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Should Macroeconomic Forecasters Use Daily Financial Data and How?

Author

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  • Elena Andreou

    () (Department of Economics, University of Cyprus, Nicosia, Cyprus)

  • Eric Ghysels

    () (Department of Economics, University of North Carolina, Chapel Hill, NC, USA; Department of Finance, Kenan-Flagler Business School, University of North Carolina, Chapel Hill, NC, USA)

  • Andros Kourtellos

    () (Department of Economics, University of Cyprus, Nicosia, Cyprus; The Rimini Centre for Economic Analysis (RCEA), Rimini, Italy)

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 MIDAS regressions. Our analysis is designed to elucidate the value of daily information and provide real-time forecast updates of the current (nowcasting) and future quarters. Our findings show that while on average the predictive ability of all models worsens substantially following the financial crisis, 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.

Suggested Citation

  • Elena Andreou & Eric Ghysels & Andros Kourtellos, 2010. "Should Macroeconomic Forecasters Use Daily Financial Data and How?," Working Paper series 42_10, Rimini Centre for Economic Analysis.
  • Handle: RePEc:rim:rimwps:42_10
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    More about this item

    Keywords

    MIDAS; macro forecasting; leads; daily financial information; daily factors;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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