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

Ulrich Fritsche () and Johannes Puckelwald ()
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Johannes Puckelwald: Universität Hamburg (University of Hamburg)

No 201804, Macroeconomics and Finance Series from University of Hamburg, Department of Socioeconomics

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.

Keywords: Sentiment analysis; text analysis; uncertainty; business cycle forecast; forecast error; expectation; adaptive expectation; latent Dirichlet allocation; structural topic model (search for similar items in EconPapers)
JEL-codes: E32 E37 C49 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-eec, nep-for and nep-mac
Date: 2018-05
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http://www.wiso.uni-hamburg.de/repec/hepdoc/macppr_4_2018.pdf First version, 2018 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:hep:macppr:201804

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