Artificial Intelligence and Predictive Modelling for Global Economic Forecasting

Daniela Witten

Artificial Intelligence and Predictive Modelling for Global Economic Forecasting

Author(s)	Daniela Witten
Country	United States
Abstract	Global economic forecasting has traditionally relied on econometric models, historical indicators, expert assessments, and macroeconomic assumptions. However, increasing economic complexity, rapid financial integration, geopolitical uncertainty, climate-related disruptions, and the availability of high-frequency data have created demand for more advanced forecasting approaches. Artificial Intelligence (AI) and predictive modelling provide new opportunities to analyse large and heterogeneous datasets, identify nonlinear relationships, detect emerging economic patterns, and generate timely forecasts. This study examines the role of AI and predictive modelling in global economic forecasting, focusing on machine learning, deep learning, natural language processing, time-series models, ensemble methods, and alternative data. A qualitative and analytical methodology based on secondary research is adopted. The study evaluates applications in GDP forecasting, inflation prediction, unemployment estimation, trade forecasting, financial-market analysis, and crisis detection. The analysis suggests that AI-based models can complement conventional econometric techniques by improving forecasting flexibility, processing large datasets, and capturing complex relationships. Nevertheless, AI models face challenges involving data quality, structural breaks, model interpretability, overfitting, forecast instability, geopolitical shocks, and dependence on historical patterns. The study argues that AI should complement rather than completely replace traditional economic modelling and expert judgment. A hybrid forecasting framework combining econometric models, machine learning, alternative data, scenario analysis, and human expertise can provide a more robust approach to global economic forecasting. The study concludes that responsible integration of AI into economic forecasting can strengthen the timeliness, breadth, and adaptability of economic intelligence while maintaining transparency and methodological rigor.
Keywords	: Artificial Intelligence, Predictive Modelling, Economic Forecasting, Machine Learning, Global Economy, Macroeconomics, Time-Series Analysis, Deep Learning, Economic Prediction.
Field	Engineering
Published In	Volume 4, Issue 3, May-June 2022
Published On	2022-05-10

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American Journal of Advanced Multidisciplinary Research and Innovation

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Artificial Intelligence and Predictive Modelling for Global Economic Forecasting

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