Machine Learning vs Traditional Forecasting Methods: An Application to South African GDP
Stellenbosch Working Paper Series No. WP12/2019Publication date: August 2019
Author(s):
This study employs traditional autoregressive and vector autoregressive forecasting models, as well as machine learning methods of forecasting, in order to compare the performance of each of these techniques. Each technique is used to forecast the percentage change of quarterly South African Gross Domestic Product, quarter-on-quarter. It is found that machine learning methods outperform traditional methods according to the chosen criteria of minimising root mean squared error and maximising correlation with the actual trend of the data. Overall, the outcomes suggest that machine learning methods are a viable option for policy-makers to use, in order to aid their decision-making process regarding trends in macroeconomic data. As this study is limited by data availability, it is recommended that policy-makers consider further exploration of these techniques.
JEL Classification:C32, C45, C53, C88
Keywords:Machine learning, Forecasting, Elastic-net, Random Forests, Support Vector Machines, Recurrent Neural Networks
Download: PDF (762 KB)Login
(for staff & registered students)
BER Weekly
14 Mar 2025 Budget 2.0 – less VAT, but still a lot of tax and little spending cuts from Budget 1.0After originally being scheduled for 19 February and then postponed at the last moment, the National Budget for 2025 was tabled by the National Treasury (NT) on 12 March. In some ways, Budget 2.0 was largely the same as Budget (1.0). While the contentious 2%pt-VAT hike was watered down, it still comes with a heavy tax burden, largely shouldered by the...
Read the full issue
BER Weekly
14 Mar 2025 Budget 2.0 – less VAT, but still a lot of tax and little spending cuts from Budget 1.0After originally being scheduled for 19 February and then postponed at the last moment, the National Budget for 2025 was tabled by the National Treasury (NT) on 12 March. In some ways, Budget 2.0 was largely the same as Budget (1.0). While the contentious 2%pt-VAT hike was watered down, it still comes with a heavy tax burden, largely shouldered by the...
Read the full issue