Forecasting Swiss exports using Bayesian forecast reconciliation

Florian Eckert, Rob J. Hyndman, Anastasios Panagiotelis

Research output: Contribution to journalArticleResearchpeer-review

Abstract

This paper proposes a novel forecast reconciliation framework using Bayesian state-space methods. It allows for the joint reconciliation at all forecast horizons and uses predictive distributions rather than past variation of forecast errors. Informative priors are used to assign weights to specific predictions, which makes it possible to reconcile forecasts such that they accommodate specific judgmental predictions or managerial decisions. The reconciled forecasts adhere to hierarchical constraints, which facilitates communication and supports aligned decision-making at all levels of complex hierarchical structures. An extensive forecasting study is conducted on a large collection of 13,118 time series that measure Swiss merchandise exports, grouped hierarchically by export destination and product category. We find strong evidence that in addition to producing coherent forecasts, reconciliation also leads to substantial improvements in forecast accuracy. The use of state-space methods is particularly promising for optimal decision-making under conditions with increased model uncertainty and data volatility.

Original languageEnglish
Pages (from-to)693-710
Number of pages18
JournalEuropean Journal of Operational Research
Volume291
Issue number2
DOIs
Publication statusPublished - Jun 2021

Keywords

  • Decision-making
  • Forecasting
  • Hierarchical reconciliation
  • Optimal combination

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