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Information combination and forecast (st)ability evidence from vintages of time-series data

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  • Altavilla, Carlo
  • Ciccarelli, Matteo

Abstract

This paper explores the role of model and vintage combination in forecasting, with a novel approach that exploits the information contained in the revision history of a given variable. We analyse the forecast performance of eleven widely used models to predict inflation and GDP growth, in the three dimensions of accuracy, uncertainty and stability by using the real-time data set for macroeconomists developed at the Federal Reserve Bank of Philadelphia. Instead of following the common practice of investigating only therelationship between first available and fully revised data, we analyse the entire revision history for each variable and extract a signal from the entire distribution of vintages of a given variable to improve forecast accuracy and precision. The novelty of our study relies on the interpretation of the vintages of a real time data base as related realizations or units of a panel data set. The results suggest that imposing appropriate weights on competing models of inflation forecasts and output growth — reflecting the relative ability each model has over different sub-sample periods — substantially increases the forecast performance. More interestingly, our results indicate that augmenting the information set with a signal extracted from all available vintages of time-series consistently leads to a substantial improvement in forecast accuracy, precision and stability. JEL Classification: C32, C33, C53

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Bibliographic Info

Paper provided by European Central Bank in its series Working Paper Series with number 0846.

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Date of creation: Dec 2007
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Handle: RePEc:ecb:ecbwps:20070846

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Keywords: data and model uncertainty; forecast combination; real-time data;

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References

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  1. Altavilla, Carlo & Ciccarelli, Matteo, 2007. "Inflation Forecasts, monetary policy and unemployment dynamics: evidence from the US and the euro area," Working Paper Series 0725, European Central Bank.
  2. Pesaran, M. Hashem & Smith, Ron, 1995. "Estimating long-run relationships from dynamic heterogeneous panels," Journal of Econometrics, Elsevier, vol. 68(1), pages 79-113, July.
  3. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
  4. Evan F. Koenig & Sheila Dolmas & Jeremy Piger, 2000. "The use and abuse of "real-time" data in economic forecasting," International Finance Discussion Papers 684, Board of Governors of the Federal Reserve System (U.S.).
  5. Andrew Ang & Geert Bekaert & Min Wei, 2005. "Do Macro Variables, Asset Markets or Surveys Forecast Inflation Better?," NBER Working Papers 11538, National Bureau of Economic Research, Inc.
  6. Swanson, N.R., 1996. "Forecasting Using First Available Versus Fully Revised Economic Time Series data," Papers 4-96-7, Pennsylvania State - Department of Economics.
  7. Aruoba, Boragan, 2005. "Data Revisions Are Not Well-Behaved," CEPR Discussion Papers 5271, C.E.P.R. Discussion Papers.
  8. Swanson, N.R. & van Dijk, D.J.C., 2001. "Are statistical reporting agencies getting it right? Data rationality and business cycle asymmetry," Econometric Institute Research Papers EI 2001-28, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  9. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  10. Olivier Blanchard & John Simon, 2001. "The Long and Large Decline in U.S. Output Volatility," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 32(1), pages 135-174.
  11. Zou, Hui & Yang, Yuhong, 2004. "Combining time series models for forecasting," International Journal of Forecasting, Elsevier, vol. 20(1), pages 69-84.
  12. Croushore, Dean, 2006. "Forecasting with Real-Time Macroeconomic Data," Handbook of Economic Forecasting, Elsevier.
  13. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
  14. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521634809.
  15. Canova, Fabio, 1993. "Modelling and forecasting exchange rates with a Bayesian time-varying coefficient model," Journal of Economic Dynamics and Control, Elsevier, vol. 17(1-2), pages 233-261.
  16. Stark, Tom & Croushore, Dean, 2002. "Forecasting with a real-time data set for macroeconomists," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 507-531, December.
  17. Dean Croushore & Tom Stark, 1999. "A real-time data set for marcoeconomists: does the data vintage matter?," Working Papers 99-21, Federal Reserve Bank of Philadelphia.
  18. Patterson, K. D., 2003. "Exploiting information in vintages of time-series data," International Journal of Forecasting, Elsevier, vol. 19(2), pages 177-197.
  19. Guerrero, Victor M., 1993. "Combining historical and preliminary information to obtain timely time series data," International Journal of Forecasting, Elsevier, vol. 9(4), pages 477-485, December.
  20. Dean Croushore & Tom Stark, 1999. "A real-time data set for macroeconomists," Working Papers 99-4, Federal Reserve Bank of Philadelphia.
  21. Athanasios Orphanides and Simon van Norden, 2001. "The Reliability of Inflation Forecasts Based on Output Gaps in Real Time," Computing in Economics and Finance 2001 247, Society for Computational Economics.
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Citations

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Cited by:
  1. Maximo Camacho & Gabriel Perez-Quiros, 2010. "Introducing the euro-sting: Short-term indicator of euro area growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 663-694.
  2. Carlo Altavilla & Matteo Ciccarelli, 2011. "Monetary Policy Analysis in Real-Time. Vintage combination from a real-time dataset," CSEF Working Papers 274, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
  3. Guido Bulligan & Roberto Golinelli & Giuseppe Parigi, 2010. "Forecasting monthly industrial production in real-time: from single equations to factor-based models," Empirical Economics, Springer, vol. 39(2), pages 303-336, October.
  4. Carlo Altavilla & Matteo Ciccarelli, 2008. "Inflation models, optimal monetary policy and uncertain unemployment dynamics: Evidence from the US and the euro area," Discussion Papers 8_2008, D.E.S. (Department of Economic Studies), University of Naples "Parthenope", Italy.
  5. Enrico D'Elia, 2014. "Predictions vs. preliminary sample estimates: the case of eurozone quarterly GDP," Working Papers 2, Department of the Treasury, Ministry of the Economy and of Finance.
  6. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
  7. D'Elia, Enrico, 2010. "Predictions vs preliminary sample estimates," MPRA Paper 36070, University Library of Munich, Germany.

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