Learning the Macroeconomic Language

TL;DR

This paper introduces a hybrid macroeconomic forecasting model that combines DSGE theory with large language models, effectively leveraging synthetic data to improve out-of-sample predictions through 2025.

Contribution

It presents a novel approach integrating DSGE models with LLMs, enabling macroeconomic forecasting with limited data and demonstrating the model's ability to learn macroeconomic language.

Findings

The hybrid model outperforms traditional methods in forecasting accuracy.

Synthetic data generated from DSGE models enhances LLM training.

The approach effectively captures key macroeconomic features.

Abstract

We show how state-of-the-art large language models (LLMs), seemingly inapplicable to the small samples typical of macroeconomics, can be trained effectively for macroeconomic forecasting. We estimate a dynamic stochastic general equilibrium (DSGE) model on an initial segment of the data to obtain a posterior distribution over structural parameters. We sample from this posterior to generate millions of theory-consistent synthetic panels that, when mixed with actual macroeconomic data, form the training corpus for a time-series transformer with attention. The trained model is then used to forecast out-of-sample through 2025. The results show that this hybrid forecaster, which combines the theoretical coherence of DSGE models with the representational power of modern LLMs, learns key features of the macroeconomic language.