Hierarchical Forecasting

George Athanasopoulos, Puwasala Gamakumara, Anastasios Panagiotelis, Rob J. Hyndman, Mohamed Affan

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Researchpeer-review

24 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationMacroeconomic Forecasting in the Era of Big Data
Subtitle of host publicationTheory and Practice
EditorsPeter Fuleky
Place of PublicationCham Switzerland
Number of pages31
ISBN (Electronic)9783030311506
ISBN (Print)9783030311490
Publication statusPublished - 2020

Publication series

NameAdvanced Studies in Theoretical and Applied Econometrics
ISSN (Print)1570-5811
ISSN (Electronic)2214-7977

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