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Economists in the 2008 financial crisis: Slow to see, fast to act

Daniel Levy (), Tamir Mayer and Alon Raviv ()

Journal of Financial Stability, 2022, vol. 60, issue C

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.

Keywords: 2008 financial crisis; great recessionNBER working papers; Machine learning; LDA textual analysis; Dynamic topic modeling (search for similar items in EconPapers)
JEL-codes: E32 E44 E50 F30 G01 G20 (search for similar items in EconPapers)
Date: 2022
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Journal Article: Economists in the 2008 Financial Crisis: Slow to See, Fast to Act (2022) Downloads
Working Paper: Economists in the 2008 Financial Crisis: Slow to See, Fast to Act (2022) Downloads
Working Paper: Economists in the 2008 Financial Crisis: Slow to See, Fast to Act (2022) Downloads
Working Paper: Economists in the 2008 Financial Crisis: Slow to See, Fast to Act (2022) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finsta:v:60:y:2022:i:c:s1572308922000158

DOI: 10.1016/j.jfs.2022.100986

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