Time-Series Topic Analysis Using Singular Spectrum Transformation for Detecting Political Business Cycles

TKFD Working Paper Series No. 20-03


Herein, we present a novel topic variation detection method that combines a topic extraction method and a change point detection method. It extracts topics from time-series text data as a feature of each time and detects change points from the changing patterns of the extracted topics. We applied this method to analyze the valuable albeit underutilized text data containing the Japanese Prime Minister's (PM’s) detailed daily activities for over 32 years. The proposed method and data provide novel insights into the empirical analyses of political business cycles, which is a classical issue in economics and political science. For instance, as our approach enables us to directly observe and analyze the PM’s actions, it can overcome empirical challenges encountered by previous researchers owing to the unobservability of the PM’s behavior. Our empirical observations are primarily consistent with recent theoretical developments regarding this topic. Despite limitations, by employing a completely novel method and data, our approach enhances our understanding and provides new insights into this classic issue.

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加藤 創太/Sota Kato

Sota Kato


Areas of Expertise

  • Comparative political economy
  • political methodology
  • public opinion

Research Program

Future Design: Research on Decision-Making Methods for Intergenerational Sustainability

Plans for Sustainable and Politically Acceptable Fiscal Consolidation and Social Insurance Reform from the Perspectives of Behavioral Economics and Political Science

中西 崇文/Takafumi Nakanishi

Takafumi Nakanishi


Areas of Expertise

  • Data mining
  • sensitivity information processing
  • media content analysis

Hirokazu Shimauchi

阿山 晴取/Budrul Ahsan

Budrul Ahsan


Areas of Expertise

  • Deep learning
  • data mining
  • recommendation system
  • genome analysis