Whatever it takes to understand a central banker
Description
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.
Files
Steps to reproduce
See the contained readme.md for a complete overview Install packages with renv To install the packages used in this project, you can use the `renv` package. Install the package with the following command: install.packages("renv") renv::restore() We publish the core text data and all foreign data. data/foreign_data contains all foreign data used in the analysis. The data is stored in .csv, .xlsx, and .rds formats. It is necessary for running the scripts and includes the following: The cobham folder contains five XLSX datasets from the Monetary Frameworks Database by Cobham (2021). These datasets can also be accessed at https://monetaryframeworks.org (last accessed: 01.10.2023). The krippner folder contains an XLSX dataset with the shadow short rate from Krippner (2021). The dataset is available at www.ljkmfa.com (last accessed: 01.10.2023). The world_bank folder contains three CSV datasets from the World Bank Development Indicators (WDI) database. The file swanson_2021.xlsx contains Forward Guidance Shocks by Swanson (2021). The dataset is accessible at Swanson Research Published (last accessed: 01.04.2024). The files WuXiaShadowRate.xlsx and shadowrate_ecb.csv contain the Wu-Xia Shadow Rates for the Federal Reserve (XLSX) and the ECB (CSV). These datasets can be accessed at Wu-Xia Shadow Rates (last accessed: 01.10.2023). data/raw contains the core text data used in the analysis. The data is stored in .rds format and is essential for training the language models.