Abstract:
Inflationary dynamics underscore the need for advanced methodologies to enhance forecasting accuracy. This paper explores the potential of Artificial Intelligence (AI) in generating short-term inflation forecasts for Argentina during the 2023-2024 period. The methodology leverages OpenAI's GPT-4o Mini model, a Large Language Model (LLM), to produce conditional predictions by supplying historical Consumer Price Index (CPI) data and explicitly restricting its knowledge base to the forecast date. Additionally, forecasts are benchmarked against the inflation expectations survey conducted by Argentina's Central Bank, known as the Relevamiento de Expectativas de Mercado (REM). While predicting high inflation spikes remains challenging for both approaches, our results indicate that the AI model achieves comparable performance to REM for medium to low monthly inflation rates. For instance, for forecasts made at a given month t (e.g., August 2024) and evaluated across the subsequent seven forecast horizons when monthly inflation is around 4%, the Mean Squared Error (MSE) for GPT-4o Mini's median predictions was 0.90 and the Mean Absolute Error (MAE) was 0.85, closely aligning with REM's performance, which recorded an MSE of 0.68 and an MAE of 0.73.
