Comparative Analysis of Traditional Versus AI-Enhanced Economic Models
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| Pages: | 22 : 38 |
| Abstract: | This paper conducts a comparative analysis between traditional economic forecasting models and AI-enhanced models in the context of macroeconomic forecasting. The main goal is to assess the advantages and limitations of each approach, highlighting the extent to which machine learning techniques and other AI methods can improve the accuracy of economic forecasts compared to classical econometric models. The study includes a literature review, a clearly defined comparative methodology, and an empirical analysis based on real European data. Relevant case studies - ranging from inflation forecasting in Romania to GDP nowcasting in the euro area - are presented, illustrating the performance of traditional models (such as autoregressive or general equilibrium models) versus AI-based models (such as artificial neural networks or random forest algorithms). The results indicate that AI models can often provide more accurate forecasts in the short run and in detecting changes in the economic regime, while traditional models remain valuable for economic interpretability and theoretical consistency. In conclusion, we recommend a complementary, hybrid approach that combines the theoretical robustness of classical economic models with the processing power and flexibility of AI models to obtain more reliable forecasts and to support economic policy decisions. The implications of these findings for practitioners are discussed and future research directions are suggested, such as integrating big data and increasing the interpretability of artificial intelligence models. |
| JEL classification: | C4, E4, G2 |
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C4, E4, G2
