What question did this study set out to answer?

The aim is to evaluate the forecasting ability of large language models compared to traditional models in predicting inflation.

March 10, 2026

Macroeconomic and Financial Forecasting in the Aftermath of the Covid Shock: Inflation Forecasting With Large Language Models

Puntos clave

The aim is to evaluate the forecasting ability of large language models compared to traditional models in predicting inflation.
Compared inflation forecasts from large language models against traditional models and Kalman filter approach.
Utilized monthly inflation data from the United States and euro area.
Forecast period extended from January 2020 to September 2025.
All large language models demonstrated a significant improvement in forecasting performance compared to traditional models.
LLMs provided more accurate inflation forecasts during the study period.

Resumen

ABSTRACT We compare the inflation forecasting performance of large language models (LLMs) to traditional models and the Kalman filter approach developed by Hall et al. (2026). Using monthly data for inflation in both the United States and the euro area, we forecast over the period 2020:M1 to 2025:M9; our findings show that all the LLMs significantly improved forecasting performance against the traditional models.

Macroeconomic and Financial Forecasting in the Aftermath of the Covid Shock: Inflation Forecasting With Large Language Models

Puntos clave

Resumen

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