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On the optimality of expert-adjusted forecasts

Author

Listed:
  • Henk Kranendonk

    (CPB Netherlands Bureau for Economic Policy Analysis)

  • Debby Lanser

    (CPB Netherlands Bureau for Economic Policy Analysis)

  • P.H. Franses

Abstract

Official forecasts of international institutions are never purely model-based. Preliminary results of models are adjusted with expert opinions. What is the impact of these adjustments for the forecasts? Are they necessary to get 'optimal' forecasts? When model-based forecasts are adjusted by experts, the loss function of these forecasts is not a mean squared error loss function. In fact, the overall loss function is unknown. To examine the quality of these forecasts, one can rely on the tests for forecast optimality under unknown loss function as developed in Patton and Timmermann (2007). We apply one of these tests to ten variables for which we have model-based forecasts and expert-adjusted forecasts, all generated by the Netherlands Bureau for Economic Policy Analysis (CPB). For almost all variables the added expertise yields better forecasts in terms of fit. In terms of optimality, the effect of adjustments for the forecasts is limited, because for most variables the assumption that the forecast are not optimal can be rejected for both the model-based and the expert-adjusted forecasts.

Suggested Citation

  • Henk Kranendonk & Debby Lanser & P.H. Franses, 2007. "On the optimality of expert-adjusted forecasts," CPB Discussion Paper 92, CPB Netherlands Bureau for Economic Policy Analysis.
    • Handle: RePEc:cpb:discus:92
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    References listed on IDEAS

    as
    1. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-862, November.
    2. F. J. H. Don & J. P. Verbruggen, 2006. "Models and methods for economic policy: 60 years of evolution at CPB," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 60(2), pages 145-170, May.
    3. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521634809, January.
    4. Granger, C. W. J. & Newbold, Paul, 1986. "Forecasting Economic Time Series," Elsevier Monographs, Elsevier, edition 2, number 9780122951831 edited by Shell, Karl.
    5. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-883, November.
    6. Patton, Andrew J. & Timmermann, Allan, 2007. "Testing Forecast Optimality Under Unknown Loss," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1172-1184, December.
    7. Nordhaus, William D, 1987. "Forecasting Efficiency: Concepts and Applications," The Review of Economics and Statistics, MIT Press, vol. 69(4), pages 667-674, November.
    8. Henk Kranendonk & Johan Verbruggen, 2007. "SAFFIER; a multi-purpose model of the Dutch economy for short-term and medium-term analyses," CPB Document 144, CPB Netherlands Bureau for Economic Policy Analysis.
    9. Clements, Michael P, 1995. "Rationality and the Role of Judgement in Macroeconomic Forecasting," Economic Journal, Royal Economic Society, vol. 105(429), pages 410-420, March.
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    Cited by:

    1. Philip Hans Franses & Michael McAleer & Rianne Legerstee, 2009. "Expert opinion versus expertise in forecasting," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 63(3), pages 334-346, August.
    2. Frank A. G. den Butter & Pieter W. Jansen, 2013. "Beating the random walk: a performance assessment of long-term interest rate forecasts," Applied Financial Economics, Taylor & Francis Journals, vol. 23(9), pages 749-765, May.
    3. Franses, Philip Hans, 2008. "Merging models and experts," International Journal of Forecasting, Elsevier, vol. 24(1), pages 31-33.
    4. Kolkman, Daan, 2020. "The usefulness of algorithmic models in policy making," SocArXiv hpma8, Center for Open Science.

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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