Forecasting Japanese elections: A nonlinear machine-learning approach
For political scientists and election forecasters, this work demonstrates that nonlinear methods can modestly improve election forecasting for a single country, though the gains are incremental.
This study applies nonlinear machine-learning models to forecast Japanese lower-house elections, achieving moderately but consistently improved predictive accuracy over a classical linear model in both in-sample and out-of-sample evaluations.
Despite Japan being one of the world's largest advanced democracies, the development of election forecasting models for its national elections remains limited. This study introduces nonlinear machine-learning forecasting models, based on decision tree and ensemble learning methods, for predicting the outcomes of Japanese lower-house elections. To assess the methodological benefits of our approach, we replicated the theoretical framework and dataset of Lewis-Beck and Tien's (LBT) foundational statistical forecasting model for Japanese elections. Our models demonstrated moderately but consistently improved predictive accuracy compared to LBT's model in both in-sample and out-of-sample evaluations, suggesting that nonlinear algorithms offer an alternative approach to classical linear methods in capturing complex electoral dynamics. This study represents one of the earlier applications of nonlinear machine-learning techniques to single-country election forecasting. It offers a replicable framework that, when combined with the country-specific electoral theories of other nations, may enhance the predictive performance of forecasting models in broader national contexts.