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<p>In this paper, we first present a spatial autoregressive model with covariate data missing at random, and propose IPW-Adaptive LASSO estimators by combining the inverse probability weighting (IPW) method of handling missing data and adaptive LASSO shrinkage method of selecting key regressors. Subsequently, asymptotic properties of the estimators are derived under some regular conditions. The results show that the proposed method can identify the true model consistently, and the resulting estimators can be as efficient as the oracle estimators. Third, Monte Carlo simulations are used to evaluate the finite sample performances of these estimators. Finally, the proposed method is applied to an analysis of the Boston housing price data.</p>

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Variable selection of the spatial autoregressive model with covariate data missing at random

  • 期刊论文

  • 发表日期：

    2026年2月18日

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  • Yu Liu,
  • Yuexin Fang

摘要

In this paper, we first present a spatial autoregressive model with covariate data missing at random, and propose IPW-Adaptive LASSO estimators by combining the inverse probability weighting (IPW) method of handling missing data and adaptive LASSO shrinkage method of selecting key regressors. Subsequently, asymptotic properties of the estimators are derived under some regular conditions. The results show that the proposed method can identify the true model consistently, and the resulting estimators can be as efficient as the oracle estimators. Third, Monte Carlo simulations are used to evaluate the finite sample performances of these estimators. Finally, the proposed method is applied to an analysis of the Boston housing price data.