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Adjusting for information content when comparing forecast performance

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  • Michael K Andersson
  • Ted Aranki
  • André Reslow

Abstract

Cross institutional forecast evaluations may be severely distorted by the fact that forecasts are made at different points in time, and thus with different amount of information. This paper proposes a method to account for these differences. The method computes the timing effect and the forecaster's ability simultaneously. Monte Carlo simulation demonstrate that evaluations that do not adjust for the differences in information content may be misleading. In addition, the method is applied on a real-world data set of 10 Swedish forecasters for the period 1999-2015. The results show that the ranking of the forecasters is affected by the proposed adjustment.
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Suggested Citation

  • Michael K Andersson & Ted Aranki & André Reslow, 2017. "Adjusting for information content when comparing forecast performance," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(7), pages 784-794, November.
    • Handle: RePEc:wly:jforec:v:36:y:2017:i:7:p:784-794
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    References listed on IDEAS

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    1. Gneiting, Tilmann, 2011. "Making and Evaluating Point Forecasts," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 746-762.
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    Cited by:

    1. Cipullo, Davide & Reslow, André, 2019. "Biased Forecasts to Affect Voting Decisions? The Brexit Case," Working Paper Series 364, Sveriges Riksbank (Central Bank of Sweden).
    2. Cipullo, Davide & Reslow, André, 2022. "Electoral cycles in macroeconomic forecasts," Journal of Economic Behavior & Organization, Elsevier, vol. 202(C), pages 307-340.
    3. Reslow, André, 2019. "Inefficient Use of Competitors'Forecasts?," Working Paper Series 380, Sveriges Riksbank (Central Bank of Sweden).
    4. Kontogeorgos, Georgios & Lambrias, Kyriacos, 2019. "An analysis of the Eurosystem/ECB projections," Working Paper Series 2291, European Central Bank.
    5. Ard Reijer & Andreas Johansson, 2019. "Nowcasting Swedish GDP with a large and unbalanced data set," Empirical Economics, Springer, vol. 57(4), pages 1351-1373, October.

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    More about this item

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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