Forecasting Swiss Exports Using Bayesian Forecast Reconciliation

Florian Eckert, Rob Hyndman and Anastasios Panagiotelis

No 14/19, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

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: 32
Date: 2019
New Economics Papers: this item is included in nep-ecm, nep-eec, nep-for, nep-int and nep-mac
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https://www.monash.edu/business/ebs/research/publications/ebs/wp14-2019.pdf (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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