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Do Forecasters Inform or Reassure? Evaluation of the German Real-Time Data

Author

Listed:
  • Konstantin A. Kholodilin
  • Boriss Siliverstovs

Abstract

The paper evaluates the quality of the German national accounting data (GDP and its useside components) as measured by the magnitude and dispersion of the forecast / revision errors. It is demonstrated that government consumption series are the least reliable, whereas real GDP and real private consumption data are the most reliable. In addition, early forecasts of GDP, private consumption, and investment growth rates are shown to be systematically upward biased. Finally, early forecasts of all the variables seem to be no more accurate than naïve forecasts based on the historical mean of the final data.

Suggested Citation

  • Konstantin A. Kholodilin & Boriss Siliverstovs, 2009. "Do Forecasters Inform or Reassure? Evaluation of the German Real-Time Data," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 55(4), pages 269-294.
    • Handle: RePEc:aeq:aeqaeq:v55_y2009_i4_q4_p269-294
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    Cited by:

    1. Ullrich Heilemann & Herman O. Stekler, 2013. "Has The Accuracy of Macroeconomic Forecasts for Germany Improved?," German Economic Review, Verein für Socialpolitik, vol. 14(2), pages 235-253, May.
    2. António Rua & Fátima Cardoso, 2011. "The Quarterly National Accounts in real-time: an analysis of the revisions over the last decade," Economic Bulletin and Financial Stability Report Articles and Banco de Portugal Economic Studies, Banco de Portugal, Economics and Research Department.
    3. Helmut Herwartz & Konstantin A. Kholodilin, 2014. "In‐Sample and Out‐of‐Sample Prediction of stock Market Bubbles: Cross‐Sectional Evidence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(1), pages 15-31, January.
    4. Katharina Glass, 2018. "Predictability of Euro Area Revisions," Macroeconomics and Finance Series 201801, University of Hamburg, Department of Socioeconomics.
    5. Helder Ferreira de Mendonça & Vítor Ribeiro Laufer Calafate, 2021. "Lack of fiscal transparency and economic growth expectations: an empirical assessment from a large emerging economy," Empirical Economics, Springer, vol. 61(6), pages 2985-3027, December.
    6. Jacopo Cimadomo, 2016. "Real-Time Data And Fiscal Policy Analysis: A Survey Of The Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 30(2), pages 302-326, April.
    7. António Rua & Carlos Melo Gouveia & Fátima Cardoso, 2023. "From first to last: the National Accounts revisions," Economic Bulletin and Financial Stability Report Articles and Banco de Portugal Economic Studies, Banco de Portugal, Economics and Research Department.
    8. Katharina Glass & Ulrich Fritsche, 2015. "Real-time Macroeconomic Data and Uncertainty," Macroeconomics and Finance Series 201406, University of Hamburg, Department of Socioeconomics.
    9. de Mendonça, Helder Ferreira & Díaz, Raime Rolando Rodríguez, 2023. "Can ignorance about the interest rate and macroeconomic surprises affect the stock market return? Evidence from a large emerging economy," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    10. de Mendonça, Helder Ferreira & Baca, Adriana Cabrera, 2022. "Fiscal opacity and reduction of income inequality through taxation: Effects on economic growth," The Quarterly Review of Economics and Finance, Elsevier, vol. 83(C), pages 69-82.
    11. Gabriel Caldas Montes & Paulo Henrique Lourenço Luna, 2022. "Do fiscal opacity, fiscal impulse, and fiscal credibility affect disagreement about economic growth forecasts? Empirical evidence from Brazil considering the period of political instability and presid," Review of Development Economics, Wiley Blackwell, vol. 26(4), pages 2356-2393, November.

    More about this item

    Keywords

    Quality of statistical data; real-time data; signal-to-noise ratio; forecasts; revisions;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C89 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other

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