Transfer Learning for Spatial Autoregressive Models

• URL: http://arxiv.org/abs/2405.15600v1
• Date: Mon, 20 May 2024 03:14:15 GMT
• Title: Transfer Learning for Spatial Autoregressive Models
• Authors: Hao Zeng, Wei Zhong, Xingbai Xu,
• Abstract summary: The spatial autoregressive (SAR) model has been widely applied in various empirical economic studies. We propose a new transfer learning framework for the SAR model to borrow the information from similar source data to improve both estimation and prediction. In the empirical application, we apply our method to the election prediction in swing states in the 2020 U.S. presidential election.
• Score: 10.825562180226424
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The spatial autoregressive (SAR) model has been widely applied in various empirical economic studies to characterize the spatial dependence among subjects. However, the precision of estimating the SAR model diminishes when the sample size of the target data is limited. In this paper, we propose a new transfer learning framework for the SAR model to borrow the information from similar source data to improve both estimation and prediction. When the informative source data sets are known, we introduce a two-stage algorithm, including a transferring stage and a debiasing stage, to estimate the unknown parameters and also establish the theoretical convergence rates for the resulting estimators. If we do not know which sources to transfer, a transferable source detection algorithm is proposed to detect informative sources data based on spatial residual bootstrap to retain the necessary spatial dependence. Its detection consistency is also derived. Simulation studies demonstrate that using informative source data, our transfer learning algorithm significantly enhances the performance of the classical two-stage least squares estimator. In the empirical application, we apply our method to the election prediction in swing states in the 2020 U.S. presidential election, utilizing polling data from the 2016 U.S. presidential election along with other demographic and geographical data. The empirical results show that our method outperforms traditional estimation methods.

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