Macroeconomic Forecasting with Large Language Models
This work addresses the applicability of LLMs for macroeconomic forecasting, which is an incremental evaluation against existing methods.
The paper compared the accuracy of Large Language Models (LLMs) to traditional methods for forecasting macroeconomic time series using the FRED-MD database, finding that LLMs offer insights into their strengths and limitations but without reporting specific numerical results.
This paper presents a comparative analysis evaluating the accuracy of Large Language Models (LLMs) against traditional macro time series forecasting approaches. In recent times, LLMs have surged in popularity for forecasting due to their ability to capture intricate patterns in data and quickly adapt across very different domains. However, their effectiveness in forecasting macroeconomic time series data compared to conventional methods remains an area of interest. To address this, we conduct a rigorous evaluation of LLMs against traditional macro forecasting methods, using as common ground the FRED-MD database. Our findings provide valuable insights into the strengths and limitations of LLMs in forecasting macroeconomic time series, shedding light on their applicability in real-world scenarios