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Macroeconomic Forecasting with Mixed Frequency Data : Forecasting US output growth and inflation

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Author Info

  • Clements, Michael P

    (Department of Economics, University of Warwick)

  • Galvão, Ana Beatriz

    (Bank of Portugal)

Abstract

Although many macroeconomic series such as US real output growth are sampled quarterly, many potentially useful predictors are observed at a higher frequency. We look at whether a recently developed mixed data-frequency sampling (MIDAS) approach can improve forecasts of output growth and inflation. We carry out a number of related real-time forecast comparisons using various indicators as explanatory variables. We find that MIDAS model forecasts of output growth are more accurate at horizons less than one quarter using coincident indicators ; that MIDAS models are an effective way of combining information from multiple indicators ; and that the forecast accuracy of the unemployment-rate Phillips curve for inflation is enhanced using the MIDAS approach.

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Bibliographic Info

Paper provided by University of Warwick, Department of Economics in its series The Warwick Economics Research Paper Series (TWERPS) with number 773.

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Length: 36 pages
Date of creation: 2006
Date of revision:
Handle: RePEc:wrk:warwec:773

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Keywords: Data frequency ; multiple predictors ; combination ; real-time forecasting;

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References

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Citations

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Cited by:
  1. Mihaela Bratu, 2012. "A Strategy to Improve the Survey of Professional Forecasters (SPF) Predictions Using Bias-Corrected-Accelerated (BCA) Bootstrap Forecast Intervals," International Journal of Synergy and Research, ToKnowPress, ToKnowPress, vol. 1(2), pages 45-59.
  2. Ana Beatriz Galv�o, 2007. "Changes in Predictive Ability with Mixed Frequency Data," Working Papers, Queen Mary, University of London, School of Economics and Finance 595, Queen Mary, University of London, School of Economics and Finance.
  3. Asgharian, Hossein & Hou, Ai Jun & Javed, Farrukh, 2013. "Importance of the macroeconomic variables for variance prediction A GARCH-MIDAS approach," Knut Wicksell Working Paper Series 2013/4, Knut Wicksell Centre for Financial Studies, Lund University.
  4. C. Emre Alper & Salih Fendoglu & Burak Saltoglu, 2009. "MIDAS Volatility Forecast Performance Under Market Stress: Evidence from Emerging and Developed Stock Markets," Working Papers, Bogazici University, Department of Economics 2009/04, Bogazici University, Department of Economics.
  5. Alper, C. Emre & Fendoglu, Salih & Saltoglu, Burak, 2008. "Forecasting Stock Market Volatilities Using MIDAS Regressions: An Application to the Emerging Markets," MPRA Paper 7460, University Library of Munich, Germany.

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