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Sampling Distributions of Post‐Sample Forecasting Errors

Author

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  • Spyros Makridakis
  • Robert L. Winkler

Abstract

Forecasting errors fall in two clearly different categories: (a) the residual errors from fitting a model to the available data and (b) the post‐sample forecasting errors. The emphasis of statistical theory and forecasting methodology has been on model fitting errors, even though the greatest concern in applied work should be with post‐sample errors. The purpose of this paper is to investigate empirically sampling distributions of post‐sample forecasting errors. The characteristics of such distributions are studied and compared with characteristics of distributions of model fitting errors. The discrepancies between characteristics of model fitting and post‐sample errors are quite large and somewhat variable.

Suggested Citation

  • Spyros Makridakis & Robert L. Winkler, 1989. "Sampling Distributions of Post‐Sample Forecasting Errors," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 38(2), pages 331-342, June.
    • Handle: RePEc:bla:jorssc:v:38:y:1989:i:2:p:331-342
    DOI: 10.2307/2348063
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    Cited by:

    1. Saoud, Patrick & Kourentzes, Nikolaos & Boylan, John E., 2022. "Approximations for the Lead Time Variance: a Forecasting and Inventory Evaluation," Omega, Elsevier, vol. 110(C).
    2. Marián Vávra, 2020. "Assessing distributional properties of forecast errors for fan-chart modelling," Empirical Economics, Springer, vol. 59(6), pages 2841-2858, December.
    3. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    4. Dimitrios Sarris & Evangelos Spiliotis & Vassilios Assimakopoulos, 2020. "Exploiting resampling techniques for model selection in forecasting: an empirical evaluation using out-of-sample tests," Operational Research, Springer, vol. 20(2), pages 701-721, June.

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