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Variable selection for semiparametric varying-coefficient spatial autoregressive models with a diverging number of parameters

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  • Guowang Luo
  • Mixia Wu

Abstract

In this article, we consider variable selection in semiparametric varying-coefficient spatial autoregressive models with a diverging number of parameters. With the nonparametric functions approximated by B-spline basis functions and combining 2SLS method with the SCAD penalty, we propose a variable selection procedure. Under mild conditions, we establish the consistency and oracle property of the resulting estimators for parameter components and consistency of the regularized estimator for nonparametric component. Some simulation studies are conducted to assess the finite sample performance of the proposed variable selection procedure, and the developed methodology is illustrated by an analysis of the Boston housing price data.

Suggested Citation

  • Guowang Luo & Mixia Wu, 2021. "Variable selection for semiparametric varying-coefficient spatial autoregressive models with a diverging number of parameters," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 50(9), pages 2062-2079, May.
    • Handle: RePEc:taf:lstaxx:v:50:y:2021:i:9:p:2062-2079
    DOI: 10.1080/03610926.2019.1659367
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    Cited by:

    1. Liu, Yu & Zhuang, Xiaoyang, 2023. "Shrinkage estimation of semi-parametric spatial autoregressive panel data model with fixed effects," Statistics & Probability Letters, Elsevier, vol. 194(C).

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