Bayesian forecasting with the structural damped trend model

Tsionas, Mike G. (2021) Bayesian forecasting with the structural damped trend model. International Journal of Production Economics, 234: 108046. ISSN 0925-5273

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In this paper we consider the structural damped trend model which is standard in the arsenal of forecasting analysis. We consider both the multiple sources of error (MSOE) as well as the single source of errors (SSOE). Relative to existing research, we propose Bayesian analysis for estimation and forecasting based on Markov Chain Monte Carlo techniques and, especially, the Gibbs sampler with data augmentation. Monte Carlo and empirical applications (from the M3 competition as well as data from the Bank of International Settlements) show the superior performance of the MOSE versus the SSOE model. We also document superior performance of the Bayesian MSOE model versus its sampling-theory counterpart. Additional evidence is provided by a Bayesian optimal model pool approach which determines optimal weights in combining predictive posterior distributions.

Item Type:
Journal Article
Journal or Publication Title:
International Journal of Production Economics
Additional Information:
This is the author’s version of a work that was accepted for publication in International Journal of Production Economics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Production Economics, 234, 2021 DOI: 10.1016/j.ijpe.2021.108046
Uncontrolled Keywords:
?? damped trend modelbayesian analysisout-of-sample forecastingforecast accuracybusiness, management and accounting(all)economics and econometricsmanagement science and operations researchindustrial and manufacturing engineering ??
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Deposited On:
07 Oct 2021 13:05
Last Modified:
17 Feb 2024 01:16