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

Author

Listed:
  • Daniel Levy

    (Department of Economics, Bar-Ilan University, Israel; Department of Economics, Emory University, US; ICEA, Wilfrid Laurier University, Canada; Rimini Centre for Economic Analysis; ISET, TSU, Georgia)

  • Tamir Mayer

    (Graduate School of Business Administration, Bar-Ilan University, Israel)

  • Alon Raviv

    (Graduate School of Business Administration, Bar-Ilan University, Israel)

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.

Suggested Citation

  • Daniel Levy & Tamir Mayer & Alon Raviv, 2022. "Economists in the 2008 Financial Crisis: Slow to See, Fast to Act," Working Paper series 22-04, Rimini Centre for Economic Analysis.
    • Handle: RePEc:rim:rimwps:22-04
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    3. Naveed, Hafiz Muhammad & HongXing, Yao & Memon, Bilal Ahmed & Ali, Shoaib & Alhussam, Mohammed Ismail & Sohu, Jan Muhammad, 2023. "Artificial neural network (ANN)-based estimation of the influence of COVID-19 pandemic on dynamic and emerging financial markets," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
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    5. Kurowski, Łukasz & Smaga, Paweł, 2023. "Analysing financial stability reports as crisis predictors with the use of text-mining," The Journal of Economic Asymmetries, Elsevier, vol. 28(C).
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    More about this item

    Keywords

    Financial crisis; Economic Crisis; Great recession; NBER working papers; LDA textual analysis; Topic modeling; Dynamic Topic Modeling; Machine learning;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • E50 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - General
    • F30 - International Economics - - International Finance - - - General
    • G01 - Financial Economics - - General - - - Financial Crises
    • G20 - Financial Economics - - Financial Institutions and Services - - - General

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