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Some Observations on Adaptive Forecasting

Author

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  • H. Theil

    (Econometric Institute, Rotterdam)

  • S. Wage

    (Econometric Institute, Rotterdam)

Abstract

The procedure of "adaptive" or "exponential" forecasting is based on weighted averages of two sources of evidence; one is the latest evidence (the most recent observation), the other the value computed one period before. As such, it is an easy, quick and cheap method; very little information is needed for a forecast; also, the most recent information is used. This article serves a dual purpose. One is to simplify the forecasting procedure and to clarify its characteristics in the simplest possible manner. This objective is pursued in Sections 2-4. The second purpose is to formulate a probabilistic model underlying the prediction procedure and to select weights which minimize the mean-square prediction error. Sections 5 and 6 are devoted to that purpose.

Suggested Citation

  • H. Theil & S. Wage, 1964. "Some Observations on Adaptive Forecasting," Management Science, INFORMS, vol. 10(2), pages 198-206, January.
    • Handle: RePEc:inm:ormnsc:v:10:y:1964:i:2:p:198-206
    DOI: 10.1287/mnsc.10.2.198
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    Cited by:

    1. Semenychev, V.K. & Kurkin, E.I. & Semenychev, E.V. & Danilova, A.A., 2017. "Multimodel forecasting of non-renewable resources production," Energy, Elsevier, vol. 130(C), pages 448-460.
    2. P. A. Nazarov & Kazakova, Maria, 2014. "Theoretical Basis of Prediction of Main Budget Parameters of Country," Published Papers r90221, Russian Presidential Academy of National Economy and Public Administration.
    3. P. A. Nazarov & Kazakova, Maria, 2014. "Development of Prediction Model of Basic Budget Parameters in Russian Federation," Published Papers r90220, Russian Presidential Academy of National Economy and Public Administration.
    4. J Keith Ord & Ralph D Snyder & Anne B Koehler & Rob J Hyndman & Mark Leeds, 2005. "Time Series Forecasting: The Case for the Single Source of Error State Space," Monash Econometrics and Business Statistics Working Papers 7/05, Monash University, Department of Econometrics and Business Statistics.
    5. Roger F. Miller & Harold W. Watts, 1967. "A Model of Household Investment in Financial Assets," NBER Chapters, in: Determinants of Investment Behavior, pages 357-410, National Bureau of Economic Research, Inc.
    6. Atkinson, A. C. & Koopman, S. J. & Shephard, N., 1997. "Detecting shocks: Outliers and breaks in time series," Journal of Econometrics, Elsevier, vol. 80(2), pages 387-422, October.
    7. Rodney L. Jacobs & Robert A. Jones, 1977. "A Bayesian Approach to Adaptive Expectations," UCLA Economics Working Papers 093, UCLA Department of Economics.
    8. Souza, R. C. & Brasil, G. H., 1988. "Formulação estrutural - abordagens clássica e bayeseiana: semelhanças e dessemelhanças," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 8(1), June.
    9. Shepherd, Ben, 2012. "When are adaptive expectations rational? A generalization," Economics Letters, Elsevier, vol. 115(1), pages 4-6.
    10. Francis X. Diebold & Lutz Kilian & Marc Nerlove, 2006. "Time Series Analysis," PIER Working Paper Archive 06-019, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
      • Diebold, F.X. & Kilian, L. & Nerlove, Marc, 2006. "Time Series Analysis," Working Papers 28556, University of Maryland, Department of Agricultural and Resource Economics.
    11. Godfrey, Gregory A. & Powell, Warren B., 2000. "Adaptive estimation of daily demands with complex calendar effects for freight transportation," Transportation Research Part B: Methodological, Elsevier, vol. 34(6), pages 451-469, August.
    12. Zomchak, Larysa & Umrysh, Hryhorii, 2017. "Моделювання Й Прогнозування Виробництва М’Яса Та Яєць В Україні За Допомогою Сезонної Arima-Моделі," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 3(3), September.
    13. P. A. Nazarov & Kazakova, Maria, 2014. "Methodological Principles of Prediction of Tax Revenues of Budgetary System," Published Papers r90219, Russian Presidential Academy of National Economy and Public Administration.
    14. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.

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