A Pedant's Approach to Exponential Smoothing
Abstract
An approach to exponential smoothing that relies on a linear single source of error state space model is outlined. A maximum likelihood method for the estimation of associated smoothing parameters is developed. Commonly used restrictions on the smoothing parameters are rationalised. Issues surrounding model identification and selection are also considered. It is argued that the proposed revised version of exponential smoothing provides a better framework for forecasting than either the Box-Jenkins or the traditional multi-disturbance state space approaches.Download Info
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Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 5/05.Length: 24 pages
Date of creation: Mar 2005
Date of revision:
Handle: RePEc:msh:ebswps:2005-5
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Related research
Keywords: Time Series Analysis; Prediction; Exponential Smoothing; ARIMA Models; Kalman Filter; State Space Models;Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2005-04-16 (All new papers)
- NEP-ECM-2005-04-16 (Econometrics)
References
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