Transfer Learning for Spatial Autoregressive Models with Application to U.S. Presidential Election Prediction

TL;DR

This paper introduces tranSAR, a transfer learning framework for spatial autoregressive models that improves election prediction accuracy in swing states by leveraging source data and includes a transferable source detection method.

Contribution

The paper develops a novel transfer learning approach for SAR models, with a two-stage algorithm and source detection, enhancing election prediction in small sample, spatially dependent data.

Findings

Improves estimation accuracy over classical SAR models.

Effectively predicts 2024 U.S. presidential election outcome.

Outperforms traditional methods in simulation and real data.

Abstract

It is important to incorporate spatial geographic information into U.S. presidential election analysis, especially for swing states. The state-level analysis also faces significant challenges of limited spatial data availability. To address the challenges of spatial dependence and small sample sizes in predicting U.S. presidential election results using spatially dependent data, we propose a novel transfer learning framework within the SAR model, called as tranSAR. Classical SAR model estimation often loses accuracy with small target data samples. Our framework enhances estimation and prediction by leveraging information from similar source data. We introduce a two-stage algorithm, consisting of a transferring stage and a debiasing stage, to estimate parameters and establish theoretical convergence rates for the estimators. Additionally, if the informative source data are unknown, we propose a transferable source detection algorithm using spatial residual bootstrap to maintain spatial dependence and derive its detection consistency. Simulation studies show our algorithm substantially improves the classical two-stage least squares estimator. We demonstrate our method's effectiveness in predicting outcomes in U.S. presidential swing states, where it outperforms traditional methods. In addition, our tranSAR model predicts that the Democratic party will win the 2024 U.S. presidential election.