Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/272971 
Year of Publication: 
2022
Series/Report no.: 
Working Paper No. 2022-01
Publisher: 
Bar-Ilan University, Department of Economics, Ramat-Gan
Abstract: 
We study the economics and finance scholars' reaction to the 2008 financial crisis using machine learning language analyses methods of Latent Dirichlet Allocation and dynamic topic modelling algorithms, to analyze the texts of 14,270 NBER working papers covering the 1999-2016 period. We find that academic scholars as a group were insufficiently engaged in crises' studies before 2008. As the crisis unraveled, however, they switched their focus to studying the crisis, its causes, and consequences. Thus, the scholars were "slow-to-see," but they were "fast-to-act." Their initial response to the ongoing Covid-19 crisis is consistent with these conclusions.
Subjects: 
Financial crisis
Economic Crisis
Great recession
NBER working papers
LDA textual analysis
Topic modeling
Dynamic Topic Modeling
Machine learning
JEL: 
E32
E44
E50
F30
G01
G20
Document Type: 
Working Paper

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