Forecasting Swiss Exports using Bayesian Forecast Reconciliation

Florian Eckert (eckert@kof.ethz.ch), Rob Hyndman and Anastasios Panagiotelis (anastasios.panagiotelis@monash.edu)
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Florian Eckert: KOF Swiss Economic Institute, ETH Zurich, Switzerland
Anastasios Panagiotelis: Department of Econometrics & Business Statistics, Monash University, Australia

No 19-457, KOF Working papers from KOF Swiss Economic Institute, ETH Zurich

Abstract: 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.

Keywords: Hierarchical Forecasting; Bayesian Forecast Reconciliation; Swiss Exports; Optimal Forecast Combination. (search for similar items in EconPapers)
JEL-codes: C32 C53 E17 (search for similar items in EconPapers)
Pages: 21 pages
Date: 2019-07
New Economics Papers: this item is included in nep-ets, nep-for, nep-int and nep-mac
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https://doi.org/10.3929/ethz-b-000354388 (application/pdf)

Related works:
Journal Article: Forecasting Swiss exports using Bayesian forecast reconciliation (2021)
Working Paper: Forecasting Swiss Exports Using Bayesian Forecast Reconciliation (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:kof:wpskof:19-457

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