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Bayesian forecasting with the structural damped trend model

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  • Tsionas, Mike G.

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.

Suggested Citation

  • Tsionas, Mike G., 2021. "Bayesian forecasting with the structural damped trend model," International Journal of Production Economics, Elsevier, vol. 234(C).
    • Handle: RePEc:eee:proeco:v:234:y:2021:i:c:s0925527321000220
    DOI: 10.1016/j.ijpe.2021.108046
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    References listed on IDEAS

    as
    1. Sbrana, Giacomo & Silvestrini, Andrea, 2020. "Forecasting with the damped trend model using the structural approach," International Journal of Production Economics, Elsevier, vol. 226(C).
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    4. Villegas, Marco A. & Pedregal, Diego J., 2019. "Automatic selection of unobserved components models for supply chain forecasting," International Journal of Forecasting, Elsevier, vol. 35(1), pages 157-169.
    5. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    6. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    7. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    8. Roberts, G. O. & Smith, A. F. M., 1994. "Simple conditions for the convergence of the Gibbs sampler and Metropolis-Hastings algorithms," Stochastic Processes and their Applications, Elsevier, vol. 49(2), pages 207-216, February.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Damped trend model; Bayesian analysis; Out-of-sample forecasting; Forecast accuracy;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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