Machine Learning vs Traditional Forecasting Methods: An Application to South African GDP

Lisa-Cheree Martin ()
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Lisa-Cheree Martin: Department of Economics, Stellenbosch University

No 12/2019, Working Papers from Stellenbosch University, Department of Economics

Abstract: 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.

Keywords: Machine learning; Forecasting; Elastic-net; Random Forests; Support Vector Machines; Recurrent Neural Networks (search for similar items in EconPapers)
JEL-codes: C32 C45 C53 C88 (search for similar items in EconPapers)
Date: 2019
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for and nep-pay
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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