Replication Files for Baumgärtner, Zahner (2025)
Description
Replication Files for ------------------------------------------------------------------------------------------------------------------------------ ------- Whatever it takes to understand a central banker - Embedding their words using neural networks. ----------- ------------------------------------------------------------------------------------------------------------------------------ ------------------------------------------- Contents and Replication Instructions This repository contains the replication code and dataset for the study. The current version reflects the code and data as of March 13, 2025. We maintain an updated GitHub repository at the following link, where we implement improvements, fix potential issues, and address code optimizations: https://github.com/martinbaumgaertner/whatever_it_takes This replication package includes: (i) detailed ReadMe file with clear, step-by-step instructions for replication. (ii) a mapping of scripts to outputs, specifying which scripts generate each figure and table in the paper. (iii) All necessary datasets and source code to fully replicate the analysis, including embedding generation, model training, and final application. --------------------------------------------------- Code Annotations and System Requirements The code includes extensive comments for ease of understanding, along with specific guidance and alternative approaches for users without access to high-performance computing (HPC) resources. The replication process has been successfully executed on modern hardware (e.g., a recent MacBook Pro) within approximately a few dozen hours. However, users with less powerful systems may encounter performance limitations. To address this, we have explicitly annotated hardware-related constraints in the code and provided shortcuts, such as the use of pre-trained models, to facilitate replication. Some larger shortcuts (e.g., those used in Section 3.3 for measuring “rhetorical stability”) are not included in this replication package due to file size constraints (several GBs). However, these files are available upon request—please feel free to contact us if needed.
Files
Steps to reproduce
See the contained readme.md for a complete overview 1. 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() 2. Download the contents of the `.zip` archive into your desired folder on your computer. The folder will contain: –– readme.md –– R-Scripts (`clean_data.R`, `03_1_descriptive_plots.R`, ...): Scripts named according to the chapters they replicate. For example, the script `"03_1_descriptive_plots.R"` replicates the descriptive plots of Chapter 3.1. The scripts can be run independently. –– Data: We publish the core Textdata and all foreign data: data/foreign_data contains all foreign data used in the analysis. The data is stored in the following formats: `.csv`, `.xlsx`, and `.rds`. The data is necessary for running the scripts. 3. FRED-API: To replicate all figures and tables, a FRED API key is required. This allows the code to directly load data from the FRED database, minimizing user intervention. You can obtain a free FRED API key here: https://www.stlouisfed.org. For questions, please contact **Johannes Zahner** at johannes.zahner@googlemail.com.