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Transfer learning for semiparametric varying coefficient spatial autoregressive models

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  • Xuan Chen

    (China University of Petroleum)

  • Yunquan Song

    (China University of Petroleum)

Abstract

Transfer learning is widely recognized for its effectiveness in leveraging external information to enhance the learning performance and predictive accuracy of target domain models. However, research on transfer learning within the context of the semiparametric varying-coefficient spatial autoregressive model is currently absent. In this study, we address this gap by introducing a transfer learning approach tailored to this model. Our method aims to improve estimation and prediction accuracy by effectively transferring knowledge from source data to the target model. We propose different algorithms for the cases where the transferable sources are known and unknown, respectively. Through extensive simulation experiments and real-world applications, we validate the efficacy of our proposed approach.

Suggested Citation

  • Xuan Chen & Yunquan Song, 2025. "Transfer learning for semiparametric varying coefficient spatial autoregressive models," Statistical Papers, Springer, vol. 66(2), pages 1-22, February.
    • Handle: RePEc:spr:stpapr:v:66:y:2025:i:2:d:10.1007_s00362-025-01662-5
    DOI: 10.1007/s00362-025-01662-5
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    References listed on IDEAS

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    1. Su, Liangjun, 2012. "Semiparametric GMM estimation of spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 167(2), pages 543-560.
    2. Ruiqin Tian & Miaojie Xia & Dengke Xu, 2024. "Profile quasi-maximum likelihood estimation for semiparametric varying-coefficient spatial autoregressive panel models with fixed effects," Statistical Papers, Springer, vol. 65(8), pages 5109-5143, October.
    3. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    4. Zhang, Yuanqing & Sun, Yanqing, 2015. "Estimation of partially specified dynamic spatial panel data models with fixed-effects," Regional Science and Urban Economics, Elsevier, vol. 51(C), pages 37-46.
    5. Guowang Luo & Mixia Wu, 2021. "Variable selection for semiparametric varying-coefficient spatial autoregressive models with a diverging number of parameters," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 50(9), pages 2062-2079, May.
    6. Lung-fei Lee, 2003. "Best Spatial Two-Stage Least Squares Estimators for a Spatial Autoregressive Model with Autoregressive Disturbances," Econometric Reviews, Taylor & Francis Journals, vol. 22(4), pages 307-335.
    7. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
    8. Sai Li & Linjun Zhang & T. Tony Cai & Hongzhe Li, 2024. "Estimation and Inference for High-Dimensional Generalized Linear Models with Knowledge Transfer," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 119(546), pages 1274-1285, April.
    9. Jingru Mu & Guannan Wang & Li Wang, 2020. "Spatial autoregressive partially linear varying coefficient models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 32(2), pages 428-451, April.
    10. Sun, Yanqing & Zhang, Yuanqing & Huang, Jianhua Z., 2019. "Estimation of a semiparametric varying-coefficient mixed regressive spatial autoregressive model," Econometrics and Statistics, Elsevier, vol. 9(C), pages 140-155.
    11. Sai Li & T. Tony Cai & Hongzhe Li, 2022. "Transfer learning for high‐dimensional linear regression: Prediction, estimation and minimax optimality," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(1), pages 149-173, February.
    12. Lung-Fei Lee, 2004. "Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models," Econometrica, Econometric Society, vol. 72(6), pages 1899-1925, November.
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