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Empirical Likelihood Inference for the Semiparametric Varying-Coefficient Spatial Autoregressive Model

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Abstract

In this paper empirical likelihood (EL)-based inference for a semiparametric varying-coefficient spatial autoregressive model is investigated. The maximum EL estimators for the parametric component and the nonparametric component are established. Furthermore, asymptotic properties of the proposed estimators and EL ratios are derived, and the corresponding confidence regions/bands are constructed. Their finite sample performances are studied via simulation and an example.

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Authors and Affiliations

  1. College of Big Data Statistics, Guizhou University of Finance and Economics, Guiyang, 550025, China

    Guowang Luo

  2. College of Statistics and Data Science, Faculty of Science, Beijing University of Technology, Beijing, 100124, China

    Mixia Wu

  3. Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China

    Zhen Pang

Authors
  1. Guowang Luo
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  2. Mixia Wu
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  3. Zhen Pang
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Corresponding author

Correspondence to Zhen Pang.

Additional information

This research was supported by the National Natural Science Foundation of China under Grant No. 11771032, Scientific Research Foundation for Young Talents of Department of Education of Guizhou Province, and Scientific Research Foundation of Guizhou University of Finance and Economics under Grant No. 2021YJ027.

This paper was recommended for publication by Editor LI Qizhai.

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Luo, G., Wu, M. & Pang, Z. Empirical Likelihood Inference for the Semiparametric Varying-Coefficient Spatial Autoregressive Model. J Syst Sci Complex 34, 2310–2333 (2021). https://doi.org/10.1007/s11424-021-1088-y

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  • DOI: https://doi.org/10.1007/s11424-021-1088-y

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