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.

研究探讨不同机器学习算法在现在预测马达加斯加国内生产总值 (GDP) 方面的预测能力。我们使用 10 个马达加斯加季度宏观经济领先指标，对流行的回归模型进行训练，包括线性正则化回归 (Ridge，Lasso，Elastic-net)，维度约简模型 (主成分回归)，k 最近邻算法 (k-NN 回归)，支持向量回归 (线性 SVR)，以及基于树的集成模型 (Random Forest 和 XGBoost 回归)，并使用简单的计量经济模型作为基准。通过计算均方根误差 (RMSE)，平均绝对误差 (MAE) 和平均绝对百分比误差 (MAPE) 来衡量每个模型的现在预测准确性。我们的发现表明，通过汇总个体预测形成的集成模型始终优于传统计量经济模型。我们得出结论，机器学习模型可以更准确和及时地预测马达加斯加的经济表现，并为决策者提供数据驱动的决策指导。