Improving Business Cycle Forecasts’ Accuracy - What Can We Learn from Past Errors?
AbstractThis 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 InfoPaper provided by Rheinisch-Westfälisches Institut für Wirtschaftsforschung in its series RWI Discussion Papers with number 0051.
Length: 22 pages
Date of creation: Oct 2006
Date of revision:
Find related papers by JEL classification:
- E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-04-09 (All new papers)
- NEP-ETS-2007-04-09 (Econometric Time Series)
- NEP-FOR-2007-04-09 (Forecasting)
- NEP-MAC-2007-04-09 (Macroeconomics)
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- 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.
- repec:fth:eeccco:137 is not listed on IDEAS
- Allan Timmermann, 2007.
"An Evaluation of the World Economic Outlook Forecasts,"
IMF Staff Papers,
Palgrave Macmillan, vol. 54(1), pages 1-33, May.
- Allan Timmermann, 2006. "An Evaluation of the World Economic Outlook Forecasts," IMF Working Papers 06/59, International Monetary Fund.
- 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.
- Oller, Lars-Erik & Barot, Bharat, 2000.
"The accuracy of European growth and inflation forecasts,"
International Journal of Forecasting,
Elsevier, vol. 16(3), pages 293-315.
- Öller, Lars-Erik & Barot, Bharat, 2000. "The Accuracy of European Growth and Inflation Forecasts," Working Paper 72, National Institute of Economic Research.
- 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.
- Campbell, Bryan & Dufour, Jean-Marie, 1995.
"Exact Nonparametric Orthogonality and Random Walk Tests,"
The Review of Economics and Statistics,
MIT Press, vol. 77(1), pages 1-16, February.
- 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.
- Francis X. Diebold & Robert S. Mariano, 1994.
"Comparing Predictive Accuracy,"
NBER Technical Working Papers
0169, National Bureau of Economic Research, Inc.
- Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
- 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.
- 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.
- Roland Doehrn & Christoph M. Schmidt, 2011.
"Information or Institution? On the Determinants of Forecast Accuracy,"
Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik),
Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 231(1), pages 9-27, February.
- 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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