Home > Research > Publications & Outputs > Bayesian forecasting with the structural damped...

Electronic data

  • BLINDpaper

    Rights statement: 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

    Accepted author manuscript, 716 KB, PDF document

    Available under license: CC BY-NC-ND

Links

Text available via DOI:

View graph of relations

Bayesian forecasting with the structural damped trend model

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published
Article number108046
<mark>Journal publication date</mark>30/04/2021
<mark>Journal</mark>International Journal of Production Economics
Volume234
Number of pages9
Publication StatusPublished
Early online date19/02/21
<mark>Original language</mark>English

Abstract

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.

Bibliographic note

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