Nowcasting Madagascar's real GDP using machine learning algorithms
Franck Ramaharo Gerzhino Rasolofomanana
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.