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Analyzing Fixed-event Forecast Revisions

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  • Michael McAleer

    (Erasmus University Rotterdam, Tinbergen Institute, The Netherlands, Complutense University of Madrid, and Institute of Economic Research, Kyoto University)

  • Philip Hans Franses

    (Econometric Institute Erasmus School of Economics Erasmus University Rotterdam)

  • Chia-Lin Chang

    (Department of Applied Economics Department of Finance National Chung Hsing University Taichung, Taiwan)

Abstract

It is common practice to evaluate fixed-event forecast revisions in macroeconomics by regressing current revisions on one-period lagged revisions. Under weak-form efficiency, the correlation between the current and one-period lagged revisions should be zero. The empirical findings in the literature suggest that the null hypothesis of zero correlation between the current and one-period lagged revisions is rejected quite frequently, where the correlation can be either positive or negative. In this paper we propose a methodology to be able to interpret such non-zero correlations in a straightforward manner. Our approach is based on the assumption that forecasts can be decomposed into both an econometric model and expert intuition. The interpretation of the sign of the correlation between the current and one-period lagged revisions depends on the process governing intuition, and the correlation between intuition and news.

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Bibliographic Info

Paper provided by Kyoto University, Institute of Economic Research in its series KIER Working Papers with number 779.

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Date of creation: Jun 2011
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Handle: RePEc:kyo:wpaper:779

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Keywords: Evaluating forecasts; Macroeconomic forecasting; Rationality; Intuition; Weak-form efficiency; Fixed-event forecasts.;

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References

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  1. Franses, Philip Hans & Kranendonk, Henk C. & Lanser, Debby, 2011. "One model and various experts: Evaluating Dutch macroeconomic forecasts," International Journal of Forecasting, Elsevier, Elsevier, vol. 27(2), pages 482-495, April.
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  3. Lawrence, Michael & O'Connor, Marcus, 2000. "Sales forecasting updates: how good are they in practice?," International Journal of Forecasting, Elsevier, Elsevier, vol. 16(3), pages 369-382.
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  6. Cho, Dong W., 2002. "Do revisions improve forecasts?," International Journal of Forecasting, Elsevier, Elsevier, vol. 18(1), pages 107-115.
  7. Chang, Chia-Lin & Franses, Philip Hans & McAleer, Michael, 2011. "How accurate are government forecasts of economic fundamentals? The case of Taiwan," International Journal of Forecasting, Elsevier, Elsevier, vol. 27(4), pages 1066-1075, October.
  8. Carlos Capistrán & Allan Timmermann, 2008. "Disagreement and Biases in Inflation Expectations," CREATES Research Papers 2008-56, School of Economics and Management, University of Aarhus.
  9. Ager, P. & Kappler, M. & Osterloh, S., 2009. "The accuracy and efficiency of the Consensus Forecasts: A further application and extension of the pooled approach," International Journal of Forecasting, Elsevier, Elsevier, vol. 25(1), pages 167-181.
  10. Philip Hans Franses & Rianne Legerstee, 2010. "Do experts' adjustments on model-based SKU-level forecasts improve forecast quality?," Journal of Forecasting, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 29(3), pages 331-340.
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  13. Franses, Ph.H.B.F. & McAleer, M.J. & Legerstee, R., 2008. "Expert opinion versus expertise in forecasting," Econometric Institute Research Papers EI 2008-30, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
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  17. Clements, M.C., 1996. "Evaluating the Rationality of Fixed-Event Forecasts," The Warwick Economics Research Paper Series (TWERPS) 457, University of Warwick, Department of Economics.
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  19. Dovern, Jonas & Weisser, Johannes, 2011. "Accuracy, unbiasedness and efficiency of professional macroeconomic forecasts: An empirical comparison for the G7," International Journal of Forecasting, Elsevier, Elsevier, vol. 27(2), pages 452-465.
  20. Isengildina, Olga & Irwin, Scott H. & Good, Darrel L., 2004. "Are Revisions To Usda Crop Production Forecasts Smoothed?," 2004 Conference, April 19-20, 2004, St. Louis, Missouri, NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management 19027, NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
  21. Giampiero M. Gallo & Clive W.J. Granger & Yongil Jeon, 2002. "Copycats and Common Swings: The Impact of the Use of Forecasts in Information Sets," IMF Staff Papers, Palgrave Macmillan, vol. 49(1), pages 2.
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Cited by:
  1. Chang, Chia-Lin & Ke, Yu-Pei, 2014. "Testing Price Pressure, Information, Feedback Trading, and Smoothing Effects for Energy Exchange Traded Funds," MPRA Paper 57625, University Library of Munich, Germany.

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