Nowcasting Madagascar's real GDP using machine learning algorithms

Franck Ramaharo and Gerzhino Rasolofomanana

MPRA Paper from University Library of Munich, Germany

Abstract: We investigate the predictive power of different machine learning algorithms to nowcast Madagascar's gross domestic product (GDP). We trained popular regression models, including linear regularized regression (Ridge, Lasso, Elastic-net), dimensionality reduction model (principal component regression), k-nearest neighbors algorithm (k-NN regression), support vector regression (linear SVR), and tree-based ensemble models (Random forest and XGBoost regressions), on 10 Malagasy quarterly macroeconomic leading indicators over the period 2007Q1-2022Q4, and we used simple econometric models as a benchmark. We measured the nowcast accuracy of each model by calculating the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Our findings reveal that the Ensemble Model, formed by aggregating individual predictions, consistently outperforms traditional econometric models. We conclude that machine learning models can deliver more accurate and timely nowcasts of Malagasy economic performance and provide policymakers with additional guidance for data-driven decision making.

Keywords: nowcasting; gross domestic product; machine learning; Madagascar (search for similar items in EconPapers)
JEL-codes: C02 C53 C63 E17 (search for similar items in EconPapers)
Date: 2023-12-23
New Economics Papers: this item is included in nep-big and nep-cmp
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Related works:
Working Paper: Nowcasting Madagascar's real GDP using machine learning algorithms (2023)
Working Paper: Nowcasting Madagascar's real GDP using machine learning algorithms (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:119574

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