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On the optimality of expert-adjusted forecasts

Author

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  • Franses, Ph.H.B.F.
  • Kranendonk, H.C.
  • Lanser, D.

Abstract

Official forecasts of international institutions are never purely model-based. The 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). We find that 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 are 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

  • Franses, Ph.H.B.F. & Kranendonk, H.C. & Lanser, D., 2007. "On the optimality of expert-adjusted forecasts," Econometric Institute Research Papers EI 2007-38, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:10874
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    1. repec:osf:socarx:hpma8_v1 is not listed on IDEAS
    2. 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.
    3. 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.
    4. Franses, Philip Hans, 2008. "Merging models and experts," International Journal of Forecasting, Elsevier, vol. 24(1), pages 31-33.
    5. Kolkman, Daan, 2020. "The usefulness of algorithmic models in policy making," SocArXiv hpma8, Center for Open Science.

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    Keywords

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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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