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Prediction Intervals For Expert-Adjusted Forecasts

Author

Listed:
  • Philip Hans Franses

    (Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, The Netherlands)

Abstract

This paper proposes a simple method to compute prediction intervals for expert-adjusted forecasts in case the analyst does not have the underlying model forecasts and thus has to create own approximate model forecasts, based on data available to the analyst. An illustration to airline revenues data shows that experts can substantially reduce forecast uncertainty.

Suggested Citation

  • Philip Hans Franses, 2018. "Prediction Intervals For Expert-Adjusted Forecasts," Advances in Decision Sciences, Asia University, Taiwan, vol. 22(1), pages 308-320, December.
    • Handle: RePEc:aag:wpaper:v:22:y:2018:i:1:p:308-320
    as

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    References listed on IDEAS

    as
    1. Chatfield, Chris, 1993. "Calculating Interval Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 121-135, April.
    2. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521520911, October.
    3. Chatfield, Chris, 1993. "Calculating Interval Forecasts: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 143-144, April.
    4. Fildes, Robert & Goodwin, Paul & Lawrence, Michael & Nikolopoulos, Konstantinos, 2009. "Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning," International Journal of Forecasting, Elsevier, vol. 25(1), pages 3-23.
    5. Franses,Philip Hans, 2014. "Expert Adjustments of Model Forecasts," Cambridge Books, Cambridge University Press, number 9781107081598, October.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Prediction intervals; expert-adjusted forecasts; approximate model forecasts; forecast uncertainty; airline revenues;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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