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

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Author Info
Carlo Altavilla () (University of Naples “Parthenope”, Via Medina, 40 - 80133 Naples, Italy.)
Matteo Ciccarelli () (Corresponding author: European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany.)

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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 the relationship 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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Paper provided by European Central Bank in its series Working Paper Series with number 846.

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Length: 71 pages
Date of creation: Dec 2007
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Handle: RePEc:ecb:ecbwps:20070846

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Related research
Keywords: Real-time data; forecast combination; data and model uncertainty.;

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  1. Patterson, K. D., 2003. "Exploiting information in vintages of time-series data," International Journal of Forecasting, Elsevier, vol. 19(2), pages 177-197. [Downloadable!] (restricted)
  2. 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. [Downloadable!] (restricted)
  3. 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. [Downloadable!] (restricted)
  4. Zou, Hui & Yang, Yuhong, 2004. "Combining time series models for forecasting," International Journal of Forecasting, Elsevier, vol. 20(1), pages 69-84. [Downloadable!] (restricted)
  5. Evan F. Koenig & Sheila Dolmas & Jeremy Piger, 2003. "The Use and Abuse of Real-Time Data in Economic Forecasting," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 618-628, 07. [Downloadable!] (restricted)
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  6. Dean Croushore & Tom Stark, 2003. "A Real-Time Data Set for Macroeconomists: Does the Data Vintage Matter?," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 605-617, 04. [Downloadable!] (restricted)
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  7. Swanson, N.R., 1996. "Forecasting Using First Available Versus Fully Revised Economic Time Series data," Papers 4-96-7, Pennsylvania State - Department of Economics.
  8. 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.
  9. 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. [Downloadable!] (restricted)
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  10. Andrew Ang & Geert Bekaert & Min Wei, 2006. "Do macro variables, asset markets, or surveys forecast inflation better?," Finance and Economics Discussion Series 2006-15, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
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  11. Aruoba, Boragan, 2005. "Data Revisions Are Not Well-Behaved," CEPR Discussion Papers 5271, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
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  12. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November. [Downloadable!] (restricted)
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  13. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October. [Downloadable!] (restricted)
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  14. Matteo Ciccarelli & Carlo Altavilla, 2007. "Inflation forecasts, monetary policy and unemployment dynamics: evidence from the US and the euro area," Working Paper Series 725, European Central Bank. [Downloadable!]
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  15. 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. [Downloadable!] (restricted)
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  16. Croushore, Dean, 2006. "Forecasting with Real-Time Macroeconomic Data," Handbook of Economic Forecasting, Elsevier. [Downloadable!] (restricted)
  17. 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(2001-1), pages 135-174. [Downloadable!]
  18. 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.
  19. Pesaran, M.H. & Smith, R., 1992. "Estimating Long-Run Relationships From Dynamic Heterogeneous Panels," Cambridge Working Papers in Economics 9215, Faculty of Economics, University of Cambridge.
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  20. N.R. Swanson & D.J.C. van Dijk, 2001. "Are statistical reporting agencies getting it right? Data rationality and business cycle asymmetry," Econometric Institute Report 230, Erasmus University Rotterdam, Econometric Institute. [Downloadable!]
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