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Variable Selection of High-Dimensional Spatial Autoregressive Panel Models with Fixed Effects

Author

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  • Miaojie Xia
  • Yuqi Zhang
  • Ruiqin Tian
  • Antonio Di Crescenzo

Abstract

This paper studies the variable selection of high-dimensional spatial autoregressive panel models with fixed effects in which a matrix transformation method is applied to eliminate the fixed effects. Then, a penalized quasi-maximum likelihood is developed for variable selection and parameter estimation in the transformed panel model. Under some regular conditions, the consistency and oracle properties of the proposed estimator are established. Some Monte-Carlo experiments and a real data analysis are conducted to examine the finite sample performance of the proposed variable selection procedure, showing that the proposed variable selection method works satisfactorily.

Suggested Citation

  • Miaojie Xia & Yuqi Zhang & Ruiqin Tian & Antonio Di Crescenzo, 2023. "Variable Selection of High-Dimensional Spatial Autoregressive Panel Models with Fixed Effects," Journal of Mathematics, Hindawi, vol. 2023, pages 1-16, May.
    • Handle: RePEc:hin:jjmath:9837117
    DOI: 10.1155/2023/9837117
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