Bin Jiang, George Athanasopoulos, Rob J Hyndman, Anastasios Panagiotelis, Farshid Vahid
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 is introduced. An extensive empirical study is carried out investigating forecasts at different horizons, using a variety of methods and with information sets containing different numbers of predictors. The results share both differences and similarities with the conclusions from the literature on forecasting US macroeconomic variables. The major difference is that forecasts based on dynamic factor models perform relatively poorly compared to forecasts based on other methods which is the opposite of the conclusion made by Stock and Watson (2012) for the US. On the other hand, a conclusion that can be made for both the Australian and US data is that there is little to no improvement in forecast accuracy when the number of predictors is expanded beyond 20-40 variables.