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Exponential Smoothing: A Prediction Error Decomposition Principle

Ralph Snyder ()

No 15/04, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: In the exponential smoothing approach to forecasting, restrictions are often imposed on the smoothing parameters which ensure that certain components are exponentially weighted averages. In this paper, a new general restriction is derived on the basis that the one-step ahead prediction error can be decomposed into permanent and transient components. It is found that this general restriction reduces to the common restrictions used for simple, trend and seasonal exponential smoothing. As such, the prediction error argument provides the rationale for these restrictions.

Keywords: time series analysis; prediction; exponential smoothing; ARIMA models; state space models. (search for similar items in EconPapers)
JEL-codes: C22 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ets
Date: 2004-08
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