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Predictions vs. preliminary sample estimates: the case of eurozone quarterly GDP

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  • Enrico D'Elia

Abstract

Economic agents are aware to incur in a loss basing their decisions on their own extrapolations instead of sound statistical data, but the loss could be smaller than the one related to waiting for the dissemination of final data. A broad guidance in deciding when statistical offices should release preliminary and final estimates of the key statistics may come from comparing the loss attached to users’ predictions to the loss associated to possible preliminary estimates from incomplete samples provides. Also the cost of delaying decisions for many economic agents may support the dissemination of very early estimates of economic indicators even if their accuracy is not fully satisfactory from a strict statistical viewpoint. Analysing the vintages of releases of quarterly Euro area GDP supports the view that even very inefficient predictions may beat some official preliminary releases of GDP, suggesting that the current calendar of data dissemination deserves some adjustment. In particular, actual “flash” estimates could be anticipated, while some later intermediate releases are likely less informative for the users.

Suggested Citation

  • 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.
    • Handle: RePEc:itt:wpaper:2014-2
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    1. Zou, Hui & Yang, Yuhong, 2004. "Combining time series models for forecasting," International Journal of Forecasting, Elsevier, vol. 20(1), pages 69-84.
    2. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    3. Olivier J. Blanchard & Jean-Paul L'Huillier & Guido Lorenzoni, 2013. "News, Noise, and Fluctuations: An Empirical Exploration," American Economic Review, American Economic Association, vol. 103(7), pages 3045-3070, December.
    4. Jansen, W. Jos & Jin, Xiaowen & de Winter, Jasper M., 2016. "Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts," International Journal of Forecasting, Elsevier, vol. 32(2), pages 411-436.
    5. Domenico Giannone & Jérôme Henry & Magdalena Lalik & Michele Modugno, 2012. "An Area-Wide Real-Time Database for the Euro Area," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1000-1013, November.
    6. Winston,Gordon C., 2008. "The Timing of Economic Activities," Cambridge Books, Cambridge University Press, number 9780521070928.
    7. K. Barhoumi & S. Benk & R. Cristadoro & A. Den Reijer & A. Jakaitiene & P. Jelonek & A. Rua & K. Ruth & C. Van Nieuwenhuyze & G. Rünstler, 2008. "Short-term forecasting of GDP using large monthly datasets – A pseudo real-time forecast evaluation exercise," Working Paper Research 133, National Bank of Belgium.
    8. Elena Angelini & Gonzalo Camba‐Mendez & Domenico Giannone & Lucrezia Reichlin & Gerhard Rünstler, 2011. "Short‐term forecasts of euro area GDP growth," Econometrics Journal, Royal Economic Society, vol. 14(1), pages 25-44, February.
    9. Reichlin, Lucrezia & Giannone, Domenico & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
    10. Cecilia Frale & Massimiliano Marcellino & Gian Luigi Mazzi & Tommaso Proietti, 2011. "EUROMIND: a monthly indicator of the euro area economic conditions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(2), pages 439-470, April.
    11. N. Gregory Mankiw & Matthew D. Shapiro, 1986. "News or Noise? An Analysis of GNP Revisions," NBER Working Papers 1939, National Bureau of Economic Research, Inc.
    12. Nigel Pain & Franck Sédillot, 2006. "Indicator models of real GDP growth in the major OECD economies," OECD Economic Studies, OECD Publishing, vol. 2005(1), pages 167-217.
    13. Dennis Fixler & Bruce Grimm, 2006. "GDP Estimates: Rationality Tests and Turning Point Performance," Journal of Productivity Analysis, Springer, vol. 25(3), pages 213-229, June.
    14. Marie Diron, 2008. "Short-term forecasts of euro area real GDP growth: an assessment of real-time performance based on vintage data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(5), pages 371-390.
    15. Sims, Christopher A., 2003. "Implications of rational inattention," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 665-690, April.
    16. 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.
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    More about this item

    Keywords

    Accuracy; data dissemination; eurozone GDP; forecast; preliminary estimates; timeliness;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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