---
title: Structural Estimation with Unstructured Data
url: https://www.ml-quant.com/papers/repec/nbr-nberwo-35487/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-10-16
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:nbr:nberwo:35487
source_url: https://econpapers.repec.org/RePEc:nbr:nberwo:35487
featured: 2026-10-16
citations: unknown
topic: Econometrics & Forecasting
---


# Structural Estimation with Unstructured Data

Incorporating unstructured text from Federal Reserve transcripts into a DSGE model sharpens monetary policy inference and implies a flatter, less backward-looking Phillips curve.

- Source: https://econpapers.repec.org/RePEc:nbr:nberwo:35487
- Identifier: RePEc:nbr:nberwo:35487
- Released: 2026-10-07
- First featured: Quant Letter No. 135 (2026-10-16): https://www.ml-quant.com/issues/2026-10-16/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Econometrics & Forecasting
- Authors: Sara Casella, Jesús Fernández-Villaverde, Stephen Hansen, Ryohei Oishi, Minchul Shin

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