High-frequency credit spread information and macroeconomic forecast revision

Bruno Deschamps, Christos Ioannidis, Kook Ka

    Research output: Journal PublicationArticlepeer-review

    2 Citations (Scopus)

    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
    Pages (from-to)358-372
    Number of pages15
    JournalInternational Journal of Forecasting
    Volume36
    Issue number2
    DOIs
    Publication statusPublished - 1 Apr 2020

    Keywords

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

    ASJC Scopus subject areas

    • Business and International Management

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