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Improving Business Cycle Forecasts’ Accuracy - What Can We Learn from Past Errors?

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  • Roland Döhrn

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Abstract

This paper addresses the question whether forecasters could have been able to produce better forecasts by using the available information more efficiently (informational efficiency of forecast). It is tested whether forecast errors covariate with indicators such as survey results, monetary data, business cycle indicators, or financial data. Because of the short sampling period and data problems, a non parametric ranked sign test is applied. The analysis is carried out for GDP and its main components. The study differentiates between two types of errors: Type I error occurs when forecasters neglect the information provided by an indicator.As type II error a situation is labelled in which forecasters have given too much weight to an indicator. In a number of cases forecast errors and the indicators are correlated, though mostly at a rather low level of significance. In most cases type I errors have been found. Additional tests reveal that there is little evidence of institution specific as well as forecast horizon specific effects. In many cases, co-variations found for GDP are not refected in one of the expenditure side components et vice versa.

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Bibliographic Info

Paper provided by Rheinisch-Westfälisches Institut für Wirtschaftsforschung in its series RWI Discussion Papers with number 0051.

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Length: 22 pages
Date of creation: Oct 2006
Date of revision:
Handle: RePEc:rwi:dpaper:0051

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Keywords: Short term forecast; Forecast evaluation; informational efficiency;

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References

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  1. Campbell, Bryan & Ghysels, Eric, 1995. "Federal Budget Projections: A Nonparametric Assessment of Bias and Efficiency," The Review of Economics and Statistics, MIT Press, vol. 77(1), pages 17-31, February.
  2. Öller, Lars-Erik & Barot, Bharat, 2000. "The Accuracy of European Growth and Inflation Forecasts," Working Paper 72, National Institute of Economic Research.
  3. Gebhardt Kirschgässner & Marcel Savioz, 2001. "Monetary Policy and Forecasts for Real GDP Growth: An Empirical Investigation for the Federal Republic of Germany," German Economic Review, Verein für Socialpolitik, vol. 2(4), pages 339-365, November.
  4. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
  5. Holden, K & Peel, D A, 1990. "On Testing for Unbiasedness and Efficiency of Forecasts," The Manchester School of Economic & Social Studies, University of Manchester, vol. 58(2), pages 120-27, June.
  6. repec:fth:eeccco:137 is not listed on IDEAS
  7. Dufour, J.M. & Campbell, B., 1993. "Exact Nonparametric Orthogonality and Random Walk Tests," Cahiers de recherche 9326, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  8. Allan Timmermann, 2007. "An Evaluation of the World Economic Outlook Forecasts," IMF Staff Papers, Palgrave Macmillan, vol. 54(1), pages 1-33, May.
  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.
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Cited by:
  1. Roland Döhrn & Christoph M. Schmidt, 2010. "Information or Institution? – On the Determinants of Forecast Accuracy," Ruhr Economic Papers 0201, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.

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