Macroeconomic forecasting for Australia using a large number of predictors

Anastasios Panagiotelis, George Athanasopoulos, Rob J. Hyndman, Bin Jiang, Farshid Vahid

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16 Citations (Scopus)


A popular approach to forecasting macroeconomic variables is to utilize a large number of predictors. Several regularization and shrinkage methods can be used to exploit such high-dimensional datasets, and have been shown to improve forecast accuracy for the US economy. To assess whether similar results hold for economies with different characteristics, an Australian dataset containing observations on 151 aggregate and disaggregate economic series as well as 185 international variables, is introduced. An extensive empirical study is carried out investigating forecasts at different horizons, using a variety of methods and with information sets containing an increasing number of predictors. In contrast to other countries the results show that it is difficult to forecast Australian key macroeconomic variables more accurately than some simple benchmarks. In line with other studies we also find that there is little to no improvement in forecast accuracy when the number of predictors is expanded beyond 20–40 variables and international factors do not seem to help.

Original languageEnglish
Pages (from-to)616-633
Number of pages18
JournalInternational Journal of Forecasting
Issue number2
Publication statusPublished - 1 Apr 2019


  • Bagging
  • Bayesian VAR
  • Dynamic factor model
  • Least angular regression
  • Regularization
  • Ridge regression
  • Shrinkage

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