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Rank-based instrumental variable estimation for semiparametric varying coefficient spatial autoregressive models

Author

Listed:
  • Yangbing Tang

    (Beijing University of Technology)

  • Zhongzhan Zhang

    (Beijing University of Technology
    Beijing Institute of Scientific and Engineering Computing)

  • Jiang Du

    (Beijing University of Technology
    Beijing Institute of Scientific and Engineering Computing)

Abstract

In this paper, it is aim to propose an instrumental variable rank estimation method for varying coefficient spatial autoregressive models. The newly proposed method provides a highly efficient and robust alternative to the existing quasi-maximum likelihood estimation or GMM estimation, and can be implemented using the existing R software package conveniently. Under mild conditions, the consistency and asymptotic normality of the resulting estimators are established. The finite sample properties of the proposed method are investigated through Monte Carlo simulation studies. Finally, the Boston house price data and crime data of Tokyo are analyzed to illustrate the usefulness of the proposed estimation method.

Suggested Citation

  • Yangbing Tang & Zhongzhan Zhang & Jiang Du, 2024. "Rank-based instrumental variable estimation for semiparametric varying coefficient spatial autoregressive models," Statistical Papers, Springer, vol. 65(3), pages 1805-1839, May.
    • Handle: RePEc:spr:stpapr:v:65:y:2024:i:3:d:10.1007_s00362-023-01466-5
    DOI: 10.1007/s00362-023-01466-5
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