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<p>The spatial Durbin model has been widely applied in regional economics, real estate, and environmental policy studies for its ability to capture both direct and indirect effects. However, when the target domain suffers from limited sample size or distributional discrepancies with available source domains, conventional single-domain estimation often encounters sample representativeness limitations and external estimation bias. To address this challenge, this paper introduces the concept of transfer learning into the spatial econometric framework and proposes a transfer learning method for spatial Durbin model, which enables knowledge transfer across multiple domains under spatial dependence. Furthermore, under the scenario where transferable sources are unknown, we develop a transferable source detection mechanism that combines instrumental variable transformation and cross-validation to automatically identify source domains most similar to the target domain. Both simulation and empirical analysis demonstrate that our methods outperform the baseline methods.</p>

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Transfer Learning for High-dimensional Spatial Durbin Model

  • 期刊论文

  • 发表日期：

    2026年6月27日

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  • Shuyu Xiao,
  • Yunquan Song,
  • Hui Zhou

摘要

The spatial Durbin model has been widely applied in regional economics, real estate, and environmental policy studies for its ability to capture both direct and indirect effects. However, when the target domain suffers from limited sample size or distributional discrepancies with available source domains, conventional single-domain estimation often encounters sample representativeness limitations and external estimation bias. To address this challenge, this paper introduces the concept of transfer learning into the spatial econometric framework and proposes a transfer learning method for spatial Durbin model, which enables knowledge transfer across multiple domains under spatial dependence. Furthermore, under the scenario where transferable sources are unknown, we develop a transferable source detection mechanism that combines instrumental variable transformation and cross-validation to automatically identify source domains most similar to the target domain. Both simulation and empirical analysis demonstrate that our methods outperform the baseline methods.