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首页> 外文期刊>Journal of Time Series Analysis >SIGNIFICANT VARIABLE SELECTION AND AUTOREGRESSIVE ORDER DETERMINATION FOR TIME-SERIES PARTIALLY LINEAR MODELS
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SIGNIFICANT VARIABLE SELECTION AND AUTOREGRESSIVE ORDER DETERMINATION FOR TIME-SERIES PARTIALLY LINEAR MODELS

机译：时间序列部分线性模型的显着变量选择和自回归阶数确定

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摘要

This paper is concerned with the regression coefficient and autoregressive order shrinkage and selection via the smoothly clipped absolute deviation (SCAD) penalty for a partially linear model with time-series errors. By combining the profile semi-parametric least squares method and SCAD penalty technique, a new penalized estimation for the regression and autoregressive parameters in the model is proposed. We show that the asymptotic property of the resultant estimator is the same as if the order of autoregressive error structure and non-zero regression coefficients are known in advance, thus achieving the oracle property in the sense of Fan and Li (2001). In addition, based on a prewhitening technique, we construct a two-stage local linear estimator (TSLLE) for the non-parametric component. It is shown that the TSLLE is more asymtotically efficient than the one that ignores the autoregressive time-series error structure. Some simulation studies are conducted to illustrate the finite sample performance of the proposed procedure. An example of application on electricity usage data is also illustrated.
机译：本文关注具有时间序列误差的部分线性模型的回归系数和自回归阶数收缩以及通过平滑限幅绝对偏差（SCAD）罚分的选择。通过将轮廓半参数最小二乘方法和SCAD惩罚技术相结合，提出了模型中回归参数和自回归参数的一种新的惩罚估计方法。我们表明，所得估计量的渐近性质与自动回归误差结构的阶数和非零回归系数的已知相同，从而实现了Fan和Li（2001）的预言性质。另外，基于预白化技术，我们为非参数分量构造了两级局部线性估计器（TSLLE）。结果表明，TSLLE比那些忽略自回归时间序列错误结构的算法在渐近效率上更高。进行了一些仿真研究，以说明所提出程序的有限样本性能。还说明了用电量数据的应用示例。

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  • 来源
    《Journal of Time Series Analysis》 |2014年第5期|478-490|共13页
  • 作者

    DEGAO LI; GUODONG LI; JINHONG YOU;

  • 作者单位

    School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China,College of Mathematics Physics and Information Engineering, Jiaxing University, Jiaxing, China;

    Department of Statistics and Actuarial Science, University of Hong Kong, Hong Kong, China;

    School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China,Key Laboratory of Mathematical Economics (SUFE), Ministry of Education of China, Shanghai, China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    partially linear; autoregressive error; consistency; asymptotic normality;

    机译：部分线性自回归误差一致性;渐近正态性;

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