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Should macroeconomic forecasters use daily financial data and how?

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  • Eric Ghysels

    (UNC)

  • Andros Kourtellos

    (University of Cyprus)

  • Elena Andreou

    (University of Cyprus)

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    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.

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    Bibliographic Info

    Paper provided by Society for Economic Dynamics in its series 2012 Meeting Papers with number 1196.

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    Date of creation: 2012
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    Handle: RePEc:red:sed012:1196

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    Cited by:
    1. J. Isaac Miller, 2012. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Working Papers 1211, Department of Economics, University of Missouri.

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