Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/286794 
Year of Publication: 
2021
Citation: 
[Journal:] Empirical Economics [ISSN:] 1435-8921 [Volume:] 62 [Issue:] 5 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2021 [Pages:] 2373-2415
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
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.
Subjects: 
Textual analysis
Sentiment
Macroeconomic forecasting
Forecast evaluation
Germany
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.