们提出了一种非参数回归方法，该方法使用核方法以自然的方式接纳连续和分类数据。提出了一种数据驱动的带宽选择方法，并建立了估计器的渐近正态性。我们还建立了交叉验证的平滑参数与其基准最佳平滑参数的收敛率。模拟表明，在存在混合数据的情况下，新的估计器比传统的非参数估计器表现得更好。对广泛使用且公开的动态专利数据组的实证应用表明，我们提出的估计器的样本外平方预测误差仅为一些已用于对该数据集建模的流行参数方法所获得的误差的 14-20%。 (C) 2003 Elsevier B.V. 保留所有权利。
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. (C) 2003 Elsevier B.V. All rights reserved.