Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/286794 
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Erscheinungsjahr: 
2021
Quellenangabe: 
[Journal:] Empirical Economics [ISSN:] 1435-8921 [Volume:] 62 [Issue:] 5 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2021 [Pages:] 2373-2415
Verlag: 
Springer, Berlin, Heidelberg
Zusammenfassung: 
Based on German business cycle forecast reports covering 10 German institutions for the period 1993–2017, the paper analyses the information content of German forecasters’ narratives for German business cycle forecasts. The paper applies textual analysis to convert qualitative text data into quantitative sentiment indices. First, a sentiment analysis utilizes dictionary methods and text regression methods, using recursive estimation. Next, the paper analyses the different characteristics of sentiments. In a third step, sentiment indices are used to test the efficiency of numerical forecasts. Using 12-month-ahead fixed horizon forecasts, fixed-effects panel regression results suggest some informational content of sentiment indices for growth and inflation forecasts. Finally, a forecasting exercise analyses the predictive power of sentiment indices for GDP growth and inflation. The results suggest weak evidence, at best, for in-sample and out-of-sample predictive power of the sentiment indices.
Schlagwörter: 
Textual analysis
Sentiment
Macroeconomic forecasting
Forecast evaluation
Germany
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