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Text Algorithms in Economics

Author

Listed:
  • Elliott Ash

    (Center for Law and Economics, ETH Zurich, Zurich, Switzerland)

  • Stephen Hansen

    (Department of Economics, University College London, London, United Kingdom)

Abstract

This article provides an overview of the methods used for algorithmic text analysis in economics, with a focus on three key contributions. First, we introduce methods for representing documents as high-dimensional count vectors over vocabulary terms, for representing words as vectors, and for representing word sequences as embedding vectors. Second, we define four core empirical tasks that encompass most text-as-data research in economics and enumerate the various approaches that have been taken so far to accomplish these tasks. Finally, we flag limitations in the current literature, with a focus on the challenge of validating algorithmic output.

Suggested Citation

  • Elliott Ash & Stephen Hansen, 2023. "Text Algorithms in Economics," Annual Review of Economics, Annual Reviews, vol. 15(1), pages 659-688, September.
    • Handle: RePEc:anr:reveco:v:15:y:2023:p:659-688
    DOI: 10.1146/annurev-economics-082222-074352
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    Cited by:

    1. Vito Cormun & Kim Ristolainen, 2024. "Exchange Rate Narratives," Discussion Papers 167, Aboa Centre for Economics.
    2. Franz Ulrich Ruch & Temel Taskin, 2024. "Global Demand and Supply Sentiment: Evidence From Earnings Calls," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(2), pages 314-334, April.
    3. Pablo Ottonello & Wenting Song & Sebastian Sotelo, 2024. "An Anatomy of Firms’ Political Speech," Staff Working Papers 24-37, Bank of Canada.
    4. Lorenz Gschwent & Bjorn Hammarfelt & Martin Karlsson & Mathias Kifmann, 2024. "The Rise of Health Economics: Transforming the Landscape of Economic Research," Papers 2410.06313, arXiv.org.
    5. Michael Bauer & Daniel Huber & Eric Offner & Marlene Renkel & Ole Wilms & Michael D. Bauer, 2024. "Corporate Green Pledges," CESifo Working Paper Series 11507, CESifo.
    6. Maria Saveria Mavillonio, 2024. "Natural Language Processing Techniques for Long Financial Document," Discussion Papers 2024/317, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    7. Ilyana Kuziemko & Nicolas Longuet-Marx & Suresh Naidu, 2024. "“Compensate the Losers?†Economic Policy and Partisan Realignment in the US," Working Papers 321, Princeton University, Department of Economics, Center for Economic Policy Studies..
    8. Ashwin, Julian, 2024. "Financial news media and volatility: Is there more to newspapers than news?," Journal of Financial Markets, Elsevier, vol. 69(C).
    9. Hana Jomni & Nikita Zakharov, 2024. "Do Terrorist Attacks Polarize Politicians? Evidence from the European Parliamentary Speeches on Migration," Discussion Paper Series 50 JEL Classification: D7, Department of International Economic Policy, University of Freiburg, revised Nov 2024.

    More about this item

    Keywords

    text as data; topic models; word embeddings; large language models; transformer models;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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