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- Title
Comparative Analysis of Machine Learning and Autoregressive Models for Forecasting Economic Growth: A Case Study.
- Authors
Messaoudi, Malika; Khouidmi, Houari
- Abstract
This study presents a comparative analysis of machine learning models, specifically gradient boosting machine (GBM) and random forest (RF), against the traditional vector autoregressive (VAR) model for forecasting economic growth in Algeria. By utilizing a dataset comprising key macroeconomic indicators—Gross Domestic Product (GDP), money supply (M), and inflation (I)—we aim to evaluate the predictive accuracy and robustness of these models. Our findings indicate that the RF model outperforms both GBM and VAR in terms of accuracy and reliability, providing a valuable understanding of the economic dynamics of Algeria. These results highlight the potential of advanced machine learning techniques in improving economic forecasting and informing policy decisions in emerging economies.
- Subjects
MACHINE learning; ECONOMIC development; ECONOMIC forecasting; AUTOREGRESSIVE models; GROSS domestic product; ECONOMIC indicators
- Publication
International Journal of Sustainable Development & Planning, 2024, Vol 19, Issue 8, p3049
- ISSN
1743-7601
- Publication type
Article
- DOI
10.18280/ijsdp.190820