Exponential smoothing and non-negative data
AbstractThe most common forecasting methods in business are based on exponential smoothing and the most common time series in business are inherently non-negative. Therefore it is of interest to consider the properties of the potential stochastic models underlying exponential smoothing when applied to non-negative data. We explore exponential smoothing state space models for non-negative data under various assumptions about the innovations, or error, process. We first demonstrate that prediction distributions from some commonly used state space models may have an infinite variance beyond a certain forecasting horizon. For multiplicative error models which do not have this flaw, we show that sample paths will converge almost surely to zero even when the error distribution is non-Gaussian. We propose a new model with similar properties to exponential smoothing, but which does not have these problems, and we develop some distributional properties for our new model. We then explore the implications of our results for inference, and compare the short-term forecasting performance of the various models using data on the weekly sales of over three hundred items of costume jewelry. The main findings of the research are that the Gaussian approximation is adequate for estimation and one-step-ahead forecasting. However, as the forecasting horizon increases, the approximate prediction intervals become increasingly problematic. When the model is to be used for simulation purposes, a suitably specified scheme must be employed.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by The George Washington University, Department of Economics, Research Program on Forecasting in its series Working Papers with number 2008-003.
Length: 24 pages
Date of creation: Jul 2008
Date of revision:
Contact details of provider:
Postal: Monroe Hall #340, 2115 G Street, NW, Washington, DC 20052
Phone: (202) 994-6150
Fax: (202) 994-6147
Web page: http://www.gwu.edu/~forcpgm
More information through EDIRC
forecasting; time series; exponential smoothing; positive-valued processes; seasonality; state space models.;
Find related papers by JEL classification:
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
- C5 - Mathematical and Quantitative Methods - - Econometric Modeling
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-05-23 (All new papers)
- NEP-ECM-2009-05-23 (Econometrics)
- NEP-ETS-2009-05-23 (Econometric Time Series)
- NEP-FOR-2009-05-23 (Forecasting)
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Alysha M De Livera, 2010. "Automatic forecasting with a modified exponential smoothing state space framework," Monash Econometrics and Business Statistics Working Papers 10/10, Monash University, Department of Econometrics and Business Statistics.
- de Silva, Ashton J, 2010. "Forecasting Australian Macroeconomic variables, evaluating innovations state space approaches," MPRA Paper 27411, University Library of Munich, Germany.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Tara M. Sinclair).
If references are entirely missing, you can add them using this form.