High-frequency credit spread information and macroeconomic forecast revision

Bruno Deschamps, Christos Ioannidis, Kook Ka

Research output: Contribution to journalArticlepeer-review

Abstract

We examine whether professional forecasters incorporate high-frequency information about credit conditions when revising their economic forecasts. Using a mixed data sampling regression approach, we find that daily credit spreads have significant predictive ability for monthly forecast revisions of output growth, at both the aggregate and individual forecast levels. The relationships are shown to be notably strong during ‘bad’ economic conditions, which suggests that forecasters anticipate more pronounced effects of credit tightening during economic downturns, indicating an amplification effect of financial developments on macroeconomic aggregates. The forecasts do not incorporate all financial information received in equal measures, implying the presence of information rigidities in the incorporation of credit spread information.
Original languageEnglish
JournalInternational Journal of Forecasting
Early online date4 Oct 2019
DOIs
Publication statusE-pub ahead of print - 4 Oct 2019

Bibliographical note

© 2019 International Institute of Forecasters. Published by Elsevier B.V.

Keywords

  • Credit spread
  • Forecast revision
  • GDP forecast
  • High-frequency data
  • Mixed data sampling (MIDAS)

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