Title : High-Frequency Credit Spread Information and Macroeconomic Forecast Revision
Author : Bruno Deschamps(Nottingham University Business School China), Christos Ioannidis(Aston Business School), Kook Ka(BOK)
We examine whether professional forecasters incorporate high-frequency information about credit conditions when revising their economic forecasts. Using Mixed Data Sampling regression approach, we find that daily credit spreads have significant predictive ability for monthly forecast revisions of output growth, at both aggregate and individual forecast levels. The relations are shown to be notably strong during ‘bad’ economic conditions, suggesting that forecasters anticipate more pronounced effects of credit tightening during economic downturns, indicating the amplification effect of financial developments on macroeconomic aggregates. Forecasts do not incorporate the totality of financial information received in equal measures, implying the presence of information rigidities in the incorporation of credit spread information.