Nowcasting Swiss GDP Growth From Public Lead Texts: Simple Methods Are Sufficient
Year: 2026
Type: article
Abstract: ABSTRACT Public lead texts from Swiss newspapers contain most of the signal needed to nowcast Swiss GDP growth in real time. I build an indicator from daily topic‐specific sentiment and recession measures extracted from three Swiss newspapers and evaluate it in pseudo‐real time. The indicator is competitive with established Swiss business‐cycle indicators and simple statistical benchmarks once enough within‐quarter news has arrived, and the gains remain after excluding COVID‐19 and the Global Financial Crisis. I then compare three design choices that arise in text‐based nowcasting systems: lead texts versus full articles, keyword‐based scoring versus large‐language‐model classification, and static versus dynamic factor aggregation. None delivers systematic forecast gains over the baseline; the LLM variant is more costly and harder to hold fixed in real time, and the full‐article indicators often perform worse. The main contribution is therefore a design result: in this public‐data setting, lead texts and simple methods already recover most of the useful nowcasting signal.
DOI: 10.1111/obes.70073
Author: Marc Burri
Institution: University of Neuchâtel
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,840
OpenAlex ID: W7153979116