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High-Frequency Credit Spread Information and Macroeconomic Forecast Revision

Bruno Deschamps (), Christos Ioannidis () and Kook Ka ()
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Bruno Deschamps: Nottingham University Business School China
Christos Ioannidis: Aston Business School, Aston University
Kook Ka: Economic Research Institute, Bank of Korea

No 2019-17, Working Papers from Economic Research Institute, Bank of Korea

Abstract: 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.

Keywords: Forecast Revision; GDP Forecast; Credit Spread; High-Frequency Data; Mixed Data Sampling (MIDAS) (search for similar items in EconPapers)
JEL-codes: C53 E32 E44 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets, nep-fdg, nep-for and nep-mac
Date: 2019-05-03
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