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GDP nowcasting with machine learning: Evaluating forecast accuracy for South Africa during economic crises

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  • Chaouch, Anouar
  • Sassi, Salim Ben

Abstract

This paper investigates how traditional econometric and machine-learning models perform in nowcasting South Africa’s real GDP growth across distinct macroeconomic regimes, including the Global Financial Crisis, the COVID-19 shock, and the post-pandemic recovery. Using a large panel of 109 monthly indicators, we estimate autoregressive and factor models, penalized linear regressions (Lasso, Ridge, Elastic Net), tree-based ensembles (Random Forest, XGBoost, Decision Trees), and their hybrids within an expanding-window framework that incorporates indicator-specific publication lags to mimic genuine real-time conditions.

Suggested Citation

  • Chaouch, Anouar & Sassi, Salim Ben, 2026. "GDP nowcasting with machine learning: Evaluating forecast accuracy for South Africa during economic crises," Economic Analysis and Policy, Elsevier, vol. 92(C), pages 1117-1141.
    • Handle: RePEc:eee:ecanpo:v:92:y:2026:i:c:p:1117-1141
    DOI: 10.1016/j.eap.2026.06.050
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