Macroeconomic forecasting for Australia using a large number of predictors
Bin Jiang (),
George Athanasopoulos (),
Rob Hyndman (),
Anastasios Panagiotelis and
Farshid Vahid ()
No 2/17, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics
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.
Keywords: Australian economy; Bayesian VAR; bagging; dynamic factor model; ridge regression; least angular regression; shrinkage; regularization. (search for similar items in EconPapers)
JEL-codes: C52 C53 C55 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-for and nep-mac
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Journal Article: Macroeconomic forecasting for Australia using a large number of predictors (2019)
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