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Exponential Smoothing Methods of Forecasting and General ARMA Time Series Representations

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Author Info
Shami, R.G.
Snyder, R.D.

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Abstract

The focus of this paper is on the relationship between the exponential smoothing methods of forecasting and the integrated autoregressive-moving average models underlying them. In this paper we derive, for the first time, the general linear relationship between their parameters. A method, suitable for implementation on computer, is proposed to determine the pertinent quantities in this relationship. It is illustrated on common forms of exponential smoothing.

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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 3/98.

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Length: 13 pages
Date of creation: 1998
Date of revision:
Handle: RePEc:msh:ebswps:1998-3

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Related research
Keywords: FORECASTS ; TIME SERIES;

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Find related papers by JEL classification:
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions

Cited by:
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  1. Shami, R.G. & Forbes, C.S., 2000. "A structural Time Series Model with Markov Switching," Monash Econometrics and Business Statistics Working Papers 10/2000, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
Statistics
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This page was last updated on 2009-12-16.


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