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Should Macroeconomic Forecasters Use Daily Financial Data and How?

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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.

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Bibliographic Info

Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 42_10.

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Date of creation: Jan 2010
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Handle: RePEc:rim:rimwps:42_10

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

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References

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Citations

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Cited by:
  1. Pierre Guerin & Massimiliano Marcellino, 2011. "Markov-Switching MIDAS Models," Economics Working Papers ECO2011/03, European University Institute.
  2. Cenesizoglu, Tolga, 2011. "Size, book-to-market ratio and macroeconomic news," Journal of Empirical Finance, Elsevier, vol. 18(2), pages 248-270, March.
  3. Christian Schumacher, 2011. "Forecasting with Factor Models Estimated on Large Datasets: A Review of the Recent Literature and Evidence for German GDP," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 231(1), pages 28-49, February.
  4. Marcelle Chauvet & Zeynep Senyuz & Emre Yoldas, 2012. "What does financial volatility tell us about macroeconomic fluctuations?," Finance and Economics Discussion Series 2012-09, Board of Governors of the Federal Reserve System (U.S.).
  5. Liebermann, Joelle, 2012. "Real-time forecasting in a data-rich environment," Research Technical Papers 07/RT/12, Central Bank of Ireland.
  6. Götz Thomas & Hecq Alain & Urbain Jean-Pierre, 2012. "Forecasting Mixed Frequency Time Series with ECM-MIDAS Models," Research Memorandum 012, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
  7. Vladimir Kuzin & Massimiliano Marcellino & Christian Schumacher, 2009. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area," Economics Working Papers ECO2009/32, European University Institute.
  8. Götz Thomas B. & Hecq Alain & Urbain Jean-Pierre, 2012. "Real-Time Forecast Density Combinations (Forecasting US GDP Growth Using Mixed-Frequency Data)," Research Memorandum 021, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
  9. Ching Wai (Jeremy) Chiu & Bjørn Eraker & Andrew T. Foerster & Tae Bong Kim & Hernán D. Seoane, 2011. "Estimating VAR's sampled at mixed or irregular spaced frequencies : a Bayesian approach," Research Working Paper RWP 11-11, Federal Reserve Bank of Kansas City.
  10. Cecilia Frale & Libero Monteforte, . "FaMIDAS: A Mixed Frequency Factor Model with MIDAS structure," Working Papers 3, Department of the Treasury, Ministry of the Economy and of Finance.
  11. Tolga Cenesizoglu, 2010. "Size, Book-to-Market Ratio and Macroeconomic News," Cahiers de recherche 1033, CIRPEE.

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