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Hierarchical Forecasting

  • George AthanasopoulosEmail author
  • Puwasala Gamakumara
  • Anastasios Panagiotelis
  • Rob J. Hyndman
  • Mohamed Affan
Chapter
Part of the Advanced Studies in Theoretical and Applied Econometrics book series (ASTA, volume 52)

Abstract

Accurate forecasts of macroeconomic variables are crucial inputs into the decisions of economic agents and policy makers. Exploiting inherent aggregation structures of such variables, we apply forecast reconciliation methods to generate forecasts that are coherent with the aggregation constraints. We generate both point and probabilistic forecasts for the first time in the macroeconomic setting. Using Australian GDP we show that forecast reconciliation not only returns coherent forecasts but also improves the overall forecast accuracy in both point and probabilistic frameworks.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • George Athanasopoulos
    • 1
    Email author
  • Puwasala Gamakumara
    • 2
  • Anastasios Panagiotelis
    • 1
  • Rob J. Hyndman
    • 2
  • Mohamed Affan
    • 3
  1. 1.Department of Econometrics and Business StatisticsMonash UniversityCaulfieldAustralia
  2. 2.Department of Econometrics and Business StatisticsMonash UniversityClaytonAustralia
  3. 3.Maldives Monetary Authority (MMA)MaléRepublic of Maldives

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