Journal
ECONOMICS LETTERS
Volume 201, Issue -, Pages -Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.econlet.2021.109783
Keywords
Natural language processing; Machine learning; Narrative economics
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According to Shiller (2017), economic and financial narratives often emerge as a consequence of their virality rather than their veracity, and constitute an important yet understudied driver of aggregate fluctuations. The study found that business cycle narratives tend to consolidate around a dominant explanation during expansions and fragment into competing explanations during contractions. Furthermore, the presence of past reference events is strongly associated with increased narrative consolidation.
According to Shiller (2017), economic and financial narratives often emerge as a consequence of their virality, rather than their veracity, and constitute an important, but understudied driver of aggregate fluctuations. Using a unique dataset of newspaper articles over the 1950-2019 period and state-ofthe-art methods from natural language processing, we characterize the properties of business cycle narratives. Our main finding is that narratives tend to consolidate around a dominant explanation during expansions and fragment into competing explanations during contractions. We also show that the existence of past reference events is strongly associated with increased narrative consolidation. (c) 2021 Elsevier B.V. All rights reserved.
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