论文标题
非参数添加剂模型的一次性方法
A One-Covariate-at-a-Time Method for Nonparametric Additive Models
论文作者
论文摘要
本文提出了一种一次性多次多次测试（OCMT）方法，以选择高维非参数添加回归模型中的重要变量。与Chudik，Kapetanios和Pesaran（2018）类似，我们一次考虑一个单个非参数添加剂组件的统计意义，并考虑了问题的多重测试性质。一阶段和多阶段的程序均考虑。前者只有在所有信号的边际效应都足够强的情况下，就真正的正速率效果很好。后者有助于拾取边缘效应较弱的隐藏信号。模拟证明了拟议程序的良好有限样本性能。作为经验应用，我们在中国农村城市移民的纵向调查中提取的数据集中使用OCMT程序。我们发现，与竞争方法相比，我们的程序在样本外预测根均方根错误方面效果很好。
This paper proposes a one-covariate-at-a-time multiple testing (OCMT) approach to choose significant variables in high-dimensional nonparametric additive regression models. Similarly to Chudik, Kapetanios and Pesaran (2018), we consider the statistical significance of individual nonparametric additive components one at a time and take into account the multiple testing nature of the problem. One-stage and multiple-stage procedures are both considered. The former works well in terms of the true positive rate only if the marginal effects of all signals are strong enough; the latter helps to pick up hidden signals that have weak marginal effects. Simulations demonstrate the good finite sample performance of the proposed procedures. As an empirical application, we use the OCMT procedure on a dataset we extracted from the Longitudinal Survey on Rural Urban Migration in China. We find that our procedure works well in terms of the out-of-sample forecast root mean square errors, compared with competing methods.
