Economists in the 2008 Financial Crisis: Slow to See, Fast to Act

Daniel Levy (), Tamir Mayer and Alon Raviv

MPRA Paper from University Library of Munich, Germany

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; Financial Crises; Economic Crisis; Great Recession; Textual Analysis; LDA Topic Modeling; Dynamic Topic Modeling; Machine Learning; Securitization; Repo; Sudden Stop (search for similar items in EconPapers)
JEL-codes: A11 C38 C55 E32 E44 E52 E58 F30 G01 G20 G21 G28 (search for similar items in EconPapers)
Date: 2022-02-13
New Economics Papers: this item is included in nep-ban, nep-big, nep-cmp, nep-cwa, nep-his, nep-hme, nep-hpe and nep-mac
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Citations: View citations in EconPapers (6)

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Related works:
Journal Article: Economists in the 2008 financial crisis: Slow to see, fast to act (2022)
Journal Article: Economists in the 2008 Financial Crisis: Slow to See, Fast to Act (2022)
Working Paper: Economists in the 2008 Financial Crisis: Slow to See, Fast to Act (2022)
Working Paper: Economists in the 2008 Financial Crisis: Slow to See, Fast to Act (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:112008

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