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<p>In this paper, we investigate empirical likelihood-based statistical inferences for a class of semiparametric spatial autoregressive models featuring single-index varying coefficients. Combining bias correction and instrumental variable adjustment techniques, we propose a calibrated empirical likelihood estimation method for model parameters and functional coefficients. Under standard regularity conditions, we establish some asymptotic properties of the resulting estimators. The empirical performance of our proposed method is validated through comprehensive simulation studies and an application to real spatial data. Monte Carlo results demonstrate that the proposed calibrated empirical likelihood estimation is efficient.</p>

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Calibrated empirical likelihood for single-index varying coefficient spatial autoregressive models

  • 期刊论文

  • 发表日期：

    2026年1月17日

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  • Peixin Zhao,
  • Xiaoyan Liu,
  • Xinrong Tang,
  • Suli Cheng,
  • Xiaoshuang Zhou

摘要

In this paper, we investigate empirical likelihood-based statistical inferences for a class of semiparametric spatial autoregressive models featuring single-index varying coefficients. Combining bias correction and instrumental variable adjustment techniques, we propose a calibrated empirical likelihood estimation method for model parameters and functional coefficients. Under standard regularity conditions, we establish some asymptotic properties of the resulting estimators. The empirical performance of our proposed method is validated through comprehensive simulation studies and an application to real spatial data. Monte Carlo results demonstrate that the proposed calibrated empirical likelihood estimation is efficient.