The Use of AI in Economic Forecasting
DOI:
https://doi.org/10.47941/ijecop.2990Keywords:
Artificial Intelligence, Economic Forecasting, Forecast Accuracy, Machine Learning Models, Explainability, InterpretabilityAbstract
Purpose: The general objective of this study was to investigate the use of AI on the accuracy of economic forecasting.
Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.
Findings: The findings reveal that there exists a contextual and methodological gap relating to the use of AI in economic forecasting. Preliminary empirical review revealed that Artificial Intelligence significantly enhanced the accuracy of economic forecasting by effectively managing complex, nonlinear, and high-frequency data that traditional models struggled to interpret. AI models adapted better to volatile conditions and offered more reliable predictions during crises, though issues like model transparency, data quality, and interpretability posed challenges. It was also found that AI-driven forecasting represented a fundamental shift in economic analysis, transforming it from static trend projection to dynamic, learning-based processes that required institutional readiness and interdisciplinary collaboration for full implementation.
Recommendations: The Adaptive Expectations theory, Complexity Economics theory and Technological Determinism theory may be used to anchor future studies on economic forecasting. The study recommended combining AI with traditional models, strengthening data infrastructure, and promoting ethical, explainable AI use. It also called for training practitioners, creating policy guidelines, and expanding research into AI’s effectiveness in diverse economic settings.
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