This article examines the specific aspects of econometric modeling in the dynamic and complex conditions of the modern economy. The paper highlights contemporary trends such as the integration of big data, machine learning, and artificial intelligence, which play a crucial role in forecasting the impacts of inflation, unemployment, climate change, and pandemics. The methodology employs a systematic literature review, drawing on scientific articles from the Scopus, Web of Science, and ResearchGate databases over the last five years (2020-2025). The results indicate that ML-hybrid models enhance forecast accuracy (with reductions in RMSE and MAE), although the adverse effects of climate change and data uncertainty pose significant challenges. The conclusions and recommendations propose increasing the robustness of models in policy formulation, strengthening interdisciplinary collaboration, and implementing ethical standards, thereby contributing to sustainable development and economic recovery.
| Mualliflar | Rajabov, Alibek, Ражабов , Алибек, Rajabov , Alibek |
|---|---|
| Jurnal | Иқтисодий тараққиёт ва таҳлил |
| Nashr sanasi | 2025-08-29 |
| Jild | 3 |
| Son | 8 |
| Betlar | 104-110 |
| Til | O‘zbek |
| DOI | 10.60078/2992-877x-2025-vol3-iss8-pp104-110 |
DOI: 10.60078/2992-877x-2025-vol3-iss8-pp104-110 · Maqolaning asl sahifasi
econometric modeling, modern economy, machine learning, big data, DSGE models, DSGE modelsclimate change, sustainable development, эконометрическое моделирование, современная экономика, машинное обучение, большие данные, модели DSGE, прогнозирование инфляции, изменение климата, устойчивое развитие, ekonometrik modellashtirish, zamonaviy iqtisodiyot, mashinaviy oʻrganish, katta ma’lumotlar, DSGE modellar, inflyatsiya prognozi, iqlim oʻzgarishi, barqaror rivojlanish
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