Whatever it takes to understand a central banker

Replication Code for Whatever it takes to undestand a Central Banker Dictionary approaches are at the forefront of current techniques for quantifying central bank communication. This paper proposes **embeddings**—a language model trained using machine learning techniques—to locate words and documents in a multidimensional vector space. To accomplish this, we gather a text corpus that is unparalleled in size and diversity within the central bank communication literature, as well as introduce a novel approach to text quantification from computational linguistics. The combination of both allows us to provide high-quality, central bank-specific textual representations and demonstrate their applicability by developing an index that tracks deviations in the Fed's communication towards inflation targeting. Our findings indicate that these deviations in communication significantly affect market expectations and impact monetary policy actions, substantially reducing the inflation response parameter in an estimated Taylor Rule.