Forecasting GDP in Europe with Textual Data
Computational Engineering, Finance, and Science
2024-01-17 v1 Artificial Intelligence
Computation and Language
Abstract
We evaluate the informational content of news-based sentiment indicators for forecasting Gross Domestic Product (GDP) and other macroeconomic variables of the five major European economies. Our data set includes over 27 million articles for 26 major newspapers in 5 different languages. The evidence indicates that these sentiment indicators are significant predictors to forecast macroeconomic variables and their predictive content is robust to controlling for other indicators available to forecasters in real-time.
Keywords
Cite
@article{arxiv.2401.07179,
title = {Forecasting GDP in Europe with Textual Data},
author = {Luca Barbaglia and Sergio Consoli and Sebastiano Manzan},
journal= {arXiv preprint arXiv:2401.07179},
year = {2024}
}
Comments
34 pages, 6 figures, published in Journal of Applied Econometrics (Early view)