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Julian Ashwin

25 November 2021
WORKING PAPER SERIES - No. 2616
Details
Abstract
This paper shows that newspaper articles contain timely economic signals that can materially improve nowcasts of real GDP growth for the euro area. Our text data is drawn from fifteen popular European newspapers, that collectively represent the four largest Euro area economies, and are machine translated into English. Daily sentiment metrics are created from these news articles and we assess their value for nowcasting. By comparing to competitive and rigorous benchmarks, we find that newspaper text is helpful in nowcasting GDP growth especially in the first half of the quarter when other lower-frequency soft indicators are not available. The choice of the sentiment measure matters when tracking economic shocks such as the Great Recession and the Great Lockdown. Non-linear machine learning models can help capture extreme movements in growth, but require sufficient training data in order to be effective so become more useful later in our sample.
JEL Code
C43 : Mathematical and Quantitative Methods→Econometric and Statistical Methods: Special Topics→Index Numbers and Aggregation
C45 : Mathematical and Quantitative Methods→Econometric and Statistical Methods: Special Topics→Neural Networks and Related Topics
C55 : Mathematical and Quantitative Methods→Econometric Modeling→Modeling with Large Data Sets?
C82 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Methodology for Collecting, Estimating, and Organizing Macroeconomic Data, Data Access
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications