Arbeitspapier
Economists in the 2008 financial crisis: Slow to see, fast to act
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Working Paper ; No. 2022-01
- Klassifikation
-
Wirtschaft
Business Fluctuations; Cycles
Financial Markets and the Macroeconomy
Monetary Policy, Central Banking, and the Supply of Money and Credit: General
International Finance: General
Financial Crises
Financial Institutions and Services: General
- Thema
-
Financial crisis
Economic Crisis
Great recession
NBER working papers
LDA textual analysis
Topic modeling
Dynamic Topic Modeling
Machine learning
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Levy, Daniel C.
Mayer, Tamir
Raviv, Alon
- Ereignis
-
Veröffentlichung
- (wer)
-
Bar-Ilan University, Department of Economics
- (wo)
-
Ramat-Gan
- (wann)
-
2022
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Levy, Daniel C.
- Mayer, Tamir
- Raviv, Alon
- Bar-Ilan University, Department of Economics
Entstanden
- 2022
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