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

Author

Listed:
  • Ciccarelli, Matteo
  • Altavilla, Carlo
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

Suggested Citation

  • Ciccarelli, Matteo & Altavilla, Carlo, 2007. "Information combination and forecast (st)ability evidence from vintages of time-series data," Working Paper Series 846, European Central Bank.
    • Handle: RePEc:ecb:ecbwps:2007846
    Note: 224580
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    File URL: https://www.ecb.europa.eu//pub/pdf/scpwps/ecbwp846.pdf
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    References listed on IDEAS

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    Cited by:

    1. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    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. D'Elia, Enrico, 2010. "Predictions vs preliminary sample estimates," MPRA Paper 36070, University Library of Munich, Germany.
    4. Marie Bessec, 2010. "Étalonnages du taux de croissance du PIB français sur la base des enquêtes de conjoncture," Économie et Prévision, Programme National Persée, vol. 193(2), pages 77-99.
    5. 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.
    6. Van Belle, Jente & Crevits, Ruben & Verbeke, Wouter, 2023. "Improving forecast stability using deep learning," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1333-1350.
    7. D’Elia Enrico, 2014. "Predictions vs. Preliminary Sample Estimates: The Case of Eurozone Quarterly GDP," Journal of Official Statistics, Sciendo, vol. 30(3), pages 499-520, September.
    8. 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.
    9. 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.

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    More about this item

    Keywords

    data and model uncertainty; forecast combination; real-time data;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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