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Should macroeconomic forecasters use daily financial data and how?

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  • Elena Andreou
  • Eric Ghysels
  • Andros Kourtellos

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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File URL: http://papers.econ.ucy.ac.cy/RePEc/papers/09-10.pdf
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Bibliographic Info

Paper provided by University of Cyprus Department of Economics in its series University of Cyprus Working Papers in Economics with number 09-2010.

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Length: 67 pages
Date of creation: Nov 2010
Date of revision:
Handle: RePEc:ucy:cypeua:09-2010

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Web page: http://www.econ.ucy.ac.cy

Related research

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

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References

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  1. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2007. "Regression Models with Mixed Sampling Frequencies," University of Cyprus Working Papers in Economics 8-2007, University of Cyprus Department of Economics.
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Citations

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Cited by:
  1. Liebermann, Joelle, 2012. "Real-time forecasting in a data-rich environment," MPRA Paper 39452, University Library of Munich, Germany.
  2. Guérin, Pierre & Marcellino, Massimiliano, 2011. "Markov-switching MIDAS models," CEPR Discussion Papers 8234, C.E.P.R. Discussion Papers.
  3. 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.
  4. 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.
  5. Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian, 2011. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area," International Journal of Forecasting, Elsevier, vol. 27(2), pages 529-542, April.
  6. 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.).
  7. 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.
  8. 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).
  9. Cenesizoglu, Tolga, 2011. "Size, book-to-market ratio and macroeconomic news," Journal of Empirical Finance, Elsevier, vol. 18(2), pages 248-270, March.
  10. 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).
  11. Tolga Cenesizoglu, 2010. "Size, Book-to-Market Ratio and Macroeconomic News," Cahiers de recherche 1033, CIRPEE.

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