Should macroeconomic forecasters use daily financial data and how?

Eric Ghysels, Andros Kourtellos () and Elena Andreou
Additional contact information
Eric Ghysels: UNC
Elena Andreou: University of Cyprus

No 1196, 2012 Meeting Papers from Society for Economic Dynamics

Abstract: We introduce easy to implement regression-based methods for predicting quarterly real economic activity that use daily financial data and rely on forecast combinations of Mixed Data Sampling (MIDAS) regressions. We also extract a novel small set of daily financial factors from a large panel of about one thousand daily financial assets. Our analysis is designed to elucidate the value of daily information and provide real-time forecast updates of the current (nowcasting) and future quarters of real GDP growth. Our findings show that while on average the predictive ability of all models worsens substantially following the financial crisis that started in 2007, the models we propose suffer relatively less losses than the traditional ones. Moreover, these predictive gains are primarily driven by the classes of government securities, equities, and especially corporate risk.

Date: 2012
New Economics Papers: this item is included in nep-for and nep-mac
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Related works:
Working Paper: Should Macroeconomic Forecasters Use Daily Financial Data and How? (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:red:sed012:1196

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