Forecasting GDP in Europe with Textual Data
This work addresses forecasting challenges for economists and policymakers in Europe, but it is incremental as it applies existing sentiment analysis methods to new data.
The authors tackled the problem of forecasting GDP and other macroeconomic variables in major European economies using news-based sentiment indicators, finding that these indicators are significant predictors and robust to other real-time controls.
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.