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Invertibility Conditions for Exponential Smoothing Models

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Author Info
Rob J. Hyndman ()
Muhammad Akram
Blyth Archibald

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Abstract

In this article we discuss invertibility conditions for some state space models, including the models that underly simple exponential smoothing, Holt's linear method, Holt-Winters' additive method and damped trend versions of Holt's and Holt-Winters' methods. The parameter space for which the model is invertible is compared to the usual parameter regions. We find that the usual parameter restrictions (requiring all smoothing parameters to lie between 0 and 1) do not always lead to invertible models. Conversely, some invertible models have parameters which lie outside the usual region. We also find that all seasonal exponential smoothing methods are non-invertible when the usual equations are used. However, this does not affect the forecast mean. Alternative models are presented which solve the problem while retaining the basic exponential smoothing ideas.

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Publisher Info
Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 3/03.

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Length: 19 pages
Date of creation: Apr 2003
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Handle: RePEc:msh:ebswps:2003-3

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Related research
Keywords: exponential smoothing invertibility state space models.

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Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454. [Downloadable!] (restricted)
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  2. Lawton, Richard, 1998. "How should additive Holt-Winters estimates be corrected?," International Journal of Forecasting, Elsevier, vol. 14(3), pages 393-403, September. [Downloadable!] (restricted)
  3. Ralph D. Snyder & Anne B. Koehler & Rob J. Hyndman & J. Keith Ord, 2002. "Exponential Smoothing for Inventory Control: Means and Variances of Lead-Time Demand," Monash Econometrics and Business Statistics Working Papers 3/02, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
  4. Hyndman, R.J. & Koehler, A.B. & Ord, J.K. & Snyder, R.D., 2001. "Prediction Intervals for Exponential Smoothing State Space Models," Monash Econometrics and Business Statistics Working Papers 11/2001, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
  5. Snyder, Ralph D & Ord, J Keith & Koehler, Anne B, 2001. "Prediction Intervals for ARIMA Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(2), pages 217-25, April.
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  6. Ord, J.K. & Koehler, A. & Snyder, R.D., 1995. "Estimation and Prediction for a Class of Dynamic Nonlinear Statistical Models," Monash Econometrics and Business Statistics Working Papers 4/95, Monash University, Department of Econometrics and Business Statistics.
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Ralph D Snyder, 2005. "A Pedant's Approach to Exponential Smoothing," Monash Econometrics and Business Statistics Working Papers 5/05, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
  2. Ralph D. Snyder, 2004. "Exponential Smoothing: A Prediction Error Decomposition Principle," Monash Econometrics and Business Statistics Working Papers 15/04, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
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