Open access
Date
2019-07Type
- Working Paper
ETH Bibliography
yes
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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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000354388Publication status
publishedJournal / series
KOF Working PapersVolume
Publisher
KOF Swiss Economic Institute, ETH ZurichSubject
Hierarchical Forecasting; Bayesian Forecast Reconciliation; Swiss Exports; Optimal Forecast CombinationOrganisational unit
02525 - KOF Konjunkturforschungsstelle / KOF Swiss Economic Institute
06330 - KOF FB Konjunktur / KOF Macroeconomic forecasting
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ETH Bibliography
yes
Altmetrics