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
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Upcoming Seminars
Monday 28 July 202512:00-13:00
Dr Neil Rankin: Ceo Of Predictive Insights & Stellenbosch University
Topic: "TBC"
12:00-13:00
Prof Willem Boshoff: Stellenbosch University
Topic: "Two competing approaches in South African competition policy: merger control and anti-cartel enforcement over the past 30 years"
12:00-13:00
Professor Johan Fourie: Stellenbosch University
Topic: "Economic History: TBC"
BER Weekly
18 Jul 2025 Encouraging data, but messy politics while US tariff deadline loomsThe big global data prints of the week came on Tuesday, with better-than-expected Chinese GDP growth for Q2 and US core CPI coming in lower than expected, but still (finally) reflecting some signs of tariffs being passed on to consumers. Locally, the uptick in mining production and retail sales was positive for Q2 GDP dynamics. In addition to the data,...
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