- Working Paper
- Japanese Politics
Forecasting Japanese Elections by Applying Ensemble Learning Methods
March 29, 2021
TKFD Working Paper Series No. 20-05
Abstract
This paper applies machine learning methods, namely, the decision tree algorithm and ensemble learning methods to forecast Japanese lower house elections. By applying these two machine learning methods, we developed several non-linear forecasting models. We then compared our forecasting results with those generated by Lewis-Beck and Tien (2012), a pioneering model on this topic, using the same data and the same explanatory variables. We found that, even without tuning, all of our ensemble-learning-based forecasting models exceeded Lewis-Beck and Tien’s model in mean accuracy. All of them also provided better explanations for mean variance. Despite small sample size inherent for country-specific forecasting models, our non-linear forecasting models of Japanese election generated better performance than linear models. We believe, by combining with substantive theories of each country’s political situation, our methodological approach can improve predictability of country-specific forecasting models of other countries.
Download Working Paper (PDF: 656 KB)
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- FORMER ADVISER
- Budrul Ahsan
- Budrul Ahsan
- Areas of Expertise
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- Deep learning
- data mining
- recommendation system
- genome analysis
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- RESEARCH DIRECTOR
- Sota Kato
- Sota Kato
- Areas of Expertise
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- Comparative political economy
- political methodology (formal modeling, machine learning)
- Japanese politics
- Research Program
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- Future Design: Research on Decision-Making Methods for Intergenerational Sustainability ( –2021)
- Contingency Plans for a Potential Financial Crisis
- Plans for Sustainable and Politically Acceptable Fiscal Consolidation and Social Insurance Reform from the Perspectives of Behavioral Economics and Political Science(-2024)
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- FORMER RESEARCH ASSISTANT
- Shun Ibaragi
- Shun Ibaragi
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