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