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Incorporating a Tracking Signal into State Space Models for Exponential Smoothing

Author

Listed:
  • Ralph D. Snyder

    ()

  • Anne B. Koehler

Abstract

It is a common practice to complement a forecasting method such as simple exponential smoothing with a monitoring scheme to detect those situations where forecasts have failed to adapt to structural change. It will be suggested in this paper that the equations for simple exponential smoothing can be augmented by a common monitoring statistic to provide a method that automatically adapts to structural change without human intervention. It is shown that the resulting equations conform to those of damped trend corrected exponential smoothing. In a similar manner, exponential smoothing with drift, when augmented by the same monitoring statistic, produces equations that split the trend into long term and short term components.

Suggested Citation

  • Ralph D. Snyder & Anne B. Koehler, 2006. "Incorporating a Tracking Signal into State Space Models for Exponential Smoothing," Monash Econometrics and Business Statistics Working Papers 16/06, Monash University, Department of Econometrics and Business Statistics.
  • Handle: RePEc:msh:ebswps:2006-16
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    File URL: http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2006/wp16-06.pdf
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    More about this item

    Keywords

    Forecasting; exponential smoothing; tracking signals.;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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