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Information or Institution? – On the Determinants of Forecast Accuracy

  • Roland Döhrn

    ()

  • Christoph M. Schmidt

The accuracy of macroeconomic forecast depends on various factors, most importantly the mix of analytical methods used by the individual forecasters, the way that their personal experience is shaping their identifi cation strategies, but also their effi - ciency in translating new information into revised forecasts. In this paper we use a broad sample of forecasts of German GDP and its components to analyze the impact of institutions and information on forecast accuracy. We fi nd that forecast errors are a linear function of the forecast horizon. This result is robust over a variety of diff erent specifi cations. As better information seems to be the key to achieving better forecasts, approaches for acquiring reliable information early seem to be a good investment. By contrast, the institutional factors tend to be small and statistically insignifi cant. It has to remain open, whether this is the consequence of the effi ciency-enhancing competition among German research institutions or rather the refl ection of an abundance of forecast suppliers.

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Paper provided by Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen in its series Ruhr Economic Papers with number 0201.

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Length: 32 pages
Date of creation: Sep 2010
Date of revision:
Handle: RePEc:rwi:repape:0201
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  1. Wolfgang Nierhaus & Jan-Egbert Sturm, 2003. "Methoden der Konjunkturprognose," Ifo Schnelldienst, Ifo Institute for Economic Research at the University of Munich, vol. 56(04), pages 7-23, 02.
  2. Lamont, Owen A., 2002. "Macroeconomic forecasts and microeconomic forecasters," Journal of Economic Behavior & Organization, Elsevier, vol. 48(3), pages 265-280, July.
  3. Jörg Döpke & Ulrich Fritsche, 2006. "Growth and inflation forecasts for Germany a panel-based assessment of accuracy and efficiency," Empirical Economics, Springer, vol. 31(3), pages 777-798, September.
  4. Roland Döhrn, 2006. "Improving Business Cycle Forecasts’ Accuracy - What Can We Learn from Past Errors?," RWI Discussion Papers 0051, Rheinisch-Westfälisches Institut für Wirtschaftsforschung.
  5. 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.
  6. Torsten Schmidt & Simeon Vosen, 2009. "Forecasting Private Consumption: Survey-based Indicators vs. Google Trends," Ruhr Economic Papers 0155, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
  7. John P. Haisken-DeNew & Christoph M. Schmidt, 2000. "Interindustry and Interregion Differentials: Mechanics and Interpretation," The Review of Economics and Statistics, MIT Press, vol. 79(3), pages 516-521, August.
  8. Kajal Lahiri & Gultekin Isiklar & Prakash Loungani, 2006. "How quickly do forecasters incorporate news? Evidence from cross-country surveys," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(6), pages 703-725.
  9. Dopke, Jorg & Fritsche, Ulrich, 2006. "When do forecasters disagree? An assessment of German growth and inflation forecast dispersion," International Journal of Forecasting, Elsevier, vol. 22(1), pages 125-135.
  10. Klinger, Sabine & Heilemann, Ullrich, 2005. "Zu wenig Wettbewerb? Zu Stand und Entwicklung der Genauigkeit makroökonomischer Prognosen," Technical Reports 2005,16, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
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