Macroeconomic forecasting for Australia using a large number of predictors
Rob Hyndman (),
Bin Jiang and
International Journal of Forecasting, 2019, vol. 35, issue 2, 616-633
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.
Keywords: Bayesian VAR; Bagging; Dynamic factor model; Ridge regression; Least angular regression; Shrinkage; Regularization (search for similar items in EconPapers)
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Working Paper: Macroeconomic forecasting for Australia using a large number of predictors (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:35:y:2019:i:2:p:616-633
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