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A new approach to forecasting based on exponential smoothing with independent regressors

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  • Ahmad Farid Osman
  • Maxwell L. King

Abstract

In There is evidence that exponential smoothing methods as well as time varying parameter models perform relatively well in forecasting comparisons. The aim of this paper is to introduce a new forecasting technique by integrating the exponential smoothing model with regressors whose coefficients are time varying. In doing this, we construct an exponential smoothing model with regressors by extending Holt's linear exponential smoothing model. We then translate it into an equivalent state space structure so that the parameters can be estimated via the maximum likely-hood estimation procedure. Due to the potential problem in the updating equation for the regressor coefficients when the change in regressor is too small, we propose an alternative structure of the state space model which allows the updating process to be put on hold until sufficient information is available. An empirical study of forecast accuracy shows that the new model performs better than the existing exponential smoothing model as well as the linear regression model.

Suggested Citation

  • Ahmad Farid Osman & Maxwell L. King, 2015. "A new approach to forecasting based on exponential smoothing with independent regressors," Monash Econometrics and Business Statistics Working Papers 2/15, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2015-2
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    File URL: http://business.monash.edu/econometrics-and-business-statistics/research/publications/ebs/wp02-15.pdf
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    References listed on IDEAS

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    3. Makridakis, Spyros & Chatfield, Chris & Hibon, Michele & Lawrence, Michael & Mills, Terence & Ord, Keith & Simmons, LeRoy F., 1993. "The M2-competition: A real-time judgmentally based forecasting study," International Journal of Forecasting, Elsevier, vol. 9(1), pages 5-22, April.
    4. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    5. George Athanasopoulos & Rob J. Hyndman, 2006. "Modelling and forecasting Australian domestic tourism," Monash Econometrics and Business Statistics Working Papers 19/06, Monash University, Department of Econometrics and Business Statistics.
    6. Wolff, Christian C P, 1987. "Time-Varying Parameters and the Out-of-Sample Forecasting Performance of Structural Exchange Rate Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(1), pages 87-97, January.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    State space model; Single source of error; Time varying parameter; Time series; Forecast accuracy;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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