High-frequency credit spread information and macroeconomic forecast revision
Christos Ioannidis and
International Journal of Forecasting, 2020, vol. 36, issue 2, 358-372
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.
Keywords: Forecast revision; GDP forecast; Credit spread; High-frequency data; Mixed data sampling (MIDAS) (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:36:y:2020:i:2:p:358-372
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