Structural Estimation with Unstructured Data

Sara Casella, Jesus Fernandez-Villaverde, Stephen Hansen, Ryohei Oishi and Minchul Shin

No 35487, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: Standard macroeconomic data do not cleanly separate the systematic and nonsystematic components of monetary policy. We show that incorporating unstructured text data into the structural estimation of a DSGE model can sharpen this distinction. We augment a standard state-space model with a non-core measurement block that links structural shocks to time series derived from FOMC transcripts, using a spike-and-slab prior to let the data select which series are informative. In a medium-scale New Keynesian model for the U.S., incorporating text improves predictive performance and materially alters structural inference: the new model estimates a lower response of the policy rate to inflation, higher price stickiness and lower price indexation, implying a flatter and less backward-looking price Phillips curve.

JEL-codes: C11 C32 C55 E37 E52 (search for similar items in EconPapers)
Date: 2026-07
Note: EFG
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Working Paper: Structural Estimation with Unstructured Data (2026)
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