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Topic Variation Detection Method for Detecting Political Business Cycles

In this paper, we present a new topic variation detection method that combines a topic extraction method with a change point detection method. It extracts topics from time-series text data as the feature of each time and detects change points from the changing patterns of the extracted topics. We applied the method to analyze effective albeit underutilized text data that contained the Japanese Prime Minister's (PM's) detailed daily activities of over 32 years. Our method and data provide novel insights into the empirical analysis of the political business cycle, a classical issue in economics and political science. For example, because our approach enabled us to directly observe and analyze the PM's actions, it overcame empirical challenges that previous researchers encountered owing to the unobservability of the PM's behavior. Our empirical observations are mostly consistent with recent theoretical developments regarding this topic. Despite limitations, by employing a completely new method and data, our approach enhances our understanding and provides new insights into this classic issue.