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

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  • Levy, Daniel
  • Mayer, Tamir
  • Raviv, Alon

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

  • Levy, Daniel & Mayer, Tamir & Raviv, Alon, 2022. "Economists in the 2008 Financial Crisis: Slow to See, Fast to Act," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, issue Forthcomi.
    • Handle: RePEc:zbw:espost:249769
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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).
    4. Lei Li & Kun Qin & Desheng Wu, 2023. "A Hybrid Approach for the Assessment of Risk Spillover to ESG Investment in Financial Networks," Sustainability, MDPI, vol. 15(7), pages 1-16, April.
    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).
    6. W. Benedikt Schmal, 2022. "From Rules to Regs: A Structural Topic Model of Collusion Research," Papers 2210.02957, arXiv.org.

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    More about this item

    Keywords

    Financial Crisis; 2008 Financial Crisis; Economic Crisis; Great Recession; NBER Working Papers; LDA Textual Analysis; Topic Modeling; Dynamic Topic Modeling; Machine Learning;
    All these keywords.

    JEL classification:

    • A11 - General Economics and Teaching - - General Economics - - - Role of Economics; Role of Economists
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • 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
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • F30 - International Economics - - International Finance - - - General
    • G01 - Financial Economics - - General - - - Financial Crises
    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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