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


  • 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)


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:4:p:483-515

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    References listed on IDEAS

    1. Burret Heiko T. & Köhler Ekkehard A. & Feld Lars P., 2013. "Sustainability of Public Debt in Germany – Historical Considerations and Time Series Evidence," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 233(3), pages 291-335, June.
    2. Stephen Hansen & Michael McMahon & Andrea Prat, 2018. "Transparency and Deliberation Within the FOMC: A Computational Linguistics Approach," The Quarterly Journal of Economics, Oxford University Press, vol. 133(2), pages 801-870.
    3. George Morgan & Karen Morgan & Alan Parker, 1990. "Analysis," Challenge, Taylor & Francis Journals, vol. 33(5), pages 55-57, September.
    4. Grün, Bettina & Hornik, Kurt, 2011. "topicmodels: An R Package for Fitting Topic Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i13).
    5. Vegard H. Larsen & Leif Anders Thorsrud, 2015. "The Value of News," Working Papers No 6/2015, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    6. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
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    Cited by:

    1. 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).
    2. Lino Wehrheim, 2017. "Economic History Goes Digital: Topic Modeling the Journal of Economic History," Working Papers 177, Bavarian Graduate Program in Economics (BGPE).
    3. 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.
    4. 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.
    5. 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).

    More about this item


    automatic text analysis; economic history; latent dirichlet allocation; time series analysis;

    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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