https://github.com/fipelle/replication-hasenzagl-et-al-2020
Replication code for "A Model of the Fed's View on Inflation".
https://github.com/fipelle/replication-hasenzagl-et-al-2020
bayesian-estimation inflation-dynamics okun-law output-gap phillips-curve unobserved-components
Last synced: 4 months ago
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Replication code for "A Model of the Fed's View on Inflation".
- Host: GitHub
- URL: https://github.com/fipelle/replication-hasenzagl-et-al-2020
- Owner: fipelle
- License: bsd-3-clause
- Created: 2020-07-09T16:11:47.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2022-07-30T15:13:34.000Z (almost 3 years ago)
- Last Synced: 2023-11-08T17:42:34.779Z (over 1 year ago)
- Topics: bayesian-estimation, inflation-dynamics, okun-law, output-gap, phillips-curve, unobserved-components
- Language: Jupyter Notebook
- Homepage: https://arxiv.org/abs/2006.14110
- Size: 5.79 MB
- Stars: 6
- Watchers: 3
- Forks: 11
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## A Model of the Fed's View on Inflation
This repository contains the source code for replicating the results in the paper:
[Hasenzagl, T., Pellegrino, F., Reichlin, L., & Ricco, G. (2020). A Model of the Fed's View on Inflation.](https://arxiv.org/abs/2006.14110)
If you have any questions, comments, or suggestions please create a new issue or email the authors.
## Code structure
The main directory is organized as follows:
* *annex_global_data*: Contains a directory with the data files, `tc_mwg.jl`, and `iis_charts.ipynb` for the model with global variables. To estimate this model use these files instead of the files with the same names in the *data* and *code* directories. The dataset for the global model includes the Baltic Dry Index (BDI) which is available here: https://www.balticexchange.com/en/index.html.
* *code_main*: Contains all of the Julia code necessary for replication.
+ The *Metropolis-Within-Gibbs* subdirectory contains the code for the Metropolis-Within-Gibbs algorithm.
* *csv_output*: Used for storing the .csv output files.
* *data*: Contains the data used in the estimation. The data is saved in .csv and .xlsx files.
* *docs*: Contains the paper and online appendix.
* *img*: Used for storing the output figures.
The code is written in Julia 1.6.4 (https://julialang.org/).
The code uses a number of Julia packages. All necessary packages can be installed using the `import_packages.jl` script. To do so, start Julia and use the following command at the Julia REPL prompt:
`julia> include("import_packages.jl")`
## Running the code
The main file is `user_main.jl`. This script runs the following exercises:
* The in-sample estimation is run by setting `run_type=1` in `user_main.jl`.
* The conditional forecasting exercise is run by setting `run_type=2` and specifying the start date of the forecasting exercise, and the conditioning variables and time periods. Note that the paper does not include a conditional forecasting exercise.
* The out-of-sample forecasting exercise is run by setting `run_type=3` and specifying the start date of the forecasting exercise.
After choosing the `run_type` run the script by starting Julia and using the following command at the Julia REPL prompt:
`julia> include("user_main.jl")`
## Figures and Tables
The figures and tables are created in two Jupyter (https://jupyter.org/) notebooks:
* `iis_charts.ipynb`: creates all figures relating to the in-sample estimation.
* `oos_charts.ipynb`: creates all figures relating to the out-of-sample forecasting exercise and the RMSE of the trend-cycle model relative to the RMSE of a random walk with drift.
## Citation
If you are using any part of the code for academic work (including, but not limited to, conference and peer-reviewed papers), please cite using the following bibtex code:
```bibtex
@misc{hasenzagl2020inflation,
title={A Model of the Fed's View on Inflation},
author={Hasenzagl, Thomas and Pellegrino, Filippo and Reichlin, Lucrezia and Ricco, Giovanni},
year={2020},
eprint={2006.14110},
archivePrefix={arXiv},
primaryClass={econ.EM}
}
```