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Deciphering Professional Forecasters’ Stories - Analyzing a Corpus of Textual Predictions for the German Economy

Author

Listed:
  • Ulrich Fritsche

    (Universität Hamburg (University of Hamburg))

  • Johannes Puckelwald

    (Universität Hamburg (University of Hamburg))

Abstract

We analyze a corpus of 564 business cycle forecast reports for the German economy. The dataset covers nine institutions and 27 years. From the entire reports we select the parts that refer exclusively to the forecast of the German economy. Sentiment and frequency analysis confirm that the mode of the textual expressions varies with the business cycle in line with the hypothesis of adaptive expectations. A calculated 'uncertainty index' based on the occurrence of modal words matches with the economic policy uncertainty index by Baker et al. (2016). The latent Dirichlet allocation (LDA) model and the structural topic model (STM) indicate that topics are significantly state- and time-dependent and different across institutions. Positive or negative forecast 'surprises' experienced in the previous year have an impact on the content of topics.

Suggested Citation

  • Ulrich Fritsche & Johannes Puckelwald, 2018. "Deciphering Professional Forecasters’ Stories - Analyzing a Corpus of Textual Predictions for the German Economy," Macroeconomics and Finance Series 201804, University of Hamburg, Department of Socioeconomics.
    • Handle: RePEc:hep:macppr:201804
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    File URL: http://www.wiso.uni-hamburg.de/repec/hepdoc/macppr_4_2018.pdf
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    References listed on IDEAS

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    Cited by:

    1. Müller, Karsten, 2020. "German forecasters' narratives: How informative are German business cycle forecast reports?," Working Papers 23, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    2. Foltas, Alexander, 2020. "Testing investment forecast efficiency with textual data," Working Papers 19, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    3. 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.
    4. Döpke, Jörg & Fritsche, Ulrich & Müller, Karsten, 2019. "Has macroeconomic forecasting changed after the Great Recession? Panel-based evidence on forecast accuracy and forecaster behavior from Germany," Journal of Macroeconomics, Elsevier, vol. 62(C).

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    More about this item

    Keywords

    Sentiment analysis; text analysis; uncertainty; business cycle forecast; forecast error; expectation; adaptive expectation; latent Dirichlet allocation; structural topic model;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other

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