Nowcasting euro area GDP with news sentiment: a tale of two crises

Lorena Saiz, Julian Ashwin and Eleni Kalamara

No 2616, Working Paper Series from European Central Bank

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 Classification: C43, C45, C55, C82, E37

Keywords: business cycles; COVID-19; forecasting; machine learning; text analysis (search for similar items in EconPapers)
Date: 2021-11
New Economics Papers: this item is included in nep-big, nep-cmp, nep-eec, nep-for and nep-mac
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20212616

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