---
title: Assessing Monetary Policy Globally: Evidence from LLMs and Semi-structural Models
url: https://www.ml-quant.com/papers/repec/fip-fedgfn-103907/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-10-30
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:fip:fedgfn:103907
source_url: https://econpapers.repec.org/RePEc:fip:fedgfn:103907
featured: 2026-10-30
citations: unknown
topic: Econometrics & Forecasting
---


# Assessing Monetary Policy Globally: Evidence from LLMs and Semi-structural Models

The paper employs large language models and semi-structural models to evaluate central bank communication and its role in shaping policy expectations.

- Source: https://econpapers.repec.org/RePEc:fip:fedgfn:103907
- Identifier: RePEc:fip:fedgfn:103907
- Released: 2026-10-26
- First featured: Quant Letter No. 137 (2026-10-30): https://www.ml-quant.com/issues/2026-10-30/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Econometrics & Forecasting
- Authors: Diego Vilán, WanTing Xu

## Related

- [DSGE Models in Central Banks: Progress and Prospects](https://www.ml-quant.com/papers/repec/bfr-banfra-1066/): A comprehensive review traces how DSGE models became standard policy tools and assesses their value for connecting theory, data and policy experiments despite known limitations.
- [From Tone to Trajectory: Continuous Sentiment and the Shape of Monetary Policy Communication (Martin Feldkircher, Márton Kardos, Kristoffer Laigaard Nielbo)](https://www.ml-quant.com/papers/repec/onb-oenbwp-281/): The sequencing and emphasis of sentiment within central bank statements predict rate decisions and inflation expectations beyond average tone alone.
- [Perceived Monetary Policy Rules: Evidence from Professional Forecasters in Australia](https://www.ml-quant.com/papers/repec/adl-wpaper-2026-02/): Time-varying beliefs about the central bank's reaction function collapsed at the zero bound and rose only after observed tightening, not from guidance alone.
- [Zero-Shot Conditional Forecasting and the Information Content of Central Bank Paths](https://www.ml-quant.com/papers/repec/ces-ceswps-12972/): A pre-trained time-series model reading central bank published paths cuts forecast errors better than the banks themselves and hard-conditioned VARs, revealing exploitable institutional differences.
- [Estimation and testing of forecast rationality with many moments](https://www.ml-quant.com/papers/arxiv/2309.09481/): The article examines the use of P-GMM moment selection procedure in estimating and testing forecast rationality, using data from the Federal Reserve Bank of Philadelphia's Survey of Professional Forecasters.
- [FinCARE: Financial Causal Analysis with Reasoning and Evidence](https://www.ml-quant.com/papers/arxiv/2510.20221/): KG+LLM for Financial Causal Discovery: Combines SEC knowledge graphs, LLM reasoning, and causal discovery to build more accurate finance‑grounded causal models.