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Do forecasters inform or reassure? Evaluation of the German real-time data

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Abstract

The paper evaluates the quality of the German national accounting data (GDP and its use-side 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.

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File URL: http://www.kof.ethz.ch/publications/science/pdf/wp_215.pdf
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Paper provided by KOF Swiss Economic Institute, ETH Zurich in its series KOF Working papers with number 09-215.

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Length: 29 pages
Date of creation: Feb 2009
Date of revision:
Handle: RePEc:kof:wpskof:09-215
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Keywords: Quality of statistical data; real-time data; signal-to-noise ratio; forecasts; revisions;

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Find related papers by 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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  1. Jan Jacobs & Jan-Egbert Sturm, 2004. "Do Ifo Indicators Help Explain Revisions in German Industrial Production?," CESifo Working Paper Series 1205, CESifo Group Munich.
  2. Francis X. Diebold & Lutz Kilian, 1997. "Measuring predictability: theory and macroeconomic applications," Working Papers 97-23, Federal Reserve Bank of Philadelphia.
  3. 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.
  4. Prakash Loungani, 2000. "How Accurate Are Private Sector Forecasts; Cross-Country Evidence From Consensus Forecasts of Output Growth," IMF Working Papers 00/77, International Monetary Fund.
  5. Jon Faust & John H. Rogers & Jonathan H. Wright, 2000. "News and noise in G-7 GDP announcements," International Finance Discussion Papers 690, Board of Governors of the Federal Reserve System (U.S.).
  6. Lahiri, Kajal & Sheng, Xuguang, 2010. "Learning and heterogeneity in GDP and inflation forecasts," International Journal of Forecasting, Elsevier, vol. 26(2), pages 265-292, April.
  7. Knetsch, Thomas A. & Reimers, Hans-Eggert, 2006. "How to treat benchmark revisions? The case of German production and orders statistics," Discussion Paper Series 1: Economic Studies 2006,38, Deutsche Bundesbank, Research Centre.
  8. Isiklar, Gultekin & Lahiri, Kajal, 2007. "How far ahead can we forecast? Evidence from cross-country surveys," International Journal of Forecasting, Elsevier, vol. 23(2), pages 167-187.
  9. Ashiya, Masahiro, 2007. "Forecast accuracy of the Japanese government: Its year-ahead GDP forecast is too optimistic," Japan and the World Economy, Elsevier, vol. 19(1), pages 68-85, January.
  10. McNees, Stephen K., 1989. "Forecasts and actuals: The trade-off between timeliness and accuracy," International Journal of Forecasting, Elsevier, vol. 5(3), pages 409-416.
  11. de Gooijer, Jan G. & Klein, Andre, 1992. "On the cumulated multi-step-ahead predictions of vector autoregressive moving average processes," International Journal of Forecasting, Elsevier, vol. 7(4), pages 501-513, March.
  12. Batchelor, Roy, 2007. "Bias in macroeconomic forecasts," International Journal of Forecasting, Elsevier, vol. 23(2), pages 189-203.
  13. Graham Elliott & Allan Timmermann & Ivana Komunjer, 2005. "Estimation and Testing of Forecast Rationality under Flexible Loss," Review of Economic Studies, Oxford University Press, vol. 72(4), pages 1107-1125.
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Cited by:

  1. Jacopo Cimadomo, 2011. "Real-time data and fiscal policy analysis: a survey of the literature," Working Papers 11-25, Federal Reserve Bank of Philadelphia.
  2. Fátima Cardoso & António Rua, 2011. "The Quarterly National Accounts in real-time: an analysis of the revisions over the last decade," Economic Bulletin and Financial Stability Report Articles, Banco de Portugal, Economics and Research Department.
  3. Helmut Herwartz & Konstantin A. Kholodilin, 2011. "In-Sample and Out-of-Sample Prediction of Stock Market Bubbles: Cross-Sectional Evidence," Discussion Papers of DIW Berlin 1173, DIW Berlin, German Institute for Economic Research.
  4. Katharina Glass & Ulrich Fritsche, 2014. "Real-time Macroeconomic Data and Uncertainty," Macroeconomics and Finance Series 201406, Hamburg University, Department Wirtschaft und Politik, revised Jul 2015.
  5. 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, 05.

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