Forecasting Swiss exports using Bayesian forecast reconciliation

This paper conducts an extensive forecasting study on 13,118 time series measuring Swiss goods exports, grouped hierarchically by export destination and product category. We apply existing state of the art methods in forecast reconciliation and introduce a novel Bayesian reconciliation framework. This approach allows for explicit estimation of reconciliation biases, leading to several innovations: Prior judgment can be used to assign weights to specific forecasts and the occurrence of negative reconciled forecasts can be ruled out. Overall we find strong evidence that in addition to producing coherent forecasts, reconciliation also leads to improvements in forecast accuracy.

• Publication year

  2020

• Venue

  European Journal of Operational Research

• Publication date

  2020-10-03

• Fields of study

  Computer Science, Economics

• Identifiers
  DOI 10.1016/J.EJOR.2020.09.046
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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