Transfer Learning for Spatial Autoregressive Models with Application to U.S. Presidential Election Prediction
The research proposes tranSAR, a novel transfer learning framework within Spatial Autoregressive (SAR) models to predict U.S. presidential election results county by county in swing states. It uses a two-stage algorithm to leverage similar source data, significantly improving estimation and prediction accuracy despite small sample sizes and spatial dependence.