Nowcasting GDP Growth by Reading Newspapers

Clément Bortoli, Stéphanie Combes and Thomas Renault
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Clément Bortoli: INSEE - Institut national de la statistique et des études économiques (INSEE)
Stéphanie Combes: INSEE - Institut national de la statistique et des études économiques (INSEE)

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Abstract: GDP statistics in France are published on a quarterly basis, 30 days after the end of the quarter. In this article, we consider media content as an additional data source to traditional economic tools to improve short-term forecast/nowcast of French GDP. We use a database of more than a million articles published in the newspaper Le Monde between 1990 and 2017 to create a new synthetic indicator capturing media sentiment about the state of the economy. We compare an autoregressive model augmented by the media sentiment indicator with a simple autoregressive model. We also consider an autoregressive model augmented with the Insee Business Climate indicator. Adding a media indicator improves French GDP forecasts compared to these two reference models. We also test an automated approach using penalised regression, where we use the frequencies at which words or expressions appear in the articles as regressors, rather than aggregated information. Although this approach is easier to implement than the former, its results are less accurate.

Keywords: economic analysis; nowcasting; GDP; media; Big Data; sentiment analysis; machine learning; natural language analysis (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (8)

Published in Economie et Statistique / Economics and Statistics, 2018, Big Data and Statistics - Part 1, 505-506, pp.17-33. ⟨10.24187/ecostat.2018.505d.1964⟩

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Journal Article: Nowcasting GDP Growth by Reading the Newspapers (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03205161

DOI: 10.24187/ecostat.2018.505d.1964

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