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Forward or Backward Looking? The Economic Discourse and the Observed Reality

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  • Lüdering Jochen

    (Center for International Developmental and Environmental Research (ZEU), Justus-Liebig-Universität Gießen, Gießen, Germany)

  • Winker Peter

    (Center for International Developmental and Environmental Research (ZEU), Justus-Liebig-Universität Gießen, Gießen, Germany; and Department of Statistics and Econometrics, Justus-Liebig-Universität Gießen, Gießen, Germany)

Abstract

Is academic research anticipating economic shake-ups or merely reflecting the past? Exploiting the corpus of articles published in the Journal of Economics and Statistics (Jahrbücher für Nationalökonomie und Statistik) for the years 1949 to 2010, this pilot study proposes a quantitative framework for addressing these questions. The framework comprises two steps. First, methods from computational linguistics are used to identify relevant topics and their relative importance over time. In particular, Latent Dirichlet Analysis is applied to the corpus after some preparatory work. Second, for some of the topics which are closely related to specific economic indicators, the developments of topic weights and indicator values are confronted in dynamic regression and VAR models. The results indicate that for some topics of interest, the discourse in the journal leads developments in the real economy, while for other topics it is the other way round.

Suggested Citation

  • Lüdering Jochen & Winker Peter, 2016. "Forward or Backward Looking? The Economic Discourse and the Observed Reality," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 236(4), pages 483-515, August.
    • Handle: RePEc:jns:jbstat:v:236:y:2016:i:1:p:483-515:n:2
    DOI: 10.1515/jbnst-2015-1026
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    Cited by:

    1. Savin, Ivan & Ott, Ingrid & Konop, Chris, 2022. "Tracing the evolution of service robotics: Insights from a topic modeling approach," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    2. Ivan Savin & Kristina Chukavina & Andrey Pushkarev, 2023. "Topic-based classification and identification of global trends for startup companies," Small Business Economics, Springer, vol. 60(2), pages 659-689, February.
    3. Levy, Daniel & Mayer, Tamir & Raviv, Alon, 2022. "Economists in the 2008 financial crisis: Slow to see, fast to act," Journal of Financial Stability, Elsevier, vol. 60(C).
    4. Jochen Lüdering & Peter Tillmann, 2016. "Monetary Policy on Twitter and its Effect on Asset Prices: Evidence from Computational Text Analysis," MAGKS Papers on Economics 201612, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    5. Lino Wehrheim, 2017. "Economic History Goes Digital: Topic Modeling the Journal of Economic History," Working Papers 177, Bavarian Graduate Program in Economics (BGPE).
    6. Sami Diaf & Jörg Döpke & Ulrich Fritsche & Ida Rockenbach, 2020. "Sharks and minnows in a shoal of words: Measuring latent ideological positions of German economic research institutes based on text mining techniques," Macroeconomics and Finance Series 202001, University of Hamburg, Department of Socioeconomics.
    7. Winker, Peter, 2023. "Visualizing Topic Uncertainty in Topic Modelling," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277584, Verein für Socialpolitik / German Economic Association.
    8. David Lenz & Peter Winker, 2020. "Measuring the diffusion of innovations with paragraph vector topic models," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-18, January.
    9. Lino Wehrheim, 2019. "Economic history goes digital: topic modeling the Journal of Economic History," Cliometrica, Springer;Cliometric Society (Association Francaise de Cliométrie), vol. 13(1), pages 83-125, January.
    10. Lüdering, Jochen & Tillmann, Peter, 2020. "Monetary policy on twitter and asset prices: Evidence from computational text analysis," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    11. Jan Kinne & David Lenz, 2021. "Predicting innovative firms using web mining and deep learning," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-18, April.
    12. Diaf, Sami & Döpke, Jörg & Fritsche, Ulrich & Rockenbach, Ida, 2022. "Sharks and minnows in a shoal of words: Measuring latent ideological positions based on text mining techniques," European Journal of Political Economy, Elsevier, vol. 75(C).

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    More about this item

    Keywords

    automatic text analysis; economic history; latent dirichlet allocation; time series analysis;
    All these keywords.

    JEL classification:

    • B23 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Econometrics; Quantitative and Mathematical Studies
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other

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