Applications of Generative Artificial Intelligence in Socioeconomic Forecasting

This article examines the application of using artificial intelligence (AI) and large language models (LLMs) for forecasting key macroeconomic indicators, including gross domestic product (GDP), inflation, unemployment rates, interest rates, and the Gini coefficient. It analyzes the capabilities of these novel approaches compared to traditional forecasting methods – such as econometric, equilibrium, and agent-based models – across different time horizons. The paper summarizes both academic research and practical implementations, including central bank experiments on using fundamental models and LLM-like architectures (such as GPT) for macroeconomic forecasting. Special attention is given to the ability of LLMs to analyze textual information and generate predictions that are comparable in accuracy to, and in some instances superior to, those produced by professional experts. The review also covers the latest fundamental time-series models, such as TimeGPT, TimesFM, and Moirai, which employ transformer architectures tailored to economic data. The main findings indicate that AI and LLMs provide a significant advantage in terms of flexibility, adaptability, and the capacity to process diverse information sources, especially in environments characterized by high volatility or information saturation. However, challenges remain regarding the interpretability, stability, and long-term consistency of predictions. The article concludes that the best prospects for advancing macroeconomic forecasting lie in hybrid approaches that combine the computational power and adaptability of AI with the theoretical rigor and explainability of traditional economic and mathematical models.