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Working Paper
Structural Estimation with Unstructured Data
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 ...
Working Paper
Remote Work Across Jobs, Companies and Space
The pandemic catalyzed an enduring shift to remote work. To measure this shift, we develop a large language model (LLM), fine tune and assess it using 30,000 human classifications, and apply it to nearly 600 million job vacancy postings across five English-speaking countries. Our model achieves a 98% classification accuracy, greatly outperforming dictionary-based approaches, and matching or surpassing the performance of frontier AI models at a fraction of the cost. From 2019 to 2026, the share of postings that indicate new employees can work remotely at least one day per week rose more than ...
Working Paper
Risk Management in Monetary Policy: A Review with Asset Pricing Implications
We review recent research on how the Fed's risk-management approach shapes the overall policy stance and how it affects financial market conditions. The evidence shows that the policy stance contains a forward-looking, conditional component that has long been an integral part of the Fed's policymaking toolkit. Asymmetric forward-looking policy tilts—motivated by risk-management considerations and revealed via the Fed’s communication—complement and extend beyond the effects of direct policy actions. Drawing on the transcripts of FOMC meetings between 1976 and 2019, we provide an ...