Using the Press to Construct a New Indicator of Inflation Perceptions in France

Olivier de Bandt, Jean-Charles Bricongne, Julien Denes, Alexandre Dhenin, Annabelle De Gaye and Pierre-Antoine Robert

Working papers from Banque de France

Abstract: The paper applies Natural Language Processing techniques (NLP) to the quasi-universe of newspaper articles for France, concentrating on the period 2004-2022, in order to measure inflation attention as well as perceptions by households and firms for that country. The indicator, constructed along the lines of a balance of opinions, is well correlated with actual HICP inflation. It also exhibits good forecasting properties for the European Commission survey on households inflation expectations, as well as overall HICP inflation. The method used is a supervised approach that we describe step-by-step. It performs better on our data than the Latent-Dirichlet-Allocation (LDA)-based approach of Angelico et al. (2022). The indicator can be used as an early real-time indicator of future inflation developments and expectations. It also provides a new set of indicators at a time when central banks monitor inflation through new types of surveys of households and firms.

Keywords: Inflation; Natural Language Processing; Households and Firms; Expectations; Machine Learning (search for similar items in EconPapers)
JEL-codes: C53 C55 D84 E31 E58 (search for similar items in EconPapers)
Pages: 62 pages
Date: 2023
New Economics Papers: this item is included in nep-ban, nep-big and nep-cmp
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Persistent link: https://EconPapers.repec.org/RePEc:bfr:banfra:921

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